├── .gitignore
├── 01_constructors.ipynb
├── 02_basicinfo.ipynb
├── 03_missingvalues.ipynb
├── 04_loadsave.ipynb
├── 05_columns.ipynb
├── 06_rows.ipynb
├── 07_factors.ipynb
├── 08_joins.ipynb
├── 09_reshaping.ipynb
├── 10_transforms.ipynb
├── 11_performance.ipynb
├── 12_pitfalls.ipynb
├── 13_extras.ipynb
├── LICENSE
├── Manifest.toml
├── Project.toml
└── README.md
/.gitignore:
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1 | .ipynb_checkpoints/
2 |
--------------------------------------------------------------------------------
/03_missingvalues.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Introduction to DataFrames\n",
8 | "**[Bogumił Kamiński](http://bogumilkaminski.pl/about/), February 13, 2023**"
9 | ]
10 | },
11 | {
12 | "cell_type": "code",
13 | "execution_count": 1,
14 | "metadata": {},
15 | "outputs": [],
16 | "source": [
17 | "using DataFrames"
18 | ]
19 | },
20 | {
21 | "cell_type": "markdown",
22 | "metadata": {},
23 | "source": [
24 | "## Handling missing values\n",
25 | "\n",
26 | "A singelton type `Missing` allows us to deal with missing values."
27 | ]
28 | },
29 | {
30 | "cell_type": "code",
31 | "execution_count": 2,
32 | "metadata": {},
33 | "outputs": [
34 | {
35 | "data": {
36 | "text/plain": [
37 | "(missing, Missing)"
38 | ]
39 | },
40 | "execution_count": 2,
41 | "metadata": {},
42 | "output_type": "execute_result"
43 | }
44 | ],
45 | "source": [
46 | "missing, typeof(missing)"
47 | ]
48 | },
49 | {
50 | "cell_type": "markdown",
51 | "metadata": {},
52 | "source": [
53 | "Arrays automatically create an appropriate union type."
54 | ]
55 | },
56 | {
57 | "cell_type": "code",
58 | "execution_count": 3,
59 | "metadata": {},
60 | "outputs": [
61 | {
62 | "data": {
63 | "text/plain": [
64 | "4-element Vector{Union{Missing, Int64}}:\n",
65 | " 1\n",
66 | " 2\n",
67 | " missing\n",
68 | " 3"
69 | ]
70 | },
71 | "execution_count": 3,
72 | "metadata": {},
73 | "output_type": "execute_result"
74 | }
75 | ],
76 | "source": [
77 | "x = [1, 2, missing, 3]"
78 | ]
79 | },
80 | {
81 | "cell_type": "markdown",
82 | "metadata": {},
83 | "source": [
84 | "`ismissing` checks if passed value is missing."
85 | ]
86 | },
87 | {
88 | "cell_type": "code",
89 | "execution_count": 4,
90 | "metadata": {},
91 | "outputs": [
92 | {
93 | "data": {
94 | "text/plain": [
95 | "(false, true, false, Bool[0, 0, 1, 0])"
96 | ]
97 | },
98 | "execution_count": 4,
99 | "metadata": {},
100 | "output_type": "execute_result"
101 | }
102 | ],
103 | "source": [
104 | "ismissing(1), ismissing(missing), ismissing(x), ismissing.(x)"
105 | ]
106 | },
107 | {
108 | "cell_type": "markdown",
109 | "metadata": {},
110 | "source": [
111 | "We can extract the type combined with Missing from a `Union` via `nonmissingtype`\n",
112 | "\n",
113 | "(This is useful for arrays!)"
114 | ]
115 | },
116 | {
117 | "cell_type": "code",
118 | "execution_count": 5,
119 | "metadata": {},
120 | "outputs": [
121 | {
122 | "data": {
123 | "text/plain": [
124 | "(Union{Missing, Int64}, Int64)"
125 | ]
126 | },
127 | "execution_count": 5,
128 | "metadata": {},
129 | "output_type": "execute_result"
130 | }
131 | ],
132 | "source": [
133 | "eltype(x), nonmissingtype(eltype(x))"
134 | ]
135 | },
136 | {
137 | "cell_type": "markdown",
138 | "metadata": {},
139 | "source": [
140 | "`missing` comparisons produce `missing`."
141 | ]
142 | },
143 | {
144 | "cell_type": "code",
145 | "execution_count": 6,
146 | "metadata": {},
147 | "outputs": [
148 | {
149 | "data": {
150 | "text/plain": [
151 | "(missing, missing, missing)"
152 | ]
153 | },
154 | "execution_count": 6,
155 | "metadata": {},
156 | "output_type": "execute_result"
157 | }
158 | ],
159 | "source": [
160 | "missing == missing, missing != missing, missing < missing"
161 | ]
162 | },
163 | {
164 | "cell_type": "markdown",
165 | "metadata": {},
166 | "source": [
167 | "This is also true when `missing`s are compared with values of other types."
168 | ]
169 | },
170 | {
171 | "cell_type": "code",
172 | "execution_count": 7,
173 | "metadata": {},
174 | "outputs": [
175 | {
176 | "data": {
177 | "text/plain": [
178 | "(missing, missing, missing)"
179 | ]
180 | },
181 | "execution_count": 7,
182 | "metadata": {},
183 | "output_type": "execute_result"
184 | }
185 | ],
186 | "source": [
187 | "1 == missing, 1 != missing, 1 < missing"
188 | ]
189 | },
190 | {
191 | "cell_type": "markdown",
192 | "metadata": {},
193 | "source": [
194 | "`isequal`, `isless`, and `===` produce results of type `Bool`. Notice that `missing` is considered greater than any numeric value."
195 | ]
196 | },
197 | {
198 | "cell_type": "code",
199 | "execution_count": 8,
200 | "metadata": {},
201 | "outputs": [
202 | {
203 | "data": {
204 | "text/plain": [
205 | "(true, true, false, true)"
206 | ]
207 | },
208 | "execution_count": 8,
209 | "metadata": {},
210 | "output_type": "execute_result"
211 | }
212 | ],
213 | "source": [
214 | "isequal(missing, missing), missing === missing, isequal(1, missing), isless(1, missing)"
215 | ]
216 | },
217 | {
218 | "cell_type": "markdown",
219 | "metadata": {},
220 | "source": [
221 | "In the next few examples, we see that many (not all) functions handle `missing`."
222 | ]
223 | },
224 | {
225 | "cell_type": "code",
226 | "execution_count": 9,
227 | "metadata": {},
228 | "outputs": [
229 | {
230 | "data": {
231 | "text/plain": [
232 | "4-element Vector{Missing}:\n",
233 | " missing\n",
234 | " missing\n",
235 | " missing\n",
236 | " missing"
237 | ]
238 | },
239 | "execution_count": 9,
240 | "metadata": {},
241 | "output_type": "execute_result"
242 | }
243 | ],
244 | "source": [
245 | "map(x -> x(missing), [sin, cos, zero, sqrt]) # part 1"
246 | ]
247 | },
248 | {
249 | "cell_type": "code",
250 | "execution_count": 10,
251 | "metadata": {},
252 | "outputs": [
253 | {
254 | "data": {
255 | "text/plain": [
256 | "5-element Vector{Missing}:\n",
257 | " missing\n",
258 | " missing\n",
259 | " missing\n",
260 | " missing\n",
261 | " missing"
262 | ]
263 | },
264 | "execution_count": 10,
265 | "metadata": {},
266 | "output_type": "execute_result"
267 | }
268 | ],
269 | "source": [
270 | "map(x -> x(missing, 1), [+, - , *, /, div]) # part 2 "
271 | ]
272 | },
273 | {
274 | "cell_type": "code",
275 | "execution_count": 11,
276 | "metadata": {},
277 | "outputs": [
278 | {
279 | "data": {
280 | "text/plain": [
281 | "5-element Vector{Any}:\n",
282 | " missing\n",
283 | " missing\n",
284 | " (missing, missing)\n",
285 | " missing\n",
286 | " Union{Missing, Float64}[1.0, 2.0, missing]"
287 | ]
288 | },
289 | "execution_count": 11,
290 | "metadata": {},
291 | "output_type": "execute_result"
292 | }
293 | ],
294 | "source": [
295 | "using Statistics # needed for mean\n",
296 | "map(x -> x([1,2,missing]), [minimum, maximum, extrema, mean, float]) # part 3"
297 | ]
298 | },
299 | {
300 | "cell_type": "markdown",
301 | "metadata": {},
302 | "source": [
303 | "`skipmissing` returns iterator skipping missing values. We can use `collect` and `skipmissing` to create an array that excludes these missing values."
304 | ]
305 | },
306 | {
307 | "cell_type": "code",
308 | "execution_count": 12,
309 | "metadata": {},
310 | "outputs": [
311 | {
312 | "data": {
313 | "text/plain": [
314 | "2-element Vector{Int64}:\n",
315 | " 1\n",
316 | " 2"
317 | ]
318 | },
319 | "execution_count": 12,
320 | "metadata": {},
321 | "output_type": "execute_result"
322 | }
323 | ],
324 | "source": [
325 | "collect(skipmissing([1, missing, 2, missing]))"
326 | ]
327 | },
328 | {
329 | "cell_type": "markdown",
330 | "metadata": {},
331 | "source": [
332 | "Here we use `replace` to create a new array that replaces all missing values with some value (`NaN` in this case)."
333 | ]
334 | },
335 | {
336 | "cell_type": "code",
337 | "execution_count": 13,
338 | "metadata": {},
339 | "outputs": [
340 | {
341 | "data": {
342 | "text/plain": [
343 | "4-element Vector{Float64}:\n",
344 | " 1.0\n",
345 | " NaN\n",
346 | " 2.0\n",
347 | " NaN"
348 | ]
349 | },
350 | "execution_count": 13,
351 | "metadata": {},
352 | "output_type": "execute_result"
353 | }
354 | ],
355 | "source": [
356 | "replace([1.0, missing, 2.0, missing], missing=>NaN)"
357 | ]
358 | },
359 | {
360 | "cell_type": "markdown",
361 | "metadata": {},
362 | "source": [
363 | "Another way to do this:"
364 | ]
365 | },
366 | {
367 | "cell_type": "code",
368 | "execution_count": 14,
369 | "metadata": {},
370 | "outputs": [
371 | {
372 | "data": {
373 | "text/plain": [
374 | "4-element Vector{Float64}:\n",
375 | " 1.0\n",
376 | " NaN\n",
377 | " 2.0\n",
378 | " NaN"
379 | ]
380 | },
381 | "execution_count": 14,
382 | "metadata": {},
383 | "output_type": "execute_result"
384 | }
385 | ],
386 | "source": [
387 | "coalesce.([1.0, missing, 2.0, missing], NaN)"
388 | ]
389 | },
390 | {
391 | "cell_type": "markdown",
392 | "metadata": {},
393 | "source": [
394 | "You can also use `recode` from CategoricalArrays.jl if you have a default output value."
395 | ]
396 | },
397 | {
398 | "cell_type": "code",
399 | "execution_count": 15,
400 | "metadata": {},
401 | "outputs": [],
402 | "source": [
403 | "using CategoricalArrays"
404 | ]
405 | },
406 | {
407 | "cell_type": "code",
408 | "execution_count": 16,
409 | "metadata": {},
410 | "outputs": [
411 | {
412 | "data": {
413 | "text/plain": [
414 | "4-element Vector{Bool}:\n",
415 | " 0\n",
416 | " 1\n",
417 | " 0\n",
418 | " 1"
419 | ]
420 | },
421 | "execution_count": 16,
422 | "metadata": {},
423 | "output_type": "execute_result"
424 | }
425 | ],
426 | "source": [
427 | "recode([1.0, missing, 2.0, missing], false, missing=>true)"
428 | ]
429 | },
430 | {
431 | "cell_type": "markdown",
432 | "metadata": {},
433 | "source": [
434 | "There are also `replace!` and `recode!` functions that work in place."
435 | ]
436 | },
437 | {
438 | "cell_type": "markdown",
439 | "metadata": {},
440 | "source": [
441 | "Here is an example how you can to missing inputation in a data frame."
442 | ]
443 | },
444 | {
445 | "cell_type": "code",
446 | "execution_count": 17,
447 | "metadata": {},
448 | "outputs": [
449 | {
450 | "data": {
451 | "text/html": [
452 | "
"
453 | ],
454 | "text/latex": [
455 | "\\begin{tabular}{r|cc}\n",
456 | "\t& a & b\\\\\n",
457 | "\t\\hline\n",
458 | "\t& Int64? & String?\\\\\n",
459 | "\t\\hline\n",
460 | "\t1 & 1 & a \\\\\n",
461 | "\t2 & 2 & b \\\\\n",
462 | "\t3 & \\emph{missing} & \\emph{missing} \\\\\n",
463 | "\\end{tabular}\n"
464 | ],
465 | "text/plain": [
466 | "\u001b[1m3×2 DataFrame\u001b[0m\n",
467 | "\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\u001b[1m b \u001b[0m\n",
468 | " │\u001b[90m Int64? \u001b[0m\u001b[90m String? \u001b[0m\n",
469 | "─────┼──────────────────\n",
470 | " 1 │ 1 a\n",
471 | " 2 │ 2 b\n",
472 | " 3 │\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m"
473 | ]
474 | },
475 | "execution_count": 17,
476 | "metadata": {},
477 | "output_type": "execute_result"
478 | }
479 | ],
480 | "source": [
481 | "df = DataFrame(a=[1,2,missing], b=[\"a\", \"b\", missing])"
482 | ]
483 | },
484 | {
485 | "cell_type": "markdown",
486 | "metadata": {},
487 | "source": [
488 | "we change `df.a` vector in place."
489 | ]
490 | },
491 | {
492 | "cell_type": "code",
493 | "execution_count": 18,
494 | "metadata": {},
495 | "outputs": [
496 | {
497 | "data": {
498 | "text/plain": [
499 | "3-element Vector{Union{Missing, Int64}}:\n",
500 | " 1\n",
501 | " 2\n",
502 | " 100"
503 | ]
504 | },
505 | "execution_count": 18,
506 | "metadata": {},
507 | "output_type": "execute_result"
508 | }
509 | ],
510 | "source": [
511 | "replace!(df.a, missing=>100)"
512 | ]
513 | },
514 | {
515 | "cell_type": "markdown",
516 | "metadata": {},
517 | "source": [
518 | "Now we overwrite `df.b` with a new vector, because the replacement type is different than what `eltype(df.b)` accepts."
519 | ]
520 | },
521 | {
522 | "cell_type": "code",
523 | "execution_count": 19,
524 | "metadata": {},
525 | "outputs": [
526 | {
527 | "data": {
528 | "text/plain": [
529 | "3-element Vector{Any}:\n",
530 | " \"a\"\n",
531 | " \"b\"\n",
532 | " 100"
533 | ]
534 | },
535 | "execution_count": 19,
536 | "metadata": {},
537 | "output_type": "execute_result"
538 | }
539 | ],
540 | "source": [
541 | "df.b = coalesce.(df.b, 100)"
542 | ]
543 | },
544 | {
545 | "cell_type": "code",
546 | "execution_count": 20,
547 | "metadata": {},
548 | "outputs": [
549 | {
550 | "data": {
551 | "text/html": [
552 | ""
553 | ],
554 | "text/latex": [
555 | "\\begin{tabular}{r|cc}\n",
556 | "\t& a & b\\\\\n",
557 | "\t\\hline\n",
558 | "\t& Int64? & Any\\\\\n",
559 | "\t\\hline\n",
560 | "\t1 & 1 & a \\\\\n",
561 | "\t2 & 2 & b \\\\\n",
562 | "\t3 & 100 & 100 \\\\\n",
563 | "\\end{tabular}\n"
564 | ],
565 | "text/plain": [
566 | "\u001b[1m3×2 DataFrame\u001b[0m\n",
567 | "\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\u001b[1m b \u001b[0m\n",
568 | " │\u001b[90m Int64? \u001b[0m\u001b[90m Any \u001b[0m\n",
569 | "─────┼─────────────\n",
570 | " 1 │ 1 a\n",
571 | " 2 │ 2 b\n",
572 | " 3 │ 100 100"
573 | ]
574 | },
575 | "execution_count": 20,
576 | "metadata": {},
577 | "output_type": "execute_result"
578 | }
579 | ],
580 | "source": [
581 | "df"
582 | ]
583 | },
584 | {
585 | "cell_type": "markdown",
586 | "metadata": {},
587 | "source": [
588 | "You can use `unique` or `levels` to get unique values with or without missings, respectively."
589 | ]
590 | },
591 | {
592 | "cell_type": "code",
593 | "execution_count": 21,
594 | "metadata": {},
595 | "outputs": [
596 | {
597 | "data": {
598 | "text/plain": [
599 | "(Union{Missing, Int64}[1, missing, 2], [1, 2])"
600 | ]
601 | },
602 | "execution_count": 21,
603 | "metadata": {},
604 | "output_type": "execute_result"
605 | }
606 | ],
607 | "source": [
608 | "unique([1, missing, 2, missing]), levels([1, missing, 2, missing])"
609 | ]
610 | },
611 | {
612 | "cell_type": "markdown",
613 | "metadata": {},
614 | "source": [
615 | "In this next example, we convert `x` to `y` with `allowmissing`, where `y` has a type that accepts missings."
616 | ]
617 | },
618 | {
619 | "cell_type": "code",
620 | "execution_count": 22,
621 | "metadata": {},
622 | "outputs": [
623 | {
624 | "data": {
625 | "text/plain": [
626 | "3-element Vector{Union{Missing, Int64}}:\n",
627 | " 1\n",
628 | " 2\n",
629 | " 3"
630 | ]
631 | },
632 | "execution_count": 22,
633 | "metadata": {},
634 | "output_type": "execute_result"
635 | }
636 | ],
637 | "source": [
638 | "x = [1,2,3]\n",
639 | "y = allowmissing(x)"
640 | ]
641 | },
642 | {
643 | "cell_type": "markdown",
644 | "metadata": {},
645 | "source": [
646 | "Then, we convert back with `disallowmissing`. This would fail if `y` contained missing values!"
647 | ]
648 | },
649 | {
650 | "cell_type": "code",
651 | "execution_count": 23,
652 | "metadata": {},
653 | "outputs": [
654 | {
655 | "data": {
656 | "text/plain": [
657 | "([1, 2, 3], Union{Missing, Int64}[1, 2, 3], [1, 2, 3])"
658 | ]
659 | },
660 | "execution_count": 23,
661 | "metadata": {},
662 | "output_type": "execute_result"
663 | }
664 | ],
665 | "source": [
666 | "z = disallowmissing(y)\n",
667 | "x,y,z"
668 | ]
669 | },
670 | {
671 | "cell_type": "markdown",
672 | "metadata": {},
673 | "source": [
674 | "`disallowmissing` has `error` keyword argument that can be used to decide how it should behave when it encounters a column that actually contains a `missing` value"
675 | ]
676 | },
677 | {
678 | "cell_type": "code",
679 | "execution_count": 24,
680 | "metadata": {},
681 | "outputs": [
682 | {
683 | "data": {
684 | "text/html": [
685 | ""
686 | ],
687 | "text/latex": [
688 | "\\begin{tabular}{r|ccc}\n",
689 | "\t& x1 & x2 & x3\\\\\n",
690 | "\t\\hline\n",
691 | "\t& Float64? & Float64? & Float64?\\\\\n",
692 | "\t\\hline\n",
693 | "\t1 & 1.0 & 1.0 & 1.0 \\\\\n",
694 | "\t2 & 1.0 & 1.0 & 1.0 \\\\\n",
695 | "\\end{tabular}\n"
696 | ],
697 | "text/plain": [
698 | "\u001b[1m2×3 DataFrame\u001b[0m\n",
699 | "\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\n",
700 | " │\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m\n",
701 | "─────┼──────────────────────────────\n",
702 | " 1 │ 1.0 1.0 1.0\n",
703 | " 2 │ 1.0 1.0 1.0"
704 | ]
705 | },
706 | "execution_count": 24,
707 | "metadata": {},
708 | "output_type": "execute_result"
709 | }
710 | ],
711 | "source": [
712 | "df = allowmissing(DataFrame(ones(2,3), :auto))"
713 | ]
714 | },
715 | {
716 | "cell_type": "code",
717 | "execution_count": 25,
718 | "metadata": {},
719 | "outputs": [
720 | {
721 | "data": {
722 | "text/plain": [
723 | "missing"
724 | ]
725 | },
726 | "execution_count": 25,
727 | "metadata": {},
728 | "output_type": "execute_result"
729 | }
730 | ],
731 | "source": [
732 | "df[1,1] = missing"
733 | ]
734 | },
735 | {
736 | "cell_type": "code",
737 | "execution_count": 26,
738 | "metadata": {},
739 | "outputs": [
740 | {
741 | "data": {
742 | "text/html": [
743 | ""
744 | ],
745 | "text/latex": [
746 | "\\begin{tabular}{r|ccc}\n",
747 | "\t& x1 & x2 & x3\\\\\n",
748 | "\t\\hline\n",
749 | "\t& Float64? & Float64? & Float64?\\\\\n",
750 | "\t\\hline\n",
751 | "\t1 & \\emph{missing} & 1.0 & 1.0 \\\\\n",
752 | "\t2 & 1.0 & 1.0 & 1.0 \\\\\n",
753 | "\\end{tabular}\n"
754 | ],
755 | "text/plain": [
756 | "\u001b[1m2×3 DataFrame\u001b[0m\n",
757 | "\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\n",
758 | " │\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m\u001b[90m Float64? \u001b[0m\n",
759 | "─────┼───────────────────────────────\n",
760 | " 1 │\u001b[90m missing \u001b[0m 1.0 1.0\n",
761 | " 2 │ 1.0 1.0 1.0"
762 | ]
763 | },
764 | "execution_count": 26,
765 | "metadata": {},
766 | "output_type": "execute_result"
767 | }
768 | ],
769 | "source": [
770 | "df"
771 | ]
772 | },
773 | {
774 | "cell_type": "code",
775 | "execution_count": 27,
776 | "metadata": {},
777 | "outputs": [
778 | {
779 | "ename": "LoadError",
780 | "evalue": "ArgumentError: Missing value found in column :x1 in row 1",
781 | "output_type": "error",
782 | "traceback": [
783 | "ArgumentError: Missing value found in column :x1 in row 1",
784 | "",
785 | "Stacktrace:",
786 | " [1] disallowmissing(df::DataFrame, cols::Colon; error::Bool)",
787 | " @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\abstractdataframe\\abstractdataframe.jl:2197",
788 | " [2] disallowmissing",
789 | " @ C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\abstractdataframe\\abstractdataframe.jl:2180 [inlined]",
790 | " [3] disallowmissing(df::DataFrame)",
791 | " @ DataFrames C:\\Users\\bogum\\.julia\\packages\\DataFrames\\LteEl\\src\\abstractdataframe\\abstractdataframe.jl:2180",
792 | " [4] top-level scope",
793 | " @ In[27]:1"
794 | ]
795 | }
796 | ],
797 | "source": [
798 | "disallowmissing(df) # an error is thrown"
799 | ]
800 | },
801 | {
802 | "cell_type": "code",
803 | "execution_count": 28,
804 | "metadata": {},
805 | "outputs": [
806 | {
807 | "data": {
808 | "text/html": [
809 | ""
810 | ],
811 | "text/latex": [
812 | "\\begin{tabular}{r|ccc}\n",
813 | "\t& x1 & x2 & x3\\\\\n",
814 | "\t\\hline\n",
815 | "\t& Float64? & Float64 & Float64\\\\\n",
816 | "\t\\hline\n",
817 | "\t1 & \\emph{missing} & 1.0 & 1.0 \\\\\n",
818 | "\t2 & 1.0 & 1.0 & 1.0 \\\\\n",
819 | "\\end{tabular}\n"
820 | ],
821 | "text/plain": [
822 | "\u001b[1m2×3 DataFrame\u001b[0m\n",
823 | "\u001b[1m Row \u001b[0m│\u001b[1m x1 \u001b[0m\u001b[1m x2 \u001b[0m\u001b[1m x3 \u001b[0m\n",
824 | " │\u001b[90m Float64? \u001b[0m\u001b[90m Float64 \u001b[0m\u001b[90m Float64 \u001b[0m\n",
825 | "─────┼─────────────────────────────\n",
826 | " 1 │\u001b[90m missing \u001b[0m 1.0 1.0\n",
827 | " 2 │ 1.0 1.0 1.0"
828 | ]
829 | },
830 | "execution_count": 28,
831 | "metadata": {},
832 | "output_type": "execute_result"
833 | }
834 | ],
835 | "source": [
836 | "disallowmissing(df, error=false) # column :x1 is left untouched as it contains missing"
837 | ]
838 | },
839 | {
840 | "cell_type": "markdown",
841 | "metadata": {},
842 | "source": [
843 | "In this next example, we show that the type of each column in `x` is initially `Int64`. After using `allowmissing!` to accept missing values in columns 1 and 3, the types of those columns become `Union{Int64,Missing}`."
844 | ]
845 | },
846 | {
847 | "cell_type": "code",
848 | "execution_count": 29,
849 | "metadata": {},
850 | "outputs": [
851 | {
852 | "name": "stdout",
853 | "output_type": "stream",
854 | "text": [
855 | "Before: DataType[Int64, Int64, Int64]\n",
856 | "After: Type[Union{Missing, Int64}, Int64, Union{Missing, Int64}]\n"
857 | ]
858 | }
859 | ],
860 | "source": [
861 | "x = DataFrame(rand(Int, 2,3), :auto)\n",
862 | "println(\"Before: \", eltype.(eachcol(x)))\n",
863 | "allowmissing!(x, 1) # make first column accept missings\n",
864 | "allowmissing!(x, :x3) # make :x3 column accept missings\n",
865 | "println(\"After: \", eltype.(eachcol(x)))"
866 | ]
867 | },
868 | {
869 | "cell_type": "markdown",
870 | "metadata": {},
871 | "source": [
872 | "In this next example, we'll use `completecases` to find all the rows of a `DataFrame` that have complete data."
873 | ]
874 | },
875 | {
876 | "cell_type": "code",
877 | "execution_count": 30,
878 | "metadata": {},
879 | "outputs": [
880 | {
881 | "data": {
882 | "text/html": [
883 | ""
884 | ],
885 | "text/latex": [
886 | "\\begin{tabular}{r|cc}\n",
887 | "\t& A & B\\\\\n",
888 | "\t\\hline\n",
889 | "\t& Int64? & String?\\\\\n",
890 | "\t\\hline\n",
891 | "\t1 & 1 & A \\\\\n",
892 | "\t2 & \\emph{missing} & B \\\\\n",
893 | "\t3 & 3 & \\emph{missing} \\\\\n",
894 | "\t4 & 4 & C \\\\\n",
895 | "\\end{tabular}\n"
896 | ],
897 | "text/plain": [
898 | "\u001b[1m4×2 DataFrame\u001b[0m\n",
899 | "\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n",
900 | " │\u001b[90m Int64? \u001b[0m\u001b[90m String? \u001b[0m\n",
901 | "─────┼──────────────────\n",
902 | " 1 │ 1 A\n",
903 | " 2 │\u001b[90m missing \u001b[0m B\n",
904 | " 3 │ 3 \u001b[90m missing \u001b[0m\n",
905 | " 4 │ 4 C"
906 | ]
907 | },
908 | "execution_count": 30,
909 | "metadata": {},
910 | "output_type": "execute_result"
911 | }
912 | ],
913 | "source": [
914 | "x = DataFrame(A=[1, missing, 3, 4], B=[\"A\", \"B\", missing, \"C\"])"
915 | ]
916 | },
917 | {
918 | "cell_type": "code",
919 | "execution_count": 31,
920 | "metadata": {},
921 | "outputs": [
922 | {
923 | "name": "stdout",
924 | "output_type": "stream",
925 | "text": [
926 | "Complete cases:\n",
927 | "Bool[1, 0, 0, 1]\n"
928 | ]
929 | }
930 | ],
931 | "source": [
932 | "println(\"Complete cases:\\n\", completecases(x))"
933 | ]
934 | },
935 | {
936 | "cell_type": "markdown",
937 | "metadata": {},
938 | "source": [
939 | "We can use `dropmissing` or `dropmissing!` to remove the rows with incomplete data from a `DataFrame` and either create a new `DataFrame` or mutate the original in-place."
940 | ]
941 | },
942 | {
943 | "cell_type": "code",
944 | "execution_count": 32,
945 | "metadata": {},
946 | "outputs": [
947 | {
948 | "data": {
949 | "text/html": [
950 | ""
951 | ],
952 | "text/latex": [
953 | "\\begin{tabular}{r|cc}\n",
954 | "\t& A & B\\\\\n",
955 | "\t\\hline\n",
956 | "\t& Int64 & String\\\\\n",
957 | "\t\\hline\n",
958 | "\t1 & 1 & A \\\\\n",
959 | "\t2 & 4 & C \\\\\n",
960 | "\\end{tabular}\n"
961 | ],
962 | "text/plain": [
963 | "\u001b[1m2×2 DataFrame\u001b[0m\n",
964 | "\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n",
965 | " │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
966 | "─────┼───────────────\n",
967 | " 1 │ 1 A\n",
968 | " 2 │ 4 C"
969 | ]
970 | },
971 | "execution_count": 32,
972 | "metadata": {},
973 | "output_type": "execute_result"
974 | }
975 | ],
976 | "source": [
977 | "y = dropmissing(x)\n",
978 | "dropmissing!(x)"
979 | ]
980 | },
981 | {
982 | "cell_type": "code",
983 | "execution_count": 33,
984 | "metadata": {},
985 | "outputs": [
986 | {
987 | "data": {
988 | "text/html": [
989 | ""
990 | ],
991 | "text/latex": [
992 | "\\begin{tabular}{r|cc}\n",
993 | "\t& A & B\\\\\n",
994 | "\t\\hline\n",
995 | "\t& Int64 & String\\\\\n",
996 | "\t\\hline\n",
997 | "\t1 & 1 & A \\\\\n",
998 | "\t2 & 4 & C \\\\\n",
999 | "\\end{tabular}\n"
1000 | ],
1001 | "text/plain": [
1002 | "\u001b[1m2×2 DataFrame\u001b[0m\n",
1003 | "\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n",
1004 | " │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
1005 | "─────┼───────────────\n",
1006 | " 1 │ 1 A\n",
1007 | " 2 │ 4 C"
1008 | ]
1009 | },
1010 | "execution_count": 33,
1011 | "metadata": {},
1012 | "output_type": "execute_result"
1013 | }
1014 | ],
1015 | "source": [
1016 | "x"
1017 | ]
1018 | },
1019 | {
1020 | "cell_type": "code",
1021 | "execution_count": 34,
1022 | "metadata": {},
1023 | "outputs": [
1024 | {
1025 | "data": {
1026 | "text/html": [
1027 | ""
1028 | ],
1029 | "text/latex": [
1030 | "\\begin{tabular}{r|cc}\n",
1031 | "\t& A & B\\\\\n",
1032 | "\t\\hline\n",
1033 | "\t& Int64 & String\\\\\n",
1034 | "\t\\hline\n",
1035 | "\t1 & 1 & A \\\\\n",
1036 | "\t2 & 4 & C \\\\\n",
1037 | "\\end{tabular}\n"
1038 | ],
1039 | "text/plain": [
1040 | "\u001b[1m2×2 DataFrame\u001b[0m\n",
1041 | "\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n",
1042 | " │\u001b[90m Int64 \u001b[0m\u001b[90m String \u001b[0m\n",
1043 | "─────┼───────────────\n",
1044 | " 1 │ 1 A\n",
1045 | " 2 │ 4 C"
1046 | ]
1047 | },
1048 | "execution_count": 34,
1049 | "metadata": {},
1050 | "output_type": "execute_result"
1051 | }
1052 | ],
1053 | "source": [
1054 | "y"
1055 | ]
1056 | },
1057 | {
1058 | "cell_type": "markdown",
1059 | "metadata": {},
1060 | "source": [
1061 | "When we call `describe` on a `DataFrame` with dropped missing values, the columns do not allow missing values any more by default."
1062 | ]
1063 | },
1064 | {
1065 | "cell_type": "code",
1066 | "execution_count": 35,
1067 | "metadata": {},
1068 | "outputs": [
1069 | {
1070 | "data": {
1071 | "text/html": [
1072 | "1 | A | 2.5 | 1 | 2.5 | 4 | 0 | Int64 |
2 | B | | A | | C | 0 | String |
"
1073 | ],
1074 | "text/latex": [
1075 | "\\begin{tabular}{r|ccccccc}\n",
1076 | "\t& variable & mean & min & median & max & nmissing & eltype\\\\\n",
1077 | "\t\\hline\n",
1078 | "\t& Symbol & Union… & Any & Union… & Any & Int64 & DataType\\\\\n",
1079 | "\t\\hline\n",
1080 | "\t1 & A & 2.5 & 1 & 2.5 & 4 & 0 & Int64 \\\\\n",
1081 | "\t2 & B & & A & & C & 0 & String \\\\\n",
1082 | "\\end{tabular}\n"
1083 | ],
1084 | "text/plain": [
1085 | "\u001b[1m2×7 DataFrame\u001b[0m\n",
1086 | "\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmissing \u001b[0m\u001b[1m eltype \u001b[0m\n",
1087 | " │\u001b[90m Symbol \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m DataType \u001b[0m\n",
1088 | "─────┼────────────────────────────────────────────────────────\n",
1089 | " 1 │ A 2.5 1 2.5 4 0 Int64\n",
1090 | " 2 │ B \u001b[90m \u001b[0m A \u001b[90m \u001b[0m C 0 String"
1091 | ]
1092 | },
1093 | "execution_count": 35,
1094 | "metadata": {},
1095 | "output_type": "execute_result"
1096 | }
1097 | ],
1098 | "source": [
1099 | "describe(x)"
1100 | ]
1101 | },
1102 | {
1103 | "cell_type": "markdown",
1104 | "metadata": {},
1105 | "source": [
1106 | "Alternatively you can pass `disallowmissing` keyword argument to `dropmissing` and `dropmissing!`"
1107 | ]
1108 | },
1109 | {
1110 | "cell_type": "code",
1111 | "execution_count": 36,
1112 | "metadata": {},
1113 | "outputs": [
1114 | {
1115 | "data": {
1116 | "text/html": [
1117 | ""
1118 | ],
1119 | "text/latex": [
1120 | "\\begin{tabular}{r|cc}\n",
1121 | "\t& A & B\\\\\n",
1122 | "\t\\hline\n",
1123 | "\t& Int64? & String?\\\\\n",
1124 | "\t\\hline\n",
1125 | "\t1 & 1 & A \\\\\n",
1126 | "\t2 & \\emph{missing} & B \\\\\n",
1127 | "\t3 & 3 & \\emph{missing} \\\\\n",
1128 | "\t4 & 4 & C \\\\\n",
1129 | "\\end{tabular}\n"
1130 | ],
1131 | "text/plain": [
1132 | "\u001b[1m4×2 DataFrame\u001b[0m\n",
1133 | "\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n",
1134 | " │\u001b[90m Int64? \u001b[0m\u001b[90m String? \u001b[0m\n",
1135 | "─────┼──────────────────\n",
1136 | " 1 │ 1 A\n",
1137 | " 2 │\u001b[90m missing \u001b[0m B\n",
1138 | " 3 │ 3 \u001b[90m missing \u001b[0m\n",
1139 | " 4 │ 4 C"
1140 | ]
1141 | },
1142 | "execution_count": 36,
1143 | "metadata": {},
1144 | "output_type": "execute_result"
1145 | }
1146 | ],
1147 | "source": [
1148 | "x = DataFrame(A=[1, missing, 3, 4], B=[\"A\", \"B\", missing, \"C\"])"
1149 | ]
1150 | },
1151 | {
1152 | "cell_type": "code",
1153 | "execution_count": 37,
1154 | "metadata": {},
1155 | "outputs": [
1156 | {
1157 | "data": {
1158 | "text/html": [
1159 | ""
1160 | ],
1161 | "text/latex": [
1162 | "\\begin{tabular}{r|cc}\n",
1163 | "\t& A & B\\\\\n",
1164 | "\t\\hline\n",
1165 | "\t& Int64? & String?\\\\\n",
1166 | "\t\\hline\n",
1167 | "\t1 & 1 & A \\\\\n",
1168 | "\t2 & 4 & C \\\\\n",
1169 | "\\end{tabular}\n"
1170 | ],
1171 | "text/plain": [
1172 | "\u001b[1m2×2 DataFrame\u001b[0m\n",
1173 | "\u001b[1m Row \u001b[0m│\u001b[1m A \u001b[0m\u001b[1m B \u001b[0m\n",
1174 | " │\u001b[90m Int64? \u001b[0m\u001b[90m String? \u001b[0m\n",
1175 | "─────┼─────────────────\n",
1176 | " 1 │ 1 A\n",
1177 | " 2 │ 4 C"
1178 | ]
1179 | },
1180 | "execution_count": 37,
1181 | "metadata": {},
1182 | "output_type": "execute_result"
1183 | }
1184 | ],
1185 | "source": [
1186 | "dropmissing!(x, disallowmissing=false)"
1187 | ]
1188 | },
1189 | {
1190 | "cell_type": "markdown",
1191 | "metadata": {},
1192 | "source": [
1193 | "### Making functions `missing`-aware\n",
1194 | "\n",
1195 | "If we have a function that does not handle `missing` values we can wrap it using `passmissing` function so that if any of its positional arguments is missing we will get a `missing` value in return. In the example below we change how `string` function behaves:"
1196 | ]
1197 | },
1198 | {
1199 | "cell_type": "code",
1200 | "execution_count": 38,
1201 | "metadata": {},
1202 | "outputs": [
1203 | {
1204 | "data": {
1205 | "text/plain": [
1206 | "\"missing\""
1207 | ]
1208 | },
1209 | "execution_count": 38,
1210 | "metadata": {},
1211 | "output_type": "execute_result"
1212 | }
1213 | ],
1214 | "source": [
1215 | "string(missing)"
1216 | ]
1217 | },
1218 | {
1219 | "cell_type": "code",
1220 | "execution_count": 39,
1221 | "metadata": {},
1222 | "outputs": [
1223 | {
1224 | "data": {
1225 | "text/plain": [
1226 | "\"missing missing\""
1227 | ]
1228 | },
1229 | "execution_count": 39,
1230 | "metadata": {},
1231 | "output_type": "execute_result"
1232 | }
1233 | ],
1234 | "source": [
1235 | "string(missing, \" \", missing)"
1236 | ]
1237 | },
1238 | {
1239 | "cell_type": "code",
1240 | "execution_count": 40,
1241 | "metadata": {},
1242 | "outputs": [
1243 | {
1244 | "data": {
1245 | "text/plain": [
1246 | "\"123\""
1247 | ]
1248 | },
1249 | "execution_count": 40,
1250 | "metadata": {},
1251 | "output_type": "execute_result"
1252 | }
1253 | ],
1254 | "source": [
1255 | "string(1,2,3)"
1256 | ]
1257 | },
1258 | {
1259 | "cell_type": "code",
1260 | "execution_count": 41,
1261 | "metadata": {},
1262 | "outputs": [
1263 | {
1264 | "data": {
1265 | "text/plain": [
1266 | "(::Missings.PassMissing{typeof(string)}) (generic function with 2 methods)"
1267 | ]
1268 | },
1269 | "execution_count": 41,
1270 | "metadata": {},
1271 | "output_type": "execute_result"
1272 | }
1273 | ],
1274 | "source": [
1275 | "lift_string = passmissing(string)"
1276 | ]
1277 | },
1278 | {
1279 | "cell_type": "code",
1280 | "execution_count": 42,
1281 | "metadata": {},
1282 | "outputs": [
1283 | {
1284 | "data": {
1285 | "text/plain": [
1286 | "missing"
1287 | ]
1288 | },
1289 | "execution_count": 42,
1290 | "metadata": {},
1291 | "output_type": "execute_result"
1292 | }
1293 | ],
1294 | "source": [
1295 | "lift_string(missing)"
1296 | ]
1297 | },
1298 | {
1299 | "cell_type": "code",
1300 | "execution_count": 43,
1301 | "metadata": {},
1302 | "outputs": [
1303 | {
1304 | "data": {
1305 | "text/plain": [
1306 | "missing"
1307 | ]
1308 | },
1309 | "execution_count": 43,
1310 | "metadata": {},
1311 | "output_type": "execute_result"
1312 | }
1313 | ],
1314 | "source": [
1315 | "lift_string(missing, \" \", missing)"
1316 | ]
1317 | },
1318 | {
1319 | "cell_type": "code",
1320 | "execution_count": 44,
1321 | "metadata": {},
1322 | "outputs": [
1323 | {
1324 | "data": {
1325 | "text/plain": [
1326 | "\"123\""
1327 | ]
1328 | },
1329 | "execution_count": 44,
1330 | "metadata": {},
1331 | "output_type": "execute_result"
1332 | }
1333 | ],
1334 | "source": [
1335 | "lift_string(1, 2, 3)"
1336 | ]
1337 | },
1338 | {
1339 | "cell_type": "markdown",
1340 | "metadata": {},
1341 | "source": [
1342 | "### Aggregating rows containing missing values"
1343 | ]
1344 | },
1345 | {
1346 | "cell_type": "markdown",
1347 | "metadata": {},
1348 | "source": [
1349 | "Create an example data frame containing missing values:"
1350 | ]
1351 | },
1352 | {
1353 | "cell_type": "code",
1354 | "execution_count": 45,
1355 | "metadata": {},
1356 | "outputs": [
1357 | {
1358 | "data": {
1359 | "text/html": [
1360 | "1 | 1 | 1 |
2 | missing | 2 |
3 | missing | missing |
"
1361 | ],
1362 | "text/latex": [
1363 | "\\begin{tabular}{r|cc}\n",
1364 | "\t& a & b\\\\\n",
1365 | "\t\\hline\n",
1366 | "\t& Int64? & Int64?\\\\\n",
1367 | "\t\\hline\n",
1368 | "\t1 & 1 & 1 \\\\\n",
1369 | "\t2 & \\emph{missing} & 2 \\\\\n",
1370 | "\t3 & \\emph{missing} & \\emph{missing} \\\\\n",
1371 | "\\end{tabular}\n"
1372 | ],
1373 | "text/plain": [
1374 | "\u001b[1m3×2 DataFrame\u001b[0m\n",
1375 | "\u001b[1m Row \u001b[0m│\u001b[1m a \u001b[0m\u001b[1m b \u001b[0m\n",
1376 | " │\u001b[90m Int64? \u001b[0m\u001b[90m Int64? \u001b[0m\n",
1377 | "─────┼──────────────────\n",
1378 | " 1 │ 1 1\n",
1379 | " 2 │\u001b[90m missing \u001b[0m 2\n",
1380 | " 3 │\u001b[90m missing \u001b[0m\u001b[90m missing \u001b[0m"
1381 | ]
1382 | },
1383 | "execution_count": 45,
1384 | "metadata": {},
1385 | "output_type": "execute_result"
1386 | }
1387 | ],
1388 | "source": [
1389 | "df = DataFrame(a = [1, missing, missing], b=[1, 2, missing])"
1390 | ]
1391 | },
1392 | {
1393 | "cell_type": "markdown",
1394 | "metadata": {},
1395 | "source": [
1396 | "If we just run `sum` on the rows we get two missing entries:"
1397 | ]
1398 | },
1399 | {
1400 | "cell_type": "code",
1401 | "execution_count": 46,
1402 | "metadata": {},
1403 | "outputs": [
1404 | {
1405 | "data": {
1406 | "text/plain": [
1407 | "3-element Vector{Union{Missing, Int64}}:\n",
1408 | " 2\n",
1409 | " missing\n",
1410 | " missing"
1411 | ]
1412 | },
1413 | "execution_count": 46,
1414 | "metadata": {},
1415 | "output_type": "execute_result"
1416 | }
1417 | ],
1418 | "source": [
1419 | "sum.(eachrow(df))"
1420 | ]
1421 | },
1422 | {
1423 | "cell_type": "markdown",
1424 | "metadata": {},
1425 | "source": [
1426 | "One can apply `skipmissing` on the rows to avoid this problem:"
1427 | ]
1428 | },
1429 | {
1430 | "cell_type": "code",
1431 | "execution_count": 47,
1432 | "metadata": {},
1433 | "outputs": [
1434 | {
1435 | "ename": "LoadError",
1436 | "evalue": "ArgumentError: reducing over an empty collection of unknown element type is not allowed.\nYou may be able to prevent this error by supplying an `init` value to the reducer.",
1437 | "output_type": "error",
1438 | "traceback": [
1439 | "ArgumentError: reducing over an empty collection of unknown element type is not allowed.\nYou may be able to prevent this error by supplying an `init` value to the reducer.",
1440 | "",
1441 | "Stacktrace:",
1442 | " [1] reduce_empty_iter(op::Base.BottomRF{typeof(Base.add_sum)}, itr::Base.SkipMissing{DataFrameRow{DataFrame, DataFrames.Index}}, #unused#::Base.EltypeUnknown)",
1443 | " @ Base .\\reduce.jl:380",
1444 | " [2] reduce_empty_iter",
1445 | " @ .\\reduce.jl:378 [inlined]",
1446 | " [3] foldl_impl",
1447 | " @ .\\reduce.jl:49 [inlined]",
1448 | " [4] mapfoldl_impl",
1449 | " @ .\\reduce.jl:44 [inlined]",
1450 | " [5] #mapfoldl#288",
1451 | " @ .\\reduce.jl:170 [inlined]",
1452 | " [6] mapfoldl",
1453 | " @ .\\reduce.jl:170 [inlined]",
1454 | " [7] #mapreduce#292",
1455 | " @ .\\reduce.jl:302 [inlined]",
1456 | " [8] mapreduce",
1457 | " @ .\\reduce.jl:302 [inlined]",
1458 | " [9] #sum#295",
1459 | " @ .\\reduce.jl:530 [inlined]",
1460 | " [10] sum",
1461 | " @ .\\reduce.jl:530 [inlined]",
1462 | " [11] #sum#296",
1463 | " @ .\\reduce.jl:559 [inlined]",
1464 | " [12] sum",
1465 | " @ .\\reduce.jl:559 [inlined]",
1466 | " [13] _broadcast_getindex_evalf",
1467 | " @ .\\broadcast.jl:683 [inlined]",
1468 | " [14] _broadcast_getindex",
1469 | " @ .\\broadcast.jl:656 [inlined]",
1470 | " [15] getindex",
1471 | " @ .\\broadcast.jl:610 [inlined]",
1472 | " [16] copyto_nonleaf!(dest::Vector{Int64}, bc::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Tuple{Base.OneTo{Int64}}, typeof(sum), Tuple{Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(skipmissing), Tuple{Base.Broadcast.Extruded{DataFrames.DataFrameRows{DataFrame}, Tuple{Bool}, Tuple{Int64}}}}}}, iter::Base.OneTo{Int64}, state::Int64, count::Int64)",
1473 | " @ Base.Broadcast .\\broadcast.jl:1068",
1474 | " [17] copy",
1475 | " @ .\\broadcast.jl:920 [inlined]",
1476 | " [18] materialize(bc::Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(sum), Tuple{Base.Broadcast.Broadcasted{Base.Broadcast.DefaultArrayStyle{1}, Nothing, typeof(skipmissing), Tuple{DataFrames.DataFrameRows{DataFrame}}}}})",
1477 | " @ Base.Broadcast .\\broadcast.jl:873",
1478 | " [19] top-level scope",
1479 | " @ In[47]:1"
1480 | ]
1481 | }
1482 | ],
1483 | "source": [
1484 | "sum.(skipmissing.(eachrow(df)))"
1485 | ]
1486 | },
1487 | {
1488 | "cell_type": "markdown",
1489 | "metadata": {},
1490 | "source": [
1491 | "However, we get an error. The problem is that the last row of `df` contains only missing values, and since `eachrow` is type unstable the `eltype` of the result of `skipmissing` is unknown (so it is marked `Any`)"
1492 | ]
1493 | },
1494 | {
1495 | "cell_type": "code",
1496 | "execution_count": 48,
1497 | "metadata": {},
1498 | "outputs": [
1499 | {
1500 | "data": {
1501 | "text/plain": [
1502 | "Any[]"
1503 | ]
1504 | },
1505 | "execution_count": 48,
1506 | "metadata": {},
1507 | "output_type": "execute_result"
1508 | }
1509 | ],
1510 | "source": [
1511 | "collect(skipmissing(eachrow(df)[end]))"
1512 | ]
1513 | },
1514 | {
1515 | "cell_type": "markdown",
1516 | "metadata": {},
1517 | "source": [
1518 | "In such cases it is useful to switch to `Tables.namedtupleiterator` which is type stable as discussed in 01_constructors.ipynb notebook."
1519 | ]
1520 | },
1521 | {
1522 | "cell_type": "code",
1523 | "execution_count": 49,
1524 | "metadata": {},
1525 | "outputs": [
1526 | {
1527 | "data": {
1528 | "text/plain": [
1529 | "3-element Vector{Int64}:\n",
1530 | " 2\n",
1531 | " 2\n",
1532 | " 0"
1533 | ]
1534 | },
1535 | "execution_count": 49,
1536 | "metadata": {},
1537 | "output_type": "execute_result"
1538 | }
1539 | ],
1540 | "source": [
1541 | "sum.(skipmissing.(Tables.namedtupleiterator(df)))"
1542 | ]
1543 | },
1544 | {
1545 | "cell_type": "markdown",
1546 | "metadata": {},
1547 | "source": [
1548 | "Later in the tutorial you will learn that you can efficiently calculate such sums using the `select` function:"
1549 | ]
1550 | },
1551 | {
1552 | "cell_type": "code",
1553 | "execution_count": 50,
1554 | "metadata": {},
1555 | "outputs": [
1556 | {
1557 | "data": {
1558 | "text/html": [
1559 | ""
1560 | ],
1561 | "text/latex": [
1562 | "\\begin{tabular}{r|c}\n",
1563 | "\t& a\\_b\\_sum\\_skipmissing\\\\\n",
1564 | "\t\\hline\n",
1565 | "\t& Int64\\\\\n",
1566 | "\t\\hline\n",
1567 | "\t1 & 2 \\\\\n",
1568 | "\t2 & 2 \\\\\n",
1569 | "\t3 & 0 \\\\\n",
1570 | "\\end{tabular}\n"
1571 | ],
1572 | "text/plain": [
1573 | "\u001b[1m3×1 DataFrame\u001b[0m\n",
1574 | "\u001b[1m Row \u001b[0m│\u001b[1m a_b_sum_skipmissing \u001b[0m\n",
1575 | " │\u001b[90m Int64 \u001b[0m\n",
1576 | "─────┼─────────────────────\n",
1577 | " 1 │ 2\n",
1578 | " 2 │ 2\n",
1579 | " 3 │ 0"
1580 | ]
1581 | },
1582 | "execution_count": 50,
1583 | "metadata": {},
1584 | "output_type": "execute_result"
1585 | }
1586 | ],
1587 | "source": [
1588 | "select(df, AsTable(:) => ByRow(sum∘skipmissing))"
1589 | ]
1590 | },
1591 | {
1592 | "cell_type": "markdown",
1593 | "metadata": {},
1594 | "source": [
1595 | "Note that it correctly handles the rows with all missing values."
