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Python | Pandas Series.describe()

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  • Last Updated : 15 Feb, 2019
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Pandas series is a One-dimensional ndarray with axis labels. The labels need not be unique but must be a hashable type. The object supports both integer- and label-based indexing and provides a host of methods for performing operations involving the index.

Pandas Series.describe() function generate a descriptive statistics that summarize the central tendency, dispersion and shape of a dataset’s distribution for the given series object. All the calculations are performed by excluding NaN values.

Syntax: Series.describe(percentiles=None, include=None, exclude=None)

Parameter :
percentiles : The percentiles to include in the output.
include : A white list of data types to include in the result. Ignored for Series.
exclude : A black list of data types to omit from the result. Ignored for Series

Returns : Summary statistics of the Series

Example #1: Use Series.describe() function to find the summary statistics of the given series object.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([80, 25, 3, 25, 24, 6])
  
# Create the Index
index_ = ['Coca Cola', 'Sprite', 'Coke', 'Fanta', 'Dew', 'ThumbsUp']
  
# set the index
sr.index = index_
  
# Print the series
print(sr)


Output :

Now we will use Series.describe() function to find the summary statistics of the underlying data in the given series object.




# find summary statistics of the underlying 
# data in the given series object.
result = sr.describe()
  
# Print the result
print(result)


Output :

As we can see in the output, the Series.describe() function has successfully returned the summary statistics of the given series object.
 
Example #2 : Use Series.describe() function to find the summary statistics of the underlying data in the given series object. The given series object contains some missing values.




# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([100, None, None, 18, 65, None, 32, 10, 5, 24, None])
  
# Create the Index
index_ = pd.date_range('2010-10-09', periods = 11, freq ='M')
  
# set the index
sr.index = index_
  
# Print the series
print(sr)


Output :

Now we will use Series.describe() function to find the summary statistics of the underlying data in the given series object.




# find summary statistics of the underlying 
# data in the given series object.
result = sr.describe()
  
# Print the result
print(result)


Output :

As we can see in the output, the Series.describe() function has successfully returned the summary statistics of the given series object. NaN values has been ignored while calculating these statistical values.


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