It would be helpful if you could post the full stack trace, so that we can see which line your error occurs at. In general, the more information you can provide in a question, the better.

In this case, it looks like your full_model_pipeline may somehow become a numpy array. Since you have a one-element pipeline, you could try changing

full_model_pipeline = Pipeline(steps =[
                        ('full_pipeline',full_pipeline),
                        ('model',LinearRegression())
                    ])

full_model_pipeline.fit(X_train,y_train)

to

model = LinearRegression()
model.fit(X_train, y_train)

If you want to use a numpy array instead of a pandas.Dataframe, you can simply pass the array as either the x or y argument to countplot.

E.g.

import numpy
import seaborn

data = numpy.array([1, 2, 2, 3, 3, 3])
ax = seaborn.countplot(x=data)

This does not seem to work with multidimensional arrays. Without knowing more about what data you are plotting, it is hard to say for sure how to produce the particular plot you want. However, since your array has only length 1 in the second dimension, how about simply reshaping the array to be one dimensional?

E.g.

train_y.shape = len(train_y)
ax = sns.countplot(x=train_y)

As an aside, it is preferred to use pandas.DataFrames instead of numpy.arrays. The examples in the docs, which I assume you have tried to emulate here, use DataFrames. You can convert your array into a DataFrame, and specify the name of the variable you will later plot.

E.g.

import numpy
import seaborn as sns
import pandas

data = numpy.array([1, 2, 2, 3, 3, 1, 1, 1, 2])
df = pandas.DataFrame(data=data, columns=["variable"])

Here, "variable" is the name of a column in the df 'table'. Then when you plot with countplot, specify this column as the argument to x.

ax = sns.countplot(x="variable", data=df)

The solution:

The given dataset was already an array, so I didn’t need to call .values.

If you debug your program by simply printing ax, you'll quickly find out that ax is a two-dimensional array: one dimension for the rows, one for the columns.

Thus, you need two indices to index ax to retrieve the actual AxesSubplot instance, like:

ax[1,1].plot(...)

If you want to iterate through the subplots in the way you do it now, by flattening ax first:

ax = ax.flatten()

and now ax is a one dimensional array. I don't know if rows or columns are stepped through first, but if it's the wrong around, use the transpose:

ax = ax.T.flatten()

Of course, by now it makes more sense to simply create each subplot on the fly, because that already has an index, and the other two numbers are fixed:

for x < plots_tot:
     ax = plt.subplot(nrows, ncols, x+1)

Note: you have x <= plots_tot, but with x starting at 0, you'll get an IndexError next with your current code (after flattening your array). Matplotlib is (unfortunately) 1-indexed for subplots. I prefer using a 0-indexed variable (Python style), and just add +1 for the subplot index (like above).

You are using the right method but in a wrong way :)

nan_to_num is a method of numpy module, not numpy.ndarray. So instead of calling nan_to_num on you data, call it on numpy module giving your data as a paramter:

import numpy as np
data = np.array([1,2,3,np.nan,np.nan,5])
data_without_nan = np.nan_to_num(data)

prints:

array([1., 2., 3., 0., 0., 5.])

In your example:

import numpy as np
val = np.nan_to_num(setTo.ravel())

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.

Using .values on a pandas dataframe gives you a numpy array. This will not contain column names and such. You do this when setting X like this:

X = dataset[['Read?', 'x1', .. ,'x47']].values 

But then you try to get the column names from X (which it does not have) by writing X.columns here:

coeff_df = pd.DataFrame(regressor.coef_, X.columns, columns=['Coefficient'])

So store your column names in a variable or input them again, like this:

coeff_df = pd.DataFrame(regressor.coef_, ['Read?', 'x1', .. ,'x47'], columns=['Coefficient'])