Exploratory Data Analysis with Python

90% of Data Work = Cleaning. SQL & Python Side‑by‑Side. Cleaning isn’t just prep it’s analysis. Here’s how SQL and Python mirror each other when tackling: Missing values Duplicates Formatting Outliers 👉Full break down here : https://lnkd.in/gUuRJExK #DataScience #SQL #Python #Analytics #BigData #MachineLearning #CareerGrowth

📊 Day 5 of #100DaysOfBusinessAnalytics Today I explored descriptive statistics of my dataset using Python (Pandas). Using the "describe()" function, I was able to quickly understand key metrics such as: • Mean • Minimum and Maximum values • Standard deviation • Count of data points 👉 This helps in getting a quick overview of the dataset and identifying patterns or anomalies. Understanding these basic statistics is an important step before performing deeper analysis. Looking forward to extracting more insights from the data! 🚀 #100DaysOfBusinessAnalytics #BusinessAnalytics #DataAnalytics #Python #Pandas #PowerBI

🐍Python for Data Analysis – Key Essentials Python is a powerful tool for data analysis, covering everything from basics to advanced insights. Starting with core concepts like data types and control flow, it extends to data manipulation using Pandas and NumPy, and visualization with Matplotlib and Seaborn. ✔ Clean data ✔ Analyze trends ✔ Visualize insights ✔ Make data-driven decisions Simple tools, powerful outcomes. Python brings together data handling, visualization, and statistics in one place—making it easier to understand and explain data. #Python #DataAnalytics #Insights #LearningJourney

A quick refresher on Statistics in Python! From basics like mean & median to advanced topics like hypothesis testing and distributions, this guide neatly covers the key functions every data analyst should know. Definitely a handy reference for real-world data analysis 💡 #DataAnalytics #Python #Statistics

🔤 Strings in Python – Quick Guide Strings are used to store text data in Python. They are simple, powerful, and used everywhere — from data cleaning to report generation. Creating Strings s1 = 'Hello' s2 = "Python" s3 = """Multi-line string""" Access & Slicing text = "Python" text[0] # P text[-1] # n text[0:3] # Pyt Common Operations "Hello" + " World" # Concatenation "Hi " * 3 # Repetition Useful String Methods text = " hello world " text.upper() # HELLO WORLD text.lower() # hello world text.strip() # remove spaces text.replace("world","Python") text.split() String Formatting (Best Practice) name = "Maha" print(f"Hello {name}") Important: Strings are immutable (cannot be changed directly) text = "hello" text = "H" + text[1:] #Python #PythonBasics #DataAnalytics #Programming #LearnPython #Coding #DataScience #PythonForBeginners #100DaysOfCode

🚀 Data Visualization Practice using Python I recently worked on a hands-on practice project where I explored different types of data visualizations using Python. 🔹 Created Line Charts to understand trends 🔹 Built Scatter Plots to analyze data distribution 🔹 Designed Bar Charts for category comparison 🔹 Worked with datasets to generate meaningful insights 📊 Tools & Technologies: Python | Matplotlib | Data Analysis This practice helped me strengthen my understanding of how to transform raw data into meaningful visual insights. Looking forward to applying these skills in real-world data analytics projects! #DataAnalytics #Python #DataVisualization #Matplotlib #LearningJourney #DataScience

Python helps automate repetitive analysis tasks. Libraries I use frequently: • Pandas → data cleaning & analysis • NumPy → calculations • Matplotlib → visualization Automation saves hours of manual work. #python #dataanalysis

📊 Stock Market Data Analysis using Python I analyzed Reliance stock data (Sep 2025 – Feb 2026) using Python. 🔹 Cleaned and processed real stock data   🔹 Calculated moving averages (5, 10, 20 days)   🔹 Identified Buy/Sell signals using MA crossover   🔹 Visualized trends with Matplotlib  📈 Key Insights:   • Strong uptrend from Sep-Dec   • Downtrend observed in Jan-Feb   • Buy/Sell signals helped identify trend reversals  Tools Used: Python | Pandas | Matplotlib

5 Python one-liners every data analyst should know I used to write 10+ lines for things that take 1. Here are 5 Python one-liners that changed how I work: Each of these saved me time on real projects at Lambton College and in my analytics work. The best part? They work on any dataset — from 100 rows to 1 million. Save this post for your next Python project. 📌 Which one do you use most? Let me know below 👇 #Python #DataAnalytics #Pandas #DataScience #Analytics #LearningInPublic

Python - pandas operations for working with Raw Data in our daily task. Python Pandas is a critical library for data manipulation, cleaning, and analysis, built on top of NumPy. It revolves around two primary data structures: the Series (1D) and the DataFrame (2D). The 9 operations cover with data flow: £ Cleaning and prepation data £ Transformating data sets for analysis £ Aggregation and summarizing information £ working with time based data £ Extraction meaningful insights I hope you you like it 💕 follow: Visweswara Rao Pilla #Python #pandas #Dataanalytics #Datacleaning #dataanalyst #interviewtips