I think I understood what you wanted to do and what you did not understand (mainly about the way to modifiy objects with pandas). I assume that you wanted to:

1. compute your aggregation by payment date in data
2. and then set its index to 'Payment date' field

Short answer: if you want to have this result into data, simply execute:

data = data.groupby('Payment date ')['Payment amount'].sum().to_frame()

'Payment date ' will be your new index, to_frame prevents your single column resulting dataframe to be squeezed into a pandas Series (which I think was your first intention to avoid, resetting your index to then set it back).

Let's dive into your code.

First line

data = data.groupby('Payment Date ')

First line is ok, but might not do exactly what you want. You are taking data, which I assume is a pandas DataFrame and reaffect it a pandas DataFrameGroupBy object. This kind of object does not hold any data, you can see it simply as a mapping between index(s) of your original DataFrame and associated groups (here, payment dates).

Anyway, you got your groupby object into data.

Second line

data['Payment Amount '].sum().reset_index()

This line does nothing. It shows the result of the computation in your Jupyter notebook, but nothing has been changed in data. data is still the same DataFrameGroupBy object.

Third line

data = data.set_index('Payment Date ', inplace  = True)

An exception is raised, saying that a DataFrameGroupBy objet has no set_index method. This is because data has not been changed by your second line of code. Even so, I would encourage you to avoid using inplace=True anytime in your code. You should always go with explicit reassignements.

Your code could look like (if you don't like the short answer above):

data = data.groupby('Payment date ')
data = data['Payment amount'].sum().reset_index()
data = data.set_index('Payment date ')  # No inplace=True!

For groupby need some aggregation function(s), like mean, sum, max:

df.sort_values(['col5'],ascending=False).groupby('col1').mean().reset_index()

Or:

df.sort_values(['col5'],ascending=False).groupby('col1', as_index=False).mean()

On checking I found my polars version :

pl.__version__

0.17.3

https://pola-rs.github.io/polars/py-polars/html/reference/dataframe/api/polars.DataFrame.groupby.html

I need to do:

df.groupby("a").agg(pl.col("b").sum())  # there is no underscore in groupby

#output

shape: (3, 2)
a   b
str i64
"a" 2
"c" 3
"b" 5

and the document says :

Deprecated since version 0.19.0: This method has been renamed to DataFrame.group_by().

This is the new document for polars version 0.19

https://pola-rs.github.io/polars/py-polars/html/reference/dataframe/api/polars.DataFrame.group_by.html#polars-dataframe-group-by

I mean, isn't it groupby(), not group_by()?

"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)