NUMPY Queries

1.Import the numpy package under the name np

Import numpy as np


2.Print the numpy version and the configuration

>>> print(np.__version__)
1.11.2
>>> np.show_config()

3.Create a null vector of size 10

>>> Z = np.zeros(10)

>>> Z

4.How to get the documentation of the numpy add function from the command line?

>>>np.info(np.add)

5.Create a null vector of size 10 but the fifth value which is 1

>>>Z = np.zeros(10)
>>>Z[4] = 1
>>>print(Z)

6.Create a vector with values ranging from 10 to 49 

>>>Z = np.arange(10,50)
>>>print(Z)

7.Reverse a vector (first element becomes last)

>>>Z = np.arange(50)

>>>Z = Z[::-1]

8.Create a 3x3 matrix with values ranging from 0 to 8

>>>Z = np.arange(9).reshape(3,3)
>>>print(Z)

9.Find indices of non-zero elements from [1,2,0,0,4,0]

>>>nz = np.nonzero([1,2,0,0,4,0])
>>>print(nz)

10.Create a 3x3 identity matrix

>>>Z = np.eye(3)
>>>print(Z)

or

>>>Z=np.identity(3)

11.Create a 3x3x3 array with random values

>>>X=np.random.random((3,3,3))

12.Create a 10x10 array with random values and find the minimum and maximum values

>>>Z = np.random.random((10,10))
>>>Zmin, Zmax = Z.min(), Z.max()
>>>print(Zmin, Zmax)

13.Create a random vector of size 30 and find the mean value
>>>Z = np.random.random(30)
>>>m = Z.mean()
>>>print(m)

14.Create a 2d array with 1 on the border and 0 inside

Z = np.ones((10,10))
Z[1:-1,1:-1] = 0

15.What is the result of the following expression?

>>> 0 * np.nan
nan
>>> np.nan == np.nan
False
>>> np.inf > np.nan
False
>>> np.nan
nan
>>> np.nan - np.nan
nan
>>> 0.3 == 3 * 0.1
False

16.Create a 5x5 matrix with values 1,2,3,4 just below the diagonal

Z = np.diag(1+np.arange(4),k=-1)
print(Z)

17.Create a 8x8 matrix and fill it with a checkerboard pattern

Z = np.zeros((8,8),dtype=int)
Z[1::2,::2] = 1
Z[::2,1::2] = 1
print(Z)

18.Consider a (6,7,8) shape array, what is the index (x,y,z) of the 100th element?
>>> print(np.unravel_index(100,(6,7,8)))
(1, 5, 4)


19.Create a checkerboard 8x8 matrix using the tile function

>>>Z = np.tile( np.array([[0,1],[1,0]]), (4,4))

20.Normalize a 5x5 random matrix
>>>Z = np.random.random((5,5))
>>>Zmax, Zmin = Z.max(), Z.min()
>>>Z = (Z - Zmin)/(Zmax - Zmin)

>>>print(Z)

21.Create a custom dtype that describes a color as four unisgned bytes (RGBA)
>>> color = np.dtype([("r", np.ubyte, 1),
                  ("g", np.ubyte, 1),
                  ("b", np.ubyte, 1),
                  ("a", np.ubyte, 1)])
>>> color
dtype([('r', 'u1'), ('g', 'u1'), ('b', 'u1'), ('a', 'u1')])

22.Multiply a 5x3 matrix by a 3x2 matrix (real matrix product)

>>>X=np.dot(np.ones((5,3)),np.ones((3,2)))

23.Given a 1D array, negate all elements which are between 3 and 8, in place.
>>> Z = np.arange(11)
>>> Z[(3 < Z) & (Z <= 8)] *= -1
>>> Z
array([ 0,  1,  2,  3, -4, -5, -6, -7, -8,  9, 10])

24.Create a 5x5 matrix with row values ranging from 0 to 4

X=np.ones((5,5))
X+=np.arange(5)

25.

What is pickle in Python?

The pickle module implements binary protocols for serializing and de-serializing a Python object structure. “Pickling” is the process whereby a Python object hierarchy is converted into a byte stream, and “unpickling” is the inverse operation, whereby a byte stream (from a binary file or bytes-like object) is converted back into an object hierarchy. Pickling (and unpickling) is alternatively known as “serialization”, “marshalling,” or “flattening”; however, to avoid confusion, the terms used here are “pickling” and “unpickling”.

Excercise and Answers

1) Create an arbitrary one dimensional array called "v".
Ans:>>>import numpy as np
>>>v=np.arange(5)




2) Create a new array which consists of the odd indices of previously created array "v"?
Ans:
>>>import numpy as np
>>>v=np.arange(5)
>>> odd_elements = v[1::2]
>>> odd_elements
array([1, 3, 5, 7, 9])




3) Create a new array in backwards ordering from v
Ans:
>>>import numpy as np
>>>v=np.arange(5)
>>> rv=v[::-1]
>>> rv
array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])



4) What will be the output of the following code:

   a = np.array([1, 2, 3, 4, 5])
   b = a[1:4]    #######  array([1, 2, 3, 4, 5])
   b[0] = 200   #######  array([200,   3,   4])
   print(a[1])   ####### 200
>>> a
array([  1, 200,   3,   4,   5])




5) Create a two dimensional array called "m".
Ans:import numpy as np
>>> m=np.arange(10).reshape(2,5)
>>> m.ndim
2
>>> m
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

6) Create a new array from m, in which the elements of each row are in reverse order.

Ans:

import numpy as np
>>> m=np.arange(10).reshape(2,5)
>>> m.ndim
2
>>> m
array([[0, 1, 2, 3, 4],

       [5, 6, 7, 8, 9]])>>> m[::,::-1]

array([[4, 3, 2, 1, 0],

       [9, 8, 7, 6, 5]])

7) Another one, where the rows are in reverse order.

Ans:import numpy as np

>>> m=np.arange(10).reshape(2,5)
>>> m.ndim
2
>>> m
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])
>>>>>> m[::-1]
array([[5, 6, 7, 8, 9],
       [0, 1, 2, 3, 4]])




8) Create an array from m, where columns and rows are in reverse order.

Ans:

import numpy as np

>>> m=np.arange(10).reshape(2,5)
>>> m.ndim
2
>>> m
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

>>> m[::-1,::-1]

array([[9, 8, 7, 6, 5],

       [4, 3, 2, 1, 0]])

9) Cut of the first and last row and the first and last column?

Ans:

import numpy as np

>>> m=np.arange(10).reshape(2,5)
>>> m.ndim
2
>>> m
array([[0, 1, 2, 3, 4],
       [5, 6, 7, 8, 9]])

>>> m[1:-1,1:-1]

array([], shape=(0, 3), dtype=int64)

 

Time Comparison between Python Lists and Numpy Arrays

import numpy as np
from timeit import Timer
size_of_vec = 1000
def pure_python_version():
    X = range(size_of_vec)
    Y = range(size_of_vec)
    Z = []
    for i in range(len(X)):
        Z.append(X[i] + Y[i])
def numpy_version():
    X = np.arange(size_of_vec)
    Y = np.arange(size_of_vec)
    Z = X + Y
#timer_obj = Timer("x = x + 1", "x = 0")
timer_obj1 = Timer("pure_python_version()", "from __main__ import pure_python_version")
timer_obj2 = Timer("numpy_version()", "from __main__ import numpy_version")
print(timer_obj1.timeit(10))
print(timer_obj2.timeit(10))


0.0022348780039465055
6.224898970685899e-05

what is linspace?

The syntax of linspace:
linspace(start, stop, num=50, endpoint=True, retstep=False)


Example:
>>> x=np.linspace(1,10)
>>> x
array([  1.        ,   1.18367347,   1.36734694,   1.55102041,
         1.73469388,   1.91836735,   2.10204082,   2.28571429,
         2.46938776,   2.65306122,   2.83673469,   3.02040816,
         3.20408163,   3.3877551 ,   3.57142857,   3.75510204,
         3.93877551,   4.12244898,   4.30612245,   4.48979592,
         4.67346939,   4.85714286,   5.04081633,   5.2244898 ,
         5.40816327,   5.59183673,   5.7755102 ,   5.95918367,
         6.14285714,   6.32653061,   6.51020408,   6.69387755,
         6.87755102,   7.06122449,   7.24489796,   7.42857143,
         7.6122449 ,   7.79591837,   7.97959184,   8.16326531,
         8.34693878,   8.53061224,   8.71428571,   8.89795918,
         9.08163265,   9.26530612,   9.44897959,   9.63265306,
         9.81632653,  10.        ])

>>> x=np.linspace(1,10,7)
>>> x
array([  1. ,   2.5,   4. ,   5.5,   7. ,   8.5,  10. ])

>>> samples, spacing = np.linspace(1, 10, retstep=True)  

>>> samples

array([  1.        ,   1.18367347,   1.36734694,   1.55102041,

         1.73469388,   1.91836735,   2.10204082,   2.28571429,

         2.46938776,   2.65306122,   2.83673469,   3.02040816,

         3.20408163,   3.3877551 ,   3.57142857,   3.75510204,

         3.93877551,   4.12244898,   4.30612245,   4.48979592,

         4.67346939,   4.85714286,   5.04081633,   5.2244898 ,

         5.40816327,   5.59183673,   5.7755102 ,   5.95918367,

         6.14285714,   6.32653061,   6.51020408,   6.69387755,

         6.87755102,   7.06122449,   7.24489796,   7.42857143,

         7.6122449 ,   7.79591837,   7.97959184,   8.16326531,

         8.34693878,   8.53061224,   8.71428571,   8.89795918,

         9.08163265,   9.26530612,   9.44897959,   9.63265306,

         9.81632653,  10.        ])

>>> spacing

0.1836734693877551

>>> samples, spacing = np.linspace(1, 10, 20, endpoint=True, retstep=True)

>>> samples

array([  1.        ,   1.47368421,   1.94736842,   2.42105263,

         2.89473684,   3.36842105,   3.84210526,   4.31578947,

         4.78947368,   5.26315789,   5.73684211,   6.21052632,

         6.68421053,   7.15789474,   7.63157895,   8.10526316,

         8.57894737,   9.05263158,   9.52631579,  10.        ])

>>> spacing

0.47368421052631576

>>> samples, spacing = np.linspace(1, 10, 20, endpoint=True, retstep=True)

>>> samples

array([  1.        ,   1.47368421,   1.94736842,   2.42105263,

         2.89473684,   3.36842105,   3.84210526,   4.31578947,

         4.78947368,   5.26315789,   5.73684211,   6.21052632,

         6.68421053,   7.15789474,   7.63157895,   8.10526316,

         8.57894737,   9.05263158,   9.52631579,  10.        ])

>>> spacing

0.47368421052631576

>>> samples, spacing = np.linspace(1, 10, 20, endpoint=False, retstep=True)

>>> samples

array([ 1.  ,  1.45,  1.9 ,  2.35,  2.8 ,  3.25,  3.7 ,  4.15,  4.6 ,

        5.05,  5.5 ,  5.95,  6.4 ,  6.85,  7.3 ,  7.75,  8.2 ,  8.65,

        9.1 ,  9.55])

>>> spacing

0.45

>>> 

Difference between range and arange?

1.

>>> x=np.arange(1,10)
>>> x
array([1, 2, 3, 4, 5, 6, 7, 8, 9])
>>> print(x)
[1 2 3 4 5 6 7 8 9]        #No commas
>>> type(x)
<type 'numpy.ndarray'>


>>> x=range(1,10)
>>> x
[1, 2, 3, 4, 5, 6, 7, 8, 9]
>>> print(x)
[1, 2, 3, 4, 5, 6, 7, 8, 9]  # is separated by commas
>>> type(x)
<type 'list'>


2.
>>> x=np.arange(10.5)  # Floating point value
>>> x
array([  0.,   1.,   2.,   3.,   4.,   5.,   6.,   7.,   8.,   9.,  10.])
>>> print(x)
[  0.   1.   2.   3.   4.   5.   6.   7.   8.   9.  10.]

>>> x=range(10.5)

Traceback (most recent call last):

  File "<stdin>", line 1, in <module>

TypeError: range() integer end argument expected, got float.

3.

What is arange?

There are functions provided by Numpy to create evenly spaced values within a given interval. One uses a given distance 'arange' and the other one 'linspace' needs the number of elements and creates the distance automatically.


The syntax of arange:
arange([start,] stop[, step,], dtype=None)

It is nearly equivalent to the Python built-in function "range", but arange returns an ndarray rather than a list iterator as range does.