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Getting Started with Python Data Analysis

You're reading from   Getting Started with Python Data Analysis Learn to use powerful Python libraries for effective data processing and analysis

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Product type Paperback
Published in Nov 2015
Publisher
ISBN-13 9781785285110
Length 188 pages
Edition 1st Edition
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Toc

Table of Contents (10) Chapters Close

Preface 1. Introducing Data Analysis and Libraries FREE CHAPTER 2. NumPy Arrays and Vectorized Computation 3. Data Analysis with Pandas 4. Data Visualization 5. Time Series 6. Interacting with Databases 7. Data Analysis Application Examples 8. Machine Learning Models with scikit-learn Index

Working with date and time objects

Python supports date and time handling in the date time and time modules from the standard library:

>>> import datetime
>>> datetime.datetime(2000, 1, 1)
datetime.datetime(2000, 1, 1, 0, 0)

Sometimes, dates are given or expected as strings, so a conversion from or to strings is necessary, which is realized by two functions: strptime and strftime, respectively:

>>> datetime.datetime.strptime("2000/1/1", "%Y/%m/%d")
datetime.datetime(2000, 1, 1, 0, 0)
>>> datetime.datetime(2000, 1, 1, 0, 0).strftime("%Y%m%d")
'20000101'

Real-world data usually comes in all kinds of shapes and it would be great if we did not need to remember the exact date format specifies for parsing. Thankfully, Pandas abstracts away a lot of the friction, when dealing with strings representing dates or time. One of these helper functions is to_datetime:

>>> import pandas as pd
>>> import numpy as...
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