Fix code

coal-price-forecast.ipynb

@@ -1506,7 +1506,7 @@
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "/var/folders/fj/ycln97zn6b1ckstg6ksdmgl80000gp/T/ipykernel_13160/2627926293.py:1: SettingWithCopyWarning:\n",
+      "/var/folders/fj/ycln97zn6b1ckstg6ksdmgl80000gp/T/ipykernel_16548/2627926293.py:1: SettingWithCopyWarning:\n",
       "\n",
       "\n",
       "A value is trying to be set on a copy of a slice from a DataFrame.\n",
@@ -9861,51 +9861,20 @@
     "plt.show()"
    ]
   },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
   {
    "cell_type": "code",
    "execution_count": 22,
    "metadata": {},
-   "outputs": [
-    {
-     "ename": "ValueError",
-     "evalue": "all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 145 and the array at index 1 has size 780",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mValueError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn[22], line 10\u001b[0m\n\u001b[1;32m      7\u001b[0m x \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mcoal_price_data\u001b[39m\u001b[38;5;124m\"\u001b[39m][\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mnewcastle\u001b[39m\u001b[38;5;124m\"\u001b[39m]\n\u001b[1;32m      8\u001b[0m y \u001b[38;5;241m=\u001b[39m df[\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mM2SL\u001b[39m\u001b[38;5;124m\"\u001b[39m][y_axis]\n\u001b[0;32m---> 10\u001b[0m slope, intercept, r, p, std_err \u001b[38;5;241m=\u001b[39m \u001b[43mstats\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mlinregress\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     12\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mslope: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mslope\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n\u001b[1;32m     13\u001b[0m \u001b[38;5;28mprint\u001b[39m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mintercept: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mintercept\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m)\n",
-      "File \u001b[0;32m/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/scipy/stats/_stats_mstats_common.py:167\u001b[0m, in \u001b[0;36mlinregress\u001b[0;34m(x, y, alternative)\u001b[0m\n\u001b[1;32m    162\u001b[0m ymean \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mmean(y, \u001b[38;5;28;01mNone\u001b[39;00m)\n\u001b[1;32m    164\u001b[0m \u001b[38;5;66;03m# Average sums of square differences from the mean\u001b[39;00m\n\u001b[1;32m    165\u001b[0m \u001b[38;5;66;03m#   ssxm = mean( (x-mean(x))^2 )\u001b[39;00m\n\u001b[1;32m    166\u001b[0m \u001b[38;5;66;03m#   ssxym = mean( (x-mean(x)) * (y-mean(y)) )\u001b[39;00m\n\u001b[0;32m--> 167\u001b[0m ssxm, ssxym, _, ssym \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mcov\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m1\u001b[39;49m\u001b[43m)\u001b[49m\u001b[38;5;241m.\u001b[39mflat\n\u001b[1;32m    169\u001b[0m \u001b[38;5;66;03m# R-value\u001b[39;00m\n\u001b[1;32m    170\u001b[0m \u001b[38;5;66;03m#   r = ssxym / sqrt( ssxm * ssym )\u001b[39;00m\n\u001b[1;32m    171\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ssxm \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0.0\u001b[39m \u001b[38;5;129;01mor\u001b[39;00m ssym \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0.0\u001b[39m:\n\u001b[1;32m    172\u001b[0m     \u001b[38;5;66;03m# If the denominator was going to be 0\u001b[39;00m\n",
-      "File \u001b[0;32m/opt/homebrew/anaconda3/envs/py312/lib/python3.12/site-packages/numpy/lib/function_base.py:2683\u001b[0m, in \u001b[0;36mcov\u001b[0;34m(m, y, rowvar, bias, ddof, fweights, aweights, dtype)\u001b[0m\n\u001b[1;32m   2681\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m rowvar \u001b[38;5;129;01mand\u001b[39;00m y\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m0\u001b[39m] \u001b[38;5;241m!=\u001b[39m \u001b[38;5;241m1\u001b[39m:\n\u001b[1;32m   2682\u001b[0m         y \u001b[38;5;241m=\u001b[39m y\u001b[38;5;241m.\u001b[39mT\n\u001b[0;32m-> 2683\u001b[0m     X \u001b[38;5;241m=\u001b[39m \u001b[43mnp\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconcatenate\u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mX\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43my\u001b[49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43maxis\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2685\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m ddof \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m   2686\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m bias \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m:\n",
-      "\u001b[0;31mValueError\u001b[0m: all the input array dimensions except for the concatenation axis must match exactly, but along dimension 1, the array at index 0 has size 145 and the array at index 1 has size 780"
-     ]
-    }
-   ],
+   "outputs": [],
    "source": [
-    "def myfunc(x):\n",
-    "    return slope * x + intercept\n",
-    "\n",
-    "\n",
-    "y_axis = \"M2SL\"\n",
-    "\n",
-    "x = df[\"coal_price_data\"][\"newcastle\"]\n",
-    "y = df[\"M2SL\"][y_axis]\n",
-    "\n",
-    "slope, intercept, r, p, std_err = stats.linregress(x, y)\n",
-    "\n",
-    "print(f\"slope: {slope}\")\n",
-    "print(f\"intercept: {intercept}\")\n",
-    "print(f\"r: {r}\")\n",
-    "print(f\"p: {p}\")\n",
-    "print(f\"std_err: {std_err}\")\n",
-    "\n",
-    "\n",
-    "mymodel = list(map(myfunc, x))\n",
-    "\n",
-    "plt.scatter(x, y)\n",
-    "plt.plot(x, mymodel, color=\"orange\")\n",
-    "plt.xlabel(\"Newcastle\")\n",
-    "plt.ylabel(y_axis)\n",
-    "plt.show()"
+    "y_axis = \"M2SL\""
    ]
   },
   {