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|
{
"cells": [
{
"cell_type": "code",
"execution_count": 1,
"id": "israeli-perth",
"metadata": {},
"outputs": [],
"source": [
"import pandas as pd"
]
},
{
"cell_type": "code",
"execution_count": 199,
"id": "pacific-fellow",
"metadata": {},
"outputs": [],
"source": [
"pd.options.plotting.backend = \"matplotlib\""
]
},
{
"cell_type": "code",
"execution_count": 137,
"id": "brave-marsh",
"metadata": {},
"outputs": [],
"source": [
"vals = range(21, 25)\n",
"indexes = [0,1,1,1] # first file has no סמל ועדה column\n",
"dfs = [pd.read_csv(f\"{e}.csv\", index_col=i, encoding=\"iso-8859-8\") for e, i in zip(vals, indexes)]\n",
"\n",
"# cleanup NaN columns\n",
"del dfs[2][\"Unnamed: 37\"]\n",
"del dfs[3][\"Unnamed: 46\"]"
]
},
{
"cell_type": "code",
"execution_count": 138,
"id": "ordered-executive",
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Index(['סמל ועדה', 'סמל ישוב', 'בזב', 'מצביעים', 'פסולים', 'כשרים', 'אמת', 'ב',\n",
" 'ג', 'ודעם', 'ז', 'זץ', 'ט', 'י', 'יז', 'ינ', 'יף', 'יק', 'יר', 'כ',\n",
" 'כך', 'כן', 'ל', 'מחל', 'מרצ', 'נ', 'ני', 'נר', 'עם', 'פה', 'ףז', 'צי',\n",
" 'צכ', 'צף', 'ץ', 'ק', 'קי', 'קך', 'קץ', 'ר', 'רנ', 'רף', 'רק', 'שס',\n",
" 'ת'],\n",
" dtype='object')\n"
]
}
],
"source": [
"print(dfs[3].keys())"
]
},
{
"cell_type": "code",
"execution_count": 139,
"id": "foreign-segment",
"metadata": {},
"outputs": [],
"source": [
"static_keys = [\"סמל ועדה\", \"שם ישוב\", \"בזב\", \"מצביעים\", \"פסולים\", \"כשרים\"]"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "ranging-calculation",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 141,
"id": "analyzed-optimization",
"metadata": {},
"outputs": [],
"source": [
"df2 = dfs[2].sort_values(by=['בזב'], ascending=False)[:50]\n",
"df3 = dfs[3].sort_values(by=['בזב'], ascending=False)[:50]"
]
},
{
"cell_type": "code",
"execution_count": 200,
"id": "associate-wesley",
"metadata": {},
"outputs": [],
"source": [
"def scatter_party(p):\n",
" rate3 = df3[\"מצביעים\"] / df3[\"בזב\"]\n",
" rate2 = df2[\"מצביעים\"] / df2[\"בזב\"]\n",
" p3 = df3[p] / rate3\n",
" p2 = df2[p] / rate2\n",
" odf = pd.DataFrame({\n",
" \"votes\": rate3 / rate2,\n",
" \"party\": p3 / p2,\n",
" \"name\": df3[p]\n",
" })\n",
" return odf[odf[\"party\"] < 1].plot.scatter(x=\"votes\", y=\"party\")"
]
},
{
"cell_type": "code",
"execution_count": 201,
"id": "unexpected-alcohol",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='votes', ylabel='party'>"
]
},
"execution_count": 201,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scatter_party(\"מחל\")"
]
},
{
"cell_type": "code",
"execution_count": 202,
"id": "frank-sleeping",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='votes', ylabel='party'>"
]
},
"execution_count": 202,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": "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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scatter_party(\"שס\")"
]
},
{
"cell_type": "code",
"execution_count": 203,
"id": "industrial-procurement",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='votes', ylabel='party'>"
]
},
"execution_count": 203,
"metadata": {},
"output_type": "execute_result"
},
{
"data": {
"image/png": 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\n",
"text/plain": [
"<Figure size 432x288 with 1 Axes>"
]
},
"metadata": {
"needs_background": "light"
},
"output_type": "display_data"
}
],
"source": [
"scatter_party(\"ודעם\")"
]
},
{
"cell_type": "code",
"execution_count": null,
"id": "charming-hardware",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "worth-despite",
"metadata": {},
"outputs": [],
"source": []
}
],
"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": "3.9.2"
}
},
"nbformat": 4,
"nbformat_minor": 5
}
|
