├── Varimax.png
├── Scree-plot.png
├── Thank-you.png
├── LICENSE
├── README.md
└── Project-code.ipynb


/Varimax.png:
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https://raw.githubusercontent.com/Sagar-Darji/Personality-prediction/HEAD/Varimax.png


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/Scree-plot.png:
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https://raw.githubusercontent.com/Sagar-Darji/Personality-prediction/HEAD/Scree-plot.png


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/Thank-you.png:
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https://raw.githubusercontent.com/Sagar-Darji/Personality-prediction/HEAD/Thank-you.png


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/LICENSE:
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 1 | MIT License
 2 | 
 3 | Copyright (c) 2021 Sagar-Darji
 4 | 
 5 | Permission is hereby granted, free of charge, to any person obtaining a copy
 6 | of this software and associated documentation files (the "Software"), to deal
 7 | in the Software without restriction, including without limitation the rights
 8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
 9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 | 
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 | 
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 | 


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/README.md:
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   1 | # Personality-prediction
   2 | > From the given database `response.csv` Find out the personality traits using this personality prediction project. 
   3 | 
   4 | ## Let's get started
   5 | [![](https://img.shields.io/badge/author-@SagarDarji-blue.svg?style=flat)](https://www.linkedin.com/in/sagar-darji-7b7011165/)
   6 | 
   7 | The system will predict one's personality and their traits through basic survey.This system will help the human resource to select right candidate for desired job profile, which in turn provide expert workforce for the organization.
   8 | 
   9 | ### Applications in psychology:
  10 | Factor analysis has been used in the study of human intelligence and human personality as a method for comparing the outcomes of (hopefully) objective tests and to construct matrices to define correlations between these outcomes, as well as finding the factors for these results. The field of psychology that measures human intelligence using quantitative testing in this way is known as psychometrics (psycho=mental, metrics=measurement). 
  11 | 
  12 | ### Advantages:
  13 | - Offers a much more objective method of testing traits such as intelligence in humans
  14 | - Allows for a satisfactory comparison between the results of intelligence tests
  15 | - Provides support for theories that would be difficult to prove otherwise
  16 | 
  17 | ## Algorithm
  18 | ```python
  19 | Refine the Data
  20 | Prepare the Data
  21 | Choose the Factor
  22 |   variable
  23 |   correlation matrix
  24 |   using any method of factor analysis such as EFA
  25 |   Decide no. of factors
  26 |   factor loading of factors
  27 |   rotation of factor loadings
  28 |   provide appropriate no. of factors
  29 |   
  30 | ```
  31 | 
  32 | ## Now understand and implement the code 
  33 | > Import all libraries which we needed to perform this `python code`
  34 | ```python
  35 | #Librerias
  36 | import pandas as pd 
  37 | import numpy as np
  38 | import matplotlib.pyplot as plt
  39 | ```
  40 | Make Dataframe using `Pandas` and shaped it 
  41 | ```python
  42 | #Data
  43 | df = pd.read_csv("responses.csv")
  44 | df.shape
  45 | ```
  46 | > Out: (1010, 15)
  47 | 
  48 | In `response.csv` [1010 rows, 150 rows] 
  49 | > Which means this data collected by surveying 1010 individuals and there is 150 types of different prefrence & fields.
  50 | 
  51 | MUSIC PREFERENCES (19) 0:19
  52 | 
  53 | MOVIE PREFERENCES (12) 19:31
  54 | 
  55 | HOBBIES & INTERESTS (32) 31:63
  56 | 
  57 | PHOBIAS (10) 63:73
  58 | 
  59 | HEALTH HABITS (3) 73:76
  60 | 
  61 | PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS (57) 76:133
  62 | 
  63 | SPENDING HABITS (7) 133:140
  64 | 
  65 | DEMOGRAPHICS (10 ) 140:150
  66 | 
  67 | We will take only: PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS (57) `76:133`
  68 | 
  69 | 
  70 | ```python
  71 | df = df.iloc[:, 76:133]
  72 | df.head(5)
  73 | ```
  74 | > Out:
  75 | 
  76 | <table border="1" class="dataframe">
  77 |   <thead>
  78 |     <tr style="text-align: right;">
  79 |       <th></th>
  80 |       <th>Daily events</th>
  81 |       <th>Prioritising workload</th>
  82 |       <th>Writing notes</th>
  83 |       <th>Workaholism</th>
  84 |       <th>Thinking ahead</th>
  85 |       <th>Final judgement</th>
  86 |       <th>Reliability</th>
  87 |       <th>Keeping promises</th>
  88 |       <th>Loss of interest</th>
  89 |       <th>Friends versus money</th>
  90 |       <th>...</th>
  91 |       <th>Happiness in life</th>
  92 |       <th>Energy levels</th>
  93 |       <th>Small - big dogs</th>
  94 |       <th>Personality</th>
  95 |       <th>Finding lost valuables</th>
  96 |       <th>Getting up</th>
  97 |       <th>Interests or hobbies</th>
  98 |       <th>Parents' advice</th>
  99 |       <th>Questionnaires or polls</th>
 100 |       <th>Internet usage</th>
 101 |     </tr>
 102 |   </thead>
 103 |   <tbody>
 104 |     <tr>
 105 |       <td>0</td>
 106 |       <td>2.0</td>
 107 |       <td>2.0</td>
 108 |       <td>5.0</td>
 109 |       <td>4.0</td>
 110 |       <td>2.0</td>
 111 |       <td>5.0</td>
 112 |       <td>4.0</td>
 113 |       <td>4.0</td>
 114 |       <td>1.0</td>
 115 |       <td>3.0</td>
 116 |       <td>...</td>
 117 |       <td>4.0</td>
 118 |       <td>5.0</td>
 119 |       <td>1.0</td>
 120 |       <td>4.0</td>
 121 |       <td>3.0</td>
 122 |       <td>2.0</td>
 123 |       <td>3.0</td>
 124 |       <td>4.0</td>
 125 |       <td>3.0</td>
 126 |       <td>few hours a day</td>
 127 |     </tr>
 128 |     <tr>
 129 |       <td>1</td>
 130 |       <td>3.0</td>
 131 |       <td>2.0</td>
 132 |       <td>4.0</td>
 133 |       <td>5.0</td>
 134 |       <td>4.0</td>
 135 |       <td>1.0</td>
 136 |       <td>4.0</td>
 137 |       <td>4.0</td>
 138 |       <td>3.0</td>
 139 |       <td>4.0</td>
 140 |       <td>...</td>
 141 |       <td>4.0</td>
 142 |       <td>3.0</td>
 143 |       <td>5.0</td>
 144 |       <td>3.0</td>
 145 |       <td>4.0</td>
 146 |       <td>5.0</td>
 147 |       <td>3.0</td>
 148 |       <td>2.0</td>
 149 |       <td>3.0</td>
 150 |       <td>few hours a day</td>
 151 |     </tr>
 152 |     <tr>
 153 |       <td>2</td>
 154 |       <td>1.0</td>
 155 |       <td>2.0</td>
 156 |       <td>5.0</td>
 157 |       <td>3.0</td>
 158 |       <td>5.0</td>
 159 |       <td>3.0</td>
 160 |       <td>4.0</td>
 161 |       <td>5.0</td>
 162 |       <td>1.0</td>
 163 |       <td>5.0</td>
 164 |       <td>...</td>
 165 |       <td>4.0</td>
 166 |       <td>4.0</td>
 167 |       <td>3.0</td>
 168 |       <td>3.0</td>
 169 |       <td>3.0</td>
 170 |       <td>4.0</td>
 171 |       <td>5.0</td>
 172 |       <td>3.0</td>
 173 |       <td>1.0</td>
 174 |       <td>few hours a day</td>
 175 |     </tr>
 176 |     <tr>
 177 |       <td>3</td>
 178 |       <td>4.0</td>
 179 |       <td>4.0</td>
 180 |       <td>4.0</td>
 181 |       <td>5.0</td>
 182 |       <td>3.0</td>
 183 |       <td>1.0</td>
 184 |       <td>3.0</td>
 185 |       <td>4.0</td>
 186 |       <td>5.0</td>
 187 |       <td>2.0</td>
 188 |       <td>...</td>
 189 |       <td>2.0</td>
 190 |       <td>2.0</td>
 191 |       <td>1.0</td>
 192 |       <td>2.0</td>
 193 |       <td>1.0</td>
 194 |       <td>1.0</td>
 195 |       <td>NaN</td>
 196 |       <td>2.0</td>
 197 |       <td>4.0</td>
 198 |       <td>most of the day</td>
 199 |     </tr>
 200 |     <tr>
 201 |       <td>4</td>
 202 |       <td>3.0</td>
 203 |       <td>1.0</td>
 204 |       <td>2.0</td>
 205 |       <td>3.0</td>
 206 |       <td>5.0</td>
 207 |       <td>5.0</td>
 208 |       <td>5.0</td>
 209 |       <td>4.0</td>
 210 |       <td>2.0</td>
 211 |       <td>3.0</td>
 212 |       <td>...</td>
 213 |       <td>3.0</td>
 214 |       <td>5.0</td>
 215 |       <td>3.0</td>
 216 |       <td>3.0</td>
 217 |       <td>2.0</td>
 218 |       <td>4.0</td>
 219 |       <td>3.0</td>
 220 |       <td>3.0</td>
 221 |       <td>3.0</td>
 222 |       <td>few hours a day</td>
 223 |     </tr>
 224 |   </tbody>
 225 | </table>
 226 | <p>5 rows × 57 columns</p>
 227 | </div>
 228 | 
 229 | 
 230 | # 1. Prepare the Data
 231 | 
 232 | ```python
 233 | #Drop NAs
 234 | df = df.dropna()
 235 | #...............................................................................................
 236 | #Encode categorical data
 237 | from sklearn.preprocessing import LabelEncoder
 238 | 
 239 | df = df.apply(LabelEncoder().fit_transform)
 240 | df
 241 | ```
 242 | 
 243 | `dropna()` method will remove Null value from dataframe.
 244 | 
 245 | Why are we encoding the data?
 246 | > In order to analys data require all i/p & o/p variable to be nummeric. This means that if our data contains categorical dat, we must encode it to number before you can fit and evalute a model.
 247 | 
 248 | There is two type of encoding
 249 | 1. `Integer encoding`
 250 | > each unique label is mapped to an integer.
 251 | 2. `One hot encoding`
 252 | > It refers to splitting the column which contains numerical categorical data to many columns depending on the number of categories present in that column. Each column contains “0” or “1” corresponding to which column it has been placed.
 253 | 
 254 | | Before Encoding | After Encoding |
 255 | |--------|--------|
 256 | | Height | Height |
 257 | | Tall | 0 |
 258 | | Short | 1 |
 259 | | Medium | 2 |
 260 | | Medium | 2 |
 261 | | Short | 1 |
 262 | | Tall | 0 |
 263 | 
 264 | Here, We have used `One hot encoding`.
 265 | 
 266 | > Out:
 267 | 
 268 | <div>
 269 | <table border="1" class="dataframe">
 270 |   <thead>
 271 |     <tr style="text-align: right;">
 272 |       <th></th>
 273 |       <th>Daily events</th>
 274 |       <th>Prioritising workload</th>
 275 |       <th>Writing notes</th>
 276 |       <th>Workaholism</th>
 277 |       <th>Thinking ahead</th>
 278 |       <th>Final judgement</th>
 279 |       <th>Reliability</th>
 280 |       <th>Keeping promises</th>
 281 |       <th>Loss of interest</th>
 282 |       <th>Friends versus money</th>
 283 |       <th>...</th>
 284 |       <th>Happiness in life</th>
 285 |       <th>Energy levels</th>
 286 |       <th>Small - big dogs</th>
 287 |       <th>Personality</th>
 288 |       <th>Finding lost valuables</th>
 289 |       <th>Getting up</th>
 290 |       <th>Interests or hobbies</th>
 291 |       <th>Parents' advice</th>
 292 |       <th>Questionnaires or polls</th>
 293 |       <th>Internet usage</th>
 294 |     </tr>
 295 |   </thead>
 296 |   <tbody>
 297 |     <tr>
 298 |       <td>0</td>
 299 |       <td>1</td>
 300 |       <td>1</td>
 301 |       <td>4</td>
 302 |       <td>3</td>
 303 |       <td>1</td>
 304 |       <td>4</td>
 305 |       <td>3</td>
 306 |       <td>3</td>
 307 |       <td>0</td>
 308 |       <td>2</td>
 309 |       <td>...</td>
 310 |       <td>3</td>
 311 |       <td>4</td>
 312 |       <td>0</td>
 313 |       <td>3</td>
 314 |       <td>2</td>
 315 |       <td>1</td>
 316 |       <td>2</td>
 317 |       <td>3</td>
 318 |       <td>2</td>
 319 |       <td>0</td>
 320 |     </tr>
 321 |     <tr>
 322 |       <td>1</td>
 323 |       <td>2</td>
 324 |       <td>1</td>
 325 |       <td>3</td>
 326 |       <td>4</td>
 327 |       <td>3</td>
 328 |       <td>0</td>
 329 |       <td>3</td>
 330 |       <td>3</td>
 331 |       <td>2</td>
 332 |       <td>3</td>
 333 |       <td>...</td>
 334 |       <td>3</td>
 335 |       <td>2</td>
 336 |       <td>4</td>
 337 |       <td>2</td>
 338 |       <td>3</td>
 339 |       <td>4</td>
 340 |       <td>2</td>
 341 |       <td>1</td>
 342 |       <td>2</td>
 343 |       <td>0</td>
 344 |     </tr>
 345 |     <tr>
 346 |       <td>2</td>
 347 |       <td>0</td>
 348 |       <td>1</td>
 349 |       <td>4</td>
 350 |       <td>2</td>
 351 |       <td>4</td>
 352 |       <td>2</td>
 353 |       <td>3</td>
 354 |       <td>4</td>
 355 |       <td>0</td>
 356 |       <td>4</td>
 357 |       <td>...</td>
 358 |       <td>3</td>
 359 |       <td>3</td>
 360 |       <td>2</td>
 361 |       <td>2</td>
 362 |       <td>2</td>
 363 |       <td>3</td>
 364 |       <td>4</td>
 365 |       <td>2</td>
 366 |       <td>0</td>
 367 |       <td>0</td>
 368 |     </tr>
 369 |     <tr>
 370 |       <td>4</td>
 371 |       <td>2</td>
 372 |       <td>0</td>
 373 |       <td>1</td>
 374 |       <td>2</td>
 375 |       <td>4</td>
 376 |       <td>4</td>
 377 |       <td>4</td>
 378 |       <td>3</td>
 379 |       <td>1</td>
 380 |       <td>2</td>
 381 |       <td>...</td>
 382 |       <td>2</td>
 383 |       <td>4</td>
 384 |       <td>2</td>
 385 |       <td>2</td>
 386 |       <td>1</td>
 387 |       <td>3</td>
 388 |       <td>2</td>
 389 |       <td>2</td>
 390 |       <td>2</td>
 391 |       <td>0</td>
 392 |     </tr>
 393 |     <tr>
 394 |       <td>5</td>
 395 |       <td>1</td>
 396 |       <td>1</td>
 397 |       <td>2</td>
 398 |       <td>2</td>
 399 |       <td>2</td>
 400 |       <td>0</td>
 401 |       <td>2</td>
 402 |       <td>3</td>
 403 |       <td>2</td>
 404 |       <td>1</td>
 405 |       <td>...</td>
 406 |       <td>2</td>
 407 |       <td>3</td>
 408 |       <td>3</td>
 409 |       <td>2</td>
 410 |       <td>2</td>
 411 |       <td>2</td>
 412 |       <td>4</td>
 413 |       <td>2</td>
 414 |       <td>3</td>
 415 |       <td>0</td>
 416 |     </tr>
 417 |     <tr>
 418 |       <td>...</td>
 419 |       <td>...</td>
 420 |       <td>...</td>
 421 |       <td>...</td>
 422 |       <td>...</td>
 423 |       <td>...</td>
 424 |       <td>...</td>
 425 |       <td>...</td>
 426 |       <td>...</td>
 427 |       <td>...</td>
 428 |       <td>...</td>
 429 |       <td>...</td>
 430 |       <td>...</td>
 431 |       <td>...</td>
 432 |       <td>...</td>
 433 |       <td>...</td>
 434 |       <td>...</td>
 435 |       <td>...</td>
 436 |       <td>...</td>
 437 |       <td>...</td>
 438 |       <td>...</td>
 439 |       <td>...</td>
 440 |     </tr>
 441 |     <tr>
 442 |       <td>1005</td>
 443 |       <td>2</td>
 444 |       <td>1</td>
 445 |       <td>0</td>
 446 |       <td>3</td>
 447 |       <td>1</td>
 448 |       <td>2</td>
 449 |       <td>2</td>
 450 |       <td>2</td>
 451 |       <td>3</td>
 452 |       <td>3</td>
 453 |       <td>...</td>
 454 |       <td>3</td>
 455 |       <td>2</td>
 456 |       <td>2</td>
 457 |       <td>2</td>
 458 |       <td>3</td>
 459 |       <td>4</td>
 460 |       <td>3</td>
 461 |       <td>3</td>
 462 |       <td>2</td>
 463 |       <td>0</td>
 464 |     </tr>
 465 |     <tr>
 466 |       <td>1006</td>
 467 |       <td>0</td>
 468 |       <td>2</td>
 469 |       <td>0</td>
 470 |       <td>4</td>
 471 |       <td>4</td>
 472 |       <td>4</td>
 473 |       <td>4</td>
 474 |       <td>3</td>
 475 |       <td>0</td>
 476 |       <td>1</td>
 477 |       <td>...</td>
 478 |       <td>3</td>
 479 |       <td>3</td>
 480 |       <td>2</td>
 481 |       <td>4</td>
 482 |       <td>2</td>
 483 |       <td>0</td>
 484 |       <td>2</td>
 485 |       <td>3</td>
 486 |       <td>2</td>
 487 |       <td>1</td>
 488 |     </tr>
 489 |     <tr>
 490 |       <td>1007</td>
 491 |       <td>2</td>
 492 |       <td>0</td>
 493 |       <td>0</td>
 494 |       <td>0</td>
 495 |       <td>3</td>
 496 |       <td>0</td>
 497 |       <td>2</td>
 498 |       <td>4</td>
 499 |       <td>0</td>
 500 |       <td>3</td>
 501 |       <td>...</td>
 502 |       <td>2</td>
 503 |       <td>0</td>
 504 |       <td>2</td>
 505 |       <td>1</td>
 506 |       <td>2</td>
 507 |       <td>4</td>
 508 |       <td>0</td>
 509 |       <td>3</td>
 510 |       <td>4</td>
 511 |       <td>2</td>
 512 |     </tr>
 513 |     <tr>
 514 |       <td>1008</td>
 515 |       <td>2</td>
 516 |       <td>0</td>
 517 |       <td>4</td>
 518 |       <td>0</td>
 519 |       <td>2</td>
 520 |       <td>3</td>
 521 |       <td>3</td>
 522 |       <td>3</td>
 523 |       <td>4</td>
 524 |       <td>2</td>
 525 |       <td>...</td>
 526 |       <td>2</td>
 527 |       <td>1</td>
 528 |       <td>1</td>
 529 |       <td>3</td>
 530 |       <td>0</td>
 531 |       <td>4</td>
 532 |       <td>2</td>
 533 |       <td>2</td>
 534 |       <td>2</td>
 535 |       <td>2</td>
 536 |     </tr>
 537 |     <tr>
 538 |       <td>1009</td>
 539 |       <td>2</td>
 540 |       <td>4</td>
 541 |       <td>3</td>
 542 |       <td>4</td>
 543 |       <td>3</td>
 544 |       <td>2</td>
 545 |       <td>4</td>
 546 |       <td>4</td>
 547 |       <td>2</td>
 548 |       <td>3</td>
 549 |       <td>...</td>
 550 |       <td>3</td>
 551 |       <td>1</td>
 552 |       <td>2</td>
 553 |       <td>3</td>
 554 |       <td>0</td>
 555 |       <td>1</td>
 556 |       <td>1</td>
 557 |       <td>2</td>
 558 |       <td>4</td>
 559 |       <td>0</td>
 560 |     </tr>
 561 |   </tbody>
 562 | </table>
 563 | <p>864 rows × 57 columns</p>
 564 | </div>
 565 | 
 566 | ## 2. Choose the Factors
 567 | 
 568 | ```python
 569 | pip install factor_analyzer 
 570 | ```
 571 |     Requirement already satisfied: factor_analyzer in c:\users\dell\anaconda3\lib\site-packages (0.3.2)
 572 |     Requirement already satisfied: pandas in c:\users\dell\anaconda3\lib\site-packages (from factor_analyzer) (0.25.1)
 573 |     Requirement already satisfied: scipy in c:\users\dell\anaconda3\lib\site-packages (from factor_analyzer) (1.3.1)
 574 |     Requirement already satisfied: numpy in c:\users\dell\anaconda3\lib\site-packages (from factor_analyzer) (1.16.5)
 575 |     Requirement already satisfied: scikit-learn in c:\users\dell\anaconda3\lib\site-packages (from factor_analyzer) (0.21.3)
 576 |     Requirement already satisfied: pytz>=2017.2 in c:\users\dell\anaconda3\lib\site-packages (from pandas->factor_analyzer) (2019.3)
 577 |     Requirement already satisfied: python-dateutil>=2.6.1 in c:\users\dell\anaconda3\lib\site-packages (from pandas->factor_analyzer) (2.8.0)
 578 |     Requirement already satisfied: joblib>=0.11 in c:\users\dell\anaconda3\lib\site-packages (from scikit-learn->factor_analyzer) (0.13.2)
 579 |     Requirement already satisfied: six>=1.5 in c:\users\dell\anaconda3\lib\site-packages (from python-dateutil>=2.6.1->pandas->factor_analyzer) (1.12.0)
 580 |     Note: you may need to restart the kernel to use updated packages.
 581 | 
 582 | `Factor Analyzer`
 583 | > Reduce large no. variables into fewer no. of factors. This is a python module to perform exploratory and factor analysis with several optional rotations. It also includes a class to perform confirmatory factor analysis (CFA), with curtain predefined techniques. 
 584 | 
 585 | What is Factor Roatation
 586 | > minimize the complexity of the factor loadings to make the structure simpler to interpret.
 587 | 
 588 | There is two type of rotation
 589 | 1. Orthogonal rotation
 590 | > constrain the factors to be uncorrelated. Althogh often favored, In many cases it is unrealistic to expect the factor to be uncorrelated and forcing then to be uncorrelated make it less likely that the rotation produces a solution with simple structure.
 591 |   Method:
 592 |   1. `varimax`
 593 |      > it maximizes the sum of the variances of the squared loadings and makes the structure simpler.
 594 |      Mathematical equation of `varimax`
 595 |       ![png](Varimax.png)
 596 |   1. `quatimax`
 597 |   1. `equimax`
 598 | 4. Oblique rotation
 599 | > permit the factors to be correlated with one another often produces solution with a simpler structure.
 600 | 
 601 | Here, Our data is uncorrelated so we have used Orthogonal's `varimax` rotation method.
 602 |  
 603 | Now, We determine no. of factor using Scree plot 
 604 | > we can use also eigenvalue to determine no. of factor but that is more complex and by Scree plot its is to find.
 605 | 
 606 | ```python
 607 | #Try the model with all the variables 
 608 | from factor_analyzer import FactorAnalyzer         # pip install factor_analyzer 
 609 | fa = FactorAnalyzer(rotation="varimax")
 610 | fa.fit(df) 
 611 | 
 612 | # Check Eigenvalues
 613 | ev, v = fa.get_eigenvalues()
 614 | ev
 615 | 
 616 | # Create scree plot using matplotlib
 617 | plt.scatter(range(1,df.shape[1]+1),ev)
 618 | plt.plot(range(1,df.shape[1]+1),ev)
 619 | plt.title('Scree Plot')
 620 | plt.xlabel('Factors')
 621 | plt.ylabel('Eigenvalue')
 622 | plt.grid()
 623 | plt.show()
 624 | ```
 625 | > Out:
 626 | 
 627 | ![png](Scree-plot.png)
 628 | 
 629 | How we find no. of factor?
 630 | >  A scree plot shows the eigenvalues on the y-axis and the number of factors on the x-axis. It always displays a downward curve.The point where the slope of the curve is clearly leveling off (the “elbow) indicates the number of factors that should be generated by the analysis.
 631 | 
 632 | As you can see the most usefull factors for explain the data are between 5-6 until falling significantly.
 633 | 
 634 | We will fit the model with 5 Factors:
 635 | 
 636 | ```python
 637 | #Factor analysis with 5 fators
 638 | fa = FactorAnalyzer(5, rotation="varimax")
 639 | fa.fit(df)
 640 | AF = fa.loadings_
 641 | AF = pd.DataFrame(AF)
 642 | AF.index = df.columns
 643 | AF
 644 | ```
 645 | > Out:
 646 | <div>
 647 | <table border="1" class="dataframe">
 648 |   <thead>
 649 |     <tr style="text-align: right;">
 650 |       <th></th>
 651 |       <th>0</th>
 652 |       <th>1</th>
 653 |       <th>2</th>
 654 |       <th>3</th>
 655 |       <th>4</th>
 656 |     </tr>
 657 |   </thead>
 658 |   <tbody>
 659 |     <tr>
 660 |       <td>Daily events</td>
 661 |       <td>0.250416</td>
 662 |       <td>0.058953</td>
 663 |       <td>0.206877</td>
 664 |       <td>0.026094</td>
 665 |       <td>0.028915</td>
 666 |     </tr>
 667 |     <tr>
 668 |       <td>Prioritising workload</td>
 669 |       <td>-0.012803</td>
 670 |       <td>-0.150045</td>
 671 |       <td>0.555946</td>
 672 |       <td>0.078913</td>
 673 |       <td>0.128156</td>
 674 |     </tr>
 675 |     <tr>
 676 |       <td>Writing notes</td>
 677 |       <td>-0.006039</td>
 678 |       <td>-0.015927</td>
 679 |       <td>0.420849</td>
 680 |       <td>0.225307</td>
 681 |       <td>0.261380</td>
 682 |     </tr>
 683 |     <tr>
 684 |       <td>Workaholism</td>
 685 |       <td>0.069524</td>
 686 |       <td>0.029275</td>
 687 |       <td>0.527082</td>
 688 |       <td>0.088573</td>
 689 |       <td>0.032979</td>
 690 |     </tr>
 691 |     <tr>
 692 |       <td>Thinking ahead</td>
 693 |       <td>0.023475</td>
 694 |       <td>0.127909</td>
 695 |       <td>0.530457</td>
 696 |       <td>0.035213</td>
 697 |       <td>0.055426</td>
 698 |     </tr>
 699 |     <tr>
 700 |       <td>Final judgement</td>
 701 |       <td>0.046188</td>
 702 |       <td>0.112493</td>
 703 |       <td>0.119861</td>
 704 |       <td>0.381338</td>
 705 |       <td>-0.039756</td>
 706 |     </tr>
 707 |     <tr>
 708 |       <td>Reliability</td>
 709 |       <td>0.061028</td>
 710 |       <td>-0.102481</td>
 711 |       <td>0.539373</td>
 712 |       <td>0.073534</td>
 713 |       <td>-0.003491</td>
 714 |     </tr>
 715 |     <tr>
 716 |       <td>Keeping promises</td>
 717 |       <td>0.053358</td>
 718 |       <td>-0.034661</td>
 719 |       <td>0.420538</td>
 720 |       <td>0.121450</td>
 721 |       <td>-0.033511</td>
 722 |     </tr>
 723 |     <tr>
 724 |       <td>Loss of interest</td>
 725 |       <td>0.273777</td>
 726 |       <td>0.226286</td>
 727 |       <td>0.003524</td>
 728 |       <td>-0.149262</td>
 729 |       <td>0.101882</td>
 730 |     </tr>
 731 |     <tr>
 732 |       <td>Friends versus money</td>
 733 |       <td>0.021279</td>
 734 |       <td>-0.111839</td>
 735 |       <td>0.022026</td>
 736 |       <td>0.381357</td>
 737 |       <td>-0.045824</td>
 738 |     </tr>
 739 |     <tr>
 740 |       <td>Funniness</td>
 741 |       <td>0.312861</td>
 742 |       <td>0.131400</td>
 743 |       <td>-0.043014</td>
 744 |       <td>-0.018258</td>
 745 |       <td>-0.026083</td>
 746 |     </tr>
 747 |     <tr>
 748 |       <td>Fake</td>
 749 |       <td>0.091188</td>
 750 |       <td>0.469616</td>
 751 |       <td>-0.024535</td>
 752 |       <td>-0.191798</td>
 753 |       <td>0.019356</td>
 754 |     </tr>
 755 |     <tr>
 756 |       <td>Criminal damage</td>
 757 |       <td>0.154868</td>
 758 |       <td>0.177732</td>
 759 |       <td>-0.112659</td>
 760 |       <td>-0.240721</td>
 761 |       <td>0.266761</td>
 762 |     </tr>
 763 |     <tr>
 764 |       <td>Decision making</td>
 765 |       <td>-0.287128</td>
 766 |       <td>0.102033</td>
 767 |       <td>0.267415</td>
 768 |       <td>0.129336</td>
 769 |       <td>0.158694</td>
 770 |     </tr>
 771 |     <tr>
 772 |       <td>Elections</td>
 773 |       <td>0.074306</td>
 774 |       <td>-0.015585</td>
 775 |       <td>0.222003</td>
 776 |       <td>0.131404</td>
 777 |       <td>-0.083563</td>
 778 |     </tr>
 779 |     <tr>
 780 |       <td>Self-criticism</td>
 781 |       <td>-0.016858</td>
 782 |       <td>0.398420</td>
 783 |       <td>0.229116</td>
 784 |       <td>0.114144</td>
 785 |       <td>0.069707</td>
 786 |     </tr>
 787 |     <tr>
 788 |       <td>Judgment calls</td>
 789 |       <td>0.182082</td>
 790 |       <td>-0.010461</td>
 791 |       <td>0.102263</td>
 792 |       <td>0.035675</td>
 793 |       <td>0.086474</td>
 794 |     </tr>
 795 |     <tr>
 796 |       <td>Hypochondria</td>
 797 |       <td>-0.040254</td>
 798 |       <td>0.258913</td>
 799 |       <td>-0.034874</td>
 800 |       <td>0.042981</td>
 801 |       <td>0.213548</td>
 802 |     </tr>
 803 |     <tr>
 804 |       <td>Empathy</td>
 805 |       <td>-0.050152</td>
 806 |       <td>-0.073697</td>
 807 |       <td>0.059441</td>
 808 |       <td>0.324982</td>
 809 |       <td>0.133754</td>
 810 |     </tr>
 811 |     <tr>
 812 |       <td>Eating to survive</td>
 813 |       <td>-0.010608</td>
 814 |       <td>0.183045</td>
 815 |       <td>0.003261</td>
 816 |       <td>-0.015131</td>
 817 |       <td>-0.018874</td>
 818 |     </tr>
 819 |     <tr>
 820 |       <td>Giving</td>
 821 |       <td>0.082276</td>
 822 |       <td>-0.154549</td>
 823 |       <td>0.112481</td>
 824 |       <td>0.376723</td>
 825 |       <td>0.234000</td>
 826 |     </tr>
 827 |     <tr>
 828 |       <td>Compassion to animals</td>
 829 |       <td>-0.083505</td>
 830 |       <td>-0.002767</td>
 831 |       <td>-0.010424</td>
 832 |       <td>0.262183</td>
 833 |       <td>0.192734</td>
 834 |     </tr>
 835 |     <tr>
 836 |       <td>Borrowed stuff</td>
 837 |       <td>-0.097017</td>
 838 |       <td>-0.023047</td>
 839 |       <td>0.323253</td>
 840 |       <td>0.171017</td>
 841 |       <td>0.071189</td>
 842 |     </tr>
 843 |     <tr>
 844 |       <td>Loneliness</td>
 845 |       <td>-0.199197</td>
 846 |       <td>0.542350</td>
 847 |       <td>-0.019272</td>
 848 |       <td>0.045942</td>
 849 |       <td>0.190369</td>
 850 |     </tr>
 851 |     <tr>
 852 |       <td>Cheating in school</td>
 853 |       <td>0.216223</td>
 854 |       <td>-0.063183</td>
 855 |       <td>-0.384634</td>
 856 |       <td>-0.083940</td>
 857 |       <td>0.208210</td>
 858 |     </tr>
 859 |     <tr>
 860 |       <td>Health</td>
 861 |       <td>-0.012267</td>
 862 |       <td>0.027867</td>
 863 |       <td>0.131645</td>
 864 |       <td>0.184296</td>
 865 |       <td>0.437826</td>
 866 |     </tr>
 867 |     <tr>
 868 |       <td>Changing the past</td>
 869 |       <td>-0.016622</td>
 870 |       <td>0.482307</td>
 871 |       <td>-0.161320</td>
 872 |       <td>0.073843</td>
 873 |       <td>0.159231</td>
 874 |     </tr>
 875 |     <tr>
 876 |       <td>God</td>
 877 |       <td>0.047894</td>
 878 |       <td>0.032281</td>
 879 |       <td>0.027136</td>
 880 |       <td>0.453873</td>
 881 |       <td>-0.025963</td>
 882 |     </tr>
 883 |     <tr>
 884 |       <td>Dreams</td>
 885 |       <td>0.207076</td>
 886 |       <td>-0.187723</td>
 887 |       <td>0.078634</td>
 888 |       <td>0.037709</td>
 889 |       <td>-0.124853</td>
 890 |     </tr>
 891 |     <tr>
 892 |       <td>Charity</td>
 893 |       <td>0.163161</td>
 894 |       <td>0.116834</td>
 895 |       <td>0.156898</td>
 896 |       <td>0.354953</td>
 897 |       <td>-0.067795</td>
 898 |     </tr>
 899 |     <tr>
 900 |       <td>Number of friends</td>
 901 |       <td>0.514994</td>
 902 |       <td>-0.321738</td>
 903 |       <td>-0.086711</td>
 904 |       <td>0.241070</td>
 905 |       <td>-0.006859</td>
 906 |     </tr>
 907 |     <tr>
 908 |       <td>Punctuality</td>
 909 |       <td>0.004662</td>
 910 |       <td>0.090531</td>
 911 |       <td>-0.143569</td>
 912 |       <td>0.069648</td>
 913 |       <td>0.078111</td>
 914 |     </tr>
 915 |     <tr>
 916 |       <td>Lying</td>
 917 |       <td>-0.095933</td>
 918 |       <td>-0.193370</td>
 919 |       <td>0.001775</td>
 920 |       <td>0.138092</td>
 921 |       <td>0.006950</td>
 922 |     </tr>
 923 |     <tr>
 924 |       <td>Waiting</td>
 925 |       <td>0.032019</td>
 926 |       <td>-0.067715</td>
 927 |       <td>-0.000820</td>
 928 |       <td>0.075966</td>
 929 |       <td>-0.329606</td>
 930 |     </tr>
 931 |     <tr>
 932 |       <td>New environment</td>
 933 |       <td>0.470076</td>
 934 |       <td>-0.129745</td>
 935 |       <td>-0.058912</td>
 936 |       <td>0.005400</td>
 937 |       <td>-0.230743</td>
 938 |     </tr>
 939 |     <tr>
 940 |       <td>Mood swings</td>
 941 |       <td>-0.086477</td>
 942 |       <td>0.353226</td>
 943 |       <td>-0.041005</td>
 944 |       <td>0.031490</td>
 945 |       <td>0.404388</td>
 946 |     </tr>
 947 |     <tr>
 948 |       <td>Appearence and gestures</td>
 949 |       <td>0.227246</td>
 950 |       <td>-0.004762</td>
 951 |       <td>0.105894</td>
 952 |       <td>0.068825</td>
 953 |       <td>0.303119</td>
 954 |     </tr>
 955 |     <tr>
 956 |       <td>Socializing</td>
 957 |       <td>0.537811</td>
 958 |       <td>-0.096245</td>
 959 |       <td>-0.048127</td>
 960 |       <td>0.135323</td>
 961 |       <td>-0.039204</td>
 962 |     </tr>
 963 |     <tr>
 964 |       <td>Achievements</td>
 965 |       <td>0.252835</td>
 966 |       <td>0.048658</td>
 967 |       <td>-0.042799</td>
 968 |       <td>-0.082401</td>
 969 |       <td>0.111902</td>
 970 |     </tr>
 971 |     <tr>
 972 |       <td>Responding to a serious letter</td>
 973 |       <td>-0.126985</td>
 974 |       <td>0.087976</td>
 975 |       <td>-0.026876</td>
 976 |       <td>0.022940</td>
 977 |       <td>0.013346</td>
 978 |     </tr>
 979 |     <tr>
 980 |       <td>Children</td>
 981 |       <td>0.079877</td>
 982 |       <td>-0.134254</td>
 983 |       <td>0.033040</td>
 984 |       <td>0.440103</td>
 985 |       <td>0.075663</td>
 986 |     </tr>
 987 |     <tr>
 988 |       <td>Assertiveness</td>
 989 |       <td>0.353462</td>
 990 |       <td>-0.094372</td>
 991 |       <td>0.002509</td>
 992 |       <td>-0.067185</td>
 993 |       <td>0.044117</td>
 994 |     </tr>
 995 |     <tr>
 996 |       <td>Getting angry</td>
 997 |       <td>0.051167</td>
 998 |       <td>0.176922</td>
 999 |       <td>-0.086069</td>
1000 |       <td>-0.070837</td>
1001 |       <td>0.532025</td>
1002 |     </tr>
1003 |     <tr>
1004 |       <td>Knowing the right people</td>
1005 |       <td>0.478657</td>
1006 |       <td>0.022868</td>
1007 |       <td>0.113503</td>
1008 |       <td>-0.045359</td>
1009 |       <td>0.227230</td>
1010 |     </tr>
1011 |     <tr>
1012 |       <td>Public speaking</td>
1013 |       <td>-0.385674</td>
1014 |       <td>0.104662</td>
1015 |       <td>0.069712</td>
1016 |       <td>0.030447</td>
1017 |       <td>0.190834</td>
1018 |     </tr>
1019 |     <tr>
1020 |       <td>Unpopularity</td>
1021 |       <td>-0.082146</td>
1022 |       <td>0.229228</td>
1023 |       <td>0.079173</td>
1024 |       <td>0.241031</td>
1025 |       <td>-0.031212</td>
1026 |     </tr>
1027 |     <tr>
1028 |       <td>Life struggles</td>
1029 |       <td>-0.226293</td>
1030 |       <td>0.057892</td>
1031 |       <td>-0.059615</td>
1032 |       <td>0.384875</td>
1033 |       <td>0.392060</td>
1034 |     </tr>
1035 |     <tr>
1036 |       <td>Happiness in life</td>
1037 |       <td>0.288585</td>
1038 |       <td>-0.541050</td>
1039 |       <td>0.158473</td>
1040 |       <td>0.051235</td>
1041 |       <td>-0.064525</td>
1042 |     </tr>
1043 |     <tr>
1044 |       <td>Energy levels</td>
1045 |       <td>0.499978</td>
1046 |       <td>-0.478860</td>
1047 |       <td>0.037918</td>
1048 |       <td>0.122773</td>
1049 |       <td>-0.025001</td>
1050 |     </tr>
1051 |     <tr>
1052 |       <td>Small - big dogs</td>
1053 |       <td>0.206696</td>
1054 |       <td>0.040211</td>
1055 |       <td>-0.143225</td>
1056 |       <td>-0.203991</td>
1057 |       <td>-0.131298</td>
1058 |     </tr>
1059 |     <tr>
1060 |       <td>Personality</td>
1061 |       <td>0.259646</td>
1062 |       <td>-0.393197</td>
1063 |       <td>0.064236</td>
1064 |       <td>0.049013</td>
1065 |       <td>-0.056988</td>
1066 |     </tr>
1067 |     <tr>
1068 |       <td>Finding lost valuables</td>
1069 |       <td>-0.127907</td>
1070 |       <td>-0.011367</td>
1071 |       <td>0.163354</td>
1072 |       <td>0.391951</td>
1073 |       <td>-0.101749</td>
1074 |     </tr>
1075 |     <tr>
1076 |       <td>Getting up</td>
1077 |       <td>0.012217</td>
1078 |       <td>0.150551</td>
1079 |       <td>-0.312297</td>
1080 |       <td>0.082580</td>
1081 |       <td>0.121198</td>
1082 |     </tr>
1083 |     <tr>
1084 |       <td>Interests or hobbies</td>
1085 |       <td>0.465627</td>
1086 |       <td>-0.253289</td>
1087 |       <td>0.065015</td>
1088 |       <td>0.144827</td>
1089 |       <td>-0.078694</td>
1090 |     </tr>
1091 |     <tr>
1092 |       <td>Parents' advice</td>
1093 |       <td>0.022594</td>
1094 |       <td>-0.032871</td>
1095 |       <td>0.243628</td>
1096 |       <td>0.282252</td>
1097 |       <td>0.113225</td>
1098 |     </tr>
1099 |     <tr>
1100 |       <td>Questionnaires or polls</td>
1101 |       <td>-0.045177</td>
1102 |       <td>0.114865</td>
1103 |       <td>0.154309</td>
1104 |       <td>0.188501</td>
1105 |       <td>-0.032532</td>
1106 |     </tr>
1107 |     <tr>
1108 |       <td>Internet usage</td>
1109 |       <td>-0.046077</td>
1110 |       <td>0.075435</td>
1111 |       <td>-0.007799</td>
1112 |       <td>-0.081575</td>
1113 |       <td>0.048144</td>
1114 |     </tr>
1115 |   </tbody>
1116 | </table>
1117 | </div>
1118 | 
1119 | ```python
1120 | #Get Top variables for each Factor 
1121 | F = AF.unstack()
1122 | F = pd.DataFrame(F).reset_index()
1123 | F = F.sort_values(['level_0',0], ascending=False).groupby('level_0').head(5)    # Top 5 
1124 | F = F.sort_values(by="level_0")
1125 | F.columns=["FACTOR","Variable","Varianza_Explica"]
1126 | F = F.reset_index().drop(["index"],axis=1)
1127 | F
1128 | ```
1129 | > Out:
1130 | <div>
1131 | <table border="1" class="dataframe">
1132 |   <thead>
1133 |     <tr style="text-align: right;">
1134 |       <th></th>
1135 |       <th>FACTOR</th>
1136 |       <th>Variable</th>
1137 |       <th>Varianza_Explica</th>
1138 |     </tr>
1139 |   </thead>
1140 |   <tbody>
1141 |     <tr>
1142 |       <td>0</td>
1143 |       <td>0</td>
1144 |       <td>New environment</td>
1145 |       <td>0.470076</td>
1146 |     </tr>
1147 |     <tr>
1148 |       <td>1</td>
1149 |       <td>0</td>
1150 |       <td>Energy levels</td>
1151 |       <td>0.499978</td>
1152 |     </tr>
1153 |     <tr>
1154 |       <td>2</td>
1155 |       <td>0</td>
1156 |       <td>Number of friends</td>
1157 |       <td>0.514994</td>
1158 |     </tr>
1159 |     <tr>
1160 |       <td>3</td>
1161 |       <td>0</td>
1162 |       <td>Socializing</td>
1163 |       <td>0.537811</td>
1164 |     </tr>
1165 |     <tr>
1166 |       <td>4</td>
1167 |       <td>0</td>
1168 |       <td>Knowing the right people</td>
1169 |       <td>0.478657</td>
1170 |     </tr>
1171 |     <tr>
1172 |       <td>5</td>
1173 |       <td>1</td>
1174 |       <td>Mood swings</td>
1175 |       <td>0.353226</td>
1176 |     </tr>
1177 |     <tr>
1178 |       <td>6</td>
1179 |       <td>1</td>
1180 |       <td>Self-criticism</td>
1181 |       <td>0.398420</td>
1182 |     </tr>
1183 |     <tr>
1184 |       <td>7</td>
1185 |       <td>1</td>
1186 |       <td>Fake</td>
1187 |       <td>0.469616</td>
1188 |     </tr>
1189 |     <tr>
1190 |       <td>8</td>
1191 |       <td>1</td>
1192 |       <td>Changing the past</td>
1193 |       <td>0.482307</td>
1194 |     </tr>
1195 |     <tr>
1196 |       <td>9</td>
1197 |       <td>1</td>
1198 |       <td>Loneliness</td>
1199 |       <td>0.542350</td>
1200 |     </tr>
1201 |     <tr>
1202 |       <td>10</td>
1203 |       <td>2</td>
1204 |       <td>Writing notes</td>
1205 |       <td>0.420849</td>
1206 |     </tr>
1207 |     <tr>
1208 |       <td>11</td>
1209 |       <td>2</td>
1210 |       <td>Workaholism</td>
1211 |       <td>0.527082</td>
1212 |     </tr>
1213 |     <tr>
1214 |       <td>12</td>
1215 |       <td>2</td>
1216 |       <td>Thinking ahead</td>
1217 |       <td>0.530457</td>
1218 |     </tr>
1219 |     <tr>
1220 |       <td>13</td>
1221 |       <td>2</td>
1222 |       <td>Prioritising workload</td>
1223 |       <td>0.555946</td>
1224 |     </tr>
1225 |     <tr>
1226 |       <td>14</td>
1227 |       <td>2</td>
1228 |       <td>Reliability</td>
1229 |       <td>0.539373</td>
1230 |     </tr>
1231 |     <tr>
1232 |       <td>15</td>
1233 |       <td>3</td>
1234 |       <td>Friends versus money</td>
1235 |       <td>0.381357</td>
1236 |     </tr>
1237 |     <tr>
1238 |       <td>16</td>
1239 |       <td>3</td>
1240 |       <td>Life struggles</td>
1241 |       <td>0.384875</td>
1242 |     </tr>
1243 |     <tr>
1244 |       <td>17</td>
1245 |       <td>3</td>
1246 |       <td>Finding lost valuables</td>
1247 |       <td>0.391951</td>
1248 |     </tr>
1249 |     <tr>
1250 |       <td>18</td>
1251 |       <td>3</td>
1252 |       <td>Children</td>
1253 |       <td>0.440103</td>
1254 |     </tr>
1255 |     <tr>
1256 |       <td>19</td>
1257 |       <td>3</td>
1258 |       <td>God</td>
1259 |       <td>0.453873</td>
1260 |     </tr>
1261 |     <tr>
1262 |       <td>20</td>
1263 |       <td>4</td>
1264 |       <td>Appearence and gestures</td>
1265 |       <td>0.303119</td>
1266 |     </tr>
1267 |     <tr>
1268 |       <td>21</td>
1269 |       <td>4</td>
1270 |       <td>Life struggles</td>
1271 |       <td>0.392060</td>
1272 |     </tr>
1273 |     <tr>
1274 |       <td>22</td>
1275 |       <td>4</td>
1276 |       <td>Mood swings</td>
1277 |       <td>0.404388</td>
1278 |     </tr>
1279 |     <tr>
1280 |       <td>23</td>
1281 |       <td>4</td>
1282 |       <td>Health</td>
1283 |       <td>0.437826</td>
1284 |     </tr>
1285 |     <tr>
1286 |       <td>24</td>
1287 |       <td>4</td>
1288 |       <td>Getting angry</td>
1289 |       <td>0.532025</td>
1290 |     </tr>
1291 |   </tbody>
1292 | </table>
1293 | </div>
1294 | 
1295 | 
1296 | ```python
1297 | #Show the Top for each Factor 
1298 | F = F.pivot(columns='FACTOR')["Variable"]
1299 | F.apply(lambda x: pd.Series(x.dropna().to_numpy()))
1300 | ```
1301 | > Out:
1302 | <div>
1303 | <table border="1" class="dataframe">
1304 |   <thead>
1305 |     <tr style="text-align: right;">
1306 |       <th>FACTOR</th>
1307 |       <th>0</th>
1308 |       <th>1</th>
1309 |       <th>2</th>
1310 |       <th>3</th>
1311 |       <th>4</th>
1312 |     </tr>
1313 |   </thead>
1314 |   <tbody>
1315 |     <tr>
1316 |       <td>0</td>
1317 |       <td>New environment</td>
1318 |       <td>Mood swings</td>
1319 |       <td>Writing notes</td>
1320 |       <td>Friends versus money</td>
1321 |       <td>Appearence and gestures</td>
1322 |     </tr>
1323 |     <tr>
1324 |       <td>1</td>
1325 |       <td>Energy levels</td>
1326 |       <td>Self-criticism</td>
1327 |       <td>Workaholism</td>
1328 |       <td>Life struggles</td>
1329 |       <td>Life struggles</td>
1330 |     </tr>
1331 |     <tr>
1332 |       <td>2</td>
1333 |       <td>Number of friends</td>
1334 |       <td>Fake</td>
1335 |       <td>Thinking ahead</td>
1336 |       <td>Finding lost valuables</td>
1337 |       <td>Mood swings</td>
1338 |     </tr>
1339 |     <tr>
1340 |       <td>3</td>
1341 |       <td>Socializing</td>
1342 |       <td>Changing the past</td>
1343 |       <td>Prioritising workload</td>
1344 |       <td>Children</td>
1345 |       <td>Health</td>
1346 |     </tr>
1347 |     <tr>
1348 |       <td>4</td>
1349 |       <td>Knowing the right people</td>
1350 |       <td>Loneliness</td>
1351 |       <td>Reliability</td>
1352 |       <td>God</td>
1353 |       <td>Getting angry</td>
1354 |     </tr>
1355 |   </tbody>
1356 | </table>
1357 | </div>
1358 | 
1359 | ---
1360 | 
1361 | FACTOR 1: Energy levels, Number of friends, Socializing...
1362 | 
1363 | Could be: Extraversion
1364 | 
1365 | ---
1366 | 
1367 | FACTOR 2: Self-ciricism, Fake, Loneliness...
1368 | 
1369 | Looks very similar to "Neuroticism"
1370 | 
1371 | ---
1372 | 
1373 | Factor 3: Thinking ahead, Prioritising workload...
1374 | 
1375 | very similar to "Conscientiousness"
1376 | 
1377 | ---
1378 | 
1379 | Factor 4: Children, God, Finding lost valuables
1380 | 
1381 | This factor could be something like "religious" or "conservative", maybe have lowest scores of a "Openness" in Big Five model.
1382 | 
1383 | ---
1384 | 
1385 | Factor 5: Appearence and gestures, Mood swings
1386 | 
1387 | Mmmm it could be "Agreeableness". What do you think it could be represent?
1388 | 
1389 | ---
1390 | 
1391 | ## Conclusion
1392 | The first three Factors are very clear: Extraversion, Neuroticism and Conscientiousness. The other two not to much. Anyway is a very interesting approximation 
1393 | 
1394 | Maybe doing first a PCA for remove hight correlate variables like "God" and "Final judgement"could help.
1395 | 
1396 | What do you think?
1397 | 
1398 | # Thank you      
1399 | ![png](Thank-you.png)                                       
1400 | > I appreciate especially your `Heart`
1401 | 


--------------------------------------------------------------------------------
/Project-code.ipynb:
--------------------------------------------------------------------------------
   1 | {
   2 |  "cells": [
   3 |   {
   4 |    "cell_type": "code",
   5 |    "execution_count": 3,
   6 |    "metadata": {},
   7 |    "outputs": [],
   8 |    "source": [
   9 |     "#Librerias\n",
  10 |     "import pandas as pd \n",
  11 |     "import numpy as np\n",
  12 |     "import matplotlib.pyplot as plt "
  13 |    ]
  14 |   },
  15 |   {
  16 |    "cell_type": "code",
  17 |    "execution_count": 4,
  18 |    "metadata": {},
  19 |    "outputs": [
  20 |     {
  21 |      "data": {
  22 |       "text/plain": [
  23 |        "(1010, 150)"
  24 |       ]
  25 |      },
  26 |      "execution_count": 4,
  27 |      "metadata": {},
  28 |      "output_type": "execute_result"
  29 |     }
  30 |    ],
  31 |    "source": [
  32 |     "#Data\n",
  33 |     "df = pd.read_csv(\"responses.csv\")\n",
  34 |     "df.shape"
  35 |    ]
  36 |   },
  37 |   {
  38 |    "cell_type": "markdown",
  39 |    "metadata": {},
  40 |    "source": [
  41 |     "MUSIC PREFERENCES (19) 0:19\n",
  42 |     "\n",
  43 |     "MOVIE PREFERENCES (12) 19:31\n",
  44 |     "\n",
  45 |     "HOBBIES & INTERESTS (32) 31:63\n",
  46 |     "\n",
  47 |     "PHOBIAS (10) 63:73\n",
  48 |     "\n",
  49 |     "HEALTH HABITS (3) 73:76\n",
  50 |     "\n",
  51 |     "PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS (57) 76:133\n",
  52 |     "\n",
  53 |     "SPENDING HABITS (7) 133:140\n",
  54 |     "\n",
  55 |     "DEMOGRAPHICS (10 ) 140:150\n",
  56 |     "\n",
  57 |     "We will take only: PERSONALITY TRAITS, VIEWS ON LIFE & OPINIONS (57) 76:133"
  58 |    ]
  59 |   },
  60 |   {
  61 |    "cell_type": "code",
  62 |    "execution_count": 5,
  63 |    "metadata": {},
  64 |    "outputs": [
  65 |     {
  66 |      "data": {
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  78 |        "    .dataframe thead th {\n",
  79 |        "        text-align: right;\n",
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  81 |        "</style>\n",
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  84 |        "    <tr style=\"text-align: right;\">\n",
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 201 |        "      <td>NaN</td>\n",
 202 |        "      <td>2.0</td>\n",
 203 |        "      <td>4.0</td>\n",
 204 |        "      <td>most of the day</td>\n",
 205 |        "    </tr>\n",
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 207 |        "      <td>4</td>\n",
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 213 |        "      <td>5.0</td>\n",
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 216 |        "      <td>2.0</td>\n",
 217 |        "      <td>3.0</td>\n",
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 219 |        "      <td>3.0</td>\n",
 220 |        "      <td>5.0</td>\n",
 221 |        "      <td>3.0</td>\n",
 222 |        "      <td>3.0</td>\n",
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 226 |        "      <td>3.0</td>\n",
 227 |        "      <td>3.0</td>\n",
 228 |        "      <td>few hours a day</td>\n",
 229 |        "    </tr>\n",
 230 |        "  </tbody>\n",
 231 |        "</table>\n",
 232 |        "<p>5 rows × 57 columns</p>\n",
 233 |        "</div>"
 234 |       ],
 235 |       "text/plain": [
 236 |        "   Daily events  Prioritising workload  Writing notes  Workaholism  \\\n",
 237 |        "0           2.0                    2.0            5.0          4.0   \n",
 238 |        "1           3.0                    2.0            4.0          5.0   \n",
 239 |        "2           1.0                    2.0            5.0          3.0   \n",
 240 |        "3           4.0                    4.0            4.0          5.0   \n",
 241 |        "4           3.0                    1.0            2.0          3.0   \n",
 242 |        "\n",
 243 |        "   Thinking ahead  Final judgement  Reliability  Keeping promises  \\\n",
 244 |        "0             2.0              5.0          4.0               4.0   \n",
 245 |        "1             4.0              1.0          4.0               4.0   \n",
 246 |        "2             5.0              3.0          4.0               5.0   \n",
 247 |        "3             3.0              1.0          3.0               4.0   \n",
 248 |        "4             5.0              5.0          5.0               4.0   \n",
 249 |        "\n",
 250 |        "   Loss of interest  Friends versus money  ...  Happiness in life  \\\n",
 251 |        "0               1.0                   3.0  ...                4.0   \n",
 252 |        "1               3.0                   4.0  ...                4.0   \n",
 253 |        "2               1.0                   5.0  ...                4.0   \n",
 254 |        "3               5.0                   2.0  ...                2.0   \n",
 255 |        "4               2.0                   3.0  ...                3.0   \n",
 256 |        "\n",
 257 |        "   Energy levels  Small - big dogs  Personality  Finding lost valuables  \\\n",
 258 |        "0            5.0               1.0          4.0                     3.0   \n",
 259 |        "1            3.0               5.0          3.0                     4.0   \n",
 260 |        "2            4.0               3.0          3.0                     3.0   \n",
 261 |        "3            2.0               1.0          2.0                     1.0   \n",
 262 |        "4            5.0               3.0          3.0                     2.0   \n",
 263 |        "\n",
 264 |        "   Getting up  Interests or hobbies  Parents' advice  Questionnaires or polls  \\\n",
 265 |        "0         2.0                   3.0              4.0                      3.0   \n",
 266 |        "1         5.0                   3.0              2.0                      3.0   \n",
 267 |        "2         4.0                   5.0              3.0                      1.0   \n",
 268 |        "3         1.0                   NaN              2.0                      4.0   \n",
 269 |        "4         4.0                   3.0              3.0                      3.0   \n",
 270 |        "\n",
 271 |        "    Internet usage  \n",
 272 |        "0  few hours a day  \n",
 273 |        "1  few hours a day  \n",
 274 |        "2  few hours a day  \n",
 275 |        "3  most of the day  \n",
 276 |        "4  few hours a day  \n",
 277 |        "\n",
 278 |        "[5 rows x 57 columns]"
 279 |       ]
 280 |      },
 281 |      "execution_count": 5,
 282 |      "metadata": {},
 283 |      "output_type": "execute_result"
 284 |     }
 285 |    ],
 286 |    "source": [
 287 |     "df = df.iloc[:, 76:133]\n",
 288 |     "df.head(5)"
 289 |    ]
 290 |   },
 291 |   {
 292 |    "cell_type": "markdown",
 293 |    "metadata": {},
 294 |    "source": [
 295 |     "# 1. Prepare the Data"
 296 |    ]
 297 |   },
 298 |   {
 299 |    "cell_type": "code",
 300 |    "execution_count": 6,
 301 |    "metadata": {},
 302 |    "outputs": [
 303 |     {
 304 |      "data": {
 305 |       "text/html": [
 306 |        "<div>\n",
 307 |        "<style scoped>\n",
 308 |        "    .dataframe tbody tr th:only-of-type {\n",
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 310 |        "    }\n",
 311 |        "\n",
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 338 |        "      <th>Personality</th>\n",
 339 |        "      <th>Finding lost valuables</th>\n",
 340 |        "      <th>Getting up</th>\n",
 341 |        "      <th>Interests or hobbies</th>\n",
 342 |        "      <th>Parents' advice</th>\n",
 343 |        "      <th>Questionnaires or polls</th>\n",
 344 |        "      <th>Internet usage</th>\n",
 345 |        "    </tr>\n",
 346 |        "  </thead>\n",
 347 |        "  <tbody>\n",
 348 |        "    <tr>\n",
 349 |        "      <td>0</td>\n",
 350 |        "      <td>1</td>\n",
 351 |        "      <td>1</td>\n",
 352 |        "      <td>4</td>\n",
 353 |        "      <td>3</td>\n",
 354 |        "      <td>1</td>\n",
 355 |        "      <td>4</td>\n",
 356 |        "      <td>3</td>\n",
 357 |        "      <td>3</td>\n",
 358 |        "      <td>0</td>\n",
 359 |        "      <td>2</td>\n",
 360 |        "      <td>...</td>\n",
 361 |        "      <td>3</td>\n",
 362 |        "      <td>4</td>\n",
 363 |        "      <td>0</td>\n",
 364 |        "      <td>3</td>\n",
 365 |        "      <td>2</td>\n",
 366 |        "      <td>1</td>\n",
 367 |        "      <td>2</td>\n",
 368 |        "      <td>3</td>\n",
 369 |        "      <td>2</td>\n",
 370 |        "      <td>0</td>\n",
 371 |        "    </tr>\n",
 372 |        "    <tr>\n",
 373 |        "      <td>1</td>\n",
 374 |        "      <td>2</td>\n",
 375 |        "      <td>1</td>\n",
 376 |        "      <td>3</td>\n",
 377 |        "      <td>4</td>\n",
 378 |        "      <td>3</td>\n",
 379 |        "      <td>0</td>\n",
 380 |        "      <td>3</td>\n",
 381 |        "      <td>3</td>\n",
 382 |        "      <td>2</td>\n",
 383 |        "      <td>3</td>\n",
 384 |        "      <td>...</td>\n",
 385 |        "      <td>3</td>\n",
 386 |        "      <td>2</td>\n",
 387 |        "      <td>4</td>\n",
 388 |        "      <td>2</td>\n",
 389 |        "      <td>3</td>\n",
 390 |        "      <td>4</td>\n",
 391 |        "      <td>2</td>\n",
 392 |        "      <td>1</td>\n",
 393 |        "      <td>2</td>\n",
 394 |        "      <td>0</td>\n",
 395 |        "    </tr>\n",
 396 |        "    <tr>\n",
 397 |        "      <td>2</td>\n",
 398 |        "      <td>0</td>\n",
 399 |        "      <td>1</td>\n",
 400 |        "      <td>4</td>\n",
 401 |        "      <td>2</td>\n",
 402 |        "      <td>4</td>\n",
 403 |        "      <td>2</td>\n",
 404 |        "      <td>3</td>\n",
 405 |        "      <td>4</td>\n",
 406 |        "      <td>0</td>\n",
 407 |        "      <td>4</td>\n",
 408 |        "      <td>...</td>\n",
 409 |        "      <td>3</td>\n",
 410 |        "      <td>3</td>\n",
 411 |        "      <td>2</td>\n",
 412 |        "      <td>2</td>\n",
 413 |        "      <td>2</td>\n",
 414 |        "      <td>3</td>\n",
 415 |        "      <td>4</td>\n",
 416 |        "      <td>2</td>\n",
 417 |        "      <td>0</td>\n",
 418 |        "      <td>0</td>\n",
 419 |        "    </tr>\n",
 420 |        "    <tr>\n",
 421 |        "      <td>4</td>\n",
 422 |        "      <td>2</td>\n",
 423 |        "      <td>0</td>\n",
 424 |        "      <td>1</td>\n",
 425 |        "      <td>2</td>\n",
 426 |        "      <td>4</td>\n",
 427 |        "      <td>4</td>\n",
 428 |        "      <td>4</td>\n",
 429 |        "      <td>3</td>\n",
 430 |        "      <td>1</td>\n",
 431 |        "      <td>2</td>\n",
 432 |        "      <td>...</td>\n",
 433 |        "      <td>2</td>\n",
 434 |        "      <td>4</td>\n",
 435 |        "      <td>2</td>\n",
 436 |        "      <td>2</td>\n",
 437 |        "      <td>1</td>\n",
 438 |        "      <td>3</td>\n",
 439 |        "      <td>2</td>\n",
 440 |        "      <td>2</td>\n",
 441 |        "      <td>2</td>\n",
 442 |        "      <td>0</td>\n",
 443 |        "    </tr>\n",
 444 |        "    <tr>\n",
 445 |        "      <td>5</td>\n",
 446 |        "      <td>1</td>\n",
 447 |        "      <td>1</td>\n",
 448 |        "      <td>2</td>\n",
 449 |        "      <td>2</td>\n",
 450 |        "      <td>2</td>\n",
 451 |        "      <td>0</td>\n",
 452 |        "      <td>2</td>\n",
 453 |        "      <td>3</td>\n",
 454 |        "      <td>2</td>\n",
 455 |        "      <td>1</td>\n",
 456 |        "      <td>...</td>\n",
 457 |        "      <td>2</td>\n",
 458 |        "      <td>3</td>\n",
 459 |        "      <td>3</td>\n",
 460 |        "      <td>2</td>\n",
 461 |        "      <td>2</td>\n",
 462 |        "      <td>2</td>\n",
 463 |        "      <td>4</td>\n",
 464 |        "      <td>2</td>\n",
 465 |        "      <td>3</td>\n",
 466 |        "      <td>0</td>\n",
 467 |        "    </tr>\n",
 468 |        "    <tr>\n",
 469 |        "      <td>...</td>\n",
 470 |        "      <td>...</td>\n",
 471 |        "      <td>...</td>\n",
 472 |        "      <td>...</td>\n",
 473 |        "      <td>...</td>\n",
 474 |        "      <td>...</td>\n",
 475 |        "      <td>...</td>\n",
 476 |        "      <td>...</td>\n",
 477 |        "      <td>...</td>\n",
 478 |        "      <td>...</td>\n",
 479 |        "      <td>...</td>\n",
 480 |        "      <td>...</td>\n",
 481 |        "      <td>...</td>\n",
 482 |        "      <td>...</td>\n",
 483 |        "      <td>...</td>\n",
 484 |        "      <td>...</td>\n",
 485 |        "      <td>...</td>\n",
 486 |        "      <td>...</td>\n",
 487 |        "      <td>...</td>\n",
 488 |        "      <td>...</td>\n",
 489 |        "      <td>...</td>\n",
 490 |        "      <td>...</td>\n",
 491 |        "    </tr>\n",
 492 |        "    <tr>\n",
 493 |        "      <td>1005</td>\n",
 494 |        "      <td>2</td>\n",
 495 |        "      <td>1</td>\n",
 496 |        "      <td>0</td>\n",
 497 |        "      <td>3</td>\n",
 498 |        "      <td>1</td>\n",
 499 |        "      <td>2</td>\n",
 500 |        "      <td>2</td>\n",
 501 |        "      <td>2</td>\n",
 502 |        "      <td>3</td>\n",
 503 |        "      <td>3</td>\n",
 504 |        "      <td>...</td>\n",
 505 |        "      <td>3</td>\n",
 506 |        "      <td>2</td>\n",
 507 |        "      <td>2</td>\n",
 508 |        "      <td>2</td>\n",
 509 |        "      <td>3</td>\n",
 510 |        "      <td>4</td>\n",
 511 |        "      <td>3</td>\n",
 512 |        "      <td>3</td>\n",
 513 |        "      <td>2</td>\n",
 514 |        "      <td>0</td>\n",
 515 |        "    </tr>\n",
 516 |        "    <tr>\n",
 517 |        "      <td>1006</td>\n",
 518 |        "      <td>0</td>\n",
 519 |        "      <td>2</td>\n",
 520 |        "      <td>0</td>\n",
 521 |        "      <td>4</td>\n",
 522 |        "      <td>4</td>\n",
 523 |        "      <td>4</td>\n",
 524 |        "      <td>4</td>\n",
 525 |        "      <td>3</td>\n",
 526 |        "      <td>0</td>\n",
 527 |        "      <td>1</td>\n",
 528 |        "      <td>...</td>\n",
 529 |        "      <td>3</td>\n",
 530 |        "      <td>3</td>\n",
 531 |        "      <td>2</td>\n",
 532 |        "      <td>4</td>\n",
 533 |        "      <td>2</td>\n",
 534 |        "      <td>0</td>\n",
 535 |        "      <td>2</td>\n",
 536 |        "      <td>3</td>\n",
 537 |        "      <td>2</td>\n",
 538 |        "      <td>1</td>\n",
 539 |        "    </tr>\n",
 540 |        "    <tr>\n",
 541 |        "      <td>1007</td>\n",
 542 |        "      <td>2</td>\n",
 543 |        "      <td>0</td>\n",
 544 |        "      <td>0</td>\n",
 545 |        "      <td>0</td>\n",
 546 |        "      <td>3</td>\n",
 547 |        "      <td>0</td>\n",
 548 |        "      <td>2</td>\n",
 549 |        "      <td>4</td>\n",
 550 |        "      <td>0</td>\n",
 551 |        "      <td>3</td>\n",
 552 |        "      <td>...</td>\n",
 553 |        "      <td>2</td>\n",
 554 |        "      <td>0</td>\n",
 555 |        "      <td>2</td>\n",
 556 |        "      <td>1</td>\n",
 557 |        "      <td>2</td>\n",
 558 |        "      <td>4</td>\n",
 559 |        "      <td>0</td>\n",
 560 |        "      <td>3</td>\n",
 561 |        "      <td>4</td>\n",
 562 |        "      <td>2</td>\n",
 563 |        "    </tr>\n",
 564 |        "    <tr>\n",
 565 |        "      <td>1008</td>\n",
 566 |        "      <td>2</td>\n",
 567 |        "      <td>0</td>\n",
 568 |        "      <td>4</td>\n",
 569 |        "      <td>0</td>\n",
 570 |        "      <td>2</td>\n",
 571 |        "      <td>3</td>\n",
 572 |        "      <td>3</td>\n",
 573 |        "      <td>3</td>\n",
 574 |        "      <td>4</td>\n",
 575 |        "      <td>2</td>\n",
 576 |        "      <td>...</td>\n",
 577 |        "      <td>2</td>\n",
 578 |        "      <td>1</td>\n",
 579 |        "      <td>1</td>\n",
 580 |        "      <td>3</td>\n",
 581 |        "      <td>0</td>\n",
 582 |        "      <td>4</td>\n",
 583 |        "      <td>2</td>\n",
 584 |        "      <td>2</td>\n",
 585 |        "      <td>2</td>\n",
 586 |        "      <td>2</td>\n",
 587 |        "    </tr>\n",
 588 |        "    <tr>\n",
 589 |        "      <td>1009</td>\n",
 590 |        "      <td>2</td>\n",
 591 |        "      <td>4</td>\n",
 592 |        "      <td>3</td>\n",
 593 |        "      <td>4</td>\n",
 594 |        "      <td>3</td>\n",
 595 |        "      <td>2</td>\n",
 596 |        "      <td>4</td>\n",
 597 |        "      <td>4</td>\n",
 598 |        "      <td>2</td>\n",
 599 |        "      <td>3</td>\n",
 600 |        "      <td>...</td>\n",
 601 |        "      <td>3</td>\n",
 602 |        "      <td>1</td>\n",
 603 |        "      <td>2</td>\n",
 604 |        "      <td>3</td>\n",
 605 |        "      <td>0</td>\n",
 606 |        "      <td>1</td>\n",
 607 |        "      <td>1</td>\n",
 608 |        "      <td>2</td>\n",
 609 |        "      <td>4</td>\n",
 610 |        "      <td>0</td>\n",
 611 |        "    </tr>\n",
 612 |        "  </tbody>\n",
 613 |        "</table>\n",
 614 |        "<p>864 rows × 57 columns</p>\n",
 615 |        "</div>"
 616 |       ],
 617 |       "text/plain": [
 618 |        "      Daily events  Prioritising workload  Writing notes  Workaholism  \\\n",
 619 |        "0                1                      1              4            3   \n",
 620 |        "1                2                      1              3            4   \n",
 621 |        "2                0                      1              4            2   \n",
 622 |        "4                2                      0              1            2   \n",
 623 |        "5                1                      1              2            2   \n",
 624 |        "...            ...                    ...            ...          ...   \n",
 625 |        "1005             2                      1              0            3   \n",
 626 |        "1006             0                      2              0            4   \n",
 627 |        "1007             2                      0              0            0   \n",
 628 |        "1008             2                      0              4            0   \n",
 629 |        "1009             2                      4              3            4   \n",
 630 |        "\n",
 631 |        "      Thinking ahead  Final judgement  Reliability  Keeping promises  \\\n",
 632 |        "0                  1                4            3                 3   \n",
 633 |        "1                  3                0            3                 3   \n",
 634 |        "2                  4                2            3                 4   \n",
 635 |        "4                  4                4            4                 3   \n",
 636 |        "5                  2                0            2                 3   \n",
 637 |        "...              ...              ...          ...               ...   \n",
 638 |        "1005               1                2            2                 2   \n",
 639 |        "1006               4                4            4                 3   \n",
 640 |        "1007               3                0            2                 4   \n",
 641 |        "1008               2                3            3                 3   \n",
 642 |        "1009               3                2            4                 4   \n",
 643 |        "\n",
 644 |        "      Loss of interest  Friends versus money  ...  Happiness in life  \\\n",
 645 |        "0                    0                     2  ...                  3   \n",
 646 |        "1                    2                     3  ...                  3   \n",
 647 |        "2                    0                     4  ...                  3   \n",
 648 |        "4                    1                     2  ...                  2   \n",
 649 |        "5                    2                     1  ...                  2   \n",
 650 |        "...                ...                   ...  ...                ...   \n",
 651 |        "1005                 3                     3  ...                  3   \n",
 652 |        "1006                 0                     1  ...                  3   \n",
 653 |        "1007                 0                     3  ...                  2   \n",
 654 |        "1008                 4                     2  ...                  2   \n",
 655 |        "1009                 2                     3  ...                  3   \n",
 656 |        "\n",
 657 |        "      Energy levels  Small - big dogs  Personality  Finding lost valuables  \\\n",
 658 |        "0                 4                 0            3                       2   \n",
 659 |        "1                 2                 4            2                       3   \n",
 660 |        "2                 3                 2            2                       2   \n",
 661 |        "4                 4                 2            2                       1   \n",
 662 |        "5                 3                 3            2                       2   \n",
 663 |        "...             ...               ...          ...                     ...   \n",
 664 |        "1005              2                 2            2                       3   \n",
 665 |        "1006              3                 2            4                       2   \n",
 666 |        "1007              0                 2            1                       2   \n",
 667 |        "1008              1                 1            3                       0   \n",
 668 |        "1009              1                 2            3                       0   \n",
 669 |        "\n",
 670 |        "      Getting up  Interests or hobbies  Parents' advice  \\\n",
 671 |        "0              1                     2                3   \n",
 672 |        "1              4                     2                1   \n",
 673 |        "2              3                     4                2   \n",
 674 |        "4              3                     2                2   \n",
 675 |        "5              2                     4                2   \n",
 676 |        "...          ...                   ...              ...   \n",
 677 |        "1005           4                     3                3   \n",
 678 |        "1006           0                     2                3   \n",
 679 |        "1007           4                     0                3   \n",
 680 |        "1008           4                     2                2   \n",
 681 |        "1009           1                     1                2   \n",
 682 |        "\n",
 683 |        "      Questionnaires or polls  Internet usage  \n",
 684 |        "0                           2               0  \n",
 685 |        "1                           2               0  \n",
 686 |        "2                           0               0  \n",
 687 |        "4                           2               0  \n",
 688 |        "5                           3               0  \n",
 689 |        "...                       ...             ...  \n",
 690 |        "1005                        2               0  \n",
 691 |        "1006                        2               1  \n",
 692 |        "1007                        4               2  \n",
 693 |        "1008                        2               2  \n",
 694 |        "1009                        4               0  \n",
 695 |        "\n",
 696 |        "[864 rows x 57 columns]"
 697 |       ]
 698 |      },
 699 |      "execution_count": 6,
 700 |      "metadata": {},
 701 |      "output_type": "execute_result"
 702 |     }
 703 |    ],
 704 |    "source": [
 705 |     "#Drop NAs\n",
 706 |     "df = df.dropna()\n",
 707 |     "#...............................................................................................\n",
 708 |     "#Encode categorical data\n",
 709 |     "from sklearn.preprocessing import LabelEncoder\n",
 710 |     "\n",
 711 |     "df = df.apply(LabelEncoder().fit_transform)\n",
 712 |     "df"
 713 |    ]
 714 |   },
 715 |   {
 716 |    "cell_type": "markdown",
 717 |    "metadata": {},
 718 |    "source": [
 719 |     "# 2. Choose the factors "
 720 |    ]
 721 |   },
 722 |   {
 723 |    "cell_type": "code",
 724 |    "execution_count": 16,
 725 |    "metadata": {},
 726 |    "outputs": [
 727 |     {
 728 |      "name": "stdout",
 729 |      "output_type": "stream",
 730 |      "text": [
 731 |       "Requirement already satisfied: factor_analyzer in c:\\users\\dell\\anaconda3\\lib\\site-packages (0.3.2)\n",
 732 |       "Requirement already satisfied: pandas in c:\\users\\dell\\anaconda3\\lib\\site-packages (from factor_analyzer) (0.25.1)\n",
 733 |       "Requirement already satisfied: scipy in c:\\users\\dell\\anaconda3\\lib\\site-packages (from factor_analyzer) (1.3.1)\n",
 734 |       "Requirement already satisfied: numpy in c:\\users\\dell\\anaconda3\\lib\\site-packages (from factor_analyzer) (1.16.5)\n",
 735 |       "Requirement already satisfied: scikit-learn in c:\\users\\dell\\anaconda3\\lib\\site-packages (from factor_analyzer) (0.21.3)\n",
 736 |       "Requirement already satisfied: pytz>=2017.2 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandas->factor_analyzer) (2019.3)\n",
 737 |       "Requirement already satisfied: python-dateutil>=2.6.1 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from pandas->factor_analyzer) (2.8.0)\n",
 738 |       "Requirement already satisfied: joblib>=0.11 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from scikit-learn->factor_analyzer) (0.13.2)\n",
 739 |       "Requirement already satisfied: six>=1.5 in c:\\users\\dell\\anaconda3\\lib\\site-packages (from python-dateutil>=2.6.1->pandas->factor_analyzer) (1.12.0)\n",
 740 |       "Note: you may need to restart the kernel to use updated packages.\n"
 741 |      ]
 742 |     }
 743 |    ],
 744 |    "source": [
 745 |     "pip install factor_analyzer "
 746 |    ]
 747 |   },
 748 |   {
 749 |    "cell_type": "code",
 750 |    "execution_count": 9,
 751 |    "metadata": {},
 752 |    "outputs": [
 753 |     {
 754 |      "data": {
 755 |       "image/png": 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\n",
 756 |       "text/plain": [
 757 |        "<Figure size 432x288 with 1 Axes>"
 758 |       ]
 759 |      },
 760 |      "metadata": {
 761 |       "needs_background": "light"
 762 |      },
 763 |      "output_type": "display_data"
 764 |     }
 765 |    ],
 766 |    "source": [
 767 |     "#Try the model with all the variables \n",
 768 |     "from factor_analyzer import FactorAnalyzer         # pip install factor_analyzer \n",
 769 |     "fa = FactorAnalyzer(rotation=\"varimax\")\n",
 770 |     "fa.fit(df) \n",
 771 |     "\n",
 772 |     "# Check Eigenvalues\n",
 773 |     "ev, v = fa.get_eigenvalues()\n",
 774 |     "ev\n",
 775 |     "\n",
 776 |     "# Create scree plot using matplotlib\n",
 777 |     "plt.scatter(range(1,df.shape[1]+1),ev)\n",
 778 |     "plt.plot(range(1,df.shape[1]+1),ev)\n",
 779 |     "plt.title('Scree Plot')\n",
 780 |     "plt.xlabel('Factors')\n",
 781 |     "plt.ylabel('Eigenvalue')\n",
 782 |     "plt.grid()\n",
 783 |     "plt.show()"
 784 |    ]
 785 |   },
 786 |   {
 787 |    "cell_type": "code",
 788 |    "execution_count": 15,
 789 |    "metadata": {},
 790 |    "outputs": [
 791 |     {
 792 |      "data": {
 793 |       "image/png": 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\n",
 794 |       "text/plain": [
 795 |        "<IPython.core.display.Image object>"
 796 |       ]
 797 |      },
 798 |      "metadata": {},
 799 |      "output_type": "display_data"
 800 |     }
 801 |    ],
 802 |    "source": [
 803 |     "from IPython.display import display, Image\n",
 804 |     "display(Image(filename='Varimax.png'))"
 805 |    ]
 806 |   },
 807 |   {
 808 |    "cell_type": "markdown",
 809 |    "metadata": {},
 810 |    "source": [
 811 |     "As you can see the most usefull factors for explain the data are between 5-6 until falling significantly.\n",
 812 |     "\n",
 813 |     "We will fit the model with 5 Factors:"
 814 |    ]
 815 |   },
 816 |   {
 817 |    "cell_type": "code",
 818 |    "execution_count": 18,
 819 |    "metadata": {},
 820 |    "outputs": [
 821 |     {
 822 |      "data": {
 823 |       "text/html": [
 824 |        "<div>\n",
 825 |        "<style scoped>\n",
 826 |        "    .dataframe tbody tr th:only-of-type {\n",
 827 |        "        vertical-align: middle;\n",
 828 |        "    }\n",
 829 |        "\n",
 830 |        "    .dataframe tbody tr th {\n",
 831 |        "        vertical-align: top;\n",
 832 |        "    }\n",
 833 |        "\n",
 834 |        "    .dataframe thead th {\n",
 835 |        "        text-align: right;\n",
 836 |        "    }\n",
 837 |        "</style>\n",
 838 |        "<table border=\"1\" class=\"dataframe\">\n",
 839 |        "  <thead>\n",
 840 |        "    <tr style=\"text-align: right;\">\n",
 841 |        "      <th></th>\n",
 842 |        "      <th>0</th>\n",
 843 |        "      <th>1</th>\n",
 844 |        "      <th>2</th>\n",
 845 |        "      <th>3</th>\n",
 846 |        "      <th>4</th>\n",
 847 |        "    </tr>\n",
 848 |        "  </thead>\n",
 849 |        "  <tbody>\n",
 850 |        "    <tr>\n",
 851 |        "      <td>Daily events</td>\n",
 852 |        "      <td>0.250416</td>\n",
 853 |        "      <td>0.058953</td>\n",
 854 |        "      <td>0.206877</td>\n",
 855 |        "      <td>0.026094</td>\n",
 856 |        "      <td>0.028915</td>\n",
 857 |        "    </tr>\n",
 858 |        "    <tr>\n",
 859 |        "      <td>Prioritising workload</td>\n",
 860 |        "      <td>-0.012803</td>\n",
 861 |        "      <td>-0.150045</td>\n",
 862 |        "      <td>0.555946</td>\n",
 863 |        "      <td>0.078913</td>\n",
 864 |        "      <td>0.128156</td>\n",
 865 |        "    </tr>\n",
 866 |        "    <tr>\n",
 867 |        "      <td>Writing notes</td>\n",
 868 |        "      <td>-0.006039</td>\n",
 869 |        "      <td>-0.015927</td>\n",
 870 |        "      <td>0.420849</td>\n",
 871 |        "      <td>0.225307</td>\n",
 872 |        "      <td>0.261380</td>\n",
 873 |        "    </tr>\n",
 874 |        "    <tr>\n",
 875 |        "      <td>Workaholism</td>\n",
 876 |        "      <td>0.069524</td>\n",
 877 |        "      <td>0.029275</td>\n",
 878 |        "      <td>0.527082</td>\n",
 879 |        "      <td>0.088573</td>\n",
 880 |        "      <td>0.032979</td>\n",
 881 |        "    </tr>\n",
 882 |        "    <tr>\n",
 883 |        "      <td>Thinking ahead</td>\n",
 884 |        "      <td>0.023475</td>\n",
 885 |        "      <td>0.127909</td>\n",
 886 |        "      <td>0.530457</td>\n",
 887 |        "      <td>0.035213</td>\n",
 888 |        "      <td>0.055426</td>\n",
 889 |        "    </tr>\n",
 890 |        "    <tr>\n",
 891 |        "      <td>Final judgement</td>\n",
 892 |        "      <td>0.046188</td>\n",
 893 |        "      <td>0.112493</td>\n",
 894 |        "      <td>0.119861</td>\n",
 895 |        "      <td>0.381338</td>\n",
 896 |        "      <td>-0.039756</td>\n",
 897 |        "    </tr>\n",
 898 |        "    <tr>\n",
 899 |        "      <td>Reliability</td>\n",
 900 |        "      <td>0.061028</td>\n",
 901 |        "      <td>-0.102481</td>\n",
 902 |        "      <td>0.539373</td>\n",
 903 |        "      <td>0.073534</td>\n",
 904 |        "      <td>-0.003491</td>\n",
 905 |        "    </tr>\n",
 906 |        "    <tr>\n",
 907 |        "      <td>Keeping promises</td>\n",
 908 |        "      <td>0.053358</td>\n",
 909 |        "      <td>-0.034661</td>\n",
 910 |        "      <td>0.420538</td>\n",
 911 |        "      <td>0.121450</td>\n",
 912 |        "      <td>-0.033511</td>\n",
 913 |        "    </tr>\n",
 914 |        "    <tr>\n",
 915 |        "      <td>Loss of interest</td>\n",
 916 |        "      <td>0.273777</td>\n",
 917 |        "      <td>0.226286</td>\n",
 918 |        "      <td>0.003524</td>\n",
 919 |        "      <td>-0.149262</td>\n",
 920 |        "      <td>0.101882</td>\n",
 921 |        "    </tr>\n",
 922 |        "    <tr>\n",
 923 |        "      <td>Friends versus money</td>\n",
 924 |        "      <td>0.021279</td>\n",
 925 |        "      <td>-0.111839</td>\n",
 926 |        "      <td>0.022026</td>\n",
 927 |        "      <td>0.381357</td>\n",
 928 |        "      <td>-0.045824</td>\n",
 929 |        "    </tr>\n",
 930 |        "    <tr>\n",
 931 |        "      <td>Funniness</td>\n",
 932 |        "      <td>0.312861</td>\n",
 933 |        "      <td>0.131400</td>\n",
 934 |        "      <td>-0.043014</td>\n",
 935 |        "      <td>-0.018258</td>\n",
 936 |        "      <td>-0.026083</td>\n",
 937 |        "    </tr>\n",
 938 |        "    <tr>\n",
 939 |        "      <td>Fake</td>\n",
 940 |        "      <td>0.091188</td>\n",
 941 |        "      <td>0.469616</td>\n",
 942 |        "      <td>-0.024535</td>\n",
 943 |        "      <td>-0.191798</td>\n",
 944 |        "      <td>0.019356</td>\n",
 945 |        "    </tr>\n",
 946 |        "    <tr>\n",
 947 |        "      <td>Criminal damage</td>\n",
 948 |        "      <td>0.154868</td>\n",
 949 |        "      <td>0.177732</td>\n",
 950 |        "      <td>-0.112659</td>\n",
 951 |        "      <td>-0.240721</td>\n",
 952 |        "      <td>0.266761</td>\n",
 953 |        "    </tr>\n",
 954 |        "    <tr>\n",
 955 |        "      <td>Decision making</td>\n",
 956 |        "      <td>-0.287128</td>\n",
 957 |        "      <td>0.102033</td>\n",
 958 |        "      <td>0.267415</td>\n",
 959 |        "      <td>0.129336</td>\n",
 960 |        "      <td>0.158694</td>\n",
 961 |        "    </tr>\n",
 962 |        "    <tr>\n",
 963 |        "      <td>Elections</td>\n",
 964 |        "      <td>0.074306</td>\n",
 965 |        "      <td>-0.015585</td>\n",
 966 |        "      <td>0.222003</td>\n",
 967 |        "      <td>0.131404</td>\n",
 968 |        "      <td>-0.083563</td>\n",
 969 |        "    </tr>\n",
 970 |        "    <tr>\n",
 971 |        "      <td>Self-criticism</td>\n",
 972 |        "      <td>-0.016858</td>\n",
 973 |        "      <td>0.398420</td>\n",
 974 |        "      <td>0.229116</td>\n",
 975 |        "      <td>0.114144</td>\n",
 976 |        "      <td>0.069707</td>\n",
 977 |        "    </tr>\n",
 978 |        "    <tr>\n",
 979 |        "      <td>Judgment calls</td>\n",
 980 |        "      <td>0.182082</td>\n",
 981 |        "      <td>-0.010461</td>\n",
 982 |        "      <td>0.102263</td>\n",
 983 |        "      <td>0.035675</td>\n",
 984 |        "      <td>0.086474</td>\n",
 985 |        "    </tr>\n",
 986 |        "    <tr>\n",
 987 |        "      <td>Hypochondria</td>\n",
 988 |        "      <td>-0.040254</td>\n",
 989 |        "      <td>0.258913</td>\n",
 990 |        "      <td>-0.034874</td>\n",
 991 |        "      <td>0.042981</td>\n",
 992 |        "      <td>0.213548</td>\n",
 993 |        "    </tr>\n",
 994 |        "    <tr>\n",
 995 |        "      <td>Empathy</td>\n",
 996 |        "      <td>-0.050152</td>\n",
 997 |        "      <td>-0.073697</td>\n",
 998 |        "      <td>0.059441</td>\n",
 999 |        "      <td>0.324982</td>\n",
1000 |        "      <td>0.133754</td>\n",
1001 |        "    </tr>\n",
1002 |        "    <tr>\n",
1003 |        "      <td>Eating to survive</td>\n",
1004 |        "      <td>-0.010608</td>\n",
1005 |        "      <td>0.183045</td>\n",
1006 |        "      <td>0.003261</td>\n",
1007 |        "      <td>-0.015131</td>\n",
1008 |        "      <td>-0.018874</td>\n",
1009 |        "    </tr>\n",
1010 |        "    <tr>\n",
1011 |        "      <td>Giving</td>\n",
1012 |        "      <td>0.082276</td>\n",
1013 |        "      <td>-0.154549</td>\n",
1014 |        "      <td>0.112481</td>\n",
1015 |        "      <td>0.376723</td>\n",
1016 |        "      <td>0.234000</td>\n",
1017 |        "    </tr>\n",
1018 |        "    <tr>\n",
1019 |        "      <td>Compassion to animals</td>\n",
1020 |        "      <td>-0.083505</td>\n",
1021 |        "      <td>-0.002767</td>\n",
1022 |        "      <td>-0.010424</td>\n",
1023 |        "      <td>0.262183</td>\n",
1024 |        "      <td>0.192734</td>\n",
1025 |        "    </tr>\n",
1026 |        "    <tr>\n",
1027 |        "      <td>Borrowed stuff</td>\n",
1028 |        "      <td>-0.097017</td>\n",
1029 |        "      <td>-0.023047</td>\n",
1030 |        "      <td>0.323253</td>\n",
1031 |        "      <td>0.171017</td>\n",
1032 |        "      <td>0.071189</td>\n",
1033 |        "    </tr>\n",
1034 |        "    <tr>\n",
1035 |        "      <td>Loneliness</td>\n",
1036 |        "      <td>-0.199197</td>\n",
1037 |        "      <td>0.542350</td>\n",
1038 |        "      <td>-0.019272</td>\n",
1039 |        "      <td>0.045942</td>\n",
1040 |        "      <td>0.190369</td>\n",
1041 |        "    </tr>\n",
1042 |        "    <tr>\n",
1043 |        "      <td>Cheating in school</td>\n",
1044 |        "      <td>0.216223</td>\n",
1045 |        "      <td>-0.063183</td>\n",
1046 |        "      <td>-0.384634</td>\n",
1047 |        "      <td>-0.083940</td>\n",
1048 |        "      <td>0.208210</td>\n",
1049 |        "    </tr>\n",
1050 |        "    <tr>\n",
1051 |        "      <td>Health</td>\n",
1052 |        "      <td>-0.012267</td>\n",
1053 |        "      <td>0.027867</td>\n",
1054 |        "      <td>0.131645</td>\n",
1055 |        "      <td>0.184296</td>\n",
1056 |        "      <td>0.437826</td>\n",
1057 |        "    </tr>\n",
1058 |        "    <tr>\n",
1059 |        "      <td>Changing the past</td>\n",
1060 |        "      <td>-0.016622</td>\n",
1061 |        "      <td>0.482307</td>\n",
1062 |        "      <td>-0.161320</td>\n",
1063 |        "      <td>0.073843</td>\n",
1064 |        "      <td>0.159231</td>\n",
1065 |        "    </tr>\n",
1066 |        "    <tr>\n",
1067 |        "      <td>God</td>\n",
1068 |        "      <td>0.047894</td>\n",
1069 |        "      <td>0.032281</td>\n",
1070 |        "      <td>0.027136</td>\n",
1071 |        "      <td>0.453873</td>\n",
1072 |        "      <td>-0.025963</td>\n",
1073 |        "    </tr>\n",
1074 |        "    <tr>\n",
1075 |        "      <td>Dreams</td>\n",
1076 |        "      <td>0.207076</td>\n",
1077 |        "      <td>-0.187723</td>\n",
1078 |        "      <td>0.078634</td>\n",
1079 |        "      <td>0.037709</td>\n",
1080 |        "      <td>-0.124853</td>\n",
1081 |        "    </tr>\n",
1082 |        "    <tr>\n",
1083 |        "      <td>Charity</td>\n",
1084 |        "      <td>0.163161</td>\n",
1085 |        "      <td>0.116834</td>\n",
1086 |        "      <td>0.156898</td>\n",
1087 |        "      <td>0.354953</td>\n",
1088 |        "      <td>-0.067795</td>\n",
1089 |        "    </tr>\n",
1090 |        "    <tr>\n",
1091 |        "      <td>Number of friends</td>\n",
1092 |        "      <td>0.514994</td>\n",
1093 |        "      <td>-0.321738</td>\n",
1094 |        "      <td>-0.086711</td>\n",
1095 |        "      <td>0.241070</td>\n",
1096 |        "      <td>-0.006859</td>\n",
1097 |        "    </tr>\n",
1098 |        "    <tr>\n",
1099 |        "      <td>Punctuality</td>\n",
1100 |        "      <td>0.004662</td>\n",
1101 |        "      <td>0.090531</td>\n",
1102 |        "      <td>-0.143569</td>\n",
1103 |        "      <td>0.069648</td>\n",
1104 |        "      <td>0.078111</td>\n",
1105 |        "    </tr>\n",
1106 |        "    <tr>\n",
1107 |        "      <td>Lying</td>\n",
1108 |        "      <td>-0.095933</td>\n",
1109 |        "      <td>-0.193370</td>\n",
1110 |        "      <td>0.001775</td>\n",
1111 |        "      <td>0.138092</td>\n",
1112 |        "      <td>0.006950</td>\n",
1113 |        "    </tr>\n",
1114 |        "    <tr>\n",
1115 |        "      <td>Waiting</td>\n",
1116 |        "      <td>0.032019</td>\n",
1117 |        "      <td>-0.067715</td>\n",
1118 |        "      <td>-0.000820</td>\n",
1119 |        "      <td>0.075966</td>\n",
1120 |        "      <td>-0.329606</td>\n",
1121 |        "    </tr>\n",
1122 |        "    <tr>\n",
1123 |        "      <td>New environment</td>\n",
1124 |        "      <td>0.470076</td>\n",
1125 |        "      <td>-0.129745</td>\n",
1126 |        "      <td>-0.058912</td>\n",
1127 |        "      <td>0.005400</td>\n",
1128 |        "      <td>-0.230743</td>\n",
1129 |        "    </tr>\n",
1130 |        "    <tr>\n",
1131 |        "      <td>Mood swings</td>\n",
1132 |        "      <td>-0.086477</td>\n",
1133 |        "      <td>0.353226</td>\n",
1134 |        "      <td>-0.041005</td>\n",
1135 |        "      <td>0.031490</td>\n",
1136 |        "      <td>0.404388</td>\n",
1137 |        "    </tr>\n",
1138 |        "    <tr>\n",
1139 |        "      <td>Appearence and gestures</td>\n",
1140 |        "      <td>0.227246</td>\n",
1141 |        "      <td>-0.004762</td>\n",
1142 |        "      <td>0.105894</td>\n",
1143 |        "      <td>0.068825</td>\n",
1144 |        "      <td>0.303119</td>\n",
1145 |        "    </tr>\n",
1146 |        "    <tr>\n",
1147 |        "      <td>Socializing</td>\n",
1148 |        "      <td>0.537811</td>\n",
1149 |        "      <td>-0.096245</td>\n",
1150 |        "      <td>-0.048127</td>\n",
1151 |        "      <td>0.135323</td>\n",
1152 |        "      <td>-0.039204</td>\n",
1153 |        "    </tr>\n",
1154 |        "    <tr>\n",
1155 |        "      <td>Achievements</td>\n",
1156 |        "      <td>0.252835</td>\n",
1157 |        "      <td>0.048658</td>\n",
1158 |        "      <td>-0.042799</td>\n",
1159 |        "      <td>-0.082401</td>\n",
1160 |        "      <td>0.111902</td>\n",
1161 |        "    </tr>\n",
1162 |        "    <tr>\n",
1163 |        "      <td>Responding to a serious letter</td>\n",
1164 |        "      <td>-0.126985</td>\n",
1165 |        "      <td>0.087976</td>\n",
1166 |        "      <td>-0.026876</td>\n",
1167 |        "      <td>0.022940</td>\n",
1168 |        "      <td>0.013346</td>\n",
1169 |        "    </tr>\n",
1170 |        "    <tr>\n",
1171 |        "      <td>Children</td>\n",
1172 |        "      <td>0.079877</td>\n",
1173 |        "      <td>-0.134254</td>\n",
1174 |        "      <td>0.033040</td>\n",
1175 |        "      <td>0.440103</td>\n",
1176 |        "      <td>0.075663</td>\n",
1177 |        "    </tr>\n",
1178 |        "    <tr>\n",
1179 |        "      <td>Assertiveness</td>\n",
1180 |        "      <td>0.353462</td>\n",
1181 |        "      <td>-0.094372</td>\n",
1182 |        "      <td>0.002509</td>\n",
1183 |        "      <td>-0.067185</td>\n",
1184 |        "      <td>0.044117</td>\n",
1185 |        "    </tr>\n",
1186 |        "    <tr>\n",
1187 |        "      <td>Getting angry</td>\n",
1188 |        "      <td>0.051167</td>\n",
1189 |        "      <td>0.176922</td>\n",
1190 |        "      <td>-0.086069</td>\n",
1191 |        "      <td>-0.070837</td>\n",
1192 |        "      <td>0.532025</td>\n",
1193 |        "    </tr>\n",
1194 |        "    <tr>\n",
1195 |        "      <td>Knowing the right people</td>\n",
1196 |        "      <td>0.478657</td>\n",
1197 |        "      <td>0.022868</td>\n",
1198 |        "      <td>0.113503</td>\n",
