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Merge pull request #58 from sikepuri-algorithm/manuscript
Manuscript
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‎docs/extras/5tex/index.mdx

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- `sample.pdf` という名前のファイルができるので、それを開く。
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:::tip
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Mac を使っている場合は、バックスラッシュ（ `\` ）は、`Alt` を押しながら `¥` を押せば、打つことができます。
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:::
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:::tip
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LuaLaTeX 以外も使うことができます。pLaTeX で jsarticle を使う例も載せておきます。
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‎docs/index.mdx

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## 更新履歴
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10/10 第一周の分を執筆 [ここから](/docs/python/01google-colaboratory/)
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11/6 range 関数についてを補足 [この下](http://localhost:3000/docs/python/08array/#%E5%95%8F%E9%A1%8C-1)
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10/10 練習問題を追加
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10/30 第三週の分を執筆 [ここから](/docs/python/07for/)
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10/23 第二週の分を執筆 [ここから](/docs/python/05function/)
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10/16 練習問題をさらに追加
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10/23 第二週の分を執筆 [ここから](/docs/python/05function/)
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10/10 練習問題を追加
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10/30 第三週の分を執筆 [ここから](/docs/python/07for/)
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10/10 第一周の分を執筆 [ここから](/docs/python/01google-colaboratory/)

‎docs/python/05function/index.mdx

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<Answer>
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<ViewSource path="_samples/introduce_language.ipynb" />
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:::note
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`f-string` を使わないと次のようになります。
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<ViewSource path="_samples/introduce_language2.ipynb" />
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:::
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</Answer>

‎docs/python/07for/_samples/upto_sum.ipynb

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{
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"nbformat_minor": 2,
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"metadata": {},
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"cells": [
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{
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"metadata": {},
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"source": [
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"sum(range(1, 11))"
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],
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"cell_type": "code",
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"55"
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]
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},
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"metadata": {},
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"execution_count": 1
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}
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],
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"execution_count": null
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‎docs/python/07for/index.mdx

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### 練習問題 1
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1 から n までの和を求める関数を作って、実際に 1 から 10 までの和を求めてみましょう。
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$1$ から $n$ までの和を求める関数を作って、実際に 1 から 10 までの和を求めてみましょう。
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<Answer>
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<ViewSource path="_samples/upto.ipynb" />
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<ViewSource path="_samples/upto.ipynb" />
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実は、このプログラムは次のようにしてもっと簡単に書くことができます。しかし、次項の配列の内容がわかっていないと理解できないため、次項で詳しく紹介します。
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<ViewSource path="_samples/upto_sum.ipynb" />
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</Answer>
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### 練習問題 2

‎docs/python/08array/_samples/average_numpy.ipynb

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{
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"nbformat": 4,
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"nbformat_minor": 2,
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"metadata": {},
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"cells": [
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{
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"metadata": {},
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"source": [
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"import numpy\n",
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"\n",
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"scores = [26, 78, 83, 20, 10, 11, 22, 16, 41, 95]\n",
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"numpy.mean(scores)"
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],
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"cell_type": "code",
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"40.2"
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]
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},
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"metadata": {},
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"execution_count": 1
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}
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],
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"execution_count": null
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}
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}

‎docs/python/08array/_samples/average_statistics.ipynb

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"cells": [
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{
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"metadata": {},
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"source": [
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"import statistics\n",
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"\n",
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"scores = [26, 78, 83, 20, 10, 11, 22, 16, 41, 95]\n",
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"statistics.mean(scores)"
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],
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"cell_type": "code",
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"40.2"
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]
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},
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"metadata": {},
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"execution_count": 1
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‎docs/python/08array/_samples/range.ipynb

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"metadata": {},
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"cells": [
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{
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"metadata": {},
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"source": [
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"print(range(1, 11))\n",
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"print(list(range(1, 11)))"
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],
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"cell_type": "code",
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"outputs": [
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{
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"output_type": "stream",
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"name": "stdout",
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"text": [
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"range(1, 11)\n",
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"[1, 2, 3, 4, 5, 6, 7, 8, 9, 10]\n"
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]
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}
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],
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"execution_count": null
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‎docs/python/08array/_samples/sum.ipynb

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"metadata": {},
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"cells": [
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"metadata": {},
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"source": [
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"sum([1, 2, 3])"
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],
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"cell_type": "code",
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"outputs": [
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"6"
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"metadata": {},
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"execution_count": 2
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‎docs/python/08array/_samples/sum2.ipynb

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"metadata": {},
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"cells": [
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{
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"metadata": {},
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"source": [
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"sum([2, 5, 7])"
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],
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"cell_type": "code",
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"outputs": [
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{
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"output_type": "execute_result",
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"data": {
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"text/plain": [
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"14"
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"metadata": {},
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"execution_count": 1
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}
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"execution_count": null
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}
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]
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}

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