Python in Excel provides a user-friendly and data analysis-optimized approach, especially suitable for general analysts beginning with Python. Notably, it defaults to loading all data inputs as Pandas DataFrames, a wise decision considering Excel users’ familiarity with tabular structures. Basic Python, on its own, isn’t inherently tailored for such specific data analysis tasks.

This article outlines three distinct methods for sourcing data to create a DataFrame in Python within Excel.

To follow along, download the accompanying exercise file below:

From a basic range

This approach is straightforward: you can initiate a Python cell and assign an Excel range to an object, which we’ll call state_land_range:

From a table

This method, while being the most straightforward, shares a common vulnerability with all alphanumeric data sources: the potential for data to become outdated. If an additional row or column is introduced to the data source, the specified C2:E7 input range would no longer encompass all the data, leading to missing information.

This concern highlights the advantage of directing the Python input data to an Excel table instead. By utilizing structured references in Excel, you can ensure that all data within the table is captured, effectively addressing the issue of dynamically changing data ranges.

From Power Query

Finally, there’s the possibility of importing data into Python from various external sources. Python boasts a remarkable array of tools for data importation. However, when working with Python in Excel, it’s important to note that it does not support reading from external files directly. To circumvent this limitation, you can utilize Power Query, an equally capable and robust tool.

For instance, consider a dataset containing state land areas that’s stored in a CSV file on the web. You can load this dataset into Power Query and name the resulting query largeststates. Once this is done, you can seamlessly connect to this query within Python using the xl() function, thus integrating external data sources into your Excel-Python workflow.

This method might feel somewhat less intuitive initially, as it requires typing out the query instead of simply selecting the data visually. It’s important to explicitly add the headers=True argument to this function, as this isn’t automatically included like in other methods.

Including this parameter ensures that Python recognizes the first row of the dataset as headers, which is crucial for accurate data interpretation and manipulation.

Which to use? As always, “it depends”

Just like with many features in Excel, there are multiple ways to achieve what appears to be the same goal. And as is often the case with Excel, the answer to which method is the best is the quintessential analyst’s response: “It depends!”

Loading data into a Pandas DataFrame from an Excel range is simple and precise, but it doesn’t automatically update if the data in Excel changes outside the specified range. Using an Excel table for loading is dynamic, as it includes new data automatically, but it requires you to adhere to the table’s structure, which might not always be ideal. Power Query offers advanced data transformation and automation capabilities, but it’s more complex to set up and relies heavily on the configuration of the Power Query itself. Each method offers different benefits, with the choice depending on your need for simplicity, dynamic data inclusion, or complex data manipulation.

What questions do you have about creating a Pandas DataFrame for Python in Excel? Let me know in the comments.

The post How to create a Pandas DataFrame with Python in Excel (three ways) first appeared on Stringfest Analytics.