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numpy.ma.py
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from typing import *
# usage.dask: 1
MaskError: object
# usage.matplotlib: 17
# usage.pandas: 2
# usage.scipy: 6
# usage.sklearn: 2
# usage.xarray: 6
MaskedArray: object
# usage.scipy: 1
add: object
# usage.statsmodels: 1
allclose: object
# usage.dask: 2
# usage.matplotlib: 19
# usage.pandas: 1
# usage.scipy: 41
# usage.skimage: 5
# usage.sklearn: 4
# usage.statsmodels: 1
# usage.xarray: 1
array: object
# usage.matplotlib: 2
asarray: object
# usage.dask: 1
# usage.sklearn: 4
average: object
# usage.dask: 1
# usage.xarray: 1
concatenate: object
# usage.scipy: 1
corrcoef: object
# usage.dask: 1
filled: object
# usage.dask: 2
# usage.scipy: 4
fix_invalid: object
# usage.dask: 1
getdata: object
# usage.matplotlib: 2
getmask: object
# usage.dask: 1
getmaskarray: object
# usage.scipy: 1
log: object
# usage.matplotlib: 1
# usage.scipy: 1
mask_or: object
# usage.scipy: 1
mask_rowcols: object
# usage.dask: 2
# usage.matplotlib: 10
# usage.scipy: 1
# usage.skimage: 4
masked: object
# usage.pandas: 9
masked_all: object
# usage.dask: 23
# usage.matplotlib: 5
# usage.pandas: 2
# usage.scipy: 3
# usage.sklearn: 2
masked_array: object
# usage.dask: 1
masked_equal: object
# usage.dask: 1
masked_inside: object
# usage.dask: 1
# usage.matplotlib: 10
masked_invalid: object
# usage.matplotlib: 3
masked_less_equal: object
# usage.dask: 1
masked_outside: object
# usage.dask: 1
masked_values: object
# usage.dask: 1
# usage.scipy: 3
masked_where: object
# usage.scipy: 3
maximum: object
# usage.sklearn: 1
mean: object
# usage.scipy: 1
# usage.sklearn: 1
median: object
# usage.scipy: 3
minimum: object
# usage.dask: 1
# usage.matplotlib: 5
# usage.scipy: 1
nomask: object
# usage.dask: 1
set_fill_value: object
# usage.scipy: 1
sort: object
# usage.scipy: 1
var: object
def arange(*args: Literal["v", "t"]):
"""
usage.scipy: 14
"""
...
def empty(*args: Literal["v", "t"]):
"""
usage.scipy: 3
"""
...
def ones(*args: Literal["v", "t"]):
"""
usage.xarray: 1
"""
...
def zeros(*args: Literal["v", "t"]):
"""
usage.pandas: 1
usage.skimage: 5
"""
...