Errata

also found in https://jakevdp.github.io/PythonDataScienceHandbook/
This is actually more of a conceptual error.

Combining Datasets: Merge and Join:
In actuality, it is the Dataset on the Left Side in the pd.merge() function that generally drives the order of the key column in the Resultant Dataset. So it's more often the index on the right that gets discarded, not both.

i.e. df1 in df3 = pd.merge(df1, df2)
(here the index in df3 will be driven by df1).

The confusion arises due to the key column (in this case 'employee') is
already sorted in alpha order in df1.

Try reversing the position of the datasets
i.e. set df3 = pd.merge(df2, df1)
(...and you will see the index of df3 driven by df2, not df1!)

This same issue is often arises when coding in SQL.

n Python, slices are also no-copies views. Therefore, the sentence «This is one area in which Numpy array slicing differs from Python list slicing.» is wrong.

The only difference is when we are using advanced/fancy indexing. In this case, NumPy creates copies.

wrong: np.ones((3, 1) + np.arange(3)

correct: np.arange(3).reshape((3, 1)) + np.arange(3)

correct: np.arange(3)[:, np.newaxis] + np.arange(3)

>Using the equivalence of A AND B and NOT (A OR B)

This is not equivalent.

A = true
B = true

A and B = true
NOT (A OR B) = NOT (true) = false

The example that follows is also incorrect since it is based off that.

The wanted equivalence is, I suppose,

A and B = NOT (NOT (A) OR NOT (B)) by the Morgan's law

In[11]: population_dict = {'California': 38332521,
'Texas': 26448193,
'New York': 19651127,
'Florida': 19552860,
'Illinois': 12882135}
population = pd.Series(population_dict)
population

Out[11]: California 38332521
Florida 19552860
Illinois 12882135
New York 19651127
Texas 26448193
dtype: int64
----
Using version 0.23.4, the keys are output in the same order as in the dictionary
(not alphabetized), so "In[13]: population['California':'Illinois']" retrieves the entire series rather than the first three elements (ordered alphabetically by keys : 'California', 'Florida', 'Illinois')

The same issue is seen in later examples

n[8]: df3 = make_df('AB', [0, 1])
df4 = make_df('CD', [0, 1])
print(df3); print(df4); print(pd.concat([df3, df4], axis='col'))

---------------------------------------------------------------------------------

pd.concat() fails ; needs to be "axis='columns'"

In the Dispatch methods, in the code "planets.groupby('method')['year'].describe().unstack()", calling the unstack method (using parenthesis) returns a 'pandas.core.series.Series' whereas it should return "<bound method DataFrame.unstack of method". Therefore, the parenthesis should be omitted in unstack to get desired result.

Correct code : planets.groupby('method')['year'].describe().unstack

This:
In[25]: from pandas_datareader import data
goog = data.DataReader('GOOG', start='2004', end='2016', data_source='google'
goog.head( )

In[26]: goog = goog['Close']

In[27]: %matplotlib inline
import matplotlib.pyplot as plt
import seaborn; seaborn.set()

In[28]: goog.plot( )

Should be:
In[25]: from pandas_datareader import data
aapl = data.DataReader('AAPL', start='2004', end='2016', data_source='yahoo')
aapl.head( )

In[26]: aapl = aapl['Close']

In[27]: %matplotlib inline
import matplotlib.pyplot as plt
import seaborn; seaborn.set()

In[28]: aapl.plot( )

---
Google Finance has discontinued its API, so this feature has been deprecated in the Pandas DataReader.
Therefore, financial data should be imported from Yahoo Finance instead.

This:
In[29]: goog.plot(alpha=0.5, style='-')
goog.resample('BA').mean().plot(style=':')
goog.asfreq('BA').plot(style='--');
plt.legend(['input', 'resample', 'asfreq'],
loc='upper left');

In[30]: fig, ax = plt.subplots(2, sharex=True)
data = goog.iloc[:10]

data.asfreq('D').plot(ax=ax[0], marker='o')

data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')
data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')
ax[1].legend(["back-fill", "forward-fill"]);

Should be:
In[29]: aapl.plot(alpha=0.5, style='-')
aapl.resample('BA').mean().plot(style=':')
aapl.asfreq('BA').plot(style='--');
plt.legend(['input', 'resample', 'asfreq'],
loc='upper left');

In[30]: fig, ax = plt.subplots(2, sharex=True)
data = aapl.iloc[:10]

data.asfreq('D').plot(ax=ax[0], marker='o')

data.asfreq('D', method='bfill').plot(ax=ax[1], style='-o')
data.asfreq('D', method='ffill').plot(ax=ax[1], style='--o')
ax[1].legend(["back-fill", "forward-fill"]);

---
Name 'goog' is not defined because Google Finance has discontinued its API, so this feature has been deprecated in the Pandas DataReader.
Therefore, financial data should be imported from Yahoo Finance instead.

