value_counts(self, normalize: 'bool' = False, sort: 'bool' = True, ascending: 'bool' = False, bins=None, dropna: 'bool' = True)
The resulting object will be in descending order so that the first element is the most frequently-occurring element. Excludes NA values by default.
If True then the object returned will contain the relative frequencies of the unique values.
Sort by frequencies.
Sort in ascending order.
Rather than count values, group them into half-open bins, a convenience for pd.cut
, only works with numeric data.
Don't include counts of NaN.
Return a Series containing counts of unique values.
DataFrame.count
Number of non-NA elements in a DataFrame.
DataFrame.value_counts
Equivalent method on DataFrames.
Series.count
Number of non-NA elements in a Series.
>>> 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 dtype: int64
With :None:None:`normalize`
set to :None:None:`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 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.
This example is valid syntax, but we were not able to check execution>>> s.value_counts(bins=3) (0.996, 2.0] 2 (2.0, 3.0] 2 (3.0, 4.0] 1 dtype: int64
dropna
With :None:None:`dropna`
set to :None:None:`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 dtype: int64See :
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