Open Canvas 2.22e serial key or number

Open Canvas 2.22e serial key or number

Open Canvas 2.22e serial key or number

Open Canvas 2.22e serial key or number

pycse - Python3 Computations in Science and Engineering

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 otherprint(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 otherprint(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 partsp1, p2 = np.hsplit(A, 2) print(p1) print(p2) #split into 4 partsp1, 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 partsp1, 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 partsp1, 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 partsp1, 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 1print(A[1]) print(A[1, :]) # row 1, all columnsprint(A[:, 2]) # get third columnprint(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.

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, Open Canvas 2.22e serial key or number

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Open Canvas 2.22e serial key or number

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