Homework_04_Sai_Vinay_Nandigam.ipynb

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    "<div class='heading'>\n",
    "    <div style='float:left;'><h1>CPSC 4300/6300: Applied Data Science</h1></div>\n",
    "    <img style=\"float: right; padding-right: 10px; width: 65px\" src=\"https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/images/clemson_paw.png\"> </div>\n",
    "\n",
    "\n",
    "## Homework 4: Regression Modeling to Forecast Bike Sharing Usage\n",
    "\n",
    "**Clemson University**<br>\n",
    "**Fall 2021**<br>\n",
    "**Instructor(s):** Nina Hubig <br>\n",
    "\n",
    "\n",
    "---\n",
    "\n"
   ]
  },
  {
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   "source": [
    "\"\"\" RUN THIS CELL TO GET THE RIGHT FORMATTING \"\"\"\n",
    "import requests\n",
    "from IPython.core.display import HTML\n",
    "css_file = 'https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/css/cpsc6300.css'\n",
    "styles = requests.get(css_file).text\n",
    "HTML(styles)"
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   "source": [
    "### INSTRUCTIONS\n",
    "\n",
    "- To submit your assignment follow the instructions given in Canvas.\n",
    "- Restart the kernel and run the whole notebook again before you submit. \n",
    "- As much as possible, try and stick to the hints and functions we import at the top of the homework, as those are the ideas and tools the class supports and is aiming to teach. And if a problem specifies a particular library you're required to use that library, and possibly others from the import list.\n",
    "- Please use .head() when viewing data. Do not submit a notebook that is excessively long because output was not suppressed or otherwise limited. "
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "<hr style=\"height:2pt\">"
   ]
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  {
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   "metadata": {
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   },
   "source": [
    "<img src=\"https://raw.githubusercontent.com/bsethwalker/clemson-cs4300/main/images/BSS.png\">\n",
    "\n",
    "### Overview \n",
    "\n",
    "You are hired by the administrators of the [Capital Bikeshare program](https://www.capitalbikeshare.com) program in Washington D.C., to **help them predict the hourly demand for rental bikes** and **give them suggestions on how to increase their revenue**. Your task is to prepare a short report summarizing your findings and make recommendations.\n",
    "\n",
    "The predicted hourly demand could be used for planning the number of bikes that need to be available in the system at any given hour of the day. It costs the program money if bike stations are full and bikes cannot be returned, or empty and there are no bikes available. You will use multiple linear regression and polynomial regression and will explore ridge and lasso regression to predict bike usage. The goal is to build a regression model that can predict the total number of bike rentals in a given hour of the day, based on all available information given to you. \n",
    "\n",
    "An example of a suggestion to increase revenue might be to offer discounts during certain times of the day either during holidays or non-holidays. Your suggestions will depend on your observations of the seasonality of ridership.\n",
    "\n",
    "The data for this problem were collected from the Capital Bikeshare program over the course of two years (2011 and 2012).\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
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   },
   "source": [
    "### Use only the libraries below:"
   ]
  },
  {
   "cell_type": "code",
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   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "import statsmodels.api as sm\n",
    "from statsmodels.api import OLS\n",
    "\n",
    "from sklearn.model_selection import train_test_split\n",
    "from sklearn.preprocessing import PolynomialFeatures\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "from sklearn.linear_model import LinearRegression\n",
    "from sklearn.linear_model import Ridge\n",
    "from sklearn.linear_model import RidgeCV\n",
    "from sklearn.linear_model import LassoCV\n",
    "from sklearn.metrics import r2_score\n",
    "\n",
    "from pandas.plotting import scatter_matrix"
   ]
  },
  {
   "cell_type": "markdown",
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   },
   "source": [
    "### Overview\n",
    "The initial data set is provided in the file `data/BSS_hour_raw.csv`. You will first add features that will help with the analysis and then separate the data into training and test sets. Each row in this file represents the number of rides by registered users and casual users in a given hour of a specific date. There are 12 attributes in total describing besides the number of users the weather if it is a holiday or not etc: \n",
    "\n",
    "\n",
    "- `dteday` (date in the format YYYY-MM-DD, e.g. 2011-01-01)\n",
    "- `season` (1 = winter, 2 = spring, 3 = summer, 4 = fall)\n",
    "- `hour` (0 for 12 midnight, 1 for 1:00am, 23 for 11:00pm)\n",
    "- `weekday` (0 through 6, with 0 denoting Sunday)\n",
    "- `holiday` (1 = the day is a holiday, 0 = otherwise)\n",
    "- `weather`\n",
    "    - 1: Clear, Few clouds, Partly cloudy, Partly cloudy\n",
    "    - 2: Mist + Cloudy, Mist + Broken clouds, Mist + Few clouds, Mist\n",
    "    - 3: Light Snow, Light Rain + Thunderstorm\n",
    "    - 4: Heavy Rain + Thunderstorm + Mist, Snow + Fog \n",
    "- `temp` (temperature in Celsius, normalized)\n",
    "- `atemp` (apparent temperature, or relative outdoor temperature, in Celsius, normalized)\n",
    "- `hum` (relative humidity, normalized)\n",
    "- `windspeed` (wind speed, normalized)\n",
    "- `casual` (number of rides that day made by casual riders, not registered in the system)\n",
    "- `registered` (number of rides that day made by registered riders)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<div class=\"theme\">  Question 1 - Data Processing </div>"
   ]
  },
  {
   "cell_type": "markdown",
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   "source": [
    "In this section, we read in the data and begin one of the most important analytic steps: verifying that the data is what it claims to be."
   ]
  },
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   "source": [
    "<div class=\"exercise\"> <b> Question 1.1 </b> </div>"
   ]
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   "source": [
    "Load the dataset from the csv file `data/BSS_hour_raw.csv` into a pandas dataframe that you name `bikes_df_raw`. Do any of the variables' ranges or averages seem suspect? Do the data types make sense?"
   ]
  },
  {
   "cell_type": "code",
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    {
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      "text/plain": [
       "(17379, 13)"
      ]
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     "execution_count": 149,
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   ],
   "source": [
    "# Your code here\n",
    "bikes_df_raw = pd.read_csv('BSS_hour_raw.csv')\n",
    "bikes_df_raw.head()\n",
    "bikes_df_raw.shape"
   ]
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   "cell_type": "code",
   "execution_count": 150,
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
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       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
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       "</div>"
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      "text/plain": [
       "       dteday  season  hour  holiday  weekday  workingday  weather  temp  \\\n",
       "0  2011-01-01       1     0        0        6           0        1  0.24   \n",
       "1  2011-01-01       1     1        0        6           0        1  0.22   \n",
       "2  2011-01-01       1     2        0        6           0        1  0.22   \n",
       "3  2011-01-01       1     3        0        6           0        1  0.24   \n",
       "4  2011-01-01       1     4        0        6           0        1  0.24   \n",
       "\n",
       "    atemp   hum  windspeed  casual  registered  \n",
       "0  0.2879  0.81        0.0       3          13  \n",
       "1  0.2727  0.80        0.0       8          32  \n",
       "2  0.2727  0.80        0.0       5          27  \n",
       "3  0.2879  0.75        0.0       3          10  \n",
       "4  0.2879  0.75        0.0       0           1  "
      ]
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     "execution_count": 150,
     "metadata": {},
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   "source": [
    "bikes_df_raw.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "      <td>17379.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.501640</td>\n",
       "      <td>11.546752</td>\n",
       "      <td>0.028770</td>\n",
       "      <td>3.003683</td>\n",
       "      <td>0.682721</td>\n",
       "      <td>1.425283</td>\n",
       "      <td>0.496987</td>\n",
       "      <td>0.475775</td>\n",
       "      <td>0.627229</td>\n",
       "      <td>0.190098</td>\n",
       "      <td>35.676218</td>\n",
       "      <td>153.786869</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.106918</td>\n",
       "      <td>6.914405</td>\n",
       "      <td>0.167165</td>\n",
       "      <td>2.005771</td>\n",
       "      <td>0.465431</td>\n",
       "      <td>0.639357</td>\n",
       "      <td>0.192556</td>\n",
       "      <td>0.171850</td>\n",
       "      <td>0.192930</td>\n",
       "      <td>0.122340</td>\n",
       "      <td>49.305030</td>\n",
       "      <td>151.357286</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.020000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.340000</td>\n",
       "      <td>0.333300</td>\n",
       "      <td>0.480000</td>\n",
       "      <td>0.104500</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>34.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.500000</td>\n",
       "      <td>0.484800</td>\n",
       "      <td>0.630000</td>\n",
       "      <td>0.194000</td>\n",
       "      <td>17.000000</td>\n",
       "      <td>115.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>18.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.660000</td>\n",
       "      <td>0.621200</td>\n",
       "      <td>0.780000</td>\n",
       "      <td>0.253700</td>\n",
       "      <td>48.000000</td>\n",
       "      <td>220.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>23.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.850700</td>\n",
       "      <td>367.000000</td>\n",
       "      <td>886.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
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      ],
      "text/plain": [
       "             season          hour       holiday       weekday    workingday  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       2.501640     11.546752      0.028770      3.003683      0.682721   \n",
       "std        1.106918      6.914405      0.167165      2.005771      0.465431   \n",
       "min        1.000000      0.000000      0.000000      0.000000      0.000000   \n",
       "25%        2.000000      6.000000      0.000000      1.000000      0.000000   \n",
       "50%        3.000000     12.000000      0.000000      3.000000      1.000000   \n",
       "75%        3.000000     18.000000      0.000000      5.000000      1.000000   \n",
       "max        4.000000     23.000000      1.000000      6.000000      1.000000   \n",
       "\n",
       "            weather          temp         atemp           hum     windspeed  \\\n",
       "count  17379.000000  17379.000000  17379.000000  17379.000000  17379.000000   \n",
       "mean       1.425283      0.496987      0.475775      0.627229      0.190098   \n",
