In [8]:

    

%load_ext autoreload
%autoreload 2


    

    




The autoreload extension is already loaded. To reload it, use:
  %reload_ext autoreload

In [9]:

    

%matplotlib inline
%matplotlib notebook


    

In [12]:

    

import numpy as np
import scipy as sp
import matplotlib.pyplot as plt

import pandas as pd
import cufflinks as cf

import plotly.plotly as py


    

In [18]:

    

s = sp.randn(100)
print(len(s))
print("Mean : {0:8.6f}".format(s.mean()))


    

    




Mean : 0.115381

In [20]:

    

df = pd.read_csv("https://plot.ly/~MattSundquist/20387.csv")
df.describe()


    

    




/Users/gabrielus/.pyenv/versions/3.5.1/envs/plotly/lib/python3.5/site-packages/numpy/lib/function_base.py:3823: RuntimeWarning:

Invalid value encountered in percentile







    
Out[20]:






  

    

      

      
line0_volts
      
line0_time, line1_time, line3_time
      
line1_volts
      
line2_volts
      
line2_time
      
line3_volts
    
  
  

    

      
count
      
200.000000
      
200.000000
      
2.000000e+02
      
20.000000
      
20.000000
      
2.000000e+02
    
    

      
mean
      
0.434498
      
0.995000
      
-1.665335e-18
      
0.620175
      
0.950000
      
6.708533e-02
    
    

      
std
      
0.243705
      
0.578792
      
7.088812e-01
      
0.324605
      
0.591608
      
3.402314e-01
    
    

      
min
      
0.136695
      
0.000000
      
-1.000000e+00
      
0.000000
      
0.000000
      
-4.781305e-01
    
    

      
25%
      
0.224812
      
0.497500
      
-6.956525e-01
      
NaN
      
NaN
      
-1.651344e-01
    
    

      
50%
      
0.369728
      
0.995000
      
6.123234e-17
      
NaN
      
NaN
      
3.713929e-17
    
    

      
75%
      
0.608055
      
1.492500
      
6.956525e-01
      
NaN
      
NaN
      
2.722606e-01
    
    

      
max
      
1.000000
      
1.990000
      
1.000000e+00
      
1.064711
      
1.900000
      
7.883040e-01
    
  

In [28]:

    

df.columns = ["volts_1", "time_1", "volts_2", "volts_2",
                "time_2", "volts_4"]


    

In [27]:

    

df.volts_1.std()
df.std()


    

    
Out[27]:





volts_1    0.243705
time_1     0.578792
volts_2    0.708881
volts_2    0.324605
time_2     0.591608
volts_4    0.340231
dtype: float64

In [30]:

    

fig1 = plt.figure()
# Make a legend for specific lines.
import matplotlib.pyplot as plt
import numpy as np


t1 = np.arange(0.0, 2.0, 0.1)
t2 = np.arange(0.0, 2.0, 0.01)

# note that plot returns a list of lines.  The "l1, = plot" usage
# extracts the first element of the list into l1 using tuple
# unpacking.  So l1 is a Line2D instance, not a sequence of lines
l1, = plt.plot(t2, np.exp(-t2))
l2, l3 = plt.plot(t2, np.sin(2 * np.pi * t2), '--go', t1, np.log(1 + t1), '.')
l4, = plt.plot(t2, np.exp(-t2) * np.sin(2 * np.pi * t2), 'rs-.')

plt.xlabel('time')
plt.ylabel('volts')
plt.title('Damped oscillation')

plt.show()


    

In [1]:

    

py.iplot_mpl(fig1, strip_style = True, filename='examples/damped_oscillation')


    

    




---------------------------------------------------------------------------
NameError                                 Traceback (most recent call last)
<ipython-input-1-48cbcb91e010> in <module>()
----> 1 py.iplot_mpl(fig1, strip_style = True, filename='examples/damped_oscillation')

NameError: name 'py' is not defined

In [39]:

    

import mpld3
mpld3.display(fig1)


    

    
Out[39]:

In [43]:

    

df = pd.read_csv('https://plot.ly/~Dreamshot/5718/electoral-college-votes-by-us-state/.csv')

for col in df.columns:
    df[col] = df[col].astype(str)

# change column names
df.columns = ["state", "votes"]

df.head()


    

In [49]:

    

scl = [[0.0, 'rgb(242,240,247)'],[0.2, 'rgb(218,218,235)'],[0.4, 'rgb(188,189,220)'],\
            [0.6, 'rgb(158,154,200)'],[0.8, 'rgb(117,107,177)'],[1.0, 'rgb(84,39,143)']]

df['text'] = df['state'] 
    
data = [dict(
    type='choropleth',
    colorscale = scl,
    autocolorscale = False,
    locations = df['state'],
    z = df['votes'].astype(float),
    locationmode = 'USA-states',
    text = df['text'],
    hoverinfo = 'location+z',
    marker = dict(
        line = dict (
            color = 'rgb(255,255,255)',
            width = 2
        )
    ),
    colorbar = dict(
        title = "Votes"
    )
)]

layout = dict(
    title = '2016 Electoral College Votes',
    geo = dict(
        scope='usa',
        projection=dict( type='albers usa' ),
        showlakes = True,
        lakecolor = 'rgb(255, 255, 255)'
    )
)
    
fig = dict(data=data, layout=layout)


    

In [50]:

    

py.iplot(fig, validate=False, filename='examples/electoral-map')


    

    
Out[50]:

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