#!/usr/bin/env python
# coding: utf-8

# # EventVestor: Contract Wins
# 
# In this notebook, we'll take a look at EventVestor's *Contract Wins* dataset, available on the [Quantopian Store](https://www.quantopian.com/store). This dataset spans January 01, 2007 through the current day, and documents major contract wins by companies.
# 
# ### Blaze
# 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](http://blaze.pydata.org). 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:
# * [Query building for Blaze](http://blaze.pydata.org/en/latest/queries.html)
# * [Pandas-to-Blaze dictionary](http://blaze.pydata.org/en/latest/rosetta-pandas.html)
# * [SQL-to-Blaze dictionary](http://blaze.pydata.org/en/latest/rosetta-sql.html).
# 
# 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)`
# 
# ### Free samples and limits
# 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[2]:


# 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


# In[3]:


# Let's use blaze to understand the data a bit using Blaze dshape()
contract_win.dshape


# In[4]:


# And how many rows are there?
# N.B. we're using a Blaze function to do this, not len()
contract_win.count()


# In[5]:


# Let's see what the data looks like. We'll grab the first three rows.
contract_win[:3]


# Let's go over the columns:
# - **event_id**: the unique identifier for this contract win.
# - **asof_date**: EventVestor's timestamp of event capture.
# - **trade_date**: for event announcements made before trading ends, trade_date is the same as event_date. For announcements issued after market close, trade_date is next market open day.
# - **symbol**: stock ticker symbol of the affected company.
# - **event_type**: this should always be *Contract Win*.
# - **contract_amount**: the amount of amount_units the contract is for.
# - **amount_units**: the currency or other units for the value of the contract. Most commonly in millions of dollars.
# - **contract_entity**: name of the customer, if available
# - **event_rating**: this is always 1. The meaning of this is uncertain.
# - **timestamp**: this is our timestamp on when we registered the data.
# - **sid**: the equity's unique identifier. Use this instead of the symbol.

# 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.

# In[6]:


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


# Finally, suppose we want the above as a DataFrame:

# In[7]:


ba_df = odo(wins, pd.DataFrame)
# Printing a pandas DataFrame displays the first 30 and last 30 items, and truncates the middle.
ba_df