In [20]:

    

import numpy as np
b = np.array([(1,2), (2, 1)])

print(b)
print()
print(2*b)
print()
print(b*b)
print()
print(b**2)
print()
print(1/b)  # Warning!


    

    




[[1 2]
 [2 1]]

[[2 4]
 [4 2]]

[[1 4]
 [4 1]]

[[1 4]
 [4 1]]

[[ 1.   0.5]
 [ 0.5  1. ]]

In [21]:

    

a = np.array([1, 2, 3])
b = np.array([2, 3, 4])
a * b, a.dot(b)


    

    
Out[21]:





(array([ 2,  6, 12]), 20)

In [29]:

    

A = np.matrix([
    [1, 2], 
    [2, 1]
])

w, v = np.linalg.eig(A)
print(w)  # Eigenvalues
print(v)  # Eingenvector
A


    

    




[ 3. -1.]
[[ 0.70710678 -0.70710678]
 [ 0.70710678  0.70710678]]






    
Out[29]:





matrix([[1, 2],
        [2, 1]])

In [4]:

    

import pandas as pd


    

In [5]:

    

df = pd.read_csv("./auxFiles/pandasExample.csv")


    

In [6]:

    

df.head()


    

    
Out[6]:







  

    

      

      
Stores
      
Code
      
x
      
y
      
Group
      
Region
    
  
  

    

      
0
      
Bayswater
      
Bay
      
-37.825908
      
145.279284
      
East
      
East
    
    

      
1
      
Box Hill
      
Box
      
-37.831194
      
145.131673
      
East
      
East
    
    

      
2
      
Croydon
      
Cro
      
-37.766767
      
145.303188
      
East
      
East
    
    

      
3
      
Nunawading
      
Nun
      
-37.819239
      
145.166277
      
East
      
East
    
    

      
4
      
Scoresby
      
Sco
      
-37.895472
      
145.241515
      
East
      
East
    
  

In [7]:

    

df.shape


    

    
Out[7]:





(41, 6)

In [8]:

    

df.columns


    

    
Out[8]:





Index(['Stores', 'Code', 'x', 'y', 'Group', 'Region'], dtype='object')

In [9]:

    

df.dtypes


    

    
Out[9]:





Stores     object
Code       object
x         float64
y         float64
Group      object
Region     object
dtype: object

In [10]:

    

xDf = df['x']
yDf = df['y']


    

In [67]:

    

import matplotlib.pyplot as plt

%matplotlib inline

rcParams["figure.figsize"] = [12.0, 6.0]


    

In [68]:

    

x = numpy.linspace(0, 2*numpy.pi, 50)
y1 = numpy.sin(x)
y2 = numpy.sin(2*x)

plot(x, y1)


    

    
Out[68]:





[<matplotlib.lines.Line2D at 0x7faa817d50b8>]






    

In [69]:

    

plot(x, y1, x, y2)


    

    
Out[69]:





[<matplotlib.lines.Line2D at 0x7faa81798550>,
 <matplotlib.lines.Line2D at 0x7faa81798c88>]






    

In [70]:

    

scatter(x, y1)


    

    
Out[70]:





<matplotlib.collections.PathCollection at 0x7faa816dea58>






    

In [71]:

    

hist(rand(1000), 10);


    

In [72]:

    

yerror = rand(50)
errorbar(x, y1, yerror)


    

    
Out[72]:





<Container object of 3 artists>






    

In [73]:

    

data = rand(100)
boxplot(data);


    

In [74]:

    

import matplotlib.image as image
img = image.imread('./auxFiles/logo.png')
imshow(img)


    

    
Out[74]:





<matplotlib.image.AxesImage at 0x7faa81617d30>






    

In [99]:

    

from IPython.display import HTML
HTML('<iframe src=http://www.unimelb.edu.au width=700 height=350></iframe>')


    

    
Out[99]:

In [ ]:

    

from IPython.display import Image
Image("./auxFiles/logo.png")


    

In [ ]:

    

from IPython.display import YouTubeVideo
YouTubeVideo('HW29067qVWk')


    

In [100]:

    

# %load https://matplotlib.org/_downloads/unicode_minus.py


    

In [109]:

    

?


    

In [ ]:

    

%time