Merge pull request #3073 from c-chaitanya/doc-prod

fixing documents

Author: nicolaskruchten

doc/python/2D-Histogram.md

@@ -137,7 +137,7 @@ fig = go.Figure(go.Histogram2d(x=x, y=y, histnorm='probability',
 fig.show()
 ```
 ### Sharing bin settings between 2D Histograms
-This example shows how to use [bingroup](https://plotly.com/python/reference/histogram/#histogram-bingroup) attribute to have a compatible bin settings for both histograms. To define `start`, `end` and `size` value of x-axis and y-axis seperatly, set [ybins](https://plotly.com/python/reference/histogram2dcontour/#histogram2dcontour-ybins) and `xbins`.
+This example shows how to use [bingroup](https://plotly.com/python/reference/histogram/#histogram-bingroup) attribute to have a compatible bin settings for both histograms. To define `start`, `end` and `size` value of x-axis and y-axis separately, set [ybins](https://plotly.com/python/reference/histogram2dcontour/#histogram2dcontour-ybins) and `xbins`.
 
 ```python
 import plotly.graph_objects as go

doc/python/3d-isosurface-plots.md

@@ -130,7 +130,7 @@ fig = go.Figure(data=go.Isosurface(
 fig.show()
 ```
 
-#### Isosurface with Addtional Slices
+#### Isosurface with Additional Slices
 
 Here we visualize slices parallel to the axes on top of isosurfaces. For a clearer visualization, the `fill` ratio of isosurfaces is decreased below 1 (completely filled).
 
@@ -235,4 +235,4 @@ fig.show()
 ```
 
 #### Reference
-See https://plotly.com/python/reference/isosurface/ for more information and chart attribute options!
+See https://plotly.com/python/reference/isosurface/ for more information and chart attribute options!

doc/python/annotated-heatmap.md

@@ -82,7 +82,7 @@ fig.show()
 ```
 
 #### Custom Text and X & Y Labels
-set `annotation_text` to a matrix with the same dimmensions as `z`
+set `annotation_text` to a matrix with the same dimensions as `z`
 
 ```python
 import plotly.figure_factory as ff
@@ -203,4 +203,4 @@ fig.show()
 
 #### Reference
 
-For more info on Plotly heatmaps, see: https://plotly.com/python/reference/heatmap/.<br> For more info on using colorscales with Plotly see: https://plotly.com/python/heatmap-and-contour-colorscales/ <br>For more info on `ff.create_annotated_heatmap()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_annotated_heatmap.html#plotly.figure_factory.create_annotated_heatmap)
+For more info on Plotly heatmaps, see: https://plotly.com/python/reference/heatmap/.<br> For more info on using colorscales with Plotly see: https://plotly.com/python/heatmap-and-contour-colorscales/ <br>For more info on `ff.create_annotated_heatmap()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_annotated_heatmap.html#plotly.figure_factory.create_annotated_heatmap)

doc/python/axes.md

@@ -66,7 +66,7 @@ The axis type is auto-detected by looking at data from the first [trace](/python
 
 ### Forcing an axis to be categorical
 
-It is possible to force the axis type by setting explicitely `xaxis_type`. In the example below the automatic X axis type would be `linear` (because there are not more than twice as many unique strings as unique numbers) but we force it to be `category`.
+It is possible to force the axis type by setting explicitly `xaxis_type`. In the example below the automatic X axis type would be `linear` (because there are not more than twice as many unique strings as unique numbers) but we force it to be `category`.
 
 ```python
 import plotly.express as px
@@ -139,7 +139,7 @@ IFrame(snippet_url + 'axes', width='100%', height=630)
 
 #### Moving Tick Labels Inside the Plot
 
-The `ticklabelposition` attribute moves tick labels inside the plotting area, and modifies the auto-range behaviour to accomodate the labels.
+The `ticklabelposition` attribute moves tick labels inside the plotting area, and modifies the auto-range behaviour to accommodate the labels.
 
