[英]Python matplotlib multiple bars
如何在 matplotlib 中繪制多個條形圖,當我嘗試多次調用 bar 函數時,它們重疊,如下圖所示,只能看到最高值紅色。 如何在 x 軸上繪制帶有日期的多個條形圖?
到目前為止,我試過這個:
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()
我懂了:
結果應該是這樣的,但是日期在 x 軸上,條形彼此相鄰:
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 值也重疊”是什么意思,下面的代碼能解決你的問題嗎?
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()
使用日期作為 x 值的問題在於,如果您想要第二張圖片中的條形圖,那么它們將是錯誤的。 您應該使用堆疊條形圖(顏色相互疊加)或按日期分組(x 軸上的“假”日期,基本上只是對數據點進行分組)。
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()
在尋找類似的解決方案但沒有找到足夠靈活的東西后,我決定為它編寫自己的函數。 它允許您在每組中擁有任意數量的條,並指定組的寬度以及組內條的各個寬度。
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()
輸出:
pandas.DataFrame.plot
進行繪圖。
dict
將值列表加載到 pandas 中,並指定x
作為索引。 索引將自動設置為 x 軸,列將繪制為條形圖。pandas.DataFrame.plot
使用matplotlib
作為默認后端。.bar_label
的詳細信息,請參閱如何在條形圖上添加值標簽。python 3.8.11
、 pandas 1.3.2
、 matplotlib 3.4.3
中測試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')
我修改了 pascscha 擴展界面的解決方案,希望這對其他人有幫助! 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()
我做了這個解決方案:如果您想在一個圖中繪制多個圖,請確保在繪制下一個圖之前您已設置正確的matplotlib.pyplot.hold(True)
以添加另一個圖。
關於 X 軸上的日期時間值,使用條形對齊的解決方案對我有用。 當您使用matplotlib.pyplot.bar()
創建另一個條形圖時,只需使用align='edge|center'
並設置width='+|-distance'
。
當您正確設置所有條形圖(圖)時,您會看到條形圖正常。
不要用 matplotlib 這樣做會更復雜。 最好是使用seaborn:
在這里: https : //seaborn.pydata.org/generated/seaborn.barplot.html
$ pip install matplotlib==3.5.0 安裝這個庫,因為大多數筆記本都支持舊版本(早於 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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