Efficient Selection of Rows in Pandas DataFrame Based on Multiple Conditions Across Columns

Introduction

When working with pandas DataFrames, selecting rows based on multiple conditions across columns can be a challenging task. In this article, we will explore an efficient way to achieve this using various techniques from the pandas library.

The problem at hand is to create a new DataFrame where specific combinations of values in two columns (topic1 and topic2) appear a certain number of times. We are given an example with a small dataset:

We want to select arbitrary rows so that topic1 == 4 appears twice and topic2 == 4 appears three times in the new DataFrame. We also need to stop once this condition is fulfilled.

Conventional Approach

The provided starter code uses a convoluted loop-based approach to achieve the desired result:

rows_list = []
counter1 = 0
counter2 = 0

for index, row in data.iterrows():
    if counter1 < bucket1_topic1:
        if row.topic1 == 4:
            counter1 +=1
            rows_list.append([row[1], row.topic1, row.topic2])

    if counter2 < bucket1_topic2:
        if row.topic2 == 4 and row.topic1 != 4:
            counter2 +=1
            if [row[1], row.topic1, row.topic2] not in rows_list:
                rows_list.append([row[1], row.topic1, row.topic2])

However, this approach has several issues:

• It is time-consuming because it involves iterating over the entire DataFrame.
• It can be prone to errors due to the complexity of the loop logic.

Alternative Approach Using GroupBy and CumCount

A more efficient and elegant solution uses pandas’ groupby and cumcount functions:

df = (data.sample(frac=1)
      .assign(t1_grp=lambda x: x.groupby(["topic1"]).cumcount(),
              t2_grp=lambda x: x.groupby(["topic2"]).cumcount())
)

final_df = df[(df["topic1"].isin([1, 2, 3])) |
              (df["topic2"].isin([1, 2, 3])) |
              ((df["topic1"] == 4) & (df["t1_grp"] < 2)) |
              ((df["topic2"] == 4) & (df["t2_grp"] < 3))]

final_df = final_df.drop(columns=["t1_grp", "t2_grp"])

This approach works as follows:

• data.sample(frac=1) reshuffles the rows of the DataFrame, so that it’s possible to select arbitrary rows.
• .assign(t1_grp=lambda x: x.groupby(["topic1"]).cumcount()) calculates running group counts for each unique value in topic1. The same is done for topic2.
• We then filter the resulting DataFrame based on the required conditions using logical subsetting.

Conclusion

The alternative approach presented here avoids looping and instead leverages pandas’ built-in functions to achieve efficient row selection. By understanding how these functions work, developers can write more concise, readable, and maintainable code.

Last modified on 2023-07-25