hjwp · January 26, 2016 15:53
diff --git a/uk_income_histogram.ipynb b/uk_income_histogram.ipynb
 {
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "source: https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[8370, 8670, 8970, 9260, 9570, 9880, 10200, 10500, 10700, 10900, 11100, 11300, 11600, 11800, 12000, 12200, 12400, 12700, 12900, 13100, 13300, 13500, 13800, 14000, 14200, 14500, 14700, 14900, 15200, 15400, 15700, 15900, 16100, 16400, 16700, 16900, 17200, 17500, 17700, 18000, 18300, 18600, 18800, 19100, 19400, 19700, 20000, 20400, 20700, 21000, 21400, 21700, 22100, 22400, 22800, 23200, 23500, 23900, 24300, 24800, 25200, 25600, 26100, 26500, 27000, 27500, 28100, 28600, 29100, 29700, 30300, 30900, 31500, 32200, 32900, 33600, 34300, 35100, 35900, 36700, 37700, 38700, 39700, 40700, 41800, 42800, 44100, 45600, 47200, 49200, 51500, 54300, 57700, 62000, 67900, 76100, 88000, 106000, 150000]\n"
     ]
    }
   ],
   "source": [
    "before_tax_percentiles_str = '''8,370\n",
    "8,670\n",
    "8,970\n",
    "9,260\n",
    "9,570\n",
    "9,880\n",
    "10,200\n",
    "10,500\n",
    "10,700\n",
    "10,900\n",
    "11,100\n",
    "11,300\n",
    "11,600\n",
    "11,800\n",
    "12,000\n",
    "12,200\n",
    "12,400\n",
    "12,700\n",
    "12,900\n",
    "13,100\n",
    "13,300\n",
    "13,500\n",
    "13,800\n",
    "14,000\n",
    "14,200\n",
    "14,500\n",
    "14,700\n",
    "14,900\n",
    "15,200\n",
    "15,400\n",
    "15,700\n",
    "15,900\n",
    "16,100\n",
    "16,400\n",
    "16,700\n",
    "16,900\n",
    "17,200\n",
    "17,500\n",
    "17,700\n",
    "18,000\n",
    "18,300\n",
    "18,600\n",
    "18,800\n",
    "19,100\n",
    "19,400\n",
    "19,700\n",
    "20,000\n",
    "20,400\n",
    "20,700\n",
    "21,000\n",
    "21,400\n",
    "21,700\n",
    "22,100\n",
    "22,400\n",
    "22,800\n",
    "23,200\n",
    "23,500\n",
    "23,900\n",
    "24,300\n",
    "24,800\n",
    "25,200\n",
    "25,600\n",
    "26,100\n",
    "26,500\n",
    "27,000\n",
    "27,500\n",
    "28,100\n",
    "28,600\n",
    "29,100\n",
    "29,700\n",
    "30,300\n",
    "30,900\n",
    "31,500\n",
    "32,200\n",
    "32,900\n",
    "33,600\n",
    "34,300\n",
    "35,100\n",
    "35,900\n",
    "36,700\n",
    "37,700\n",
    "38,700\n",
    "39,700\n",
    "40,700\n",
    "41,800\n",
    "42,800\n",
    "44,100\n",
    "45,600\n",
    "47,200\n",
    "49,200\n",
    "51,500\n",
    "54,300\n",
    "57,700\n",
    "62,000\n",
    "67,900\n",
    "76,100\n",
    "88,000\n",
    "106,000\n",
    "150,000\n",
    "'''.split()\n",
    "before_tax_percentiles = [int(s.replace(',', '')) for s in before_tax_percentiles_str]\n",
    "print(before_tax_percentiles)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from matplotlib import pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
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Da4HvA98Dfgd8YpTtfhk4A1g/ynrMzKyDhr17StIU4NqIeMtYNZimIHkmIhZJqrU6b2Bg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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x7fc6550fd278>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(before_tax_percentiles, bins=50)\n",
    "plt.xlabel(\"Income before tax\")\n",
    "plt.ylabel(\"Percentage of population\")\n",
    "plt.show()"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3.4",
   "language": "python",
   "name": "python3.4"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.4.3"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
 }
	{
	"cells": [
	{
	"cell_type": "code",
	"execution_count": 4,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"%matplotlib inline"
	]
	},
	{
	"cell_type": "markdown",
