Errata

Chapter 10 uses DataFrame.groupby(...,axis="columns") on several occasions, which is deprecated.

Text:
"A signed integer can represent both positive and negative integers, while an unsigned integer can only represent nonzero integers."

Suggestion:
"A signed integer can represent both positive and negative integers, while an unsigned integer can only represent non-negative integers, including zero."

It says:
"Integer division not resulting in a whole number will always yield a floating-point number"

Suggestion:
"Integer division will always yield a floating-point number"

It says: "NumPy has some basic set operations for one-dimensional ndarrays. A commonly used one is numpy.unique, which returns the sorted unique values in an array:"

The sentence might imply that `numpy.unique` only works for one-dimensional arrays, which is not true. The `numpy.unique` function also works for n-dimensional arrays, although by default it flattens the array to one dimension before finding the unique values.

In [98]: movies["genre"] = movies.pop("genres").str.split("|")

should be movies["genres"] = movies.pop("genre").str.split("|")

In [31]: np.empty((2, 3, 2))
Out[31]:
array([[[0., 0.],
[0., 0.],
[0., 0.]],
[[0., 0.],
[0., 0.],
[0., 0.]]])

La función np.empty no inicializa los valores del array, por lo que los valores que muestra son arbitrarios y no necesariamente ceros. El resultado es más consistente con np.zeros.

In [46]: np.empty((2, 3, 2))
Out[46]:
array([[[4.67296746e-307, 1.69121096e-306],
[1.78020984e-306, 1.55762979e-307],
[1.78022342e-306, 8.06635958e-308]],

[[1.86921415e-306, 1.00132737e-307],
[1.33508506e-307, 9.45701377e-308],
[1.11257937e-307, 2.00755374e-317]]])

In the “Open Access” HTML version, Table 11.2: datetime format specification:
It says:
"%j - Day of the year as a zero-padded integer (from 001 to 336)"
According to the official Python document (and common sense:) ), the value range should be "from 001 to 366"

In [45]: numeric_strings = np.array(["1.25", "-9.6", "42"], dtype=np.string_)

`np.string_` was removed in the NumPy 2.0 release. Use `np.bytes_` instead.

"Suppose you want to keep only rows containing at most a certain number of missing observations. You can indicate this with the thresh argument:"

The thresh argument to numpy.Dataframe.dropna() does not govern how many NA values are allowed.
Instead it requires that many non-NA values to be present.

The code to generate figure 9.27 does not match the generated figure, as the generated figure has a hue to the bars (indicating the day) which is missing from:

In [113]: sns.catplot(x="day", y="tip_pct", row="time",
.....: col="smoker",
.....: kind="bar", data=tips[tips.tip_pct < 1])

This can be corrected with:

In [113]: sns.catplot(x="day", y="tip_pct", row="time",
.....: col="smoker", hue="day",
.....: kind="bar", data=tips[tips.tip_pct < 1])

Figure 9.28 box plots have hues in the image, but the code to generate them does not match.

In [114]: sns.catplot(x="tip_pct", y="day", kind="box",
.....: data=tips[tips.tip_pct < 0.5])

should be

In [114]: sns.catplot(x="tip_pct", y="day", kind="box", hue="day",
.....: data=tips[tips.tip_pct < 0.5])

To match figure 9.28.

The code example is "grouped_pct.agg([("average", "mean"), ("stdev", np.std)])". There is a FutureWarning to use "grouped_pct.agg([("average", "mean"), ("stdev", "std")]) instead.

According to the up code block: def~~
In[116]: names Out[116]: table maybe wrong.
It should be
name sex births year prop
year sex
1880 F 0 Mary F 7065 1880 0.077643
1 Anna F 2604 1880 0.028618
2 Emma F 2003 1880 0.022013
3 Elizabeth F 1939 1880 0.021309
4 Minnie F 1746 1880 0.019188
... ... ... ... ... ... ... ...
2010 M 1690779 Zymaire M 5 2010 0.000003
1690780 Zyonne M 5 2010 0.000003
1690781 Zyquarius M 5 2010 0.000003
1690782 Zyran M 5 2010 0.000003
1690783 Zzyzx M 5 2010 0.000003

"When an entire row or column contains all NA values, the sum is 0, whereas if any value is not NA, then the result is NA. "

This sentence should be: "When an entire row or column contains all NA values, the sum is 0, whereas if any value is not NA, then the result includes the value(s) not NA."

df
one two
a 1.40 NaN
b 7.10 -4.5
c NaN NaN
d 0.75 -1.3

df.sum(axis="columns")
a 1.40
b 2.60
c 0.00
d -0.55
dtype: float64

df.sum(axis="columns", skipna=False)
a NaN
b 2.60
c NaN
d -0.55
dtype: float64

In[283] and In[285] look exactly the same even though line above says that you could include more concise syntax.

The sentence is:
Relatedly, drop_duplicates returns a DataFrame with rows where the duplicated array is False filtered out:

The sentence should be:
Relatedly, drop_duplicates returns a DataFrame with rows where the duplicated array is True filtered out: