Guide: How To Move a Column to the Front in Pandas DataFrame?

In this guide, you can learn how to move columns by name to the front in Pandas DataFrame.

There are situations where you might need to move a specific column to the front of the DataFrame:

for better visibility
to facilitate further analysis
to fulfill business requirements
export purposes

Let's guide you through the process of moving a column by name to the front of a Pandas DataFrame, helping you organize your data more effectively.

For sorting column in DataFrame check: How to Change the Order of Columns in Pandas DataFrame

Data

To illustrate the example let's create simple Pandas DataFrame:

import pandas as pd

# Sample DataFrame
data = {
	'id': [1, 2, 3, 4, 5],
	'name': ['John', 'Alice', 'Bob', 'Charlie', 'Emma'],
	'value': [10, 20, 30, 40, 50],
	'category': ['A', 'B', 'C', 'B', 'A']
}

df = pd.DataFrame(data)
df

	id	name	value	category
0	1	John	10	A
1	2	Alice	20	B
2	3	Bob	30	C
3	4	Charlie	40	B
4	5	Emma	50	A

Move Column by `pop()` and `insert()`

To move a specific column to the front of the DataFrame, you can use: Pandas pop() and insert() functions:

col = 'category'
column_to_move = df.pop(col)
df.insert(0, col, column_to_move)

Column category will be moved as first column in the DataFrame:

	category	id	name	value
0	A	1	John	10
1	B	2	Alice	20
2	C	3	Bob	30
3	B	4	Charlie	40
4	A	5	Emma	50

Move Column by selecting a list of columns

As an alternative solution we can use Python list comprehension to predefine the column order.

To move single column to front of the DataFrame by using list comprehension we can:

col_to_move = 'category'
df[[col_to_move] + [ col for col in df.columns if col != col_to_move ]]

The result is the same as the on using pop

Move Multiple Columns

To move multiple Pandas columns to the start of the DataFrame we can build a list with the desired column order:

cols_to_move = ['category', 'name']
df[ cols_to_move + [ col for col in df.columns if col not in cols_to_move ]]

Now the columns 'category', 'name' are move at the beginning:

	category	name	id	value
0	A	John	1	10
1	B	Alice	2	20
2	C	Bob	3	30
3	B	Charlie	4	40
4	A	Emma	5	50

Conclusion

Reordering columns in a Pandas DataFrame allows you to organize your data effectively for analysis, sharing and visualization.

Hopefully, now you can easily move a specific columns to the front of the DataFrame.

Resources:

Additional resources on reordering and sorting columns in Pandas: