Pandas DataFrame Tutorial: Data Operations and Visualization

🐍📰 In this tutorial, you'll learn how to start exploring a dataset with Pandas and Python. You'll learn how to access specific rows and columns to answer questions about your data. #python

Creating Excel files from Python can enhance your applications with export and reporting features. This tutorial covers openpyxl and pandas approaches. #python #excel #coding #backenddev #codewolfy https://lnkd.in/dqC9AUYr

Day 4 of Python. Pandas begins. Today I started working with Pandas. Not to learn functions. But to understand how data behaves inside Python. The moment it clicked: Pandas is SQL-like thinking inside Python. Rows are records. Columns are attributes. Indexes define identity. What I focused on today: Series vs DataFrame Reading CSV files Understanding index and column structure Exploring data using head(), info(), and describe() This is where Python becomes useful for data work. With Pandas, I can: Clean data before it hits a database Apply business logic programmatically Prepare datasets for pipelines and ML Combine SQL thinking with Python control The goal isn’t analysis yet. The goal is structure and understanding. Next: filtering, transformations, and chaining operations. If you work with Pandas: What confused you the most when you first started — indexing or filtering? #datawithanurag #dataxbootcamp

In my latest article on the Towards Data Science  blogging platform, “Think Your Python Code Is Slow? Stop Guessing and Start Measuring”, I walk through how to use cProfile and SnakeViz to identify real bottlenecks and optimise with confidence. If you care about the performance of your Python code, this is essential reading. Read the full article for free using the link below. https://lnkd.in/eycxySZ5

Announcing Orbital for Python 0.3.0: Accelerated Tree-Based Models in SQL We are pleased to announce the release of Orbital for Python 0.3.0, a significant update to our library designed to streamline the deployment of machine learning models for Python and Scikit-learn users. Orbital for Python allows you to transform Scikit-learn pipelines directly into native SQL queries, enabling model inference to execute within your database and eliminating the need for separate Python environments for production scoring. For those familiar with the R ecosystem, Orbital for R provides a similar capability that allows you to predict in databases using tidymodels workflows. Version 0.3.0 optimizes tree-based models, addressing the challenge of long, complex SQL queries that can be difficult for database optimizers to parse and execute efficiently. This release specifically enhances the performance and compatibility of Decision Trees, Random Forests, and Gradient Boosted Trees. Learn more about Orbital 0.3.0 and its new capabilities: https://lnkd.in/gGZqw8sA

🧠 Scala vs Python: Data Types Explained Simply Before jumping into frameworks or big projects, it’s important to understand data types and operators — they define how your code behaves. 🔹 Key difference > Scala → Statically typed (types checked at compile time) > Python → Dynamically typed (types checked at runtime) 🔢 Common Data Types Integer > Scala: val x: Int = 10 > Python: x = 10 Long > Scala: val y: Long = 100000L > Python: y = 100000 (handled by int) String / Char > Scala has separate String and Char > Python uses str for both characters and strings Boolean > Scala: true / false > Python: True / False ➕ Operators Explained Arithmetic: + - * / % Comparison: == != > < >= <= Logical > Scala: && || ! > Python: and or not Bitwise > & | ^ << >> 💡 Why this matters > Prevents runtime errors > Improves readability > Helps in interviews and real projects 📌 Takeaway Scala is strict and type-safe. Python is flexible and beginner-friendly. Knowing both makes you a stronger developer. #Scala #Python #DataTypes #LearnToCode #ProgrammingBasics #TechCareers

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....

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....

🚀 Python Polars: A Lightning-Fast DataFrame Library Recently explored a tutorial on Polars, and it’s a great reminder of how modern data tools are evolving beyond traditional pandas workflows. Polars stands out for its speed, efficient memory usage, and lazy execution model—making it especially useful for large datasets, ETL pipelines, and analytics engineering use cases. Definitely worth exploring if performance and scalability are becoming bottlenecks in data workflows.

Functions in Python: Write Once, Reuse Everywhere Day 8 of #30DaysOfPython 🐍 Until now, we have been writing logic step by step using conditions and loops. Today, we learned how to group that logic into reusable blocks using functions. This is where Python code becomes clean, reusable, and scalable. Example 1: A simple function 👇 def calculate_discount(price): return price * 0.9 final_price = calculate_discount(2500) print(final_price) 👉 Output: 2250.0 Here: 🔹 def → defines a function 🔹 price → input (parameter) 🔹 return → sends the result back Example 2: Reusing the same function 👇 prices = [1500, 2500, 4000] for p in prices: print(calculate_discount(p)) This shows the real power of functions — one logic, multiple values. Example 3: Function with business logic 👇 def sale_type(amount): if amount > 3000: return "High value sale" else: return "Regular sale" print(sale_type(4000)) 👉 Output: High value sale This is how rules and classifications are handled in real projects. DA Insight 💡 Functions help us: ✔ Avoid repeating code ✔ Keep logic in one place ✔ Make code easier to read and maintain ✔ Apply the same rule across datasets Think of it as: Excel → Reusable formulas SQL → Stored logic / expressions Power BI → Measures Python → Functions Next up: Day 9 – Built-in Functions (Python’s shortcuts) 🚀 #30DaysOfPython #PythonForDataAnalysis #DataAnalytics #LearningInPublic #DataAnalyst #Upskilling