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Created June 13, 2017 17:07
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20170614.ipynb
{
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{
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"execution_count": 175,
"metadata": {},
"outputs": [
{
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" <span id=\"f9ccf774-94ff-4029-b43b-0d675f4e84cd\">Loading BokehJS ...</span>\n",
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" \"BokehJS does not appear to have successfully loaded. If loading BokehJS from CDN, this \\n\"+\n",
" \"may be due to a slow or bad network connection. Possible fixes:\\n\"+\n",
" \"</p>\\n\"+\n",
" \"<ul>\\n\"+\n",
" \"<li>re-rerun `output_notebook()` to attempt to load from CDN again, or</li>\\n\"+\n",
" \"<li>use INLINE resources instead, as so:</li>\\n\"+\n",
" \"</ul>\\n\"+\n",
" \"<code>\\n\"+\n",
" \"from bokeh.resources import INLINE\\n\"+\n",
" \"output_notebook(resources=INLINE)\\n\"+\n",
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"\n",
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" Bokeh.embed.inject_css(\"https://cdn.pydata.org/bokeh/release/bokeh-0.12.5.min.css\");\n",
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],
"source": [
"import numpy as np\n",
"import pandas as pd\n",
"from bokeh.io import output_notebook, show\n",
"from bokeh.charts import *\n",
"from bokeh.models.ranges import *\n",
"output_notebook()"
]
},
{
"cell_type": "code",
"execution_count": 158,
"metadata": {
"collapsed": true
},
"outputs": [],
"source": [
"df = pd.read_csv('./database.csv', low_memory=False, na_values='Unknown')"
]
},
{
"cell_type": "code",
"execution_count": 176,
"metadata": {},
"outputs": [
{
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" </div>\n",
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" }\n",
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" var render_items = [{\"docid\":\"efcee48c-f88d-44c0-9d41-1ffba6aa3196\",\"elementid\":\"583feea0-d1eb-4d64-99ad-8a41b36afd1d\",\"modelid\":\"9a921ea8-53ac-4fa7-8103-d60a1a85e04b\"}];\n",
" \n",
" Bokeh.embed.embed_items(docs_json, render_items);\n",
" };\n",
" if (document.readyState != \"loading\") fn();\n",
" else document.addEventListener(\"DOMContentLoaded\", fn);\n",
" })();\n",
" },\n",
" function(Bokeh) {\n",
" }\n",
" ];\n",
" \n",
" function run_inline_js() {\n",
" \n",
" if ((window.Bokeh !== undefined) || (force === true)) {\n",
" for (var i = 0; i < inline_js.length; i++) {\n",
" inline_js[i](window.Bokeh);\n",
" }if (force === true) {\n",
" display_loaded();\n",
" }} else if (Date.now() < window._bokeh_timeout) {\n",
" setTimeout(run_inline_js, 100);\n",
" } else if (!window._bokeh_failed_load) {\n",
" console.log(\"Bokeh: BokehJS failed to load within specified timeout.\");\n",
" window._bokeh_failed_load = true;\n",
" } else if (force !== true) {\n",
" var cell = $(document.getElementById(\"583feea0-d1eb-4d64-99ad-8a41b36afd1d\")).parents('.cell').data().cell;\n",
" cell.output_area.append_execute_result(NB_LOAD_WARNING)\n",
" }\n",
" \n",
" }\n",
" \n",
" if (window._bokeh_is_loading === 0) {\n",
" console.log(\"Bokeh: BokehJS loaded, going straight to plotting\");\n",
" run_inline_js();\n",
" } else {\n",
" load_libs(js_urls, function() {\n",
" console.log(\"Bokeh: BokehJS plotting callback run at\", now());\n",
" run_inline_js();\n",
" });\n",
" }\n",
" }(this));\n",
"</script>"
]
},
"metadata": {},
"output_type": "display_data"
}
],
"source": [
"is_solved = df['Crime Solved'] == 'Yes'\n",
"cnt = df[is_solved]['Year'].value_counts(sort=False)\n",
"ttl = df['Year'].value_counts(sort=False)\n",
"data = cnt / ttl\n",
"\n",
"s = pd.DataFrame()\n",
"s['Prob'] = data\n",
"s['Year'] = data.index\n",
"\n",
"reg = np.poly1d(np.polyfit(s['Year'], s['Prob'], deg=1))\n",
"s['Pred'] = reg(s['Year'])\n",
"\n",
"r = '{:.3f}'.format(s['Year'].corr(s['Prob']))\n",
"xbar = s['Year'].mean()\n",
"ybar = s['Prob'].mean()\n",
"\n",
"# p = Line(s, x='Year', y='Prob')\n",
"p = Scatter(s, x='Year', y='Prob', title='Prob vs Year')\n",
"p.line(x=s['Year'], y=s['Pred'], line_width=2)\n",
"p.text(x=[xbar], y=[ybar], text=[r])\n",
"\n",
"show(p)"
]
},
{
"cell_type": "code",
"execution_count": 202,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"-34.068733678129384"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"from math import sqrt\n",
"\n",
"is_male = df['Victim Sex'] == 'Male'\n",
"is_female = df['Victim Sex'] == 'Female'\n",
"\n",
"dfm = df[is_male]['Victim Age']\n",
"dff = df[is_female]['Victim Age']\n",
"\n",
"bar_m, std_m, n_m = dfm.mean(), dfm.std(), dfm.size\n",
"bar_f, std_f, n_f = dff.mean(), dff.std(), dff.size\n",
"\n",
"nom = (bar_m - bar_f) - 0\n",
"dem = sqrt(std_m**2 / n_m + std_f**2 / n_f)\n",
"z = nom / dem\n",
"\n",
"z"
]
},
{
"cell_type": "code",
"execution_count": 201,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"317422 0.7409253296872933\n",
"299899 0.6628031437250541\n"
]
},
{
"data": {
"text/plain": [
"67.13633023616113"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"is_white = df['Victim Race'] == 'White'\n",
"is_black = df['Victim Race'] == 'Black'\n",
"is_solved = df['Crime Solved'] == 'Yes'\n",
"\n",
"n1 = len(df[is_white])\n",
"X1 = len(df[is_white & is_solved])\n",
"n2 = len(df[is_black])\n",
"X2 = len(df[is_black & is_solved])\n",
"\n",
"hat_p1 = X1 / n1\n",
"hat_p2 = X2 / n2\n",
"bar_p = (X1 + X2) / (n1 + n2)\n",
"bar_q = 1 - bar_p\n",
"\n",
"print(n1, hat_p1)\n",
"print(n2, hat_p2)\n",
"\n",
"nom = (hat_p1 - hat_p2) - 0\n",
"dem = sqrt(bar_p * bar_q * (1 / n1 + 1 / n2))\n",
"z = nom / dem\n",
"\n",
"z"
]
}
],
"metadata": {
"kernelspec": {
"display_name": "Python 3",
"language": "python",
"name": "python3"
},
"language_info": {
"codemirror_mode": {
"name": "ipython",
"version": 3
},
"file_extension": ".py",
"mimetype": "text/x-python",
"name": "python",
"nbconvert_exporter": "python",
"pygments_lexer": "ipython3",
"version": "3.5.3"
}
},
"nbformat": 4,
"nbformat_minor": 2
}
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