 ```python
 import plotly.express as px

doc/python/builtin-colorscales.md

@@ -23,7 +23,7 @@ jupyter:
     version: 3.7.6
   plotly:
     description: A reference for the built-in named continuous (sequential, diverging
-      and cylclical) color scales in Plotly.
+      and cyclical) color scales in Plotly.
     display_as: file_settings
     has_thumbnail: true
     ipynb: ~notebook_demo/187

doc/python/carpet-contour.md

@@ -37,7 +37,7 @@ jupyter:
 
 ### Basic Carpet Plot
 
-Set the `x` and `y` coorindates, using `x` and `y` attributes. If `x` coorindate values are ommitted a cheater plot will be created. To save parameter values use `a` and `b` attributes. To make changes to the axes, use `aaxis` or `baxis` attributes. For a more detailed list of axes attributes refer to [python reference](https://plotly.com/python/reference/carpet/#carpet-aaxis).
+Set the `x` and `y` coordinates, using `x` and `y` attributes. If `x` coordinate values are omitted a cheater plot will be created. To save parameter values use `a` and `b` attributes. To make changes to the axes, use `aaxis` or `baxis` attributes. For a more detailed list of axes attributes refer to [python reference](https://plotly.com/python/reference/carpet/#carpet-aaxis).
 
 ```python
 import plotly.graph_objects as go
@@ -286,4 +286,4 @@ fig.show()
 
 ### Reference
 
-See https://plotly.com/python/reference/contourcarpet/ for more information and chart attribute options!
+See https://plotly.com/python/reference/contourcarpet/ for more information and chart attribute options!

doc/python/carpet-plot.md

@@ -39,7 +39,7 @@ jupyter:
 ### Set X and Y Coordinates
 
 
-To set the `x` and `y` coordinates use `x` and `y` attributes. If `x` coordindate values are ommitted a cheater plot will be created. The plot below has a `y` array specified but requires `a` and `b` parameter values before an axis may be plotted.
+To set the `x` and `y` coordinates use `x` and `y` attributes. If `x` coordinate values are omitted a cheater plot will be created. The plot below has a `y` array specified but requires `a` and `b` parameter values before an axis may be plotted.
 <!-- #endregion -->
 
 ```python
@@ -189,4 +189,4 @@ To add points and lines see [Carpet Scatter Plots](https://plotly.com/python/car
 
 ### Reference
 
-See https://plotly.com/python/reference/carpet/ for more information and chart attribute options!
+See https://plotly.com/python/reference/carpet/ for more information and chart attribute options!

doc/python/categorical-axes.md

@@ -40,7 +40,7 @@ This page shows examples of how to configure [2-dimensional Cartesian axes](/pyt
 
 The different types of Cartesian axes are configured via the `xaxis.type` or `yaxis.type` attribute, which can take on the following values:
 
-- `'linear'` (see the [linear axes tutoria](/python/axes/))
+- `'linear'` (see the [linear axes tutorial](/python/axes/))
 - `'log'` (see the [log plot tutorial](/python/log-plots/))
 - `'date'` (see the [tutorial on timeseries](/python/time-series/))
 - `'category'` see below
@@ -55,7 +55,7 @@ The axis type is auto-detected by looking at data from the first [trace](/python
 
 ### Forcing an axis to be categorical
 
-It is possible to force the axis type by setting explicitely `xaxis_type`. In the example below the automatic X axis type would be `linear` (because there are not more than twice as many unique strings as unique numbers) but we force it to be `category`.
+It is possible to force the axis type by setting explicitly `xaxis_type`. In the example below the automatic X axis type would be `linear` (because there are not more than twice as many unique strings as unique numbers) but we force it to be `category`.
 
 ```python
 import plotly.express as px

doc/python/colorscales.md

@@ -22,7 +22,7 @@ jupyter:
     pygments_lexer: ipython3
     version: 3.7.6
   plotly:
-    description: How to set, create and control continous color scales and color bars
+    description: How to set, create and control continuous color scales and color bars
       in scatter, bar, map and heatmap figures.
     display_as: file_settings
     has_thumbnail: true
@@ -46,7 +46,7 @@ In the same way as the X or Y position of a mark in cartesian coordinates can be
 This document explains the following four continuous-color-related concepts:
 
 - **color scales** represent a mapping between the range 0 to 1 and some color domain within which colors are to be interpolated (unlike [discrete color sequences](/python/discrete-color/) which are never interpolated). Color scale defaults depend on the `layout.colorscales` attributes of the active [template](/python/templates/), and can be explicitly specified using the `color_continuous_scale` argument for many [Plotly Express](/python/plotly-express/) functions or the `colorscale` argument in various `graph_objects` such as `layout.coloraxis` or `marker.colorscale` in `go.Scatter` traces or `colorscale` in `go.Heatmap` traces. For example `[(0,"blue"), (1,"red")]` is a simple color scale that interpolated between blue and red via purple, which can also be implicitly represented as `["blue", "red"]` and happens to be one of the [built-in color scales](/python/builtin-colorscales) and therefore referred to as `"bluered"` or `plotly.colors.sequential.Bluered`.
-- **color ranges** represent the minimum to maximum range of data to be mapped onto the 0 to 1 input range of the color scale. Color ranges default to the range of the input data and can be explicitly specified using either the `range_color` or `color_continous_midpoint` arguments for many Plotly Express functions, or `cmin`/`cmid`/`cmax` or `zmin`/`zmid`/`zmax` for various `graph_objects` such as `layout.coloraxis.cmin` or `marker.cmin` in `go.Scatter` traces or `cmin` in `go.Heatmap` traces. For example, if a color range of `[100, 200]` is used with the color scale above, then any mark with a color value of 100 or less will be blue, and 200 or more will be red. Marks with values in between will be various shades of purple.
+- **color ranges** represent the minimum to maximum range of data to be mapped onto the 0 to 1 input range of the color scale. Color ranges default to the range of the input data and can be explicitly specified using either the `range_color` or `color_continuous_midpoint` arguments for many Plotly Express functions, or `cmin`/`cmid`/`cmax` or `zmin`/`zmid`/`zmax` for various `graph_objects` such as `layout.coloraxis.cmin` or `marker.cmin` in `go.Scatter` traces or `cmin` in `go.Heatmap` traces. For example, if a color range of `[100, 200]` is used with the color scale above, then any mark with a color value of 100 or less will be blue, and 200 or more will be red. Marks with values in between will be various shades of purple.
 - **color bars** are legend-like visible representations of the color range and color scale with optional tick labels and tick marks. Color bars can be configured with attributes inside `layout.coloraxis.colorbar` or in places like `marker.colorbar` in `go.Scatter` traces or `colorbar` in `go.Heatmap` traces.
 - **color axes** connect color scales, color ranges and color bars to a trace's data. By default, any colorable attribute in a trace is attached to its own local color axis, but color axes may also be shared across attributes and traces by setting e.g. `marker.coloraxis` in `go.Scatter` traces or `coloraxis` in `go.Heatmap` traces. Local color axis attributes are configured within traces e.g. `marker.showscale` whereas shared color axis attributes are configured within the Layout e.g. `layout.coloraxis.showscale`.
 
@@ -60,7 +60,7 @@ For example, in the `tips` dataset, the `size` column contains numbers:
 import plotly.express as px
 df = px.data.tips()
 fig = px.scatter(df, x="total_bill", y="tip", color="size",
-                 title="Numeric 'size' values mean continous color")
+                 title="Numeric 'size' values mean continuous color")
 
 fig.show()
 ```
@@ -85,7 +85,7 @@ df = px.data.tips()
 df["size"] = df["size"].astype(str)
 df["size"] = df["size"].astype(float)
 fig = px.scatter(df, x="total_bill", y="tip", color="size",
-                 title="Numeric 'size' values mean continous color")
+                 title="Numeric 'size' values mean continuous color")
 
 fig.show()
 ```
@@ -151,7 +151,7 @@ fig = px.imshow(data, color_continuous_scale=px.colors.sequential.Cividis_r)
 fig.show()
 ```
 
-### Explicity Constructing a Color scale
+### Explicitly Constructing a Color scale
 
 The Plotly Express `color_continuous_scale` argument accepts explicitly-constructed color scales as well:
 

doc/python/compare-webgl-svg.md

@@ -36,7 +36,7 @@ jupyter:
 ### Comparing Scatter Plots with 75,000 Random Points
 
 
-Now in Ploty you can implement WebGL with `Scattergl()` in place of `Scatter()` <br>
+Now in Plotly you can implement WebGL with `Scattergl()` in place of `Scatter()` <br>
 for increased speed, improved interactivity, and the ability to plot even more data!
 
 

doc/python/configuration-options.md

@@ -252,7 +252,7 @@ fig.show(config={
 
 ### Add optional shape-drawing buttons to modebar
 
-Some modebar buttons of Cartesian plots are optional and have to be added explictly, using the `modeBarButtonsToAdd` config attribute. These buttons are used for drawing or erasing shapes. See [the tutorial on shapes and shape drawing](python/shapes#drawing-shapes-on-cartesian-plots) for more details.
+Some modebar buttons of Cartesian plots are optional and have to be added explicitly, using the `modeBarButtonsToAdd` config attribute. These buttons are used for drawing or erasing shapes. See [the tutorial on shapes and shape drawing](python/shapes#drawing-shapes-on-cartesian-plots) for more details.
 
 ```python
 import plotly.graph_objects as go

doc/python/county-choropleth.md

@@ -59,7 +59,7 @@ conda install geopandas
 <!-- #endraw -->
 
 #### FIPS and Values
-Every US state and county has an assined ID regulated by the US Federal Government under the term FIPS (Federal Information Processing Standards) codes. There are state codes and county codes: the 2016 state and county FIPS codes can be found at the [US Census Website](https://www.census.gov/geographies/reference-files/2016/demo/popest/2016-fips.html).
+Every US state and county has an assigned ID regulated by the US Federal Government under the term FIPS (Federal Information Processing Standards) codes. There are state codes and county codes: the 2016 state and county FIPS codes can be found at the [US Census Website](https://www.census.gov/geographies/reference-files/2016/demo/popest/2016-fips.html).
 
 Combine a state FIPS code (eg. `06` for California) with a county FIPS code of the state (eg. `059` for Orange county) and this new state-county FIPS code (`06059`) uniquely refers to the specified state and county.
 
@@ -197,7 +197,7 @@ Below is a choropleth that uses several other parameters. For a full list of all
 
 - `simplify_county` determines the simplification factor for the counties. The larger the number, the fewer vertices and edges each polygon has. See http://toblerity.org/shapely/manual.html#object.simplify for more information.
 - `simplify_state` simplifies the state outline polygon. See the [documentation](http://toblerity.org/shapely/manual.html#object.simplify) for more information.
-Default for both `simplify_county` and `simplif_state` is 0.02
+Default for both `simplify_county` and `simplify_state` is 0.02
 
 Note: This choropleth uses a divergent categorical colorscale. See http://react-colorscales.getforge.io/ for other cool colorscales.
 
@@ -277,4 +277,4 @@ Also see Mapbox county choropleths made in Python: [https://plotly.com/python/ma
 
 ### Reference
 
-For more info on `ff.create_choropleth()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_choropleth.html)
+For more info on `ff.create_choropleth()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_choropleth.html)

doc/python/creating-and-updating-figures.md

@@ -43,7 +43,7 @@ The `plotly` Python package exists to create, manipulate and [render](/python/re
 
 ### Figures As Dictionaries
 
-At a low level, figures can be represented as dictionaries and displayed using functions from the `plotly.io` module. The `fig` dictonary in the example below describes a figure. It contains a single `bar` trace and a title.
+At a low level, figures can be represented as dictionaries and displayed using functions from the `plotly.io` module. The `fig` dictionary in the example below describes a figure. It contains a single `bar` trace and a title.
 
 ```python
 fig = dict({

doc/python/custom-buttons.md

@@ -348,7 +348,7 @@ fig.show()
 
 #### Update Button
 The `"update"` method should be used when modifying the data and layout sections of the graph.<br>
-This example demonstrates how to update which traces are displayed while simulaneously updating layout attributes such as the chart title and annotations.
+This example demonstrates how to update which traces are displayed while simultaneously updating layout attributes such as the chart title and annotations.
 
 ```python
 import plotly.graph_objects as go

doc/python/dendrogram.md

@@ -35,7 +35,7 @@ jupyter:
 
 #### Basic Dendrogram
 
-A [dendrogram](https://en.wikipedia.org/wiki/Dendrogram) is a diagram representing a tree. The [figure factory](/python/figure-factories/) called `create_dendrogram` performs [hierachical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) on data and represents the resulting tree. Values on the tree depth axis correspond to distances between clusters.
+A [dendrogram](https://en.wikipedia.org/wiki/Dendrogram) is a diagram representing a tree. The [figure factory](/python/figure-factories/) called `create_dendrogram` performs [hierarchical clustering](https://en.wikipedia.org/wiki/Hierarchical_clustering) on data and represents the resulting tree. Values on the tree depth axis correspond to distances between clusters.
 
 Dendrogram plots are commonly used in computational biology to show the clustering of genes or samples, sometimes in the margin of heatmaps.
 
@@ -178,4 +178,4 @@ fig.show()
 
 ### Reference
 
-For more info on `ff.create_dendrogram()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_dendrogram.html)
+For more info on `ff.create_dendrogram()`, see the [full function reference](https://plotly.com/python-api-reference/generated/plotly.figure_factory.create_dendrogram.html)

doc/python/discrete-color.md

@@ -45,7 +45,7 @@ In the same way as the X or Y position of a mark in cartesian coordinates can be
 This document explains the following discrete-color-related concepts:
 
 - **color sequences** are lists of colors to be mapped onto discrete data values. No interpolation occurs when using color sequences, unlike with [continuous color scales](/python/colorscales/), and each color is used as-is. Color sequence defaults depend on the `layout.colorway` attribute of the active [template](/python/templates/), and can be explicitly specified using the `color_discrete_sequence` argument for many [Plotly Express](/python/plotly-express/) functions.
-- **legends** are visible representations of the mapping between colors and data values. Legend markers also change shape when used with various kinds of traces, such as symbols or lines for scatter-like traces. [Legends are configurable](/python/legend/) under the `layout.legend` attribute. Legends are the discrete equivalent of [continous color bars](/python/colorscales/)
+- **legends** are visible representations of the mapping between colors and data values. Legend markers also change shape when used with various kinds of traces, such as symbols or lines for scatter-like traces. [Legends are configurable](/python/legend/) under the `layout.legend` attribute. Legends are the discrete equivalent of [continuous color bars](/python/colorscales/)
 
 ### Discrete Color with Plotly Express
 
@@ -68,7 +68,7 @@ The `size` column, however, contains numbers:
 import plotly.express as px
 df = px.data.tips()
 fig = px.scatter(df, x="total_bill", y="tip", color="size",
-                 title="Numeric 'size' values mean continous color")
+                 title="Numeric 'size' values mean continuous color")
 
 fig.show()
 ```
@@ -94,7 +94,7 @@ df["size"] = df["size"].astype(str) #convert to string
 df["size"] = df["size"].astype(float) #convert back to numeric
 
 fig = px.scatter(df, x="total_bill", y="tip", color="size",
-                 title="Numeric 'size' values mean continous color")
+                 title="Numeric 'size' values mean continuous color")
 
 fig.show()
 ```