Expanding - Nixtla

`expanding_mean`

expanding_mean(x, skipna=False)

Compute the expanding_mean of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`skipna`	`bool`	If True, exclude NaN values from calculations. When False (default), any NaN value causes the result to be NaN, maintaining backwards compatibility. When True, NaN values are ignored (matching pandas default behavior).	required

Returns:

Type	Description
np.ndarray: Array with the expanding statistic

Examples:

>>> import numpy as np
>>> x = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
>>> # Default behavior: NaN propagates
>>> expanding_mean(x)
array([1., 1.5, nan, nan, nan])
>>> # With skipna=True: NaN values are excluded
>>> expanding_mean(x, skipna=True)
array([1., 1.5, 1.5, 2.33..., 3.0])

`expanding_std`

expanding_std(x, skipna=False)

Compute the expanding_std of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`skipna`	`bool`	If True, exclude NaN values from calculations. When False (default), any NaN value causes the result to be NaN, maintaining backwards compatibility. When True, NaN values are ignored (matching pandas default behavior).	required

Returns:

Type	Description
np.ndarray: Array with the expanding statistic

Examples:

>>> import numpy as np
>>> x = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
>>> # Default behavior: NaN propagates
>>> expanding_std(x)
array([1., 1.5, nan, nan, nan])
>>> # With skipna=True: NaN values are excluded
>>> expanding_std(x, skipna=True)
array([1., 1.5, 1.5, 2.33..., 3.0])

`expanding_min`

expanding_min(x, skipna=False)

Compute the expanding_min of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`skipna`	`bool`	If True, exclude NaN values from calculations. When False (default), any NaN value causes the result to be NaN, maintaining backwards compatibility. When True, NaN values are ignored (matching pandas default behavior).	required

Returns:

Type	Description
np.ndarray: Array with the expanding statistic

Examples:

>>> import numpy as np
>>> x = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
>>> # Default behavior: NaN propagates
>>> expanding_min(x)
array([1., 1.5, nan, nan, nan])
>>> # With skipna=True: NaN values are excluded
>>> expanding_min(x, skipna=True)
array([1., 1.5, 1.5, 2.33..., 3.0])

`expanding_max`

expanding_max(x, skipna=False)

Compute the expanding_max of the input array. Parameters:

Name	Type	Description	Default
`x`	`np.ndarray`	Input array.	required
`skipna`	`bool`	If True, exclude NaN values from calculations. When False (default), any NaN value causes the result to be NaN, maintaining backwards compatibility. When True, NaN values are ignored (matching pandas default behavior).	required

Returns:

Type	Description
np.ndarray: Array with the expanding statistic

Examples:

>>> import numpy as np
>>> x = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
>>> # Default behavior: NaN propagates
>>> expanding_max(x)
array([1., 1.5, nan, nan, nan])
>>> # With skipna=True: NaN values are excluded
>>> expanding_max(x, skipna=True)
array([1., 1.5, 1.5, 2.33..., 3.0])

`expanding_quantile`

expanding_quantile(x, p, skipna=False)

Compute the expanding_quantile of the input array. Parameters:

Name	Type	Description	Default
`x`	`ndarray`	Input array.	required
`p`	`float`	Quantile to compute.	required
`skipna`	`bool`	If True, exclude NaN values from calculations. When False (default), any NaN value causes the result to be NaN, maintaining backwards compatibility. When True, NaN values are ignored (matching pandas default behavior).	`False`

Returns:

Type	Description
`ndarray`	np.ndarray: Array with the expanding statistic

Examples:

>>> import numpy as np
>>> x = np.array([1.0, 2.0, np.nan, 4.0, 5.0])
>>> # Default behavior: NaN propagates
>>> expanding_quantile(x, 0.5)
array([1., 1.5, nan, nan, nan])
>>> # With skipna=True: NaN values are excluded
>>> expanding_quantile(x, 0.5, skipna=True)
array([1., 1.5, 1.5, 2., 2.5])

​

​expanding_mean

​expanding_std

​expanding_min

​expanding_max

​expanding_quantile

`expanding_mean`

`expanding_std`

`expanding_min`

`expanding_max`

`expanding_quantile`