docs: replace getting_started page with quickstart

docs/conf.py

@@ -104,6 +104,7 @@ def skip_submodules(
      "spec/v3": "https://zarr-specs.readthedocs.io/en/latest/v3/core/v3.0.html",
      "license": "https://github.com/zarr-developers/zarr-python/blob/main/LICENSE.txt",
      "tutorial": "user-guide",
+     "getting-started": "quickstart",
 }
 
 # The language for content autogenerated by Sphinx. Refer to documentation

docs/index.rst

@@ -8,13 +8,14 @@ Zarr-Python
     :maxdepth: 1
     :hidden:
 
-    getting_started
+    quickstart
     about
     user-guide/index
     api/index
     release
     contributing
     roadmap
+    installation
 
 **Version**: |version|
 
@@ -34,20 +35,20 @@ Zarr is a file storage format for chunked, compressed, N-dimensional arrays base
     .. grid-item-card::
         :img-top: _static/index_getting_started.svg
 
-        Getting Started
-        ^^^^^^^^^^^^^^^
+        Quick Start
+        ^^^^^^^^^^^
 
-        New to Zarr? Check out the getting started guide. It contains an
+        New to Zarr? Check out the quick start guide. It contains a brief
         introduction to Zarr's main concepts and links to additional tutorials.
 
         +++
 
-        .. button-ref:: getting_started
+        .. button-ref:: quickstart
             :expand:
             :color: dark
             :click-parent:
 
