Australia_weather_data_analysis.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "29adddce",
   "metadata": {},
   "source": [
    "## WEATHER ANALYSIS - AUSTRALIA 2022"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b920a7a5",
   "metadata": {},
   "source": [
    "### INTRODUCTION"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a2c9f266",
   "metadata": {},
   "source": [
    "- The \"Australia_weather_2022-01-01_to_2022-12-31.csv\" dataset used in this analysis is an timebased quantitive data, about the road accidents due to various weather condition in India during 2018.\n",
    "\n",
    "- The data is a csv file. Analysis is done using pandas.\n",
    "\n",
    "- The data is be saved and exported to power bi for dynamic visualisations & dashboards.\n",
    "\n",
    "- 'matplotlib' and 'searbon' packages are used for basic statistics and deriving insights from the data."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "abdc02f8",
   "metadata": {},
   "source": [
    "### SCOPE"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "d5c05f7a",
   "metadata": {},
   "source": [
    "- **Data filename:** Australia_weather_2022-01-01_to_2022-12-31.csv\n",
    "- **Data type:** Timebased Quantitive data (weather data)\n",
    "- **Year in process:** 2022\n",
    "- **Country/Place in process:** Australia (all Stations)\n",
    "- **Processes used:** Downloading, Preparing, Evaluation, Processing, Visualisation "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4d5635a",
   "metadata": {},
   "source": [
    "### QUESTIONS/QUERIES TAKEN UP"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "545ea9b3",
   "metadata": {},
   "source": [
    "- find the unique weather of the values in the data\n",
    "- find the number of days when the weather was exactly 'Rain' only.\n",
    "- find from where the maximum readings of weather patterns taken from.\n",
    "- find the number of times when 'wind speed is greater than 30 Kmph'\n",
    "- What is the variance of the humidity\n",
    "- Find the mean visibility\n",
    "- What is the standard deviation of temprature in this data\n",
    "- Find number of days when visibility is above 11 and was raining.\n",
    "- Create the column of day_name and month\n",
    "- On which day, humidity is maximum.\n",
    "- Which months had the temperatures less than 3 degrees celcius\n",
    "- what was the monthly mean temprature throughout the year\n",
    "- What is the range of UV index parameters\n",
    "- Make a subset of the dataframe with the following parameters as columns -- date/time, temprature, dew point, humidity, wind speed, weather conditions.\n",
    "- find the correlation of the new data set\n",
    "- what is the mean value of each column against each weather conditions in new dataset\n",
    "- what is the min and max value of each column against each weather conditions\n",
    "- Plot maximum visibility with each month\n",
    "- Show a scatterplot with Temprature vs windspeed.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "30ac0d09",
   "metadata": {},
   "source": [
    "These quesions will curate the exploratory story of the data. It will help in further storytelling."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "2ee550e4",
   "metadata": {},
   "source": [
    "### Phase 1: Prepare"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a4e5286e",
   "metadata": {},
   "source": [
    "This step involves data import,it includes import of libraries that facilitate data import, analysis and visualisation."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "82b4055a",
   "metadata": {},
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "import seaborn as sns\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "import matplotlib\n",
    "import warnings\n",
    "warnings.filterwarnings('ignore')\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "39287787",
   "metadata": {},
   "outputs": [],
   "source": [
    "na_vals = [' ', 'nan', 'Nan', 'NaN','?', 'missing', 'Missing']      #the list containing all values deemed as null values\n",
    "\n",
    "df = pd.read_csv(r'C:\\Users\\R5 EJ274t\\Desktop\\DATA-PROJECTS\\Australia-weather-analysis-2022\\Dataset\\Australia_weather_2022-01-01_to_2022-12-31.csv', na_values = na_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "20363bdb",
   "metadata": {},
   "outputs": [
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       "      <th>tempmax</th>\n",
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       "      <th>feelslikemin</th>\n",
       "      <th>feelslike</th>\n",
       "      <th>dew</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>solarenergy</th>\n",
       "      <th>uvindex</th>\n",
       "      <th>severerisk</th>\n",
       "      <th>sunrise</th>\n",
       "      <th>sunset</th>\n",
       "      <th>moonphase</th>\n",
       "      <th>conditions</th>\n",
       "      <th>description</th>\n",
       "      <th>icon</th>\n",
       "      <th>stations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>australia</td>\n",
       "      <td>01-01-2022</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>57.1</td>\n",
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       "      <td>2022-01-01T05:52:07</td>\n",
       "      <td>2022-01-01T20:21:29</td>\n",
       "      <td>0.94</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>Clearing in the afternoon.</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
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       "      <th>1</th>\n",
       "      <td>australia</td>\n",
       "      <td>02-01-2022</td>\n",
       "      <td>32.6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>...</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-02T05:52:52</td>\n",
       "      <td>2022-01-02T20:21:39</td>\n",
       "      <td>0.97</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with late aft...</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
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       "      <th>2</th>\n",
       "      <td>australia</td>\n",
       "      <td>03-01-2022</td>\n",
       "      <td>28.6</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>29.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.3</td>\n",
       "      <td>15.4</td>\n",
       "      <td>70.8</td>\n",
       "      <td>...</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-03T05:53:40</td>\n",
       "      <td>2022-01-03T20:21:46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>australia</td>\n",
       "      <td>04-01-2022</td>\n",
       "      <td>26.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>20.2</td>\n",
       "      <td>27.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.6</td>\n",
       "      <td>72.8</td>\n",
       "      <td>...</td>\n",
       "      <td>30.8</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-04T05:54:28</td>\n",
       "      <td>2022-01-04T20:21:52</td>\n",
       "      <td>0.04</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>australia</td>\n",
       "      <td>05-01-2022</td>\n",
       "      <td>25.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>25.7</td>\n",
       "      <td>13.8</td>\n",
       "      <td>20.0</td>\n",
       "      <td>15.0</td>\n",
       "      <td>74.4</td>\n",
       "      <td>...</td>\n",
       "      <td>18.3</td>\n",
       "      <td>10</td>\n",
       "      <td>NaN</td>\n",
       "      <td>2022-01-05T05:55:17</td>\n",
       "      <td>2022-01-05T20:21:56</td>\n",
       "      <td>0.07</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
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       "<p>5 rows × 33 columns</p>\n",
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      ],
      "text/plain": [
       "        name    datetime  tempmax  tempmin  temp  feelslikemax  feelslikemin  \\\n",
       "0  australia  01-01-2022     30.0     11.7  21.3          29.0          11.7   \n",
       "1  australia  02-01-2022     32.6     13.9  22.9          31.1          13.9   \n",
       "2  australia  03-01-2022     28.6     14.9  21.2          29.1          14.9   \n",
       "3  australia  04-01-2022     26.8     13.9  20.2          27.1          13.9   \n",
       "4  australia  05-01-2022     25.7     13.8  20.0          25.7          13.8   \n",
       "\n",
       "   feelslike   dew  humidity  ...  solarenergy  uvindex  severerisk  \\\n",
       "0       21.1  11.4      57.1  ...         31.7       10         NaN   \n",
       "1       22.6  14.0      63.1  ...         30.0       10         NaN   \n",
       "2       21.3  15.4      70.8  ...         27.8       10         NaN   \n",
       "3       20.3  14.6      72.8  ...         30.8       10         NaN   \n",
       "4       20.0  15.0      74.4  ...         18.3       10         NaN   \n",
       "\n",
       "               sunrise               sunset  moonphase  \\\n",
       "0  2022-01-01T05:52:07  2022-01-01T20:21:29       0.94   \n",
       "1  2022-01-02T05:52:52  2022-01-02T20:21:39       0.97   \n",
       "2  2022-01-03T05:53:40  2022-01-03T20:21:46       0.00   \n",
       "3  2022-01-04T05:54:28  2022-01-04T20:21:52       0.04   \n",
       "4  2022-01-05T05:55:17  2022-01-05T20:21:56       0.07   \n",
       "\n",
       "               conditions                                        description  \\\n",
       "0        Partially cloudy                         Clearing in the afternoon.   \n",
