matplotlib条形图中极限处的误差线 [英] error bars at the limits in matplotlib barchart
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问题描述
我正在尝试使误差线显示在置信区间的极限处,而不是在中心.
I'm trying to get the errorbars to show at the confidence interval's limits, and not in the center.
这是我想要的:
但是我得到的是:
要绘制条形图,我使用了它:
To plot the bar chart I used this:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(32000,200000,3650),
np.random.normal(43000,100000,3650),
np.random.normal(43500,140000,3650),
np.random.normal(48000,70000,3650)],
index=[1992,1993,1994,1995])
df1 = df.T
df1.columns = ['1992', '1993','1994','1995']
a = df1.describe()
means = a.loc['mean'].values.tolist()
stdevs = a.loc['std'].values.tolist()
counts = a.loc['count'].values.tolist()
index = np.arange(len(df1.columns))
CI = []
for i in range(len(means)):
CIval = 1.96*stdevs[i]/(counts[i]**(0.5))
CI.append(CIval)
#print(means, CI)
plt.figure()
fig, ax = plt.subplots(figsize=(10,10))
ax.set_xticks(index)
ax.set_xticklabels(df1.columns)
plt.bar(index, means, xerr = 0.1, yerr=CI)
plt.tight_layout()
plt.show()
推荐答案
错误栏按预期显示.您已将x误差设置为0.1值,但是在预期结果图像中,没有x误差栏,因此我们可以将其删除.其次,我们可以增加错误栏的 capsize
,以便在调用 plt.bar()时使用
capsize =
可以真正看到它们.代码>:
The error bars are showing as expected. You have set a 0.1 value for the x error, however in your expected result image, there is no x errorbar so we can remove that. Secondly, we can increase the capsize
of your error bars so that they are actually visible by using the capsize=
in the call to plt.bar()
:
import pandas as pd
import numpy as np
import matplotlib.pyplot as plt
np.random.seed(12345)
df = pd.DataFrame([np.random.normal(32000,200000,3650),
np.random.normal(43000,100000,3650),
np.random.normal(43500,140000,3650),
np.random.normal(48000,70000,3650)],
index=[1992,1993,1994,1995])
df1 = df.T
df1.columns = ['1992', '1993','1994','1995']
a = df1.describe()
means = a.loc['mean'].values.tolist()
stdevs = a.loc['std'].values.tolist()
counts = a.loc['count'].values.tolist()
index = np.arange(len(df1.columns))
CI = []
for i in range(len(means)):
CIval = 1.96*stdevs[i]/(counts[i]**(0.5))
CI.append(CIval)
fig, ax = plt.subplots(figsize=(10,10))
ax.set_xticks(index)
ax.set_xticklabels(df1.columns)
plt.bar(index, means, yerr=CI, capsize=10)
plt.tight_layout()
plt.show()
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