Python数据分析_第03课：NumPy介绍_笔记

迈不过友情╰ 2021-11-02 11:12 338阅读 0赞

### 文章目录 ###

*  NumPy对向量的处理
 *   *  效率比较
     *  numpy数组
     *  创建多维数组
     *  选取数组元素
     *  numpy数据类型
     *  数据类型转换
     *  数据类型对象
     *  字符编码
     *  type类的属性
     *  创建自定义数据类型
     *  数组与标量的运算
     *  一维数组的索引与切片
     *  多维数组的切片与索引
     *  布尔型索引
     *  花式索引
     *  数组转置
     *  改变数组的维度
     *  组合数组
     *  数组的分割
     *  数组的属性
     *  数组的转换

GitHub: [https://github.com/RealEmperor/Python-for-Data-Analysis][https_github.com_RealEmperor_Python-for-Data-Analysis]

# NumPy对向量的处理 #

向量相加-Python方式

# 向量相加-Python
    def pythonsum(n):
        a = list(range(n))
        b = list(range(n))
        c = []
        for i in range(len(a)):
            a[i] = i ** 2
            b[i] = i ** 3
            c.append(a[i] + b[i])
        return c

向量相加-NumPy方式

# 向量相加-NumPy
    import numpy
    
    
    def numpysum(n):
        a = numpy.arange(n) ** 2
        b = numpy.arange(n) ** 3
        c = a + b
        return c

## 效率比较 ##

# 效率比较
    from datetime import datetime
    import numpy as np
    
    size = 1000
    
    start = datetime.now()
    c = pythonsum(size)
    delta = datetime.now() - start
    print("The last 2 elements of the sum", c[-2:])
    print("PythonSum elapsed time in microseconds", delta.microseconds)
    
    start = datetime.now()
    c = numpysum(size)
    delta = datetime.now() - start
    print("The last 2 elements of the sum", c[-2:])
    print("NumPySum elapsed time in microseconds", delta.microseconds)

The last 2 elements of the sum [995007996, 998001000]
    PythonSum elapsed time in microseconds 997
    The last 2 elements of the sum [995007996 998001000]
    NumPySum elapsed time in microseconds 0

## numpy数组 ##

# numpy数组
    a = np.arange(5)
    a.dtype

dtype('int32')

array([0, 1, 2, 3, 4])

a.shape

(5,)

## 创建多维数组 ##

# 创建多维数组
    m = np.array([np.arange(2), np.arange(2)])
    
    print(m)
    
    print(m.shape)
    
    print(m.dtype)

[[0 1]
     [0 1]]
    (2, 2)
    int32

np.zeros(10)

array([ 0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.,  0.])

np.zeros((3, 6))

array([[ 0.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  0.],
           [ 0.,  0.,  0.,  0.,  0.,  0.]])

np.empty((2, 3, 2))

array([[[  1.12319318e-311,   3.16202013e-322],
            [  0.00000000e+000,   0.00000000e+000],
            [  0.00000000e+000,   2.32203106e-056]],
    
           [[  1.39425677e+165,   7.12024499e-067],
            [  1.08628251e-071,   2.19317805e-076],
            [  1.90269056e-052,   1.31378093e-071]]])

np.arange(15)

array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

## 选取数组元素 ##

# 选取数组元素
    a = np.array([[1, 2], [3, 4]])
    
    print("In: a")
    print(a)
    
    print("In: a[0,0]")
    print(a[0, 0])
    
    print("In: a[0,1]")
    print(a[0, 1])
    
    print("In: a[1,0]")
    print(a[1, 0])
    
    print("In: a[1,1]")
    print(a[1, 1])

In: a
    [[1 2]
     [3 4]]
    In: a[0,0]
    1
    In: a[0,1]
    2
    In: a[1,0]
    3
    In: a[1,1]
    4

## numpy数据类型 ##

# numpy数据类型
    print("In: float64(42)")
    print(np.float64(42))

In: float64(42)
    42.0

print("In: int8(42.0)")
    print(np.int8(42.0))

In: int8(42.0)
    42

print("In: bool(42)")
    print(np.bool(42))

In: bool(42)
    True

print(np.bool(0))

False

print("In: bool(42.0)")
    print(np.bool(42.0))

In: bool(42.0)
    True

print("In: float(True)")
    print(np.float(True))
    print(np.float(False))

In: float(True)
    1.0
    0.0

print("In: arange(7, dtype=uint16)")
    print(np.arange(7, dtype=np.uint16))

In: arange(7, dtype=uint16)
    [0 1 2 3 4 5 6]

print("In: int(42.0 + 1.j)")
    try:
        print(np.int(42.0 + 1.j))
    except TypeError:
        print("TypeError")
    # Type error

In: int(42.0 + 1.j)
    TypeError

print("In: float(42.0 + 1.j)")
    print(float(42.0 + 1.j))
    # Type error

In: float(42.0 + 1.j)
    
    
    
    ---------------------------------------------------------------------------
    
    TypeError                                 Traceback (most recent call last)
    
    <ipython-input-21-5c8b6ddc18ab> in <module>()
          1 print("In: float(42.0 + 1.j)")
    ----> 2 print(float(42.0 + 1.j))
          3 # Type error
    
    
    TypeError: can't convert complex to float

## 数据类型转换 ##

# 数据类型转换
    arr = np.array([1, 2, 3, 4, 5])
    arr.dtype

dtype('int32')

float_arr = arr.astype(np.float64)
    float_arr.dtype

dtype('float64')

arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])
    arr

array([  3.7,  -1.2,  -2.6,   0.5,  12.9,  10.1])

arr.astype(np.int32)

array([ 3, -1, -2,  0, 12, 10])

numeric_strings = np.array(['1.25', '-9.6', '42'], dtype=np.string_)
    numeric_strings.astype(float)

array([  1.25,  -9.6 ,  42.  ])

## 数据类型对象 ##

# 数据类型对象
    a = np.array([[1, 2], [3, 4]])
    
    print(a.dtype.byteorder)
    
    print(a.dtype.itemsize)

=
    4

## 字符编码 ##

# 字符编码
    print(np.arange(7, dtype='f'))
    print(np.arange(7, dtype='D'))
    
    print(np.dtype(float))
    
    print(np.dtype('f'))
    
    print(np.dtype('d'))
    
    print(np.dtype('f8'))
    
    print(np.dtype('Float64'))

[ 0.  1.  2.  3.  4.  5.  6.]
    [ 0.+0.j  1.+0.j  2.+0.j  3.+0.j  4.+0.j  5.+0.j  6.+0.j]
    float64
    float32
    float64
    float64
    float64
    
    
    C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:13: DeprecationWarning: Numeric-style type codes are deprecated and will result in an error in the future.
      del sys.path[0]

## type类的属性 ##

# dtype类的属性
    t = np.dtype('Float64')
    
    print(t.char)
    
    print(t.type)
    
    print(t.str)

d
    <class 'numpy.float64'>
    <f8
    
    
    C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:2: DeprecationWarning: Numeric-style type codes are deprecated and will result in an error in the future.

## 创建自定义数据类型 ##

# 创建自定义数据类型
    t = np.dtype([('name', np.str_, 40), ('numitems', np.int32), ('price', np.float32)])
    print(t)
    
    print(t['name'])
    
    itemz = np.array([('Meaning of life DVD', 42, 3.14), ('Butter', 13, 2.72)], dtype=t)
    
    print(itemz[1])

[('name', '<U40'), ('numitems', '<i4'), ('price', '<f4')]
    <U40
    ('Butter', 13,  2.72000003)

## 数组与标量的运算 ##

# 数组与标量的运算
    arr = np.array([[1., 2., 3.], [4., 5., 6.]])
    arr

array([[ 1.,  2.,  3.],
           [ 4.,  5.,  6.]])

arr * arr

array([[  1.,   4.,   9.],
           [ 16.,  25.,  36.]])

arr - arr

array([[ 0.,  0.,  0.],
           [ 0.,  0.,  0.]])

1 / arr

array([[ 1.        ,  0.5       ,  0.33333333],
           [ 0.25      ,  0.2       ,  0.16666667]])

arr ** 0.5

array([[ 1.        ,  1.41421356,  1.73205081],
           [ 2.        ,  2.23606798,  2.44948974]])

## 一维数组的索引与切片 ##

# 一维数组的索引与切片
    a = np.arange(9)
    
    print(a[3:7])
    
    print(a[:7:2])
    
    print(a[::-1])
    
    s = slice(3, 7, 2)
    print(a[s])
    
    s = slice(None, None, -1)
    
    print(a[s])

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

## 多维数组的切片与索引 ##

# 多维数组的切片与索引
    b = np.arange(24).reshape(2, 3, 4)
    
    print(b.shape)
    
    print(b)
    
    print(b[0, 0, 0])
    
    print(b[:, 0, 0])
    
    print(b[0])
    
    print(b[0, :, :])
    
    print(b[0, ...])
    
