Python numpy convert dtype object to dtype float32 in case of a 2d array

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pythonconvertfloat32numpyobjectdtype
90%

Unless explicitly specified (more on this later), np,array tries to infer a good data type for the array that it creates

Example_snippet/controller/utility/_python.js/ In [13]: data1 = [6, 7.5, 8, 0. . .
```In[13]: data1 = [6, 7.5, 8, 0, 1]

In[14]: arr1 = np.array(data1)

In[15]: arr1
Out[15]: array([6., 7.5, 8., 0., 1.])```
Step 2 continued with In [16]: data2 = [[1, 2, 3, 4]. . .
```In[16]: data2 = [
[1, 2, 3, 4],
[5, 6, 7, 8]
]

In[17]: arr2 = np.array(data2)

In[18]: arr2
Out[18]:
array([
[1, 2, 3, 4],
[5, 6, 7, 8]
])

In[19]: arr2.ndim
Out[19]: 2

In[20]: arr2.shape
Out[20]: (2, 4)```
Step 3 continued with In [21]: arr1.dtype Out[21]: d. . .
```In[21]: arr1.dtype
Out[21]: dtype('float64')

In[22]: arr2.dtype
Out[22]: dtype('int64')```
Step 4 continued with In [23]: np.zeros(10) Out[23]:. . .
```In[23]: np.zeros(10)
Out[23]: array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])

In[24]: np.zeros((3, 6))
Out[24]:
array([
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.],
[0., 0., 0., 0., 0., 0.]
])

In[25]: np.empty((2, 3, 2))
Out[25]:
array([
[
[4.94065646e-324, 4.94065646e-324],
[3.87491056e-297, 2.46845796e-130],
[4.94065646e-324, 4.94065646e-324]
],

[
[1.90723115e+083, 5.73293533e-053],
[-2.33568637e+124, -6.70608105e-012],
[4.42786966e+160, 1.27100354e+025]
]
])```
Step 5 continued with In [26]: np.arange(15) Out[26]. . .
```In[26]: np.arange(15)
Out[26]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14])```
88%

Array interpretation of a, No copy is performed if the input is already an ndarray with matching dtype and order

Example_snippet/controller/utility/_python.js/ >>> a = [1, 2] >>> np.asarray(. . .
```>>> a = [1, 2] >>>
np.asarray(a)
array([1, 2])```
`np.array(input, dtype = np.float32)`