diff --git "a/Day31-35/31-35.\347\216\251\350\275\254Linux\346\223\215\344\275\234\347\263\273\347\273\237.md" "b/Day31-35/31-35.\347\216\251\350\275\254Linux\346\223\215\344\275\234\347\263\273\347\273\237.md" index 2bcc59878..93103633d 100644 --- "a/Day31-35/31-35.\347\216\251\350\275\254Linux\346\223\215\344\275\234\347\263\273\347\273\237.md" +++ "b/Day31-35/31-35.\347\216\251\350\275\254Linux\346\223\215\344\275\234\347\263\273\347\273\237.md" @@ -230,7 +230,7 @@ Linux系统的命令通常都是如下所示的格式： [root@iZwz97tbgo9lkabnat2lo8Z ~]# !454 ``` - > 说明：查看到历史命令之后，可以用`!历史命令编号`来重新执行该命令；通过`history -c`可以清除历史命令。 + > **说明**：查看到历史命令之后，可以用`!历史命令编号`来重新执行该命令；通过`history -c`可以清除历史命令。 ### 实用程序 @@ -308,7 +308,7 @@ Linux系统的命令通常都是如下所示的格式： ... ``` - > 说明：上面用到了一个名为`wget`的命令，它是一个网络下载器程序，可以从指定的URL下载资源。 + > **说明**：上面用到了一个名为`wget`的命令，它是一个网络下载器程序，可以从指定的URL下载资源。 6. 拷贝/移动文件 - **cp** / **mv**。 @@ -350,7 +350,7 @@ Linux系统的命令通常都是如下所示的格式： 52: ... ``` - > 说明：`grep`在搜索字符串时可以使用正则表达式，如果需要使用正则表达式可以用`grep -E`或者直接使用`egrep`。 + > **说明**：`grep`在搜索字符串时可以使用正则表达式，如果需要使用正则表达式可以用`grep -E`或者直接使用`egrep`。 9. 创建链接和查看链接 - **ln** / **readlink**。 @@ -372,7 +372,7 @@ Linux系统的命令通常都是如下所示的格式： CentOS Linux release 7.4.1708 (Core) ``` - > 说明：链接可以分为硬链接和软链接（符号链接）。硬链接可以认为是一个指向文件数据的指针，就像Python中对象的引用计数，每添加一个硬链接，文件的对应链接数就增加1，只有当文件的链接数为0时，文件所对应的存储空间才有可能被其他文件覆盖。我们平常删除文件时其实并没有删除硬盘上的数据，我们删除的只是一个指针，或者说是数据的一条使用记录，所以类似于“文件粉碎机”之类的软件在“粉碎”文件时除了删除文件指针，还会在文件对应的存储区域填入数据来保证文件无法再恢复。软链接类似于Windows系统下的快捷方式，当软链接链接的文件被删除时，软链接也就失效了。 + > **说明**：链接可以分为硬链接和软链接（符号链接）。硬链接可以认为是一个指向文件数据的指针，就像Python中对象的引用计数，每添加一个硬链接，文件的对应链接数就增加1，只有当文件的链接数为0时，文件所对应的存储空间才有可能被其他文件覆盖。我们平常删除文件时其实并没有删除硬盘上的数据，我们删除的只是一个指针，或者说是数据的一条使用记录，所以类似于“文件粉碎机”之类的软件在“粉碎”文件时除了删除文件指针，还会在文件对应的存储区域填入数据来保证文件无法再恢复。软链接类似于Windows系统下的快捷方式，当软链接链接的文件被删除时，软链接也就失效了。 10. 压缩/解压缩和归档/解归档 - **gzip** / **gunzip** / **xz**。 @@ -429,7 +429,7 @@ Linux系统的命令通常都是如下所示的格式： [root@iZwz97tbgo9lkabnat2lo8Z ~]# xargs < a.txt > b.txt ``` - > 说明：这个命令就像上面演示的那样常在管道（实现进程间通信的一种方式）和重定向（重新指定输入输出的位置）操作中用到，后面的内容中会讲到管道操作和输入输出重定向操作。 + > **说明**：这个命令就像上面演示的那样常在管道（实现进程间通信的一种方式）和重定向（重新指定输入输出的位置）操作中用到，后面的内容中会讲到管道操作和输入输出重定向操作。 13. 显示文件或目录 - **basename** / **dirname**。 diff --git "a/Day41-55/48.\345\211\215\345\220\216\347\253\257\345\210\206\347\246\273\345\274\200\345\217\221\345\205\245\351\227\250.md" "b/Day41-55/48.\345\211\215\345\220\216\347\253\257\345\210\206\347\246\273\345\274\200\345\217\221\345\205\245\351\227\250.md" index 974a9cc47..0675a7558 100644 --- "a/Day41-55/48.\345\211\215\345\220\216\347\253\257\345\210\206\347\246\273\345\274\200\345\217\221\345\205\245\351\227\250.md" +++ "b/Day41-55/48.\345\211\215\345\220\216\347\253\257\345\210\206\347\246\273\345\274\200\345\217\221\345\205\245\351\227\250.md" @@ -133,21 +133,7 @@ class SubjectMapper(ModelMapper): 学科信息 @@ -157,7 +143,9 @@ class SubjectMapper(ModelMapper):
- {{ subject.name }} + + {{ subject.name }} +
{{ subject.intro }}
diff --git "a/Day41-55/49.RESTful\346\236\266\346\236\204\345\222\214DRF\345\205\245\351\227\250.md" "b/Day41-55/49.RESTful\346\236\266\346\236\204\345\222\214DRF\345\205\245\351\227\250.md" index 0004429fc..04eb692ad 100644 --- "a/Day41-55/49.RESTful\346\236\266\346\236\204\345\222\214DRF\345\205\245\351\227\250.md" +++ "b/Day41-55/49.RESTful\346\236\266\346\236\204\345\222\214DRF\345\205\245\351\227\250.md" @@ -156,48 +156,14 @@ urlpatterns = [ 通过Vue.js渲染页面。 -```Python +```HTML 老师信息 @@ -217,9 +183,11 @@ urlpatterns = [
{{ teacher.intro }}
- 好评  ({{ teacher.good_count }}) + 好评   + ({{ teacher.good_count }})      - 差评  ({{ teacher.bad_count }}) + 差评   + ({{ teacher.bad_count }})
@@ -355,7 +323,7 @@ JSON Web Token通常简称为JWT，它是一种开放标准（RFC 7519）。随 2. 在令牌过期之前，无法作废已经颁发的令牌，要解决这个问题，还需要额外的中间层和代码来辅助。 3. JWT是用户的身份令牌，一旦泄露，任何人都可以获得该用户的所有权限。为了降低令牌被盗用后产生的风险，JWT的有效期应该设置得比较短。对于一些比较重要的权限，使用时应通过其他方式再次对用户进行认证，例如短信验证码等。 -#### 使用PyJWT生成和验证令牌 +#### 使用PyJWT 在Python代码中，可以使用三方库`PyJWT`生成和验证JWT，下面是安装`PyJWT`的命令。 diff --git "a/Day76-90/code/.ipynb_checkpoints/1-pandas\345\205\245\351\227\250-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/1-pandas\345\205\245\351\227\250-checkpoint.ipynb" index b5b3acc8b..d10293f1e 100644 --- "a/Day76-90/code/.ipynb_checkpoints/1-pandas\345\205\245\351\227\250-checkpoint.ipynb" +++ "b/Day76-90/code/.ipynb_checkpoints/1-pandas\345\205\245\351\227\250-checkpoint.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -22,14 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -44,7 +37,7 @@ "dtype: int64" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -52,13 +45,13 @@ "source": [ "# 创建\n", "# Series是一维的数据\n", - "s = Series(data = [120,136,128,99],index = ['Math','Python','En','Chinese'])\n", + "s = Series(data=[120,136,128,99], index=['Math','Python','En','Chinese'])\n", "s" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -67,7 +60,7 @@ "(4,)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -78,16 +71,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([120, 136, 128, 99], dtype=int64)" + "array([120, 136, 128, 99])" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -108,7 +101,7 @@ "numpy.ndarray" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -128,7 +121,7 @@ "120.75" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -148,7 +141,7 @@ "136" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -168,7 +161,7 @@ "15.903353943953666" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -179,36 +172,33 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Math 14400\n", - "Python 18496\n", - "En 16384\n", - "Chinese 9801\n", + "Math 122\n", + "Python 138\n", + "En 130\n", + "Chinese 101\n", "dtype: int64" ] }, - "execution_count": 11, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s.pow(2)" + "s.add(1)\n", + "s" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -238,64 +228,64 @@ " \n", " \n", " \n", - " a\n", - " 113\n", - " 116\n", - " 75\n", + " a\n", + " 109\n", + " 120\n", + " 23\n", " \n", " \n", - " b\n", - " 19\n", - " 145\n", - " 23\n", + " b\n", + " 54\n", + " 39\n", + " 54\n", " \n", " \n", - " c\n", - " 57\n", - " 107\n", - " 113\n", + " c\n", + " 97\n", + " 22\n", + " 106\n", " \n", " \n", - " d\n", - " 95\n", + " d\n", + " 21\n", + " 96\n", " 3\n", - " 66\n", " \n", " \n", - " e\n", - " 28\n", - " 121\n", - " 120\n", + " e\n", + " 23\n", + " 145\n", + " 147\n", " \n", " \n", - " f\n", - " 141\n", - " 85\n", - " 132\n", + " f\n", + " 80\n", + " 62\n", + " 83\n", " \n", " \n", - " h\n", - " 124\n", - " 39\n", - " 10\n", + " h\n", + " 70\n", + " 31\n", + " 134\n", " \n", " \n", - " i\n", - " 80\n", - " 35\n", - " 17\n", + " i\n", + " 132\n", + " 51\n", + " 115\n", " \n", " \n", - " j\n", - " 68\n", - " 99\n", - " 31\n", + " j\n", + " 95\n", + " 143\n", + " 111\n", " \n", " \n", - " k\n", - " 74\n", - " 12\n", - " 11\n", + " k\n", + " 66\n", + " 94\n", + " 7\n", " \n", " \n", "\n", @@ -303,19 +293,19 @@ ], "text/plain": [ " Python En Math\n", - "a 113 116 75\n", - "b 19 145 23\n", - "c 57 107 113\n", - "d 95 3 66\n", - "e 28 121 120\n", - "f 141 85 132\n", - "h 124 39 10\n", - "i 80 35 17\n", - "j 68 99 31\n", - "k 74 12 11" + "a 109 120 23\n", + "b 54 39 54\n", + "c 97 22 106\n", + "d 21 96 3\n", + "e 23 145 147\n", + "f 80 62 83\n", + "h 70 31 134\n", + "i 132 51 115\n", + "j 95 143 111\n", + "k 66 94 7" ] }, - "execution_count": 12, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +314,7 @@ "# DataFrame是二维的数据\n", "# excel就非常相似\n", "# 所有进行数据分析，数据挖掘的工具最基础的结果：行和列，行表示样本，列表示的是属性\n", - "df = DataFrame(data = np.random.randint(0,150,size = (10,3)),index = list('abcdefhijk'),columns=['Python','En','Math'])\n", + "df = DataFrame(data=np.random.randint(0, 150, size=(10, 3)), index=list('abcdefhijk'), columns=['Python', 'En', 'Math'])\n", "df" ] }, @@ -553,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -561,50 +551,57 @@ { "data": { "text/plain": [ - "Python 79.9\n", - "En 76.2\n", - "Math 59.8\n", + "Python 74.7\n", + "En 80.3\n", + "Math 78.3\n", "dtype: float64" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.mean(axis = 0)" + "df.mean(axis=0)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "a 101.333333\n", - "b 62.333333\n", - "c 92.333333\n", - "d 54.666667\n", - "e 89.666667\n", - "f 119.333333\n", - "h 57.666667\n", - "i 44.000000\n", - "j 66.000000\n", - "k 32.333333\n", + "a 84.000000\n", + "b 49.000000\n", + "c 75.000000\n", + "d 40.000000\n", + "e 105.000000\n", + "f 75.000000\n", + "h 78.333333\n", + "i 99.333333\n", + "j 116.333333\n", + "k 55.666667\n", "dtype: float64" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.mean(axis = 1)" + "df.mean(axis=1)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -623,7 +620,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/.ipynb_checkpoints/2-pandas-\347\264\242\345\274\225-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/2-pandas-\347\264\242\345\274\225-checkpoint.ipynb" new file mode 100644 index 000000000..98c1704a5 --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/2-pandas-\347\264\242\345\274\225-checkpoint.ipynb" @@ -0,0 +1,372 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series, DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s = Series(np.random.randint(0,150,size = 100),index = np.arange(10,110),dtype=np.int16,name = 'Python')\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s[10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s[[10,20]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 切片操作\n", + "s[10:20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s[::2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s[::-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 可以使用pandas为开发者提供方法，去进行检索\n", + "s.loc[10]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "s.loc[[10,20]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.loc[10:20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.loc[::2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.loc[::-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.index" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# iloc 索引从0开始，数字化自然索引\n", + "s.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.iloc[[0,10]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.iloc[0:20]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "s.iloc[::-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# DataFrame是二维，索引大同小异，\n", + "df = DataFrame(data = np.random.randint(0,150,size= (10,3)),index=list('ABCDEFHIJK'),columns=['Python','En','Math'])\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['A']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['Python']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df[['Python','En']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df['Python':'Math']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df['A':'D']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc['Python']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df.loc['A']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[['A','H']]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc['A':'E']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[::2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.loc[::-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.iloc['A']" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.iloc[0]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df.iloc[[0,5]]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.iloc[0:5]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df.iloc[::-2]" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": { + "scrolled": true + }, + "outputs": [], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.iloc[::2,1:]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/.ipynb_checkpoints/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256-checkpoint.ipynb" new file mode 100644 index 000000000..17346ba23 --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256-checkpoint.ipynb" @@ -0,0 +1,5834 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
100455079129
101728257138111
10254115818151
10313805412940
10411391393498
..................
1951351041027640
1967572283931
197136261236281
1985048103606
199771391145147
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 4 55 0 79 129\n", + "101 72 82 57 138 111\n", + "102 54 115 81 81 51\n", + "103 13 80 54 129 40\n", + "104 113 91 39 34 98\n", + ".. ... ... ... ... ...\n", + "195 135 104 102 76 40\n", + "196 75 72 28 39 31\n", + "197 136 26 123 62 81\n", + "198 50 48 103 60 6\n", + "199 77 13 91 145 147\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = DataFrame(np.random.randint(0,150,size = (100,5)),index = np.arange(100,200),columns=['Python','En','Math','Physic','Chem'])\n", + "df\n", + "df[100]['Python']" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python False\n", + "En False\n", + "Math False\n", + "Physic False\n", + "Chem False\n", + "dtype: bool" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 判断DataFrame是否存在空数据\n", + "df.isnull().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python True\n", + "En True\n", + "Math True\n", + "Physic True\n", + "Chem True\n", + "dtype: bool" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.notnull().all()" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "500" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "100*5" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(50):\n", + " # 行索引\n", + " index = np.random.randint(100,200,size =1)[0]\n", + "\n", + " cols = df.columns\n", + "\n", + " # 列索引\n", + " col = np.random.choice(cols)\n", + "\n", + " df.loc[index,col] = None" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(20):\n", + " # 行索引\n", + " index = np.random.randint(100,200,size =1)[0]\n", + "\n", + " cols = df.columns\n", + "\n", + " # 列索引\n", + " col = np.random.choice(cols)\n", + "\n", + "# not a number 不是一个数\n", + " df.loc[index,col] = np.NAN" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
100122.010.05.028.057.0
101NaN129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.0NaN86.0116.017.0
10585.0NaN42.0121.066.0
106142.065.01.0124.083.0
107136.0141.0NaN86.0113.0
10815.037.0124.0110.0102.0
10963.030.0NaN69.058.0
110NaNNaN113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.0NaN97.0NaN
11470.0138.069.068.0134.0
115NaN136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.0NaN22.0
12028.093.0121.0NaN4.0
121136.0NaN25.097.019.0
122111.070.012.038.058.0
123NaN103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.0NaNNaN55.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.0NaNNaN
..................
170144.0124.077.092.082.0
17136.098.0NaN43.080.0
17251.0NaN68.034.074.0
173149.0NaN18.0141.0NaN
1748.0139.0146.0112.0NaN
175115.0NaN64.062.09.0
176NaN7.0140.045.0148.0
177NaN43.068.0109.018.0
17831.0100.0NaN49.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.0NaN140.0
1824.0100.0147.0116.0110.0
18355.087.093.0NaN34.0
184NaN109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.0NaN
18794.0NaN138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.0NaN29.0132.0
19068.0135.094.028.0125.0
19130.060.098.0NaN15.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.0NaN
19519.0102.0135.041.040.0
19658.0NaN70.082.064.0
197NaN97.0129.076.013.0
198131.015.0NaN44.0114.0
19979.0NaN95.0128.0NaN
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 122.0 10.0 5.0 28.0 57.0\n", + "101 NaN 129.0 16.0 114.0 26.0\n", + "102 97.0 121.0 122.0 29.0 65.0\n", + "103 141.0 73.0 120.0 147.0 1.0\n", + "104 126.0 NaN 86.0 116.0 17.0\n", + "105 85.0 NaN 42.0 121.0 66.0\n", + "106 142.0 65.0 1.0 124.0 83.0\n", + "107 136.0 141.0 NaN 86.0 113.0\n", + "108 15.0 37.0 124.0 110.0 102.0\n", + "109 63.0 30.0 NaN 69.0 58.0\n", + "110 NaN NaN 113.0 109.0 16.0\n", + "111 5.0 51.0 87.0 58.0 126.0\n", + "112 53.0 97.0 76.0 37.0 45.0\n", + "113 42.0 148.0 NaN 97.0 NaN\n", + "114 70.0 138.0 69.0 68.0 134.0\n", + "115 NaN 136.0 113.0 22.0 94.0\n", + "116 31.0 137.0 6.0 20.0 28.0\n", + "117 148.0 74.0 134.0 4.0 124.0\n", + "118 102.0 81.0 138.0 128.0 32.0\n", + "119 27.0 111.0 13.0 NaN 22.0\n", + "120 28.0 93.0 121.0 NaN 4.0\n", + "121 136.0 NaN 25.0 97.0 19.0\n", + "122 111.0 70.0 12.0 38.0 58.0\n", + "123 NaN 103.0 147.0 86.0 8.0\n", + "124 10.0 10.0 46.0 63.0 149.0\n", + "125 7.0 75.0 97.0 108.0 31.0\n", + "126 88.0 6.0 NaN NaN 55.0\n", + "127 33.0 74.0 106.0 50.0 46.0\n", + "128 74.0 28.0 26.0 100.0 76.0\n", + "129 76.0 18.0 101.0 NaN NaN\n", + ".. ... ... ... ... ...\n", + "170 144.0 124.0 77.0 92.0 82.0\n", + "171 36.0 98.0 NaN 43.0 80.0\n", + "172 51.0 NaN 68.0 34.0 74.0\n", + "173 149.0 NaN 18.0 141.0 NaN\n", + "174 8.0 139.0 146.0 112.0 NaN\n", + "175 115.0 NaN 64.0 62.0 9.0\n", + "176 NaN 7.0 140.0 45.0 148.0\n", + "177 NaN 43.0 68.0 109.0 18.0\n", + "178 31.0 100.0 NaN 49.0 123.0\n", + "179 29.0 46.0 69.0 57.0 90.0\n", + "180 146.0 86.0 18.0 22.0 46.0\n", + "181 71.0 50.0 40.0 NaN 140.0\n", + "182 4.0 100.0 147.0 116.0 110.0\n", + "183 55.0 87.0 93.0 NaN 34.0\n", + "184 NaN 109.0 124.0 87.0 82.0\n", + "185 10.0 118.0 139.0 50.0 51.0\n", + "186 32.0 12.0 71.0 36.0 NaN\n", + "187 94.0 NaN 138.0 13.0 149.0\n", + "188 65.0 101.0 123.0 128.0 86.0\n", + "189 43.0 94.0 NaN 29.0 132.0\n", + "190 68.0 135.0 94.0 28.0 125.0\n", + "191 30.0 60.0 98.0 NaN 15.0\n", + "192 89.0 16.0 10.0 135.0 4.0\n", + "193 104.0 139.0 97.0 29.0 17.0\n", + "194 5.0 29.0 41.0 99.0 NaN\n", + "195 19.0 102.0 135.0 41.0 40.0\n", + "196 58.0 NaN 70.0 82.0 64.0\n", + "197 NaN 97.0 129.0 76.0 13.0\n", + "198 131.0 15.0 NaN 44.0 114.0\n", + "199 79.0 NaN 95.0 128.0 NaN\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python True\n", + "En True\n", + "Math True\n", + "Physic True\n", + "Chem True\n", + "dtype: bool" + ] + }, + "execution_count": 18, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().any()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 14\n", + "En 14\n", + "Math 15\n", + "Physic 11\n", + "Chem 13\n", + "dtype: int64" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 24, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 14\n", + "En 14\n", + "Math 15\n", + "Physic 11\n", + "Chem 13\n", + "dtype: int64" + ] + }, + "execution_count": 24, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
100122.010.05.028.057.0
101100.0129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.0100.086.0116.017.0
10585.0100.042.0121.066.0
106142.065.01.0124.083.0
107136.0141.0100.086.0113.0
10815.037.0124.0110.0102.0
10963.030.0100.069.058.0
110100.0100.0113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.0100.097.0100.0
11470.0138.069.068.0134.0
115100.0136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.0100.022.0
12028.093.0121.0100.04.0
121136.0100.025.097.019.0
122111.070.012.038.058.0
123100.0103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.0100.0100.055.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.0100.0100.0
..................
170144.0124.077.092.082.0
17136.098.0100.043.080.0
17251.0100.068.034.074.0
173149.0100.018.0141.0100.0
1748.0139.0146.0112.0100.0
175115.0100.064.062.09.0
176100.07.0140.045.0148.0
177100.043.068.0109.018.0
17831.0100.0100.049.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.0100.0140.0
1824.0100.0147.0116.0110.0
18355.087.093.0100.034.0
184100.0109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.0100.0
18794.0100.0138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.0100.029.0132.0
19068.0135.094.028.0125.0
19130.060.098.0100.015.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.0100.0
19519.0102.0135.041.040.0
19658.0100.070.082.064.0
197100.097.0129.076.013.0
198131.015.0100.044.0114.0
19979.0100.095.0128.0100.0
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 122.0 10.0 5.0 28.0 57.0\n", + "101 100.0 129.0 16.0 114.0 26.0\n", + "102 97.0 121.0 122.0 29.0 65.0\n", + "103 141.0 73.0 120.0 147.0 1.0\n", + "104 126.0 100.0 86.0 116.0 17.0\n", + "105 85.0 100.0 42.0 121.0 66.0\n", + "106 142.0 65.0 1.0 124.0 83.0\n", + "107 136.0 141.0 100.0 86.0 113.0\n", + "108 15.0 37.0 124.0 110.0 102.0\n", + "109 63.0 30.0 100.0 69.0 58.0\n", + "110 100.0 100.0 113.0 109.0 16.0\n", + "111 5.0 51.0 87.0 58.0 126.0\n", + "112 53.0 97.0 76.0 37.0 45.0\n", + "113 42.0 148.0 100.0 97.0 100.0\n", + "114 70.0 138.0 69.0 68.0 134.0\n", + "115 100.0 136.0 113.0 22.0 94.0\n", + "116 31.0 137.0 6.0 20.0 28.0\n", + "117 148.0 74.0 134.0 4.0 124.0\n", + "118 102.0 81.0 138.0 128.0 32.0\n", + "119 27.0 111.0 13.0 100.0 22.0\n", + "120 28.0 93.0 121.0 100.0 4.0\n", + "121 136.0 100.0 25.0 97.0 19.0\n", + "122 111.0 70.0 12.0 38.0 58.0\n", + "123 100.0 103.0 147.0 86.0 8.0\n", + "124 10.0 10.0 46.0 63.0 149.0\n", + "125 7.0 75.0 97.0 108.0 31.0\n", + "126 88.0 6.0 100.0 100.0 55.0\n", + "127 33.0 74.0 106.0 50.0 46.0\n", + "128 74.0 28.0 26.0 100.0 76.0\n", + "129 76.0 18.0 101.0 100.0 100.0\n", + ".. ... ... ... ... ...\n", + "170 144.0 124.0 77.0 92.0 82.0\n", + "171 36.0 98.0 100.0 43.0 80.0\n", + "172 51.0 100.0 68.0 34.0 74.0\n", + "173 149.0 100.0 18.0 141.0 100.0\n", + "174 8.0 139.0 146.0 112.0 100.0\n", + "175 115.0 100.0 64.0 62.0 9.0\n", + "176 100.0 7.0 140.0 45.0 148.0\n", + "177 100.0 43.0 68.0 109.0 18.0\n", + "178 31.0 100.0 100.0 49.0 123.0\n", + "179 29.0 46.0 69.0 57.0 90.0\n", + "180 146.0 86.0 18.0 22.0 46.0\n", + "181 71.0 50.0 40.0 100.0 140.0\n", + "182 4.0 100.0 147.0 116.0 110.0\n", + "183 55.0 87.0 93.0 100.0 34.0\n", + "184 100.0 109.0 124.0 87.0 82.0\n", + "185 10.0 118.0 139.0 50.0 51.0\n", + "186 32.0 12.0 71.0 36.0 100.0\n", + "187 94.0 100.0 138.0 13.0 149.0\n", + "188 65.0 101.0 123.0 128.0 86.0\n", + "189 43.0 94.0 100.0 29.0 132.0\n", + "190 68.0 135.0 94.0 28.0 125.0\n", + "191 30.0 60.0 98.0 100.0 15.0\n", + "192 89.0 16.0 10.0 135.0 4.0\n", + "193 104.0 139.0 97.0 29.0 17.0\n", + "194 5.0 29.0 41.0 99.0 100.0\n", + "195 19.0 102.0 135.0 41.0 40.0\n", + "196 58.0 100.0 70.0 82.0 64.0\n", + "197 100.0 97.0 129.0 76.0 13.0\n", + "198 131.0 15.0 100.0 44.0 114.0\n", + "199 79.0 100.0 95.0 128.0 100.0\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 固定值填充\n", + "df2.fillna(value=100)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 71.662791\n", + "En 75.627907\n", + "Math 77.929412\n", + "Physic 73.471910\n", + "Chem 69.080460\n", + "dtype: float64" + ] + }, + "execution_count": 27, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.mean()" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
1001221052857
101711291611426
102971211222965
103141731201471
104126758611617
10585754212166
10614265112483
1071361417786113
1081537124110102
1096330776958
110717511310916
1115518758126
1125397763745
11342148779769
114701386968134
115711361132294
1163113762028
117148741344124
1181028113812832
11927111137322
1202893121734
12113675259719
12211170123858
12371103147868
12410104663149
1257759710831
126886777355
12733741065046
12874282610076
12976181017369
..................
170144124779282
1713698774380
1725175683474
173149751814169
174813914611269
1751157564629
17671714045148
17771436810918
178311007749123
1792946695790
18014686182246
18171504073140
1824100147116110
1835587937334
184711091248782
185101181395051
1863212713669
187947513813149
1886510112312886
18943947729132
190681359428125
1913060987315
1928916101354
193104139972917
194529419969
195191021354140
1965875708264
19771971297613
198131157744114
19979759512869
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 122 10 5 28 57\n", + "101 71 129 16 114 26\n", + "102 97 121 122 29 65\n", + "103 141 73 120 147 1\n", + "104 126 75 86 116 17\n", + "105 85 75 42 121 66\n", + "106 142 65 1 124 83\n", + "107 136 141 77 86 113\n", + "108 15 37 124 110 102\n", + "109 63 30 77 69 58\n", + "110 71 75 113 109 16\n", + "111 5 51 87 58 126\n", + "112 53 97 76 37 45\n", + "113 42 148 77 97 69\n", + "114 70 138 69 68 134\n", + "115 71 136 113 22 94\n", + "116 31 137 6 20 28\n", + "117 148 74 134 4 124\n", + "118 102 81 138 128 32\n", + "119 27 111 13 73 22\n", + "120 28 93 121 73 4\n", + "121 136 75 25 97 19\n", + "122 111 70 12 38 58\n", + "123 71 103 147 86 8\n", + "124 10 10 46 63 149\n", + "125 7 75 97 108 31\n", + "126 88 6 77 73 55\n", + "127 33 74 106 50 46\n", + "128 74 28 26 100 76\n", + "129 76 18 101 73 69\n", + ".. ... ... ... ... ...\n", + "170 144 124 77 92 82\n", + "171 36 98 77 43 80\n", + "172 51 75 68 34 74\n", + "173 149 75 18 141 69\n", + "174 8 139 146 112 69\n", + "175 115 75 64 62 9\n", + "176 71 7 140 45 148\n", + "177 71 43 68 109 18\n", + "178 31 100 77 49 123\n", + "179 29 46 69 57 90\n", + "180 146 86 18 22 46\n", + "181 71 50 40 73 140\n", + "182 4 100 147 116 110\n", + "183 55 87 93 73 34\n", + "184 71 109 124 87 82\n", + "185 10 118 139 50 51\n", + "186 32 12 71 36 69\n", + "187 94 75 138 13 149\n", + "188 65 101 123 128 86\n", + "189 43 94 77 29 132\n", + "190 68 135 94 28 125\n", + "191 30 60 98 73 15\n", + "192 89 16 10 135 4\n", + "193 104 139 97 29 17\n", + "194 5 29 41 99 69\n", + "195 19 102 135 41 40\n", + "196 58 75 70 82 64\n", + "197 71 97 129 76 13\n", + "198 131 15 77 44 114\n", + "199 79 75 95 128 69\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 均值\n", + "df3 = df2.fillna(value=df2.mean())\n", + "df3.astype(np.int16)" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 6, 18, 1, 17, 19, 5, 17, 16, 13, 3])" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nd = np.random.randint(0,20,size = 10)\n", + "nd" + ] + }, + { + "cell_type": "code", + "execution_count": 33, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 1, 3, 5, 6, 13, 16, 17, 17, 18, 19])" + ] + }, + "execution_count": 33, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "nd.sort()\n", + "nd" + ] + }, + { + "cell_type": "code", + "execution_count": 34, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.5" + ] + }, + "execution_count": 34, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "(13 + 16)/2" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "14.5" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "np.median(nd)" + ] + }, + { + "cell_type": "code", + "execution_count": 36, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
100122.010.05.028.057.0
10168.0129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.082.586.0116.017.0
10585.082.542.0121.066.0
106142.065.01.0124.083.0
107136.0141.086.086.0113.0
10815.037.0124.0110.0102.0
10963.030.086.069.058.0
11068.082.5113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.086.097.065.0
11470.0138.069.068.0134.0
11568.0136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.069.022.0
12028.093.0121.069.04.0
121136.082.525.097.019.0
122111.070.012.038.058.0
12368.0103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.086.069.055.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.069.065.0
..................
170144.0124.077.092.082.0
17136.098.086.043.080.0
17251.082.568.034.074.0
173149.082.518.0141.065.0
1748.0139.0146.0112.065.0
175115.082.564.062.09.0
17668.07.0140.045.0148.0
17768.043.068.0109.018.0
17831.0100.086.049.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.069.0140.0
1824.0100.0147.0116.0110.0
18355.087.093.069.034.0
18468.0109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.065.0
18794.082.5138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.086.029.0132.0
19068.0135.094.028.0125.0
19130.060.098.069.015.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.065.0
19519.0102.0135.041.040.0
19658.082.570.082.064.0
19768.097.0129.076.013.0
198131.015.086.044.0114.0
19979.082.595.0128.065.0
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 122.0 10.0 5.0 28.0 57.0\n", + "101 68.0 129.0 16.0 114.0 26.0\n", + "102 97.0 121.0 122.0 29.0 65.0\n", + "103 141.0 73.0 120.0 147.0 1.0\n", + "104 126.0 82.5 86.0 116.0 17.0\n", + "105 85.0 82.5 42.0 121.0 66.0\n", + "106 142.0 65.0 1.0 124.0 83.0\n", + "107 136.0 141.0 86.0 86.0 113.0\n", + "108 15.0 37.0 124.0 110.0 102.0\n", + "109 63.0 30.0 86.0 69.0 58.0\n", + "110 68.0 82.5 113.0 109.0 16.0\n", + "111 5.0 51.0 87.0 58.0 126.0\n", + "112 53.0 97.0 76.0 37.0 45.0\n", + "113 42.0 148.0 86.0 97.0 65.0\n", + "114 70.0 138.0 69.0 68.0 134.0\n", + "115 68.0 136.0 113.0 22.0 94.0\n", + "116 31.0 137.0 6.0 20.0 28.0\n", + "117 148.0 74.0 134.0 4.0 124.0\n", + "118 102.0 81.0 138.0 128.0 32.0\n", + "119 27.0 111.0 13.0 69.0 22.0\n", + "120 28.0 93.0 121.0 69.0 4.0\n", + "121 136.0 82.5 25.0 97.0 19.0\n", + "122 111.0 70.0 12.0 38.0 58.0\n", + "123 68.0 103.0 147.0 86.0 8.0\n", + "124 10.0 10.0 46.0 63.0 149.0\n", + "125 7.0 75.0 97.0 108.0 31.0\n", + "126 88.0 6.0 86.0 69.0 55.0\n", + "127 33.0 74.0 106.0 50.0 46.0\n", + "128 74.0 28.0 26.0 100.0 76.0\n", + "129 76.0 18.0 101.0 69.0 65.0\n", + ".. ... ... ... ... ...\n", + "170 144.0 124.0 77.0 92.0 82.0\n", + "171 36.0 98.0 86.0 43.0 80.0\n", + "172 51.0 82.5 68.0 34.0 74.0\n", + "173 149.0 82.5 18.0 141.0 65.0\n", + "174 8.0 139.0 146.0 112.0 65.0\n", + "175 115.0 82.5 64.0 62.0 9.0\n", + "176 68.0 7.0 140.0 45.0 148.0\n", + "177 68.0 43.0 68.0 109.0 18.0\n", + "178 31.0 100.0 86.0 49.0 123.0\n", + "179 29.0 46.0 69.0 57.0 90.0\n", + "180 146.0 86.0 18.0 22.0 46.0\n", + "181 71.0 50.0 40.0 69.0 140.0\n", + "182 4.0 100.0 147.0 116.0 110.0\n", + "183 55.0 87.0 93.0 69.0 34.0\n", + "184 68.0 109.0 124.0 87.0 82.0\n", + "185 10.0 118.0 139.0 50.0 51.0\n", + "186 32.0 12.0 71.0 36.0 65.0\n", + "187 94.0 82.5 138.0 13.0 149.0\n", + "188 65.0 101.0 123.0 128.0 86.0\n", + "189 43.0 94.0 86.0 29.0 132.0\n", + "190 68.0 135.0 94.0 28.0 125.0\n", + "191 30.0 60.0 98.0 69.0 15.0\n", + "192 89.0 16.0 10.0 135.0 4.0\n", + "193 104.0 139.0 97.0 29.0 17.0\n", + "194 5.0 29.0 41.0 99.0 65.0\n", + "195 19.0 102.0 135.0 41.0 40.0\n", + "196 58.0 82.5 70.0 82.0 64.0\n", + "197 68.0 97.0 129.0 76.0 13.0\n", + "198 131.0 15.0 86.0 44.0 114.0\n", + "199 79.0 82.5 95.0 128.0 65.0\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 36, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 中位数填充\n", + "df2.median()\n", + "df4 = df2.fillna(df2.median())\n", + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": 37, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
100122.010.05.028.057.0
101NaN129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.0NaN86.0116.017.0
10585.0NaN42.0121.066.0
106142.065.01.0124.083.0
107136.0141.0NaN86.0113.0
10815.037.0124.0110.0102.0
10963.030.0NaN69.058.0
110NaNNaN113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.0NaN97.0NaN
11470.0138.069.068.0134.0
115NaN136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.0NaN22.0
12028.093.0121.0NaN4.0
121136.0NaN25.097.019.0
122111.070.012.038.058.0
123NaN103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.0NaNNaN55.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.0NaNNaN
..................
170144.0124.077.092.082.0
17136.098.0NaN43.080.0
17251.0NaN68.034.074.0
173149.0NaN18.0141.0NaN
1748.0139.0146.0112.0NaN
175115.0NaN64.062.09.0
176NaN7.0140.045.0148.0
177NaN43.068.0109.018.0
17831.0100.0NaN49.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.0NaN140.0
1824.0100.0147.0116.0110.0
18355.087.093.0NaN34.0
184NaN109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.0NaN
18794.0NaN138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.0NaN29.0132.0
19068.0135.094.028.0125.0
19130.060.098.0NaN15.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.0NaN
19519.0102.0135.041.040.0
19658.0NaN70.082.064.0
197NaN97.0129.076.013.0
198131.015.0NaN44.0114.0
19979.0NaN95.0128.0NaN
\n", + "

100 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 122.0 10.0 5.0 28.0 57.0\n", + "101 NaN 129.0 16.0 114.0 26.0\n", + "102 97.0 121.0 122.0 29.0 65.0\n", + "103 141.0 73.0 120.0 147.0 1.0\n", + "104 126.0 NaN 86.0 116.0 17.0\n", + "105 85.0 NaN 42.0 121.0 66.0\n", + "106 142.0 65.0 1.0 124.0 83.0\n", + "107 136.0 141.0 NaN 86.0 113.0\n", + "108 15.0 37.0 124.0 110.0 102.0\n", + "109 63.0 30.0 NaN 69.0 58.0\n", + "110 NaN NaN 113.0 109.0 16.0\n", + "111 5.0 51.0 87.0 58.0 126.0\n", + "112 53.0 97.0 76.0 37.0 45.0\n", + "113 42.0 148.0 NaN 97.0 NaN\n", + "114 70.0 138.0 69.0 68.0 134.0\n", + "115 NaN 136.0 113.0 22.0 94.0\n", + "116 31.0 137.0 6.0 20.0 28.0\n", + "117 148.0 74.0 134.0 4.0 124.0\n", + "118 102.0 81.0 138.0 128.0 32.0\n", + "119 27.0 111.0 13.0 NaN 22.0\n", + "120 28.0 93.0 121.0 NaN 4.0\n", + "121 136.0 NaN 25.0 97.0 19.0\n", + "122 111.0 70.0 12.0 38.0 58.0\n", + "123 NaN 103.0 147.0 86.0 8.0\n", + "124 10.0 10.0 46.0 63.0 149.0\n", + "125 7.0 75.0 97.0 108.0 31.0\n", + "126 88.0 6.0 NaN NaN 55.0\n", + "127 33.0 74.0 106.0 50.0 46.0\n", + "128 74.0 28.0 26.0 100.0 76.0\n", + "129 76.0 18.0 101.0 NaN NaN\n", + ".. ... ... ... ... ...\n", + "170 144.0 124.0 77.0 92.0 82.0\n", + "171 36.0 98.0 NaN 43.0 80.0\n", + "172 51.0 NaN 68.0 34.0 74.0\n", + "173 149.0 NaN 18.0 141.0 NaN\n", + "174 8.0 139.0 146.0 112.0 NaN\n", + "175 115.0 NaN 64.0 62.0 9.0\n", + "176 NaN 7.0 140.0 45.0 148.0\n", + "177 NaN 43.0 68.0 109.0 18.0\n", + "178 31.0 100.0 NaN 49.0 123.0\n", + "179 29.0 46.0 69.0 57.0 90.0\n", + "180 146.0 86.0 18.0 22.0 46.0\n", + "181 71.0 50.0 40.0 NaN 140.0\n", + "182 4.0 100.0 147.0 116.0 110.0\n", + "183 55.0 87.0 93.0 NaN 34.0\n", + "184 NaN 109.0 124.0 87.0 82.0\n", + "185 10.0 118.0 139.0 50.0 51.0\n", + "186 32.0 12.0 71.0 36.0 NaN\n", + "187 94.0 NaN 138.0 13.0 149.0\n", + "188 65.0 101.0 123.0 128.0 86.0\n", + "189 43.0 94.0 NaN 29.0 132.0\n", + "190 68.0 135.0 94.0 28.0 125.0\n", + "191 30.0 60.0 98.0 NaN 15.0\n", + "192 89.0 16.0 10.0 135.0 4.0\n", + "193 104.0 139.0 97.0 29.0 17.0\n", + "194 5.0 29.0 41.0 99.0 NaN\n", + "195 19.0 102.0 135.0 41.0 40.0\n", + "196 58.0 NaN 70.0 82.0 64.0\n", + "197 NaN 97.0 129.0 76.0 13.0\n", + "198 131.0 15.0 NaN 44.0 114.0\n", + "199 79.0 NaN 95.0 128.0 NaN\n", + "\n", + "[100 rows x 5 columns]" + ] + }, + "execution_count": 37, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 众数填充，数量最多的那个数\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 42, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
100828999101125
101431109325
10256103566190
1034710014713899
1043846827544
10518111223126
106562610614139
10731377567144
10835471026063
109861265788149
11019140303533
11176151133
11231549111969
1136437502321
11472571381521
115551201043225
11696248922146
11763086489
11828461258274
119853970132111
12010990447439
121214810311465
12211029998057
123109888113571
12470103134121121
12551921172743
1266929759105
12765905214822
12841291711913
1292410010728139
..................
207012777241631
2071936192822
20721166154861
207347214011234
2074261081233233
207546130135124113
207633181363820
20771071112954119
207884551293787
20799550451984
2080124746514053
20812635149145127
20821921101389
20838410131714
208428741056889
20852393849788
2086861332612513
208721124401155
20882015353137
208996123123564
20902243927860
20911631176058
20926518131334
209369491094058
2094128461082111
20952659854149
209611147909266
209759773140104
2098102675119
209997197714348
\n", + "

2000 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 82 89 99 101 125\n", + "101 4 31 109 32 5\n", + "102 56 103 56 61 90\n", + "103 47 100 147 138 99\n", + "104 38 46 82 75 44\n", + "105 18 11 122 3 126\n", + "106 56 26 106 14 139\n", + "107 3 137 75 67 144\n", + "108 35 47 102 60 63\n", + "109 86 126 57 88 149\n", + "110 19 140 30 35 33\n", + "111 76 1 5 11 33\n", + "112 31 54 91 119 69\n", + "113 64 37 50 23 21\n", + "114 72 57 138 15 21\n", + "115 55 120 104 32 25\n", + "116 96 24 89 22 146\n", + "117 63 0 8 64 89\n", + "118 28 46 125 82 74\n", + "119 85 39 70 132 111\n", + "120 109 90 44 74 39\n", + "121 2 148 103 114 65\n", + "122 110 29 99 80 57\n", + "123 109 88 81 135 71\n", + "124 70 103 134 121 121\n", + "125 51 92 117 27 43\n", + "126 6 92 97 59 105\n", + "127 65 90 52 148 22\n", + "128 4 129 17 119 13\n", + "129 24 100 107 28 139\n", + "... ... ... ... ... ...\n", + "2070 127 77 24 16 31\n", + "2071 93 61 9 28 22\n", + "2072 116 61 54 8 61\n", + "2073 4 72 140 112 34\n", + "2074 26 108 123 32 33\n", + "2075 46 130 135 124 113\n", + "2076 33 18 136 38 20\n", + "2077 107 11 129 54 119\n", + "2078 84 55 129 37 87\n", + "2079 95 50 45 19 84\n", + "2080 124 74 65 140 53\n", + "2081 26 35 149 145 127\n", + "2082 19 21 101 3 89\n", + "2083 84 10 131 71 4\n", + "2084 28 74 105 68 89\n", + "2085 23 93 84 97 88\n", + "2086 86 133 26 125 13\n", + "2087 21 124 40 115 5\n", + "2088 20 15 35 31 37\n", + "2089 96 123 123 5 64\n", + "2090 22 43 92 78 60\n", + "2091 16 31 17 60 58\n", + "2092 65 18 13 13 34\n", + "2093 69 49 109 40 58\n", + "2094 128 46 10 82 111\n", + "2095 26 59 8 54 149\n", + "2096 111 47 90 92 66\n", + "2097 5 97 73 140 104\n", + "2098 102 6 7 5 119\n", + "2099 97 19 77 143 48\n", + "\n", + "[2000 rows x 5 columns]" + ] + }, + "execution_count": 42, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = DataFrame(np.random.randint(0,150,size = (2000,5)),index = np.arange(100,2100),columns=['Python','En','Math','Physic','Chem'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 43, + "metadata": {}, + "outputs": [], + "source": [ + "for i in range(1000):\n", + " # 行索引\n", + " index = np.random.randint(100,2100,size =1)[0]\n", + "\n", + " cols = df.columns\n", + "\n", + " # 列索引\n", + " col = np.random.choice(cols)\n", + "\n", + " df.loc[index,col] = None" + ] + }, + { + "cell_type": "code", + "execution_count": 44, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 190\n", + "En 200\n", + "Math 194\n", + "Physic 189\n", + "Chem 181\n", + "dtype: int64" + ] + }, + "execution_count": 44, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 45, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.0NaN90.0
10347.0100.0147.0138.099.0
10438.046.0NaN75.044.0
\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 82.0 89.0 99.0 101.0 125.0\n", + "101 4.0 31.0 109.0 32.0 5.0\n", + "102 56.0 103.0 56.0 NaN 90.0\n", + "103 47.0 100.0 147.0 138.0 99.0\n", + "104 38.0 46.0 NaN 75.0 44.0" + ] + }, + "execution_count": 45, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.head()" + ] + }, + { + "cell_type": "code", + "execution_count": 46, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
209526.059.08.054.0149.0
2096NaN47.090.092.066.0
20975.097.073.0140.0104.0
2098102.06.07.05.0119.0
209997.019.077.0NaN48.0
\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "2095 26.0 59.0 8.0 54.0 149.0\n", + "2096 NaN 47.0 90.0 92.0 66.0\n", + "2097 5.0 97.0 73.0 140.0 104.0\n", + "2098 102.0 6.0 7.0 5.0 119.0\n", + "2099 97.0 19.0 77.0 NaN 48.0" + ] + }, + "execution_count": 46, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.tail()" + ] + }, + { + "cell_type": "code", + "execution_count": 48, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([ 82., 4., 56., 47., 38., 18., 3., 35., 86., 19., 76.,\n", + " 31., 64., 72., 55., 96., 63., 28., 85., 109., 2., 110.,\n", + " 70., 51., 6., 65., 24., 48., 44., 11., 114., 129., 87.,\n", + " 108., 125., nan, 140., 132., 91., 34., 54., 30., 12., 98.,\n", + " 142., 79., 13., 77., 40., 139., 39., 81., 112., 36., 22.,\n", + " 5., 120., 17., 127., 119., 59., 146., 89., 103., 8., 97.,\n", + " 130., 73., 83., 122., 95., 100., 41., 21., 136., 80., 101.,\n", + " 50., 27., 71., 16., 141., 126., 102., 145., 15., 52., 94.,\n", + " 10., 33., 137., 9., 128., 88., 26., 84., 93., 1., 7.,\n", + " 131., 107., 148., 0., 105., 66., 32., 115., 118., 58., 53.,\n", + " 29., 42., 57., 62., 25., 60., 69., 133., 68., 20., 106.,\n", + " 147., 78., 90., 124., 149., 92., 75., 117., 143., 99., 37.,\n", + " 123., 45., 61., 121., 135., 138., 116., 14., 104., 74., 46.,\n", + " 111., 23., 43., 49., 144., 113., 67., 134.])" + ] + }, + "execution_count": 48, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 去重之后的数据\n", + "df['Python'].unique()" + ] + }, + { + "cell_type": "code", + "execution_count": 49, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "143.0 20\n", + "136.0 20\n", + "102.0 19\n", + "105.0 19\n", + "26.0 19\n", + "69.0 19\n", + "31.0 18\n", + "148.0 18\n", + "75.0 18\n", + "139.0 18\n", + "1.0 18\n", + "35.0 17\n", + "140.0 17\n", + "110.0 17\n", + "125.0 17\n", + "146.0 17\n", + "141.0 17\n", + "64.0 16\n", + "30.0 16\n", + "79.0 16\n", + "73.0 16\n", + "40.0 16\n", + "10.0 15\n", + "6.0 15\n", + "65.0 15\n", + "81.0 15\n", + "28.0 15\n", + "48.0 15\n", + "92.0 15\n", + "103.0 15\n", + " ..\n", + "104.0 9\n", + "12.0 9\n", + "116.0 9\n", + "13.0 9\n", + "59.0 9\n", + "93.0 9\n", + "124.0 9\n", + "85.0 8\n", + "135.0 8\n", + "131.0 8\n", + "68.0 8\n", + "66.0 8\n", + "62.0 8\n", + "120.0 8\n", + "17.0 8\n", + "25.0 8\n", + "145.0 7\n", + "58.0 7\n", + "134.0 7\n", + "113.0 7\n", + "123.0 7\n", + "39.0 7\n", + "34.0 7\n", + "43.0 7\n", + "74.0 6\n", + "144.0 6\n", + "132.0 6\n", + "142.0 5\n", + "67.0 5\n", + "49.0 5\n", + "Name: Python, Length: 150, dtype: int64" + ] + }, + "execution_count": 49, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df['Python'].value_counts()" + ] + }, + { + "cell_type": "code", + "execution_count": 53, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "8.0 21\n", + "96.0 19\n", + "118.0 19\n", + "24.0 19\n", + "43.0 19\n", + "27.0 19\n", + "19.0 19\n", + "41.0 18\n", + "0.0 18\n", + "3.0 18\n", + "52.0 18\n", + "4.0 17\n", + "137.0 17\n", + "1.0 17\n", + "101.0 17\n", + "51.0 17\n", + "39.0 17\n", + "100.0 17\n", + "127.0 17\n", + "115.0 16\n", + "33.0 16\n", + "112.0 16\n", + "92.0 16\n", + "126.0 16\n", + "133.0 15\n", + "32.0 15\n", + "89.0 15\n", + "95.0 15\n", + "36.0 15\n", + "93.0 15\n", + " ..\n", + "12.0 9\n", + "28.0 9\n", + "106.0 9\n", + "45.0 9\n", + "80.0 9\n", + "84.0 9\n", + "58.0 9\n", + "79.0 9\n", + "71.0 9\n", + "83.0 9\n", + "142.0 9\n", + "7.0 9\n", + "6.0 8\n", + "61.0 8\n", + "149.0 8\n", + "34.0 8\n", + "20.0 8\n", + "38.0 8\n", + "130.0 8\n", + "104.0 7\n", + "120.0 7\n", + "56.0 7\n", + "146.0 7\n", + "98.0 7\n", + "134.0 6\n", + "123.0 6\n", + "35.0 6\n", + "87.0 5\n", + "42.0 5\n", + "119.0 4\n", + "Name: En, Length: 150, dtype: int64" + ] + }, + "execution_count": 53, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "en = df['En'].value_counts()\n", + "en" + ] + }, + { + "cell_type": "code", + "execution_count": 54, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "8.0" + ] + }, + "execution_count": 54, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "en.index[0]" + ] + }, + { + "cell_type": "code", + "execution_count": 52, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Python 75.0\n", + "En 74.0\n", + "Math 77.5\n", + "Physic 73.0\n", + "Chem 72.0\n", + "dtype: float64 \n" + ] + } + ], + "source": [ + "s = df.median()\n", + "print(s,type(s))" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "metadata": {}, + "outputs": [], + "source": [ + "zhongshu = []\n", + "for col in df.columns:\n", + " zhongshu.append(df[col].value_counts().index[0])" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 143.0\n", + "En 8.0\n", + "Math 80.0\n", + "Physic 31.0\n", + "Chem 125.0\n", + "dtype: float64" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = Series(zhongshu,index = df.columns)\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 60, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.031.090.0
10347.0100.0147.0138.099.0
10438.046.080.075.044.0
10518.011.0122.03.0126.0
10656.026.0106.014.0139.0
1073.0137.075.067.0144.0
10835.047.0102.060.063.0
10986.0126.080.088.0149.0
11019.0140.080.035.033.0
11176.08.05.011.033.0
11231.054.091.0119.069.0
11364.037.050.023.021.0
11472.057.0138.015.021.0
11555.0120.0104.032.025.0
11696.024.089.031.0146.0
11763.08.08.064.089.0
11828.08.0125.082.074.0
11985.039.070.0132.0111.0
120109.090.080.074.039.0
1212.08.0103.0114.065.0
122110.029.099.080.057.0
123109.088.081.0135.071.0
12470.0103.0134.0121.0121.0
12551.092.0117.031.043.0
1266.092.097.059.0105.0
12765.090.052.0148.022.0
1284.0129.017.0119.013.0
12924.0100.0107.028.0139.0
..................
2070127.077.024.016.0125.0
207193.061.09.028.022.0
2072116.061.054.08.061.0
20734.072.0140.031.034.0
2074143.0108.0123.032.033.0
207546.08.0135.0124.0113.0
2076143.018.0136.038.0125.0
2077143.011.0129.054.0119.0
207884.055.0129.037.087.0
207995.050.045.019.084.0
2080124.074.065.031.053.0
208126.035.0149.0145.0127.0
208219.021.0101.03.089.0
208384.08.0131.071.04.0
208428.074.0105.068.089.0
208523.093.084.097.088.0
208686.0133.026.0125.013.0
208721.0124.040.031.05.0
208820.015.035.031.037.0
208996.0123.0123.05.064.0
209022.043.092.078.060.0
209116.031.017.060.058.0
209265.018.013.013.034.0
209369.049.0109.040.058.0
2094128.046.010.082.0111.0
209526.059.08.054.0149.0
2096143.047.090.092.066.0
20975.097.073.0140.0104.0
2098102.06.07.05.0119.0
209997.019.077.031.048.0
\n", + "

2000 rows × 5 columns

\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 82.0 89.0 99.0 101.0 125.0\n", + "101 4.0 31.0 109.0 32.0 5.0\n", + "102 56.0 103.0 56.0 31.0 90.0\n", + "103 47.0 100.0 147.0 138.0 99.0\n", + "104 38.0 46.0 80.0 75.0 44.0\n", + "105 18.0 11.0 122.0 3.0 126.0\n", + "106 56.0 26.0 106.0 14.0 139.0\n", + "107 3.0 137.0 75.0 67.0 144.0\n", + "108 35.0 47.0 102.0 60.0 63.0\n", + "109 86.0 126.0 80.0 88.0 149.0\n", + "110 19.0 140.0 80.0 35.0 33.0\n", + "111 76.0 8.0 5.0 11.0 33.0\n", + "112 31.0 54.0 91.0 119.0 69.0\n", + "113 64.0 37.0 50.0 23.0 21.0\n", + "114 72.0 57.0 138.0 15.0 21.0\n", + "115 55.0 120.0 104.0 32.0 25.0\n", + "116 96.0 24.0 89.0 31.0 146.0\n", + "117 63.0 8.0 8.0 64.0 89.0\n", + "118 28.0 8.0 125.0 82.0 74.0\n", + "119 85.0 39.0 70.0 132.0 111.0\n", + "120 109.0 90.0 80.0 74.0 39.0\n", + "121 2.0 8.0 103.0 114.0 65.0\n", + "122 110.0 29.0 99.0 80.0 57.0\n", + "123 109.0 88.0 81.0 135.0 71.0\n", + "124 70.0 103.0 134.0 121.0 121.0\n", + "125 51.0 92.0 117.0 31.0 43.0\n", + "126 6.0 92.0 97.0 59.0 105.0\n", + "127 65.0 90.0 52.0 148.0 22.0\n", + "128 4.0 129.0 17.0 119.0 13.0\n", + "129 24.0 100.0 107.0 28.0 139.0\n", + "... ... ... ... ... ...\n", + "2070 127.0 77.0 24.0 16.0 125.0\n", + "2071 93.0 61.0 9.0 28.0 22.0\n", + "2072 116.0 61.0 54.0 8.0 61.0\n", + "2073 4.0 72.0 140.0 31.0 34.0\n", + "2074 143.0 108.0 123.0 32.0 33.0\n", + "2075 46.0 8.0 135.0 124.0 113.0\n", + "2076 143.0 18.0 136.0 38.0 125.0\n", + "2077 143.0 11.0 129.0 54.0 119.0\n", + "2078 84.0 55.0 129.0 37.0 87.0\n", + "2079 95.0 50.0 45.0 19.0 84.0\n", + "2080 124.0 74.0 65.0 31.0 53.0\n", + "2081 26.0 35.0 149.0 145.0 127.0\n", + "2082 19.0 21.0 101.0 3.0 89.0\n", + "2083 84.0 8.0 131.0 71.0 4.0\n", + "2084 28.0 74.0 105.0 68.0 89.0\n", + "2085 23.0 93.0 84.0 97.0 88.0\n", + "2086 86.0 133.0 26.0 125.0 13.0\n", + "2087 21.0 124.0 40.0 31.0 5.0\n", + "2088 20.0 15.0 35.0 31.0 37.0\n", + "2089 96.0 123.0 123.0 5.0 64.0\n", + "2090 22.0 43.0 92.0 78.0 60.0\n", + "2091 16.0 31.0 17.0 60.0 58.0\n", + "2092 65.0 18.0 13.0 13.0 34.0\n", + "2093 69.0 49.0 109.0 40.0 58.0\n", + "2094 128.0 46.0 10.0 82.0 111.0\n", + "2095 26.0 59.0 8.0 54.0 149.0\n", + "2096 143.0 47.0 90.0 92.0 66.0\n", + "2097 5.0 97.0 73.0 140.0 104.0\n", + "2098 102.0 6.0 7.0 5.0 119.0\n", + "2099 97.0 19.0 77.0 31.0 48.0\n", + "\n", + "[2000 rows x 5 columns]" + ] + }, + "execution_count": 60, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = df.fillna(s)\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 61, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 0\n", + "En 0\n", + "Math 0\n", + "Physic 0\n", + "Chem 0\n", + "dtype: int64" + ] + }, + "execution_count": 61, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 190\n", + "En 200\n", + "Math 194\n", + "Physic 189\n", + "Chem 181\n", + "dtype: int64" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "cell_type": "code", + "execution_count": 65, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.0NaN90.0
10347.0100.0147.0138.099.0
10438.046.0NaN75.044.0
10518.011.0122.03.0126.0
10656.026.0106.014.0139.0
1073.0137.075.067.0144.0
10835.047.0102.060.063.0
10986.0126.0NaN88.0149.0
11019.0140.0NaN35.033.0
11176.0NaN5.011.033.0
11231.054.091.0119.069.0
11364.037.050.023.021.0
11472.057.0138.015.021.0
11555.0120.0104.032.025.0
11696.024.089.0NaN146.0
11763.0NaN8.064.089.0
11828.0NaN125.082.074.0
11985.039.070.0132.0111.0
\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 82.0 89.0 99.0 101.0 125.0\n", + "101 4.0 31.0 109.0 32.0 5.0\n", + "102 56.0 103.0 56.0 NaN 90.0\n", + "103 47.0 100.0 147.0 138.0 99.0\n", + "104 38.0 46.0 NaN 75.0 44.0\n", + "105 18.0 11.0 122.0 3.0 126.0\n", + "106 56.0 26.0 106.0 14.0 139.0\n", + "107 3.0 137.0 75.0 67.0 144.0\n", + "108 35.0 47.0 102.0 60.0 63.0\n", + "109 86.0 126.0 NaN 88.0 149.0\n", + "110 19.0 140.0 NaN 35.0 33.0\n", + "111 76.0 NaN 5.0 11.0 33.0\n", + "112 31.0 54.0 91.0 119.0 69.0\n", + "113 64.0 37.0 50.0 23.0 21.0\n", + "114 72.0 57.0 138.0 15.0 21.0\n", + "115 55.0 120.0 104.0 32.0 25.0\n", + "116 96.0 24.0 89.0 NaN 146.0\n", + "117 63.0 NaN 8.0 64.0 89.0\n", + "118 28.0 NaN 125.0 82.0 74.0\n", + "119 85.0 39.0 70.0 132.0 111.0" + ] + }, + "execution_count": 65, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = df.iloc[:20]\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 70, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.090.090.0
10347.0100.0147.0138.099.0
10438.046.075.075.044.0
10518.011.0122.03.0126.0
10656.026.0106.014.0139.0
1073.0137.075.067.0144.0
10835.047.0102.060.063.0
10986.0126.088.088.0149.0
11019.0140.035.035.033.0
11176.05.05.011.033.0
11231.054.091.0119.069.0
11364.037.050.023.021.0
11472.057.0138.015.021.0
11555.0120.0104.032.025.0
11696.024.089.0146.0146.0
11763.08.08.064.089.0
11828.0125.0125.082.074.0
11985.039.070.0132.0111.0
\n", + "
" + ], + "text/plain": [ + " Python En Math Physic Chem\n", + "100 82.0 89.0 99.0 101.0 125.0\n", + "101 4.0 31.0 109.0 32.0 5.0\n", + "102 56.0 103.0 56.0 90.0 90.0\n", + "103 47.0 100.0 147.0 138.0 99.0\n", + "104 38.0 46.0 75.0 75.0 44.0\n", + "105 18.0 11.0 122.0 3.0 126.0\n", + "106 56.0 26.0 106.0 14.0 139.0\n", + "107 3.0 137.0 75.0 67.0 144.0\n", + "108 35.0 47.0 102.0 60.0 63.0\n", + "109 86.0 126.0 88.0 88.0 149.0\n", + "110 19.0 140.0 35.0 35.0 33.0\n", + "111 76.0 5.0 5.0 11.0 33.0\n", + "112 31.0 54.0 91.0 119.0 69.0\n", + "113 64.0 37.0 50.0 23.0 21.0\n", + "114 72.0 57.0 138.0 15.0 21.0\n", + "115 55.0 120.0 104.0 32.0 25.0\n", + "116 96.0 24.0 89.0 146.0 146.0\n", + "117 63.0 8.0 8.0 64.0 89.0\n", + "118 28.0 125.0 125.0 82.0 74.0\n", + "119 85.0 39.0 70.0 132.0 111.0" + ] + }, + "execution_count": 70, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "'''method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n", + " Method to use for filling holes in reindexed Series\n", + " pad / ffill: propagate last valid observation forward to next valid\n", + " backfill / bfill: use NEXT valid observation to fill gap'''\n", + "df3.fillna(method='bfill',axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 71, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "(2000, 5)" + ] + }, + "execution_count": 71, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "#数据量足够大，空数据比较少，直接删除\n", + "df.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "df.dro" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/.ipynb_checkpoints/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225-checkpoint.ipynb" new file mode 100644 index 000000000..d8e0d1ee9 --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225-checkpoint.ipynb" @@ -0,0 +1,494 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "# 数据分析BI-------->人工智能AI\n", + "# 数据分析和数据挖掘一个意思，\n", + "# 工具和软件：Excel 免费版\n", + "# SPSS（一人一年10000）、SAS（一人一年5000）、Matlab 收费\n", + "# R、Python（全方位语言，流行） 免费\n", + "# Python + numpy + scipy + pandas + matplotlib + seaborn + pyEcharts + sklearn + kereas（Tensorflow）+…… \n", + "# 代码，自动化（数据输入----输出结果）\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "a 63\n", + "b 107\n", + "c 16\n", + "d 35\n", + "e 140\n", + "f 83\n", + "dtype: int32" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 多层索引，行列\n", + "# 单层索引\n", + "s = Series(np.random.randint(0,150,size = 6),index=list('abcdef'))\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "张三 期中 114\n", + " 期末 131\n", + "李四 期中 3\n", + " 期末 63\n", + "王五 期中 107\n", + " 期末 34\n", + "dtype: int32" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 多层索引，两层，三层以上（规则一样）\n", + "s2 = Series(np.random.randint(0,150,size = 6),index = pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]))\n", + "s2" + ] + }, + { + "cell_type": "code", + "execution_count": 1, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "ename": "NameError", + "evalue": "name 'DataFrame' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'En'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Math'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_product\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'李四'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'王五'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'期中'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'期末'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined" + ] + } + ], + "source": [ + "df = DataFrame(np.random.randint(0,150,size = (6,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末']]) )\n", + "\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
张三期中A153117
B8256123
期末A14278
B695017
李四期中A9187143
B12011839
期末A567655
B11105121
王五期中A147781
B128126146
期末A4945114
B1212677
\n", + "
" + ], + "text/plain": [ + " Python En Math\n", + "张三 期中 A 15 31 17\n", + " B 82 56 123\n", + " 期末 A 14 2 78\n", + " B 69 50 17\n", + "李四 期中 A 91 87 143\n", + " B 120 118 39\n", + " 期末 A 56 76 55\n", + " B 11 105 121\n", + "王五 期中 A 147 78 1\n", + " B 128 126 146\n", + " 期末 A 49 45 114\n", + " B 121 26 77" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 三层索引\n", + "df3 = DataFrame(np.random.randint(0,150,size = (12,3)),columns=['Python','En','Math'],index =pd.MultiIndex.from_product([['张三','李四','王五'],['期中','期末'],['A','B']]) )\n", + "\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "73" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 先获取列后获取行\n", + "df['Python']['张三']['期中']" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df.copy()" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
张三期中73525
期末373656
李四期中14981142
期末711380
王五期中1194103
期末2512183
\n", + "
" + ], + "text/plain": [ + " Python En Math\n", + "张三 期中 73 5 25\n", + " 期末 37 36 56\n", + "李四 期中 149 81 142\n", + " 期末 71 138 0\n", + "王五 期中 11 94 103\n", + " 期末 25 121 83" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.sort_index()" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "73" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 先获取行，后获取列\n", + "df.loc['张三'].loc['期中']['Python']" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
张三期中73525
期末373656
\n", + "
" + ], + "text/plain": [ + " Python En Math\n", + "张三 期中 73 5 25\n", + " 期末 37 36 56" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.iloc[[0,1]]" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/.ipynb_checkpoints/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227-checkpoint.ipynb" new file mode 100644 index 000000000..4bcaad27c --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227-checkpoint.ipynb" @@ -0,0 +1,1000 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
期中期末期中期末期中期末
A1311011731517
B6234531012457
C247636117123105
D11246794246122
E661131044510108
F11110844113221
\n", + "
" + ], + "text/plain": [ + " Python En Math \n", + " 期中 期末 期中 期末 期中 期末\n", + "A 131 101 1 73 15 17\n", + "B 62 34 53 101 24 57\n", + "C 24 76 36 117 123 105\n", + "D 112 46 79 42 46 122\n", + "E 66 113 104 45 10 108\n", + "F 111 108 4 41 132 21" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 多层列索引\n", + "df = DataFrame(np.random.randint(0,150,size = (6,6)),index = list('ABCDEF'),\n", + " columns=pd.MultiIndex.from_product([['Python','En','Math'],['期中','期末']]))\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 期中 84.3\n", + " 期末 79.7\n", + "En 期中 46.2\n", + " 期末 69.8\n", + "Math 期中 58.3\n", + " 期末 71.7\n", + "dtype: float64" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# round保留2位小数\n", + "df.mean().round(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
期中期末期中期末期中期末
A1311011731517
B6234531012457
C247636117123105
D11246794246122
E661131044510108
F11110844113221
\n", + "
" + ], + "text/plain": [ + " Python En Math \n", + " 期中 期末 期中 期末 期中 期末\n", + "A 131 101 1 73 15 17\n", + "B 62 34 53 101 24 57\n", + "C 24 76 36 117 123 105\n", + "D 112 46 79 42 46 122\n", + "E 66 113 104 45 10 108\n", + "F 111 108 4 41 132 21" + ] + }, + "execution_count": 10, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
A116.037.016.0
B48.077.040.5
C50.076.5114.0
D79.060.584.0
E89.574.559.0
F109.522.576.5
\n", + "
" + ], + "text/plain": [ + " Python En Math\n", + "A 116.0 37.0 16.0\n", + "B 48.0 77.0 40.5\n", + "C 50.0 76.5 114.0\n", + "D 79.0 60.5 84.0\n", + "E 89.5 74.5 59.0\n", + "F 109.5 22.5 76.5" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# axis = 0代表行\n", + "# axis = 1代表列\n", + "df.mean(axis = 1,level = 0)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
期中期末
A49.063.7
B46.364.0
C61.099.3
D79.070.0
E60.088.7
F82.356.7
\n", + "
" + ], + "text/plain": [ + " 期中 期末\n", + "A 49.0 63.7\n", + "B 46.3 64.0\n", + "C 61.0 99.3\n", + "D 79.0 70.0\n", + "E 60.0 88.7\n", + "F 82.3 56.7" + ] + }, + "execution_count": 15, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.mean(axis = 1,level = 1).round(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonEnMath
期中期末期中期末期中期末
A1311011731517
B6234531012457
C247636117123105
D11246794246122
E661131044510108
F11110844113221
\n", + "
" + ], + "text/plain": [ + " Python En Math \n", + " 期中 期末 期中 期末 期中 期末\n", + "A 131 101 1 73 15 17\n", + "B 62 34 53 101 24 57\n", + "C 24 76 36 117 123 105\n", + "D 112 46 79 42 46 122\n", + "E 66 113 104 45 10 108\n", + "F 111 108 4 41 132 21" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EnMathPython
A期中115131
期末7317101
B期中532462
期末1015734
C期中3612324
期末11710576
D期中7946112
期末4212246
E期中1041066
期末45108113
F期中4132111
期末4121108
\n", + "
" + ], + "text/plain": [ + " En Math Python\n", + "A 期中 1 15 131\n", + " 期末 73 17 101\n", + "B 期中 53 24 62\n", + " 期末 101 57 34\n", + "C 期中 36 123 24\n", + " 期末 117 105 76\n", + "D 期中 79 46 112\n", + " 期末 42 122 46\n", + "E 期中 104 10 66\n", + " 期末 45 108 113\n", + "F 期中 4 132 111\n", + " 期末 41 21 108" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 行和列的多层索引，进行转换\n", + "# Stack the prescribed level(s) from columns to index.\n", + "# 从列变成行\n", + "df2 = df.stack(level = 1)\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EnMathPython
ABCDEFABCDEFABCDEF
期中1533679104415241234610132131622411266111
期末73101117424541175710512210821101347646113108
\n", + "
" + ], + "text/plain": [ + " En Math Python \n", + " A B C D E F A B C D E F A B C D E F\n", + "期中 1 53 36 79 104 4 15 24 123 46 10 132 131 62 24 112 66 111\n", + "期末 73 101 117 42 45 41 17 57 105 122 108 21 101 34 76 46 113 108" + ] + }, + "execution_count": 21, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 从行变成列\n", + "df2.unstack(level= 0 )" + ] + }, + { + "cell_type": "code", + "execution_count": 22, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
EnMathPython
期中期末期中期末期中期末
A1731517131101
B5310124576234
C361171231052476
D79424612211246
E104451010866113
F44113221111108
\n", + "
" + ], + "text/plain": [ + " En Math Python \n", + " 期中 期末 期中 期末 期中 期末\n", + "A 1 73 15 17 131 101\n", + "B 53 101 24 57 62 34\n", + "C 36 117 123 105 24 76\n", + "D 79 42 46 122 112 46\n", + "E 104 45 10 108 66 113\n", + "F 4 41 132 21 111 108" + ] + }, + "execution_count": 22, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2.unstack(level = 1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/.ipynb_checkpoints/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220-checkpoint.ipynb" new file mode 100644 index 000000000..7df4f33a7 --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220-checkpoint.ipynb" @@ -0,0 +1,1209 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 数据分析数据挖掘\n", + "# 有数据情况下：\n", + "# 数据预处理\n", + "# 数据清洗（空数据，异常值）\n", + "# 数据集成（多个数据合并到一起，级联）数据可能存放在多个表中\n", + "# 数据转化\n", + "# 数据规约（属性减少（不重要的属性删除），数据减少去重操作）" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 12, 67, 29, 46, 103, 53, 53, 139, 87],\n", + " [126, 33, 55, 104, 45, 70, 96, 133, 116, 43],\n", + " [ 84, 45, 17, 42, 19, 11, 125, 43, 54, 39],\n", + " [ 97, 68, 99, 90, 28, 60, 135, 84, 111, 63],\n", + " [114, 56, 30, 81, 48, 73, 119, 65, 20, 22]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[115, 128, 122, 127, 4, 135, 26, 25, 131, 139],\n", + " [ 66, 119, 37, 136, 101, 40, 102, 127, 148, 127],\n", + " [ 89, 80, 140, 133, 51, 142, 47, 27, 54, 23],\n", + " [ 64, 127, 33, 128, 60, 106, 67, 94, 110, 76],\n", + " [ 6, 21, 23, 96, 10, 62, 26, 79, 149, 43],\n", + " [116, 143, 132, 118, 68, 21, 57, 133, 124, 124]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 首先看numpy数组的集成\n", + "nd1 = np.random.randint(0,150,size = (5,10))\n", + "\n", + "nd2 = np.random.randint(0,150,size = (6,10))\n", + "display(nd1,nd2)" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[ 5, 12, 67, 29, 46, 103, 53, 53, 139, 87],\n", + " [126, 33, 55, 104, 45, 70, 96, 133, 116, 43],\n", + " [ 84, 45, 17, 42, 19, 11, 125, 43, 54, 39],\n", + " [ 97, 68, 99, 90, 28, 60, 135, 84, 111, 63],\n", + " [114, 56, 30, 81, 48, 73, 119, 65, 20, 22],\n", + " [115, 128, 122, 127, 4, 135, 26, 25, 131, 139],\n", + " [ 66, 119, 37, 136, 101, 40, 102, 127, 148, 127],\n", + " [ 89, 80, 140, 133, 51, 142, 47, 27, 54, 23],\n", + " [ 64, 127, 33, 128, 60, 106, 67, 94, 110, 76],\n", + " [ 6, 21, 23, 96, 10, 62, 26, 79, 149, 43],\n", + " [116, 143, 132, 118, 68, 21, 57, 133, 124, 124]])" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 原来数据一个5行，一个是6行，级联之后变成了11行\n", + "nd3 = np.concatenate([nd1,nd2],axis = 0)\n", + "nd3" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[110, 38, 144, 92, 38, 2, 67, 2, 103, 81],\n", + " [ 56, 61, 61, 22, 108, 145, 95, 44, 40, 100],\n", + " [ 65, 74, 85, 123, 47, 117, 35, 55, 120, 20],\n", + " [ 15, 9, 4, 84, 71, 133, 140, 13, 71, 91],\n", + " [ 94, 31, 41, 5, 7, 32, 50, 24, 18, 120]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "array([[ 65, 149, 86, 138, 98],\n", + " [136, 49, 102, 45, 140],\n", + " [ 13, 124, 94, 81, 73],\n", + " [ 82, 38, 0, 75, 94],\n", + " [146, 28, 143, 61, 49]])" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "nd1 = np.random.randint(0,150,size = (5,10))\n", + "\n", + "nd2 = np.random.randint(0,150,size = (5,5))\n", + "display(nd1,nd2)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([[110, 38, 144, 92, 38, 2, 67, 2, 103, 81, 65, 149, 86,\n", + " 138, 98],\n", + " [ 56, 61, 61, 22, 108, 145, 95, 44, 40, 100, 136, 49, 102,\n", + " 45, 140],\n", + " [ 65, 74, 85, 123, 47, 117, 35, 55, 120, 20, 13, 124, 94,\n", + " 81, 73],\n", + " [ 15, 9, 4, 84, 71, 133, 140, 13, 71, 91, 82, 38, 0,\n", + " 75, 94],\n", + " [ 94, 31, 41, 5, 7, 32, 50, 24, 18, 120, 146, 28, 143,\n", + " 61, 49]])" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# axis = 0行级联（第一维度的级联），axis = 1（第二个维度的级联，列的级联）\n", + "np.concatenate((nd1,nd2),axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# pandas级联操作，pandas基于numpy\n", + "# pandas的级联类似" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A1135380
B1354052
C1441864
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 113 53 80\n", + "B 135 40 52\n", + "C 144 18 64" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
D126118146
E1478127
F87631
G359533
H13011791
I12498122
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "D 126 118 146\n", + "E 147 81 27\n", + "F 87 63 1\n", + "G 35 95 33\n", + "H 130 117 91\n", + "I 124 98 122" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "df1 = DataFrame(np.random.randint(0,150,size = (3,3)),index = list('ABC'),columns=['Python','Math','En'])\n", + "\n", + "df2 = DataFrame(np.random.randint(0,150,size = (6,3)),index = list('DEFGHI'),columns=['Python','Math','En'])\n", + "\n", + "display(df1,df2)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A1135380
B1354052
C1441864
D126118146
E1478127
F87631
G359533
H13011791
I12498122
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 113 53 80\n", + "B 135 40 52\n", + "C 144 18 64\n", + "D 126 118 146\n", + "E 147 81 27\n", + "F 87 63 1\n", + "G 35 95 33\n", + "H 130 117 91\n", + "I 124 98 122" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# pandas汇总数据，数据集成\n", + "df1.append(df2)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A1135380
B1354052
C1441864
D126118146
E1478127
F87631
G359533
H13011791
I12498122
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 113 53 80\n", + "B 135 40 52\n", + "C 144 18 64\n", + "D 126 118 146\n", + "E 147 81 27\n", + "F 87 63 1\n", + "G 35 95 33\n", + "H 130 117 91\n", + "I 124 98 122" + ] + }, + "execution_count": 9, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat([df1,df2])" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\python36\\lib\\site-packages\\ipykernel_launcher.py:1: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=False'.\n", + "\n", + "To retain the current behavior and silence the warning, pass 'sort=True'.\n", + "\n", + " \"\"\"Entry point for launching an IPython kernel.\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEnPythonMathEn
A113.053.080.0NaNNaNNaN
B135.040.052.0NaNNaNNaN
C144.018.064.0NaNNaNNaN
DNaNNaNNaN126.0118.0146.0
ENaNNaNNaN147.081.027.0
FNaNNaNNaN87.063.01.0
GNaNNaNNaN35.095.033.0
HNaNNaNNaN130.0117.091.0
INaNNaNNaN124.098.0122.0
\n", + "
" + ], + "text/plain": [ + " Python Math En Python Math En\n", + "A 113.0 53.0 80.0 NaN NaN NaN\n", + "B 135.0 40.0 52.0 NaN NaN NaN\n", + "C 144.0 18.0 64.0 NaN NaN NaN\n", + "D NaN NaN NaN 126.0 118.0 146.0\n", + "E NaN NaN NaN 147.0 81.0 27.0\n", + "F NaN NaN NaN 87.0 63.0 1.0\n", + "G NaN NaN NaN 35.0 95.0 33.0\n", + "H NaN NaN NaN 130.0 117.0 91.0\n", + "I NaN NaN NaN 124.0 98.0 122.0" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat([df1,df2],axis = 1,ignore_index = False)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A225813
B995735
C512824
E560111
F13723121
G4978115
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 22 58 13\n", + "B 99 57 35\n", + "C 51 28 24\n", + "E 5 60 111\n", + "F 137 23 121\n", + "G 49 78 115" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A11811381
B5122126
C0115128
E10013094
F4993140
G705994
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 118 113 81\n", + "B 51 22 126\n", + "C 0 115 128\n", + "E 100 130 94\n", + "F 49 93 140\n", + "G 70 59 94" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# 期中\n", + "df1 = DataFrame(np.random.randint(0,150,size = (6,3)),index = list('ABCEFG'),columns=['Python','Math','En'])\n", + "\n", + "# 期末\n", + "df2 = DataFrame(np.random.randint(0,150,size = (6,3)),index = list('ABCEFG'),columns=['Python','Math','En'])\n", + "\n", + "display(df1,df2)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
期中A225813
B995735
C512824
E560111
F13723121
G4978115
期末A11811381
B5122126
C0115128
E10013094
F4993140
G705994
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "期中 A 22 58 13\n", + " B 99 57 35\n", + " C 51 28 24\n", + " E 5 60 111\n", + " F 137 23 121\n", + " G 49 78 115\n", + "期末 A 118 113 81\n", + " B 51 22 126\n", + " C 0 115 128\n", + " E 100 130 94\n", + " F 49 93 140\n", + " G 70 59 94" + ] + }, + "execution_count": 16, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = pd.concat([df1,df2],axis = 0,keys = ['期中','期末'])\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A期中225813
期末11811381
B期中995735
期末5122126
C期中512824
期末0115128
E期中560111
期末10013094
F期中13723121
期末4993140
G期中4978115
期末705994
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 期中 22 58 13\n", + " 期末 118 113 81\n", + "B 期中 99 57 35\n", + " 期末 51 22 126\n", + "C 期中 51 28 24\n", + " 期末 0 115 128\n", + "E 期中 5 60 111\n", + " 期末 100 130 94\n", + "F 期中 137 23 121\n", + " 期末 49 93 140\n", + "G 期中 49 78 115\n", + " 期末 70 59 94" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3.unstack(level = 0).stack()" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/.ipynb_checkpoints/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge-checkpoint.ipynb" new file mode 100644 index 000000000..06fd9f690 --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge-checkpoint.ipynb" @@ -0,0 +1,1272 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 上一讲，append，concat数据集成方法\n", + "# merge融合，根据某一共同属性进行级联，高级用法" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namesexid
0A男1
1B女2
2C女3
3D女4
4E男5
5F男6
\n", + "
" + ], + "text/plain": [ + " name sex id\n", + "0 A 男 1\n", + "1 B 女 2\n", + "2 C 女 3\n", + "3 D 女 4\n", + "4 E 男 5\n", + "5 F 男 6" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1 = DataFrame({'name':['A','B','C','D','E','F'],\n", + " 'sex':['男','女','女','女','男','男'],\n", + " 'id':[1,2,3,4,5,6]})\n", + "df1" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
agesalaryid
022120001
125150002
227200003
321300004
418100005
52980007
\n", + "
" + ], + "text/plain": [ + " age salary id\n", + "0 22 12000 1\n", + "1 25 15000 2\n", + "2 27 20000 3\n", + "3 21 30000 4\n", + "4 18 10000 5\n", + "5 29 8000 7" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = DataFrame({'age':[22,25,27,21,18,29],'salary':[12000,15000,20000,30000,10000,8000],'id':[1,2,3,4,5,7]})\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "d:\\python36\\lib\\site-packages\\pandas\\core\\frame.py:6692: FutureWarning: Sorting because non-concatenation axis is not aligned. A future version\n", + "of pandas will change to not sort by default.\n", + "\n", + "To accept the future behavior, pass 'sort=False'.\n", + "\n", + "To retain the current behavior and silence the warning, pass 'sort=True'.\n", + "\n", + " sort=sort)\n" + ] + }, + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ageidnamesalarysex
0NaN1ANaN男
1NaN2BNaN女
2NaN3CNaN女
3NaN4DNaN女
4NaN5ENaN男
5NaN6FNaN男
022.01NaN12000.0NaN
125.02NaN15000.0NaN
227.03NaN20000.0NaN
321.04NaN30000.0NaN
418.05NaN10000.0NaN
529.07NaN8000.0NaN
\n", + "
" + ], + "text/plain": [ + " age id name salary sex\n", + "0 NaN 1 A NaN 男\n", + "1 NaN 2 B NaN 女\n", + "2 NaN 3 C NaN 女\n", + "3 NaN 4 D NaN 女\n", + "4 NaN 5 E NaN 男\n", + "5 NaN 6 F NaN 男\n", + "0 22.0 1 NaN 12000.0 NaN\n", + "1 25.0 2 NaN 15000.0 NaN\n", + "2 27.0 3 NaN 20000.0 NaN\n", + "3 21.0 4 NaN 30000.0 NaN\n", + "4 18.0 5 NaN 10000.0 NaN\n", + "5 29.0 7 NaN 8000.0 NaN" + ] + }, + "execution_count": 4, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.append(df2)" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namesexidagesalaryid
0A男122120001
1B女225150002
2C女327200003
3D女421300004
4E男518100005
5F男62980007
\n", + "
" + ], + "text/plain": [ + " name sex id age salary id\n", + "0 A 男 1 22 12000 1\n", + "1 B 女 2 25 15000 2\n", + "2 C 女 3 27 20000 3\n", + "3 D 女 4 21 30000 4\n", + "4 E 男 5 18 10000 5\n", + "5 F 男 6 29 8000 7" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pd.concat([df1,df2],axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namesexidagesalary
0A男12212000
1B女22515000
2C女32720000
3D女42130000
4E男51810000
\n", + "
" + ], + "text/plain": [ + " name sex id age salary\n", + "0 A 男 1 22 12000\n", + "1 B 女 2 25 15000\n", + "2 C 女 3 27 20000\n", + "3 D 女 4 21 30000\n", + "4 E 男 5 18 10000" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.merge(df2)" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
namesexidagesalary
0A男122.012000.0
1B女225.015000.0
2C女327.020000.0
3D女421.030000.0
4E男518.010000.0
5F男6NaNNaN
6NaNNaN729.08000.0
\n", + "
" + ], + "text/plain": [ + " name sex id age salary\n", + "0 A 男 1 22.0 12000.0\n", + "1 B 女 2 25.0 15000.0\n", + "2 C 女 3 27.0 20000.0\n", + "3 D 女 4 21.0 30000.0\n", + "4 E 男 5 18.0 10000.0\n", + "5 F 男 6 NaN NaN\n", + "6 NaN NaN 7 29.0 8000.0" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df1.merge(df2,how = 'outer')" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A401590
B595283
C14138137
D897853
E811013
F757986
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 40 15 90\n", + "B 59 52 83\n", + "C 14 138 137\n", + "D 89 78 53\n", + "E 81 101 3\n", + "F 75 79 86" + ] + }, + "execution_count": 8, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = DataFrame(np.random.randint(0,150,size = (6,3)),index = list('ABCDEF'),columns=['Python','Math','En'])\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": false + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Python 59.7\n", + "Math 77.2\n", + "En 75.3\n", + "dtype: float64" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "s = df.mean().round(1)\n", + "s" + ] + }, + { + "cell_type": "code", + "execution_count": 20, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
score_mean
Python59.7
Math77.2
En75.3
\n", + "
" + ], + "text/plain": [ + " score_mean\n", + "Python 59.7\n", + "Math 77.2\n", + "En 75.3" + ] + }, + "execution_count": 20, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = DataFrame(s)\n", + "df2.columns = ['score_mean']\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 30, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
score_mean59.777.275.3
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "score_mean 59.7 77.2 75.3" + ] + }, + "execution_count": 30, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = df2.T\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 32, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEn
A40.015.090.0
B59.052.083.0
C14.0138.0137.0
D89.078.053.0
E81.0101.03.0
F75.079.086.0
score_mean59.777.275.3
\n", + "
" + ], + "text/plain": [ + " Python Math En\n", + "A 40.0 15.0 90.0\n", + "B 59.0 52.0 83.0\n", + "C 14.0 138.0 137.0\n", + "D 89.0 78.0 53.0\n", + "E 81.0 101.0 3.0\n", + "F 75.0 79.0 86.0\n", + "score_mean 59.7 77.2 75.3" + ] + }, + "execution_count": 32, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4 = df.append(df3)\n", + "df4" + ] + }, + { + "cell_type": "code", + "execution_count": 38, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
score_mean
A48.3
B64.7
C96.3
D73.3
E61.7
F80.0
score_mean70.7
\n", + "
" + ], + "text/plain": [ + " score_mean\n", + "A 48.3\n", + "B 64.7\n", + "C 96.3\n", + "D 73.3\n", + "E 61.7\n", + "F 80.0\n", + "score_mean 70.7" + ] + }, + "execution_count": 38, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df5 = DataFrame(df4.mean(axis = 1).round(1))\n", + "df5.columns = ['score_mean']\n", + "df5" + ] + }, + { + "cell_type": "code", + "execution_count": 40, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
PythonMathEnscore_mean
A40.015.090.048.3
B59.052.083.064.7
C14.0138.0137.096.3
D89.078.053.073.3
E81.0101.03.061.7
F75.079.086.080.0
score_mean59.777.275.370.7
\n", + "
" + ], + "text/plain": [ + " Python Math En score_mean\n", + "A 40.0 15.0 90.0 48.3\n", + "B 59.0 52.0 83.0 64.7\n", + "C 14.0 138.0 137.0 96.3\n", + "D 89.0 78.0 53.0 73.3\n", + "E 81.0 101.0 3.0 61.7\n", + "F 75.0 79.0 86.0 80.0\n", + "score_mean 59.7 77.2 75.3 70.7" + ] + }, + "execution_count": 40, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df4.merge(df5,left_index=True,right_index=True)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/.ipynb_checkpoints/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234-checkpoint.ipynb" "b/Day76-90/code/.ipynb_checkpoints/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234-checkpoint.ipynb" new file mode 100644 index 000000000..e9dddcc7e --- /dev/null +++ "b/Day76-90/code/.ipynb_checkpoints/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234-checkpoint.ipynb" @@ -0,0 +1,877 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 1, + "metadata": {}, + "outputs": [], + "source": [ + "# 分组聚合透视\n", + "# 很多时候属性是相似的\n", + "\n", + "import numpy as np\n", + "\n", + "import pandas as pd\n", + "\n", + "from pandas import Series,DataFrame" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
HandSmokesexweightIQ
0rightyesmale80100
1leftyesfemale50120
2leftnofemale4890
3rightnomale75130
4rightyesmale68140
5rightnomale10080
6rightnofemale4094
7rightnofemale90110
8leftnomale88100
9rightyesfemale76160
\n", + "
" + ], + "text/plain": [ + " Hand Smoke sex weight IQ\n", + "0 right yes male 80 100\n", + "1 left yes female 50 120\n", + "2 left no female 48 90\n", + "3 right no male 75 130\n", + "4 right yes male 68 140\n", + "5 right no male 100 80\n", + "6 right no female 40 94\n", + "7 right no female 90 110\n", + "8 left no male 88 100\n", + "9 right yes female 76 160" + ] + }, + "execution_count": 2, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "# 走右手习惯，是否抽烟，性别，对体重，智商，有一定影响\n", + "\n", + "df = DataFrame({'Hand':['right','left','left','right','right','right','right','right','left','right'],\n", + " 'Smoke':['yes','yes','no','no','yes','no','no','no','no','yes'],\n", + " 'sex':['male','female','female','male','male','male','female','female','male','female'],\n", + " 'weight':[80,50,48,75,68,100,40,90,88,76],\n", + " 'IQ':[100,120,90,130,140,80,94,110,100,160]})\n", + "df" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# 分组聚合查看规律，某一条件下规律" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weightIQ
Hand
left62.0103.3
right75.6116.3
\n", + "
" + ], + "text/plain": [ + " weight IQ\n", + "Hand \n", + "left 62.0 103.3\n", + "right 75.6 116.3" + ] + }, + "execution_count": 7, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = df.groupby(by = ['Hand'])[['weight','IQ']].mean().round(1)\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weight
Hand
left62.0
right75.6
\n", + "
" + ], + "text/plain": [ + " weight\n", + "Hand \n", + "left 62.0\n", + "right 75.6" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(by = ['Hand'])[['weight']].apply(np.mean).round(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [], + "source": [ + "df2 = df.groupby(by = ['Hand'])[['weight']].transform(np.mean).round(1)\n" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
weight_mean
075.6
162.0
262.0
375.6
475.6
575.6
675.6
775.6
862.0
975.6
\n", + "
" + ], + "text/plain": [ + " weight_mean\n", + "0 75.6\n", + "1 62.0\n", + "2 62.0\n", + "3 75.6\n", + "4 75.6\n", + "5 75.6\n", + "6 75.6\n", + "7 75.6\n", + "8 62.0\n", + "9 75.6" + ] + }, + "execution_count": 17, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df2 = df2.add_suffix('_mean')\n", + "df2" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": { + "collapsed": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
HandSmokesexweightIQweight_mean
0rightyesmale8010075.6
1leftyesfemale5012062.0
2leftnofemale489062.0
3rightnomale7513075.6
4rightyesmale6814075.6
5rightnomale1008075.6
6rightnofemale409475.6
7rightnofemale9011075.6
8leftnomale8810062.0
9rightyesfemale7616075.6
\n", + "
" + ], + "text/plain": [ + " Hand Smoke sex weight IQ weight_mean\n", + "0 right yes male 80 100 75.6\n", + "1 left yes female 50 120 62.0\n", + "2 left no female 48 90 62.0\n", + "3 right no male 75 130 75.6\n", + "4 right yes male 68 140 75.6\n", + "5 right no male 100 80 75.6\n", + "6 right no female 40 94 75.6\n", + "7 right no female 90 110 75.6\n", + "8 left no male 88 100 62.0\n", + "9 right yes female 76 160 75.6" + ] + }, + "execution_count": 19, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df3 = df.merge(df2,left_index=True,right_index=True)\n", + "df3" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/plain": [ + "Hand\n", + "left ([3, 3], [62.0, 103.3])\n", + "right ([7, 7], [75.6, 116.3])\n", + "dtype: object" + ] + }, + "execution_count": 26, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "def count(x):\n", + " \n", + " return (x.count(),x.mean().round(1))\n", + "\n", + "df.groupby(by = ['Hand'])[['weight','IQ']].apply(count)" + ] + }, + { + "cell_type": "code", + "execution_count": 28, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IQ
Handsex
leftfemale120
male100
rightfemale160
male140
\n", + "
" + ], + "text/plain": [ + " IQ\n", + "Hand sex \n", + "left female 120\n", + " male 100\n", + "right female 160\n", + " male 140" + ] + }, + "execution_count": 28, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.groupby(by = ['Hand','sex'])[['IQ']].max()" + ] + }, + { + "cell_type": "code", + "execution_count": 29, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 29, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data = df.groupby(by = ['Hand'])['IQ','weight']\n", + "data" + ] + }, + { + "cell_type": "code", + "execution_count": 31, + "metadata": { + "scrolled": true + }, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IQweight
maxmeanmaxmean
Hand
left120103.38862.0
right160116.310075.6
\n", + "
" + ], + "text/plain": [ + " IQ weight \n", + " max mean max mean\n", + "Hand \n", + "left 120 103.3 88 62.0\n", + "right 160 116.3 100 75.6" + ] + }, + "execution_count": 31, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.agg(['max','mean']).round(1)" + ] + }, + { + "cell_type": "code", + "execution_count": 35, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
\n", + "\n", + "\n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
IQweight
Hand
left12062.0
right16075.6
\n", + "
" + ], + "text/plain": [ + " IQ weight\n", + "Hand \n", + "left 120 62.0\n", + "right 160 75.6" + ] + }, + "execution_count": 35, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "data.agg({'IQ':'max','weight':'mean'}).round(1)" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.7.7" + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git "a/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" "b/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" index b5b3acc8b..d10293f1e 100644 --- "a/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" +++ "b/Day76-90/code/1-pandas\345\205\245\351\227\250.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "metadata": {}, "outputs": [], "source": [ @@ -13,7 +13,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -22,14 +22,7 @@ }, { "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": 4, + "execution_count": 5, "metadata": { "scrolled": false }, @@ -44,7 +37,7 @@ "dtype: int64" ] }, - "execution_count": 4, + "execution_count": 5, "metadata": {}, "output_type": "execute_result" } @@ -52,13 +45,13 @@ "source": [ "# 创建\n", "# Series是一维的数据\n", - "s = Series(data = [120,136,128,99],index = ['Math','Python','En','Chinese'])\n", + "s = Series(data=[120,136,128,99], index=['Math','Python','En','Chinese'])\n", "s" ] }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -67,7 +60,7 @@ "(4,)" ] }, - "execution_count": 5, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -78,16 +71,16 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 7, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "array([120, 136, 128, 99], dtype=int64)" + "array([120, 136, 128, 99])" ] }, - "execution_count": 6, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } @@ -99,7 +92,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -108,7 +101,7 @@ "numpy.ndarray" ] }, - "execution_count": 7, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -119,7 +112,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [ { @@ -128,7 +121,7 @@ "120.75" ] }, - "execution_count": 8, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -139,7 +132,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 10, "metadata": {}, "outputs": [ { @@ -148,7 +141,7 @@ "136" ] }, - "execution_count": 9, + "execution_count": 10, "metadata": {}, "output_type": "execute_result" } @@ -159,7 +152,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 11, "metadata": {}, "outputs": [ { @@ -168,7 +161,7 @@ "15.903353943953666" ] }, - "execution_count": 10, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -179,36 +172,33 @@ }, { "cell_type": "code", - "execution_count": 11, - "metadata": { - "collapsed": true - }, + "execution_count": 20, + "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "Math 14400\n", - "Python 18496\n", - "En 16384\n", - "Chinese 9801\n", + "Math 122\n", + "Python 138\n", + "En 130\n", + "Chinese 101\n", "dtype: int64" ] }, - "execution_count": 11, + "execution_count": 20, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "s.pow(2)" + "s.add(1)\n", + "s" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, + "execution_count": 21, + "metadata": {}, "outputs": [ { "data": { @@ -238,64 +228,64 @@ " \n", " \n", " \n", - " a\n", - " 113\n", - " 116\n", - " 75\n", + " a\n", + " 109\n", + " 120\n", + " 23\n", " \n", " \n", - " b\n", - " 19\n", - " 145\n", - " 23\n", + " b\n", + " 54\n", + " 39\n", + " 54\n", " \n", " \n", - " c\n", - " 57\n", - " 107\n", - " 113\n", + " c\n", + " 97\n", + " 22\n", + " 106\n", " \n", " \n", - " d\n", - " 95\n", + " d\n", + " 21\n", + " 96\n", " 3\n", - " 66\n", " \n", " \n", - " e\n", - " 28\n", - " 121\n", - " 120\n", + " e\n", + " 23\n", + " 145\n", + " 147\n", " \n", " \n", - " f\n", - " 141\n", - " 85\n", - " 132\n", + " f\n", + " 80\n", + " 62\n", + " 83\n", " \n", " \n", - " h\n", - " 124\n", - " 39\n", - " 10\n", + " h\n", + " 70\n", + " 31\n", + " 134\n", " \n", " \n", - " i\n", - " 80\n", - " 35\n", - " 17\n", + " i\n", + " 132\n", + " 51\n", + " 115\n", " \n", " \n", - " j\n", - " 68\n", - " 99\n", - " 31\n", + " j\n", + " 95\n", + " 143\n", + " 111\n", " \n", " \n", - " k\n", - " 74\n", - " 12\n", - " 11\n", + " k\n", + " 66\n", + " 94\n", + " 7\n", " \n", " \n", "\n", @@ -303,19 +293,19 @@ ], "text/plain": [ " Python En Math\n", - "a 113 116 75\n", - "b 19 145 23\n", - "c 57 107 113\n", - "d 95 3 66\n", - "e 28 121 120\n", - "f 141 85 132\n", - "h 124 39 10\n", - "i 80 35 17\n", - "j 68 99 31\n", - "k 74 12 11" + "a 109 120 23\n", + "b 54 39 54\n", + "c 97 22 106\n", + "d 21 96 3\n", + "e 23 145 147\n", + "f 80 62 83\n", + "h 70 31 134\n", + "i 132 51 115\n", + "j 95 143 111\n", + "k 66 94 7" ] }, - "execution_count": 12, + "execution_count": 21, "metadata": {}, "output_type": "execute_result" } @@ -324,7 +314,7 @@ "# DataFrame是二维的数据\n", "# excel就非常相似\n", "# 所有进行数据分析，数据挖掘的工具最基础的结果：行和列，行表示样本，列表示的是属性\n", - "df = DataFrame(data = np.random.randint(0,150,size = (10,3)),index = list('abcdefhijk'),columns=['Python','En','Math'])\n", + "df = DataFrame(data=np.random.randint(0, 150, size=(10, 3)), index=list('abcdefhijk'), columns=['Python', 'En', 'Math'])\n", "df" ] }, @@ -553,7 +543,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 22, "metadata": { "scrolled": true }, @@ -561,50 +551,57 @@ { "data": { "text/plain": [ - "Python 79.9\n", - "En 76.2\n", - "Math 59.8\n", + "Python 74.7\n", + "En 80.3\n", + "Math 78.3\n", "dtype: float64" ] }, - "execution_count": 19, + "execution_count": 22, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.mean(axis = 0)" + "df.mean(axis=0)" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "a 101.333333\n", - "b 62.333333\n", - "c 92.333333\n", - "d 54.666667\n", - "e 89.666667\n", - "f 119.333333\n", - "h 57.666667\n", - "i 44.000000\n", - "j 66.000000\n", - "k 32.333333\n", + "a 84.000000\n", + "b 49.000000\n", + "c 75.000000\n", + "d 40.000000\n", + "e 105.000000\n", + "f 75.000000\n", + "h 78.333333\n", + "i 99.333333\n", + "j 116.333333\n", + "k 55.666667\n", "dtype: float64" ] }, - "execution_count": 20, + "execution_count": 23, "metadata": {}, "output_type": "execute_result" } ], "source": [ - "df.mean(axis = 1)" + "df.mean(axis=1)" ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { @@ -623,7 +620,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.7.3" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" "b/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" index ddbde0ae5..98c1704a5 100644 --- "a/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" +++ "b/Day76-90/code/2-pandas-\347\264\242\345\274\225.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -10,88 +10,14 @@ "\n", "import pandas as pd\n", "\n", - "from pandas import Series,DataFrame" + "from pandas import Series, DataFrame" ] }, { "cell_type": "code", - "execution_count": 3, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "11 111\n", - "12 113\n", - "13 103\n", - "14 147\n", - "15 63\n", - "16 11\n", - "17 130\n", - "18 38\n", - "19 17\n", - "20 32\n", - "21 112\n", - "22 75\n", - "23 68\n", - "24 124\n", - "25 138\n", - "26 56\n", - "27 1\n", - "28 88\n", - "29 113\n", - "30 63\n", - "31 42\n", - "32 65\n", - "33 104\n", - "34 105\n", - "35 0\n", - "36 95\n", - "37 119\n", - "38 86\n", - "39 124\n", - " ... \n", - "80 127\n", - "81 139\n", - "82 110\n", - "83 65\n", - "84 127\n", - "85 108\n", - "86 33\n", - "87 91\n", - "88 134\n", - "89 65\n", - "90 110\n", - "91 144\n", - "92 40\n", - "93 3\n", - "94 3\n", - "95 59\n", - "96 97\n", - "97 64\n", - "98 126\n", - "99 94\n", - "100 20\n", - "101 107\n", - "102 59\n", - "103 146\n", - "104 83\n", - "105 59\n", - "106 25\n", - "107 0\n", - "108 78\n", - "109 93\n", - "Name: Python, Length: 100, dtype: int16" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s = Series(np.random.randint(0,150,size = 100),index = np.arange(10,110),dtype=np.int16,name = 'Python')\n", "s" @@ -99,107 +25,27 @@ }, { "cell_type": "code", - "execution_count": 4, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "0", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# 索引操作\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0ms\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\series.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 866\u001b[0m \u001b[0mkey\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 867\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 868\u001b[1;33m \u001b[0mresult\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_value\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 869\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 870\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_scalar\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mresult\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_value\u001b[1;34m(self, series, key)\u001b[0m\n\u001b[0;32m 4373\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4374\u001b[0m return self._engine.get_value(s, k,\n\u001b[1;32m-> 4375\u001b[1;33m tz=getattr(series.dtype, 'tz', None))\n\u001b[0m\u001b[0;32m 4376\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4377\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mlen\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m0\u001b[0m \u001b[1;32mand\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mholds_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_boolean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_value\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.Int64HashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 0" - ] - } - ], - "source": [ - "# 索引操作\n", - "s[0]" - ] - }, - { - "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s[10]" ] }, { "cell_type": "code", - "execution_count": 6, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "20 32\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s[[10,20]]" ] }, { "cell_type": "code", - "execution_count": 7, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "20 32\n", - "21 112\n", - "22 75\n", - "23 68\n", - "24 124\n", - "25 138\n", - "26 56\n", - "27 1\n", - "28 88\n", - "29 113\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 7, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# 切片操作\n", "s[10:20]" @@ -207,164 +53,27 @@ }, { "cell_type": "code", - "execution_count": 8, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "12 113\n", - "14 147\n", - "16 11\n", - "18 38\n", - "20 32\n", - "22 75\n", - "24 124\n", - "26 56\n", - "28 88\n", - "30 63\n", - "32 65\n", - "34 105\n", - "36 95\n", - "38 86\n", - "40 6\n", - "42 57\n", - "44 72\n", - "46 43\n", - "48 87\n", - "50 83\n", - "52 99\n", - "54 132\n", - "56 17\n", - "58 116\n", - "60 33\n", - "62 51\n", - "64 80\n", - "66 121\n", - "68 81\n", - "70 0\n", - "72 50\n", - "74 31\n", - "76 114\n", - "78 60\n", - "80 127\n", - "82 110\n", - "84 127\n", - "86 33\n", - "88 134\n", - "90 110\n", - "92 40\n", - "94 3\n", - "96 97\n", - "98 126\n", - "100 20\n", - "102 59\n", - "104 83\n", - "106 25\n", - "108 78\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s[::2]" ] }, { "cell_type": "code", - "execution_count": 9, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "109 93\n", - "107 0\n", - "105 59\n", - "103 146\n", - "101 107\n", - "99 94\n", - "97 64\n", - "95 59\n", - "93 3\n", - "91 144\n", - "89 65\n", - "87 91\n", - "85 108\n", - "83 65\n", - "81 139\n", - "79 14\n", - "77 96\n", - "75 76\n", - "73 29\n", - "71 68\n", - "69 4\n", - "67 57\n", - "65 58\n", - "63 106\n", - "61 42\n", - "59 135\n", - "57 56\n", - "55 12\n", - "53 135\n", - "51 74\n", - "49 129\n", - "47 110\n", - "45 1\n", - "43 90\n", - "41 120\n", - "39 124\n", - "37 119\n", - "35 0\n", - "33 104\n", - "31 42\n", - "29 113\n", - "27 1\n", - "25 138\n", - "23 68\n", - "21 112\n", - "19 17\n", - "17 130\n", - "15 63\n", - "13 103\n", - "11 111\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 9, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s[::-2]" ] }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34" - ] - }, - "execution_count": 10, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# 可以使用pandas为开发者提供方法，去进行检索\n", "s.loc[10]" @@ -372,249 +81,56 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "20 32\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s.loc[[10,20]]" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "11 111\n", - "12 113\n", - "13 103\n", - "14 147\n", - "15 63\n", - "16 11\n", - "17 130\n", - "18 38\n", - "19 17\n", - "20 32\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s.loc[10:20]" ] }, { "cell_type": "code", - "execution_count": 13, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "12 113\n", - "14 147\n", - "16 11\n", - "18 38\n", - "20 32\n", - "22 75\n", - "24 124\n", - "26 56\n", - "28 88\n", - "30 63\n", - "32 65\n", - "34 105\n", - "36 95\n", - "38 86\n", - "40 6\n", - "42 57\n", - "44 72\n", - "46 43\n", - "48 87\n", - "50 83\n", - "52 99\n", - "54 132\n", - "56 17\n", - "58 116\n", - "60 33\n", - "62 51\n", - "64 80\n", - "66 121\n", - "68 81\n", - "70 0\n", - "72 50\n", - "74 31\n", - "76 114\n", - "78 60\n", - "80 127\n", - "82 110\n", - "84 127\n", - "86 33\n", - "88 134\n", - "90 110\n", - "92 40\n", - "94 3\n", - "96 97\n", - "98 126\n", - "100 20\n", - "102 59\n", - "104 83\n", - "106 25\n", - "108 78\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 13, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s.loc[::2]" ] }, { "cell_type": "code", - "execution_count": 14, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "109 93\n", - "107 0\n", - "105 59\n", - "103 146\n", - "101 107\n", - "99 94\n", - "97 64\n", - "95 59\n", - "93 3\n", - "91 144\n", - "89 65\n", - "87 91\n", - "85 108\n", - "83 65\n", - "81 139\n", - "79 14\n", - "77 96\n", - "75 76\n", - "73 29\n", - "71 68\n", - "69 4\n", - "67 57\n", - "65 58\n", - "63 106\n", - "61 42\n", - "59 135\n", - "57 56\n", - "55 12\n", - "53 135\n", - "51 74\n", - "49 129\n", - "47 110\n", - "45 1\n", - "43 90\n", - "41 120\n", - "39 124\n", - "37 119\n", - "35 0\n", - "33 104\n", - "31 42\n", - "29 113\n", - "27 1\n", - "25 138\n", - "23 68\n", - "21 112\n", - "19 17\n", - "17 130\n", - "15 63\n", - "13 103\n", - "11 111\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 14, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s.loc[::-2]" ] }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Int64Index([ 10, 11, 12, 13, 14, 15, 16, 17, 18, 19, 20, 21, 22,\n", - " 23, 24, 25, 26, 27, 28, 29, 30, 31, 32, 33, 34, 35,\n", - " 36, 37, 38, 39, 40, 41, 42, 43, 44, 45, 46, 47, 48,\n", - " 49, 50, 51, 52, 53, 54, 55, 56, 57, 58, 59, 60, 61,\n", - " 62, 63, 64, 65, 66, 67, 68, 69, 70, 71, 72, 73, 74,\n", - " 75, 76, 77, 78, 79, 80, 81, 82, 83, 84, 85, 86, 87,\n", - " 88, 89, 90, 91, 92, 93, 94, 95, 96, 97, 98, 99, 100,\n", - " 101, 102, 103, 104, 105, 106, 107, 108, 109],\n", - " dtype='int64')" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s.index" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "34" - ] - }, - "execution_count": 15, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# iloc 索引从0开始，数字化自然索引\n", "s.iloc[0]" @@ -622,257 +138,36 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "20 32\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 17, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s.iloc[[0,10]]" ] }, { "cell_type": "code", - "execution_count": 18, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "10 34\n", - "11 111\n", - "12 113\n", - "13 103\n", - "14 147\n", - "15 63\n", - "16 11\n", - "17 130\n", - "18 38\n", - "19 17\n", - "20 32\n", - "21 112\n", - "22 75\n", - "23 68\n", - "24 124\n", - "25 138\n", - "26 56\n", - "27 1\n", - "28 88\n", - "29 113\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s.iloc[0:20]" ] }, { "cell_type": "code", - "execution_count": 20, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "109 93\n", - "107 0\n", - "105 59\n", - "103 146\n", - "101 107\n", - "99 94\n", - "97 64\n", - "95 59\n", - "93 3\n", - "91 144\n", - "89 65\n", - "87 91\n", - "85 108\n", - "83 65\n", - "81 139\n", - "79 14\n", - "77 96\n", - "75 76\n", - "73 29\n", - "71 68\n", - "69 4\n", - "67 57\n", - "65 58\n", - "63 106\n", - "61 42\n", - "59 135\n", - "57 56\n", - "55 12\n", - "53 135\n", - "51 74\n", - "49 129\n", - "47 110\n", - "45 1\n", - "43 90\n", - "41 120\n", - "39 124\n", - "37 119\n", - "35 0\n", - "33 104\n", - "31 42\n", - "29 113\n", - "27 1\n", - "25 138\n", - "23 68\n", - "21 112\n", - "19 17\n", - "17 130\n", - "15 63\n", - "13 103\n", - "11 111\n", - "Name: Python, dtype: int16" - ] - }, - "execution_count": 20, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "s.iloc[::-2]" ] }, { "cell_type": "code", - "execution_count": 21, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
B13565135
C1379146
D4724145
E899716
F6426109
H4846111
I164997
J122126100
K6013662
