Models

In [5]:

    

from IPython.display import HTML
#http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynbiframe src=http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb width=400 height=350></iframe>"
HTML("<iframe src=http://http://nbviewer.ipython.org/github/ipython/ipython/blob/1.x/examples/notebooks/Part%205%20-%20Rich%20Display%20System.ipynb width=400 height=350></iframe>")


    

    
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from IPython.display import HTML
# http://markus.com/content/images/2013/Nov/nnet.png
HTML("<iframe src=http://markus.com/content/images/2013/Nov/nnet.png </iframe>")


    

    
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In [3]:

    

from IPython.display import Math
Math(r'F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx')


    

    
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$$F(k) = \int_{-\infty}^{\infty} f(x) e^{2\pi i k} dx$$

In [4]:

    

from IPython.display import Latex
Latex(r"""\begin{eqnarray}
\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
\nabla \cdot \vec{\mathbf{B}} & = 0 
\end{eqnarray}""")


    

    
Out[4]:





\begin{eqnarray}
\nabla \times \vec{\mathbf{B}} -\, \frac1c\, \frac{\partial\vec{\mathbf{E}}}{\partial t} & = \frac{4\pi}{c}\vec{\mathbf{j}} \\
\nabla \cdot \vec{\mathbf{E}} & = 4 \pi \rho \\
\nabla \times \vec{\mathbf{E}}\, +\, \frac1c\, \frac{\partial\vec{\mathbf{B}}}{\partial t} & = \vec{\mathbf{0}} \\
\nabla \cdot \vec{\mathbf{B}} & = 0 
\end{eqnarray}

In [1]:

    

#Scatter plot
%matplotlib inline
import pandas as pd
import pylab as pl
import matplotlib.pyplot as plt
import numpy as np
from pandas import *
from pylab import *

#Read Data Source
testdata=pd.read_csv('TestData.csv')

#Build Dataframe
df = pd.DataFrame(data = testdata, columns=['AccountNo_t', 'Amount_t'])

#Declare 'fig2' variable assign 'plt.figure()' value
fig2 = plt.figure(figsize=(8,4), dpi=200)

#Create Random Scatter Plot
N = 50
x = np.random.rand(N)
y = np.random.rand(N)
colors = np.random.rand(N)
area = np.pi * (15 * np.random.rand(N))**2 # 0 to 15 point radiuses

plt.scatter(x, y, s=area, c=colors, alpha=0.5)
plt.show()

#Save fig
fig2.savefig("fig2.png", dpi=200)


    

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