For example, sometimes a comma-separated value file might have substrings that naturally contain commas, such as dates or addresses. When you write your own parser, however, it's hard to anticipate all the exceptional cases you might run across. One of the most helpful ones is string.split(), which turns a big string into a list of smaller strings based on some delimiting character, such as a space or comma. If a module or library doesn't exist that fits your parsing needs, then you'll have to extract the information from the text yourself using Python's string manipulation methods. Other helpful libraries such as this include lxml and xml.dom for parsing XML, and BeautifulSoup for parsing HTML. In this lesson, you will work with the Python "csv" module that can read comma-delimited values and turn them into a Python list.
Usually the wisest is to see if some Python module exists that will examine the text for you and turn it into an object that you can then work with. There are several different approaches to parsing. Parsing the response involves writing some code to read through the lines and tags in the XML and isolating only those three values. This is called parsing the text.įor example, a Web service may return you many lines of XML describing all the readings at a weather station, when all you're really interested in are the coordinates of the weather station and the annual average temperature. If no tool or script exists, you'll need to do some programmatic work to read the file and separate out the pieces of text that you really need. When faced with these files, you should first understand if your GIS software already comes with a tool or script that can read or convert the data to a format it can use.
But often GIS data is collected and shared in more "raw" formats such as a spreadsheet in CSV (comma-separated value) format, a list of coordinates in a text file, or an XML response received through a Web service. In Lesson 3, we talked about how to read data in ArcGIS's native formats, such as feature classes. One of the best ways to increase your effectiveness as a GIS programmer is to learn how to manipulate text-based information.
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