On Feb 13, 2017, at 08:19, John Marino <john.d.marino@gmail.com> wrote:

I was surprised to find that having a dataframe with a datetime column that contains a NULL, even if that column is not used, causes an error. (ValueError: NaTType does not support timetuple) Is this expected behavior?

Here is a simple example. Attached is the jupyter notebook I used.

import pandas as pd
pd.options.display.max_rows = 10

from bokeh.plotting import figure, output_notebook, output_file, show
output_file('broken.html')

# some fake data
data = pd.DataFrame({'time':['09:30','10:00','10:05','10:30'], 'prc':[5,5.25,6,7]})
data.time = pd.to_datetime(data.time)
# data['time_not_used'] = data.time # causes no problems

# create an column we will not use, but set a value to NaT
data['time_not_used'] = data.time.where(data.time!='2017-02-13 10:05:00')

print(data.info())

print(data)

p = figure(x_axis_type='datetime')
p.circle(x='time', y='prc', source=data, color='red', size=10)
show(p) # throws "ValueError: NaTType does not support timetuple"

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<ErrorExample.ipynb>

On Feb 13, 2017, at 09:08, Bryan Van de ven <bryan@bokeh.org> wrote:

Hi,

Bokeh has no way of knowing whether a column is truly "used" or no. E.g. maybe you refer to an "unused" column in some CustomJS callback, Bokeh has no way of determining this, so has to act conservatively. Accordingly, anything that goes into a CDS is "used", in the sense that it is serialized and included in the document. The corollary is that every type in a CDS has to be serializable. I'm guessing Bokeh does not know what to do with NAT types. There's certainly no corresponding type on the JS side. Maybe they can just be converted to nulls, but it will require discussion and/or investigation, so I'd suggest a GitHub issue.

Thanks,

Bryan

On Feb 13, 2017, at 08:19, John Marino <john.d.marino@gmail.com> wrote:

I was surprised to find that having a dataframe with a datetime column that contains a NULL, even if that column is not used, causes an error. (ValueError: NaTType does not support timetuple) Is this expected behavior?

Here is a simple example. Attached is the jupyter notebook I used.

import pandas as pd
pd.options.display.max_rows = 10

from bokeh.plotting import figure, output_notebook, output_file, show
output_file('broken.html')

# some fake data
data = pd.DataFrame({'time':['09:30','10:00','10:05','10:30'], 'prc':[5,5.25,6,7]})
data.time = pd.to_datetime(data.time)
# data['time_not_used'] = data.time # causes no problems

# create an column we will not use, but set a value to NaT
data['time_not_used'] = data.time.where(data.time!='2017-02-13 10:05:00')

print(data.info())

print(data)

p = figure(x_axis_type='datetime')
p.circle(x='time', y='prc', source=data, color='red', size=10)
show(p) # throws "ValueError: NaTType does not support timetuple"

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<ErrorExample.ipynb>

On Monday, February 13, 2017 at 10:10:09 AM UTC-5, Bryan Van de ven wrote:

I should add, as an immediate workaround, things should work if you delete such unused columns from the CDS .data dict before generating output.

Bryan

On Feb 13, 2017, at 09:08, Bryan Van de ven bry...@continuum.io wrote:

Hi,

Bokeh has no way of knowing whether a column is truly “used” or no. E.g. maybe you refer to an “unused” column in some CustomJS callback, Bokeh has no way of determining this, so has to act conservatively. Accordingly, anything that goes into a CDS is “used”, in the sense that it is serialized and included in the document. The corollary is that every type in a CDS has to be serializable. I’m guessing Bokeh does not know what to do with NAT types. There’s certainly no corresponding type on the JS side. Maybe they can just be converted to nulls, but it will require discussion and/or investigation, so I’d suggest a GitHub issue.

Thanks,

Bryan

On Feb 13, 2017, at 08:19, John Marino john.d...@gmail.com wrote:

I was surprised to find that having a dataframe with a datetime column that contains a NULL, even if that column is not used, causes an error. (ValueError: NaTType does not support timetuple) Is this expected behavior?

Here is a simple example. Attached is the jupyter notebook I used.

import pandas as pd

pd.options.display.max_rows = 10

from bokeh.plotting import figure, output_notebook, output_file, show

output_file(‘broken.html’)

some fake data

data = pd.DataFrame({‘time’:[‘09:30’,‘10:00’,‘10:05’,‘10:30’], ‘prc’:[5,5.25,6,7]})

data.time = pd.to_datetime(data.time)

data[‘time_not_used’] = data.time # causes no problems

create an column we will not use, but set a value to NaT

data[‘time_not_used’] = data.time.where(data.time!=‘2017-02-13 10:05:00’)

print(data.info())

print(data)

p = figure(x_axis_type=‘datetime’)

p.circle(x=‘time’, y=‘prc’, source=data, color=‘red’, size=10)

show(p) # throws “ValueError: NaTType does not support timetuple”

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<ErrorExample.ipynb>