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1 {
2 "cells": [
3 {
4 "cell_type": "markdown",
5 "metadata": {},
6 "source": [
7 "---\n",
8 "\n",
9 "_You are currently looking at **version 1.3** of this notebook. To download notebooks and datafiles, as well as get help on Jupyter notebooks in the Coursera platform, visit the [Jupyter Notebook FAQ](https://www.coursera.org/learn/python-machine-learning/resources/bANLa) course resource._\n",
10 "\n",
11 "---"
12 ]
13 },
14 {
15 "cell_type": "markdown",
16 "metadata": {},
17 "source": [
18 "# Assignment 1 - Introduction to Machine Learning"
19 ]
20 },
21 {
22 "cell_type": "markdown",
23 "metadata": {},
24 "source": [
25 "For this assignment, you will be using the Breast Cancer Wisconsin (Diagnostic) Database to create a classifier that can help diagnose patients. First, read through the description of the dataset (below)."
26 ]
27 },
28 {
29 "cell_type": "code",
30 "execution_count": 1,
31 "metadata": {},
32 "outputs": [
33 {
34 "name": "stdout",
35 "output_type": "stream",
36 "text": [
37 "Breast Cancer Wisconsin (Diagnostic) Database\n",
38 "=============================================\n",
39 "\n",
40 "Notes\n",
41 "-----\n",
42 "Data Set Characteristics:\n",
43 " :Number of Instances: 569\n",
44 "\n",
45 " :Number of Attributes: 30 numeric, predictive attributes and the class\n",
46 "\n",
47 " :Attribute Information:\n",
48 " - radius (mean of distances from center to points on the perimeter)\n",
49 " - texture (standard deviation of gray-scale values)\n",
50 " - perimeter\n",
51 " - area\n",
52 " - smoothness (local variation in radius lengths)\n",
53 " - compactness (perimeter^2 / area - 1.0)\n",
54 " - concavity (severity of concave portions of the contour)\n",
55 " - concave points (number of concave portions of the contour)\n",
56 " - symmetry \n",
57 " - fractal dimension (\"coastline approximation\" - 1)\n",
58 "\n",
59 " The mean, standard error, and \"worst\" or largest (mean of the three\n",
60 " largest values) of these features were computed for each image,\n",
61 " resulting in 30 features. For instance, field 3 is Mean Radius, field\n",
62 " 13 is Radius SE, field 23 is Worst Radius.\n",
63 "\n",
64 " - class:\n",
65 " - WDBC-Malignant\n",
66 " - WDBC-Benign\n",
67 "\n",
68 " :Summary Statistics:\n",
69 "\n",
70 " ===================================== ====== ======\n",
71 " Min Max\n",
72 " ===================================== ====== ======\n",
73 " radius (mean): 6.981 28.11\n",
74 " texture (mean): 9.71 39.28\n",
75 " perimeter (mean): 43.79 188.5\n",
76 " area (mean): 143.5 2501.0\n",
77 " smoothness (mean): 0.053 0.163\n",
78 " compactness (mean): 0.019 0.345\n",
79 " concavity (mean): 0.0 0.427\n",
80 " concave points (mean): 0.0 0.201\n",
81 " symmetry (mean): 0.106 0.304\n",
82 " fractal dimension (mean): 0.05 0.097\n",
83 " radius (standard error): 0.112 2.873\n",
84 " texture (standard error): 0.36 4.885\n",
85 " perimeter (standard error): 0.757 21.98\n",
86 " area (standard error): 6.802 542.2\n",
87 " smoothness (standard error): 0.002 0.031\n",
88 " compactness (standard error): 0.002 0.135\n",
89 " concavity (standard error): 0.0 0.396\n",
90 " concave points (standard error): 0.0 0.053\n",
91 " symmetry (standard error): 0.008 0.079\n",
92 " fractal dimension (standard error): 0.001 0.03\n",
93 " radius (worst): 7.93 36.04\n",
94 " texture (worst): 12.02 49.54\n",
95 " perimeter (worst): 50.41 251.2\n",
96 " area (worst): 185.2 4254.0\n",
97 " smoothness (worst): 0.071 0.223\n",
98 " compactness (worst): 0.027 1.058\n",
99 " concavity (worst): 0.0 1.252\n",
100 " concave points (worst): 0.0 0.291\n",
101 " symmetry (worst): 0.156 0.664\n",
102 " fractal dimension (worst): 0.055 0.208\n",
103 " ===================================== ====== ======\n",
104 "\n",
105 " :Missing Attribute Values: None\n",
106 "\n",
107 " :Class Distribution: 212 - Malignant, 357 - Benign\n",
108 "\n",
109 " :Creator: Dr. William H. Wolberg, W. Nick Street, Olvi L. Mangasarian\n",
110 "\n",
111 " :Donor: Nick Street\n",
112 "\n",
113 " :Date: November, 1995\n",
114 "\n",
115 "This is a copy of UCI ML Breast Cancer Wisconsin (Diagnostic) datasets.\n",
116 "https://goo.gl/U2Uwz2\n",
117 "\n",
118 "Features are computed from a digitized image of a fine needle\n",
119 "aspirate (FNA) of a breast mass. They describe\n",
120 "characteristics of the cell nuclei present in the image.\n",
121 "\n",
122 "Separating plane described above was obtained using\n",
123 "Multisurface Method-Tree (MSM-T) [K. P. Bennett, \"Decision Tree\n",
124 "Construction Via Linear Programming.\" Proceedings of the 4th\n",
125 "Midwest Artificial Intelligence and Cognitive Science Society,\n",
126 "pp. 97-101, 1992], a classification method which uses linear\n",
127 "programming to construct a decision tree. Relevant features\n",
128 "were selected using an exhaustive search in the space of 1-4\n",
129 "features and 1-3 separating planes.\n",
130 "\n",
131 "The actual linear program used to obtain the separating plane\n",
132 "in the 3-dimensional space is that described in:\n",
133 "[K. P. Bennett and O. L. Mangasarian: \"Robust Linear\n",
134 "Programming Discrimination of Two Linearly Inseparable Sets\",\n",
135 "Optimization Methods and Software 1, 1992, 23-34].\n",
136 "\n",
137 "This database is also available through the UW CS ftp server:\n",
138 "\n",
139 "ftp ftp.cs.wisc.edu\n",
140 "cd math-prog/cpo-dataset/machine-learn/WDBC/\n",
141 "\n",
142 "References\n",
143 "----------\n",
144 " - W.N. Street, W.H. Wolberg and O.L. Mangasarian. Nuclear feature extraction \n",
145 " for breast tumor diagnosis. IS&T/SPIE 1993 International Symposium on \n",
146 " Electronic Imaging: Science and Technology, volume 1905, pages 861-870,\n",
147 " San Jose, CA, 1993.\n",
148 " - O.L. Mangasarian, W.N. Street and W.H. Wolberg. Breast cancer diagnosis and \n",
149 " prognosis via linear programming. Operations Research, 43(4), pages 570-577, \n",
150 " July-August 1995.\n",
151 " - W.H. Wolberg, W.N. Street, and O.L. Mangasarian. Machine learning techniques\n",
152 " to diagnose breast cancer from fine-needle aspirates. Cancer Letters 77 (1994) \n",
153 " 163-171.\n",
154 "\n"
155 ]
156 }
157 ],
158 "source": [
159 "import numpy as np\n",
160 "import pandas as pd\n",
161 "from sklearn.datasets import load_breast_cancer\n",
162 "\n",
163 "cancer = load_breast_cancer()\n",
164 "\n",
165 "print(cancer.DESCR) # Print the data set description"
166 ]
167 },
168 {
169 "cell_type": "markdown",
170 "metadata": {},
171 "source": [
172 "The object returned by `load_breast_cancer()` is a scikit-learn Bunch object, which is similar to a dictionary."
173 ]
174 },
175 {
176 "cell_type": "code",
177 "execution_count": 2,
178 "metadata": {},
179 "outputs": [
180 {
181 "data": {
182 "text/plain": [
183 "dict_keys(['data', 'target', 'target_names', 'DESCR', 'feature_names'])"
184 ]
185 },
186 "execution_count": 2,
187 "metadata": {},
188 "output_type": "execute_result"
189 }
190 ],
191 "source": [
192 "cancer.keys()"
193 ]
194 },
195 {
196 "cell_type": "code",
197 "execution_count": 3,
198 "metadata": {
199 "collapsed": true
200 },
201 "outputs": [],
202 "source": [
203 "# data : Bunch\n",
204 "# Dictionary-like object, the interesting attributes are:\n",
205 "# ‘data’, the data to learn,\n",
206 "# ‘target’, the classification labels, \n",
207 "# ‘target_names’, the meaning of the labels, \n",
208 "# ‘feature_names’, the meaning of the features, \n",
209 "# ‘DESCR’, the full description of the dataset, \n",
210 "# ‘filename’, the physical location of iris csv dataset "
211 ]
212 },
213 {
214 "cell_type": "markdown",
215 "metadata": {},
216 "source": [
217 "### Question 0 (Example)\n",
218 "\n",
219 "How many features does the breast cancer dataset have?\n",
220 "\n",
221 "*This function should return an integer.*"
222 ]
223 },
224 {
225 "cell_type": "code",
226 "execution_count": 4,
227 "metadata": {},
228 "outputs": [
229 {
230 "data": {
231 "text/plain": [
232 "30"
233 ]
234 },
235 "execution_count": 4,
236 "metadata": {},
237 "output_type": "execute_result"
238 }
239 ],
240 "source": [
241 "# You should write your whole answer within the function provided. The autograder will call\n",
242 "# this function and compare the return value against the correct solution value\n",
243 "def answer_zero():\n",
244 " # This function returns the number of features of the breast cancer dataset, which is an integer. \n",
245 " # The assignment question description will tell you the general format the autograder is expecting\n",
246 " return len(cancer['feature_names'])\n",
247 "\n",
248 "# You can examine what your function returns by calling it in the cell. If you have questions\n",
249 "# about the assignment formats, check out the discussion forums for any FAQs\n",
250 "answer_zero() "
251 ]
252 },
253 {
254 "cell_type": "markdown",
255 "metadata": {},
256 "source": [
257 "### Question 1\n",
258 "\n",
259 "Scikit-learn works with lists, numpy arrays, scipy-sparse matrices, and pandas DataFrames, so converting the dataset to a DataFrame is not necessary for training this model. Using a DataFrame does however help make many things easier such as munging data, so let's practice creating a classifier with a pandas DataFrame. \n",
260 "\n",
261 "\n",
262 "\n",
263 "Convert the sklearn.dataset `cancer` to a DataFrame. \n",
264 "\n",
265 "*This function should return a `(569, 31)` DataFrame with * \n",
266 "\n",
267 "*columns = *\n",
268 "\n",
269 " ['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
270 " 'mean smoothness', 'mean compactness', 'mean concavity',\n",
271 " 'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
272 " 'radius error', 'texture error', 'perimeter error', 'area error',\n",
273 " 'smoothness error', 'compactness error', 'concavity error',\n",
274 " 'concave points error', 'symmetry error', 'fractal dimension error',\n",
275 " 'worst radius', 'worst texture', 'worst perimeter', 'worst area',\n",
276 " 'worst smoothness', 'worst compactness', 'worst concavity',\n",
277 " 'worst concave points', 'worst symmetry', 'worst fractal dimension',\n",
278 " 'target']\n",
279 "\n",
280 "*and index = *\n",
281 "\n",
282 " RangeIndex(start=0, stop=569, step=1)"
283 ]
284 },
285 {
286 "cell_type": "code",
287 "execution_count": 5,
288 "metadata": {},
289 "outputs": [
290 {
291 "data": {
292 "text/html": [
293 "<div>\n",
294 "<table border=\"1\" class=\"dataframe\">\n",
295 " <thead>\n",
296 " <tr style=\"text-align: right;\">\n",
297 " <th></th>\n",
298 " <th>mean radius</th>\n",
299 " <th>mean texture</th>\n",
300 " <th>mean perimeter</th>\n",
301 " <th>mean area</th>\n",
302 " <th>mean smoothness</th>\n",
303 " <th>mean compactness</th>\n",
304 " <th>mean concavity</th>\n",
305 " <th>mean concave points</th>\n",
306 " <th>mean symmetry</th>\n",
307 " <th>mean fractal dimension</th>\n",
308 " <th>...</th>\n",
309 " <th>worst texture</th>\n",
310 " <th>worst perimeter</th>\n",
311 " <th>worst area</th>\n",
312 " <th>worst smoothness</th>\n",
313 " <th>worst compactness</th>\n",
314 " <th>worst concavity</th>\n",
315 " <th>worst concave points</th>\n",
316 " <th>worst symmetry</th>\n",
317 " <th>worst fractal dimension</th>\n",
318 " <th>target</th>\n",
319 " </tr>\n",
320 " </thead>\n",
321 " <tbody>\n",
322 " <tr>\n",
323 " <th>0</th>\n",
324 " <td>17.990</td>\n",
325 " <td>10.38</td>\n",
326 " <td>122.80</td>\n",
327 " <td>1001.0</td>\n",
328 " <td>0.11840</td>\n",
329 " <td>0.27760</td>\n",
330 " <td>0.300100</td>\n",
331 " <td>0.147100</td>\n",
332 " <td>0.2419</td>\n",
333 " <td>0.07871</td>\n",
334 " <td>...</td>\n",
335 " <td>17.33</td>\n",
336 " <td>184.60</td>\n",
337 " <td>2019.0</td>\n",
338 " <td>0.16220</td>\n",
339 " <td>0.66560</td>\n",
340 " <td>0.71190</td>\n",
341 " <td>0.26540</td>\n",
342 " <td>0.4601</td>\n",
343 " <td>0.11890</td>\n",
344 " <td>0</td>\n",
345 " </tr>\n",
346 " <tr>\n",
347 " <th>1</th>\n",
348 " <td>20.570</td>\n",
349 " <td>17.77</td>\n",
350 " <td>132.90</td>\n",
351 " <td>1326.0</td>\n",
352 " <td>0.08474</td>\n",
353 " <td>0.07864</td>\n",
354 " <td>0.086900</td>\n",
355 " <td>0.070170</td>\n",
356 " <td>0.1812</td>\n",
357 " <td>0.05667</td>\n",
358 " <td>...</td>\n",
359 " <td>23.41</td>\n",
360 " <td>158.80</td>\n",
361 " <td>1956.0</td>\n",
362 " <td>0.12380</td>\n",
363 " <td>0.18660</td>\n",
364 " <td>0.24160</td>\n",
365 " <td>0.18600</td>\n",
366 " <td>0.2750</td>\n",
367 " <td>0.08902</td>\n",
368 " <td>0</td>\n",
369 " </tr>\n",
370 " <tr>\n",
371 " <th>2</th>\n",
372 " <td>19.690</td>\n",
373 " <td>21.25</td>\n",
374 " <td>130.00</td>\n",
375 " <td>1203.0</td>\n",
376 " <td>0.10960</td>\n",
377 " <td>0.15990</td>\n",
378 " <td>0.197400</td>\n",
379 " <td>0.127900</td>\n",
380 " <td>0.2069</td>\n",
381 " <td>0.05999</td>\n",
382 " <td>...</td>\n",
383 " <td>25.53</td>\n",
384 " <td>152.50</td>\n",
385 " <td>1709.0</td>\n",
386 " <td>0.14440</td>\n",
387 " <td>0.42450</td>\n",
388 " <td>0.45040</td>\n",
389 " <td>0.24300</td>\n",
390 " <td>0.3613</td>\n",
391 " <td>0.08758</td>\n",
392 " <td>0</td>\n",
393 " </tr>\n",
394 " <tr>\n",
395 " <th>3</th>\n",
396 " <td>11.420</td>\n",
397 " <td>20.38</td>\n",
398 " <td>77.58</td>\n",
399 " <td>386.1</td>\n",
400 " <td>0.14250</td>\n",
401 " <td>0.28390</td>\n",
402 " <td>0.241400</td>\n",
403 " <td>0.105200</td>\n",
404 " <td>0.2597</td>\n",
405 " <td>0.09744</td>\n",
406 " <td>...</td>\n",
407 " <td>26.50</td>\n",
408 " <td>98.87</td>\n",
409 " <td>567.7</td>\n",
410 " <td>0.20980</td>\n",
411 " <td>0.86630</td>\n",
412 " <td>0.68690</td>\n",
413 " <td>0.25750</td>\n",
414 " <td>0.6638</td>\n",
415 " <td>0.17300</td>\n",
416 " <td>0</td>\n",
417 " </tr>\n",
418 " <tr>\n",
419 " <th>4</th>\n",
420 " <td>20.290</td>\n",
421 " <td>14.34</td>\n",
422 " <td>135.10</td>\n",
423 " <td>1297.0</td>\n",
424 " <td>0.10030</td>\n",
425 " <td>0.13280</td>\n",
426 " <td>0.198000</td>\n",
427 " <td>0.104300</td>\n",
428 " <td>0.1809</td>\n",
429 " <td>0.05883</td>\n",
430 " <td>...</td>\n",
431 " <td>16.67</td>\n",
432 " <td>152.20</td>\n",
433 " <td>1575.0</td>\n",
434 " <td>0.13740</td>\n",
435 " <td>0.20500</td>\n",
436 " <td>0.40000</td>\n",
437 " <td>0.16250</td>\n",
438 " <td>0.2364</td>\n",
439 " <td>0.07678</td>\n",
440 " <td>0</td>\n",
441 " </tr>\n",
442 " <tr>\n",
443 " <th>5</th>\n",
444 " <td>12.450</td>\n",
445 " <td>15.70</td>\n",
446 " <td>82.57</td>\n",
447 " <td>477.1</td>\n",
448 " <td>0.12780</td>\n",
449 " <td>0.17000</td>\n",
450 " <td>0.157800</td>\n",
451 " <td>0.080890</td>\n",
452 " <td>0.2087</td>\n",
453 " <td>0.07613</td>\n",
454 " <td>...</td>\n",
455 " <td>23.75</td>\n",
456 " <td>103.40</td>\n",
457 " <td>741.6</td>\n",
458 " <td>0.17910</td>\n",
459 " <td>0.52490</td>\n",
460 " <td>0.53550</td>\n",
461 " <td>0.17410</td>\n",
462 " <td>0.3985</td>\n",
463 " <td>0.12440</td>\n",
464 " <td>0</td>\n",
465 " </tr>\n",
466 " <tr>\n",
467 " <th>6</th>\n",
468 " <td>18.250</td>\n",
469 " <td>19.98</td>\n",
470 " <td>119.60</td>\n",
471 " <td>1040.0</td>\n",
472 " <td>0.09463</td>\n",
473 " <td>0.10900</td>\n",
474 " <td>0.112700</td>\n",
475 " <td>0.074000</td>\n",
476 " <td>0.1794</td>\n",
477 " <td>0.05742</td>\n",
478 " <td>...</td>\n",
479 " <td>27.66</td>\n",
480 " <td>153.20</td>\n",
481 " <td>1606.0</td>\n",
482 " <td>0.14420</td>\n",
483 " <td>0.25760</td>\n",
484 " <td>0.37840</td>\n",
485 " <td>0.19320</td>\n",
486 " <td>0.3063</td>\n",
487 " <td>0.08368</td>\n",
488 " <td>0</td>\n",
489 " </tr>\n",
490 " <tr>\n",
491 " <th>7</th>\n",
492 " <td>13.710</td>\n",
493 " <td>20.83</td>\n",
494 " <td>90.20</td>\n",
495 " <td>577.9</td>\n",
496 " <td>0.11890</td>\n",
497 " <td>0.16450</td>\n",
498 " <td>0.093660</td>\n",
499 " <td>0.059850</td>\n",
500 " <td>0.2196</td>\n",
501 " <td>0.07451</td>\n",
502 " <td>...</td>\n",
503 " <td>28.14</td>\n",
504 " <td>110.60</td>\n",
505 " <td>897.0</td>\n",
506 " <td>0.16540</td>\n",
507 " <td>0.36820</td>\n",
508 " <td>0.26780</td>\n",
509 " <td>0.15560</td>\n",
510 " <td>0.3196</td>\n",
511 " <td>0.11510</td>\n",
512 " <td>0</td>\n",
513 " </tr>\n",
514 " <tr>\n",
515 " <th>8</th>\n",
516 " <td>13.000</td>\n",
517 " <td>21.82</td>\n",
518 " <td>87.50</td>\n",
519 " <td>519.8</td>\n",
520 " <td>0.12730</td>\n",
521 " <td>0.19320</td>\n",
522 " <td>0.185900</td>\n",
523 " <td>0.093530</td>\n",
524 " <td>0.2350</td>\n",
525 " <td>0.07389</td>\n",
526 " <td>...</td>\n",
527 " <td>30.73</td>\n",
528 " <td>106.20</td>\n",
529 " <td>739.3</td>\n",
530 " <td>0.17030</td>\n",
531 " <td>0.54010</td>\n",
532 " <td>0.53900</td>\n",
533 " <td>0.20600</td>\n",
534 " <td>0.4378</td>\n",
535 " <td>0.10720</td>\n",
536 " <td>0</td>\n",
537 " </tr>\n",
538 " <tr>\n",
539 " <th>9</th>\n",
540 " <td>12.460</td>\n",
541 " <td>24.04</td>\n",
542 " <td>83.97</td>\n",
543 " <td>475.9</td>\n",
544 " <td>0.11860</td>\n",
545 " <td>0.23960</td>\n",
546 " <td>0.227300</td>\n",
547 " <td>0.085430</td>\n",
548 " <td>0.2030</td>\n",
549 " <td>0.08243</td>\n",
550 " <td>...</td>\n",
551 " <td>40.68</td>\n",
552 " <td>97.65</td>\n",
553 " <td>711.4</td>\n",
554 " <td>0.18530</td>\n",
555 " <td>1.05800</td>\n",
556 " <td>1.10500</td>\n",
557 " <td>0.22100</td>\n",
558 " <td>0.4366</td>\n",
559 " <td>0.20750</td>\n",
560 " <td>0</td>\n",
561 " </tr>\n",
562 " <tr>\n",
563 " <th>10</th>\n",
564 " <td>16.020</td>\n",
565 " <td>23.24</td>\n",
566 " <td>102.70</td>\n",
567 " <td>797.8</td>\n",
568 " <td>0.08206</td>\n",
569 " <td>0.06669</td>\n",
570 " <td>0.032990</td>\n",
571 " <td>0.033230</td>\n",
572 " <td>0.1528</td>\n",
573 " <td>0.05697</td>\n",
574 " <td>...</td>\n",
575 " <td>33.88</td>\n",
576 " <td>123.80</td>\n",
577 " <td>1150.0</td>\n",
578 " <td>0.11810</td>\n",
579 " <td>0.15510</td>\n",
580 " <td>0.14590</td>\n",
581 " <td>0.09975</td>\n",
582 " <td>0.2948</td>\n",
583 " <td>0.08452</td>\n",
584 " <td>0</td>\n",
585 " </tr>\n",
586 " <tr>\n",
587 " <th>11</th>\n",
588 " <td>15.780</td>\n",
589 " <td>17.89</td>\n",
590 " <td>103.60</td>\n",
591 " <td>781.0</td>\n",
592 " <td>0.09710</td>\n",
593 " <td>0.12920</td>\n",
594 " <td>0.099540</td>\n",
595 " <td>0.066060</td>\n",
596 " <td>0.1842</td>\n",
597 " <td>0.06082</td>\n",
598 " <td>...</td>\n",
599 " <td>27.28</td>\n",
600 " <td>136.50</td>\n",
601 " <td>1299.0</td>\n",
602 " <td>0.13960</td>\n",
603 " <td>0.56090</td>\n",
604 " <td>0.39650</td>\n",
605 " <td>0.18100</td>\n",
606 " <td>0.3792</td>\n",
607 " <td>0.10480</td>\n",
608 " <td>0</td>\n",
609 " </tr>\n",
610 " <tr>\n",
611 " <th>12</th>\n",
612 " <td>19.170</td>\n",
613 " <td>24.80</td>\n",
614 " <td>132.40</td>\n",
615 " <td>1123.0</td>\n",
616 " <td>0.09740</td>\n",
617 " <td>0.24580</td>\n",
618 " <td>0.206500</td>\n",
619 " <td>0.111800</td>\n",
620 " <td>0.2397</td>\n",
621 " <td>0.07800</td>\n",
622 " <td>...</td>\n",
623 " <td>29.94</td>\n",
624 " <td>151.70</td>\n",
625 " <td>1332.0</td>\n",
626 " <td>0.10370</td>\n",
627 " <td>0.39030</td>\n",
628 " <td>0.36390</td>\n",
629 " <td>0.17670</td>\n",
630 " <td>0.3176</td>\n",
631 " <td>0.10230</td>\n",
632 " <td>0</td>\n",
633 " </tr>\n",
634 " <tr>\n",
635 " <th>13</th>\n",
636 " <td>15.850</td>\n",
637 " <td>23.95</td>\n",
638 " <td>103.70</td>\n",
639 " <td>782.7</td>\n",
640 " <td>0.08401</td>\n",
641 " <td>0.10020</td>\n",
642 " <td>0.099380</td>\n",
643 " <td>0.053640</td>\n",
644 " <td>0.1847</td>\n",
645 " <td>0.05338</td>\n",
646 " <td>...</td>\n",
647 " <td>27.66</td>\n",
648 " <td>112.00</td>\n",
649 " <td>876.5</td>\n",
650 " <td>0.11310</td>\n",
651 " <td>0.19240</td>\n",
652 " <td>0.23220</td>\n",
653 " <td>0.11190</td>\n",
654 " <td>0.2809</td>\n",
655 " <td>0.06287</td>\n",
656 " <td>0</td>\n",
657 " </tr>\n",
658 " <tr>\n",
659 " <th>14</th>\n",
660 " <td>13.730</td>\n",
661 " <td>22.61</td>\n",
662 " <td>93.60</td>\n",
663 " <td>578.3</td>\n",
664 " <td>0.11310</td>\n",
665 " <td>0.22930</td>\n",
666 " <td>0.212800</td>\n",
667 " <td>0.080250</td>\n",
668 " <td>0.2069</td>\n",
669 " <td>0.07682</td>\n",
670 " <td>...</td>\n",
671 " <td>32.01</td>\n",
672 " <td>108.80</td>\n",
673 " <td>697.7</td>\n",
674 " <td>0.16510</td>\n",
675 " <td>0.77250</td>\n",
676 " <td>0.69430</td>\n",
677 " <td>0.22080</td>\n",
678 " <td>0.3596</td>\n",
679 " <td>0.14310</td>\n",
680 " <td>0</td>\n",
681 " </tr>\n",
682 " <tr>\n",
683 " <th>15</th>\n",
684 " <td>14.540</td>\n",
685 " <td>27.54</td>\n",
686 " <td>96.73</td>\n",
687 " <td>658.8</td>\n",
688 " <td>0.11390</td>\n",
689 " <td>0.15950</td>\n",
690 " <td>0.163900</td>\n",
691 " <td>0.073640</td>\n",
692 " <td>0.2303</td>\n",
693 " <td>0.07077</td>\n",
694 " <td>...</td>\n",
695 " <td>37.13</td>\n",
696 " <td>124.10</td>\n",
697 " <td>943.2</td>\n",
698 " <td>0.16780</td>\n",
699 " <td>0.65770</td>\n",
700 " <td>0.70260</td>\n",
701 " <td>0.17120</td>\n",
702 " <td>0.4218</td>\n",
703 " <td>0.13410</td>\n",
704 " <td>0</td>\n",
705 " </tr>\n",
706 " <tr>\n",
707 " <th>16</th>\n",
708 " <td>14.680</td>\n",
709 " <td>20.13</td>\n",
710 " <td>94.74</td>\n",
711 " <td>684.5</td>\n",
712 " <td>0.09867</td>\n",
713 " <td>0.07200</td>\n",
714 " <td>0.073950</td>\n",
715 " <td>0.052590</td>\n",
716 " <td>0.1586</td>\n",
717 " <td>0.05922</td>\n",
718 " <td>...</td>\n",
719 " <td>30.88</td>\n",
720 " <td>123.40</td>\n",
721 " <td>1138.0</td>\n",
722 " <td>0.14640</td>\n",
723 " <td>0.18710</td>\n",
724 " <td>0.29140</td>\n",
725 " <td>0.16090</td>\n",
726 " <td>0.3029</td>\n",
727 " <td>0.08216</td>\n",
728 " <td>0</td>\n",
729 " </tr>\n",
730 " <tr>\n",
731 " <th>17</th>\n",
732 " <td>16.130</td>\n",
733 " <td>20.68</td>\n",
734 " <td>108.10</td>\n",
735 " <td>798.8</td>\n",
736 " <td>0.11700</td>\n",
737 " <td>0.20220</td>\n",
738 " <td>0.172200</td>\n",
739 " <td>0.102800</td>\n",
740 " <td>0.2164</td>\n",
741 " <td>0.07356</td>\n",
742 " <td>...</td>\n",
743 " <td>31.48</td>\n",
744 " <td>136.80</td>\n",
745 " <td>1315.0</td>\n",
746 " <td>0.17890</td>\n",
747 " <td>0.42330</td>\n",
748 " <td>0.47840</td>\n",
749 " <td>0.20730</td>\n",
750 " <td>0.3706</td>\n",
751 " <td>0.11420</td>\n",
752 " <td>0</td>\n",
753 " </tr>\n",
754 " <tr>\n",
755 " <th>18</th>\n",
756 " <td>19.810</td>\n",
757 " <td>22.15</td>\n",
758 " <td>130.00</td>\n",
759 " <td>1260.0</td>\n",
760 " <td>0.09831</td>\n",
761 " <td>0.10270</td>\n",
762 " <td>0.147900</td>\n",
763 " <td>0.094980</td>\n",
764 " <td>0.1582</td>\n",
765 " <td>0.05395</td>\n",
766 " <td>...</td>\n",
767 " <td>30.88</td>\n",
768 " <td>186.80</td>\n",
769 " <td>2398.0</td>\n",
770 " <td>0.15120</td>\n",
771 " <td>0.31500</td>\n",
772 " <td>0.53720</td>\n",
773 " <td>0.23880</td>\n",
774 " <td>0.2768</td>\n",
775 " <td>0.07615</td>\n",
776 " <td>0</td>\n",
777 " </tr>\n",
778 " <tr>\n",
779 " <th>19</th>\n",
780 " <td>13.540</td>\n",
781 " <td>14.36</td>\n",
782 " <td>87.46</td>\n",
783 " <td>566.3</td>\n",
784 " <td>0.09779</td>\n",
785 " <td>0.08129</td>\n",
786 " <td>0.066640</td>\n",
787 " <td>0.047810</td>\n",
788 " <td>0.1885</td>\n",
789 " <td>0.05766</td>\n",
790 " <td>...</td>\n",
791 " <td>19.26</td>\n",
792 " <td>99.70</td>\n",
793 " <td>711.2</td>\n",
794 " <td>0.14400</td>\n",
795 " <td>0.17730</td>\n",
796 " <td>0.23900</td>\n",
797 " <td>0.12880</td>\n",
798 " <td>0.2977</td>\n",
799 " <td>0.07259</td>\n",
800 " <td>1</td>\n",
801 " </tr>\n",
802 " <tr>\n",
803 " <th>20</th>\n",
804 " <td>13.080</td>\n",
805 " <td>15.71</td>\n",
806 " <td>85.63</td>\n",
807 " <td>520.0</td>\n",
808 " <td>0.10750</td>\n",
809 " <td>0.12700</td>\n",
810 " <td>0.045680</td>\n",
811 " <td>0.031100</td>\n",
812 " <td>0.1967</td>\n",
813 " <td>0.06811</td>\n",
814 " <td>...</td>\n",
815 " <td>20.49</td>\n",
816 " <td>96.09</td>\n",
817 " <td>630.5</td>\n",
818 " <td>0.13120</td>\n",
819 " <td>0.27760</td>\n",
820 " <td>0.18900</td>\n",
821 " <td>0.07283</td>\n",
822 " <td>0.3184</td>\n",
823 " <td>0.08183</td>\n",
824 " <td>1</td>\n",
825 " </tr>\n",
826 " <tr>\n",
827 " <th>21</th>\n",
828 " <td>9.504</td>\n",
829 " <td>12.44</td>\n",
830 " <td>60.34</td>\n",
831 " <td>273.9</td>\n",
832 " <td>0.10240</td>\n",
833 " <td>0.06492</td>\n",
834 " <td>0.029560</td>\n",
835 " <td>0.020760</td>\n",
836 " <td>0.1815</td>\n",
837 " <td>0.06905</td>\n",
838 " <td>...</td>\n",
839 " <td>15.66</td>\n",
840 " <td>65.13</td>\n",
841 " <td>314.9</td>\n",
842 " <td>0.13240</td>\n",
843 " <td>0.11480</td>\n",
844 " <td>0.08867</td>\n",
845 " <td>0.06227</td>\n",
846 " <td>0.2450</td>\n",
847 " <td>0.07773</td>\n",
848 " <td>1</td>\n",
849 " </tr>\n",
850 " <tr>\n",
851 " <th>22</th>\n",
852 " <td>15.340</td>\n",
853 " <td>14.26</td>\n",
854 " <td>102.50</td>\n",
855 " <td>704.4</td>\n",
856 " <td>0.10730</td>\n",
857 " <td>0.21350</td>\n",
858 " <td>0.207700</td>\n",
859 " <td>0.097560</td>\n",
860 " <td>0.2521</td>\n",
861 " <td>0.07032</td>\n",
862 " <td>...</td>\n",
863 " <td>19.08</td>\n",
864 " <td>125.10</td>\n",
865 " <td>980.9</td>\n",
866 " <td>0.13900</td>\n",
867 " <td>0.59540</td>\n",
868 " <td>0.63050</td>\n",
869 " <td>0.23930</td>\n",
870 " <td>0.4667</td>\n",
871 " <td>0.09946</td>\n",
872 " <td>0</td>\n",
873 " </tr>\n",
874 " <tr>\n",
875 " <th>23</th>\n",
876 " <td>21.160</td>\n",
877 " <td>23.04</td>\n",
878 " <td>137.20</td>\n",
879 " <td>1404.0</td>\n",
880 " <td>0.09428</td>\n",
881 " <td>0.10220</td>\n",
882 " <td>0.109700</td>\n",
883 " <td>0.086320</td>\n",
884 " <td>0.1769</td>\n",
885 " <td>0.05278</td>\n",
886 " <td>...</td>\n",
887 " <td>35.59</td>\n",
888 " <td>188.00</td>\n",
889 " <td>2615.0</td>\n",
890 " <td>0.14010</td>\n",
891 " <td>0.26000</td>\n",
892 " <td>0.31550</td>\n",
893 " <td>0.20090</td>\n",
894 " <td>0.2822</td>\n",
895 " <td>0.07526</td>\n",
896 " <td>0</td>\n",
897 " </tr>\n",
898 " <tr>\n",
899 " <th>24</th>\n",
900 " <td>16.650</td>\n",
901 " <td>21.38</td>\n",
902 " <td>110.00</td>\n",
903 " <td>904.6</td>\n",
904 " <td>0.11210</td>\n",
905 " <td>0.14570</td>\n",
906 " <td>0.152500</td>\n",
907 " <td>0.091700</td>\n",
908 " <td>0.1995</td>\n",
909 " <td>0.06330</td>\n",
910 " <td>...</td>\n",
911 " <td>31.56</td>\n",
912 " <td>177.00</td>\n",
913 " <td>2215.0</td>\n",
914 " <td>0.18050</td>\n",
915 " <td>0.35780</td>\n",
916 " <td>0.46950</td>\n",
917 " <td>0.20950</td>\n",
918 " <td>0.3613</td>\n",
919 " <td>0.09564</td>\n",
920 " <td>0</td>\n",
921 " </tr>\n",
922 " <tr>\n",
923 " <th>25</th>\n",
924 " <td>17.140</td>\n",
925 " <td>16.40</td>\n",
926 " <td>116.00</td>\n",
927 " <td>912.7</td>\n",
928 " <td>0.11860</td>\n",
929 " <td>0.22760</td>\n",
930 " <td>0.222900</td>\n",
931 " <td>0.140100</td>\n",
932 " <td>0.3040</td>\n",
933 " <td>0.07413</td>\n",
934 " <td>...</td>\n",
935 " <td>21.40</td>\n",
936 " <td>152.40</td>\n",
937 " <td>1461.0</td>\n",
938 " <td>0.15450</td>\n",
939 " <td>0.39490</td>\n",
940 " <td>0.38530</td>\n",
941 " <td>0.25500</td>\n",
942 " <td>0.4066</td>\n",
943 " <td>0.10590</td>\n",
944 " <td>0</td>\n",
945 " </tr>\n",
946 " <tr>\n",
947 " <th>26</th>\n",
948 " <td>14.580</td>\n",
949 " <td>21.53</td>\n",
950 " <td>97.41</td>\n",
951 " <td>644.8</td>\n",
952 " <td>0.10540</td>\n",
953 " <td>0.18680</td>\n",
954 " <td>0.142500</td>\n",
955 " <td>0.087830</td>\n",
956 " <td>0.2252</td>\n",
957 " <td>0.06924</td>\n",
958 " <td>...</td>\n",
959 " <td>33.21</td>\n",
960 " <td>122.40</td>\n",
961 " <td>896.9</td>\n",
962 " <td>0.15250</td>\n",
963 " <td>0.66430</td>\n",
964 " <td>0.55390</td>\n",
965 " <td>0.27010</td>\n",
966 " <td>0.4264</td>\n",
967 " <td>0.12750</td>\n",
968 " <td>0</td>\n",
969 " </tr>\n",
970 " <tr>\n",
971 " <th>27</th>\n",
972 " <td>18.610</td>\n",
973 " <td>20.25</td>\n",
974 " <td>122.10</td>\n",
975 " <td>1094.0</td>\n",
976 " <td>0.09440</td>\n",
977 " <td>0.10660</td>\n",
978 " <td>0.149000</td>\n",
979 " <td>0.077310</td>\n",
980 " <td>0.1697</td>\n",
981 " <td>0.05699</td>\n",
982 " <td>...</td>\n",
983 " <td>27.26</td>\n",
984 " <td>139.90</td>\n",
985 " <td>1403.0</td>\n",
986 " <td>0.13380</td>\n",
987 " <td>0.21170</td>\n",
988 " <td>0.34460</td>\n",
989 " <td>0.14900</td>\n",
990 " <td>0.2341</td>\n",
991 " <td>0.07421</td>\n",
992 " <td>0</td>\n",
993 " </tr>\n",
994 " <tr>\n",
995 " <th>28</th>\n",
996 " <td>15.300</td>\n",
997 " <td>25.27</td>\n",
998 " <td>102.40</td>\n",
999 " <td>732.4</td>\n",
1000 " <td>0.10820</td>\n",
1001 " <td>0.16970</td>\n",
1002 " <td>0.168300</td>\n",
1003 " <td>0.087510</td>\n",
1004 " <td>0.1926</td>\n",
1005 " <td>0.06540</td>\n",
1006 " <td>...</td>\n",
1007 " <td>36.71</td>\n",
1008 " <td>149.30</td>\n",
1009 " <td>1269.0</td>\n",
1010 " <td>0.16410</td>\n",
1011 " <td>0.61100</td>\n",
1012 " <td>0.63350</td>\n",
1013 " <td>0.20240</td>\n",
1014 " <td>0.4027</td>\n",
1015 " <td>0.09876</td>\n",
1016 " <td>0</td>\n",
1017 " </tr>\n",
1018 " <tr>\n",
1019 " <th>29</th>\n",
1020 " <td>17.570</td>\n",
1021 " <td>15.05</td>\n",
1022 " <td>115.00</td>\n",
1023 " <td>955.1</td>\n",
1024 " <td>0.09847</td>\n",
1025 " <td>0.11570</td>\n",
1026 " <td>0.098750</td>\n",
1027 " <td>0.079530</td>\n",
1028 " <td>0.1739</td>\n",
1029 " <td>0.06149</td>\n",
1030 " <td>...</td>\n",
1031 " <td>19.52</td>\n",
1032 " <td>134.90</td>\n",
1033 " <td>1227.0</td>\n",
1034 " <td>0.12550</td>\n",
1035 " <td>0.28120</td>\n",
1036 " <td>0.24890</td>\n",
1037 " <td>0.14560</td>\n",
1038 " <td>0.2756</td>\n",
1039 " <td>0.07919</td>\n",
1040 " <td>0</td>\n",
1041 " </tr>\n",
1042 " <tr>\n",
1043 " <th>...</th>\n",
1044 " <td>...</td>\n",
1045 " <td>...</td>\n",
1046 " <td>...</td>\n",
1047 " <td>...</td>\n",
1048 " <td>...</td>\n",
1049 " <td>...</td>\n",
1050 " <td>...</td>\n",
1051 " <td>...</td>\n",
1052 " <td>...</td>\n",
1053 " <td>...</td>\n",
1054 " <td>...</td>\n",
1055 " <td>...</td>\n",
1056 " <td>...</td>\n",
1057 " <td>...</td>\n",
1058 " <td>...</td>\n",
1059 " <td>...</td>\n",
1060 " <td>...</td>\n",
1061 " <td>...</td>\n",
1062 " <td>...</td>\n",
1063 " <td>...</td>\n",
1064 " <td>...</td>\n",
1065 " </tr>\n",
1066 " <tr>\n",
1067 " <th>539</th>\n",
1068 " <td>7.691</td>\n",
1069 " <td>25.44</td>\n",
1070 " <td>48.34</td>\n",
1071 " <td>170.4</td>\n",
1072 " <td>0.08668</td>\n",
1073 " <td>0.11990</td>\n",
1074 " <td>0.092520</td>\n",
1075 " <td>0.013640</td>\n",
1076 " <td>0.2037</td>\n",
1077 " <td>0.07751</td>\n",
1078 " <td>...</td>\n",
1079 " <td>31.89</td>\n",
1080 " <td>54.49</td>\n",
1081 " <td>223.6</td>\n",
1082 " <td>0.15960</td>\n",
1083 " <td>0.30640</td>\n",
1084 " <td>0.33930</td>\n",
1085 " <td>0.05000</td>\n",
1086 " <td>0.2790</td>\n",
1087 " <td>0.10660</td>\n",
1088 " <td>1</td>\n",
1089 " </tr>\n",
1090 " <tr>\n",
1091 " <th>540</th>\n",
1092 " <td>11.540</td>\n",
1093 " <td>14.44</td>\n",
1094 " <td>74.65</td>\n",
1095 " <td>402.9</td>\n",
1096 " <td>0.09984</td>\n",
1097 " <td>0.11200</td>\n",
1098 " <td>0.067370</td>\n",
1099 " <td>0.025940</td>\n",
1100 " <td>0.1818</td>\n",
1101 " <td>0.06782</td>\n",
1102 " <td>...</td>\n",
1103 " <td>19.68</td>\n",
1104 " <td>78.78</td>\n",
1105 " <td>457.8</td>\n",
1106 " <td>0.13450</td>\n",
1107 " <td>0.21180</td>\n",
1108 " <td>0.17970</td>\n",
1109 " <td>0.06918</td>\n",
1110 " <td>0.2329</td>\n",
1111 " <td>0.08134</td>\n",
1112 " <td>1</td>\n",
1113 " </tr>\n",
1114 " <tr>\n",
1115 " <th>541</th>\n",
1116 " <td>14.470</td>\n",
1117 " <td>24.99</td>\n",
1118 " <td>95.81</td>\n",
1119 " <td>656.4</td>\n",
1120 " <td>0.08837</td>\n",
1121 " <td>0.12300</td>\n",
1122 " <td>0.100900</td>\n",
1123 " <td>0.038900</td>\n",
1124 " <td>0.1872</td>\n",
1125 " <td>0.06341</td>\n",
1126 " <td>...</td>\n",
1127 " <td>31.73</td>\n",
1128 " <td>113.50</td>\n",
1129 " <td>808.9</td>\n",
1130 " <td>0.13400</td>\n",
1131 " <td>0.42020</td>\n",
1132 " <td>0.40400</td>\n",
1133 " <td>0.12050</td>\n",
1134 " <td>0.3187</td>\n",
1135 " <td>0.10230</td>\n",
1136 " <td>1</td>\n",
1137 " </tr>\n",
1138 " <tr>\n",
1139 " <th>542</th>\n",
1140 " <td>14.740</td>\n",
1141 " <td>25.42</td>\n",
1142 " <td>94.70</td>\n",
1143 " <td>668.6</td>\n",
1144 " <td>0.08275</td>\n",
1145 " <td>0.07214</td>\n",
1146 " <td>0.041050</td>\n",
1147 " <td>0.030270</td>\n",
1148 " <td>0.1840</td>\n",
1149 " <td>0.05680</td>\n",
1150 " <td>...</td>\n",
1151 " <td>32.29</td>\n",
1152 " <td>107.40</td>\n",
1153 " <td>826.4</td>\n",
1154 " <td>0.10600</td>\n",
1155 " <td>0.13760</td>\n",
1156 " <td>0.16110</td>\n",
1157 " <td>0.10950</td>\n",
1158 " <td>0.2722</td>\n",
1159 " <td>0.06956</td>\n",
1160 " <td>1</td>\n",
1161 " </tr>\n",
1162 " <tr>\n",
1163 " <th>543</th>\n",
1164 " <td>13.210</td>\n",
1165 " <td>28.06</td>\n",
1166 " <td>84.88</td>\n",
1167 " <td>538.4</td>\n",
1168 " <td>0.08671</td>\n",
1169 " <td>0.06877</td>\n",
1170 " <td>0.029870</td>\n",
1171 " <td>0.032750</td>\n",
1172 " <td>0.1628</td>\n",
1173 " <td>0.05781</td>\n",
1174 " <td>...</td>\n",
1175 " <td>37.17</td>\n",
1176 " <td>92.48</td>\n",
1177 " <td>629.6</td>\n",
1178 " <td>0.10720</td>\n",
1179 " <td>0.13810</td>\n",
1180 " <td>0.10620</td>\n",
1181 " <td>0.07958</td>\n",
1182 " <td>0.2473</td>\n",
1183 " <td>0.06443</td>\n",
1184 " <td>1</td>\n",
1185 " </tr>\n",
1186 " <tr>\n",
1187 " <th>544</th>\n",
1188 " <td>13.870</td>\n",
1189 " <td>20.70</td>\n",
1190 " <td>89.77</td>\n",
1191 " <td>584.8</td>\n",
1192 " <td>0.09578</td>\n",
1193 " <td>0.10180</td>\n",
1194 " <td>0.036880</td>\n",
1195 " <td>0.023690</td>\n",
1196 " <td>0.1620</td>\n",
1197 " <td>0.06688</td>\n",
1198 " <td>...</td>\n",
1199 " <td>24.75</td>\n",
1200 " <td>99.17</td>\n",
1201 " <td>688.6</td>\n",
1202 " <td>0.12640</td>\n",
1203 " <td>0.20370</td>\n",
1204 " <td>0.13770</td>\n",
1205 " <td>0.06845</td>\n",
1206 " <td>0.2249</td>\n",
1207 " <td>0.08492</td>\n",
1208 " <td>1</td>\n",
1209 " </tr>\n",
1210 " <tr>\n",
1211 " <th>545</th>\n",
1212 " <td>13.620</td>\n",
1213 " <td>23.23</td>\n",
1214 " <td>87.19</td>\n",
1215 " <td>573.2</td>\n",
1216 " <td>0.09246</td>\n",
1217 " <td>0.06747</td>\n",
1218 " <td>0.029740</td>\n",
1219 " <td>0.024430</td>\n",
1220 " <td>0.1664</td>\n",
1221 " <td>0.05801</td>\n",
1222 " <td>...</td>\n",
1223 " <td>29.09</td>\n",
1224 " <td>97.58</td>\n",
1225 " <td>729.8</td>\n",
1226 " <td>0.12160</td>\n",
1227 " <td>0.15170</td>\n",
1228 " <td>0.10490</td>\n",
1229 " <td>0.07174</td>\n",
1230 " <td>0.2642</td>\n",
1231 " <td>0.06953</td>\n",
1232 " <td>1</td>\n",
1233 " </tr>\n",
1234 " <tr>\n",
1235 " <th>546</th>\n",
1236 " <td>10.320</td>\n",
1237 " <td>16.35</td>\n",
1238 " <td>65.31</td>\n",
1239 " <td>324.9</td>\n",
1240 " <td>0.09434</td>\n",
1241 " <td>0.04994</td>\n",
1242 " <td>0.010120</td>\n",
1243 " <td>0.005495</td>\n",
1244 " <td>0.1885</td>\n",
1245 " <td>0.06201</td>\n",
1246 " <td>...</td>\n",
1247 " <td>21.77</td>\n",
1248 " <td>71.12</td>\n",
1249 " <td>384.9</td>\n",
1250 " <td>0.12850</td>\n",
1251 " <td>0.08842</td>\n",
1252 " <td>0.04384</td>\n",
1253 " <td>0.02381</td>\n",
1254 " <td>0.2681</td>\n",
1255 " <td>0.07399</td>\n",
1256 " <td>1</td>\n",
1257 " </tr>\n",
1258 " <tr>\n",
1259 " <th>547</th>\n",
1260 " <td>10.260</td>\n",
1261 " <td>16.58</td>\n",
1262 " <td>65.85</td>\n",
1263 " <td>320.8</td>\n",
1264 " <td>0.08877</td>\n",
1265 " <td>0.08066</td>\n",
1266 " <td>0.043580</td>\n",
1267 " <td>0.024380</td>\n",
1268 " <td>0.1669</td>\n",
1269 " <td>0.06714</td>\n",
1270 " <td>...</td>\n",
1271 " <td>22.04</td>\n",
1272 " <td>71.08</td>\n",
1273 " <td>357.4</td>\n",
1274 " <td>0.14610</td>\n",
1275 " <td>0.22460</td>\n",
1276 " <td>0.17830</td>\n",
1277 " <td>0.08333</td>\n",
1278 " <td>0.2691</td>\n",
1279 " <td>0.09479</td>\n",
1280 " <td>1</td>\n",
1281 " </tr>\n",
1282 " <tr>\n",
1283 " <th>548</th>\n",
1284 " <td>9.683</td>\n",
1285 " <td>19.34</td>\n",
1286 " <td>61.05</td>\n",
1287 " <td>285.7</td>\n",
1288 " <td>0.08491</td>\n",
1289 " <td>0.05030</td>\n",
1290 " <td>0.023370</td>\n",
1291 " <td>0.009615</td>\n",
1292 " <td>0.1580</td>\n",
1293 " <td>0.06235</td>\n",
1294 " <td>...</td>\n",
1295 " <td>25.59</td>\n",
1296 " <td>69.10</td>\n",
1297 " <td>364.2</td>\n",
1298 " <td>0.11990</td>\n",
1299 " <td>0.09546</td>\n",
1300 " <td>0.09350</td>\n",
1301 " <td>0.03846</td>\n",
1302 " <td>0.2552</td>\n",
1303 " <td>0.07920</td>\n",
1304 " <td>1</td>\n",
1305 " </tr>\n",
1306 " <tr>\n",
1307 " <th>549</th>\n",
1308 " <td>10.820</td>\n",
1309 " <td>24.21</td>\n",
1310 " <td>68.89</td>\n",
1311 " <td>361.6</td>\n",
1312 " <td>0.08192</td>\n",
1313 " <td>0.06602</td>\n",
1314 " <td>0.015480</td>\n",
1315 " <td>0.008160</td>\n",
1316 " <td>0.1976</td>\n",
1317 " <td>0.06328</td>\n",
1318 " <td>...</td>\n",
1319 " <td>31.45</td>\n",
1320 " <td>83.90</td>\n",
1321 " <td>505.6</td>\n",
1322 " <td>0.12040</td>\n",
1323 " <td>0.16330</td>\n",
1324 " <td>0.06194</td>\n",
1325 " <td>0.03264</td>\n",
1326 " <td>0.3059</td>\n",
1327 " <td>0.07626</td>\n",
1328 " <td>1</td>\n",
1329 " </tr>\n",
1330 " <tr>\n",
1331 " <th>550</th>\n",
1332 " <td>10.860</td>\n",
1333 " <td>21.48</td>\n",
1334 " <td>68.51</td>\n",
1335 " <td>360.5</td>\n",
1336 " <td>0.07431</td>\n",
1337 " <td>0.04227</td>\n",
1338 " <td>0.000000</td>\n",
1339 " <td>0.000000</td>\n",
1340 " <td>0.1661</td>\n",
1341 " <td>0.05948</td>\n",
1342 " <td>...</td>\n",
1343 " <td>24.77</td>\n",
1344 " <td>74.08</td>\n",
1345 " <td>412.3</td>\n",
1346 " <td>0.10010</td>\n",
1347 " <td>0.07348</td>\n",
1348 " <td>0.00000</td>\n",
1349 " <td>0.00000</td>\n",
1350 " <td>0.2458</td>\n",
1351 " <td>0.06592</td>\n",
1352 " <td>1</td>\n",
1353 " </tr>\n",
1354 " <tr>\n",
1355 " <th>551</th>\n",
1356 " <td>11.130</td>\n",
1357 " <td>22.44</td>\n",
1358 " <td>71.49</td>\n",
1359 " <td>378.4</td>\n",
1360 " <td>0.09566</td>\n",
1361 " <td>0.08194</td>\n",
1362 " <td>0.048240</td>\n",
1363 " <td>0.022570</td>\n",
1364 " <td>0.2030</td>\n",
1365 " <td>0.06552</td>\n",
1366 " <td>...</td>\n",
1367 " <td>28.26</td>\n",
1368 " <td>77.80</td>\n",
1369 " <td>436.6</td>\n",
1370 " <td>0.10870</td>\n",
1371 " <td>0.17820</td>\n",
1372 " <td>0.15640</td>\n",
1373 " <td>0.06413</td>\n",
1374 " <td>0.3169</td>\n",
1375 " <td>0.08032</td>\n",
1376 " <td>1</td>\n",
1377 " </tr>\n",
1378 " <tr>\n",
1379 " <th>552</th>\n",
1380 " <td>12.770</td>\n",
1381 " <td>29.43</td>\n",
1382 " <td>81.35</td>\n",
1383 " <td>507.9</td>\n",
1384 " <td>0.08276</td>\n",
1385 " <td>0.04234</td>\n",
1386 " <td>0.019970</td>\n",
1387 " <td>0.014990</td>\n",
1388 " <td>0.1539</td>\n",
1389 " <td>0.05637</td>\n",
1390 " <td>...</td>\n",
1391 " <td>36.00</td>\n",
1392 " <td>88.10</td>\n",
1393 " <td>594.7</td>\n",
1394 " <td>0.12340</td>\n",
1395 " <td>0.10640</td>\n",
1396 " <td>0.08653</td>\n",
1397 " <td>0.06498</td>\n",
1398 " <td>0.2407</td>\n",
1399 " <td>0.06484</td>\n",
1400 " <td>1</td>\n",
1401 " </tr>\n",
1402 " <tr>\n",
1403 " <th>553</th>\n",
1404 " <td>9.333</td>\n",
1405 " <td>21.94</td>\n",
1406 " <td>59.01</td>\n",
1407 " <td>264.0</td>\n",
1408 " <td>0.09240</td>\n",
1409 " <td>0.05605</td>\n",
1410 " <td>0.039960</td>\n",
1411 " <td>0.012820</td>\n",
1412 " <td>0.1692</td>\n",
1413 " <td>0.06576</td>\n",
1414 " <td>...</td>\n",
1415 " <td>25.05</td>\n",
1416 " <td>62.86</td>\n",
1417 " <td>295.8</td>\n",
1418 " <td>0.11030</td>\n",
1419 " <td>0.08298</td>\n",
1420 " <td>0.07993</td>\n",
1421 " <td>0.02564</td>\n",
1422 " <td>0.2435</td>\n",
1423 " <td>0.07393</td>\n",
1424 " <td>1</td>\n",
1425 " </tr>\n",
1426 " <tr>\n",
1427 " <th>554</th>\n",
1428 " <td>12.880</td>\n",
1429 " <td>28.92</td>\n",
1430 " <td>82.50</td>\n",
1431 " <td>514.3</td>\n",
1432 " <td>0.08123</td>\n",
1433 " <td>0.05824</td>\n",
1434 " <td>0.061950</td>\n",
1435 " <td>0.023430</td>\n",
1436 " <td>0.1566</td>\n",
1437 " <td>0.05708</td>\n",
1438 " <td>...</td>\n",
1439 " <td>35.74</td>\n",
1440 " <td>88.84</td>\n",
1441 " <td>595.7</td>\n",
1442 " <td>0.12270</td>\n",
1443 " <td>0.16200</td>\n",
1444 " <td>0.24390</td>\n",
1445 " <td>0.06493</td>\n",
1446 " <td>0.2372</td>\n",
1447 " <td>0.07242</td>\n",
1448 " <td>1</td>\n",
1449 " </tr>\n",
1450 " <tr>\n",
1451 " <th>555</th>\n",
1452 " <td>10.290</td>\n",
1453 " <td>27.61</td>\n",
1454 " <td>65.67</td>\n",
1455 " <td>321.4</td>\n",
1456 " <td>0.09030</td>\n",
1457 " <td>0.07658</td>\n",
1458 " <td>0.059990</td>\n",
1459 " <td>0.027380</td>\n",
1460 " <td>0.1593</td>\n",
1461 " <td>0.06127</td>\n",
1462 " <td>...</td>\n",
1463 " <td>34.91</td>\n",
1464 " <td>69.57</td>\n",
1465 " <td>357.6</td>\n",
1466 " <td>0.13840</td>\n",
1467 " <td>0.17100</td>\n",
1468 " <td>0.20000</td>\n",
1469 " <td>0.09127</td>\n",
1470 " <td>0.2226</td>\n",
1471 " <td>0.08283</td>\n",
1472 " <td>1</td>\n",
1473 " </tr>\n",
1474 " <tr>\n",
1475 " <th>556</th>\n",
1476 " <td>10.160</td>\n",
1477 " <td>19.59</td>\n",
1478 " <td>64.73</td>\n",
1479 " <td>311.7</td>\n",
1480 " <td>0.10030</td>\n",
1481 " <td>0.07504</td>\n",
1482 " <td>0.005025</td>\n",
1483 " <td>0.011160</td>\n",
1484 " <td>0.1791</td>\n",
1485 " <td>0.06331</td>\n",
1486 " <td>...</td>\n",
1487 " <td>22.88</td>\n",
1488 " <td>67.88</td>\n",
1489 " <td>347.3</td>\n",
1490 " <td>0.12650</td>\n",
1491 " <td>0.12000</td>\n",
1492 " <td>0.01005</td>\n",
1493 " <td>0.02232</td>\n",
1494 " <td>0.2262</td>\n",
1495 " <td>0.06742</td>\n",
1496 " <td>1</td>\n",
1497 " </tr>\n",
1498 " <tr>\n",
1499 " <th>557</th>\n",
1500 " <td>9.423</td>\n",
1501 " <td>27.88</td>\n",
1502 " <td>59.26</td>\n",
1503 " <td>271.3</td>\n",
1504 " <td>0.08123</td>\n",
1505 " <td>0.04971</td>\n",
1506 " <td>0.000000</td>\n",
1507 " <td>0.000000</td>\n",
1508 " <td>0.1742</td>\n",
1509 " <td>0.06059</td>\n",
1510 " <td>...</td>\n",
1511 " <td>34.24</td>\n",
1512 " <td>66.50</td>\n",
1513 " <td>330.6</td>\n",
1514 " <td>0.10730</td>\n",
1515 " <td>0.07158</td>\n",
1516 " <td>0.00000</td>\n",
1517 " <td>0.00000</td>\n",
1518 " <td>0.2475</td>\n",
1519 " <td>0.06969</td>\n",
1520 " <td>1</td>\n",
1521 " </tr>\n",
1522 " <tr>\n",
1523 " <th>558</th>\n",
1524 " <td>14.590</td>\n",
1525 " <td>22.68</td>\n",
1526 " <td>96.39</td>\n",
1527 " <td>657.1</td>\n",
1528 " <td>0.08473</td>\n",
1529 " <td>0.13300</td>\n",
1530 " <td>0.102900</td>\n",
1531 " <td>0.037360</td>\n",
1532 " <td>0.1454</td>\n",
1533 " <td>0.06147</td>\n",
1534 " <td>...</td>\n",
1535 " <td>27.27</td>\n",
1536 " <td>105.90</td>\n",
1537 " <td>733.5</td>\n",
1538 " <td>0.10260</td>\n",
1539 " <td>0.31710</td>\n",
1540 " <td>0.36620</td>\n",
1541 " <td>0.11050</td>\n",
1542 " <td>0.2258</td>\n",
1543 " <td>0.08004</td>\n",
1544 " <td>1</td>\n",
1545 " </tr>\n",
1546 " <tr>\n",
1547 " <th>559</th>\n",
1548 " <td>11.510</td>\n",
1549 " <td>23.93</td>\n",
1550 " <td>74.52</td>\n",
1551 " <td>403.5</td>\n",
1552 " <td>0.09261</td>\n",
1553 " <td>0.10210</td>\n",
1554 " <td>0.111200</td>\n",
1555 " <td>0.041050</td>\n",
1556 " <td>0.1388</td>\n",
1557 " <td>0.06570</td>\n",
1558 " <td>...</td>\n",
1559 " <td>37.16</td>\n",
1560 " <td>82.28</td>\n",
1561 " <td>474.2</td>\n",
1562 " <td>0.12980</td>\n",
1563 " <td>0.25170</td>\n",
1564 " <td>0.36300</td>\n",
1565 " <td>0.09653</td>\n",
1566 " <td>0.2112</td>\n",
1567 " <td>0.08732</td>\n",
1568 " <td>1</td>\n",
1569 " </tr>\n",
1570 " <tr>\n",
1571 " <th>560</th>\n",
1572 " <td>14.050</td>\n",
1573 " <td>27.15</td>\n",
1574 " <td>91.38</td>\n",
1575 " <td>600.4</td>\n",
1576 " <td>0.09929</td>\n",
1577 " <td>0.11260</td>\n",
1578 " <td>0.044620</td>\n",
1579 " <td>0.043040</td>\n",
1580 " <td>0.1537</td>\n",
1581 " <td>0.06171</td>\n",
1582 " <td>...</td>\n",
1583 " <td>33.17</td>\n",
1584 " <td>100.20</td>\n",
1585 " <td>706.7</td>\n",
1586 " <td>0.12410</td>\n",
1587 " <td>0.22640</td>\n",
1588 " <td>0.13260</td>\n",
1589 " <td>0.10480</td>\n",
1590 " <td>0.2250</td>\n",
1591 " <td>0.08321</td>\n",
1592 " <td>1</td>\n",
1593 " </tr>\n",
1594 " <tr>\n",
1595 " <th>561</th>\n",
1596 " <td>11.200</td>\n",
1597 " <td>29.37</td>\n",
1598 " <td>70.67</td>\n",
1599 " <td>386.0</td>\n",
1600 " <td>0.07449</td>\n",
1601 " <td>0.03558</td>\n",
1602 " <td>0.000000</td>\n",
1603 " <td>0.000000</td>\n",
1604 " <td>0.1060</td>\n",
1605 " <td>0.05502</td>\n",
1606 " <td>...</td>\n",
1607 " <td>38.30</td>\n",
1608 " <td>75.19</td>\n",
1609 " <td>439.6</td>\n",
1610 " <td>0.09267</td>\n",
1611 " <td>0.05494</td>\n",
1612 " <td>0.00000</td>\n",
1613 " <td>0.00000</td>\n",
1614 " <td>0.1566</td>\n",
1615 " <td>0.05905</td>\n",
1616 " <td>1</td>\n",
1617 " </tr>\n",
1618 " <tr>\n",
1619 " <th>562</th>\n",
1620 " <td>15.220</td>\n",
1621 " <td>30.62</td>\n",
1622 " <td>103.40</td>\n",
1623 " <td>716.9</td>\n",
1624 " <td>0.10480</td>\n",
1625 " <td>0.20870</td>\n",
1626 " <td>0.255000</td>\n",
1627 " <td>0.094290</td>\n",
1628 " <td>0.2128</td>\n",
1629 " <td>0.07152</td>\n",
1630 " <td>...</td>\n",
1631 " <td>42.79</td>\n",
1632 " <td>128.70</td>\n",
1633 " <td>915.0</td>\n",
1634 " <td>0.14170</td>\n",
1635 " <td>0.79170</td>\n",
1636 " <td>1.17000</td>\n",
1637 " <td>0.23560</td>\n",
1638 " <td>0.4089</td>\n",
1639 " <td>0.14090</td>\n",
1640 " <td>0</td>\n",
1641 " </tr>\n",
1642 " <tr>\n",
1643 " <th>563</th>\n",
1644 " <td>20.920</td>\n",
1645 " <td>25.09</td>\n",
1646 " <td>143.00</td>\n",
1647 " <td>1347.0</td>\n",
1648 " <td>0.10990</td>\n",
1649 " <td>0.22360</td>\n",
1650 " <td>0.317400</td>\n",
1651 " <td>0.147400</td>\n",
1652 " <td>0.2149</td>\n",
1653 " <td>0.06879</td>\n",
1654 " <td>...</td>\n",
1655 " <td>29.41</td>\n",
1656 " <td>179.10</td>\n",
1657 " <td>1819.0</td>\n",
1658 " <td>0.14070</td>\n",
1659 " <td>0.41860</td>\n",
1660 " <td>0.65990</td>\n",
1661 " <td>0.25420</td>\n",
1662 " <td>0.2929</td>\n",
1663 " <td>0.09873</td>\n",
1664 " <td>0</td>\n",
1665 " </tr>\n",
1666 " <tr>\n",
1667 " <th>564</th>\n",
1668 " <td>21.560</td>\n",
1669 " <td>22.39</td>\n",
1670 " <td>142.00</td>\n",
1671 " <td>1479.0</td>\n",
1672 " <td>0.11100</td>\n",
1673 " <td>0.11590</td>\n",
1674 " <td>0.243900</td>\n",
1675 " <td>0.138900</td>\n",
1676 " <td>0.1726</td>\n",
1677 " <td>0.05623</td>\n",
1678 " <td>...</td>\n",
1679 " <td>26.40</td>\n",
1680 " <td>166.10</td>\n",
1681 " <td>2027.0</td>\n",
1682 " <td>0.14100</td>\n",
1683 " <td>0.21130</td>\n",
1684 " <td>0.41070</td>\n",
1685 " <td>0.22160</td>\n",
1686 " <td>0.2060</td>\n",
1687 " <td>0.07115</td>\n",
1688 " <td>0</td>\n",
1689 " </tr>\n",
1690 " <tr>\n",
1691 " <th>565</th>\n",
1692 " <td>20.130</td>\n",
1693 " <td>28.25</td>\n",
1694 " <td>131.20</td>\n",
1695 " <td>1261.0</td>\n",
1696 " <td>0.09780</td>\n",
1697 " <td>0.10340</td>\n",
1698 " <td>0.144000</td>\n",
1699 " <td>0.097910</td>\n",
1700 " <td>0.1752</td>\n",
1701 " <td>0.05533</td>\n",
1702 " <td>...</td>\n",
1703 " <td>38.25</td>\n",
1704 " <td>155.00</td>\n",
1705 " <td>1731.0</td>\n",
1706 " <td>0.11660</td>\n",
1707 " <td>0.19220</td>\n",
1708 " <td>0.32150</td>\n",
1709 " <td>0.16280</td>\n",
1710 " <td>0.2572</td>\n",
1711 " <td>0.06637</td>\n",
1712 " <td>0</td>\n",
1713 " </tr>\n",
1714 " <tr>\n",
1715 " <th>566</th>\n",
1716 " <td>16.600</td>\n",
1717 " <td>28.08</td>\n",
1718 " <td>108.30</td>\n",
1719 " <td>858.1</td>\n",
1720 " <td>0.08455</td>\n",
1721 " <td>0.10230</td>\n",
1722 " <td>0.092510</td>\n",
1723 " <td>0.053020</td>\n",
1724 " <td>0.1590</td>\n",
1725 " <td>0.05648</td>\n",
1726 " <td>...</td>\n",
1727 " <td>34.12</td>\n",
1728 " <td>126.70</td>\n",
1729 " <td>1124.0</td>\n",
1730 " <td>0.11390</td>\n",
1731 " <td>0.30940</td>\n",
1732 " <td>0.34030</td>\n",
1733 " <td>0.14180</td>\n",
1734 " <td>0.2218</td>\n",
1735 " <td>0.07820</td>\n",
1736 " <td>0</td>\n",
1737 " </tr>\n",
1738 " <tr>\n",
1739 " <th>567</th>\n",
1740 " <td>20.600</td>\n",
1741 " <td>29.33</td>\n",
1742 " <td>140.10</td>\n",
1743 " <td>1265.0</td>\n",
1744 " <td>0.11780</td>\n",
1745 " <td>0.27700</td>\n",
1746 " <td>0.351400</td>\n",
1747 " <td>0.152000</td>\n",
1748 " <td>0.2397</td>\n",
1749 " <td>0.07016</td>\n",
1750 " <td>...</td>\n",
1751 " <td>39.42</td>\n",
1752 " <td>184.60</td>\n",
1753 " <td>1821.0</td>\n",
1754 " <td>0.16500</td>\n",
1755 " <td>0.86810</td>\n",
1756 " <td>0.93870</td>\n",
1757 " <td>0.26500</td>\n",
1758 " <td>0.4087</td>\n",
1759 " <td>0.12400</td>\n",
1760 " <td>0</td>\n",
1761 " </tr>\n",
1762 " <tr>\n",
1763 " <th>568</th>\n",
1764 " <td>7.760</td>\n",
1765 " <td>24.54</td>\n",
1766 " <td>47.92</td>\n",
1767 " <td>181.0</td>\n",
1768 " <td>0.05263</td>\n",
1769 " <td>0.04362</td>\n",
1770 " <td>0.000000</td>\n",
1771 " <td>0.000000</td>\n",
1772 " <td>0.1587</td>\n",
1773 " <td>0.05884</td>\n",
1774 " <td>...</td>\n",
1775 " <td>30.37</td>\n",
1776 " <td>59.16</td>\n",
1777 " <td>268.6</td>\n",
1778 " <td>0.08996</td>\n",
1779 " <td>0.06444</td>\n",
1780 " <td>0.00000</td>\n",
1781 " <td>0.00000</td>\n",
1782 " <td>0.2871</td>\n",
1783 " <td>0.07039</td>\n",
1784 " <td>1</td>\n",
1785 " </tr>\n",
1786 " </tbody>\n",
1787 "</table>\n",
1788 "<p>569 rows × 31 columns</p>\n",
1789 "</div>"
1790 ],
1791 "text/plain": [
1792 " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
1793 "0 17.990 10.38 122.80 1001.0 0.11840 \n",
1794 "1 20.570 17.77 132.90 1326.0 0.08474 \n",
1795 "2 19.690 21.25 130.00 1203.0 0.10960 \n",
1796 "3 11.420 20.38 77.58 386.1 0.14250 \n",
1797 "4 20.290 14.34 135.10 1297.0 0.10030 \n",
1798 "5 12.450 15.70 82.57 477.1 0.12780 \n",
1799 "6 18.250 19.98 119.60 1040.0 0.09463 \n",
1800 "7 13.710 20.83 90.20 577.9 0.11890 \n",
1801 "8 13.000 21.82 87.50 519.8 0.12730 \n",
1802 "9 12.460 24.04 83.97 475.9 0.11860 \n",
1803 "10 16.020 23.24 102.70 797.8 0.08206 \n",
1804 "11 15.780 17.89 103.60 781.0 0.09710 \n",
1805 "12 19.170 24.80 132.40 1123.0 0.09740 \n",
1806 "13 15.850 23.95 103.70 782.7 0.08401 \n",
1807 "14 13.730 22.61 93.60 578.3 0.11310 \n",
1808 "15 14.540 27.54 96.73 658.8 0.11390 \n",
1809 "16 14.680 20.13 94.74 684.5 0.09867 \n",
1810 "17 16.130 20.68 108.10 798.8 0.11700 \n",
1811 "18 19.810 22.15 130.00 1260.0 0.09831 \n",
1812 "19 13.540 14.36 87.46 566.3 0.09779 \n",
1813 "20 13.080 15.71 85.63 520.0 0.10750 \n",
1814 "21 9.504 12.44 60.34 273.9 0.10240 \n",
1815 "22 15.340 14.26 102.50 704.4 0.10730 \n",
1816 "23 21.160 23.04 137.20 1404.0 0.09428 \n",
1817 "24 16.650 21.38 110.00 904.6 0.11210 \n",
1818 "25 17.140 16.40 116.00 912.7 0.11860 \n",
1819 "26 14.580 21.53 97.41 644.8 0.10540 \n",
1820 "27 18.610 20.25 122.10 1094.0 0.09440 \n",
1821 "28 15.300 25.27 102.40 732.4 0.10820 \n",
1822 "29 17.570 15.05 115.00 955.1 0.09847 \n",
1823 ".. ... ... ... ... ... \n",
1824 "539 7.691 25.44 48.34 170.4 0.08668 \n",
1825 "540 11.540 14.44 74.65 402.9 0.09984 \n",
1826 "541 14.470 24.99 95.81 656.4 0.08837 \n",
1827 "542 14.740 25.42 94.70 668.6 0.08275 \n",
1828 "543 13.210 28.06 84.88 538.4 0.08671 \n",
1829 "544 13.870 20.70 89.77 584.8 0.09578 \n",
1830 "545 13.620 23.23 87.19 573.2 0.09246 \n",
1831 "546 10.320 16.35 65.31 324.9 0.09434 \n",
1832 "547 10.260 16.58 65.85 320.8 0.08877 \n",
1833 "548 9.683 19.34 61.05 285.7 0.08491 \n",
1834 "549 10.820 24.21 68.89 361.6 0.08192 \n",
1835 "550 10.860 21.48 68.51 360.5 0.07431 \n",
1836 "551 11.130 22.44 71.49 378.4 0.09566 \n",
1837 "552 12.770 29.43 81.35 507.9 0.08276 \n",
1838 "553 9.333 21.94 59.01 264.0 0.09240 \n",
1839 "554 12.880 28.92 82.50 514.3 0.08123 \n",
1840 "555 10.290 27.61 65.67 321.4 0.09030 \n",
1841 "556 10.160 19.59 64.73 311.7 0.10030 \n",
1842 "557 9.423 27.88 59.26 271.3 0.08123 \n",
1843 "558 14.590 22.68 96.39 657.1 0.08473 \n",
1844 "559 11.510 23.93 74.52 403.5 0.09261 \n",
1845 "560 14.050 27.15 91.38 600.4 0.09929 \n",
1846 "561 11.200 29.37 70.67 386.0 0.07449 \n",
1847 "562 15.220 30.62 103.40 716.9 0.10480 \n",
1848 "563 20.920 25.09 143.00 1347.0 0.10990 \n",
1849 "564 21.560 22.39 142.00 1479.0 0.11100 \n",
1850 "565 20.130 28.25 131.20 1261.0 0.09780 \n",
1851 "566 16.600 28.08 108.30 858.1 0.08455 \n",
1852 "567 20.600 29.33 140.10 1265.0 0.11780 \n",
1853 "568 7.760 24.54 47.92 181.0 0.05263 \n",
1854 "\n",
1855 " mean compactness mean concavity mean concave points mean symmetry \\\n",
1856 "0 0.27760 0.300100 0.147100 0.2419 \n",
1857 "1 0.07864 0.086900 0.070170 0.1812 \n",
1858 "2 0.15990 0.197400 0.127900 0.2069 \n",
1859 "3 0.28390 0.241400 0.105200 0.2597 \n",
1860 "4 0.13280 0.198000 0.104300 0.1809 \n",
1861 "5 0.17000 0.157800 0.080890 0.2087 \n",
1862 "6 0.10900 0.112700 0.074000 0.1794 \n",
1863 "7 0.16450 0.093660 0.059850 0.2196 \n",
1864 "8 0.19320 0.185900 0.093530 0.2350 \n",
1865 "9 0.23960 0.227300 0.085430 0.2030 \n",
1866 "10 0.06669 0.032990 0.033230 0.1528 \n",
1867 "11 0.12920 0.099540 0.066060 0.1842 \n",
1868 "12 0.24580 0.206500 0.111800 0.2397 \n",
1869 "13 0.10020 0.099380 0.053640 0.1847 \n",
1870 "14 0.22930 0.212800 0.080250 0.2069 \n",
1871 "15 0.15950 0.163900 0.073640 0.2303 \n",
1872 "16 0.07200 0.073950 0.052590 0.1586 \n",
1873 "17 0.20220 0.172200 0.102800 0.2164 \n",
1874 "18 0.10270 0.147900 0.094980 0.1582 \n",
1875 "19 0.08129 0.066640 0.047810 0.1885 \n",
1876 "20 0.12700 0.045680 0.031100 0.1967 \n",
1877 "21 0.06492 0.029560 0.020760 0.1815 \n",
1878 "22 0.21350 0.207700 0.097560 0.2521 \n",
1879 "23 0.10220 0.109700 0.086320 0.1769 \n",
1880 "24 0.14570 0.152500 0.091700 0.1995 \n",
1881 "25 0.22760 0.222900 0.140100 0.3040 \n",
1882 "26 0.18680 0.142500 0.087830 0.2252 \n",
1883 "27 0.10660 0.149000 0.077310 0.1697 \n",
1884 "28 0.16970 0.168300 0.087510 0.1926 \n",
1885 "29 0.11570 0.098750 0.079530 0.1739 \n",
1886 ".. ... ... ... ... \n",
1887 "539 0.11990 0.092520 0.013640 0.2037 \n",
1888 "540 0.11200 0.067370 0.025940 0.1818 \n",
1889 "541 0.12300 0.100900 0.038900 0.1872 \n",
1890 "542 0.07214 0.041050 0.030270 0.1840 \n",
1891 "543 0.06877 0.029870 0.032750 0.1628 \n",
1892 "544 0.10180 0.036880 0.023690 0.1620 \n",
1893 "545 0.06747 0.029740 0.024430 0.1664 \n",
1894 "546 0.04994 0.010120 0.005495 0.1885 \n",
1895 "547 0.08066 0.043580 0.024380 0.1669 \n",
1896 "548 0.05030 0.023370 0.009615 0.1580 \n",
1897 "549 0.06602 0.015480 0.008160 0.1976 \n",
1898 "550 0.04227 0.000000 0.000000 0.1661 \n",
1899 "551 0.08194 0.048240 0.022570 0.2030 \n",
1900 "552 0.04234 0.019970 0.014990 0.1539 \n",
1901 "553 0.05605 0.039960 0.012820 0.1692 \n",
1902 "554 0.05824 0.061950 0.023430 0.1566 \n",
1903 "555 0.07658 0.059990 0.027380 0.1593 \n",
1904 "556 0.07504 0.005025 0.011160 0.1791 \n",
1905 "557 0.04971 0.000000 0.000000 0.1742 \n",
1906 "558 0.13300 0.102900 0.037360 0.1454 \n",
1907 "559 0.10210 0.111200 0.041050 0.1388 \n",
1908 "560 0.11260 0.044620 0.043040 0.1537 \n",
1909 "561 0.03558 0.000000 0.000000 0.1060 \n",
1910 "562 0.20870 0.255000 0.094290 0.2128 \n",
1911 "563 0.22360 0.317400 0.147400 0.2149 \n",
1912 "564 0.11590 0.243900 0.138900 0.1726 \n",
1913 "565 0.10340 0.144000 0.097910 0.1752 \n",
1914 "566 0.10230 0.092510 0.053020 0.1590 \n",
1915 "567 0.27700 0.351400 0.152000 0.2397 \n",
1916 "568 0.04362 0.000000 0.000000 0.1587 \n",
1917 "\n",
1918 " mean fractal dimension ... worst texture worst perimeter \\\n",
1919 "0 0.07871 ... 17.33 184.60 \n",
1920 "1 0.05667 ... 23.41 158.80 \n",
1921 "2 0.05999 ... 25.53 152.50 \n",
1922 "3 0.09744 ... 26.50 98.87 \n",
1923 "4 0.05883 ... 16.67 152.20 \n",
1924 "5 0.07613 ... 23.75 103.40 \n",
1925 "6 0.05742 ... 27.66 153.20 \n",
1926 "7 0.07451 ... 28.14 110.60 \n",
1927 "8 0.07389 ... 30.73 106.20 \n",
1928 "9 0.08243 ... 40.68 97.65 \n",
1929 "10 0.05697 ... 33.88 123.80 \n",
1930 "11 0.06082 ... 27.28 136.50 \n",
1931 "12 0.07800 ... 29.94 151.70 \n",
1932 "13 0.05338 ... 27.66 112.00 \n",
1933 "14 0.07682 ... 32.01 108.80 \n",
1934 "15 0.07077 ... 37.13 124.10 \n",
1935 "16 0.05922 ... 30.88 123.40 \n",
1936 "17 0.07356 ... 31.48 136.80 \n",
1937 "18 0.05395 ... 30.88 186.80 \n",
1938 "19 0.05766 ... 19.26 99.70 \n",
1939 "20 0.06811 ... 20.49 96.09 \n",
1940 "21 0.06905 ... 15.66 65.13 \n",
1941 "22 0.07032 ... 19.08 125.10 \n",
1942 "23 0.05278 ... 35.59 188.00 \n",
1943 "24 0.06330 ... 31.56 177.00 \n",
1944 "25 0.07413 ... 21.40 152.40 \n",
1945 "26 0.06924 ... 33.21 122.40 \n",
1946 "27 0.05699 ... 27.26 139.90 \n",
1947 "28 0.06540 ... 36.71 149.30 \n",
1948 "29 0.06149 ... 19.52 134.90 \n",
1949 ".. ... ... ... ... \n",
1950 "539 0.07751 ... 31.89 54.49 \n",
1951 "540 0.06782 ... 19.68 78.78 \n",
1952 "541 0.06341 ... 31.73 113.50 \n",
1953 "542 0.05680 ... 32.29 107.40 \n",
1954 "543 0.05781 ... 37.17 92.48 \n",
1955 "544 0.06688 ... 24.75 99.17 \n",
1956 "545 0.05801 ... 29.09 97.58 \n",
1957 "546 0.06201 ... 21.77 71.12 \n",
1958 "547 0.06714 ... 22.04 71.08 \n",
1959 "548 0.06235 ... 25.59 69.10 \n",
1960 "549 0.06328 ... 31.45 83.90 \n",
1961 "550 0.05948 ... 24.77 74.08 \n",
1962 "551 0.06552 ... 28.26 77.80 \n",
1963 "552 0.05637 ... 36.00 88.10 \n",
1964 "553 0.06576 ... 25.05 62.86 \n",
1965 "554 0.05708 ... 35.74 88.84 \n",
1966 "555 0.06127 ... 34.91 69.57 \n",
1967 "556 0.06331 ... 22.88 67.88 \n",
1968 "557 0.06059 ... 34.24 66.50 \n",
1969 "558 0.06147 ... 27.27 105.90 \n",
1970 "559 0.06570 ... 37.16 82.28 \n",
1971 "560 0.06171 ... 33.17 100.20 \n",
1972 "561 0.05502 ... 38.30 75.19 \n",
1973 "562 0.07152 ... 42.79 128.70 \n",
1974 "563 0.06879 ... 29.41 179.10 \n",
1975 "564 0.05623 ... 26.40 166.10 \n",
1976 "565 0.05533 ... 38.25 155.00 \n",
1977 "566 0.05648 ... 34.12 126.70 \n",
1978 "567 0.07016 ... 39.42 184.60 \n",
1979 "568 0.05884 ... 30.37 59.16 \n",
1980 "\n",
1981 " worst area worst smoothness worst compactness worst concavity \\\n",
1982 "0 2019.0 0.16220 0.66560 0.71190 \n",
1983 "1 1956.0 0.12380 0.18660 0.24160 \n",
1984 "2 1709.0 0.14440 0.42450 0.45040 \n",
1985 "3 567.7 0.20980 0.86630 0.68690 \n",
1986 "4 1575.0 0.13740 0.20500 0.40000 \n",
1987 "5 741.6 0.17910 0.52490 0.53550 \n",
1988 "6 1606.0 0.14420 0.25760 0.37840 \n",
1989 "7 897.0 0.16540 0.36820 0.26780 \n",
1990 "8 739.3 0.17030 0.54010 0.53900 \n",
1991 "9 711.4 0.18530 1.05800 1.10500 \n",
1992 "10 1150.0 0.11810 0.15510 0.14590 \n",
1993 "11 1299.0 0.13960 0.56090 0.39650 \n",
1994 "12 1332.0 0.10370 0.39030 0.36390 \n",
1995 "13 876.5 0.11310 0.19240 0.23220 \n",
1996 "14 697.7 0.16510 0.77250 0.69430 \n",
1997 "15 943.2 0.16780 0.65770 0.70260 \n",
1998 "16 1138.0 0.14640 0.18710 0.29140 \n",
1999 "17 1315.0 0.17890 0.42330 0.47840 \n",
2000 "18 2398.0 0.15120 0.31500 0.53720 \n",
2001 "19 711.2 0.14400 0.17730 0.23900 \n",
2002 "20 630.5 0.13120 0.27760 0.18900 \n",
2003 "21 314.9 0.13240 0.11480 0.08867 \n",
2004 "22 980.9 0.13900 0.59540 0.63050 \n",
2005 "23 2615.0 0.14010 0.26000 0.31550 \n",
2006 "24 2215.0 0.18050 0.35780 0.46950 \n",
2007 "25 1461.0 0.15450 0.39490 0.38530 \n",
2008 "26 896.9 0.15250 0.66430 0.55390 \n",
2009 "27 1403.0 0.13380 0.21170 0.34460 \n",
2010 "28 1269.0 0.16410 0.61100 0.63350 \n",
2011 "29 1227.0 0.12550 0.28120 0.24890 \n",
2012 ".. ... ... ... ... \n",
2013 "539 223.6 0.15960 0.30640 0.33930 \n",
2014 "540 457.8 0.13450 0.21180 0.17970 \n",
2015 "541 808.9 0.13400 0.42020 0.40400 \n",
2016 "542 826.4 0.10600 0.13760 0.16110 \n",
2017 "543 629.6 0.10720 0.13810 0.10620 \n",
2018 "544 688.6 0.12640 0.20370 0.13770 \n",
2019 "545 729.8 0.12160 0.15170 0.10490 \n",
2020 "546 384.9 0.12850 0.08842 0.04384 \n",
2021 "547 357.4 0.14610 0.22460 0.17830 \n",
2022 "548 364.2 0.11990 0.09546 0.09350 \n",
2023 "549 505.6 0.12040 0.16330 0.06194 \n",
2024 "550 412.3 0.10010 0.07348 0.00000 \n",
2025 "551 436.6 0.10870 0.17820 0.15640 \n",
2026 "552 594.7 0.12340 0.10640 0.08653 \n",
2027 "553 295.8 0.11030 0.08298 0.07993 \n",
2028 "554 595.7 0.12270 0.16200 0.24390 \n",
2029 "555 357.6 0.13840 0.17100 0.20000 \n",
2030 "556 347.3 0.12650 0.12000 0.01005 \n",
2031 "557 330.6 0.10730 0.07158 0.00000 \n",
2032 "558 733.5 0.10260 0.31710 0.36620 \n",
2033 "559 474.2 0.12980 0.25170 0.36300 \n",
2034 "560 706.7 0.12410 0.22640 0.13260 \n",
2035 "561 439.6 0.09267 0.05494 0.00000 \n",
2036 "562 915.0 0.14170 0.79170 1.17000 \n",
2037 "563 1819.0 0.14070 0.41860 0.65990 \n",
2038 "564 2027.0 0.14100 0.21130 0.41070 \n",
2039 "565 1731.0 0.11660 0.19220 0.32150 \n",
2040 "566 1124.0 0.11390 0.30940 0.34030 \n",
2041 "567 1821.0 0.16500 0.86810 0.93870 \n",
2042 "568 268.6 0.08996 0.06444 0.00000 \n",
2043 "\n",
2044 " worst concave points worst symmetry worst fractal dimension target \n",
2045 "0 0.26540 0.4601 0.11890 0 \n",
2046 "1 0.18600 0.2750 0.08902 0 \n",
2047 "2 0.24300 0.3613 0.08758 0 \n",
2048 "3 0.25750 0.6638 0.17300 0 \n",
2049 "4 0.16250 0.2364 0.07678 0 \n",
2050 "5 0.17410 0.3985 0.12440 0 \n",
2051 "6 0.19320 0.3063 0.08368 0 \n",
2052 "7 0.15560 0.3196 0.11510 0 \n",
2053 "8 0.20600 0.4378 0.10720 0 \n",
2054 "9 0.22100 0.4366 0.20750 0 \n",
2055 "10 0.09975 0.2948 0.08452 0 \n",
2056 "11 0.18100 0.3792 0.10480 0 \n",
2057 "12 0.17670 0.3176 0.10230 0 \n",
2058 "13 0.11190 0.2809 0.06287 0 \n",
2059 "14 0.22080 0.3596 0.14310 0 \n",
2060 "15 0.17120 0.4218 0.13410 0 \n",
2061 "16 0.16090 0.3029 0.08216 0 \n",
2062 "17 0.20730 0.3706 0.11420 0 \n",
2063 "18 0.23880 0.2768 0.07615 0 \n",
2064 "19 0.12880 0.2977 0.07259 1 \n",
2065 "20 0.07283 0.3184 0.08183 1 \n",
2066 "21 0.06227 0.2450 0.07773 1 \n",
2067 "22 0.23930 0.4667 0.09946 0 \n",
2068 "23 0.20090 0.2822 0.07526 0 \n",
2069 "24 0.20950 0.3613 0.09564 0 \n",
2070 "25 0.25500 0.4066 0.10590 0 \n",
2071 "26 0.27010 0.4264 0.12750 0 \n",
2072 "27 0.14900 0.2341 0.07421 0 \n",
2073 "28 0.20240 0.4027 0.09876 0 \n",
2074 "29 0.14560 0.2756 0.07919 0 \n",
2075 ".. ... ... ... ... \n",
2076 "539 0.05000 0.2790 0.10660 1 \n",
2077 "540 0.06918 0.2329 0.08134 1 \n",
2078 "541 0.12050 0.3187 0.10230 1 \n",
2079 "542 0.10950 0.2722 0.06956 1 \n",
2080 "543 0.07958 0.2473 0.06443 1 \n",
2081 "544 0.06845 0.2249 0.08492 1 \n",
2082 "545 0.07174 0.2642 0.06953 1 \n",
2083 "546 0.02381 0.2681 0.07399 1 \n",
2084 "547 0.08333 0.2691 0.09479 1 \n",
2085 "548 0.03846 0.2552 0.07920 1 \n",
2086 "549 0.03264 0.3059 0.07626 1 \n",
2087 "550 0.00000 0.2458 0.06592 1 \n",
2088 "551 0.06413 0.3169 0.08032 1 \n",
2089 "552 0.06498 0.2407 0.06484 1 \n",
2090 "553 0.02564 0.2435 0.07393 1 \n",
2091 "554 0.06493 0.2372 0.07242 1 \n",
2092 "555 0.09127 0.2226 0.08283 1 \n",
2093 "556 0.02232 0.2262 0.06742 1 \n",
2094 "557 0.00000 0.2475 0.06969 1 \n",
2095 "558 0.11050 0.2258 0.08004 1 \n",
2096 "559 0.09653 0.2112 0.08732 1 \n",
2097 "560 0.10480 0.2250 0.08321 1 \n",
2098 "561 0.00000 0.1566 0.05905 1 \n",
2099 "562 0.23560 0.4089 0.14090 0 \n",
2100 "563 0.25420 0.2929 0.09873 0 \n",
2101 "564 0.22160 0.2060 0.07115 0 \n",
2102 "565 0.16280 0.2572 0.06637 0 \n",
2103 "566 0.14180 0.2218 0.07820 0 \n",
2104 "567 0.26500 0.4087 0.12400 0 \n",
2105 "568 0.00000 0.2871 0.07039 1 \n",
2106 "\n",
2107 "[569 rows x 31 columns]"
2108 ]
2109 },
2110 "execution_count": 5,
2111 "metadata": {},
2112 "output_type": "execute_result"
2113 }
2114 ],
2115 "source": [
2116 "def answer_one():\n",
2117 " \n",
2118 " cancerdf = pd.concat([pd.DataFrame(cancer.data), pd.DataFrame(cancer.target)], axis=1)\n",
2119 " cancerdf.columns = np.append(cancer.feature_names,'target')\n",
2120 " return cancerdf\n",
2121 "\n",
2122 "answer_one()"
2123 ]
2124 },
2125 {
2126 "cell_type": "markdown",
2127 "metadata": {},
2128 "source": [
2129 "### Question 2\n",
2130 "What is the class distribution? (i.e. how many instances of `malignant` (encoded 0) and how many `benign` (encoded 1)?)\n",
2131 "\n",
2132 "*This function should return a Series named `target` of length 2 with integer values and index =* `['malignant', 'benign']`"
2133 ]
2134 },
2135 {
2136 "cell_type": "code",
2137 "execution_count": 6,
2138 "metadata": {},
2139 "outputs": [
2140 {
2141 "data": {
2142 "text/plain": [
2143 "benign 357\n",
2144 "malignant 212\n",
2145 "Name: target, dtype: int64"
2146 ]
2147 },
2148 "execution_count": 6,
2149 "metadata": {},
2150 "output_type": "execute_result"
2151 }
2152 ],
2153 "source": [
2154 "def answer_two():\n",
2155 " cancerdf = answer_one()\n",
2156 " \n",
2157 " # Counts frequency of unique values. Useful!\n",
2158 " target = cancerdf['target'].value_counts()\n",
2159 " target.index = ['benign','malignant']\n",
2160 "\n",
2161 " return target\n",
2162 "\n",
2163 "answer_two()"
2164 ]
2165 },
2166 {
2167 "cell_type": "markdown",
2168 "metadata": {},
2169 "source": [
2170 "### Question 3\n",
2171 "Split the DataFrame into `X` (the data) and `y` (the labels).\n",
2172 "\n",
2173 "*This function should return a tuple of length 2:* `(X, y)`*, where* \n",
2174 "* `X`*, a pandas DataFrame, has shape* `(569, 30)`\n",
2175 "* `y`*, a pandas Series, has shape* `(569,)`."
2176 ]
2177 },
2178 {
2179 "cell_type": "code",
2180 "execution_count": 7,
2181 "metadata": {
2182 "scrolled": true
2183 },
2184 "outputs": [
2185 {
2186 "data": {
2187 "text/plain": [
2188 "( mean radius mean texture mean perimeter mean area mean smoothness \\\n",
2189 " 0 17.990 10.38 122.80 1001.0 0.11840 \n",
2190 " 1 20.570 17.77 132.90 1326.0 0.08474 \n",
2191 " 2 19.690 21.25 130.00 1203.0 0.10960 \n",
2192 " 3 11.420 20.38 77.58 386.1 0.14250 \n",
2193 " 4 20.290 14.34 135.10 1297.0 0.10030 \n",
2194 " 5 12.450 15.70 82.57 477.1 0.12780 \n",
2195 " 6 18.250 19.98 119.60 1040.0 0.09463 \n",
2196 " 7 13.710 20.83 90.20 577.9 0.11890 \n",
2197 " 8 13.000 21.82 87.50 519.8 0.12730 \n",
2198 " 9 12.460 24.04 83.97 475.9 0.11860 \n",
2199 " 10 16.020 23.24 102.70 797.8 0.08206 \n",
2200 " 11 15.780 17.89 103.60 781.0 0.09710 \n",
2201 " 12 19.170 24.80 132.40 1123.0 0.09740 \n",
2202 " 13 15.850 23.95 103.70 782.7 0.08401 \n",
2203 " 14 13.730 22.61 93.60 578.3 0.11310 \n",
2204 " 15 14.540 27.54 96.73 658.8 0.11390 \n",
2205 " 16 14.680 20.13 94.74 684.5 0.09867 \n",
2206 " 17 16.130 20.68 108.10 798.8 0.11700 \n",
2207 " 18 19.810 22.15 130.00 1260.0 0.09831 \n",
2208 " 19 13.540 14.36 87.46 566.3 0.09779 \n",
2209 " 20 13.080 15.71 85.63 520.0 0.10750 \n",
2210 " 21 9.504 12.44 60.34 273.9 0.10240 \n",
2211 " 22 15.340 14.26 102.50 704.4 0.10730 \n",
2212 " 23 21.160 23.04 137.20 1404.0 0.09428 \n",
2213 " 24 16.650 21.38 110.00 904.6 0.11210 \n",
2214 " 25 17.140 16.40 116.00 912.7 0.11860 \n",
2215 " 26 14.580 21.53 97.41 644.8 0.10540 \n",
2216 " 27 18.610 20.25 122.10 1094.0 0.09440 \n",
2217 " 28 15.300 25.27 102.40 732.4 0.10820 \n",
2218 " 29 17.570 15.05 115.00 955.1 0.09847 \n",
2219 " .. ... ... ... ... ... \n",
2220 " 539 7.691 25.44 48.34 170.4 0.08668 \n",
2221 " 540 11.540 14.44 74.65 402.9 0.09984 \n",
2222 " 541 14.470 24.99 95.81 656.4 0.08837 \n",
2223 " 542 14.740 25.42 94.70 668.6 0.08275 \n",
2224 " 543 13.210 28.06 84.88 538.4 0.08671 \n",
2225 " 544 13.870 20.70 89.77 584.8 0.09578 \n",
2226 " 545 13.620 23.23 87.19 573.2 0.09246 \n",
2227 " 546 10.320 16.35 65.31 324.9 0.09434 \n",
2228 " 547 10.260 16.58 65.85 320.8 0.08877 \n",
2229 " 548 9.683 19.34 61.05 285.7 0.08491 \n",
2230 " 549 10.820 24.21 68.89 361.6 0.08192 \n",
2231 " 550 10.860 21.48 68.51 360.5 0.07431 \n",
2232 " 551 11.130 22.44 71.49 378.4 0.09566 \n",
2233 " 552 12.770 29.43 81.35 507.9 0.08276 \n",
2234 " 553 9.333 21.94 59.01 264.0 0.09240 \n",
2235 " 554 12.880 28.92 82.50 514.3 0.08123 \n",
2236 " 555 10.290 27.61 65.67 321.4 0.09030 \n",
2237 " 556 10.160 19.59 64.73 311.7 0.10030 \n",
2238 " 557 9.423 27.88 59.26 271.3 0.08123 \n",
2239 " 558 14.590 22.68 96.39 657.1 0.08473 \n",
2240 " 559 11.510 23.93 74.52 403.5 0.09261 \n",
2241 " 560 14.050 27.15 91.38 600.4 0.09929 \n",
2242 " 561 11.200 29.37 70.67 386.0 0.07449 \n",
2243 " 562 15.220 30.62 103.40 716.9 0.10480 \n",
2244 " 563 20.920 25.09 143.00 1347.0 0.10990 \n",
2245 " 564 21.560 22.39 142.00 1479.0 0.11100 \n",
2246 " 565 20.130 28.25 131.20 1261.0 0.09780 \n",
2247 " 566 16.600 28.08 108.30 858.1 0.08455 \n",
2248 " 567 20.600 29.33 140.10 1265.0 0.11780 \n",
2249 " 568 7.760 24.54 47.92 181.0 0.05263 \n",
2250 " \n",
2251 " mean compactness mean concavity mean concave points mean symmetry \\\n",
2252 " 0 0.27760 0.300100 0.147100 0.2419 \n",
2253 " 1 0.07864 0.086900 0.070170 0.1812 \n",
2254 " 2 0.15990 0.197400 0.127900 0.2069 \n",
2255 " 3 0.28390 0.241400 0.105200 0.2597 \n",
2256 " 4 0.13280 0.198000 0.104300 0.1809 \n",
2257 " 5 0.17000 0.157800 0.080890 0.2087 \n",
2258 " 6 0.10900 0.112700 0.074000 0.1794 \n",
2259 " 7 0.16450 0.093660 0.059850 0.2196 \n",
2260 " 8 0.19320 0.185900 0.093530 0.2350 \n",
2261 " 9 0.23960 0.227300 0.085430 0.2030 \n",
2262 " 10 0.06669 0.032990 0.033230 0.1528 \n",
2263 " 11 0.12920 0.099540 0.066060 0.1842 \n",
2264 " 12 0.24580 0.206500 0.111800 0.2397 \n",
2265 " 13 0.10020 0.099380 0.053640 0.1847 \n",
2266 " 14 0.22930 0.212800 0.080250 0.2069 \n",
2267 " 15 0.15950 0.163900 0.073640 0.2303 \n",
2268 " 16 0.07200 0.073950 0.052590 0.1586 \n",
2269 " 17 0.20220 0.172200 0.102800 0.2164 \n",
2270 " 18 0.10270 0.147900 0.094980 0.1582 \n",
2271 " 19 0.08129 0.066640 0.047810 0.1885 \n",
2272 " 20 0.12700 0.045680 0.031100 0.1967 \n",
2273 " 21 0.06492 0.029560 0.020760 0.1815 \n",
2274 " 22 0.21350 0.207700 0.097560 0.2521 \n",
2275 " 23 0.10220 0.109700 0.086320 0.1769 \n",
2276 " 24 0.14570 0.152500 0.091700 0.1995 \n",
2277 " 25 0.22760 0.222900 0.140100 0.3040 \n",
2278 " 26 0.18680 0.142500 0.087830 0.2252 \n",
2279 " 27 0.10660 0.149000 0.077310 0.1697 \n",
2280 " 28 0.16970 0.168300 0.087510 0.1926 \n",
2281 " 29 0.11570 0.098750 0.079530 0.1739 \n",
2282 " .. ... ... ... ... \n",
2283 " 539 0.11990 0.092520 0.013640 0.2037 \n",
2284 " 540 0.11200 0.067370 0.025940 0.1818 \n",
2285 " 541 0.12300 0.100900 0.038900 0.1872 \n",
2286 " 542 0.07214 0.041050 0.030270 0.1840 \n",
2287 " 543 0.06877 0.029870 0.032750 0.1628 \n",
2288 " 544 0.10180 0.036880 0.023690 0.1620 \n",
2289 " 545 0.06747 0.029740 0.024430 0.1664 \n",
2290 " 546 0.04994 0.010120 0.005495 0.1885 \n",
2291 " 547 0.08066 0.043580 0.024380 0.1669 \n",
2292 " 548 0.05030 0.023370 0.009615 0.1580 \n",
2293 " 549 0.06602 0.015480 0.008160 0.1976 \n",
2294 " 550 0.04227 0.000000 0.000000 0.1661 \n",
2295 " 551 0.08194 0.048240 0.022570 0.2030 \n",
2296 " 552 0.04234 0.019970 0.014990 0.1539 \n",
2297 " 553 0.05605 0.039960 0.012820 0.1692 \n",
2298 " 554 0.05824 0.061950 0.023430 0.1566 \n",
2299 " 555 0.07658 0.059990 0.027380 0.1593 \n",
2300 " 556 0.07504 0.005025 0.011160 0.1791 \n",
2301 " 557 0.04971 0.000000 0.000000 0.1742 \n",
2302 " 558 0.13300 0.102900 0.037360 0.1454 \n",
2303 " 559 0.10210 0.111200 0.041050 0.1388 \n",
2304 " 560 0.11260 0.044620 0.043040 0.1537 \n",
2305 " 561 0.03558 0.000000 0.000000 0.1060 \n",
2306 " 562 0.20870 0.255000 0.094290 0.2128 \n",
2307 " 563 0.22360 0.317400 0.147400 0.2149 \n",
2308 " 564 0.11590 0.243900 0.138900 0.1726 \n",
2309 " 565 0.10340 0.144000 0.097910 0.1752 \n",
2310 " 566 0.10230 0.092510 0.053020 0.1590 \n",
2311 " 567 0.27700 0.351400 0.152000 0.2397 \n",
2312 " 568 0.04362 0.000000 0.000000 0.1587 \n",
2313 " \n",
2314 " mean fractal dimension ... worst radius \\\n",
2315 " 0 0.07871 ... 25.380 \n",
2316 " 1 0.05667 ... 24.990 \n",
2317 " 2 0.05999 ... 23.570 \n",
2318 " 3 0.09744 ... 14.910 \n",
2319 " 4 0.05883 ... 22.540 \n",
2320 " 5 0.07613 ... 15.470 \n",
2321 " 6 0.05742 ... 22.880 \n",
2322 " 7 0.07451 ... 17.060 \n",
2323 " 8 0.07389 ... 15.490 \n",
2324 " 9 0.08243 ... 15.090 \n",
2325 " 10 0.05697 ... 19.190 \n",
2326 " 11 0.06082 ... 20.420 \n",
2327 " 12 0.07800 ... 20.960 \n",
2328 " 13 0.05338 ... 16.840 \n",
2329 " 14 0.07682 ... 15.030 \n",
2330 " 15 0.07077 ... 17.460 \n",
2331 " 16 0.05922 ... 19.070 \n",
2332 " 17 0.07356 ... 20.960 \n",
2333 " 18 0.05395 ... 27.320 \n",
2334 " 19 0.05766 ... 15.110 \n",
2335 " 20 0.06811 ... 14.500 \n",
2336 " 21 0.06905 ... 10.230 \n",
2337 " 22 0.07032 ... 18.070 \n",
2338 " 23 0.05278 ... 29.170 \n",
2339 " 24 0.06330 ... 26.460 \n",
2340 " 25 0.07413 ... 22.250 \n",
2341 " 26 0.06924 ... 17.620 \n",
2342 " 27 0.05699 ... 21.310 \n",
2343 " 28 0.06540 ... 20.270 \n",
2344 " 29 0.06149 ... 20.010 \n",
2345 " .. ... ... ... \n",
2346 " 539 0.07751 ... 8.678 \n",
2347 " 540 0.06782 ... 12.260 \n",
2348 " 541 0.06341 ... 16.220 \n",
2349 " 542 0.05680 ... 16.510 \n",
2350 " 543 0.05781 ... 14.370 \n",
2351 " 544 0.06688 ... 15.050 \n",
2352 " 545 0.05801 ... 15.350 \n",
2353 " 546 0.06201 ... 11.250 \n",
2354 " 547 0.06714 ... 10.830 \n",
2355 " 548 0.06235 ... 10.930 \n",
2356 " 549 0.06328 ... 13.030 \n",
2357 " 550 0.05948 ... 11.660 \n",
2358 " 551 0.06552 ... 12.020 \n",
2359 " 552 0.05637 ... 13.870 \n",
2360 " 553 0.06576 ... 9.845 \n",
2361 " 554 0.05708 ... 13.890 \n",
2362 " 555 0.06127 ... 10.840 \n",
2363 " 556 0.06331 ... 10.650 \n",
2364 " 557 0.06059 ... 10.490 \n",
2365 " 558 0.06147 ... 15.480 \n",
2366 " 559 0.06570 ... 12.480 \n",
2367 " 560 0.06171 ... 15.300 \n",
2368 " 561 0.05502 ... 11.920 \n",
2369 " 562 0.07152 ... 17.520 \n",
2370 " 563 0.06879 ... 24.290 \n",
2371 " 564 0.05623 ... 25.450 \n",
2372 " 565 0.05533 ... 23.690 \n",
2373 " 566 0.05648 ... 18.980 \n",
2374 " 567 0.07016 ... 25.740 \n",
2375 " 568 0.05884 ... 9.456 \n",
2376 " \n",
2377 " worst texture worst perimeter worst area worst smoothness \\\n",
2378 " 0 17.33 184.60 2019.0 0.16220 \n",
2379 " 1 23.41 158.80 1956.0 0.12380 \n",
2380 " 2 25.53 152.50 1709.0 0.14440 \n",
2381 " 3 26.50 98.87 567.7 0.20980 \n",
2382 " 4 16.67 152.20 1575.0 0.13740 \n",
2383 " 5 23.75 103.40 741.6 0.17910 \n",
2384 " 6 27.66 153.20 1606.0 0.14420 \n",
2385 " 7 28.14 110.60 897.0 0.16540 \n",
2386 " 8 30.73 106.20 739.3 0.17030 \n",
2387 " 9 40.68 97.65 711.4 0.18530 \n",
2388 " 10 33.88 123.80 1150.0 0.11810 \n",
2389 " 11 27.28 136.50 1299.0 0.13960 \n",
2390 " 12 29.94 151.70 1332.0 0.10370 \n",
2391 " 13 27.66 112.00 876.5 0.11310 \n",
2392 " 14 32.01 108.80 697.7 0.16510 \n",
2393 " 15 37.13 124.10 943.2 0.16780 \n",
2394 " 16 30.88 123.40 1138.0 0.14640 \n",
2395 " 17 31.48 136.80 1315.0 0.17890 \n",
2396 " 18 30.88 186.80 2398.0 0.15120 \n",
2397 " 19 19.26 99.70 711.2 0.14400 \n",
2398 " 20 20.49 96.09 630.5 0.13120 \n",
2399 " 21 15.66 65.13 314.9 0.13240 \n",
2400 " 22 19.08 125.10 980.9 0.13900 \n",
2401 " 23 35.59 188.00 2615.0 0.14010 \n",
2402 " 24 31.56 177.00 2215.0 0.18050 \n",
2403 " 25 21.40 152.40 1461.0 0.15450 \n",
2404 " 26 33.21 122.40 896.9 0.15250 \n",
2405 " 27 27.26 139.90 1403.0 0.13380 \n",
2406 " 28 36.71 149.30 1269.0 0.16410 \n",
2407 " 29 19.52 134.90 1227.0 0.12550 \n",
2408 " .. ... ... ... ... \n",
2409 " 539 31.89 54.49 223.6 0.15960 \n",
2410 " 540 19.68 78.78 457.8 0.13450 \n",
2411 " 541 31.73 113.50 808.9 0.13400 \n",
2412 " 542 32.29 107.40 826.4 0.10600 \n",
2413 " 543 37.17 92.48 629.6 0.10720 \n",
2414 " 544 24.75 99.17 688.6 0.12640 \n",
2415 " 545 29.09 97.58 729.8 0.12160 \n",
2416 " 546 21.77 71.12 384.9 0.12850 \n",
2417 " 547 22.04 71.08 357.4 0.14610 \n",
2418 " 548 25.59 69.10 364.2 0.11990 \n",
2419 " 549 31.45 83.90 505.6 0.12040 \n",
2420 " 550 24.77 74.08 412.3 0.10010 \n",
2421 " 551 28.26 77.80 436.6 0.10870 \n",
2422 " 552 36.00 88.10 594.7 0.12340 \n",
2423 " 553 25.05 62.86 295.8 0.11030 \n",
2424 " 554 35.74 88.84 595.7 0.12270 \n",
2425 " 555 34.91 69.57 357.6 0.13840 \n",
2426 " 556 22.88 67.88 347.3 0.12650 \n",
2427 " 557 34.24 66.50 330.6 0.10730 \n",
2428 " 558 27.27 105.90 733.5 0.10260 \n",
2429 " 559 37.16 82.28 474.2 0.12980 \n",
2430 " 560 33.17 100.20 706.7 0.12410 \n",
2431 " 561 38.30 75.19 439.6 0.09267 \n",
2432 " 562 42.79 128.70 915.0 0.14170 \n",
2433 " 563 29.41 179.10 1819.0 0.14070 \n",
2434 " 564 26.40 166.10 2027.0 0.14100 \n",
2435 " 565 38.25 155.00 1731.0 0.11660 \n",
2436 " 566 34.12 126.70 1124.0 0.11390 \n",
2437 " 567 39.42 184.60 1821.0 0.16500 \n",
2438 " 568 30.37 59.16 268.6 0.08996 \n",
2439 " \n",
2440 " worst compactness worst concavity worst concave points worst symmetry \\\n",
2441 " 0 0.66560 0.71190 0.26540 0.4601 \n",
2442 " 1 0.18660 0.24160 0.18600 0.2750 \n",
2443 " 2 0.42450 0.45040 0.24300 0.3613 \n",
2444 " 3 0.86630 0.68690 0.25750 0.6638 \n",
2445 " 4 0.20500 0.40000 0.16250 0.2364 \n",
2446 " 5 0.52490 0.53550 0.17410 0.3985 \n",
2447 " 6 0.25760 0.37840 0.19320 0.3063 \n",
2448 " 7 0.36820 0.26780 0.15560 0.3196 \n",
2449 " 8 0.54010 0.53900 0.20600 0.4378 \n",
2450 " 9 1.05800 1.10500 0.22100 0.4366 \n",
2451 " 10 0.15510 0.14590 0.09975 0.2948 \n",
2452 " 11 0.56090 0.39650 0.18100 0.3792 \n",
2453 " 12 0.39030 0.36390 0.17670 0.3176 \n",
2454 " 13 0.19240 0.23220 0.11190 0.2809 \n",
2455 " 14 0.77250 0.69430 0.22080 0.3596 \n",
2456 " 15 0.65770 0.70260 0.17120 0.4218 \n",
2457 " 16 0.18710 0.29140 0.16090 0.3029 \n",
2458 " 17 0.42330 0.47840 0.20730 0.3706 \n",
2459 " 18 0.31500 0.53720 0.23880 0.2768 \n",
2460 " 19 0.17730 0.23900 0.12880 0.2977 \n",
2461 " 20 0.27760 0.18900 0.07283 0.3184 \n",
2462 " 21 0.11480 0.08867 0.06227 0.2450 \n",
2463 " 22 0.59540 0.63050 0.23930 0.4667 \n",
2464 " 23 0.26000 0.31550 0.20090 0.2822 \n",
2465 " 24 0.35780 0.46950 0.20950 0.3613 \n",
2466 " 25 0.39490 0.38530 0.25500 0.4066 \n",
2467 " 26 0.66430 0.55390 0.27010 0.4264 \n",
2468 " 27 0.21170 0.34460 0.14900 0.2341 \n",
2469 " 28 0.61100 0.63350 0.20240 0.4027 \n",
2470 " 29 0.28120 0.24890 0.14560 0.2756 \n",
2471 " .. ... ... ... ... \n",
2472 " 539 0.30640 0.33930 0.05000 0.2790 \n",
2473 " 540 0.21180 0.17970 0.06918 0.2329 \n",
2474 " 541 0.42020 0.40400 0.12050 0.3187 \n",
2475 " 542 0.13760 0.16110 0.10950 0.2722 \n",
2476 " 543 0.13810 0.10620 0.07958 0.2473 \n",
2477 " 544 0.20370 0.13770 0.06845 0.2249 \n",
2478 " 545 0.15170 0.10490 0.07174 0.2642 \n",
2479 " 546 0.08842 0.04384 0.02381 0.2681 \n",
2480 " 547 0.22460 0.17830 0.08333 0.2691 \n",
2481 " 548 0.09546 0.09350 0.03846 0.2552 \n",
2482 " 549 0.16330 0.06194 0.03264 0.3059 \n",
2483 " 550 0.07348 0.00000 0.00000 0.2458 \n",
2484 " 551 0.17820 0.15640 0.06413 0.3169 \n",
2485 " 552 0.10640 0.08653 0.06498 0.2407 \n",
2486 " 553 0.08298 0.07993 0.02564 0.2435 \n",
2487 " 554 0.16200 0.24390 0.06493 0.2372 \n",
2488 " 555 0.17100 0.20000 0.09127 0.2226 \n",
2489 " 556 0.12000 0.01005 0.02232 0.2262 \n",
2490 " 557 0.07158 0.00000 0.00000 0.2475 \n",
2491 " 558 0.31710 0.36620 0.11050 0.2258 \n",
2492 " 559 0.25170 0.36300 0.09653 0.2112 \n",
2493 " 560 0.22640 0.13260 0.10480 0.2250 \n",
2494 " 561 0.05494 0.00000 0.00000 0.1566 \n",
2495 " 562 0.79170 1.17000 0.23560 0.4089 \n",
2496 " 563 0.41860 0.65990 0.25420 0.2929 \n",
2497 " 564 0.21130 0.41070 0.22160 0.2060 \n",
2498 " 565 0.19220 0.32150 0.16280 0.2572 \n",
2499 " 566 0.30940 0.34030 0.14180 0.2218 \n",
2500 " 567 0.86810 0.93870 0.26500 0.4087 \n",
2501 " 568 0.06444 0.00000 0.00000 0.2871 \n",
2502 " \n",
2503 " worst fractal dimension \n",
2504 " 0 0.11890 \n",
2505 " 1 0.08902 \n",
2506 " 2 0.08758 \n",
2507 " 3 0.17300 \n",
2508 " 4 0.07678 \n",
2509 " 5 0.12440 \n",
2510 " 6 0.08368 \n",
2511 " 7 0.11510 \n",
2512 " 8 0.10720 \n",
2513 " 9 0.20750 \n",
2514 " 10 0.08452 \n",
2515 " 11 0.10480 \n",
2516 " 12 0.10230 \n",
2517 " 13 0.06287 \n",
2518 " 14 0.14310 \n",
2519 " 15 0.13410 \n",
2520 " 16 0.08216 \n",
2521 " 17 0.11420 \n",
2522 " 18 0.07615 \n",
2523 " 19 0.07259 \n",
2524 " 20 0.08183 \n",
2525 " 21 0.07773 \n",
2526 " 22 0.09946 \n",
2527 " 23 0.07526 \n",
2528 " 24 0.09564 \n",
2529 " 25 0.10590 \n",
2530 " 26 0.12750 \n",
2531 " 27 0.07421 \n",
2532 " 28 0.09876 \n",
2533 " 29 0.07919 \n",
2534 " .. ... \n",
2535 " 539 0.10660 \n",
2536 " 540 0.08134 \n",
2537 " 541 0.10230 \n",
2538 " 542 0.06956 \n",
2539 " 543 0.06443 \n",
2540 " 544 0.08492 \n",
2541 " 545 0.06953 \n",
2542 " 546 0.07399 \n",
2543 " 547 0.09479 \n",
2544 " 548 0.07920 \n",
2545 " 549 0.07626 \n",
2546 " 550 0.06592 \n",
2547 " 551 0.08032 \n",
2548 " 552 0.06484 \n",
2549 " 553 0.07393 \n",
2550 " 554 0.07242 \n",
2551 " 555 0.08283 \n",
2552 " 556 0.06742 \n",
2553 " 557 0.06969 \n",
2554 " 558 0.08004 \n",
2555 " 559 0.08732 \n",
2556 " 560 0.08321 \n",
2557 " 561 0.05905 \n",
2558 " 562 0.14090 \n",
2559 " 563 0.09873 \n",
2560 " 564 0.07115 \n",
2561 " 565 0.06637 \n",
2562 " 566 0.07820 \n",
2563 " 567 0.12400 \n",
2564 " 568 0.07039 \n",
2565 " \n",
2566 " [569 rows x 30 columns], 0 0\n",
2567 " 1 0\n",
2568 " 2 0\n",
2569 " 3 0\n",
2570 " 4 0\n",
2571 " 5 0\n",
2572 " 6 0\n",
2573 " 7 0\n",
2574 " 8 0\n",
2575 " 9 0\n",
2576 " 10 0\n",
2577 " 11 0\n",
2578 " 12 0\n",
2579 " 13 0\n",
2580 " 14 0\n",
2581 " 15 0\n",
2582 " 16 0\n",
2583 " 17 0\n",
2584 " 18 0\n",
2585 " 19 1\n",
2586 " 20 1\n",
2587 " 21 1\n",
2588 " 22 0\n",
2589 " 23 0\n",
2590 " 24 0\n",
2591 " 25 0\n",
2592 " 26 0\n",
2593 " 27 0\n",
2594 " 28 0\n",
2595 " 29 0\n",
2596 " ..\n",
2597 " 539 1\n",
2598 " 540 1\n",
2599 " 541 1\n",
2600 " 542 1\n",
2601 " 543 1\n",
2602 " 544 1\n",
2603 " 545 1\n",
2604 " 546 1\n",
2605 " 547 1\n",
2606 " 548 1\n",
2607 " 549 1\n",
2608 " 550 1\n",
2609 " 551 1\n",
2610 " 552 1\n",
2611 " 553 1\n",
2612 " 554 1\n",
2613 " 555 1\n",
2614 " 556 1\n",
2615 " 557 1\n",
2616 " 558 1\n",
2617 " 559 1\n",
2618 " 560 1\n",
2619 " 561 1\n",
2620 " 562 0\n",
2621 " 563 0\n",
2622 " 564 0\n",
2623 " 565 0\n",
2624 " 566 0\n",
2625 " 567 0\n",
2626 " 568 1\n",
2627 " Name: target, dtype: int64)"
2628 ]
2629 },
2630 "execution_count": 7,
2631 "metadata": {},
2632 "output_type": "execute_result"
2633 }
2634 ],
2635 "source": [
2636 "# The last column 'target' has the labels.\n",
2637 "# Can also use cancerdf.ix[:,:-1] to select all columns but the last one.\n",
2638 "# cancerdf.ix[:,-1] to select just the last column.\n",
2639 "\n",
2640 "def answer_three():\n",
2641 " cancerdf = answer_one()\n",
2642 " \n",
2643 " X = cancerdf.drop('target', axis=1) # All columns but the last one.\n",
2644 " y = cancerdf['target'] # Just the last column.\n",
2645 " \n",
2646 " return X, y\n",
2647 "\n",
2648 "answer_three()"
2649 ]
2650 },
2651 {
2652 "cell_type": "markdown",
2653 "metadata": {},
2654 "source": [
2655 "### Question 4\n",
2656 "Using `train_test_split`, split `X` and `y` into training and test sets `(X_train, X_test, y_train, and y_test)`.\n",
2657 "\n",
2658 "**Set the random number generator state to 0 using `random_state=0` to make sure your results match the autograder!**\n",
2659 "\n",
2660 "*This function should return a tuple of length 4:* `(X_train, X_test, y_train, y_test)`*, where* \n",
2661 "* `X_train` *has shape* `(426, 30)`\n",
2662 "* `X_test` *has shape* `(143, 30)`\n",
2663 "* `y_train` *has shape* `(426,)`\n",
2664 "* `y_test` *has shape* `(143,)`"
2665 ]
2666 },
2667 {
2668 "cell_type": "code",
2669 "execution_count": 8,
2670 "metadata": {},
2671 "outputs": [
2672 {
2673 "data": {
2674 "text/plain": [
2675 "( mean radius mean texture mean perimeter mean area mean smoothness \\\n",
2676 " 293 11.850 17.46 75.54 432.7 0.08372 \n",
2677 " 332 11.220 19.86 71.94 387.3 0.10540 \n",
2678 " 565 20.130 28.25 131.20 1261.0 0.09780 \n",
2679 " 278 13.590 17.84 86.24 572.3 0.07948 \n",
2680 " 489 16.690 20.20 107.10 857.6 0.07497 \n",
2681 " 346 12.060 18.90 76.66 445.3 0.08386 \n",
2682 " 357 13.870 16.21 88.52 593.7 0.08743 \n",
2683 " 355 12.560 19.07 81.92 485.8 0.08760 \n",
2684 " 112 14.260 19.65 97.83 629.9 0.07837 \n",
2685 " 68 9.029 17.33 58.79 250.5 0.10660 \n",
2686 " 526 13.460 18.75 87.44 551.1 0.10750 \n",
2687 " 206 9.876 17.27 62.92 295.4 0.10890 \n",
2688 " 65 14.780 23.94 97.40 668.3 0.11720 \n",
2689 " 437 14.040 15.98 89.78 611.2 0.08458 \n",
2690 " 126 13.610 24.69 87.76 572.6 0.09258 \n",
2691 " 429 12.720 17.67 80.98 501.3 0.07896 \n",
2692 " 392 15.490 19.97 102.40 744.7 0.11600 \n",
2693 " 343 19.680 21.68 129.90 1194.0 0.09797 \n",
2694 " 334 12.300 19.02 77.88 464.4 0.08313 \n",
2695 " 440 10.970 17.20 71.73 371.5 0.08915 \n",
2696 " 441 17.270 25.42 112.40 928.8 0.08331 \n",
2697 " 137 11.430 15.39 73.06 399.8 0.09639 \n",
2698 " 230 17.050 19.08 113.40 895.0 0.11410 \n",
2699 " 7 13.710 20.83 90.20 577.9 0.11890 \n",
2700 " 408 17.990 20.66 117.80 991.7 0.10360 \n",
2701 " 523 13.710 18.68 88.73 571.0 0.09916 \n",
2702 " 361 13.300 21.57 85.24 546.1 0.08582 \n",
2703 " 553 9.333 21.94 59.01 264.0 0.09240 \n",
2704 " 478 11.490 14.59 73.99 404.9 0.10460 \n",
2705 " 303 10.490 18.61 66.86 334.3 0.10680 \n",
2706 " .. ... ... ... ... ... \n",
2707 " 459 9.755 28.20 61.68 290.9 0.07984 \n",
2708 " 510 11.740 14.69 76.31 426.0 0.08099 \n",
2709 " 151 8.219 20.70 53.27 203.9 0.09405 \n",
2710 " 244 19.400 23.50 129.10 1155.0 0.10270 \n",
2711 " 543 13.210 28.06 84.88 538.4 0.08671 \n",
2712 " 544 13.870 20.70 89.77 584.8 0.09578 \n",
2713 " 265 20.730 31.12 135.70 1419.0 0.09469 \n",
2714 " 288 11.260 19.96 73.72 394.1 0.08020 \n",
2715 " 423 13.660 19.13 89.46 575.3 0.09057 \n",
2716 " 147 14.950 18.77 97.84 689.5 0.08138 \n",
2717 " 177 16.460 20.11 109.30 832.9 0.09831 \n",
2718 " 99 14.420 19.77 94.48 642.5 0.09752 \n",
2719 " 448 14.530 19.34 94.25 659.7 0.08388 \n",
2720 " 431 12.400 17.68 81.47 467.8 0.10540 \n",
2721 " 115 11.930 21.53 76.53 438.6 0.09768 \n",
2722 " 72 17.200 24.52 114.20 929.4 0.10710 \n",
2723 " 537 11.690 24.44 76.37 406.4 0.12360 \n",
2724 " 174 10.660 15.15 67.49 349.6 0.08792 \n",
2725 " 87 19.020 24.59 122.00 1076.0 0.09029 \n",
2726 " 551 11.130 22.44 71.49 378.4 0.09566 \n",
2727 " 486 14.640 16.85 94.21 666.0 0.08641 \n",
2728 " 314 8.597 18.60 54.09 221.2 0.10740 \n",
2729 " 396 13.510 18.89 88.10 558.1 0.10590 \n",
2730 " 472 14.920 14.93 96.45 686.9 0.08098 \n",
2731 " 70 18.940 21.31 123.60 1130.0 0.09009 \n",
2732 " 277 18.810 19.98 120.90 1102.0 0.08923 \n",
2733 " 9 12.460 24.04 83.97 475.9 0.11860 \n",
2734 " 359 9.436 18.32 59.82 278.6 0.10090 \n",
2735 " 192 9.720 18.22 60.73 288.1 0.06950 \n",
2736 " 559 11.510 23.93 74.52 403.5 0.09261 \n",
2737 " \n",
2738 " mean compactness mean concavity mean concave points mean symmetry \\\n",
2739 " 293 0.05642 0.026880 0.022800 0.1875 \n",
2740 " 332 0.06779 0.005006 0.007583 0.1940 \n",
2741 " 565 0.10340 0.144000 0.097910 0.1752 \n",
2742 " 278 0.04052 0.019970 0.012380 0.1573 \n",
2743 " 489 0.07112 0.036490 0.023070 0.1846 \n",
2744 " 346 0.05794 0.007510 0.008488 0.1555 \n",
2745 " 357 0.05492 0.015020 0.020880 0.1424 \n",
2746 " 355 0.10380 0.103000 0.043910 0.1533 \n",
2747 " 112 0.22330 0.300300 0.077980 0.1704 \n",
2748 " 68 0.14130 0.313000 0.043750 0.2111 \n",
2749 " 526 0.11380 0.042010 0.031520 0.1723 \n",
2750 " 206 0.07232 0.017560 0.019520 0.1934 \n",
2751 " 65 0.14790 0.126700 0.090290 0.1953 \n",
2752 " 437 0.05895 0.035340 0.029440 0.1714 \n",
2753 " 126 0.07862 0.052850 0.030850 0.1761 \n",
2754 " 429 0.04522 0.014020 0.018350 0.1459 \n",
2755 " 392 0.15620 0.189100 0.091130 0.1929 \n",
2756 " 343 0.13390 0.186300 0.110300 0.2082 \n",
2757 " 334 0.04202 0.007756 0.008535 0.1539 \n",
2758 " 440 0.11130 0.094570 0.036130 0.1489 \n",
2759 " 441 0.11090 0.120400 0.057360 0.1467 \n",
2760 " 137 0.06889 0.035030 0.028750 0.1734 \n",
2761 " 230 0.15720 0.191000 0.109000 0.2131 \n",
2762 " 7 0.16450 0.093660 0.059850 0.2196 \n",
2763 " 408 0.13040 0.120100 0.088240 0.1992 \n",
2764 " 523 0.10700 0.053850 0.037830 0.1714 \n",
2765 " 361 0.06373 0.033440 0.024240 0.1815 \n",
2766 " 553 0.05605 0.039960 0.012820 0.1692 \n",
2767 " 478 0.08228 0.053080 0.019690 0.1779 \n",
2768 " 303 0.06678 0.022970 0.017800 0.1482 \n",
2769 " .. ... ... ... ... \n",
2770 " 459 0.04626 0.015410 0.010430 0.1621 \n",
2771 " 510 0.09661 0.067260 0.026390 0.1499 \n",
2772 " 151 0.13050 0.132100 0.021680 0.2222 \n",
2773 " 244 0.15580 0.204900 0.088860 0.1978 \n",
2774 " 543 0.06877 0.029870 0.032750 0.1628 \n",
2775 " 544 0.10180 0.036880 0.023690 0.1620 \n",
2776 " 265 0.11430 0.136700 0.086460 0.1769 \n",
2777 " 288 0.11810 0.092740 0.055880 0.2595 \n",
2778 " 423 0.11470 0.096570 0.048120 0.1848 \n",
2779 " 147 0.11670 0.090500 0.035620 0.1744 \n",
2780 " 177 0.15560 0.179300 0.088660 0.1794 \n",
2781 " 99 0.11410 0.093880 0.058390 0.1879 \n",
2782 " 448 0.07800 0.088170 0.029250 0.1473 \n",
2783 " 431 0.13160 0.077410 0.027990 0.1811 \n",
2784 " 115 0.07849 0.033280 0.020080 0.1688 \n",
2785 " 72 0.18300 0.169200 0.079440 0.1927 \n",
2786 " 537 0.15520 0.045150 0.045310 0.2131 \n",
2787 " 174 0.04302 0.000000 0.000000 0.1928 \n",
2788 " 87 0.12060 0.146800 0.082710 0.1953 \n",
2789 " 551 0.08194 0.048240 0.022570 0.2030 \n",
2790 " 486 0.06698 0.051920 0.027910 0.1409 \n",
2791 " 314 0.05847 0.000000 0.000000 0.2163 \n",
2792 " 396 0.11470 0.085800 0.053810 0.1806 \n",
2793 " 472 0.08549 0.055390 0.032210 0.1687 \n",
2794 " 70 0.10290 0.108000 0.079510 0.1582 \n",
2795 " 277 0.05884 0.080200 0.058430 0.1550 \n",
2796 " 9 0.23960 0.227300 0.085430 0.2030 \n",
2797 " 359 0.05956 0.027100 0.014060 0.1506 \n",
2798 " 192 0.02344 0.000000 0.000000 0.1653 \n",
2799 " 559 0.10210 0.111200 0.041050 0.1388 \n",
2800 " \n",
2801 " mean fractal dimension ... worst radius \\\n",
2802 " 293 0.05715 ... 13.060 \n",
2803 " 332 0.06028 ... 11.980 \n",
2804 " 565 0.05533 ... 23.690 \n",
2805 " 278 0.05520 ... 15.500 \n",
2806 " 489 0.05325 ... 19.180 \n",
2807 " 346 0.06048 ... 13.640 \n",
2808 " 357 0.05883 ... 15.110 \n",
2809 " 355 0.06184 ... 13.370 \n",
2810 " 112 0.07769 ... 15.300 \n",
2811 " 68 0.08046 ... 10.310 \n",
2812 " 526 0.06317 ... 15.350 \n",
2813 " 206 0.06285 ... 10.420 \n",
2814 " 65 0.06654 ... 17.310 \n",
2815 " 437 0.05898 ... 15.660 \n",
2816 " 126 0.06130 ... 16.890 \n",
2817 " 429 0.05544 ... 13.820 \n",
2818 " 392 0.06744 ... 21.200 \n",
2819 " 343 0.05715 ... 22.750 \n",
2820 " 334 0.05945 ... 13.350 \n",
2821 " 440 0.06640 ... 12.360 \n",
2822 " 441 0.05407 ... 20.380 \n",
2823 " 137 0.05865 ... 12.320 \n",
2824 " 230 0.06325 ... 19.590 \n",
2825 " 7 0.07451 ... 17.060 \n",
2826 " 408 0.06069 ... 21.080 \n",
2827 " 523 0.06843 ... 15.110 \n",
2828 " 361 0.05696 ... 14.200 \n",
2829 " 553 0.06576 ... 9.845 \n",
2830 " 478 0.06574 ... 12.400 \n",
2831 " 303 0.06600 ... 11.060 \n",
2832 " .. ... ... ... \n",
2833 " 459 0.05952 ... 10.670 \n",
2834 " 510 0.06758 ... 12.450 \n",
2835 " 151 0.08261 ... 9.092 \n",
2836 " 244 0.06000 ... 21.650 \n",
2837 " 543 0.05781 ... 14.370 \n",
2838 " 544 0.06688 ... 15.050 \n",
2839 " 265 0.05674 ... 32.490 \n",
2840 " 288 0.06233 ... 11.860 \n",
2841 " 423 0.06181 ... 15.140 \n",
2842 " 147 0.06493 ... 16.250 \n",
2843 " 177 0.06323 ... 17.790 \n",
2844 " 99 0.06390 ... 16.330 \n",
2845 " 448 0.05746 ... 16.300 \n",
2846 " 431 0.07102 ... 12.880 \n",
2847 " 115 0.06194 ... 13.670 \n",
2848 " 72 0.06487 ... 23.320 \n",
2849 " 537 0.07405 ... 12.980 \n",
2850 " 174 0.05975 ... 11.540 \n",
2851 " 87 0.05629 ... 24.560 \n",
2852 " 551 0.06552 ... 12.020 \n",
2853 " 486 0.05355 ... 16.460 \n",
2854 " 314 0.07359 ... 8.952 \n",
2855 " 396 0.06079 ... 14.800 \n",
2856 " 472 0.05669 ... 17.180 \n",
2857 " 70 0.05461 ... 24.860 \n",
2858 " 277 0.04996 ... 19.960 \n",
2859 " 9 0.08243 ... 15.090 \n",
2860 " 359 0.06959 ... 12.020 \n",
2861 " 192 0.06447 ... 9.968 \n",
2862 " 559 0.06570 ... 12.480 \n",
2863 " \n",
2864 " worst texture worst perimeter worst area worst smoothness \\\n",
2865 " 293 25.75 84.35 517.8 0.13690 \n",
2866 " 332 25.78 76.91 436.1 0.14240 \n",
2867 " 565 38.25 155.00 1731.0 0.11660 \n",
2868 " 278 26.10 98.91 739.1 0.10500 \n",
2869 " 489 26.56 127.30 1084.0 0.10090 \n",
2870 " 346 27.06 86.54 562.6 0.12890 \n",
2871 " 357 25.58 96.74 694.4 0.11530 \n",
2872 " 355 22.43 89.02 547.4 0.10960 \n",
2873 " 112 23.73 107.00 709.0 0.08949 \n",
2874 " 68 22.65 65.50 324.7 0.14820 \n",
2875 " 526 25.16 101.90 719.8 0.16240 \n",
2876 " 206 23.22 67.08 331.6 0.14150 \n",
2877 " 65 33.39 114.60 925.1 0.16480 \n",
2878 " 437 21.58 101.20 750.0 0.11950 \n",
2879 " 126 35.64 113.20 848.7 0.14710 \n",
2880 " 429 20.96 88.87 586.8 0.10680 \n",
2881 " 392 29.41 142.10 1359.0 0.16810 \n",
2882 " 343 34.66 157.60 1540.0 0.12180 \n",
2883 " 334 28.46 84.53 544.3 0.12220 \n",
2884 " 440 26.87 90.14 476.4 0.13910 \n",
2885 " 441 35.46 132.80 1284.0 0.14360 \n",
2886 " 137 22.02 79.93 462.0 0.11900 \n",
2887 " 230 24.89 133.50 1189.0 0.17030 \n",
2888 " 7 28.14 110.60 897.0 0.16540 \n",
2889 " 408 25.41 138.10 1349.0 0.14820 \n",
2890 " 523 25.63 99.43 701.9 0.14250 \n",
2891 " 361 29.20 92.94 621.2 0.11400 \n",
2892 " 553 25.05 62.86 295.8 0.11030 \n",
2893 " 478 21.90 82.04 467.6 0.13520 \n",
2894 " 303 24.54 70.76 375.4 0.14130 \n",
2895 " .. ... ... ... ... \n",
2896 " 459 36.92 68.03 349.9 0.11100 \n",
2897 " 510 17.60 81.25 473.8 0.10730 \n",
2898 " 151 29.72 58.08 249.8 0.16300 \n",
2899 " 244 30.53 144.90 1417.0 0.14630 \n",
2900 " 543 37.17 92.48 629.6 0.10720 \n",
2901 " 544 24.75 99.17 688.6 0.12640 \n",
2902 " 265 47.16 214.00 3432.0 0.14010 \n",
2903 " 288 22.33 78.27 437.6 0.10280 \n",
2904 " 423 25.50 101.40 708.8 0.11470 \n",
2905 " 147 25.47 107.10 809.7 0.09970 \n",
2906 " 177 28.45 123.50 981.2 0.14150 \n",
2907 " 99 30.86 109.50 826.4 0.14310 \n",
2908 " 448 28.39 108.10 830.5 0.10890 \n",
2909 " 431 22.91 89.61 515.8 0.14500 \n",
2910 " 115 26.15 87.54 583.0 0.15000 \n",
2911 " 72 33.82 151.60 1681.0 0.15850 \n",
2912 " 537 32.19 86.12 487.7 0.17680 \n",
2913 " 174 19.20 73.20 408.3 0.10760 \n",
2914 " 87 30.41 152.90 1623.0 0.12490 \n",
2915 " 551 28.26 77.80 436.6 0.10870 \n",
2916 " 486 25.44 106.00 831.0 0.11420 \n",
2917 " 314 22.44 56.65 240.1 0.13470 \n",
2918 " 396 27.20 97.33 675.2 0.14280 \n",
2919 " 472 18.22 112.00 906.6 0.10650 \n",
2920 " 70 26.58 165.90 1866.0 0.11930 \n",
2921 " 277 24.30 129.00 1236.0 0.12430 \n",
2922 " 9 40.68 97.65 711.4 0.18530 \n",
2923 " 359 25.02 75.79 439.6 0.13330 \n",
2924 " 192 20.83 62.25 303.8 0.07117 \n",
2925 " 559 37.16 82.28 474.2 0.12980 \n",
2926 " \n",
2927 " worst compactness worst concavity worst concave points worst symmetry \\\n",
2928 " 293 0.17580 0.13160 0.09140 0.3101 \n",
2929 " 332 0.09669 0.01335 0.02022 0.3292 \n",
2930 " 565 0.19220 0.32150 0.16280 0.2572 \n",
2931 " 278 0.07622 0.10600 0.05185 0.2335 \n",
2932 " 489 0.29200 0.24770 0.08737 0.4677 \n",
2933 " 346 0.13520 0.04506 0.05093 0.2880 \n",
2934 " 357 0.10080 0.05285 0.05556 0.2362 \n",
2935 " 355 0.20020 0.23880 0.09265 0.2121 \n",
2936 " 112 0.41930 0.67830 0.15050 0.2398 \n",
2937 " 68 0.43650 1.25200 0.17500 0.4228 \n",
2938 " 526 0.31240 0.26540 0.14270 0.3518 \n",
2939 " 206 0.12470 0.06213 0.05588 0.2989 \n",
2940 " 65 0.34160 0.30240 0.16140 0.3321 \n",
2941 " 437 0.12520 0.11170 0.07453 0.2725 \n",
2942 " 126 0.28840 0.37960 0.13290 0.3470 \n",
2943 " 429 0.09605 0.03469 0.03612 0.2165 \n",
2944 " 392 0.39130 0.55530 0.21210 0.3187 \n",
2945 " 343 0.34580 0.47340 0.22550 0.4045 \n",
2946 " 334 0.09052 0.03619 0.03983 0.2554 \n",
2947 " 440 0.40820 0.47790 0.15550 0.2540 \n",
2948 " 441 0.41220 0.50360 0.17390 0.2500 \n",
2949 " 137 0.16480 0.13990 0.08476 0.2676 \n",
2950 " 230 0.39340 0.50180 0.25430 0.3109 \n",
2951 " 7 0.36820 0.26780 0.15560 0.3196 \n",
2952 " 408 0.37350 0.33010 0.19740 0.3060 \n",
2953 " 523 0.25660 0.19350 0.12840 0.2849 \n",
2954 " 361 0.16670 0.12120 0.05614 0.2637 \n",
2955 " 553 0.08298 0.07993 0.02564 0.2435 \n",
2956 " 478 0.20100 0.25960 0.07431 0.2941 \n",
2957 " 303 0.10440 0.08423 0.06528 0.2213 \n",
2958 " .. ... ... ... ... \n",
2959 " 459 0.11090 0.07190 0.04866 0.2321 \n",
2960 " 510 0.27930 0.26900 0.10560 0.2604 \n",
2961 " 151 0.43100 0.53810 0.07879 0.3322 \n",
2962 " 244 0.29680 0.34580 0.15640 0.2920 \n",
2963 " 543 0.13810 0.10620 0.07958 0.2473 \n",
2964 " 544 0.20370 0.13770 0.06845 0.2249 \n",
2965 " 265 0.26440 0.34420 0.16590 0.2868 \n",
2966 " 288 0.18430 0.15460 0.09314 0.2955 \n",
2967 " 423 0.31670 0.36600 0.14070 0.2744 \n",
2968 " 147 0.25210 0.25000 0.08405 0.2852 \n",
2969 " 177 0.46670 0.58620 0.20350 0.3054 \n",
2970 " 99 0.30260 0.31940 0.15650 0.2718 \n",
2971 " 448 0.26490 0.37790 0.09594 0.2471 \n",
2972 " 431 0.26290 0.24030 0.07370 0.2556 \n",
2973 " 115 0.23990 0.15030 0.07247 0.2438 \n",
2974 " 72 0.73940 0.65660 0.18990 0.3313 \n",
2975 " 537 0.32510 0.13950 0.13080 0.2803 \n",
2976 " 174 0.06791 0.00000 0.00000 0.2710 \n",
2977 " 87 0.32060 0.57550 0.19560 0.3956 \n",
2978 " 551 0.17820 0.15640 0.06413 0.3169 \n",
2979 " 486 0.20700 0.24370 0.07828 0.2455 \n",
2980 " 314 0.07767 0.00000 0.00000 0.3142 \n",
2981 " 396 0.25700 0.34380 0.14530 0.2666 \n",
2982 " 472 0.27910 0.31510 0.11470 0.2688 \n",
2983 " 70 0.23360 0.26870 0.17890 0.2551 \n",
2984 " 277 0.11600 0.22100 0.12940 0.2567 \n",
2985 " 9 1.05800 1.10500 0.22100 0.4366 \n",
2986 " 359 0.10490 0.11440 0.05052 0.2454 \n",
2987 " 192 0.02729 0.00000 0.00000 0.1909 \n",
2988 " 559 0.25170 0.36300 0.09653 0.2112 \n",
2989 " \n",
2990 " worst fractal dimension \n",
2991 " 293 0.07007 \n",
2992 " 332 0.06522 \n",
2993 " 565 0.06637 \n",
2994 " 278 0.06263 \n",
2995 " 489 0.07623 \n",
2996 " 346 0.08083 \n",
2997 " 357 0.07113 \n",
2998 " 355 0.07188 \n",
2999 " 112 0.10820 \n",
3000 " 68 0.11750 \n",
3001 " 526 0.08665 \n",
3002 " 206 0.07380 \n",
3003 " 65 0.08911 \n",
3004 " 437 0.07234 \n",
3005 " 126 0.07900 \n",
3006 " 429 0.06025 \n",
3007 " 392 0.10190 \n",
3008 " 343 0.07918 \n",
3009 " 334 0.07207 \n",
3010 " 440 0.09532 \n",
3011 " 441 0.07944 \n",
3012 " 137 0.06765 \n",
3013 " 230 0.09061 \n",
3014 " 7 0.11510 \n",
3015 " 408 0.08503 \n",
3016 " 523 0.09031 \n",
3017 " 361 0.06658 \n",
3018 " 553 0.07393 \n",
3019 " 478 0.09180 \n",
3020 " 303 0.07842 \n",
3021 " .. ... \n",
3022 " 459 0.07211 \n",
3023 " 510 0.09879 \n",
3024 " 151 0.14860 \n",
3025 " 244 0.07614 \n",
3026 " 543 0.06443 \n",
3027 " 544 0.08492 \n",
3028 " 265 0.08218 \n",
3029 " 288 0.07009 \n",
3030 " 423 0.08839 \n",
3031 " 147 0.09218 \n",
3032 " 177 0.09519 \n",
3033 " 99 0.09353 \n",
3034 " 448 0.07463 \n",
3035 " 431 0.09359 \n",
3036 " 115 0.08541 \n",
3037 " 72 0.13390 \n",
3038 " 537 0.09970 \n",
3039 " 174 0.06164 \n",
3040 " 87 0.09288 \n",
3041 " 551 0.08032 \n",
3042 " 486 0.06596 \n",
3043 " 314 0.08116 \n",
3044 " 396 0.07686 \n",
3045 " 472 0.08273 \n",
3046 " 70 0.06589 \n",
3047 " 277 0.05737 \n",
3048 " 9 0.20750 \n",
3049 " 359 0.08136 \n",
3050 " 192 0.06559 \n",
3051 " 559 0.08732 \n",
3052 " \n",
3053 " [426 rows x 30 columns],\n",
3054 " mean radius mean texture mean perimeter mean area mean smoothness \\\n",
3055 " 512 13.400 20.52 88.64 556.7 0.11060 \n",
3056 " 457 13.210 25.25 84.10 537.9 0.08791 \n",
3057 " 439 14.020 15.66 89.59 606.5 0.07966 \n",
3058 " 298 14.260 18.17 91.22 633.1 0.06576 \n",
3059 " 37 13.030 18.42 82.61 523.8 0.08983 \n",
3060 " 515 11.340 18.61 72.76 391.2 0.10490 \n",
3061 " 382 12.050 22.72 78.75 447.8 0.06935 \n",
3062 " 310 11.700 19.11 74.33 418.7 0.08814 \n",
3063 " 538 7.729 25.49 47.98 178.8 0.08098 \n",
3064 " 345 10.260 14.71 66.20 321.6 0.09882 \n",
3065 " 421 14.690 13.98 98.22 656.1 0.10310 \n",
3066 " 90 14.620 24.02 94.57 662.7 0.08974 \n",
3067 " 412 9.397 21.68 59.75 268.8 0.07969 \n",
3068 " 157 16.840 19.46 108.40 880.2 0.07445 \n",
3069 " 89 14.640 15.24 95.77 651.9 0.11320 \n",
3070 " 172 15.460 11.89 102.50 736.9 0.12570 \n",
3071 " 318 9.042 18.90 60.07 244.5 0.09968 \n",
3072 " 233 20.510 27.81 134.40 1319.0 0.09159 \n",
3073 " 389 19.550 23.21 128.90 1174.0 0.10100 \n",
3074 " 250 20.940 23.56 138.90 1364.0 0.10070 \n",
3075 " 31 11.840 18.70 77.93 440.6 0.11090 \n",
3076 " 283 16.240 18.77 108.80 805.1 0.10660 \n",
3077 " 482 13.470 14.06 87.32 546.3 0.10710 \n",
3078 " 211 11.840 18.94 75.51 428.0 0.08871 \n",
3079 " 372 21.370 15.10 141.30 1386.0 0.10010 \n",
3080 " 401 11.930 10.91 76.14 442.7 0.08872 \n",
3081 " 159 10.900 12.96 68.69 366.8 0.07515 \n",
3082 " 14 13.730 22.61 93.60 578.3 0.11310 \n",
3083 " 364 13.400 16.95 85.48 552.4 0.07937 \n",
3084 " 337 18.770 21.43 122.90 1092.0 0.09116 \n",
3085 " .. ... ... ... ... ... \n",
3086 " 500 15.040 16.74 98.73 689.4 0.09883 \n",
3087 " 338 10.050 17.53 64.41 310.8 0.10070 \n",
3088 " 427 10.800 21.98 68.79 359.9 0.08801 \n",
3089 " 406 16.140 14.86 104.30 800.0 0.09495 \n",
3090 " 96 12.180 17.84 77.79 451.1 0.10450 \n",
3091 " 490 12.250 22.44 78.18 466.5 0.08192 \n",
3092 " 384 13.280 13.72 85.79 541.8 0.08363 \n",
3093 " 281 11.740 14.02 74.24 427.3 0.07813 \n",
3094 " 325 12.670 17.30 81.25 489.9 0.10280 \n",
3095 " 190 14.220 23.12 94.37 609.9 0.10750 \n",
3096 " 380 11.270 12.96 73.16 386.3 0.12370 \n",
3097 " 366 20.200 26.83 133.70 1234.0 0.09905 \n",
3098 " 469 11.620 18.18 76.38 408.8 0.11750 \n",
3099 " 225 14.340 13.47 92.51 641.2 0.09906 \n",
3100 " 271 11.290 13.04 72.23 388.0 0.09834 \n",
3101 " 547 10.260 16.58 65.85 320.8 0.08877 \n",
3102 " 550 10.860 21.48 68.51 360.5 0.07431 \n",
3103 " 492 18.010 20.56 118.40 1007.0 0.10010 \n",
3104 " 185 10.080 15.11 63.76 317.5 0.09267 \n",
3105 " 306 13.200 15.82 84.07 537.3 0.08511 \n",
3106 " 208 13.110 22.54 87.02 529.4 0.10020 \n",
3107 " 242 11.300 18.19 73.93 389.4 0.09592 \n",
3108 " 313 11.540 10.72 73.73 409.1 0.08597 \n",
3109 " 542 14.740 25.42 94.70 668.6 0.08275 \n",
3110 " 514 15.050 19.07 97.26 701.9 0.09215 \n",
3111 " 236 23.210 26.97 153.50 1670.0 0.09509 \n",
3112 " 113 10.510 20.19 68.64 334.2 0.11220 \n",
3113 " 527 12.340 12.27 78.94 468.5 0.09003 \n",
3114 " 76 13.530 10.94 87.91 559.2 0.12910 \n",
3115 " 162 19.590 18.15 130.70 1214.0 0.11200 \n",
3116 " \n",
3117 " mean compactness mean concavity mean concave points mean symmetry \\\n",
3118 " 512 0.14690 0.144500 0.081720 0.2116 \n",
3119 " 457 0.05205 0.027720 0.020680 0.1619 \n",
3120 " 439 0.05581 0.020870 0.026520 0.1589 \n",
3121 " 298 0.05220 0.024750 0.013740 0.1635 \n",
3122 " 37 0.03766 0.025620 0.029230 0.1467 \n",
3123 " 515 0.08499 0.043020 0.025940 0.1927 \n",
3124 " 382 0.10730 0.079430 0.029780 0.1203 \n",
3125 " 310 0.05253 0.015830 0.011480 0.1936 \n",
3126 " 538 0.04878 0.000000 0.000000 0.1870 \n",
3127 " 345 0.09159 0.035810 0.020370 0.1633 \n",
3128 " 421 0.18360 0.145000 0.063000 0.2086 \n",
3129 " 90 0.08606 0.031020 0.029570 0.1685 \n",
3130 " 412 0.06053 0.037350 0.005128 0.1274 \n",
3131 " 157 0.07223 0.051500 0.027710 0.1844 \n",
3132 " 89 0.13390 0.099660 0.070640 0.2116 \n",
3133 " 172 0.15550 0.203200 0.109700 0.1966 \n",
3134 " 318 0.19720 0.197500 0.049080 0.2330 \n",
3135 " 233 0.10740 0.155400 0.083400 0.1448 \n",
3136 " 389 0.13180 0.185600 0.102100 0.1989 \n",
3137 " 250 0.16060 0.271200 0.131000 0.2205 \n",
3138 " 31 0.15160 0.121800 0.051820 0.2301 \n",
3139 " 283 0.18020 0.194800 0.090520 0.1876 \n",
3140 " 482 0.11550 0.057860 0.052660 0.1779 \n",
3141 " 211 0.06900 0.026690 0.013930 0.1533 \n",
3142 " 372 0.15150 0.193200 0.125500 0.1973 \n",
3143 " 401 0.05242 0.026060 0.017960 0.1601 \n",
3144 " 159 0.03718 0.003090 0.006588 0.1442 \n",
3145 " 14 0.22930 0.212800 0.080250 0.2069 \n",
3146 " 364 0.05696 0.021810 0.014730 0.1650 \n",
3147 " 337 0.14020 0.106000 0.060900 0.1953 \n",
3148 " .. ... ... ... ... \n",
3149 " 500 0.13640 0.077210 0.061420 0.1668 \n",
3150 " 338 0.07326 0.025110 0.017750 0.1890 \n",
3151 " 427 0.05743 0.036140 0.014040 0.2016 \n",
3152 " 406 0.08501 0.055000 0.045280 0.1735 \n",
3153 " 96 0.07057 0.024900 0.029410 0.1900 \n",
3154 " 490 0.05200 0.017140 0.012610 0.1544 \n",
3155 " 384 0.08575 0.050770 0.028640 0.1617 \n",
3156 " 281 0.04340 0.022450 0.027630 0.2101 \n",
3157 " 325 0.07664 0.031930 0.021070 0.1707 \n",
3158 " 190 0.24130 0.198100 0.066180 0.2384 \n",
3159 " 380 0.11110 0.079000 0.055500 0.2018 \n",
3160 " 366 0.16690 0.164100 0.126500 0.1875 \n",
3161 " 469 0.14830 0.102000 0.055640 0.1957 \n",
3162 " 225 0.07624 0.057240 0.046030 0.2075 \n",
3163 " 271 0.07608 0.032650 0.027550 0.1769 \n",
3164 " 547 0.08066 0.043580 0.024380 0.1669 \n",
3165 " 550 0.04227 0.000000 0.000000 0.1661 \n",
3166 " 492 0.12890 0.117000 0.077620 0.2116 \n",
3167 " 185 0.04695 0.001597 0.002404 0.1703 \n",
3168 " 306 0.05251 0.001461 0.003261 0.1632 \n",
3169 " 208 0.14830 0.087050 0.051020 0.1850 \n",
3170 " 242 0.13250 0.154800 0.028540 0.2054 \n",
3171 " 313 0.05969 0.013670 0.008907 0.1833 \n",
3172 " 542 0.07214 0.041050 0.030270 0.1840 \n",
3173 " 514 0.08597 0.074860 0.043350 0.1561 \n",
3174 " 236 0.16820 0.195000 0.123700 0.1909 \n",
3175 " 113 0.13030 0.064760 0.030680 0.1922 \n",
3176 " 527 0.06307 0.029580 0.026470 0.1689 \n",
3177 " 76 0.10470 0.068770 0.065560 0.2403 \n",
3178 " 162 0.16660 0.250800 0.128600 0.2027 \n",
3179 " \n",
3180 " mean fractal dimension ... worst radius \\\n",
3181 " 512 0.07325 ... 16.410 \n",
3182 " 457 0.05584 ... 14.350 \n",
3183 " 439 0.05586 ... 14.910 \n",
3184 " 298 0.05586 ... 16.220 \n",
3185 " 37 0.05863 ... 13.300 \n",
3186 " 515 0.06211 ... 12.470 \n",
3187 " 382 0.06659 ... 12.570 \n",
3188 " 310 0.06128 ... 12.610 \n",
3189 " 538 0.07285 ... 9.077 \n",
3190 " 345 0.07005 ... 10.880 \n",
3191 " 421 0.07406 ... 16.460 \n",
3192 " 90 0.05866 ... 16.110 \n",
3193 " 412 0.06724 ... 9.965 \n",
3194 " 157 0.05268 ... 18.220 \n",
3195 " 89 0.06346 ... 16.340 \n",
3196 " 172 0.07069 ... 18.790 \n",
3197 " 318 0.08743 ... 10.060 \n",
3198 " 233 0.05592 ... 24.470 \n",
3199 " 389 0.05884 ... 20.820 \n",
3200 " 250 0.05898 ... 25.580 \n",
3201 " 31 0.07799 ... 16.820 \n",
3202 " 283 0.06684 ... 18.550 \n",
3203 " 482 0.06639 ... 14.830 \n",
3204 " 211 0.06057 ... 13.300 \n",
3205 " 372 0.06183 ... 22.690 \n",
3206 " 401 0.05541 ... 13.800 \n",
3207 " 159 0.05743 ... 12.360 \n",
3208 " 14 0.07682 ... 15.030 \n",
3209 " 364 0.05701 ... 14.730 \n",
3210 " 337 0.06083 ... 24.540 \n",
3211 " .. ... ... ... \n",
3212 " 500 0.06869 ... 16.760 \n",
3213 " 338 0.06331 ... 11.160 \n",
3214 " 427 0.05977 ... 12.760 \n",
3215 " 406 0.05875 ... 17.710 \n",
3216 " 96 0.06635 ... 12.830 \n",
3217 " 490 0.05976 ... 14.170 \n",
3218 " 384 0.05594 ... 14.240 \n",
3219 " 281 0.06113 ... 13.310 \n",
3220 " 325 0.05984 ... 13.710 \n",
3221 " 190 0.07542 ... 15.740 \n",
3222 " 380 0.06914 ... 12.840 \n",
3223 " 366 0.06020 ... 24.190 \n",
3224 " 469 0.07255 ... 13.360 \n",
3225 " 225 0.05448 ... 16.770 \n",
3226 " 271 0.06270 ... 12.320 \n",
3227 " 547 0.06714 ... 10.830 \n",
3228 " 550 0.05948 ... 11.660 \n",
3229 " 492 0.06077 ... 21.530 \n",
3230 " 185 0.06048 ... 11.870 \n",
3231 " 306 0.05894 ... 14.410 \n",
3232 " 208 0.07310 ... 14.550 \n",
3233 " 242 0.07669 ... 12.580 \n",
3234 " 313 0.06100 ... 12.340 \n",
3235 " 542 0.05680 ... 16.510 \n",
3236 " 514 0.05915 ... 17.580 \n",
3237 " 236 0.06309 ... 31.010 \n",
3238 " 113 0.07782 ... 11.160 \n",
3239 " 527 0.05808 ... 13.610 \n",
3240 " 76 0.06641 ... 14.080 \n",
3241 " 162 0.06082 ... 26.730 \n",
3242 " \n",
3243 " worst texture worst perimeter worst area worst smoothness \\\n",
3244 " 512 29.66 113.30 844.4 0.15740 \n",
3245 " 457 34.23 91.29 632.9 0.12890 \n",
3246 " 439 19.31 96.53 688.9 0.10340 \n",
3247 " 298 25.26 105.80 819.7 0.09445 \n",
3248 " 37 22.81 84.46 545.9 0.09701 \n",
3249 " 515 23.03 79.15 478.6 0.14830 \n",
3250 " 382 28.71 87.36 488.4 0.08799 \n",
3251 " 310 26.55 80.92 483.1 0.12230 \n",
3252 " 538 30.92 57.17 248.0 0.12560 \n",
3253 " 345 19.48 70.89 357.1 0.13600 \n",
3254 " 421 18.34 114.10 809.2 0.13120 \n",
3255 " 90 29.11 102.90 803.7 0.11150 \n",
3256 " 412 27.99 66.61 301.0 0.10860 \n",
3257 " 157 28.07 120.30 1032.0 0.08774 \n",
3258 " 89 18.24 109.40 803.6 0.12770 \n",
3259 " 172 17.04 125.00 1102.0 0.15310 \n",
3260 " 318 23.40 68.62 297.1 0.12210 \n",
3261 " 233 37.38 162.70 1872.0 0.12230 \n",
3262 " 389 30.44 142.00 1313.0 0.12510 \n",
3263 " 250 27.00 165.30 2010.0 0.12110 \n",
3264 " 31 28.12 119.40 888.7 0.16370 \n",
3265 " 283 25.09 126.90 1031.0 0.13650 \n",
3266 " 482 18.32 94.94 660.2 0.13930 \n",
3267 " 211 24.99 85.22 546.3 0.12800 \n",
3268 " 372 21.84 152.10 1535.0 0.11920 \n",
3269 " 401 20.14 87.64 589.5 0.13740 \n",
3270 " 159 18.20 78.07 470.0 0.11710 \n",
3271 " 14 32.01 108.80 697.7 0.16510 \n",
3272 " 364 21.70 93.76 663.5 0.12130 \n",
3273 " 337 34.37 161.10 1873.0 0.14980 \n",
3274 " .. ... ... ... ... \n",
3275 " 500 20.43 109.70 856.9 0.11350 \n",
3276 " 338 26.84 71.98 384.0 0.14020 \n",
3277 " 427 32.04 83.69 489.5 0.13030 \n",
3278 " 406 19.58 115.90 947.9 0.12060 \n",
3279 " 96 20.92 82.14 495.2 0.11400 \n",
3280 " 490 31.99 92.74 622.9 0.12560 \n",
3281 " 384 17.37 96.59 623.7 0.11660 \n",
3282 " 281 18.26 84.70 533.7 0.10360 \n",
3283 " 325 21.10 88.70 574.4 0.13840 \n",
3284 " 190 37.18 106.40 762.4 0.15330 \n",
3285 " 380 20.53 84.93 476.1 0.16100 \n",
3286 " 366 33.81 160.00 1671.0 0.12780 \n",
3287 " 469 25.40 88.14 528.1 0.17800 \n",
3288 " 225 16.90 110.40 873.2 0.12970 \n",
3289 " 271 16.18 78.27 457.5 0.13580 \n",
3290 " 547 22.04 71.08 357.4 0.14610 \n",
3291 " 550 24.77 74.08 412.3 0.10010 \n",
3292 " 492 26.06 143.40 1426.0 0.13090 \n",
3293 " 185 21.18 75.39 437.0 0.15210 \n",
3294 " 306 20.45 92.00 636.9 0.11280 \n",
3295 " 208 29.16 99.48 639.3 0.13490 \n",
3296 " 242 27.96 87.16 472.9 0.13470 \n",
3297 " 313 12.87 81.23 467.8 0.10920 \n",
3298 " 542 32.29 107.40 826.4 0.10600 \n",
3299 " 514 28.06 113.80 967.0 0.12460 \n",
3300 " 236 34.51 206.00 2944.0 0.14810 \n",
3301 " 113 22.75 72.62 374.4 0.13000 \n",
3302 " 527 19.27 87.22 564.9 0.12920 \n",
3303 " 76 12.49 91.36 605.5 0.14510 \n",
3304 " 162 26.39 174.90 2232.0 0.14380 \n",
3305 " \n",
3306 " worst compactness worst concavity worst concave points worst symmetry \\\n",
3307 " 512 0.38560 0.51060 0.20510 0.3585 \n",
3308 " 457 0.10630 0.13900 0.06005 0.2444 \n",
3309 " 439 0.10170 0.06260 0.08216 0.2136 \n",
3310 " 298 0.21670 0.15650 0.07530 0.2636 \n",
3311 " 37 0.04619 0.04833 0.05013 0.1987 \n",
3312 " 515 0.15740 0.16240 0.08542 0.3060 \n",
3313 " 382 0.32140 0.29120 0.10920 0.2191 \n",
3314 " 310 0.10870 0.07915 0.05741 0.3487 \n",
3315 " 538 0.08340 0.00000 0.00000 0.3058 \n",
3316 " 345 0.16360 0.07162 0.04074 0.2434 \n",
3317 " 421 0.36350 0.32190 0.11080 0.2827 \n",
3318 " 90 0.17660 0.09189 0.06946 0.2522 \n",
3319 " 412 0.18870 0.18680 0.02564 0.2376 \n",
3320 " 157 0.17100 0.18820 0.08436 0.2527 \n",
3321 " 89 0.30890 0.26040 0.13970 0.3151 \n",
3322 " 172 0.35830 0.58300 0.18270 0.3216 \n",
3323 " 318 0.37480 0.46090 0.11450 0.3135 \n",
3324 " 233 0.27610 0.41460 0.15630 0.2437 \n",
3325 " 389 0.24140 0.38290 0.18250 0.2576 \n",
3326 " 250 0.31720 0.69910 0.21050 0.3126 \n",
3327 " 31 0.57750 0.69560 0.15460 0.4761 \n",
3328 " 283 0.47060 0.50260 0.17320 0.2770 \n",
3329 " 482 0.24990 0.18480 0.13350 0.3227 \n",
3330 " 211 0.18800 0.14710 0.06913 0.2535 \n",
3331 " 372 0.28400 0.40240 0.19660 0.2730 \n",
3332 " 401 0.15750 0.15140 0.06876 0.2460 \n",
3333 " 159 0.08294 0.01854 0.03953 0.2738 \n",
3334 " 14 0.77250 0.69430 0.22080 0.3596 \n",
3335 " 364 0.16760 0.13640 0.06987 0.2741 \n",
3336 " 337 0.48270 0.46340 0.20480 0.3679 \n",
3337 " .. ... ... ... ... \n",
3338 " 500 0.21760 0.18560 0.10180 0.2177 \n",
3339 " 338 0.14020 0.10550 0.06499 0.2894 \n",
3340 " 427 0.16960 0.19270 0.07485 0.2965 \n",
3341 " 406 0.17220 0.23100 0.11290 0.2778 \n",
3342 " 96 0.09358 0.04980 0.05882 0.2227 \n",
3343 " 490 0.18040 0.12300 0.06335 0.3100 \n",
3344 " 384 0.26850 0.28660 0.09173 0.2736 \n",
3345 " 281 0.08500 0.06735 0.08290 0.3101 \n",
3346 " 325 0.12120 0.10200 0.05602 0.2688 \n",
3347 " 190 0.93270 0.84880 0.17720 0.5166 \n",
3348 " 380 0.24290 0.22470 0.13180 0.3343 \n",
3349 " 366 0.34160 0.37030 0.21520 0.3271 \n",
3350 " 469 0.28780 0.31860 0.14160 0.2660 \n",
3351 " 225 0.15250 0.16320 0.10870 0.3062 \n",
3352 " 271 0.15070 0.12750 0.08750 0.2733 \n",
3353 " 547 0.22460 0.17830 0.08333 0.2691 \n",
3354 " 550 0.07348 0.00000 0.00000 0.2458 \n",
3355 " 492 0.23270 0.25440 0.14890 0.3251 \n",
3356 " 185 0.10190 0.00692 0.01042 0.2933 \n",
3357 " 306 0.13460 0.01120 0.02500 0.2651 \n",
3358 " 208 0.44020 0.31620 0.11260 0.4128 \n",
3359 " 242 0.48480 0.74360 0.12180 0.3308 \n",
3360 " 313 0.16260 0.08324 0.04715 0.3390 \n",
3361 " 542 0.13760 0.16110 0.10950 0.2722 \n",
3362 " 514 0.21010 0.28660 0.11200 0.2282 \n",
3363 " 236 0.41260 0.58200 0.25930 0.3103 \n",
3364 " 113 0.20490 0.12950 0.06136 0.2383 \n",
3365 " 527 0.20740 0.17910 0.10700 0.3110 \n",
3366 " 76 0.13790 0.08539 0.07407 0.2710 \n",
3367 " 162 0.38460 0.68100 0.22470 0.3643 \n",
3368 " \n",
3369 " worst fractal dimension \n",
3370 " 512 0.11090 \n",
3371 " 457 0.06788 \n",
3372 " 439 0.06710 \n",
3373 " 298 0.07676 \n",
3374 " 37 0.06169 \n",
3375 " 515 0.06783 \n",
3376 " 382 0.09349 \n",
3377 " 310 0.06958 \n",
3378 " 538 0.09938 \n",
3379 " 345 0.08488 \n",
3380 " 421 0.09208 \n",
3381 " 90 0.07246 \n",
3382 " 412 0.09206 \n",
3383 " 157 0.05972 \n",
3384 " 89 0.08473 \n",
3385 " 172 0.10100 \n",
3386 " 318 0.10550 \n",
3387 " 233 0.08328 \n",
3388 " 389 0.07602 \n",
3389 " 250 0.07849 \n",
3390 " 31 0.14020 \n",
3391 " 283 0.10630 \n",
3392 " 482 0.09326 \n",
3393 " 211 0.07993 \n",
3394 " 372 0.08666 \n",
3395 " 401 0.07262 \n",
3396 " 159 0.07685 \n",
3397 " 14 0.14310 \n",
3398 " 364 0.07582 \n",
3399 " 337 0.09870 \n",
3400 " .. ... \n",
3401 " 500 0.08549 \n",
3402 " 338 0.07664 \n",
3403 " 427 0.07662 \n",
3404 " 406 0.07012 \n",
3405 " 96 0.07376 \n",
3406 " 490 0.08203 \n",
3407 " 384 0.07320 \n",
3408 " 281 0.06688 \n",
3409 " 325 0.06888 \n",
3410 " 190 0.14460 \n",
3411 " 380 0.09215 \n",
3412 " 366 0.07632 \n",
3413 " 469 0.09270 \n",
3414 " 225 0.06072 \n",
3415 " 271 0.08022 \n",
3416 " 547 0.09479 \n",
3417 " 550 0.06592 \n",
3418 " 492 0.07625 \n",
3419 " 185 0.07697 \n",
3420 " 306 0.08385 \n",
3421 " 208 0.10760 \n",
3422 " 242 0.12970 \n",
3423 " 313 0.07434 \n",
3424 " 542 0.06956 \n",
3425 " 514 0.06954 \n",
3426 " 236 0.08677 \n",
3427 " 113 0.09026 \n",
3428 " 527 0.07592 \n",
3429 " 76 0.07191 \n",
3430 " 162 0.09223 \n",
3431 " \n",
3432 " [143 rows x 30 columns],\n",
3433 " 293 1\n",
3434 " 332 1\n",
3435 " 565 0\n",
3436 " 278 1\n",
3437 " 489 0\n",
3438 " 346 1\n",
3439 " 357 1\n",
3440 " 355 1\n",
3441 " 112 1\n",
3442 " 68 1\n",
3443 " 526 1\n",
3444 " 206 1\n",
3445 " 65 0\n",
3446 " 437 1\n",
3447 " 126 0\n",
3448 " 429 1\n",
3449 " 392 0\n",
3450 " 343 0\n",
3451 " 334 1\n",
3452 " 440 1\n",
3453 " 441 0\n",
3454 " 137 1\n",
3455 " 230 0\n",
3456 " 7 0\n",
3457 " 408 0\n",
3458 " 523 1\n",
3459 " 361 1\n",
3460 " 553 1\n",
3461 " 478 1\n",
3462 " 303 1\n",
3463 " ..\n",
3464 " 459 1\n",
3465 " 510 1\n",
3466 " 151 1\n",
3467 " 244 0\n",
3468 " 543 1\n",
3469 " 544 1\n",
3470 " 265 0\n",
3471 " 288 1\n",
3472 " 423 1\n",
3473 " 147 1\n",
3474 " 177 0\n",
3475 " 99 0\n",
3476 " 448 1\n",
3477 " 431 1\n",
3478 " 115 1\n",
3479 " 72 0\n",
3480 " 537 1\n",
3481 " 174 1\n",
3482 " 87 0\n",
3483 " 551 1\n",
3484 " 486 1\n",
3485 " 314 1\n",
3486 " 396 1\n",
3487 " 472 1\n",
3488 " 70 0\n",
3489 " 277 0\n",
3490 " 9 0\n",
3491 " 359 1\n",
3492 " 192 1\n",
3493 " 559 1\n",
3494 " Name: target, dtype: int64,\n",
3495 " 512 0\n",
3496 " 457 1\n",
3497 " 439 1\n",
3498 " 298 1\n",
3499 " 37 1\n",
3500 " 515 1\n",
3501 " 382 1\n",
3502 " 310 1\n",
3503 " 538 1\n",
3504 " 345 1\n",
3505 " 421 1\n",
3506 " 90 1\n",
3507 " 412 1\n",
3508 " 157 1\n",
3509 " 89 1\n",
3510 " 172 0\n",
3511 " 318 1\n",
3512 " 233 0\n",
3513 " 389 0\n",
3514 " 250 0\n",
3515 " 31 0\n",
3516 " 283 0\n",
3517 " 482 1\n",
3518 " 211 1\n",
3519 " 372 0\n",
3520 " 401 1\n",
3521 " 159 1\n",
3522 " 14 0\n",
3523 " 364 1\n",
3524 " 337 0\n",
3525 " ..\n",
3526 " 500 1\n",
3527 " 338 1\n",
3528 " 427 1\n",
3529 " 406 1\n",
3530 " 96 1\n",
3531 " 490 1\n",
3532 " 384 1\n",
3533 " 281 1\n",
3534 " 325 1\n",
3535 " 190 0\n",
3536 " 380 1\n",
3537 " 366 0\n",
3538 " 469 1\n",
3539 " 225 1\n",
3540 " 271 1\n",
3541 " 547 1\n",
3542 " 550 1\n",
3543 " 492 0\n",
3544 " 185 1\n",
3545 " 306 1\n",
3546 " 208 1\n",
3547 " 242 1\n",
3548 " 313 1\n",
3549 " 542 1\n",
3550 " 514 0\n",
3551 " 236 0\n",
3552 " 113 1\n",
3553 " 527 1\n",
3554 " 76 1\n",
3555 " 162 0\n",
3556 " Name: target, dtype: int64)"
3557 ]
3558 },
3559 "execution_count": 8,
3560 "metadata": {},
3561 "output_type": "execute_result"
3562 }
3563 ],
3564 "source": [
3565 "from sklearn.model_selection import train_test_split\n",
3566 "\n",
3567 "def answer_four():\n",
3568 " X, y = answer_three()\n",
3569 " \n",
3570 " X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
3571 " \n",
3572 " return X_train, X_test, y_train, y_test\n",
3573 "\n",
3574 "answer_four()"
3575 ]
3576 },
3577 {
3578 "cell_type": "markdown",
3579 "metadata": {},
3580 "source": [
3581 "### Question 5\n",
3582 "Using KNeighborsClassifier, fit a k-nearest neighbors (knn) classifier with `X_train`, `y_train` and using one nearest neighbor (`n_neighbors = 1`).\n",
3583 "\n",
3584 "*This function should return a * `sklearn.neighbors.classification.KNeighborsClassifier`."
3585 ]
3586 },
3587 {
3588 "cell_type": "code",
3589 "execution_count": 9,
3590 "metadata": {},
3591 "outputs": [
3592 {
3593 "data": {
3594 "text/plain": [
3595 "KNeighborsClassifier(algorithm='auto', leaf_size=30, metric='minkowski',\n",
3596 " metric_params=None, n_jobs=1, n_neighbors=1, p=2,\n",
3597 " weights='uniform')"
3598 ]
3599 },
3600 "execution_count": 9,
3601 "metadata": {},
3602 "output_type": "execute_result"
3603 }
3604 ],
3605 "source": [
3606 "from sklearn.neighbors import KNeighborsClassifier\n",
3607 "\n",
3608 "def answer_five():\n",
3609 " X_train, X_test, y_train, y_test = answer_four()\n",
3610 " \n",
3611 " knn = KNeighborsClassifier(n_neighbors = 1)\n",
3612 " knn.fit(X_train, y_train)\n",
3613 " \n",
3614 " return knn\n",
3615 "\n",
3616 "answer_five()"
3617 ]
3618 },
3619 {
3620 "cell_type": "markdown",
3621 "metadata": {},
3622 "source": [
3623 "### Question 6\n",
3624 "Using your knn classifier, predict the class label using the mean value for each feature.\n",
3625 "\n",
3626 "Hint: You can use `cancerdf.mean()[:-1].values.reshape(1, -1)` which gets the mean value for each feature, ignores the target column, and reshapes the data from 1 dimension to 2 (necessary for the precict method of KNeighborsClassifier).\n",
3627 "\n",
3628 "*This function should return a numpy array either `array([ 0.])` or `array([ 1.])`*"
3629 ]
3630 },
3631 {
3632 "cell_type": "code",
3633 "execution_count": 10,
3634 "metadata": {},
3635 "outputs": [
3636 {
3637 "data": {
3638 "text/plain": [
3639 "array([1])"
3640 ]
3641 },
3642 "execution_count": 10,
3643 "metadata": {},
3644 "output_type": "execute_result"
3645 }
3646 ],
3647 "source": [
3648 "def answer_six():\n",
3649 " cancerdf = answer_one()\n",
3650 " knn = answer_five()\n",
3651 " # Remember a dimension is added to whichever position has 1 in reshape.\n",
3652 " # If you have reshape(1,-1) a dimension is added to the rows, i.e, the shape goes from being '30,' to '1,30'.\n",
3653 " # If you do reshape(-1,1) a dimension is added the columns.\n",
3654 " means = cancerdf.mean()[:-1].values.reshape(1, -1)\n",
3655 " #return means.shape\n",
3656 " \n",
3657 " pred = knn.predict(means)\n",
3658 " return pred\n",
3659 " \n",
3660 "answer_six()"
3661 ]
3662 },
3663 {
3664 "cell_type": "markdown",
3665 "metadata": {},
3666 "source": [
3667 "### Question 7\n",
3668 "Using your knn classifier, predict the class labels for the test set `X_test`.\n",
3669 "\n",
3670 "*This function should return a numpy array with shape `(143,)` and values either `0.0` or `1.0`.*"
3671 ]
3672 },
3673 {
3674 "cell_type": "code",
3675 "execution_count": 11,
3676 "metadata": {},
3677 "outputs": [
3678 {
3679 "data": {
3680 "text/plain": [
3681 "array([1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 0, 1, 0, 0, 0, 0, 1, 1,\n",
3682 " 1, 0, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 1, 0, 0, 1, 0, 1, 0, 0,\n",
3683 " 1, 1, 1, 0, 0, 1, 0, 1, 1, 1, 1, 1, 1, 0, 0, 0, 1, 1, 0, 1, 0, 0, 0,\n",
3684 " 1, 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 0, 0, 0, 1, 0, 1, 1, 1, 0, 0, 1, 0,\n",
3685 " 1, 0, 1, 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 0, 1, 0, 1, 1, 0, 0, 1, 1,\n",
3686 " 1, 0, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 1, 0,\n",
3687 " 0, 1, 1, 1, 0])"
3688 ]
3689 },
3690 "execution_count": 11,
3691 "metadata": {},
3692 "output_type": "execute_result"
3693 }
3694 ],
3695 "source": [
3696 "def answer_seven():\n",
3697 " X_train, X_test, y_train, y_test = answer_four()\n",
3698 " knn = answer_five()\n",
3699 " \n",
3700 " pred1 = knn.predict(X_test)\n",
3701 " return pred1\n",
3702 "\n",
3703 "answer_seven()"
3704 ]
3705 },
3706 {
3707 "cell_type": "markdown",
3708 "metadata": {},
3709 "source": [
3710 "### Question 8\n",
3711 "Find the score (mean accuracy) of your knn classifier using `X_test` and `y_test`.\n",
3712 "\n",
3713 "*This function should return a float between 0 and 1*"
3714 ]
3715 },
3716 {
3717 "cell_type": "code",
3718 "execution_count": 12,
3719 "metadata": {},
3720 "outputs": [
3721 {
3722 "data": {
3723 "text/plain": [
3724 "0.91608391608391604"
3725 ]
3726 },
3727 "execution_count": 12,
3728 "metadata": {},
3729 "output_type": "execute_result"
3730 }
3731 ],
3732 "source": [
3733 "def answer_eight():\n",
3734 " X_train, X_test, y_train, y_test = answer_four()\n",
3735 " knn = answer_five()\n",
3736 " \n",
3737 " return knn.score(X_test, y_test)\n",
3738 "\n",
3739 "answer_eight()"
3740 ]
3741 },
3742 {
3743 "cell_type": "markdown",
3744 "metadata": {},
3745 "source": [
3746 "### Optional plot\n",
3747 "\n",
3748 "Try using the plotting function below to visualize the differet predicition scores between training and test sets, as well as malignant and benign cells."
3749 ]
3750 },
3751 {
3752 "cell_type": "code",
3753 "execution_count": 13,
3754 "metadata": {
3755 "collapsed": true
3756 },
3757 "outputs": [],
3758 "source": [
3759 "def accuracy_plot():\n",
3760 " import matplotlib.pyplot as plt\n",
3761 "\n",
3762 " %matplotlib notebook\n",
3763 "\n",
3764 " X_train, X_test, y_train, y_test = answer_four()\n",
3765 "\n",
3766 " # Find the training and testing accuracies by target value (i.e. malignant, benign)\n",
3767 " mal_train_X = X_train[y_train==0]\n",
3768 " mal_train_y = y_train[y_train==0]\n",
3769 " ben_train_X = X_train[y_train==1]\n",
3770 " ben_train_y = y_train[y_train==1]\n",
3771 "\n",
3772 " mal_test_X = X_test[y_test==0]\n",
3773 " mal_test_y = y_test[y_test==0]\n",
3774 " ben_test_X = X_test[y_test==1]\n",
3775 " ben_test_y = y_test[y_test==1]\n",
3776 "\n",
3777 " knn = answer_five()\n",
3778 "\n",
3779 " scores = [knn.score(mal_train_X, mal_train_y), knn.score(ben_train_X, ben_train_y), \n",
3780 " knn.score(mal_test_X, mal_test_y), knn.score(ben_test_X, ben_test_y)]\n",
3781 "\n",
3782 "\n",
3783 " plt.figure()\n",
3784 "\n",
3785 " # Plot the scores as a bar chart\n",
3786 " bars = plt.bar(np.arange(4), scores, color=['#4c72b0','#4c72b0','#55a868','#55a868'])\n",
3787 "\n",
3788 " # directly label the score onto the bars\n",
3789 " for bar in bars:\n",
3790 " height = bar.get_height()\n",
3791 " plt.gca().text(bar.get_x() + bar.get_width()/2, height*.90, '{0:.{1}f}'.format(height, 2), \n",
3792 " ha='center', color='w', fontsize=11)\n",
3793 "\n",
3794 " # remove all the ticks (both axes), and tick labels on the Y axis\n",
3795 " plt.tick_params(top='off', bottom='off', left='off', right='off', labelleft='off', labelbottom='on')\n",
3796 "\n",
3797 " # remove the frame of the chart\n",
3798 " for spine in plt.gca().spines.values():\n",
3799 " spine.set_visible(False)\n",
3800 "\n",
3801 " plt.xticks([0,1,2,3], ['Malignant\\nTraining', 'Benign\\nTraining', 'Malignant\\nTest', 'Benign\\nTest'], alpha=0.8);\n",
3802 " plt.title('Training and Test Accuracies for Malignant and Benign Cells', alpha=0.8)"
3803 ]
3804 },
3805 {
3806 "cell_type": "markdown",
3807 "metadata": {},
3808 "source": [
3809 "Uncomment the plotting function to see the visualization.\n",
3810 "\n",
3811 "**Comment out** the plotting function when submitting your notebook for grading. "
3812 ]
3813 },
3814 {
3815 "cell_type": "code",
3816 "execution_count": 14,
3817 "metadata": {},
3818 "outputs": [
3819 {
3820 "data": {
3821 "application/javascript": [
3822 "/* Put everything inside the global mpl namespace */\n",
3823 "window.mpl = {};\n",
3824 "\n",
3825 "\n",
3826 "mpl.get_websocket_type = function() {\n",
3827 " if (typeof(WebSocket) !== 'undefined') {\n",
3828 " return WebSocket;\n",
3829 " } else if (typeof(MozWebSocket) !== 'undefined') {\n",
3830 " return MozWebSocket;\n",
3831 " } else {\n",
3832 " alert('Your browser does not have WebSocket support.' +\n",
3833 " 'Please try Chrome, Safari or Firefox ≥ 6. ' +\n",
3834 " 'Firefox 4 and 5 are also supported but you ' +\n",
3835 " 'have to enable WebSockets in about:config.');\n",
3836 " };\n",
3837 "}\n",
3838 "\n",
3839 "mpl.figure = function(figure_id, websocket, ondownload, parent_element) {\n",
3840 " this.id = figure_id;\n",
3841 "\n",
3842 " this.ws = websocket;\n",
3843 "\n",
3844 " this.supports_binary = (this.ws.binaryType != undefined);\n",
3845 "\n",
3846 " if (!this.supports_binary) {\n",
3847 " var warnings = document.getElementById(\"mpl-warnings\");\n",
3848 " if (warnings) {\n",
3849 " warnings.style.display = 'block';\n",
3850 " warnings.textContent = (\n",
3851 " \"This browser does not support binary websocket messages. \" +\n",
3852 " \"Performance may be slow.\");\n",
3853 " }\n",
3854 " }\n",
3855 "\n",
3856 " this.imageObj = new Image();\n",
3857 "\n",
3858 " this.context = undefined;\n",
3859 " this.message = undefined;\n",
3860 " this.canvas = undefined;\n",
3861 " this.rubberband_canvas = undefined;\n",
3862 " this.rubberband_context = undefined;\n",
3863 " this.format_dropdown = undefined;\n",
3864 "\n",
3865 " this.image_mode = 'full';\n",
3866 "\n",
3867 " this.root = $('<div/>');\n",
3868 " this._root_extra_style(this.root)\n",
3869 " this.root.attr('style', 'display: inline-block');\n",
3870 "\n",
3871 " $(parent_element).append(this.root);\n",
3872 "\n",
3873 " this._init_header(this);\n",
3874 " this._init_canvas(this);\n",
3875 " this._init_toolbar(this);\n",
3876 "\n",
3877 " var fig = this;\n",
3878 "\n",
3879 " this.waiting = false;\n",
3880 "\n",
3881 " this.ws.onopen = function () {\n",
3882 " fig.send_message(\"supports_binary\", {value: fig.supports_binary});\n",
3883 " fig.send_message(\"send_image_mode\", {});\n",
3884 " if (mpl.ratio != 1) {\n",
3885 " fig.send_message(\"set_dpi_ratio\", {'dpi_ratio': mpl.ratio});\n",
3886 " }\n",
3887 " fig.send_message(\"refresh\", {});\n",
3888 " }\n",
3889 "\n",
3890 " this.imageObj.onload = function() {\n",
3891 " if (fig.image_mode == 'full') {\n",
3892 " // Full images could contain transparency (where diff images\n",
3893 " // almost always do), so we need to clear the canvas so that\n",
3894 " // there is no ghosting.\n",
3895 " fig.context.clearRect(0, 0, fig.canvas.width, fig.canvas.height);\n",
3896 " }\n",
3897 " fig.context.drawImage(fig.imageObj, 0, 0);\n",
3898 " };\n",
3899 "\n",
3900 " this.imageObj.onunload = function() {\n",
3901 " this.ws.close();\n",
3902 " }\n",
3903 "\n",
3904 " this.ws.onmessage = this._make_on_message_function(this);\n",
3905 "\n",
3906 " this.ondownload = ondownload;\n",
3907 "}\n",
3908 "\n",
3909 "mpl.figure.prototype._init_header = function() {\n",
3910 " var titlebar = $(\n",
3911 " '<div class=\"ui-dialog-titlebar ui-widget-header ui-corner-all ' +\n",
3912 " 'ui-helper-clearfix\"/>');\n",
3913 " var titletext = $(\n",
3914 " '<div class=\"ui-dialog-title\" style=\"width: 100%; ' +\n",
3915 " 'text-align: center; padding: 3px;\"/>');\n",
3916 " titlebar.append(titletext)\n",
3917 " this.root.append(titlebar);\n",
3918 " this.header = titletext[0];\n",
3919 "}\n",
3920 "\n",
3921 "\n",
3922 "\n",
3923 "mpl.figure.prototype._canvas_extra_style = function(canvas_div) {\n",
3924 "\n",
3925 "}\n",
3926 "\n",
3927 "\n",
3928 "mpl.figure.prototype._root_extra_style = function(canvas_div) {\n",
3929 "\n",
3930 "}\n",
3931 "\n",
3932 "mpl.figure.prototype._init_canvas = function() {\n",
3933 " var fig = this;\n",
3934 "\n",
3935 " var canvas_div = $('<div/>');\n",
3936 "\n",
3937 " canvas_div.attr('style', 'position: relative; clear: both; outline: 0');\n",
3938 "\n",
3939 " function canvas_keyboard_event(event) {\n",
3940 " return fig.key_event(event, event['data']);\n",
3941 " }\n",
3942 "\n",
3943 " canvas_div.keydown('key_press', canvas_keyboard_event);\n",
3944 " canvas_div.keyup('key_release', canvas_keyboard_event);\n",
3945 " this.canvas_div = canvas_div\n",
3946 " this._canvas_extra_style(canvas_div)\n",
3947 " this.root.append(canvas_div);\n",
3948 "\n",
3949 " var canvas = $('<canvas/>');\n",
3950 " canvas.addClass('mpl-canvas');\n",
3951 " canvas.attr('style', \"left: 0; top: 0; z-index: 0; outline: 0\")\n",
3952 "\n",
3953 " this.canvas = canvas[0];\n",
3954 " this.context = canvas[0].getContext(\"2d\");\n",
3955 "\n",
3956 " var backingStore = this.context.backingStorePixelRatio ||\n",
3957 "\tthis.context.webkitBackingStorePixelRatio ||\n",
3958 "\tthis.context.mozBackingStorePixelRatio ||\n",
3959 "\tthis.context.msBackingStorePixelRatio ||\n",
3960 "\tthis.context.oBackingStorePixelRatio ||\n",
3961 "\tthis.context.backingStorePixelRatio || 1;\n",
3962 "\n",
3963 " mpl.ratio = (window.devicePixelRatio || 1) / backingStore;\n",
3964 "\n",
3965 " var rubberband = $('<canvas/>');\n",
3966 " rubberband.attr('style', \"position: absolute; left: 0; top: 0; z-index: 1;\")\n",
3967 "\n",
3968 " var pass_mouse_events = true;\n",
3969 "\n",
3970 " canvas_div.resizable({\n",
3971 " start: function(event, ui) {\n",
3972 " pass_mouse_events = false;\n",
3973 " },\n",
3974 " resize: function(event, ui) {\n",
3975 " fig.request_resize(ui.size.width, ui.size.height);\n",
3976 " },\n",
3977 " stop: function(event, ui) {\n",
3978 " pass_mouse_events = true;\n",
3979 " fig.request_resize(ui.size.width, ui.size.height);\n",
3980 " },\n",
3981 " });\n",
3982 "\n",
3983 " function mouse_event_fn(event) {\n",
3984 " if (pass_mouse_events)\n",
3985 " return fig.mouse_event(event, event['data']);\n",
3986 " }\n",
3987 "\n",
3988 " rubberband.mousedown('button_press', mouse_event_fn);\n",
3989 " rubberband.mouseup('button_release', mouse_event_fn);\n",
3990 " // Throttle sequential mouse events to 1 every 20ms.\n",
3991 " rubberband.mousemove('motion_notify', mouse_event_fn);\n",
3992 "\n",
3993 " rubberband.mouseenter('figure_enter', mouse_event_fn);\n",
3994 " rubberband.mouseleave('figure_leave', mouse_event_fn);\n",
3995 "\n",
3996 " canvas_div.on(\"wheel\", function (event) {\n",
3997 " event = event.originalEvent;\n",
3998 " event['data'] = 'scroll'\n",
3999 " if (event.deltaY < 0) {\n",
4000 " event.step = 1;\n",
4001 " } else {\n",
4002 " event.step = -1;\n",
4003 " }\n",
4004 " mouse_event_fn(event);\n",
4005 " });\n",
4006 "\n",
4007 " canvas_div.append(canvas);\n",
4008 " canvas_div.append(rubberband);\n",
4009 "\n",
4010 " this.rubberband = rubberband;\n",
4011 " this.rubberband_canvas = rubberband[0];\n",
4012 " this.rubberband_context = rubberband[0].getContext(\"2d\");\n",
4013 " this.rubberband_context.strokeStyle = \"#000000\";\n",
4014 "\n",
4015 " this._resize_canvas = function(width, height) {\n",
4016 " // Keep the size of the canvas, canvas container, and rubber band\n",
4017 " // canvas in synch.\n",
4018 " canvas_div.css('width', width)\n",
4019 " canvas_div.css('height', height)\n",
4020 "\n",
4021 " canvas.attr('width', width * mpl.ratio);\n",
4022 " canvas.attr('height', height * mpl.ratio);\n",
4023 " canvas.attr('style', 'width: ' + width + 'px; height: ' + height + 'px;');\n",
4024 "\n",
4025 " rubberband.attr('width', width);\n",
4026 " rubberband.attr('height', height);\n",
4027 " }\n",
4028 "\n",
4029 " // Set the figure to an initial 600x600px, this will subsequently be updated\n",
4030 " // upon first draw.\n",
4031 " this._resize_canvas(600, 600);\n",
4032 "\n",
4033 " // Disable right mouse context menu.\n",
4034 " $(this.rubberband_canvas).bind(\"contextmenu\",function(e){\n",
4035 " return false;\n",
4036 " });\n",
4037 "\n",
4038 " function set_focus () {\n",
4039 " canvas.focus();\n",
4040 " canvas_div.focus();\n",
4041 " }\n",
4042 "\n",
4043 " window.setTimeout(set_focus, 100);\n",
4044 "}\n",
4045 "\n",
4046 "mpl.figure.prototype._init_toolbar = function() {\n",
4047 " var fig = this;\n",
4048 "\n",
4049 " var nav_element = $('<div/>')\n",
4050 " nav_element.attr('style', 'width: 100%');\n",
4051 " this.root.append(nav_element);\n",
4052 "\n",
4053 " // Define a callback function for later on.\n",
4054 " function toolbar_event(event) {\n",
4055 " return fig.toolbar_button_onclick(event['data']);\n",
4056 " }\n",
4057 " function toolbar_mouse_event(event) {\n",
4058 " return fig.toolbar_button_onmouseover(event['data']);\n",
4059 " }\n",
4060 "\n",
4061 " for(var toolbar_ind in mpl.toolbar_items) {\n",
4062 " var name = mpl.toolbar_items[toolbar_ind][0];\n",
4063 " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
4064 " var image = mpl.toolbar_items[toolbar_ind][2];\n",
4065 " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
4066 "\n",
4067 " if (!name) {\n",
4068 " // put a spacer in here.\n",
4069 " continue;\n",
4070 " }\n",
4071 " var button = $('<button/>');\n",
4072 " button.addClass('ui-button ui-widget ui-state-default ui-corner-all ' +\n",
4073 " 'ui-button-icon-only');\n",
4074 " button.attr('role', 'button');\n",
4075 " button.attr('aria-disabled', 'false');\n",
4076 " button.click(method_name, toolbar_event);\n",
4077 " button.mouseover(tooltip, toolbar_mouse_event);\n",
4078 "\n",
4079 " var icon_img = $('<span/>');\n",
4080 " icon_img.addClass('ui-button-icon-primary ui-icon');\n",
4081 " icon_img.addClass(image);\n",
4082 " icon_img.addClass('ui-corner-all');\n",
4083 "\n",
4084 " var tooltip_span = $('<span/>');\n",
4085 " tooltip_span.addClass('ui-button-text');\n",
4086 " tooltip_span.html(tooltip);\n",
4087 "\n",
4088 " button.append(icon_img);\n",
4089 " button.append(tooltip_span);\n",
4090 "\n",
4091 " nav_element.append(button);\n",
4092 " }\n",
4093 "\n",
4094 " var fmt_picker_span = $('<span/>');\n",
4095 "\n",
4096 " var fmt_picker = $('<select/>');\n",
4097 " fmt_picker.addClass('mpl-toolbar-option ui-widget ui-widget-content');\n",
4098 " fmt_picker_span.append(fmt_picker);\n",
4099 " nav_element.append(fmt_picker_span);\n",
4100 " this.format_dropdown = fmt_picker[0];\n",
4101 "\n",
4102 " for (var ind in mpl.extensions) {\n",
4103 " var fmt = mpl.extensions[ind];\n",
4104 " var option = $(\n",
4105 " '<option/>', {selected: fmt === mpl.default_extension}).html(fmt);\n",
4106 " fmt_picker.append(option)\n",
4107 " }\n",
4108 "\n",
4109 " // Add hover states to the ui-buttons\n",
4110 " $( \".ui-button\" ).hover(\n",
4111 " function() { $(this).addClass(\"ui-state-hover\");},\n",
4112 " function() { $(this).removeClass(\"ui-state-hover\");}\n",
4113 " );\n",
4114 "\n",
4115 " var status_bar = $('<span class=\"mpl-message\"/>');\n",
4116 " nav_element.append(status_bar);\n",
4117 " this.message = status_bar[0];\n",
4118 "}\n",
4119 "\n",
4120 "mpl.figure.prototype.request_resize = function(x_pixels, y_pixels) {\n",
4121 " // Request matplotlib to resize the figure. Matplotlib will then trigger a resize in the client,\n",
4122 " // which will in turn request a refresh of the image.\n",
4123 " this.send_message('resize', {'width': x_pixels, 'height': y_pixels});\n",
4124 "}\n",
4125 "\n",
4126 "mpl.figure.prototype.send_message = function(type, properties) {\n",
4127 " properties['type'] = type;\n",
4128 " properties['figure_id'] = this.id;\n",
4129 " this.ws.send(JSON.stringify(properties));\n",
4130 "}\n",
4131 "\n",
4132 "mpl.figure.prototype.send_draw_message = function() {\n",
4133 " if (!this.waiting) {\n",
4134 " this.waiting = true;\n",
4135 " this.ws.send(JSON.stringify({type: \"draw\", figure_id: this.id}));\n",
4136 " }\n",
4137 "}\n",
4138 "\n",
4139 "\n",
4140 "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
4141 " var format_dropdown = fig.format_dropdown;\n",
4142 " var format = format_dropdown.options[format_dropdown.selectedIndex].value;\n",
4143 " fig.ondownload(fig, format);\n",
4144 "}\n",
4145 "\n",
4146 "\n",
4147 "mpl.figure.prototype.handle_resize = function(fig, msg) {\n",
4148 " var size = msg['size'];\n",
4149 " if (size[0] != fig.canvas.width || size[1] != fig.canvas.height) {\n",
4150 " fig._resize_canvas(size[0], size[1]);\n",
4151 " fig.send_message(\"refresh\", {});\n",
4152 " };\n",
4153 "}\n",
4154 "\n",
4155 "mpl.figure.prototype.handle_rubberband = function(fig, msg) {\n",
4156 " var x0 = msg['x0'] / mpl.ratio;\n",
4157 " var y0 = (fig.canvas.height - msg['y0']) / mpl.ratio;\n",
4158 " var x1 = msg['x1'] / mpl.ratio;\n",
4159 " var y1 = (fig.canvas.height - msg['y1']) / mpl.ratio;\n",
4160 " x0 = Math.floor(x0) + 0.5;\n",
4161 " y0 = Math.floor(y0) + 0.5;\n",
4162 " x1 = Math.floor(x1) + 0.5;\n",
4163 " y1 = Math.floor(y1) + 0.5;\n",
4164 " var min_x = Math.min(x0, x1);\n",
4165 " var min_y = Math.min(y0, y1);\n",
4166 " var width = Math.abs(x1 - x0);\n",
4167 " var height = Math.abs(y1 - y0);\n",
4168 "\n",
4169 " fig.rubberband_context.clearRect(\n",
4170 " 0, 0, fig.canvas.width, fig.canvas.height);\n",
4171 "\n",
4172 " fig.rubberband_context.strokeRect(min_x, min_y, width, height);\n",
4173 "}\n",
4174 "\n",
4175 "mpl.figure.prototype.handle_figure_label = function(fig, msg) {\n",
4176 " // Updates the figure title.\n",
4177 " fig.header.textContent = msg['label'];\n",
4178 "}\n",
4179 "\n",
4180 "mpl.figure.prototype.handle_cursor = function(fig, msg) {\n",
4181 " var cursor = msg['cursor'];\n",
4182 " switch(cursor)\n",
4183 " {\n",
4184 " case 0:\n",
4185 " cursor = 'pointer';\n",
4186 " break;\n",
4187 " case 1:\n",
4188 " cursor = 'default';\n",
4189 " break;\n",
4190 " case 2:\n",
4191 " cursor = 'crosshair';\n",
4192 " break;\n",
4193 " case 3:\n",
4194 " cursor = 'move';\n",
4195 " break;\n",
4196 " }\n",
4197 " fig.rubberband_canvas.style.cursor = cursor;\n",
4198 "}\n",
4199 "\n",
4200 "mpl.figure.prototype.handle_message = function(fig, msg) {\n",
4201 " fig.message.textContent = msg['message'];\n",
4202 "}\n",
4203 "\n",
4204 "mpl.figure.prototype.handle_draw = function(fig, msg) {\n",
4205 " // Request the server to send over a new figure.\n",
4206 " fig.send_draw_message();\n",
4207 "}\n",
4208 "\n",
4209 "mpl.figure.prototype.handle_image_mode = function(fig, msg) {\n",
4210 " fig.image_mode = msg['mode'];\n",
4211 "}\n",
4212 "\n",
4213 "mpl.figure.prototype.updated_canvas_event = function() {\n",
4214 " // Called whenever the canvas gets updated.\n",
4215 " this.send_message(\"ack\", {});\n",
4216 "}\n",
4217 "\n",
4218 "// A function to construct a web socket function for onmessage handling.\n",
4219 "// Called in the figure constructor.\n",
4220 "mpl.figure.prototype._make_on_message_function = function(fig) {\n",
4221 " return function socket_on_message(evt) {\n",
4222 " if (evt.data instanceof Blob) {\n",
4223 " /* FIXME: We get \"Resource interpreted as Image but\n",
4224 " * transferred with MIME type text/plain:\" errors on\n",
4225 " * Chrome. But how to set the MIME type? It doesn't seem\n",
4226 " * to be part of the websocket stream */\n",
4227 " evt.data.type = \"image/png\";\n",
4228 "\n",
4229 " /* Free the memory for the previous frames */\n",
4230 " if (fig.imageObj.src) {\n",
4231 " (window.URL || window.webkitURL).revokeObjectURL(\n",
4232 " fig.imageObj.src);\n",
4233 " }\n",
4234 "\n",
4235 " fig.imageObj.src = (window.URL || window.webkitURL).createObjectURL(\n",
4236 " evt.data);\n",
4237 " fig.updated_canvas_event();\n",
4238 " fig.waiting = false;\n",
4239 " return;\n",
4240 " }\n",
4241 " else if (typeof evt.data === 'string' && evt.data.slice(0, 21) == \"data:image/png;base64\") {\n",
4242 " fig.imageObj.src = evt.data;\n",
4243 " fig.updated_canvas_event();\n",
4244 " fig.waiting = false;\n",
4245 " return;\n",
4246 " }\n",
4247 "\n",
4248 " var msg = JSON.parse(evt.data);\n",
4249 " var msg_type = msg['type'];\n",
4250 "\n",
4251 " // Call the \"handle_{type}\" callback, which takes\n",
4252 " // the figure and JSON message as its only arguments.\n",
4253 " try {\n",
4254 " var callback = fig[\"handle_\" + msg_type];\n",
4255 " } catch (e) {\n",
4256 " console.log(\"No handler for the '\" + msg_type + \"' message type: \", msg);\n",
4257 " return;\n",
4258 " }\n",
4259 "\n",
4260 " if (callback) {\n",
4261 " try {\n",
4262 " // console.log(\"Handling '\" + msg_type + \"' message: \", msg);\n",
4263 " callback(fig, msg);\n",
4264 " } catch (e) {\n",
4265 " console.log(\"Exception inside the 'handler_\" + msg_type + \"' callback:\", e, e.stack, msg);\n",
4266 " }\n",
4267 " }\n",
4268 " };\n",
4269 "}\n",
4270 "\n",
4271 "// from http://stackoverflow.com/questions/1114465/getting-mouse-location-in-canvas\n",
4272 "mpl.findpos = function(e) {\n",
4273 " //this section is from http://www.quirksmode.org/js/events_properties.html\n",
4274 " var targ;\n",
4275 " if (!e)\n",
4276 " e = window.event;\n",
4277 " if (e.target)\n",
4278 " targ = e.target;\n",
4279 " else if (e.srcElement)\n",
4280 " targ = e.srcElement;\n",
4281 " if (targ.nodeType == 3) // defeat Safari bug\n",
4282 " targ = targ.parentNode;\n",
4283 "\n",
4284 " // jQuery normalizes the pageX and pageY\n",
4285 " // pageX,Y are the mouse positions relative to the document\n",
4286 " // offset() returns the position of the element relative to the document\n",
4287 " var x = e.pageX - $(targ).offset().left;\n",
4288 " var y = e.pageY - $(targ).offset().top;\n",
4289 "\n",
4290 " return {\"x\": x, \"y\": y};\n",
4291 "};\n",
4292 "\n",
4293 "/*\n",
4294 " * return a copy of an object with only non-object keys\n",
4295 " * we need this to avoid circular references\n",
4296 " * http://stackoverflow.com/a/24161582/3208463\n",
4297 " */\n",
4298 "function simpleKeys (original) {\n",
4299 " return Object.keys(original).reduce(function (obj, key) {\n",
4300 " if (typeof original[key] !== 'object')\n",
4301 " obj[key] = original[key]\n",
4302 " return obj;\n",
4303 " }, {});\n",
4304 "}\n",
4305 "\n",
4306 "mpl.figure.prototype.mouse_event = function(event, name) {\n",
4307 " var canvas_pos = mpl.findpos(event)\n",
4308 "\n",
4309 " if (name === 'button_press')\n",
4310 " {\n",
4311 " this.canvas.focus();\n",
4312 " this.canvas_div.focus();\n",
4313 " }\n",
4314 "\n",
4315 " var x = canvas_pos.x * mpl.ratio;\n",
4316 " var y = canvas_pos.y * mpl.ratio;\n",
4317 "\n",
4318 " this.send_message(name, {x: x, y: y, button: event.button,\n",
4319 " step: event.step,\n",
4320 " guiEvent: simpleKeys(event)});\n",
4321 "\n",
4322 " /* This prevents the web browser from automatically changing to\n",
4323 " * the text insertion cursor when the button is pressed. We want\n",
4324 " * to control all of the cursor setting manually through the\n",
4325 " * 'cursor' event from matplotlib */\n",
4326 " event.preventDefault();\n",
4327 " return false;\n",
4328 "}\n",
4329 "\n",
4330 "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
4331 " // Handle any extra behaviour associated with a key event\n",
4332 "}\n",
4333 "\n",
4334 "mpl.figure.prototype.key_event = function(event, name) {\n",
4335 "\n",
4336 " // Prevent repeat events\n",
4337 " if (name == 'key_press')\n",
4338 " {\n",
4339 " if (event.which === this._key)\n",
4340 " return;\n",
4341 " else\n",
4342 " this._key = event.which;\n",
4343 " }\n",
4344 " if (name == 'key_release')\n",
4345 " this._key = null;\n",
4346 "\n",
4347 " var value = '';\n",
4348 " if (event.ctrlKey && event.which != 17)\n",
4349 " value += \"ctrl+\";\n",
4350 " if (event.altKey && event.which != 18)\n",
4351 " value += \"alt+\";\n",
4352 " if (event.shiftKey && event.which != 16)\n",
4353 " value += \"shift+\";\n",
4354 "\n",
4355 " value += 'k';\n",
4356 " value += event.which.toString();\n",
4357 "\n",
4358 " this._key_event_extra(event, name);\n",
4359 "\n",
4360 " this.send_message(name, {key: value,\n",
4361 " guiEvent: simpleKeys(event)});\n",
4362 " return false;\n",
4363 "}\n",
4364 "\n",
4365 "mpl.figure.prototype.toolbar_button_onclick = function(name) {\n",
4366 " if (name == 'download') {\n",
4367 " this.handle_save(this, null);\n",
4368 " } else {\n",
4369 " this.send_message(\"toolbar_button\", {name: name});\n",
4370 " }\n",
4371 "};\n",
4372 "\n",
4373 "mpl.figure.prototype.toolbar_button_onmouseover = function(tooltip) {\n",
4374 " this.message.textContent = tooltip;\n",
4375 "};\n",
4376 "mpl.toolbar_items = [[\"Home\", \"Reset original view\", \"fa fa-home icon-home\", \"home\"], [\"Back\", \"Back to previous view\", \"fa fa-arrow-left icon-arrow-left\", \"back\"], [\"Forward\", \"Forward to next view\", \"fa fa-arrow-right icon-arrow-right\", \"forward\"], [\"\", \"\", \"\", \"\"], [\"Pan\", \"Pan axes with left mouse, zoom with right\", \"fa fa-arrows icon-move\", \"pan\"], [\"Zoom\", \"Zoom to rectangle\", \"fa fa-square-o icon-check-empty\", \"zoom\"], [\"\", \"\", \"\", \"\"], [\"Download\", \"Download plot\", \"fa fa-floppy-o icon-save\", \"download\"]];\n",
4377 "\n",
4378 "mpl.extensions = [\"eps\", \"jpeg\", \"pdf\", \"png\", \"ps\", \"raw\", \"svg\", \"tif\"];\n",
4379 "\n",
4380 "mpl.default_extension = \"png\";var comm_websocket_adapter = function(comm) {\n",
4381 " // Create a \"websocket\"-like object which calls the given IPython comm\n",
4382 " // object with the appropriate methods. Currently this is a non binary\n",
4383 " // socket, so there is still some room for performance tuning.\n",
4384 " var ws = {};\n",
4385 "\n",
4386 " ws.close = function() {\n",
4387 " comm.close()\n",
4388 " };\n",
4389 " ws.send = function(m) {\n",
4390 " //console.log('sending', m);\n",
4391 " comm.send(m);\n",
4392 " };\n",
4393 " // Register the callback with on_msg.\n",
4394 " comm.on_msg(function(msg) {\n",
4395 " //console.log('receiving', msg['content']['data'], msg);\n",
4396 " // Pass the mpl event to the overriden (by mpl) onmessage function.\n",
4397 " ws.onmessage(msg['content']['data'])\n",
4398 " });\n",
4399 " return ws;\n",
4400 "}\n",
4401 "\n",
4402 "mpl.mpl_figure_comm = function(comm, msg) {\n",
4403 " // This is the function which gets called when the mpl process\n",
4404 " // starts-up an IPython Comm through the \"matplotlib\" channel.\n",
4405 "\n",
4406 " var id = msg.content.data.id;\n",
4407 " // Get hold of the div created by the display call when the Comm\n",
4408 " // socket was opened in Python.\n",
4409 " var element = $(\"#\" + id);\n",
4410 " var ws_proxy = comm_websocket_adapter(comm)\n",
4411 "\n",
4412 " function ondownload(figure, format) {\n",
4413 " window.open(figure.imageObj.src);\n",
4414 " }\n",
4415 "\n",
4416 " var fig = new mpl.figure(id, ws_proxy,\n",
4417 " ondownload,\n",
4418 " element.get(0));\n",
4419 "\n",
4420 " // Call onopen now - mpl needs it, as it is assuming we've passed it a real\n",
4421 " // web socket which is closed, not our websocket->open comm proxy.\n",
4422 " ws_proxy.onopen();\n",
4423 "\n",
4424 " fig.parent_element = element.get(0);\n",
4425 " fig.cell_info = mpl.find_output_cell(\"<div id='\" + id + \"'></div>\");\n",
4426 " if (!fig.cell_info) {\n",
4427 " console.error(\"Failed to find cell for figure\", id, fig);\n",
4428 " return;\n",
4429 " }\n",
4430 "\n",
4431 " var output_index = fig.cell_info[2]\n",
4432 " var cell = fig.cell_info[0];\n",
4433 "\n",
4434 "};\n",
4435 "\n",
4436 "mpl.figure.prototype.handle_close = function(fig, msg) {\n",
4437 " var width = fig.canvas.width/mpl.ratio\n",
4438 " fig.root.unbind('remove')\n",
4439 "\n",
4440 " // Update the output cell to use the data from the current canvas.\n",
4441 " fig.push_to_output();\n",
4442 " var dataURL = fig.canvas.toDataURL();\n",
4443 " // Re-enable the keyboard manager in IPython - without this line, in FF,\n",
4444 " // the notebook keyboard shortcuts fail.\n",
4445 " IPython.keyboard_manager.enable()\n",
4446 " $(fig.parent_element).html('<img src=\"' + dataURL + '\" width=\"' + width + '\">');\n",
4447 " fig.close_ws(fig, msg);\n",
4448 "}\n",
4449 "\n",
4450 "mpl.figure.prototype.close_ws = function(fig, msg){\n",
4451 " fig.send_message('closing', msg);\n",
4452 " // fig.ws.close()\n",
4453 "}\n",
4454 "\n",
4455 "mpl.figure.prototype.push_to_output = function(remove_interactive) {\n",
4456 " // Turn the data on the canvas into data in the output cell.\n",
4457 " var width = this.canvas.width/mpl.ratio\n",
4458 " var dataURL = this.canvas.toDataURL();\n",
4459 " this.cell_info[1]['text/html'] = '<img src=\"' + dataURL + '\" width=\"' + width + '\">';\n",
4460 "}\n",
4461 "\n",
4462 "mpl.figure.prototype.updated_canvas_event = function() {\n",
4463 " // Tell IPython that the notebook contents must change.\n",
4464 " IPython.notebook.set_dirty(true);\n",
4465 " this.send_message(\"ack\", {});\n",
4466 " var fig = this;\n",
4467 " // Wait a second, then push the new image to the DOM so\n",
4468 " // that it is saved nicely (might be nice to debounce this).\n",
4469 " setTimeout(function () { fig.push_to_output() }, 1000);\n",
4470 "}\n",
4471 "\n",
4472 "mpl.figure.prototype._init_toolbar = function() {\n",
4473 " var fig = this;\n",
4474 "\n",
4475 " var nav_element = $('<div/>')\n",
4476 " nav_element.attr('style', 'width: 100%');\n",
4477 " this.root.append(nav_element);\n",
4478 "\n",
4479 " // Define a callback function for later on.\n",
4480 " function toolbar_event(event) {\n",
4481 " return fig.toolbar_button_onclick(event['data']);\n",
4482 " }\n",
4483 " function toolbar_mouse_event(event) {\n",
4484 " return fig.toolbar_button_onmouseover(event['data']);\n",
4485 " }\n",
4486 "\n",
4487 " for(var toolbar_ind in mpl.toolbar_items){\n",
4488 " var name = mpl.toolbar_items[toolbar_ind][0];\n",
4489 " var tooltip = mpl.toolbar_items[toolbar_ind][1];\n",
4490 " var image = mpl.toolbar_items[toolbar_ind][2];\n",
4491 " var method_name = mpl.toolbar_items[toolbar_ind][3];\n",
4492 "\n",
4493 " if (!name) { continue; };\n",
4494 "\n",
4495 " var button = $('<button class=\"btn btn-default\" href=\"#\" title=\"' + name + '\"><i class=\"fa ' + image + ' fa-lg\"></i></button>');\n",
4496 " button.click(method_name, toolbar_event);\n",
4497 " button.mouseover(tooltip, toolbar_mouse_event);\n",
4498 " nav_element.append(button);\n",
4499 " }\n",
4500 "\n",
4501 " // Add the status bar.\n",
4502 " var status_bar = $('<span class=\"mpl-message\" style=\"text-align:right; float: right;\"/>');\n",
4503 " nav_element.append(status_bar);\n",
4504 " this.message = status_bar[0];\n",
4505 "\n",
4506 " // Add the close button to the window.\n",
4507 " var buttongrp = $('<div class=\"btn-group inline pull-right\"></div>');\n",
4508 " var button = $('<button class=\"btn btn-mini btn-primary\" href=\"#\" title=\"Stop Interaction\"><i class=\"fa fa-power-off icon-remove icon-large\"></i></button>');\n",
4509 " button.click(function (evt) { fig.handle_close(fig, {}); } );\n",
4510 " button.mouseover('Stop Interaction', toolbar_mouse_event);\n",
4511 " buttongrp.append(button);\n",
4512 " var titlebar = this.root.find($('.ui-dialog-titlebar'));\n",
4513 " titlebar.prepend(buttongrp);\n",
4514 "}\n",
4515 "\n",
4516 "mpl.figure.prototype._root_extra_style = function(el){\n",
4517 " var fig = this\n",
4518 " el.on(\"remove\", function(){\n",
4519 "\tfig.close_ws(fig, {});\n",
4520 " });\n",
4521 "}\n",
4522 "\n",
4523 "mpl.figure.prototype._canvas_extra_style = function(el){\n",
4524 " // this is important to make the div 'focusable\n",
4525 " el.attr('tabindex', 0)\n",
4526 " // reach out to IPython and tell the keyboard manager to turn it's self\n",
4527 " // off when our div gets focus\n",
4528 "\n",
4529 " // location in version 3\n",
4530 " if (IPython.notebook.keyboard_manager) {\n",
4531 " IPython.notebook.keyboard_manager.register_events(el);\n",
4532 " }\n",
4533 " else {\n",
4534 " // location in version 2\n",
4535 " IPython.keyboard_manager.register_events(el);\n",
4536 " }\n",
4537 "\n",
4538 "}\n",
4539 "\n",
4540 "mpl.figure.prototype._key_event_extra = function(event, name) {\n",
4541 " var manager = IPython.notebook.keyboard_manager;\n",
4542 " if (!manager)\n",
4543 " manager = IPython.keyboard_manager;\n",
4544 "\n",
4545 " // Check for shift+enter\n",
4546 " if (event.shiftKey && event.which == 13) {\n",
4547 " this.canvas_div.blur();\n",
4548 " // select the cell after this one\n",
4549 " var index = IPython.notebook.find_cell_index(this.cell_info[0]);\n",
4550 " IPython.notebook.select(index + 1);\n",
4551 " }\n",
4552 "}\n",
4553 "\n",
4554 "mpl.figure.prototype.handle_save = function(fig, msg) {\n",
4555 " fig.ondownload(fig, null);\n",
4556 "}\n",
4557 "\n",
4558 "\n",
4559 "mpl.find_output_cell = function(html_output) {\n",
4560 " // Return the cell and output element which can be found *uniquely* in the notebook.\n",
4561 " // Note - this is a bit hacky, but it is done because the \"notebook_saving.Notebook\"\n",
4562 " // IPython event is triggered only after the cells have been serialised, which for\n",
4563 " // our purposes (turning an active figure into a static one), is too late.\n",
4564 " var cells = IPython.notebook.get_cells();\n",
4565 " var ncells = cells.length;\n",
4566 " for (var i=0; i<ncells; i++) {\n",
4567 " var cell = cells[i];\n",
4568 " if (cell.cell_type === 'code'){\n",
4569 " for (var j=0; j<cell.output_area.outputs.length; j++) {\n",
4570 " var data = cell.output_area.outputs[j];\n",
4571 " if (data.data) {\n",
4572 " // IPython >= 3 moved mimebundle to data attribute of output\n",
4573 " data = data.data;\n",
4574 " }\n",
4575 " if (data['text/html'] == html_output) {\n",
4576 " return [cell, data, j];\n",
4577 " }\n",
4578 " }\n",
4579 " }\n",
4580 " }\n",
4581 "}\n",
4582 "\n",
4583 "// Register the function which deals with the matplotlib target/channel.\n",
4584 "// The kernel may be null if the page has been refreshed.\n",
4585 "if (IPython.notebook.kernel != null) {\n",
4586 " IPython.notebook.kernel.comm_manager.register_target('matplotlib', mpl.mpl_figure_comm);\n",
4587 "}\n"
4588 ],
4589 "text/plain": [
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4592 },
4593 "metadata": {},
4594 "output_type": "display_data"
4595 },
4596 {
4597 "data": {
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4615 "metadata": {},
4616 "source": [
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4625 },
4626 "outputs": [],
4627 "source": []
4628 }
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4631 "coursera": {
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