PDEP-10: Add pyarrow as a required dependency (#52711)

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+# PDEP-10: PyArrow as a required dependency for default string inference implementation
+
+- Created: 17 April 2023
+- Status: Accepted
+- Discussion: [#52711](https://github.com/pandas-dev/pandas/pull/52711)
+              [#52509](https://github.com/pandas-dev/pandas/issues/52509)
+- Author: [Matthew Roeschke](https://github.com/mroeschke)
+          [Patrick Hoefler](https://github.com/phofl)
+- Revision: 1
+
+## Abstract
+
+This PDEP proposes that:
+
+- PyArrow becomes a required runtime dependency starting with pandas 3.0
+- The minimum version of PyArrow supported starting with pandas 3.0 is version 7 of PyArrow.
+- When the minimum version of PyArrow is bumped, PyArrow will be bumped to the highest version that has
+  been released for at least 2 years.
+- The pandas 2.1 release notes will have a big warning that PyArrow will become a required dependency starting
+  with pandas 3.0. We will pin a feedback issue on the pandas issue tracker. The note in the release notes will point
+  to that issue.
+- Starting in pandas 2.2, pandas raises a ``FutureWarning`` when PyArrow is not installed in the users
+  environment when pandas is imported. This will ensure that only one warning is raised and users can
+  easily silence it if necessary. This warning will point to the feedback issue.
+- Starting in pandas 3.0, the default type inferred for string data will be `ArrowDtype` with `pyarrow.string`
+  instead of `object`. Additionally, we will infer all dtypes that are listed below as well instead of storing as object.
+
+This will bring **immediate benefits to users**, as well as opening up the door for significant further
+benefits in the future.
+
+## Background
+
+PyArrow is an optional dependency of pandas that provides a wide range of supplemental features to pandas:
+
+- Since pandas 0.21.0, PyArrow provided I/O reading functionality for Parquet
+- Since pandas 1.2.0, pandas integrated PyArrow into the `ExtensionArray` interface to provide an
+  optional string data type backed by PyArrow
+- Since pandas 1.4.0, PyArrow provided I/0 reading functionality for CSV
+- Since pandas 1.5.0, pandas provided an `ArrowExtensionArray` and `ArrowDtype` to support all PyArrow
+  data types within the `ExtensionArray` interface
+- Since pandas 2.0.0, all I/O readers have the option to return PyArrow-backed data types, and many methods
+  now utilize PyArrow compute functions to
+accelerate PyArrow-backed data in pandas, notibly string and datetime types.
+
+As of pandas 2.0, one can feasibly utilize PyArrow as an alternative data representation to NumPy with advantages such as:
+
+1. Consistent `NA` support for all data types;
+2. Broader support of data types such as `decimal`, `date` and nested types;
+3. Better interoperability with other dataframe libraries based on Arrow.
+
+## Motivation
+
+While all the functionality described in the previous paragraph is currently optional, PyArrow has significant
+integration into many areas of pandas. With our roadmap noting that pandas strives for better Apache Arrow
+interoperability [^1] and many projects [^2], within or beyond the Python ecosystem, adopting or interacting with
+the Arrow format, making PyArrow a required dependency provides an additional signal of confidence in the Arrow
+ecosystem (as well as improving interoperability with it).
+
+### Immediate User Benefit 1: pyarrow strings
+
+Currently, when users pass string data into pandas constructors without specifying a data type, the resulting data type
+is `object`, which has significantly much worse memory usage and performance as compared to pyarrow strings.
+With pyarrow string support available since 1.2.0, requiring pyarrow for 3.0 will allow pandas to default
+the inferred type to the more efficient pyarrow string type.
+
+```python
+In [1]: import pandas as pd
+
+In [2]: pd.Series(["a"]).dtype
+# Current behavior
+Out[2]: dtype('O')
+
+# Future behavior in 3.0
+Out[2]: string[pyarrow]
+```
+
+Dask developers investigated performance and memory of pyarrow strings [here](https://www.coiled.io/blog/pyarrow-strings-in-dask-dataframes),
+and found them to be a significant improvement over the current `object` dtype.
+
+Little demo:
+```python
+import string
+import random
+
+import pandas as pd
+
+
+def random_string() -> str:
+    return "".join(random.choices(string.printable, k=random.randint(10, 100)))
+
+
+ser_object = pd.Series([random_string() for _ in range(1_000_000)])
+ser_string = ser_object.astype("string[pyarrow]")\
+```
+
+PyArrow backed strings are significantly faster than NumPy object strings:
+
+*str.len*
+
+```python
+In[1]: %timeit ser_object.str.len()
+118 ms ± 260 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
+
+In[2]: %timeit ser_string.str.len()
+24.2 ms ± 187 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
+```
+
+*str.startswith*
+
+```python
+In[3]: %timeit ser_object.str.startswith("a")
+136 ms ± 300 µs per loop (mean ± std. dev. of 7 runs, 10 loops each)
+
+In[4]: %timeit ser_string.str.startswith("a")
+11 ms ± 19.8 µs per loop (mean ± std. dev. of 7 runs, 100 loops each)
+```
+
+### Immediate User Benefit 2: Nested Datatypes
+
+Currently, if you try storing `dict`s in a pandas `Series`, you will again get the horrendeous `object` dtype:
+```python
+In [6]: pd.Series([{'a': 1, 'b': 2}, {'a': 2, 'b': 99}])
+Out[6]:
+0     {'a': 1, 'b': 2}
+1    {'a': 2, 'b': 99}
+dtype: object
+```
+
+If `pyarrow` were required, this could have been auto-inferred to be `pyarrow.struct`, which again
+would come with memory and performance improvements.
+
+### Immediate User Benefit 3: Interoperability
+
+Other Arrow-backed dataframe libraries are growing in popularity. Having the same memory representation
+would improve interoperability with them, as operations such as:
+```python
+import pandas as pd
+import polars as pl
+
+df = pd.DataFrame(
+  {
+    'a': ['one', 'two'],
+    'b': [{'name': 'Billy', 'age': 3}, {'name': 'Bob', 'age': 4}],
+  }
+)
+pl.from_pandas(df)
+```
+could be zero-copy. Users making use of multiple dataframe libraries would more easily be able to
+switch between them.
+
+### Future User Benefits:
+
+Requiring PyArrow would simplify the related development within pandas and potentially improve NumPy
+functionality that would be better suited by PyArrow including:
+
+- Avoiding runtime checking if PyArrow is available to perform PyArrow object inference during constructor or indexing operations
+
+- NumPy object dtype will be avoided as much as possible. This means that every dtype that has a PyArrow equivalent is inferred automatically as such. This includes:
+  - decimal
+  - binary
+  - nested types (list or dict data)
+  - strings
+  - time
+  - date
+
+#### Developer benefits
+
+First, this would simplify development of pyarrow-backed datatypes, as it would avoid
+optional dependency checks.
+
+Second, it could potentially remove redundant functionality:
+- fastparquet engine in `read_parquet`;
+- potentially simplifying the `read_csv` logic (needs more investigation);
+- factorization;
+- datetime/timezone ops.
+
+## Drawbacks
+
+Including PyArrow would naturally increase the installation size of pandas. For example, installing pandas and PyArrow
+using pip from wheels, numpy and pandas requires about `70MB`, and including PyArrow requires an additional `120MB`.
+An increase of installation size would have negative implication using pandas in space-constrained development or deployment environments
+such as AWS Lambda.
+
+Additionally, if a user is installing pandas in an environment where wheels are not available through a `pip install` or `conda install`,
+the user will need to also build Arrow C++ and related dependencies when installing from source. These environments include
+
+- Alpine linux (commonly used as a base for Docker containers)
+- WASM (pyodide and pyscript)
+- Python development versions
+
+Lastly, pandas development and releases will need to be mindful of PyArrow's development and release cadance. For example when
+supporting a newly released Python version, pandas will also need to be mindful of PyArrow's wheel support for that Python version
+before releasing a new pandas version.
+
+## F.A.Q.
+
+**Q: Why can't pandas just use numpy string and numpy void datatypes instead of pyarrow string and pyarrow struct?**
+
+**A**: NumPy strings aren't yet available, whereas pyarrow strings are. NumPy void datatype would be different to pyarrow struct,
+  not bringing the same interoperabitlity benefit with other arrow-based dataframe libraries.
+
+**Q: Are all pyarrow dtypes ready? Isn't it too soon to make them the default?**
+
+**A**: They will likely be ready by 3.0 - however, we're not making them the default (yet).
+  For example, `pd.Series([1, 2, 3])` will continue to be auto-inferred to be
+  `np.int64`.  We will only change the default for dtypes which currently have no `numpy`-backed equivalent and which are
+  stored as `object` dtype, such as strings and nested datatypes.
+
+### PDEP-10 History
+
+- 17 April 2023: Initial version
+- 8 May 2023: Changed proposal to make pyarrow required in pandas 3.0 instead of 2.1
+
+[^1] <https://pandas.pydata.org/docs/development/roadmap.html#apache-arrow-interoperability>
+[^2] <https://arrow.apache.org/powered_by/>