Finding help in python

How to find help in Python3

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In this shot, we will go over the smart ways to find built-in functions on a particular data type without the use of Google.

dir()

One easy way to find all of the attributes of a particular data type is by applying the dir function. If we call dir() without any parameters, it returns a list of all the globally available attributes. If you pass a particular object, then dir() lists the attributes present inside that object.

Let’s apply dir() on the list and analyze the output.

print('Globally available attributes: ',dir())
 
print('Attributes available in list: ',dir(list))
 
print('Globally available builtin functions are: ',dir(__builtins__))
 
'''
 
__builtins__ object contains many functions that are very
 
much helpful for daily use like for converting float to int
 
we use int function actually the int function is present inside
 
the __builtins__ object when we write int(some_float_value)
 
then python automatically converts it into __builtins__.
 
int(some_float_value), similarly list(),tuple(),
 
len(),max(). are present inside __builtins_ object.
 
In the output we can see all the attributes present inside
 
the __builtins__ object.
 
'''

The dir(list) function call returns all the attributes that are present inside the list data type. dir(object) returns the names of attributes that are present inside the object. Here, the object can be anything, such as data type, function, class, etc.

help()

In Python, everything is an object. Data types, functions, and classes are all objects. You can use help() on an attribute if you want to know more information. An attribute can be any object, such as a datatype, predefined functions, user-defined functions, predefined classes, and user-defined classes.

print(help(list.count))
 
print(help(list.sort))
 
print(help(list))
 
class Student:
 
name='' # Let's take default name as ''
 
rollno=0 # Let's take default roll no is 0
 
section=1 # Let's take by default section as 1
 
def __init__(self,name,rollno,section):
 
self.name=name
 
self.rollno=rollno
 
self.section=section
 
def student_name(self):
 
print('Student name is ',self.name)
 
print(help(Student))

The above code returns a detailed description of the functions count and sort , as well as detailed information about the class list and the user-defined class, Student .

In the output, we can easily see what arguments we can pass, what the return type is, what the default arguments are of a function, what attributes are present inside a class, and more. We need to remember one thing: if we want to know about any attribute that is present inside a particular data type or object, then we need to call the help function.

For example, we can call help(data_type.attribute_name) or help(object.attribute_name) . If we want to apply help on global objects, we can direct the call as help(object) .

_annotations_ and _doc_

If we want to know even more information about the arguments, then we can use the __ annotations __ and __ doc __ attributes of the function.

The built-in type does not have the __ annotations __ attribute. We can use __ annotations __ on any user-defined functions, provided the programmer wrote the annotations in code. If they didn’t, then an empty dictionary will be returned.

def acceleration(d: 'Distance in meters',t: 'Time in seconds')->'Return the acceleration value in m/s':
 
'''
 
This function calculates the acceleration by taking distance and time as parameters.
 
'''
 
return d/t
 
print('acceleration.__annotations__ = ',acceleration.__annotations__)
 
print('acceleration.__doc__ = ',acceleration.__doc__)

_dict_

Annotations and docstrings are very helpful when we borrow functions from other people’s code or when we try to extend the work of others. In many situations, we don’t bother with the implementation of the function, we are simply interested in knowing what arguments to pass when calling a function, and what the return type is. This information about annotations and docstrings is very helpful.

We can also use the __dict__ attribute instead of the dir function to learn about all the attributes of an object. However, __dict__ returns a dictionary instead of the list that contains the property name and property value as key-value pairs of the dictionary.

The below code is an example of the __dict__ property:

print('list.__dict__ = ',list.__dict__)
class Student:
 
name='' # Let's take default name as ''
 
rollno=0 # Let's take default roll no is 0
 
section=1 # Let's take by default section as 1
 
def __init__(self,name,rollno,section):
 
self.name=name
 
self.rollno=rollno
 
self.section=section
 
def student_name(self):
 
print('Student name is ',self.name)
print('Student.__dict__ = ',Student.__dict__)
s1=Student('Harsha','03','15')
print('s1.__dict__ = ',s1.__dict__)
'''
Below is a small code to illustarte that functions are also
 
objects in python for the below function just i am assigning two
attributes one is author and other is created data.If we use
__dict__ on function we can see that the dictionary is written
that contains all the aatributes of function along with the values
'''
def test_func(a,b):
 
return 'test'
test_func.author='Harsha'
test_func.date='28-08-2021'
print('__dict__ attribute on user defined function test_func : ',test_func.__dict__)

Takeaways

Let’s say you participate in a hackathon, or, while writing a company selection exam, you suddenly forget the functions that help solve a problem. You can use the dir and help functions to search for all the available built-in functions in the builtins module, as well as in specific objects like list, tuple, etc.

In other cases, like if you are using someone else’s code and you want to know all the available attributes that are present inside a built-in or user-defined object, then you can use the functions dir() and help() and the attributes __annotations__ , __doc__ , and __dict__ .

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Five ways to get help in Python

In an earlier blog post I offered five ways to get help in R. This post serves as the Python equivalent, assuming you are working in Jupyter notebooks.

Here you can see all necessary and optional arguments of the function with additional notes and examples.

2. Check the package’s documentation

Step 0 suggested to start with a web search, but when in doubt about Python code it’s often better to go straight to the source: the package’s documentation. The Help menu links out to some of the most popular Python packages; you can find the docs for other packages with a web search.

3. Visualize your code

When it comes to data, we analysts have a simple rule: When in doubt, visualize it. The same principle can fortunately be applied to Python coding with PythonTutor.com. This free service will visually step through the code so you see exactly how inputs transform to outputs.

If your code is throwing errors and you just can’t pinpoint where in the process things are breaking down, check out this tool.

Try your own code or use my example from the previous:

4. Compose a minimally reproducible example

If you’ve gone through the steps so far are still stumped… first, take some time away from the problem and come back. It’s amazing how often getting this distance works.

Second… did you restart your computer? 🙈 (That works often, too!)

If all that fails, you may be approaching the point at which you need to “phone a friend…” or at least some person on the internet.

Now, if you are going to do that, you want to make your problem as easy as possible to follow along with: preferably by including a copy-and-pasteable code program containing a small dataset, what you are trying to achieve, the steps you are taking and where you are failing. This is known as a minimally reproducible example (MRE).

If you’ve worked in R you may be familiar with the datasets that ship out of the box like iris or mtcars . These are great datasets to use for an MRE because everybody’s got them. Python doesn’t ship with any datasets, so you can either make your own or find a package that includes some.

I’d suggest using the seaborn data visualization package, which comes with some standard datasets. Some of these are pretty large, so if you’d like to make your MRE more “minimal” you could even just take a few lines of the dataset like so:

There’s more to a good MRE than a dataset, so check out this post for more tips.

5. … then hit the forums

OK, you’ve searched the web, the help documentation, tried stepping through your code and wrote an MRE. It’s time to hit the forums.

There are so many great user forums out there; r/Python and StackOverflow come to mind. But you want to do your homework beforehand; the latter in particular is notorious for chewing out unprepared posters. It’s understandable — people aren’t there to provide pro bono consulting — but it can feel a little jarring.

More recently, groups have sprung up on Slack and Discord. These are also great places to network, get and give help.

6. What else?

As mentioned previously, it’s smart to have go-to resources or procedures so that when you get blocked you don’t drift aimlessly. At the same time, there’s almost always a better way of doing anything so you want to keep your steps open for improvement.

In that spirit, what go-to steps do you take when you need help in Python? Please share them in the comments.

Video notebook

The notebook used in the video demonstration follows.

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