Format: application/octet-stream
1 {
2 "cells": [
3 {
4 "cell_type": "code",
5 "execution_count": 1,
6 "id": "00df6599",
7 "metadata": {},
8 "outputs": [],
9 "source": [
10 "import pandas as pd"
11 ]
12 },
13 {
14 "cell_type": "code",
15 "execution_count": 2,
16 "id": "e1ca1a42",
17 "metadata": {},
18 "outputs": [],
19 "source": [
20 "df= pd.read_csv('question 2.csv')"
21 ]
22 },
23 {
24 "cell_type": "code",
25 "execution_count": 3,
26 "id": "38250004",
27 "metadata": {},
28 "outputs": [
29 {
30 "name": "stdout",
31 "output_type": "stream",
32 "text": [
33 "<class 'pandas.core.frame.DataFrame'>\n",
34 "RangeIndex: 943 entries, 0 to 942\n",
35 "Data columns (total 5 columns):\n",
36 " # Column Non-Null Count Dtype \n",
37 "--- ------ -------------- ----- \n",
38 " 0 user_id 943 non-null int64 \n",
39 " 1 age 943 non-null int64 \n",
40 " 2 gender 943 non-null object\n",
41 " 3 occupation 943 non-null object\n",
42 " 4 zip_code 943 non-null object\n",
43 "dtypes: int64(2), object(3)\n",
44 "memory usage: 37.0+ KB\n"
45 ]
46 }
47 ],
48 "source": [
49 "df.info()"
50 ]
51 },
52 {
53 "cell_type": "code",
54 "execution_count": 4,
55 "id": "7bc6d6ee",
56 "metadata": {},
57 "outputs": [
58 {
59 "data": {
60 "text/html": [
61 "<div>\n",
62 "<style scoped>\n",
63 " .dataframe tbody tr th:only-of-type {\n",
64 " vertical-align: middle;\n",
65 " }\n",
66 "\n",
67 " .dataframe tbody tr th {\n",
68 " vertical-align: top;\n",
69 " }\n",
70 "\n",
71 " .dataframe thead th {\n",
72 " text-align: right;\n",
73 " }\n",
74 "</style>\n",
75 "<table border=\"1\" class=\"dataframe\">\n",
76 " <thead>\n",
77 " <tr style=\"text-align: right;\">\n",
78 " <th></th>\n",
79 " <th>user_id</th>\n",
80 " <th>age</th>\n",
81 " <th>gender</th>\n",
82 " <th>occupation</th>\n",
83 " <th>zip_code</th>\n",
84 " </tr>\n",
85 " </thead>\n",
86 " <tbody>\n",
87 " <tr>\n",
88 " <th>0</th>\n",
89 " <td>1</td>\n",
90 " <td>24</td>\n",
91 " <td>M</td>\n",
92 " <td>technician</td>\n",
93 " <td>85711</td>\n",
94 " </tr>\n",
95 " <tr>\n",
96 " <th>1</th>\n",
97 " <td>2</td>\n",
98 " <td>53</td>\n",
99 " <td>F</td>\n",
100 " <td>other</td>\n",
101 " <td>94043</td>\n",
102 " </tr>\n",
103 " <tr>\n",
104 " <th>2</th>\n",
105 " <td>3</td>\n",
106 " <td>23</td>\n",
107 " <td>M</td>\n",
108 " <td>writer</td>\n",
109 " <td>32067</td>\n",
110 " </tr>\n",
111 " <tr>\n",
112 " <th>3</th>\n",
113 " <td>4</td>\n",
114 " <td>24</td>\n",
115 " <td>M</td>\n",
116 " <td>technician</td>\n",
117 " <td>43537</td>\n",
118 " </tr>\n",
119 " <tr>\n",
120 " <th>4</th>\n",
121 " <td>5</td>\n",
122 " <td>33</td>\n",
123 " <td>F</td>\n",
124 " <td>other</td>\n",
125 " <td>15213</td>\n",
126 " </tr>\n",
127 " </tbody>\n",
128 "</table>\n",
129 "</div>"
130 ],
131 "text/plain": [
132 " user_id age gender occupation zip_code\n",
133 "0 1 24 M technician 85711\n",
134 "1 2 53 F other 94043\n",
135 "2 3 23 M writer 32067\n",
136 "3 4 24 M technician 43537\n",
137 "4 5 33 F other 15213"
138 ]
139 },
140 "execution_count": 4,
141 "metadata": {},
142 "output_type": "execute_result"
143 }
144 ],
145 "source": [
146 "df.head()"
147 ]
148 },
149 {
150 "cell_type": "code",
151 "execution_count": 5,
152 "id": "01afb3dc",
153 "metadata": {},
154 "outputs": [],
155 "source": [
156 "min_age=df.groupby(df.occupation).min().age\n",
157 "min_age.name= 'Min Age'"
158 ]
159 },
160 {
161 "cell_type": "code",
162 "execution_count": 6,
163 "id": "430ac10a",
164 "metadata": {},
165 "outputs": [],
166 "source": [
167 "max_age= df.groupby(df.occupation).max().age\n",
168 "max_age.name= 'Max Age'"
169 ]
170 },
171 {
172 "cell_type": "code",
173 "execution_count": 7,
174 "id": "f3f6d4b0",
175 "metadata": {},
176 "outputs": [],
177 "source": [
178 "result= pd.DataFrame([min_age,max_age], columns=['Min Age', 'Max Age'])"
179 ]
180 },
181 {
182 "cell_type": "code",
183 "execution_count": 8,
184 "id": "8eb468a8",
185 "metadata": {},
186 "outputs": [
187 {
188 "data": {
189 "text/html": [
190 "<div>\n",
191 "<style scoped>\n",
192 " .dataframe tbody tr th:only-of-type {\n",
193 " vertical-align: middle;\n",
194 " }\n",
195 "\n",
196 " .dataframe tbody tr th {\n",
197 " vertical-align: top;\n",
198 " }\n",
199 "\n",
200 " .dataframe thead th {\n",
201 " text-align: right;\n",
202 " }\n",
203 "</style>\n",
204 "<table border=\"1\" class=\"dataframe\">\n",
205 " <thead>\n",
206 " <tr style=\"text-align: right;\">\n",
207 " <th></th>\n",
208 " <th>Min Age</th>\n",
209 " <th>Max Age</th>\n",
210 " </tr>\n",
211 " <tr>\n",
212 " <th>occupation</th>\n",
213 " <th></th>\n",
214 " <th></th>\n",
215 " </tr>\n",
216 " </thead>\n",
217 " <tbody>\n",
218 " <tr>\n",
219 " <th>administrator</th>\n",
220 " <td>21</td>\n",
221 " <td>70</td>\n",
222 " </tr>\n",
223 " <tr>\n",
224 " <th>artist</th>\n",
225 " <td>19</td>\n",
226 " <td>48</td>\n",
227 " </tr>\n",
228 " <tr>\n",
229 " <th>doctor</th>\n",
230 " <td>28</td>\n",
231 " <td>64</td>\n",
232 " </tr>\n",
233 " <tr>\n",
234 " <th>educator</th>\n",
235 " <td>23</td>\n",
236 " <td>63</td>\n",
237 " </tr>\n",
238 " <tr>\n",
239 " <th>engineer</th>\n",
240 " <td>22</td>\n",
241 " <td>70</td>\n",
242 " </tr>\n",
243 " <tr>\n",
244 " <th>entertainment</th>\n",
245 " <td>15</td>\n",
246 " <td>50</td>\n",
247 " </tr>\n",
248 " <tr>\n",
249 " <th>executive</th>\n",
250 " <td>22</td>\n",
251 " <td>69</td>\n",
252 " </tr>\n",
253 " <tr>\n",
254 " <th>healthcare</th>\n",
255 " <td>22</td>\n",
256 " <td>62</td>\n",
257 " </tr>\n",
258 " <tr>\n",
259 " <th>homemaker</th>\n",
260 " <td>20</td>\n",
261 " <td>50</td>\n",
262 " </tr>\n",
263 " <tr>\n",
264 " <th>lawyer</th>\n",
265 " <td>21</td>\n",
266 " <td>53</td>\n",
267 " </tr>\n",
268 " <tr>\n",
269 " <th>librarian</th>\n",
270 " <td>23</td>\n",
271 " <td>69</td>\n",
272 " </tr>\n",
273 " <tr>\n",
274 " <th>marketing</th>\n",
275 " <td>24</td>\n",
276 " <td>55</td>\n",
277 " </tr>\n",
278 " <tr>\n",
279 " <th>none</th>\n",
280 " <td>11</td>\n",
281 " <td>55</td>\n",
282 " </tr>\n",
283 " <tr>\n",
284 " <th>other</th>\n",
285 " <td>13</td>\n",
286 " <td>64</td>\n",
287 " </tr>\n",
288 " <tr>\n",
289 " <th>programmer</th>\n",
290 " <td>20</td>\n",
291 " <td>63</td>\n",
292 " </tr>\n",
293 " <tr>\n",
294 " <th>retired</th>\n",
295 " <td>51</td>\n",
296 " <td>73</td>\n",
297 " </tr>\n",
298 " <tr>\n",
299 " <th>salesman</th>\n",
300 " <td>18</td>\n",
301 " <td>66</td>\n",
302 " </tr>\n",
303 " <tr>\n",
304 " <th>scientist</th>\n",
305 " <td>23</td>\n",
306 " <td>55</td>\n",
307 " </tr>\n",
308 " <tr>\n",
309 " <th>student</th>\n",
310 " <td>7</td>\n",
311 " <td>42</td>\n",
312 " </tr>\n",
313 " <tr>\n",
314 " <th>technician</th>\n",
315 " <td>21</td>\n",
316 " <td>55</td>\n",
317 " </tr>\n",
318 " <tr>\n",
319 " <th>writer</th>\n",
320 " <td>18</td>\n",
321 " <td>60</td>\n",
322 " </tr>\n",
323 " </tbody>\n",
324 "</table>\n",
325 "</div>"
326 ],
327 "text/plain": [
328 " Min Age Max Age\n",
329 "occupation \n",
330 "administrator 21 70\n",
331 "artist 19 48\n",
332 "doctor 28 64\n",
333 "educator 23 63\n",
334 "engineer 22 70\n",
335 "entertainment 15 50\n",
336 "executive 22 69\n",
337 "healthcare 22 62\n",
338 "homemaker 20 50\n",
339 "lawyer 21 53\n",
340 "librarian 23 69\n",
341 "marketing 24 55\n",
342 "none 11 55\n",
343 "other 13 64\n",
344 "programmer 20 63\n",
345 "retired 51 73\n",
346 "salesman 18 66\n",
347 "scientist 23 55\n",
348 "student 7 42\n",
349 "technician 21 55\n",
350 "writer 18 60"
351 ]
352 },
353 "execution_count": 8,
354 "metadata": {},
355 "output_type": "execute_result"
356 }
357 ],
358 "source": [
359 "df=pd.concat([min_age,max_age], axis=1)\n",
360 "df"
361 ]
362 },
363 {
364 "cell_type": "code",
365 "execution_count": 9,
366 "id": "040b2a87",
367 "metadata": {},
368 "outputs": [],
369 "source": [
370 "df.to_csv('answer2.csv')"
371 ]
372 },
373 {
374 "cell_type": "code",
375 "execution_count": null,
376 "id": "25520200",
377 "metadata": {},
378 "outputs": [],
379 "source": []
380 }
381 ],
382 "metadata": {
383 "kernelspec": {
384 "display_name": "Python 3",
385 "language": "python",
386 "name": "python3"
387 },
388 "language_info": {
389 "codemirror_mode": {
390 "name": "ipython",
391 "version": 3
392 },
393 "file_extension": ".py",
394 "mimetype": "text/x-python",
395 "name": "python",
396 "nbconvert_exporter": "python",
397 "pygments_lexer": "ipython3",
398 "version": "3.8.8"
399 }
400 },
401 "nbformat": 4,
402 "nbformat_minor": 5
403 }
404