Instant Data Intensive Apps with pandas How-to [Instant]
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- Learn something new in an Instant! A short, fast, focused guide delivering immediate results
- Follow simple recipes that will teach common tasks when performing data analysis with Pandas
- Build a data product for displaying information over the web
- Create visualizations of the data including displaying tables and line graphs
Book DetailsLanguage : English
eBook : 50 pages
Release Date : May 2013
ISBN : 1782165584
ISBN 13 : 9781782165583
Author(s) : Trent Hauck
Topics and Technologies : All Books, Big Data and Business Intelligence, Instant, Open Source
Table of ContentsPreface
Instant Data-intensive Apps with pandas How-to
- Instant Data-intensive Apps with pandas How-to
- Working with files (Simple)
- Slicing pandas objects (Simple)
- Subsetting data (Simple)
- Working with dates (Medium)
- Modifying data with functions (Simple)
- Combining datasets (Medium)
- Using indexes to manipulate objects (Medium)
- Getting data from the Web (Simple)
- Combining pandas with scikit-learn (Advanced)
- Integrating pandas with statistics packages (Advanced)
- Using Flask for the backend (Advanced)
- Visualizing pandas objects (Advanced)
- Reporting with pandas objects (Medium)
Download the code and support files for this book.
Please let us know if you have found any errors not listed on this list by completing our errata submission form. Our editors will check them and add them to this list. Thank you.
Errata- 8 submitted: last submission 26 Aug 2013
Page : 10
df.ix[1:3 ,[ 'one', 'two']] = 10 Should be: df.ix[1:3, [ 'one', 'two']] = 10
Page : 12
"...it creates a copy of df.CatA with NaN in places where df.CatA doesn't equals to 'a'." should be "where df.CatA is not equal to 'a'."
Category: Grammar | Page number: 20
The following: # a yet undiscussed data structure, in the same way the a # DataFrame is a collection of Series, a Panel is a collection of # DataFrames
The following: # a yet undiscussed data structure, in the same way the # DataFrame is a collection of Series, a Panel is a collection of # DataFrames
Category: Code | Page number: 9
The following:df = pd.DataFrame(np.random.normal(0, 1, dim), columns ['one', 'two', 'three'])It should read: df = pd.DataFrame(np.random.normal(0, 1, dim), columns=['one', 'two', 'three'])
Category: Code | Page number: 20
The following: > from pandas.i git push -u origin master o.data import DataReader Should be: > from pandas.io.data import DataReader
Category: Code | Page number: 9
The following:> df[['one', 'two']][:2] one two 0 -0.492156 1.978798 -0.476418 -0.225360 should be > df[['one', 'two']][:2] one two 0 -0.492156 1.978798 1 -0.476418 -0.225360
Category: Code | Page number: 23
df = pd.read_csv(url, df = pd.read_csv) should be df = pd.read_csv(url)
Category: Code | Page number: 19
> pd.merge(df1, tickers, right_index=True, left_on='TickerID')
The output should be:
Price TickerID Ticker
2000-01-01 76.937336 0 MSFT
2000-01-02 127.788659 0 MSFT
2000-01-03 99.990745 0 MSFT
2000-01-04 112.125489 0 MSFT
2000-01-05 101.941148 0 MSFT
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What you will learn from this book
- Learn data manipulation in Pandas including subsetting data, data transformation, and data reshaping
- Combine data sets to get an idea of what the output with tabular data looks like
- Perform aggregate data operations such as counts and sums for exploratory analysis
- Use basic machine learning to identify stock performance similarities
- Create visualizations of data including displaying tables and line graphs
- Integrate other libraries with Pandas and carry out a simple analysis
- Create a web application to display Pandas data in a web page
Pandas helps to alleviate a genuinely complex situation in data analytics libraries. Many incumbent languages aren't approachable or are fairly unproductive in general computing tasks in comparison to Python. However with Pandas it's easy to begin working with tabular datasets in a language that's easier to learn and use.
Instant Data Intensive Apps with pandas How-to starts with Pandas’ functionalities such as joining datasets, cleaning data, and other data munging tasks. It quickly moves onto building a data reporting tool, which consists of analysis in Pandas to determine what’s relevant and present that relevant data in an easy-to-consume manner.
Instant Data Intensive Apps with pandas How-to starts with data manipulation and other practical tasks for a fundamental understanding, and through successive recipes you will gain a more profitable understanding of Pandas.
Throughout this book the recipes are presented in a structured way. It starts with data transformation techniques, but builds up to more complex examples such as performing statistical analysis and integrating Pandas objects with web applications. The other recipes cover visualization and machine learning, among other things.
Instant Data Intensive Apps with pandas How-to will get the reader up and running quickly with Pandas and put the user in a position to move up the learning curve faster.
Filled with practical, step-by-step instructions and clear explanations for the most important and useful tasks. This book has a practical approach with step-by-step recipes to help readers get to grips with Pandas.
Who this book is for
Users of other data analysis tools will find value in seeing tasks they commonly encounter translated to Pandas and users of Python will encounter an introduction to a very impressive tool in a syntax they inherently know. In terms of general skills, it is assumed that the reader understands basic data structures such as arrays or lists dictionaries or hash map as well as having some understanding of command line work. Installing Pandas is not covered, but the online documentation is straightforward. Also, readers are encouraged to use IPython to interact and experiment with the code.