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South Staffordshire Council
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\n",
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Spelthorne Borough Council
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tree
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3477
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\n",
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Swale Borough Council
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\n",
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Tandridge District Council
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2032
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1895
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\n",
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Torbay Council
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2079
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6572
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218
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4417
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\n",
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\n",
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+ "179 tree Harlow District Council \n",
+ "180 tree Horsham District Council \n",
+ "181 tree Lake District National Park Authority \n",
+ "182 tree Leicester City Council \n",
+ "183 tree Liverpool City Council \n",
+ "184 tree London Borough of Barnet \n",
+ "185 tree London Borough of Brent \n",
+ "186 tree London Borough of Lambeth \n",
+ "187 tree London Borough of Southwark \n",
+ "188 tree London Borough of Tower Hamlets \n",
+ "189 tree London Borough of Waltham Forest \n",
+ "190 tree Maidstone Borough Council \n",
+ "191 tree New Forest District Council \n",
+ "192 tree New Forest National Park Authority \n",
+ "193 tree Newcastle City Council \n",
+ "194 tree North Lincolnshire Council \n",
+ "195 tree North Norfolk District Council \n",
+ "196 tree North Somerset Council \n",
+ "197 tree Northumberland County Council \n",
+ "198 tree Peterborough City Council \n",
+ "199 tree Plymouth City Council \n",
+ "200 tree Rossendale Borough Council \n",
+ "201 tree Rother District Council \n",
+ "202 tree Royal Borough of Kensington and Chelsea \n",
+ "203 tree Salford City Council \n",
+ "204 tree Sandwell Metropolitan Borough Council \n",
+ "205 tree Sefton Metropolitan Borough Council \n",
+ "206 tree South Cambridgeshire District Council \n",
+ "207 tree South Gloucestershire Council \n",
+ "208 tree South Staffordshire Council \n",
+ "209 tree Spelthorne Borough Council \n",
+ "210 tree St Albans City and District Council \n",
+ "211 tree Stockport Metropolitan Borough Council \n",
+ "212 tree Swale Borough Council \n",
+ "213 tree Tandridge District Council \n",
+ "214 tree Tewkesbury Borough Council \n",
+ "215 tree Thanet District Council \n",
+ "216 tree Torbay Council \n",
+ "217 tree West Berkshire Council \n",
+ "218 tree Wirral Borough Council \n",
+ "\n",
+ " organisation platform_count \\\n",
+ "164 local-authority:ASF 1224 \n",
+ "165 local-authority:BAI 549 \n",
+ "166 local-authority:BST 4961 \n",
+ "167 local-authority:BUC 7591 \n",
+ "168 local-authority:CAS 1629 \n",
+ "169 local-authority:WSM 4286 \n",
+ "170 local-authority:DAR 1202 \n",
+ "171 local-authority:DNC 2832 \n",
+ "172 local-authority:DOV 1365 \n",
+ "173 local-authority:ECA 2557 \n",
+ "174 local-authority:ERY 3476 \n",
+ "175 local-authority:EPS 4345 \n",
+ "176 local-authority:GAT 2716 \n",
+ "177 local-authority:GLO 1358 \n",
+ "178 local-authority:HAL 449 \n",
+ "179 local-authority:HAR 1467 \n",
+ "180 local-authority:HOR 7996 \n",
+ "181 national-park-authority:Q27159704 2093 \n",
+ "182 local-authority:LCE 5005 \n",
+ "183 local-authority:LIV 1571 \n",
+ "184 local-authority:BNE 6198 \n",
+ "185 local-authority:BEN 1229 \n",
+ "186 local-authority:LBH 2551 \n",
+ "187 local-authority:SWK 3035 \n",
+ "188 local-authority:TWH 1769 \n",
+ "189 local-authority:WFT 2043 \n",
+ "190 local-authority:MAI 10126 \n",
+ "191 local-authority:NEW 6702 \n",
+ "192 national-park-authority:Q72617158 2574 \n",
+ "193 local-authority:NET 7336 \n",
+ "194 local-authority:NLN 6989 \n",
+ "195 local-authority:NNO 1509 \n",
+ "196 local-authority:NSM 4129 \n",
+ "197 local-authority:NBL 3614 \n",
+ "198 local-authority:PTE 2379 \n",
+ "199 local-authority:PLY 1252 \n",
+ "200 local-authority:ROS 1729 \n",
+ "201 local-authority:ROH 1922 \n",
+ "202 local-authority:KEC 4560 \n",
+ "203 local-authority:SLF 1296 \n",
+ "204 local-authority:SAW 659 \n",
+ "205 local-authority:SFT 4794 \n",
+ "206 local-authority:SCA 2478 \n",
+ "207 local-authority:SGC 6086 \n",
+ "208 local-authority:SST 10505 \n",
+ "209 local-authority:SPE 300 \n",
+ "210 local-authority:SAL 3477 \n",
+ "211 local-authority:SKP 8177 \n",
+ "212 local-authority:SWL 1596 \n",
+ "213 local-authority:TAN 1678 \n",
+ "214 local-authority:TEW 2032 \n",
+ "215 local-authority:THA 1895 \n",
+ "216 local-authority:TOB 2079 \n",
+ "217 local-authority:WBK 6572 \n",
+ "218 local-authority:WRL 4417 \n",
+ "\n",
+ " dataset_resource_count match \n",
+ "164 0.0 mismatch \n",
+ "165 NaN mismatch \n",
+ "166 4940.0 mismatch \n",
+ "167 591.0 mismatch \n",
+ "168 0.0 mismatch \n",
+ "169 0.0 mismatch \n",
+ "170 0.0 mismatch \n",
+ "171 0.0 mismatch \n",
+ "172 0.0 mismatch \n",
+ "173 0.0 mismatch \n",
+ "174 0.0 mismatch \n",
+ "175 0.0 mismatch \n",
+ "176 NaN mismatch \n",
+ "177 0.0 mismatch \n",
+ "178 0.0 mismatch \n",
+ "179 0.0 mismatch \n",
+ "180 656.0 mismatch \n",
+ "181 0.0 mismatch \n",
+ "182 0.0 mismatch \n",
+ "183 0.0 mismatch \n",
+ "184 3217.0 mismatch \n",
+ "185 0.0 mismatch \n",
+ "186 0.0 mismatch \n",
+ "187 0.0 mismatch \n",
+ "188 0.0 mismatch \n",
+ "189 0.0 mismatch \n",
+ "190 0.0 mismatch \n",
+ "191 NaN mismatch \n",
+ "192 2574.0 match \n",
+ "193 NaN mismatch \n",
+ "194 6979.0 mismatch \n",
+ "195 0.0 mismatch \n",
+ "196 0.0 mismatch \n",
+ "197 0.0 mismatch \n",
+ "198 0.0 mismatch \n",
+ "199 0.0 mismatch \n",
+ "200 0.0 mismatch \n",
+ "201 0.0 mismatch \n",
+ "202 0.0 mismatch \n",
+ "203 0.0 mismatch \n",
+ "204 0.0 mismatch \n",
+ "205 0.0 mismatch \n",
+ "206 0.0 mismatch \n",
+ "207 0.0 mismatch \n",
+ "208 0.0 mismatch \n",
+ "209 2656.0 mismatch \n",
+ "210 0.0 mismatch \n",
+ "211 0.0 mismatch \n",
+ "212 0.0 mismatch \n",
+ "213 0.0 mismatch \n",
+ "214 0.0 mismatch \n",
+ "215 0.0 mismatch \n",
+ "216 0.0 mismatch \n",
+ "217 0.0 mismatch \n",
+ "218 0.0 mismatch "
+ ]
+ },
+ "execution_count": 26,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "testing.loc[testing['dataset'] == 'tree']"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "id": "20bd0fdd",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "image/png": 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",
+ "text/plain": [
+ "
"
+ ]
+ },
+ "metadata": {},
+ "output_type": "display_data"
+ }
+ ],
+ "source": [
+ "f, ax = plt.subplots(figsize=(12, 6))\n",
+ "\n",
+ "data = testing.groupby(['dataset', 'match']).size().unstack(fill_value=0)\n",
+ "\n",
+ "data.plot(kind='bar', ax=ax)\n",
+ "plt.xticks(rotation=45, ha='right')\n",
+ "\n",
+ "ax.set_ylabel('Count')\n",
+ "\n",
+ "plt.show()"
+ ]
+ },
+ {
+ "cell_type": "markdown",
+ "id": "67f40c81",
+ "metadata": {},
+ "source": [
+ "---\n",
+ "## Randomly check resources\n",
+ "\n",
+ "Take ten resources at random and investigate them to get a sample of the problems.\n",
+ "\n",
+ "Some things to check for each:\n",
+ "\n",
+ "1. the entity_count of the platform vs the dataset_resource entity_count. If they are equal then all is good\n",
+ "2. if they are not equal then download the transformed_resource (I can probably give you the link structure to follow) and download the entities from the platform. Get the entities that aren’t in the resource but are on the platform. you can then look at these entities to check the history of what’s going on.\n",
+ "3. check the entity_count on the dataset_resource is equal to the entity_count in the transformed resource. This wil tell us if there’s a processing issue\n",
+ "\n",
+ "the link to transformed resources wil be something like:\n",
+ "files.planning.data.gov.uk/-collection/transformed//.csv"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "id": "20ee654b",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "['47b2c02399036050fe507e4b90ee532f25293b19f7cec9fb308336e625f7109b',\n",
+ " '3081bb2dec818694e2a6eaccd39ad37abc7dca39131dd8b085d413931ee21109',\n",
+ " 'bb6a8eb7e6f4720030aec3bd5fdff525700d8ae045cd7b32fd08f4c8614efc5e',\n",
+ " '6eb49d0579ac5162126f793800ead81a74761b880e4066fc623a01ec72678952',\n",
+ " '5837666a86b654bdf27164f2fa5bdc6c74552efccda5c4ff982356df89fa1787',\n",
+ " '8b88687d996f7b9bbcfae4f4ba7c515f55eef34212a0064eb922891d3c99a97e',\n",
+ " 'd632df6f6b49074036d830502a8f22df6b3e75593d9a8cd783d4ccf33d6dd67a',\n",
+ " 'dd14d735a5cc249c6a3c4751e134c2040a83e58aa0c29475faed0825b54c2927',\n",
+ " 'ae9aa5602c72b8a76da0eac063f7190e9211fd198244e244c89d65b41306a192',\n",
+ " '85e3264ae354c8701d3147d13c54a4bc6b5edbefebfe6add3cf010243303ef95']"
+ ]
+ },
+ "execution_count": 41,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "# Get a random sample of 10 resources\n",
+ "sample_df = report_he.sample(n=10, random_state=42)\n",
+ "sample_resources = sample_df['resource'].tolist()\n",
+ "\n",
+ "sample_resources"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 63,
+ "id": "b09eba1f",
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/html": [
+ "