ch04.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "6bbf8bd2-7a31-4b0f-b110-55d493c36751",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Chapter 4: NumPy Basics: Arrays and Vectorized Computation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "dd10f7c8-ef90-4783-8f65-9573bd4bc215",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "cd66244a-eb43-470e-8c99-de5237b8ed3b",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_arr = np.arange(1_000_000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "708b3bb1-76bd-4028-b273-82e711862b5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "my_list = list(range(1_000_000))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "82d65a03-bdce-43eb-b70a-c91b04ff59cc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "1.38 ms ± 15.1 μs per loop (mean ± std. dev. of 7 runs, 1,000 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit my_arr2 = my_arr * 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "0b95ec8d-6849-4b4f-8f98-07991d033b64",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "56.4 ms ± 526 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit my_list2 = [x * 2 for x in my_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "da8d2c8d-ed84-4366-952d-192168631db4",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "b6b03f23-3534-4961-849d-ea7ea31bca12",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([[1.5, -0.1, 3], [0, -3, 6.5]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "6973c325-0b4f-41ea-96ac-136f99c67de7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.5, -0.1,  3. ],\n",
       "       [ 0. , -3. ,  6.5]])"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "91fa4f95-3574-451b-a10e-f20267ec17fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 15.,  -1.,  30.],\n",
       "       [  0., -30.,  65.]])"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data * 10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "23b1a0a9-2776-4764-b122-b6a3b5bab86f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 3. , -0.2,  6. ],\n",
       "       [ 0. , -6. , 13. ]])"
      ]
     },
     "execution_count": 24,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data + data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "332f3f37-a2a5-4c17-ba47-b30e00e23e67",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 3)"
      ]
     },
     "execution_count": 26,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "18a6ad1f-b3cc-47d1-a237-74acf20ed4b3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "7410bf7b-20f3-48c9-b6de-773b48b172d3",
   "metadata": {},
   "outputs": [],
   "source": [
    "data1 = [6, 7.5, 8, 0, 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "bf04b508-3ce0-40e7-9edd-4044f1cd7e4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array(data1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "49f8cfae-7f60-4f7c-93ff-c87f7cb20c6e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([6. , 7.5, 8. , 0. , 1. ])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "78983058-deb4-427d-b0f7-714a802bd17c",
   "metadata": {},
   "outputs": [],
   "source": [
    "data2 = [[1, 2, 3, 4], [5, 6, 7, 8]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "d53879e5-61d9-4b4d-be12-43b715788a46",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array(data2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "ed830b9a-e704-4b01-a522-539c6366f86b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3, 4],\n",
       "       [5, 6, 7, 8]])"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "d29fe8f5-c84e-4ce1-97a6-0b61f07f3cf8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.ndim"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "3f4343d5-6c78-4dea-8339-bbcf3f8f9332",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2, 4)"
      ]
     },
     "execution_count": 48,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "1740a2f6-aef0-4059-a270-4ca9888da3a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 50,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "a2af4aae-47a9-416a-a53c-5627f8f3bab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 52,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "20204a3c-d992-463c-8cc2-3c22ffc5863c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 0., 0., 0., 0., 0., 0., 0., 0., 0.])"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "ce84a295-983b-4e88-9b0a-17fb0b74a269",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 56,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.zeros((3, 6))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "95644b82-c088-4716-bf9b-e7d484f64348",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[1.08775020e-311, 3.16202013e-322],\n",
       "        [0.00000000e+000, 0.00000000e+000],\n",
       "        [1.14587773e-312, 3.92322211e+179]],\n",
       "\n",
       "       [[1.40013485e-076, 1.10979059e-047],\n",
       "        [1.76177705e-076, 4.01657322e-057],\n",
       "        [3.64054348e+175, 3.40418724e+175]]])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# numpy.empty returns uninitialized memory and thus may contain nonzero \"garbage\" values. \n",
    "np.empty((2, 3, 2))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "e11d544a-7dd5-477c-b124-9e333b42e260",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4,  5,  6,  7,  8,  9, 10, 11, 12, 13, 14])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.arange(15)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "17af7431-09fa-4cba-8221-c952ea2c8320",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr1 = np.array([1, 2, 3], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "94d3a419-2ce3-4dc8-8656-0d4acffbbfa7",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array([1, 2, 3], dtype=np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "b623e8c8-7a2d-4c8b-9836-6268167c6ff0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 68,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr1.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "24f20b6b-dca1-43b2-ab35-26ad2f6e4a55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "5e815c5a-1f82-4f38-b92a-ebbde6cbe52c",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([1, 2, 3, 4, 5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "9f2903f3-5dc3-4a24-a4c7-9d694c8742a1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('int32')"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "4f4a2145-ccbc-4541-a73e-f5517d0d08a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "float_arr = arr.astype(np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "4582ef66-360d-46b9-b4c1-4836c1872a8d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1., 2., 3., 4., 5.])"
      ]
     },
     "execution_count": 82,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "db797b5b-9994-4899-9885-ae2821dc1e44",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "float_arr.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3fcd0ee0-5698-4a3c-89f5-7820c0bfa495",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([3.7, -1.2, -2.6, 0.5, 12.9, 10.1])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "edd89832-9dac-486a-9ee2-e56d5c48dfcd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3.7, -1.2, -2.6,  0.5, 12.9, 10.1])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "ca1abbd8-8ec8-4e13-945f-3b38bc60008b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 3, -1, -2,  0, 12, 10])"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.astype(np.int32)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "5a917247-c047-48d7-ad55-99ad984c1fdf",
   "metadata": {},
   "outputs": [],
   "source": [
    "numeric_strings = np.array([\"1.25\", \"-9.6\", \"42\"], dtype=np.string_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "7dbff520-61ea-45f7-b818-6a8e1c913507",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1.25, -9.6 , 42.  ])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_strings.astype(float)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "dba09557-7761-46cb-9fd9-ee34a6ce5b48",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('S4')"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_strings.dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "42cc3721-1ff0-459d-932b-e4594424fa2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "dtype('float64')"
      ]
     },
     "execution_count": 98,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "numeric_strings.astype(float).dtype"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "677aac3d-6016-4f1f-a2ca-c85043be19ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "int_array = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "83eb2b67-ccc6-4b65-bcec-dddc690c3a36",
   "metadata": {},
   "outputs": [],
   "source": [
    "calibers = np.array([.22, .270, .357, .380, .44, .50], dtype=np.float64)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "daaeb55b-224a-4c88-9fd8-02cc5c4a7599",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0., 1., 2., 3., 4., 5., 6., 7., 8., 9.])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "int_array.astype(calibers.dtype)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "05524e56-b164-43c7-97ef-9fa8f5962507",
   "metadata": {},
   "outputs": [],
   "source": [
    "zeros_uint32 = np.zeros(8, dtype=\"u4\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 110,
   "id": "6a478efe-a0a5-4e7c-8cd2-0ffb1823a0ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 0, 0, 0, 0, 0, 0, 0], dtype=uint32)"
      ]
     },
     "execution_count": 110,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "zeros_uint32"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 112,
   "id": "d43a2193-876b-46ba-9c8c-85ffcc8ab571",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Calling astype always creates a new array (a copy of the data), even if the new data type is the same as the old data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 114,
   "id": "965133ee-5250-4923-9871-2b2901c6eac3",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Arrays are important because they enable you to express batch operations on data without writing any for loops. \n",
    "# NumPy users call this vectorization. \n",
    "# Any arithmetic operations between equal-size arrays apply the operation element-wise."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "16a54aaa-76b6-4d0d-b2b8-d11f4981b1fd",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([[1., 2., 3.], [4., 5., 6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 118,
   "id": "c3c3e5db-86d4-4ca4-a5b3-424764284ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]])"
      ]
     },
     "execution_count": 118,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 120,
   "id": "f0aa5faa-3b21-452c-bb7d-c809cb22c25a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  4.,  9.],\n",
       "       [16., 25., 36.]])"
      ]
     },
     "execution_count": 120,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr * arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 122,
   "id": "1ae0a240-9aaf-4451-98cd-3e5c4bf840ca",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0.],\n",
       "       [0., 0., 0.]])"
      ]
     },
     "execution_count": 122,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr - arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e5c1c7e7-111a-4940-8c89-eaf265c6885f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A scalar in NumPy is a single numeric element that has a specific data type."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 124,
   "id": "c45fddb2-ce84-4417-879f-0cea51ca50c2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1.        , 0.5       , 0.33333333],\n",
       "       [0.25      , 0.2       , 0.16666667]])"
      ]
     },
     "execution_count": 124,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "1 / arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "7b43efc6-aaa2-4023-9f11-5e2ccfea0c33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.,  4.,  9.],\n",
       "       [16., 25., 36.]])"
      ]
     },
     "execution_count": 126,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr ** 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 128,
   "id": "f4726f95-d8df-4b3e-a00c-817faf45d1b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array([[0., 4., 1.], [7., 2., 12.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 130,
   "id": "938d5d77-c8e6-49bf-a42d-1d39670e22a0",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.,  4.,  1.],\n",
       "       [ 7.,  2., 12.]])"
      ]
     },
     "execution_count": 130,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 132,
   "id": "b821372d-2f85-4119-a130-836dcfa2fb50",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[False,  True, False],\n",
       "       [ True, False,  True]])"
      ]
     },
     "execution_count": 132,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2 > arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "be78a904-db8a-4fb8-8282-cf1870ed2d8f",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "7f134d80-3414-4b64-8eed-705454f64ecb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9a86ce58-803e-4aba-8068-14098bd0c81b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "5"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "1af417b9-6a4d-4b5e-8750-b0f36dd20f6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([5, 6, 7])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[5:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "19fd00e4-d7c1-4356-82d5-bc72535e030d",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr [5:8] = 12"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "970ae6ad-0fd7-410c-b90c-4b3679204b5f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 12, 12, 12,  8,  9])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "f1b6c54c-2c03-4c09-9563-49dc95eee64b",
   "metadata": {},
   "outputs": [],
   "source": [
    "# array slices are views on the original array and any modifications to the view will be reflected in the source array."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "af334f56-543a-4144-9f2e-71c1e984919d",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_slice = arr[5:8]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "4f852094-9647-4009-8f1d-412385f1921f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([12, 12, 12])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "a9d4700a-a1ad-4f0e-acc6-033571dfecb0",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_slice[1] = 12345"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "f56ffc55-a62e-4696-b461-7e10a8cf3d0a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([    0,     1,     2,     3,     4,    12, 12345,    12,     8,\n",
       "           9])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "491e93f4-dce0-4cf1-aef2-2dcbaabed684",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr_slice[:] = 64"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "331a3025-bba5-4121-9390-88403b2cce7f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 64, 64, 64,  8,  9])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "04fa3d08-390c-4cab-9d46-dda13366cfec",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([64, 64, 64])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# explicitly copy the array\n",
    "arr[5:8].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1c5313e7-2a66-4dbc-96a9-ee434a67cc90",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d = np.array([[1, 2, 3], [4, 5, 6], [7, 8, 9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "e57d3ec0-f370-4587-a98a-3495348101ea",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b475b3a6-b30d-4a65-9bb8-679febd22679",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0][2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "28bf1c0a-a5fe-4ae2-a19d-490ac33b443b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[0, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "61d7cfc7-9ddc-41b3-a11e-4ea46068f005",
   "metadata": {},
   "outputs": [],
   "source": [
    "# axis 0 as the \"rows\" of the array and axis 1 as the \"columns.\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "12e793a7-2260-4e2a-bb8e-77d1844e374f",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr3d = np.array([[[1, 2, 3], [4, 5, 6]], [[7, 8, 9], [10, 11, 12]]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "47f2e00a-de00-499e-a55d-ef82410cb56b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "824a30d2-ef95-4c68-b59c-396a3f267fc9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "6b7269f2-6d6e-4dcc-891a-9cbd5e37f20d",
   "metadata": {},
   "outputs": [],
   "source": [
    "old_values = arr3d[0].copy()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "ce967713-1ed2-48b2-b43a-1fb29d972d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr3d[0] = 42"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "85b571bc-362c-477d-86ee-43ae7d85caa8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[42, 42, 42],\n",
       "        [42, 42, 42]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "20240898-a69d-4ff1-a42c-938b25d35cfa",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr3d[0] = old_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "4f324c26-bd5f-40d6-8e27-cd4215b1b90d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[[ 1,  2,  3],\n",
       "        [ 4,  5,  6]],\n",
       "\n",
       "       [[ 7,  8,  9],\n",
       "        [10, 11, 12]]])"
      ]
     },
     "execution_count": 55,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "05b9179a-9611-4fc6-b049-c20b4f2499a9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr3d[1, 0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "4a1da0bd-f13c-49ae-832d-f2ce4dba09e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = arr3d[1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "68495d68-205a-417d-80fa-2ab268df1c86",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 7,  8,  9],\n",
       "       [10, 11, 12]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "3dc09420-0ccd-425e-bfd6-c30f919f3482",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 8, 9])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x[0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0db90075-69c9-4702-8668-e2c4d8eba54c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  2,  3,  4, 64, 64, 64,  8,  9])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 71,
   "id": "00edda4a-880c-49b4-b661-e9d89553c857",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 1,  2,  3,  4, 64])"
      ]
     },
     "execution_count": 71,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[1:6]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 73,
   "id": "f2d5f0a2-b41b-42dd-8368-d33712ae8c8b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 73,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 75,
   "id": "18cb1b0e-b55d-4423-8cc5-0164e93c3406",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 2, 3],\n",
       "       [4, 5, 6]])"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "5ce6da14-acd0-48f1-8467-f95dc1446267",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[2, 3],\n",
       "       [5, 6]])"
      ]
     },
     "execution_count": 77,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2, 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "d01cbbc8-5da5-406e-94d6-3a609d958935",
   "metadata": {},
   "outputs": [],
   "source": [
    "lower_dim_slice = arr2d[1, :2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "7794e8b4-54c2-45fb-9ab6-e8c101156503",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(2,)"
      ]
     },
     "execution_count": 81,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_dim_slice.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "78e1f85f-80cb-4dcb-9c8b-d31009cb329d",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([4, 5])"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "lower_dim_slice"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "fa17ef81-34a7-4a1a-8ff6-516f5ffb9ccd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([3, 6])"
      ]
     },
     "execution_count": 85,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:2, 2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "id": "7f02b7e6-cb86-40bc-9fdf-ae789a2423af",
   "metadata": {},
   "outputs": [],
   "source": [
    "# a colon by itself means to take the entire axis."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 89,
   "id": "dc550c36-3129-4c81-a217-1198f917a13f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1],\n",
       "       [4],\n",
       "       [7]])"
      ]
     },
     "execution_count": 89,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d[:, :1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 91,
   "id": "d3ac31e2-d165-432b-8f0e-ba0260fa4f56",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2d[:2, 1:] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 93,
   "id": "38a40b95-5a95-4dca-9eac-1348c5fc883f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1, 0, 0],\n",
       "       [4, 0, 0],\n",
       "       [7, 8, 9]])"
      ]
     },
     "execution_count": 93,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "3b56dab5-4f91-49c4-bb1f-dcbe60a7d57a",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = np.array([\"Bob\", \"Joe\", \"Will\", \"Bob\", \"Will\", \"Joe\", \"Joe\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "9692c93d-454b-4708-be35-4c7a8cd02521",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = np.array([[4, 7], [0, 2], [-5, 6], [0, 0], [1, 2],\n",
    "                 [-12, -4], [3, 4]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "322b539d-0784-46b5-993f-7d0395f297da",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bob', 'Joe', 'Will', 'Bob', 'Will', 'Joe', 'Joe'], dtype='<U4')"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "b1d2d020-7aa5-4eae-ab78-8e334e68bd63",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  4,   7],\n",
       "       [  0,   2],\n",
       "       [ -5,   6],\n",
       "       [  0,   0],\n",
       "       [  1,   2],\n",
       "       [-12,  -4],\n",
       "       [  3,   4]])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "f902b3c2-a25e-4cb5-85b5-07e2c5c397a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True, False, False, False])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names == \"Bob\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "d46c12de-5351-439d-b735-0b61c5d1a8f9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 7],\n",
       "       [0, 0]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[names == \"Bob\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "15f66c35-dba8-4fa9-a363-16e0b9ef1c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7],\n",
       "       [7]])"
      ]
     },
     "execution_count": 49,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A slice index 1:: selects a range of columns from the chosen rows, resulting in a two-dimensional array.\n",
    "data[names == \"Bob\", 1:]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "ca321613-284a-4a76-a3dd-9ef63ee624c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([7, 7])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# A single index 1: selects a specific column from the chosen rows, resulting in a one-dimensional array.\n",
    "data[names == \"Bob\", 1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "01ed827e-c2a9-4d32-8747-72c92c1d6075",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False,  True,  True,  True])"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "names != \"Bob\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "461a1783-7509-4f7f-a052-34e849c70338",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False,  True,  True, False,  True,  True,  True])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "~(names == \"Bob\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "cfcac3a0-10b3-4227-a6fb-d5d166f7e2c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   2],\n",
       "       [ -5,   6],\n",
       "       [  1,   2],\n",
       "       [-12,  -4],\n",
       "       [  3,   4]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[~(names == \"Bob\")]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "f0c2b6b1-f482-4e86-9e60-2664ffb04181",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = names == \"Bob\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "1dcd12a3-ca63-4285-90df-fb9df6fb4273",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  0,   2],\n",
       "       [ -5,   6],\n",
       "       [  1,   2],\n",
       "       [-12,  -4],\n",
       "       [  3,   4]])"
      ]
     },
     "execution_count": 33,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[~cond]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "d893041a-7708-4654-a1cd-7b31a6adf7bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = (names == \"Bob\") | (names == \"Will\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "a3c304f1-725c-4f58-a859-2d1ee67f4c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True,  True,  True, False, False])"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mask"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "fbe1d2e0-188b-407c-bd93-31dc7a4a07ba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  7],\n",
       "       [-5,  6],\n",
       "       [ 0,  0],\n",
       "       [ 1,  2]])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data[mask]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "7fff7eca-8622-4cc2-8bc0-3b937c0d4d33",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The Python keywords and and or do not work with Boolean arrays. Use & (and) and | (or) instead."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "2482c9b8-38af-4bd8-9baa-a76cee411be1",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[data < 0] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "7eff828b-4f63-46e5-8afc-d0ccfec5783c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4, 7],\n",
       "       [0, 2],\n",
       "       [0, 6],\n",
       "       [0, 0],\n",
       "       [1, 2],\n",
       "       [0, 0],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "67fe0096-f713-4236-a144-b0b4a878cc99",
   "metadata": {},
   "outputs": [],
   "source": [
    "data[names != \"Joe\"] = 7"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "9492e684-67fb-4dff-b6cc-cc62629dda59",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7, 7],\n",
       "       [0, 2],\n",
       "       [7, 7],\n",
       "       [7, 7],\n",
       "       [7, 7],\n",
       "       [0, 0],\n",
       "       [3, 4]])"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "806cca3c-43aa-4705-b23f-8da2f9d4a15f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# fancy indexing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "d09dd94d-3b7a-42a9-8bab-b0101658dfe2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.zeros((8, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 61,
   "id": "15b5179f-e2f2-4ce8-985d-d9aab6bb5067",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [0., 0., 0., 0.]])"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 63,
   "id": "b5bfe35b-02c6-43af-9909-c0353c7d38ea",
   "metadata": {},
   "outputs": [],
   "source": [
    "for i in range(8):\n",
    "    arr[i] = i"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "05bb89d7-c971-4499-8849-a3c7d923e250",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0., 0., 0., 0.],\n",
       "       [1., 1., 1., 1.],\n",
       "       [2., 2., 2., 2.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [4., 4., 4., 4.],\n",
       "       [5., 5., 5., 5.],\n",
       "       [6., 6., 6., 6.],\n",
       "       [7., 7., 7., 7.]])"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 67,
   "id": "8b77802c-73b1-46cc-a46a-ebe1b5553126",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[4., 4., 4., 4.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [0., 0., 0., 0.],\n",
       "       [6., 6., 6., 6.]])"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[4, 3, 0, 6]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "267b719f-c507-4f88-8770-28bd79c9e2f5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[5., 5., 5., 5.],\n",
       "       [3., 3., 3., 3.],\n",
       "       [1., 1., 1., 1.]])"
