使用日期作为 x 值的麻烦在于，如果你想要一个条形图，就像你的第二张图片，它们将是错误的。您应该使用一个堆叠的条形图(颜色相互叠加)或按日期分组(X 轴上的“假”日期，基本上只是对数据点进行分组)。

import numpy as np
import matplotlib.pyplot as plt


N = 3
ind = np.arange(N)  # the x locations for the groups
width = 0.27       # the width of the bars


fig = plt.figure()
ax = fig.add_subplot(111)


yvals = [4, 9, 2]
rects1 = ax.bar(ind, yvals, width, color='r')
zvals = [1,2,3]
rects2 = ax.bar(ind+width, zvals, width, color='g')
kvals = [11,12,13]
rects3 = ax.bar(ind+width*2, kvals, width, color='b')


ax.set_ylabel('Scores')
ax.set_xticks(ind+width)
ax.set_xticklabels( ('2011-Jan-4', '2011-Jan-5', '2011-Jan-6') )
ax.legend( (rects1[0], rects2[0], rects3[0]), ('y', 'z', 'k') )


def autolabel(rects):
for rect in rects:
h = rect.get_height()
ax.text(rect.get_x()+rect.get_width()/2., 1.05*h, '%d'%int(h),
ha='center', va='bottom')


autolabel(rects1)
autolabel(rects2)
autolabel(rects3)


plt.show()

import matplotlib.pyplot as plt
from matplotlib.dates import date2num
import datetime


x = [
datetime.datetime(2011, 1, 4, 0, 0),
datetime.datetime(2011, 1, 5, 0, 0),
datetime.datetime(2011, 1, 6, 0, 0)
]
x = date2num(x)


y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]


ax = plt.subplot(111)
ax.bar(x-0.2, y, width=0.2, color='b', align='center')
ax.bar(x, z, width=0.2, color='g', align='center')
ax.bar(x+0.2, k, width=0.2, color='r', align='center')
ax.xaxis_date()


plt.show()

我不知道“ y 值也重叠”是什么意思，下面的代码能解决你的问题吗？

ax = plt.subplot(111)
w = 0.3
ax.bar(x-w, y, width=w, color='b', align='center')
ax.bar(x, z, width=w, color='g', align='center')
ax.bar(x+w, k, width=w, color='r', align='center')
ax.xaxis_date()
ax.autoscale(tight=True)


plt.show()

我做了这个解决方案: 如果您希望在一个图形中绘制多个图形，请确保在绘制下一个图形之前已经设置了正确的 matplotlib.pyplot.hold(True) 能够增加另一块地。

关于 X 轴上的日期时间值，一个使用条形对齐的解决方案适合我。使用 matplotlib.pyplot.bar()创建另一个条形图时，只需使用 align='edge|center'并设置 width='+|-distance'。

当您设置所有的条形图(情节)正确时，您将看到条形图正常。

我知道这是关于 matplotlib的，但是使用 pandas和 seaborn可以节省你很多时间:

df = pd.DataFrame(zip(x*3, ["y"]*3+["z"]*3+["k"]*3, y+z+k), columns=["time", "kind", "data"])
plt.figure(figsize=(10, 6))
sns.barplot(x="time", hue="kind", y="data", data=df)
plt.show()

在寻找了一个类似的解决方案，但没有找到足够灵活的解决方案之后，我决定为它编写自己的函数。它允许您根据需要为每个组设置尽可能多的条形图，并指定组的宽度以及组内条形图的各个宽度。

享受:

from matplotlib import pyplot as plt




def bar_plot(ax, data, colors=None, total_width=0.8, single_width=1, legend=True):
"""Draws a bar plot with multiple bars per data point.


Parameters
----------
ax : matplotlib.pyplot.axis
The axis we want to draw our plot on.


data: dictionary
A dictionary containing the data we want to plot. Keys are the names of the
data, the items is a list of the values.


Example:
data = {
"x":[1,2,3],
"y":[1,2,3],
"z":[1,2,3],
}


colors : array-like, optional
A list of colors which are used for the bars. If None, the colors
will be the standard matplotlib color cyle. (default: None)


total_width : float, optional, default: 0.8
The width of a bar group. 0.8 means that 80% of the x-axis is covered
by bars and 20% will be spaces between the bars.


single_width: float, optional, default: 1
The relative width of a single bar within a group. 1 means the bars
will touch eachother within a group, values less than 1 will make
these bars thinner.


legend: bool, optional, default: True
If this is set to true, a legend will be added to the axis.
"""


# Check if colors where provided, otherwhise use the default color cycle
if colors is None:
colors = plt.rcParams['axes.prop_cycle'].by_key()['color']


# Number of bars per group
n_bars = len(data)


# The width of a single bar
bar_width = total_width / n_bars


# List containing handles for the drawn bars, used for the legend
bars = []


# Iterate over all data
for i, (name, values) in enumerate(data.items()):
# The offset in x direction of that bar
x_offset = (i - n_bars / 2) * bar_width + bar_width / 2


# Draw a bar for every value of that type
for x, y in enumerate(values):
bar = ax.bar(x + x_offset, y, width=bar_width * single_width, color=colors[i % len(colors)])


