From fad25be6ef29dd888d0f88c5f35e5c06e786532b Mon Sep 17 00:00:00 2001 From: Soumak Majumdar <41966015+SoumakMajumdar@users.noreply.github.com> Date: Mon, 31 Dec 2018 20:06:31 +0530 Subject: [PATCH] Assignment 3 completed --- SoumakMajumdar.ipynb | 3266 +++++++++++++++++++++++++++++++++++++++++- 1 file changed, 3236 insertions(+), 30 deletions(-) diff --git a/SoumakMajumdar.ipynb b/SoumakMajumdar.ipynb index 9e2543a..0ae2d07 100644 --- a/SoumakMajumdar.ipynb +++ b/SoumakMajumdar.ipynb @@ -1,32 +1,3238 @@ { - "cells": [ - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" + "nbformat": 4, + "nbformat_minor": 0, + "metadata": { + "colab": { + "name": "SoumakMajumdar.ipynb", + "version": "0.3.2", + "provenance": [] + }, + "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": "3.5.2" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} + "cells": [ + { + "metadata": { + "id": "2LTtpUJEibjg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "# Pandas Exercise :\n", + "\n", + "\n", + "#### import necessary modules" + ] + }, + { + "metadata": { + "id": "_QrMy3uCnCHE", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "import numpy as np\n", + "import pandas as pd" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "tp-cTCyWi8mR", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Load url = \"https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data\" to a dataframe named wine_df\n", + "\n", + "This is a wine dataset\n", + "\n" + ] + }, + { + "metadata": { + "id": "lfxl4ZFlnspu", + "colab_type": "code", + "colab": {} + }, + "cell_type": "code", + "source": [ + "wine_df = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', header = None)" + ], + "execution_count": 0, + "outputs": [] + }, + { + "metadata": { + "id": "BF9MMjoZjSlg", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### print first five rows" + ] + }, + { + "metadata": { + "id": "kuqa0-_nn54F", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "3cc62cd3-e5d5-44df-d876-4ae380b99130" + }, + "cell_type": "code", + "source": [ + "wine_df.head()" + ], + "execution_count": 5, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
012345678910111213
0114.231.712.4315.61272.803.060.282.295.641.043.921065
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93735
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n", + "0 1 14.23 1.71 2.43 15.6 127 2.80 3.06 0.28 2.29 5.64 1.04 3.92 \n", + "1 1 13.20 1.78 2.14 11.2 100 2.65 2.76 0.26 1.28 4.38 1.05 3.40 \n", + "2 1 13.16 2.36 2.67 18.6 101 2.80 3.24 0.30 2.81 5.68 1.03 3.17 \n", + "3 1 14.37 1.95 2.50 16.8 113 3.85 3.49 0.24 2.18 7.80 0.86 3.45 \n", + "4 1 13.24 2.59 2.87 21.0 118 2.80 2.69 0.39 1.82 4.32 1.04 2.93 \n", + "\n", + " 13 \n", + "0 1065 \n", + "1 1050 \n", + "2 1185 \n", + "3 1480 \n", + "4 735 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 5 + } + ] + }, + { + "metadata": { + "id": "Tet6P2DvjY3T", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### assign wine_df to a different variable wine_df_copy and then delete all odd rows of wine_df_copy" + ] + }, + { + "metadata": { + "id": "l6Yf77iboFyW", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 204 + }, + "outputId": "00f4aee0-d2fe-4680-925f-f40cb4d6340c" + }, + "cell_type": "code", + "source": [ + "#wine_df_copy = wine_df\n", + "wine_df_copy = wine_df.copy()\n", + "\n", + "#wine_df_copy.head()\n", + "\n", + "#for x in range(149):\n", + "\n", + "wine_df_copy.drop(wine_df_copy.index[1::2], inplace=True)\n", + "wine_df_copy.head()" + ], + "execution_count": 7, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
012345678910111213
0114.231.712.4315.61272.83.060.282.295.641.043.921065
2113.162.362.6718.61012.83.240.302.815.681.033.171185
4113.242.592.8721.01182.82.690.391.824.321.042.93735
6114.391.872.4514.6962.52.520.301.985.251.023.581290
8114.831.642.1714.0972.82.980.291.985.201.082.851045
\n", + "
