Merge branch 'master' into pass-b64

Author: archmoj

.circleci/config.yml

@@ -374,7 +374,7 @@ jobs:
 
   full_build:
     docker:
-      - image: continuumio/miniconda3
+      - image: continuumio/miniconda3:24.3.0-0
         environment:
           LANG: en_US.UTF-8
     resource_class: large
@@ -447,8 +447,8 @@ jobs:
     resource_class: xlarge
     docker:
       # specify the version you desire here
-      # use `-browsers` prefix for selenium tests, for example, `3.6.1-browsers`
-      - image: cimg/python:3.9-browsers
+      # use `-browsers` prefix for selenium tests, for example, `3.9-browsers`
+      - image: cimg/python:3.10-browsers
 
     steps:
       - add_ssh_keys:
@@ -599,3 +599,4 @@ workflows:
       - python_38_orca
       - python_39_percy
       - build-doc
+

CHANGELOG.md

@@ -2,6 +2,34 @@
 All notable changes to this project will be documented in this file.
 This project adheres to [Semantic Versioning](http://semver.org/).
 
+## [5.23.0] - TBD
+
+### Updated
+- Specify Python version 3.8-3.11 for development virtual environments and pin `pytest` at version 8.1.1 to match.
+- Update `IntegerValidator` to handle `extras` option to allow supporting additional keyword values. For example, 'bold' and 'normal' as well as integers as used in font weights [#4612].
+
+## [5.22.0] - 2024-05-01
+
+### Updated
+- Updated Plotly.js from version 2.31.1 to version 2.32.0. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#2320----2024-04-23) for more information. These changes are reflected in the auto-generated `plotly.graph_objects` module. Notable changes include:
+  - Add "bold" weight, "italic" style and "small-caps" variant options to fonts [#6956]
+  - Fix applying autotickangles on axes with showdividers as well as cases where tickson is set to "boundaries" [#6967], with thanks to @my-tien for the contribution!
+  - Fix positioning of multi-line axis titles with standoff [#6970], with thanks to @my-tien for the contribution!
+
+## [5.21.0] - 2024-04-17
+
+### Updated
+- Updated Plotly.js from version 2.30.0 to version 2.31.1. See the [plotly.js CHANGELOG](https://github.com/plotly/plotly.js/blob/master/CHANGELOG.md#2311----2024-04-15) for more information. These changes are reflected in the auto-generated `plotly.graph_objects` module. Notable changes include:
+  - Add `zorder` attribute to various cartesian traces for controlling stacking order of SVG traces drawn into a subplot [[#6918](https://github.com/plotly/plotly.js/pull/6918), [#6953](https://github.com/plotly/plotly.js/pull/6953)],
+    This feature was anonymously sponsored: thank you to our sponsor!
+  - Add "between" option to shape layer for placing them above grid lines and below traces [[#6927](https://github.com/plotly/plotly.js/pull/6927)],
+    with thanks to @my-tien for the contribution!
+  - Add "raw" `sizemode` to cone trace [[#6938](https://github.com/plotly/plotly.js/pull/6938)]
+  - Add `layout.hoversubplots` to enable hover effects across multiple cartesian suplots sharing one axis [[#6947](https://github.com/plotly/plotly.js/pull/6947), [#6950](https://github.com/plotly/plotly.js/pull/6950)]
+
+### Fixed
+- Fixed `orjson` loading issue [[#4562](https://github.com/plotly/plotly.py/pull/4562)]
+
 ## [5.20.0] - 2024-03-13
 
 ### Updated

README.md

@@ -31,9 +31,16 @@
     </tr>
 </table>
 
+<div align="center">
+  <a href="https://dash.plotly.com/project-maintenance">
+    <img src="https://dash.plotly.com/assets/images/maintained-by-plotly.png" width="400px" alt="Maintained by Plotly">
+  </a>
+</div>
+
+
 ## Quickstart
 
-`pip install plotly==5.20.0`
+`pip install plotly==5.22.0`
 
 Inside [Jupyter](https://jupyter.org/install) (installable with `pip install "jupyterlab>=3" "ipywidgets>=7.6"`):
 
@@ -78,13 +85,13 @@ Built on top of [plotly.js](https://github.com/plotly/plotly.js), `plotly.py` is
 plotly.py may be installed using pip...
 
 ```
-pip install plotly==5.20.0
+pip install plotly==5.22.0
 ```
 
 or conda.
 
 ```
-conda install -c plotly plotly=5.20.0
+conda install -c plotly plotly=5.22.0
 ```
 
 ### JupyterLab Support
@@ -106,7 +113,7 @@ The instructions above apply to JupyterLab 3.x. **For JupyterLab 2 or earlier**,
 
 ```
 # JupyterLab 2.x renderer support
-jupyter labextension install jupyterlab-plotly@5.20.0 @jupyter-widgets/jupyterlab-manager
+jupyter labextension install jupyterlab-plotly@5.22.0 @jupyter-widgets/jupyterlab-manager
 ```
 
 Please check out our [Troubleshooting guide](https://plotly.com/python/troubleshooting/) if you run into any problems with JupyterLab.

binder/requirements.txt

@@ -1,11 +1,11 @@
 jupytext
-plotly==5.20.0
+plotly==5.22.0
 jupyter
 notebook
-pandas==1.2.0
-statsmodels==0.12.1
+pandas==2.2.2
+statsmodels==0.14.2
 scipy
-patsy==0.5.1
+patsy==0.5.6
 numpy
 plotly-geo
 psutil

contributing.md

@@ -125,13 +125,16 @@ learn and become confident about git, like http://try.github.io/.
 
 ### Create a virtual environment for plotly development
 
-You can use either [conda][conda-env] or [virtualenv][virtualenv] to create a virtual environment for plotly development, e.g.
+You can use either [conda][conda-env] or [virtualenv][virtualenv] to create a virtual environment for plotly development, e.g.:
 
 ```bash
-conda create -n plotly-dev python
+conda create -n plotly-dev python=3.11
 conda activate plotly-dev
 ```
 
+As of May 2024 our dependencies have been tested against Python versions 3.8 to 3.11.
+We will support Python 3.12 and higher versions soon.
+
 [conda-env]: https://docs.conda.io/projects/conda/en/latest/user-guide/tasks/manage-environments.html#creating-an-environment-with-commands
 [virtualenv]: http://docs.python-guide.org/en/latest/dev/virtualenvs/
 

doc/apidoc/conf.py

@@ -26,7 +26,7 @@
 # The short X.Y version
 version = ""
 # The full version, including alpha/beta/rc tags
-release = "5.20.0"
+release = "5.22.0"
 
 
 # -- General configuration ---------------------------------------------------

doc/python/3d-volume.md

@@ -86,7 +86,7 @@ np.random.seed(0)
 l = 30
 X, Y, Z = np.mgrid[:l, :l, :l]
 vol = np.zeros((l, l, l))
-pts = (l * np.random.rand(3, 15)).astype(np.int)
+pts = (l * np.random.rand(3, 15)).astype(int)
 vol[tuple(indices for indices in pts)] = 1
 from scipy import ndimage
 vol = ndimage.gaussian_filter(vol, 4)

doc/python/axes.md

@@ -367,7 +367,7 @@ fig.show()
 
 *New in 5.19*
 
-If `tickangle` is not explicitly set, its default value is `auto`, meaning if the label needs to be rotated to avoid labels overlapping, it will rotate by either 30 or 90 degrees. Using `autotickangles`, you can also specify a list of angles for `tickangle` to use. If `tickangle` is `auto` and you provide a list of angles to `autotickangles`, the label angle will be set to the first value in the list that prevents overlap. 
+If `tickangle` is not explicitly set, its default value is `auto`, meaning if the label needs to be rotated to avoid labels overlapping, it will rotate by either 30 or 90 degrees. Using `autotickangles`, you can also specify a list of angles for `tickangle` to use. If `tickangle` is `auto` and you provide a list of angles to `autotickangles`, the label angle will be set to the first value in the list that prevents overlap.
 
 ```python
 import plotly.express as px
@@ -390,14 +390,18 @@ Here is an example.
 import plotly.graph_objects as go
 import pandas as pd
 
-# Load and filter Apple stock data for 2016
 apple_df = pd.read_csv(
-    "https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv",
-    parse_dates=["Date"],
-    index_col="Date"
+    "https://raw.githubusercontent.com/plotly/datasets/master/finance-charts-apple.csv"
 )
 
-apple_df_2016 = apple_df["2016"]
+# Convert 'Date' column to datetime format
+apple_df['Date'] = pd.to_datetime(apple_df['Date'])
+
+# Set 'Date' column as index
+apple_df.set_index('Date', inplace=True)
+
+# Filter for 2016
+apple_df_2016 = apple_df.loc['2016']
 
 # Create figure and add line
 fig = go.Figure()
@@ -599,7 +603,7 @@ 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 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.
 
 ```python
 import plotly.express as px
@@ -857,7 +861,7 @@ fig.show()
 
 #### <code>nonnegative</code>, <code>tozero</code>, and <code>normal</code> Rangemode
 
-When you don't specify a range, autorange is used. It's also used for bounds set to `None` when providing a `range`. 
+When you don't specify a range, autorange is used. It's also used for bounds set to `None` when providing a `range`.
 
 The axis auto-range calculation logic can be configured using the `rangemode` axis parameter.
 
@@ -898,7 +902,7 @@ fig.update_xaxes(autorangeoptions=dict(maxallowed=5))
 fig.show()
 ```
 
-##### Clip Minimum and Maximum 
+##### Clip Minimum and Maximum
 
 You can also clip an axis range at a specific maximum or minimum value with `autorangeoptions.clipmax` and `autorangeoptions.clipmin`.
 
@@ -916,7 +920,7 @@ fig.show()
 
 ##### Specify Values to be Included
 
-Use `autorangeoptions.include` to specify a value that should always be included within the calculated autorange. In this example, we specify that for the autorange calculated on the x-axis, 5 should be included. 
+Use `autorangeoptions.include` to specify a value that should always be included within the calculated autorange. In this example, we specify that for the autorange calculated on the x-axis, 5 should be included.
 
 ```python
 import plotly.express as px

doc/python/bar-charts.md

@@ -304,6 +304,56 @@ fig.update_layout(barmode='stack')
 fig.show()
 ```
 
+### Stacked Bar Chart From Aggregating a DataFrame 
+
+Stacked bar charts are a powerful way to present results summarizing categories generated using the Pandas aggregate commands. `pandas.DataFrame.agg` produces a wide data set format incompatible with `px.bar`. Transposing and updating the indexes to achieve `px.bar` compatibility is a somewhat involved option. Here is one straightforward alternative, which presents the aggregated data as a stacked bar using plotly.graph_objects.
+
+```python
+from plotly import graph_objects as go
+import pandas as pd
+
+# Get one year of gapminder data
+url = 'https://raw.githubusercontent.com/plotly/datasets/master/gapminderDataFiveYear.csv'
+df = pd.read_csv(url)
+df = df[df['year']==2007]
+df["gdp"]=df["pop"]*df['gdpPercap']
+
+
+# Build the summary of interest
+df_summarized = df.groupby("continent", observed=True).agg("sum").reset_index()
+
+df_summarized["percent of world population"]=100*df_summarized["pop"]/df_summarized["pop"].sum()
+df_summarized["percent of world GDP"]=100*df_summarized["gdp"]/df_summarized["gdp"].sum()
+
+
+df = df_summarized[["continent", 
+"percent of world population",
+"percent of world GDP",
+]]
+
+# We now have a wide data frame, but it's in the opposite orientation from the one that px is designed to deal with.
+# Transposing it and rebuilding the indexes is an option, but iterating through the DF using graph objects is more succinct. 
+
+fig=go.Figure()
+for category in df_summarized["continent"].values:
+    fig.add_trace(go.Bar(
+            x=df.columns[1:],
+            # We need to get a pandas series that contains just the values to graph; 
+            # We do so by selecting the right row, selecting the right columns
+            # and then transposing and using iloc to convert to a series
+            # Here, we assume that the bar element category variable is in column 0
+            y=list(df.loc[df["continent"]==category][list(df.columns[1:])].transpose().iloc[:,0]),
+            name=str(category)
+
+
+        )
+)
+fig.update_layout(barmode="stack")
+
+fig.show()
+```
+
+
 ### Bar Chart with Hover Text
 
 ```python

doc/python/box-plots.md

@@ -458,12 +458,12 @@ x_data = ['Carmelo Anthony', 'Dwyane Wade',
 
 N = 50
 
-y0 = (10 * np.random.randn(N) + 30).astype(np.int)
-y1 = (13 * np.random.randn(N) + 38).astype(np.int)
-y2 = (11 * np.random.randn(N) + 33).astype(np.int)
-y3 = (9 * np.random.randn(N) + 36).astype(np.int)
-y4 = (15 * np.random.randn(N) + 31).astype(np.int)
-y5 = (12 * np.random.randn(N) + 40).astype(np.int)
+y0 = (10 * np.random.randn(N) + 30).astype(int)
+y1 = (13 * np.random.randn(N) + 38).astype(int)
+y2 = (11 * np.random.randn(N) + 33).astype(int)
+y3 = (9 * np.random.randn(N) + 36).astype(int)
+y4 = (15 * np.random.randn(N) + 31).astype(int)
+y5 = (12 * np.random.randn(N) + 40).astype(int)
 
 y_data = [y0, y1, y2, y3, y4, y5]