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‎CHANGELOG.md

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All notable changes to this project will be documented in this file.
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This project adheres to [Semantic Versioning](http://semver.org/).
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## [Unreleased] - TBA
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## [2.5.2] - UNRELEASED
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### Updated
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- error message for `plotly.figure_factory.create_choropleth` is more helpful for Windows users on installing `geopandas` and dependencies including `shapely`.
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## [2.5.1] - 2018-03-26
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### Fixed
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- `plotly.figure_factory.create_choropleth` now works in Windows without raising an OSError. The module now uses cross-platform path tools from `os` to manipulate and manage the shapefiles contained in this package.
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## [2.5.0] - 2018-03-12
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### Fixed
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- `import plotly.figure_factory` does not fail if `pandas` is not installed. See https://github.com/plotly/plotly.py/pull/958
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### Added
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- New parameter `fill_percent` to the `.insert` method for the dashboards API. You can now insert a box into the dashboard layout and specify what proportion of the original container box it will occupy. Run `help(plotly.dashboard_objs.Dashboard.insert)` for more information on `fill_percent`.
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### Updated
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- Updated `plotly.min.js` to version 1.35.2.
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- New features include adding an `automargin` attribute to cartesian axes and a layout `grids` attribute for easy subplot generation.
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- See [the plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#1352----2018-03-09) for additional information regarding the updates.
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- `plotly.figure_factory.create_choropleth` has changed some of the default plotting options:
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- 'offline_mode' param has been removed from call signature.
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- Persistent selection api for the centroid points is automatically enabled. See https://plot.ly/python/reference/#scatter-selected and https://plot.ly/python/reference/#scatter-unselected for details
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- FIPS values that appear on hover are 0-padded to ensure they are 5 digits.
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- `hover_info='none'` is now default for the county lines data.
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## [2.4.1] - 2018-02-21
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### Fixed
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- The required shapefiles to generate the choropleths via `plotly.figure_factory.create_choropleth` are now shipped in the package data.
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## [2.4.0] - 2018-02-16
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### Added
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- County Choropleth figure factory. Call `help(plotly.figure_factory.create_choropleth)` for examples and how to get started making choropleths of US counties with the Python API.
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Note: Calling `plotly.figure_factory.create_choropleth` will fail with an IOError due to missing shapefiles see: https://github.com/plotly/plotly.py/blob/master/CHANGELOG.md#241---2018-02-21
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## [2.3.0] - 2018-01-25
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### Fixed
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- Merged [pull request](https://github.com/plotly/plotly.py/commit/a226e07393c158e01c34c050aaf492da9d77679a) that fixes `GraphWidget` for IPython > v6
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### Updated
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- Updated `plotly.min.js` to version 1.33.1.
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- New plot types include a `violin` trace type.
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- New features include completely rewritten `scattergl` using `regl` and a completely rewritten polar chart renderer.
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- See [the plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#1331----2018-01-24) for additional information regarding the updates.
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## [2.2.3] - 2017-12-04
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### Added
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-`column_width` and `row_width` parameters for `plotly.tools.make_subplots`. Call `help(plotly.tools.make_subplots)` for documentation.
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### Updated
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- Updated `plotly.min.js` to version 1.31.2.
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- Fixes include adjustments to `table` trace for offline plotting.
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- See [the plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#1312----2017-10-23) for additional information regarding the updates.
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## [2.2.2] - 2017-11-23
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### Added
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- bullet chart figure factory. Call `help(plotly.figure_factory.create_bullet)` for examples and how to get started making bullet charts with the API.
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- Bullet chart figure factory. Call `help(plotly.figure_factory.create_bullet)` for examples and how to get started making bullet charts with the API.
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## [2.2.1] - 2017-10-26
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### Fixed
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- presentation objects now added to setup.py
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- Presentation objects now added to setup.py
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## [2.2.0] - 2017-10-26
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### Added

‎README.md

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# plotly.py
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> 📢 Announcement!
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> Seats are still available for a 2 day, Dash master class in NYC, November 18-19.
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> [Registration here](https://plotcon.plot.ly/workshops) 🎚 📈 🗽
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> 📢 Announcement!
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> Registration is open for a 2 day, Dash master class in Boston, April 14-15.
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> [Register online here](https://plotcon.plot.ly/tickets/) 🎚📈⚾️
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***
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‎circle.yml

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node:
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# use a pre-installed version of node so we don't need to download it.
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version: 4.2.2
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version: 6.0.0
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dependencies:
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‎contributing.md

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**DO THIS (in the directory where you want the repo to live)**
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```bash
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git clone https://github.com/plotly/python-api.git
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git clone https://github.com/your_github_username/plotly.py.git
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```
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### Submodules
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Second, this project uses git submodules! They're both helpful and, at times, difficult to work with. The good news is you probably don't need to think about them! Just run the following shell command to make sure that your local repo is wired properly:
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**DO THIS (run this command in your new `plotly-api` directory)**
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**DO THIS (run this command in your new `plotly.py` directory)**
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```bash
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make setup_subs

‎optional-requirements.txt

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## jupyter ##
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jupyter
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ipykernel
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## deps for _county_choropleth.py figure factory
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pyshp
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geopandas
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shapely

‎plotly/colors.py

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floats between 0 and 1 inclusive.
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Types of colormap:
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Colormaps and Colorscales:
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A colormap or a colorscale is a correspondence between values - Pythonic
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objects such as strings and floats - to colors.
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There are typically two main types of colormaps that exist: numerical and
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categorical colormaps.
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Numerical colormaps are used when a the coloring column being used takes a
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spectrum of values or numbers. Alternatively, a categorical colormap is used
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to assign a specific value in a color column to a specific color everytime it
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appears in the plot at hand. For instance, a column of strings in a dataframe
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would naturally use a categorical colormap. You can choose however to use a
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categorical colormap with a column of numbers. Be careful though, as if you
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have a large set of unique numbers in your column you'll get a lot of colors.
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Numerical:
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----------
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Numerical colormaps are used when the coloring column being used takes a
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spectrum of values or numbers.
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A classic example from the Plotly library:
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```
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rainbow_colorscale = [
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[0, 'rgb(150,0,90)'], [0.125, 'rgb(0,0,200)'],
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[0.25, 'rgb(0,25,255)'], [0.375, 'rgb(0,152,255)'],
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[0.5, 'rgb(44,255,150)'], [0.625, 'rgb(151,255,0)'],
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[0.75, 'rgb(255,234,0)'], [0.875, 'rgb(255,111,0)'],
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[1, 'rgb(255,0,0)']
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]
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```
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Notice that this colorscale is a list of lists with each inner list containing
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a number and a color. These left hand numbers in the nested lists go from 0 to
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1, and they are like pointers tell you when a number is mapped to a specific
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color.
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If you have a column of numbers `col_num` that you want to plot, and you know
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```
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min(col_num) = 0
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max(col_num) = 100
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```
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then if you pull out the number `12.5` in the list and want to figure out what
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color the corresponding chart element (bar, scatter plot, etc) is going to be,
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you'll figure out that proportionally 12.5 to 100 is the same as 0.125 to 1.
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So, the point will be mapped to 'rgb(0,0,200)'.
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All other colors between the pinned values in a colorscale are linearly
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interpolated.
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Categorical:
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------------
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Alternatively, a categorical colormap is used to assign a specific value in a
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color column to a specific color everytime it appears in the dataset.
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A column of strings in a panadas.dataframe that is chosen to serve as the
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color index would naturally use a categorical colormap. However, you can
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choose to use a categorical colormap with a column of numbers.
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Be careful! If you have a lot of unique numbers in your color column you will
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end up with a colormap that is massive and may slow down graphing performance.
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"""
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from __future__ import absolute_import
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