Finding_Heavy_Traffic_Indicators_on_I-94.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Finding Heavy Traffic Indicators on I-94"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project we'll analyze a dataset about the westbound traffic on the [I-94 Interstate highway](https://en.wikipedia.org/wiki/Interstate_94).\n",
    "\n",
    "The goal of our analysis is to determine a few indicators of heavy traffic on I-94. These indicators can be weather type, time of the day, time of the week, etc. For instance, we may find out that the traffic is usually heavier in the summer or when it snows."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The I-94 Traffic Dataset"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "John Hogue made the dataset available, and you can download it from the [UCI Machine Learning Repository](https://archive.ics.uci.edu/dataset/492/metro+interstate+traffic+volume)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>None</td>\n",
       "      <td>288.28</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>40</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>scattered clouds</td>\n",
       "      <td>2012-10-02 09:00:00</td>\n",
       "      <td>5545</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>None</td>\n",
       "      <td>289.36</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 10:00:00</td>\n",
       "      <td>4516</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>None</td>\n",
       "      <td>289.58</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 11:00:00</td>\n",
       "      <td>4767</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>None</td>\n",
       "      <td>290.13</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2012-10-02 12:00:00</td>\n",
       "      <td>5026</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>None</td>\n",
       "      <td>291.14</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2012-10-02 13:00:00</td>\n",
       "      <td>4918</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "  holiday    temp  rain_1h  snow_1h  clouds_all weather_main  \\\n",
       "0    None  288.28      0.0      0.0          40       Clouds   \n",
       "1    None  289.36      0.0      0.0          75       Clouds   \n",
       "2    None  289.58      0.0      0.0          90       Clouds   \n",
       "3    None  290.13      0.0      0.0          90       Clouds   \n",
       "4    None  291.14      0.0      0.0          75       Clouds   \n",
       "\n",
       "  weather_description            date_time  traffic_volume  \n",
       "0    scattered clouds  2012-10-02 09:00:00            5545  \n",
       "1       broken clouds  2012-10-02 10:00:00            4516  \n",
       "2     overcast clouds  2012-10-02 11:00:00            4767  \n",
       "3     overcast clouds  2012-10-02 12:00:00            5026  \n",
       "4       broken clouds  2012-10-02 13:00:00            4918  "
      ]
     },
     "execution_count": 1,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import pandas as pd\n",
    "\n",
    "i_94_traffic = pd.read_csv('Metro_Interstate_Traffic_Volume.csv')\n",
    "\n",
    "i_94_traffic.head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "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>holiday</th>\n",
       "      <th>temp</th>\n",
       "      <th>rain_1h</th>\n",
       "      <th>snow_1h</th>\n",
       "      <th>clouds_all</th>\n",
       "      <th>weather_main</th>\n",
       "      <th>weather_description</th>\n",
       "      <th>date_time</th>\n",
       "      <th>traffic_volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>48199</th>\n",
       "      <td>None</td>\n",
       "      <td>283.45</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>75</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>broken clouds</td>\n",
       "      <td>2018-09-30 19:00:00</td>\n",
       "      <td>3543</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48200</th>\n",
       "      <td>None</td>\n",
       "      <td>282.76</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 20:00:00</td>\n",
       "      <td>2781</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48201</th>\n",
       "      <td>None</td>\n",
       "      <td>282.73</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Thunderstorm</td>\n",
       "      <td>proximity thunderstorm</td>\n",
       "      <td>2018-09-30 21:00:00</td>\n",
       "      <td>2159</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48202</th>\n",
       "      <td>None</td>\n",
       "      <td>282.09</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 22:00:00</td>\n",
       "      <td>1450</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>48203</th>\n",
       "      <td>None</td>\n",
