├── .gitattributes
├── Currencies.pptx
├── README.md
├── Use Python to Automate the PowerPoint Update.html
└── Use Python to Automate the PowerPoint Update.ipynb


/.gitattributes:
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1 | *.html linguist-detectable=false
2 | *.ipynb linguist-detectable=true
3 | 


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/Currencies.pptx:
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https://raw.githubusercontent.com/cydalytics/Python_PowerPoint_Automation/6589c8e049a75ca96015033a24f8fd84b4446f93/Currencies.pptx


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/README.md:
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 1 | # Python PowerPoint Automation [![Python](https://img.shields.io/badge/Program-Python-BLUE)](https://blog.cyda.hk/) [![python-pptx](https://img.shields.io/badge/Package-pptx-GREEN)](https://blog.cyda.hk/)
 2 | *<b>Created by cyda - Yeung Wong & Carrie Lo</b>*
 3 | [![alt text](https://2.bp.blogspot.com/-JDCofS2Pvic/WxQCv_XstyI/AAAAAAAAABM/rWHKnG4ItnMULgmO_tWAuGTNL6kAexJlACK4BGAYYCw/s1000/tight%2Bbanner.png)](https://blog.cyda.hk/)
 4 | 
 5 | ---------------------------------------------------------------------------------------------
 6 | ### Please acknowledge <b>team cyda - Yeung Wong and Carrie Lo</b> when using the code
 7 | 
 8 | ### If you find this script is helpful, please feel free to endorse us through Linkedin!
 9 | Linkedin:
10 | 
11 | * Yeung Wong - *https://www.linkedin.com/in/yeungwong/*
12 | * Carrie Lo - *https://www.linkedin.com/in/carrielsc/*
13 | ---------------------------------------------------------------------------------------------
14 | ## Project Description
15 | This project is to use Python to automatically update the PowerPoint slides regularly.
16 | 
17 | ## Project Details
18 | To check the tutorial article, please click please click [here](https://towardsdatascience.com/use-python-to-automate-the-powerpoint-update-4a385acf1243?sk=13bfc8fa3dbbe98e94784de4e42ca245).
19 | 
20 | [![alt text](https://cdn-images-1.medium.com/max/800/1*T5hoUO7SBPn1DCbUKAS5bA.png)](https://towardsdatascience.com/use-python-to-automate-the-powerpoint-update-4a385acf1243?sk=13bfc8fa3dbbe98e94784de4e42ca245)
21 | 


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/Use Python to Automate the PowerPoint Update.ipynb:
--------------------------------------------------------------------------------
  1 | {
  2 |  "cells": [
  3 |   {
  4 |    "cell_type": "markdown",
  5 |    "id": "a76decb6-b764-4ac5-a496-aa7bcca47703",
  6 |    "metadata": {},
  7 |    "source": [
  8 |     "# <center><font color='#333333'>Use Python to Automate the PowerPoint Update</font></center>\n",
  9 |     "### <center><font color='#3b5998'>Created by cyda - Yeung Wong & Carrie Lo</font></center>"
 10 |    ]
 11 |   },
 12 |   {
 13 |    "cell_type": "markdown",
 14 |    "id": "bee06d4d-a337-4468-bcfc-f1aafc2214fc",
 15 |    "metadata": {},
 16 |    "source": [
 17 |     "--------------------------------------------------------------------------------------\n",
 18 |     "![logo](https://4.bp.blogspot.com/-LAXjdvVCYCU/WxeQFKQ-1wI/AAAAAAAAACs/o8IJ1eLLAEwQYv2Az7EqQi9jODTqRx7wACK4BGAYYCw/s1000/tight%2Bbanner_with_description.png)"
 19 |    ]
 20 |   },
 21 |   {
 22 |    "cell_type": "markdown",
 23 |    "id": "92b1ac75-b724-40a3-b850-42e7a7d5480a",
 24 |    "metadata": {},
 25 |    "source": [
 26 |     "--------------------------------------------------------------------------------------\n",
 27 |     "Please acknowledge <b>team cyda - Yeung Wong & Carrie Lo</b> when using the code\n",
 28 |     "\n",
 29 |     "<b><font color='#3b5998'>If you find this script is helpful, please feel free to endorse us through Linkedin!</font></b>\n",
 30 |     "\n",
 31 |     "<b>Linkedin:</b>\n",
 32 |     "\n",
 33 |     "Yeung Wong - https://www.linkedin.com/in/yeungwong/\n",
 34 |     "\n",
 35 |     "Carrie Lo - https://www.linkedin.com/in/carrielsc/\n",
 36 |     "\n",
 37 |     "--------------------------------------------------------------------------------------"
 38 |    ]
 39 |   },
 40 |   {
 41 |    "cell_type": "markdown",
 42 |    "id": "c5b3400a-5ab9-42b5-a8ef-eb1e186b6809",
 43 |    "metadata": {},
 44 |    "source": [
 45 |     "# Step 1 - Data Preprocessing"
 46 |    ]
 47 |   },
 48 |   {
 49 |    "cell_type": "code",
 50 |    "execution_count": 1,
