首页Pandas Cookbook (2017)
This book contains nearly 100 recipes, ranging from very simple to advanced. All recipes strive to be written in clear, concise, and modern idiomatic pandas code. The How it works... sections contain extremely detailed descriptions of the intricacies of each step of the recipe. Often, in the There's more... section, you will get what may seem like an entirely new recipe. This book is densely packed with an extraordinary amount of pandas code.
Pandas is one of the most powerful,
ﬂ exible, and efﬁ cient scientiﬁ c computing
packages in Python. With this book,
you will explore data in pandas through
dozens of practice problems with detailed
solutions in iPython notebooks.
This book will provide you with clean, clear
recipes, and solutions that explain how to
handle common data manipulation and
scientiﬁ c computing tasks with pandas.
You will work with different types of
datasets, and perform data manipulation
and data wrangling effectively. You will
explore the power of pandas DataFrames
and ﬁ nd out about boolean and multi-
indexing. Tasks related to statistical
and time series computations, and how
to implement them in ﬁ nancial and
scientiﬁ c applications are also covered in
By the end of this book, you will have
all the knowledge you need to master
pandas, and perform fast and accurate
scientiﬁ c computing.
Things you will learn:
• Master the fundamentals of pandas to
quickly begin exploring any dataset
• Isolate any subset of data by properly
selecting and querying the data
• Split data into independent groups
before applying aggregations and
transformations to each group
• Restructure data into a tidy form
to make data analysis and
• Prepare messy real-world datasets
for machine learning
• Combine and merge data from
different sources through pandas
• Utilize pandas unparalleled time
• Create beautiful and insightful
visualizations through pandas direct
hooks to matplotlib and seaborn
Recipes for Scientiﬁ c Computing, Time Series Analysis
and Data Visualization using Python
Recipes for Scientiﬁc Computing, Time Series Analysis and
Data Visualization using Python
BIRMINGHAM - MUMBAI
Copyright © 2017 Packt Publishing
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First published: October 2017
Production reference: 1181017
Published by Packt Publishing Ltd.
35 Livery Street
B3 2PB, UK.
Tejal Daruwale Soni
Content Development Editor
About the Author
Theodore Petrou is a data scientist and the founder of Dunder Data, a professional
educational company focusing on exploratory data analysis. He is also the head of Houston
Data Science, a meetup group with more than 2,000 members that has the primary goal of
getting local data enthusiasts together in the same room to practice data science. Before
founding Dunder Data, Ted was a data scientist at Schlumberger, a large oil services
company, where he spent the vast majority of his time exploring data.
Some of his projects included using targeted sentiment analysis to discover the root cause of
part failure from engineer text, developing customized client/server dashboarding
applications, and real-time web services to avoid the mispricing of sales items. Ted received
his masters degree in statistics from Rice University, and used his analytical skills to play
poker professionally and teach math before becoming a data scientist. Ted is a strong
supporter of learning through practice and can often be found answering questions about
pandas on Stack Overflow.
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