pandas.core.window.expanding.Expanding.quantile#

Expanding.quantile(q, interpolation='linear', numeric_only=False)[source]#

Calculate the expanding quantile.

Parameters:

quantilefloat

Quantile to compute. 0 <= quantile <= 1.

Deprecated since version 2.1.0: This will be renamed to ‘q’ in a future version.

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.

numeric_onlybool, default False

Include only float, int, boolean columns.

New in version 1.5.0.

Returns:

Series or DataFrame: Return type is the same as the original object with np.float64 dtype.

See also

pandas.Series.expanding: Calling expanding with Series data.
pandas.DataFrame.expanding: Calling expanding with DataFrames.
pandas.Series.quantile: Aggregating quantile for Series.
pandas.DataFrame.quantile: Aggregating quantile for DataFrame.

Examples

>>> ser = pd.Series([1, 2, 3, 4, 5, 6], index=['a', 'b', 'c', 'd', 'e', 'f'])
>>> ser.expanding(min_periods=4).quantile(.25)
a     NaN
b     NaN
c     NaN
d    1.75
e    2.00
f    2.25
dtype: float64