notebook.community

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Compute Project Stats

  • This notebook uses the GitHub GraphQL API to compute the number of open and closed bugs pertaining to Kubeflow GitHub Projects
    • Stats are broken down by labels
  • Results are plotted using plotly
    • Plots are currently published on plot.ly for sharing; they are publicly vieable by anyone

Setup GitHub

  • You will need a GitHub personal access token in order to use the GitHub API
  • See these instructions for creating a personal access token
    • You will need the scopes:
      • repo
      • read:org
  • Set the environment variable GITHUB_TOKEN to pass your token to the code

Setup Plot.ly Online

  • In order to use plot.ly to publish the plot you need to create a plot.ly account and get an API key
  • Follow plot.ly's getting started guide
  • Store your API key in ~/.plotly/.credentials




In [7]:



    


# Use plotly cufflinks to plot data frames
# https://plot.ly/ipython-notebooks/cufflinks/
# instructions for offline plotting
# https://plot.ly/python/getting-started/#initialization-for-offline-plotting
#
# Follow the instructions for online plotting:
# https://plot.ly/python/getting-started/
# You will need to setup an account
import plotly
import plotly.plotly as py
import plotly.graph_objs as go
import cufflinks as cf
#from importlib import reload
import itertools



    











In [8]:



    


import project_stats
#reload(project_stats)



    











In [9]:



    


c = project_stats.ProjectStats(project="0.6.0")
#c = project_stats.ProjectStats(project="0.7.0")
c.main()

Make plots showing different groups of labels

  • Columns are multi level indexes
  • See here for instructions on multilevel indexes
    • We specify a list of tuples where each tuple specifies the item to select at the corresponding level in the index




In [10]:



    


counts = ["open", "total"]
#labels = ["cuj/build-train-deploy", "cuj/multi-user", "area/katib"]
labels = ["priority/p0", "priority/p1", "priority/p2"]
columns = [(a,b) for (a,b) in itertools.product(counts, labels)]

import datetime
start=datetime.datetime(2019, 1, 1)

i = c.stats.index > start
#c.stats.iloc[i]
c.stats.loc[i, columns].iplot(kind='scatter', width=5, filename='project-stats', title='{0} Issue Count'.format(c.project))



    


















    
Out[10]:























In [11]:



    


c.stats.iloc[-1][columns]



    


















    
Out[11]:








       label      
open   priority/p0      7
       priority/p1     13
       priority/p2      7
total  priority/p0     46
       priority/p1    139
       priority/p2     38
Name: 2019-08-04 23:42:23, dtype: int64

















In [12]:



    


import datetime
start=datetime.datetime(2019, 1, 1)

i = c.stats.index > start
c.stats.iloc[i]



    


















    
Out[12]:











  
    

      
      open
      ...
      total
    

    

      label
      addition/feature
      area/0.3.0
      area/0.4.0
      area/0.5.0
      area/1.0.0
      area/bootstrap
      area/build-release
      area/centraldashboard
      area/deployment
      area/design
      ...
      lifecycle/stale
      nolabels
      platform/aws
      platform/gcp
      platform/minikube
      platform/other
      priority/p0
      priority/p1
      priority/p2
      release/v0.6
    

    

      time
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
      
    

  
  
    

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      2019-01-04 02:34:32
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      2019-02-27 17:17:57
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      2019-02-27 17:47:04
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      2019-02-27 21:06:34
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      2019-02-28 23:03:24
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      ...
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      2019-07-22 01:57:27
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      2019-07-26 19:20:07
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      2019-07-26 22:10:50
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      38
      3
    

    

      2019-07-27 03:10:37
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      38
      3
    

    

      2019-07-28 16:49:54
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      38
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      2019-07-28 22:59:43
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      2019-07-29 14:25:39
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      38
      3
    

    

      2019-07-31 00:33:27
      2
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      ...
      53
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      24
      1
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      139
      38
      3
    

    

      2019-07-31 21:24:15
      2
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      0
      ...
      53
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      24
      1
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      46
      139
      38
      3
    

    

      2019-08-01 18:30:22
      2
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      53
      2
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      24
      1
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      46
      139
      38
      3
    

    

      2019-08-03 15:19:37
      2
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      53
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      24
      1
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      139
      38
      3
    

    

      2019-08-03 19:29:51
      1
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      53
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      139
      38
      3
    

    

      2019-08-04 22:56:38
      0
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      ...
      53
      2
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      24
      1
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      139
      38
      3
    

    

      2019-08-04 23:42:23
      0
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      0
      0
      0
      1
      2
      0
      ...
      53
      2
      3
      24
      1
      1
      46
      139
      38
      3
    

  



421 rows × 106 columns

Content source: kubeflow/community

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