Following up on a post on supporting users in open source this post lists some suggestions on how to ask a maintainer to help you with a problem.

You don’t have to follow these suggestions. They are optional. They make it more likely that a project maintainer will spend time helping you. It’s important to remember that their willingness to support you for free is optional too.

Crafting minimal bug reports is essential for the life and maintenance of community-driven open source projects. Doing this well is an incredible service to the community.

Minimal Complete Verifiable Examples

I strongly recommend following Stack Overflow’s guidelines on Minimal Complete Verifiable Exmamples. I’ll include brief highlights here:

… code should be …

Minimal – Use as little code as possible that still produces the same problem

Complete – Provide all parts needed to reproduce the problem

Verifiable – Test the code you’re about to provide to make sure it reproduces the problem

Lets be clear, this is hard and takes time.

As a question-asker I find that creating an MCVE often takes 10-30 minutes for a simple problem. Fortunately this work is usually straightforward, even if I don’t know very much about the package I’m having trouble with. Most of the work to create a minimal example is about removing all of the code that was specific to my application, and as the question-asker I am probably the most qualified person to do that.

When answering questions I often point people to StackOverflow’s MCVE document. They sometimes come back with a better-but-not-yet-minimal example. This post clarifies a few common issues.

As an running example I’m going to use Pandas dataframe problems.

Don’t post data

You shouldn’t post the file that you’re working with. Instead, try to see if you can reproduce the problem with just a few lines of data rather than the whole thing.

Having to download a file, unzip it, etc. make it much less likely that someone will actually run your example in their free time.

Don’t

I’ve uploaded my data to Dropbox and you can get it here: my-data.csv.gz

import pandas as pd df = pd . read_csv ( 'my-data.csv.gz' )

Do

You should be able to copy-paste the following to get enough of my data to cause the problem:

import pandas as pd df = pd . DataFrame ({ 'account-start' : [ '2017-02-03' , '2017-03-03' , '2017-01-01' ], 'client' : [ 'Alice Anders' , 'Bob Baker' , 'Charlie Chaplin' ], 'balance' : [ - 1432.32 , 10.43 , 30000.00 ], 'db-id' : [ 1234 , 2424 , 251 ], 'proxy-id' : [ 525 , 1525 , 2542 ], 'rank' : [ 52 , 525 , 32 ], ... })

Actually don’t include your data at all

Actually, your data probably has lots of information that is very specific to your application. Your eyes gloss over it but a maintainer doesn’t know what is relevant and what isn’t, so it will take them time to digest it if you include it. Instead see if you can reproduce your same failure with artificial or random data.

Don’t

Here is enough of my data to reproduce the problem

import pandas as pd df = pd . DataFrame ({ 'account-start' : [ '2017-02-03' , '2017-03-03' , '2017-01-01' ], 'client' : [ 'Alice Anders' , 'Bob Baker' , 'Charlie Chaplin' ], 'balance' : [ - 1432.32 , 10.43 , 30000.00 ], 'db-id' : [ 1234 , 2424 , 251 ], 'proxy-id' : [ 525 , 1525 , 2542 ], 'rank' : [ 52 , 525 , 32 ], ... })

Do

My actual problem is about finding the best ranked employee over a certain time period, but we can reproduce the problem with this simpler dataset. Notice that the dates are out of order in this data (2000-01-02 comes after 2000-01-03). I found that this was critical to reproducing the error.

import pandas as pd df = pd . DataFrame ({ 'account-start' : [ '2000-01-01' , '2000-01-03' , '2000-01-02' ], 'db-id' : [ 1 , 2 , 3 ], 'name' : [ 'Alice' , 'Bob' , 'Charlie' })

As we shrink down our example problem we often discover a lot about what causes the problem. This discovery is valuable and something that only the question-asker is capable of doing efficiently.

See how small you can make things

To make it even easier, see how small you can make your data. For example if working with tabular data (like Pandas), then how many columns do you actually need to reproduce the failure? How many rows do you actually need to reproduce the failure? Do the columns need to be named as you have them now or could they be just “A” and “B” or descriptive of the types within?

Do

import pandas as pd df = pd . DataFrame ({ 'datetime' : [ '2000-01-03' , '2000-01-02' ], 'id' : [ 1 , 2 ]})

Remove unnecessary steps

Is every line in your example absolutely necessary to reproduce the error? If you’re able to delete a line of code then please do. Because you already understand your problem you are much more efficient at doing this than the maintainer is. They probably know more about the tool, but you know more about your code.

Don’t

The groupby step below is raising a warning that I don’t understand

df = pd . DataFrame (...) df = df [ df . value > 0 ] df = df . fillna ( 0 ) df . groupby ( df . x ). y . mean () # <-- this produces the error

Do

The groupby step below is raising a warning that I don’t understand

df = pd . DataFrame (...) df . groupby ( df . x ). y . mean () # <-- this produces the error

Use Syntax Highlighting

When using Github you can enclose code blocks in triple-backticks (the character on the top-left of your keyboard on US-standard QWERTY keyboards). It looks like this:

```python x = 1 ```

Provide complete tracebacks

You know all of that stuff between your code and the exception that is hard to make sense of? You should include it.

Don’t

I get a ZeroDivisionError from the following code: ```python def div(x, y): return x / y div(1, 0) ```

Do

I get a ZeroDivisionError from the following code: ```python def div(x, y): return x / y div(1, 0) ``` ```python-traceback ZeroDivisionError Traceback (most recent call last) <ipython-input-4-7b96263abbfa> in <module>() ----> 1 div(1, 0) <ipython-input-3-7685f97b4ce5> in div(x, y) 1 def div(x, y): ----> 2 return x / y 3 ZeroDivisionError: division by zero ```

If the traceback is long that’s ok. If you really want to be clean you can put it in <details> brackets.

I get a ZeroDivisionError from the following code: ```python def div(x, y): return x / y div(1, 0) ``` ### Traceback <details> ```python ZeroDivisionError Traceback (most recent call last) <ipython-input-4-7b96263abbfa> in <module>() ----> 1 div(1, 0) <ipython-input-3-7685f97b4ce5> in div(x, y) 1 def div(x, y): ----> 2 return x / y 3 ZeroDivisionError: division by zero ``` </details>

Ask Questions in Public Places

When raising issues you often have a few possible locations:

GitHub issue tracker Stack Overflow Project mailing list Project Chat room E-mail maintainers directly (never do this)

Different projects handle this differently, but they usually have a page on their documentation about where to go for help. This is often labeled “Community”, “Support” or “Where to ask for help”. Here are the recommendations from the Pandas community.

Generally it’s good to ask questions where many maintainers can see your question and help, and where other users can find your question and answer if they encounter a similar bug in the future.

While your goal may be to solve your problem, the maintainer’s goal is likely to create a record of how to solve problems like yours. This helps many more users who will have a similar problem in the future, see your well-crafted bug report, and learn from the resulting conversation.

My personal preferences

For user questions like “What is the right way to do X?” I prefer Stack Overflow.

For bug reports like “I did X, I’m pretty confident that it should work, but I get this error” I prefer Github issues

For general chit-chat I prefer Gitter, though actually, I personally spend almost no time in gitter because it isn’t easily searchable by future users. If you’ve asked me a question in Gitter I will almost certainly not respond to it, except to direct you to github, stack overflow, or this blogpost.

I only like personal e-mail if someone is proposing to fund or seriously support the project in some way

But again, different projects do this differently and have different policies. You should check the documentation of the project you’re dealing with to learn how they like to support users.

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