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Changing Data Types ​

When working with datasets in Pandas, you often encounter situations where you need to change the data type assigned to a column. This might be necessary for various reasons, such as performing calculations or ensuring proper data manipulation. Understanding how to correctly change data types can save you from unexpected errors and streamline your data analysis process.

Why Change Data Types? ​

Let's start with a common scenario: you've created a new column called AreaValues by splitting an existing column that had an object data type. Because the original column was of object type, Pandas assigns the new column the same data type. However, if you want to perform any numerical calculations with AreaValues, you need to convert it to a numeric type. Without this conversion, Pandas will treat it as text, leading to errors or incorrect results.

To change the data type of a column in Pandas, you use the .astype() method. Here’s the syntax:

python
df['Column'] = df['Column'].astype('type')
  • df is your DataFrame.
  • Column is the name of the column you want to change.
  • .astype() is the method used to change the data type.
  • type is the specific data type you want to convert your column to.

Types You Can Convert To ​

Pandas supports various data types that you can convert your columns to. Here are some common data types you might encounter:

  • float64: For decimal numbers.
  • int64: For integers.
  • object: For objects or text.
  • category: For categorical data.

Example ​

Imagine you have a column AreaValues initially created as an object type. To convert it to a numeric type, you would do the following:

python
df['AreaValues'] = df['AreaValues'].astype('float64')

Handling Missing Values ​

One important consideration is how missing values (NaNs) are handled. NaNs can be present in your dataset and are represented as float64. This means that if you have a column with missing values, converting it to an integer type (int64) directly is not possible because integers cannot represent NaNs.

For instance, if you have a column Visitors2019 with some missing values, converting it from float64 to int64 will raise an error. You need to handle the NaNs first, either by filling them with a specific value or by removing them, depending on your data handling strategy.

Conclusion ​

Changing data types in Pandas is a powerful tool that ensures your data is in the correct format for analysis. By using the .astype() method, you can seamlessly convert columns to the desired data types, making your calculations and data manipulations more efficient and error-free. Always remember to handle missing values appropriately to avoid conversion issues, and keep your dataset clean and ready for analysis.