In this notebook, we'll take a look at EventVestor's Contract Wins dataset, available on the Quantopian Store. This dataset spans January 01, 2007 through the current day, and documents major contract wins by companies.
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:
# import the dataset
from quantopian.interactive.data.eventvestor import contract_win
# or if you want to import the free dataset, use:
# from quantopian.data.eventvestor import contract_win_free
# import data operations
from odo import odo
# import other libraries we will use
import pandas as pd
# Let's use blaze to understand the data a bit using Blaze dshape()
contract_win.dshape
dshape("""var * { event_id: ?float64, asof_date: datetime, trade_date: ?datetime, symbol: ?string, event_type: ?string, event_headline: ?string, contract_amount: ?float64, amount_units: ?string, contract_entity: ?string, event_rating: ?float64, timestamp: datetime, sid: ?int64 }""")
# And how many rows are there?
# N.B. we're using a Blaze function to do this, not len()
contract_win.count()
# Let's see what the data looks like. We'll grab the first three rows.
contract_win[:3]
event_id | asof_date | trade_date | symbol | event_type | event_headline | contract_amount | amount_units | contract_entity | event_rating | timestamp | sid | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | 907471 | 2007-01-03 | 2007-01-03 | CECE | Contract Win | CECO Environmental Gets Two Orders for $55M Plus | 55.0 | $M | NaN | 1 | 2007-01-04 | 1396 |
1 | 148887 | 2007-01-04 | 2007-01-04 | ATK | Contract Win | Alliant Techsystems Gets $90M Contract from U.... | 90.0 | $M | U.S. Department of Homeland Security | 1 | 2007-01-05 | NaN |
2 | 908341 | 2007-01-04 | 2007-01-04 | BCRX | Contract Win | BioCryst Pharma Gets $102.6M Contract From US ... | 102.6 | $M | U.S. Department of Health and Human Services | 1 | 2007-01-05 | 10905 |
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 contract wins by Boeing. We'll display only the contract_amount, amount_units, contract_entity, and timestamp. We'll sort by date.
ba_sid = symbols('BA').sid
wins = contract_win[contract_win.sid == ba_sid][['timestamp', 'contract_amount','amount_units','contract_entity']].sort('timestamp')
# When displaying a Blaze Data Object, the printout is automatically truncated to ten rows.
wins
timestamp | contract_amount | amount_units | contract_entity | |
---|---|---|---|---|
0 | 2007-04-19 | 2500 | $M | South Korea |
1 | 2007-04-20 | 295 | $M | CIT Aerospace |
2 | 2007-04-24 | 1600 | $M | Aviation Capital Group |
3 | 2007-04-25 | 3600 | $M | Virgin Atlantic |
4 | 2007-04-27 | 700 | $M | SpiceJet |
5 | 2007-05-17 | 4700 | $M | TUI Group |
6 | 2007-05-30 | 2400 | $M | Russian Airline S7 |
7 | 2007-06-01 | 1900 | $M | Ryanair Holdings PLC |
8 | 2007-06-05 | 3000 | $M | Kuwait Airways |
9 | 2007-06-07 | 500 | $M | Philippine Airlines |
10 | 2007-06-19 | 1420 | $M | GE Commercial Aviation Services |
Finally, suppose we want the above as a DataFrame:
ba_df = odo(wins, pd.DataFrame)
# Printing a pandas DataFrame displays the first 30 and last 30 items, and truncates the middle.
ba_df
timestamp | contract_amount | amount_units | contract_entity | |
---|---|---|---|---|
0 | 2007-04-19 | 2500.0 | $M | South Korea |
1 | 2007-04-20 | 295.0 | $M | CIT Aerospace |
2 | 2007-04-24 | 1600.0 | $M | Aviation Capital Group |
3 | 2007-04-25 | 3600.0 | $M | Virgin Atlantic |
4 | 2007-04-27 | 700.0 | $M | SpiceJet |
5 | 2007-05-17 | 4700.0 | $M | TUI Group |
6 | 2007-05-30 | 2400.0 | $M | Russian Airline S7 |
7 | 2007-06-01 | 1900.0 | $M | Ryanair Holdings PLC |
8 | 2007-06-05 | 3000.0 | $M | Kuwait Airways |
9 | 2007-06-07 | 500.0 | $M | Philippine Airlines |
10 | 2007-06-19 | 1420.0 | $M | GE Commercial Aviation Services |
11 | 2007-06-20 | 8800.0 | $M | International Lease Finance Corp |
12 | 2007-06-21 | 2700.0 | $M | Air France KLM |
13 | 2007-07-01 | 2000.0 | $M | U.S. Air Force |
14 | 2007-07-06 | 810.0 | $M | CIT Aerospace |
15 | 2007-07-09 | 4000.0 | $M | Air Berlin |
16 | 2007-08-03 | 523.0 | $M | AeroSvit |
17 | 2007-08-04 | 1100.0 | $M | Air New Zealand |
18 | 2007-08-09 | 1400.0 | $M | Cathay Pacific Airways |
19 | 2007-08-31 | 3100.0 | $M | Norwegian Air Shuttle ASA |
20 | 2007-09-07 | 3800.0 | $M | China Southern Airlines |
21 | 2007-09-12 | 1100.0 | $M | US Air Force |
22 | 2007-10-17 | 1500.0 | $M | NaN |
23 | 2007-11-06 | 5000.0 | $M | LAN Airlines |
24 | 2007-11-09 | 5200.0 | $M | Cathay Pacific |
25 | 2007-11-12 | 3200.0 | $M | Emirates |
26 | 2007-11-14 | 523.0 | $M | transavia.com |
27 | 2007-11-23 | 716.0 | $M | KLM Royal Dutch Airlines |
28 | 2007-12-05 | 1700.0 | $M | Lion Air |
29 | 2007-12-11 | 1500.0 | $M | Babcock & Brown |
... | ... | ... | ... | ... |
191 | 2014-01-07 | 8800.0 | $M | flydubai |
192 | 2014-01-21 | 3900.0 | $M | GE Capital Aviation Services |
193 | 2014-02-06 | 228.0 | $M | Linhas Aereas de Mocambique |
194 | 2014-02-15 | 357.5 | $M | Cargolux Airlines |
195 | 2014-03-13 | 4400.0 | $M | SpiceJet Ltd. |
196 | 2014-03-20 | 830.0 | $M | Comair Limited |
197 | 2014-05-01 | 452.0 | $M | Ryanair |
198 | 2014-05-31 | 1100.0 | $M | Japan Transocean Air |
199 | 2014-06-17 | 1600.0 | $M | Turkish Airlines |
200 | 2014-06-27 | 272.0 | $M | Belarus flag carrier Belavia Airlines |
201 | 2014-07-10 | 56000.0 | $M | Emirates Airline |
202 | 2014-07-15 | 980.0 | $M | Okay Airways Company |
203 | 2014-07-15 | 2000.0 | $M | Avolon |
204 | 2014-07-16 | 1900.0 | $M | Intrepid Aviation |
205 | 2014-07-17 | 1890.0 | $M | Qatar Airways |
206 | 2014-07-17 | 152.0 | $M | NaN |
207 | 2014-08-26 | 8800.0 | $M | BOC Aviation |
208 | 2014-09-18 | 2100.0 | $M | Avolon |
209 | 2014-09-21 | 2100.0 | $M | Ethiopian Airlines |
210 | 2014-10-07 | 990.0 | $M | Alaska Airlines |
211 | 2014-12-02 | 11000.0 | $M | Ryanair |
212 | 2014-12-16 | 438.0 | $M | Jetlines |
213 | 2014-12-19 | 186.0 | $M | BOC Aviation |
214 | 2014-12-24 | 3300.0 | $M | Kuwait Airways |
215 | 2015-01-07 | 514.0 | $M | Air New Zealand |
216 | 2015-01-07 | 1240.0 | $M | Qatar Airways |
217 | 2015-01-16 | 3600.0 | $M | Air Europa |
218 | 2015-02-13 | 1600.0 | $M | Transavia Company |
219 | 2015-02-13 | 1500.0 | $M | Korean Air |
220 | 2015-03-28 | 900.0 | $M | All Nippon Airways |
221 rows × 4 columns