Download Python for data analysis : [agile tools for real-world data] by Wes McKinney PDF

By Wes McKinney

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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. Of course, the IPython project now encompasses a great deal more than just an enhanced, interactive Python shell. It also includes a rich GUI console with inline plotting, a web-based interactive notebook format, and a lightweight, fast parallel computing engine.

2228955458351768} 46 | Chapter 3: IPython: An Interactive Computing and Development Environment Many kinds of Python objects are formatted to be more readable, or pretty-printed, which is distinct from normal printing with print. 3308507317325902} IPython also provides facilities to make it easy to execute arbitrary blocks of code (via somewhat glorified copy-and-pasting) and whole Python scripts. These will be discussed shortly. Tab Completion On the surface, the IPython shell looks like a cosmetically slightly-different interactive Python interpreter.

DataFrame'> Int64Index: 1690784 entries, 0 to 1690783 Data columns: name 1690784 non-null values sex 1690784 non-null values births 1690784 non-null values year 1690784 non-null values prop 1690784 non-null values dtypes: float64(1), int64(2), object(2) When performing a group operation like this, it's often valuable to do a sanity check, like verifying that the prop column sums to 1 within all the groups. sum(), 1) Out[379]: True Now that this is done, I’m going to extract a subset of the data to facilitate further analysis: the top 1000 names for each sex/year combination.

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