pandas_3.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "0471b7a1",
   "metadata": {},
   "source": [
    "### Concatination in pandas##\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "4262fa44",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "india_weather = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "    \"humidity\": [80, 60, 78]\n",
    "})\n",
    "india_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "b281a9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "</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>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 3,
     "metadata": {},
     "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": 4,
   "id": "5188bb0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "df_test=pd.concat([india_weather,us_weather],)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "fffe5b73",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "<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>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "867fa21f",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>index</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>0</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1</td>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2</td>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0</td>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1</td>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>2</td>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   index      city  temperature  humidity\n",
       "0      0    mumbai           32        80\n",
       "1      1     delhi           45        60\n",
       "2      2  banglore           30        78\n",
       "3      0  new york           21        68\n",
       "4      1   chicago           14        65\n",
       "5      2   orlando           35        75"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# reseting a index\n",
    "\n",
    "df_test.reset_index()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "30ecffd8",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "3  new york           21        68\n",
       "4   chicago           14        65\n",
       "5   orlando           35        75"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_test.reset_index(drop=True)  #removes the older index"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "b9a8881b",
   "metadata": {},
   "outputs": [
    {
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       "    <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>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "3  new york           21        68\n",
       "4   chicago           14        65\n",
       "5   orlando           35        75"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([india_weather,us_weather],ignore_index=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "ccb45238",
   "metadata": {},
   "outputs": [
    {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</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>80</td>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "       city  temperature  humidity      city  temperature  humidity\n",
       "0    mumbai           32        80  new york           21        68\n",
       "1     delhi           45        60   chicago           14        65\n",
       "2  banglore           30        78   orlando           35        75"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# side by side concatre\n",
    "pd.concat([india_weather,us_weather],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "217d7f2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>class</th>\n",
       "      <th>max_speed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>falcon</th>\n",
       "      <td>bird</td>\n",
       "      <td>389.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parrot</th>\n",
       "      <td>bird</td>\n",
       "      <td>24.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>lion</th>\n",
       "      <td>mammal</td>\n",
       "      <td>80.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monkey</th>\n",
       "      <td>mammal</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         class  max_speed\n",
       "falcon    bird      389.0\n",
       "parrot    bird       24.0\n",
       "lion    mammal       80.5\n",
       "monkey  mammal        NaN"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([('bird', 389.0),\n",
    "                   ('bird', 24.0),\n",
    "                   ('mammal', 80.5),\n",
    "                   ('mammal', np.nan)],\n",
    "                 index=['falcon', 'parrot', 'lion', 'monkey'],\n",
    "                  columns=('class', 'max_speed'))\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "8df8df73",
   "metadata": {},
   "outputs": [],
   "source": [
    "index = pd.MultiIndex.from_tuples([('bird', 'falcon'),\n",
    "...                                    ('bird', 'parrot'),\n",
    "...                                    ('mammal', 'lion'),\n",
    "...                                    ('mammal', 'monkey')],\n",
    "...                                   names=['class', 'name'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "32f89a4e",
   "metadata": {},
   "outputs": [],
   "source": [
    "columns = pd.MultiIndex.from_tuples([('speed', 'max'),\n",
    "...                                      ('species', 'type')])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "ec09483a",
   "metadata": {},
   "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 tr th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe thead tr:last-of-type th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>speed</th>\n",
       "      <th>species</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th>max</th>\n",
       "      <th>type</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>class</th>\n",
       "      <th>name</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">bird</th>\n",
       "      <th>falcon</th>\n",
       "      <td>389.0</td>\n",
       "      <td>fly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>parrot</th>\n",
       "      <td>24.0</td>\n",
       "      <td>fly</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"2\" valign=\"top\">mammal</th>\n",
       "      <th>lion</th>\n",
       "      <td>80.5</td>\n",
       "      <td>run</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>monkey</th>\n",
       "      <td>NaN</td>\n",
       "      <td>jump</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "               speed species\n",
       "                 max    type\n",
       "class  name                 \n",
       "bird   falcon  389.0     fly\n",
       "       parrot   24.0     fly\n",
       "mammal lion     80.5     run\n",
       "       monkey    NaN    jump"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df = pd.DataFrame([(389.0, 'fly'),\n",
    "...                    ( 24.0, 'fly'),\n",
    "...                    ( 80.5, 'run'),\n",
    "...                    (np.nan, 'jump')],\n",
    "...                   index=index,\n",
    "...                   columns=columns)\n",
    "df"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f67e4137",
   "metadata": {},
   "source": [
    "### test case 1: Number if columns diff"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "9a22f2ec",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "india_weather = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "    \"humidity\": [80, 60, 78]\n",
    "})\n",
    "india_weather\n",
    "\n",
    "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": 25,
   "id": "ded94ac0",
   "metadata": {},
   "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>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>80</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0    mumbai           32        80\n",
       "1     delhi           45        60\n",
       "2  banglore           30        78\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([india_weather,us_weather],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d1f1869f",
   "metadata": {},
   "source": [
    "## case 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "b5eb107a",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</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>80</td>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity      city  temperature  humidity\n",
       "0    mumbai           32        80  new york           21        68\n",
       "1     delhi           45        60   chicago           14        65\n",
       "2  banglore           30        78   orlando           35        75"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([india_weather,us_weather],axis=1)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "56e96581",
   "metadata": {},
   "outputs": [],
   "source": [
    "### case3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bb7035a5",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>humi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity  humi\n",
       "0  new york           21        68   681\n",
       "1   chicago           14        65   651\n",
       "2   orlando           35        75   175"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "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",
    "    \"humi\": [681, 651, 175]\n",
    "})\n",
    "us_weather"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "302ffb21",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>humi</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>80</td>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "      <td>681</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>60</td>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "      <td>651</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>78</td>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "      <td>175</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity      city  temperature  humidity  humi\n",
       "0    mumbai           32        80  new york           21        68   681\n",
       "1     delhi           45        60   chicago           14        65   651\n",
       "2  banglore           30        78   orlando           35        75   175"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([india_weather,us_weather],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "c93b3275",
   "metadata": {},
   "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>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",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature\n",
       "0    mumbai           32\n",
       "1     delhi           45\n",
       "2  banglore           30"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temperature_df = pd.DataFrame({\n",
    "    \"city\": [\"mumbai\",\"delhi\",\"banglore\"],\n",
    "    \"temperature\": [32,45,30],\n",
    "}, index=[0,1,2])\n",
    "temperature_df\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "5625b3df",
   "metadata": {},
   "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>city</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     city  windspeed\n",
       "1   delhi          7\n",
       "0  mumbai         12"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "windspeed_df = pd.DataFrame({\n",
    "    \"city\": [\"delhi\",\"mumbai\"],\n",
    "    \"windspeed\": [7,12],\n",
    "}, index=[1,0])\n",
    "windspeed_df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "ef55ff04",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>city</th>\n",
       "      <th>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32</td>\n",
       "      <td>mumbai</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45</td>\n",
       "      <td>delhi</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature    city  windspeed\n",
       "0    mumbai           32  mumbai       12.0\n",
       "1     delhi           45   delhi        7.0\n",
       "2  banglore           30     NaN        NaN"
      ]
     },
     "execution_count": 36,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([temperature_df,windspeed_df],axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "89e40b01",
   "metadata": {},
   "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",
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       "\n",
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       "</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>windspeed</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>45.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>banglore</td>\n",
       "      <td>30.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>delhi</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>mumbai</td>\n",
       "      <td>NaN</td>\n",
       "      <td>12.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  windspeed\n",
       "0    mumbai         32.0        NaN\n",
       "1     delhi         45.0        NaN\n",
       "2  banglore         30.0        NaN\n",
       "1     delhi          NaN        7.0\n",
       "0    mumbai          NaN       12.0"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.concat([temperature_df,windspeed_df],axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c5f8fe06",
   "metadata": {},
   "source": [
    "### merge  - the data based on some same col"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e38e7e02",
   "metadata": {},
   "source": [
    "* can merge only 2 df at a time\n",
    "* at least one col must be same in 2 df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "8f2600c3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Ij: common\n",
    "# lj: complete left\n",
    "    \n",
    "# lr: complete right\n",
    "    \n",
    "\n",
    "# Oj: complete 2 df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "72721cc3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\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>city</th>\n",
       "      <th>temperature</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature\n",
       "0  new york           21\n",
       "1   chicago           14\n",
       "2   orlando           35"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1 = pd.DataFrame({\n",
    "    \"city\": [\"new york\",\"chicago\",\"orlando\"],\n",
    "    \"temperature\": [21,14,35],\n",
    "})\n",