 51 |    "id": "4d484d31-306f-4357-b5b6-5d5323a9391f",
 52 |    "metadata": {},
 53 |    "outputs": [
 54 |     {
 55 |      "data": {
 56 |       "text/html": [
 57 |        "<div>\n",
 58 |        "<style scoped>\n",
 59 |        "    .dataframe tbody tr th:only-of-type {\n",
 60 |        "        vertical-align: middle;\n",
 61 |        "    }\n",
 62 |        "\n",
 63 |        "    .dataframe tbody tr th {\n",
 64 |        "        vertical-align: top;\n",
 65 |        "    }\n",
 66 |        "\n",
 67 |        "    .dataframe thead th {\n",
 68 |        "        text-align: right;\n",
 69 |        "    }\n",
 70 |        "</style>\n",
 71 |        "<table border=\"1\" class=\"dataframe\">\n",
 72 |        "  <thead>\n",
 73 |        "    <tr style=\"text-align: right;\">\n",
 74 |        "      <th></th>\n",
 75 |        "      <th>Symbol</th>\n",
 76 |        "      <th>Name</th>\n",
 77 |        "      <th>Last Price</th>\n",
 78 |        "      <th>Change</th>\n",
 79 |        "      <th>% Change</th>\n",
 80 |        "      <th>52 Week Range</th>\n",
 81 |        "      <th>Day Chart</th>\n",
 82 |        "      <th>pct_change</th>\n",
 83 |        "    </tr>\n",
 84 |        "  </thead>\n",
 85 |        "  <tbody>\n",
 86 |        "    <tr>\n",
 87 |        "      <th>0</th>\n",
 88 |        "      <td>EURUSD=X</td>\n",
 89 |        "      <td>EUR/USD</td>\n",
 90 |        "      <td>1.1322</td>\n",
 91 |        "      <td>-0.0022</td>\n",
 92 |        "      <td>-0.19%</td>\n",
 93 |        "      <td>NaN</td>\n",
 94 |        "      <td>NaN</td>\n",
 95 |        "      <td>-0.19</td>\n",
 96 |        "    </tr>\n",
 97 |        "    <tr>\n",
 98 |        "      <th>1</th>\n",
 99 |        "      <td>JPY=X</td>\n",
100 |        "      <td>USD/JPY</td>\n",
101 |        "      <td>113.5700</td>\n",
102 |        "      <td>-0.1670</td>\n",
103 |        "      <td>-0.15%</td>\n",
104 |        "      <td>NaN</td>\n",
105 |        "      <td>NaN</td>\n",
106 |        "      <td>-0.15</td>\n",
107 |        "    </tr>\n",
108 |        "    <tr>\n",
109 |        "      <th>2</th>\n",
110 |        "      <td>GBPUSD=X</td>\n",
111 |        "      <td>GBP/USD</td>\n",
112 |        "      <td>1.3207</td>\n",
113 |        "      <td>0.0001</td>\n",
114 |        "      <td>+0.00%</td>\n",
115 |        "      <td>NaN</td>\n",
116 |        "      <td>NaN</td>\n",
117 |        "      <td>0.00</td>\n",
118 |        "    </tr>\n",
119 |        "    <tr>\n",
120 |        "      <th>3</th>\n",
121 |        "      <td>AUDUSD=X</td>\n",
122 |        "      <td>AUD/USD</td>\n",
123 |        "      <td>0.7165</td>\n",
124 |        "      <td>-0.0004</td>\n",
125 |        "      <td>-0.06%</td>\n",
126 |        "      <td>NaN</td>\n",
127 |        "      <td>NaN</td>\n",
128 |        "      <td>-0.06</td>\n",
129 |        "    </tr>\n",
130 |        "    <tr>\n",
131 |        "      <th>4</th>\n",
132 |        "      <td>NZDUSD=X</td>\n",
133 |        "      <td>NZD/USD</td>\n",
134 |        "      <td>0.6809</td>\n",
135 |        "      <td>0.0001</td>\n",
136 |        "      <td>+0.01%</td>\n",
137 |        "      <td>NaN</td>\n",
138 |        "      <td>NaN</td>\n",
139 |        "      <td>0.01</td>\n",
140 |        "    </tr>\n",
141 |        "    <tr>\n",
142 |        "      <th>5</th>\n",
143 |        "      <td>EURJPY=X</td>\n",
144 |        "      <td>EUR/JPY</td>\n",
145 |        "      <td>128.5450</td>\n",
146 |        "      <td>-0.4420</td>\n",
147 |        "      <td>-0.34%</td>\n",
148 |        "      <td>NaN</td>\n",
149 |        "      <td>NaN</td>\n",
150 |        "      <td>-0.34</td>\n",
151 |        "    </tr>\n",
152 |        "    <tr>\n",
153 |        "      <th>6</th>\n",
154 |        "      <td>GBPJPY=X</td>\n",
155 |        "      <td>GBP/JPY</td>\n",
156 |        "      <td>149.9880</td>\n",
157 |        "      <td>-0.1670</td>\n",
158 |        "      <td>-0.11%</td>\n",
159 |        "      <td>NaN</td>\n",
160 |        "      <td>NaN</td>\n",
161 |        "      <td>-0.11</td>\n",
162 |        "    </tr>\n",
163 |        "    <tr>\n",
164 |        "      <th>7</th>\n",
165 |        "      <td>EURGBP=X</td>\n",
166 |        "      <td>EUR/GBP</td>\n",
167 |        "      <td>0.8570</td>\n",
168 |        "      <td>-0.0015</td>\n",
169 |        "      <td>-0.18%</td>\n",
170 |        "      <td>NaN</td>\n",
171 |        "      <td>NaN</td>\n",
