The purpose of this notebook is to demonstrate the use of Google Sheets as a data repository for use within a Jupyter notebook.
import requests
r = requests.get('https://docs.google.com/spreadsheets/d/1lqIWdnmjiZX2LwHZ_5TdPXDOEn8hp-ZkdONlbjA-P1k/export?format=csv&id')
data = r.content
from StringIO import StringIO
import pandas as pd
adb = pd.io.parsers.read_csv(StringIO(data),index_col=0)
adb
Tmin | Tmax | A | B | C | |
---|---|---|---|---|---|
Species | |||||
Acetaldehyde | -45 | 70 | 8.00550 | 1600.000 | 291.800 |
Acetic Acid | NaN | NaN | 7.38782 | 1533.313 | 222.309 |
Benzene | 8 | 103 | 6.90565 | 1211.033 | 220.790 |
import matplotlib.pyplot as plt
import numpy as np
%matplotlib inline
species = 'Benzene'
T = np.linspace(adb.ix[species]['Tmin'],adb.ix[species]['Tmax'])
def Psat(s,T):
return 10.0**(adb.ix[s]['A'] - adb.ix[s]['B']/(T + adb.ix[s]['C']) )
plt.plot(T,Psat(species,T))
plt.xlabel('Temperature [deg C]')
plt.ylabel('Pressure [mmHg]')
plt.title('Saturation Pressure of ' + species)
plt.grid()