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Python | Pandas Series.value_counts()

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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 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.value_counts() function return a Series containing counts of unique values. The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.
Syntax: Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True) Parameter : normalize : If True then the object returned will contain the relative frequencies of the unique values. sort : Sort by values. ascending : Sort in ascending order. bins : Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data. dropna : Don’t include counts of NaN. Returns : counts : Series
Example #1: Use Series.value_counts() function to find the unique value counts of each element in the given Series object.
# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series(['New York', 'Chicago', 'Toronto', 'Lisbon', 'Rio', 'Chicago', 'Lisbon'])
  
# Print the series
print(sr)

                    
Output : Now we will use Series.value_counts() function to find the values counts of each unique value in the given Series object.
# find the value counts
sr.value_counts()

                    
Output : As we can see in the output, the Series.value_counts() function has returned the value counts of each unique value in the given Series object. Example #2: Use Series.value_counts() function to find the unique value counts of each element in the given Series object.
# importing pandas as pd
import pandas as pd
  
# Creating the Series
sr = pd.Series([100, 214, 325, 88, None, 325, None, 325, 100])
  
# Print the series
print(sr)

                    
Output : Now we will use Series.value_counts() function to find the values counts of each unique value in the given Series object.
# find the value counts
sr.value_counts()

                    
Output : As we can see in the output, the Series.value_counts() function has returned the value counts of each unique value in the given Series object.

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Last Updated : 29 Jan, 2019
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