pandas.Series.value_counts — pandas 0.24.0rc1 documentation (original) (raw)
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. |
|---|---|
| Returns: | counts : Series |
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