@app.post("/uploadfile/")
async def create_upload_file(file: UploadFile = File(...)):
    return {"filename": file.filename, "content_type": file.content_type}

• Set a response cookie.

from fastapi import Response
@app.post("/cookie/")
def create_cookie(response: Response):
    response.set_cookie(key="fakesession", value="fake-cookie-session-value")
    return {"message": "Cookie set"}

• Read a request cookie.

from fastapi import Cookie
@app.get("/read-cookie/")
def read_cookie(fakesession: Optional[str] = Cookie(None)):
    return {"session": fakesession}

X. Error Handling & Advanced Responses

• Handle HTTPException.

from fastapi import HTTPException
@app.get("/items/{item_id}")
def read_item(item_id: str):
    if item_id not in items_db:
        raise HTTPException(status_code=404, detail="Item not found")

• Return a custom JSONResponse.

from fastapi.responses import JSONResponse
@app.get("/custom/")
def custom_response():
    return JSONResponse(status_code=202, content={"message": "Accepted"})

• Return an HTMLResponse.

from fastapi.responses import HTMLResponse
@app.get("/page", response_class=HTMLResponse)
def read_page():
    return "<h1>Hello World</h1>"

• Define a WebSocket endpoint.

from fastapi import WebSocket
@app.websocket("/ws")
async def websocket_endpoint(websocket: WebSocket):
    await websocket.accept()
    await websocket.send_text("Hello from WebSocket")
    await websocket.close()

• Handle a path operation that may not exist.

@app.get("/{full_path:path}")
def read_catch_all(full_path: str):
    return {"path": full_path}

• Mount a static files directory.

from fastapi.staticfiles import StaticFiles
app.mount("/static", StaticFiles(directory="static"), name="static")

• Add a custom exception handler.

from fastapi import Request
from fastapi.responses import JSONResponse

class MyCustomException(Exception): pass

@app.exception_handler(MyCustomException)
async def custom_exception_handler(request: Request, exc: MyCustomException):
    return JSONResponse(status_code=418, content={"message": "I'm a teapot"})

• Read a request header.

from fastapi import Header
@app.get("/headers/")
async def read_headers(user_agent: Optional[str] = Header(None)):
    return {"User-Agent": user_agent}

• Get a raw Request object.

from fastapi import Request
@app.get("/request-info")
def get_request_info(request: Request):
    return {"client_host": request.client.host}

#Python #FastAPI #API #WebDevelopment #Pydantic

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By: @DataScience4 ✨

💡 Top 70 Django ORM Operations

I. Model Definition
(in models.py)

• Define a character field.

from django.db import models
name = models.CharField(max_length=100)

• Define a foreign key relationship (many-to-one).

author = models.ForeignKey('Author', on_delete=models.CASCADE)

• Define a many-to-many relationship.

tags = models.ManyToManyField('Tag')

• Define a model's Meta class for default ordering.

class Meta:
    ordering = ['-pub_date']

• Define the string representation of an object.

def __str__(self):
    return self.headline

II. Creating Objects

• Create and save an object in one step.

entry = Entry.objects.create(headline="New Title", author=some_author)

• Create an instance and save it separately.

new_author = Author(name="John Doe")
new_author.save()

• Create multiple objects in a single query.

Author.objects.bulk_create([Author(name="A"), Author(name="B")])

• Add an object to a ManyToManyField.

entry.tags.add(tag1, tag2)

III. Retrieving Objects (Basic QuerySets)

• Retrieve all objects.

all_entries = Entry.objects.all()

• Retrieve a single object by a unique constraint (e.g., PK).

entry = Entry.objects.get(pk=1)

• Filter objects that match criteria.

entries = Entry.objects.filter(pub_date__year=2023)

• Exclude objects that match criteria.

entries = Entry.objects.exclude(headline__startswith="Old")

• Get the first object from a queryset.

first_entry = Entry.objects.order_by('pub_date').first()

• Get the last object from a queryset.

last_entry = Entry.objects.order_by('pub_date').last()

• Get an object, or create it if it doesn't exist.

obj, created = Author.objects.get_or_create(name="Unique Name")

IV. Field Lookups for Filtering

• Exact match (case-sensitive).