" + ], + "text/plain": [ + " 0 1 2 3 4 5 6 7 8 9 10 11 12 \\\n", + "0 1 14.23 1.71 2.43 15.6 127 2.8 3.06 0.28 2.29 5.64 1.04 3.92 \n", + "2 1 13.16 2.36 2.67 18.6 101 2.8 3.24 0.30 2.81 5.68 1.03 3.17 \n", + "4 1 13.24 2.59 2.87 21.0 118 2.8 2.69 0.39 1.82 4.32 1.04 2.93 \n", + "6 1 14.39 1.87 2.45 14.6 96 2.5 2.52 0.30 1.98 5.25 1.02 3.58 \n", + "8 1 14.83 1.64 2.17 14.0 97 2.8 2.98 0.29 1.98 5.20 1.08 2.85 \n", + "\n", + " 13 \n", + "0 1065 \n", + "2 1185 \n", + "4 735 \n", + "6 1290 \n", + "8 1045 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 7 + } + ] + }, + { + "metadata": { + "id": "o6Cs6T1Rjz71", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Assign the columns as below:\n", + "\n", + "The attributes are (dontated by Riccardo Leardi, riclea '@' anchem.unige.it): \n", + "1) Alcohol \n", + "2) Malic acid \n", + "3) Ash \n", + "4) Alcalinity of ash \n", + "5) Magnesium \n", + "6) Total phenols \n", + "7) Flavanoids \n", + "8) Nonflavanoid phenols \n", + "9) Proanthocyanins \n", + "10)Color intensity \n", + "11)Hue \n", + "12)OD280/OD315 of diluted wines \n", + "13)Proline " + ] + }, + { + "metadata": { + "id": "VF5R1eQColue", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "52d1786a-8d5c-4dde-9dcd-b5078760052c" + }, + "cell_type": "code", + "source": [ + "wine_df.columns = ['Class', 'Alcohol', 'Malic acid', 'Ash', \\\n", + " 'Alcalinity of ash', 'Magnesium', 'Total phenols', \\\n", + " 'Flavanoids', 'Nonflavanoid phenols', 'Proanthocyanins', \\\n", + " 'Color intensity', 'Hue', 'OD280/OD315 of diluted wines', \\\n", + " 'Proline' ]\n", + "wine_df.head()" + ], + "execution_count": 8, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
0114.231.712.4315.61272.803.060.282.295.641.043.921065
1113.201.782.1411.21002.652.760.261.284.381.053.401050
2113.162.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93735
\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 14.23 1.71 2.43 15.6 127 \n", + "1 1 13.20 1.78 2.14 11.2 100 \n", + "2 1 13.16 2.36 2.67 18.6 101 \n", + "3 1 14.37 1.95 2.50 16.8 113 \n", + "4 1 13.24 2.59 2.87 21.0 118 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.64 1.04 3.92 1065 \n", + "1 4.38 1.05 3.40 1050 \n", + "2 5.68 1.03 3.17 1185 \n", + "3 7.80 0.86 3.45 1480 \n", + "4 4.32 1.04 2.93 735 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 8 + } + ] + }, + { + "metadata": { + "id": "Zqi7hwWpkNbH", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Set the values of the first 3 rows from alcohol as NaN" + ] + }, + { + "metadata": { + "id": "GgdgtRD9ov0P", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "f87257aa-f637-4d77-e306-b2bf5fa3b67c" + }, + "cell_type": "code", + "source": [ + "wine_df.iloc[:3, 1] = 'NaN'\n", + "#wine_df.iloc[:3, 1] = None\n", + "wine_df.head()" + ], + "execution_count": 10, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
01NaN1.712.4315.61272.803.060.282.295.641.043.921065
11NaN1.782.1411.21002.652.760.261.284.381.053.401050
21NaN2.362.6718.61012.803.240.302.815.681.033.171185
3114.371.952.5016.81133.853.490.242.187.800.863.451480
4113.242.592.8721.01182.802.690.391.824.321.042.93735
\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 NaN 1.71 2.43 15.6 127 \n", + "1 1 NaN 1.78 2.14 11.2 100 \n", + "2 1 NaN 2.36 2.67 18.6 101 \n", + "3 1 14.37 1.95 2.50 16.8 113 \n", + "4 1 13.24 2.59 2.87 21.0 118 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.64 1.04 3.92 1065 \n", + "1 4.38 1.05 3.40 1050 \n", + "2 5.68 1.03 3.17 1185 \n", + "3 7.80 0.86 3.45 1480 \n", + "4 4.32 1.04 2.93 735 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 10 + } + ] + }, + { + "metadata": { + "id": "RQMNI2UHkP3o", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Create an array of 10 random numbers uptill 10 and assign it to a variable named `random`" + ] + }, + { + "metadata": { + "id": "m0Y8PoJepZN1", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "fb8b8661-bcc8-433d-b2a8-8fa8a944e1b8" + }, + "cell_type": "code", + "source": [ + "random = np.random.randint(0,10,10)\n", + "random" + ], + "execution_count": 11, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "array([1, 0, 5, 8, 2, 3, 4, 7, 7, 3])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 11 + } + ] + }, + { + "metadata": { + "id": "hELUakyXmFSu", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Use random numbers you generated as an index and assign NaN value to each of cell of the column alcohol" + ] + }, + { + "metadata": { + "id": "6uAbmp3VqM5V", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 376 + }, + "outputId": "ac856cd4-b1bc-41b2-9f16-86b5fa32144d" + }, + "cell_type": "code", + "source": [ + "for i in random:\n", + " wine_df.iloc[i, 1] = 'NaN'\n", + " # wine_df.iloc[i, 1] = None\n", + "wine_df.head(10)" + ], + "execution_count": 12, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
01NaN1.712.4315.61272.803.060.282.295.641.043.921065
11NaN1.782.1411.21002.652.760.261.284.381.053.401050
21NaN2.362.6718.61012.803.240.302.815.681.033.171185
31NaN1.952.5016.81133.853.490.242.187.800.863.451480
41NaN2.592.8721.01182.802.690.391.824.321.042.93735
