This feature was just added in Version 0.24.0, which was released a couple of days ago. If you haven't updated yet, the attribute doesn't exist! Once you update pandas the problem should resolve itself.

If you need your code to work with all versions of pandas, here's a simple way to convert a Series into a NumPy array:

import pandas as pd
import numpy as np

s = pd.Series([1.1, 2.3])
a = np.array(s)
print(a)  # [1.1 2.3]

On an advanced note, if your Series has missing values (as NaN values), these can be converted to a masked array:

s = pd.Series([1.1, np.nan])
a = np.ma.masked_invalid(s)
print(a)  # [1.1 --]

The Iris Dataset from Sklearn is in Sklearn's Bunch format:

print(type(iris))
print(iris.keys())

output:

<class 'sklearn.utils.Bunch'>
dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names', 'filename'])

So, that's why you can access it as:

x=iris.data
y=iris.target

But when you read the CSV file as DataFrame as mentioned by you:

iris = pd.read_csv('iris.csv',header=None).iloc[:,2:4]
iris.head()

output is:

    2   3
0   petal_length    petal_width
1   1.4 0.2
2   1.4 0.2
3   1.3 0.2
4   1.5 0.2

Here the column names are '1' and '2'.

First of all you should read the CSV file as:

df = pd.read_csv('iris.csv')

you should not include header=None as your csv file includes the column names i.e. the headers.

So, now what you can do is something like this:

X = df.iloc[:, [2, 3]] # Will give you columns 2 and 3 i.e 'petal_length' and 'petal_width'
y = df.iloc[:, 4] # Label column i.e 'species'

or if you want to use the column names then:

X = df[['petal_length', 'petal_width']]
y = df.iloc['species']

Also, if you want to convert labels from string to numerical format use sklearn LabelEncoder

from sklearn import preprocessing
le = preprocessing.LabelEncoder()
y = le.fit_transform(y)

You are calling the .fillna() method on a numpy array. And numpy arrays don't have that method defined.

You can probably convert the numpy array to a pandas.DataFrame and then apply the .fillna() method.

DO NOT VOTE UP RESPONSE MAIN WORK DONE BY @wayne above:

Did you run print(type(X)) on X prior to that line that gave an error? Given you got an error of AttributeError: 'numpy.ndarray' object has no attribute 'to_numpy', X appears to already be numpy object. Also, I don't think the to_numpy comes from numpy. Pandas has one. Polars has one. This drops us in the end of the problem and so we cannot provide much help without more code upstream.

solution

reload data set.

df = pd.read_csv(file_name, header=0)

[str(i) for i in ([["CPU_frequency"]])]

code above resolves.