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expanding_mean

expanding_mean(x, skipna=False)
Compute the expanding_mean of the input array. Parameters:
NameTypeDescriptionDefault
xnp.ndarrayInput array.required
skipnaboolIf 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:
TypeDescription
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])

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expanding_std

expanding_std(x, skipna=False)
Compute the expanding_std of the input array. Parameters:
NameTypeDescriptionDefault
xnp.ndarrayInput array.required
skipnaboolIf 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:
TypeDescription
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])

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expanding_min

expanding_min(x, skipna=False)
Compute the expanding_min of the input array. Parameters:
NameTypeDescriptionDefault
xnp.ndarrayInput array.required
skipnaboolIf 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:
TypeDescription
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])

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expanding_max

expanding_max(x, skipna=False)
Compute the expanding_max of the input array. Parameters:
NameTypeDescriptionDefault
xnp.ndarrayInput array.required
skipnaboolIf 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:
TypeDescription
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])

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expanding_quantile

expanding_quantile(x, p, skipna=False)
Compute the expanding_quantile of the input array. Parameters:
NameTypeDescriptionDefault
xndarrayInput array.required
pfloatQuantile to compute.required
skipnaboolIf 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:
TypeDescription
ndarraynp.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])