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 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

The immediate cause of your problem is that dataset1 and dataset2 are ndarray type, with dtype == object.

Although your values are read in as float type, when you access the column of the values array you return (at the line dataset1 = data1[:,ithattr1]), the dtype is changed to object (as you are actually pulling the data row by row, then extracting a column and numpy has both floats and strings in the row, so has to coerce to the most specific common data type - object).

You can get around this several ways. One is simply to make the arrays into lists, at which point Python coerces what looks like a float to be a float, i.e. change

ax.boxplot([dataset1,dataset2])

to

ax.boxplot([list(dataset1),list(dataset2)])

Another is to add lines explicitly setting the type:

dataset1 = dataset1.astype(np.float)
dataset2 = dataset2.astype(np.float)

This is a gotcha when you access pandas dataframes or numpy arrays containing different data types in columns by index. It's pretty hard to debug (took me a while to get it for this question and I've seen it before - see the edit history)

However, the way you're handling your data via numerical indices also means you end up having to reorder your columns etc for convenience in your loadData function. A better way would be to let pandas do all the heavy lifting on types etc...

As an example - I've put your code into what (I think) is a more conventional pandas / python writing. It's a bit shorter and doesn't require the hack to change the data to a list that I give you above. Code is below and output plot after that (using the input data snippet from your question)

import matplotlib.pyplot as plt
import pandas as pd
import numpy as np

def loadData(filename,cols):
    data = pd.read_csv(filename, quotechar='"',names=cols,header=None)
    return data

def boxplot(filename,cols):
    data1 = loadData(filename,cols)

    fig = plt.figure()
    ax = fig.add_subplot(111)

    ax.boxplot([data1['high'],data1['close/last']])
    plt.show()

cols=['date','close/last','volume','open','high','low']
filename = 'microsoft.csv'

boxplot(filename,cols)

Output

Your question leaves a lot unexplained, but the error you're receiving probably came from the last line vectorized_text = features.transform("Heathcare is good").toarray().
The variable features is itself an array (it came from features = ... .toarray()).
I'm assuming you meant to replace it with
vectorized_text = tfidf.transform("Heathcare is good").toarray()?

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.

.batch() method is usually applying to objects of tf.data.Dataset type. In your case applying this method is non needed and surely cannot be done because your data has ndarray type.

Just remove .batch() from your code and specify batch_size arg in 'fit()' method if it is needed.