Listado de la etiqueta: Check


For this one, we will explore how to get the data type of a specific column in a Pandas DataFrame.

Sample

Let us start by creating a sample DataFrame:

# import pandas
import pandas as pd
df = pd.DataFrame({
    ‘salary’: [120000, 100000, 90000, 110000, 120000, 100000, 56000],
    ‘department’: [‘game developer’, ‘database developer’, ‘front-end developer’, ‘full-stack developer’, ‘database developer’, ‘security researcher’, ‘cloud-engineer’],
    ‘rating’: [4.3, 4.4, 4.3, 3.3, 4.3, 5.0, 4.4]},
    index=[‘Alice’, ‘Michael’, ‘Joshua’, ‘Patricia’, ‘Peter’, ‘Jeff’, ‘Ruth’])
print(df)

The above should create a DataFrame with sample data as shown:

Pandas dtype Attribute

The most straightforward way to get the column’s data type in Pandas is to use the dtypes attribute.

The syntax is as shown:

The attribute returns each column and its corresponding data type.

An example is as shown:

The above should return the columns and their data types as shown:

salary          int64
department     object
rating        float64

If you want to get the data type of a specific column, you can pass the column name as an index as shown:

This should return the data type of the salary column as shown:

Pandas Column Info

Pandas also provide us with the info() method. It allows us to get detailed information about the columns within a Pandas DataFrame.

The syntax is as shown:

DataFrame.info(verbose=None, buf=None, max_cols=None, memory_usage=None, show_counts=None, null_counts=None)

It allows you to fetch the name of the columns, data type, number of non-null elements, etc.

An example is as shown:

This should return:

The above shows detailed information about the columns in the DataFrame, including the data type.

Conclusion

This tutorial covers two methods you can use to fetch the data type of a column in a Pandas DataFrame.

Thanks for reading!!



Source link