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CMPengCMPeng
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column names subject to change by whether arcpy and fgdb
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‎samples/04_gis_analysts_data_scientists/finding_a_new_home.ipynb

Lines changed: 146 additions & 17 deletions
Original file line numberDiff line numberDiff line change
@@ -61,7 +61,23 @@
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"cell_type": "markdown",
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"metadata": {},
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"source": [
64-
"## Selling your home"
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"## Selling your home\n",
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"\n",
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"Execute the following command to install the openpyxl library if not already:\n",
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"```\n",
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"!pip install openpyxl\n",
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"```"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Also, when `matplotlib` is not present, run the following command to have it install:\n",
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"```\n",
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"import sys \n",
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"!{sys.executable} -m pip install matplotlib\n",
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"```"
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]
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},
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{
@@ -186,6 +202,45 @@
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"gis = GIS('home')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"Use the boolean `has_arcpy` to flag whether `arcpy` is present on the local environement:"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 2,
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"metadata": {},
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"outputs": [],
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"source": [
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"has_arcpy = False"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 4,
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"metadata": {},
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"outputs": [
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{
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"name": "stdout",
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"output_type": "stream",
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"text": [
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"arcpy not present\n"
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]
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}
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],
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"source": [
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"import importlib\n",
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"try:\n",
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" if importlib.find_module('arcpy'):\n",
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" has_arcpy = True\n",
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" print(\"arcpy present\")\n",
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"except:\n",
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" print(\"arcpy not present\")"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -2700,6 +2755,15 @@
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" tags='datascience')"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
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"source": [
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"When arcpy is present, the `import_data` will upload the local SeDF as a FGDB to your organization, and publish to a hosted feature layer; On the ther hand, when arcpy is not present, then the `import_data` method would have the local SeDF upload to your organization as a shapefile, and then publish as a hosted Feature Layer. This minor difference will result in column/property name differences from what's defined in the original SeDF.\n",
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"\n",
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"The `has_arcpy` flag is to be used in determine which naming convention the newly created Feature Layer would be conforming to, when we are adding the Feature Layer for display based on variables."
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {},
@@ -2740,16 +2804,30 @@
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"m1"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 6,
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"metadata": {},
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"outputs": [],
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"source": [
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"cur_field_name = \"BuyerSellerIndex\"\n",
2814+
"if has_arcpy:\n",
2815+
" if cur_field_name not in mergd_lyr.layers[0].properties.fields:\n",
2816+
" cur_field_name = \"buyer_seller_index\"\n",
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"else:\n",
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" cur_field_name = \"BuyerSelle\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 68,
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"metadata": {},
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"outputs": [],
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"source": [
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"m1.add_layer(mergd_lyr, {\"renderer\":\"ClassedColorRenderer\",\n",
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" \"field_name\":\"BuyerSellerIndex\",\n",
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" \"field_name\":cur_field_name,\n",
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" \"opacity\":0.7\n",
2752-
" })"
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" })"
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]
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},
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{
@@ -2785,14 +2863,28 @@
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"m2"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 7,
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"metadata": {},
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"outputs": [],
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"source": [
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"cur_field_name = \"DaysOnMarket\"\n",
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"if has_arcpy:\n",
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" if cur_field_name not in mergd_lyr.layers[0].properties.fields:\n",
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" cur_field_name = \"days_on_market\"\n",
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"else:\n",
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" cur_field_name = \"DaysOnMark\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 70,
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"metadata": {},
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"outputs": [],
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"source": [
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"m2.add_layer(mergd_lyr, {\"renderer\":\"ClassedSizeRenderer\",\n",
2795-
" \"field_name\":\"DaysOnMarket\",\n",
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" \"field_name\":cur_field_name,\n",
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" \"opacity\":0.7\n",
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" })"
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]
@@ -4222,17 +4314,31 @@
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"m4"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 8,
