Skip to main content

​
Overview

The YellowTaxiData class provides a complete pipeline for importing, cleaning, analyzing, and exporting NYC Yellow Taxi trip data. It downloads Parquet files from the NYC TLC dataset, performs data quality checks, generates metrics, and exports results in multiple formats.

​
Constructor

YellowTaxiData(start_date, end_date)
Initializes a new instance of the YellowTaxiData class with the specified date range.
​
start_date
str
required
Start date for data analysis in ‘YYYY-MM-DD’ format (e.g., ‘2022-01-01’)
​
end_date
str
required
End date for data analysis in ‘YYYY-MM-DD’ format (e.g., ‘2022-03-31’)

​
Class Attributes

After initialization, the following attributes are available:
​
start_date
str
The start date of the analysis period
​
end_date
str
The end date of the analysis period
​
dates_list
list
List of month strings in ‘YYYY-MM’ format generated from start_date to end_date
​
end_date_weeks
DatetimeIndex
Weekly date range from start_date to end_date (ending on Sundays)
​
urls_list
list
List of URLs pointing to NYC TLC Parquet files for each month in the date range
​
data
DataFrame
Main DataFrame containing the raw and processed taxi trip data
​
weeks_ranges
DataFrame
DataFrame for storing weekly date ranges (initialized as empty)
​
months_ranges
DataFrame
DataFrame for storing monthly date ranges (initialized as empty)
​
jfk_df
DataFrame
DataFrame containing metrics for JFK airport trips (RatecodeID = 2)
​
regular_df
DataFrame
DataFrame containing metrics for regular/standard rate trips (RatecodeID = 1)
​
other_df
DataFrame
DataFrame containing metrics for all other rate codes
​
csv_df
DataFrame
DataFrame containing weekly aggregated metrics for CSV export

​
Methods

The class provides the following methods organized by functionality:

​
Data Import and Cleaning

  • import_data() - Downloads and imports Parquet files from NYC TLC dataset
  • clean_data() - Applies data quality filters and removes invalid records
  • add_more_columns() - Adds derived date columns for analysis

​
Metrics Generation

  • generate_week_metrics() - Calculates weekly aggregated statistics
  • generate_month_metrics() - Generates monthly metrics by rate code and day type
  • format_data() - Formats and prepares data for export

​
Data Export

  • export_data() - Exports all processed data to files
  • export_csv_data() - Exports weekly metrics to CSV format
  • export_excel_data() - Exports monthly metrics to Excel format with multiple sheets

​
Usage Example

Here’s a complete example from the source code: 
import time

# Initialize the YellowTaxiData object with date range
yellow_taxi_data = YellowTaxiData(start_date='2022-01-01', end_date='2022-03-31')

# Import data from NYC TLC dataset
yellow_taxi_data.import_data()

# Clean and filter data
yellow_taxi_data.clean_data()

# Add derived columns for analysis
yellow_taxi_data.add_more_columns()

# Generate weekly metrics
yellow_taxi_data.generate_week_metrics()

# Generate monthly metrics by rate code
yellow_taxi_data.generate_month_metrics()

# Format data for export
yellow_taxi_data.format_data()

# Export results to CSV and Excel
yellow_taxi_data.export_data()
All DataFrames (data, jfk_df, regular_df, other_df, csv_df) are initialized as empty pandas DataFrames and populated by calling the respective methods in sequence.

Build docs developers (and LLMs) love

Get started for freeTalk to us