Скачать или смотреть Understanding Pandas' read_csv Behavior with CategoricalDtype: Why Numbers Become Objects

Discover why using `pd.CategoricalDtype` with `read_csv` results in object categories and explore efficient workarounds for correct data types in your Pandas DataFrames.
---
This video is based on the question https://stackoverflow.com/q/64652975/ asked by the user 'Eduardo Paul' ( https://stackoverflow.com/u/12139941/ ) and on the answer https://stackoverflow.com/a/64653321/ provided by the user 'Cameron Riddell' ( https://stackoverflow.com/u/14278448/ ) 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: Pandas' read_csv with dtype=pd.CategoricalDtype() creates 'object' categories even when the input data are numbers

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.
---
Understanding Pandas' read_csv Behavior with CategoricalDtype: Why Numbers Become Objects

When working with data in Python, Pandas is one of the most popular libraries for data manipulation and analysis. However, users often encounter unexpected behavior when using its capabilities. A common issue arises when trying to use pd.read_csv with the dtype parameter set to CategoricalDtype, particularly regarding the data type of categories created from numerical values. In this guide, we will dive deep into understanding this behavior and how to manage it effectively in your analyses.

The Problem Explained

Imagine you have a CSV file containing a simple dataset of numbers, and you want to read that data into a Pandas DataFrame with categorical types. Here's a quick demonstration of what can happen:

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

When you run this code, you will see the following output:

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

Despite the input numbers being integers, the categories in the DataFrame are recognized as object types instead of int. This can lead to unexpected issues in data analysis, especially when working with numerical comparisons or calculations. On the contrary, using a list of numbers directly results in categories of type int64:

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

This results in:

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

Why Does This Happen?

The core of the issue lies in how Pandas processes data when reading from a CSV file. Here's a breakdown of the behavior:

1. String Storage of CSV Inputs

When you read data from a CSV file, all values are initially stored as strings. Pandas then performs its best guess to determine the appropriate data types. This intelligent parsing is effective for many cases, but when you specify a categorical data type (like CategoricalDtype), it prevents Pandas from performing this implicit conversion into numeric types before creating categories.

2. Setting the Categorical Type

If you don't explicitly specify dtype in the pd.read_csv function, Pandas will automatically convert numbers to their appropriate integer types. However, by defining dtype=pd.CategoricalDtype, you are directing Pandas to skip that crucial step, leading to the resulting categories being treated as object types.

Solutions to the Problem

While this behavior may seem frustrating at first, there are efficient ways to manage it. Here are some recommended approaches:

Option 1: Leave out the dtype

If your goal is to allow automatic type conversion, simply omit the dtype parameter in pd.read_csv. This enables Pandas to infer the data types correctly.

Option 2: Convert Categories Post-Creation

If you need to keep using dtype=pd.CategoricalDtype, you can convert the categories to the desired type after creating them. Here's an example to demonstrate this method:

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

This will produce the output with categories as objects:

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

Option 3: Manually Specify Categories

Another alternative is to explicitly define the categories you wish to use in the CategoricalDtype. For example:

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

Conclusion

Understanding the behavior of Pandas when working with categorical data types can be critical for effective data analysis. The automatic type inference can be incredibly helpful, but knowing when and how to specify data types, or allowing Pandas to guess, can save you from future headaches.

By adopting the solutions discussed above, you can achieve the desired data types effectiv