[100% Off] Pandas &Amp; Numpy Coding Practice
Python Data Analysis: Master Pandas DataFrames, NumPy Array Operations, Indexing, and Data Cleaning through hands-on pra
What you’ll learn
- Fluently apply Pandas methods for slicing
- indexing
- and selecting data from DataFrames and Series.
- Master fundamental NumPy array creation
- manipulation
- broadcasting
- and universal function application.
- Efficiently handle missing data (NaN values) using various techniques like dropping
- filling
- and interpolation.
- Efficiently handle missing data (NaN values) using various techniques like dropping
- filling
- and interpolation.
- Combine multiple datasets effectively using merge
- join
- and concatenate operations in Pandas.
- Reshape and pivot DataFrames for analysis using functions like pivot_table and melt.
- Write highly optimized
- vectorized code using NumPy to drastically improve performance over standard Python loops.
- Clean and preprocess messy
- real-world datasets
- preparing them for downstream machine learning models.
- Implement powerful time series indexing and manipulation techniques for chronological data analysis.
- Apply conditional selection and boolean mask indexing across both Pandas DataFrames and NumPy arrays.
- Develop strong troubleshooting skills essential for debugging complex data manipulation scripts.
Requirements
- Familiarity with Python basics (variables
- loops
- functions
- basic data structures like lists and dictionaries).
- Basic understanding of how data analysis works (conceptual).
- A working installation of Python (3.7+) and Anaconda/Jupyter environment.
Description
Welcome to “Pandas & NumPy Coding Practice”! This intensive, project-based course is specifically designed for learners who understand the basics of Python but need to bridge the gap between theoretical knowledge and practical, real-world data science applications.
Why Practice Matters
Reading documentation is essential, but true mastery of data manipulation libraries like Pandas and NumPy comes only from solving problems. This course provides hundreds of challenging, carefully curated coding exercises that cover the essential functionality of these two libraries. We move beyond simple “Hello World” examples and dive deep into complex indexing, aggregation, merging, reshaping, and handling messy data.
What Makes This Course Unique?
“Pandas & NumPy Coding Practice isn’t a lecture-heavy course. After a brief review of core concepts, 90% of the content involves practical problem sets. We focus on efficiency and best practices, teaching you to write vectorized NumPy code and idiomatic Pandas expressions that are faster and cleaner. You will work through scenarios mirroring tasks faced by professional Data Scientists, including financial data analysis, survey result processing, and cleaning real-world datasets for machine learning consumption. By the end of this course, you won’t just know what Pandas and NumPy do; you will know how to use them fluently and efficiently.
