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There is a newer version: 4.23.0.17664

This rule raises an issue when the pandas.DataFrame.values is used instead of the pandas.DataFrame.to_numpy() method.
Why is this an issue?
The values attribute and the to_numpy() method in pandas both provide a way to return a NumPy representation of the
DataFrame. However, there are some reasons why the to_numpy() method is recommended over the values
attribute:

   Future Compatibility: The values attribute is considered a legacy feature, while the to_numpy() is
  the recommended method to extract data and is considered more future-proof. 
   Data type consistency: If the DataFrame has columns with different data types, NumPy will choose a common data
  type that can hold all the data. This may lead to loss of information, unexpected type conversions, or increased memory usage. The
  to_numpy() allows you to select the common type manually, passing the dtype argument. 
   View vs Copy: The values attribute can return a view or a copy of the data depending on whether the data needs to
  be transposed. This can lead to confusion when modifying the extracted data. On the other hand, to_numpy() has copy
  argument allowing to force it always to return a new NumPy array, ensuring that any changes you make won’t affect the original
  DataFrame. 
   Missing values control: The to_numpy() allows to specify the default value used for missing values in the
  DataFrame, while the values will always use numpy.nan for missing values. 

How to fix it
Use the to_numpy() method instead of the values attribute to get a NumPy representation of the
DataFrame.
Code examples
Noncompliant code example
import pandas as pd

df = pd.DataFrame({
        'X': ['A', 'B', 'A', 'C'],
        'Y': [10, 7, 12, 5]
    })

arr = df.values # Noncompliant: using the 'values' attribute is not recommended

Compliant solution
import pandas as pd

df = pd.DataFrame({
        'X': ['A', 'B', 'A', 'C'],
        'Y': [10, 7, 12, 5]
    })

arr = df.to_numpy() # Compliant

Resources
Documentation

   Pandas Documentation - pandas.DataFrame.to_numpy() 
   Pandas Documentation - pandas.DataFrame.values