Pandas in Python 🔥 Modules 3–5 | DataFrames, Data Selection & Data Analysis | Complete Guide
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Pandas in Python 🔥 Modules 3–5 | DataFrames, Data Selection & Data Analysis | Complete Guide
18 просмотров · 2 недели назад
barabankiorg
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18 просмотров · 2 недели назад
🚀 MASTER PANDAS IN PYTHON — FROM DATA TO INSIGHTS!
Are you learning Python, Data Analytics, Data Science, AI or Machine Learning?
Then mastering Pandas is essential.
In this video, we continue our Pandas in Python learning journey with Modules 3, 4 & 5, focusing on the skills you will use regularly when working with real-world datasets.
📌 MODULE 3 — DataFrame Fundamentals
Learn how to create and understand Pandas DataFrames:
✅ Creating DataFrames
✅ CSV Files
✅ Excel Files
✅ SQL Data
✅ JSON Data
✅ shape
✅ columns
✅ index
✅ dtypes
🔎 MODULE 4 — Understanding Data
Before analyzing data, you need to understand what you're actually working with.
We cover:
✅ head()
✅ tail()
✅ sample()
✅ info()
✅ describe()
✅ shape
✅ size
✅ ndim
✅ columns
✅ index
✅ dtypes
✅ memory_usage()
🎯 MODULE 5 — Selecting Data
Learn how to select exactly the data you need:
✅ Selecting Columns
✅ Selecting Rows
✅ loc[]
✅ iloc[]
✅ at[]
✅ iat[]
✅ Data Filtering
✅ Boolean Conditions
✅ query()
💡 Why is this important?
Real-world Data Analysts and Data Scientists don't just load datasets—they need to inspect, understand, select, filter and analyze data efficiently.
This is where Pandas becomes extremely powerful.
👨💻 Who Should Watch?
🎓 B.Tech / BCA / MCA Students
🎓 Engineering & University Students
💻 Python Beginners
📊 Aspiring Data Analysts
📈 Data Science Students
🤖 AI & Machine Learning Learners
🚀 Software Developers
💼 Working Professionals
🔥 Your Pandas Journey
Create Data → Understand Data → Select Data → Clean Data → Transform Data → Analyze Data → Generate Insights
Don't just learn syntax.
Learn how Pandas is actually used to work with data.
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