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"metadata": {},
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"outputs": [],
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"source": [
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"cur_field_name = \"MarketHealthIndex\"\n",
4324+
"if has_arcpy:\n",
4325+
" if cur_field_name not in hlth_lyr.layers[0].properties.fields:\n",
4326+
" cur_field_name = \"market_health_index\"\n",
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"else:\n",
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" cur_field_name = \"MarketHeal\""
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]
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},
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{
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"cell_type": "code",
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"execution_count": 104,
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"metadata": {},
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"outputs": [],
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"source": [
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"m4.add_layer(hlth_lyr, {\"renderer\":\"ClassedColorRenderer\",\n",
4232-
" \"field_name\":\"MarketHealthIndex\",\n",
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" \"field_name\":cur_field_name,\n",
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" \"classificationMethod\":'quantile',\n",
4234-
" \"opacity\":0.7\n",
4235-
" })"
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" \"opacity\":0.7\n",
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" })"
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]
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},
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{
@@ -4398,10 +4504,10 @@
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"outputs": [],
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"source": [
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"m5.add_layer(hlth_lyr, {\"renderer\":\"ClassedColorRenderer\",\n",
4401-
" \"field_name\":\"MarketHealthIndex\",\n",
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" \"field_name\":cur_field_name,\n",
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" \"classificationMethod\":'quantile',\n",
4403-
" \"opacity\":0.7\n",
4404-
" })\n",
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" \"opacity\":0.7\n",
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" })\n",
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"m5.add_layer(drive_time_lyr)"
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]
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},
@@ -4451,9 +4557,9 @@
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"metadata": {},
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"outputs": [],
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"source": [
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"m6.add_layer(hlth_lyr, {\"renderer\":\"ClassedColorRenderer\",\n",
4560+
"m6.add_layer(hlth_lyr, { \"renderer\":\"ClassedColorRenderer\",\n",
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" \"field_name\":\"ZHVI\",\n",
4456-
" \"classificationMethod\":'quantile',\n",
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" \"classificationMethod\":'quantile',\n",
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" \"opacity\":0.7\n",
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" })\n",
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"m6.add_layer(drive_time_lyr)"
@@ -4499,16 +4605,30 @@
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"m7"
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]
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},
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{
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"cell_type": "code",
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"execution_count": 10,
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"metadata": {},
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"outputs": [],
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"source": [
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"cur_field_name = \"ForecastYoYPctChange\"\n",
4615+
"if has_arcpy:\n",
4616+
" if cur_field_name not in hlth_lyr.layers[0].properties.fields:\n",
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" cur_field_name = \"forecase_yo_y_pct_change\"\n",
4618+
"else:\n",
4619+
" cur_field_name = \"ForecastYo\""
4620+
]
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},
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{
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"cell_type": "code",
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"execution_count": 119,
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"metadata": {},
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"outputs": [],
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"source": [
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"m7.add_layer(hlth_lyr, {\"renderer\":\"ClassedColorRenderer\",\n",
4509-
" \"field_name\":\"ForecastYoYPctChange\",\n",
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" \"field_name\":cur_field_name,\n",
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" \"classificationMethod\":'quantile',\n",
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" \"opacity\":0.7\n",
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" \"opacity\":0.7\n",
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" })\n",
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"m7.add_layer(drive_time_lyr)"
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]
@@ -4855,7 +4975,11 @@
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}
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],
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"source": [
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"zip_hlth_intersect.query(where='((ZHVI > 350000) AND (ZHVI < 600000) AND (MarketHealthIndex > 8) AND (ForecastYoYPctChange> 0.06)) AND (1=1)').sdf"
4978+
"if has_arcpy:\n",
4979+
" zip_hlth_intersect_df = zip_hlth_intersect.query(where='((ZHVI > 350000) AND (ZHVI < 600000) AND (MarketHealthIndex > 8) AND (ForecastYoYPctChange> 0.06)) AND (1=1)').sdf\n",
4980+
"else:\n",
4981+
" zip_hlth_intersect_df = zip_hlth_intersect.query(where='((ZHVI > 350000) AND (ZHVI < 600000) AND (MarketHeal > 8) AND (ForecastYo> 0.06)) AND (1=1)').sdf\n",
4982+
"zip_hlth_intersect_df"
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]
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},
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{
@@ -4890,9 +5014,14 @@
48905014
"metadata": {},
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"outputs": [],
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"source": [
4893-
"m9.add_layer(zip_hlth_intersect,\n",
5017+
"if has_arcpy:\n",
5018+
" m9.add_layer(zip_hlth_intersect,\n",
48945019
" {\"definition_expression\": '((ZHVI > 350000) AND (ZHVI < 600000) AND (MarketHealthIndex > 8) AND (ForecastYoYPctChange > 0.06)) AND (1=1)',\n",
48955020
" \"classificationMethod\":'quantile'})\n",
5021+
"else:\n",
5022+
" m9.add_layer(zip_hlth_intersect,\n",
5023+
" {\"definition_expression\": '((ZHVI > 350000) AND (ZHVI < 600000) AND (MarketHeal > 8) AND (ForecastYo> 0.06)) AND (1=1)',\n",
5024+
" \"classificationMethod\":'quantile'})\n",
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"m9.zoom_to_layer(zip_hlth_intersect)"
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]
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},
@@ -4938,7 +5067,7 @@
49385067
"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
4941-
"version": "3.9.15"
5070+
"version": "3.9.16"
49425071
}
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},
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"nbformat": 4,

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