├── README.md
├── YES24API.ipynb
├── aeiou.wav
├── audio_processing.ipynb
├── classification.ipynb
├── clustering.ipynb
├── dimensionality_reduction(PCA).ipynb
├── function.ipynb
├── goodpants.wav
├── import.ipynb
├── numpy_matplotlib.ipynb
├── pandas.ipynb
├── regression.ipynb
├── requests_gradio(공동주택가격).ipynb
├── string.ipynb
├── syntax.ipynb
├── variables.ipynb
├── yesterday.wav
└── 교보API.ipynb


/README.md:
--------------------------------------------------------------------------------
1 | # class2022Fall 영어음성학 (고려대학교 영어영문학과)
2 | ## [github repository](https://github.com/hsnam95/class2022Fall)
3 | ## [NAMZ channel](https://www.youtube.com/channel/UCKHB0ZiTVk8qUdqhVtnCUrA)
4 | ## [lecture note](https://koreaoffice-my.sharepoint.com/:p:/g/personal/hnam_korea_ac_kr/EYNpUXN9in9Gs3wn43mii7cB0mSq06bCuIGYrJIeZlbE0g?e=DbqzP7)
5 | 


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/YES24API.ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyO3k41zi4htcpCGg5M4ueUk",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/YES24API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "code",
 31 |       "source": [
 32 |         "import pandas as pd\n",
 33 |         "import json\n",
 34 |         "import requests\n",
 35 |         "from bs4 import BeautifulSoup\n",
 36 |         "import re\n",
 37 |         "import numpy as np\n"
 38 |       ],
 39 |       "metadata": {
 40 |         "id": "nhZj-Bi8Atg2"
 41 |       },
 42 |       "execution_count": null,
 43 |       "outputs": []
 44 |     },
 45 |     {
 46 |       "cell_type": "code",
 47 |       "source": [
 48 |         "bookID = '176787'"
 49 |       ],
 50 |       "metadata": {
 51 |         "id": "j4Cj1FNgAyDU"
 52 |       },
 53 |       "execution_count": null,
 54 |       "outputs": []
 55 |     },
 56 |     {
 57 |       "cell_type": "code",
 58 |       "source": [
 59 |         "url = f'http://www.yes24.com/Product/communityModules/GoodsReviewList/{bookID}?Sort=1&PageNumber=1&Type=ALL'\n",
 60 |         "response = requests.get(url)\n",
 61 |         "soup = BeautifulSoup(response.content, 'html.parser')\n",
 62 |         "tmp = soup.find(string=re.compile('reviewCountText'))\n",
 63 |         "tmp = str(tmp)\n",
 64 |         "cnt = re.findall('(?<= reviewCountText = \\')\\d*(?=\\')', tmp)\n",
 65 |         "cnt = int(cnt[0])\n",
 66 |         "\n",
 67 |         "nPage = int(np.ceil(cnt/5))"
 68 |       ],
 69 |       "metadata": {
 70 |         "id": "q9xZlnJzSP5i"
 71 |       },
 72 |       "execution_count": null,
 73 |       "outputs": []
 74 |     },
 75 |     {
 76 |       "cell_type": "code",
 77 |       "source": [
 78 |         "data = {'title': [],\n",
 79 |         "        'ID': [],\n",
 80 |         "        'date': [],\n",
 81 |         "        'contRating': [],\n",
 82 |         "        'designRating': [],\n",
 83 |         "        'text': []}\n",
 84 |         "df = pd.DataFrame(data)"
 85 |       ],
 86 |       "metadata": {
 87 |         "id": "MkNNj561QaZh"
 88 |       },
 89 |       "execution_count": null,
 90 |       "outputs": []
 91 |     },
 92 |     {
 93 |       "cell_type": "code",
 94 |       "source": [
 95 |         "i = 0\n",
 96 |         "for n in range(nPage):\n",
 97 |         "  PageNumber = n + 1\n",
 98 |         "  url = f'http://www.yes24.com/Product/communityModules/GoodsReviewList/{bookID}?Sort=1&PageNumber={PageNumber}&Type=ALL'\n",
 99 |         "  response = requests.get(url)\n",
100 |         "  \n",
101 |         "  soup = BeautifulSoup(response.content, 'html.parser')\n",
102 |         "  title = soup.select('span.txt')\n",
103 |         "  ID = soup.select('a.lnk_id')\n",
104 |         "  date = soup.select('em.txt_date')\n",
105 |         "  rating = soup.select('span.review_rating > span.rating');\n",
106 |         "  contRating = rating[0:9:2]; designRating = rating[1:10:2]\n",
107 |         "  text = soup.select('div.reviewInfoBot.origin > div.review_cont')\n",
108 |         "\n",
109 |         "  for r in zip(title, ID, date, contRating, designRating, text):\n",
110 |         "    title, ID, date, contRating, designRating, text = [r[0].get_text(), r[1].get_text(), r[2].get_text(), r[3].get_text(), r[4].get_text(), r[5].get_text()]    \n",
111 |         "    row = [title, ID, date, contRating, designRating, text]\n",
112 |         "    df.loc[i, :] = row\n",
113 |         "    i +=1"
114 |       ],
115 |       "metadata": {
116 |         "id": "YJ6D1OVA4MLk"
117 |       },
118 |       "execution_count": null,
119 |       "outputs": []
120 |     },
121 |     {
122 |       "cell_type": "code",
123 |       "source": [
124 |         "import xlwt\n",
125 |         "df.to_excel('review.xls')"
126 |       ],
127 |       "metadata": {
128 |         "id": "Mgr-AdFGYVIj"
129 |       },
130 |       "execution_count": null,
131 |       "outputs": []
132 |     }
133 |   ]
134 | }


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/aeiou.wav:
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https://raw.githubusercontent.com/hsnam95/class2022Fall/b6d8b0cd9bd9d2ab443fe58ab1d1813f70db9e45/aeiou.wav


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/classification.ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyOxHGzEIPa4OH8P5u4fYMbP",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/classification.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "jXx1hl8Haz_B"
 33 |       },
 34 |       "source": [
 35 |         "## Loading a dataset"
 36 |       ]
 37 |     },
 38 |     {
 39 |       "cell_type": "code",
 40 |       "source": [
 41 |         "import pandas as pd"
 42 |       ],
 43 |       "metadata": {
 44 |         "id": "Og5Gu9lAvOAW"
 45 |       },
 46 |       "execution_count": 1,
 47 |       "outputs": []
 48 |     },
 49 |     {
 50 |       "cell_type": "markdown",
 51 |       "source": [
 52 |         "### iris dataset"
 53 |       ],
 54 |       "metadata": {
 55 |         "id": "U-C2V43QvWU7"
 56 |       }
 57 |     },
 58 |     {
 59 |       "cell_type": "code",
 60 |       "execution_count": null,
 61 |       "metadata": {
 62 |         "id": "-1qGv4bFaz_C"
 63 |       },
 64 |       "outputs": [],
 65 |       "source": [
 66 |         "from sklearn.preprocessing import LabelEncoder\n",
 67 |         "le = LabelEncoder()\n",
 68 |         "iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'label'])\n",
 69 |         "iris['label'] = le.fit_transform(iris['label'])\n",
 70 |         "iris.head()"
 71 |       ]
 72 |     },
 73 |     {
 74 |       "cell_type": "code",
 75 |       "source": [
 76 |         "iris"
 77 |       ],
 78 |       "metadata": {
 79 |         "colab": {
 80 |           "base_uri": "https://localhost:8080/",
 81 |           "height": 424
 82 |         },
 83 |         "id": "5QicxfMH8zDf",
 84 |         "outputId": "32aa129e-3b4d-4153-acad-1b8988c915f2"
 85 |       },
 86 |       "execution_count": 30,
 87 |       "outputs": [
 88 |         {
 89 |           "output_type": "execute_result",
 90 |           "data": {
 91 |             "text/plain": [
 92 |               "     sepal_length  sepal_width  petal_length  petal_width  label\n",
 93 |               "0             5.1          3.5           1.4          0.2      0\n",
 94 |               "1             4.9          3.0           1.4          0.2      0\n",
 95 |               "2             4.7          3.2           1.3          0.2      0\n",
 96 |               "3             4.6          3.1           1.5          0.2      0\n",
 97 |               "4             5.0          3.6           1.4          0.2      0\n",
 98 |               "..            ...          ...           ...          ...    ...\n",
 99 |               "145           6.7          3.0           5.2          2.3      2\n",
100 |               "146           6.3          2.5           5.0          1.9      2\n",
101 |               "147           6.5          3.0           5.2          2.0      2\n",
102 |               "148           6.2          3.4           5.4          2.3      2\n",
103 |               "149           5.9          3.0           5.1          1.8      2\n",
104 |               "\n",
105 |               "[150 rows x 5 columns]"
106 |             ],
107 |             "text/html": [
108 |               "\n",
109 |               "  <div id=\"df-5605e439-f86c-49a2-baec-8e7d920b3e8f\">\n",
110 |               "    <div class=\"colab-df-container\">\n",
111 |               "      <div>\n",
112 |               "<style scoped>\n",
113 |               "    .dataframe tbody tr th:only-of-type {\n",
114 |               "        vertical-align: middle;\n",
115 |               "    }\n",
116 |               "\n",
117 |               "    .dataframe tbody tr th {\n",
118 |               "        vertical-align: top;\n",
119 |               "    }\n",
120 |               "\n",
121 |               "    .dataframe thead th {\n",
122 |               "        text-align: right;\n",
123 |               "    }\n",
124 |               "</style>\n",
125 |               "<table border=\"1\" class=\"dataframe\">\n",
126 |               "  <thead>\n",
127 |               "    <tr style=\"text-align: right;\">\n",
128 |               "      <th></th>\n",
129 |               "      <th>sepal_length</th>\n",
130 |               "      <th>sepal_width</th>\n",
131 |               "      <th>petal_length</th>\n",
132 |               "      <th>petal_width</th>\n",
133 |               "      <th>label</th>\n",
134 |               "    </tr>\n",
135 |               "  </thead>\n",
136 |               "  <tbody>\n",
137 |               "    <tr>\n",
138 |               "      <th>0</th>\n",
139 |               "      <td>5.1</td>\n",
140 |               "      <td>3.5</td>\n",
141 |               "      <td>1.4</td>\n",
142 |               "      <td>0.2</td>\n",
143 |               "      <td>0</td>\n",
144 |               "    </tr>\n",
145 |               "    <tr>\n",
146 |               "      <th>1</th>\n",
147 |               "      <td>4.9</td>\n",
148 |               "      <td>3.0</td>\n",
149 |               "      <td>1.4</td>\n",
150 |               "      <td>0.2</td>\n",
151 |               "      <td>0</td>\n",
152 |               "    </tr>\n",
153 |               "    <tr>\n",
154 |               "      <th>2</th>\n",
155 |               "      <td>4.7</td>\n",
156 |               "      <td>3.2</td>\n",
157 |               "      <td>1.3</td>\n",
158 |               "      <td>0.2</td>\n",
159 |               "      <td>0</td>\n",
160 |               "    </tr>\n",
161 |               "    <tr>\n",
162 |               "      <th>3</th>\n",
163 |               "      <td>4.6</td>\n",
164 |               "      <td>3.1</td>\n",
165 |               "      <td>1.5</td>\n",
166 |               "      <td>0.2</td>\n",
167 |               "      <td>0</td>\n",
168 |               "    </tr>\n",
169 |               "    <tr>\n",
170 |               "      <th>4</th>\n",
171 |               "      <td>5.0</td>\n",
172 |               "      <td>3.6</td>\n",
173 |               "      <td>1.4</td>\n",
174 |               "      <td>0.2</td>\n",
175 |               "      <td>0</td>\n",
176 |               "    </tr>\n",
177 |               "    <tr>\n",
178 |               "      <th>...</th>\n",
179 |               "      <td>...</td>\n",
180 |               "      <td>...</td>\n",
181 |               "      <td>...</td>\n",
182 |               "      <td>...</td>\n",
183 |               "      <td>...</td>\n",
184 |               "    </tr>\n",
185 |               "    <tr>\n",
186 |               "      <th>145</th>\n",
187 |               "      <td>6.7</td>\n",
188 |               "      <td>3.0</td>\n",
189 |               "      <td>5.2</td>\n",
190 |               "      <td>2.3</td>\n",
191 |               "      <td>2</td>\n",
192 |               "    </tr>\n",
193 |               "    <tr>\n",
194 |               "      <th>146</th>\n",
195 |               "      <td>6.3</td>\n",
196 |               "      <td>2.5</td>\n",
197 |               "      <td>5.0</td>\n",
198 |               "      <td>1.9</td>\n",
199 |               "      <td>2</td>\n",
200 |               "    </tr>\n",
201 |               "    <tr>\n",
202 |               "      <th>147</th>\n",
203 |               "      <td>6.5</td>\n",
204 |               "      <td>3.0</td>\n",
205 |               "      <td>5.2</td>\n",
206 |               "      <td>2.0</td>\n",
207 |               "      <td>2</td>\n",
208 |               "    </tr>\n",
209 |               "    <tr>\n",
210 |               "      <th>148</th>\n",
211 |               "      <td>6.2</td>\n",
212 |               "      <td>3.4</td>\n",
213 |               "      <td>5.4</td>\n",
214 |               "      <td>2.3</td>\n",
215 |               "      <td>2</td>\n",
216 |               "    </tr>\n",
217 |               "    <tr>\n",
218 |               "      <th>149</th>\n",
219 |               "      <td>5.9</td>\n",
220 |               "      <td>3.0</td>\n",
221 |               "      <td>5.1</td>\n",
222 |               "      <td>1.8</td>\n",
223 |               "      <td>2</td>\n",
224 |               "    </tr>\n",
225 |               "  </tbody>\n",
226 |               "</table>\n",
227 |               "<p>150 rows × 5 columns</p>\n",
228 |               "</div>\n",
229 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-5605e439-f86c-49a2-baec-8e7d920b3e8f')\"\n",
230 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
231 |               "              style=\"display:none;\">\n",
232 |               "        \n",
233 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
234 |               "       width=\"24px\">\n",
235 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
236 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
237 |               "  </svg>\n",
238 |               "      </button>\n",
239 |               "      \n",
240 |               "  <style>\n",
241 |               "    .colab-df-container {\n",
242 |               "      display:flex;\n",
243 |               "      flex-wrap:wrap;\n",
244 |               "      gap: 12px;\n",
245 |               "    }\n",
246 |               "\n",
247 |               "    .colab-df-convert {\n",
248 |               "      background-color: #E8F0FE;\n",
249 |               "      border: none;\n",
250 |               "      border-radius: 50%;\n",
251 |               "      cursor: pointer;\n",
252 |               "      display: none;\n",
253 |               "      fill: #1967D2;\n",
254 |               "      height: 32px;\n",
255 |               "      padding: 0 0 0 0;\n",
256 |               "      width: 32px;\n",
257 |               "    }\n",
258 |               "\n",
259 |               "    .colab-df-convert:hover {\n",
260 |               "      background-color: #E2EBFA;\n",
261 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
262 |               "      fill: #174EA6;\n",
263 |               "    }\n",
264 |               "\n",
265 |               "    [theme=dark] .colab-df-convert {\n",
266 |               "      background-color: #3B4455;\n",
267 |               "      fill: #D2E3FC;\n",
268 |               "    }\n",
269 |               "\n",
270 |               "    [theme=dark] .colab-df-convert:hover {\n",
271 |               "      background-color: #434B5C;\n",
272 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
273 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
274 |               "      fill: #FFFFFF;\n",
275 |               "    }\n",
276 |               "  </style>\n",
277 |               "\n",
278 |               "      <script>\n",
279 |               "        const buttonEl =\n",
280 |               "          document.querySelector('#df-5605e439-f86c-49a2-baec-8e7d920b3e8f button.colab-df-convert');\n",
281 |               "        buttonEl.style.display =\n",
282 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
283 |               "\n",
284 |               "        async function convertToInteractive(key) {\n",
285 |               "          const element = document.querySelector('#df-5605e439-f86c-49a2-baec-8e7d920b3e8f');\n",
286 |               "          const dataTable =\n",
287 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
288 |               "                                                     [key], {});\n",
289 |               "          if (!dataTable) return;\n",
290 |               "\n",
291 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
292 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
293 |               "            + ' to learn more about interactive tables.';\n",
294 |               "          element.innerHTML = '';\n",
295 |               "          dataTable['output_type'] = 'display_data';\n",
296 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
297 |               "          const docLink = document.createElement('div');\n",
298 |               "          docLink.innerHTML = docLinkHtml;\n",
299 |               "          element.appendChild(docLink);\n",
300 |               "        }\n",
301 |               "      </script>\n",
302 |               "    </div>\n",
303 |               "  </div>\n",
304 |               "  "
305 |             ]
306 |           },
307 |           "metadata": {},
308 |           "execution_count": 30
309 |         }
310 |       ]
311 |     },
312 |     {
313 |       "cell_type": "markdown",
314 |       "source": [
315 |         "### breast cancer dataset"
316 |       ],
317 |       "metadata": {
318 |         "id": "BAmzSNjVvfwD"
319 |       }
320 |     },
321 |     {
322 |       "cell_type": "code",
323 |       "execution_count": 13,
324 |       "metadata": {
325 |         "id": "TUAfbcbTaz_D",
326 |         "colab": {
327 |           "base_uri": "https://localhost:8080/",
328 |           "height": 206
329 |         },
330 |         "outputId": "c7e6fdc2-e392-4f3f-cb32-0496e7235274"
331 |       },
332 |       "outputs": [
333 |         {
334 |           "output_type": "execute_result",
335 |           "data": {
336 |             "text/plain": [
337 |               "   clump_thickness  uniformity_of_cell_size  uniformity_of_cell_shape  \\\n",
338 |               "0                5                        1                         1   \n",
339 |               "1                5                        4                         4   \n",
340 |               "2                3                        1                         1   \n",
341 |               "3                6                        8                         8   \n",
342 |               "4                4                        1                         1   \n",
343 |               "\n",
344 |               "   marginal_adhesion  single_epithelial_cell_size bare_nuclei  \\\n",
345 |               "0                  1                            2           1   \n",
346 |               "1                  5                            7          10   \n",
347 |               "2                  1                            2           2   \n",
348 |               "3                  1                            3           4   \n",
349 |               "4                  3                            2           1   \n",
350 |               "\n",
351 |               "   bland_chromatin  normal_nucleoli  mitoses  label  \n",
352 |               "0                3                1        1      0  \n",
353 |               "1                3                2        1      0  \n",
354 |               "2                3                1        1      0  \n",
355 |               "3                3                7        1      0  \n",
356 |               "4                3                1        1      0  "
357 |             ],
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380 |               "      <th>clump_thickness</th>\n",
381 |               "      <th>uniformity_of_cell_size</th>\n",
382 |               "      <th>uniformity_of_cell_shape</th>\n",
383 |               "      <th>marginal_adhesion</th>\n",
384 |               "      <th>single_epithelial_cell_size</th>\n",
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387 |               "      <th>normal_nucleoli</th>\n",
388 |               "      <th>mitoses</th>\n",
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469 |               "  </svg>\n",
470 |               "      </button>\n",
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475 |               "      flex-wrap:wrap;\n",
476 |               "      gap: 12px;\n",
477 |               "    }\n",
478 |               "\n",
479 |               "    .colab-df-convert {\n",
480 |               "      background-color: #E8F0FE;\n",
481 |               "      border: none;\n",
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483 |               "      cursor: pointer;\n",
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487 |               "      padding: 0 0 0 0;\n",
488 |               "      width: 32px;\n",
489 |               "    }\n",
490 |               "\n",
491 |               "    .colab-df-convert:hover {\n",
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494 |               "      fill: #174EA6;\n",
495 |               "    }\n",
496 |               "\n",
497 |               "    [theme=dark] .colab-df-convert {\n",
498 |               "      background-color: #3B4455;\n",
499 |               "      fill: #D2E3FC;\n",
500 |               "    }\n",
501 |               "\n",
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506 |               "      fill: #FFFFFF;\n",
507 |               "    }\n",
508 |               "  </style>\n",
509 |               "\n",
510 |               "      <script>\n",
511 |               "        const buttonEl =\n",
512 |               "          document.querySelector('#df-14e8fac8-4f2a-4c51-8ff2-17b47ab57e68 button.colab-df-convert');\n",
513 |               "        buttonEl.style.display =\n",
514 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
515 |               "\n",
516 |               "        async function convertToInteractive(key) {\n",
517 |               "          const element = document.querySelector('#df-14e8fac8-4f2a-4c51-8ff2-17b47ab57e68');\n",
518 |               "          const dataTable =\n",
519 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
520 |               "                                                     [key], {});\n",
521 |               "          if (!dataTable) return;\n",
522 |               "\n",
523 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
524 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
525 |               "            + ' to learn more about interactive tables.';\n",
526 |               "          element.innerHTML = '';\n",
527 |               "          dataTable['output_type'] = 'display_data';\n",
528 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
529 |               "          const docLink = document.createElement('div');\n",
530 |               "          docLink.innerHTML = docLinkHtml;\n",
531 |               "          element.appendChild(docLink);\n",
532 |               "        }\n",
533 |               "      </script>\n",
534 |               "    </div>\n",
535 |               "  </div>\n",
536 |               "  "
537 |             ]
538 |           },
539 |           "metadata": {},
540 |           "execution_count": 13
541 |         }
542 |       ],
543 |       "source": [
544 |         "breast_cancer = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/breast-cancer-wisconsin/breast-cancer-wisconsin.data', names=['id', 'clump_thickness', 'uniformity_of_cell_size', 'uniformity_of_cell_shape', 'marginal_adhesion', 'single_epithelial_cell_size', 'bare_nuclei', 'bland_chromatin', 'normal_nucleoli', 'mitoses', 'label'])\n",
545 |         "breast_cancer.drop(['id'], axis=1, inplace=True)\n",
546 |         "breast_cancer['label'].replace([2, 4], [0, 1], inplace=True)\n",
547 |         "breast_cancer.replace('?', -999999, inplace=True)  # replace missing value with outliers\n",
548 |         "breast_cancer.head()"
549 |       ]
550 |     },
551 |     {
552 |       "cell_type": "markdown",
553 |       "source": [
554 |         "### wine dataset"
555 |       ],
556 |       "metadata": {
557 |         "id": "ssWdyrqavsbT"
558 |       }
559 |     },
560 |     {
561 |       "cell_type": "code",
562 |       "execution_count": 14,
563 |       "metadata": {
564 |         "id": "YQcjVDY0az_E",
565 |         "colab": {
566 |           "base_uri": "https://localhost:8080/",
567 |           "height": 261
568 |         },
569 |         "outputId": "a95f03bb-e5f6-463e-95c4-4323b438ac9b"
570 |       },
571 |       "outputs": [
572 |         {
573 |           "output_type": "stream",
574 |           "name": "stderr",
575 |           "text": [
576 |             "/usr/local/lib/python3.7/dist-packages/pandas/util/_decorators.py:311: ParserWarning: Length of header or names does not match length of data. This leads to a loss of data with index_col=False.\n",
577 |             "  return func(*args, **kwargs)\n"
578 |           ]
579 |         },
580 |         {
581 |           "output_type": "execute_result",
582 |           "data": {
583 |             "text/plain": [
584 |               "   label  malic_acid   ash  alcalinity_of_ash  magnesium  total_phenols  \\\n",
585 |               "0      1       14.23  1.71               2.43       15.6            127   \n",
586 |               "1      1       13.20  1.78               2.14       11.2            100   \n",
587 |               "2      1       13.16  2.36               2.67       18.6            101   \n",
588 |               "3      1       14.37  1.95               2.50       16.8            113   \n",
589 |               "4      1       13.24  2.59               2.87       21.0            118   \n",
590 |               "\n",
591 |               "   flavanoids  nonflavanoid_phenols  proanthocyanins  color_intensity   hue  \\\n",
592 |               "0        2.80                  3.06             0.28             2.29  5.64   \n",
593 |               "1        2.65                  2.76             0.26             1.28  4.38   \n",
594 |               "2        2.80                  3.24             0.30             2.81  5.68   \n",
595 |               "3        3.85                  3.49             0.24             2.18  7.80   \n",
596 |               "4        2.80                  2.69             0.39             1.82  4.32   \n",
597 |               "\n",
598 |               "   OD280/OD315_of_diluted_wines  proline  \n",
599 |               "0                          1.04     3.92  \n",
600 |               "1                          1.05     3.40  \n",
601 |               "2                          1.03     3.17  \n",
602 |               "3                          0.86     3.45  \n",
603 |               "4                          1.04     2.93  "
604 |             ],
605 |             "text/html": [
606 |               "\n",
607 |               "  <div id=\"df-beea3936-5a96-44fc-86ed-d040de56ce4a\">\n",
608 |               "    <div class=\"colab-df-container\">\n",
609 |               "      <div>\n",
610 |               "<style scoped>\n",
611 |               "    .dataframe tbody tr th:only-of-type {\n",
612 |               "        vertical-align: middle;\n",
613 |               "    }\n",
614 |               "\n",
615 |               "    .dataframe tbody tr th {\n",
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617 |               "    }\n",
618 |               "\n",
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622 |               "</style>\n",
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624 |               "  <thead>\n",
625 |               "    <tr style=\"text-align: right;\">\n",
626 |               "      <th></th>\n",
627 |               "      <th>label</th>\n",
628 |               "      <th>malic_acid</th>\n",
629 |               "      <th>ash</th>\n",
630 |               "      <th>alcalinity_of_ash</th>\n",
631 |               "      <th>magnesium</th>\n",
632 |               "      <th>total_phenols</th>\n",
633 |               "      <th>flavanoids</th>\n",
634 |               "      <th>nonflavanoid_phenols</th>\n",
635 |               "      <th>proanthocyanins</th>\n",
636 |               "      <th>color_intensity</th>\n",
637 |               "      <th>hue</th>\n",
638 |               "      <th>OD280/OD315_of_diluted_wines</th>\n",
639 |               "      <th>proline</th>\n",
640 |               "    </tr>\n",
641 |               "  </thead>\n",
642 |               "  <tbody>\n",
643 |               "    <tr>\n",
644 |               "      <th>0</th>\n",
645 |               "      <td>1</td>\n",
646 |               "      <td>14.23</td>\n",
647 |               "      <td>1.71</td>\n",
648 |               "      <td>2.43</td>\n",
649 |               "      <td>15.6</td>\n",
650 |               "      <td>127</td>\n",
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652 |               "      <td>3.06</td>\n",
653 |               "      <td>0.28</td>\n",
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655 |               "      <td>5.64</td>\n",
656 |               "      <td>1.04</td>\n",
657 |               "      <td>3.92</td>\n",
658 |               "    </tr>\n",
659 |               "    <tr>\n",
660 |               "      <th>1</th>\n",
661 |               "      <td>1</td>\n",
662 |               "      <td>13.20</td>\n",
663 |               "      <td>1.78</td>\n",
664 |               "      <td>2.14</td>\n",
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669 |               "      <td>0.26</td>\n",
670 |               "      <td>1.28</td>\n",
671 |               "      <td>4.38</td>\n",
672 |               "      <td>1.05</td>\n",
673 |               "      <td>3.40</td>\n",
674 |               "    </tr>\n",
675 |               "    <tr>\n",
676 |               "      <th>2</th>\n",
677 |               "      <td>1</td>\n",
678 |               "      <td>13.16</td>\n",
679 |               "      <td>2.36</td>\n",
680 |               "      <td>2.67</td>\n",
681 |               "      <td>18.6</td>\n",
682 |               "      <td>101</td>\n",
683 |               "      <td>2.80</td>\n",
684 |               "      <td>3.24</td>\n",
685 |               "      <td>0.30</td>\n",
686 |               "      <td>2.81</td>\n",
687 |               "      <td>5.68</td>\n",
688 |               "      <td>1.03</td>\n",
689 |               "      <td>3.17</td>\n",
690 |               "    </tr>\n",
691 |               "    <tr>\n",
692 |               "      <th>3</th>\n",
693 |               "      <td>1</td>\n",
694 |               "      <td>14.37</td>\n",
695 |               "      <td>1.95</td>\n",
696 |               "      <td>2.50</td>\n",
697 |               "      <td>16.8</td>\n",
698 |               "      <td>113</td>\n",
699 |               "      <td>3.85</td>\n",
700 |               "      <td>3.49</td>\n",
701 |               "      <td>0.24</td>\n",
702 |               "      <td>2.18</td>\n",
703 |               "      <td>7.80</td>\n",
704 |               "      <td>0.86</td>\n",
705 |               "      <td>3.45</td>\n",
706 |               "    </tr>\n",
707 |               "    <tr>\n",
708 |               "      <th>4</th>\n",
709 |               "      <td>1</td>\n",
710 |               "      <td>13.24</td>\n",
711 |               "      <td>2.59</td>\n",
712 |               "      <td>2.87</td>\n",
713 |               "      <td>21.0</td>\n",
714 |               "      <td>118</td>\n",
715 |               "      <td>2.80</td>\n",
716 |               "      <td>2.69</td>\n",
717 |               "      <td>0.39</td>\n",
718 |               "      <td>1.82</td>\n",
719 |               "      <td>4.32</td>\n",
720 |               "      <td>1.04</td>\n",
721 |               "      <td>2.93</td>\n",
722 |               "    </tr>\n",
723 |               "  </tbody>\n",
724 |               "</table>\n",
725 |               "</div>\n",
726 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-beea3936-5a96-44fc-86ed-d040de56ce4a')\"\n",
727 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
728 |               "              style=\"display:none;\">\n",
729 |               "        \n",
730 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
731 |               "       width=\"24px\">\n",
732 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
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734 |               "  </svg>\n",
735 |               "      </button>\n",
736 |               "      \n",
737 |               "  <style>\n",
738 |               "    .colab-df-container {\n",
739 |               "      display:flex;\n",
740 |               "      flex-wrap:wrap;\n",
741 |               "      gap: 12px;\n",
742 |               "    }\n",
743 |               "\n",
744 |               "    .colab-df-convert {\n",
745 |               "      background-color: #E8F0FE;\n",
746 |               "      border: none;\n",
747 |               "      border-radius: 50%;\n",
748 |               "      cursor: pointer;\n",
749 |               "      display: none;\n",
750 |               "      fill: #1967D2;\n",
751 |               "      height: 32px;\n",
752 |               "      padding: 0 0 0 0;\n",
753 |               "      width: 32px;\n",
754 |               "    }\n",
755 |               "\n",
756 |               "    .colab-df-convert:hover {\n",
757 |               "      background-color: #E2EBFA;\n",
758 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
759 |               "      fill: #174EA6;\n",
760 |               "    }\n",
761 |               "\n",
762 |               "    [theme=dark] .colab-df-convert {\n",
763 |               "      background-color: #3B4455;\n",
764 |               "      fill: #D2E3FC;\n",
765 |               "    }\n",
766 |               "\n",
767 |               "    [theme=dark] .colab-df-convert:hover {\n",
768 |               "      background-color: #434B5C;\n",
769 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
770 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
771 |               "      fill: #FFFFFF;\n",
772 |               "    }\n",
773 |               "  </style>\n",
774 |               "\n",
775 |               "      <script>\n",
776 |               "        const buttonEl =\n",
777 |               "          document.querySelector('#df-beea3936-5a96-44fc-86ed-d040de56ce4a button.colab-df-convert');\n",
778 |               "        buttonEl.style.display =\n",
779 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
780 |               "\n",
781 |               "        async function convertToInteractive(key) {\n",
782 |               "          const element = document.querySelector('#df-beea3936-5a96-44fc-86ed-d040de56ce4a');\n",
783 |               "          const dataTable =\n",
784 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
785 |               "                                                     [key], {});\n",
786 |               "          if (!dataTable) return;\n",
787 |               "\n",
788 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
789 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
790 |               "            + ' to learn more about interactive tables.';\n",
791 |               "          element.innerHTML = '';\n",
792 |               "          dataTable['output_type'] = 'display_data';\n",
793 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
794 |               "          const docLink = document.createElement('div');\n",
795 |               "          docLink.innerHTML = docLinkHtml;\n",
796 |               "          element.appendChild(docLink);\n",
797 |               "        }\n",
798 |               "      </script>\n",
799 |               "    </div>\n",
800 |               "  </div>\n",
801 |               "  "
802 |             ]
803 |           },
804 |           "metadata": {},
805 |           "execution_count": 14
806 |         }
807 |       ],
808 |       "source": [
809 |         "wine = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/wine/wine.data', names=['label', 'malic_acid', 'ash', 'alcalinity_of_ash', 'magnesium', 'total_phenols','flavanoids', 'nonflavanoid_phenols' ,'proanthocyanins', 'color_intensity', 'hue', 'OD280/OD315_of_diluted_wines', 'proline'], delimiter=\",\", index_col=False)\n",
810 |         "wine.head()"
811 |       ]
812 |     },
813 |     {
814 |       "cell_type": "markdown",
815 |       "metadata": {
816 |         "id": "kKgDxumOaz_J"
817 |       },
818 |       "source": [
819 |         "## Comparing 6 classification algorithms"
820 |       ]
821 |     },
822 |     {
823 |       "cell_type": "code",
824 |       "execution_count": 20,
825 |       "metadata": {
826 |         "id": "eWToHFv_az_J"
827 |       },
828 |       "outputs": [],
829 |       "source": [
830 |         "from sklearn.linear_model import LogisticRegression\n",
831 |         "from sklearn.naive_bayes import GaussianNB\n",
832 |         "from sklearn.svm import SVC\n",
833 |         "from sklearn.neighbors import KNeighborsClassifier\n",
834 |         "from sklearn.tree import DecisionTreeClassifier\n",
835 |         "from sklearn.neural_network import MLPClassifier\n",
836 |         "\n",
837 |         "models = [\n",
838 |         "    ('LR', LogisticRegression()),\n",
839 |         "    ('NB', GaussianNB()),\n",
840 |         "    ('SVM', SVC()),\n",
841 |         "    ('KNN', KNeighborsClassifier()),\n",
842 |         "    ('DT', DecisionTreeClassifier()),\n",
843 |         "    ('NN', MLPClassifier()),\n",
844 |         "]"
845 |       ]
846 |     },
847 |     {
848 |       "cell_type": "markdown",
849 |       "source": [
850 |         "### test a single model (e.g. Logistic Regression)"
851 |       ],
852 |       "metadata": {
853 |         "id": "LA6iBoHr6HhB"
854 |       }
855 |     },
856 |     {
857 |       "cell_type": "code",
858 |       "source": [
859 |         "from sklearn.model_selection import train_test_split\n",
860 |         "import numpy as np\n",
861 |         "\n",
862 |         "dataset_name = 'iris'\n",
863 |         "dataset = iris\n",
864 |         "\n",
865 |         "X = np.array(dataset.drop(['label'], axis=1))\n",
866 |         "y = np.array(dataset['label'])\n",
867 |         "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
868 |         "\n",
869 |         "name = 'LR'\n",
870 |         "model = LogisticRegression()\n",
871 |         "model.fit(X_train, y_train)\n",
872 |         "accuracy = model.score(X_test, y_test)\n",
873 |         "print(dataset_name, name, accuracy)"
874 |       ],
875 |       "metadata": {
876 |         "colab": {
877 |           "base_uri": "https://localhost:8080/"
878 |         },
879 |         "id": "fMWlaFbr4Qkm",
880 |         "outputId": "de0f50b5-763d-475f-b3e0-ad2be066a393"
881 |       },
882 |       "execution_count": 23,
883 |       "outputs": [
884 |         {
885 |           "output_type": "stream",
886 |           "name": "stdout",
887 |           "text": [
888 |             "iris LR 1.0\n"
889 |           ]
890 |         }
891 |       ]
892 |     },
893 |     {
894 |       "cell_type": "code",
895 |       "source": [
896 |         "model.predict(np.array([6, 3, 5, 2]).reshape(1,4))"
897 |       ],
898 |       "metadata": {
899 |         "colab": {
900 |           "base_uri": "https://localhost:8080/"
901 |         },
902 |         "id": "Ibi29aDq8Epg",
903 |         "outputId": "967edd5e-d8d2-48f2-feb5-96f4f5905e82"
904 |       },
905 |       "execution_count": 31,
906 |       "outputs": [
907 |         {
908 |           "output_type": "execute_result",
909 |           "data": {
910 |             "text/plain": [
911 |               "array([2])"
912 |             ]
913 |           },
914 |           "metadata": {},
915 |           "execution_count": 31
916 |         }
917 |       ]
918 |     },
919 |     {
920 |       "cell_type": "markdown",
921 |       "source": [
922 |         "### Evaluate all the models"
923 |       ],
924 |       "metadata": {
925 |         "id": "WMYLrQOd6obk"
926 |       }
927 |     },
928 |     {
929 |       "cell_type": "code",
930 |       "execution_count": null,
931 |       "metadata": {
932 |         "id": "ZoFEHsC2az_J"
933 |       },
934 |       "outputs": [],
935 |       "source": [
936 |         "for dataset_name, dataset in [('iris', iris), ('breast_cancer', breast_cancer), ('wine', wine)]:\n",
937 |         "  X = np.array(dataset.drop(['label'], axis=1))\n",
938 |         "  y = np.array(dataset['label'])\n",
939 |         "  X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
940 |         "\n",
941 |         "  for name, model in models:\n",
942 |         "    model.fit(X_train, y_train)\n",
943 |         "    accuracy = model.score(X_test, y_test)\n",
944 |         "    print(dataset_name, name, accuracy)"
945 |       ]
946 |     }
947 |   ]
948 | }


--------------------------------------------------------------------------------
/clustering.ipynb:
--------------------------------------------------------------------------------
  1 | {
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  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyNmMDnsPkQYGI7dd+58lVtu",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/clustering.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "u8M1FUzv4HVg"
 33 |       },
 34 |       "source": [
 35 |         "## Loading a dataset"
 36 |       ]
 37 |     },
 38 |     {
 39 |       "cell_type": "code",
 40 |       "execution_count": 25,
 41 |       "metadata": {
 42 |         "id": "9JmnJiue4HVv",
 43 |         "outputId": "edc0efe4-b0f2-469a-ae4a-906e5414f63c",
 44 |         "colab": {
 45 |           "base_uri": "https://localhost:8080/",
 46 |           "height": 424
 47 |         }
 48 |       },
 49 |       "outputs": [
 50 |         {
 51 |           "output_type": "execute_result",
 52 |           "data": {
 53 |             "text/plain": [
 54 |               "     sepal_length  sepal_width  petal_length  petal_width           label\n",
 55 |               "0             5.1          3.5           1.4          0.2     Iris-setosa\n",
 56 |               "1             4.9          3.0           1.4          0.2     Iris-setosa\n",
 57 |               "2             4.7          3.2           1.3          0.2     Iris-setosa\n",
 58 |               "3             4.6          3.1           1.5          0.2     Iris-setosa\n",
 59 |               "4             5.0          3.6           1.4          0.2     Iris-setosa\n",
 60 |               "..            ...          ...           ...          ...             ...\n",
 61 |               "145           6.7          3.0           5.2          2.3  Iris-virginica\n",
 62 |               "146           6.3          2.5           5.0          1.9  Iris-virginica\n",
 63 |               "147           6.5          3.0           5.2          2.0  Iris-virginica\n",
 64 |               "148           6.2          3.4           5.4          2.3  Iris-virginica\n",
 65 |               "149           5.9          3.0           5.1          1.8  Iris-virginica\n",
 66 |               "\n",
 67 |               "[150 rows x 5 columns]"
 68 |             ],
 69 |             "text/html": [
 70 |               "\n",
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 72 |               "    <div class=\"colab-df-container\">\n",
 73 |               "      <div>\n",
 74 |               "<style scoped>\n",
 75 |               "    .dataframe tbody tr th:only-of-type {\n",
 76 |               "        vertical-align: middle;\n",
 77 |               "    }\n",
 78 |               "\n",
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 85 |               "    }\n",
 86 |               "</style>\n",
 87 |               "<table border=\"1\" class=\"dataframe\">\n",
 88 |               "  <thead>\n",
 89 |               "    <tr style=\"text-align: right;\">\n",
 90 |               "      <th></th>\n",
 91 |               "      <th>sepal_length</th>\n",
 92 |               "      <th>sepal_width</th>\n",
 93 |               "      <th>petal_length</th>\n",
 94 |               "      <th>petal_width</th>\n",
 95 |               "      <th>label</th>\n",
 96 |               "    </tr>\n",
 97 |               "  </thead>\n",
 98 |               "  <tbody>\n",
 99 |               "    <tr>\n",
100 |               "      <th>0</th>\n",
101 |               "      <td>5.1</td>\n",
102 |               "      <td>3.5</td>\n",
103 |               "      <td>1.4</td>\n",
104 |               "      <td>0.2</td>\n",
105 |               "      <td>Iris-setosa</td>\n",
106 |               "    </tr>\n",
107 |               "    <tr>\n",
108 |               "      <th>1</th>\n",
109 |               "      <td>4.9</td>\n",
110 |               "      <td>3.0</td>\n",
111 |               "      <td>1.4</td>\n",
112 |               "      <td>0.2</td>\n",
113 |               "      <td>Iris-setosa</td>\n",
114 |               "    </tr>\n",
115 |               "    <tr>\n",
116 |               "      <th>2</th>\n",
117 |               "      <td>4.7</td>\n",
118 |               "      <td>3.2</td>\n",
119 |               "      <td>1.3</td>\n",
120 |               "      <td>0.2</td>\n",
121 |               "      <td>Iris-setosa</td>\n",
122 |               "    </tr>\n",
123 |               "    <tr>\n",
124 |               "      <th>3</th>\n",
125 |               "      <td>4.6</td>\n",
126 |               "      <td>3.1</td>\n",
127 |               "      <td>1.5</td>\n",
128 |               "      <td>0.2</td>\n",
129 |               "      <td>Iris-setosa</td>\n",
130 |               "    </tr>\n",
131 |               "    <tr>\n",
132 |               "      <th>4</th>\n",
133 |               "      <td>5.0</td>\n",
134 |               "      <td>3.6</td>\n",
135 |               "      <td>1.4</td>\n",
136 |               "      <td>0.2</td>\n",
137 |               "      <td>Iris-setosa</td>\n",
138 |               "    </tr>\n",
139 |               "    <tr>\n",
140 |               "      <th>...</th>\n",
141 |               "      <td>...</td>\n",
142 |               "      <td>...</td>\n",
143 |               "      <td>...</td>\n",
144 |               "      <td>...</td>\n",
145 |               "      <td>...</td>\n",
146 |               "    </tr>\n",
147 |               "    <tr>\n",
148 |               "      <th>145</th>\n",
149 |               "      <td>6.7</td>\n",
150 |               "      <td>3.0</td>\n",
151 |               "      <td>5.2</td>\n",
152 |               "      <td>2.3</td>\n",
153 |               "      <td>Iris-virginica</td>\n",
154 |               "    </tr>\n",
155 |               "    <tr>\n",
156 |               "      <th>146</th>\n",
157 |               "      <td>6.3</td>\n",
158 |               "      <td>2.5</td>\n",
159 |               "      <td>5.0</td>\n",
160 |               "      <td>1.9</td>\n",
161 |               "      <td>Iris-virginica</td>\n",
162 |               "    </tr>\n",
163 |               "    <tr>\n",
164 |               "      <th>147</th>\n",
165 |               "      <td>6.5</td>\n",
166 |               "      <td>3.0</td>\n",
167 |               "      <td>5.2</td>\n",
168 |               "      <td>2.0</td>\n",
169 |               "      <td>Iris-virginica</td>\n",
170 |               "    </tr>\n",
171 |               "    <tr>\n",
172 |               "      <th>148</th>\n",
173 |               "      <td>6.2</td>\n",
174 |               "      <td>3.4</td>\n",
175 |               "      <td>5.4</td>\n",
176 |               "      <td>2.3</td>\n",
177 |               "      <td>Iris-virginica</td>\n",
178 |               "    </tr>\n",
179 |               "    <tr>\n",
180 |               "      <th>149</th>\n",
181 |               "      <td>5.9</td>\n",
182 |               "      <td>3.0</td>\n",
183 |               "      <td>5.1</td>\n",
184 |               "      <td>1.8</td>\n",
185 |               "      <td>Iris-virginica</td>\n",
186 |               "    </tr>\n",
187 |               "  </tbody>\n",
188 |               "</table>\n",
189 |               "<p>150 rows × 5 columns</p>\n",
190 |               "</div>\n",
191 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-8433ba5a-1ae3-401a-8547-57024560f212')\"\n",
192 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
193 |               "              style=\"display:none;\">\n",
194 |               "        \n",
195 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
196 |               "       width=\"24px\">\n",
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198 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
199 |               "  </svg>\n",
200 |               "      </button>\n",
201 |               "      \n",
202 |               "  <style>\n",
203 |               "    .colab-df-container {\n",
204 |               "      display:flex;\n",
205 |               "      flex-wrap:wrap;\n",
206 |               "      gap: 12px;\n",
207 |               "    }\n",
208 |               "\n",
209 |               "    .colab-df-convert {\n",
210 |               "      background-color: #E8F0FE;\n",
211 |               "      border: none;\n",
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213 |               "      cursor: pointer;\n",
214 |               "      display: none;\n",
215 |               "      fill: #1967D2;\n",
216 |               "      height: 32px;\n",
217 |               "      padding: 0 0 0 0;\n",
218 |               "      width: 32px;\n",
219 |               "    }\n",
220 |               "\n",
221 |               "    .colab-df-convert:hover {\n",
222 |               "      background-color: #E2EBFA;\n",
223 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
224 |               "      fill: #174EA6;\n",
225 |               "    }\n",
226 |               "\n",
227 |               "    [theme=dark] .colab-df-convert {\n",
228 |               "      background-color: #3B4455;\n",
229 |               "      fill: #D2E3FC;\n",
230 |               "    }\n",
231 |               "\n",
232 |               "    [theme=dark] .colab-df-convert:hover {\n",
233 |               "      background-color: #434B5C;\n",
234 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
235 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
236 |               "      fill: #FFFFFF;\n",
237 |               "    }\n",
238 |               "  </style>\n",
239 |               "\n",
240 |               "      <script>\n",
241 |               "        const buttonEl =\n",
242 |               "          document.querySelector('#df-8433ba5a-1ae3-401a-8547-57024560f212 button.colab-df-convert');\n",
243 |               "        buttonEl.style.display =\n",
244 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
245 |               "\n",
246 |               "        async function convertToInteractive(key) {\n",
247 |               "          const element = document.querySelector('#df-8433ba5a-1ae3-401a-8547-57024560f212');\n",
248 |               "          const dataTable =\n",
249 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
250 |               "                                                     [key], {});\n",
251 |               "          if (!dataTable) return;\n",
252 |               "\n",
253 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
254 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
255 |               "            + ' to learn more about interactive tables.';\n",
256 |               "          element.innerHTML = '';\n",
257 |               "          dataTable['output_type'] = 'display_data';\n",
258 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
259 |               "          const docLink = document.createElement('div');\n",
260 |               "          docLink.innerHTML = docLinkHtml;\n",
261 |               "          element.appendChild(docLink);\n",
262 |               "        }\n",
263 |               "      </script>\n",
264 |               "    </div>\n",
265 |               "  </div>\n",
266 |               "  "
267 |             ]
268 |           },
269 |           "metadata": {},
270 |           "execution_count": 25
271 |         }
272 |       ],
273 |       "source": [
274 |         "import pandas as pd\n",
275 |         "iris = pd.read_csv('https://archive.ics.uci.edu/ml/machine-learning-databases/iris/iris.data', names=['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'label'])\n",
276 |         "iris"
277 |       ]
278 |     },
279 |     {
280 |       "cell_type": "code",
281 |       "source": [
282 |         "X = iris.drop(['label'], axis=1)\n",
283 |         "X"
284 |       ],
285 |       "metadata": {
286 |         "id": "_pEBhIzlYojp",
287 |         "outputId": "dc46b10a-eb3f-420e-88c3-e4bbe05e0752",
288 |         "colab": {
289 |           "base_uri": "https://localhost:8080/",
290 |           "height": 424
291 |         }
292 |       },
293 |       "execution_count": 26,
294 |       "outputs": [
295 |         {
296 |           "output_type": "execute_result",
297 |           "data": {
298 |             "text/plain": [
299 |               "     sepal_length  sepal_width  petal_length  petal_width\n",
300 |               "0             5.1          3.5           1.4          0.2\n",
301 |               "1             4.9          3.0           1.4          0.2\n",
302 |               "2             4.7          3.2           1.3          0.2\n",
303 |               "3             4.6          3.1           1.5          0.2\n",
304 |               "4             5.0          3.6           1.4          0.2\n",
305 |               "..            ...          ...           ...          ...\n",
306 |               "145           6.7          3.0           5.2          2.3\n",
307 |               "146           6.3          2.5           5.0          1.9\n",
308 |               "147           6.5          3.0           5.2          2.0\n",
309 |               "148           6.2          3.4           5.4          2.3\n",
310 |               "149           5.9          3.0           5.1          1.8\n",
311 |               "\n",
312 |               "[150 rows x 4 columns]"
313 |             ],
314 |             "text/html": [
315 |               "\n",
316 |               "  <div id=\"df-0e347578-78d6-4985-b01a-5999431aa395\">\n",
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318 |               "      <div>\n",
319 |               "<style scoped>\n",
320 |               "    .dataframe tbody tr th:only-of-type {\n",
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322 |               "    }\n",
323 |               "\n",
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327 |               "\n",
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330 |               "    }\n",
331 |               "</style>\n",
332 |               "<table border=\"1\" class=\"dataframe\">\n",
333 |               "  <thead>\n",
334 |               "    <tr style=\"text-align: right;\">\n",
335 |               "      <th></th>\n",
336 |               "      <th>sepal_length</th>\n",
337 |               "      <th>sepal_width</th>\n",
338 |               "      <th>petal_length</th>\n",
339 |               "      <th>petal_width</th>\n",
340 |               "    </tr>\n",
341 |               "  </thead>\n",
342 |               "  <tbody>\n",
343 |               "    <tr>\n",
344 |               "      <th>0</th>\n",
345 |               "      <td>5.1</td>\n",
346 |               "      <td>3.5</td>\n",
347 |               "      <td>1.4</td>\n",
348 |               "      <td>0.2</td>\n",
349 |               "    </tr>\n",
350 |               "    <tr>\n",
351 |               "      <th>1</th>\n",
352 |               "      <td>4.9</td>\n",
353 |               "      <td>3.0</td>\n",
354 |               "      <td>1.4</td>\n",
355 |               "      <td>0.2</td>\n",
356 |               "    </tr>\n",
357 |               "    <tr>\n",
358 |               "      <th>2</th>\n",
359 |               "      <td>4.7</td>\n",
360 |               "      <td>3.2</td>\n",
361 |               "      <td>1.3</td>\n",
362 |               "      <td>0.2</td>\n",
363 |               "    </tr>\n",
364 |               "    <tr>\n",
365 |               "      <th>3</th>\n",
366 |               "      <td>4.6</td>\n",
367 |               "      <td>3.1</td>\n",
368 |               "      <td>1.5</td>\n",
369 |               "      <td>0.2</td>\n",
370 |               "    </tr>\n",
371 |               "    <tr>\n",
372 |               "      <th>4</th>\n",
373 |               "      <td>5.0</td>\n",
374 |               "      <td>3.6</td>\n",
375 |               "      <td>1.4</td>\n",
376 |               "      <td>0.2</td>\n",
377 |               "    </tr>\n",
378 |               "    <tr>\n",
379 |               "      <th>...</th>\n",
380 |               "      <td>...</td>\n",
381 |               "      <td>...</td>\n",
382 |               "      <td>...</td>\n",
383 |               "      <td>...</td>\n",
384 |               "    </tr>\n",
385 |               "    <tr>\n",
386 |               "      <th>145</th>\n",
387 |               "      <td>6.7</td>\n",
388 |               "      <td>3.0</td>\n",
389 |               "      <td>5.2</td>\n",
390 |               "      <td>2.3</td>\n",
391 |               "    </tr>\n",
392 |               "    <tr>\n",
393 |               "      <th>146</th>\n",
394 |               "      <td>6.3</td>\n",
395 |               "      <td>2.5</td>\n",
396 |               "      <td>5.0</td>\n",
397 |               "      <td>1.9</td>\n",
398 |               "    </tr>\n",
399 |               "    <tr>\n",
400 |               "      <th>147</th>\n",
401 |               "      <td>6.5</td>\n",
402 |               "      <td>3.0</td>\n",
403 |               "      <td>5.2</td>\n",
404 |               "      <td>2.0</td>\n",
405 |               "    </tr>\n",
406 |               "    <tr>\n",
407 |               "      <th>148</th>\n",
408 |               "      <td>6.2</td>\n",
409 |               "      <td>3.4</td>\n",
410 |               "      <td>5.4</td>\n",
411 |               "      <td>2.3</td>\n",
412 |               "    </tr>\n",
413 |               "    <tr>\n",
414 |               "      <th>149</th>\n",
415 |               "      <td>5.9</td>\n",
416 |               "      <td>3.0</td>\n",
417 |               "      <td>5.1</td>\n",
418 |               "      <td>1.8</td>\n",
419 |               "    </tr>\n",
420 |               "  </tbody>\n",
421 |               "</table>\n",
422 |               "<p>150 rows × 4 columns</p>\n",
423 |               "</div>\n",
424 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e347578-78d6-4985-b01a-5999431aa395')\"\n",
425 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
426 |               "              style=\"display:none;\">\n",
427 |               "        \n",
428 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
429 |               "       width=\"24px\">\n",
430 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
431 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
432 |               "  </svg>\n",
433 |               "      </button>\n",
434 |               "      \n",
435 |               "  <style>\n",
436 |               "    .colab-df-container {\n",
437 |               "      display:flex;\n",
438 |               "      flex-wrap:wrap;\n",
439 |               "      gap: 12px;\n",
440 |               "    }\n",
441 |               "\n",
442 |               "    .colab-df-convert {\n",
443 |               "      background-color: #E8F0FE;\n",
444 |               "      border: none;\n",
445 |               "      border-radius: 50%;\n",
446 |               "      cursor: pointer;\n",
447 |               "      display: none;\n",
448 |               "      fill: #1967D2;\n",
449 |               "      height: 32px;\n",
450 |               "      padding: 0 0 0 0;\n",
451 |               "      width: 32px;\n",
452 |               "    }\n",
453 |               "\n",
454 |               "    .colab-df-convert:hover {\n",
455 |               "      background-color: #E2EBFA;\n",
456 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
457 |               "      fill: #174EA6;\n",
458 |               "    }\n",
459 |               "\n",
460 |               "    [theme=dark] .colab-df-convert {\n",
461 |               "      background-color: #3B4455;\n",
462 |               "      fill: #D2E3FC;\n",
463 |               "    }\n",
464 |               "\n",
465 |               "    [theme=dark] .colab-df-convert:hover {\n",
466 |               "      background-color: #434B5C;\n",
467 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
468 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
469 |               "      fill: #FFFFFF;\n",
470 |               "    }\n",
471 |               "  </style>\n",
472 |               "\n",
473 |               "      <script>\n",
474 |               "        const buttonEl =\n",
475 |               "          document.querySelector('#df-0e347578-78d6-4985-b01a-5999431aa395 button.colab-df-convert');\n",
476 |               "        buttonEl.style.display =\n",
477 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
478 |               "\n",
479 |               "        async function convertToInteractive(key) {\n",
480 |               "          const element = document.querySelector('#df-0e347578-78d6-4985-b01a-5999431aa395');\n",
481 |               "          const dataTable =\n",
482 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
483 |               "                                                     [key], {});\n",
484 |               "          if (!dataTable) return;\n",
485 |               "\n",
486 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
487 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
488 |               "            + ' to learn more about interactive tables.';\n",
489 |               "          element.innerHTML = '';\n",
490 |               "          dataTable['output_type'] = 'display_data';\n",
491 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
492 |               "          const docLink = document.createElement('div');\n",
493 |               "          docLink.innerHTML = docLinkHtml;\n",
494 |               "          element.appendChild(docLink);\n",
495 |               "        }\n",
496 |               "      </script>\n",
497 |               "    </div>\n",
498 |               "  </div>\n",
499 |               "  "
500 |             ]
501 |           },
502 |           "metadata": {},
503 |           "execution_count": 26
504 |         }
505 |       ]
506 |     },
507 |     {
508 |       "cell_type": "markdown",
509 |       "metadata": {
510 |         "id": "om3V-ZFw4HVu"
511 |       },
512 |       "source": [
513 |         "## Comparing Algorithms"
514 |       ]
515 |     },
516 |     {
517 |       "cell_type": "markdown",
518 |       "source": [
519 |         "### K-Means"
520 |       ],
521 |       "metadata": {
522 |         "id": "ydWddMsnUqx4"
523 |       }
524 |     },
525 |     {
526 |       "cell_type": "code",
527 |       "source": [
528 |         "from sklearn.cluster import KMeans\n",
529 |         "model = KMeans(n_clusters=3)\n",
530 |         "model.fit_predict(X)"
531 |       ],
532 |       "metadata": {
533 |         "id": "Spo5ISoBUp02",
534 |         "outputId": "40412121-810e-4ead-f5e6-8a2ef41b38fa",
535 |         "colab": {
536 |           "base_uri": "https://localhost:8080/"
537 |         }
538 |       },
539 |       "execution_count": 41,
540 |       "outputs": [
541 |         {
542 |           "output_type": "execute_result",
543 |           "data": {
544 |             "text/plain": [
545 |               "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
546 |               "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
547 |               "       1, 1, 1, 1, 1, 1, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
548 |               "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
549 |               "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2,\n",
550 |               "       2, 2, 2, 0, 0, 2, 2, 2, 2, 0, 2, 0, 2, 0, 2, 2, 0, 0, 2, 2, 2, 2,\n",
551 |               "       2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 2, 0, 2, 2, 0], dtype=int32)"
552 |             ]
553 |           },
554 |           "metadata": {},
555 |           "execution_count": 41
556 |         }
557 |       ]
558 |     },
559 |     {
560 |       "cell_type": "markdown",
561 |       "source": [
562 |         "###Gaussian Mixture Model(GMM)"
563 |       ],
564 |       "metadata": {
565 |         "id": "e35GqbOOXzWq"
566 |       }
567 |     },
568 |     {
569 |       "cell_type": "code",
570 |       "execution_count": 29,
571 |       "metadata": {
572 |         "id": "cRY2FK1H4HVx",
573 |         "outputId": "5d49abb9-cfaf-42bc-eae5-01e318dda612",
574 |         "colab": {
575 |           "base_uri": "https://localhost:8080/"
576 |         }
577 |       },
578 |       "outputs": [
579 |         {
580 |           "output_type": "execute_result",
581 |           "data": {
582 |             "text/plain": [
583 |               "array([1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
584 |               "       1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,\n",
585 |               "       1, 1, 1, 1, 1, 1, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2,\n",
586 |               "       2, 2, 0, 2, 0, 2, 0, 2, 2, 2, 2, 0, 2, 2, 2, 2, 2, 0, 2, 2, 2, 2,\n",
587 |               "       2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
588 |               "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,\n",
589 |               "       0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0])"
590 |             ]
591 |           },
592 |           "metadata": {},
593 |           "execution_count": 29
594 |         }
595 |       ],
596 |       "source": [
597 |         "from sklearn.mixture import GaussianMixture\n",
598 |         "model = GaussianMixture(n_components=3)\n",
599 |         "model.fit_predict(X)"
600 |       ]
601 |     },
602 |     {
603 |       "cell_type": "markdown",
604 |       "metadata": {
605 |         "id": "iincSgPb4HVx"
606 |       },
607 |       "source": [
608 |         "![](https://firebasestorage.googleapis.com/v0/b/programmingwithgilbert.appspot.com/o/Videos%2FScikit%20Learn%20Tutorials%2FScikit%20Learn%20Tutorial%20%237%20-%20Clustering%20Algorithms%2FAlgorithm%20Comparison.png?alt=media&token=c71334ff-e81f-465d-8009-655fe0132017)"
609 |       ]
610 |     }
611 |   ]
612 | }


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/function.ipynb:
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 1 | {
 2 |   "nbformat": 4,
 3 |   "nbformat_minor": 0,
 4 |   "metadata": {
 5 |     "colab": {
 6 |       "name": "function.ipynb",
 7 |       "provenance": [],
 8 |       "authorship_tag": "ABX9TyNIN+fAOH3niQgAxlxuusQz",
 9 |       "include_colab_link": true
10 |     },
11 |     "kernelspec": {
12 |       "name": "python3",
13 |       "display_name": "Python 3"
14 |     }
15 |   },
16 |   "cells": [
17 |     {
18 |       "cell_type": "markdown",
19 |       "metadata": {
20 |         "id": "view-in-github",
21 |         "colab_type": "text"
22 |       },
23 |       "source": [
24 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/function.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
25 |       ]
26 |     },
27 |     {
28 |       "cell_type": "code",
29 |       "metadata": {
30 |         "id": "hpc_mhgAye2S"
31 |       },
32 |       "source": [
33 |         "def add(a,b):\n",
34 |         "  return a+b\n",
35 |         "\n",
36 |         "def subtract(a,b):\n",
37 |         "  return a-b\n",
38 |         "\n",
39 |         "def multiply(a,b):\n",
40 |         "  return a*b\n",
41 |         "\n",
42 |         "def divide(a,b):\n",
43 |         "  return a/b"
44 |       ],
45 |       "execution_count": null,
46 |       "outputs": []
47 |     },
48 |     {
49 |       "cell_type": "code",
50 |       "metadata": {
51 |         "colab": {
52 |           "base_uri": "https://localhost:8080/"
53 |         },
54 |         "id": "8Y4oa05Eykn4",
55 |         "outputId": "7e186590-61dc-49a9-e22f-0bcb617fd3b7"
56 |       },
57 |       "source": [
58 |         "c = add(1,2); print(c)\n",
59 |         "c = subtract(1,2); print(c)\n",
60 |         "c = multiply(1,2); print(c)\n",
61 |         "c = divide(1,2); print(c)"
62 |       ],
63 |       "execution_count": null,
64 |       "outputs": [
65 |         {
66 |           "output_type": "stream",
67 |           "text": [
68 |             "3\n",
69 |             "-1\n",
70 |             "2\n",
71 |             "0.5\n"
72 |           ],
73 |           "name": "stdout"
74 |         }
75 |       ]
76 |     }
77 |   ]
78 | }


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/goodpants.wav:
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https://raw.githubusercontent.com/hsnam95/class2022Fall/b6d8b0cd9bd9d2ab443fe58ab1d1813f70db9e45/goodpants.wav


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/import.ipynb:
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  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "import.ipynb",
  7 |       "provenance": [],
  8 |       "authorship_tag": "ABX9TyMFMvQMVl0NnE6plZo3NTbg",
  9 |       "include_colab_link": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "python3",
 13 |       "display_name": "Python 3"
 14 |     },
 15 |     "language_info": {
 16 |       "name": "python"
 17 |     }
 18 |   },
 19 |   "cells": [
 20 |     {
 21 |       "cell_type": "markdown",
 22 |       "metadata": {
 23 |         "id": "view-in-github",
 24 |         "colab_type": "text"
 25 |       },
 26 |       "source": [
 27 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/import.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "code",
 32 |       "metadata": {
 33 |         "id": "JXicrkYOzPRo"
 34 |       },
 35 |       "source": [
 36 |         "import numpy as np\n",
 37 |         "n = np.random.randn(100)"
 38 |       ],
 39 |       "execution_count": null,
 40 |       "outputs": []
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "metadata": {
 45 |         "id": "oKT4vn4MwyLA",
 46 |         "colab": {
 47 |           "base_uri": "https://localhost:8080/",
 48 |           "height": 350
 49 |         },
 50 |         "outputId": "2b0aeb6e-b426-450b-964c-4374a09dfeb0"
 51 |       },
 52 |       "source": [
 53 |         "import matplotlib\n",
 54 |         "matplotlib.pyplot.hist(n)"
 55 |       ],
 56 |       "execution_count": null,
 57 |       "outputs": [
 58 |         {
 59 |           "output_type": "execute_result",
 60 |           "data": {
 61 |             "text/plain": [
 62 |               "(array([ 3.,  4.,  9., 11., 26., 19., 16., 10.,  1.,  1.]),\n",
 63 |               " array([-2.51307585, -1.99199295, -1.47091005, -0.94982715, -0.42874425,\n",
 64 |               "         0.09233865,  0.61342155,  1.13450445,  1.65558734,  2.17667024,\n",
 65 |               "         2.69775314]),\n",
 66 |               " <a list of 10 Patch objects>)"
 67 |             ]
 68 |           },
 69 |           "metadata": {},
 70 |           "execution_count": 8
 71 |         },
 72 |         {
 73 |           "output_type": "display_data",
 74 |           "data": {
 75 |             "image/png": "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\n",
 76 |             "text/plain": [
 77 |               "<Figure size 432x288 with 1 Axes>"
 78 |             ]
 79 |           },
 80 |           "metadata": {
 81 |             "needs_background": "light"
 82 |           }
 83 |         }
 84 |       ]
 85 |     },
 86 |     {
 87 |       "cell_type": "code",
 88 |       "metadata": {
 89 |         "colab": {
 90 |           "base_uri": "https://localhost:8080/",
 91 |           "height": 350
 92 |         },
 93 |         "id": "zQJ4LnbA-Hvz",
 94 |         "outputId": "bec7eeff-8cae-4c3d-f090-eaf78cdb5fa9"
 95 |       },
 96 |       "source": [
 97 |         "import matplotlib.pyplot\n",
 98 |         "matplotlib.pyplot.hist(n)"
 99 |       ],
100 |       "execution_count": null,
101 |       "outputs": [
102 |         {
103 |           "output_type": "execute_result",
104 |           "data": {
105 |             "text/plain": [
106 |               "(array([ 4.,  4.,  4.,  8., 19., 21., 16., 13.,  6.,  5.]),\n",
107 |               " array([-2.54804234, -2.0585933 , -1.56914426, -1.07969522, -0.59024618,\n",
108 |               "        -0.10079714,  0.3886519 ,  0.87810094,  1.36754998,  1.85699902,\n",
109 |               "         2.34644806]),\n",
110 |               " <a list of 10 Patch objects>)"
111 |             ]
112 |           },
113 |           "metadata": {},
114 |           "execution_count": 4
115 |         },
116 |         {
117 |           "output_type": "display_data",
118 |           "data": {
119 |             "image/png": "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\n",
120 |             "text/plain": [
121 |               "<Figure size 432x288 with 1 Axes>"
122 |             ]
123 |           },
124 |           "metadata": {
125 |             "needs_background": "light"
126 |           }
127 |         }
128 |       ]
129 |     },
130 |     {
131 |       "cell_type": "code",
132 |       "metadata": {
133 |         "id": "AWU6awi53c07",
134 |         "outputId": "9b49fb47-6bac-4887-8815-e5ed130f6c56",
135 |         "colab": {
136 |           "base_uri": "https://localhost:8080/",
137 |           "height": 350
138 |         }
139 |       },
140 |       "source": [
141 |         "import matplotlib.pyplot as plt\n",
142 |         "plt.hist(n)"
143 |       ],
144 |       "execution_count": null,
145 |       "outputs": [
146 |         {
147 |           "output_type": "execute_result",
148 |           "data": {
149 |             "text/plain": [
150 |               "(array([ 4.,  4.,  4.,  8., 19., 21., 16., 13.,  6.,  5.]),\n",
151 |               " array([-2.54804234, -2.0585933 , -1.56914426, -1.07969522, -0.59024618,\n",
152 |               "        -0.10079714,  0.3886519 ,  0.87810094,  1.36754998,  1.85699902,\n",
153 |               "         2.34644806]),\n",
154 |               " <a list of 10 Patch objects>)"
155 |             ]
156 |           },
157 |           "metadata": {},
158 |           "execution_count": 5
159 |         },
160 |         {
161 |           "output_type": "display_data",
162 |           "data": {
163 |             "image/png": "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\n",
164 |             "text/plain": [
165 |               "<Figure size 432x288 with 1 Axes>"
166 |             ]
167 |           },
168 |           "metadata": {
169 |             "needs_background": "light"
170 |           }
171 |         }
172 |       ]
173 |     },
174 |     {
175 |       "cell_type": "markdown",
176 |       "source": [
177 |         "## from import를 쓰면 import as처럼 축약해서 부를 수 있는 효과"
178 |       ],
179 |       "metadata": {
180 |         "id": "v1zA2xRWzpdu"
181 |       }
182 |     },
183 |     {
184 |       "cell_type": "code",
185 |       "metadata": {
186 |         "colab": {
187 |           "base_uri": "https://localhost:8080/",
188 |           "height": 350
189 |         },
190 |         "id": "moIjn3Kv-Hqy",
191 |         "outputId": "4b1e3880-1576-4b63-b4d0-192704221a8d"
192 |       },
193 |       "source": [
194 |         "from matplotlib import pyplot\n",
195 |         "pyplot.hist(n)"
196 |       ],
197 |       "execution_count": null,
198 |       "outputs": [
199 |         {
200 |           "output_type": "execute_result",
201 |           "data": {
202 |             "text/plain": [
203 |               "(array([ 3.,  4.,  9., 11., 26., 19., 16., 10.,  1.,  1.]),\n",
204 |               " array([-2.51307585, -1.99199295, -1.47091005, -0.94982715, -0.42874425,\n",
205 |               "         0.09233865,  0.61342155,  1.13450445,  1.65558734,  2.17667024,\n",
206 |               "         2.69775314]),\n",
207 |               " <a list of 10 Patch objects>)"
208 |             ]
209 |           },
210 |           "metadata": {},
211 |           "execution_count": 11
212 |         },
213 |         {
214 |           "output_type": "display_data",
215 |           "data": {
216 |             "image/png": "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\n",
217 |             "text/plain": [
218 |               "<Figure size 432x288 with 1 Axes>"
219 |             ]
220 |           },
221 |           "metadata": {
222 |             "needs_background": "light"
223 |           }
224 |         }
225 |       ]
226 |     },
227 |     {
228 |       "cell_type": "code",
229 |       "metadata": {
230 |         "colab": {
231 |           "base_uri": "https://localhost:8080/",
232 |           "height": 350
233 |         },
234 |         "id": "ORo259i7_Uqi",
235 |         "outputId": "8faa9ebd-66ff-4f86-e588-f114023e649d"
236 |       },
237 |       "source": [
238 |         "from matplotlib.pyplot import hist\n",
239 |         "hist(n)"
240 |       ],
241 |       "execution_count": null,
242 |       "outputs": [
243 |         {
244 |           "output_type": "execute_result",
245 |           "data": {
246 |             "text/plain": [
247 |               "(array([ 3.,  4.,  9., 11., 26., 19., 16., 10.,  1.,  1.]),\n",
248 |               " array([-2.51307585, -1.99199295, -1.47091005, -0.94982715, -0.42874425,\n",
249 |               "         0.09233865,  0.61342155,  1.13450445,  1.65558734,  2.17667024,\n",
250 |               "         2.69775314]),\n",
251 |               " <a list of 10 Patch objects>)"
252 |             ]
253 |           },
254 |           "metadata": {},
255 |           "execution_count": 14
256 |         },
257 |         {
258 |           "output_type": "display_data",
259 |           "data": {
260 |             "image/png": "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\n",
261 |             "text/plain": [
262 |               "<Figure size 432x288 with 1 Axes>"
263 |             ]
264 |           },
265 |           "metadata": {
266 |             "needs_background": "light"
267 |           }
268 |         }
269 |       ]
270 |     }
271 |   ]
272 | }


--------------------------------------------------------------------------------
/numpy_matplotlib.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "kernelspec": {
  6 |       "display_name": "Python 3",
  7 |       "language": "python",
  8 |       "name": "python3"
  9 |     },
 10 |     "language_info": {
 11 |       "codemirror_mode": {
 12 |         "name": "ipython",
 13 |         "version": 3
 14 |       },
 15 |       "file_extension": ".py",
 16 |       "mimetype": "text/x-python",
 17 |       "name": "python",
 18 |       "nbconvert_exporter": "python",
 19 |       "pygments_lexer": "ipython3",
 20 |       "version": "3.7.1"
 21 |     },
 22 |     "colab": {
 23 |       "name": "numpy_matplotlib.ipynb",
 24 |       "provenance": [],
 25 |       "include_colab_link": true
 26 |     }
 27 |   },
 28 |   "cells": [
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "view-in-github",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/numpy_matplotlib.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "markdown",
 41 |       "metadata": {
 42 |         "id": "JcCdLoe_4bY7"
 43 |       },
 44 |       "source": [
 45 |         "### create nd arrays (≈matrices)"
 46 |       ]
 47 |     },
 48 |     {
 49 |       "cell_type": "code",
 50 |       "metadata": {
 51 |         "id": "5ap7xyofShBB"
 52 |       },
 53 |       "source": [
 54 |         "import numpy as np"
 55 |       ],
 56 |       "execution_count": null,
 57 |       "outputs": []
 58 |     },
 59 |     {
 60 |       "cell_type": "code",
 61 |       "metadata": {
 62 |         "id": "2rqpSPZCShBC",
 63 |         "colab": {
 64 |           "base_uri": "https://localhost:8080/"
 65 |         },
 66 |         "outputId": "8a4d14a6-b816-432a-caae-902c8c640f33"
 67 |       },
 68 |       "source": [
 69 |         "x = np.array([1,2,3])\n",
 70 |         "print(x)\n",
 71 |         "print(type(x))\n",
 72 |         "print(x.shape)"
 73 |       ],
 74 |       "execution_count": null,
 75 |       "outputs": [
 76 |         {
 77 |           "output_type": "stream",
 78 |           "name": "stdout",
 79 |           "text": [
 80 |             "[1 2 3]\n",
 81 |             "<class 'numpy.ndarray'>\n",
 82 |             "(3,)\n"
 83 |           ]
 84 |         }
 85 |       ]
 86 |     },
 87 |     {
 88 |       "cell_type": "code",
 89 |       "metadata": {
 90 |         "id": "EjC88C75ShBC",
 91 |         "colab": {
 92 |           "base_uri": "https://localhost:8080/"
 93 |         },
 94 |         "outputId": "7143e4dd-2864-4b57-855e-56eb92a6bd75"
 95 |       },
 96 |       "source": [
 97 |         "x = np.arange(0,10,2)\n",
 98 |         "print(x)\n",
 99 |         "print(type(x))\n",
100 |         "print(x.shape)"
101 |       ],
102 |       "execution_count": null,
103 |       "outputs": [
104 |         {
105 |           "output_type": "stream",
106 |           "name": "stdout",
107 |           "text": [
108 |             "[0 2 4 6 8]\n",
109 |             "<class 'numpy.ndarray'>\n",
110 |             "(5,)\n"
111 |           ]
112 |         }
113 |       ]
114 |     },
115 |     {
116 |       "cell_type": "code",
117 |       "metadata": {
118 |         "id": "-MwkCEjTShBC",
119 |         "colab": {
120 |           "base_uri": "https://localhost:8080/"
121 |         },
122 |         "outputId": "11427674-886c-4f82-ca48-a1465f53223e"
123 |       },
124 |       "source": [
125 |         "x = np.linspace(0,10,6)\n",
126 |         "print(x)\n",
127 |         "print(type(x))\n",
128 |         "print(x.shape)"
129 |       ],
130 |       "execution_count": null,
131 |       "outputs": [
132 |         {
133 |           "output_type": "stream",
134 |           "name": "stdout",
135 |           "text": [
136 |             "[ 0.  2.  4.  6.  8. 10.]\n",
137 |             "<class 'numpy.ndarray'>\n",
138 |             "(6,)\n"
139 |           ]
140 |         }
141 |       ]
142 |     },
143 |     {
144 |       "cell_type": "code",
145 |       "metadata": {
146 |         "id": "tjk79SQ27fNv",
147 |         "colab": {
148 |           "base_uri": "https://localhost:8080/"
149 |         },
150 |         "outputId": "3ff3368b-6bb1-4664-933e-8504613780b1"
151 |       },
152 |       "source": [
153 |         "x = np.array([[1,2,3], [4,5,6]])\n",
154 |         "print(x)\n",
155 |         "print(type(x))\n",
156 |         "print(x.shape)"
157 |       ],
158 |       "execution_count": null,
159 |       "outputs": [
160 |         {
161 |           "output_type": "stream",
162 |           "name": "stdout",
163 |           "text": [
164 |             "[[1 2 3]\n",
165 |             " [4 5 6]]\n",
166 |             "<class 'numpy.ndarray'>\n",
167 |             "(2, 3)\n"
168 |           ]
169 |         }
170 |       ]
171 |     },
172 |     {
173 |       "cell_type": "code",
174 |       "metadata": {
175 |         "id": "C1ogg6iZ7yir",
176 |         "colab": {
177 |           "base_uri": "https://localhost:8080/"
178 |         },
179 |         "outputId": "baf27f4e-10a7-40bb-82f4-bf470d8de90c"
180 |       },
181 |       "source": [
182 |         "x = np.array([[[1,2,3], [4,5,6]], [[7,8,9], [10,11,12]]])\n",
183 |         "print(x)\n",
184 |         "print(type(x))\n",
185 |         "print(x.shape)"
186 |       ],
187 |       "execution_count": null,
188 |       "outputs": [
189 |         {
190 |           "output_type": "stream",
191 |           "name": "stdout",
192 |           "text": [
193 |             "[[[ 1  2  3]\n",
194 |             "  [ 4  5  6]]\n",
195 |             "\n",
196 |             " [[ 7  8  9]\n",
197 |             "  [10 11 12]]]\n",
198 |             "<class 'numpy.ndarray'>\n",
199 |             "(2, 2, 3)\n"
200 |           ]
201 |         }
202 |       ]
203 |     },
204 |     {
205 |       "cell_type": "code",
206 |       "metadata": {
207 |         "id": "efgeNHtD8ZlT",
208 |         "colab": {
209 |           "base_uri": "https://localhost:8080/"
210 |         },
211 |         "outputId": "047fb9dd-8c90-4dbf-9f12-35123bed1052"
212 |       },
213 |       "source": [
214 |         "x = np.ones([2, 2, 3])\n",
215 |         "print(x)\n",
216 |         "print(type(x))\n",
217 |         "print(x.shape)"
218 |       ],
219 |       "execution_count": null,
220 |       "outputs": [
221 |         {
222 |           "output_type": "stream",
223 |           "name": "stdout",
224 |           "text": [
225 |             "[[[1. 1. 1.]\n",
226 |             "  [1. 1. 1.]]\n",
227 |             "\n",
228 |             " [[1. 1. 1.]\n",
229 |             "  [1. 1. 1.]]]\n",
230 |             "<class 'numpy.ndarray'>\n",
231 |             "(2, 2, 3)\n"
232 |           ]
233 |         }
234 |       ]
235 |     },
236 |     {
237 |       "cell_type": "code",
238 |       "metadata": {
239 |         "id": "b_F-ccY08zzl",
240 |         "colab": {
241 |           "base_uri": "https://localhost:8080/"
242 |         },
243 |         "outputId": "d56dd759-bbd1-48d5-d208-4a2084fe1cec"
244 |       },
245 |       "source": [
246 |         "x = np.zeros([2, 2, 3])\n",
247 |         "print(x)\n",
248 |         "print(type(x))\n",
249 |         "print(x.shape)\n",
250 |         "x.reshape(3,2,2)"
251 |       ],
252 |       "execution_count": null,
253 |       "outputs": [
254 |         {
255 |           "output_type": "stream",
256 |           "name": "stdout",
257 |           "text": [
258 |             "[[[0. 0. 0.]\n",
259 |             "  [0. 0. 0.]]\n",
260 |             "\n",
261 |             " [[0. 0. 0.]\n",
262 |             "  [0. 0. 0.]]]\n",
263 |             "<class 'numpy.ndarray'>\n",
264 |             "(2, 2, 3)\n"
265 |           ]
266 |         },
267 |         {
268 |           "output_type": "execute_result",
269 |           "data": {
270 |             "text/plain": [
271 |               "array([[[0., 0.],\n",
272 |               "        [0., 0.]],\n",
273 |               "\n",
274 |               "       [[0., 0.],\n",
275 |               "        [0., 0.]],\n",
276 |               "\n",
277 |               "       [[0., 0.],\n",
278 |               "        [0., 0.]]])"
279 |             ]
280 |           },
281 |           "metadata": {},
282 |           "execution_count": 27
283 |         }
284 |       ]
285 |     },
286 |     {
287 |       "cell_type": "markdown",
288 |       "metadata": {
289 |         "id": "0hQuDJ5g9gpE"
290 |       },
291 |       "source": [
292 |         "### create random numbers"
293 |       ]
294 |     },
295 |     {
296 |       "cell_type": "code",
297 |       "source": [
298 |         "np.random.normal(0, 1, [2,3,4])"
299 |       ],
300 |       "metadata": {
301 |         "id": "pCVnElBgan70",
302 |         "outputId": "b06de733-6380-4b28-870b-61ddb453ad8a",
303 |         "colab": {
304 |           "base_uri": "https://localhost:8080/"
305 |         }
306 |       },
307 |       "execution_count": null,
308 |       "outputs": [
309 |         {
310 |           "output_type": "execute_result",
311 |           "data": {
312 |             "text/plain": [
313 |               "array([[[ 0.28610219, -0.7264868 , -0.50719279,  1.04473795],\n",
314 |               "        [-2.6269445 ,  0.80373735, -3.94639222, -0.11259909],\n",
315 |               "        [-0.271832  , -1.03545502, -0.11481238, -0.80041025]],\n",
316 |               "\n",
317 |               "       [[-0.94959226,  1.11390672, -0.47629241, -1.43499953],\n",
318 |               "        [ 1.01427831,  0.93052177, -0.51619177,  1.99354224],\n",
319 |               "        [-0.68265827,  0.97492243, -0.33173391,  0.0293543 ]]])"
320 |             ]
321 |           },
322 |           "metadata": {},
323 |           "execution_count": 5
324 |         }
325 |       ]
326 |     },
327 |     {
328 |       "cell_type": "code",
329 |       "metadata": {
330 |         "id": "QRatmkx79otL"
331 |       },
332 |       "source": [
333 |         "import matplotlib.pyplot as plt"
334 |       ],
335 |       "execution_count": null,
336 |       "outputs": []
337 |     },
338 |     {
339 |       "cell_type": "code",
340 |       "metadata": {
341 |         "id": "z1oOAB6dShBD",
342 |         "colab": {
343 |           "base_uri": "https://localhost:8080/",
344 |           "height": 775
345 |         },
346 |         "outputId": "db329362-279b-4166-f67a-c2d40d28a0a5"
347 |       },
348 |       "source": [
349 |         "x = np.random.normal(0, 1, 100)\n",
350 |         "print(x)\n",
351 |         "plt.hist(x, bins=10)"
352 |       ],
353 |       "execution_count": null,
354 |       "outputs": [
355 |         {
356 |           "output_type": "stream",
357 |           "name": "stdout",
358 |           "text": [
359 |             "[-1.30410375e+00  2.81304056e-01 -6.68872336e-01 -1.64063589e-01\n",
360 |             " -1.22158374e+00 -1.04653440e+00 -1.60460987e-01  7.24631153e-01\n",
361 |             "  1.66127871e-01 -7.27988751e-01  1.63277291e-01  5.09236054e-01\n",
362 |             " -1.98943416e-01 -1.46600112e+00  3.33479774e-01  1.60454386e-02\n",
363 |             " -8.55730740e-02 -5.11190740e-01 -1.08187184e+00 -1.81470665e+00\n",
364 |             "  9.02654879e-01  2.61542981e+00 -9.61451307e-01 -3.76245505e-01\n",
365 |             "  1.10573440e+00 -2.90233466e-01  9.83471186e-01 -9.17783309e-01\n",
366 |             " -6.85692679e-01  6.80350119e-01  3.02418935e-01 -6.67165338e-01\n",
367 |             " -1.13758564e+00  5.51581315e-01  4.08377252e-01  2.41734447e-01\n",
368 |             " -5.31870253e-01  2.46012558e+00  6.24542625e-02  3.04243491e+00\n",
369 |             "  1.75779308e+00  1.52166416e+00  1.23817111e+00 -7.97803951e-01\n",
370 |             "  3.90696889e-01 -6.19225451e-01  1.42304945e+00 -1.28138928e+00\n",
371 |             "  1.05451851e+00  2.99641058e-01 -7.78909324e-01  2.55184996e-01\n",
372 |             "  1.04213387e+00  4.01985262e+00  4.10956638e-02  9.72650554e-01\n",
373 |             "  4.38663065e-01 -1.24154905e+00  4.68534855e-01  6.32756145e-01\n",
374 |             "  8.42972324e-01 -1.69977165e+00  2.21320141e+00 -9.63472499e-01\n",
375 |             "  6.40706441e-01  1.74025374e+00 -1.57992412e-01 -2.12527110e+00\n",
376 |             " -3.41463121e-01 -5.23844235e-01  5.30544156e-01 -5.80984627e-01\n",
377 |             "  3.95706884e-03 -1.23409175e+00 -6.23110796e-01  8.33769043e-01\n",
378 |             " -1.39568461e+00 -2.23386940e-01  5.97831290e-01 -8.15726743e-02\n",
379 |             "  1.36391643e-01 -6.62688401e-01  4.45915336e-01 -8.10861118e-01\n",
380 |             " -2.91582565e-01  7.36019302e-01  1.59291496e+00 -8.13765805e-01\n",
381 |             " -1.15831445e+00 -3.46717461e-01  5.96119975e-01 -1.09709991e+00\n",
382 |             "  1.25127130e+00 -1.88386233e-01  1.79404880e+00  3.97428197e-01\n",
383 |             "  2.39494165e-01  4.14585331e-01  5.97507814e-01 -3.59018225e-01]\n"
384 |           ]
385 |         },
386 |         {
387 |           "output_type": "execute_result",
388 |           "data": {
389 |             "text/plain": [
390 |               "(array([ 3., 15., 21., 21., 22.,  9.,  4.,  3.,  1.,  1.]),\n",
391 |               " array([-2.1252711 , -1.51075872, -0.89624635, -0.28173398,  0.33277839,\n",
392 |               "         0.94729076,  1.56180314,  2.17631551,  2.79082788,  3.40534025,\n",
393 |               "         4.01985262]),\n",
394 |               " <a list of 10 Patch objects>)"
395 |             ]
396 |           },
397 |           "metadata": {},
398 |           "execution_count": 33
399 |         },
400 |         {
401 |           "output_type": "display_data",
402 |           "data": {
403 |             "image/png": "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\n",
404 |             "text/plain": [
405 |               "<Figure size 432x288 with 1 Axes>"
406 |             ]
407 |           },
408 |           "metadata": {
409 |             "needs_background": "light"
410 |           }
411 |         }
412 |       ]
413 |     },
414 |     {
415 |       "cell_type": "markdown",
416 |       "metadata": {
417 |         "id": "G0exvwBbShBE"
418 |       },
419 |       "source": [
420 |         "### generate a sine curve"
421 |       ]
422 |     },
423 |     {
424 |       "cell_type": "code",
425 |       "metadata": {
426 |         "id": "HgQmRXzMShBE",
427 |         "colab": {
428 |           "base_uri": "https://localhost:8080/",
429 |           "height": 282
430 |         },
431 |         "outputId": "a266cc4f-3373-43c7-d8a8-3290c8bf4248"
432 |       },
433 |       "source": [
434 |         "plt.figure()\n",
435 |         "\n",
436 |         "theta1 = np.linspace(0, 2*np.pi, 100)\n",
437 |         "s1 = np.sin(theta1)\n",
438 |         "theta2 = np.linspace(0, 2*np.pi, 100) + np.pi/2\n",
439 |         "s2 = np.sin(theta2)\n",
440 |         "\n",
441 |         "plt.subplot(2, 1, 1) # (rows, columns, panel number)\n",
442 |         "plt.plot(theta1, s1, '.')\n",
443 |         "plt.subplot(2, 1, 2) # (rows, columns, panel number)\n",
444 |         "plt.plot(theta2, s2, '.')"
445 |       ],
446 |       "execution_count": null,
447 |       "outputs": [
448 |         {
449 |           "output_type": "execute_result",
450 |           "data": {
451 |             "text/plain": [
452 |               "[<matplotlib.lines.Line2D at 0x7f3b55111d90>]"
453 |             ]
454 |           },
455 |           "metadata": {},
456 |           "execution_count": 8
457 |         },
458 |         {
459 |           "output_type": "display_data",
460 |           "data": {
461 |             "text/plain": [
462 |               "<Figure size 432x288 with 2 Axes>"
463 |             ],
464 |             "image/png": 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HsuSA46x+nAz71k8/ULUuIv1LC+9aD3m0sNjAYSXDNozXVqr0PESzRf/SnZs5dNPeVX+IC4sNxpo/9ZaWVj9eFfaLDe49Ost9R2dTWzMKdRHpZL3wxp1azTC8Yx5taFbk+3ZPDKWYjCbIYTnMW2/+ovPO6dqTSob9hvEaBqmtGYW6iLTLEt4bMs7ZDSNjcjlrJXkYFPBH1jkManp62kex/LB9AuLjB4+sac3UgFrNFOoiFbdeeLeywn1teI8qK8ys7u7Ta54PcWjWqIK8Xfv/qPVCPc9ZZREpruSquKKFd1KnII+qtTKotNZMp1Bv9dU1OSpSXq3i7vmXX1tpvXZrmxQ1ByoV5EnrhfpYzbinPquljCIl02nZ4PhYbWViMrbv90q1VnqR/Al91yPPqOUiUiKdWihjBtfvmWT7uWcVOrzVWulRq1Kvn5rj3qOzarmIlEAvLZR9uyei/X5WkHfQbd16suWi6lyk2JJVeOwtlE4U5F2k9dGTLRdV5yLFlVaFLzWc6/fsKHwLJSsFeY/SWi6qzkWKqVsVHnMLpRMFeUbJlouqc5FiqVIVnqQg74Oqc5HiqVoVnqQgH4Cqc5GwksdwJI+6rkIVnqQgH5Cqc5Ew0m42kzwmtuxVeJKCPCeqzkVGI60PvrDYYO7V+ZXvwap9rynIc6TqXGS4uvXBW+Fdxe8vBfkQdKvO87y9k0jVVLkP3o2CfEjSqvMN4zU2b9rIgcMnKv8XTySL5EFXVe2Dd6MgH7K0rf7DuIu2SFmlTWqWYVt9nhTkI9Cqzg8cPrFqckaToCKddZvUvPmKC0MPr1AU5CO0d9eWlY+FmgQV6Wy9SU1ZTUE+QpoEFemNJjWzUZCPWKdJ0L27tqzapaa/rFJFmtTsj4I8kGR13vqomJzQUatFqkaTmv1TkAeU3LygiVCpKk1qDk5BXhCaCJUq0qRmPhTkBaGJUKkiTWrmQ0FeINoNKlWhSc18KcgLSLtBpcw0qZm/WugBSLpLd27m5isuZO7V+VUTQEdOngk9NJGBJNspyUlNhXj/FOQF15oEHTNWtVnqp+ZCD00kk/qpOQ4cPrHSTmn9ndak5uDUWik4tVmkDNROGS5V5BFQm0Vip3bKcCnII6I2i8RG7ZTRUGslImqzSEzUThkdBXlkdLa5FJ223I+egjxS2tIvRaQt92EoyCOlLf1SRNpyH4aCPGLa0i9FoS33YeUS5GZ2NfBtYAw46O7fyOO60htNgkpImtQMb+Dlh2Y2BhwArgHeBtxoZm8b9LqSjdaaSyhaIx5eHuvI9wAn3P2ku88DdwPX5XBd6YPWmsuoaI14ceTRWtkOPJv4ehZ4Z/svMrP9wH6AycnJHF5W0qjNIqOgdkqxjGxnp7vf5u7T7j69bdu2Ub1sJanNIsOmdkqx5BHkzwE7El9PNJ+TwNRmkbypnVJMebRWHgXeYmYXsBzgNwAfy+G6MiC1WSRPaqcU18AVubsvAp8GHgaOAz929ycGva7kQ20WyYvaKcWVyzpyd38IeCiPa8lwJLf0a9OQZNFps4/aKcWhnZ0VoTaL9EPtlDjoPPIKUZtFslI7JQ4K8grSahbpprUypX5qbs3fFbVTisncfeQvOj097TMzMyN/XXldsu+pNou0tLdSDt20F0Bn3ReEmdXdfbr9efXIK0o3qJCkTjeDOHLyjFopEVCQV5xuUCG6GUT8FOQVpxtUiG4GET8FuegGFRWlm0GUh4JcVmiteXVofXi5aPmhrKK15tWg9eHloiCXVFprXk46vbCc1FqRVGqzlI/aKeWlIJeOOq01b7VZtN48Dp3WiLfaKRI/BbmsK+3kxPbdfwrzYtIa8WpQkMu6km2Wvbu2rJko03rz4tIa8WpQkEtPWm2WFp1tXmxaI14tCnLJTBOhxaZJzepRkEtfdOhW8WhSs7oU5DIQHbpVDJrUrDYFuQxEh24VgyY1q01BLgPToVvhaFJTQEEuOdIk6GhpUlNaFOSSK02CDp8mNaWdglyGQpOgw6FJTUmjIJeh6DYJquo8u7QqXJOa0qIgl6FJmwRVdZ5dtypck5oCCnIZAVXn/VEVLr1SkMtIqDrPRlW4ZKEgl5FSdd6dqnDph4JcRq6X6rxKa6KTm3paa+9VhUsW5u4jf9Hp6WmfmZkZ+etK8SQr0FZ1XgNqNaPhXvqWS7KFUrPl99xwGDO4fs+kqnBZxczq7j7d/rwqcgkqrTq3RKCVseXS+uHVfpMO3KnVDMNVhUsmCnIphLTt/WVquaS1T1rvJ3lGSqzvT8JSkEthJO9CdNF556xpucwvNLj1gWPRtVw6tU9a2+qTt9GL4f1I8SjIpZDK0HJJW4HS3j5pjb2I45d4KMil0GJrufSyAqUoY5XyGGjVipl9FPgq8FZgj7v3tBRFq1akX72ucgFWqvXk47yCMzlh2bp+Mry1AkWGYVirVo4BHwG+O+B1RHrSa8vlvqOzK9UwZqmVO6SH/XqP26vt1vWT4a0VKDJKAwW5ux8HMLN8RiPSo04tlw3jNQxeP6d7yQHHWT1ZmgzgrI9XTVgmrt8e3mqhyKiMrEduZvuB/QCTk5OjelkpsbRVLq3KObljFDOWltoq90QAZ32cDOzk9RXeEsq6PXIz+wVwXsp/+rK7P9D8Nf8J/JN65FIU3XrY7QGf9XF7YLeur/CWYeu7R+7u7xvOkESGp31JX6fKvd/H7YGtAJeQcjlrRRW5iMjwdarIawNe9MNmNgu8C/h3M3t4kOuJiEh2QU4/NLMXgVMjf+HOtgIvhR7EAGIfP8T/HmIfP8T/HmIfP6z/Hna6+7b2J4MEedGY2Uzax5VYxD5+iP89xD5+iP89xD5+6P89DNRaERGR8BTkIiKRU5Avuy30AAYU+/gh/vcQ+/gh/vcQ+/ihz/egHrmISORUkYuIRE5BLiISuUoHuZntMLPDZvakmT1hZreEHlMWZvYGM3vEzH7fHP/XQo+pH2Y2Zma/M7MHQ4+lH2b2tJk9bmaPmVl0W5bN7Fwzu8fMnjKz42b2rtBjysLMLmr+2bf++bOZfTb0uLIws39sfg8fM7O7zOwNmX5/lXvkZnY+cL67HzWzc4A68CF3fzLw0Hpiy+cHn+3ur5jZBuA3wC3ufiTw0DIxs88B08Cb3P3a0OPJysyeBqbdPcrNKGZ2J/Brdz9oZhuBTe7+cuhx9cPMxoDngHe6e5E2HXZkZttZ/t59m7u/ZmY/Bh5y9+/3eo1KV+Tu/oK7H20+/gtwHNgedlS982WvNL/c0Pwnqp/MZjYBfAA4GHosVWRmfwNcDtwO4O7zsYZ405XA/8QS4gnjwFlmNg5sAp7P8psrHeRJZjYFXAL8NuxIsmm2JR4DTgM/d/eoxg98C/g80Ag9kAE48DMzqzfP3Y/JBcCLwPea7a2DZnZ26EEN4AbgrtCDyMLdnwP+GXgGeAH4P3f/WZZrKMgBM3sjcC/wWXf/c+jxZOHuS+7+DmAC2GNmF4ceU6/M7FrgtLvXQ49lQO92993ANcDNZnZ56AFlMA7sBr7j7pcAfwW+EHZI/Wm2hT4I/FvosWRhZpuB61j+ofq3wNlm9oks16h8kDd7y/cCh9z9vtDj6Vfz4/Bh4OrQY8ngMuCDzR7z3cB7zeyHYYeUXbOiwt1PA/cDe8KOKJNZYDbxSe4eloM9RtcAR939j6EHktH7gP919xfdfQG4D/i7LBeodJA3JwtvB467+zdDjycrM9tmZuc2H58FXAU8FXZUvXP3L7r7hLtPsfyR+JfunqkSCc3Mzm5OlNNsSbyf5ZuSR8Hd/wA8a2YXNZ+6Eohisj/FjUTWVml6BthrZpuamXQly/N1PRvZPTsL6jLgk8DjzT4zwJfc/aGAY8rifODO5kx9Dfixu0e5hC9ibwbub96AfBz4kbv/NOyQMvsMcKjZmjgJfCrweDJr/hC9CviH0GPJyt1/a2b3AEeBReB3ZNyqX+nlhyIiZVDp1oqISBkoyEVEIqcgFxGJnIJcRCRyCnIRkcgpyEVEIqcgFxGJ3P8DTBmcjC+j74gAAAAASUVORK5CYII=\n"
465 |           },
466 |           "metadata": {
467 |             "needs_background": "light"
468 |           }
469 |         }
470 |       ]
471 |     },
472 |     {
473 |       "cell_type": "code",
474 |       "metadata": {
475 |         "id": "TuH489v9BvQ7",
476 |         "colab": {
477 |           "base_uri": "https://localhost:8080/"
478 |         },
479 |         "outputId": "80f021a2-50ec-4eb5-aed7-4faf9a306496"
480 |       },
481 |       "source": [
482 |         "np.corrcoef(s1,s2)"
483 |       ],
484 |       "execution_count": null,
485 |       "outputs": [
486 |         {
487 |           "output_type": "execute_result",
488 |           "data": {
489 |             "text/plain": [
490 |               "array([[1.00000000e+00, 2.41915449e-17],\n",
491 |               "       [2.41915449e-17, 1.00000000e+00]])"
492 |             ]
493 |           },
494 |           "metadata": {},
495 |           "execution_count": 11
496 |         }
497 |       ]
498 |     }
499 |   ]
500 | }


--------------------------------------------------------------------------------
/pandas.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "pandas.ipynb",
  7 |       "provenance": [],
  8 |       "authorship_tag": "ABX9TyMt4W/bQNRo+t800DtLn3Ib",
  9 |       "include_colab_link": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "python3",
 13 |       "display_name": "Python 3"
 14 |     },
 15 |     "language_info": {
 16 |       "name": "python"
 17 |     }
 18 |   },
 19 |   "cells": [
 20 |     {
 21 |       "cell_type": "markdown",
 22 |       "metadata": {
 23 |         "id": "view-in-github",
 24 |         "colab_type": "text"
 25 |       },
 26 |       "source": [
 27 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/pandas.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "markdown",
 32 |       "source": [
 33 |         "### Importing libraries"
 34 |       ],
 35 |       "metadata": {
 36 |         "id": "t6YRFy8vHw3f"
 37 |       }
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "source": [
 42 |         "import pandas as pd "
 43 |       ],
 44 |       "metadata": {
 45 |         "id": "km1FLv12HwfE"
 46 |       },
 47 |       "execution_count": null,
 48 |       "outputs": []
 49 |     },
 50 |     {
 51 |       "cell_type": "markdown",
 52 |       "source": [
 53 |         "### Loading csv file"
 54 |       ],
 55 |       "metadata": {
 56 |         "id": "l9hcO5nhFNBs"
 57 |       }
 58 |     },
 59 |     {
 60 |       "cell_type": "code",
 61 |       "execution_count": null,
 62 |       "metadata": {
 63 |         "id": "IlcjthG78XD5"
 64 |       },
 65 |       "outputs": [],
 66 |       "source": [
 67 |         "df = pd.read_csv(\"https://download.mlcc.google.com/mledu-datasets/california_housing_train.csv\", sep=\",\")\n",
 68 |         "df"
 69 |       ]
 70 |     },
 71 |     {
 72 |       "cell_type": "code",
 73 |       "source": [
 74 |         "print(df.size); print(df.ndim); print(df.shape)"
 75 |       ],
 76 |       "metadata": {
 77 |         "id": "TccKwiUEB3S-"
 78 |       },
 79 |       "execution_count": null,
 80 |       "outputs": []
 81 |     },
 82 |     {
 83 |       "cell_type": "code",
 84 |       "source": [
 85 |         "df.describe()"
 86 |       ],
 87 |       "metadata": {
 88 |         "id": "kP3NoboG8iAs"
 89 |       },
 90 |       "execution_count": null,
 91 |       "outputs": []
 92 |     },
 93 |     {
 94 |       "cell_type": "markdown",
 95 |       "source": [
 96 |         "### Saving to CSV"
 97 |       ],
 98 |       "metadata": {
 99 |         "id": "YDgsE5VsG7EK"
100 |       }
101 |     },
102 |     {
103 |       "cell_type": "code",
104 |       "source": [
105 |         "df.to_csv('california_housing_train.csv')"
106 |       ],
107 |       "metadata": {
108 |         "id": "p0HH9Mf6G6xp"
109 |       },
110 |       "execution_count": null,
111 |       "outputs": []
112 |     },
113 |     {
114 |       "cell_type": "markdown",
115 |       "metadata": {
116 |         "id": "NysUV4ad32LJ"
117 |       },
118 |       "source": [
119 |         "### Constructing a DataFrame using dict"
120 |       ]
121 |     },
122 |     {
123 |       "cell_type": "code",
124 |       "execution_count": null,
125 |       "metadata": {
126 |         "id": "CI7tYc4Q32LK"
127 |       },
128 |       "outputs": [],
129 |       "source": [
130 |         "# Some input data - a small sample of the iris dataset\n",
131 |         "data = {'sepal_length': [6.9, 6.9, 4.8, 5.4, 4.6],\n",
132 |         "        'sepal_width': [3.2, 3.1, 3.4, 3.0, 3.6],\n",
133 |         "        'petal_length': [5.7, 5.1, 1.9, 4.5, 1.0],\n",
134 |         "        'petal_width': [2.3, 2.3, 0.2, 1.5, 0.2],\n",
135 |         "        'species': ['virginica', 'virginica', 'setosa', 'versicolor', 'setosa']}\n",
136 |         "\n",
137 |         "df = pd.DataFrame(data)\n",
138 |         "df "
139 |       ]
140 |     },
141 |     {
142 |       "cell_type": "markdown",
143 |       "source": [
144 |         "### Constructing a DataFrame using pd.Series"
145 |       ],
146 |       "metadata": {
147 |         "id": "4xktp6-HLv2W"
148 |       }
149 |     },
150 |     {
151 |       "cell_type": "code",
152 |       "source": [
153 |         "sepal_length = pd.Series([6.9, 6.9, 4.8, 5.4, 4.6])\n",
154 |         "sepal_width = pd.Series([3.2, 3.1, 3.4, 3.0, 3.6])\n",
155 |         "petal_length = pd.Series([5.7, 5.1, 1.9, 4.5, 1.0])\n",
156 |         "petal_width = pd.Series([2.3, 2.3, 0.2, 1.5, 0.2])\n",
157 |         "species = pd.Series(['virginica', 'virginica', 'setosa', 'versicolor', 'setosa'])\n",
158 |         "\n",
159 |         "df = pd.DataFrame({'sepal_length': sepal_length, 'sepal_width': sepal_width, 'petal_length': petal_length, 'petal_width': petal_width, 'SPECIES': species})\n",
160 |         "df"
161 |       ],
162 |       "metadata": {
163 |         "id": "tOXBW0W4_c3e"
164 |       },
165 |       "execution_count": null,
166 |       "outputs": []
167 |     },
168 |     {
169 |       "cell_type": "markdown",
170 |       "metadata": {
171 |         "id": "btyJ4nIB32LM"
172 |       },
173 |       "source": [
174 |         "### Changing column names"
175 |       ]
176 |     },
177 |     {
178 |       "cell_type": "code",
179 |       "execution_count": null,
180 |       "metadata": {
181 |         "id": "iBsTsL0J32LN"
182 |       },
183 |       "outputs": [],
184 |       "source": [
185 |         "# Change the columns\n",
186 |         "df.columns = ['sepal_length', 'sepal_width', 'petal_length', 'petal_width', 'species']\n",
187 |         "df"
188 |       ]
189 |     },
190 |     {
191 |       "cell_type": "markdown",
192 |       "metadata": {
193 |         "id": "8NRfX2U332LO"
194 |       },
195 |       "source": [
196 |         "### Accessing columns"
197 |       ]
198 |     },
199 |     {
200 |       "cell_type": "code",
201 |       "execution_count": null,
202 |       "metadata": {
203 |         "id": "DBAAnC1I32LO"
204 |       },
205 |       "outputs": [],
206 |       "source": [
207 |         "# Access one column \n",
208 |         "df.loc[:, 'sepal_length']"
209 |       ]
210 |     },
211 |     {
212 |       "cell_type": "code",
213 |       "execution_count": null,
214 |       "metadata": {
215 |         "id": "gDBR--u832LO"
216 |       },
217 |       "outputs": [],
218 |       "source": [
219 |         "# Access one column (short-hand)\n",
220 |         "df['sepal_length']"
221 |       ]
222 |     },
223 |     {
224 |       "cell_type": "code",
225 |       "source": [
226 |         "# Access one column (shortest-hand)\n",
227 |         "df.sepal_length"
228 |       ],
229 |       "metadata": {
230 |         "id": "ix7c1jzFQRTe"
231 |       },
232 |       "execution_count": null,
233 |       "outputs": []
234 |     },
235 |     {
236 |       "cell_type": "code",
237 |       "execution_count": null,
238 |       "metadata": {
239 |         "id": "DaXVg9Gs32LP"
240 |       },
241 |       "outputs": [],
242 |       "source": [
243 |         "# Access two columns\n",
244 |         "df.loc[:, ['sepal_length', 'sepal_width']]"
245 |       ]
246 |     },
247 |     {
248 |       "cell_type": "code",
249 |       "execution_count": null,
250 |       "metadata": {
251 |         "id": "zyXCD9yl32LP"
252 |       },
253 |       "outputs": [],
254 |       "source": [
255 |         "# Access two columns (short-hand)\n",
256 |         "df[['sepal_length', 'sepal_width']]"
257 |       ]
258 |     },
259 |     {
260 |       "cell_type": "markdown",
261 |       "metadata": {
262 |         "id": "G7IgmghJ32LP"
263 |       },
264 |       "source": [
265 |         "### Accessing rows"
266 |       ]
267 |     },
268 |     {
269 |       "cell_type": "code",
270 |       "execution_count": null,
271 |       "metadata": {
272 |         "id": "jS7CLQR532LP"
273 |       },
274 |       "outputs": [],
275 |       "source": [
276 |         "# Access one row \n",
277 |         "df.loc[3, :]"
278 |       ]
279 |     },
280 |     {
281 |       "cell_type": "code",
282 |       "execution_count": null,
283 |       "metadata": {
284 |         "id": "_eZBMb0q32LP"
285 |       },
286 |       "outputs": [],
287 |       "source": [
288 |         "# Access two rows\n",
289 |         "df.loc[[3, 0], :]"
290 |       ]
291 |     },
292 |     {
293 |       "cell_type": "code",
294 |       "execution_count": null,
295 |       "metadata": {
296 |         "id": "DtxCf_Rf32LQ"
297 |       },
298 |       "outputs": [],
299 |       "source": [
300 |         "# Access a range of rows\n",
301 |         "df.loc[2:5, :]"
302 |       ]
303 |     },
304 |     {
305 |       "cell_type": "code",
306 |       "execution_count": null,
307 |       "metadata": {
308 |         "id": "Zm8edm9Z32LQ"
309 |       },
310 |       "outputs": [],
311 |       "source": [
312 |         "# Filter keeping only rows with `sepal_length` > 5\n",
313 |         "df[df.sepal_length > 5]"
314 |       ]
315 |     },
316 |     {
317 |       "cell_type": "markdown",
318 |       "source": [
319 |         "### Accessing a cell"
320 |       ],
321 |       "metadata": {
322 |         "id": "lcXgjiuwM4SR"
323 |       }
324 |     },
325 |     {
326 |       "cell_type": "code",
327 |       "source": [
328 |         "df.loc[:, 'sepal_length'][4]"
329 |       ],
330 |       "metadata": {
331 |         "id": "an1xbTN4Q1vA",
332 |         "outputId": "459d6495-fb29-49fc-9ff3-81b08df472e8",
333 |         "colab": {
334 |           "base_uri": "https://localhost:8080/"
335 |         }
336 |       },
337 |       "execution_count": null,
338 |       "outputs": [
339 |         {
340 |           "output_type": "execute_result",
341 |           "data": {
342 |             "text/plain": [
343 |               "4.6"
344 |             ]
345 |           },
346 |           "metadata": {},
347 |           "execution_count": 68
348 |         }
349 |       ]
350 |     },
351 |     {
352 |       "cell_type": "code",
353 |       "source": [
354 |         "df['sepal_length'][4]"
355 |       ],
356 |       "metadata": {
357 |         "id": "PJw7-zu4M4or",
358 |         "outputId": "3d0bb0e1-ff8b-48ff-ab3c-f459b43b7942",
359 |         "colab": {
360 |           "base_uri": "https://localhost:8080/"
361 |         }
362 |       },
363 |       "execution_count": null,
364 |       "outputs": [
365 |         {
366 |           "output_type": "execute_result",
367 |           "data": {
368 |             "text/plain": [
369 |               "4.6"
370 |             ]
371 |           },
372 |           "metadata": {},
373 |           "execution_count": 67
374 |         }
375 |       ]
376 |     },
377 |     {
378 |       "cell_type": "code",
379 |       "source": [
380 |         "df.sepal_length[4]"
381 |       ],
382 |       "metadata": {
383 |         "id": "HkMdk-H-Q1lL",
384 |         "outputId": "1fb2ebe4-9d50-4c4a-d123-e0c2dd1d834f",
385 |         "colab": {
386 |           "base_uri": "https://localhost:8080/"
387 |         }
388 |       },
389 |       "execution_count": null,
390 |       "outputs": [
391 |         {
392 |           "output_type": "execute_result",
393 |           "data": {
394 |             "text/plain": [
395 |               "4.6"
396 |             ]
397 |           },
398 |           "metadata": {},
399 |           "execution_count": 66
400 |         }
401 |       ]
402 |     },
403 |     {
404 |       "cell_type": "markdown",
405 |       "metadata": {
406 |         "id": "26641I8q32LR"
407 |       },
408 |       "source": [
409 |         "### Adding/Removing new columns"
410 |       ]
411 |     },
412 |     {
413 |       "cell_type": "code",
414 |       "execution_count": null,
415 |       "metadata": {
416 |         "id": "-EZ10v9s32LR"
417 |       },
418 |       "outputs": [],
419 |       "source": [
420 |         "df['new_column'] = 1\n",
421 |         "df"
422 |       ]
423 |     },
424 |     {
425 |       "cell_type": "code",
426 |       "execution_count": null,
427 |       "metadata": {
428 |         "id": "O2P8oNUm32LR"
429 |       },
430 |       "outputs": [],
431 |       "source": [
432 |         "df['new_column'] = [1, 2, 3, 4, 5]\n",
433 |         "df"
434 |       ]
435 |     },
436 |     {
437 |       "cell_type": "code",
438 |       "source": [
439 |         "df = df.drop('new_column', axis=1) # axis=1: column\n",
440 |         "df"
441 |       ],
442 |       "metadata": {
443 |         "id": "WTTZwNEZSS2C"
444 |       },
445 |       "execution_count": null,
446 |       "outputs": []
447 |     },
448 |     {
449 |       "cell_type": "markdown",
450 |       "source": [
451 |         "### Adding/Removing new rows"
452 |       ],
453 |       "metadata": {
454 |         "id": "f5sF36S2Tob3"
455 |       }
456 |     },
457 |     {
458 |       "cell_type": "code",
459 |       "source": [
460 |         "df.loc[5, :] = ''\n",
461 |         "df"
462 |       ],
463 |       "metadata": {
464 |         "id": "BGdOrz-kRbhv"
465 |       },
466 |       "execution_count": null,
467 |       "outputs": []
468 |     },
469 |     {
470 |       "cell_type": "code",
471 |       "source": [
472 |         "df = df.drop(5, axis=0) # axis=0: row\n",
473 |         "df"
474 |       ],
475 |       "metadata": {
476 |         "id": "0q5Oov0CS_aZ"
477 |       },
478 |       "execution_count": null,
479 |       "outputs": []
480 |     }
481 |   ]
482 | }


--------------------------------------------------------------------------------
/regression.ipynb:
--------------------------------------------------------------------------------
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  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyPDkP9CJ6xeHWvHhhmjzSKZ",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/regression.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "EJYv5IDxwcnS"
 33 |       },
 34 |       "source": [
 35 |         "## Loading a dataset"
 36 |       ]
 37 |     },
 38 |     {
 39 |       "cell_type": "code",
 40 |       "execution_count": 1,
 41 |       "metadata": {
 42 |         "id": "UbhqZ2xOwcnX"
 43 |       },
 44 |       "outputs": [],
 45 |       "source": [
 46 |         "import pandas as pd"
 47 |       ]
 48 |     },
 49 |     {
 50 |       "cell_type": "markdown",
 51 |       "metadata": {
 52 |         "id": "01oxANUgwcnX"
 53 |       },
 54 |       "source": [
 55 |         "### computer hardware dataset"
 56 |       ]
 57 |     },
 58 |     {
 59 |       "cell_type": "code",
 60 |       "execution_count": 2,
 61 |       "metadata": {
 62 |         "id": "G6LdpzV9wcnZ",
 63 |         "outputId": "025a729f-10c9-48d3-fa78-1930e6b797c8",
 64 |         "colab": {
 65 |           "base_uri": "https://localhost:8080/",
 66 |           "height": 206
 67 |         }
 68 |       },
 69 |       "outputs": [
 70 |         {
 71 |           "output_type": "execute_result",
 72 |           "data": {
 73 |             "text/plain": [
 74 |               "   vendor  model  MYCT  MMIN   MMAX  CACH  CHMIN  CHMAX  PRP  label\n",
 75 |               "0       0     29   125   256   6000   256     16    128  198    199\n",
 76 |               "1       1     62    29  8000  32000    32      8     32  269    253\n",
 77 |               "2       1     63    29  8000  32000    32      8     32  220    253\n",
 78 |               "3       1     64    29  8000  32000    32      8     32  172    253\n",
 79 |               "4       1     65    29  8000  16000    32      8     16  132    132"
 80 |             ],
 81 |             "text/html": [
 82 |               "\n",
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 84 |               "    <div class=\"colab-df-container\">\n",
 85 |               "      <div>\n",
 86 |               "<style scoped>\n",
 87 |               "    .dataframe tbody tr th:only-of-type {\n",
 88 |               "        vertical-align: middle;\n",
 89 |               "    }\n",
 90 |               "\n",
 91 |               "    .dataframe tbody tr th {\n",
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 95 |               "    .dataframe thead th {\n",
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 98 |               "</style>\n",
 99 |               "<table border=\"1\" class=\"dataframe\">\n",
100 |               "  <thead>\n",
101 |               "    <tr style=\"text-align: right;\">\n",
102 |               "      <th></th>\n",
103 |               "      <th>vendor</th>\n",
104 |               "      <th>model</th>\n",
105 |               "      <th>MYCT</th>\n",
106 |               "      <th>MMIN</th>\n",
107 |               "      <th>MMAX</th>\n",
108 |               "      <th>CACH</th>\n",
109 |               "      <th>CHMIN</th>\n",
110 |               "      <th>CHMAX</th>\n",
111 |               "      <th>PRP</th>\n",
112 |               "      <th>label</th>\n",
113 |               "    </tr>\n",
114 |               "  </thead>\n",
115 |               "  <tbody>\n",
116 |               "    <tr>\n",
117 |               "      <th>0</th>\n",
118 |               "      <td>0</td>\n",
119 |               "      <td>29</td>\n",
120 |               "      <td>125</td>\n",
121 |               "      <td>256</td>\n",
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124 |               "      <td>16</td>\n",
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126 |               "      <td>198</td>\n",
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129 |               "    <tr>\n",
130 |               "      <th>1</th>\n",
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132 |               "      <td>62</td>\n",
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134 |               "      <td>8000</td>\n",
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139 |               "      <td>269</td>\n",
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142 |               "    <tr>\n",
143 |               "      <th>2</th>\n",
144 |               "      <td>1</td>\n",
145 |               "      <td>63</td>\n",
146 |               "      <td>29</td>\n",
147 |               "      <td>8000</td>\n",
148 |               "      <td>32000</td>\n",
149 |               "      <td>32</td>\n",
150 |               "      <td>8</td>\n",
151 |               "      <td>32</td>\n",
152 |               "      <td>220</td>\n",
153 |               "      <td>253</td>\n",
154 |               "    </tr>\n",
155 |               "    <tr>\n",
156 |               "      <th>3</th>\n",
157 |               "      <td>1</td>\n",
158 |               "      <td>64</td>\n",
159 |               "      <td>29</td>\n",
160 |               "      <td>8000</td>\n",
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163 |               "      <td>8</td>\n",
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182 |               "</table>\n",
183 |               "</div>\n",
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187 |               "        \n",
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199 |               "      gap: 12px;\n",
200 |               "    }\n",
201 |               "\n",
202 |               "    .colab-df-convert {\n",
203 |               "      background-color: #E8F0FE;\n",
204 |               "      border: none;\n",
205 |               "      border-radius: 50%;\n",
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207 |               "      display: none;\n",
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209 |               "      height: 32px;\n",
210 |               "      padding: 0 0 0 0;\n",
211 |               "      width: 32px;\n",
212 |               "    }\n",
213 |               "\n",
214 |               "    .colab-df-convert:hover {\n",
215 |               "      background-color: #E2EBFA;\n",
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217 |               "      fill: #174EA6;\n",
218 |               "    }\n",
219 |               "\n",
220 |               "    [theme=dark] .colab-df-convert {\n",
221 |               "      background-color: #3B4455;\n",
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223 |               "    }\n",
224 |               "\n",
225 |               "    [theme=dark] .colab-df-convert:hover {\n",
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231 |               "  </style>\n",
232 |               "\n",
233 |               "      <script>\n",
234 |               "        const buttonEl =\n",
235 |               "          document.querySelector('#df-1fd5930c-4388-49fe-a563-7667283caa47 button.colab-df-convert');\n",
236 |               "        buttonEl.style.display =\n",
237 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
238 |               "\n",
239 |               "        async function convertToInteractive(key) {\n",
240 |               "          const element = document.querySelector('#df-1fd5930c-4388-49fe-a563-7667283caa47');\n",
241 |               "          const dataTable =\n",
242 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
243 |               "                                                     [key], {});\n",
244 |               "          if (!dataTable) return;\n",
245 |               "\n",
246 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
247 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
248 |               "            + ' to learn more about interactive tables.';\n",
249 |               "          element.innerHTML = '';\n",
250 |               "          dataTable['output_type'] = 'display_data';\n",
251 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
252 |               "          const docLink = document.createElement('div');\n",
253 |               "          docLink.innerHTML = docLinkHtml;\n",
254 |               "          element.appendChild(docLink);\n",
255 |               "        }\n",
256 |               "      </script>\n",
257 |               "    </div>\n",
258 |               "  </div>\n",
259 |               "  "
260 |             ]
261 |           },
262 |           "metadata": {},
263 |           "execution_count": 2
264 |         }
265 |       ],
266 |       "source": [
267 |         "from sklearn.preprocessing import LabelEncoder\n",
268 |         "le = LabelEncoder()\n",
269 |         "computer_hardware = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/cpu-performance/machine.data', header=None, names=['vendor', 'model', 'MYCT', 'MMIN', 'MMAX', 'CACH', 'CHMIN', 'CHMAX', 'PRP', 'label'])\n",
270 |         "computer_hardware['vendor'] = le.fit_transform(computer_hardware['vendor'])\n",
271 |         "computer_hardware['model'] = le.fit_transform(computer_hardware['model'])\n",
272 |         "computer_hardware.head()"
273 |       ]
274 |     },
275 |     {
276 |       "cell_type": "markdown",
277 |       "metadata": {
278 |         "id": "V-quACWgzeVa"
279 |       },
280 |       "source": [
281 |         "### parkinsons dataset"
282 |       ]
283 |     },
284 |     {
285 |       "cell_type": "code",
286 |       "execution_count": 3,
287 |       "metadata": {
288 |         "id": "LpnoApn2wcna",
289 |         "outputId": "0218d7bc-0205-4711-9455-8eb8d09514ea",
290 |         "colab": {
291 |           "base_uri": "https://localhost:8080/",
292 |           "height": 300
293 |         }
294 |       },
295 |       "outputs": [
296 |         {
297 |           "output_type": "execute_result",
298 |           "data": {
299 |             "text/plain": [
300 |               "   subject#  age  sex  test_time   label  total_UPDRS  Jitter(%)  Jitter(Abs)  \\\n",
301 |               "0         1   72    0     5.6431  28.199       34.398    0.00662     0.000034   \n",
302 |               "1         1   72    0    12.6660  28.447       34.894    0.00300     0.000017   \n",
303 |               "2         1   72    0    19.6810  28.695       35.389    0.00481     0.000025   \n",
304 |               "3         1   72    0    25.6470  28.905       35.810    0.00528     0.000027   \n",
305 |               "4         1   72    0    33.6420  29.187       36.375    0.00335     0.000020   \n",
306 |               "\n",
307 |               "   Jitter:RAP  Jitter:PPQ5  ...  Shimmer(dB)  Shimmer:APQ3  Shimmer:APQ5  \\\n",
308 |               "0     0.00401      0.00317  ...        0.230       0.01438       0.01309   \n",
309 |               "1     0.00132      0.00150  ...        0.179       0.00994       0.01072   \n",
310 |               "2     0.00205      0.00208  ...        0.181       0.00734       0.00844   \n",
311 |               "3     0.00191      0.00264  ...        0.327       0.01106       0.01265   \n",
312 |               "4     0.00093      0.00130  ...        0.176       0.00679       0.00929   \n",
313 |               "\n",
314 |               "   Shimmer:APQ11  Shimmer:DDA       NHR     HNR     RPDE      DFA      PPE  \n",
315 |               "0        0.01662      0.04314  0.014290  21.640  0.41888  0.54842  0.16006  \n",
316 |               "1        0.01689      0.02982  0.011112  27.183  0.43493  0.56477  0.10810  \n",
317 |               "2        0.01458      0.02202  0.020220  23.047  0.46222  0.54405  0.21014  \n",
318 |               "3        0.01963      0.03317  0.027837  24.445  0.48730  0.57794  0.33277  \n",
319 |               "4        0.01819      0.02036  0.011625  26.126  0.47188  0.56122  0.19361  \n",
320 |               "\n",
321 |               "[5 rows x 22 columns]"
322 |             ],
323 |             "text/html": [
324 |               "\n",
325 |               "  <div id=\"df-6bc104d2-48d3-4923-b679-a1a63a3eefaa\">\n",
326 |               "    <div class=\"colab-df-container\">\n",
327 |               "      <div>\n",
328 |               "<style scoped>\n",
329 |               "    .dataframe tbody tr th:only-of-type {\n",
330 |               "        vertical-align: middle;\n",
331 |               "    }\n",
332 |               "\n",
333 |               "    .dataframe tbody tr th {\n",
334 |               "        vertical-align: top;\n",
335 |               "    }\n",
336 |               "\n",
337 |               "    .dataframe thead th {\n",
338 |               "        text-align: right;\n",
339 |               "    }\n",
340 |               "</style>\n",
341 |               "<table border=\"1\" class=\"dataframe\">\n",
342 |               "  <thead>\n",
343 |               "    <tr style=\"text-align: right;\">\n",
344 |               "      <th></th>\n",
345 |               "      <th>subject#</th>\n",
346 |               "      <th>age</th>\n",
347 |               "      <th>sex</th>\n",
348 |               "      <th>test_time</th>\n",
349 |               "      <th>label</th>\n",
350 |               "      <th>total_UPDRS</th>\n",
351 |               "      <th>Jitter(%)</th>\n",
352 |               "      <th>Jitter(Abs)</th>\n",
353 |               "      <th>Jitter:RAP</th>\n",
354 |               "      <th>Jitter:PPQ5</th>\n",
355 |               "      <th>...</th>\n",
356 |               "      <th>Shimmer(dB)</th>\n",
357 |               "      <th>Shimmer:APQ3</th>\n",
358 |               "      <th>Shimmer:APQ5</th>\n",
359 |               "      <th>Shimmer:APQ11</th>\n",
360 |               "      <th>Shimmer:DDA</th>\n",
361 |               "      <th>NHR</th>\n",
362 |               "      <th>HNR</th>\n",
363 |               "      <th>RPDE</th>\n",
364 |               "      <th>DFA</th>\n",
365 |               "      <th>PPE</th>\n",
366 |               "    </tr>\n",
367 |               "  </thead>\n",
368 |               "  <tbody>\n",
369 |               "    <tr>\n",
370 |               "      <th>0</th>\n",
371 |               "      <td>1</td>\n",
372 |               "      <td>72</td>\n",
373 |               "      <td>0</td>\n",
374 |               "      <td>5.6431</td>\n",
375 |               "      <td>28.199</td>\n",
376 |               "      <td>34.398</td>\n",
377 |               "      <td>0.00662</td>\n",
378 |               "      <td>0.000034</td>\n",
379 |               "      <td>0.00401</td>\n",
380 |               "      <td>0.00317</td>\n",
381 |               "      <td>...</td>\n",
382 |               "      <td>0.230</td>\n",
383 |               "      <td>0.01438</td>\n",
384 |               "      <td>0.01309</td>\n",
385 |               "      <td>0.01662</td>\n",
386 |               "      <td>0.04314</td>\n",
387 |               "      <td>0.014290</td>\n",
388 |               "      <td>21.640</td>\n",
389 |               "      <td>0.41888</td>\n",
390 |               "      <td>0.54842</td>\n",
391 |               "      <td>0.16006</td>\n",
392 |               "    </tr>\n",
393 |               "    <tr>\n",
394 |               "      <th>1</th>\n",
395 |               "      <td>1</td>\n",
396 |               "      <td>72</td>\n",
397 |               "      <td>0</td>\n",
398 |               "      <td>12.6660</td>\n",
399 |               "      <td>28.447</td>\n",
400 |               "      <td>34.894</td>\n",
401 |               "      <td>0.00300</td>\n",
402 |               "      <td>0.000017</td>\n",
403 |               "      <td>0.00132</td>\n",
404 |               "      <td>0.00150</td>\n",
405 |               "      <td>...</td>\n",
406 |               "      <td>0.179</td>\n",
407 |               "      <td>0.00994</td>\n",
408 |               "      <td>0.01072</td>\n",
409 |               "      <td>0.01689</td>\n",
410 |               "      <td>0.02982</td>\n",
411 |               "      <td>0.011112</td>\n",
412 |               "      <td>27.183</td>\n",
413 |               "      <td>0.43493</td>\n",
414 |               "      <td>0.56477</td>\n",
415 |               "      <td>0.10810</td>\n",
416 |               "    </tr>\n",
417 |               "    <tr>\n",
418 |               "      <th>2</th>\n",
419 |               "      <td>1</td>\n",
420 |               "      <td>72</td>\n",
421 |               "      <td>0</td>\n",
422 |               "      <td>19.6810</td>\n",
423 |               "      <td>28.695</td>\n",
424 |               "      <td>35.389</td>\n",
425 |               "      <td>0.00481</td>\n",
426 |               "      <td>0.000025</td>\n",
427 |               "      <td>0.00205</td>\n",
428 |               "      <td>0.00208</td>\n",
429 |               "      <td>...</td>\n",
430 |               "      <td>0.181</td>\n",
431 |               "      <td>0.00734</td>\n",
432 |               "      <td>0.00844</td>\n",
433 |               "      <td>0.01458</td>\n",
434 |               "      <td>0.02202</td>\n",
435 |               "      <td>0.020220</td>\n",
436 |               "      <td>23.047</td>\n",
437 |               "      <td>0.46222</td>\n",
438 |               "      <td>0.54405</td>\n",
439 |               "      <td>0.21014</td>\n",
440 |               "    </tr>\n",
441 |               "    <tr>\n",
442 |               "      <th>3</th>\n",
443 |               "      <td>1</td>\n",
444 |               "      <td>72</td>\n",
445 |               "      <td>0</td>\n",
446 |               "      <td>25.6470</td>\n",
447 |               "      <td>28.905</td>\n",
448 |               "      <td>35.810</td>\n",
449 |               "      <td>0.00528</td>\n",
450 |               "      <td>0.000027</td>\n",
451 |               "      <td>0.00191</td>\n",
452 |               "      <td>0.00264</td>\n",
453 |               "      <td>...</td>\n",
454 |               "      <td>0.327</td>\n",
455 |               "      <td>0.01106</td>\n",
456 |               "      <td>0.01265</td>\n",
457 |               "      <td>0.01963</td>\n",
458 |               "      <td>0.03317</td>\n",
459 |               "      <td>0.027837</td>\n",
460 |               "      <td>24.445</td>\n",
461 |               "      <td>0.48730</td>\n",
462 |               "      <td>0.57794</td>\n",
463 |               "      <td>0.33277</td>\n",
464 |               "    </tr>\n",
465 |               "    <tr>\n",
466 |               "      <th>4</th>\n",
467 |               "      <td>1</td>\n",
468 |               "      <td>72</td>\n",
469 |               "      <td>0</td>\n",
470 |               "      <td>33.6420</td>\n",
471 |               "      <td>29.187</td>\n",
472 |               "      <td>36.375</td>\n",
473 |               "      <td>0.00335</td>\n",
474 |               "      <td>0.000020</td>\n",
475 |               "      <td>0.00093</td>\n",
476 |               "      <td>0.00130</td>\n",
477 |               "      <td>...</td>\n",
478 |               "      <td>0.176</td>\n",
479 |               "      <td>0.00679</td>\n",
480 |               "      <td>0.00929</td>\n",
481 |               "      <td>0.01819</td>\n",
482 |               "      <td>0.02036</td>\n",
483 |               "      <td>0.011625</td>\n",
484 |               "      <td>26.126</td>\n",
485 |               "      <td>0.47188</td>\n",
486 |               "      <td>0.56122</td>\n",
487 |               "      <td>0.19361</td>\n",
488 |               "    </tr>\n",
489 |               "  </tbody>\n",
490 |               "</table>\n",
491 |               "<p>5 rows × 22 columns</p>\n",
492 |               "</div>\n",
493 |               "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bc104d2-48d3-4923-b679-a1a63a3eefaa')\"\n",
494 |               "              title=\"Convert this dataframe to an interactive table.\"\n",
495 |               "              style=\"display:none;\">\n",
496 |               "        \n",
497 |               "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
498 |               "       width=\"24px\">\n",
499 |               "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
500 |               "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
501 |               "  </svg>\n",
502 |               "      </button>\n",
503 |               "      \n",
504 |               "  <style>\n",
505 |               "    .colab-df-container {\n",
506 |               "      display:flex;\n",
507 |               "      flex-wrap:wrap;\n",
508 |               "      gap: 12px;\n",
509 |               "    }\n",
510 |               "\n",
511 |               "    .colab-df-convert {\n",
512 |               "      background-color: #E8F0FE;\n",
513 |               "      border: none;\n",
514 |               "      border-radius: 50%;\n",
515 |               "      cursor: pointer;\n",
516 |               "      display: none;\n",
517 |               "      fill: #1967D2;\n",
518 |               "      height: 32px;\n",
519 |               "      padding: 0 0 0 0;\n",
520 |               "      width: 32px;\n",
521 |               "    }\n",
522 |               "\n",
523 |               "    .colab-df-convert:hover {\n",
524 |               "      background-color: #E2EBFA;\n",
525 |               "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
526 |               "      fill: #174EA6;\n",
527 |               "    }\n",
528 |               "\n",
529 |               "    [theme=dark] .colab-df-convert {\n",
530 |               "      background-color: #3B4455;\n",
531 |               "      fill: #D2E3FC;\n",
532 |               "    }\n",
533 |               "\n",
534 |               "    [theme=dark] .colab-df-convert:hover {\n",
535 |               "      background-color: #434B5C;\n",
536 |               "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
537 |               "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
538 |               "      fill: #FFFFFF;\n",
539 |               "    }\n",
540 |               "  </style>\n",
541 |               "\n",
542 |               "      <script>\n",
543 |               "        const buttonEl =\n",
544 |               "          document.querySelector('#df-6bc104d2-48d3-4923-b679-a1a63a3eefaa button.colab-df-convert');\n",
545 |               "        buttonEl.style.display =\n",
546 |               "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
547 |               "\n",
548 |               "        async function convertToInteractive(key) {\n",
549 |               "          const element = document.querySelector('#df-6bc104d2-48d3-4923-b679-a1a63a3eefaa');\n",
550 |               "          const dataTable =\n",
551 |               "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
552 |               "                                                     [key], {});\n",
553 |               "          if (!dataTable) return;\n",
554 |               "\n",
555 |               "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
556 |               "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
557 |               "            + ' to learn more about interactive tables.';\n",
558 |               "          element.innerHTML = '';\n",
559 |               "          dataTable['output_type'] = 'display_data';\n",
560 |               "          await google.colab.output.renderOutput(dataTable, element);\n",
561 |               "          const docLink = document.createElement('div');\n",
562 |               "          docLink.innerHTML = docLinkHtml;\n",
563 |               "          element.appendChild(docLink);\n",
564 |               "        }\n",
565 |               "      </script>\n",
566 |               "    </div>\n",
567 |               "  </div>\n",
568 |               "  "
569 |             ]
570 |           },
571 |           "metadata": {},
572 |           "execution_count": 3
573 |         }
574 |       ],
575 |       "source": [
576 |         "parkinsons = pd.read_csv('http://archive.ics.uci.edu/ml/machine-learning-databases/parkinsons/telemonitoring/parkinsons_updrs.data')\n",
577 |         "parkinsons.rename(columns={'motor_UPDRS':'label'}, inplace=True)\n",
578 |         "parkinsons.head()"
579 |       ]
580 |     },
581 |     {
582 |       "cell_type": "markdown",
583 |       "metadata": {
584 |         "id": "6KNf3AKIwcnb"
585 |       },
586 |       "source": [
587 |         "## Comparing Models"
588 |       ]
589 |     },
590 |     {
591 |       "cell_type": "code",
592 |       "execution_count": 4,
593 |       "metadata": {
594 |         "id": "o1el3KYcwcnf"
595 |       },
596 |       "outputs": [],
597 |       "source": [
598 |         "from sklearn.linear_model import LinearRegression, Lasso, Ridge\n",
599 |         "from sklearn.svm import LinearSVR\n",
600 |         "from sklearn.tree import DecisionTreeRegressor\n",
601 |         "from sklearn.neural_network import MLPRegressor\n",
602 |         "\n",
603 |         "models = [\n",
604 |         "    ('LR', LinearRegression()),\n",
605 |         "    ('L', Lasso()),\n",
606 |         "    ('R', Ridge()),\n",
607 |         "    ('SVR', LinearSVR()),\n",
608 |         "    ('DT', DecisionTreeRegressor()),\n",
609 |         "    ('NN', MLPRegressor()),\n",
610 |         "]"
611 |       ]
612 |     },
613 |     {
614 |       "cell_type": "markdown",
615 |       "source": [
616 |         "### test a single model (e.g. Linear Regression)"
617 |       ],
618 |       "metadata": {
619 |         "id": "5FGF4M9l9Skt"
620 |       }
621 |     },
622 |     {
623 |       "cell_type": "code",
624 |       "source": [
625 |         "from sklearn.model_selection import train_test_split\n",
626 |         "import numpy as np\n",
627 |         "\n",
628 |         "dataset_name = 'computer_hardware'\n",
629 |         "dataset = computer_hardware\n",
630 |         "\n",
631 |         "X = np.array(dataset.drop(['label'], axis=1))\n",
632 |         "y = np.array(dataset['label'])\n",
633 |         "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
634 |         "\n",
635 |         "name ='LR'\n",
636 |         "model = LinearRegression()\n",
637 |         "model.fit(X_train, y_train)\n",
638 |         "accuracy = model.score(X_test, y_test)\n",
639 |         "print(dataset_name, name, accuracy)"
640 |       ],
641 |       "metadata": {
642 |         "colab": {
643 |           "base_uri": "https://localhost:8080/"
644 |         },
645 |         "id": "JJT5EHhp9aRU",
646 |         "outputId": "455e902e-632a-4921-81eb-89cc2f982b9d"
647 |       },
648 |       "execution_count": 5,
649 |       "outputs": [
650 |         {
651 |           "output_type": "stream",
652 |           "name": "stdout",
653 |           "text": [
654 |             "computer_hardware LR 0.9438611618299354\n"
655 |           ]
656 |         }
657 |       ]
658 |     },
659 |     {
660 |       "cell_type": "code",
661 |       "source": [
662 |         "model.predict(np.array([22,   122,    40,  8000, 16000,    32,     8,    16, 214]).reshape(1,9))"
663 |       ],
664 |       "metadata": {
665 |         "colab": {
666 |           "base_uri": "https://localhost:8080/"
667 |         },
668 |         "id": "MI1VXq7S_W63",
669 |         "outputId": "26b27d81-6419-481f-a09d-1ace73d44e5f"
670 |       },
671 |       "execution_count": 13,
672 |       "outputs": [
673 |         {
674 |           "output_type": "execute_result",
675 |           "data": {
676 |             "text/plain": [
677 |               "array([181.53279469])"
678 |             ]
679 |           },
680 |           "metadata": {},
681 |           "execution_count": 13
682 |         }
683 |       ]
684 |     },
685 |     {
686 |       "cell_type": "markdown",
687 |       "source": [
688 |         "### Evaluate all the models"
689 |       ],
690 |       "metadata": {
691 |         "id": "SwAsHS1g9b1m"
692 |       }
693 |     },
694 |     {
695 |       "cell_type": "code",
696 |       "execution_count": null,
697 |       "metadata": {
698 |         "id": "CDKoYk0-wcnf"
699 |       },
700 |       "outputs": [],
701 |       "source": [
702 |         "for dataset_name, dataset in [('computer_hardware', computer_hardware), ('parkinsons', parkinsons)]:\n",
703 |         "    X = np.array(dataset.drop(['label'], axis=1))\n",
704 |         "    y = np.array(dataset['label'])\n",
705 |         "    X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=42)\n",
706 |         "\n",
707 |         "    for name, model in models:\n",
708 |         "        model.fit(X_train, y_train)\n",
709 |         "        accuracy = model.score(X_test, y_test)\n",
710 |         "        print(dataset_name, name, accuracy)"
711 |       ]
712 |     }
713 |   ]
714 | }


--------------------------------------------------------------------------------
/requests_gradio(공동주택가격).ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "name": "requests_gradio.ipynb",
  7 |       "provenance": [],
  8 |       "machine_shape": "hm",
  9 |       "include_colab_link": true
 10 |     },
 11 |     "kernelspec": {
 12 |       "name": "python3",
 13 |       "display_name": "Python 3"
 14 |     },
 15 |     "language_info": {
 16 |       "name": "python"
 17 |     }
 18 |   },
 19 |   "cells": [
 20 |     {
 21 |       "cell_type": "markdown",
 22 |       "metadata": {
 23 |         "id": "view-in-github",
 24 |         "colab_type": "text"
 25 |       },
 26 |       "source": [
 27 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/requests_gradio(%EA%B3%B5%EB%8F%99%EC%A3%BC%ED%83%9D%EA%B0%80%EA%B2%A9).ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 28 |       ]
 29 |     },
 30 |     {
 31 |       "cell_type": "markdown",
 32 |       "source": [
 33 |         "## import libraries"
 34 |       ],
 35 |       "metadata": {
 36 |         "id": "n4d_TfR3M4_7"
 37 |       }
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "source": [
 42 |         "!pip install gradio\n",
 43 |         "import gradio as gr\n",
 44 |         "import requests\n",
 45 |         "import pandas as pd\n",
 46 |         "\n",
 47 |         "!pip install xmltodict\n",
 48 |         "import json\n",
 49 |         "import xmltodict"
 50 |       ],
 51 |       "metadata": {
 52 |         "id": "8eZHGJhmqd9a"
 53 |       },
 54 |       "execution_count": null,
 55 |       "outputs": []
 56 |     },
 57 |     {
 58 |       "cell_type": "markdown",
 59 |       "source": [
 60 |         "## test API"
 61 |       ],
 62 |       "metadata": {
 63 |         "id": "LDOeErLXgJUu"
 64 |       }
 65 |     },
 66 |     {
 67 |       "cell_type": "code",
 68 |       "source": [
 69 |         "input1 = '11290' # 법정동코드 (e.g. 성북구)\n",
 70 |         "input2 = '202210' # 조회월\n",
 71 |         "\n",
 72 |         "URL = ('http://openapi.molit.go.kr:8081/OpenAPI_ToolInstallPackage/service/rest/RTMSOBJSvc/getRTMSDataSvcAptTrade?LAWD_CD='+input1+'&DEAL_YMD='+input2+'&serviceKey=miH%2BZXg85lQy4%2FkmhffvygXDIFiTwisriSLxtyECSzw6hxTVK7yI8QKIOc7mP7LEqCnhBGuj6iW1gZW65dum7A%3D%3D')\n",
 73 |         "response = requests.get(URL)"
 74 |       ],
 75 |       "metadata": {
 76 |         "id": "lHHEwqpyJF7r"
 77 |       },
 78 |       "execution_count": 75,
 79 |       "outputs": []
 80 |     },
 81 |     {
 82 |       "cell_type": "markdown",
 83 |       "source": [
 84 |         "https://codebeautify.org/"
 85 |       ],
 86 |       "metadata": {
 87 |         "id": "lpbP7XcMalRT"
 88 |       }
 89 |     },
 90 |     {
 91 |       "cell_type": "markdown",
 92 |       "source": [
 93 |         "#### xmltodict: xml text to dict\n",
 94 |         "#### json.loads: json text to dict\n",
 95 |         "#### json.dumps: dict to json text\n",
 96 |         "\n"
 97 |       ],
 98 |       "metadata": {
 99 |         "id": "7EdvyvDpZk58"
100 |       }
101 |     },
102 |     {
103 |       "cell_type": "code",
104 |       "source": [
105 |         "dict_data = xmltodict.parse(response.text) \n",
106 |         "# json_txt = json.dumps(dict_data, ensure_ascii = False)  \n",
107 |         "# dict_data = json.loads(json_txt)  \n"
108 |       ],
109 |       "metadata": {
110 |         "id": "tV6YoUcsdN8c"
111 |       },
112 |       "execution_count": 77,
113 |       "outputs": []
114 |     },
115 |     {
116 |       "cell_type": "code",
117 |       "source": [
118 |         "item = dict_data['response']['body']['items']['item']"
119 |       ],
120 |       "metadata": {
121 |         "id": "AXxaFHiM-vUU"
122 |       },
123 |       "execution_count": 78,
124 |       "outputs": []
125 |     },
126 |     {
127 |       "cell_type": "code",
128 |       "source": [
129 |         "df = pd.DataFrame(item)\n",
130 |         "df"
131 |       ],
132 |       "metadata": {
133 |         "id": "8ll_27m1x35t"
134 |       },
135 |       "execution_count": null,
136 |       "outputs": []
137 |     },
138 |     {
139 |       "cell_type": "markdown",
140 |       "source": [
141 |         "## make a function"
142 |       ],
143 |       "metadata": {
144 |         "id": "7Ij1aHSygY-A"
145 |       }
146 |     },
147 |     {
148 |       "cell_type": "code",
149 |       "source": [
150 |         "def test(input1, input2):\n",
151 |         "  URL = ('http://openapi.molit.go.kr:8081/OpenAPI_ToolInstallPackage/service/rest/RTMSOBJSvc/getRTMSDataSvcAptTrade?LAWD_CD='+input1+'&DEAL_YMD='+input2+'&serviceKey=miH%2BZXg85lQy4%2FkmhffvygXDIFiTwisriSLxtyECSzw6hxTVK7yI8QKIOc7mP7LEqCnhBGuj6iW1gZW65dum7A%3D%3D')  \n",
152 |         "  response = requests.get(URL)\n",
153 |         "  dict_data = xmltodict.parse(response.content)\n",
154 |         "  item = dict_data['response']['body']['items']['item']\n",
155 |         "\n",
156 |         "  df = pd.DataFrame(item)\n",
157 |         "  return df"
158 |       ],
159 |       "metadata": {
160 |         "id": "Wf_JH79cDKc3"
161 |       },
162 |       "execution_count": 80,
163 |       "outputs": []
164 |     },
165 |     {
166 |       "cell_type": "markdown",
167 |       "source": [
168 |         "## Build a demo"
169 |       ],
170 |       "metadata": {
171 |         "id": "Ca8G_kDVgp3x"
172 |       }
173 |     },
174 |     {
175 |       "cell_type": "code",
176 |       "source": [
177 |         "demo = gr.Interface(fn=test, inputs=[\"text\", \"text\"], outputs=\"dataframe\")\n",
178 |         "demo.launch(share=True)"
179 |       ],
180 |       "metadata": {
181 |         "id": "nopFEK52GSuz",
182 |         "colab": {
183 |           "base_uri": "https://localhost:8080/",
184 |           "height": 646
185 |         },
186 |         "outputId": "c880cb79-ee9c-4b27-f1a3-14ace03f4b08"
187 |       },
188 |       "execution_count": 81,
189 |       "outputs": [
190 |         {
191 |           "output_type": "stream",
192 |           "name": "stdout",
193 |           "text": [
194 |             "Colab notebook detected. To show errors in colab notebook, set `debug=True` in `launch()`\n",
195 |             "Running on public URL: https://426b5fc078f6a3d9.gradio.app\n",
196 |             "\n",
197 |             "This share link expires in 72 hours. For free permanent hosting and GPU upgrades (NEW!), check out Spaces: https://huggingface.co/spaces\n"
198 |           ]
199 |         },
200 |         {
201 |           "output_type": "display_data",
202 |           "data": {
203 |             "text/plain": [
204 |               "<IPython.core.display.HTML object>"
205 |             ],
206 |             "text/html": [
207 |               "<div><iframe src=\"https://426b5fc078f6a3d9.gradio.app\" width=\"100%\" height=\"500\" allow=\"autoplay; camera; microphone; clipboard-read; clipboard-write;\" frameborder=\"0\" allowfullscreen></iframe></div>"
208 |             ]
209 |           },
210 |           "metadata": {}
211 |         },
212 |         {
213 |           "output_type": "execute_result",
214 |           "data": {
215 |             "text/plain": [
216 |               "(<gradio.routes.App at 0x7f3f62fb8c50>,\n",
217 |               " 'http://127.0.0.1:7860/',\n",
218 |               " 'https://426b5fc078f6a3d9.gradio.app')"
219 |             ]
220 |           },
221 |           "metadata": {},
222 |           "execution_count": 81
223 |         }
224 |       ]
225 |     }
226 |   ]
227 | }


--------------------------------------------------------------------------------
/string.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "kernelspec": {
  6 |       "display_name": "Python 3",
  7 |       "language": "python",
  8 |       "name": "python3"
  9 |     },
 10 |     "language_info": {
 11 |       "codemirror_mode": {
 12 |         "name": "ipython",
 13 |         "version": 3
 14 |       },
 15 |       "file_extension": ".py",
 16 |       "mimetype": "text/x-python",
 17 |       "name": "python",
 18 |       "nbconvert_exporter": "python",
 19 |       "pygments_lexer": "ipython3",
 20 |       "version": "3.7.1"
 21 |     },
 22 |     "colab": {
 23 |       "name": "string.ipynb",
 24 |       "provenance": [],
 25 |       "include_colab_link": true
 26 |     }
 27 |   },
 28 |   "cells": [
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "view-in-github",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/string.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "metadata": {
 42 |         "id": "zwpWU02YmAur",
 43 |         "outputId": "f181d85d-17a2-4571-e914-3d332031f533"
 44 |       },
 45 |       "source": [
 46 |         "s = 'abcdef'\n",
 47 |         "print(s[0], s[5], s[-1], s[-6])\n",
 48 |         "print(s[1:3], s[1:], s[:3], s[:])"
 49 |       ],
 50 |       "execution_count": null,
 51 |       "outputs": [
 52 |         {
 53 |           "name": "stdout",
 54 |           "output_type": "stream",
 55 |           "text": [
 56 |             "<class 'str'>\n",
 57 |             "a f f a\n",
 58 |             "bc bcdef abc abcdef\n"
 59 |           ]
 60 |         }
 61 |       ]
 62 |     },
 63 |     {
 64 |       "cell_type": "code",
 65 |       "metadata": {
 66 |         "id": "mpkdZBeWmAux",
 67 |         "outputId": "a5f03d66-f3c7-4ac5-bb23-d4ee9780172d"
 68 |       },
 69 |       "source": [
 70 |         "n = [100, 200, 300]\n",
 71 |         "print(n[0], n[2], n[-1], n[-3])\n",
 72 |         "print(n[1:2], n[1:], n[:2], n[:])"
 73 |       ],
 74 |       "execution_count": null,
 75 |       "outputs": [
 76 |         {
 77 |           "name": "stdout",
 78 |           "output_type": "stream",
 79 |           "text": [
 80 |             "100 300 300 100\n",
 81 |             "[200] [200, 300] [100, 200] [100, 200, 300]\n"
 82 |           ]
 83 |         }
 84 |       ]
 85 |     },
 86 |     {
 87 |       "cell_type": "code",
 88 |       "metadata": {
 89 |         "id": "ulnefmnFmAuy",
 90 |         "outputId": "013b6af1-433f-4d95-e343-71631a426f84"
 91 |       },
 92 |       "source": [
 93 |         "len(s)"
 94 |       ],
 95 |       "execution_count": null,
 96 |       "outputs": [
 97 |         {
 98 |           "data": {
 99 |             "text/plain": [
100 |               "6"
101 |             ]
102 |           },
103 |           "execution_count": 4,
104 |           "metadata": {},
105 |           "output_type": "execute_result"
106 |         }
107 |       ]
108 |     },
109 |     {
110 |       "cell_type": "code",
111 |       "metadata": {
112 |         "id": "a45rgiNMmAuy",
113 |         "outputId": "9b1a4417-a513-4f77-8ae9-64352e6da015"
114 |       },
115 |       "source": [
116 |         "s[1]+s[3]+s[4:]*10"
117 |       ],
118 |       "execution_count": null,
119 |       "outputs": [
120 |         {
121 |           "data": {
122 |             "text/plain": [
123 |               "'bdefefefefefefefefefef'"
124 |             ]
125 |           },
126 |           "execution_count": 5,
127 |           "metadata": {},
128 |           "output_type": "execute_result"
129 |         }
130 |       ]
131 |     },
132 |     {
133 |       "cell_type": "code",
134 |       "metadata": {
135 |         "id": "IevwoMpmmAuz",
136 |         "outputId": "69636955-73a9-49f7-ea43-efdb38a982c1"
137 |       },
138 |       "source": [
139 |         "s.upper()"
140 |       ],
141 |       "execution_count": null,
142 |       "outputs": [
143 |         {
144 |           "data": {
145 |             "text/plain": [
146 |               "'ABCDEF'"
147 |             ]
148 |           },
149 |           "execution_count": 7,
150 |           "metadata": {},
151 |           "output_type": "execute_result"
152 |         }
153 |       ]
154 |     },
155 |     {
156 |       "cell_type": "code",
157 |       "metadata": {
158 |         "id": "LRU0Yt1amAuz",
159 |         "outputId": "3fdbe8c7-9962-40ba-f624-2ac1aa2c7e44"
160 |       },
161 |       "source": [
162 |         "s = ' this is a house built this year.\\n' \t\n",
163 |         "s"
164 |       ],
165 |       "execution_count": null,
166 |       "outputs": [
167 |         {
168 |           "data": {
169 |             "text/plain": [
170 |               "' this is a house built this year.\\n'"
171 |             ]
172 |           },
173 |           "execution_count": 10,
174 |           "metadata": {},
175 |           "output_type": "execute_result"
176 |         }
177 |       ]
178 |     },
179 |     {
180 |       "cell_type": "code",
181 |       "metadata": {
182 |         "id": "nMHteyDfmAu0",
183 |         "outputId": "2f55caa2-dd53-4fac-e876-05ca87c5d63d"
184 |       },
185 |       "source": [
186 |         "result = s.find('house') \t    # index of first instance of string t inside s (-1 if not found)\n",
187 |         "result"
188 |       ],
189 |       "execution_count": null,
190 |       "outputs": [
191 |         {
192 |           "data": {
193 |             "text/plain": [
194 |               "11"
195 |             ]
196 |           },
197 |           "execution_count": 11,
198 |           "metadata": {},
199 |           "output_type": "execute_result"
200 |         }
201 |       ]
202 |     },
203 |     {
204 |       "cell_type": "code",
205 |       "metadata": {
206 |         "id": "gv82JUX7mAu0",
207 |         "outputId": "18540837-5eb4-4cea-f087-2f7fd1581afa"
208 |       },
209 |       "source": [
210 |         "result = s.find('this')\t      # index of first instance of string t inside s (-1 if not found)\n",
211 |         "result"
212 |       ],
213 |       "execution_count": null,
214 |       "outputs": [
215 |         {
216 |           "data": {
217 |             "text/plain": [
218 |               "1"
219 |             ]
220 |           },
221 |           "execution_count": 12,
222 |           "metadata": {},
223 |           "output_type": "execute_result"
224 |         }
225 |       ]
226 |     },
227 |     {
228 |       "cell_type": "code",
229 |       "metadata": {
230 |         "id": "ua3TjwLcmAu2",
231 |         "outputId": "ac192f91-46ce-4ff0-a040-f0d63541124e"
232 |       },
233 |       "source": [
234 |         "s = s.strip()      \t          # a copy of s without leading or trailing whitespace\n",
235 |         "s"
236 |       ],
237 |       "execution_count": null,
238 |       "outputs": [
239 |         {
240 |           "data": {
241 |             "text/plain": [
242 |               "'this is a house built this year.'"
243 |             ]
244 |           },
245 |           "execution_count": 14,
246 |           "metadata": {},
247 |           "output_type": "execute_result"
248 |         }
249 |       ]
250 |     },
251 |     {
252 |       "cell_type": "code",
253 |       "metadata": {
254 |         "id": "l37g5DqQmAu2",
255 |         "outputId": "c1eefe2c-0edc-4678-c67b-b8306ca13471"
256 |       },
257 |       "source": [
258 |         "tokens = s.split(' ')\t        # split s into a list wherever a t is found (whitespace by default)\n",
259 |         "tokens"
260 |       ],
261 |       "execution_count": null,
262 |       "outputs": [
263 |         {
264 |           "data": {
265 |             "text/plain": [
266 |               "['this', 'is', 'a', 'house', 'built', 'this', 'year.']"
267 |             ]
268 |           },
269 |           "execution_count": 15,
270 |           "metadata": {},
271 |           "output_type": "execute_result"
272 |         }
273 |       ]
274 |     },
275 |     {
276 |       "cell_type": "code",
277 |       "metadata": {
278 |         "id": "64sewKE8mAu2",
279 |         "outputId": "e2e8dc1f-6b83-41ef-b746-7712099f2776"
280 |       },
281 |       "source": [
282 |         "s = ' '.join(tokens)\t        # combine the words of the text into a string using s as the glue\n",
283 |         "s"
284 |       ],
285 |       "execution_count": null,
286 |       "outputs": [
287 |         {
288 |           "data": {
289 |             "text/plain": [
290 |               "'this is a house built this year.'"
291 |             ]
292 |           },
293 |           "execution_count": 16,
294 |           "metadata": {},
295 |           "output_type": "execute_result"
296 |         }
297 |       ]
298 |     },
299 |     {
300 |       "cell_type": "code",
301 |       "metadata": {
302 |         "id": "jfiqbBiImAu3",
303 |         "outputId": "13300aff-f13a-4f48-c6ec-cbc5d01dd55b"
304 |       },
305 |       "source": [
306 |         "s = s.replace('this', 'that') # replace instances of t with u inside s\n",
307 |         "s"
308 |       ],
309 |       "execution_count": null,
310 |       "outputs": [
311 |         {
312 |           "data": {
313 |             "text/plain": [
314 |               "'that is a house built that year.'"
315 |             ]
316 |           },
317 |           "execution_count": 17,
318 |           "metadata": {},
319 |           "output_type": "execute_result"
320 |         }
321 |       ]
322 |     }
323 |   ]
324 | }


