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.

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

Apply aggregation to the grouped result first. for instance, dfgrp1 what does it produces when you print it? an object reference, which you cannot make into frame. However, the result that you see as result of groupby, employing agregation, will allow you to use to_frame()

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

You could let seaborn do the grouping. It is one of its fortes. So, directly sns.boxplot(data=df, x='weekday', y='NO2').

import seaborn as sns
import matplotlib.pyplot as plt
import numpy as np
import pandas as pd

N = 100
df_data = pd.DataFrame({'year': np.random.randint(2015, 2021, N),
                        'weekday': np.random.randint(0, 7, N),
                        'NO2': np.random.uniform(5, 40, N)})
year_filter = df_data['year'] != 2020

fig, axes = plt.subplots(ncols=2, squeeze=False)
sns.boxplot(data=df_data[year_filter], x='weekday', y='NO2', ax=axes[0, 0])
sns.boxplot(data=df_data[year_filter], x='year', y='NO2', ax=axes[0, 1])
plt.tight_layout()
plt.show()