8000 Using 'date' type for scatter xaxis fails when coordinates stored in Numpy array · Issue #1927 · plotly/plotly.py · GitHub
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Using 'date' type for scatter xaxis fails when coordinates stored in Numpy array #1927

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jordanweaver opened this issue Nov 25, 2019 · 4 comments
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Using 'date' type for scatter xaxis fails when coordinates stored in Numpy array #1927

jordanweaver opened this issue Nov 25, 2019 · 4 comments

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@jordanweaver
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jordanweaver commented Nov 25, 2019 •
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It's possible (nay, likely) that I'm misunderstanding something but the docs indicate that Numpy arrays containing timestamps should be allowed. Yet, I'm currently having to cast this into a Pandas Series for it to work:

# plotly date axis bug?
import dash
import dash_core_components as dcc
import dash_html_components as html
import plotly.graph_objects as go

import numpy as np
import pandas as pd


app = dash.Dash(__name__)

timestamps = np.array(
    [
        '2017-05-24T23:00:21',
        '2017-05-24T23:00:23',
        '2017-05-24T23:00:25',
        '2017-05-24T23:00:27',
        '2017-05-24T23:00:29'
    ],
    dtype='datetime64[ns]'
)
fake_data = np.arange(len(timestamps))

fig_working = go.Figure().add_scatter(
    x=pd.Series(timestamps),
    y=fake_data
).update_layout(xaxis_type='date')


fig_working_not_so_much = go.Figure().add_scatter(
    x=timestamps,
    y=fake_data
).update_layout(xaxis_type='date')


app.layout = html.Div(
    [
        html.H1("Working with a pandas.Series:"),
        dcc.Graph(figure=fig_working),

        html.H1("Same data in a numpy.array:"),
        dcc.Graph(figure=fig_working_not_so_much),

        html.H1("😿")
    ]
)


if __name__ == '__main__':
    app.run_server(debug=True)

This yields:

image
@jordanweaver jordanweaver changed the title Using 'date' type for scatter xaxis fails when for coordinates stored in Numpy array Using 'date' type for scatter xaxis fails when coordinates stored in Numpy array Nov 25, 2019
@eceforge
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So I'm moderately sure this has to do with the fact that pandas stores its datatime64[ns] internally as 'int64' where as numpy stores it as 'uint64'. It's supposed to be number of nanoseconds since epoch, so I can't imagine there would be a need for negative counts, but I don't know enough about the intent pandas had there.

@nicolaskruchten
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< 8000 a class="user-mention notranslate" data-hovercard-type="user" data-hovercard-url="/users/jonmmease/hovercard" data-octo-click="hovercard-link-click" data-octo-dimensions="link_type:self" href="https://github.com/jonmmease">@jonmmease maybe let's take a look at this in the context of the #2955 thing?

@nicolaskruchten
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@jonmmease was this fixed by #3022 actually?

@nicolaskruchten
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I'm not sure when this was fixed, but it no longer happens on master, and so at the very least will be fixed in 5.0 :)

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