    print(b[0, 1])
    
    print(b[0, 1, ::2])
    
    print(b[..., 1])
    
    print(b[:, 1])
    
    print(b[0, :, 1])
    
    print(b[0, :, -1])
    
    print(b[0, ::-1, -1])
    
    print(b[0, ::2, -1])
    
    print(b[::-1])
    
    s = slice(None, None, -1)
    print(b[(s, s, s)])

(2, 3, 4)
    [[[ 0  1  2  3]
      [ 4  5  6  7]
      [ 8  9 10 11]]
    
     [[12 13 14 15]
      [16 17 18 19]
      [20 21 22 23]]]
    0
    [ 0 12]
    [[ 0  1  2  3]
     [ 4  5  6  7]
     [ 8  9 10 11]]
    [[ 0  1  2  3]
     [ 4  5  6  7]
     [ 8  9 10 11]]
    [[ 0  1  2  3]
     [ 4  5  6  7]
     [ 8  9 10 11]]
    [4 5 6 7]
    [4 6]
    [[ 1  5  9]
     [13 17 21]]
    [[ 4  5  6  7]
     [16 17 18 19]]
    [1 5 9]
    [ 3  7 11]
    [11  7  3]
    [ 3 11]
    [[[12 13 14 15]
      [16 17 18 19]
      [20 21 22 23]]
    
     [[ 0  1  2  3]
      [ 4  5  6  7]
      [ 8  9 10 11]]]
    [[[23 22 21 20]
      [19 18 17 16]
      [15 14 13 12]]
    
     [[11 10  9  8]
      [ 7  6  5  4]
      [ 3  2  1  0]]]

## 布尔型索引 ##

# 布尔型索引
    names = np.array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'])
    data = np.random.randn(7, 4)

names

array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'], 
          dtype='<U4')

data

array([[-1.01504197, -1.53005352,  0.75853162,  0.35188611],
           [ 0.42691628,  0.64738979, -0.49456879,  0.6420728 ],
           [ 0.79245449, -0.88837584,  1.15706385, -1.40232002],
           [ 0.10355699, -1.15478154,  0.99303669, -1.42431578],
           [-1.08005402, -0.88447402, -0.19597154,  0.50873589],
           [ 1.52942389,  0.37328854, -1.01259458, -0.70578044],
           [-0.50512187,  1.40398143,  0.324043  , -0.58337786]])

names == 'Bob'

array([ True, False, False,  True, False, False, False], dtype=bool)

data[names == 'Bob']

array([[-1.01504197, -1.53005352,  0.75853162,  0.35188611],
           [ 0.10355699, -1.15478154,  0.99303669, -1.42431578]])

data[names == 'Bob', 2:]

array([[ 0.75853162,  0.35188611],
           [ 0.99303669, -1.42431578]])

data[names == 'Bob', 3]

array([ 0.35188611, -1.42431578])

names != 'Bob'

array([False,  True,  True, False,  True,  True,  True], dtype=bool)

data[~(names == 'Bob')]

array([[ 0.42691628,  0.64738979, -0.49456879,  0.6420728 ],
           [ 0.79245449, -0.88837584,  1.15706385, -1.40232002],
           [-1.08005402, -0.88447402, -0.19597154,  0.50873589],
           [ 1.52942389,  0.37328854, -1.01259458, -0.70578044],
           [-0.50512187,  1.40398143,  0.324043  , -0.58337786]])

mask = (names == 'Bob') | (names == 'Will')
    mask

array([ True, False,  True,  True,  True, False, False], dtype=bool)

data[mask]

array([[-1.01504197, -1.53005352,  0.75853162,  0.35188611],
           [ 0.79245449, -0.88837584,  1.15706385, -1.40232002],
           [ 0.10355699, -1.15478154,  0.99303669, -1.42431578],
           [-1.08005402, -0.88447402, -0.19597154,  0.50873589]])

data[data < 0] = 0
    data

array([[ 0.        ,  0.        ,  0.75853162,  0.35188611],
           [ 0.42691628,  0.64738979,  0.        ,  0.6420728 ],
           [ 0.79245449,  0.        ,  1.15706385,  0.        ],
           [ 0.10355699,  0.        ,  0.99303669,  0.        ],
           [ 0.        ,  0.        ,  0.        ,  0.50873589],
           [ 1.52942389,  0.37328854,  0.        ,  0.        ],
           [ 0.        ,  1.40398143,  0.324043  ,  0.        ]])

data[names != 'Joe'] = 7
    data

array([[ 7.        ,  7.        ,  7.        ,  7.        ],
           [ 0.42691628,  0.64738979,  0.        ,  0.6420728 ],
           [ 7.        ,  7.        ,  7.        ,  7.        ],
           [ 7.        ,  7.        ,  7.        ,  7.        ],
           [ 7.        ,  7.        ,  7.        ,  7.        ],
           [ 1.52942389,  0.37328854,  0.        ,  0.        ],
           [ 0.        ,  1.40398143,  0.324043  ,  0.        ]])

## 花式索引 ##

# 花式索引
    arr = np.empty((8, 4))
    for i in range(8):
        arr[i] = i
    arr

array([[ 0.,  0.,  0.,  0.],
           [ 1.,  1.,  1.,  1.],
           [ 2.,  2.,  2.,  2.],
           [ 3.,  3.,  3.,  3.],
           [ 4.,  4.,  4.,  4.],
           [ 5.,  5.,  5.,  5.],
           [ 6.,  6.,  6.,  6.],
           [ 7.,  7.,  7.,  7.]])

arr[[4, 3, 0, 6]]

array([[ 4.,  4.,  4.,  4.],
           [ 3.,  3.,  3.,  3.],
           [ 0.,  0.,  0.,  0.],
           [ 6.,  6.,  6.,  6.]])

arr[[-3, -5, -7]]

array([[ 5.,  5.,  5.,  5.],
           [ 3.,  3.,  3.,  3.],
           [ 1.,  1.,  1.,  1.]])

arr = np.arange(32).reshape((8, 4))
    arr

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]])

arr[[1, 5, 7, 2], [0, 3, 1, 2]]

array([ 4, 23, 29, 10])

arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]

array([[ 4,  7,  5,  6],
           [20, 23, 21, 22],
           [28, 31, 29, 30],
           [ 8, 11,  9, 10]])

arr[np.ix_([1, 5, 7, 2], [0, 3, 1, 2])]

array([[ 4,  7,  5,  6],
           [20, 23, 21, 22],
           [28, 31, 29, 30],
           [ 8, 11,  9, 10]])

## 数组转置 ##

# 数组转置
    arr = np.arange(15).reshape((3, 5))
    arr

array([[ 0,  1,  2,  3,  4],
           [ 5,  6,  7,  8,  9],
           [10, 11, 12, 13, 14]])

arr.T

array([[ 0,  5, 10],
           [ 1,  6, 11],
           [ 2,  7, 12],
           [ 3,  8, 13],
           [ 4,  9, 14]])

## 改变数组的维度 ##

# 改变数组的维度
    b = np.arange(24).reshape(2, 3, 4)
    
    print(b)
    
    print(b.ravel())
    
    print(b.flatten())
    
    b.shape = (6, 4)
    
    print(b)
    
    print(b.transpose())
    
    b.resize((2, 12))
    
    print(b)

[[[ 0  1  2  3]
      [ 4  5  6  7]
      [ 8  9 10 11]]
    
     [[12 13 14 15]
      [16 17 18 19]
      [20 21 22 23]]]
    [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
    [ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14 15 16 17 18 19 20 21 22 23]
    [[ 0  1  2  3]
     [ 4  5  6  7]
     [ 8  9 10 11]
     [12 13 14 15]
     [16 17 18 19]
     [20 21 22 23]]
    [[ 0  4  8 12 16 20]
     [ 1  5  9 13 17 21]
     [ 2  6 10 14 18 22]
     [ 3  7 11 15 19 23]]
    [[ 0  1  2  3  4  5  6  7  8  9 10 11]
     [12 13 14 15 16 17 18 19 20 21 22 23]]

## 组合数组 ##

# 组合数组
    a = np.arange(9).reshape(3, 3)
    
    print(a)
    
    b = 2 * a
    
    print(b)
    
    print(np.hstack((a, b)))
    
    print(np.concatenate((a, b), axis=1))
    
    print(np.vstack((a, b)))
    
    print(np.concatenate((a, b), axis=0))
    
    print(np.dstack((a, b)))
    
    oned = np.arange(2)
    
    print(oned)
    
    twice_oned = 2 * oned
    
    print(twice_oned)
    
    print(np.column_stack((oned, twice_oned)))
    
    print(np.column_stack((a, b)))
    
    print(np.column_stack((a, b)) == np.hstack((a, b)))
    
    print(np.row_stack((oned, twice_oned)))
    
    print(np.row_stack((a, b)))
    
    print(np.row_stack((a, b)) == np.vstack((a, b)))

[[0 1 2]
     [3 4 5]
     [6 7 8]]
    [[ 0  2  4]
     [ 6  8 10]
     [12 14 16]]
    [[ 0  1  2  0  2  4]
     [ 3  4  5  6  8 10]
     [ 6  7  8 12 14 16]]
    [[ 0  1  2  0  2  4]
     [ 3  4  5  6  8 10]
     [ 6  7  8 12 14 16]]
    [[ 0  1  2]
     [ 3  4  5]
     [ 6  7  8]
     [ 0  2  4]
     [ 6  8 10]
     [12 14 16]]
    [[ 0  1  2]
     [ 3  4  5]
     [ 6  7  8]
     [ 0  2  4]
     [ 6  8 10]
     [12 14 16]]
    [[[ 0  0]
      [ 1  2]
      [ 2  4]]
    
     [[ 3  6]
      [ 4  8]
      [ 5 10]]
    
     [[ 6 12]
      [ 7 14]
      [ 8 16]]]
    [0 1]
    [0 2]
    [[0 0]
     [1 2]]
    [[ 0  1  2  0  2  4]
     [ 3  4  5  6  8 10]
     [ 6  7  8 12 14 16]]
    [[ True  True  True  True  True  True]
     [ True  True  True  True  True  True]
     [ True  True  True  True  True  True]]
    [[0 1]
     [0 2]]
    [[ 0  1  2]
     [ 3  4  5]
     [ 6  7  8]
     [ 0  2  4]
     [ 6  8 10]
     [12 14 16]]
    [[ True  True  True]
     [ True  True  True]
     [ True  True  True]
     [ True  True  True]
     [ True  True  True]
     [ True  True  True]]

## 数组的分割 ##

# 数组的分割
    a = np.arange(6).reshape(2, 3)
    
    print(a)
    
    print(np.hsplit(a, 3))
    
    print(np.split(a, 3, axis=1))
    
    print(np.vsplit(a, 2))
    
    print(np.split(a, 2, axis=0))
    
    c = np.arange(27).reshape(3, 3, 3)
    
    print(c)
    
    print(np.dsplit(c, 3))

[[0 1 2]
     [3 4 5]]
    [array([[0],
           [3]]), array([[1],
           [4]]), array([[2],
           [5]])]
    [array([[0],
           [3]]), array([[1],
           [4]]), array([[2],
           [5]])]
    [array([[0, 1, 2]]), array([[3, 4, 5]])]
    [array([[0, 1, 2]]), array([[3, 4, 5]])]
    [[[ 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]]]
    [array([[[ 0],
            [ 3],
            [ 6]],
    
           [[ 9],
            [12],
            [15]],
    
           [[18],
            [21],
            [24]]]), array([[[ 1],
            [ 4],
            [ 7]],
    
           [[10],
            [13],
            [16]],
    
           [[19],
            [22],
            [25]]]), array([[[ 2],
            [ 5],
            [ 8]],
    
           [[11],
            [14],
            [17]],
    
           [[20],
            [23],
            [26]]])]

## 数组的属性 ##

# 数组的属性
    b = np.arange(24).reshape(2, 12)
    b.ndim
    b.size
    b.itemsize
    b.nbytes
    
    b = np.array([1. + 1.j, 3. + 2.j])
    b.real
    b.imag
    
    b = np.arange(4).reshape(2, 2)
    b.flat
    b.flat[2]

## 数组的转换 ##

# 数组的转换
    b = np.array([1. + 1.j, 3. + 2.j])
    print(b)
    
    print(b.tolist())
    
    print(b.tostring())
    
    print(np.fromstring(
        b'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x00@',
        dtype=complex))
    
    print(np.fromstring('20:42:52', sep=':', dtype=int))
    
    print(b)
    
    print(b.astype(int))
    
    print(b.astype('complex'))

[ 1.+1.j  3.+2.j]
    [(1+1j), (3+2j)]
    b'\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\xf0?\x00\x00\x00\x00\x00\x00\x08@\x00\x00\x00\x00\x00\x00\x00@'
    [ 1.+1.j  3.+2.j]
    [20 42 52]
    [ 1.+1.j  3.+2.j]
    [1 3]
    [ 1.+1.j  3.+2.j]
    
    
    C:\ProgramData\Anaconda3\lib\site-packages\ipykernel_launcher.py:17: ComplexWarning: Casting complex values to real discards the imaginary part

参考资料：炼数成金Python数据分析课程

[https_github.com_RealEmperor_Python-for-Data-Analysis]: https://github.com/RealEmperor/Python-for-Data-Analysis