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numpy_tips.md

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numpy tips

  • Use the shape method to find the dimensions of the array. (rows, columns)
    • m.shape # (2, 3)
  • arange returns evenly spaced values within a given interval.
    • np.arange(0, 30, 2) # start at 0 count up by 2, stop before 30
    • array([ 0, 2, 4, 6, 8, 10, 12, 14, 16, 18, 20, 22, 24, 26, 28])
  • reshape returns an array with the same data with a new shape.
    • n.reshape(3, 5) # reshape array to be 3x5
    • array([[ 0, 2, 4, 6, 8], [10, 12, 14, 16, 18], [20, 22, 24, 26, 28]])
  • linspace returns evenly spaced numbers over a specified interval.
    • o = np.linspace(0, 4, 9)
    • array([ 0. , 0.5, 1. , 1.5, 2. , 2.5, 3. , 3.5, 4. ])
  • resize changes the shape and size of array in-place.
    • o.resize(3, 3)
    • array([[ 0. , 0.5, 1. ], [ 1.5, 2. , 2.5], [ 3. , 3.5, 4. ]])
  • ones returns a new array of given shape and type, filled with ones.
    • np.ones((3, 2))
    • array([[ 1., 1.], [ 1., 1.], [ 1., 1.]])
  • zeros returns a new array of given shape and type, filled with zeros.
    • np.zeros((2, 3))
  • eye returns a 2-D array with ones on the diagonal and zeros elsewhere.
    • np.eye(3)
array([[ 1.,  0.,  0.],
       [ 0.,  1.,  0.],
       [ 0.,  0.,  1.]])
  • diag extracts a diagonal or constructs a diagonal array.
>>> y = np.array([4, 5, 6])
>>> y
>>> array([4, 5, 6])
>>>
>>> np.diag(y)
>>> array([[4, 0, 0],
           [0, 5, 0],
           [0, 0, 6]])
  • Create an array using repeating list (or see np.tile)
    • np.array([1, 2, 3] * 3)
    • array([1, 2, 3, 1, 2, 3, 1, 2, 3])
  • Repeat elements of an array using repeat.
    • np.repeat([1, 2, 3], 4)
    • array([1, 1, 1, 1, 2, 2, 2, 2, 3, 3, 3, 3])

Combining Arrays

>>> p = np.ones([2, 3], int)
>>> p
>>> array([[1, 1, 1],
           [1, 1, 1]])
  • Use vstack to stack arrays in sequence vertically (row wise).
>>> np.vstack([p, 2*p])
>>> array([[1, 1, 1],
           [1, 1, 1],
           [2, 2, 2],
           [2, 2, 2]])
  • Use hstack to stack arrays in sequence horizontally (column wise).
>>> np.hstack([p, 2*p])
>>> array([[1, 1, 1, 2, 2, 2],
           [1, 1, 1, 2, 2, 2]])
  • Dot Product:
    • x.dot(y)
  • Use .T to get the transpose.
    • z.T
  • Use .dtype to see the data type of the elements in the array.
    • z.dtype
  • Use .astype to cast to a specific type.
    • z = z.astype('f')

Math Functions

a = np.array([-4, -2, 1, 3, 5])
a.mean()
0.59999999999999998
a.std()
3.2619012860600183

# argmax and argmin return the index of the maximum and minimum values in the array.
a.argmax()
4 
a.argmin()
0

# PS. also works on multidimensional array 
# but will flatten the array , then do operations

Indexing / Slicing

s = np.arange(13)**2
s
array([  0,   1,   4,   9,  16,  25,  36,  49,  64,  81, 100, 121, 144])

# 切片
s[-5::-2]
array([64, 36, 16,  4,  0])

# Let's look at a multidimensional array.
r = np.arange(36)
r.resize((6, 6))
r
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 31, 32, 33, 34, 35]])

# Use bracket notation to slice: array[row, column]
r[2, 2]
14

# And use : to select a range of rows or columns
r[3, 3:6]
array([21, 22, 23])

# Here we are selecting all the rows up to (and not including) row 2,
# and all the columns up to (and not including) the last column.
r[:2, :-1]
array([[ 0,  1,  2,  3,  4],
       [ 6,  7,  8,  9, 10]])

# This is a slice of the last row, and only every other element.
r[-1, ::2]
array([30, 32, 34])

# We can also perform conditional indexing. 
# Here we are selecting values from the array that
# that are greater than 27. (Also see `np.where`)
r[r > 27]
array([28, 29, 30, 31, 32, 33, 34, 35])

# Here we are assigning all values in the array 
# that are greater than 30 to the value of 30.
r[r > 30] = 30
r
array([[ 0,  1,  2,  3,  4,  5],
       [ 6,  7,  8,  9, 10, 11],
       [12, 13, 14, 15, 16, 17],
       [18, 19, 20, 21, 22, 23],
       [24, 25, 26, 27, 28, 29],
       [30, 30, 30, 30, 30, 30]])
  • Be careful with copying and modifying arrays in NumPy!
  • use r.copy to create a copy that will not affect the original array
    • r_copy = r.copy()

Iterating Over Arrays

  • Let's create a new 4 by 3 array of random numbers 0-9.
test = np.random.randint(0, 10, (4,3))
test
array([[5, 2, 9],
       [6, 0, 1],
       [0, 4, 5],
       [3, 8, 0]])
  • Use zip to iterate over multiple iterables.
test2 = test**2
test2
array([[25,  4, 81],
       [36,  0,  1],
       [ 0, 16, 25],
       [ 9, 64,  0]])

for i, j in zip(test, test2):
    print(i,'+',j,'=',i+j)
[5 2 9] + [25  4 81] = [30  6 90]
[6 0 1] + [36  0  1] = [42  0  2]
[0 4 5] + [ 0 16 25] = [ 0 20 30]
[3 8 0] + [ 9 64  0] = [12 72  0]

数据 slice

  • 从2维度数据中 抽取第一列
train_X = data[:,0]
  • 从2维度数据中 抽取第1,3列
train_X = data[:,[0,2]]
  • 从2维度数据中 抽取第一列 ，返回一个 Nx1 的 2维度数组
train_X = data[:, [0] ]

数据过滤

  • 返回第2列 值是1的 第1列数据
pos = data[:, 1] == 1
Train_X[ pos , 0  ]

数据 拼接

  • 在2维数组 第一列 ， 插入 1列 1
    • 注意，要确保 Train_X 是2维数据，如果不是，使用 reshape 转成2维数组
np.insert( Train_X , 0, 1 , axis =1 )

数据转换

  • bool 数据转 int 数组
>>> y
array([False, False,  True,  True], dtype=bool)
>>> 1*y                      # Method 1
array([0, 0, 1, 1])
>>> y.astype(int)            # Method 2
array([0, 0, 1, 1])
  • constant 转 2维数组
np.array( 0.4 , ndmin=2  )