-            To the getting started guide
+            To the Quick Start
 
     .. grid-item-card::
         :img-top: _static/index_user_guide.svg

docs/quickstart.rst

@@ -0,0 +1,190 @@
+.. only:: doctest
+
+   >>> import shutil
+   >>> shutil.rmtree('data', ignore_errors=True)
+   >>>
+   >>> import numpy as np
+   >>> np.random.seed(0)
+
+Quickstart
+==========
+
+Welcome to the Zarr-Python Quickstart guide! This page will help you get up and running with
+the Zarr library in Python to efficiently manage and analyze multi-dimensional arrays.
+
+Zarr is a powerful library for storage of n-dimensional arrays, supporting chunking,
+compression, and various backends, making it a versatile choice for scientific and
+large-scale data.
+
+Installation
+------------
+
+Zarr requires Python 3.11 or higher. You can install it via `pip`:
+
+.. code-block:: bash
+
+    pip install zarr
+
+or `conda`:
+
+.. code-block:: bash
+
+    conda install --channel conda-forge zarr
+
+Creating an Array
+-----------------
+
+To get started, you can create a simple Zarr array::
+
+    >>> import zarr
+    >>> import numpy as np
+    >>>
+    >>> # Create a 2D Zarr array
+    >>> z = zarr.create_array(
+    ...    store="data/example-1.zarr",
+    ...    shape=(100, 100),
+    ...    chunks=(10, 10),
+    ...    dtype="f4"
+    ... )
+    >>>
+    >>> # Assign data to the array
+    >>> z[:, :] = np.random.random((100, 100))
+    >>> z.info
+    Type               : Array
+    Zarr format        : 3
+    Data type          : DataType.float32
+    Shape              : (100, 100)
+    Chunk shape        : (10, 10)
+    Order              : C
+    Read-only          : False
+    Store type         : LocalStore
+    Codecs             : [{'endian': <Endian.little: 'little'>}, {'level': 0, 'checksum': False}]
+    No. bytes          : 40000 (39.1K)
+
+Here, we created a 2D array of shape ``(100, 100)``, chunked into blocks of
+``(10, 10)``, and filled it with random floating-point data. This array was
+written to a ``LocalStore`` in the ``data/example-1.zarr`` directory.
+
+Compression and Filters
+~~~~~~~~~~~~~~~~~~~~~~~
+
+Zarr supports data compression and filters. For example, to use Blosc compression::
+
+    >>> z = zarr.create_array(
+    ...    "data/example-3.zarr",
+    ...    mode="w", shape=(100, 100),
+    ...    chunks=(10, 10), dtype="f4",
+    ...    compressor=zarr.codecs.BloscCodec(cname="zstd", clevel=3, shuffle=zarr.codecs.BloscShuffle.SHUFFLE)
+    ... )
+    >>> z[:, :] = np.random.random((100, 100))
+    >>>
+    >>> z.info
+    Type               : Array
+    Zarr format        : 3
+    Data type          : DataType.float32
+    Shape              : (100, 100)
+    Chunk shape        : (10, 10)
+    Order              : C
+    Read-only          : False
+    Store type         : LocalStore
+    Codecs             : [{'endian': <Endian.little: 'little'>}, {'level': 0, 'checksum': False}]
+    No. bytes          : 40000 (39.1K)
+
+This compresses the data using the Zstandard codec with shuffle enabled for better compression.
+
+Hierarchical Groups
+-------------------
+
+Zarr allows you to create hierarchical groups, similar to directories::
+
+    >>> # Create nested groups and add arrays
+    >>> root = zarr.group("data/example-2.zarr")
+    >>> foo = root.create_group(name="foo")
+    >>> bar = root.create_array(
+    ...     name="bar", shape=(100, 10), chunks=(10, 10)
+    ... )
+    >>> spam = foo.create_array(name="spam", shape=(10,), dtype="i4")
+    >>>
+    >>> # Assign values
+    >>> bar[:, :] = np.random.random((100, 10))
+    >>> spam[:] = np.arange(10)
+    >>>
+    >>> # print the hierarchy
+    >>> root.tree()
+    /
+    └── foo
+        └── spam (10,) int32
+    <BLANKLINE>
+
+This creates a group with two datasets: ``foo`` and ``bar``.
+
+Persistent Storage
+------------------
+
+Zarr supports persistent storage to disk or cloud-compatible backends. While examples above
+utilized a :class:`zarr.storage.LocalStore`, a number of other storage options are available,
+including the :class:`zarr.storage.ZipStore` and :class:`zarr.storage.FsspecStore`::
+
+    >>> # Store the array in a ZIP file
+    >>> store = zarr.storage.ZipStore("data/example-3.zip", mode='w')
+    >>>
+    >>> z = zarr.create_array(
+    ...     store=store,
+    ...     mode="w",
+    ...     shape=(100, 100),
+    ...     chunks=(10, 10),
+    ...     dtype="f4"
+    ... )
+    >>>
+    >>> # write to the array
+    >>> z[:, :] = np.random.random((100, 100))
+    >>>
+    >>> # the ZipStore must be explicitly closed
+    >>> store.close()
+
+To open an existing array::
+
+    >>> # Open the ZipStore in read-only mode
+    >>> store = zarr.storage.ZipStore("data/example-3.zip", read_only=True)
+    >>>
+    >>> z = zarr.open_array(store, mode='r')
+    >>>
+    >>> # read the data as a NumPy Array
+    >>> z[:]
+    array([[0.66734236, 0.15667458, 0.98720884, ..., 0.36229587, 0.67443246,
+            0.34315267],
+        [0.65787303, 0.9544212 , 0.4830079 , ..., 0.33097172, 0.60423803,
+            0.45621237],
+        [0.27632037, 0.9947008 , 0.42434934, ..., 0.94860053, 0.6226942 ,
+            0.6386924 ],
+        ...,
+        [0.12854576, 0.934397  , 0.19524333, ..., 0.11838563, 0.4967675 ,
+            0.43074256],
+        [0.82029045, 0.4671437 , 0.8090906 , ..., 0.7814118 , 0.42650765,
+            0.95929915],
+        [0.4335856 , 0.7565437 , 0.7828931 , ..., 0.48119593, 0.66220033,
+            0.6652362 ]], shape=(100, 100), dtype=float32)
+
+Cloud Storage Backends
+~~~~~~~~~~~~~~~~~~~~~~
+
+Zarr integrates seamlessly with cloud storage such as Amazon S3 and Google Cloud Storage
+using external libraries like `s3fs <https://s3fs.readthedocs.io>`_ or
+`gcsfs <https://gcsfs.readthedocs.io>`_.
+
+For example, to use S3::
+
+    >>> import s3fs # doctest: +SKIP
+    >>>
+    >>> z = zarr.create_array("s3://example-bucket/foo", mode="w", shape=(100, 100), chunks=(10, 10)) # doctest: +SKIP
+    >>> z[:, :] = np.random.random((100, 100)) # doctest: +SKIP
+
+Read more about Zarr's storage options  in the :ref:`User Guide <user-guide-storage>`.
+
+Next Steps
+----------
+
+Now that you're familiar with the basics, explore the following resources:
+
+- `User Guide <user-guide>`_
+- `API Reference <api>`_