Replacing Column Value with Keys and Values in a Dictionary of List Values Using pandas

Introduction

In this article, we will explore how to replace column values in a pandas DataFrame based on keys and values from a dictionary. We’ll cover various approaches and provide code examples for clarity.

Problem Statement

Given a DataFrame and a dictionary where the dictionary contains list values, our goal is to find matching keys and values in the dictionary and use them to replace specific words or phrases in the text column of the DataFrame.

Background

To tackle this problem, we need to understand some fundamental concepts in pandas and Python:

1. DataFrames: pandas DataFrames are two-dimensional labeled data structures with columns of potentially different types.
2. Series: A Series is a one-dimensional labeled array of values.
3. String Methods: pandas offers several string methods that allow us to manipulate strings, such as str.lower(), str.split(), and str.contains().
4. Regex (Regular Expressions): We’ll use regular expressions in Python’s re module to match patterns in the text column.

Initial Attempt

Let’s look at an initial approach that was mentioned in the Stack Overflow question:

## Initial Approach

The initial solution uses a loop to iterate over each key-value pair in the dictionary and checks if any of the values from the list exist within the `text` column. If they do, it replaces all occurrences of those values with the corresponding key.

```python
import pandas as pd

# Create DataFrame
df = pd.DataFrame({'text': ['can you open the door?','shall you write the address?']})

dic = {'Should': ['can','could'], 'Could': ['shall'], 'Would': ['will']}

for key, val in dic.items():
    if df['text'].str.lower().str.split().map(lambda x: x[0]).str.contains('|'.join(val)).any():
        df['text'] = df['text'].str.replace('|'.join(val), key, regex=False)

print(df)

This approach works for lists with a single value but is inefficient and not scalable.

Optimized Approach

We can improve upon this solution by creating a dictionary that maps values to keys in a more efficient manner:

## Optimized Approach

Instead of using the initial loop-based approach, we'll create a new dictionary where each value from the original dictionary's list becomes a key and its corresponding original key becomes the value. This way, we can directly map values to their desired replacements.

```python
import pandas as pd
import re

# Create DataFrame
df = pd.DataFrame({'text': ['can you open the door?','shall you write the address?']})

dic2 = {v:k for k,l in dic.items() for v in l}

regex = '|'.join(map(re.escape, dic2))

df['text2'] = df['text'].str.replace(f'\b({regex})\b',
                                    lambda m: dic2.get(m.group()),
                                    case=False, # only if case doesn't matter
                                    regex=True)

print(df)

This approach is more efficient and scalable, as it avoids the need for loops and uses the built-in string replacement methods of pandas.

Further Optimization

We can further optimize this solution by using regular expressions to avoid unnecessary replacements. We’ll use the \b character class in our regex pattern to ensure that we only match whole words:

## Further Optimized Approach

Using the `re` module's raw strings feature, we can define a regex pattern that only matches whole words.

```python
import pandas as pd
import re

# Create DataFrame
df = pd.DataFrame({'text': ['can you open the door?','shall you write the address?']})

dic2 = {v:k for k,l in dic.items() for v in l}

regex = '|'.join(map(re.escape, dic2))

pattern = f'\b({regex})\b'

df['text2'] = df['text'].str.replace(pattern,
                                    lambda m: dic2.get(m.group()),
                                    case=False, # only if case doesn't matter
                                    regex=True)

print(df)

By using the \b character class in our regex pattern, we ensure that we only match whole words and avoid unnecessary replacements.

Conclusion

In this article, we explored how to replace column values in a pandas DataFrame based on keys and values from a dictionary. We discussed various approaches, provided code examples for clarity, and offered suggestions for further optimization. By following these steps, you can efficiently update your DataFrame’s columns using dictionaries of list values.

Last modified on 2025-03-31