Are you struggling with regular expressions for parsing text? Well, have no fear because we’re here to introduce you to the world of Python Parsing using pyparsing!
To begin with, what exactly is pyparsing. It’s a library that allows us to create and execute simple grammars in Python without needing to learn a new syntax or match expressions with regular expressions. Instead, we use classes provided by the library to construct our grammar directly in Python code!
Here’s an example: let’s say you want to parse “Hello, World!” (or any greeting of the form “
# Import the pyparsing library
import pyparsing
# Define a Word object that will match any alphabetic character
# and assign it to the variable "greet"
greet = pyparsing.Word(pyparsing.alphas)
# Create a grammar that consists of a Word object, followed by a comma,
# followed by another Word object, and ending with an exclamation point
# Assign this grammar to the variable "hello"
hello = greet + "," + greet + "!"
# Create a string that we want to parse using our grammar
# In this case, the string is "Hello, World!"
# Assign this string to the variable "greeting"
greeting = "Hello, World!"
# Print the original string and an arrow to indicate the parsing process
print(greeting, "->", end=" ")
# Use the parseString method to parse the string using our grammar
# and print the result
print(greet.parseString(greeting))
# Output: Hello, World! -> ['Hello', ',', 'World', '!']
This code defines a grammar for our greeting using the `Word()` class to match alphabetic characters and the `+` operator to concatenate them into words separated by commas and exclamation points. We then test it with an example input string (`hello`) and print out the result of parsing that string using the `parseString()` method.
The output will be:
# Define a class called Word that will match alphabetic characters
class Word:
# Initialize the class with a constructor
def __init__(self):
# Create an empty list to store the parsed words
self.words = []
# Define a method called parseString that takes in a string as input
def parseString(self, string):
# Create an empty string to store the current word being parsed
current_word = ""
# Loop through each character in the input string
for char in string:
# Check if the character is alphabetic
if char.isalpha():
# If it is, add it to the current word
current_word += char
# If the character is not alphabetic
else:
# Check if the current word is not empty
if current_word != "":
# If it is not empty, add it to the list of words
self.words.append(current_word)
# Reset the current word to an empty string
current_word = ""
# Add the non-alphabetic character to the list of words
self.words.append(char)
# Check if the current word is not empty
if current_word != "":
# If it is not empty, add it to the list of words
self.words.append(current_word)
# Create an instance of the Word class
word = Word()
# Call the parseString method with the input string "Hello, World!"
word.parseString("Hello, World!")
# Print out the result of parsing the input string
print(word.words)
# Output: ['Hello', ',', 'World', '!']
Pretty cool, right? But what if we want to parse more complex text with multiple rules and conditions? No problem! Let’s say you have a CSV file that looks like this:
# This script is used to parse a CSV file with data on people's names, ages, and genders.
# The first line of the CSV file contains the headers for each column: Name, Age, and Gender.
# These headers will be used to access the data in each column.
Name, Age, Gender # The headers are separated by commas to indicate different columns.
Alice, 25, Female # The first row of data contains information for Alice, who is 25 years old and identifies as female.
Bob, 30, Male # The second row of data contains information for Bob, who is 30 years old and identifies as male.
Charlie, 45, Other # The third row of data contains information for Charlie, who is 45 years old and identifies as other.
# The data in each row is separated by commas to indicate different columns.
# This allows for easy access and organization of the data.
# Overall, this script is used to organize and access data from a CSV file, making it easier to work with and analyze.
We can use pyparsing to parse each line of the CSV file and extract its values:
# Import necessary libraries
import csv # Importing the csv library to read and write CSV files
from io import StringIO # Importing the StringIO library to handle string inputs and outputs
from pyparsing import (Word, nums, alphas, Group, delimitedList) # Importing the pyparsing library for parsing strings
# Define the structure of a CSV line
csv_line = Word(alphas) + "," + Word(nums) + "," + Word(alphas) # Creating a pyparsing object to match a line in the CSV file
# The line should contain alphabetic characters, followed by a comma, followed by numeric characters, followed by a comma, followed by alphabetic characters
# Define the possible values for the "gender" field
gender = Keyword("Female") | Keyword("Male") | Keyword("Other") # Creating a pyparsing object to match the possible values for the "gender" field
# Define a function to parse the CSV file
def parse_csv():
with open('input.csv', 'r') as f: # Opening the CSV file in read mode
reader = csv.reader(f, delimiter=",") # Creating a csv reader object to read the file, specifying the delimiter as a comma
for line in reader: # Looping through each line in the file
yield from parse_line(line) # Yielding the results of the parse_line function for each line in the file
# Define a function to parse a single line
def parse_line(line):
yield from Group(csv_line + "\n")(StringIO(line)) # Using the pyparsing object to parse the line and yield the results as a list
# Loop through the results of the parse_csv function and print them
for result in parse_csv():
print(result.asList()) # Printing the results of the parse_csv function as a list
In this example, we define a grammar for each field of the CSV line using `Word()`, and then combine them with commas to create our full grammar (`csv_line`). We also define a set of possible values for the gender column as keywords.
We use a generator function called `parse_csv()` that reads from an input file, parses each line using another generator function called `parse_line()`, and yields the results. The `yield from` syntax is used to pass on the yielded results of `parse_line()`.
Finally, we iterate over the results of `parse_csv()` and print them out as lists.
The output will be:
# This script takes in an input file and parses each line using the generator function parse_line()
# The yield from syntax is used to pass on the yielded results of parse_line()
# Finally, the results of parse_csv() are iterated over and printed out as lists
# Define the generator function parse_line() which takes in a line of text as input
def parse_line(line):
# Split the line by commas and store the result in a list
line_list = line.split(',')
# Strip any whitespace from each element in the list
line_list = [element.strip() for element in line_list]
# Yield the list as a result
yield line_list
# Define the generator function parse_csv() which takes in a file as input
def parse_csv(file):
# Open the file and iterate over each line
with open(file, 'r') as f:
for line in f:
# Use the yield from syntax to pass on the yielded results of parse_line()
yield from parse_line(line)
# Call the parse_csv() function and pass in the input file as an argument
results = parse_csv('input_file.txt')
# Iterate over the results and print them out as lists
for result in results:
print(result)
And that’s it! With pyparsing, you can easily create and execute simple grammars for parsing text without the headache of regular expressions. Give it a try and let us know what you think in the comments below!