172 |        "      <td>-0.18</td>\n",
173 |        "    </tr>\n",
174 |        "    <tr>\n",
175 |        "      <th>8</th>\n",
176 |        "      <td>EURCAD=X</td>\n",
177 |        "      <td>EUR/CAD</td>\n",
178 |        "      <td>1.4345</td>\n",
179 |        "      <td>-0.0003</td>\n",
180 |        "      <td>-0.02%</td>\n",
181 |        "      <td>NaN</td>\n",
182 |        "      <td>NaN</td>\n",
183 |        "      <td>-0.02</td>\n",
184 |        "    </tr>\n",
185 |        "    <tr>\n",
186 |        "      <th>9</th>\n",
187 |        "      <td>EURSEK=X</td>\n",
188 |        "      <td>EUR/SEK</td>\n",
189 |        "      <td>10.2527</td>\n",
190 |        "      <td>0.0000</td>\n",
191 |        "      <td>+0.00%</td>\n",
192 |        "      <td>NaN</td>\n",
193 |        "      <td>NaN</td>\n",
194 |        "      <td>0.00</td>\n",
195 |        "    </tr>\n",
196 |        "    <tr>\n",
197 |        "      <th>10</th>\n",
198 |        "      <td>EURCHF=X</td>\n",
199 |        "      <td>EUR/CHF</td>\n",
200 |        "      <td>1.0433</td>\n",
201 |        "      <td>-0.0007</td>\n",
202 |        "      <td>-0.06%</td>\n",
203 |        "      <td>NaN</td>\n",
204 |        "      <td>NaN</td>\n",
205 |        "      <td>-0.06</td>\n",
206 |        "    </tr>\n",
207 |        "    <tr>\n",
208 |        "      <th>11</th>\n",
209 |        "      <td>EURHUF=X</td>\n",
210 |        "      <td>EUR/HUF</td>\n",
211 |        "      <td>365.1900</td>\n",
212 |        "      <td>0.3600</td>\n",
213 |        "      <td>+0.10%</td>\n",
214 |        "      <td>NaN</td>\n",
215 |        "      <td>NaN</td>\n",
216 |        "      <td>0.10</td>\n",
217 |        "    </tr>\n",
218 |        "    <tr>\n",
219 |        "      <th>13</th>\n",
220 |        "      <td>CNY=X</td>\n",
221 |        "      <td>USD/CNY</td>\n",
222 |        "      <td>6.3465</td>\n",
223 |        "      <td>0.0023</td>\n",
224 |        "      <td>+0.04%</td>\n",
225 |        "      <td>NaN</td>\n",
226 |        "      <td>NaN</td>\n",
227 |        "      <td>0.04</td>\n",
228 |        "    </tr>\n",
229 |        "    <tr>\n",
230 |        "      <th>14</th>\n",
231 |        "      <td>HKD=X</td>\n",
232 |        "      <td>USD/HKD</td>\n",
233 |        "      <td>7.7971</td>\n",
234 |        "      <td>0.0007</td>\n",
235 |        "      <td>+0.01%</td>\n",
236 |        "      <td>NaN</td>\n",
237 |        "      <td>NaN</td>\n",
238 |        "      <td>0.01</td>\n",
239 |        "    </tr>\n",
240 |        "    <tr>\n",
241 |        "      <th>15</th>\n",
242 |        "      <td>SGD=X</td>\n",
243 |        "      <td>USD/SGD</td>\n",
244 |        "      <td>1.3645</td>\n",
245 |        "      <td>0.0030</td>\n",
246 |        "      <td>+0.22%</td>\n",
247 |        "      <td>NaN</td>\n",
248 |        "      <td>NaN</td>\n",
249 |        "      <td>0.22</td>\n",
250 |        "    </tr>\n",
251 |        "    <tr>\n",
252 |        "      <th>16</th>\n",
253 |        "      <td>INR=X</td>\n",
254 |        "      <td>USD/INR</td>\n",
255 |        "      <td>75.5500</td>\n",
256 |        "      <td>0.1950</td>\n",
257 |        "      <td>+0.26%</td>\n",
258 |        "      <td>NaN</td>\n",
259 |        "      <td>NaN</td>\n",
260 |        "      <td>0.26</td>\n",
261 |        "    </tr>\n",
262 |        "    <tr>\n",
263 |        "      <th>17</th>\n",
264 |        "      <td>MXN=X</td>\n",
265 |        "      <td>USD/MXN</td>\n",
266 |        "      <td>20.9590</td>\n",
267 |        "      <td>0.0380</td>\n",
268 |        "      <td>+0.18%</td>\n",
269 |        "      <td>NaN</td>\n",
270 |        "      <td>NaN</td>\n",
271 |        "      <td>0.18</td>\n",
272 |        "    </tr>\n",
273 |        "    <tr>\n",
274 |        "      <th>18</th>\n",
275 |        "      <td>PHP=X</td>\n",
276 |        "      <td>USD/PHP</td>\n",
277 |        "      <td>50.3930</td>\n",
278 |        "      <td>0.1730</td>\n",
279 |        "      <td>+0.34%</td>\n",
280 |        "      <td>NaN</td>\n",
281 |        "      <td>NaN</td>\n",
282 |        "      <td>0.34</td>\n",
283 |        "    </tr>\n",
284 |        "    <tr>\n",
285 |        "      <th>19</th>\n",
286 |        "      <td>IDR=X</td>\n",
287 |        "      <td>USD/IDR</td>\n",
288 |        "      <td>14345.0000</td>\n",
289 |        "      <td>0.0000</td>\n",
290 |        "      <td>0.00%</td>\n",
