Introducción a Python

Clase 6

Pablo Cappagli

> python

>>> print ‘Hola mundo!’

Hola mundo!

>>> exit()

>

Python tiene cinco data types estandar:

● Numbers

● String

● List

● Tuples

● Dictionary

>>> var1 = 12

>>> var2 = 12.0

>>> type(var1), type(var2)

<type 'int'> <type 'float'>

Numbers

str = 'Hola Mundo!'

print str

print str[0]

print str[2:5]

print str[2:]

print str * 2

print str + ‘ ’ + ‘Chau’

Strings

Hola Mundo!

H

la

la Mundo!

Hola Mundo!Hola Mundo!

Hola Mundo! Chau

Lists

['abcd', 786, 2.23, 'juan', 70.200000000000003]

abcd

[786, 2.23]

[2.23, 'juan', 70.200000000000003]

[123, 'juan', 123, 'juan']

['abcd', 786, 2.23, 'juan', 70.200000000000003, 123, 'juan']

list = [ 'abcd', 786 , 2.23, 'juan', 70.2 ]

tinylist = [123, 'juan']

print list

print list[0]

print list[1:3]

print list[2:]

print tinylist * 2

print list + tinylist

Tuples

('abcd', 786, 2.23, 'juan', 70.2)

abcd

(786, 2.23)

(2.23, 'juan', 70.2)

(123, 'juan', 123, 'juan')

('abcd', 786, 2.23, 'juan', 70.2, 123, 'juan')

tuple = ( 'abcd', 786 , 2.23, 'juan', 70.2 )

tinytuple = (123, 'juan')

print tuple

print tuple[0]

print tuple[1:3]

print tuple[2:]

print tinytuple * 2

print tuple + tinytuple

Dictionary

‘Este es el primero’

‘Este es el segundo’

{'dept': ‘ventas', 'code': 6734, 'name': 'juan'}

['dept', 'code', 'name']

[‘ventas', 6734, 'juan']

dict = {}

dict[‘uno'] = ‘Este es el primero’

dict[2] = ‘Este es el segundo’

tinydict = {'name': 'juan','code':6734, 'dept': ‘ventas'}

print dict[‘uno']

print dict[2]

print tinydict

print tinydict.keys()

print tinydict.values()

redrum = "-->".join(["Here", "is", "Jhonny!"])

print redrum

fecha = "%d de %s, %d" % (12, ‘noviembre', 1955)

print fecha

nombre = "%(nombre)s %(apellido)s" %{'nombre':'Nicolas', 'apellido':'Chiaraviglio'}

print nombre

Strings

Here-->is-->Jhonny!

12 de noviembre, 1955

Nicolas Chiaraviglio

choice = 'b'

if choice == 'a':

print("You chose 'a'.")

elif choice == 'b':

print("You chose 'b'.")

else:

print("Invalid choice.")

age = 7

if age < 0:

print "This can hardly be true!"

elif age == 1:

print "about 14 human years"

elif age == 2:

print "about 22 human years"

elif age > 2:

human = 22 + (age -2)*5

print "Human years: ", human

Conditionals

for i in [1, 2, 3, 4]:

print i

for x in range(0,10):

print x

for c in "python":

print c

frutas = ['manzana', 'banana', 'pera', 'frutilla']

for fr in frutas:

print fr

else:

print 'no hay mas fruta'

for line in open("a.txt"):

print line

Foor loop

count = 0

while (count < 9):

print 'The count is:', count

count = count + 1

count = 0

while count < 5:

print count, " is less than 5"

count = count + 1

else:

print count, " is not less than 5"

count = 0

while True:

print count, " is less than 5"

count = count + 1

if count >= 5:

print count, " is not less than 5"

break

While loop

def happyBirthdayEmily():

print("Happy Birthday, dear Emily.")

happyBirthdayEmily()

def happyBirthday(person):

print("Happy Birthday, dear " + person + ".")

happyBirthday('Robertito')

def f(x, y):

z = x**2 + x/y

return z

z = f(2.3, 5.1)

print z

Functions

Is an extension to Python, adding support forlarge, multi-dimensional arrays and matrices,along with a large library of high-levelmathematical functions to operate on thesearrays.

Numpy Array

[2, 3, 1, 0], [2, 3, 1, 0]

import numpy as np

x1 = np.array([2, 3, 1, 0])

x2 = np.array((2, 3, 1, 0))

print x1, x2

Numpy Array

[[ 1 2 3 4 5]

[ 6 7 8 9 10]]

int32

(2L, 5L)

10

import numpy as np

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

print z

print z.dtype

print z.shape

print z.size

Numpy Array

[[ 0. 0. 0.]

[ 0. 0. 0.]]

[[0 0 0]

[0 0 0]]

[[1 1 1]

[1 1 1]]

import numpy as np

z = np.zeros([2, 3])

print z

z = np.zeros([2, 3]).astype(np.int32)

print z

z = np.ones([2, 3], dtype=np.int32)

print z

Numpy Array

[ 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13.]

[ 2. 2.1 2.2 2.3 2.4 2.5 2.6 2.7 2.8 2.9]

[ 1. 1.6 2.2 2.8 3.4 4. ]

import numpy as np

z = np.arange(2, 14, dtype=np.float32)

print z

z = np.arange(2, 3, 0.1)

print z

z = np.linspace(1., 4., 6)

print z

Numpy Array

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

2 9 9 7

[[0 1 2 3 4]

[5 6 7 8 9]]

8 9

[0 1 2 3 4]

[0 5]

import numpy as np

z = np.arange(10)

print z

print z[2], z[9], z[-1], z[-3]

z.shape = (2,5) # ahora z es 2D

print z

print z[1,3], z[1,-1]

print z[0]

print z[:,0]

Numpy

import numpy as np

y = np.arange(1,36, dtype = np.float64).reshape(5,7)

print y[1:5:2,::3]

z = np.cos(y)

z = np.exp(y)

z = np.round(y)

z = np.abs(y)

w = np.prod(y) #w = np.prod(y, axis=1)

w = np.sum(y)

w = np.cumsum(y)

w = np.mean(y)

w = np.max(y)

Numpy

import numpy as np

N = 10000000

M = 501

my_filter = np.ones(M) astype(np.float16)

my_array = np.random.rand(N).astype(np.float16)

conv_array = np.convolve(my_array, my_filter)

SciPy is an open source Python library used byscientists, analysts, and engineers doing scientificcomputing and technical computing.

SciPy contains modules for optimization linear algebra, integration, interpolation, special functions, FFT, signal and image processing, ODE solvers and other tasks common in science and engineering.

Numpy - Scipy

import numpy as np

from scipy import signal

N = 10000000

M = 501

my_filter = np.ones(M).astype(np.float16)

my_array = np.random.rand(N).astype(np.float16)

conv_array = np.convolve(my_array, my_filter)

convfft_array = signal.fftconvolve(my_array, my_filter)

Numpy - Scipy

import numpy as np

from scipy import signal

import time

N = 10000000

M = 501

my_filter = np.ones(M).astype(np.float16)

my_array = np.random.rand(N).astype(np.float16)

start_time = time.time()

conv_array = np.convolve(my_array, my_filter)

print("--- %s seconds ---" % (time.time() - start_time))

start_time = time.time()

convfft_array = signal.fftconvolve(my_array, my_filter)

print("--- %s seconds ---" % (time.time() - start_time))

--- 36.4789998531 seconds ---

--- 1.4470000267 seconds ---

matplotlibis plotting library forthe Python programming language and its numerical mathematics extension NumPy

Numpy + Matplotlib

import numpy as np

import matplotlib.pyplot as plt

N = 10000

M = 201

my_filter = np.ones(M).astype(np.float16)/M

my_array = np.random.rand(N).astype(np.float16)

conv_array = np.convolve(my_array, my_filter)

plt.figure()

plt.plot(my_array, 'b')

plt.plot(conv_array, 'g')

plt.xlim([0, N])

plt.ylim([-0.5,1.5])

plt.show()

coming soon….