From 9111e232f11dc9e48129b8284d317c0aeb5a5dcf Mon Sep 17 00:00:00 2001
From: Young Woo Kim <skysaver00@ajou.ac.kr>
Date: Sun, 20 Dec 2020 16:14:40 +0900
Subject: [PATCH] Upload New File
---
.../Numpy_\354\230\210\354\240\234.ipynb" | 893 ++++++++++++++++++
1 file changed, 893 insertions(+)
create mode 100644 "Python Numpy, Pandas \354\230\210\354\240\234\355\214\214\354\235\274/Numpy_\354\230\210\354\240\234.ipynb"
diff --git "a/Python Numpy, Pandas \354\230\210\354\240\234\355\214\214\354\235\274/Numpy_\354\230\210\354\240\234.ipynb" "b/Python Numpy, Pandas \354\230\210\354\240\234\355\214\214\354\235\274/Numpy_\354\230\210\354\240\234.ipynb"
new file mode 100644
index 0000000..92b7197
--- /dev/null
+++ "b/Python Numpy, Pandas \354\230\210\354\240\234\355\214\214\354\235\274/Numpy_\354\230\210\354\240\234.ipynb"
@@ -0,0 +1,893 @@
+{
+ "cells": [
+ {
+ "cell_type": "code",
+ "execution_count": 1,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "1.19.3\n",
+ "1.1.5\n"
+ ]
+ }
+ ],
+ "source": [
+ "import numpy as np\n",
+ "import pandas as pd\n",
+ "print (np.__version__)\n",
+ "print (pd.__version__)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#np array 만들기\n",
+ "#여기서부터는 array = 배열로 취급"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 2,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1, 2, 3, 4, 5]\n",
+ "[10, 20, 30, 40, 50]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "list"
+ ]
+ },
+ "execution_count": 2,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "one = [1,2,3,4,5]\n",
+ "print (one)\n",
+ "ten = [10,20,30,40,50]\n",
+ "print (ten)\n",
+ "\n",
+ "type (one)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 3,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1 2 3 4 5]\n",
+ "[10 20 30 40 50]\n"
+ ]
+ },
+ {
+ "data": {
+ "text/plain": [
+ "numpy.ndarray"
+ ]
+ },
+ "execution_count": 3,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "onearr = np.array(one)\n",
+ "tenarr = np.array(ten)\n",
+ "print (onearr)\n",
+ "print (tenarr)\n",
+ "\n",
+ "type (onearr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 4,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "dtype('int32')"
+ ]
+ },
+ "execution_count": 4,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "onearr.dtype"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 5,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(5,)"
+ ]
+ },
+ "execution_count": 5,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "onearr.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 6,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "(4, 3)"
+ ]
+ },
+ "execution_count": 6,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "arr2nd = np.array([[1,0,0],[0,1,0],[0,0,1],[1,1,1]])\n",
+ "arr2nd.shape"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 7,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "AttributeError",
+ "evalue": "'list' object has no attribute 'dtype'",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mAttributeError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m<ipython-input-7-81347d222ef3>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[1;32m----> 1\u001b[1;33m \u001b[0mone\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m",
+ "\u001b[1;31mAttributeError\u001b[0m: 'list' object has no attribute 'dtype'"
+ ]
+ }
+ ],
+ "source": [
+ "one.dtype"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 8,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[11 22 33 44 55]\n",
+ "[1, 2, 3, 4, 5, 10, 20, 30, 40, 50]\n"
+ ]
+ }
+ ],
+ "source": [
+ "sumarr = onearr + tenarr\n",
+ "sumlist = one + ten\n",
+ "print (sumarr)\n",
+ "print (sumlist)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#기본 배열 만드는 함수 -> 빠르게 배열 만들기"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 9,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0. 0. 0.]\n",
+ "[[0. 0. 0.]\n",
+ " [0. 0. 0.]\n",
+ " [0. 0. 0.]\n",
+ " [0. 0. 0.]\n",
+ " [0. 0. 0.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.zeros(3)\n",
+ "arr2 = np.zeros((5, 3))\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 10,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1. 1. 1.]\n",
+ "[[1. 1. 1.]\n",
+ " [1. 1. 1.]\n",
+ " [1. 1. 1.]\n",
+ " [1. 1. 1.]\n",
+ " [1. 1. 1.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.ones(3)\n",
+ "arr2 = np.ones((5, 3))\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 11,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[1.]]\n",
+ "[[1. 0.]\n",
+ " [0. 1.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.identity(1)\n",
+ "arr2 = np.identity(2)\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 12,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 0 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23\n",
+ " 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47\n",
+ " 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71\n",
+ " 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 94 95\n",
+ " 96 97 98 99]\n",
+ "[96 97 98 99]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.arange(100)\n",
+ "arr2 = np.arange(96,100)\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#기본적인 배열의 연산"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 13,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[1 2 3 4 5]\n",
+ "[10 20 30 40 50]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (onearr)\n",
+ "print (tenarr)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 14,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[11 22 33 44 55]\n",
+ "[ -9 -18 -27 -36 -45]\n",
+ "[ 10 40 90 160 250]\n",
+ "[0.1 0.1 0.1 0.1 0.1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "plus = onearr + tenarr\n",
+ "minus = onearr - tenarr\n",
+ "mul = onearr * tenarr\n",
+ "div = onearr / tenarr\n",
+ "\n",
+ "print (plus)\n",
+ "print (minus)\n",
+ "print (mul)\n",
+ "print (div)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 15,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 15 26 37]\n",
+ " [105 206 307]]\n",
+ "[[ 5 14 23]\n",
+ " [ 95 194 293]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.array([[10, 20, 30], [100, 200, 300]])\n",
+ "arr2 = np.array(([5, 6, 7], [5, 6, 7]))\n",
+ "\n",
+ "print (arr1 + arr2)\n",
+ "print (arr1 - arr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 19,
+ "metadata": {
+ "scrolled": true
+ },
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[10. 0.]\n",
+ " [ 0. 40.]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.array([[10, 20], [30, 40]])\n",
+ "arr2 = np.identity(2)\n",
+ "\n",
+ "print (arr1 * arr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#numpy에서의 브로드캐스트\n",
+ "#numpy는 브로드 캐스트를 지원합니다. 몰론 두 array가 행이나 열의 길이가 같아야 합니다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 21,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[10 20]\n",
+ " [30 40]\n",
+ " [50 60]]\n",
+ "(3, 2)\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.array([[10, 20],[30, 40],[50,60]])\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr1.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 22,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "(2,)\n",
+ "(3, 1)\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr2 = np.array([100,200])\n",
+ "arr3 = np.array([[100], [200], [300]])\n",
+ "\n",
+ "print (arr2.shape)\n",
+ "print (arr3.shape)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 23,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[110 220]\n",
+ " [130 240]\n",
+ " [150 260]]\n",
+ "[[110 120]\n",
+ " [230 240]\n",
+ " [350 360]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "horizontal = arr1 + arr2\n",
+ "print (horizontal)\n",
+ "\n",
+ "vertical = arr1 + arr3\n",
+ "print (vertical)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 24,
+ "metadata": {},
+ "outputs": [
+ {
+ "ename": "ValueError",
+ "evalue": "operands could not be broadcast together with shapes (5,) (4,) ",
+ "output_type": "error",
+ "traceback": [
+ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m",
+ "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)",
+ "\u001b[1;32m<ipython-input-24-3603de6b54e0>\u001b[0m in \u001b[0;36m<module>\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[0mdifarr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0marray\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m100\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m200\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m300\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;36m400\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 2\u001b[1;33m \u001b[0msumarr2\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0monearr\u001b[0m \u001b[1;33m+\u001b[0m \u001b[0mdifarr\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3\u001b[0m \u001b[0mprint\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0msumarr2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n",
+ "\u001b[1;31mValueError\u001b[0m: operands could not be broadcast together with shapes (5,) (4,) "
+ ]
+ }
+ ],
+ "source": [
+ "difarr = np.array([100,200,300,400])\n",
+ "sumarr2 = onearr + difarr\n",
+ "print (sumarr2)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#배열의 값 찾기\n",
+ "#파이썬, C언어에서 하는 배열 값 찾기와 비슷하다.\n",
+ "\n",
+ "#1차원에서는 기본적으로 1개의 값만 입력하면 되지만, 2차원은 2개의 값을 입력한다."