51NaN1.762.4515.21123.273.390.341.976.751.052.851450
6114.391.872.4514.6962.502.520.301.985.251.023.581290
71NaN2.152.6117.61212.602.510.311.255.051.063.581295
81NaN1.642.1714.0972.802.980.291.985.201.082.851045
9113.861.352.2716.0982.983.150.221.857.221.013.551045
\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 NaN 1.71 2.43 15.6 127 \n", + "1 1 NaN 1.78 2.14 11.2 100 \n", + "2 1 NaN 2.36 2.67 18.6 101 \n", + "3 1 NaN 1.95 2.50 16.8 113 \n", + "4 1 NaN 2.59 2.87 21.0 118 \n", + "5 1 NaN 1.76 2.45 15.2 112 \n", + "6 1 14.39 1.87 2.45 14.6 96 \n", + "7 1 NaN 2.15 2.61 17.6 121 \n", + "8 1 NaN 1.64 2.17 14.0 97 \n", + "9 1 13.86 1.35 2.27 16.0 98 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "5 3.27 3.39 0.34 1.97 \n", + "6 2.50 2.52 0.30 1.98 \n", + "7 2.60 2.51 0.31 1.25 \n", + "8 2.80 2.98 0.29 1.98 \n", + "9 2.98 3.15 0.22 1.85 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.64 1.04 3.92 1065 \n", + "1 4.38 1.05 3.40 1050 \n", + "2 5.68 1.03 3.17 1185 \n", + "3 7.80 0.86 3.45 1480 \n", + "4 4.32 1.04 2.93 735 \n", + "5 6.75 1.05 2.85 1450 \n", + "6 5.25 1.02 3.58 1290 \n", + "7 5.05 1.06 3.58 1295 \n", + "8 5.20 1.08 2.85 1045 \n", + "9 7.22 1.01 3.55 1045 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 12 + } + ] + }, + { + "metadata": { + "id": "PHyK_vRsmRwV", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### How many missing values do we have? " + ] + }, + { + "metadata": { + "id": "nV0XfOyJqfB2", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 272 + }, + "outputId": "ee3ad5ce-0729-49ee-d2bc-883d3ed51d87" + }, + "cell_type": "code", + "source": [ + "wine_df.isnull().sum()" + ], + "execution_count": 13, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "Class 0\n", + "Alcohol 0\n", + "Malic acid 0\n", + "Ash 0\n", + "Alcalinity of ash 0\n", + "Magnesium 0\n", + "Total phenols 0\n", + "Flavanoids 0\n", + "Nonflavanoid phenols 0\n", + "Proanthocyanins 0\n", + "Color intensity 0\n", + "Hue 0\n", + "OD280/OD315 of diluted wines 0\n", + "Proline 0\n", + "dtype: int64" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 13 + } + ] + }, + { + "metadata": { + "id": "-Fd4WBklmf1_", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "#### Delete the rows that contain missing values " + ] + }, + { + "metadata": { + "id": "HcIncMVRqzp9", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 1986 + }, + "outputId": "d602d144-5cd0-4bb3-b101-d0a437479dc5" + }, + "cell_type": "code", + "source": [ + "wine_df.dropna()" + ], + "execution_count": 14, + "outputs": [ + { + "output_type": "execute_result", + "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", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
01NaN1.712.4315.61272.803.060.282.295.6400001.043.921065
11NaN1.782.1411.21002.652.760.261.284.3800001.053.401050
21NaN2.362.6718.61012.803.240.302.815.6800001.033.171185
31NaN1.952.5016.81133.853.490.242.187.8000000.863.451480
41NaN2.592.8721.01182.802.690.391.824.3200001.042.93735
51NaN1.762.4515.21123.273.390.341.976.7500001.052.851450
6114.391.872.4514.6962.502.520.301.985.2500001.023.581290
71NaN2.152.6117.61212.602.510.311.255.0500001.063.581295
81NaN1.642.1714.0972.802.980.291.985.2000001.082.851045
9113.861.352.2716.0982.983.150.221.857.2200001.013.551045
10114.12.162.3018.01052.953.320.222.385.7500001.253.171510
11114.121.482.3216.8952.202.430.261.575.0000001.172.821280
12113.751.732.4116.0892.602.760.291.815.6000001.152.901320
13114.751.732.3911.4913.103.690.432.815.4000001.252.731150
14114.381.872.3812.01023.303.640.292.967.5000001.203.001547
15113.631.812.7017.21122.852.910.301.467.3000001.282.881310
16114.31.922.7220.01202.803.140.331.976.2000001.072.651280
17113.831.572.6220.01152.953.400.401.726.6000001.132.571130
18114.191.592.4816.51083.303.930.321.868.7000001.232.821680
19113.643.102.5615.21162.703.030.171.665.1000000.963.36845
20114.061.632.2816.01263.003.170.242.105.6500001.093.71780
21112.933.802.6518.61022.412.410.251.984.5000001.033.52770
22113.711.862.3616.61012.612.880.271.693.8000001.114.001035
23112.851.602.5217.8952.482.370.261.463.9300001.093.631015
24113.51.812.6120.0962.532.610.281.663.5200001.123.82845
25113.052.053.2225.01242.632.680.471.923.5800001.133.20830
26113.391.772.6216.1932.852.940.341.454.8000000.923.221195
27113.31.722.1417.0942.402.190.271.353.9500001.022.771285
28113.871.902.8019.41072.952.970.371.764.5000001.253.40915
29114.021.682.2116.0962.652.330.261.984.7000001.043.591035
.............................................
