numpy_exercises []

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Q. Given two one dimensional arrays, how to combine them into a 2d numpy array?

A. use <nowiki>np.column_stack((c1, c2))</nowiki>

<code>
 % ipython
Python 3.8.5 (default, Sep  4 2020, 07:30:14) 
Type 'copyright', 'credits' or 'license' for more information
IPython 7.18.1 -- An enhanced Interactive Python. Type '?' for help.

In [1]: 
import numpy as np
c1 = np.array([0.39656302, 0.14878788, 0.14948021, 0.26837201, 0.40427337])
c2 = np.array([0.68676044, 0.41183541, 0.72868058, 0.24776482, 0.52792127])
m = np.column_stack((c1, c2))
m
Out[1]: 
array([[0.39656302, 0.68676044],
       [0.14878788, 0.41183541],
       [0.14948021, 0.72868058],
       [0.26837201, 0.24776482],
       [0.40427337, 0.52792127]])

In [2]: 
m.shape
Out[2]: 
(5, 2)

In [3]: 
m.ndim
Out[3]: 
2
</code>
You can also get the same result by doing <nowiki>np.vstack((c1, c2)).T</nowiki> . But I like the column_stack() approach as it gives the correct shape right away and does not require a transpose.

<code>
In [4]: 
m2 = np.vstack((c1, c2)).T
m2
Out[4]: 
array([[0.39656302, 0.68676044],
       [0.14878788, 0.41183541],
       [0.14948021, 0.72868058],
       [0.26837201, 0.24776482],
       [0.40427337, 0.52792127]])

In [5]: 
np.array_equal(m, m2)
Out[5]: 
True
</code>

Ref:-
  * https://openlibrary.org/books/OL26834151M/Python_for_Data_Analysis_Data_Wrangling_with_Pandas_NumPy_and_IPython -> Appendix A "Advanced Numpy" -> "A.2 Advanced Array Manipulation" -> "Concatenating and Splitting Arrays" -> "Table A-1. Array concatenation functions" - contains a list of similar functions and their description.
  * https://numpy.org/doc/stable/reference/generated/numpy.array_equal.html - np.array_equal() can be used to check if two arrays are equal
  * https://stackoverflow.com/questions/17710672/create-2-dimensional-array-with-2-one-dimensional-array

tags | combine two numpy arrays into 2d array