pandas.DataFrame.value_counts#
- DataFrame.value_counts(subset=None, normalize=False, sort=True, ascending=False, dropna=True)[source]#
Return a Series containing counts of unique rows in the DataFrame.
New in version 1.1.0.
- Parameters
- subsetlist-like, optional
Columns to use when counting unique combinations.
- normalizebool, default False
Return proportions rather than frequencies.
- sortbool, default True
Sort by frequencies.
- ascendingbool, default False
Sort in ascending order.
- dropnabool, default True
Don’t include counts of rows that contain NA values.
New in version 1.3.0.
- Returns
- Series
See also
Series.value_countsEquivalent method on Series.
Notes
The returned Series will have a MultiIndex with one level per input column. By default, rows that contain any NA values are omitted from the result. By default, the resulting Series will be in descending order so that the first element is the most frequently-occurring row.
Examples
>>> df = pd.DataFrame({'num_legs': [2, 4, 4, 6], ... 'num_wings': [2, 0, 0, 0]}, ... index=['falcon', 'dog', 'cat', 'ant']) >>> df num_legs num_wings falcon 2 2 dog 4 0 cat 4 0 ant 6 0
>>> df.value_counts() num_legs num_wings 4 0 2 2 2 1 6 0 1 dtype: int64
>>> df.value_counts(sort=False) num_legs num_wings 2 2 1 4 0 2 6 0 1 dtype: int64
>>> df.value_counts(ascending=True) num_legs num_wings 2 2 1 6 0 1 4 0 2 dtype: int64
>>> df.value_counts(normalize=True) num_legs num_wings 4 0 0.50 2 2 0.25 6 0 0.25 dtype: float64
With dropna set to False we can also count rows with NA values.
>>> df = pd.DataFrame({'first_name': ['John', 'Anne', 'John', 'Beth'], ... 'middle_name': ['Smith', pd.NA, pd.NA, 'Louise']}) >>> df first_name middle_name 0 John Smith 1 Anne <NA> 2 John <NA> 3 Beth Louise
>>> df.value_counts() first_name middle_name Beth Louise 1 John Smith 1 dtype: int64
>>> df.value_counts(dropna=False) first_name middle_name Anne NaN 1 Beth Louise 1 John Smith 1 NaN 1 dtype: int64