I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.
Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
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Here is the code-
import pandas as pd
import seaborn as sns
import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.charts import Bar, output_file, show
df = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df)
p = Bar(df, values=blend(‘M1’,‘M2’,‘M3’,‘M4’,‘P1’,‘P2’,‘P3’,‘P4’,
name=‘Severity Count’, labels_name=‘severity’),
label=cat(columns=‘Code’,
sort=False), stack=cat(columns=‘severity’),
legend=‘top_right’, title=‘Check if any Particular Code leads to a specific severity’, plot_width=1100,
plot_height=600, tools=“wheel_zoom,pan,reset,resize,save”,
color=[‘#87CEFA’,‘#9BC4E2’,‘#6CA6CD’,‘#325C74’,‘#EE7942’,‘#CD6839’,‘#8A360F’,‘#5C4033’], webgl=True)
output_file(“/home/pragyan/Documents/test2.html”)
show(p)
The code runs well. No error. Runs as expected. But slow. Can anything be done?
On Tuesday, July 26, 2016 at 4:32:42 PM UTC+5:30, Bryan Van de ven wrote:
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
–
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For more options, visit https://groups.google.com/a/continuum.io/d/optout.
How big is the csv?
On Jul 26, 2016, at 06:47, Pragyan Bezbaruah [email protected] wrote:
Here is the code-
import pandas as pd
import seaborn as sns
import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.charts import Bar, output_file, showdf = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df)
p = Bar(df, values=blend(‘M1’,‘M2’,‘M3’,‘M4’,‘P1’,‘P2’,‘P3’,‘P4’,
name=‘Severity Count’, labels_name=‘severity’),
label=cat(columns=‘Code’,
sort=False), stack=cat(columns=‘severity’),
legend=‘top_right’, title=‘Check if any Particular Code leads to a specific severity’, plot_width=1100,
plot_height=600, tools=“wheel_zoom,pan,reset,resize,save”,
color=[‘#87CEFA’,‘#9BC4E2’,‘#6CA6CD’,‘#325C74’,‘#EE7942’,‘#CD6839’,‘#8A360F’,‘#5C4033’], webgl=True)output_file(“/home/pragyan/Documents/test2.html”)
show(p)The code runs well. No error. Runs as expected. But slow. Can anything be done?
On Tuesday, July 26, 2016 at 4:32:42 PM UTC+5:30, Bryan Van de ven wrote:
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
–
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For more options, visit https://groups.google.com/a/continuum.io/d/optout.
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Have you tried to add timers to each section to see where it is slow?
i.e. (Python 2)
print datetime.now() - startTime
from datetime import datetime
startTime = datetime.now()
#do a section of code
print datetime.now() - startTime
#do a section of code
``
On Tuesday, 26 July 2016 12:47:55 UTC+1, Pragyan Bezbaruah wrote:
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
–
You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.
To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
To post to this group, send email to [email protected].
To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/14c55cb7-993a-4f7c-a831-1ffe03397799%40continuum.io.
For more options, visit https://groups.google.com/a/continuum.io/d/optout.
Here is the code-
import pandas as pd
import seaborn as sns
import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.charts import Bar, output_file, showdf = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df)
p = Bar(df, values=blend(‘M1’,‘M2’,‘M3’,‘M4’,‘P1’,‘P2’,‘P3’,‘P4’,
name=‘Severity Count’, labels_name=‘severity’),
label=cat(columns=‘Code’,
sort=False), stack=cat(columns=‘severity’),
legend=‘top_right’, title=‘Check if any Particular Code leads to a specific severity’, plot_width=1100,
plot_height=600, tools=“wheel_zoom,pan,reset,resize,save”,
color=[‘#87CEFA’,‘#9BC4E2’,‘#6CA6CD’,‘#325C74’,‘#EE7942’,‘#CD6839’,‘#8A360F’,‘#5C4033’], webgl=True)output_file(“/home/pragyan/Documents/test2.html”)
show(p)The code runs well. No error. Runs as expected. But slow. Can anything be done?
On Tuesday, July 26, 2016 at 4:32:42 PM UTC+5:30, Bryan Van de ven wrote:
Also, you seem to be creating a DataFrame from a DataFrame -
df = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df) #this line is redundant?
``
On Tuesday, 26 July 2016 15:37:27 UTC+1, Will Kew wrote:
Have you tried to add timers to each section to see where it is slow?
i.e. (Python 2)
print datetime.now() - startTimefrom datetime import datetime startTime = datetime.now() #do a section of code print datetime.now() - startTime #do a section of code
``
On Tuesday, 26 July 2016 12:47:55 UTC+1, Pragyan Bezbaruah wrote:
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
–
You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.
To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
To post to this group, send email to [email protected].
To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/14c55cb7-993a-4f7c-a831-1ffe03397799%40continuum.io.
For more options, visit https://groups.google.com/a/continuum.io/d/optout.
Here is the code-
import pandas as pd
import seaborn as sns
import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.charts import Bar, output_file, showdf = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df)
p = Bar(df, values=blend(‘M1’,‘M2’,‘M3’,‘M4’,‘P1’,‘P2’,‘P3’,‘P4’,
name=‘Severity Count’, labels_name=‘severity’),
label=cat(columns=‘Code’,
sort=False), stack=cat(columns=‘severity’),
legend=‘top_right’, title=‘Check if any Particular Code leads to a specific severity’, plot_width=1100,
plot_height=600, tools=“wheel_zoom,pan,reset,resize,save”,
color=[‘#87CEFA’,‘#9BC4E2’,‘#6CA6CD’,‘#325C74’,‘#EE7942’,‘#CD6839’,‘#8A360F’,‘#5C4033’], webgl=True)output_file(“/home/pragyan/Documents/test2.html”)
show(p)The code runs well. No error. Runs as expected. But slow. Can anything be done?
On Tuesday, July 26, 2016 at 4:32:42 PM UTC+5:30, Bryan Van de ven wrote:
100MB file. Has like 78k records. Hehe. 
On Tuesday, July 26, 2016 at 5:58:18 PM UTC+5:30, Bryan Van de ven wrote:
How big is the csv?
On Jul 26, 2016, at 06:47, Pragyan Bezbaruah [email protected] wrote:
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
–
You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.
To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
To post to this group, send email to [email protected].
To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/14c55cb7-993a-4f7c-a831-1ffe03397799%40continuum.io.
For more options, visit https://groups.google.com/a/continuum.io/d/optout.
Here is the code-
import pandas as pd
import seaborn as sns
import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.charts import Bar, output_file, showdf = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df)
p = Bar(df, values=blend(‘M1’,‘M2’,‘M3’,‘M4’,‘P1’,‘P2’,‘P3’,‘P4’,
name=‘Severity Count’, labels_name=‘severity’),
label=cat(columns=‘Code’,
sort=False), stack=cat(columns=‘severity’),
legend=‘top_right’, title=‘Check if any Particular Code leads to a specific severity’, plot_width=1100,
plot_height=600, tools=“wheel_zoom,pan,reset,resize,save”,
color=[‘#87CEFA’,‘#9BC4E2’,‘#6CA6CD’,‘#325C74’,‘#EE7942’,‘#CD6839’,‘#8A360F’,‘#5C4033’], webgl=True)output_file(“/home/pragyan/Documents/test2.html”)
show(p)The code runs well. No error. Runs as expected. But slow. Can anything be done?
On Tuesday, July 26, 2016 at 4:32:42 PM UTC+5:30, Bryan Van de ven wrote:
–
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To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/4166ab21-f6d3-4b89-901d-b1078397cd18%40continuum.io.
For more options, visit https://groups.google.com/a/continuum.io/d/optout.
Yes. I followed some forum and they did that. I dont know its purpose but with/without it, the time is similar.
And about the datetime, I didnt think it that way. Will check and let you know. 
Thanks.
On Tuesday, July 26, 2016 at 8:08:24 PM UTC+5:30, Will Kew wrote:
Also, you seem to be creating a DataFrame from a DataFrame -
df = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df) #this line is redundant?
``
On Tuesday, 26 July 2016 15:37:27 UTC+1, Will Kew wrote:
Have you tried to add timers to each section to see where it is slow?
i.e. (Python 2)
print datetime.now() - startTimefrom datetime import datetime startTime = datetime.now() #do a section of code print datetime.now() - startTime #do a section of code
``
On Tuesday, 26 July 2016 12:47:55 UTC+1, Pragyan Bezbaruah wrote:
Without code to inspect or run it is impossible to speculate about what might be going on. Can you provide a complete, minimal, run-able test script to reproduce what you are seeing?
Bryan
On Jul 26, 2016, at 04:59, Pragyan Bezbaruah [email protected] wrote:
I have tried webgl in Stacked Bar Chart but Not much improvement.
I have a csv file. Analytics is done in pandas and visualisation is done using Bokeh.Is there any other way to fast up rendering?
Rendering takes like 5-6 seconds in the Python CLI but when I give the show command, it takes like a minute to show up the visualisation.
–
You received this message because you are subscribed to the Google Groups “Bokeh Discussion - Public” group.
To unsubscribe from this group and stop receiving emails from it, send an email to [email protected].
To post to this group, send email to [email protected].
To view this discussion on the web visit https://groups.google.com/a/continuum.io/d/msgid/bokeh/14c55cb7-993a-4f7c-a831-1ffe03397799%40continuum.io.
For more options, visit https://groups.google.com/a/continuum.io/d/optout.
Here is the code-
import pandas as pd
import seaborn as sns
import pandas as pd
from bokeh.charts import Bar, output_file, show
from bokeh.charts.attributes import cat, color
from bokeh.charts.operations import blend
from bokeh.charts.utils import df_from_json
from bokeh.charts import Bar, output_file, showdf = pd.read_csv(“/home/pragyan/Documents/Pandas-Table/samplereqd-test2.csv”)
df = pd.DataFrame(df)
p = Bar(df, values=blend(‘M1’,‘M2’,‘M3’,‘M4’,‘P1’,‘P2’,‘P3’,‘P4’,
name=‘Severity Count’, labels_name=‘severity’),
label=cat(columns=‘Code’,
sort=False), stack=cat(columns=‘severity’),
legend=‘top_right’, title=‘Check if any Particular Code leads to a specific severity’, plot_width=1100,
plot_height=600, tools=“wheel_zoom,pan,reset,resize,save”,
color=[‘#87CEFA’,‘#9BC4E2’,‘#6CA6CD’,‘#325C74’,‘#EE7942’,‘#CD6839’,‘#8A360F’,‘#5C4033’], webgl=True)output_file(“/home/pragyan/Documents/test2.html”)
show(p)The code runs well. No error. Runs as expected. But slow. Can anything be done?
On Tuesday, July 26, 2016 at 4:32:42 PM UTC+5:30, Bryan Van de ven wrote: