如何基于DataFrame行内两列条件为指定单元格赋值？

Your core logic is on the right track, but there's a key issue with how you're checking for matching rows—Pandas doesn't allow using a DataFrame directly in an if condition (this will throw a ValueError because it can't reduce the DataFrame to a single boolean). Let's fix that and also address a deprecation issue with append().

Step-by-Step Corrected Implementation #

Here's a robust version of your code that works as intended:

import pandas as pd

# Define a boolean mask to identify rows matching your criteria
mask = (df_['Camera'] == camera1) & (df_['Return'].isnull())

# Check if any rows match the mask
if mask.any():
    # Update the 'Return' column for all matching rows
    df_.loc[mask, 'Return'] = time_
else:
    # Append new row (using pd.concat instead of deprecated append())
    # Convert dfin to a single-row DataFrame if it's a dict/list of values
    dfin_row = pd.DataFrame([dfin])
    df_ = pd.concat([df_, dfin_row], ignore_index=True)

# Save the updated DataFrame to CSV
df_.to_csv(csv_file, index=False)

Key Improvements: #

• Valid Condition Check: Using mask.any() safely checks if there are any rows that meet your criteria (returns True if at least one row matches the mask).
• Deprecated append() Replacement: Pandas' append() method is no longer recommended, so we use pd.concat() instead—this is the official way to add rows to a DataFrame moving forward. If dfin is already a single-row DataFrame, you can skip the conversion step.
• Cleaner Code: Defining the mask once and reusing it makes the code more readable and avoids repeating the same filter logic.

Edge Cases to Consider: #

• If multiple rows match your criteria (same camera1 with null Return), this code will update all of them with time_. If you only want to update the first matching row, modify the mask to mask & (df_.index == df_[mask].index[0]).
• Ensure time_ is stored in a format compatible with your CSV (e.g., a string like '2024-05-20 14:30:00').

内容的提问来源于stack exchange，提问作者Novato