pandas.Series.value_counts

Series.value_counts(normalize=False, sort=True, ascending=False, bins=None, dropna=True)

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:

normalizebool, default False

If True then the object returned will contain the relative frequencies 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 for pd.cut, only works with numeric data.

dropnabool, default True

Don’t include counts of NaN.

Returns:

Series

Series containing counts of unique values.

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    2
1.0    1
2.0    1
4.0    1
Name: count, 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
1.0    0.2
2.0    0.2
4.0    0.2
Name: proportion, 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)
(0.996, 2.0]    2
(2.0, 3.0]      2
(3.0, 4.0]      1
Name: count, dtype: int64

dropna

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

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

Categorical Dtypes

Rows with categorical type will be counted as one group if they have same categories and order. In the example below, even though a, c, and d all have the same data types of category, only c and d will be counted as one group since a doesn’t have the same categories.

>>> df = pd.DataFrame({"a": [1], "b": ["2"], "c": [3], "d": [3]})
>>> df = df.astype({"a": "category", "c": "category", "d": "category"})
>>> df
   a  b  c  d
0  1  2  3  3

>>> df.dtypes
a    category
b      object
c    category
d    category
dtype: object

>>> df.dtypes.value_counts()
category    2
category    1
object      1
Name: count, dtype: int64