如何用汇总值注释海洋条形图 [英] How to annotate a seaborn barplot with the aggregated value
本文介绍了如何用汇总值注释海洋条形图的处理方法，对大家解决问题具有一定的参考价值，需要的朋友们下面随着小编来一起学习吧！
问题描述
如何修改以下代码以在条形图的每个条形上显示平均值以及不同的误差条?
将 numpy 导入为 np将熊猫作为pd导入导入matplotlib.pyplot作为plt将 seaborn 作为 sns 导入sns.set_style(白色")a，b，c，d = []，[]，[]，[]对于范围内的 i (1,5):np.random.seed(i)a.append(np.random.uniform(35,55))b.append(np.random.uniform(40,70))c.append(np.random.uniform(63,85))d.append(np.random.uniform(59,80))data_df =pd.DataFrame({'stages':[1,2,3,4],'S1':a,'S2':b,'S3':c,'S4':d})打印(延迟:")显示(data_df)S1 S2 S3 S40 43.340440 61.609735 63.002516 65.3489841 43.719898 40.777787 75.092575 68.1417702 46.015958 61.244435 69.399904 69.7273803 54.340597 56.416967 84.399056 74.011136meansd_df = data_df.describe().loc [['mean'，'std']，::.drop('stages'，axis = 1)显示(meansd_df)sns.set()sns.set_style('darkgrid'，{"axes.facecolor":".92"})#(1)sns.set_context('notebook')无花果，ax = plt.subplots(figsize =(8,6))x = meand_df.columnsy = meand_df.loc['mean',:]yerr = meand_df.loc['std',:]plt.xlabel(时间"，大小= 14)plt.ylim(-0.3, 100)宽度 = 0.45对于 zip(x,y,yerr) 中的 i, j,k: # (2)ax.bar(i,j, width, yerr = k, edgecolor = "black",error_kw = dict(lw = 1，capsize = 8，capthick = 1))#(3)ax.set(ylabel ='Delay')从matplotlib导入代码ax.yaxis.set_major_locator(ticker.MultipleLocator(10))plt.savefig("Over.png", dpi=300, bbox_inches='tight') 解决方案
- 给定示例数据，对于
注释资源 - 来自
matplotlib v3.4.2- 在 matplotlib 条形图上添加值标签
- 如何注释堆积条形图的每一段
- 带有居中标签的堆叠条形图
- 如何在海洋条形图中绘制和注释多个数据列
- matplotlib 中的堆栈条形图并为每个部分添加标签
- 如何在条形图上添加多个注释
- 如何绘制和注释分组条形图
使用
绘制seaborn.barplot- 在 3.4.2 版本之前使用
matplotlib estimator参数的默认值是mean，所以条形的高度是组的平均值.- 钢筋高度是使用
.get_height从p中提取的，可用于注释钢筋.
fig, ax = plt.subplots(figsize=(8, 6))sns.barplot(x ='set'，y ='val'，data = df，capsize = 0.2，ax = ax)# 显示平均值对于ax.patches中的p:h，w，x = p.get_height()，p.get_width()，p.get_x()xy =(x + w/2.，h/2)text = f'意思是:\n{h:0.2f}'ax.annotate(text = text，xy = xy，ha ='center'，va ='center')ax.set(xlabel ='Delay'，ylabel ='Time')plt.show()How can the following code be modified to show the mean as well as the different error bars on each bar of the bar plot?
