NumPy is one of the most popular library in Python that is used in the field of science and engineering. NumPy APIs are used in other popular libraries like Pandas, SciPy, Matplotlib, scikit-learn, scikit-image, etc. Read our Python NumPy tutorials with real examples in PythonGuides.com.
While working on a data analysis project, I needed to round decimal values consistently across large NumPy arrays. The issue is that while Python has the built-in round() function, it doesn’t always work ideally with NumPy arrays and can behave unexpectedly with certain decimal values. This is where NumPy’s np.round() function becomes incredibly useful. In … Read more >>
When I was working on a data analysis project where I needed to process some text data stored in a NumPy array. When I tried to use the .split() method on my array, I got this frustrating error: AttributeError: ‘numpy.ndarray’ object has no attribute ‘split’. This is a common error that many Python developers encounter … Read more >>
I was working on a data analysis project where I needed to calculate the absolute values of a large dataset. The issue is, while Python has a built-in abs() function, it’s not optimized for large numerical arrays. This is where NumPy’s np.abs() function comes to the rescue. In this article, I’ll cover everything you need … Read more >>
While I was working on an image processing project, I needed to reduce noise in some satellite images of national parks in the USA. The images had some grain that was making feature extraction difficult. This is where the mean filter (also called average filter) saved the day. In this article, I’ll share multiple ways … Read more >>
When working with NumPy arrays in Python, you might encounter the error message “ValueError: setting an array element with a sequence.” This error typically occurs when you’re trying to assign a sequence (like a list) to a single element in a NumPy array. In this article, I’ll explain what causes this error and show you … Read more >>
np.repeat duplicates each element of a NumPy array in place, which is not the same as repeating the array: That difference is the thing to get straight first. After it, the argument that catches people is axis, which flattens your array when you leave it out. All output below is from real runs on Python … Read more >>
When working with numerical data in Python, normalization is a common preprocessing step that can significantly improve the performance of machine learning algorithms. Recently, I was analyzing US housing price data and needed to normalize the values between 0 and 1 to make my model more effective. The issue was that the raw data had … Read more >>
A 2D array in Python is a table of values arranged in rows and columns. Python has no built-in 2D array type, so you use either a nested list (a list of rows, like [[1, 2], [3, 4]]) or a NumPy 2D array created with np.array(), np.zeros() and similar functions. Nested lists are fine for … Read more >>
np.unique() returns the distinct values of a NumPy array, sorted, with duplicates removed. Add return_counts=True and you get how often each one appeared: The sorting is not optional, which is the first thing that surprises people coming from pandas. The axis argument is the second. Runs below are on NumPy 2.5.3, pandas 3.0.6, Python 3.12.5. … Read more >>
np.uint8 is NumPy’s unsigned 8-bit integer data type: every value uses one byte and must be a whole number from 0 to 255. That makes it the standard type for image pixels and other compact data. Create a uint8 array with np.array(values, dtype=np.uint8) or convert one with arr.astype(np.uint8), but watch out: values outside 0–255 wrap … Read more >>