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You're reading from  Hands-On Data Analysis with Pandas - Second Edition

Product typeBook
Published inApr 2021
Reading LevelIntermediate
PublisherPackt
ISBN-139781800563452
Edition2nd Edition
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Stefanie Molin
Stefanie Molin
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Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
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Creating a pandas DataFrame

Now that we understand the data structures we will be working with, we can discuss the different ways we can create them. Before we dive into the code however, it's important to know how to get help right from Python. Should we ever find ourselves unsure of how to use something in Python, we can utilize the built-in help() function. We simply run help(), passing in the package, module, class, object, method, or function that we want to read the documentation on. We can, of course, look up the documentation online; however, in most cases, the docstrings (the documentation text written in the code) that are returned with help() will be equivalent to this since they are used to generate the documentation.

Assuming we first ran import pandas as pd, we can run help(pd) to display information about the pandas package; help(pd.DataFrame) for all the methods and attributes of DataFrame objects (note we can also pass in a DataFrame object instead); and help...

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Hands-On Data Analysis with Pandas - Second Edition
Published in: Apr 2021Publisher: PacktISBN-13: 9781800563452

Author (1)

author image
Stefanie Molin

Stefanie Molin is a data scientist and software engineer at Bloomberg LP in NYC, tackling tough problems in information security, particularly revolving around anomaly detection, building tools for gathering data, and knowledge sharing. She has extensive experience in data science, designing anomaly detection solutions, and utilizing machine learning in both R and Python in the AdTech and FinTech industries. She holds a B.S. in operations research from Columbia University's Fu Foundation School of Engineering and Applied Science, with minors in economics, and entrepreneurship and innovation. In her free time, she enjoys traveling the world, inventing new recipes, and learning new languages spoken among both people and computers.
Read more about Stefanie Molin