图解：如何防止甘特图中的钢筋厚度变化？

2024-10-06 12:21:10 发布

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我试图使用plotly.express制作一个plotly{a1}图，如示例中所示，但是plotly以某种方式根据给定的名称改变了条的厚度（参见图片，红色>；绿色>；蓝色）：

enter image description here

代码如下所示：

import pandas as pd
import plotly.express as px

df = pd.read_csv('stackoverflow.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()

其中stackoverflow.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厚度，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： enter image description here

我认为这与以AB*或AC*开头的资源有关。不幸的是，资源的名称依赖于真实世界的名称，因此我不能随意更改它们。当资源的名称类似于FooBar-Axx-FOO和xx = [CC, BS, CT...]时，条的厚度也会发生变化。有人知道为什么会发生这种情况或如何预防吗

附言：是的

fig.update_xaxes(
    dtick="1000",
    tickformat="%M:%S")

必须在甘特图中显示秒而不是天。。。有没有更好的方法来实现这一点

更新： conda我用于创建问题的环境yaml文件：

name: stack
channels:
  - conda-forge
  - pytorch
  - plotly
dependencies:
    - python>=3.5,<3.8
    - pandas
    - pip
    - pip:
      - plotly

稍微修改过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("stackoverflow.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

Tags： csvimport名称dftaskfigupdateabs
1条回答
网友
1楼 · 发布于 2024-10-06 12:21:10

（这是一个正在进行的答案，很容易改变）

一个可能的解决方案：

但是只有一个可能的解决方案，因为我仍然无法100%地再现您的代码片段和相应的绘图。但我们将在细节中更仔细地看一看。你需要最新的Plotly版本和Kaleido。但这些都是非常直接的安装，对于plotly来说是一个巨大的进步。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()

绘图0:

enter image description here

详细信息：

我们必须一步一步地做。首先，当我运行您提供的代码时，我得到：

图1:

enter image description here

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("stackoverflow.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，我必须注释掉以下几行内容。但还是和你的数字不一样

图2:

enter image description here

代码2（相同的数据集）：

df = pd.read_csv(StringIO(csv))
# df = pd.read_csv("stackoverflow.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。而可能的解决方案则更为奇怪。如果您查看postPlotly: How to inspect and make changes to a plotly figure?，您将看到如何使用f2 = fig.full_figure_for_development显示大多数地物属性。您对f2所做的任何更改也应该可以对fig所做的任何更改。但在这种情况下不是这样。为了得到与您期望的输出类似的结果，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()

图3:

enter image description here

也许我们会有进展？但是现在你可能会想，“为什么不呢？”。好吧，结果如下：

图4:

enter image description here

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()

我觉得这整件事有点奇怪。但是，请尝试一下，让我知道这一切是如何为你工作的

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