Got it, let's break this down step by step for your large dataset—first we'll tackle identifying rows with NaN values (without targeting a specific column like 'Computer'), then we'll cover how to merge rows using Pandas.

Step 1: Locate All Rows Containing NaN Values #

Since you can't directly filter by the 'Computer' column, we'll use a general approach to find any row with at least one NaN, regardless of which column it's in. Pandas has built-in vectorized methods that work efficiently even on large datasets:

import pandas as pd

# Assume your dataset is stored in a DataFrame called `df`
# Create a boolean mask where True means the row has at least one NaN
has_nan = df.isna().any(axis=1)

# Get the indices of rows with NaN values
nan_row_indices = df[has_nan].index.tolist()

# If you want to view the actual rows (optional)
nan_rows = df.loc[has_nan]
print("Rows containing NaN values:\n", nan_rows)

• isna().any(axis=1) checks each row for any NaN value and returns a boolean Series.
• Using .index.tolist() gives you a clean list of row indices where NaNs exist—perfect for targeting those rows later.

Step 2: Merging Rows with Pandas #

Merging rows can mean a few different things, so I'll cover the two most common scenarios you might run into:

Scenario 1: Merge Two Specific Rows (e.g., by Index) #

If you want to combine two individual rows (like filling NaNs in one row with values from another), use combine_first()—it prioritizes non-NaN values from the first row and fills gaps with values from the second:

# Let's say you want to merge rows at indices 5 and 6
row_a = df.loc[5]
row_b = df.loc[6]

# Merge the rows: keep non-NaN values from row_a, fill missing ones with row_b
merged_row = row_a.combine_first(row_b)

# Update the DataFrame: replace row 5 with the merged row, then drop row 6
df.loc[5] = merged_row
df = df.drop(6).reset_index(drop=True)  # Reset index to avoid gaps

Scenario 2: Merge Rows with a Common Identifier (e.g., Duplicate IDs) #

If your dataset has duplicate entries (like rows with the same 'ID' but split data across rows), group by the identifier and aggregate to combine non-NaN values:

# Assume your DataFrame has an 'ID' column that groups related rows
# Use groupby + first() to take the first non-NaN value for each column in the group
merged_df = df.groupby('ID', as_index=False).first()

# If you need more control (e.g., combine all non-NaN values into a list), use a lambda:
merged_df = df.groupby('ID', as_index=False).agg(
    lambda x: ', '.join(x.dropna().astype(str)) if not x.dropna().empty else pd.NA
)

• groupby('ID').first() is super efficient for large datasets since it uses Pandas' optimized built-in functions.
• The lambda approach works if you need to concatenate values instead of picking the first one—adjust it based on your needs.

Quick Note for Large Datasets #

For big datasets, avoid using slow loops or custom functions where possible. Stick to vectorized operations (like isna(), combine_first()) and built-in aggregation methods—they're designed to handle large volumes of data efficiently.

内容的提问来源于stack exchange，提问作者Joono