This Python script uses pandas to import a CSV file containing journal entries, cleans the data, and converts columns to the appropriate data types. It handles operations such as date conversions, numeric conversions, and cleaning string data. We assume the data includes columns for date, amount, description, and account number.
import pandas as pd
# Sample file path
file_path = 'journal_entries.csv'
# Load the CSV file
data = pd.read_csv(file_path)
# Display the first few rows of the dataframe
print("Original Data:")
print(data.head())
# Data Cleaning Steps
# Convert the 'Date' column to datetime format
data['Date'] = pd.to_datetime(data['Date'], errors='coerce')
# Remove any rows where 'Date' could not be converted
data = data.dropna(subset=['Date'])
# Convert 'Amount' to a numeric format, coerce errors and fill NaNs with 0
data['Amount'] = pd.to_numeric(data['Amount'], errors='coerce').fillna(0)
# Strip any leading/trailing whitespace from 'Description'
data['Description'] = data['Description'].str.strip()
# Replace invalid account numbers with NaN and then handle NaNs if needed
# Assume valid account numbers are purely numeric
data['Account Number'] = pd.to_numeric(data['Account Number'], errors='coerce')
data['Account Number'] = data['Account Number'].fillna(0).astype(int)
# Optionally, remove duplicates if necessary
data = data.drop_duplicates()
# Output cleaned data
print("Cleaned Data:")
print(data.head())
# Save the cleaned data back to a CSV if required
data.to_csv('cleaned_journal_entries.csv', index=False)