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Lesson 11

Accessing Elements in NumPy Arrays

This lesson covers:

  • Accessing specific elements in NumPy arrays

Accessing elements in an array or a DataFrame is a common task. To begin this lesson, clear the workspace set up some vectors and a $5\times5$ array. These vectors and matrix will make it easy to determine which elements are selected by a command.

Using arange and reshape to create 3 arrays:

  • 5-by-5 array x containing the values 0,1,...,24
  • 5-element, 1-dimensional array y containing 0,1,...,4
  • 5-by-1 array z containing 0,1,...,4
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Zero-based indexing

Python indexing is 0 based so that the first element has position 0, the second has position 1 and so on until the last element has position n-1 in an array that contains n elements in total.

Problem: Scalar selection

Select the number 2 in all three, x, y, and z.

Question: Which index is rows and which index is columns?

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Problem: Scalar selection of a single row

Select the 2nd row in x and z using a single integer value.

Question: What is the dimension of x and the second row of x

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Problem: Slice selection of a single row

Use a slice to select the 2nd row of x and the 2nd element of y and z.

Question: What are the dimension selections?

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Problem: List selection of a single row

Use a list to select the 2nd row of x and the 2nd element of y and z.

Question: What are the dimension selections?

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Problem: Selecting a single Column

Select the 2nd column of x using a scalar integer, a slice and a list.

Question: What the the dimensions of the selected elements?

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Problem: Selecting a block of specific columns

Select the 2nd and 3rd columns of x using a slice.

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Problem: Selecting a block of specific rows

Select the 2nd and 4th rows of x using both a slice and a list.

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Problem: Selecting a block of specific rows and columns

Combine these be combined to select the 2nd and 3rd columns and 2nd and 4th rows.

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Problem: Use ix_ to select rows and columns using lists

Use ix_ to select the 2nd and 4th rows and 1st and 3rd columns of x.

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Problem: Convert a DataFrame to a NumPy array

Use .to_numpy to convert a DataFrame to a NumPy array.

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# Setup: Create a DataFrame
import pandas as pd
import numpy as np

names = ["a", "b", "c", "d", "e"]
x = np.arange(25).reshape((5,5))
x_df = pd.DataFrame(x, index=names, columns=names)
print(x_df)
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Problem: Use np.asarray to convert to an array

Use np.asarray to convert a DataFrame to a NumPy array.

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Exercises

Exercise: Block selection

Select the second and third rows of a and the first and last column. Use at least three different methods including all slices, np.ix_, and mixed slice-list selection.

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# Setup: Data for Exercises

import numpy as np
rs = np.random.RandomState(20000214)
a = rs.randint(1, 10, (4,3))
b = rs.randint(1, 10, (6,4))

print(f"a = \n {a}")
print()
print(f"b = \n {b}")
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Exercise: Row Assign

Assign the first three elements of the first row of b to a.

Note Assignment sets one selected block in one array equal to another block.

x[0:2,0:3] = y[1:3,1:4]
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Exercise: Block Assign

Assign the block consisting the first and third columns and the second and last rows of b to the last two rows and last two columns of a

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