In [1]:

    

import numpy as np


    

In [3]:

    

# Array creation
shape = (3,4)
dtype = np.int32
order = 'C'


    

In [17]:

    

# Creates array without initializing entries
array1 = np.empty(shape, dtype)
array1


    

    
Out[17]:





array([[-2043364424,       32730,  1725429712,       21959],
       [          0,           0,           0,           0],
       [          0,           0,           0,           0]], dtype=int32)

In [18]:

    

# Returns array with same shape and type as given array
array2 = np.empty_like(array1)
array2


    

    
Out[18]:





array([[-2043364488,       32730, -2043364488,       32730],
       [          0,           0,           0,           0],
       [ 1760273608,       32730,  1760273672,       32730]], dtype=int32)

In [24]:

    

# Returns a 2-D matrix with ones on diagonal and zeros elsewhere
array3 = np.eye(N=4)
print(array3)

array4 = np.eye(N=4,M=2)
print(array4)

array5 = np.eye(N=4, k=1)
print(array5)
# k defines the amount by which you want to shift the main diagonal


    

    




[[ 1.  0.  0.  0.]
 [ 0.  1.  0.  0.]
 [ 0.  0.  1.  0.]
 [ 0.  0.  0.  1.]]
[[ 1.  0.]
 [ 0.  1.]
 [ 0.  0.]
 [ 0.  0.]]
[[ 0.  1.  0.  0.]
 [ 0.  0.  1.  0.]
 [ 0.  0.  0.  1.]
 [ 0.  0.  0.  0.]]

In [25]:

    

# To get a identity matrix
np.identity(4)


    

    
Out[25]:





array([[ 1.,  0.,  0.,  0.],
       [ 0.,  1.,  0.,  0.],
       [ 0.,  0.,  1.,  0.],
       [ 0.,  0.,  0.,  1.]])

In [27]:

    

np.ones((4,3,2), dtype=np.float32)


    

    
Out[27]:





array([[[ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.]],

       [[ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.]],

       [[ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.]],

       [[ 1.,  1.],
        [ 1.,  1.],
        [ 1.,  1.]]], dtype=float32)

In [28]:

    

np.ones_like(array1)


    

    
Out[28]:





array([[1, 1, 1, 1],
       [1, 1, 1, 1],
       [1, 1, 1, 1]], dtype=int32)

In [29]:

    

np.zeros_like(array1)


    

    
Out[29]:





array([[0, 0, 0, 0],
       [0, 0, 0, 0],
       [0, 0, 0, 0]], dtype=int32)

In [30]:

    

np.zeros(shape)


    

    
Out[30]:





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

In [32]:

    

# To create an array with your specified value in all the cells
np.full((4,3), fill_value=10.)


    

    
Out[32]:





array([[ 10.,  10.,  10.],
       [ 10.,  10.,  10.],
       [ 10.,  10.,  10.],
       [ 10.,  10.,  10.]])

In [36]:

    

np.full_like(array1, fill_value=1+9j, dtype=complex)


    

    
Out[36]:





array([[ 1.+9.j,  1.+9.j,  1.+9.j,  1.+9.j],
       [ 1.+9.j,  1.+9.j,  1.+9.j,  1.+9.j],
       [ 1.+9.j,  1.+9.j,  1.+9.j,  1.+9.j]])

In [54]:

    

# So far we discussed a new array creation and 
# now we discuss how to create a new array using existing data
ref_array = 10*np.random.rand(3,4)
ref_array


    

    
Out[54]:





array([[ 3.87194212,  6.0227274 ,  9.40008761,  1.55617568],
       [ 1.62449474,  8.24526602,  8.9153371 ,  0.13569454],
       [ 1.31895575,  5.89019258,  8.51156338,  2.12524318]])

In [58]:

    

array1 = np.array(ref_array, dtype=np.int, copy=True, order='C')
print(array1)

