├── data_go_th_api_key.ipynb
├── read_tis-620 file.ipynb
├── bangkok open data.ipynb
├── data_go_th_api.ipynb
├── README.md
└── pandas_transform_google_form_data2.ipynb


/data_go_th_api_key.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# data.go.th: Data API\n",
  8 |     "\n",
  9 |     "web: https://opendata.data.go.th/\n"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "---\n",
 17 |     "* author:  [Prasert Kanawattanachai](prasert.k@chula.ac.th)\n",
 18 |     "* YouTube: https://www.youtube.com/prasertcbs\n",
 19 |     "* github: https://github.com/prasertcbs/\n",
 20 |     "* kaggle: https://www.kaggle.com/prasertk/\n",
 21 |     "* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)\n",
 22 |     "---\n"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "<a href=https://youtu.be/SAeqkzbpikk><img src=https://i.ytimg.com/vi/SAeqkzbpikk/mqdefault.jpg width=560 width=315/><p>Play YouTube Video</p></a>"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 1,
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "import pandas as pd\n",
 39 |     "import requests\n"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 2,
 45 |    "metadata": {
 46 |     "colab": {
 47 |      "base_uri": "https://localhost:8080/",
 48 |      "height": 64
 49 |     },
 50 |     "colab_type": "code",
 51 |     "id": "fdr0pYIf7P-_",
 52 |     "outputId": "c919deae-c99b-44b0-8924-4d2355ca0b63"
 53 |    },
 54 |    "outputs": [
 55 |     {
 56 |      "name": "stdout",
 57 |      "output_type": "stream",
 58 |      "text": [
 59 |       "pandas version:  1.4.2\n",
 60 |       "requests version: 2.28.0\n"
 61 |      ]
 62 |     },
 63 |     {
 64 |      "data": {
 65 |       "text/plain": [
 66 |        "Timestamp('2022-07-12 19:44:50.631955')"
 67 |       ]
 68 |      },
 69 |      "execution_count": 2,
 70 |      "metadata": {},
 71 |      "output_type": "execute_result"
 72 |     }
 73 |    ],
 74 |    "source": [
 75 |     "print(f\"pandas version:  {pd.__version__}\")\n",
 76 |     "print(f\"requests version: {requests.__version__}\")\n",
 77 |     "\n",
 78 |     "pd.Timestamp.now()\n",
 79 |     "# pd.Timestamp.now().strftime('%Y-%m-%d')\n"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "markdown",
 84 |    "metadata": {},
 85 |    "source": [
 86 |     "## Data API\n"
 87 |    ]
 88 |   },
 89 |   {
 90 |    "cell_type": "code",
 91 |    "execution_count": 3,
 92 |    "metadata": {},
 93 |    "outputs": [],
 94 |    "source": [
 95 |     "from getpass import getpass\n",
 96 |     "api_key = getpass()"
 97 |    ]
 98 |   },
 99 |   {
100 |    "cell_type": "markdown",
101 |    "metadata": {},
102 |    "source": [
103 |     "- ข้อมูลโรงแรมที่เป็นมิตรกับสิ่งแวดล้อม (Green Hotel)\n",
104 |     "  * https://opendata.data.go.th/dataset/db0103-002\n",
105 |     "  * https://opend.data.go.th/get-ckan/datastore_search?resource_id=d13e3be5-9020-4a78-9a7e-760b2dcbf6be&limit=5\n",
106 |     "- จำนวนคนทำงานในที่พักแรม\n",
107 |     "  * https://opendata.data.go.th/dataset/os_17_00010"
108 |    ]
109 |   },
110 |   {
111 |    "cell_type": "code",
112 |    "execution_count": 4,
113 |    "metadata": {},
114 |    "outputs": [
115 |     {
116 |      "data": {
117 |       "text/html": [
118 |        "<div>\n",
119 |        "<style scoped>\n",
120 |        "    .dataframe tbody tr th:only-of-type {\n",
121 |        "        vertical-align: middle;\n",
122 |        "    }\n",
123 |        "\n",
124 |        "    .dataframe tbody tr th {\n",
125 |        "        vertical-align: top;\n",
126 |        "    }\n",
127 |        "\n",
128 |        "    .dataframe thead th {\n",
129 |        "        text-align: right;\n",
130 |        "    }\n",
131 |        "</style>\n",
132 |        "<table border=\"1\" class=\"dataframe\">\n",
133 |        "  <thead>\n",
134 |        "    <tr style=\"text-align: right;\">\n",
135 |        "      <th></th>\n",
136 |        "      <th>_id</th>\n",
137 |        "      <th>No</th>\n",
138 |        "      <th>Agency</th>\n",
139 |        "      <th>Project</th>\n",
140 |        "      <th>Budget Year</th>\n",
141 |        "      <th>Award</th>\n",
142 |        "      <th>Tumbol</th>\n",
143 |        "      <th>Amphur</th>\n",
144 |        "      <th>Province</th>\n",
145 |        "      <th>Postcode</th>\n",
146 |        "      <th>Telephone</th>\n",
147 |        "      <th>Start_Date</th>\n",
148 |        "      <th>End_Date</th>\n",
149 |        "    </tr>\n",
150 |        "  </thead>\n",
151 |        "  <tbody>\n",
152 |        "    <tr>\n",
153 |        "      <th>0</th>\n",
154 |        "      <td>1</td>\n",
155 |        "      <td>1</td>\n",
156 |        "      <td>โรงแรมดุสิตธานี พัทยา ชลบุรี</td>\n",
157 |        "      <td>Green Hotel</td>\n",
158 |        "      <td>2556</td>\n",
159 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
160 |        "      <td>นาเกลือ</td>\n",
161 |        "      <td>บางละมุง</td>\n",
162 |        "      <td>ชลบุรี</td>\n",
163 |        "      <td>20150</td>\n",
164 |        "      <td>038 425 611</td>\n",
165 |        "      <td>2556</td>\n",
166 |        "      <td>2558</td>\n",
167 |        "    </tr>\n",
168 |        "    <tr>\n",
169 |        "      <th>1</th>\n",
170 |        "      <td>2</td>\n",
171 |        "      <td>2</td>\n",
172 |        "      <td>โรงแรมสยามเบย์ ชอว์ พัทยา ชลบุรี</td>\n",
173 |        "      <td>Green Hotel</td>\n",
174 |        "      <td>2556</td>\n",
175 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
176 |        "      <td></td>\n",
177 |        "      <td>บางละมุง</td>\n",
178 |        "      <td>ชลบุรี</td>\n",
179 |        "      <td>20150</td>\n",
180 |        "      <td>038 428 678</td>\n",
181 |        "      <td>2556</td>\n",
182 |        "      <td>2558</td>\n",
183 |        "    </tr>\n",
184 |        "    <tr>\n",
185 |        "      <th>2</th>\n",
186 |        "      <td>3</td>\n",
187 |        "      <td>3</td>\n",
188 |        "      <td>โรงแรมไทยการ์เด้นรีสอร์ท พัทยา ชลบุรี</td>\n",
189 |        "      <td>Green Hotel</td>\n",
190 |        "      <td>2556</td>\n",
191 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
192 |        "      <td>หนองปรือ</td>\n",
193 |        "      <td>บางละมุง</td>\n",
194 |        "      <td>ชลบุรี</td>\n",
195 |        "      <td>20150</td>\n",
196 |        "      <td>038 370 614</td>\n",
197 |        "      <td>2556</td>\n",
198 |        "      <td>2558</td>\n",
199 |        "    </tr>\n",
200 |        "    <tr>\n",
201 |        "      <th>3</th>\n",
202 |        "      <td>4</td>\n",
203 |        "      <td>4</td>\n",
204 |        "      <td>โรงแรมลองบีช รีสอร์ท พัทยา ชลบุรี</td>\n",
205 |        "      <td>Green Hotel</td>\n",
206 |        "      <td>2556</td>\n",
207 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
208 |        "      <td></td>\n",
209 |        "      <td>บางละมุง</td>\n",
210 |        "      <td>ชลบุรี</td>\n",
211 |        "      <td>20150</td>\n",
212 |        "      <td>038-414-616-26</td>\n",
213 |        "      <td>2556</td>\n",
214 |        "      <td>2558</td>\n",
215 |        "    </tr>\n",
216 |        "    <tr>\n",
217 |        "      <th>4</th>\n",
218 |        "      <td>5</td>\n",
219 |        "      <td>5</td>\n",
220 |        "      <td>โรงแรมบ้านอัมพวารีสอร์ทแอนด์สปา สมุทรสงคราม</td>\n",
221 |        "      <td>Green Hotel</td>\n",
222 |        "      <td>2556</td>\n",
223 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
224 |        "      <td>อัมพวา</td>\n",
225 |        "      <td>อัมพวา</td>\n",
226 |        "      <td>สมุทรสงคราม</td>\n",
227 |        "      <td>75110</td>\n",
228 |        "      <td>034 752 222</td>\n",
229 |        "      <td>2556</td>\n",
230 |        "      <td>2558</td>\n",
231 |        "    </tr>\n",
232 |        "    <tr>\n",
233 |        "      <th>5</th>\n",
234 |        "      <td>6</td>\n",
235 |        "      <td>6</td>\n",
236 |        "      <td>โรงแรมบ้านทะเลดาว รีสอร์ท ประจวบคีรีขันธ์</td>\n",
237 |        "      <td>Green Hotel</td>\n",
238 |        "      <td>2556</td>\n",
239 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
240 |        "      <td></td>\n",
241 |        "      <td>หัวหิน</td>\n",
242 |        "      <td>ประจวบคีรีขันธ์</td>\n",
243 |        "      <td>77110</td>\n",
244 |        "      <td>083 253 6024</td>\n",
245 |        "      <td>2556</td>\n",
246 |        "      <td>2558</td>\n",
247 |        "    </tr>\n",
248 |        "    <tr>\n",
249 |        "      <th>6</th>\n",
250 |        "      <td>7</td>\n",
251 |        "      <td>7</td>\n",
252 |        "      <td>โรงแรมบ้านบาหยัน รีสอร์ท หัวหิน ประจวบคีรีขันธ์</td>\n",
253 |        "      <td>Green Hotel</td>\n",
254 |        "      <td>2556</td>\n",
255 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
256 |        "      <td></td>\n",
257 |        "      <td>หัวหิน</td>\n",
258 |        "      <td>ประจวบคีรีขันธ์</td>\n",
259 |        "      <td>77110</td>\n",
260 |        "      <td>032 533 544</td>\n",
261 |        "      <td>2556</td>\n",
262 |        "      <td>2558</td>\n",
263 |        "    </tr>\n",
264 |        "    <tr>\n",
265 |        "      <th>7</th>\n",
266 |        "      <td>8</td>\n",
267 |        "      <td>8</td>\n",
268 |        "      <td>โรงแรมสยามเคมเปนสกี้ กรุงเทพฯ</td>\n",
269 |        "      <td>Green Hotel</td>\n",
270 |        "      <td>2556</td>\n",
271 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
272 |        "      <td>ปทุมวัน</td>\n",
273 |        "      <td>ปทุมวัน</td>\n",
274 |        "      <td>กรุงเทพมหานคร</td>\n",
275 |        "      <td>10330</td>\n",
276 |        "      <td>02 162 9000</td>\n",
277 |        "      <td>2556</td>\n",
278 |        "      <td>2558</td>\n",
279 |        "    </tr>\n",
280 |        "    <tr>\n",
281 |        "      <th>8</th>\n",
282 |        "      <td>9</td>\n",
283 |        "      <td>9</td>\n",
284 |        "      <td>โรงแรมอมารีดอนเมือง แอร์พอร์ต กรุงเทพฯ</td>\n",
285 |        "      <td>Green Hotel</td>\n",
286 |        "      <td>2556</td>\n",
287 |        "      <td>ระดับดีเยี่ยม (ทอง)</td>\n",
288 |        "      <td>สีกัน</td>\n",
289 |        "      <td>ดอนเมือง</td>\n",
290 |        "      <td>กรุงเทพมหานคร</td>\n",
291 |        "      <td>10210</td>\n",
292 |        "      <td>02 566 1941</td>\n",
293 |        "      <td>2556</td>\n",
294 |        "      <td>2558</td>\n",
295 |        "    </tr>\n",
296 |        "    <tr>\n",
297 |        "      <th>9</th>\n",
298 |        "      <td>10</td>\n",
299 |        "      <td>10</td>\n",
300 |        "      <td>โรงแรมสยามเบย์วิว พัทยา ชลบุรี</td>\n",
301 |        "      <td>Green Hotel</td>\n",
302 |        "      <td>2556</td>\n",
303 |        "      <td>ระดับดีมาก (เงิน)</td>\n",
304 |        "      <td>หนองปรือ</td>\n",
305 |        "      <td>บางละมุง</td>\n",
306 |        "      <td>ชลบุรี</td>\n",
307 |        "      <td>20150</td>\n",
308 |        "      <td>038 423 871</td>\n",
309 |        "      <td>2556</td>\n",
310 |        "      <td>2558</td>\n",
311 |        "    </tr>\n",
312 |        "  </tbody>\n",
313 |        "</table>\n",
314 |        "</div>"
315 |       ],
316 |       "text/plain": [
317 |        "   _id  No                                            Agency      Project  \\\n",
318 |        "0    1   1                      โรงแรมดุสิตธานี พัทยา ชลบุรี  Green Hotel   \n",
319 |        "1    2   2                  โรงแรมสยามเบย์ ชอว์ พัทยา ชลบุรี  Green Hotel   \n",
320 |        "2    3   3             โรงแรมไทยการ์เด้นรีสอร์ท พัทยา ชลบุรี  Green Hotel   \n",
321 |        "3    4   4                 โรงแรมลองบีช รีสอร์ท พัทยา ชลบุรี  Green Hotel   \n",
322 |        "4    5   5     โรงแรมบ้านอัมพวารีสอร์ทแอนด์สปา สมุทรสงคราม    Green Hotel   \n",
323 |        "5    6   6         โรงแรมบ้านทะเลดาว รีสอร์ท ประจวบคีรีขันธ์  Green Hotel   \n",
324 |        "6    7   7  โรงแรมบ้านบาหยัน รีสอร์ท หัวหิน ประจวบคีรีขันธ์   Green Hotel   \n",
325 |        "7    8   8                     โรงแรมสยามเคมเปนสกี้ กรุงเทพฯ  Green Hotel   \n",
326 |        "8    9   9           โรงแรมอมารีดอนเมือง แอร์พอร์ต กรุงเทพฯ   Green Hotel   \n",
327 |        "9   10  10                    โรงแรมสยามเบย์วิว พัทยา ชลบุรี  Green Hotel   \n",
328 |        "\n",
329 |        "   Budget Year                Award    Tumbol    Amphur         Province  \\\n",
330 |        "0         2556  ระดับดีเยี่ยม (ทอง)   นาเกลือ  บางละมุง           ชลบุรี   \n",
331 |        "1         2556  ระดับดีเยี่ยม (ทอง)            บางละมุง           ชลบุรี   \n",
332 |        "2         2556  ระดับดีเยี่ยม (ทอง)  หนองปรือ  บางละมุง           ชลบุรี   \n",
333 |        "3         2556  ระดับดีเยี่ยม (ทอง)            บางละมุง           ชลบุรี   \n",
334 |        "4         2556  ระดับดีเยี่ยม (ทอง)    อัมพวา    อัมพวา      สมุทรสงคราม   \n",
335 |        "5         2556  ระดับดีเยี่ยม (ทอง)              หัวหิน  ประจวบคีรีขันธ์   \n",
336 |        "6         2556  ระดับดีเยี่ยม (ทอง)              หัวหิน  ประจวบคีรีขันธ์   \n",
337 |        "7         2556  ระดับดีเยี่ยม (ทอง)   ปทุมวัน   ปทุมวัน    กรุงเทพมหานคร   \n",
338 |        "8         2556  ระดับดีเยี่ยม (ทอง)     สีกัน  ดอนเมือง    กรุงเทพมหานคร   \n",
339 |        "9         2556    ระดับดีมาก (เงิน)  หนองปรือ  บางละมุง           ชลบุรี   \n",
340 |        "\n",
341 |        "  Postcode       Telephone  Start_Date  End_Date  \n",
342 |        "0    20150     038 425 611        2556      2558  \n",
343 |        "1    20150     038 428 678        2556      2558  \n",
344 |        "2    20150     038 370 614        2556      2558  \n",
345 |        "3    20150  038-414-616-26        2556      2558  \n",
346 |        "4    75110     034 752 222        2556      2558  \n",
347 |        "5    77110    083 253 6024        2556      2558  \n",
348 |        "6    77110     032 533 544        2556      2558  \n",
349 |        "7    10330     02 162 9000        2556      2558  \n",
350 |        "8    10210     02 566 1941        2556      2558  \n",
351 |        "9    20150     038 423 871        2556      2558  "
352 |       ]
353 |      },
354 |      "execution_count": 4,
355 |      "metadata": {},
356 |      "output_type": "execute_result"
357 |     }
358 |    ],
359 |    "source": [
360 |     "# Request headers\n",
361 |     "headers = {\n",
362 |     "    \"api-key\": api_key,\n",
363 |     "}\n",
364 |     "\n",
365 |     "params = {\"resource_id\": \"d13e3be5-9020-4a78-9a7e-760b2dcbf6be\", \"limit\": 10}\n",
366 |     "r = requests.get(\n",
367 |     "    \"https://opend.data.go.th/get-ckan/datastore_search\", params, headers=headers\n",
368 |     ")\n",
369 |     "if r.ok:\n",
370 |     "    j = r.json()\n",
371 |     "    records = j[\"result\"][\"records\"]\n",
372 |     "df = pd.DataFrame(records)\n",
373 |     "df\n"
374 |    ]
375 |   },
376 |   {
377 |    "cell_type": "code",
378 |    "execution_count": null,
379 |    "metadata": {},
380 |    "outputs": [],
381 |    "source": [
382 |     "r.json()"
383 |    ]
384 |   }
385 |  ],
386 |  "metadata": {
387 |   "kernelspec": {
388 |    "display_name": "Python 3.9.12 ('base')",
389 |    "language": "python",
390 |    "name": "python3"
391 |   },
392 |   "language_info": {
393 |    "codemirror_mode": {
394 |     "name": "ipython",
395 |     "version": 3
396 |    },
397 |    "file_extension": ".py",
398 |    "mimetype": "text/x-python",
399 |    "name": "python",
400 |    "nbconvert_exporter": "python",
401 |    "pygments_lexer": "ipython3",
402 |    "version": "3.9.12"
403 |   },
404 |   "vscode": {
405 |    "interpreter": {
406 |     "hash": "629cb9f199f624aadf5cdcebfeb0fdc9652b7a7e432d484384b677cab7914fce"
407 |    }
408 |   },
409 |   "widgets": {
410 |    "application/vnd.jupyter.widget-state+json": {
411 |     "state": {},
412 |     "version_major": 2,
413 |     "version_minor": 0
414 |    }
415 |   }
416 |  },
417 |  "nbformat": 4,
418 |  "nbformat_minor": 4
419 | }
420 | 


--------------------------------------------------------------------------------
/read_tis-620 file.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "86e1940f",
  6 |    "metadata": {
  7 |     "toc-hr-collapsed": false
  8 |    },
  9 |    "source": [
 10 |     "# read/write TIS-620 file\n",
 11 |     "* standard encoding: https://docs.python.org/3.7/library/codecs.html#standard-encodings\n"
 12 |    ]
 13 |   },
 14 |   {
 15 |    "cell_type": "markdown",
 16 |    "metadata": {},
 17 |    "source": [
 18 |     "---\n",
 19 |     "* author:  [Prasert Kanawattanachai](prasert.k@chula.ac.th)\n",
 20 |     "* YouTube: https://www.youtube.com/prasertcbs\n",
 21 |     "* github: https://github.com/prasertcbs/\n",
 22 |     "* kaggle: https://www.kaggle.com/prasertk/\n",
 23 |     "* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)\n",
 24 |     "---\n"
 25 |    ]
 26 |   },
 27 |   {
 28 |    "cell_type": "code",
 29 |    "execution_count": 1,
 30 |    "metadata": {},
 31 |    "outputs": [],
 32 |    "source": [
 33 |     "import pandas as pd\n"
 34 |    ]
 35 |   },
 36 |   {
 37 |    "cell_type": "markdown",
 38 |    "id": "86e1940f",
 39 |    "metadata": {
 40 |     "toc-hr-collapsed": false
 41 |    },
 42 |    "source": [
 43 |     "# pandas read TIS-620 file from URL\n"
 44 |    ]
 45 |   },
 46 |   {
 47 |    "cell_type": "code",
 48 |    "execution_count": null,
 49 |    "metadata": {},
 50 |    "outputs": [
 51 |     {
 52 |      "data": {
 53 |       "text/html": [
 54 |        "<div>\n",
 55 |        "<style scoped>\n",
 56 |        "    .dataframe tbody tr th:only-of-type {\n",
 57 |        "        vertical-align: middle;\n",
 58 |        "    }\n",
 59 |        "\n",
 60 |        "    .dataframe tbody tr th {\n",
 61 |        "        vertical-align: top;\n",
 62 |        "    }\n",
 63 |        "\n",
 64 |        "    .dataframe thead th {\n",
 65 |        "        text-align: right;\n",
 66 |        "    }\n",
 67 |        "</style>\n",
 68 |        "<table border=\"1\" class=\"dataframe\">\n",
 69 |        "  <thead>\n",
 70 |        "    <tr style=\"text-align: right;\">\n",
 71 |        "      <th></th>\n",
 72 |        "      <th>abbr</th>\n",
 73 |        "      <th>province_th</th>\n",
 74 |        "      <th>province_en</th>\n",
 75 |        "    </tr>\n",
 76 |        "  </thead>\n",
 77 |        "  <tbody>\n",
 78 |        "    <tr>\n",
 79 |        "      <th>0</th>\n",
 80 |        "      <td>กจ</td>\n",
 81 |        "      <td>กาญจนบุรี</td>\n",
 82 |        "      <td>Kanchanaburi</td>\n",
 83 |        "    </tr>\n",
 84 |        "    <tr>\n",
 85 |        "      <th>1</th>\n",
 86 |        "      <td>กทม</td>\n",
 87 |        "      <td>กรุงเทพมหานคร</td>\n",
 88 |        "      <td>Bangkok</td>\n",
 89 |        "    </tr>\n",
 90 |        "    <tr>\n",
 91 |        "      <th>2</th>\n",
 92 |        "      <td>จบ</td>\n",
 93 |        "      <td>จันทบุรี</td>\n",
 94 |        "      <td>Chanthaburi</td>\n",
 95 |        "    </tr>\n",
 96 |        "    <tr>\n",
 97 |        "      <th>3</th>\n",
 98 |        "      <td>ฉช</td>\n",
 99 |        "      <td>ฉะเชิงเทรา</td>\n",
100 |        "      <td>Chachoengsao</td>\n",
101 |        "    </tr>\n",
102 |        "    <tr>\n",
103 |        "      <th>4</th>\n",
104 |        "      <td>ชน</td>\n",
105 |        "      <td>ชัยนาท</td>\n",
106 |        "      <td>Chainat</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>...</th>\n",
110 |        "      <td>...</td>\n",
111 |        "      <td>...</td>\n",
112 |        "      <td>...</td>\n",
113 |        "    </tr>\n",
114 |        "    <tr>\n",
115 |        "      <th>72</th>\n",
116 |        "      <td>ยล</td>\n",
117 |        "      <td>ยะลา</td>\n",
118 |        "      <td>Yala</td>\n",
119 |        "    </tr>\n",
120 |        "    <tr>\n",
121 |        "      <th>73</th>\n",
122 |        "      <td>รน</td>\n",
123 |        "      <td>ระนอง</td>\n",
124 |        "      <td>Ranong</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>74</th>\n",
128 |        "      <td>สข</td>\n",
129 |        "      <td>สงขลา</td>\n",
130 |        "      <td>Songkhla</td>\n",
131 |        "    </tr>\n",
132 |        "    <tr>\n",
133 |        "      <th>75</th>\n",
134 |        "      <td>สฎ</td>\n",
135 |        "      <td>สุราษฎร์ธานี</td>\n",
136 |        "      <td>Surat Thani</td>\n",
137 |        "    </tr>\n",
138 |        "    <tr>\n",
139 |        "      <th>76</th>\n",
140 |        "      <td>สต</td>\n",
141 |        "      <td>สตูล</td>\n",
142 |        "      <td>Satun</td>\n",
143 |        "    </tr>\n",
144 |        "  </tbody>\n",
145 |        "</table>\n",
146 |        "<p>77 rows × 3 columns</p>\n",
147 |        "</div>"
148 |       ],
149 |       "text/plain": [
150 |        "   abbr    province_th   province_en\n",
151 |        "0    กจ      กาญจนบุรี  Kanchanaburi\n",
152 |        "1   กทม  กรุงเทพมหานคร       Bangkok\n",
153 |        "2    จบ       จันทบุรี   Chanthaburi\n",
154 |        "3    ฉช     ฉะเชิงเทรา  Chachoengsao\n",
155 |        "4    ชน         ชัยนาท       Chainat\n",
156 |        "..  ...            ...           ...\n",
157 |        "72   ยล           ยะลา          Yala\n",
158 |        "73   รน          ระนอง        Ranong\n",
159 |        "74   สข          สงขลา      Songkhla\n",
160 |        "75   สฎ   สุราษฎร์ธานี   Surat Thani\n",
161 |        "76   สต           สตูล         Satun\n",
162 |        "\n",
163 |        "[77 rows x 3 columns]"
164 |       ]
165 |      },
166 |      "metadata": {},
167 |      "output_type": "display_data"
168 |     }
169 |    ],
170 |    "source": [
171 |     "url = \"https://raw.githubusercontent.com/prasertcbs/basic-dataset/master/province_tis.csv\"\n",
172 |     "# url = \"https://data.go.th/dataset/c24ec42f-db0d-4a33-9e13-a98cd22de74d/resource/3d222c43-dea2-43f5-ad0d-7f0e58d4ad54/download/flightrule_11_2021.csv\"\n",
173 |     "# df = pd.read_csv(url, encoding=\"utf-8\")\n",
174 |     "df = pd.read_csv(url, encoding=\"iso8859_11\")\n",
175 |     "# df = pd.read_csv(url, encoding=\"tis-620\")\n",
176 |     "# df = pd.read_csv(url, encoding=\"thai\")\n",
177 |     "# df = pd.read_csv(url, encoding=\"cp874\")\n",
178 |     "df\n"
179 |    ]
180 |   },
181 |   {
182 |    "cell_type": "code",
183 |    "execution_count": 9,
184 |    "metadata": {},
185 |    "outputs": [],
186 |    "source": [
187 |     "df.to_csv(\"province_utf-8.csv\", index=False)\n",
188 |     "\n"
189 |    ]
190 |   },
191 |   {
192 |    "cell_type": "markdown",
193 |    "metadata": {},
194 |    "source": [
195 |     "# Save as TIS-620\n"
196 |    ]
197 |   },
198 |   {
199 |    "cell_type": "code",
200 |    "execution_count": 5,
201 |    "metadata": {},
202 |    "outputs": [],
203 |    "source": [
204 |     "df.to_csv(\"province_tis-620.csv\", index=False, encoding=\"iso8859_11\")\n",
205 |     "\n"
206 |    ]
207 |   },
208 |   {
209 |    "cell_type": "markdown",
210 |    "metadata": {},
211 |    "source": [
212 |     "# Read TIS-620 file from local\n"
213 |    ]
214 |   },
215 |   {
216 |    "cell_type": "code",
217 |    "execution_count": 6,
218 |    "metadata": {},
219 |    "outputs": [
220 |     {
221 |      "name": "stdout",
222 |      "output_type": "stream",
223 |      "text": [
224 |       "abbr,province_th,province_en\n",
225 |       "กจ,กาญจนบุรี,Kanchanaburi\n",
226 |       "กทม,กรุงเทพมหานคร,Bangkok\n",
227 |       "จบ,จันทบุรี,Chanthaburi\n",
228 |       "ฉช,ฉะเชิงเทรา,Chachoengsao\n",
229 |       "ชน,ชัยนาท,Chainat\n",
230 |       "ชบ,ชลบุรี,Chonburi\n",
231 |       "ตร,ตราด,Trat\n",
232 |       "นฐ,นครปฐม,Nakhon Pathom\n",
233 |       "นบ,นนทบุรี,Nonthaburi\n",
234 |       "นย,นครนายก,Nakhon Nayok\n",
235 |       "ปข,ประจวบคีรีขันธ์,Prachuap Khiri Khan\n",
236 |       "ปจ,ปราจีนบุรี,Prachinburi\n",
237 |       "ปท,ปทุมธานี,Pathum Thani\n",
238 |       "พบ,เพชรบุรี,Phetchaburi\n",
239 |       "รบ,ราชบุรี,Ratchaburi\n",
240 |       "รย,ระยอง,Rayong\n",
241 |       "ลบ,ลพบุรี,Lopburi\n",
242 |       "สก,สระแก้ว,Sa Kaeo\n",
243 |       "สค,สมุทรสาคร,Samut Sakhon\n",
244 |       "สบ,สระบุรี,Saraburi\n",
245 |       "สป,สมุทรปราการ,Samut Prakan\n",
246 |       "สพ,สุพรรณบุรี,Suphan Buri\n",
247 |       "สส,สมุทรสงคราม,Samut Songkhram\n",
248 |       "สห,สิงห์บุรี,Sing Buri\n",
249 |       "อท,อ่างทอง,Ang Thong\n",
250 |       "อย,พระนครศรีอยุธยา,Phra Nakhon Si Ayutthaya\n",
251 |       "กพ,กำแพงเพชร,Kamphaeng Phet\n",
252 |       "ชม,เชียงใหม่,Chiang Mai\n",
253 |       "ชร,เชียงราย,Chiang Rai\n",
254 |       "ตก,ตาก,Tak\n",
255 |       "นน,น่าน,Nan\n",
256 |       "นว,นครสวรรค์,Nakhon Sawan\n",
257 |       "พจ,พิจิตร,Phichit\n",
258 |       "พช,เพชรบูรณ์,Phetchabun\n",
259 |       "พย,พะเยา,Phayao\n",
260 |       "พร,แพร่,Phrae\n",
261 |       "พล,พิษณุโลก,Phitsanulok\n",
262 |       "มส,แม่ฮ่องสอน,Mae Hong Son\n",
263 |       "ลป,ลำปาง,Lampang\n",
264 |       "ลพ,ลำพูน,Lamphun\n",
265 |       "สท,สุโขทัย,Sukhothai\n",
266 |       "อต,อุตรดิตถ์,Uttaradit\n",
267 |       "อน,อุทัยธานี,Uthai Thani\n",
268 |       "กส,กาฬสินธุ์,Kalasin\n",
269 |       "ขก,ขอนแก่น,Khon Kaen\n",
270 |       "ชย,ชัยภูมิ,Chaiyaphum\n",
271 |       "นค,หนองคาย,Nong Khai\n",
272 |       "นพ,นครพนม,Nakhon Phanom\n",
273 |       "นภ,หนองบัวลำภู,Nong Bua Lamphu\n",
274 |       "นม,นครราชสีมา,Nakhon Ratchasima\n",
275 |       "บก,บึงกาฬ,Bueng Kan\n",
276 |       "บร,บุรีรัมย์,Buriram\n",
277 |       "มค,มหาสารคาม,Maha Sarakham\n",
278 |       "มห,มุกดาหาร,Mukdahan\n",
279 |       "ยส,ยโสธร,Yasothon\n",
280 |       "รอ,ร้อยเอ็ด,Roi Et\n",
281 |       "ลย,เลย,Loei\n",
282 |       "ศก,ศรีสะเกษ,Sisaket\n",
283 |       "สน,สกลนคร,Sakon Nakhon\n",
284 |       "สร,สุรินทร์,Surin\n",
285 |       "อจ,อำนาจเจริญ,Amnat Charoen\n",
286 |       "อด,อุดรธานี,Udon Thani\n",
287 |       "อบ,อุบลราชธานี,Ubon Ratchathani\n",
288 |       "กบ,กระบี่,Krabi\n",
289 |       "ชพ,ชุมพร,Chumphon\n",
290 |       "ตง,ตรัง,Trang\n",
291 |       "นธ,นราธิวาส,Narathiwat\n",
292 |       "นศ,นครศรีธรรมราช,Nakhon Si Thammarat\n",
293 |       "ปน,ปัตตานี,Pattani\n",
294 |       "พง,พังงา,Phang Nga\n",
295 |       "พท,พัทลุง,Phattalung\n",
296 |       "ภก,ภูเก็ต,Phuket\n",
297 |       "ยล,ยะลา,Yala\n",
298 |       "รน,ระนอง,Ranong\n",
299 |       "สข,สงขลา,Songkhla\n",
300 |       "สฎ,สุราษฎร์ธานี,Surat Thani\n",
301 |       "สต,สตูล,Satun\n",
302 |       "\n"
303 |      ]
304 |     }
305 |    ],
306 |    "source": [
307 |     "with open(\"province_tis-620.csv\", \"r\", encoding=\"iso8859_11\") as f:\n",
308 |     "    print(f.read())\n",
309 |     "\n"
310 |    ]
311 |   },
312 |   {
313 |    "cell_type": "markdown",
314 |    "metadata": {},
315 |    "source": [
316 |     "# read text file from URL\n"
317 |    ]
318 |   },
319 |   {
320 |    "cell_type": "code",
321 |    "execution_count": null,
322 |    "metadata": {},
323 |    "outputs": [
324 |     {
325 |      "name": "stdout",
326 |      "output_type": "stream",
327 |      "text": [
328 |       "p_id,name,name_en,region,area_km2\n",
329 |       "กจ,กาญจนบุรี,Kanchanaburi,C,19483.148\n",
330 |       "กทม,กรุงเทพมหานคร,Bangkok,C,1568.737\n",
331 |       "จบ,จันทบุรี,Chanthaburi,C,6338\n",
332 |       "ฉช,ฉะเชิงเทรา,Chachoengsao,C,5351\n",
333 |       "ชน,ชัยนาท,Chainat,C,2469.746\n",
334 |       "ชบ,ชลบุรี,Chonburi,C,4611.829\n",
335 |       "ตร,ตราด,Trat,C,2819\n",
336 |       "นฐ,นครปฐม,Nakhon Pathom,C,2168.327\n",
337 |       "นบ,นนทบุรี,Nonthaburi,C,622.303\n",
338 |       "นย,นครนายก,Nakhon Nayok,C,2122\n",
339 |       "ปข,ประจวบคีรีขันธ์,Prachuap Khiri Khan,C,6367.62\n",
340 |       "ปจ,ปราจีนบุรี,Prachinburi,C,4762.362\n",
341 |       "ปท,ปทุมธานี,Pathum Thani,C,1525.856\n",
342 |       "พบ,เพชรบุรี,Phetchaburi,C,6225.138\n",
343 |       "รบ,ราชบุรี,Ratchaburi,C,5196.462\n",
344 |       "รย,ระยอง,Rayong,C,3552\n",
345 |       "ลบ,ลพบุรี,Lopburi,C,6199.753\n",
346 |       "สก,สระแก้ว,Sa Kaeo,C,7195.436\n",
347 |       "สค,สมุทรสาคร,Samut Sakhon,C,872.347\n",
348 |       "สบ,สระบุรี,Saraburi,C,3576.486\n",
349 |       "สป,สมุทรปราการ,Samut Prakan,C,1004.092\n",
350 |       "สพ,สุพรรณบุรี,Suphan Buri,C,5358.008\n",
351 |       "สส,สมุทรสงคราม,Samut Songkhram,C,416.707\n",
352 |       "สห,สิงห์บุรี,Sing Buri,C,822.478\n",
353 |       "อท,อ่างทอง,Ang Thong,C,968.372\n",
354 |       "อย,พระนครศรีอยุธยา,Phra Nakhon Si Ayutthaya,C,2556.64\n",
355 |       "กพ,กำแพงเพชร,Kamphaeng Phet,N,8607.49\n",
356 |       "ชม,เชียงใหม่,Chiang Mai,N,20107.057\n",
357 |       "ชร,เชียงราย,Chiang Rai,N,11678.369\n",
358 |       "ตก,ตาก,Tak,N,16406.65\n",
359 |       "นน,น่าน,Nan,N,11472.072\n",
360 |       "นว,นครสวรรค์,Nakhon Sawan,N,9597.677\n",
361 |       "พจ,พิจิตร,Phichit,N,4531.013\n",
362 |       "พช,เพชรบูรณ์,Phetchabun,N,12668.416\n",
363 |       "พย,พะเยา,Phayao,N,6335.06\n",
364 |       "พร,แพร่,Phrae,N,6538.598\n",
365 |       "พล,พิษณุโลก,Phitsanulok,N,10815.854\n",
366 |       "มส,แม่ฮ่องสอน,Mae Hong Son,N,12681.259\n",
367 |       "ลป,ลำปาง,Lampang,N,12533.961\n",
368 |       "ลพ,ลำพูน,Lamphun,N,4505.882\n",
369 |       "สท,สุโขทัย,Sukhothai,N,6596.092\n",
370 |       "อต,อุตรดิตถ์,Uttaradit,N,7838.592\n",
371 |       "อน,อุทัยธานี,Uthai Thani,N,6730.246\n",
372 |       "กส,กาฬสินธุ์,Kalasin,NE,6946.746\n",
