Hướng dẫn plot array of arrays python - vẽ mảng của mảng python

Tôi có một danh sách raws các mảng mà tôi muốn vẽ trong máy tính xách tay ipython. Đây là mã tôi đang cố gắng để làm việc:

fig, axes = subplots(len(raws),1, sharex=True, tight_layout=True, figsize=(12, 6), dpi=72)
for r in range(len(raws)):
    axes[r].plot(raws)

Tôi đã bị mất hàng giờ nếu không phải là ngày cố gắng tìm ra cách lập chỉ mục danh sách raws, sao cho tôi có thể vẽ từng mảng MXN trên trục riêng của nó trong đó n là số điểm thời gian, tức là, trục x và m là Số lượng hàm chuỗi thời gian được lấy mẫu tại mỗi điểm.

Khi tôi viết mã:

for r in range(len(raws)):
        axes[r].plot(raws[r])

Tôi nhận được một giá trịerror: Đặt một phần tử mảng với một chuỗi.

Cho thông tin của bạn:

    len(raws) = 2
    type(raws) = 'list'
    np.shape(raws[0][0]) = (306, 10001)
    raws = 
[(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
          1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
       [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
         -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
       [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
         -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
       ..., 
       [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
          4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
       [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
          3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
       [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
          7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
         -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
       [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
         -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
       [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
          2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
       ..., 
       [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
         -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
       [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
          7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
       [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
         -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]

Cải thiện bài viết

Lưu bài viết

  • Đọc
  • Bàn luận
  • Cải thiện bài viết

    Lưu bài viết

    Để vẽ đồ thị trong Python, chúng tôi sẽ sử dụng thư viện matplotlib. Matplotlib được sử dụng cùng với dữ liệu numpy để vẽ bất kỳ loại đồ thị nào. Từ matplotlib, chúng tôi sử dụng hàm cụ thể, tức là pyplot (), được sử dụng để vẽ dữ liệu hai chiều.pyplot(), which is used to plot two-dimensional data.

    Các chức năng khác nhau được sử dụng được giải thích dưới đây:

    • np.arange (bắt đầu, kết thúc): hàm này trả về các giá trị cách đều nhau từ khoảng [bắt đầu, kết thúc).This function returns equally spaced values from the interval [start, end).
    • plt.title (): Nó được sử dụng để đưa ra một tiêu đề cho biểu đồ. Tiêu đề được truyền làm tham số cho hàm này.It is used to give a title to the graph. Title is passed as the parameter to this function.
    • plt.xlabel (): Nó đặt tên nhãn tại trục x. Tên của trục x được truyền làm đối số cho hàm này.It sets the label name at X-axis. Name of X-axis is passed as argument to this function.
    • plt.ylabel (): Nó đặt tên nhãn tại trục y. Tên của trục y được truyền như là đối số cho hàm này.It sets the label name at Y-axis. Name of Y-axis is passed as argument to this function.
    • plt.plot (): Nó biểu thị các giá trị của các tham số được truyền cho nó cùng nhau.It plots the values of parameters passed to it together.
    • plt.show (): Nó hiển thị tất cả các biểu đồ cho bảng điều khiển.It shows all the graph to the console.

    Ví dụ 1 :

    Python3

    import numpy as np

    import matplotlib.pyplot as plt

    x ____10

    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    1
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    2
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    3
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    4
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    6
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    0 x
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    9
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    0

        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    1
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    2
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    4
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    5
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    7
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    8
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

    raws0

    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    0raws2
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

    raws4

    & nbsp; đầu ra: & nbsp;
    Output : 

    Ví dụ 2:

    Python3

    import numpy as np

    import matplotlib.pyplot as plt

    x ____10

    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    1
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    2
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    3
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    4
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    6
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    0 x
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    9
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    0

