如何在Python Pandas中无需新增列，基于自定义函数对DataFrame进行排序？

Hey there! Let's figure out how to get your DataFrame sorted by the length of the message column without adding extra columns, and fix why your original code might not have worked.

Why Your Original Code Didn't Behave as Expected #

When you ran df.sort_values(df['message'].apply(len), ascending=False), you were on the right track—but by default, sort_values() returns a new sorted DataFrame instead of modifying your original one. If you didn't assign the result back to a variable (or use inplace=True), you wouldn't see the sorted output. Also, this approach isn't the most idiomatic way to handle custom sorting in modern Pandas.

The Cleanest Modern Approach: Use the key Parameter (Pandas 1.1.0+) #

Pandas added the key parameter to sort_values() in version 1.1.0, which was made exactly for this kind of custom sorting scenario. It lets you define a transformation for the target column without creating a new one. Here's how to use it:

# Create a new sorted DataFrame (recommended to avoid altering your original data)
sorted_df = df.sort_values(by='message', key=lambda x: x.str.len(), ascending=False)

# If you want to modify the original DataFrame directly
df.sort_values(by='message', key=lambda x: x.str.len(), ascending=False, inplace=True)

Alternative for Older Pandas Versions #

If you're stuck with a Pandas version before 1.1.0, you can use argsort() to get the indices of sorted message lengths, then reindex your DataFrame:

# Get indices ordered by message length (descending)
sorted_indices = df['message'].str.len().argsort()[::-1]
# Reindex the DataFrame to apply the sort
sorted_df = df.iloc[sorted_indices]

Fixing Your Original Code (If You Prefer) #

If you want to make your initial approach work, just assign the result back to your variable:

df = df.sort_values(df['message'].apply(len), ascending=False)

That said, using the key parameter is cleaner and more readable for this use case.

内容的提问来源于stack exchange，提问作者MichaelYonas