This Python code works with a Pandas DataFrame (`df`) and...

May 18, 2025 at 03:58 AM

unused_columns = [
    'Agency Name', 'Incident Address', 'Street Name', 'Cross Street 1', 'Cross Street 2',
    'Intersection Street 1', 'Intersection Street 2', 'Address Type', 'Park Facility Name',
    'Park Borough', 'School Name', 'School Number', 'School Region', 'School Code',
    'School Phone Number', 'School Address', 'School City', 'School State', 'School Zip',
    'School Not Found', 'School or Citywide Complaint', 'Vehicle Type', 'Taxi Company Borough',
    'Taxi Pick Up Location', 'Bridge Highway Name', 'Bridge Highway Direction', 'Road Ramp',
    'Bridge Highway Segment', 'Garage Lot Name', 'Ferry Direction', 'Ferry Terminal Name',
    'Landmark', 'X Coordinate (State Plane)', 'Y Coordinate (State Plane)', 'Due Date',
    'Resolution Action Updated Date', 'Community Board', 'Facility Type', 'Location'
]

print(f"Original DataFrame shape: {df.shape}")
columns_to_remove = [col for col in unused_columns if col in df.columns]
df.drop(columns=columns_to_remove, inplace=True)
print(f"DataFrame shape after dropping unused columns: {df.shape}")
print("\nRemaining columns in the DataFrame:")
print(df.columns.tolist())

This Python code works with a Pandas DataFrame (df) and performs the following actions:

Define a List of Columns to Remove:
A list named unused_columns specifies column names that are deemed unnecessary or irrelevant for the analysis.
Print the Original DataFrame Shape:
The shape of the DataFrame (df.shape) is printed before any operation is performed, which gives the number of rows and columns in the DataFrame.
Identify Columns to Be Removed:
The code uses a list comprehension to create a new list called columns_to_remove. It checks the unused_columns list against the actual column names (df.columns) in the DataFrame to ensure only columns that exist in the DataFrame are included for removal.
Drop the Identified Columns:
The drop method is used to remove the columns listed in columns_to_remove from the DataFrame df. The parameter inplace=True ensures the changes are applied directly to the original DataFrame (no new copy is created).
Print the Updated DataFrame Shape:
After the columns are removed, the shape (number of rows and columns) of the modified DataFrame is printed to show how it changed.
List the Remaining Columns:
The script prints a list of column names still present in the DataFrame after dropping the unused ones.

Summary:

The purpose of the code is to clean up a DataFrame (df) by removing a predefined set of unused or irrelevant columns (unused_columns) and to display the before-and-after state of the DataFrame in terms of its shape and column names. This is useful for preparing the data for further analysis by keeping only the necessary columns.

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