## Creating arrays in python

Posted February 26, 2013 at 09:00 AM | categories: python | tags: | View Comments

Updated March 06, 2013 at 07:39 PM

Often, we will have a set of 1-D arrays, and we would like to construct a 2D array with those vectors as either the rows or columns of the array. This may happen because we have data from different sources we want to combine, or because we organize the code with variables that are easy to read, and then want to combine the variables. Here are examples of doing that to get the vectors as the columns.

import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print np.column_stack([a, b]) # this means stack the arrays vertically, e.g. on top of each other print np.vstack([a, b]).T

[[1 4] [2 5] [3 6]] [[1 4] [2 5] [3 6]]

Or rows:

import numpy as np a = np.array([1, 2, 3]) b = np.array([4, 5, 6]) print np.row_stack([a, b]) # this means stack the arrays vertically, e.g. on top of each other print np.vstack([a, b])

[[1 2 3] [4 5 6]] [[1 2 3] [4 5 6]]

The opposite operation is to extract the rows or columns of a 2D array into smaller arrays. We might want to do that to extract a row or column from a calculation for further analysis, or plotting for example. There are splitting functions in numpy. They are somewhat confusing, so we examine some examples. The numpy.hsplit command splits an array “horizontally”. The best way to think about it is that the “splits” move horizontally across the array. In other words, you draw a vertical split, move over horizontally, draw another vertical split, etc… You must specify the number of splits that you want, and the array must be evenly divisible by the number of splits.

import numpy as np A = np.array([[1, 2, 3, 5], [4, 5, 6, 9]]) # split into two parts p1, p2 = np.hsplit(A, 2) print p1 print p2 #split into 4 parts p1, p2, p3, p4 = np.hsplit(A, 4) print p1 print p2 print p3 print p4

[[1 2] [4 5]] [[3 5] [6 9]] [[1] [4]] [[2] [5]] [[3] [6]] [[5] [9]]

In the numpy.vsplit command the “splits” go “vertically” down the array. Note that the split commands return 2D arrays.

import numpy as np A = np.array([[1, 2, 3, 5], [4, 5, 6, 9]]) # split into two parts p1, p2 = np.vsplit(A, 2) print p1 print p2 print p2.shape

[[1 2 3 5]] [[4 5 6 9]] (1, 4)

An alternative approach is array unpacking. In this example, we unpack the array into two variables. The array unpacks by row.

import numpy as np A = np.array([[1, 2, 3, 5], [4, 5, 6, 9]]) # split into two parts p1, p2 = A print p1 print p2

[1 2 3 5] [4 5 6 9]

To get the columns, just transpose the array.

import numpy as np A = np.array([[1, 2, 3, 5], [4, 5, 6, 9]]) # split into two parts p1, p2, p3, p4 = A.T print p1 print p2 print p3 print p4 print p4.shape

[1 4] [2 5] [3 6] [5 9] (2,)

Note that now, we have 1D arrays.

You can also access rows and columns by indexing. We index an array by [row, column]. To get a row, we specify the row number, and all the columns in that row like this [row, :]. Similarly, to get a column, we specify that we want all rows in that column like this: [:, column]. This approach is useful when you only want a few columns or rows.

import numpy as np A = np.array([[1, 2, 3, 5], [4, 5, 6, 9]]) # get row 1 print A[1] print A[1, :] # row 1, all columns print A[:, 2] # get third column print A[:, 2].shape

[4 5 6 9] [4 5 6 9] [3 6] (2,)

Note that even when we specify a column, it is returned as a 1D array.

Copyright (C) 2013 by John Kitchin. See the License for information about copying.