Convert column Values to Columns in a Pandas DataFrame
Borislav Hadzhiev
Last updated: Apr 12, 2024
Reading time·5 min
# Table of Contents
- Convert column Values to Columns in a Pandas DataFrame
- Convert column Values to Columns in a Pandas DataFrame using pivot
- Convert column Values to Columns in a Pandas DataFrame using groupby() and unstack()
# Convert column Values to Columns in a Pandas DataFrame
Use the DataFrame.pivot_table() method to convert column values to columns
in a Pandas DataFrame.
The method will return a spreadsheet-style pivot table as a DataFrame.
main.py
import pandas as pd df = pd.DataFrame({ 'Frontend': ['React', 'Vue', 'Angular'], 'Styles': ['Bootstrap', 'Vuetify', 'Material'], }) print(df) print('-' * 50) df2 = df.pivot_table(values='Styles', index=df.index, columns='Frontend', aggfunc='first') print(df2)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
Frontend Styles 0 React Bootstrap 1 Vue Vuetify 2 Angular Material -------------------------------------------------- Frontend Angular React Vue 0 NaN Bootstrap NaN 1 NaN NaN Vuetify 2 Material NaN NaN
We passed the following arguments to the DataFrame.pivot_table method:
values- the column (or multiple columns in a list) to aggregate.index- the index column.- columns - the column values that you want to use as column headers.
aggfunc- the aggregation function.
# Convert column Values to Columns in a Pandas DataFrame using pivot
You can also use the
DataFrame.pivot()
method to convert the column values of a DataFrame to columns.
main.py
import pandas as pd df = pd.DataFrame({ 'id': [1, 2, 3], 'Frontend': ['React', 'Vue', 'Angular'], 'Styles': ['Bootstrap', 'Vuetify', 'Material'], }) print(df) print('-' * 50) df2 = df.pivot(index='id', columns='Frontend', values='Styles') print(df2)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
id Frontend Styles 0 1 React Bootstrap 1 2 Vue Vuetify 2 3 Angular Material -------------------------------------------------- Frontend Angular React Vue id 1 NaN Bootstrap NaN 2 NaN NaN Vuetify 3 Material NaN NaN
The DataFrame.pivot method returns a reshaped DataFrame that is organized by
the given index/column values.
We could've also accessed the Styles column using bracket notation instead of
supplying the values argument.
main.py
import pandas as pd df = pd.DataFrame({ 'id': [1, 2, 3], 'Frontend': ['React', 'Vue', 'Angular'], 'Styles': ['Bootstrap', 'Vuetify', 'Material'], }) print(df) print('-' * 50) df2 = df.pivot(index='id', columns='Frontend')['Styles'] print(df2)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
id Frontend Styles 0 1 React Bootstrap 1 2 Vue Vuetify 2 3 Angular Material -------------------------------------------------- Frontend Angular React Vue id 1 NaN Bootstrap NaN 2 NaN NaN Vuetify 3 Material NaN NaN
# Convert column Values to Columns in a Pandas DataFrame using groupby() and unstack()
You can also use the groupby() and unstack() methods to convert the column
values in a DataFrame to columns.
main.py
import pandas as pd df = pd.DataFrame({ 'id': [1, 2, 3], 'Frontend': ['React', 'Vue', 'Angular'], 'Styles': ['Bootstrap', 'Vuetify', 'Material'], }) print(df) print('-' * 50) df2 = df.reset_index().groupby(['id', 'Frontend'])[ 'Styles'].aggregate('first').unstack() print(df2)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
id Frontend Styles 0 1 React Bootstrap 1 2 Vue Vuetify 2 3 Angular Material -------------------------------------------------- Frontend Angular React Vue id 1 NaN Bootstrap NaN 2 NaN NaN Vuetify 3 Material NaN NaN
The code sample:
- Resets the
DataFrameindex using reset_index().
main.py
# index id Frontend Styles # 0 0 1 React Bootstrap # 1 1 2 Vue Vuetify # 2 2 3 Angular Material print(df.reset_index())
- The
groupby()
method groups the
DataFrameby a series of columns. - We then aggregate using DataFrame.first().
- The
DataFrame.unstack()
method returns a
DataFramewith a new level of column labels whose innermost level consists of the pivoted index labels.
You can achieve a similar result by using DataFrame.set_index().
main.py
import pandas as pd df = pd.DataFrame({ 'Frontend': ['React', 'Vue', 'Angular'], 'Styles': ['Bootstrap', 'Vuetify', 'Material'], }) print(df) print('-' * 50) df2 = df.set_index([df.index, 'Frontend'])['Styles'].unstack() print(df2)
The code for this article is available on GitHub
Running the code sample produces the following output.
shell
Frontend Styles 0 React Bootstrap 1 Vue Vuetify 2 Angular Material -------------------------------------------------- Frontend Angular React Vue 0 NaN Bootstrap NaN 1 NaN NaN Vuetify 2 Material NaN NaN
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