According to what I understand. You are loading loanapp_c.csv in ds using this code:

ds = pd.read_csv('desktop/python ML/loanapp_c.csv')

ds over here is a DataFrame object. What you are doing is calling to_dataframe on an object which a DataFrame already.

Removing this dataset = ds.to_dataframe() from your code should solve the error

I think the column name that contains "Number" is something like " Number" or "Number ". I'm assuming you might have a residual space in the column name. Please run print "<{}>".format(data.columns[1]) and see what you get. If it's something like < Number>, it can be fixed with:

data.columns = data.columns.str.strip()

See pandas.Series.str.strip

In general, AttributeError: 'DataFrame' object has no attribute '...', where ... is some column name, is caused because . notation has been used to reference a nonexistent column name or pandas method.

pandas methods are accessed with a .. pandas columns can also be accessed with a . (e.g. data.col) or with brackets (e.g. ['col'] or [['col1', 'col2']]).

data.columns = data.columns.str.strip() is a fast way to quickly remove leading and trailing spaces from all column names. Otherwise verify the column or attribute is correctly spelled.

you can do this to read a parquet file:

import pandas as pd
txt = pd.read_parquet("/data/file.parquet")
txt_ecommerce = txt.loc[txt.POS_Cardholder_Presence =="ECommerce"]

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)