numpy基本函数与操作——一篇就够了！

Dear 丶 2022-06-01 13:46 224阅读 0赞

对numpy基本函数操作进行了整理，掌握这些便可以算是入了个门，基本全部敲一遍就掌握差不多了！开发环境为jupyter notebook 基本是一个输入一个输出  
需要源码的可以去我的github下载

import numpy as np
    # 读文件的操作 分隔符为逗号 类型是str
    world_alcohol = np.genfromtxt('world_alcohol.txt', delimiter = ',', dtype = str )

print(type(world_alcohol))

world_alcohol

array([['Year', 'WHO region', 'Country', 'Beverage Types', 'Display Value'],
           ['1986', 'Western Pacific', 'Viet Nam', 'Wine', '0'],
           ['1986', 'Americas', 'Uruguay', 'Other', '0.5'],
           ..., 
           ['1987', 'Africa', 'Malawi', 'Other', '0.75'],
           ['1989', 'Americas', 'Bahamas', 'Wine', '1.5'],
           ['1985', 'Africa', 'Malawi', 'Spirits', '0.31']],
          dtype='<U52')

# 调用help函数 查看api详情
    print(help(np.genfromtxt))

Help on function genfromtxt in module numpy.lib.npyio:
    
    genfromtxt(fname, dtype=<class 'float'>, comments='#', delimiter=None, skip_header=0, skip_footer=0, converters=None, missing_values=None, filling_values=None, usecols=None, names=None, excludelist=None, deletechars=None, replace_space='_', autostrip=False, case_sensitive=True, defaultfmt='f%i', unpack=None, usemask=False, loose=True, invalid_raise=True, max_rows=None)
        Load data from a text file, with missing values handled as specified.
    
        Each line past the first `skip_header` lines is split at the `delimiter`
        character, and characters following the `comments` character are discarded.
    
        Parameters
        ----------
        fname : file, str, pathlib.Path, list of str, generator
            File, filename, list, or generator to read.  If the filename
            extension is `.gz` or `.bz2`, the file is first decompressed. Note
            that generators must return byte strings in Python 3k.  The strings
            in a list or produced by a generator are treated as lines.
        dtype : dtype, optional
            Data type of the resulting array.
            If None, the dtypes will be determined by the contents of each
            column, individually.
        comments : str, optional
            The character used to indicate the start of a comment.
            All the characters occurring on a line after a comment are discarded
        delimiter : str, int, or sequence, optional
            The string used to separate values.  By default, any consecutive
            whitespaces act as delimiter.  An integer or sequence of integers
            can also be provided as width(s) of each field.
        skiprows : int, optional
            `skiprows` was removed in numpy 1.10. Please use `skip_header` instead.
        skip_header : int, optional
            The number of lines to skip at the beginning of the file.
        skip_footer : int, optional
            The number of lines to skip at the end of the file.
        converters : variable, optional
            The set of functions that convert the data of a column to a value.
            The converters can also be used to provide a default value
            for missing data: ``converters = {3: lambda s: float(s or 0)}``.
        missing : variable, optional
            `missing` was removed in numpy 1.10. Please use `missing_values`
            instead.
        missing_values : variable, optional
            The set of strings corresponding to missing data.
        filling_values : variable, optional
            The set of values to be used as default when the data are missing.
        usecols : sequence, optional
            Which columns to read, with 0 being the first.  For example,
            ``usecols = (1, 4, 5)`` will extract the 2nd, 5th and 6th columns.
        names : {None, True, str, sequence}, optional
            If `names` is True, the field names are read from the first valid line
            after the first `skip_header` lines.
            If `names` is a sequence or a single-string of comma-separated names,
            the names will be used to define the field names in a structured dtype.
            If `names` is None, the names of the dtype fields will be used, if any.
        excludelist : sequence, optional
            A list of names to exclude. This list is appended to the default list
            ['return','file','print']. Excluded names are appended an underscore:
            for example, `file` would become `file_`.
        deletechars : str, optional
            A string combining invalid characters that must be deleted from the
            names.
        defaultfmt : str, optional
            A format used to define default field names, such as "f%i" or "f_%02i".
        autostrip : bool, optional
            Whether to automatically strip white spaces from the variables.
        replace_space : char, optional
            Character(s) used in replacement of white spaces in the variables
            names. By default, use a '_'.
        case_sensitive : {True, False, 'upper', 'lower'}, optional
            If True, field names are case sensitive.
            If False or 'upper', field names are converted to upper case.
            If 'lower', field names are converted to lower case.
        unpack : bool, optional
            If True, the returned array is transposed, so that arguments may be
            unpacked using ``x, y, z = loadtxt(...)``
        usemask : bool, optional
            If True, return a masked array.
            If False, return a regular array.
        loose : bool, optional
            If True, do not raise errors for invalid values.
        invalid_raise : bool, optional
            If True, an exception is raised if an inconsistency is detected in the
            number of columns.
            If False, a warning is emitted and the offending lines are skipped.
        max_rows : int,  optional
            The maximum number of rows to read. Must not be used with skip_footer
            at the same time.  If given, the value must be at least 1. Default is
            to read the entire file.
    
            .. versionadded:: 1.10.0
    
        Returns
        -------
        out : ndarray
            Data read from the text file. If `usemask` is True, this is a
            masked array.
    
        See Also
        --------
        numpy.loadtxt : equivalent function when no data is missing.
    
        Notes
        -----
        * When spaces are used as delimiters, or when no delimiter has been given
          as input, there should not be any missing data between two fields.
        * When the variables are named (either by a flexible dtype or with `names`,
          there must not be any header in the file (else a ValueError
          exception is raised).
        * Individual values are not stripped of spaces by default.
          When using a custom converter, make sure the function does remove spaces.
    
        References
        ----------
        .. [1] NumPy User Guide, section `I/O with NumPy
               <http://docs.scipy.org/doc/numpy/user/basics.io.genfromtxt.html>`_.
    
        Examples
        ---------
        >>> from io import StringIO
        >>> import numpy as np
    
        Comma delimited file with mixed dtype
    
        >>> s = StringIO("1,1.3,abcde")
        >>> data = np.genfromtxt(s, dtype=[('myint','i8'),('myfloat','f8'),
        ... ('mystring','S5')], delimiter=",")
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
    
        Using dtype = None
    
        >>> s.seek(0) # needed for StringIO example only
        >>> data = np.genfromtxt(s, dtype=None,
        ... names = ['myint','myfloat','mystring'], delimiter=",")
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
    
        Specifying dtype and names
    
        >>> s.seek(0)
        >>> data = np.genfromtxt(s, dtype="i8,f8,S5",
        ... names=['myint','myfloat','mystring'], delimiter=",")
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('myint', '<i8'), ('myfloat', '<f8'), ('mystring', '|S5')])
    
        An example with fixed-width columns
    
        >>> s = StringIO("11.3abcde")
        >>> data = np.genfromtxt(s, dtype=None, names=['intvar','fltvar','strvar'],
        ...     delimiter=[1,3,5])
        >>> data
        array((1, 1.3, 'abcde'),
              dtype=[('intvar', '<i8'), ('fltvar', '<f8'), ('strvar', '|S5')])
    
    None

import numpy as np
    # 构造一维向量
    np.array(np.arange(3))

array([0, 1, 2])

# 构造二维矩阵
    s = np.array([[1,2,3], [4,5,6]])

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

s.dtype

dtype('int32')

# 索引
    s[0,0]

# 矩阵中类型必须一致
    import numpy as np
    numbers = np.array([1,2,3,4.])
    print(numbers)
    print(numbers.dtype)

[ 1.  2.  3.  4.]
    float64

# 值的判定 返回布尔数组
    numbers == 3

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

# 可用布尔数组作为索引 查出具体的值
    equal_to_3 = (numbers == 3)
    numbers[equal_to_3]

array([ 3.])

matrix = np.array(
        [[11,26,38],
         [32,65,96],
         [21,78,84],
        ]
    )

matrix

array([[11, 26, 38],
           [32, 65, 96],
           [21, 78, 84]])

# 返回第二列中是否等于78的布尔数组
    column_equalto65 = matrix[:,1] == 78

# 由列变为行
    column_equalto65

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

# 返回第二行中等于78的一行数据
    matrix[column_equalto65]

array([[21, 78, 84]])

# 这样的索引表示取第二列 :代表所有的行 返回结果为行向量
    matrix[:,1]

array([26, 65, 78])

# 求和操作 指定维度（axis）为1 则表示每一行求和
    matrix.sum(axis = 1)

array([ 75, 193, 183])

# 求和操作 指定维度（axis）为0 则表示每一列求和
    matrix.sum(axis = 0)

array([ 64, 169, 218])

# 通过reshape方法 指定3行5列 重构矩阵
    import numpy as np
    a = np.array([np.arange(15)])
    print(a)
    a = a.reshape((3,5))
    print(a)
    b = np.arange(16).reshape(2,8)
    print(b)

[[ 0  1  2  3  4  5  6  7  8  9 10 11 12 13 14]]
    [[ 0  1  2  3  4]
     [ 5  6  7  8  9]
     [10 11 12 13 14]]
    [[ 0  1  2  3  4  5  6  7]
     [ 8  9 10 11 12 13 14 15]]

# 输出维度
    a.ndim

# 输出类型
    a.dtype

dtype('int32')

# 输出类型名
    a.dtype.name

'int32'

# 随机模块的应用 指定3行4列矩阵
    np.random.random((3,4))

array([[ 0.19375842,  0.36607602,  0.2676583 ,  0.45307936],
           [ 0.43905375,  0.28215774,  0.89246178,  0.2877808 ],
           [ 0.52287865,  0.3748371 ,  0.85626729,  0.37688939]])

# arange的应用 从1开始每次加5 直到小于15
    np.arange(1, 15, 5)

array([ 1,  6, 11])

# linspace的应用 起点值为0 终点为2pi 平均取100个值
    from numpy import pi
    s = np.linspace(0, 2*pi, 100)
    print(s)

[ 0.          0.06346652  0.12693304  0.19039955  0.25386607  0.31733259
      0.38079911  0.44426563  0.50773215  0.57119866  0.63466518  0.6981317
      0.76159822  0.82506474  0.88853126  0.95199777  1.01546429  1.07893081
      1.14239733  1.20586385  1.26933037  1.33279688  1.3962634   1.45972992
      1.52319644  1.58666296  1.65012947  1.71359599  1.77706251  1.84052903
      1.90399555  1.96746207  2.03092858  2.0943951   2.15786162  2.22132814
      2.28479466  2.34826118  2.41172769  2.47519421  2.53866073  2.60212725
      2.66559377  2.72906028  2.7925268   2.85599332  2.91945984  2.98292636
      3.04639288  3.10985939  3.17332591  3.23679243  3.30025895  3.36372547
      3.42719199  3.4906585   3.55412502  3.61759154  3.68105806  3.74452458
      3.8079911   3.87145761  3.93492413  3.99839065  4.06185717  4.12532369
      4.1887902   4.25225672  4.31572324  4.37918976  4.44265628  4.5061228
      4.56958931  4.63305583  4.69652235  4.75998887  4.82345539  4.88692191
      4.95038842  5.01385494  5.07732146  5.14078798  5.2042545   5.26772102
      5.33118753  5.39465405  5.45812057  5.52158709  5.58505361  5.64852012
      5.71198664  5.77545316  5.83891968  5.9023862   5.96585272  6.02931923
      6.09278575  6.15625227  6.21971879  6.28318531]

# 取三角函数值
    np.sin(np.linspace(0, 2*pi, 100))

array([  0.00000000e+00,   6.34239197e-02,   1.26592454e-01,
             1.89251244e-01,   2.51147987e-01,   3.12033446e-01,
             3.71662456e-01,   4.29794912e-01,   4.86196736e-01,
             5.40640817e-01,   5.92907929e-01,   6.42787610e-01,
             6.90079011e-01,   7.34591709e-01,   7.76146464e-01,
             8.14575952e-01,   8.49725430e-01,   8.81453363e-01,
             9.09631995e-01,   9.34147860e-01,   9.54902241e-01,
             9.71811568e-01,   9.84807753e-01,   9.93838464e-01,
             9.98867339e-01,   9.99874128e-01,   9.96854776e-01,
             9.89821442e-01,   9.78802446e-01,   9.63842159e-01,
             9.45000819e-01,   9.22354294e-01,   8.95993774e-01,
             8.66025404e-01,   8.32569855e-01,   7.95761841e-01,
             7.55749574e-01,   7.12694171e-01,   6.66769001e-01,
             6.18158986e-01,   5.67059864e-01,   5.13677392e-01,
             4.58226522e-01,   4.00930535e-01,   3.42020143e-01,
             2.81732557e-01,   2.20310533e-01,   1.58001396e-01,
             9.50560433e-02,   3.17279335e-02,  -3.17279335e-02,
            -9.50560433e-02,  -1.58001396e-01,  -2.20310533e-01,
            -2.81732557e-01,  -3.42020143e-01,  -4.00930535e-01,
            -4.58226522e-01,  -5.13677392e-01,  -5.67059864e-01,
            -6.18158986e-01,  -6.66769001e-01,  -7.12694171e-01,
            -7.55749574e-01,  -7.95761841e-01,  -8.32569855e-01,
            -8.66025404e-01,  -8.95993774e-01,  -9.22354294e-01,
            -9.45000819e-01,  -9.63842159e-01,  -9.78802446e-01,
            -9.89821442e-01,  -9.96854776e-01,  -9.99874128e-01,
            -9.98867339e-01,  -9.93838464e-01,  -9.84807753e-01,
            -9.71811568e-01,  -9.54902241e-01,  -9.34147860e-01,
            -9.09631995e-01,  -8.81453363e-01,  -8.49725430e-01,
            -8.14575952e-01,  -7.76146464e-01,  -7.34591709e-01,
            -6.90079011e-01,  -6.42787610e-01,  -5.92907929e-01,
            -5.40640817e-01,  -4.86196736e-01,  -4.29794912e-01,
            -3.71662456e-01,  -3.12033446e-01,  -2.51147987e-01,
            -1.89251244e-01,  -1.26592454e-01,  -6.34239197e-02,
            -2.44929360e-16])

# 做数学运算
    a = np.array([12, 45, 16, 56])
    b = np.arange(4)
    print(a)
    print(b)
    c = a - b
    print(c)
    c = c - 1
    print(c)
    b = b ** 2
    print(b)
    # 返回布尔数组
    print(a > 16)

[12 45 16 56]
    [0 1 2 3]
    [12 44 14 53]
    [11 43 13 52]
    [0 1 4 9]
    [False  True False  True]

# 矩阵乘法
    a = np.array([
        [1,2],
        [3,4]
    ])
    b = np.array([
        [3,4],
        [1,2]
    ])
    # 对应项相乘
    print(a * b)
    print('-'*10)
    # 矩阵乘法
    print(a.dot(b))
    print('-'*10)
    print(np.dot(a, b))
    print('-'*10)

[[3 8]
     [3 8]]
    ----------
    [[ 5  8]
     [13 20]]
    ----------
    [[ 5  8]
     [13 20]]
    ----------

# e次幂、开根号的计算
    B = np.arange(4)
    print(np.exp(B))
    print(np.sqrt(B))

[  1.           2.71828183   7.3890561   20.08553692]
    [ 0.          1.          1.41421356  1.73205081]

# floor 表示向下取整
    a = np.floor(10*np.random.random((3, 4)))
    print(a)
    print('-'*20)
    # 利用ravel()方法将矩阵拉成向量
    print(a.ravel())
    print('-'*20)
    a.shape = (6, 2)
    print(a)
    print('-'*20)
    # 求转置
    print(a.T)
    # -1 代表默认让系统自己计算列数
    print(a.reshape(3, -1))

[[ 4.  8.  1.  7.]
     [ 2.  6.  8.  9.]
     [ 8.  9.  5.  6.]]
    --------------------
    [ 4.  8.  1.  7.  2.  6.  8.  9.  8.  9.  5.  6.]
    --------------------
    [[ 4.  8.]
     [ 1.  7.]
     [ 2.  6.]
     [ 8.  9.]
     [ 8.  9.]
     [ 5.  6.]]
    --------------------
    [[ 4.  1.  2.  8.  8.  5.]
     [ 8.  7.  6.  9.  9.  6.]]
    [[ 4.  8.  1.  7.]
     [ 2.  6.  8.  9.]
     [ 8.  9.  5.  6.]]

# 数据拼接 
    a = np.floor(10*np.random.random((2, 2)))
    b = np.floor(10*np.random.random((2, 2)))
    print(a)
    print('-'*20)
    print(b)
    print('-'*20)
    # 按行拼 增加样本特征
    print(np.hstack((a, b)))
    print('-'*20)
    # 按列拼 增加样本数
    print(np.vstack((a, b)))
    print('-'*20)

[[ 5.  6.]
     [ 8.  0.]]
    --------------------
    [[ 9.  9.]
     [ 9.  8.]]
    --------------------
    [[ 5.  6.  9.  9.]
     [ 8.  0.  9.  8.]]
    --------------------
    [[ 5.  6.]
     [ 8.  0.]
     [ 9.  9.]
     [ 9.  8.]]
    --------------------

# 数据的切割
    a = np.floor(10*np.random.random((2, 12)))
    b = np.floor(10*np.random.random((2, 12)))
    print(a)
    print('-'*20)
    print(b)
    print('-'*20)
    # 表示按行切开
    print(np.hsplit(a, 3))
    print('-'*20)
    # 表示从某位置切割 (3, 4) 切两下 最左边记为0
    print(np.hsplit(a, (3, 4)))
    print('-'*20)
    # 表示按列切开
    print(np.vsplit(b , 2))

[[ 2.  2.  1.  9.  3.  9.  3.  6.  8.  1.  0.  2.]
     [ 6.  3.  7.  7.  0.  0.  5.  3.  5.  8.  5.  0.]]
    --------------------
    [[ 0.  0.  6.  4.  3.  1.  8.  9.  7.  7.  8.  5.]
     [ 8.  2.  4.  1.  5.  2.  0.  8.  2.  4.  8.  0.]]
    --------------------
    [array([[ 2.,  2.,  1.,  9.],
           [ 6.,  3.,  7.,  7.]]), array([[ 3.,  9.,  3.,  6.],
           [ 0.,  0.,  5.,  3.]]), array([[ 8.,  1.,  0.,  2.],
           [ 5.,  8.,  5.,  0.]])]
    --------------------
    [array([[ 2.,  2.,  1.],
           [ 6.,  3.,  7.]]), array([[ 9.],
           [ 7.]]), array([[ 3.,  9.,  3.,  6.,  8.,  1.,  0.,  2.],
           [ 0.,  0.,  5.,  3.,  5.,  8.,  5.,  0.]])]
    --------------------
    [array([[ 0.,  0.,  6.,  4.,  3.,  1.,  8.,  9.,  7.,  7.,  8.,  5.]]), array([[ 8.,  2.,  4.,  1.,  5.,  2.,  0.,  8.,  2.,  4.,  8.,  0.]])]

# 对象的复制 传引用的方式
    a = np.arange(12)
    b = a
    b.shape = (3, -1)
    print(a.shape)
    print(id(a))
    print(id(b))

(3, 4)
    2262218295696
    2262218295696

# 用view方法创建拷贝对象
    # a、c指向不同的内存 但共用了一堆值 改变c的值 a的值也会改变
    c = a.view()
    c.shape = (4, -1)
    print(a.shape)
    print(id(a))
    print(id(c))
    
    c[1, 1] = 123456
    print(c)
    print(a)

(3, 4)
    2262218295696
    2262218297216
    [[     0      1      2]
     [     3 123456      5]
     [     6      7      8]
     [     9     10     11]]
    [[     0      1      2      3]
     [123456      5      6      7]
     [     8      9     10     11]]

# 用copy进行深拷贝对象 改变d的值 a不会改变
    d = a.copy()
    print(d is a)
    d[0, 0] = 2356
    print(a)

False
    [[     0      1      2      3]
     [123456      5      6      7]
     [     8      9     10     11]]

# 根据索引做运算
    data = np.sin(np.arange(20)).reshape(5, 4)
    print(data)
    # 求出每一列中最大元素的索引
    ind = data.argmax(axis=0)
    print(ind)
    # 将索引传进去 range(data.shape[1])值为[0,1,2,3]代表四列
    data_max = data[ind, range(data.shape[1])]
    print(data_max)

[[ 0.          0.84147098  0.90929743  0.14112001]
     [-0.7568025  -0.95892427 -0.2794155   0.6569866 ]
     [ 0.98935825  0.41211849 -0.54402111 -0.99999021]
     [-0.53657292  0.42016704  0.99060736  0.65028784]
     [-0.28790332 -0.96139749 -0.75098725  0.14987721]]
    [2 0 3 1]
    [ 0.98935825  0.84147098  0.99060736  0.6569866 ]

# 运用tile进行扩展
    a = np.arange(0, 40, 10)
    print(a)
    b = np.tile(a, (2, 2))
    print(b)

[ 0 10 20 30]
    [[ 0 10 20 30  0 10 20 30]
     [ 0 10 20 30  0 10 20 30]]

# 排序操作
    a = np.array([
        [4, 3, 5],
        [1, 2, 1],
    ])
    # 按行进行排序
    print(np.sort(a, axis = 1))
    # 按列进行排序
    print(np.sort(a, axis = 0))
    b = np.array([2, 6, 1, 3])
    # 对索引进行排序
    c = np.argsort(b)
    print(c)
    # 按排序的索引进行输出 则是从小到大输出
    print(b[c])

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