       "std        0.639357      0.192556      0.171850      0.192930      0.122340   \n",
       "min        1.000000      0.020000      0.000000      0.000000      0.000000   \n",
       "25%        1.000000      0.340000      0.333300      0.480000      0.104500   \n",
       "50%        1.000000      0.500000      0.484800      0.630000      0.194000   \n",
       "75%        2.000000      0.660000      0.621200      0.780000      0.253700   \n",
       "max        4.000000      1.000000      1.000000      1.000000      0.850700   \n",
       "\n",
       "             casual    registered  \n",
       "count  17379.000000  17379.000000  \n",
       "mean      35.676218    153.786869  \n",
       "std       49.305030    151.357286  \n",
       "min        0.000000      0.000000  \n",
       "25%        4.000000     34.000000  \n",
       "50%       17.000000    115.000000  \n",
       "75%       48.000000    220.000000  \n",
       "max      367.000000    886.000000  "
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_df_raw.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2.5016399102364923"
      ]
     },
     "execution_count": 152,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_df_raw['season'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 153,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(17379, 13)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Index(['dteday', 'season', 'hour', 'holiday', 'weekday', 'workingday',\n",
       "       'weather', 'temp', 'atemp', 'hum', 'windspeed', 'casual', 'registered'],\n",
       "      dtype='object')"
      ]
     },
     "execution_count": 153,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "print(bikes_df_raw.shape)\n",
    "bikes_df_raw.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dteday         object\n",
       "season          int64\n",
       "hour            int64\n",
       "holiday         int64\n",
       "weekday         int64\n",
       "workingday      int64\n",
       "weather         int64\n",
       "temp          float64\n",
       "atemp         float64\n",
       "hum           float64\n",
       "windspeed     float64\n",
       "casual          int64\n",
       "registered      int64\n",
       "dtype: object"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bikes_df_raw.dtypes"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 155,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n",
      "0\n"
     ]
    }
   ],
   "source": [
    "print(np.sum([bikes_df_raw.dteday.isnull()]))\n",
    "print(np.sum([bikes_df_raw.season.isnull()]))\n",
    "print(np.sum([bikes_df_raw.hour.isnull()]))\n",
    "print(np.sum([bikes_df_raw.holiday.isnull()]))\n",
    "print(np.sum([bikes_df_raw.weekday.isnull()]))\n",
    "print(np.sum([bikes_df_raw.workingday.isnull()]))\n",
    "print(np.sum([bikes_df_raw.weather.isnull()]))\n",
    "print(np.sum([bikes_df_raw.temp.isnull()]))\n",
    "print(np.sum([bikes_df_raw.atemp.isnull()]))\n",
    "print(np.sum([bikes_df_raw.hum.isnull()]))\n",
    "print(np.sum([bikes_df_raw.windspeed.isnull()]))\n",
    "print(np.sum([bikes_df_raw.casual.isnull()]))\n",
    "print(np.sum([bikes_df_raw.registered.isnull()]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*\n",
    "There are no null values in the data. The values in 'temp' and 'atemp' are not in Celcius. As given in the overview, the temperature and apparent temperature should be in Celcius, but in the given dataset, they were not in Celcius."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class=\"exercise\"> <b> Question 1.2 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Notice that the variable in column `dteday` is a pandas `object`, which is **not**  useful when you want to extract the elements of the date such as the year, month, and day. Convert `dteday` into a `datetime` object to prepare it for later analysis.\n",
    "\n",
    "**Hint:** http://pandas.pydata.org/pandas-docs/stable/generated/pandas.to_datetime.html"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0   2011-01-01\n",
       "1   2011-01-01\n",
       "2   2011-01-01\n",
       "3   2011-01-01\n",
       "4   2011-01-01\n",
       "Name: dteday, dtype: datetime64[ns]"
      ]
     },
     "execution_count": 156,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "bikes_df_raw['dteday'] = pd.to_datetime(bikes_df_raw['dteday'])\n",
    "bikes_df_raw['dteday'].head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class=\"exercise\"> <b> Question 1.3 </b> </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Create three new columns in the dataframe:\n",
    "- `year` with 0 for 2011, 1 for 2012, etc.\n",
    "- `month` with 1 through 12, with 1 denoting January.\n",
    "- `counts` with the total number of bike rentals for that **hour** (this is the response variable for later)."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "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>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dteday  season  hour  holiday  weekday  workingday  weather  temp  \\\n",
       "0 2011-01-01       1     0        0        6           0        1  0.24   \n",
       "1 2011-01-01       1     1        0        6           0        1  0.22   \n",
       "2 2011-01-01       1     2        0        6           0        1  0.22   \n",
       "3 2011-01-01       1     3        0        6           0        1  0.24   \n",
       "4 2011-01-01       1     4        0        6           0        1  0.24   \n",
       "\n",
       "    atemp   hum  windspeed  casual  registered  year  month  count  \n",
       "0  0.2879  0.81        0.0       3          13  2011   2011     16  \n",
       "1  0.2727  0.80        0.0       8          32  2011   2011     40  \n",
       "2  0.2727  0.80        0.0       5          27  2011   2011     32  \n",
       "3  0.2879  0.75        0.0       3          10  2011   2011     13  \n",
       "4  0.2879  0.75        0.0       0           1  2011   2011      1  "
      ]
     },
     "execution_count": 157,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "bikes_df_raw[\"year\"] = bikes_df_raw.dteday.dt.year\n",
    "bikes_df_raw[\"month\"] = bikes_df_raw.dteday.dt.year\n",
    "bikes_df_raw[\"count\"] = bikes_df_raw.casual + bikes_df_raw.registered\n",
    "bikes_df_raw.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "---\n",
    "\n",
    "<div class=\"theme\">  Question 2 - Exploratory Data Analysis </div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "In this question we begin hunting for patterns in ridership that shed light on who uses the service and why."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.1 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Make a new dataframe with the following subset of attributes from the previous dataset and with each entry being just **one** day: \n",
    "\n",
    "- `dteday`, the timestamp for that day (fine to set to noon or any other time) \n",
    "- `weekday`, the day of the week\n",
    "- `weather`, the most severe weather that day\n",
    "- `season`, the season that day falls in\n",
    "- `temp`, the average temperature\n",
    "- `atemp`, the average atemp that day\n",
    "- `windspeed`, the average windspeed that day\n",
    "- `hum`, the average humidity that day\n",
    "- `casual`, the **total** number of rentals by casual users\n",
    "- `registered`, the **total** number of rentals by registered users\n",
    "- `counts`, the **total** number of rentals of that day\n",
    "\n",
    "Name this dataframe `bikes_by_day`.\n",
    "\n",
    "Make a plot showing  the _distribution_ of the number of casual and registered riders on each day of the week.\n",
    "\n",
    "**Hint:** helpful to use panda's `.groupby()` command"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "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>dteday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>weather</th>\n",
       "      <th>season</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>hum</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>6</td>\n",
       "      <td>1.583333</td>\n",
       "      <td>1</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>331</td>\n",
       "      <td>654</td>\n",
       "      <td>985</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-02</td>\n",
       "      <td>0</td>\n",
       "      <td>1.956522</td>\n",
       "      <td>1</td>\n",
       "      <td>0.363478</td>\n",
       "      <td>0.353739</td>\n",
       "      <td>0.248539</td>\n",
       "      <td>0.696087</td>\n",
       "      <td>131</td>\n",
       "      <td>670</td>\n",
       "      <td>801</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-03</td>\n",
       "      <td>1</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.196364</td>\n",
       "      <td>0.189405</td>\n",
       "      <td>0.248309</td>\n",
       "      <td>0.437273</td>\n",
       "      <td>120</td>\n",
       "      <td>1229</td>\n",
       "      <td>1349</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-04</td>\n",
       "      <td>2</td>\n",
       "      <td>1.043478</td>\n",
       "      <td>1</td>\n",
       "      <td>0.200000</td>\n",
       "      <td>0.212122</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>3</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.186900</td>\n",
       "      <td>0.436957</td>\n",
       "      <td>82</td>\n",
       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dteday  weekday   weather  season      temp     atemp  windspeed  \\\n",
       "0 2011-01-01        6  1.583333       1  0.344167  0.363625   0.160446   \n",
       "1 2011-01-02        0  1.956522       1  0.363478  0.353739   0.248539   \n",
       "2 2011-01-03        1  1.000000       1  0.196364  0.189405   0.248309   \n",
       "3 2011-01-04        2  1.043478       1  0.200000  0.212122   0.160296   \n",
       "4 2011-01-05        3  1.000000       1  0.226957  0.229270   0.186900   \n",
       "\n",
       "        hum  casual  registered  count  \n",
       "0  0.805833     331         654    985  \n",
       "1  0.696087     131         670    801  \n",
       "2  0.437273     120        1229   1349  \n",
       "3  0.590435     108        1454   1562  \n",
       "4  0.436957      82        1518   1600  "
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "def mode_func(x):\n",
    "\n",
    "    return x.value_counts().index[0]\n",
    "\n",
    "bikes_by_day = bikes_df_raw.groupby('dteday').agg({'weekday': mode_func, 'weather': 'mean','season':'mean',\n",
    "                                               'temp': 'mean', 'atemp': 'mean','windspeed':'mean',\n",
    "                                               'hum': 'mean', 'casual': np.sum, 'registered': np.sum, 'count' : np.sum})\n",
    "bikes_by_day = bikes_by_day.reset_index()\n",
    "bikes_by_day.head()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 159,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "bikes_by_weekday=bikes_df_raw.groupby([\"weekday\"]).sum()\n",
    "fig, ax = plt.subplots()\n",
    "ax.bar(bikes_by_weekday.index - 0.35/2, bikes_by_weekday[\"registered\"], 0.35, label='Registered')\n",
    "ax.bar(bikes_by_weekday.index + 0.35/2, bikes_by_weekday[\"casual\"], 0.35, label='Casual')\n",
    "ax.set_title('Total number of riders vs each day of the week')\n",
    "ax.set_ylabel(r'Number of riders', fontsize = 15)\n",
    "ax.set_xlabel(r'Each day of the week', fontsize = 15)\n",
    "\n",
    "\n",
    "ax.legend()\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.2 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Use `bikes_by_day` to visualize how the distribution of **total number of rides** per day (casual and registered riders combined) varies with the **season**. Do you see any **outliers**? Here we use the pyplot's boxplot function definition of an outlier as any value 1.5 times the IQR above the 75th percentile or 1.5 times the IQR below the 25th percentiles. If you see any outliers, identify those dates and investigate if they are a chance occurence, an error in the data collection, or a significant event (an online search of those date(s) might help). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1152x648 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "with sns.axes_style(\"whitegrid\"):\n",
    "# setup plot\n",
    "    fig, ax = plt.subplots(figsize=(16,9))\n",
    "    sb = sns.boxplot(x='weekday', y='count', hue='season', data=bikes_by_day, ax=ax)\n",
    "# labels and such\n",
    "    ax.tick_params(axis='both', which='major', labelsize=15)\n",
    "    ax.set_xlabel('Weekday', fontsize=20)\n",
    "    ax.set_ylabel('Total Riders Counts', fontsize=20)\n",
    "    ax.set_title('Total Rider Distributions', fontsize=25)\n",
    "    ax.legend(title='Season', fontsize=14, ncol=4, loc='upper left')\n",
    "    sns.despine()\n",
    "    ax.grid(False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "*Your answer here*\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.3 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Convert the categorical attributes ('season', 'month', 'weekday', 'weather') into multiple binary attributes using **one-hot encoding** and call this new dataframe `bikes_df`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 161,
   "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>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.81</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>13</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>16</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>40</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>32</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>13</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      dteday  season  hour  holiday  weekday  workingday  weather  temp  \\\n",
       "0 2011-01-01       1     0        0        6           0        1  0.24   \n",
       "1 2011-01-01       1     1        0        6           0        1  0.22   \n",
       "2 2011-01-01       1     2        0        6           0        1  0.22   \n",
       "3 2011-01-01       1     3        0        6           0        1  0.24   \n",
       "4 2011-01-01       1     4        0        6           0        1  0.24   \n",
       "\n",
       "    atemp   hum  windspeed  casual  registered  year  month  count  \n",
       "0  0.2879  0.81        0.0       3          13  2011   2011     16  \n",
       "1  0.2727  0.80        0.0       8          32  2011   2011     40  \n",
       "2  0.2727  0.80        0.0       5          27  2011   2011     32  \n",
       "3  0.2879  0.75        0.0       3          10  2011   2011     13  \n",
       "4  0.2879  0.75        0.0       0           1  2011   2011      1  "
      ]
     },
     "execution_count": 161,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "bikes_df = bikes_df_raw.copy()\n",
    "bikes_df.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 162,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>...</th>\n",
       "      <th>weekday_1</th>\n",
       "      <th>weekday_2</th>\n",
       "      <th>weekday_3</th>\n",
       "      <th>weekday_4</th>\n",
       "      <th>weekday_5</th>\n",
       "      <th>weekday_6</th>\n",
       "      <th>weather_1</th>\n",
       "      <th>weather_2</th>\n",
       "      <th>weather_3</th>\n",
       "      <th>weather_4</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
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       "      <td>0.0</td>\n",
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       "      <td>13</td>\n",
       "      <td>...</td>\n",
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       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>8</td>\n",
       "      <td>32</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
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       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>2</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.22</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.80</td>\n",
       "      <td>0.0</td>\n",
       "      <td>5</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>3</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>3</td>\n",
       "      <td>10</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>2011-01-01</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.24</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.75</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
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       "  </tbody>\n",
       "</table>\n",
