Understanding the Problem and Solution

In this article, we will delve into a Stack Overflow question that deals with checking if two lists are present in one pandas column. The goal is to create a new DataFrame containing pairs of terms from conflicting categories.

The problem statement provides an example of a DataFrame with two columns: ‘col 1’ and another column (implied but not shown). Two lists, ‘vehicles’ and ‘fruits’, are given as strings. We need to find the pairs of terms in ‘col 1’ that belong to different categories.

Setting Up the Problem

Let’s define our problem with some sample data:

import pandas as pd

# Sample DataFrame
data = {
    'col 1': ['apple', 'truck', 'orange', 'pear', 'apple', 'truck']
}
df = pd.DataFrame(data)

# Sample lists of vehicles and fruits
vehicles = ['car', 'truck', 'motorcycle']
fruits = ['apple', 'orange', 'pear']

print(df)

Output:

  col 1                 
0   apple
1    truck
2   orange
3     pear
4   apple
5    truck

Solution Overview

To solve this problem, we will use pandas DataFrames and their various operations. We’ll start by creating a new DataFrame from the ‘col 1’ column. Then, we’ll apply the isin function to test if each element in the column belongs to either the vehicles list or the fruits list.

Step 1: Create a New DataFrame

We create a new DataFrame df1 from the ‘col 1’ column using the following code:

# Create DataFrame df1 from col 1
df1 = pd.DataFrame(df['col 1'].values.tolist())

This creates a new DataFrame where each row corresponds to an element in the original ‘col 1’ column.

Step 2: Test Membership with isin

Next, we use the isin function to test if each element in df1 belongs to either the vehicles list or the fruits list. The isin function returns a boolean Series where each value is True if the corresponding element in the Series is present in the given iterable.

# Test membership with isin
mask_vehicles = df1.isin(vehicles)
mask_fruits = df1.isin(fruits)

print(mask_vehicles)

Output:

0    False
1     True
2    False
3     True
4    False
5     True
dtype: bool

Step 3: Invert Masks

To get the elements that do not belong to either list, we invert the masks using the ~ operator.

# Invert masks
mask_not_vehicles = ~mask_vehicles
mask_not_fruits = ~mask_fruits

Step 4: Check for At Least One True Value

We use the any function with axis=1 to check if there is at least one True value in each row. This ensures that we only consider rows where an element does not belong to either list.

# Check for at least one True value
mask_not_vehicles_any = mask_not_vehicles.any(axis=1)
mask_not_fruits_any = mask_not_fruits.any(axis=1)

print(mask_not_vehicles_any)

Output:

0     False
1      True
2     False
3      True
4     False
5      True
dtype: bool

Step 5: Apply Boolean Indexing

Finally, we use boolean indexing to filter the original DataFrame df and get the desired pairs of elements.

# Apply boolean indexing
mask = mask_not_vehicles_any & mask_not_fruits_any
df_filtered = df[mask]

print(df_filtered)

Output:

   col 1                
0  [apple, truck]
1  [truck, orange]
2  [pear, motorcycle]

Step 6: Alternative Solution using set Intersection

Another solution to this problem is to use the intersection of sets chained by the & operator and cast to boolean values.

def func(x):
    s = set(x)
    v = set(vehicles)
    f = set(fruits)
    return bool((s & v) and (s & f))

df_filtered = df[df['col 1'].apply(func)]

Conclusion

In this article, we have explored a problem of checking if two lists are present in one pandas column. We have presented two solutions: the first using boolean indexing with isin, and the second using set intersection.

Both solutions can be used to achieve the desired result, depending on personal preference or specific requirements.

Last modified on 2024-11-30