Extract function

1 files changed, 71 insertions, 36 deletions

diff --git a/stats.py.ipynb b/stats.py.ipynb
index dbead51..2294c64 100644
--- a/stats.py.ipynb
+++ b/stats.py.ipynb
@@ -12,8 +12,8 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 152,
-   "id": "referenced-windows",
+   "execution_count": 199,
+   "id": "pacific-fellow",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -72,7 +72,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "human-morocco",
+   "id": "ranging-calculation",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -80,7 +80,7 @@
   {
    "cell_type": "code",
    "execution_count": 141,
-   "id": "confirmed-general",
+   "id": "analyzed-optimization",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -90,23 +90,76 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 155,
-   "id": "vulnerable-disposal",
+   "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='rates', ylabel='likud'>"
+       "<AxesSubplot:xlabel='votes', ylabel='party'>"
       ]
      },
-     "execution_count": 155,
+     "execution_count": 202,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -118,37 +171,28 @@
     }
    ],
    "source": [
-    "rate3 = df3[\"מצביעים\"] / df3[\"בזב\"]\n",
-    "rate2 = df2[\"מצביעים\"] / df2[\"בזב\"]\n",
-    "lik3 = df3[\"מחל\"] / rate3\n",
-    "lik2 = df2[\"מחל\"] / rate2\n",
-    "odf = pd.DataFrame({\n",
-    "    \"rates\": rate3 / rate2,\n",
-    "    \"likud\": lik3 / lik2\n",
-    "})\n",
-    "odf.sort_values(by=\"rates\", ascending=True)[:100]\n",
-    "odf.plot.scatter(x=\"rates\", y=\"likud\")"
+    "scatter_party(\"שס\")"
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 159,
-   "id": "fossil-grove",
+   "execution_count": 203,
+   "id": "industrial-procurement",
    "metadata": {},
    "outputs": [
     {
      "data": {
       "text/plain": [
-       "<AxesSubplot:xlabel='rates', ylabel='mesh'>"
+       "<AxesSubplot:xlabel='votes', ylabel='party'>"
       ]
      },
-     "execution_count": 159,
+     "execution_count": 203,
      "metadata": {},
      "output_type": "execute_result"
     },
     {
      "data": {
-      "image/png": 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\n",
+      "image/png": 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\n",
       "text/plain": [
        "<Figure size 432x288 with 1 Axes>"
       ]
@@ -160,22 +204,13 @@
     }
    ],
    "source": [
-    "rate3 = df3[\"מצביעים\"] / df3[\"בזב\"]\n",
-    "rate2 = df2[\"מצביעים\"] / df2[\"בזב\"]\n",
-    "mesh3 = df3[\"ודעם\"] / rate3\n",
-    "mesh2 = df2[\"ודעם\"] / rate2\n",
-    "odf = pd.DataFrame({\n",
-    "    \"rates\": rate3 / rate2,\n",
-    "    \"mesh\": mesh3 / mesh2\n",
-    "})\n",
-    "odf.sort_values(by=\"rates\", ascending=True)[:100]\n",
-    "odf.plot.scatter(x=\"rates\", y=\"mesh\")"
+    "scatter_party(\"ודעם\")"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "prostate-meter",
+   "id": "charming-hardware",
    "metadata": {},
    "outputs": [],
    "source": []
@@ -183,7 +218,7 @@
   {
    "cell_type": "code",
    "execution_count": null,
-   "id": "enabling-equality",
+   "id": "worth-despite",
    "metadata": {},
    "outputs": [],
    "source": []