如何使用 matplotlib 获取甘特图

Posted 2023-05-08

【中文标题】如何使用 matplotlib 获取甘特图

【英文标题】：How to get gantt plot using matplotlib

【发布时间】：2017-09-08 03:00:27

【问题描述】：

我得到了一些数据，例如：

a0:86:c6:52:4e:e8,0.006568,0.006620,Out
a0:86:c6:52:4e:e8,0.006663,0.006695,In
a0:86:c6:52:4e:e8,0.008089,0.008141,Out
a0:86:c6:52:4e:e8,0.008185,0.008217,In
01:00:5e:00:00:fb,0.033096,0.035016,Out
33:33:00:00:00:fb,0.034997,0.037077,Out
01:00:5e:7f:ff:fa,0.039969,0.042057,Out
ff:ff:ff:ff:ff:ff,0.059823,0.061639,Out
a0:86:c6:52:4e:e8,0.068865,0.068917,Out
a0:86:c6:52:4e:e8,0.068962,0.068994,In
a0:86:c6:52:4e:e8,0.083492,0.083544,Out
a0:86:c6:52:4e:e8,0.083588,0.083620,In
...

实际上，它会持续 120 秒。我想使用matplotlib将这些数据绘制成甘特图。 原因是大多数甘特图api只支持时间格式YY-MM-DD HH:MM:SS，而不是那么小（微秒）的大小。

请告诉我该怎么做？

【问题讨论】：

您的数据中有 4 列。目前尚不清楚它们应如何排列在图表中。请通过edit提出您的问题添加信息。

其实列名可以是Task、Start、Finish、Resource单独如 甘特图。

【参考方案1】：

为了在 matplotlib 中生成甘特图，可以使用plt.broken_barh 函数。 matplotlib 页面上有a broken_barh example。

为了对数据进行过滤和分组，可以使用 pandas，尤其是数据框的 groupby 函数。

这是一个完整的例子：

inp = u"""a0:86:c6:52:4e:e8,0.006568,0.006620,Out
a0:86:c6:52:4e:e8,0.006663,0.006695,In
a0:86:c6:52:4e:e8,0.008089,0.008141,Out
a0:86:c6:52:4e:e8,0.008185,0.008217,In
01:00:5e:00:00:fb,0.033096,0.035016,Out
33:33:00:00:00:fb,0.034997,0.037077,Out
01:00:5e:7f:ff:fa,0.039969,0.042057,Out
ff:ff:ff:ff:ff:ff,0.059823,0.061639,Out
a0:86:c6:52:4e:e8,0.068865,0.068917,Out
a0:86:c6:52:4e:e8,0.068962,0.068994,In
a0:86:c6:52:4e:e8,0.083492,0.083544,Out
a0:86:c6:52:4e:e8,0.083588,0.083620,In"""

import pandas as pd
import io
import matplotlib.pyplot as plt

df = pd.read_csv(io.StringIO(inp), header=None, names=["Task", "Start", "Finish", "Resource"] )
df["Diff"] = df.Finish - df.Start

color = "In":"turquoise", "Out":"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("Resource"):
        data = r[1][["Start", "Diff"]]
        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()

产生以下情节：

【讨论】：

谢谢！这很有帮助。在 groupby 之后，您将如何更改 y 轴上对象的顺序？这对我的项目非常重要。