You can remove your for loop and do this instead:

Copydata = df.groupby(['ta', 'tb', 'tc'])['income'].sum()

Then plot it.

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

.size is a DataFrameGroupBy function so that takes precedence with dot notation (.size). This is why the safer method to access columns is with brackets ['size']:

df.groupby('sex')['size'].mean()

sex
female    160.500000
male      178.333333
Name: size, dtype: float64

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

Use groupby.ngroup:

df1 = df.sort_values('Year')
df1['year code'] = df1.groupby('Year').ngroup()
df1 = df1.reset_index(drop=True)

# df.sort_values('Year').assign(Code=df.groupby('Year').ngroup()).reset_index(drop=True)

    Team  Rank  Year   Points year code
0   Riders  1   2014    876      0
1   Devils  2   2014    863      0
2   Kings   3   2014    741      0
3   Royals  4   2014    701      0
4   Riders  2   2015    789      1
5   Devils  3   2015    673      1
6   kings   4   2015    812      1
7   Royals  1   2015    804      1
8   Kings   1   2016    756      2
9   Riders  2   2016    694      2
10  Kings   1   2017    788      3
11  Riders  2   2017    690      3

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

The error you got tells you exactly what the problem is: numpy arrays do not have the attributegroupby. I figure you wanted to use the method groupby(), but numpy doesn't have that either. What you want to do is use a pandas dataframe and then group by a specific column, like this

df = pd.DataFrame({'Animal': ['Falcon', 'Falcon', 'Parrot', 'Parrot'], 'Max Speed': [380., 370., 24., 26.]})
df.groupby(['Animal'])

Example taken from here