1199 |        "      <td>-0.045359</td>\n",
1200 |        "      <td>0.227230</td>\n",
1201 |        "    </tr>\n",
1202 |        "    <tr>\n",
1203 |        "      <td>Public speaking</td>\n",
1204 |        "      <td>-0.385674</td>\n",
1205 |        "      <td>0.104662</td>\n",
1206 |        "      <td>0.069712</td>\n",
1207 |        "      <td>0.030447</td>\n",
1208 |        "      <td>0.190834</td>\n",
1209 |        "    </tr>\n",
1210 |        "    <tr>\n",
1211 |        "      <td>Unpopularity</td>\n",
1212 |        "      <td>-0.082146</td>\n",
1213 |        "      <td>0.229228</td>\n",
1214 |        "      <td>0.079173</td>\n",
1215 |        "      <td>0.241031</td>\n",
1216 |        "      <td>-0.031212</td>\n",
1217 |        "    </tr>\n",
1218 |        "    <tr>\n",
1219 |        "      <td>Life struggles</td>\n",
1220 |        "      <td>-0.226293</td>\n",
1221 |        "      <td>0.057892</td>\n",
1222 |        "      <td>-0.059615</td>\n",
1223 |        "      <td>0.384875</td>\n",
1224 |        "      <td>0.392060</td>\n",
1225 |        "    </tr>\n",
1226 |        "    <tr>\n",
1227 |        "      <td>Happiness in life</td>\n",
1228 |        "      <td>0.288585</td>\n",
1229 |        "      <td>-0.541050</td>\n",
1230 |        "      <td>0.158473</td>\n",
1231 |        "      <td>0.051235</td>\n",
1232 |        "      <td>-0.064525</td>\n",
1233 |        "    </tr>\n",
1234 |        "    <tr>\n",
1235 |        "      <td>Energy levels</td>\n",
1236 |        "      <td>0.499978</td>\n",
1237 |        "      <td>-0.478860</td>\n",
1238 |        "      <td>0.037918</td>\n",
1239 |        "      <td>0.122773</td>\n",
1240 |        "      <td>-0.025001</td>\n",
1241 |        "    </tr>\n",
1242 |        "    <tr>\n",
1243 |        "      <td>Small - big dogs</td>\n",
1244 |        "      <td>0.206696</td>\n",
1245 |        "      <td>0.040211</td>\n",
1246 |        "      <td>-0.143225</td>\n",
1247 |        "      <td>-0.203991</td>\n",
1248 |        "      <td>-0.131298</td>\n",
1249 |        "    </tr>\n",
1250 |        "    <tr>\n",
1251 |        "      <td>Personality</td>\n",
1252 |        "      <td>0.259646</td>\n",
1253 |        "      <td>-0.393197</td>\n",
1254 |        "      <td>0.064236</td>\n",
1255 |        "      <td>0.049013</td>\n",
1256 |        "      <td>-0.056988</td>\n",
1257 |        "    </tr>\n",
1258 |        "    <tr>\n",
1259 |        "      <td>Finding lost valuables</td>\n",
1260 |        "      <td>-0.127907</td>\n",
1261 |        "      <td>-0.011367</td>\n",
1262 |        "      <td>0.163354</td>\n",
1263 |        "      <td>0.391951</td>\n",
1264 |        "      <td>-0.101749</td>\n",
1265 |        "    </tr>\n",
1266 |        "    <tr>\n",
1267 |        "      <td>Getting up</td>\n",
1268 |        "      <td>0.012217</td>\n",
1269 |        "      <td>0.150551</td>\n",
1270 |        "      <td>-0.312297</td>\n",
1271 |        "      <td>0.082580</td>\n",
1272 |        "      <td>0.121198</td>\n",
1273 |        "    </tr>\n",
1274 |        "    <tr>\n",
1275 |        "      <td>Interests or hobbies</td>\n",
1276 |        "      <td>0.465627</td>\n",
1277 |        "      <td>-0.253289</td>\n",
1278 |        "      <td>0.065015</td>\n",
1279 |        "      <td>0.144827</td>\n",
1280 |        "      <td>-0.078694</td>\n",
1281 |        "    </tr>\n",
1282 |        "    <tr>\n",
1283 |        "      <td>Parents' advice</td>\n",
1284 |        "      <td>0.022594</td>\n",
1285 |        "      <td>-0.032871</td>\n",
1286 |        "      <td>0.243628</td>\n",
1287 |        "      <td>0.282252</td>\n",
1288 |        "      <td>0.113225</td>\n",
1289 |        "    </tr>\n",
1290 |        "    <tr>\n",
1291 |        "      <td>Questionnaires or polls</td>\n",
1292 |        "      <td>-0.045177</td>\n",
1293 |        "      <td>0.114865</td>\n",
1294 |        "      <td>0.154309</td>\n",
1295 |        "      <td>0.188501</td>\n",
1296 |        "      <td>-0.032532</td>\n",
1297 |        "    </tr>\n",
1298 |        "    <tr>\n",
1299 |        "      <td>Internet usage</td>\n",
1300 |        "      <td>-0.046077</td>\n",
1301 |        "      <td>0.075435</td>\n",
1302 |        "      <td>-0.007799</td>\n",
1303 |        "      <td>-0.081575</td>\n",
1304 |        "      <td>0.048144</td>\n",
1305 |        "    </tr>\n",
1306 |        "  </tbody>\n",
1307 |        "</table>\n",
1308 |        "</div>"
1309 |       ],
1310 |       "text/plain": [
1311 |        "                                       0         1         2         3  \\\n",
1312 |        "Daily events                    0.250416  0.058953  0.206877  0.026094   \n",
1313 |        "Prioritising workload          -0.012803 -0.150045  0.555946  0.078913   \n",
1314 |        "Writing notes                  -0.006039 -0.015927  0.420849  0.225307   \n",
1315 |        "Workaholism                     0.069524  0.029275  0.527082  0.088573   \n",
1316 |        "Thinking ahead                  0.023475  0.127909  0.530457  0.035213   \n",
1317 |        "Final judgement                 0.046188  0.112493  0.119861  0.381338   \n",
1318 |        "Reliability                     0.061028 -0.102481  0.539373  0.073534   \n",
1319 |        "Keeping promises                0.053358 -0.034661  0.420538  0.121450   \n",
1320 |        "Loss of interest                0.273777  0.226286  0.003524 -0.149262   \n",
1321 |        "Friends versus money            0.021279 -0.111839  0.022026  0.381357   \n",
1322 |        "Funniness                       0.312861  0.131400 -0.043014 -0.018258   \n",
1323 |        "Fake                            0.091188  0.469616 -0.024535 -0.191798   \n",
1324 |        "Criminal damage                 0.154868  0.177732 -0.112659 -0.240721   \n",
1325 |        "Decision making                -0.287128  0.102033  0.267415  0.129336   \n",
1326 |        "Elections                       0.074306 -0.015585  0.222003  0.131404   \n",
1327 |        "Self-criticism                 -0.016858  0.398420  0.229116  0.114144   \n",
1328 |        "Judgment calls                  0.182082 -0.010461  0.102263  0.035675   \n",
1329 |        "Hypochondria                   -0.040254  0.258913 -0.034874  0.042981   \n",
1330 |        "Empathy                        -0.050152 -0.073697  0.059441  0.324982   \n",
1331 |        "Eating to survive              -0.010608  0.183045  0.003261 -0.015131   \n",
1332 |        "Giving                          0.082276 -0.154549  0.112481  0.376723   \n",
1333 |        "Compassion to animals          -0.083505 -0.002767 -0.010424  0.262183   \n",
1334 |        "Borrowed stuff                 -0.097017 -0.023047  0.323253  0.171017   \n",
1335 |        "Loneliness                     -0.199197  0.542350 -0.019272  0.045942   \n",
1336 |        "Cheating in school              0.216223 -0.063183 -0.384634 -0.083940   \n",
1337 |        "Health                         -0.012267  0.027867  0.131645  0.184296   \n",
1338 |        "Changing the past              -0.016622  0.482307 -0.161320  0.073843   \n",
1339 |        "God                             0.047894  0.032281  0.027136  0.453873   \n",
1340 |        "Dreams                          0.207076 -0.187723  0.078634  0.037709   \n",
1341 |        "Charity                         0.163161  0.116834  0.156898  0.354953   \n",
1342 |        "Number of friends               0.514994 -0.321738 -0.086711  0.241070   \n",
1343 |        "Punctuality                     0.004662  0.090531 -0.143569  0.069648   \n",
1344 |        "Lying                          -0.095933 -0.193370  0.001775  0.138092   \n",
1345 |        "Waiting                         0.032019 -0.067715 -0.000820  0.075966   \n",
1346 |        "New environment                 0.470076 -0.129745 -0.058912  0.005400   \n",
1347 |        "Mood swings                    -0.086477  0.353226 -0.041005  0.031490   \n",
1348 |        "Appearence and gestures         0.227246 -0.004762  0.105894  0.068825   \n",
1349 |        "Socializing                     0.537811 -0.096245 -0.048127  0.135323   \n",
1350 |        "Achievements                    0.252835  0.048658 -0.042799 -0.082401   \n",
1351 |        "Responding to a serious letter -0.126985  0.087976 -0.026876  0.022940   \n",
1352 |        "Children                        0.079877 -0.134254  0.033040  0.440103   \n",
1353 |        "Assertiveness                   0.353462 -0.094372  0.002509 -0.067185   \n",
1354 |        "Getting angry                   0.051167  0.176922 -0.086069 -0.070837   \n",
1355 |        "Knowing the right people        0.478657  0.022868  0.113503 -0.045359   \n",
1356 |        "Public speaking                -0.385674  0.104662  0.069712  0.030447   \n",
1357 |        "Unpopularity                   -0.082146  0.229228  0.079173  0.241031   \n",
1358 |        "Life struggles                 -0.226293  0.057892 -0.059615  0.384875   \n",
1359 |        "Happiness in life               0.288585 -0.541050  0.158473  0.051235   \n",
1360 |        "Energy levels                   0.499978 -0.478860  0.037918  0.122773   \n",
1361 |        "Small - big dogs                0.206696  0.040211 -0.143225 -0.203991   \n",
1362 |        "Personality                     0.259646 -0.393197  0.064236  0.049013   \n",
1363 |        "Finding lost valuables         -0.127907 -0.011367  0.163354  0.391951   \n",
1364 |        "Getting up                      0.012217  0.150551 -0.312297  0.082580   \n",
1365 |        "Interests or hobbies            0.465627 -0.253289  0.065015  0.144827   \n",
1366 |        "Parents' advice                 0.022594 -0.032871  0.243628  0.282252   \n",
1367 |        "Questionnaires or polls        -0.045177  0.114865  0.154309  0.188501   \n",
1368 |        "Internet usage                 -0.046077  0.075435 -0.007799 -0.081575   \n",
1369 |        "\n",
1370 |        "                                       4  \n",
1371 |        "Daily events                    0.028915  \n",
1372 |        "Prioritising workload           0.128156  \n",
1373 |        "Writing notes                   0.261380  \n",
1374 |        "Workaholism                     0.032979  \n",
1375 |        "Thinking ahead                  0.055426  \n",
1376 |        "Final judgement                -0.039756  \n",
1377 |        "Reliability                    -0.003491  \n",
1378 |        "Keeping promises               -0.033511  \n",
1379 |        "Loss of interest                0.101882  \n",
1380 |        "Friends versus money           -0.045824  \n",
1381 |        "Funniness                      -0.026083  \n",
1382 |        "Fake                            0.019356  \n",
1383 |        "Criminal damage                 0.266761  \n",
1384 |        "Decision making                 0.158694  \n",
1385 |        "Elections                      -0.083563  \n",
1386 |        "Self-criticism                  0.069707  \n",
1387 |        "Judgment calls                  0.086474  \n",
1388 |        "Hypochondria                    0.213548  \n",
1389 |        "Empathy                         0.133754  \n",
1390 |        "Eating to survive              -0.018874  \n",
1391 |        "Giving                          0.234000  \n",
1392 |        "Compassion to animals           0.192734  \n",
1393 |        "Borrowed stuff                  0.071189  \n",
1394 |        "Loneliness                      0.190369  \n",
1395 |        "Cheating in school              0.208210  \n",
1396 |        "Health                          0.437826  \n",
1397 |        "Changing the past               0.159231  \n",
1398 |        "God                            -0.025963  \n",
1399 |        "Dreams                         -0.124853  \n",
1400 |        "Charity                        -0.067795  \n",
1401 |        "Number of friends              -0.006859  \n",
1402 |        "Punctuality                     0.078111  \n",
1403 |        "Lying                           0.006950  \n",
1404 |        "Waiting                        -0.329606  \n",
1405 |        "New environment                -0.230743  \n",
1406 |        "Mood swings                     0.404388  \n",
1407 |        "Appearence and gestures         0.303119  \n",
1408 |        "Socializing                    -0.039204  \n",
1409 |        "Achievements                    0.111902  \n",
1410 |        "Responding to a serious letter  0.013346  \n",
1411 |        "Children                        0.075663  \n",
1412 |        "Assertiveness                   0.044117  \n",
1413 |        "Getting angry                   0.532025  \n",
1414 |        "Knowing the right people        0.227230  \n",
1415 |        "Public speaking                 0.190834  \n",
1416 |        "Unpopularity                   -0.031212  \n",
1417 |        "Life struggles                  0.392060  \n",
1418 |        "Happiness in life              -0.064525  \n",
1419 |        "Energy levels                  -0.025001  \n",
1420 |        "Small - big dogs               -0.131298  \n",
1421 |        "Personality                    -0.056988  \n",
1422 |        "Finding lost valuables         -0.101749  \n",
1423 |        "Getting up                      0.121198  \n",
1424 |        "Interests or hobbies           -0.078694  \n",
1425 |        "Parents' advice                 0.113225  \n",
1426 |        "Questionnaires or polls        -0.032532  \n",
1427 |        "Internet usage                  0.048144  "
1428 |       ]
1429 |      },
1430 |      "execution_count": 18,
1431 |      "metadata": {},
1432 |      "output_type": "execute_result"
1433 |     }
1434 |    ],
1435 |    "source": [
1436 |     "#Factor analysis with 5 fators\n",
1437 |     "fa = FactorAnalyzer(5, rotation=\"varimax\")\n",
1438 |     "fa.fit(df)\n",
1439 |     "AF = fa.loadings_\n",
1440 |     "AF = pd.DataFrame(AF)\n",
1441 |     "AF.index = df.columns\n",
1442 |     "AF"
1443 |    ]
1444 |   },
1445 |   {
1446 |    "cell_type": "code",
1447 |    "execution_count": 19,
1448 |    "metadata": {},
1449 |    "outputs": [
1450 |     {
1451 |      "data": {
1452 |       "text/html": [
1453 |        "<div>\n",
1454 |        "<style scoped>\n",
1455 |        "    .dataframe tbody tr th:only-of-type {\n",
1456 |        "        vertical-align: middle;\n",
1457 |        "    }\n",
1458 |        "\n",
1459 |        "    .dataframe tbody tr th {\n",
1460 |        "        vertical-align: top;\n",
1461 |        "    }\n",
1462 |        "\n",
1463 |        "    .dataframe thead th {\n",
1464 |        "        text-align: right;\n",
1465 |        "    }\n",
1466 |        "</style>\n",
1467 |        "<table border=\"1\" class=\"dataframe\">\n",
1468 |        "  <thead>\n",
1469 |        "    <tr style=\"text-align: right;\">\n",
1470 |        "      <th></th>\n",
1471 |        "      <th>FACTOR</th>\n",
1472 |        "      <th>Variable</th>\n",
1473 |        "      <th>Varianza_Explica</th>\n",
1474 |        "    </tr>\n",
1475 |        "  </thead>\n",
