Remove blank lines-->fix cookbook rendering issue

[thoo]

Fixes #23128

[codecov]

Codecov Report

Merging #23144 into master will not change coverage.
The diff coverage is n/a.

@@           Coverage Diff           @@
##           master   #23144   +/-   ##
=======================================
  Coverage   92.13%   92.13%           
=======================================
  Files         170      170           
  Lines       51073    51073           
=======================================
  Hits        47056    47056           
  Misses       4017     4017

Flag	Coverage Δ
#multiple	90.56% <ø> (ø)	⬆️
#single	42.28% <ø> (ø)	⬆️

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[jreback]

what are you removing lines? if anything they improve code readability

[thoo]

@jreback Yes I was removing lines. Ipython directive has a bug to cut off a function block if there is a blank line. Please let me know if there is another way to do it.

[datapythonista]

It'd be nice to find a better way to fix the problem with the ipython directive, but unless someone knows how to do that, I think we can merge this, which is probably better than having the function incomplete (ipython only renders until the first blank line). See: https://pandas-docs.github.io/pandas-docs-travis/cookbook.html#correlation

[jreback]

better way is to use multiple ipython blocks (caveat is that they cannot span a single function definition)
simpler and more descriptive (as u. can comment on code)
having large blocks of code is an anti-pattern

[datapythonista]

But can we split the code of a function across different ipython blocks?

I'm thinking that may be a better option is to use a python code block instead of an ipython one. That should fix the problem and we still keep the readability of the code.

Closing this PR. @thoo do you want to take care of that? The idea is to use .. code-block:: python instead. And that requires that instead of the code directly without the outputs, we use >>> and the outputs, for example:

>>> def func():
...      print('hello world')
>>> func()
hello world

[thoo]

@datapythonista Sure. Let me take a look. Hopefully it is straight forward.

[thoo]

@datapythonista Will this work?

>>> def distcorr(x, y):
...     n = len(x)
...     a = np.zeros(shape=(n, n))
...     b = np.zeros(shape=(n, n))

...     for i in range(n):
...         for j in range(i + 1, n):
...             a[i, j] = abs(x[i] - x[j])
...             b[i, j] = abs(y[i] - y[j])
...     a += a.T
...     b += b.T

...     a_bar = np.vstack([np.nanmean(a, axis=0)] * n)
...     b_bar = np.vstack([np.nanmean(b, axis=0)] * n)

...     A = a - a_bar - a_bar.T + np.full(shape=(n, n), fill_value=a_bar.mean())
...     B = b - b_bar - b_bar.T + np.full(shape=(n, n), fill_value=b_bar.mean())

...     cov_ab = np.sqrt(np.nansum(A * B)) / n
...     std_a = np.sqrt(np.sqrt(np.nansum(A**2)) / n)
...     std_b = np.sqrt(np.sqrt(np.nansum(B**2)) / n)

...     return cov_ab / std_a / std_b

>>> df = pd.DataFrame(np.random.normal(size=(100, 3)))

>>> df.corr(method=distcorr)
       0         1         2
0  1.000000  0.209812  0.132786
1  0.209812  1.000000  0.153526
2  0.132786  0.153526  1.000000

[datapythonista]

I think you need the ... in the blank lines too. Can you check how to run doctests on the Internet, and find a way to run it to make sure please.