機器學習：第一章python中numpy庫(1)

1.概述

numpy庫是Python語言的一個擴充程序庫。支持高級大量的維度數組與矩陣運算,此外也針對數組運算提供大量的數學函數庫。

2.操作

①對讀取文件的操作

    import numpy
    //genfromtxt是一個對文件操作的一個函數
    new_text = numpy.genfromtxt("C:\\Users\\Administrator\\Desktop\\new_text.txt",delimiter=",",dtype=str)
    //打印出文件的存儲類型
    print(type(new_text))
    print(new_text)
    //help函數是爲了查詢這個genfromtxt有什麼功能，以及有什麼參數
    print(help(numpy.genfromtxt)

運行結果：
<class 'numpy.ndarray'>
[['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']]
Help on function genfromtxt in module numpy:

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.

②一維數組和二維數組的建立

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

    運行結果：[ 5 10 15 20]
            [[ 5 10 15]
             [20 25 30]
             [35 40 45]]

   ##### ③
    shape()：一維元素查元素個數，二維數組表示幾行幾列
    vector=numpy.array([5,10,15,20])
    matrix=numpy.array([[5,10,15],[20,25,30],[35,40,45]])
    print(vector.shape)
    print(matrix.shape)
    運行結果：(4,)
            (3, 3)
 //一個矩陣或者數組裏面的元素必須是同種類型，如果有一個優先級高的，所有的元素會向優先級轉換
import numpy
numbers=numpy.array([1,2,3,'4'])
print(numbers)
numbers.dtype
運行結果：['1' '2' '3' '4']

⑤數組和矩陣的切片操作

  vector=numpy.array([5,10,15,20])
  //左閉右開，輸出第0個元素到第二個元素
  print(vector[0:3])
 matrix=numpy.array([[5,10,15],[20,25,30],[35,40,45]])
 //表示第一列的所有元素
 print(matrix[:,1])
 //表示第0列到第1列的所有元素
 matrix=numpy.array([[5,10,15],[20,25,30],[35,40,45]])
 print(matrix[:,0:2])
 運行結果：
 [ 5 10 15]
 [10 25 40]
 [[ 5 10]
 [20 25]
 [35 40]]

⑥判斷字符的應用

 vector=numpy.array([5,10,15,20])
 vector==10
 運行結果：array([False,  True, False, False])
 //bollean類型也可以當索引來用
 vector=numpy.array([5,10,15,20])
 equal_to_ten=(vector==10)
 print (equal_to_ten)
 print(vector[equal_to_ten])
 運行結果：
 [False  True False False]
 [10]
matrix=numpy.array([[5,10,15],[20,25,30],[35,40,45]])
second_column_25=(matrix[:,1]==25)
print (second_column_25)
print(matrix[second_column_25,:])
運行結果：
[False  True False]
[[20 25 30]]

⑦矩陣的列求和和行求和

 import numpy
 matrix=numpy.array([[5,10,15],[20,25,30],[35,40,45]])
 //代表每一行的和
 matrix.sum(axis=1)
 運行結果:
 array([ 30,  75, 120])

 import numpy
 matrix=numpy.array([[5,10,15],[20,25,30],[35,40,45]])
 //代表每一列的和
 matrix.sum(axis=0)
 運行結果:
 array([60, 75, 90])