Convert a Row to a Column Header in a Pandas DataFrame
Borislav Hadzhiev
Last updated: Apr 11, 2024
Reading time·6 min
# Convert a Row to a Column Header in a Pandas DataFrame
Set the column property to the result of accessing the iloc indexer at the
given index to convert a row to a column header in a Pandas DataFrame.
The iloc indexer is used for integer, location-based indexing for selection
by position.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df.columns = df.iloc[1] print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- 1 Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3
Notice that there is a duplicate row.
You can use the df.drop() method to drop the duplicate.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df.columns = df.iloc[1] print('-' * 50) print(df) df = df.drop(df.index[1]) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- 1 Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- 1 Bobby 13 180.2 0 Alice 10 175.1 2 Carl 15 190.3
You can also reset the index by using the DataFrame.reset_index() method.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df.columns = df.iloc[1] print('-' * 50) print(df) df = df.drop(df.index[1]) print('-' * 50) print(df) df = df.reset_index(drop=True) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- 1 Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- 1 Bobby 13 180.2 0 Alice 10 175.1 2 Carl 15 190.3 -------------------------------------------------- 1 Bobby 13 180.2 0 Alice 10 175.1 1 Carl 15 190.3
The df.drop(df.index[1]) method call removes all rows that have the same label
as the second row (index 1).
If the index is not unique, use a RangeIndex object.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df.columns = df.iloc[1] print('-' * 50) print(df) df = df.iloc[pd.RangeIndex(len(df)).drop(1)] print('-' * 50) print(df)
The code for this article is available on GitHub
We called the drop() method on the RangeIndex object to delete the row at
index 1.
The DataFrame.iloc indexer returns the row at the given index.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df.iloc[1]) print('-' * 50) print(df.iloc[2])
Running the code sample produces the following output.
shell
name Bobby experience 13 salary 180.2 Name: 1, dtype: object -------------------------------------------------- name Carl experience 15 salary 190.3 Name: 2, dtype: object
Note that the .iloc indexer raises an IndexError if the supplied index is
out of bounds.
# Using the DataFrame.rename() method to convert a row to a column header
You can also use the DataFrame.rename method to convert a row to a column header in Pandas.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df = df.rename(columns=df.iloc[1]) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3
If you want to delete the duplicate row, use the drop() method.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df = df.rename(columns=df.iloc[1]) print('-' * 50) print(df) df = df.drop(df.index[1]) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 2 Carl 15 190.3
You can also restart the index after removing the duplicate row.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df = df.rename(columns=df.iloc[1]) print('-' * 50) print(df) df = df.drop(df.index[1]) print('-' * 50) print(df) df = df.reset_index(drop=True) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 1 Carl 15 190.3
Notice that the index starts at 0 after calling
DataFrame.reset_index().
If you want to convert a given row to a column header without reassigning the
DataFrame, set the inplace argument to True.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) df.rename(columns=df.iloc[1], inplace=True) print('-' * 50) print(df) df.drop(df.index[1], inplace=True) print('-' * 50) print(df) df.reset_index(drop=True, inplace=True) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample above produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 2 Carl 15 190.3 -------------------------------------------------- Bobby 13 180.2 0 Alice 10 175.1 1 Carl 15 190.3
# Convert a row to a column header by creating a new DataFrame
You can also convert a row to a column header by creating a new DataFrame.
main.py
import pandas as pd df = pd.DataFrame({ 'name': ['Alice', 'Bobby', 'Carl'], 'experience': [10, 13, 15], 'salary': [175.1, 180.2, 190.3], }) print(df) headers = df.iloc[0] new_df = pd.DataFrame(df.values[1:], columns=headers) print('-' * 50) print(new_df) df.reset_index(drop=True, inplace=True) print('-' * 50) print(df)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3 -------------------------------------------------- 0 Alice 10 175.1 0 Bobby 13 180.2 1 Carl 15 190.3 -------------------------------------------------- name experience salary 0 Alice 10 175.1 1 Bobby 13 180.2 2 Carl 15 190.3
We created a new DataFrame and set the columns to the first row (index 0) by
supplying the columns
keyword argument.
You can optionally use the reset_index method to reset the index of the
DataFrame and use the default index instead.
# Additional Resources
You can learn more about the related topics by checking out the following tutorials:
- AttributeError module 'pandas' has no attribute 'DataFrame'
- ModuleNotFoundError: No module named 'pandas' in Python
- FutureWarning: pandas.Int64Index is deprecated and will be removed from pandas in a future version
- You are trying to merge on int64 and object columns [Fixed]
- Copy a column from one DataFrame to another in Pandas
- ValueError: cannot reindex on an axis with duplicate labels
- ValueError: Length mismatch: Expected axis has X elements, new values have Y elements
- ValueError: cannot reshape array of size X into shape Y
- Object arrays cannot be loaded when allow_pickle=False
- ValueError: Columns must be same length as key [Solved]
- ValueError: DataFrame constructor not properly called [Fix]
- Convert a Row to a Column Header in a Pandas DataFrame
- Drop Unnamed: 0 columns from a Pandas DataFrame in Python
- IndexError: single positional indexer is out-of-bounds [Fix]
- IndexError: single positional indexer is out-of-bounds [Fix]
- Arrays used as indices must be of integer (or boolean) type
- Boolean index did not match indexed array along dimension 0
- AttributeError: Can only use .dt accessor with datetimelike values
- Count number of non-NaN values in each column of DataFrame
- Add a column with incremental Numbers to a Pandas DataFrame
- Replace whole String if it contains Substring in Pandas
- Usecols do not match columns, columns expected but not found
- Pandas: Create new row for each element in List in DataFrame
- ValueError: Length of values does not match length of index
- How to add a Level to Pandas MultiIndex in Python
- Select all Columns starting with a given String in Pandas
- Pandas: Cannot setitem on a Categorical with a new category
- Pandas: Get a List of Categories or Categorical Columns
- Pandas ValueError: ('Lengths must match to compare')
- Pandas: Drop columns if Name contains a given String
- Pandas: Convert GroupBy results to Dictionary of Lists
- Cannot perform 'rand_' with a dtyped [int64] array and scalar of type [bool]
- How to remove Time from DateTime in Pandas [5 Ways]
- Pandas: Check if a Date is during the Weekend or Weekday
- Pandas: Find the percentage of Missing values in each Column
- Create Date column from Year, Month and Day in Pandas
- ValueError: Grouper for 'X' not 1-dimensional [Solved]
- Cannot subset columns with tuple with more than one element
- Pandas: Select first N or last N columns of DataFrame
- Pandas: Select Rows between two values in DataFrame
- Pandas: How to Filter a DataFrame by value counts
- Pandas: GroupBy columns with NaN (missing) values
- Pandas: Split a Column of Lists into Multiple Columns
- ValueError: Expected object or value with
pd.read_json()
