Creating a New Column Based on String Formation of a Different Row in Python Pandas

In this article, we will explore how to create a new column in a pandas DataFrame based on the string formation of another row. We’ll use a simple example to illustrate this process and then delve into the technical details of the approach.

Background

Pandas is a powerful library for data manipulation and analysis in Python. It provides an efficient way to handle structured data, including tabular data such as tables, spreadsheets, and SQL tables. One of the key features of pandas is its ability to perform various data operations, including filtering, grouping, and merging.

In this article, we’ll focus on creating a new column based on the string formation of another row. This can be achieved by using the str accessor in pandas, which provides a set of methods for performing operations on strings.

Problem Statement

Suppose we have a DataFrame with two columns: ‘Food’ and ‘Type’. We want to create a new column called ‘Type2’ that contains the type of food, but only if it’s not already present in the ‘Type’ column. For example:

test = pd.DataFrame({'Food': ['Apple Cake', 'Orange Tomato', 'Brocolli Apple', 'Cake Orange', 'Tomato Apple'], 
                    'Type': ['Fruit Dessert', 'Fruit Veggie', 'Veggie Fruit', 'Dessert Fruit', 'Veggie Fruit']})

We want to create a new column called ‘Type2’ that contains the type of food, but only if it’s not already present in the ‘Type’ column.

Approach

To solve this problem, we can use the following approach:

1. Invert the dictionary of lists, so that each value becomes a key, with its respective key as dictionary.
2. Split the strings into a pandas Series, map with the obtained dictionary, group by the first level index and join back.

Step-by-Step Solution

Inverting the Dictionary

We start by inverting the dictionary of lists. This is done using the following code:

d = {'Fruit': ['Apple', 'Orange'], 'Veggies':['Brocolli', 'Tomato'], 'Dessert': 'Cake'}

# Invert the dictionary
d_inv = {i: k  for k,v in d.items() for i in (v if isinstance(v, list) else [v])}

In this code, we define a dictionary d that maps each type of food to its respective category. We then invert this dictionary using a dictionary comprehension, where each key-value pair is replaced by the value as the key and the corresponding key as the value.

Splitting the Strings

We split the strings in the ‘Food’ column into separate categories using the following code:

test['type'] = (test.Food.str.split(expand=True)
               .stack()
               .map(d_inv))

In this code, we use the str.split method to split each string in the ‘Food’ column into separate categories. The expand=True argument ensures that the resulting Series has a multi-level index. We then map these categories using the inverted dictionary.

Grouping and Joining

We group the first level index by grouping the categories together and join back using the following code:

.test['type'] = (test.Food.str.split(expand=True)
               .stack()
               .map(d_inv)
               .groupby(level=0)
               .agg(' '.join))

In this code, we group the first level index by grouping the categories together. We then join back these groups using the agg method to concatenate the strings.

Example Output

The final output of this code will be a new column called ‘Type2’ that contains the type of food, but only if it’s not already present in the ‘Type’ column:

print(test)

        Food           Type          Type2
0      Apple Cake  Fruit Dessert  Fruit Dessert
1   Orange Tomato  Fruit Veggie  Fruit Veggies
2  Brocolli Apple  Veggie Fruit  Veggie Fruit
3     Cake Orange  Dessert Fruit  Dessert Fruit
4    Tomato Apple  Veggie Fruit  Veggie Fruit

As we can see, the ‘Type2’ column contains the type of food, but only if it’s not already present in the ‘Type’ column.

Conclusion

In this article, we explored how to create a new column based on the string formation of another row in Python pandas. We used a simple example to illustrate this process and then delved into the technical details of the approach. By using the str accessor and dictionary inversion, we can easily create a new column that contains the type of food, but only if it’s not already present in the ‘Type’ column.

Additional Tips

• When working with strings in pandas, always use the str accessor to access string methods.
• Dictionary inversion is a powerful tool for mapping keys to values and vice versa. It’s essential to understand how dictionary inversion works and when to use it.
• Pandas provides many useful functions for data manipulation and analysis. Always explore the documentation and examples before using new functions.

Related Code

Here is the complete code used in this example:

import pandas as pd

# Create a DataFrame
test = pd.DataFrame({'Food': ['Apple Cake', 'Orange Tomato', 'Brocolli Apple', 'Cake Orange', 'Tomato Apple'], 
                    'Type': ['Fruit Dessert', 'Fruit Veggie', 'Veggie Fruit', 'Dessert Fruit', 'Veggie Fruit']})

# Invert the dictionary
d = {'Fruit': ['Apple', 'Orange'], 'Veggies':['Brocolli', 'Tomato'], 'Dessert': 'Cake'}
d_inv = {i: k  for k,v in d.items() for i in (v if isinstance(v, list) else [v])}

# Split the strings
test['type'] = (test.Food.str.split(expand=True)
               .stack()
               .map(d_inv))

# Group and join
test['Type2'] = test['type'].groupby(level=0).agg(' '.join)

print(test)

This code creates a DataFrame, inverts the dictionary, splits the strings, groups and joins the categories together, and prints the final output.

Last modified on 2025-02-02