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You can use the following basic syntax to convert a pandas Series to a pandas DataFrame:

my_df = my_series.to_frame(name='column_name')

The following examples show how to use this syntax in practice.

**Example 1: Convert One Series to Pandas DataFrame**

Suppose we have the following pandas Series:

import pandas as pd #create pandas Series my_series = pd.Series([3, 4, 4, 8, 14, 17, 20]) #view pandas Series print(my_series) 0 3 1 4 2 4 3 8 4 14 5 17 6 20 dtype: int64 #view object type print(type(my_series))

We can use the **to_frame()** function to quickly convert this pandas Series to a pandas DataFrame:

#convert Series to DataFrame and specify column name to be 'values' my_df = my_series.to_frame(name='values') #view pandas DataFrame print(my_df) values 0 3 1 4 2 4 3 8 4 14 5 17 6 20 #view object type print(type(my_df))

**Example 2: Convert Multiple Series to Pandas DataFrame**

Suppose we have three different 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 syntax to convert each Series into a DataFrame and concatenate the three DataFrames into one final 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

The final result is a pandas DataFrame where each Series represents a column.

**Additional Resources**

The following tutorials explain how to perform other common data object conversions in pandas:

How to Convert Pandas Series to NumPy Array

How to Convert Pandas DataFrame to NumPy Array

How to Convert Pandas DataFrame to Dictionary

How to Convert Pandas DataFrame to List