Notice: This page requires JavaScript to function properly.
Please enable JavaScript in your browser settings or update your browser.Boolean Indexing in 2D Arrays | Indexing and Slicing
course content

Course Content

Ultimate NumPy

1. NumPy Basics

Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays

2. Indexing and Slicing

Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays

3. Commonly used NumPy Functions

Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation

4. Math with NumPy

BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

Ultimate NumPy

Boolean Indexing in 2D ArraysBoolean Indexing in 2D Arrays

Boolean indexing in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the same number of dimensions as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be 1D.

Here is an example:

Let’s visualize it:

Boolean indexing in a 2D array

The boolean array on the right is the resulting boolean array of these two combined conditions. Once again, purple elements match the conditions, and green elements don’t. So when applying array_2d[(array_2d < 3) | (array_2d >= 8)], we get a 1D array of purple elements.

Task

You are analyzing the temperatures recorded in two different cities over four days. The temperatures are stored in the city_temperatures array where each row represents a city and each column represents a day. Your task is to filter out the temperatures that are less than or equal to 15 degrees OR greater than 30 degrees Celsius using boolean indexing.

Everything was clear?

Section 2. Chapter 9
toggle bottom row
course content

Course Content

Ultimate NumPy

1. NumPy Basics

Introduction to NumPyBasic Array CreationCreating Higher Dimensional ArraysCreation Functions for 1D ArraysCreation Functions for 2D ArraysGeneral Array Creation FunctionsRandom Arrays

2. Indexing and Slicing

Basic IndexingMultidimensional IndexingSlicingSlicing in 2D ArraysInteger Array IndexingInteger Array Indexing in 2D ArraysBoolean IndexingMore about Comparisons Boolean Indexing in 2D ArraysAssigning Values to Indexed ElementsAssigning Values to Indexed Subarrays

3. Commonly used NumPy Functions

Sorting ArraysSorting 2D ArraysCopying ArraysReshaping ArraysFlattening ArraysArray Concatenation

4. Math with NumPy

BroadcastingBasic Mathematical OperationsStatistical OperationsBasic Linear Algebra with NumPy

Ultimate NumPy

Boolean Indexing in 2D ArraysBoolean Indexing in 2D Arrays

Boolean indexing in 2D and higher-dimensional arrays works similarly to 1D arrays. However, the boolean array must have the same number of dimensions as the initial array (e.g., 2D for two-dimensional arrays). The returned array, however, will be 1D.

Here is an example:

Let’s visualize it:

Boolean indexing in a 2D array

The boolean array on the right is the resulting boolean array of these two combined conditions. Once again, purple elements match the conditions, and green elements don’t. So when applying array_2d[(array_2d < 3) | (array_2d >= 8)], we get a 1D array of purple elements.

Task

You are analyzing the temperatures recorded in two different cities over four days. The temperatures are stored in the city_temperatures array where each row represents a city and each column represents a day. Your task is to filter out the temperatures that are less than or equal to 15 degrees OR greater than 30 degrees Celsius using boolean indexing.

Everything was clear?

Section 2. Chapter 9
toggle bottom row
some-alt