use python code blocks in readme

Author: mrocklin

README.rst

@@ -41,7 +41,9 @@ Design goals
 Usage
 -----
 
-Create an array::
+Create an array
+
+.. code-block:: python
 
     >>> import numpy as np
     >>> import zarr
@@ -51,15 +53,19 @@ Create an array::
       cname: blosclz; clevel: 5; shuffle: 1 (BYTESHUFFLE)
       nbytes: 38.1M; cbytes: 0; initialized: 0/100
 
-Fill it with some data::
+Fill it with some data
+
+.. code-block:: python
 
     >>> z[:] = np.arange(10000000, dtype='i4').reshape(10000, 1000)
     >>> z
     zarr.ext.SynchronizedArray((10000, 1000), int32, chunks=(1000, 100))
       cname: blosclz; clevel: 5; shuffle: 1 (BYTESHUFFLE)
       nbytes: 38.1M; cbytes: 2.0M; ratio: 19.3; initialized: 100/100
 
-Obtain a NumPy array by slicing::
+Obtain a NumPy array by slicing
+
+.. code-block:: python
 
     >>> z[:]
     array([[      0,       1,       2, ...,     997,     998,     999],
@@ -86,7 +92,9 @@ Obtain a NumPy array by slicing::
            [9998000, 9998001, 9998002, ..., 9998097, 9998098, 9998099],
            [9999000, 9999001, 9999002, ..., 9999097, 9999098, 9999099]], dtype=int32)
 
-Resize the array and add more data::
+Resize the array and add more data
+
+.. code-block:: python
 
     >>> z.resize(20000, 1000)
     >>> z
@@ -100,7 +108,9 @@ Resize the array and add more data::
       nbytes: 76.3M; cbytes: 4.0M; ratio: 19.3; initialized: 200/200
 
 For convenience, an ``append()`` method is also available, which can be used to
-append data to any axis::
+append data to any axis
+
+.. code-block:: python
 
     >>> a = np.arange(10000000, dtype='i4').reshape(10000, 1000)
     >>> z = zarr.array(a, chunks=(1000, 100))
@@ -119,7 +129,9 @@ append data to any axis::
       cname: blosclz; clevel: 5; shuffle: 1 (BYTESHUFFLE)
       nbytes: 152.6M; cbytes: 7.6M; ratio: 20.2; initialized: 400/400
 
-Create a persistent array (data stored on disk)::
+Create a persistent array (data stored on disk)
+
+.. code-block:: python
 
     >>> path = 'example.zarr'
     >>> z = zarr.open(path, shape=(10000, 1000), dtype='i4', chunks=(1000, 100))
@@ -133,7 +145,9 @@ Create a persistent array (data stored on disk)::
 There is no need to close a persistent array. Data are automatically flushed
 to disk.
 
-If you're working with really big arrays, try the 'lazy' option::
+If you're working with really big arrays, try the 'lazy' option
+
+.. code-block:: python
 
     >>> path = 'big.zarr'
     >>> z = zarr.open(path, shape=(1e8, 1e7), dtype='i4', chunks=(1000, 1000), lazy=True)