pt_csv

Module Path: pt_dev_utility.pt_csv

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Description

CSV data processing utilities with pandas integration. This module provides functions for reading and processing CSV files with flexible column selection for Python 3.8+.

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Functions

read_csv_as_dict_list(csv_file_path: str, custom_headers: Optional[List[str]] = None, selected_columns: Optional[List[str]] = None) -> List[Dict[str, Any]]

Description: Read CSV file and return selected columns as list of dictionaries.

This function provides flexible CSV reading with options for custom headers and column selection, returning data in a dictionary format suitable for further processing.

Parameters:

• csv_file_path (str) - Path to the CSV file to be read

• custom_headers (Optional[List[str]]) - Custom column names to use instead of CSV headers. If None, uses the first row of the CSV as headers

• selected_columns (Optional[List[str]]) - List of column names to include in output. If None, includes all columns

Returns: - List[Dict[str, Any]] - List of dictionaries where each dictionary represents a row with column names as keys

Raises: - Exception - If CSV loading or processing fails

Example Usage:

import pt_dev_utility as pdu

# Example 1: Read all columns with default headers
data = pdu.read_csv_as_dict_list('employees.csv')
print(data)
# Output: [{'name': 'John', 'age': 30, 'city': 'NYC'}, 
#          {'name': 'Jane', 'age': 25, 'city': 'LA'}]

# Example 2: Read with custom headers
data = pdu.read_csv_as_dict_list(
    'data.csv',
    custom_headers=['employee_name', 'employee_age', 'location']
)
print(data[0])
# Output: {'employee_name': 'John', 'employee_age': 30, 'location': 'NYC'}

# Example 3: Read specific columns only
data = pdu.read_csv_as_dict_list(
    'employees.csv',
    selected_columns=['name', 'age']
)
print(data)
# Output: [{'name': 'John', 'age': 30}, {'name': 'Jane', 'age': 25}]

# Example 4: Combine custom headers with column selection
data = pdu.read_csv_as_dict_list(
    'data.csv',
    custom_headers=['full_name', 'years_old', 'city'],
    selected_columns=['full_name', 'years_old']
)
print(data)
# Output: [{'full_name': 'John', 'years_old': 30}, 
#          {'full_name': 'Jane', 'years_old': 25}]

# Example 5: Process the data
for row in data:
    print(f"Name: {row['name']}, Age: {row['age']}")

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Use Cases

• Data Analysis - Load CSV data for analysis and processing
• ETL Operations - Extract and transform CSV data before loading
• Configuration Files - Read configuration data from CSV files
• Report Generation - Process CSV reports with custom formatting
• Data Migration - Convert CSV data to different formats

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Notes

• Uses pandas internally for robust CSV parsing
• Supports all pandas-compatible CSV formats
• Memory efficient for large files
• Handles missing values and data type inference automatically
• Custom headers override the first row of the CSV file