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 25,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0 1 2 3 4 5 6 7 8 9]\n",
+ "6\n",
+ "0\n",
+ "[3 4 5 6]\n",
+ "[0 1 2 3]\n",
+ "[6 7 8 9]\n",
+ "[0 1 2 3 4 5 6 7 8 9]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.arange(10)\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr1[6])\n",
+ "print (arr1[0])\n",
+ "print (arr1[3:7])\n",
+ "print (arr1[:4])\n",
+ "print (arr1[6:])\n",
+ "print (arr1[:])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 26,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[1 2 3]\n",
+ " [4 5 6]\n",
+ " [7 8 9]]\n",
+ "9\n",
+ "1\n",
+ "[1 2 3]\n",
+ "[[2 3]\n",
+ " [5 6]\n",
+ " [8 9]]\n",
+ "[[1]\n",
+ " [4]\n",
+ " [7]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr2 = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+ "\n",
+ "print (arr2)\n",
+ "print (arr2[2,2])\n",
+ "print (arr2[0,0])\n",
+ "print (arr2[0,:])\n",
+ "print (arr2[0:,1:])\n",
+ "print (arr2[:3,:1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#numpy의 함수들\n",
+ "#진짜 많다 여기 있는게 다가 아니다"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 34,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[0.9446711 0.83497248 0.83201387 0.13935773 0.17415421 0.49383566\n",
+ " 0.62534832 0.95301777 0.775304 0.42802872]\n",
+ "[ 0.49760198 0.42642281 -0.17887658 0.97600137 -1.09890443 1.14944642\n",
+ " -1.61367583 0.68313963 -0.69266217 -0.20675362]\n",
+ "[0.45892907 0.79511622 0.23510859 0.83475971 0.61042409 0.98928205\n",
+ " 0.04701599 0.47026134 0.10542542 0.6393744 ]\n",
+ "3\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.random.random(10)\n",
+ "arr2 = np.random.randn(10)\n",
+ "arr3 = np.random.rand(10)\n",
+ "arr4 = np.random.randint(10)\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr2)\n",
+ "print (arr3)\n",
+ "print (arr4)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 35,
+ "metadata": {},
+ "outputs": [
+ {
+ "data": {
+ "text/plain": [
+ "array([[0.69049139, 0.33961382, 0.4720473 , 0.05657901, 0.44823818,\n",
+ " 0.62798771, 0.18857805, 0.58646854, 0.78296022, 0.23064811],\n",
+ " [0.89095585, 0.56933846, 0.66368549, 0.36591346, 0.78302718,\n",
+ " 0.04110933, 0.23313533, 0.3403511 , 0.68102568, 0.99330884],\n",
+ " [0.18974105, 0.01787721, 0.24513353, 0.61273492, 0.82943479,\n",
+ " 0.88117367, 0.39032357, 0.85654216, 0.57124692, 0.9269831 ]])"
+ ]
+ },
+ "execution_count": 35,
+ "metadata": {},
+ "output_type": "execute_result"
+ }
+ ],
+ "source": [
+ "np.random.rand(3, 10)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 36,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[9.44671104 8.34972483 8.32013866 1.39357733 1.74154213 4.9383566\n",
+ " 6.2534832 9.53017769 7.75304001 4.28028724]\n",
+ "[10. 9. 9. 2. 2. 5. 7. 10. 8. 5.]\n",
+ "[9. 8. 8. 1. 1. 4. 6. 9. 7. 4.]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = arr1 * 10\n",
+ "\n",
+ "print (arr1)\n",
+ "print (np.ceil(arr1))\n",
+ "print (np.floor(arr1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 37,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[1. 1.41421356 1.73205081]\n",
+ " [2. 2.23606798 2.44948974]\n",
+ " [2.64575131 2.82842712 3. ]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.array([[1,2,3],[4,5,6],[7,8,9]])\n",
+ "\n",
+ "print (np.sqrt(arr1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 38,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 10 -100]\n",
+ " [ 10 -1000]]\n",
+ "[[ 10 100]\n",
+ " [ 10 1000]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr2 = np.array([[10, -100],[10, -1000]])\n",
+ "\n",
+ "print (arr2)\n",
+ "print (np.abs(arr2))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 39,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[2.71828183e+00 7.38905610e+00 2.00855369e+01]\n",
+ " [5.45981500e+01 1.48413159e+02 4.03428793e+02]\n",
+ " [1.09663316e+03 2.98095799e+03 8.10308393e+03]]\n",
+ "[[0. 0.30103 0.47712125]\n",
+ " [0.60205999 0.69897 0.77815125]\n",
+ " [0.84509804 0.90308999 0.95424251]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (np.exp(arr1))\n",
+ "print (np.log10(arr1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 40,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[ 0.54030231 -0.41614684 -0.9899925 ]\n",
+ " [-0.65364362 0.28366219 0.96017029]\n",
+ " [ 0.75390225 -0.14550003 -0.91113026]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (np.cos(arr1))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 41,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[[9 8 7]\n",
+ " [6 5 6]\n",
+ " [7 8 9]]\n",
+ "[[1 2 3]\n",
+ " [4 5 4]\n",
+ " [3 2 1]]\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr3 = np.array([[9,8,7],[6,5,4],[3,2,1]])\n",
+ "\n",
+ "print (np.maximum(arr1, arr3))\n",
+ "print (np.minimum(arr1, arr3))"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": [
+ "#행렬의 정렬"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 42,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[31. 75. 13. 88. 97. 83. 97. 52. 42. 40. 27. 39. 95. 78. 86. 9. 72. 85.\n",
+ " 55. 75. 8. 17. 77. 78. 55. 1. 81. 89. 62. 68.]\n",
+ "[31 75 13 88 97 83 97 52 42 40 27 39 95 78 86 9 72 85 55 75 8 17 77 78\n",
+ " 55 1 81 89 62 68]\n",
+ "int32\n"
+ ]
+ }
+ ],
+ "source": [
+ "arr1 = np.random.rand(30)\n",
+ "\n",
+ "arr1 = arr1 * 100\n",
+ "arr1 = np.floor(arr1)\n",
+ "\n",
+ "print (arr1)\n",
+ "\n",
+ "arr1 = arr1.astype('int32')\n",
+ "\n",
+ "print (arr1)\n",
+ "print (arr1.dtype)"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": 43,
+ "metadata": {},
+ "outputs": [
+ {
+ "name": "stdout",
+ "output_type": "stream",
+ "text": [
+ "[ 1 8 9 13 17 27 31 39 40 42 52 55 55 62 68 72 75 75 77 78 78 81 83 85\n",
+ " 86 88 89 95 97 97]\n",
+ "[97 97 95 89 88 86 85 83 81 78 78 77 75 75 72 68 62 55 55 52 42 40 39 31\n",
+ " 27 17 13 9 8 1]\n"
+ ]
+ }
+ ],
+ "source": [
+ "print (np.sort(arr1))\n",
+ "print (np.sort(arr1)[::-1])"
+ ]
+ },
+ {
+ "cell_type": "code",
+ "execution_count": null,
+ "metadata": {},
+ "outputs": [],
+ "source": []
+ }
+ ],
+ "metadata": {
+ "kernelspec": {
+ "display_name": "Python 3",
+ "language": "python",
+ "name": "python3"
+ },
+ "language_info": {
+ "codemirror_mode": {
+ "name": "ipython",
+ "version": 3
+ },
+ "file_extension": ".py",
+ "mimetype": "text/x-python",
+ "name": "python",
+ "nbconvert_exporter": "python",
+ "pygments_lexer": "ipython3",
+ "version": "3.8.6"
+ }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 4
+}
--
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