Prevent Pandas Integers from Turning into Floats with Int64

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

The "Date Stored as Text" Nightmare. 📅 We have all seen it. You try to sort by date, and Excel sorts it alphabetically because the date is "240115". Text-to-Columns works... until you get a new file next week. Python's to_datetime function is the ultimate fix. You teach it the format once, and it never gets confused again. Swipe to see how to force dates to behave. 👉 #codingtips #analytics #data #dataanalysis #pythonforexcel #excel #datacleaning 

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

I started using Pandas last week. After a month of Python and NumPy, I thought I was ready. First impression: it feels like Excel. But smarter. In code. NumPy gave me arrays—rows of numbers I could analyze mathematically. Pandas gives me DataFrames—full tables with column names, mixed data types, and the ability to ask real questions of real data. The difference hit me immediately: With NumPy I was working with arrays I created myself. With Pandas I loaded an actual CSV file. Real column names. Real messy data. Real supply chain numbers. And in 3 lines of code: pd.read_csv() df.head() df.info() I could already see which suppliers had missing data, what their delivery rates looked like, and which columns needed cleaning. That's not practice anymore. That's actual analysis. This is where Python stops being theoretical and starts being useful. #Python #Pandas #LearningInPublic #SupplyChain #DataAnalytics

Pandas is about to get replaced. Not tomorrow. But in 2 years, half of you will have switched to Polars. And the other half will be wondering why their scripts are still slow. Polars is: → 5-30x faster than Pandas (on real benchmarks) → Memory-efficient (no more OOM errors on 10GB datasets) → Written in Rust (lazy evaluation, query optimization built in) → Has a cleaner, more consistent API than Pandas → Native support for streaming data (no chunking required) My free notebook walks through the fundamentals: → Polars DataFrames — creation, inspection, indexing → The expressions API (the thing that makes Polars fast) → Filtering, selecting, sorting — the Pandas equivalents → group_by with expressions (way cleaner than agg) → Lazy evaluation — query optimizer explained → Side-by-side Pandas vs Polars benchmarks If you've never heard of Polars, you're about to. Get ahead of the curve. https://lnkd.in/gDXKkV75 Day 2/7. #Polars #Python #DataEngineering #DataAnalytics #Pandas #Rust #DataFrames #OpenSource

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

Stop loading massive datasets into memory and crashing your pipeline. 🛑 I used to load multi-gigabyte CSVs into Pandas, only to watch my memory usage spike to 100% and trigger an OOM kill. Switching to Python generators transformed how we handle large-scale data ingestion. Before (messy): import pandas as pd data = pd.read_csv("large_file.csv") for row in data.itertuples(): process(row) After (clean): import pandas as pd def stream_data(file_path): for chunk in pd.read_csv(file_path, chunksize=10000): yield from chunk.itertuples() for row in stream_data("large_file.csv"): process(row) Why this matters for data engineers: By processing data in chunks rather than loading the entire file, you keep your memory footprint constant regardless of file size. This allows your small containers to handle massive files without crashing. What is your go-to method for memory-efficient data processing in Python? #DataEngineering #Python #BigData #DataPipelines #SoftwareEngineering

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

5 Pandas functions I use almost every day. If you come from SQL, these will feel familiar right away. 1. query() Filter rows the same way you would use a WHERE clause. 2. groupby() Aggregate your data by category. The Python equivalent of GROUP BY. 3. merge() Combine two DataFrames together. Works just like a JOIN. 4. value_counts() Count how often each value appears in a column. Great for a quick data quality check. 5. fillna() Replace missing values with a default. One line instead of a whole if-else block. The full code is in the image. Which one do you use the most? #Python #Pandas #DataScience #SQL #LearningInPublic

Knowing Python isn't enough... You need to know how to work with real data. That's where Pandas comes in. Day 5 of my 30-day Data Science challenge Here's what I simplified into this cheat sheet 👇 Data Loading → read_csv, read_excel, read_json Data Inspection → head(), info(), describe() Data Cleaning → dropna(), fillna(), rename() Data Selection → loc, iloc, df['col'] Data Manipulation → groupby(), merge(), sort_values() Filtering → df[df['col'] > value], query() This is something I keep coming back to every single day. Save this — you'll need it Which Pandas function do you use the most? 👇 #Pandas #Python #DataScience #LearningInPublic #DataScienceFresher