Khalil micaleel

Stacked and Grouped Bar Plot

Oddly enough ggplot2 has no support for a stacked and grouped (position="dodge") bar plot. The seaborn python package, although excellent, also does not provide an alternative. However, I knew it was surely possible to make such a plot in regular matplotlib. Matplotlib, although sometimes clunky, gives you enough flexibility to precisely place plotting elements which is needed for a stacked and grouped bar plot.

Below is a working example of making a stacked and grouped bar plot.

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np

Useful Pandas Snippets

A personal diary of DataFrame munging over the years.

Data Types and Conversion

Convert Series datatype to numeric (will error if column has non-numeric values)
(h/t @makmanalp)

Status

READY/IN DEVELOPMENT/HOLD

Migrations

YES | NO

Description

A few sentences describing the overall goals of the pull request's commits.

	# Elasticsearch Cheatsheet - an overview of commonly used Elasticsearch API commands

	# cat paths
	/_cat/allocation
	/_cat/shards
	/_cat/shards/{index}
	/_cat/master
	/_cat/nodes
	/_cat/indices
	/_cat/indices/{index}

	#!/usr/bin/env python

	"""
	This module provides methods to read values from a INI file.
	"""

	import ConfigParser

	class Config(object):

	#!/usr/bin/env python

	from pocket import Pocket
	import webbrowser, sys

	# Get consumer key from cmd line
	consumer_key = sys.argv[1]

	request_token = Pocket.get_request_token(
	consumer_key=consumer_key,

	[diff]
	tool = Kaleidoscope
	[difftool]
	prompt = false
	[difftool "Kaleidoscope"]
	cmd = ksdiff --partial-changeset --relative-path \"$MERGED\" -- \"$LOCAL\" \"$REMOTE\"
	[merge]
	tool = Kaleidoscope
	keepBackup = false
	[mergetool]

	#!/usr/bin/env bash

	## create an ubuntu 14.04 hvm instance, then from your home directory:

	# 1. download this script
	# wget https://gist.githubusercontent.com/waylonflinn/506f563573600d944923/raw/install-python-data-science.sh

	# 2. make it executable
	# chmod a+x install-python-data-science.sh

	#-- coding: utf-8 --

	import re
	import nltk
	from nltk.tokenize import RegexpTokenizer
	from nltk import bigrams, trigrams
	import math


	stopwords = nltk.corpus.stopwords.words('portuguese')

	import string
	import collections

	from nltk import word_tokenize
	from nltk.stem import PorterStemmer
	from nltk.corpus import stopwords
	from sklearn.cluster import KMeans
	from sklearn.feature_extraction.text import TfidfVectorizer
	from pprint import pprint