# Check and convert the data type of a NumPy array based on a predefined set of data types.
## pip install numpytypechecker
### Tested against Windows / Python 3.11 / Anaconda
```python
dtypecheck(array, filterna=True, float2int=True, dtypes=(np.uint8,
np.int8,
np.uint16,
np.int16,
np.uint32,
np.int32,
np.uint64,
np.int64,
np.uintp,
np.intp,
np.float16,
np.float32,
np.float64,
'M',
'm',
'O',
'P',
'S',
'U',
'V',
'p',
's',
np.complex64,
np.complex128,
np.datetime64,
np.timedelta64,
np.void, bool, np.bool_,
object
)):
r"""
Check and convert the data type of a NumPy array based on a predefined set of data types.
Parameters:
- array (numpy.ndarray): Input NumPy array.
- filterna (bool, optional): If True, remove NaN values from the array before type checking.
Default is True.
- float2int (bool, optional): If True, convert float arrays to integer if they contain only integers.
Default is True.
- dtypes (tuple, optional): Tuple of NumPy data types to check against. Default includes various numeric,
datetime, timedelta, complex, boolean, and object types.
Returns:
- numpy.ndarray: NumPy array with the converted data type.
Examples:
from numpytypechecker import dtypecheck
import numpy as np
# Example
a1D = np.array([1, 2, 3, 4])
a2D = np.array([[1, 2], [3, 4]])
a3D = np.array([[[1, 2], [3, 4]], [[5, 6], [7, 8]]])
b1 = np.array([2., 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9])
b2 = np.array([[1., 1., 1., 0.],
[1., 1., 0., 1.],
[0., 0., -3., 0.],
[0., 0., np.nan, -4.]])
print(dtypecheck(a1D, filterna=True, float2int=True, ).dtype)
print(dtypecheck(a2D, filterna=True, float2int=True, ).dtype)
print(dtypecheck(a3D, filterna=True, float2int=True, ).dtype)
print(dtypecheck(b1, filterna=True, float2int=True, ).dtype)
print(dtypecheck(b2, filterna=False, float2int=True, ).dtype)
# uint8
# uint8
# uint8
# float64
# float64
```
Raw data
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