148313.323.242.3821.5921.930.760.451.258.4200000.551.62650
149313.083.902.3621.51131.411.390.341.149.4000000.571.33550
150313.53.122.6224.01231.401.570.221.258.6000000.591.30500
151312.792.672.4822.01121.481.360.241.2610.8000000.481.47480
152313.111.902.7525.51162.201.280.261.567.1000000.611.33425
153313.233.302.2818.5981.800.830.611.8710.5200000.561.51675
154312.581.292.1020.01031.480.580.531.407.6000000.581.55640
155313.175.192.3222.0931.740.630.611.557.9000000.601.48725
156313.844.122.3819.5891.800.830.481.569.0100000.571.64480
157312.453.032.6427.0971.900.580.631.147.5000000.671.73880
158314.341.682.7025.0982.801.310.532.7013.0000000.571.96660
159313.481.672.6422.5892.601.100.522.2911.7500000.571.78620
160312.363.832.3821.0882.300.920.501.047.6500000.561.58520
161313.693.262.5420.01071.830.560.500.805.8800000.961.82680
162312.853.272.5822.01061.650.600.600.965.5800000.872.11570
163312.963.452.3518.51061.390.700.400.945.2800000.681.75675
164313.782.762.3022.0901.350.680.411.039.5800000.701.68615
165313.734.362.2622.5881.280.470.521.156.6200000.781.75520
166313.453.702.6023.01111.700.920.431.4610.6800000.851.56695
167312.823.372.3019.5881.480.660.400.9710.2600000.721.75685
168313.582.582.6924.51051.550.840.391.548.6600000.741.80750
169313.44.602.8625.01121.980.960.271.118.5000000.671.92630
170312.23.032.3219.0961.250.490.400.735.5000000.661.83510
171312.772.392.2819.5861.390.510.480.649.8999990.571.63470
172314.162.512.4820.0911.680.700.441.249.7000000.621.71660
173313.715.652.4520.5951.680.610.521.067.7000000.641.74740
174313.43.912.4823.01021.800.750.431.417.3000000.701.56750
175313.274.282.2620.01201.590.690.431.3510.2000000.591.56835
176313.172.592.3720.01201.650.680.531.469.3000000.601.62840
177314.134.102.7424.5962.050.760.561.359.2000000.611.60560
\n", + "

178 rows × 14 columns

\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 NaN 1.71 2.43 15.6 127 \n", + "1 1 NaN 1.78 2.14 11.2 100 \n", + "2 1 NaN 2.36 2.67 18.6 101 \n", + "3 1 NaN 1.95 2.50 16.8 113 \n", + "4 1 NaN 2.59 2.87 21.0 118 \n", + "5 1 NaN 1.76 2.45 15.2 112 \n", + "6 1 14.39 1.87 2.45 14.6 96 \n", + "7 1 NaN 2.15 2.61 17.6 121 \n", + "8 1 NaN 1.64 2.17 14.0 97 \n", + "9 1 13.86 1.35 2.27 16.0 98 \n", + "10 1 14.1 2.16 2.30 18.0 105 \n", + "11 1 14.12 1.48 2.32 16.8 95 \n", + "12 1 13.75 1.73 2.41 16.0 89 \n", + "13 1 14.75 1.73 2.39 11.4 91 \n", + "14 1 14.38 1.87 2.38 12.0 102 \n", + "15 1 13.63 1.81 2.70 17.2 112 \n", + "16 1 14.3 1.92 2.72 20.0 120 \n", + "17 1 13.83 1.57 2.62 20.0 115 \n", + "18 1 14.19 1.59 2.48 16.5 108 \n", + "19 1 13.64 3.10 2.56 15.2 116 \n", + "20 1 14.06 1.63 2.28 16.0 126 \n", + "21 1 12.93 3.80 2.65 18.6 102 \n", + "22 1 13.71 1.86 2.36 16.6 101 \n", + "23 1 12.85 1.60 2.52 17.8 95 \n", + "24 1 13.5 1.81 2.61 20.0 96 \n", + "25 1 13.05 2.05 3.22 25.0 124 \n", + "26 1 13.39 1.77 2.62 16.1 93 \n", + "27 1 13.3 1.72 2.14 17.0 94 \n", + "28 1 13.87 1.90 2.80 19.4 107 \n", + "29 1 14.02 1.68 2.21 16.0 96 \n", + ".. ... ... ... ... ... ... \n", + "148 3 13.32 3.24 2.38 21.5 92 \n", + "149 3 13.08 3.90 2.36 21.5 113 \n", + "150 3 13.5 3.12 2.62 24.0 123 \n", + "151 3 12.79 2.67 2.48 22.0 112 \n", + "152 3 13.11 1.90 2.75 25.5 116 \n", + "153 3 13.23 3.30 2.28 18.5 98 \n", + "154 3 12.58 1.29 2.10 20.0 103 \n", + "155 3 13.17 5.19 2.32 22.0 93 \n", + "156 3 13.84 4.12 2.38 19.5 89 \n", + "157 3 12.45 3.03 2.64 27.0 97 \n", + "158 3 14.34 1.68 2.70 25.0 98 \n", + "159 3 13.48 1.67 2.64 22.5 89 \n", + "160 3 12.36 3.83 2.38 21.0 88 \n", + "161 3 13.69 3.26 2.54 20.0 107 \n", + "162 3 12.85 3.27 2.58 22.0 106 \n", + "163 3 12.96 3.45 2.35 18.5 106 \n", + "164 3 13.78 2.76 2.30 22.0 90 \n", + "165 3 13.73 4.36 2.26 22.5 88 \n", + "166 3 13.45 3.70 2.60 23.0 111 \n", + "167 3 12.82 3.37 2.30 19.5 88 \n", + "168 3 13.58 2.58 2.69 24.5 105 \n", + "169 3 13.4 4.60 2.86 25.0 112 \n", + "170 3 12.2 3.03 2.32 19.0 96 \n", + "171 3 12.77 2.39 2.28 19.5 86 \n", + "172 3 14.16 2.51 2.48 20.0 91 \n", + "173 3 13.71 5.65 2.45 20.5 95 \n", + "174 3 13.4 3.91 2.48 23.0 102 \n", + "175 3 13.27 4.28 2.26 20.0 120 \n", + "176 3 13.17 2.59 2.37 20.0 120 \n", + "177 3 14.13 4.10 2.74 24.5 96 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "5 3.27 3.39 0.34 1.97 \n", + "6 2.50 2.52 0.30 1.98 \n", + "7 2.60 2.51 0.31 1.25 \n", + "8 2.80 2.98 0.29 1.98 \n", + "9 2.98 3.15 0.22 1.85 \n", + "10 2.95 3.32 0.22 2.38 \n", + "11 2.20 2.43 0.26 1.57 \n", + "12 2.60 2.76 0.29 1.81 \n", + "13 3.10 3.69 0.43 2.81 \n", + "14 3.30 3.64 0.29 2.96 \n", + "15 2.85 2.91 0.30 1.46 \n", + "16 2.80 3.14 0.33 1.97 \n", + "17 2.95 3.40 0.40 1.72 \n", + "18 3.30 3.93 0.32 1.86 \n", + "19 2.70 3.03 0.17 1.66 \n", + "20 3.00 3.17 0.24 2.10 \n", + "21 2.41 2.41 0.25 1.98 \n", + "22 2.61 2.88 0.27 1.69 \n", + "23 2.48 2.37 0.26 1.46 \n", + "24 2.53 2.61 0.28 1.66 \n", + "25 2.63 2.68 0.47 1.92 \n", + "26 2.85 2.94 0.34 1.45 \n", + "27 2.40 2.19 0.27 1.35 \n", + "28 2.95 2.97 0.37 1.76 \n", + "29 2.65 2.33 0.26 1.98 \n", + ".. ... ... ... ... \n", + "148 1.93 0.76 0.45 1.25 \n", + "149 1.41 1.39 0.34 1.14 \n", + "150 1.40 1.57 0.22 1.25 \n", + "151 1.48 1.36 0.24 1.26 \n", + "152 2.20 1.28 0.26 1.56 \n", + "153 1.80 0.83 0.61 1.87 \n", + "154 1.48 0.58 0.53 1.40 \n", + "155 1.74 0.63 0.61 1.55 \n", + "156 1.80 0.83 0.48 1.56 \n", + "157 1.90 0.58 0.63 1.14 \n", + "158 2.80 1.31 0.53 2.70 \n", + "159 2.60 1.10 0.52 2.29 \n", + "160 2.30 0.92 0.50 1.04 \n", + "161 1.83 0.56 0.50 0.80 \n", + "162 1.65 0.60 0.60 0.96 \n", + "163 1.39 0.70 0.40 0.94 \n", + "164 1.35 0.68 0.41 1.03 \n", + "165 1.28 0.47 0.52 1.15 \n", + "166 1.70 0.92 0.43 1.46 \n", + "167 1.48 0.66 0.40 0.97 \n", + "168 1.55 0.84 0.39 1.54 \n", + "169 1.98 0.96 0.27 1.11 \n", + "170 1.25 0.49 0.40 0.73 \n", + "171 1.39 0.51 0.48 0.64 \n", + "172 1.68 0.70 0.44 1.24 \n", + "173 1.68 0.61 0.52 1.06 \n", + "174 1.80 0.75 0.43 1.41 \n", + "175 1.59 0.69 0.43 1.35 \n", + "176 1.65 0.68 0.53 1.46 \n", + "177 2.05 0.76 0.56 1.35 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.640000 1.04 3.92 1065 \n", + "1 4.380000 1.05 3.40 1050 \n", + "2 5.680000 1.03 3.17 1185 \n", + "3 7.800000 0.86 3.45 1480 \n", + "4 4.320000 1.04 2.93 735 \n", + "5 6.750000 1.05 2.85 1450 \n", + "6 5.250000 1.02 3.58 1290 \n", + "7 5.050000 1.06 3.58 1295 \n", + "8 5.200000 1.08 2.85 1045 \n", + "9 7.220000 1.01 3.55 1045 \n", + "10 5.750000 1.25 3.17 1510 \n", + "11 5.000000 1.17 2.82 1280 \n", + "12 5.600000 1.15 2.90 1320 \n", + "13 5.400000 