key_operations_on_dataframes.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "ZcMqCohOsK7B"
   },
   "source": [
    "# Read and write CSV and XLS files"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 880
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 1094,
     "status": "error",
     "timestamp": 1546783678701,
     "user": {
      "displayName": "Applied Course",
      "photoUrl": "https://lh3.googleusercontent.com/-EsJzSyawCkQ/AAAAAAAAAAI/AAAAAAAADag/xYU9KO6AZf4/s64/photo.jpg",
      "userId": "06629147635963609455"
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     "user_tz": -330
    },
    "id": "YAt3VZ-6sK7C",
    "outputId": "1b927f4b-d50a-45f5-c0ea-b8a73ad0963e"
   },
   "outputs": [
    {
     "data": {
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       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('C:\\\\VipinML\\\\InputData\\\\weather_data.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "oT1rKPabsK7I",
    "outputId": "a0c7b7a6-bc29-47b6-884f-d4979a839451"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "\n",
       "    .dataframe tbody tr th {\n",
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       "    }\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>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>35</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>2</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>24</td>\n",
       "      <td>7</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>4</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>31</td>\n",
       "      <td>2</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day  temperature  windspeed  event\n",
       "0  1/1/2017           32          6   Rain\n",
       "1  1/2/2017           35          7  Sunny\n",
       "2  1/3/2017           28          2   Snow\n",
       "3  1/4/2017           24          7   Snow\n",
       "4  1/5/2017           32          4   Rain\n",
       "5  1/6/2017           31          2  Sunny"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#INSTALL: pip3 install xlrd\n",
    "\n",
    "#read excel file \n",
    "df = pd.read_excel('C:\\\\VipinML\\\\InputData\\\\weather_data.xlsx')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "PlblL3yHsK7M"
   },
   "outputs": [],
   "source": [
    "#write DF to csv\n",
    "df.to_csv('new.csv') \n",
    "df.to_csv('new_noIndex.csv', index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "6ZF5HkthsK7O"
   },
   "outputs": [],
   "source": [
    "# INSTALL: pip3 install openpyxl\n",
    "\n",
    "#write DF to Excel\n",
    "df.to_excel('new.xlsx', sheet_name='weather_data')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "RzT9J4ZysK7R"
   },
   "source": [
    "# GROUP-BY"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "g4YY64sbsK7S",
    "outputId": "5e9af13a-1976-4737-eeee-994757c87cd0"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "    }\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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "df = pd.read_csv('C:\\\\VipinML\\\\InputData\\\\weather_data_cities.csv')\n",
    "df #weather by cities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "YYXjYb-2sK7V",
    "outputId": "029b2173-57e3-4085-bc3b-1f62b6f81bba"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>90</td>\n",
       "      <td>5</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>85</td>\n",
       "      <td>12</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>87</td>\n",
       "      <td>15</td>\n",
       "      <td>Fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>92</td>\n",
       "      <td>5</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>45</td>\n",
       "      <td>20</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>50</td>\n",
       "      <td>13</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>54</td>\n",
       "      <td>8</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>paris</td>\n",
       "      <td>42</td>\n",
       "      <td>10</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day      city  temperature  windspeed   event\n",
       "0   1/1/2017  new york           32          6    Rain\n",
       "1   1/2/2017  new york           36          7   Sunny\n",
       "2   1/3/2017  new york           28         12    Snow\n",
       "3   1/4/2017  new york           33          7   Sunny\n",
       "4   1/1/2017    mumbai           90          5   Sunny\n",
       "5   1/2/2017    mumbai           85         12     Fog\n",
       "6   1/3/2017    mumbai           87         15     Fog\n",
       "7   1/4/2017    mumbai           92          5    Rain\n",
       "8   1/1/2017     paris           45         20   Sunny\n",
       "9   1/2/2017     paris           50         13  Cloudy\n",
       "10  1/3/2017     paris           54          8  Cloudy\n",
       "11  1/4/2017     paris           42         10  Cloudy"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "g = df.groupby('city')\n",
    "g.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "SuhY5rNfsK7Z",
    "outputId": "f563434e-d686-4877-b78a-55ad077827d3"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "mumbai\n",
      "        day    city  temperature  windspeed  event\n",
      "4  1/1/2017  mumbai           90          5  Sunny\n",
      "5  1/2/2017  mumbai           85         12    Fog\n",
      "6  1/3/2017  mumbai           87         15    Fog\n",
      "7  1/4/2017  mumbai           92          5   Rain\n",
      "new york\n",
      "        day      city  temperature  windspeed  event\n",
      "0  1/1/2017  new york           32          6   Rain\n",
      "1  1/2/2017  new york           36          7  Sunny\n",
      "2  1/3/2017  new york           28         12   Snow\n",
      "3  1/4/2017  new york           33          7  Sunny\n",
      "paris\n",
      "         day   city  temperature  windspeed   event\n",
      "8   1/1/2017  paris           45         20   Sunny\n",
      "9   1/2/2017  paris           50         13  Cloudy\n",
      "10  1/3/2017  paris           54          8  Cloudy\n",
      "11  1/4/2017  paris           42         10  Cloudy\n"
     ]
    }
   ],
   "source": [
    "for city, city_df in g:\n",
    "    print(city)\n",
    "    print(city_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "A3GmoRWCsK7f",
    "outputId": "e5434152-365a-4aeb-fe6c-4d8ad03108ed"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>event</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>32</td>\n",
       "      <td>6</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>36</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>28</td>\n",
       "      <td>12</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>new york</td>\n",
       "      <td>33</td>\n",
       "      <td>7</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        day      city  temperature  windspeed  event\n",
       "0  1/1/2017  new york           32          6   Rain\n",
       "1  1/2/2017  new york           36          7  Sunny\n",
       "2  1/3/2017  new york           28         12   Snow\n",
       "3  1/4/2017  new york           33          7  Sunny"
      ]
     },
     "execution_count": 18,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#or to get specific group\n",
    "g.get_group('new york')\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ccxPVzgVsK7i",
    "outputId": "4b194921-deae-44e2-e21c-758ec30f03f6"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "               day  temperature  windspeed  event\n",
      "city                                             \n",
      "mumbai    1/4/2017           92         15  Sunny\n",
      "new york  1/4/2017           36         12  Sunny\n",
      "paris     1/4/2017           54         20  Sunny\n"
     ]
    }
   ],
   "source": [
    "#Find maximum temperature in each of the cities\n",
    "print(g.max())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "20hwbkrqsK7m",
    "outputId": "c5bd07da-c28f-4dd4-e0f0-df9959dcc8e1"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "          temperature  windspeed\n",
      "city                            \n",
      "mumbai          88.50       9.25\n",
      "new york        32.25       8.00\n",
      "paris           47.75      12.75\n"
     ]
    }
   ],
   "source": [
    "print(g.mean())\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "ayKxFWPOsK7p",
    "outputId": "db8ab616-7d7a-4cd9-f1c2-8fa0a31a6eda"
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "         temperature                                                   \\\n",
      "               count   mean       std   min    25%   50%    75%   max   \n",
      "city                                                                    \n",
      "mumbai           4.0  88.50  3.109126  85.0  86.50  88.5  90.50  92.0   \n",
      "new york         4.0  32.25  3.304038  28.0  31.00  32.5  33.75  36.0   \n",
      "paris            4.0  47.75  5.315073  42.0  44.25  47.5  51.00  54.0   \n",
      "\n",
      "         windspeed                                                 \n",
      "             count   mean       std  min   25%   50%    75%   max  \n",
      "city                                                               \n",
      "mumbai         4.0   9.25  5.057997  5.0  5.00   8.5  12.75  15.0  \n",
      "new york       4.0   8.00  2.708013  6.0  6.75   7.0   8.25  12.0  \n",
      "paris          4.0  12.75  5.251984  8.0  9.50  11.5  14.75  20.0  \n"
     ]
    }
   ],
   "source": [
    "print(g.describe())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "S0ejCDYUsK7s"
   },
   "source": [
    "# concatenate Data Frames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "guywqgX3sK7u",
    "outputId": "3e4f0bd5-dbf4-48b4-ab58-bcfed907d22f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity  temperature\n",
