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@kauffmanes
kauffmanes / install_anaconda.md
Last active May 24, 2024 08:51
Install Anaconda on Windows Subsystem for Linux (WSL)

Thanks everyone for commenting/contributing! I made this in college for a class and I no longer really use the technology. I encourage you all to help each other, but I probably won't be answering questions anymore.

This article is also on my blog: https://emilykauffman.com/blog/install-anaconda-on-wsl

Note: $ denotes the start of a command. Don't actually type this.

Steps to Install Anaconda on Windows Ubuntu Terminal

  1. Install WSL (Ubuntu for Windows - can be found in Windows Store). I recommend the latest version (I'm using 18.04) because there are some bugs they worked out during 14/16 (microsoft/WSL#785)
  2. Go to https://repo.continuum.io/archive to find the list of Anaconda releases
  3. Select the release you want. I have a 64-bit computer, so I chose the latest release ending in x86_64.sh. If I had a 32-bit computer, I'd select the x86.sh version. If you accidentally try to install the wrong one, you'll get a warning in the terminal. I chose `Anaconda3-5.2.0-Li
@sainathadapa
sainathadapa / glimpse.py
Last active December 19, 2023 15:33
glimpse-python
def glimpse(df, maxvals=10, maxlen=110):
print('Shape: ', df.shape)
def pad(y):
max_len = max([len(x) for x in y])
return [x.ljust(max_len) for x in y]
# Column Name
toprnt = pad(df.columns.tolist())
@conormm
conormm / r-to-python-data-wrangling-basics.md
Last active April 24, 2024 18:22
R to Python: Data wrangling with dplyr and pandas

R to python data wrangling snippets

The dplyr package in R makes data wrangling significantly easier. The beauty of dplyr is that, by design, the options available are limited. Specifically, a set of key verbs form the core of the package. Using these verbs you can solve a wide range of data problems effectively in a shorter timeframe. Whilse transitioning to Python I have greatly missed the ease with which I can think through and solve problems using dplyr in R. The purpose of this document is to demonstrate how to execute the key dplyr verbs when manipulating data using Python (with the pandas package).

dplyr is organised around six key verbs: