Merge branch 'master' into plot/layout_rect

Author: mwaskom

.github/CONTRIBUTING.md

@@ -16,7 +16,7 @@ If you think you've encountered a bug in seaborn, please report it on the [Githu
 - A clear explanation of why you think something is wrong
 - The specific versions of seaborn and matplotlib that you are working with
 
-Bug reports are easiest to address if they can be demonstrated using one of the example datasets from the seaborn docs (i.e. with `seaborn.load_dataset`). Otherwise, it is preferable that your example generate synthetic data to reproduce the problem. If you can only demonstrate the issue with your actual dataset, you will need to share it, ideally as a csv. Note that you can upload a csv directly to a github issue thread, but it must have a `.txt` suffix.
+Bug reports are easiest to address if they can be demonstrated using one of the example datasets from the seaborn docs (i.e. with `seaborn.load_dataset`). Otherwise, it is preferable that your example generate synthetic data to reproduce the problem. If you can only demonstrate the issue with your actual dataset, you will need to share it, ideally as a csv (do not share data as a pickle file).
 
 If you've encountered an error, searching the specific text of the message before opening a new issue can often help you solve the problem quickly and avoid making a duplicate report.
 

README.md

@@ -6,7 +6,7 @@ seaborn: statistical data visualization
 =======================================
 
 [![PyPI Version](https://img.shields.io/pypi/v/seaborn.svg)](https://pypi.org/project/seaborn/)
-[![License](https://img.shields.io/pypi/l/seaborn.svg)](https://github.com/mwaskom/seaborn/blob/master/LICENSE)
+[![License](https://img.shields.io/pypi/l/seaborn.svg)](https://github.com/mwaskom/seaborn/blob/master/LICENSE.md)
 [![DOI](https://joss.theoj.org/papers/10.21105/joss.03021/status.svg)](https://doi.org/10.21105/joss.03021)
 [![Tests](https://github.com/mwaskom/seaborn/workflows/CI/badge.svg)](https://github.com/mwaskom/seaborn/actions)
 [![Code Coverage](https://codecov.io/gh/mwaskom/seaborn/branch/master/graph/badge.svg)](https://codecov.io/gh/mwaskom/seaborn)

doc/_docstrings/barplot.ipynb

@@ -22,7 +22,7 @@
    "id": "b53b65b8-5670-4905-aa39-36db04f4b813",
    "metadata": {},
    "source": [
-    "With long data, assign `x` and `y` to group by a categorical varaible and plot aggregated values, with confidence intervals:"
+    "With long data, assign `x` and `y` to group by a categorical variable and plot aggregated values, with confidence intervals:"
    ]
   },
   {

doc/_docstrings/histplot.ipynb

@@ -312,7 +312,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Step functions, esepcially when unfilled, make it easy to compare cumulative histograms:"
+    "Step functions, especially when unfilled, make it easy to compare cumulative histograms:"
    ]
   },
   {

doc/_docstrings/objects.Est.ipynb

@@ -109,12 +109,30 @@
     "p.add(so.Range(), so.Est(seed=0))"
    ]
   },
+  {
+   "cell_type": "markdown",
+   "id": "df807ef8-b5fb-4eac-b539-1bd4e797ddc2",
+   "metadata": {},
+   "source": [
+    "To compute a weighted estimate (and confidence interval), assign a `weight` variable in the layer where you use the stat:"
+   ]
+  },
   {
    "cell_type": "code",
    "execution_count": null,
    "id": "5e4a0594-e1ee-4f72-971e-3763dd626e8b",
    "metadata": {},
    "outputs": [],
+   "source": [
+    "p.add(so.Range(), so.Est(), weight=\"price\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "0d0c34d7-fb76-44cf-9079-3ec7f45741d0",
+   "metadata": {},
+   "outputs": [],
    "source": []
   }
  ],

doc/_docstrings/pointplot.ipynb

@@ -22,7 +22,7 @@
    "id": "f25d3647-9fad-47b2-b49d-db6f5b5c3795",
    "metadata": {},
    "source": [
-    "Group by a categorical varaible and plot aggregated values, with confidence intervals:"
+    "Group by a categorical variable and plot aggregated values, with confidence intervals:"
    ]
   },
   {
@@ -138,7 +138,7 @@
    "id": "00273ada-cd12-410a-a268-38243d6514ae",
    "metadata": {},
    "source": [
-    "Dodge by a specific amount, relative to the width alloted for each level:"
+    "Dodge by a specific amount, relative to the width allotted for each level:"
    ]
   },
   {
@@ -164,7 +164,7 @@
    "id": "e205e7c8-6b11-44e6-b43f-7416c427215d",
    "metadata": {},
    "source": [
-    "When variables are not explicity assigned and the dataset is two-dimensional, the plot will aggregate over each column:"
+    "When variables are not explicitly assigned and the dataset is two-dimensional, the plot will aggregate over each column:"
    ]
   },
   {

doc/_docstrings/scatterplot.ipynb

@@ -167,7 +167,7 @@
    "cell_type": "raw",
    "metadata": {},
    "source": [
-    "Control the range of marker areas with ``sizes``, and set ``lengend=\"full\"`` to force every unique value to appear in the legend:"
+    "Control the range of marker areas with ``sizes``, and set ``legend=\"full\"`` to force every unique value to appear in the legend:"
    ]
   },
   {

doc/_tutorial/data_structure.ipynb

@@ -19,7 +19,7 @@
     "As a data visualization library, seaborn requires that you provide it with data. This chapter explains the various ways to accomplish that task. Seaborn supports several different dataset formats, and most functions accept data represented with objects from the `pandas <https://pandas.pydata.org/>`_ or `numpy <https://numpy.org/>`_ libraries as well as built-in Python types like lists and dictionaries. Understanding the usage patterns associated with these different options will help you quickly create useful visualizations for nearly any dataset.\n",
     "\n",
     ".. note::\n",
-    "   As of current writing (v0.13.0), the full breadth of options covered here are supported by most, but not all, of the functions in seaborn. Namely, a few older functinos (e.g., :func:`lmplot` and :func:`regplot`) anre more limited in what they accept."
+    "   As of current writing (v0.13.0), the full breadth of options covered here are supported by most, but not all, of the functions in seaborn. Namely, a few older functions (e.g., :func:`lmplot` and :func:`regplot`) are more limited in what they accept."
    ]
   },
   {

pyproject.toml

@@ -26,7 +26,7 @@ requires-python = ">=3.8"
 dependencies = [
     "numpy>=1.20,!=1.24.0",
     "pandas>=1.2",
-    "matplotlib>=3.3,!=3.6.1",
+    "matplotlib>=3.4,!=3.6.1",
 ]
 
 [project.optional-dependencies]

seaborn/_base.py

@@ -933,7 +933,8 @@ def iter_data(
 
             grouping_keys = []
             for var in grouping_vars:
-                grouping_keys.append(levels.get(var, []))
+                key = levels.get(var)
+                grouping_keys.append([] if key is None else key)
 
             iter_keys = itertools.product(*grouping_keys)
             if reverse: