Plotly 带有 integer x 轴的甘特图的快速时间表？

Question

I'm using plotly express timeline to produce a Gantt chart following this example: https://medium.com/dev-genius/gantt-charts-in-python-with-plotly-e7213f932f1e

It automatically sets the x-axis to use dates but I'd actually like to just use integers (ie Project Kick-Off +1, Project Kick-Off +6, etc).

Is there a way to make a timeline plot NOT use dates for the xaxis?

If I try using integers it'll treat them like milliseconds.

Answer 1

The answer:

Yes, it's possible, Just give integers as start and end "dates", calculate the difference between them ( delta ), and make these changes to your fig :

fig.layout.xaxis.type = 'linear'
fig.data[0].x = df.delta.tolist()

Plot

The details:

There actually is a way to achieve this, although the docs state that:

The px.timeline function by default sets the X-axis to be of type=date, so it can be configured like any time-series chart.

And therefore every other functionality in px.timeline() seems to revolve around that fact. But if you just ignore that and use integers as values for Start and Finish , then you can tweak a few attributes to get what you want. You just need to calculate the differences between each Start and Stop . For example like this:

df = pd.DataFrame([
    dict(Task="Job A", Start=1, Finish=4),
    dict(Task="Job B", Start=2, Finish=6),
    dict(Task="Job C", Start=3, Finish=10)
])
df['delta'] = df['Finish'] - df['Start']

And then tweaks a little further:

fig.layout.xaxis.type = 'linear'
fig.data[0].x = df.delta.tolist()

Complete code:

import plotly.express as px
import pandas as pd

df = pd.DataFrame([
    dict(Task="Job A", Start=1, Finish=4),
    dict(Task="Job B", Start=2, Finish=6),
    dict(Task="Job C", Start=3, Finish=10)
])
df['delta'] = df['Finish'] - df['Start']

fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task")
fig.update_yaxes(autorange="reversed") 

fig.layout.xaxis.type = 'linear'
fig.data[0].x = df.delta.tolist()
f = fig.full_figure_for_development(warn=False)
fig.show()

Answer 2

I tried the other answer listed here, but that doesn't work if I specify a color . If I try, the data fig.data has multiple Bar objects, and I don't think it contains the data necessary to assign all the deltas. However, I did find that I could monkeypatch plotly code to not convert it to time objects and I get the correct result:

import plotly.express as px
import pandas as pd

def my_process_dataframe_timeline(args):
    """
    Massage input for bar traces for px.timeline()
    """
    print("my method")
    args["is_timeline"] = True
    if args["x_start"] is None or args["x_end"] is None:
        raise ValueError("Both x_start and x_end are required")

    x_start = args["data_frame"][args["x_start"]]
    x_end = args["data_frame"][args["x_end"]]

    # note that we are not adding any columns to the data frame here, so no risk of overwrite
    args["data_frame"][args["x_end"]] = (x_end - x_start)
    args["x"] = args["x_end"]
    del args["x_end"]
    args["base"] = args["x_start"]
    del args["x_start"]
    return args
px._core.process_dataframe_timeline = my_process_dataframe_timeline

df = pd.DataFrame([
    dict(Task="Job A", Start=1, Finish=4, color="1"),
    dict(Task="Job B", Start=2, Finish=6, color="2"),
    dict(Task="Job C", Start=3, Finish=10, color="1")
])
df['delta'] = df['Finish'] - df['Start']

fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="color")
fig.update_yaxes(autorange="reversed") 

fig.layout.xaxis.type = 'linear'
fig.show()

Obviously not desirable to do this... it would be nice to get formal support.

Answer 3

I think this is much simpler solution than above when we have to specify color

for d in fig.data:
  filt = df['color'] == d.name
  d.x = df[filt]['Delta'].tolist()

Answer 4

To illustrate how the answer from JounghooLee works, I want to add this example

import plotly.express as px
import pandas as pd

df = pd.DataFrame([
    dict(Task="Job A", Start=1, Finish=10),
    dict(Task="Job B", Start=3, Finish=6),
    dict(Task="Job C", Start=3, Finish=10)
])

df['delta'] = df['Finish'] - df['Start']

fig = px.timeline(df, x_start="Start", x_end="Finish", y="Task", color="Task")


print(fig.data[0].x)
print(fig.data[1].x)
print(fig.data[2].x)

a = [True, False, False]
b = [False, True, False]
c = [False, False, True]

aSeries = pd.Series(a)
bSeries = pd.Series(b)
cSeries = pd.Series(c)

fig.layout.xaxis.type = 'linear'

# the dataFrame df is filtered by the pandas series, to add the specific delta to each of the bars
fig.data[0].x = df[aSeries]['delta'].tolist()
fig.data[1].x = df[bSeries]['delta'].tolist()
fig.data[2].x = df[cSeries]['delta'].tolist()

print(fig.data[0].x)
print(fig.data[1].x)
print(fig.data[2].x)


fig.show()

The prints give the following output:

[0.]
[0.]
[0.]
(9,)
(3,)
(7,)

and the fig.data contains three bars

If there is no color assigned, it does not work, because then fig.data contains just 1 bar

The print(fig.data[0].x) returns

[0. 0. 0.]

in this case.

See also this https://stackoverflow.com/a/71141827/7447940

Answer 5

Another option is to use a plotly barplot and use the base argument to indicate where to put the bar start and the x value would be the duration of the task:

df = pd.DataFrame([
    dict(task="Job A", start=1, end=4),
    dict(task="Job B", start=2, end=6),
    dict(task="Job C", start=3, end=10)
])
df['delta'] = df['end'] - df['start']

fig = px.bar(df, 
base = "start",
x = "delta",
y = "task",
orientation = 'h'
)

fig.update_yaxes(autorange="reversed")
fig.show()

The barplot also accepts the color argument and correctly groups the tasks by the column you indicate, for example:

df = pd.DataFrame([
    dict(task="Job A", start=1, end=4, color = "A"),
    dict(task="Job B", start=2, end=6, color = "B"),
    dict(task="Job C", start=3, end=10, color = "A")
])
df['delta'] = df['end'] - df['start']

fig = px.bar(df, 
base = "start",
x = "delta",
y = "task",
color = "color",
orientation = 'h'
)

fig.update_yaxes(autorange="reversed")
fig.show()