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replace nan with null
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@ikerey
ikerey committed Nov 28, 2024
1 parent c6aecc9 commit 132b14b
Showing 1 changed file with 30 additions and 29 deletions.
59 changes: 30 additions & 29 deletions data-processing/notebooks/04_prepare_tabular_data.ipynb
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},
{
"cell_type": "code",
"execution_count": 1,
"execution_count": 2,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -117,7 +117,7 @@
},
{
"cell_type": "code",
"execution_count": 2,
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
Expand Down Expand Up @@ -151,29 +151,29 @@
},
{
"cell_type": "code",
"execution_count": 3,
"execution_count": 4,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"{'Agricultural drought exposure': <datasets.datasets.Dataset object at 0x7f79df77a3f0>,\n",
" 'Agricultural drought hazard': <datasets.datasets.Dataset object at 0x7f79df77a420>,\n",
" 'Boundaries': <datasets.datasets.Dataset object at 0x7f79df77a480>,\n",
" 'Contextual layers': <datasets.datasets.Dataset object at 0x7f79df77a540>,\n",
" 'EO-based flood exposure': <datasets.datasets.Dataset object at 0x7f79df77a600>,\n",
" 'EO-based flood hazard': <datasets.datasets.Dataset object at 0x7f79df77a4b0>,\n",
" 'Hydrographic data': <datasets.datasets.Dataset object at 0x7f79df77a4e0>,\n",
" 'Hydrometeorological Data': <datasets.datasets.Dataset object at 0x7f79df77a690>,\n",
" 'In-situ Data': <datasets.datasets.Dataset object at 0x7f79df77a6c0>,\n",
" 'Meteorological drought exposure': <datasets.datasets.Dataset object at 0x7f79df77a6f0>,\n",
" 'Meteorological drought hazard': <datasets.datasets.Dataset object at 0x7f79df77a720>,\n",
" 'Model-based flood exposure': <datasets.datasets.Dataset object at 0x7f79df77a750>,\n",
" 'Model-based flood hazard': <datasets.datasets.Dataset object at 0x7f79df77a780>,\n",
" 'Populated infrastructures': <datasets.datasets.Dataset object at 0x7f79df77a7b0>,\n",
" 'Transportation Network Infrastructures': <datasets.datasets.Dataset object at 0x7f79df77a7e0>,\n",
" 'Water-related infrastructures': <datasets.datasets.Dataset object at 0x7f79df77a810>}\n"
"{'Agricultural drought exposure': <datasets.datasets.Dataset object at 0x7f3df02993d0>,\n",
" 'Agricultural drought hazard': <datasets.datasets.Dataset object at 0x7f3d16b4e630>,\n",
" 'Boundaries': <datasets.datasets.Dataset object at 0x7f3d4bf21850>,\n",
" 'Contextual layers': <datasets.datasets.Dataset object at 0x7f3d16bca630>,\n",
" 'EO-based flood exposure': <datasets.datasets.Dataset object at 0x7f3d16bca600>,\n",
" 'EO-based flood hazard': <datasets.datasets.Dataset object at 0x7f3d16bca660>,\n",
" 'Hydrographic data': <datasets.datasets.Dataset object at 0x7f3d16bca720>,\n",
" 'Hydrometeorological Data': <datasets.datasets.Dataset object at 0x7f3d16bca7e0>,\n",
" 'In-situ Data': <datasets.datasets.Dataset object at 0x7f3d16bca690>,\n",
" 'Meteorological drought exposure': <datasets.datasets.Dataset object at 0x7f3d16bca6c0>,\n",
" 'Meteorological drought hazard': <datasets.datasets.Dataset object at 0x7f3d16bca870>,\n",
" 'Model-based flood exposure': <datasets.datasets.Dataset object at 0x7f3d16bca8a0>,\n",
" 'Model-based flood hazard': <datasets.datasets.Dataset object at 0x7f3d16bca8d0>,\n",
" 'Populated infrastructures': <datasets.datasets.Dataset object at 0x7f3d16bca900>,\n",
" 'Transportation Network Infrastructures': <datasets.datasets.Dataset object at 0x7f3d16bca930>,\n",
" 'Water-related infrastructures': <datasets.datasets.Dataset object at 0x7f3d16bca960>}\n"
]
}
],
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},
{
"cell_type": "code",
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"{'Administrative Boundaries - adm0': <datasets.datasets.Layer at 0x7f79df77a930>,\n",
" 'Administrative Boundaries - adm1': <datasets.datasets.Layer at 0x7f79df77a840>,\n",
" 'Administrative Boundaries - adm2': <datasets.datasets.Layer at 0x7f79df77b110>,\n",
" 'Administrative Boundaries - adm3': <datasets.datasets.Layer at 0x7f79df77b140>,\n",
" 'Hydrological Basins': <datasets.datasets.Layer at 0x7f79df77b1a0>}"
"{'Administrative Boundaries - adm0': <datasets.datasets.Layer at 0x7f3d16bc84d0>,\n",
" 'Administrative Boundaries - adm1': <datasets.datasets.Layer at 0x7f3d16bcbef0>,\n",
" 'Administrative Boundaries - adm2': <datasets.datasets.Layer at 0x7f3d16bcab10>,\n",
" 'Administrative Boundaries - adm3': <datasets.datasets.Layer at 0x7f3d16bcaa20>,\n",
" 'Hydrological Basins': <datasets.datasets.Layer at 0x7f3d16bcacf0>}"
]
},
"execution_count": 4,
"execution_count": 5,
"metadata": {},
"output_type": "execute_result"
}
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},
{
"cell_type": "code",
"execution_count": 5,
"execution_count": 6,
"metadata": {},
"outputs": [],
"source": [
Expand All @@ -352,7 +352,7 @@
},
{
"cell_type": "code",
"execution_count": 6,
"execution_count": 7,
"metadata": {},
"outputs": [
{
Expand Down Expand Up @@ -408,13 +408,14 @@
"\n",
" for _index, row in df.iterrows():\n",
" code = str(row[code_column])\n",
" y_values = convert_string_to_float_list(row[\"y_axis_values\"])\n",
"\n",
" chart_data = {\n",
" \"id\": chart_id,\n",
" \"location_code\": str(row[code_column]),\n",
" \"year\": int(row[\"year\"]),\n",
" \"x_values\": literal_eval(row[\"x_axis_values\"]),\n",
" \"y_values\": convert_string_to_float_list(row[\"y_axis_values\"]),\n",
" \"y_values\": [None if math.isnan(value) else value for value in y_values],\n",
" \"createdAt\": datetime.now().isoformat(),\n",
" \"updatedAt\": datetime.now().isoformat(),\n",
" \"layer\": layer_id,\n",
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