How to save Tensorflow predictions to data frame?
Thanks to Ankit Seth I found the answer.

#this returns output layer of forward prop with trained parameters of the model.
pred =  forward_propagation(X, parameters)
predictions_sigm = pred.eval(feed_dict = {X: X_test})
predictions_sigm = sigmoid(predictions)

#pred.eval returns linear regression layer for each class separately, so we need to pick index of maximum of them. I don't use softmax layer, since the output should be the same.

#init list of classes
y_list = []

for i in range(predictions_sigm.shape[1]):
    class_list = []
    class_list.append(predictions_sigm[0][i])   
    class_list.append(predictions_sigm[1][i])
    class_list.append(predictions_sigm[2][i])
    class_list.append(predictions_sigm[3][i])

    #get index of maximum value of sigmoid. it will correspond to class
    y = np.argmax(class_list)
    y_list.append(y)

   #then append y_list to dataframe
   df['predicted_y'] = y_list