Python np.logical_and 操作數無法與形狀一起廣播

Question

我有Y_1和X_1作為 (506,1) 作為形狀的矩陣，兩者都是。 我想知道 X_1 值大於 5 小於 6 時的 Y_1 值。就像這樣。

np.logical_and(Y_1[X_1 > 5], Y_1[X_1 < 6])

但我得到了這個錯誤。

ValueError：操作數無法與形狀一起廣播 (490,) (173,)

這是 X_1 和 Y_1 中值的數據：

Values for Y_1
 [[24. ]
 [21.6]
 [34.7]
 [33.4]
 [36.2]
 [28.7]
 [22.9]
 [27.1]
 [16.5]
 [18.9]
 [15. ]
 [18.9]
 [21.7]
 [20.4]
 [18.2]
 [19.9]
 [23.1]
 [17.5]
 [20.2]
 [18.2]
 [13.6]
 [19.6]
 [15.2]
 [14.5]
 [15.6]
 [13.9]
 [16.6]
 [14.8]
 [18.4]
 [21. ]
 [12.7]
 [14.5]
 [13.2]
 [13.1]
 [13.5]
 [18.9]
 [20. ]
 [21. ]
 [24.7]
 [30.8]
 [34.9]
 [26.6]
 [25.3]
 [24.7]
 [21.2]
 [19.3]
 [20. ]
 [16.6]
 [14.4]
 [19.4]
 [19.7]
 [20.5]
 [25. ]
 [23.4]
 [18.9]
 [35.4]
 [24.7]
 [31.6]
 [23.3]
 [19.6]
 [18.7]
 [16. ]
 [22.2]
 [25. ]
 [33. ]
 [23.5]
 [19.4]
 [22. ]
 [17.4]
 [20.9]
 [24.2]
 [21.7]
 [22.8]
 [23.4]
 [24.1]
 [21.4]
 [20. ]
 [20.8]
 [21.2]
 [20.3]
 [28. ]
 [23.9]
 [24.8]
 [22.9]
 [23.9]
 [26.6]
 [22.5]
 [22.2]
 [23.6]
 [28.7]
 [22.6]
 [22. ]
 [22.9]
 [25. ]
 [20.6]
 [28.4]
 [21.4]
 [38.7]
 [43.8]
 [33.2]
 [27.5]
 [26.5]
 [18.6]
 [19.3]
 [20.1]
 [19.5]
 [19.5]
 [20.4]
 [19.8]
 [19.4]
 [21.7]
 [22.8]
 [18.8]
 [18.7]
 [18.5]
 [18.3]
 [21.2]
 [19.2]
 [20.4]
 [19.3]
 [22. ]
 [20.3]
 [20.5]
 [17.3]
 [18.8]
 [21.4]
 [15.7]
 [16.2]
 [18. ]
 [14.3]
 [19.2]
 [19.6]
 [23. ]
 [18.4]
 [15.6]
 [18.1]
 [17.4]
 [17.1]
 [13.3]
 [17.8]
 [14. ]
 [14.4]
 [13.4]
 [15.6]
 [11.8]
 [13.8]
 [15.6]
 [14.6]
 [17.8]
 [15.4]
 [21.5]
 [19.6]
 [15.3]
 [19.4]
 [17. ]
 [15.6]
 [13.1]
 [41.3]
 [24.3]
 [23.3]
 [27. ]
 [50. ]
 [50. ]
 [50. ]
 [22.7]
 [25. ]
 [50. ]
 [23.8]
 [23.8]
 [22.3]
 [17.4]
 [19.1]
 [23.1]
 [23.6]
 [22.6]
 [29.4]
 [23.2]
 [24.6]
 [29.9]
 [37.2]
 [39.8]
 [36.2]
 [37.9]
 [32.5]
 [26.4]
 [29.6]
 [50. ]
 [32. ]
 [29.8]
 [34.9]
 [37. ]
 [30.5]
 [36.4]
 [31.1]
 [29.1]
 [50. ]
 [33.3]
 [30.3]
 [34.6]
 [34.9]
 [32.9]
 [24.1]
 [42.3]
 [48.5]
 [50. ]
 [22.6]
 [24.4]
 [22.5]
 [24.4]
 [20. ]
 [21.7]
 [19.3]
 [22.4]
 [28.1]
 [23.7]
 [25. ]
 [23.3]
 [28.7]
 [21.5]
 [23. ]
 [26.7]
 [21.7]
 [27.5]
 [30.1]
 [44.8]
 [50. ]
 [37.6]
 [31.6]
 [46.7]
 [31.5]
 [24.3]
 [31.7]
 [41.7]
 [48.3]
 [29. ]
 [24. ]
 [25.1]
 [31.5]
 [23.7]
 [23.3]
 [22. ]
 [20.1]
 [22.2]
 [23.7]
 [17.6]
 [18.5]
 [24.3]
 [20.5]
 [24.5]
 [26.2]
 [24.4]
 [24.8]
 [29.6]
 [42.8]
 [21.9]
 [20.9]
 [44. ]
 [50. ]
 [36. ]
 [30.1]
 [33.8]
 [43.1]
 [48.8]
 [31. ]
 [36.5]
 [22.8]
 [30.7]
 [50. ]
 [43.5]
 [20.7]
 [21.1]
 [25.2]
 [24.4]
 [35.2]
 [32.4]
 [32. ]
 [33.2]
 [33.1]
 [29.1]
 [35.1]
 [45.4]
 [35.4]
 [46. ]
 [50. ]
 [32.2]
 [22. ]
 [20.1]
 [23.2]
 [22.3]
 [24.8]
 [28.5]
 [37.3]
 [27.9]
 [23.9]
 [21.7]
 [28.6]
 [27.1]
 [20.3]
 [22.5]
 [29. ]
 [24.8]
 [22. ]
 [26.4]
 [33.1]
 [36.1]
 [28.4]
 [33.4]
 [28.2]
 [22.8]
 [20.3]
 [16.1]
 [22.1]
 [19.4]
 [21.6]
 [23.8]
 [16.2]
 [17.8]
 [19.8]
 [23.1]
 [21. ]
 [23.8]
 [23.1]
 [20.4]
 [18.5]
 [25. ]
 [24.6]
 [23. ]
 [22.2]
 [19.3]
 [22.6]
 [19.8]
 [17.1]
 [19.4]
 [22.2]
 [20.7]
 [21.1]
 [19.5]
 [18.5]
 [20.6]
 [19. ]
 [18.7]
 [32.7]
 [16.5]
 [23.9]
 [31.2]
 [17.5]
 [17.2]
 [23.1]
 [24.5]
 [26.6]
 [22.9]
 [24.1]
 [18.6]
 [30.1]
 [18.2]
 [20.6]
 [17.8]
 [21.7]
 [22.7]
 [22.6]
 [25. ]
 [19.9]
 [20.8]
 [16.8]
 [21.9]
 [27.5]
 [21.9]
 [23.1]
 [50. ]
 [50. ]
 [50. ]
 [50. ]
 [50. ]
 [13.8]
 [13.8]
 [15. ]
 [13.9]
 [13.3]
 [13.1]
 [10.2]
 [10.4]
 [10.9]
 [11.3]
 [12.3]
 [ 8.8]
 [ 7.2]
 [10.5]
 [ 7.4]
 [10.2]
 [11.5]
 [15.1]
 [23.2]
 [ 9.7]
 [13.8]
 [12.7]
 [13.1]
 [12.5]
 [ 8.5]
 [ 5. ]
 [ 6.3]
 [ 5.6]
 [ 7.2]
 [12.1]
 [ 8.3]
 [ 8.5]
 [ 5. ]
 [11.9]
 [27.9]
 [17.2]
 [27.5]
 [15. ]
 [17.2]
 [17.9]
 [16.3]
 [ 7. ]
 [ 7.2]
 [ 7.5]
 [10.4]
 [ 8.8]
 [ 8.4]
 [16.7]
 [14.2]
 [20.8]
 [13.4]
 [11.7]
 [ 8.3]
 [10.2]
 [10.9]
 [11. ]
 [ 9.5]
 [14.5]
 [14.1]
 [16.1]
 [14.3]
 [11.7]
 [13.4]
 [ 9.6]
 [ 8.7]
 [ 8.4]
 [12.8]
 [10.5]
 [17.1]
 [18.4]
 [15.4]
 [10.8]
 [11.8]
 [14.9]
 [12.6]
 [14.1]
 [13. ]
 [13.4]
 [15.2]
 [16.1]
 [17.8]
 [14.9]
 [14.1]
 [12.7]
 [13.5]
 [14.9]
 [20. ]
 [16.4]
 [17.7]
 [19.5]
 [20.2]
 [21.4]
 [19.9]
 [19. ]
 [19.1]
 [19.1]
 [20.1]
 [19.9]
 [19.6]
 [23.2]
 [29.8]
 [13.8]
 [13.3]
 [16.7]
 [12. ]
 [14.6]
 [21.4]
 [23. ]
 [23.7]
 [25. ]
 [21.8]
 [20.6]
 [21.2]
 [19.1]
 [20.6]
 [15.2]
 [ 7. ]
 [ 8.1]
 [13.6]
 [20.1]
 [21.8]
 [24.5]
 [23.1]
 [19.7]
 [18.3]
 [21.2]
 [17.5]
 [16.8]
 [22.4]
 [20.6]
 [23.9]
 [22. ]
 [11.9]] 
Values for X_1
 [[6.575]
 [6.421]
 [7.185]
 [6.998]
 [7.147]
 [6.43 ]
 [6.012]
 [6.172]
 [5.631]
 [6.004]
 [6.377]
 [6.009]
 [5.889]
 [5.949]
 [6.096]
 [5.834]
 [5.935]
 [5.99 ]
 [5.456]
 [5.727]
 [5.57 ]
 [5.965]
 [6.142]
