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 )