How to Check the Data Type in Pandas DataFrame?
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.
Consider an 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.
#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:
Method 1: Using pandas.DataFrame.dtypes
For user to check DataType of particular Dataset or particular column from dataset can use this method. This method return a list of data types for each column or also return 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')
Method 2: Using pandas.DataFrame.select_dtypes
Unlike checking Data Type user can alternatively perform check to get the data for particular datatype if it is existing otherwise get an empty dataset in return. This method return 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:
Example 2:
- Python3
# Returns columns excluding int64 df.select_dtypes(exclude = 'int64' ) |
Output :
Example 3 :
- Python3
# Print an empty list as there is # no column of bool type df.select_dtypes(include = "bool" ) |
Output :
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Last Updated on October 19, 2021 by admin