Excel Pivot Export from Pandas Dataframe

Concatenating Pandas DataFrames and Exporting to Excel Pivot Table

When working with data analysis and reporting, we often need to combine data from multiple sources or dataframes into a single consolidated view. Pandas, a powerful data manipulation library in Python, provides several options for concatenating dataframes, including pd.concat() and pd.append().

In this technical blog post, we will specifically focus on concatenating multiple Pandas dataframes and exporting the resulting dataframe to an Excel pivot table. We will explore two methods:

1. Method 1: Concatenating Dataframes with Default Column Names

In this method, we will use the pd.concat() function to concatenate the dataframes while simultaneously generating default column names for the resulting dataframe. Here's the code:

dfs = [data1_summary, data2_summary, data3_summary]
df = pd.concat(x.set_axis(range(len(x.columns)), axis=1) for x in dfs)

print(df)

Output:

     0       1       2
0   L1   100.0   400.0   700.0
1   L2   200.0   500.0   800.0
2   L3   300.0   600.0   900.0
0   L5  1000.0  1300.0     NaN
1   L6  1100.0  1400.0     NaN
0   L7  1900.0  2900.0  3500.0
1   L8  2000.0  2300.0  3600.0
2   L9  2100.0  2400.0  3700.0
3  L10  2200.0  2800.0  3900.0

As you can see, the resulting dataframe has default column names ('0', '1', '2', ...) and the rows are concatenated from the input dataframes.

2. Method 2: Concatenating Dataframes and Removing Header Columns

In this method, we will first drop the header columns from each dataframe and then concatenate them using pd.concat(). We will also set the axis labels for the resulting dataframe.

dfs = [data1_summary, data2_summary, data3_summary]
df = pd.concat(x.drop('Header', axis=1)
               .set_axis(range(len(x.columns) - 1), axis=1) for x in dfs)

print(df)

Output:

        0       1       2
0   100.0   400.0   700.0
1   200.0   500.0   800.0
2   300.0   600.0   900.0
0  1000.0  1300.0     NaN
1  1100.0  1400.0     NaN
0  1900.0  2900.0  3500.0
1  2000.0  2300.0  3600.0
2  2100.0  2400.0  3700.0
3  2200.0  2800.0  3900.0

In this case, the header columns are removed, and default column names are generated for the resulting dataframe.

Once you have concatenated the dataframes, you can export the resulting dataframe to an Excel pivot table using any of the following methods:

• Using the Pandas to_excel() function with the pivot_table=True argument.
• Using third-party libraries such as openpyxl or xlwt.
• Copy and paste the data into an Excel spreadsheet and manually create a pivot table.

The method you choose will depend on your specific requirements and preferences.

By following these steps, you can easily concatenate multiple Pandas dataframes and export the result to an Excel pivot table, allowing you to perform comprehensive data analysis and reporting tasks.