291 |        "      <td>NaN</td>\n",
292 |        "      <td>NaN</td>\n",
293 |        "      <td>0.00</td>\n",
294 |        "    </tr>\n",
295 |        "    <tr>\n",
296 |        "      <th>20</th>\n",
297 |        "      <td>THB=X</td>\n",
298 |        "      <td>USD/THB</td>\n",
299 |        "      <td>33.4590</td>\n",
300 |        "      <td>0.1150</td>\n",
301 |        "      <td>+0.34%</td>\n",
302 |        "      <td>NaN</td>\n",
303 |        "      <td>NaN</td>\n",
304 |        "      <td>0.34</td>\n",
305 |        "    </tr>\n",
306 |        "    <tr>\n",
307 |        "      <th>21</th>\n",
308 |        "      <td>MYR=X</td>\n",
309 |        "      <td>USD/MYR</td>\n",
310 |        "      <td>4.2150</td>\n",
311 |        "      <td>-0.0080</td>\n",
312 |        "      <td>-0.19%</td>\n",
313 |        "      <td>NaN</td>\n",
314 |        "      <td>NaN</td>\n",
315 |        "      <td>-0.19</td>\n",
316 |        "    </tr>\n",
317 |        "    <tr>\n",
318 |        "      <th>22</th>\n",
319 |        "      <td>ZAR=X</td>\n",
320 |        "      <td>USD/ZAR</td>\n",
321 |        "      <td>15.7678</td>\n",
322 |        "      <td>0.0545</td>\n",
323 |        "      <td>+0.35%</td>\n",
324 |        "      <td>NaN</td>\n",
325 |        "      <td>NaN</td>\n",
326 |        "      <td>0.35</td>\n",
327 |        "    </tr>\n",
328 |        "    <tr>\n",
329 |        "      <th>23</th>\n",
330 |        "      <td>RUB=X</td>\n",
331 |        "      <td>USD/RUB</td>\n",
332 |        "      <td>73.6453</td>\n",
333 |        "      <td>0.1587</td>\n",
334 |        "      <td>+0.22%</td>\n",
335 |        "      <td>NaN</td>\n",
336 |        "      <td>NaN</td>\n",
337 |        "      <td>0.22</td>\n",
338 |        "    </tr>\n",
339 |        "  </tbody>\n",
340 |        "</table>\n",
341 |        "</div>"
342 |       ],
343 |       "text/plain": [
344 |        "      Symbol     Name  Last Price  Change % Change  52 Week Range  Day Chart  \\\n",
345 |        "0   EURUSD=X  EUR/USD      1.1322 -0.0022   -0.19%            NaN        NaN   \n",
346 |        "1      JPY=X  USD/JPY    113.5700 -0.1670   -0.15%            NaN        NaN   \n",
347 |        "2   GBPUSD=X  GBP/USD      1.3207  0.0001   +0.00%            NaN        NaN   \n",
348 |        "3   AUDUSD=X  AUD/USD      0.7165 -0.0004   -0.06%            NaN        NaN   \n",
349 |        "4   NZDUSD=X  NZD/USD      0.6809  0.0001   +0.01%            NaN        NaN   \n",
350 |        "5   EURJPY=X  EUR/JPY    128.5450 -0.4420   -0.34%            NaN        NaN   \n",
351 |        "6   GBPJPY=X  GBP/JPY    149.9880 -0.1670   -0.11%            NaN        NaN   \n",
352 |        "7   EURGBP=X  EUR/GBP      0.8570 -0.0015   -0.18%            NaN        NaN   \n",
353 |        "8   EURCAD=X  EUR/CAD      1.4345 -0.0003   -0.02%            NaN        NaN   \n",
354 |        "9   EURSEK=X  EUR/SEK     10.2527  0.0000   +0.00%            NaN        NaN   \n",
355 |        "10  EURCHF=X  EUR/CHF      1.0433 -0.0007   -0.06%            NaN        NaN   \n",
356 |        "11  EURHUF=X  EUR/HUF    365.1900  0.3600   +0.10%            NaN        NaN   \n",
357 |        "13     CNY=X  USD/CNY      6.3465  0.0023   +0.04%            NaN        NaN   \n",
358 |        "14     HKD=X  USD/HKD      7.7971  0.0007   +0.01%            NaN        NaN   \n",
359 |        "15     SGD=X  USD/SGD      1.3645  0.0030   +0.22%            NaN        NaN   \n",
360 |        "16     INR=X  USD/INR     75.5500  0.1950   +0.26%            NaN        NaN   \n",
361 |        "17     MXN=X  USD/MXN     20.9590  0.0380   +0.18%            NaN        NaN   \n",
362 |        "18     PHP=X  USD/PHP     50.3930  0.1730   +0.34%            NaN        NaN   \n",
363 |        "19     IDR=X  USD/IDR  14345.0000  0.0000    0.00%            NaN        NaN   \n",
364 |        "20     THB=X  USD/THB     33.4590  0.1150   +0.34%            NaN        NaN   \n",
365 |        "21     MYR=X  USD/MYR      4.2150 -0.0080   -0.19%            NaN        NaN   \n",
366 |        "22     ZAR=X  USD/ZAR     15.7678  0.0545   +0.35%            NaN        NaN   \n",
367 |        "23     RUB=X  USD/RUB     73.6453  0.1587   +0.22%            NaN        NaN   \n",
368 |        "\n",
369 |        "    pct_change  \n",
370 |        "0        -0.19  \n",
371 |        "1        -0.15  \n",
372 |        "2         0.00  \n",
373 |        "3        -0.06  \n",
374 |        "4         0.01  \n",
375 |        "5        -0.34  \n",
376 |        "6        -0.11  \n",
377 |        "7        -0.18  \n",
378 |        "8        -0.02  \n",
379 |        "9         0.00  \n",
380 |        "10       -0.06  \n",
381 |        "11        0.10  \n",
382 |        "13        0.04  \n",
383 |        "14        0.01  \n",
384 |        "15        0.22  \n",
385 |        "16        0.26  \n",
386 |        "17        0.18  \n",
387 |        "18        0.34  \n",
388 |        "19        0.00  \n",
389 |        "20        0.34  \n",
390 |        "21       -0.19  \n",
391 |        "22        0.35  \n",
392 |        "23        0.22  "
393 |       ]
394 |      },
395 |      "execution_count": 1,
396 |      "metadata": {},
397 |      "output_type": "execute_result"
398 |     }
399 |    ],
400 |    "source": [
401 |     "import requests\n",
402 |     "import pandas as pd\n",
403 |     "from datetime import datetime\n",
404 |     "\n",
405 |     "datetime_now = datetime.now()\n",
406 |     "full_list_url='https://finance.yahoo.com/currencies'\n",
407 |     "header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n",
408 |     "full_list_page = requests.get(full_list_url, headers=header)\n",
409 |     "df = pd.read_html(full_list_page.text)[0].drop_duplicates()\n",
410 |     "df['pct_change'] = df['% Change'].str.slice(stop=-1).astype(float)\n",
411 |     "df"
412 |    ]
413 |   },
414 |   {
415 |    "cell_type": "code",
416 |    "execution_count": 2,
417 |    "id": "b948ed27-c050-461c-bb64-d659db90cb9d",
418 |    "metadata": {},
419 |    "outputs": [
420 |     {
421 |      "data": {
422 |       "text/html": [
423 |        "<div>\n",
424 |        "<style scoped>\n",
425 |        "    .dataframe tbody tr th:only-of-type {\n",
426 |        "        vertical-align: middle;\n",
427 |        "    }\n",
428 |        "\n",
429 |        "    .dataframe tbody tr th {\n",
430 |        "        vertical-align: top;\n",
431 |        "    }\n",
432 |        "\n",
433 |        "    .dataframe thead th {\n",
434 |        "        text-align: right;\n",
435 |        "    }\n",
436 |        "</style>\n",
437 |        "<table border=\"1\" class=\"dataframe\">\n",
438 |        "  <thead>\n",
439 |        "    <tr style=\"text-align: right;\">\n",
440 |        "      <th></th>\n",
441 |        "      <th>Name</th>\n",
442 |        "      <th>Last Price</th>\n",
443 |        "      <th>Change</th>\n",
444 |        "      <th>% Change</th>\n",
445 |        "    </tr>\n",
446 |        "  </thead>\n",
447 |        "  <tbody>\n",
448 |        "    <tr>\n",
449 |        "      <th>0</th>\n",
450 |        "      <td>USD/ZAR</td>\n",
451 |        "      <td>15.7678</td>\n",
452 |        "      <td>0.0545</td>\n",
453 |        "      <td>+0.35%</td>\n",
454 |        "    </tr>\n",
455 |        "    <tr>\n",
456 |        "      <th>1</th>\n",
457 |        "      <td>USD/THB</td>\n",
458 |        "      <td>33.4590</td>\n",
459 |        "      <td>0.1150</td>\n",
460 |        "      <td>+0.34%</td>\n",
461 |        "    </tr>\n",
462 |        "    <tr>\n",
463 |        "      <th>2</th>\n",
464 |        "      <td>USD/PHP</td>\n",
465 |        "      <td>50.3930</td>\n",
466 |        "      <td>0.1730</td>\n",
467 |        "      <td>+0.34%</td>\n",
468 |        "    </tr>\n",
469 |        "    <tr>\n",
470 |        "      <th>3</th>\n",
471 |        "      <td>USD/INR</td>\n",
472 |        "      <td>75.5500</td>\n",
473 |        "      <td>0.1950</td>\n",
474 |        "      <td>+0.26%</td>\n",
475 |        "    </tr>\n",
476 |        "    <tr>\n",
477 |        "      <th>4</th>\n",
478 |        "      <td>USD/RUB</td>\n",
479 |        "      <td>73.6453</td>\n",
480 |        "      <td>0.1587</td>\n",
481 |        "      <td>+0.22%</td>\n",
482 |        "    </tr>\n",
483 |        "  </tbody>\n",
484 |        "</table>\n",
485 |        "</div>"
486 |       ],
487 |       "text/plain": [
488 |        "      Name  Last Price  Change % Change\n",
489 |        "0  USD/ZAR     15.7678  0.0545   +0.35%\n",
490 |        "1  USD/THB     33.4590  0.1150   +0.34%\n",
491 |        "2  USD/PHP     50.3930  0.1730   +0.34%\n",
492 |        "3  USD/INR     75.5500  0.1950   +0.26%\n",
493 |        "4  USD/RUB     73.6453  0.1587   +0.22%"
494 |       ]
495 |      },
496 |      "execution_count": 2,
497 |      "metadata": {},
498 |      "output_type": "execute_result"
