Python Data Cleaning Phases: Audit, Clean, Validate

This is the only data cleaning Python cheat sheet you'll ever need. (Save it so you don't miss it) Whether you're just starting out, want to clean data faster, or keep making the same mistakes, this covers it all. 𝐖𝐡𝐚𝐭'𝐬 𝐢𝐧𝐬𝐢𝐝𝐞: → Load essential libraries → Inspect your dataset → Remove duplicate records → Handle missing values → Standardize text data → Fix data types → Remove invalid data → Handle outliers → Rename and reorganize columns → Validate and export Data cleaning takes 80% of a data scientist's time. This cheat sheet cuts that in half. 𝐖𝐚𝐧𝐭 𝐭𝐨 𝐠𝐞𝐭 𝐬𝐭𝐚𝐫𝐭𝐞𝐝 𝐰𝐢𝐭𝐡 𝐏𝐲𝐭𝐡𝐨𝐧? Here are 5 free resources to learn Python from scratch: → Harvard CS50's Introduction to Programming with Python https://lnkd.in/dSbbXQEg → Automate the Boring Stuff with Python (free book) https://lnkd.in/d-MWq4jT → University of Helsinki Python MOOC https://lnkd.in/dg4uqdk4 → LearnPython.org (interactive tutorial) https://lnkd.in/dti-Ex3j → Google's Python Class https://lnkd.in/dXngytpG Which step do you struggle with most when cleaning data? 👇 ♻️ Repost to help someone level up their Python skills 📩 I share tips on data analytics & data science in my free newsletter. Join 24,000+ readers → https://lnkd.in/dUfe4Ac6

Getting better day by day in EDA by learning and applying the data cleaning skills for Analysis Dashboard projects with the help of Python. If you want to become a better version then earlier than you must need to master the skill of data cleaning and surely this will help you.

I've completed Intermediate Python by DataCamp Here are a few things I learnt... ✓ Matplotlib It's used for data visualization on python, it's used for creating meaningful and insightful plots such as line plots, scattered plots, Histogram. I also learnt how to build a histogram, choosing the right plot, customisation, interpretation, and much more. ✓ Dictionary and Pandas This is an alternative to the Python list, and the pandas DataFrame, the de facto standard to work with tabular data in Python. I got a hands-on practice in creating and manipulating datasets, and also learnt how to access the information needed from such data structures. I also learnt about square brackets, loc and iloc. ✓ Logic, Control Flow and Filtering Boolean logic is the foundation of decision-making in Python programs. I leearnt about different comparison operators, how to combine them with Boolean operators, and how to use the Boolean outcomes in control structures. I also learnt how to filter data in pandas DataFrames using logic. ✓ Loops There are several techniques you can use to repeatedly execute Python code, one of them is the Loops. While loops are like repeated 'if statements', the 'for loop' iterates over all kinds of data structures. I also learnt about loop over list of lists, loop over dictionary, looo over dataFrame and more. ✓ Case Study: Hackers Walk I practiced a case study in the final chapter of the course, Using some of the things I've learnt so far, I used hacker statistics to calculate your chances of winning a bet. Some of the things I also learnt are : Random Numbers, Random float Roll the dice Random Walk Visualize the walk Simulate multiple walks Visualize all walks Plot the distribution Calculate the odds and more

Stop guessing Python methods Know what to use and when Start learning → https://lnkd.in/dBMXaiCv ⬇️ Core Python data structures SET • add() → add element • remove() / discard() → delete • union() → merge sets • intersection() → common values • difference() → unique values • issubset() → check relation Use case Remove duplicates fast LIST • append() → add item • extend() → add multiple • insert() → add at index • remove() → delete value • pop() → delete by index • sort() → order items • reverse() → flip order Use case Ordered data DICTIONARY • get() → safe access • keys() → all keys • values() → all values • items() → key value pairs • update() → merge data • pop() → remove key • setdefault() → default value Use case Key value mapping Rule Pick structure first Then pick method ⬇️ Learn Python the right way Python Courses Guide https://lnkd.in/dtFbRP96 Become Data Analyst https://lnkd.in/dz3AXtmy Best AI Courses https://lnkd.in/dqQDSEEA Question Which one do you use most list or dict #Python #Programming #DataStructures #Coding #ProgrammingValley

Introduction to Python Python is one of those tools I almost talked myself out of learning… before even starting. Fear can be funny like that. (Same thing happened with Tableau… now look who uses Tableau ....Meeee) I’ve just started my journey into Python for data analysis, and here’s what I’ve learned so far: Python is a general-purpose programming language widely used in data analysis, automation, and machine learning. It was created by Guido van Rossum and released in 1991. Right now, I’m learning with Jupyter Notebook and starting with the basics: VARIABLES Variables are simply containers for storing data. Think of them like labeled boxes: Label = variable name Content = value Example: x = 22 name = 'Sero' price = 9.99 Python automatically understands the data type: 22 → integer 'Sero' → string 9.99 → float You can also check using: print(type(x)) A few things I found interesting: 1. Variables are case-sensitive (x ≠ X) 2. You can assign multiple variables at once 3. You can assign one value to multiple variables Still early days… but consistency over perfection.What was your first experience learning Python like?

🚀 Today’s Learning in Python Pandas 🐍📊 Explored some powerful Pandas functions that help in data analysis and understanding datasets efficiently. These functions are widely used in real-world projects for summarizing, cleaning, and extracting insights from data. ✅ value_counts() – Counts the frequency of unique values in a column. Useful for checking repeated categories or values. Python df["City"].value_counts() ✅ unique() – Returns all unique values from a column. Helpful to know different categories available in the dataset. Python df["City"].unique() ✅ nunique() – Gives the total number of unique values in a column. Great for quick summary statistics. Python df["City"].nunique() ✅ groupby() – Groups rows based on a column and performs aggregate operations like sum, mean, count, max, min, etc. Very useful for business insights and reporting. Python df.groupby("Department")["Salary"].mean() 📌 Learning these functions makes data exploration faster and easier. They are essential for every Data Analyst and Data Science beginner. #Python #Pandas #DataAnalytics #DataScience #LearningJourney #LinkedInPost #CodingJourney #DataCleaning #MachineLearning

Are you ready to unlock the potential of Python and transform your data management skills? Join us for "Mastering Python for Data Grouping and Validation," a dynamic course designed specifically for adults who want to dive into the exciting world of data. In this course, you'll learn to group information effectively, validate data for accuracy, and perform reservation calculations—all while building robust projection models. Whether you're looking to give your career a boost or simply want to explore new skills, we've got you covered! Here’s a sneak peek of what you can expect in our engaging curriculum: 1. Introduction to Python: We'll start with the basics and get your development environment set up in no time. 2. Grouping Information: Dive deep into lists, dictionaries, and data grouping using the powerful Pandas library. 3. Data Validation Techniques: Understand the importance of data accuracy and learn to implement critical validation rules in Python. 4. Reservation Calculations: Create reservation systems and explore real-world applications to see your skills in action. 5. Building Projection Models: Get acquainted with forecasting techniques and use Python for dynamic financial projections. 6. Project and Evaluation: Apply your newfound skills in a final project, receive peer feedback, and wrap up with insights to further your learning journey. This course is not just about learning Python; it's about equipping you with the tools and insights needed for true success in today’s data-driven world. So, whether you're a complete beginner or someone looking to polish your skills, we promise a mix of serious learning with a touch of humor along the way. Get ready to elevate your data skills! Excited to begin? Visit us at https://lnkd.in/gJ7TmVCx to learn more and enroll today!

