What this PR does / why we need it: Hey guys! I spent some time working on an "interactive" version of the Chartify tutorial notebook. We've created RMOTR Notebooks for our students, to help them get started.

With this version, anybody can just start working on the tutorial immediately in just a couple of seconds.

Special notes for your reviewer: I'm also working on the Chartify examples notebook, I'll update it as soon as it's done.

Any feedback is greatly appreciated!

Release note:

NONE

What this PR does / why we need it:

Pandas 1.0 has been out for a few months, and seems stable enough that we should start supporting it!

In addition, I noticed some inconsistencies between versions used in the package requirements itself, and in the requirements specified for local development. These have now been rectified (and future-proofed) by including the package requirements.txt from the dev one instead of specifying everything twice.

Special notes for your reviewer:

On my local setup (Windows with WSL) there are some problems with chromedriver that I haven't been able to figure out. Because of this, saving images as png doesn't work, and thus that test has been skipped when I've tested locally. I doubt that it would make a difference for this change, but might be worth double-checking in the PR builds.

Release note:

Bump Pandas requirement to 1.0

Is this a BUG REPORT or FEATURE REQUEST?:

Uncomment only one, leave it on its own line:

type: bug

type: feature

Environment:

• Chartify version(s): beta (pip install)
• Operating System(s): Linux 4.19.3-300.fc29.x86_64 x86_64
• Python version(s): 3.7

What happened: I'm getting an extra two orders of magnitude (or more if I set a floating point on the tick value) for values with floating points in stacked bar charts.

What you expected to happen: When setting the y axis tick values to '0%", I should get percentage values ranging from 0-100%.

How to reproduce it (as minimally and precisely as possible): Plot all chromosome data using stacked bar plot

RdGy = chartify.color_palettes['RdGy'] shifted_RdGy = RdGy.shift_palette('black', percent=20) shifted_RdGy.show()

(chartify.Chart(blank_labels=True, x_axis_type='categorical') .style.set_color_palette('diverging', palette=shifted_RdGy) .plot.bar_stacked( data_frame=results, categorical_columns='#ID', categorical_order_by='labels', categorical_order_ascending=True, numeric_column='Covered_percent', stack_column='sample', normalize=False) .set_legend_location('outside_right', orientation='vertical') .axes.set_yaxis_tick_format('0.0%') .axes.set_xaxis_tick_orientation('vertical') .show('png'))

Anything else we need to know?: Included the df and ipynb if you want to replicate. Allen_concatData.txt pileup.zip

Bokeh 1.2.0:

/usr/local/lib/python3.6/dist-packages/confidence/analysis/base.py in _summary_plot(self, level_name, level_df, remaining_groups, groupby)
    233 
    234             ch = self._ordinal_summary_plot(level_name, level_df,
--> 235                                             remaining_groups, groupby)
    236         else:
    237             ch = self._categorical_summary_plot(level_name, level_df,
/usr/local/lib/python3.6/dist-packages/confidence/analysis/base.py in _ordinal_summary_plot(self, level_name, level_df, remaining_groups, groupby)
    249         return self._ordinal_plot('point_estimate', df, groupby, level_name,
    250                                   remaining_groups, absolute=True,
--> 251                                   title=title, y_axis_label=y_axis_label)
    252 
    253     def _ordinal_plot(self, center_name, df, groupby, level_name,
/usr/local/lib/python3.6/dist-packages/confidence/analysis/base.py in _ordinal_plot(self, center_name, df, groupby, level_name, remaining_groups, absolute, title, y_axis_label)
    278         ch.set_title(title)
    279         if colors:
--> 280             ch.set_legend_location('outside_bottom')
    281         return ch
    282 
/usr/local/lib/python3.6/dist-packages/chartify/_core/chart.py in set_legend_location(self, location, orientation)
    332                 self._subtitle_glyph.text)
    333         elif location == 'outside_bottom':
--> 334             add_outside_legend('bottom_center', 'below')
    335         elif location == 'outside_right':
    336             add_outside_legend('top_left', 'right')
/usr/local/lib/python3.6/dist-packages/chartify/_core/chart.py in add_outside_legend(legend_location, layout_location)
    320                 return self
    321             new_legend = self.figure.legend[0]
--> 322             new_legend.plot = None
    323             new_legend.orientation = orientation
    324             self.figure.add_layout(new_legend, layout_location)
/usr/local/lib/python3.6/dist-packages/bokeh/core/has_props.py in __setattr__(self, name, value)
    286 
    287             raise AttributeError("unexpected attribute '%s' to %s, %s attributes are %s" %
--> 288                 (name, self.__class__.__name__, text, nice_join(matches)))
    289 
    290     def __str__(self):
AttributeError: unexpected attribute 'plot' to Legend, possible ```

Hi, I updated to the latest version of bokeh yesterday and get now this warning: `BokehDeprecationWarning: 'legend' keyword is deprecated, use explicit 'legend_label', 'legend_field', or 'legend_group' keywords instead

followed by: ValueError: Bad 'legend' parameter value: None

The issue seems to be related to the following section of the plot.py code 1397 if color_column: 1398 legend = bokeh.core.properties.field('color_column') 1399 else: 1400 legend = None

Please could you tell me how to fix this. Thank you

What this PR does / why we need it:

Which issue(s) this PR fixes

Fixes #

Special notes for your reviewer:

Release note:

Chartify doesn't work with bokeh 2.3.0, so require <2.3.0 for now, until a fix is in place.

/Pelle

What this PR does / why we need it:

Which issue(s) this PR fixes

Fixes #

Special notes for your reviewer:

Release note:

Just a heads-up that Bokeh 2.0 will be coming out in the next week or so. At a minimum you may want to release packages that versions to <2.0 if they are not already. Whenever you look to update packages for Bokeh 2.0 compat we'd be happy to help over at the Development topic on the Discourse:

https://discourse.bokeh.org/c/development/7

type: bug

Environment:

• Chartify version(s): 2.3.5
• Operating System(s): MacOS 10.14.2
• Python version(s): 3.6.5

What happened: Calling line method with all 0 y_column raises OverflowError

What you expected to happen: Return an instance of chart

How to reproduce it (as minimally and precisely as possible):

import chartify
import pandas as pd

df = pd.DataFrame(data={'col1': [1, 2, 3], 'col2': [0, 0, 0]})

ch = chartify.Chart()
ch.plot.line(data_frame=df,
             x_column='col1',
             y_column='col2')

Is this a BUG REPORT or FEATURE REQUEST?:

type: feature

Maybe this is already possible with Chartify and I missed it, but it would be great if Chartify provided an easy way to overlay/add horizontal and vertical lines (of different colors, different dash patterns) to a graph! e.g. to represent levels, or dates of specific events, on the graph.

Is this a BUG REPORT or FEATURE REQUEST?:

type: feature

Anything else we need to know?:

A tutorial using mybinder.org (like bokeh's tutorial) makes it much more convenient to test the library before installing it.

What this PR does / why we need it: The new changes made on this branch are related to image enhancement, the image quality of the chartify is low when we copy the png to a different platform, as I started to work on this issue. This pull request is related to #37, and #86. I found out that bokeh has the same problem and they have the same alternative as we do, i.e., resizing. But they couldn't work on the dpi(dots per inch) functionality because of the lack of human resources and the technical difficulty they have to change something this huge. I didn't try to do something which bokeh ignored, but I came up with my alternative with the help of the PIL library. I used PIL to add some features to this file. Like colour enhancement, brightness, contrast, etc. The changes could be boiled down to type and filter methods. The type is enhancement classes implementing a common interface, containing a single method. As with filter, it is a pre-defined set of filters, which can be used with the Image.filter() method. I had to add --virtual-time-budget(set the system to wait the specified number of virtual milliseconds before deeming the page to be ready.) This is related to #27.

Which issue(s) this PR fixes #37, #86, #27.

What this PR does / why we need it: We need a security policies measurement for the chartify package. I tried to add some which resonate with the values that I have seen as a contributor to it. I tried my best to align the security measures with chartify vulnerabilities that I could identify.

Which issue(s) this PR fixes#132

Fixes # Security policies

Special notes for your reviewer: I guess there should be more points that certainly need to be on the document. If anyone has any points, create a patch or comment on the pull request.

Reopens spotify/chartify#133

After merging this into a release branch I realised it doesn't work and isn't trivial to fix. See this repo for inspiration on how to fix it: https://github.com/fmenabe/python-yamlordereddictloader

FEATURE REQUEST:

type: feature

It would be great if you could add the cumulative option type to the histogram plot as there is in:

https://plotly.com/python/histograms/#cumulative-histogram

Hey there!

I'd like to report a security issue but cannot find contact instructions on your repository.

If not a hassle, might you kindly add a SECURITY.md file with an email, or another contact method? GitHub recommends this best practice to ensure security issues are responsibly disclosed, and it would serve as a simple instruction for security researchers in the future.

Thank you for your consideration, and I look forward to hearing from you!

(cc @huntr-helper)

First:

Then note the empty output:

bug

Environment:

• Chartify version(s): latest
• Operating System(s): macos
• Python version(s): 3.8

What happened: trying first example, in colab.research.google.com

What you expected to happen: something to show

How to reproduce it (as minimally and precisely as possible):

Anything else we need to know?: