Python | Pandas DataFrame.ftypes (original) (raw)

Last Updated : 20 Feb, 2019

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.ftypes** attribute return the ftypes (indication of sparse/dense and dtype) in DataFrame. It returns a Series with the data type of each column.

Syntax: DataFrame.ftypesParameter : NoneReturns : series

Example #1: Use DataFrame.ftypes attribute to check if the columns are sparse or dense in the given Dataframe.

Python3 `

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)

Output : Now we will use DataFrame.ftypes attribute to check the ftype of the columns in the given dataframe.

Python3 1== `

check if the column are

dense or sparse

result = df.ftypes

Print the result

print(result)

Output : As we can see in the output, the DataFrame.ftypes attribute has successfully returned a series containing the ftypes of each column in the given dataframe.Example #2: Use DataFrame.ftypes attribute to check if the columns are sparse or dense in the given Dataframe.