python:numpy学習ノート

目次

numpy学習ノート

1、np.set_printoptions()

2、numpyでの乗算

3、np.flatten()

numpy学習ノート
1、np.set_printoptions()

set_printoptions(precision=None, threshold=None, edgeitems=None, linewidth=None, suppress=None, nanstr=None, infstr=None, formatter=None)

この関数は、スクリーン配列印刷の表示方法precision:int,optional,float出力の精度、すなわち小数点後次元数、デフォルト8(Number of digits of precision for floating point output(default 8))threshold:int,optionalを設定するために使用され、配列数が大きすぎる場合、いくつかの数字を表示するように設定します.残りは省略記号(Total number of array elements which trigger summarization rather than full repr(default 1000).edgeitems:int,optional,エッジ数(Number of array items in summary at beginning and end of each dimension(default 3).linewidth : int, optional,The number of characters per line for the purpose of inserting line breaks (default 75). suppress:bool,optional,科学計数法で表される浮動小数点数を圧縮するかどうか(Whether or not suppress printing of small floating point values using scientific notation(default False).nanstr : str, optional,String representation of floating point not-a-number (default nan). infstr : str, optional,String representation of floating point infinity (default inf). formatter : dict of callables, optional If not None, the keys should indicate the type(s) that the respective formatting function applies to. Callables should return a string. Types that are not specified (by their corresponding keys) are handled by the default formatters. Individual types for which a formatter can be set are: eg:np.set_printoptions(threshold=np.nan)印刷時の表示方式を設定、threshold=np.nanは配列を出力するときに完全に出力することを意味し、省略記号で中間データを

>>> np.set_printoptions(precision=4)
>>> print np.array([1.123456789])
[ 1.1235]

>>> np.set_printoptions(threshold=5)
>>> print np.arange(10)
[0 1 2 ..., 7 8 9]

>>> eps = np.finfo(float).eps
>>> x = np.arange(4.)
>>> x**2 - (x + eps)**2
array([ -4.9304e-32,  -4.4409e-16,   0.0000e+00,   0.0000e+00])
>>> np.set_printoptions(suppress=True)
>>> x**2 - (x + eps)**2
array([-0., -0.,  0.,  0.])
>>> np.set_printoptions(formatter={'all':lambda x: 'int: '+str(-x)})
>>> x = np.arange(3)
>>> x
array([int: 0, int: -1, int: -2])
>>> np.set_printoptions()  # formatter gets reset
>>> x
array([0, 1, 2])

2、numpyでの乗算

>>> import numpy as np
>>> a=np.array([[1,2,3],[2,3,4]])
>>> a
array([[1, 2, 3],
       [2, 3, 4]])
>>> a.shape
(2, 3)
>>> a.size
6
>>> b=np.array([1,2,3])
>>> b
array([1, 2, 3])
>>> b.shape
(3,)
>>> a*b #      （  ）
array([[ 1,  4,  9],
       [ 2,  6, 12]])
>>> np.multiply(a,b)
array([[ 1,  4,  9],
       [ 2,  6, 12]])

>>> a@b #  
array([14, 20])
>>> np.dot(a,b)
array([14, 20])
>>> b.T #           
array([1, 2, 3])
>>> a.T
array([[1, 2],
       [2, 3],
       [3, 4]])
>>> 

3、np.flatten()
この関数は、1次元に折り畳まれた配列を返します.

>>> a = np.array([[1,2], [3,4]])
>>> a.flatten()
array([1, 2, 3, 4])
>>> a.flatten('F') #      
array([1, 3, 2, 4])