1.25 2.73 1150 \n", + "14 7.500000 1.20 3.00 1547 \n", + "15 7.300000 1.28 2.88 1310 \n", + "16 6.200000 1.07 2.65 1280 \n", + "17 6.600000 1.13 2.57 1130 \n", + "18 8.700000 1.23 2.82 1680 \n", + "19 5.100000 0.96 3.36 845 \n", + "20 5.650000 1.09 3.71 780 \n", + "21 4.500000 1.03 3.52 770 \n", + "22 3.800000 1.11 4.00 1035 \n", + "23 3.930000 1.09 3.63 1015 \n", + "24 3.520000 1.12 3.82 845 \n", + "25 3.580000 1.13 3.20 830 \n", + "26 4.800000 0.92 3.22 1195 \n", + "27 3.950000 1.02 2.77 1285 \n", + "28 4.500000 1.25 3.40 915 \n", + "29 4.700000 1.04 3.59 1035 \n", + ".. ... ... ... ... \n", + "148 8.420000 0.55 1.62 650 \n", + "149 9.400000 0.57 1.33 550 \n", + "150 8.600000 0.59 1.30 500 \n", + "151 10.800000 0.48 1.47 480 \n", + "152 7.100000 0.61 1.33 425 \n", + "153 10.520000 0.56 1.51 675 \n", + "154 7.600000 0.58 1.55 640 \n", + "155 7.900000 0.60 1.48 725 \n", + "156 9.010000 0.57 1.64 480 \n", + "157 7.500000 0.67 1.73 880 \n", + "158 13.000000 0.57 1.96 660 \n", + "159 11.750000 0.57 1.78 620 \n", + "160 7.650000 0.56 1.58 520 \n", + "161 5.880000 0.96 1.82 680 \n", + "162 5.580000 0.87 2.11 570 \n", + "163 5.280000 0.68 1.75 675 \n", + "164 9.580000 0.70 1.68 615 \n", + "165 6.620000 0.78 1.75 520 \n", + "166 10.680000 0.85 1.56 695 \n", + "167 10.260000 0.72 1.75 685 \n", + "168 8.660000 0.74 1.80 750 \n", + "169 8.500000 0.67 1.92 630 \n", + "170 5.500000 0.66 1.83 510 \n", + "171 9.899999 0.57 1.63 470 \n", + "172 9.700000 0.62 1.71 660 \n", + "173 7.700000 0.64 1.74 740 \n", + "174 7.300000 0.70 1.56 750 \n", + "175 10.200000 0.59 1.56 835 \n", + "176 9.300000 0.60 1.62 840 \n", + "177 9.200000 0.61 1.60 560 \n", + "\n", + "[178 rows x 14 columns]" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 14 + } + ] + }, + { + "metadata": { + "id": "DlpG8drhmz7W", + "colab_type": "text" + }, + "cell_type": "markdown", + "source": [ + "### BONUS: Play with the data set below" + ] + }, + { + "metadata": { + "id": "VeBKR2JVrGrV", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 34 + }, + "outputId": "360b9a4d-251b-4c22-e744-452b59fd1947" + }, + "cell_type": "code", + "source": [ + "Class = wine_df['Class'].unique()\n", + "\n", + "print(Class)" + ], + "execution_count": 15, + "outputs": [ + { + "output_type": "stream", + "text": [ + "[1 2 3]\n" + ], + "name": "stdout" + } + ] + }, + { + "metadata": { + "id": "xk6drVufrH1e", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "3552b3f9-c289-46c4-8632-815fd66a4bf5" + }, + "cell_type": "code", + "source": [ + "class1 = wine_df[wine_df['Class'] == Class[0]]\n", + "\n", + "class1.head()" + ], + "execution_count": 16, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
01NaN1.712.4315.61272.803.060.282.295.641.043.921065
11NaN1.782.1411.21002.652.760.261.284.381.053.401050
21NaN2.362.6718.61012.803.240.302.815.681.033.171185
31NaN1.952.5016.81133.853.490.242.187.800.863.451480
41NaN2.592.8721.01182.802.690.391.824.321.042.93735
\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "0 1 NaN 1.71 2.43 15.6 127 \n", + "1 1 NaN 1.78 2.14 11.2 100 \n", + "2 1 NaN 2.36 2.67 18.6 101 \n", + "3 1 NaN 1.95 2.50 16.8 113 \n", + "4 1 NaN 2.59 2.87 21.0 118 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "0 2.80 3.06 0.28 2.29 \n", + "1 2.65 2.76 0.26 1.28 \n", + "2 2.80 3.24 0.30 2.81 \n", + "3 3.85 3.49 0.24 2.18 \n", + "4 2.80 2.69 0.39 1.82 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "0 5.64 1.04 3.92 1065 \n", + "1 4.38 1.05 3.40 1050 \n", + "2 5.68 1.03 3.17 1185 \n", + "3 7.80 0.86 3.45 1480 \n", + "4 4.32 1.04 2.93 735 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 16 + } + ] + }, + { + "metadata": { + "id": "MhuwHF3VrMul", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "78f6298b-ea9c-43fc-e401-dc3c87ed6e1d" + }, + "cell_type": "code", + "source": [ + "class2 = wine_df[wine_df['Class'] == Class[1]]\n", + "\n", + "class2.head()" + ], + "execution_count": 17, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
59212.370.941.3610.6881.980.570.280.421.951.051.82520
60212.331.102.2816.01012.051.090.630.413.271.251.67680
61212.641.362.0216.81002.021.410.530.625.750.981.59450
62213.671.251.9218.0942.101.790.320.733.801.232.46630
63212.371.132.1619.0873.503.100.191.874.451.222.87420
\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "59 2 12.37 0.94 1.36 10.6 88 \n", + "60 2 12.33 1.10 2.28 16.0 101 \n", + "61 2 12.64 1.36 2.02 16.8 100 \n", + "62 2 13.67 1.25 1.92 18.0 94 \n", + "63 2 12.37 1.13 2.16 19.0 87 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "59 1.98 0.57 0.28 0.42 \n", + "60 2.05 1.09 0.63 0.41 \n", + "61 2.02 1.41 0.53 0.62 \n", + "62 2.10 1.79 0.32 0.73 \n", + "63 3.50 3.10 0.19 1.87 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "59 1.95 1.05 1.82 520 \n", + "60 3.27 1.25 1.67 680 \n", + "61 5.75 0.98 1.59 450 \n", + "62 3.80 1.23 2.46 630 \n", + "63 4.45 1.22 2.87 420 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 17 + } + ] + }, + { + "metadata": { + "id": "u4k25nzSrQId", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 221 + }, + "outputId": "15c9f2da-a283-4002-fa36-3b268995a761" + }, + "cell_type": "code", + "source": [ + "class3 = wine_df[wine_df['Class'] == Class[2]]\n", + "\n", + "class3.head()" + ], + "execution_count": 18, + "outputs": [ + { + "output_type": "execute_result", + "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", + "
ClassAlcoholMalic acidAshAlcalinity of ashMagnesiumTotal phenolsFlavanoidsNonflavanoid phenolsProanthocyaninsColor intensityHueOD280/OD315 of diluted winesProline
130312.861.352.3218.01221.511.250.210.944.100.761.29630
131312.882.992.4020.01041.301.220.240.835.400.741.42530
132312.812.312.4024.0981.151.090.270.835.700.661.36560
133312.73.552.3621.51061.701.200.170.845.000.781.29600
134312.511.242.2517.5852.000.580.601.255.450.751.51650
\n", + "
" + ], + "text/plain": [ + " Class Alcohol Malic acid Ash Alcalinity of ash Magnesium \\\n", + "130 3 12.86 1.35 2.32 18.0 122 \n", + "131 3 12.88 2.99 2.40 20.0 104 \n", + "132 3 12.81 2.31 2.40 24.0 98 \n", + "133 3 12.7 3.55 2.36 21.5 106 \n", + "134 3 12.51 1.24 2.25 17.5 85 \n", + "\n", + " Total phenols Flavanoids Nonflavanoid phenols Proanthocyanins \\\n", + "130 1.51 1.25 0.21 0.94 \n", + "131 1.30 1.22 0.24 0.83 \n", + "132 1.15 1.09 0.27 0.83 \n", + "133 1.70 1.20 0.17 0.84 \n", + "134 2.00 0.58 0.60 1.25 \n", + "\n", + " Color intensity Hue OD280/OD315 of diluted wines Proline \n", + "130 4.10 0.76 1.29 630 \n", + "131 5.40 