How to Convert First Row to Header Column in Pandas DataFrame

1. Overview

This guide describes how to convert first or other rows as a header in Pandas DataFrame.

We will cover several different examples with details.

If you are using read_csv() method you can learn more

2. Setup

We are going to work with simple DataFrame created by:

import pandas as pd
df = pd.DataFrame([('head_1', 'head_2', 'head_3' ),
                   ('val_11','val_12','val_13'),
                   ('val_21','val_22','val_23'),
                  ('val_31','val_32','val_33')])

Final DataFrame looks like:

	0	1	2
0	head_1	head_2	head_3
1	val_11	val_12	val_13
2	val_21	val_22	val_23
3	val_31	val_32	val_33

From this DataFrame we can conclude that the first row of it should be used as a header.

3. Using First Row as a Header with df.rename()

The first solution is to combine two Pandas methods:

The method .rename(columns=) expects to be iterable with the column names. To select the first row we are going to use iloc - df.iloc[0].

Finally we need to drop the first row which was used as a header by drop(df.index[0]):

df.rename(columns=df.iloc[0]).drop(df.index[0])

The result is:

	head_1	head_2	head_3
1	val_11	val_12	val_13
2	val_21	val_22	val_23
3	val_31	val_32	val_33

For other rows we can change the index - 0. For example to use the last row as header: -1 - df.iloc[-1].

To make the change permanent we need to use inplace = True or reassign the DataFrame.

4. Using First Row as a Header with pd.DataFrame()

Another solution is to create new DataFrame by using the values from the first one - up to the first row: df.values[1:]

Use the column header from the first row of the existing DataFrame.

pd.DataFrame(df.values[1:], columns=df.iloc[0])

The result is exactly the same as the previous solution.

There are several differences:

This solution might be slower for bigger DataFrames
It creates new DataFrame
It may change the dtypes of the new DataFrame

5. Conclusion

In this short post we saw how to use a row as a header in Pandas. We covered also several Pandas methods like: iloc(), rename() and drop()