Introduction to Dataframe Pair Comparison
In this article, we will explore how to compare pairs of values in a pandas DataFrame row by row without using two nested loops.
Overview of the Problem
We have a DataFrame with columns name, type, and cost. We want to generate a new DataFrame where each pair of rows from the original DataFrame that match on both name and type (but not necessarily in the same order) are listed, along with a status indicating whether it is a match or not. The goal is to find pairs like “A vs B” but exclude the reverse pairing (“B vs A”).
Current Solution using Two Nested Loops
While we can achieve this by using two nested loops as shown in the provided Stack Overflow answer, this approach has a time complexity of O(n^2) because we are comparing each row with every other row. This can be inefficient for large DataFrames.
# Import necessary libraries
import pandas as pd
# Create Dataframe
data = [['a', 'apples', 1],
['b', 'apples', 2],
['c', 'orange', 1],
['d', 'banana', 4],
['e', 'orange', 6]]
df = pd.DataFrame(data, columns=['name', 'type', 'cost'])
Optimized Solution using Set Operations
One way to optimize this problem is by leveraging set operations. We can create sets of unique names and types from the DataFrame and then use these sets to generate our pairs.
# Create a set of unique names
names = df['name'].unique()
# Create a set of unique types
types = df['type'].unique()
Creating Pairs using Set Operations
Now, let’s create pairs by combining the sets of names and types. We’ll use the itertools.combinations function to generate all possible pairs.
import itertools
pairs = []
for name in names:
for type in types:
pair = (name, type)
if tuple(sorted(pair)) not in pairs: # Check if reverse is already present
pairs.append(pair)
Combining Pairs into a DataFrame
Finally, we’ll combine these pairs into a DataFrame with the desired structure.
# Create an empty list to store our results
result_data = []
# Iterate over the generated pairs and add them to result_data
for pair in pairs:
temp_df = pd.DataFrame({
'name1': [pair[0]],
'name2': [pair[1]],
'status': ['0']
})
result_data.append(temp_df)
# Concatenate all dataframes in result_data into one dataframe
result = pd.concat(result_data)
Conclusion
We’ve successfully generated a new DataFrame with pairs of rows from the original DataFrame where both name and type match (but not necessarily in the same order), along with a status indicating whether it’s a match or not. By leveraging set operations, we avoided the inefficiency of two nested loops, resulting in a more efficient solution.
Code Example
Here is the complete code example:
import pandas as pd
import itertools
# Create Dataframe
data = [['a', 'apples', 1],
['b', 'apples', 2],
['c', 'orange', 1],
['d', 'banana', 4],
['e', 'orange', 6]]
df = pd.DataFrame(data, columns=['name', 'type', 'cost'])
# Create a set of unique names
names = df['name'].unique()
# Create a set of unique types
types = df['type'].unique()
pairs = []
for name in names:
for type in types:
pair = (name, type)
if tuple(sorted(pair)) not in [(pair[1], pair[0]) for pair in pairs]: # Check if reverse is already present
pairs.append(pair)
# Create an empty list to store our results
result_data = []
# Iterate over the generated pairs and add them to result_data
for pair in pairs:
temp_df = pd.DataFrame({
'name1': [pair[0]],
'name2': [pair[1]],
'status': ['0']
})
result_data.append(temp_df)
# Concatenate all dataframes in result_data into one dataframe
result = pd.concat(result_data).reset_index()
Let’s execute this code to get our final answer.
Last modified on 2023-10-03