Comparing Data with Python Libraries

Comparing Data Using Python Comparing groups within a dataset is another aspect of analysis. Here, we will use some tools from Python. Libraries & Data Prep First, we need to load our libraries and prepare our data. Below is the code for the libraries we need. import seaborn as sns from pydataset import data import matplotlib.pyplot as plt The first and last lines load the libraries we need for data visualization....

Day 3 of 🐍:-- 🔹 Multi-Valued Data Types in Python In Python, multi-valued data types are used to store multiple values in a single variable. They help organize data efficiently and make programs more powerful. 🚀 Why use Multi-Valued Data Types? >>Store related data together >>Reduce code complexity >>Improve readability and performance 📌 Common Multi-Valued Data Types: ✅ List >>Ordered collection >>Allows duplicates >>Mutable (can be changed) ✅ Tuple >>Ordered collection >>Allows duplicates >>Immutable (cannot be changed) 📌String In Python, a string is a sequence of characters used to store and manipulate text. >> Strings are written inside single (' '), double (" ") or triple quotes (''' ''') >>Strings are immutable (cannot be changed after creation) ✅ Dictionary >>Stores data as key–value pairs >>Keys must be unique 💡 Mastering these data types is a big step toward becoming confident in Python programming! #Python #DataTypes #LearningPython #DataScience #Programming

🚀 Revisiting Python Fundamentals Day 3: Mutable vs Immutable Data Types In Python, not all data behaves the same. Some data can change after it’s created. Some data cannot — no matter what you do. That’s the difference between mutable and immutable data types. Let’s understand this with a simple idea 👇 Think of writing something in ink 🖊️ Once written, it stays the same. Now think of writing with a pencil ✏️ You can erase and update it anytime. That’s exactly how Python works. 🔒 Immutable Data Types (Cannot be changed) Once created, their value stays fixed: int float str tuple Example: name = "Alex" name[0] = "a" # ❌ Error 🔓 Mutable Data Types (Can be changed) These allow updates after creation: list set dict Example: skills = ["Python", "SQL"] skills.append("ML") # ✅ Allowed #Python #MutableImmutable #PythonBasics #LearnPython #CodingJourney

🐍📰 How to Drop Null Values in pandas Learn how to use .dropna() to drop null values from pandas DataFrames so you can clean missing data and keep your Python analysis accurate #python

🚀 Revisiting Python Fundamentals Day 4: Multi-Valued Data Types In real-world programs, we rarely work with just one value. We work with: lists of skills ->collections of data ->groups of related information ->That’s where multi-valued data types come in. They allow Python to store multiple values inside a single variable. But Python keeps things simple. 🟦 Sequential Data Types (Order Matters) Here, the position of each element is important. list → ordered & changeable tuple → ordered & unchangeable string → sequence of characters range → sequence of numbers Example: skills = ["Python", "SQL", "ML"] 🟩 Non-Sequential Data Types (Order Doesn’t Matter) Here, the focus is on values and meaning, not position. set → stores unique values dict → stores data as key-value pairs Example: student = {"name": "Alex", "age": 21} 🧠 Key Takeaway Sequential → order matters Non-Sequential → order doesn’t matter #Python #DataTypes #MultiValuedData #PythonBasics #LearnPython #CodingJourney

Annotating Visualizations in Python Annotating data allows you to communicate vital information in a visualization for an audience. In the example below, we will look at how to annotate a visualization while using Python. Libraries and Data Preparation We will begin by loading the needed libraries and preparing the data. In the code below, lines 1 and 3 load our visualization libraries. Line 2 loads the function we will need to load our data....

Annotating Visualizations in Python Annotating data allows you to communicate vital information in a visualization for an audience. In the example below, we will look at how to annotate a visualization while using Python. Libraries and Data Preparation We will begin by loading the needed libraries and preparing the data. In the code below, lines 1 and 3 load our visualization libraries. Line 2 loads the function we will need to load our data....

🔍 Python & MySQL — Chapter 4: Fetching Data Most applications are about reading data efficiently, not just storing it. In Chapter 4, we learn how to fetch data from MySQL databases using Python. 🔧 You’ll learn: ✅ SELECT queries using Python ✅ Fetching one row vs multiple rows ✅ Looping through database results ✅ Displaying data cleanly in Python 🎥 Full lesson is on YouTube — link in the comments 👇 Essential knowledge for analytics, dashboards, and backend APIs. 💬 Comment “SELECT” if you’re enjoying the series. #Python #MySQL #SQLSelect #DataHandling #Backend #YouTube

Do you use Python for analytics? I’ve started expanding my knowledge of Python recently. And heard that some teams rely on Python almost exclusively, even instead of SQL, which surprised me, since SQL has always covered most of my analytics needs. During my job search, though, it’s still unclear how essential advanced Python really is. In my experience, SQL (with a bit of Python when needed) usually gets the job done. So I’m curious: - What do you use Python for in your day-to-day analytics work? - Where does it clearly outperform SQL? - Is it your main tool, or just a complement? Would love to hear your thoughts — and maybe get some motivation to keep going 🙂

While working with SQL and Python side-by-side, one realization stood out to me — not every data problem should be solved in Python, and not every dataset should be pulled into memory. To understand this better, I performed the same data analysis tasks using both SQL queries and Python’s Pandas library, comparing how each approach behaves in practice. For this comparison, I worked on tasks such as: - Filtering and selecting data - Applying conditions, ranges, and pattern matching - Sorting and aggregating data - Grouping records and filtering grouped results - Combining datasets using joins and unions This comparison made the strengths of each tool clear: - SQL excels at querying and aggregating large, structured datasets directly at the database layer. - Pandas offers flexibility for in-memory analysis, exploratory work, and integration with visualization and statistical libraries. Instead of thinking in terms of “SQL vs Python”, this exercise helped me think in terms of where the computation should happen. Understanding when to push logic to the database and when to work in Python becomes critical for building efficient, scalable data workflows. The complete comparison notebook and queries are documented here: https://lnkd.in/dvUWH8Bg #SQL #Pandas #DataAnalytics #DataScience #LearningJourney #ContinuousLearning