Renaming columns in Data Analysis Python - Step-by-Step Execution

Concept Flow - Renaming columns

Start with DataFrame

↓

Choose columns to rename

↓

Create mapping: old_name -> new_name

↓

Apply rename method

↓

Get DataFrame with new column names

↓

End

We start with a DataFrame, decide which columns to rename, create a mapping from old to new names, apply the rename, and get the updated DataFrame.

Execution Sample

Data Analysis Python

import pandas as pd

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

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

This code renames columns 'A' to 'Alpha' and 'B' to 'Beta' in a simple DataFrame.

Execution Table

Step	Action	DataFrame Columns Before	Rename Mapping	DataFrame Columns After
1	Create DataFrame	['A', 'B']	{}	['A', 'B']
2	Define rename mapping	['A', 'B']	{'A': 'Alpha', 'B': 'Beta'}	['A', 'B']
3	Apply rename method	['A', 'B']	{'A': 'Alpha', 'B': 'Beta'}	['Alpha', 'Beta']
4	Store renamed DataFrame	['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 2	After Step 3	Final
df.columns	N/A	['A', 'B']	['A', 'B']	['A', 'B']	['A', 'B']
rename_mapping	N/A	N/A	{'A': 'Alpha', 'B': 'Beta'}	{'A': 'Alpha', 'B': 'Beta'}	{'A': 'Alpha', 'B': 'Beta'}
df_renamed.columns	N/A	N/A	N/A	['Alpha', 'Beta']	['Alpha', 'Beta']

Key Moments - 2 Insights

Why does the original DataFrame 'df' keep its old column names after rename?

Because the rename method returns a new DataFrame by default and does not change 'df' in place. See execution_table step 3 and 4 where 'df_renamed' has new columns but 'df' remains unchanged.

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

The rename mapping for that column is ignored and the DataFrame columns stay the same. Only existing columns are renamed. This is why the mapping keys must match existing column names.

Visual Quiz - 3 Questions

Look at the execution_table at step 3, what are the columns of the DataFrame returned by rename?

A ['Alpha', 'Beta']

B ['A', 'B']

C ['Alpha', 'B']

D ['A', 'Beta']

💡 Hint

Check the 'DataFrame Columns After' column at step 3 in execution_table.

According to variable_tracker, what is the value of df.columns after step 4?

A ['Alpha', 'Beta']

B ['A', 'B']

C ['Alpha', 'B']

D ['A', 'Beta']

💡 Hint

Look at the row for df.columns in variable_tracker after step 4.

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

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

B Assign df = df.rename(columns=...)

C All of the above

D Use df.columns = new_names list

💡 Hint

All these methods can change column names; see common pandas usage.

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 rename in place.
Only columns in the mapping are renamed.
Unmapped columns stay the same.

Full Transcript

We start with a DataFrame with columns 'A' and 'B'. We create a mapping to rename 'A' to 'Alpha' and 'B' to 'Beta'. Applying df.rename with this mapping returns a new DataFrame with columns 'Alpha' and 'Beta'. The original DataFrame keeps its columns unless we specify inplace=True. This process helps us change column names clearly and safely.