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@pybokeh
Last active December 21, 2015 11:49
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IPython Notebook - Plotting Yearly Unemployment Rate
{
"metadata": {
"name": "BeautifulSoup_Ex1"
},
"nbformat": 3,
"nbformat_minor": 0,
"worksheets": [
{
"cells": [
{
"cell_type": "code",
"collapsed": false,
"input": "from bs4 import BeautifulSoup\nfrom urllib import request\nimport re\n\n# Fetch yearly US unemployment data\nyrly_unemployment_data = request.urlopen('http://www.bls.gov/cps/cpsaat01.htm')\nsoup = BeautifulSoup(yrly_unemployment_data)\n\n# Unemployment rate values appear to be inside id tags, except for 2 rows\nyrs_initial = soup.find_all('th',id=re.compile('cps_eeann_year.r.'))\n\n# Exclude those 2 non-unemployment data\nyrs_final = [int(yr.text) for yr in yrs_initial if yr.text.isnumeric()]",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 14
},
{
"cell_type": "code",
"collapsed": false,
"input": "# The unemployment rate values appear to be inside the class=\"datavalue\" attribute\ndata = soup.find_all(class_=\"datavalue\")\n\n# The actual unemployment rate is the 8th value in the table data and it repeats every 10 rows\nunemploy_rate_data = data[8::10]\n\n# Creating list of unemployment rate\nunemploy_rate = [float(rate.text) for rate in unemploy_rate_data]\n\n# We have a list of years and a list of unemployment rates, we need to make a dictionary out of them using zip\nunemployment_dict = {year:rate for year, rate in zip(yrs_final, unemploy_rate)}",
"language": "python",
"metadata": {},
"outputs": [],
"prompt_number": 15
},
{
"cell_type": "code",
"collapsed": false,
"input": "bar_width = 0.5\n\nyrs = 50\nax = subplot()\ncurrent_axis = plt.gca()\nfor xticklabels in current_axis.get_xticklabels():\n xticklabels.set_fontsize(6)\n \nbar(np.arange(0,yrs), unemploy_rate[-yrs:], bar_width, alpha=0.7)\ntitle(\"U.S. Unemployment Rate of last \"+str(yrs)+\" Years\")\nylabel(\"Unemployment Rate %\")\nxticks(np.arange(0,yrs),yrs_final[-yrs:],rotation=-90)\nyticks(np.arange(0,11))\ngrid()\nshow()",
"language": "python",
"metadata": {},
"outputs": [
{
"metadata": {},
"output_type": "display_data",
"png": 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naNWqVYWS3r59G0OHDkWrVq0QGhqK/fv3l3ks+YfSMfLsK65Hnj1/PZ65yLM3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\nAAAASUVORK5CYII=\n",
"text": "<matplotlib.figure.Figure at 0xb062084c>"
}
],
"prompt_number": 16
}
],
"metadata": {}
}
]
}
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