1476 |        "  <tbody>\n",
1477 |        "    <tr>\n",
1478 |        "      <td>0</td>\n",
1479 |        "      <td>0</td>\n",
1480 |        "      <td>New environment</td>\n",
1481 |        "      <td>0.470076</td>\n",
1482 |        "    </tr>\n",
1483 |        "    <tr>\n",
1484 |        "      <td>1</td>\n",
1485 |        "      <td>0</td>\n",
1486 |        "      <td>Energy levels</td>\n",
1487 |        "      <td>0.499978</td>\n",
1488 |        "    </tr>\n",
1489 |        "    <tr>\n",
1490 |        "      <td>2</td>\n",
1491 |        "      <td>0</td>\n",
1492 |        "      <td>Number of friends</td>\n",
1493 |        "      <td>0.514994</td>\n",
1494 |        "    </tr>\n",
1495 |        "    <tr>\n",
1496 |        "      <td>3</td>\n",
1497 |        "      <td>0</td>\n",
1498 |        "      <td>Socializing</td>\n",
1499 |        "      <td>0.537811</td>\n",
1500 |        "    </tr>\n",
1501 |        "    <tr>\n",
1502 |        "      <td>4</td>\n",
1503 |        "      <td>0</td>\n",
1504 |        "      <td>Knowing the right people</td>\n",
1505 |        "      <td>0.478657</td>\n",
1506 |        "    </tr>\n",
1507 |        "    <tr>\n",
1508 |        "      <td>5</td>\n",
1509 |        "      <td>1</td>\n",
1510 |        "      <td>Mood swings</td>\n",
1511 |        "      <td>0.353226</td>\n",
1512 |        "    </tr>\n",
1513 |        "    <tr>\n",
1514 |        "      <td>6</td>\n",
1515 |        "      <td>1</td>\n",
1516 |        "      <td>Self-criticism</td>\n",
1517 |        "      <td>0.398420</td>\n",
1518 |        "    </tr>\n",
1519 |        "    <tr>\n",
1520 |        "      <td>7</td>\n",
1521 |        "      <td>1</td>\n",
1522 |        "      <td>Fake</td>\n",
1523 |        "      <td>0.469616</td>\n",
1524 |        "    </tr>\n",
1525 |        "    <tr>\n",
1526 |        "      <td>8</td>\n",
1527 |        "      <td>1</td>\n",
1528 |        "      <td>Changing the past</td>\n",
1529 |        "      <td>0.482307</td>\n",
1530 |        "    </tr>\n",
1531 |        "    <tr>\n",
1532 |        "      <td>9</td>\n",
1533 |        "      <td>1</td>\n",
1534 |        "      <td>Loneliness</td>\n",
1535 |        "      <td>0.542350</td>\n",
1536 |        "    </tr>\n",
1537 |        "    <tr>\n",
1538 |        "      <td>10</td>\n",
1539 |        "      <td>2</td>\n",
1540 |        "      <td>Writing notes</td>\n",
1541 |        "      <td>0.420849</td>\n",
1542 |        "    </tr>\n",
1543 |        "    <tr>\n",
1544 |        "      <td>11</td>\n",
1545 |        "      <td>2</td>\n",
1546 |        "      <td>Workaholism</td>\n",
1547 |        "      <td>0.527082</td>\n",
1548 |        "    </tr>\n",
1549 |        "    <tr>\n",
1550 |        "      <td>12</td>\n",
1551 |        "      <td>2</td>\n",
1552 |        "      <td>Thinking ahead</td>\n",
1553 |        "      <td>0.530457</td>\n",
1554 |        "    </tr>\n",
1555 |        "    <tr>\n",
1556 |        "      <td>13</td>\n",
1557 |        "      <td>2</td>\n",
1558 |        "      <td>Prioritising workload</td>\n",
1559 |        "      <td>0.555946</td>\n",
1560 |        "    </tr>\n",
1561 |        "    <tr>\n",
1562 |        "      <td>14</td>\n",
1563 |        "      <td>2</td>\n",
1564 |        "      <td>Reliability</td>\n",
1565 |        "      <td>0.539373</td>\n",
1566 |        "    </tr>\n",
1567 |        "    <tr>\n",
1568 |        "      <td>15</td>\n",
1569 |        "      <td>3</td>\n",
1570 |        "      <td>Friends versus money</td>\n",
1571 |        "      <td>0.381357</td>\n",
1572 |        "    </tr>\n",
1573 |        "    <tr>\n",
1574 |        "      <td>16</td>\n",
1575 |        "      <td>3</td>\n",
1576 |        "      <td>Life struggles</td>\n",
1577 |        "      <td>0.384875</td>\n",
1578 |        "    </tr>\n",
1579 |        "    <tr>\n",
1580 |        "      <td>17</td>\n",
1581 |        "      <td>3</td>\n",
1582 |        "      <td>Finding lost valuables</td>\n",
1583 |        "      <td>0.391951</td>\n",
1584 |        "    </tr>\n",
1585 |        "    <tr>\n",
1586 |        "      <td>18</td>\n",
1587 |        "      <td>3</td>\n",
1588 |        "      <td>Children</td>\n",
1589 |        "      <td>0.440103</td>\n",
1590 |        "    </tr>\n",
1591 |        "    <tr>\n",
1592 |        "      <td>19</td>\n",
1593 |        "      <td>3</td>\n",
1594 |        "      <td>God</td>\n",
1595 |        "      <td>0.453873</td>\n",
1596 |        "    </tr>\n",
1597 |        "    <tr>\n",
1598 |        "      <td>20</td>\n",
1599 |        "      <td>4</td>\n",
1600 |        "      <td>Appearence and gestures</td>\n",
1601 |        "      <td>0.303119</td>\n",
1602 |        "    </tr>\n",
1603 |        "    <tr>\n",
1604 |        "      <td>21</td>\n",
1605 |        "      <td>4</td>\n",
1606 |        "      <td>Life struggles</td>\n",
1607 |        "      <td>0.392060</td>\n",
1608 |        "    </tr>\n",
1609 |        "    <tr>\n",
1610 |        "      <td>22</td>\n",
1611 |        "      <td>4</td>\n",
1612 |        "      <td>Mood swings</td>\n",
1613 |        "      <td>0.404388</td>\n",
1614 |        "    </tr>\n",
1615 |        "    <tr>\n",
1616 |        "      <td>23</td>\n",
1617 |        "      <td>4</td>\n",
1618 |        "      <td>Health</td>\n",
1619 |        "      <td>0.437826</td>\n",
1620 |        "    </tr>\n",
1621 |        "    <tr>\n",
1622 |        "      <td>24</td>\n",
1623 |        "      <td>4</td>\n",
1624 |        "      <td>Getting angry</td>\n",
1625 |        "      <td>0.532025</td>\n",
1626 |        "    </tr>\n",
1627 |        "  </tbody>\n",
1628 |        "</table>\n",
1629 |        "</div>"
1630 |       ],
1631 |       "text/plain": [
1632 |        "    FACTOR                  Variable  Varianza_Explica\n",
1633 |        "0        0           New environment          0.470076\n",
1634 |        "1        0             Energy levels          0.499978\n",
1635 |        "2        0         Number of friends          0.514994\n",
1636 |        "3        0               Socializing          0.537811\n",
1637 |        "4        0  Knowing the right people          0.478657\n",
1638 |        "5        1               Mood swings          0.353226\n",
1639 |        "6        1            Self-criticism          0.398420\n",
1640 |        "7        1                      Fake          0.469616\n",
1641 |        "8        1         Changing the past          0.482307\n",
1642 |        "9        1                Loneliness          0.542350\n",
1643 |        "10       2             Writing notes          0.420849\n",
1644 |        "11       2               Workaholism          0.527082\n",
1645 |        "12       2            Thinking ahead          0.530457\n",
1646 |        "13       2     Prioritising workload          0.555946\n",
1647 |        "14       2               Reliability          0.539373\n",
1648 |        "15       3      Friends versus money          0.381357\n",
1649 |        "16       3            Life struggles          0.384875\n",
1650 |        "17       3    Finding lost valuables          0.391951\n",
1651 |        "18       3                  Children          0.440103\n",
1652 |        "19       3                       God          0.453873\n",
1653 |        "20       4   Appearence and gestures          0.303119\n",
1654 |        "21       4            Life struggles          0.392060\n",
1655 |        "22       4               Mood swings          0.404388\n",
1656 |        "23       4                    Health          0.437826\n",
1657 |        "24       4             Getting angry          0.532025"
1658 |       ]
1659 |      },
1660 |      "execution_count": 19,
1661 |      "metadata": {},
1662 |      "output_type": "execute_result"
1663 |     }
1664 |    ],
1665 |    "source": [
1666 |     "#Get Top variables for each Factor \n",
1667 |     "F = AF.unstack()\n",
1668 |     "F = pd.DataFrame(F).reset_index()\n",
1669 |     "F = F.sort_values(['level_0',0], ascending=False).groupby('level_0').head(5)    # Top 5 \n",
1670 |     "F = F.sort_values(by=\"level_0\")\n",
1671 |     "F.columns=[\"FACTOR\",\"Variable\",\"Varianza_Explica\"]\n",
1672 |     "F = F.reset_index().drop([\"index\"],axis=1)\n",
1673 |     "F"
1674 |    ]
1675 |   },
1676 |   {
1677 |    "cell_type": "code",
1678 |    "execution_count": 20,
1679 |    "metadata": {},
1680 |    "outputs": [
1681 |     {
1682 |      "data": {
1683 |       "text/html": [
1684 |        "<div>\n",
1685 |        "<style scoped>\n",
1686 |        "    .dataframe tbody tr th:only-of-type {\n",
1687 |        "        vertical-align: middle;\n",
1688 |        "    }\n",
1689 |        "\n",
1690 |        "    .dataframe tbody tr th {\n",
1691 |        "        vertical-align: top;\n",
1692 |        "    }\n",
1693 |        "\n",
1694 |        "    .dataframe thead th {\n",
1695 |        "        text-align: right;\n",
1696 |        "    }\n",
1697 |        "</style>\n",
1698 |        "<table border=\"1\" class=\"dataframe\">\n",
1699 |        "  <thead>\n",
1700 |        "    <tr style=\"text-align: right;\">\n",
1701 |        "      <th>FACTOR</th>\n",
1702 |        "      <th>0</th>\n",
1703 |        "      <th>1</th>\n",
1704 |        "      <th>2</th>\n",
1705 |        "      <th>3</th>\n",
1706 |        "      <th>4</th>\n",
1707 |        "    </tr>\n",
1708 |        "  </thead>\n",
1709 |        "  <tbody>\n",
1710 |        "    <tr>\n",
1711 |        "      <td>0</td>\n",
1712 |        "      <td>New environment</td>\n",
1713 |        "      <td>Mood swings</td>\n",
1714 |        "      <td>Writing notes</td>\n",
1715 |        "      <td>Friends versus money</td>\n",
1716 |        "      <td>Appearence and gestures</td>\n",
1717 |        "    </tr>\n",
1718 |        "    <tr>\n",
1719 |        "      <td>1</td>\n",
1720 |        "      <td>Energy levels</td>\n",
1721 |        "      <td>Self-criticism</td>\n",
1722 |        "      <td>Workaholism</td>\n",
1723 |        "      <td>Life struggles</td>\n",
1724 |        "      <td>Life struggles</td>\n",
1725 |        "    </tr>\n",
1726 |        "    <tr>\n",
1727 |        "      <td>2</td>\n",
1728 |        "      <td>Number of friends</td>\n",
1729 |        "      <td>Fake</td>\n",
1730 |        "      <td>Thinking ahead</td>\n",
1731 |        "      <td>Finding lost valuables</td>\n",
1732 |        "      <td>Mood swings</td>\n",
1733 |        "    </tr>\n",
1734 |        "    <tr>\n",
1735 |        "      <td>3</td>\n",
1736 |        "      <td>Socializing</td>\n",
1737 |        "      <td>Changing the past</td>\n",
1738 |        "      <td>Prioritising workload</td>\n",
1739 |        "      <td>Children</td>\n",
1740 |        "      <td>Health</td>\n",
1741 |        "    </tr>\n",
1742 |        "    <tr>\n",
1743 |        "      <td>4</td>\n",
1744 |        "      <td>Knowing the right people</td>\n",
1745 |        "      <td>Loneliness</td>\n",
1746 |        "      <td>Reliability</td>\n",
1747 |        "      <td>God</td>\n",
1748 |        "      <td>Getting angry</td>\n",
1749 |        "    </tr>\n",
1750 |        "  </tbody>\n",
1751 |        "</table>\n",
1752 |        "</div>"
1753 |       ],
1754 |       "text/plain": [
1755 |        "FACTOR                         0                  1                      2  \\\n",
1756 |        "0                New environment        Mood swings          Writing notes   \n",
1757 |        "1                  Energy levels     Self-criticism            Workaholism   \n",
1758 |        "2              Number of friends               Fake         Thinking ahead   \n",
1759 |        "3                    Socializing  Changing the past  Prioritising workload   \n",
1760 |        "4       Knowing the right people         Loneliness            Reliability   \n",
1761 |        "\n",
1762 |        "FACTOR                       3                        4  \n",
1763 |        "0         Friends versus money  Appearence and gestures  \n",
1764 |        "1               Life struggles           Life struggles  \n",
1765 |        "2       Finding lost valuables              Mood swings  \n",
1766 |        "3                     Children                   Health  \n",
1767 |        "4                          God            Getting angry  "
1768 |       ]
1769 |      },
1770 |      "execution_count": 20,
1771 |      "metadata": {},
1772 |      "output_type": "execute_result"
1773 |     }
1774 |    ],
1775 |    "source": [
1776 |     "#Show the Top for each Factor \n",
1777 |     "F = F.pivot(columns='FACTOR')[\"Variable\"]\n",
1778 |     "F.apply(lambda x: pd.Series(x.dropna().to_numpy()))"
1779 |    ]
1780 |   },
1781 |   {
1782 |    "cell_type": "markdown",
1783 |    "metadata": {},
1784 |    "source": [
1785 |     "FACTOR 1: Energy levels, Number of friends, Socializing...\n",
1786 |     "\n",
1787 |     "Could be: Extraversion\n",
1788 |     "\n",
1789 |     "FACTOR 2: Self-ciricism, Fake, Loneliness...\n",
1790 |     "\n",
1791 |     "Looks very similar to \"Neuroticism\"\n",
1792 |     "\n",
1793 |     "Factor 3: Thinking ahead, Prioritising workload...\n",
1794 |     "\n",
1795 |     "very similar to \"Conscientiousness\"\n",
1796 |     "\n",
1797 |     "Factor 4: Children, God, Finding lost valuables\n",
1798 |     "\n",
1799 |     "This factor could be something like \"religious\" or \"conservative\", maybe have lowest scores of a \"Openness\" in Big Five model.\n",
1800 |     "\n",
1801 |     "Factor 5: Appearence and gestures, Mood swings\n",
1802 |     "\n",
1803 |     "Mmmm it could be \"Agreeableness\". What do you think it could be represent?"
1804 |    ]
1805 |   },
1806 |   {
1807 |    "cell_type": "markdown",
1808 |    "metadata": {},
1809 |    "source": [
1810 |     "# Conclusion:"
1811 |    ]
1812 |   },
1813 |   {
1814 |    "cell_type": "markdown",
1815 |    "metadata": {},
1816 |    "source": [
1817 |     "The first three Factors are very clear: Extraversion, Neuroticism and Conscientiousness. The other two not to much. Anyway is a very interesting approximation\n",
1818 |     "\n",
1819 |     "Maybe doing first a PCA for remove hight correlate variables like \"God\" and \"Final judgement\"could help.\n",
1820 |     "\n",
1821 |     "What do you think?\n",
1822 |     "\n",
1823 |     "Thanks you!"
1824 |    ]
1825 |   }
1826 |  ],
1827 |  "metadata": {
1828 |   "kernelspec": {
1829 |    "display_name": "Python 3",
1830 |    "language": "python",
1831 |    "name": "python3"
1832 |   },
1833 |   "language_info": {
1834 |    "codemirror_mode": {
1835 |     "name": "ipython",
1836 |     "version": 3
1837 |    },
1838 |    "file_extension": ".py",
1839 |    "mimetype": "text/x-python",
1840 |    "name": "python",
1841 |    "nbconvert_exporter": "python",
1842 |    "pygments_lexer": "ipython3",
1843 |    "version": "3.7.4"
1844 |   }
1845 |  },
1846 |  "nbformat": 4,
1847 |  "nbformat_minor": 2
1848 | }
1849 | 


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