       "0    mumbai        80           32\n",
       "1     delhi        60           45\n",
       "2  banglore        78           30"
      ]
     },
     "execution_count": 26,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "india_weather = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "    \"humidity\": [80, 60, 78]\n",
    "})\n",
    "\n",
    "india_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "56m1pVT7sK7x",
    "outputId": "09a547be-9af2-424b-b2b4-30c44f3def49"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity  temperature\n",
       "0  new york        68           21\n",
       "1   chicago        65           14\n",
       "2   orlando        75           35"
      ]
     },
     "execution_count": 27,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "us_weather = pd.DataFrame({\n",
    "    \"city\": [\"new york\",\"chicago\",\"orlando\"],\n",
    "    \"temperature\": [21,14,35],\n",
    "    \"humidity\": [68, 65, 75]\n",
    "})\n",
    "us_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "x3GQCVNfsK71",
    "outputId": "5995671b-881f-470a-d6de-5ed6705663b3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity  temperature\n",
       "0    mumbai        80           32\n",
       "1     delhi        60           45\n",
       "2  banglore        78           30\n",
       "0  new york        68           21\n",
       "1   chicago        65           14\n",
       "2   orlando        75           35"
      ]
     },
     "execution_count": 28,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#concate two dataframes\n",
    "df = pd.concat([india_weather, us_weather])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "rfSAVjmdsK75",
    "outputId": "38011dfe-ecf8-4c39-ab74-e00a3090708b"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity  temperature\n",
       "0    mumbai        80           32\n",
       "1     delhi        60           45\n",
       "2  banglore        78           30\n",
       "3  new york        68           21\n",
       "4   chicago        65           14\n",
       "5   orlando        75           35"
      ]
     },
     "execution_count": 29,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#if you want continuous index\n",
    "df = pd.concat([india_weather, us_weather], ignore_index=True)\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "abTtEcqnsK7-",
    "outputId": "038a4293-956f-4021-e47e-00f46881bbf3"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>80</td>\n",
       "      <td>32</td>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>60</td>\n",
       "      <td>45</td>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>78</td>\n",
       "      <td>30</td>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity  temperature      city  humidity  temperature\n",
       "0    mumbai        80           32  new york        68           21\n",
       "1     delhi        60           45   chicago        65           14\n",
       "2  banglore        78           30   orlando        75           35"
      ]
     },
     "execution_count": 30,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.concat([india_weather, us_weather],axis=1)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "id": "8uulelVEsK8C"
   },
   "source": [
    "# Merge DataFrames"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "q7aFBEFMsK8C",
    "outputId": "e6f800a0-9d59-4670-db19-af7d763f028e"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature\n",
       "0     mumbai           32\n",
       "1      delhi           45\n",
       "2   banglore           30\n",
       "3  hyderabad           40"
      ]
     },
     "execution_count": 34,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature_df = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\", 'hyderabad'],\n",
    "    \"temperature\": [32,45,30,40]})\n",
    "temperature_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "UfeQu7TrsK8G",
    "outputId": "db0f0046-439a-4dd4-84a9-3f55a4411c67"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>delhi</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity\n",
       "0     delhi        68\n",
       "1    mumbai        65\n",
       "2  banglore        75"
      ]
     },
     "execution_count": 35,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "humidity_df = pd.DataFrame({\n",
    "    \"city\": [\"delhi\",\"mumbai\",\"banglore\"],\n",
    "    \"humidity\": [68, 65, 75]})\n",
    "humidity_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Ee1Xn1JhsK8J",
    "outputId": "1ea25755-be1b-43c7-bac5-c72c33be986f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        65\n",
       "1     delhi           45        68\n",
       "2  banglore           30        75"
      ]
     },
     "execution_count": 36,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#merge two dataframes with out explicitly mention index\n",
