Merge branch 'main' into dependabot/github_actions/pypa/cibuildwheel-2.21.3

Author: mroeschke

pandas/_libs/tslibs/timedeltas.pyx

@@ -1864,10 +1864,12 @@ class Timedelta(_Timedelta):
 
     Parameters
     ----------
-    value : Timedelta, timedelta, np.timedelta64, str, or int
+    value : Timedelta, timedelta, np.timedelta64, str, int or float
         Input value.
     unit : str, default 'ns'
-        Denote the unit of the input, if input is an integer.
+        If input is an integer, denote the unit of the input.
+        If input is a float, denote the unit of the integer parts.
+        The decimal parts with resolution lower than 1 nanosecond are ignored.
 
         Possible values:
 

pandas/core/frame.py

@@ -10823,7 +10823,7 @@ def round(
         self, decimals: int | dict[IndexLabel, int] | Series = 0, *args, **kwargs
     ) -> DataFrame:
         """
-        Round a DataFrame to a variable number of decimal places.
+        Round numeric columns in a DataFrame to a variable number of decimal places.
 
         Parameters
         ----------

pyproject.toml

@@ -2,8 +2,8 @@
 # Minimum requirements for the build system to execute.
 # See https://github.com/scipy/scipy/pull/12940 for the AIX issue.
 requires = [
-    "meson-python==0.13.1",
-    "meson==1.2.1",
+    "meson-python>=0.13.1",
+    "meson>=1.2.1,<2",
     "wheel",
     "Cython~=3.0.5",  # Note: sync with setup.py, environment.yml and asv.conf.json
     # Force numpy higher than 2.0rc1, so that built wheels are compatible

web/pandas/community/ecosystem.md

@@ -239,6 +239,17 @@ Console](https://docs.spyder-ide.org/current/panes/ipythonconsole.html), and Spy
 render Numpydoc documentation on pandas objects in rich text with Sphinx
 both automatically and on-demand.
 
+### [marimo](https://marimo.io)
+
+marimo is a reactive notebook for Python and SQL that enhances productivity when working with dataframes. It provides several features to make data manipulation and visualization more interactive and fun:
+
+1. Rich, interactive displays: marimo can display pandas dataframes in interactive tables or charts with filtering and sorting capabilities.
+2. Data selection: Users can select data in tables or pandas-backed plots, and the selections are automatically sent to Python as pandas dataframes.
+3. No-code transformations: Users can interactively transform pandas dataframes using a GUI, without writing code. The generated code can be copied and pasted into the notebook.
+4. Custom filters: marimo allows the creation of pandas-backed filters using UI elements like sliders and dropdowns.
+5. Dataset explorer: marimo automatically discovers and displays all dataframes in the notebook, allowing users to explore and visualize data interactively.
+6. SQL integration: marimo allows users to write SQL queries against any pandas dataframes existing in memory.
+
 ## API
 
 ### [pandas-datareader](https://github.com/pydata/pandas-datareader)