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Email Required, but never shown. The Overflow Blog. Podcast Making Agile work for data science. Stack Gives Back Featured on Meta. New post summary designs on greatest hits now, everywhere else eventually. Related Hot Network Questions. So either of these setups would work in instantiating the myreader object:. If you managed to turn the raw text data into a file, which you then opened as a file-object, then this would work:.
What is myreader. It should be treated as a collection-type object to iterate through. For example, this is how to print each age value for each line:.
Each row in the iterator is a list object. Index 0 is the name value, and 1 is the age value. We have to re-load it with data, i. We can pass myreader into the list function, which converts myreader into a list of lists:. Forcing the myreader object to turn into a list is basically the same thing as creating an empty list, and looping through myreader and appending each object of the iteration to the empty list:.
Take a look at the last line of this interactive output. That is, what does the age value look like to us humans and what do we want it to mean, versus, what does the Python interpreter think it is?
That means our original goal of adding up ages will not work with simple, naive addition of values:. We will be using pandas for serious data-crunching. What can we use to convert a text string of numbers into actual numbers? The int function:. Of course there are variations depending on how much you like brevity. DictReader class, which you can read about in the official documentation.
Basically, csv. DictReader works just like csv. Initializing the csv. DictReader class is the same process as csv. The DictReader method assumes that the first line is the column headers, and thus are not meant to be actual data. Not much changes beyond that. There are several different approaches to parsing. 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.
In this lesson, you will work with the Python "csv" module that can read comma-delimited values and turn them into a Python list. Other helpful libraries such as this include lxml and xml. 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.
One of the most helpful ones is string. When you write your own parser, however, it's hard to anticipate all the exceptional cases you might run across. For example, sometimes a comma-separated value file might have substrings that naturally contain commas, such as dates or addresses. In these cases, splitting the string using a simple comma as the delimiter is not sufficient and you need to add extra logic. Another pitfall when parsing is the use of "magic numbers" to slice off a particular number of characters in a string, to refer to a specific column number in a spreadsheet, and so on.
If the structure of the data changes, or if the script is applied to data with a slightly different structure, the code could be rendered inoperable and would require some precision surgery to fix.
People who read your code and see a number other than 0 to begin a series or 1 to increment a counter will often be left wondering how the number was derived and what it refers to. In programming, numbers other than 0 or 1 are magic numbers that should typically be avoided, or at least accompanied by a comment explaining what the number refers to. There are an infinite number of parsing scenarios that you can encounter. This lesson will attempt to teach you the general approach by walking through just one module and example.
In your final project for this course, you may choose to explore parsing other types of files. A common text-based data interchange format is the comma-separated value CSV file. This is often used when transferring spreadsheets or other tabular data. Each line in the file represents a row of the dataset, and the columns in the data are separated by commas.
The file often begins with a header line containing all the field names. Spreadsheet programs like Microsoft Excel can understand the CSV structure and display all the values in a row-column grid. A CSV file may look a little messier when you open it in a text editor, but it can be helpful to always continue thinking of it as a grid structure.
If you had a Python list of rows and a Python list of column values for each row, you could use looping logic to pull out any value you needed. This is exactly what the Python csv module gives you.
It's easiest to learn about the csv module by looking at a real example. The scenario below shows how the csv module can be used to parse information out of a GPS track file. This example reads a text file collected from a GPS unit.
The lines in the file represent readings taken from the GPS unit as the user traveled along a path. In this section of the lesson, you'll learn one way to parse out the coordinates from each reading.
The next section of the lesson uses a variation of this example to show how you could write the user's track to a polyline feature class. Please note, line breaks have been added to the file shown below to ensure that the text fits within the page margins. Click on this link to the gps track.
Notice that the file starts with a header line, explaining the meaning of the values contained in the readings from the GPS unit. Each subsequent line contains one reading. The goal for this example is to create a Python list containing the X,Y coordinates from each reading. Specifically, the script should be able to read the above file and print a text string like the one shown below.
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