Get Python for data analysis : [agile tools for real-world data] PDF

By Wes McKinney

ISBN-10: 1449319793

ISBN-13: 9781449319793

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We’ll explore more tools for working with this kind of data. 1 (K Figure 2-1. gov sample data Parsing all of the interesting information in these “agent” strings may seem like a daunting task. Luckily, once you have mastered Python’s built-in string functions and regular expression capabilities, it is really not so bad. 681 4 Now, suppose you wanted to decompose the top time zones into Windows and nonWindows users. As a simplification, let’s say that a user is on Windows if the string 'Windows' is in the agent string.

This chapter is intended to provide a standalone, rich overview of the functionality provided by IPython. 2 (default, May 27 2012, 21:26:12) Type "copyright", "credits" or "license" for more information. 12 ? -> %quickref -> help -> object? -> -- An enhanced Interactive Python. Introduction and overview of IPython's features. Quick reference. Python's own help system. ' for extra details. In [1]: a = 5 In [2]: a Out[2]: 5 You can execute arbitrary Python statements by typing them in and pressing .

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

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