Although numpy.ndarray has a mean, max, std etc. method, it does not have a median method. For a list of all methods available for an ndarray, see the numpy documentation for ndarray.

It is available as a function that takes the array as an argument:

>>> import numpy as np
>>> a = np.array([1,2,3,4,5,6,7,8,9,10])
>>> np.median(a)
5.5

As you will see in the documentation for ndarray.mean, ndarray.mean and np.mean are "equivalent functions," so this is just a matter of semantics.

The error is reproducible if the array is of dtype=object:

import numpy  as np

label0 = np.random.random((50, 3)).astype(object)
np.cov(label0, rowvar=False)

AttributeError: 'float' object has no attribute 'shape'

If possible you should convert it to a numeric type. For example:

np.cov(label0.astype(float), rowvar=False)  # works

Note: object arrays are rarely useful (they are slow and not all NumPy functions deal gracefully with these - like in this case), so it could make sense to check where it came from and also fix that.

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())

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'])

Given that numpy.ndarray is the Python type of "numpy arrays", the error is just saying that arrays don't have a diff method. diff is a function defined in the numpy module.

Instead of np.log(close_prices).diff(), do

np.diff(np.log(close_prices))

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