1596 | ]
1597 | }
1598 | ],
1599 | "metadata": {
1600 | "@webio": {
1601 | "lastCommId": null,
1602 | "lastKernelId": null
1603 | },
1604 | "kernelspec": {
1605 | "display_name": "Julia 1.9.0",
1606 | "language": "julia",
1607 | "name": "julia-1.9"
1608 | },
1609 | "language_info": {
1610 | "file_extension": ".jl",
1611 | "mimetype": "application/julia",
1612 | "name": "julia",
1613 | "version": "1.9.0"
1614 | }
1615 | },
1616 | "nbformat": 4,
1617 | "nbformat_minor": 1
1618 | }
1619 |
--------------------------------------------------------------------------------
/07_factors.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# Introduction to DataFrames\n",
8 | "**[Bogumił Kamiński](http://bogumilkaminski.pl/about/), February 13, 2023**"
9 | ]
10 | },
11 | {
12 | "cell_type": "code",
13 | "execution_count": 1,
14 | "metadata": {},
15 | "outputs": [],
16 | "source": [
17 | "using DataFrames # load package"
18 | ]
19 | },
20 | {
21 | "cell_type": "code",
22 | "execution_count": 2,
23 | "metadata": {},
24 | "outputs": [],
25 | "source": [
26 | "using CategoricalArrays # CategoricalArrays.jl is independent from DataFrames.jl but it is often used in combination"
27 | ]
28 | },
29 | {
30 | "cell_type": "markdown",
31 | "metadata": {},
32 | "source": [
33 | "## Working with CategoricalArrays"
34 | ]
35 | },
36 | {
37 | "cell_type": "markdown",
38 | "metadata": {},
39 | "source": [
40 | "### Constructor"
41 | ]
42 | },
43 | {
44 | "cell_type": "code",
45 | "execution_count": 3,
46 | "metadata": {},
47 | "outputs": [
48 | {
49 | "data": {
50 | "text/plain": [
51 | "4-element CategoricalArray{String,1,UInt32}:\n",
52 | " \"A\"\n",
53 | " \"B\"\n",
54 | " \"B\"\n",
55 | " \"C\""
56 | ]
57 | },
58 | "execution_count": 3,
59 | "metadata": {},
60 | "output_type": "execute_result"
61 | }
62 | ],
63 | "source": [
64 | "x = categorical([\"A\", \"B\", \"B\", \"C\"]) # unordered"
65 | ]
66 | },
67 | {
68 | "cell_type": "code",
69 | "execution_count": 4,
70 | "metadata": {},
71 | "outputs": [
72 | {
73 | "data": {
74 | "text/plain": [
75 | "4-element CategoricalArray{String,1,UInt32}:\n",
76 | " \"A\"\n",
77 | " \"B\"\n",
78 | " \"B\"\n",
79 | " \"C\""
80 | ]
81 | },
82 | "execution_count": 4,
83 | "metadata": {},
84 | "output_type": "execute_result"
85 | }
86 | ],
87 | "source": [
88 | "y = categorical([\"A\", \"B\", \"B\", \"C\"], ordered=true) # ordered, by default order is sorting order"
89 | ]
90 | },
91 | {
92 | "cell_type": "code",
93 | "execution_count": 5,
94 | "metadata": {},
95 | "outputs": [
96 | {
97 | "data": {
98 | "text/plain": [
99 | "5-element CategoricalArray{Union{Missing, String},1,UInt32}:\n",
100 | " \"A\"\n",
101 | " \"B\"\n",
102 | " \"B\"\n",
103 | " \"C\"\n",
104 | " missing"
105 | ]
106 | },
107 | "execution_count": 5,
108 | "metadata": {},
109 | "output_type": "execute_result"
110 | }
111 | ],
112 | "source": [
113 | "z = categorical([\"A\",\"B\",\"B\",\"C\", missing]) # unordered with missings"
114 | ]
115 | },
116 | {
117 | "cell_type": "code",
118 | "execution_count": 6,
119 | "metadata": {},
120 | "outputs": [
121 | {
122 | "data": {
123 | "text/plain": [
124 | "10-element CategoricalArray{String,1,UInt32}:\n",
125 | " \"Q1: [1.0, 2.8)\"\n",
126 | " \"Q1: [1.0, 2.8)\"\n",
127 | " \"Q2: [2.8, 4.6)\"\n",
128 | " \"Q2: [2.8, 4.6)\"\n",
129 | " \"Q3: [4.6, 6.4)\"\n",
130 | " \"Q3: [4.6, 6.4)\"\n",
131 | " \"Q4: [6.4, 8.2)\"\n",
132 | " \"Q4: [6.4, 8.2)\"\n",
133 | " \"Q5: [8.2, 10.0]\"\n",
134 | " \"Q5: [8.2, 10.0]\""
135 | ]
136 | },
137 | "execution_count": 6,
138 | "metadata": {},
139 | "output_type": "execute_result"
140 | }
141 | ],
142 | "source": [
143 | "c = cut(1:10, 5) # ordered, into equal counts, possible to rename labels and give custom breaks"
144 | ]
145 | },
146 | {
147 | "cell_type": "markdown",
148 | "metadata": {},
149 | "source": [
150 | "(we will cover grouping later, but let us here use it to analyze the results, we use Chain.jl for chaining)"
151 | ]
152 | },
153 | {
154 | "cell_type": "code",
155 | "execution_count": 7,
156 | "metadata": {},
157 | "outputs": [],
158 | "source": [
159 | "using Chain"
160 | ]
161 | },
162 | {
163 | "cell_type": "code",
164 | "execution_count": 8,
165 | "metadata": {},
166 | "outputs": [
167 | {
168 | "data": {
169 | "text/html": [
170 | "1 | Q1: [-4.37425549047856, -1.2786607637653769) | 10000 |
2 | Q2: [-1.2786607637653769, -0.8430443474683171) | 10000 |
3 | Q3: [-0.8430443474683171, -0.5258259568979863) | 10000 |
4 | Q4: [-0.5258259568979863, -0.2593167185316153) | 10000 |
5 | Q5: [-0.2593167185316153, -0.0018062175416992735) | 10000 |
6 | Q6: [-0.0018062175416992735, 0.25137476303234196) | 10000 |
7 | Q7: [0.25137476303234196, 0.5222753668380758) | 10000 |
8 | Q8: [0.5222753668380758, 0.838323113350727) | 10000 |
9 | Q9: [0.838323113350727, 1.2837083260172553) | 10000 |
10 | Q10: [1.2837083260172553, 4.269831619555335] | 10000 |
"
171 | ],
172 | "text/latex": [
173 | "\\begin{tabular}{r|cc}\n",
174 | "\t& x & nrow\\\\\n",
175 | "\t\\hline\n",
176 | "\t& Cat… & Int64\\\\\n",
177 | "\t\\hline\n",
178 | "\t1 & Q1: [-4.37425549047856, -1.2786607637653769) & 10000 \\\\\n",
179 | "\t2 & Q2: [-1.2786607637653769, -0.8430443474683171) & 10000 \\\\\n",
180 | "\t3 & Q3: [-0.8430443474683171, -0.5258259568979863) & 10000 \\\\\n",
181 | "\t4 & Q4: [-0.5258259568979863, -0.2593167185316153) & 10000 \\\\\n",
182 | "\t5 & Q5: [-0.2593167185316153, -0.0018062175416992735) & 10000 \\\\\n",
183 | "\t6 & Q6: [-0.0018062175416992735, 0.25137476303234196) & 10000 \\\\\n",
184 | "\t7 & Q7: [0.25137476303234196, 0.5222753668380758) & 10000 \\\\\n",
185 | "\t8 & Q8: [0.5222753668380758, 0.838323113350727) & 10000 \\\\\n",
186 | "\t9 & Q9: [0.838323113350727, 1.2837083260172553) & 10000 \\\\\n",
187 | "\t10 & Q10: [1.2837083260172553, 4.269831619555335] & 10000 \\\\\n",
188 | "\\end{tabular}\n"
189 | ],
190 | "text/plain": [
191 | "\u001b[1m10×2 DataFrame\u001b[0m\n",
192 | "\u001b[1m Row \u001b[0m│\u001b[1m x \u001b[0m\u001b[1m nrow \u001b[0m\n",
193 | " │\u001b[90m Cat… \u001b[0m\u001b[90m Int64 \u001b[0m\n",
194 | "─────┼──────────────────────────────────────────\n",
195 | " 1 │ Q1: [-4.37425549047856, -1.27866… 10000\n",
196 | " 2 │ Q2: [-1.2786607637653769, -0.843… 10000\n",
197 | " 3 │ Q3: [-0.8430443474683171, -0.525… 10000\n",
198 | " 4 │ Q4: [-0.5258259568979863, -0.259… 10000\n",
199 | " 5 │ Q5: [-0.2593167185316153, -0.001… 10000\n",
200 | " 6 │ Q6: [-0.0018062175416992735, 0.2… 10000\n",
201 | " 7 │ Q7: [0.25137476303234196, 0.5222… 10000\n",
202 | " 8 │ Q8: [0.5222753668380758, 0.83832… 10000\n",
203 | " 9 │ Q9: [0.838323113350727, 1.283708… 10000\n",
204 | " 10 │ Q10: [1.2837083260172553, 4.2698… 10000"
205 | ]
206 | },
207 | "execution_count": 8,
208 | "metadata": {},
209 | "output_type": "execute_result"
210 | }
211 | ],
212 | "source": [
213 | "@chain DataFrame(x=cut(randn(100000), 10)) begin\n",
214 | " groupby(:x)\n",
215 | " combine(nrow) # just to make sure cut works right\n",
216 | "end"
217 | ]
218 | },
219 | {
220 | "cell_type": "code",
221 | "execution_count": 9,
222 | "metadata": {},
223 | "outputs": [
224 | {
225 | "data": {
226 | "text/plain": [
227 | "5-element CategoricalArray{Int64,1,UInt32}:\n",
228 | " 1\n",
229 | " 2\n",
230 | " 2\n",
231 | " 3\n",
232 | " 3"
233 | ]
234 | },
235 | "execution_count": 9,
236 | "metadata": {},
237 | "output_type": "execute_result"
238 | }
239 | ],
240 | "source": [
241 | "v = categorical([1,2,2,3,3]) # contains integers not strings"
242 | ]
243 | },
244 | {
245 | "cell_type": "code",
246 | "execution_count": 10,
247 | "metadata": {},
248 | "outputs": [
249 | {
250 | "data": {
251 | "text/plain": [
252 | "5-element Vector{Union{Missing, String}}:\n",
253 | " \"A\"\n",
254 | " \"B\"\n",
255 | " \"B\"\n",
256 | " \"C\"\n",
257 | " missing"
258 | ]
259 | },
260 | "execution_count": 10,
261 | "metadata": {},
262 | "output_type": "execute_result"
263 | }
264 | ],
265 | "source": [
266 | "Vector{Union{String, Missing}}(z) # sometimes you need to convert back to a standard vector"
267 | ]
268 | },
269 | {
270 | "cell_type": "markdown",
271 | "metadata": {},
272 | "source": [
273 | "### Managing levels"
274 | ]
275 | },
276 | {
277 | "cell_type": "code",
278 | "execution_count": 11,
279 | "metadata": {},
280 | "outputs": [
281 | {
282 | "data": {
283 | "text/plain": [
284 | "5-element Vector{CategoricalVector{T, UInt32} where T}:\n",
285 | " CategoricalValue{String, UInt32}[\"A\", \"B\", \"B\", \"C\"]\n",
286 | " CategoricalValue{String, UInt32}[\"A\", \"B\", \"B\", \"C\"]\n",
287 | " Union{Missing, CategoricalValue{String, UInt32}}[\"A\", \"B\", \"B\", \"C\", missing]\n",
288 | " CategoricalValue{String, UInt32}[\"Q1: [1.0, 2.8)\", \"Q1: [1.0, 2.8)\", \"Q2: [2.8, 4.6)\", \"Q2: [2.8, 4.6)\", \"Q3: [4.6, 6.4)\", \"Q3: [4.6, 6.4)\", \"Q4: [6.4, 8.2)\", \"Q4: [6.4, 8.2)\", \"Q5: [8.2, 10.0]\", \"Q5: [8.2, 10.0]\"]\n",
289 | " CategoricalValue{Int64, UInt32}[1, 2, 2, 3, 3]"
290 | ]
291 | },
292 | "execution_count": 11,
293 | "metadata": {},
294 | "output_type": "execute_result"
