numpy的用法-01

import numpy

world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
print(type(world_alcohol))
print(help(numpy.genfromtxt))

import numpy
​
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
print(type(world_alcohol))
print(help(numpy.genfromtxt))
<class 'numpy.ndarray'>
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, encoding='bytes')
    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 line after
        the first `skip_header` lines.  This line can optionally be proceeded
        by a comment delimeter. 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
    encoding : str, optional
        Encoding used to decode the inputfile. Does not apply when `fname` is
        a file object.  The special value 'bytes' enables backward compatibility
        workarounds that ensure that you receive byte arrays when possible
        and passes latin1 encoded strings to converters. Override this value to
        receive unicode arrays and pass strings as input to converters.  If set
        to None the system default is used. The default value is 'bytes'.
    
        .. versionadded:: 1.14.0
    
    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')])

vector = numpy.array([5, 10, 15, 20])
matrix = numpy.array([[5, 10, 15], [20, 25, 30], [35, 40, 45]])
print(vector)
print(matrix)
[ 5 10 15 20]
[[ 5 10 15]
 [20 25 30]
 [35 40 45]]

vector = numpy.array([1, 2, 3, 4])
print(vector.shape)
matrix = numpy.array([[5, 10, 15], [20, 25, 30]])
print(matrix.shape)
(4,)
(2, 3)

numbers = numpy.array([1, 2, 3, 4])
numbers.dtype
dtype('int32')

world_alcohol
array([[      nan,       nan,       nan,       nan,       nan],
       [1.986e+03,       nan,       nan,       nan, 0.000e+00],
       [1.986e+03,       nan,       nan,       nan, 5.000e-01],
       ...,
       [1.987e+03,       nan,       nan,       nan, 7.500e-01],
       [1.989e+03,       nan,       nan,       nan, 1.500e+00],
       [1.985e+03,       nan,       nan,       nan, 3.100e-01]])

world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",", dtype="U75", skip_header=1)
print(world_alcohol)
[[u'1986' u'Western Pacific' u'Viet Nam' u'Wine' u'0']
 [u'1986' u'Americas' u'Uruguay' u'Other' u'0.5']
 [u'1985' u'Africa' u"Cte d'Ivoire" u'Wine' u'1.62']
 ..., 
 [u'1987' u'Africa' u'Malawi' u'Other' u'0.75']
 [u'1989' u'Americas' u'Bahamas' u'Wine' u'1.5']
 [u'1985' u'Africa' u'Malawi' u'Spirits' u'0.31']]

uruguay_other_1986 = world_alcohol[1,4]
third_country = world_alcohol[2,2]
print uruguay_other_1986
print third_country
0.5
Cte d'Ivoire

vector = numpy.array([5, 10, 15, 20]) 
print(vector[0:3])  #包头不包尾 0 1 2 
[ 5 10 15]

matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
print(matrix[:,1]) # :前后面不加参数，代表选取所有的行  matrix['行的操作'，'列的操作']
[10 25 40]

matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
print(matrix[:,0:2]) #包头不包尾 0 1 列
[[ 5 10]
 [20 25]
 [35 40]]
In [12]:

matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
print(matrix[1:3,0:2]) #1 2 行 0 1 列
[[20 25]
 [35 40]]