pandas.Index.value_counts#

Index.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:

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