Python In Excel – Basic Data Frame Extraction in Pandas for Excel Step by Step Tutorial (MUST KNOW!)

Discover how to seamlessly integrate Python in Excel and efficiently extract data from pandas DataFrames in this tutorial focusing on .loc indexing, slicing cell ranges, and selectively retrieving entire rows or columns. We’ll walk through extracting precise row and column subsets, preserving column headers and row labels, and converting DataFrames into lists for easy analysis or reporting. Whether you’re handling small tables or big data, these fundamental Python data manipulation techniques will serve you well on your data-wrangling journey. Quickly load Excel data into Python, manipulate it using powerful pandas methods, and present your results back in Excel—all while optimizing your workflow for clear and concise data extraction.

Video Timeline:

0:00 – Tutorial Overview: Selecting contiguous and non-contiguous ranges, entire columns, and entire rows. Various ways to output data: with row labels and column headers, without row labels but with column headers, or only column headers.

0:50 – Loading Excel Data: How to load Excel data into a pandas DataFrame using the "=PY" tab and selecting the Excel cells with column headers.

1:25 – Extracting Contiguous Ranges: How to extract a contiguous range from the Excel data using the pandas .loc[] indexer.

3:10 – Output with Labels and Headers: How to output extracted DataFrames with row labels and column headers by referencing the variable name of the extracted DataFrame.

5:10 – Extracting Non-Contiguous Cells: How to extract non-contiguous cells (i.e., cells not adjacent in the original table) by passing lists (values separated by commas, enclosed in square brackets) into either the rows or columns argument of .loc[].

6:55 – Selecting All Columns: How to select all columns by using only a colon (no values) in the columns parameter of .loc[].

7:50 – Selecting All Rows: How to select all rows by using only a colon (no values) in the rows parameter of .loc[].

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Cheers,
Dan 😊