	"metadata": {},
	"source": [
	"source: https://www.gov.uk/government/statistics/percentile-points-from-1-to-99-for-total-income-before-and-after-tax\n"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 5,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"name": "stdout",
	"output_type": "stream",
	"text": [
	"[8370, 8670, 8970, 9260, 9570, 9880, 10200, 10500, 10700, 10900, 11100, 11300, 11600, 11800, 12000, 12200, 12400, 12700, 12900, 13100, 13300, 13500, 13800, 14000, 14200, 14500, 14700, 14900, 15200, 15400, 15700, 15900, 16100, 16400, 16700, 16900, 17200, 17500, 17700, 18000, 18300, 18600, 18800, 19100, 19400, 19700, 20000, 20400, 20700, 21000, 21400, 21700, 22100, 22400, 22800, 23200, 23500, 23900, 24300, 24800, 25200, 25600, 26100, 26500, 27000, 27500, 28100, 28600, 29100, 29700, 30300, 30900, 31500, 32200, 32900, 33600, 34300, 35100, 35900, 36700, 37700, 38700, 39700, 40700, 41800, 42800, 44100, 45600, 47200, 49200, 51500, 54300, 57700, 62000, 67900, 76100, 88000, 106000, 150000]\n"
	]
	}
	],
	"source": [
	"before_tax_percentiles_str = '''8,370\n",
	"8,670\n",
	"8,970\n",
	"9,260\n",
	"9,570\n",
	"9,880\n",
	"10,200\n",
	"10,500\n",
	"10,700\n",
	"10,900\n",
	"11,100\n",
	"11,300\n",
	"11,600\n",
	"11,800\n",
	"12,000\n",
	"12,200\n",
	"12,400\n",
	"12,700\n",
	"12,900\n",
	"13,100\n",
	"13,300\n",
	"13,500\n",
	"13,800\n",
	"14,000\n",
	"14,200\n",
	"14,500\n",
	"14,700\n",
	"14,900\n",
	"15,200\n",
	"15,400\n",
	"15,700\n",
	"15,900\n",
	"16,100\n",
	"16,400\n",
	"16,700\n",
	"16,900\n",
	"17,200\n",
	"17,500\n",
	"17,700\n",
	"18,000\n",
	"18,300\n",
	"18,600\n",
	"18,800\n",
	"19,100\n",
	"19,400\n",
	"19,700\n",
	"20,000\n",
	"20,400\n",
	"20,700\n",
	"21,000\n",
	"21,400\n",
	"21,700\n",
	"22,100\n",
	"22,400\n",
	"22,800\n",
	"23,200\n",
	"23,500\n",
	"23,900\n",
	"24,300\n",
	"24,800\n",
	"25,200\n",
	"25,600\n",
	"26,100\n",
	"26,500\n",
	"27,000\n",
	"27,500\n",
	"28,100\n",
	"28,600\n",
	"29,100\n",
	"29,700\n",
	"30,300\n",
	"30,900\n",
	"31,500\n",
	"32,200\n",
	"32,900\n",
	"33,600\n",
	"34,300\n",
	"35,100\n",
	"35,900\n",
	"36,700\n",
	"37,700\n",
	"38,700\n",
	"39,700\n",
	"40,700\n",
	"41,800\n",
	"42,800\n",
	"44,100\n",
	"45,600\n",
	"47,200\n",
	"49,200\n",
	"51,500\n",
	"54,300\n",
	"57,700\n",
	"62,000\n",
	"67,900\n",
	"76,100\n",
	"88,000\n",
	"106,000\n",
	"150,000\n",
	"'''.split()\n",
	"before_tax_percentiles = [int(s.replace(',', '')) for s in before_tax_percentiles_str]\n",
	"print(before_tax_percentiles)"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 6,
	"metadata": {
	"collapsed": true
	},
	"outputs": [],
	"source": [
	"from matplotlib import pyplot as plt"
	]
	},
	{
	"cell_type": "code",
	"execution_count": 7,
	"metadata": {
	"collapsed": false
	},
	"outputs": [
	{
	"data": {
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Da4HvA98Dfgd8YpTtfhk4A1g/ynrMzKyDhr17StIU4NqIeMtYNZimIHkmIhZJqrU6b2Bg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\n",
	"text/plain": [
	"<matplotlib.figure.Figure at 0x7fc6550fd278>"
	]
	},
	"metadata": {},
	"output_type": "display_data"
	}
	],
	"source": [
	"plt.hist(before_tax_percentiles, bins=50)\n",
	"plt.xlabel(\"Income before tax\")\n",
	"plt.ylabel(\"Percentage of population\")\n",
	"plt.show()"
	]
	}
	],
	"metadata": {
	"kernelspec": {
	"display_name": "Python 3.4",
	"language": "python",
	"name": "python3.4"
	},
	"language_info": {
	"codemirror_mode": {
	"name": "ipython",
	"version": 3
	},
	"file_extension": ".py",
	"mimetype": "text/x-python",
	"name": "python",
	"nbconvert_exporter": "python",
	"pygments_lexer": "ipython3",
	"version": "3.4.3"
	}
	},
	"nbformat": 4,
	"nbformat_minor": 0
	}