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* Guide updates for v2 * Guide updates * More guide updates * Update self ssl troubleshooting section * Add hyperlinks and code files * Formatting readme * Formatting readme * Formatting readme * Change nut graph and add Insights Desktop topic * Readme update * Adding diagrams * Add client diagram * Desktop and Cloud diagrams * Desktop and Client revisions diagrams * Connection section * Create connection w/ url table * Start command logic * Stop instructions * superscript note * Connection images, connection section * new images * image updates * image updates * image updates * more doc * new images * Adds tool table, general feature section * Table styling * Table styling * Table styling markdowm * more table styling markdowm * more styling * Changes not honored * Table update * Magic commands and shortcuts * Edits and readme clean up * More edits and readme clean up * readme clean up * readme * readme * readme update * Docker section * Readme update * Readme update connection section * Readme update * Readme update * Readme update * Readme update * Readme update * more edits * more edits * tweaks * move section * added code sample * Sample tweaks * Sample tweaks * Sample tweaks * Sample update
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Doug Carroll
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## Create a Panda Data Frame with Geocode Ready Data | ||
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Creates a Panda data frame that can be imported and persisted into Insights workbooks for further analysis. | ||
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__Steps__ | ||
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1) In the first cell paste this code and then click Run (or _Shift-Enter_) | ||
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```python | ||
import pandas as pd | ||
import numpy as np | ||
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df = pd.DataFrame(np.array([ | ||
["1001 19th St North", "Arlington", "VA", "Esri R&D"], | ||
["380 New York St", "Redlands", "CA", "Esri Headquarters"], | ||
["920 SW #rd Avenue", "Portland", "OR", "Esri R&D"], | ||
["75 Broad St", "New York City", "NY", "Esri Regional Office"] | ||
]), columns=["Address", "City", "State", "Office"]) | ||
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df | ||
``` | ||
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2) In second cell paste this code (or use the magic command _Ctrl-Alt-B_ passing in ```df```) and then click Run. Notice the layer named ```Layer``` is created in the _Data Pane_ | ||
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```python | ||
%insights_return(df) | ||
``` | ||
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3) Next find the layer named ```Layer``` in the _Data Pane_ and click the ellipse. From the context menu select __Enable Location__. Choose _Address_ and _Esri World Geocoder_. Then click Run. | ||
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4) Notice a new field _Location_ is created in the layer. This is a geometry field. Next drag the layer onto the canvas to create a Map (by dropping the layer in a _drop zone_ named _Map_). Once the map is created you can visualise and style each location and dive deeper into spatial and non-spatial analytics. |
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import pandas as pd | ||
import numpy as np | ||
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df = pd.DataFrame(np.array([ | ||
["1001 19th St North", "Arlington", "VA", "Esri R&D"], | ||
["380 New York St", "Redlands", "CA", "Esri Headquarters"], | ||
["920 SW #rd Avenue", "Portland", "OR", "Esri R&D"], | ||
["75 Broad St", "New York City", "NY", "Esri Regional Office"] | ||
]), columns=["Address", "City", "State", "Office"]) | ||
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df |
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## Kernel Gateway Deployment Planning | ||
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Want to learn more about deploying a Jupyter Kernel Gateway? These conceptual diagrams were designed to help organizations visualize different Jupyter Kernel Gateway configurations. | ||
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### Insights Desktop | ||
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![Insights Desktop and Kernel Gateway](diagrams/jkg-desktop-diagram.png) | ||
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### ArcGIS Enterprise with Insights and Kernel Gateway | ||
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#### Dedicated | ||
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![Dedicated Kernel Gateway](diagrams/jkg-dedicated-diagram.png) | ||
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#### Co-Located | ||
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![Co-Located Kernel Gateway](diagrams/jkg-colocated-diagram.png) | ||
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#### Client Kernel Gateway System Design | ||
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![Client Kernel Gateway](diagrams/jkg-client-diagram.png) | ||
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### On-premises ArcGIS Enterprise with Insights Insights and Cloud Kernel Gateway | ||
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![Cloud Kernel Gateway](diagrams/jkg-cloud-diagram.png) | ||
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*Other configurations and designs may exisit. |
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name: insights-base | ||
channels: | ||
- defaults | ||
- esri | ||
dependencies: | ||
- python=3.7 | ||
- jupyter_kernel_gateway=2.1.0 | ||
- pandas | ||
- numpy | ||
- shapely | ||
- requests | ||
- msgpack-python | ||
- matplotlib | ||
- geopandas | ||
- arcgis | ||
- r-itertools | ||
- r-essentials |
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