Understanding Plain Lists and Converting to Dictionaries in Python

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In this article, we will explore how to convert a plain list of text into a dictionary in Python. We will use the pandas library, but also discuss other methods that do not require it.

Introduction

The problem presented is a simple one: take a plain list of text and convert it into a dictionary where each key is a heading, and its corresponding value is a list of items under that heading. The input list is as follows:

Heading 1
item 1
item 2

Heading 2
item 1

Heading 3
item 1
item 2
item 3

And the desired output is:

{
 "Heading 1": ["item 1", "item 2"],
 "Heading 2": ["item 1"],
 "Heading 3": ["item 1", "item 2", "item 3"]
}

Using Python’s Built-in re Module

We can use the built-in re module in Python to solve this problem. The regular expression we’ll be using is:

^ Heading \s+ (.*)$

This will match any line that starts with ‘Heading’, followed by one or more spaces, and then captures the text after those spaces.

Code for Using Regular Expressions

Here’s an example of how you could use regular expressions to solve this problem:

import re

def convert_to_dict(filename):
    dictionary = {}
    with open(filename) as f:
        for line in f:
            # Use regular expression to match the heading and its corresponding text.
            if re.match(r'^\s*Heading\s+\S+$', line):
                current = line.strip()
            else:
                # If it's not a heading, add it to the dictionary under the current heading.
                dictionary[current] = []
                dictionary[current].append(line)

    return dictionary

This function reads in a file line by line. When it encounters a line that matches the regular expression for headings, it sets current to that line. If the line does not match the heading, it adds current as a key and line as its value.

Using Pandas

However, we can also use pandas to solve this problem. We’ll create a DataFrame with two columns: one for headings, and another for items. Then, we’ll group the DataFrame by ‘Heading’ and append the corresponding items to each row’s item list.

Code for Using Pandas

Here’s an example of how you could use pandas to solve this problem:

import pandas as pd

def convert_to_dict(filename):
    # Create a DataFrame with two columns: one for headings, and another for items.
    df = pd.DataFrame(columns=['Heading', 'Items'])
    
    with open(filename) as f:
        current_heading = None
        current_items = []
        
        for line in f:
            if line.strip().startswith('Heading'):
                # If it's a heading, add the last row to the DataFrame and reset variables.
                df.loc[len(df)] = [line.strip(), ' '.join(current_items)]
                current_heading = line.strip()
                current_items = []
            
            else:
                # If it's not a heading, add it to the list of items for the current heading.
                current_items.append(line.strip())
    
    # Add the last row to the DataFrame if there was a last heading.
    if current_heading is not None:
        df.loc[len(df)] = [current_heading, ' '.join(current_items)]
    
    # Group by 'Heading' and append corresponding items to each row's item list
    dictionary = {}
    for index, row in df.iterrows():
        key = row['Heading']
        if key not in dictionary:
            dictionary[key] = []
        dictionary[key].append(row['Items'])
    
    return dictionary

Choosing Between Methods

Now we have two methods to solve this problem. The regular expression method is simpler and doesn’t require pandas, but it does require some manual processing of the file. The pandas method uses more built-in functionality, but requires creating a DataFrame.

The choice between these methods depends on what you’re trying to accomplish. If you need something simple and don’t want to worry about data manipulation, the regular expression method might be sufficient. However, if you’re working with larger files or more complex data structures, pandas can be much more powerful.

Conclusion

In this article, we explored how to convert a plain list of text into a dictionary in Python using both regular expressions and pandas. The regular expression method uses the re module to match headings and their corresponding items, while the pandas method creates a DataFrame with two columns: one for headings, and another for items.

We also discussed the choice between these methods depending on your specific needs and what you’re trying to accomplish.

Last modified on 2023-08-11