srikanthbojja

Steps to create a good project base for developing in a conda environment

Step-by-step (uncomplete) tutorial for setting up a base Python library given the following requirements:

• Use conda (instead of pipenv or others) because this is both a package manager and an environment manager, and installing the Python scientific stack (Numpy, Pandas, Scipy, Matplotlib, etc.) is straightforward
• Use Visual Studio Code (instead of PyCharm, Spyder or others) because it's free, runs on Windows and is one of the mostly used IDE
• Document and automate as many production steps as possible including linting (flake8), formatting (black), packaging (setup.py, setup.cfg), versionning (git), testing (pytest, pytest-cov, tox), documenting (sphinx, readthedocs), building (setuptools) and distributing (twine, keyring)
• Include IPython Notebooks and have them tested (pytest-nbval)

Table of content

srikanthbojja

Whether you're trying to give back to the open source community or collaborating on your own projects, knowing how to properly fork and generate pull requests is essential. Unfortunately, it's quite easy to make mistakes or not know what you should do when you're initially learning the process. I know that I certainly had considerable initial trouble with it, and I found a lot of the information on GitHub and around the internet to be rather piecemeal and incomplete - part of the process described here, another there, common hangups in a different place, and so on.

In an attempt to coallate this information for myself and others, this short tutorial is what I've found to be fairly standard procedure for creating a fork, doing your work, issuing a pull request, and merging that pull request back into the original project.

Creating a Fork

Just head over to the GitHub page and click the "Fork" button. It's just that simple. Once you've done that, you can use your favorite git client to clone your repo or j

srikanthbojja

d3js: Create an HTML table using d3.js

srikanthbojja

"""
GET_SAS_AS_DASK.PY
2019-05-02
kingfischer16

Functionality to read SAS data from a SAS server (or locally) and return
dask.dataframe.

General idea: Using SASPY, build a list of pandas.DataFrames that are blocks
called via a SAS session. These blocks then make up the dask.DataFrame. Helper

srikanthbojja

#
# Example of automatical rotating proxy middleware for scrapy-rotating-proxies using proxybroker
#

import codecs
import logging
from subprocess import call

from rotating_proxies.expire import Proxies
from rotating_proxies.middlewares import RotatingProxyMiddleware

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Downloading Videos from Pluralsight

Disclaimer

Pluralsight do not permit users to download their videos.
If you are an user of pluralsight you have agreed with their ToS,
and are thusly refrained from doing so.
Use this knowledge at your own risk.

youtube-dl for Windows

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function isChatMessage(message) {
    if (message.__x_isSentByMe) {
        return false;
    }
    if (message.__x_isNotification) {
        return false;
    }
    if (!message.__x_isUserCreatedType) {
        return false;
    }

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setTimeout(function() {
function getAllModules() {
    return new Promise((resolve) => {
        const id = _.uniqueId("fakeModule_");
        window["webpackJsonp"](
            [],
            {
                [id]: function(module, exports, __webpack_require__) {
                    resolve(__webpack_require__.c);
                }

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# A python script which anonymizes email addresses in all files in current directory and sub-directories.
# e.g. A file with the following contents:

# [email protected]
# Sid Phn#- 6385833322
# [email protected]
# [email protected]
# [email protected]

# Would change to:

srikanthbojja

1. Dowloand and install Anacaonda Python

   https://www.anaconda.com/download/

2. Download OASIS from the GitHub

   https://github.com/j-friedrich/OASIS/archive/master.zip

3. Unzip it