Entry.objects.filter(headline__exact="Hello World")

• Case-insensitive exact match.

Entry.objects.filter(headline__iexact="hello world")

• Case-sensitive contains.

Entry.objects.filter(headline__contains="World")

• Case-insensitive contains.

Entry.objects.filter(headline__icontains="world")

• Match against a list of values.

Entry.objects.filter(pk__in=[1, 3, 5])

• Greater than (__gt) or greater than or equal (__gte).

Entry.objects.filter(rating__gt=4)

• Less than (__lt) or less than or equal (__lte).

Entry.objects.filter(rating__lte=3)

• Starts with or ends with a string.

Entry.objects.filter(headline__startswith="The")

• Match within a range.

Entry.objects.filter(pub_date__range=["2023-01-01", "2023-03-31"])

• Filter by a date component.

Entry.objects.filter(pub_date__year=2023, pub_date__month=5)

• Check if a field is NULL.

Entry.objects.filter(pub_date__isnull=True)

V. Slicing & Ordering QuerySets

• Slice a queryset (similar to Python lists).

first_ten = Entry.objects.all()[:10]

• Order results in ascending order.

Entry.objects.order_by('headline')

• Order results in descending order.

Entry.objects.order_by('-pub_date')

• Order results randomly.

Entry.objects.order_by('?')

VI. Modifying Objects

• Update a single object's fields.

entry = Entry.objects.get(pk=1)
entry.rating = 5
entry.save()

• Update multiple objects in a single query.

Entry.objects.filter(pub_date__year=2022).update(rating=0)

• Remove an object from a ManyToManyField.

entry.tags.remove(tag1)

VII. Deleting Objects

• Delete a single object.

entry = Entry.objects.get(pk=1)
entry.delete()

• Delete multiple objects in a single query.

Entry.objects.filter(rating=0).delete()

VIII. Spanning Relationships

• Filter based on a related model's field.

Entry.objects.filter(author__name="John Doe")

• Use a field lookup on a related model.

Entry.objects.filter(author__name__startswith="John")

• Access related objects from the "one" side of a relationship.

author = Author.objects.get(name="John Doe")
authors_entries = author.entry_set.all()

IX. Aggregation
(Requires from django.db.models import ...)

• Calculate aggregate values over an entire queryset.

from django.db.models import Avg
avg_rating = Entry.objects.aggregate(Avg('rating'))

• Average (Avg).

Entry.objects.aggregate(average_rating=Avg('rating'))

• Sum (Sum).

Entry.objects.aggregate(total_ratings=Sum('rating'))

• Maximum (Max) and Minimum (Min).

Entry.objects.aggregate(max_r=Max('rating'), min_r=Min('rating'))

• Count (Count).

Entry.objects.aggregate(num_entries=Count('pk'))

• Count distinct values.

Entry.objects.aggregate(num_authors=Count('author', distinct=True))

X. Annotation

• Add a calculated field to each object in a queryset.

from django.db.models import Count
authors = Author.objects.annotate(entry_count=Count('entry'))

• Access an annotated value.

for author in authors:
    print(author.name, author.entry_count)

• Get results as a list of dictionaries.

Entry.objects.values('headline', 'author__name')

XI. F() Expressions

• Import F() expressions.

from django.db.models import F

• Update a field based on its own value.

Entry.objects.filter(pk=1).update(rating=F('rating') + 1)

• Filter by comparing two fields on the same model.

Entry.objects.filter(rating__gt=F('author__rating'))

XII. Q() Objects

• Import Q() objects for complex queries.

from django.db.models import Q

• Create an OR query.

Entry.objects.filter(Q(headline__startswith='A') | Q(headline__startswith='B'))

• Create a NOT query.

Entry.objects.filter(~Q(headline__icontains='boring'))

XIII. QuerySet Evaluation

• Get the count of objects efficiently.

count = Entry.objects.count()

• Check if any objects exist efficiently.

exists = Entry.objects.filter(rating=5).exists()

• Get the count (evaluates the full queryset).

count = len(Entry.objects.all())

• Check for existence (evaluates the full queryset).

if Entry.objects.filter(rating=5): # True if queryset is not empty
    ...

