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Summary

Description
English: Graph of US Civilian Labor Participation Rate from 1948 to 2011 by gender. Men are represented in light blue, women in pink, and the total in black.
Date
Source Bureau of Labor Statistics within the United States Department of Labor
Author User:Int21h
Permission
( Reusing this file)
Creative Commons CC-Zero This file is made available under the Creative Commons CC0 1.0 Universal Public Domain Dedication.
The person who associated a work with this deed has dedicated the work to the public domain by waiving all of their rights to the work worldwide under copyright law, including all related and neighboring rights, to the extent allowed by law. You can copy, modify, distribute and perform the work, even for commercial purposes, all without asking permission.

Other versions

Source code

#!/usr/bin/env python

##
# Create an SVG graph of BLS timeseries data using matplotlib and 
# BeautifulSoup.
#

import matplotlib.figure
import datetime
import bs4
import matplotlib.backends.backend_cairo
import string
import urllib2

def main():
	url = 'http://data.bls.gov/timeseries/%s?years_option=specific_years&include_graphs=true&to_year=2011&from_year=1948'
	total_series = 'LNS11300000'
	men_series = 'LNS11300001'
	women_series = 'LNS11300002'
	
	fig,ax = init_figure()
	add_plot(ax, scrape_bls(bs4.BeautifulSoup(urllib2.urlopen(url % total_series))), 'black')
	add_plot(ax, scrape_bls(bs4.BeautifulSoup(urllib2.urlopen(url % men_series))), 'lightblue')
	add_plot(ax, scrape_bls(bs4.BeautifulSoup(urllib2.urlopen(url % women_series))), 'pink')
	save_figure(fig, 'US Labor Participation Rate 1948-2011 by gender.svg')

##
# Scrape the BLS soup for the data.
#
def scrape_bls(soup):
	table = soup.find_all('table', attrs={'class': 'regular-data'})
	assert len(table) == 1
	table = table0
	data = []
	years_lc = [[t for t in r if type(t) is bs4.element.Tag for r in table.contents4 if type(r) is bs4.element.Tag
	for row in years_lc:
		year = int(row0.text)
		months = float(t.text) for t in row1:] if any(c in string.printable and c != ' ' for c in t.text)]
		data.append((year,months))
	return data

##
# Add plot to figure. Extra parameters are passed to 
# matplotlib.axes.Axes.plot().
#
def add_plot(axis, data, *args):
	x=[]
	y=[]
	for year,months in data:
		for month,n in enumerate(months):
			x.append(datetime.date(year, month+1, 1))
			y.append(n)
	axis.plot(x, y, *args)

##
# Initialize figure.
#
def init_figure():
	figure = matplotlib.figure.Figure()
	axis = figure.add_subplot(111)
	
	axis.xaxis.set_major_locator(matplotlib.dates.YearLocator(6))
	axis.xaxis.set_major_formatter(matplotlib.dates.DateFormatter('%Y'))
	
	axis.grid(True)
	
	return figure,axis

##
# Save figure
#
def save_figure(figure, filename):
	figure.canvas = matplotlib.backends.backend_cairo.FigureCanvasCairo(figure)
	figure.savefig(filename, transparent=True)

if __name__ == "__main__":
	main()

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depicts

8 October 2011

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Date/TimeThumbnailDimensionsUserComment
current 11:47, 9 November 2016 Thumbnail for version as of 11:47, 9 November 2016720 × 540 (81 KB)Kopiersperreupdated to 2016
03:52, 9 October 2011 Thumbnail for version as of 03:52, 9 October 2011720 × 540 (76 KB)Int21hcleanup, standardization.
09:59, 8 October 2011 Thumbnail for version as of 09:59, 8 October 2011720 × 540 (83 KB)Int21h{{Information |Description={{en|1=Graph of US Civilian Labor Participaton Rate from 1948 to 2010 by gender}} |Source= Bureau of Labor Statistics within the [[:wikipedia:United States Department of Labor|United Stat
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