How to Check the Data Type in Pandas DataFrame?

Last Updated : 08 Sep, 2022

Pandas DataFrame is a Two-dimensional data structure of mutable size and heterogeneous tabular data. There are different Built-in data types available in Python. Two methods used to check the datatypes are pandas.DataFrame.dtypes and pandas.DataFrame.select_dtypes.

Creating a Dataframe to Check DataType in Pandas DataFrame

Consider a dataset of a shopping store having data about Customer Serial Number, Customer Name, Product ID of the purchased item, Product Cost, and Date of Purchase.

Python3

#importing pandas as pd import pandas as pd  # Create the dataframe  df = pd.DataFrame({ 'Cust_No': [1,2,3], 'Cust_Name': ['Alex', 'Bob', 'Sophie'], 'Product_id': [12458,48484,11311], 'Product_cost': [65.25, 25.95, 100.99], 'Purchase_Date': [pd.Timestamp('20180917'),                   pd.Timestamp('20190910'),                   pd.Timestamp('20200610')] })  # Print the dataframe  df

Output:

Check the Data Type in Pandas using pandas.DataFrame.dtypes

For users to check the DataType of a particular Dataset or particular column from the dataset can use this method. This method returns a list of data types for each column or also returns just a data type of a particular column

Example 1:

Python3

# Print a list datatypes of all columns df.dtypes

Output:

Example 2:

Python3

# print datatype of particular column df.Cust_No.dtypes

Output:

dtype('int64')

Example 3:

Python3

# Checking the Data Type of a Particular Column df['Product_cost'].dtypes

Output:

dtype('float64')

Check the Data Type in Pandas using pandas.DataFrame.select_dtypes

Unlike checking Data Type user can alternatively perform a check to get the data for a particular Datatype if it is existing otherwise get an empty dataset in return. This method returns a subset of the DataFrame’s columns based on the column dtypes.

Example 1:

Python3

# Returns Two column of int64 df.select_dtypes(include = 'int64')

Output: