Python-Numpy(2)Array数组操作

最新推荐文章于 2024-04-07 15:10:04 发布

KongX_B

最新推荐文章于 2024-04-07 15:10:04 发布

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分类专栏： python 文章标签： python numpy

本文链接：https://blog.youkuaiyun.com/kong1287988804/article/details/77113157

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本文介绍了使用Python的NumPy库进行数组操作的方法，包括数组比较、条件筛选、数据类型转换、数学运算及缺失值处理等核心功能。通过具体实例展示了如何创建、比较和修改一维及二维数组，并演示了如何利用布尔索引选取特定元素。

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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

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

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#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])

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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, :])

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#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

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vector = numpy.array([5, 10, 15, 20])
equal_to_ten_or_five = (vector == 10) | (vector == 5)
print equal_to_ten_or_five

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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)

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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

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#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

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vector = numpy.array([5, 10, 15, 20])
vector.sum()

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# 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)

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matrix = numpy.array([
                [5, 10, 15], 
                [20, 25, 30],
                [35, 40, 45]
             ])
matrix.sum(axis=0)

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#replace nan value with 0
world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")
#print world_alcohol
is_value_empty = numpy.isnan(world_alcohol[:,4])
#print is_value_empty
world_alcohol[is_value_empty, 4] = '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

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