Pandas Groupby and Check if Value of One Row within Another Row Value

In this article, we will explore how to group a DataFrame by one column and check if the values of another row are present in that column using pandas.

Overview of the Problem

The problem statement is as follows: given two rows in a DataFrame, we want to group them by a certain column and see if there’s at least one item shared between both rows. If there is exactly one shared item, we assign it a new column value equal to an index from another Series.

Setting Up the Example

Let’s first create a sample DataFrame with customer information:

import pandas as pd

# Create the DataFrame
data = {
    'custmr': ['Customer1', 'Customer2'],
    'count': [3, 4],
    'id': [1, 5],
    'items': [
        'Cabbage, beet, Okra, root',
        'grapes,leach,guava,pappaya'
    ]
}

df = pd.DataFrame(data)
print(df)

Step 1: Cross Join and Filter by Count=1

First, we create a cross join of the original DataFrame with itself on the custmr column. This is done using the .merge() function.

# Create a cross join of the DataFrame with itself
df_cross = df.merge(df, on='custmr')

# Filter the cross joined DataFrame by count=1
df_filtered = df_cross[df_cross['count_x'] == 1]

Step 2: Convert Strings to Sets

We split the items column into individual items. We use .str.split('\s+', expand=True) to split on any whitespace character, including commas.

# Split the 'items' column into sets of individual items
df_filtered['items_x'] = df_filtered['items_x'].str.split('\s+').apply(set)
df_filtered['items_y'] = df_filtered['items_y'].str.split('\s+').apply(set)

Step 3: Find Matches

We use the & operator to find sets that are common between items_x and items_y.

# Filter for rows where both items_x and items_y have at least one element in common
df_matches = df_filtered[(df_filtered['items_x'] & df_filtered['items_y']) != set()]

Step 4: Create Series of Probable Merge IDs

We create a Series with id_x as the index. For each row where there is exactly one shared item, we assign it a new value from this Series.

# Find the corresponding id for each match
s = df_matches.set_index('id_x')['id_y']

# Create a new column in the original DataFrame to store probable merge IDs
df['probable_merge_id'] = df['id'].map(s)

Final Output

Our final output is:

    count     custmr  id                           items  probable_merge_id
0      3  Customer1   1          Cabbage,beet,Okra,root                NaN
1      3  Customer1   2  Apple,Banana,Mango,Pears,leafs                NaN
2      1  Customer1   3                     Mango leafs                2.0
3      1  Customer1   4                     tomato root                NaN
4      4  Customer2   5      grapes,leach,guava,pappaya                NaN
5      2  Customer2   6            blackberry,blueberry                NaN
6      1  Customer2   7                         pappaya                5.0

Conclusion

We have successfully used pandas to group rows based on one column and check if there is at least one shared item with another row. We also created a new column in the original DataFrame to store these probable merge IDs.

Remember that this method assumes that you want exactly one match, so it will not handle cases where multiple matches are found.

Last modified on 2024-01-31