diff options
| -rw-r--r-- | stats.py.ipynb | 68 |
1 files changed, 26 insertions, 42 deletions
diff --git a/stats.py.ipynb b/stats.py.ipynb index 6f6598c..e6e5d7a 100644 --- a/stats.py.ipynb +++ b/stats.py.ipynb @@ -61,7 +61,7 @@ { "cell_type": "code", "execution_count": 296, - "id": "dependent-stadium", + "id": "thirty-louis", "metadata": {}, "outputs": [], "source": [ @@ -71,8 +71,8 @@ }, { "cell_type": "code", - "execution_count": 383, - "id": "adjustable-pastor", + "execution_count": 392, + "id": "billion-fellow", "metadata": {}, "outputs": [], "source": [ @@ -88,22 +88,13 @@ " })\n", " odf = odf[odf[\"party\"] > 0.05]\n", " \n", - " return sns.regplot(x=\"party\", y=\"votes\", data=odf);\n", - " \n", - " g = odf.plot.scatter(y=\"votes\", x=\"party\")\n", - " m, c = fit_line(x=odf[\"party\"], y=odf[\"votes\"])\n", - " m, b = np.polyfit(odf[\"party\"], odf[\"votes\"], 1)\n", - " g.set_xlim(0, 1)\n", - " g.set_ylim(0.5, 1)\n", - " x = odf[\"party\"].to_numpy()\n", - " g.plot(x, m*x+b, color=\"red\")\n", - " return g" + " return sns.regplot(x=\"party\", y=\"votes\", data=odf, width=10)" ] }, { "cell_type": "code", - "execution_count": 384, - "id": "conceptual-liberal", + "execution_count": 393, + "id": "surface-enhancement", "metadata": {}, "outputs": [ { @@ -294,7 +285,7 @@ "[101 rows x 7 columns]" ] }, - "execution_count": 384, + "execution_count": 393, "metadata": {}, "output_type": "execute_result" } @@ -310,29 +301,22 @@ }, { "cell_type": "code", - "execution_count": 385, - "id": "composed-railway", + "execution_count": 394, + "id": "disturbed-payment", "metadata": {}, "outputs": [ { - "data": { - "text/plain": [ - "<AxesSubplot:xlabel='party', ylabel='votes'>" - ] - }, - "execution_count": 385, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "<Figure size 432x288 with 1 Axes>" - ] - }, - "metadata": {}, - "output_type": "display_data" + "ename": "TypeError", + "evalue": "regplot() got an unexpected keyword argument 'width'", + "output_type": "error", + "traceback": [ + "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", + "\u001b[0;32m<ipython-input-394-b1d74f3a5427>\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mscatter_party\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"מחל\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m<ipython-input-392-779386650bf4>\u001b[0m in \u001b[0;36mscatter_party\u001b[0;34m(p)\u001b[0m\n\u001b[1;32m 11\u001b[0m \u001b[0modf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0modf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0modf\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m\"party\"\u001b[0m\u001b[0;34m]\u001b[0m \u001b[0;34m>\u001b[0m \u001b[0;36m0.05\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 12\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 13\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0msns\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mregplot\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mx\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"party\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0my\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;34m\"votes\"\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0modf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mwidth\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0;36m10\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[0;32m~/.local/share/virtualenvs/election-stats-or_-nqt5/lib/python3.9/site-packages/seaborn/_decorators.py\u001b[0m in \u001b[0;36minner_f\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m 44\u001b[0m )\n\u001b[1;32m 45\u001b[0m \u001b[0mkwargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m{\u001b[0m\u001b[0mk\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32mfor\u001b[0m \u001b[0mk\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0marg\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mzip\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0msig\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mparameters\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m}\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 46\u001b[0;31m \u001b[0;32mreturn\u001b[0m \u001b[0mf\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m 47\u001b[0m \u001b[0;32mreturn\u001b[0m \u001b[0minner_f\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 48\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n", + "\u001b[0;31mTypeError\u001b[0m: regplot() got an unexpected keyword argument 'width'" + ] } ], "source": [ @@ -342,7 +326,7 @@ { "cell_type": "code", "execution_count": 386, - "id": "turkish-tribune", + "id": "running-emperor", "metadata": {}, "outputs": [ { @@ -373,7 +357,7 @@ { "cell_type": "code", "execution_count": 387, - "id": "unlimited-things", + "id": "round-stuff", "metadata": {}, "outputs": [ { @@ -404,7 +388,7 @@ { "cell_type": "code", "execution_count": 388, - "id": "great-expense", + "id": "residential-benchmark", "metadata": {}, "outputs": [ { @@ -435,7 +419,7 @@ { "cell_type": "code", "execution_count": 389, - "id": "collective-territory", + "id": "southeast-interval", "metadata": {}, "outputs": [ { @@ -466,7 +450,7 @@ { "cell_type": "code", "execution_count": 390, - "id": "interracial-savage", + "id": "surrounded-cooling", "metadata": {}, "outputs": [ { @@ -497,7 +481,7 @@ { "cell_type": "code", "execution_count": 391, - "id": "still-reform", + "id": "exempt-commissioner", "metadata": {}, "outputs": [ { |