The 'correct' way of achieving your goal is to not expand the mask to 2D. Instead index with `[:, mask]`

with the 1D mask. This indicates to numpy that you want axis 0 unchanged and `mask`

applied along axis 1.

a = np.arange(12).reshape(3, 4) b = np.array((1, 0, 1, 0), '?') a # array([ [0, 1, 2, 3], #[4, 5, 6, 7], #[8, 9, 10, 11] ]) b # array([True, False, True, False]) a[: , b] # array([ [0, 2], #[4, 6], #[8, 10] ])

If your `mask`

is 2D and just happens to have the same number of `True`

s in each row then either use @tel's answer. Or create an index array:

B = b ^ b[: 3, None] B # array([ [False, True, False, True], #[True, False, True, False], #[False, True, False, True] ]) J = np.where(B)[1].reshape(len(B), -1)

And now either

np.take_along_axis(a, J, 1) # array([ [1, 3], #[4, 6], #[9, 11] ])

I believe what you want can be done by calling `new_data.reshape(837, -1)`

. Here's a brief example:

```
arr = np.arange(8 * 6).reshape(8, 6)
maskpiece = np.array([True, False] * 3)
mask = np.broadcast_to(maskpiece, (8, 6))
print('the original array\n%s\n' % arr)
print('the flat masked array\n%s\n' % arr[mask])
print('the masked array reshaped into 2D\n%s\n' % arr[mask].reshape(8, -1))
```

Output:

```
the original array
[[0 1 2 3 4 5]
[6 7 8 9 10 11]
[12 13 14 15 16 17]
[18 19 20 21 22 23]
[24 25 26 27 28 29]
[30 31 32 33 34 35]
[36 37 38 39 40 41]
[42 43 44 45 46 47]]
the flat masked array
[0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46]
the masked array reshaped into 2 D
[[0 2 4]
[6 8 10]
[12 14 16]
[18 20 22]
[24 26 28]
[30 32 34]
[36 38 40]
[42 44 46]]
```

I have created a 2D mask appropriately named anycodings_python mask to have the same shape as the array anycodings_python data, the array I want to apply it upon. anycodings_python However when I do so, the data loses it's anycodings_python shape and becomes 1D.,If your mask is 2D and just happens to anycodings_python have the same number of Trues in each anycodings_python row then either use @tel's answer. Or anycodings_python create an index array:,I am wondering is there any numpy tricks to anycodings_python be used to achieve this goal without using anycodings_python reshape?,The 'correct' way of achieving your goal anycodings_python is to not expand the mask to 2D. Instead anycodings_python index with [:, mask] with the 1D mask. anycodings_python This indicates to numpy that you want anycodings_python axis 0 unchanged and mask applied along anycodings_python axis 1.

I am wondering is there any numpy tricks to anycodings_python be used to achieve this goal without using anycodings_python reshape?

>>> data.shape(837, 44) >>> m = altitudes < 50000 >>> m.shape(44, ) >>> np.sum(m) # calculates my expected dimension for axis 1 10 >>> mask = [m for i in range(data.shape[0]) ] >>> mask.shape(837, 44) >>> new_data = data[mask] >>> new_data.shape(8370, ) # same as 837 * 10(dimension wanted)

The 'correct' way of achieving your goal anycodings_python is to not expand the mask to 2D. Instead anycodings_python index with [:, mask] with the 1D mask. anycodings_python This indicates to numpy that you want anycodings_python axis 0 unchanged and mask applied along anycodings_python axis 1.

a = np.arange(12).reshape(3, 4) b = np.array((1, 0, 1, 0), '?') a # array([ [0, 1, 2, 3], #[4, 5, 6, 7], #[8, 9, 10, 11] ]) b # array([True, False, True, False]) a[: , b] # array([ [0, 2], #[4, 6], #[8, 10] ])

If your mask is 2D and just happens to anycodings_python have the same number of Trues in each anycodings_python row then either use @tel's answer. Or anycodings_python create an index array:

B = b ^ b[: 3, None] B # array([ [False, True, False, True], #[True, False, True, False], #[False, True, False, True] ]) J = np.where(B)[1].reshape(len(B), -1)

And now either

np.take_along_axis(a, J, 1) # array([ [1, 3], #[4, 6], #[9, 11] ])

I believe what you want can be done by anycodings_python calling new_data.reshape(837, -1). anycodings_python Here's a brief example:

```
arr = np.arange(8 * 6).reshape(8, 6)
maskpiece = np.array([True, False] * 3)
mask = np.broadcast_to(maskpiece, (8, 6))
print('the original array\n%s\n' % arr)
print('the flat masked array\n%s\n' % arr[mask])
print('the masked array reshaped into 2D\n%s\n' % arr[mask].reshape(8, -1))
```

Output:

```
the original array
[[0 1 2 3 4 5]
[6 7 8 9 10 11]
[12 13 14 15 16 17]
[18 19 20 21 22 23]
[24 25 26 27 28 29]
[30 31 32 33 34 35]
[36 37 38 39 40 41]
[42 43 44 45 46 47]]
the flat masked array
[0 2 4 6 8 10 12 14 16 18 20 22 24 26 28 30 32 34 36 38 40 42 44 46]
the masked array reshaped into 2 D
[[0 2 4]
[6 8 10]
[12 14 16]
[18 20 22]
[24 26 28]
[30 32 34]
[36 38 40]
[42 44 46]]
```

Masked arrays are arrays that may have missing or invalid entries. The numpy.ma module provides a nearly work-alike replacement for numpy that supports data arrays with masks.,In many circumstances, datasets can be incomplete or tainted by the presence of invalid data. For example, a sensor may have failed to record a data, or recorded an invalid value. The numpy.ma module provides a convenient way to address this issue, by introducing masked arrays.,Arithmetic and comparison operations are supported by masked arrays. As much as possible, invalid entries of a masked array are not processed, meaning that the corresponding data entries should be the same before and after the operation.,through the data attribute. The output is a view of the array as a numpy.ndarray or one of its subclasses, depending on the type of the underlying data at the masked array creation.

```
>>>
import numpy as np
>>>
import numpy.ma as ma >>>
x = np.array([1, 2, 3, -1, 5])
```

>>> mx = ma.masked_array(x, mask = [0, 0, 0, 1, 0])

>>> mx.mean() 2.75

```
>>>
import numpy as np
>>>
import numpy.ma as ma
```

>>> y = ma.array([1, 2, 3], mask = [0, 1, 0])

>>> z = ma.masked_values([1.0, 1.e20, 3.0, 4.0], 1.e20)

when applied on arrays, they return the array of the element-by-element comparisons,we count the number of elements in the array a that are: less than 6 and even,you can create sub-arrays using lists or ndarrays of indices,conditions are applied to all elements of the array

`[1]:`

`import numpy as np`

`[2]:`

# we create a matrix of shape(3 x 4) a = np.random.randint(-10, 10, 12).reshape(3, 4) a

array([ [2, -3, 9, 6], [-6, 7, -10, -4], [2, -2, -8, -5] ])

`[3]:`