How to describe the Dataframe in python?

Data Science, AI and ML
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Pandas DataFrame.describe()

The describe() method is used for calculating some statistical data like percentile, mean and std of the numerical values of the Series or DataFrame. It analyzes both numeric and object series and also the DataFrame column sets of mixed data types.

Syntax

DataFrame.describe(percentiles=None, include=None, exclude=None)

Parameters

  • percentile: It is an optional parameter which is a list like data type of numbers that should fall between 0 and 1. Its default value is [.25, .5, .75], which returns the 25th, 50th, and 75th percentiles.
  • include: It is also an optional parameter that includes the list of the data types while describing the DataFrame. Its default value is None.
  • exclude: It is also an optional parameter that exclude the list of data types while describing DataFrame. Its default value is None.

Returns

It returns the statistical summary of the Series and DataFrame.

Example1

import pandas as pd  
import numpy as np  
a1 = pd.Series([1, 2, 3])  
a1.describe()

Output

count     3.0
mean      2.0
std       1.0
min       1.0
25%       1.5
50%       2.0
75%       2.5
max       3.0
dtype: float64

Example2

import pandas as pd  
import numpy as np  
a1 = pd.Series(['p', 'q', 'q', 'r'])  
a1.describe()

Output

count      4
unique     3
top        q
freq       2
dtype: object

Example3

import pandas as pd  
import numpy as np  
a1 = pd.Series([1, 2, 3])  
a1.describe()  
a1 = pd.Series(['p', 'q', 'q', 'r'])  
a1.describe()  
info = pd.DataFrame({'categorical': pd.Categorical(['s','t','u']),  
'numeric': [1, 2, 3],  
'object': ['p', 'q', 'r']  
 })  
info.describe(include=[np.number])  
info.describe(include=[np.object])  
info.describe(include=['category'])

Output

	categorical
count	3
unique	3
top     u
freq	1

Example4

import pandas as pd  
import numpy as np  
a1 = pd.Series([1, 2, 3])  
a1.describe()  
a1 = pd.Series(['p', 'q', 'q', 'r'])  
a1.describe()  
info = pd.DataFrame({'categorical': pd.Categorical(['s','t','u']),  
'numeric': [1, 2, 3],  
'object': ['p', 'q', 'r']  
 })  
info.describe()  
info.describe(include='all')  
info.numeric.describe()  
info.describe(include=[np.number])  
info.describe(include=[np.object])  
info.describe(include=['category'])  
info.describe(exclude=[np.number])  
info.describe(exclude=[np.object])

Output

  categorical  numeric
count     3         3.0
unique    3         NaN
top       u         NaN
freq      1         NaN
mean      NaN       2.0
std       NaN       1.0
min       NaN       1.0
25%       NaN       1.5
50%       NaN       2.0
75%       NaN       2.5
max       NaN       3.0