The python standard library map function takes a function and a list, and applies the function to each element of the list. For more information, visit the official Python re docs.
We could easily take this list and use it to instantiate User Profile objects within our system, display user profiles on a web-page, or persist profile data in a database. Is there a way not to read chunks of the file in memory? Save my name, email, and website in this browser for the next time I comment.
Notify me of followup comments via e-mail. All rights reserved Terms of Service. If you are new to Python regular expressions, the following two articles will help: Getting started with python reg-ex using re. Sign up to join this community. The best answers are voted up and rise to the top.
Stack Overflow for Teams — Collaborate and share knowledge with a private group. Create a free Team What is Teams? Learn more. Parse complex text files using Python Ask Question. Asked 4 years ago. Active 4 years ago. Viewed 42k times. Once you understand the input data, the next step is to determine what would be a more usable format.
Well, this depends entirely on how you plan on using the data. For further data analysis, I highly recommend reading the data into a pandas DataFrame. If you a Python data analyst then you are most likely familiar with pandas. It is a Python package that provides the DataFrame class and other functions to do insanely powerful data analysis with minimal effort.
It is an abstraction on top of Numpy which provides multi-dimensional arrays, similar to Matlab. The DataFrame is a 2D array, but it can have multiple row and column indices, which pandas calls MultiIndex , that essentially allows it to store multi-dimensional data. Although, we would want to read the data into a feature-rich data structure like a pandas DataFrame , it would be very inefficient to create an empty DataFrame and directly write data to it.
A DataFrame is a complex data structure, and writing something to a DataFrame item by item is computationally expensive. Once the list or dict is created, pandas allows us to easily convert it to a DataFrame as you will see later on.
The image below shows the standard process when it comes to parsing any file. If your data is in a standard format or close enough, then there is probably an existing package that you can use to read your data with minimal effort. You can handle this easily with pandas.
Python is incredible when it comes to dealing with strings. It is worth internalising all the common string operations. We can use these methods to extract data from a string as you can see in the simple example below. As you saw in the previous two sections, if the parsing problem is simple we might get away with just using an existing parser or some string methods. How do we go about parsing a complex text file?
The data it contains is pretty simple though as you can see below:. The sample text looks similar to a CSV in that it uses commas to separate out some information. There is a title and some metadata at the top of the file. School, Grade and Student number are keys. Name and Score are fields. In other words, School, Grade, and Student Number together form a compound key.
The data is given in a hierarchical format. First, a School is declared, then a Grade. This is followed by two tables providing Name and Score for each Student number. Then Grade is incremented. This is followed by another set of tables.
The mode is an optional parameter. For example, to open a file whose name is the-zen-of-python. The open function returns a file object which you will use to read text from a text file. To close the file automatically without calling the close method, you use the with statement like this:.
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