• Iteration (evaluates the queryset).

for entry in Entry.objects.all():
    print(entry.headline)

XIV. Optimization

• Fetch related ForeignKey objects in the same query.

entries = Entry.objects.select_related('author').all()

• Fetch related ManyToManyField objects in a separate efficient query.

entries = Entry.objects.prefetch_related('tags').all()

• Load only specific model fields.

entries = Entry.objects.only('headline')

• Defer loading of specific model fields.

entries = Entry.objects.defer('body_text')

• Execute raw, unmanaged SQL.

authors = Author.objects.raw('SELECT * FROM myapp_author')

• Get results as a list of tuples.

Entry.objects.values_list('headline', 'pub_date')

XV. Transactions

• Import the transaction module.

from django.db import transaction

• Run a block of code within a database transaction.

with transaction.atomic():
    # All database operations here are either committed together or rolled back.
    author.save()
    entry.save()

XVI. Managers & Model Methods

• Create a custom Manager for common queries.

class PublishedEntryManager(models.Manager):
    def get_queryset(self):
        return super().get_queryset().filter(status='published')

• Add a custom method to a QuerySet via its Manager.

Entry.objects.get_queryset().by_author("John Doe")

• Add a custom method to a model for object-specific logic.

class Entry(models.Model):
    #...
    def is_recent(self):
        return self.pub_date > timezone.now() - timedelta(days=1)

#Python #Django #ORM #Database #Backend

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By: @DataScience4 ✨

✨ inference | AI Coding Glossary ✨

📖 The phase where a trained model processes new inputs to produce outputs.

🏷️ #Python

✨ zero-shot learning | AI Coding Glossary ✨

📖 A machine learning setting where a model handles classes or tasks that were not encountered during training.

🏷️ #Python

💡 Top 90 Python Coding Interview Questions (Set 2)

(Note: These are distinct from the first set, covering more variations, design questions, and advanced topics. Code snippets are conceptual.)

I. Arrays & Matrices

• 3Sum: Find all unique triplets that sum to zero.

# Sort the array, then use a two-pointer approach for each element.

• Rotate Image: Rotate an NxN matrix by 90 degrees in-place.

# Transpose the matrix, then reverse each row.

• Set Matrix Zeroes: If an element is 0, set its entire row and column to 0.

# Use the first row/col as markers to avoid extra space.

• Spiral Matrix: Traverse a matrix in a spiral order.

# Use four pointers for boundaries (top, bottom, left, right) and shrink them inward.

• Sort Colors (Dutch National Flag problem): Sort an array of 0s, 1s, and 2s.

# Use three pointers (low, mid, high) to partition in a single pass.

• Find Peak Element: Find an element that is greater than its neighbors.

# Use binary search. Compare mid with its right neighbor to decide which way to go.

• Maximum Product Subarray: Find the max product of a contiguous subarray.

# Track current max and min product because two negatives make a positive.

• Longest Consecutive Sequence: Find the length of the longest run of consecutive numbers.

# Use a hash set for O(1) lookups. Only start counting from the beginning of a sequence.

• Insert Interval: Insert a new interval into a list of non-overlapping intervals.

# Iterate through intervals, add non-overlapping ones, merge with overlapping ones.

• Merge Intervals: Merge all overlapping intervals.

# Sort intervals by start time, then iterate and merge.

• Jump Game: Can you reach the last index?

# Greedy approach. Keep track of the maximum reachable index.

• Next Permutation: Find the next lexicographically greater permutation.

# Find the first decreasing element from the right, swap it with the next largest, then reverse the rest.

• Find the Duplicate Number: Array n+1 integers from 1 to n. Find the one duplicate.

# Use Floyd's Tortoise and Hare (cycle detection) algorithm.

• Word Search: Find a word in a 2D grid of characters.

# Backtracking (DFS) from each cell. Mark visited cells to avoid reuse.

• Task Scheduler: Schedule tasks with a cooling period.