# Add a handle to the last drawn bar, which we'll need for the legend
bars.append(bar[0])


# Draw legend if we need
if legend:
ax.legend(bars, data.keys())




if __name__ == "__main__":
# Usage example:
data = {
"a": [1, 2, 3, 2, 1],
"b": [2, 3, 4, 3, 1],
"c": [3, 2, 1, 4, 2],
"d": [5, 9, 2, 1, 8],
"e": [1, 3, 2, 2, 3],
"f": [4, 3, 1, 1, 4],
}


fig, ax = plt.subplots()
bar_plot(ax, data, total_width=.8, single_width=.9)
plt.show()

产出:

我修改了 Pascscha 的解决方案，扩展了界面，希望这对其他人有帮助! 关键特性:

• 每个条组的条目数可变
• 可定制的颜色
• X 蜱的处理
• 完全可定制的酒吧标签上的酒吧

def bar_plot(ax, data, group_stretch=0.8, bar_stretch=0.95,
legend=True, x_labels=True, label_fontsize=8,
colors=None, barlabel_offset=1,
bar_labeler=lambda k, i, s: str(round(s, 3))):
"""
Draws a bar plot with multiple bars per data point.
:param dict data: The data we want to plot, wher keys are the names of each
bar group, and items is a list of bar values for the corresponding group.
:param float group_stretch: 1 means groups occupy the most (largest groups
touch side to side if they have equal number of bars).
:param float bar_stretch: If 1, bars within a group will touch side to side.
:param bool x_labels: If true, x-axis will contain labels with the group
names given at data, centered at the bar group.
:param int label_fontsize: Font size for the label on top of each bar.
:param float barlabel_offset: Distance, in y-values, between the top of the
bar and its label.
:param function bar_labeler: If not None, must be a functor with signature
``f(group_name, i, scalar)->str``, where each scalar is the entry found at
data[group_name][i]. When given, returns a label to put on the top of each
bar. Otherwise no labels on top of bars.
"""
sorted_data = list(sorted(data.items(), key=lambda elt: elt[0]))
sorted_k, sorted_v  = zip(*sorted_data)
max_n_bars = max(len(v) for v in data.values())
group_centers = np.cumsum([max_n_bars
for _ in sorted_data]) - (max_n_bars / 2)
bar_offset = (1 - bar_stretch) / 2
bars = defaultdict(list)
#
if colors is None:
colors = {g_name: [f"C{i}" for _ in values]
for i, (g_name, values) in enumerate(data.items())}
#
for g_i, ((g_name, vals), g_center) in enumerate(zip(sorted_data,
group_centers)):
n_bars = len(vals)
group_beg = g_center - (n_bars / 2) + (bar_stretch / 2)
for val_i, val in enumerate(vals):
bar = ax.bar(group_beg + val_i + bar_offset,
height=val, width=bar_stretch,
color=colors[g_name][val_i])[0]
bars[g_name].append(bar)
if  bar_labeler is not None:
x_pos = bar.get_x() + (bar.get_width() / 2.0)
y_pos = val + barlabel_offset
barlbl = bar_labeler(g_name, val_i, val)
ax.text(x_pos, y_pos, barlbl, ha="center", va="bottom",
fontsize=label_fontsize)
if legend:
ax.legend([bars[k][0] for k in sorted_k], sorted_k)
#
ax.set_xticks(group_centers)
if x_labels:
ax.set_xticklabels(sorted_k)
else:
ax.set_xticklabels()
return bars, group_centers

样本运行:

fig, ax = plt.subplots()
data = {"Foo": [1, 2, 3, 4], "Zap": [0.1, 0.2], "Quack": [6], "Bar": [1.1, 2.2, 3.3, 4.4, 5.5]}
bar_plot(ax, data, group_stretch=0.8, bar_stretch=0.95, legend=True,
labels=True, label_fontsize=8, barlabel_offset=0.05,
bar_labeler=lambda k, i, s: str(round(s, 3)))
fig.show()

• 给定现有的答案，给定 OP 中的数据，最简单的解决方案是将数据加载到数据框架中并使用 pandas.DataFrame.plot绘图。
  • 用 dict将值列表加载到熊猫中，并指定 x作为索引。索引将自动设置为 x 轴，列将绘制为条形图。
  • pandas.DataFrame.plot使用 matplotlib作为默认后端。
• 有关使用 .bar_label的详细信息，请参阅 如何在条形图上添加价值标签。
• 测试 python 3.8.11，pandas 1.3.2，matplotlib 3.4.3

import pandas as pd


# using the existing lists from the OP, create the dataframe
df = pd.DataFrame(data={'y': y, 'z': z, 'k': k}, index=x)


# since there's no time component and x was a datetime dtype, set the index to be just the date
df.index = df.index.date


# display(df)
y  z   k
2011-01-04  4  1  11
2011-01-05  9  2  12
2011-01-06  2  3  13


# plot bars or kind='barh' for horizontal bars; adjust figsize accordingly
ax = df.plot(kind='bar', rot=0, xlabel='Date', ylabel='Value', title='My Plot', figsize=(6, 4))


# add some labels
for c in ax.containers:
# set the bar label
ax.bar_label(c, fmt='%.0f', label_type='edge')
    

# add a little space at the top of the plot for the annotation
ax.margins(y=0.1)


# move the legend out of the plot
ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')

• 当有更多的柱子时使用的水平条

ax = df.plot(kind='barh', ylabel='Date', title='My Plot', figsize=(5, 4))
ax.set(xlabel='Value')
for c in ax.containers:
# set the bar label
ax.bar_label(c, fmt='%.0f', label_type='edge')
    

ax.margins(x=0.1)


# move the legend out of the plot
ax.legend(title='Columns', bbox_to_anchor=(1, 1.02), loc='upper left')