-
Notifications
You must be signed in to change notification settings - Fork 0
Expand file tree
/
Copy pathlesson_26.html
More file actions
1069 lines (928 loc) · 163 KB
/
Copy pathlesson_26.html
File metadata and controls
1069 lines (928 loc) · 163 KB
1
2
3
4
5
6
7
8
9
10
11
12
13
14
15
16
17
18
19
20
21
22
23
24
25
26
27
28
29
30
31
32
33
34
35
36
37
38
39
40
41
42
43
44
45
46
47
48
49
50
51
52
53
54
55
56
57
58
59
60
61
62
63
64
65
66
67
68
69
70
71
72
73
74
75
76
77
78
79
80
81
82
83
84
85
86
87
88
89
90
91
92
93
94
95
96
97
98
99
100
101
102
103
104
105
106
107
108
109
110
111
112
113
114
115
116
117
118
119
120
121
122
123
124
125
126
127
128
129
130
131
132
133
134
135
136
137
138
139
140
141
142
143
144
145
146
147
148
149
150
151
152
153
154
155
156
157
158
159
160
161
162
163
164
165
166
167
168
169
170
171
172
173
174
175
176
177
178
179
180
181
182
183
184
185
186
187
188
189
190
191
192
193
194
195
196
197
198
199
200
201
202
203
204
205
206
207
208
209
210
211
212
213
214
215
216
217
218
219
220
221
222
223
224
225
226
227
228
229
230
231
232
233
234
235
236
237
238
239
240
241
242
243
244
245
246
247
248
249
250
251
252
253
254
255
256
257
258
259
260
261
262
263
264
265
266
267
268
269
270
271
272
273
274
275
276
277
278
279
280
281
282
283
284
285
286
287
288
289
290
291
292
293
294
295
296
297
298
299
300
301
302
303
304
305
306
307
308
309
310
311
312
313
314
315
316
317
318
319
320
321
322
323
324
325
326
327
328
329
330
331
332
333
334
335
336
337
338
339
340
341
342
343
344
345
346
347
348
349
350
351
352
353
354
355
356
357
358
359
360
361
362
363
364
365
366
367
368
369
370
371
372
373
374
375
376
377
378
379
380
381
382
383
384
385
386
387
388
389
390
391
392
393
394
395
396
397
398
399
400
401
402
403
404
405
406
407
408
409
410
411
412
413
414
415
416
417
418
419
420
421
422
423
424
425
426
427
428
429
430
431
432
433
434
435
436
437
438
439
440
441
442
443
444
445
446
447
448
449
450
451
452
453
454
455
456
457
458
459
460
461
462
463
464
465
466
467
468
469
470
471
472
473
474
475
476
477
478
479
480
481
482
483
484
485
486
487
488
489
490
491
492
493
494
495
496
497
498
499
500
501
502
503
504
505
506
507
508
509
510
511
512
513
514
515
516
517
518
519
520
521
522
523
524
525
526
527
528
529
530
531
532
533
534
535
536
537
538
539
540
541
542
543
544
545
546
547
548
549
550
551
552
553
554
555
556
557
558
559
560
561
562
563
564
565
566
567
568
569
570
571
572
573
574
575
576
577
578
579
580
581
582
583
584
585
586
587
588
589
590
591
592
593
594
595
596
597
598
599
600
601
602
603
604
605
606
607
608
609
610
611
612
613
614
615
616
617
618
619
620
621
622
623
624
625
626
627
628
629
630
631
632
633
634
635
636
637
638
639
640
641
642
643
644
645
646
647
648
649
650
651
652
653
654
655
656
657
658
659
660
661
662
663
664
665
666
667
668
669
670
671
672
673
674
675
676
677
678
679
680
681
682
683
684
685
686
687
688
689
690
691
692
693
694
695
696
697
698
699
700
701
702
703
704
705
706
707
708
709
710
711
712
713
714
715
716
717
718
719
720
721
722
723
724
725
726
727
728
729
730
731
732
733
734
735
736
737
738
739
740
741
742
743
744
745
746
747
748
749
750
751
752
753
754
755
756
757
758
759
760
761
762
763
764
765
766
767
768
769
770
771
772
773
774
775
776
777
778
779
780
781
782
783
784
785
786
787
788
789
790
791
792
793
794
795
796
797
798
799
800
801
802
803
804
805
806
807
808
809
810
811
812
813
814
815
816
817
818
819
820
821
822
823
824
825
826
827
828
829
830
831
832
833
834
835
836
837
838
839
840
841
842
843
844
845
846
847
848
849
850
851
852
853
854
855
856
857
858
859
860
861
862
863
864
865
866
867
868
869
870
871
872
873
874
875
876
877
878
879
880
881
882
883
884
885
886
887
888
889
890
891
892
893
894
895
896
897
898
899
900
901
902
903
904
905
906
907
908
909
910
911
912
913
914
915
916
917
918
919
920
921
922
923
924
925
926
927
928
929
930
931
932
933
934
935
936
937
938
939
940
941
942
943
944
945
946
947
948
949
950
951
952
953
954
955
956
957
958
959
960
961
962
963
964
965
966
967
968
969
970
971
972
973
974
975
976
977
978
979
980
981
982
983
984
985
986
987
988
989
990
991
992
993
994
995
996
997
998
999
1000
<!DOCTYPE html>
<html lang="en">
<head>
<meta charset="UTF-8"><meta name="viewport" content="width=device-width,initial-scale=1.0">
<title>Lesson 26: NumPy Array Operations: Splitting, Searching, Sorting, Filtering & Random Numbers — Techbase Python</title>
<link rel="preconnect" href="https://fonts.googleapis.com"><link rel="preconnect" href="https://fonts.gstatic.com" crossorigin><link href="https://fonts.googleapis.com/css2?family=Plus+Jakarta+Sans:wght@400;500;600;700;800&family=Space+Grotesk:wght@400;500;600;700&family=JetBrains+Mono:wght@400;500&display=swap" rel="stylesheet"><style>
:root{--acc:#059669;--acd:#047857;--acp:#ecfdf5;--acb:rgba(5,150,105,.22);--acs:rgba(5,150,105,.28);
--bg:#f8fafc;--sf:#fff;--br:#e2e8f0;--tx:#0f172a;--t2:#334155;--mu:#64748b;
--D:'Space Grotesk',sans-serif;--B:'Plus Jakarta Sans',sans-serif;--M:'JetBrains Mono',monospace}
*,*::before,*::after{box-sizing:border-box;margin:0;padding:0}
html{scroll-behavior:smooth;-webkit-font-smoothing:antialiased}
body{background:var(--bg);color:var(--tx);font-family:var(--B);font-size:18px;line-height:1.8;overflow-x:hidden}
/* NAV */
.l-nav{position:fixed;top:0;left:0;right:0;z-index:200;background:rgba(255,255,255,.97);
backdrop-filter:blur(14px);border-bottom:1px solid var(--br);height:64px;
display:flex;align-items:center;padding:0 1.5rem;justify-content:space-between;
box-shadow:0 1px 8px rgba(0,0,0,.06)}
.nav-logo-wrap{display:flex;align-items:center;gap:.75rem;text-decoration:none}
.nav-logo-img{height:44px;width:auto;display:block;object-fit:contain}
.nav-logo-text{font-family:var(--D);font-weight:700;font-size:.95rem;color:var(--tx);
display:flex;flex-direction:column;line-height:1.2}
.nav-logo-text span.course-lbl{font-size:.65rem;font-weight:600;color:var(--acc);
text-transform:uppercase;letter-spacing:.1em}
.l-nav-back{font-size:.85rem;color:var(--mu);text-decoration:none;font-weight:600;
display:flex;align-items:center;gap:.3rem;transition:color .2s;padding:.4rem .8rem;
border-radius:8px;border:1.5px solid var(--br)}
.l-nav-back:hover{color:var(--acc);border-color:var(--acb)}
.l-nav-lbl{font-family:var(--M);font-size:.72rem;color:var(--mu)}
/* PROGRESS */
.pw{position:fixed;top:64px;left:0;right:0;z-index:199;height:4px;background:var(--br)}
.pb{height:100%;background:linear-gradient(90deg,var(--acc),var(--acd));
transition:width .5s cubic-bezier(.4,0,.2,1);width:0%;position:relative}
.pb::after{content:'';position:absolute;right:0;top:0;bottom:0;width:24px;
background:inherit;filter:brightness(1.4);animation:pg 1.5s ease-in-out infinite}
@keyframes pg{0%,100%{opacity:1}50%{opacity:.35}}
/* HERO */
.l-hero{padding:88px 1.5rem 2.5rem;background:linear-gradient(160deg,#ecfdf5 0%,#f8fafc 55%,#d1fae5 100%);border-bottom:1px solid var(--br)}
.l-hero-in{max-width:880px;margin:0 auto;display:flex;align-items:flex-start;gap:2rem;flex-wrap:wrap}
.l-hero-logo{flex-shrink:0}
.l-hero-logo img{height:80px;width:auto;opacity:.92}
.l-hero-text{flex:1}
.badge{display:inline-flex;align-items:center;border-radius:100px;padding:.25rem 1rem;
font-family:var(--M);font-size:.68rem;letter-spacing:.08em;text-transform:uppercase;
font-weight:700;color:var(--acc);background:var(--acp);
border:1.5px solid var(--acb);margin-bottom:.9rem}
.l-hero h1{font-family:var(--D);font-weight:700;font-size:clamp(1.6rem,4vw,2.4rem);
letter-spacing:-.02em;line-height:1.15;margin-bottom:.5rem}
.l-hero-sub{font-size:1rem;color:var(--mu)}
/* WELCOME BOX */
.wb{background:#fff;border:1.5px solid var(--acb);border-left:5px solid var(--acc);
border-radius:14px;padding:1.5rem 1.8rem;max-width:880px;margin:1.5rem auto 0;
box-shadow:0 2px 14px var(--acs)}
.wb h2{font-family:var(--D);font-weight:700;font-size:1.1rem;color:var(--acd);
margin-bottom:.65rem;display:flex;align-items:center;gap:.45rem}
.wb p,.wb li{font-size:1rem;line-height:1.85;color:var(--t2)}
.wb ul{margin-left:1.3rem;margin-top:.4rem}
.wm{display:flex;gap:.65rem;margin-top:1rem;flex-wrap:wrap}
.wm span{background:var(--acp);color:var(--acd);border:1px solid var(--acb);
border-radius:7px;padding:.25rem .8rem;font-family:var(--M);font-size:.72rem;font-weight:600}
/* PHASE */
.phase{background:var(--sf);border:1.5px solid var(--br);border-radius:16px;
margin-bottom:1.6rem;overflow:hidden;
transition:opacity .35s,filter .35s,transform .35s;
box-shadow:0 2px 10px rgba(0,0,0,.04)}
.phase.locked{opacity:.28;pointer-events:none;filter:blur(2px);transform:scale(.99)}
.ph{padding:1.1rem 1.5rem;background:#f0fdf6;border-bottom:1px solid var(--br);
display:flex;align-items:center;gap:.9rem}
.pn{font-family:var(--M);font-size:.68rem;font-weight:700;letter-spacing:.1em;
text-transform:uppercase;color:var(--acc);white-space:nowrap}
.pt{font-family:var(--D);font-weight:700;font-size:1rem;color:var(--tx);flex:1}
.pc{width:28px;height:28px;border-radius:50%;background:var(--br);flex-shrink:0;
display:flex;align-items:center;justify-content:center;font-size:.85rem;
transition:background .3s,transform .45s}
.pc.done{background:#10b981;color:#fff;transform:scale(1.15) rotate(360deg)}
.pb2{padding:1.8rem 2rem}
/* RICH CONTENT */
.v2-p{margin-bottom:1.1rem;font-size:1rem;color:var(--t2);line-height:1.88}
.v2-h3{font-family:var(--D);font-weight:700;font-size:1.1rem;color:var(--tx);
margin:1.7rem 0 .6rem;border-top:2px solid var(--acp);padding-top:.45rem}
.v2-h4{font-family:var(--D);font-weight:600;font-size:1rem;color:var(--t2);margin:1.1rem 0 .4rem}
.v2-h5,.v2-h6{font-family:var(--D);font-weight:600;font-size:.95rem;color:var(--mu);margin:.8rem 0 .35rem}
.v2-ul,.v2-ol{margin:.65rem 0 1.1rem 1.5rem;font-size:1rem;color:var(--t2);line-height:1.9}
.v2-ul li,.v2-ol li{margin-bottom:.35rem}
.v2-bq{border-left:4px solid var(--acc);background:var(--acp);border-radius:0 12px 12px 0;
padding:1rem 1.3rem;margin:1.1rem 0;font-size:1rem;color:#064e3b;font-style:italic}
.v2-bq p{margin:0;color:inherit}
.v2-hr{border:none;border-top:1px solid var(--br);margin:1.3rem 0}
.v2-table-wrap{overflow-x:auto;margin:1.1rem 0;border-radius:10px;border:1px solid var(--br)}
.v2-table{width:100%;border-collapse:collapse;font-size:.95rem}
.v2-table th{background:var(--acp);color:#064e3b;font-family:var(--D);font-weight:700;
padding:11px 14px;text-align:left;border-bottom:2px solid var(--acb)}
.v2-table td{padding:9px 14px;border-bottom:1px solid var(--br);color:var(--t2);vertical-align:top}
.v2-table tr:last-child td{border:none}
.v2-table tr:hover td{background:var(--acp)}
.pb2 code{background:var(--acp);color:var(--acd);border-radius:5px;padding:2px 7px;
font-family:var(--M);font-size:.85em}
.pb2 a{color:var(--acc);text-decoration:underline;text-underline-offset:2px}
.pb2 strong{color:var(--tx)}
/* CODE BLOCKS */
.v2-code-wrap{margin:1.3rem 0;border-radius:12px;overflow:hidden;
border:1px solid #1e293b;box-shadow:0 5px 20px rgba(0,0,0,.2)}
.v2-code-bar{background:#1e293b;padding:.6rem 1.1rem;display:flex;align-items:center;gap:.4rem}
.v2-dot{width:11px;height:11px;border-radius:50%}
.v2-dot.r{background:#ff5f57}.v2-dot.y{background:#febc2e}.v2-dot.g{background:#28c840}
.v2-code-lang{margin-left:.5rem;font-family:var(--M);font-size:.65rem;color:#64748b;
text-transform:uppercase;letter-spacing:.08em;flex:1}
.copy-btn{background:rgba(255,255,255,.08);color:#94a3b8;border:none;border-radius:6px;
padding:.28rem .7rem;font-family:var(--M);font-size:.68rem;cursor:pointer;
transition:background .2s,color .2s}
.copy-btn:hover{background:rgba(255,255,255,.2);color:#fff}
.copy-btn.copied{background:#10b981;color:#fff}
.v2-code-body{background:#0f172a;padding:1.4rem 1.6rem;overflow-x:auto}
.v2-code-body pre{margin:0}
.v2-code-body code{font-family:var(--M);font-size:.85rem;line-height:2;
color:#e2e8f0;white-space:pre;background:transparent;padding:0;border-radius:0}
/* TASK BOX */
.task-box{background:#d1fae5;border:2px dashed #059669;border-radius:12px;
padding:1.2rem 1.4rem;margin:1.5rem 0}
.task-lbl{font-family:var(--M);font-size:.68rem;font-weight:700;letter-spacing:.1em;
text-transform:uppercase;color:#064e3b;margin-bottom:.55rem;display:flex;align-items:center;gap:.4rem}
.task-body{font-size:1rem;color:#064e3b;line-height:1.85}
.task-body code{background:rgba(0,0,0,.1);border-radius:4px;padding:2px 7px;
font-family:var(--M);font-size:.85em}
/* EXERCISE / CHALLENGE */
.chal-box{background:linear-gradient(135deg,#ecfdf5 0%,#fff 100%);
border:2px solid var(--acc);border-radius:14px;padding:1.3rem 1.5rem;margin-bottom:1.2rem}
.chal-lbl{font-family:var(--M);font-size:.68rem;font-weight:700;letter-spacing:.1em;
text-transform:uppercase;color:var(--acd);margin-bottom:.65rem}
.chal-body{font-size:1rem;color:var(--tx);line-height:1.85}
.chal-body code{background:var(--acp);color:var(--acd);border-radius:4px;
padding:2px 6px;font-family:var(--M);font-size:.85em}
.reveal-btn{display:inline-flex;align-items:center;gap:.5rem;background:transparent;
color:var(--acd);font-family:var(--D);font-weight:700;font-size:.9rem;
padding:.65rem 1.3rem;border-radius:9px;border:2px solid var(--acc);cursor:pointer;
transition:background .2s,color .2s;margin:.65rem 0}
.reveal-btn:hover{background:var(--acc);color:#fff}
.reveal-content{display:none;margin-top:.65rem;animation:fi .3s ease}
.reveal-content.open{display:block}
@keyframes fi{from{opacity:0;transform:translateY(-6px)}to{opacity:1;transform:none}}
/* BUILD IT */
.build-box{background:#f0fdf4;border:2px solid #16a34a;border-radius:16px;
padding:1.8rem;margin:2rem 0;display:none}
.build-box.visible{display:block;animation:fi .4s ease}
.build-lbl{font-family:var(--M);font-size:.68rem;font-weight:700;letter-spacing:.1em;
text-transform:uppercase;color:#15803d;margin-bottom:.5rem}
.build-name{font-family:var(--D);font-weight:700;font-size:1.2rem;color:#14532d;margin-bottom:.7rem}
.build-req{font-size:1rem;color:#166534;line-height:1.85;margin-bottom:.9rem}
.build-req code{background:rgba(22,163,74,.12);border-radius:4px;
padding:2px 7px;font-family:var(--M);font-size:.85em}
.build-req strong{color:#14532d}
/* GITHUB */
.gh-acc{border:1.5px solid var(--br);border-radius:12px;overflow:hidden;margin:1.5rem 0}
.gh-hd{padding:1.1rem 1.3rem;background:#f8fafc;cursor:pointer;
display:flex;align-items:center;justify-content:space-between;
font-family:var(--D);font-weight:600;font-size:.95rem;user-select:none;transition:background .2s}
.gh-hd:hover{background:#f1f5f9}
.gh-bd{display:none;padding:1.3rem;border-top:1px solid var(--br)}
.gh-bd.open{display:block;animation:fi .25s ease}
.gh-st{display:flex;gap:.9rem;margin-bottom:1.1rem;align-items:flex-start}
.gh-n{width:28px;height:28px;border-radius:50%;background:var(--acc);color:#fff;
font-family:var(--D);font-weight:700;font-size:.78rem;
display:flex;align-items:center;justify-content:center;flex-shrink:0;margin-top:3px}
.gh-st p{font-size:.95rem;color:var(--t2);line-height:1.75}
.gh-st code{background:var(--acp);color:var(--acd);border-radius:4px;
padding:2px 7px;font-family:var(--M);font-size:.85em}
.gh-nop{background:#fef3c7;border:1.5px solid #fde68a;border-radius:10px;
padding:1.1rem 1.3rem;font-size:.95rem;color:#78350f;line-height:1.8;margin-bottom:1.1rem}
