Python3.6（Win10）下scipy.stats.mode()调用异常求助

Hey there, let's break down why you're hitting that warning and how to fix it quickly.

The Problem #

Your Gender column in the loan prediction dataset has missing values (NaN), and the older version of scipy that comes with Python 3.6/Anaconda 3 doesn't handle NaNs well in the mode() function. That's exactly what that RuntimeWarning is telling you—scipy can't properly check for NaNs in your input, which might lead to incorrect results or errors.

Solution 1: Use Pandas' Built-in mode() (Recommended) #

Pandas has a far more user-friendly mode() method for Series that automatically ignores NaNs. This is the simplest fix:

import pandas as pd
data = pd.read_csv('Loan3_train.csv')
# Grab the first mode (in case there are multiple ties for most frequent value)
gender_mode = data['Gender'].mode().iloc[0]
print(gender_mode)

This will directly give you the most frequent non-null value in the Gender column without any warnings.

Solution 2: Fix the scipy.stats.mode Approach (If You Need to Use Scipy) #

If you specifically want to stick with scipy's mode() function, you just need to filter out NaNs first, and adjust how you access the result:

from scipy.stats import mode
import pandas as pd

data = pd.read_csv('Loan3_train.csv')
# Drop all rows where Gender has a missing value
gender_non_null = data['Gender'].dropna()
# Scipy returns a tuple—[0] gets the mode array, then [0] pulls the single mode value
gender_mode = mode(gender_non_null)[0][0]
print(gender_mode)

By removing NaNs before passing the data to scipy, you eliminate the warning. The extra [0] is necessary because scipy returns the mode as an array even when there's only one most frequent value.

Why the Warning Happens #

C:\ProgramData\Anaconda3\lib\site-packages\scipy\stats\stats.py:253: RuntimeWarning: The input array could not be properly checked for nan value...

Older scipy versions (compatible with Python 3.6) don't have robust handling for NaN values in the mode() function. When you pass a Series containing NaNs, scipy can't validate the input correctly, triggering that warning. In some cases, this could even lead to incorrect mode calculations if NaNs are treated as a valid category.

内容的提问来源于stack exchange，提问作者user8270077