fixing documents

Author: c-chaitanya

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!