Merge pull request #5542 from yangsong97/fix-documentation-typos

camdecoster · web-flow · commit 0a46b63304d9 · 2026-03-25T12:43:51.000-06:00
Fix typos and grammar errors in docs and source code
diff --git a/CHANGELOG.md b/CHANGELOG.md
@@ -801,7 +801,7 @@ Items in this section may be considered backwards-incompatible changes for the p
     - if either `x` or `y` (but not both) may now be provided as a list of column references into `data_frame` or columns of data, in which case the imputed data frame will be treated as "wide" data and `melt()`ed internally before applying the usual mapping rules, with function-specific defaults.
     - if neither `x` nor `y` is provided but `data_frame` is, the data frame will be treated as "wide" with defaults depending on the value of `orientation` (and `orientation` has accordingly been added to `scatter`, `line`, `density_heatmap`, and `density_contour` for this purpose). Previously this would have resulted in an empty figure.
     - if both `x` and `y` are provided to `histogram`, and if `x`, `y` and `z` are provided to `density_heatmap` or `density_contour`, then `histfunc` now defaults to `sum` so as to avoid ignoring the provided data, and to cause `histogram` and `bar` to behave more similarly.
-    - `violinmode`, `boxmode` and `stripmode` now default to `overlay` if `x` (`y`) in in `v` (`h`) orientation is also mapped to `color`, to avoid strange spacing issues with the previous default of `group` in all cases.
+    - `violinmode`, `boxmode` and `stripmode` now default to `overlay` if `x` (`y`) in `v` (`h`) orientation is also mapped to `color`, to avoid strange spacing issues with the previous default of `group` in all cases.
 - The Plotly Express arguments `color_discrete_map`, `symbol_map` and `line_dash_map` now accept the string `"identity"` which causes the corresponding input data to be used as-is rather than mapped into `color_discrete_sequence`, `symbol_sequence` or `line_dash_sequence`, respectively. ([#2336](https://github.com/plotly/plotly.py/pull/2336))
 - Plotly Express now accepts `px.Constant` or `px.Range` objects in the place of column references so as to express constant or increasing integer values. ([#2336](https://github.com/plotly/plotly.py/pull/2336))
 
diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md
@@ -178,7 +178,7 @@ When creating your pull request, please follow the guidelines below.
 ### Code pull request
 
 - *Make sure you have reviewed the full [contributing notes (this file)](https://github.com/plotly/plotly.py/blob/main/CONTRIBUTING.md) and understand the structure of the package.*
-- If your PR modifies code of `plotly.graph_objects`, the modifications should be made to the the code generator, *not* the generated files.
+- If your PR modifies code of `plotly.graph_objects`, the modifications should be made to the code generator, *not* the generated files.
 - You have added tests or modified existing tests, as needed.
 - For a new feature, you have added documentation examples (please see the doc checklist as well).
 - You have added a CHANGELOG entry if changing anything substantial.
diff --git a/_plotly_utils/basevalidators.py b/_plotly_utils/basevalidators.py
@@ -878,7 +878,7 @@ def __init__(
 
     def description(self):
         desc = """\
-    The '{plotly_name}' property is a integer and may be specified as:""".format(
+    The '{plotly_name}' property is an integer and may be specified as:""".format(
             plotly_name=self.plotly_name
         )
 
diff --git a/_plotly_utils/colors/__init__.py b/_plotly_utils/colors/__init__.py
@@ -419,10 +419,10 @@ def convert_colors_to_same_type(
 
     Takes a single color or an iterable of colors, as well as a list of scale
     values, and outputs a 2-pair of the list of color(s) converted all to an
-    rgb or tuple color type, aswell as the scale as the second element. If
+    rgb or tuple color type, as well as the scale as the second element. If
     colors is a Plotly Scale name, then 'scale' will be forced to the scale
     from the respective colorscale and the colors in that colorscale will also
-    be coverted to the selected colortype. If colors is None, then there is an
+    be converted to the selected colortype. If colors is None, then there is an
     option to return portion of the DEFAULT_PLOTLY_COLORS
 
     :param (str|tuple|list) colors: either a plotly scale name, an rgb or hex
@@ -567,8 +567,8 @@ def make_colorscale(colors, scale=None):
 
     Takes a list of colors and scales and constructs a colorscale based
     on the colors in sequential order. If 'scale' is left empty, a linear-
-    interpolated colorscale will be generated. If 'scale' is a specificed
-    list, it must be the same legnth as colors and must contain all floats
+    interpolated colorscale will be generated. If 'scale' is a specified
+    list, it must be the same length as colors and must contain all floats
     For documentation regarding to the form of the output, see
     https://plot.ly/python/reference/#mesh3d-colorscale
 
diff --git a/_plotly_utils/png.py b/_plotly_utils/png.py
@@ -350,7 +350,7 @@ class ChunkError(FormatError):
 
 
 class Default:
-    """The default for the greyscale paramter."""
+    """The default for the greyscale parameter."""
 
 
 class Writer:
diff --git a/doc/python/axes.md b/doc/python/axes.md
@@ -693,7 +693,7 @@ fig.show()
 
 *New in 5.17*
 
-You can also set just a lower or upper bound manually and have autorange applied to the other bound by setting it to `None`. In the following example, we set a an upper bound of 4.5 on the x axes, while specifying `None` for the lower bound, meaning it will use autorange. On the y axes, we set the lower bound, and use `None` for the upper bound, meaning that uses autorange.
+You can also set just a lower or upper bound manually and have autorange applied to the other bound by setting it to `None`. In the following example, we set an upper bound of 4.5 on the x axes, while specifying `None` for the lower bound, meaning it will use autorange. On the y axes, we set the lower bound, and use `None` for the upper bound, meaning that uses autorange.
 
 ```python
 import plotly.express as px
diff --git a/doc/python/carpet-plot.md b/doc/python/carpet-plot.md
@@ -117,7 +117,7 @@ fig.show()
 ### Cheater plot layout
 
 
-The layout of cheater plots is not unique and depends upon the `cheaterslope` and axis `cheatertype` parameters. If `x` is not specified, each row of the `x` array is constructed based on the the formula `a + cheaterslope * b`, where `a` and `b` are either the value or the integer index of `a` and `b` respectively, depending on the corresponding axis `cheatertype`. Although the layout of the axis below is different than the plots above, it represents the same data as the axes above.
+The layout of cheater plots is not unique and depends upon the `cheaterslope` and axis `cheatertype` parameters. If `x` is not specified, each row of the `x` array is constructed based on the formula `a + cheaterslope * b`, where `a` and `b` are either the value or the integer index of `a` and `b` respectively, depending on the corresponding axis `cheatertype`. Although the layout of the axis below is different than the plots above, it represents the same data as the axes above.
 
 ```python
 import plotly.graph_objects as go
diff --git a/doc/python/categorical-axes.md b/doc/python/categorical-axes.md
@@ -79,7 +79,7 @@ fig.update_traces(marker_size=10)
 fig.show()
 ```
 
-[Box plots]() and [violin plots]() are often shown with one categorical and one continuous axis.
+[Box plots](/python/box-plots/) and [violin plots](/python/violin/) are often shown with one categorical and one continuous axis.
 
 ```python
 import plotly.express as px
diff --git a/doc/python/ecdf-plots.md b/doc/python/ecdf-plots.md
@@ -90,7 +90,7 @@ fig.show()
 
 ### Reversed and Complementary CDF plots
 
-By default, the Y value represents the fraction of the data that is *at or below* the value on on the X axis. Setting `ecdfmode` to `"reversed"` reverses this, with the Y axis representing the fraction of the data *at or above* the X value. Setting `ecdfmode` to `"complementary"` plots `1-ECDF`, meaning that the Y values represent the fraction of the data *above* the X value.
+By default, the Y value represents the fraction of the data that is *at or below* the value on the X axis. Setting `ecdfmode` to `"reversed"` reverses this, with the Y axis representing the fraction of the data *at or above* the X value. Setting `ecdfmode` to `"complementary"` plots `1-ECDF`, meaning that the Y values represent the fraction of the data *above* the X value.
 
 In `standard` mode (the default), the right-most point is at 1 (or the total count/sum, depending on `ecdfnorm`) and the right-most point is above 0.
 
diff --git a/doc/python/marker-style.md b/doc/python/marker-style.md
@@ -309,7 +309,7 @@ fig.show()
 
 To maximise visibility of each point, set the color as an `rgba` string that includes an alpha value of 0.5.
 
-This example sets the marker color to `'rgba(135, 206, 250, 0.5)'`. The rgb values of 135, 206, and 250 are from the definition of the `LightSkyBlue` named CSS color that is is used in the previous examples (See https://www.color-hex.com/color/87cefa). The marker line will remain opaque.
+This example sets the marker color to `'rgba(135, 206, 250, 0.5)'`. The rgb values of 135, 206, and 250 are from the definition of the `LightSkyBlue` named CSS color that is used in the previous examples (See https://www.color-hex.com/color/87cefa). The marker line will remain opaque.
 
 ```python
 import plotly.graph_objects as go
diff --git a/doc/python/ml-regression.md b/doc/python/ml-regression.md
@@ -324,7 +324,7 @@ fig.show()
 
 ### Enhanced prediction error analysis using `plotly.express`
 
-Add marginal histograms to quickly diagnoses any prediction bias your model might have. The built-in `OLS` functionality let you visualize how well your model generalizes by comparing it with the theoretical optimal fit (black dotted line).
+Add marginal histograms to quickly diagnose any prediction bias your model might have. The built-in `OLS` functionality let you visualize how well your model generalizes by comparing it with the theoretical optimal fit (black dotted line).
 
 ```python
 import plotly.express as px
diff --git a/doc/python/parallel-categories-diagram.md b/doc/python/parallel-categories-diagram.md
@@ -114,7 +114,7 @@ fig.show()
 
 #### Multi-Color Parallel Categories Diagram
 
-The color of the ribbons can be specified with the `line.color` property. Similar to other trace types, this property may be set to an array of numbers, which are then mapped to colors according to the the colorscale specified in the `line.colorscale` property.
+The color of the ribbons can be specified with the `line.color` property. Similar to other trace types, this property may be set to an array of numbers, which are then mapped to colors according to the colorscale specified in the `line.colorscale` property.
 
 Here is an example of visualizing the survival rate of passengers in the titanic dataset, where the ribbons are colored based on survival outcome.
 
diff --git a/doc/python/pattern-hatching-texture.md b/doc/python/pattern-hatching-texture.md
@@ -88,7 +88,7 @@ fig = px.bar(df, x="medal", y="count", color="nation",
 fig.show()
 ```
 
-Here we use `pattern_shape_map` to explictly assign a shape to each value of `nation`, regardless of order:
+Here we use `pattern_shape_map` to explicitly assign a shape to each value of `nation`, regardless of order:
 
 ```python
 import plotly.express as px
diff --git a/doc/python/selections.md b/doc/python/selections.md
@@ -153,7 +153,7 @@ fig.show()
 ## Referencing Selections on Multiple Cartesian Subplots
 
 
-You can add selections to multiple Cartesian subplots by specifying `xref` and/or `yref`. Here, we add one selection on the plot with axis ids `x` and `y2` and two selections to the the plot with axis ids `x` and `y`.
+You can add selections to multiple Cartesian subplots by specifying `xref` and/or `yref`. Here, we add one selection on the plot with axis ids `x` and `y2` and two selections to the plot with axis ids `x` and `y`.
 
 ```python
 import plotly.graph_objects as go
diff --git a/doc/python/text-and-annotations.md b/doc/python/text-and-annotations.md
@@ -162,7 +162,7 @@ fig.show()
 
 ### Controlling Maximum Text Size
 
-The `textfont_size` parameter of the the [pie](/python/pie-charts), [bar](/python/bar-charts)-like, [sunburst](/python/sunburst-charts) and [treemap](/python/treemaps) traces can be used to set the **maximum font size** used in the chart. Note that the `textfont` parameter sets the `insidetextfont` and `outsidetextfont` parameter, which can also be set independently.
+The `textfont_size` parameter of the [pie](/python/pie-charts), [bar](/python/bar-charts)-like, [sunburst](/python/sunburst-charts) and [treemap](/python/treemaps) traces can be used to set the **maximum font size** used in the chart. Note that the `textfont` parameter sets the `insidetextfont` and `outsidetextfont` parameter, which can also be set independently.
 
 ```python
 import plotly.express as px
diff --git a/doc/python/v4-migration.md b/doc/python/v4-migration.md
@@ -249,9 +249,9 @@ fig.show()
 ```
 
 #### Implementation of shared axes with `make_subplots`
-The implementation of shared axis support in the `make_subplots` function has been simplified.  Prior to version 4, shared y-axes were implemented by associating a single `yaxis` object with multiple `xaxis` objects, and vica versa.
+The implementation of shared axis support in the `make_subplots` function has been simplified.  Prior to version 4, shared y-axes were implemented by associating a single `yaxis` object with multiple `xaxis` objects, and vice versa.
 
-In version 4, every 2D Cartesian subplot has a dedicated x-axis and and a dedicated y-axis. Axes are now "shared" by being linked together using the `matches` axis property.
+In version 4, every 2D Cartesian subplot has a dedicated x-axis and a dedicated y-axis. Axes are now "shared" by being linked together using the `matches` axis property.
 
 For legacy code that makes use of the `make_subplots` and add trace APIs, this change does not require any action on the user's part.  However, legacy code that uses `make_subplots` to create a figure with shared axes, and then manipulates the axes directly, may require updates.  The output of the `.print_grid` method on a figure created using `make_subplots` can be used to identify which axis objects are associated with each subplot.
 
diff --git a/doc/unconverted/python/2d-projection-of-3d-surface.md b/doc/unconverted/python/2d-projection-of-3d-surface.md
@@ -41,7 +41,7 @@ Namely, if $x, y, z$ are numpy arrays of shape (m, n), defined by a discretizati
 
 This method allows to project a surface onto planes perpendicular to the z, x or y-direction in the 3d space
 and interpret the projection as a planar surface colored according to the z, x or y value at each point
-of the coresponding plane.
+of the corresponding plane.
 
 First, define the surface and its discretization:
 
diff --git a/doc/unconverted/python/normality-test.md b/doc/unconverted/python/normality-test.md
@@ -189,7 +189,7 @@ Each test will return at least two things:
 
 Each test calculates a test-specific statistic. This statistic can aid in the interpretation of the result, although it may require a deeper proficiency with statistics and a deeper knowledge of the specific statistical test. Instead, the p-value can be used to quickly and accurately interpret the statistic in practical applications.
 
-The tests assume that that the sample was drawn from a Gaussian distribution. Technically this is called the null hypothesis, or H0. A threshold level is chosen called alpha, typically 5% (or 0.05), that is used to interpret the p-value.
+The tests assume that the sample was drawn from a Gaussian distribution. Technically this is called the null hypothesis, or H0. A threshold level is chosen called alpha, typically 5% (or 0.05), that is used to interpret the p-value.
 
 In the SciPy implementation of these tests, you can interpret the p value as follows.
  - **_p <= alpha_**: reject H0, not normal.
diff --git a/doc/unconverted/python/outlier-test.md b/doc/unconverted/python/outlier-test.md
@@ -56,7 +56,7 @@ table = FF.create_table(df)
 py.iplot(table, filename='wind-data-sample')
 ```
 
-In any set of data, an `outlier` is a a datum point that is not consistent with the other data points. If the data sampled from a particular distribution then with high probability, an outlier would not belong to that distribution. There are various tests used for testing if a particular point is an outlier, and this is done with the same null-hypothesis testing used in Normality Tests.
+In any set of data, an `outlier` is a datum point that is not consistent with the other data points. If the data sampled from a particular distribution then with high probability, an outlier would not belong to that distribution. There are various tests used for testing if a particular point is an outlier, and this is done with the same null-hypothesis testing used in Normality Tests.
 
 
 #### Q Test
diff --git a/plotly/__init__.py b/plotly/__init__.py
@@ -57,7 +57,7 @@
         "__version__",
     ]
 
-    # Set default template (for >= 3.7 this is done in ploty/io/__init__.py)
+    # Set default template (for >= 3.7 this is done in plotly/io/__init__.py)
     from plotly.io import templates
 
     templates._default = "plotly"
diff --git a/plotly/basedatatypes.py b/plotly/basedatatypes.py
@@ -555,7 +555,7 @@ class is a subclass of both BaseFigure and widgets.DOMWidget.
         for trace_ind, trace in enumerate(data):
             # By setting the trace's parent to be this figure, we tell the
             # trace object to use the figure's _data and _data_defaults
-            # dicts to get/set it's properties, rather than using the trace
+            # dicts to get/set its properties, rather than using the trace
             # object's internal _orphan_props dict.
             trace._parent = self
 
@@ -1921,7 +1921,7 @@ def _set_in(d, key_path_str, v):
 
             val_parent = val_parent[key_path_el]
 
-        # Assign value to to final parent dict or list
+        # Assign value to final parent dict or list
         # --------------------------------------------
         # ### Get reference to final key path element ###
         last_key = key_path[-1]
@@ -4281,7 +4281,7 @@ def _subplot_not_empty(self, xref, yref, selector="all"):
                 ret |= any(
                     t == (xref, yref)
                     for t in [
-                        # if a object exists but has no xaxis or yaxis keys, then it
+                        # if an object exists but has no xaxis or yaxis keys, then it
                         # is plotted with xaxis/xref 'x' and yaxis/yref 'y'
                         (
                             "x" if d[xaxiskw] is None else d[xaxiskw],
diff --git a/plotly/basewidget.py b/plotly/basewidget.py
@@ -34,7 +34,7 @@ class BaseFigureWidget(BaseFigure, anywidget.AnyWidget):
     # ### Python -> JS message properties ###
     # These properties are used to send messages from Python to the
     # frontend. Messages are sent by assigning the message contents to the
-    # appropriate _py2js_* property and then immediatly assigning None to the
+    # appropriate _py2js_* property and then immediately assigning None to the
     # property.
     #
     # See JSDoc comments in the FigureModel class in js/src/Figure.js for
diff --git a/plotly/callbacks.py b/plotly/callbacks.py
@@ -102,14 +102,14 @@ def buttons(self):
         -  0: No button or un-initialized
         -  1: Primary button (usually left)
         -  2: Secondary button (usually right)
-        -  4: Auxilary button (usually middle or mouse wheel button)
+        -  4: Auxiliary button (usually middle or mouse wheel button)
         -  8: 4th button (typically the "Browser Back" button)
         - 16: 5th button (typically the "Browser Forward" button)
 
         Combinations of buttons are represented as the decimal form of the
         bitmask of the values above.
 
-        For example, pressing both the primary (1) and auxilary (4) buttons
+        For example, pressing both the primary (1) and auxiliary (4) buttons
         will result in a code of 5
 
         Returns
diff --git a/plotly/figure_factory/_facet_grid.py b/plotly/figure_factory/_facet_grid.py
@@ -819,7 +819,7 @@ def create_facet_grid(
     else:
         SUBPLOT_SPACING = 0.015
 
-    # seperate kwargs for marker and else
+    # separate kwargs for marker and else
     if "marker" in kwargs:
         kwargs_marker = kwargs["marker"]
     else:
diff --git a/plotly/figure_factory/_trisurf.py b/plotly/figure_factory/_trisurf.py
@@ -270,7 +270,7 @@ def create_trisurf(
     :param (array) z: data values of z in a 1D array
     :param (array) simplices: an array of shape (ntri, 3) where ntri is
         the number of triangles in the triangularization. Each row of the
-        array contains the indicies of the verticies of each triangle
+        array contains the indices of the vertices of each triangle
     :param (str|tuple|list) colormap: either a plotly scale name, an rgb
         or hex color, a color tuple or a list of colors. An rgb color is
         of the form 'rgb(x, y, z)' where x, y, z belong to the interval
diff --git a/plotly/figure_factory/utils.py b/plotly/figure_factory/utils.py
@@ -236,7 +236,7 @@ def annotation_dict_for_label(
 
 def list_of_options(iterable, conj="and", period=True):
     """
-    Returns an English listing of objects seperated by commas ','
+    Returns an English listing of objects separated by commas ','
 
     For example, ['foo', 'bar', 'baz'] becomes 'foo, bar and baz'
     if the conjunction 'and' is selected.
diff --git a/plotly/io/__init__.py b/plotly/io/__init__.py
@@ -64,7 +64,7 @@
         ],
     )
 
-    # Set default template (for < 3.7 this is done in ploty/__init__.py)
+    # Set default template (for < 3.7 this is done in plotly/__init__.py)
     from plotly.io import templates
 
     templates._default = "plotly"
diff --git a/plotly/matplotlylib/renderer.py b/plotly/matplotlylib/renderer.py
diff --git a/plotly/offline/offline.py b/plotly/offline/offline.py
diff --git a/test_init/test_lazy_imports.py b/test_init/test_lazy_imports.py

Original file line number	Diff line number	Diff line change
`@@ -878,7 +878,7 @@ def __init__(`
`878`	`878`
`879`	`879`	`def description(self):`
`880`	`880`	`desc = """\`
`881`		`- The '{plotly_name}' property is a integer and may be specified as:""".format(`
	`881`	`+ The '{plotly_name}' property is an integer and may be specified as:""".format(`
`882`	`882`	`plotly_name=self.plotly_name`
`883`	`883`	`)`
`884`	`884`