How to use Python Pandas for Data Analytics

Hello Everyone, As part of my Data Cleaning and Visualization skills upskilling, I've created Product Analytics Dashboard. The dashboard shows "Descriptive Statistics" about 1000 products downloaded from Kaggle in form of CSV. Before preparing charts, I performed process of data cleaning. (It was a manual process because of only 1000 records but it can definitely be automated through Python scripts). The charts created are basic Bar, Column, Line and Pie types. I've used all best practices used in Visualizations. Please have a look at my dashboard and suggest improvements. #DataCleaning #DataVisualizations #CSV #Dashboard #Python #PivotTables #Excel #Analytics

Session 3 Today’s session focused on strengthening my understanding of Python Data Structures, which are fundamental for efficient data handling, manipulation, and analysis in Data Science. 🔍 Topics Covered 📘 List – Ordered, mutable collection used for flexible data storage 📗 Tuple – Ordered, immutable structure ideal for fixed datasets 📙 Set – Unordered collection of unique elements, useful for removing duplicates 📒 Dictionary – Key–value mapping for fast lookups and structured information 🧩 Array, Queue, Deque – Sequential data structures that support optimized insertion, deletion, and access patterns 📊 Data Frame & Series – Core components of the Pandas library that enable powerful, table-like data management and analysis Each of these structures provided insight into how data can be organized efficiently, which is a crucial step toward mastering Data Science workflows. Looking forward to applying these concepts to real-world datasets in future sessions! #DataScience #Python #LearningJourney #Pandas #ProgrammingFundamentals #Upskilling

1️⃣ Data Acquisition using Pandas Caption: 🚀 Exploring Data Acquisition with Pandas! Under the guidance of Prof. Ashish Sawant, I explored how to import and manage datasets efficiently using Python’s Pandas library. Data acquisition is the foundation of every data-driven project. I practiced reading data from various sources like CSV, Excel, JSON, and SQL. Also learned to inspect data using .head(), .info(), and .describe(). Clean and structured data is the first step toward meaningful analysis. This practical gave me a clear understanding of how data flows into the analytics pipeline. For more info,you can visit :- GitHub :-https://lnkd.in/edWY72Hg G drive:https://lnkd.in/ewkPtNtH #DataScience #Pandas #Python #DataAcquisition #LearningByDoing

When it comes to data transformation, Pandas and NumPy are two of the most important tools every data engineer should master. Together, they make the manipulation of data faster, cleaner, and more efficient. With NumPy, you are able to explore how n-dimensional arrays enable high-performance numerical computations. Tasks that would normally take multiple loops in pure Python can be done in just one line using vectorization and broadcasting. Then came Pandas, built on top of NumPy, which provides powerful tools for handling real-world datasets. Working on data often require us to Load and inspect data from CSV and JSON files, Handle missing values and duplicates, Perform transformations using groupby, merge, and pivot operations. Using Pandas and NumPy helps with faster computations and cleaner data pipelines. What really stood out is how these two libraries simplify the data preparation process, turning raw, messy data into something structured and ready for analysis or storage. Every dataset tells a story and today, I’m learning the language that lets me read it. #SamsonDataEngineeringJourneyWith10alytics #DataEngineeringWith10alytics #NumPy #Pandas #Python #DataTransformation #LearningInPublic #DataEngineering

When I first heard about Pandas… I thought it’s just another Python library. But when I actually started working with it.. I realised Pandas is literally like Excel on steroids 🔥 It helps you clean data, fix missing values, filter, merge, visualize patterns… basically everything that prepares data before Machine Learning. Most of the real work in Data Science is not building the model… it’s shaping the data correctly — and Pandas is the #1 tool for that. Where Pandas is useful? •Data cleaning •Data transformation •Exploratory data analysis •Feature selection •Preparing data for ML models •Working with CSV / Excel files easily I’ve also made a Pandas Cheatsheet atteched below #pandas #python #datascience #ml #learningjourney #dataanalysis

🐍 Power of Python in Data Analysis: Pandas & NumPy In my data analysis journey, Pandas and NumPy have been two of the most powerful libraries I’ve worked with. Having some hands-on experience with them has really helped me understand data more efficiently. 💡 Here’s how they make a difference: 🔹 NumPy – Helps manage and process large numerical datasets quickly using arrays and mathematical operations. 🔹 Pandas – Makes it simple to clean, transform, and analyze data using DataFrames with just a few lines of code. From handling missing values to merging datasets and performing statistical analysis — these libraries make data work smooth, fast, and enjoyable. Every time I use them, I find new ways to make analysis more efficient and insightful. #Python #Pandas #NumPy #DataAnalysis #DataScience #DataAnalytics #Learning #DataEngineer #PowerBI #SQL

📘 Experiment 1: Data Acquisition Using Pandas As part of my Data Science and Statistics lab, I explored the fundamentals of data acquisition and loading using the Pandas library in Python. This experiment focused on efficiently importing and managing datasets from different file formats such as CSV, Excel, and JSON. Key learning outcomes included:  • Utilizing Pandas functions for reading and exploring datasets  • Performing initial data inspection using .head(), .tail(), .info(), and .describe()  • Understanding dataset structure, size, and dimensions for better preprocessing This experiment provided a solid foundation in data handling and preparation — essential skills for performing effective data analysis and building reliable machine learning workflows. 📁 Explore the repository here:  👉https://lnkd.in/epWys7e7 #DataScience  #MachineLearning  #Python  #Pandas  #Statistics  #JupyterNotebook  #DataAnalysis  #GitHub  #LearningByDoing Ashish Sawant Sir

🚀 Day of Deep Learning in Python Data Science! Today was packed with essential Python concepts that are game-changers for data analysis and manipulation. Here's what I covered: Core Python Skills: 📁 File Handling - mastering data input/output operations 🔄 Map, Filter & Reduce - functional programming for cleaner, more efficient code NumPy Mastery: Introduction to NumPy and its performance benefits Basic operations and matrix manipulations Advanced slicing and stacking techniques Pandas Deep Dive: Setting up and understanding DataFrames Reading/Writing Excel and CSV files Handling missing values (NA) effectively GroupBy operations for data aggregation Concatenating and merging datasets Data Visualization: 📊 Creating compelling visuals with Matplotlib and Seaborn Every day is a step closer to becoming proficient in data science. The journey from raw data to meaningful insights is challenging but incredibly rewarding! What's your favorite Python library for data analysis? Drop your thoughts below! 👇 #Python #DataScience #MachineLearning #NumPy #Pandas #DataVisualization #LearningJourney #Codebasics

💡 Did you know that Python sets automatically remove duplicates — no extra code needed? When I first learned Python, I used lists for everything — until I discovered sets. That tiny curly-brace {} structure changed how I handled data forever. Here’s why sets deserve more love 👇 ✅ They store unique elements — perfect for cleaning data. ✅ They’re super fast for lookups (faster than lists). ✅ They support math-like operations: union() → combine data intersection() → find common elements difference() → filter out unwanted values And my personal favorite — a.symmetric_difference(b) 💥 helps find what’s different between two datasets. Whether you’re deduplicating a CSV file, comparing user lists, or cleaning logs — sets are your secret weapon in data engineering and analytics. 👉 What’s one Python trick that saved you hours of work? Drop it in the comments — let’s build a cheat sheet together! #Python #DataEngineering #CodingTips #DataCleaning #PythonSets #100DaysOfCode #LearnPython #DataScience #BigData #CodeNewbie