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

# # Third Party Libraries With Rich Output

# A number of third party libraries defined their own custom display logic. This gives their objcts rich output by default when used in the Notebook.

# In[1]:


from IPython.display import display


# ## Pandas

# [Pandas](http://pandas.pydata.org/) is a data analysis library for Python. Its `DataFrame` objects have an HTML table representation in the Notebook.

# In[2]:


import pandas


# Here is a small amount of stock data for APPL:

# In[3]:


get_ipython().run_cell_magic('writefile', 'data.csv', 'Date,Open,High,Low,Close,Volume,Adj Close\n2012-06-01,569.16,590.00,548.50,584.00,14077000,581.50\n2012-05-01,584.90,596.76,522.18,577.73,18827900,575.26\n2012-04-02,601.83,644.00,555.00,583.98,28759100,581.48\n2012-03-01,548.17,621.45,516.22,599.55,26486000,596.99\n2012-02-01,458.41,547.61,453.98,542.44,22001000,540.12\n2012-01-03,409.40,458.24,409.00,456.48,12949100,454.53\n')


# Read this as into a `DataFrame`:

# In[4]:


df = pandas.read_csv('data.csv')


# And view the HTML representation:

# In[5]:


df


# ## SymPy

# [SymPy](http://sympy.org/) is a symbolic computing library for Python. Its equation objects have LaTeX representations that are rendered in the Notebook.

# In[6]:


from sympy.interactive.printing import init_printing
init_printing(use_latex='mathjax')


# In[7]:


from __future__ import division
import sympy as sym
from sympy import *
x, y, z = symbols("x y z")
k, m, n = symbols("k m n", integer=True)
f, g, h = map(Function, 'fgh')


# In[8]:


Rational(3,2)*pi + exp(I*x) / (x**2 + y)


# In[9]:


a = 1/x + (x*sin(x) - 1)/x
a


# In[10]:


(1/cos(x)).series(x, 0, 6)


# ## Vincent

# [Vincent](https://vincent.readthedocs.org/en/latest/) is a visualization library that uses the [Vega](http://trifacta.github.io/vega/) visualization grammar to build [d3.js](http://d3js.org/) based visualizations in the Notebook and on http://nbviewer.ipython.org. `Visualization` objects in Vincetn have rich HTML and JavaSrcript representations.

# In[11]:


import vincent
import pandas as pd


# In[12]:


import pandas.io.data as web
import datetime
all_data = {}
date_start = datetime.datetime(2010, 1, 1)
date_end = datetime.datetime(2014, 1, 1)
for ticker in ['AAPL', 'IBM', 'YHOO', 'MSFT']:
    all_data[ticker] = web.DataReader(ticker, 'yahoo', date_start, date_end)
price = pd.DataFrame({tic: data['Adj Close']
                      for tic, data in all_data.items()})


# In[13]:


vincent.initialize_notebook()


# In[14]:


line = vincent.Line(price[['AAPL', 'IBM', 'YHOO', 'MSFT']], width=600, height=300)
line.axis_titles(x='Date', y='Price')
line.legend(title='Ticker')
display(line)