{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "7584fbf6",
   "metadata": {},
   "source": [
    "# File input and output with arrays\n",
    "\n",
    "NumPy is able to store data in some text or binary formats on disk and load it from there. However, in this section I only discuss NumPy's own binary format, as mostly pandas or other tools are used to load text or table data (see [Read, persist and provide data](../../data-processing/index.rst).\n",
    "\n",
    "`np.save` and `np.load` are the two most important functions for efficiently saving and loading array data to disk. Arrays are saved by default in an uncompressed raw binary format with the file extension `.npy`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "d6c92106",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "\n",
    "data = np.random.randn(7, 3)\n",
    "\n",
    "np.save(\"my_data\", data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "f9b70227",
   "metadata": {},
   "source": [
    "If the file path does not already end in `.npy`, the extension is appended. The array on the hard disk can then be loaded with `np.load`:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ed098f7c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.30602873,  0.01517455, -1.17839484],\n",
       "       [ 1.34255075, -0.08358613, -0.71584898],\n",
       "       [-2.18879985, -0.24203697, -0.64047467],\n",
       "       [ 1.22940807,  0.81555884, -0.92011422],\n",
       "       [-1.2429792 , -0.16863888,  1.0725737 ],\n",
       "       [-1.08591637,  1.19503417, -2.37629923],\n",
       "       [-0.56407652, -0.42741029,  0.79441985]])"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(\"my_data.npy\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "467f7c50",
   "metadata": {},
   "source": [
    "You can save multiple arrays in an uncompressed archive by using `np.savez` and passing the arrays as keyword arguments:"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "3df6c190",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez(\"data_archive.npz\", a=data, b=np.square(data))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "14ea0da5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5.31776850e+00, 2.30267094e-04, 1.38861441e+00],\n",
       "       [1.80244252e+00, 6.98664125e-03, 5.12439756e-01],\n",
       "       [4.79084477e+00, 5.85818951e-02, 4.10207800e-01],\n",
       "       [1.51144420e+00, 6.65136217e-01, 8.46610174e-01],\n",
       "       [1.54499728e+00, 2.84390717e-02, 1.15041434e+00],\n",
       "       [1.17921437e+00, 1.42810668e+00, 5.64679805e+00],\n",
       "       [3.18182316e-01, 1.82679555e-01, 6.31102899e-01]])"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "archive = np.load(\"data_archive.npz\")\n",
    "\n",
    "archive[\"b\"]"
   ]
  }
 ],
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