Destructuring-bind dictionary contents
I am trying to ‘destructure’ a dictionary and associate values with variables names after its keys. Something like
python
Why is Random Forest with a single tree much better than a Decision Tree classifier?
I apply the
decision tree classifier and the random forest classifier to my data with the following code:
Parse HTML table to Python list?
I’d like to take an HTML table and parse through it to get a list of dictionaries. Each list element would be a dictionary corresponding to a row in the table.
How to pipe input to python line by line from linux program?
I want to pipe the output of ps -ef to python line by line.
How to get a GCP Bearer token programmatically with python
gcloud auth print-access-token gives me a Bearer token that I can use later on; however, this is a shell command. How would I obtain one programmatically via the Google Cloud Python API?
Update and render a value from Flask periodically
I want to display my CPU usage dynamically. I don’t want to reload the page to see a new value. I know how to get the CPU usage in Python. Right now I render a template with the value. How can I continually update a page with a value from Flask?
How can I translate dates and times from natural language to datetime?
I’m looking for a way to translate ‘tomorrow at 6am’ or ‘next monday at noon’ to the appropriate datetime objects. I thought of engineering a complex set of rules, but is there another way? Answers: Thank you for visiting the Q&A section on Magenaut. Please note that all the answers may not help you solve … Read more
What is the difference between join and merge in Pandas?
Suppose I have two DataFrames like so:
Using Extensions with Selenium (Python)
I am currently using Selenium to run instances of Chrome to test web pages. Each time my script runs, a clean instance of Chrome starts up (clean of extensions, bookmarks, browsing history, etc). I was wondering if it’s possible to run my script with Chrome extensions. I’ve tried searching for a Python example, but nothing came up when I googled this.
How to find which columns contain any NaN value in Pandas dataframe
Given a pandas dataframe containing possible NaN values scattered here and there: