这在最新版本的 Bokeh 中发生了变化（我猜是 0.12.7）。这是这样做的新方法。

from bokeh.io import show, output_file
from bokeh.models import ColumnDataSource, FactorRange
from bokeh.plotting import figure

output_file("bars.html")

fruits = ['Apples', 'Pears', 'Nectarines', 'Plums', 'Grapes', 'Strawberries']
years = ['2015', '2016', '2017']

data = {'fruits' : fruits,
        '2015'   : [2, 1, 4, 3, 2, 4],
        '2016'   : [5, 3, 3, 2, 4, 6],
        '2017'   : [3, 2, 4, 4, 5, 3]}

# this creates [ ("Apples", "2015"), ("Apples", "2016"), ("Apples", "2017"), ("Pears", "2015), ... ]
x = [ (fruit, year) for fruit in fruits for year in years ]
counts = sum(zip(data['2015'], data['2016'], data['2017']), ()) # like an hstack

source = ColumnDataSource(data=dict(x=x, counts=counts))

p = figure(x_range=FactorRange(*x), plot_height=250, title="Fruit Counts by Year",
           toolbar_location=None, tools="")

p.vbar(x='x', top='counts', width=0.9, source=source)

p.y_range.start = 0
p.x_range.range_padding = 0.1
p.xaxis.major_label_orientation = 1
p.xgrid.grid_line_color = None

show(p)

只是熊猫，没有散景（在运行前将数据复制到剪贴板）：

import pandas, seaborn
DF = pandas.read_clipboard()
DF.plot.bar(x='year')

如果您首先融合 Pandas 数据框，则可以在 Bokeh 高级条形图中使用分组。

import pandas as pd
from bokeh.plotting import figure, show

# Use output_notebook if you are using an IPython or Jupyter notebook
from bokeh.io import output_notebook
output_notebook()

# Get your data into the dataframe
df = pd.read_csv("data.csv")

# Create a "melted" version of your dataframe
melted_df = pd.melt(df, id_vars=['year'], value_vars=['Recalled', 'Recovered'])

melted_df.head()

这是您融化的数据框的格式：

+---+------+----------+--------+
|   | year | variable | value  |
+---+------+----------+--------+
| 0 | 1994 | Recalled | 11.472 |
| 1 | 1995 | Recalled |  11.81 |
| 2 | 1996 | Recalled | 10.632 |
| 3 | 1997 | Recalled | 13.857 |
| 4 | 1998 | Recalled | 13.861 |
+---+------+----------+--------+

然后只需使用融化的数据框作为散景条形图中的数据：

p = Bar(melted_df, label="year", values="value", group="variable", legend="top_left", ylabel='Values')
show(p)

这是我仅使用 matplotlib 和 numpy 的答案。

与接受的答案相比，我的代码似乎很长。因此，如果有人可以帮助我改进它，那就太好了！

代码在这里：

## Reading the data
year = np.arange(1994,2015,1)
type = ["Recalled", "Recovered"]
value_1 = [11.472, 11.81, 10.632, 13.857, 13.861, 13.375, 11.278, 12.827, 12.687, 10.859, np.nan, 11.782, 12.089,\
       14.351, 14.921, 11.759, 12.987, 13.262, 9.98, 10.626, 12.199]
value_2 = [10.207, 10.326, 10.094, 12.944, 12.588, 11.951, np.nan, np.nan, np.nan, np.nan, np.nan, 11.047, 10.194,\
       13.401, 13.886, 10.815, 11.482, 10.73, 9.52, 9.591, 10.27] 



## Changing data into ndarray format
dpoints = np.array([type[0], year[0], value_1[0]])
conditions =  np.unique(dpoints[:,0])
categories =  np.unique(dpoints[:,1]).tolist()

for i in range(1,len(year),1):
    dpoints = np.vstack([dpoints,np.array([type[0],year[i],value_1[i]])])
for i in range(0,len(year),1):
    dpoints = np.vstack([dpoints,np.array([type[1],year[i],value_2[i]])])

# Plot it!
fig = plt.figure(figsize=(16,5))
ax = plt.subplot()
#the space between each set of bars
space = 0.2
n = len(conditions)
width = (1 - space) / (len(conditions))

# Create a set of bars at each position
for i,cond in enumerate(conditions):
    indeces = range(1, len(categories)+1)
    vals = dpoints[dpoints[:,0] == cond][:,2].astype(np.float)
    pos = [j - (1 - space) / 2. + i * width for j in indeces]

    ax.bar(pos, vals, width=width, label =cond,lw = 0,color=  ["blue","r"][i],alpha = 0.6)


    # Set the x-axis tick labels to be equal to the categories
    ax.set_xticks(indeces)
    ax.set_xticklabels(categories)
    ax.set_xlim(0,22)
    plt.setp(plt.xticks()[1], rotation=0,fontsize = 12)
    ax.set_ylabel('Variables',fontsize =15)
    ax.set_xlabel("Year",fontsize =15)


    # Add a legend
    handles, labels = ax.get_legend_handles_labels()
    ax.legend(handles[::-1], labels[::-1], loc='upper left',frameon =False,fontsize =12)    

图在这里