如何在多行列的Pandas DataFrame末尾添加指定统计值字段？

Absolutely feasible! This is a super common task in pandas, and it works perfectly even with larger DataFrames (20+ rows, xx+ columns). Let me break down how to pull this off, depending on exactly what you're looking for:

1. Add a column to count specific values per row #

If you want to tally how many times a target value appears in each individual row (e.g., counting how many "Yes" entries are in each row), here's the code:

# Replace with your actual DataFrame name and target value
df = your_dataframe_here
target_value = "Yes"  # Could also be a number like 1, or any value matching your data

# Add the new count column at the end of the DataFrame
df["特定值行计数"] = df.apply(lambda row: (row == target_value).sum(), axis=1)

How this works: #

• axis=1 tells pandas to operate row-by-row instead of column-by-column
• (row == target_value) creates a boolean Series where each cell is True if it matches your target
• .sum() converts those True values to 1 and adds them up, giving the total per row

For faster performance with very large DataFrames, use this vectorized alternative (avoids the slower apply method):

df["特定值行计数"] = df.isin([target_value]).sum(axis=1)

2. Add a column with the total count of specific values across the entire DataFrame #

If you want every row in the new column to show the total number of times your target value appears anywhere in the DataFrame, use this simpler approach:

target_value = "Yes"
# Calculate total occurrences across all rows and columns
global_total = (df == target_value).sum().sum()
# Add the new column with this total repeated in every row
df["全局特定值总数"] = global_total

How this works: #

• The first .sum() counts occurrences per column
• The second .sum() adds those column totals together to get the overall count
• Assigning this single value to a new column fills every row with that total

Final Step: Export to Excel #

Once you've added your custom count column, just run your existing export command as usual:

df.to_excel("your_output_file.xlsx", index=False)  # index=False removes default row numbers if you don't want them

This will export the DataFrame with your new column included, exactly as you need before writing to Excel.

内容的提问来源于stack exchange，提问作者MarkoZg