Renaming columns in Pandas - Step-by-Step Execution

Concept Flow - Renaming columns

Start with DataFrame

↓

Choose rename method

↓

Map old names to new names

↓

Apply rename

↓

Get DataFrame with new column names

↓

End

We start with a DataFrame, decide how to rename columns, map old names to new, apply the rename, and get the updated DataFrame.

Execution Sample

Pandas

import pandas as pd

df = pd.DataFrame({
    'A': [1, 2], 'B': [3, 4]
})

# Rename columns
new_df = df.rename(columns={'A': 'Alpha', 'B': 'Beta'})

This code creates a DataFrame with columns 'A' and 'B', then renames them to 'Alpha' and 'Beta'.

Execution Table

Step	Action	DataFrame Columns Before	Rename Mapping	DataFrame Columns After
1	Create DataFrame	['A', 'B']	{}	['A', 'B']
2	Prepare rename mapping	['A', 'B']	{'A': 'Alpha', 'B': 'Beta'}	['A', 'B']
3	Apply rename	['A', 'B']	{'A': 'Alpha', 'B': 'Beta'}	['Alpha', 'Beta']
4	Result stored in new_df	['A', 'B']	{'A': 'Alpha', 'B': 'Beta'}	['Alpha', 'Beta']

💡 Rename applied, columns changed from ['A', 'B'] to ['Alpha', 'Beta']

Variable Tracker

Variable	Start	After Step 1	After Step 3	Final
df.columns	N/A	Index(['A', 'B'], dtype='object')	Index(['A', 'B'], dtype='object')	Index(['A', 'B'], dtype='object')
new_df.columns	N/A	N/A	Index(['Alpha', 'Beta'], dtype='object')	Index(['Alpha', 'Beta'], dtype='object')

Key Moments - 2 Insights

Why does df.columns not change after rename, but new_df.columns does?

Because rename returns a new DataFrame by default and does not modify df in place. See execution_table step 3 and 4 where new_df has renamed columns but df remains the same.

What happens if you rename a column not present in the DataFrame?

The rename mapping for missing columns is ignored, so only existing columns are renamed. This is why the mapping keys must match existing column names to have effect.

Visual Quiz - 3 Questions

Look at the execution table, what are the columns of new_df after step 3?

A ['A', 'B']

B ['Alpha', 'B']

C ['Alpha', 'Beta']

D ['A', 'Beta']

💡 Hint

Check the 'DataFrame Columns After' column in execution_table row with Step 3

At which step does the rename mapping get prepared?

A Step 2

B Step 3

C Step 1

D Step 4

💡 Hint

Look at the 'Action' column in execution_table to find when rename mapping is set

If you want to rename columns in the original df without creating a new DataFrame, what should you do?

A Assign new_df back to df

B All of the above

C Use df.columns = ['Alpha', 'Beta']

D Use df.rename(columns=..., inplace=True)

💡 Hint

All these methods change the original DataFrame columns; see variable_tracker for df.columns changes

Concept Snapshot

Renaming columns in pandas:
Use df.rename(columns={'old': 'new'}) to rename columns.
By default, rename returns a new DataFrame.
Use inplace=True to modify original df.
Only columns in mapping keys are renamed.
Unmapped columns stay the same.

Full Transcript

We start with a DataFrame with columns 'A' and 'B'. We prepare a rename mapping to change 'A' to 'Alpha' and 'B' to 'Beta'. Applying df.rename with this mapping returns a new DataFrame with renamed columns. The original DataFrame remains unchanged unless inplace=True is used. This process helps change column names clearly and safely.