我认为同时具有相等的条形宽度（垂直方向上的宽度）和不同的子图大小的唯一方法实际上是手动在图中定位轴。
为此，您可以指定条形图的尺寸（以英寸为单位）以及子图之间要以此条形图宽度为单位的间距。根据这些数字以及要绘制的数据量，可以计算出总的图形高度（以英寸为单位）。然后
fig.add_axes根据数据量和先前子图中的间距（通过）定位每个子图。因此，您很好地填写了情节。添加一组新数据将使图变大。
data = [ {"name": "Category 1", "entries": [ {"name": "Entry 1", "value": 5}, {"name": "Entry 2", "value": 2}, ]}, {"name": "Category 2", "entries": [ {"name": "Entry 1", "value": 1}, ]}, {"name": "Category 3", "entries": [ {"name": "Entry 1", "value": 1}, {"name": "Entry 2", "value": 10}, {"name": "Entry 3", "value": 4}, ]}, {"name": "Category 4", "entries": [ {"name": "Entry 1", "value": 6}, ]},]import matplotlib.pyplot as pltimport numpy as npdef plot_data(data, barwidth = 0.2, # inch per bar spacing = 3, # spacing between subplots in units of barwidth figx = 5, # figure width in inch left = 4, # left margin in units of bar width right=2): # right margin in units of bar width tc = len(data) # "total_categories", holds how many charts to create max_values = [] # holds the maximum number of bars to create for category in data: max_values.append( len(category["entries"])) max_values = np.array(max_values) # total figure height: figy = ((np.sum(max_values)+tc) + (tc+1)*spacing)*barwidth #inch fig = plt.figure(figsize=(figx,figy)) ax = None for index, category in enumerate(data): entries = [] values = [] for entry in category["entries"]: entries.append(entry["name"]) values.append(entry["value"]) if not entries: continue # do not create empty charts y_ticks = range(1, len(entries) + 1) # coordinates of new axes [left, bottom, width, height] coord = [left*barwidth/figx, 1-barwidth*((index+1)*spacing+np.sum(max_values[:index+1])+index+1)/figy, 1-(left+right)*barwidth/figx, (max_values[index]+1)*barwidth/figy ] ax = fig.add_axes(coord, sharex=ax) ax.barh(y_ticks, values) ax.set_ylim(0, max_values[index] + 1) # limit the y axis for fixed height ax.set_yticks(y_ticks) ax.set_yticklabels(entries) ax.invert_yaxis() ax.set_title(category["name"], loc="left")plot_data(data)plt.savefig(__file__+".png")plt.show()
