Pandas中‘pad’与‘ffill’是否完全等同？

Great question! Let’s get straight to the answer: pad and ffill are absolute synonyms in Pandas—they have zero functional differences and can be used completely interchangeably.

Here’s a quick breakdown to confirm:

• Both methods exist to handle missing values via forward-filling: they replace NaN entries with the last non-missing value that appears before the gap in your dataset.
• Pandas offers both names purely for user convenience. Some developers prefer ffill because it’s an explicit shorthand for "forward fill," while others favor the brevity of pad.

To prove they behave exactly the same, here’s a simple code example:

import pandas as pd
import numpy as np

# Create a sample dataset with missing values
df = pd.DataFrame({'scores': [85, np.nan, 90, np.nan, 78, np.nan]})

# Fill missing values using ffill
df_ffill = df.fillna(method='ffill')
# Fill missing values using pad
df_pad = df.fillna(method='pad')

# Verify the results are identical
print(df_ffill.equals(df_pad))  # Output: True

This equivalence also applies to the direct Series methods: calling pd.Series.ffill() and pd.Series.pad() on the same data will always produce identical outputs.

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