pandas.api.typing.Expanding.quantile#

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

Calculate the expanding quantile.

At each step the specified quantile is computed from all prior observations, using the given interpolation method for values between data points.

Parameters:
qfloat

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

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.

Returns:
Series or DataFrame

Return type is the same as the original object with np.float64 dtype.

See also

Series.expanding

Calling expanding with Series data.

DataFrame.expanding

Calling expanding with DataFrames.

Series.quantile

Aggregating quantile for Series.

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(0.25)
a     NaN
b     NaN
c     NaN
d    1.75
e    2.00
f    2.25
dtype: float64
On this page