Python 2.7.10 |Anaconda 2.3.0 (64-bit)| (default, May 28 2015, 16:44:52) [MSC v.1500 64 bit (AMD64)]

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IPython 3.2.0 -- An enhanced Interactive Python.

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? -> Introduction and overview of IPython's features.

%quickref -> Quick reference.

help -> Python's own help system.

object? -> Details about 'object', use 'object??' for extra details.

%guiref -> A brief reference about the graphical user interface.

In [1]: d={

...: 'Name':'Angela Merkel',

...: 'Country':'Germany',

...: 'Profession':'Chancelor',

...: 'Age':60

...: }

In [2]: type(d)

Out[2]: dict

In [3]: print d['Name'],d['Age']

Angela Merkel 60

In [4]: d.keys()

Out[4]: ['Country', 'Age', 'Profession', 'Name']

In [5]: d.values()

Out[5]: ['Germany', 60, 'Chancelor', 'Angela Merkel']

In [6]: d.items()

Out[6]:

[('Country', 'Germany'),

('Age', 60),

('Profession', 'Chancelor'),

('Name', 'Angela Merkel')]

In [7]: birthday=True

In [8]: if birthday is

File "<ipython-input-8-cdd6aa25a1a1>", line 1

if birthday is

SyntaxError: invalid syntax

In [9]: if birthday is True:

...: d['Age']+=1;

...:

In [10]: print d['Age']

In [11]: for item in d.iteritems():

...: print item

...:

('Country', 'Germany')

('Age', 61)

('Profession', 'Chancelor')

('Name', 'Angela Merkel')

In [12]: for value in d.itervalues():

...: print type(value)

...:

In [13]: s=set(['u','d','ud','du','d','du'])

...: s

...:

Out[13]: {'d', 'du', 'u', 'ud'}

In [14]: t=set(['d','dd','uu','u'])

In [15]: s.union(t)

Out[15]: {'d', 'dd', 'du', 'u', 'ud', 'uu'}

In [16]: s.intersection(t)

Out[16]: {'d', 'u'}

In [17]: s.difference(t)

Out[17]: {'du', 'ud'}

In [18]: t.difference(s)

Out[18]: {'dd', 'uu'}

In [19]: s.symmetric_difference(t)

Out[19]: {'dd', 'du', 'ud', 'uu'}

In [20]: from random import randint

...: l=[randint(0,10) for i in range(1000)]

...: len(l)

...:

Out[20]: 1000

In [21]: l[:20]

Out[21]: [2, 7, 9, 9, 7, 1, 1, 1, 9, 9, 7, 5, 9, 7, 4, 9, 9, 8, 8, 6]

In [22]: s=set(l)

...: s

...:

Out[22]: {0, 1, 2, 3, 4, 5, 6, 7, 8, 9, 10}

In [23]: v=[0.5,0.75,1.0,1.5,2.0]

In [24]: m=[v,v,v]

...: m

...:

Out[24]:

[[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0]]

In [25]: m[1]

Out[25]: [0.5, 0.75, 1.0, 1.5, 2.0]

In [26]: m[1][0]

Out[26]: 0.5

In [27]: v1=[0.5,1.5]

...: v2=[1,2]

...: m=[v1,v2]

...: c=[m,m]

...: c

...:

Out[27]: [[[0.5, 1.5], [1, 2]], [[0.5, 1.5], [1, 2]]]

In [28]: c[1][1][0]

Out[28]: 1

In [29]: v=[0.5,0.75,1.0,1.5,2.0]

...: m=[v,v,v]

...: m

...:

Out[29]:

[[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0]]

In [30]: v[0]='Python'

...: m

...:

Out[30]:

[['Python', 0.75, 1.0, 1.5, 2.0],

['Python', 0.75, 1.0, 1.5, 2.0],

['Python', 0.75, 1.0, 1.5, 2.0]]

In [31]: v=[0.5,0.75,1.0,1.5,2.0]

...: m=3*[deepcopy(v),]

...: m

...:

Traceback (most recent call last):

File "<ipython-input-31-166915cf6c14>", line 2, in <module>

m=3*[deepcopy(v),]

NameError: name 'deepcopy' is not defined

In [32]: from copy import deepcopy

In [33]: v=[0.5,0.75,1.0,1.5,2.0]

...: m=3*[deepcopy(v),]

...: m

...:

Out[33]:

[[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0]]

In [34]: v[0]='Python'

In [35]: m

Out[35]:

[[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0],

[0.5, 0.75, 1.0, 1.5, 2.0]]

In [36]: import numpy as np

In [37]: a=np.array([0,0.5,1.0,1.5,2.0])

...: type(a)

...:

Out[37]: numpy.ndarray

In [38]: a[:2]

Out[38]: array([ 0. , 0.5])

In [39]: a.sum()

Out[39]: 5.0

In [40]: a.std()

Out[40]: 0.70710678118654757

In [41]: a.cumsum()

Out[41]: array([ 0. , 0.5, 1.5, 3. , 5. ])

In [42]: a*2

Out[42]: array([ 0., 1., 2., 3., 4.])

In [43]: a**2

Out[43]: array([ 0. , 0.25, 1. , 2.25, 4. ])

In [44]: np.sqrt(a)

Out[44]: array([ 0. , 0.70710678, 1. , 1.22474487, 1.41421356])

In [45]: b=np.array([a,a*2])

...: b

...:

Out[45]:

array([[ 0. , 0.5, 1. , 1.5, 2. ],

[ 0. , 1. , 2. , 3. , 4. ]])

In [46]: b[0]

Out[46]: array([ 0. , 0.5, 1. , 1.5, 2. ])

In [47]: b[0,2]

Out[47]: 1.0

In [48]: b.sum()

Out[48]: 15.0

In [49]: b.sum(axis=0)

Out[49]: array([ 0. , 1.5, 3. , 4.5, 6. ])

In [50]: b.sum(axis=1)

Out[50]: array([ 5., 10.])

In [51]: c=np.zeros((2,3,4),dtype='i',order='C')

In [52]: c

Out[52]:

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

[0, 0, 0, 0],

[0, 0, 0, 0]],

[[0, 0, 0, 0],

[0, 0, 0, 0],

[0, 0, 0, 0]]])

In [53]: d=np.ones_like(c,dtype='f16',order='C')

In [53]:

In [54]: d

Out[54]:

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 [55]: import random

...: I=5000

...:

In [56]: %time mat=[[random.gauss(0,1) for j in range(I)] for i in range(I)]

Wall time: 25.8 s

In [57]: %time reduce(lambda x,y:x+y,[reduce(lambda x,y:x+y,row) for row in mat])

Wall time: 2.97 s

Out[57]: 445.49932903342665

In [58]: %time mat=np.random.standard_normal((I,I))

Wall time: 1.13 s

In [59]: %time mat.sum()

Wall time: 28 ms

Out[59]: 2638.7219038800572

In [60]: dt=np.dtype([('Name','S10'),('Age','i4'),('Height','f'),('Children/Pets','i4',2)])

In [61]: s=np.array([('Smith',45,1.83,(0,1)),('Jones',53,1.72,(2,2))],dtype=dt)

In [62]: s

Out[62]:

array([('Smith', 45, 1.8300000429153442, [0, 1]),

('Jones', 53, 1.7200000286102295, [2, 2])],

dtype=[('Name', 'S10'), ('Age', '<i4'), ('Height', '<f4'), ('Children/Pets', '<i4', (2,))])

In [63]: s['Name']

Out[63]:

array(['Smith', 'Jones'],

dtype='|S10')

In [64]: s['Height'].mean()

Out[64]: 1.7750001

In [65]: s[1]['Age']

Out[65]: 53

In [66]: r=np.random.standard_normal((4,3))

...: s=np.random.standard_normal((4,3))

...:

In [67]: r+s

Out[67]:

array([[-1.08236119, -0.40845649, -1.39591288],

[ 0.67282979, -1.67472306, 1.02340313],

[ 0.25657081, 2.4666021 , -1.08896318],

[-0.65978059, -0.89335707, 2.04573807]])

In [68]: 2*r+3

Out[68]:

array([[ 0.4383362 , 2.06321283, 1.73050739],

[ 6.24047385, 1.95914397, 5.98931023],

[ 3.30047169, 5.48339121, 3.1089062 ],

[ 3.36953153, 1.69323078, 4.93485884]])

In [69]: s=np.random.standard_normal(3)

In [70]: r+s

Out[70]:

array([[-0.16614389, -0.12067005, -0.28967217],

[ 2.73492494, -0.17270448, 1.83972925],

[ 1.26492386, 1.58941914, 0.39952724],

[ 1.29945378, -0.30566107, 1.31250355]])

In [71]: s=np.random.standard_normal(4)

In [72]: r+s

Traceback (most recent call last):

File "<ipython-input-72-500bf6181e22>", line 1, in <module>

r+s

ValueError: operands could not be broadcast together with shapes (4,3) (4,)

In [73]: r.transpose()+s

Out[73]:

array([[-1.28805207, 2.92514733, 1.00283501, -0.78751221],

[-0.47561376, 0.78448238, 2.09429476, -1.62566259],

[-0.64196647, 2.79956551, 0.90705226, -0.00484856]])

In [74]: np.shape(r,T)

Traceback (most recent call last):

File "<ipython-input-74-65fbd7bcb393>", line 1, in <module>

np.shape(r,T)

NameError: name 'T' is not defined

In [75]: np.shape(4,s)

Traceback (most recent call last):

File "<ipython-input-75-550edbb0f81b>", line 1, in <module>

np.shape(4,s)

TypeError: shape() takes exactly 1 argument (2 given)

In [76]: np.shape(r,s)

Traceback (most recent call last):

File "<ipython-input-76-2f6caeb9b36c>", line 1, in <module>

np.shape(r,s)

TypeError: shape() takes exactly 1 argument (2 given)

In [77]: np.shape(r)

Out[77]: (4L, 3L)

In [78]: def f(x):

...: return 3*x+5

...:

In [79]: f(0.5)

Out[79]: 6.5

In [80]: f(r)

Out[80]:

array([[ 1.1575043 , 3.59481924, 3.09576109],

[ 9.86071078, 3.43871595, 9.48396534],

[ 5.45070754, 8.72508681, 5.1633593 ],

[ 5.5542973 , 3.03984618, 7.90228826]])

In [81]: import math

In [82]: math.sin(r)

Traceback (most recent call last):

File "<ipython-input-82-36a33c1d845d>", line 1, in <module>

math.sin(r)

TypeError: only length-1 arrays can be converted to Python scalars

In [83]: np.sin(r)

Out[83]:

array([[-0.95825405, -0.45145347, -0.59297325],

[ 0.99877806, -0.49725153, 0.99710266],

[ 0.14967133, 0.94633337, 0.05442619],

[ 0.18371629, -0.60787737, 0.82342984]])

In [84]: np.sin(np.pi)

Out[84]: 1.2246467991473532e-16

In [85]: x=np.random.standard_normal((5,10000000))\

...: y=2*x+3

File "<ipython-input-85-77676bfcb2ae>", line 1

x=np.random.standard_normal((5,10000000))y=2*x+3

SyntaxError: invalid syntax

In [86]: x=np.random.standard_normal((5,10000000))

...: y=2*x+3

...: C=np.array((x,y),order='C')

...: F=np.array((x,y),order='F')

...: x=0.0,y=0.0

File "<ipython-input-86-f82a1956aebd>", line 5

x=0.0,y=0.0

SyntaxError: can't assign to literal

In [87]: x=np.random.standard_normal((5,10000000))

...: y=2*x+3

...: C=np.array((x,y),order='C')

...: F=np.array((x,y),order='F')

...: x=0.0;y=0.0

...:

In [88]: C[:2].round(2)

Out[88]:

array([[[ 1.5 , 0.72, 1. , ..., 1.81, -1.73, -0.89],

[ 0.06, 2.27, 0.51, ..., -0.31, 0.13, -0.55],

[ 1.77, 1.52, -0.48, ..., -0.22, 1.05, 0.67],

[-0.96, 0.53, -1.19, ..., -0.3 , -1.59, -1.75],

[-1.12, -1.62, 1.14, ..., 0.34, 1.15, -0.11]],

[[ 6. , 4.43, 5.01, ..., 6.63, -0.46, 1.21],

[ 3.12, 7.54, 4.02, ..., 2.38, 3.27, 1.89],

[ 6.54, 6.04, 2.03, ..., 2.56, 5.1 , 4.33],

[ 1.08, 4.06, 0.63, ..., 2.39, -0.18, -0.49],

[ 0.76, -0.25, 5.29, ..., 3.68, 5.29, 2.79]]])

In [89]: %timeit C.sum()

10 loops, best of 3: 103 ms per loop

In [90]: %timeit F.sum()

10 loops, best of 3: 103 ms per loop

In [91]: %timeit C[0].sum(axis=0)

10 loops, best of 3: 92.4 ms per loop

In [92]: %timeit C[0].sum(axis=1)

10 loops, best of 3: 50.8 ms per loop

In [93]: %timeit F.sum(axis=0)

1 loops, best of 3: 770 ms per loop

In [94]: %timeit F.sum(axis=1)

1 loops, best of 3: 2.1 s per loop

In [95]: