Thanks for the great book. The equation between fig 4.10 and 4.11 shows unrendered, as \[ \hat{F}_n(t) = \left\{ \begin{array}{ll} 0 & \text{for }t. I wanted to submit a PR, but couldn't find the book sources!

"Note that are rows are uniquely defined by their respective Ids."

In section 7.1.4, there are two minor typos:

Exercise 7.2
Are they* worth taking note of ...

Exercise 7.3
Using numpy.insert, add* a new row/column ...

I think there is a typo in section 3.5.4. Modify in place or return a modified copy?:

The list.sorted method modifies the list it is applied on in place:

x = [5, 3, 2, 4, 1]
x.sort()  # modifies x in place and returns nothing

(e.g. list.sort instead of list.sorted)?

In 6.3.2. Pareto Distribution I think the sentence

This time, however, will be interested in not what is typical, but ...

is IMHO missing the subject.
I would write:

This time, however, we will be interested in not what is typical, but ...

a warning comes up that the command
cmap=cm.get_cmap("copper"), # colour map
will soon be depreciated

substituting it with
cmap=plt.colormaps.get_cmap("copper"), # colour map
as suggested by the warning text seems to give the same output

In this section: https://datawranglingpy.gagolewski.com/chapter/000-preface.html#we-need-data-and-domain-knowledge

The sentence "Still, we need to familiar with the reality behind the numbers we have at hand, for example, by working closely with various experts in the field of our interest or pursuing study/research therein." Is missing a "be" at the start of the sentence

In 5.4.3 Slicing the comment in part of the very first code snippet

...
x[::-1]  # every second element
## array([50, 40, 30, 20, 10])
...

should say something like

...
x[::-1]  # every element in reverse order
## array([50, 40, 30, 20, 10])
...

Small typo here... 3

In 5.4.3 Slicing
it looks like the inline code *= has. ot been rendered correctly

This did not modify the original vector, because we applied `*=` on a different object, which has not even been memorised after that operation took place.

I came across multiple instances where it was not clear what exaxctly was meant by normalisation.
For example Exercise 8.3 asks for standardisation (calculating the Z-score), normalisation(?) and min-max scaling (min-max normalization). I assume that normalisation means calculating the mean normalisation. However, min-max scaling is a normalisation technique as well.
I'd recommend to spell out specifically what kind of normalisation needs to be calculated in excercises, to prevent confussion.

There are 4 missing 4 figures in chapter 9.
9.2, 9.4, 9.6, 9.8

table 14.1 values are in 100s rather than 1000s GPB

it should be corrected to
0-20
20-40
40-60 ..

small typo in the Important box final paragraph.
It should be corrected to:

"Generally, for two matrices, their column/row numbers must match or be equal to 1. Also, if one operand is a one-dimensional array, it will be promoted to a row vector."

Thank you for this free and easy to follow introduction to Data Wrangling in Python.

In section 1.2.3 under point 5:
Code is presented as normal text.

To make this code work I needed to change the double quote characters and reformat the text.
import matplotlib.pyplot as plt # basic plotting library plt.bar( ['Python', 'JavaScript', 'HTML', 'CSS'], # a list of strings [80, 30, 10, 15] # a list of integers (the corresponding bar heights) ) ) plt.title('What makes you happy?') plt.show()