pandas.DataFrame.quantile
- DataFrame. quantile ( q=0.5, axis=0, numeric_only=True, interpolation='linear' ) [source]
-
Return values at the given quantile over requested axis.
- Parameters
-
- q :float or array-like, default 0.5 (50% quantile)
-
Value between 0 <= q <= 1, the quantile(s) to compute.
- axis :{0, 1, ‘index’, ‘columns’}, default 0
-
Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.
- numeric_only :bool, default True
-
If False, the quantile of datetime and timedelta data will be computed as well.
- interpolation :{‘linear’, ‘lower’, ‘higher’, ‘midpoint’, ‘nearest’}
-
This optional parameter specifies the interpolation method to use, when the desired quantile lies between two data points i and j:
linear: i + (j - i) * fraction, where fraction is the fractional part of the index surrounded by i and j.
lower: i.
higher: j.
nearest: i or j whichever is nearest.
midpoint: (i + j) / 2.
- Returns
-
- Series or DataFrame
-
-
If
q
is an array, a DataFrame will be returned where the -
index is
q
, the columns are the columns of self, and the values are the quantiles. -
If
q
is a float, a Series will be returned where the -
index is the columns of self and the values are the quantiles.
-
If
See also
-
core.window.Rolling.quantile
-
Rolling quantile.
-
numpy.percentile
-
Numpy function to compute the percentile.
Examples
>>> 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 Name: 0.1, 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.
https://pandas.pydata.org/pandas-docs/version/1.4.0/reference/api/pandas.DataFrame.quantile.html