eetuko

import matplotlib as mpl
import matplotlib.pyplot as plt
from cycler import cycler

DARK_BLUE = "#2b2d42"
MEDIUM_GREY = "#8d99ae"
LIGHT_GREY = "#edf2f4"
LIGHT_RED = "#ef233c"
DARK_RED = "#d90429"

eetuko

license: gpl-3.0
height: 540
scrolling: no
border: yes

eetuko

In data science, exploratory data analysis is an endless game. Here is a quick summary of the process that I follow to get it done. It is easy to get sidetracked, lost in the details. The commitment to follow bullets points helps to stick to your main goal: get a global overview. The important shift is to iterate at least three times with a different focus at every iteration:

• Function: What is the purpose of the system? Is it to satisfy customers? How? Something else?
• Structure: How are the building blocks arranged in space?
• Processes: How are the building blocks arranged in time?

Depending on the type of question it might make more sense to adapt the ratio between time and space, but the two are important.

---

eetuko

# Credit https://forums.docker.com/t/how-to-delete-cache/5753
alias docker_clean_images='docker rmi $(docker images -a --filter=dangling=true -q)'
alias docker_clean_ps='docker rm $(docker ps --filter=status=exited --filter=status=created -q)'

alias docker_delete_all_images='docker rmi $(docker images -a -q)'
alias docker_delete_all_containers='docker container rm $(docker container ls -aq)'

eetuko

#!/usr/bin/env python
import gzip
import io
import pprint
import upsetplot
import pandas as pd
from collections import defaultdict
from matplotlib import pyplot as plt
from matplotlib_venn import venn2, venn3
from urllib.request import Request, urlopen