Added npz example and credited Aalto for the table

content/scientific-data.rst

@@ -140,55 +140,55 @@
      - | Long term
        | storage/sharing:
 
    * - :ref:`Pickle <pickle>`
      - ❌
      - 🟨
      - ✅
      - 🟨
      - 🟨
      - ❌
 
    * - :ref:`CSV <csv>`
      - ✅
      - ❌
      - ❌
      - ✅
      - 🟨
      - ✅
 
    * - :ref:`Feather <feather>`
      - ❌
      - ✅
      - ❌
      - ✅
      - ❌
      - ❌
 
    * - :ref:`Parquet <parquet>`
      - ❌
      - ✅
      - 🟨
      - ✅
      - 🟨
      - ✅
 
    * - :ref:`npy <npy>`
      - ❌
      - 🟨
      - ❌
      - ❌
      - ✅
      - ❌
 
    * - :ref:`HDF5 <hdf5>`
      - ❌
      - ✅
      - ❌
      - ❌
      - ✅
      - ✅
 
    * - :ref:`NetCDF4 <netcdf4>`
      - ❌
      - ✅
      - ❌
@@ -196,7 +196,7 @@
      - ✅
      - ✅
 
    * - :ref:`JSON <json>`
      - ✅
      - ❌
      - 🟨
@@ -204,7 +204,7 @@
      - ❌
      - ✅
 
    * - :ref:`Excel <excel>`
      - ❌
      - ❌
      - ❌
@@ -212,7 +212,7 @@
      - ❌
      - 🟨
 
    * - :ref:`Graph formats <graph>`
      - 🟨
      - 🟨
      - ❌
@@ -226,6 +226,8 @@
     - 🟨 : Ok / depends on a case
     - ❌ : Bad
 
+    Adapted from Aalto university's `Python for scientific computing <https://aaltoscicomp.github.io/python-for-scicomp/work-with-data/#what-is-a-data-format>`__.
+
 Some of these formats (e.g. JSON and CSV) are saved as text files (ASCII), thus they are 
 human-readable. This makes them easier to visually check them (e.g. for format errors) and 
 are supported out of the box by many tools. However, they tend to be slower during I/O and

content/stack.rst

@@ -359,14 +359,44 @@ Views and copies of arrays
 I/O with NumPy
 ^^^^^^^^^^^^^^
 
-- Numpy provides functions for reading data from file and for writing data
-  into the files
-- Simple text files
-
-    - :meth:`numpy.loadtxt`
-    - :meth:`numpy.savetxt`
-    - Data in regular column layout
-    - Can deal with comments and different column delimiters
+Numpy provides functions for reading from/writing to files. Both ASCII and binary
+formats are supported with the CSV and npy/npz formats:
+
+.. tabs::
+
+  .. tab:: CSV
+
+    The ``numpy.loadtxt()`` and ``numpy.savetxt()`` functions can be used. They 
+    save in a regular column layout and can deal with different delimiters, 
+    column titles and numerical representations.
+
+      .. code-block:: python
+
+        a = np.array([1, 2, 3, 4])
+        np.savetxt("my_array.csv", a)
+        b = np.loadtxt("my_array.csv")
+        a == b
+        # True
+
+  .. tab:: Binary
+
+    The npy format is a binary format used to dump arrays of any 
+    shape. Several arrays can be saved into a single npz file, which is 
+    simply a zipped collection of different npy files. All the arrays to 
+    be saved into a npz file can be passed as kwargs to the ``numpy.savez()``
+    function. The data can then be recovered using the ``numpy.load()`` method,
+    which returns a dictionary-like object in which each key points to one of the arrays:
+
+    .. code-block:: python
+
+      a = np.array([1, 2, 3, 4])
+      b = np.array([5, 6, 7, 8])
+
+      np.savez("my_arrays.npz", array_1=a, array_2=b)
+      data = np.load("my_arrays.npz")
+      data['array_1'] == a
+      data['array_2'] == b
+      # Both are true
 
 
 Random numbers