Python | Pandas DataFrame.dtypes

Pandas DataFrame is a two-dimensional size-mutable, potentially heterogeneous tabular data structure with labeled axes (rows and columns). Arithmetic operations align on both row and column labels. It can be thought of as a dict-like container for Series objects. This is the primary data structure of the Pandas.

Pandas DataFrame.dtypes attribute return the dtypes in the DataFrame. It returns a Series with the data type of each column.

Syntax: DataFrame.dtypes

Parameter : None

Returns : dtype of each column

Example #1: Use DataFrame.dtypes attribute to find out the data type (dtype) of each column in the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the DataFrame
df = pd.DataFrame({'Weight':[45, 88, 56, 15, 71],
                   'Name':['Sam', 'Andrea', 'Alex', 'Robin', 'Kia'],
                   'Age':[14, 25, 55, 8, 21]})
  
# Create the index
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
  
# Set the index
df.index = index_
  
# Print the DataFrame
print(df)

chevron_right


Output :

Now we will use DataFrame.dtypes attribute to find out the data type of each column in the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# return the dtype of each column
result = df.dtypes
  
# Print the result
print(result)

chevron_right


Output :

As we can see in the output, the DataFrame.dtypes attribute has successfully returned the data types of each column in the given dataframe.
 
Example #2: Use DataFrame.dtypes attribute to find out the data type (dtype) of each column in the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# importing pandas as pd
import pandas as pd
  
# Creating the DataFrame
df = pd.DataFrame({"A":[12, 4, 5, None, 1], 
                   "B":[7, 2, 54, 3, None], 
                   "C":[20, 16, 11, 3, 8], 
                   "D":[14, 3, None, 2, 6]}) 
  
# Create the index
index_ = ['Row_1', 'Row_2', 'Row_3', 'Row_4', 'Row_5']
  
# Set the index
df.index = index_
  
# Print the DataFrame
print(df)

chevron_right


Output :

Now we will use DataFrame.dtypes attribute to find out the data type of each column in the given dataframe.

filter_none

edit
close

play_arrow

link
brightness_4
code

# return the dtype of each column
result = df.dtypes
  
# Print the result
print(result)

chevron_right


Output :

As we can see in the output, the DataFrame.dtypes attribute has successfully returned the data types of each column in the given dataframe.



My Personal Notes arrow_drop_up

Check out this Author's contributed articles.

If you like GeeksforGeeks and would like to contribute, you can also write an article using contribute.geeksforgeeks.org or mail your article to contribute@geeksforgeeks.org. See your article appearing on the GeeksforGeeks main page and help other Geeks.

Please Improve this article if you find anything incorrect by clicking on the "Improve Article" button below.