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export_data()

Exports all processed data to both CSV and Excel formats. 
def export_data(self)

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Description

Convenience method that calls both export_csv_data() and export_excel_data() to generate all output files in a single operation.

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Parameters

No parameters required.

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Returns

No return value.

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Output Files

Generates two files in the current working directory:
  1. processed_data.csv - Weekly metrics with pipe delimiter
  2. processed_data.xlsx - Monthly metrics with three sheets (JFK, Regular, Others)

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Implementation Details

From source/main.py:146-148 
def export_data(self):
    self.export_csv_data()
    self.export_excel_data()

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Usage Example

# After processing all data
yellow_taxi_data.export_data()

# Output:
# - processed_data.csv (weekly aggregated metrics)
# - processed_data.xlsx (monthly metrics by rate code)
Ensure generate_week_metrics(), generate_month_metrics(), and format_data() have been called before exporting to ensure data is complete and properly formatted.

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export_csv_data()

Exports weekly aggregated metrics to a CSV file. 
def export_csv_data(self)

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Description

Writes the csv_df DataFrame containing weekly metrics to a pipe-delimited CSV file.

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Parameters

No parameters required.

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Returns

No return value.

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Output File Details

Filename: processed_data.csv Location: Current working directory Delimiter: Pipe character (|) Index: Not included in output Columns:
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year_week
str
Week identifier in ‘YYYY-WWW’ format
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min_trip_time
float
Minimum trip time in seconds (rounded to 2 decimals)
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max_trip_time
float
Maximum trip time in seconds (rounded to 2 decimals)
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mean_trip_time
float
Average trip time in seconds (rounded to 2 decimals)
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min_trip_distance
float
Minimum trip distance in miles (rounded to 2 decimals)
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max_trip_distance
float
Maximum trip distance in miles (rounded to 2 decimals)
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mean_trip_distance
float
Average trip distance in miles (rounded to 2 decimals)
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min_trip_amount
float
Minimum trip amount in dollars (rounded to 2 decimals)
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max_trip_amount
float
Maximum trip amount in dollars (rounded to 2 decimals)
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mean_trip_amount
float
Average trip amount in dollars (rounded to 2 decimals)
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total_services
int
Total number of trips for the week
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percentage_variation
float
Week-over-week percentage change in total services (rounded to 2 decimals)

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Implementation Details

From source/main.py:134-135 
def export_csv_data(self):
    self.csv_df.to_csv('processed_data.csv', sep='|', index=False)

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Example Output

year_week|min_trip_time|max_trip_time|mean_trip_time|min_trip_distance|max_trip_distance|mean_trip_distance|min_trip_amount|max_trip_amount|mean_trip_amount|total_services|percentage_variation
2022-001|60.0|7245.0|876.34|0.01|45.67|3.42|2.8|487.5|18.76|152340|
2022-002|60.0|6832.0|891.22|0.01|43.21|3.38|2.8|456.3|18.54|149872|-1.62
The pipe delimiter (|) is used instead of comma to avoid conflicts with decimal values and potential commas in data fields.

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export_excel_data()

Exports monthly metrics to an Excel file with multiple sheets. 
def export_excel_data(self)

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Description

Writes monthly metrics segmented by rate code type to a multi-sheet Excel workbook. Each rate code category (JFK, Regular, Others) is exported to a separate sheet.

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Parameters

No parameters required.

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Returns

No return value.

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Output File Details

Filename: processed_data.xlsx Location: Current working directory Engine: OpenPyXL Index: Not included in output Sheets:
  1. JFK - Metrics for JFK airport trips (RatecodeID = 2)
  2. Regular - Metrics for standard rate trips (RatecodeID = 1)
  3. Others - Metrics for all other rate codes

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Sheet Structure

All sheets contain the same columns:
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year_month
str
Month identifier in ‘YYYY-MM’ format
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day_type
int
1 for weekdays (Monday-Friday), 2 for weekends (Saturday-Sunday)
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services
int
Total number of trips for the month and day type
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distances
float
Sum of all trip distances in miles for the month and day type
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passengers
int
Sum of all passengers transported for the month and day type

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Implementation Details

From source/main.py:138-143 
def export_excel_data(self):
    common_columns = ['year_month', 'day_type', 'services', 'distances', 'passengers']
    with pd.ExcelWriter("processed_data.xlsx", engine="openpyxl") as writer:
        self.jfk_df[common_columns].to_excel(writer, sheet_name="JFK", index=False)
        self.regular_df[common_columns].to_excel(writer, sheet_name="Regular", index=False)
        self.other_df[common_columns].to_excel(writer, sheet_name="Others", index=False)

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Usage Example

# Export only Excel file
yellow_taxi_data.export_excel_data()

# Result: processed_data.xlsx with three sheets

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Excel Sheet Preview

JFK Sheet Example:
year_monthday_typeservicesdistancespassengers
2022-01145623567823.4578934
2022-01212456154321.6721543
2022-02147821589234.1282341
2022-02213124162345.8922678
OpenPyXL must be installed in your environment. Install with: pip install openpyxl
Each sheet represents a different rate code category, allowing easy comparison of metrics across trip types and time periods.

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