    "df1\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "bd5ac54b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\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>city</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>chicago</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  humidity\n",
       "0   chicago        65\n",
       "1  new york        68\n",
       "2   orlando        75"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df2 = pd.DataFrame({\n",
    "    \"city\": [\"chicago\",\"new york\",\"orlando\"],\n",
    "    \"humidity\": [65,68,75],\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 44,
   "id": "e156bf55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</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>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>75</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65\n",
       "2   orlando           35        75"
      ]
     },
     "execution_count": 44,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,how='inner', on='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "2a4586a4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "\n",
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       "        text-align: right;\n",
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       "</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>chicago</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>new york</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>san diego</td>\n",
       "      <td>71</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  humidity\n",
       "0    chicago        65\n",
       "1   new york        68\n",
       "2  san diego        71"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "\n",
    "df1 = pd.DataFrame({\n",
    "    \"city\": [\"new york\",\"chicago\",\"orlando\", \"baltimore\"],\n",
    "    \"temperature\": [21,14,35, 38],\n",
    "})\n",
    "df1\n",
    "\n",
    "df2 = pd.DataFrame({\n",
    "    \"city\": [\"chicago\",\"new york\",\"san diego\"],\n",
    "    \"humidity\": [65,68,71],\n",
    "})\n",
    "df2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "a17fbe9f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
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       "\n",
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       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
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       "    }\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>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity\n",
       "0  new york           21        68\n",
       "1   chicago           14        65"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,on='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "662cacd3",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>orlando</td>\n",
       "      <td>35</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>baltimore</td>\n",
       "      <td>38</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "        city  temperature  humidity\n",
       "0   new york           21      68.0\n",
       "1    chicago           14      65.0\n",
       "2    orlando           35       NaN\n",
       "3  baltimore           38       NaN"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,how='left',on='city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "ac15e6f7",
   "metadata": {},
   "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>city</th>\n",
       "      <th>temperature</th>\n",
       "      <th>humidity</th>\n",
       "      <th>_merge</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>new york</td>\n",
       "      <td>21</td>\n",
       "      <td>68</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>chicago</td>\n",
       "      <td>14</td>\n",
       "      <td>65</td>\n",
       "      <td>both</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       city  temperature  humidity _merge\n",
       "0  new york           21        68   both\n",
       "1   chicago           14        65   both"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.merge(df1,df2,on='city',indicator=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251b5492",
   "metadata": {},
   "source": [
    "### Cleaning of data\n",
    "# imp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c1231392",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Handling the missing data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "c5965641",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\n",
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       "\n",
       "    .dataframe thead th {\n",
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       "</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>32.0</td>\n",
       "      <td>6.0</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>9.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>28.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>7.0</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/7/2017</td>\n",
       "      <td>32.0</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/10/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed   event\n",
       "0   1/1/2017         32.0        6.0    Rain\n",
       "1   1/4/2017          NaN        9.0   Sunny\n",
       "2   1/5/2017         28.0        NaN    Snow\n",
       "3   1/6/2017          NaN        7.0     NaN\n",
       "4   1/7/2017         32.0        NaN    Rain\n",
       "5   1/8/2017          NaN        NaN   Sunny\n",
       "6   1/9/2017          NaN        NaN     NaN\n",
       "7  1/10/2017         34.0        8.0  Cloudy\n",
       "8  1/11/2017         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 59,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df=pd.read_csv('datasets/weather_data_nan.csv')\n",
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "00a34c6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/10/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed   event\n",
       "0   1/1/2017         32.0        6.0    Rain\n",
       "1   1/4/2017          0.0        9.0   Sunny\n",
       "2   1/5/2017         28.0        0.0    Snow\n",
       "3   1/6/2017          0.0        7.0       0\n",
       "4   1/7/2017         32.0        0.0    Rain\n",
       "5   1/8/2017          0.0        0.0   Sunny\n",
       "6   1/9/2017          0.0        0.0       0\n",
       "7  1/10/2017         34.0        8.0  Cloudy\n",
       "8  1/11/2017         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# change NaN value with 0\n",
    "df.fillna(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "bf53b947",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>1.0</td>\n",
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       "      <td>2.0</td>\n",
       "      <td>Rain</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>1.0</td>\n",
       "      <td>2.0</td>\n",
       "      <td>Sunny</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
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       "      <td>1.0</td>\n",
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       "      <td>no event</td>\n",
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       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/10/2017</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed     event\n",
       "0   1/1/2017         32.0        6.0      Rain\n",
       "1   1/4/2017          1.0        9.0     Sunny\n",
       "2   1/5/2017         28.0        2.0      Snow\n",
       "3   1/6/2017          1.0        7.0  no event\n",
       "4   1/7/2017         32.0        2.0      Rain\n",
       "5   1/8/2017          1.0        2.0     Sunny\n",
       "6   1/9/2017          1.0        2.0  no event\n",
       "7  1/10/2017         34.0        8.0    Cloudy\n",
       "8  1/11/2017         40.0       12.0     Sunny"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({\n",
    "    \"temperature\":1,\n",
    "    \"windspeed\":2,\n",
    "    \"event\":\"no event\"\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "8e1559e7",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>temperature</th>\n",
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       "      <th>event</th>\n",
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       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>False</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "      <td>True</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "      <td>False</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     day  temperature  windspeed  event\n",
       "0  False        False      False  False\n",
       "1  False         True      False  False\n",
       "2  False        False       True  False\n",
       "3  False         True      False   True\n",
       "4  False        False       True  False\n",
       "5  False         True       True  False\n",
       "6  False         True       True   True\n",
       "7  False        False      False  False\n",
       "8  False        False      False  False"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "c974b233",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>33.2</td>\n",
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       "      <td>Sunny</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/5/2017</td>\n",
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       "      <td>8.0</td>\n",
       "      <td>Rain</td>\n",
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       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/8/2017</td>\n",
       "      <td>33.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Sunny</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/9/2017</td>\n",
       "      <td>33.2</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/10/2017</td>\n",
       "      <td>34.0</td>\n",
       "      <td>8.0</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/11/2017</td>\n",
       "      <td>40.0</td>\n",
       "      <td>12.0</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day  temperature  windspeed   event\n",
       "0   1/1/2017         32.0        6.0    Rain\n",
       "1   1/4/2017         33.2        9.0   Sunny\n",
       "2   1/5/2017         28.0        8.0    Snow\n",
       "3   1/6/2017         33.2        7.0   Sunny\n",
       "4   1/7/2017         32.0        8.0    Rain\n",
       "5   1/8/2017         33.2        8.0   Sunny\n",
       "6   1/9/2017         33.2        8.0   Sunny\n",
       "7  1/10/2017         34.0        8.0  Cloudy\n",
       "8  1/11/2017         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 79,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.fillna({\n",
    "    \"temperature\":df['temperature'].mean(),\n",
    "    \"windspeed\":df['windspeed'].median(),\n",
    "    \"event\": df['event'].mode()[0]\n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "98ad0162",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'Sunny'"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['event'].mode()[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "cc5e585c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# dropna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "ec3a4c88",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <td>40.0</td>\n",
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      ],
      "text/plain": [
       "         day  temperature  windspeed   event\n",
       "0   1/1/2017         32.0        6.0    Rain\n",
       "7  1/10/2017         34.0        8.0  Cloudy\n",
       "8  1/11/2017         40.0       12.0   Sunny"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# drop NaN value rows\n",
    "df.dropna(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "16f9ab98",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "      <th>day</th>\n",
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       "      <th>0</th>\n",
       "      <td>1/1/2017</td>\n",
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       "      <td>1/4/2017</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/5/2017</td>\n",
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       "      <th>3</th>\n",
       "      <td>1/6/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/7/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>1/8/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/9/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>1/10/2017</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>1/11/2017</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "         day\n",
       "0   1/1/2017\n",
       "1   1/4/2017\n",
       "2   1/5/2017\n",
       "3   1/6/2017\n",
       "4   1/7/2017\n",
       "5   1/8/2017\n",
       "6   1/9/2017\n",
       "7  1/10/2017\n",
       "8  1/11/2017"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dropna(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "d9cf7fef",
   "metadata": {},
   "outputs": [],
   "source": [
    "# df.isnull().fillna()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "2fcf2b88",
   "metadata": {},
   "outputs": [],
   "source": [
    "ddf = pd.read_csv('datasets/weather_data_replace.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "353307b1",
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>-99999</td>\n",
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       "      <td>Sunny</td>\n",
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       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
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       "      <th>3</th>\n",
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       "      <td>32</td>\n",
       "      <td>-99999</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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>0</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       -99999          7  Sunny\n",
       "2  1/3/2017           28     -99999   Snow\n",
       "3  1/4/2017       -99999          7      0\n",
       "4  1/5/2017           32     -99999   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "a70dacaa",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    <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",
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       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1/2/2017</td>\n",