373 |       "ขก,ขอนแก่น,Khon Kaen,NE,10885.991\n",
374 |       "ชย,ชัยภูมิ,Chaiyaphum,NE,12778.287\n",
375 |       "นค,หนองคาย,Nong Khai,NE,3027.28\n",
376 |       "นพ,นครพนม,Nakhon Phanom,NE,5512.668\n",
377 |       "นภ,หนองบัวลำภู,Nong Bua Lamphu,NE,3859.086\n",
378 |       "นม,นครราชสีมา,Nakhon Ratchasima,NE,20493.964\n",
379 |       "บก,บึงกาฬ,Bueng Kan,NE,4305\n",
380 |       "บร,บุรีรัมย์,Buriram,NE,10322.885\n",
381 |       "มค,มหาสารคาม,Maha Sarakham,NE,5291.683\n",
382 |       "มห,มุกดาหาร,Mukdahan,NE,4339.83\n",
383 |       "ยส,ยโสธร,Yasothon,NE,4161.664\n",
384 |       "รอ,ร้อยเอ็ด,Roi Et,NE,8299.449\n",
385 |       "ลย,เลย,Loei,NE,11424.612\n",
386 |       "ศก,ศรีสะเกษ,Sisaket,NE,8839.976\n",
387 |       "สน,สกลนคร,Sakon Nakhon,NE,9605.764\n",
388 |       "สร,สุรินทร์,Surin,NE,8124.056\n",
389 |       "อจ,อำนาจเจริญ,Amnat Charoen,NE,3161.248\n",
390 |       "อด,อุดรธานี,Udon Thani,NE,11730.302\n",
391 |       "อบ,อุบลราชธานี,Ubon Ratchathani,NE,16112.65\n",
392 |       "กบ,กระบี่,Krabi,S,4708.512\n",
393 |       "ชพ,ชุมพร,Chumphon,S,6010.849\n",
394 |       "ตง,ตรัง,Trang,S,4917.519\n",
395 |       "นธ,นราธิวาส,Narathiwat,S,4475.43\n",
396 |       "นศ,นครศรีธรรมราช,Nakhon Si Thammarat,S,9942.502\n",
397 |       "ปน,ปัตตานี,Pattani,S,1940.356\n",
398 |       "พง,พังงา,Phang Nga,S,4170.895\n",
399 |       "พท,พัทลุง,Phattalung,S,3424.473\n",
400 |       "ภก,ภูเก็ต,Phuket,S,543.034\n",
401 |       "ยล,ยะลา,Yala,S,4521.078\n",
402 |       "รน,ระนอง,Ranong,S,3298.045\n",
403 |       "สข,สงขลา,Songkhla,S,7393.889\n",
404 |       "สฎ,สุราษฎร์ธานี,Surat Thani,S,12891.469\n",
405 |       "สต,สตูล,Satun,S,2478.977\n",
406 |       "\n"
407 |      ]
408 |     }
409 |    ],
410 |    "source": [
411 |     "import requests\n",
412 |     "\n",
413 |     "url = \"https://raw.githubusercontent.com/prasertcbs/basic-dataset/master/province.csv\"\n",
414 |     "# url=\"https://data.go.th/dataset/78e7405b-947e-4752-a10c-67c4b819ec74/resource/1cdb37ad-f52b-4a94-a829-c1876421ea83/download/usage_64_sep.csv\"\n",
415 |     "r = requests.get(url)  # r -> response\n",
416 |     "if r.ok:\n",
417 |     "    s = r.text\n",
418 |     "    print(s)\n",
419 |     "    # print(s.splitlines())\n"
420 |    ]
421 |   },
422 |   {
423 |    "cell_type": "markdown",
424 |    "metadata": {},
425 |    "source": [
426 |     "# read TIS-620 from URL\n",
427 |     "\n",
428 |     "-   standard encoding: https://docs.python.org/3.7/library/codecs.html#standard-encodings\n"
429 |    ]
430 |   },
431 |   {
432 |    "cell_type": "code",
433 |    "execution_count": null,
434 |    "metadata": {},
435 |    "outputs": [],
436 |    "source": [
437 |     "import requests\n",
438 |     "\n",
439 |     "# url = \"https://raw.githubusercontent.com/prasertcbs/basic-dataset/master/province_tis.csv\"\n",
440 |     "url = \"https://data.go.th/dataset/78e7405b-947e-4752-a10c-67c4b819ec74/resource/1cdb37ad-f52b-4a94-a829-c1876421ea83/download/usage_64_sep.csv\"\n",
441 |     "\n",
442 |     "r = requests.get(url)\n",
443 |     "if r.ok:\n",
444 |     "    s = r.content.decode(\"iso8859_11\")\n",
445 |     "    # s = r.content.decode('cp874') # code page 874\n",
446 |     "    # s = r.content.decode(\"thai\")\n",
447 |     "    # s = r.content.decode('tis-620')\n",
448 |     "    print(s)\n",
449 |     "    # print(s.splitlines())\n"
450 |    ]
451 |   }
452 |  ],
453 |  "metadata": {
454 |   "interpreter": {
455 |    "hash": "eefe6ca76f6e878a3e3929bbec9156982baa217b1f16ff8dc984bf661b4791cc"
456 |   },
457 |   "kernelspec": {
458 |    "display_name": "Python 3.8.12 ('base')",
459 |    "language": "python",
460 |    "name": "python3"
461 |   },
462 |   "language_info": {
463 |    "codemirror_mode": {
464 |     "name": "ipython",
465 |     "version": 3
466 |    },
467 |    "file_extension": ".py",
468 |    "mimetype": "text/x-python",
469 |    "name": "python",
470 |    "nbconvert_exporter": "python",
471 |    "pygments_lexer": "ipython3",
472 |    "version": "3.8.13"
473 |   },
474 |   "orig_nbformat": 4
475 |  },
476 |  "nbformat": 4,
477 |  "nbformat_minor": 2
478 | }
479 | 


--------------------------------------------------------------------------------
/bangkok open data.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# [Bangkok open data](https://data.bangkok.go.th/dataset/)\n",
  8 |     "\n",
  9 |     "---\n",
 10 |     "* author:  [Prasert Kanawattanachai](prasert.k@chula.ac.th)\n",
 11 |     "* YouTube: https://www.youtube.com/prasertcbs\n",
 12 |     "* github: https://github.com/prasertcbs/\n",
 13 |     "* kaggle: https://www.kaggle.com/prasertk/\n",
 14 |     "* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)\n",
 15 |     "---\n"
 16 |    ]
 17 |   },
 18 |   {
 19 |    "cell_type": "code",
 20 |    "execution_count": null,
 21 |    "metadata": {},
 22 |    "outputs": [],
 23 |    "source": [
 24 |     "import requests\n",
 25 |     "import pandas as pd\n"
 26 |    ]
 27 |   },
 28 |   {
 29 |    "cell_type": "code",
 30 |    "execution_count": 10,
 31 |    "metadata": {},
 32 |    "outputs": [
 33 |     {
 34 |      "data": {
 35 |       "text/html": [
 36 |        "<div>\n",
 37 |        "<style scoped>\n",
 38 |        "    .dataframe tbody tr th:only-of-type {\n",
 39 |        "        vertical-align: middle;\n",
 40 |        "    }\n",
 41 |        "\n",
 42 |        "    .dataframe tbody tr th {\n",
 43 |        "        vertical-align: top;\n",
 44 |        "    }\n",
 45 |        "\n",
 46 |        "    .dataframe thead th {\n",
 47 |        "        text-align: right;\n",
 48 |        "    }\n",
 49 |        "</style>\n",
 50 |        "<table border=\"1\" class=\"dataframe\">\n",
 51 |        "  <thead>\n",
 52 |        "    <tr style=\"text-align: right;\">\n",
 53 |        "      <th></th>\n",
 54 |        "      <th>id</th>\n",
 55 |        "      <th>station_area</th>\n",
 56 |        "      <th>pm10_min</th>\n",
 57 |        "      <th>pm10_max</th>\n",
 58 |        "      <th>pm10_overstd</th>\n",
 59 |        "      <th>pm10_count</th>\n",
 60 |        "      <th>no2_min</th>\n",
 61 |        "      <th>no2_max</th>\n",
 62 |        "      <th>no2_overstd</th>\n",
 63 |        "      <th>no2_count</th>\n",
 64 |        "      <th>...</th>\n",
 65 |        "      <th>o38h_min</th>\n",
 66 |        "      <th>o38h_max</th>\n",
 67 |        "      <th>o38h_overstd</th>\n",
 68 |        "      <th>o38h_count</th>\n",
 69 |        "      <th>pm2_5_min</th>\n",
 70 |        "      <th>pm2_5_max</th>\n",
 71 |        "      <th>pm2_5_overstd</th>\n",
 72 |        "      <th>pm2_5_count</th>\n",
 73 |        "      <th>adddate</th>\n",
 74 |        "      <th>updatedate</th>\n",
 75 |        "    </tr>\n",
 76 |        "  </thead>\n",
 77 |        "  <tbody>\n",
 78 |        "    <tr>\n",
 79 |        "      <th>0</th>\n",
 80 |        "      <td>6</td>\n",
 81 |        "      <td>ค่ามาตรฐาน</td>\n",
 82 |        "      <td>-</td>\n",
 83 |        "      <td>-</td>\n",
 84 |        "      <td>120</td>\n",
 85 |        "      <td>-</td>\n",
 86 |        "      <td>-</td>\n",
 87 |        "      <td>-</td>\n",
 88 |        "      <td>170</td>\n",
 89 |        "      <td>-</td>\n",
 90 |        "      <td>...</td>\n",
 91 |        "      <td>-</td>\n",
 92 |        "      <td>-</td>\n",
 93 |        "      <td>70</td>\n",
 94 |        "      <td>-</td>\n",
 95 |        "      <td>-</td>\n",
 96 |        "      <td>-</td>\n",
 97 |        "      <td>50</td>\n",
 98 |        "      <td>-</td>\n",
 99 |        "      <td>2020-05-23</td>\n",
100 |        "      <td>2020-09-15</td>\n",
101 |        "    </tr>\n",
102 |        "    <tr>\n",
103 |        "      <th>1</th>\n",
104 |        "      <td>5</td>\n",
105 |        "      <td>สรุป</td>\n",
106 |        "      <td>13</td>\n",
107 |        "      <td>185</td>\n",
108 |        "      <td>59</td>\n",
109 |        "      <td>1434</td>\n",
110 |        "      <td>0</td>\n",
111 |        "      <td>158.4</td>\n",
112 |        "      <td>0</td>\n",
113 |        "      <td>33142</td>\n",
114 |        "      <td>...</td>\n",
115 |        "      <td>0</td>\n",
116 |        "      <td>79.4</td>\n",
117 |        "      <td>9</td>\n",
118 |        "      <td>8665</td>\n",
119 |        "      <td>9</td>\n",
120 |        "      <td>76</td>\n",
121 |        "      <td>16</td>\n",
122 |        "      <td>354</td>\n",
123 |        "      <td>2020-05-23</td>\n",
124 |        "      <td>2020-09-15</td>\n",
125 |        "    </tr>\n",
126 |        "    <tr>\n",
127 |        "      <th>2</th>\n",
128 |        "      <td>4</td>\n",
129 |        "      <td>ราชเทวี</td>\n",
130 |        "      <td>18</td>\n",
131 |        "      <td>125</td>\n",
132 |        "      <td>1</td>\n",
133 |        "      <td>354</td>\n",
134 |        "      <td>2</td>\n",
135 |        "      <td>100</td>\n",
136 |        "      <td>0</td>\n",
137 |        "      <td>8372</td>\n",
138 |        "      <td>...</td>\n",
139 |        "      <td>0</td>\n",
140 |        "      <td>79.4</td>\n",
141 |        "      <td>9</td>\n",
142 |        "      <td>8665</td>\n",
143 |        "      <td>9</td>\n",
144 |        "      <td>76</td>\n",
145 |        "      <td>16</td>\n",
146 |        "      <td>354</td>\n",
147 |        "      <td>2020-05-23</td>\n",
148 |        "      <td>2020-09-15</td>\n",
149 |        "    </tr>\n",
150 |        "    <tr>\n",
151 |        "      <th>3</th>\n",
152 |        "      <td>3</td>\n",
153 |        "      <td>ราษฎร์บูรณะ</td>\n",
154 |        "      <td>36</td>\n",
155 |        "      <td>165</td>\n",
156 |        "      <td>16</td>\n",
157 |        "      <td>361</td>\n",
158 |        "      <td>0</td>\n",
159 |        "      <td>116.2</td>\n",
160 |        "      <td>0</td>\n",
161 |        "      <td>8355</td>\n",
162 |        "      <td>...</td>\n",
163 |        "      <td>-</td>\n",
164 |        "      <td>-</td>\n",
165 |        "      <td>-</td>\n",
166 |        "      <td>-</td>\n",
167 |        "      <td>-</td>\n",
168 |        "      <td>-</td>\n",
169 |        "      <td>-</td>\n",
170 |        "      <td>-</td>\n",
171 |        "      <td>2020-05-23</td>\n",
172 |        "      <td>2020-09-15</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>4</th>\n",
176 |        "      <td>2</td>\n",
177 |        "      <td>พระโขนง</td>\n",
178 |        "      <td>13</td>\n",
179 |        "      <td>155</td>\n",
180 |        "      <td>14</td>\n",
181 |        "      <td>362</td>\n",
182 |        "      <td>0</td>\n",
183 |        "      <td>158.4</td>\n",
184 |        "      <td>0</td>\n",
185 |        "      <td>8286</td>\n",
186 |        "      <td>...</td>\n",
187 |        "      <td>-</td>\n",
188 |        "      <td>-</td>\n",
189 |        "      <td>-</td>\n",
190 |        "      <td>-</td>\n",
191 |        "      <td>-</td>\n",
192 |        "      <td>-</td>\n",
193 |        "      <td>-</td>\n",
194 |        "      <td>-</td>\n",
195 |        "      <td>2020-05-23</td>\n",
196 |        "      <td>2020-09-15</td>\n",
197 |        "    </tr>\n",
198 |        "    <tr>\n",
199 |        "      <th>5</th>\n",
200 |        "      <td>1</td>\n",
201 |        "      <td>ดินแดง</td>\n",
202 |        "      <td>29</td>\n",
203 |        "      <td>185</td>\n",
204 |        "      <td>28</td>\n",
205 |        "      <td>357</td>\n",
206 |        "      <td>0</td>\n",
207 |        "      <td>136.6</td>\n",
208 |        "      <td>0</td>\n",
209 |        "      <td>8129</td>\n",
210 |        "      <td>...</td>\n",
211 |        "      <td>-</td>\n",
212 |        "      <td>-</td>\n",
213 |        "      <td>-</td>\n",
214 |        "      <td>-</td>\n",
215 |        "      <td>-</td>\n",
216 |        "      <td>-</td>\n",
217 |        "      <td>-</td>\n",
218 |        "      <td>-</td>\n",
219 |        "      <td>2020-05-23</td>\n",
220 |        "      <td>2020-10-15</td>\n",
221 |        "    </tr>\n",
222 |        "  </tbody>\n",
223 |        "</table>\n",
224 |        "<p>6 rows × 32 columns</p>\n",
225 |        "</div>"
226 |       ],
227 |       "text/plain": [
228 |        "   id station_area pm10_min pm10_max  pm10_overstd pm10_count no2_min no2_max  \\\n",
229 |        "0   6   ค่ามาตรฐาน        -        -           120          -       -       -   \n",
230 |        "1   5         สรุป       13      185            59       1434       0   158.4   \n",
231 |        "2   4      ราชเทวี       18      125             1        354       2     100   \n",
232 |        "3   3  ราษฎร์บูรณะ       36      165            16        361       0   116.2   \n",
233 |        "4   2      พระโขนง       13      155            14        362       0   158.4   \n",
234 |        "5   1       ดินแดง       29      185            28        357       0   136.6   \n",
235 |        "\n",
236 |        "   no2_overstd no2_count  ... o38h_min o38h_max  o38h_overstd o38h_count  \\\n",
237 |        "0          170         -  ...        -        -            70          -   \n",
238 |        "1            0     33142  ...        0     79.4             9       8665   \n",
239 |        "2            0      8372  ...        0     79.4             9       8665   \n",
240 |        "3            0      8355  ...        -        -             -          -   \n",
241 |        "4            0      8286  ...        -        -             -          -   \n",
242 |        "5            0      8129  ...        -        -             -          -   \n",
243 |        "\n",
244 |        "  pm2_5_min pm2_5_max  pm2_5_overstd pm2_5_count     adddate  updatedate  \n",
245 |        "0         -         -             50           -  2020-05-23  2020-09-15  \n",
246 |        "1         9        76             16         354  2020-05-23  2020-09-15  \n",
247 |        "2         9        76             16         354  2020-05-23  2020-09-15  \n",
248 |        "3         -         -              -           -  2020-05-23  2020-09-15  \n",
249 |        "4         -         -              -           -  2020-05-23  2020-09-15  \n",
250 |        "5         -         -              -           -  2020-05-23  2020-10-15  \n",
251 |        "\n",
252 |        "[6 rows x 32 columns]"
253 |       ]
254 |      },
255 |      "execution_count": 10,
256 |      "metadata": {},
257 |      "output_type": "execute_result"
258 |     }
259 |    ],
260 |    "source": [
261 |     "url=\"https://data.bangkok.go.th/dataset/52a5da69-c086-425a-bcb3-fccfadd824f5/resource/1aecc616-c570-4efb-9398-7dd4e39356b2/download/env_5ec8dad38033f.csv\"\n",
262 |     "df=pd.read_csv(url)\n",
263 |     "df"
264 |    ]
265 |   },
266 |   {
267 |    "cell_type": "code",
268 |    "execution_count": 5,
269 |    "metadata": {},
270 |    "outputs": [
271 |     {
272 |      "data": {
273 |       "text/html": [
274 |        "<div>\n",
275 |        "<style scoped>\n",
276 |        "    .dataframe tbody tr th:only-of-type {\n",
277 |        "        vertical-align: middle;\n",
278 |        "    }\n",
279 |        "\n",
280 |        "    .dataframe tbody tr th {\n",
281 |        "        vertical-align: top;\n",
282 |        "    }\n",
283 |        "\n",
284 |        "    .dataframe thead th {\n",
285 |        "        text-align: right;\n",
286 |        "    }\n",
287 |        "</style>\n",
288 |        "<table border=\"1\" class=\"dataframe\">\n",
289 |        "  <thead>\n",
290 |        "    <tr style=\"text-align: right;\">\n",
291 |        "      <th></th>\n",
292 |        "      <th>year</th>\n",
293 |        "      <th>No.</th>\n",
294 |        "      <th>Province</th>\n",
295 |        "      <th>แหล่งน้ำ</th>\n",
296 |        "      <th>WQI score</th>\n",
297 |        "      <th>Type</th>\n",
298 |        "    </tr>\n",
299 |        "  </thead>\n",
300 |        "  <tbody>\n",
301 |        "    <tr>\n",
302 |        "      <th>0</th>\n",
303 |        "      <td>2561</td>\n",
304 |        "      <td>64</td>\n",
305 |        "      <td>กรุงเทพมหานคร</td>\n",
306 |        "      <td>แม่น้ำเจ้าพระยา</td>\n",
307 |        "      <td>35</td>\n",
308 |        "      <td>4</td>\n",
309 |        "    </tr>\n",
310 |        "    <tr>\n",
311 |        "      <th>1</th>\n",
312 |        "      <td>2562</td>\n",
313 |        "      <td>64</td>\n",
314 |        "      <td>กรุงเทพมหานคร</td>\n",
315 |        "      <td>แม่น้ำเจ้าพระยา</td>\n",
316 |        "      <td>36</td>\n",
317 |        "      <td>4</td>\n",
318 |        "    </tr>\n",
319 |        "    <tr>\n",
320 |        "      <th>2</th>\n",
321 |        "      <td>2563</td>\n",
322 |        "      <td>64</td>\n",
323 |        "      <td>กรุงเทพมหานคร</td>\n",
324 |        "      <td>แม่น้ำเจ้าพระยา</td>\n",
325 |        "      <td>38</td>\n",
326 |        "      <td>4</td>\n",
327 |        "    </tr>\n",
328 |        "  </tbody>\n",
329 |        "</table>\n",
330 |        "</div>"
331 |       ],
332 |       "text/plain": [
333 |        "   year  No.       Province         แหล่งน้ำ  WQI score  Type\n",
334 |        "0  2561   64  กรุงเทพมหานคร  แม่น้ำเจ้าพระยา         35     4\n",
335 |        "1  2562   64  กรุงเทพมหานคร  แม่น้ำเจ้าพระยา         36     4\n",
336 |        "2  2563   64  กรุงเทพมหานคร  แม่น้ำเจ้าพระยา         38     4"
337 |       ]
338 |      },
339 |      "execution_count": 5,
340 |      "metadata": {},
341 |      "output_type": "execute_result"
342 |     }
343 |    ],
344 |    "source": [
345 |     "url=\"https://data.bangkok.go.th/dataset/d7139135-3988-40fa-8703-d546eb480960/resource/70c4f872-6e4b-42db-b8c4-474ef29e0245/download/wq-4-wqi-..xlsx\"\n",
346 |     "df=pd.read_excel(url)\n",
347 |     "df"
348 |    ]
349 |   },
350 |   {
351 |    "cell_type": "code",
352 |    "execution_count": 17,
353 |    "metadata": {},
354 |    "outputs": [
355 |     {
356 |      "data": {
357 |       "text/html": [
358 |        "<div>\n",
359 |        "<style scoped>\n",
360 |        "    .dataframe tbody tr th:only-of-type {\n",
361 |        "        vertical-align: middle;\n",
362 |        "    }\n",
363 |        "\n",
364 |        "    .dataframe tbody tr th {\n",
365 |        "        vertical-align: top;\n",
366 |        "    }\n",
367 |        "\n",
368 |        "    .dataframe thead th {\n",
369 |        "        text-align: right;\n",
370 |        "    }\n",
371 |        "</style>\n",
372 |        "<table border=\"1\" class=\"dataframe\">\n",
373 |        "  <thead>\n",
374 |        "    <tr style=\"text-align: right;\">\n",
375 |        "      <th></th>\n",
376 |        "      <th>_id</th>\n",
377 |        "      <th>drag_g_id</th>\n",
378 |        "      <th>dcode</th>\n",
379 |        "      <th>scode</th>\n",
380 |        "      <th>community_name</th>\n",
381 |        "      <th>num_volunteers</th>\n",
382 |        "      <th>population</th>\n",
383 |        "      <th>amount_waste_kg</th>\n",
384 |        "      <th>general_waste_kg</th>\n",
385 |        "      <th>organic_waste_kg</th>\n",
386 |        "      <th>toxic_waste_kg</th>\n",
387 |        "      <th>infectious_waste_kg</th>\n",
388 |        "      <th>recycle_waste_kg</th>\n",
389 |        "      <th>sum_month</th>\n",
390 |        "      <th>sum_year</th>\n",
391 |        "      <th>remark</th>\n",
392 |        "    </tr>\n",
393 |        "  </thead>\n",
394 |        "  <tbody>\n",
395 |        "    <tr>\n",
396 |        "      <th>0</th>\n",
397 |        "      <td>1</td>\n",
398 |        "      <td>1025001</td>\n",
399 |        "      <td>1025</td>\n",
400 |        "      <td>102504</td>\n",
401 |        "      <td>ชุมชนวัดดาวดึงษาราม</td>\n",
402 |        "      <td>2</td>\n",
403 |        "      <td>2095</td>\n",
404 |        "      <td>36266</td>\n",
405 |        "      <td>17408</td>\n",
406 |        "      <td>10880</td>\n",
407 |        "      <td>725</td>\n",
408 |        "      <td>0</td>\n",
409 |        "      <td>7253</td>\n",
410 |        "      <td>7</td>\n",
411 |        "      <td>2564</td>\n",
412 |        "      <td>0</td>\n",
413 |        "    </tr>\n",
414 |        "    <tr>\n",
415 |        "      <th>1</th>\n",
416 |        "      <td>2</td>\n",
417 |        "      <td>1025002</td>\n",
418 |        "      <td>1025</td>\n",
419 |        "      <td>102501</td>\n",
420 |        "      <td>ชุมชนวัดเทพนารี</td>\n",
421 |        "      <td>1</td>\n",
422 |        "      <td>990</td>\n",
423 |        "      <td>20425</td>\n",
424 |        "      <td>9804</td>\n",
425 |        "      <td>6128</td>\n",
426 |        "      <td>409</td>\n",
427 |        "      <td>0</td>\n",
428 |        "      <td>4085</td>\n",
429 |        "      <td>7</td>\n",
430 |        "      <td>2564</td>\n",
431 |        "      <td>0</td>\n",
432 |        "    </tr>\n",
433 |        "    <tr>\n",
434 |        "      <th>2</th>\n",
435 |        "      <td>3</td>\n",
436 |        "      <td>1025003</td>\n",
437 |        "      <td>1025</td>\n",
438 |        "      <td>102502</td>\n",
439 |        "      <td>ชุมชนเติมสุข</td>\n",
440 |        "      <td>1</td>\n",
441 |        "      <td>440</td>\n",
442 |        "      <td>20115</td>\n",
443 |        "      <td>9655</td>\n",
444 |        "      <td>6035</td>\n",
445 |        "      <td>402</td>\n",
446 |        "      <td>0</td>\n",
447 |        "      <td>4023</td>\n",
448 |        "      <td>7</td>\n",
449 |        "      <td>2564</td>\n",
450 |        "      <td>0</td>\n",
451 |        "    </tr>\n",
452 |        "    <tr>\n",
453 |        "      <th>3</th>\n",
454 |        "      <td>4</td>\n",
455 |        "      <td>1025004</td>\n",
456 |        "      <td>1025</td>\n",
457 |        "      <td>102502</td>\n",
458 |        "      <td>ชุมชนริมคลองบางพลัด</td>\n",
459 |        "      <td>1</td>\n",
460 |        "      <td>534</td>\n",
461 |        "      <td>25548</td>\n",
462 |        "      <td>12263</td>\n",
463 |        "      <td>7664</td>\n",
464 |        "      <td>511</td>\n",
465 |        "      <td>0</td>\n",
466 |        "      <td>5110</td>\n",
467 |        "      <td>7</td>\n",
468 |        "      <td>2564</td>\n",
469 |        "      <td>0</td>\n",
470 |        "    </tr>\n",
471 |        "    <tr>\n",
472 |        "      <th>4</th>\n",
473 |        "      <td>5</td>\n",
474 |        "      <td>1025005</td>\n",
475 |        "      <td>1025</td>\n",
476 |        "      <td>102502</td>\n",
477 |        "      <td>ชุมชนวัดฉัตรแก้วจงกลนี</td>\n",
478 |        "      <td>1</td>\n",
479 |        "      <td>1498</td>\n",
480 |        "      <td>19215</td>\n",
481 |        "      <td>9223</td>\n",
482 |        "      <td>5765</td>\n",
483 |        "      <td>384</td>\n",
484 |        "      <td>0</td>\n",
485 |        "      <td>3843</td>\n",
486 |        "      <td>7</td>\n",
487 |        "      <td>2564</td>\n",
488 |        "      <td>0</td>\n",
489 |        "    </tr>\n",
490 |        "    <tr>\n",
491 |        "      <th>5</th>\n",
492 |        "      <td>6</td>\n",
493 |        "      <td>1025006</td>\n",
494 |        "      <td>1025</td>\n",
495 |        "      <td>102502</td>\n",
496 |        "      <td>ชุมชนแสงทอง</td>\n",
497 |        "      <td>1</td>\n",
498 |        "      <td>1922</td>\n",
499 |        "      <td>37914</td>\n",
500 |        "      <td>18199</td>\n",
501 |        "      <td>11374</td>\n",
502 |        "      <td>758</td>\n",
503 |        "      <td>0</td>\n",
504 |        "      <td>7583</td>\n",
505 |        "      <td>7</td>\n",
506 |        "      <td>2564</td>\n",
507 |        "      <td>0</td>\n",
508 |        "    </tr>\n",
509 |        "    <tr>\n",
510 |        "      <th>6</th>\n",
511 |        "      <td>7</td>\n",
512 |        "      <td>1025007</td>\n",
513 |        "      <td>1025</td>\n",
514 |        "      <td>102501</td>\n",
515 |        "      <td>ชุมชนสวนปรก</td>\n",
516 |        "      <td>1</td>\n",
517 |        "      <td>276</td>\n",
518 |        "      <td>18900</td>\n",
519 |        "      <td>9072</td>\n",
520 |        "      <td>5670</td>\n",
521 |        "      <td>378</td>\n",
522 |        "      <td>0</td>\n",
523 |        "      <td>3780</td>\n",
524 |        "      <td>7</td>\n",
525 |        "      <td>2564</td>\n",
526 |        "      <td>0</td>\n",
527 |        "    </tr>\n",
528 |        "  </tbody>\n",
529 |        "</table>\n",
530 |        "</div>"
531 |       ],
532 |       "text/plain": [
533 |        "   _id  drag_g_id  dcode   scode          community_name  num_volunteers  \\\n",
534 |        "0    1    1025001   1025  102504     ชุมชนวัดดาวดึงษาราม               2   \n",
535 |        "1    2    1025002   1025  102501         ชุมชนวัดเทพนารี               1   \n",
536 |        "2    3    1025003   1025  102502            ชุมชนเติมสุข               1   \n",
537 |        "3    4    1025004   1025  102502     ชุมชนริมคลองบางพลัด               1   \n",
538 |        "4    5    1025005   1025  102502  ชุมชนวัดฉัตรแก้วจงกลนี               1   \n",
539 |        "5    6    1025006   1025  102502             ชุมชนแสงทอง               1   \n",
540 |        "6    7    1025007   1025  102501             ชุมชนสวนปรก               1   \n",
541 |        "\n",
542 |        "   population  amount_waste_kg  general_waste_kg  organic_waste_kg  \\\n",
543 |        "0        2095            36266             17408             10880   \n",
544 |        "1         990            20425              9804              6128   \n",
545 |        "2         440            20115              9655              6035   \n",
546 |        "3         534            25548             12263              7664   \n",
547 |        "4        1498            19215              9223              5765   \n",
548 |        "5        1922            37914             18199             11374   \n",
549 |        "6         276            18900              9072              5670   \n",
550 |        "\n",
551 |        "   toxic_waste_kg  infectious_waste_kg  recycle_waste_kg  sum_month  sum_year  \\\n",
552 |        "0             725                    0              7253          7      2564   \n",
553 |        "1             409                    0              4085          7      2564   \n",
554 |        "2             402                    0              4023          7      2564   \n",
555 |        "3             511                    0              5110          7      2564   \n",
556 |        "4             384                    0              3843          7      2564   \n",
557 |        "5             758                    0              7583          7      2564   \n",
558 |        "6             378                    0              3780          7      2564   \n",
559 |        "\n",
560 |        "   remark  \n",
561 |        "0       0  \n",
562 |        "1       0  \n",
563 |        "2       0  \n",
564 |        "3       0  \n",
565 |        "4       0  \n",
566 |        "5       0  \n",
567 |        "6       0  "
568 |       ]
569 |      },
570 |      "execution_count": 17,
571 |      "metadata": {},
572 |      "output_type": "execute_result"
573 |     }
574 |    ],
575 |    "source": [
576 |     "def bkkapi(resource_id):\n",
577 |     "\turl=f\"https://data.bangkok.go.th/api/3/action/datastore_search?resource_id={resource_id}\"\n",
578 |     "\tr=requests.get(url)\n",
579 |     "\tj=r.json()\n",
580 |     "\t# j['result']['records']\n",
581 |     "\tdf=pd.DataFrame(j['result']['records'])\n",
582 |     "\treturn df\n",
583 |     "\n",
584 |     "# https://data.bangkok.go.th/dataset/65eb2ee6-efc6-4045-8e3e-ef451b844953/resource/95bd1379-177e-4dd8-9374-c0ce307bede9/download/-..-64.csv\n",
585 |     "dt=bkkapi(\"95bd1379-177e-4dd8-9374-c0ce307bede9\")\n",
586 |     "dt"
587 |    ]
588 |   },
589 |   {
590 |    "cell_type": "markdown",
591 |    "metadata": {},
592 |    "source": [
593 |     "## read CSV\n",
594 |     "- ข้อมูลสถิติการจัดเก็บภาษีเปรียบเทียบยอดประมาณการ\n",
595 |     "  -   https://data.bangkok.go.th/dataset/statvat0864/resource/5cd749fe-11a8-457d-b88f-2151ac33fe27\n"
596 |    ]
597 |   },
598 |   {
599 |    "cell_type": "code",
600 |    "execution_count": null,
601 |    "metadata": {},
602 |    "outputs": [],
603 |    "source": [
604 |     "url = \"https://data.bangkok.go.th/dataset/c051d4a7-8418-49c1-86b0-5a5ea8511c33/resource/5cd749fe-11a8-457d-b88f-2151ac33fe27/download/fin_600ea1e3c81a7.csv\"\n",
605 |     "df = pd.read_csv(url)\n",
606 |     "df\n"
607 |    ]
608 |   },
609 |   {
610 |    "cell_type": "markdown",
611 |    "metadata": {},
612 |    "source": [
613 |     "## read Excel\n",
614 |     "\n",
615 |     "-   รายงานปริมาณน้ำที่ผ่านการบำบัดแล้วกลับมาใช้ประโยชน์ (พ.ศ.2561-2563)\n",
616 |     "    -   https://data.bangkok.go.th/dataset/kpisbangkok_1100/resource/272b8fbc-6146-40d6-be7c-335ea30c2f7e\n"
617 |    ]
618 |   },
619 |   {
620 |    "cell_type": "code",
621 |    "execution_count": null,
622 |    "metadata": {},
623 |    "outputs": [],
624 |    "source": [
625 |     "url = \"https://data.bangkok.go.th/dataset/e53ef05f-32b2-4b06-b800-cad2702f426e/resource/272b8fbc-6146-40d6-be7c-335ea30c2f7e/download/wq-2-reused_water-2561-2563.xlsx\"\n",
626 |     "print(url)\n",
627 |     "df = pd.read_excel(url)\n",
628 |     "df[:5]\n"
629 |    ]
630 |   },
631 |   {
632 |    "cell_type": "code",
633 |    "execution_count": null,
634 |    "metadata": {},
635 |    "outputs": [],
636 |    "source": [
637 |     "def download(url):\n",
638 |     "\tif (url[-4:] == \".xls\") or (url[-5:] == \".xlsx\"):\n",
639 |     "\t\tdf = pd.read_excel(url)\n",
640 |     "\telse:\n",
641 |     "\t\tdf = pd.read_csv(url)\n",
642 |     "\treturn df\n",
643 |     "\n",
644 |     "# url = \"https://data.bangkok.go.th/dataset/c051d4a7-8418-49c1-86b0-5a5ea8511c33/resource/5cd749fe-11a8-457d-b88f-2151ac33fe27/download/fin_600ea1e3c81a7.csv\"\n",
645 |     "url = \"https://data.bangkok.go.th/dataset/e53ef05f-32b2-4b06-b800-cad2702f426e/resource/272b8fbc-6146-40d6-be7c-335ea30c2f7e/download/wq-2-reused_water-2561-2563.xlsx\"\n",
646 |     "df=download(url)\n",
647 |     "df[:5]"
648 |    ]
649 |   },
650 |   {
651 |    "cell_type": "markdown",
652 |    "metadata": {},
653 |    "source": [
654 |     "## CKAN API\n",
655 |     "-   รายงานปริมาณน้ำที่ผ่านการบำบัดแล้วกลับมาใช้ประโยชน์ (พ.ศ.2561-2563)\n",
656 |     "    -   https://data.bangkok.go.th/dataset/kpisbangkok_1100/resource/272b8fbc-6146-40d6-be7c-335ea30c2f7e\n"
657 |    ]
658 |   },
659 |   {
660 |    "cell_type": "code",
661 |    "execution_count": null,
662 |    "metadata": {},
663 |    "outputs": [],
664 |    "source": [
665 |     "# resource_id=\"5cd749fe-11a8-457d-b88f-2151ac33fe27\"\n",
666 |     "resource_id = \"272b8fbc-6146-40d6-be7c-335ea30c2f7e\"\n",
667 |     "url = f\"https://data.bangkok.go.th/api/3/action/datastore_search?resource_id={resource_id}\"\n",
668 |     "print(url)\n",
669 |     "r = requests.get(url)\n",
670 |     "\n",
671 |     "j = r.json()\n",
672 |     "# j\n",
673 |     "j[\"result\"][\"records\"]\n",
674 |     "\n"
675 |    ]
676 |   },
677 |   {
678 |    "cell_type": "code",
679 |    "execution_count": null,
680 |    "metadata": {},
681 |    "outputs": [],
682 |    "source": [
683 |     "df = pd.DataFrame(j[\"result\"][\"records\"])\n",
684 |     "df\n"
685 |    ]
686 |   },
687 |   {
688 |    "cell_type": "code",
689 |    "execution_count": null,
690 |    "metadata": {},
691 |    "outputs": [],
692 |    "source": [
693 |     "df.loc[:, 'Year':]"
694 |    ]
695 |   },
696 |   {
697 |    "cell_type": "code",
698 |    "execution_count": null,
699 |    "metadata": {},
700 |    "outputs": [],
701 |    "source": []
702 |   }
703 |  ],
704 |  "metadata": {
705 |   "kernelspec": {
706 |    "display_name": "Python 3.9.12 ('base')",
707 |    "language": "python",
708 |    "name": "python3"
709 |   },
710 |   "language_info": {
711 |    "codemirror_mode": {
712 |     "name": "ipython",
713 |     "version": 3
714 |    },
715 |    "file_extension": ".py",
716 |    "mimetype": "text/x-python",
717 |    "name": "python",
718 |    "nbconvert_exporter": "python",
719 |    "pygments_lexer": "ipython3",
720 |    "version": "3.9.12"
721 |   },
722 |   "orig_nbformat": 4,
723 |   "vscode": {
724 |    "interpreter": {
725 |     "hash": "629cb9f199f624aadf5cdcebfeb0fdc9652b7a7e432d484384b677cab7914fce"
726 |    }
727 |   }
728 |  },
729 |  "nbformat": 4,
730 |  "nbformat_minor": 2
731 | }
732 | 


--------------------------------------------------------------------------------
/data_go_th_api.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "metadata": {},
  6 |    "source": [
  7 |     "# data.go.th\n",
  8 |     "\n",
  9 |     "web: https://data.go.th/\n"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "metadata": {},
 15 |    "source": [
 16 |     "---\n",
 17 |     "* author:  [Prasert Kanawattanachai](prasert.k@chula.ac.th)\n",
 18 |     "* YouTube: https://www.youtube.com/prasertcbs\n",
 19 |     "* github: https://github.com/prasertcbs/\n",
 20 |     "* kaggle: https://www.kaggle.com/prasertk/\n",
 21 |     "* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)\n",
 22 |     "---\n"
 23 |    ]
 24 |   },
 25 |   {
 26 |    "cell_type": "markdown",
 27 |    "metadata": {},
 28 |    "source": [
 29 |     "<a href=https://youtu.be/sKGYvSm28qY><img src=https://i.ytimg.com/vi/sKGYvSm28qY/mqdefault.jpg width=560 width=315/><p>Play YouTube Video</p></a>"
 30 |    ]
 31 |   },
 32 |   {
 33 |    "cell_type": "code",
 34 |    "execution_count": 1,
 35 |    "metadata": {},
 36 |    "outputs": [],
 37 |    "source": [
 38 |     "import pandas as pd\n",
 39 |     "import requests\n"
 40 |    ]
 41 |   },
 42 |   {
 43 |    "cell_type": "code",
 44 |    "execution_count": 2,
 45 |    "metadata": {
 46 |     "colab": {
 47 |      "base_uri": "https://localhost:8080/",
 48 |      "height": 64
 49 |     },
 50 |     "colab_type": "code",
 51 |     "id": "fdr0pYIf7P-_",
 52 |     "outputId": "c919deae-c99b-44b0-8924-4d2355ca0b63"
 53 |    },
 54 |    "outputs": [
 55 |     {
 56 |      "name": "stdout",
 57 |      "output_type": "stream",
 58 |      "text": [
 59 |       "pandas version:  1.4.2\n",
 60 |       "requests version: 2.28.0\n"
 61 |      ]
 62 |     },
 63 |     {
 64 |      "data": {
 65 |       "text/plain": [
 66 |        "Timestamp('2022-07-13 05:48:50.328872')"
 67 |       ]
 68 |      },
 69 |      "execution_count": 2,
 70 |      "metadata": {},
 71 |      "output_type": "execute_result"
 72 |     }
 73 |    ],
 74 |    "source": [
 75 |     "print(f\"pandas version:  {pd.__version__}\")\n",
 76 |     "print(f\"requests version: {requests.__version__}\")\n",
 77 |     "\n",
 78 |     "pd.Timestamp.now()\n",
 79 |     "# pd.Timestamp.now().strftime('%Y-%m-%d')\n"
 80 |    ]
 81 |   },
 82 |   {
 83 |    "cell_type": "markdown",
 84 |    "metadata": {},
 85 |    "source": [
 86 |     "# read CSV\n",
 87 |     "* https://data.go.th/dataset/mrta-crmk\n"
 88 |    ]
 89 |   },
 90 |   {
 91 |    "cell_type": "code",
 92 |    "execution_count": 3,
 93 |    "metadata": {},
 94 |    "outputs": [
 95 |     {
 96 |      "data": {
 97 |       "text/html": [
 98 |        "<div>\n",
 99 |        "<style scoped>\n",
100 |        "    .dataframe tbody tr th:only-of-type {\n",
101 |        "        vertical-align: middle;\n",
102 |        "    }\n",
103 |        "\n",
104 |        "    .dataframe tbody tr th {\n",
105 |        "        vertical-align: top;\n",
106 |        "    }\n",
107 |        "\n",
108 |        "    .dataframe thead th {\n",
109 |        "        text-align: right;\n",
110 |        "    }\n",
111 |        "</style>\n",
112 |        "<table border=\"1\" class=\"dataframe\">\n",
113 |        "  <thead>\n",
114 |        "    <tr style=\"text-align: right;\">\n",
115 |        "      <th></th>\n",
116 |        "      <th>เดือน</th>\n",
117 |        "      <th>ปี</th>\n",
118 |        "      <th>โครงการ</th>\n",
119 |        "      <th>จำนวนผู้โดยสารรวม</th>\n",
120 |        "      <th>จำนวนผู้โดยสารเฉลี่ยรายวัน</th>\n",
121 |        "      <th>จำนวนผู้โดยสารเฉลี่ยรายวันธรรมดา</th>\n",
122 |        "      <th>จำนวนผู้โดยสารเฉลี่ยรายวันหยุด</th>\n",
123 |        "    </tr>\n",
124 |        "  </thead>\n",
125 |        "  <tbody>\n",
126 |        "    <tr>\n",
127 |        "      <th>0</th>\n",
128 |        "      <td>มกราคม</td>\n",
129 |        "      <td>2557</td>\n",
130 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
131 |        "      <td>8,613,608</td>\n",
132 |        "      <td>277,858</td>\n",
133 |        "      <td>316,029</td>\n",
134 |        "      <td>184,553</td>\n",
135 |        "    </tr>\n",
136 |        "    <tr>\n",
137 |        "      <th>1</th>\n",
138 |        "      <td>กุมภาพันธ์</td>\n",
139 |        "      <td>2557</td>\n",
140 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
141 |        "      <td>8,212,613</td>\n",
142 |        "      <td>293,308</td>\n",
143 |        "      <td>329,204</td>\n",
144 |        "      <td>217,526</td>\n",
145 |        "    </tr>\n",
146 |        "    <tr>\n",
147 |        "      <th>2</th>\n",
148 |        "      <td>มีนาคม</td>\n",
149 |        "      <td>2557</td>\n",
150 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
151 |        "      <td>8,123,428</td>\n",
152 |        "      <td>262,046</td>\n",
153 |        "      <td>287,668</td>\n",
154 |        "      <td>208,239</td>\n",
155 |        "    </tr>\n",
156 |        "    <tr>\n",
157 |        "      <th>3</th>\n",
158 |        "      <td>เมษายน</td>\n",
159 |        "      <td>2557</td>\n",
160 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
161 |        "      <td>6,990,396</td>\n",
162 |        "      <td>233,013</td>\n",
163 |        "      <td>285,863</td>\n",
164 |        "      <td>153,738</td>\n",
165 |        "    </tr>\n",
166 |        "    <tr>\n",
167 |        "      <th>4</th>\n",
168 |        "      <td>พฤษภาคม</td>\n",
169 |        "      <td>2557</td>\n",
170 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
171 |        "      <td>7,265,594</td>\n",
172 |        "      <td>234,374</td>\n",
173 |        "      <td>276,809</td>\n",
174 |        "      <td>175,618</td>\n",
175 |        "    </tr>\n",
176 |        "    <tr>\n",
177 |        "      <th>...</th>\n",
178 |        "      <td>...</td>\n",
179 |        "      <td>...</td>\n",
180 |        "      <td>...</td>\n",
181 |        "      <td>...</td>\n",
182 |        "      <td>...</td>\n",
183 |        "      <td>...</td>\n",
184 |        "      <td>...</td>\n",
185 |        "    </tr>\n",
186 |        "    <tr>\n",
187 |        "      <th>157</th>\n",
188 |        "      <td>มกราคม</td>\n",
189 |        "      <td>2565</td>\n",
190 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
191 |        "      <td>949885</td>\n",
192 |        "      <td>30641</td>\n",
193 |        "      <td>36093</td>\n",
194 |        "      <td>20729</td>\n",
195 |        "    </tr>\n",
196 |        "    <tr>\n",
197 |        "      <th>158</th>\n",
198 |        "      <td>กุมภาพันธ์</td>\n",
199 |        "      <td>2565</td>\n",
200 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
201 |        "      <td>980551</td>\n",
202 |        "      <td>35020</td>\n",
203 |        "      <td>40477</td>\n",
204 |        "      <td>23499</td>\n",
205 |        "    </tr>\n",
206 |        "    <tr>\n",
207 |        "      <th>159</th>\n",
208 |        "      <td>มีนาคม</td>\n",
209 |        "      <td>2565</td>\n",
210 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
211 |        "      <td>949885</td>\n",
212 |        "      <td>32684</td>\n",
213 |        "      <td>36292</td>\n",
214 |        "      <td>22313</td>\n",
215 |        "    </tr>\n",
216 |        "    <tr>\n",
217 |        "      <th>160</th>\n",
218 |        "      <td>เมษายน</td>\n",
219 |        "      <td>2565</td>\n",
220 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
221 |        "      <td>903,899</td>\n",
222 |        "      <td>30,130</td>\n",
223 |        "      <td>37,815</td>\n",
224 |        "      <td>20,080</td>\n",
225 |        "    </tr>\n",
226 |        "    <tr>\n",
227 |        "      <th>161</th>\n",
228 |        "      <td>พฤษภาคม</td>\n",
229 |        "      <td>2565</td>\n",
230 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
231 |        "      <td>1198303</td>\n",
232 |        "      <td>38655</td>\n",
233 |        "      <td>47597</td>\n",
234 |        "      <td>26274</td>\n",
235 |        "    </tr>\n",
236 |        "  </tbody>\n",
237 |        "</table>\n",
238 |        "<p>162 rows × 7 columns</p>\n",
239 |        "</div>"
240 |       ],
241 |       "text/plain": [
242 |        "          เดือน    ปี                               โครงการ จำนวนผู้โดยสารรวม  \\\n",
243 |        "0        มกราคม  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล         8,613,608   \n",
244 |        "1    กุมภาพันธ์  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล         8,212,613   \n",
245 |        "2        มีนาคม  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล         8,123,428   \n",
246 |        "3        เมษายน  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล         6,990,396   \n",
247 |        "4       พฤษภาคม  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล         7,265,594   \n",
248 |        "..          ...   ...                                   ...               ...   \n",
249 |        "157      มกราคม  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม            949885   \n",
250 |        "158  กุมภาพันธ์  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม            980551   \n",
251 |        "159      มีนาคม  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม            949885   \n",
252 |        "160      เมษายน  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม           903,899   \n",
253 |        "161     พฤษภาคม  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม           1198303   \n",
254 |        "\n",
255 |        "    จำนวนผู้โดยสารเฉลี่ยรายวัน จำนวนผู้โดยสารเฉลี่ยรายวันธรรมดา  \\\n",
256 |        "0                      277,858                          316,029   \n",
257 |        "1                      293,308                          329,204   \n",
258 |        "2                      262,046                          287,668   \n",
259 |        "3                      233,013                          285,863   \n",
260 |        "4                      234,374                          276,809   \n",
261 |        "..                         ...                              ...   \n",
262 |        "157                      30641                            36093   \n",
263 |        "158                      35020                            40477   \n",
264 |        "159                      32684                            36292   \n",
265 |        "160                     30,130                           37,815   \n",
266 |        "161                      38655                            47597   \n",
267 |        "\n",
268 |        "    จำนวนผู้โดยสารเฉลี่ยรายวันหยุด  \n",
269 |        "0                          184,553  \n",
270 |        "1                          217,526  \n",
271 |        "2                          208,239  \n",
272 |        "3                          153,738  \n",
273 |        "4                          175,618  \n",
274 |        "..                             ...  \n",
275 |        "157                          20729  \n",
276 |        "158                          23499  \n",
277 |        "159                          22313  \n",
278 |        "160                         20,080  \n",
279 |        "161                          26274  \n",
280 |        "\n",
281 |        "[162 rows x 7 columns]"
282 |       ]
283 |      },
284 |      "execution_count": 3,
285 |      "metadata": {},
286 |      "output_type": "execute_result"
287 |     }
288 |    ],
289 |    "source": [
290 |     "url = \"https://data.go.th/dataset/fd781923-6c64-4cbc-90b8-83ad77c96ecd/resource/a542d7d4-bc27-4c03-81ef-bef0a5213210/download/-..-65.csv\"\n",
291 |     "df = pd.read_csv(url)\n",
292 |     "df\n"
293 |    ]
294 |   },
295 |   {
296 |    "cell_type": "markdown",
297 |    "metadata": {},
298 |    "source": [
299 |     "# read CSV (TIS-620, cp874, Windows-874)\n",
300 |     "* https://data.go.th/dataset/bangkok-fir-may-2565"
301 |    ]
302 |   },
303 |   {
304 |    "cell_type": "code",
305 |    "execution_count": 14,
306 |    "metadata": {},
307 |    "outputs": [
308 |     {
309 |      "data": {
310 |       "text/html": [
311 |        "<div>\n",
312 |        "<style scoped>\n",
313 |        "    .dataframe tbody tr th:only-of-type {\n",
314 |        "        vertical-align: middle;\n",
315 |        "    }\n",
316 |        "\n",
317 |        "    .dataframe tbody tr th {\n",
318 |        "        vertical-align: top;\n",
319 |        "    }\n",
320 |        "\n",
321 |        "    .dataframe thead th {\n",
322 |        "        text-align: right;\n",
323 |        "    }\n",
324 |        "</style>\n",
325 |        "<table border=\"1\" class=\"dataframe\">\n",
326 |        "  <thead>\n",
327 |        "    <tr style=\"text-align: right;\">\n",
328 |        "      <th></th>\n",
329 |        "      <th>flight_rule</th>\n",
330 |        "      <th>เที่ยวบินรายเดือน</th>\n",
331 |        "      <th>เที่ยวบินสะสม</th>\n",
332 |        "      <th>เฉลี่ยต่อวัน</th>\n",
333 |        "      <th>การเปลี่ยนแปลง</th>\n",
334 |        "    </tr>\n",
335 |        "  </thead>\n",
336 |        "  <tbody>\n",
337 |        "    <tr>\n",
338 |        "      <th>0</th>\n",
339 |        "      <td>VFR</td>\n",
340 |        "      <td>5643</td>\n",
341 |        "      <td>47737</td>\n",
342 |        "      <td>225.17</td>\n",
343 |        "      <td>8.31</td>\n",
344 |        "    </tr>\n",
345 |        "    <tr>\n",
346 |        "      <th>1</th>\n",
347 |        "      <td>IFR</td>\n",
348 |        "      <td>34309</td>\n",
349 |        "      <td>224719</td>\n",
350 |        "      <td>1.00</td>\n",
351 |        "      <td>18.34</td>\n",
352 |        "    </tr>\n",
353 |        "    <tr>\n",
354 |        "      <th>2</th>\n",
355 |        "      <td>TOTAL</td>\n",
356 |        "      <td>39952</td>\n",
357 |        "      <td>272456</td>\n",
358 |        "      <td>226.17</td>\n",
359 |        "      <td>26.65</td>\n",
360 |        "    </tr>\n",
361 |        "  </tbody>\n",
362 |        "</table>\n",
363 |        "</div>"
364 |       ],
365 |       "text/plain": [
366 |        "  flight_rule  เที่ยวบินรายเดือน  เที่ยวบินสะสม  เฉลี่ยต่อวัน  การเปลี่ยนแปลง\n",
367 |        "0         VFR               5643          47737        225.17            8.31\n",
368 |        "1         IFR              34309         224719          1.00           18.34\n",
369 |        "2       TOTAL              39952         272456        226.17           26.65"
370 |       ]
371 |      },
372 |      "execution_count": 14,
373 |      "metadata": {},
374 |      "output_type": "execute_result"
375 |     }
376 |    ],
377 |    "source": [
378 |     "url = \"https://data.go.th/dataset/31170cfe-eaf5-4583-83aa-000e4de39064/resource/0899889b-ae8e-44d7-a2ba-da81bcdb24a9/download/flightrule_05_2022.csv\"\n",
379 |     "\n",
380 |     "# df = pd.read_csv(url)\n",
381 |     "\n",
382 |     "# df = pd.read_csv(url, encoding=\"iso8859-11\")\n",
383 |     "df = pd.read_csv(url, encoding=\"tis-620\")\n",
384 |     "# df = pd.read_csv(url, encoding=\"cp874\")\n",
385 |     "# df = pd.read_csv(url, encoding=\"thai\")\n",
386 |     "\n",
387 |     "df\n"
388 |    ]
389 |   },
390 |   {
391 |    "cell_type": "markdown",
392 |    "metadata": {},
393 |    "source": [
394 |     "# read excel from URL\n",
395 |     "* https://opendata.data.go.th/dataset/mrta-crmk"
396 |    ]
397 |   },
398 |   {
399 |    "cell_type": "code",
400 |    "execution_count": 15,
401 |    "metadata": {},
402 |    "outputs": [
403 |     {
404 |      "data": {
405 |       "text/html": [
406 |        "<div>\n",
407 |        "<style scoped>\n",
408 |        "    .dataframe tbody tr th:only-of-type {\n",
409 |        "        vertical-align: middle;\n",
410 |        "    }\n",
411 |        "\n",
412 |        "    .dataframe tbody tr th {\n",
413 |        "        vertical-align: top;\n",
414 |        "    }\n",
415 |        "\n",
416 |        "    .dataframe thead th {\n",
417 |        "        text-align: right;\n",
418 |        "    }\n",
419 |        "</style>\n",
420 |        "<table border=\"1\" class=\"dataframe\">\n",
421 |        "  <thead>\n",
422 |        "    <tr style=\"text-align: right;\">\n",
423 |        "      <th></th>\n",
424 |        "      <th>เดือน</th>\n",
425 |        "      <th>ปี</th>\n",
426 |        "      <th>โครงการ</th>\n",
427 |        "      <th>จำนวนผู้โดยสารรวม</th>\n",
428 |        "      <th>จำนวนผู้โดยสารเฉลี่ยรายวัน</th>\n",
429 |        "      <th>จำนวนผู้โดยสารเฉลี่ยรายวันธรรมดา</th>\n",
430 |        "      <th>จำนวนผู้โดยสารเฉลี่ยรายวันหยุด</th>\n",
431 |        "    </tr>\n",
432 |        "  </thead>\n",
433 |        "  <tbody>\n",
434 |        "    <tr>\n",
435 |        "      <th>0</th>\n",
436 |        "      <td>มกราคม</td>\n",
437 |        "      <td>2557</td>\n",
438 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
439 |        "      <td>8613608</td>\n",
440 |        "      <td>277858</td>\n",
441 |        "      <td>316029</td>\n",
442 |        "      <td>184553</td>\n",
443 |        "    </tr>\n",
444 |        "    <tr>\n",
445 |        "      <th>1</th>\n",
446 |        "      <td>กุมภาพันธ์</td>\n",
447 |        "      <td>2557</td>\n",
448 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
449 |        "      <td>8212613</td>\n",
450 |        "      <td>293308</td>\n",
451 |        "      <td>329204</td>\n",
452 |        "      <td>217526</td>\n",
453 |        "    </tr>\n",
454 |        "    <tr>\n",
455 |        "      <th>2</th>\n",
456 |        "      <td>มีนาคม</td>\n",
457 |        "      <td>2557</td>\n",
458 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
459 |        "      <td>8123428</td>\n",
460 |        "      <td>262046</td>\n",
461 |        "      <td>287668</td>\n",
462 |        "      <td>208239</td>\n",
463 |        "    </tr>\n",
464 |        "    <tr>\n",
465 |        "      <th>3</th>\n",
466 |        "      <td>เมษายน</td>\n",
467 |        "      <td>2557</td>\n",
468 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
469 |        "      <td>6990396</td>\n",
470 |        "      <td>233013</td>\n",
471 |        "      <td>285863</td>\n",
472 |        "      <td>153738</td>\n",
473 |        "    </tr>\n",
474 |        "    <tr>\n",
475 |        "      <th>4</th>\n",
476 |        "      <td>พฤษภาคม</td>\n",
477 |        "      <td>2557</td>\n",
478 |        "      <td>โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล</td>\n",
479 |        "      <td>7265594</td>\n",
480 |        "      <td>234374</td>\n",
481 |        "      <td>276809</td>\n",
482 |        "      <td>175618</td>\n",
483 |        "    </tr>\n",
484 |        "    <tr>\n",
485 |        "      <th>...</th>\n",
486 |        "      <td>...</td>\n",
487 |        "      <td>...</td>\n",
488 |        "      <td>...</td>\n",
489 |        "      <td>...</td>\n",
490 |        "      <td>...</td>\n",
491 |        "      <td>...</td>\n",
492 |        "      <td>...</td>\n",
493 |        "    </tr>\n",
494 |        "    <tr>\n",
495 |        "      <th>166</th>\n",
496 |        "      <td>มกราคม</td>\n",
497 |        "      <td>2565</td>\n",
498 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
499 |        "      <td>949885</td>\n",
500 |        "      <td>30641</td>\n",
501 |        "      <td>36093</td>\n",
502 |        "      <td>20729</td>\n",
503 |        "    </tr>\n",
504 |        "    <tr>\n",
505 |        "      <th>167</th>\n",
506 |        "      <td>กุมภาพันธ์</td>\n",
507 |        "      <td>2565</td>\n",
508 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
509 |        "      <td>980551</td>\n",
510 |        "      <td>35020</td>\n",
511 |        "      <td>40477</td>\n",
512 |        "      <td>23499</td>\n",
513 |        "    </tr>\n",
514 |        "    <tr>\n",
515 |        "      <th>168</th>\n",
516 |        "      <td>มีนาคม</td>\n",
517 |        "      <td>2565</td>\n",
518 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
519 |        "      <td>1013287</td>\n",
520 |        "      <td>32684</td>\n",
521 |        "      <td>36292</td>\n",
522 |        "      <td>22313</td>\n",
523 |        "    </tr>\n",
524 |        "    <tr>\n",
525 |        "      <th>169</th>\n",
526 |        "      <td>เมษายน</td>\n",
527 |        "      <td>2565</td>\n",
528 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
529 |        "      <td>903899</td>\n",
530 |        "      <td>30130</td>\n",
531 |        "      <td>37815</td>\n",
532 |        "      <td>20080</td>\n",
533 |        "    </tr>\n",
534 |        "    <tr>\n",
535 |        "      <th>170</th>\n",
536 |        "      <td>พฤษภาคม</td>\n",
537 |        "      <td>2565</td>\n",
538 |        "      <td>โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม</td>\n",
539 |        "      <td>1198303</td>\n",
540 |        "      <td>38655</td>\n",
541 |        "      <td>47597</td>\n",
542 |        "      <td>26274</td>\n",
543 |        "    </tr>\n",
544 |        "  </tbody>\n",
545 |        "</table>\n",
546 |        "<p>171 rows × 7 columns</p>\n",
547 |        "</div>"
548 |       ],
549 |       "text/plain": [
550 |        "          เดือน    ปี                               โครงการ  \\\n",
551 |        "0        มกราคม  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล   \n",
552 |        "1    กุมภาพันธ์  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล   \n",
553 |        "2        มีนาคม  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล   \n",
554 |        "3        เมษายน  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล   \n",
555 |        "4       พฤษภาคม  2557  โครงการรถไฟฟ้ามหานคร สายเฉลิมรัชมงคล   \n",
556 |        "..          ...   ...                                   ...   \n",
557 |        "166      มกราคม  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม   \n",
558 |        "167  กุมภาพันธ์  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม   \n",
559 |        "168      มีนาคม  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม   \n",
560 |        "169      เมษายน  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม   \n",
561 |        "170     พฤษภาคม  2565   โครงการรถไฟฟ้ามหานคร สายฉลองรัชธรรม   \n",
562 |        "\n",
563 |        "     จำนวนผู้โดยสารรวม  จำนวนผู้โดยสารเฉลี่ยรายวัน  \\\n",
564 |        "0              8613608                      277858   \n",
565 |        "1              8212613                      293308   \n",
566 |        "2              8123428                      262046   \n",
567 |        "3              6990396                      233013   \n",
568 |        "4              7265594                      234374   \n",
569 |        "..                 ...                         ...   \n",
570 |        "166             949885                       30641   \n",
571 |        "167             980551                       35020   \n",
572 |        "168            1013287                       32684   \n",
573 |        "169             903899                       30130   \n",
574 |        "170            1198303                       38655   \n",
575 |        "\n",
576 |        "     จำนวนผู้โดยสารเฉลี่ยรายวันธรรมดา  จำนวนผู้โดยสารเฉลี่ยรายวันหยุด  \n",
577 |        "0                              316029                          184553  \n",
578 |        "1                              329204                          217526  \n",
579 |        "2                              287668                          208239  \n",
580 |        "3                              285863                          153738  \n",
581 |        "4                              276809                          175618  \n",
582 |        "..                                ...                             ...  \n",
583 |        "166                             36093                           20729  \n",
584 |        "167                             40477                           23499  \n",
585 |        "168                             36292                           22313  \n",
586 |        "169                             37815                           20080  \n",
587 |        "170                             47597                           26274  \n",
588 |        "\n",
589 |        "[171 rows x 7 columns]"
590 |       ]
591 |      },
592 |      "execution_count": 15,
593 |      "metadata": {},
594 |      "output_type": "execute_result"
595 |     }
596 |    ],
597 |    "source": [
598 |     "url = \"https://data.go.th/dataset/fd781923-6c64-4cbc-90b8-83ad77c96ecd/resource/8364f303-8f2f-4693-aa0c-2c96dc7cb075/download/-..-65.xlsx\"\n",
599 |     "df = pd.read_excel(url)\n",
600 |     "df\n"
601 |    ]
602 |   },
603 |   {
604 |    "cell_type": "code",
605 |    "execution_count": 16,
606 |    "metadata": {},
607 |    "outputs": [],
608 |    "source": [
609 |     "df.to_csv('mrt.csv', index=False)\n",
610 |     "df.to_excel('mrt.xlsx')"
611 |    ]
612 |   },
613 |   {
614 |    "cell_type": "markdown",
615 |    "metadata": {},
616 |    "source": [
617 |     "## Data API\n"
618 |    ]
619 |   },
620 |   {
621 |    "cell_type": "code",
622 |    "execution_count": null,
623 |    "metadata": {},
624 |    "outputs": [],
625 |    "source": [
626 |     "from getpass import getpass\n",
627 |     "api_key = getpass()"
628 |    ]
629 |   },
630 |   {
631 |    "cell_type": "markdown",
632 |    "metadata": {},
633 |    "source": [
634 |     "- ข้อมูลโรงแรมที่เป็นมิตรกับสิ่งแวดล้อม (Green Hotel)\n",
635 |     "  * https://opendata.data.go.th/dataset/db0103-002\n",
636 |     "- จำนวนคนทำงานในที่พักแรม\n",
637 |     "  * https://opendata.data.go.th/dataset/os_17_00010"
638 |    ]
639 |   },
640 |   {
641 |    "cell_type": "code",
642 |    "execution_count": null,
643 |    "metadata": {},
644 |    "outputs": [],
645 |    "source": [
646 |     "# Request headers\n",
647 |     "headers = {\n",
648 |     "    \"api-key\": api_key,\n",
649 |     "}\n",
650 |     "\n",
651 |     "params = {\"resource_id\": \"6c865fd0-93e4-4642-8e57-7063b3162896\", \"limit\": 10}\n",
652 |     "r = requests.get(\n",
653 |     "    \"https://opend.data.go.th/get-ckan/datastore_search\", params, headers=headers\n",
654 |     ")\n",
655 |     "if r.ok:\n",
656 |     "    j = r.json()\n",
657 |     "    records = j[\"result\"][\"records\"]\n",
658 |     "df = pd.DataFrame(records)\n",
659 |     "df\n"
660 |    ]
661 |   },
662 |   {
663 |    "cell_type": "markdown",
664 |    "metadata": {},
665 |    "source": [
666 |     "# functions\n"
667 |    ]
668 |   },
669 |   {
670 |    "cell_type": "code",
671 |    "execution_count": null,
672 |    "metadata": {},
673 |    "outputs": [],
674 |    "source": [
675 |     "import requests\n",
676 |     "import pandas as pd\n",
677 |     "\n",
678 |     "def gov_open_data(api_key, resource_id: str, nrows=10000):\n",
679 |     "    \"\"\"\n",
680 |     "    >>> gov_open_data(\"your api-key\", \"ce7f4a78-71db-4754-9084-edca971903bd\", nrows=3)\n",
681 |     "    \"\"\"\n",
682 |     "    # Request headers\n",
683 |     "    headers = {\n",
684 |     "        \"api-key\": api_key,\n",
685 |     "    }\n",
686 |     "\n",
687 |     "    params = {\"resource_id\": resource_id, \"limit\": nrows}\n",
688 |     "    r = requests.get(\n",
689 |     "        \"https://opend.data.go.th/get-ckan/datastore_search\", params, headers=headers\n",
690 |     "    )\n",
691 |     "\n",
692 |     "    j = r.json()\n",
693 |     "    records = j[\"result\"][\"records\"]\n",
694 |     "    return pd.DataFrame(records)\n",
695 |     "\n"
696 |    ]
697 |   },
698 |   {
699 |    "cell_type": "code",
700 |    "execution_count": null,
701 |    "metadata": {},
702 |    "outputs": [],
703 |    "source": [
704 |     "# ข้อมูลพิกัด LAT/LONG ที่ตั้งตำบล\n",
705 |     "# https://data.go.th/dataset/item_c6d42e1b-3219-47e1-b6b7-dfe914f27910\n",
706 |     "gov_open_data(api_key, \"48039a2a-2f01-448c-b2a2-bb0d541dedcd\", nrows=3)\n"
707 |    ]
708 |   },
709 |   {
710 |    "cell_type": "code",
711 |    "execution_count": null,
712 |    "metadata": {},
713 |    "outputs": [],
714 |    "source": []
715 |   }
716 |  ],
717 |  "metadata": {
718 |   "kernelspec": {
719 |    "display_name": "Python 3.9.12 ('base')",
720 |    "language": "python",
721 |    "name": "python3"
722 |   },
723 |   "language_info": {
724 |    "codemirror_mode": {
725 |     "name": "ipython",
726 |     "version": 3
727 |    },
728 |    "file_extension": ".py",
729 |    "mimetype": "text/x-python",
730 |    "name": "python",
731 |    "nbconvert_exporter": "python",
732 |    "pygments_lexer": "ipython3",
733 |    "version": "3.9.12"
734 |   },
735 |   "vscode": {
736 |    "interpreter": {
737 |     "hash": "629cb9f199f624aadf5cdcebfeb0fdc9652b7a7e432d484384b677cab7914fce"
738 |    }
739 |   },
740 |   "widgets": {
741 |    "application/vnd.jupyter.widget-state+json": {
742 |     "state": {},
743 |     "version_major": 2,
744 |     "version_minor": 0
745 |    }
746 |   }
747 |  },
748 |  "nbformat": 4,
749 |  "nbformat_minor": 4
750 | }
751 | 


--------------------------------------------------------------------------------
/README.md:
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  1 | # pandas for Data Science
  2 | | YouTube                                                                                                     | Title                                                                                                                                         |
  3 | |:------------------------------------------------------------------------------------------------------------|:----------------------------------------------------------------------------------------------------------------------------------------------|
  4 | | <a href=https://youtu.be/f3CLdRl-zyQ><img src=https://i.ytimg.com/vi/f3CLdRl-zyQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/f3CLdRl-zyQ">สอน Python สำหรับ Data science: การติดตั้ง Anaconda Python และ Jupyter Notebook บน Windows</a>             |
  5 | | <a href=https://youtu.be/QCqWo-Imv_A><img src=https://i.ytimg.com/vi/QCqWo-Imv_A/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/QCqWo-Imv_A">สอน Jupyter notebook: รู้จัก Jupyter notebook</a>                                                         |
  6 | | <a href=https://youtu.be/-Zoa7acOr5A><img src=https://i.ytimg.com/vi/-Zoa7acOr5A/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/-Zoa7acOr5A">สอน Jupyter notebook: แนะนำหลักการและชุดคำสั่งพื้นฐาน</a>                                                    |
  7 | | <a href=https://youtu.be/KKdItXMpo9Q><img src=https://i.ytimg.com/vi/KKdItXMpo9Q/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/KKdItXMpo9Q">สอน Jupyter notebook: การใช้คำสั่งเบื้องต้น ตอนที่ 1</a>                                                       |
  8 | | <a href=https://youtu.be/z6IcUuGcINU><img src=https://i.ytimg.com/vi/z6IcUuGcINU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/z6IcUuGcINU">สอน Jupyter notebook: การใช้คำสั่งเบื้องต้น ตอนที่ 2</a>                                                       |
  9 | | <a href=https://youtu.be/UZYBVVmzo1I><img src=https://i.ytimg.com/vi/UZYBVVmzo1I/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/UZYBVVmzo1I">สอน Jupyter notebook: การใช้ markdown ตอนที่ 1</a>                                                        |
 10 | | <a href=https://youtu.be/UQb5G26RA2M><img src=https://i.ytimg.com/vi/UQb5G26RA2M/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/UQb5G26RA2M">สอน Jupyter notebook: การใช้ markdown ตอนที่ 2</a>                                                        |
 11 | | <a href=https://youtu.be/ww4Nc4rJcKQ><img src=https://i.ytimg.com/vi/ww4Nc4rJcKQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/ww4Nc4rJcKQ">สอน Jupyter notebook: วิธีการเขียน Markdown เพื่อสร้างสารบัญให้กับ Jupyter Notebook</a>                         |
 12 | | <a href=https://youtu.be/OGYALEqTIQk><img src=https://i.ytimg.com/vi/OGYALEqTIQk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/OGYALEqTIQk">เทคนิคการใช้ IPython.display เพื่อเปิดไฟล์เสียง แสดงรูป JSON และ YouTube ใน Jupyter Notebook</a>               |
 13 | | <a href=https://youtu.be/YPgImo9kcbg><img src=https://i.ytimg.com/vi/YPgImo9kcbg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/YPgImo9kcbg">สอน Jupyter notebook: การแสดงหน้าเว็บ รูปภาพและ YouTube ใน notebook</a>                                   |
 14 | | <a href=https://youtu.be/71OIZZUFosY><img src=https://i.ytimg.com/vi/71OIZZUFosY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/71OIZZUFosY">สอน Jupyter notebook: เทคนิคการใช้ autocomplete และ help เพื่อเขียนโค้ด</a>                                  |
 15 | | <a href=https://youtu.be/wpFNY1nQeJU><img src=https://i.ytimg.com/vi/wpFNY1nQeJU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/wpFNY1nQeJU">สอน Jupyter notebook: เทคนิคการ start Jupyter notebook บน Windows</a>                                   |
 16 | | <a href=https://youtu.be/WNc3bFQSR00><img src=https://i.ytimg.com/vi/WNc3bFQSR00/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/WNc3bFQSR00">สอน Jupyter notebook: การ run Jupyter notebook ใน browser ที่ต้องการ</a>                                  |
 17 | | <a href=https://youtu.be/jOYOUrgR16c><img src=https://i.ytimg.com/vi/jOYOUrgR16c/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/jOYOUrgR16c">สอน Jupyter Notebook: การ run notebook cell ที่มี Python Prompt (เครื่องหมายมากกว่า 3 ตัว)</a>                |
 18 | | <a href=https://youtu.be/_88c01sc9zw><img src=https://i.ytimg.com/vi/_88c01sc9zw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/_88c01sc9zw">สอน Jupyter notebook: การ copy cells ข้าม notebooks</a>                                                 |
 19 | | <a href=https://youtu.be/gHUCt9T5_tw><img src=https://i.ytimg.com/vi/gHUCt9T5_tw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/gHUCt9T5_tw">สอน Jupyter notebook: การเก็บและแชร์ notebooks บน github</a>                                             |
 20 | | <a href=https://youtu.be/jdUQNXZJm60><img src=https://i.ytimg.com/vi/jdUQNXZJm60/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/jdUQNXZJm60">สอน Jupyter Notebook: ลองใช้ Jupyter notebook บน Cloud ด้วย Google Colaboratory (Colab)</a>              |
 21 | | <a href=https://youtu.be/zy2J9r-l5yQ><img src=https://i.ytimg.com/vi/zy2J9r-l5yQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/zy2J9r-l5yQ">การใช้ pip เพื่อจัดการ Python packages ใน Google Colaboratory</a>                                          |
 22 | | <a href=https://youtu.be/gWJr20O7rBI><img src=https://i.ytimg.com/vi/gWJr20O7rBI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/gWJr20O7rBI">เทคนิคการใช้ Jupyter notebook บน Visual Studio Code</a>                                                  |
 23 | | <a href=https://youtu.be/W661Zy939Q0><img src=https://i.ytimg.com/vi/W661Zy939Q0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/W661Zy939Q0">สอน Python สำหรับ Data science: รู้จักกับ pandas</a>                                                        |
 24 | | <a href=https://youtu.be/dX6COdB7yRo><img src=https://i.ytimg.com/vi/dX6COdB7yRo/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/dX6COdB7yRo">สอน Python สำหรับ Data science: pandas.Series</a>                                                       |
 25 | | <a href=https://youtu.be/NrcAJFJo1R4><img src=https://i.ytimg.com/vi/NrcAJFJo1R4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/NrcAJFJo1R4">สอน Python สำหรับ Data science: รู้จักกับ pandas DataFrame</a>                                              |
 26 | | <a href=https://youtu.be/ciXCePAEvxE><img src=https://i.ytimg.com/vi/ciXCePAEvxE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/ciXCePAEvxE">สอน Python สำหรับ Data science: การสำรวจข้อมูลเบื้องต้นด้วย pandas</a>                                        |
 27 | | <a href=https://youtu.be/7d-I9v2iO1k><img src=https://i.ytimg.com/vi/7d-I9v2iO1k/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/7d-I9v2iO1k">สอน Python สำหรับ Data science: การแสดงแถวข้อมูลใน pandas โดยใช้ head, tail และ sample</a>                 |
 28 | | <a href=https://youtu.be/JpaouEK59ss><img src=https://i.ytimg.com/vi/JpaouEK59ss/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/JpaouEK59ss">สอน Python สำหรับ Data science: ประเภทข้อมูล (data types) ใน pandas</a>                                   |
 29 | | <a href=https://youtu.be/phjPyZ_oO9Q><img src=https://i.ytimg.com/vi/phjPyZ_oO9Q/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/phjPyZ_oO9Q">การกำหนด max_rows, max_columns, float_format เพื่อแสดงข้อมูลใน pandas DataFrame</a>                        |
 30 | | <a href=https://youtu.be/8YzTrOJIpCM><img src=https://i.ytimg.com/vi/8YzTrOJIpCM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/8YzTrOJIpCM">การสร้างกราฟด้วย pandas: การสร้างฮีสโตแกรม (histogram) และ density plot</a>                                |
 31 | | <a href=https://youtu.be/G25GGFrSKPM><img src=https://i.ytimg.com/vi/G25GGFrSKPM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/G25GGFrSKPM">การสร้างกราฟด้วย pandas: การสร้าง boxplot</a>                                                             |
 32 | | <a href=https://youtu.be/G4JYU2zG34E><img src=https://i.ytimg.com/vi/G4JYU2zG34E/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/G4JYU2zG34E">การสร้างกราฟด้วย pandas: การสร้าง boxplot หลายรูปพร้อมกัน</a>                                                |
 33 | | <a href=https://youtu.be/PDgNr8c-YKE><img src=https://i.ytimg.com/vi/PDgNr8c-YKE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/PDgNr8c-YKE">การสร้างกราฟด้วย pandas: การสร้างกราฟ XY หรือ Scatter Plot</a>                                             |
 34 | | <a href=https://youtu.be/Y1zLp4jng-Q><img src=https://i.ytimg.com/vi/Y1zLp4jng-Q/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Y1zLp4jng-Q">การสร้างกราฟด้วย pandas: การสร้างกราฟแท่ง (bar graph)</a>                                                  |
 35 | | <a href=https://youtu.be/26Nmks3vctU><img src=https://i.ytimg.com/vi/26Nmks3vctU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/26Nmks3vctU">การสร้างกราฟด้วย pandas: การสร้างกราฟ stacked bar</a>                                                     |
 36 | | <a href=https://youtu.be/2mwIUYDV1OI><img src=https://i.ytimg.com/vi/2mwIUYDV1OI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/2mwIUYDV1OI">การสร้างกราฟด้วย pandas: กราฟเส้น (line graph)</a>                                                        |
 37 | | <a href=https://youtu.be/IiMBov4qx3M><img src=https://i.ytimg.com/vi/IiMBov4qx3M/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/IiMBov4qx3M">การสร้างกราฟด้วย pandas: การสร้างกราฟเส้นแสดงการเปลี่ยนแปลงเป็น % เปรียบเทียบ</a>                               |
 38 | | <a href=https://youtu.be/SkZ-736AEKw><img src=https://i.ytimg.com/vi/SkZ-736AEKw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/SkZ-736AEKw">การสร้างกราฟด้วย pandas: การสร้างกราฟเส้นหลายเส้นและหลายรูปพร้อม ๆ กัน</a>                                     |
 39 | | <a href=https://youtu.be/mXLpEzSVk1I><img src=https://i.ytimg.com/vi/mXLpEzSVk1I/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/mXLpEzSVk1I">การสร้างกราฟด้วย pandas: การสร้างกราฟที่มีตัวอักษรไทยด้วย pandas</a>                                            |
 40 | | <a href=https://youtu.be/NWSckTiUq_E><img src=https://i.ytimg.com/vi/NWSckTiUq_E/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/NWSckTiUq_E">การสร้างกราฟด้วย pandas: การกำหนด style ให้กับกราฟ</a>                                                     |
 41 | | <a href=https://youtu.be/xZtT9JBzIng><img src=https://i.ytimg.com/vi/xZtT9JBzIng/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/xZtT9JBzIng">สอน pandas: การสร้าง correlation matrix พร้อมแสดงผลแบบ heatmap</a>                                       |
 42 | | <a href=https://youtu.be/71wHi9O0QUQ><img src=https://i.ytimg.com/vi/71wHi9O0QUQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/71wHi9O0QUQ">สอน Python สำหรับ Data science: การอ่านไฟล์ CSV, TSV, FWF, Excel, Zip มาสร้างเป็น pandas DataFrame</a>      |
 43 | | <a href=https://youtu.be/Wo7tu3-Ltf4><img src=https://i.ytimg.com/vi/Wo7tu3-Ltf4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Wo7tu3-Ltf4">สอน Python สำหรับ Data science: การอ่านไฟล์ข้อมูลจาก local, web, dropbox, github ด้วย pandas</a>             |
 44 | | <a href=https://youtu.be/vmH1c-uCCW4><img src=https://i.ytimg.com/vi/vmH1c-uCCW4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/vmH1c-uCCW4">สอน pandas: การอ่านไฟล์ที่แชร์จาก OneDrive มาสร้างเป็น DataFrame</a>                                          |
 45 | | <a href=https://youtu.be/KBmxzulupNw><img src=https://i.ytimg.com/vi/KBmxzulupNw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/KBmxzulupNw">สอน Python สำหรับ Data science: การอ่านไฟล์ที่ไม่มี header row</a>                                            |
 46 | | <a href=https://youtu.be/Us3efwySjCI><img src=https://i.ytimg.com/vi/Us3efwySjCI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Us3efwySjCI">สอน Python สำหรับ Data science: การสร้าง pandas DataFrame จาก Clipboard</a>                              |
 47 | | <a href=https://youtu.be/Zfp5YVZtwYc><img src=https://i.ytimg.com/vi/Zfp5YVZtwYc/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Zfp5YVZtwYc">สอน Python สำหรับ Data science: การอ่านไฟล์ที่มีข้อมูลวันเวลา (date/time data) ด้วย pandas</a>                   |
 48 | | <a href=https://youtu.be/Ea3UcXdy7uY><img src=https://i.ytimg.com/vi/Ea3UcXdy7uY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Ea3UcXdy7uY">สอน Python สำหรับ Data science: การอ่านและรวมไฟล์ CSV หลาย ๆ ไฟล์</a>                                      |
 49 | | <a href=https://youtu.be/0TEbg9wTcx0><img src=https://i.ytimg.com/vi/0TEbg9wTcx0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/0TEbg9wTcx0">สอน Python สำหรับ Data science: การ save pandas DataFrame เป็นไฟล์ CSV</a>                                |
 50 | | <a href=https://youtu.be/FQwS8hFnmBo><img src=https://i.ytimg.com/vi/FQwS8hFnmBo/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/FQwS8hFnmBo">สอน Python สำหรับ Data science: การอ่านไฟล์ Excel ด้วย pandas</a>                                          |
 51 | | <a href=https://youtu.be/d_PLKgC9aFQ><img src=https://i.ytimg.com/vi/d_PLKgC9aFQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/d_PLKgC9aFQ">สอน Python สำหรับ Data science: การอ่านชีทหลาย ๆ ชีทจาก Excel มาสร้างเป็น DataFrame (ตอนที่ 1)</a>             |
 52 | | <a href=https://youtu.be/9b_eUwMYo6w><img src=https://i.ytimg.com/vi/9b_eUwMYo6w/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/9b_eUwMYo6w">สอน Python สำหรับ Data science: การอ่านชีทหลาย ๆ ชีทจาก Excel มาสร้างเป็น DataFrame (ตอนที่ 2)</a>             |
 53 | | <a href=https://youtu.be/LuMjGIEwfTQ><img src=https://i.ytimg.com/vi/LuMjGIEwfTQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/LuMjGIEwfTQ">สอน Python สำหรับ Data science: การ save หลาย ๆ pandas DataFrame ให้เป็นไฟล์ Excel</a>                     |
 54 | | <a href=https://youtu.be/sDUJxIZn0W4><img src=https://i.ytimg.com/vi/sDUJxIZn0W4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/sDUJxIZn0W4">สอน Python สำหรับ Data science: การอ่านข้อมูลจาก google sheets มาสร้างเป็น pandas DataFrame</a>              |
 55 | | <a href=https://youtu.be/sYUyG_gkIoI><img src=https://i.ytimg.com/vi/sYUyG_gkIoI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/sYUyG_gkIoI">สอน pandas: การอ่านไฟล์ csv, tsv, json, excel ที่แชร์บน Google Drive</a>                                    |
 56 | | <a href=https://youtu.be/mVXUZSyIq40><img src=https://i.ytimg.com/vi/mVXUZSyIq40/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/mVXUZSyIq40">สอน pandas: การอ่านและแปลงข้อมูลจาก Google Form เช่น แปลงข้อความ "พอใจมาก" ให้เป็นเลข 4</a>                   |
 57 | | <a href=https://youtu.be/9wRKKyAaz3A><img src=https://i.ytimg.com/vi/9wRKKyAaz3A/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/9wRKKyAaz3A">การดึงข้อมูลตัวชี้วัดจาก World Bank ด้วย pandas</a>                                                            |
 58 | | <a href=https://youtu.be/ucjGGJHzC9I><img src=https://i.ytimg.com/vi/ucjGGJHzC9I/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/ucjGGJHzC9I">สอน Python สำหรับ Data science: การอ่านข้อมูลจาก web ที่อยู่ในรูปตาราง html ด้วย read_html</a>                   |
 59 | | <a href=https://youtu.be/vaOdo845soQ><img src=https://i.ytimg.com/vi/vaOdo845soQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/vaOdo845soQ">สอน Python สำหรับ Data science: การอ่านไฟล์ JSON มาสร้างเป็น pandas dataframe</a>                           |
 60 | | <a href=https://youtu.be/EUHT7pFaywU><img src=https://i.ytimg.com/vi/EUHT7pFaywU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/EUHT7pFaywU">การใช้ GET requests ในการดึงข้อมูล text file (CSV, JSON) และรูปภาพจาก web URL</a>                           |
 61 | | <a href=https://youtu.be/IQhJAr-hcFE><img src=https://i.ytimg.com/vi/IQhJAr-hcFE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/IQhJAr-hcFE">สอน Python สำหรับ data science: การใช้ Pandas ดึงข้อมูลจาก Bank Of Thailand API (REST API)</a>              |
 62 | | <a href=https://youtu.be/QVrZL4T8NBk><img src=https://i.ytimg.com/vi/QVrZL4T8NBk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/QVrZL4T8NBk">การอ่าน R datasets มาเป็น pandas DataFrame</a>                                                           |
 63 | | <a href=https://youtu.be/Oe_fY0FJpNY><img src=https://i.ytimg.com/vi/Oe_fY0FJpNY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Oe_fY0FJpNY">สอน pandas: การอ่านและเขียนไฟล์ SPSS (read and write SPSS sav and zsav file)</a>                          |
 64 | | <a href=https://youtu.be/eYBiRr3ENco><img src=https://i.ytimg.com/vi/eYBiRr3ENco/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/eYBiRr3ENco">สอน Python สำหรับ Data science: การอ่านข้อมูลจาก sqlite3 มาเป็น pandas DataFrame</a>                        |
 65 | | <a href=https://youtu.be/zjB8dgt6Sgc><img src=https://i.ytimg.com/vi/zjB8dgt6Sgc/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/zjB8dgt6Sgc">สอน Python สำหรับ Data science: การแปลง pandas DataFrame ให้เป็นตารางใน sqlite3</a>                       |
 66 | | <a href=https://youtu.be/HBLgIguv4PM><img src=https://i.ytimg.com/vi/HBLgIguv4PM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/HBLgIguv4PM">สอน Python สำหรับ data science: การดึงข้อมูลจาก Microsoft SQL Server มาสร้างเป็น pandas DataFrame</a>        |
 67 | | <a href=https://youtu.be/i27D8RHPDO8><img src=https://i.ytimg.com/vi/i27D8RHPDO8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/i27D8RHPDO8">สอน pandas: save DataFrame ไปเป็นตารางใน MS SQL Server</a>                                              |
 68 | | <a href=https://youtu.be/HEATMtHbdK8><img src=https://i.ytimg.com/vi/HEATMtHbdK8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/HEATMtHbdK8">สอน pandas: การเชื่อมต่อกับฐานข้อมูล PostgreSQL</a>                                                          |
 69 | | <a href=https://youtu.be/1vTwq3Tli34><img src=https://i.ytimg.com/vi/1vTwq3Tli34/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/1vTwq3Tli34">สอน Python สำหรับ data science: การดึงข้อมูลจาก MySQL มาสร้างเป็น pandas DataFrame</a>                       |
 70 | | <a href=https://youtu.be/M6du6UnpQvU><img src=https://i.ytimg.com/vi/M6du6UnpQvU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/M6du6UnpQvU">สอน pandas:  save DataFrame ไปเป็นตารางใน MySQL, PostgreSQL และ SQLite</a>                              |
 71 | | <a href=https://youtu.be/spCJLwi4emE><img src=https://i.ytimg.com/vi/spCJLwi4emE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/spCJLwi4emE">สอน Python สำหรับ Data science: การใช้งาน pandas index เบื้องต้น</a>                                        |
 72 | | <a href=https://youtu.be/uvotRtVAT68><img src=https://i.ytimg.com/vi/uvotRtVAT68/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/uvotRtVAT68">สอน Python สำหรับ Data science: การเลือกคอลัมน์ใน pandas DataFrame ตอนที่ 1</a>                              |
 73 | | <a href=https://youtu.be/A9iqVNC35So><img src=https://i.ytimg.com/vi/A9iqVNC35So/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/A9iqVNC35So">สอน Python สำหรับ Data science: การเลือกคอลัมน์ใน pandas DataFrame ตอนที่ 2</a>                              |
 74 | | <a href=https://youtu.be/QS1-851VIhY><img src=https://i.ytimg.com/vi/QS1-851VIhY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/QS1-851VIhY">สอน Python สำหรับ Data science: การเลือกแถวข้อมูลตามเงื่อนไข</a>                                             |
 75 | | <a href=https://youtu.be/_T9HUhE6XD4><img src=https://i.ytimg.com/vi/_T9HUhE6XD4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/_T9HUhE6XD4">สอน Python สำหรับ Data science: การลบแถวและคอลัมน์ใน DataFrame</a>                                        |
 76 | | <a href=https://youtu.be/sZZkBpc_eo0><img src=https://i.ytimg.com/vi/sZZkBpc_eo0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/sZZkBpc_eo0">สอน Python สำหรับ Data science: การเปลี่ยนชื่อคอลัมน์ใน pandas DataFrame</a>                                  |
 77 | | <a href=https://youtu.be/Wa2xatlpOsI><img src=https://i.ytimg.com/vi/Wa2xatlpOsI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Wa2xatlpOsI">สอน Python สำหรับ Data science: การเรียงลำดับข้อมูลใน pandas ด้วย sort_values และ sort_index</a>             |
 78 | | <a href=https://youtu.be/k5rjyOhG3kg><img src=https://i.ytimg.com/vi/k5rjyOhG3kg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/k5rjyOhG3kg">สอน Python สำหรับ Data science: การใช้ loc เพื่อเลือกแถวและคอลัมน์</a>                                        |
 79 | | <a href=https://youtu.be/nV6rUkXu-IY><img src=https://i.ytimg.com/vi/nV6rUkXu-IY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/nV6rUkXu-IY">สอน Python สำหรับ Data science: การใช้ loc ร่วมกับ regex เพื่อเลือกชื่อคอลัมน์</a>                                |
 80 | | <a href=https://youtu.be/2mRwQ2_p0Ws><img src=https://i.ytimg.com/vi/2mRwQ2_p0Ws/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/2mRwQ2_p0Ws">สอน Python สำหรับ Data science: การใช้ iloc เพื่อเลือกแถวและคอลัมน์ใน pandas DataFrame</a>                    |
 81 | | <a href=https://youtu.be/GT1KhvEzoV8><img src=https://i.ytimg.com/vi/GT1KhvEzoV8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/GT1KhvEzoV8">สอน Python สำหรับ Data science: การใช้ at และ iat ในการเข้าถึงและกำหนดค่าแบบ scalar ใน DataFrame</a>        |
 82 | | <a href=https://youtu.be/w9n1xZq38Pg><img src=https://i.ytimg.com/vi/w9n1xZq38Pg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/w9n1xZq38Pg">สอน Python สำหรับ Data science: การสร้าง two-level index ให้ pandas DataFrame</a>                         |
 83 | | <a href=https://youtu.be/SycL_UqzIaY><img src=https://i.ytimg.com/vi/SycL_UqzIaY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/SycL_UqzIaY">สอน Python สำหรับ Data science: การสร้าง three-level index ให้ pandas DataFrame</a>                       |
 84 | | <a href=https://youtu.be/tLlhdC8V5gw><img src=https://i.ytimg.com/vi/tLlhdC8V5gw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/tLlhdC8V5gw">สอน Python สำหรับ Data science: การใช้ pandas IndexSlice เพื่อดึงข้อมูล multi-level index ตอนที่ 1</a>          |
 85 | | <a href=https://youtu.be/JpKeTfjAqKM><img src=https://i.ytimg.com/vi/JpKeTfjAqKM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/JpKeTfjAqKM">สอน Python สำหรับ Data science: การใช้ pandas IndexSlice เพื่อดึงข้อมูล multi-level index ตอนที่ 2</a>          |
 86 | | <a href=https://youtu.be/U2YMOfGcsvg><img src=https://i.ytimg.com/vi/U2YMOfGcsvg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/U2YMOfGcsvg">การดึงราคาหุ้นในตลาดหุ้นไทยและต่างประเทศด้วย pandas (get daily stock price)</a>                               |
 87 | | <a href=https://youtu.be/Xsfttmd3xRM><img src=https://i.ytimg.com/vi/Xsfttmd3xRM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Xsfttmd3xRM">สอน pandas: เข้าใจการทำงานของ multilevel column names เช่น ข้อมูลราคาหุ้นรายวัน</a>                           |
 88 | | <a href=https://youtu.be/Op4e6amDblE><img src=https://i.ytimg.com/vi/Op4e6amDblE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Op4e6amDblE">สอน Python สำหรับ Data science: การทำงานกับข้อมูลวันและเวลาใน pandas</a>                                    |
 89 | | <a href=https://youtu.be/vc-_oaJhU2I><img src=https://i.ytimg.com/vi/vc-_oaJhU2I/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/vc-_oaJhU2I">สอน Python สำหรับ Data science: การจัดการข้อมูลแบบ TimeSeries (DatetimeIndex)</a>                          |
 90 | | <a href=https://youtu.be/f-R_BV1g6hY><img src=https://i.ytimg.com/vi/f-R_BV1g6hY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/f-R_BV1g6hY">สอน Python สำหรับ Data science: การใช้ groupby เพื่อจัดกลุ่มข้อมูลใน pandas DataFrame</a>                       |
 91 | | <a href=https://youtu.be/wPFmdm_kT3Y><img src=https://i.ytimg.com/vi/wPFmdm_kT3Y/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/wPFmdm_kT3Y">สอน Python สำหรับ Data science: การใช้ Series.str.replace เพื่อ clean ข้อมูล</a>                             |
 92 | | <a href=https://youtu.be/7IuM9iVI98s><img src=https://i.ytimg.com/vi/7IuM9iVI98s/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/7IuM9iVI98s">สอน Python สำหรับ Data science: การปรับแต่งข้อมูลในคอลัมน์ เช่น ลบส่วนที่ไม่ใช่เลข 0-9 ออกจากข้อมูล</a>               |
 93 | | <a href=https://youtu.be/AMlh2HO_r_E><img src=https://i.ytimg.com/vi/AMlh2HO_r_E/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/AMlh2HO_r_E">สอน pandas: การแปลงตัวเลขที่มีสัญลักษณ์สกุลเงินและหน่วย (clean currency text) เช่น $70K เป็น 70000</a>             |
 94 | | <a href=https://youtu.be/wO2KzbYKNFY><img src=https://i.ytimg.com/vi/wO2KzbYKNFY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/wO2KzbYKNFY">สอน Python สำหรับ Data science: การใช้ pandas drop_duplicates เพื่อลบแถวซ้ำ</a>                             |
 95 | | <a href=https://youtu.be/Ysgqv1ICWU4><img src=https://i.ytimg.com/vi/Ysgqv1ICWU4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Ysgqv1ICWU4">สอน Python สำหรับ Data science: การจัดการกับ missing values ตอนที่ 1</a>                                    |
 96 | | <a href=https://youtu.be/Fib0R8yJOYE><img src=https://i.ytimg.com/vi/Fib0R8yJOYE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Fib0R8yJOYE">สอน Python สำหรับ Data science: การจัดการกับ missing values ตอนที่ 2</a>                                    |
 97 | | <a href=https://youtu.be/g3T4KTzfK3U><img src=https://i.ytimg.com/vi/g3T4KTzfK3U/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/g3T4KTzfK3U">สอน Python สำหรับ Data science: การแทนที่ missing values</a>                                              |
 98 | | <a href=https://youtu.be/ulDIl1E_VVA><img src=https://i.ytimg.com/vi/ulDIl1E_VVA/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/ulDIl1E_VVA">สอน Python สำหรับ Data science: ตัวแปรแบบ category เบื้องต้น</a>                                            |
 99 | | <a href=https://youtu.be/9sZHNXoDGr0><img src=https://i.ytimg.com/vi/9sZHNXoDGr0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/9sZHNXoDGr0">สอน Python สำหรับ Data science: การสร้าง ordered category ใน pandas DataFrame</a>                        |
100 | | <a href=https://youtu.be/W9NzO7iLVbY><img src=https://i.ytimg.com/vi/W9NzO7iLVbY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/W9NzO7iLVbY">สอน Python สำหรับ Data science: การใช้ cut เพื่อแบ่งข้อมูลออกเป็น category</a>                                 |
101 | | <a href=https://youtu.be/fNJb52eMLNg><img src=https://i.ytimg.com/vi/fNJb52eMLNg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/fNJb52eMLNg">สอน Python สำหรับ Data science: การรวมหลาย ๆ DataFrame เข้าด้วยกันด้วย pandas.concat</a>                    |
102 | | <a href=https://youtu.be/mqG4Uq3lpwI><img src=https://i.ytimg.com/vi/mqG4Uq3lpwI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/mqG4Uq3lpwI">สอน Python สำหรับ Data science: การใช้ aggregate function ใน Series และ DataFrame</a>                    |
103 | | <a href=https://youtu.be/29RJJ_Ia3SI><img src=https://i.ytimg.com/vi/29RJJ_Ia3SI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/29RJJ_Ia3SI">สอน pandas: เข้าใจการทำงาน axis=0 (by index) และ axis=1 (by columns)</a>                                |
104 | | <a href=https://youtu.be/CaAe6ui-pOk><img src=https://i.ytimg.com/vi/CaAe6ui-pOk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/CaAe6ui-pOk">สอน Python สำหรับ Data science: การสร้างตารางไขว้ (crosstab) ด้วย pandas</a>                               |
105 | | <a href=https://youtu.be/ohNwrsXyaFM><img src=https://i.ytimg.com/vi/ohNwrsXyaFM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/ohNwrsXyaFM">สอน Python สำหรับ Data science: การสร้าง pivot table ด้วย pandas เบื้องต้น</a>                               |
106 | | <a href=https://youtu.be/vZ6Y5CoAtWY><img src=https://i.ytimg.com/vi/vZ6Y5CoAtWY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/vZ6Y5CoAtWY">สอน Python สำหรับ Data science: การ melt/unpivot pandas DataFrame</a>                                   |
107 | | <a href=https://youtu.be/Jc5KOan46Ik><img src=https://i.ytimg.com/vi/Jc5KOan46Ik/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Jc5KOan46Ik">สอน Python สำหรับ Data science: การสร้าง pandas pivot table แสดงความถี่ และการแสดง missing values</a>      |
108 | | <a href=https://youtu.be/vuDCEq3pWLk><img src=https://i.ytimg.com/vi/vuDCEq3pWLk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/vuDCEq3pWLk">สอน Python สำหรับ Data science: การใช้ aggregate function ใน pivot table ของ pandas</a>                  |
109 | | <a href=https://youtu.be/xl1VC_nZ1b8><img src=https://i.ytimg.com/vi/xl1VC_nZ1b8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/xl1VC_nZ1b8">สอน Python สำหรับ Data science: การใช้ diff() และ pct_change() เพื่อหาผลต่างระหว่างสองแถว</a>                |
110 | | <a href=https://youtu.be/Y2j2eSG82-c><img src=https://i.ytimg.com/vi/Y2j2eSG82-c/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Y2j2eSG82-c">สอน Python สำหรับ Data science: การใช้ diff() และ pct_change() ร่วมกับ groupby ใน pandas DataFrame</a>     |
111 | | <a href=https://youtu.be/1T8rTfSEhoc><img src=https://i.ytimg.com/vi/1T8rTfSEhoc/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/1T8rTfSEhoc">สอน Python สำหรับ Data science: การหาค่าแรกและค่าสุดท้ายของแต่ละ group ใน pandas DataFrame</a>               |
112 | | <a href=https://youtu.be/7xt8pC_hTDc><img src=https://i.ytimg.com/vi/7xt8pC_hTDc/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/7xt8pC_hTDc">สอน pandas: การคำนวณราคาหุ้นเทียบกับวันที่ใช้เป็นฐานด้วย groupby().first()</a>                                   |
113 | | <a href=https://youtu.be/itnud30V0eE><img src=https://i.ytimg.com/vi/itnud30V0eE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/itnud30V0eE">สอน pandas การหาแถวที่มีค่าสูงสุดและต่ำสุดด้วย nlargest, nsmallest</a>                                          |
114 | | <a href=https://youtu.be/xjQzlpFjbI8><img src=https://i.ytimg.com/vi/xjQzlpFjbI8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/xjQzlpFjbI8">สอน Python สำหรับ Data science: สร้างคอลัมน์แสดงลำดับ (rank) ด้วย pandas</a>                                 |
115 | | <a href=https://youtu.be/DC8_bjrxxpk><img src=https://i.ytimg.com/vi/DC8_bjrxxpk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/DC8_bjrxxpk">สอน Python สำหรับ Data science: การ standardize data (z-score) ด้วย pandas</a>                           |
116 | | <a href=https://youtu.be/rGF1jY5tvCk><img src=https://i.ytimg.com/vi/rGF1jY5tvCk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/rGF1jY5tvCk">การสร้าง dummy variable และใช้งานกับ Linear Regression ของ scikit-learn และ statsmodels</a>               |
117 | | <a href=https://youtu.be/AJDJsKHd5L8><img src=https://i.ytimg.com/vi/AJDJsKHd5L8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/AJDJsKHd5L8">สอน Python สำหรับ Data science: การ merge DataFrames แบบ inner join</a>                                 |
118 | | <a href=https://youtu.be/Ymywt7I4PKg><img src=https://i.ytimg.com/vi/Ymywt7I4PKg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Ymywt7I4PKg">สอน Python สำหรับ Data science: การ merge DataFrames แบบ left join</a>                                  |
119 | | <a href=https://youtu.be/GRYfWdJ_46k><img src=https://i.ytimg.com/vi/GRYfWdJ_46k/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/GRYfWdJ_46k">สอน Python สำหรับ Data science: รู้จักกับ lambda function</a>                                               |
120 | | <a href=https://youtu.be/EQWDtr8iPpo><img src=https://i.ytimg.com/vi/EQWDtr8iPpo/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/EQWDtr8iPpo">สอน Python สำหรับ Data science: การใช้ lambda function กับ pandas Series และ DataFrame</a>                |
121 | | <a href=https://youtu.be/RQYyPAJuoX8><img src=https://i.ytimg.com/vi/RQYyPAJuoX8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/RQYyPAJuoX8">สอน Python สำหรับ Data science: การใช้ map ใน pandas</a>                                                 |
122 | | <a href=https://youtu.be/TCN5t-TIosM><img src=https://i.ytimg.com/vi/TCN5t-TIosM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/TCN5t-TIosM">สอน Python สำหรับ Data science: การใช้ applymap ใน pandas</a>                                            |
123 | | <a href=https://youtu.be/3vZocRESZF4><img src=https://i.ytimg.com/vi/3vZocRESZF4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/3vZocRESZF4">สอน Python สำหรับ Data science: การใช้ apply ใน pandas</a>                                               |
124 | | <a href=https://youtu.be/cHuBLNYnR9c><img src=https://i.ytimg.com/vi/cHuBLNYnR9c/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/cHuBLNYnR9c">สอน pandas: จัดการข้อมูลแยกตามกลุ่มด้วยหลักการ Split-Apply-Combine</a>                                        |
125 | | <a href=https://youtu.be/akY6EDyP4w8><img src=https://i.ytimg.com/vi/akY6EDyP4w8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/akY6EDyP4w8">สอน Python สำหรับ Data science: การใช้ query ในการกรองข้อมูลใน pandas DataFrame</a>                        |
126 | | <a href=https://youtu.be/pQdDrM4weiM><img src=https://i.ytimg.com/vi/pQdDrM4weiM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/pQdDrM4weiM">สอน Python สำหรับ Data science: การใช้ any() และ all() ในการตรวจสอบ True/False</a>                       |
127 | | <a href=https://youtu.be/M2ClgVsNHrw><img src=https://i.ytimg.com/vi/M2ClgVsNHrw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/M2ClgVsNHrw">สอน Python สำหรับ Data science: การสลับแถว (shuffle) ใน pandas DataFrame</a>                             |
128 | | <a href=https://youtu.be/s8LCVtdGFfQ><img src=https://i.ytimg.com/vi/s8LCVtdGFfQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/s8LCVtdGFfQ">สอน Python สำหรับ Data science: การสุ่มแถวจากข้อมูลที่แบ่งออกเป็นกลุ่ม ๆ</a>                                      |
129 | | <a href=https://youtu.be/mDsOckSft-A><img src=https://i.ytimg.com/vi/mDsOckSft-A/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/mDsOckSft-A">สอน Python สำหรับ Data science: การสุ่มแถวจากข้อมูลที่แบ่งออกเป็นกลุ่ม ๆ  โดยระบุจำนวนที่ต้องการ</a>                  |
130 | | <a href=https://youtu.be/q9X3XUz6qAI><img src=https://i.ytimg.com/vi/q9X3XUz6qAI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/q9X3XUz6qAI">สอน Python สำหรับ Data science: การแบ่ง DataFrame ออกเป็น training และ test datasets (ตอนที่ 1)</a>         |
131 | | <a href=https://youtu.be/KOFwPXhN-QQ><img src=https://i.ytimg.com/vi/KOFwPXhN-QQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/KOFwPXhN-QQ">สอน Python สำหรับ Data science: การแบ่ง DataFrame ออกเป็น training และ test datasets (ตอนที่ 2)</a>         |
132 | | <a href=https://youtu.be/5nj8ToExnOA><img src=https://i.ytimg.com/vi/5nj8ToExnOA/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/5nj8ToExnOA">สอน pandas: การเข้าถึงแต่ละแถวใน DataFrame (iterate rows in dataframe)</a>                                |
133 | | <a href=https://youtu.be/qBivg-iAIkI><img src=https://i.ytimg.com/vi/qBivg-iAIkI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/qBivg-iAIkI">สอน Python สำหรับ Data science: กำหนดจำนวนแถวที่จะแสดงผลและรูปแบบการแสดงตัวเลข</a>                          |
134 | | <a href=https://youtu.be/CobiB4GhFNo><img src=https://i.ytimg.com/vi/CobiB4GhFNo/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/CobiB4GhFNo">สอน Python สำหรับ Data science: การปรับแต่ง style ในการแสดงข้อมูล DataFrame เบื้องต้น</a>                      |
135 | | <a href=https://youtu.be/YSpWell_Gs0><img src=https://i.ytimg.com/vi/YSpWell_Gs0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/YSpWell_Gs0">สอน Python สำหรับ Data science: การปรับแต่ง style ในการแสดงข้อมูล DataFrame ด้วย custom function</a>         |
136 | | <a href=https://youtu.be/z120LhX8tmI><img src=https://i.ytimg.com/vi/z120LhX8tmI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/z120LhX8tmI">สอน Python สำหรับ Data science: การแสดงรูปและ hyperlinkใน pandas DataFrame</a>                           |
137 | | <a href=https://youtu.be/RvjFTz_JTUQ><img src=https://i.ytimg.com/vi/RvjFTz_JTUQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/RvjFTz_JTUQ">สอนไพธอน Python 3: การดาวน์โหลดรูปภาพจาก URL เพื่อแสดงผลบน jupyter notebook</a>                            |
138 | | <a href=https://youtu.be/8S3RjNE6U4E><img src=https://i.ytimg.com/vi/8S3RjNE6U4E/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/8S3RjNE6U4E">สอนทำ web scraping ด้วย Python: การ scrape รูปภาพจากหน้าเว็บด้วย BeautifulSoup</a>                          |
139 | | <a href=https://youtu.be/e3ngA0uGcDY><img src=https://i.ytimg.com/vi/e3ngA0uGcDY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/e3ngA0uGcDY">สอนทำ web scraping ด้วย Python: การ scrape url link และ img เพื่อสร้างเป็น DataFrame ตอนที่ 1</a>             |
140 | | <a href=https://youtu.be/7Qv6IpWFYFg><img src=https://i.ytimg.com/vi/7Qv6IpWFYFg/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/7Qv6IpWFYFg">สอนทำ web scraping ด้วย Python: การ scrape url link และ img เพื่อสร้างเป็น DataFrame ตอนที่ 2</a>             |
141 | | <a href=https://youtu.be/Y-_t5Hll09I><img src=https://i.ytimg.com/vi/Y-_t5Hll09I/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Y-_t5Hll09I">สอน web scraping: การ scape นักเตะทีม Liverpool มาเก็บใน pandas.DataFrame</a>                             |
142 | | <a href=https://youtu.be/Okc0TwCvZD4><img src=https://i.ytimg.com/vi/Okc0TwCvZD4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Okc0TwCvZD4">สอนการ download รูปภาพจาก url ที่เก็บใน pandas DataFrame</a>                                               |
143 | | <a href=https://youtu.be/vnF1L-YYdN8><img src=https://i.ytimg.com/vi/vnF1L-YYdN8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/vnF1L-YYdN8">สอน web scraping ในการดึงข้อความและรูปภาพเพื่อแสดงบน Notebook และบันทึกเป็นไฟล์</a>                             |
144 | | <a href=https://youtu.be/7kyIhEWSzoY><img src=https://i.ytimg.com/vi/7kyIhEWSzoY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/7kyIhEWSzoY">สอน web scraping ในการดึงข้อความและรูปภาพมาเก็บในฐานข้อมูล MySQL</a>                                         |
145 | | <a href=https://youtu.be/9OUwRrshD1s><img src=https://i.ytimg.com/vi/9OUwRrshD1s/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/9OUwRrshD1s">สอน pandas: explode (unnest) multivalue column เช่น คอลัมน์ที่เก็บค่าแบบ list</a>                             |
146 | | <a href=https://youtu.be/OFyQhtlgje4><img src=https://i.ytimg.com/vi/OFyQhtlgje4/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/OFyQhtlgje4">สอน pandas: ทำความสะอาดข้อมูล JSON</a>                                                                   |
147 | | <a href=https://youtu.be/-KNJoFlDc0g><img src=https://i.ytimg.com/vi/-KNJoFlDc0g/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/-KNJoFlDc0g">สอน pandas: ดึงข้อมูล JSON จาก public RESTful APIs เช่น อัตราแลกเปลี่ยน พยากรณ์อากาศ Pokemon</a>               |
148 | | <a href=https://youtu.be/kVnEv2rUDHE><img src=https://i.ytimg.com/vi/kVnEv2rUDHE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/kVnEv2rUDHE">สอน pandas: รู้จักกับ JSON string format แบบต่าง ๆ ที่ pandas รองรับ</a>                                       |
149 | | <a href=https://youtu.be/0H1QBeNA4oU><img src=https://i.ytimg.com/vi/0H1QBeNA4oU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/0H1QBeNA4oU">สอน pandas: การจัดการกับคอลัมน์ใน DataFrame ที่เก็บค่าแบบ dict และ list</a>                                    |
150 | | <a href=https://youtu.be/W76aJHwjJF8><img src=https://i.ytimg.com/vi/W76aJHwjJF8/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/W76aJHwjJF8">สอน pandas: การดึงข้อมูลดัชนีตลาดหุ้นทั่วโลกแบบรายวัน (get daily stock market index)</a>                         |
151 | | <a href=https://youtu.be/Ag1C9MqJ6SI><img src=https://i.ytimg.com/vi/Ag1C9MqJ6SI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Ag1C9MqJ6SI">สอน pandas: การอ่านไฟล์ SAS (xport, sas7bdat)</a>                                                        |
152 | | <a href=https://youtu.be/Ouuks0qWIEQ><img src=https://i.ytimg.com/vi/Ouuks0qWIEQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/Ouuks0qWIEQ">สอน pandas: การ stack และ unstack DataFrame ที่มี MultiIndex</a>                                          |
153 | | <a href=https://youtu.be/s-70yr3b5LY><img src=https://i.ytimg.com/vi/s-70yr3b5LY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/s-70yr3b5LY">สอน pandas: การเชื่อมต่อกับ Azure SQL Database</a>                                                         |
154 | | <a href=https://youtu.be/stm-Jc_0pOk><img src=https://i.ytimg.com/vi/stm-Jc_0pOk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/stm-Jc_0pOk">สอน pandas: การทำงานกับคอลัมน์ที่มีชื่อเป็นตัวเลข (numeric column name)</a>                                      |
155 | | <a href=https://youtu.be/oCb41OOZMi0><img src=https://i.ytimg.com/vi/oCb41OOZMi0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/oCb41OOZMi0">สอน pandas: การจัดรูปแบบการแสดงผลตัวเลช เช่น จำนวนจุดทศนิยม เปอร์เซ็นต์ ใส่ , คั่นทุก 3 หลัก</a>                     |
156 | | <a href=https://youtu.be/lmD2wFH5vmw><img src=https://i.ytimg.com/vi/lmD2wFH5vmw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/lmD2wFH5vmw">สอน pandas: การเลือก row/column label ด้วย filter เช่น เลือกชื่อคอลัมน์ที่มีคำว่า math</a>                         |
157 | | <a href=https://youtu.be/rie7-xldNwM><img src=https://i.ytimg.com/vi/rie7-xldNwM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/rie7-xldNwM">สอน pandas: การรวมชื่อคอลัมน์แบบ MultiIndex เข้าด้วยกัน</a>                                                   |
158 | | <a href=https://youtu.be/tko93L_QPIE><img src=https://i.ytimg.com/vi/tko93L_QPIE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/tko93L_QPIE">สอน pandas: การดึงอัตราแลกเปลี่ยนเงินตราต่างประเทศจาก RESTful api</a>                                        |
159 | | <a href=https://youtu.be/rciKuP7VeHQ><img src=https://i.ytimg.com/vi/rciKuP7VeHQ/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/rciKuP7VeHQ">สอน pandas: การหาค่าเฉลี่ยดัชนีตลาดหุ้นแยกตามสัปดาห์ เดือน ไตรมาส และปี</a>                                       |
160 | | <a href=https://youtu.be/GqbYVfZPYos><img src=https://i.ytimg.com/vi/GqbYVfZPYos/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/GqbYVfZPYos">สอนไพธอน Python: การใช้ tqdm เพื่อสร้าง progress meter</a>                                                 |
161 | | <a href=https://youtu.be/fIT4ASdOo7M><img src=https://i.ytimg.com/vi/fIT4ASdOo7M/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/fIT4ASdOo7M">สอนไพธอน Python: การใช้ io.StringIO เพื่อทำ memory file (อ่าน string ให้เหมือนอ่านไฟล์)</a>                    |
162 | | <a href=https://youtu.be/5PMyTvaFbSo><img src=https://i.ytimg.com/vi/5PMyTvaFbSo/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/5PMyTvaFbSo">สอน pandas: การใช้คำสั่ง SQL เพื่อดึงข้อมูลใน DataFrame ด้วย pandasql package</a>                               |
163 | | <a href=https://youtu.be/-YpVn7C3mqE><img src=https://i.ytimg.com/vi/-YpVn7C3mqE/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/-YpVn7C3mqE">สอน pandas: การ merge ข้อมูลราคาน้ำมันและทองคำรายเดือน (merge oil and gold prices)</a>                      |
164 | | <a href=https://youtu.be/FzfKJaxbIME><img src=https://i.ytimg.com/vi/FzfKJaxbIME/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/FzfKJaxbIME">สอน pandas: การ merge ราคาน้ำมันและดัชนีตลาดหุ้นรายวันเข้าด้วยกัน (merge oil prices and stock index)</a>         |
165 | | <a href=https://youtu.be/bwdg6uHiCAM><img src=https://i.ytimg.com/vi/bwdg6uHiCAM/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/bwdg6uHiCAM">สอน pandas: การอ่านและรวมหลาย ๆ ชีทจาก Google Sheets มาสร้างเป็น DataFrame</a>                             |
166 | | <a href=https://youtu.be/KTGvdg9c_qU><img src=https://i.ytimg.com/vi/KTGvdg9c_qU/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/KTGvdg9c_qU">สอน pandas: เทคนิคการใช้ regular expression ในการกรองข้อมูลแบบต่าง ๆ</a>                                    |
167 | | <a href=https://youtu.be/rEdibMiUQg0><img src=https://i.ytimg.com/vi/rEdibMiUQg0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/rEdibMiUQg0">สอน data science: การดึงข้อความภาษาไทยด้วย regex (extract Thai characters with regular expression)</a>    |
168 | | <a href=https://youtu.be/7spx-I6Omxk><img src=https://i.ytimg.com/vi/7spx-I6Omxk/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/7spx-I6Omxk">สอน pandas: การทำความสะอาดข้อมูลด้วย regular expression (str.extract และ str.split)</a>                   |
169 | | <a href=https://youtu.be/4d5XZAsV-sw><img src=https://i.ytimg.com/vi/4d5XZAsV-sw/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/4d5XZAsV-sw">สอน pandas: การสร้าง dummy/one-hot จาก multivalued column</a>                                           |
170 | | <a href=https://youtu.be/iH1IUA_pZRI><img src=https://i.ytimg.com/vi/iH1IUA_pZRI/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/iH1IUA_pZRI">การแปลง pandas DataFrame ให้เป็น Sparse เพื่อทำ Market Basket Analysis</a>                                 |
171 | | <a href=https://youtu.be/8DBgdVFTsu0><img src=https://i.ytimg.com/vi/8DBgdVFTsu0/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/8DBgdVFTsu0">สอน Jupyter notebook: แนะนำ Azure Notebook เพื่อใช้ Jupyter Notebook บน Cloud</a>                         |
172 | | <a href=https://youtu.be/3PZO2gp6FqY><img src=https://i.ytimg.com/vi/3PZO2gp6FqY/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/3PZO2gp6FqY">สอน data science: ทำความสะอาดข้อมูลแบบ multilevel ด้วย pandas (clean multilevel table)</a>                |
173 | | <a href=https://youtu.be/I-8iP1fQS80><img src=https://i.ytimg.com/vi/I-8iP1fQS80/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/I-8iP1fQS80">สอน pandas: แปลง DataFrame เป็น HTML, Markdown และ Excel (DataFrame to HTML, Markdown, Excel table)</a> |
174 | | <a href=https://youtu.be/NfM8VXB2d0U><img src=https://i.ytimg.com/vi/NfM8VXB2d0U/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/NfM8VXB2d0U">สอน pandas: การทำงานกับคอลัมน์ที่เก็บค่าเป็น dictionary</a>                                                    |
175 | | <a href=https://youtu.be/U90EY-9qhRA><img src=https://i.ytimg.com/vi/U90EY-9qhRA/mqdefault.jpg />&nbsp;</a> | <a href="https://youtu.be/U90EY-9qhRA">สอน data science: preprocess ข้อมูลที่ได้จาก Google Form</a>                                                |
176 | 


--------------------------------------------------------------------------------
/pandas_transform_google_form_data2.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "markdown",
   5 |    "metadata": {},
   6 |    "source": [
   7 |     "# read and transform Google Form data (checkboxes, multiple choice grid items)\n",
   8 |     "* Google Form: https://forms.gle/7a35kfYmzyJJb5GD9\n",
   9 |     "* Google Sheets: https://docs.google.com/spreadsheets/d/1W0EaP8WGWaOK8XYDQA3Z4HIZaOQXXYj6U6kzqMuVrUI/edit#gid=1695829581"
  10 |    ]
  11 |   },
  12 |   {
  13 |    "cell_type": "markdown",
  14 |    "metadata": {},
  15 |    "source": [
  16 |     "---\n",
  17 |     "* author:  [Prasert Kanawattanachai](prasert.k@chula.ac.th)\n",
  18 |     "* YouTube: https://www.youtube.com/prasertcbs\n",
  19 |     "* github: https://github.com/prasertcbs/\n",
  20 |     "* [Chulalongkorn Business School](https://www.cbs.chula.ac.th/en/)\n",
  21 |     "---"
  22 |    ]
  23 |   },
  24 |   {
  25 |    "cell_type": "code",
  26 |    "execution_count": 1,
  27 |    "metadata": {},
  28 |    "outputs": [],
  29 |    "source": [
  30 |     "import pandas as pd\n",
  31 |     "import numpy as np\n",
  32 |     "import re"
  33 |    ]
  34 |   },
  35 |   {
  36 |    "cell_type": "code",
  37 |    "execution_count": 2,
  38 |    "metadata": {},
  39 |    "outputs": [
  40 |     {
  41 |      "name": "stdout",
  42 |      "output_type": "stream",
  43 |      "text": [
  44 |       "pandas version: 1.2.1\n",
  45 |       "numpy  version: 1.19.2\n",
  46 |       "2021-02-20 09:20:15.132975\n"
  47 |      ]
  48 |     }
  49 |    ],
  50 |    "source": [
  51 |     "print(f'pandas version: {pd.__version__}')\n",
  52 |     "print(f'numpy  version: {np.__version__}')\n",
  53 |     "print(pd.Timestamp.now())"
  54 |    ]
  55 |   },
  56 |   {
  57 |    "cell_type": "code",
  58 |    "execution_count": 54,
  59 |    "metadata": {
  60 |     "scrolled": true
  61 |    },
  62 |    "outputs": [
  63 |     {
  64 |      "data": {
  65 |       "text/html": [
  66 |        "<div>\n",
  67 |        "<style scoped>\n",
  68 |        "    .dataframe tbody tr th:only-of-type {\n",
  69 |        "        vertical-align: middle;\n",
  70 |        "    }\n",
  71 |        "\n",
  72 |        "    .dataframe tbody tr th {\n",
  73 |        "        vertical-align: top;\n",
  74 |        "    }\n",
  75 |        "\n",
  76 |        "    .dataframe thead th {\n",
  77 |        "        text-align: right;\n",
  78 |        "    }\n",
  79 |        "</style>\n",
  80 |        "<table border=\"1\" class=\"dataframe\">\n",
  81 |        "  <thead>\n",
  82 |        "    <tr style=\"text-align: right;\">\n",
  83 |        "      <th></th>\n",
  84 |        "      <th>Timestamp</th>\n",
  85 |        "      <th>gender</th>\n",
  86 |        "      <th>age (years)</th>\n",
  87 |        "      <th>skills</th>\n",
  88 |        "      <th>satisfaction [product]</th>\n",
  89 |        "      <th>satisfaction [price]</th>\n",
  90 |        "      <th>satisfaction [service]</th>\n",
  91 |        "      <th>satisfaction [overall]</th>\n",
  92 |        "    </tr>\n",
  93 |        "  </thead>\n",
  94 |        "  <tbody>\n",
  95 |        "    <tr>\n",
  96 |        "      <th>0</th>\n",
  97 |        "      <td>2021-02-19 22:36:35.982</td>\n",
  98 |        "      <td>Male</td>\n",
  99 |        "      <td>24.0</td>\n",
 100 |        "      <td>Word, Excel, PowerPoint, Access</td>\n",
 101 |        "      <td>very dissatisfied</td>\n",
 102 |        "      <td>dissatisfied</td>\n",
 103 |        "      <td>neutral</td>\n",
 104 |        "      <td>satisfied</td>\n",
 105 |        "    </tr>\n",
 106 |        "    <tr>\n",
 107 |        "      <th>1</th>\n",
 108 |        "      <td>2021-02-19 22:38:17.913</td>\n",
 109 |        "      <td>Male</td>\n",
 110 |        "      <td>22.0</td>\n",
 111 |        "      <td>Excel, SQL</td>\n",
 112 |        "      <td>very satisfied</td>\n",
 113 |        "      <td>satisfied</td>\n",
 114 |        "      <td>neutral</td>\n",
 115 |        "      <td>dissatisfied</td>\n",
 116 |        "    </tr>\n",
 117 |        "    <tr>\n",
 118 |        "      <th>2</th>\n",
 119 |        "      <td>2021-02-19 22:49:14.802</td>\n",
 120 |        "      <td>Prefer not to say</td>\n",
 121 |        "      <td>35.0</td>\n",
 122 |        "      <td>Word, Excel, PowerPoint, SPSS</td>\n",
 123 |        "      <td>very dissatisfied</td>\n",
 124 |        "      <td>dissatisfied</td>\n",
 125 |        "      <td>neutral</td>\n",
 126 |        "      <td>dissatisfied</td>\n",
 127 |        "    </tr>\n",
 128 |        "    <tr>\n",
 129 |        "      <th>3</th>\n",
 130 |        "      <td>2021-02-19 23:01:24.057</td>\n",
 131 |        "      <td>Female</td>\n",
 132 |        "      <td>23.0</td>\n",
 133 |        "      <td>Word, Excel, PowerPoint, SQL, Python, R, JavaS...</td>\n",
 134 |        "      <td>very satisfied</td>\n",
 135 |        "      <td>satisfied</td>\n",
 136 |        "      <td>neutral</td>\n",
 137 |        "      <td>dissatisfied</td>\n",
 138 |        "    </tr>\n",
 139 |        "    <tr>\n",
 140 |        "      <th>4</th>\n",
 141 |        "      <td>2021-02-20 08:27:19.584</td>\n",
 142 |        "      <td>Female</td>\n",
 143 |        "      <td>27.0</td>\n",
 144 |        "      <td>Word, Excel, PowerPoint</td>\n",
 145 |        "      <td>neutral</td>\n",
 146 |        "      <td>satisfied</td>\n",
 147 |        "      <td>very satisfied</td>\n",
 148 |        "      <td>very satisfied</td>\n",
 149 |        "    </tr>\n",
 150 |        "    <tr>\n",
 151 |        "      <th>5</th>\n",
 152 |        "      <td>2021-02-20 08:27:36.059</td>\n",
 153 |        "      <td>Female</td>\n",
 154 |        "      <td>19.0</td>\n",
 155 |        "      <td>Excel, Python, R</td>\n",
 156 |        "      <td>satisfied</td>\n",
 157 |        "      <td>satisfied</td>\n",
 158 |        "      <td>satisfied</td>\n",
 159 |        "      <td>satisfied</td>\n",
 160 |        "    </tr>\n",
 161 |        "    <tr>\n",
 162 |        "      <th>6</th>\n",
 163 |        "      <td>2021-02-20 08:28:19.725</td>\n",
 164 |        "      <td>Prefer not to say</td>\n",
 165 |        "      <td>30.0</td>\n",
 166 |        "      <td>SQL, R, SPSS, SAS</td>\n",
 167 |        "      <td>dissatisfied</td>\n",
 168 |        "      <td>satisfied</td>\n",
 169 |        "      <td>neutral</td>\n",
 170 |        "      <td>very satisfied</td>\n",
 171 |        "    </tr>\n",
 172 |        "    <tr>\n",
 173 |        "      <th>7</th>\n",
 174 |        "      <td>2021-02-20 09:16:02.448</td>\n",
 175 |        "      <td>Prefer not to say</td>\n",
 176 |        "      <td>24.0</td>\n",
 177 |        "      <td>PowerPoint</td>\n",
 178 |        "      <td>satisfied</td>\n",
 179 |        "      <td>very satisfied</td>\n",
 180 |        "      <td>satisfied</td>\n",
 181 |        "      <td>very satisfied</td>\n",
 182 |        "    </tr>\n",
 183 |        "    <tr>\n",
 184 |        "      <th>8</th>\n",
 185 |        "      <td>2021-02-20 09:22:23.851</td>\n",
 186 |        "      <td>Male</td>\n",
 187 |        "      <td>NaN</td>\n",
 188 |        "      <td>Word</td>\n",
 189 |        "      <td>very dissatisfied</td>\n",
 190 |        "      <td>dissatisfied</td>\n",
 191 |        "      <td>neutral</td>\n",
 192 |        "      <td>None</td>\n",
 193 |        "    </tr>\n",
 194 |        "    <tr>\n",
 195 |        "      <th>9</th>\n",
 196 |        "      <td>2021-02-20 09:23:22.259</td>\n",
 197 |        "      <td>NaN</td>\n",
 198 |        "      <td>NaN</td>\n",
 199 |        "      <td>Excel</td>\n",
 200 |        "      <td>NaN</td>\n",
 201 |        "      <td>NaN</td>\n",
 202 |        "      <td>NaN</td>\n",
 203 |        "      <td>satisfied</td>\n",
 204 |        "    </tr>\n",
 205 |        "  </tbody>\n",
 206 |        "</table>\n",
 207 |        "</div>"
 208 |       ],
 209 |       "text/plain": [
 210 |        "                Timestamp             gender  age (years)  \\\n",
 211 |        "0 2021-02-19 22:36:35.982               Male         24.0   \n",
 212 |        "1 2021-02-19 22:38:17.913               Male         22.0   \n",
 213 |        "2 2021-02-19 22:49:14.802  Prefer not to say         35.0   \n",
 214 |        "3 2021-02-19 23:01:24.057             Female         23.0   \n",
 215 |        "4 2021-02-20 08:27:19.584             Female         27.0   \n",
 216 |        "5 2021-02-20 08:27:36.059             Female         19.0   \n",
 217 |        "6 2021-02-20 08:28:19.725  Prefer not to say         30.0   \n",
 218 |        "7 2021-02-20 09:16:02.448  Prefer not to say         24.0   \n",
 219 |        "8 2021-02-20 09:22:23.851               Male          NaN   \n",
 220 |        "9 2021-02-20 09:23:22.259                NaN          NaN   \n",
 221 |        "\n",
 222 |        "                                              skills satisfaction [product]  \\\n",
 223 |        "0                    Word, Excel, PowerPoint, Access      very dissatisfied   \n",
 224 |        "1                                         Excel, SQL         very satisfied   \n",
 225 |        "2                      Word, Excel, PowerPoint, SPSS      very dissatisfied   \n",
 226 |        "3  Word, Excel, PowerPoint, SQL, Python, R, JavaS...         very satisfied   \n",
 227 |        "4                            Word, Excel, PowerPoint                neutral   \n",
 228 |        "5                                   Excel, Python, R              satisfied   \n",
 229 |        "6                                  SQL, R, SPSS, SAS           dissatisfied   \n",
 230 |        "7                                         PowerPoint              satisfied   \n",
 231 |        "8                                               Word      very dissatisfied   \n",
 232 |        "9                                              Excel                    NaN   \n",
 233 |        "\n",
 234 |        "  satisfaction [price] satisfaction [service] satisfaction [overall]  \n",
 235 |        "0         dissatisfied                neutral              satisfied  \n",
 236 |        "1            satisfied                neutral           dissatisfied  \n",
 237 |        "2         dissatisfied                neutral           dissatisfied  \n",
 238 |        "3            satisfied                neutral           dissatisfied  \n",
 239 |        "4            satisfied         very satisfied         very satisfied  \n",
 240 |        "5            satisfied              satisfied              satisfied  \n",
 241 |        "6            satisfied                neutral         very satisfied  \n",
 242 |        "7       very satisfied              satisfied         very satisfied  \n",
 243 |        "8         dissatisfied                neutral                   None  \n",
 244 |        "9                  NaN                    NaN              satisfied  "
 245 |       ]
 246 |      },
 247 |      "execution_count": 54,
 248 |      "metadata": {},
 249 |      "output_type": "execute_result"
 250 |     }
 251 |    ],
 252 |    "source": [
 253 |     "# google sheets generated by google form\n",
 254 |     "# https://docs.google.com/spreadsheets/d/1W0EaP8WGWaOK8XYDQA3Z4HIZaOQXXYj6U6kzqMuVrUI/edit#gid=1695829581\n",
 255 |     "# note: replace 'edit#' with 'export?format=xlsx&'\n",
 256 |     "gs_url='https://docs.google.com/spreadsheets/d/1W0EaP8WGWaOK8XYDQA3Z4HIZaOQXXYj6U6kzqMuVrUI/edit#gid=1695829581'\n",
 257 |     "url=re.sub('edit#', 'export?format=xlsx&', gs_url)\n",
 258 |     "# url='https://docs.google.com/spreadsheets/d/1W0EaP8WGWaOK8XYDQA3Z4HIZaOQXXYj6U6kzqMuVrUI/export?format=xlsx&gid=1695829581'\n",
 259 |     "df = pd.read_excel(url)\n",
 260 |     "df"
 261 |    ]
 262 |   },
 263 |   {
 264 |    "cell_type": "code",
 265 |    "execution_count": 55,
 266 |    "metadata": {},
 267 |    "outputs": [
 268 |     {
 269 |      "data": {
 270 |       "text/plain": [
 271 |        "Index(['Timestamp', 'gender', 'age (years)', 'skills',\n",
 272 |        "       'satisfaction [product]', 'satisfaction [price]',\n",
 273 |        "       'satisfaction [service]', 'satisfaction [overall]'],\n",
 274 |        "      dtype='object')"
 275 |       ]
 276 |      },
 277 |      "execution_count": 55,
 278 |      "metadata": {},
 279 |      "output_type": "execute_result"
 280 |     }
 281 |    ],
 282 |    "source": [
 283 |     "df.columns"
 284 |    ]
 285 |   },
 286 |   {
 287 |    "cell_type": "code",
 288 |    "execution_count": 56,
 289 |    "metadata": {},
 290 |    "outputs": [
 291 |     {
 292 |      "data": {
 293 |       "text/plain": [
 294 |        "Index(['Timestamp', 'gender', 'age (years)', 'skills', 'product', 'price',\n",
 295 |        "       'service', 'overall'],\n",
 296 |        "      dtype='object')"
 297 |       ]
 298 |      },
 299 |      "execution_count": 56,
 300 |      "metadata": {},
 301 |      "output_type": "execute_result"
 302 |     }
 303 |    ],
 304 |    "source": [
 305 |     "df.columns.str.replace(r'(satisfaction|\\[|\\])', '', regex=True).str.strip()"
 306 |    ]
 307 |   },
 308 |   {
 309 |    "cell_type": "code",
 310 |    "execution_count": 57,
 311 |    "metadata": {
 312 |     "scrolled": true
 313 |    },
 314 |    "outputs": [
 315 |     {
 316 |      "data": {
 317 |       "text/html": [
 318 |        "<div>\n",
 319 |        "<style scoped>\n",
 320 |        "    .dataframe tbody tr th:only-of-type {\n",
 321 |        "        vertical-align: middle;\n",
 322 |        "    }\n",
 323 |        "\n",
 324 |        "    .dataframe tbody tr th {\n",
 325 |        "        vertical-align: top;\n",
 326 |        "    }\n",
 327 |        "\n",
 328 |        "    .dataframe thead th {\n",
 329 |        "        text-align: right;\n",
 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>timestamp</th>\n",
 337 |        "      <th>gender</th>\n",
 338 |        "      <th>age</th>\n",
 339 |        "      <th>skills</th>\n",
 340 |        "      <th>product</th>\n",
 341 |        "      <th>price</th>\n",
 342 |        "      <th>service</th>\n",
 343 |        "      <th>overall</th>\n",
 344 |        "    </tr>\n",
 345 |        "  </thead>\n",
 346 |        "  <tbody>\n",
 347 |        "    <tr>\n",
 348 |        "      <th>0</th>\n",
 349 |        "      <td>2021-02-19 22:36:35.982</td>\n",
 350 |        "      <td>Male</td>\n",
 351 |        "      <td>24.0</td>\n",
 352 |        "      <td>Word, Excel, PowerPoint, Access</td>\n",
 353 |        "      <td>very dissatisfied</td>\n",
 354 |        "      <td>dissatisfied</td>\n",
 355 |        "      <td>neutral</td>\n",
 356 |        "      <td>satisfied</td>\n",
 357 |        "    </tr>\n",
 358 |        "    <tr>\n",
 359 |        "      <th>1</th>\n",
 360 |        "      <td>2021-02-19 22:38:17.913</td>\n",
 361 |        "      <td>Male</td>\n",
 362 |        "      <td>22.0</td>\n",
 363 |        "      <td>Excel, SQL</td>\n",
 364 |        "      <td>very satisfied</td>\n",
 365 |        "      <td>satisfied</td>\n",
 366 |        "      <td>neutral</td>\n",
 367 |        "      <td>dissatisfied</td>\n",
 368 |        "    </tr>\n",
 369 |        "    <tr>\n",
 370 |        "      <th>2</th>\n",
 371 |        "      <td>2021-02-19 22:49:14.802</td>\n",
 372 |        "      <td>Prefer not to say</td>\n",
 373 |        "      <td>35.0</td>\n",
 374 |        "      <td>Word, Excel, PowerPoint, SPSS</td>\n",
 375 |        "      <td>very dissatisfied</td>\n",
 376 |        "      <td>dissatisfied</td>\n",
 377 |        "      <td>neutral</td>\n",
 378 |        "      <td>dissatisfied</td>\n",
 379 |        "    </tr>\n",
 380 |        "    <tr>\n",
 381 |        "      <th>3</th>\n",
 382 |        "      <td>2021-02-19 23:01:24.057</td>\n",
 383 |        "      <td>Female</td>\n",
 384 |        "      <td>23.0</td>\n",
 385 |        "      <td>Word, Excel, PowerPoint, SQL, Python, R, JavaS...</td>\n",
 386 |        "      <td>very satisfied</td>\n",
 387 |        "      <td>satisfied</td>\n",
 388 |        "      <td>neutral</td>\n",
 389 |        "      <td>dissatisfied</td>\n",
 390 |        "    </tr>\n",
 391 |        "    <tr>\n",
 392 |        "      <th>4</th>\n",
 393 |        "      <td>2021-02-20 08:27:19.584</td>\n",
 394 |        "      <td>Female</td>\n",
 395 |        "      <td>27.0</td>\n",
 396 |        "      <td>Word, Excel, PowerPoint</td>\n",
 397 |        "      <td>neutral</td>\n",
 398 |        "      <td>satisfied</td>\n",
 399 |        "      <td>very satisfied</td>\n",
 400 |        "      <td>very satisfied</td>\n",
 401 |        "    </tr>\n",
 402 |        "    <tr>\n",
 403 |        "      <th>5</th>\n",
 404 |        "      <td>2021-02-20 08:27:36.059</td>\n",
 405 |        "      <td>Female</td>\n",
 406 |        "      <td>19.0</td>\n",
 407 |        "      <td>Excel, Python, R</td>\n",
 408 |        "      <td>satisfied</td>\n",
 409 |        "      <td>satisfied</td>\n",
 410 |        "      <td>satisfied</td>\n",
 411 |        "      <td>satisfied</td>\n",
 412 |        "    </tr>\n",
 413 |        "    <tr>\n",
 414 |        "      <th>6</th>\n",
 415 |        "      <td>2021-02-20 08:28:19.725</td>\n",
 416 |        "      <td>Prefer not to say</td>\n",
 417 |        "      <td>30.0</td>\n",
 418 |        "      <td>SQL, R, SPSS, SAS</td>\n",
 419 |        "      <td>dissatisfied</td>\n",
 420 |        "      <td>satisfied</td>\n",
 421 |        "      <td>neutral</td>\n",
 422 |        "      <td>very satisfied</td>\n",
 423 |        "    </tr>\n",
 424 |        "    <tr>\n",
 425 |        "      <th>7</th>\n",
 426 |        "      <td>2021-02-20 09:16:02.448</td>\n",
 427 |        "      <td>Prefer not to say</td>\n",
 428 |        "      <td>24.0</td>\n",
 429 |        "      <td>PowerPoint</td>\n",
 430 |        "      <td>satisfied</td>\n",
 431 |        "      <td>very satisfied</td>\n",
 432 |        "      <td>satisfied</td>\n",
 433 |        "      <td>very satisfied</td>\n",
 434 |        "    </tr>\n",
 435 |        "    <tr>\n",
 436 |        "      <th>8</th>\n",
 437 |        "      <td>2021-02-20 09:22:23.851</td>\n",
 438 |        "      <td>Male</td>\n",
 439 |        "      <td>NaN</td>\n",
 440 |        "      <td>Word</td>\n",
 441 |        "      <td>very dissatisfied</td>\n",
 442 |        "      <td>dissatisfied</td>\n",
 443 |        "      <td>neutral</td>\n",
 444 |        "      <td>None</td>\n",
 445 |        "    </tr>\n",
 446 |        "    <tr>\n",
 447 |        "      <th>9</th>\n",
 448 |        "      <td>2021-02-20 09:23:22.259</td>\n",
 449 |        "      <td>NaN</td>\n",
 450 |        "      <td>NaN</td>\n",
 451 |        "      <td>Excel</td>\n",
 452 |        "      <td>NaN</td>\n",
 453 |        "      <td>NaN</td>\n",
 454 |        "      <td>NaN</td>\n",
 455 |        "      <td>satisfied</td>\n",
 456 |        "    </tr>\n",
 457 |        "  </tbody>\n",
 458 |        "</table>\n",
 459 |        "</div>"
 460 |       ],
 461 |       "text/plain": [
 462 |        "                timestamp             gender   age  \\\n",
 463 |        "0 2021-02-19 22:36:35.982               Male  24.0   \n",
 464 |        "1 2021-02-19 22:38:17.913               Male  22.0   \n",
 465 |        "2 2021-02-19 22:49:14.802  Prefer not to say  35.0   \n",
 466 |        "3 2021-02-19 23:01:24.057             Female  23.0   \n",
 467 |        "4 2021-02-20 08:27:19.584             Female  27.0   \n",
 468 |        "5 2021-02-20 08:27:36.059             Female  19.0   \n",
 469 |        "6 2021-02-20 08:28:19.725  Prefer not to say  30.0   \n",
 470 |        "7 2021-02-20 09:16:02.448  Prefer not to say  24.0   \n",
 471 |        "8 2021-02-20 09:22:23.851               Male   NaN   \n",
 472 |        "9 2021-02-20 09:23:22.259                NaN   NaN   \n",
 473 |        "\n",
 474 |        "                                              skills            product  \\\n",
 475 |        "0                    Word, Excel, PowerPoint, Access  very dissatisfied   \n",
 476 |        "1                                         Excel, SQL     very satisfied   \n",
 477 |        "2                      Word, Excel, PowerPoint, SPSS  very dissatisfied   \n",
 478 |        "3  Word, Excel, PowerPoint, SQL, Python, R, JavaS...     very satisfied   \n",
 479 |        "4                            Word, Excel, PowerPoint            neutral   \n",
 480 |        "5                                   Excel, Python, R          satisfied   \n",
 481 |        "6                                  SQL, R, SPSS, SAS       dissatisfied   \n",
 482 |        "7                                         PowerPoint          satisfied   \n",
 483 |        "8                                               Word  very dissatisfied   \n",
 484 |        "9                                              Excel                NaN   \n",
 485 |        "\n",
 486 |        "            price         service         overall  \n",
 487 |        "0    dissatisfied         neutral       satisfied  \n",
 488 |        "1       satisfied         neutral    dissatisfied  \n",
 489 |        "2    dissatisfied         neutral    dissatisfied  \n",
 490 |        "3       satisfied         neutral    dissatisfied  \n",
 491 |        "4       satisfied  very satisfied  very satisfied  \n",
 492 |        "5       satisfied       satisfied       satisfied  \n",
 493 |        "6       satisfied         neutral  very satisfied  \n",
 494 |        "7  very satisfied       satisfied  very satisfied  \n",
 495 |        "8    dissatisfied         neutral            None  \n",
 496 |        "9             NaN             NaN       satisfied  "
 497 |       ]
 498 |      },
 499 |      "execution_count": 57,
 500 |      "metadata": {},
 501 |      "output_type": "execute_result"
 502 |     }
 503 |    ],
 504 |    "source": [
 505 |     "df.columns = ['timestamp', 'gender', 'age', 'skills', 'product', 'price', 'service', 'overall']\n",
 506 |     "df"
 507 |    ]
 508 |   },
 509 |   {
 510 |    "cell_type": "markdown",
 511 |    "metadata": {},
 512 |    "source": [
 513 |     "## recode gender "
 514 |    ]
 515 |   },
 516 |   {
 517 |    "cell_type": "code",
 518 |    "execution_count": 58,
 519 |    "metadata": {},
 520 |    "outputs": [
 521 |     {
 522 |      "data": {
 523 |       "text/plain": [
 524 |        "0      M\n",
 525 |        "1      M\n",
 526 |        "2      X\n",
 527 |        "3      F\n",
 528 |        "4      F\n",
 529 |        "5      F\n",
 530 |        "6      X\n",
 531 |        "7      X\n",
 532 |        "8      M\n",
 533 |        "9    NaN\n",
 534 |        "Name: gender, dtype: object"
 535 |       ]
 536 |      },
 537 |      "execution_count": 58,
 538 |      "metadata": {},
 539 |      "output_type": "execute_result"
 540 |     }
 541 |    ],
 542 |    "source": [
 543 |     "d = {'Female': 'F',\n",
 544 |     "     'Male': 'M',\n",
 545 |     "     'Prefer not to say': 'X'}\n",
 546 |     "\n",
 547 |     "df.gender.map(d, na_action='ignore')"
 548 |    ]
 549 |   },
 550 |   {
 551 |    "cell_type": "code",
 552 |    "execution_count": 59,
 553 |    "metadata": {},
 554 |    "outputs": [],
 555 |    "source": [
 556 |     "df.gender=df.gender.map(d, na_action='ignore')"
 557 |    ]
 558 |   },
 559 |   {
 560 |    "cell_type": "code",
 561 |    "execution_count": 60,
 562 |    "metadata": {},
 563 |    "outputs": [
 564 |     {
 565 |      "data": {
 566 |       "text/html": [
 567 |        "<div>\n",
 568 |        "<style scoped>\n",
 569 |        "    .dataframe tbody tr th:only-of-type {\n",
 570 |        "        vertical-align: middle;\n",
 571 |        "    }\n",
 572 |        "\n",
 573 |        "    .dataframe tbody tr th {\n",
 574 |        "        vertical-align: top;\n",
 575 |        "    }\n",
 576 |        "\n",
 577 |        "    .dataframe thead th {\n",
 578 |        "        text-align: right;\n",
 579 |        "    }\n",
 580 |        "</style>\n",
 581 |        "<table border=\"1\" class=\"dataframe\">\n",
 582 |        "  <thead>\n",
 583 |        "    <tr style=\"text-align: right;\">\n",
 584 |        "      <th></th>\n",
 585 |        "      <th>timestamp</th>\n",
 586 |        "      <th>gender</th>\n",
 587 |        "      <th>age</th>\n",
 588 |        "      <th>skills</th>\n",
 589 |        "      <th>product</th>\n",
 590 |        "      <th>price</th>\n",
 591 |        "      <th>service</th>\n",
 592 |        "      <th>overall</th>\n",
 593 |        "    </tr>\n",
 594 |        "  </thead>\n",
 595 |        "  <tbody>\n",
 596 |        "    <tr>\n",
 597 |        "      <th>0</th>\n",
 598 |        "      <td>2021-02-19 22:36:35.982</td>\n",
 599 |        "      <td>M</td>\n",
 600 |        "      <td>24.0</td>\n",
 601 |        "      <td>Word, Excel, PowerPoint, Access</td>\n",
 602 |        "      <td>very dissatisfied</td>\n",
 603 |        "      <td>dissatisfied</td>\n",
 604 |        "      <td>neutral</td>\n",
 605 |        "      <td>satisfied</td>\n",
 606 |        "    </tr>\n",
 607 |        "    <tr>\n",
 608 |        "      <th>1</th>\n",
 609 |        "      <td>2021-02-19 22:38:17.913</td>\n",
 610 |        "      <td>M</td>\n",
 611 |        "      <td>22.0</td>\n",
 612 |        "      <td>Excel, SQL</td>\n",
 613 |        "      <td>very satisfied</td>\n",
 614 |        "      <td>satisfied</td>\n",
 615 |        "      <td>neutral</td>\n",
 616 |        "      <td>dissatisfied</td>\n",
 617 |        "    </tr>\n",
 618 |        "    <tr>\n",
 619 |        "      <th>2</th>\n",
 620 |        "      <td>2021-02-19 22:49:14.802</td>\n",
 621 |        "      <td>X</td>\n",
 622 |        "      <td>35.0</td>\n",
 623 |        "      <td>Word, Excel, PowerPoint, SPSS</td>\n",
 624 |        "      <td>very dissatisfied</td>\n",
 625 |        "      <td>dissatisfied</td>\n",
 626 |        "      <td>neutral</td>\n",
 627 |        "      <td>dissatisfied</td>\n",
 628 |        "    </tr>\n",
 629 |        "    <tr>\n",
 630 |        "      <th>3</th>\n",
 631 |        "      <td>2021-02-19 23:01:24.057</td>\n",
 632 |        "      <td>F</td>\n",
 633 |        "      <td>23.0</td>\n",
 634 |        "      <td>Word, Excel, PowerPoint, SQL, Python, R, JavaS...</td>\n",
 635 |        "      <td>very satisfied</td>\n",
 636 |        "      <td>satisfied</td>\n",
 637 |        "      <td>neutral</td>\n",
 638 |        "      <td>dissatisfied</td>\n",
 639 |        "    </tr>\n",
 640 |        "    <tr>\n",
 641 |        "      <th>4</th>\n",
 642 |        "      <td>2021-02-20 08:27:19.584</td>\n",
 643 |        "      <td>F</td>\n",
 644 |        "      <td>27.0</td>\n",
 645 |        "      <td>Word, Excel, PowerPoint</td>\n",
 646 |        "      <td>neutral</td>\n",
 647 |        "      <td>satisfied</td>\n",
 648 |        "      <td>very satisfied</td>\n",
 649 |        "      <td>very satisfied</td>\n",
 650 |        "    </tr>\n",
 651 |        "    <tr>\n",
 652 |        "      <th>5</th>\n",
 653 |        "      <td>2021-02-20 08:27:36.059</td>\n",
 654 |        "      <td>F</td>\n",
 655 |        "      <td>19.0</td>\n",
 656 |        "      <td>Excel, Python, R</td>\n",
 657 |        "      <td>satisfied</td>\n",
 658 |        "      <td>satisfied</td>\n",
 659 |        "      <td>satisfied</td>\n",
 660 |        "      <td>satisfied</td>\n",
 661 |        "    </tr>\n",
 662 |        "    <tr>\n",
 663 |        "      <th>6</th>\n",
 664 |        "      <td>2021-02-20 08:28:19.725</td>\n",
 665 |        "      <td>X</td>\n",
 666 |        "      <td>30.0</td>\n",
 667 |        "      <td>SQL, R, SPSS, SAS</td>\n",
 668 |        "      <td>dissatisfied</td>\n",
 669 |        "      <td>satisfied</td>\n",
 670 |        "      <td>neutral</td>\n",
 671 |        "      <td>very satisfied</td>\n",
 672 |        "    </tr>\n",
 673 |        "    <tr>\n",
 674 |        "      <th>7</th>\n",
 675 |        "      <td>2021-02-20 09:16:02.448</td>\n",
 676 |        "      <td>X</td>\n",
 677 |        "      <td>24.0</td>\n",
 678 |        "      <td>PowerPoint</td>\n",
 679 |        "      <td>satisfied</td>\n",
 680 |        "      <td>very satisfied</td>\n",
 681 |        "      <td>satisfied</td>\n",
 682 |        "      <td>very satisfied</td>\n",
 683 |        "    </tr>\n",
 684 |        "    <tr>\n",
 685 |        "      <th>8</th>\n",
 686 |        "      <td>2021-02-20 09:22:23.851</td>\n",
 687 |        "      <td>M</td>\n",
 688 |        "      <td>NaN</td>\n",
 689 |        "      <td>Word</td>\n",
 690 |        "      <td>very dissatisfied</td>\n",
 691 |        "      <td>dissatisfied</td>\n",
 692 |        "      <td>neutral</td>\n",
 693 |        "      <td>None</td>\n",
 694 |        "    </tr>\n",
 695 |        "    <tr>\n",
 696 |        "      <th>9</th>\n",
 697 |        "      <td>2021-02-20 09:23:22.259</td>\n",
 698 |        "      <td>NaN</td>\n",
 699 |        "      <td>NaN</td>\n",
 700 |        "      <td>Excel</td>\n",
 701 |        "      <td>NaN</td>\n",
 702 |        "      <td>NaN</td>\n",
 703 |        "      <td>NaN</td>\n",
 704 |        "      <td>satisfied</td>\n",
 705 |        "    </tr>\n",
 706 |        "  </tbody>\n",
 707 |        "</table>\n",
 708 |        "</div>"
 709 |       ],
 710 |       "text/plain": [
 711 |        "                timestamp gender   age  \\\n",
 712 |        "0 2021-02-19 22:36:35.982      M  24.0   \n",
 713 |        "1 2021-02-19 22:38:17.913      M  22.0   \n",
 714 |        "2 2021-02-19 22:49:14.802      X  35.0   \n",
 715 |        "3 2021-02-19 23:01:24.057      F  23.0   \n",
 716 |        "4 2021-02-20 08:27:19.584      F  27.0   \n",
 717 |        "5 2021-02-20 08:27:36.059      F  19.0   \n",
 718 |        "6 2021-02-20 08:28:19.725      X  30.0   \n",
 719 |        "7 2021-02-20 09:16:02.448      X  24.0   \n",
 720 |        "8 2021-02-20 09:22:23.851      M   NaN   \n",
 721 |        "9 2021-02-20 09:23:22.259    NaN   NaN   \n",
 722 |        "\n",
 723 |        "                                              skills            product  \\\n",
 724 |        "0                    Word, Excel, PowerPoint, Access  very dissatisfied   \n",
 725 |        "1                                         Excel, SQL     very satisfied   \n",
 726 |        "2                      Word, Excel, PowerPoint, SPSS  very dissatisfied   \n",
 727 |        "3  Word, Excel, PowerPoint, SQL, Python, R, JavaS...     very satisfied   \n",
 728 |        "4                            Word, Excel, PowerPoint            neutral   \n",
 729 |        "5                                   Excel, Python, R          satisfied   \n",
 730 |        "6                                  SQL, R, SPSS, SAS       dissatisfied   \n",
 731 |        "7                                         PowerPoint          satisfied   \n",
 732 |        "8                                               Word  very dissatisfied   \n",
 733 |        "9                                              Excel                NaN   \n",
 734 |        "\n",
 735 |        "            price         service         overall  \n",
 736 |        "0    dissatisfied         neutral       satisfied  \n",
 737 |        "1       satisfied         neutral    dissatisfied  \n",
 738 |        "2    dissatisfied         neutral    dissatisfied  \n",
 739 |        "3       satisfied         neutral    dissatisfied  \n",
 740 |        "4       satisfied  very satisfied  very satisfied  \n",
 741 |        "5       satisfied       satisfied       satisfied  \n",
 742 |        "6       satisfied         neutral  very satisfied  \n",
 743 |        "7  very satisfied       satisfied  very satisfied  \n",
 744 |        "8    dissatisfied         neutral            None  \n",
 745 |        "9             NaN             NaN       satisfied  "
 746 |       ]
 747 |      },
 748 |      "execution_count": 60,
 749 |      "metadata": {},
 750 |      "output_type": "execute_result"
 751 |     }
 752 |    ],
 753 |    "source": [
 754 |     "df"
 755 |    ]
 756 |   },
 757 |   {
 758 |    "cell_type": "code",
 759 |    "execution_count": 61,
 760 |    "metadata": {},
 761 |    "outputs": [
 762 |     {
 763 |      "data": {
 764 |       "text/plain": [
 765 |        "0    1.0\n",
 766 |        "1    5.0\n",
 767 |        "2    1.0\n",
 768 |        "3    5.0\n",
 769 |        "4    3.0\n",
 770 |        "5    4.0\n",
 771 |        "6    2.0\n",
 772 |        "7    4.0\n",
 773 |        "8    1.0\n",
 774 |        "9    NaN\n",
 775 |        "Name: product, dtype: float64"
 776 |       ]
 777 |      },
 778 |      "execution_count": 61,
 779 |      "metadata": {},
 780 |      "output_type": "execute_result"
 781 |     }
 782 |    ],
 783 |    "source": [
 784 |     "df['product'].map({'very dissatisfied':1, 'dissatisfied':2, 'neutral':3, 'satisfied':4, 'very satisfied':5}, na_action='ignore')"
 785 |    ]
 786 |   },
 787 |   {
 788 |    "cell_type": "code",
 789 |    "execution_count": 62,
 790 |    "metadata": {},
 791 |    "outputs": [
 792 |     {
 793 |      "data": {
 794 |       "text/plain": [
 795 |        "['very dissatisfied', 'very satisfied', 'very dissatisfied', 'very satisfied', 'neutral', 'satisfied', 'dissatisfied', 'satisfied', 'very dissatisfied', NaN]\n",
 796 |        "Categories (5, object): ['very dissatisfied' < 'dissatisfied' < 'neutral' < 'satisfied' < 'very satisfied']"
 797 |       ]
 798 |      },
 799 |      "execution_count": 62,
 800 |      "metadata": {},
 801 |      "output_type": "execute_result"
 802 |     }
 803 |    ],
 804 |    "source": [
 805 |     "pd.Categorical(df['product'], \n",
 806 |     "               categories=['very dissatisfied', 'dissatisfied', 'neutral', 'satisfied', 'very satisfied'], ordered=True)"
 807 |    ]
 808 |   },
 809 |   {
 810 |    "cell_type": "code",
 811 |    "execution_count": 63,
 812 |    "metadata": {},
 813 |    "outputs": [
 814 |     {
 815 |      "data": {
 816 |       "text/plain": [
 817 |        "0    very dissatisfied\n",
 818 |        "1       very satisfied\n",
 819 |        "2    very dissatisfied\n",
 820 |        "3       very satisfied\n",
 821 |        "4              neutral\n",
 822 |        "5            satisfied\n",
 823 |        "6         dissatisfied\n",
 824 |        "7            satisfied\n",
 825 |        "8    very dissatisfied\n",
 826 |        "9                  NaN\n",
 827 |        "Name: product, dtype: object"
 828 |       ]
 829 |      },
 830 |      "execution_count": 63,
 831 |      "metadata": {},
 832 |      "output_type": "execute_result"
 833 |     }
 834 |    ],
 835 |    "source": [
 836 |     "df['product']"
 837 |    ]
 838 |   },
 839 |   {
 840 |    "cell_type": "code",
 841 |    "execution_count": 64,
 842 |    "metadata": {},
 843 |    "outputs": [],
 844 |    "source": [
 845 |     "for c in ['product', 'price', 'service', 'overall']:\n",
 846 |     "    df[f'{c}_n']=df[c].map({'very dissatisfied':1, 'dissatisfied':2, 'neutral':3, 'satisfied':4, 'very satisfied':5})    \n",
 847 |     "    df[c]=pd.Categorical(df[c], \n",
 848 |     "               categories=['very dissatisfied', 'dissatisfied', 'neutral', 'satisfied', 'very satisfied'], ordered=True)  \n",
 849 |     "    df[f'{c}_cat']=df[c].cat.codes + 1"
 850 |    ]
 851 |   },
 852 |   {
 853 |    "cell_type": "code",
 854 |    "execution_count": 65,
 855 |    "metadata": {},
 856 |    "outputs": [
 857 |     {
 858 |      "name": "stdout",
 859 |      "output_type": "stream",
 860 |      "text": [
 861 |       "<class 'pandas.core.frame.DataFrame'>\n",
 862 |       "RangeIndex: 10 entries, 0 to 9\n",
 863 |       "Data columns (total 16 columns):\n",
 864 |       " #   Column       Non-Null Count  Dtype         \n",
 865 |       "---  ------       --------------  -----         \n",
 866 |       " 0   timestamp    10 non-null     datetime64[ns]\n",
 867 |       " 1   gender       9 non-null      object        \n",
 868 |       " 2   age          8 non-null      float64       \n",
 869 |       " 3   skills       10 non-null     object        \n",
 870 |       " 4   product      9 non-null      category      \n",
 871 |       " 5   price        9 non-null      category      \n",
 872 |       " 6   service      9 non-null      category      \n",
 873 |       " 7   overall      9 non-null      category      \n",
 874 |       " 8   product_n    9 non-null      float64       \n",
 875 |       " 9   product_cat  10 non-null     int8          \n",
 876 |       " 10  price_n      9 non-null      float64       \n",
 877 |       " 11  price_cat    10 non-null     int8          \n",
 878 |       " 12  service_n    9 non-null      float64       \n",
 879 |       " 13  service_cat  10 non-null     int8          \n",
 880 |       " 14  overall_n    9 non-null      float64       \n",
 881 |       " 15  overall_cat  10 non-null     int8          \n",
 882 |       "dtypes: category(4), datetime64[ns](1), float64(5), int8(4), object(2)\n",
 883 |       "memory usage: 1.7+ KB\n"
 884 |      ]
 885 |     }
 886 |    ],
 887 |    "source": [
 888 |     "df.info()"
 889 |    ]
 890 |   },
 891 |   {
 892 |    "cell_type": "code",
 893 |    "execution_count": 66,
 894 |    "metadata": {},
 895 |    "outputs": [
 896 |     {
 897 |      "data": {
 898 |       "text/plain": [
 899 |        "0    1\n",
 900 |        "1    5\n",
 901 |        "2    1\n",
 902 |        "3    5\n",
 903 |        "4    3\n",
 904 |        "5    4\n",
 905 |        "6    2\n",
 906 |        "7    4\n",
 907 |        "8    1\n",
 908 |        "9    0\n",
 909 |        "dtype: int8"
 910 |       ]
 911 |      },
 912 |      "execution_count": 66,
 913 |      "metadata": {},
 914 |      "output_type": "execute_result"
 915 |     }
 916 |    ],
 917 |    "source": [
 918 |     "df['product'].cat.codes + 1"
 919 |    ]
 920 |   },
 921 |   {
 922 |    "cell_type": "code",
 923 |    "execution_count": 67,
 924 |    "metadata": {},
 925 |    "outputs": [
 926 |     {
 927 |      "data": {
 928 |       "text/plain": [
 929 |        "0    3\n",
 930 |        "1    1\n",
 931 |        "2    1\n",
 932 |        "3    1\n",
 933 |        "4    4\n",
 934 |        "5    3\n",
 935 |        "6    4\n",
 936 |        "7    4\n",
 937 |        "8   -1\n",
 938 |        "9    3\n",
 939 |        "dtype: int8"
 940 |       ]
 941 |      },
 942 |      "execution_count": 67,
 943 |      "metadata": {},
 944 |      "output_type": "execute_result"
 945 |     }
 946 |    ],
 947 |    "source": [
 948 |     "df['overall'].cat.codes"
 949 |    ]
 950 |   },
 951 |   {
 952 |    "cell_type": "code",
 953 |    "execution_count": 68,
 954 |    "metadata": {},
 955 |    "outputs": [
 956 |     {
 957 |      "data": {
 958 |       "text/plain": [
 959 |        "3.6666666666666665"
 960 |       ]
 961 |      },
 962 |      "execution_count": 68,
 963 |      "metadata": {},
 964 |      "output_type": "execute_result"
 965 |     }
 966 |    ],
 967 |    "source": [
 968 |     "df['overall_n'].mean()"
 969 |    ]
 970 |   },
 971 |   {
 972 |    "cell_type": "code",
 973 |    "execution_count": 69,
 974 |    "metadata": {},
 975 |    "outputs": [
 976 |     {
 977 |      "data": {
 978 |       "text/plain": [
 979 |        "3.3"
 980 |       ]
 981 |      },
 982 |      "execution_count": 69,
 983 |      "metadata": {},
 984 |      "output_type": "execute_result"
 985 |     }
 986 |    ],
 987 |    "source": [
 988 |     "(df['overall'].cat.codes + 1).mean()"
 989 |    ]
 990 |   },
 991 |   {
 992 |    "cell_type": "code",
 993 |    "execution_count": 70,
 994 |    "metadata": {},
 995 |    "outputs": [
 996 |     {
 997 |      "data": {
 998 |       "text/html": [
 999 |        "<div>\n",
1000 |        "<style scoped>\n",
1001 |        "    .dataframe tbody tr th:only-of-type {\n",
1002 |        "        vertical-align: middle;\n",
1003 |        "    }\n",
1004 |        "\n",
1005 |        "    .dataframe tbody tr th {\n",
1006 |        "        vertical-align: top;\n",
1007 |        "    }\n",
1008 |        "\n",
1009 |        "    .dataframe thead th {\n",
1010 |        "        text-align: right;\n",
1011 |        "    }\n",
1012 |        "</style>\n",
1013 |        "<table border=\"1\" class=\"dataframe\">\n",
1014 |        "  <thead>\n",
1015 |        "    <tr style=\"text-align: right;\">\n",
1016 |        "      <th></th>\n",
1017 |        "      <th>timestamp</th>\n",
1018 |        "      <th>gender</th>\n",
1019 |        "      <th>age</th>\n",
1020 |        "      <th>skills</th>\n",
1021 |        "      <th>product</th>\n",
1022 |        "      <th>price</th>\n",
1023 |        "      <th>service</th>\n",
1024 |        "      <th>overall</th>\n",
1025 |        "      <th>product_n</th>\n",
1026 |        "      <th>product_cat</th>\n",
1027 |        "      <th>price_n</th>\n",
1028 |        "      <th>price_cat</th>\n",
1029 |        "      <th>service_n</th>\n",
1030 |        "      <th>service_cat</th>\n",
1031 |        "      <th>overall_n</th>\n",
1032 |        "      <th>overall_cat</th>\n",
1033 |        "    </tr>\n",
1034 |        "  </thead>\n",
1035 |        "  <tbody>\n",
1036 |        "    <tr>\n",
1037 |        "      <th>0</th>\n",
1038 |        "      <td>2021-02-19 22:36:35.982</td>\n",
1039 |        "      <td>M</td>\n",
1040 |        "      <td>24.0</td>\n",
1041 |        "      <td>Word, Excel, PowerPoint, Access</td>\n",
1042 |        "      <td>very dissatisfied</td>\n",
1043 |        "      <td>dissatisfied</td>\n",
1044 |        "      <td>neutral</td>\n",
1045 |        "      <td>satisfied</td>\n",
1046 |        "      <td>1.0</td>\n",
1047 |        "      <td>1</td>\n",
1048 |        "      <td>2.0</td>\n",
1049 |        "      <td>2</td>\n",
1050 |        "      <td>3.0</td>\n",
1051 |        "      <td>3</td>\n",
1052 |        "      <td>4.0</td>\n",
1053 |        "      <td>4</td>\n",
1054 |        "    </tr>\n",
1055 |        "    <tr>\n",
1056 |        "      <th>4</th>\n",
1057 |        "      <td>2021-02-20 08:27:19.584</td>\n",
1058 |        "      <td>F</td>\n",
1059 |        "      <td>27.0</td>\n",
1060 |        "      <td>Word, Excel, PowerPoint</td>\n",
1061 |        "      <td>neutral</td>\n",
1062 |        "      <td>satisfied</td>\n",
1063 |        "      <td>very satisfied</td>\n",
1064 |        "      <td>very satisfied</td>\n",
1065 |        "      <td>3.0</td>\n",
1066 |        "      <td>3</td>\n",
1067 |        "      <td>4.0</td>\n",
1068 |        "      <td>4</td>\n",
1069 |        "      <td>5.0</td>\n",
1070 |        "      <td>5</td>\n",
1071 |        "      <td>5.0</td>\n",
1072 |        "      <td>5</td>\n",
1073 |        "    </tr>\n",
1074 |        "    <tr>\n",
1075 |        "      <th>5</th>\n",
1076 |        "      <td>2021-02-20 08:27:36.059</td>\n",
1077 |        "      <td>F</td>\n",
1078 |        "      <td>19.0</td>\n",
1079 |        "      <td>Excel, Python, R</td>\n",
1080 |        "      <td>satisfied</td>\n",
1081 |        "      <td>satisfied</td>\n",
1082 |        "      <td>satisfied</td>\n",
1083 |        "      <td>satisfied</td>\n",
1084 |        "      <td>4.0</td>\n",
1085 |        "      <td>4</td>\n",
1086 |        "      <td>4.0</td>\n",
1087 |        "      <td>4</td>\n",
1088 |        "      <td>4.0</td>\n",
1089 |        "      <td>4</td>\n",
1090 |        "      <td>4.0</td>\n",
1091 |        "      <td>4</td>\n",
1092 |        "    </tr>\n",
1093 |        "    <tr>\n",
1094 |        "      <th>6</th>\n",
1095 |        "      <td>2021-02-20 08:28:19.725</td>\n",
1096 |        "      <td>X</td>\n",
1097 |        "      <td>30.0</td>\n",
1098 |        "      <td>SQL, R, SPSS, SAS</td>\n",
1099 |        "      <td>dissatisfied</td>\n",
1100 |        "      <td>satisfied</td>\n",
1101 |        "      <td>neutral</td>\n",
1102 |        "      <td>very satisfied</td>\n",
1103 |        "      <td>2.0</td>\n",
1104 |        "      <td>2</td>\n",
1105 |        "      <td>4.0</td>\n",
1106 |        "      <td>4</td>\n",
1107 |        "      <td>3.0</td>\n",
1108 |        "      <td>3</td>\n",
1109 |        "      <td>5.0</td>\n",
1110 |        "      <td>5</td>\n",
1111 |        "    </tr>\n",
1112 |        "    <tr>\n",
1113 |        "      <th>7</th>\n",
1114 |        "      <td>2021-02-20 09:16:02.448</td>\n",
1115 |        "      <td>X</td>\n",
1116 |        "      <td>24.0</td>\n",
1117 |        "      <td>PowerPoint</td>\n",
1118 |        "      <td>satisfied</td>\n",
1119 |        "      <td>very satisfied</td>\n",
1120 |        "      <td>satisfied</td>\n",
1121 |        "      <td>very satisfied</td>\n",
1122 |        "      <td>4.0</td>\n",
1123 |        "      <td>4</td>\n",
1124 |        "      <td>5.0</td>\n",
1125 |        "      <td>5</td>\n",
1126 |        "      <td>4.0</td>\n",
1127 |        "      <td>4</td>\n",
1128 |        "      <td>5.0</td>\n",
1129 |        "      <td>5</td>\n",
1130 |        "    </tr>\n",
1131 |        "    <tr>\n",
1132 |        "      <th>9</th>\n",
1133 |        "      <td>2021-02-20 09:23:22.259</td>\n",
1134 |        "      <td>NaN</td>\n",
1135 |        "      <td>NaN</td>\n",
1136 |        "      <td>Excel</td>\n",
1137 |        "      <td>NaN</td>\n",
1138 |        "      <td>NaN</td>\n",
1139 |        "      <td>NaN</td>\n",
1140 |        "      <td>satisfied</td>\n",
1141 |        "      <td>NaN</td>\n",
1142 |        "      <td>0</td>\n",
1143 |        "      <td>NaN</td>\n",
1144 |        "      <td>0</td>\n",
1145 |        "      <td>NaN</td>\n",
1146 |        "      <td>0</td>\n",
1147 |        "      <td>4.0</td>\n",
1148 |        "      <td>4</td>\n",
1149 |        "    </tr>\n",
1150 |        "  </tbody>\n",
1151 |        "</table>\n",
1152 |        "</div>"
1153 |       ],
1154 |       "text/plain": [
1155 |        "                timestamp gender   age                           skills  \\\n",
1156 |        "0 2021-02-19 22:36:35.982      M  24.0  Word, Excel, PowerPoint, Access   \n",
1157 |        "4 2021-02-20 08:27:19.584      F  27.0          Word, Excel, PowerPoint   \n",
1158 |        "5 2021-02-20 08:27:36.059      F  19.0                 Excel, Python, R   \n",
1159 |        "6 2021-02-20 08:28:19.725      X  30.0                SQL, R, SPSS, SAS   \n",
1160 |        "7 2021-02-20 09:16:02.448      X  24.0                       PowerPoint   \n",
1161 |        "9 2021-02-20 09:23:22.259    NaN   NaN                            Excel   \n",
1162 |        "\n",
1163 |        "             product           price         service         overall  \\\n",
1164 |        "0  very dissatisfied    dissatisfied         neutral       satisfied   \n",
1165 |        "4            neutral       satisfied  very satisfied  very satisfied   \n",
1166 |        "5          satisfied       satisfied       satisfied       satisfied   \n",
1167 |        "6       dissatisfied       satisfied         neutral  very satisfied   \n",
1168 |        "7          satisfied  very satisfied       satisfied  very satisfied   \n",
1169 |        "9                NaN             NaN             NaN       satisfied   \n",
1170 |        "\n",
1171 |        "   product_n  product_cat  price_n  price_cat  service_n  service_cat  \\\n",
1172 |        "0        1.0            1      2.0          2        3.0            3   \n",
1173 |        "4        3.0            3      4.0          4        5.0            5   \n",
1174 |        "5        4.0            4      4.0          4        4.0            4   \n",
1175 |        "6        2.0            2      4.0          4        3.0            3   \n",
1176 |        "7        4.0            4      5.0          5        4.0            4   \n",
1177 |        "9        NaN            0      NaN          0        NaN            0   \n",
1178 |        "\n",
1179 |        "   overall_n  overall_cat  \n",
1180 |        "0        4.0            4  \n",
1181 |        "4        5.0            5  \n",
1182 |        "5        4.0            4  \n",
1183 |        "6        5.0            5  \n",
1184 |        "7        5.0            5  \n",
1185 |        "9        4.0            4  "
1186 |       ]
1187 |      },
1188 |      "execution_count": 70,
1189 |      "metadata": {},
1190 |      "output_type": "execute_result"
1191 |     }
1192 |    ],
1193 |    "source": [
1194 |     "df[df['overall'] > 'neutral']"
1195 |    ]
1196 |   },
1197 |   {
1198 |    "cell_type": "code",
1199 |    "execution_count": 71,
1200 |    "metadata": {},
1201 |    "outputs": [
1202 |     {
1203 |      "data": {
1204 |       "text/html": [
1205 |        "<div>\n",
1206 |        "<style scoped>\n",
1207 |        "    .dataframe tbody tr th:only-of-type {\n",
1208 |        "        vertical-align: middle;\n",
1209 |        "    }\n",
1210 |        "\n",
1211 |        "    .dataframe tbody tr th {\n",
1212 |        "        vertical-align: top;\n",
1213 |        "    }\n",
1214 |        "\n",
1215 |        "    .dataframe thead th {\n",
1216 |        "        text-align: right;\n",
1217 |        "    }\n",
1218 |        "</style>\n",
1219 |        "<table border=\"1\" class=\"dataframe\">\n",
1220 |        "  <thead>\n",
1221 |        "    <tr style=\"text-align: right;\">\n",
1222 |        "      <th></th>\n",
1223 |        "      <th>timestamp</th>\n",
1224 |        "      <th>gender</th>\n",
1225 |        "      <th>age</th>\n",
1226 |        "      <th>skills</th>\n",
1227 |        "      <th>product</th>\n",
1228 |        "      <th>price</th>\n",
1229 |        "      <th>service</th>\n",
1230 |        "      <th>overall</th>\n",
1231 |        "      <th>product_n</th>\n",
1232 |        "      <th>product_cat</th>\n",
1233 |        "      <th>price_n</th>\n",
1234 |        "      <th>price_cat</th>\n",
1235 |        "      <th>service_n</th>\n",
1236 |        "      <th>service_cat</th>\n",
1237 |        "      <th>overall_n</th>\n",
1238 |        "      <th>overall_cat</th>\n",
1239 |        "    </tr>\n",
1240 |        "  </thead>\n",
1241 |        "  <tbody>\n",
1242 |        "    <tr>\n",
1243 |        "      <th>0</th>\n",
1244 |        "      <td>2021-02-19 22:36:35.982</td>\n",
1245 |        "      <td>M</td>\n",
1246 |        "      <td>24.0</td>\n",
1247 |        "      <td>Word, Excel, PowerPoint, Access</td>\n",
1248 |        "      <td>very dissatisfied</td>\n",
1249 |        "      <td>dissatisfied</td>\n",
1250 |        "      <td>neutral</td>\n",
1251 |        "      <td>satisfied</td>\n",
1252 |        "      <td>1.0</td>\n",
1253 |        "      <td>1</td>\n",
1254 |        "      <td>2.0</td>\n",
1255 |        "      <td>2</td>\n",
1256 |        "      <td>3.0</td>\n",
1257 |        "      <td>3</td>\n",
1258 |        "      <td>4.0</td>\n",
1259 |        "      <td>4</td>\n",
1260 |        "    </tr>\n",
1261 |        "    <tr>\n",
1262 |        "      <th>4</th>\n",
1263 |        "      <td>2021-02-20 08:27:19.584</td>\n",
1264 |        "      <td>F</td>\n",
1265 |        "      <td>27.0</td>\n",
1266 |        "      <td>Word, Excel, PowerPoint</td>\n",
1267 |        "      <td>neutral</td>\n",
1268 |        "      <td>satisfied</td>\n",
1269 |        "      <td>very satisfied</td>\n",
1270 |        "      <td>very satisfied</td>\n",
1271 |        "      <td>3.0</td>\n",
1272 |        "      <td>3</td>\n",
1273 |        "      <td>4.0</td>\n",
1274 |        "      <td>4</td>\n",
1275 |        "      <td>5.0</td>\n",
1276 |        "      <td>5</td>\n",
1277 |        "      <td>5.0</td>\n",
1278 |        "      <td>5</td>\n",
1279 |        "    </tr>\n",
1280 |        "    <tr>\n",
1281 |        "      <th>5</th>\n",
1282 |        "      <td>2021-02-20 08:27:36.059</td>\n",
1283 |        "      <td>F</td>\n",
1284 |        "      <td>19.0</td>\n",
1285 |        "      <td>Excel, Python, R</td>\n",
1286 |        "      <td>satisfied</td>\n",
1287 |        "      <td>satisfied</td>\n",
1288 |        "      <td>satisfied</td>\n",
1289 |        "      <td>satisfied</td>\n",
1290 |        "      <td>4.0</td>\n",
1291 |        "      <td>4</td>\n",
1292 |        "      <td>4.0</td>\n",
1293 |        "      <td>4</td>\n",
1294 |        "      <td>4.0</td>\n",
1295 |        "      <td>4</td>\n",
1296 |        "      <td>4.0</td>\n",
1297 |        "      <td>4</td>\n",
1298 |        "    </tr>\n",
1299 |        "    <tr>\n",
1300 |        "      <th>6</th>\n",
1301 |        "      <td>2021-02-20 08:28:19.725</td>\n",
1302 |        "      <td>X</td>\n",
1303 |        "      <td>30.0</td>\n",
1304 |        "      <td>SQL, R, SPSS, SAS</td>\n",
1305 |        "      <td>dissatisfied</td>\n",
1306 |        "      <td>satisfied</td>\n",
1307 |        "      <td>neutral</td>\n",
1308 |        "      <td>very satisfied</td>\n",
1309 |        "      <td>2.0</td>\n",
1310 |        "      <td>2</td>\n",
1311 |        "      <td>4.0</td>\n",
1312 |        "      <td>4</td>\n",
1313 |        "      <td>3.0</td>\n",
1314 |        "      <td>3</td>\n",
1315 |        "      <td>5.0</td>\n",
1316 |        "      <td>5</td>\n",
1317 |        "    </tr>\n",
1318 |        "    <tr>\n",
1319 |        "      <th>7</th>\n",
1320 |        "      <td>2021-02-20 09:16:02.448</td>\n",
1321 |        "      <td>X</td>\n",
1322 |        "      <td>24.0</td>\n",
1323 |        "      <td>PowerPoint</td>\n",
1324 |        "      <td>satisfied</td>\n",
1325 |        "      <td>very satisfied</td>\n",
1326 |        "      <td>satisfied</td>\n",
1327 |        "      <td>very satisfied</td>\n",
1328 |        "      <td>4.0</td>\n",
1329 |        "      <td>4</td>\n",
1330 |        "      <td>5.0</td>\n",
1331 |        "      <td>5</td>\n",
1332 |        "      <td>4.0</td>\n",
1333 |        "      <td>4</td>\n",
1334 |        "      <td>5.0</td>\n",
1335 |        "      <td>5</td>\n",
1336 |        "    </tr>\n",
1337 |        "    <tr>\n",
1338 |        "      <th>9</th>\n",
1339 |        "      <td>2021-02-20 09:23:22.259</td>\n",
1340 |        "      <td>NaN</td>\n",
1341 |        "      <td>NaN</td>\n",
1342 |        "      <td>Excel</td>\n",
1343 |        "      <td>NaN</td>\n",
1344 |        "      <td>NaN</td>\n",
1345 |        "      <td>NaN</td>\n",
1346 |        "      <td>satisfied</td>\n",
1347 |        "      <td>NaN</td>\n",
1348 |        "      <td>0</td>\n",
1349 |        "      <td>NaN</td>\n",
1350 |        "      <td>0</td>\n",
1351 |        "      <td>NaN</td>\n",
1352 |        "      <td>0</td>\n",
1353 |        "      <td>4.0</td>\n",
1354 |        "      <td>4</td>\n",
1355 |        "    </tr>\n",
1356 |        "  </tbody>\n",
1357 |        "</table>\n",
1358 |        "</div>"
1359 |       ],
1360 |       "text/plain": [
1361 |        "                timestamp gender   age                           skills  \\\n",
1362 |        "0 2021-02-19 22:36:35.982      M  24.0  Word, Excel, PowerPoint, Access   \n",
1363 |        "4 2021-02-20 08:27:19.584      F  27.0          Word, Excel, PowerPoint   \n",
1364 |        "5 2021-02-20 08:27:36.059      F  19.0                 Excel, Python, R   \n",
1365 |        "6 2021-02-20 08:28:19.725      X  30.0                SQL, R, SPSS, SAS   \n",
1366 |        "7 2021-02-20 09:16:02.448      X  24.0                       PowerPoint   \n",
1367 |        "9 2021-02-20 09:23:22.259    NaN   NaN                            Excel   \n",
1368 |        "\n",
1369 |        "             product           price         service         overall  \\\n",
1370 |        "0  very dissatisfied    dissatisfied         neutral       satisfied   \n",
1371 |        "4            neutral       satisfied  very satisfied  very satisfied   \n",
1372 |        "5          satisfied       satisfied       satisfied       satisfied   \n",
1373 |        "6       dissatisfied       satisfied         neutral  very satisfied   \n",
1374 |        "7          satisfied  very satisfied       satisfied  very satisfied   \n",
1375 |        "9                NaN             NaN             NaN       satisfied   \n",
1376 |        "\n",
1377 |        "   product_n  product_cat  price_n  price_cat  service_n  service_cat  \\\n",
1378 |        "0        1.0            1      2.0          2        3.0            3   \n",
1379 |        "4        3.0            3      4.0          4        5.0            5   \n",
1380 |        "5        4.0            4      4.0          4        4.0            4   \n",
1381 |        "6        2.0            2      4.0          4        3.0            3   \n",
1382 |        "7        4.0            4      5.0          5        4.0            4   \n",
1383 |        "9        NaN            0      NaN          0        NaN            0   \n",
1384 |        "\n",
1385 |        "   overall_n  overall_cat  \n",
1386 |        "0        4.0            4  \n",
1387 |        "4        5.0            5  \n",
1388 |        "5        4.0            4  \n",
1389 |        "6        5.0            5  \n",
1390 |        "7        5.0            5  \n",
1391 |        "9        4.0            4  "
1392 |       ]
1393 |      },
1394 |      "execution_count": 71,
1395 |      "metadata": {},
1396 |      "output_type": "execute_result"
1397 |     }
1398 |    ],
1399 |    "source": [
1400 |     "df[df['overall_n'] > 3]"
1401 |    ]
1402 |   },
1403 |   {
1404 |    "cell_type": "code",
1405 |    "execution_count": 72,
1406 |    "metadata": {},
1407 |    "outputs": [
1408 |     {
1409 |      "data": {
1410 |       "text/plain": [
1411 |        "0    1\n",
1412 |        "1    5\n",
1413 |        "2    1\n",
1414 |        "3    5\n",
1415 |        "4    3\n",
1416 |        "5    4\n",
1417 |        "6    2\n",
1418 |        "7    4\n",
1419 |        "8    1\n",
1420 |        "9    0\n",
1421 |        "Name: product_cat, dtype: int8"
1422 |       ]
1423 |      },
1424 |      "execution_count": 72,
1425 |      "metadata": {},
1426 |      "output_type": "execute_result"
1427 |     }
1428 |    ],
1429 |    "source": [
1430 |     "df['product_cat']"
1431 |    ]
1432 |   },
1433 |   {
1434 |    "cell_type": "code",
1435 |    "execution_count": 73,
1436 |    "metadata": {},
1437 |    "outputs": [
1438 |     {
1439 |      "data": {
1440 |       "text/plain": [
1441 |        "0    0\n",
1442 |        "1    4\n",
1443 |        "2    0\n",
1444 |        "3    4\n",
1445 |        "4    2\n",
1446 |        "5    3\n",
1447 |        "6    1\n",
1448 |        "7    3\n",
1449 |        "8    0\n",
1450 |        "9   -1\n",
1451 |        "dtype: int8"
1452 |       ]
1453 |      },
1454 |      "execution_count": 73,
1455 |      "metadata": {},
1456 |      "output_type": "execute_result"
1457 |     }
1458 |    ],
1459 |    "source": [
1460 |     "df['product'].cat.codes"
1461 |    ]
1462 |   },
1463 |   {
1464 |    "cell_type": "code",
1465 |    "execution_count": 74,
1466 |    "metadata": {},
1467 |    "outputs": [
1468 |     {
1469 |      "data": {
1470 |       "text/plain": [
1471 |        "'very dissatisfied'"
1472 |       ]
1473 |      },
1474 |      "execution_count": 74,
1475 |      "metadata": {},
1476 |      "output_type": "execute_result"
1477 |     }
1478 |    ],
1479 |    "source": [
1480 |     "df['product'][0]"
1481 |    ]
1482 |   },
1483 |   {
1484 |    "cell_type": "code",
1485 |    "execution_count": 75,
1486 |    "metadata": {},
1487 |    "outputs": [
1488 |     {
1489 |      "data": {
1490 |       "text/html": [
1491 |        "<div>\n",
1492 |        "<style scoped>\n",
1493 |        "    .dataframe tbody tr th:only-of-type {\n",
1494 |        "        vertical-align: middle;\n",
1495 |        "    }\n",
1496 |        "\n",
1497 |        "    .dataframe tbody tr th {\n",
1498 |        "        vertical-align: top;\n",
1499 |        "    }\n",
1500 |        "\n",
1501 |        "    .dataframe thead th {\n",
1502 |        "        text-align: right;\n",
1503 |        "    }\n",
1504 |        "</style>\n",
1505 |        "<table border=\"1\" class=\"dataframe\">\n",
1506 |        "  <thead>\n",
1507 |        "    <tr style=\"text-align: right;\">\n",
1508 |        "      <th></th>\n",
1509 |        "      <th>Access</th>\n",
1510 |        "      <th>Excel</th>\n",
1511 |        "      <th>JavaScript</th>\n",
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1514 |        "      <th>R</th>\n",
1515 |        "      <th>SAS</th>\n",
1516 |        "      <th>SPSS</th>\n",
1517 |        "      <th>SQL</th>\n",
1518 |        "      <th>Word</th>\n",
1519 |        "    </tr>\n",
1520 |        "  </thead>\n",
1521 |        "  <tbody>\n",
1522 |        "    <tr>\n",
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1527 |        "      <td>1</td>\n",
1528 |        "      <td>0</td>\n",
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1534 |        "    </tr>\n",
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1536 |        "      <th>1</th>\n",
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1543 |        "      <td>0</td>\n",
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1547 |        "    </tr>\n",
1548 |        "    <tr>\n",
1549 |        "      <th>2</th>\n",
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1551 |        "      <td>1</td>\n",
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1557 |        "      <td>1</td>\n",
1558 |        "      <td>0</td>\n",
1559 |        "      <td>1</td>\n",
1560 |        "    </tr>\n",
1561 |        "    <tr>\n",
1562 |        "      <th>3</th>\n",
1563 |        "      <td>0</td>\n",
1564 |        "      <td>1</td>\n",
1565 |        "      <td>1</td>\n",
1566 |        "      <td>1</td>\n",
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1569 |        "      <td>0</td>\n",
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1571 |        "      <td>1</td>\n",
1572 |        "      <td>1</td>\n",
1573 |        "    </tr>\n",
1574 |        "    <tr>\n",
1575 |        "      <th>4</th>\n",
1576 |        "      <td>0</td>\n",
1577 |        "      <td>1</td>\n",
1578 |        "      <td>0</td>\n",
1579 |        "      <td>1</td>\n",
1580 |        "      <td>0</td>\n",
1581 |        "      <td>0</td>\n",
1582 |        "      <td>0</td>\n",
1583 |        "      <td>0</td>\n",
1584 |        "      <td>0</td>\n",
1585 |        "      <td>1</td>\n",
1586 |        "    </tr>\n",
1587 |        "    <tr>\n",
1588 |        "      <th>5</th>\n",
1589 |        "      <td>0</td>\n",
1590 |        "      <td>1</td>\n",
1591 |        "      <td>0</td>\n",
1592 |        "      <td>0</td>\n",
1593 |        "      <td>1</td>\n",
1594 |        "      <td>1</td>\n",
1595 |        "      <td>0</td>\n",
1596 |        "      <td>0</td>\n",
1597 |        "      <td>0</td>\n",
1598 |        "      <td>0</td>\n",
1599 |        "    </tr>\n",
1600 |        "    <tr>\n",
1601 |        "      <th>6</th>\n",
1602 |        "      <td>0</td>\n",
1603 |        "      <td>0</td>\n",
1604 |        "      <td>0</td>\n",
1605 |        "      <td>0</td>\n",
1606 |        "      <td>0</td>\n",
1607 |        "      <td>1</td>\n",
1608 |        "      <td>1</td>\n",
1609 |        "      <td>1</td>\n",
1610 |        "      <td>1</td>\n",
1611 |        "      <td>0</td>\n",
1612 |        "    </tr>\n",
1613 |        "    <tr>\n",
1614 |        "      <th>7</th>\n",
1615 |        "      <td>0</td>\n",
1616 |        "      <td>0</td>\n",
1617 |        "      <td>0</td>\n",
1618 |        "      <td>1</td>\n",
1619 |        "      <td>0</td>\n",
1620 |        "      <td>0</td>\n",
1621 |        "      <td>0</td>\n",
1622 |        "      <td>0</td>\n",
1623 |        "      <td>0</td>\n",
1624 |        "      <td>0</td>\n",
1625 |        "    </tr>\n",
1626 |        "    <tr>\n",
1627 |        "      <th>8</th>\n",
1628 |        "      <td>0</td>\n",
1629 |        "      <td>0</td>\n",
1630 |        "      <td>0</td>\n",
1631 |        "      <td>0</td>\n",
1632 |        "      <td>0</td>\n",
1633 |        "      <td>0</td>\n",
1634 |        "      <td>0</td>\n",
1635 |        "      <td>0</td>\n",
1636 |        "      <td>0</td>\n",
1637 |        "      <td>1</td>\n",
1638 |        "    </tr>\n",
1639 |        "    <tr>\n",
1640 |        "      <th>9</th>\n",
1641 |        "      <td>0</td>\n",
1642 |        "      <td>1</td>\n",
1643 |        "      <td>0</td>\n",
1644 |        "      <td>0</td>\n",
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1647 |        "      <td>0</td>\n",
1648 |        "      <td>0</td>\n",
1649 |        "      <td>0</td>\n",
1650 |        "      <td>0</td>\n",
1651 |        "    </tr>\n",
1652 |        "  </tbody>\n",
1653 |        "</table>\n",
1654 |        "</div>"
1655 |       ],
1656 |       "text/plain": [
1657 |        "   Access  Excel  JavaScript  PowerPoint  Python  R  SAS  SPSS  SQL  Word\n",
1658 |        "0       1      1           0           1       0  0    0     0    0     1\n",
1659 |        "1       0      1           0           0       0  0    0     0    1     0\n",
1660 |        "2       0      1           0           1       0  0    0     1    0     1\n",
1661 |        "3       0      1           1           1       1  1    0     0    1     1\n",
1662 |        "4       0      1           0           1       0  0    0     0    0     1\n",
1663 |        "5       0      1           0           0       1  1    0     0    0     0\n",
1664 |        "6       0      0           0           0       0  1    1     1    1     0\n",
1665 |        "7       0      0           0           1       0  0    0     0    0     0\n",
1666 |        "8       0      0           0           0       0  0    0     0    0     1\n",
1667 |        "9       0      1           0           0       0  0    0     0    0     0"
1668 |       ]
1669 |      },
1670 |      "execution_count": 75,
1671 |      "metadata": {},
1672 |      "output_type": "execute_result"
1673 |     }
1674 |    ],
1675 |    "source": [
1676 |     "ds=df.skills.str.get_dummies(', ')\n",
1677 |     "ds"
1678 |    ]
1679 |   },
1680 |   {
1681 |    "cell_type": "code",
1682 |    "execution_count": 76,
1683 |    "metadata": {},
1684 |    "outputs": [
1685 |     {
1686 |      "data": {
1687 |       "text/html": [
1688 |        "<div>\n",
1689 |        "<style scoped>\n",
1690 |        "    .dataframe tbody tr th:only-of-type {\n",
1691 |        "        vertical-align: middle;\n",
1692 |        "    }\n",
1693 |        "\n",
1694 |        "    .dataframe tbody tr th {\n",
1695 |        "        vertical-align: top;\n",
1696 |        "    }\n",
1697 |        "\n",
1698 |        "    .dataframe thead th {\n",
1699 |        "        text-align: right;\n",
1700 |        "    }\n",
1701 |        "</style>\n",
1702 |        "<table border=\"1\" class=\"dataframe\">\n",
1703 |        "  <thead>\n",
1704 |        "    <tr style=\"text-align: right;\">\n",
