`riptable.rt_fastarraynumba`

Functions

`fill_backward`(arr[, fill_val, inplace, limit])	Replace NaN and invalid array values by propagating the next encountered valid value
`fill_forward`(arr[, fill_val, inplace, limit])	Replace NaN and invalid array values by propagating the last encountered valid value

riptable.rt_fastarraynumba.fill_backward(arr, fill_val=None, inplace=False, limit=0)

Replace NaN and invalid array values by propagating the next encountered valid value backward.

Note that this method can be called either as a FastArray method (riptable.rt_fastarraynumba.fill_backward()) or a function (riptable.fill_backward()) that takes an array or FastArray as input. The function returns either an array or a FastArray, depending on the original input.

Parameters:

fill_val (scalar, default None) – The value to use where there is no valid value to propagate backward. If fill_val is not specified, NaN and invalid values aren’t replaced where there is no valid value to propagate backward.
inplace (bool, default False) – If False, return a copy of the array. If True, modify original data. This will modify any other views on this object.
limit (int, default 0) – The maximium number of consecutive NaN or invalid values to fill. If there is a gap with more than this number of consecutive NaN or invalid values, the gap will be only partially filled. If no limit is specified, all consecutive NaN and invalid values are replaced.

Returns:

The FastArray will be the same size and have the same dtype as the original input.

Return type:

FastArray

See also

riptable.rt_fastarraynumba.fill_forward: Replace NaN and invalid values with the last valid value.
riptable.rt_fastarraynumba.fill_backward: Replace NaN and invalid values with the next valid value.
riptable.fill_backward: Replace NaN and invalid values with the next valid value.
FastArray.fillna: Replace NaN and invalid values with a specified value or nearby data.
FastArray.replacena: Replace NaN and invalid values with a specified value.
Dataset.fillna: Replace NaN and invalid values with a specified value or nearby data.
Categorical.fill_backward: Replace NaN and invalid values with the next valid group value.
GroupBy.fill_backward: Replace NaN and invalid values with the next valid group value.

Examples

Use a fill_val to replace values where there’s no valid value to propagate backward:

>>> a = rt.FastArray([0.0, rt.nan, rt.nan, rt.nan, 4.0, rt.nan])
>>> a.fill_backward(fill_val = 0)
FastArray([0., 4., 4., 4., 4., 0.])

Using riptable.fill_backward():

>>> a = rt.FastArray([0.0, rt.nan, rt.nan, rt.nan, 4.0, rt.nan])
>>> rt.fill_backward(a, fill_val = 0)
FastArray([0., 4., 4., 4., 4., 0.])

Replace only the first NaN or invalid value in any consecutive series of NaN or invalid values:

>>> a.fill_backward(limit = 1)
FastArray([ 0., nan, nan,  4.,  4., nan])

riptable.rt_fastarraynumba.fill_forward(arr, fill_val=None, inplace=False, limit=0)

Replace NaN and invalid array values by propagating the last encountered valid value forward.

Note that this method can be called either as a FastArray method (riptable.rt_fastarraynumba.fill_forward()) or a function (riptable.fill_forward()) that takes an array or FastArray as input. The function returns either an array or a FastArray, depending on the original input.