Python namespace packages are an advanced Python feature. You may have heard them mentioned in relation to the __init__.py file. Specifically, if you don’t include at least an empty __init__.py file in your package, then your package becomes a namespace package. For the most part, namespace packages and regular packages won’t behave differently when it comes to using them in your project. In fact, you’ve probably accidentally forgotten to include an __init__.py file in at least one package but didn’t […]

Python’s iterators and iterables are two different but related tools that come in handy when you need to iterate over a data stream or container. Iterators power and control the iteration process, while iterables typically hold data that you want to iterate over one value at a time. Iterators and iterables are fundamental components of Python programming, and you’ll have to deal with them in almost all your programs. Learning how they work and how to create them is key […]

The main data structure that you’ll use in NumPy is the N-dimensional array. An array can have one or more dimensions to structure your data. In some programs, you may need to change how you organize your data within a NumPy array. You can use NumPy’s reshape() to rearrange the data. The shape of an array describes the number of dimensions in the array and the length of each dimension. In this tutorial, you’ll learn how to change the shape […]

The terminal can be intimidating to work with when you’re used to working with graphical user interfaces (GUIs). However, it’s an important tool that you need to get used to in your journey as a Python developer. And once you level up your skill of using the terminal, it becomes an extremely powerful tool in your repertoire. With just a few commands in the terminal, you can do tasks that are impossible or at least very tedious to do in […]

# app.py import pandas as pd from dash import Dash, Input, Output, dcc, html data = ( pd.read_csv(“avocado.csv”) .assign(Date=lambda data: pd.to_datetime(data[“Date”], format=”%Y-%m-%d”)) .sort_values(by=”Date”) ) regions = data[“region”].sort_values().unique() avocado_types = data[“type”].sort_values().unique() external_stylesheets = [ { “href”

Because Python has limited parallelism when using threads, using worker processes is a common way to take advantage of multiple CPU cores. The multiprocessing module is built-in to the standard library, so it’s frequently used for this purpose. But while multiple processes let you take advantage of multiple CPUs, moving data between processes can be very slow. And that can reduce some of the performance benefits of using worker processes. Let’s see: Why processes can have performance problems that threads […]

Do you want to build a compact visual progress indicator for your Python script using print(), but your output doesn’t show up when you’d expect it to? Or are you piping the logs of your script to another application, but you can’t manage to access them in real time? In both cases, data buffering is the culprit, and you can solve your troubles by flushing the output of print(). In this tutorial, you’ll learn how to: Flush the output data […]