Pandas DataFrame赋值报错及按条件修改'Bare Nuclei'值的技术问询

Hey there! Let's fix that frustrating "Series objects are mutable, thus they cannot be hashed" error you're hitting when trying to modify your breast cancer dataset based on the Bare Nuclei != '?' condition.

First, Load Your Data Correctly #

First up, let's make sure your data is loaded properly (I noticed your read_csv call was incomplete):

import pandas as pd
import numpy as np

column_names = ['Sample code number', 'Clump Thickness', 'Uniformity of Cell Size', 
                'Uniformity of Cell Shape', 'Marginal Adhesion', 'Single Epithelial Cell Size', 
                'Bare Nuclei', 'Bland Chromatin', 'Normal Nucleoli', 'Mitoses', 'Class']

# Full working read_csv call with correct names parameter
data = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', 
                   names=column_names)

Why You're Getting the Error #

That error pops up when you accidentally use a Series object as a hashable key (like for DataFrame column indexing, or in any context that requires a fixed hash value). For example, if you wrote something like this:

# ❌ Wrong: Using a Series as the column index in .loc
data.loc[data['Bare Nuclei'] != '?', data['Bare Nuclei']] = some_value

Here, data['Bare Nuclei'] is a mutable Series, which can't be hashed—so pandas throws that error.

Another common mistake is chained indexing (like data[data['Bare Nuclei'] != '?']['Mitoses'] = 0), which can lead to SettingWithCopyWarning and might not even modify your original DataFrame correctly.

Correct Ways to Modify Values Based on the Condition #

Use pandas' .loc accessor to safely target rows and columns directly. This avoids hash errors and ensures you're modifying the original DataFrame.

Example 1: Convert 'Bare Nuclei' to Numeric for Valid Rows

If you want to turn the non-'?' values in 'Bare Nuclei' into integers:

# ✅ Correct: Use .loc to target rows and column by name
data.loc[data['Bare Nuclei'] != '?', 'Bare Nuclei'] = data.loc[data['Bare Nuclei'] != '?', 'Bare Nuclei'].astype(int)

Example 2: Set a Specific Column Value for Valid Rows

If you want to set, say, the 'Mitoses' column to 0 for all rows where 'Bare Nuclei' isn't '?':

# ✅ Correct: Directly target rows and column with .loc
data.loc[data['Bare Nuclei'] != '?', 'Mitoses'] = 0

Bonus: Clean Up Missing Values (Optional)

Since '?' acts as a missing value marker here, you can also replace it with NaN first for easier handling:

# Replace '?' with NaN
data['Bare Nuclei'] = data['Bare Nuclei'].replace('?', np.nan)

# Convert the column to numeric (NaN will stay as missing)
data['Bare Nuclei'] = pd.to_numeric(data['Bare Nuclei'])

# Now you can filter non-missing rows like this
data.loc[data['Bare Nuclei'].notna(), 'SomeColumn'] = new_values

Key Takeaways #

• Always use .loc[row_condition, 'column_name'] when modifying DataFrame values—this avoids hash errors and ensures you're working on the original data.
• Never pass a Series as the column index in .loc; use the column name as a string instead.
• Avoid chained indexing (like df[cond][col]) as it can lead to unexpected behavior.

内容的提问来源于stack exchange，提问作者Rosand Liu