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Introduction

In this week of the course you'll learn the fundamentals of one of the most important toolkits Python has for data cleaning and processing -- pandas. You'll learn how to read in data into DataFrame structures, how to query these structures, and the details about such structures are indexed. The module ends with a programming assignment and a discussion question.

Introduction to Data Science in Python

University of Michigan

Course 1 of 5 in the Applied Data Science with Python Specialization

This course will introduce the learner to the basics of the python programming environment, including fundamental python programming techniques such as lambdas, reading and manipulating csv files, and the numpy library. The course will introduce data manipulation and cleaning techniques using the popular python pandas data science library and introduce the abstraction of the Series and DataFrame as the central data structures for data analysis, along with tutorials on how to use functions such as groupby, merge, and pivot tables effectively. By the end of this course, students will be able to take tabular data, clean it, manipulate it, and run basic inferential statistical analyses. This course should be taken before any of the other Applied Data Science with Python courses: Applied Plotting, Charting & Data Representation in Python, Applied Machine Learning in Python, Applied Text Mining in Python, Applied Social Network Analysis in Python.

Skills You'll Learn

Python Programming, Numpy, Pandas, Data Cleansing

Taught By

Christopher Brooks

Transcript

This week we're going to deepen our investigation to how Python can be used to

manipulate, clean, and query data by looking at the Pandas data tool kit.

Pandas was created by Wes McKinney in 2008, and

is an open source project under a very permissive license.

As an open source project it's got a strong community,

with 100 software developers all committing code to help make it better.

There's a couple of places you should consider going to for help with Pandas.

Obviously, the course discussion forums make for a great resource.

But I don't think they're the best resource for

Pandas problems in particular.

I think the best resource is a question answering site called Stack Overflow.

Stack Overflow is used broadly within the software development community to post

questions about programming, programming languages, and programming toolkits.

What's special about Stack Overflow is that it's heavily curated by

the community.

And the Pandas community, in particular, uses it as their number one resource for

helping new members.

It's quite possible if you post a question to Stack Overflow, and

tag it as being Pandas and

Python related, that a Pandas developer will actually respond to your question.

In addition to posting questions, Stack Overflow is a great place to go

to see what issues people are having and how they can be solved.

You can learn a lot from browsing Stacks at Stack Overflow.

A second resource you might want to consider are books.

In 2012 Wes McKinney wrote the definitive Pandas reference book called Python for

Data Analysis and published by O'Reilly.

While it's starting to get a bit dated,

he recently announced that a new edition of the book is coming, and

I still consider this a go to book for understanding how Pandas works.

I also appreciate the more brief book Leaning the Pandas Library by

Matt Harrison.

It's not a comprehensive book on data analysis and statistics.

But if you just want to learn the basics of Pandas and

want to do so quickly, I think it's a well laid out volume.

The field of data science is rapidly changing.

There's new toolkits and method being created everyday.

It can be tough to stay on top of it all.

And Marco Rodriguez and

Tim Golden maintained a wonderful blog aggregator site called Planet Python.

You can visit the webpage at planetpython.org, subscribe with an RSS

reader, or get the latest articles from the @PlanetPython Twitter feed.

There's lots of regular Python data science contributors, and

I highly recommend it if you follow RSS feeds.

Here's my last plug on how to deepen your learning.

Kyle Polich runs an excellent podcast called Data Skeptic.

Well, it isn't Python based per se, it's well produced and

it has a wonderful mixture of interviews with experts in the field as well as short

educational lessons.

Much of the word he describes is specific to machine learning methods.

But if that's something you are planning to explore through this specialization

this course is in, I would really encourage you to subscribe to his podcast.

Well that's it for a little bit of an introduction to this week.

Next we're going to dive right into Pandas and talk about the series data structure.

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