## Graphing Finance Data

```import urllib2

###
import matplotlib
import matplotlib.pyplot as plt
import matplotlib.ticker as mticker
import matplotlib.dates as mdates
import numpy as np
###

def grabQuandl(ticker):

netIncomeAr = []
revAr = []
ROCAr = []

try:
print urlAttempt

####
splitNI = netIncome.split('\n')
print 'Net Income:'
for eachNI in splitNI[1:-1]:
print eachNI
netIncomeAr.append(eachNI)

print '___________'
splitRev = revenue.split('\n')
print 'Revenue:'
for eachRev in splitRev[1:-1]:
print eachRev
revAr.append(eachRev)

print '___________'
splitROC = ROC.split('\n')
print 'Return on Capital:'
for eachROC in splitROC[1:-1]:
print eachROC
ROCAr.append(eachROC)
####

incomeDate, income = np.loadtxt(netIncomeAr, delimiter=',', unpack=True,
converters={ 0: mdates.strpdate2num('%Y-%m-%d')})

fig = plt.figure()
ax1 = plt.subplot2grid((6,4), (0,0), rowspan=6, colspan=4)
ax1.plot(incomeDate,income)
plt.show()

except Exception, e:
print 'failed the main quandl loop for reason of',str(e)

grabQuandl('YHOO')
```

The next tutorial:

• Programming for Fundamental Investing

• Getting Company Data

• Price to Book ratio example

• Python Stock Screener for Price to Book

• Python Screener for PEG Ratio

• Adding Price to Earnings

• Getting all Russell 3000 stock tickers

• Getting all Russell 3000 stock tickers part 2

• More stock Screening

• Completing Basic Stock Screener

• Connecting with Quandl for Annual Earnings Data

• Organizing Earnings Data

• Graphing Finance Data
• Finishing the Graphing

• Adding the Graphing to the Screener

• Preparing figure to Accept Finance Data

• Adding Historical Earnings to Stock Screener Chart Data

• Completing the Fundamental Investing Stock Screeners