情节：如何防止甘特图中条形的粗细变化？
Plotly: How to prevent varying thickness of bars in a Gantt diagram?
我正在尝试使用示例中的 plotly.express 制作 plotly Gantt 图，但是 plotly 以某种方式根据给定的值改变了条形厚度名称（见图，红色 > 绿色 > 蓝色）：
代码如下所示：
import pandas as pd
import plotly.express as px
df = pd.read_csv('Whosebug.csv')
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S",
ticklabelmode="instant")
fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
Whosebug.csv 为：
Task,Start,Finish,Workstation,Resource
1,1970-01-01 01:00:00.000,1970-01-01 01:00:05.400,1,ABL
2,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,2,ABS
3,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,3,ABU
4,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,4,ACC
5,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,4,ACC
6,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,4,ACC
7,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,5,ABS
8,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,6,ACT
9,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,7,ACC
10,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,7,ACC
11,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,7,ACC
12,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,8,ABS
13,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,9,ABU
14,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,10,ACC
15,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,11,ABS
16,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,12,ABU
17,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,13,ACC
18,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,13,ACC
19,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,14,ABS
20,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,15,ABP
21,1970-01-01 01:00:00.000,1970-01-01 01:00:01.500,16,ABZ
22,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,17,ACC
23,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,17,ACC
24,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,18,ABS
25,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,19,AAW
26,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,20,ACC
27,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,20,ACC
28,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,21,ABS
29,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,22,ABU
30,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,23,ACC
我希望所有的条都具有相同的粗细，令人惊讶的是，当我将 Resource 的名称更改为某个随机的 3 字符值时，这会起作用：
我认为这与以AB*或AC*开头的资源有关。不幸的是，资源的名称取决于真实世界的名称，所以我不能随意更改它们。当资源名称类似于 FooBar-Axx-FOO 和 xx = [CC, BS, CT...] 时，也会发生变化的条形粗细。有谁知道为什么会发生这种情况或如何防止这种情况？
P.S.:
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S")
有必要在甘特图中显示秒而不是天...是否有更好的方法来实现此目的？
更新：
conda 我用来创建问题的环境 yaml 文件：
name: stack
channels:
- conda-forge
- pytorch
- plotly
dependencies:
- python>=3.5,<3.8
- pandas
- pip
- pip:
- plotly
稍作修改的代码仍然会产生如上所示的相同问题：
import pandas as pd
import plotly.express as px
from io import StringIO
csv = """Task,Start,Finish,Workstation,Resource
1,1970-01-01 01:00:00.000,1970-01-01 01:00:05.400,1,ABL
2,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,2,ABS
3,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,3,ABU
4,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,4,ACC
5,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,4,ACC
6,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,4,ACC
7,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,5,ABS
8,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,6,ACT
9,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,7,ACC
10,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,7,ACC
11,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,7,ACC
12,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,8,ABS
13,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,9,ABU
14,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,10,ACC
15,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,11,ABS
16,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,12,ABU
17,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,13,ACC
18,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,13,ACC
19,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,14,ABS
20,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,15,ABP
21,1970-01-01 01:00:00.000,1970-01-01 01:00:01.500,16,ABZ
22,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,17,ACC
23,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,17,ACC
24,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,18,ABS
25,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,19,AAW
26,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,20,ACC
27,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,20,ACC
28,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,21,ABS
29,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,22,ABU
30,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,23,ACC"""
df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("Whosebug.csv")
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S",
ticklabelmode="instant")
fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
conda list的输出：
pip 20.2.4 py_0 conda-forge
plotly 4.12.0 pypi_0 pypi
python 3.7.8 h6f2ec95_1_cpython conda-forge
（这是一个正在进行中的答案，可能会发生变化）
可能的解决方案：
但只有一个 可能的 解决方案，因为我仍然无法 100% 重现您的代码片段和相应的情节。但我们将在细节中更仔细地研究它。你需要最新的 Plotly 版本和 Kaleido。但这些都是非常直接的安装，并且是 plotly 向前迈出的一大步。至少在我看来……
代码 0:
df = pd.read_csv(StringIO(csv))
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
f2 = fig.full_figure_for_development(warn=False)
f2.layout.barmode = 'group'
f2.show()
地块 0：
详情：
我们必须一步一步来。首先，当我 运行 你提供的代码时，我得到这个：
地块 1：
代码 1：
import pandas as pd
import plotly.express as px
from io import StringIO
csv = """Task,Start,Finish,Workstation,Resource
1,1970-01-01 01:00:00.000,1970-01-01 01:00:05.400,1,ABL
2,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,2,ABS
3,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,3,ABU
4,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,4,ACC
5,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,4,ACC
6,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,4,ACC
7,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,5,ABS
8,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,6,ACT
9,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,7,ACC
10,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,7,ACC
11,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,7,ACC
12,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,8,ABS
13,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,9,ABU
14,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,10,ACC
15,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,11,ABS
16,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,12,ABU
17,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,13,ACC
18,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,13,ACC
19,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,14,ABS
20,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,15,ABP
21,1970-01-01 01:00:00.000,1970-01-01 01:00:01.500,16,ABZ
22,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,17,ACC
23,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,17,ACC
24,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,18,ABS
25,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,19,AAW
26,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,20,ACC
27,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,20,ACC
28,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,21,ABS
29,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,22,ABU
30,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,23,ACC"""
df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("Whosebug.csv")
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S",
ticklabelmode="instant")
fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
为了接近您提供的屏幕截图，我必须注释掉几行，如下所示。但是还是和你的不一样
情节 2：
代码 2（相同数据集）：
df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("Whosebug.csv")
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
# fig.update_yaxes(autorange="reversed")
# fig.update_xaxes(
# dtick="1000",
# tickformat="%M:%S",
# ticklabelmode="instant")
# fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
我觉得有点奇怪。可能的解决方案甚至更奇怪。如果您查看 post ，您将了解如何使用 f2 = fig.full_figure_for_development 显示大部分图形属性。您对 f2 所做的任何更改也应该可以对 fig 进行。但在这种情况下不是。为了获得类似于您想要的输出的结果，我必须执行以下操作：
代码 3：
df = pd.read_csv(StringIO(csv))
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
f2 = fig.full_figure_for_development(warn=False)
f2.layout.barmode = 'group'
f2.show()
情节 3：
也许我们正在取得进展？但现在您可能会想“为什么不 fig.layout.barmode = 'group'？”。那么，这是结果：
情节 4：
代码 4：
df = pd.read_csv(StringIO(csv))
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
f2 = fig.full_figure_for_development(warn=False)
# f2.layout.barmode = 'group'
# f2.show()
fig.layout.barmode = 'group'
fig.show()
我觉得整件事有点奇怪。但是请尝试一下，让我知道这一切对您有何影响！
我正在尝试使用示例中的 plotly.express 制作 plotly Gantt 图，但是 plotly 以某种方式根据给定的值改变了条形厚度名称（见图，红色 > 绿色 > 蓝色）：
代码如下所示：
import pandas as pd
import plotly.express as px
df = pd.read_csv('Whosebug.csv')
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S",
ticklabelmode="instant")
fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
Whosebug.csv 为：
Task,Start,Finish,Workstation,Resource
1,1970-01-01 01:00:00.000,1970-01-01 01:00:05.400,1,ABL
2,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,2,ABS
3,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,3,ABU
4,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,4,ACC
5,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,4,ACC
6,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,4,ACC
7,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,5,ABS
8,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,6,ACT
9,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,7,ACC
10,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,7,ACC
11,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,7,ACC
12,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,8,ABS
13,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,9,ABU
14,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,10,ACC
15,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,11,ABS
16,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,12,ABU
17,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,13,ACC
18,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,13,ACC
19,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,14,ABS
20,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,15,ABP
21,1970-01-01 01:00:00.000,1970-01-01 01:00:01.500,16,ABZ
22,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,17,ACC
23,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,17,ACC
24,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,18,ABS
25,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,19,AAW
26,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,20,ACC
27,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,20,ACC
28,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,21,ABS
29,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,22,ABU
30,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,23,ACC
我希望所有的条都具有相同的粗细，令人惊讶的是，当我将 Resource 的名称更改为某个随机的 3 字符值时，这会起作用：
我认为这与以AB*或AC*开头的资源有关。不幸的是，资源的名称取决于真实世界的名称，所以我不能随意更改它们。当资源名称类似于 FooBar-Axx-FOO 和 xx = [CC, BS, CT...] 时，也会发生变化的条形粗细。有谁知道为什么会发生这种情况或如何防止这种情况？
P.S.:
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S")
有必要在甘特图中显示秒而不是天...是否有更好的方法来实现此目的？
更新：
conda 我用来创建问题的环境 yaml 文件：
name: stack
channels:
- conda-forge
- pytorch
- plotly
dependencies:
- python>=3.5,<3.8
- pandas
- pip
- pip:
- plotly
稍作修改的代码仍然会产生如上所示的相同问题：
import pandas as pd
import plotly.express as px
from io import StringIO
csv = """Task,Start,Finish,Workstation,Resource
1,1970-01-01 01:00:00.000,1970-01-01 01:00:05.400,1,ABL
2,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,2,ABS
3,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,3,ABU
4,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,4,ACC
5,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,4,ACC
6,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,4,ACC
7,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,5,ABS
8,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,6,ACT
9,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,7,ACC
10,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,7,ACC
11,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,7,ACC
12,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,8,ABS
13,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,9,ABU
14,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,10,ACC
15,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,11,ABS
16,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,12,ABU
17,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,13,ACC
18,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,13,ACC
19,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,14,ABS
20,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,15,ABP
21,1970-01-01 01:00:00.000,1970-01-01 01:00:01.500,16,ABZ
22,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,17,ACC
23,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,17,ACC
24,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,18,ABS
25,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,19,AAW
26,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,20,ACC
27,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,20,ACC
28,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,21,ABS
29,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,22,ABU
30,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,23,ACC"""
df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("Whosebug.csv")
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S",
ticklabelmode="instant")
fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
conda list的输出：
pip 20.2.4 py_0 conda-forge
plotly 4.12.0 pypi_0 pypi
python 3.7.8 h6f2ec95_1_cpython conda-forge
（这是一个正在进行中的答案，可能会发生变化）
可能的解决方案：
但只有一个 可能的 解决方案，因为我仍然无法 100% 重现您的代码片段和相应的情节。但我们将在细节中更仔细地研究它。你需要最新的 Plotly 版本和 Kaleido。但这些都是非常直接的安装，并且是 plotly 向前迈出的一大步。至少在我看来……
代码 0:
df = pd.read_csv(StringIO(csv))
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
f2 = fig.full_figure_for_development(warn=False)
f2.layout.barmode = 'group'
f2.show()
地块 0：
详情：
我们必须一步一步来。首先，当我 运行 你提供的代码时，我得到这个：
地块 1：
代码 1：
import pandas as pd
import plotly.express as px
from io import StringIO
csv = """Task,Start,Finish,Workstation,Resource
1,1970-01-01 01:00:00.000,1970-01-01 01:00:05.400,1,ABL
2,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,2,ABS
3,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,3,ABU
4,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,4,ACC
5,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,4,ACC
6,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,4,ACC
7,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,5,ABS
8,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,6,ACT
9,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,7,ACC
10,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,7,ACC
11,1970-01-01 01:00:03.300,1970-01-01 01:00:05.300,7,ACC
12,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,8,ABS
13,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,9,ABU
14,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,10,ACC
15,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,11,ABS
16,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,12,ABU
17,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,13,ACC
18,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,13,ACC
19,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,14,ABS
20,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,15,ABP
21,1970-01-01 01:00:00.000,1970-01-01 01:00:01.500,16,ABZ
22,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,17,ACC
23,1970-01-01 01:00:01.300,1970-01-01 01:00:03.300,17,ACC
24,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,18,ABS
25,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,19,AAW
26,1970-01-01 01:00:00.000,1970-01-01 01:00:02.000,20,ACC
27,1970-01-01 01:00:02.000,1970-01-01 01:00:03.300,20,ACC
28,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,21,ABS
29,1970-01-01 01:00:00.000,1970-01-01 01:00:01.000,22,ABU
30,1970-01-01 01:00:00.000,1970-01-01 01:00:01.300,23,ACC"""
df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("Whosebug.csv")
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
fig.update_yaxes(autorange="reversed")
fig.update_xaxes(
dtick="1000",
tickformat="%M:%S",
ticklabelmode="instant")
fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
为了接近您提供的屏幕截图，我必须注释掉几行，如下所示。但是还是和你的不一样
情节 2：
代码 2（相同数据集）：
df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("Whosebug.csv")
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
# fig.update_yaxes(autorange="reversed")
# fig.update_xaxes(
# dtick="1000",
# tickformat="%M:%S",
# ticklabelmode="instant")
# fig.update_layout(xaxis_range=[df.Start.min(), df.Finish.max()])
fig.show()
我觉得有点奇怪。可能的解决方案甚至更奇怪。如果您查看 post f2 = fig.full_figure_for_development 显示大部分图形属性。您对 f2 所做的任何更改也应该可以对 fig 进行。但在这种情况下不是。为了获得类似于您想要的输出的结果，我必须执行以下操作：
代码 3：
df = pd.read_csv(StringIO(csv))
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
f2 = fig.full_figure_for_development(warn=False)
f2.layout.barmode = 'group'
f2.show()
情节 3：
也许我们正在取得进展？但现在您可能会想“为什么不 fig.layout.barmode = 'group'？”。那么，这是结果：
情节 4：
代码 4：
df = pd.read_csv(StringIO(csv))
fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Resource", text="Task", width=1600, height=800)
f2 = fig.full_figure_for_development(warn=False)
# f2.layout.barmode = 'group'
# f2.show()
fig.layout.barmode = 'group'
fig.show()
我觉得整件事有点奇怪。但是请尝试一下，让我知道这一切对您有何影响！