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How to reshape 1D array to 2D array in numpy

ARYAN CHAVDA answered on August 6, 2022 Popularity 9/10 Helpfulness 4/10

answer How to reshape 1D array to 2D array in numpy

related numpy reshape 1d to 2d

How to reshape 1D array to 2D array in numpy

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a = np.array([1, 2, 3, 4, 5, 6])
a2 = a[np.newaxis, :]
print(a2.shape)

Popularity 9/10 Helpfulness 4/10 Language python

Source: numpy.org

Tags: 2d numpy python reshape

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Contributed on Aug 06 2022

ARYAN CHAVDA

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numpy reshape 1d to 2d

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Use the reshape method with -1

import numpy as np

my_array = np.((1, 2, 3, 4))
print(my_array.shape) # shape (4,)

my_array = my_array.reshape(-1,1)
print(my_array.shape) # shape is (4,1)

my_array = my_array.reshape(1,-1)
print(my_array.shape) # shape is (-1,4)

As per numpy documentation says "One shape dimension can be -1. In this case, the value is inferred from the length of the array and remaining dimensions."

By default when you do array operations with 1D array on ND arrays. The broadcasting rules states the 1D array must have the same lenght as the Nth array dimension (the last one). See the exemples below

array_substract = np.array((1,1)) # shape (2,)

array_0 = np.zeros((5, 5, 2))
print(array_0 - np.array((1,2))) # This will compute without any error

array_1 = np.zeros((2, 2, 5))
try:
    print(array_1 - np.array((1,2)))
except ValueError as err:
    print(repr(err)) # Will show ValueError('operands could not be
                     # broadcast together with shapes (2,2,5) (2,) ')
# To solve this we use the solution stated above
print(array_0 - np.array((1,2)).reshape(1,1,-1))

For more info

https://numpy.org/doc/stable/reference/generated/numpy.reshape.html#numpy-reshape

https://numpy.org/doc/stable/user/basics.broadcasting.html#broadcasting

Popularity 8/10 Helpfulness 8/10 Language python

Source: Grepper

Tags: 2d line li

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Contributed on Mar 29 2023

The pythonic skaven

0 Answers Avg Quality 2/10

How to reshape 1D array to 2D array in numpy

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