This code defines a function `plot_complaint_times_by_location` that generates a bar...

May 18, 2025 at 05:57 AM

def plot_complaint_times_by_location(df, location_col='Borough'):
    if 'Complaint Type' not in df.columns or 'Request_Closing_Time' not in df.columns or location_col not in df.columns:
        print("Required columns not found in DataFrame.")
        return

    grouped = df.groupby(['Complaint Type', location_col])['Request_Closing_Time'].mean().reset_index()

    plt.figure(figsize=(12, 8))
    sns.barplot(data=grouped,
                x='Request_Closing_Time',
                y='Complaint Type',
                hue=location_col,
                palette='viridis',
                errorbar=None)
    
    plt.xscale('log')
    plt.title('Avg Request Closing Time by Complaint Type and Location (Log Scale)')
    plt.xlabel('Average Closing Time (log scale)')
    plt.ylabel('Complaint Type')
    plt.legend(title=location_col, bbox_to_anchor=(1.05, 1), loc='upper left')
    plt.tight_layout()
    plt.show()

plot_complaint_times_by_location(df, location_col='Borough')

This code defines a function plot_complaint_times_by_location that generates a bar plot to visualize the average time taken to resolve complaints for different types of complaints and their occurrences across locations. Here is a detailed breakdown of its functionality:

Input Parameters:
- df: A pandas DataFrame containing the data. It must have the following columns:
  - 'Complaint Type'
  - 'Request_Closing_Time' (representing the time taken to close a complaint)
  - A column specified by location_col (default is 'Borough').
- location_col: A string representing the column name corresponding to the location data (e.g., 'Borough').
Column Validation:
- The function checks if the required columns ('Complaint Type', 'Request_Closing_Time', and the one specified by location_col) exist in the DataFrame.
- If any of these columns is missing, the function prints an error message and exits.
Group Data:
- The function groups the data by Complaint Type and location_col.
- It calculates the mean of 'Request_Closing_Time' for each combination of Complaint Type and location.
- This grouped information is reset to a new DataFrame called grouped for further plotting.
Plotting:
- The function creates a horizontal bar plot using Seaborn's barplot:
  - x='Request_Closing_Time': The x-axis represents the average request closing time.
  - y='Complaint Type': The y-axis represents the complaint types.
  - hue=location_col: Adding a color distinction based on the location column.
  - palette='viridis': A color palette used for visualization.
  - errorbar=None: Disables error bars.
- The x-axis is scaled logarithmically using plt.xscale('log') to better visualize wide-ranging values of closing times.
Plot Aesthetics:
- A title is added: 'Avg Request Closing Time by Complaint Type and Location (Log Scale)'.
- Labels are set for the x-axis ('Average Closing Time (log scale)') and y-axis ('Complaint Type').
- A legend is placed outside the plot on the upper right, describing the locations.
Display:
- plt.tight_layout() adjusts the spacing of elements to avoid overlap.
- plt.show() displays the final plot.

Summary:

This function visualizes the average closing times for complaints, grouped by both complaint type and location (e.g., borough). It provides insights into how long (on average) certain complaints take to resolve in different locations. The logarithmic scale helps make the data more interpretable when the closing times vary significantly.

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