      ]
     },
     "execution_count": 69,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[-3, -5, -7]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "7d850fc1-8e41-427c-b657-1521768908c0",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(32).reshape((8, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "65df5cf2-80a8-4eda-a7db-a4964461f9c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 4,  5,  6,  7],\n",
       "       [ 8,  9, 10, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22, 23],\n",
       "       [24, 25, 26, 27],\n",
       "       [28, 29, 30, 31]])"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "01caf5fc-8cd8-4956-909b-bbf3d770dd4e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 23, 29, 10])"
      ]
     },
     "execution_count": 11,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# The result of fancy indexing with as many integer arrays as there are axes is always one-dimensional.\n",
    "arr[[1, 5, 7, 2], [0, 3, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "8e1b4082-9175-4074-9e2e-3ec21a3318d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 4,  7,  5,  6],\n",
       "       [20, 23, 21, 22],\n",
       "       [28, 31, 29, 30],\n",
       "       [ 8, 11,  9, 10]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[1, 5, 7, 2]][:, [0, 3, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e9d6ea6c-1f9d-449f-b862-2b683222cf55",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4, 23, 29, 10])"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr[[1, 5, 7, 2], [0, 3, 1, 2]]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "fb52570f-972a-4a6b-b474-6aece06b67f0",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr[[1, 5, 7, 2], [0, 3, 1, 2]] = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "e7afdb6b-1ffa-44e7-8765-ae36abe63eba",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3],\n",
       "       [ 0,  5,  6,  7],\n",
       "       [ 8,  9,  0, 11],\n",
       "       [12, 13, 14, 15],\n",
       "       [16, 17, 18, 19],\n",
       "       [20, 21, 22,  0],\n",
       "       [24, 25, 26, 27],\n",
       "       [28,  0, 30, 31]])"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "23272396-37e6-4927-8144-20dad2cc2ff1",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(15).reshape((3, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "ba1b82d5-276b-4afa-917a-d77a23251d56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2,  3,  4],\n",
       "       [ 5,  6,  7,  8,  9],\n",
       "       [10, 11, 12, 13, 14]])"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "4e514f1e-2c26-4316-a741-0b607d40e8d1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  5, 10],\n",
       "       [ 1,  6, 11],\n",
       "       [ 2,  7, 12],\n",
       "       [ 3,  8, 13],\n",
       "       [ 4,  9, 14]])"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "771c3829-4373-4ba8-8d0f-d95140c7e1a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([[0, 1, 0], [1, 2, -2], [6, 3, 2], [-1, 0, -1],[1, 0, 1]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "97114a0f-095b-4890-a063-67d6547ebb3f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  0],\n",
       "       [ 1,  2, -2],\n",
       "       [ 6,  3,  2],\n",
       "       [-1,  0, -1],\n",
       "       [ 1,  0,  1]])"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "8f6aded9-36f6-4199-aea8-4e938b3afce1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, 20, 12],\n",
       "       [20, 14,  2],\n",
       "       [12,  2, 10]])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(arr.T, arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "28a17079-eef5-4606-983b-6368114dda02",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[39, 20, 12],\n",
       "       [20, 14,  2],\n",
       "       [12,  2, 10]])"
      ]
     },
     "execution_count": 25,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T @ arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "fffd46d8-de65-409d-a02e-1c0c14b11105",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  0],\n",
       "       [ 1,  2, -2],\n",
       "       [ 6,  3,  2],\n",
       "       [-1,  0, -1],\n",
       "       [ 1,  0,  1]])"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "42f439ac-71d1-4f63-868c-9cb844491ddd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  6, -1,  1],\n",
       "       [ 1,  2,  3,  0,  0],\n",
       "       [ 0, -2,  2, -1,  1]])"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "6b26382e-7841-42f8-a7f0-f10354f7a10c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  6, -1,  1],\n",
       "       [ 1,  2,  3,  0,  0],\n",
       "       [ 0, -2,  2, -1,  1]])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.swapaxes(0, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "9c284090-3153-4065-b330-610fc950d9f3",
   "metadata": {},
   "outputs": [],
   "source": [
    "samples = np.random.standard_normal(size=(4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "fda43d48-8dc8-467a-a8f7-5c3c10a211ac",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.17775941, -0.2087727 ,  1.14114225,  0.18912813],\n",
       "       [-0.81045175, -0.66473119, -1.81580027, -0.87849856],\n",
       "       [ 1.13355148,  1.6301108 ,  0.39556307,  0.01281782],\n",
       "       [-1.10423843, -0.27976042,  0.13787067,  0.83876571]])"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "samples"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "b4164097-c81d-45e8-947e-2aa6f7715bef",
   "metadata": {},
   "outputs": [],
   "source": [
    "from random import normalvariate"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "27782701-3236-4a8b-93ce-32355fe04291",
   "metadata": {},
   "outputs": [],
   "source": [
    "N = 1_000_000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "b829c382-1cbc-455a-b2a0-31b03f2cd38d",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "687 ms ± 11.4 ms per loop (mean ± std. dev. of 7 runs, 1 loop each)\n"
     ]
    }
   ],
   "source": [
    "%timeit samples = [normalvariate(0, 1) for _ in range(N)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "cd21c446-9fa8-45e9-b936-e4cec56e0125",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "22 ms ± 273 μs per loop (mean ± std. dev. of 7 runs, 10 loops each)\n"
     ]
    }
   ],
   "source": [
    "%timeit np.random.standard_normal(N)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "066b4e05-cd90-411e-a93c-855548445ca2",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(seed=12345)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "f94a9311-2552-4011-90b4-83a54f5f3d9e",
   "metadata": {},
   "outputs": [],
   "source": [
    "data = rng.standard_normal((2, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "e9a78dae-b562-47a2-9006-1a5a8e054c81",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.42382504,  1.26372846, -0.87066174],\n",
       "       [-0.25917323, -0.07534331, -0.74088465]])"
      ]
     },
     "execution_count": 51,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "c7564553-f1cf-4e40-98d8-86ee554698c4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "numpy.random._generator.Generator"
      ]
     },
     "execution_count": 53,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(rng)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f54ceb85-ac26-489d-821f-01e1d8044df9",
   "metadata": {},
   "outputs": [],
   "source": [
    "# A universal function, or ufunc, is a function that performs element-wise operations on data in ndarrays. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "5a40b0b6-9558-4bab-9627-2033e6a9ca65",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "86ed56d0-07ae-4fd7-9818-10d131811a56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 58,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 60,
   "id": "03fc6eef-ba88-4efd-9d20-7f217f74382e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 1.        , 1.41421356, 1.73205081, 2.        ,\n",
       "       2.23606798, 2.44948974, 2.64575131, 2.82842712, 3.        ])"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.sqrt(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "4b6ef4d4-4a1b-43e0-9b7b-aa2b40b7d54a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.00000000e+00, 2.71828183e+00, 7.38905610e+00, 2.00855369e+01,\n",
       "       5.45981500e+01, 1.48413159e+02, 4.03428793e+02, 1.09663316e+03,\n",
       "       2.98095799e+03, 8.10308393e+03])"
      ]
     },
     "execution_count": 62,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.exp(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 64,
   "id": "fd4e5586-e4f4-47f7-80d4-fcb83728dfa1",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = rng.standard_normal(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 68,
   "id": "e0208841-a327-43fc-b691-d6458b8b2985",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = rng.standard_normal(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "860b81af-41eb-4fa7-b455-cc50e8e3b7eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.3677927 ,  0.6488928 ,  0.36105811, -1.95286306,  2.34740965,\n",
       "        0.96849691, -0.75938718,  0.90219827])"
      ]
     },
     "execution_count": 70,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "500d948f-be96-49ff-be7f-04699acb1c78",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.46695317, -0.06068952,  0.78884434, -1.25666813,  0.57585751,\n",
       "        1.39897899,  1.32229806, -0.29969852])"
      ]
     },
     "execution_count": 72,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 74,
   "id": "47428b6b-78bf-49ec-b354-faa000cc96eb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.46695317,  0.6488928 ,  0.78884434, -1.25666813,  2.34740965,\n",
       "        1.39897899,  1.32229806,  0.90219827])"
      ]
     },
     "execution_count": 74,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.maximum(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "09e60965-b665-4338-9314-7111bc62bdaf",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = rng.standard_normal(7) * 5"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "d2dbbc74-415b-4bc2-a60b-e2d3c10e6ecf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.51459671, -8.10791367, -0.7909463 ,  2.24741966, -6.71800536,\n",
       "       -0.40843795,  8.62369966])"
      ]
     },
     "execution_count": 80,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "14aec6f2-6923-4da3-8de6-06bf72330e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "remainder, whole_part = np.modf(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 84,
   "id": "2f2f0a89-0801-484f-b617-ef2f58a524ad",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.51459671, -0.10791367, -0.7909463 ,  0.24741966, -0.71800536,\n",
       "       -0.40843795,  0.62369966])"
      ]
     },
     "execution_count": 84,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "remainder"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "d22d3d0f-4b9f-47d8-9353-3737b4be1f82",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4., -8., -0.,  2., -6., -0.,  8.])"
      ]
     },
     "execution_count": 86,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "whole_part"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 88,
   "id": "94ad9b5b-a91d-47b7-89e9-dce88ad07cb6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 4.51459671, -8.10791367, -0.7909463 ,  2.24741966, -6.71800536,\n",
       "       -0.40843795,  8.62369966])"
      ]
     },
     "execution_count": 88,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "id": "d47d80d0-6869-4c24-b8c4-54492e18cf51",
   "metadata": {},
   "outputs": [],
   "source": [
    "out = np.zeros_like(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "id": "57393b3b-2352-4dde-9177-3992e2ec51c5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.51459671, -7.10791367,  0.2090537 ,  3.24741966, -5.71800536,\n",
       "        0.59156205,  9.62369966])"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(arr, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "ebce724f-4887-4288-be53-241a0bf5a235",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.51459671, -7.10791367,  0.2090537 ,  3.24741966, -5.71800536,\n",
       "        0.59156205,  9.62369966])"
      ]
     },
     "execution_count": 94,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.add(arr, 1, out=out)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "id": "89069ada-b950-4e99-a1c5-200cdd679c9a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 5.51459671, -7.10791367,  0.2090537 ,  3.24741966, -5.71800536,\n",
       "        0.59156205,  9.62369966])"
      ]
     },
     "execution_count": 96,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "out"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "id": "37b42467-a856-4fe3-9039-88ae12453637",
   "metadata": {},
   "outputs": [],
   "source": [
    "points = np.arange(-5, 5, 0.01) # 100 equally spaced points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 138,
   "id": "38ade7d6-83d7-4307-9332-9874dc306f36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-5.0000000e+00, -4.9900000e+00, -4.9800000e+00, -4.9700000e+00,\n",
       "       -4.9600000e+00, -4.9500000e+00, -4.9400000e+00, -4.9300000e+00,\n",
       "       -4.9200000e+00, -4.9100000e+00, -4.9000000e+00, -4.8900000e+00,\n",
       "       -4.8800000e+00, -4.8700000e+00, -4.8600000e+00, -4.8500000e+00,\n",
       "       -4.8400000e+00, -4.8300000e+00, -4.8200000e+00, -4.8100000e+00,\n",
       "       -4.8000000e+00, -4.7900000e+00, -4.7800000e+00, -4.7700000e+00,\n",
       "       -4.7600000e+00, -4.7500000e+00, -4.7400000e+00, -4.7300000e+00,\n",
       "       -4.7200000e+00, -4.7100000e+00, -4.7000000e+00, -4.6900000e+00,\n",
       "       -4.6800000e+00, -4.6700000e+00, -4.6600000e+00, -4.6500000e+00,\n",
       "       -4.6400000e+00, -4.6300000e+00, -4.6200000e+00, -4.6100000e+00,\n",
       "       -4.6000000e+00, -4.5900000e+00, -4.5800000e+00, -4.5700000e+00,\n",
       "       -4.5600000e+00, -4.5500000e+00, -4.5400000e+00, -4.5300000e+00,\n",
       "       -4.5200000e+00, -4.5100000e+00, -4.5000000e+00, -4.4900000e+00,\n",
       "       -4.4800000e+00, -4.4700000e+00, -4.4600000e+00, -4.4500000e+00,\n",
       "       -4.4400000e+00, -4.4300000e+00, -4.4200000e+00, -4.4100000e+00,\n",
       "       -4.4000000e+00, -4.3900000e+00, -4.3800000e+00, -4.3700000e+00,\n",
       "       -4.3600000e+00, -4.3500000e+00, -4.3400000e+00, -4.3300000e+00,\n",
       "       -4.3200000e+00, -4.3100000e+00, -4.3000000e+00, -4.2900000e+00,\n",
       "       -4.2800000e+00, -4.2700000e+00, -4.2600000e+00, -4.2500000e+00,\n",
       "       -4.2400000e+00, -4.2300000e+00, -4.2200000e+00, -4.2100000e+00,\n",
       "       -4.2000000e+00, -4.1900000e+00, -4.1800000e+00, -4.1700000e+00,\n",
       "       -4.1600000e+00, -4.1500000e+00, -4.1400000e+00, -4.1300000e+00,\n",
       "       -4.1200000e+00, -4.1100000e+00, -4.1000000e+00, -4.0900000e+00,\n",
       "       -4.0800000e+00, -4.0700000e+00, -4.0600000e+00, -4.0500000e+00,\n",
       "       -4.0400000e+00, -4.0300000e+00, -4.0200000e+00, -4.0100000e+00,\n",
       "       -4.0000000e+00, -3.9900000e+00, -3.9800000e+00, -3.9700000e+00,\n",
       "       -3.9600000e+00, -3.9500000e+00, -3.9400000e+00, -3.9300000e+00,\n",
       "       -3.9200000e+00, -3.9100000e+00, -3.9000000e+00, -3.8900000e+00,\n",
       "       -3.8800000e+00, -3.8700000e+00, -3.8600000e+00, -3.8500000e+00,\n",
       "       -3.8400000e+00, -3.8300000e+00, -3.8200000e+00, -3.8100000e+00,\n",
       "       -3.8000000e+00, -3.7900000e+00, -3.7800000e+00, -3.7700000e+00,\n",
       "       -3.7600000e+00, -3.7500000e+00, -3.7400000e+00, -3.7300000e+00,\n",
       "       -3.7200000e+00, -3.7100000e+00, -3.7000000e+00, -3.6900000e+00,\n",
       "       -3.6800000e+00, -3.6700000e+00, -3.6600000e+00, -3.6500000e+00,\n",
       "       -3.6400000e+00, -3.6300000e+00, -3.6200000e+00, -3.6100000e+00,\n",
       "       -3.6000000e+00, -3.5900000e+00, -3.5800000e+00, -3.5700000e+00,\n",
       "       -3.5600000e+00, -3.5500000e+00, -3.5400000e+00, -3.5300000e+00,\n",
       "       -3.5200000e+00, -3.5100000e+00, -3.5000000e+00, -3.4900000e+00,\n",
       "       -3.4800000e+00, -3.4700000e+00, -3.4600000e+00, -3.4500000e+00,\n",
       "       -3.4400000e+00, -3.4300000e+00, -3.4200000e+00, -3.4100000e+00,\n",
       "       -3.4000000e+00, -3.3900000e+00, -3.3800000e+00, -3.3700000e+00,\n",
       "       -3.3600000e+00, -3.3500000e+00, -3.3400000e+00, -3.3300000e+00,\n",
       "       -3.3200000e+00, -3.3100000e+00, -3.3000000e+00, -3.2900000e+00,\n",
       "       -3.2800000e+00, -3.2700000e+00, -3.2600000e+00, -3.2500000e+00,\n",
       "       -3.2400000e+00, -3.2300000e+00, -3.2200000e+00, -3.2100000e+00,\n",
       "       -3.2000000e+00, -3.1900000e+00, -3.1800000e+00, -3.1700000e+00,\n",
       "       -3.1600000e+00, -3.1500000e+00, -3.1400000e+00, -3.1300000e+00,\n",
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       "        4.4400000e+00,  4.4500000e+00,  4.4600000e+00,  4.4700000e+00,\n",
       "        4.4800000e+00,  4.4900000e+00,  4.5000000e+00,  4.5100000e+00,\n",
       "        4.5200000e+00,  4.5300000e+00,  4.5400000e+00,  4.5500000e+00,\n",
       "        4.5600000e+00,  4.5700000e+00,  4.5800000e+00,  4.5900000e+00,\n",
       "        4.6000000e+00,  4.6100000e+00,  4.6200000e+00,  4.6300000e+00,\n",
       "        4.6400000e+00,  4.6500000e+00,  4.6600000e+00,  4.6700000e+00,\n",
       "        4.6800000e+00,  4.6900000e+00,  4.7000000e+00,  4.7100000e+00,\n",
       "        4.7200000e+00,  4.7300000e+00,  4.7400000e+00,  4.7500000e+00,\n",
       "        4.7600000e+00,  4.7700000e+00,  4.7800000e+00,  4.7900000e+00,\n",
       "        4.8000000e+00,  4.8100000e+00,  4.8200000e+00,  4.8300000e+00,\n",
       "        4.8400000e+00,  4.8500000e+00,  4.8600000e+00,  4.8700000e+00,\n",
       "        4.8800000e+00,  4.8900000e+00,  4.9000000e+00,  4.9100000e+00,\n",
       "        4.9200000e+00,  4.9300000e+00,  4.9400000e+00,  4.9500000e+00,\n",
       "        4.9600000e+00,  4.9700000e+00,  4.9800000e+00,  4.9900000e+00])"
      ]
     },
     "execution_count": 138,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "points"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "id": "73c72b06-be0e-47f9-97bf-16a908fa0365",
   "metadata": {},
   "outputs": [],
   "source": [
    "xs, ys = np.meshgrid(points, points)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "1b79ee68-82c7-47cf-a011-2b69a57fa3f8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.  , -5.  , -5.  , ..., -5.  , -5.  , -5.  ],\n",
       "       [-4.99, -4.99, -4.99, ..., -4.99, -4.99, -4.99],\n",
       "       [-4.98, -4.98, -4.98, ..., -4.98, -4.98, -4.98],\n",
       "       ...,\n",
       "       [ 4.97,  4.97,  4.97, ...,  4.97,  4.97,  4.97],\n",
       "       [ 4.98,  4.98,  4.98, ...,  4.98,  4.98,  4.98],\n",
       "       [ 4.99,  4.99,  4.99, ...,  4.99,  4.99,  4.99]])"
      ]
     },
     "execution_count": 102,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "ys"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "5931605a-19fd-461f-81f5-f7102528f659",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       ...,\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99],\n",
       "       [-5.  , -4.99, -4.98, ...,  4.97,  4.98,  4.99]])"
      ]
     },
     "execution_count": 104,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "xs"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "84ad413b-928f-4cbf-9f55-414247b6a687",
   "metadata": {},
   "outputs": [],
   "source": [
    "z = np.sqrt(xs ** 2 + ys ** 2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 108,
   "id": "7cda0854-fc7d-4ac5-a644-fa46a6369436",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[7.07106781, 7.06400028, 7.05693985, ..., 7.04988652, 7.05693985,\n",
       "        7.06400028],\n",
       "       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,\n",
       "        7.05692568],\n",
       "       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,\n",
       "        7.04985815],\n",
       "       ...,\n",
       "       [7.04988652, 7.04279774, 7.03571603, ..., 7.0286414 , 7.03571603,\n",
       "        7.04279774],\n",
       "       [7.05693985, 7.04985815, 7.04278354, ..., 7.03571603, 7.04278354,\n",
       "        7.04985815],\n",
       "       [7.06400028, 7.05692568, 7.04985815, ..., 7.04279774, 7.04985815,\n",
       "        7.05692568]])"
      ]
     },
     "execution_count": 108,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "z"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 116,
   "id": "2c3dbf31-8282-4e40-a8e5-98db73c694c1",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 134,
   "id": "9ac8f88f-a775-4919-a8e8-70a2edda9937",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0.5, 1.0, 'Image plot of $sqrt{x^2 + y^2}$ for a grid of values')"
      ]
     },
     "execution_count": 134,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.imshow(z, cmap=plt.cm.gray, extent=[-5, 5, -5, 5])\n",
    "plt.colorbar()\n",
    "plt.title(\"Image plot of $sqrt{x^2 + y^2}$ for a grid of values\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 140,
   "id": "c2f538c9-ad08-4732-89fc-2bfaf69707ac",
   "metadata": {},
   "outputs": [],
   "source": [
    "xarr = np.array([1.1, 1.2, 1.3, 1.4, 1.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 142,
   "id": "e707a690-d1b0-42b9-b353-4ea62dee9a0d",
   "metadata": {},
   "outputs": [],
   "source": [
    "yarr = np.array([2.1, 2.2, 2.3, 2.4, 2.5])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "b26601f3-7499-4fdb-816b-429af65b4758",
   "metadata": {},
   "outputs": [],
   "source": [
    "cond = np.array([True, False, True, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 146,
   "id": "2f6e7829-ae47-4ae0-b871-7945865cd8cc",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = [(x if c else y)\n",
    "          for x, y, c in zip(xarr, yarr, cond)]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 148,
   "id": "6e6d29f3-c4d3-4ea5-9ae5-3aa33c8d2f42",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[1.1, 2.2, 1.3, 1.4, 2.5]"
      ]
     },
     "execution_count": 148,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 152,
   "id": "df4a8e83-44ef-4f75-b59a-d9cebae02886",
   "metadata": {},
   "outputs": [],