🚀 Day 4: Understanding Python Data Types for Data Science 🐍📊 As I continue building my foundation in Data Science with Python, today I explored one of the most important concepts in programming — Python Data Types. Data types define the kind of data a variable can store. Understanding them is essential because almost every data science task involves working with different types of data. What I explored today: 🔹 Integer (int) Used to store whole numbers. Example: age = 25 🔹 Float (float) Used to store decimal numbers. Example: price = 99.99 🔹 String (str) Used to store text data. Example: course = "Data Science with Python" 🔹 Boolean (bool) Represents logical values. Example: is_student = True 🔹 Common Data Structures in Python • List → Ordered collection of items Example: numbers = [1, 2, 3, 4] • Tuple → Ordered but immutable collection Example: coordinates = (10, 20) • Dictionary → Key-value data structure Example: student = {"name": "John", "age": 22} • Set → Unordered collection of unique elements Example: unique_numbers = {1, 2, 3} 🔹 Why Data Types Matter in Data Science In real-world datasets, data can be numbers, text, categories, or logical values. Understanding data types helps in data cleaning, transformation, and analysis. 📌 Today's takeaway: Mastering Python data types is a crucial step toward working with real datasets and building strong data analysis skills. A special thanks to my mentor, Nallagoni Omkar sir 🙏 , for guiding me and helping me strengthen my Python fundamentals for Data Science. Next up: Python Operators! 🚀 #Python #DataScience #ProgrammingFundamentals #LearningInPublic #CodingJourney #StudentOfDataScience #MachineLearning #NeverStopLearning #omkarnallagoni Nallagoni Omkar

🐍 If you don’t understand Python data types, you’re coding blind. Most beginners jump into frameworks… But the real foundation? Data types. Master these, and everything else becomes easier. Here’s what you actually need: 🔹 int Whole numbers → Example: age = 25 🔹 float Decimal numbers → Example: price = 19.99 🔹 str Text data → Example: name = "John" 🔹 list Ordered & changeable → Use when data can grow or change 🔹 tuple Ordered but fixed → Use when data should not change 🔹 dict Key → value pairs → Best for structured, fast lookups 🔹 set Unique values only → Perfect for removing duplicates 💡 Pro insight: Choosing the right data type isn’t just syntax… It directly impacts performance, readability, and scalability. 👉 List when data changes 👉 Tuple when it shouldn’t 👉 Dict for fast access That’s how better code is written. 🎯 Want to build strong Python fundamentals? Start here: 💻 Python Automation 🔗 https://lnkd.in/dyJ4mYs9 📊 Data + Python 🔗 https://lnkd.in/dTdWqpf5 🧠 AI with Python 🔗 https://lnkd.in/duHcQ8sT 🚀 Strong fundamentals = faster growth in tech. 👉 Which Python data type do you use the most?

Continuing my Data Analytics journey with Python We went deeper into the fundamentals of Python, focusing on variables, data types, and operators: key building blocks for data analysis. We started with variables, which are containers used to store values. I learned important rules for naming variables: - Use letters, digits, or underscores - Must start with a letter or underscore - Cannot contain spaces - Cannot use reserved keywords - Variables are case sensitive Next, we explored data types, which define the kind of data a variable holds and the operations that can be performed on it: - Numeric types (Numbers): - Integers: whole numbers without decimals (e.g., age, IDs, counts) - Floats: numbers with decimals (e.g., sales, weight) - Python performs division as a float by default - Text (Strings): - Used to store text and must be enclosed in single or double quotes - Boolean (Logic): - Represents truth values: "True" or "False" - Collection types: - Structures that hold multiple items in memory (e.g., lists), similar to tables but more flexible We also learned about operators, which are symbols used to perform operations on variables and values. The three main types covered were: - Arithmetic operators - Comparison operators - Logical operators This session really helped me understand how data is structured and manipulated in Python, an essential step toward effective data analysis. Grateful to our tutor Agmuasie Belay Birhanie for the clear explanations and practical approach to teaching. #DataAnalytics #Python #LearningInPublic #TechJourney #DataTypes #ProgrammingBasics #Growth Africa Agility Foundation Pelumi Adebayo