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()?

DataFrame has no attribute group. However, it is possible to access data in a column in your dataframe with the same syntax used to access attributes and methods, i.e. if you have a column col, you may access the series related to this column through

df.col

What happened here is that your data is probably different from what she used in the tutorial. Or at least, the columns she has are different than the columns you have.

To solve that problem, you can either (I) simply rename your columns to match the columns from the tutorial or (II) replace data.group with the corresponding column name that you have in your df

Following the documentation for the pandas.DataFrame.groupby method, there are a couple ways you could fix this. The way I'd recommend is to explicitly specify the grouping column using the by parameter (although you don't need to), and then providing an aggregation function (looks like you want the mean). Any of these will work:

# Option 1
New.groupby(['P5PN']).mean()

# Option 2
New.groupby('P5PN').mean()

# Option 3
New.groupby(by=['P5PN']).mean()

The issue here is that while you are specifying a grouping column, you are not telling pandas how to aggregate the measures in the other columns of your data.

Note: you might want to update the title of your question to "'SeriesGroupBy' object has no attribute 'shape'" to match the actual error you're getting.

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!