4 Pandas Hacks to Boost Your Data Science Performance

I used to be really confused about NumPy and Pandas before/while learning them. They both seem similar at first. Here’s a simple way I understood them: 1. Numpy was built first (2005) to solve Python numerical problems. Python lists were slow for numerical work. And numpy made it faster and easier with C-based arrays. And when I learned about substitution, like you don't even have to use loops for those kinda tasks. 2. Pandas came later(2008) because Numpy was great with numbers, but real-world data is messy. So, to work with missing data and to work with other apps like Excel and SQL, it was created. The important part is that in most real projects, you don’t really choose one over the other; you use both together. Use NumPy when: 1. Working with pure numerical computations (linear algebra, mathematical operations) 2. Handling arrays, images, or signal data 3. You need performance and memory efficiency Use Pandas when: 1. Working with tabular or relational data (like Excel or SQL) 2. Dealing with missing or messy real-world data 3. Performing data cleaning, aggregation, or analysis 4. Working with time series data So in practice: NumPy handles the fast numerical backbone, and Pandas builds on top of it to make data handling more practical and readable. #pandas #numpy #NumpyVsPandas

The loop that takes 47 seconds becomes 0.3 seconds. Day 11 of 30 -- Advanced Pandas Optimization No new hardware. No rewrite. Just one change. Replace iterrows() with a vectorized expression. Here is what most Pandas developers do not realize: A DataFrame is just a NumPy array -- contiguous C memory. When you write df.iterrows(), Python converts every row to a Python dict. You are running a Python for-loop over a C array. That is where the 47 seconds comes from. Write df['total'] = df['qty'] * df['price'] instead. That is a C loop on the raw array. 157x faster. Today's topic covers: Why Pandas can be slow -- the Python loop trap explained Speed hierarchy -- iterrows 47s vs apply 28s vs itertuples 5s vs vectorized 0.3s dtype optimization -- 6 dtype conversions that cut memory by 70% before writing a single query Auto dtype downcast function that optimizes an entire DataFrame in 10 lines pd.eval and query for complex expressions without intermediate arrays Chunked processing -- 50M rows on a laptop with 6GB RAM Real scenario -- retail analytics, 48GB to 6GB, 4 hours to 8 minutes 8 optimization techniques including the SettingWithCopyWarning trap 5 mistakes including growing DataFrames in loops and loading unused columns Key insight: Pandas is not slow. Writing Python loops over Pandas DataFrames is slow. #Python #Pandas #DataEngineering #Performance #SoftwareEngineering #100DaysOfCode #PythonDeveloper #TechContent #BuildInPublic #TechIndia #DataScience #Analytics #PythonProgramming #LinkedInCreator #LearnPython #PythonTutorial

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

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

Did you know that... A 2023 study found that 99.81% of figures generated in a sample of 100,000 Jupyter notebooks did not contain alt text. But take heart! You can use the MatplotAlt Python library to add alt text to your matplotlib figures, making them more accessible to blind and visually impaired (BVI) users. Alt text can be added manually: add_alt_text(‘Your custom description’, methods=[‘html’, ‘markdown’]) Or automatically: show_with_alt(desc_level=3, methods=[‘html’, ’img_file’]) Both functions take a “methods” parameter to control output formats. The automatic version uses a “desc_level” parameter based on a four-level semantic model to control the depth of detail. Learn more about MatplotAlt here: https://lnkd.in/efApReeQ Access the GitHub page here: https://lnkd.in/e2pD2E7m Install via PyPI: pip install matplotalt ...Now you know! And knowledge is power. What else do you use to make your data and visualizations more accessible? I’d love to hear your thoughts in the comments! -Your friendly neighborhood Data Scientist

PYTHON GUIDE FOR BIGINNERS ✅ Python basics: syntax, indentation, variables, data types, casting, input/output ✅ Operators and control flow: arithmetic, comparison, logical, bitwise, loops, conditions ✅ Data structures: lists, tuples, sets, dictionaries, strings ✅ Functions: arguments, return values, scope, recursion, lambda, map / filter / reduce ✅ Modules and files: imports, standard library, CSV, JSON, file modes ✅ Error handling: try/except/finally, custom exceptions ✅ OOP: classes, inheritance, polymorphism, encapsulation, abstraction, magic methods ✅ Libraries: NumPy, Pandas, Matplotlib, Seaborn, scikit-learn ✅ APIs and web: requests, BeautifulSoup, Selenium, SQLite, MySQL, Flask #Python #Beginners #Programming #Pandas #OOP #Flask

Pandas is essentially Excel in Python — but way more powerful. Here's what you need to know: 📌 Two Core Data Structures: • Series — 1D, single column, homogeneous • DataFrame — 2D, multiple columns, heterogeneous 📌 Essential Operations Covered: • Importing CSV/Excel/SQL datasets • Indexing with .loc (label-based) & .iloc (position-based) • Data Cleaning — handling missing values with dropna() & fillna() • Removing duplicates with drop_duplicates() • Broadcasting — performing operations across entire columns • Joins & Merges — combining multiple datasets • Lambda & Apply — handling invalid values efficiently 📌 Pro Tip: Always use inplace=True if you want changes reflected in your original DataFrame! The best part? All of this with just a few lines of code. 🚀 Starting with a clean dataset is half the battle in Data Science. Master Pandas, and you're already ahead of the curve. #DataScience #Python #Pandas #MachineLearning #DataAnalysis

Learning never stops. Over the last weeks we’ve been diving deep into Python, SQL, and NoSQL – building small projects, breaking things on purpose, and then fixing them again. It’s a great way to understand not only how to write queries and scripts, but also how data actually flows through real applications. Step by step, it’s starting to connect: Python for logic and automation, SQL for structured data, and NoSQL for flexible, modern workloads. Looking forward to turning this practice into real‑world projects soon. https://lnkd.in/dcPkK-hX #sql #nosql #python