Fix code

coal-price-forecast.ipynb

@@ -37,7 +37,7 @@
     "    csv_file = csv_file.split(\".\")[0]\n",
     "    df[csv_file] = pd.read_csv(f\"../coal-price-data/fred/{csv_file}.csv\")\n",
     "    df[csv_file][\"datetime\"] = pd.to_datetime(df[csv_file][csv_date], format=\"%Y-%m-%d\")\n",
-    "    df_m2_filtered = df[csv_file].loc[\n",
+    "    df[csv_file] = df[csv_file].loc[\n",
     "        (df[csv_file][\"datetime\"] >= csv_date_start) & (df[csv_file][\"datetime\"] < csv_date_end)\n",
     "    ]"
    ]
@@ -75,34 +75,34 @@
        "  </thead>\n",
        "  <tbody>\n",
        "    <tr>\n",
-       "      <th>0</th>\n",
-       "      <td>1959-01-01</td>\n",
-       "      <td>286.6</td>\n",
-       "      <td>1959-01-01</td>\n",
+       "      <th>634</th>\n",
+       "      <td>2011-11-01</td>\n",
+       "      <td>9612.6</td>\n",
+       "      <td>2011-11-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>1</th>\n",
-       "      <td>1959-02-01</td>\n",
-       "      <td>287.7</td>\n",
-       "      <td>1959-02-01</td>\n",
+       "      <th>635</th>\n",
+       "      <td>2011-12-01</td>\n",
+       "      <td>9660.1</td>\n",
+       "      <td>2011-12-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>2</th>\n",
-       "      <td>1959-03-01</td>\n",
-       "      <td>289.2</td>\n",
-       "      <td>1959-03-01</td>\n",
+       "      <th>636</th>\n",
+       "      <td>2012-01-01</td>\n",
+       "      <td>9733.3</td>\n",
+       "      <td>2012-01-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>3</th>\n",
-       "      <td>1959-04-01</td>\n",
-       "      <td>290.1</td>\n",
-       "      <td>1959-04-01</td>\n",
+       "      <th>637</th>\n",
+       "      <td>2012-02-01</td>\n",
+       "      <td>9785.7</td>\n",
+       "      <td>2012-02-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
-       "      <th>4</th>\n",
-       "      <td>1959-05-01</td>\n",
-       "      <td>292.2</td>\n",
-       "      <td>1959-05-01</td>\n",
+       "      <th>638</th>\n",
+       "      <td>2012-03-01</td>\n",
+       "      <td>9830.6</td>\n",
+       "      <td>2012-03-01</td>\n",
        "    </tr>\n",
        "    <tr>\n",
        "      <th>...</th>\n",
@@ -111,6 +111,12 @@
        "      <td>...</td>\n",
        "    </tr>\n",
        "    <tr>\n",
+       "      <th>774</th>\n",
+       "      <td>2023-07-01</td>\n",
+       "      <td>20863.8</td>\n",
+       "      <td>2023-07-01</td>\n",
+       "    </tr>\n",
+       "    <tr>\n",
        "      <th>775</th>\n",
        "      <td>2023-08-01</td>\n",
        "      <td>20825.6</td>\n",
@@ -134,32 +140,26 @@
        "      <td>20767.5</td>\n",
        "      <td>2023-11-01</td>\n",
        "    </tr>\n",
-       "    <tr>\n",
-       "      <th>779</th>\n",
-       "      <td>2023-12-01</td>\n",
-       "      <td>20865.2</td>\n",
-       "      <td>2023-12-01</td>\n",
-       "    </tr>\n",
        "  </tbody>\n",
        "</table>\n",
-       "<p>780 rows × 3 columns</p>\n",
+       "<p>145 rows × 3 columns</p>\n",
        "</div>"
       ],
       "text/plain": [
        "           DATE     M2SL   datetime\n",
-       "0    1959-01-01    286.6 1959-01-01\n",
-       "1    1959-02-01    287.7 1959-02-01\n",
-       "2    1959-03-01    289.2 1959-03-01\n",
-       "3    1959-04-01    290.1 1959-04-01\n",
-       "4    1959-05-01    292.2 1959-05-01\n",
+       "634  2011-11-01   9612.6 2011-11-01\n",
+       "635  2011-12-01   9660.1 2011-12-01\n",
+       "636  2012-01-01   9733.3 2012-01-01\n",
+       "637  2012-02-01   9785.7 2012-02-01\n",
+       "638  2012-03-01   9830.6 2012-03-01\n",
        "..          ...      ...        ...\n",
+       "774  2023-07-01  20863.8 2023-07-01\n",
        "775  2023-08-01  20825.6 2023-08-01\n",
        "776  2023-09-01  20755.4 2023-09-01\n",
        "777  2023-10-01  20725.7 2023-10-01\n",
        "778  2023-11-01  20767.5 2023-11-01\n",
-       "779  2023-12-01  20865.2 2023-12-01\n",
        "\n",
-       "[780 rows x 3 columns]"
+       "[145 rows x 3 columns]"
       ]
      },
      "execution_count": 3,
@@ -173,7 +173,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 4,
    "metadata": {},
    "outputs": [
     {
@@ -194,13 +194,13 @@
       "ICI_1                     277.62\n",
       "datetime     2023-12-01 00:00:00\n",
       "dtype: object\n",
-      "DATE                 1959-01-01\n",
-      "M2SL                      286.6\n",
-      "datetime    1959-01-01 00:00:00\n",
+      "DATE                 2011-11-01\n",
+      "M2SL                     9612.6\n",
+      "datetime    2011-11-01 00:00:00\n",
       "dtype: object\n",
-      "DATE                 2023-12-01\n",
+      "DATE                 2023-11-01\n",
       "M2SL                    21703.5\n",
-      "datetime    2023-12-01 00:00:00\n",
+      "datetime    2023-11-01 00:00:00\n",
       "dtype: object\n"
      ]
     }
@@ -214,7 +214,7 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 5,
    "metadata": {},
    "outputs": [
     {
@@ -1455,120 +1455,1342 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "y = \"M2SL\"\n",
-    "fig = px.line(df_m2_filtered, x=\"datetime\", y=y, labels={\"Month\": \"Date\"})\n",
-    "fig.update_layout(\n",
-    "    template=\"simple_white\",\n",
-    "    font=dict(size=18),\n",
-    "    title_text=y,\n",
-    "    width=650,\n",
-    "    title_x=0.5,\n",
-    "    height=400,\n",
-    ")\n",
-    "fig.show()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_coal"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "df_m2_filtered"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "print(len(df_coal.newcastle))\n",
-    "print(len(df_m2_filtered[\"M2SL\"]))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = df_coal.newcastle\n",
-    "y = df_coal.ICI_1\n",
-    "\n",
-    "slope, intercept, r, p, std_err = stats.linregress(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x = df_coal.newcastle\n",
-    "y = df_m2_filtered[\"M2SL\"]\n",
-    "\n",
-    "slope, intercept, r, p, std_err = stats.linregress(x, y)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "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}\")"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 6,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.plotly.v1+json": {
+       "config": {
+        "plotlyServerURL": "https://plot.ly"
+       },
+       "data": [
+        {
+         "hovertemplate": "datetime=%{x}<br>M2SL=%{y}<extra></extra>",
+         "legendgroup": "",
+         "line": {
+          "color": "#636efa",
+          "dash": "solid"
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+         "marker": {
+          "symbol": "circle"
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+         "mode": "lines",
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+         "orientation": "v",
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+            "linecolor": "rgb(36,36,36)",
+            "showgrid": false,
+            "showline": true,
+            "ticks": "outside"
+           },
+           "baxis": {
+            "gridcolor": "rgb(232,232,232)",
+            "linecolor": "rgb(36,36,36)",
+            "showgrid": false,
+            "showline": true,
+            "ticks": "outside"
+           },
+           "bgcolor": "white",
+           "caxis": {
+            "gridcolor": "rgb(232,232,232)",
+            "linecolor": "rgb(36,36,36)",
+            "showgrid": false,
+            "showline": true,
+            "ticks": "outside"
+           }
+          },
+          "title": {
+           "x": 0.05
+          },
+          "xaxis": {
+           "automargin": true,
+           "gridcolor": "rgb(232,232,232)",
+           "linecolor": "rgb(36,36,36)",
+           "showgrid": false,
+           "showline": true,
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zeroline": false,
+           "zerolinecolor": "rgb(36,36,36)"
+          },
+          "yaxis": {
+           "automargin": true,
+           "gridcolor": "rgb(232,232,232)",
+           "linecolor": "rgb(36,36,36)",
+           "showgrid": false,
+           "showline": true,
+           "ticks": "outside",
+           "title": {
+            "standoff": 15
+           },
+           "zeroline": false,
+           "zerolinecolor": "rgb(36,36,36)"
+          }
+         }
+        },
+        "title": {
+         "text": "M2SL",
+         "x": 0.5
+        },
+        "width": 650,
+        "xaxis": {
+         "anchor": "y",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "datetime"
+         }
+        },
+        "yaxis": {
+         "anchor": "x",
+         "domain": [
+          0,
+          1
+         ],
+         "title": {
+          "text": "M2SL"
+         }
+        }
+       }
+      }
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y = \"M2SL\"\n",
+    "fig = px.line(df[y], x=\"datetime\", y=y, labels={\"Month\": \"Date\"})\n",
+    "fig.update_layout(\n",
+    "    template=\"simple_white\",\n",
+    "    font=dict(size=18),\n",
+    "    title_text=y,\n",
+    "    width=650,\n",
+    "    title_x=0.5,\n",
+    "    height=400,\n",
+    ")\n",
+    "fig.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df[\"coal_price_data\"].newcastle\n",
+    "y = df[\"coal_price_data\"].ICI_1\n",
+    "\n",
+    "slope, intercept, r, p, std_err = stats.linregress(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "slope: 0.600533935403765\n",
+      "intercept: 33.65381401159914\n",
+      "r: 0.9606500704209069\n",
+      "p: 1.9310655623962052e-81\n",
+      "std_err: 0.01452032511898455\n"
+     ]
+    }
+   ],
+   "source": [
+    "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}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "x = df[\"coal_price_data\"][\"newcastle\"]\n",
+    "y = df[\"M2SL\"][\"M2SL\"]\n",
+    "\n",
+    "slope, intercept, r, p, std_err = stats.linregress(x, y)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "slope: -20.46182026230733\n",
+      "intercept: 17246.85603449831\n",
+      "r: -0.4331490046040797\n",
+      "p: 5.279632704944257e-08\n",
+      "std_err: 3.5605661278914575\n"
+     ]
+    }
+   ],
+   "source": [
+    "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}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
    "source": [
     "def myfunc(x):\n",
     "    return slope * x + intercept\n",