analysis-ky-roadclosures_final_analysis_after_refactoring.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This notebook is being developed as part of the Code Kentucky Python Data Analyst pathway.\n",
    "\n",
    "---\n",
    "\n",
    "Goal: Quantify the impact of road closures based on three metrics:\n",
    "1) Total number of closures<br>\n",
    "2) Frequency of closures<br>\n",
    "3) Duration of closures.<br>\n",
    "\n",
    "These are a few of the sample questions that I hope to answer:<br>\n",
    "1) How many closures occur statewide each year? (Typical bar graph showing count per year?)<br>\n",
    "2) How many road closures occur in each county per year? (Normal bar graph with year as x-axis and count of closures?)<br>\n",
    "3) How often, or how frequently, is a single road being closed due to rainfall? (Horizonatal bar graph with roadname as Y axis or pivot table output?)<br>\n",
    "4) What is the average duration of road closures?\n",
    "\n",
    "DISCLAIMER:  Results may vary.  In addition to historic data, this notebook is also utilizing current year data.  The data source is updated every 1 hour but only when there are active road closures due to weather related events.\n",
    "\n",
    "-Chris Lambert\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "| Field           | Data Type    | Description                                                                                               | Examples                   |\n",
    "|-----------------|--------------|-----------------------------------------------------------------------------------------------------------|----------------------------|\n",
    "| District        | Integer      | KYTC divides the state into 12 geographic regions. Districts start from 1 in the West to 12 in the East. | 1, 2, 3, etc.              |\n",
    "| County          | Object       | The name of the county where the event occurred. County names are proper case.                            | Fayette, Frankfort, etc.  |\n",
    "| Route           | Object       | The route name associated with the incident.                                                             | KY-80, US-60, I-69        |\n",
    "| Road_Name       | Object       | The name of the road associated with the transportation records.                                          | DONALDSON CREEK RD, etc. |\n",
    "| Begin_MP        | Float        | The milepost where the event or condition begins on the road.                                              | 10.5, 20.3, etc.           |\n",
    "| End_MP          | Float        | The milepost where the event or condition ends on the road.                                                | 15.2, 25.7, etc.           |\n",
    "| Comments        | Object       | Additional comments or information related to the transportation event.                                   | N/A                        |\n",
    "| Reported_On     | Datetime     | The date and time when the transportation event was reported. All reports are in Eastern Standard Time.   | YYYY-MM-DD HH:MM:SS       |\n",
    "| End_Date        | Datetime     | The date and time when the transportation event concluded or was resolved. All reports are in EST.        | YYYY-MM-DD HH:MM:SS       |\n",
    "| latitude        | Float        | The latitude coordinate associated with the location of the transportation event.                         | 38.1234, 39.5678, etc.    |\n",
    "| longitude       | Float        | The longitude coordinate associated with the location of the transportation event.                        | -84.5678, -85.1234, etc.  |\n",
    "| Duration_Default| Timedelta    | The default duration of the transportation event.                                                        | 0 days 01:30:00, etc.     |\n",
    "| Duration_Hours  | Float        | The duration of the transportation event in hours.                                                        | 1.5, 2.75, etc.            |\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Pandas is used for data manipulation and analysis.\n",
    "#Matplotlib is used for data visualization.\n",
    "#os is used to create folders\n",
    "#urlretrieve is used to download files from the internet\n",
    "\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt \n",
    "import os\n",
    "from urllib.request import urlretrieve\n",
    "\n",
    "#Create folders if it does not exist\n",
    "os.makedirs('data-raw', exist_ok=True)\n",
    "os.makedirs('data-clean', exist_ok=True)\n",
    "os.makedirs('data-reportready', exist_ok=True)\n",
    "os.makedirs('temp', exist_ok=True)\n",
    "\n",
    "#Alternative method for retrieving the data.  This allows for discovery of the raw data with other tools.\n",
    "#urlretrieve('https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2021.csv', 'data-raw/KYTC-TOC-Weather-Closures-Historic-2021.csv')\n",
    "#urlretrieve('https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2022.csv', 'data-raw/KYTC-TOC-Weather-Closures-Historic-2022.csv')\n",
    "#urlretrieve('https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2023.csv', 'data-raw/KYTC-TOC-Weather-Closures-Historic-2023.csv')\n",
    "#urlretrieve('https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2024.csv', 'data-raw/KYTC-TOC-Weather-Closures-Historic-2024.csv')\n",
    "#urlretrieve('https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2025.csv', 'data-raw/KYTC-TOC-Weather-Closures-Historic-2025.csv')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Import each year into individual dataframes.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Import files directly from the website into memory\n",
    "df2021=pd.read_csv(\"https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2021.csv\")\n",
    "df2022=pd.read_csv(\"https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2022.csv\")\n",
    "df2023=pd.read_csv(\"https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2023.csv\")\n",
    "df2024=pd.read_csv(\"https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2024.csv\")\n",
    "df2025=pd.read_csv(\"https://storage.googleapis.com/kytc-its-2020-openrecords/toc/KYTC-TOC-Weather-Closures-Historic-2025.csv\")\n",
    "\n",
    "#Export the dataset to a csv file as a method of copying the data to the local machine.\n",
    "df2021.to_csv(\"data-raw/KYTC-TOC-Weather-Closures-Historic-2021.csv\", index=False)\n",
    "df2022.to_csv(\"data-raw/KYTC-TOC-Weather-Closures-Historic-2022.csv\", index=False)\n",
    "df2023.to_csv(\"data-raw/KYTC-TOC-Weather-Closures-Historic-2023.csv\", index=False)\n",
    "df2024.to_csv(\"data-raw/KYTC-TOC-Weather-Closures-Historic-2024.csv\", index=False)\n",
    "df2025.to_csv(\"data-raw/KYTC-TOC-Weather-Closures-Historic-2025.csv\", index=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "After reviewing the data, I noticed the following issues:\n",
    "- 'Route_Link' in 2021 uses an ESRI formatted hyperlink.\n",
    "- 'Route_Link' 2022+ uses Google Map formatted hyperlink.\n",
    "- 'Route_Link' in 2023 contained additional characters at the end of the URL\n",
    "- The latitide and longitude are in different orders, depending on the map provider.\n",
    "- 'Reported_On' and 'End_Date' columns contain what looks like different timestamps for some of the years.\n",
    "- 'Comments' contained line breaks that needed to be cleaned."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Functions to Clean Various Data Issues\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Function to clean the data.\n",
    "def clean_tl(df):\n",
    "    #clening the datetimes in the dataframes\n",
    "    df['End_Date'] = df['End_Date'].str.replace('+00:00', '')\n",
    "    df['Reported_On'] = pd.to_datetime(df['Reported_On'])\n",
    "    df['End_Date'] = pd.to_datetime(df['End_Date'])\n",
    "    #calculating the duration of the incidents\n",
    "    df['Duration_Default'] = (df['End_Date']) - (df['Reported_On'])\n",
    "    #converting the duration to hours to make it easier for reporting software\n",
    "    df['Duration_Hours'] = (df['Duration_Default'].dt.total_seconds() / 3600).round(4)\n",
    "    #removing line breaks from the comments column.\n",
    "    df['Comments'] = df['Comments'].replace(r'[\\r\\n]+', ' ', regex=True) \n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the ESRI link (notice the longitude then latitide)\n",
    "def clean_esri_link(df):\n",
    "    df['Route_Link'] = df['Route_Link'].str.replace('https://kytc.maps.arcgis.com/apps/webappviewer/index.html?id=327a38decc8c4e5cb882dc6cd0f9d45d&zoom=14&center=', '')\n",
    "    df[['longitude', 'latitude']] = df['Route_Link'].str.split(\",\", expand=True)\n",
    "    df = df.drop('Route_Link', axis=1, inplace=True)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the Google Maps link\n",
    "def clean_google_link(df):\n",
    "    df['Route_Link'] = df['Route_Link'].str.replace('https://goky.ky.gov/?lat=','')\n",
    "    df['Route_Link'] = df['Route_Link'].str.replace('&lng=',',')\n",
    "    df['Route_Link'] = df['Route_Link'].str.replace('&.*', '', regex=True)\n",
    "    df[['latitude','longitude']] = df.Route_Link.str.split(\",\",expand=True)\n",
    "    df = df.drop('Route_Link', axis=1, inplace=True)\n",
    "    return df\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2021 Dataset\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the 2021 dataset.\n",
    "\n",
    "#Calling my function to clean an ESRI map URL.\n",
    "clean_esri_link(df2021)\n",
    "\n",
    "#Calling my function to clean the dataframe\n",
    "clean_tl(df2021)\n",
    "\n",
    "#Reorder the columns to make it compatible with the 2022+ datasets.\n",
    "order=['District','County','Route','Road_Name','Begin_MP','End_MP','Comments','Reported_On','End_Date','latitude','longitude','Duration_Default','Duration_Hours']\n",
    "\n",
    "#Export the clean 2021 dataset to a csv file to show progress.\n",
    "df2021.to_csv(\"data-clean/kytc-closures-2021-clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment the lines below to verify datatypes and review data in dataframe.\n",
    "#print(df2021.dtypes) #verify the datatypes in the dataframe\n",
    "#print(df2021.head(3)) #print the first 3 rows of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2022 Dataset\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the 2022 dataset.\n",
    "\n",
    "#Calling my function to clean a Google map URL.\n",
    "clean_google_link(df2022)\n",
    "\n",
    "#calling my function to clean the dataframe\n",
    "clean_tl(df2022)\n",
    "\n",
    "#Export the clean 2022 dataset to a csv file to show progress.\n",
    "df2022.to_csv(\"data-clean/kytc-closures-2022-clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment the lines below to verify datatypes and review data in dataframe.\n",
    "#print(df2022.dtypes) #verify the datatypes in the dataframe\n",
    "#print(df2022.head(3)) #print the first 3 rows of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2023 Dataset\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Clean the 2023 dataset.\n",
    "\n",
    "#Calling my function to clean a Google map URL.\n",
    "clean_google_link(df2023)\n",
    "\n",
    "#calling my function to clean the dataframe\n",
    "clean_tl(df2023)\n",
    "\n",
    "#Export the clean 2023 dataset to a csv file to show progress.\n",
    "df2023.to_csv(\"data-clean/kytc-closures-2023-clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment the lines below to verify datatypes and review data in dataframe.\n",
    "#print(df2023.dtypes) #verify the datatypes in the dataframe\n",
    "#print(df2023.head(3)) #print the first 3 rows of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2024 Dataset\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyze the 2024 dataset.\n",
    "#Since this is 2024, this dataset is updated every 1 hour during weather events.\n",
    "#This will produce different calculations throughout the year.\n",
    "\n",
    "#Calling my function to clean a Google map URL.\n",
    "clean_google_link(df2024)\n",
    "\n",
    "#calling my function to clean the dataframe\n",
    "clean_tl(df2024)\n",
    "\n",
    "#Export the clean 2023 dataset to a csv file to show progress.\n",
    "df2024.to_csv(\"data-clean/kytc-closures-2024-clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "2025 Dataset (year to date)\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Analyze the 2025 dataset.\n",
    "#Since this is 2025, this dataset is updated every 1 hour during weather events.\n",
    "#This will produce different calculations throughout the year.\n",
    "\n",
    "#Calling my function to clean a Google map URL.\n",
    "clean_google_link(df2025)\n",
    "\n",
    "#calling my function to clean the dataframe\n",
    "clean_tl(df2025)\n",
    "\n",
    "#Export the clean 2023 dataset to a csv file to show progress.\n",
    "df2025.to_csv(\"data-clean/kytc-closures-2025-clean.csv\", index=False)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Uncomment the lines below to verify datatypes and review data in dataframe.\n",
    "#print(df2024.dtypes) #verify the datatypes in the dataframe\n",
    "#print(df2024.head(3)) #print the first 3 rows of the dataframe"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Order and Merge Dataframes<br>\n",
    "Create Reporting Datasets in Different File Formets<br>\n",
    "CSV, XLXS, and Parquet are all popular formats among various types of users and use cases.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [],
   "source": [
    "#Merge the following dataframes together.\n",
    "#In 2021, the columns are in a slightly different order.\n",
    "\n",
    "#I've developed a column list to force uniformity between the datasets.\n",
    "col_order=['District','County','Route','Road_Name','Begin_MP','End_MP','Comments','Reported_On','End_Date','latitude','longitude','Duration_Default','Duration_Hours']\n",
    "df2021=df2021[col_order]\n",
    "df2022=df2022[col_order]\n",
    "df2023=df2023[col_order]\n",
    "df2024=df2024[col_order]\n",
    "df2025=df2025[col_order]\n",
    "\n",
    "#One last analysis of the data revealed erroneous time calculations.  I should have caught this earlier.\n",
    "#There are 6 records that calculate negative duration_hours.  Since these are erroneous, I have removed them.\n",
    "#While not all data is completely accurate, it is completely possible to see closures lasting 100s of days for repair.\n",
    "df2021 = df2021[df2021['Duration_Hours'] > 0]\n",
    "df2022 = df2022[df2022['Duration_Hours'] > 0]\n",
    "df2023 = df2023[df2023['Duration_Hours'] > 0]\n",
    "df2024 = df2024[df2024['Duration_Hours'] > 0]\n",
    "df2025 = df2025[df2025['Duration_Hours'] > 0]\n",
    "\n",
    "#Merge the dataframes together into a single, cleaned reporting dataset.\n",
    "df = pd.concat([df2021, df2022, df2023, df2024, df2025])\n",
    "\n",
    "#Export the final reporting dataset to CSV\n",
    "#CSV is normally my default export file format since it easy to read and manipulate using simple text editors.\n",
    "df.to_csv(\"data-reportready/kytc-closures-2021-2025-report_dataset.csv\", index=False) \n",
    "\n",
    "#Export the file reporting dataset to XLSX\n",
    "#XLSX is useful and prefered over CSV by most MS Office users.\n",
    "#Requires the openpyxl library to be installed with Pandas\n",
    "df.to_excel(\"data-reportready/kytc-closures-2021-2025-report_dataset.xlsx\", index=False) \n",
    "\n",
    "#Parquest is a compressed, columnar storage format, which is good for analytics.\n",
    "#Parquet is used by many of the modren data platforms.\n",
    "df.to_parquet(\"data-reportready/kytc-closures-2021-2025-report_dataset.parquet\", index=False) \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Develop Visualizations (matplotlib)<br>\n",
    "First, verify the data types.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "#df.info() #Print the dataframe info to the console.  This is useful for debugging and understanding the data."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This first section will deal with counting the number of closures per Year, District, and then County\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Extract the year from \"Reported_On\" value to use on the plot.\n",
    "df['Year'] = df['Reported_On'].dt.year\n",
    "\n",
    "#Count the number of closures per year\n",
    "closure_counts = df['Year'].value_counts()\n",
    "\n",
    "# Plot the data\n",
    "plt.bar(closure_counts.index, closure_counts.values)\n",
    "plt.xlabel('Year Report')\n",
    "plt.ylabel('Number of Closures')\n",
    "plt.title('Total Number of Road Closures per Year \\n (During Inclement Weather Events)') #ading the \\n forces a line break in the title.\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#Calculate the number of closures per district and then plot the data.\n",
    "closure_counts_by_district = df['District'].value_counts()\n",
    "\n",
    "# Plot the data\n",
    "plt.bar(closure_counts_by_district.index, closure_counts_by_district.values)\n",
    "plt.xlabel('District')\n",
    "plt.ylabel('Number of Closures')\n",
    "plt.title('Total Number of Closures by District \\n (During Inclement Weather Events)')\n",
    "\n",
    "# Set the x-axis ticks to show all 12 districts\n",
    "plt.xticks(closure_counts_by_district.index)\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the number of closures per County\n",
    "closure_counts_by_county = df['County'].value_counts()\n",
    "\n",
    "# Select the top 10 counties\n",
    "top_10_counties = closure_counts_by_county.head(10) #adjust for more counties if needed.\n",
    "\n",
    "# Sort the counties by the number of closures in descending order\n",
    "top_10_counties = top_10_counties.sort_values(ascending=True)\n",
    "\n",
    "# Plot the data\n",
    "plt.barh(top_10_counties.index, top_10_counties.values)\n",
    "plt.xlabel('Number of Closures')\n",
    "plt.ylabel('County')\n",
    "plt.title('Top 10 Counties with the Highest Number of Closures \\n (During Inclement Weather Events)')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This section will deal with summarizing the duration of closures per year, district, and county.\n",
    "\n",
    "---"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Total the duration_hours per year\n",
    "total_duration = df.groupby(df['Reported_On'].dt.year)['Duration_Hours'].sum()\n",
    "\n",
    "plt.bar(total_duration.index, total_duration.values)\n",
    "\n",
    "plt.xlabel('Year')\n",
    "plt.ylabel('Total Duration (Hours)')\n",
    "plt.title('Total Duration (Hours) of Road Closures per Year \\n (During Inclement Weather Events)')\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the total hours of closures by District and plot the data.\n",
    "total_duration_by_district = df.groupby('District')['Duration_Hours'].sum()\n",
    "\n",
    "# Plot the data\n",
    "plt.bar(total_duration_by_district.index, total_duration_by_district.values)\n",
    "plt.xlabel('District')\n",
    "plt.ylabel('Total Duration (Hours)')\n",
    "plt.title('Total Duration of Closures by District \\n (During Inclement Weather Events)')\n",
    "\n",
    "# Set the x-axis ticks to show all 12 districts\n",
    "plt.xticks(total_duration_by_district.index)\n",
    "\n",
    "plt.show()\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Calculate the total hours of closures by County\n",
    "total_duration_by_county = df.groupby('County')['Duration_Hours'].sum()\n",
    "\n",
    "# Select the top 10 counties\n",
    "top_10_counties = total_duration_by_county.nlargest(10) #adjust for more counties if needed.\n",
    "\n",
    "# Sort the counties by the total duration in descending order\n",
    "top_10_counties = top_10_counties.sort_values(ascending=True)\n",
    "\n",
    "# Plot the data\n",
    "plt.barh(top_10_counties.index, top_10_counties.values)\n",
    "plt.xlabel('Total Duration (Hours)')\n",
    "plt.ylabel('County')\n",
    "plt.title('Top 10 Counties with the Highest Total Duration of Closures \\n (During Inclement Weather Events)')\n",
    "\n",
    "plt.show()\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Optional Feature: Check for PowerBI file and PDF export in the repo.\n",
    "\n",
    "---"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": ".venv",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
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