modin.pandas.Series.value_counts¶

Series.value_counts(normalize: bool = False, sort: bool = True, ascending: bool = False, bins: int | None = None, dropna: bool = 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 (bool, default False) – If True then the object returned will contain the relative frequencies of the unique values. Being different from native pandas, Snowpark pandas will return a Series with decimal.Decimal values.

• sort (bool, default True) – Sort by frequencies when True. Preserve the order of the data when False. When there is a tie between counts, the order is still deterministic where the order in the original data is preserved, but may be different from the result from native pandas. Snowpark pandas will always respect the order of insertion during ties. Native pandas is not deterministic when sort=True since the original order/order of insertion is based on the Python hashmap which may produce different results on different versions. Refer to: https://github.com/pandas-dev/pandas/issues/15833

• ascending (bool, default False) – Whether to sort the frequencies in ascending order or descending order.

• bins (int, optional) – Rather than count values, group them into half-open bins, a convenience for pd.cut, only works with numeric data. This argument is not supported yet.

• dropna (bool, default True) – Don’t include counts of NaN.

Return type:

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

>>> s = pd.Series([3, 1, 2, 3, 4, np.nan])
>>> s.value_counts()
3.0    2
1.0    1
2.0    1
4.0    1
Name: count, dtype: int64

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

>>> 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

Copy

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

Copy