By Wes McKinney
Python for info Analysis is worried with the nuts and bolts of manipulating, processing, cleansing, and crunching information in Python. it's also a realistic, glossy creation to medical computing in Python, adapted for data-intensive functions. this can be a ebook concerning the elements of the Python language and libraries you'll have to successfully remedy a vast set of information research difficulties. This ebook isn't really an exposition on analytical tools utilizing Python because the implementation language.
Written by way of Wes McKinney, the most writer of the pandas library, this hands-on booklet is full of functional instances experiences. It's excellent for analysts new to Python and for Python programmers new to clinical computing.
• Use the IPython interactive shell as your fundamental improvement environment
• research uncomplicated and complicated NumPy (Numerical Python) features
• start with info research instruments within the pandas library
• Use high-performance instruments to load, fresh, remodel, merge, and reshape data
• Create scatter plots and static or interactive visualizations with matplotlib
• observe the pandas groupby facility to slice, cube, and summarize datasets
• degree facts by way of time cut-off dates, even if it's particular situations, fastened classes, or intervals
• tips on how to clear up difficulties in net analytics, social sciences, finance, and economics, via particular examples
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Extra info for Python for Data Analysis: Data Wrangling with Pandas, NumPy, and IPython
In : a = 5 In : a Out: 5 You can execute arbitrary Python statements by typing them in and pressing
While it does not provide any computational or data analytical tools by itself, IPython is designed from the ground up to maximize your productivity in both interactive computing and software development. It encourages an execute-explore workflow instead of the typical edit-compile-run workflow of many other programming languages. It also provides very tight integration with the operating system’s shell and file system. Since much of data analysis coding involves exploration, trial and error, and iteration, IPython will, in almost all cases, help you get the job done faster.
954903], 1. gov/pubmed/22415991'} Note that Python indices start at 0 and not 1 like some other languages (like R). It’s now easy to access individual values within records by passing a string for the key you wish to access: In : records['tz'] Out: u'America/New_York' The u here in front of the quotation stands for unicode, a standard form of string encoding. Note that IPython shows the time zone string object representation here rather than its print equivalent: In : print records['tz'] America/New_York Counting Time Zones in Pure Python Suppose we were interested in the most often-occurring time zones in the data set (the tz field).