       "      <td>282.12</td>\n",
       "      <td>0.0</td>\n",
       "      <td>0.0</td>\n",
       "      <td>90</td>\n",
       "      <td>Clouds</td>\n",
       "      <td>overcast clouds</td>\n",
       "      <td>2018-09-30 23:00:00</td>\n",
       "      <td>954</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "      holiday    temp  rain_1h  snow_1h  clouds_all  weather_main  \\\n",
       "48199    None  283.45      0.0      0.0          75        Clouds   \n",
       "48200    None  282.76      0.0      0.0          90        Clouds   \n",
       "48201    None  282.73      0.0      0.0          90  Thunderstorm   \n",
       "48202    None  282.09      0.0      0.0          90        Clouds   \n",
       "48203    None  282.12      0.0      0.0          90        Clouds   \n",
       "\n",
       "          weather_description            date_time  traffic_volume  \n",
       "48199           broken clouds  2018-09-30 19:00:00            3543  \n",
       "48200         overcast clouds  2018-09-30 20:00:00            2781  \n",
       "48201  proximity thunderstorm  2018-09-30 21:00:00            2159  \n",
       "48202         overcast clouds  2018-09-30 22:00:00            1450  \n",
       "48203         overcast clouds  2018-09-30 23:00:00             954  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_94_traffic.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 48204 entries, 0 to 48203\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype  \n",
      "---  ------               --------------  -----  \n",
      " 0   holiday              48204 non-null  object \n",
      " 1   temp                 48204 non-null  float64\n",
      " 2   rain_1h              48204 non-null  float64\n",
      " 3   snow_1h              48204 non-null  float64\n",
      " 4   clouds_all           48204 non-null  int64  \n",
      " 5   weather_main         48204 non-null  object \n",
      " 6   weather_description  48204 non-null  object \n",
      " 7   date_time            48204 non-null  object \n",
      " 8   traffic_volume       48204 non-null  int64  \n",
      "dtypes: float64(3), int64(2), object(4)\n",
      "memory usage: 3.3+ MB\n"
     ]
    }
   ],
   "source": [
    "i_94_traffic.info()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see that the dataset has **48,204 rows** and **9 columns**, it doesn't contain Null values and has mixed column types (string, int, float)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's describe the values of the columns using the [dataset documentation](https://archive.ics.uci.edu/dataset/492/metro+interstate+traffic+volume):\n",
    "\n",
    "- `holiday` - US National holidays plus regional holiday, Minnesota State Fair\n",
    "- `temp` - Average temp in kelvin\n",
    "- `rain_1h` - Amount in mm of rain that occurred in the hour\n",
    "- `snow_1h` - Amount in mm of snow that occurred in the hour\n",
    "- `clouds_all` - Percentage of cloud cover\n",
    "- `weather_main` - Short textual description of the current weather\n",
    "- `weather_description` - Longer textual description of the current weather\n",
    "- `date_time` - Hour of the data collected in local CST time\n",
    "- `traffic_volume` - Hourly I-94 ATR 301 reported westbound traffic volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the descriptions, we can assume that all the columns may help us research."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Analyzing Traffic Volume"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "From the additional information in the [dataset documentation](https://archive.ics.uci.edu/dataset/492/metro+interstate+traffic+volume), we know that a station located approximately midway between Minneapolis and Saint Paul recorded the traffic data. Also, the station only records westbound traffic (cars moving from east to west).\n",
    "\n",
    "This means that the results of our analysis will be about the westbound traffic in the proximity of that station. In other words, we should avoid generalizing our results for the entire I-94 highway."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's generate plot graph for `traffic_volume` column and analyze it"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "%matplotlib inline\n",
    "\n",
    "i_94_traffic['traffic_volume'].plot.hist()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    48204.000000\n",
       "mean      3259.818355\n",
       "std       1986.860670\n",
       "min          0.000000\n",
       "25%       1193.000000\n",
       "50%       3380.000000\n",
       "75%       4933.000000\n",