This:
In[31]: fig, ax = plt.subplots(3, sharey=True)

goog = goog.asfreq('D', method='pad')

goog.plot(ax=ax[0])
goog.shift(900).plot(ax=ax[1])
goog.tshift(900).plot(ax=ax[2])

local_max = pd.to_datetime('2007-11-05')
offset = pd.Timedelta(900, 'D')

ax[0].legend(['input'], loc=2)
ax[0].get_xticklabels()[2].set(weight='heavy', color='red')
ax[0].axvline(local_max, alpha=0.3, color='red')

ax[1].legend(['shift(900)'], loc=2)
ax[1].get_xticklabels()[2].set(weight='heavy', color='red')
ax[1].axvline(local_max + offset, alpha=0.3, color='red')

ax[2].legend(['tshift(900)'], loc=2)
ax[2].get_xticklabels()[1].set(weight='heavy', color='red')
ax[2].axvline(local_max + offset, alpha=0.3, color='red');

In[32]: ROI = 100 * (goog.tshift(-365) / goog - 1)
ROI.plot()
plt.ylabel('% Return on Investment');

Should be:
In[31]: fig, ax = plt.subplots(3, sharey=True)

aapl = aapl.asfreq('D', method='pad')

aapl.plot(ax=ax[0])
aapl.shift(900).plot(ax=ax[1])
aapl.tshift(900).plot(ax=ax[2])

local_max = pd.to_datetime('2007-11-05')
offset = pd.Timedelta(900, 'D')

ax[0].legend(['input'], loc=2)
ax[0].get_xticklabels()[2].set(weight='heavy', color='red')
ax[0].axvline(local_max, alpha=0.3, color='red')

ax[1].legend(['shift(900)'], loc=2)
ax[1].get_xticklabels()[2].set(weight='heavy', color='red')
ax[1].axvline(local_max + offset, alpha=0.3, color='red')

ax[2].legend(['tshift(900)'], loc=2)
ax[2].get_xticklabels()[1].set(weight='heavy', color='red')
ax[2].axvline(local_max + offset, alpha=0.3, color='red');

In[32]: ROI = 100 * (aapl.tshift(-365) / aapl - 1)
ROI.plot()
plt.ylabel('% Return on Investment');

---
Name 'goog' is not defined because Google Finance has discontinued its API, so this feature has been deprecated in the Pandas DataReader.
Therefore, financial data should be imported from Yahoo Finance instead.

This:
In[33]: rolling = goog.rolling(365, center=True)

data = pd.DataFrame({'input': goog,
'one-year rolling_mean': rolling.mean(),
'one-year rolling_std': rolling.std()})
ax = data.plot(style=['-', '--', ':'])
ax.lines[0].set_alpha(0.3)

Should be:
In[33]: rolling = aapl.rolling(365, center=True)

data = pd.DataFrame({'input': aapl,
'one-year rolling_mean': rolling.mean(),
'one-year rolling_std': rolling.std()})
ax = data.plot(style=['-', '--', ':'])
ax.lines[0].set_alpha(0.3)

---
Name 'goog' is not defined because Google Finance has discontinued its API, so this feature has been deprecated in the Pandas DataReader.
Therefore, financial data should be imported from Yahoo Finance instead.

In the last paragraph of page 344:
"Often one point of confusion is how the target array differs from the other features
columns. The distinguishing feature of the target array is that it is usually the quantity we want to predict from the data: in statistical terms, it is the dependent variable. For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the species column would be considered the feature."

Since the species column contains the value to predict using model, it should be interpreted as target array rather than feature. The last sentence should be corrected as follows:
"For example, in the preceding data we may wish to construct a model that can predict the species of flower based on the other measurements; in this case, the species column would be considered the target array."