# Now why we copied
array1[0,1] = 10
print(array1)
print(ref_array)

# Now if we didn't copy
array2 = np.array(ref_array, copy=False)
array2[0,2] = 15
print('\n', array2)

print(ref_array)
# As you can notice that if we didn.t copy than the array from which
# we copied also got the changes


    

    




[[ 3  6 15  1]
 [ 1  8  8  0]
 [ 1  5  8  2]]
[[ 3 10 15  1]
 [ 1  8  8  0]
 [ 1  5  8  2]]
[[  3.87194212   6.0227274   15.           1.55617568]
 [  1.62449474   8.24526602   8.9153371    0.13569454]
 [  1.31895575   5.89019258   8.51156338   2.12524318]]

 [[  3.87194212   6.0227274   15.           1.55617568]
 [  1.62449474   8.24526602   8.9153371    0.13569454]
 [  1.31895575   5.89019258   8.51156338   2.12524318]]
[[  3.87194212   6.0227274   15.           1.55617568]
 [  1.62449474   8.24526602   8.9153371    0.13569454]
 [  1.31895575   5.89019258   8.51156338   2.12524318]]

In [60]:

    

# To create an array from objects that can be converted to arrays
# like lists, tuples, ndarrays
array2 = np.asarray(ref_array)
print(array2)

array3 = np.asarray([[4,5,6],[1,2,3]])
print(array3)


    

    




[[  3.87194212   6.0227274   15.           1.55617568]
 [  1.62449474   8.24526602   8.9153371    0.13569454]
 [  1.31895575   5.89019258   8.51156338   2.12524318]]
[[4 5 6]
 [1 2 3]]

In [65]:

    

# To get a contiguous array in memory
array4 = np.ascontiguousarray(ref_array)
array4


    

    
Out[65]:





array([[  3.87194212,   6.0227274 ,  15.        ,   1.55617568],
       [  1.62449474,   8.24526602,   8.9153371 ,   0.13569454],
       [  1.31895575,   5.89019258,   8.51156338,   2.12524318]])

In [66]:

    

# To simply copy an array
array5 = np.copy(ref_array)
array5


    

    
Out[66]:





array([[  3.87194212,   6.0227274 ,  15.        ,   1.55617568],
       [  1.62449474,   8.24526602,   8.9153371 ,   0.13569454],
       [  1.31895575,   5.89019258,   8.51156338,   2.12524318]])

In [73]:

    

# if you want to create an array from a buffer say a srtring
dt = np.dtype(int)
dt = dt.newbyteorder('>')
np.frombuffer('hello', dtype=dt)


    

    




---------------------------------------------------------------------------
AttributeError                            Traceback (most recent call last)
<ipython-input-73-b66b204e14a8> in <module>()
      2 dt = np.dtype(int)
      3 dt = dt.newbyteorder('>')
----> 4 np.frombuffer('hello', dtype=dt)

AttributeError: 'str' object has no attribute '__buffer__'

In [90]:

    

# To create an array from a file
dt = np.dtype(int)
dt = dt.newbyteorder('>')
np.fromfile(file='vimTutorial.txt', dtype=dt)


    

    
Out[90]:





array([5432882060864155506, 2336646335973647726, 8391061973054070892,
       7306655128079005285, 7882828167451910189, 2338616291577788005,
       2337208112243304814, 7286934626993310315, 2318545067618364783,
       8531260732088852588, 7597120811504241260, 2318545058932418676,
       2336646335973647726, 8361506904289124397, 4476706050239985769,
       8367818302828869481, 8388076856227689313, 8532472353939220577,
       7955438811540518769, 2318577692278988901, 8676593839527851109,
       8223699782014168679, 2334669371110750240, 3260078864715506789,
       7810777106429012069, 2333823799648150388, 7309940812453342836,
       7306000102812836725, 8247057878230919456, 3260078864715507557,
       8367804030775140457, 7958816508742757743, 7234224000819227936,
       8388065495758692722, 7017580623428612207, 7597137600240968313,
       2333838166331453962, 6998643565789474592, 7450488841110975599,
       2335519323670606880, 7885631858047087215, 7863412941110931045,
       8679597993174856289, 7166464447898218345, 7959097922184968480,
       7166760222146562570, 7239290304709293167, 2334102023117759776,
       7382085253699171698, 8317992520494181484, 2334393358181426720,
       8606223186924938272, 3260078864715506789, 7810777106428097135,
       7863394305471377266, 2338610020903580776, 7286935721886117734,
       2336358228040115232, 3260078864715506789, 7810777106429012069,
       2333831547736319348, 7286943397164943882, 8439795446548033312,
       8461811178324387941, 2336349455195857251, 8388358299364696109,
       4476706050241031780, 8007507255763170408, 7286933497047836517,
       8295746435327008876, 7597120715852098878, 2338616291577788005,
       2338616291694765344, 8319381555862597478, 2336358229560920665,
       8031361121693606005, 8315087945798083173, 8246970979836780908,
       7791355347073000303, 7885065649355169908, 8007525917467697524,
       2338608891343959144, 7022273377766040165, 8223695404000373101,
       7310197658759689330, 7794999917943857268, 8007513822902379381,
       8246765392737889385, 7954799975608443493, 8223680046332667497,
       7810684787197043978, 5125146120803348256, 7885651649257303840,
       7093015981566271599, 7358994394099550823, 7430989130017042208,
       7885651649257303840, 8319381555862597478, 2334669371110732857,
       3478784615854929007, 2336646335968605295, 2336358229562388085,
       7881973838692759856,  723442862983570783, 8606223187290439200,
       8389960259432641647, 2334669397122359411, 8029185171371553380,
       2333258732058845300, 7522454419870542196, 7597412181984288867,
       8462953572274433568, 3260078864715509103, 8531260753575964261,
       8679598062012556639, 8606223186923495469, 4476706050240898401,
       8242546231384961568, 8388065496026672485, 6879089492928522606,
       2338042715741254501, 2334106446932964457, 8029390821770210848,
       7237970109630082415, 8289507829152048416, 8099849692699238445,
       4476706050240898401, 7163010831661277299, 8029184900670714465,
       8315063571842231341, 8007449882320465696, 7453211592774153320,
       7286947833716304239, 8463143792037554548, 7597412181984288867,
       8462953572274422644, 8245014809904234016, 8389960285208077600,
       8389960315154276462, 7311721677710127988, 7597412181984288867,
       8462953572274407973, 2318577692278988903, 8007528129186786657,
       8386661733127102576, 8030878509237759520, 8027138987362640242,
       7020676860844665203,  737028136615699830, 7597420233974769016,
       8367737852509971824, 7809632516211504232, 7286936800163230752,
       8026368316985929315, 7286946681507050085, 8532473519263479657,
       8387989990317301870, 7311721677592600673, 2336358229560932979,
       3418357897278091125, 8299974020358745959, 2318577692244799596,
       7017564189512264812, 2337196010451920238, 7166107094903451762,
       7311146972515344503, 7598812959781255284, 2339461068539062638,
       2333181731952026890, 4189881675908146223, 7954895362297179454,
       2338616291409551726, 7450396821335274866, 7308888531255911968,
       8028902326299882600, 2336916818971160096, 8388065496094240620,
       7286936800061622842, 2698552757331439470, 7311364641150479678,
       2338616291459952228, 2334402159945785455, 7161696966637675365,
       2337199021122937888, 8604536553637569911, 2333267458274059636,
       2333181749282958704, 8367819397849686132, 8007510565540471596,
       2554437569255583598, 7311364640027262496, 8389960306514750561,
       7162166479645075488, 8028902326299882600, 2336916818971160096,
       8388065496009895527, 7286951123660007782, 7594586436884045859,
       2842626828842132335, 7885065649349733694, 2338616291695882341,
       2333181693397921121, 7954518444758559086, 2333181749282958704,
       8367795234408523128, 7305812163298353523, 2333181761999432812,
       2333831547685269092,  723454973463382348, 4994000836928482622,
       2338616291677533797, 2333181706182092064, 8389960315154559776,
       5064663116504190277,  736938264369317449, 5495884962651775021,
       4476706050240832877, 8031718175528215840, 5064663116504190277,
        753825366319068271, 2335232299646085479, 7503123901613154416,
       7021802451680764020, 7522454368329889133, 7308906927691293728,
       8388065829642596965, 8319028370339143777, 7954518526182911520,
       4212871355116319855, 2338319792666080360, 7022273386020762656,
       8027139070174240870, 7596557766071820358, 5281672665404097824,
       3260078864715510373, 8390884940243607668, 7522454436882969632,
       8027139070174524192, 5064663116504190277, 2333273764695401760,
       8101821151424638830, 2337199021206562080, 7166760222146562618,
       8223609669250198846, 2338616291627917667, 7286952188807507829,
       8390335247412061728, 8388065495759154541, 7881702199194839406,
       7286940118797281381, 2339172973881485676, 7280643219995311648,
       8389960298041271141, 2333181740475424876, 7597120811168068709,
       8223682275661016946, 2333267458273930868, 7309940644394517842,
       6061965423633828431, 2318577692278988912, 7809632514823233646,
       7311348170056688928, 7090185817211347043, 8462953572275872110,
       7214878127870469490, 2326476802065847328, 7885631857677901869,
       4476706050240507766, 7286955517629702263, 7598528186186342497,
       2336358229560942880, 3260078864715508078, 8315178130095763232,
       8388065495943051380, 2337490748086446447, 7935465027847550322,
       8317992425144462654, 2338616291510612837, 8247252502074913896,
       7286935721886117734, 2338608890057091438, 7280643262944984608,
       8389960242258016544, 8388065495842645864, 7811888726977897504,
       8387242320834076717, 4476706050240700787, 8387121397853334846,
       2338616291426398821, 7166472174543531888, 8367816099376428911,
       8242748880955929704, 7286933553167953778,  723454957444145257,
       7142758753865330464, 7595160721523286132, 7522454363801085216,
       8604243002365342565, 7021784069120616202, 4211821625955871859,
       2335524463803506804, 8007527017041913961, 7451324783446750240,
       8315159430931423329, 7954518444797620000, 7597118964399695214,
       8367814986918159208,  737028368605671023, 7593948775364719727,
       2334106373868774688, 7595160721523286115, 7022067434117425516,
       7286951076349240168, 7597122915048580468, 2336927750938632237,
       4476706050239916385, 7092154703066785640, 7811888726973379685,
       8679573420895659372, 8079507476358393632, 7450488841093606512,
        739562798608897824, 4860635706782326829, 4476706050239850593,
       7955438456873250080, 8604529940713927434, 4208725368189772142,
       8745818331605200225, 7954518220531069807, 7882742334225214817,
       7954609424754173216, 8587274200660792402, 5489118477147532915,
       7310032883566928997, 8659331872345911853, 7881702273299802730])

In [92]:

    

# To create a character array
np.chararray


    

    




---------------------------------------------------------------------------
TypeError                                 Traceback (most recent call last)
<ipython-input-92-48335dcfc097> in <module>()
      1 # To create a character array
----> 2 np.chararray(['a','a','b'])

~/anaconda3/lib/python3.6/site-packages/numpy/core/defchararray.py in __new__(subtype, shape, itemsize, unicode, buffer, offset, strides, order)
   1833         if buffer is None:
   1834             self = ndarray.__new__(subtype, shape, (dtype, itemsize),
-> 1835                                    order=order)
   1836         else:
   1837             self = ndarray.__new__(subtype, shape, (dtype, itemsize),

TypeError: 'str' object cannot be interpreted as an integer

In [95]:

    

# it creates an array from starting=1 to end=10 taking strides=3
np.arange(start=1, stop=10, step=3)


    

    
Out[95]:





array([1, 4, 7])

In [98]:

    

# To divide the number into equal number of sample
np.linspace(start=1,stop=10, num=10, endpoint=True)
# endp[oint specifies whether to include the endpoint in the smaples


    

    
Out[98]:





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

In [104]:

    

# To get numbers on a logspace
# It starts at base**start and ends with base**stop
array1= np.logspace(start=1, stop=10, num=10, endpoint=True, base=10)
print (array1, '\n')

array2 = np.logspace(start=1, stop=10, num=10, base=2)
print(array2)


    

    




[  1.00000000e+01   1.00000000e+02   1.00000000e+03   1.00000000e+04
   1.00000000e+05   1.00000000e+06   1.00000000e+07   1.00000000e+08
   1.00000000e+09   1.00000000e+10] 

[    2.     4.     8.    16.    32.    64.   128.   256.   512.  1024.]

In [107]:

    

# Each output is a constant multiple of previous output
array3 = np.geomspace(start=1, stop=10, num=10)
print(array3, '\n')

for a in range(1,10):
    print(array3[a]/array3[a-1])

array4 = np.geomspace(start=1, stop=1000, num=4)
print('\n', array4)


    

    




[  1.           1.29154967   1.66810054   2.15443469   2.7825594
   3.59381366   4.64158883   5.9948425    7.74263683  10.        ] 

1.29154966501
1.29154966501
1.29154966501
1.29154966501
1.29154966501
1.29154966501
1.29154966501
1.29154966501
1.29154966501

 [    1.    10.   100.  1000.]

In [110]:

    

# To extract a diagonal from a matrix
print(ref_array, '\n')

np.diag(ref_array, k=1)


    

    




[[  3.87194212   6.0227274   15.           1.55617568]
 [  1.62449474   8.24526602   8.9153371    0.13569454]
 [  1.31895575   5.89019258   8.51156338   2.12524318]] 







    
Out[110]:





array([ 6.0227274 ,  8.9153371 ,  2.12524318])

In [115]:

    

array5 = np.diagflat(ref_array , k=0)
for a in array5:
    print(a, '\n')


    

    




[ 3.87194212  0.          0.          0.          0.          0.          0.
  0.          0.          0.          0.          0.        ] 

[ 0.         6.0227274  0.         0.         0.         0.         0.         0.
  0.         0.         0.         0.       ] 

[  0.   0.  15.   0.   0.   0.   0.   0.   0.   0.   0.   0.] 

[ 0.          0.          0.          1.55617568  0.          0.          0.
  0.          0.          0.          0.          0.        ] 

[ 0.          0.          0.          0.          1.62449474  0.          0.
  0.          0.          0.          0.          0.        ] 

[ 0.          0.          0.          0.          0.          8.24526602
  0.          0.          0.          0.          0.          0.        ] 

[ 0.         0.         0.         0.         0.         0.         8.9153371
  0.         0.         0.         0.         0.       ] 

[ 0.          0.          0.          0.          0.          0.          0.
  0.13569454  0.          0.          0.          0.        ] 

[ 0.          0.          0.          0.          0.          0.          0.
  0.          1.31895575  0.          0.          0.        ] 

[ 0.          0.          0.          0.          0.          0.          0.
  0.          0.          5.89019258  0.          0.        ] 

[ 0.          0.          0.          0.          0.          0.          0.
  0.          0.          0.          8.51156338  0.        ] 

[ 0.          0.          0.          0.          0.          0.          0.
  0.          0.          0.          0.          2.12524318] 

In [122]:

    

# To generate a vandemonde matrix
# The columns of output matrix are powers of input vector
x = np.vander(np.array([1,2,3,5]), N=3, increasing=True)
x


    

    
Out[122]:





array([[ 1,  1,  1],
       [ 1,  2,  4],
       [ 1,  3,  9],
       [ 1,  5, 25]])

In [126]:

    

# Matrix
# Interpret the input as a matrix
x = np.array([[1,2], [3,4]])
m = np.mat(x)
print(x[0,0])
print(m[0,0])

# What this allows is allow you to use matrix operations on the numpy array


    

    




1
1

In [138]:

    

# Basic operations
ref_array = 10*np.random.rand(3,4)
ref_array


    

    
Out[138]:





array([[ 3.46824036,  7.67681693,  8.2756161 ,  7.32539822],
       [ 4.8000464 ,  6.80180895,  5.63463621,  8.51450354],
       [ 0.82627203,  0.50071077,  1.99507292,  6.79505002]])

In [154]:

    

# Chaning array shape
ref_array = np.arange(10)
print(ref_array)


    

    




[0 1 2 3 4 5 6 7 8 9]

In [142]:

    

ref_array = ref_array.reshape(5,2)
ref_array


    

    
Out[142]:





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

In [144]:

    

# To get a contiguous flattened array
np.ravel(ref_array)


    

    
Out[144]:





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

In [149]:

    

# Transposiing
ref_array = np.transpose(ref_array)
ref_array


    

    
Out[149]:





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

In [156]:

    

# if you want to expand shape of an array
ref_array = np.expand_dims(ref_array, axis=2)
ref_array


    

    
Out[156]:





array([[[0]],

       [[1]],

       [[2]],

       [[3]],

       [[4]],

       [[5]],

       [[6]],

       [[7]],

       [[8]],

       [[9]]])

In [163]:

    

# Joining arrays
a1 = np.random.randint(50, size=(4,3,2))
a2 = np.random.randint(50, size=(4,3,3))
print(a1, '\n', a2)


    

    




[[[14 37]
  [49  2]
  [37 23]]

 [[23 47]
  [47 46]
  [14 21]]

 [[27 10]
  [49 42]
  [45 23]]

 [[49 29]
  [22 20]
  [17 24]]] 
 [[[ 7 46 21]
  [39 32 11]
  [23 25  7]]

 [[22 39 20]
  [25 22  7]
  [15 30 20]]

 [[43 11  6]
  [29 39 44]
  [30 25  0]]

 [[14 35 12]
  [ 3 18  5]
  [41 48 44]]]

In [164]:

    

np.concatenate((a1,a2), axis=2)


    

    
Out[164]:





array([[[14, 37,  7, 46, 21],
        [49,  2, 39, 32, 11],
        [37, 23, 23, 25,  7]],

       [[23, 47, 22, 39, 20],
        [47, 46, 25, 22,  7],
        [14, 21, 15, 30, 20]],

       [[27, 10, 43, 11,  6],
        [49, 42, 29, 39, 44],
        [45, 23, 30, 25,  0]],

       [[49, 29, 14, 35, 12],
        [22, 20,  3, 18,  5],
        [17, 24, 41, 48, 44]]])

In [167]:

    

# Stacking two same dimensional arrays along a new axis
a1 = np.random.randint(50, size=(4,3))
a2 = np.random.randint(50, size=(4,3))
print(a1, '\n', a2, '\n')

np.stack((a1, a2), axis=0)


    

    




[[ 1 41 42]
 [20 12 18]
 [ 0 25 22]
 [41 43 22]] 
 [[46 27  2]
 [48 10 27]
 [ 0  7 40]
 [35 11 20]] 







    
Out[167]:





array([[[ 1, 41, 42],
        [20, 12, 18],
        [ 0, 25, 22],
        [41, 43, 22]],

       [[46, 27,  2],
        [48, 10, 27],
        [ 0,  7, 40],
        [35, 11, 20]]])

In [168]:

    

# To stack arrays horizontally i.e. columsn wise)
np.hstack((a1, a2))


    

    
Out[168]:





array([[ 1, 41, 42, 46, 27,  2],
       [20, 12, 18, 48, 10, 27],
       [ 0, 25, 22,  0,  7, 40],
       [41, 43, 22, 35, 11, 20]])

In [169]:

    

# To stack arrays vertically i.e. row wise
np.vstack((a1, a2))


    

    
Out[169]:





array([[ 1, 41, 42],
       [20, 12, 18],
       [ 0, 25, 22],
       [41, 43, 22],
       [46, 27,  2],
       [48, 10, 27],
       [ 0,  7, 40],
       [35, 11, 20]])

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# to create blocks of


    

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