Why inplace=True in Pandas Can Be Risky

Day 9/120 – Today I learned something most beginners ignore… but pros don’t 😳🔥 Yesterday → Lists Today → CONTROL over data 👇 👉 Tuples & Sets in Python Here’s the problem 🤯 Lists can be changed anytime… But what if your data SHOULD NOT change? ❌ Example: Coordinates 📍 Dates 📅 Configurations ⚙️ That’s where TUPLES come in 👇 data = (10, 20, 30) ✔ Cannot be modified ✔ Safe & reliable Now comes something even more powerful 👇 👉 SETS nums = {1, 2, 2, 3, 3} Output? 😳 {1, 2, 3} ✔ No duplicates ✔ Clean data This is HUGE in Data Analytics 📊 Now I can: ✔ Protect data (Tuples) ✔ Clean data (Sets) This is getting serious now 🔥 Comment “DATA” if you're learning with me 💪 #Day9 #Python #DataAnalytics #LearningInPublic #CodingJourney #Consistency

Day 23 Today I learned how to handle missing values in pandas.The problem:My dataset has empty rows. If I leave them, my analysis will be wrong.What I learned:· .dropna() – removes any row with missing data· .fillna(value) – fills empty spots with a value I chooseExample:```pythondf.dropna()  # deletes rows with missing valuesdf.fillna(0) # fills empty spots with 0```What I will try tomorrow:Figure out which method works better for my loan dataset.That's it. Short day. Still learning.#M4ACE LearningChallenge#LearningInPublic#30DaysOfAIML#Python #Pandas

4 of my #100DaysOfCode Moving from simple variables into actual Data Structures using Python Lists. As I grow in data analytics, I know organizing and manipulating data is the core of the job, so getting comfortable with lists is a critical foundation. Here is what I tackled in day 4. Randomisation: Using the Mersenne Twister (import random) and randint() to generate unpredictable outcomes. Lists: Creating, altering, and managing data structures using brackets []. List Methods: How to use .append(), .extend(), .insert(), and .pop(). Indexing: Accessing specific data points (and successfully conquering negative indexing!). To put it all together, we built a fully functional Rock, Paper, Scissors game that plays against the user.

LeetCode Problem 380 Insert Delete GetRandom O(1): "Implement the RandomizedSet class: RandomizedSet() Initializes the RandomizedSet object. bool insert(int val) Inserts an item val into the set if not present. Returns true if the item was not present, false otherwise. bool remove(int val) Removes an item val from the set if present. Returns true if the item was present, false otherwise. int getRandom() Returns a random element from the current set of elements (it's guaranteed that at least one element exists when this method is called). Each element must have the same probability of being returned. You must implement the functions of the class such that each function works in average O(1) time complexity." Approach: Maintain two data structures one hash_table to get elements in constant time and other a list so that getRandom() function can be implemented by using random.choice(list). Time Complexity: O(1) Space Complexity: O(n) #Python #LeetCode #DSA #Algorithms #HashTable #Lists #Arrays #OptimalSolution #DataStructures

Combining data from multiple sources is one of the most common tasks in data analysis and data engineering and in pandas, pd.concat() is the primary tool for getting it done. But there is more to it than just passing two DataFrames and getting one back. Understanding when to use axis=0 vs axis=1, how join handles mismatched columns, why concatenating inside a loop is a performance trap, and when to use concat vs merge. These are the details that separate clean, efficient data pipelines from slow, buggy ones. Get comfortable with pd.concat() and combining data from multiple sources becomes one of the fastest steps in your workflow. Read the full post here: https://lnkd.in/es7KJ7Y9 #Python #Pandas #DataScience #DataEngineering #Analytics #ETL

Ever had your Pandas integers mysteriously turn into floats? 🧐 It’s a common headache: you have a column of IDs or counts, one missing value (NaN) appears, and suddenly your 1 becomes 1.0. The secret is in the capitalization: int64 vs Int64. 🔹 int64 (numpy-backed): The default. High performance, but cannot handle nulls. If a NaN sneaks in, Pandas "upcasts" the whole column to floats to accommodate it. 🔹 Int64 (pandas-nullable): The "modern" way. It uses a mask to support pd.NA. Your integers stay as integers even with missing data. No more 1.0 where you expected a 1! Pro-tip: Use .astype('Int64') during your data cleaning phase to keep your schemas clean and predictable. #Python #Pandas #DataScience #DataEngineering #CodingTips #Dataanalyst

Day 24/75 — This one Python function helped me understand my data better 👇 When I started analyzing datasets, I felt overwhelmed. Too many rows. Too much information. Then I discovered this: df.groupby('city')['price'].mean() 💡 What it does: 👉 Groups data by a category 👉 Calculates insights (like average, sum, count) Example: Instead of looking at thousands of rows… I can instantly see: 📊 Average price per city 🚨 Why this is powerful: • Turns raw data into insights • Helps you compare groups easily • Makes analysis faster and clearer 👨💻 Now I use it all the time to: • Compare categories • Find patterns • Simplify data Small function… But a big upgrade in how I analyze data. Have you used groupby() before? 👇 #DataScience #Python #Pandas #DataAnalysis #LearningInPublic

Advanced pandas tricks that make you 10x faster at data wrangling. Most people learn pandas basics and stop. This free notebook covers what comes after. → MultiIndex: hierarchical indexing for complex datasets → .pipe() — chain custom functions into your workflow → Method chaining: write entire analyses in one readable block → Memory optimization: reduce DataFrame memory by 70%+ → Vectorized operations: why your for loop is 100x slower → Performance patterns the documentation buries If your pandas code has more than 2 for loops, this notebook will change how you write it. Every trick has before/after benchmarks. See the speed difference yourself. Free: https://lnkd.in/g7HsJfGy Day 3/7. #Python #Pandas #DataAnalyst #DataScience #DataWrangling #Performance #FreeResources #DataAnalytics

🚀#Day10 of #Learning Today I continued exploring Pandas DataFrames and practiced several useful functions for analyzing and organizing data. 🔹 DataFrame Functions – Worked with built-in functions for exploring and understanding data. 🔹 value_counts() – Used value counts to analyze frequency distributions in data. 🔹 sort_values() – Sorted data based on column values. 🔹 Sorting by Multiple Columns – Learned how to sort using more than one column for more refined organization. 🔹 sort_index() – Practiced sorting data based on index labels. 🔹 set_index() and reset_index() – Learned how to set columns as an index and reset them when needed. Today’s learning improved my understanding of organizing, summarizing, and structuring data efficiently Github Repo : https://lnkd.in/gZ8r-ku4 #Python #Pandas #MachineLearning #LearningJourney

Day 6/10 🚀 This is where your data starts to take shape. Collections — the backbone of every Python program. Without the right one? Slower code, messy logic. With the right one? Faster lookups, cleaner design. 📋 What I covered today: 01 → Lists — slicing & comprehensions 02 → Tuples — immutability & unpacking 03 → Dictionaries — CRUD & O(1) lookup 04 → Sets — unique values & operations 05 → Frozenset 06 → Advanced — defaultdict, Counter, namedtuple 07 → Iterators — iter() & next() 08 → Mini Project — Inventory Management System Built a simple system using dictionaries to manage stock & pricing — a real-world pattern used in inventory and data pipelines. Day 1 ✅ Day 2 ✅ Day 3 ✅ Day 4 ✅ Day 5 ✅ Day 6 ✅ 4 more to go. Drop a 🐍 if you’ve ever used a list when a set would’ve been better 😄 #Python #Collections #DataEngineering #LearningInPublic #CleanCode #10DaysOfPython #DataStructures