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 SeriesReturns : 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.
