pandas.Series.value_counts#

Series.value_counts(normalize=False,sort=True,ascending=False,bins=None,dropna=True)[source]#

Return a Series containing counts of unique values.

The resulting object will be in descending order so that thefirst element is the most frequently-occurring element.Excludes NA values by default.

Parameters:

normalizebool, default False

If True then the object returned will contain the relativefrequencies of the unique values.

sortbool, default True

Sort by frequencies when True. Preserve the order of the data when False.

ascendingbool, default False

Sort in ascending order.

binsint, optional

Rather than count values, group them into half-open bins,a convenience forpd.cut, only works with numeric data.

dropnabool, default True

Don’t include counts of NaN.

Returns:

Series

See also

Series.count

Number of non-NA elements in a Series.

DataFrame.count

Number of non-NA elements in a DataFrame.

DataFrame.value_counts

Equivalent method on DataFrames.

Examples

>>>index=pd.Index([3,1,2,3,4,np.nan])>>>index.value_counts()3.0    21.0    12.0    14.0    1Name: count, dtype: int64

Withnormalize set toTrue, returns the relative frequency bydividing all values by the sum of values.

>>>s=pd.Series([3,1,2,3,4,np.nan])>>>s.value_counts(normalize=True)3.0    0.41.0    0.22.0    0.24.0    0.2Name: proportion, dtype: float64

bins

Bins can be useful for going from a continuous variable to acategorical variable; instead of counting uniqueapparitions of values, divide the index in the specifiednumber of half-open bins.

>>>s.value_counts(bins=3)(0.996, 2.0]    2(2.0, 3.0]      2(3.0, 4.0]      1Name: count, dtype: int64

dropna

Withdropna set toFalse we can also see NaN index values.

>>>s.value_counts(dropna=False)3.0    21.0    12.0    14.0    1NaN    1Name: count, dtype: int64