/* SESSION END */
.se{background:linear-gradient(135deg,var(--acc),var(--acd));color:#fff;
border-radius:16px;padding:1.8rem 2rem;margin:2rem 0;display:none}
.se.visible{display:block;animation:fi .4s ease}
.se h2{font-family:var(--D);font-weight:700;font-size:1.15rem;margin-bottom:.85rem}
.se ul{list-style:none;display:flex;flex-direction:column;gap:.45rem}
.se li{font-size:.95rem;opacity:.93;display:flex;align-items:center;gap:.5rem}
.ln-btn{display:inline-flex;align-items:center;gap:.4rem;background:var(--sf);
color:var(--t2);font-family:var(--D);font-weight:600;font-size:.88rem;
padding:.7rem 1.3rem;border-radius:8px;text-decoration:none;
border:1.5px solid var(--br);transition:border-color .2s,color .2s}
.ln-btn:hover{border-color:var(--acc);color:var(--acc)}
.ln-btn.primary{background:var(--acc);color:#fff;border-color:var(--acc)}
.ln-btn.primary:hover{opacity:.9}
.ln{display:flex;align-items:center;justify-content:space-between;
padding-top:1.5rem;border-top:1px solid rgba(255,255,255,.25);
margin-top:1.5rem;flex-wrap:wrap;gap:.75rem}
/* UNLOCK BUTTON */
.ub{display:inline-flex;align-items:center;gap:.5rem;background:var(--acc);
color:#fff;font-family:var(--D);font-weight:700;font-size:.95rem;
padding:.8rem 1.7rem;border-radius:10px;border:none;cursor:pointer;
transition:transform .2s,box-shadow .2s;box-shadow:0 4px 14px var(--acs);margin-top:1.3rem}
.ub:hover{transform:translateY(-2px);box-shadow:0 8px 22px var(--acs)}
.ub:active{transform:scale(.97)}
/* TOAST */
#toast{position:fixed;bottom:95px;right:1.5rem;z-index:999;background:#1e293b;color:#fff;
border-radius:10px;padding:.8rem 1.3rem;font-family:var(--D);font-size:.9rem;
font-weight:600;box-shadow:0 8px 26px rgba(0,0,0,.25);
transform:translateY(20px);opacity:0;transition:all .3s;pointer-events:none}
#toast.show{transform:translateY(0);opacity:1}
/* GO TO TOP */
#go-top{position:fixed;bottom:1.5rem;right:1.5rem;z-index:300;
width:48px;height:48px;border-radius:50%;background:var(--acc);color:#fff;
border:none;cursor:pointer;font-size:1.2rem;box-shadow:0 4px 16px var(--acs);
display:flex;align-items:center;justify-content:center;
opacity:0;transform:translateY(10px);transition:opacity .3s,transform .3s,box-shadow .2s;
pointer-events:none}
#go-top.visible{opacity:1;transform:translateY(0);pointer-events:auto}
#go-top:hover{box-shadow:0 8px 26px var(--acs);transform:translateY(-2px)}
/* CONFETTI */
#cc{position:fixed;top:0;left:0;width:100%;height:100%;pointer-events:none;z-index:998;display:none}
/* FOOTER */
.lf{display:flex;align-items:center;justify-content:center;gap:1rem;
padding:1.6rem 1.5rem;border-top:1px solid var(--br);background:var(--sf)}
.lf img{height:32px;width:auto;opacity:.7}
.lf-text{font-family:var(--M);font-size:.75rem;color:var(--mu)}
</style>
</head>
<body>
<nav class="l-nav">
<a href="../index.html" class="nav-logo-wrap">
<img src="data:image/png;base64,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" alt="Techbase" class="nav-logo-img">
<div class="nav-logo-text">
Techbase<span class="course-lbl">Python Course</span>
</div>
</a>
<span class="l-nav-lbl">Lesson 26 of 45</span>
<a href="../index.html" class="l-nav-back">← All Lessons</a>
</nav>
<div class="pw"><div class="pb" id="pbar"></div></div>
<div class="l-hero">
<div class="l-hero-in">
<div class="l-hero-logo">
<img src="data:image/png;base64,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" alt="Techbase Consultant Services">
</div>
<div class="l-hero-text">
<div class="badge">Python · Lesson 26</div>
<h1>NumPy Array Operations: Splitting, Searching, Sorting, Filtering & Random Numbers</h1>
<div class="l-hero-sub">9 phases · Build: Project Overview</div>
</div>
</div>
</div>
<section style="background:linear-gradient(160deg,#ecfdf5 0%,#f8fafc 55%,#d1fae5 100%);padding-bottom:1.5rem;border-bottom:1px solid var(--br)">
<div style="max-width:880px;margin:0 auto;padding:0 1.5rem"><div class="wb"><h2>👋 Welcome to Lesson 26</h2><div style="font-size:1rem;line-height:1.85;color:var(--t2)"><p class="v2-p">Welcome to Lesson 26! In this lesson, you will master five incredibly powerful NumPy skills that data scientists, engineers, and programmers use every single day:</p>
<ol class="v2-ol"><li><strong>Splitting</strong> · breaking one array into multiple smaller arrays</li><li><strong>Searching</strong> · finding where values live inside an array</li><li><strong>Sorting</strong> · arranging array elements in order</li><li><strong>Filtering</strong> · keeping only elements that meet a condition</li><li><strong>Random Numbers</strong> · generating random integers, floats, and arrays</li></ol>
<p class="v2-p">By the end of this lesson, you will be able to write a realistic mini-project that reads a dataset of student exam scores, cleans it, filters it, sorts it, splits it into groups, and generates random mock data for testing.</p>
<blockquote class="v2-bq"><p class="v2-p"><strong>Real-world relevance:</strong> Every major field · data science, machine learning, finance, science, and engineering · relies on these five operations constantly. If you master them, you can analyse real datasets with confidence.</p></blockquote>
<hr class="v2-hr"></div><div class="wm"><span>📚 9 phases</span><span>🏗️ Project Overview</span><span>🐍 GitHub Repo</span></div></div></div>
</section>
<main style="max-width:880px;margin:0 auto;padding:2rem 1.5rem 5rem">
<div class="phase" id="phase1"><div class="ph"><div class="pn">Phase 1 of 9</div><div class="pt">Lesson Introduction</div><div class="pc" id="chk1"></div></div><div class="pb2"><p class="v2-p">Welcome to Lesson 26! In this lesson, you will master five incredibly powerful NumPy skills that data scientists, engineers, and programmers use every single day:</p>
<ol class="v2-ol"><li><strong>Splitting</strong> · breaking one array into multiple smaller arrays</li><li><strong>Searching</strong> · finding where values live inside an array</li><li><strong>Sorting</strong> · arranging array elements in order</li><li><strong>Filtering</strong> · keeping only elements that meet a condition</li><li><strong>Random Numbers</strong> · generating random integers, floats, and arrays</li></ol>
<p class="v2-p">By the end of this lesson, you will be able to write a realistic mini-project that reads a dataset of student exam scores, cleans it, filters it, sorts it, splits it into groups, and generates random mock data for testing.</p>
<blockquote class="v2-bq"><p class="v2-p"><strong>Real-world relevance:</strong> Every major field · data science, machine learning, finance, science, and engineering · relies on these five operations constantly. If you master them, you can analyse real datasets with confidence.</p></blockquote>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Lesson Introduction</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(1)">Start Lesson ✓</button></div></div>
<div class="phase locked" id="phase2"><div class="ph"><div class="pn">Phase 2 of 9</div><div class="pt">Prerequisite Concepts</div><div class="pc" id="chk2"></div></div><div class="pb2"><p class="v2-p">Before we begin, make sure you are comfortable with these ideas. If any of them are unfamiliar, read the short explanation below before continuing.</p>
<h3 class="v2-h3">What is a NumPy Array?</h3>
<p class="v2-p">A NumPy array is a grid of values · like a list, but much more powerful and faster. Every value in the array has a <strong>position number called an index</strong>, starting from <code>0</code>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([10, 20, 30, 40, 50])
