How to Efficiently Swap Specific Values Between Two Pandas Columns

Data Requirement

This article demonstrates a simple data manipulation task: swapping the values in the red‑boxed region with those in the blue‑boxed region of a DataFrame.

Requirement Breakdown

Swapping entire columns is trivial—just rename them. Here we need to exchange only part of the data, so we create a mask, extract the target rows, and fill each column with the other's values using pandas.

Constructing the Data

Processing the Requirement

Method One

Create a mask column that marks rows meeting the condition, then use boolean indexing to swap the values.

# map calls a custom condition function (example only)
# The "学历" column contains numeric values, so cast to str for filtering
mask = df['学历'].astype(str).map(lambda x: '经验不限' in x or '年' in x or x.isdigit())

# Swap the two columns where the mask is True
df.loc[mask, '经验'], df.loc[mask, '学历'] = df.loc[mask, '学历'], df.loc[mask, '经验']

Method Two

Use NumPy to swap the columns by accessing the underlying array values.

# mask obtained in the previous step
df.loc[mask, ['学历', '经验']] = df.loc[mask, ['学历', '经验']].values[:, [1, 0]]

The swap succeeds as shown below.

Note: Directly assigning one column to another (e.g.,

df.loc[mask, ['经验', '学历']] = df.loc[mask, ['学历', '经验']]

) does not perform the swap because pandas creates a view that cannot be overwritten in place.

Summary

When data does not reside in the expected columns, extracting and reassigning values is necessary. Pandas provides powerful data manipulation capabilities that make such column‑level swaps straightforward, allowing you to focus on more meaningful analysis tasks.