diff --git a/CHANGELOG.md b/CHANGELOG.md index 923896f4030..10c58b1e4bd 100644 --- a/CHANGELOG.md +++ b/CHANGELOG.md @@ -135,7 +135,7 @@ This project adheres to [Semantic Versioning](http://semver.org/). faster image rendering and smaller figure size. Additional optional arguments `binary_backend`, `binary_format` and `binary_compression_level` control how to generate the b64 string ([#2691](https://github.com/plotly/plotly.py/pull/2691) -- `px.imshow` has a new `constrast_rescaling` argument in order to choose how +- `px.imshow` has a new `contrast_rescaling` argument in order to choose how to set data values corresponding to the bounds of the color range ([#2691](https://github.com/plotly/plotly.py/pull/2691) @@ -239,7 +239,7 @@ This project adheres to [Semantic Versioning](http://semver.org/). ### Added -- The `hover_data` parameter of `px` functions can now be a dictionary. This makes it possible to skip hover information for some arguments or to change the formatting of hover informatiom [#2377](https://github.com/plotly/plotly.py/pull/2377). +- The `hover_data` parameter of `px` functions can now be a dictionary. This makes it possible to skip hover information for some arguments or to change the formatting of hover information [#2377](https://github.com/plotly/plotly.py/pull/2377). - It's now possible to build a development version of Plotly.py against the build artifacts from a non-`master` branch of Plotly.js, which makes for faster QA and development cycles [#2349](https://github.com/plotly/plotly.py/pull/2349). Thanks [@zouhairm](https://github.com/zouhairm) for this Pull Request! ### Fixed @@ -252,7 +252,7 @@ This version includes several performance improvements ([#2368](https://github.c - Child graph objects (e.g. `figure.layout.xaxis`) are no longer created eagerly during graph object construction. Instead, they are created lazily the first time the property is accessed. - Property validation is now disabled for select internal operations. - - When used with Python 3.7 and above, ploty.py now takes advantage of [PEP-562](https://www.python.org/dev/peps/pep-0562/) to perform submodule imports lazily. This dramatically improves import times. + - When used with Python 3.7 and above, plotly.py now takes advantage of [PEP-562](https://www.python.org/dev/peps/pep-0562/) to perform submodule imports lazily. This dramatically improves import times. ## [4.6.0] - 2020-03-31 @@ -290,7 +290,7 @@ This version includes several performance improvements ([#2368](https://github.c - Jupyterlab extension now compatible with both Jupyterlab 1.2 and 2.0 [#2261](https://github.com/plotly/plotly.py/pull/2261) with thanks to [@consideRatio](https://github.com/consideRatio) for the contribution! - Fixed a bug when using boolean values for the color argument of px functions [#2127](https://github.com/plotly/plotly.py/pull/2127) -- Corrected import bug which was occuring with old versions of ipywidgets [#2265](https://github.com/plotly/plotly.py/pull/2265) +- Corrected import bug which was occurring with old versions of ipywidgets [#2265](https://github.com/plotly/plotly.py/pull/2265) - Fixed python 3.8 syntax warning [#2262](https://github.com/plotly/plotly.py/pull/2262), with thanks to [@sgn](https://github.com/sgn) for the contribution! ## [4.5.3] - 2020-03-05 @@ -365,7 +365,7 @@ This version includes several performance improvements ([#2368](https://github.c for more information - The tutorials of the [plotly.py documentation](https://plot.ly/python/) are now in the main [plotly.py Github repository](https://github.com/plotly/plotly.py). Contributions in order to improve or extend the documentation are very welcome! - - `plotly.express` generated plots no longer have a default height of 600 pixels, instead they inherit the default height of regular figures [#1990](https://github.com/plotly/plotly.py/pull/1990). To restore the old behavior, set `px.defaults.height=600` once per session, or set the `height` keyword arguement to any `px.function()` to 600. + - `plotly.express` generated plots no longer have a default height of 600 pixels, instead they inherit the default height of regular figures [#1990](https://github.com/plotly/plotly.py/pull/1990). To restore the old behavior, set `px.defaults.height=600` once per session, or set the `height` keyword argument to any `px.function()` to 600. ### Fixed @@ -439,7 +439,7 @@ section [#1969](https://github.com/plotly/plotly.py/pull/1969). - The width of a figure produced by the `create_gantt` figure factory now resizes responsively ([#1724](https://github.com/plotly/plotly.py/pull/1724)) ### Fixed - - The name of the steps property of `graph_objects.indicator.Guage` has been renamed from `stepss` to `steps` + - The name of the steps property of `graph_objects.indicator.Gauge` has been renamed from `stepss` to `steps` - Avoid crash in iframe renderers when running outside iPython ([#1723](https://github.com/plotly/plotly.py/pull/1723)) ## [4.1.0] - 2019-08-06 @@ -491,7 +491,7 @@ This is a major release that includes many new features, and a few breaking chan - Added support for all trace types in `make_subplots` ([#1528](https://github.com/plotly/plotly.py/pull/1528)) - Added support for secondary y-axes in `make_subplots` ([#1564](https://github.com/plotly/plotly.py/pull/1564)) - Support passing a scalar trace object (rather than a list or tuple of trace objects) as the `data` property to the `Figure` constructor ([#1614](https://github.com/plotly/plotly.py/pull/1614)) - - Added dictionary-stule `.pop` method to graph object classes ([#1614](https://github.com/plotly/plotly.py/pull/1614)) + - Added dictionary-style `.pop` method to graph object classes ([#1614](https://github.com/plotly/plotly.py/pull/1614)) - New `jupyterlab-plotly` JupyterLab extension for rendering figures in JupyterLab. Replaces the `@jupyterlab/plotly-extension` extension, and includes JupyterLab 1.0 support. - Added new suite of built-in colorscales to the `plotly.colors` module, and support for specifying this wide range of colorscales by name. Also added support for specifying colorscales as a list of colors, in which case the color spacing is assumed to be uniform ([#1647](https://github.com/plotly/plotly.py/pull/1647)). - Added `sphinx-gallery` renderer for embedding plotly figures in [Sphinx-Gallery](https://sphinx-gallery.github.io/) ([#1577](https://github.com/plotly/plotly.py/pull/1577), [plotly/plotly-sphinx-gallery](https://github.com/plotly/plotly-sphinx-gallery)). @@ -1096,7 +1096,7 @@ must be installed: properties are ignored rather than causing an exception. - A `to_ordered_dict` method has been added to the `Figure` and `FigureWidget` classes. This method returns a representation of the figure as a nested - structure of `OrdererdDict` and `list` instances where the keys in each + structure of `OrderedDict` and `list` instances where the keys in each `OrderedDict` are sorted alphabetically. This method replaces the `get_ordered` method that was available in version 2, and makes it possible to traverse the nested structure of a figure in a deterministic order. @@ -1517,7 +1517,7 @@ gone. ## [1.12.10] - 2016-11-28 ### Updated - `FF.create_violin` and `FF.create_scatterplotmatrix` now by default do not print subplot grid information in output -- Removed alert that occured when downloading plot images offline. Please note: for higher resolution images and more export options, consider making requests to our image servers. See: `help(py.image)` for more details. +- Removed alert that occurred when downloading plot images offline. Please note: for higher resolution images and more export options, consider making requests to our image servers. See: `help(py.image)` for more details. ### Added - Plot configuration options for offline plots. See the list of [configuration options](https://github.com/Rikorose/plotly.py/blob/master/plotly/offline/offline.py#L189) and [examples](https://plot.ly/javascript/configuration-options/) for more information. diff --git a/build_for_conda.md b/build_for_conda.md index 57a2480e602..8fb74e0d7cd 100644 --- a/build_for_conda.md +++ b/build_for_conda.md @@ -16,4 +16,4 @@ Finally, build and test the created version: `conda build plotly` -Currently, the updated (version 1.12.4) conda package sits at https://anaconda.org/chohner/plotly. There seems to be an old offial package at https://anaconda.org/plotly/plotly. +Currently, the updated (version 1.12.4) conda package sits at https://anaconda.org/chohner/plotly. There seems to be an old official package at https://anaconda.org/plotly/plotly. diff --git a/doc/python/3d-mesh.md b/doc/python/3d-mesh.md index 33199a230b9..cf6587a8487 100644 --- a/doc/python/3d-mesh.md +++ b/doc/python/3d-mesh.md @@ -83,7 +83,7 @@ IFrame(snippet_url + '3d-mesh', width='100%', height=630) ### Mesh Tetrahedron -In this example we use the `ì`, `j` and `k` parameters to specify manually the geometry of the triangles of the mesh. +In this example we use the `i`, `j` and `k` parameters to specify manually the geometry of the triangles of the mesh. ```python import plotly.graph_objects as go diff --git a/doc/python/linear-fits.md b/doc/python/linear-fits.md index ca80970cbf9..7f1f1a2971f 100644 --- a/doc/python/linear-fits.md +++ b/doc/python/linear-fits.md @@ -23,7 +23,7 @@ jupyter: version: 3.6.8 plotly: description: Add linear Ordinary Least Squares (OLS) regression trendlines or - non-linear Locally Weighted Scatterplot Smoothing (LOEWSS) trendlines to scatterplots + non-linear Locally Weighted Scatterplot Smoothing (LOWESS) trendlines to scatterplots in Python. display_as: statistical language: python @@ -76,4 +76,4 @@ import plotly.express as px df = px.data.gapminder().query("year == 2007") fig = px.scatter(df, x="gdpPercap", y="lifeExp", color="continent", trendline="lowess") fig.show() -``` \ No newline at end of file +``` diff --git a/doc/python/ml-regression.md b/doc/python/ml-regression.md index faee293eef4..4ac41288c35 100644 --- a/doc/python/ml-regression.md +++ b/doc/python/ml-regression.md @@ -174,7 +174,7 @@ fig.show() Notice how linear regression fits a straight line, but kNN can take non-linear shapes. Moreover, it is