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 the first element is the most frequently-occurring element. Excludes NA values by default.

Parameters: normalize : boolean, default False If True then the object returned will contain the relative frequencies of the unique values. sort : boolean, default True Sort by values. ascending : boolean, default False Sort in ascending order. bins : integer, optional Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data. dropna : boolean, default True Don’t include counts of NaN. counts : Series

Series.count
Number of non-NA elements in a Series.
DataFrame.count
Number of non-NA elements in a DataFrame.

Examples

>>> index = pd.Index([3, 1, 2, 3, 4, np.nan])
>>> index.value_counts()
3.0    2
4.0    1
2.0    1
1.0    1
dtype: int64


With normalize set to True, returns the relative frequency by dividing all values by the sum of values.

>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts(normalize=True)
3.0    0.4
4.0    0.2
2.0    0.2
1.0    0.2
dtype: float64


bins

Bins can be useful for going from a continuous variable to a categorical variable; instead of counting unique apparitions of values, divide the index in the specified number of half-open bins.

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


dropna

With dropna set to False we can also see NaN index values.

>>> s.value_counts(dropna=False)
3.0    2
NaN    1
4.0    1
2.0    1
1.0    1
dtype: int64

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