如何使用matplotlib获取甘特图，开始时间和结束时间最多为毫秒

Posted 2023-05-08

【中文标题】如何使用matplotlib获取甘特图，开始时间和结束时间最多为毫秒

【英文标题】：How to get gantt plot using matplotlib for task with start time and end time upto millisecs

【发布时间】：2019-11-10 05:23:36

【问题描述】：

我在每个任务的数据框中都有数据，包括开始时间、结束时间和状态。 我想为此绘制一个甘特图。我尝试关注关于 *** 的其他问题（link），但他们使用了数值，因此无法使用它们。下面是代码。

import pandas as pd   
import matplotlib.pyplot as plt 
data = [['A', '2019-06-27 18:33:58.033', '2019-06-27 19:54:04.658', 'Success'], ['B', '2019-06-27 19:54:04.957', '2019-06-27 19:54:14.570', 'Success'], ['B', '2019-06-27 19:54:04.963', '2019-06-27 19:54:19.928', 'Failed']]
#Converting List to a dataframe
df = pd.DataFrame(data, columns = ['Task', 'Start Time', 'End Time', 'Status']) 
#Calculating the Time Difference
df['Duration'] = pd.to_datetime(df['End Time']) - pd.to_datetime(df['Start Time'])

color = "Success":"turquoise", "Failed":"crimson"
fig,ax=plt.subplots(figsize=(6,3))
labels=[]

for i, task in enumerate(df.groupby("Task")):
    labels.append(task[0])
    for r in task[1].groupby("Status"):
        data = r[1][["Start Time", "Duration"]]
        ax.broken_barh(data.values, (i-0.4,0.8), color=color[r[0]] )

ax.set_yticks(range(len(labels)))
ax.set_yticklabels(labels) 
ax.set_xlabel("time [ms]")
plt.tight_layout()       
plt.show()

它没有显示正确的图表，可能是由于时间格式。如果我使用十进制数字代替时间，上面的代码效果很好。这里有任何帮助。

【问题讨论】：

看看这个***.com/questions/11042290/…，broken_barh 期望数字因此你可以做同样的事情来改变你的日期时间

【参考方案1】：

我可以在 matplotlib 中使用时间来绘制图形，但无法为成功和失败使用不同的颜色条。欢迎使用此功能的解决方案。

import pandas as pd    
from datetime import datetime
import matplotlib.dates as dates
import matplotlib.pyplot as plt
data = [['A', '2019-06-27 18:33:58.033', '2019-06-27 19:54:04.658', 'Success'], ['B', '2019-06-27 19:54:04.957', '2019-06-27 19:58:14.570', 'Success'], ['C', '2019-06-27 19:54:04.963', '2019-06-27 19:54:19.928', 'Failed']]
df = pd.DataFrame(data, columns = ['Task', 'Start_Time', 'End_Time', 'Status']) 

df_phase = df
df_phase['Start_Time'] = pd.to_datetime(df_phase['Start_Time'], format='%Y-%m-%d %H:%M:%S.%f')
df_phase['End_Time'] = pd.to_datetime(df_phase['End_Time'], format='%Y-%m-%d %H:%M:%S.%f')

#Convert DF columns into lists
sdate = df_phase['Start_Time'].tolist()
edate = df_phase['End_Time'].tolist()
tasks = df_phase['Task'].tolist()

#Convert time to Matplotlib number format
edate, sdate = [dates.date2num(item) for item in (edate, sdate)]
time_diff = edate - sdate
ypos = range(len(tasks))
fig, ax = plt.subplots()
ax.barh(ypos, time_diff, left=sdate, height=0.8, align='center', color='blue',edgecolor='black')
plt.yticks(ypos, tasks)
ax.axis('tight')

# We need to tell matplotlib that these are dates...
ax.xaxis_date()
plt.show()

输出图像：

【参考方案2】：

虽然这里是您的代码与 Rishi 的代码略有合并，但似乎迟到了 -

import pandas as pd   
import matplotlib.pyplot as plt 
data = [['A', '2019-06-27 18:33:58.033', '2019-06-27 19:54:04.658', 'Success'], ['B', '2019-06-27 19:54:04.957', '2019-06-27 19:54:14.570', 'Success'], ['C', '2019-06-27 19:54:04.963', '2019-06-27 20:54:19.928', 'Failed']]
#Converting List to a dataframe
df = pd.DataFrame(data, columns = ['Task', 'Start_Time', 'End_Time', 'Status']) 
#Calculating the Time Difference
#df['Duration'] = pd.to_datetime(df['End Time']) - pd.to_datetime(df['Start Time'])
df_phase = df
df_phase['Start_Time'] = pd.to_datetime(df_phase['Start_Time'], format='%Y-%m-%d %H:%M:%S')
df_phase['End_Time'] = pd.to_datetime(df_phase['End_Time'], format='%Y-%m-%d %H:%M:%S')

color = "Success":"turquoise", "Failed":"crimson"
#Convert DF columns into lists
sdate = df_phase['Start_Time'].tolist()
edate = df_phase['End_Time'].tolist()
tasks = df_phase['Task'].tolist()
#Convert time to Matplotlib number format
edate, sdate = [dates.date2num(item) for item in (edate, sdate)]
df_phase['Duration']=edate - sdate
fig,ax=plt.subplots(figsize=(6,3))
labels=[]

for i, task in enumerate(df_phase.groupby("Task")):
    labels.append(task[0])
    for r in task[1].groupby("Status"):
        data = r[1][["Start_Time", "Duration"]]
        ax.broken_barh(data.values, (i-0.4,0.8), color=color[r[0]] )

ax.set_yticks(range(len(labels)))
ax.set_yticklabels(labels) 
ax.set_xlabel("time [ms]")
plt.tight_layout()       
plt.show()