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.
As a Python developer with over a decade of experience, I’ve found that data manipulation tasks often come down to filtering and finding specific values. One of the most efficient tools in the Python data science ecosystem is np.where(), especially when used with Pandas. In this comprehensive guide, I’ll walk you through everything you need … Read more >>
![NumPy shape: the Command Prompt showing shape[0] as the row count and shape[1] as the column count](https://pythonguides.com/wp-content/uploads/2026/09/python-numpy-shape-guide.webp "python numpy shape guide")
NumPy shape is an attribute, not a function. It hands you a tuple with one entry per dimension, and you read it with no parentheses at all: That single detail explains most of the errors people hit with it. The rest of this guide covers what the tuple means, what shape[0] and shape[1] actually count, … Read more >>
A NumPy empty array comes from np.empty(), which reserves memory without clearing it first: Those are two separate ideas that share a name. np.empty(5) has five slots full of junk, while np.array([]) genuinely holds nothing. Timings and output below come from Python 3.12.5, NumPy 2.5.3 on this machine. What np.empty actually returns The values are … Read more >>
I was working on a data analysis project for a US retail chain where I needed to handle missing sales data. The issue was, I needed to create placeholder arrays filled with NaN (Not a Number) values that would later be populated with actual data. In this article, I’ll share several practical methods to create … Read more >>
np.zeros() creates a NumPy array of a given shape filled with zeros. Pass one number for a 1D array or a tuple for more dimensions, and add dtype if you don’t want floats: Below I cover the shape rules, dtypes, zeros_like(), how it compares with np.empty(), and the errors people hit. Everything ran on Python … Read more >>
While working on a data analysis project, I needed to import CSV files with header rows into my Python application. While Pandas is often the go-to library for this task, I needed the performance benefits and numerical capabilities of NumPy. The challenge is that NumPy doesn’t handle headers as intuitively as Pandas does. In this … Read more >>
np.linspace returns evenly spaced numbers between two endpoints, and you tell it how many you want rather than how big the gaps are: That inclusive stop is the difference from np.arange, which leaves it out. It’s also why linspace is the safer of the two. Output below is from real runs on Python 3.12.5, NumPy … Read more >>
When I was working on a data science project, I needed to combine multiple NumPy arrays. I realized that many Python developers get confused between NumPy’s concatenate and append functions – both seem to do similar things, but there are crucial differences between them. In this article, I’ll cover how to use both methods, when … Read more >>
np.sum adds up the values in a NumPy array, either all of them or along one axis: The axis argument is what people come for, and the rule is short: the axis you name is the one that disappears from the result. All figures below are real runs on Python 3.12.5, NumPy 2.5.3. Using np.sum … Read more >>
To check if a NumPy array is empty, test arr.size == 0. size is the total number of elements, so it is 0 only when the array holds nothing, whatever its shape. Don’t use if not arr: (it raises an error for arrays with more than one element), and be careful with len(arr), which only … Read more >>