NumPy详细API第二篇

来源：互联网发布：图像自动拼接软件编辑：程序博客网时间：2024/04/30 22:21
以下代码是基于python3.5.0
import numpy# -----------------------判断数组中是否存在特定值------------------------------vector = numpy.array([5, 10, 15, 20])print(vector == 10)            # 判断数组中有没有10，返回布尔值[False  True False False]# -----------------------判断矩阵中是否存在特定值------------------------------matrix = numpy.array([                    [5, 10, 15],                    [20, 25, 30],                    [35, 40, 45]])# 返回array([[False, False, False],[False,  True, False],[False, False, False]], dtype=bool)matrix == 25#  -----------------------判断数组中是否存在特定值，并打印出值------------------------------vector = numpy.array([5, 10, 15, 20])equal_to_ten = (vector == 10)print(equal_to_ten)               # [False  True False False]print(vector[equal_to_ten])       # 10# -----------------------判断矩阵中是否存在特定值------------------------------matrix = numpy.array([                [5, 10, 15],                [20, 25, 30],                [35, 40, 45]             ])second_column_25 = (matrix[:,1] == 25)   # 判断第二列有没有等于25的值，返回值为布尔值print(second_column_25)                  # [False True False]print(matrix[second_column_25, :])       # [[20 25 30]]vector = numpy.array([5, 10, 15, 20])equal_to_ten_and_five = (vector == 10) & (vector == 5)print(equal_to_ten_and_five)             # [False False False False]# ---------------------判断数组中是否存在某些值，把一个或多个值进行重新赋值-------------------vector = numpy.array([5, 10, 15, 20])equal_to_ten_or_five = (vector == 10) | (vector == 5)   # [True True False False]vector[equal_to_ten_or_five] = 50               # 把为true的位置赋值为50print(vector)                                   # [50, 50, 15, 20]# ------------------判断矩阵中是否存在某个值，把特定位置的值进行重新赋值----------------------matrix = numpy.array([            [5, 10, 15],            [20, 25, 30],            [35, 40, 45]         ])second_column_25 = matrix[:,1] == 25print(second_column_25)                       # [False True False]matrix[second_column_25, 1] = 10              # 把第2行第2列赋值为10print(matrix)# -------------------类型装换-------------------vector = numpy.array(["1", "2", "3"])print(vector.dtype)                           # S1print(vector)                                 # ['1' '2' '3']vector = vector.astype(float)print(vector.dtype)                           # float64print(vector)                                 # [ 1.  2.  3.]# --------------------求和----------------------vector = numpy.array([5, 10, 15, 20])vector.sum()                                  # 50# -------------------按行求和axis=1--------------------matrix = numpy.array([                [5, 10, 15],                [20, 25, 30],                [35, 40, 45]             ])matrix.sum(axis=1)                            # array([ 30,  75, 120])# -------------------按列求和axis=0--------------------matrix = numpy.array([                [5, 10, 15],                [20, 25, 30],                [35, 40, 45]             ])matrix.sum(axis=0)                            # array([60, 75, 90])# ------------------------自己练习---------------------------#replace nan value with 0world_alcohol = numpy.genfromtxt("world_alcohol.txt", delimiter=",")#print world_alcoholis_value_empty = numpy.isnan(world_alcohol[:,4])#print is_value_emptyworld_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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