possible to extend linear regression to polynomial regression by using scikit-learn's `PolynomialFeatures`, which lets you fit a slope for your features raised to the power of `n`, where `n=1,2,3,4` in our example. -With Plotly, it's easy to diplay latex equations in legend and titles by simply adding `$` before and after your equation. This way, you can see the coefficients that our polynomial regression fitted. +With Plotly, it's easy to display latex equations in legend and titles by simply adding `$` before and after your equation. This way, you can see the coefficients that our polynomial regression fitted. ```python diff --git a/doc/python/sliders.md b/doc/python/sliders.md index 7dd85b65ad7..2ebe84f0403 100644 --- a/doc/python/sliders.md +++ b/doc/python/sliders.md @@ -90,7 +90,7 @@ The method determines which [plotly.js function](https://plot.ly/javascript/plot ### Sliders in Plotly Express -Plotly Express provide sliders, but with implicit animation using the `"animate"` method described above. The animation play button can be omited by removing `updatemenus` in the `layout`: +Plotly Express provide sliders, but with implicit animation using the `"animate"` method described above. The animation play button can be omitted by removing `updatemenus` in the `layout`: ```python import plotly.express as px diff --git a/doc/python/ternary-plots.md b/doc/python/ternary-plots.md index 7e07f558694..a389faba417 100644 --- a/doc/python/ternary-plots.md +++ b/doc/python/ternary-plots.md @@ -41,7 +41,7 @@ A ternary plot depicts the ratios of three variables as positions in an equilate [Plotly Express](/python/plotly-express/) is the easy-to-use, high-level interface to Plotly, which [operates on a variety of types of data](/python/px-arguments/) and produces [easy-to-style figures](/python/styling-plotly-express/). -Here we use `px.scatter_ternary` to visualize thre three-way split between the three major candidates in a municipal election. +Here we use `px.scatter_ternary` to visualize the three-way split between the three major candidates in a municipal election. ```python import plotly.express as px diff --git a/doc/unconverted/python/3d-parametric-plots.md b/doc/unconverted/python/3d-parametric-plots.md index 2f7445391b9..bf09abb36d8 100644 --- a/doc/unconverted/python/3d-parametric-plots.md +++ b/doc/unconverted/python/3d-parametric-plots.md @@ -12,7 +12,7 @@ jupyter: language: python name: python2 plotly: - description: How to 3D Parameteric Plots in Python + description: How to 3D Parametric Plots in Python display_as: 3d_charts language: python layout: base @@ -80,7 +80,7 @@ py.iplot(fig, filename='Parametric_plot') ``` -#### Parameteric Plot with Colorscale +#### Parametric Plot with Colorscale ```python deletable=true editable=true diff --git a/doc/unconverted/python/amazon-redshift.md b/doc/unconverted/python/amazon-redshift.md index 70afe5d318f..2ced7b45bbf 100644 --- a/doc/unconverted/python/amazon-redshift.md +++ b/doc/unconverted/python/amazon-redshift.md @@ -68,7 +68,7 @@ port = 5439 dbname = 'dev' ``` -As I mentioned there are numerous ways to connect to a Redshift databause and I've included two below. We can use either the SQLAlchemy package or we can use the psycopg2 package for a more direct access. +As I mentioned there are numerous ways to connect to a Redshift database and I've included two below. We can use either the SQLAlchemy package or we can use the psycopg2 package for a more direct access. Both will allow us to execute SQL queries and get results however the SQLAlchemy engine makes it a bit easier to directly return our data as a dataframe using pandas. Plotly has a tight integration with pandas as well, making it extremely easy to make interactive graphs to share with your company. diff --git a/doc/unconverted/python/anova.md b/doc/unconverted/python/anova.md index de13a5cdbfa..9405a66f8e2 100644 --- a/doc/unconverted/python/anova.md +++ b/doc/unconverted/python/anova.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/average_multiple_curves.md b/doc/unconverted/python/average_multiple_curves.md index 307052e997f..8f0c0023801 100644 --- a/doc/unconverted/python/average_multiple_curves.md +++ b/doc/unconverted/python/average_multiple_curves.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/baseline-subtraction.md b/doc/unconverted/python/baseline-subtraction.md index a0f2306a697..531c6f5b317 100644 --- a/doc/unconverted/python/baseline-subtraction.md +++ b/doc/unconverted/python/baseline-subtraction.md @@ -60,7 +60,7 @@ py.iplot(table, filename='milk-production-dataframe') ``` #### Plot with Baseline -To subtact a baseline estimate from our data, it is a good idea to first we must first calculate the baseline values then plot the data with the baseline drawn in. +To subtract a baseline estimate from our data, it is a good idea to first we must first calculate the baseline values then plot the data with the baseline drawn in. ```python baseline_values = peakutils.baseline(time_series) diff --git a/doc/unconverted/python/basic-statistics.md b/doc/unconverted/python/basic-statistics.md index 2e7efe1be3b..fc422efd72f 100644 --- a/doc/unconverted/python/basic-statistics.md +++ b/doc/unconverted/python/basic-statistics.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/chord-diagram.md b/doc/unconverted/python/chord-diagram.md index 5b7a5117769..57697a9fd90 100644 --- a/doc/unconverted/python/chord-diagram.md +++ b/doc/unconverted/python/chord-diagram.md @@ -45,10 +45,10 @@ To avoid cluttered edges we adopted the following procedure in choosing the inte - define a list, `Dist`, having as elements the distances between the following pairs of points: $$(P_1, P_1), \:(P_1, P_2), \: (P_1, P_3),\: (P_1, P_4),\: (P_1, P_5)$$ -- In order to assign the control poligon to the Bézier curve that will be the edge between two connected +- In order to assign the control polygon to the Bézier curve that will be the edge between two connected nodes, `V[i], V[j]`, we compute the distance between these nodes, and deduce the interval $k$, of two consecutive values in `Dist`, this distance belongs to. -- Since there are four such intervals indexed $k=0,1,2,3$, we define the control poligon as follows: $${\bf b}_0=V[i],\:\: {\bf b}_1=V[i]/param,\:\: {\bf b}_2=V[j]/param, \:\:{\bf b}_3=V[j],$$ where `param` is chosen from the list: `params=[1.2, 1.5, 1.8, 2.1]`. +- Since there are four such intervals indexed $k=0,1,2,3$, we define the control polygon as follows: $${\bf b}_0=V[i],\:\: {\bf b}_1=V[i]/param,\:\: {\bf b}_2=V[j]/param, \:\:{\bf b}_3=V[j],$$ where `param` is chosen from the list: `params=[1.2, 1.5, 1.8, 2.1]`. Namely, if the distance(`V[i], V[j]`), belongs to the $K^{th}$ interval associated to `Dist`, then we choose `param= params[K]`. diff --git a/doc/unconverted/python/density-plots.md b/doc/unconverted/python/density-plots.md index 4657d1fcdc1..5ad4494e0d7 100644 --- a/doc/unconverted/python/density-plots.md +++ b/doc/unconverted/python/density-plots.md @@ -26,7 +26,7 @@ jupyter: #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/discrete-frequency.md b/doc/unconverted/python/discrete-frequency.md index ca106eccd71..a561b6504ec 100644 --- a/doc/unconverted/python/discrete-frequency.md +++ b/doc/unconverted/python/discrete-frequency.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! @@ -115,7 +115,7 @@ py.iplot(fig, filename='histogram-percentage') #### Cumulative Density Function -We can also take the cumulatve sum of our dataset and then plot the cumulative density function, or `CDF`, as a scatter plot +We can also take the cumulative sum of our dataset and then plot the cumulative density function, or `CDF`, as a scatter plot ```python cumsum = np.cumsum(x) diff --git a/doc/unconverted/python/filled-chord-diagram.md b/doc/unconverted/python/filled-chord-diagram.md index a9989f093c6..4ba1e79dbc4 100644 --- a/doc/unconverted/python/filled-chord-diagram.md +++ b/doc/unconverted/python/filled-chord-diagram.md @@ -28,7 +28,7 @@ jupyter: Circular layout or [Chord diagram](https://en.wikipedia.org/wiki/Chord_diagram) is a method of visualizing data that describe relationships. It was intensively promoted through [Circos](http://circos.ca/), a software package in Perl that was initially designed for displaying genomic data. -M Bostock developed reusable charts for [chord diagrams](http://bl.ocks.org/mbostock/4062006) in d3.js. Two years ago on [stackoverflow](http://stackoverflow.com/questions/19105801/chord-diagram-in-python), the exsistence of a Python package for plotting chord diagrams was adressed, but the question was closed due to being *off topic*.
Here we show that a chord diagram can be generated in Python with Plotly. We illustrate the method of generating a chord diagram from data recorded in a square matrix. The rows and columns represent the same entities. +M Bostock developed reusable charts for [chord diagrams](http://bl.ocks.org/mbostock/4062006) in d3.js. Two years ago on [stackoverflow](http://stackoverflow.com/questions/19105801/chord-diagram-in-python), the existence of a Python package for plotting chord diagrams was addressed, but the question was closed due to being *off topic*.
Here we show that a chord diagram can be generated in Python with Plotly. We illustrate the method of generating a chord diagram from data recorded in a square matrix. The rows and columns represent the same entities. This example considers a community of 5 friends on Facebook. We record the number of comments posted by each member on the other friends' walls. The data table is given in the next cell: @@ -82,7 +82,7 @@ Let us denote by `total_comments` the total number of posts recorded in this com Theoretically the interval `[0, total_comments)` is mapped linearly onto the unit circle, identified with the interval $[0,2\pi)$. -For a better looking plot one proceeds as follows: starting from the angular position $0$, in counter-clockwise direction, one draws succesively, around the unit circle, two parallel arcs of length equal to a mapped row sum value, minus a fixed gap. Click the image below: +For a better looking plot one proceeds as follows: starting from the angular position $0$, in counter-clockwise direction, one draws successively, around the unit circle, two parallel arcs of length equal to a mapped row sum value, minus a fixed gap. Click the image below: @@ -291,7 +291,7 @@ def control_pts(angle, radius): ```python def ctrl_rib_chords(l, r, radius): - # this function returns a 2-list containing control poligons of the two quadratic Bezier + # this function returns a 2-list containing control polygons of the two quadratic Bezier #curves that are opposite sides in a ribbon #l (r) the list of angular variables of the ribbon arc ends defining #the ribbon starting (ending) arc diff --git a/doc/unconverted/python/frequency-counts.md b/doc/unconverted/python/frequency-counts.md index 9682577c93a..7ca53fed101 100644 --- a/doc/unconverted/python/frequency-counts.md +++ b/doc/unconverted/python/frequency-counts.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! @@ -46,7 +46,7 @@ import scipy #### Make the Data -We are generating a 1D dataset from a `Weibull Distribution` which has the distrubution +We are generating a 1D dataset from a `Weibull Distribution` which has the distribution $$ \begin{align*} diff --git a/doc/unconverted/python/gapminder-example.md b/doc/unconverted/python/gapminder-example.md index a0d063fb1e7..ef59e82ae4a 100644 --- a/doc/unconverted/python/gapminder-example.md +++ b/doc/unconverted/python/gapminder-example.md @@ -62,7 +62,7 @@ py.iplot(table, filename='animations-gapminder-data-preview') #### Make the Grid Since we are using the v2 api for animations in Plotly, we need to first make a `grid`. You can learn more in the [introduction to animation doc](https://plot.ly/python/animations/). -We will first define a list of _string_ years which will represent the values that our `slider` will take on. Going through the dataset, we will take out all the unique continents from the column `continent` and store them as well. Finally, we make a grid with each column representing a slice of the dataframe by _year_, _continent_ and _column name_, making sure to name each column uniquly by these variables: +We will first define a list of _string_ years which will represent the values that our `slider` will take on. Going through the dataset, we will take out all the unique continents from the column `continent` and store them as well. Finally, we make a grid with each column representing a slice of the dataframe by _year_, _continent_ and _column name_, making sure to name each column uniquely by these variables: ```python years_from_col = set(dataset['year']) @@ -259,7 +259,7 @@ Finally we make our `frames`. Here we are running again through the years and co ``` frame = {'data': [], 'name': value-name} ``` -We add a dictionary of data to this list and at the end of each loop, we ensure to add the `steps` dictionary to the steps list. At the end, we attatch the `sliders` dictionary to the figure via: +We add a dictionary of data to this list and at the end of each loop, we ensure to add the `steps` dictionary to the steps list. At the end, we attach the `sliders` dictionary to the figure via: ``` figure['layout']['sliders'] = [sliders_dict] diff --git a/doc/unconverted/python/interpolation-and-extrapolation-in-1d.md b/doc/unconverted/python/interpolation-and-extrapolation-in-1d.md index f8d43e26b2b..2ff9041c730 100644 --- a/doc/unconverted/python/interpolation-and-extrapolation-in-1d.md +++ b/doc/unconverted/python/interpolation-and-extrapolation-in-1d.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! @@ -99,7 +99,7 @@ py.iplot(fig, filename='interpolation-and-extrapolation') ``` #### Interpolation and Extrapolation of Y From X -Interpolation and Extrapolation of (x, y) points with pre-existant points and an array of specific x values. +Interpolation and Extrapolation of (x, y) points with pre-existent points and an array of specific x values. ```python points = np.array([(1, 1), (2, 4), (3, 1), (9, 3)]) diff --git a/doc/unconverted/python/interpolation-and-extrapolation-in-2d.md b/doc/unconverted/python/interpolation-and-extrapolation-in-2d.md index d5b4126e756..02d52296408 100644 --- a/doc/unconverted/python/interpolation-and-extrapolation-in-2d.md +++ b/doc/unconverted/python/interpolation-and-extrapolation-in-2d.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/linear-gauge-chart.md b/doc/unconverted/python/linear-gauge-chart.md index 982912eb332..70185c7e1a6 100644 --- a/doc/unconverted/python/linear-gauge-chart.md +++ b/doc/unconverted/python/linear-gauge-chart.md @@ -12,7 +12,7 @@ jupyter: language: python name: python2 plotly: - description: How to make interactive linear-guage charts in Python with Plotly. + description: How to make interactive linear-gauge charts in Python with Plotly. display_as: basic language: python layout: base @@ -30,7 +30,7 @@ Plotly's Python library is free and open source! [Get started](https://plot.ly/p #### Linear Gauge Chart Shell -Note the following tutorial shows how to create a linear-gauge chart with 4 gauges. It's recommended to use a `width` between 600-1000px and `ticklen` should be `width/20`. These variables are definied in the code below. +Note the following tutorial shows how to create a linear-gauge chart with 4 gauges. It's recommended to use a `width` between 600-1000px and `ticklen` should be `width/20`. These variables are defined in the code below. ```python from plotly import tools diff --git a/doc/unconverted/python/normality-test.md b/doc/unconverted/python/normality-test.md index 93adb29bbed..040fd3bad21 100644 --- a/doc/unconverted/python/normality-test.md +++ b/doc/unconverted/python/normality-test.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! @@ -361,7 +361,7 @@ We have covered a few normality tests, but this is not all of the tests that exi - Start looking into the use of nonparametric statistical methods instead of the parametric methods. - If some of the methods suggest that the sample is Gaussian and some not, then perhaps take this as an indication that your data is Gaussian-like. -_This tuorial is inspired from ["A Gentle Introduction to Normality Tests"](https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/)_ +_This tutorial is inspired from ["A Gentle Introduction to Normality Tests"](https://machinelearningmastery.com/a-gentle-introduction-to-normality-tests-in-python/)_ ```python diff --git a/doc/unconverted/python/normalization.md b/doc/unconverted/python/normalization.md index 51845b1ac64..c81f9fe9f91 100644 --- a/doc/unconverted/python/normalization.md +++ b/doc/unconverted/python/normalization.md @@ -26,7 +26,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/numerical-differentiation.md b/doc/unconverted/python/numerical-differentiation.md index 146aaae7b10..82f12e39e8f 100644 --- a/doc/unconverted/python/numerical-differentiation.md +++ b/doc/unconverted/python/numerical-differentiation.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/numerical-integration.md b/doc/unconverted/python/numerical-integration.md index 365a0a195ef..50179d0ebb3 100644 --- a/doc/unconverted/python/numerical-integration.md +++ b/doc/unconverted/python/numerical-integration.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/outlier-test.md b/doc/unconverted/python/outlier-test.md index 82f2c4e881b..41fc2aec76b 100644 --- a/doc/unconverted/python/outlier-test.md +++ b/doc/unconverted/python/outlier-test.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! @@ -146,7 +146,7 @@ data = [trace1, trace2] py.iplot(data, filename='q-test-scatter') ``` -Since our smallest value (the holoed out circle) is higher than the critical line, this validates the result of the test that the point is `NOT` an outlier. +Since our smallest value (the hollowed out circle) is higher than the critical line, this validates the result of the test that the point is `NOT` an outlier. ```python from IPython.display import display, HTML diff --git a/doc/unconverted/python/peak-integration.md b/doc/unconverted/python/peak-integration.md index 040fbdcd2af..4d6e66d77a6 100644 --- a/doc/unconverted/python/peak-integration.md +++ b/doc/unconverted/python/peak-integration.md @@ -12,7 +12,7 @@ jupyter: language: python name: python2 plotly: - description: Learn how to integrate the area between peaks and bassline in Python. + description: Learn how to integrate the area between peaks and baseline in Python. display_as: peak-analysis has_thumbnail: false language: python diff --git a/doc/unconverted/python/polygon-area.md b/doc/unconverted/python/polygon-area.md index 7aef8ce09d8..9180bf59ba3 100644 --- a/doc/unconverted/python/polygon-area.md +++ b/doc/unconverted/python/polygon-area.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/salesforce.md b/doc/unconverted/python/salesforce.md index 3a84a7b7888..974bf944f79 100644 --- a/doc/unconverted/python/salesforce.md +++ b/doc/unconverted/python/salesforce.md @@ -190,7 +190,7 @@ for name, temp_df in large_opps_df.groupby('Owner'): name=name, text=hover_text, marker=dict( - size=(temp_df.Probability / 2) # helps keep the bubbles of managable size + size=(temp_df.Probability / 2) # helps keep the bubbles of manageable size ) ) ) diff --git a/doc/unconverted/python/statistics-charts.md b/doc/unconverted/python/statistics-charts.md index 5ad1e8650d1..f4fffcede6c 100644 --- a/doc/unconverted/python/statistics-charts.md +++ b/doc/unconverted/python/statistics-charts.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! diff --git a/doc/unconverted/python/streaming-tutorial.md b/doc/unconverted/python/streaming-tutorial.md index 60e65595050..fd1f971d9bd 100644 --- a/doc/unconverted/python/streaming-tutorial.md +++ b/doc/unconverted/python/streaming-tutorial.md @@ -74,7 +74,7 @@ The `Stream Id Object` comes bundled in the `graph_objs` package. We can then ca help(go.Stream) ``` -As we can see, the `Stream Id Object` is a dictionary-like object that takes two parameters, and has all the methods that are assoicated with dictionaries. +As we can see, the `Stream Id Object` is a dictionary-like object that takes two parameters, and has all the methods that are associated with dictionaries. We will need one of these objects for each of trace that we wish to stream data to. We'll now create a single stream token for our streaming example, which will include one scatter trace. @@ -89,7 +89,7 @@ stream_1 = go.Stream( ) ``` -The `'maxpoints'` key sets the maxiumum number of points to keep on the plotting surface at any given time. +The `'maxpoints'` key sets the maximum number of points to keep on the plotting surface at any given time. More over, if you want to avoid the use of these `Stream Id Objects`, you can just create a dictionary with at least the token parameter defined, for example: ```python diff --git a/doc/unconverted/python/t-test.md b/doc/unconverted/python/t-test.md index c7984104ab3..6c74b55ab92 100644 --- a/doc/unconverted/python/t-test.md +++ b/doc/unconverted/python/t-test.md @@ -25,7 +25,7 @@ jupyter: --- #### New to Plotly? -Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by dowloading the client and [reading the primer](https://plot.ly/python/getting-started/). +Plotly's Python library is free and open source! [Get started](https://plot.ly/python/getting-started/) by downloading the client and [reading the primer](https://plot.ly/python/getting-started/).
You can set up Plotly to work in [online](https://plot.ly/python/getting-started/#initialization-for-online-plotting) or [offline](https://plot.ly/python/getting-started/#initialization-for-offline-plotting) mode, or in [jupyter notebooks](https://plot.ly/python/getting-started/#start-plotting-online).
We also have a quick-reference [cheatsheet](https://images.plot.ly/plotly-documentation/images/python_cheat_sheet.pdf) (new!) to help you get started! @@ -46,7 +46,7 @@ import scipy #### Generate Data -Let us generate some random data from the `Normal Distriubtion`. We will sample 50 points from a normal distribution with mean $\mu = 0$ and variance $\sigma^2 = 1$ and from another with mean $\mu = 2$ and variance $\sigma^2 = 1$. +Let us generate some random data from the `Normal Distribution`. We will sample 50 points from a normal distribution with mean $\mu = 0$ and variance $\sigma^2 = 1$ and from another with mean $\mu = 2$ and variance $\sigma^2 = 1$. ```python data1 = np.random.normal(0, 1, size=50) @@ -82,7 +82,7 @@ py.iplot(data, filename='normal-dists-plot') #### One Sample T Test -A `One Sample T-Test` is a statistical test used to evaluate the null hypothesis that the mean $m$ of a 1D sample dataset of independant observations is equal to the true mean $\mu$ of the population from which the data is sampled. In other words, our null hypothesis is that +A `One Sample T-Test` is a statistical test used to evaluate the null hypothesis that the mean $m$ of a 1D sample dataset of independent observations is equal to the true mean $\mu$ of the population from which the data is sampled. In other words, our null hypothesis is that $$ \begin{align*} diff --git a/doc/unconverted/python/tesla-supercharging-stations.md b/doc/unconverted/python/tesla-supercharging-stations.md index d9e0d9d3efd..28db02a40fd 100644 --- a/doc/unconverted/python/tesla-supercharging-stations.md +++ b/doc/unconverted/python/tesla-supercharging-stations.md @@ -12,7 +12,7 @@ jupyter: language: python name: python2 plotly: - description: How to plot car-travel routes between USA and Canada Telsa Supercharging + description: How to plot car-travel routes between USA and Canada Tesla Supercharging Stations in Python. display_as: maps language: python @@ -125,7 +125,7 @@ py.iplot(table, filename='supercharger-locations-sample') #### Plot the Route The server_key should be replaced with your own Google Maps Directions API key. -Be careful! Make sure you are picking a start and end point that can be driven between, eg. both in the United States of America. Otherwise, the Google Maps API cannot comupute directions and will return an empty list. +Be careful! Make sure you are picking a start and end point that can be driven between, eg. both in the United States of America. Otherwise, the Google Maps API cannot compute directions and will return an empty list. ```python def plot_route_between_tesla_stations(address_start, address_end, zoom=3, endpt_size=6): diff --git a/packages/python/chart-studio/specs/gridspec.md b/packages/python/chart-studio/specs/gridspec.md index 43179d705a4..3386a0714cd 100644 --- a/packages/python/chart-studio/specs/gridspec.md +++ b/packages/python/chart-studio/specs/gridspec.md @@ -192,7 +192,7 @@ Type checking boiler plate A `PlotlyRequestError` that prints a useful error message from the server: 1. Print `response.detail` if provided (Plotly error message) 2. Otherwise, print `response.body` if the response is plain-text -3. Otherwise, print the original `requests.expceptions.HTTPError` error message. +3. Otherwise, print the original `requests.exceptions.HTTPError` error message. Also, store the status code. diff --git a/packages/python/plotly/plotly/figure_factory/README.md b/packages/python/plotly/plotly/figure_factory/README.md index 53e01b5c79e..45b264ddccd 100644 --- a/packages/python/plotly/plotly/figure_factory/README.md +++ b/packages/python/plotly/plotly/figure_factory/README.md @@ -4,7 +4,7 @@ In the Python Plotly Library: We have basic plot types that are created using the `plotly.graph_objs` module. -These plot types include Scatter, Box and Bar types. For a complete list see the [graph_objs file](https://github.com/plotly/plotly.py/blob/master/plotly/graph_objs/graph_objs.py). They are the basis of the plots and charts instatiated by Plotly. +These plot types include Scatter, Box and Bar types. For a complete list see the [graph_objs file](https://github.com/plotly/plotly.py/blob/master/plotly/graph_objs/graph_objs.py). They are the basis of the plots and charts instantiated by Plotly. To create a basic chart like this, first we create the `data` using the tools in `plotly.graph_objs` and then we plot it. For example: @@ -25,7 +25,7 @@ py.iplot(data, filename='new-scatter-plot') There is another type of chart which uses these basic plot types to make other types of graphs, and these are the figure factories. These are wrappers that utilize the code from `plotly.graph_objs` to build charts that can use their structures. A good example of a figure factory is the [Scatterplot Matrix](https://plot.ly/python/scatterplot-matrix/) as it utilizes `go.Scatter`, `go.Box` and `go.Histogram`. -So if you have ever wanted to contribute to the Plotly Python Library by adding a new chart type we don't have, now you can! This README will help you get started by cloning the plotly.py repo, forking a new branch, creating a new figure factory, and creatng a new Pull Request to get feedback for merging. Just follow all these steps and you'll be ready to go. +So if you have ever wanted to contribute to the Plotly Python Library by adding a new chart type we don't have, now you can! This README will help you get started by cloning the plotly.py repo, forking a new branch, creating a new figure factory, and creating a new Pull Request to get feedback for merging. Just follow all these steps and you'll be ready to go. ## Getting Started: 1. In the Terminal, clone the `plotly.py` repo locally and then check out the master branch. @@ -45,7 +45,7 @@ $ git checkout -b "add-ff-type" ## Create a figure_factory File 1. Creating python file -Move to the `plotly/figure_factory` directory in the `plotly.py` repo. To do this, open up the Terminal and excute the command: +Move to the `plotly/figure_factory` directory in the `plotly.py` repo. To do this, open up the Terminal and execute the command: ``` cd plotly/figure_factory @@ -138,7 +138,7 @@ The figure `fig` must be a Plotly Figure, meaning it must have the form `fig = g 5. Useful Tips -It is often not a good idea to put all your code into your `create_foo()` function. It is best practice to not repeat yourself and this requires taking repeated blocks of code and puting them into a seperate function. +It is often not a good idea to put all your code into your `create_foo()` function. It is best practice to not repeat yourself and this requires taking repeated blocks of code and putting them into a separate function. It is best to make all other functions besides `create_foo()` secret so a user cannot access them. This is done by placing a `_` before the name of the function, so `_aux_func()` for example. @@ -161,7 +161,7 @@ and commit these changes and write a commit message. $ git commit -m "this is the work that I did" ``` -After you have added and commited all of your changes to the local branch, it is time to create your PR for the Plotly team to review. +After you have added and committed all of your changes to the local branch, it is time to create your PR for the Plotly team to review. ``` $ git push origin add-ff-type diff --git a/release.md b/release.md index 1de7a2e23f5..1f5a79bc002 100644 --- a/release.md +++ b/release.md @@ -24,7 +24,7 @@ with the expected publication date. Use the `Added`, `Changed`, `Deprecated`, `Removed`, `Fixed`, and `Security` labels for all changes to plotly.py. If the version of plotly.js has been updated, include this as the first `Updated` entry. Call out any -noteable changes as sub-bullets (new trace types in particular), and provide +notable changes as sub-bullets (new trace types in particular), and provide a link to the plotly.js CHANGELOG. ### Finalize versions @@ -213,7 +213,7 @@ npm run build && npm publish --access public --tag next ``` The `--tag next` part ensures that users won't install this version unless -they explicitly ask for the version or for the version wtih the `next` tag. +they explicitly ask for the version or for the version with the `next` tag. Do the same in the `jupyterlab-plotly` directory. @@ -302,7 +302,7 @@ Publish the final version to PyPI ### Publish to plotly anaconda channel -From `packages/python/plotly-geo`, build the conda packge +From `packages/python/plotly-geo`, build the conda package ```bash (plotly_dev) $ conda build recipe/ ``` @@ -351,7 +351,7 @@ Publish the final version to PyPI ### Publish to plotly anaconda channel -From `packages/python/plotly-geo`, build the conda packge +From `packages/python/plotly-geo`, build the conda package ```bash (plotly_dev) $ conda build recipe/ ```