Marius Marius - 3 months ago 9
Python Question

How to make good reproducible pandas examples

Having spent a decent amount of time watching both the and tags on SO, the impression that I get is that

pandas
questions are less likely to contain reproducible data. This is something that the R community has been pretty good about encouraging, and thanks to guides like this, newcomers are able to get some help on putting together these examples. People who are able to read these guides and come back with reproducible data will often have much better luck getting answers to their questions.

How can we create good reproducible examples for
pandas
questions? Simple dataframes can be put together, e.g.:

import pandas as pd
df = pd.DataFrame({'user': ['Bob', 'Jane', 'Alice'],
'income': [40000, 50000, 42000]})


But many example datasets need more complicated structure, e.g.:


  • datetime
    indices or data

  • Multiple categorical variables (is there an equivalent to R's
    expand.grid()
    function, which produces all possible combinations of some given variables?)

  • MultiIndex or Panel data



For datasets that are hard to mock up using a few lines of code, is there an equivalent to R's
dput()
that allows you to generate copy-pasteable code to regenerate your datastructure?

Answer

Note: The ideas here are pretty generic for StackOverflow, indeed questions.

Disclaimer: Writing a good question is HARD.

The Good:

  • do include small* example DataFrame, either as runnable code:

    In [1]: df = pd.DataFrame([[1, 2], [1, 3], [4, 6]], columns=['A', 'B'])
    

    or make it "copy and pasteable" using pd.read_clipboard(sep='\s\s+'), you can format the text for StackOverflow highlight and use Ctrl+K (or prepend four spaces to each line):

    In [2]: df
    Out[2]: 
       A  B
    0  1  2
    1  1  3
    2  4  6
    

    test pd.read_clipboard(sep='\s\s+') yourself.

    * I really do mean small, the vast majority of example DataFrames could be fewer than 6 rowscitation needed, and I bet I can do it in 5 rows. Can you reproduce the error with df = df.head(), if not fiddle around to see if you can make up a small DataFrame which exhibits the issue you are facing.

    * Every rule has an exception, the obvious one is for performance issues (in which case definitely use %timeit and possibly %prun), where you should generate (consider using np.random.seed so we have the exact same frame): df = pd.DataFrame(np.random.randn(100000000, 10)). Saying that, "make this code fast for me" is not strictly on topic for the site...

  • write out the outcome you desire (similarly to above)

    In [3]: iwantthis
    Out[3]: 
       A  B
    0  1  5
    1  4  6
    

    Explain what the numbers come from: the 5 is sum of the B column for the rows where A is 1.

  • do show the code you've tried:

    In [4]: df.groupby('A').sum()
    Out[4]: 
       B
    A   
    1  5
    4  6
    

    But say what's incorrect: the A column is in the index rather than a column.

  • do show you've done some research (search the docs, search StackOverflow), give a summary:

    The docstring for sum simply states "Compute sum of group values"

    The groupby docs don't give any examples for this.

    Aside: the answer here is to use df.groupby('A', as_index=False).sum().

  • if it's relevant that you have Timestamp columns, e.g. you're resampling or something, then be explicit and apply pd.to_datetime to them for good measure**.

    df['date'] = pd.to_datetime(df['date']) # this column ought to be date..
    

    ** Sometimes this is the issue itself: they were strings.

The Bad:

  • don't include a MultiIndex, which we can't copy and paste (see above), this is kind of a grievance with pandas default display but nonetheless annoying:

    In [11]: df
    Out[11]:
         C
    A B   
    1 2  3
      2  6
    

    The correct way is to include an ordinary DataFrame with a set_index call:

    In [12]: df = pd.DataFrame([[1, 2, 3], [1, 2, 6]], columns=['A', 'B', 'C']).set_index(['A', 'B'])
    
    In [13]: df
    Out[13]: 
         C
    A B   
    1 2  3
      2  6
    
  • do provide insight to what it is when giving the outcome you want:

       B
    A   
    1  1
    5  0
    

    Be specific about how you got the numbers (what are they)... double check they're correct.

  • If your code throws an error, do include the entire stacktrace (this can be edited out later if it's too noisy). Show the line number (and the corresponding line of your code which it's raising against).

The Ugly:

  • don't link to a csv we don't has access to (ideally don't link to an external source at all...)

    df = pd.read_csv('my_secret_file.csv')  # ideally with lots of parsing options
    

    Most data is proprietary we get that: Make up similar data and see if you can reproduce the problem (something small).

  • don't explain the situation vaguely in words, like you have a DataFrame which is large, mention some of the column names in passing (be sure not to mention their dtypes). Try and go into lots of detail about something which is completely meaningless without seeing the actual context. Presumably noone is even going to read to the end of this paragraph.

    Essays are bad, it's easier with small examples.

  • don't include 10+ (100+??) lines of data munging before getting to your actual question.

    Please, we see enough of this in our day jobs. We want to help, but not like this....
    Cut the intro, and just show the relevant DataFrames (or small versions of them) in the step which is causing you trouble.

Anyways, have fun learning python, numpy and pandas!