## Numpy.array reshape from multiple brackets to 2 brackets

numpy reshape 1d to 2d

numpy reshape 2d to 3d

numpy array double brackets

numpy array((1))

numpy reshape 1d to 3d

numpy array slicing

python double brackets list

I have same data which I need in a one-dimensional numpy.array, but for some reason I don't get them in the right format. My biggest problem is that I don't really know what to look for.

My data is in a form like this:

yTrue [[27.23] [26.38] [26.19] [26.21] [26.24] [27.47] [37.85] [53.35]]

but in order to calculate it I need my data to be stored as a one-dimensional array, if I'm right, so they have to look like this:

Ypred [26.63003973 26.34320268 26.05945521 25.77876403 25.50109623 25.22641923]

`type()`

tells me that both variables are the same `class:<class 'numpy.ndarray'>`

I think you're looking for the `.flat`

attribute of your array. If that isn't quite what you're looking for, take a look at this question for other ideas.

**Difference between single and double bracket Numpy array?,** In [71]: np.array([[0,0,0,0]]).shape Out[71]: (1, 4) In [72]: np.array([0,0,0,0]).shape Out[72]: (4,). The former is a 1 x 4 two-dimensional array, the� Parameters: a: array_like. Array to be reshaped. newshape: int or tuple of ints. The new shape should be compatible with the original shape. If an integer, then the result will be a 1-D array of that length.

You have a (n,1) shape array, like:

In [39]: arr = np.random.rand(5,1)*100 In [40]: arr Out[40]: array([[39.12922352], [66.79745338], [51.97361542], [97.60386022], [85.89486218]])

there are many ways to reshape it to (n,), 1d:

In [41]: arr.ravel() Out[41]: array([39.12922352, 66.79745338, 51.97361542, 97.60386022, 85.89486218]) In [42]: arr.reshape(5) Out[42]: array([39.12922352, 66.79745338, 51.97361542, 97.60386022, 85.89486218]) In [43]: arr.reshape(-1) Out[43]: array([39.12922352, 66.79745338, 51.97361542, 97.60386022, 85.89486218]) In [44]: arr.flatten() Out[44]: array([39.12922352, 66.79745338, 51.97361542, 97.60386022, 85.89486218]) In [45]: arr[:,0] Out[45]: array([39.12922352, 66.79745338, 51.97361542, 97.60386022, 85.89486218])

Take your pick, read their docs, experiment.

What you show is the `str`

representation:

In [46]: print(arr) [[39.12922352] [66.79745338] [51.97361542] [97.60386022] [85.89486218]]

**numpy: Creating Arrays,** The values in the parentheses will become the values in our array. For example, we could have a variable that contains the values 1, 2, 3, 4 and 5. but instead of giving just one list of values in square brackets we give multiple lists, with numpy � Back to post list; Next post numpy: Array shapes and reshaping arrays. 3 by 4 numpy array. If you want numpy to automatically determine what size/length a particular dimension should be, specify the dimension as -1 for that dimension.. a1.reshape(3, 4) a1.reshape(-1, 4) # same as above: a1.reshape(3, 4) a1.reshape(3, 4) a1.reshape(3, -1) # same as above: a1.reshape(3, 4) a1.reshape(2, 6) a1.reshape(2, -1) # same as above: a1.reshape(2, 6)

Thanks for the help. It worked out. My real problem was that i didnt understand the differences of the arays in that moment. Thanks

**Reshape numpy arrays—a visualization,** Reshape numpy arrays in Python — a step-by-step pictorial tutorial to 1_12 print(a1_1_by_12) # note the double square brackets! The ravel() method lets you convert multi-dimensional arrays to 1D arrays (see docs here). For instance, array[2:5] = np.arange(3) would make the third, fourth, and fifth elements (second, third, and fourth indexes) equal to 0, 1, and 2, respectively. On the other hand, to access multiple values in custom indexes, pass in a list or array into the indexing brackets.

**Advanced NumPy Array Indexing, Made Easy | by Andre Ye,** Consider the array construction array = np.array([1, 2, 3, 4, 5]) , which creates an array of numbers from 1 to 5. On the other hand, to access multiple values in custom indexes, pass in a list or array into the indexing brackets. Consider a three-dimensional array named array with shape (3, 2, 3): array([[[ 0� The analogy is summarized in Figure 11. For example directions to element A[2][1][0][0] would be 2nd Street , Building 1, Floor 0 room 0. Figure 18: Street analogy for figure 11 . We see that you can store multiple dimensions of data as a Python list. Similarly, a Numpy array is a more widely used method to store and process data.

**the absolute basics for beginners — NumPy v1.20.dev0 ,** The shape of the array is a tuple of integers giving the size of the array along each dimension. We can access the elements in the array using square brackets. You might also hear 1-D, or one-dimensional array, 2-D, or two- dimensional� Note that numpy.array is not the same as the Standard Python Library class array.array, which only handles one-dimensional arrays and offers less functionality. The more important attributes of an ndarray object are: ndarray.ndim the number of axes (dimensions) of the array.

**squeeze - Numpy and Scipy Documentation,** NumPy is a Python library for handling multi-dimensional arrays. [2]:. array([1, 2 , 3]). Note that leaving out the brackets from the above expression, i.e. calling NumPy can easily produce arrays of wanted shape filled with random numbers. Reshape Data. In some occasions, you need to reshape the data from wide to long. You can use the reshape function for this. The syntax is numpy.reshape(a, newShape, order='C') Here, a: Array that you want to reshape . newShape: The new desires shape . Order: Default is C which is an essential row style. Exampe of Reshape

##### Comments

- The shape of the first is (8,1). Check it out. You can use
`reshape`

or`ravel`

to get a (8,) shaped array.