How Do I Change Pandas Data Type?


Larry Thompson

In this tutorial, we will explore how to change data types in Pandas. Data types play a crucial role in data analysis and manipulation, as they determine the kind of operations that can be performed on a particular column or series within a DataFrame.

Understanding Data Types in Pandas

Data types are a way of categorizing and organizing data in a structured manner. In Pandas, data types are referred to as dTypes. Each column or series in a DataFrame has its own data type, which can be one of several predefined types such as integer, float, string, boolean, etc.

Checking the Current Data Type

To check the current data type of a column or series in Pandas, we can make use of the dtype attribute. Let’s say we have a DataFrame called df, and we want to check the data type of a column named 'column_name':


This will return the current data type for that specific column.

Changing Data Types

Pandas provides various methods to change the data type of a column or series. The most commonly used method is the astype() function.

To change the data type using astype(), you need to specify the desired new data type as an argument. For example, if you want to change a column named ‘column_name’ from its current data type to integer:

df['column_name'] = df['column_name'].astype(int)

This will convert the data type of the specified column to integer. However, it’s important to note that if the data in the column cannot be converted to the desired type, Pandas will raise an error.

Common Data Type Conversions

Here are some common data type conversions that you may encounter:

  • Integer to Float: Use astype(float).
  • Float to Integer: Use astype(int). Note that this will truncate any decimal places.
  • Numeric to String: Use astype(str).
  • Date/Time to String: Use dt.strftime(format). Specify the desired format for the string representation of the date/time.

If you want to change multiple columns at once, you can do so by specifying a list of column names instead of a single column name in the above examples.

Inference-based Data Type Conversion

Pandas also provides a method called .infer_objects(), which automatically infers and changes the data type of columns based on their values. This method is particularly useful when dealing with columns containing mixed data types or when you want to optimize memory usage.

df = df.infer_objects()


You have now learned how to change data types in Pandas using various methods such as astype() and .infer_objects(). Remember to always verify the data after performing any data type conversion to ensure the desired changes have been made successfully.

Keep exploring and experimenting with Pandas to unleash its full potential in your data analysis projects!

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