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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": "persistent-recall",
"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": "russian-indian",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": 141,
"id": "accessory-least",
"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": 233,
"id": "future-posting",
"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",
" g = odf[odf[\"party\"] < 1].plot.scatter(x=\"votes\", y=\"party\")\n",
" g.set_ylim(0,1)\n",
" return g"
]
},
{
"cell_type": "code",
"execution_count": 234,
"id": "sublime-packaging",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='votes', ylabel='party'>"
]
},
"execution_count": 234,
"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": 235,
"id": "discrete-eugene",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='votes', ylabel='party'>"
]
},
"execution_count": 235,
"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": 236,
"id": "embedded-malpractice",
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"<AxesSubplot:xlabel='votes', ylabel='party'>"
]
},
"execution_count": 236,
"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": "moderate-scene",
"metadata": {},
"outputs": [],
"source": []
},
{
"cell_type": "code",
"execution_count": null,
"id": "tender-secretary",
"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
}
|
