Python | Pandas dataframe.mad()

Python | Pandas dataframe.kurt()

Last Updated : 19 Nov, 2018

Python is a great language for doing data analysis, primarily because of the fantastic ecosystem of data-centric python packages. Pandas is one of those packages and makes importing and analyzing data much easier.

Pandas dataframe.kurt() function return unbiased kurtosis over requested axis using Fisher’s definition of kurtosis (kurtosis of normal == 0.0). Normalized by N-1.

Syntax: DataFrame.kurt(axis=None, skipna=None, level=None, numeric_only=None, **kwargs)

Parameters :
axis : {index (0), columns (1)}
skipna : Exclude NA/null values when computing the result
level : If the axis is a MultiIndex (hierarchical), count along a particular level, collapsing into a Series
numeric_only : Include only float, int, boolean columns. If None, will attempt to use everything, then use only numeric data. Not implemented for Series.

Returns : kurt : Series or DataFrame (if level specified)

Example #1: Use kurt() function to find the kurtosis over the index axis.

# importing pandas as pd 
import pandas as pd 
  
# Creating the dataframe  
df = pd.DataFrame({"A":[12, 4, 5, 44, 1], 
                   "B":[5, 2, 54, 3, 2], 
                   "C":[20, 16, 7, 3, 8], 
                   "D":[14, 3, 17, 2, 6]}) 
  
# Print the dataframe 
df 

Let’s use the dataframe.kurt() function to find the kurtosis.

# find the kurtosis over the index axis 
df.kurt(axis = 0) 

Output :

Example #2: Use kurt() function to find the kurtosis of dataframe which is having some Na values in it. Find kurtosis over the index axis.

# 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]}) 
  
# to find the kurtosis 
# skip the Na values when finding the kurtosis 
df.kurt(axis = 0, skipna = True) 

Output :

Python | Pandas dataframe.mad()

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