Simplify Excel Age Bucketing in Python with Pandas: Quick Code Solutions

Hello, I am a Python enthusiast sharing a practical solution to an Excel aging classification problem raised in a community chat.

1. Introduction

The question asks for a simple way to split project ages into buckets such as "less than 30 days", "30‑90 days", and "over 90 days" without cumbersome manual steps.

Initial code using apply with lambda functions:

'''<br/>项目 账龄天数<br/>B 12<br/>C 75<br/>E 92<br/>D 48<br/>A 46<br/><br/>'''<br/>data  = pd.read_clipboard()<br/>data['小于30天'] = data.apply(lambda row: row['项目'] if row['账龄天数'] < 30 else '', axis=1)<br/>data['30-90天'] = data.apply(lambda row: row['项目'] if (row['账龄天数'] > 30) & (row['账龄天数'] < 90) else '', axis=1)<br/>data['90以上'] = data.apply(lambda row: row['项目'] if row['账龄天数'] > 90 else '', axis=1)<br/>data

2. Implementation

Community members suggested using pd.cut for automatic binning:

Another illustrative example was shared:

One concern was how to return the interval label and fill the project column accordingly.

A detailed code snippet was posted:

A concise solution using where was also offered:

df['小于30天'] = df['项目'].where(df['天数'] < 30, '')<br/>df['30-90天'] = df['项目'].where((df['天数'] >= 30) & (df['天数'] <= 90), '')<br/>df['90天以上'] = df['项目'].where(df['天数'] > 90, '')<br/># Suitable for this simple requirement; not ideal for many intervals

Additional visual aid:

3. Conclusion

The article presented several pandas‑based methods to categorize Excel aging data, highlighted their trade‑offs, and thanked the contributors who shared their ideas.