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 pdimport pandas as pd# Create the dataframedf = 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 dataframedf |
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 columnsdf.dtypes |
Output:
Example 2:
Python3
# print datatype of particular columndf.Cust_No.dtypes |
Output:
dtype('int64')
Example 3:
Python3
# Checking the Data Type of a Particular Columndf['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 int64df.select_dtypes(include = 'int64') |
Output:
Example 2:
Python3
# Returns columns excluding int64df.select_dtypes(exclude = 'int64') |
Output :
Example 3 :
Python3
# Print an empty list as there is# no column of bool typedf.select_dtypes(include = "bool") |
Output :
