如何高效实现DataFrame中遇EOL行后插入反向查找的首个Branch行？

Awesome question—let's tackle this efficiently while keeping resource usage low. The core requirements are clear: whenever we hit an EOL row in the original DataFrame, we need to insert the first Branch row from the original data right after it. Here's a streamlined approach that avoids unnecessary computations and minimizes memory overhead:

Step-by-Step Approach #

1. Pre-fetch the Branch Row: Grab the first Branch entry from the original DataFrame once—no need to search for it every time we encounter an EOL.
2. Identify EOL Positions: Get all indices of EOL rows from the original DataFrame (critical, since we must base our search on the unmodified data).
3. Split and Recombine: Break the original DataFrame into segments separated by EOL rows, insert the pre-fetched Branch row after each segment, then concatenate everything back together. This batch operation is far more efficient than inserting rows one by one.

Code Implementation #

import pandas as pd

# Sample original DataFrame (matches your example)
data = {
    'Description': ['Branch', 'Forward', 'Backwards', 'Forward', 'Backwards', 'Forward', 'EOL',
                    'Forward', 'Backwards', 'Forward', 'Backwards', 'Forward', 'EOL',
                    'Forward', 'Forward', 'Forward'],
    'Type': ['Actuated'] * 16,
    'x': [0, 7.07, 7.07, 17.07, 10, 17.07, 7.07,
          -7.07, -7.07, -17.07, -10, -17.07, -7.07,
          0, 0, 10],
    'y': [0, 7.07, -2.93, -2.93, -10, -17.07, -17.07,
          -7.07, 2.93, 2.93, 10, 17.07, 17.07,
          10, 20, 0],
    'z': [0] * 16
}
df = pd.DataFrame(data)

# 1. Fetch the first Branch row from the original DataFrame (convert to DataFrame for easy concatenation)
branch_row = df[df['Description'] == 'Branch'].iloc[0].to_frame().T

# 2. Get all EOL indices from the original DataFrame
eol_indices = df[df['Description'] == 'EOL'].index.tolist()

# 3. Split the DataFrame and insert Branch rows
segments = []
start_idx = 0

for eol_idx in eol_indices:
    # Add the segment from start_idx up to and including the EOL row
    segments.append(df.loc[start_idx:eol_idx])
    # Insert the pre-fetched Branch row
    segments.append(branch_row)
    # Update start index for the next segment
    start_idx = eol_idx + 1

# Add the final segment after the last EOL (if any rows remain)
if start_idx <= df.index[-1]:
    segments.append(df.loc[start_idx:])

# 4. Combine all segments into the final DataFrame
result_df = pd.concat(segments, ignore_index=True)

# Print the result to verify
print(result_df)

Why This Is Efficient #

• Single Search for Branch: We only look up the first Branch row once, avoiding redundant filtering operations.
• Batch Concatenation: Using pd.concat with pre-defined segments is much faster than inserting rows individually (which triggers repeated reindexing and memory reshuffling).
• Original Data Alignment: We rely entirely on the original DataFrame's indices to split segments, ensuring we never use the modified/expanded data for our EOL search—exactly as required.
• Low Memory Footprint: We don't modify the original DataFrame in-place; instead, we create segments from slices (which are views, not copies, in most cases) and combine them efficiently.

内容的提问来源于stack exchange，提问作者Izak Joubert