Errata for Python for Data Analysis

The input variable df is: In [187]: df Out[187]: one two a 1.40 NaN b 7.10 -4.5 c NaN NaN d 0.75 -1.3 The code in the book gives this result: In [204]: df.sum(axis=1) Out[204]: a 1.40 b 2.60 c 0.00 d -0.55 dtype: float64 but shouldn't row "c" be "NaN" since we're summing together two NaNs? Here's what I get from my interpreter: In [186]: df.sum(axis=1) Out[186]: a 1.40 b 2.60 c NaN d -0.55 dtype: float64 In [188]: pd.__version__ Out[188]: '0.19.2'

It looks like `.sort_values(by=...) is deprecated. In [203]: frame.sort_index(by='b') FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...) In [207]: frame.sort_index(by=['a','b']) FutureWarning: by argument to sort_index is deprecated, pls use .sort_values(by=...) In [205]: frame.sort_values(by='b') fixed the problem. In [211]: pd.__version__ Out[211]: '0.19.2'

`df.iloc[where]` Selects single row or subset of rows from the DataFrame by label. Should probably be "...from the DataFrame by *integer position*."

The line of code: data[-(names == 'Bob')] Gives the deprecation warning: DeprecationWarning: numpy boolean negative, the `-` operator, is deprecated, use the `~` operator or the logical_not function instead. using numpy version 1.12.0 Using the tilde operator, as recommended silences the warning.

Text states: To exit the shell, press Ctrl-D or type the command exit() and press return. On Windows, Ctrl-Z should be used.

Magic functions can be used by default without the percent sign ... Some magic functions behave like Pyton functions and their output can be assigned to a variable: In [22]: %pwd Out [22]: '/home/west/code/pydata-book/ In [23]: foo = %pwd ---------------------------------------------------------------------------------- First, a single quote is missing from Out[22] With ipython 6.3.1, although In [22] works using pwd without the leading percent sign, In [23] fails with "NameError: name 'pwd' is not defined"

Note from the Author or Editor:
Fixing this typo

Two illegal print statements: print('It's negative') Since the strings contain single quote characters, they should be delimited by double quotes.

Note from the Author or Editor:
Thank you, fixed

The alternative syntax for a.issubset (b) and a.issuperset (b) shoule be "<=" and "=>" respectively (not N/A).

Note from the Author or Editor:
Fixing this.

1st release print copy says, “...the result of fancy indexing is always one-dimensional.” However, there are example outputs in this section with more than one dimension. Is that because some of the examples in the section are not fancy indexing? If that’s the case, it’s unclear where the section is building up to a fancy indexing example as opposed to every example being fancy indexing. The number of dimensions in the output seems to be the number of array dimensions plus one minus the number of dimensions indexed.

Note from the Author or Editor:
The text is unclear, I will clarify

"To keep things consistent, if you have an axis index containing integers, data selection will always be label-oriented." I think this should be integer-oriented.

Note from the Author or Editor:
The code example does not illustrate the intended behavior. I am changing the example to be "ser[-1]" instead of "ser[:2]" and added a note that slicing with integers ignores the integer labels

Looks like instead of method "match" there should be "get_indexer"

Note from the Author or Editor:
Fixing this to "get_indexer"

The 'iterator' option of pandas.read_csv returns a TextFileReader object, not a TextParser object, since 2012.

Note from the Author or Editor:
I am changing the test to correspond to changes in the latest version of pandas

As Out[38] shows, the 'iterator' option of pandas.read_csv returns a TextFileReader object, not a TextParser object, since 2012.

Note from the Author or Editor:
I am changing the text to correspond with changes in pandas

As Out[38] show, the 'iterator' option of pandas.read_csv returns a TextFileReader object, not a TextParser object, since 2012.

Note from the Author or Editor:
I am changing the text to correspond to the current version of pandas

Code says the example file is in path: path='examples/mta_perf/Performance_MNR.xml' Actual path from git repository is: path='datasets/mta_perf/Performance_MNR.xml'

Note from the Author or Editor:
Correct, thank you. This will need to be fixed in the source files

Book say: "match - Use re.match with the passed regular expression on each element, returning matched groups as list" Should say: "... returning Series/array of boolean values" And commands on pp 217 - 218 are not correct, because they return boolean values and there is no "access elements" at all. Instead of: In [174]: matches = data.str.match(pattern, flags=re.IGNORECASE) In [175]: matches Out[175]: Dave True Rob True Steve True Wes NaN dtype: object In [176]: matches.str.get(1) Out[176]:Dave NaN Rob NaN Steve NaN Wes NaN dtype: float64 In [177]: matches.str[0] Out[177]: Dave NaN Rob NaN Steve NaN Wes NaN dtype: float64 it may be better to use: In [174]: matches = data.str.extract(pattern, flags=re.IGNORECASE) In [175]: matches Out[175]: 0 1 2 Dave dave google com Rob rob gmail com Steve steve gmail com Wes NaN NaN NaN In [176]: matches[0] Out[176]: Dave dave Rob rob Steve steve Wes NaN Name: 0, dtype: object In [177]: matches.iloc[:, 0] Out[177]: Dave dave Rob rob Steve steve Wes NaN Name: 0, dtype: object

Note from the Author or Editor:
This behavior changed in pandas. I'm correcting the code examples and the language in the text

The code MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']], names=['state', 'color']) should be pd.MultiIndex.from_arrays([['Ohio', 'Ohio', 'Colorado'], ['Green', 'Red', 'Green']], names=['state', 'color'])

Note from the Author or Editor:
Adding the "pd."

Final entry in table is 'output' join. It should be 'outer' join.

Note from the Author or Editor:
This needs to be changed from "output" to "outer"

After the last sentence "Then, these can be concatenated together with concat:", it looks some python codes would be needed to make sense. These codes are found in https://github.com/wesm/pydata-book/blob/2nd-edition/ch14.ipynb , the below: nutrients = [] for rec in db: fnuts = pd.DataFrame(rec['nutrients']) fnuts['id'] = rec['id'] nutrients.append(fnuts) nutrients = pd.concat(nutrients, ignore_index=True)

Note from the Author or Editor:
Thanks -- I am restoring the code to the text (it was being accidentally suppressed in the output)

The paragraph states that "the output of outer will have a dimension that is the sum of the dimensions of the inputs". Since the result of outer for (3, 4) and (5,) is (3, 4, 5), is it better to replace the word "sum" with "concatenation"?

Note from the Author or Editor:
Making suggested change

It would be better to make a zipped result more pretty for the last code example as follows: In [188]: zip(last_name[sorter], first_name[sorter]) Out[188]: <zip at 0x7fa203eda1c8>

Note from the Author or Editor:
Adding "list(...)" to make the example prettier

Found two syntax errors in Python3. 1) for _ in xrange(niter): needed to be replaced by like for _ in range(niter): 2) print 'Largest one we saw: %s' % np.max(some_results) needed to be replaced by like print('Largest one we saw: {0}'.format(np.max(some_results)))

Note from the Author or Editor:
Fixing this