Python快速入手

ゝ一世哀愁。 2022-08-08 14:54 215阅读 0赞

*  列表的常用操作  
    
    
     *  列表遍历

In [18]: for x in list:
       ....:     print(x)
       ....:
    5
    4
    3
    2
    
    In [19]: for x in range(0,len(list)):
       ....:     print(list[x])
       ....:
    5
    4
    3
    2

*  列表排序

In [13]: list=[2,4,5,3]
    In [14]: list.sort()
    In [15]: list
    Out[15]: [2, 3, 4, 5]
    //从大到小
    In [16]: list.sort(reverse=True)
    In [17]: list
    Out[17]: [5, 4, 3, 2]

*  字典的常用操作  
    
    
     *  字典遍历

In [9]: x={1:2,3:4,4:3,2:1,0:0}
    In [10]: for i in x:
       ....:     print(i)
    0
    1
    2
    3
    4
    In [11]: for i,j in x.items():
       ....:     print(i,j)
       ....:
    0 0
    1 2
    2 1
    3 4
    4 3
    In [12]: for i in x.keys():
       ....:     print(i)
       ....:
    0
    1
    2
    3
    4
    
    In [13]:

*  排序

In [1]: import operator
    In [2]: x={1:2,3:4,4:3,2:1,0:0}
    //按value排序（默认从小到大）
    In [3]: sortx=sorted(x.items(),key=operator.itemgetter(1))
    In [4]: sortx
    Out[4]: [(0, 0), (2, 1), (1, 2), (4, 3), (3, 4)]
    //按key排序
    In [5]: sortx=sorted(x.items(),key=operator.itemgetter(0))
    In [6]: sortx
    Out[6]: [(0, 0), (1, 2), (2, 1), (3, 4), (4, 3)]
    //从大到小排序
    In [7]: sortx=sorted(x.items(),key=operator.itemgetter(0),reverse=True)
    In [8]: sortx
    Out[8]: [(4, 3), (3, 4), (2, 1), (1, 2), (0, 0)]

*  文件的读写

//读文件
    read=open("file","r",encoding="utf-8")
    for line in read.readlines():
        print(line)
    //写文件
    write=open("file","a",encoding="utf-8")
    write.write("something")

数组的重塑  
**reshape**

In [2]: arr=np.arange(8)
    In [3]: arr
    Out[3]: array([0, 1, 2, 3, 4, 5, 6, 7])
    In [4]: arr.reshape((4,2))
    Out[4]:
    array([[0, 1],
           [2, 3],
           [4, 5],
           [6, 7]])
    In [5]: arr.reshape((2,4))
    Out[5]:
    array([[0, 1, 2, 3],
           [4, 5, 6, 7]])
    In [6]: arr=np.arange(15)
    In [7]: arr.reshape((5,-1))//-1表示该维度的大小由数据本身推断而来
    Out[7]:
    array([[ 0,  1,  2],
           [ 3,  4,  5],
           [ 6,  7,  8],
           [ 9, 10, 11],
           [12, 13, 14]])
    In [8]: other_arr=np.ones((3,5))
    In [9]: other_arr.shape//通过shape来得到行、列数
    Out[9]: (3, 5)

**raveling**

In [10]: arr=np.arange(15).reshape((5,3))
    
    In [11]: arr
    Out[11]:
    array([[ 0,  1,  2],
           [ 3,  4,  5],
           [ 6,  7,  8],
           [ 9, 10, 11],
           [12, 13, 14]])
    In [12]: arr.ravel()
    Out[12]: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])
    In [13]: arr.ravel("f")//f表示按列展开
    Out[13]: array([ 0,  3,  6,  9, 12,  1,  4,  7, 10, 13,  2,  5,  8, 11, 14])
    In [14]: arr.ravel("c")//c表示按列展开
    Out[14]: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])
    In [15]: arr.ravel("a")//a表示按行展开也就是默认的方式
    Out[15]: array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])

**数组的合并和拆分**

In [16]: arr1=np.array([[1,2,3],[4,5,6]])
    In [17]: arr2=np.array([[7,8,9],[10,11,12]])
    In [18]: np.concatenate([arr1,arr2],axis=0)
    Out[18]:
    array([[ 1,  2,  3],
           [ 4,  5,  6],
           [ 7,  8,  9],
           [10, 11, 12]])
    In [19]: np.concatenate([arr1,arr2],axis=1)
    Out[19]:
    array([[ 1,  2,  3,  7,  8,  9],
           [ 4,  5,  6, 10, 11, 12]])
    /简单方式/
    In [20]: np.vstack((arr1,arr2))
    Out[20]:
    array([[ 1,  2,  3],
           [ 4,  5,  6],
           [ 7,  8,  9],
           [10, 11, 12]])
    
    In [21]: np.hstack((arr1,arr2))
    Out[21]:
    array([[ 1,  2,  3,  7,  8,  9],
           [ 4,  5,  6, 10, 11, 12]])
    
    In [29]: arr=randn(8,2)
    
    In [30]: arr
    Out[30]:
    array([[-0.61557794,  0.6589772 ],
           [-1.53940669,  0.72233791],
           [ 0.65838257,  0.16509701],
           [-0.85338451, -0.01873622],
           [ 2.63647146, -0.90049343],
           [ 1.00616154,  0.26211412],
           [ 0.08777359, -0.23117177],
           [ 0.19183086,  1.49532713]])
    In [32]: a,b,c,d=np.split(arr,[1,4,5])//按第一行，四行，五行，分开
    In [33]: a
    Out[33]: array([[-0.61557794,  0.6589772 ]])
    In [34]: b
    Out[34]:
    array([[-1.53940669,  0.72233791],
           [ 0.65838257,  0.16509701],
           [-0.85338451, -0.01873622]])
    
    In [35]: c
    Out[35]: array([[ 2.63647146, -0.90049343]])
    
    In [36]: d
    Out[36]:
    array([[ 1.00616154,  0.26211412],
           [ 0.08777359, -0.23117177],
           [ 0.19183086,  1.49532713]])

好用的工具：np.where()

In [4]: arr=numpy.random.randn(4,4)
    
    In [5]: arr
    Out[5]:
    array([[-1.6015138 ,  0.02734431,  1.23806435,  1.14439669],
           [-0.52331802, -0.04224807, -0.00374231,  0.56596793],
           [ 0.12013888,  0.46383992, -1.41978895,  0.76854959],
           [-3.71788919,  0.11227569, -0.37714645, -1.25479449]])
    
    In [6]: numpy.where(arr>0,1,-1)//大于零为1，小于零为-1
    Out[6]:
    array([[-1,  1,  1,  1],
           [-1, -1, -1,  1],
           [ 1,  1, -1,  1],
           [-1,  1, -1, -1]])
    
    In [7]: numpy.where(arr>0,1,arr)//arr表示结果不变
    Out[7]:
    array([[-1.6015138 ,  1.        ,  1.        ,  1.        ],
           [-0.52331802, -0.04224807, -0.00374231,  1.        ],
           [ 1.        ,  1.        , -1.41978895,  1.        ],
           [-3.71788919,  1.        , -0.37714645, -1.25479449]])

Python快速入手

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