Parsing text with Python
I hate parsing files, but it is something that I have had to do at the start of nearly every project. Parsing is not easy, and it can be a stumbling block for beginners. However, once you become comfortable with parsing files, you never have to worry about that part of the problem. That is why I recommend that beginners get comfortable with parsing files early on in their programming education. This article is aimed at Python beginners who are interested in learning to parse text files.
In this article, I will introduce you to my system for parsing files. I will briefly touch on parsing files in standard formats, but what I want to focus on is the parsing of complex text files. What do I mean by complex? Well, we will get to that, young padawan.
For reference, the slide deck that I use to present on this topic is available here. All of the code and the sample text that I use is available in my Github repo here.
Why parse files?
First, let us understand what the problem is. Why do we even need to parse files? In an imaginary world where all data existed in the same format, one could expect all programs to input and output that data. There would be no need to parse files. However, we live in a world where there is a wide variety of data formats. Some data formats are better suited to different applications. An individual program can only be expected to cater for a selection of these data formats. So, inevitably there is a need to convert data from one format to another for consumption by different programs. Sometimes data is not even in a standard format which makes things a little harder.
Parse Analyse (a string or text) into logical syntactic components.
I don’t like the above Oxford dictionary definition. So, here is my alternate definition.
Parse Convert data in a certain format into a more usable format.
The big picture
With that definition in mind, we can imagine that our input may be in any format. So, the first step, when faced with any parsing problem, is to understand the input data format. If you are lucky, there will be documentation that describes the data format. If not, you may have to decipher the data format for yourselves. That is always fun.
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. If the program that you want to feed the data into expects a CSV format, then that’s your end product. 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. SQL or database style operations can be easily performed with pandas (Comparison with SQL). Pandas also comes with a suite of IO tools which includes functions to deal with CSV, MS Excel, JSON, HDF5 and other data formats.
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. It’s a lot faster to read the data into a primitive data type like a list or a dict . 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.
Parsing text in standard format
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.
For example, let’s say we have a CSV file, data.txt:
You can handle this easily with pandas.
File Parsing and Data Analysis in Python Part I (Interactive Parsing and Data Visualisation)
1) File Parsing Definition: Parse essentially means to »resolve (a sentence) into its component parts and describe their syntactic roles». In computing, parsing is ‘an act of parsing a string or a text’. [Google Dictionary]File parsing in computer language means to give a meaning to the characters of a text file as per…
Share Project
comment 
Share ProjectThanks for choosing to leave a comment. Please keep in mind that all the comments are moderated as per our comment policy, and your email will not be published for privacy reasons. Please leave a personal & meaningful conversation.
Read more Projects by Adnan Zaib Bhat (17)
File Parsing and Data Analysis in Python Part I (Interactive Parsing and Data Visualisation)
1) File Parsing Definition: Parse essentially means to »resolve (a sentence) into its component parts and describe their syntactic roles». In computing, parsing is ‘an act of parsing a string or a text’. [Google Dictionary]File parsing in computer language means to give a meaning to the characters of a text file as per…
File Parsing and Data Analysis in Python Part I (Interactive Parsing and Data Visualisation)
1) File Parsing Definition: Parse essentially means to »resolve (a sentence) into its component parts and describe their syntactic roles». In computing, parsing is ‘an act of parsing a string or a text’. [Google Dictionary]File parsing in computer language means to give a meaning to the characters of a text file as per…
File Parsing and Data Analysis in Python Part II (Area Under Curve and Engine Performance)
1) Integration/Area Under Curve 1.1 PV Diagram In thermodynamics, a PV diagram is a plot which shows the relationship between the pressure and volume for a particular process. We know that ` dw = p.dv` is the small work done by the process at a particular instance. Hence, total work done by a process from…
Constrained Optimisation Using Lagrange Multipliers
Problem: Minimize: `5-(x-2)^2 -2(y-1)^2`; subject to the following constraint: `x + 4y = 3` 1) Lagrange Multipliers Lagrange multipliers technique is a fundamental technique to solve problems involving constrained problems. This method is utilised to find the local minima and maxima subjected to (at least one) equality…
Finding Minimum Air Cushion Pressure With Newton-Raphson Method in Python
1) Air Cushion Vehicle [source: https://bit.ly/2z5LgIl] A hovercraft, also known as an air-cushion vehicle or ACV, is an amphibious craft capable of travelling over land, water, mud, ice, and other surfaces. Hovercraft use blowers to produce a large volume of air below the hull that…
Curve Fitting: Linear, Cubic, Polynomial (1-5), Piecewise, Goodness of Fit and Regression Analysis In Python.
