Python | Pandas DataFrame.ftypes
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.ftypes
Parameter : None
Returns : series
Example #1: Use DataFrame.ftypes
attribute to check if the columns are sparse or dense in the given Dataframe.
import pandas as pd
df = pd.DataFrame({ 'Weight' :[ 45 , 88 , 56 , 15 , 71 ],
'Name' :[ 'Sam' , 'Andrea' , 'Alex' , 'Robin' , 'Kia' ],
'Age' :[ 14 , 25 , 55 , 8 , 21 ]})
index_ = [ 'Row_1' , 'Row_2' , 'Row_3' , 'Row_4' , 'Row_5' ]
df.index = index_
print (df)
|
Output :
Now we will use DataFrame.ftypes
attribute to check the ftype of the columns in the given dataframe.
result = df.ftypes
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.
import pandas as pd
arr = [ 100 , 35 , 125 , 85 , 35 ]
df = pd.SparseDataFrame(arr)
print (df)
|
Output :
Now we will use DataFrame.ftypes
attribute to check the ftype of the columns in the given dataframe.
result = df.ftypes
print (result)
|
Output :
As we can see in the output, the DataFrame.ftypes
attribute has successfully returned the ftype of the given dataframe.
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