Publisher's Synopsis
Pandas and NumPy in Practice: Python Libraries for Data Manipulation
Master the art of data manipulation with Pandas and NumPy, two of the most powerful Python libraries for data analysis and numerical computing. Pandas and NumPy in Practice is a comprehensive, hands-on guide designed for data scientists, analysts, and Python developers who want to unlock the full potential of these libraries to clean, transform, and analyze complex data.
Whether you're working with large datasets, performing statistical analysis, or building data pipelines, this book provides step-by-step tutorials, real-world examples, and practical techniques to help you manipulate data like a pro. Pandas and NumPy will become your go-to tools for efficient, scalable data manipulation, and with this guide, you'll learn how to leverage them to solve a wide variety of data-related problems.
What You'll Learn:✅ Pandas Basics - Understand the core components of Pandas such as DataFrames and Series, and perform basic data manipulation tasks like filtering, selecting, and aggregating data.
✅ Data Cleaning with Pandas - Learn powerful techniques for handling missing data, duplicating entries, and data type conversions to prepare your datasets for analysis.
✅ Data Aggregation and Grouping - Use groupby, pivot tables, and resampling methods in Pandas to summarize and aggregate data efficiently.
✅ Advanced Pandas Techniques - Dive into advanced data manipulation techniques like merging, joining, concatenating, and reshaping data.
✅ NumPy Fundamentals - Learn how to leverage NumPy arrays for high-performance numerical computations, including array indexing, slicing, and broadcasting.
✅ Mathematical and Statistical Operations - Use NumPy to perform mathematical operations, statistical analysis, and linear algebra for data exploration and analysis.
✅ Time Series Analysis with Pandas - Master time series data handling, including date parsing, frequency conversion, and resampling in Pandas.
✅ Data Visualization with Pandas - Create visualizations like line plots, scatter plots, and histograms using Pandas and libraries like Matplotlib.
✅ Working with External Data Sources - Import and export data from CSV, Excel, SQL databases, and JSON using Pandas.
✅ Optimizing Data Operations - Learn how to speed up your data processing tasks using techniques like vectorization with NumPy and Pandas.
✅ Practical Data Science Projects - Work on real-world data projects using Pandas and NumPy, such as data cleaning pipelines, financial data analysis, and data exploration for machine learning.
✅ Integrating Pandas and NumPy with Other Libraries - Leverage Pandas and NumPy in conjunction with Matplotlib, SciPy, and scikit-learn for comprehensive data analysis workflows.
With clear examples, practical projects, and insider tips, Pandas and NumPy in Practice will transform the way you work with data and give you the confidence to handle any data manipulation challenge in Python.
