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Colaboratory를 통해 생성됨
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‎04. Numpy의 연산과 함수/Numpy의 연산과 함수.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 0,
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"metadata": {
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"colab": {
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"name": "Numpy의 연산과 함수",
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"version": "0.3.2",
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"provenance": [],
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"collapsed_sections": [],
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"include_colab_link": true
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},
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"kernelspec": {
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"name": "python3",
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"display_name": "Python 3"
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}
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},
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"cells": [
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "view-in-github",
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"colab_type": "text"
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},
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"source": [
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"<a href=\"https://colab.research.google.com/github/ndb796/Python-Data-Analysis-and-Image-Processing-Tutorial/blob/master/04.%20Numpy%EC%9D%98%20%EC%97%B0%EC%82%B0%EA%B3%BC%20%ED%95%A8%EC%88%98/Numpy%EC%9D%98%20%EC%97%B0%EC%82%B0%EA%B3%BC%20%ED%95%A8%EC%88%98.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "jflbKZvWIewX",
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"colab_type": "text"
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},
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"source": [
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"## Numpy의 연산과 함수\n",
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"[강의 노트](https://github.com/ndb796/Python-Data-Analysis-and-Image-Processing-Tutorial/blob/master/04.%20Numpy%EC%9D%98%20%EC%97%B0%EC%82%B0%EA%B3%BC%20%ED%95%A8%EC%88%98/Python%20%EB%8D%B0%EC%9D%B4%ED%84%B0%20%EB%B6%84%EC%84%9D%EA%B3%BC%20%EC%9D%B4%EB%AF%B8%EC%A7%80%20%EC%B2%98%EB%A6%AC%20-%20Numpy%EC%9D%98%20%EC%97%B0%EC%82%B0%EA%B3%BC%20%ED%95%A8%EC%88%98.pdf)"
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "YMGeT2gmIqQO",
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"colab_type": "text"
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},
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"source": [
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"Python의 Numpy 라이브러리는 기본적인 상수 연산이 가능합니다."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "lB3TNlINIn33",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 52
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},
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"outputId": "9293f423-94d0-4f3d-f4fa-3f1a5850de8c"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"array = np.random.randint(1, 10, size=4).reshape(2, 2)\n",
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"result_array = array * 10\n",
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"print(result_array)"
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],
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"execution_count": 1,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[[10 30]\n",
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" [40 80]]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "3gXaKkCGI5lG",
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"colab_type": "text"
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},
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"source": [
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"**서로 다른 형태의 Numpy 연산**이 가능합니다."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "CjFI4tuPI1M2",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 52
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},
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"outputId": "74cb0c4d-0e91-4d28-99d7-1b080dfa69be"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"array1 = np.arange(4).reshape(2, 2)\n",
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"array2 = np.arange(2)\n",
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"array3 = array1 + array2\n",
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"\n",
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"print(array3)"
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],
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"execution_count": 2,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[[0 2]\n",
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" [2 4]]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "yoaNGmROI8Fk",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 87
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},
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"outputId": "d88c62ad-e4ef-49aa-b40b-fbe9a9888ba6"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"array1 = np.arange(0, 8).reshape(2, 4)\n",
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"array2 = np.arange(0, 8).reshape(2, 4)\n",
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"array3 = np.concatenate([array1, array2], axis=0)\n",
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"array4 = np.arange(0, 4).reshape(4, 1)\n",
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"\n",
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"print(array3 + array4)"
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],
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"execution_count": 3,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[[ 0 1 2 3]\n",
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" [ 5 6 7 8]\n",
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" [ 2 3 4 5]\n",
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" [ 7 8 9 10]]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "HR-bcngHJbo3",
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"colab_type": "text"
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},
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"source": [
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"Numpy의 **마스킹 연산**이 가능합니다."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "5XWPfcCEJK17",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 228
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},
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"outputId": "499685e3-925b-428f-d922-63eab1ef1d82"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"# Numpy 원소의 값을 조건에 따라 바꿀 때는 다음과 같이 합니다.\n",
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"# 반복문을 이용할 때보다 매우 빠르게 동작합니다.\n",
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"# 대체로 이미지 처리(Image Processing)에서 자주 활용됩니다.\n",
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"array1 = np.arange(16).reshape(4, 4)\n",
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"print(array1)\n",
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"\n",
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"array2 = array1 < 10\n",
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"print(array2)\n",
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"\n",
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"array1[array2] = 100\n",
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"print(array1)"
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],
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"execution_count": 4,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[[ 0 1 2 3]\n",
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" [ 4 5 6 7]\n",
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" [ 8 9 10 11]\n",
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" [12 13 14 15]]\n",
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"[[ True True True True]\n",
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" [ True True True True]\n",
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" [ True True False False]\n",
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" [False False False False]]\n",
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"[[100 100 100 100]\n",
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" [100 100 100 100]\n",
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" [100 100 10 11]\n",
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" [ 12 13 14 15]]\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "markdown",
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"metadata": {
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"id": "NznH01UXJnpn",
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"colab_type": "text"
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},
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"source": [
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"Numpy는 **다양한 집계 함수**가 존재합니다."
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "iPhu1aBhJR8O",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 87
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},
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"outputId": "59d8968e-25cd-4828-c615-098b558c1f00"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"array = np.arange(16).reshape(4, 4)\n",
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"\n",
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"print(\"최대값:\", np.max(array))\n",
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"print(\"최소값:\", np.min(array))\n",
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"print(\"합계:\", np.sum(array))\n",
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"print(\"평균값:\", np.mean(array))"
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],
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"execution_count": 5,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"최대값: 15\n",
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"최소값: 0\n",
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"합계: 120\n",
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"평균값: 7.5\n"
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],
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"name": "stdout"
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}
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]
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},
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{
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"cell_type": "code",
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"metadata": {
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"id": "2AQxs__jJP-L",
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"colab_type": "code",
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"colab": {
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"base_uri": "https://localhost:8080/",
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"height": 105
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},
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"outputId": "3446db66-a125-4f7e-c832-e1ecc181a17d"
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},
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"source": [
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"import numpy as np\n",
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"\n",
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"array = np.arange(16).reshape(4, 4)\n",
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"\n",
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"print(array)\n",
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"print(\"합계:\", np.sum(array, axis=0))"
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],
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"execution_count": 6,
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"outputs": [
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{
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"output_type": "stream",
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"text": [
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"[[ 0 1 2 3]\n",
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" [ 4 5 6 7]\n",
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" [ 8 9 10 11]\n",
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" [12 13 14 15]]\n",
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"합계: [24 28 32 36]\n"
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],
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"name": "stdout"
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}
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]
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}
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]
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}

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