       "1  Rain, Partially cloudy  Partly cloudy throughout the day with late aft...   \n",
       "2  Rain, Partially cloudy        Partly cloudy throughout the day with rain.   \n",
       "3  Rain, Partially cloudy        Partly cloudy throughout the day with rain.   \n",
       "4  Rain, Partially cloudy        Partly cloudy throughout the day with rain.   \n",
       "\n",
       "                icon                                           stations  \n",
       "0  partly-cloudy-day  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "1               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "2               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "3               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "4               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "\n",
       "[5 rows x 33 columns]"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "55ea2bcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(365, 33)"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "6b21ab7a",
   "metadata": {},
   "outputs": [
    {
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       "      <th>feelslikemin</th>\n",
       "      <th>feelslike</th>\n",
       "      <th>dew</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>solarenergy</th>\n",
       "      <th>uvindex</th>\n",
       "      <th>severerisk</th>\n",
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       "      <th>363</th>\n",
       "      <td>australia</td>\n",
       "      <td>30-12-2022</td>\n",
       "      <td>21.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>17.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>17.7</td>\n",
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       "      <td>79.3</td>\n",
       "      <td>...</td>\n",
       "      <td>17.2</td>\n",
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       "      <td>0.25</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
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       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>australia</td>\n",
       "      <td>31-12-2022</td>\n",
       "      <td>24.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>24.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>19.3</td>\n",
       "      <td>13.4</td>\n",
       "      <td>70.8</td>\n",
       "      <td>...</td>\n",
       "      <td>20.0</td>\n",
       "      <td>7</td>\n",
       "      <td>10.0</td>\n",
       "      <td>2022-12-31T05:51:12</td>\n",
       "      <td>2022-12-31T20:21:15</td>\n",
       "      <td>0.26</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with afternoo...</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
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       "<p>2 rows × 33 columns</p>\n",
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      ],
      "text/plain": [
       "          name    datetime  tempmax  tempmin  temp  feelslikemax  \\\n",
       "363  australia  30-12-2022     21.1     14.9  17.7          21.1   \n",
       "364  australia  31-12-2022     24.8     13.9  19.3          24.8   \n",
       "\n",
       "     feelslikemin  feelslike   dew  humidity  ...  solarenergy  uvindex  \\\n",
       "363          14.9       17.7  13.8      79.3  ...         17.2        6   \n",
       "364          13.9       19.3  13.4      70.8  ...         20.0        7   \n",
       "\n",
       "     severerisk              sunrise               sunset  moonphase  \\\n",
       "363        10.0  2022-12-30T05:50:29  2022-12-30T20:21:02       0.25   \n",
       "364        10.0  2022-12-31T05:51:12  2022-12-31T20:21:15       0.26   \n",
       "\n",
       "                 conditions  \\\n",
       "363  Rain, Partially cloudy   \n",
       "364  Rain, Partially cloudy   \n",
       "\n",
       "                                           description  icon  \\\n",
       "363        Partly cloudy throughout the day with rain.  rain   \n",
       "364  Partly cloudy throughout the day with afternoo...  rain   \n",
       "\n",
       "                                              stations  \n",
       "363  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "364  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "\n",
       "[2 rows x 33 columns]"
      ]
     },
     "execution_count": 91,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can check of the data have total in the end row so to exclude it from calculation\n",
    "df.tail(2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "531bd70b",
   "metadata": {},
   "outputs": [
    {
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       "      <th>datetime</th>\n",
       "      <td>datetime</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempmax</th>\n",
       "      <td>tempmax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>tempmin</th>\n",
       "      <td>tempmin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>temp</th>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feelslikemax</th>\n",
       "      <td>feelslikemax</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feelslikemin</th>\n",
       "      <td>feelslikemin</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>feelslike</th>\n",
       "      <td>feelslike</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>dew</th>\n",
       "      <td>dew</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>humidity</th>\n",
       "      <td>humidity</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precip</th>\n",
       "      <td>precip</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipprob</th>\n",
       "      <td>precipprob</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>precipcover</th>\n",
       "      <td>precipcover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>preciptype</th>\n",
       "      <td>preciptype</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snow</th>\n",
       "      <td>snow</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>snowdepth</th>\n",
       "      <td>snowdepth</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windgust</th>\n",
       "      <td>windgust</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>windspeed</th>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>winddir</th>\n",
       "      <td>winddir</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sealevelpressure</th>\n",
       "      <td>sealevelpressure</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>cloudcover</th>\n",
       "      <td>cloudcover</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>visibility</th>\n",
       "      <td>visibility</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solarradiation</th>\n",
       "      <td>solarradiation</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>solarenergy</th>\n",
       "      <td>solarenergy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>uvindex</th>\n",
       "      <td>uvindex</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>severerisk</th>\n",
       "      <td>severerisk</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sunrise</th>\n",
       "      <td>sunrise</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>sunset</th>\n",
       "      <td>sunset</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>moonphase</th>\n",
       "      <td>moonphase</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>conditions</th>\n",
       "      <td>conditions</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>description</th>\n",
       "      <td>description</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>icon</th>\n",
       "      <td>icon</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>stations</th>\n",
       "      <td>stations</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                                 0\n",
       "name                          name\n",
       "datetime                  datetime\n",
       "tempmax                    tempmax\n",
       "tempmin                    tempmin\n",
       "temp                          temp\n",
       "feelslikemax          feelslikemax\n",
       "feelslikemin          feelslikemin\n",
       "feelslike                feelslike\n",
       "dew                            dew\n",
       "humidity                  humidity\n",
       "precip                      precip\n",
       "precipprob              precipprob\n",
       "precipcover            precipcover\n",
       "preciptype              preciptype\n",
       "snow                          snow\n",
       "snowdepth                snowdepth\n",
       "windgust                  windgust\n",
       "windspeed                windspeed\n",
       "winddir                    winddir\n",
       "sealevelpressure  sealevelpressure\n",
       "cloudcover              cloudcover\n",
       "visibility              visibility\n",
       "solarradiation      solarradiation\n",
       "solarenergy            solarenergy\n",
       "uvindex                    uvindex\n",
       "severerisk              severerisk\n",
       "sunrise                    sunrise\n",
       "sunset                      sunset\n",
       "moonphase                moonphase\n",
       "conditions              conditions\n",
       "description            description\n",
       "icon                          icon\n",
       "stations                  stations"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#we can note all the headers of the columns\n",
    "df.columns.to_frame()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9635fe22",
   "metadata": {},
   "source": [
    "*NOTE: all the columns have keys (heading) with proper nomenclature so we won't be changing the names here.*"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "e816ce1b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                 object\n",
       "datetime             object\n",
       "tempmax             float64\n",
       "tempmin             float64\n",
       "temp                float64\n",
       "feelslikemax        float64\n",
       "feelslikemin        float64\n",
       "feelslike           float64\n",
       "dew                 float64\n",
       "humidity            float64\n",
       "precip              float64\n",
       "precipprob            int64\n",
       "precipcover         float64\n",
       "preciptype           object\n",
       "snow                  int64\n",
       "snowdepth           float64\n",
       "windgust            float64\n",
       "windspeed           float64\n",
       "winddir             float64\n",
       "sealevelpressure    float64\n",
       "cloudcover          float64\n",
       "visibility          float64\n",
       "solarradiation      float64\n",
       "solarenergy         float64\n",
       "uvindex               int64\n",
       "severerisk          float64\n",
       "sunrise              object\n",
       "sunset               object\n",
       "moonphase           float64\n",
       "conditions           object\n",
       "description          object\n",
       "icon                 object\n",
       "stations             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Let's check the datatypes of all the columns\n",
    "\n",
    "df.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "f2bc3adf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                365\n",
       "datetime            365\n",
       "tempmax             365\n",
       "tempmin             365\n",
       "temp                365\n",
       "feelslikemax        365\n",
       "feelslikemin        365\n",
       "feelslike           365\n",
       "dew                 365\n",
       "humidity            365\n",
       "precip              365\n",
       "precipprob          365\n",
       "precipcover         365\n",
       "preciptype          306\n",
       "snow                365\n",
       "snowdepth           365\n",
       "windgust            356\n",
       "windspeed           365\n",
       "winddir             365\n",
       "sealevelpressure    365\n",
       "cloudcover          365\n",
       "visibility          365\n",
       "solarradiation      365\n",
       "solarenergy         365\n",
       "uvindex             365\n",
       "severerisk          356\n",
       "sunrise             365\n",
       "sunset              365\n",
       "moonphase           365\n",
       "conditions          365\n",
       "description         365\n",
       "icon                365\n",
       "stations            365\n",
       "dtype: int64"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#a total of non null values in a dataframe\n",
    "df.count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b60ca696",
   "metadata": {},
   "source": [
    "### Phase 2: Data Processing"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8c4ddcc1",
   "metadata": {},
   "source": [
    "This phase includes all the cleaning, transforming and processing of data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "22cfd4d0",
   "metadata": {},
   "source": [
    "#### Step 1: Check for all the the missing or 'na' values in the dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "id": "881c13ee",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                 0\n",
       "datetime             0\n",
       "tempmax              0\n",
       "tempmin              0\n",
       "temp                 0\n",
       "feelslikemax         0\n",
       "feelslikemin         0\n",
       "feelslike            0\n",
       "dew                  0\n",
       "humidity             0\n",
       "precip               0\n",
       "precipprob           0\n",
       "precipcover          0\n",
       "preciptype          59\n",
       "snow                 0\n",
       "snowdepth            0\n",
       "windgust             9\n",
       "windspeed            0\n",
       "winddir              0\n",
       "sealevelpressure     0\n",
       "cloudcover           0\n",
       "visibility           0\n",
       "solarradiation       0\n",
       "solarenergy          0\n",
       "uvindex              0\n",
       "severerisk           9\n",
       "sunrise              0\n",
       "sunset               0\n",
       "moonphase            0\n",
       "conditions           0\n",
       "description          0\n",
       "icon                 0\n",
       "stations             0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 95,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.isnull().sum()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20fadb4a",
   "metadata": {},
   "source": [
    "#### There are 3 columns with null values.\n",
    "- preciptype ---          59\n",
    "- windgust ---             9\n",
    "- severerisk ---           9"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ad980e2d",
   "metadata": {},
   "source": [
    "This is comparable to the predicted result by df.count()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "b34f195f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan, 'rain', 'rain,snow'], dtype=object)"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#Now to handle the missing values. We need to look at individual columns.\n",
    "df['preciptype'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "id": "a4bd599c",
   "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>name</th>\n",
       "      <th>preciptype</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>australia</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>360</th>\n",
       "      <td>australia</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>361</th>\n",
       "      <td>australia</td>\n",
       "      <td>NONE</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>362</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>363</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>364</th>\n",
       "      <td>australia</td>\n",
       "      <td>rain</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>365 rows × 2 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "          name preciptype\n",
       "0    australia       NONE\n",
       "1    australia       rain\n",
       "2    australia       rain\n",
       "3    australia       rain\n",
       "4    australia       rain\n",
       "..         ...        ...\n",
       "360  australia       NONE\n",
       "361  australia       NONE\n",
       "362  australia       rain\n",
       "363  australia       rain\n",
       "364  australia       rain\n",
       "\n",
       "[365 rows x 2 columns]"
      ]
     },
     "execution_count": 97,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#preciptype gives types of precipitation with values rain or rain,snow.\n",
    "# we can add NONE in placeof missing values.\n",
    "df['preciptype'] = df['preciptype'].fillna('NONE')\n",
    "df[['name','preciptype']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "aedaab90",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ nan, 27. , 32.8, 37.1, 30.2, 35.6, 42.5, 31.7, 29.9, 50. , 48.6,\n",
       "       36.7, 33.5, 34.2, 33.8, 30.6, 31.3, 22. , 29.5, 24.5, 31. , 53.6,\n",
       "       43.2, 39.2, 36. , 20.9, 34.9, 27.4, 23.4, 33.1, 14.8, 34.6, 28.1,\n",
       "       17.3, 24.1, 37.8, 46.1, 37.4, 27.7, 52.2, 59.4, 70.6, 45. , 40.3,\n",
       "       29.2, 36.4, 13.7, 25.2, 25.9, 20.2, 32. , 55.4, 57.6, 44.6, 41.4,\n",
       "       44.3, 22.3, 25.6,  9.7, 21.6, 20.5, 40. , 28.4, 53.3, 10.4, 12.6,\n",
       "       46.4, 38.5, 42.8, 22.7, 28.8, 41. , 51.8, 10.8, 18. , 17.6, 52.9,\n",
       "       69.1, 59. , 49.3, 64.8, 71.6, 50.8, 41.8, 15.8,  7.2, 15.1, 21.2,\n",
       "       50.4,  7.6, 52.6, 51.1, 10.1, 38.9, 63.4, 69.5,  5.4, 19.1, 18.4,\n",
       "       56.2, 61.9, 68. , 55.1,  9. , 19.4, 16.9, 49.7, 58.7, 24.8, 66.2,\n",
       "       46.8, 38.2, 19.8, 54.7, 58. , 11.9, 40.7, 32.4, 42.1, 43.6, 65.9,\n",
       "       56.9, 81. , 79.9, 26.6, 16.2, 39.6, 66.6, 65.2, 56.5, 43.9, 48.2,\n",
       "       77. , 45.7, 16.6])"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['windgust'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "id": "c453f603",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0       0.0\n",
       "1       0.0\n",
       "2       0.0\n",
       "3       0.0\n",
       "4       0.0\n",
       "       ... \n",
       "360    41.8\n",
       "361    44.6\n",
       "362    25.6\n",
       "363    29.9\n",
       "364    32.8\n",
       "Name: windgust, Length: 365, dtype: float64"
      ]
     },
     "execution_count": 99,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#windgust columns provides value of the wind speed.\n",
    "#for windgust we can use zero where no value is available\n",
    "df['windgust'] = df['windgust'].fillna(0)\n",
    "df['windgust']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "0d4defb7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([nan,  3., 10., 30.])"
      ]
     },
     "execution_count": 100,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['severerisk'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "id": "1ffd406e",
   "metadata": {},
   "outputs": [],
   "source": [
    "#severerisk is used to detect of weather going severe in time called -- sevierity risk.\n",
    "#It spew values 3, 10 & 30. Therefore is not much valuable from our use case.\n",
    "# we can drop the severity risk column.\n",
    "df = df.drop('severerisk',axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "c1e9ae34",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'datetime', 'tempmax', 'tempmin', 'temp', 'feelslikemax',\n",
       "       'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob',\n",
       "       'precipcover', 'preciptype', 'snow', 'snowdepth', 'windgust',\n",
       "       'windspeed', 'winddir', 'sealevelpressure', 'cloudcover', 'visibility',\n",
       "       'solarradiation', 'solarenergy', 'uvindex', 'sunrise', 'sunset',\n",
       "       'moonphase', 'conditions', 'description', 'icon', 'stations'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "43bc0a30",
   "metadata": {},
   "source": [
    "#### Step 2: Checking and handling the data duplicates, columns and datatypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "id": "0d9a559d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking for number of duplicates in data\n",
    "df.duplicated().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5cd811a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "name                 object\n",
       "datetime             object\n",
       "tempmax             float64\n",
       "tempmin             float64\n",
       "temp                float64\n",
       "feelslikemax        float64\n",
       "feelslikemin        float64\n",
       "feelslike           float64\n",
       "dew                 float64\n",
       "humidity            float64\n",
       "precip              float64\n",
       "precipprob            int64\n",
       "precipcover         float64\n",
       "preciptype           object\n",
       "snow                  int64\n",
       "snowdepth           float64\n",
       "windgust            float64\n",
       "windspeed           float64\n",
       "winddir             float64\n",
       "sealevelpressure    float64\n",
       "cloudcover          float64\n",
       "visibility          float64\n",
       "solarradiation      float64\n",
       "solarenergy         float64\n",
       "uvindex               int64\n",
       "sunrise              object\n",
       "sunset               object\n",
       "moonphase           float64\n",
       "conditions           object\n",
       "description          object\n",
       "icon                 object\n",
       "stations             object\n",
       "dtype: object"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.dtypes"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "959adb5d",
   "metadata": {},
   "source": [
    "datetime columns is of data type object."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 105,
   "id": "bbf278b7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0      01-01-2022\n",
       "1      02-01-2022\n",
       "2      03-01-2022\n",
       "3      04-01-2022\n",
       "4      05-01-2022\n",
       "          ...    \n",
       "360    27-12-2022\n",
       "361    28-12-2022\n",
       "362    29-12-2022\n",
       "363    30-12-2022\n",
       "364    31-12-2022\n",
       "Name: datetime, Length: 365, dtype: object"
      ]
     },
     "execution_count": 105,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['datetime']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "668b1f90",
   "metadata": {},
   "outputs": [],
   "source": [
    "#converting the datetime columns to correct data type.\n",
    "df['datetime']= pd.to_datetime(df['datetime'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 107,
   "id": "41ef1f87",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('<M8[ns]')"
      ]
     },
     "execution_count": 107,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['datetime'].dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "94cbda83",
   "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>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>tempmax</th>\n",
       "      <th>tempmin</th>\n",
       "      <th>temp</th>\n",
       "      <th>feelslikemax</th>\n",
       "      <th>feelslikemin</th>\n",
       "      <th>feelslike</th>\n",
       "      <th>dew</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>solarradiation</th>\n",
       "      <th>solarenergy</th>\n",
       "      <th>uvindex</th>\n",
       "      <th>sunrise</th>\n",
       "      <th>sunset</th>\n",
       "      <th>moonphase</th>\n",
       "      <th>conditions</th>\n",
       "      <th>description</th>\n",
       "      <th>icon</th>\n",
       "      <th>stations</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>57.1</td>\n",
       "      <td>...</td>\n",
       "      <td>368.4</td>\n",
       "      <td>31.7</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-01T05:52:07</td>\n",
       "      <td>2022-01-01T20:21:29</td>\n",
       "      <td>0.94</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>Clearing in the afternoon.</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>32.6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>...</td>\n",
       "      <td>347.5</td>\n",
       "      <td>30.0</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-02T05:52:52</td>\n",
       "      <td>2022-01-02T20:21:39</td>\n",
       "      <td>0.97</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with late aft...</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>28.6</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>29.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.3</td>\n",
       "      <td>15.4</td>\n",
       "      <td>70.8</td>\n",
       "      <td>...</td>\n",
       "      <td>321.3</td>\n",
       "      <td>27.8</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-03T05:53:40</td>\n",
       "      <td>2022-01-03T20:21:46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-04-01</td>\n",
       "      <td>26.8</td>\n",
       "      <td>13.9</td>\n",
       "      <td>20.2</td>\n",
       "      <td>27.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>20.3</td>\n",
       "      <td>14.6</td>\n",
       "      <td>72.8</td>\n",
       "      <td>...</td>\n",
       "      <td>357.6</td>\n",
       "      <td>30.8</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-04T05:54:28</td>\n",
       "      <td>2022-01-04T20:21:52</td>\n",
       "      <td>0.04</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>YSCB,95925099999,94925099999,94927099999,F0067...</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4 rows × 32 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        name   datetime  tempmax  tempmin  temp  feelslikemax  feelslikemin  \\\n",
       "0  australia 2022-01-01     30.0     11.7  21.3          29.0          11.7   \n",
       "1  australia 2022-02-01     32.6     13.9  22.9          31.1          13.9   \n",
       "2  australia 2022-03-01     28.6     14.9  21.2          29.1          14.9   \n",
       "3  australia 2022-04-01     26.8     13.9  20.2          27.1          13.9   \n",
       "\n",
       "   feelslike   dew  humidity  ...  solarradiation  solarenergy  uvindex  \\\n",
       "0       21.1  11.4      57.1  ...           368.4         31.7       10   \n",
       "1       22.6  14.0      63.1  ...           347.5         30.0       10   \n",
       "2       21.3  15.4      70.8  ...           321.3         27.8       10   \n",
       "3       20.3  14.6      72.8  ...           357.6         30.8       10   \n",
       "\n",
       "               sunrise               sunset  moonphase  \\\n",
       "0  2022-01-01T05:52:07  2022-01-01T20:21:29       0.94   \n",
       "1  2022-01-02T05:52:52  2022-01-02T20:21:39       0.97   \n",
       "2  2022-01-03T05:53:40  2022-01-03T20:21:46       0.00   \n",
       "3  2022-01-04T05:54:28  2022-01-04T20:21:52       0.04   \n",
       "\n",
       "               conditions                                        description  \\\n",
       "0        Partially cloudy                         Clearing in the afternoon.   \n",
       "1  Rain, Partially cloudy  Partly cloudy throughout the day with late aft...   \n",
       "2  Rain, Partially cloudy        Partly cloudy throughout the day with rain.   \n",
       "3  Rain, Partially cloudy        Partly cloudy throughout the day with rain.   \n",
       "\n",
       "                icon                                           stations  \n",
       "0  partly-cloudy-day  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "1               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "2               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "3               rain  YSCB,95925099999,94925099999,94927099999,F0067...  \n",
       "\n",
       "[4 rows x 32 columns]"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.head(4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 109,
   "id": "24c786bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "32"
      ]
     },
     "execution_count": 109,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#checking number of columns\n",
    "len(df.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "814ec654",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df['stations'].unique())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 111,
   "id": "667207db",
   "metadata": {},
   "outputs": [],
   "source": [
    "#for better understanting changing station names as alphabets\n",
    "\n",
    "vals = df['stations'].unique()                          #getting the cordinates of stations\n",
    "st_vals = ['A','B','C','D','E']                         #array with alphabets to replace station names\n",
    "df['stations'] = df['stations'].replace(vals, st_vals)  #replacing station names in dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "3461ccb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'B', 'C', 'D', 'E'], dtype=object)"
      ]
     },
     "execution_count": 112,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stations'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f165c69f",
   "metadata": {},
   "source": [
    "### Phase 3: Exploratory Data analysis"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "eaf67104",
   "metadata": {},
   "source": [
    "- EDA is an approach to anaylse data inorder to summarize main characteristics of the data\n",
    "- It also helps to gain better understanding of the dataset\n",
    "- It helps uncover relationships between different variables\n",
    "- It extract the important variables for the problem being solved\n",
    "\n",
    "#### Q1: find the unique weather of the values in the data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 113,
   "id": "7c69a4f2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Partially cloudy', 'Rain, Partially cloudy', 'Rain, Overcast',\n",
       "       'Rain', 'Clear', 'Snow, Rain, Partially cloudy', 'Overcast'],\n",
       "      dtype=object)"
      ]
     },
     "execution_count": 113,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['conditions'].unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b9e22ac1",
   "metadata": {},
   "source": [
    "#### Q2: find the number of days when the weather was exactly 'Rain' only."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "5bafdbf7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "8"
      ]
     },
     "execution_count": 114,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = df['conditions'] == 'Rain'        #setting up a filtering condition \n",
    "df.loc[filt, 'conditions'].count()        #getting the actual count on what or number of conditions that really happened"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "75b782d9",
   "metadata": {},
   "source": [
    "The weather condition was only rain for 8 days."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 115,
   "id": "f8039c72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "294"
      ]
     },
     "execution_count": 115,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "#using preciptype to detect the same factor\n",
    "filt2 = df['preciptype'] == 'rain'\n",
    "df.loc[filt2, 'preciptype'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "237c838f",
   "metadata": {},
   "source": [
    "The precipitation condition is rain for 294 days."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "95d98fad",
   "metadata": {},
   "source": [
    "#### Q3. find from where the maximum readings of weather patterns taken from."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "f608ee55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['A', 'B', 'C', 'D', 'E'], dtype=object)"
      ]
     },
     "execution_count": 116,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['stations'].unique()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 117,
   "id": "0ad527cb",
   "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>stations</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>A</td>\n",
       "      <td>328</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>B</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>C</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>D</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>E</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  stations  count\n",
       "0        A    328\n",
       "1        B     32\n",
       "2        C      2\n",
       "3        D      2\n",
       "4        E      1"
      ]
     },
     "execution_count": 117,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "stations = df['stations'].unique()\n",
    "st_count = {'stations':[],'count':[]}\n",
    "for station in stations:\n",
    "    filt = df['stations'] == station\n",
    "    st_count['stations'].append(station)\n",
    "    st_count['count'].append(df.loc[filt, 'stations'].count())\n",
    "st_count = pd.DataFrame(st_count)\n",
    "st_count"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8156bd69",
   "metadata": {},
   "source": [
    "We can note Station A has maximum inputs with 328 days."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bf225307",
   "metadata": {},
   "source": [
    "#### Q4. find the number of times when 'wind speed is greater than 30 Kmph'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 147,
   "id": "fde3ff42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "89"
      ]
     },
     "execution_count": 147,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = df['windspeed']>30\n",
    "df.loc[filt, 'windspeed'].count()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "89c5b7af",
   "metadata": {},
   "source": [
    "For 89 days within the total 365 days wind speed is greater than 30mph"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "612fef05",
   "metadata": {},
   "source": [
    "#### Q5. What is the variance of the humidity"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "987dc629",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "91.85267951226852"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['humidity'].var()   #calculating the variance axis =0 thats horizontally"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "453b19db",
   "metadata": {},
   "source": [
    "Variance in humidity is 91.85"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "a6340c96",
   "metadata": {},
   "source": [
    "#### Q6. Find the mean visibility"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "9b842f53",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9.898082191780823"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['visibility'].mean() "
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ecc79ace",
   "metadata": {},
   "source": [
    "Mean visibility is 9.898"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "45444b0e",
   "metadata": {},
   "source": [
    "#### Q7: What is the standard deviation of temprature in this data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "a1a110b0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5.221098018524704"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['temp'].std(axis=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "56f56055",
   "metadata": {},
   "source": [
    "The standard deviation of temprature is 5.2 deg Celsius <br/>\n",
    "Please note, the standard deviation in degrees Centigrade is equal to the standard deviation in degrees Fahrenheit times 0.556."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "37c80c40",
   "metadata": {},
   "source": [
    "#### Q8: Find number of days when visibility is above 11 and was raining."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 168,
   "id": "5a45c004",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 168,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = (df['visibility'] > 11) & (df['preciptype'] == 'rain')\n",
    "len(df[filt])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6aaac9e5",
   "metadata": {},
   "source": [
    "There are **10 days** with rain, having visbility index greater than 11."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5aa2a65d",
   "metadata": {},
   "source": [
    "#### Q9: Create the column of day_name and month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 150,
   "id": "0ff6fcd2",
   "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>name</th>\n",
       "      <th>datetime</th>\n",
       "      <th>tempmax</th>\n",
       "      <th>tempmin</th>\n",
       "      <th>temp</th>\n",
       "      <th>feelslikemax</th>\n",
       "      <th>feelslikemin</th>\n",
       "      <th>feelslike</th>\n",
       "      <th>dew</th>\n",
       "      <th>humidity</th>\n",
       "      <th>...</th>\n",
       "      <th>uvindex</th>\n",
       "      <th>sunrise</th>\n",
       "      <th>sunset</th>\n",
       "      <th>moonphase</th>\n",
       "      <th>conditions</th>\n",
       "      <th>description</th>\n",
       "      <th>icon</th>\n",
       "      <th>stations</th>\n",
       "      <th>day_name</th>\n",
       "      <th>month</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>30.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.3</td>\n",
       "      <td>29.0</td>\n",
       "      <td>11.7</td>\n",
       "      <td>21.1</td>\n",
       "      <td>11.4</td>\n",
       "      <td>57.1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-01T05:52:07</td>\n",
       "      <td>2022-01-01T20:21:29</td>\n",
       "      <td>0.94</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>Clearing in the afternoon.</td>\n",
       "      <td>partly-cloudy-day</td>\n",
       "      <td>A</td>\n",
       "      <td>Saturday</td>\n",
       "      <td>Jan</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>32.6</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.9</td>\n",
       "      <td>31.1</td>\n",
       "      <td>13.9</td>\n",
       "      <td>22.6</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-02T05:52:52</td>\n",
       "      <td>2022-01-02T20:21:39</td>\n",
       "      <td>0.97</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with late aft...</td>\n",
       "      <td>rain</td>\n",
       "      <td>A</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Feb</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>australia</td>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>28.6</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.2</td>\n",
       "      <td>29.1</td>\n",
       "      <td>14.9</td>\n",
       "      <td>21.3</td>\n",
       "      <td>15.4</td>\n",
       "      <td>70.8</td>\n",
       "      <td>...</td>\n",
       "      <td>10</td>\n",
       "      <td>2022-01-03T05:53:40</td>\n",
       "      <td>2022-01-03T20:21:46</td>\n",
       "      <td>0.00</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>Partly cloudy throughout the day with rain.</td>\n",
       "      <td>rain</td>\n",
       "      <td>A</td>\n",
       "      <td>Tuesday</td>\n",
       "      <td>Mar</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>3 rows × 34 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "        name   datetime  tempmax  tempmin  temp  feelslikemax  feelslikemin  \\\n",
       "0  australia 2022-01-01     30.0     11.7  21.3          29.0          11.7   \n",
       "1  australia 2022-02-01     32.6     13.9  22.9          31.1          13.9   \n",
       "2  australia 2022-03-01     28.6     14.9  21.2          29.1          14.9   \n",
       "\n",
       "   feelslike   dew  humidity  ...  uvindex              sunrise  \\\n",
       "0       21.1  11.4      57.1  ...       10  2022-01-01T05:52:07   \n",
       "1       22.6  14.0      63.1  ...       10  2022-01-02T05:52:52   \n",
       "2       21.3  15.4      70.8  ...       10  2022-01-03T05:53:40   \n",
       "\n",
       "                sunset moonphase              conditions  \\\n",
       "0  2022-01-01T20:21:29      0.94        Partially cloudy   \n",
       "1  2022-01-02T20:21:39      0.97  Rain, Partially cloudy   \n",
       "2  2022-01-03T20:21:46      0.00  Rain, Partially cloudy   \n",
       "\n",
       "                                         description               icon  \\\n",
       "0                         Clearing in the afternoon.  partly-cloudy-day   \n",
       "1  Partly cloudy throughout the day with late aft...               rain   \n",
       "2        Partly cloudy throughout the day with rain.               rain   \n",
       "\n",
       "   stations  day_name  month  \n",
       "0         A  Saturday    Jan  \n",
       "1         A   Tuesday    Feb  \n",
       "2         A   Tuesday    Mar  \n",
       "\n",
       "[3 rows x 34 columns]"
      ]
     },
     "execution_count": 150,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['day_name'] = df['datetime'].dt.day_name()    #inserting the column in all three occasion\n",
    "df['month'] = df['datetime'].dt.month         #creating the month column\n",
    "\n",
    "# changing the month column from numbers to month name abbr.\n",
    "df['month'] = df['month'].replace({1:'Jan', 2:'Feb', 3:'Mar', 4:'Apr', 5:'May', 6:'Jun', 7:'Jul', 8:'Aug', 9:'Sep', 10:'Oct',\n",
    "                                  11:'Nov', 12:'Dec'})\n",
    "\n",
    "df.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "84526cfa",
   "metadata": {},
   "source": [
    "#### Q10. On which day, humidity is maximum."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "be0a3264",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "day_name\n",
       "Monday       99.4\n",
       "Sunday       98.4\n",
       "Saturday     97.1\n",
       "Friday       96.6\n",
       "Tuesday      96.6\n",
       "Thursday     95.3\n",
       "Wednesday    91.4\n",
       "Name: humidity, dtype: float64"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('day_name')['humidity'].max().sort_values(ascending=False)    #ascending false, denote descending"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "3b7014e3",
   "metadata": {},
   "source": [
    "The humidity is 99.4% mostly on mondays."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c68518f5",
   "metadata": {},
   "source": [
    "#### Q11. Which months had the temperatures less than 3 degrees celcius"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "e4692714",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "152    Feb\n",
       "164    Jun\n",
       "178    Jun\n",
       "195    Jul\n",
       "210    Jul\n",
       "211    Jul\n",
       "Name: month, dtype: object"
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "filt = df['temp'] < 3            #inserting a conditon to be met\n",
    "#df['temp'].unique()\n",
    "df[filt]['month']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "b41bc83e",
   "metadata": {},
   "source": [
    "#### Q12: what was the monthly mean temprature throughout the year"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "96ea8781",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "month\n",
       "Jan    16.867742\n",
       "Feb    16.246429\n",
       "Mar    15.341935\n",
       "Dec    15.170968\n",
       "Oct    13.787097\n",
       "Nov    13.316667\n",
       "Apr    13.223333\n",
       "Sep    10.993333\n",
       "May    10.932258\n",
       "Aug     9.800000\n",
       "Jun     8.333333\n",
       "Jul     8.135484\n",
       "Name: temp, dtype: float64"
      ]
     },
     "execution_count": 174,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.groupby('month').temp.mean().sort_values(ascending=False)   #group the temprature by month"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4f769001",
   "metadata": {},
   "source": [
    "#### Q13. What is the range of UV index parameters"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 129,
   "id": "98fd129b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10], dtype=int64)"
      ]
     },
     "execution_count": 129,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df['uvindex'].sort_values().unique()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "21f299f3",
   "metadata": {},
   "source": [
    "#### Q14. Make a subset of the dataframe with the following parameters as columns -- date/time, temprature, dew point, humidity, wind speed, weather conditions."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "id": "ef2f290b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'datetime', 'tempmax', 'tempmin', 'temp', 'feelslikemax',\n",
       "       'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob',\n",
       "       'precipcover', 'preciptype', 'snow', 'snowdepth', 'windgust',\n",
       "       'windspeed', 'winddir', 'sealevelpressure', 'cloudcover', 'visibility',\n",
       "       'solarradiation', 'solarenergy', 'uvindex', 'sunrise', 'sunset',\n",
       "       'moonphase', 'conditions', 'description', 'icon', 'stations',\n",
       "       'day_name', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 189,
   "id": "cc8ede3d",
   "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>Date/Time</th>\n",
       "      <th>Temperature_C</th>\n",
       "      <th>Dew_point</th>\n",
       "      <th>Humidity_%</th>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "      <th>Weather</th>\n",
       "      <th>Visibility</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>21.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>57.1</td>\n",
       "      <td>19.5</td>\n",
       "      <td>Partially cloudy</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>22.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>21.2</td>\n",
       "      <td>15.4</td>\n",
       "      <td>70.8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "      <td>10.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date/Time  Temperature_C  Dew_point  Humidity_%  Wind_speed_kmph  \\\n",
       "0 2022-01-01           21.3       11.4        57.1             19.5   \n",
       "1 2022-02-01           22.9       14.0        63.1             26.0   \n",
       "2 2022-03-01           21.2       15.4        70.8             29.5   \n",
       "\n",
       "                  Weather  Visibility  \n",
       "0        Partially cloudy        10.0  \n",
       "1  Rain, Partially cloudy        10.0  \n",
       "2  Rain, Partially cloudy        10.0  "
      ]
     },
     "execution_count": 189,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new = df[['datetime', 'temp', 'dew', 'humidity', 'windspeed', 'conditions']]\n",
    "df_new['Visibility'] = df['visibility']\n",
    "\n",
    "#replacing the name of columns to make it more readable\n",
    "df_new.rename(columns={'datetime': 'Date/Time', 'dew': 'Dew_point', 'temp':'Temperature_C', 'humidity':'Humidity_%', \n",
    "                        'windspeed':'Wind_speed_kmph', 'conditions':'Weather'}, inplace=True)\n",
    "\n",
    "df_new.head(3)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6d901f27",
   "metadata": {},
   "source": [
    "#### Q15. find the correlation of the new data set"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "id": "da9d57de",
   "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>Temperature_C</th>\n",
       "      <th>Dew_point</th>\n",
       "      <th>Humidity_%</th>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Temperature_C</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.905186</td>\n",
       "      <td>-0.272460</td>\n",
       "      <td>0.022350</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Dew_point</th>\n",
       "      <td>0.905186</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.154375</td>\n",
       "      <td>-0.082516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Humidity_%</th>\n",
       "      <td>-0.272460</td>\n",
       "      <td>0.154375</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>-0.299526</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "      <td>0.022350</td>\n",
       "      <td>-0.082516</td>\n",
       "      <td>-0.299526</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                 Temperature_C  Dew_point  Humidity_%  Wind_speed_kmph\n",
       "Temperature_C         1.000000   0.905186   -0.272460         0.022350\n",
       "Dew_point             0.905186   1.000000    0.154375        -0.082516\n",
       "Humidity_%           -0.272460   0.154375    1.000000        -0.299526\n",
       "Wind_speed_kmph       0.022350  -0.082516   -0.299526         1.000000"
      ]
     },
     "execution_count": 171,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.corr()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c71467f",
   "metadata": {},
   "source": [
    "- There is high correlation between **Dew point** and **Temprature**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7749a43a",
   "metadata": {},
   "source": [
    "##### plotting the correlation in regression plane"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "8af71992",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<Axes: xlabel='Temperature_C', ylabel='Dew_point'>"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "sns.regplot(data=df_new, x=\"Temperature_C\", y=\"Dew_point\") #taking in 2 columns to check for their correlation"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12675381",
   "metadata": {},
   "source": [
    "There is a strong correlation between the temp and the dew point temp as one increases the other incraeses as well"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "46764f6b",
   "metadata": {},
   "source": [
    "#### Q16: what is the mean value of each column against each weather conditions in new dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "57a776e7",
   "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>Date/Time</th>\n",
       "      <th>Temperature_C</th>\n",
       "      <th>Dew_point</th>\n",
       "      <th>Humidity_%</th>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "      <th>Weather</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>21.3</td>\n",
       "      <td>11.4</td>\n",
       "      <td>57.1</td>\n",
       "      <td>19.5</td>\n",
       "      <td>Partially cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2022-02-01</td>\n",
       "      <td>22.9</td>\n",
       "      <td>14.0</td>\n",
       "      <td>63.1</td>\n",
       "      <td>26.0</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2022-03-01</td>\n",
       "      <td>21.2</td>\n",
       "      <td>15.4</td>\n",
       "      <td>70.8</td>\n",
       "      <td>29.5</td>\n",
       "      <td>Rain, Partially cloudy</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   Date/Time  Temperature_C  Dew_point  Humidity_%  Wind_speed_kmph  \\\n",
       "0 2022-01-01           21.3       11.4        57.1             19.5   \n",
       "1 2022-02-01           22.9       14.0        63.1             26.0   \n",
       "2 2022-03-01           21.2       15.4        70.8             29.5   \n",
       "\n",
       "                  Weather  \n",
       "0        Partially cloudy  \n",
       "1  Rain, Partially cloudy  \n",
       "2  Rain, Partially cloudy  "
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.head(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "id": "c1456a03",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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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>Temperature_C</th>\n",
       "      <th>Dew_point</th>\n",
       "      <th>Humidity_%</th>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clear</th>\n",
       "      <td>11.887500</td>\n",
       "      <td>4.300000</td>\n",
       "      <td>66.237500</td>\n",
       "      <td>23.587500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overcast</th>\n",
       "      <td>11.200000</td>\n",
       "      <td>5.875000</td>\n",
       "      <td>71.625000</td>\n",
       "      <td>24.975000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partially cloudy</th>\n",
       "      <td>12.717568</td>\n",
       "      <td>6.693243</td>\n",
       "      <td>70.560811</td>\n",
       "      <td>21.895946</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain</th>\n",
       "      <td>10.237500</td>\n",
       "      <td>4.412500</td>\n",
       "      <td>71.025000</td>\n",
       "      <td>23.662500</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain, Overcast</th>\n",
       "      <td>14.622222</td>\n",
       "      <td>11.794444</td>\n",
       "      <td>84.397222</td>\n",
       "      <td>22.048611</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain, Partially cloudy</th>\n",
       "      <td>12.516043</td>\n",
       "      <td>7.806417</td>\n",
       "      <td>75.733155</td>\n",
       "      <td>26.369519</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow, Rain, Partially cloudy</th>\n",
       "      <td>5.483333</td>\n",
       "      <td>1.475000</td>\n",
       "      <td>77.391667</td>\n",
       "      <td>26.141667</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Temperature_C  Dew_point  Humidity_%  \\\n",
       "Weather                                                              \n",
       "Clear                             11.887500   4.300000   66.237500   \n",
       "Overcast                          11.200000   5.875000   71.625000   \n",
       "Partially cloudy                  12.717568   6.693243   70.560811   \n",
       "Rain                              10.237500   4.412500   71.025000   \n",
       "Rain, Overcast                    14.622222  11.794444   84.397222   \n",
       "Rain, Partially cloudy            12.516043   7.806417   75.733155   \n",
       "Snow, Rain, Partially cloudy       5.483333   1.475000   77.391667   \n",
       "\n",
       "                              Wind_speed_kmph  \n",
       "Weather                                        \n",
       "Clear                               23.587500  \n",
       "Overcast                            24.975000  \n",
       "Partially cloudy                    21.895946  \n",
       "Rain                                23.662500  \n",
       "Rain, Overcast                      22.048611  \n",
       "Rain, Partially cloudy              26.369519  \n",
       "Snow, Rain, Partially cloudy        26.141667  "
      ]
     },
     "execution_count": 179,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.groupby('Weather').mean()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "8fb79d7f",
   "metadata": {},
   "source": [
    "#### Q17: what is the min and max value of each column against each weather conditions"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "24bf9e65",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "    }\n",
       "\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>Date/Time</th>\n",
       "      <th>Temperature_C</th>\n",
       "      <th>Dew_point</th>\n",
       "      <th>Humidity_%</th>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clear</th>\n",
       "      <td>2022-02-05</td>\n",
       "      <td>2.0</td>\n",
       "      <td>-1.8</td>\n",
       "      <td>50.4</td>\n",
       "      <td>12.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overcast</th>\n",
       "      <td>2022-04-10</td>\n",
       "      <td>8.6</td>\n",
       "      <td>2.6</td>\n",
       "      <td>66.0</td>\n",
       "      <td>22.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partially cloudy</th>\n",
       "      <td>2022-01-01</td>\n",
       "      <td>1.3</td>\n",
       "      <td>-4.4</td>\n",
       "      <td>56.9</td>\n",
       "      <td>11.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain</th>\n",
       "      <td>2022-01-05</td>\n",
       "      <td>2.3</td>\n",
       "      <td>-2.1</td>\n",
       "      <td>60.6</td>\n",
       "      <td>13.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain, Overcast</th>\n",
       "      <td>2022-01-03</td>\n",
       "      <td>2.3</td>\n",
       "      <td>0.6</td>\n",
       "      <td>67.5</td>\n",
       "      <td>9.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain, Partially cloudy</th>\n",
       "      <td>2022-01-02</td>\n",
       "      <td>2.7</td>\n",
       "      <td>-0.9</td>\n",
       "      <td>47.2</td>\n",
       "      <td>10.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow, Rain, Partially cloudy</th>\n",
       "      <td>2022-01-06</td>\n",
       "      <td>3.2</td>\n",
       "      <td>0.2</td>\n",
       "      <td>64.4</td>\n",
       "      <td>16.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Date/Time  Temperature_C  Dew_point  Humidity_%  \\\n",
       "Weather                                                                         \n",
       "Clear                        2022-02-05            2.0       -1.8        50.4   \n",
       "Overcast                     2022-04-10            8.6        2.6        66.0   \n",
       "Partially cloudy             2022-01-01            1.3       -4.4        56.9   \n",
       "Rain                         2022-01-05            2.3       -2.1        60.6   \n",
       "Rain, Overcast               2022-01-03            2.3        0.6        67.5   \n",
       "Rain, Partially cloudy       2022-01-02            2.7       -0.9        47.2   \n",
       "Snow, Rain, Partially cloudy 2022-01-06            3.2        0.2        64.4   \n",
       "\n",
       "                              Wind_speed_kmph  \n",
       "Weather                                        \n",
       "Clear                                    12.6  \n",
       "Overcast                                 22.9  \n",
       "Partially cloudy                         11.2  \n",
       "Rain                                     13.3  \n",
       "Rain, Overcast                            9.4  \n",
       "Rain, Partially cloudy                   10.5  \n",
       "Snow, Rain, Partially cloudy             16.0  "
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.groupby('Weather').min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "id": "6b1981e5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
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       "\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>Date/Time</th>\n",
       "      <th>Temperature_C</th>\n",
       "      <th>Dew_point</th>\n",
       "      <th>Humidity_%</th>\n",
       "      <th>Wind_speed_kmph</th>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Weather</th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "      <th></th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>Clear</th>\n",
       "      <td>2022-12-28</td>\n",
       "      <td>22.9</td>\n",
       "      <td>10.3</td>\n",
       "      <td>81.0</td>\n",
       "      <td>28.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Overcast</th>\n",
       "      <td>2022-10-13</td>\n",
       "      <td>14.0</td>\n",
       "      <td>9.2</td>\n",
       "      <td>76.1</td>\n",
       "      <td>27.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Partially cloudy</th>\n",
       "      <td>2022-12-25</td>\n",
       "      <td>21.6</td>\n",
       "      <td>15.8</td>\n",
       "      <td>89.8</td>\n",
       "      <td>37.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain</th>\n",
       "      <td>2022-11-24</td>\n",
       "      <td>21.5</td>\n",
       "      <td>12.0</td>\n",
       "      <td>82.2</td>\n",
       "      <td>33.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain, Overcast</th>\n",
       "      <td>2022-12-29</td>\n",
       "      <td>21.8</td>\n",
       "      <td>20.5</td>\n",
       "      <td>99.4</td>\n",
       "      <td>43.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Rain, Partially cloudy</th>\n",
       "      <td>2022-12-31</td>\n",
       "      <td>23.5</td>\n",
       "      <td>17.6</td>\n",
       "      <td>98.3</td>\n",
       "      <td>49.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>Snow, Rain, Partially cloudy</th>\n",
       "      <td>2022-09-08</td>\n",
       "      <td>8.4</td>\n",
       "      <td>3.5</td>\n",
       "      <td>85.9</td>\n",
       "      <td>36.3</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                              Date/Time  Temperature_C  Dew_point  Humidity_%  \\\n",
       "Weather                                                                         \n",
       "Clear                        2022-12-28           22.9       10.3        81.0   \n",
       "Overcast                     2022-10-13           14.0        9.2        76.1   \n",
       "Partially cloudy             2022-12-25           21.6       15.8        89.8   \n",
       "Rain                         2022-11-24           21.5       12.0        82.2   \n",
       "Rain, Overcast               2022-12-29           21.8       20.5        99.4   \n",
       "Rain, Partially cloudy       2022-12-31           23.5       17.6        98.3   \n",
       "Snow, Rain, Partially cloudy 2022-09-08            8.4        3.5        85.9   \n",
       "\n",
       "                              Wind_speed_kmph  \n",
       "Weather                                        \n",
       "Clear                                    28.6  \n",
       "Overcast                                 27.7  \n",
       "Partially cloudy                         37.5  \n",
       "Rain                                     33.7  \n",
       "Rain, Overcast                           43.0  \n",
       "Rain, Partially cloudy                   49.1  \n",
       "Snow, Rain, Partially cloudy             36.3  "
      ]
     },
     "execution_count": 181,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df_new.groupby('Weather').max()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "41226da4",
   "metadata": {},
   "source": [
    "### Phase 4: Visualising the Data"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff141a39",
   "metadata": {},
   "source": [
    "#### 4.1 Plot maximum visibility with each month"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 195,
   "id": "dadb55ff",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['name', 'datetime', 'tempmax', 'tempmin', 'temp', 'feelslikemax',\n",
       "       'feelslikemin', 'feelslike', 'dew', 'humidity', 'precip', 'precipprob',\n",
       "       'precipcover', 'preciptype', 'snow', 'snowdepth', 'windgust',\n",
       "       'windspeed', 'winddir', 'sealevelpressure', 'cloudcover', 'visibility',\n",
       "       'solarradiation', 'solarenergy', 'uvindex', 'sunrise', 'sunset',\n",
       "       'moonphase', 'conditions', 'description', 'icon', 'stations',\n",
       "       'day_name', 'month'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 195,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 273,
   "id": "e11bdf20",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x700 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df.groupby('month')['visibility'].max().sort_values(ascending=False)\n",
    "\n",
    "plt.figure(figsize =(15, 7))\n",
    "\n",
    "plt.barh(y=df['month'].sort_values(ascending=False).unique(),width=plot, height=0.4)\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "fd93f765",
   "metadata": {},
   "source": [
    "* **Key Takeaway:** The months of *September* and *October* had higher visibility, followed by *November*."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "e26a6181",
   "metadata": {},
   "source": [
    "#### 4.2 Show a scatterplot with Temprature vs windspeed."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 245,
   "id": "96289018",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot = df.groupby('month')['windspeed', 'temp'].min()\n",
    "\n",
    "plt.scatter(df['windspeed'], df['temp'])\n",
    "\n",
    "plt.xlabel('Wind Speed')\n",
    "plt.ylabel('Temperature')\n",
    "plt.title('Temperature VS Wind Speed')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f75953ef",
   "metadata": {},
   "source": [
    "* **Key Takeaway:** as higher the windspeed the more drop in temprature. This is even prominent after 35 Kmph."
   ]
  },
  {
   "cell_type": "markdown",
   "id": "251c0976",
   "metadata": {},
   "source": [
    "### ACNOWLEDGEMENT"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "bd7c3a31",
   "metadata": {},
   "source": [
    "- **Author:** Samwaran Banerjee\n",
    "- This data has been taken from: [visual crossing](https://www.visualcrossing.com)\n",
    "- The dataset has been processed under full transparency.\n",
    "- Further case study on the same can be found on [my website](https://samwaran.github.io)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}