{
"cells": [
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# библиотека NumPy\n",
"\n",
"автор: Дьяконов Александр www.dyakonov.org/ag/\n",
"\n",
"для поддержки курсов автора, в частности https://github.com/Dyakonov/IML"
]
},
{
"cell_type": "code",
"execution_count": 1,
"metadata": {},
"outputs": [],
"source": [
"import numpy as np"
]
},
{
"cell_type": "code",
"execution_count": 2,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"Populating the interactive namespace from numpy and matplotlib\n"
]
}
],
"source": [
"# подгружаем все нужные пакеты\n",
"import pandas as pd\n",
"import numpy as np\n",
"import matplotlib.pyplot as plt\n",
"\n",
"# для встроенных картинок\n",
"%pylab inline\n",
"# чуть покрасивше картинки:\n",
"plt.style.use('seaborn-dark')\n",
"plt.rc('font', size=14)\n",
"\n",
"import warnings\n",
"warnings.filterwarnings(\"ignore\")\n",
"\n",
"pd.set_option('display.max_columns', None)"
]
},
{
"cell_type": "code",
"execution_count": 3,
"metadata": {},
"outputs": [],
"source": [
"plt.rc('font', size=14)"
]
},
{
"cell_type": "code",
"execution_count": 4,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"'1.15.1'"
]
},
"execution_count": 4,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"import numpy as np\n",
"np.__version__"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Создание вектора"
]
},
{
"cell_type": "code",
"execution_count": 7,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"(array([0., 1., 2., 3.]), array([0, 1, 2, 3]), array([0, 1, 2, 3]))"
]
},
"execution_count": 7,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"a = np.array([0, 1, 2, 3], dtype='float')\n",
"b = np.array(range(4))\n",
"c = np.arange(4)\n",
"\n",
"a, b, c"
]
},
{
"cell_type": "code",
"execution_count": 8,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"206 µs ± 1.98 µs per loop (mean ± std. dev. of 7 runs, 1000 loops each)\n"
]
}
],
"source": [
"%timeit z = [x ** 2 for x in range(1000)]"
]
},
{
"cell_type": "code",
"execution_count": 9,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"2.97 µs ± 31.7 ns per loop (mean ± std. dev. of 7 runs, 100000 loops each)\n"
]
}
],
"source": [
"%timeit z = np.arange(1000) ** 2"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## создание разные векторов"
]
},
{
"cell_type": "code",
"execution_count": 65,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[3.14 3.14 3.14 3.14 3.14]\n",
"[1 4 7]\n",
"[0. 0.25 0.5 0.75 1. ]\n",
"[1. 1. 1.]\n",
"[1 1 1]\n",
"[[1. 1.]\n",
" [1. 1.]]\n",
"[0. 0. 0.]\n",
"[1. 1. 1. 1.]\n",
"[0.77132064 0.02075195 0.63364823 0.74880388]\n",
"[-1.54540029 -0.00838385 0.62133597 -0.72008556]\n",
"[0 2 0 4 3 0 4 3 0 3]\n",
"[1 2 1 2 1 2]\n",
"[0. 0. 0.]\n"
]
},
{
"data": {
"text/plain": [
"array([4, 4, 3, 2, 2])"
]
},
"execution_count": 65,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"print (np.full(5, 3.14)) # заполнить одним элементом\n",
"print (np.arange(1, 10, 3))\n",
"print (np.linspace(0, 1, 5))\n",
"print (np.ones(3))\n",
"print (np.ones(3, dtype=int))\n",
"print (np.ones((2, 2)))\n",
"print (np.zeros(3))\n",
"print (np.empty(4))\n",
"np.random.seed(10) \n",
"print (np.random.rand(4))\n",
"print (np.random.randn(4))\n",
"print (np.random.randint(0, 5, 10))\n",
"print (np.tile([1, 2], 3))\n",
"print (np.empty(3)) # просто массив из 3х элементов\n",
"np.random.choice([2, 3, 4], 5, replace=True)"
]
},
{
"cell_type": "code",
"execution_count": 137,
"metadata": {},
"outputs": [
{
"data": {
"text/plain": [
"array([1, 2, 1, 2, 2])"
]
},
"execution_count": 137,
"metadata": {},
"output_type": "execute_result"
}
],
"source": [
"np.choose([0,1,0,1,1], [[1,1,1,1,1], [2,2,2,2,2]])"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## Динамическая типизация"
]
},
{
"cell_type": "code",
"execution_count": 24,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"int64\n",
"float64\n",
"complex128\n",
"bool\n"
]
}
],
"source": [
"x = np.array([1, 2])\n",
"print (x.dtype)\n",
"x = x + 0.0 # x += 0.0 конкретно здесь не сработает\n",
"print (x.dtype)\n",
"x = x + 2j\n",
"print (x.dtype)\n",
"x = x > 0\n",
"print (x.dtype)"
]
},
{
"cell_type": "code",
"execution_count": 85,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[-1. 0. 1. 2. 2.]\n",
"[0 0 1 1 1]\n",
"[-1. 0. 1. 2. 2.]\n",
"[-1 0 1 2 2]\n"
]
}
],
"source": [
"x = np.array([-0.6, 0.4, 1.4, 1.5, 1.6])\n",
"print (np.round(x))\n",
"print (x.astype(int))\n",
"print (np.round(x))\n",
"print (np.round(x).astype(int))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"## сравнения"
]
},
{
"cell_type": "code",
"execution_count": 67,
"metadata": {},
"outputs": [
{
"name": "stdout",
"output_type": "stream",
"text": [
"[False True False]\n",
"[False False True]\n",
"False\n",
"True\n"
]
}
],
"source": [
"x = np.array([1, 3, 2])\n",
"y = np.array([2, 1, 2])\n",
"\n",
"print (x > y)\n",
"print (x == y)\n",
"print (np.array_equal(x, y))\n",
"print (np.array_equal(x, x))"
]
},
{
"cell_type": "markdown",
"metadata": {},
"source": [
"# создание матрицы"
]
},
{
"cell_type": "code",
"execution_count": 44,
"metadata": {},
"outputs": [
{
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