In [1]:
import numpy, scipy, matplotlib.pyplot as plt, pandas, librosa

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NumPy and SciPy

Special Arrays

In [2]:
print numpy.arange(5)

[0 1 2 3 4]
In [3]:
print numpy.linspace(0, 5, 10, endpoint=False)

[ 0.   0.5  1.   1.5  2.   2.5  3.   3.5  4.   4.5]
In [4]:
print numpy.zeros(5)

[ 0.  0.  0.  0.  0.]
In [5]:
print numpy.ones(5)

[ 1.  1.  1.  1.  1.]
In [6]:
print numpy.ones((5,2))

[[ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]
 [ 1.  1.]]
In [7]:
print scipy.randn(5) # random Gaussian, zero-mean unit-variance

[ -1.12009510e+00   2.15875646e-03  -7.93208376e-01  -1.02710782e+00
   2.37388108e+00]
In [8]:
print scipy.randn(5,2)

[[-0.60527349 -0.82200312]
 [-0.67330474 -0.12914043]
 [-0.71574719 -0.5962005 ]
 [-1.03690426  0.59078457]
 [-2.22983691 -1.70858604]]

Slicing Arrays

In [9]:
x = numpy.arange(10)
print x[2:4]

[2 3]
In [10]:
print x[-1]

9
In [11]:
print x[0:8:2]

[0 2 4 6]
In [12]:
print x[4:2:-1]

[4 3]
In [13]:
print x[:4]

[0 1 2 3]
In [14]:
print x[:999]

[0 1 2 3 4 5 6 7 8 9]
In [15]:
print x[::-1]

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

Array Arithmetic

In [16]:
x = numpy.arange(5)
y = numpy.ones(5)
print x+2*y

[ 2.  3.  4.  5.  6.]
In [17]:
x = scipy.randn(5)
y = numpy.ones(5)
print numpy.dot(x, y)

-4.36027379404
In [18]:
x = scipy.randn(5,3)
y = numpy.ones((3,2))
print numpy.dot(x, y)

[[ 0.9351335   0.9351335 ]
 [-4.22851009 -4.22851009]
 [-2.66983557 -2.66983557]
 [ 3.18545804  3.18545804]
 [ 1.82532797  1.82532797]]

Boolean Operations

In [19]:
x = numpy.arange(10)
print x < 5

[ True  True  True  True  True False False False False False]
In [20]:
y = numpy.ones(10)
print x < y

[ True False False False False False False False False False]

Distance Metrics

In [21]:
from scipy.spatial import distance
print distance.euclidean([0, 0], [3, 4])
print distance.sqeuclidean([0, 0], [3, 4])
print distance.cityblock([0, 0], [3, 4])
print distance.chebyshev([0, 0], [3, 4])

5.0
25.0
7
4
In [22]:
print distance.cosine([67, 0], [89, 0])
print distance.cosine([67, 0], [0, 89])

0.0
1.0

Sorting

In [23]:
x = scipy.randn(5)
print x
x.sort()
print x

[ 0.70589021  0.14767722  0.06884379  0.37189002  0.43313129]
[ 0.06884379  0.14767722  0.37189002  0.43313129  0.70589021]
In [24]:
x = scipy.randn(5)
print x
ind = numpy.argsort(x)
print ind
print x[ind]

[ 0.9443719   0.2831604   0.85627     0.22827583 -0.03939166]
[4 3 1 2 0]
[-0.03939166  0.22827583  0.2831604   0.85627     0.9443719 ]

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