matplotlib 如何在轴值之间放置标签并添加第二个Y轴

lvmkulzt  于 2023-05-23  发布在  其他
关注(0)|答案(1)|浏览(178)

我有一个numpy 2d-array，从中生成以下图形。
Stacked 8 year state distribution for different parameter b
该图基本上解释了对于不同的系统参数b，系统如何在8个时间间隔内采取5个状态。
这有点令人困惑，因为分数从0到8并不完全正确。我希望分数从0到1，然后是另一个指定年份的Y轴。基本上正确地标记图表，这样新读者就不会感到困惑。
我该怎么做？
代码随附在下面。我想我可以循环数据，但它是什么...

数据

import numpy as np

st_02 =  np.array( [[0.00000000e+00, 0.00000000e+00, 3.00000000e-01, 3.00000000e-01, 4.00000000e-01],
 [3.97179162e-01, 9.99999998e-09, 1.49999990e-01, 3.90000000e-01, 6.28208384e-02],
 [5.09153766e-01, 1.58871677e-01, 1.34153757e-01, 1.04999974e-01, 9.28208264e-02],
 [1.52746140e-01, 2.35435852e-01, 3.03484421e-01, 1.88681959e-01, 1.19651568e-01],
 [4.58238520e-02, 1.08185636e-01, 3.23255031e-01, 3.72735451e-01, 1.50000033e-01],
 [1.37471656e-02, 2.33919697e-01, 3.57054740e-01, 2.45278398e-01, 1.50000033e-01],
 [4.12415968e-03, 2.66515660e-01, 3.85409213e-01, 1.93950958e-01, 1.50000000e-01],
 [3.00000000e-01, 5.49528058e-02, 1.55510049e-01, 3.89537165e-01, 1.00000000e-01]] ).reshape((40,1))

act_02 =  np.array( [[3.02820848e-01, 0.00000000e+00, 3.00000000e-01, 3.97179152e-01],
 [6.09999990e-01, 0.00000000e+00, 9.99999998e-09, 3.90000000e-01],
 [8.95000027e-01, 0.00000000e+00, 1.04999974e-01, 0.00000000e+00],
 [5.07833560e-01, 3.03484421e-01, 1.88681959e-01, 0.00000000e+00],
 [3.04009522e-01, 0.00000000e+00, 6.95990482e-01, 0.00000000e+00],
 [3.97666896e-01, 0.00000000e+00, 6.02333138e-01, 0.00000000e+00],
 [4.95560235e-01, 0.00000000e+00, 3.54439755e-01, 1.50000000e-01],
 [1.00000002e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]  ).reshape((32,1))

st_05 =  np.array( [[0.00000000e+00, 0.00000000e+00, 3.00000000e-01, 3.00000000e-01, 4.00000000e-01],
 [3.97179162e-01, 9.99999998e-09, 1.49999990e-01, 3.90000000e-01, 6.28208384e-02],
 [5.09153766e-01, 1.58871677e-01, 1.34153757e-01, 1.04999974e-01, 9.28208264e-02],
 [1.52746140e-01, 2.35435852e-01, 3.03484421e-01, 1.88681959e-01, 1.19651568e-01],
 [4.58238520e-02, 1.08185636e-01, 3.23255031e-01, 3.72735451e-01, 1.50000033e-01],
 [1.37471656e-02, 2.33919697e-01, 3.57054740e-01, 2.45278398e-01, 1.50000033e-01],
 [4.12415968e-03, 2.66515660e-01, 3.85409213e-01, 1.93950958e-01, 1.50000000e-01],
 [3.00000000e-01, 5.49528058e-02, 1.55510049e-01, 3.89537165e-01, 1.00000000e-01]] ).reshape((40,1))

act_05 =  np.array( [[3.02820848e-01, 0.00000000e+00, 3.00000000e-01, 3.97179152e-01],
 [6.09999990e-01, 0.00000000e+00, 9.99999998e-09, 3.90000000e-01],
 [8.95000027e-01, 0.00000000e+00, 1.04999974e-01, 0.00000000e+00],
 [5.07833560e-01, 3.03484421e-01, 1.88681959e-01, 0.00000000e+00],
 [3.04009522e-01, 0.00000000e+00, 6.95990482e-01, 0.00000000e+00],
 [3.97666896e-01, 0.00000000e+00, 6.02333138e-01, 0.00000000e+00],
 [4.95560235e-01, 0.00000000e+00, 3.54439755e-01, 1.50000000e-01],
 [1.00000002e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]  ).reshape((32,1))

st_06 =  np.array(  [[1.00000000e-08, 1.00000000e-08, 3.00000000e-01, 3.00000000e-01, 3.99999980e-01],
 [3.99999993e-01, 1.60000000e-08, 1.33743840e-01, 3.69679758e-01, 9.65763937e-02],
 [4.89679778e-01, 1.60000010e-01, 1.33374292e-01, 9.36206777e-02, 1.23325152e-01],
 [1.46903944e-01, 2.27871923e-01, 2.93689650e-01, 1.81534423e-01, 1.50000000e-01],
 [1.02809113e-01, 1.04335972e-01, 2.82777081e-01, 3.60077804e-01, 1.50000010e-01],
 [3.90920537e-01, 2.31657098e-01, 2.06555160e-01, 2.08672044e-02, 1.50000010e-01],
 [1.17276161e-01, 3.26632740e-01, 3.47239376e-01, 5.88517223e-02, 1.50000000e-01],
 [3.00000000e-01, 1.12237023e-01, 1.89735546e-01, 2.98027461e-01, 1.00000000e-01]] ).reshape((40,1))

act_06 =  np.array(  [[3.40640469e-01, 0.00000000e+00, 2.59359551e-01, 3.99999980e-01],
 [6.30320226e-01, 0.00000000e+00, 1.60000000e-08, 3.69679758e-01],
 [9.06379232e-01, 0.00000000e+00, 9.36206777e-02, 0.00000000e+00],
 [7.59727597e-01, 0.00000000e+00, 1.81534423e-01, 5.87379200e-02],
 [3.57145095e-01, 0.00000000e+00, 2.82777081e-01, 3.60077804e-01],
 [7.72577646e-01, 0.00000000e+00, 2.27422364e-01, 0.00000000e+00],
 [6.81573667e-01, 0.00000000e+00, 1.68426333e-01, 1.50000000e-01],
 [1.00000003e+00, 0.00000000e+00, 0.00000000e+00, 0.00000000e+00]]   ).reshape((32,1))

st_07 =  np.array( [[1.00000000e-08, 1.00017172e-08, 3.00000000e-01, 3.00000000e-01, 3.99999980e-01],
 [3.99999993e-01, 1.60003434e-08, 1.20731694e-01, 3.53414616e-01, 1.25853675e-01],
 [4.73414624e-01, 1.60000010e-01, 1.32073187e-01, 8.45121789e-02, 1.50000000e-01],
 [2.95048729e-01, 2.21365842e-01, 2.51231702e-01, 1.32600008e-01, 9.97536896e-02],
 [8.85146287e-02, 1.62292670e-01, 2.99497287e-01, 2.99695354e-01, 1.50000020e-01],
 [3.26249753e-01, 2.47562768e-01, 2.43728915e-01, 3.24584740e-02, 1.50000010e-01],
 [9.78749359e-02, 3.26249814e-01, 3.56887552e-01, 6.89876479e-02, 1.50000000e-01],
 [3.00000000e-01, 1.04399947e-01, 1.85196960e-01, 3.10403093e-01, 1.00000000e-01]]  ).reshape((40,1))

act_07 =  np.array( [[0.37317078, 0.,         0.22682924, 0.39999998],
 [0.64658538, 0.,         0.,         0.35341462],
 [0.84697567, 0.,         0.,         0.15302433],
 [0.86739996, 0.,         0.13260001, 0.        ],
 [0.40080732, 0.,         0.29949729, 0.29969535],
 [0.72381253, 0.,         0.27618739, 0.        ],
 [0.66201817, 0.,         0.18798178, 0.15      ],
 [1.,         0.,         0.,         0.        ]]  ).reshape((32,1))

st_08 =  np.array( [[1.00000000e-08, 2.00005688e-08, 3.00000000e-01, 3.00000000e-01, 3.99999980e-01],
 [6.06666639e-01, 1.80001138e-08, 3.00000050e-02, 2.19333347e-01, 1.44000009e-01],
 [4.01333361e-01, 2.42666649e-01, 1.84999993e-01, 2.09999971e-02, 1.50000000e-01],
 [2.33199972e-01, 2.09066664e-01, 2.84499987e-01, 1.80133315e-01, 9.31000126e-02],
 [6.99600015e-02, 1.35093342e-01, 2.95903305e-01, 3.49043302e-01, 1.50000010e-01],
 [3.70031303e-01, 2.32544662e-01, 2.20405317e-01, 2.70186583e-02, 1.50000000e-01],
 [1.79407247e-01, 1.94521463e-01, 2.72576730e-01, 2.03494530e-01, 1.50000000e-01],
 [3.00000000e-01, 1.10667201e-01, 1.86841779e-01, 3.02491020e-01, 1.00000000e-01]] ).reshape((40,1))

act_08 =  np.array( [[0.39333338, 0.,         0.,         0.60666663],
 [0.78066667, 0.,         0.,         0.21933335],
 [0.88720003, 0.,         0.,         0.11279997],
 [0.81986664, 0.,         0.18013331, 0.        ],
 [0.35505335, 0.,         0.2959033,  0.3490433 ],
 [0.93160209, 0.,         0.,         0.06839785],
 [0.57682169, 0.,         0.27317828, 0.15      ],
 [1.,         0.,         0.,         0.        ]]  ).reshape((32,1))

st_09 =  np.array(  [[0.,         0.,         0.3,        0.3,        0.39999998],
 [0.60000003, 0.,         0.02999999, 0.22000001, 0.15      ],
 [0.406,      0.24,       0.18299999, 0.02099998, 0.15      ],
 [0.17729999, 0.21040001, 0.28409999, 0.17819998, 0.15      ],
 [0.27785663, 0.11300001, 0.20784001, 0.25130335, 0.15000001],
 [0.33466034, 0.25844667, 0.23429301, 0.02259995, 0.15      ],
 [0.1567521,  0.18555348, 0.28371522, 0.2239792,  0.15      ],
 [0.3,        0.09981155, 0.21590587, 0.28428259, 0.1       ]]  ).reshape((40,1))

act_09 =  np.array( [[0.40000001, 0.,         0.,         0.59999997],
 [0.77400003, 0.,         0.,         0.226     ],
 [0.94449999, 0.,         0.,         0.05549998],
 [0.77533334, 0.,         0.,         0.22466663],
 [0.54085665, 0.,         0.20784001, 0.25130335],
 [0.93159647, 0.,         0.01204951, 0.05635399],
 [0.6740841,  0.,         0.07294154, 0.25297436],
 [1.00000001, 0.,         0.,         0.        ]]   ).reshape((32,1))

st_10 =  np.array( [[1.00000000e-08, 1.00000000e-08, 3.00000000e-01, 3.00000000e-01, 3.99999980e-01],
 [5.99999982e-01, 5.99998334e-09, 2.99999990e-02, 2.19999993e-01, 1.50000000e-01],
 [3.89999986e-01, 2.40000004e-01, 1.82999988e-01, 3.69999916e-02, 1.50000000e-01],
 [1.86899986e-01, 2.04000005e-01, 2.79300007e-01, 1.79800002e-01, 1.50000000e-01],
 [2.81270038e-01, 1.15559985e-01, 2.06399990e-01, 2.46769997e-01, 1.50000000e-01],
 [3.60351005e-01, 1.35620022e-01, 1.74356992e-01, 1.79671981e-01, 1.50000000e-01],
 [1.42976700e-01, 1.71264396e-01, 2.78781796e-01, 2.56977077e-01, 1.50000000e-01],
 [3.00000000e-01, 9.14435692e-02, 2.13814032e-01, 2.94742399e-01, 1.00000000e-01]]  ).reshape((40,1))

act_10 =  np.array(  [[0.40000003, 0.,         0.,         0.59999997],
 [0.79,       0.,         0.,         0.20999998],
 [0.93009999, 0.,         0.,         0.06989998],
 [0.77480001, 0.,         0.,         0.22519999],
 [0.72403003, 0.,         0.,         0.27596998],
 [0.78545663, 0.,         0.17967198, 0.03487139],
 [0.64218250, 0.,         0.10071049, 0.25710698],
 [1.,         0.,         0.,         0.        ]]   ).reshape((32,1))

绘图

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

state_palette = list((sns.color_palette("Spectral_r", 5).as_hex()))
action_palette = list((sns.color_palette("Spectral_r", 4).as_hex()))

# create data for E=0.1 Sigmoid/Golabi States
x = ['0.2', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0 \n (Linear model)']

states = np.c_[st_02, st_05, st_06, st_07, st_08, st_09, st_10]

# plot bars in stack manner
plt.figure(figsize=(6.5,14))
#y1
plt.bar(x, states[0], label = 'State 1', color= state_palette[0])
plt.bar(x, states[1], bottom=states[0], label = 'State 2', color= state_palette[1])
plt.bar(x, states[2], bottom=states[0]+states[1], label = 'State 3', color= state_palette[2])
plt.bar(x, states[3], bottom=states[0]+states[1]+states[2], label = 'State 4', color= state_palette[3])
plt.bar(x, states[4], bottom=states[0]+states[1]+states[2]+states[3], label = 'State 5', color= state_palette[4])
#y2
plt.bar(x, states[5], bottom= 1, color= state_palette[0])
plt.bar(x, states[6], bottom= 1+ states[5], color= state_palette[1])
plt.bar(x, states[7], bottom= 1+ states[5]+states[6], color= state_palette[2])
plt.bar(x, states[8], bottom= 1+ states[5]+states[6]+states[7], color= state_palette[3])
plt.bar(x, states[9], bottom= 1+ states[5]+states[6]+states[7]+states[8], color= state_palette[4])
#y3
plt.bar(x, states[10], bottom= 2, color= state_palette[0])
plt.bar(x, states[11], bottom= 2+ states[10], color= state_palette[1])
plt.bar(x, states[12], bottom= 2+ states[10]+states[11], color= state_palette[2])
plt.bar(x, states[13], bottom= 2+ states[10]+states[11]+states[12], color= state_palette[3])
plt.bar(x, states[14], bottom= 2+ states[10]+states[11]+states[12]+states[13], color= state_palette[4])
#y4
plt.bar(x, states[15], bottom= 3, color= state_palette[0])
plt.bar(x, states[16], bottom= 3+ states[15], color= state_palette[1])
plt.bar(x, states[17], bottom= 3+ states[15]+states[16], color= state_palette[2])
plt.bar(x, states[18], bottom= 3+ states[15]+states[16]+states[17], color= state_palette[3])
plt.bar(x, states[19], bottom= 3+ states[15]+states[16]+states[17]+states[18], color= state_palette[4])
#y5
plt.bar(x, states[20], bottom= 4, color= state_palette[0])
plt.bar(x, states[21], bottom= 4+ states[20], color= state_palette[1])
plt.bar(x, states[22], bottom= 4+ states[20]+states[21], color= state_palette[2])
plt.bar(x, states[23], bottom= 4+ states[20]+states[21]+states[22], color= state_palette[3])
plt.bar(x, states[24], bottom= 4+ states[20]+states[21]+states[22]+states[23], color= state_palette[4])

#y6
plt.bar(x, states[25], bottom= 5, color= state_palette[0])
plt.bar(x, states[26], bottom= 5+ states[25], color= state_palette[1])
plt.bar(x, states[27], bottom= 5+ states[25]+states[26], color= state_palette[2])
plt.bar(x, states[28], bottom= 5+ states[25]+states[26]+states[27], color= state_palette[3])
plt.bar(x, states[29], bottom= 5+ states[25]+states[26]+states[27]+states[28], color= state_palette[4])
#y7
plt.bar(x, states[30], bottom= 6, color= state_palette[0])
plt.bar(x, states[31], bottom= 6+ states[30], color= state_palette[1])
plt.bar(x, states[32], bottom= 6+ states[30]+states[31], color= state_palette[2])
plt.bar(x, states[33], bottom= 6+ states[30]+states[31]+states[32], color= state_palette[3])
plt.bar(x, states[34], bottom= 6+ states[30]+states[31]+states[32]+states[33], color= state_palette[4])
#y8
plt.bar(x, states[35], bottom= 7, color= state_palette[0])
plt.bar(x, states[36], bottom= 7+ states[35], color= state_palette[1])
plt.bar(x, states[37], bottom= 7+ states[35]+states[36], color= state_palette[2])
plt.bar(x, states[38], bottom= 7+ states[35]+states[36]+states[37], color= state_palette[3])
plt.bar(x, states[39], bottom= 7+ states[35]+states[36]+states[37]+states[38], color= state_palette[4])

plt.gca().invert_xaxis()
plt.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=3)
plt.xlabel('Economies of scale parameter, b')
plt.ylabel('Fraction of states')
plt.axhline(y = 1, color = 'k', linestyle = '-')
plt.axhline(y = 2, color = 'k', linestyle = '-')
plt.axhline(y = 3, color = 'k', linestyle = '-')
plt.axhline(y = 4, color = 'k', linestyle = '-')
plt.axhline(y = 5, color = 'k', linestyle = '-')
plt.axhline(y = 6, color = 'k', linestyle = '-')
plt.axhline(y = 7, color = 'k', linestyle = '-')
plt.axhline(y = 8, color = 'k', linestyle = '-')
plt.ylim([0,8])

#plt.savefig('Transient_state_distribution_PWL.png', transparent=True, dpi = 144)
plt.show()

编辑1：

我创建了子图，但无法在另一侧添加第二个Y轴。它需要有fig, axs = plt.subplot()类型的定义。
这里是更新的代码。

import matplotlib.pyplot as plt
import numpy as np
import seaborn as sns

state_palette = list((sns.color_palette("Spectral_r", 5).as_hex()))
action_palette = list((sns.color_palette("Spectral_r", 4).as_hex()))

# create data for E=0.1 Sigmoid/Golabi States
x = ['0.2', '0.5', '0.6', '0.7', '0.8', '0.9', '1.0 \n (Linear model)']

states = np.c_[st_02, st_05, st_06, st_07, st_08, st_09, st_10]
actions = np.c_[act_02, act_05, act_06, act_07, act_08, act_09, act_10]

# plot bars in stack manner
plt.figure(figsize=(6,18))
plt.subplot(8, 1, 8)
#y1
plt.bar(x, states[0], label = 'State 1', color= state_palette[0])
plt.bar(x, states[1], bottom=states[0], label = 'State 2', color= state_palette[1])
plt.bar(x, states[2], bottom=states[0]+states[1], label = 'State 3', color= state_palette[2])
plt.bar(x, states[3], bottom=states[0]+states[1]+states[2], label = 'State 4', color= state_palette[3])
plt.bar(x, states[4], bottom=states[0]+states[1]+states[2]+states[3], label = 'State 5', color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.yticks([0, 1])
plt.xlabel('Economies of scale parameter, b')

plt.subplot(8, 1, 7)
#y2
plt.bar(x, states[5], bottom= 0, color= state_palette[0])
plt.bar(x, states[6], bottom= 0+ states[5], color= state_palette[1])
plt.bar(x, states[7], bottom= 0+ states[5]+states[6], color= state_palette[2])
plt.bar(x, states[8], bottom= 0+ states[5]+states[6]+states[7], color= state_palette[3])
plt.bar(x, states[9], bottom= 0+ states[5]+states[6]+states[7]+states[8], color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.xticks([])
plt.yticks([0, 1])
plt.subplot(8, 1, 6)
#y3
plt.bar(x, states[10], bottom= 0, color= state_palette[0])
plt.bar(x, states[11], bottom= 0+ states[10], color= state_palette[1])
plt.bar(x, states[12], bottom= 0+ states[10]+states[11], color= state_palette[2])
plt.bar(x, states[13], bottom= 0+ states[10]+states[11]+states[12], color= state_palette[3])
plt.bar(x, states[14], bottom= 0+ states[10]+states[11]+states[12]+states[13], color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.xticks([])
plt.yticks([0, 1])
plt.subplot(8, 1, 5)
#y4
plt.bar(x, states[15], bottom= 0, color= state_palette[0])
plt.bar(x, states[16], bottom= 0+ states[15], color= state_palette[1])
plt.bar(x, states[17], bottom= 0+ states[15]+states[16], color= state_palette[2])
plt.bar(x, states[18], bottom= 0+ states[15]+states[16]+states[17], color= state_palette[3])
plt.bar(x, states[19], bottom= 0+ states[15]+states[16]+states[17]+states[18], color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.xticks([])
plt.yticks([0, 1])
plt.subplot(8, 1, 4)
#y5
plt.bar(x, states[20], bottom= 0, color= state_palette[0])
plt.bar(x, states[21], bottom= 0+ states[20], color= state_palette[1])
plt.bar(x, states[22], bottom= 0+ states[20]+states[21], color= state_palette[2])
plt.bar(x, states[23], bottom= 0+ states[20]+states[21]+states[22], color= state_palette[3])
plt.bar(x, states[24], bottom= 0+ states[20]+states[21]+states[22]+states[23], color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.xticks([])
plt.yticks([0, 1])
plt.subplot(8, 1, 3)
#y6
plt.bar(x, states[25], bottom= 0, color= state_palette[0])
plt.bar(x, states[26], bottom= 0+ states[25], color= state_palette[1])
plt.bar(x, states[27], bottom= 0+ states[25]+states[26], color= state_palette[2])
plt.bar(x, states[28], bottom= 0+ states[25]+states[26]+states[27], color= state_palette[3])
plt.bar(x, states[29], bottom= 0+ states[25]+states[26]+states[27]+states[28], color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.xticks([])
plt.yticks([0, 1])
plt.subplot(8, 1, 2)
#y7
plt.bar(x, states[30], bottom= 0, color= state_palette[0])
plt.bar(x, states[31], bottom= 0+ states[30], color= state_palette[1])
plt.bar(x, states[32], bottom= 0+ states[30]+states[31], color= state_palette[2])
plt.bar(x, states[33], bottom= 0+ states[30]+states[31]+states[32], color= state_palette[3])
plt.bar(x, states[34], bottom= 0+ states[30]+states[31]+states[32]+states[33], color= state_palette[4])
plt.gca().invert_xaxis()
plt.ylim([0,1])
plt.xticks([])
plt.yticks([0, 1])
plt.subplot(8, 1, 1)
#y8
plt.bar(x, states[35], bottom= 0, color= state_palette[0], label = 'State 1')
plt.bar(x, states[36], bottom= 0+ states[35], color= state_palette[1], label = 'State 2')
plt.bar(x, states[37], bottom= 0+ states[35]+states[36], color= state_palette[2], label = 'State 3')
plt.bar(x, states[38], bottom= 0+ states[35]+states[36]+states[37], color= state_palette[3], label = 'State 4')
plt.bar(x, states[39], bottom= 0+ states[35]+states[36]+states[37]+states[38], color= state_palette[4], label = 'State 5')
plt.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc="lower left", mode="expand", borderaxespad=0, ncol=3)
plt.gca().invert_xaxis()
plt.xticks([])
plt.ylim([0,1])
plt.yticks([0, 1])
plt.subplots_adjust(wspace=0, hspace=0.175)
plt.tick_params(labelcolor="none", bottom=False, left=False)

#plt.savefig('Transient_state_distribution_PWL.png', transparent=True, dpi = 144)
plt.show()
matplotlib

1条答案

按热度按时间
sr4lhrrt

sr4lhrrt1#

  • 我认为如果使用pandas，代码会更简洁，更容易阅读。
  • pandas.DataFrme.plot API在绘制分组或堆叠条形图时效率要高得多。
  • 不包括数据的数组，OP中目前大约有105行代码。使用pandas选项可以将其减少到36行代码（不包括空格），这更容易扩展。
  • 对于标签：
  • 使用fig标签
  • fig.supxlabel
  • fig.supylabel
  • fig.suptitle
  • 给定OP中的代码，使用ax = plt.gca()和ax_right = ax.twinx()，然后使用ax_right.set_ylabel(...)。
  • 使用以下代码，您可以在右侧y轴上绘制ax.yaxis.set_label_position('right')
  • .set_ylabel具有labelpad参数，用于更改轴边界框的位置。
  • 请参考Matplotlib应用程序接口（API）
  • 如果你创建一个图（没有子图），那么可以使用隐式的“pyplot”接口，否则你应该使用显式的“Axes”接口。
  • 在显式命令和隐式命令之间切换是一种不好的做法，尽管有很多方法可以这样做。
    *在python 3.11.2、pandas 2.0.1、matplotlib 3.7.1中测试
import pandas as pd
import matplotlib.pyplot as plt

# stack the data
states = np.c_[st_02, st_05, st_06, st_07, st_08, st_09, st_10]

# create a dataframe from states
df_st = pd.DataFrame(states)

# split the dataframe into 5 row chunks
n = 5
list_st = [df_st[i:i+n].reset_index(drop=True) for i in range(0, df_st.shape[0], n)]

state_palette = list((sns.color_palette("Spectral_r", 5).as_hex()))

# create the figure and axes
fig, axes = plt.subplots(nrows=len(list_st), figsize=(6, 18), sharex=True, gridspec_kw={'hspace': 0.1, 'wspace': 0})

# reverse the axes based on the preferred display options
axes = axes[::-1]

# years labels
years = range(2015, 2024)

# iterate through each axes and it's associated data
for ax, data, year in zip(axes, list_st, years):
    
    # transpose the dataframe and then reverse the column order
    data = data.T[::-1]
    
    # set the index labels (e.g. the x-axis)
    data.index = ['1.0 \n (Linear model)', '0.9', '0.8', '0.7', '0.6', '0.5', '0.2']
    
    # set the column names (e.g. the legend labels)
    data.columns = [f'State {i}' for i in range(1, 6)]
    
    # plot the data on the axes
    data.plot(kind='bar', stacked=True, ax=ax, legend=False, width=0.85, ec='k', rot=0, yticks=[0, 1], color=state_palette)
    
    # right y-axis label
    ax.yaxis.set_label_position('right')
    ax.set_ylabel(f'Year: {year}', rotation=-90, labelpad=15)
    
    # remove x-ticks on all but the bottom axes
    if ax != axes[0]:
        ax.tick_params(bottom=False)

    # create a legend for the top axes
    if ax == axes[-1]:
        ax.legend(bbox_to_anchor=(0, 1.02, 1, 0.2), loc='lower left', mode='expand', borderaxespad=0, ncol=3)

fig.supxlabel('Economies of scale parameter, b', y=0.06)
fig.supylabel('Fractions of States')
fig.suptitle('The Figure Title', y=0.93)

  • 使用ax.twinx
...

    # plot the data on the axes
    data.plot(kind='bar', stacked=True, ax=ax, legend=False, width=0.85, ec='k', rot=0, yticks=[0, 1], color=state_palette)

    # remove ax.yaxis.set_label_position('right')
    # remove ax.set_ylabel(f'Year: {year}', rotation=-90, labelpad=15)
    
    # set the left y-axis label
    ax.set_ylabel('Range', labelpad=0)
    
    # using twinx to add a right y-axis label
    ax_right = ax.twinx()
    ax_right.set_ylabel(f'Year: {year}', rotation=-90, labelpad=15)
    ax_right.set_yticks([], [])  # remove the right yticks and yticklabels

...

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