[英]Python matplotlib multiple bars
How to plot multiple bars in matplotlib, when I tried to call the bar function multiple times, they overlap and as seen the below figure the highest value red can be seen only.如何在 matplotlib 中绘制多个条形图,当我尝试多次调用 bar 函数时,它们重叠,如下图所示,只能看到最高值红色。 How can I plot the multiple bars with dates on the x-axes?如何在 x 轴上绘制带有日期的多个条形图?
So far, I tried this:到目前为止,我试过这个:
import matplotlib.pyplot as plt
import datetime
x = [
datetime.datetime(2011, 1, 4, 0, 0),
datetime.datetime(2011, 1, 5, 0, 0),
datetime.datetime(2011, 1, 6, 0, 0)
]
y = [4, 9, 2]
z = [1, 2, 3]
k = [11, 12, 13]
ax = plt.subplot(111)
ax.bar(x, y, width=0.5, color='b', align='center')
ax.bar(x, z, width=0.5, color='g', align='center')
ax.bar(x, k, width=0.5, color='r', align='center')
ax.xaxis_date()
plt.show()
I got this:我懂了:
The results should be something like, but with the dates are on the x-axes and bars are next to each other:结果应该是这样的,但是日期在 x 轴上,条形彼此相邻:
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()
I don't know what's the "y values are also overlapping" means, does the following code solve your problem?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()
The trouble with using dates as x-values, is that if you want a bar chart like in your second picture, they are going to be wrong.使用日期作为 x 值的问题在于,如果您想要第二张图片中的条形图,那么它们将是错误的。 You should either use a stacked bar chart (colours on top of each other) or group by date (a "fake" date on the x-axis, basically just grouping the data points).您应该使用堆叠条形图(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()
after looking for a similar solution and not finding anything flexible enough, I decided to write my own function for it.在寻找类似的解决方案但没有找到足够灵活的东西后,我决定为它编写自己的函数。 It allows you to have as many bars per group as you wish and specify both the width of a group as well as the individual widths of the bars within the groups.它允许您在每组中拥有任意数量的条,并指定组的宽度以及组内条的各个宽度。
Enjoy: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()
Output:输出:
I know that this is about matplotlib
, but using pandas
and seaborn
can save you a lot of time:我知道这是关于matplotlib
的,但是使用pandas
和seaborn
可以为您节省很多时间:
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()
pandas.DataFrame.plot
.给定现有答案,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中并使用pandas.DataFrame.plot
进行绘图。
dict
, and specify x
as the index.使用dict
将值列表加载到 pandas 中,并指定x
作为索引。 The index will automatically be set as the x-axis, and the columns will be plotted as the bars.索引将自动设置为 x 轴,列将绘制为条形图。pandas.DataFrame.plot
uses matplotlib
as the default backend. pandas.DataFrame.plot
使用matplotlib
作为默认后端。.bar_label
.有关使用.bar_label
的详细信息,请参阅如何在条形图上添加值标签。python 3.8.11
, pandas 1.3.2
, matplotlib 3.4.3
在python 3.8.11
、 pandas 1.3.2
、 matplotlib 3.4.3
中测试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')
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')
I modified pascscha's solution extending the interface, hopefully this helps someone else!我修改了 pascscha 扩展界面的解决方案,希望这对其他人有帮助! Key features:主要特征:
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
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()
I did this solution: if you want plot more than one plot in one figure, make sure before plotting next plots you have set right matplotlib.pyplot.hold(True)
to able adding another plots.我做了这个解决方案:如果您想在一个图中绘制多个图,请确保在绘制下一个图之前您已设置正确的matplotlib.pyplot.hold(True)
以添加另一个图。
Concerning the datetime values on the X axis, a solution using the alignment of bars works for me.关于 X 轴上的日期时间值,使用条形对齐的解决方案对我有用。 When you create another bar plot with matplotlib.pyplot.bar()
, just use align='edge|center'
and set width='+|-distance'
.当您使用matplotlib.pyplot.bar()
创建另一个条形图时,只需使用align='edge|center'
并设置width='+|-distance'
。
When you set all bars (plots) right, you will see the bars fine.当您正确设置所有条形图(图)时,您会看到条形图正常。
Dont do this with matplotlib is way more complicated.不要用 matplotlib 这样做会更复杂。 The best is to use seaborn:最好是使用seaborn:
here: https://seaborn.pydata.org/generated/seaborn.barplot.html在这里: https : //seaborn.pydata.org/generated/seaborn.barplot.html
$ pip install matplotlib==3.5.0 Install this library as most notebooks support older versions (older than 3.3) $ pip install matplotlib==3.5.0 安装这个库,因为大多数笔记本都支持旧版本(早于 3.3)
import matplotlib.pyplot as plt
import numpy as np
labels = ['I1', 'I2', 'I3', 'I4', 'I5', 'I6', 'I7', 'I8', 'I9', 'I10']
sample1 = [0.2, 0.34, 0.30, 0.35, 0.27, 0.25, 0.32, 0.34, 0.20, 0.25]
sample2 = [0.25, 0.32, 0.34, 0.20, 0.25, 0.2, 0.34, 0.30, 0.35, 0.27]
x = np.arange(len(labels)) # the label locations
width = 0.35 # the width of the bars
fig, ax = plt.subplots()
rects1 = ax.bar(x - width/2, sample1, width, label='Error 1')
rects2 = ax.bar(x + width/2, sample2, width, label='Error 2')
# Add some text for labels, title and custom x-axis tick labels, etc.
ax.set_ylabel('Error', fontweight='bold', fontsize=16)
ax.set_xlabel('Images', fontweight='bold', fontsize=16)
ax.set_title("Error Mapping", fontweight='bold', fontsize=20)
ax.set_xticks(x, labels)
ax.legend()
ax.bar_label(rects1, padding=3)
ax.bar_label(rects2, padding=3)
fig.tight_layout()
plt.show()
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