Assignment-1.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#importing of required libraries\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pylab as plt\n",
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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",
       "\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>State</th>\n",
       "      <th>Country</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Geoposoition</th>\n",
       "      <th>Season</th>\n",
       "      <th>WindSpeed (Km/h)</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Vancouver</td>\n",
       "      <td>Washington</td>\n",
       "      <td>Canada</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Seashore</td>\n",
       "      <td>Rainy</td>\n",
       "      <td>18</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Portland</td>\n",
       "      <td>Oregon</td>\n",
       "      <td>United States</td>\n",
       "      <td>Thunder</td>\n",
       "      <td>Plain</td>\n",
       "      <td>Autumn</td>\n",
       "      <td>14</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>San Francisco</td>\n",
       "      <td>California</td>\n",
       "      <td>United States</td>\n",
       "      <td>fog</td>\n",
       "      <td>Seashore</td>\n",
       "      <td>Autumn</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Seattle</td>\n",
       "      <td>Washington</td>\n",
       "      <td>United States</td>\n",
       "      <td>Snow</td>\n",
       "      <td>Seashore</td>\n",
       "      <td>Autumn</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Pachuca</td>\n",
       "      <td>Hidalgo</td>\n",
       "      <td>Mexico</td>\n",
       "      <td>fog</td>\n",
       "      <td>Central</td>\n",
       "      <td>Rainy</td>\n",
       "      <td>19</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            City       State        Country  Weather Geoposoition  Season  \\\n",
       "0      Vancouver  Washington         Canada     Snow     Seashore   Rainy   \n",
       "1       Portland      Oregon  United States  Thunder        Plain  Autumn   \n",
       "2  San Francisco  California  United States      fog     Seashore  Autumn   \n",
       "3        Seattle  Washington  United States     Snow     Seashore  Autumn   \n",
       "4        Pachuca     Hidalgo         Mexico      fog      Central   Rainy   \n",
       "\n",
       "   WindSpeed (Km/h)  \n",
       "0                18  \n",
       "1                14  \n",
       "2                13  \n",
       "3                13  \n",
       "4                19  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# importing of required dataset\n",
    "df=pd.read_csv('assignment-1.csv')\n",
    "df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "# using the PIVOT technique to categorize cities based on similar weather\n",
    "\n",
    "df1=df.pivot(columns=\"Weather\",values=\"City\")\n",
    "df1.fillna('-',inplace=True) # filling Nan to -\n",
    "# saving the obtained data drame to df1\n",
    "df1.to_csv('C:\\\\Users\\\\DELL\\\\Desktop\\\\MY STUFF\\DATA SCIENCE\\DATA\\\\Raw data\\\\similar_weather_cities.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "        vertical-align: middle;\n",
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       "    }\n",
       "\n",
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       "    }\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Cloudy</th>\n",
       "      <th>Rainy</th>\n",
       "      <th>Snow</th>\n",
       "      <th>Sunny</th>\n",
       "      <th>Thunder</th>\n",
       "      <th>Windy</th>\n",
       "      <th>fog</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Portland</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>San Francisco</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Pachuca</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>San Diego</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Toronto</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Philadelphia</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Boston</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Guadalajara</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>-</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>Merida</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "      <td>-</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "       Cloudy     Rainy       Snow        Sunny   Thunder     Windy  \\\n",
       "0           -         -  Vancouver            -         -         -   \n",
       "1           -         -          -            -  Portland         -   \n",
       "2           -         -          -            -         -         -   \n",
       "3           -         -    Seattle            -         -         -   \n",
       "4           -         -          -            -         -         -   \n",
       "5   San Diego         -          -            -         -         -   \n",
       "6           -         -          -            -         -         -   \n",
       "7           -         -          -            -         -         -   \n",
       "8           -         -          -            -         -  Montreal   \n",
       "9           -         -          -            -         -         -   \n",
       "10          -         -          -  Guadalajara         -         -   \n",
       "11          -         -    Calgary            -         -         -   \n",
       "12          -  Victoria          -            -         -         -   \n",
       "13          -         -          -       Merida         -         -   \n",
       "\n",
       "              fog  \n",
       "0               -  \n",
       "1               -  \n",
       "2   San Francisco  \n",
       "3               -  \n",
       "4         Pachuca  \n",
       "5               -  \n",
       "6         Toronto  \n",
       "7    Philadelphia  \n",
       "8               -  \n",
       "9          Boston  \n",
       "10              -  \n",
       "11              -  \n",
       "12              -  \n",
       "13              -  "
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying of all cities that have similar weather characteristics\n",
    "\n",
    "df1=pd.read_csv('C:\\\\Users\\\\DELL\\\\Desktop\\\\MY STUFF\\DATA SCIENCE\\DATA\\\\Raw data\\\\similar_weather_cities.csv')\n",
    "df1.drop('Unnamed: 0',axis=1,inplace=True)\n",
    "df1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th rowspan=\"5\" valign=\"top\">Canada</th>\n",
       "      <th>Calgary</th>\n",
       "      <th>Snow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Montreal</th>\n",
       "      <th>Windy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Toronto</th>\n",
       "      <th>fog</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Vancouver</th>\n",
       "      <th>Snow</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Victoria</th>\n",
       "      <th>Rainy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"3\" valign=\"top\">Mexico</th>\n",
       "      <th>Guadalajara</th>\n",
       "      <th>Sunny</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Merida</th>\n",
       "      <th>Sunny</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Pachuca</th>\n",
       "      <th>fog</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th rowspan=\"6\" valign=\"top\">United States</th>\n",
       "      <th>Boston</th>\n",
       "      <th>fog</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Philadelphia</th>\n",
       "      <th>fog</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Portland</th>\n",
       "      <th>Thunder</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Diego</th>\n",
       "      <th>Cloudy</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>San Francisco</th>\n",
       "      <th>fog</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Seattle</th>\n",
       "      <th>Snow</th>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "Empty DataFrame\n",
       "Columns: []\n",
       "Index: [(Canada, Calgary, Snow), (Canada, Montreal, Windy), (Canada, Toronto, fog), (Canada, Vancouver, Snow), (Canada, Victoria, Rainy), (Mexico, Guadalajara, Sunny), (Mexico, Merida, Sunny), (Mexico, Pachuca, fog), (United States, Boston, fog), (United States, Philadelphia, fog), (United States, Portland, Thunder), (United States, San Diego, Cloudy), (United States, San Francisco, fog), (United States, Seattle, Snow)]"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Using by the groupby  techniqueto display all cities of a particular country and display their weather characteristics\n",
    "\n",
    "df2=df.groupby(['Country', 'City', 'Weather'], observed=True).count()\n",
    "columns=['State','Geoposoition','Season','WindSpeed (Km/h)']\n",
    "df2.drop(columns,axis=1,inplace=True)\n",
    "df2\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "# saving the obtained datadrame df2\n",
    "df2.to_csv('C:\\\\Users\\\\DELL\\\\Desktop\\\\MY STUFF\\DATA SCIENCE\\DATA\\\\Raw data\\\\country_cities_weather.csv')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "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>Country</th>\n",
       "      <th>City</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Calgary</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Montreal</td>\n",
       "      <td>Windy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Toronto</td>\n",
       "      <td>fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Vancouver</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>Canada</td>\n",
       "      <td>Victoria</td>\n",
       "      <td>Rainy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>Guadalajara</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>Merida</td>\n",
       "      <td>Sunny</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>Mexico</td>\n",
       "      <td>Pachuca</td>\n",
       "      <td>fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>United States</td>\n",
       "      <td>Boston</td>\n",
       "      <td>fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>United States</td>\n",
       "      <td>Philadelphia</td>\n",
       "      <td>fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>United States</td>\n",
       "      <td>Portland</td>\n",
       "      <td>Thunder</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>United States</td>\n",
       "      <td>San Diego</td>\n",
       "      <td>Cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>United States</td>\n",
       "      <td>San Francisco</td>\n",
       "      <td>fog</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>United States</td>\n",
       "      <td>Seattle</td>\n",
       "      <td>Snow</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          Country           City  Weather\n",
       "0          Canada        Calgary     Snow\n",
       "1          Canada       Montreal    Windy\n",
       "2          Canada        Toronto      fog\n",
       "3          Canada      Vancouver     Snow\n",
       "4          Canada       Victoria    Rainy\n",
       "5          Mexico    Guadalajara    Sunny\n",
       "6          Mexico         Merida    Sunny\n",
       "7          Mexico        Pachuca      fog\n",
       "8   United States         Boston      fog\n",
       "9   United States   Philadelphia      fog\n",
       "10  United States       Portland  Thunder\n",
       "11  United States      San Diego   Cloudy\n",
       "12  United States  San Francisco      fog\n",
       "13  United States        Seattle     Snow"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# displaying of all cities of a particular country and display their weather characteristics\n",
    "\n",
    "df2=pd.read_csv('C:\\\\Users\\\\DELL\\\\Desktop\\\\MY STUFF\\DATA SCIENCE\\DATA\\\\Raw data\\\\country_cities_weather.csv')\n",
    "df2"
   ]
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