准备

Python

def quicksort(arr):
    if len(arr) < 1:
        return arr
    pivot = arr[len(arr) // 2]
    left = [x for x in arr if x < pivot]
    middle = [x for x in arr if x = pivot]
    right = [x for x in arr if x > pivot]
    return quicksort(left) + middle + quicksort(right)

print(quicksort([2, 4, 10, 1, 2, 5]))

• basic data types
  
• containers
  
• lists
  
• dictionaries
  
• sets
  
• tuples
  

  tuples 和 list 相似。前者可以用于 dict 的 key，而 list 不可以

• functions
  
• classes
  

Numpy

• arrays
  

  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
  print(d)              # Prints "[[ 1.  0.]
                        #          [ 0.  1.]]"
  e = np.random.random((2,2))  # Create an array filled with random values
  print(e)                     #
  

• Array indexing
  

  a = np.array([[1,2,3,4], [5,6,7,8], [9,10,11,12]])
  # [2,3][6, 7]
  b = a[:2, 1:3]
  

• datatypes
  

  x = np.array([1, 2])
  print(x.dtype) #prints "int 64"
  

• array math
  

  x = np.array([[1, 2], [3, 4]])
  print(np.sum(x))
  print(np.sum(x, axis=0)) # print echo column sum
  print(np.sum(x, axis=1)) # print echo row sum
  

  a = np.array([[1, 2, 3], [3, 4, 5]])
  b = np.array([1, 2, 3])
  print(a - b)
  

  结果：

   array([[0, 0, 0],
  [2, 2, 2]])
  

• broadcasting
  
• Doc
  

  see here：

Scipy

• Image operations
  

  Scipy 提供了很多的方法去处理图像。

  from scipy.misc import imread, imsave, imresize
  # Read an JPEG image into a numpy array
  img = imread('assets/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('assets/cat_tinted.jpg', img_tinted)
  

• Distance between points
  

  Scipy 提供了一些有用的函数去处理点之间的距离。 例如下面计算欧几里得距离。

  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
  x = np.arange(0, 3*np.pi, 0.1)
  y = np.sin(x)
  plt.plot(x, y)
  plt.show()
  

  

  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()
  

  结果如下图。

  

• subplots
  

  这个可以将多个要画的东西放到一个 figure 中来。 subplot(a, b, c) 表示创建 a 行，b 列的图，做为第 c 个图。

• images
  

  we can use the imshow function to show images.

  import numpy as np
  from scipy.misc import imread, imresize
  import matplotlib.pyplot as plt
  
  img = imread('assets/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()
  

  

IPython Tutorial

具体代码中用到的函数

主页

数据集

import pickle
def unpickle(file):
        with open(file, 'rb') as fo:
                dict = pickle.load(fo, encoding='bytes')
        return dict

    {
            'num_cases_per_batch': 10000,
            'label_names': ['airplane', 'automobile', 'bird', 'cat', 'deer', 'dog', 'frog', 'horse', 'ship', 'truck'],
            'num_vis': 3072
}

笔记

视频

PPT

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