How to Check the Data Type in a DataFrame

In this tutorial, you will learn how to check the data type of a column in a DataFrame.

TLDR solution

# shows data type for all columns
df.dtypes

# shows data type for one column
df['column'].dtypes

Step-by-Step Example

Suppose, you have the following DataFrame:

import pandas as pd

data = {'date': ['01/15/2021', '02/15/2021', '03/15/2021'],
        'fish': ['salmon', 'pufferfish', 'shark'],
        'count': [1000, 100, 10.0]
        }

df = pd.DataFrame(data)

print(df)

         date        fish  count
0  01/15/2021      salmon   1000
1  02/15/2021  pufferfish    100
2  03/15/2021       shark     10

You can use the dtypes attribute to check the data type of each column in a DataFrame:

print(df.dtypes)

date      object
fish      object
count    float64
dtype: object

Note that the date column is currently of type object/string, and count of type float.

Bonus: Change the Data Type of a Column

Let's convert the type of the date column to datetime and that of count to integer:

df['date'] = pd.to_datetime(df['date'], format='%m/%d/%Y')

df['count'] = df['count'].astype('int')

# check the data type for a subset of columns
print(df[['date', 'count']].dtypes)

date     datetime64[ns]
count             int64
dtype: object

That's it! You just learned how to check the data types of a DataFrame.