It seems you have no Values but values column, so need add [] because collision with values function.

Sample:

df = pd.DataFrame ({'values': [1,2,3,4,np.nan,8] })
print (df)
   values
0     1.0
1     2.0
2     3.0
3     4.0
4     NaN
5     8.0

#return numpy array
print (df.values)
[[  1.]
 [  2.]
 [  3.]
 [  4.]
 [ nan]
 [  8.]]

#select column values
print (df['values'])
0    1.0
1    2.0
2    3.0
3    4.0
4    NaN
5    8.0
Name: values, dtype: float64

Your solution for me works nice, I also change .Values to ['Values'].

df1 = df.groupby('Label').filter(lambda x: x['Values'].count() > 1)
print (df1)
  Label  Values
0     A     1.0
1     A     2.0
3     C     4.0
4     C     NaN
5     C     8.0

Alternative solution with transform and boolean indexing:

print (df.groupby('Label')['Values'].transform('count'))
0    2.0
1    2.0
2    1.0
3    2.0
4    2.0
5    2.0
Name: Values, dtype: float64

print (df.groupby('Label')['Values'].transform('count') > 1)
0     True
1     True
2    False
3     True
4     True
5     True
Name: Values, dtype: bool

print (df[df.groupby('Label')['Values'].transform('count') > 1])
  Label  Values
0     A     1.0
1     A     2.0
3     C     4.0
4     C     NaN
5     C     8.0

Also check What is the difference between size and count in pandas?

The problem is that train_test_split(X, y, ...) returns numpy arrays and not pandas dataframes. Numpy arrays have no attribute named columns

If you want to see what features SelectFromModel kept, you need to substitute X_train (which is a numpy.array) with X which is a pandas.DataFrame.

selected_feat= X.columns[(sel.get_support())]

This will return a list of the columns kept by the feature selector.

If you wanted to see how many features were kept you can just run this:

sel.get_support().sum()  # by default this will count 'True' as 1 and 'False' as 0

There is nothing wrong with your data, your main mistake is that you need to pay attention to the plot function used. This link tells you how to plot your data. https://matplotlib.org/tutorials/introductory/pyplot.html

However I would change the last line to something like:

import matplotlib.pyplot as plt
plt.plot(y_rus)
plt.show()

or something like:

plt.scatter(y_rus)
plt.show()

In general is a bit difficult to understand what exactly you want or the format of the data, so I hope this works. Follow the tutorial it will help you understand what you really need to continue.

Well, according to the documentation, ndarray simply has no count method.

The code you have posted contradicts the error-message you give us. In your code you create a simple Python list, but your error message indicates that you are actually using a numpy ndarray.

This is more of a programming question than a data science question and is therefore better suited for stackoverflow. The vectorizer is trying to convert the string values to lowercase using the .lower() method. However, you are passing in a numpy array, which does not have this method. Instead of passing in a numpy array, you can simply use a pandas dataframe (which does have the .lower() method, see the documentation) by removing the .values attribute in dataset.iloc[:,:-1].values.