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "B 135 65 135\n", - "C 13 79 146\n", - "D 47 24 145\n", - "E 89 97 16\n", - "F 64 26 109\n", - "H 48 46 111\n", - "I 16 49 97\n", - "J 122 126 100\n", - "K 60 136 62" - ] - }, - "execution_count": 21, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# DataFrame是二维，索引大同小异，\n", "df = DataFrame(data = np.random.randint(0,150,size= (10,3)),index=list('ABCDEFHIJK'),columns=['Python','En','Math'])\n", @@ -882,1177 +177,172 @@ }, { "cell_type": "code", - "execution_count": 22, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'A'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2656\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'A'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 2925\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnlevels\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2926\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_multilevel\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2927\u001b[1;33m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2928\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2929\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[0mindexer\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2661\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'A'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df['A']" ] }, { "cell_type": "code", - "execution_count": 23, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "A 103\n", - "B 135\n", - "C 13\n", - "D 47\n", - "E 89\n", - "F 64\n", - "H 48\n", - "I 16\n", - "J 122\n", - "K 60\n", - "Name: Python, dtype: int32" - ] - }, - "execution_count": 23, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df['Python']" ] }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEn
A10356
B13565
C1379
D4724
E8997
F6426
H4846
I1649
J122126
K60136
\n", - "
" - ], - "text/plain": [ - " Python En\n", - "A 103 56\n", - "B 135 65\n", - "C 13 79\n", - "D 47 24\n", - "E 89 97\n", - "F 64 26\n", - "H 48 46\n", - "I 16 49\n", - "J 122 126\n", - "K 60 136" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df[['Python','En']]" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
\n", - "
" - ], - "text/plain": [ - "Empty DataFrame\n", - "Columns: [Python, En, Math]\n", - "Index: []" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df['Python':'Math']" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
B13565135
C1379146
D4724145
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "B 135 65 135\n", - "C 13 79 146\n", - "D 47 24 145" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df['A':'D']" ] }, { "cell_type": "code", - "execution_count": 27, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "ename": "KeyError", - "evalue": "'Python'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2656\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2657\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'Python'", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mKeyError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'Python'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1500\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1501\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1502\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 1911\u001b[0m \u001b[1;31m# fall thru to straight lookup\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1912\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_validate_key\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1913\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_get_label\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1914\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1915\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_get_label\u001b[1;34m(self, label, axis)\u001b[0m\n\u001b[0;32m 139\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mIndexingError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'no slices here, handle elsewhere'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 140\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 141\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_xs\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlabel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 142\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 143\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_get_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\generic.py\u001b[0m in \u001b[0;36mxs\u001b[1;34m(self, key, axis, level, drop_level)\u001b[0m\n\u001b[0;32m 3583\u001b[0m drop_level=drop_level)\n\u001b[0;32m 3584\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3585\u001b[1;33m \u001b[0mloc\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3586\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3587\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mloc\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndarray\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexes\\base.py\u001b[0m in \u001b[0;36mget_loc\u001b[1;34m(self, key, method, tolerance)\u001b[0m\n\u001b[0;32m 2657\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2658\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2659\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_engine\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_loc\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_maybe_cast_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 2660\u001b[0m \u001b[0mindexer\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_indexer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmethod\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mmethod\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtolerance\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mtolerance\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2661\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mndim\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mindexer\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msize\u001b[0m \u001b[1;33m>\u001b[0m \u001b[1;36m1\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\index.pyx\u001b[0m in \u001b[0;36mpandas._libs.index.IndexEngine.get_loc\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;32mpandas\\_libs\\hashtable_class_helper.pxi\u001b[0m in \u001b[0;36mpandas._libs.hashtable.PyObjectHashTable.get_item\u001b[1;34m()\u001b[0m\n", - "\u001b[1;31mKeyError\u001b[0m: 'Python'" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.loc['Python']" ] }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 103\n", - "En 56\n", - "Math 98\n", - "Name: A, dtype: int32" - ] - }, - "execution_count": 28, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc['A']" ] }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
H4846111
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "H 48 46 111" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc[['A','H']]" ] }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
B13565135
C1379146
D4724145
E899716
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "B 135 65 135\n", - "C 13 79 146\n", - "D 47 24 145\n", - "E 89 97 16" - ] - }, - "execution_count": 30, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc['A':'E']" ] }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
C1379146
E899716
H4846111
J122126100
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "C 13 79 146\n", - "E 89 97 16\n", - "H 48 46 111\n", - "J 122 126 100" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.loc[::2]" ] }, { "cell_type": "code", - "execution_count": 32, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
K6013662
I164997
F6426109
D4724145
B13565135
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "K 60 136 62\n", - "I 16 49 97\n", - "F 64 26 109\n", - "D 47 24 145\n", - "B 135 65 135" - ] - }, - "execution_count": 32, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.loc[::-2]" ] }, { "cell_type": "code", - "execution_count": 33, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "ename": "TypeError", - "evalue": "Cannot index by location index with a non-integer key", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mTypeError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0miloc\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m'A'\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m__getitem__\u001b[1;34m(self, key)\u001b[0m\n\u001b[0;32m 1498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1499\u001b[0m \u001b[0mmaybe_callable\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcom\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mapply_if_callable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mobj\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1500\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_getitem_axis\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmaybe_callable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0maxis\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0maxis\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1501\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1502\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_is_scalar_access\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32md:\\python36\\lib\\site-packages\\pandas\\core\\indexing.py\u001b[0m in \u001b[0;36m_getitem_axis\u001b[1;34m(self, key, axis)\u001b[0m\n\u001b[0;32m 2224\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 2225\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mis_integer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mkey\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 2226\u001b[1;33m raise TypeError(\"Cannot index by location index with a \"\n\u001b[0m\u001b[0;32m 2227\u001b[0m \"non-integer key\")\n\u001b[0;32m 2228\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mTypeError\u001b[0m: Cannot index by location index with a non-integer key" - ] - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.iloc['A']" ] }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 103\n", - "En 56\n", - "Math 98\n", - "Name: A, dtype: int32" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.iloc[0]" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
F6426109
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "F 64 26 109" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.iloc[[0,5]]" ] }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
B13565135
C1379146
D4724145
E899716
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "B 135 65 135\n", - "C 13 79 146\n", - "D 47 24 145\n", - "E 89 97 16" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.iloc[0:5]" ] }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
K6013662
I164997
F6426109
D4724145
B13565135
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "K 60 136 62\n", - "I 16 49 97\n", - "F 64 26 109\n", - "D 47 24 145\n", - "B 135 65 135" - ] - }, - "execution_count": 38, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.iloc[::-2]" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
A1035698
B13565135
C1379146
D4724145
E899716
F6426109
H4846111
I164997
J122126100
K6013662
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "A 103 56 98\n", - "B 135 65 135\n", - "C 13 79 146\n", - "D 47 24 145\n", - "E 89 97 16\n", - "F 64 26 109\n", - "H 48 46 111\n", - "I 16 49 97\n", - "J 122 126 100\n", - "K 60 136 62" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df" ] }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
EnMath
A5698
C79146
E9716
H46111
J126100
\n", - "
" - ], - "text/plain": [ - " En Math\n", - "A 56 98\n", - "C 79 146\n", - "E 97 16\n", - "H 46 111\n", - "J 126 100" - ] - }, - "execution_count": 40, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.iloc[::2,1:]" ] @@ -2074,7 +364,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256.ipynb" "b/Day76-90/code/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256.ipynb" index dde9ec651..2c6fb4018 100644 --- "a/Day76-90/code/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256.ipynb" +++ "b/Day76-90/code/3-pandas\346\225\260\346\215\256\346\270\205\346\264\227\344\271\213\347\251\272\346\225\260\346\215\256.ipynb" @@ -2,7 +2,7 @@ "cells": [ { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -15,633 +15,21 @@ }, { "cell_type": "code", - "execution_count": 2, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
1001221052857
101741291611426
102971211222965
103141731201471
1041261328611617
1058534212166
10614265112483
10713614112286113
1081537124110102
1096330446958
110593811310916
1115518758126
1125397763745
1134214810797143
114701386968134
115471361132294
1163113762028
117148741344124
1181028113812832
11927111137022
1202893121684
12113643259719
12211170123858
12396103147868
12410104663149
1257759710831
12688614511655
12733741065046
12874282610076
1297618101126133
..................
170144124779282
1713698484380
17251143683474
17314911718141120
1748139146112122
17511510164629
17610714045148
17765436810918
1783110011049123
1792946695790
18014686182246
18171504090140
1824100147116110
1835587937834
18451091248782
185101181395051
18632127136124
187941613813149
1886510112312886
18943941029132
190681359428125
1913060982715
1928916101354
193104139972917
194529419991
195191021354140
19658100708264
19784971297613
19813115744114
199793795128116
\n", - "

100 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 122 10 5 28 57\n", - "101 74 129 16 114 26\n", - "102 97 121 122 29 65\n", - "103 141 73 120 147 1\n", - "104 126 132 86 116 17\n", - "105 85 3 42 121 66\n", - "106 142 65 1 124 83\n", - "107 136 141 122 86 113\n", - "108 15 37 124 110 102\n", - "109 63 30 44 69 58\n", - "110 59 38 113 109 16\n", - "111 5 51 87 58 126\n", - "112 53 97 76 37 45\n", - "113 42 148 107 97 143\n", - "114 70 138 69 68 134\n", - "115 47 136 113 22 94\n", - "116 31 137 6 20 28\n", - "117 148 74 134 4 124\n", - "118 102 81 138 128 32\n", - "119 27 111 13 70 22\n", - "120 28 93 121 68 4\n", - "121 136 43 25 97 19\n", - "122 111 70 12 38 58\n", - "123 96 103 147 86 8\n", - "124 10 10 46 63 149\n", - "125 7 75 97 108 31\n", - "126 88 6 145 116 55\n", - "127 33 74 106 50 46\n", - "128 74 28 26 100 76\n", - "129 76 18 101 126 133\n", - ".. ... ... ... ... ...\n", - "170 144 124 77 92 82\n", - "171 36 98 48 43 80\n", - "172 51 143 68 34 74\n", - "173 149 117 18 141 120\n", - "174 8 139 146 112 122\n", - "175 115 101 64 62 9\n", - "176 10 7 140 45 148\n", - "177 65 43 68 109 18\n", - "178 31 100 110 49 123\n", - "179 29 46 69 57 90\n", - "180 146 86 18 22 46\n", - "181 71 50 40 90 140\n", - "182 4 100 147 116 110\n", - "183 55 87 93 78 34\n", - "184 5 109 124 87 82\n", - "185 10 118 139 50 51\n", - "186 32 12 71 36 124\n", - "187 94 16 138 13 149\n", - "188 65 101 123 128 86\n", - "189 43 94 10 29 132\n", - "190 68 135 94 28 125\n", - "191 30 60 98 27 15\n", - "192 89 16 10 135 4\n", - "193 104 139 97 29 17\n", - "194 5 29 41 99 91\n", - "195 19 102 135 41 40\n", - "196 58 100 70 82 64\n", - "197 84 97 129 76 13\n", - "198 131 15 7 44 114\n", - "199 79 37 95 128 116\n", - "\n", - "[100 rows x 5 columns]" - ] - }, - "execution_count": 2, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = DataFrame(np.random.randint(0,150,size = (100,5)),index = np.arange(100,200),columns=['Python','En','Math','Physic','Chem'])\n", - "df" + "df.loc[100, 'En'] = None" ] }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python False\n", - "En False\n", - "Math False\n", - "Physic False\n", - "Chem False\n", - "dtype: bool" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "# 判断DataFrame是否存在空数据\n", "df.isnull().any()" @@ -649,54 +37,18 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python True\n", - "En True\n", - "Math True\n", - "Physic True\n", - "Chem True\n", - "dtype: bool" - ] - }, - "execution_count": 6, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.notnull().all()" ] }, { "cell_type": "code", - "execution_count": 12, - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "500" - ] - }, - "execution_count": 12, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "100*5" - ] - }, - { - "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -714,7 +66,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -733,666 +85,38 @@ }, { "cell_type": "code", - "execution_count": 16, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
100122.010.05.028.057.0
101NaN129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.0NaN86.0116.017.0
10585.0NaN42.0121.066.0
106142.065.01.0124.083.0
107136.0141.0NaN86.0113.0
10815.037.0124.0110.0102.0
10963.030.0NaN69.058.0
110NaNNaN113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.0NaN97.0NaN
11470.0138.069.068.0134.0
115NaN136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.0NaN22.0
12028.093.0121.0NaN4.0
121136.0NaN25.097.019.0
122111.070.012.038.058.0
123NaN103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.0NaNNaN55.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.0NaNNaN
..................
170144.0124.077.092.082.0
17136.098.0NaN43.080.0
17251.0NaN68.034.074.0
173149.0NaN18.0141.0NaN
1748.0139.0146.0112.0NaN
175115.0NaN64.062.09.0
176NaN7.0140.045.0148.0
177NaN43.068.0109.018.0
17831.0100.0NaN49.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.0NaN140.0
1824.0100.0147.0116.0110.0
18355.087.093.0NaN34.0
184NaN109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.0NaN
18794.0NaN138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.0NaN29.0132.0
19068.0135.094.028.0125.0
19130.060.098.0NaN15.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.0NaN
19519.0102.0135.041.040.0
19658.0NaN70.082.064.0
197NaN97.0129.076.013.0
198131.015.0NaN44.0114.0
19979.0NaN95.0128.0NaN
\n", - "

100 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 122.0 10.0 5.0 28.0 57.0\n", - "101 NaN 129.0 16.0 114.0 26.0\n", - "102 97.0 121.0 122.0 29.0 65.0\n", - "103 141.0 73.0 120.0 147.0 1.0\n", - "104 126.0 NaN 86.0 116.0 17.0\n", - "105 85.0 NaN 42.0 121.0 66.0\n", - "106 142.0 65.0 1.0 124.0 83.0\n", - "107 136.0 141.0 NaN 86.0 113.0\n", - "108 15.0 37.0 124.0 110.0 102.0\n", - "109 63.0 30.0 NaN 69.0 58.0\n", - "110 NaN NaN 113.0 109.0 16.0\n", - "111 5.0 51.0 87.0 58.0 126.0\n", - "112 53.0 97.0 76.0 37.0 45.0\n", - "113 42.0 148.0 NaN 97.0 NaN\n", - "114 70.0 138.0 69.0 68.0 134.0\n", - "115 NaN 136.0 113.0 22.0 94.0\n", - "116 31.0 137.0 6.0 20.0 28.0\n", - "117 148.0 74.0 134.0 4.0 124.0\n", - "118 102.0 81.0 138.0 128.0 32.0\n", - "119 27.0 111.0 13.0 NaN 22.0\n", - "120 28.0 93.0 121.0 NaN 4.0\n", - "121 136.0 NaN 25.0 97.0 19.0\n", - "122 111.0 70.0 12.0 38.0 58.0\n", - "123 NaN 103.0 147.0 86.0 8.0\n", - "124 10.0 10.0 46.0 63.0 149.0\n", - "125 7.0 75.0 97.0 108.0 31.0\n", - "126 88.0 6.0 NaN NaN 55.0\n", - "127 33.0 74.0 106.0 50.0 46.0\n", - "128 74.0 28.0 26.0 100.0 76.0\n", - "129 76.0 18.0 101.0 NaN NaN\n", - ".. ... ... ... ... ...\n", - "170 144.0 124.0 77.0 92.0 82.0\n", - "171 36.0 98.0 NaN 43.0 80.0\n", - "172 51.0 NaN 68.0 34.0 74.0\n", - "173 149.0 NaN 18.0 141.0 NaN\n", - "174 8.0 139.0 146.0 112.0 NaN\n", - "175 115.0 NaN 64.0 62.0 9.0\n", - "176 NaN 7.0 140.0 45.0 148.0\n", - "177 NaN 43.0 68.0 109.0 18.0\n", - "178 31.0 100.0 NaN 49.0 123.0\n", - "179 29.0 46.0 69.0 57.0 90.0\n", - "180 146.0 86.0 18.0 22.0 46.0\n", - "181 71.0 50.0 40.0 NaN 140.0\n", - "182 4.0 100.0 147.0 116.0 110.0\n", - "183 55.0 87.0 93.0 NaN 34.0\n", - "184 NaN 109.0 124.0 87.0 82.0\n", - "185 10.0 118.0 139.0 50.0 51.0\n", - "186 32.0 12.0 71.0 36.0 NaN\n", - "187 94.0 NaN 138.0 13.0 149.0\n", - "188 65.0 101.0 123.0 128.0 86.0\n", - "189 43.0 94.0 NaN 29.0 132.0\n", - "190 68.0 135.0 94.0 28.0 125.0\n", - "191 30.0 60.0 98.0 NaN 15.0\n", - "192 89.0 16.0 10.0 135.0 4.0\n", - "193 104.0 139.0 97.0 29.0 17.0\n", - "194 5.0 29.0 41.0 99.0 NaN\n", - "195 19.0 102.0 135.0 41.0 40.0\n", - "196 58.0 NaN 70.0 82.0 64.0\n", - "197 NaN 97.0 129.0 76.0 13.0\n", - "198 131.0 15.0 NaN 44.0 114.0\n", - "199 79.0 NaN 95.0 128.0 NaN\n", - "\n", - "[100 rows x 5 columns]" - ] - }, - "execution_count": 16, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df" ] }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python True\n", - "En True\n", - "Math True\n", - "Physic True\n", - "Chem True\n", - "dtype: bool" - ] - }, - "execution_count": 18, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.isnull().any()" ] }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 14\n", - "En 14\n", - "Math 15\n", - "Physic 11\n", - "Chem 13\n", - "dtype: int64" - ] - }, - "execution_count": 22, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1401,658 +125,28 @@ }, { "cell_type": "code", - "execution_count": 24, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 14\n", - "En 14\n", - "Math 15\n", - "Physic 11\n", - "Chem 13\n", - "dtype: int64" - ] - }, - "execution_count": 24, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df2.isnull().sum()" ] }, { "cell_type": "code", - "execution_count": 26, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
100122.010.05.028.057.0
101100.0129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.0100.086.0116.017.0
10585.0100.042.0121.066.0
106142.065.01.0124.083.0
107136.0141.0100.086.0113.0
10815.037.0124.0110.0102.0
10963.030.0100.069.058.0
110100.0100.0113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.0100.097.0100.0
11470.0138.069.068.0134.0
115100.0136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.0100.022.0
12028.093.0121.0100.04.0
121136.0100.025.097.019.0
122111.070.012.038.058.0
123100.0103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.0100.0100.055.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.0100.0100.0
..................
170144.0124.077.092.082.0
17136.098.0100.043.080.0
17251.0100.068.034.074.0
173149.0100.018.0141.0100.0
1748.0139.0146.0112.0100.0
175115.0100.064.062.09.0
176100.07.0140.045.0148.0
177100.043.068.0109.018.0
17831.0100.0100.049.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.0100.0140.0
1824.0100.0147.0116.0110.0
18355.087.093.0100.034.0
184100.0109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.0100.0
18794.0100.0138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.0100.029.0132.0
19068.0135.094.028.0125.0
19130.060.098.0100.015.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.0100.0
19519.0102.0135.041.040.0
19658.0100.070.082.064.0
197100.097.0129.076.013.0
198131.015.0100.044.0114.0
19979.0100.095.0128.0100.0
\n", - "

100 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 122.0 10.0 5.0 28.0 57.0\n", - "101 100.0 129.0 16.0 114.0 26.0\n", - "102 97.0 121.0 122.0 29.0 65.0\n", - "103 141.0 73.0 120.0 147.0 1.0\n", - "104 126.0 100.0 86.0 116.0 17.0\n", - "105 85.0 100.0 42.0 121.0 66.0\n", - "106 142.0 65.0 1.0 124.0 83.0\n", - "107 136.0 141.0 100.0 86.0 113.0\n", - "108 15.0 37.0 124.0 110.0 102.0\n", - "109 63.0 30.0 100.0 69.0 58.0\n", - "110 100.0 100.0 113.0 109.0 16.0\n", - "111 5.0 51.0 87.0 58.0 126.0\n", - "112 53.0 97.0 76.0 37.0 45.0\n", - "113 42.0 148.0 100.0 97.0 100.0\n", - "114 70.0 138.0 69.0 68.0 134.0\n", - "115 100.0 136.0 113.0 22.0 94.0\n", - "116 31.0 137.0 6.0 20.0 28.0\n", - "117 148.0 74.0 134.0 4.0 124.0\n", - "118 102.0 81.0 138.0 128.0 32.0\n", - "119 27.0 111.0 13.0 100.0 22.0\n", - "120 28.0 93.0 121.0 100.0 4.0\n", - "121 136.0 100.0 25.0 97.0 19.0\n", - "122 111.0 70.0 12.0 38.0 58.0\n", - "123 100.0 103.0 147.0 86.0 8.0\n", - "124 10.0 10.0 46.0 63.0 149.0\n", - "125 7.0 75.0 97.0 108.0 31.0\n", - "126 88.0 6.0 100.0 100.0 55.0\n", - "127 33.0 74.0 106.0 50.0 46.0\n", - "128 74.0 28.0 26.0 100.0 76.0\n", - "129 76.0 18.0 101.0 100.0 100.0\n", - ".. ... ... ... ... ...\n", - "170 144.0 124.0 77.0 92.0 82.0\n", - "171 36.0 98.0 100.0 43.0 80.0\n", - "172 51.0 100.0 68.0 34.0 74.0\n", - "173 149.0 100.0 18.0 141.0 100.0\n", - "174 8.0 139.0 146.0 112.0 100.0\n", - "175 115.0 100.0 64.0 62.0 9.0\n", - "176 100.0 7.0 140.0 45.0 148.0\n", - "177 100.0 43.0 68.0 109.0 18.0\n", - "178 31.0 100.0 100.0 49.0 123.0\n", - "179 29.0 46.0 69.0 57.0 90.0\n", - "180 146.0 86.0 18.0 22.0 46.0\n", - "181 71.0 50.0 40.0 100.0 140.0\n", - "182 4.0 100.0 147.0 116.0 110.0\n", - "183 55.0 87.0 93.0 100.0 34.0\n", - "184 100.0 109.0 124.0 87.0 82.0\n", - "185 10.0 118.0 139.0 50.0 51.0\n", - "186 32.0 12.0 71.0 36.0 100.0\n", - "187 94.0 100.0 138.0 13.0 149.0\n", - "188 65.0 101.0 123.0 128.0 86.0\n", - "189 43.0 94.0 100.0 29.0 132.0\n", - "190 68.0 135.0 94.0 28.0 125.0\n", - "191 30.0 60.0 98.0 100.0 15.0\n", - "192 89.0 16.0 10.0 135.0 4.0\n", - "193 104.0 139.0 97.0 29.0 17.0\n", - "194 5.0 29.0 41.0 99.0 100.0\n", - "195 19.0 102.0 135.0 41.0 40.0\n", - "196 58.0 100.0 70.0 82.0 64.0\n", - "197 100.0 97.0 129.0 76.0 13.0\n", - "198 131.0 15.0 100.0 44.0 114.0\n", - "199 79.0 100.0 95.0 128.0 100.0\n", - "\n", - "[100 rows x 5 columns]" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# 固定值填充\n", - "df2.fillna(value=100)" + "df.fillna(value=100)" ] }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 71.662791\n", - "En 75.627907\n", - "Math 77.929412\n", - "Physic 73.471910\n", - "Chem 69.080460\n", - "dtype: float64" - ] - }, - "execution_count": 27, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df2.mean()" ] @@ -2066,605 +160,9 @@ }, { "cell_type": "code", - "execution_count": 29, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
1001221052857
101711291611426
102971211222965
103141731201471
104126758611617
10585754212166
10614265112483
1071361417786113
1081537124110102
1096330776958
110717511310916
1115518758126
1125397763745
11342148779769
114701386968134
115711361132294
1163113762028
117148741344124
1181028113812832
11927111137322
1202893121734
12113675259719
12211170123858
12371103147868
12410104663149
1257759710831
126886777355
12733741065046
12874282610076
12976181017369
..................
170144124779282
1713698774380
1725175683474
173149751814169
174813914611269
1751157564629
17671714045148
17771436810918
178311007749123
1792946695790
18014686182246
18171504073140
1824100147116110
1835587937334
184711091248782
185101181395051
1863212713669
187947513813149
1886510112312886
18943947729132
190681359428125
1913060987315
1928916101354
193104139972917
194529419969
195191021354140
1965875708264
19771971297613
198131157744114
19979759512869
\n", - "

100 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 122 10 5 28 57\n", - "101 71 129 16 114 26\n", - "102 97 121 122 29 65\n", - "103 141 73 120 147 1\n", - "104 126 75 86 116 17\n", - "105 85 75 42 121 66\n", - "106 142 65 1 124 83\n", - "107 136 141 77 86 113\n", - "108 15 37 124 110 102\n", - "109 63 30 77 69 58\n", - "110 71 75 113 109 16\n", - "111 5 51 87 58 126\n", - "112 53 97 76 37 45\n", - "113 42 148 77 97 69\n", - "114 70 138 69 68 134\n", - "115 71 136 113 22 94\n", - "116 31 137 6 20 28\n", - "117 148 74 134 4 124\n", - "118 102 81 138 128 32\n", - "119 27 111 13 73 22\n", - "120 28 93 121 73 4\n", - "121 136 75 25 97 19\n", - "122 111 70 12 38 58\n", - "123 71 103 147 86 8\n", - "124 10 10 46 63 149\n", - "125 7 75 97 108 31\n", - "126 88 6 77 73 55\n", - "127 33 74 106 50 46\n", - "128 74 28 26 100 76\n", - "129 76 18 101 73 69\n", - ".. ... ... ... ... ...\n", - "170 144 124 77 92 82\n", - "171 36 98 77 43 80\n", - "172 51 75 68 34 74\n", - "173 149 75 18 141 69\n", - "174 8 139 146 112 69\n", - "175 115 75 64 62 9\n", - "176 71 7 140 45 148\n", - "177 71 43 68 109 18\n", - "178 31 100 77 49 123\n", - "179 29 46 69 57 90\n", - "180 146 86 18 22 46\n", - "181 71 50 40 73 140\n", - "182 4 100 147 116 110\n", - "183 55 87 93 73 34\n", - "184 71 109 124 87 82\n", - "185 10 118 139 50 51\n", - "186 32 12 71 36 69\n", - "187 94 75 138 13 149\n", - "188 65 101 123 128 86\n", - "189 43 94 77 29 132\n", - "190 68 135 94 28 125\n", - "191 30 60 98 73 15\n", - "192 89 16 10 135 4\n", - "193 104 139 97 29 17\n", - "194 5 29 41 99 69\n", - "195 19 102 135 41 40\n", - "196 58 75 70 82 64\n", - "197 71 97 129 76 13\n", - "198 131 15 77 44 114\n", - "199 79 75 95 128 69\n", - "\n", - "[100 rows x 5 columns]" - ] - }, - "execution_count": 29, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# 均值\n", "df3 = df2.fillna(value=df2.mean())\n", @@ -2673,22 +171,11 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 6, 18, 1, 17, 19, 5, 17, 16, 13, 3])" - ] - }, - "execution_count": 31, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nd = np.random.randint(0,20,size = 10)\n", "nd" @@ -2696,20 +183,9 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 1, 3, 5, 6, 13, 16, 17, 17, 18, 19])" - ] - }, - "execution_count": 33, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "nd.sort()\n", "nd" @@ -2717,645 +193,27 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.5" - ] - }, - "execution_count": 34, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "(13 + 16)/2" ] }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "14.5" - ] - }, - "execution_count": 35, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "np.median(nd)" ] }, { "cell_type": "code", - "execution_count": 36, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
100122.010.05.028.057.0
10168.0129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.082.586.0116.017.0
10585.082.542.0121.066.0
106142.065.01.0124.083.0
107136.0141.086.086.0113.0
10815.037.0124.0110.0102.0
10963.030.086.069.058.0
11068.082.5113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.086.097.065.0
11470.0138.069.068.0134.0
11568.0136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.069.022.0
12028.093.0121.069.04.0
121136.082.525.097.019.0
122111.070.012.038.058.0
12368.0103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.086.069.055.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.069.065.0
..................
170144.0124.077.092.082.0
17136.098.086.043.080.0
17251.082.568.034.074.0
173149.082.518.0141.065.0
1748.0139.0146.0112.065.0
175115.082.564.062.09.0
17668.07.0140.045.0148.0
17768.043.068.0109.018.0
17831.0100.086.049.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.069.0140.0
1824.0100.0147.0116.0110.0
18355.087.093.069.034.0
18468.0109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.065.0
18794.082.5138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.086.029.0132.0
19068.0135.094.028.0125.0
19130.060.098.069.015.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.065.0
19519.0102.0135.041.040.0
19658.082.570.082.064.0
19768.097.0129.076.013.0
198131.015.086.044.0114.0
19979.082.595.0128.065.0
\n", - "

100 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 122.0 10.0 5.0 28.0 57.0\n", - "101 68.0 129.0 16.0 114.0 26.0\n", - "102 97.0 121.0 122.0 29.0 65.0\n", - "103 141.0 73.0 120.0 147.0 1.0\n", - "104 126.0 82.5 86.0 116.0 17.0\n", - "105 85.0 82.5 42.0 121.0 66.0\n", - "106 142.0 65.0 1.0 124.0 83.0\n", - "107 136.0 141.0 86.0 86.0 113.0\n", - "108 15.0 37.0 124.0 110.0 102.0\n", - "109 63.0 30.0 86.0 69.0 58.0\n", - "110 68.0 82.5 113.0 109.0 16.0\n", - "111 5.0 51.0 87.0 58.0 126.0\n", - "112 53.0 97.0 76.0 37.0 45.0\n", - "113 42.0 148.0 86.0 97.0 65.0\n", - "114 70.0 138.0 69.0 68.0 134.0\n", - "115 68.0 136.0 113.0 22.0 94.0\n", - "116 31.0 137.0 6.0 20.0 28.0\n", - "117 148.0 74.0 134.0 4.0 124.0\n", - "118 102.0 81.0 138.0 128.0 32.0\n", - "119 27.0 111.0 13.0 69.0 22.0\n", - "120 28.0 93.0 121.0 69.0 4.0\n", - "121 136.0 82.5 25.0 97.0 19.0\n", - "122 111.0 70.0 12.0 38.0 58.0\n", - "123 68.0 103.0 147.0 86.0 8.0\n", - "124 10.0 10.0 46.0 63.0 149.0\n", - "125 7.0 75.0 97.0 108.0 31.0\n", - "126 88.0 6.0 86.0 69.0 55.0\n", - "127 33.0 74.0 106.0 50.0 46.0\n", - "128 74.0 28.0 26.0 100.0 76.0\n", - "129 76.0 18.0 101.0 69.0 65.0\n", - ".. ... ... ... ... ...\n", - "170 144.0 124.0 77.0 92.0 82.0\n", - "171 36.0 98.0 86.0 43.0 80.0\n", - "172 51.0 82.5 68.0 34.0 74.0\n", - "173 149.0 82.5 18.0 141.0 65.0\n", - "174 8.0 139.0 146.0 112.0 65.0\n", - "175 115.0 82.5 64.0 62.0 9.0\n", - "176 68.0 7.0 140.0 45.0 148.0\n", - "177 68.0 43.0 68.0 109.0 18.0\n", - "178 31.0 100.0 86.0 49.0 123.0\n", - "179 29.0 46.0 69.0 57.0 90.0\n", - "180 146.0 86.0 18.0 22.0 46.0\n", - "181 71.0 50.0 40.0 69.0 140.0\n", - "182 4.0 100.0 147.0 116.0 110.0\n", - "183 55.0 87.0 93.0 69.0 34.0\n", - "184 68.0 109.0 124.0 87.0 82.0\n", - "185 10.0 118.0 139.0 50.0 51.0\n", - "186 32.0 12.0 71.0 36.0 65.0\n", - "187 94.0 82.5 138.0 13.0 149.0\n", - "188 65.0 101.0 123.0 128.0 86.0\n", - "189 43.0 94.0 86.0 29.0 132.0\n", - "190 68.0 135.0 94.0 28.0 125.0\n", - "191 30.0 60.0 98.0 69.0 15.0\n", - "192 89.0 16.0 10.0 135.0 4.0\n", - "193 104.0 139.0 97.0 29.0 17.0\n", - "194 5.0 29.0 41.0 99.0 65.0\n", - "195 19.0 102.0 135.0 41.0 40.0\n", - "196 58.0 82.5 70.0 82.0 64.0\n", - "197 68.0 97.0 129.0 76.0 13.0\n", - "198 131.0 15.0 86.0 44.0 114.0\n", - "199 79.0 82.5 95.0 128.0 65.0\n", - "\n", - "[100 rows x 5 columns]" - ] - }, - "execution_count": 36, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# 中位数填充\n", "df2.median()\n", @@ -3365,605 +223,9 @@ }, { "cell_type": "code", - "execution_count": 37, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
100122.010.05.028.057.0
101NaN129.016.0114.026.0
10297.0121.0122.029.065.0
103141.073.0120.0147.01.0
104126.0NaN86.0116.017.0
10585.0NaN42.0121.066.0
106142.065.01.0124.083.0
107136.0141.0NaN86.0113.0
10815.037.0124.0110.0102.0
10963.030.0NaN69.058.0
110NaNNaN113.0109.016.0
1115.051.087.058.0126.0
11253.097.076.037.045.0
11342.0148.0NaN97.0NaN
11470.0138.069.068.0134.0
115NaN136.0113.022.094.0
11631.0137.06.020.028.0
117148.074.0134.04.0124.0
118102.081.0138.0128.032.0
11927.0111.013.0NaN22.0
12028.093.0121.0NaN4.0
121136.0NaN25.097.019.0
122111.070.012.038.058.0
123NaN103.0147.086.08.0
12410.010.046.063.0149.0
1257.075.097.0108.031.0
12688.06.0NaNNaN55.0
12733.074.0106.050.046.0
12874.028.026.0100.076.0
12976.018.0101.0NaNNaN
..................
170144.0124.077.092.082.0
17136.098.0NaN43.080.0
17251.0NaN68.034.074.0
173149.0NaN18.0141.0NaN
1748.0139.0146.0112.0NaN
175115.0NaN64.062.09.0
176NaN7.0140.045.0148.0
177NaN43.068.0109.018.0
17831.0100.0NaN49.0123.0
17929.046.069.057.090.0
180146.086.018.022.046.0
18171.050.040.0NaN140.0
1824.0100.0147.0116.0110.0
18355.087.093.0NaN34.0
184NaN109.0124.087.082.0
18510.0118.0139.050.051.0
18632.012.071.036.0NaN
18794.0NaN138.013.0149.0
18865.0101.0123.0128.086.0
18943.094.0NaN29.0132.0
19068.0135.094.028.0125.0
19130.060.098.0NaN15.0
19289.016.010.0135.04.0
193104.0139.097.029.017.0
1945.029.041.099.0NaN
19519.0102.0135.041.040.0
19658.0NaN70.082.064.0
197NaN97.0129.076.013.0
198131.015.0NaN44.0114.0
19979.0NaN95.0128.0NaN
\n", - "

100 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 122.0 10.0 5.0 28.0 57.0\n", - "101 NaN 129.0 16.0 114.0 26.0\n", - "102 97.0 121.0 122.0 29.0 65.0\n", - "103 141.0 73.0 120.0 147.0 1.0\n", - "104 126.0 NaN 86.0 116.0 17.0\n", - "105 85.0 NaN 42.0 121.0 66.0\n", - "106 142.0 65.0 1.0 124.0 83.0\n", - "107 136.0 141.0 NaN 86.0 113.0\n", - "108 15.0 37.0 124.0 110.0 102.0\n", - "109 63.0 30.0 NaN 69.0 58.0\n", - "110 NaN NaN 113.0 109.0 16.0\n", - "111 5.0 51.0 87.0 58.0 126.0\n", - "112 53.0 97.0 76.0 37.0 45.0\n", - "113 42.0 148.0 NaN 97.0 NaN\n", - "114 70.0 138.0 69.0 68.0 134.0\n", - "115 NaN 136.0 113.0 22.0 94.0\n", - "116 31.0 137.0 6.0 20.0 28.0\n", - "117 148.0 74.0 134.0 4.0 124.0\n", - "118 102.0 81.0 138.0 128.0 32.0\n", - "119 27.0 111.0 13.0 NaN 22.0\n", - "120 28.0 93.0 121.0 NaN 4.0\n", - "121 136.0 NaN 25.0 97.0 19.0\n", - "122 111.0 70.0 12.0 38.0 58.0\n", - "123 NaN 103.0 147.0 86.0 8.0\n", - "124 10.0 10.0 46.0 63.0 149.0\n", - "125 7.0 75.0 97.0 108.0 31.0\n", - "126 88.0 6.0 NaN NaN 55.0\n", - "127 33.0 74.0 106.0 50.0 46.0\n", - "128 74.0 28.0 26.0 100.0 76.0\n", - "129 76.0 18.0 101.0 NaN NaN\n", - ".. ... ... ... ... ...\n", - "170 144.0 124.0 77.0 92.0 82.0\n", - "171 36.0 98.0 NaN 43.0 80.0\n", - "172 51.0 NaN 68.0 34.0 74.0\n", - "173 149.0 NaN 18.0 141.0 NaN\n", - "174 8.0 139.0 146.0 112.0 NaN\n", - "175 115.0 NaN 64.0 62.0 9.0\n", - "176 NaN 7.0 140.0 45.0 148.0\n", - "177 NaN 43.0 68.0 109.0 18.0\n", - "178 31.0 100.0 NaN 49.0 123.0\n", - "179 29.0 46.0 69.0 57.0 90.0\n", - "180 146.0 86.0 18.0 22.0 46.0\n", - "181 71.0 50.0 40.0 NaN 140.0\n", - "182 4.0 100.0 147.0 116.0 110.0\n", - "183 55.0 87.0 93.0 NaN 34.0\n", - "184 NaN 109.0 124.0 87.0 82.0\n", - "185 10.0 118.0 139.0 50.0 51.0\n", - "186 32.0 12.0 71.0 36.0 NaN\n", - "187 94.0 NaN 138.0 13.0 149.0\n", - "188 65.0 101.0 123.0 128.0 86.0\n", - "189 43.0 94.0 NaN 29.0 132.0\n", - "190 68.0 135.0 94.0 28.0 125.0\n", - "191 30.0 60.0 98.0 NaN 15.0\n", - "192 89.0 16.0 10.0 135.0 4.0\n", - "193 104.0 139.0 97.0 29.0 17.0\n", - "194 5.0 29.0 41.0 99.0 NaN\n", - "195 19.0 102.0 135.0 41.0 40.0\n", - "196 58.0 NaN 70.0 82.0 64.0\n", - "197 NaN 97.0 129.0 76.0 13.0\n", - "198 131.0 15.0 NaN 44.0 114.0\n", - "199 79.0 NaN 95.0 128.0 NaN\n", - "\n", - "[100 rows x 5 columns]" - ] - }, - "execution_count": 37, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# 众数填充，数量最多的那个数\n", "df2" @@ -3971,605 +233,9 @@ }, { "cell_type": "code", - "execution_count": 42, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
100828999101125
101431109325
10256103566190
1034710014713899
1043846827544
10518111223126
106562610614139
10731377567144
10835471026063
109861265788149
11019140303533
11176151133
11231549111969
1136437502321
11472571381521
115551201043225
11696248922146
11763086489
11828461258274
119853970132111
12010990447439
121214810311465
12211029998057
123109888113571
12470103134121121
12551921172743
1266929759105
12765905214822
12841291711913
1292410010728139
..................
207012777241631
2071936192822
20721166154861
207347214011234
2074261081233233
207546130135124113
207633181363820
20771071112954119
207884551293787
20799550451984
2080124746514053
20812635149145127
20821921101389
20838410131714
208428741056889
20852393849788
2086861332612513
208721124401155
20882015353137
208996123123564
20902243927860
20911631176058
20926518131334
209369491094058
2094128461082111
20952659854149
209611147909266
209759773140104
2098102675119
209997197714348
\n", - "

2000 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 82 89 99 101 125\n", - "101 4 31 109 32 5\n", - "102 56 103 56 61 90\n", - "103 47 100 147 138 99\n", - "104 38 46 82 75 44\n", - "105 18 11 122 3 126\n", - "106 56 26 106 14 139\n", - "107 3 137 75 67 144\n", - "108 35 47 102 60 63\n", - "109 86 126 57 88 149\n", - "110 19 140 30 35 33\n", - "111 76 1 5 11 33\n", - "112 31 54 91 119 69\n", - "113 64 37 50 23 21\n", - "114 72 57 138 15 21\n", - "115 55 120 104 32 25\n", - "116 96 24 89 22 146\n", - "117 63 0 8 64 89\n", - "118 28 46 125 82 74\n", - "119 85 39 70 132 111\n", - "120 109 90 44 74 39\n", - "121 2 148 103 114 65\n", - "122 110 29 99 80 57\n", - "123 109 88 81 135 71\n", - "124 70 103 134 121 121\n", - "125 51 92 117 27 43\n", - "126 6 92 97 59 105\n", - "127 65 90 52 148 22\n", - "128 4 129 17 119 13\n", - "129 24 100 107 28 139\n", - "... ... ... ... ... ...\n", - "2070 127 77 24 16 31\n", - "2071 93 61 9 28 22\n", - "2072 116 61 54 8 61\n", - "2073 4 72 140 112 34\n", - "2074 26 108 123 32 33\n", - "2075 46 130 135 124 113\n", - "2076 33 18 136 38 20\n", - "2077 107 11 129 54 119\n", - "2078 84 55 129 37 87\n", - "2079 95 50 45 19 84\n", - "2080 124 74 65 140 53\n", - "2081 26 35 149 145 127\n", - "2082 19 21 101 3 89\n", - "2083 84 10 131 71 4\n", - "2084 28 74 105 68 89\n", - "2085 23 93 84 97 88\n", - "2086 86 133 26 125 13\n", - "2087 21 124 40 115 5\n", - "2088 20 15 35 31 37\n", - "2089 96 123 123 5 64\n", - "2090 22 43 92 78 60\n", - "2091 16 31 17 60 58\n", - "2092 65 18 13 13 34\n", - "2093 69 49 109 40 58\n", - "2094 128 46 10 82 111\n", - "2095 26 59 8 54 149\n", - "2096 111 47 90 92 66\n", - "2097 5 97 73 140 104\n", - "2098 102 6 7 5 119\n", - "2099 97 19 77 143 48\n", - "\n", - "[2000 rows x 5 columns]" - ] - }, - "execution_count": 42, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df = DataFrame(np.random.randint(0,150,size = (2000,5)),index = np.arange(100,2100),columns=['Python','En','Math','Physic','Chem'])\n", "df" @@ -4577,7 +243,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -4595,258 +261,36 @@ }, { "cell_type": "code", - "execution_count": 44, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 190\n", - "En 200\n", - "Math 194\n", - "Physic 189\n", - "Chem 181\n", - "dtype: int64" - ] - }, - "execution_count": 44, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", - "execution_count": 45, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.0NaN90.0
10347.0100.0147.0138.099.0
10438.046.0NaN75.044.0
\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 82.0 89.0 99.0 101.0 125.0\n", - "101 4.0 31.0 109.0 32.0 5.0\n", - "102 56.0 103.0 56.0 NaN 90.0\n", - "103 47.0 100.0 147.0 138.0 99.0\n", - "104 38.0 46.0 NaN 75.0 44.0" - ] - }, - "execution_count": 45, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.head()" ] }, { "cell_type": "code", - "execution_count": 46, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
209526.059.08.054.0149.0
2096NaN47.090.092.066.0
20975.097.073.0140.0104.0
2098102.06.07.05.0119.0
209997.019.077.0NaN48.0
\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "2095 26.0 59.0 8.0 54.0 149.0\n", - "2096 NaN 47.0 90.0 92.0 66.0\n", - "2097 5.0 97.0 73.0 140.0 104.0\n", - "2098 102.0 6.0 7.0 5.0 119.0\n", - "2099 97.0 19.0 77.0 NaN 48.0" - ] - }, - "execution_count": 46, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.tail()" ] }, { "cell_type": "code", - "execution_count": 48, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "array([ 82., 4., 56., 47., 38., 18., 3., 35., 86., 19., 76.,\n", - " 31., 64., 72., 55., 96., 63., 28., 85., 109., 2., 110.,\n", - " 70., 51., 6., 65., 24., 48., 44., 11., 114., 129., 87.,\n", - " 108., 125., nan, 140., 132., 91., 34., 54., 30., 12., 98.,\n", - " 142., 79., 13., 77., 40., 139., 39., 81., 112., 36., 22.,\n", - " 5., 120., 17., 127., 119., 59., 146., 89., 103., 8., 97.,\n", - " 130., 73., 83., 122., 95., 100., 41., 21., 136., 80., 101.,\n", - " 50., 27., 71., 16., 141., 126., 102., 145., 15., 52., 94.,\n", - " 10., 33., 137., 9., 128., 88., 26., 84., 93., 1., 7.,\n", - " 131., 107., 148., 0., 105., 66., 32., 115., 118., 58., 53.,\n", - " 29., 42., 57., 62., 25., 60., 69., 133., 68., 20., 106.,\n", - " 147., 78., 90., 124., 149., 92., 75., 117., 143., 99., 37.,\n", - " 123., 45., 61., 121., 135., 138., 116., 14., 104., 74., 46.,\n", - " 111., 23., 43., 49., 144., 113., 67., 134.])" - ] - }, - "execution_count": 48, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "# 去重之后的数据\n", "df['Python'].unique()" @@ -4854,166 +298,18 @@ }, { "cell_type": "code", - "execution_count": 49, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "143.0 20\n", - "136.0 20\n", - "102.0 19\n", - "105.0 19\n", - "26.0 19\n", - "69.0 19\n", - "31.0 18\n", - "148.0 18\n", - "75.0 18\n", - "139.0 18\n", - "1.0 18\n", - "35.0 17\n", - "140.0 17\n", - "110.0 17\n", - "125.0 17\n", - "146.0 17\n", - "141.0 17\n", - "64.0 16\n", - "30.0 16\n", - "79.0 16\n", - "73.0 16\n", - "40.0 16\n", - "10.0 15\n", - "6.0 15\n", - "65.0 15\n", - "81.0 15\n", - "28.0 15\n", - "48.0 15\n", - "92.0 15\n", - "103.0 15\n", - " ..\n", - "104.0 9\n", - "12.0 9\n", - "116.0 9\n", - "13.0 9\n", - "59.0 9\n", - "93.0 9\n", - "124.0 9\n", - "85.0 8\n", - "135.0 8\n", - "131.0 8\n", - "68.0 8\n", - "66.0 8\n", - "62.0 8\n", - "120.0 8\n", - "17.0 8\n", - "25.0 8\n", - "145.0 7\n", - "58.0 7\n", - "134.0 7\n", - "113.0 7\n", - "123.0 7\n", - "39.0 7\n", - "34.0 7\n", - "43.0 7\n", - "74.0 6\n", - "144.0 6\n", - "132.0 6\n", - "142.0 5\n", - "67.0 5\n", - "49.0 5\n", - "Name: Python, Length: 150, dtype: int64" - ] - }, - "execution_count": 49, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df['Python'].value_counts()" ] }, { "cell_type": "code", - "execution_count": 53, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "8.0 21\n", - "96.0 19\n", - "118.0 19\n", - "24.0 19\n", - "43.0 19\n", - "27.0 19\n", - "19.0 19\n", - "41.0 18\n", - "0.0 18\n", - "3.0 18\n", - "52.0 18\n", - "4.0 17\n", - "137.0 17\n", - "1.0 17\n", - "101.0 17\n", - "51.0 17\n", - "39.0 17\n", - "100.0 17\n", - "127.0 17\n", - "115.0 16\n", - "33.0 16\n", - "112.0 16\n", - "92.0 16\n", - "126.0 16\n", - "133.0 15\n", - "32.0 15\n", - "89.0 15\n", - "95.0 15\n", - "36.0 15\n", - "93.0 15\n", - " ..\n", - "12.0 9\n", - "28.0 9\n", - "106.0 9\n", - "45.0 9\n", - "80.0 9\n", - "84.0 9\n", - "58.0 9\n", - "79.0 9\n", - "71.0 9\n", - "83.0 9\n", - "142.0 9\n", - "7.0 9\n", - "6.0 8\n", - "61.0 8\n", - "149.0 8\n", - "34.0 8\n", - "20.0 8\n", - "38.0 8\n", - "130.0 8\n", - "104.0 7\n", - "120.0 7\n", - "56.0 7\n", - "146.0 7\n", - "98.0 7\n", - "134.0 6\n", - "123.0 6\n", - "35.0 6\n", - "87.0 5\n", - "42.0 5\n", - "119.0 4\n", - "Name: En, Length: 150, dtype: int64" - ] - }, - "execution_count": 53, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "en = df['En'].value_counts()\n", "en" @@ -5021,44 +317,20 @@ }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "8.0" - ] - }, - "execution_count": 54, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "en.index[0]" ] }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Python 75.0\n", - "En 74.0\n", - "Math 77.5\n", - "Physic 73.0\n", - "Chem 72.0\n", - "dtype: float64 \n" - ] - } - ], + "outputs": [], "source": [ "s = df.median()\n", "print(s,type(s))" @@ -5066,7 +338,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -5077,25 +349,9 @@ }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 143.0\n", - "En 8.0\n", - "Math 80.0\n", - "Physic 31.0\n", - "Chem 125.0\n", - "dtype: float64" - ] - }, - "execution_count": 57, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "s = Series(zhongshu,index = df.columns)\n", "s" @@ -5103,605 +359,9 @@ }, { "cell_type": "code", - "execution_count": 60, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.031.090.0
10347.0100.0147.0138.099.0
10438.046.080.075.044.0
10518.011.0122.03.0126.0
10656.026.0106.014.0139.0
1073.0137.075.067.0144.0
10835.047.0102.060.063.0
10986.0126.080.088.0149.0
11019.0140.080.035.033.0
11176.08.05.011.033.0
11231.054.091.0119.069.0
11364.037.050.023.021.0
11472.057.0138.015.021.0
11555.0120.0104.032.025.0
11696.024.089.031.0146.0
11763.08.08.064.089.0
11828.08.0125.082.074.0
11985.039.070.0132.0111.0
120109.090.080.074.039.0
1212.08.0103.0114.065.0
122110.029.099.080.057.0
123109.088.081.0135.071.0
12470.0103.0134.0121.0121.0
12551.092.0117.031.043.0
1266.092.097.059.0105.0
12765.090.052.0148.022.0
1284.0129.017.0119.013.0
12924.0100.0107.028.0139.0
..................
2070127.077.024.016.0125.0
207193.061.09.028.022.0
2072116.061.054.08.061.0
20734.072.0140.031.034.0
2074143.0108.0123.032.033.0
207546.08.0135.0124.0113.0
2076143.018.0136.038.0125.0
2077143.011.0129.054.0119.0
207884.055.0129.037.087.0
207995.050.045.019.084.0
2080124.074.065.031.053.0
208126.035.0149.0145.0127.0
208219.021.0101.03.089.0
208384.08.0131.071.04.0
208428.074.0105.068.089.0
208523.093.084.097.088.0
208686.0133.026.0125.013.0
208721.0124.040.031.05.0
208820.015.035.031.037.0
208996.0123.0123.05.064.0
209022.043.092.078.060.0
209116.031.017.060.058.0
209265.018.013.013.034.0
209369.049.0109.040.058.0
2094128.046.010.082.0111.0
209526.059.08.054.0149.0
2096143.047.090.092.066.0
20975.097.073.0140.0104.0
2098102.06.07.05.0119.0
209997.019.077.031.048.0
\n", - "

2000 rows × 5 columns

\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 82.0 89.0 99.0 101.0 125.0\n", - "101 4.0 31.0 109.0 32.0 5.0\n", - "102 56.0 103.0 56.0 31.0 90.0\n", - "103 47.0 100.0 147.0 138.0 99.0\n", - "104 38.0 46.0 80.0 75.0 44.0\n", - "105 18.0 11.0 122.0 3.0 126.0\n", - "106 56.0 26.0 106.0 14.0 139.0\n", - "107 3.0 137.0 75.0 67.0 144.0\n", - "108 35.0 47.0 102.0 60.0 63.0\n", - "109 86.0 126.0 80.0 88.0 149.0\n", - "110 19.0 140.0 80.0 35.0 33.0\n", - "111 76.0 8.0 5.0 11.0 33.0\n", - "112 31.0 54.0 91.0 119.0 69.0\n", - "113 64.0 37.0 50.0 23.0 21.0\n", - "114 72.0 57.0 138.0 15.0 21.0\n", - "115 55.0 120.0 104.0 32.0 25.0\n", - "116 96.0 24.0 89.0 31.0 146.0\n", - "117 63.0 8.0 8.0 64.0 89.0\n", - "118 28.0 8.0 125.0 82.0 74.0\n", - "119 85.0 39.0 70.0 132.0 111.0\n", - "120 109.0 90.0 80.0 74.0 39.0\n", - "121 2.0 8.0 103.0 114.0 65.0\n", - "122 110.0 29.0 99.0 80.0 57.0\n", - "123 109.0 88.0 81.0 135.0 71.0\n", - "124 70.0 103.0 134.0 121.0 121.0\n", - "125 51.0 92.0 117.0 31.0 43.0\n", - "126 6.0 92.0 97.0 59.0 105.0\n", - "127 65.0 90.0 52.0 148.0 22.0\n", - "128 4.0 129.0 17.0 119.0 13.0\n", - "129 24.0 100.0 107.0 28.0 139.0\n", - "... ... ... ... ... ...\n", - "2070 127.0 77.0 24.0 16.0 125.0\n", - "2071 93.0 61.0 9.0 28.0 22.0\n", - "2072 116.0 61.0 54.0 8.0 61.0\n", - "2073 4.0 72.0 140.0 31.0 34.0\n", - "2074 143.0 108.0 123.0 32.0 33.0\n", - "2075 46.0 8.0 135.0 124.0 113.0\n", - "2076 143.0 18.0 136.0 38.0 125.0\n", - "2077 143.0 11.0 129.0 54.0 119.0\n", - "2078 84.0 55.0 129.0 37.0 87.0\n", - "2079 95.0 50.0 45.0 19.0 84.0\n", - "2080 124.0 74.0 65.0 31.0 53.0\n", - "2081 26.0 35.0 149.0 145.0 127.0\n", - "2082 19.0 21.0 101.0 3.0 89.0\n", - "2083 84.0 8.0 131.0 71.0 4.0\n", - "2084 28.0 74.0 105.0 68.0 89.0\n", - "2085 23.0 93.0 84.0 97.0 88.0\n", - "2086 86.0 133.0 26.0 125.0 13.0\n", - "2087 21.0 124.0 40.0 31.0 5.0\n", - "2088 20.0 15.0 35.0 31.0 37.0\n", - "2089 96.0 123.0 123.0 5.0 64.0\n", - "2090 22.0 43.0 92.0 78.0 60.0\n", - "2091 16.0 31.0 17.0 60.0 58.0\n", - "2092 65.0 18.0 13.0 13.0 34.0\n", - "2093 69.0 49.0 109.0 40.0 58.0\n", - "2094 128.0 46.0 10.0 82.0 111.0\n", - "2095 26.0 59.0 8.0 54.0 149.0\n", - "2096 143.0 47.0 90.0 92.0 66.0\n", - "2097 5.0 97.0 73.0 140.0 104.0\n", - "2098 102.0 6.0 7.0 5.0 119.0\n", - "2099 97.0 19.0 77.0 31.0 48.0\n", - "\n", - "[2000 rows x 5 columns]" - ] - }, - "execution_count": 60, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df2 = df.fillna(s)\n", "df2" @@ -5709,285 +369,27 @@ }, { "cell_type": "code", - "execution_count": 61, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 0\n", - "En 0\n", - "Math 0\n", - "Physic 0\n", - "Chem 0\n", - "dtype: int64" - ] - }, - "execution_count": 61, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df2.isnull().sum()" ] }, { "cell_type": "code", - "execution_count": 62, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/plain": [ - "Python 190\n", - "En 200\n", - "Math 194\n", - "Physic 189\n", - "Chem 181\n", - "dtype: int64" - ] - }, - "execution_count": 62, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "df.isnull().sum()" ] }, { "cell_type": "code", - "execution_count": 65, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.0NaN90.0
10347.0100.0147.0138.099.0
10438.046.0NaN75.044.0
10518.011.0122.03.0126.0
10656.026.0106.014.0139.0
1073.0137.075.067.0144.0
10835.047.0102.060.063.0
10986.0126.0NaN88.0149.0
11019.0140.0NaN35.033.0
11176.0NaN5.011.033.0
11231.054.091.0119.069.0
11364.037.050.023.021.0
11472.057.0138.015.021.0
11555.0120.0104.032.025.0
11696.024.089.0NaN146.0
11763.0NaN8.064.089.0
11828.0NaN125.082.074.0
11985.039.070.0132.0111.0
\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 82.0 89.0 99.0 101.0 125.0\n", - "101 4.0 31.0 109.0 32.0 5.0\n", - "102 56.0 103.0 56.0 NaN 90.0\n", - "103 47.0 100.0 147.0 138.0 99.0\n", - "104 38.0 46.0 NaN 75.0 44.0\n", - "105 18.0 11.0 122.0 3.0 126.0\n", - "106 56.0 26.0 106.0 14.0 139.0\n", - "107 3.0 137.0 75.0 67.0 144.0\n", - "108 35.0 47.0 102.0 60.0 63.0\n", - "109 86.0 126.0 NaN 88.0 149.0\n", - "110 19.0 140.0 NaN 35.0 33.0\n", - "111 76.0 NaN 5.0 11.0 33.0\n", - "112 31.0 54.0 91.0 119.0 69.0\n", - "113 64.0 37.0 50.0 23.0 21.0\n", - "114 72.0 57.0 138.0 15.0 21.0\n", - "115 55.0 120.0 104.0 32.0 25.0\n", - "116 96.0 24.0 89.0 NaN 146.0\n", - "117 63.0 NaN 8.0 64.0 89.0\n", - "118 28.0 NaN 125.0 82.0 74.0\n", - "119 85.0 39.0 70.0 132.0 111.0" - ] - }, - "execution_count": 65, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "df3 = df.iloc[:20]\n", "df3" @@ -5995,233 +397,9 @@ }, { "cell_type": "code", - "execution_count": 70, - "metadata": { - "collapsed": true - }, - "outputs": [ - { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMathPhysicChem
10082.089.099.0101.0125.0
1014.031.0109.032.05.0
10256.0103.056.090.090.0
10347.0100.0147.0138.099.0
10438.046.075.075.044.0
10518.011.0122.03.0126.0
10656.026.0106.014.0139.0
1073.0137.075.067.0144.0
10835.047.0102.060.063.0
10986.0126.088.088.0149.0
11019.0140.035.035.033.0
11176.05.05.011.033.0
11231.054.091.0119.069.0
11364.037.050.023.021.0
11472.057.0138.015.021.0
11555.0120.0104.032.025.0
11696.024.089.0146.0146.0
11763.08.08.064.089.0
11828.0125.0125.082.074.0
11985.039.070.0132.0111.0
\n", - "
" - ], - "text/plain": [ - " Python En Math Physic Chem\n", - "100 82.0 89.0 99.0 101.0 125.0\n", - "101 4.0 31.0 109.0 32.0 5.0\n", - "102 56.0 103.0 56.0 90.0 90.0\n", - "103 47.0 100.0 147.0 138.0 99.0\n", - "104 38.0 46.0 75.0 75.0 44.0\n", - "105 18.0 11.0 122.0 3.0 126.0\n", - "106 56.0 26.0 106.0 14.0 139.0\n", - "107 3.0 137.0 75.0 67.0 144.0\n", - "108 35.0 47.0 102.0 60.0 63.0\n", - "109 86.0 126.0 88.0 88.0 149.0\n", - "110 19.0 140.0 35.0 35.0 33.0\n", - "111 76.0 5.0 5.0 11.0 33.0\n", - "112 31.0 54.0 91.0 119.0 69.0\n", - "113 64.0 37.0 50.0 23.0 21.0\n", - "114 72.0 57.0 138.0 15.0 21.0\n", - "115 55.0 120.0 104.0 32.0 25.0\n", - "116 96.0 24.0 89.0 146.0 146.0\n", - "117 63.0 8.0 8.0 64.0 89.0\n", - "118 28.0 125.0 125.0 82.0 74.0\n", - "119 85.0 39.0 70.0 132.0 111.0" - ] - }, - "execution_count": 70, - "metadata": {}, - "output_type": "execute_result" - } - ], + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [ "'''method : {'backfill', 'bfill', 'pad', 'ffill', None}, default None\n", " Method to use for filling holes in reindexed Series\n", @@ -6232,20 +410,9 @@ }, { "cell_type": "code", - "execution_count": 71, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "(2000, 5)" - ] - }, - "execution_count": 71, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "#数据量足够大，空数据比较少，直接删除\n", "df.shape" @@ -6277,7 +444,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225.ipynb" "b/Day76-90/code/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225.ipynb" index 01f9dd637..d8e0d1ee9 100644 --- "a/Day76-90/code/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225.ipynb" +++ "b/Day76-90/code/4-pandas\345\244\232\345\261\202\347\264\242\345\274\225.ipynb" @@ -82,95 +82,21 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": 1, "metadata": { "scrolled": true }, "outputs": [ { - "data": { - "text/html": [ - "
\n", - "\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
PythonEnMath
张三期中73525
期末373656
李四期中14981142
期末711380
王五期中1194103
期末2512183
\n", - "
" - ], - "text/plain": [ - " Python En Math\n", - "张三 期中 73 5 25\n", - " 期末 37 36 56\n", - "李四 期中 149 81 142\n", - " 期末 71 138 0\n", - "王五 期中 11 94 103\n", - " 期末 25 121 83" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" + "ename": "NameError", + "evalue": "name 'DataFrame' is not defined", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mnp\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandom\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mrandint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;36m0\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m150\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0msize\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0;36m6\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;36m3\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mcolumns\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'Python'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'En'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'Math'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mindex\u001b[0m \u001b[0;34m=\u001b[0m\u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mMultiIndex\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mfrom_product\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'张三'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'李四'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'王五'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'期中'\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m'期末'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 2\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0mdf\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mNameError\u001b[0m: name 'DataFrame' is not defined" + ] } ], "source": [ @@ -560,7 +486,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" "b/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" index 22e1c8e38..4bcaad27c 100644 --- "a/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" +++ "b/Day76-90/code/5-pandas\345\244\232\345\261\202\347\264\242\345\274\225\350\256\241\347\256\227.ipynb" @@ -992,7 +992,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220.ipynb" "b/Day76-90/code/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220.ipynb" index e128ee4a1..7df4f33a7 100644 --- "a/Day76-90/code/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220.ipynb" +++ "b/Day76-90/code/6-pandas\346\225\260\346\215\256\351\233\206\346\210\220.ipynb" @@ -1201,7 +1201,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge.ipynb" "b/Day76-90/code/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge.ipynb" index 8b2eefcef..06fd9f690 100644 --- "a/Day76-90/code/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge.ipynb" +++ "b/Day76-90/code/7-pandas\346\225\260\346\215\256\351\233\206\346\210\220merge.ipynb" @@ -1264,7 +1264,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4, diff --git "a/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" "b/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" index 7c16aff82..e9dddcc7e 100644 --- "a/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" +++ "b/Day76-90/code/8-pandas\345\210\206\347\273\204\350\201\232\345\220\210\346\223\215\344\275\234.ipynb" @@ -869,7 +869,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.6.5" + "version": "3.7.7" } }, "nbformat": 4,