An essential part of a GIS is the data that represents both the geometry (locations) of geographic features and the attributes of those features. This combination of features and attributes is what makes GIS go beyond just "mapping." Much of your work as a GIS analyst involves adding, modifying, and deleting features and their attributes from the GIS.
Beyond maintaining the data, you also need to know how to query and select the data that is most important to your projects. Sometimes you'll want to query a dataset to find only the records that match a certain criteria (for example, single-family homes constructed before 1980) and calculate some statistics based on only the selected records (for example, percentage of those homes that experienced termite infestation).
All of the above tasks of maintaining, querying, and summarizing data can become tedious and error prone if performed manually. Python scripting is often a faster and more accurate way to read and write large amounts of data. There are already many pre-existing tools for data selection and management in ArcGIS Pro. Any of these can be used in a Python script. For more customized scenarios where you want to read through a table yourself and modify records one-by-one, arcpy contains special objects, called cursors, that you can use to examine each record in a table. You'll quickly see how the looping logic that you learned in Lesson 2 becomes useful when you are cycling through tables using cursors.
Using a script to work with your data introduces some other subtle advantages over manual data entry. For example, in a script, you can add checks to ensure that the data entered conforms to a certain format. You can also chain together multiple steps of selection logic that would be time-consuming to perform in ArcGIS Pro.
This lesson explains ways to read and write GIS data using Python. We'll start off by looking at how you can create and open datasets within a script. Then, we'll practice reading and writing data using both geoprocessing tools and cursor objects. Although this is most applicable to vector datasets, we'll also look at some ways you can manipulate rasters with Python. Once you're familiar with these concepts, Project 3 will give you a chance to practice what you've learned.