WEB: add pandas 3.0 announcement blog post (#63796)

Co-authored-by: Irv Lustig <irv@princeton.com>
Co-authored-by: Richard Shadrach <45562402+rhshadrach@users.noreply.github.com>

Author: 3 people

doc/_templates/docs-announcement-banner.html

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-Announcement: pandas 3.0.0 release candidate ready for testing! Read more <a href="https://pandas.pydata.org/community/blog/pandas-3.0-release-candidate.html">in the blog post</a>
+Announcement: pandas 3.0 released! Read more <a href="https://pandas.pydata.org/community/blog/pandas-3.0.html">in the blog post</a>

web/pandas/community/blog/pandas-3.0.md

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+Title: pandas 3.0 released!
+Date: 2026-01-21
+
+# pandas 3.0 released!
+
+We're excited to announce the release of pandas 3.0.0. This major
+long-awaited release brings significant improvements to pandas, but also
+features some potentially breaking changes.
+
+## Highlights of pandas 3.0
+
+pandas 3.0 introduces several major enhancements:
+
+- **Dedicated string data type by default**: string columns are now inferred as
+  the new `str` dtype instead of `object`, providing better performance and type
+  safety
+- **Consistent copy/view behaviour with Copy-on-Write (CoW)** (a.k.a. getting
+  rid of the `SettingWithCopyWarning`): more predictable and consistent behavior
+  for all operations, with improved performance through avoiding unnecessary
+  copies
+- **New default resolution for datetime-like data**: no longer defaulting to
+  nanoseconds, but generally microseconds (or the resolution of the input), when
+  constructing datetime or timedelta data (avoiding out-of-bounds errors
+  for dates with a year before 1678 or after 2262)
+- **New `pd.col` syntax**: initial support for `pd.col()` as a simplified syntax
+  for creating callables in `DataFrame.assign`
+
+Further, pandas 3.0 includes a lot of other improvements and bug fixes. You can
+find the complete list of changes in the
+[release notes](https://pandas.pydata.org/docs/dev/whatsnew/v3.0.0.html).
+
+## Upgrading to pandas 3.0
+
+The pandas 3.0 release removed functionality that was deprecated in previous releases
+(see [here](https://pandas.pydata.org/docs/whatsnew/v3.0.0.html#whatsnew-300-prior-deprecations)
+for an overview). It is recommended to first upgrade to pandas 2.3 and to ensure
+your code is working without warnings, before upgrading to pandas 3.0.
+
+Further, as a major release, pandas 3.0 includes some breaking changes that may
+require updates to your code. The two most significant changes are the new
+string dtype and the copy/view behaviour changes, detailed below. An overview of
+all potentially breaking changes can be found in the [Backwards incompatible API
+changes](https://pandas.pydata.org/docs/whatsnew/v3.0.0.html#backwards-incompatible-api-changes)
+section of the release notes.
+
+### 1. Dedicated string data type by default
+
+Starting with pandas 3.0, string columns are automatically inferred as `str`
+dtype instead of the numpy `object` (which can store any Python object).
+
+**Example:**
+```python
+# Old behavior (pandas < 3.0)
+>>> ser = pd.Series(["a", "b"])
+>>> ser
+0    a
+1    b
+dtype: object  # <-- numpy object dtype
+
+# New behavior (pandas 3.0)
+>>> ser = pd.Series(["a", "b"])
+>>> ser.dtype
+>>> ser
+0    a
+1    b
+dtype: str  # <-- new string dtype
+```
+
+This change improves performance and type safety, but may require code updates,
+especially for library code that currently looks for "object" dtype when
+expecting string data.
+
+For more details, see the
+[migration guide for the new string data type](https://pandas.pydata.org/docs/dev/user_guide/migration-3-strings.html).
+
+This new data type will use the `pyarrow` library under the hood, if installed,
+to provide the performance improvements. Therefore we strongly recommend to
+install `pyarrow` alongside pandas (but `pyarrow` is not a required dependency
+installed by default).
+
+### 2. Consistent copy/view behaviour with Copy-on-Write (CoW)
+
+Copy-on-Write is now the default and only mode in pandas 3.0. This makes
+behavior more consistent and predictable, and avoids a lot of defensive copying
+(improving performance), but requires updates to certain coding patterns.
+
+The most impactfull change is that **chained assignment will no longer work**.
+As a result, the `SettingWithCopyWarning` is also removed (since there is no
+longer ambiguity whether it would work or not), and defensive `.copy()` calls
+to silence the warning are no longer needed.
+
+**Example:**
+```python
+# Old behavior (pandas < 3.0) - chained assignment
+df["foo"][df["bar"] > 5] =   # This might modify df (unpredictable)
+
+# New behavior (pandas 3.0) - must do the modification in one step (e.g. with .loc)
+df.loc[df["bar"] > 5, "foo"] = 100
+```
+
+In general, any result of an indexing operation or method now always behaves as
+if it were a copy, so modifications of the result won't affect the original
+DataFrame.
+
+For more details, see the
+[Copy-on-Write migration guide](https://pandas.pydata.org/docs/dev/user_guide/copy_on_write.html#migrating-to-copy-on-write).
+
+## Obtaining pandas 3.0
+
+You can install the latest pandas 3.0 release from PyPI:
+
+```bash
+python -m pip install --upgrade pandas==3.0.*
+```
+
+Or from conda-forge using conda/mamba:
+
+```bash
+conda install -c conda-forge pandas=3.0
+```
+
+## Running into an issue or regression?
+
+Please report any problem you encounter with the release on the pandas [issue tracker](https://github.com/pandas-dev/pandas/issues).
+
+Thanks to all the contributors who made this release possible!