In this notebook, we'll take a look at EventVestor's CEO Changes dataset, available on the Quantopian Store. This dataset spans January 01, 2007 through the current day.
Before we dig into the data, we want to tell you about how you generally access Quantopian Store data sets. These datasets are available through an API service known as Blaze. Blaze provides the Quantopian user with a convenient interface to access very large datasets.
Blaze provides an important function for accessing these datasets. Some of these sets are many millions of records. Bringing that data directly into Quantopian Research directly just is not viable. So Blaze allows us to provide a simple querying interface and shift the burden over to the server side.
It is common to use Blaze to reduce your dataset in size, convert it over to Pandas and then to use Pandas for further computation, manipulation and visualization.
Helpful links:
Once you've limited the size of your Blaze object, you can convert it to a Pandas DataFrames using:
from odo import odo
odo(expr, pandas.DataFrame)
One other key caveat: we limit the number of results returned from any given expression to 10,000 to protect against runaway memory usage. To be clear, you have access to all the data server side. We are limiting the size of the responses back from Blaze.
There is a free version of this dataset as well as a paid one. The free one includes about three years of historical data, though not up to the current day.
With preamble in place, let's get started:
In [3]:
# import the dataset
from quantopian.interactive.data.eventvestor import ceo_change
# or if you want to import the free dataset, use:
# from quantopian.data.eventvestor import ceo_change_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
In [4]:
# Let's use blaze to understand the data a bit using Blaze dshape()
ceo_change.dshape
Out[4]:
In [5]:
# And how many rows are there?
# N.B. we're using a Blaze function to do this, not len()
ceo_change.count()
Out[5]:
In [6]:
# Let's see what the data looks like. We'll grab the first three rows.
ceo_change[:3]
Out[6]:
Let's go over the columns:
We've done much of the data processing for you. Fields like timestamp and sid are standardized across all our Store Datasets, so the datasets are easy to combine. We have standardized the sid across all our equity databases.
We can select columns and rows with ease. Below, we'll fetch all entries for Microsoft. We're really only interested in the CEO coming in, the CEO going out, and the date, so we'll display only those columns.
In [7]:
# get the sid for MSFT
symbols('MSFT')
Out[7]:
In [8]:
# knowing that the MSFT sid is 5061:
msft = ceo_change[ceo_change.sid==5061][['timestamp','in_ceoname', 'out_ceoname','change_status']].sort('timestamp')
msft
Out[8]:
Note that the in_ceoname and out_ceoname in these cases were NaNs because there was a long transition period. Steve Ballmer announced his resignation on 2013-08-24, and formally stepped down on 2014-02-05.
Let's try another one:
In [9]:
# get the sid for AMD
sid_amd = symbols('AMD').sid
amd = ceo_change[ceo_change.sid==sid_amd][['timestamp','in_ceoname', 'out_ceoname','change_status']].sort('timestamp')
amd
Out[9]:
Now suppose want to know how many CEO changes there were in the past year in which a female CEO was incoming.
In [10]:
females_in = ceo_change[ceo_change['in_ceogender']=='Female']
# Note that whenever you print a Blaze Data Object here, it will be automatically truncated to ten rows.
females_in = females_in[females_in.asof_date > '2014-09-17']
len(females_in)
Out[10]:
Finally, suppose want this as a DataFrame:
In [11]:
females_in_df = odo(females_in, pd.DataFrame)
females_in_df.sort('symbol', inplace=True)
# let's get the first three rows
females_in_df[:3]
Out[11]:
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