CPython VS Pandas

Go on Python's official site and download it.

To follow along, the reader must have a good knowledge of Python.

from selenium import webdriver from webdriver_manager.chrome import ChromeDriverManager import time # driver = webdriver.Chrome(ChromeDriverManager().install()) # #chromedriver_autoinstaller.install() # driver = webdriver.Chrome(ChromeDriverManager().install()) driver = webdriver.Chrome(ChromeDriverManager(version = '101.0.4951.41').install()) driver.get("http://www.python.org") # path = '/Users/jiggz/Desktop/chromedriver 2' driver.get('https://www.youtube.com/')

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You are most welcome! In fact I had my issues with this too and can relate. Btw., I am sure Python would benefit from issues that mention concrete shortcomings, that is, if you are up to another good deed.

https://github.com/python/cpython/tree/main/Lib/tkinter https://github.com/python/cpython/tree/main/Lib/idlelib https://github.com/python/cpython/blob/main/Lib/turtle.py

It does. Note that it calls popcount_digit on each digit of the integer (a 'digit' here is 32 bits), which calls _Py_popcount32, which does map to a hardware instruction if possible: https://github.com/python/cpython/blob/f62ad4f2c4214fdc05cc4...

For anyone who hasn't already heard PyScript, which debuted at PyCon 2022, is a browser-embedded python environment, built on top of an existing project called Pyodide. This project, to the shock of long-term Pythonistas and web developers, seamlessly blends (well almost) JavaScript and Python in a bi-directional environment allowing developers to utilize Python staples such as NumPy or Pandas in the browser.

It is worthwhile to learn how to use Pandas for handling data - https://pandas.pydata.org/

I use pandas to read the file

For example, line 424 to 436 in mask.py in pandas, there’s an @overload decorator, then a method astype with some hinting, then ... inside; followed by a few other methods just like it with the same name, followed by the actual implementation. pandas mask

If you are using python, try pandas, for the analysis (the describe method seems to be what you are looking for), and Seaborn for visualisation, and quick overview of your data. Good luck!

Pandas seems popular to try to turn Python into something more like R, which is certainly useful for many types of use-cases when you have tables of data you want to do some operations on to spit out some results and plot nice graphs. But I am sure there are many use-cases that would require other libraries, or a combination of libraries.

Pandas can do all that.

We’ve learned a lot while setting up Spark on AWS EMR. While this post will focus on how to use PySpark with Pandas, let us know in the comments if you’re interested in a future article on how we set up Spark on AWS EMR.