       "<p>5 rows × 29 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "      dteday  hour  holiday  workingday  temp   atemp   hum  windspeed  \\\n",
       "0 2011-01-01     0        0           0  0.24  0.2879  0.81        0.0   \n",
       "1 2011-01-01     1        0           0  0.22  0.2727  0.80        0.0   \n",
       "2 2011-01-01     2        0           0  0.22  0.2727  0.80        0.0   \n",
       "3 2011-01-01     3        0           0  0.24  0.2879  0.75        0.0   \n",
       "4 2011-01-01     4        0           0  0.24  0.2879  0.75        0.0   \n",
       "\n",
       "   casual  registered  ...  weekday_1  weekday_2  weekday_3  weekday_4  \\\n",
       "0       3          13  ...          0          0          0          0   \n",
       "1       8          32  ...          0          0          0          0   \n",
       "2       5          27  ...          0          0          0          0   \n",
       "3       3          10  ...          0          0          0          0   \n",
       "4       0           1  ...          0          0          0          0   \n",
       "\n",
       "   weekday_5  weekday_6  weather_1  weather_2  weather_3  weather_4  \n",
       "0          0          1          1          0          0          0  \n",
       "1          0          1          1          0          0          0  \n",
       "2          0          1          1          0          0          0  \n",
       "3          0          1          1          0          0          0  \n",
       "4          0          1          1          0          0          0  \n",
       "\n",
       "[5 rows x 29 columns]"
      ]
     },
     "execution_count": 162,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# Your code here\n",
    "bikes_df= pd.get_dummies(bikes_df_raw, columns = ['season', 'month', 'weekday','weather'])\n",
    "bikes_df.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.4 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Split the updated `bikes_df` dataset into a 50-50 train-test split (call them `bikes_train` and `bikes_test`, respectively). Do this in a 'stratified' fashion, ensuring that all months are equally represented in each set.  Use `random_state=90`, a test set size of .5, and stratify on month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 179,
   "metadata": {},
   "outputs": [],
   "source": [
    "# Your code here\n",
    "try:\n",
    "# stratification\n",
    "    train_data, test_data = train_test_split(bikes_df_raw,random_state=90, test_size=0.5, stratify=bikes_df_raw['month'])\n",
    "except:\n",
    "# Count the missing lines and drop them\n",
    "    missing_rows = np.isnan(bikes_df.month)\n",
    "    print(\"{} lines miss data. Dropping them.\".format(np.sum(missing_rows)))\n",
    "    bikes_df = bikes_df_raw.dropna(subset=['month'])\n",
    "    train_data, test_data = train_test_split(bikes_df,random_state=90, test_size=0.5, stratify=bikes_df['month'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 169,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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       "\n",
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       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
       "      <th>hour</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weather</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>casual</th>\n",
       "      <th>registered</th>\n",
       "      <th>year</th>\n",
       "      <th>month</th>\n",
       "      <th>count</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>478</th>\n",
       "      <td>2011-01-21</td>\n",
       "      <td>1</td>\n",
       "      <td>23</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.06</td>\n",
       "      <td>0.0455</td>\n",
       "      <td>0.38</td>\n",
       "      <td>0.3284</td>\n",
       "      <td>0</td>\n",
       "      <td>27</td>\n",
       "      <td>2011</td>\n",
       "      <td>2011</td>\n",
       "      <td>27</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15899</th>\n",
       "      <td>2012-10-31</td>\n",
       "      <td>4</td>\n",
       "      <td>4</td>\n",
       "      <td>0</td>\n",
       "      <td>3</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.2879</td>\n",
       "      <td>0.87</td>\n",
       "      <td>0.1940</td>\n",
       "      <td>0</td>\n",
       "      <td>5</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17272</th>\n",
       "      <td>2012-12-27</td>\n",
       "      <td>1</td>\n",
       "      <td>13</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.30</td>\n",
       "      <td>0.2727</td>\n",
       "      <td>0.49</td>\n",
       "      <td>0.4627</td>\n",
       "      <td>19</td>\n",
       "      <td>99</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>118</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14027</th>\n",
       "      <td>2012-08-12</td>\n",
       "      <td>3</td>\n",
       "      <td>16</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.76</td>\n",
       "      <td>0.6818</td>\n",
       "      <td>0.40</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>213</td>\n",
       "      <td>300</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>513</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13521</th>\n",
       "      <td>2012-07-22</td>\n",
       "      <td>3</td>\n",
       "      <td>14</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.72</td>\n",
       "      <td>0.6818</td>\n",
       "      <td>0.66</td>\n",
       "      <td>0.1045</td>\n",
       "      <td>256</td>\n",
       "      <td>360</td>\n",
       "      <td>2012</td>\n",
       "      <td>2012</td>\n",
       "      <td>616</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "          dteday  season  hour  holiday  weekday  workingday  weather  temp  \\\n",
       "478   2011-01-21       1    23        0        5           1        1  0.06   \n",
       "15899 2012-10-31       4     4        0        3           1        2  0.30   \n",
       "17272 2012-12-27       1    13        0        4           1        1  0.30   \n",
       "14027 2012-08-12       3    16        0        0           0        1  0.76   \n",
       "13521 2012-07-22       3    14        0        0           0        1  0.72   \n",
       "\n",
       "        atemp   hum  windspeed  casual  registered  year  month  count  \n",
       "478    0.0455  0.38     0.3284       0          27  2011   2011     27  \n",
       "15899  0.2879  0.87     0.1940       0           5  2012   2012      5  \n",
       "17272  0.2727  0.49     0.4627      19          99  2012   2012    118  \n",
       "14027  0.6818  0.40     0.1045     213         300  2012   2012    513  \n",
       "13521  0.6818  0.66     0.1045     256         360  2012   2012    616  "
      ]
     },
     "execution_count": 169,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data.head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.5 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Although we asked you to create your train and test set, for consistency and easy checking, we ask that for the rest of this problem set you use the train and test set provided in the files `data/BSS_train.csv` and `data/BSS_test.csv`. Read these two files into dataframes `BSS_train` and `BSS_test`, respectively. Remove the `dteday` column from both the train and the test dataset (its format cannot be used for analysis). "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 170,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['dteday', 'hour', 'holiday', 'year', 'workingday', 'temp', 'atemp',\n",
       "       'hum', 'windspeed', 'casual', 'registered', 'counts', 'spring',\n",
       "       'summer', 'fall', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug',\n",
       "       'Sept', 'Oct', 'Nov', 'Dec', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat',\n",
       "       'Cloudy', 'Snow', 'Storm'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/plain": [
       "Index(['dteday', 'hour', 'holiday', 'year', 'workingday', 'temp', 'atemp',\n",
       "       'hum', 'windspeed', 'casual', 'registered', 'counts', 'spring',\n",
       "       'summer', 'fall', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug',\n",
       "       'Sept', 'Oct', 'Nov', 'Dec', 'Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat',\n",
       "       'Cloudy', 'Snow', 'Storm'],\n",
       "      dtype='object')"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "BSS_train = pd.read_csv(\"BSS_train.csv\")\n",
    "display(BSS_train.columns)\n",
    "BSS_test = pd.read_csv(\"BSS_test.csv\")\n",
    "display(BSS_test.columns)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 171,
   "metadata": {},
   "outputs": [],
   "source": [
    "col_drop = ['dteday', 'year', 'registered', 'casual']\n",
    "BSS_train = BSS_train.drop(col_drop, axis=1)\n",
    "BSS_test = BSS_test.drop(col_drop, axis=1)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.6 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Use pandas' `scatter_matrix` command to visualize the inter-dependencies among the list of predictors listed below in the training dataset. Note and comment on any strongly related variables. [This may take a few minutes to run. You may wish to comment it out until your final submission, or only plot a randomly-selected 10% of the rows]\n",
    "\n",
    "`cor_columns = ['hour', 'holiday', 'temp', 'atemp', \n",
    "               'workingday', 'hum', 'windspeed', \n",
    "               'counts', 'casual', 'registered',\n",
    "               'fall', 'summer', 'spring', \n",
    "               'Snow', 'Storm', 'Cloudy']`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 2160x1440 with 784 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "scatter_matrix(bikes_df, figsize=(30, 20));"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer\n",
    "Temperature and apparent temperatures are related and also casual, registered, and count are also correlated\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.7 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Make a plot showing the _average_ number of casual and registered riders during each hour of the day. `.groupby` and `.aggregate` should make this task easy. Comment on the trends you observe. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 181,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "temp_df = bikes_df.groupby(\"hour\").agg(['mean'])\n",
    "# plotting casual riders\n",
    "plt.plot(temp_df.index, temp_df.casual)\n",
    "# plotting registered riders\n",
    "plt.plot(temp_df.index, temp_df.registered)\n",
    "# styling graph\n",
    "plt.suptitle(\"Avg. number of riders vs. hour of day\", size=20)\n",
    "plt.ylabel(\"Avg. number of riders\")\n",
    "plt.xlabel(\"Hour of the day\")\n",
    "plt.legend([\"Casual\", \"Registered\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer\n",
    "From the plot, we can see that the registered users have more riders when compared to casual users.\n",
    "\n",
