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, 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 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: quantiles : Series or DataFrame
- If
q
is an array, a DataFrame will be returned where the index isq
, 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.
See also
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 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