   "source": [
    "result = np.where(cond, xarr, yarr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "33ffd379-a780-4634-997f-fe2e95a9b304",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1.1, 2.2, 1.3, 1.4, 2.5])"
      ]
     },
     "execution_count": 154,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 156,
   "id": "2b026d0c-59ed-4573-9f27-83ec43f3ed66",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = rng.standard_normal((4, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 158,
   "id": "dd308721-2654-4b2e-9e51-b59952557496",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.61815943,  0.77736134,  0.8286332 , -0.95898831],\n",
       "       [-1.20938829, -1.41229201,  0.54154683,  0.7519394 ],\n",
       "       [-0.65876032, -1.22867499,  0.25755777,  0.31290292],\n",
       "       [-0.13081169,  1.26998312, -0.09296246, -0.06615089]])"
      ]
     },
     "execution_count": 158,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 160,
   "id": "0b4f07f7-983f-47a7-b34a-fa5444dbd9a8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ True,  True,  True, False],\n",
       "       [False, False,  True,  True],\n",
       "       [False, False,  True,  True],\n",
       "       [False,  True, False, False]])"
      ]
     },
     "execution_count": 160,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr > 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 164,
   "id": "24722940-ad98-4c09-b79c-2a31da67f365",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2,  2,  2, -2],\n",
       "       [-2, -2,  2,  2],\n",
       "       [-2, -2,  2,  2],\n",
       "       [-2,  2, -2, -2]])"
      ]
     },
     "execution_count": 164,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(arr > 0, 2, -2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 172,
   "id": "864b7d5e-52f6-4476-850d-2e5be046dd83",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 2.        ,  2.        ,  2.        , -0.95898831],\n",
       "       [-1.20938829, -1.41229201,  2.        ,  2.        ],\n",
       "       [-0.65876032, -1.22867499,  2.        ,  2.        ],\n",
       "       [-0.13081169,  2.        , -0.09296246, -0.06615089]])"
      ]
     },
     "execution_count": 172,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.where(arr > 0, 2, arr) # set only positive values to 2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 174,
   "id": "01e6eeb5-894d-46d2-bf87-3033102ae690",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = rng.standard_normal((5, 4))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 176,
   "id": "700a09fb-e489-4016-8d89-ece512380f17",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-1.10821447,  0.13595685,  1.34707776,  0.06114402],\n",
       "       [ 0.0709146 ,  0.43365454,  0.27748366,  0.53025239],\n",
       "       [ 0.53672097,  0.61835001, -0.79501746,  0.30003095],\n",
       "       [-1.60270159,  0.26679883, -1.26162378, -0.07127081],\n",
       "       [ 0.47404973, -0.41485376,  0.0977165 , -1.64041784]])"
      ]
     },
     "execution_count": 176,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 178,
   "id": "a28588b0-5a0b-47d8-baaf-e43a319c9190",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.08719744457434529"
      ]
     },
     "execution_count": 178,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 180,
   "id": "3817d3e7-27d7-4146-83b1-b7b608f84078",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.08719744457434529"
      ]
     },
     "execution_count": 180,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.mean(arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 182,
   "id": "4338a59f-16e2-4951-83fa-ec98c7693724",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-1.743948891486906"
      ]
     },
     "execution_count": 182,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 184,
   "id": "434e050e-9a02-48d9-95d8-c6be21aaaf75",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.10899104,  0.3280763 ,  0.16502112, -0.66719934, -0.37087634])"
      ]
     },
     "execution_count": 184,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.mean(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 186,
   "id": "0ddddb56-0175-4b04-b902-abee73388421",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-1.62923076,  1.03990647, -0.33436331, -0.82026129])"
      ]
     },
     "execution_count": 186,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.sum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 188,
   "id": "1360e3d6-ade4-4a85-a74a-e6f628557303",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([0, 1, 2, 3, 4, 5, 6, 7])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 190,
   "id": "b60e1b52-1a43-4a39-b62b-ad8d6bd8a863",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0,  1,  3,  6, 10, 15, 21, 28])"
      ]
     },
     "execution_count": 190,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 194,
   "id": "7d97d030-cb5c-498e-812e-de1586a87c21",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.array([[0, 1, 2], [3, 4, 5], [6, 7, 8]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 196,
   "id": "19df6b23-1786-455d-8392-2ca44cbc0950",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 1, 2],\n",
       "       [3, 4, 5],\n",
       "       [6, 7, 8]])"
      ]
     },
     "execution_count": 196,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 198,
   "id": "16f62f69-df9b-46c4-8e17-80ef00909c79",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  2],\n",
       "       [ 3,  5,  7],\n",
       "       [ 9, 12, 15]])"
      ]
     },
     "execution_count": 198,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.cumsum(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 200,
   "id": "0d340e88-41a6-4d19-81e4-c19210b609c9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0,  1,  3],\n",
       "       [ 3,  7, 12],\n",
       "       [ 6, 13, 21]])"
      ]
     },
     "execution_count": 200,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr.cumsum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 204,
   "id": "6aa563d6-5155-4121-a124-f03d0b7a5f27",
   "metadata": {},
   "outputs": [],
   "source": [
    "# The key aspect of the axis is the direction, not the rows and columns. \n",
    "# 1 indicates the horizontal axis, with the direction from left to right; \n",
    "# 0 indicates the vertical axis, with the direction from top to bottom."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 206,
   "id": "a82171d3-7904-4c0f-a265-86713846262a",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = rng.standard_normal(100)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 208,
   "id": "36cbc0d6-e6d7-45b8-96f2-b64f565b82dc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-8.57258824e-01,  6.88281788e-01, -1.15452958e+00,  6.50452389e-01,\n",
       "       -1.38835995e+00, -9.07382457e-01, -1.09542531e+00,  7.14569494e-03,\n",
       "        5.34359903e-01, -1.06580785e+00, -1.81472740e-01,  1.62195180e+00,\n",
       "       -3.17391946e-01, -8.15814967e-01,  3.86579017e-01, -2.23638926e-01,\n",
       "       -7.01690809e-01, -1.79571318e+00,  8.18325622e-01, -5.71032902e-01,\n",
       "        7.85525063e-04, -1.06364272e+00,  1.30171450e+00,  7.47872942e-01,\n",
       "        9.80875909e-01, -1.10418688e-01,  4.67918531e-01,  8.90607150e-01,\n",
       "        1.02300937e+00,  3.12383389e-01, -6.19046857e-02, -3.59479647e-01,\n",
       "       -7.48643984e-01, -9.65478907e-01,  3.60034657e-01, -2.44552532e-01,\n",
       "       -1.99585661e+00, -1.55247617e-01,  1.06383087e+00, -2.75171567e-01,\n",
       "       -1.85333593e+00, -1.24341928e-01,  7.84974522e-01,  2.01998597e-01,\n",
       "       -4.28074443e-01,  1.84828890e+00,  1.89995289e+00, -9.84250348e-02,\n",
       "        8.13445440e-01,  3.92494389e-01,  7.81442900e-01,  1.45327152e+00,\n",
       "        8.20186045e-01,  8.77053446e-02, -6.53505648e-01, -8.11886879e-01,\n",
       "       -2.55381724e-02,  1.15818454e+00,  3.00520870e-01,  5.30566461e-02,\n",
       "        2.57271524e-01,  3.57428624e-02,  5.47236686e-01, -1.12296158e+00,\n",
       "       -1.97524767e+00, -4.25150050e-01, -1.14907382e+00,  1.61513805e+00,\n",
       "       -1.58476860e-01, -2.52873345e-01, -1.53815403e+00,  2.82086033e-01,\n",
       "       -6.23612134e-01,  1.12182226e+00,  8.41221031e-01, -7.75896101e-01,\n",
       "        4.10716440e-01, -2.72241610e+00, -6.73304799e-01,  1.24622153e+00,\n",
       "        7.90208028e-01,  1.75340888e-01, -2.92946016e-02, -1.41951426e+00,\n",
       "       -1.35996632e+00,  2.23411559e-01,  1.76177943e+00, -2.17088985e+00,\n",
       "        6.28488171e-01,  6.01196526e-01,  9.50757858e-01, -8.69246667e-01,\n",
       "       -5.29007071e-01,  4.56840956e-02, -1.02755181e+00, -1.22928930e+00,\n",
       "       -8.83358472e-01, -7.08934637e-02,  3.74053351e-01, -2.45937384e-02])"
      ]
     },
     "execution_count": 208,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 210,
   "id": "04de6482-cefd-4201-a3f2-054c9ca4958c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "48"
      ]
     },
     "execution_count": 210,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(arr > 0).sum() # Number of positive values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 212,
   "id": "759dd837-7a12-4879-84b6-056cfc0dfa6a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "52"
      ]
     },
     "execution_count": 212,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(arr <= 0).sum() # Number of non-positive values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 214,
   "id": "c5b70311-2c07-44a6-b07b-5d5889d255c7",
   "metadata": {},
   "outputs": [],
   "source": [
    "bools = np.array([False, False, True, False])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 220,
   "id": "51b94c6e-68c7-4e8e-b3f0-a9315a7411e1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 220,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools.any() # any() tests whether one or more values in an array is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 222,
   "id": "039d6d56-7fb7-4c50-8d81-2fb1461b0e72",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "False"
      ]
     },
     "execution_count": 222,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "bools.all() # all() checks if every value is True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 224,
   "id": "35cf88b6-920c-4c41-a203-04484fee9b1c",
   "metadata": {},
   "outputs": [],
   "source": [
    "# These methods also work with non-Boolean arrays, where nonzero elements are treated as True."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 226,
   "id": "ca3bf0f0-8ad9-47d1-bbce-b96572b2dea8",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = rng.standard_normal(6)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 228,
   "id": "720c313e-49fa-475c-9367-26ff38d99acf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 0.07726066, -0.68391322, -0.72083767,  1.12062282, -0.05481416,\n",
       "       -0.08241372])"
      ]
     },
     "execution_count": 228,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 230,
   "id": "11ed035a-b9e2-4ccc-b61d-142ecbac13b4",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.sort()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 232,
   "id": "ec13378b-f2bd-4190-bc4b-ad32ba8a4333",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-0.72083767, -0.68391322, -0.08241372, -0.05481416,  0.07726066,\n",
       "        1.12062282])"
      ]
     },
     "execution_count": 232,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 234,
   "id": "ff735f1e-897b-4df0-8ede-493d06487ea2",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = rng.standard_normal((5, 3))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 236,
   "id": "478f0f8f-fca2-4354-9afd-1c432728c241",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 0.9359865 ,  1.23853712,  1.27279553],\n",
       "       [ 0.40589222, -0.05032522,  0.28931754],\n",
       "       [ 0.17930568,  1.39748056,  0.29204679],\n",
       "       [ 0.63840567, -0.02788771,  1.37105185],\n",
       "       [-2.05280763,  0.38050908,  0.75539067]])"
      ]
     },
     "execution_count": 236,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 238,
   "id": "53e1b815-d6e5-4d03-a64d-ab01518b4a0e",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.sort(axis=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 240,
   "id": "ead76029-284d-4f24-9b96-b24d4b782c36",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.05280763, -0.05032522,  0.28931754],\n",
       "       [ 0.17930568, -0.02788771,  0.29204679],\n",
       "       [ 0.40589222,  0.38050908,  0.75539067],\n",
       "       [ 0.63840567,  1.23853712,  1.27279553],\n",
       "       [ 0.9359865 ,  1.39748056,  1.37105185]])"
      ]
     },
     "execution_count": 240,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 242,
   "id": "32631aac-183a-4dd8-a374-1ea01583a792",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr.sort(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 244,
   "id": "9d0963f9-6ffe-4339-b739-eb3deef0e8ae",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[-2.05280763, -0.05032522,  0.28931754],\n",
       "       [-0.02788771,  0.17930568,  0.29204679],\n",
       "       [ 0.38050908,  0.40589222,  0.75539067],\n",
       "       [ 0.63840567,  1.23853712,  1.27279553],\n",
       "       [ 0.9359865 ,  1.37105185,  1.39748056]])"
      ]
     },
     "execution_count": 244,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 246,
   "id": "6c50e921-7e67-4339-a79d-98808478bc0b",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr2 = np.array([5, -10, 7, 1, 0, -3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 248,
   "id": "ec1d4eea-5809-4988-b892-42654eecc9ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "sorted_arr2 = np.sort(arr2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 250,
   "id": "514485f5-e3ab-4710-bcae-33b95d150a80",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([-10,  -3,   0,   1,   5,   7])"
      ]
     },
     "execution_count": 250,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted_arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 252,
   "id": "f497b72f-d69f-49da-8393-229b7a45829e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([  5, -10,   7,   1,   0,  -3])"
      ]
     },
     "execution_count": 252,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arr2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 256,
   "id": "c1bfb073-f413-4a08-8672-34db8c9820d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "names = np.array([\"Bob\", \"Will\", \"Joe\", \"Bob\", \"Will\", \"Joe\", \"Joe\"])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 258,
   "id": "7be22476-205c-44b4-a6aa-f193d6a99e2b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['Bob', 'Joe', 'Will'], dtype='<U4')"
      ]
     },
     "execution_count": 258,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(names)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 260,
   "id": "120b0de3-fc48-4145-b212-3eeb45f51dcf",
   "metadata": {},
   "outputs": [],
   "source": [
    "ints = np.array([3, 3, 3, 2, 2, 1, 1, 4, 4])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 262,
   "id": "da67f0af-0bf9-4530-9503-24000d2e37d5",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([1, 2, 3, 4])"
      ]
     },
     "execution_count": 262,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.unique(ints)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 266,
   "id": "f2e0d890-a3da-4b44-9b0b-7624c849dd68",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['Bob', 'Joe', 'Will']"
      ]
     },
     "execution_count": 266,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sorted(set(names)) # Contrast numpy.unique with the pure Python alternative"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 268,
   "id": "e9a958cf-ad3b-47c5-aa1a-24d8b8655d8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = np.array([6, 0, 0, 3, 2, 5, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 272,
   "id": "8e403840-5bdb-4a26-bd60-ea556907c2f6",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False, False,  True,  True, False,  True])"
      ]
     },
     "execution_count": 272,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.in1d(values, [2, 3, 6])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 274,
   "id": "8e39fac4-0b84-41f4-b718-4ceaeb333e5a",
   "metadata": {},
   "outputs": [],
   "source": [
    "arr = np.arange(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 276,
   "id": "93ad40ef-95a1-4de4-8ad9-3113786fd734",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.save(\"some_array\", arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 278,
   "id": "1aa44bc3-efdf-4273-9688-262d5dab778e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 278,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.load(\"some_array.npy\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 280,
   "id": "d3ab0b77-d29f-40a6-8d72-c95a87e232e0",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez(\"array_archive.npz\", a=arr, b=arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 282,
   "id": "5adb54a7-c077-43dc-9b14-98f0ff17894d",
   "metadata": {},
   "outputs": [],
   "source": [
    "arch = np.load(\"array_archive.npz\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 284,
   "id": "f4385f54-207a-4b05-a8f8-9c97d5ae1a94",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, 3, 4, 5, 6, 7, 8, 9])"
      ]
     },
     "execution_count": 284,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "arch[\"b\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 288,
   "id": "d81731a7-a7da-424b-8752-3eb98b71f5a6",
   "metadata": {},
   "outputs": [],
   "source": [
    "np.savez_compressed(\"arrays_compressed.npz\", a=arr, b=arr)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 290,
   "id": "4320c070-7016-41b3-acb4-9709eb8da7ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = np.array([[1., 2., 3.], [4., 5., 6.]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 298,
   "id": "2f2f7cb5-2471-4c91-a331-a2f9d2ac2dbf",
   "metadata": {},
   "outputs": [],
   "source": [
    "y = np.array([[6., 23.], [-1, 7], [8, 9]])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 300,
   "id": "bd9773f4-4d6e-4be5-9316-377d208cd1fb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[1., 2., 3.],\n",
       "       [4., 5., 6.]])"
      ]
     },
     "execution_count": 300,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 302,
   "id": "f3797ba5-1b01-4d8a-af97-f6eca0300468",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6., 23.],\n",
       "       [-1.,  7.],\n",
       "       [ 8.,  9.]])"
      ]
     },
     "execution_count": 302,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 304,
   "id": "c416faff-be40-404b-b9b1-88a69a053de9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 28.,  64.],\n",
       "       [ 67., 181.]])"
      ]
     },
     "execution_count": 304,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.dot(y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 306,
   "id": "f2bca3c7-ae48-4837-a6ef-06efb2910e3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 28.,  64.],\n",
       "       [ 67., 181.]])"
      ]
     },
     "execution_count": 306,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "np.dot(x, y)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 308,
   "id": "71cc40b3-8e29-4137-9e8e-853093ac8fdd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ 6., 15.])"
      ]
     },
     "execution_count": 308,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x @ np.ones(3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 310,
   "id": "d3e0dbcb-c3ab-4c35-b7db-d3f729293fc6",
   "metadata": {},
   "outputs": [],
   "source": [
    "from numpy.linalg import inv, qr"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 312,
   "id": "1f8ffa0b-1771-4325-83b5-15a4f42c19f1",
   "metadata": {},
   "outputs": [],
   "source": [
    "X = rng.standard_normal((5, 5))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 314,
   "id": "ca6e3118-3d29-497b-9c44-978553ff0a8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "mat = X.T @ X"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 318,
   "id": "5500d8fa-f7a5-4b94-9e20-bca092455c4b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[  3.49932285,   2.84436268,   3.59557002, -16.55376878,\n",
       "          4.47325573],\n",
       "       [  2.84436268,   2.56666253,   2.9001963 , -13.57742   ,\n",
       "          3.76776505],\n",
       "       [  3.59557002,   2.9001963 ,   4.48232906, -18.34525499,\n",
       "          4.70660032],\n",
       "       [-16.55376878, -13.57742   , -18.34525499,  84.01018808,\n",
       "        -22.04840478],\n",
       "       [  4.47325573,   3.76776505,   4.70660032, -22.04840478,\n",
       "          6.05251342]])"
      ]
     },
     "execution_count": 318,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "inv(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 320,
   "id": "befbeaff-4763-4d3c-93bc-fa7f1107336c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 1.00000000e+00, -1.73873408e-15,  3.87463259e-16,\n",
       "        -8.39878841e-15, -3.26849986e-16],\n",
       "       [ 8.52161656e-16,  1.00000000e+00, -7.04958114e-16,\n",
       "        -6.52066434e-15, -3.77552457e-15],\n",
       "       [-1.36100122e-15, -1.07066293e-15,  1.00000000e+00,\n",
       "         1.14130951e-14, -1.43770470e-15],\n",
       "       [-8.34150822e-16, -7.38245558e-16, -3.05805686e-16,\n",
       "         1.00000000e+00,  7.41529958e-16],\n",
       "       [ 3.76280560e-15,  1.83772397e-15,  3.80914905e-15,\n",
       "         1.21053835e-14,  1.00000000e+00]])"
      ]
     },
     "execution_count": 320,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mat @ inv(mat)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 322,
   "id": "49633c51-4f12-469b-9175-23edd1f6db1e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ 6.63732514, -2.12482716, -0.41721791,  0.4141581 , -1.74958717],\n",
       "       [-2.12482716,  5.34294773, -0.06468282, -0.38396683, -3.10408173],\n",
       "       [-0.41721791, -0.06468282,  2.34761515,  0.73648716,  1.20596393],\n",
       "       [ 0.4141581 , -0.38396683,  0.73648716,  0.55469981,  1.38090681],\n",
       "       [-1.74958717, -3.10408173,  1.20596393,  1.38090681,  7.4832719 ]])"
      ]
     },
     "execution_count": 322,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "X.T.dot(X)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 324,