        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    1
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    2
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    4
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    5
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    7
        len(raws) = 2
        type(raws) = 'list'
        np.shape(raws[0][0]) = (306, 10001)
        raws = 
    [(array([[ -4.13211217e-12,  -4.13287303e-12,  -4.01705259e-12, ...,
              1.36386023e-12,   1.65182851e-12,   2.00368966e-12],
           [  1.08914129e-12,   1.47828466e-12,   1.82257607e-12, ...,
             -2.70151520e-12,  -2.48631967e-12,  -2.28625548e-12],
           [ -7.80962369e-14,  -1.27119591e-13,  -1.73610315e-13, ...,
             -1.13219629e-13,  -1.15031720e-13,  -1.12106621e-13],
           ..., 
           [  2.52774254e-12,   2.32293195e-12,   2.02644002e-12, ...,
              4.20064191e-12,   3.94858906e-12,   3.69495394e-12],
           [ -4.38122146e-12,  -4.96229676e-12,  -5.47782145e-12, ...,
              3.93820033e-12,   4.18850823e-12,   4.34950629e-12],
           [ -1.07284424e-13,  -9.23447993e-14,  -7.89852400e-14, ...,
              7.92079631e-14,   5.60172215e-14,   3.04448868e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ])), (array([[ -6.71363108e-12,  -5.80501003e-12,  -4.95944514e-12, ...,
             -3.25087343e-12,  -2.68982494e-12,  -2.13637448e-12],
           [ -5.04818633e-12,  -4.65757005e-12,  -4.16084140e-12, ...,
             -4.26120531e-13,   2.20744290e-13,   7.81245614e-13],
           [  1.97329506e-13,   1.64543867e-13,   1.32679812e-13, ...,
              2.11645494e-13,   1.94795729e-13,   1.75781773e-13],
           ..., 
           [  3.04245661e-12,   2.28376461e-12,   1.54118900e-12, ...,
             -1.14020908e-14,  -8.04647589e-13,  -1.52676489e-12],
           [ -1.83485962e-13,  -5.22949893e-13,  -8.60038852e-13, ...,
              7.70312553e-12,   7.20825156e-12,   6.58362857e-12],
           [ -7.26357906e-14,  -7.11700989e-14,  -6.88759767e-14, ...,
             -1.04171843e-13,  -1.03084861e-13,  -9.68462427e-14]]), array([ 60.   ,  60.001,  60.002, ...,  69.998,  69.999,  70.   ]))]
    
    8
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

    raws0

    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    0matplotlib.pyplot as plt0
    for r in range(len(raws)):
            axes[r].plot(raws[r])
    
    5

    raws4

    & nbsp; đầu ra: & nbsp;
    Output : 

    Hướng dẫn plot array of arrays python - vẽ mảng của mảng python


    Làm thế nào để bạn vẽ một mảng các mảng trong Python?

    Khoa học dữ liệu thực tế sử dụng Python..
    Đặt kích thước hình và điều chỉnh phần đệm giữa và xung quanh các ô phụ ..
    Tạo hai mảng, x và y, sử dụng numpy ..
    Đặt tiêu đề của đường cong bằng phương thức Tiêu đề () ..
    Sơ đồ các điểm dữ liệu x và y, với màu đỏ ..
    Để hiển thị hình, sử dụng phương thức show () ..

    Bạn có thể có một mảng các mảng trong Python không?

    Khoa học dữ liệu thực tế sử dụng mảng hai chiều Python là một mảng trong một mảng.Nó là một mảng các mảng.Trong loại mảng này, vị trí của một phần tử dữ liệu được giới thiệu bởi hai chỉ số thay vì một.Two dimensional array is an array within an array. It is an array of arrays. In this type of array the position of an data element is referred by two indices instead of one.

    Bạn có thể vẽ mảng numpy không?

    Để vẽ đồ thị trong Python, chúng tôi sẽ sử dụng thư viện matplotlib.Matplotlib được sử dụng cùng với dữ liệu numpy để vẽ bất kỳ loại đồ thị nào.Từ matplotlib, chúng tôi sử dụng hàm cụ thể, tức là pyplot (), được sử dụng để vẽ dữ liệu hai chiều.Matplotlib is used along with NumPy data to plot any type of graph. From matplotlib we use the specific function i.e. pyplot(), which is used to plot two-dimensional data.

    Làm thế nào để bạn vẽ một mảng của một dòng trong Python?

    Để tạo biểu đồ dòng, hãy truyền một mảng hoặc danh sách các số làm đối số cho hàm plt.plot () của matplotlib.Lệnh plt.Show () là cần thiết ở cuối để hiển thị cốt truyện.pass an array or list of numbers as an argument to Matplotlib's plt. plot() function. The command plt. show() is needed at the end to show the plot.