Praktikum

In [4]:

    

import numpy
import pandas
import scipy.optimize

data = pandas.read_csv('data.csv')
data['C'] = data['A'] * data['B']

def f(x, C0, C1):
    return C0 * x + C1

popt, pcov = scipy.optimize.curve_fit(f, data['T'], data['C'])
x = numpy.linspace(data['T'].min(), data['T'].max(), 100)
y = popt[0] * x + popt[1]


    

In [5]:

    

import tabulate
table = tabulate.tabulate(data, tablefmt="html", headers=['#'] + list(data.columns.values), floatfmt='0.4f')


    

In [6]:

    

import IPython.display
IPython.display.display(IPython.display.HTML(table))


    

    







     #
      T
     A
       B
      C


0.0000
15.0000
0.1734
459.0000
79.5906
1.0000
16.0000
0.1782
450.0000
80.1900
2.0000
17.0000
0.1831
441.0000
80.7471
3.0000
18.0000
0.1880
435.0000
81.7800
4.0000
19.0000
0.1928
427.0000
82.3256
5.0000
20.0000
0.1976
419.0000
82.7944
6.0000
21.0000
0.2024
411.0000
83.1864

In [7]:

    

import bokeh
import bokeh.plotting
import bokeh.io
bokeh.io.output_notebook();


    

    






    

        
        Loading BokehJS ...
    






    

In [8]:

    

fig = bokeh.plotting.figure(width=800, height=450)
fig.scatter(data['T'], data['C'], size=10)
fig.line(x, y, color='green')
fig.xaxis.axis_label = "T"
fig.yaxis.axis_label = "C"
bokeh.io.show(fig);