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Merge 'upstream/next' into jy-24upd-add-basic-guides
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guide/03-the-gis/working-with-different-authentication-schemes.ipynb

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"## Storing your credentials locally\n",
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"If you are signing in frequently to a particular GIS and would like to store the credentials locally on your computer, then you could do so using the `profile` parameter of your `GIS`.\n",
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"\n",
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"Persistent profiles for the GIS can be created by giving the GIS authorization credentials and specifying a profile name. The profile stores all of the authorization credentials (except the password) in the user’s home directory in an unencrypted config file named `.arcgisprofile.` The profile securely stores the password in an O.S. specific password manager through the [keyring](https://pypi.python.org/pypi/keyring) python module. (Note: Linux systems may need additional software installed and configured for proper security) Once a profile has been saved, passing the profile parameter by itself uses the authorization credentials saved in the configuration file/password manager by that profile name. Multiple profiles can be created and used in parallel.\n"
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"Persistent profiles for the GIS can be created by giving the GIS authorization credentials and specifying a profile name. The profile stores all of the authorization credentials (except the password) in the user’s home directory in an unencrypted config file named `.arcgisprofile.` The profile securely stores the password in an OS specific password manager through the [keyring](https://pypi.python.org/pypi/keyring) python module. (Note: Linux systems may need additional software installed and configured for proper security) Once a profile has been saved, passing the profile parameter by itself uses the authorization credentials saved in the configuration file/password manager by that profile name. Multiple profiles can be created and used in parallel.\n"
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]
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},
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{

guide/09-finding-places-with-geocoding/part3_finding_point_of_interests.ipynb

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guide/09-finding-places-with-geocoding/part4_batch_geocoding.ipynb

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guide/14-deep-learning/feature_categorization.ipynb

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samples/03_org_administrators/clone_portal_users_groups_and_content_rn.ipynb

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samples/03_org_administrators/move_existing_user_content_to_a_new_user.ipynb

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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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"execution_count": null,
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"metadata": {
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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},
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"scrolled": true
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},
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"outputs": [],
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"cell_type": "code",
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"execution_count": null,
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"metadata": {
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"collapsed": true
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"collapsed": true,
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"jupyter": {
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"outputs_hidden": true
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}
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},
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"outputs": [],
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"source": [
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" print(\"Item may have been already assigned to the user.\")\n",
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"\n",
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"for folder in folders:\n",
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" gis.content.create_folder(folder['title'], new_userid)\n",
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" gis.content.folders.create(folder['title'], new_userid)\n",
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" folderitems = olduser.items(folder=folder['title'])\n",
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" for item in folderitems:\n",
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" item.reassign_to(new_userid, target_folder=folder['title'])"
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"metadata": {
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"anaconda-cloud": {},
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"kernelspec": {
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"display_name": "Python 3",
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"display_name": "Python 3 (ipykernel)",
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"language": "python",
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"name": "python3"
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},
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"name": "python",
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"nbconvert_exporter": "python",
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"pygments_lexer": "ipython3",
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"version": "3.9.11"
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"version": "3.11.0"
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}
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},
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"nbformat": 4,
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"nbformat_minor": 1
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"nbformat_minor": 4
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}

samples/04_gis_analysts_data_scientists/analyze_us_tornadoes.ipynb

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samples/04_gis_analysts_data_scientists/analyzing_growth_factors_of_airbnb_properties_in_new_york_city.ipynb

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samples/04_gis_analysts_data_scientists/analyzing_violent_crime.ipynb

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"metadata": {},
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"outputs": [],
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"source": [
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"crime_map.add_layer(violent_crimes)"
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"crime_map.content.add(violent_crimes)"
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]
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"crime_spots_map.add_layer(crime_hot_spots)"
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"crime_spots_map.content.add(crime_hot_spots)"
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]
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"liquor_hot_spots_map.add_layer(liquor_vendor_hot_spots_lyr)"
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"liquor_hot_spots_map.content.add(liquor_vendor_hot_spots_lyr)"
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]
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"poverty_data_hot_spots_map.add_layer(poverty_data_hot_spots)"
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"poverty_data_hot_spots_map.content.add(poverty_data_hot_spots)"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"intersected_map.add_layer(intersect_cri_pov_liq)"
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"intersected_map.content.add(intersect_cri_pov_liq)"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"unemployment_rate_hot_spots_map.add_layer(unemployment_rate_hot_spots)"
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"unemployment_rate_hot_spots_map.content.add(unemployment_rate_hot_spots)"
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]
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},
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"overlay_map.add_layer(overlay_unemp_crime)"
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"overlay_map.content.add(overlay_unemp_crime)"
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]
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{
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"metadata": {},
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"outputs": [],
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"source": [
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"selected_high_schools_map.add_layer(selected_schools)"
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"selected_high_schools_map.content.add(selected_schools)"
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]
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},
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{

samples/04_gis_analysts_data_scientists/automate_road_surface_investigation_using_deep_learning.ipynb

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