Pyroad

Detailed Python developer roadmap

Hello! My name is Mikhail Emelyanov, I am embedded software engineer, and I was inspired to write this little roadmap on the capabilities of Python language by a certain commonality among the existing Python tutorials found on the web.

The usual suggestions to study, say, “Algorithms and Data Structures” or “Databases” are especially jarring. You can spend years studying these topics, and even after decades you'd still be able to find something you didn't know yet even without ever venturing outside the scope of Algorithms!

Using video game analogies, we can say that novice programmers often stand on the shore of the lake of boiling lava with an island with the ever-coveted jobs in the center, while the islands in between, which you have to jump on, gradually increasing your skills in successive mini-quests, are either missing, or arranged haphazardly, or their fairly smooth sequence breaks off, never having managed to get you any farther from the shore. Let's try to build a path of hint islands, a number of which, although not without effort, will finally allow us to reach our goal.

The roadmap is very easy to use. Just as you would in a normal text, go from left to right and from top to bottom. If you're just starting to learn Python, follow the green sections of the roadmap. If your accumulated experience, curiosity, or necessity pushes you deeper, start exploring the sections marked in gray. Orange marks the topics that require in-depth study, those are better to tackle (at least without digging especially deep to begin with) on the third pass.

This article definitely contains mistakes and inaccuracies of different calibers, and of course, many required subsections are missing; so, if you notice any of these, feel free to comment, and if you feel the Force, you're welcome to fork the GitHub repository with the roadmap's source code and contribute whatever you feel is necessary; all corrections and additions are strongly encouraged. It also contains all the parts of the map in Mermaid diagram format, as well as png/svg illustrations.

When diving into Python, don't forget the excellent official documentation at docs.python.org. By studying it, at least in brief, and gradually reading deeper into the right sections, you will be able to see that many of the “hacks”, “findings” and other obscure matters have long since been considered, described and have detailed examples of use.

I would also recommend leetcode.com for learning basic Python syntax to the fullest extent. If you filter the tasks by “Easy” level, and then add an additional sorting by the “Acceptance” column, you'll be presented with a straightforward primer with smoothly increasing task difficulty, rather than the intimidating competitive platform.

Well, that’s enough stalling for the moment. Let's get started!

Data Structures

As a reminder, if you are a novice developer, go from left to right through the entries marked in green. Create instances of each type, try adding and removing elements, and experiment with them via the debugger. See how big the resulting objects are, and try to figure out why list and array containing the same data are different in size. Study the features of each type, read and figure out which data structure will work best for which tasks.

Don't forget that this is just a guide, a table of contents for a book that you will have to write yourself. Actively seek information on the web, consult sources and official documentation. Dive Stackoverflow just for fun, there's plenty of exciting reading there!

If you start to make progress, move on to the next section, and don't feel bad if you can't. Don't envision your mind as the sword of Alexander the Great cutting the Gordian knot in one fluid, precise move, worthy of Instagram's front page. Be as a carpenter’s plane, stripping away one thin layer at a time, and sooner or later the misunderstanding will go away, even if this seems to you to be a chasm ten thousand leagues deep.

flowchart TD

subgraph Data_Structures
direction LR
List(list) -.-> Tuple -.-> Dict -.-> Set -.-> Array -.-> Linked_List -.->Tree -.-> Python_specific_data_structures

subgraph Tuple
direction LR
tuple(tuple)
namedtuple(namedtuple)
end

subgraph Dict
direction LR
dict(dict)
HashProblem("Hash collisions")
defaultdict(defaultdict)
Counter(Counter)
end

subgraph Set
direction LR
set(set)
FrozenSet("frozen set")
end

subgraph Array
direction LR
array(array)
bytes(bytes)
bytearray(bytearray)
end

subgraph Linked_List
direction LR
SinglyLinkedList("Singly linked list")
subgraph Doubly_Linked_List
direction LR
deque(deque)
Queue(Queue)
end
end

subgraph Tree
direction LR
tree("Binary tree")
heap(heap)
B-tree(B-tree)
RedBlackTree("Red–black tree")
AVLTree("AVL tree")
trie(trie)
end

subgraph Python_specific_data_structures
direction LR
enum(enum)
range(range)
dataclass(dataclass)
struct(struct)
string(string)
datetime(datetime)
end
end

classDef trainee fill:#6ADA6A, stroke-width:3px

class List trainee;
class tuple trainee;
class dict trainee;
class set trainee;
class array trainee;
class bytes trainee;
class deque trainee;
class heap trainee;
class range trainee;
class string trainee;
class datetime trainee;

Data Management

Try to manipulate your data, feel how you can mold anything you want out of this malleable clay. Try creating a data structure with many elements (a million, for example), sort them, quickly find the values you want with bisect, and write the results in a JSON file.

If everything goes according to plan, try to dig into the less obvious topics: apply regex to solve some simple task or save previously obtained data in Pickle format, understanding the reason for binary file formats after observing the size of the resulting files.

This is where you will find the first entries marked in orange. Google what TensorFlow and Keras are and what tasks they solve. Perhaps, this could be your future job, your vocation!

flowchart TD

subgraph Data_Management
direction LR
slice(slice) -.-> Sorting -.-> Comprehension -.-> String_Management -.-> Datetime_Management -.-> bisect(bisect) -.-> Functools -.->File -.-> Data_Analysis -.-> Neural_Networks
subgraph Sorting
direction LR
sort(sort)
sorted(sorted)
end

subgraph Comprehension
direction LR
listcomprehension(list)
dictcomprehension(dict)
setcomprehension(set)
end

subgraph String_Management
direction LR
String_Built-in_Functions("Built-in functions")
regex(regex)
end

subgraph Datetime_Management
direction LR
encode(encode)
decode(decode)
dtmath(math)
end

subgraph Functools
direction LR
fmap(map)
ffilter(filter)
freduce(reduce)
fpartial(partial)
fmore(...)
end

subgraph File
direction LR
Read_Write("read/write")
Text_Binary("text/binary")
JSON(JSON)
Pickle("Pickle")
Protocol_Buffers("Protocol Buffers")
paths(paths)
end

subgraph Data_Analysis
direction LR
Data_Built-in_Functions("Built-in functions")
NumPy(NumPy)
Pandas(Pandas)
Matplotlib_Seaborn("Matplotlib/Seaborn")
end

subgraph Neural_Networks
direction LR
PyTorch(PyTorch)
TensorFlow("TensorFlow/Keras")
end

end

classDef trainee fill:#6ADA6A, stroke-width:3px
classDef middle fill:#FF9900, stroke-width:3px

class slice trainee;
class sort trainee;
class sorted trainee;
class listcomprehension trainee;
class bisect trainee;
class fmap trainee;
class ffilter trainee;
class freduce trainee;
class String_Built-in_Functions trainee;
class encode trainee;
class decode trainee;
class Read_Write trainee;
class Text_Binary trainee;
class JSON trainee;
class paths trainee;
class Data_Built-in_Functions trainee;

class PyTorch middle;
class TensorFlow middle;

Data Flows

Add more specific capabilities to your data management skills. All of the topics covered are essential in the practical programming process and are present in all modern languages in one form or another. That way, if you eventually switch from Python to Java, C# or C++, the knowledge you've acquired won't become dead weight.

flowchart TD

subgraph Data_Flows
direction LR

itertools -.-> enumerate -.-> Generator -.-> Decorator -.-> Context_managers

subgraph itertools
direction LR

subgraph Infinite_Iterators
icount(count)
icycle(cycle)
irepeat(repeat)
end

subgraph Finite_Iterators
accumulate(accumulate)
chain(chain)
compress(compress)
dropwhile(dropwhile)
takewhile(takewhile)
fimore(...)
end

subgraph Combinatorics
product(product)
combinations(combinations)
permutations(permutations)
combinations_with_replacement(combinations_with_replacement)
end
end

enumerate(enumerate)

subgraph Generator
direction LR
yield("yield")
Generator_expression("Generator expression")
yield_from("yield from")
end

subgraph Decorator
direction LR
decorator(decorator)
LRUCache("LRU Cache")
param_decorator("Parameterized decorator")
end

subgraph Context_managers
direction LR
enter_exit_cm("__enter__, __exit__")
contextlib(contextlib)
end

end

classDef trainee fill:#6ADA6A, stroke-width:3px

class icount trainee;
class icycle trainee;
class irepeat trainee;
class accumulate trainee;
class chain trainee;
class compress trainee;
class dropwhile trainee;
class takewhile trainee;
class product trainee;
class combinations trainee;
class enumerate trainee;
class yield trainee;
class decorator trainee;
class enter_exit_cm trainee;

Objec