Analyzing Data from Multiple Files



In [4]:

    
import glob

# The glob library contains a single function, also called glob, 
# that finds files whose names match a pattern.



In [5]:

    
print(glob.glob('data/inflammation*.csv'))









    



['data/inflammation-01.csv', 'data/inflammation-02.csv', 'data/inflammation-03.csv', 'data/inflammation-04.csv', 'data/inflammation-05.csv', 'data/inflammation-06.csv', 'data/inflammation-07.csv', 'data/inflammation-08.csv', 'data/inflammation-09.csv', 'data/inflammation-10.csv', 'data/inflammation-11.csv', 'data/inflammation-12.csv']

This is has created a list with values of filenames that matched the glob above.



In [6]:

    
# Lets load two libraries
import numpy                # Fundamental package for scientific computing with Python.
import matplotlib.pyplot    # Provides a MATLAB-like plotting framework.
# this allows plots to appear directly in the notebook
%matplotlib inline



In [7]:

    
filenames = glob.glob('data/inflammation*.csv')
filenames = filenames[0:3]



In [8]:

    
filenames









    Out[8]:





['data/inflammation-01.csv',
 'data/inflammation-02.csv',
 'data/inflammation-03.csv']



In [9]:

    
for f in filenames:
    print(f)









    



data/inflammation-01.csv
data/inflammation-02.csv
data/inflammation-03.csv



In [10]:

    
for f in filenames:
    print(f)
    
    data = numpy.loadtxt(fname=f, delimiter=',') # load the data in to into the data variable
    fig = matplotlib.pyplot.figure(figsize=(10.0, 3.0)) # create a plotting figure
    
    axes1 = fig.add_subplot(1, 3, 1)  
    axes2 = fig.add_subplot(1, 3, 2)
    axes3 = fig.add_subplot(1, 3, 3)

    axes1.set_ylabel('average')
    axes1.plot(data.mean(axis=0))

    axes2.set_ylabel('max')
    axes2.plot(data.max(axis=0))

    axes3.set_ylabel('min')
    axes3.plot(data.min(axis=0))

    fig.tight_layout()
    matplotlib.pyplot.show()









    



data/inflammation-01.csv






    












    



data/inflammation-02.csv






    












    



data/inflammation-03.csv

Creating Functions



In [1]:

    
def fahr_to_kelvin(temp):
    return ((temp - 32) * (5/9)) + 273.15



In [2]:

    
fahr_to_kelvin(100)









    Out[2]:





310.92777777777775



In [3]:

    
fahr_to_kelvin(60)









    Out[3]:





288.7055555555555



In [9]:

    
def kelvin_to_celsius(temp_k):
    return temp_k - 273.15



In [10]:

    
kelvin_to_celsius(310.92777777777775)









    Out[10]:





37.77777777777777



In [4]:

    
def print_hello_world():
    print("Hello World")
    print("Hello everybody")



In [5]:

    
print_hello_world()









    



Hello World
Hello everybody



In [7]:

    
def print_hello_name(name):
    print("Hello %s" % name)



In [8]:

    
print_hello_name("Andre")









    



Hello Andre



In [11]:

    
def get_value_from_input(value):
    if value < 10:
        return value
    else:
        return value * 10



In [12]:

    
x = get_value_from_input(5)
x









    Out[12]:





5



In [13]:

    
y = get_value_from_input(15)



In [14]:

    
y









    Out[14]:





150



In [ ]: