Plotting DataFrame with timedelta64 (y-axis)

[s-celles]

Similar to #799 but on y-axis and pandas-dev/pandas#16953

import pandas as pd
from pandas.compat import StringIO
import plotly
import plotly.graph_objs as go


dat = """c1,c2,c3
1000,2000,1500
9000,8000,1600"""

df = pd.read_csv(StringIO(dat))

df = df.apply(lambda x: pd.to_timedelta(x, unit='ms'))

print(df)
print(df.dtypes)
print(df.index)

trace1 = go.Bar(
    x=df.index,
    y=df.c1,
    name='c1'
)
trace2 = go.Bar(
    x=df.index,
    y=df.c2,
    name='c2'
)

trace3 = go.Bar(
    x=df.index,
    y=df.c3,
    name='c3'
)

data = [trace1, trace2, trace3]
layout = go.Layout(
    barmode='group'
)

plotly.offline.plot({
"data": data,
"layout": layout
})

displays

y-axis values are not correctly displayed

[s-celles]

A workaround is to do:

for col in df.columns:
    df[col] = df[col] + pd.to_datetime('1970/01/01')

but it will be nice if plotly.py could handle timedelta64 on y-axis

[jaladh-singhal]

Is there any progress regarding this, I really need to use timedelta64 on Y-axis?

[nhoover]

That's really not much of a workaround. Showing Jan 1, 1970 at the bottom... Timedelta is really a standard feature used all the time and plots with the y-axis being timedelta are very common.

[harahu]

For reference, there are two other issues closely related to this one, covering the x axis (#799), and color axis (#3368). It would probably make sense to tackle all three issues holistically, rather than separately.

[ThomasGl]

Hey, it's a bit late to the party. But, I wrote a solution for this issue. You can have dash, including its iterative features working and a datetime format in any sense. This, includes the autorender to solve for many x_sample points, so that it won't crash your axis.

Such is the following:

For the x_axis make absolutely sure that the format of your list is list[str] and they are consistent. ALSO, match the format in those strings in the tickformat of your list, the reference for time is standard as used in datetime objects, for future reference check: https://plotly.com/python/reference/layout/xaxis/#layout-xaxis-tickformat

e.g.:

mock_list = ["00:00:00", "00:00:01"]

mock_list to be the x values in a scatter plot for instance
then adjust the axis as follows:

fig.update_xaxes( tickformat="%H:%M:%S")

[dizcza]

Thanks @ThomasGl. However, this works only for the last subplot in the figure.

The X axis is displayed in "%H:%M:%S".
The 3rd (bottom) subplot hover X data is in "%H:%M:%S".
But 1st and 2nd subplots hover X data is still in Jan 1, 1970, ...%H:%M:%S. How to make them also %H:%M:%S?

Tried

    for xaxis in range(1, 4):
        fig['layout'][f'xaxis{xaxis}']['tickformat'] = "%H:%M:%S.%f"

with no help.

[ThomasGl]

Thanks @ThomasGl. However, this works only for the last subplot in the figure.

The X axis is displayed in "%H:%M:%S".
The 3rd (bottom) subplot hover X data is in "%H:%M:%S".
But 1st and 2nd subplots hover X data is still in Jan 1, 1970, ...%H:%M:%S. How to make them also %H:%M:%S?

Tried

    for xaxis in range(1, 4):
        fig['layout'][f'xaxis{xaxis}']['tickformat'] = "%H:%M:%S.%f"

with no help.

Hi. Ill take at look at it over the weekend, but can you share a bit more of information upon the issue you are having?

[ThomasGl]

@dizcza also take note that you must adjust the axis for each subplot. As the engine responsible to generate the graphs renders each one as a new "fig" object with defaults params

[dizcza]

Here is the code I'm using:

import plotly.graph_objects as go
from plotly.subplots import make_subplots


def add_traces(fig, record_data_dict: dict):
    # only one key/value for now in this dict
    for sensor, record_data in record_data_dict.items():
        y = np.random.randn(1000, 3)
        # convert s to ms
        time_ms = (record_data.time * 1000).astype(np.int32)
        td = pd.to_timedelta(time_ms, unit='ms') + pd.Timestamp("1970/01/01")
        idx = np.arange(len(y)).astype(str)
        for dim in range(3):
            trace = go.Scatter(
                x=td,
                y=y[:, dim],
                hovertext=idx,
                name="AAA",
                legendgroup=sensor,
                showlegend=dim == 0,
                marker=dict(color=colors[sensor]),
                line=dict(color=colors[sensor]),
                opacity=0.8
            )
            fig.add_trace(trace, row=dim + 1, col=1)
 
 
 
 def plot_fig(record_dir=DATA_DIR / "2023.02.28"):
    fig = make_subplots(rows=3, shared_xaxes=True)
    record = Record(record_dir)
    add_traces(fig, record.data)
    fig['layout']['xaxis3']['title'] = "Time, s"
    fig.update_layout(
        title=record_dir.name,
        legend_title="Sensor",
    )
    fig.update_xaxes(tickformat="%H:%M:%S.%f")

and here is the plot

The 1st and 2nd plots hover data is incorrect: it starts with Jan 1, 1970.

[dizcza]

@dizcza also take note that you must adjust the axis for each subplot. As the engine responsible to generate the graphs renders each one as a new "fig" object with defaults params

How can I do so? In my case, I have only one figure.

[ThomasGl]

Each subplot renders the engine plot for figure, in the sense that you have as many fig objects as you have subplots, thus in your case you have 4 fig objects, One containing subplots and then 3 as you have 3 subplots.

As for starting in Jan 1, 1970. this is a standard initial date, in case of missing compiling data, meaning, if you don't have a "DD:MM:YYYY" string like in the element responsible to render it, check in the documentation for the dash plots in case it changed, or it has some slightly different format. This could be generated using a list comprehension.

Yet, as for correcting timestamp, pass the line with fig.update_xaxes to the last line in the function add_traces

By the way, I can't know if theres an error with your data without the the function call arg to plot_fig, by that I mean that I need whatever DATA_DIR contains in order to recreate your plots