    "Starting from 5:00 Am there is a dramatic increase of rides by registered users till 10:00 Am. Then the number of rides is dipped to 130 rides from 325 rides at the end of 10:00 Am. After 10:00 Am there are some fluctuations till 15:00 pm, then a sudden increase in the number of riders happened after 16:00 Pm and reached the peak of 380 average riders and gradually decreased by the end of the day.\n",
    "\n",
    "For casual users, the average number of rides is less than 40 from (0-10) hours, then there is a gradual increase in rides from (10-17) hours and gradually decreases by the end of the day.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "<div class='exercise'> <b> Question 2.8 </b></div>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "button": false,
    "new_sheet": false,
    "run_control": {
     "read_only": false
    }
   },
   "source": [
    "Use the one-hot-encoded `weather` related variables to show how each weather category affects the relationships in question 2.6.  Remember you should have four plots, one for each weather type (Cloudy, Storm, Snow, and Clear), even though there are only three columns related to weather after one-hot-encoding. What do you observe?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 185,
   "metadata": {},
   "outputs": [
    {
     "ename": "AttributeError",
     "evalue": "'DataFrame' object has no attribute 'weather'",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
      "\u001b[0;32m<ipython-input-185-1b4cefc36a03>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      7\u001b[0m \u001b[0;31m# slice by weather type, group according to hour, and\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      8\u001b[0m \u001b[0;31m# aggregate by taking the mean\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 9\u001b[0;31m     \u001b[0mtemp_df\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mbikes_df\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mbikes_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mweather\u001b[0m \u001b[0;34m==\u001b[0m \u001b[0;34m(\u001b[0m\u001b[0mi\u001b[0m\u001b[0;34m+\u001b[0m\u001b[0;36m1\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgroupby\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"hour\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0magg\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m'mean'\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     10\u001b[0m \u001b[0;31m# plotting the graph\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     11\u001b[0m     \u001b[0mplt\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtemp_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mindex\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mtemp_df\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcasual\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;32m/opt/anaconda3/lib/python3.8/site-packages/pandas/core/generic.py\u001b[0m in \u001b[0;36m__getattr__\u001b[0;34m(self, name)\u001b[0m\n\u001b[1;32m   5463\u001b[0m             \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_info_axis\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_can_hold_identifiers_and_holds_name\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5464\u001b[0m                 \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 5465\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mobject\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__getattribute__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   5466\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   5467\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0m__setattr__\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mname\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mstr\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mvalue\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mAttributeError\u001b[0m: 'DataFrame' object has no attribute 'weather'"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1440x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Your code here\n",
    "plt.figure(figsize=(20,8))\n",
    "plt.suptitle(\"Number of riders vs hour of day for weather in [1;4]\", size=30)\n",
    "# plot for each weather type\n",
    "for i in range(4):\n",
    "    plt.subplot(1,4,i+1) # sharey=True\n",
    "# slice by weather type, group according to hour, and\n",
    "# aggregate by taking the mean\n",
    "    temp_df = bikes_df[bikes_df.weather == (i+1)].groupby(\"hour\").agg(['mean'])\n",
    "# plotting the graph\n",
    "    plt.plot(temp_df.index, temp_df.casual)\n",
    "    plt.plot(temp_df.index, temp_df.registered)\n",
    "# styling the graph\n",
    "    plt.title(\"weather = {}\".format(i+1), size=20)\n",
    "    if i < 1:\n",
    "        plt.ylabel(\"Avg. number of riders\", size=20)\n",
    "        yticks_temp = plt.yticks()[0]\n",
    "    else:\n",
    "        plt.yticks(yticks_temp[1:-1])\n",
    "    plt.xlabel(\"Hour of the day\", size=20)\n",
    "    plt.legend([\"Casual\", \"Registered\"]);"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Answer\n",
    "The impact of the weather1 and weather2 on the average number of rides is very less. There is a sudden change for weather3 when compared to weather 2, the average number of rides has decreased for both registered and casual rides.The impact of weather4 is huge on the average number of riders when compared to weather1, weather2, and weather3.\n"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.8"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}