DataFrame.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')
[source]
Return values at the given quantile over requested axis, a la numpy.percentile.
Parameters: |
q : float or array-like, default 0.5 (50% quantile) 0 <= q <= 1, the quantile(s) to compute axis : {0, 1, ‘index’, ‘columns’} (default 0) 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise numeric_only : boolean, default True If False, the quantile of datetime and timedelta data will be computed as well interpolation : {‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’} New in version 0.18.0. This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points
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Returns: |
quantiles : Series or DataFrame
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See also
>>> df = pd.DataFrame(np.array([[1, 1], [2, 10], [3, 100], [4, 100]]), columns=['a', 'b']) >>> df.quantile(.1) a 1.3 b 3.7 dtype: float64 >>> df.quantile([.1, .5]) a b 0.1 1.3 3.7 0.5 2.5 55.0
Specifying numeric_only=False
will also compute the quantile of datetime and timedelta data.
>>> df = pd.DataFrame({'A': [1, 2], 'B': [pd.Timestamp('2010'), pd.Timestamp('2011')], 'C': [pd.Timedelta('1 days'), pd.Timedelta('2 days')]}) >>> df.quantile(0.5, numeric_only=False) A 1.5 B 2010-07-02 12:00:00 C 1 days 12:00:00 Name: 0.5, dtype: object
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Licensed under the 3-clause BSD License.
http://pandas.pydata.org/pandas-docs/version/0.23.4/generated/pandas.DataFrame.quantile.html