Скачать или смотреть How to Specify a Column's dtype by Index in Pandas' pd.read_excel

Discover how to efficiently set data types during Excel file import using column indices instead of names in Pandas.
---
This video is based on the question https://stackoverflow.com/q/64047518/ asked by the user 'MerveD' ( https://stackoverflow.com/u/14333679/ ) and on the answer https://stackoverflow.com/a/64047666/ provided by the user 'Umar.H' ( https://stackoverflow.com/u/9375102/ ) at 'Stack Overflow' website. Thanks to these great users and Stackexchange community for their contributions.

Visit these links for original content and any more details, such as alternate solutions, latest updates/developments on topic, comments, revision history etc. For example, the original title of the Question was: How to specify a columns dtype by its index rather than its name in pandas pd.read_excel

Also, Content (except music) licensed under CC BY-SA https://meta.stackexchange.com/help/l...
The original Question post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license, and the original Answer post is licensed under the 'CC BY-SA 4.0' ( https://creativecommons.org/licenses/... ) license.

If anything seems off to you, please feel free to write me at vlogize [AT] gmail [DOT] com.
---
How to Specify a Column's dtype by Index in Pandas' pd.read_excel

When working with Excel files in Python, especially using the Pandas library, you may encounter situations where you need to read data without using the column names for specifying data types. Instead, you might prefer to work with column indices. This guide will guide you through how to achieve that effectively while using the pd.read_excel function.

The Problem

You want to read data from an Excel file while specifying data types (dtype) based on the column indices. Traditional usage involves naming the columns, which can be inconvenient or infeasible in some cases, especially if you want your code to be more flexible or if the column names are not known in advance.

For example, when reading an Excel file, the typical approach looks like this:

[[See Video to Reveal this Text or Code Snippet]]

However, if you'd like to reference the columns by their index positions instead, how can you do that?

The Solution

To specify the dtype by index rather than by name, you can follow these steps:

Step 1: Load the Excel file with headers skipped

When you specify header=None, you tell Pandas that the first row of your Excel sheet does not contain headers, allowing you to access the columns using their index positions. Here’s how you can do it:

[[See Video to Reveal this Text or Code Snippet]]

Step 2: Understand the parameter skiprows

Make sure to use skiprows=1 to exclude the original header from being treated as data. This is crucial because without it, all columns will read as strings (dtype) by default.

Step 3: Create an example to visualize

Let's consider creating an example DataFrame and saving it to Excel to see this in practice.

[[See Video to Reveal this Text or Code Snippet]]

Step 4: Note on the DataFrame structure

After executing the above code, you will note that the DataFrame columns are now accessible as index-based types.

Important Notes

Make sure to customize the dtype parameter correspondingly if you add more columns.

You'll need to set your DataFrame columns manually after using the above method, as they will default to numerical indices.

Conclusion

Specifying a column's dtype by its index in Pandas while reading Excel files not only simplifies code but also makes it more adaptable for various datasets. By mastering the use of header=None and skiprows, you can efficiently manipulate how your data is loaded into your Python environment.

Try it out for your data loading tasks, and see how using indices can streamline your data processing workflow!