1705 |        "      <th></th>\n",
1706 |        "      <th>timestamp</th>\n",
1707 |        "      <th>gender</th>\n",
1708 |        "      <th>age</th>\n",
1709 |        "      <th>skills</th>\n",
1710 |        "      <th>product</th>\n",
1711 |        "      <th>price</th>\n",
1712 |        "      <th>service</th>\n",
1713 |        "      <th>overall</th>\n",
1714 |        "      <th>product_n</th>\n",
1715 |        "      <th>product_cat</th>\n",
1716 |        "      <th>...</th>\n",
1717 |        "      <th>Access</th>\n",
1718 |        "      <th>Excel</th>\n",
1719 |        "      <th>JavaScript</th>\n",
1720 |        "      <th>PowerPoint</th>\n",
1721 |        "      <th>Python</th>\n",
1722 |        "      <th>R</th>\n",
1723 |        "      <th>SAS</th>\n",
1724 |        "      <th>SPSS</th>\n",
1725 |        "      <th>SQL</th>\n",
1726 |        "      <th>Word</th>\n",
1727 |        "    </tr>\n",
1728 |        "  </thead>\n",
1729 |        "  <tbody>\n",
1730 |        "    <tr>\n",
1731 |        "      <th>0</th>\n",
1732 |        "      <td>2021-02-19 22:36:35.982</td>\n",
1733 |        "      <td>M</td>\n",
1734 |        "      <td>24.0</td>\n",
1735 |        "      <td>Word, Excel, PowerPoint, Access</td>\n",
1736 |        "      <td>very dissatisfied</td>\n",
1737 |        "      <td>dissatisfied</td>\n",
1738 |        "      <td>neutral</td>\n",
1739 |        "      <td>satisfied</td>\n",
1740 |        "      <td>1.0</td>\n",
1741 |        "      <td>1</td>\n",
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1751 |        "      <td>0</td>\n",
1752 |        "      <td>1</td>\n",
1753 |        "    </tr>\n",
1754 |        "    <tr>\n",
1755 |        "      <th>1</th>\n",
1756 |        "      <td>2021-02-19 22:38:17.913</td>\n",
1757 |        "      <td>M</td>\n",
1758 |        "      <td>22.0</td>\n",
1759 |        "      <td>Excel, SQL</td>\n",
1760 |        "      <td>very satisfied</td>\n",
1761 |        "      <td>satisfied</td>\n",
1762 |        "      <td>neutral</td>\n",
1763 |        "      <td>dissatisfied</td>\n",
1764 |        "      <td>5.0</td>\n",
1765 |        "      <td>5</td>\n",
1766 |        "      <td>...</td>\n",
1767 |        "      <td>0</td>\n",
1768 |        "      <td>1</td>\n",
1769 |        "      <td>0</td>\n",
1770 |        "      <td>0</td>\n",
1771 |        "      <td>0</td>\n",
1772 |        "      <td>0</td>\n",
1773 |        "      <td>0</td>\n",
1774 |        "      <td>0</td>\n",
1775 |        "      <td>1</td>\n",
1776 |        "      <td>0</td>\n",
1777 |        "    </tr>\n",
1778 |        "    <tr>\n",
1779 |        "      <th>2</th>\n",
1780 |        "      <td>2021-02-19 22:49:14.802</td>\n",
1781 |        "      <td>X</td>\n",
1782 |        "      <td>35.0</td>\n",
1783 |        "      <td>Word, Excel, PowerPoint, SPSS</td>\n",
1784 |        "      <td>very dissatisfied</td>\n",
1785 |        "      <td>dissatisfied</td>\n",
1786 |        "      <td>neutral</td>\n",
1787 |        "      <td>dissatisfied</td>\n",
1788 |        "      <td>1.0</td>\n",
1789 |        "      <td>1</td>\n",
1790 |        "      <td>...</td>\n",
1791 |        "      <td>0</td>\n",
1792 |        "      <td>1</td>\n",
1793 |        "      <td>0</td>\n",
1794 |        "      <td>1</td>\n",
1795 |        "      <td>0</td>\n",
1796 |        "      <td>0</td>\n",
1797 |        "      <td>0</td>\n",
1798 |        "      <td>1</td>\n",
1799 |        "      <td>0</td>\n",
1800 |        "      <td>1</td>\n",
1801 |        "    </tr>\n",
1802 |        "    <tr>\n",
1803 |        "      <th>3</th>\n",
1804 |        "      <td>2021-02-19 23:01:24.057</td>\n",
1805 |        "      <td>F</td>\n",
1806 |        "      <td>23.0</td>\n",
1807 |        "      <td>Word, Excel, PowerPoint, SQL, Python, R, JavaS...</td>\n",
1808 |        "      <td>very satisfied</td>\n",
1809 |        "      <td>satisfied</td>\n",
1810 |        "      <td>neutral</td>\n",
1811 |        "      <td>dissatisfied</td>\n",
1812 |        "      <td>5.0</td>\n",
1813 |        "      <td>5</td>\n",
1814 |        "      <td>...</td>\n",
1815 |        "      <td>0</td>\n",
1816 |        "      <td>1</td>\n",
1817 |        "      <td>1</td>\n",
1818 |        "      <td>1</td>\n",
1819 |        "      <td>1</td>\n",
1820 |        "      <td>1</td>\n",
1821 |        "      <td>0</td>\n",
1822 |        "      <td>0</td>\n",
1823 |        "      <td>1</td>\n",
1824 |        "      <td>1</td>\n",
1825 |        "    </tr>\n",
1826 |        "    <tr>\n",
1827 |        "      <th>4</th>\n",
1828 |        "      <td>2021-02-20 08:27:19.584</td>\n",
1829 |        "      <td>F</td>\n",
1830 |        "      <td>27.0</td>\n",
1831 |        "      <td>Word, Excel, PowerPoint</td>\n",
1832 |        "      <td>neutral</td>\n",
1833 |        "      <td>satisfied</td>\n",
1834 |        "      <td>very satisfied</td>\n",
1835 |        "      <td>very satisfied</td>\n",
1836 |        "      <td>3.0</td>\n",
1837 |        "      <td>3</td>\n",
1838 |        "      <td>...</td>\n",
1839 |        "      <td>0</td>\n",
1840 |        "      <td>1</td>\n",
1841 |        "      <td>0</td>\n",
1842 |        "      <td>1</td>\n",
1843 |        "      <td>0</td>\n",
1844 |        "      <td>0</td>\n",
1845 |        "      <td>0</td>\n",
1846 |        "      <td>0</td>\n",
1847 |        "      <td>0</td>\n",
1848 |        "      <td>1</td>\n",
1849 |        "    </tr>\n",
1850 |        "    <tr>\n",
1851 |        "      <th>5</th>\n",
1852 |        "      <td>2021-02-20 08:27:36.059</td>\n",
1853 |        "      <td>F</td>\n",
1854 |        "      <td>19.0</td>\n",
1855 |        "      <td>Excel, Python, R</td>\n",
1856 |        "      <td>satisfied</td>\n",
1857 |        "      <td>satisfied</td>\n",
1858 |        "      <td>satisfied</td>\n",
1859 |        "      <td>satisfied</td>\n",
1860 |        "      <td>4.0</td>\n",
1861 |        "      <td>4</td>\n",
1862 |        "      <td>...</td>\n",
1863 |        "      <td>0</td>\n",
1864 |        "      <td>1</td>\n",
1865 |        "      <td>0</td>\n",
1866 |        "      <td>0</td>\n",
1867 |        "      <td>1</td>\n",
1868 |        "      <td>1</td>\n",
1869 |        "      <td>0</td>\n",
1870 |        "      <td>0</td>\n",
1871 |        "      <td>0</td>\n",
1872 |        "      <td>0</td>\n",
1873 |        "    </tr>\n",
1874 |        "    <tr>\n",
1875 |        "      <th>6</th>\n",
1876 |        "      <td>2021-02-20 08:28:19.725</td>\n",
1877 |        "      <td>X</td>\n",
1878 |        "      <td>30.0</td>\n",
1879 |        "      <td>SQL, R, SPSS, SAS</td>\n",
1880 |        "      <td>dissatisfied</td>\n",
1881 |        "      <td>satisfied</td>\n",
1882 |        "      <td>neutral</td>\n",
1883 |        "      <td>very satisfied</td>\n",
1884 |        "      <td>2.0</td>\n",
1885 |        "      <td>2</td>\n",
1886 |        "      <td>...</td>\n",
1887 |        "      <td>0</td>\n",
1888 |        "      <td>0</td>\n",
1889 |        "      <td>0</td>\n",
1890 |        "      <td>0</td>\n",
1891 |        "      <td>0</td>\n",
1892 |        "      <td>1</td>\n",
1893 |        "      <td>1</td>\n",
1894 |        "      <td>1</td>\n",
1895 |        "      <td>1</td>\n",
1896 |        "      <td>0</td>\n",
1897 |        "    </tr>\n",
1898 |        "    <tr>\n",
1899 |        "      <th>7</th>\n",
1900 |        "      <td>2021-02-20 09:16:02.448</td>\n",
1901 |        "      <td>X</td>\n",
1902 |        "      <td>24.0</td>\n",
1903 |        "      <td>PowerPoint</td>\n",
1904 |        "      <td>satisfied</td>\n",
1905 |        "      <td>very satisfied</td>\n",
1906 |        "      <td>satisfied</td>\n",
1907 |        "      <td>very satisfied</td>\n",
1908 |        "      <td>4.0</td>\n",
1909 |        "      <td>4</td>\n",
1910 |        "      <td>...</td>\n",
1911 |        "      <td>0</td>\n",
1912 |        "      <td>0</td>\n",
1913 |        "      <td>0</td>\n",
1914 |        "      <td>1</td>\n",
1915 |        "      <td>0</td>\n",
1916 |        "      <td>0</td>\n",
1917 |        "      <td>0</td>\n",
1918 |        "      <td>0</td>\n",
1919 |        "      <td>0</td>\n",
1920 |        "      <td>0</td>\n",
1921 |        "    </tr>\n",
1922 |        "    <tr>\n",
1923 |        "      <th>8</th>\n",
1924 |        "      <td>2021-02-20 09:22:23.851</td>\n",
1925 |        "      <td>M</td>\n",
1926 |        "      <td>NaN</td>\n",
1927 |        "      <td>Word</td>\n",
1928 |        "      <td>very dissatisfied</td>\n",
1929 |        "      <td>dissatisfied</td>\n",
1930 |        "      <td>neutral</td>\n",
1931 |        "      <td>NaN</td>\n",
1932 |        "      <td>1.0</td>\n",
1933 |        "      <td>1</td>\n",
1934 |        "      <td>...</td>\n",
1935 |        "      <td>0</td>\n",
1936 |        "      <td>0</td>\n",
1937 |        "      <td>0</td>\n",
1938 |        "      <td>0</td>\n",
1939 |        "      <td>0</td>\n",
1940 |        "      <td>0</td>\n",
1941 |        "      <td>0</td>\n",
1942 |        "      <td>0</td>\n",
1943 |        "      <td>0</td>\n",
1944 |        "      <td>1</td>\n",
1945 |        "    </tr>\n",
1946 |        "    <tr>\n",
1947 |        "      <th>9</th>\n",
1948 |        "      <td>2021-02-20 09:23:22.259</td>\n",
1949 |        "      <td>NaN</td>\n",
1950 |        "      <td>NaN</td>\n",
1951 |        "      <td>Excel</td>\n",
1952 |        "      <td>NaN</td>\n",
1953 |        "      <td>NaN</td>\n",
1954 |        "      <td>NaN</td>\n",
1955 |        "      <td>satisfied</td>\n",
1956 |        "      <td>NaN</td>\n",
1957 |        "      <td>0</td>\n",
1958 |        "      <td>...</td>\n",
1959 |        "      <td>0</td>\n",
1960 |        "      <td>1</td>\n",
1961 |        "      <td>0</td>\n",
1962 |        "      <td>0</td>\n",
1963 |        "      <td>0</td>\n",
1964 |        "      <td>0</td>\n",
1965 |        "      <td>0</td>\n",
1966 |        "      <td>0</td>\n",
1967 |        "      <td>0</td>\n",
1968 |        "      <td>0</td>\n",
1969 |        "    </tr>\n",
1970 |        "  </tbody>\n",
1971 |        "</table>\n",
1972 |        "<p>10 rows × 26 columns</p>\n",
1973 |        "</div>"
1974 |       ],
1975 |       "text/plain": [
1976 |        "                timestamp gender   age  \\\n",
1977 |        "0 2021-02-19 22:36:35.982      M  24.0   \n",
1978 |        "1 2021-02-19 22:38:17.913      M  22.0   \n",
1979 |        "2 2021-02-19 22:49:14.802      X  35.0   \n",
1980 |        "3 2021-02-19 23:01:24.057      F  23.0   \n",
1981 |        "4 2021-02-20 08:27:19.584      F  27.0   \n",
1982 |        "5 2021-02-20 08:27:36.059      F  19.0   \n",
1983 |        "6 2021-02-20 08:28:19.725      X  30.0   \n",
1984 |        "7 2021-02-20 09:16:02.448      X  24.0   \n",
1985 |        "8 2021-02-20 09:22:23.851      M   NaN   \n",
1986 |        "9 2021-02-20 09:23:22.259    NaN   NaN   \n",
1987 |        "\n",
1988 |        "                                              skills            product  \\\n",
1989 |        "0                    Word, Excel, PowerPoint, Access  very dissatisfied   \n",
1990 |        "1                                         Excel, SQL     very satisfied   \n",
1991 |        "2                      Word, Excel, PowerPoint, SPSS  very dissatisfied   \n",
1992 |        "3  Word, Excel, PowerPoint, SQL, Python, R, JavaS...     very satisfied   \n",
1993 |        "4                            Word, Excel, PowerPoint            neutral   \n",
1994 |        "5                                   Excel, Python, R          satisfied   \n",
1995 |        "6                                  SQL, R, SPSS, SAS       dissatisfied   \n",
1996 |        "7                                         PowerPoint          satisfied   \n",
1997 |        "8                                               Word  very dissatisfied   \n",
1998 |        "9                                              Excel                NaN   \n",
1999 |        "\n",
2000 |        "            price         service         overall  product_n  product_cat  \\\n",
2001 |        "0    dissatisfied         neutral       satisfied        1.0            1   \n",
2002 |        "1       satisfied         neutral    dissatisfied        5.0            5   \n",
2003 |        "2    dissatisfied         neutral    dissatisfied        1.0            1   \n",
2004 |        "3       satisfied         neutral    dissatisfied        5.0            5   \n",
2005 |        "4       satisfied  very satisfied  very satisfied        3.0            3   \n",
2006 |        "5       satisfied       satisfied       satisfied        4.0            4   \n",
2007 |        "6       satisfied         neutral  very satisfied        2.0            2   \n",
2008 |        "7  very satisfied       satisfied  very satisfied        4.0            4   \n",
2009 |        "8    dissatisfied         neutral             NaN        1.0            1   \n",
2010 |        "9             NaN             NaN       satisfied        NaN            0   \n",
2011 |        "\n",
2012 |        "   ...  Access  Excel  JavaScript  PowerPoint  Python  R  SAS  SPSS  SQL  Word  \n",
2013 |        "0  ...       1      1           0           1       0  0    0     0    0     1  \n",
2014 |        "1  ...       0      1           0           0       0  0    0     0    1     0  \n",
2015 |        "2  ...       0      1           0           1       0  0    0     1    0     1  \n",
2016 |        "3  ...       0      1           1           1       1  1    0     0    1     1  \n",
2017 |        "4  ...       0      1           0           1       0  0    0     0    0     1  \n",
2018 |        "5  ...       0      1           0           0       1  1    0     0    0     0  \n",
2019 |        "6  ...       0      0           0           0       0  1    1     1    1     0  \n",
2020 |        "7  ...       0      0           0           1       0  0    0     0    0     0  \n",
2021 |        "8  ...       0      0           0           0       0  0    0     0    0     1  \n",
2022 |        "9  ...       0      1           0           0       0  0    0     0    0     0  \n",
2023 |        "\n",
2024 |        "[10 rows x 26 columns]"
2025 |       ]
2026 |      },
2027 |      "execution_count": 76,
2028 |      "metadata": {},
2029 |      "output_type": "execute_result"
2030 |     }
2031 |    ],
2032 |    "source": [
2033 |     "dt=pd.concat([df, ds], axis=1).copy()\n",
2034 |     "dt"
2035 |    ]
2036 |   },
2037 |   {
2038 |    "cell_type": "code",
2039 |    "execution_count": 77,
2040 |    "metadata": {},
2041 |    "outputs": [
2042 |     {
2043 |      "data": {
2044 |       "text/plain": [
2045 |        "Index(['timestamp', 'gender', 'age', 'skills', 'product', 'price', 'service',\n",
2046 |        "       'overall', 'product_n', 'product_cat', 'price_n', 'price_cat',\n",
2047 |        "       'service_n', 'service_cat', 'overall_n', 'overall_cat', 'Access',\n",
2048 |        "       'Excel', 'JavaScript', 'PowerPoint', 'Python', 'R', 'SAS', 'SPSS',\n",
2049 |        "       'SQL', 'Word'],\n",
2050 |        "      dtype='object')"
2051 |       ]
2052 |      },
2053 |      "execution_count": 77,
2054 |      "metadata": {},
2055 |      "output_type": "execute_result"
2056 |     }
2057 |    ],
2058 |    "source": [
2059 |     "dt.columns"
2060 |    ]
2061 |   },
2062 |   {
2063 |    "cell_type": "code",
2064 |    "execution_count": 78,
2065 |    "metadata": {},
2066 |    "outputs": [
2067 |     {
2068 |      "data": {
2069 |       "text/html": [
2070 |        "<div>\n",
2071 |        "<style scoped>\n",
2072 |        "    .dataframe tbody tr th:only-of-type {\n",
2073 |        "        vertical-align: middle;\n",
2074 |        "    }\n",
2075 |        "\n",
2076 |        "    .dataframe tbody tr th {\n",
2077 |        "        vertical-align: top;\n",
2078 |        "    }\n",
2079 |        "\n",
2080 |        "    .dataframe thead th {\n",
2081 |        "        text-align: right;\n",
2082 |        "    }\n",
2083 |        "</style>\n",
2084 |        "<table border=\"1\" class=\"dataframe\">\n",
2085 |        "  <thead>\n",
2086 |        "    <tr style=\"text-align: right;\">\n",
2087 |        "      <th></th>\n",
2088 |        "      <th>count</th>\n",
2089 |        "      <th>mean</th>\n",
2090 |        "      <th>std</th>\n",
2091 |        "      <th>min</th>\n",
2092 |        "      <th>25%</th>\n",
2093 |        "      <th>50%</th>\n",
2094 |        "      <th>75%</th>\n",
2095 |        "      <th>max</th>\n",
2096 |        "    </tr>\n",
2097 |        "  </thead>\n",
2098 |        "  <tbody>\n",
2099 |        "    <tr>\n",
2100 |        "      <th>age</th>\n",
2101 |        "      <td>8.0</td>\n",
2102 |        "      <td>25.500000</td>\n",
2103 |        "      <td>5.042675</td>\n",
2104 |        "      <td>19.0</td>\n",
2105 |        "      <td>22.75</td>\n",
2106 |        "      <td>24.0</td>\n",
2107 |        "      <td>27.75</td>\n",
2108 |        "      <td>35.0</td>\n",
2109 |        "    </tr>\n",
2110 |        "    <tr>\n",
2111 |        "      <th>product_n</th>\n",
2112 |        "      <td>9.0</td>\n",
2113 |        "      <td>2.888889</td>\n",
2114 |        "      <td>1.691482</td>\n",
2115 |        "      <td>1.0</td>\n",
2116 |        "      <td>1.00</td>\n",
2117 |        "      <td>3.0</td>\n",
2118 |        "      <td>4.00</td>\n",
2119 |        "      <td>5.0</td>\n",
2120 |        "    </tr>\n",
2121 |        "    <tr>\n",
2122 |        "      <th>product_cat</th>\n",
2123 |        "      <td>10.0</td>\n",
2124 |        "      <td>2.600000</td>\n",
2125 |        "      <td>1.837873</td>\n",
2126 |        "      <td>0.0</td>\n",
2127 |        "      <td>1.00</td>\n",
2128 |        "      <td>2.5</td>\n",
2129 |        "      <td>4.00</td>\n",
2130 |        "      <td>5.0</td>\n",
2131 |        "    </tr>\n",
2132 |        "    <tr>\n",
2133 |        "      <th>price_n</th>\n",
2134 |        "      <td>9.0</td>\n",
2135 |        "      <td>3.444444</td>\n",
2136 |        "      <td>1.130388</td>\n",
2137 |        "      <td>2.0</td>\n",
2138 |        "      <td>2.00</td>\n",
2139 |        "      <td>4.0</td>\n",
2140 |        "      <td>4.00</td>\n",
2141 |        "      <td>5.0</td>\n",
2142 |        "    </tr>\n",
2143 |        "    <tr>\n",
2144 |        "      <th>price_cat</th>\n",
2145 |        "      <td>10.0</td>\n",
2146 |        "      <td>3.100000</td>\n",
2147 |        "      <td>1.523884</td>\n",
2148 |        "      <td>0.0</td>\n",
2149 |        "      <td>2.00</td>\n",
2150 |        "      <td>4.0</td>\n",
2151 |        "      <td>4.00</td>\n",
2152 |        "      <td>5.0</td>\n",
2153 |        "    </tr>\n",
2154 |        "    <tr>\n",
2155 |        "      <th>service_n</th>\n",
2156 |        "      <td>9.0</td>\n",
2157 |        "      <td>3.444444</td>\n",
2158 |        "      <td>0.726483</td>\n",
2159 |        "      <td>3.0</td>\n",
2160 |        "      <td>3.00</td>\n",
2161 |        "      <td>3.0</td>\n",
2162 |        "      <td>4.00</td>\n",
2163 |        "      <td>5.0</td>\n",
2164 |        "    </tr>\n",
2165 |        "    <tr>\n",
2166 |        "      <th>service_cat</th>\n",
2167 |        "      <td>10.0</td>\n",
2168 |        "      <td>3.100000</td>\n",
2169 |        "      <td>1.286684</td>\n",
2170 |        "      <td>0.0</td>\n",
2171 |        "      <td>3.00</td>\n",
2172 |        "      <td>3.0</td>\n",
2173 |        "      <td>3.75</td>\n",
2174 |        "      <td>5.0</td>\n",
2175 |        "    </tr>\n",
2176 |        "    <tr>\n",
2177 |        "      <th>overall_n</th>\n",
2178 |        "      <td>9.0</td>\n",
2179 |        "      <td>3.666667</td>\n",
2180 |        "      <td>1.322876</td>\n",
2181 |        "      <td>2.0</td>\n",
2182 |        "      <td>2.00</td>\n",
2183 |        "      <td>4.0</td>\n",
2184 |        "      <td>5.00</td>\n",
2185 |        "      <td>5.0</td>\n",
2186 |        "    </tr>\n",
2187 |        "    <tr>\n",
2188 |        "      <th>overall_cat</th>\n",
2189 |        "      <td>10.0</td>\n",
2190 |        "      <td>3.300000</td>\n",
2191 |        "      <td>1.702939</td>\n",
2192 |        "      <td>0.0</td>\n",
2193 |        "      <td>2.00</td>\n",
2194 |        "      <td>4.0</td>\n",
2195 |        "      <td>4.75</td>\n",
2196 |        "      <td>5.0</td>\n",
2197 |        "    </tr>\n",
2198 |        "    <tr>\n",
2199 |        "      <th>Access</th>\n",
2200 |        "      <td>10.0</td>\n",
2201 |        "      <td>0.100000</td>\n",
2202 |        "      <td>0.316228</td>\n",
2203 |        "      <td>0.0</td>\n",
2204 |        "      <td>0.00</td>\n",
2205 |        "      <td>0.0</td>\n",
2206 |        "      <td>0.00</td>\n",
2207 |        "      <td>1.0</td>\n",
2208 |        "    </tr>\n",
2209 |        "    <tr>\n",
2210 |        "      <th>Excel</th>\n",
2211 |        "      <td>10.0</td>\n",
2212 |        "      <td>0.700000</td>\n",
2213 |        "      <td>0.483046</td>\n",
2214 |        "      <td>0.0</td>\n",
2215 |        "      <td>0.25</td>\n",
2216 |        "      <td>1.0</td>\n",
2217 |        "      <td>1.00</td>\n",
2218 |        "      <td>1.0</td>\n",
2219 |        "    </tr>\n",
2220 |        "    <tr>\n",
2221 |        "      <th>JavaScript</th>\n",
2222 |        "      <td>10.0</td>\n",
2223 |        "      <td>0.100000</td>\n",
2224 |        "      <td>0.316228</td>\n",
2225 |        "      <td>0.0</td>\n",
2226 |        "      <td>0.00</td>\n",
2227 |        "      <td>0.0</td>\n",
2228 |        "      <td>0.00</td>\n",
2229 |        "      <td>1.0</td>\n",
2230 |        "    </tr>\n",
2231 |        "    <tr>\n",
2232 |        "      <th>PowerPoint</th>\n",
2233 |        "      <td>10.0</td>\n",
2234 |        "      <td>0.500000</td>\n",
2235 |        "      <td>0.527046</td>\n",
2236 |        "      <td>0.0</td>\n",
2237 |        "      <td>0.00</td>\n",
2238 |        "      <td>0.5</td>\n",
2239 |        "      <td>1.00</td>\n",
2240 |        "      <td>1.0</td>\n",
2241 |        "    </tr>\n",
2242 |        "    <tr>\n",
2243 |        "      <th>Python</th>\n",
2244 |        "      <td>10.0</td>\n",
2245 |        "      <td>0.200000</td>\n",
2246 |        "      <td>0.421637</td>\n",
2247 |        "      <td>0.0</td>\n",
2248 |        "      <td>0.00</td>\n",
2249 |        "      <td>0.0</td>\n",
2250 |        "      <td>0.00</td>\n",
2251 |        "      <td>1.0</td>\n",
2252 |        "    </tr>\n",
2253 |        "    <tr>\n",
2254 |        "      <th>R</th>\n",
2255 |        "      <td>10.0</td>\n",
2256 |        "      <td>0.300000</td>\n",
2257 |        "      <td>0.483046</td>\n",
2258 |        "      <td>0.0</td>\n",
2259 |        "      <td>0.00</td>\n",
2260 |        "      <td>0.0</td>\n",
2261 |        "      <td>0.75</td>\n",
2262 |        "      <td>1.0</td>\n",
2263 |        "    </tr>\n",
2264 |        "    <tr>\n",
2265 |        "      <th>SAS</th>\n",
2266 |        "      <td>10.0</td>\n",
2267 |        "      <td>0.100000</td>\n",
2268 |        "      <td>0.316228</td>\n",
2269 |        "      <td>0.0</td>\n",
2270 |        "      <td>0.00</td>\n",
2271 |        "      <td>0.0</td>\n",
2272 |        "      <td>0.00</td>\n",
2273 |        "      <td>1.0</td>\n",
2274 |        "    </tr>\n",
2275 |        "    <tr>\n",
2276 |        "      <th>SPSS</th>\n",
2277 |        "      <td>10.0</td>\n",
2278 |        "      <td>0.200000</td>\n",
2279 |        "      <td>0.421637</td>\n",
2280 |        "      <td>0.0</td>\n",
2281 |        "      <td>0.00</td>\n",
2282 |        "      <td>0.0</td>\n",
2283 |        "      <td>0.00</td>\n",
2284 |        "      <td>1.0</td>\n",
2285 |        "    </tr>\n",
2286 |        "    <tr>\n",
2287 |        "      <th>SQL</th>\n",
2288 |        "      <td>10.0</td>\n",
2289 |        "      <td>0.300000</td>\n",
2290 |        "      <td>0.483046</td>\n",
2291 |        "      <td>0.0</td>\n",
2292 |        "      <td>0.00</td>\n",
2293 |        "      <td>0.0</td>\n",
2294 |        "      <td>0.75</td>\n",
2295 |        "      <td>1.0</td>\n",
2296 |        "    </tr>\n",
2297 |        "    <tr>\n",
2298 |        "      <th>Word</th>\n",
2299 |        "      <td>10.0</td>\n",
2300 |        "      <td>0.500000</td>\n",
2301 |        "      <td>0.527046</td>\n",
2302 |        "      <td>0.0</td>\n",
2303 |        "      <td>0.00</td>\n",
2304 |        "      <td>0.5</td>\n",
2305 |        "      <td>1.00</td>\n",
2306 |        "      <td>1.0</td>\n",
2307 |        "    </tr>\n",
2308 |        "  </tbody>\n",
2309 |        "</table>\n",
2310 |        "</div>"
2311 |       ],
2312 |       "text/plain": [
2313 |        "             count       mean       std   min    25%   50%    75%   max\n",
2314 |        "age            8.0  25.500000  5.042675  19.0  22.75  24.0  27.75  35.0\n",
2315 |        "product_n      9.0   2.888889  1.691482   1.0   1.00   3.0   4.00   5.0\n",
2316 |        "product_cat   10.0   2.600000  1.837873   0.0   1.00   2.5   4.00   5.0\n",
2317 |        "price_n        9.0   3.444444  1.130388   2.0   2.00   4.0   4.00   5.0\n",
2318 |        "price_cat     10.0   3.100000  1.523884   0.0   2.00   4.0   4.00   5.0\n",
2319 |        "service_n      9.0   3.444444  0.726483   3.0   3.00   3.0   4.00   5.0\n",
2320 |        "service_cat   10.0   3.100000  1.286684   0.0   3.00   3.0   3.75   5.0\n",
2321 |        "overall_n      9.0   3.666667  1.322876   2.0   2.00   4.0   5.00   5.0\n",
2322 |        "overall_cat   10.0   3.300000  1.702939   0.0   2.00   4.0   4.75   5.0\n",
2323 |        "Access        10.0   0.100000  0.316228   0.0   0.00   0.0   0.00   1.0\n",
2324 |        "Excel         10.0   0.700000  0.483046   0.0   0.25   1.0   1.00   1.0\n",
2325 |        "JavaScript    10.0   0.100000  0.316228   0.0   0.00   0.0   0.00   1.0\n",
2326 |        "PowerPoint    10.0   0.500000  0.527046   0.0   0.00   0.5   1.00   1.0\n",
2327 |        "Python        10.0   0.200000  0.421637   0.0   0.00   0.0   0.00   1.0\n",
2328 |        "R             10.0   0.300000  0.483046   0.0   0.00   0.0   0.75   1.0\n",
2329 |        "SAS           10.0   0.100000  0.316228   0.0   0.00   0.0   0.00   1.0\n",
2330 |        "SPSS          10.0   0.200000  0.421637   0.0   0.00   0.0   0.00   1.0\n",
2331 |        "SQL           10.0   0.300000  0.483046   0.0   0.00   0.0   0.75   1.0\n",
2332 |        "Word          10.0   0.500000  0.527046   0.0   0.00   0.5   1.00   1.0"
2333 |       ]
2334 |      },
2335 |      "execution_count": 78,
2336 |      "metadata": {},
2337 |      "output_type": "execute_result"
2338 |     }
2339 |    ],
2340 |    "source": [
2341 |     "dt.describe().T"
2342 |    ]
2343 |   },
2344 |   {
2345 |    "cell_type": "code",
2346 |    "execution_count": 79,
2347 |    "metadata": {},
2348 |    "outputs": [
2349 |     {
2350 |      "data": {
2351 |       "text/plain": [
2352 |        "Access        1\n",
2353 |        "Excel         7\n",
2354 |        "JavaScript    1\n",
2355 |        "PowerPoint    5\n",
2356 |        "Python        2\n",
2357 |        "R             3\n",
2358 |        "SAS           1\n",
2359 |        "SPSS          2\n",
2360 |        "SQL           3\n",
2361 |        "Word          5\n",
2362 |        "dtype: int64"
2363 |       ]
2364 |      },
2365 |      "execution_count": 79,
2366 |      "metadata": {},
2367 |      "output_type": "execute_result"
2368 |     }
2369 |    ],
2370 |    "source": [
2371 |     "dt.loc[:, 'Access':'Word'].sum()"
2372 |    ]
2373 |   },
2374 |   {
2375 |    "cell_type": "code",
2376 |    "execution_count": 80,
2377 |    "metadata": {},
2378 |    "outputs": [
2379 |     {
2380 |      "data": {
2381 |       "text/plain": [
2382 |        "0    4\n",
2383 |        "1    2\n",
2384 |        "2    4\n",
2385 |        "3    7\n",
2386 |        "4    3\n",
2387 |        "5    3\n",
2388 |        "6    4\n",
2389 |        "7    1\n",
2390 |        "8    1\n",
2391 |        "9    1\n",
2392 |        "dtype: int64"
2393 |       ]
2394 |      },
2395 |      "execution_count": 80,
2396 |      "metadata": {},
2397 |      "output_type": "execute_result"
2398 |     }
2399 |    ],
2400 |    "source": [
2401 |     "dt.loc[:, 'Access':'Word'].sum(axis=1)"
2402 |    ]
2403 |   }
2404 |  ],
2405 |  "metadata": {
2406 |   "kernelspec": {
2407 |    "display_name": "Python 3",
2408 |    "language": "python",
2409 |    "name": "python3"
2410 |   },
2411 |   "language_info": {
2412 |    "codemirror_mode": {
2413 |     "name": "ipython",
2414 |     "version": 3
2415 |    },
2416 |    "file_extension": ".py",
2417 |    "mimetype": "text/x-python",
2418 |    "name": "python",
2419 |    "nbconvert_exporter": "python",
2420 |    "pygments_lexer": "ipython3",
2421 |    "version": "3.7.9"
2422 |   },
2423 |   "widgets": {
2424 |    "application/vnd.jupyter.widget-state+json": {
2425 |     "state": {},
2426 |     "version_major": 2,
2427 |     "version_minor": 0
2428 |    }
2429 |   }
2430 |  },
2431 |  "nbformat": 4,
2432 |  "nbformat_minor": 4
2433 | }
2434 | 


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