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Scipy Lectures - Basic 1
February 26, 2016
In [2]: # Convencion para importar numpy
import numpy as np
In [3]: # Creando Arrays
## de forma manual
a = np.array([1,2,3,4])
a
Out[3]: array([1, 2, 3, 4])
In [9]: a = np.array([[1,2,3],[4,5,6]])
a
Out[9]: array([[1, 2, 3],
[4, 5, 6]])
In [10]: ## Funciones para crear arrays
a = np.arange(10) # 0, ... , (n-1)
a
Out[10]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [11]: b = np.arange(3,10,2) # init, end ,stepb
Out[11]: array([3, 5, 7, 9])
In [15]: c = np.linspace(7,11,5)
c
Out[15]: array([ 7., 8., 9., 10., 11.])
In [16]: a = np.ones((3,3))
a
Out[16]: array([[ 1., 1., 1.],
[ 1., 1., 1.],
[ 1., 1., 1.]])
In [18]: a = np.zeros((3,3))
a
Out[18]: array([[ 0., 0., 0.],
[ 0., 0., 0.],
[ 0., 0., 0.]])
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In [19]: a = np.eye(4)
a
Out[19]: array([[ 1., 0., 0., 0.],
[ 0., 1., 0., 0.],
[ 0., 0., 1., 0.],
[ 0., 0., 0., 1.]])
In [20]: a = np.random.rand(4) # numeros aleatorios uniformes
entre 0 y 1
a
Out[20]: array([ 0.86582314, 0.67382439, 0.14029105,
0.20395629])
In [21]: a = np.random.randn(4) # distribucion gaussiana
a
Out[21]: array([-0.95808113, -0.46274233, 0.69001548,
-1.25042222])
In [23]: # Visualizacion Basica
%matplotlib inline
import matplotlib.pyplot as plt
In [25]: x = np.linspace(1,5,15)
y = np.linspace(3,8,15)
plt.plot(x,y)
Out[25]: []
In [26]: plt.plot(x,y,o)
Out[26]: []
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In [27]: image = np.random.rand(30, 30)
plt.imshow(image, cmap=plt.cm.hot)
plt.colorbar()
Out[27]:
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In [28]: # Indexing and slicing
a = np.arange(10)
a
Out[28]: array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])
In [29]: a[2] , a [1] , a [-1]
Out[29]: (2, 1, 9)
In [30]: a[::-1] # reverso
Out[30]: array([9, 8, 7, 6, 5, 4, 3, 2, 1, 0])
In [4]: a = np.arange(5,20)
a
Out[4]: array([ 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17,
18, 19])
In [9]: # en general name_array[init:end:step]
a[3:12:2]
Out[9]: array([ 8, 10, 12, 14, 16])
In [ ]:
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