学习Python
# -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ # -*- coding: utf-8 -*- """ Spyder Editor This is a temporary script file. """ #数据类型 #在 Python 3.0之前是“语句”,在 Python 3.0中是“函数”,因此print需要加括号 x=3 print (type(x)) print (x) print (x+12) print(x**2) print(12*19) t = True f = False print (type(t)) # Prints "<type 'bool'>" print (t and f) # Logical AND; prints "False" print (t or f) # Logical OR; prints "True" print (not t) # Logical NOT; prints "False" print (t != f) # Logical XOR; prints "True" print '\n\n' #字符串 hello = 'hello' # String literals can use single quotes world = "world" # or double quotes; it does not matter. print (hello) # Prints "hello" print (len(hello)) # String length; prints "5" hw = hello + ' ' + world # String concatenation print (hw) # prints "hello world" hw12 = '%s %s %d' % (hello, world, 12) # sprintf style string formatting print (hw12) # prints "hello world 12" #字符串函数 s = "hello" print (s.capitalize()) # Capitalize a string; prints "Hello" print (s.upper()) # Convert a string to uppercase; prints "HELLO" print (s.rjust(7) ) # Right-justify a string, padding with spaces; prints " hello" print (s.center(7)) # Center a string, padding with spaces; prints " hello " print (s.replace('l', '(ell)')) # Replace all instances of one substring with another; # prints "he(ell)(ell)o" print (' world '.strip()) # Strip leading and trailing whitespace; prints "world" #剥离前置后置空格 print '\n\n' #list xs = [3, 1, 2] # Create a list print(xs) print (xs, xs[2] ) # Prints "[3, 1, 2] 2" print (xs[-3]) # Negative indices count from the end of the list; prints "2" xs[2] = 'foo' # Lists can contain elements of different types print (xs) # Prints "[3, 1, 'foo']" xs.append('bar') # Add a new element to the end of the list print (xs) # Prints "[3, 1, 'foo', 'bar']" x = xs.pop() # Remove and return the last element of the list print (x, xs ) # Prints "bar [3, 1, 'foo']" #Slicing nums = range(5) # range is a built-in(内置) function that creates a list of integers print (nums[-4]) # Prints "[0, 1, 2, 3, 4]" print (nums[2:4]) # Get a slice from index 2 to 4 (exclusive); prints "[2, 3]" print (nums[2:]) # Get a slice from index 2 to the end; prints "[2, 3, 4]" print (nums[:2])# Get a slice from the start to index 2 (exclusive); prints "[0, 1, 2]" print (nums[:]) # Get a slice of the whole list; prints ["0, 1, 2, 3, 4]" print (nums[:-1]) # Slice indices can be negative; prints ["0, 1, 2, 3]" nums[2:4] = [8, 9] # Assign a new sublist to a slice print (nums) # Prints "[0, 1, 8, 9, 4]" print '\n\n' #loops animals = ['cat', 'dog', 'monkey'] for animal in animals: print (animal) # Prints "cat", "dog", "monkey", each on its own line. animals = ['cat', 'dog', 'monkey'] for idx, animal in enumerate(animals): print ('#%d: %s' % (idx + 1, animal)) # Prints "#1: cat", "#2: dog", "#3: monkey", each on its own line print '\n\n' #list comprehension(列表推导式): nums = [0, 1, 2, 3, 4] squares = [] for x in nums: squares.append(x ** 2) print (squares) # Prints [0, 1, 4, 9, 16] nums = [0, 1, 2, 3, 4] squares = [x ** 2 for x in nums] print (squares) # Prints [0, 1, 4, 9, 16] nums = [0, 1, 2, 3, 4] even_squares = [x ** 2 for x in nums if x % 2 == 0] print (even_squares) # Prints "[0, 4, 16]" print '\n\n' #Dictionaries d = {'cat': 'cute', 'dog': 'furry'} # Create a new dictionary with some data print (d['cat']) # Get an entry from a dictionary; prints "cute" print ('cat' in d) # Check if a dictionary has a given key; prints "True" d['fish'] = 'wet' # Set an entry in a dictionary print (d['fish']) # Prints "wet" # print d['monkey'] # KeyError: 'monkey' not a key of d print (d.get('monkey', 'N/A')) # Get an element with a default; prints "N/A" print (d.get('fish', 'N/A') ) # Get an element with a default; prints "wet" del d['fish'] # Remove an element from a dictionary print (d.get('fish', 'N/A')) # "fish" is no longer a key; prints "N/A" print '\n\n' d = {'person': 2, 'cat': 4, 'spider': 8} for animal, legs in d.iteritems(): print 'A %s has %d legs' % (animal, legs) # Prints "A person has 2 legs", "A spider has 8 legs", "A cat has 4 legs" print '\n\n' #Dictionary comprehensions字典推导式: nums = [0, 1, 2, 3, 4] even_num_to_square = {x: x ** 2 for x in nums if x % 2 == 0} print even_num_to_square # Prints "{0: 0, 2: 4, 4: 16}" print '\n\n' #Sets animals = {'cat', 'dog'} print 'cat' in animals # Check if an element is in a set; prints "True" print 'fish' in animals # prints "False" animals.add('fish') # Add an element to a set print 'fish' in animals # Prints "True" print len(animals) # Number of elements in a set; prints "3" animals.add('cat') # Adding an element that is already in the set does nothing print len(animals) # Prints "3" animals.remove('cat') # Remove an element from a set print len(animals) # Prints "2" print '\n' #Set Loops:集合中的元素无序,不能确定访问的先后顺序 animals = {'cat', 'dog', 'fish'} for idx, animal in enumerate(animals): print '#%d: %s' % (idx + 1, animal) # Prints "#1: fish", "#2: dog", "#3: cat" print '\n' #Set comprehensions(集合推导式): from math import sqrt nums = {int(sqrt(x)) for x in range(30)} print nums # Prints "set([0, 1, 2, 3, 4, 5])" print '\n\n' #Tuples(元组) d = {(x, x + 1): x for x in range(10)} # Create a dictionary with tuple keys t = (5, 6) # Create a tuple print type(t) # Prints "<type 'tuple'>" print d print d[t] # Prints "5" print d[(1, 2)] # Prints "1" print '\n\n' #Functions def sign(x): if x > 0: return 'positive' elif x < 0: return 'negative' else: return 'zero' for x in [-1, 0, 1]: print sign(x) # Prints "negative", "zero", "positive" print '\n' def hello(name, loud=False): if loud: print 'HELLO, %s!' % name.upper() else: print 'Hello, %s' % name hello('Bob') # Prints "Hello, Bob" hello('Fred', loud=True) # Prints "HELLO, FRED!" print '\n\n' #Classes class Greeter(object): # Constructor def __init__(self, name): self.name = name # Create an instance variable # Instance method def greet(self, loud=False): if loud: print 'HELLO, %s!' % self.name.upper() else: print 'Hello, %s' % self.name g = Greeter('Fred') # Construct an instance of the Greeter class g.greet() # Call an instance method; prints "Hello, Fred" g.greet(loud=True) # Call an instance method; prints "HELLO, FRED!" print '\n\n' #Numpy #Arrays import numpy as np a = np.array([1, 2, 3]) # Create a rank 1 array print type(a) # Prints "<type 'numpy.ndarray'>" print a.shape # Prints "(3,)" print a[0], a[1], a[2] # Prints "1 2 3" a[0] = 5 # Change an element of the array print a # Prints "[5, 2, 3]" b = np.array([[1,2,3],[4,5,6]]) # Create a rank 2 array print b.shape # Prints "(2, 3)" print b[0, 0], b[0, 1], b[1, 0] # Prints "1 2 4" print '\n' import numpy as np a = np.zeros((2,2)) # Create an array of all zeros print a # Prints "[[ 0. 0.] # [ 0. 0.]]" b = np.ones((1,2)) # Create an array of all ones print b # Prints "[[ 1. 1.]]" c = np.full((2,2), 7) # Create a constant array print c # Prints "[[ 7. 7.] # [ 7. 7.]]" d = np.eye(2) # Create a 2x2 identity matrix(单位矩阵) print d # Prints "[[ 1. 0.] # [ 0. 1.]]" e = np.random.random((2,2)) # Create an array filled with random values print e # Might print "[[ 0.91940167 0.08143941] # [ 0.68744134 0.87236687]]" print '\n\n' #Array indexing import numpy as np # Create the following rank 2 array with shape (3, 4) # [[ 1 2 3 4] # [ 5 6 7 8] # [ 9 10 11 12]] a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) print a # Use slicing to pull out the subarray consisting of the first 2 rows # and columns 1 and 2; b is the following array of shape (2, 2): # [[2 3] # [6 7]] b = a[:2, 1:3] print b #b数组只是a数组的引用 # A slice of an array is a view into the same data, so modifying it # will modify the original array. print a[0, 1] # Prints "2" b[0, 0] = 77 # b[0, 0] is the same piece of data as a[0, 1] print a[0, 1] # Prints "77" print '\n\n' import numpy as np # Create the following rank 2 array with shape (3, 4) # [[ 1 2 3 4] # [ 5 6 7 8] # [ 9 10 11 12]] a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]]) # Two ways of accessing the data in the middle row of the array. # Mixing integer indexing with slices yields an array of lower rank, # while using only slices yields an array of the same rank as the # original array: row_r1 = a[1, :] # Rank 1 view of the second row of a row_r2 = a[1:2, :] # Rank 2 view of the second row of a print row_r1, row_r1.shape # Prints "[5 6 7 8] (4,)" print row_r2, row_r2.shape # Prints "[[5 6 7 8]] (1, 4)" # We can make the same distinction when accessing columns of an array: col_r1 = a[:, 1] col_r2 = a[:, 1:2] print col_r1, col_r1.shape # Prints "[ 2 6 10] (3,)" print col_r2, col_r2.shape # Prints "[[ 2] # [ 6] # [10]] (3, 1)" print '\n\n\n\n' import numpy as np a = np.array([[1,2], [3, 4], [5, 6]]) # An example of integer array indexing. # The returned array will have shape (3,) and print a[[0, 1, 2], [0, 1, 0]] # Prints "[1 4 5]" # The above example of integer array indexing is equivalent to this: print np.array([a[0, 0], a[1, 1], a[2, 0]]) # Prints "[1 4 5]" # When using integer array indexing, you can reuse the same # element from the source array: print a[[0, 0], [1, 1]] # Prints "[2 2]" # Equivalent to the previous integer array indexing example print np.array([a[0, 1], a[0, 1]]) # Prints "[2 2]" # Create a new array from which we will select elements a = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]]) print a # prints "array([[ 1, 2, 3], # [ 4, 5, 6], # [ 7, 8, 9], # [10, 11, 12]])" # Create an array of indices b = np.array([0, 2, 0, 1]) # Select one element from each row of a using the indices in b print a[np.arange(4), b] # Prints "[ 1 6 7 11]" # Mutate one element from each row of a using the indices in b a[np.arange(4), b] += 10 print a # prints "array([[11, 2, 3], # [ 4, 5, 16], # [17, 8, 9], # [10, 21, 12]]) a = np.array([[1,2], [3, 4], [5, 6]]) bool_idx = (a > 2) # Find the elements of a that are bigger than 2; # this returns a numpy array of Booleans of the same # shape as a, where each slot of bool_idx tells # whether that element of a is > 2. print bool_idx # Prints "[[False False] # [ True True] # [ True True]]" # We use boolean array indexing to construct a rank 1 array # consisting of the elements of a corresponding to the True values # of bool_idx print a[bool_idx] # Prints "[3 4 5 6]" # We can do all of the above in a single concise statement: print a[a > 2] # Prints "[3 4 5 6]" print '\n\n' #Datatypes import numpy as np x = np.array([1, 2]) # Let numpy choose the datatype print x.dtype # Prints "int64" x = np.array([1.0, 2.0]) # Let numpy choose the datatype print x.dtype # Prints "float64" x = np.array([1, 2], dtype=np.int64) # Force a particular datatype print x.dtype # Prints "int64" #Array math import numpy as np x = np.array([[1,2],[3,4]], dtype=np.float64) y = np.array([[5,6],[7,8]], dtype=np.float64) # Elementwise sum; both produce the array # [[ 6.0 8.0] # [10.0 12.0]] print x + y print np.add(x, y) # Elementwise difference; both produce the array # [[-4.0 -4.0] # [-4.0 -4.0]] print x - y print np.subtract(x, y) # Elementwise product; both produce the array # [[ 5.0 12.0] # [21.0 32.0]] print x * y print np.multiply(x, y) # Elementwise division; both produce the array # [[ 0.2 0.33333333] # [ 0.42857143 0.5 ]] print x / y print np.divide(x, y) # Elementwise square root; produces the array # [[ 1. 1.41421356] # [ 1.73205081 2. ]] print np.sqrt(x) import numpy as np x = np.array([[1,2],[3,4]]) y = np.array([[5,6],[7,8]]) v = np.array([9,10]) w = np.array([11, 12]) # Inner product of vectors; both produce 219 print v.dot(w) print np.dot(v, w) # Matrix / vector product; both produce the rank 1 array [29 67] print x.dot(v) print np.dot(x, v) # Matrix / matrix product; both produce the rank 2 array # [[19 22] # [43 50]] print x.dot(y) print np.dot(x, y) import numpy as np x = np.array([[1,2],[3,4]]) print np.sum(x) # Compute sum of all elements; prints "10" print np.sum(x, axis=0) # Compute sum of each column; prints "[4 6]" print np.sum(x, axis=1) # Compute sum of each row; prints "[3 7]" import numpy as np x = np.array([[1,2], [3,4]]) print x # Prints "[[1 2] # [3 4]]" print x.T # Prints "[[1 3] # [2 4]]" # Note that taking the transpose of a rank 1 array does nothing: v = np.array([1,2,3]) print v # Prints "[1 2 3]" print v.T # Prints "[1 2 3]" #Broadcasting import numpy as np # We will add the vector v to each row of the matrix x, # storing the result in the matrix y x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]]) v = np.array([1, 0, 1]) y = np.empty_like(x) # Create an empty matrix with the same shape as x # Add the vector v to each row of the matrix x with an explicit loop for i in range(4): y[i, :] = x[i, :] + v # Now y is the following # [[ 2 2 4] # [ 5 5 7] # [ 8 8 10] # [11 11 13]] print y import numpy as np # We will add the vector v to each row of the matrix x, # storing the result in the matrix y x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]]) v = np.array([1, 0, 1]) vv = np.tile(v, (4, 1)) # Stack 4 copies of v on top of each other print vv # Prints "[[1 0 1] # [1 0 1] # [1 0 1] # [1 0 1]]" y = x + vv # Add x and vv elementwise print y # Prints "[[ 2 2 4 # [ 5 5 7] # [ 8 8 10] # [11 11 13]]" import numpy as np # We will add the vector v to each row of the matrix x, # storing the result in the matrix y x = np.array([[1,2,3], [4,5,6], [7,8,9], [10, 11, 12]]) v = np.array([1, 0, 1]) y = x + v # Add v to each row of x using broadcasting print y # Prints "[[ 2 2 4] # [ 5 5 7] # [ 8 8 10] # [11 11 13]]" import numpy as np # Compute outer product of vectors v = np.array([1,2,3]) # v has shape (3,) w = np.array([4,5]) # w has shape (2,) # To compute an outer product, we first reshape v to be a column # vector of shape (3, 1); we can then broadcast it against w to yield # an output of shape (3, 2), which is the outer product of v and w: # [[ 4 5] # [ 8 10] # [12 15]] print np.reshape(v, (3, 1)) * w # Add a vector to each row of a matrix x = np.array([[1,2,3], [4,5,6]]) # x has shape (2, 3) and v has shape (3,) so they broadcast to (2, 3), # giving the following matrix: # [[2 4 6] # [5 7 9]] print x + v # Add a vector to each column of a matrix # x has shape (2, 3) and w has shape (2,). # If we transpose x then it has shape (3, 2) and can be broadcast # against w to yield a result of shape (3, 2); transposing this result # yields the final result of shape (2, 3) which is the matrix x with # the vector w added to each column. Gives the following matrix: # [[ 5 6 7] # [ 9 10 11]] print (x.T + w).T # Another solution is to reshape w to be a row vector of shape (2, 1); # we can then broadcast it directly against x to produce the same # output. print x + np.reshape(w, (2, 1)) # Multiply a matrix by a constant: # x has shape (2, 3). Numpy treats scalars as arrays of shape (); # these can be broadcast together to shape (2, 3), producing the # following array: # [[ 2 4 6] # [ 8 10 12]] print x * 2 print '\n\n' #Numpy Documentation #Image operations from scipy.misc import imread, imsave, imresize # Read an JPEG image into a numpy array img = imread('cat.jpg') print img.dtype, img.shape # Prints "uint8 (400, 248, 3)" # We can tint the image by scaling each of the color channels # by a different scalar constant. The image has shape (400, 248, 3); # we multiply it by the array [1, 0.95, 0.9] of shape (3,); # numpy broadcasting means that this leaves the red channel unchanged, # and multiplies the green and blue channels by 0.95 and 0.9 # respectively. img_tinted = img * [1, 0.95, 0.9] # Resize the tinted image to be 300 by 300 pixels. img_tinted = imresize(img_tinted, (300, 300)) # Write the tinted image back to disk imsave('cat_tinted.jpg', img_tinted) print '\n\n' #MATLAB files #Distance between points import numpy as np from scipy.spatial.distance import pdist, squareform # Create the following array where each row is a point in 2D space: # [[0 1] # [1 0] # [2 0]] x = np.array([[0, 1], [1, 0], [2, 0]]) print x # Compute the Euclidean distance between all rows of x. # d[i, j] is the Euclidean distance between x[i, :] and x[j, :], # and d is the following array: # [[ 0. 1.41421356 2.23606798] # [ 1.41421356 0. 1. ] # [ 2.23606798 1. 0. ]] d = squareform(pdist(x, 'euclidean')) print d #Matplotlib #Plotting import numpy as np import matplotlib.pyplot as plt # Compute the x and y coordinates for points on a sine curve x = np.arange(0, 3 * np.pi, 0.1) y = np.sin(x) # Plot the points using matplotlib plt.plot(x, y) plt.show() # You must call plt.show() to make graphics appear. import numpy as np import matplotlib.pyplot as plt # Compute the x and y coordinates for points on sine and cosine curves x = np.arange(0, 3 * np.pi, 0.1) y_sin = np.sin(x) y_cos = np.cos(x) # Plot the points using matplotlib plt.plot(x, y_sin) plt.plot(x, y_cos) plt.xlabel('x axis label') plt.ylabel('y axis label') plt.title('Sine and Cosine') plt.legend(['Sine', 'Cosine']) plt.show() #Subplots import numpy as np import matplotlib.pyplot as plt # Compute the x and y coordinates for points on sine and cosine curves x = np.arange(0, 3 * np.pi, 0.1) y_sin = np.sin(x) y_cos = np.cos(x) # Set up a subplot grid that has height 2 and width 1, # and set the first such subplot as active. plt.subplot(2, 1, 1) # Make the first plot plt.plot(x, y_sin) plt.title('Sine') # Set the second subplot as active, and make the second plot. plt.subplot(2, 1, 2) plt.plot(x, y_cos) plt.title('Cosine') # Show the figure. plt.show() print'\n\n' #Images import numpy as np from scipy.misc import imread, imresize import matplotlib.pyplot as plt img = imread('cat.jpg') img_tinted = img * [1, 0.95, 0.9] # Show the original image plt.subplot(1, 2, 1) plt.imshow(img) # Show the tinted image plt.subplot(1, 2, 2) # A slight gotcha with imshow is that it might give strange results # if presented with data that is not uint8. To work around this, we # explicitly cast the image to uint8 before displaying it. plt.imshow(np.uint8(img_tinted)) plt.show()
keep calm and carry on