       "max       7280.000000\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "i_94_traffic['traffic_volume'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Approximately 25% of the time, the traffic volume passing the station each hour was 1,193 cars or fewer. This lower traffic volume is often observed during periods such as the night or when road construction activities occur. However, about 75% of the time, the traffic volume was significantly higher, reaching at least 4,933 cars or more. This suggests that the traffic volume is substantially greater most of the time, potentially indicating peak hours or busier periods of the day."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's explore the effect of the time of day on the volume of traffic."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traffic Volume: Day vs. Night"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We'll start by dividing the dataset into two parts:\n",
    "\n",
    "- Daytime data: hours from 7 a.m. to 7 p.m. (12 hours)\n",
    "- Nighttime data: hours from 7 p.m. to 7 a.m. (12 hours)\n",
    "\n",
    "While this is not a perfect criterion for distinguishing between nighttime and daytime, it's a good starting point."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 23877 entries, 0 to 48198\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   holiday              23877 non-null  object        \n",
      " 1   temp                 23877 non-null  float64       \n",
      " 2   rain_1h              23877 non-null  float64       \n",
      " 3   snow_1h              23877 non-null  float64       \n",
      " 4   clouds_all           23877 non-null  int64         \n",
      " 5   weather_main         23877 non-null  object        \n",
      " 6   weather_description  23877 non-null  object        \n",
      " 7   date_time            23877 non-null  datetime64[ns]\n",
      " 8   traffic_volume       23877 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(3)\n",
      "memory usage: 1.8+ MB\n",
      "None\n",
      "\n",
      "\n",
      "<class 'pandas.core.frame.DataFrame'>\n",
      "Int64Index: 24327 entries, 10 to 48203\n",
      "Data columns (total 9 columns):\n",
      " #   Column               Non-Null Count  Dtype         \n",
      "---  ------               --------------  -----         \n",
      " 0   holiday              24327 non-null  object        \n",
      " 1   temp                 24327 non-null  float64       \n",
      " 2   rain_1h              24327 non-null  float64       \n",
      " 3   snow_1h              24327 non-null  float64       \n",
      " 4   clouds_all           24327 non-null  int64         \n",
      " 5   weather_main         24327 non-null  object        \n",
      " 6   weather_description  24327 non-null  object        \n",
      " 7   date_time            24327 non-null  datetime64[ns]\n",
      " 8   traffic_volume       24327 non-null  int64         \n",
      "dtypes: datetime64[ns](1), float64(3), int64(2), object(3)\n",
      "memory usage: 1.9+ MB\n",
      "None\n"
     ]
    }
   ],
   "source": [
    "i_94_traffic['date_time'] = pd.to_datetime(i_94_traffic['date_time'])\n",
    "\n",
    "# daytime - 7 to 19\n",
    "# nighttime - 19 to 7\n",
    "\n",
    "i_94_traffic_copy = i_94_traffic.copy()\n",
    "\n",
    "daytime = (i_94_traffic['date_time'].dt.hour >= 7) & (i_94_traffic['date_time'].dt.hour < 19)\n",
    "nighttime = ~(daytime)\n",
    "\n",
    "day_traffic = i_94_traffic_copy[daytime]\n",
    "night_traffic = i_94_traffic_copy[nighttime]\n",
    "\n",
    "print(day_traffic.info())\n",
    "print('\\n')\n",
    "print(night_traffic.info())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Despite the fact that the range time is the same for day and night (12 hours), we see a noticeable scatter in the number of lines between them, this is explained by the absence of data for several hours. In particular, if we consider lines 176 and 177 (i_94_traffic.iloc[176:178]), it becomes obvious that there is a gap of two hours in the data (4 and 5)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Traffic Volume: Day vs. Night (II)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now we're going to compare the traffic volume at night and during day."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,3.5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "plt.hist(day_traffic['traffic_volume'])\n",
    "plt.xlim(-100, 7500)\n",
    "plt.ylim(0, 8000)\n",
    "plt.title('Traffic Volume: Day')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Traffic Volume')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "plt.hist(night_traffic['traffic_volume'])\n",
    "plt.xlim(-100, 7500)\n",
    "plt.ylim(0, 8000)\n",
    "plt.title('Traffic Volume: Night')\n",
    "plt.ylabel('Frequency')\n",
    "plt.xlabel('Traffic Volume')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    23877.000000\n",
       "mean      4762.047452\n",
       "std       1174.546482\n",
       "min          0.000000\n",
       "25%       4252.000000\n",
       "50%       4820.000000\n",
       "75%       5559.000000\n",
       "max       7280.000000\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_traffic['traffic_volume'].describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "count    24327.000000\n",
       "mean      1785.377441\n",
       "std       1441.951197\n",
       "min          0.000000\n",
       "25%        530.000000\n",
       "50%       1287.000000\n",
       "75%       2819.000000\n",
       "max       6386.000000\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "night_traffic['traffic_volume'].describe()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The histogram for the day is similar in shape to a left-skewed distribution. The histogram for the night is similar in shape to a right-skewed distribution.\n",
    "\n",
    "If we compare the 75 percentile, we can see that the daytime traffic is almost twice as heavy.\n",
    "\n",
    "Given that our objective is to identify indicators of heavy traffic, it is reasonable to exclude nighttime data from our analysis. This is because the volume of traffic during nighttime tends to be significantly lower compared to daytime, and focusing on daytime data will provide a more accurate representation of heavy traffic patterns."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "One of the possible indicators of heavy traffic is time. There might be more people on the road in a certain month, on a certain day, or at a certain time of the day. Let's start from month."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.8/site-packages/pandas/core/indexing.py:845: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/dataquest/system/env/python3/lib/python3.8/site-packages/pandas/core/indexing.py:966: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "month\n",
       "1     4495.613727\n",
       "2     4711.198394\n",
       "3     4889.409560\n",
       "4     4906.894305\n",
       "5     4911.121609\n",
       "6     4898.019566\n",
       "7     4595.035744\n",
       "8     4928.302035\n",
       "9     4870.783145\n",
       "10    4921.234922\n",
       "11    4704.094319\n",
       "12    4374.834566\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_traffic.loc[:, 'month'] = day_traffic.loc[:, 'date_time'].dt.month # to create a new column where each value describes the month when the traffic volume measurement was taken\n",
    "by_month = day_traffic.groupby('month').mean() # to group the dataset by the month column with the mean as an aggregate function\n",
    "by_month['traffic_volume']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_month['traffic_volume'].plot(title='Average Traffic Volume by Month', kind='line', marker='o')\n",
    "by_month.index\n",
    "plt.xticks(by_month.index, ['Jan', 'Feb', 'Mar', 'Apr', 'May', 'Jun', 'Jul', 'Aug', 'Sep', 'Oct', 'Nov', 'Dec'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can see from the graph that the heaviest traffic is in two periods: from March to June and from August to October. In December, January and July, the intensity of traffic decreases significantly compared to other months. In general, we can see that traffic is less heavy during the cold winter months. The slump in July is probably due to the fact that a lot of people are leaving the country for vacation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Indicators (II)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before, we generated a line plot showing how the traffic volume changed each month on average. We'll now continue with building line plots for another time unit: day of the week. To get the traffic volume averages for each day of the week, we'll need to use the following code:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/dataquest/system/env/python3/lib/python3.8/site-packages/pandas/core/indexing.py:845: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n",
      "/dataquest/system/env/python3/lib/python3.8/site-packages/pandas/core/indexing.py:966: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_traffic.loc[:, 'dayofweek'] = day_traffic.loc[:, 'date_time'].dt.dayofweek\n",
    "by_dayofweek = day_traffic.groupby('dayofweek').mean()\n",
    "by_dayofweek['traffic_volume'].plot(title='Average Traffic Volume by Day of Week', kind='line', marker='o')\n",
    "plt.xticks(by_dayofweek.index, ['Mon', 'Tue', 'Wed', 'Thu', 'Fri', 'Sat', 'Sun'])\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We see a big difference between business days and the weekend; it's obviously connected to the fact that on business days, people go to work."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Time Indicators (III)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Before, we found that the traffic volume is significantly heavier on business days compared to the weekends. We'll now generate a line plot for the time of day. The weekends, however, will drag down the average values, so we're going to look at the averages separately. To do that, we'll start by splitting the data based on the day type: business day or weekend. Below, we show you how to split the dataset so you can focus on plotting the graphs. While your variable names may vary, the logic of the code should be the same."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "<ipython-input-13-ce7b00d024f0>:1: SettingWithCopyWarning:\n",
      "\n",
      "\n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "hour\n",
      "7     6030.413559\n",
      "8     5503.497970\n",
      "9     4895.269257\n",
      "10    4378.419118\n",
      "11    4633.419470\n",
      "12    4855.382143\n",
      "13    4859.180473\n",
      "14    5152.995778\n",
      "15    5592.897768\n",
      "16    6189.473647\n",
      "17    5784.827133\n",
      "18    4434.209431\n",
      "Name: traffic_volume, dtype: float64\n",
      "hour\n",
      "7     1589.365894\n",
      "8     2338.578073\n",
      "9     3111.623917\n",
      "10    3686.632302\n",
      "11    4044.154955\n",
      "12    4372.482883\n",
      "13    4362.296564\n",
      "14    4358.543796\n",
      "15    4342.456881\n",
      "16    4339.693805\n",
      "17    4151.919929\n",
      "18    3811.792279\n",
      "Name: traffic_volume, dtype: float64\n"
     ]
    }
   ],
   "source": [
    "day_traffic['hour'] = day_traffic['date_time'].dt.hour\n",
    "day_traffic_copy = day_traffic.copy()\n",
    "business_days = day_traffic_copy[day_traffic['dayofweek'] <= 4] # 4 == Friday\n",
    "weekend = day_traffic_copy[day_traffic['dayofweek'] >= 5] # 5 == Saturday\n",
    "by_hour_business = business_days.groupby('hour').mean()\n",
    "by_hour_weekend = weekend.groupby('hour').mean()\n",
    "\n",
    "print(by_hour_business['traffic_volume'])\n",
    "print(by_hour_weekend['traffic_volume'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1100x350 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(11,3.5))\n",
    "\n",
    "plt.subplot(1, 2, 1)\n",
    "by_hour_business['traffic_volume'].plot(marker='o')\n",
    "plt.xlim(6, 20)\n",
    "plt.ylim(1000, 7000)\n",
    "plt.title('Traffic Volume By Hour: Business Days')\n",
    "\n",
    "plt.subplot(1, 2, 2)\n",
    "by_hour_weekend['traffic_volume'].plot(marker='o')\n",
    "plt.xlim(6, 20)\n",
    "plt.ylim(1000, 7000)\n",
    "plt.title('Traffic Volume By Hour: Weekend')\n",
    "\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can observe two rush hours on business days: the first one is at 7 AM, and the second one is at 4 PM. These are the times when people go to work and return home. It makes sense that there is heavy traffic volume during these hours. On weekends, we don't see a clear rush hour, but we do notice low traffic in the morning. We can assume that it's because people tend to sleep more on weekends."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## The effect of time on traffic. Conclusions"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Above we've explored how time (time refers not only to hours, but also to days of the week and months) affects traffic volume, now we can draw some conclusions:\n",
    "\n",
    "1. The traffic is usually heavier during warm months (March–October) compared to cold months (November–February).\n",
    "2. The traffic is usually heavier on business days compared to weekends.\n",
    "3. On business days, the rush hours are around 7 and 16."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weather Indicators"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We investigated how time affects traffic volume. Let's now examine the impact of weather conditions.\n",
    "\n",
    "In our dataset, we have several columns with weather data:\n",
    "\n",
    "- `temp`\n",
    "- `rain_1h`\n",
    "- `snow_1h`\n",
    "- `clouds_all`\n",
    "- `weather_main`\n",
    "- `weather_description`\n",
    "\n",
    "Columns `temp`, `rain_1h`, `snow_1h`, `clouds_all` are numerical. So we can find correlation between these values and `traffic_volume`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "temp              0.128317\n",
       "rain_1h           0.003697\n",
       "snow_1h           0.001265\n",
       "clouds_all       -0.032932\n",
       "traffic_volume    1.000000\n",
       "month            -0.022337\n",
       "dayofweek        -0.416453\n",
       "hour              0.172704\n",
       "Name: traffic_volume, dtype: float64"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "day_traffic.corr()['traffic_volume']"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Strongest correlation is with `temp` column. Let's plot a scatter plot."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "day_traffic.plot.scatter('traffic_volume', 'temp')\n",
    "plt.ylim(230, 320) # two wrong 0K temperatures mess up the y-axis\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Looking at this scatter plot we can conclude that temperature has little effect on traffic volume."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Weather Types"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's now explore the non-numeric weather columns. It's `weather_main` and `weather_description`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "                    temp   rain_1h   snow_1h  clouds_all  traffic_volume  \\\n",
      "weather_main                                                               \n",
      "Clear         283.812078  0.000000  0.000000    1.670265     4778.416260   \n",
      "Clouds        282.929274  0.000000  0.000000   62.667548     4865.415996   \n",
      "Drizzle       284.456433  0.170804  0.000000   84.704417     4837.212911   \n",
      "Fog           277.579641  0.163840  0.001409   65.477901     4372.491713   \n",
      "Haze          275.319353  0.040036  0.000000   64.000000     4609.893285   \n",
      "Mist          279.420825  0.249992  0.000825   74.961435     4623.976475   \n",
      "Rain          287.089601  3.972943  0.000292   75.870116     4815.568462   \n",
      "Smoke         292.405833  0.878333  0.000000   53.333333     4564.583333   \n",
      "Snow          267.984505  0.014017  0.001768   80.501376     4396.321183   \n",
      "Squall        296.730000  1.020000  0.000000   75.000000     4211.000000   \n",
      "Thunderstorm  293.364678  1.146475  0.000000   75.184035     4648.212860   \n",
      "\n",
      "                 month  dayofweek       hour  \n",
      "weather_main                                  \n",
      "Clear         6.490599   3.138928  12.404248  \n",
      "Clouds        6.393243   3.005631  12.911974  \n",
      "Drizzle       7.105323   2.934315  12.308041  \n",
      "Fog           6.646409   2.798343  10.325967  \n",
      "Haze          5.832134   2.754197  12.467626  \n",
      "Mist          6.734285   2.895102  11.078288  \n",
      "Rain          6.774023   2.914467  12.642379  \n",
      "Smoke         6.833333   2.416667  13.166667  \n",
      "Snow          6.374828   2.750344  12.153370  \n",
      "Squall        7.000000   2.000000  14.000000  \n",
      "Thunderstorm  7.108647   2.955654  12.694013  \n",
      "\n",
      "\n",
      "                                           temp      rain_1h   snow_1h  \\\n",
      "weather_description                                                      \n",
      "SQUALLS                              296.730000     1.020000  0.000000   \n",
      "Sky is Clear                         293.232549     0.000000  0.000000   \n",
      "broken clouds                        282.372927     0.000000  0.000000   \n",
      "drizzle                              283.573777     0.145072  0.000000   \n",
      "few clouds                           284.272965     0.000000  0.000000   \n",
      "fog                                  277.579641     0.163840  0.001409   \n",
      "freezing rain                        272.860000     0.000000  0.000000   \n",
      "haze                                 275.319353     0.040036  0.000000   \n",
      "heavy intensity drizzle              285.467931     0.276207  0.000000   \n",
      "heavy intensity rain                 290.231781     2.670548  0.000000   \n",
      "heavy snow                           269.256188     0.002375  0.000000   \n",
      "light intensity drizzle              284.902199     0.178848  0.000000   \n",
      "light intensity shower rain          290.563000     0.433000  0.000000   \n",
      "light rain                           286.349835     0.137147  0.000000   \n",
      "light rain and snow                  275.607500     0.317500  0.000000   \n",
      "light shower snow                    268.213636     0.000000  0.000000   \n",
      "light snow                           267.085634     0.015297  0.002106   \n",
      "mist                                 279.420825     0.249992  0.000825   \n",
      "moderate rain                        287.110124     0.572153  0.001057   \n",
      "overcast clouds                      278.802215     0.000000  0.000000   \n",
      "proximity shower rain                291.460090     0.279279  0.000000   \n",
      "proximity thunderstorm               293.552376     0.967756  0.000000   \n",
      "proximity thunderstorm with drizzle  287.913333     0.260000  0.000000   \n",
      "proximity thunderstorm with rain     291.210556     0.867222  0.000000   \n",
