Automate Data Cleaning with 5 Python Scripts

𝐃𝐚𝐲 5 | 𝐍𝐮𝐦𝐏𝐲 𝐀𝐫𝐫𝐚𝐲 𝐂𝐫𝐞𝐚𝐭𝐢𝐨𝐧 & 𝐕𝐞𝐜𝐭𝐨𝐫 𝐎𝐩𝐞𝐫𝐚𝐭𝐢𝐨𝐧𝐬 📊 Not long ago, these Python libraries for analysis felt unfamiliar and almost mysterious. But by showing up daily and working through them step by step, the logic is starting to click. What once looked strange is becoming intuitive, and it’s getting more interesting each day. Today was about multi-dimensional arrays and understanding how NumPy handles operations across axes. ✔️ Created NumPy arrays from nested lists ✔️ Extracted negative values using boolean indexing ✔️ Performed element-wise multiplication ✔️ Calculated sums across different axes ✔️ Built and reshaped a 3D array with arange() ✔️ Practiced slicing specific rows ✔️ Compared values across rows using vectorized logic Day 5 done. Looking forward to day 6 🚀 𝐎𝐬𝐭𝐢𝐧𝐚𝐭𝐨 𝐑𝐢𝐠𝐨𝐫𝐞 #NumPy #Python #DataAnalytics #DataScience #LearningInPublic #50_days_of_data_analysis_with_python

𝗗𝗮𝘆 𝟭3: 𝗧𝘂𝗽𝗹𝗲𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 🐍 🔹 Tuples are ordered and immutable collections of items, separated by commas and enclosed in round brackets. 🔹 Items can be of multiple data types 🔹 Once created, tuples cannot be changed, added to, or deleted 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗧𝘂𝗽𝗹𝗲𝘀:  ✔ Single-item tuple (comma is mandatory) ✔ Using parentheses: (1, 2, 3) ✔ Without parentheses: tup = 1, 2, 3 𝗖𝗼𝗺𝗺𝗼𝗻 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀: ✔ Indexing and slicing (same as lists) ✔ Unpacking – assign tuple values to multiple variables ✔ Concatenation – combine tuples using + 📝𝗡𝗼𝘁𝗲:  🔸 Tuples cannot be modified directly.  🔸 To make changes, convert the tuple to a list, modify it, and convert it back to a tuple. Building strong Python fundamentals, one concept at a time 🚀 #Python #Tuples #LearningPython #LearningInPublic #AspiringDataScientist #Consistency

🐻❄Pandas Tip: Instead of looping through rows, use vectorized operations in Pandas. They are faster, cleaner, and more Pythonic.Vectorized operations mean performing calculations on entire columns (arrays) at once, instead of processing data row by row using loops. Example: Python under pandas library: df["total"] = df["price"] * df["quantity"] 🚀 This approach improves performance significantly, especially on large datasets. Why Avoid Loops in Pandas? Using loops (for, iterrows()): 😐Slow for large datasets 😐Harder to read and maintain 😐Doesn’t utilize Pandas’ full power Using vectorization: 😊Faster execution 😊Cleaner and shorter code 😊Better memory usage #Python #Pandas #DataEngineering #DataScience

Our first-ever Data Vis course just dropped!! ❤️🔥 Learn Matplotlib (a powerful Python library) and create beautiful line plots, scatter plots, bar graphs, pie charts, subplots, and more. There are two chapters + a cheatsheet! Written by Estrella Popoca. 🤿 Dive in today: https://lnkd.in/eStpG7G3 Hint: February Monthly Challenge may depend on it... 🤐 #datascience #datavisualization #matplotlib

🌟 New Blog Just Published! 🌟 📌 5 Python Scripts to Automate Feature Engineering in Seconds 🚀 📖 Imagine you could turn a half-day of manual tweaking into a handful of seconds of script. That’s the promise of automating feature engineering. Feature engineering is the art of converting raw...... 🔗 Read more: https://lnkd.in/ghsJDpDJ 🚀✨ #python-feature-eng #feature-automation #data-prep-scripts

𝗗𝗮𝘆 𝟭𝟰: 𝗦𝗲𝘁𝘀 𝗶𝗻 𝗣𝘆𝘁𝗵𝗼𝗻 🐍 𝗦𝗲𝘁𝘀: 🔹 Sets are unordered collections of items 🔹 Elements are unique (no duplicate values) 🔹 Items are separated by commas and enclosed in curly brackets { } 🔹 Sets do not maintain order 𝗖𝗿𝗲𝗮𝘁𝗶𝗻𝗴 𝗮 𝗦𝗲𝘁: 🔹 s = {1, 2, 3} 🔹 Use set() to create an empty set : s = set() ⚠️ {} creates an empty dictionary, not a set 𝗦𝗲𝘁 𝗢𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀:  1️⃣𝗨𝗻𝗶𝗼𝗻 (|) → combines elements  2️⃣𝗜𝗻𝘁𝗲𝗿𝘀𝗲𝗰𝘁𝗶𝗼𝗻 (&) → common elements  3️⃣𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 (-) → elements in one set but not the other  4️⃣𝗦𝘆𝗺𝗺𝗲𝘁𝗿𝗶𝗰 𝗗𝗶𝗳𝗳𝗲𝗿𝗲𝗻𝗰𝗲 (^) → elements not common 📝Important Notes: 🔹 Sets do not support indexing or slicing 🔹 Elements must be immutable (int, string, tuple) Building strong Python fundamentals, one concept at a time 🚀 #Python #Tuples #LearningPython #LearningInPublic #AspiringDataScientist #Consistency

𝐃𝐚𝐲 𝟔: 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬 𝐢𝐧 𝐏𝐲𝐭𝐡𝐨𝐧 🐍 Conditional statements are used to execute specific blocks of code based on whether a condition is true or false. 𝐓𝐲𝐩𝐞𝐬 𝐨𝐟 𝐂𝐨𝐧𝐝𝐢𝐭𝐢𝐨𝐧𝐚𝐥 𝐒𝐭𝐚𝐭𝐞𝐦𝐞𝐧𝐭𝐬: 1️⃣ if statement: Execute a code block only when the condition is True. 2️⃣ if–else statement:   🔹If condition is True → if code block runs 🔹 If condition is False → else code block runs 3️⃣ if–elif–else ladder: Used to check multiple conditions in sequence. 4️⃣ Nested if: An if statement inside another if for complex checks. 5️⃣Ternary Operator (Conditional Expressions): 🔹A single-line shorthand for a simple if-else statement. 🔹Ex: print("Yes") if condition else print("No") #Python #LearningDaily #DataScienceJourney #LearningInPublic #Consistency

🐍 Day 22 — Functions in Python Day 22 of #python365ai 🧩 Functions group reusable code. Example: def greet(name): print("Hello", name) 📌 Why this matters: Functions improve readability and reduce repetition. 📘 Practice task: Write a function that adds two numbers. #python365ai #PythonFunctions #CleanCode #LearnPython

Flagging outliers in time series is tricky. You need to decompose the series, calculate the residuals, choose a threshold, and then check if the results make sense. That's a lot of manual steps. And a lot of room for error. TimeCopilot handles it differently. You pass your data to detect_anomalies() and get: • Prediction intervals built with conformal methods • Anomalies flagged based on the confidence level you choose • Visualization with forecasts and anomalies together No separate tools. No manual calculations. 🚀Full tutorial: https://lnkd.in/ePEjshey #TimeSeries #AnomalyDetection #Python #DataScience