# 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 […]