pyedahelper: A new method for Data Analysis and Machine Learning

𝗧𝗵𝗶𝗻𝗸 𝗣𝘆𝘁𝗵𝗼𝗻 𝗶𝘀 𝗵𝗮𝗿𝗱? 🐍 𝗟𝗲𝘁’𝘀 𝗯𝗿𝗲𝗮𝗸 𝗶𝘁 𝗱𝗼𝘄𝗻 𝘁𝗵𝗲 𝗯𝗮𝘀𝗶𝗰𝘀 𝗶𝗻 𝟲𝟬 𝘀𝗲𝗰𝗼𝗻𝗱𝘀! Here are the basic data types you should know as a beginner: ➡️ 𝗩𝗮𝗿𝗶𝗮𝗯𝗹𝗲𝘀 are containers that store data in Python. ➡️ 𝗦𝘁𝗿𝗶𝗻𝗴𝘀: In Python, strings are immutable, meaning they cannot be changed once created. ➡️ 𝗡𝘂𝗺𝗯𝗲𝗿𝘀:- Integers: They store whole numbers without a decimal part. Ex - 57, 70, etc. Float: It stores a fractional Number with a whole and decimal part. Ex - 57.7, 70.9. 𝗜𝗳 𝘆𝗼𝘂 𝗳𝗼𝘂𝗻𝗱 𝘁𝗵𝗶𝘀 𝘂𝘀𝗲𝗳𝘂𝗹,  👉 Follow me for more simple Python tips and tricks.🔥 Repost to help others💡 Codebasics Indian Data Club #python #businessintelligence #analysis #data #learning #dailygrowth #DataAnalysis

🚀Excited to share my fourth Python practical on 🧩Missing Value Treatment : In this practical, I explored techniques to handle missing data using Pandas. The main objective of this experiment is to ensure data completeness and accuracy by identifying, analyzing, and appropriately treating missing values — an essential step in preparing data for reliable analysis and model building. 📁 Here's the Google drive : linkhttps://lnkd.in/gxfhQ8cB 🔗GitHub account : https://lnkd.in/gcCiRDfS #Python #DataAnalysis #LearningJourney #CentralTendency

Experiment no 3✨ Central Tendency of Measure using Python In this practical, I learned how to find the Mean, Median, and Mode using Python. I created a small dataset of ages and used Python code to understand how these values represent the central part or average trend of data. 💡 Mean gives the average value, 📍 Median shows the middle value, and 🔁 Mode represents the most frequent value in the dataset. 💻 Tools Used: Jupyter Notebook Python This activity helped me understand basic statistics and how Python makes it simple to calculate and analyze data efficiently. 📊🐍 #DataScience #Statistics #Matplotlib #MachineLearning Guided by : Ashish Sawant Sir Github :https://lnkd.in/dQMZdU5A Drive :https://lnkd.in/dVVdnzeb

Did you know you can read and write files in Python without using Pandas? Well, I only discovered this recently while exploring Python more deeply and it has been eye-opening. Python has what is known as file modes, and they are the little helpers that let you handle files directly. Some of these modes are: - 'r' → for reading - 'w' → for writing - 'a' → for appending - Add 'b' → to handle binary files (e.g., 'rb', 'wb', 'ab') I recently worked on a project where I wrote and read a CSV file using these modes. Attached to this post is a snapshot of part of the code, and you can find the full script on my GitHub via the link below: https://lnkd.in/dG88jyAa #Python #DataEngineering #Programming #GitHub #Data

📊 Week 7 Recap: Diving into Statistics with Python on the #ALXPython Track! Week 7 was entirely focused on Statistics with Python. Statistics is essential for analysing and interpreting data in projects, and Python's rich libraries make it a powerful tool for this purpose. This enables improved analysis, teamwork, and project transparency. Hence, I learnt how to use Python for statistics by exploring some basic functionality of Python in statistics, including: ◦ Calculating sample and population metrics in Python. ◦ Using Python to do descriptive statistics. ◦ Calculating probabilities. ◦ Defining and using hypothesis testing. ◦ Understanding the concept of the data science process. The week's concepts centered entirely on statistics with Python and its applications. I ended my week with the usual test. #ALX #Statistics #Python #DataScience #HypothesisTesting #DataAnalysis

🚀Experiment 4: Handling Missing Values in Data using Pandas 🐼 Missing data — one of the most common (and tricky!) challenges in any dataset. In this lab, I learned how to detect, understand, and treat missing values effectively using Python’s Pandas library. 🔍 What I explored:  • Identifying missing data using functions like isnull() & notnull()  • Cleaning data through imputation, removal, and replacement techniques  • Understanding the impact of missing data on model performance This hands-on exercise helped me grasp how data preprocessing lays the foundation for reliable analysis and better machine learning outcomes. 📁 Explore the repository here :  👉 https://lnkd.in/epWys7e7 #DataScience #Python  #Pandas  #DataCleaning  #MachineLearning  #Statistics  #JupyterNotebook Ashish Sawant sir

🐍 Day 3/30 Variables & Data Types in Python Today, I learned about variables and data types the building blocks of any Python program. A variable is simply a name that stores a value. Python automatically detects the data type based on the value assigned. ✔️ Example: name = "Scaler" # string age = 20 # integer height = 5.9 # float is_student = True # boolean print(name, age, height, is_student) 🧠 Tip: Use the type() function to check the data type of a variable. print(type(name)) print(type(age)) #Python #LearnPython #PythonBasics #Scaler #CodingJourney #CodingForBeginners #30DaysOfCode #30DaysOfPythonjourney!

Here's how to customize card previews for Python in Excel. I'm fairly certain this is the first and possibly only blog post dedicated to this subject. I hope you find it useful and that you'll consider sharing with your network if you think they'll find it useful. https://lnkd.in/gPbiRMcs In the blog post, I describe how to construct the card preview in this gif in Python in Excel. #data #analytics #excel #python

Python is now part of Excel, and that changes everything. You can now analyze, summarize, and clean your data right inside Excel using Python, without switching tools or exporting files. With the simple =PY() function, you can build a data frame, query it, and generate instant statistical summaries using .describe(). 📊 Learn how to use Python directly in Excel to automate and elevate your analysis at the Financial Modeling Academy (FMA): https://bit.ly/FMARoutes #FinancialModeling #PythonInExcel #DataAnalytics #FinanceProfessionals #ExcelTips #Automation #FMA

📊 Experiment 4 – Data Preprocessing and Handling Missing Values In this practical, I learned how to handle missing data and perform preprocessing using Python’s Pandas library. By working with the Titanic dataset, I practiced: 🔹 Checking for missing values 🔹 Removing missing records using dropna() 🔹 Filling missing data using fillna() 🔹 Understanding dataset summary and structure with describe() This experiment helped me understand how data cleaning is a crucial step before any analysis or model building. 📁 GitHub:https://lnkd.in/eTtC53qu 🎓 Guided by: Ashish Sawant #Python #Pandas #DataPreprocessing #DataCleaning #MachineLearning #DataScience #Coding #Learning #JupyterNotebook #CSE #PRMCEAM