fix some typos

Author: LiamConnors

doc/python/bar-charts.md

@@ -653,7 +653,7 @@ def pictogram_bar(data, title, icon_size, max_icons_per_column=10, units_per_ico
         title=title,
         xaxis=dict(
             tickvals=tick_locations,
-            # Label ecah category
+            # Label each category
             ticktext=list(data.keys()),
             tickangle=-45,
             showgrid=False,

doc/python/choropleth-maps.md

@@ -258,7 +258,7 @@ fig = go.Figure(data=go.Choropleth(
 
 fig.update_layout(
     title_text = '2011 US Agriculture Exports by State',
-    geo_scope='usa', # limite map scope to USA
+    geo_scope='usa', # limit map scope to USA
 )
 
 fig.show()

doc/python/configuration-options.md

@@ -40,7 +40,7 @@ The `.show()` method that you use to display your figures also accepts a `config
 
 You can set the configuration options for your figure by passing a dictionary to this parameter which contains the options you want to set.
 
-If you don't set an option's value, it will be automatically be set to the default value for that option.
+If you don't set an option's value, it will automatically be set to the default value for that option.
 
 For the complete list of configuration options and their defaults see: https://github.com/plotly/plotly.js/blob/master/src/plot_api/plot_config.js
 
@@ -348,7 +348,7 @@ fig = go.Figure(
         title='Google Stock Price Over Time with Mode Bar Disabled',
         xaxis=dict(
             title='Date',
-            # Try zooming in or out using the modebar buttons. These only apply to the yaxis in this exampe.
+            # Try zooming in or out using the modebar buttons. These only apply to the yaxis in this example.
             modebardisable='zoominout'
         ),
         yaxis=dict(

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

@@ -281,7 +281,7 @@ import plotly.graph_objects as go
 df = px.data.iris()
 
 fig = px.scatter(df, x="sepal_width", y="sepal_length", color="species", facet_col="species",
-                 title="Adding Traces To Subplots Witin A Plotly Express Figure")
+                 title="Adding Traces To Subplots Within A Plotly Express Figure")
 
 reference_line = go.Scatter(x=[2, 4],
                             y=[4, 8],
@@ -624,7 +624,7 @@ Figures created with the plotly.py graphing library also support:
 
 #### Chaining Figure Operations
 
-All of the figure update operations described above are methods that return a reference to the figure being modified. This makes it possible the chain multiple figure modification operations together into a single expression.
+All of the figure update operations described above are methods that return a reference to the figure being modified. This makes it possible to chain multiple figure modification operations together into a single expression.
 
 Here is an example of a chained expression that creates:
 

doc/python/custom-buttons.md

@@ -146,7 +146,7 @@ fig.update_scenes(
     aspectmode="manual"
 )
 
-# Add drowdowns
+# Add dropdowns
 # button_layer_1_height = 1.08
 button_layer_1_height = 1.12
 button_layer_2_height = 1.065

doc/python/horizontal-bar-charts.md

@@ -37,7 +37,7 @@ See more examples of bar charts (including vertical bar charts) and styling opti
 
 ### Horizontal Bar Chart with Plotly Express
 
-[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/). For a horizontal bar char, use the `px.bar` function with `orientation='h'`.
+[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/). For a horizontal bar chart, use the `px.bar` function with `orientation='h'`.
 
 #### Basic Horizontal Bar Chart with Plotly Express
 

doc/python/icicle-charts.md

@@ -187,7 +187,7 @@ fig =go.Figure(go.Icicle(
     "North America", "Europe", "Australia", "North America - Football", "Soccer",
     "North America - Rugby", "Europe - Football", "Rugby",
     "Europe - American Football","Australia - Football", "Association",
-    "Australian Rules", "Autstralia - American Football", "Australia - Rugby",
+    "Australian Rules", "Australia - American Football", "Australia - Rugby",
     "Rugby League", "Rugby Union"
   ],
   labels= ["Sports",

doc/python/linear-fits.md

@@ -116,7 +116,7 @@ fig = px.scatter(df, x="gdpPercap", y="lifeExp", log_x=True,
 fig.show()
 ```
 
-### Locally WEighted Scatterplot Smoothing (LOWESS)
+### Locally Weighted Scatterplot Smoothing (LOWESS)
 
 Plotly Express also supports non-linear [LOWESS](https://en.wikipedia.org/wiki/Local_regression) trendlines. In order use this feature, you will need to [install `statsmodels` and its dependencies](https://www.statsmodels.org/stable/install.html).
 

doc/python/ml-knn.md

@@ -124,7 +124,7 @@ fig.show()
 
 Just like the previous example, we will first train our kNN model on the training set.
 
-Instead of predicting the conference for the test set, we can predict the confidence map for the entire area that wraps around the dimensions of our dataset. To do this, we use [`np.meshgrid`](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html) to create a grid, where the distance between each point is denoted by the `mesh_size` variable.
+Instead of predicting the confidence for the test set, we can predict the confidence map for the entire area that wraps around the dimensions of our dataset. To do this, we use [`np.meshgrid`](https://numpy.org/doc/stable/reference/generated/numpy.meshgrid.html) to create a grid, where the distance between each point is denoted by the `mesh_size` variable.
 
 Then, for each of those points, we will use our model to give a confidence score, and plot it with a [contour plot](https://plotly.com/python/contour-plots/).
 

doc/python/performance.md

@@ -181,7 +181,7 @@ it is also possible to use [datashader](/python/datashader/).
 
 The `render_mode` argument to supported Plotly Express functions (e.g. `scatter` and `scatter_polar`) can be used to enable WebGL rendering.
 
-> **Note** The default `render_mode` is `"auto"`, in which case Plotly Express will automatically set `render_mode="webgl"` if the input data is more than 1,000 rows long. In this case, WebGl can be disabled by setting `render_mode=svg`.
+> **Note** The default `render_mode` is `"auto"`, in which case Plotly Express will automatically set `render_mode="webgl"` if the input data is more than 1,000 rows long. In this case, WebGL can be disabled by setting `render_mode=svg`.
 
 Here is an example that creates a 100,000 point scatter plot using Plotly Express with WebGL rendering explicitly enabled.