numpy的用法-02

import numpy

#1.array把数组转化为矩阵
In [9]:
    #it will compare the second value to each element in the vector
    # If the values are equal, the Python interpreter returns True; otherwise, it returns False
    vector = numpy.array([5, 10, 15, 20])
    vector == 10
    import numpy
    #it will compare the second value to each element in the vector
    # If the values are equal, the Python interpreter returns True; otherwise, it returns False
    vector = numpy.array([5, 10, 15, 20])
    vector == 10
Out[9]:
    array([False,  True, False, False], dtype=bool)

#2.二维数组
In [10]:
    matrix = numpy.array([
                        [5, 10, 15], 
                        [20, 25, 30],
                        [35, 40, 45]
                     ])
    matrix == 25
Out[10]:
    array([[False, False, False],
           [False,  True, False],
           [False, False, False]], dtype=bool)

#3.判断是否含有10，并输出
In [6]:
    #Compares vector to the value 10, which generates a new Boolean vector [False, True, False, False]. It assigns this result to equal_to_ten
    vector = numpy.array([5, 10, 15, 20])
    equal_to_ten = (vector == 10) 
    print equal_to_ten
    print(vector[equal_to_ten])
Out[6]:
    [False  True False False]
    [10]


#4.
In [8]:
    matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
    second_column_25 = (matrix[:,1] == 25) #输出第二列 对应的True或false
    print(second_column_25)
    print(matrix[second_column_25, :]) #second_column_25中为True的行全部输出
Out [8]:   
    [False  True False]
    [[20 25 30]]

#5.与操作  同时满足
In [11]:
    #We can also perform comparisons with multiple conditions
    vector = numpy.array([5, 10, 15, 20])
    equal_to_ten_and_five = (vector == 10) & (vector == 5)
    print equal_to_ten_and_five
Out [11]:
    [False False False False]

#6.或操作  其中之一满足，或都满足
In [12]:
    vector = numpy.array([5, 10, 15, 20])
    equal_to_ten_or_five = (vector == 10) | (vector == 5)
Out [12]:
    print equal_to_ten_or_five
    [ True  True False False]
    
#7.或操作   复制
In [13]:
    vector = numpy.array([5, 10, 15, 20])
    equal_to_ten_or_five = (vector == 10) | (vector == 5)
    vector[equal_to_ten_or_five] = 50
    print(vector)
Out [13]:
    [50 50 15 20]
    
#8、查看某一列是否存在某个值，并进行修改
In [12]:
    matrix = numpy.array([
                [5, 10, 15], 
                [20, 25, 30],
                [35, 40, 45]
             ])
    second_column_25 = matrix[:,1] == 25
    print second_column_25
    matrix[second_column_25, 1] = 10
    print matrix
Out [12]:
    [False  True False]
    [[ 5 10 15]
     [20 10 30]
     [35 40 45]]

#9、 astype 数据的类型修改
In [14]:
    #We can convert the data type of an array with the ndarray.astype() method.
    vector = numpy.array(["1", "2", "3"])
    print vector.dtype
    print vector
    vector = vector.astype(float)
    print vector.dtype
    print vector
Out [14]:
    |S1
    ['1' '2' '3']
    float64
    [ 1.  2.  3.]
    
#10、 求和
In [19]:
    vector = numpy.array([5, 10, 15, 20])
    vector.sum()
Out[19]:
    50
    
In [20]:
    #11.sum(axis=1) 每一列求和 sum(axis=0) 每一行求和
    # The axis dictates which dimension we perform the operation on
    #1 means that we want to perform the operation on each row, and 0 means on each column
    matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
    matrix.sum(axis=1)
Out[20]:
    array([ 30,  75, 120])
    
In [21]:
    matrix = numpy.array([
                    [5, 10, 15], 
                    [20, 25, 30],
                    [35, 40, 45]
                 ])
    matrix.sum(axis=0)

Out[21]:
    array([60, 75, 90])

In [25]:
    #replace nan value with 0
    world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
    #print world_alcohol
    is_value_empty = numpy.isnan(world_alcohol[:,4]) #不是数组返回nan 否则返回数字
    #print is_value_empty
    world_alcohol[is_value_empty, 4] = '0' #最后一列是nan 值为0
    alcohol_consumption = world_alcohol[:,4]
    alcohol_consumption = alcohol_consumption.astype(float)
    total_alcohol = alcohol_consumption.sum()
    average_alcohol = alcohol_consumption.mean()
    print total_alcohol
    print average_alcohol
Out [25]:   
    1137.78
    1.14006012024