    "df = pd.merge(temperature_df, humidity_df, on='city')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "Wa9EQCNlsK8M",
    "outputId": "09630629-d670-4229-b7b1-9f2694ecd865"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>75.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>hyderabad</td>\n",
       "      <td>40</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0     mumbai           32      65.0\n",
       "1      delhi           45      68.0\n",
       "2   banglore           30      75.0\n",
       "3  hyderabad           40       NaN"
      ]
     },
     "execution_count": 38,
     "metadata": {
      "tags": []
     },
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#OUTER-JOIN\n",
    "df = pd.merge(temperature_df, humidity_df, on='city', how='outer')\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "colab_type": "text",
    "collapsed": true,
    "id": "ecGaNHTlsK8P"
   },
   "source": [
    "# Numerical Indexing (.loc vs iloc)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "fgNWHOKNmeVP"
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 359
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 864,
     "status": "ok",
     "timestamp": 1548261944924,
     "user": {
      "displayName": "Applied AI Course",
      "photoUrl": "https://lh3.googleusercontent.com/-EsJzSyawCkQ/AAAAAAAAAAI/AAAAAAAADag/xYU9KO6AZf4/s64/photo.jpg",
      "userId": "06629147635963609455"
     },
     "user_tz": -330
    },
    "id": "9KvFO9WfsK8Q",
    "outputId": "a611e5b1-a1ee-4154-caeb-62c7a16153b8"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "      <th>4</th>\n",
       "      <td>9</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0\n",
       "49   1\n",
       "48   2\n",
       "47   3\n",
       "46   4\n",
       "45   5\n",
       "1    6\n",
       "2    7\n",
       "3    8\n",
       "4    9\n",
       "5   19"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([1,2,3,4,5,6,7,8,9,19], index=[49,48,47,46,45, 1, 2, 3, 4, 5])\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "colab": {
     "base_uri": "https://localhost:8080/",
     "height": 163
    },
    "colab_type": "code",
    "executionInfo": {
     "elapsed": 892,
     "status": "error",
     "timestamp": 1548261952054,
     "user": {
      "displayName": "Applied AI Course",
      "photoUrl": "https://lh3.googleusercontent.com/-EsJzSyawCkQ/AAAAAAAAAAI/AAAAAAAADag/xYU9KO6AZf4/s64/photo.jpg",
      "userId": "06629147635963609455"
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     "user_tz": -330
    },
    "id": "IIFtfjoksK8T",
    "outputId": "dc52c58a-e351-4838-cbf4-bb7aa3aa0b7f"
   },
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<style scoped>\n",
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       "49  1\n",
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       "46  4\n",
       "45  5\n",
       "1   6\n",
       "2   7"
      ]
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     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "wN2y3aGlsK8X",
    "outputId": "82c7ea79-f2c2-49fc-dc07-8891bb03960d"
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   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
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       "    .dataframe tbody tr th {\n",
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       "\n",
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       "      <th></th>\n",
       "      <th>0</th>\n",
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       "      <th>49</th>\n",
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       "    0\n",
       "49  1\n",
       "48  2"
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     "execution_count": 14,
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   "source": [
    "df.iloc[:2]"
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  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "HG6l67wDsK8a",
    "outputId": "e3f6b5c1-72b5-4071-8e8a-17f7c7020038"
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   "outputs": [
    {
     "data": {
      "text/plain": [
       "0    5\n",
       "Name: 45, dtype: int64"
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     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.loc[45]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "BoEBmjE7sK8d",
    "outputId": "ec07eb43-a744-4c35-bfb3-7fa78c196bb5"
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    {
     "data": {
      "text/plain": [
       "0    6\n",
       "Name: 1, dtype: int64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
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   ],
   "source": [
    "df.iloc[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 0,
   "metadata": {
    "colab": {},
    "colab_type": "code",
    "id": "cjkIxjeksK8g"
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   "outputs": [],
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