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

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

The groupby is not an inplace operation and you need to assign the result to a variable in order to access get_group. So, simply just assign it back to the table:

import pandas as pd 

table = pd.read_csv("April24_HD_T2_MMStack_Default_edges.csv")
table.drop(table.index[0:3], inplace = True)
table = table.groupby('TRACK_ID', sort = False)

zero = table.get_group('0')

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()