 [5.813]
 [5.924]
 [5.599]
 [5.813]
 [6.047]
 [6.495]
 [6.674]
 [5.713]
 [6.072]
 [5.95 ]
 [5.701]
 [6.096]
 [5.933]
 [5.841]
 [5.85 ]
 [5.966]
 [6.595]
 [7.024]
 [6.77 ]
 [6.169]
 [6.211]
 [6.069]
 [5.682]
 [5.786]
 [6.03 ]
 [5.399]
 [5.602]
 [5.963]
 [6.115]
 [6.511]
 [5.998]
 [5.888]
 [7.249]
 [6.383]
 [6.816]
 [6.145]
 [5.927]
 [5.741]
 [5.966]
 [6.456]
 [6.762]
 [7.104]
 [6.29 ]
 [5.787]
 [5.878]
 [5.594]
 [5.885]
 [6.417]
 [5.961]
 [6.065]
 [6.245]
 [6.273]
 [6.286]
 [6.279]
 [6.14 ]
 [6.232]
 [5.874]
 [6.727]
 [6.619]
 [6.302]
 [6.167]
 [6.389]
 [6.63 ]
 [6.015]
 [6.121]
 [7.007]
 [7.079]
 [6.417]
 [6.405]
 [6.442]
 [6.211]
 [6.249]
 [6.625]
 [6.163]
 [8.069]
 [7.82 ]
 [7.416]
 [6.727]
 [6.781]
 [6.405]
 [6.137]
 [6.167]
 [5.851]
 [5.836]
 [6.127]
 [6.474]
 [6.229]
 [6.195]
 [6.715]
 [5.913]
 [6.092]
 [6.254]
 [5.928]
 [6.176]
 [6.021]
 [5.872]
 [5.731]
 [5.87 ]
 [6.004]
 [5.961]
 [5.856]
 [5.879]
 [5.986]
 [5.613]
 [5.693]
 [6.431]
 [5.637]
 [6.458]
 [6.326]
 [6.372]
 [5.822]
 [5.757]
 [6.335]
 [5.942]
 [6.454]
 [5.857]
 [6.151]
 [6.174]
 [5.019]
 [5.403]
 [5.468]
 [4.903]
 [6.13 ]
 [5.628]
 [4.926]
 [5.186]
 [5.597]
 [6.122]
 [5.404]
 [5.012]
 [5.709]
 [6.129]
 [6.152]
 [5.272]
 [6.943]
 [6.066]
 [6.51 ]
 [6.25 ]
 [7.489]
 [7.802]
 [8.375]
 [5.854]
 [6.101]
 [7.929]
 [5.877]
 [6.319]
 [6.402]
 [5.875]
 [5.88 ]
 [5.572]
 [6.416]
 [5.859]
 [6.546]
 [6.02 ]
 [6.315]
 [6.86 ]
 [6.98 ]
 [7.765]
 [6.144]
 [7.155]
 [6.563]
 [5.604]
 [6.153]
 [7.831]
 [6.782]
 [6.556]
 [7.185]
 [6.951]
 [6.739]
 [7.178]
 [6.8  ]
 [6.604]
 [7.875]
 [7.287]
 [7.107]
 [7.274]
 [6.975]
 [7.135]
 [6.162]
 [7.61 ]
 [7.853]
 [8.034]
 [5.891]
 [6.326]
 [5.783]
 [6.064]
 [5.344]
 [5.96 ]
 [5.404]
 [5.807]
 [6.375]
 [5.412]
 [6.182]
 [5.888]
 [6.642]
 [5.951]
 [6.373]
 [6.951]
 [6.164]
 [6.879]
 [6.618]
 [8.266]
 [8.725]
 [8.04 ]
 [7.163]
 [7.686]
 [6.552]
 [5.981]
 [7.412]
 [8.337]
 [8.247]
 [6.726]
 [6.086]
 [6.631]
 [7.358]
 [6.481]
 [6.606]
 [6.897]
 [6.095]
 [6.358]
 [6.393]
 [5.593]
 [5.605]
 [6.108]
 [6.226]
 [6.433]
 [6.718]
 [6.487]
 [6.438]
 [6.957]
 [8.259]
 [6.108]
 [5.876]
 [7.454]
 [8.704]
 [7.333]
 [6.842]
 [7.203]
 [7.52 ]
 [8.398]
 [7.327]
 [7.206]
 [5.56 ]
 [7.014]
 [8.297]
 [7.47 ]
 [5.92 ]
 [5.856]
 [6.24 ]
 [6.538]
 [7.691]
 [6.758]
 [6.854]
 [7.267]
 [6.826]
 [6.482]
 [6.812]
 [7.82 ]
 [6.968]
 [7.645]
 [7.923]
 [7.088]
 [6.453]
 [6.23 ]
 [6.209]
 [6.315]
 [6.565]
 [6.861]
 [7.148]
 [6.63 ]
 [6.127]
 [6.009]
 [6.678]
 [6.549]
 [5.79 ]
 [6.345]
 [7.041]
 [6.871]
 [6.59 ]
 [6.495]
 [6.982]
 [7.236]
 [6.616]
 [7.42 ]
 [6.849]
 [6.635]
 [5.972]
 [4.973]
 [6.122]
 [6.023]
 [6.266]
 [6.567]
 [5.705]
 [5.914]
 [5.782]
 [6.382]
 [6.113]
 [6.426]
 [6.376]
 [6.041]
 [5.708]
 [6.415]
 [6.431]
 [6.312]
 [6.083]
 [5.868]
 [6.333]
 [6.144]
 [5.706]
 [6.031]
 [6.316]
 [6.31 ]
 [6.037]
 [5.869]
 [5.895]
 [6.059]
 [5.985]
 [5.968]
 [7.241]
 [6.54 ]
 [6.696]
 [6.874]
 [6.014]
 [5.898]
 [6.516]
 [6.635]
 [6.939]
 [6.49 ]
 [6.579]
 [5.884]
 [6.728]
 [5.663]
 [5.936]
 [6.212]
 [6.395]
 [6.127]
 [6.112]
 [6.398]
 [6.251]
 [5.362]
 [5.803]
 [8.78 ]
 [3.561]
 [4.963]
 [3.863]
 [4.97 ]
 [6.683]
 [7.016]
 [6.216]
 [5.875]
 [4.906]
 [4.138]
 [7.313]
 [6.649]
 [6.794]
 [6.38 ]
 [6.223]
 [6.968]
 [6.545]
 [5.536]
 [5.52 ]
 [4.368]
 [5.277]
 [4.652]
 [5.   ]
 [4.88 ]
 [5.39 ]
 [5.713]
 [6.051]
 [5.036]
 [6.193]
 [5.887]
 [6.471]
 [6.405]
 [5.747]
 [5.453]
 [5.852]
 [5.987]
 [6.343]
 [6.404]
 [5.349]
 [5.531]
 [5.683]
 [4.138]
 [5.608]
 [5.617]
 [6.852]
 [5.757]
 [6.657]
 [4.628]
 [5.155]
 [4.519]
 [6.434]
 [6.782]
 [5.304]
 [5.957]
 [6.824]
 [6.411]
 [6.006]
 [5.648]
 [6.103]
 [5.565]
 [5.896]
 [5.837]
 [6.202]
 [6.193]
 [6.38 ]
 [6.348]
 [6.833]
 [6.425]
 [6.436]
 [6.208]
 [6.629]
 [6.461]
 [6.152]
 [5.935]
 [5.627]
 [5.818]
 [6.406]
 [6.219]
 [6.485]
 [5.854]
 [6.459]
 [6.341]
 [6.251]
 [6.185]
 [6.417]
 [6.749]
 [6.655]
 [6.297]
 [7.393]
 [6.728]
 [6.525]
 [5.976]
 [5.936]
 [6.301]
 [6.081]
 [6.701]
 [6.376]
 [6.317]
 [6.513]
 [6.209]
 [5.759]
 [5.952]
 [6.003]
 [5.926]
 [5.713]
 [6.167]
 [6.229]
 [6.437]
 [6.98 ]
 [5.427]
 [6.162]
 [6.484]
 [5.304]
 [6.185]
 [6.229]
 [6.242]
 [6.75 ]
 [7.061]
 [5.762]
 [5.871]
 [6.312]
 [6.114]
 [5.905]
 [5.454]
 [5.414]
 [5.093]
 [5.983]
 [5.983]
 [5.707]
 [5.926]
 [5.67 ]
 [5.39 ]
 [5.794]
 [6.019]
 [5.569]
 [6.027]
 [6.593]
 [6.12 ]
 [6.976]
 [6.794]
 [6.03 ]]

Answer 1

您在這里使用了邏輯和錯誤的方式。 您在此處傳遞的 arguments 即 Y_1[X_1>5] 和 Y_1[X_1<6] 具有錯誤中給出的不同形狀。 它們的形狀必須相同。 X_1 中有 490 個元素大於 5，173 個元素小於 6。

它應該像所有元素的 np.logical_and(X_1>5,X_1<6) 。