v.4.0.2

loop-calling.ipynb

@@ -2758,22 +2758,10 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 8,
    "id": "607bbcc1",
    "metadata": {},
-   "outputs": [
+   "outputs": [],
-    {
-     "ename": "NameError",
-     "evalue": "name 'os' is not defined",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
-      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
-      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[2]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m list_pds = []\n\u001b[32m      2\u001b[39m folder = \u001b[33m'export_landmarks'\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m file \u001b[38;5;28;01min\u001b[39;00m os.listdir(folder):\n\u001b[32m      4\u001b[39m     \u001b[38;5;28;01mif\u001b[39;00m file.endswith(\u001b[33m'.csv'\u001b[39m):\n\u001b[32m      5\u001b[39m         \u001b[38;5;66;03m# Use os.path.join to ensure the correct file path\u001b[39;00m\n\u001b[32m      6\u001b[39m         df = pd.read_csv(os.path.join(folder, file))\n",
-      "\u001b[31mNameError\u001b[39m: name 'os' is not defined"
-     ]
-    }
-   ],
    "source": [
     "import os,sys,pandas as pd\n",
     "\n",
@@ -2786,7 +2774,9 @@
     "        list_pds.append(df)\n",
     "\n",
     "# 2. Combine them all at once (Vertical Stack)\n",
-    "global_pd = pd.concat(list_pds, ignore_index=True)"
+    "global_pd = pd.concat(list_pds, ignore_index=True)\n",
+    "\n",
+    "global_pd.to_csv('all_landmarks.csv')"
    ]
   },
   {
@@ -2796,17 +2786,21 @@
    "metadata": {},
    "outputs": [],
    "source": [
-    "len(global_pd)\n",
+    "# len(global_pd)\n",
     "\n",
-    "global_pd['garmin_type'].unique()\n",
+    "# global_pd['garmin_type'].unique()\n",
-    "global_pd['garmin_subtype'].unique()\n",
+    "# global_pd['garmin_subtype'].unique()\n",
     "\n",
-    "water_df = global_pd[global_pd['semantic_tags_json'].str.contains('water', na=False)]\n",
+    "# water_df = global_pd[global_pd['semantic_tags_json'].str.contains('water', na=False)]\n",
-    "spring = global_pd[global_pd['semantic_tags_json'].str.contains('spring', na=False)]\n",
+    "# spring = global_pd[global_pd['semantic_tags_json'].str.contains('spring', na=False)]\n",
     "\n",
-    "#{\"amenity\": \"drinking_water\"}\n",
+    "# global_water_pd = pd.concat([water_df,spring],ignore_index=True)\n",
-    "#11%\n",
+    "# global_water_pd.to_csv('water-landmarks.csv')\n",
-    "#{\"natural\": \"spring\"}"
+    "# #{\"amenity\": \"drinking_water\"}\n",
+    "# #11%\n",
+    "# #{\"natural\": \"spring\"}\n",
+    "# import json\n",
+    "# amenities = global_pd[global_pd['semantic_tags_json'].str.contains('amenity')]\n"
    ]
   },
   {
@@ -2814,8 +2808,53 @@
    "execution_count": null,
    "id": "0791e550",
    "metadata": {},
-   "outputs": [],
+   "outputs": [
-   "source": []
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "['0x66' '0x64']\n",
+      "['0x00' '0x14' '0x0e' '0x08' '0x09' '0x16']\n"
+     ]
+    }
+   ],
+   "source": [
+    "#extracting \"хижа\"\n",
+    "\n",
+    "living = global_pd[global_pd['semantic_tags_json'].str.contains('хижа', na=False)]\n",
+    "print(living['garmin_type'].unique())\n",
+    "print(living['garmin_subtype'].unique())\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "1080c073",
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Found 179303 matching entries.\n"
+     ]
+    }
+   ],
+   "source": [
+    "target_types = ['0x66', '0x64']\n",
+    "target_subtypes = ['0x00', '0x14', '0x0e', '0x08', '0x09', '0x16']\n",
+    "\n",
+    "# Query the big DataFrame for any rows matching both criteria\n",
+    "housing_living = global_pd[\n",
+    "    global_pd['garmin_type'].isin(target_types) & \n",
+    "    global_pd['garmin_subtype'].isin(target_subtypes)\n",
+    "]\n",
+    "\n",
+    "print(f\"Found {len(housing_living)} matching entries.\")\n",
+    "\n",
+    "housing_living.to_csv('landmark-living-housing-water.csv')"
+   ]
   }
  ],
  "metadata": {