Notebooks Coverage

Report on last executions.

48% 2019-02-17

_images/nbcov-2019-02-17.png
index coverage exe time last execution name title success time nb cells nb runs nb valid
0 0% nan   centrale/azure_pig.ipynb HDInsight, PIG   nan 35 0  
1 100% 0.083 2019-02-17 centrale/centrale_201606.ipynb Big Data, Azure, Machine Learning, Python True 2.019 2 2 2
2 100% 0.075 2019-02-17 centrale/centrale_201606_sysrec.ipynb Recommandations sur le web True 1.034 1 1 1
3 100% 1.049 2019-02-17 ensae/2017_1a_ensae_nocture.ipynb ENSAE 1A : nocturne True 3.022 10 10 10
4 100% 0.317 2019-02-17 ensae/kaggle_review_2016.ipynb Revue de compétitions Kaggle (2016) True 2.267 2 2 2
5 100% 0.326 2019-02-17 ensae/kaggle_review_2017.ipynb Revue de compétitions Kaggle (2017) True 2.297 2 2 2
6 100% 5.809 2019-02-17 meshs/automation_finance_trading.ipynb Les algorithmes, outils de décision automatique. True 7.030 10 10 10
7 0% nan   msexp/onnx_deploy.ipynb Deploy machine learned models with ONNX   nan 169 0  
8 100% 7.318 2019-02-17 pydata/10_plotting_libraries.ipynb 10 plotting libraries True 9.083 16 16 16
9 100% 15.832 2019-02-10 pydata/big_datashader.ipynb datashader True 17.315 28 28 28
10 80% 1.070 2019-02-17 pydata/gui_geoplotlib.ipynb geoplotlib True 3.197 5 4 4
11 100% 1.063 2019-02-17 pydata/im_biopython.ipynb biopython True 2.039 6 6 6
12 100% 0.725 2019-02-17 pydata/im_cartopy.ipynb cartopy True 2.040 4 4 4
13 100% 2.983 2019-02-10 pydata/im_ete3.ipynb ete3 True 4.244 11 11 11
14 100% 2.443 2019-02-17 pydata/im_lifelines.ipynb lifelines True 4.027 7 7 7
15 90% 1.026 2019-02-17 pydata/im_matplotlib.ipynb matplotlib True 2.041 11 10 10
16 100% 4.483 2019-02-17 pydata/im_missingno.ipynb missingno True 6.048 13 13 13
17 100% 11.650 2019-02-17 pydata/im_mpl_scatter_density.ipynb mpl-scatter-density True 13.062 6 6 6
18 100% 1.070 2019-02-17 pydata/im_networkx.ipynb networkx True 2.024 4 4 4
19 100% 5.576 2019-02-17 pydata/im_plotnine.ipynb plotnine True 7.050 9 9 9
20 100% 0.746 2019-02-17 pydata/im_reportlab.ipynb reportlab True 2.337 4 4 4
21 100% 1.553 2019-02-17 pydata/im_scikit_plot.ipynb scikit-plot True 3.043 5 5 5
22 100% 3.101 2019-02-17 pydata/im_seaborn.ipynb seaborn True 4.028 5 5 5
23 100% 1.424 2019-02-17 pydata/js_bokeh.ipynb bokeh True 3.030 7 7 7
24 50% 0.115 2019-02-17 pydata/js_lightning_python.ipynb lightning-python True 2.029 4 2 2
25 0% nan   pydata/js_mpld3.ipynb mpld3   nan 17 0  
26 100% 9.043 2019-02-17 pydata/js_plotly.ipynb plotly True 10.063 13 13 13
27 100% 3.644 2019-02-17 pydata/js_pydy_mass_spring_damper.ipynb pydy True 5.029 32 32 32
28 80% 2.348 2019-02-17 pydata/js_pyecharts.ipynb pyecharts True 4.062 5 4 4
29 100% 1.091 2019-02-17 pydata/js_pygal.ipynb pygal True 2.049 7 7 7
30 100% 2.640 2019-02-17 pydata/js_pythreejs.ipynb pythreejs True 4.055 5 5 5
31 100% 0.691 2019-02-17 pydata/js_vega.ipynb vega True 2.023 6 6 6
32 100% 0.571 2019-02-17 pydata/jsonly_treant.ipynb treant-js True 2.537 7 7 7
33 100% 2.764 2019-02-17 pydata/pyjs_bqplot.ipynb bqplot True 5.064 19 19 19
34 100% 1.538 2019-02-17 pydata/pyjs_brython.ipynb brython, brythonmagic True 3.022 18 18 18
35 100% 0.800 2019-02-17 pydata/pyjsc_vispy.ipynb vispy True 2.277 8 8 8
36 0% nan   pyparis/onnx_deploy_pyparis.ipynb Deploy machine learned models with ONNX   nan 80 0  
_images/nbcov.png