summary.txt

-----------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


22. Quick Interview Cheat Sheet
Input
input()

Takes user input and returns a string.

Type checking
type(value)

Tells you the type of a value.

Conversion
int()
float()
str()
bool()

Explicitly converts values.

String methods
.upper()
.lower()
.find()
.replace()
.startswith()
Membership
in
not in
Arithmetic
+   -   *   /   //   %   **
Comparison
>   <   >=   <=   ==   !=
Logical
and
or
not
Conditions
if
elif
else


--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------


1. Python fundamnetals 
Variables
Data types
Input/output
if / elif / else
for
while
range()
Functions
Parameters
return
Scope
Lists
Tuples
Sets
Dictionaries
Strings
Basic slicing
List/dict comprehensions
Exception handling
Modules/imports



2. Programming fundamentals ⭐
This is actually more important than knowing 100 Python tricks.
We'll work on:

Breaking a problem into steps
Loops and conditions
Functions
State changes
Searching
Counting
Frequency maps
Sorting
Two pointers
Sliding window
Recursion
Basic complexity analysis
Debugging
Edge cases



3. Imp data structures 
list
dict
set
tuple
string

You should become extremely comfortable with:
append()
pop()
remove()
sort()
sorted()
reverse()
split()
join()
strip()
replace()
get()
items()
keys()
values()


And understand when to choose:

list → ordered collection
set → uniqueness / fast membership
dict → key-value lookup
tuple → fixed/grouped values
string → text



4. Intermediate Python
Then we'll move into things that actually start appearing in real projects:

File handling
JSON
CSV
APIs
requests
Environment variables
Logging
Exceptions
Classes/OOP
Dataclasses
Virtual environments
Packages
pip
Type hints
Testing
Git
Debugging

This is where Python starts becoming a professional development tool.




5. Data Engineering-specific Python
If data engineering:

Python
   ↓
Files
   ↓
CSV / JSON
   ↓
SQL
   ↓
APIs
   ↓
Data cleaning
   ↓
Pandas
   ↓
ETL / ELT
   ↓
Databases
   ↓
Data pipelines
   ↓
Airflow
   ↓
Cloud



6. Interview prep
Qt- Given a list of numbers, find the number that appears most frequently.


--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------





Python needed for MerchantGuard 

# The Python you actually need

Before we start `risk_evaluator.py`, these are the concepts I want you to know:

### Level 1 — Absolutely required

- Variables
- Strings
- Integers
- Lists
- Dictionaries
- `None`
- `if`
- `for`
- Functions
- Arguments
- Return values
- Classes
- Objects
- `self`
- Methods
- Imports
- Exceptions
- `try/except`

### Level 2 — Required for your code

- Type hints
- `dataclass`
- `field`
- List/dict comprehensions
- `lambda`
- `staticmethod`
- `with`
- `Path`
- f-strings
- `any()`
- `all()`
- `sum()`
- `min()`
- `max()`
- `set`
- `defaultdict`

### Level 3 — Understand conceptually

- Regex
- HTTP requests
- HTML parsing
- DNS
- WHOIS
- TLS
- JSON
- Pandas
- Playwright



--------------------------------------------------------------------------------



## Python needed for starting risk_evaluator.py



1. Variables
2. Strings, integers, booleans, `None`
3. Lists
4. Dictionaries
5. `if / elif / else`
6. `for` loops
7. Functions
8. Parameters and return values
9. `import`
10. Classes and objects
11. `self`
12. Methods
13. `try / except`
14. `with`
15. Type hints
16. `dataclass`   

17. List/dict comprehensions
18. `*args` / `**kwargs` — basic understanding
19. `lambda` — basic understanding
20. `Path`
21. f-strings
22. `enumerate`
23. `any`, `all`, `sum`, `min`, `max`


---------------------------------------------------------------------------------------------------------------------------------------------------------------------------



1. Strings
indexing, slicing
common methods
looping through strings
formatting

2. Functions — deeper practice
parameters/arguments
return
default arguments
scope
practical function design

3. List/Dictionary Comprehensions
very common Python syntax
useful for data processing and interviews

4. Exception Handling
try
except
else
finally
raise

5. File Handling
reading/writing files
with open(...)
practical text/CSV usage

6. Modules & Packages
import
standard library
creating your own modules

7. Object-Oriented Programming
classes/objects
__init__
instance attributes/methods
inheritance
when OOP is actually useful

8. Pythonic Tools & Patterns
enumerate()
zip()
sorted()
lambda
map() / filter() — enough to understand/use, not overdo

9. Working with Real Data
JSON
CSV
APIs
basic requests

10. Testing & Debugging
assertions
unittest / pytest basics
debugging strategies
logging

11. Python + SQL / Data Engineering
database interaction
Pandas
basic ETL concepts

12. Python Interview Problem Solving
frequency counting
searching
two pointers
sliding window
stacks/queues
recursion
complexity analysis