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.
Index.value_counts() function returns object 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: Index.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)
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
Index.value_counts() function to count the number of unique values in the given Index.
Let’s find the count of all unique values in the index.
The function has returned the count of all unique values in the given index. Notice the object returned by the function contains the occurrence of the values in descending order.
Example #2: Use
Index.value_counts() function to find the count of all unique values in the given index.
Let’s count the occurrence of all the unique values in the Index.
The function has returned the count of all unique values in the index.
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