Curve Fitting Curve fitting is a process of determining a possible curve for a given set of values. This is useful in order to estimate any value that is not in the given range. In other words, it can be used to interpolate or extrapolate data. A good curve fit is one which will be able to predict and explain the trend…
Polynomial Curve-fitting, RMS Error and Goodness of the Curve Fit (Order = 1 — 10) in MatLab
Curve fitting is a process of determining a possible curve for a given set of values. This is useful in order to estimate any value that is not in the given range. In other words, it can be used to interpolate or extrapolate data. Polynomial curve fitting or Polynomial Regression is a process where the given data-set curve…
Genetic Algorithm. Finding The Global Maxima of The Stalagmite Function in MatLab
1. Genetic Algorithm A Genetic Algorithm is an optimisation technique which is used to minimise a certain function. When used in optimisation, generally its purpose is to minimise the error of a function. It is often used in curve fitting. This algorithm is named ‘Genetic’, because of its structure. It is a ‘…
Curve-Fitting by Piece-wise Curves and Spline Function in MatLab
Curve fitting is a process of determining a possible curve for a given set of values. This is useful in order to estimate any value that is not in the given range. In other words, it can be used to interpolate or extrapolate data. We can fit a curve either by using the polynomial regression (check out: Polynomial Curve-fitting,…
Coding PV-Plot of an Otto Cycle and Calculating the Thermal Efficiency
A code in MatLab to plot the PV Diagram and Thermal Efficiency of an Otto Cycle. Explanations are given in the comments following the code parts. The relationship between the crank-angle and Volume-traced is used in the code to derive the adiabatic curves. Thermal Efficiency has been calculated by the general otto…
Otto Cycle Plot Generator Using Python Function
Otto Cycle An Otto cycle is an idealized thermodynamic cycle that describes the functioning of a typical petrol or spark-ignition piston engine (Otto engine). The Otto cycle is a description of what happens to a mass of gas as it is subjected to changes in pressure, temperature, volume, addition,…
Interactive NASA Polynomial File Parsing in MatLab
**This project parses the NASA Polynomials file in an interactive programme where the user can enter the name of a species (which exists in the data file), and the code creates and stores the plots of specific heats, entropies, and enthalpies of the species in particular folders. ** A) File Parsing Meaning:…
Robotic Arm Simulation In Python (2D)
2D Animation In Python: A basic robotic arm has two essential parts: the main arm and the manipulator. The main arm is the backbone or the support and can rotate with the base and lean in or out based on the requirements. Thus, the arm defines the reach of the robot. The manipulator or grabber is the end part attached…
Plotting Drag Forces vs (Velocities and Drag Coeff) Over A Moving Body in Python
This program code in python helps to visualise the effect of the drag forces on a moving body at different speeds and at different drag coefficients. Drag Force The drag force is a type of fluid friction or resistance which is experienced by a body moving inside a fluid medium. Depending upon the type of the…
% Various methods for Row-vector making a= [ 1 2 3 4 5] %entering direct values a1= [1,2,3,4,5] % using commas to seperate the elements b=1:50 %incremental without the use of \’\'[]\’\’ c=[1:50] %Incremental with \’\'[]\’\’ c=[1:0.1:2] % With an increment of 0.1 a= rand(1,5) %using random…
2-D Robotic Arm Manipulator Animation
% Forward Kinematic of a 2-D Robotic Arm Manipulator clear all close all clc l1= 2; %langth of base arm l2=1; %length of hand/grabber t1=linspace(0,90,20); %range of angles, i.e., the space for the movements t2=linspace(0,90,20); c=1; for i=1:length(t1) T1=t1(i); for j=1:length(t2) T2= t2(j); x0=0; y0=0; x1= l1*cosd(T1);…
Second Order Differential Equations and Simulating the Motion of a Damped Oscillator
Programme for the animation of a Damped Pendulum Motion close all %clears all figures clear all %clears all variables clc %clears the command window %Given conditions for the pendulum b =0.05; %Damping constant/coefficient g = 9.81; %acc. due to gravity in m/s^2 l=1; % length of the pendulum in m m=0.1; % mass of…