matplotlib Code

"""
parse and load ge.csv into a record array
"""
import time, datetime, csv
import dateutil.parser
import matplotlib.mlab as mlab
import matplotlib.dates as mdates
import matplotlib.cbook as cbook
import numpy as np

# this is how you can use the function mlab.csv2rec to do the same
r = mlab.csv2rec('data/ge.csv')
r.sort()  #sort by date, the first column

# compute the average approximate dollars traded over the last 10 days
# hint: closing price * volume trades approx equals dollars trades
recent = r[-10:]
dollars = (recent.close * recent.volume).mean()
print '$%1.2fM'%(dollars/1e6)

# plot the adjusted closing price vs time since 2003 - hint, you must
# use date2num to convert the date to a float for mpl.  Make two axes,
# one for price and one for volume.  Use a bar chart for volume

import matplotlib.pyplot as plt
# filter to get dates since 2003
r = r[r.date > datetime.date(2003,1,1)]

fig = plt.figure()
fig.subplots_adjust(hspace=0)
ax1 = fig.add_subplot(211)
ax1.plot(r.date, r.adj_close, '-');

ax1.grid()
for label in ax1.get_xticklabels():
    label.set_visible(False)

ax2 = fig.add_subplot(212, sharex=ax1)
ax2.bar(r.date, r.volume);

ax2.grid()
for label in ax2.get_xticklabels():
    label.set_rotation(30)
    label.set_horizontalalignment('right')

fig.autofmt_xdate()
plt.show()