Python | Pandas Index.value_counts ()

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`Index.value_counts() ` Pandas `Index.value_counts() ` returns an object containing the number of unique values. The resulting object will be in descending order, so the first item is the most common item. Excludes NA values ‚Äã‚Äãby default.

Syntax: Index.value_counts (normalize = False, sort = True, ascending = False, bins = None, dropna = True)

Parameters:
normalize: If True then the object returned will contain the relative frequencies of the unique values.
sort: Sort by values ‚Äã‚Äã
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 ` Index.value_counts () ` to count the number of unique values ‚Äã‚Äãin a given index.

 ` # import pandas as pd `` import pandas as pd # Create index idx = pd.Index ([ ’Harry’ , ’Mike’ , ’ Arther’ , ’Nick’ , ’ Harry’ , ’Arther’ ], name = ’Student’ ) # Print index print (idx) `

Output:

Let’s find the count of all unique values ‚Äã‚Äãin the index.

 ` # find the number of unique values ‚Äã‚Äãin the index ` ` idx.value_counts () `

Output:

The function returned a counter of all unique values ‚Äã‚Äãin the given index. Note that the object returned by the function contains occurrences of values ‚Äã‚Äãin descending order.

Example # 2: Use ` Index.value_counts () ` to find the count all unique values ‚Äã‚Äãin this index.

 ` # import pandas as pd ` ` import ` ` pandas as pd ` ` # Create Index ` ` idx ` ` = ` ` pd .Index ([` ` 21 ` `, ` ` 10 ` `, ` ` 30 ` `, ` ` 40 ` `, ` ` 50 ` `, ` ` 10 ` `, ` ` 50 ` `]) ` ` # Print index ` ` print ` ` (idx) `

Output:

Let’s count the occurrence of all unique values ‚Äã‚Äãin the index.

 ` # to count all ` ` # unique values in the index. ` ` idx.value_counts () `

Output:

The function returned a counter of all unique values in the index.

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