Matching Rows in a DataFrame with Multiple Conditions

When working with dataframes, it’s not uncommon to encounter situations where you need to match rows based on multiple conditions. In this article, we’ll explore how to efficiently match rows in one dataframe against another using a combination of boolean masks and the merge function.

Background

In pandas, dataframes are powerful tools for data manipulation and analysis. However, when dealing with complex matching scenarios, traditional methods can become cumbersome and inefficient. This is where the merge function comes to the rescue.

The merge function allows you to combine two dataframes based on a common column or set of columns. In this case, we’ll use it to match rows in one dataframe against another based on multiple conditions.

Problem Statement

Suppose we have two dataframes, df1 and df2, with the following structures:

df_1 = pd.DataFrame({'num': [1, 2, 3], 'time': [100, 200, 300]})
df_2 = pd.DataFrame({'num': [1, 2, 3], 'time': [101, 104, 200]})

We want to match rows in df1 against df2 based on the following conditions:

• The ’num’ column in df1 does not equal the corresponding value in df2.
• The time in df1 is within 10 units of the corresponding value in df2.

The resulting matched rows should be stored in a new dataframe, df3, with columns for each condition.

Naive Approach

In our example code, we’ve attempted to implement this matching process using a custom function, find_match. However, as mentioned in the question, this approach is inefficient due to its nested loops and use of reset_index.

def find_match(row_df_1, df_2):
    time = row_df_1['time']
    match_in_df2 = df_2[(df_2['num'] != row_df_1['num']) & (df_2['time'].between(time-10, time+10))]
    
    if len(match_in_df2) > 0:
        match_in_df2.reset_index(inplace=True, drop=True)
        return pd.Series([row_df_1['num'], match_in_df2.loc[0,'num'], row_df_1['time'], match_in_df2.loc[0,'time']], index=df_3.columns)

for index, row_df_1 in df_1.iterrows():
    df_3 = df_3.append(find_match(row_df_1, df_2), ignore_index=True)

Optimized Approach

Fortunately, we can improve upon this approach by leveraging the merge function and boolean masks.

First, we’ll merge the two dataframes on their common columns, using a cross join with no matching value (how='cross'). This will result in a temporary dataframe with all possible combinations of rows from both dataframes.

out = pd.merge(df_1, df_2, how='cross', suffixes=('_df1', '_df2'))

Next, we’ll create two boolean masks to select the rows that meet our conditions:

• m1 checks whether the ’num’ column in df1 does not equal the corresponding value in df2.
• m2 checks whether the absolute difference between the time values in df1 and df2 is less than or equal to 10.

m1 = out['num_df1'] != out['num_df2']
m2 = abs(out['time_df2'] - out['time_df1']) <= 10

Finally, we’ll apply these masks to select the desired rows from the merged dataframe:

out = out[m1 & m2]

This approach is more efficient than the naive method because it avoids nested loops and uses vectorized operations instead.

Example Code

Here’s the complete example code that demonstrates this optimized approach:

# Import necessary libraries
import pandas as pd

# Define two dataframes
df_1 = pd.DataFrame({'num': [1, 2, 3], 'time': [100, 200, 300]})
df_2 = pd.DataFrame({'num': [1, 2, 3], 'time': [101, 104, 200]})

# Merge the dataframes with a cross join
out = pd.merge(df_1, df_2, how='cross', suffixes=('_df1', '_df2'))

# Create boolean masks to select rows that meet our conditions
m1 = out['num_df1'] != out['num_df2']
m2 = abs(out['time_df2'] - out['time_df1']) <= 10

# Apply the masks to select the desired rows
out = out[m1 & m2]

print(out)

Output

The resulting matched dataframe, df3, will have columns for each condition:

   num_df1  time_df1  num_df2  time_df2
0        1       100        2       104
5        2       200        3       200

This approach demonstrates how to efficiently match rows in one dataframe against another based on multiple conditions using the merge function and boolean masks.

Last modified on 2024-03-04