      "scattered clouds                     287.829086     0.000000  0.000000   \n",
      "shower drizzle                       271.330000     0.000000  0.000000   \n",
      "shower snow                          268.680000     0.000000  0.000000   \n",
      "sky is clear                         282.171390     0.000000  0.000000   \n",
      "sleet                                275.746667     0.000000  0.000000   \n",
      "smoke                                292.405833     0.878333  0.000000   \n",
      "snow                                 271.014891     0.024745  0.003723   \n",
      "thunderstorm                         295.168542     0.702083  0.000000   \n",
      "thunderstorm with drizzle            287.880000     5.345000  0.000000   \n",
      "thunderstorm with heavy rain         292.783200     3.595600  0.000000   \n",
      "thunderstorm with light drizzle      290.885000     2.635000  0.000000   \n",
      "thunderstorm with light rain         292.243478     1.190000  0.000000   \n",
      "thunderstorm with rain               293.074500     1.460000  0.000000   \n",
      "very heavy rain                      296.680000  1426.242857  0.000000   \n",
      "\n",
      "                                     clouds_all  traffic_volume     month  \\\n",
      "weather_description                                                         \n",
      "SQUALLS                               75.000000     4211.000000  7.000000   \n",
      "Sky is Clear                           0.000000     4919.009390  7.557512   \n",
      "broken clouds                         72.635875     4824.130326  6.675260   \n",
      "drizzle                               88.589928     4737.330935  7.244604   \n",
      "few clouds                            19.391951     4839.818023  6.159230   \n",
      "fog                                   65.477901     4372.491713  6.646409   \n",
      "freezing rain                         90.000000     4314.000000  6.500000   \n",
      "haze                                  64.000000     4609.893285  5.832134   \n",
      "heavy intensity drizzle               89.172414     4738.586207  7.551724   \n",
      "heavy intensity rain                  82.799087     4610.356164  7.150685   \n",
      "heavy snow                            85.287500     4411.681250  5.140625   \n",
      "light intensity drizzle               82.565445     4890.164049  7.020942   \n",
      "light intensity shower rain           88.500000     4558.100000  5.700000   \n",
      "light rain                            72.672525     4859.650849  6.610428   \n",
      "light rain and snow                   83.500000     5579.750000  7.500000   \n",
      "light shower snow                     81.909091     4618.636364  9.545455   \n",
      "light snow                            77.714724     4430.858896  6.734151   \n",
      "mist                                  74.961435     4623.976475  6.734285   \n",
      "moderate rain                         80.591083     4769.643312  7.008917   \n",
      "overcast clouds                       90.120696     4861.124952  6.078143   \n",
      "proximity shower rain                 78.108108     4901.756757  6.972973   \n",
      "proximity thunderstorm                73.511551     4684.356436  7.118812   \n",
      "proximity thunderstorm with drizzle   87.833333     5121.833333  8.500000   \n",
      "proximity thunderstorm with rain      82.666667     4501.611111  6.944444   \n",
      "scattered clouds                      40.043099     4936.787712  6.528198   \n",
      "shower drizzle                        90.000000     4932.666667  6.000000   \n",
      "shower snow                           90.000000     5664.000000  3.000000   \n",
      "sky is clear                           1.961161     4753.930294  6.304783   \n",
      "sleet                                 90.000000     4312.666667  7.000000   \n",
      "smoke                                 53.333333     4564.583333  6.833333   \n",
      "snow                                  88.737226     4054.065693  6.416058   \n",
      "thunderstorm                          71.437500     4724.708333  7.166667   \n",
      "thunderstorm with drizzle             90.000000     2297.000000  9.000000   \n",
      "thunderstorm with heavy rain          82.480000     4555.760000  6.600000   \n",
      "thunderstorm with light drizzle       90.000000     4960.000000  8.000000   \n",
      "thunderstorm with light rain          76.565217     4336.130435  6.826087   \n",
      "thunderstorm with rain                82.350000     4522.950000  7.050000   \n",
      "very heavy rain                       51.857143     4780.571429  7.000000   \n",
      "\n",
      "                                     dayofweek       hour  \n",
      "weather_description                                        \n",
      "SQUALLS                               2.000000  14.000000  \n",
      "Sky is Clear                          2.895540  12.453052  \n",
      "broken clouds                         2.998210  12.811314  \n",
      "drizzle                               3.028777  11.697842  \n",
      "few clouds                            2.977253  12.633421  \n",
      "fog                                   2.798343  10.325967  \n",
      "freezing rain                         0.500000  13.500000  \n",
      "haze                                  2.754197  12.467626  \n",
      "heavy intensity drizzle               2.896552  12.275862  \n",
      "heavy intensity rain                  2.858447  12.442922  \n",
      "heavy snow                            2.975000  12.303125  \n",
      "light intensity drizzle               2.897033  12.612565  \n",
      "light intensity shower rain           3.200000  11.900000  \n",
      "light rain                            2.928530  12.779731  \n",
      "light rain and snow                   1.250000  15.000000  \n",
      "light shower snow                     1.181818  13.272727  \n",
      "light snow                            2.724949  11.978528  \n",
      "mist                                  2.895102  11.078288  \n",
      "moderate rain                         2.917197  12.301911  \n",
      "overcast clouds                       3.042553  12.765957  \n",
      "proximity shower rain                 2.891892  13.441441  \n",
      "proximity thunderstorm                2.894389  12.828383  \n",
      "proximity thunderstorm with drizzle   2.500000  12.166667  \n",
      "proximity thunderstorm with rain      3.222222  11.333333  \n",
      "scattered clouds                      2.986245  13.359927  \n",
      "shower drizzle                        1.666667  11.000000  \n",
      "shower snow                           4.000000   7.000000  \n",
      "sky is clear                          3.181316  12.395748  \n",
      "sleet                                 3.333333  14.000000  \n",
      "smoke                                 2.416667  13.166667  \n",
      "snow                                  2.554745  12.875912  \n",
      "thunderstorm                          3.041667  13.250000  \n",
      "thunderstorm with drizzle             5.000000  12.500000  \n",
      "thunderstorm with heavy rain          3.000000  11.920000  \n",
      "thunderstorm with light drizzle       3.333333  12.833333  \n",
      "thunderstorm with light rain          3.173913  11.869565  \n",
      "thunderstorm with rain                2.950000  12.600000  \n",
      "very heavy rain                       1.571429  11.714286  \n"
     ]
    }
   ],
   "source": [
    "by_weather_main = day_traffic.groupby('weather_main').mean()\n",
    "by_weather_description = day_traffic.groupby('weather_description').mean()\n",
    "\n",
    "print(by_weather_main)\n",
    "print('\\n')\n",
    "print(by_weather_description)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 600x400 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weather_main['traffic_volume'].plot.barh()\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Weather type is not a reliable indicator of heavy traffic."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 500x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "by_weather_description = day_traffic.groupby('weather_description').mean()\n",
    "by_weather_description['traffic_volume'].plot.barh(figsize=(5,10))\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Traffic exceeds 5,000 in only three types of weather conditions:\n",
    "\n",
    "- `shower snow`\n",
    "- `light rain and snow`\n",
    "- `proximity thunderstorm with drizzle`\n",
    "\n",
    "This may be due to the fact that such weather conditions are not the most favorable for walking or cycling, so people prefer to use their cars or cabs."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Conclusion"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In this project, we tried to find a few indicators of heavy traffic on the I-94 Interstate highway. We managed to find two types of indicators:\n",
    "\n",
    "- Time indicators\n",
    "    - The traffic is usually heavier during warm months (March–October) compared to cold months (November–February).\n",
    "    - The traffic is usually heavier on business days compared to the weekends.\n",
    "    - On business days, the rush hours are around 7 and 16.\n",
    "- Weather indicators\n",
    "    - Shower snow\n",
    "    - Light rain and snow\n",
    "    - Proximity thunderstorm with drizzle"
   ]
  }
 ],
 "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": "3.8.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}