pandas.DataFrame.quantile — pandas 3.0.0rc0+31.g944c527c0a documentation (original) (raw)

DataFrame.quantile(q=0.5, axis=0, numeric_only=False, interpolation='linear', method='single')[source]#

Return values at the given quantile over requested axis.

Parameters:

qfloat or array-like, default 0.5 (50% quantile)

Value between 0 <= q <= 1, the quantile(s) to compute.

axis{0 or ‘index’, 1 or ‘columns’}, default 0

Equals 0 or ‘index’ for row-wise, 1 or ‘columns’ for column-wise.

numeric_onlybool, default False

Include only float, int or boolean data.

Changed in version 2.0.0: The default value of numeric_only is now False.

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.

method{‘single’, ‘table’}, default ‘single’

Whether to compute quantiles per-column (‘single’) or over all columns (‘table’). When ‘table’, the only allowed interpolation methods are ‘nearest’, ‘lower’, and ‘higher’.

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.

See also

core.window.rolling.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(0.1) a 1.3 b 3.7 Name: 0.1, dtype: float64 df.quantile([0.1, 0.5]) a b 0.1 1.3 3.7 0.5 2.5 55.0

Specifying method=’table’ will compute the quantile over all columns.

df.quantile(0.1, method="table", interpolation="nearest") a 1 b 1 Name: 0.1, dtype: int64 df.quantile([0.1, 0.5], method="table", interpolation="nearest") a b 0.1 1 1 0.5 3 100

Specifying numeric_only=False will compute the quantiles for all columns.

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