![]() This example shows how to use slice assignment with all parameters specified. Thus, the notation a replaces all sequence elements but the first one. Recall that the notation a selects the sequence starting at index “start”, ending in index “stop” (exclusive), and considering only every “step”-th sequence element. Then we use slice assignment to replace the 15 trailing sequence values with the value 16. The code snippet creates an array containing 16 times the value 4. In NumPy’s slice assignment feature, you specify the values to be replaced on the left-hand side of the equation and the values that replace them on the right-hand side of the equation. But before we dive into the code, let’s explore the most important concepts you need as a basic understanding. In this mini code project, you want to “clean” your data by replacing every Sunday sensor value with the average sensor value of the last seven days. Now, you realize that the Sunday sensor values are faulty because they partially measured the temperature at your home and not at the outside location. Every Sunday, you uninstall the temperature sensor from the garden and take it in your house to digitize the sensor values. Say, you have installed a temperature sensor in your garden to measure temperature data over a period of many weeks. In this one-liner example, you learn about how to quickly handle smaller cleaning tasks in a single line of code. Real-world data is seldomly clean: It may contain errors because of faulty sensor, or it may contain missing data because of damaged sensors. Take your time to study it and watch the explainer video to polish your NumPy slicing skills once and for all. Let’s dive into a practical example about NumPy slice assignments from my Python One-Liners book next. > a = np.array()įor 2D arrays, you can use the advanced slice notation-selection comma-separated by axis-to replace whole columns like so: > a = np.array(, The left and right operands don’t have the same array shape-but NumPy figures it out through broadcasting. The next example shows how to replace every other value of a 1D array with the same value. The NumPy slice assignment operation doesn’t need the same shape on the left and right side because NumPy will use broadcasting to bring the array-like data structure providing the replacement data values into the same shape as the array to be overwritten. Here’s a minimal example of slice assignment: > import numpy as np For example, a = would overwrite every other value of NumPy array a. ![]() The right side of the slice assignment operation provides the exact number of elements to replace the selected slice. NumPy slice assignment allows you to use slicing on the left-hand side of an assignment operation to overwrite a specific subsequence of a NumPy array at once.
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