In [1]:

    

import numpy as np
np.__version__


    

    
Out[1]:





'1.12.1'

In [3]:

    

# Create a simple numpy array
ar1 = np.array([1,2,3,4,5])
ar1


    

    
Out[3]:





array([1, 2, 3, 4, 5])

In [4]:

    

# Find the type
type(ar1)


    

    
Out[4]:





numpy.ndarray

In [6]:

    

np.size(ar1)


    

    
Out[6]:





5

In [9]:

    

ar1.dtype


    

    
Out[9]:





dtype('int64')

In [10]:

    

ar2 = np.array([1.0,2.0,3.0])
ar2.dtype


    

    
Out[10]:





dtype('float64')

In [11]:

    

ar3 = np.array([1,1.0])
ar3.dtype


    

    
Out[11]:





dtype('float64')

In [12]:

    

ar3


    

    
Out[12]:





array([ 1.,  1.])

In [14]:

    

ar4 = np.array([1,1.0,'Hello'])
ar4.dtype
ar4


    

    
Out[14]:





array(['1', '1.0', 'Hello'], 
      dtype='<U32')

In [15]:

    

# shorthand to repeat a sequence n times
ar5 = np.array([0]*10)
ar5


    

    
Out[15]:





array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

In [17]:

    

# Convert Python range to np array
ar6 = np.array(range(10))
ar6


    

    
Out[17]:





array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])

In [20]:

    

# create a numpy of zeroes
np.zeros(10)


    

    
Out[20]:





array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

In [21]:

    

# force to get only integers
np.zeros(10,dtype='int')


    

    
Out[21]:





array([0, 0, 0, 0, 0, 0, 0, 0, 0, 0])

In [25]:

    

# make a range starting at 1 for 10 values
np.arange(1,11)


    

    
Out[25]:





array([ 1,  2,  3,  4,  5,  6,  7,  8,  9, 10])

In [23]:

    

# range value but increment by 2
np.arange(0,10,2)


    

    
Out[23]:





array([0, 2, 4, 6, 8])

In [28]:

    

# integers in reverse order
np.arange(15,10,-1)


    

    
Out[28]:





array([15, 14, 13, 12, 11])

In [34]:

    

# linspace similar to array but produces exact number of items between start and end values
np.linspace(0,5,2)


    

    
Out[34]:





array([ 0.,  5.])

In [37]:

    

# multiply array elements
ar7 = np.arange(0,10)
ar7 * 2


    

    
Out[37]:





array([ 0,  2,  4,  6,  8, 10, 12, 14, 16, 18])

In [38]:

    

# addition of 2 arrays
ar8 = np.arange(10,20)
ar7 + ar8


    

    
Out[38]:





array([10, 12, 14, 16, 18, 20, 22, 24, 26, 28])

In [40]:

    

# create a matrix
np.array([[1,2,3],[4,5,6]])


    

    
Out[40]:





array([[1, 2, 3],
       [4, 5, 6]])

In [44]:

    

m = np.arange(0,20).reshape(5,4)
m


    

    
Out[44]:





array([[ 0,  1,  2,  3],
       [ 4,  5,  6,  7],
       [ 8,  9, 10, 11],
       [12, 13, 14, 15],
       [16, 17, 18, 19]])

In [45]:

    

# Get the size of the matrix
np.size(m)


    

    
Out[45]:





20

In [46]:

    

# Get the size of the row (0 -> row)
np.size(m,0)


    

    
Out[46]:





5

In [47]:

    

# Get the size of the column (1 -> Col)
np.size(m,1)


    

    
Out[47]:





4

In [48]:

    

# select an element -> row 1 col2
m[1,2]


    

    
Out[48]:





6

In [49]:

    

# select a row
m[2,]


    

    
Out[49]:





array([ 8,  9, 10, 11])

In [51]:

    

# select a column
m[:,3]


    

    
Out[51]:





array([ 3,  7, 11, 15, 19])

In [52]:

    

# which items are less than 2
arr8 = np.arange(10)
arr8 < 2


    

    
Out[52]:





array([ True,  True, False, False, False, False, False, False, False, False], dtype=bool)

In [54]:

    

(arr8 < 2) | (arr8 > 5)


    

    
Out[54]:





array([ True,  True, False, False, False, False,  True,  True,  True,  True], dtype=bool)

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