在pythonnumpy中按条件索引？

Question

我正在尝试从Matlab迁移到Python。我正在将Matlab中的一些代码重写为Python进行测试。我已经安装了Anaconda，目前正在使用Spyder IDE。我使用Matlab创建了一个函数，返回更接近函数输入参数的商用API 5L直径（diametro）和管道厚度（espesor）的值。我是用Matlab表格做的。你知道吗

请注意，直径（diametro_entrada）和厚度（espesor_entrada）的输入单位是米[m]，函数中的厚度单位是毫米[mm]，这就是为什么最后我必须将espesor_entrada乘以1000

    function tabla_seleccion=tablaAPI(diametro_entrada,espesor_entrada)
%Proporciona la tabla de caños API 5L, introducir diámetro en [m] y espesor
%en [m]
    Diametro_m=[0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;0.3556;...
    0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;0.4064;...
    0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;0.4570;...
    0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;0.5080;...
    0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;0.559;...
    0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;0.610;...
    0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;0.660;...
    0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;0.711;...
    0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;0.762;...
    0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813;0.813];

Espesor_mm=[4.8;5.2;5.3;5.6;6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;31.8;...
    4.8;5.2;5.6;6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8;...
    4.8;5.6;6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8;...
    5.6;6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8;33.3;34.9;...
    5.6;6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8;33.3;34.9;36.5;38.1;...
    6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8;33.3;34.9;36.5;38.1;39.7;...
    6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;...
    6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;...
    6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8;...
    6.4;7.1;7.9;8.7;9.5;10.3;11.1;11.9;12.7;14.3;15.9;17.5;19.1;20.6;22.2;23.8;25.4;27.0;28.6;30.2;31.8];

TablaAPI=table(Diametro_m,Espesor_mm);
tabla_seleccion=TablaAPI(abs(TablaAPI.Diametro_m-diametro_entrada)<0.05 & abs(TablaAPI.Espesor_mm-(espesor_entrada*1000))<1.2,:);
end

通过输入直径（d）和输入厚度（e），我得到了直径小于0.05，厚度小于1.2的商用管道。你知道吗

我想用Python和Numpy或者其他包来复制这个。 首先，我定义了两个Numpy数组，它们的名称与Matlab中的名称相同，但用逗号分隔而不是分号，并且每行末尾没有“…”，然后定义另一个Numpy数组为：

TablaAPI=numpy.array([Diametro_m,Espesor_mm])   

我想知道我是否可以像在Matlab中那样索引那个数组，或者我必须定义一些完全不同的东西。你知道吗

非常感谢！你知道吗

Answer 1

你当然可以！你知道吗

下面是一个如何使用numpy的示例：

使用Numpy

import math
import numpy as np

# Declare your Diametro_m, Espesor_mmhere just like you did in your example

# Transpose and merge the columns
arr = np.concatenate((Diametro_m, Espesor_mm.T), axis=1)
selection = arr[np.ix_(abs(arr[:0])<0.05,abs(arr[:1]-(math.e*1000)) > <1.2 )]

Example usage from John Zwinck's answer

使用数据帧

如果您需要执行更重的查询或混合列数据类型，数据帧也可能非常适合您的应用程序。如果您选择以下选项，则此代码应适用于您：

# These imports go at the top of your document
import pandas as pd
import numpy as np
import math


# Declare your Diametro_m, Espesor_mmhere just like you did in your example

df_d = pd.DataFrame(data=Diametro_m,
          index=np.array(range(1, len(Diametro_m))),
          columns=np.array(range(1, len(Diametro_m))))

df_e = pd.DataFrame(data=Espesor_mm,
          index=np.array(range(1, len(Diametro_m))),
          columns=np.array(range(1, len(Diametro_m))))

# Merge the dataframes
merged_df = pd.merge(left=df_d , left_index=True
                  right=df_e , right_index=True,
                  how='inner')

# Now you can perform your selections like this:
selection = merged_df.loc[abs(merged_df['df_d']) <0.05, abs(merged_df['df_e']-(math.e*1000))) <1.2]

# This "mask" of the dataframe will return all results that satisfy your query.
print(selection)

Answer 2

因为您没有给出预期输出的示例，所以有点猜测您真正想要的是什么，但是这里有一个版本是使用numpy的。你知道吗

# rewritten arrays for numpy
Diametro_m=[0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,0.3556,
    0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,0.4064,
    0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,0.4570,
    0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,0.5080,
    0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,0.559,
    0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,0.610,
    0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,0.660,
    0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,0.711,
    0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,0.762,
    0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813,0.813]

Espesor_mm=[4.8,5.2,5.3,5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,31.8,
    4.8,5.2,5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,
    4.8,5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,
    5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,33.3,34.9,
    5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,33.3,34.9,36.5,38.1,
    6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,33.3,34.9,36.5,38.1,39.7,
    6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,
    6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,
    6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,
    6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8]


import numpy as np

diametro_entrada = 0.4
espesor_entrada = 5

Diametro_m = np.array(Diametro_m)
Espesor_mm = np.array(Espesor_mm)
# Diametro_m and Espesor_mm has shape (223,)
# if not change so that they have that shape
table = np.array([Diametro_m, Espesor_mm]).T

mask = np.where((np.abs(Diametro_m - diametro_entrada) < 0.05) &
                (np.abs(Espesor_mm - espesor_entrada) < 1.2)
                )
result = table[mask]
print('with numpy')
print(result)

或者你可以用python。。。你知道吗

# redo with python only
# based on a simple dict and list comprehension
D_m = [0.3556, 0.4064, 0.4570, 0.5080, 0.559, 0.610, 0.660, 0.711, 0.762, 0.813]
E_mm = [[4.8,5.2,5.3,5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,31.8],
    [4.8,5.2,5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8],
    [4.8,5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8],
    [5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,33.3,34.9],
    [5.6,6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,33.3,34.9,36.5,38.1],
    [6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8,33.3,34.9,36.5,38.1,39.7],
    [6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4],
    [6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4],
    [6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8],
    [6.4,7.1,7.9,8.7,9.5,10.3,11.1,11.9,12.7,14.3,15.9,17.5,19.1,20.6,22.2,23.8,25.4,27.0,28.6,30.2,31.8]]

table2 = dict(zip(D_m, E_mm))
result2 = []
for D, E in table2.items():
    if abs(D - diametro_entrada) < 0.05:
        Et = [t for t in E if abs(t - espesor_entrada) < 1.2]
        result2 += [(D, t) for t in Et]
print('with vanilla python')
print('\n'.join((str(r) for r in result2)))

一旦您使用python，就有无数种方法可以做到这一点，您可以很容易地使用pandas或sqlite来实现这一点。我个人倾向于尽可能少的依赖关系，在这种情况下，我会选择csv文件作为输入，然后不使用numpy来完成，如果这是一个真正的大规模问题，我会考虑sqlite/numpy/pandas。你知道吗

祝你好运，我想你不会后悔的。你知道吗