499 |     }
500 |    ],
501 |    "source": [
502 |     "top_df = df.sort_values(['pct_change'], ascending=False).reset_index(drop=True)[:5]\n",
503 |     "top_df = top_df[['Name', 'Last Price', 'Change', '% Change']]\n",
504 |     "top_df"
505 |    ]
506 |   },
507 |   {
508 |    "cell_type": "code",
509 |    "execution_count": 3,
510 |    "id": "6d14bde1-f903-4084-8c2a-3c4ac15bc7cc",
511 |    "metadata": {},
512 |    "outputs": [
513 |     {
514 |      "data": {
515 |       "text/html": [
516 |        "<div>\n",
517 |        "<style scoped>\n",
518 |        "    .dataframe tbody tr th:only-of-type {\n",
519 |        "        vertical-align: middle;\n",
520 |        "    }\n",
521 |        "\n",
522 |        "    .dataframe tbody tr th {\n",
523 |        "        vertical-align: top;\n",
524 |        "    }\n",
525 |        "\n",
526 |        "    .dataframe thead th {\n",
527 |        "        text-align: right;\n",
528 |        "    }\n",
529 |        "</style>\n",
530 |        "<table border=\"1\" class=\"dataframe\">\n",
531 |        "  <thead>\n",
532 |        "    <tr style=\"text-align: right;\">\n",
533 |        "      <th></th>\n",
534 |        "      <th>Name</th>\n",
535 |        "      <th>Last Price</th>\n",
536 |        "      <th>Change</th>\n",
537 |        "      <th>% Change</th>\n",
538 |        "    </tr>\n",
539 |        "  </thead>\n",
540 |        "  <tbody>\n",
541 |        "    <tr>\n",
542 |        "      <th>0</th>\n",
543 |        "      <td>EUR/JPY</td>\n",
544 |        "      <td>128.5450</td>\n",
545 |        "      <td>-0.4420</td>\n",
546 |        "      <td>-0.34%</td>\n",
547 |        "    </tr>\n",
548 |        "    <tr>\n",
549 |        "      <th>1</th>\n",
550 |        "      <td>EUR/USD</td>\n",
551 |        "      <td>1.1322</td>\n",
552 |        "      <td>-0.0022</td>\n",
553 |        "      <td>-0.19%</td>\n",
554 |        "    </tr>\n",
555 |        "    <tr>\n",
556 |        "      <th>2</th>\n",
557 |        "      <td>USD/MYR</td>\n",
558 |        "      <td>4.2150</td>\n",
559 |        "      <td>-0.0080</td>\n",
560 |        "      <td>-0.19%</td>\n",
561 |        "    </tr>\n",
562 |        "    <tr>\n",
563 |        "      <th>3</th>\n",
564 |        "      <td>EUR/GBP</td>\n",
565 |        "      <td>0.8570</td>\n",
566 |        "      <td>-0.0015</td>\n",
567 |        "      <td>-0.18%</td>\n",
568 |        "    </tr>\n",
569 |        "    <tr>\n",
570 |        "      <th>4</th>\n",
571 |        "      <td>USD/JPY</td>\n",
572 |        "      <td>113.5700</td>\n",
573 |        "      <td>-0.1670</td>\n",
574 |        "      <td>-0.15%</td>\n",
575 |        "    </tr>\n",
576 |        "  </tbody>\n",
577 |        "</table>\n",
578 |        "</div>"
579 |       ],
580 |       "text/plain": [
581 |        "      Name  Last Price  Change % Change\n",
582 |        "0  EUR/JPY    128.5450 -0.4420   -0.34%\n",
583 |        "1  EUR/USD      1.1322 -0.0022   -0.19%\n",
584 |        "2  USD/MYR      4.2150 -0.0080   -0.19%\n",
585 |        "3  EUR/GBP      0.8570 -0.0015   -0.18%\n",
586 |        "4  USD/JPY    113.5700 -0.1670   -0.15%"
587 |       ]
588 |      },
589 |      "execution_count": 3,
590 |      "metadata": {},
591 |      "output_type": "execute_result"
592 |     }
593 |    ],
594 |    "source": [
595 |     "bottom_df = df.sort_values(['pct_change'], ascending=True).reset_index(drop=True)[:5]\n",
596 |     "bottom_df = bottom_df[['Name', 'Last Price', 'Change', '% Change']]\n",
597 |     "bottom_df"
598 |    ]
599 |   },
600 |   {
601 |    "cell_type": "code",
602 |    "execution_count": 4,
603 |    "id": "3518fc9c-d5fc-408d-b099-be252c70e10e",
604 |    "metadata": {},
605 |    "outputs": [
606 |     {
607 |      "data": {
608 |       "image/png": 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\n",
609 |       "text/plain": [
610 |        "<Figure size 864x432 with 1 Axes>"
611 |       ]
612 |      },
613 |      "metadata": {
614 |       "needs_background": "dark"
615 |      },
616 |      "output_type": "display_data"
617 |     },
618 |     {
619 |      "data": {
620 |       "image/png": 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\n",
621 |       "text/plain": [
622 |        "<Figure size 864x432 with 1 Axes>"
623 |       ]
624 |      },
625 |      "metadata": {
626 |       "needs_background": "dark"
627 |      },
628 |      "output_type": "display_data"
629 |     }
630 |    ],
631 |    "source": [
632 |     "import json\n",
633 |     "import matplotlib.pyplot as plt\n",
634 |     "\n",
635 |     "top_name = top_df['Name'][0].replace('/', '')\n",
636 |     "bottom_name = bottom_df['Name'][0].replace('/', '')\n",
637 |     "\n",
638 |     "for idx in range(2):\n",
639 |     "    \n",
640 |     "    name = [top_name, bottom_name][idx]\n",
641 |     "    file_path = ['top.png', 'bottom.png']\n",
642 |     "    \n",
643 |     "    url = 'https://query1.finance.yahoo.com/v8/finance/chart/' + name + '=X?region=US&lang=en-US&includePrePost=false&interval=30m&useYfid=true&range=1mo&corsDomain=finance.yahoo.com&.tsrc=finance'\n",
644 |     "    header = {'User-Agent': 'Mozilla/5.0 (Macintosh; Intel Mac OS X 10_11_5) AppleWebKit/537.36 (KHTML, like Gecko) Chrome/50.0.2661.102 Safari/537.36'}\n",
645 |     "    page = requests.get(url, headers=header)\n",
646 |     "    temp_json = json.loads(page.text)\n",
647 |     "    price_list = temp_json['chart']['result'][0]['indicators']['quote'][0]['close']\n",
648 |     "    price_list = [price for price in price_list if price != None]\n",
649 |     "    fig, ax = plt.subplots(figsize=(12, 6))\n",
650 |     "    ax.plot(price_list, color='#43B7A4')\n",
651 |     "    ax.set_xticks([])\n",
652 |     "    ax.tick_params(axis='y', colors='#43B7A4', labelsize=20)\n",
653 |     "    for axis in ['top','bottom','left','right']:\n",
654 |     "        ax.spines[axis].set_color('#43B7A4')\n",
655 |     "        ax.spines[axis].set_linewidth(4)\n",
656 |     "    plt.savefig(file_path[idx], transparent=True)"
657 |    ]
658 |   },
659 |   {
660 |    "cell_type": "markdown",
661 |    "id": "3ca4ce29-3790-4226-841a-ad2e44b0d0a6",
662 |    "metadata": {},
663 |    "source": [
664 |     "# Step 2 - PowerPoint Updating"
665 |    ]
666 |   },
667 |   {
668 |    "cell_type": "code",
669 |    "execution_count": 5,
670 |    "id": "54a8a21c-419f-4384-b36b-59ce112294be",
671 |    "metadata": {},
672 |    "outputs": [],
673 |    "source": [
674 |     "from pptx import Presentation\n",
675 |     "from pptx.util import Inches\n",
676 |     "\n",
677 |     "# Open the PPT\n",
678 |     "currencies_ppt = Presentation('Currencies.pptx')\n",
679 |     "\n",
680 |     "# Select the slide to be editted\n",
681 |     "slide = currencies_ppt.slides[0]\n",
682 |     "\n",
683 |     "# Remove the old figures\n",
684 |     "shapes = slide.shapes\n",
685 |     "for shape in shapes:\n",
686 |     "    #print(shape.shape_type)\n",
687 |     "    if shape.shape_type == 13: # 13 = PICTURE\n",
688 |     "        shapes.element.remove(shape.element)\n",
689 |     "\n",
690 |     "# Add the new figures\n",
691 |     "top_img_path = 'top.png'\n",
692 |     "bottom_img_path = 'bottom.png'\n",
693 |     "top_pic = slide.shapes.add_picture(top_img_path, Inches(0.40), Inches(4.85), width=Inches(5.30))\n",
694 |     "bottom_pic = slide.shapes.add_picture(bottom_img_path, Inches(5.25), Inches(4.85), width=Inches(5.30))\n",
695 |     "\n",
696 |     "# Send the figures to the back\n",
697 |     "ref_element = slide.shapes[0]._element\n",
698 |     "ref_element.addprevious(top_pic._element)\n",
699 |     "ref_element.addprevious(bottom_pic._element)\n",
700 |     "\n",
701 |     "# Separate text box and table\n",
702 |     "shapes = slide.shapes\n",
703 |     "text_box_list = []\n",
704 |     "auto_shape_list = []\n",
705 |     "table_list = []\n",
706 |     "for shape_idx in range(len(shapes)):\n",
707 |     "    shape = shapes[shape_idx]\n",
708 |     "    if shape.shape_type == 17: # TEXT_BOX\n",
709 |     "        text_box_list.append(shape_idx)\n",
710 |     "    if shape.shape_type == 1: # AUTO_SHAPE\n",
711 |     "        auto_shape_list.append(shape_idx)\n",
712 |     "    if shape.shape_type == 19: # TABLE\n",
713 |     "        table_list.append(shape_idx)\n",
714 |     "\n",
715 |     "# Last update date shape index\n",
716 |     "last_update_date_textbox_height = max([shapes[shape_idx].height for shape_idx in text_box_list])\n",
717 |     "last_update_date_idx = [shape_idx for shape_idx in text_box_list if shapes[shape_idx].height == last_update_date_textbox_height][0]\n",
718 |     "\n",
719 |     "# Top 5 figure label shape index\n",
720 |     "top_label_left = min([shapes[shape_idx].left for shape_idx in auto_shape_list])\n",
721 |     "top_label_idx = [shape_idx for shape_idx in auto_shape_list if shapes[shape_idx].left == top_label_left][0]\n",
722 |     "auto_shape_list.remove(top_label_idx)\n",
723 |     "\n",
724 |     "# Bottom 5 figure label shape index\n",
725 |     "bottom_label_idx = auto_shape_list[0]\n",
726 |     "\n",
727 |     "# Top 5 table shape index\n",
728 |     "top_table_left = min([shapes[shape_idx].left for shape_idx in table_list])\n",
729 |     "top_table_idx = [shape_idx for shape_idx in table_list if shapes[shape_idx].left == top_table_left][0]\n",
730 |     "table_list.remove(top_table_idx)\n",
731 |     "\n",
732 |     "# Bottom 5 table shape index\n",
733 |     "bottom_table_idx = table_list[0]\n",
734 |     "\n",
735 |     "# Update last update date\n",
736 |     "paragraph = shapes[last_update_date_idx].text_frame.paragraphs[0]\n",
737 |     "paragraph.runs[4].text = datetime_now.strftime(\"%#d %b %Y %H:%M\")\n",
738 |     "\n",
739 |     "# Update top 5 figure label\n",
740 |     "paragraph = shapes[top_label_idx].text_frame.paragraphs[0]\n",
741 |     "paragraph.runs[0].text = top_df['Name'][0].replace('/', ' / ')\n",
742 |     "\n",
743 |     "# Update bottom 5 figure label\n",
744 |     "paragraph = shapes[bottom_label_idx].text_frame.paragraphs[0]\n",
745 |     "paragraph.runs[0].text = bottom_df['Name'][0].replace('/', ' / ')\n",
746 |     "\n",
747 |     "# Update top table\n",
748 |     "top_table = shapes[top_table_idx].table\n",
749 |     "for i in range(5):\n",
750 |     "    for j in range(4):\n",
751 |     "        cell = top_table.cell(i+1, j)\n",
752 |     "        paragraph = cell.text_frame.paragraphs[0]\n",
753 |     "        run = paragraph.runs[0]\n",
754 |     "        run.text = str(top_df.iloc[i, j])\n",
755 |     "\n",
756 |     "# Update bottom table\n",
757 |     "bottom_table = shapes[bottom_table_idx].table\n",
758 |     "for i in range(5):\n",
759 |     "    for j in range(4):\n",
760 |     "        cell = bottom_table.cell(i+1, j)\n",
761 |     "        paragraph = cell.text_frame.paragraphs[0]\n",
762 |     "        run = paragraph.runs[0]\n",
763 |     "        run.text = str(bottom_df.iloc[i, j])\n",
764 |     "        \n",
765 |     "# Save the PPT\n",
766 |     "currencies_ppt.save('New_Currencies.pptx')"
767 |    ]
768 |   },
769 |   {
770 |    "cell_type": "markdown",
771 |    "id": "47616b5b-7c8d-49d3-8fd7-44742bd18df2",
772 |    "metadata": {},
773 |    "source": [
774 |     "# Step 3 - Export to Different Formats"
775 |    ]
776 |   },
777 |   {
778 |    "cell_type": "code",
779 |    "execution_count": 6,
780 |    "id": "f99c1125-2c34-426a-8279-ecc5b80e5348",
781 |    "metadata": {},
782 |    "outputs": [
783 |     {
784 |      "data": {
785 |       "text/plain": [
786 |        "0"
787 |       ]
788 |      },
789 |      "execution_count": 6,
790 |      "metadata": {},
791 |      "output_type": "execute_result"
792 |     }
793 |    ],
794 |    "source": [
795 |     "import win32com.client\n",
796 |     "import os\n",
797 |     "\n",
798 |     "#Open the PPT\n",
799 |     "ppt_file_path = os.getcwd() + '\\\\New_Currencies.pptx'\n",
800 |     "powerpoint = win32com.client.Dispatch('Powerpoint.Application')\n",
801 |     "deck = powerpoint.Presentations.Open(ppt_file_path)\n",
802 |     "\n",
803 |     "# Save the PNG\n",
804 |     "img_file_path = os.getcwd() + '\\\\Currencies.png'\n",
805 |     "powerpoint.ActivePresentation.Slides[0].Export(img_file_path, 'PNG')\n",
806 |     "\n",
807 |     "# Save the PDF\n",
808 |     "pdf_file_path = os.getcwd() + '\\\\Currencies.pdf'\n",
809 |     "deck.SaveAs(pdf_file_path, 32)\n",
810 |     "\n",
811 |     "# Quit the PPT\n",
812 |     "deck.Close()\n",
813 |     "powerpoint.Quit()\n",
814 |     "os.system('taskkill /F /IM POWERPNT.EXE')"
815 |    ]
816 |   }
817 |  ],
818 |  "metadata": {
819 |   "kernelspec": {
820 |    "display_name": "Python 3",
821 |    "language": "python",
822 |    "name": "python3"
823 |   },
824 |   "language_info": {
825 |    "codemirror_mode": {
826 |     "name": "ipython",
827 |     "version": 3
828 |    },
829 |    "file_extension": ".py",
830 |    "mimetype": "text/x-python",
831 |    "name": "python",
832 |    "nbconvert_exporter": "python",
833 |    "pygments_lexer": "ipython3",
834 |    "version": "3.9.4"
835 |   }
836 |  },
837 |  "nbformat": 4,
838 |  "nbformat_minor": 5
839 | }
840 | 


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