Python: Performance issues with large datasets in nested loops using Pandas
I've looked through the documentation and I'm still confused about I'm currently working on a data processing task using Pandas to loop through a large DataFrame (about 1 million rows). I have the following nested loop structure to calculate a new column based on the values from two other columns: ```python import pandas as pd # Sample data data = {'A': range(1, 1000001), 'B': range(1, 1000001)} df = pd.DataFrame(data) # Adding a new column with nested loops for i in range(len(df)): for j in range(len(df)): if df.iloc[i]['A'] > df.iloc[j]['B']: df.at[i, 'C'] = df.at[i, 'C'] + 1 if 'C' in df.columns else 1 ``` However, this approach is extremely slow and I often run into performance issues, especially when trying to scale. After running the code, I'm encountering long wait times and sometimes the script hangs. I know that nested loops are generally inefficient for this type of operation, and I’ve read that vectorized operations in Pandas would be faster. I've tried using `apply()` and `lambda` functions instead, but I'm still unsure how to implement this correctly without reverting to the nested loop approach. Here’s what I attempted: ```python df['C'] = df.apply(lambda row: sum(row['A'] > df['B']), axis=1) ``` While this is somewhat faster, it still doesn't seem optimal. Is there a more efficient way to achieve this without running into performance bottlenecks? Can someone guide me on how to effectively utilize Pandas for this scenario? Any best practices would be greatly appreciated! I am using Pandas version 1.3.3. I'd really appreciate any guidance on this.