Append a one dimensional numpy array in a new x-value of a two dimensional numpy array

numpy reshape
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numpy concatenate
numpy vstack
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numpy insert
np.append not working

I'm trying to append a one dimensional numpy array to a two dimensional, so the one dimensional one is inserted on the place of another x-value.

Example:

all_polys = [[5,6],[8,9]] (before the error down below there is nothing stored in it yet)

poly = [1,2]

Expected Result:

all_polys = [[5,6],[8,9],[1,2]]

My Code:

all_polys = numpy.array([[]])
poly = np.expand_dims(poly, axis=0)
print(poly)
print(all_polys)
all_polys = np.concatenate(all_polys, poly)

The Error:

TypeError: only integer scalar arrays can be converted to a scalar index

Print Output before error:

[['400' '815' '650' '815' '650' '745' '400' '745']] (poly with added dimension)

[] (all_polies)

This really frustrates me. What I am doing wrong? It must be a little detail I overlooked, I guess.

Starting with a 2d array, and a 1d array:

In [26]: all_polys = np.array([[5,6],[8,9]])                                    
In [27]: poly = np.array([1,2])                                                 

vstack does a nice job of making sure all inputs are 2d, and then concatenating:

In [28]: np.vstack((all_polys, poly))                                           
Out[28]: 
array([[5, 6],
       [8, 9],
       [1, 2]])

You had the right ides with expand_dims:

In [29]: np.concatenate((all_polys, np.expand_dims(poly, axis=0)))              
Out[29]: 
array([[5, 6],
       [8, 9],
       [1, 2]])

But the np.array([[]]) is a poor starting point. Why use that? Are you doing this iteratively?

For iterative work we recommend using lists:

In [30]: alist = []                                                             
In [31]: alist.append([5,6])                                                    
In [32]: alist.append([8,9])                                                    
In [33]: alist.append([1,2])                                                    
In [34]: np.array(alist)                                                        
Out[34]: 
array([[5, 6],
       [8, 9],
       [1, 2]])

I discourage the use of np.append. It is misused too often.

numpy.append — NumPy v1.20.dev0 Manual, If axis is not specified, values can be any shape and will be flattened before use. Note that append does not occur in-place: a new array is allocated and filled. array at index 0 has 2 dimension(s) and the array at index 1 has 1 dimension(s). First let's discuss some useful array attributes. We'll start by defining three random arrays, a one-dimensional, two-dimensional, and three-dimensional array. We'll use NumPy's random number generator, which we will seed with a set value in order to ensure that the same random arrays are generated each time this code is run:

You can try the append function instead of expand_dims

import numpy as np
all_polys = [[5,6],
             [8,9]]
all_polys = np.append(all_polys,[ [1,2] ], axis=0)
print(all_polys)
#Output=
#all_polys = [[5,6],
#             [8,9],
#             [1,2]]

the absolute basics for beginners — NumPy v1.20.dev0 , An array is a grid of values and it contains information about the raw data, how to You might also hear 1-D, or one-dimensional array, 2-D, or two-dimensional array, and so on. np.concatenate((x, y), axis=0) array([[1, 2], [3, 4], [5, 6]]) Using arr.reshape() will give a new shape to an array without changing the data. Append a one dimensional numpy array in a new x-value of a two dimensional numpy array March 2019 Python three dimensional array of zeros w/out using numpy

You just need to do this:

all_polys = np.concatenate((all_polys, poly[None,:]), axis=0)

The two arrays we are concatenating are all_polys, which looks like [[5,6],[8,9]], and poly[None,:], which looks like [[1.2]].

By axis=0, we're specifying that the concatenation must happen along the outermost (first) dimension of these arrays.

numpy.append() in Python, Python numpy append() function is used to merge two arrays. This function returns a new array and the original array remains unchanged. 1 NumPy append() Syntax; 2 Python numpy.append() Examples in append return concatenate((arr, values), axis=axis) ValueError: all the input array dimensions except for the� Create a simple two dimensional array. First, redo the examples from above. And then create your own: how about odd numbers counting backwards on the first row, and even numbers on the second? Use the functions len(), numpy.shape() on these arrays. How do they relate to each other? And to the ndim attribute of the arrays?

you should do like this.

arr = [old array]
newArr = numpy.append(arr, [new_array])

Use of append function will works.

numpy.append() : How to append elements at the end of a Numpy , end of this new copied array. So, basically it returns a copy of numpy array provided with values appended to it. 1 : values will be appended to arr at axis 1 i.e. new columns will be added Create two 2D Numpy Array like Matrix ValueError: all the input arrays must have same number of dimensions. 0:29 create one dimensional array 0:45 create two dimensional array 1:11 ndim property 1:35 itemsize property 1:57 dtype property 2:05 change data type of element 2:52 size property 3:12 shape

1.4.1. The NumPy array object — Scipy lecture notes, values of an experiment/simulation at discrete time steps; signal recorded by a 2 x 3 array. >>> b. array([[0, 1, 2],. [3, 4, 5]]). >>> b.ndim. 2. >>> b.shape. (2, 3). >> > len(b) # returns the size of the first dimension. 2. >>> c = np.array([[[1], [2]], [[3], for the notebook, so that plots are displayed in the notebook and not in a new� ma.empty (shape[, dtype, order]): Return a new array of given shape and type, without initializing entries. ma.empty_like (a[, dtype, order, subok]): Return a new array with the same shape and type as a given array.

Python NumPy array tutorial, The ndarray stands for N-dimensional array where N is any number. The values will be appended at the end of the array and a new import numpy a = numpy.array([1, 2, 3]) newArray = numpy.append (a, [10, 11, 12]) print(newArray) import numpy addition = lambda x: x + 2 a = numpy.array([1, 2, 3, 4,� Here we have created a one-dimensional array of length 2. Each element of this array is a structure that contains three items, a 32-bit integer, a 32-bit float, and a string of length 10 or less. If we index this array at the second position we get the second structure: >>>

numpy.append, numpy.append - This function adds values at the end of an input array. The append operation is not inplace, a new array is allocated. Also the Also the dimensions of the input arrays must match otherwise ValueError will be generated. Parameter & Description. 1. arr. Input array. 2. values. To be appended to arr. It must� numpy.interp¶ numpy.interp (x, xp, fp, left=None, right=None, period=None) [source] ¶ One-dimensional linear interpolation. Returns the one-dimensional piecewise linear interpolant to a function with given values at discrete data-points.

Comments
  • Does all_polys start as a list [[5,6],[8,9], as an array, np.array([[5,6,8,9]]), or as this useless thing np.array([[]])? Is poly a list [1,2] or an array, np.array([1,2])?
  • all_polys started as that "useless thing" and poly was an array filled with a flexible amount of numbers. I changed it, so that all_polys becomes redundant. Instead a list is initiated, with a certain amount of elements, to which i append more lists.
  • @hpaulj: Nice. Was surprised by your use of vstack with a mix of 2d and 1d array. Expected it to throw an error, as it doesn't seem to satisfy the rule "The arrays must have the same shape along all but the first axis" -- they aren't even of the same rank.
  • Is there also a way to do that to arrays with a different ammount of saved entries? Like all_polys = [[1,4]] and poly = [7,8,4,3] to create [[1,4],[7,8,4,3]]? I get a value error: all the input array dimensions except for the concatenation axis must match exactly
  • For the example in your question the arrays already have a different number of stored elements. all_polys has 4 elements, and poly has 2 elements. I think the question in your comment now is about flattening the arrays before concatenating. For that you can use something like my_flat = np.concatenate((all_polys.ravel(), poly.ravel()), axis=0). BTW, if you don't pass axis=0, the default of 0 will be used by concatenate(), which will work just fine for us.