1-2: Working with multiple stocks

This lesson covers more in-depth manipulation of stock data in .csv files with pandas. In this lesson, we create empty dataframes with pandas and selectively include data from a number of .csv files of different stock symbols. This lesson provides examples of selecting stock information based upon date ranges from various stock symbols, and provides guidance on how to avoid ingesting invalid (NaN values) into the dataframe. An example provided by the lesson can be found here.

Inner join vs outer join

An inner join of a table provides the common items between the two tables being joined. An example Venn-diagram of the operation is provided below:

An outer join of a table provides the common items between two tables, as well, however it also provides the left or right quantities of the two tables being joined. An example Venn-diagram of a left outer join and a right outer join are provided below:

The default method of join for a pandas dataframe is the left outer join.

Normalizing price data

The preferred method to normalize data within a pandas dataframe is to conduct the following operation in Python:

my_dataframe = my_dataframe / mydataframe.ix[0, :]

Quizzes

How many days were U.S. stocks traded at NYSE in 2014?

• 365
• 260
• 252