Python for Finance: Analyze Big Financial Data

Further Reading

At the time of this writing, the definitive resource in printed form for pandas is the book

by the main author of the library:

McKinney, Wes (2012): Data Analysis with Python. O’Reilly, Sebastopol, CA.

Of course, the Web — especially the website of pandas itself — also provides a wealth of

information:

Again, it is good to start on the home page of the library: http://pandas.pydata.org.

There is rather comprehensive online documentation available at

http://pandas.pydata.org/pandas-docs/stable/.

The documentation in PDF format with 1,500+ pages illustrates how much

functionality pandas has to offer: http://pandas.pydata.org/pandas-

docs/stable/pandas.pdf.

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Considering only daily closing prices, you have approximately 30 × 252 = 7,560 closing prices for a single stock

over a period of 30 years. It is not uncommon to have more than 10,000 daily (bid/ask) ticks for a single stock.

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For a similar example using matplotlib only, see Chapter 5.

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See Chapter 7 for more information on input-output operations with Python.

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Note that the data provider only provides this type of data for a couple of days back from the current date.

Therefore, you might need to use different (i.e., more current) dates to implement the same example.