In [20]: import pandas as pd
    ...: import numpy as np
    ...:
    ...: df = pd.DataFrame(np.arange(5))

In [21]:

In [21]: df.dtypes.value_counts()
Out[21]:
int64    1
dtype: int64

.get_dtype_counts() is deprecated since version 0.25.0

"sklearn.datasets" is a scikit package, where it contains a method load_iris().

load_iris(), by default return an object which holds data, target and other members in it. In order to get actual values you have to read the data and target content itself.

Whereas 'iris.csv', holds feature and target together.

FYI: If you set return_X_y as True in load_iris(), then you will directly get features and target.

from sklearn import datasets
data,target = datasets.load_iris(return_X_y=True)

I'm assuming you are using the Kaggle Heart Disease Dataset (https://www.kaggle.com/datasets/johnsmith88/heart-disease-dataset?resource=download) and this code got me the desired result.

import pandas as pd

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

print(df['target'].value_counts())

Giving this result:

target
1    526
0    499
Name: count, dtype: int64

As the dtype does not match yours I guess you are not working with this dataset so a sample of yours will be usefull as @user19077881 suggested.

I used the documentation from https://pandas.pydata.org/docs/reference/api/pandas.Series.value_counts.html as you might already have judging by your initial code

df[col] will return a DataFrame object (not a Series) if df contains more than one column with the same name. Make sure you do not have more than one column with the same name in df as that is the likely source of this error.

It's possible that this error arises when you have multiple columns with the same name in your dataframe