How to Check Data Types in Pandas

You can use the function of dtype() to check the data type of columns for Pandas dataframes. You can either check a single column or all the columns. The following is the sample code.

Check Data Type for All Columns in Pandas

import pandas as pd
car_data = {'Brand': ['Tesla', 'Tesla','Tesla','Ford'], 
     'Location': ['CA', 'CA','NY','MA'],
    'Year':[2019,2018,2020,2019],
    'DateTime':[pd.Timestamp('20190310'), pd.Timestamp('20180311'), pd.Timestamp('20200101'), pd.Timestamp('20190324')]}
car_data=pd.DataFrame(data=car_data)
print(car_data)

   Brand Location  Year   DateTime
0  Tesla       CA  2019 2019-03-10
1  Tesla       CA  2018 2018-03-11
2  Tesla       NY  2020 2020-01-01
3   Ford       MA  2019 2019-03-24

# Check data types of all columns
car_data.dtypes

Brand       object
Location    object
Year         int64
dtype: object

Check Data Types of a Specific Column

# Check Data Types of a Specific Column
car_data.Year.dtypes

dtype('int64')

# Check Data Types of a Specific Column
car_data.DateTime.dtypes

dtype('<M8[ns]')

Note that, datetime64[ns] is a general dtype, while <M8[ns] is a specific dtype.

Additional Note

Note that, in Pandas, it uses object to indicate that it is string. The following is the quote from Stackoverflow about this topic. This is the link for the disucssion on Stackoverflow.

Since release 0.11.1 there is an auto-conversion from dtype=str to dtype=object whenever it is seen, so it does not matter what you use, although I would advise avoiding str altogether and just use dtype=object.