Do you want to replace values in NumPy array? In this NumPy Python tutorial, I will explain how to replace values in NumPy array by index in Python using different methods with examples.
To replace values in a NumPy array by index in Python, use simple indexing for single values (e.g., array = new_value), slicing for multiple values (array[start:end] = new_values_array), boolean indexing for condition-based replacement (array[array > threshold] = new_value), and fancy indexing to change specific positions (array[[index1, index2]] = [new_value1, new_value2]).
Replace values in NumPy array by index in Python
To replace values in NumPy array by index in Python is a fundamental operation in data manipulation and analysis. Here, are the four different functions and methods available in NumPy:
- Simple Value Replacement
- Replacing Multiple Values
- Boolean Indexing
- Fancy Indexing
Let’s see them one by one using some examples:
1. NumPy replace value in Python
To replace a value in NumPy array by index in Python, assign a new value to the desired index. For instance:
import numpy as np temperatures = np.array([58, 66, 52, 69, 77]) temperatures = 59 print("Updated Temperatures:", temperatures)
Updated Temperatures: [59 66 52 69 77]
After implementing the code in the Pycharm editor, the screenshot is mentioned below.
2. Replace values in NumPy array by replacing multiple values
To replace multiple values, we can use slicing in Python. Slices include the start index and exclude the end index. For instance:
import numpy as np sunny_days = np.array([152, 259, 245, 233, 294]) sunny_days[1:3] = [260, 250] print("Updated Sunny Days:", sunny_days)
Updated Sunny Days: [152 260 250 233 294]
A screenshot is mentioned below, after implementing the code in the Pycharm editor.
3. Replace value in NumPy array by boolean indexing
With boolean indexing, we can replace values in NumPy array by index in Python that meet a certain condition. For example:
import numpy as np sales = np.array([45, 55, 40, 50, 60]) sales[sales < 50] = -1 print("Updated Sales Data:", sales)
Updated Sales Data: [-1 55 -1 50 60]
After executing the code in Pycharm, one can see the output in the below screenshot.
4. Replace values in matrix Python with fancy indexing
Fancy indexing involves passing an array of indices to access multiple elements to replace values in NumPy array by index in Python. For example:
import numpy as np populations = np.array([120, 85, 95, 110, 100]) populations[[0, 4]] = populations[[4, 0]] print("Updated Populations:", populations)
Updated Populations: [100 85 95 110 120]
The following screenshot illustrates the results obtained from executing the code in the PyCharm editor.
NumPy replace 0 with 1 in a Python array
Let’s see a situation where we have to replace 0 in the array by 1 through Python:
import numpy as np species_presence = np.array([1, 0, 1, 0, 0]) species_presence[species_presence == 0] = 1 print("Updated Species Presence:", species_presence)
Updated Species Presence: [1 1 1 1 1]
Displayed below is a screenshot capturing the outcome of the code execution in the PyCharm editor.
This article explains how to replace values in NumPy array by index in Python using four different ways such as simple indexing, multiple values at a time, boolean indexing, and fancy indexing with illustrative examples. I have also explained how in NumPy array, replace 0 with 1.
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I am Bijay Kumar, a Microsoft MVP in SharePoint. Apart from SharePoint, I started working on Python, Machine learning, and artificial intelligence for the last 5 years. During this time I got expertise in various Python libraries also like Tkinter, Pandas, NumPy, Turtle, Django, Matplotlib, Tensorflow, Scipy, Scikit-Learn, etc… for various clients in the United States, Canada, the United Kingdom, Australia, New Zealand, etc. Check out my profile.