DataFrameGroupBy.quantile()

DataFrameGroupBy.quantile(q=0.5, axis=0, numeric_only=True, interpolation='linear')

Return values at the given quantile over requested axis, a la numpy.percentile.

Parameters:

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 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 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.

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

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 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.

Examples

>>> df = 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

Links:

http://pandas.pydata.org/pandas-docs/version/0.19.2/generated/pandas.core.groupby.DataFrameGroupBy.quantile.html

doc_Pandas

2017-01-12 04:47:01

Comments