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You can use the following syntax to convert a pandas pivot table to a pandas DataFrame:

df = pivot_name.reset_index()

The following example shows how to use this syntax in practice.

**Example: Convert Pivot Table to DataFrame**

Suppose we have the following pandas DataFrame:

import pandas as pd #create DataFrame df = pd.DataFrame({'team': ['A', 'A', 'A', 'A', 'B', 'B', 'B', 'B'], 'position': ['G', 'G', 'F', 'F', 'G', 'G', 'F', 'F'], 'points': [11, 8, 10, 6, 6, 5, 9, 12]}) #view DataFrame df team position points 0 A G 11 1 A G 8 2 A F 10 3 A F 6 4 B G 6 5 B G 5 6 B F 9 7 B F 12

We can use the following code to create a pivot table that displays the mean points scored by team and position:

#create pivot table df_pivot = pd.pivot_table(df, values='points', index='team', columns='position') #view pivot table df_pivot position F G team A 8.0 9.5 B 10.5 5.5

We can then use the **reset_index()** function to convert this pivot table to a pandas DataFrame:

#convert pivot table to DataFrame df2 = df_pivot.reset_index() #view DataFrame df2 team F G 0 A 8.0 9.5 1 B 10.5 5.5

The result is a pandas DataFrame with two rows and three columns.

We can also use the following syntax to rename the columns of the DataFrame:

#convert pivot table to DataFrame df2.columns = ['team', 'Forward_Pts', 'Guard_Pts'] #view updated DataFrame df2 team Forward_Pts Guard_Pts 0 A 8.0 9.5 1 B 10.5 5.5

**Additional Resources**

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

Pandas: How to Reshape DataFrame from Long to Wide

Pandas: How to Reshape DataFrame from Wide to Long

Pandas: How to Group and Aggregate by Multiple Columns