   "id": "187a0ca5-7523-40c9-9bb0-d69c2a553a6c",
   "metadata": {},
   "outputs": [],
   "source": [
    "#! blockstart\n",
    "import random\n",
    "position = 0\n",
    "walk = [position]\n",
    "nsteps = 1000\n",
    "for _ in range(nsteps):\n",
    "    step = 1 if random.randint(0, 1) else -1\n",
    "    position += step\n",
    "    walk.append(position)\n",
    "#! blockend"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 326,
   "id": "9b3055aa-9d1d-4284-97d8-5a109e94b837",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x21b16a53b60>]"
      ]
     },
     "execution_count": 326,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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tltNmxXCPFyfmQpicDWGgvVXvLhEREZWk+8wKALzzne/E+fPnEY/HcezYMbzoRS/Su0sAys+syMXgDFCVdivyJzAzb4WIiIzNEMGKUUkf6NPLUUTjhacUb1YMzuh4RhAREZkFg5UyurxOdOdOKZ6aL/xQl2YkjFBCfyvGy8waERERGQmDlU2ofaivJ9I4eyVS8HOzGc31+/LVdVxdS+jcGyIiotIYrGxCWuZR5nacXAgjIwIBj0O1/ooZ+Fx27OzIJtZydoWIiIyMwcom1CrZ5pNr/YapSrsVTLIlIiIzYLCyCWmZ5+RCGMncKcWTJs9XkTDJloiIzIDByiYG27OnFCdSGTlPpZIzgcwgP2vEAw2JiMi4GKxswmIRCg7+S2dEeWeQWWusSKR8nLNXoogl0zr3hoiISB2DlQoo81amlyKIJTNodVjls4PMqsfnRKfbgXRGxMn5sN7dISIiUsVgpQLK5RJpCWi0zweLxbzJtQAgCIJqAjEREZGRMFipgLLWitmLwRWTjxSYY94KEREZE4OVCgx3e2G3CgjFUjg6uQDA/Mm1Em5fJiIio2OwUgGHzYLhbi8AYHopCsD8ybUSKcl2ai6MdEbUuTdEREQbMVipkDI4sVoEXNPj1bE32hkKuNFit2I9mZYDMSIiIiNhsFIh5bLPvi4PXHarjr3RjtUiYKQvG3ixOBwRERkRg5UKScsl2f9vjCUgyTiLwxERkYExWKnQaF9+2adR8lUkY33ZQIwHGhIRkRExWKmQ12XHNT0eAMCBnW36dkZj44paK6LIJFsiIjIWm94dMJNPvPEAzixGcHBXh95d0dS1vV5YLQJWogkshOLo9bv07hIREZGMMytVGOn14dXX9evdDc257Fbs7coeHcDicEREZDQMVghAPoF44jLzVoiIyFgYrBAAVrIlIiLjYrBCABTnH7HWChERGQyDFQKQ3459cWUNoVhS594QERHlMVghAEBbqwM72loAACe4FERERAbCYIVkY/3MWyEiIuNhsEIyKcmWeStERGQkDFZINs6ZFSIiMiAGKySTloFOL4QRT6V17g0REVEWgxWS7Whrgb/FjlRGxOmFiN7dISIiAsBghRQEQcjnrXApiIiIDILBChVgcTgiIjIaBitUYHyHlGTLAw2JiMgYGKxQgbG+7IGGk7MhZDKizr0hIiJisEJF9na54bBZEE2kcXFlTe/uEBERMVihQjarBSO9XgCst0JERMbAYIU2yCfZMm+FiIj0p1uwcv78ebztbW/D0NAQWlpasHfvXtxzzz1IJBJ6dYlyxvqzeSucWSEiIiOw6XXHU1NTyGQy+PSnP419+/bh2Wefxdvf/nZEo1Hcf//9enWLANZaISIiQ9EtWHnlK1+JV77ylfK/9+zZg5MnT+KBBx5gsKKz0T4vBAFYDMdxJRxHl9epd5eIiKiJGSpnJRgMoqOjo2ybeDyOUChU8EXaanXYMBRwAzBWvZVvPHkJxy6s6t0NIiKqM8MEK2fPnsXf/d3f4c477yzb7siRI/D7/fLX4OBgnXrYXMYNlrfyzKUg3vd/n8bhLz+pd1eIiKjONA9W7r33XgiCUPbriSeeKPid2dlZvPKVr8Stt96KP/7jPy57+3fffTeCwaD8NTMzo/WfQFDkrRik7P7xmeyMylwwhsVQTOfeEBFRPWmes3L48GHcdtttZdvs3r1b/v/Z2Vm89KUvxQte8AJ85jOf2fT2nU4nnE7mUNSatH35hEFmVpRB08RcCN0+l469ISKietI8WAkEAggEAhW1vXz5Ml760pfi4MGDePDBB2GxGGZVqumN5YKV6eUoovEU3E7dcrEBFC5HTc6G8NJru3XsDRER1ZNu0cHs7Cxe8pKXYHBwEPfffz+uXLmC+fl5zM/P69UlUgh4nOjxOSGKwAmdl4KS6Qym5sPyv42U9EtERLWn23D5kUcewZkzZ3DmzBkMDAwU/EwUeYCeEYz3+7EQWsTkXAiHdpffpVVL565EkUhl5H+z/gsRUXPRbWblrW99K0RRVP0iY5CSbCcu6xscSDMp+7o9AIDzy2sIx5J6domIiOqISSJUUv6MIH2DFWkm5aZ9AfT5s4m1ymUhIiJqbAxWqCSp1srJ+TCS6cwmrWtHSq4d6/MpZnuYt0JE1CwYrFBJA+0t8DptSKQzOHsloksfRFGUZ3bG+n2Gme0hIqL6YbBCJVksAkb79c1buXx1HcH1JOxWAdf0eHkiNBFRE2KwQmXJyy46BQfS/e7r9sJhs8gzK6cWwgU7hIiIqHExWKGy8ssu+uSISMm1Uj8G2lvgddmQTIs4s6jP0hQREdUXgxUqS6pkOzkb0mVbuTK5FgAEQVDM9jDJloioGTBYobKGu72wWwWEYilcWl2v+/1L1XOlmZXs/2fzVphkS0TUHBisUFkOmwXX9HgB1D9vZTWawOWr2QBptCBY0TePhoiI6ovBCm1KWnaZrPOyizRzsrOjFT6XPd8fxYnQmQwrHhMRNToGK7QpvWqbFCfXSvZ1e+CwWhCO67M0RURE9cVghTalV20TKYFWmtmR2K0WXNPrKWhDRESNi8EKbWq0L5uzMheMYSWaqNv9SjM54zt8G3423sckWyKiZsFghTblddmxu7MVQH5pptZiyTTOXokCyO/+UZICGCbZEhE1PgYrVJGxOheHOzkfRjojotPtQLfXubE/ffn6L0RE1NgYrFBFxuuctyIXg+v3QRCEDT8f6fNBEID5UAzLkXhd+kRERPpgsEIVqfcZQXJybf/GfBUA8Dht2N3prmufiIhIHwxWqCLS9uFzVyJYT6Rrfn9S4mzxTiClMRaHIyJqCgxWqCJdXicCHgcyIjA1X9vgIJ0RMTUXBqCeXCuR81a4I4iIqKExWKGKCIIg11updXAwvRTFejKNFrsVQwF3yXZysTrWWiEiamgMVqhi9TqTR8pXGe3zwmrZmFyb7082eDq3FMVaIlXTPhERkX4YrFDF6pVkO6nYCVROl9eJLq8TogicyC0bERFR42GwQhWTZlam5kJIpTM1ux+5cm2ZfJXiPjFvhYiocTFYoYrt7nSj1WFFPJXB9FK0JvchimK+xkqZnUASvU6EJiKi+mGwQhWzWASM1ngHzkIojpVoAlaLgGt7vZu2l2ZfWMmWiKhxMVihqtQ6yVZKrt3X5YHLbq24P1Pz4ZouTRERkX4YrFBVan0mT6XJtZKdHa3wOG2IpzI4V6OlKSIi0heDFapK/oygIERR1Pz2pRmb8QqDlezSlFfuExERNR4GK1SV4R4PrBYBq2tJzAVjmt/+RO5U50qSayXylurLzFshImpEDFaoKi67FcPdHgDaLwUF15OYWVkHUPkyEKBIsuX2ZSKihsRghapWq+JwJ3LBxo62FrS1OirvjyLptxZLU0REpC8GK1S1MbkQm7Y5ItUm10qGezywWQQE15OYrcHSFBER6YvBClUtn2Sr7cxKtcm1EqfNiuGeXJLtZSbZEhE1GgYrVDVpGejS6jqCa0nNblfazVNNcm1xn2p9bhEREdUfgxWqmr/VjoH2FgDaJbXGU2mcWYwAAMZ3bH4mUDGeEURE1LgYrNCW5GcytFl2Ob0QQSojwt9iR7/fVX1/+mtbrI6IiPTDYIW2ROvtwpOKfBVBEKr+fSlYuXx1HVfXEpr0iYiIjMEQwUo8Hsf1118PQRBw/PhxvbtDFRjXeCZDmqGpNrlW4nPZsbOjVdM+ERGRMRgiWPnzP/9z9Pf3690NqoI0k3FmMYJYMr3t25NmaKrdtlzQpxqfCE1ERPrQPVj5wQ9+gEceeQT333+/3l2hKvT5XWhvtSOVEXF6IbKt28pkRMUyUPXJtZJanwhNRET60DVYWVhYwNvf/nb80z/9E1pbWyv6nXg8jlAoVPBF9ScIgqJybOkk20Qqg3947Bwura6VbHNhZQ3RRBpOmwV7Au4t96mS/jSyTEbEF39xHqcXwmXb/euxS3jq4mrZNj94Zg6Pn17SsntEDePCchT/+Pg0UumM3l1pGroFK6Io4q1vfSvuvPNOHDp0qOLfO3LkCPx+v/w1ODhYw15SOZUUh/vKf1zER753An/9/RMl20izKtf2emGzbv0pKfXn7JWoJktTZvNvE/P4y29P4IPffKZkm6curuJPv/Y03v0vT5Vsc/nqOt755Sfx9i8+gXiq+a4j0Wb+6ruT+PBDk/jGk5f17krT0DxYuffeeyEIQtmvJ554An/3d3+HUCiEu+++u6rbv/vuuxEMBuWvmZkZrf8EqlAlOSLHL14FADw9U3q2Y7vJtZIenxMdbgfSGREn58vPLjSi4zNXAQDPXA4inVE/I0lqc2l1HcuRuGqbZy5dhSgC68n0tpf4iBqNKIry6+j4pau69qWZ2LS+wcOHD+O2224r22b37t34yEc+gl/+8pdwOp0FPzt06BDe9KY34Qtf+ILq7zqdzg2/Q/qQgosTcyFkMiIslo1bjqVARtpSrHZAYT65duv5KkB2aWq834fHTi9hci6E5w62bev2zEa6jrFkBtNLEezr9m5so5gFm5wL4XeGuzZts38LRfqIGtVCKI6VaLY8Ance1o/mwUogEEAgENi03Sc+8Ql85CMfkf89OzuLV7ziFfjqV7+K5z//+Vp3i2pgT5cHLrsFa4k0zi9HsafLU/DzWDKN04v5kfnkbAg37tv43NjqmUBqxnLBSrPlrYiiWLAcNzEbUg1WituoBSvKNnwzJiqkfG+Zmg8hnRFhVRmokbZ0y1nZuXMn9u/fL39dc801AIC9e/diYGBAr25RFawWAdf2lt6Bc2ohXLAcodZmMRzDlXAcggCM9G78cK1Ws54RNB+KyaM9QP3vT6QyOL0YLtum+PvNFvQRbUb5+oglMzh3hUul9aD71mUyt3Jn8hSPysu12RNwo9Wx/Yk+Kcl2ai5cMm+jEW241iqByOnFMJJpUdFmYyCyHIljPhST/31iLoxME11Hos1U8r5G2jNMsLJ7926Ioojrr79e765QFcrNZEjf29ftyf1744ej1Ga7+SqSoYAbLXYr1pNpTC9FNblNM1C71qIolm1zbimKtUSqoI30xjvQ3gKHzYJIPIWLK6W3nRM1m4m57PtY/rXGYKUeDBOskDmVK7svffDdejC7rKe2pVhqo0W+CpBdmhrp8xbcdjOQrv9/ObADVouA1bVkwQyJss2Lr+lCl9cJUQSminZNSW2eO9AmL8s103UkKie4nsTMyjoA4L/m3teY11UfDFZoW0Z6fbAIwFIkjkXFh2M6I+JE7kPuZSPdJbcUT2qYXCsZb8LicNJo78BgG/blEp0nLqsvDY31+UrOiOVnunyan6xNZHbSe9qOthbcuLcTgPosJmmPwQptS4vDKu8CUn7wnV+OYi2RhstuwZ4uj2op/Eg8JS/VSB+MWhjry50I3SQjHuVob6zfp3qtMxkxP4u1w6eYESsMRKTARHk7zXIdiTYzqQjmr+nxlpzFJO0xWKFtU0uylV7UI70+WC350vyTc4ptf7n2vT4XOj3a1c5Rfsg2w4hHOdpra3WoXuuZ1TVE4ik4bBbs7fLIicjKQGQtkcK5XPA43u+T84i4Jk+UpSyz4LJbMdytPotJ2mOwQtumtlygXE4obBMq2UYr1/ZmRzzL0QQWQupVWhvJhmutMrMi/f+1PV7YrRa5zdR8WD7fZGo+DFEEAh4nur0ujPR6IQjAYjiOK+HGv45Em5FnHvtKv69RbTBYoW1TG6UXJ86qbSmuRb4KALjsVuztcuf60fj5FsXXcTy3DHZpdR3B9aRqm10drXA7rIinMvJsSnEbt9OGoYB0HflmTM0tnkrjTK7I5XiuqrPaLCbVBoMV2jbpBXt+eQ3hWBKiKMq5ENLIQ21LsZQUqmW+itwnacTTBNOzxaM9f6sdO9paAOQDEGUuCgBYLAJGi2bE1Ga6mGRLlHV6IYJURoS/xY5+vwuA+iwm1QaDFdq2DrcDfbkX79R8GFfCcSxFErAI2ZwVYOOW4mQ6g1PzuVGKRjVWlOTZngafEVAb7QEb84jUtogXJ9Cqt2muZGWiUpQzj4KQLa+vNotJtcFghTQh70C5HJRHGXu7PGhxWDe2mQ3izGIEiXQGXqcNgx0ttetPg3/ISqO9ttb8aA/IBxkTs0EsReJYCElHGmwMRCZmQ0ilM3LCszJ45I4goiy10+H9rXYMtBfOYlJtMFghTSgTzYqXHPJt8qN0KYgYVYxSNO1P7r4vrqwhFGvcEY9yCUh5HccUQYZ0rYc63XA7bRvaTMyGcG4pingqA7fDil0drRvaTC9HEY0XVrslaialNgRwqbQ+GKyQJsYUyy6lqtKOF3yAbhylaKmt1SHnbZxo4BFPqSRl6d9nFiN46uIqgI1vssM9HtgsAoLrSRydXAAAjPb5YFGcIBvwONHjk6rdNu51JConoyhyWbxs3SxLznpjsEKakD4cTy2E8fSMNNovfFErtxT/5OSVXJvaBCsAFAmkjfsmUmq01+d3oa3VjlRGxLePz6q2cdqs8vkm/3rskmobgNsziS6srCGaSMNps2BPboecZIxLpXXBYIU0MdDeAp/LhmRaxOWr2WqqxaN95Zbiabn4mPbJtZJyJ0I3gnKjPUEQ5L+/3LWWvjetKAZXqg3fjKlZ5YtcemGzFn5sKmcxi88+I+0wWCFNCIJQMCrv97vQ7nZsaKf8wHRYLfLIvhYaPcm23GgP2BicqM1ibVw+UgtoGvs6Em0mn4e38fXR53ehPTeLeXohUu+uNQ0GK6QZ5bJPqaq0yg/M4R4PHLbaPQXH5BFPGIlUpmb3o5dyoz2g8Fp3e53o8m480kD5ONksAoZ7NgaPUpuTC2Ek0413HYk2I83Oqr2vKQdqLA5XOwxWSDPKUbraCKS4Ta2SayU72lrgb7EjmRZxaiG8+S+YTLnRHlDZtVa++e7r9sBps25oM9jeCq/ThkQqg7NXOHKk5jNRIpFdMs5ztGqOwQppRq3y6VbaaEUQBPk+GjHfYrOzlYYCbjhzM1el2vhcdrnOTak2BdVum6AisJqp+RC+9MsL2z4YcyWawKcfPYtwme30sWQan/3pOSyUOck3nRHx+Z9NM3isg8VwDFfCUp0ir2obJqHXHoMV0sy+bg+8ThusFgHXDaiP9ttaHXJ+xYGd7TXvk7IQXaORp6ZLBH02qwXPHWwDABwYLH2tn5d7HMo9Hs1eVvz9X38Gf/GtZ/HDE4vbup1P/PA0jvxgCp969GzJNl/4+Xnc9/0T+F8PT5Vs89BvZnHvdyfxP7717Lb6Q5uTBjpDATdaHTbVNtL7zIm5EDKZxj/pXQ/qV55oC+xWCz7/RzcgFEuhv610Vdq/f9PzML0UlT9Ia2msQXcEKUd7o33qoz0A+F+/fx2evLCK3x3tLtnmg783iucPdeLWQwMl2zTzmnwilZHPunr60lW8fKxny7d1fOZq9nZmSl/HfJurm7b5zaUgMhmxoDYOaSu/BFR656I0i7mWSOP8chR7umq3caBZMVghTR3c1bFpm9E+n7ysUGvSG8yJuXBDvalLo709ZUZ7QPZNdEhlp5BSj8+FP3j+zrJtlAX9RFGsSdVhozq9GEYyXXhS+FakM6JcWG9yrvR1lALrc0tRrCVSqo+v1I9IPIWZ1TXs6iz/GNPWlSpyqWSzWjDS58PTM1cxORdisFIDXAaihra3yw2HzYJIPIWLK2t6d0czlYz2tDTc7YXdKiAUS+HS6npd7tMolEtf21kGm16KIJbM7qZaiSYwr5KTEoolcWE5+zzNVg3emBguimLBTGGzLs3VS6kq0cW4xb+2GKxQQ7NZLXJSXCO9iUxuklyrNYfNguHuxruOlVDOpsyHYliOxLd0O8XXTS1ZufhoCLVrPbOyjnAspWjTfEtz9RKJp+SCiZttCGCSbW0xWKGGN96A+RaVTE1rrdErApdSvPSz1b+/ktsp/p7aslPx87gRd7oZhXQSea/PhU7PxjpFSjyhvLYYrFDDa7QRTzWjPS3lz0BpnKBvM5lMfslFqra81eeR9Hv529l4HYvbqF3rjbfTGM9rI9qsPIDSSK8PFgFYisSxWGbbOW0NgxVqeGMNdrZNNaM9LTXjGUEzq2uIxFNw2Cx4zXX9ALb29yvzTG49mN11pTqzMlvYZmo+jFRR1WCpzeuftwOCACyG47gS3trSFJVXab4KALQ4rHJi7USTzT7WA4MVanijfd6GelPfrJpmrUhbpGeDMaxGE3W9b71I1/raHi+uG5SqlFY/szQfimElmoDVIuB1B3YAyOaeBNfzxeESqQxOL2YTal+1vw9uhxXxVAbncrNoxX26YXcHhnK7gJptaa5eJnJLbpW+1rgUVDsMVqjhtTps8vbdRnhTr3dyrcTrsmNXZ2u2Dw1wHSuhHFlLH0TSluKt3M6+Lg96fC7syNUhUn6onVrIbpH2t2SrCstVgxXB0XIkjvlQLFdfx6dYmmuOx6OekukMTs1nKwQrzz0rp5ErZuuNwQo1hfzZHebPt6h2tKelRq4IrEb6O8f7fej2utDldZbcUlz+dgpnw9SSlZUViQVBUB2lS212d7rhcdoa6nltNGcWI0ikM/A6bfKRFJvh41E7DFaoKTRKku1WRntaapTrWKniBMut/v35Qyd9Bf9VfqgVz5ipHXEg96evsA1H8tqTrvVov6/iIojS43F+ea3s+U9UPQYr1BTksztM/qa+ldGelpopyfZKOI5F+QC7ohmRKv9+edZEnlnZeB2LkznlNrlqt8o2xcHT9HIU0Xh1S1NUXjXJtZIOtwN9fheA6mffqDwGK9QUpDd3s7+pb2W0pyXpOp69EsF6Il33+68nKcAY6nTD7cyWvN/K9u3gehIzK9mqv8UzImcWI4in0gVbpKWfDfd4YLMIuLqWxGwwuxW2eIamy+tEt7w01fgBZD3J17rK8gDy7NtlLgVpicEKNYWAx4ken/nf1Lcy2tNSt9eJgMeBjAicXGjskaNaIrM026G2pbiUE7kgZEdbC9paHQCAfr8Lba12pDIiTi9ECrZI781tf3XarIp6KyGsJVLyzqDxgj5xKUhryq3m1R5p0azFE2uNwQo1jXzym3nfRPIJn/XPVwEAQRDkujWNnkSodq13dbSW3FJc+nY2BpjKBNqJ2aDcZqTXC7s1/7asTNicmg9DFKXZFJdKG/M+r43m0mr2SAOH1SIHjJUa4+NREwxWqGnkp2fN+SaiHO3Vs3JtsWZJslWbWbFYBNUtxeUUL91IlNex1JKDMsm2OLlWrQ1pQ3o8hns8cNiq+5iUgtBTC2EkUpXNvtHmGKxQ0zD79Ox2RntaaoZlh2g8hell9SMNqv3780t3hbNhyiTbUst7yvvarM3JhTCSFS5NUXnbWW4daG+B12VDMi3izGJE6641LQYr1DSkEejJeXO+qW9ntKcl6TpOzYeQzoi69aOWpuZDEMVsjk6Xt/BIg2pmMuKptPyBtWFmRdqhNhfCsyUK/UmzOJevruOX55ZV2wy2t8LjtCGRyuDsFX44aqHULFYlBEFQzJo19lJpPTFYoaYx2N4Kr9OGRNqcb+p6J9dKhjrdaHVYEUtmML1kvutYiXLXWm1LcSmnFyJIZUS0tdrR73cV/GxPwA2nzYJoIo0rRVukJVI1WwDy4ZXFMzQWi8DKqRqTk2t3bC03TPkcIW3oHqx873vfw/Of/3y0tLQgEAjg9a9/vd5dogZlsQgYlUbFJsxbmSixnFBvhXkb5ruOlSh3rdW2FJe+nXwF3OKt5jarBSOKkftQIL9FWmlcUfzP7bBiV0frhjbMW9HOSjSBudzjOrrF3LBxPh6a0zVY+frXv47bb78df/iHf4inn34aP/vZz/AHf/AHenaJGpw8AjXhiKe4DoeeGn0kX+5aF28pLns7mywnKL9fso2iD6N9PlgsG+vrsJKtdqRruLszu7y2FfIS3+zms29Uma09EhpIpVK466678Dd/8zd429veJn//2muv1atL1ATMeraNFqM9LTXyyDGZzsjVR0stuY33+zE1H8bEbBD/aayn5G1tNhtWWC+lkjal+pN/XouiqEvBwEahRXmAfd0eOKwWhOMpzKysY2fnxtkwqo5uMytPPvkkLl++DIvFggMHDqCvrw+vetWrMDExUfb34vE4QqFQwRdRpZQjUDONeKQ30O2M9rQ0VvTh2EjOXokgkcrA47RhsF39Q6aSZZdMRpQLwpWaDVN+fztthru9sFsFhGIpXFpdL9mnmZU1fO7xaVMmmNdL8XlQW2G3WnBNryd3e6UHRmuJFD7z07O4Eo5v+b6ahW7Byrlz5wAA9957L/7iL/4CDz30ENrb2/HiF78YKysrJX/vyJEj8Pv98tfg4GC9ukwNoNI3daNRq/mhp2t6vLBaBKyuJeUZn0YhXevRPq/qkgtQ2fblCytriCbScNos2BNwq7YZ7fWhxW6F3Spgf4nHttfnks+buX6wXbWNw2bBcLcXQPkA6n8+NIn/+dAk/u8TMyXbNDutahlJuUblHo/PPTaNv/7+FD529OS27qsZaB6s3HvvvRAEoezXE088gUwmG9l/6EMfwu///u/j4MGDePDBByEIAr72ta+VvP27774bwWBQ/pqZ4YuOKlfpm7rRGCW5VuKyW7Gvq7K8DbOp5FortxRfXUuUuJ3siHqk1wubVf2ttsVhxef/8Ab841tvQKfHqdpGEAR85vZD+PTtB3Ftr7dkn+RZwzL5WMdnrgIAns79lwqtJ9I4l9spuN1dd9U8HsdnzLUsrQfN55MPHz6M2267rWyb3bt3IxzOrgmPjY3J33c6ndizZw8uXrxY8nedTiecTvUXNVElxvt9mJwLYXIuhFfu79W7OxUxUnKtZLzfh5MLYUzOhfDyMnkbZlPJLJa0pXhmZR2TcyHcuDdQ5nbKB5jP39O5aZ+eM+DHcwbK3854vw//eqx08CidIg2YM8G8HqbmQ8iI2bPEun2uzX+hjEpm36TH4cxittqtnvWTjE7zYCUQCCAQ2PjCLXbw4EE4nU6cPHkSN910EwAgmUzi/Pnz2LVrl9bdIpKN9/vwtWPVnZyrJy1He1oa6/fhG09dNl2ycjmiKBZsNy5nvM+fDVZm1YMVtTOBailfEVf98VAGKKfmI0imMwXnEJG2j9lInw+CAMyHYliOxDfMnCmT5pNpEacWwti/xbouzUC3Z6rP58Odd96Je+65B4888ghOnjyJd7zjHQCAW2+9Va9uURMw20FjJ5SjPe/2RntaasTaHpdW1xGKpWC3CvJyYSmb/f1aJGpWY7Qv29/ZYAyr0Y1LU8qgMpHO4PRCYxb02w4tHzOP04bdne6C2y28r8KgstGWU7Wma1j9N3/zN7jttttw++2344YbbsCFCxfwox/9CO3t6klkRFqQ3tTngjGsqLypG41RKtcWkxIIL62uI7ie1Lk32pBmH4a7vZtOyZeb5l8Mx7AUicMiZJNo68HrsmNXbous2jJPcT+5FLSRXLlWo9daubwVPh7V0TVYsdvtuP/++7GwsIBQKISjR49ifHxczy5REyh4UzfBaKbeI/RK+Vvt2NGWLQVvhutYiWqutdTmzJUIYsm06u0MBdxocVg17mWZPpU5k0Z6jKSCdo20fKeFVDqDKY1PNS93QvkEH4+qcMGSmlL+BGbjv0FoPdrTktlPsi5WzSxWr8+FDrcD6Uw230D9duqbg1Bqtkd5ivR/PTig2qbZTS9FEU9l4HZY5eWb7co/HirBY+41c2vu8TgxF0amQQ8G1QKDFWpK4ybJW1GO9oyybVkpfx2NH/RVYrKK6qWCIJSs5KvX0l2p57XyFOkXDXdl+1jBQYzNRLpmpY402Arp8Ti3FMVaIiV/X5k0/5rn9sNhsyAST+Hiypom99uIGKxQUzLL2TbK0Z7aAXZ6a6QzaVajCflgQimvaTOlll2kf9d76U66v7NXIlhP5JemlLtchntypeBNVhix1mpRHqDL60SX1wlRzM6cSJRJ8/1tLRjJ1c9plBnKWmCwQk1pvMSbutHUYrSnJek6nlmMIJ4y7nWshPRBsauzFV6XvaLfUQvWIvEUzi9nR8ha5T5UqtvrRMDjQEYETiqWppTLUpWWgm82lW5Zr5baUmnxzJtZzyyrJwYr1JS6FG/qU/PGHc3oNUKvVJ/fhbZWO1IZEafmzb0VVr7WVQQY0ofMibkw0rl8A+k8oF6fq2RV2loRBEGurqv84CtOHC6X+NmMsvV1pORabZdb87O4fDy2g8EKNSVBEOR6K0aeejVyci1QmLdhhmTlcraSZzIU8MBlt2A9mcb5XAKr3lvN88Xhsv1IpjPyLEt+JF/YptnNBWO4upaEzSJguMej6W2rXevi1/UYH49NMVihpmX00UwtR3taMvp1rNRWtohbLQJGegv/fr1nw4qL1amdIt2IBf22Q7mN2GXXdqu5dK2n5sNIpTOqW6RHer0QBGAxHOcJzCUwWKGmVcnZHXpSjvakHAMjaoRReiyZxln5SIPqAsPi55Hes2Hj8odjCOmMmD+jSJH3NFpUCr7Z1fJU810drfA4bYinMji3FFXdIu122jCUO5nbyDO9emKwQk2r+E3daJSjPaetfoXFqpXP2wiZtk7E1Hw4tzvDgW5vdXkmyu3byXRGzt3Ra6v57k43Wh1WxJIZTC9FVGeMlKXg+eGoTK7V/jGzWAR5d9nEbLBk0nyjlQHQGoMValrKN3Wp5oGR6L2cUKmhgBtOmwXRRD5vw2yka52dcahu15VyR9DphQgS6Qy8LhsG2ls072clsktT0odjqOTzqFGW77QwMVu4LKM1+Vpf5uOxVQxWqGllRzzGrcCqVxXUatmsFowY+DpWYjvXeqTXC4sALEcT+PHJRQDZD55qgx4tKYvDlUr4baQaOdsRXEvi8tVsvZlaDQzGFcn8pZYJ5RnKJn88SmGwQk3NyKOZWo/2tGTk61iJ7Zy/5LJbsbcrm1P0r8cubfl2tCTd/9HJhZKnSOeTbJt72WEit4ttoL0F/pbK6utUS5nQPFkiaV5qM70cRTSeAhVisEJNzahJtvUY7WnJqNexEumMKNfa2WpSrPR700vR3L/1nQ0r7o/aKdJSm+JS8M2mHlvNh3s8sFkEBNeTWC2RNB/wONHjy1a7NXLtJ70wWKGmpkxqM9I5KdJob7CjdqM9LZU6I8cMppciiCUzaN3GAXbFwYnedXGu6fHCWpC8ubE/3V6XXAp+aj684efNoh7LrU6bFcM9+ZmtUknzZjmzTA8MVqipDfd4YLUIWF1LYj4U07s7skkTLQEBwEivDxYBWIrEsRg2znWshPTBMNJb+AFfDeXsl8Nqwb5ufbeau+xW7OvK96HU7JxZzsiqpcm5+rzWlLfPx6N6DFaoqbnsVgznPlgmLhvnDcIsybWSFocVe7qk82aMcx0rocW1Vn4QXdPrgd2q/1urcjal1N9m5hkxLcSSaZxezG0131HbYIWPx/bo/4oi0lm9k0MffnYOj52+UraNmZJrJZWMCjMZEV/8xXmcWTTOssN2kmsl7W4H+v2u7O0Y5DFT/j2lTpHO7whqziTbUwvZM53aW+3o9blqel/Kx6PUc0Rqc3I+jGQ6U9P+VOoXZ5fxwW8+g4efnde1HwxWqOnVc1fEXHAd7/jnJ/H2Lz6BWFL9lOJYMo0zubovZkiulVRycuwPnp3HX357Ah/85rP16lZZoihqtgxwYFd79r8727fdLy1I/Rju9pQ8RVoa4Uul4JuNsnJtrbeaj/X74LBZ4LBZSr6uB9tb4XXakEhncGbRGLWffnZmCV/+1UX8JLctXy82Xe+dyADk0WUdaoT85lIQogjEkhmcXojgOQMbp4OVo70+f21He1qqpG7H8ZlVAMAzl4JIZ8Qt54hoZT4Uw0o0AatFwLW96rMPlfoft4zhpn0B3HpwQKPebc/BXe34//7gQNn8mV0drXA7rIgm0ji3FMU1Pdu7BmYzUcflVp/Ljs//4Q2AiJJJ81Ltp1+fX8HkbEiuA6UnoxSn5MwKNb3xXL2DS6vrCK4na3pfhSevqs9AKHMo9CwsVi3pDf/88hoiJepESAGh8pRiPUnXel/X9g+w6/W78Mbf2gmbAfJVJK++rl8+aFFNQWHEJsyTqPc5TjfuDeDGfYGybeo5eKqE3mddSYzzqiLSib/VLpdGr/UbtjIvplSOzEQd6j7UQofbIc8EnVB5o1WeIg0YI4nQrNdaS5Us3zWidEaUn6dGevyN9HgsReJYCMUhCCgb9NYDgxUiKJNsa/sGoUxkLB2sGGPadSvyZ6BsvI6zuVOkJUZ4MzbztdbKWJPuQDm/HMVaIg2X3YKhgHFONVcup+pd+0kavA11uuF26ps1wmCFCIVnd9TKajSB2WC+BonaKcXZaqrhXJ/M9wE6XmYKu3jWygjLDnJyrQmvtVaUz329PxzrSXr+jfT6dM+dUhru9sJuFRCKpXBpdV3XvmixU04rDFaIUJ9D3aQPxh1tLXDaLFhTOaXYqKO9SpUbpUuzGFLCp94jx+B6EjMruSMNDJDIqBepFPzVtWRBMN3ojPRBrOSwWeRznPSe7TLSzCODFSLkZwTOLEZKbineLikQum7AX/KUYqOO9ioljdJPL0SQSBVuhZX+tv9yYId8SvFiOF73PkpOKILHtlaHbv3Qm9NmLQggm4VREkfVlJuhrKf8NdK/OCWDFSIAfX4X2lvtSGVEnF6oTX0DeZTS5ytZiM7sCZ8D7S3wutTrREh/2/N2tsunFOuZt2L2a60l5RlZzUAURTl/zAgfxMXyB4Pq93hE4yn5IEwjzDwyWCECIAiCYstgbd4g5FHKDl/JstpGmnbdCkEQVJOVr64lCk6RNsIpzZMGXQbQQz2WQY3kSjiOpUgCFgG41oC1ZcakPCIdH4+p+TBEEej2OtHlderWDwmDFaKcWp54GkumcfZKVL4ftQ/r7GjPONOuW6WWrCz9v3SKtBFOl50w8Mi63prtTBrp79zb5UGLY3v1dWpBOh5hNhjDajShSx/yM0/GCOYZrBDl1PKMoKn5bFXaTrcD3V5n4SnFudOeF8NxLEeNO9qrlFqSbfEp0npvl42n0vIyFWdWIBeGu3x1HVfX9PlwrCejz2B6XXbs6mwFoN9rxGgJyAxWiHKkEYTaluLtKj6DpOCU4tysw6TBR3uVkq+jYrdP8YyRFLRcXFlDKFbbqsFqTi9EkMqIaGu1ywcQNjN/ix2DHbnCiAapnFpLRk6ulYzXeFl6M0ZKrgUYrBDJhgLukluKt0ttJFd8SrHRR3uV2tftgcNqQTiekrcGF58irTyl+IQOI0dlsrOZjjSopUpOzW4U+eejMT6I1dT7NHilZDoj13syQnItwGCFSGazWkpuKd4utVFKcd6KGUZ7lbBbLbimN7cVdi5YcIr0+A5FsFaHQnylTHIn0AbjBkjqrIdwLIkLy2sAjP346/l4nLsSRSKVgcdpw86O1rrfvxoGK0QKtUg0TGdETM1trEpbvF20nifA1pp0OOTEbEg+RbrD7UCvL7/komdSZyNda600S5Ltidxrsd/vQrvbuPV1pMfj7JUI1hO1qf1UinLm0WKQek8MVogUajH1Or0UwXoyjRa7Fbs73fn7yr0ZnV9ew1xwXR7tGWXadTuUCbTKJSDlkoteSbYZxQF2Zl9y05J0Lc5cqV1hRCMwy3Jrl9eJgMeBjAhMzdf3NWLEbf0MVogUalH/Q/owHu3zFlSlVZ5S/I0nLwMw/mivUsrrWGrJJV81OLyh2m0tXVhZQzSRhtNmwZ6Ae/NfaBK9Phc63A6kMyJOLYT17k7N5D+IjT2rlq39pM9SqdF2AgEMVogKqG0p3q5yoxRpFuVfj10q2caMRvp8EARgPhTD42eWAGz823a0ZWuuJNP1/XCURtYjvV7YrHwLlBQW9GvcpaDiZG8j0+PxEEWxYBnIKPhKJVJQ21K8XeW2AEqzC3JZa4OP9irlcdrkJS/pbyueWVF+ONZz5GiWkbUejFBZuJYSqQxOL5rnVHM9Ho/LV9cRiqVgtwq4xkD1nnQNVk6dOoXXvva1CAQC8Pl8eOELX4gf//jHenaJSNM3iOwopfTOk+IPTDO8gVZKOZNS6hRpPd6MeSZQafk8osY8I+j0YhjJtAify4aB9ha9u7Mp6Tk6NR9CWuPaT6VIr4993V44bMaZz9C1J7fccgtSqRR+9KMf4dixY7j++uvx6le/GvPz83p2i5qclvUmFkJxrEQTsFrURynFH5hGmnbdLuXfUuoUaT3OpJlkcm1J+Q/HcN0+HOupuDij0e3udKPVYUUsmcH0Um0OWC1m1G39ugUrS0tLOHPmDD7wgQ/guuuuw/DwMD760Y9ibW0NExMTenWLSNMTaKXb2Nflgcu+sSqtdEoxANOM9ipVuE1b/Y1PeY6Q1lWD1SyGY7gSjsMiAKO9xnozNoKhgAcue20KIxqB2basWyyCfBRCvfJWjDrzqFuw0tnZidHRUXzxi19ENBpFKpXCpz/9afT09ODgwYMlfy8ejyMUChV8EWlJuaU4vM1S8Jtl1SvzNswy2qtUQbXeEn//ni43HDYLIvEULq6s1bxP0uMxFHCb+kiDWrFaBIz0Gi/J9qHfzOIXZ5fLtvnxyUX88MRC2TbFZ1SZQb2TbCcNmFwL6BisCIKAo0eP4qmnnoLX64XL5cL//t//Gw8//DDa2tpK/t6RI0fg9/vlr8HBwfp1mpqCckuxVEBqqyp5c3zernYAwIGd7du6L6Pp9rrkmaLrB9tU29itFoz0ZpfH6vFmzOTazY0bLG9leimKw19+Cv/PPz1RcmkqHEvi//3iMfy//3Ss5CnFZq2vU8/HYzWawGwwuwty1GDXSPNg5d5774UgCGW/nnjiCYiiiHe+853o7u7GY489hl//+td47Wtfi1e/+tWYm5sreft33303gsGg/DUzM6P1n0CkyFvZ3hvExNzmx6y/66X78D9ftx/veum+bd2XEX3qzQfxqTc/r+y0e35HUO3fjI26Hm8keuQRlfObS1cBAOFYqmTexuRsCIl0BqmMWDLonVldQziegsNmwb7ujcneRqV8PKSDQWtFyufa2dEKn8te0/uqlk3rGzx8+DBuu+22sm12796NH/3oR3jooYewuroKny/7YHzyk5/E0aNH8YUvfAEf+MAHVH/X6XTC6XRq3W2iAuP9PvxwanFbW2qD60n5IL9yIzmP04bbf3vXlu/HyPbv8GP/jvKzGPXcEdQo5y/VkvJMGlEUdV+aVD4vJmZD2Ne9MVFd+TqdnAvipuFAydu5tscLu4nq61zTky0mubqWxHwohj5/7fLajBzMax6sBAIBBAIbnyjF1tay69MWS+GTxmKxIJOpXzVLIjVj/fmzbbZKmnLe0daCtlbzV6WtFS2udSUi8VS+no3B1uONZKTXC4sALEcTWAzH0aM4z0kPyufF5GwIr71+R9k2pZ5HRk0c3YzLbsVwtwdT82FMXA7VNFiRlpqMeI10Cy9f8IIXoL29HXfccQeefvppnDp1Cn/2Z3+G6elp3HLLLXp1iwhA/sV6amHrpeCNWLLaiEZ6vRAEYDEcx5VwvGb3IwWPvT4XOj2cnS3FZbdir1QYUee8FWU1VWDzQKR8G3OcCaSmXkm2Rn7P0i1YCQQCePjhhxGJRPCyl70Mhw4dwuOPP45vf/vbeO5zn6tXt4gA5LcUJ9Mizixurb6BkadUjcTttGEod0ZPLSvZ8vGonFEq2c6HYlhdy+/Im5zbmLeRSGVwZjGfCH+uxCnFZl4ClPNWapjXFUumcfZK9r3OiFu7dV24O3ToEP7t3/4Ny8vLCIVC+MUvfoFXvepVenaJCEDxOSlbe4Mw4vkaRrXda10JM4+s602vE7GLTVzO3v/uzlZYLQJWognMF53ZdWohW5XW32IveUrxUiSOhVAcggB5a7aZ1OPxmJoPIyMCnW4Hur3Gm3k0T5YRUZ2Nb+PE03gqLc/IjG+SYEqFSZ21YuaRdb1t57mvJen+n7ezHftyS1PFzxHl41rqlGLpd4Y63XA7NU/VrLnxvuzfdWl1HcH17dV+KsXo1X0ZrBCVML6N0czphQhSGRFtrXb0+/VNUDSDWi87JNMZnJo37hS30UgzXReW1xDaZmHE7VDOhpWaXVDWMiqV22HkXIxK+Fvtcs2iWr1G8sm1xnx9MFghKkF6YzuxhfoGyjdQI45SjEa61tPLUUTjKc1v/8xiBIl0Bt4GO9KgVtrdDjnIntpmYcTtUJ7jVKo4mvwhu8NXcoDRCEuAtT6h3OhnZjFYISphX7cHDqsF4XhKrpdSKSNvATSigMeJHp8Tokq+gRYmGDxWbUzDM7K2QlmnaLzPr0gyzT8/slVps8HUWJ8/fxDjXAipdH4XX36pyJizBpXQ8syyYumMKAelRn3PYrBCVILdasE1vVvbwmn2aWc91HJ7ZiOMrOtN7yRbaXZyR1sL/K12+fkxs5LP27i4soZIrirt3i63fEpxPJWRa+pEG6S+Ti0rC08vRbCeTKPFbsXuTrfmt68FBitEZUiJbdVMvSrPIDHzSK7eaplkm9+2zMejUnpvXy5OiG5rdWBHW3YJT3p9SW1Ger2wWS0FpxRLP5uaD0MUgW6vE10G3OVSKek6nFmMIJbcuDV7O6SAdLQvWy3XiBisEJWxldHlhZU1RBNpOG0W7AkYc5RiRLUayYuimF+PN/HIut6ka3V6ceuFEbdDbTas+DmittxaPEM32SCzan1+F9pa7UhlRJxe2Frtp1ImTTATzGCFqIytjC6lttJojyojXeuTC2Ek09p9OF5aXUc4loLDasFwj3kOsNPbQHsL/C12JNMiTi/WP8lWbTas+PWodor2hjYNsmVdEIT836ZxcTgz5PTwnZSojJE+HwQhW0lzOVJZKfj8iNC4L3wjGmxvhddpQyKVkStpakF6PK7p9ZjqADu9FRZGrO9SUEGdIkWQUZxkqkycVmuTLddv/A/iSo3X4Bytwmtk3ICOr1yiMjxOm5xwVukbBJNrt0aZbyBVLtWC2gcaVaaWSZ3lnJrP1ynqU9QpGlPkbVy+uo7FcLYq7Whf/iTm4R6PfErxpdV1TM1Lu4XM//jXInicD8WwEk3AahFwTc/GE62NgsEK0SbUtkyW0yjTznqo9lpXgsm1W6dXkq20zDFeVE21X5G38a2nLgMAhgJutDryVWmlU4oB4DtPzyKRysDjtGFnR2sd/4LakB6PE3MhZDLV1X4qRXps93V54LJbNbnNWmCwQrSJakYzi+EYroSlM0iMO0oxqnwCpXZr8pzp2jpl8KjVh2MlSs2GKZem/vXYJQDqQWhxm9E+LywG3eVSjaGAG06bBWuJNM4vRzW5TbO8PhisEG0iP7rc/ANUGqXsKRrtUWWUI/lqqwarWY7EMR+K5ZYKjP1mbER7uzxw2CyIxFOYWV2r2/2Wmw2TniPlaqeMFbVplFk1m9WCEY0r2ZrlNHIGK0SbkN7ozi1FsZYoXwq+kZL59DDc7YXdKiAUS+HSanVVg9VIb+i7O93wmPAAO73ZrRZ5hrBeSbaFdYo2foAWv7YqaWP0WYNqbOfMMjUTc+bY2s1ghWgTXbliUtlS8OW3cBr9fA2jc9gsGO7OfjhqMXKcZHLttsln0tQpWFHWKRpSqVNU/NpSe60VP96N9Phr+XgojzQw+jVisEJUgUpHM2aZUjUyLUeOZlmPN7JSBwjWinQ/I30+1TpFe3J5GwDQ43Mi4NlYlVZ5SrHdauxdLtXS8vUhzWDtaGtBW6tj27dXSwxWiCqQH82UfsOONMgZJHobqyJHaDM8E2j76n1G0GZbzW2KpalyrzPpZ/u6vXDYGuejbqTXB4sALEXiWAzFSrY7vxTFgz+bLjjQsZiZgvnGeQSJaqiSYkxTuVFKj8+JTpXRHlVGq8JX64l0PsGSweOWjfRmCyMuhuO4Eq6sMOJ2VFL6/cDO9oL/qnneLqlNm3adM4AWhxV7uqQDVku/Ru797gT+6ruT+GZui7caMy2TMlghqoD0xnlyPlxypMLkWm1IBb7mgjGsRhNbvp0T8yFkRCDgcaLb59r8F0iV22nDUK4wopb1b0qppJrqe19+DT7yuv14++/sKdnmrTfuxkdetx9/evO1mvdRb2Ob7AgSRRHHZ64CAJ6+dLXk7aidrWRUDFaIKrCroxUepw3xVAbnltTrGzBfRRtelx27OrMFvLbz4cjHQzv1qmS7GI5hKRKHRQBGe0s/bv5WO97827vQ4ihdxMxlt+LNv70LHW5j52JsxWbF+uaCMVxdS5ZtU3CkwQ7jD7AYrBBVIFsKXtrCqZ5LMTFnnlGK0WmR1GmG807MovhMnlqRHrM9XZ6ygUiz2+zxUC4PnZgLI61S0O/0Qv5Ig36/8WceGawQVWiszLk1yXQGp+YjuXbGH6UYnRZnoEwyuVYz9ZpZMVMOhZ6kx+P88hrCseSGnyuDmPVkPndLrc1YX+GRBkbFYIWoQtJoRm1p4sxiBIl0Bl6nDYMdLfXuWsORr/UWPxxT6YxcE4c5RNsnBQ/Ty1FE4+ULI24Hl+4q0+F2yAc8qtV+Kn7dqL1nme1aM1ghqpByC2dxKXhpBmC03xyjFKOTrvXZKxGsJ9JV//65pSjiqQzcDit2NcABdnrr8jrRLRdGrN3sCreaVy4/07txKUh6P9rXLe0aKt3GLNeawQpRhYZ7PLBZBATXk5gNFtY3MNsoxei6vU4EPA5kRODkQvmqwWqkx2O0z9cQB9gZQa1PYI7EUzi/vJa7L86GbUZ+PIpmTYJrSVy+mq1K+18PDmTbFD1mhUcamONaM1ghqpDTZsVwrhJm8WgmvwXQHC98oxMEAWPbSOo005ZMs9Cq/k0p0odnn9/VkDt4tDZW4vGQEv0H2lvw23s6AWw8GFR5pMEelSMNjIjBClEV1BI/RVHMnwnExEDNbOcMFJ7RpL2xEiN5rTC5tjpSIH5qIYxEKl/7STnLO9LrhdUiYDmawEIoX9BPPtKg16t6pIERmaOXRAahNvV6aXUd4VgKDqtFXiOm7dvqGSiiKLJAXw1Ij8fUfBjJMiXct4qzYdUZaG+B12VDMi3K9VIAZbDih8tuxd4uqaBfcEObMRO9PhisEFVBbQun9CY73ONpqDNI9DYmfziGVOtElDKbK4hlswgY7mHwqJXB9mxhxEQqg7NXIpv/QpXMlvCpN0EQFDO9+UCk+GwltZILZrzWfGclqoL04r58dR1X17Kl4JlcWxu7O91odVgRS2YwvVT5h+OkYieE08bCYlqxWIRtLc2Vk0hlcHohV03VRKN9vRWXU4gl0zhzRapK61Nto/x/M71nMVghqoLPZZfrqEhv2JudEktbY7UI8um61SwFcftr7dTqBGa5TpHLhoF21imqVPHjcWohW622vdWO3tx5WMVtFsMxXAnHIQiQX19mwGCFqErjfYUjFXmUYoLzNcxmK8XhJpmvUjO1qmSrTFBnnaLKSTMjJ3K7fZTPfek6Sm0urqwhFEvKbfYE3Gh12HTo9dYwWCGqkjLxcyWawFyu5sooZ1Y0t5UkW54JVDvKM5uKCyNuB7f+b82+bg8cVgvC8RRmVtZVn/ttrQ7saMvOVp2YDZk2+ZzBClGVxhRv2NKb7O7ObPIhaWusyg/Hq2sJuSAWg0ftDXd7YbcKCMVSuLS6rtntmjHh0wjsVguu6c1XqS21BDqqKLkwadJrzWCFqErSiOTslSievHC14HukrWt6snUiVteSmA/FNm0vLScMdrTA32KvdfeajsNmwXB3Ns9Bq3oroijiBGfDtkxaln7mclBxHlbhdVSWXDBjci3AYIWoaj0+JzrcDqQzIr51/DIA841SzMJlt2JfV27kqHLadTEWFqs9rZNsZ1bWEY6zTtFWSY/HD56dx1oiDZfdgqGAR7XNf5xfwfnl7AnMZnuNMFghqpIgCPKoRDp6ncFK7ZQ6A0UNk2trT+szgqRiZdf0emA3STVVIyl+Lxrp9cFadB6W1ObC8hpEEej1udDpcda3o9tU02fGfffdhxtvvBGtra1oa2tTbXPx4kW85jWvgdvtRiAQwJ/8yZ8gkUjUsltE21YcnJhtStVMlHkrm2Fybe3ld2hVf2aTGvkx62OAuRUjfT4oN1CpPfd3tBUui5rx9VHTYCWRSODWW2/FO97xDtWfp9Np3HLLLYhGo3j88cfxla98BV//+tfx3//7f69lt4i2TTmFGvA40e116dibxlbpmTTKglic6aqd0b5szspsMIbV6PYHlmZN+DQKj9OG3Z35wwjVrqOy2m2pNkZX02Dlr/7qr/De974Xz3nOc1R//sgjj2BychJf+tKXcODAAbz85S/H3/7t3+Kzn/0sQqHaHJZFpAXlMoMZRylmIo24Z1bWEVxPlmwnFcTqcDvkglikPa/Ljl2drQC0SbLlbNj2KYOPUkug4wVtzHetdV0g/MUvfoH9+/ejv79f/t4rXvEKxONxHDt2TPV34vE4QqFQwRdRvQ0F3GixZ0u5m3GUYib+VrtcJ6JcnoSykjALi9WW2pk0xdIZEZ//2TTOlTlHaDkSx3wolq2marKETyORHg+LAFzbo16VVvk+NWbCJTddg5X5+Xn09PQUfK+9vR0OhwPz8/Oqv3PkyBH4/X75a3BwsB5dJSpgtQh47mD2BX9gsE3fzjSB8QryVricUD+VFOv77tOzuPe7k/jLb0+UbCPNzOzqYJ2i7XjeznYA2XoqLQ7187CuH2yDIAABj0M+MsRMqg5W7r33XgiCUPbriSeeqPj21EZAoiiWHBndfffdCAaD8tfMzEy1fwKRJj76+uvwt7c+F/9prGfzxrQtleSt5KugMliptUrK7h+fuQoAePrS1ZIF/cxaTdVoXrC3E//ntuvx8TdcX7LNni4PPnv7IfzDHTeYcuax6lD28OHDuO2228q22b17d0W31dvbi1/96lcF31tdXUUymdww4yJxOp1wOs215Yoa0+6AG7sD7s0b0rZtdkZQOiOWLIhF2ssXRowglkzDZd84mpceq3Cu2u1gR2vJNpwN277XXr9j0zYvN/HAqupgJRAIIBAIaHLnL3jBC3Dfffdhbm4OfX19ALJJt06nEwcPHtTkPojI/KQA5MxiBPFUGk5b4Yfj+eVoyYJYpL1urxMBjwNLkQSm5sO4vmgpNJMRC2bBJmaDqsEKZ8OoUjXNWbl48SKOHz+OixcvIp1O4/jx4zh+/DgikWzC1c0334yxsTHcfvvteOqpp/DDH/4Qf/qnf4q3v/3t8Pn45CWirD6/C22tdqQyIk7Nb0zYlJYT1ApikfYEQVCcN7Mxj+jiyhoi8ZT8b7XclrVECudYVJEqVNNg5S//8i9x4MAB3HPPPYhEIjhw4AAOHDgg57RYrVZ873vfg8vlwgtf+EL8t//23/C6170O999/fy27RUQmo6waLFU8VZrk9te6K7c0V5xbpNZmaj4MUQS6vKxTRJurafr15z//eXz+858v22bnzp146KGHatkNImoAY30+/OzMsuoovdRps1Q75c4Ikh6Pfd0enFmMlGjDc5yocjyIgYhModRIXhRFngmkA2kWa2o+hHSmcLeP9Hj8/vMGAADzoRiWI3HVNpwNo0owWCEiU5A+1E7MhZBRfDguhuNYjiZgEYCRXvWCWKS93Z1utDqsiCUzmF4qzCOSZk1+a6gdu0tUu52Uk2sZYNLmGKwQkSkMBdxw2iyIJtLyMfdAfslhb5dHdQst1YbVIsjBoXKZ50o4jsVwPFuVttcnByPKNql0Rt5qzqU7qgSDFSIyBZvVIpdkV47SuZygH7WlOemxGQq44XbaVAvInVuKIp7KwO2wYpfKlmaiYgxWiMg08mfSKGt4sLCYXtSSbOVk59xjNaZyVIL0/6N9Pli41ZwqwGCFiExjXGWULo3kmftQf+OKYxCkkvrFyc5Sm3NLUawlUkVtGGBSZRisEJFpFI/kQ7EkLiyvZX/GLbB1d02PF1aLgJVoAvOhGICNJfS7vS4EPE6IIuQ8Fc6GUbUYrBCRaYz2+mARgKVIHIvhGKbmsh9+/X4X2t0OnXvXfFx2K/Z1ZY83mJwNIRpPYTqX/KycNVHOiImiyNkwqhqDFSIyjRaHFXtyH44TsyFFMTh+6OllXDHbNTUfgigCPT4nAh6napvZYAxX15KwWQQM9/AcJ6oMgxUiMhVpuWdyNsTlBANQJtCWqkqb3xEUxMTlfHXb4gMpiUphsEJEpqJcUmCipv7GFEm2pSoJS/+emg/jmcssBkfVq+nZQEREWpM+HI/PXMViOJvUyeRa/UjXfmZlHb8UlrPfKwoed3W0wu2wIppI46HfzKm2ISqHMytEZCrSiPzy1XUk0yL8LXYMtLfo3Kvm1dbqwI627PU/n9uZVTzTZbEIGM0FNdNLGxNwiTbDYIWITKXD7UCf3yX/e6zPB0FgYTE9KQMPr9OGwfaNVWmLgxPOrFA1GKwQkekol334oac/5WNQqiqtss1gRwt8Lntd+kaNgcEKEZmOWg0P0o8yWbZU8KhsM97H5FqqDoMVIjId5QciZ1b0V8njMdzjgS0348LHjKrFYIWITOe6gTZYhGx+xN4uFhbTW7/fhR5ftgjcgcE21TZOmxXjO7IzKgd2qrchKoVbl4nIdPrbWvC5t94An8sGu5VjLr0JgoDPvuUQ5oMxDPd4S7b721ufi2cvB3HTvkAde0eNQBClozJNKhQKwe/3IxgMwufj1CIREZEZVPP5zSEJERERGRqDFSIiIjI0BitERERkaAxWiIiIyNAYrBAREZGhMVghIiIiQ2OwQkRERIbGYIWIiIgMjcEKERERGRqDFSIiIjI0BitERERkaAxWiIiIyNAYrBAREZGh2fTuwHZJh0aHQiGde0JERESVkj63pc/xckwfrITDYQDA4OCgzj0hIiKiaoXDYfj9/rJtBLGSkMbAMpkMZmdn4fV6IQiCprcdCoUwODiImZkZ+Hw+TW+bCvFa1w+vdf3wWtcPr3X9aHWtRVFEOBxGf38/LJbyWSmmn1mxWCwYGBio6X34fD4++euE17p+eK3rh9e6fnit60eLa73ZjIqECbZERERkaAxWiIiIyNAYrJThdDpxzz33wOl06t2VhsdrXT+81vXDa10/vNb1o8e1Nn2CLRERETU2zqwQERGRoTFYISIiIkNjsEJERESGxmCFiIiIDI3BSgmf/OQnMTQ0BJfLhYMHD+Kxxx7Tu0umd+TIEdxwww3wer3o7u7G6173Opw8ebKgjSiKuPfee9Hf34+Wlha85CUvwcTEhE49bhxHjhyBIAh4z3veI3+P11o7ly9fxpvf/GZ0dnaitbUV119/PY4dOyb/nNdaG6lUCn/xF3+BoaEhtLS0YM+ePfjwhz+MTCYjt+G13pqf/vSneM1rXoP+/n4IgoBvfetbBT+v5LrG43G8+93vRiAQgNvtxn/+z/8Zly5d0qaDIm3wla98RbTb7eJnP/tZcXJyUrzrrrtEt9stXrhwQe+umdorXvEK8cEHHxSfffZZ8fjx4+Itt9wi7ty5U4xEInKbj370o6LX6xW//vWvi88884z4hje8Qezr6xNDoZCOPTe3X//61+Lu3bvF6667Trzrrrvk7/Naa2NlZUXctWuX+Na3vlX81a9+JU5PT4v//u//Lp45c0Zuw2utjY985CNiZ2en+NBDD4nT09Pi1772NdHj8Ygf//jH5Ta81lvz/e9/X/zQhz4kfv3rXxcBiN/85jcLfl7Jdb3zzjvFHTt2iEePHhWffPJJ8aUvfan43Oc+V0ylUtvuH4MVFb/1W78l3nnnnQXfGxkZET/wgQ/o1KPGtLi4KAIQH330UVEURTGTyYi9vb3iRz/6UblNLBYT/X6/+KlPfUqvbppaOBwWh4eHxaNHj4ovfvGL5WCF11o773//+8Wbbrqp5M95rbVzyy23iH/0R39U8L3Xv/714pvf/GZRFHmttVIcrFRyXa9evSra7XbxK1/5itzm8uXLosViER9++OFt94nLQEUSiQSOHTuGm2++ueD7N998M37+85/r1KvGFAwGAQAdHR0AgOnpaczPzxdce6fTiRe/+MW89lv0rne9C7fccgte/vKXF3yf11o73/nOd3Do0CHceuut6O7uxoEDB/DZz35W/jmvtXZuuukm/PCHP8SpU6cAAE8//TQef/xx/N7v/R4AXutaqeS6Hjt2DMlksqBNf38/9u/fr8m1N/1BhlpbWlpCOp1GT09Pwfd7enowPz+vU68ajyiKeN/73oebbroJ+/fvBwD5+qpd+wsXLtS9j2b3la98BU8++ST+4z/+Y8PPeK21c+7cOTzwwAN43/vehw9+8IP49a9/jT/5kz+B0+nEW97yFl5rDb3//e9HMBjEyMgIrFYr0uk07rvvPrzxjW8EwOd1rVRyXefn5+FwONDe3r6hjRafnQxWShAEoeDfoihu+B5t3eHDh/Gb3/wGjz/++Iaf8dpv38zMDO666y488sgjcLlcJdvxWm9fJpPBoUOH8Nd//dcAgAMHDmBiYgIPPPAA3vKWt8jteK2376tf/Sq+9KUv4ctf/jLGx8dx/PhxvOc970F/fz/uuOMOuR2vdW1s5bpqde25DFQkEAjAarVuiAQXFxc3RJW0Ne9+97vxne98Bz/+8Y8xMDAgf7+3txcAeO01cOzYMSwuLuLgwYOw2Wyw2Wx49NFH8YlPfAI2m02+nrzW29fX14exsbGC742OjuLixYsA+LzW0p/92Z/hAx/4AG677TY85znPwe233473vve9OHLkCABe61qp5Lr29vYikUhgdXW1ZJvtYLBSxOFw4ODBgzh69GjB948ePYobb7xRp141BlEUcfjwYXzjG9/Aj370IwwNDRX8fGhoCL29vQXXPpFI4NFHH+W1r9Lv/u7v4plnnsHx48flr0OHDuFNb3oTjh8/jj179vBaa+SFL3zhhi34p06dwq5duwDwea2ltbU1WCyFH1tWq1XeusxrXRuVXNeDBw/CbrcXtJmbm8Ozzz6rzbXfdopuA5K2Ln/uc58TJycnxfe85z2i2+0Wz58/r3fXTO0d73iH6Pf7xZ/85Cfi3Nyc/LW2tia3+ehHPyr6/X7xG9/4hvjMM8+Ib3zjG7ntUCPK3UCiyGutlV//+teizWYT77vvPvH06dPiP//zP4utra3il770JbkNr7U27rjjDnHHjh3y1uVvfOMbYiAQEP/8z/9cbsNrvTXhcFh86qmnxKeeekoEIH7sYx8Tn3rqKblkRyXX9c477xQHBgbEf//3fxeffPJJ8WUvexm3Ltfa3//934u7du0SHQ6H+LznPU/eXktbB0D168EHH5TbZDIZ8Z577hF7e3tFp9MpvuhFLxKfeeYZ/TrdQIqDFV5r7Xz3u98V9+/fLzqdTnFkZET8zGc+U/BzXmtthEIh8a677hJ37twpulwucc+ePeKHPvQhMR6Py214rbfmxz/+ser78x133CGKYmXXdX19XTx8+LDY0dEhtrS0iK9+9avFixcvatI/QRRFcfvzM0RERES1wZwVIiIiMjQGK0RERGRoDFaIiIjI0BisEBERkaExWCEiIiJDY7BCREREhsZghYiIiAyNwQoREREZGoMVIiIiMjQGK0RERGRoDFaIiIjI0BisEBERkaH9/9DdGiFjVGL9AAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(walk[:100])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 328,
   "id": "e2ee942f-c322-4134-a884-d93363610436",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 100"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 330,
   "id": "47d89f0f-537c-4e08-8095-6cd85dce9c1b",
   "metadata": {},
   "outputs": [],
   "source": [
    "rng = np.random.default_rng(seed=12345) # fresh random generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 332,
   "id": "79dee5b1-ed7d-4631-8fe3-11a85f2c4140",
   "metadata": {},
   "outputs": [],
   "source": [
    "draws = rng.integers(0, 2, size=nsteps)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 334,
   "id": "12f04de4-5e81-4119-aec9-0fcbba6fd495",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = np.where(draws == 0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 336,
   "id": "8113e723-1fb4-4ccd-b527-b22ade0bdf4a",
   "metadata": {},
   "outputs": [],
   "source": [
    "walk = steps.cumsum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 338,
   "id": "18304436-eb38-41c3-95c4-5e30d683c792",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-8"
      ]
     },
     "execution_count": 338,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walk.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 340,
   "id": "c9102138-3aaf-49ad-bbca-1e575b38bdfc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "9"
      ]
     },
     "execution_count": 340,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walk.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 342,
   "id": "1da3a13c-32c6-4e16-8838-a7496204ba6b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0"
      ]
     },
     "execution_count": 342,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(np.abs(walk) >= 10).argmax()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 344,
   "id": "205dc4de-fae8-43a1-a8db-983d5572f282",
   "metadata": {},
   "outputs": [],
   "source": [
    "nwalks = 5000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 348,
   "id": "268a469c-0980-40d4-87f9-8b449826530e",
   "metadata": {},
   "outputs": [],
   "source": [
    "nsteps = 1000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 350,
   "id": "60e07b25-7213-48db-9415-89d49b45092c",
   "metadata": {},
   "outputs": [],
   "source": [
    "draws = rng.integers(0, 2, size=(nwalks, nsteps)) # 0 or 1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 352,
   "id": "817ce8f0-ffc3-4ba7-bf2f-7206f67924e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "steps = np.where(draws > 0, 1, -1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 354,
   "id": "7aa687d0-44c2-484e-9ea4-94011259296a",
   "metadata": {},
   "outputs": [],
   "source": [
    "walks = steps.cumsum(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 356,
   "id": "b3d61f03-0165-4c2d-85ea-ae68c28e1210",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[ -1,  -2,  -1, ..., -48, -49, -48],\n",
       "       [ -1,   0,  -1, ...,  10,   9,   8],\n",
       "       [  1,   0,  -1, ..., -32, -31, -32],\n",
       "       ...,\n",
       "       [ -1,   0,  -1, ...,  36,  37,  38],\n",
       "       [  1,   0,   1, ...,  -8,  -7,  -8],\n",
       "       [ -1,   0,   1, ...,  12,  11,  12]])"
      ]
     },
     "execution_count": 356,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 358,
   "id": "b25eacd7-a845-4984-9205-eef233a97c3c",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "120"
      ]
     },
     "execution_count": 358,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walks.max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 360,
   "id": "010eef07-6cf3-4b59-bbb6-6f654da0bea2",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-109"
      ]
     },
     "execution_count": 360,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "walks.min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 364,
   "id": "81e849c9-048a-49d1-8801-ea290d440d98",
   "metadata": {},
   "outputs": [],
   "source": [
    "hits30 = (np.abs(walks) >= 30).any(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 366,
   "id": "43a93f25-7a96-4d33-bf05-b6f844e9feed",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([ True, False,  True, ...,  True,  True, False])"
      ]
     },
     "execution_count": 366,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 368,
   "id": "a4237c50-78f4-468e-adc4-e700242227a3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3375"
      ]
     },
     "execution_count": 368,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "hits30.sum() # Number that hit 30 or -30"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 372,
   "id": "4a487051-a292-4c9f-9f19-2fb72bf1c78c",
   "metadata": {},
   "outputs": [],
   "source": [
    "crossing_times = (np.abs(walks[hits30]) >= 30).argmax(axis=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 374,
   "id": "a810c7d2-bc84-428c-9f7e-4d284e164dc7",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([605, 253, 391, ..., 385, 159, 157], dtype=int64)"
      ]
     },
     "execution_count": 374,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crossing_times"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 376,
   "id": "b3a14d68-015d-4850-b9d1-e22e9414d097",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "496.96207407407405"
      ]
     },
     "execution_count": 376,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "crossing_times.mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 378,
   "id": "950b1277-418c-49f6-92ad-c3106a37d64f",
   "metadata": {},
   "outputs": [],
   "source": [
    "draws = 0.25 * rng.standard_normal((nwalks, nsteps))"
   ]
  }
 ],
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