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numpy/README.md

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+# Anatomy of a numpy array
+## one dimension, float64
+![1d array](ndarray-memory-layout-1d.svg)
+
+## two dimensions, square, float64
+![2d array - square](ndarray-memory-layout-2d-square.svg)
+
+## two dimensions, rectangular, int32
+![2d array - rectangular](ndarray-memory-layout-2d-rectangular.svg)
+
+## what about Python lists?
+![memory layout of a Python list](python-list-memory-layout.svg)
+
+## interesting attributes of numpy arrays
+  - `x.data`, `x.data.hex()`, `x.data.format`, `x.tobytes()`
+  - `x.flags`:
+    - `OWNDATA`
+    - `C_CONTIGUOUS`
+    - `F_CONTIGUOUS`
+    - more [flags](https://numpy.org/doc/stable/reference/generated/numpy.ndarray.flags.html)
+
+## If your arrays are bigger than RAM
+  - [`numpy.memmap`](https://numpy.org/doc/stable/reference/generated/numpy.memmap.html): an array-like
+      object that maps memory to an array stored on disk, used for accessing small segments of large
+      files on disk, without reading the entire file into memory. Use with caution!
+
+  - [`HDF5`](https://support.hdfgroup.org/documentation/hdf5/latest/_intro_h_d_f5.html): hierarchical
+      data format, with aribitrary metadata and multilanguage support
+      with [`h5py`](https://docs.h5py.org/en/stable/) with an array-lie interface
+  - other projects, for example [`xarray`](https://docs.xarray.dev/en/stable/) 
+      and [`zarr`](https://zarr.readthedocs.io/en/stable/)