import numpy as np import pandas as pd import matplotlib.pyplot as plt import seaborn as sns sns.set_style("white") a,b,c,d = [],[],[],[] for i in range(1,5): np.random.seed(i) a.append(np.random.uniform(35,55)) b.append(np.random.uniform(40,70)) c.append(np.random.uniform(63,85)) d.append(np.random.uniform(59,80)) data_df =pd.DataFrame({'stages':[1,2,3,4],'S1':a,'S2':b,'S3':c,'S4':d}) print("Delay:") display(data_df) S1 S2 S3 S4 0 43.340440 61.609735 63.002516 65.348984 1 43.719898 40.777787 75.092575 68.141770 2 46.015958 61.244435 69.399904 69.727380 3 54.340597 56.416967 84.399056 74.011136 meansd_df=data_df.describe().loc[['mean', 'std'],:].drop('stages', axis = 1) display(meansd_df) sns.set() sns.set_style('darkgrid',{"axes.facecolor": ".92"}) # (1) sns.set_context('notebook') fig, ax = plt.subplots(figsize = (8,6)) x = meansd_df.columns y = meansd_df.loc['mean',:] yerr = meansd_df.loc['std',:] plt.xlabel("Time", size=14) plt.ylim(-0.3, 100) width = 0.45 for i, j,k in zip(x,y,yerr): # (2) ax.bar(i,j, width, yerr = k, edgecolor = "black", error_kw=dict(lw=1, capsize=8, capthick=1)) # (3) ax.set(ylabel = 'Delay') from matplotlib import ticker ax.yaxis.set_major_locator(ticker.MultipleLocator(10)) plt.savefig("Over.png", dpi=300, bbox_inches='tight')解决方案- Given the example data, for a
seaborn.barplotwith capped error bars,data_dfmust be converted from a wide format, to a tidy (long) format, which can be accomplished withpandas.DataFrame.stackorpandas.DataFrame.melt- It is also important to keep in mind that a bar plot shows only the mean (or other estimator) value
Sample Data and DataFrame
.iloc[:, 1:]is used to skip the'stages'column at column index 0.
import pandas as pd import numpy as np import seaborn as sns import matplotlib.pyplot as plt # given data_df from the OP, select the columns except stage and reshape to long format df = data_df.iloc[:, 1:].melt(var_name='set', value_name='val') # display(df.head()) set val 0 S1 43.340440 1 S1 43.719898 2 S1 46.015958 3 S1 54.340597 4 S2 61.609735Updated as of
matplotlib v3.4.2- Use
matplotlib.pyplot.bar_label - See the matplotlib: Bar Label Demo page for additional formatting options.
- Some formatting can be done with the
fmtparameter, but more sophisticated formatting should be done with thelabelsparameter, as show in the bottom Demo example, and in How to add multiple annotations to a barplot.
- Some formatting can be done with the
- Tested with
seaborn v0.11.1, which is usingmatplotlibas the plot engine.
fig, ax = plt.subplots(figsize=(8, 6)) # add the plot sns.barplot(x='set', y='val', data=df, capsize=0.2, ax=ax) # add the annotation ax.bar_label(ax.containers[-1], fmt='Mean:\n%.2f', label_type='center') ax.set(ylabel='Mean Time') plt.show()Annotation Resources - from
matplotlib v3.4.2- Adding value labels on a matplotlib bar chart
- How to annotate each segment of a stacked bar chart
- Stacked Bar Chart with Centered Labels
- How to plot and annotate multiple data columns in a seaborn barplot
- stack bar plot in matplotlib and add label to each section
- How to add multiple annotations to a barplot
- How to plot and annotate a grouped bar chart
plot with
seaborn.barplot- Using
matplotlibbefore version 3.4.2 - The default for the
estimatorparameter ismean, so the height of the bar is the mean of the group. - The bar height is extracted from
pwith.get_height, which can be used to annotate the bar.
fig, ax = plt.subplots(figsize=(8, 6)) sns.barplot(x='set', y='val', data=df, capsize=0.2, ax=ax) # show the mean for p in ax.patches: h, w, x = p.get_height(), p.get_width(), p.get_x() xy = (x + w / 2., h / 2) text = f'Mean:\n{h:0.2f}' ax.annotate(text=text, xy=xy, ha='center', va='center') ax.set(xlabel='Delay', ylabel='Time') plt.show()这篇关于如何用汇总值注释海洋条形图的文章就介绍到这了，希望我们推荐的答案对大家有所帮助，也希望大家多多支持IT屋！
查看全文