--------------------------------------------------------------------------------
/syntax.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "kernelspec": {
  6 |       "display_name": "Python 3",
  7 |       "language": "python",
  8 |       "name": "python3"
  9 |     },
 10 |     "language_info": {
 11 |       "codemirror_mode": {
 12 |         "name": "ipython",
 13 |         "version": 3
 14 |       },
 15 |       "file_extension": ".py",
 16 |       "mimetype": "text/x-python",
 17 |       "name": "python",
 18 |       "nbconvert_exporter": "python",
 19 |       "pygments_lexer": "ipython3",
 20 |       "version": "3.7.1"
 21 |     },
 22 |     "colab": {
 23 |       "name": "syntax.ipynb",
 24 |       "provenance": [],
 25 |       "include_colab_link": true
 26 |     }
 27 |   },
 28 |   "cells": [
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "view-in-github",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/syntax.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "metadata": {
 42 |         "id": "Fu2mzgrqSLAU",
 43 |         "outputId": "283876b5-0fc9-4961-d92d-d242b3c222de"
 44 |       },
 45 |       "source": [
 46 |         "a = [1, 2, 3, 4]\n",
 47 |         "for i in a:\n",
 48 |         "    print(i)"
 49 |       ],
 50 |       "execution_count": null,
 51 |       "outputs": [
 52 |         {
 53 |           "output_type": "stream",
 54 |           "text": [
 55 |             "1\n",
 56 |             "2\n",
 57 |             "3\n",
 58 |             "4\n"
 59 |           ],
 60 |           "name": "stdout"
 61 |         }
 62 |       ]
 63 |     },
 64 |     {
 65 |       "cell_type": "code",
 66 |       "metadata": {
 67 |         "id": "XSJxLJVCSLAW",
 68 |         "outputId": "921b10b7-3bc8-4345-a6b2-16253a900c95"
 69 |       },
 70 |       "source": [
 71 |         "a = [1, 2, 3, 4]\n",
 72 |         "for i in range(len(a)):\n",
 73 |         "    print(a[i])"
 74 |       ],
 75 |       "execution_count": null,
 76 |       "outputs": [
 77 |         {
 78 |           "output_type": "stream",
 79 |           "text": [
 80 |             "1\n",
 81 |             "2\n",
 82 |             "3\n",
 83 |             "4\n"
 84 |           ],
 85 |           "name": "stdout"
 86 |         }
 87 |       ]
 88 |     },
 89 |     {
 90 |       "cell_type": "code",
 91 |       "metadata": {
 92 |         "id": "VTfxVRdgSLAW",
 93 |         "outputId": "7b430ba8-015e-4296-c883-ce2b3f9ed43b"
 94 |       },
 95 |       "source": [
 96 |         "a = ['red', 'green', 'blue', 'purple']\n",
 97 |         "for i in a:\n",
 98 |         "    print(i)"
 99 |       ],
100 |       "execution_count": null,
101 |       "outputs": [
102 |         {
103 |           "output_type": "stream",
104 |           "text": [
105 |             "red\n",
106 |             "green\n",
107 |             "blue\n",
108 |             "purple\n"
109 |           ],
110 |           "name": "stdout"
111 |         }
112 |       ]
113 |     },
114 |     {
115 |       "cell_type": "code",
116 |       "metadata": {
117 |         "id": "Vc-7pW85SLAW",
118 |         "outputId": "9394e177-c8c8-44db-ff28-1e9566072bb3"
119 |       },
120 |       "source": [
121 |         "a = ['red', 'green', 'blue', 'purple']\n",
122 |         "for i in range(len(a)):\n",
123 |         "    print(a[i])"
124 |       ],
125 |       "execution_count": null,
126 |       "outputs": [
127 |         {
128 |           "output_type": "stream",
129 |           "text": [
130 |             "red\n",
131 |             "green\n",
132 |             "blue\n",
133 |             "purple\n"
134 |           ],
135 |           "name": "stdout"
136 |         }
137 |       ]
138 |     },
139 |     {
140 |       "cell_type": "code",
141 |       "metadata": {
142 |         "id": "dD-ephD3SLAX",
143 |         "outputId": "69d729f8-996a-41be-d84b-f1bd2a7c809c"
144 |       },
145 |       "source": [
146 |         "a = 0\n",
147 |         "if a == 0:\n",
148 |         "    print(a)\n",
149 |         "else:\n",
150 |         "    print(a+1)"
151 |       ],
152 |       "execution_count": null,
153 |       "outputs": [
154 |         {
155 |           "output_type": "stream",
156 |           "text": [
157 |             "0\n"
158 |           ],
159 |           "name": "stdout"
160 |         }
161 |       ]
162 |     },
163 |     {
164 |       "cell_type": "code",
165 |       "metadata": {
166 |         "id": "M0AZaDMvSLAY",
167 |         "outputId": "b25a5fe6-defa-4549-85b0-0c146ff7a2a0"
168 |       },
169 |       "source": [
170 |         "for i in range(1, 3):\n",
171 |         "    for j in range(3, 5):\n",
172 |         "        print(i*j)"
173 |       ],
174 |       "execution_count": null,
175 |       "outputs": [
176 |         {
177 |           "output_type": "stream",
178 |           "text": [
179 |             "3\n",
180 |             "4\n",
181 |             "6\n",
182 |             "8\n"
183 |           ],
184 |           "name": "stdout"
185 |         }
186 |       ]
187 |     },
188 |     {
189 |       "cell_type": "code",
190 |       "metadata": {
191 |         "id": "ZPpZmVIHSLAY",
192 |         "outputId": "0bbcc529-c1d8-4774-e1ad-9de8b9e065ce"
193 |       },
194 |       "source": [
195 |         "for i in range(1, 3):\n",
196 |         "    for j in range(3, 5):\n",
197 |         "        if j >=4:\n",
198 |         "            print(i*j)"
199 |       ],
200 |       "execution_count": null,
201 |       "outputs": [
202 |         {
203 |           "output_type": "stream",
204 |           "text": [
205 |             "4\n",
206 |             "8\n"
207 |           ],
208 |           "name": "stdout"
209 |         }
210 |       ]
211 |     },
212 |     {
213 |       "cell_type": "code",
214 |       "metadata": {
215 |         "id": "JQXk5vi0SLAY",
216 |         "outputId": "cd3306f3-6d6d-446e-dd4a-f7f4e11dd967"
217 |       },
218 |       "source": [
219 |         "a = [1, 2, 3, 4]\n",
220 |         "b = [i for i in a if i >=2]\n",
221 |         "b"
222 |       ],
223 |       "execution_count": null,
224 |       "outputs": [
225 |         {
226 |           "output_type": "execute_result",
227 |           "data": {
228 |             "text/plain": [
229 |               "[2, 3, 4]"
230 |             ]
231 |           },
232 |           "metadata": {
233 |             "tags": []
234 |           },
235 |           "execution_count": 12
236 |         }
237 |       ]
238 |     },
239 |     {
240 |       "cell_type": "code",
241 |       "metadata": {
242 |         "id": "MDmBGZ3LSLAZ",
243 |         "outputId": "bd06f885-7334-43d9-d92d-848c615f6771"
244 |       },
245 |       "source": [
246 |         "a = ['red', 'green', 'blue', 'purple']\n",
247 |         "b = [i for i in a if len(i) >=4]\n",
248 |         "b"
249 |       ],
250 |       "execution_count": null,
251 |       "outputs": [
252 |         {
253 |           "output_type": "execute_result",
254 |           "data": {
255 |             "text/plain": [
256 |               "['green', 'blue', 'purple']"
257 |             ]
258 |           },
259 |           "metadata": {
260 |             "tags": []
261 |           },
262 |           "execution_count": 13
263 |         }
264 |       ]
265 |     },
266 |     {
267 |       "cell_type": "code",
268 |       "source": [
269 |         "a = ['red', 'green', 'blue', 'purple']\n",
270 |         "b = [1, 2, 3, 4]\n",
271 |         "c = {a[i]:b[i] for i in range(4)}\n",
272 |         "c"
273 |       ],
274 |       "metadata": {
275 |         "id": "Z6JMoDOHu48B",
276 |         "outputId": "2bf14607-9cf5-40be-d60b-b7cbda48c2a8",
277 |         "colab": {
278 |           "base_uri": "https://localhost:8080/"
279 |         }
280 |       },
281 |       "execution_count": null,
282 |       "outputs": [
283 |         {
284 |           "output_type": "execute_result",
285 |           "data": {
286 |             "text/plain": [
287 |               "{'blue': 3, 'green': 2, 'purple': 4, 'red': 1}"
288 |             ]
289 |           },
290 |           "metadata": {},
291 |           "execution_count": 17
292 |         }
293 |       ]
294 |     }
295 |   ]
296 | }


--------------------------------------------------------------------------------
/variables.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "kernelspec": {
  6 |       "display_name": "Python 3",
  7 |       "language": "python",
  8 |       "name": "python3"
  9 |     },
 10 |     "language_info": {
 11 |       "codemirror_mode": {
 12 |         "name": "ipython",
 13 |         "version": 3
 14 |       },
 15 |       "file_extension": ".py",
 16 |       "mimetype": "text/x-python",
 17 |       "name": "python",
 18 |       "nbconvert_exporter": "python",
 19 |       "pygments_lexer": "ipython3",
 20 |       "version": "3.7.1"
 21 |     },
 22 |     "colab": {
 23 |       "name": "variables.ipynb",
 24 |       "provenance": [],
 25 |       "include_colab_link": true
 26 |     }
 27 |   },
 28 |   "cells": [
 29 |     {
 30 |       "cell_type": "markdown",
 31 |       "metadata": {
 32 |         "id": "view-in-github",
 33 |         "colab_type": "text"
 34 |       },
 35 |       "source": [
 36 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/variables.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 37 |       ]
 38 |     },
 39 |     {
 40 |       "cell_type": "code",
 41 |       "metadata": {
 42 |         "id": "goAyoU7vRheI",
 43 |         "outputId": "068731d9-7186-453a-9a6b-b437acc64bc5",
 44 |         "colab": {
 45 |           "base_uri": "https://localhost:8080/"
 46 |         }
 47 |       },
 48 |       "source": [
 49 |         "a = 1; print(type(a))"
 50 |       ],
 51 |       "execution_count": null,
 52 |       "outputs": [
 53 |         {
 54 |           "output_type": "stream",
 55 |           "name": "stdout",
 56 |           "text": [
 57 |             "<class 'int'>\n"
 58 |           ]
 59 |         }
 60 |       ]
 61 |     },
 62 |     {
 63 |       "cell_type": "code",
 64 |       "metadata": {
 65 |         "id": "txxnoqJuRheJ",
 66 |         "outputId": "fac1233b-b1e0-41b4-ea3c-94ad0c7104ec",
 67 |         "colab": {
 68 |           "base_uri": "https://localhost:8080/"
 69 |         }
 70 |       },
 71 |       "source": [
 72 |         "a = 1.1; print(type(a))"
 73 |       ],
 74 |       "execution_count": null,
 75 |       "outputs": [
 76 |         {
 77 |           "output_type": "stream",
 78 |           "name": "stdout",
 79 |           "text": [
 80 |             "<class 'float'>\n"
 81 |           ]
 82 |         }
 83 |       ]
 84 |     },
 85 |     {
 86 |       "cell_type": "code",
 87 |       "metadata": {
 88 |         "id": "SyP0PB86RheL",
 89 |         "outputId": "e10d274f-e7fd-4cdd-a822-847fb83560ec"
 90 |       },
 91 |       "source": [
 92 |         "a = '123'; print(type(a)); print(a[1])"
 93 |       ],
 94 |       "execution_count": null,
 95 |       "outputs": [
 96 |         {
 97 |           "output_type": "stream",
 98 |           "text": [
 99 |             "<class 'str'>\n",
100 |             "2\n"
101 |           ],
102 |           "name": "stdout"
103 |         }
104 |       ]
105 |     },
106 |     {
107 |       "cell_type": "code",
108 |       "metadata": {
109 |         "id": "EKYj0dvIHPo0",
110 |         "outputId": "a1237ded-1b0a-4ec6-e393-09846b2194a5",
111 |         "colab": {
112 |           "base_uri": "https://localhost:8080/"
113 |         }
114 |       },
115 |       "source": [
116 |         "a = 'hello'; print(type(a)); print(a[1])"
117 |       ],
118 |       "execution_count": null,
119 |       "outputs": [
120 |         {
121 |           "output_type": "stream",
122 |           "name": "stdout",
123 |           "text": [
124 |             "<class 'str'>\n",
125 |             "e\n"
126 |           ]
127 |         }
128 |       ]
129 |     },
130 |     {
131 |       "cell_type": "code",
132 |       "metadata": {
133 |         "id": "Df6PhU-tHXr2",
134 |         "outputId": "3e99729c-3214-4756-e692-1522f494a01e",
135 |         "colab": {
136 |           "base_uri": "https://localhost:8080/"
137 |         }
138 |       },
139 |       "source": [
140 |         "a = [1, 2, 3, 4]; print(type(a)); print(a[0])"
141 |       ],
142 |       "execution_count": null,
143 |       "outputs": [
144 |         {
145 |           "output_type": "stream",
146 |           "name": "stdout",
147 |           "text": [
148 |             "<class 'list'>\n",
149 |             "1\n"
150 |           ]
151 |         }
152 |       ]
153 |     },
154 |     {
155 |       "cell_type": "code",
156 |       "metadata": {
157 |         "id": "DfS-gknCRheL",
158 |         "outputId": "ff76fc0a-73db-43fc-bb86-946381a6cb4d",
159 |         "colab": {
160 |           "base_uri": "https://localhost:8080/"
161 |         }
162 |       },
163 |       "source": [
164 |         "a = [1,'a', [3, '4']]; print(type(a)); print(a[0]); print(a[1]); print(a[2]); print(a[2][1])"
165 |       ],
166 |       "execution_count": null,
167 |       "outputs": [
168 |         {
169 |           "output_type": "stream",
170 |           "name": "stdout",
171 |           "text": [
172 |             "<class 'list'>\n",
173 |             "1\n",
174 |             "a\n",
175 |             "[3, '4']\n",
176 |             "4\n"
177 |           ]
178 |         }
179 |       ]
180 |     },
181 |     {
182 |       "cell_type": "code",
183 |       "metadata": {
184 |         "id": "2sivfloGRheM",
185 |         "outputId": "5c768bb7-e02b-4c87-a76e-3e992cdb244e"
186 |       },
187 |       "source": [
188 |         "a =\t{\"a\": \"apple\", \"b\": \"orange\", \"c\": 2014}\n",
189 |         "print(type(a))\n",
190 |         "print(a[\"a\"])"
191 |       ],
192 |       "execution_count": null,
193 |       "outputs": [
194 |         {
195 |           "output_type": "stream",
196 |           "text": [
197 |             "<class 'dict'>\n",
198 |             "apple\n"
199 |           ],
200 |           "name": "stdout"
201 |         }
202 |       ]
203 |     }
204 |   ]
205 | }


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/yesterday.wav:
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https://raw.githubusercontent.com/hsnam95/class2022Fall/b6d8b0cd9bd9d2ab443fe58ab1d1813f70db9e45/yesterday.wav


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/교보API.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |   "nbformat": 4,
  3 |   "nbformat_minor": 0,
  4 |   "metadata": {
  5 |     "colab": {
  6 |       "provenance": [],
  7 |       "authorship_tag": "ABX9TyMahaEgXMGv5CwHvGDqA5DT",
  8 |       "include_colab_link": true
  9 |     },
 10 |     "kernelspec": {
 11 |       "name": "python3",
 12 |       "display_name": "Python 3"
 13 |     },
 14 |     "language_info": {
 15 |       "name": "python"
 16 |     }
 17 |   },
 18 |   "cells": [
 19 |     {
 20 |       "cell_type": "markdown",
 21 |       "metadata": {
 22 |         "id": "view-in-github",
 23 |         "colab_type": "text"
 24 |       },
 25 |       "source": [
 26 |         "<a href=\"https://colab.research.google.com/github/hsnam95/class2022Fall/blob/main/%EA%B5%90%EB%B3%B4API.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
 27 |       ]
 28 |     },
 29 |     {
 30 |       "cell_type": "code",
 31 |       "source": [
 32 |         "import pandas as pd\n",
 33 |         "import json\n",
 34 |         "import requests"
 35 |       ],
 36 |       "metadata": {
 37 |         "id": "nhZj-Bi8Atg2"
 38 |       },
 39 |       "execution_count": 28,
 40 |       "outputs": []
 41 |     },
 42 |     {
 43 |       "cell_type": "code",
 44 |       "source": [
 45 |         "bookID = 'S000000620181'\n",
 46 |         "url='https://product.kyobobook.co.kr/api/review/list?page=1&pageLimit=10000&reviewSort=001&revwPatrCode=000&saleCmdtid='+bookID"
 47 |       ],
 48 |       "metadata": {
 49 |         "id": "j4Cj1FNgAyDU"
 50 |       },
 51 |       "execution_count": 13,
 52 |       "outputs": []
 53 |     },
 54 |     {
 55 |       "cell_type": "code",
 56 |       "source": [
 57 |         "response = requests.get(url)\n",
 58 |         "response"
 59 |       ],
 60 |       "metadata": {
 61 |         "id": "P2y8pa1teetY"
 62 |       },
 63 |       "execution_count": null,
 64 |       "outputs": []
 65 |     },
 66 |     {
 67 |       "cell_type": "code",
 68 |       "source": [
 69 |         "json_txt = response.text\n",
 70 |         "dict_data = json.loads(json_txt)"
 71 |       ],
 72 |       "metadata": {
 73 |         "id": "nU26-rUceiyY"
 74 |       },
 75 |       "execution_count": 15,
 76 |       "outputs": []
 77 |     },
 78 |     {
 79 |       "cell_type": "code",
 80 |       "source": [
 81 |         "df = pd.DataFrame(dict_data['data']['reviewList'])"
 82 |       ],
 83 |       "metadata": {
 84 |         "id": "DMlya8yXe5DH"
 85 |       },
 86 |       "execution_count": 31,
 87 |       "outputs": []
 88 |     },
 89 |     {
 90 |       "cell_type": "code",
 91 |       "source": [
 92 |         "df.to_excel('review.xls')"
 93 |       ],
 94 |       "metadata": {
 95 |         "id": "shEXbNuF25m0"
 96 |       },
 97 |       "execution_count": null,
 98 |       "outputs": []
 99 |     },
100 |     {
101 |       "cell_type": "code",
102 |       "source": [
103 |         "df.to_csv('review.csv')"
104 |       ],
105 |       "metadata": {
106 |         "id": "_DVrUrYXykvL"
107 |       },
108 |       "execution_count": null,
109 |       "outputs": []
110 |     },
111 |     {
112 |       "cell_type": "code",
113 |       "source": [
114 |         "df.to_csv('review.csv', encoding='utf-8-sig')"
115 |       ],
116 |       "metadata": {
117 |         "id": "Q46X-kXa2-Ai"
118 |       },
119 |       "execution_count": null,
120 |       "outputs": []
121 |     }
122 |   ]
123 | }


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