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Excel Pivot Export from Pandas Dataframe

Introduction:

Excel pivot tables are a powerful tool for data analysis and summarization. They allow you to quickly and easily create interactive reports that can be used to identify trends, patterns, and outliers in your data.

However, pivot tables can be challenging to export as Pandas dataframes. By default, pivot tables will export with their column and row headers as the first row of the dataframe. This can make it difficult to work with the data in Pandas, as the column and row headers will need to be removed before the data can be used.

There are a few different ways to export a pivot table to a Pandas dataframe without the column and row headers.

Method 1: Use the drop() method

One way to remove the column and row headers from a pivot table is to use the drop() method. The drop() method takes a list of column or row indices as input, and returns a new dataframe with those columns or rows removed.

import pandas as pd

# Create a pivot table
pivot_table = pd.pivot_table(df, values='value', index='row_index', columns='column_index')

# Drop the column and row headers
pivot_table = pivot_table.drop([0, 1], axis=0)  # Drop the first two rows
pivot_table = pivot_table.drop([0, 1], axis=1)  # Drop the first two columns

# Print the dataframe
print(pivot_table)

This method will create a new dataframe with the column and row headers removed. The resulting dataframe will have the same number of columns and rows as the original pivot table, minus the headers.

Method 2: Use the set_axis() method

Another way to remove the column and row headers from a pivot table is to use the set_axis() method. The set_axis() method takes a list of new column or row indices as input, and returns a new dataframe with the old indices replaced by the new indices.

import pandas as pd

# Create a pivot table
pivot_table = pd.pivot_table(df, values='value', index='row_index', columns='column_index')

# Set the column and row indices
pivot_table = pivot_table.set_axis(['New Column 1', 'New Column 2', 'New Column 3'], axis=1)
pivot_table = pivot_table.set_axis(['New Row 1', 'New Row 2', 'New Row 3'], axis=0)

# Print the dataframe
print(pivot_table)

This method will create a new dataframe with the column and row headers replaced by the new indices. The resulting dataframe will have the same number of columns and rows as the original pivot table, but the headers will be different.

Conclusion:

There are a few different ways to export a pivot table to a Pandas dataframe without the column and row headers. The two methods discussed in this blog post are the drop() method and the set_axis() method. Both methods are relatively simple to use, and they can be used to create a dataframe that is easier to work with in Pandas.

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