Combine Multiple Excel Worksheets Into a Single Pandas Dataframe

Introduction

One of the most commonly used pandas functions is read_excel . This short article shows how you can read in all the tabs in an Excel workbook and combine them into a single pandas dataframe using one command.

For those of you that want the TLDR, here is the command:

df = pd.concat(pd.read_excel('2018_Sales_Total.xlsx', sheet_name=None), ignore_index=True)

Read on for an explanation of when to use this and how it works.

Excel Worksheets

For the purposes of this example, we assume that the Excel workbook is structured like this:

The process I will describe works when:

• The data is not duplicated across tabs (sheet1 is one full month and the subsequent sheets have only a single month’s worth of data)
• The columns are all named the same
• You wish to read in all tabs and combine them

Understanding read_excel

The read_excel function is a feature packed pandas function. For this specific case, we can use the sheet_name parameter to streamline the reading in of all the sheets in our Excel file.

Most of the time, you will read in a specific sheet from an Excel file:

import pandas as pd

workbook_url = 'https://github.com/chris1610/pbpython/raw/master/data/2018_Sales_Total_Tabs.xlsx'
single_df = pd.read_excel(workbook_url, sheet_name='Sheet1')

If you carefully look at the documentation, you may notice that if you use sheet_name=None , you can read in all the sheets in the workbook at one time. Let’s try it:

all_dfs = pd.read_excel(workbook_url, sheet_name=None)

Pandas will read in all the sheets and return a collections.OrderedDict object. For the purposes of the readability of this article, I’m defining the full url and passing it to read_excel . In practice, you may decide to make this one command.

Let’s inspect the resulting all_dfs :

all_dfs.keys()

odict_keys(['Sheet1', 'Sheet2', 'Sheet3', 'Sheet4', 'Sheet5', 'Sheet6'])

If you want to access a single sheet as a dataframe:

all_dfs['Sheet1'].head()

	account number	name	sku	quantity	unit price	ext price	date
0	412290	Jerde-Hilpert	S2-77896	43	76.66	3296.38	2018-03-04 23:10:28
1	383080	Will LLC	S1-93683	28	90.86	2544.08	2018-03-05 05:11:49
2	729833	Koepp Ltd	S1-30248	13	44.84	582.92	2018-03-05 17:33:52
3	424914	White-Trantow	S2-82423	38	50.93	1935.34	2018-03-05 21:40:10
4	672390	Kuhn-Gusikowski	S1-50961	34	48.20	1638.80	2018-03-06 11:59:00

If we want to join all the individual dataframes into one single dataframe, use pd.concat:

df = pd.concat(all_dfs, ignore_index=True)

In this case, we use ignore_index since the automatically generated indices of Sheet1 , Sheet2 , etc. are not meaningful.

If your data meets the structure outlined above, this one liner will return a single pandas dataframe that combines the data in each Excel worksheet:

df = pd.concat(pd.read_excel(workbook_url, sheet_name=None), ignore_index=True)

Summary

This trick can be useful in the right circumstances. It also illustrates how much power there is in a pandas command that “just” reads in an Excel file. The full notebook is available on github if you would like to try it out for yourself.

from Practical Business Python
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