You can use the following syntax to quickly merge two or more series together into a single pandas DataFrame:
df = pd.concat([series1, series2, ...], axis=1)
The following examples show how to use this syntax in practice.
Example 1: Merge Two Series in Pandas
The following code shows how to merge together two pandas Series into a single pandas DataFrame:
import pandas as pd #define series series1 = pd.Series(['Mavs', 'Rockets', 'Spurs'], name='Team') series2 = pd.Series([109, 103, 98], name='Points') #merge series into DataFrame df = pd.concat([series1, series2], axis=1) #view DataFrame df Team Points 0 Mavs 109 1 Rockets 103 2 Spurs 98
Note that if one series is longer than the other, pandas will automatically provide NaN values for missing values in the resulting DataFrame:
import pandas as pd #define series series1 = pd.Series(['Mavs', 'Rockets', 'Spurs'], name='Team') series2 = pd.Series([109, 103], name='Points') #merge series into DataFrame df = pd.concat([series1, series2], axis=1) #view DataFrame df Team Points 0 Mavs 109 1 Rockets 103 2 Spurs NaN
Example 2: Merge Multiple Series in Pandas
The following code shows how to merge multiple series into a single pandas DataFrame:
import pandas as pd #define series series1 = pd.Series(['Mavs', 'Rockets', 'Spurs'], name='Team') series2 = pd.Series([109, 103, 98], name='Points') series3 = pd.Series([22, 18, 15], name='Assists') series4 = pd.Series([30, 35, 28], name='Rebounds') #merge series into DataFrame df = pd.concat([series1, series2, series3, series4], axis=1) #view DataFrame df Team Points Assists Rebounds 0 Mavs 109 22 30 1 Rockets 103 18 35 2 Spurs 98 15 28
Additional Resources
How to Merge Two Pandas DataFrames on Index
How to Merge Pandas DataFrames on Multiple Columns
How to Stack Multiple Pandas DataFrames