Often you may want to create a pandas DataFrame from one or more pandas Series.

The following examples show how to create a pandas DataFrame using existing series as either the rows or columns of the DataFrame.

**Example 1: Create Pandas DataFrame Using Series as Columns**

Suppose we have the following three pandas Series:

import pandas as pd #define three Series name = pd.Series(['A', 'B', 'C', 'D', 'E']) points = pd.Series([34, 20, 21, 57, 68]) assists = pd.Series([8, 12, 14, 9, 11])

We can use the following code to convert each series into a DataFrame and then concatenate them all into one DataFrame:

#convert each Series to a DataFrame name_df = name.to_frame(name='name') points_df = points.to_frame(name='points') assists_df = assists.to_frame(name='assists') #concatenate three Series into one DataFrame df = pd.concat([name_df, points_df, assists_df], axis=1) #view final DataFrame print(df) name points assists 0 A 34 8 1 B 20 12 2 C 21 14 3 D 57 9 4 E 68 11

Notice that the three series are each represented as columns in the final DataFrame.

**Example 2: Create Pandas DataFrame Using Series as Rows**

Suppose we have the following three pandas Series:

import pandas as pd #define three Series row1 = pd.Series(['A', 34, 8]) row2 = pd.Series(['B', 20, 12]) row3 = pd.Series(['C', 21, 14])

We can use the following code to combine each of the Series into a pandas DataFrame, using each Series as a row in the DataFrame:

#create DataFrame using Series as rows df = pd.DataFrame([row1, row2, row3]) #create column names for DataFrame df.columns = ['col1', 'col2', 'col3'] #view resulting DataFrame print(df) col1 col2 col3 0 A 34 8 1 B 20 12 2 C 21 14

Notice that the three series are each represented as rows in the final DataFrame.

**Additional Resources**

The following tutorials explain how to perform other common operations in Python:

How to Convert Pandas Series to DataFrame

How to Convert Pandas Series to NumPy Array

How to Convert NumPy Array to Pandas DataFrame