riptable.rt_fastarraynumba
Functions
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Replace NaN and invalid array values by propagating the next encountered valid value |
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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)[source]
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 (rt.FastArray.fill_backward()
) or a function (rt.fill_backward()
) that takes an array orFastArray
as input. The function returns either an array or aFastArray
, depending on the original input.- Parameters:
arr (array) – The array for which the NaN and invalid array values are replaced. If
fill_backward()
is called as a class method,arr
is theFastArray
instance. Iffill_backward()
is called as a function, an array orFastArray
must be passed toarr
.fill_val (scalar, default
None
) – The value to use where there is no valid value to propagate backward. Iffill_val
is not specified, NaN and invalid values aren’t replaced where there is no valid value to propagate backward.inplace (bool, default
False
) – IfFalse
, return a copy of the array. IfTrue
, modify original data. This modifies 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 is only partially filled. If no
limit
is specified, all consecutive NaN and invalid values are replaced.
- Returns:
The
FastArray
is the same size and have the same dtype as the original input.- Return type:
See also
rt_fastarraynumba.fill_forward()
Replace NaN and invalid values with the last valid value.
rt_fastarraynumba.fill_backward()
Replace NaN and invalid values with the next valid value.
rt_fastarray.FastArray.fillna()
Replace NaN and invalid values with a specified value or nearby data.
rt_fastarray.FastArray.replacena()
Replace NaN and invalid values with a specified value.
rt_dataset.Dataset.fillna()
Replace NaN and invalid values with a specified value or nearby data.
rt_categorical.Categorical.fill_backward()
Replace NaN and invalid values with the next valid group value.
rt_groupby.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.])
Call
fill_backward()
as a function:>>> 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)[source]
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 (rt.FastArray.fill_forward()
) or a function (rt.fill_forward()
) that takes an array orFastArray
as input. The function returns either an array or aFastArray
, depending on the original input.- Parameters:
arr (array) – The array for which the NaN and invalid array values are replaced. If
fill_forward()
is called as a class method,arr
is theFastArray
instance. Iffill_forward()
is called as a function, an array orFastArray
must be passed toarr
.fill_val (scalar, default
None
) – The value to use where there is no valid value to propagate forward. Iffill_val
is not specified, NaN and invalid values aren’t replaced where there is no valid value to propagate forward.inplace (bool, default
False
) – IfFalse
, return a copy of the array. IfTrue
, modify original data. This modifies 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 are only partially filled. If no
limit
is specified, all consecutive NaN and invalid values are replaced.
- Returns:
The
FastArray
are the same size and have the same dtype as the original input.- Return type:
See also
rt_fastarraynumba.fill_backward()
Replace NaN and invalid values with the next valid value.
rt_fastarraynumba.fill_forward()
Replace NaN and invalid values with the last valid value.
rt_numpy.fill_forward()
Replace NaN and invalid values with the last valid value.
rt_fastarray.FastArray.fillna()
Replace NaN and invalid values with a specified value or nearby data.
rt_fastarray.FastArray.replacena()
Replace NaN and invalid values with a specified value.
rt_dataset.Dataset.fillna()
Replace NaN and invalid values with a specified value or nearby data.
rt_categorical.Categorical.fill_forward()
Replace NaN and invalid values with the last valid group value.
rt_groupby.GroupBy.fill_forward()
Replace NaN and invalid values with the last valid group value.
Examples
Use a
fill_val
to replace values where there’s no valid value to propagate forward:>>> a = rt.FastArray([rt.nan, 1.0, rt.nan, rt.nan, rt.nan, 5.0]) >>> a.fill_forward(fill_val = 0) FastArray([0., 1., 1., 1., 1., 5.])
Call
fill_forward()
as a function:>>> a = rt.FastArray([0.0, rt.nan, rt.nan, rt.nan, 4.0, rt.nan]) >>> rt.fill_forward(a, fill_val = 0) FastArray([0., 0., 0., 0., 4., 4.])
Replace only the first NaN or invalid value in any consecutive series of NaN or invalid values:
>>> a.fill_forward(limit = 1) FastArray([ 0., 0., nan, nan, 4., 4.])