You will learn how to create a bubble chart in Seaborn.
-
-
The RecursionError occurs in Python when a recursive function exceeds the maximum recursion depth, a limit set to prevent a stack overflow. I provide you insights into understanding and resolving this common error in recursive function implementations.
-
Tracebacks in Python provide a detailed snapshot of the call stack at the point where an exception occurs, making them an invaluable resource for debugging. I show you how to interpret tracebacks and leverage them to diagnose and resolve errors in your Python code.
-
Transform from squinting at error messages to systematically hunting down bugs. Learn how Python’s built-in debugger (PDB) can reduce your debugging time by 60-80% and help you write more reliable code.
-
A colormap, or cmap, is a mapping from a range of values to a range of colors. In Matplotlib, cmaps are used to colorize data in plots. There are many built-in cmaps in Matplotlib, and you can also create your own. To use a cmap in Matplotlib, you can use the plt.cm.get_cmap() function. This function takes a cmap name as an argument and returns a colormap object. You can then use the colormap object to colorize your data.
-
Plotly offers extensive customization options for your plots, including adjusting the title font size. This guide demonstrates two methods to achieve this: using the layout argument and the update_layout method.
-
One of the features of Plotly is that it allows you to customize the appearance of your charts, including the title. We will show you how to create a title with multiple lines in Plotly, using different methods and options.
-
OpenCV (Open Source Computer Vision Library) is a powerful open-source tool for computer vision and image processing tasks. Aruco markers are a type of augmented reality marker used for detecting and tracking objects in computer vision applications. We’ll explore how to use OpenCV to detect and work with Aruco markers in your projects.
-
If you are working with big data and performance-related scalable systems in Python, you might have encountered the dreaded MemoryError: Unable to allocate array in Numpy. This error occurs when Numpy tries to create an array that is larger than the available memory on your machine. I will explain why this error happens, how to avoid it, and how to fix it if it occurs.
-
Master the complete troubleshooting process for NumPy’s “fails to pass a sanity check” error. Learn the root causes, platform-specific fixes, and prevention strategies. ⚠️ Critical Error: This error means NumPy detected a problem with its installation that prevents safe operation. Do NOT ignore it—your code will fail or produce incorrect results.
-
I will explain how to use numpy mgrid, a powerful tool for creating multidimensional grids. Numpy mgrid is a function that returns a dense multi-dimensional “meshgrid”. A meshgrid is an array that contains the coordinates of a rectangular grid.
-
I will explain how to use numpy logspace, a handy function for creating logarithmically spaced arrays. Such arrays are particularly useful in various scientific and engineering applications where data or phenomena span several orders of magnitude. For example, in frequency analysis, signal processing, or when dealing with exponential scales, logarithmic spacing is often more appropriate than linear spacing to represent data effectively and explore a wide range of values. Numpy logspace is a function that returns an array of numbers that are evenly spaced on a log scale. The syntax of the function is:
-
If you are working with NumPy arrays, you may encounter a TypeError when you try to convert a float array to an integer array using the astype() method. For example, if you have an array like this:
-
I will explain the difference between the sep and end parameters in Python print statement. These parameters are useful for formatting the output of your print statements and making them more readable and customizable. The sep parameter specifies the separator between the values that are printed. By default, it is a single space character. For example, if you write:
-
I will show you how to use numpy to permute the elements of an array along a given axis. Permuting means rearranging the order of the elements in a way that preserves their shape and size. For example, if we have an array of shape (2, 3, 4), we can permute the elements along the first axis (axis=0) to get a new array of shape (2, 3, 4) with different rows.