       "      <td>0</td>\n",
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       "      <td>Sunny</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1/3/2017</td>\n",
       "      <td>28</td>\n",
       "      <td>0</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>0</td>\n",
       "      <td>7</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>0</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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>0</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            0          7  Sunny\n",
       "2  1/3/2017           28          0   Snow\n",
       "3  1/4/2017            0          7      0\n",
       "4  1/5/2017           32          0   Rain\n",
       "5  1/6/2017           31          2  Sunny\n",
       "6  1/6/2017           34          5      0"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.replace([-99999],0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "f01158d8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "    .dataframe tbody tr th:only-of-type {\n",
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       "  <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>100</td>\n",
       "      <td>200</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>-99999</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1/4/2017</td>\n",
       "      <td>100</td>\n",
       "      <td>200</td>\n",
       "      <td>no event</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1/5/2017</td>\n",
       "      <td>32</td>\n",
       "      <td>-99999</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",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>1/6/2017</td>\n",
       "      <td>34</td>\n",
       "      <td>5</td>\n",
       "      <td>no event</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          100        200     Sunny\n",
       "2  1/3/2017           28     -99999      Snow\n",
       "3  1/4/2017          100        200  no event\n",
       "4  1/5/2017           32     -99999      Rain\n",
       "5  1/6/2017           31          2     Sunny\n",
       "6  1/6/2017           34          5  no event"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# replacing\n",
    "\n",
    "df.replace({\n",
    "    \"temperature\":-99999,\n",
    "    \"windspeed\":7,\n",
    "    \"event\": \"0\"\n",
    "},\n",
    "{\n",
    "    \"temperature\":100,\n",
    "    \"windspeed\":200,\n",
    "    \"event\": \"no event\"  \n",
    "})"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "46ea6565",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Group by\n",
    "\n",
    "df=pd.read_csv('datasets/')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "4c8d1d06",
   "metadata": {},
   "outputs": [
    {
     "ename": "KeyError",
     "evalue": "'city'",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mKeyError\u001b[0m                                  Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_1600/396875322.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mf\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mdf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'city'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\frame.py\u001b[0m in \u001b[0;36mgroupby\u001b[1;34m(self, by, axis, level, as_index, sort, group_keys, squeeze, observed, dropna)\u001b[0m\n\u001b[0;32m   7629\u001b[0m         \u001b[1;31m# error: Argument \"squeeze\" to \"DataFrameGroupBy\" has incompatible type\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7630\u001b[0m         \u001b[1;31m# \"Union[bool, NoDefault]\"; expected \"bool\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 7631\u001b[1;33m         return DataFrameGroupBy(\n\u001b[0m\u001b[0;32m   7632\u001b[0m             \u001b[0mobj\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m   7633\u001b[0m             \u001b[0mkeys\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mby\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\groupby.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, obj, keys, axis, level, grouper, exclusions, selection, as_index, sort, group_keys, squeeze, observed, mutated, dropna)\u001b[0m\n\u001b[0;32m    887\u001b[0m             \u001b[1;32mfrom\u001b[0m \u001b[0mpandas\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mgrouper\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mget_grouper\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    888\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 889\u001b[1;33m             grouper, exclusions, obj = get_grouper(\n\u001b[0m\u001b[0;32m    890\u001b[0m                 \u001b[0mobj\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    891\u001b[0m                 \u001b[0mkeys\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;32mC:\\ProgramData\\Anaconda3\\lib\\site-packages\\pandas\\core\\groupby\\grouper.py\u001b[0m in \u001b[0;36mget_grouper\u001b[1;34m(obj, key, axis, level, sort, observed, mutated, validate, dropna)\u001b[0m\n\u001b[0;32m    860\u001b[0m                 \u001b[0min_axis\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlevel\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgpr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mFalse\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    861\u001b[0m             \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 862\u001b[1;33m                 \u001b[1;32mraise\u001b[0m \u001b[0mKeyError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m    863\u001b[0m         \u001b[1;32melif\u001b[0m \u001b[0misinstance\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgpr\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mGrouper\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mgpr\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mkey\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m    864\u001b[0m             \u001b[1;31m# Add key to exclusions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
      "\u001b[1;31mKeyError\u001b[0m: 'city'"
     ]
    }
   ],
   "source": [
    "f=df.groupby('city')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "aac8bc44",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'g' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_1600/122988605.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_group\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"mumbai\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'g' is not defined"
     ]
    }
   ],
   "source": [
    "g.get_group(\"mumbai\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "c45ab54f",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'g' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[1;32mC:\\Users\\KIRANS~1\\AppData\\Local\\Temp/ipykernel_22436/2978112660.py\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mg\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmean\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
      "\u001b[1;31mNameError\u001b[0m: name 'g' is not defined"
     ]
    }
   ],
   "source": [
    "g.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "edb8bdf0",
   "metadata": {},
   "outputs": [],
   "source": []
  }
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