What are some ways to use Python to interact with external data sources in ArcGIS Pro?

Python is a powerful and versatile programming language that can enhance your GIS workflows and projects. With Python, you can access and manipulate data from various external sources, such as web services, databases, files, and APIs. In this article, you will learn some ways to use Python to interact with external data sources in ArcGIS Pro, the leading desktop GIS application from Esri.

1 Use arcpy to access data

ArcGIS Pro comes with arcpy, a Python module that provides access to the geoprocessing tools and data management capabilities of ArcGIS. With arcpy, you can use Python scripts to perform tasks such as creating, updating, deleting, and querying data in various formats and locations. For example, you can use arcpy to connect to a spatial database, such as PostgreSQL or SQL Server, and execute SQL statements to manipulate the data. You can also use arcpy to read and write data from web services, such as ArcGIS Online or ArcGIS Server, using the REST API.

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Stephen Amade Reuben

Full Stack Web Developer | Mobile Developer | Banking Professional
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ArcPy is a package that provides useful and productive way to perform GIS analysis and manage GIS data using Python. ArcPy provides functions to read, write and manipulate GIS data. For example: """ import arcpy #Reading a shapefile fc = "pat/to/your/shapefile.shp" cursor = arcpy.da.SearchCursor(fc, ["SHAPE@", "ATTRIBUTE1", "ATTRIBUTE2"] for row in cursor: pring(row) """

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2 Use pandas to analyze data

Pandas is a popular Python library that provides high-performance data structures and analysis tools for working with tabular and time series data. With pandas, you can use Python scripts to load, filter, transform, aggregate, and visualize data from various sources, such as CSV files, Excel files, JSON files, or databases. For example, you can use pandas to read a CSV file from a web URL and perform some statistical calculations on the data. You can also use pandas to connect to a database using SQLAlchemy, a Python library that provides a unified interface for various database engines, and execute SQL queries to retrieve data.

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Stephen Amade Reuben

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Using Python and Pandas with arcPy for External Data Reading: Python's 'pandas' library is a powerful tool for data manipulation and analysis. It excels in handling tabular data, making it an excellent choice for processing non-spatial data before integrating it into ArcGIS Pro. Considering a scenario where a CSV file containing point data and we want to create a point feature class in ArcGIS Pro. """ import pandas as pd import arcpy #Read csv data to pandas DataFrame csv_path = 'path/to/you/data.csv' df = pd.read_csv(csv_path) # use pd.read_excel('path/to/your/data.xlsx') to read excel. #convert pandas DataFrame to a point feature class arcpy.da.NumPyArrayToFeatureClass(df.to_records(), 'SHAPE@'. 'path/to/your/output_points.shp' """

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3 Use requests to communicate with APIs

Requests is a simple and elegant Python library that allows you to send HTTP requests and handle responses from web servers. With requests, you can use Python scripts to communicate with various web APIs that provide access to data and services on the internet. For example, you can use requests to fetch data from an open data portal, such as CKAN or Socrata, using the API endpoints and parameters. You can also use requests to post data to a web service, such as Geocodio or Mapbox, using the API key and payload.

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4 Use arcpy and pandas together

One of the advantages of using Python for GIS is that you can combine different libraries and modules to achieve your goals. For example, you can use arcpy and pandas together to leverage the strengths of both tools. You can use arcpy to access spatial data from ArcGIS Pro and convert it to a pandas DataFrame, a tabular data structure that supports various operations and methods. You can then use pandas to perform data analysis and manipulation on the DataFrame, such as filtering, grouping, joining, or plotting. You can also use pandas to create a DataFrame from an external data source and convert it to an arcpy FeatureClass, a spatial data structure that supports geometry and attributes.

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5 Use Jupyter Notebook to document and share your work

Jupyter Notebook is an interactive web-based environment that allows you to create and share documents that contain code, text, images, and outputs. With Jupyter Notebook, you can use Python scripts to interact with external data sources in ArcGIS Pro and document your workflow and results in a clear and reproducible way. You can also use Jupyter Notebook to share your work with others, such as colleagues, clients, or the public, by exporting your notebook to various formats, such as HTML, PDF, or Markdown, or by hosting your notebook on a web server, such as GitHub or Binder.

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6 Here’s what else to consider

This is a space to share examples, stories, or insights that don’t fit into any of the previous sections. What else would you like to add?

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What are some ways to use Python to interact with external data sources in ArcGIS Pro?

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3

4

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6

1 Use arcpy to access data

2 Use pandas to analyze data

3 Use requests to communicate with APIs

4 Use arcpy and pandas together

5 Use Jupyter Notebook to document and share your work

6 Here’s what else to consider

Geographic Information Systems (GIS)

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What are some ways to use Python to interact with external data sources in ArcGIS Pro?

1

2

3

4

5

6

1 Use arcpy to access data

2 Use pandas to analyze data

3 Use requests to communicate with APIs

4 Use arcpy and pandas together

5 Use Jupyter Notebook to document and share your work

6 Here’s what else to consider

Geographic Information Systems (GIS)

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