# Greedy approach using a frequency map or a max-heap.

• Is Subsequence: Check if one string is a subsequence of another.

# Simple two-pointer approach.

• Dot Product of Two Sparse Vectors:

# Store non-zero elements in a hash map or list of pairs to optimize.

II. Linked Lists

• Remove Duplicates from Sorted List II: Delete all nodes that have duplicate numbers.

# Use a sentinel/dummy head and a predecessor pointer.

• Rotate List: Rotate the list to the right by k places.

# Find length, connect tail to head to make a cycle, then break at the new tail.

• Partition List: Partition around a value x.

# Use two pointers to build two separate lists (less than `x` and >= `x`), then link them.

• Swap Nodes in Pairs: Swap every two adjacent nodes.

# Use careful pointer manipulation with a dummy head.

• Odd Even Linked List: Group odd nodes followed by even nodes.

# Use two pointers, one for the odd list tail and one for the even list tail.

• Flatten a Multilevel Doubly Linked List:

# Use DFS. When a child is found, insert its flattened list between the current node and its next.

• LRU Cache: Design a Least Recently Used cache.

# Use a hash map for O(1) key lookups and a doubly linked list to manage recency.

III. Stacks & Queues

• Largest Rectangle in Histogram:

# Use a monotonic increasing stack to track indices of bars.

• Car Fleet: Calculate the number of car fleets that will arrive at the destination.

# Sort cars by position. Use a stack to merge fleets based on arrival time.

• Asteroid Collision:

# Use a stack to simulate collisions, handling different sizes and directions.

• Decode String (e.g., 3[a2[c]]):

# Use a stack to keep track of counts and previous strings.

• Simplify Path (Unix-style):

# Split by '/' and use a stack to handle '..' and '.'.

• Moving Average from Data Stream:

# Use a `collections.deque` with a fixed `maxlen` to efficiently calculate the sum.

• Basic Calculator II: Implement a calculator with +, -, *, /.

# Use a stack to handle operator precedence, processing `*` and `/` immediately.

IV. Trees & Graphs

• Binary Tree Zigzag Level Order Traversal:

# BFS with a deque. Alternate the direction of appending to the level result list.

• Construct Binary Tree from Preorder and Inorder Traversal:

# Use preorder to find the root and inorder to find the left/right subtrees. Recurse.

• Populating Next Right Pointers in Each Node:

# Use level-order traversal (BFS) or a clever DFS/recursive approach.

• Binary Tree Maximum Path Sum:

# Recursive DFS. At each node, return the max path down, but update a global max with the full path through that node.

• Balanced Binary Tree Check:

# DFS. For each node, get the height of left/right subtrees. If diff > 1, it's unbalanced.

• Symmetric Tree: Check if a tree is a mirror of itself.

# Use a recursive helper function that compares two nodes `is_mirror(t1, t2)`.

• Path Sum II: Find all root-to-leaf paths that sum to a target.

# Backtracking (DFS) with a path list and current sum.

• Validate IP Address:

# String parsing with careful checks for IPv4 (numeric ranges) and IPv6 (hex, length).

• Find Leaves of Binary Tree: Collect leaves, remove them, repeat.

# Use a modified DFS that returns the height of a node. Group nodes by height.

• Graph Valid Tree: Check if a graph is a valid tree.

# A valid tree has `n-1` edges and is fully connected (no cycles). Use DFS/BFS or Union-Find.

• Pacific Atlantic Water Flow:

# Start DFS/BFS from all cells on the ocean borders and find the intersection of reachable cells.

• Redundant Connection: Find a redundant edge in a graph that makes a cycle.

# Use Union-Find. The first edge that connects two nodes already in the same set is redundant.

• Network Delay Time: Find the time for a signal to reach all nodes.

# Dijkstra's algorithm to find the shortest path from the source to all other nodes.

• Cheapest Flights Within K Stops:

# Modified Dijkstra's or Bellman-Ford, keeping track of the number of stops.

• Alien Dictionary: Find the order of characters from a sorted list of words.

# Build a dependency graph, then perform a topological sort.

V. Dynamic Programming & Recursion

• Decode Ways: Number of ways to decode a string of digits.

# DP: `dp[i]` is the number of ways to decode `s[:i]`. Consider one and two-digit decodings.

• Partition Equal Subset Sum:

# DP (knapsack variation). Check if a subset sums to `total_sum / 2`.

• Best Time to Buy and Sell Stock II: You can buy and sell multiple times.

# Greedy. Add profit `prices[i] - prices[i-1]` whenever it's positive.

• Coin Change 2: Number of combinations that make up an amount.

# Unbounded knapsack DP. `dp[i]` is the number of ways to make amount `i`.

• Minimum Path Sum: Find the min path sum from top-left to bottom-right in a grid.

# 2D DP. `dp[i][j] = grid[i][j] + min(dp[i-1][j], dp[i][j-1])`.

• House Robber II: Houses are in a circle.

# Run the standard House Robber algorithm twice: once excluding the first house, once excluding the last.

• Palindromic Substrings: Count how many substrings are palindromic.

# Expand from center for every possible center (2n-1 centers).

• Longest Palindromic Subsequence:

# 2D DP. `dp[i][j]` = length of LPS in `s[i:j+1]`.

• Target Sum: Find ways to assign + or - to numbers to reach a target.

# DP or backtracking with memoization.

• Letter Combinations of a Phone Number:

# Classic backtracking problem.

• Knight's Shortest Path on a Chessboard:

# Use Breadth-First Search (BFS).

• Generate All Subsequences of a String:

# Backtracking: at each character, either include it or don't.

• Interleaving String:

# 2D DP. `dp[i][j]` is true if `s1[:i]` and `s2[:j]` can form `s3[:i+j]`.

• Paint House:

# Simple 1D DP. Track the min cost to paint the current house each of the three colors.

VI. Sorting, Searching & Heaps

• K Closest Points to Origin:

# Use a max-heap of size k or `heapq.nsmallest`.

• Find Kth Largest Element in a Stream:

# Maintain a min-heap of size k.

• Median of Two Sorted Arrays:

# Binary search on the smaller array to find the correct partition.

• Find Median from Data Stream:

# Use two heaps: a max-heap for the lower half and a min-heap for the upper half.

• Time Based Key-Value Store:

# Store values in a list sorted by timestamp for each key. Use binary search (`bisect_right`) for lookups.

• Reorganize String: Rearrange so no two adjacent characters are the same.

# Greedy approach with a max-heap or by counting character frequencies.

• Wiggle Sort II: Reorder so nums[0] < nums[1] > nums[2] < ....

# Find the median, then use a three-way partition and clever index mapping.

• Pancake Sorting:

# Find the max, flip it to the front, then flip it to its correct sorted position. Repeat.

• Custom Sort String:

# Use a hash map to count chars in the string, then build the result based on the custom order.

VII. Design Questions

• Design a Logger Rate Limiter:

# Use a hash map to store the last printed timestamp for each message.

• Design Tic-Tac-Toe:

# Store counts for rows, columns, and diagonals to check for a win in O(1).

• Design an In-Memory File System:

# Use a Trie-like structure where each node is a directory/file.

• Design a Hit Counter:

# Use a queue or deque to store timestamps of hits within the last 5 minutes.

• Design TinyURL:

# Map a long URL to a unique hash or incrementing ID, then convert to a base-62 string.

• Design a Web Crawler:

# Use BFS to explore links and a set to keep track of visited URLs.

• Design Twitter's Data Structures:

# Hash maps for users, tweets. A list/deque for a user's own tweets. A more complex structure for the news feed.

• Design Search Autocomplete System:

# Use a Trie (Prefix Tree) where nodes store search frequency.

VIII. Bit Manipulation & Math

• Power of Two:

# Check if `n > 0` and `(n & (n - 1)) == 0`.

• Pow(x, n): Implement pow(x, n).

# Use exponentiation by squaring for an O(log n) solution.

• Majority Element:

# Boyer-Moore Voting Algorithm for an O(n) time, O(1) space solution.

• Excel Sheet Column Number:

# Base-26 conversion from string to integer.

• Valid Number:

# Use a state machine or a series of careful conditional checks.

• Integer to English Words:

# Handle numbers in chunks of three (hundreds, tens, ones) with helper functions.

• Sqrt(x): Compute and return the square root of x.

# Use binary search or Newton's method.

• Gray Code:

# Formula: `i ^ (i >> 1)`.

• Shuffle an Array:

# Implement the Fisher-Yates shuffle algorithm.

IX. Python Concepts

• Explain the GIL (Global Interpreter Lock):

# Conceptual: A mutex that allows only one thread to execute Python bytecode at a time in CPython.

• Difference between __str__ and __repr__:

# __str__ is for end-users (readable), __repr__ is for developers (unambiguous).

• Implement a Context Manager (with statement):

class MyContext:
    def __enter__(self): # setup
        return self
    def __exit__(self, exc_type, exc_val, exc_tb): # teardown
        pass

• Implement itertools.groupby logic:

# Iterate through the sorted iterable, collecting items into a sublist until the key changes.

#Python #CodingInterview #DataStructures #Algorithms #SystemDesign

━━━━━━━━━━━━━━━
By: @DataScience4 ✨

💡 Top 10 Python Clean Code Practices

1. Use List Comprehensions for Simple Loops
(Replaces verbose for loops for creating lists.)

Cluttered Way:

squares = []
for i in range(10):
    squares.append(i * i)

Clean Way:

squares = [i * i for i in range(10)]

2. Use enumerate for Index and Value
(Avoids manual index tracking with range(len(...))).)

Cluttered Way:

items = ['a', 'b', 'c']
for i in range(len(items)):
    print(i, items[i])

Clean Way:

items = ['a', 'b', 'c']
for i, item in enumerate(items):
    print(i, item)

3. Use Context Managers for Resources
(Ensures resources like files are properly closed, even if errors occur.)

Cluttered Way:

f = open('my_file.txt', 'w')
try:
    f.write('hello world')
finally:
    f.close()

Clean Way:

with open('my_file.txt', 'w') as f:
    f.write('hello world')

4. Use Dictionary .get() for Safe Key Access
(Prevents KeyError and avoids verbose if key in dict checks.)

Cluttered Way:

my_dict = {'name': 'Alice'}
if 'age' in my_dict:
    age = my_dict['age']
else:
    age = 0

Clean Way:

my_dict = {'name': 'Alice'}
age = my_dict.get('age', 0)

5. Use F-Strings for Formatting
(More readable and often faster than other string formatting methods.)

Cluttered Way:

name = "Bob"
age = 30
message = "Hello, " + name + "! You are " + str(age) + " years old."
# Or: message = "Hello, {}! You are {} years old.".format(name, age)

Clean Way:

name = "Bob"
age = 30
message = f"Hello, {name}! You are {age} years old."

6. Use Unpacking to Swap Variables
(A concise, Pythonic way to swap values without a temporary variable.)

Cluttered Way:

a = 5
b = 10
temp = a
a = b
b = temp

Clean Way:

a = 5
b = 10
a, b = b, a

7. Check for Empty Sequences Correctly
(Leverages Python's "truthiness" for more readable code.)

Cluttered Way:

my_list = []
if len(my_list) == 0:
    print("List is empty!")

Clean Way:

my_list = []
if not my_list:
    print("List is empty!")

8. Use Underscores for Unused Variables
(Signals to other developers that a variable is intentionally ignored.)

Cluttered Way:

# 'i' is created but never used
for i in range(5):
    print("Hello")

Clean Way:

for _ in range(5):
    print("Hello")

9. Chain Comparison Operators
(Makes numerical range checks more intuitive and readable.)

Cluttered Way:

x = 10
if x > 5 and x < 15:
    print("x is between 5 and 15")

Clean Way:

x = 10
if 5 < x < 15:
    print("x is between 5 and 15")

10. Return from a Function Early
(Reduces nesting and improves readability by handling edge cases or invalid states first.)

Cluttered Way:

def process_data(data):
    if data is not None:
        if isinstance(data, list) and len(data) > 0:
            # ... deep nested logic here ...
            print("Processing data...")
            return "Done"
        else:
            return "Error: Invalid data format."
    else:
        return "Error: No data provided."

Clean Way:

def process_data(data):
    if data is None:
        return "Error: No data provided."
    if not isinstance(data, list) or not data:
        return "Error: Invalid data format."

    # ... logic is now at the top level ...
    print("Processing data...")
    return "Done"

#Python #CleanCode #Programming #BestPractices #CodingTips

━━━━━━━━━━━━━━━
By: @DataScience4 ✨

💡 Top 10 More Python Clean Code Practices

1. Use Ternary Operators for Simple Conditionals
(Replaces a multi-line if/else block for simple assignments.)

Cluttered Way:

is_adult = False
age = 20
if age >= 18:
    is_adult = True

Clean Way:

age = 20
is_adult = True if age >= 18 else False

2. Use str.join() for Concatenating Strings in a List
(More efficient and readable than using + in a loop.)

Cluttered Way:

words = ["hello", "world", "this", "is", "python"]
sentence = ""
for word in words:
    sentence += word + " "

Clean Way:

words = ["hello", "world", "this", "is", "python"]
sentence = " ".join(words)

3. Use collections.defaultdict for Grouping or Counting
(Avoids manual key checking when appending to lists or incrementing counters.)

Cluttered Way:

data = [('fruit', 'apple'), ('veg', 'carrot'), ('fruit', 'banana')]
grouped = {}
for category, item in data:
    if category not in grouped:
        grouped[category] = []
    grouped[category].append(item)

Clean Way:

from collections import defaultdict

data = [('fruit', 'apple'), ('veg', 'carrot'), ('fruit', 'banana')]
grouped = defaultdict(list)
for category, item in data:
    grouped[category].append(item)

4. Use collections.namedtuple for Simple Data Objects
(Provides readable attribute access instead of relying on numeric indices.)

Cluttered Way:

point = (10, 20)
# Unclear what point[0] or point[1] represents
if point[0] > 5:
    print("X is greater than 5")

Clean Way:

from collections import namedtuple

Point = namedtuple('Point', ['x', 'y'])
point = Point(10, 20)
if point.x > 5:
    print("X is greater than 5")

5. Use Argument Unpacking (* and **)
(Passes all items from a list or dictionary as arguments to a function.)

Cluttered Way:

def print_coords(x, y, z):
    print(f"X: {x}, Y: {y}, Z: {z}")

coords = [1, 2, 3]
print_coords(coords[0], coords[1], coords[2])

Clean Way:

def print_coords(x, y, z):
    print(f"X: {x}, Y: {y}, Z: {z}")

coords_list = [1, 2, 3]
print_coords(*coords_list)

coords_dict = {'x': 4, 'y': 5, 'z': 6}
print_coords(**coords_dict)

6. Use any() and all() for Boolean Checks on Iterables
(More declarative and concise than manual loops with a flag variable.)

Cluttered Way:

numbers = [-1, -2, 5, -4]
has_positive = False
for num in numbers:
    if num > 0:
        has_positive = True
        break

Clean Way:

numbers = [-1, -2, 5, -4]
has_positive = any(num > 0 for num in numbers)

7. Prefer Generator Expressions for Large Datasets
(They don't store the entire sequence in memory, making them highly efficient.)

Cluttered Way (can cause high memory usage):

total = sum([i * i for i in range(1000000)])

Clean Way (memory efficient):

total = sum(i * i for i in range(1000000))

8. Use Destructuring for More Powerful Unpacking
(A clean way to assign elements from a sequence to multiple variables.)

Cluttered Way:

numbers = [1, 2, 3, 4, 5]
first = numbers[0]
last = numbers[-1]

Clean Way:

numbers = [1, 2, 3, 4, 5]
first, *middle, last = numbers
# first = 1, middle = [2, 3, 4], last = 5

9. Use isinstance() for Type Checking
(It's safer and more robust than type() because it correctly handles inheritance.)

Cluttered Way (brittle, fails on subclasses):

class MyList(list): pass
my_list_instance = MyList()
if type(my_list_instance) == list:
    print("It's a list!") # This will not print

Clean Way (correctly handles subclasses):

class MyList(list): pass
my_list_instance = MyList()
if isinstance(my_list_instance, list):
    print("It's an instance of list or its subclass!") # This prints

10. Use the else Block in try/except
(Clearly separates the code that runs on success from the try block being monitored.)

Cluttered Way:

try:
    data = my_ risky_operation()
    # It's not clear if this next part can also raise an error
    process_data(data)
except ValueError:
    handle_error()

Clean Way:

try:
    data = my_risky_operation()
except ValueError:
    handle_error()
else:
    # This code only runs if the 'try' block succeeds with NO exception
    process_data(data)

#Python #CleanCode #Programming #BestPractices #CodeReadability

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By: @DataScience4 ✨

✨ few-shot learning | AI Coding Glossary ✨

📖 A setting where a model adapts to a new task using only a small number of labeled examples.

🏷️ #Python

Learning Common Algorithms with Python

• This lesson covers fundamental algorithms implemented in Python. Understanding these concepts is crucial for building efficient software. We will explore searching, sorting, and recursion.

• Linear Search: This is the simplest search algorithm. It sequentially checks each element of the list until a match is found or the whole list has been searched. Its time complexity is O(n).

def linear_search(data, target):
    for i in range(len(data)):
        if data[i] == target:
            return i  # Return the index of the found element
    return -1 # Return -1 if the element is not found

# Example
my_list = [4, 2, 7, 1, 9, 5]
print(f"Linear Search: Element 7 found at index {linear_search(my_list, 7)}")

• Binary Search: A much more efficient search algorithm, but it requires the list to be sorted first. It works by repeatedly dividing the search interval in half. Its time complexity is O(log n).

def binary_search(sorted_data, target):
    low = 0
    high = len(sorted_data) - 1
    
    while low <= high:
        mid = (low + high) // 2
        if sorted_data[mid] < target:
            low = mid + 1
        elif sorted_data[mid] > target:
            high = mid - 1
        else:
            return mid # Element found
    return -1 # Element not found

# Example
my_sorted_list = [1, 2, 4, 5, 7, 9]
print(f"Binary Search: Element 7 found at index {binary_search(my_sorted_list, 7)}")

• Bubble Sort: A simple sorting algorithm that repeatedly steps through the list, compares adjacent elements and swaps them if they are in the wrong order. The process is repeated until the list is sorted. Its time complexity is O(n^2).

def bubble_sort(data):
    n = len(data)
    for i in range(n):
        # Last i elements are already in place
        for j in range(0, n-i-1):
            if data[j] > data[j+1]:
                # Swap the elements
                data[j], data[j+1] = data[j+1], data[j]
    return data

# Example
my_list_to_sort = [4, 2, 7, 1, 9, 5]
print(f"Bubble Sort: Sorted list is {bubble_sort(my_list_to_sort)}")

• Recursion (Factorial): Recursion is a method where a function calls itself to solve a problem. A classic example is calculating the factorial of a number (n!). It must have a base case to stop the recursion.

def factorial(n):
    # Base case: if n is 1 or 0, factorial is 1
    if n == 0 or n == 1:
        return 1
    # Recursive step: n * factorial of (n-1)
    else:
        return n * factorial(n - 1)

# Example
num = 5
print(f"Recursion: Factorial of {num} is {factorial(num)}")

#Python #Algorithms #DataStructures #Coding #Programming #LearnToCode

━━━━━━━━━━━━━━━
By: @DataScience4 ✨

✨ Quiz: Python MarkItDown: Convert Documents Into LLM-Ready Markdown ✨

📖 Practice MarkItDown basics. Convert PDFs, Word documents, Excel documents, and HTML documents to Markdown. Try the quiz.

🏷️ #intermediate #ai #tools

✨ Python 3.14 Released and Other Python News for November 2025 ✨

📖 Python 3.14 is officially out, Python 3.15 begins, and Python 3.9 reaches end of life. Plus, Django 6.0 first beta released, new PEPs, and more Python news.

🏷️ #community #news