0.74 1.42 530 \n", + "132 5.70 0.66 1.36 560 \n", + "133 5.00 0.78 1.29 600 \n", + "134 5.45 0.75 1.51 650 " + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 18 + } + ] + }, + { + "metadata": { + "id": "dnyQJo6srVDd", + "colab_type": "code", + "colab": { + "base_uri": "https://localhost:8080/", + "height": 2336 + }, + "outputId": "57ed260a-73d1-4322-9847-09f87ac6d9ce" + }, + "cell_type": "code", + "source": [ + "import matplotlib.pyplot as plt\n", + "\n", + "plt.pie(class1['Magnesium'])" + ], + "execution_count": 19, + "outputs": [ + { + "output_type": "execute_result", + "data": { + "text/plain": [ + "([,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ],\n", + " [Text(1.09778,0.0699051,''),\n", + " Text(1.08276,0.193966,''),\n", + " Text(1.0578,0.301777,''),\n", + " Text(1.01945,0.413179,''),\n", + " Text(0.964955,0.528075,''),\n", + " Text(0.897879,0.635463,''),\n", + " Text(0.826948,0.725367,''),\n", + " Text(0.743409,0.810767,''),\n", + " Text(0.650657,0.886931,''),\n", + " Text(0.561092,0.946137,''),\n", + " Text(0.462188,0.998189,''),\n", + " Text(0.360074,1.0394,''),\n", + " Text(0.262918,1.06812,''),\n", + " Text(0.16571,1.08745,''),\n", + " Text(0.060008,1.09836,''),\n", + " Text(-0.0578079,1.09848,''),\n", + " Text(-0.184742,1.08438,''),\n", + " Text(-0.31077,1.05519,''),\n", + " Text(-0.426416,1.01399,''),\n", + " Text(-0.537231,0.959887,''),\n", + " Text(-0.649323,0.887907,''),\n", + " Text(-0.746246,0.808157,''),\n", + " Text(-0.824401,0.728261,''),\n", + " Text(-0.891793,0.643977,''),\n", + " Text(-0.949213,0.555873,''),\n", + " Text(-1.00457,0.448148,''),\n", + " Text(-1.04725,0.336564,''),\n", + " Text(-1.07413,0.237172,''),\n", + " Text(-1.09252,0.128047,''),\n", + " Text(-1.09988,0.0165236,''),\n", + " Text(-1.09616,-0.0918773,''),\n", + " Text(-1.08077,-0.204799,''),\n", + " Text(-1.0533,-0.317105,''),\n", + " Text(-1.00857,-0.439076,''),\n", + " Text(-0.948098,-0.557773,''),\n", + " Text(-0.884317,-0.654204,''),\n", + " Text(-0.810767,-0.743409,''),\n", + " Text(-0.729086,-0.823671,''),\n", + " Text(-0.644869,-0.891147,''),\n", + " Text(-0.540113,-0.958268,''),\n", + " Text(-0.418788,-1.01716,''),\n", + " Text(-0.311299,-1.05503,''),\n", + " Text(-0.209127,-1.07994,''),\n", + " Text(-0.0979136,-1.09563,''),\n", + " Text(0.017625,-1.09986,''),\n", + " Text(0.137342,-1.09139,''),\n", + " Text(0.252744,-1.07057,''),\n", + " Text(0.360074,-1.0394,''),\n", + " Text(0.464187,-0.997262,''),\n", + " Text(0.566767,-0.942748,''),\n", + " Text(0.658183,-0.88136,''),\n", + " Text(0.737298,-0.816328,''),\n", + " Text(0.817066,-0.73648,''),\n", + " Text(0.895006,-0.639503,''),\n", + " Text(0.963364,-0.530971,''),\n", + " Text(1.01883,-0.41471,''),\n", + " Text(1.06033,-0.292762,''),\n", + " Text(1.08608,-0.174417,''),\n", + " Text(1.09839,-0.059458,'')])" + ] + }, + "metadata": { + "tags": [] + }, + "execution_count": 19 + }, + { + "output_type": "display_data", + "data": { + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAcwAAAE5CAYAAAAdhBAsAAAABHNCSVQICAgIfAhkiAAAAAlwSFlz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\nQmCC\n", + "text/plain": [ + "" + ] + }, + "metadata": { + "tags": [] + } + } + ] + } + ] +} \ No newline at end of file