# 0 1 2 3 4 ← index numbers</code></pre></div></div>
<p class="v2-p">Think of an array like a row of numbered mailboxes. Index 0 is the first mailbox, index 1 is the second, and so on.</p>
<h3 class="v2-h3">What is an Index?</h3>
<p class="v2-p">An <strong>index</strong> is just the position of an element inside an array. It starts at <code>0</code>, not <code>1</code>. So the first element is at index <code>0</code>, the second at index <code>1</code>, etc.</p>
<h3 class="v2-h3">What is a Boolean?</h3>
<p class="v2-p">A <strong>boolean</strong> is a value that is either <code>True</code> or <code>False</code>. Think of it like a light switch · it is either ON (<code>True</code>) or OFF (<code>False</code>).</p>
<h3 class="v2-h3">Quick Setup · Always Import NumPy First</h3>
<p class="v2-p">Every code example in this lesson needs NumPy to be imported. Always start your program with:</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np</code></pre></div></div>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Prerequisite Concepts</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(2)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase3"><div class="ph"><div class="pn">Phase 3 of 9</div><div class="pt">Section 1 · Splitting Arrays</div><div class="pc" id="chk3"></div></div><div class="pb2"><h3 class="v2-h3">What is Splitting? Why Do We Need It?</h3>
<p class="v2-p">Imagine you have a long list of 1,000 exam scores and you want to give each of three teachers a portion of the scores to grade. You would <strong>split</strong> the big list into three smaller ones. That is exactly what NumPy splitting does.</p>
<p class="v2-p"><strong>Splitting</strong> is the reverse of joining. Joining merges multiple arrays into one. Splitting breaks one array into multiple parts.</p>
<blockquote class="v2-bq"><p class="v2-p"><strong>Analogy:</strong> Think of a loaf of bread. Joining is baking multiple loaves together. Splitting is slicing one loaf into separate pieces.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">The <code>np.array_split()</code> Function</h3>
<p class="v2-p"><strong>What it does:</strong> Splits one array into a given number of smaller arrays.</p>
<p class="v2-p"><strong>Syntax:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>result = np.array_split(array, number_of_splits)</code></pre></div></div>
<ul class="v2-ul"><li><code>array</code> · the array you want to split</li><li><code>number_of_splits</code> · how many pieces to make</li><li><code>result</code> · a Python <strong>list</strong> of smaller arrays</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Important:</strong> The return value is a <strong>list</strong> of arrays, not a single array!</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 1 · Split into 3 Equal Parts</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
newarr = np.array_split(arr, 3)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[array([1, 2]), array([3, 4]), array([5, 6])]</code></pre></div></div>
<p class="v2-p"><strong>Line-by-line explanation:</strong></p>
<ul class="v2-ul"><li><code>arr = np.array([1, 2, 3, 4, 5, 6])</code> · creates an array with 6 elements</li><li><code>np.array_split(arr, 3)</code> · splits arr into 3 equal pieces: [1,2], [3,4], [5,6]</li><li><code>print(newarr)</code> · prints the list of 3 small arrays</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Notice:</strong> Each piece has exactly 2 elements because 6 ÷ 3 = 2.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 2 · Split into 4 Parts (Unequal Division)</h3>
<p class="v2-p">What happens when the array cannot be divided equally? NumPy handles this gracefully · it adjusts the sizes from the end.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
newarr = np.array_split(arr, 4)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[array([1, 2]), array([3, 4]), array([5]), array([6])]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li>6 elements split into 4 parts: the first two parts get 2 elements, the last two parts get 1 element each.</li><li>NumPy does NOT crash or complain · it adjusts automatically.</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Thinking prompt:</strong> What would happen if you tried to split 6 elements into 7 parts? Some parts would be empty! Try it and see.</p></blockquote>
<blockquote class="v2-bq"><p class="v2-p"><strong>Important note:</strong> The regular <code>np.split()</code> function would crash here if the division is not exactly equal. Always prefer <code>np.array_split()</code> for flexibility.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Accessing Individual Split Pieces</h3>
<p class="v2-p">The result of <code>array_split()</code> is a list. You can access each piece using list index notation <code>[0]</code>, <code>[1]</code>, <code>[2]</code>, etc.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6])
newarr = np.array_split(arr, 3)
print(newarr[0]) # First piece
print(newarr[1]) # Second piece
print(newarr[2]) # Third piece</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[1 2]
[3 4]
[5 6]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Splitting 2-D Arrays (Tables)</h3>
<p class="v2-p">A 2-D array is like a table with rows and columns. Splitting a 2-D array works by splitting along rows by default.</p>
<h4 class="v2-h4">Example · Split a 2-D Array Into 3 Parts (Row-wise)</h4>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([[1, 2],
[3, 4],
[5, 6],
[7, 8],
[9, 10],
[11, 12]])
newarr = np.array_split(arr, 3)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[array([[1, 2],
[3, 4]]),
array([[5, 6],
[7, 8]]),
array([[ 9, 10],
[11, 12]])]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong> The 6-row table is split into three 2-row tables.</p>
<hr class="v2-hr">
<h4 class="v2-h4">Example · Split a 2-D Array Along Columns (axis=1)</h4>
<p class="v2-p">You can split along <strong>columns</strong> instead of rows by using <code>axis=1</code>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12],
[13, 14, 15],
[16, 17, 18]])
newarr = np.array_split(arr, 3, axis=1)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[array([[ 1],
[ 4],
[ 7],
[10],
[13],
[16]]),
array([[ 2],
[ 5],
[ 8],
[11],
[14],
[17]]),
array([[ 3],
[ 6],
[ 9],
[12],
[15],
[18]])]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li><code>axis=0</code> means split along rows (default)</li><li><code>axis=1</code> means split along columns</li><li>The 3-column table is split into three 1-column tables</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">The <code>hsplit()</code> Shortcut</h3>
<p class="v2-p"><code>np.hsplit()</code> is a shorthand for splitting along columns (horizontal split). It is equivalent to <code>array_split(arr, n, axis=1)</code>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([[1, 2, 3],
[4, 5, 6],
[7, 8, 9],
[10, 11, 12],
[13, 14, 15],
[16, 17, 18]])
newarr = np.hsplit(arr, 3)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> Same as the <code>axis=1</code> example above.</p>
<blockquote class="v2-bq"><p class="v2-p"><strong>Tip:</strong> Similarly, <code>np.vsplit()</code> splits vertically (same as <code>axis=0</code>), and <code>np.dsplit()</code> splits along the depth axis for 3-D arrays.</p></blockquote>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">> Important note: The regular np.split() function would crash here if the division is not exactly equal. Always prefer np.array_split() for flexibility. · </div></div><button class="ub" onclick="unlockNext(3)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase4"><div class="ph"><div class="pn">Phase 4 of 9</div><div class="pt">Section 2 · Searching Arrays</div><div class="pc" id="chk4"></div></div><div class="pb2"><h3 class="v2-h3">What is Searching? Why Do We Need It?</h3>
<p class="v2-p">Imagine you have an array of 10,000 temperature readings and you need to find all positions where the temperature exceeded 40°C. You could loop through every element yourself, but NumPy gives you a much faster, cleaner tool: <code>np.where()</code>.</p>
<p class="v2-p"><strong>Searching</strong> means finding the <strong>index positions</strong> where elements match a condition.</p>
<hr class="v2-hr">
<h3 class="v2-h3">The <code>np.where()</code> Function</h3>
<p class="v2-p"><strong>What it does:</strong> Returns the index positions where a given condition is <code>True</code>.</p>
<p class="v2-p"><strong>Syntax:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>result = np.where(condition)</code></pre></div></div>
<ul class="v2-ul"><li><code>condition</code> · a comparison like <code>arr == 4</code> or <code>arr > 10</code></li><li><code>result</code> · a <strong>tuple</strong> containing an array of matching index positions</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>What is a tuple?</strong> A tuple is like a list but surrounded by <code>()</code> instead of <code>[]</code>. It cannot be changed after creation.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 1 · Find Where a Specific Value Appears</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 4, 4])
x = np.where(arr == 4)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>(array([3, 5, 6]),)</code></pre></div></div>
<p class="v2-p"><strong>Line-by-line explanation:</strong></p>
<ul class="v2-ul"><li><code>arr == 4</code> · checks every element: is this equal to 4?</li><li><code>np.where(arr == 4)</code> · returns the positions where the answer is True</li><li>The value <code>4</code> appears at index 3, 5, and 6</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Thinking prompt:</strong> What does the outer <code>(...)</code> wrapping the result mean? It means <code>np.where</code> returns a tuple. To get just the array inside, you can write <code>x[0]</code>.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 2 · Find All Odd Numbers</h3>
<p class="v2-p">The <code>%</code> symbol is the <strong>modulo operator</strong>. It gives the <strong>remainder</strong> after division.</p>
<ul class="v2-ul"><li><code>7 % 2 = 1</code> (because 7 ÷ 2 = 3 remainder <strong>1</strong>)</li><li><code>8 % 2 = 0</code> (because 8 ÷ 2 = 4 remainder <strong>0</strong>)</li><li>If <code>number % 2 == 1</code>, the number is <strong>odd</strong></li><li>If <code>number % 2 == 0</code>, the number is <strong>even</strong></li></ul>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([10, 14, 93, 41, 8, 7])
x = np.where(arr % 2 == 1)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>(array([2, 3, 5]),)</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li>Check each element: 10%2=0 (even), 14%2=0 (even), 93%2=1 (<strong>odd</strong> → index 2), 41%2=1 (<strong>odd</strong> → index 3), 8%2=0 (even), 7%2=1 (<strong>odd</strong> → index 5)</li><li>Odd numbers are at positions 2, 3, and 5</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">Example 3 · Find All Even Numbers</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([10, 14, 93, 41, 8, 7])
x = np.where(arr % 2 == 0)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>(array([0, 1, 4]),)</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong> Even numbers (10, 14, 8) are at index positions 0, 1, and 4.</p>
<hr class="v2-hr">
<h3 class="v2-h3">The <code>np.searchsorted()</code> Function · Binary Search</h3>
<p class="v2-p">Sometimes you have a <strong>sorted</strong> array and you want to know <strong>where to insert a new value</strong> so the array stays sorted. This is called a <strong>binary search</strong>.</p>
<p class="v2-p"><strong>What is binary search?</strong> Instead of checking every element one by one, binary search repeatedly cuts the array in half to find the right position very quickly. It is like looking up a word in a dictionary by opening to the middle first.</p>
<p class="v2-p"><strong>Syntax:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>result = np.searchsorted(sorted_array, value)</code></pre></div></div>
<ul class="v2-ul"><li><code>sorted_array</code> · must already be sorted in ascending order</li><li><code>value</code> · the number you want to insert</li><li>Returns the <strong>index</strong> where you should insert the value to keep the array sorted</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">Example 1 · Basic searchsorted</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([6, 7, 8, 9])
x = np.searchsorted(arr, 7)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>1</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li>The array is <code>[6, 7, 8, 9]</code></li><li>If we insert <code>7</code> at index <strong>1</strong>, the array stays sorted: <code>[6, 7, 7, 8, 9]</code></li><li>The method searches from the <strong>left</strong> by default, so it finds the first position where 7 is no longer larger than the next value</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">Example 2 · Search From the Right Side</h3>
<p class="v2-p">By default, <code>searchsorted()</code> returns the <strong>leftmost</strong> valid position. If you want the <strong>rightmost</strong> position, use <code>side='right'</code>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([6, 7, 8, 9])
x = np.searchsorted(arr, 7, side='right')
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>2</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li>Searching from the right: the value <code>7</code> should be inserted at index <strong>2</strong> to keep sorted order: <code>[6, 7, 7, 8, 9]</code></li><li><code>side='left'</code> → insert before existing 7 → index 1</li><li><code>side='right'</code> → insert after existing 7 → index 2</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">Example 3 · Search for Multiple Values at Once</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 3, 5, 7])
x = np.searchsorted(arr, [2, 4, 6])
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[1 2 3]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li><code>2</code> would go at index <strong>1</strong> (between 1 and 3)</li><li><code>4</code> would go at index <strong>2</strong> (between 3 and 5)</li><li><code>6</code> would go at index <strong>3</strong> (between 5 and 7)</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Real-world use:</strong> Binary search with <code>searchsorted</code> is used in stock market order books, database lookups, and anywhere you need to find where to insert a value quickly in a sorted list.</p></blockquote>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Searching Arrays</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(4)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase5"><div class="ph"><div class="pn">Phase 5 of 9</div><div class="pt">Section 3 · Sorting Arrays</div><div class="pc" id="chk5"></div></div><div class="pb2"><h3 class="v2-h3">What is Sorting? Why Do We Need It?</h3>
<p class="v2-p">Sorting means arranging elements in a specific order · usually from smallest to largest (<strong>ascending</strong>) or largest to smallest (<strong>descending</strong>). We sort data constantly in real life: ranking student scores, ordering products by price, alphabetising names.</p>
<hr class="v2-hr">
<h3 class="v2-h3">The <code>np.sort()</code> Function</h3>
<p class="v2-p"><strong>What it does:</strong> Returns a sorted copy of the array, leaving the original unchanged.</p>
<p class="v2-p"><strong>Syntax:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>sorted_arr = np.sort(array)</code></pre></div></div>
<blockquote class="v2-bq"><p class="v2-p"><strong>Important:</strong> <code>np.sort()</code> does NOT change the original array. It creates a <strong>new sorted copy</strong>. This is safe behaviour · your original data is preserved.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 1 · Sort Numbers</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([3, 2, 0, 1])
print(np.sort(arr))</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[0 1 2 3]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong> The numbers are rearranged from smallest to largest.</p>
<blockquote class="v2-bq"><p class="v2-p"><strong>Thinking prompt:</strong> What happens to the original <code>arr</code> after sorting? It stays unchanged! Try printing <code>arr</code> after the sort to confirm.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 2 · Sort Strings Alphabetically</h3>
<p class="v2-p">NumPy can also sort arrays of text (strings)! It sorts them alphabetically, just like a dictionary.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array(['banana', 'cherry', 'apple'])
print(np.sort(arr))</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>['apple' 'banana' 'cherry']</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong> 'apple' comes first alphabetically, then 'banana', then 'cherry'.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Example 3 · Sort Booleans</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([True, False, True])
print(np.sort(arr))</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[False True True]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong> In Python/NumPy, <code>False</code> is treated as <code>0</code> and <code>True</code> as <code>1</code>. So <code>False</code> sorts before <code>True</code>.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Sorting a 2-D Array</h3>
<p class="v2-p">When you sort a 2-D array, NumPy sorts <strong>each row independently</strong>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([[3, 2, 4],
[5, 0, 1]])
print(np.sort(arr))</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[[2 3 4]
[0 1 5]]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li>Row 1: <code>[3, 2, 4]</code> → sorted to <code>[2, 3, 4]</code></li><li>Row 2: <code>[5, 0, 1]</code> → sorted to <code>[0, 1, 5]</code></li><li>Each row is sorted separately</li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Thinking prompt:</strong> What if you wanted to sort the columns instead of the rows? You could use <code>np.sort(arr, axis=0)</code>. Try it!</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">How to Sort in Descending Order (Largest First)</h3>
<p class="v2-p"><code>np.sort()</code> only sorts in ascending order by default. To sort in descending order, sort first, then reverse using <code>[::-1]</code>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([3, 1, 4, 1, 5, 9, 2, 6])
sorted_desc = np.sort(arr)[::-1]
print(sorted_desc)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[9 6 5 4 3 2 1 1]</code></pre></div></div>
<p class="v2-p"><strong>Explanation:</strong></p>
<ul class="v2-ul"><li><code>np.sort(arr)</code> gives <code>[1, 1, 2, 3, 4, 5, 6, 9]</code></li><li><code>[::-1]</code> reverses it to <code>[9, 6, 5, 4, 3, 2, 1, 1]</code></li></ul>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Sorting Arrays</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(5)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase6"><div class="ph"><div class="pn">Phase 6 of 9</div><div class="pt">Section 4 · Filtering Arrays</div><div class="pc" id="chk6"></div></div><div class="pb2"><h3 class="v2-h3">What is Filtering? Why Do We Need It?</h3>
<p class="v2-p">Filtering means <strong>selecting only the elements that meet a condition</strong> and creating a new array with just those elements.</p>
<p class="v2-p">Think of it like a coffee filter: you pour in a mixture, and only what passes through the filter gets collected. In NumPy, only elements that pass your condition get collected.</p>
<p class="v2-p"><strong>Real-world examples:</strong></p>
<ul class="v2-ul"><li>Keep only exam scores above 50 (passing scores)</li><li>Keep only temperatures below 0°C (freezing days)</li><li>Keep only even product IDs</li><li>Keep only positive financial transactions</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">What is a Boolean Index List?</h3>
<p class="v2-p">A <strong>boolean index list</strong> is a list of <code>True</code> and <code>False</code> values, one for each element in an array. When you use it to index an array:</p>
<ul class="v2-ul"><li>Elements with <code>True</code> → <strong>included</strong> in the result</li><li>Elements with <code>False</code> → <strong>excluded</strong> from the result</li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">Example 1 · Manual Boolean Filter (Hard-coded)</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([41, 42, 43, 44])
x = [True, False, True, False]
newarr = arr[x]
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[41 43]</code></pre></div></div>
<p class="v2-p"><strong>Line-by-line explanation:</strong></p>
<ul class="v2-ul"><li><code>arr = [41, 42, 43, 44]</code> · four elements at index 0, 1, 2, 3</li><li><code>x = [True, False, True, False]</code> · index 0 = True, index 1 = False, index 2 = True, index 3 = False</li><li><code>arr[x]</code> · keep index 0 (41 ✓), skip index 1 (42 ✗), keep index 2 (43 ✓), skip index 3 (44 ✗)</li><li>Result: <code>[41, 43]</code></li></ul>
<blockquote class="v2-bq"><p class="v2-p"><strong>Thinking prompt:</strong> Why was 42 excluded? Because the value at its position in the filter list was <code>False</code>.</p></blockquote>
<hr class="v2-hr">
<h3 class="v2-h3">Example 2 · Building a Filter with a Loop</h3>
<p class="v2-p">In practice, you almost never hard-code <code>True</code> and <code>False</code>. Instead, you build the filter automatically using a loop.</p>
<p class="v2-p"><strong>Scenario:</strong> Keep only values higher than 42.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([41, 42, 43, 44])
# Step 1: Create an empty list to hold True/False values
filter_arr = []
# Step 2: Go through each element in arr
for element in arr:
# Step 3: If the element is higher than 42, add True; otherwise add False
if element > 42:
filter_arr.append(True)
else:
filter_arr.append(False)
# Step 4: Apply the filter to the array
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[False, False, True, True]
[43 44]</code></pre></div></div>
<p class="v2-p"><strong>Line-by-line explanation:</strong></p>
<ul class="v2-ul"><li>We loop through <code>[41, 42, 43, 44]</code></li><li><code>41 > 42</code>? No → <code>False</code></li><li><code>42 > 42</code>? No → <code>False</code></li><li><code>43 > 42</code>? Yes → <code>True</code></li><li><code>44 > 42</code>? Yes → <code>True</code></li><li><code>filter_arr = [False, False, True, True]</code></li><li><code>arr[filter_arr]</code> keeps only elements at <code>True</code> positions → <code>[43, 44]</code></li></ul>
<hr class="v2-hr">
<h3 class="v2-h3">Example 3 · Loop Filter for Even Numbers</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
filter_arr = []
for element in arr:
if element % 2 == 0: # Is the remainder zero? (even number?)
filter_arr.append(True)
else:
filter_arr.append(False)
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[False, True, False, True, False, True, False]
[2 4 6]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Example 4 · The NumPy Shortcut: Direct Array Conditions ⭐</h3>
<p class="v2-p">The loop method works but is long. NumPy provides a much shorter and more elegant way: you can write the condition <strong>directly on the array</strong>, and NumPy creates the boolean array automatically!</p>
<p class="v2-p"><strong>Filtering values > 42:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([41, 42, 43, 44])
filter_arr = arr > 42
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[False False True True]
[43 44]</code></pre></div></div>
<p class="v2-p"><strong>What happened?</strong> <code>arr > 42</code> does NOT give a single True/False. It checks every element and returns a <strong>boolean array</strong>: <code>[False, False, True, True]</code>. This is called <strong>vectorised comparison</strong> · NumPy processes all elements at once, which is much faster than a Python loop.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Example 5 · Direct Filter for Even Numbers (Short Version)</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
filter_arr = arr % 2 == 0
newarr = arr[filter_arr]
print(filter_arr)
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[False True False True False True False]
[2 4 6]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">One-Line Filtering</h3>
<p class="v2-p">You can even combine the filter and the indexing into a single line:</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4, 5, 6, 7])
newarr = arr[arr % 2 == 0]
print(newarr)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[2 4 6]</code></pre></div></div>
<p class="v2-p">This is the most common style used by professional data scientists.</p>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Filtering Arrays</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(6)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase7"><div class="ph"><div class="pn">Phase 7 of 9</div><div class="pt">Section 5 · Random Numbers in NumPy</div><div class="pc" id="chk7"></div></div><div class="pb2"><h3 class="v2-h3">What is a Random Number?</h3>
<p class="v2-p">A <strong>random number</strong> is a number that cannot be predicted logically before it is generated. This is different from simply "a number that changes" · true randomness means there is no pattern.</p>
<p class="v2-p"><strong>Pseudo-random vs True Random:</strong></p>
<ul class="v2-ul"><li><strong>Pseudo-random:</strong> Generated by a mathematical algorithm. It looks random but is technically predictable if you know the algorithm and starting point (called a "seed"). This is what NumPy uses.</li><li><strong>True random:</strong> Generated from unpredictable physical sources like keyboard timings, mouse movements, or atmospheric noise.</li></ul>
<p class="v2-p">For most purposes · simulations, testing, data science, games · <strong>pseudo-random numbers are perfectly fine</strong>.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Importing the Random Module</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random</code></pre></div></div>
<p class="v2-p">Or you can use <code>np.random</code> after importing NumPy:</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
# then use: np.random.randint(...)</code></pre></div></div>
<p class="v2-p">Both approaches work. In this lesson, we use <code>from numpy import random</code> to keep examples concise.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a Single Random Integer</h3>
<p class="v2-p"><code>random.randint(n)</code> returns a random integer from <strong>0 up to but not including n</strong>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.randint(100)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> A random integer between 0 and 99 (e.g., <code>47</code>)</p>
<blockquote class="v2-bq"><p class="v2-p"><strong>Note:</strong> Your output will be different each time you run this! That is the whole point.</p></blockquote>
<p class="v2-p"><strong>Real-world analogy:</strong> This is like rolling a 100-sided die and reading the number.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a Single Random Float</h3>
<p class="v2-p"><code>random.rand()</code> returns a random decimal number between <strong>0.0 and 1.0</strong> (never exactly 1.0).</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.rand()
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> A random float like <code>0.5488135039273248</code></p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a 1-D Array of Random Integers</h3>
<p class="v2-p">Use <code>randint(high, size=(n))</code> to generate an array of <code>n</code> random integers from 0 to <code>high - 1</code>.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.randint(100, size=(5))
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> 5 random integers, e.g., <code>[51 92 14 71 60]</code></p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a 2-D Array of Random Integers</h3>
<p class="v2-p">Use <code>size=(rows, columns)</code> to make a 2-D array.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.randint(100, size=(3, 5))
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> A 3-row, 5-column table of random integers, e.g.:</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[[44 38 69 21 5]
[81 63 27 72 8]
[96 3 44 56 78]]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a 1-D Array of Random Floats</h3>
<p class="v2-p"><code>rand(n)</code> generates <code>n</code> random floats between 0.0 and 1.0.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.rand(5)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> 5 random decimals, e.g., <code>[0.548 0.715 0.603 0.545 0.424]</code></p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a 2-D Array of Random Floats</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.rand(3, 5)
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> A 3-row, 5-column table of random floats between 0 and 1.</p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating Random Values from a Custom List: <code>choice()</code></h3>
<p class="v2-p"><code>random.choice()</code> picks a random value from an array you provide.</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.choice([3, 5, 7, 9])
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> One of: <code>3</code>, <code>5</code>, <code>7</code>, or <code>9</code> (randomly chosen)</p>
<hr class="v2-hr">
<h3 class="v2-h3">Generating a 2-D Array from a Custom List</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
x = random.choice([3, 5, 7, 9], size=(3, 5))
print(x)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong> A 3×5 grid where every value is one of 3, 5, 7, or 9, e.g.:</p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>[[9 5 3 7 9]
[5 5 7 3 9]
[7 5 5 7 9]]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Quick Summary of Random Functions</h3>
<div class="v2-table-wrap"><table class="v2-table"><thead><tr><th>Function</th><th>What it does</th><th>Example</th></tr></thead><tbody><tr><td><code>random.randint(n)</code></td><td>One random integer from 0 to n-1</td><td><code>random.randint(10)</code> → <code>7</code></td></tr><tr><td><code>random.randint(n, size=(k))</code></td><td>Array of k random integers</td><td><code>random.randint(10, size=(5))</code></td></tr><tr><td><code>random.randint(n, size=(r, c))</code></td><td>2-D array of random integers</td><td><code>random.randint(10, size=(3, 4))</code></td></tr><tr><td><code>random.rand()</code></td><td>One random float between 0.0 and 1.0</td><td><code>random.rand()</code> → <code>0.73</code></td></tr><tr><td><code>random.rand(k)</code></td><td>Array of k random floats</td><td><code>random.rand(5)</code></td></tr><tr><td><code>random.rand(r, c)</code></td><td>2-D array of random floats</td><td><code>random.rand(3, 5)</code></td></tr><tr><td><code>random.choice(arr)</code></td><td>One random value from arr</td><td><code>random.choice([1,2,3])</code> → <code>2</code></td></tr><tr><td><code>random.choice(arr, size=(r, c))</code></td><td>2-D array from arr's values</td><td><code>random.choice([1,2,3], size=(2,3))</code></td></tr></tbody></table></div>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Random Numbers in NumPy</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(7)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase8"><div class="ph"><div class="pn">Phase 8 of 9</div><div class="pt">Guided Practice Exercises</div><div class="pc" id="chk8"></div></div><div class="pb2"><div class="chal-box"><div class="chal-lbl">🎯 Your Challenge</div><div class="chal-body"><h3 class="v2-h3">Exercise 1 · Splitting a Sensor Dataset</h3>
<p class="v2-p"><strong>Objective:</strong> Practice splitting arrays into equal parts.</p>
<p class="v2-p"><strong>Scenario:</strong> You have 9 temperature readings from 3 weather stations. Split the data so each station gets its own set of readings.</p>
<p class="v2-p"><strong>Steps:</strong></p>
<ol class="v2-ol"><li>Create the array: <code>[22, 25, 19, 30, 28, 24, 15, 18, 21]</code></li><li>Split it into 3 equal parts</li><li>Print each part separately</li></ol>
<p class="v2-p"><strong>Code:</strong></p></div></div><div class="task-box"><div class="task-lbl">✏️ Task</div><div class="task-body">Practise what you just learned about <strong>Guided Practice Exercises</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="reveal-btn" onclick="toggleReveal(this)">Reveal Answer 👁️</button><div class="reveal-content"><div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
temperatures = np.array([22, 25, 19, 30, 28, 24, 15, 18, 21])
parts = np.array_split(temperatures, 3)
print("Station 1:", parts[0])
print("Station 2:", parts[1])
print("Station 3:", parts[2])</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>Station 1: [22 25 19]
Station 2: [30 28 24]
Station 3: [15 18 21]</code></pre></div></div>
<p class="v2-p"><strong>Self-check:</strong> How many elements should each station have? What happens if you split 9 elements into 4 groups?</p>
<hr class="v2-hr">
<h3 class="v2-h3">Exercise 2 · Finding Passing Scores</h3>
<p class="v2-p"><strong>Objective:</strong> Practice using <code>np.where()</code> to find index positions.</p>
<p class="v2-p"><strong>Scenario:</strong> You have 7 student exam scores. Find which students passed (scored 50 or above).</p>
<p class="v2-p"><strong>Steps:</strong></p>
<ol class="v2-ol"><li>Create the scores array: <code>[45, 72, 38, 91, 55, 49, 83]</code></li><li>Use <code>np.where()</code> to find positions where score >= 50</li><li>Print the positions</li><li>Print the actual passing scores</li></ol>
<p class="v2-p"><strong>Code:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
scores = np.array([45, 72, 38, 91, 55, 49, 83])
pass_positions = np.where(scores >= 50)
print("Positions of passing students:", pass_positions)
print("Passing scores:", scores[pass_positions])</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>Positions of passing students: (array([1, 3, 4, 6]),)
Passing scores: [72 91 55 83]</code></pre></div></div>
<p class="v2-p"><strong>Self-check:</strong> How many students passed? What score is at position 3?</p>
<hr class="v2-hr">
<h3 class="v2-h3">Exercise 3 · Sorting a Leaderboard</h3>
<p class="v2-p"><strong>Objective:</strong> Practice sorting arrays.</p>
<p class="v2-p"><strong>Scenario:</strong> Sort student scores to build a leaderboard (highest first).</p>
<p class="v2-p"><strong>Code:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
scores = np.array([45, 72, 38, 91, 55, 49, 83])
ascending = np.sort(scores)
descending = np.sort(scores)[::-1]
print("Sorted (low to high):", ascending)
print("Leaderboard (high to low):", descending)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>Sorted (low to high): [38 45 49 55 72 83 91]
Leaderboard (high to low): [91 83 72 55 49 45 38]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Exercise 4 · Filtering Scores Above Average</h3>
<p class="v2-p"><strong>Objective:</strong> Practice direct boolean filtering.</p>
<p class="v2-p"><strong>Scenario:</strong> Find and display only scores that are above the average.</p>
<p class="v2-p"><strong>Code:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
scores = np.array([45, 72, 38, 91, 55, 49, 83])
average = np.mean(scores) # np.mean() calculates the average
print("Average score:", average)
above_average = scores[scores > average]
print("Above-average scores:", above_average)</code></pre></div></div>
<p class="v2-p"><strong>Expected Output:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>Average score: 61.857142857142854
Above-average scores: [72 91 83]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Exercise 5 · Generating Mock Exam Data</h3>
<p class="v2-p"><strong>Objective:</strong> Practice using <code>random</code> to generate test data.</p>
<p class="v2-p"><strong>Scenario:</strong> Generate a random dataset of 20 student exam scores (between 0 and 100) for testing your code.</p>
<p class="v2-p"><strong>Code:</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
import numpy as np
mock_scores = random.randint(101, size=(20))
print("Mock scores:", mock_scores)
print("Highest:", np.max(mock_scores))
print("Lowest:", np.min(mock_scores))
print("Average:", np.mean(mock_scores).round(2))</code></pre></div></div>
<p class="v2-p"><strong>Expected Output (yours will differ):</strong></p>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">code</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>Mock scores: [51 92 14 71 60 20 82 86 74 74 87 99 23 2 21 52 1 87 29 37]
Highest: 99
Lowest: 1
Average: 53.15</code></pre></div></div>
<hr class="v2-hr"></div><button class="ub" onclick="unlockNext(8)">Mark Complete and Unlock Next ✓</button></div></div>
<div class="phase locked" id="phase9"><div class="ph"><div class="pn">Phase 9 of 9</div><div class="pt">Common Beginner Mistakes</div><div class="pc" id="chk9"></div></div><div class="pb2"><h3 class="v2-h3">Mistake 1 · Using <code>np.split()</code> Instead of <code>np.array_split()</code></h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code># WRONG - this will crash if division is unequal
arr = np.array([1, 2, 3, 4, 5])
# np.split(arr, 3) # ValueError! 5 cannot be split into 3 equal parts
# RIGHT - this adjusts automatically
result = np.array_split(arr, 3) # Works fine: [1,2], [3,4], [5]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Mistake 2 · Forgetting That <code>where()</code> Returns a Tuple</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([1, 2, 3, 4])
result = np.where(arr > 2)
print(result) # (array([2, 3]),) ← it's a tuple!
print(result[0]) # array([2, 3]) ← to get just the array, use [0]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Mistake 3 · Assuming <code>np.sort()</code> Modifies the Original Array</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([3, 1, 2])
np.sort(arr) # This does NOT change arr!
print(arr) # Still [3 1 2] ← ORIGINAL UNCHANGED
sorted_arr = np.sort(arr) # You must capture the result in a variable
print(sorted_arr) # [1 2 3]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Mistake 4 · Building Filter Lists Manually When You Can Use Direct Conditions</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
arr = np.array([10, 20, 30, 40, 50])
# SLOW and unnecessary
filter_arr = []
for element in arr:
if element > 25:
filter_arr.append(True)
else:
filter_arr.append(False)
result = arr[filter_arr]
# FAST and Pythonic - do this instead!
result = arr[arr > 25] # One line, faster, cleaner
print(result) # [30 40 50]</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Mistake 5 · Using <code>searchsorted()</code> on an Unsorted Array</h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>import numpy as np
# WRONG - array is not sorted!
arr = np.array([5, 2, 8, 1, 9])
# np.searchsorted(arr, 6) # The result would be meaningless!
# RIGHT - sort first
arr_sorted = np.sort(arr) # [1 2 5 8 9]
position = np.searchsorted(arr_sorted, 6)
print(position) # 3 (insert 6 between 5 and 8)</code></pre></div></div>
<hr class="v2-hr">
<h3 class="v2-h3">Mistake 6 · Confusing <code>randint()</code> and <code>rand()</code></h3>
<div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">python</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
# randint → random INTEGERS
x = random.randint(10) # One integer from 0 to 9
y = random.randint(10, size=5) # Five integers from 0 to 9
# rand → random FLOATS between 0.0 and 1.0
a = random.rand() # One float
b = random.rand(5) # Five floats</code></pre></div></div>
<hr class="v2-hr"><div class="task-box"><div class="task-lbl">✏️ Your Task</div><div class="task-body">Practise what you just learned about <strong>Common Beginner Mistakes</strong>. Open your editor, type the examples above by hand, modify them, and observe what changes.</div></div><button class="ub" onclick="unlockNext(9)">Phase Complete — Unlock Next ✓</button></div></div>
<div class="build-box" id="build-it"><div class="build-lbl">🏗️ Build It — Mini Project</div><div class="build-name">Project Overview</div><div class="build-req"><p class="v2-p">This project combines all five skills from this lesson into a realistic data analysis tool.</p>
<h3 class="v2-h3">Project Overview</h3>
<p class="v2-p">You are a teacher. You have a class of 12 students. You want to:</p>
<ol class="v2-ol"><li>Generate mock scores (using random)</li><li>Sort the scores (from highest to lowest)</li><li>Filter out failing scores (below 50)</li><li>Find where specific score thresholds occur</li><li>Split the class into three performance groups</li></ol>
<hr class="v2-hr">
<h3 class="v2-h3">Stage 1 · Setup: Generate Student Scores</h3></div><button class="reveal-btn" onclick="toggleCode(this)">Reveal Starter Code 💻</button><div class="reveal-content"><div class="v2-code-wrap"><div class="v2-code-bar"><span class="v2-dot r"></span><span class="v2-dot y"></span><span class="v2-dot g"></span><span class="v2-code-lang">starter.py</span><button class="copy-btn" onclick="copyCode(this)">❐ Copy</button></div><div class="v2-code-body"><pre><code>from numpy import random
import numpy as np
# Set a seed so we get the same "random" results each run (useful for testing)
random.seed(42)
# Generate 12 scores between 30 and 100
scores = random.randint(30, 101, size=(12))
student_ids = np.array([f"S{i+1:02d}" for i in range(12)])
print("=== RAW SCORES ===")
for sid, score in zip(student_ids, scores):
print(f" {sid}: {score}")</code></pre></div></div><div style="display:flex;gap:.8rem;flex-wrap:wrap;margin-top:1.1rem"><button class="ub" onclick="downloadStarter(`from numpy import random
import numpy as np
# Set a seed so we get the same "random" results each run (useful for testing)
random.seed(42)
# Generate 12 scores between 30 and 100
scores = random.randint(30, 101, size=(12))
student_ids = np.array([f"S{i+1:02d}" for i in range(12)])
print("=== RAW SCORES ===")
for sid, score in zip(student_ids, scores):
print(f" {sid}: {score}")`)" style="background:#10b981;box-shadow:0 4px 14px rgba(16,185,129,.3)">Download starter.py ↓</button><button class="ub" onclick="showGithub()" style="background:#0f172a;box-shadow:none">🐙 Save to GitHub →</button></div></div></div>
<div class="gh-acc" id="gh-steps" style="display:none">
<div class="gh-hd" onclick="toggleGh(this)">🐙 Save Python Project to GitHub <span>▼</span></div>
<div class="gh-bd">