基于条件批量生成命名DataFrame的Python代码优化方案求助

Absolutely! Your current manual approach works but gets unwieldy as you add more product types. Here's a much cleaner, scalable way to handle this using a dictionary to store all your vintage tables, paired with a simple loop:

import os
import numpy as np
import pandas as pd

data1 = pd.read_csv('C:/Users/Oz/Desktop/vintage/vintage1.csv', encoding='latin-1')
product_list = data1['product_types'].unique()

def vintage_table(df):
    # Optional: Add df = df.copy() here if you don't want to modify the original DataFrame
    df['Disbursement_Date'] = pd.to_datetime(df.Disbursement_Date)
    df['Closing_Date'] = pd.to_datetime(df.Closing_Date)
    df['NPL_date'] = pd.to_datetime(df.NPL_date, errors='ignore')
    df['NPL_date_period'] = df.loc[df.NPL_date > '2015-01-01', 'NPL_date'].apply(lambda x: x.strftime('%Y-%m'))
    df['Dis_date_period'] = df.Disbursement_Date.apply(lambda x: x.strftime('%Y-%m'))
    df['diff'] = ((df.NPL_date - df.Disbursement_Date) / np.timedelta64(3, 'M')).round(0)
    df = df.groupby(['Dis_date_period','NPL_date_period']).agg({'Dis_amount' : 'sum', 'NPL_amount' : 'sum', 'diff' : 'mean'})
    df.reset_index(level=0, inplace=True)
    df['Vintage_Ratio'] = df['NPL_amount']/df['Dis_amount']
    table = pd.pivot_table(df, values='Vintage_Ratio', index='Dis_date_period', columns=['diff'],).fillna(0)
    return table

# Batch processing with a dictionary to store results
vintage_results = {}
for product in product_list:
    # Filter data for the current product type
    filtered_data = data1[data1['product_types'] == product]
    # Generate and store the vintage table
    vintage_results[product] = vintage_table(filtered_data)

# Access individual tables like this:
# vintage_results['consumer']  # For consumer product
# vintage_results['mortgage']  # For mortgage product

Why this works better: #

• Scalability: No need to write new lines of code if you add more product types later—the loop handles everything automatically.
• Cleaner code: Eliminates repetitive manual assignment and reduces redundancy.
• Easy access: Retrieve any vintage table instantly using the product type name as the dictionary key.
• Maintainability: If you need to adjust how you process the data, you only modify one loop instead of multiple duplicate blocks.

A quick note: Your vintage_table function modifies the input DataFrame directly. If you want to avoid altering the original data1 dataframe, add df = df.copy() at the very start of the function—this ensures you're working with a duplicate instead of the original data.

内容的提问来源于stack exchange，提问作者ajan40