ADA - BasicPandas.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Basic commands for pandas\n",
    "- Just like a library in R, \"pandas\" is a popular open-source data manipulation and analysis library. It provides easy-to-use data structures and functions for efficiently working with structured data.\n",
    "- A 10-minuts to pandas, covering nearly everything we need. https://pandas.pydata.org/docs/user_guide/10min.html\n",
    "- Ask ChatGPT"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To use pandas in your Python environment, you need to install it first using the following command:\n",
    "- remove ! in front to install"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Requirement already satisfied: pandas in /Users/zhengfan/opt/anaconda3/lib/python3.8/site-packages (1.1.3)\r\n",
      "Requirement already satisfied: python-dateutil>=2.7.3 in /Users/zhengfan/opt/anaconda3/lib/python3.8/site-packages (from pandas) (2.8.1)\r\n",
      "Requirement already satisfied: pytz>=2017.2 in /Users/zhengfan/opt/anaconda3/lib/python3.8/site-packages (from pandas) (2020.1)\r\n",
      "Requirement already satisfied: numpy>=1.15.4 in /Users/zhengfan/opt/anaconda3/lib/python3.8/site-packages (from pandas) (1.19.2)\r\n",
      "Requirement already satisfied: six>=1.5 in /Users/zhengfan/opt/anaconda3/lib/python3.8/site-packages (from python-dateutil>=2.7.3->pandas) (1.15.0)\r\n"
     ]
    }
   ],
   "source": [
    "!pip install pandas"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Once installed, you can import it in your Python scripts or Jupyter notebooks using:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- pd is therefore just a shortcut pandas, you can choose what you like.\n",
    "- we can use it to make a simple data frame"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "   Var_1  Var_2 this_is_my_name\n",
      "0    1.0   test             foo\n",
      "1    1.0  train             foo\n",
      "2    1.0   test             foo\n",
      "3    1.0  train             foo\n"
     ]
    }
   ],
   "source": [
    "df_sample = pd.DataFrame(\n",
    "    {\n",
    "        \"Var_1\": 1.0,\n",
    "        \"Var_2\": pd.Categorical([\"test\", \"train\", \"test\", \"train\"]),\n",
    "        \"this_is_my_name\": \"foo\",\n",
    "    }\n",
    ")\n",
    "print(df_sample)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### We need to check the data type before doing any numerical exercise\n",
    "- check python data type here: https://www.w3schools.com/python/python_datatypes.asp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Var_1               float64\n",
       "Var_2              category\n",
       "this_is_my_name      object\n",
       "dtype: object"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    4.0\n",
       "1    4.0\n",
       "2    4.0\n",
       "3    4.0\n",
       "Name: Var_1, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_sample['Var_1'] + 3"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Check data dimension"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(4, 3)"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# the dimension of the data set\n",
    "df_sample.shape"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### For illustration purpose, I will use a publicly available dataset from a online Monash python class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "df = pd.read_csv(\"https://monashdatafluency.github.io/python-workshop-base/modules/data/surveys.csv\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Viewing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
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       "      <th>record_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>site_id</th>\n",
       "      <th>species_id</th>\n",
       "      <th>sex</th>\n",
       "      <th>hindfoot_length</th>\n",
       "      <th>weight</th>\n",
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       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>NL</td>\n",
       "      <td>M</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
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       "      <th>1</th>\n",
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       "      <td>7</td>\n",
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       "      <td>1977</td>\n",
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      "text/plain": [
       "   record_id  month  day  year  site_id species_id sex  hindfoot_length  \\\n",
       "0          1      7   16  1977        2         NL   M             32.0   \n",
       "1          2      7   16  1977        3         NL   M             33.0   \n",
       "\n",
       "   weight  \n",
       "0     NaN  \n",
       "1     NaN  "
      ]
     },
     "execution_count": 8,
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   ],
   "source": [
    "df.head(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
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       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>site_id</th>\n",
       "      <th>species_id</th>\n",
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       "      <th>35547</th>\n",
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       "      <td>7</td>\n",
       "      <td>DO</td>\n",
       "      <td>M</td>\n",
       "      <td>36.0</td>\n",
       "      <td>51.0</td>\n",
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       "      <th>35548</th>\n",
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       "      <td>NaN</td>\n",
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      "text/plain": [
       "       record_id  month  day  year  site_id species_id  sex  hindfoot_length  \\\n",
       "35547      35548     12   31  2002        7         DO    M             36.0   \n",
       "35548      35549     12   31  2002        5        NaN  NaN              NaN   \n",
       "\n",
       "       weight  \n",
       "35547    51.0  \n",
       "35548     NaN  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
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   ],
   "source": [
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['record_id', 'month', 'day', 'year', 'site_id', 'species_id', 'sex',\n",
       "       'hindfoot_length', 'weight'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shows a quick statistic summary of your data:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
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       "      <th></th>\n",
       "      <th>record_id</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "      <th>year</th>\n",
       "      <th>site_id</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>35549.000000</td>\n",
       "      <td>35549.000000</td>\n",
       "      <td>35549.000000</td>\n",
       "      <td>35549.000000</td>\n",
       "      <td>35549.000000</td>\n",
       "      <td>31438.000000</td>\n",
       "      <td>32283.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>17775.000000</td>\n",
       "      <td>6.474022</td>\n",
       "      <td>16.105966</td>\n",
       "      <td>1990.475231</td>\n",
       "      <td>11.397001</td>\n",
       "      <td>29.287932</td>\n",
       "      <td>42.672428</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>10262.256696</td>\n",
       "      <td>3.396583</td>\n",
       "      <td>8.256691</td>\n",
       "      <td>7.493355</td>\n",
       "      <td>6.799406</td>\n",
       "      <td>9.564759</td>\n",
       "      <td>36.631259</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1977.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>4.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>8888.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>1984.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>21.000000</td>\n",
       "      <td>20.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>17775.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>16.000000</td>\n",
       "      <td>1990.000000</td>\n",
       "      <td>11.000000</td>\n",
       "      <td>32.000000</td>\n",
       "      <td>37.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>26662.000000</td>\n",
       "      <td>9.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1997.000000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>36.000000</td>\n",
       "      <td>48.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>35549.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>31.000000</td>\n",
       "      <td>2002.000000</td>\n",
       "      <td>24.000000</td>\n",
       "      <td>70.000000</td>\n",
       "      <td>280.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          record_id         month           day          year       site_id  \\\n",
       "count  35549.000000  35549.000000  35549.000000  35549.000000  35549.000000   \n",
       "mean   17775.000000      6.474022     16.105966   1990.475231     11.397001   \n",
       "std    10262.256696      3.396583      8.256691      7.493355      6.799406   \n",
       "min        1.000000      1.000000      1.000000   1977.000000      1.000000   \n",
       "25%     8888.000000      4.000000      9.000000   1984.000000      5.000000   \n",
       "50%    17775.000000      6.000000     16.000000   1990.000000     11.000000   \n",
       "75%    26662.000000      9.000000     23.000000   1997.000000     17.000000   \n",
       "max    35549.000000     12.000000     31.000000   2002.000000     24.000000   \n",
       "\n",
       "       hindfoot_length        weight  \n",
       "count     31438.000000  32283.000000  \n",
       "mean         29.287932     42.672428  \n",
       "std           9.564759     36.631259  \n",
       "min           2.000000      4.000000  \n",
       "25%          21.000000     20.000000  \n",
       "50%          32.000000     37.000000  \n",
       "75%          36.000000     48.000000  \n",
       "max          70.000000    280.000000  "
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting a single column, which yields a Series"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         NaN\n",
       "1         NaN\n",
       "2         NaN\n",
       "3         NaN\n",
       "4         NaN\n",
       "         ... \n",
       "35544     NaN\n",
       "35545     NaN\n",
       "35546    14.0\n",
       "35547    51.0\n",
       "35548     NaN\n",
       "Name: weight, Length: 35549, dtype: float64"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[\"weight\"]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Get some rows"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
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       "      <td>NaN</td>\n",
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       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>7</td>\n",
       "      <td>16</td>\n",
       "      <td>1977</td>\n",
       "      <td>2</td>\n",
       "      <td>DM</td>\n",
       "      <td>F</td>\n",
       "      <td>37.0</td>\n",
       "      <td>NaN</td>\n",
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      "text/plain": [
       "   record_id  month  day  year  site_id species_id sex  hindfoot_length  \\\n",
       "0          1      7   16  1977        2         NL   M             32.0   \n",
       "1          2      7   16  1977        3         NL   M             33.0   \n",
       "2          3      7   16  1977        2         DM   F             37.0   \n",
       "\n",
       "   weight  \n",
       "0     NaN  \n",
       "1     NaN  \n",
       "2     NaN  "
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df[0:3]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Selecting by label"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
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       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>day</th>\n",
       "      <th>site_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>16</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>16</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>16</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35544</th>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35545</th>\n",
       "      <td>31</td>\n",
       "      <td>15</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35546</th>\n",
       "      <td>31</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35547</th>\n",
       "      <td>31</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>35548</th>\n",
       "      <td>31</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>35549 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "       day  site_id\n",
       "0       16        2\n",
       "1       16        3\n",
       "2       16        2\n",
       "3       16        7\n",
       "4       16        3\n",
       "...    ...      ...\n",
       "35544   31       15\n",
       "35545   31       15\n",
       "35546   31       10\n",
       "35547   31        7\n",
       "35548   31        5\n",
       "\n",
       "[35549 rows x 2 columns]"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.loc[:, [\"day\", \"site_id\"]]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Performing a descriptive statistic:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "record_id          17775.000000\n",
       "month                  6.474022\n",
       "day                   16.105966\n",
       "year                1990.475231\n",
       "site_id               11.397001\n",
       "hindfoot_length       29.287932\n",
       "weight                42.672428\n",
       "dtype: float64"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0         339.166667\n",
       "1         339.666667\n",
       "2         340.333333\n",
       "3         341.166667\n",
       "4         340.500000\n",
       "            ...     \n",
       "35544    7521.000000\n",
       "35545    7521.200000\n",
       "35546    5375.857143\n",
       "35547    5383.857143\n",
       "35548    7519.800000\n",
       "Length: 35549, dtype: float64"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.mean(1) # calculate row mean"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "6.474021772764353"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.month.mean()"
   ]
  }
 ],
 "metadata": {
  "authors": [
   {
    "name": "Zheng Fan"
   }
  ],
  "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": "3.8.5"
  },
  "title": "ECOM90025 Advanced Data Analysis - Pandas Basic"
 },
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 "nbformat_minor": 5
}