Mastering pandas : a complete guide to pandas, from installation to advanced data analysis techniques / Ashish Kumar.
Perform advanced data manipulation tasks using pandas and become an expert data analyst. Key Features Manipulate and analyze your data expertly using the power of pandas Work with missing data and time series data and become a true pandas expert Includes expert tips and techniques on making your dat...
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| Online Access: |
Full Text (via O'Reilly/Safari) |
|---|---|
| Main Author: | |
| Format: | eBook |
| Language: | English |
| Published: |
Birmingham, UK :
Packt Publishing,
2019.
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| Edition: | Second edition. |
| Subjects: |
MARC
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| 245 | 1 | 0 | |a Mastering pandas : |b a complete guide to pandas, from installation to advanced data analysis techniques / |c Ashish Kumar. |
| 250 | |a Second edition. | ||
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| 520 | |a Perform advanced data manipulation tasks using pandas and become an expert data analyst. Key Features Manipulate and analyze your data expertly using the power of pandas Work with missing data and time series data and become a true pandas expert Includes expert tips and techniques on making your data analysis tasks easier Book Description pandas is a popular Python library used by data scientists and analysts worldwide to manipulate and analyze their data. This book presents useful data manipulation techniques in pandas to perform complex data analysis in various domains. An update to our highly successful previous edition with new features, examples, updated code, and more, this book is an in-depth guide to get the most out of pandas for data analysis. Designed for both intermediate users as well as seasoned practitioners, you will learn advanced data manipulation techniques, such as multi-indexing, modifying data structures, and sampling your data, which allow for powerful analysis and help you gain accurate insights from it. With the help of this book, you will apply pandas to different domains, such as Bayesian statistics, predictive analytics, and time series analysis using an example-based approach. And not just that; you will also learn how to prepare powerful, interactive business reports in pandas using the Jupyter notebook. By the end of this book, you will learn how to perform efficient data analysis using pandas on complex data, and become an expert data analyst or data scientist in the process. What you will learn Speed up your data analysis by importing data into pandas Keep relevant data points by selecting subsets of your data Create a high-quality dataset by cleaning data and fixing missing values Compute actionable analytics with grouping and aggregation in pandas Master time series data analysis in pandas Make powerful reports in pandas using Jupyter notebooks Who this book is for This book is for data scientists, analysts and Python developers who wish to explore advanced data analysis and scientific computing techniques using pandas. Some fundamental understanding of Python programming and familiarity with the basic data analysis concepts is all you need to get started with this book. | ||
| 505 | 0 | |a Section 1: Overview of Data Analysis and pandas. Chapter 1: Introduction to pandas and Data Analysis ; Chapter 2: Installation of pandas and Supporting Software -- Section 2: Data Structures and I/O in pandas. Chapter 3: Using NumPy and Data Structures with pandas ; Chapter 4: I/Os of Different Data Formats with pandas -- Section 3: Mastering Different Data Operations in pandas. Chapter 5: Indexing and Selecting in pandas ; Chapter 6: Grouping, Merging, and Reshaping Data in pandas ; Chapter 7: Special Data Operations in pandas ; Chapter 8: Time Series and Plotting Using Matplotlib -- Section 4: Going a Step Beyond with pandas. Chapter 9: Making Powerful Reports In Jupyter Using pandas ; Chapter 10: A Tour of Statistics with pandas and NumPy ; Chapter 11: A Brief Tour of Bayesian Statistics and Maximum Likelihood Estimates ; Chapter 12: Data Case Studies Using pandas ; Chapter 13: The pandas Library Architecture ; Chapter 14: pandas Compared with Other Tools ; Chapter 15: A Brief Tour of Machine Learning. | |
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