295 | }
296 | ],
297 | "source": [
298 | "arr = [x,y,z,c,v]"
299 | ]
300 | },
301 | {
302 | "cell_type": "code",
303 | "execution_count": 12,
304 | "metadata": {},
305 | "outputs": [
306 | {
307 | "data": {
308 | "text/plain": [
309 | "5-element BitVector:\n",
310 | " 0\n",
311 | " 1\n",
312 | " 0\n",
313 | " 1\n",
314 | " 0"
315 | ]
316 | },
317 | "execution_count": 12,
318 | "metadata": {},
319 | "output_type": "execute_result"
320 | }
321 | ],
322 | "source": [
323 | "isordered.(arr) # chcek if categorical array is orderd"
324 | ]
325 | },
326 | {
327 | "cell_type": "code",
328 | "execution_count": 13,
329 | "metadata": {},
330 | "outputs": [
331 | {
332 | "data": {
333 | "text/plain": [
334 | "(CategoricalValue{String, UInt32}[\"A\", \"B\", \"B\", \"C\"], true)"
335 | ]
336 | },
337 | "execution_count": 13,
338 | "metadata": {},
339 | "output_type": "execute_result"
340 | }
341 | ],
342 | "source": [
343 | "ordered!(x, true), isordered(x) # make x ordered"
344 | ]
345 | },
346 | {
347 | "cell_type": "code",
348 | "execution_count": 14,
349 | "metadata": {},
350 | "outputs": [
351 | {
352 | "data": {
353 | "text/plain": [
354 | "(CategoricalValue{String, UInt32}[\"A\", \"B\", \"B\", \"C\"], false)"
355 | ]
356 | },
357 | "execution_count": 14,
358 | "metadata": {},
359 | "output_type": "execute_result"
360 | }
361 | ],
362 | "source": [
363 | "ordered!(x, false), isordered(x) # and unordered again"
364 | ]
365 | },
366 | {
367 | "cell_type": "code",
368 | "execution_count": 15,
369 | "metadata": {},
370 | "outputs": [
371 | {
372 | "data": {
373 | "text/plain": [
374 | "5-element Vector{Vector}:\n",
375 | " [\"A\", \"B\", \"C\"]\n",
376 | " [\"A\", \"B\", \"C\"]\n",
377 | " [\"A\", \"B\", \"C\"]\n",
378 | " [\"Q1: [1.0, 2.8)\", \"Q2: [2.8, 4.6)\", \"Q3: [4.6, 6.4)\", \"Q4: [6.4, 8.2)\", \"Q5: [8.2, 10.0]\"]\n",
379 | " [1, 2, 3]"
380 | ]
381 | },
382 | "execution_count": 15,
383 | "metadata": {},
384 | "output_type": "execute_result"
385 | }
386 | ],
387 | "source": [
388 | "levels.(arr) # list levels"
389 | ]
390 | },
391 | {
392 | "cell_type": "code",
393 | "execution_count": 16,
394 | "metadata": {},
395 | "outputs": [
396 | {
397 | "data": {
398 | "text/plain": [
399 | "5-element Vector{Vector}:\n",
400 | " [\"A\", \"B\", \"C\"]\n",
401 | " [\"A\", \"B\", \"C\"]\n",
402 | " Union{Missing, String}[\"A\", \"B\", \"C\", missing]\n",
403 | " [\"Q1: [1.0, 2.8)\", \"Q2: [2.8, 4.6)\", \"Q3: [4.6, 6.4)\", \"Q4: [6.4, 8.2)\", \"Q5: [8.2, 10.0]\"]\n",
404 | " [1, 2, 3]"
405 | ]
406 | },
407 | "execution_count": 16,
408 | "metadata": {},
409 | "output_type": "execute_result"
410 | }
411 | ],
412 | "source": [
413 | "unique.(arr) # missing will be included"
414 | ]
415 | },
416 | {
417 | "cell_type": "code",
418 | "execution_count": 17,
419 | "metadata": {},
420 | "outputs": [
421 | {
422 | "data": {
423 | "text/plain": [
424 | "true"
425 | ]
426 | },
427 | "execution_count": 17,
428 | "metadata": {},
429 | "output_type": "execute_result"
430 | }
431 | ],
432 | "source": [
433 | "y[1] < y[2] # can compare as y is ordered"
434 | ]
435 | },
436 | {
437 | "cell_type": "code",
438 | "execution_count": 18,
439 | "metadata": {},
440 | "outputs": [
441 | {
442 | "ename": "LoadError",
443 | "evalue": "ArgumentError: Unordered CategoricalValue objects cannot be tested for order using <. Use isless instead, or call the ordered! function on the parent array to change this",
444 | "output_type": "error",
445 | "traceback": [
446 | "ArgumentError: Unordered CategoricalValue objects cannot be tested for order using <. Use isless instead, or call the ordered! function on the parent array to change this",
447 | "",
448 | "Stacktrace:",
449 | " [1] <(x::CategoricalValue{Int64, UInt32}, y::CategoricalValue{Int64, UInt32})",
450 | " @ CategoricalArrays C:\\Users\\bogum\\.julia\\packages\\CategoricalArrays\\tJ8hD\\src\\value.jl:166",
451 | " [2] top-level scope",
452 | " @ In[18]:1"
453 | ]
454 | }
455 | ],
456 | "source": [
457 | "v[1] < v[2] # not comparable, v is unordered although it contains integers"
458 | ]
459 | },
460 | {
461 | "cell_type": "code",
462 | "execution_count": 19,
463 | "metadata": {},
464 | "outputs": [
465 | {
466 | "ename": "LoadError",
467 | "evalue": "ArgumentError: cannot compare a `CategoricalValue` to value `v` of type `String`: wrap `v` using `CategoricalValue(v, catvalue)` or `CategoricalValue(v, catarray)` first",
468 | "output_type": "error",
469 | "traceback": [
470 | "ArgumentError: cannot compare a `CategoricalValue` to value `v` of type `String`: wrap `v` using `CategoricalValue(v, catvalue)` or `CategoricalValue(v, catarray)` first",
471 | "",
472 | "Stacktrace:",
473 | " [1] <(x::CategoricalValue{String, UInt32}, y::String)",
474 | " @ CategoricalArrays C:\\Users\\bogum\\.julia\\packages\\CategoricalArrays\\tJ8hD\\src\\value.jl:173",
475 | " [2] top-level scope",
476 | " @ In[19]:1"
477 | ]
478 | }
479 | ],
480 | "source": [
481 | "y[2] < \"A\" # comparison against type underlying categorical value is not allowed"
482 | ]
483 | },
484 | {
485 | "cell_type": "code",
486 | "execution_count": 20,
487 | "metadata": {},
488 | "outputs": [
489 | {
490 | "data": {
491 | "text/plain": [
492 | "false"
493 | ]
494 | },
495 | "execution_count": 20,
496 | "metadata": {},
497 | "output_type": "execute_result"
498 | }
499 | ],
500 | "source": [
501 | "y[2] < CategoricalValue(\"A\", y) # you need to explicitly convert a value to a level"
502 | ]
503 | },
504 | {
505 | "cell_type": "code",
506 | "execution_count": 21,
507 | "metadata": {},
508 | "outputs": [
509 | {
510 | "ename": "LoadError",
511 | "evalue": "ArgumentError: level Z not found in source pool",
512 | "output_type": "error",
513 | "traceback": [
514 | "ArgumentError: level Z not found in source pool",
515 | "",
516 | "Stacktrace:",
517 | " [1] CategoricalValue(value::String, source::CategoricalVector{String, UInt32, String, CategoricalValue{String, UInt32}, Union{}})",
518 | " @ CategoricalArrays C:\\Users\\bogum\\.julia\\packages\\CategoricalArrays\\tJ8hD\\src\\value.jl:13",
519 | " [2] top-level scope",
520 | " @ In[21]:1"
521 | ]
522 | }
523 | ],
524 | "source": [
525 | "y[2] < CategoricalValue(\"Z\", y) # but it is treated as a level, and thus only valid levels are allowed"
526 | ]
527 | },
528 | {
529 | "cell_type": "code",
530 | "execution_count": 22,
531 | "metadata": {},
532 | "outputs": [
533 | {
534 | "data": {
535 | "text/plain": [
536 | "4-element CategoricalArray{String,1,UInt32}:\n",
537 | " \"A\"\n",
538 | " \"B\"\n",
539 | " \"B\"\n",
540 | " \"C\""
541 | ]
542 | },
543 | "execution_count": 22,
544 | "metadata": {},
545 | "output_type": "execute_result"
546 | }
547 | ],
548 | "source": [
549 | "levels!(y, [\"C\", \"B\", \"A\"]) # you can reorder levels, mostly useful for ordered CategoricalArrays"
550 | ]
551 | },
552 | {
553 | "cell_type": "code",
554 | "execution_count": 23,
555 | "metadata": {},
556 | "outputs": [
557 | {
558 | "data": {
559 | "text/plain": [
560 | "false"
561 | ]
562 | },
563 | "execution_count": 23,
564 | "metadata": {},
565 | "output_type": "execute_result"
566 | }
567 | ],
568 | "source": [
569 | "y[1] < y[2] # observe that the order is changed"
570 | ]
571 | },
572 | {
573 | "cell_type": "code",
574 | "execution_count": 24,
575 | "metadata": {},
576 | "outputs": [
577 | {
578 | "ename": "LoadError",
579 | "evalue": "ArgumentError: cannot remove level \"C\" as it is used at position 4 and allowmissing=false.",
580 | "output_type": "error",
581 | "traceback": [
582 | "ArgumentError: cannot remove level \"C\" as it is used at position 4 and allowmissing=false.",
583 | "",
584 | "Stacktrace:",
585 | " [1] levels!(A::CategoricalVector{Union{Missing, String}, UInt32, String, CategoricalValue{String, UInt32}, Missing}, newlevels::Vector{String}; allowmissing::Bool, allow_missing::Nothing)",
586 | " @ CategoricalArrays C:\\Users\\bogum\\.julia\\packages\\CategoricalArrays\\tJ8hD\\src\\array.jl:859",
587 | " [2] levels!(A::CategoricalVector{Union{Missing, String}, UInt32, String, CategoricalValue{String, UInt32}, Missing}, newlevels::Vector{String})",
588 | " @ CategoricalArrays C:\\Users\\bogum\\.julia\\packages\\CategoricalArrays\\tJ8hD\\src\\array.jl:793",
589 | " [3] top-level scope",
590 | " @ In[24]:1"
591 | ]
592 | }
593 | ],
594 | "source": [
595 | "levels!(z, [\"A\", \"B\"]) # you have to specify all levels that are present"
596 | ]
597 | },
598 | {
599 | "cell_type": "code",
600 | "execution_count": 25,
601 | "metadata": {},
602 | "outputs": [
603 | {
604 | "data": {
605 | "text/plain": [
606 | "5-element CategoricalArray{Union{Missing, String},1,UInt32}:\n",
607 | " \"A\"\n",
608 | " \"B\"\n",
609 | " \"B\"\n",
610 | " missing\n",
611 | " missing"
612 | ]
613 | },
614 | "execution_count": 25,
615 | "metadata": {},
616 | "output_type": "execute_result"
617 | }
618 | ],
619 | "source": [
620 | "levels!(z, [\"A\", \"B\"], allowmissing=true) # unless the underlying array allows for missings and force removal of levels"
621 | ]
622 | },
623 | {
624 | "cell_type": "code",
625 | "execution_count": 26,
626 | "metadata": {},
627 | "outputs": [
628 | {
629 | "data": {
630 | "text/plain": [
631 | "5-element CategoricalArray{Union{Missing, String},1,UInt32}:\n",
632 | " \"B\"\n",
633 | " \"B\"\n",
634 | " \"B\"\n",
635 | " missing\n",
636 | " missing"
637 | ]
638 | },
639 | "execution_count": 26,
640 | "metadata": {},
641 | "output_type": "execute_result"
642 | }
643 | ],
644 | "source": [
645 | "z[1] = \"B\"\n",
646 | "z # now z has only \"B\" entries"
647 | ]
648 | },
649 | {
650 | "cell_type": "code",
651 | "execution_count": 27,
652 | "metadata": {},
653 | "outputs": [
654 | {
655 | "data": {
656 | "text/plain": [
657 | "2-element Vector{String}:\n",
658 | " \"A\"\n",
659 | " \"B\""
660 | ]
661 | },
662 | "execution_count": 27,
663 | "metadata": {},
664 | "output_type": "execute_result"
665 | }
666 | ],
667 | "source": [
668 | "levels(z) # but it remembers the levels it had (the reason is mostly performance)"
669 | ]
670 | },
671 | {
672 | "cell_type": "code",
673 | "execution_count": 28,
674 | "metadata": {},
675 | "outputs": [
676 | {
677 | "data": {
678 | "text/plain": [
679 | "1-element Vector{String}:\n",
680 | " \"B\""
681 | ]
682 | },
683 | "execution_count": 28,
684 | "metadata": {},
685 | "output_type": "execute_result"
686 | }
687 | ],
688 | "source": [
689 | "droplevels!(z) # this way we can clean it up\n",
690 | "levels(z)"
691 | ]
692 | },
693 | {
694 | "cell_type": "markdown",
695 | "metadata": {},
696 | "source": [
697 | "### Data manipulation"
698 | ]
699 | },
700 | {
701 | "cell_type": "code",
702 | "execution_count": 29,
703 | "metadata": {},
704 | "outputs": [
705 | {
706 | "data": {
707 | "text/plain": [
708 | "(CategoricalValue{String, UInt32}[\"A\", \"B\", \"B\", \"C\"], [\"A\", \"B\", \"C\"])"
709 | ]
710 | },
711 | "execution_count": 29,
712 | "metadata": {},
713 | "output_type": "execute_result"
714 | }
715 | ],
716 | "source": [
717 | "x, levels(x)"
718 | ]
719 | },
720 | {
721 | "cell_type": "code",
722 | "execution_count": 30,
723 | "metadata": {},
724 | "outputs": [
725 | {
726 | "data": {
727 | "text/plain": [
728 | "(CategoricalValue{String, UInt32}[\"A\", \"0\", \"B\", \"C\"], [\"A\", \"B\", \"C\", \"0\"])"
729 | ]
730 | },
731 | "execution_count": 30,
732 | "metadata": {},
733 | "output_type": "execute_result"
734 | }
735 | ],
736 | "source": [
737 | "x[2] = \"0\"\n",
738 | "x, levels(x) # new level added at the end (works only for unordered)"
739 | ]
740 | },
741 | {
742 | "cell_type": "code",
743 | "execution_count": 31,
744 | "metadata": {},
745 | "outputs": [
746 | {
747 | "data": {
748 | "text/plain": [
749 | "(CategoricalValue{Int64, UInt32}[1, 2, 2, 3, 3], [1, 2, 3])"
750 | ]
751 | },
752 | "execution_count": 31,
753 | "metadata": {},
754 | "output_type": "execute_result"
755 | }
756 | ],
757 | "source": [
758 | "v, levels(v)"
759 | ]
760 | },
761 | {
762 | "cell_type": "code",
763 | "execution_count": 32,
764 | "metadata": {},
765 | "outputs": [
766 | {
767 | "ename": "LoadError",
768 | "evalue": "MethodError: no method matching +(::CategoricalValue{Int64, UInt32}, ::CategoricalValue{Int64, UInt32})\n\n\u001b[0mClosest candidates are:\n\u001b[0m +(::Any, ::Any, \u001b[91m::Any\u001b[39m, \u001b[91m::Any...\u001b[39m)\n\u001b[0m\u001b[90m @\u001b[39m \u001b[90mBase\u001b[39m \u001b[90m\u001b[4moperators.jl:578\u001b[24m\u001b[39m\n",
769 | "output_type": "error",
770 | "traceback": [
771 | "MethodError: no method matching +(::CategoricalValue{Int64, UInt32}, ::CategoricalValue{Int64, UInt32})\n\n\u001b[0mClosest candidates are:\n\u001b[0m +(::Any, ::Any, \u001b[91m::Any\u001b[39m, \u001b[91m::Any...\u001b[39m)\n\u001b[0m\u001b[90m @\u001b[39m \u001b[90mBase\u001b[39m \u001b[90m\u001b[4moperators.jl:578\u001b[24m\u001b[39m\n",
772 | "",
773 | "Stacktrace:",
774 | " [1] top-level scope",
775 | " @ In[32]:1"
776 | ]
777 | }
778 | ],
779 | "source": [
780 | "v[1] + v[2] # even though the underlying data is Int, we cannot operate on it"
781 | ]
782 | },
783 | {
784 | "cell_type": "code",
785 | "execution_count": 33,
786 | "metadata": {},
787 | "outputs": [
788 | {
789 | "data": {
790 | "text/plain": [
791 | "5-element Vector{Int64}:\n",
792 | " 1\n",
793 | " 2\n",
794 | " 2\n",
795 | " 3\n",
796 | " 3"
797 | ]
798 | },
799 | "execution_count": 33,
800 | "metadata": {},
801 | "output_type": "execute_result"
802 | }
803 | ],
804 | "source": [
805 | "Vector{Int}(v) # you have either to retrieve the data by conversion (may be expensive)"
806 | ]
807 | },
808 | {
809 | "cell_type": "code",
810 | "execution_count": 34,
811 | "metadata": {},
812 | "outputs": [
813 | {
814 | "data": {
815 | "text/plain": [
816 | "3"
817 | ]
818 | },
819 | "execution_count": 34,
820 | "metadata": {},
821 | "output_type": "execute_result"
822 | }
823 | ],
824 | "source": [
825 | "unwrap(v[1]) + unwrap(v[2]) # or get a single value"
826 | ]
827 | },
828 | {
829 | "cell_type": "code",
830 | "execution_count": 35,
831 | "metadata": {},
832 | "outputs": [
833 | {
834 | "data": {
835 | "text/plain": [
836 | "5-element Vector{Int64}:\n",
837 | " 1\n",
838 | " 2\n",
839 | " 2\n",
840 | " 3\n",
841 | " 3"
842 | ]
843 | },
844 | "execution_count": 35,
845 | "metadata": {},
846 | "output_type": "execute_result"
847 | }
848 | ],
849 | "source": [
850 | "unwrap.(v) # this will work for arrays witout missings"
851 | ]
852 | },
853 | {
854 | "cell_type": "code",
855 | "execution_count": 36,
856 | "metadata": {},
857 | "outputs": [
858 | {
859 | "data": {
860 | "text/plain": [
861 | "5-element Vector{Union{Missing, String}}:\n",
862 | " \"B\"\n",
863 | " \"B\"\n",
864 | " \"B\"\n",
865 | " missing\n",
866 | " missing"
867 | ]
868 | },
869 | "execution_count": 36,
870 | "metadata": {},
871 | "output_type": "execute_result"
872 | }
873 | ],
874 | "source": [
875 | "unwrap.(z) # also works on missing values"
876 | ]
877 | },
878 | {
879 | "cell_type": "code",
880 | "execution_count": 37,
881 | "metadata": {},
882 | "outputs": [
883 | {
884 | "data": {
885 | "text/plain": [
886 | "5-element Vector{Union{Missing, String}}:\n",
887 | " \"B\"\n",
888 | " \"B\"\n",
889 | " \"B\"\n",
890 | " missing\n",
891 | " missing"
892 | ]
893 | },
894 | "execution_count": 37,
895 | "metadata": {},
896 | "output_type": "execute_result"
897 | }
898 | ],
899 | "source": [
900 | "Vector{Union{String, Missing}}(z) # or do the conversion"
901 | ]
902 | },
903 | {
904 | "cell_type": "code",
905 | "execution_count": 38,
906 | "metadata": {},
907 | "outputs": [
908 | {
909 | "data": {
910 | "text/plain": [
911 | "6-element Vector{Union{Missing, Int64}}:\n",
912 | " 10\n",
913 | " 2\n",
914 | " 3\n",
915 | " 4\n",
916 | " 5\n",
917 | " missing"
918 | ]
919 | },
920 | "execution_count": 38,
921 | "metadata": {},
922 | "output_type": "execute_result"
923 | }
924 | ],
925 | "source": [
926 | "recode([1,2,3,4,5,missing], 1=>10) # recode some values in an array; has also in place recode! equivalent"
927 | ]
928 | },
929 | {
930 | "cell_type": "code",
931 | "execution_count": 39,
932 | "metadata": {},
933 | "outputs": [
934 | {
935 | "data": {
936 | "text/plain": [
937 | "6-element Vector{Union{Missing, Int64, String}}:\n",
938 | " 10\n",
939 | " 20\n",
940 | " \"a\"\n",
941 | " \"a\"\n",
942 | " \"a\"\n",
943 | " missing"
944 | ]
945 | },
946 | "execution_count": 39,
947 | "metadata": {},
948 | "output_type": "execute_result"
949 | }
950 | ],
951 | "source": [
952 | "recode([1,2,3,4,5,missing], \"a\", 1=>10, 2=>20) # here we provided a default value for not mapped recodings"
953 | ]
954 | },
955 | {
956 | "cell_type": "code",
957 | "execution_count": 40,
958 | "metadata": {},
959 | "outputs": [
960 | {
961 | "data": {
962 | "text/plain": [
963 | "6-element Vector{Union{Int64, String}}:\n",
964 | " 10\n",
965 | " 2\n",
966 | " 3\n",
967 | " 4\n",
968 | " 5\n",
969 | " \"missing\""
970 | ]
971 | },
972 | "execution_count": 40,
973 | "metadata": {},
974 | "output_type": "execute_result"
975 | }
976 | ],
977 | "source": [
978 | "recode([1,2,3,4,5,missing], 1=>10, missing=>\"missing\") # to recode Missing you have to do it explicitly"
979 | ]
980 | },
981 | {
982 | "cell_type": "code",
983 | "execution_count": 41,
984 | "metadata": {},
985 | "outputs": [
986 | {
987 | "data": {
988 | "text/plain": [
989 | "(Union{Missing, CategoricalValue{Int64, UInt32}}[1, 2, 3, 4, 5, missing], [1, 2, 3, 4, 5])"
990 | ]
991 | },
992 | "execution_count": 41,
993 | "metadata": {},
994 | "output_type": "execute_result"
995 | }
996 | ],
997 | "source": [
998 | "t = categorical([1:5; missing])\n",
999 | "t, levels(t)"
1000 | ]
1001 | },
1002 | {
1003 | "cell_type": "code",
1004 | "execution_count": 42,
1005 | "metadata": {},
1006 | "outputs": [
1007 | {
1008 | "data": {
1009 | "text/plain": [
1010 | "(Union{Missing, CategoricalValue{Int64, UInt32}}[2, 2, 2, 4, 5, missing], [2, 4, 5])"
1011 | ]
1012 | },
1013 | "execution_count": 42,
1014 | "metadata": {},
1015 | "output_type": "execute_result"
1016 | }
1017 | ],
1018 | "source": [
1019 | "recode!(t, [1,3]=>2)\n",
1020 | "t, levels(t) # note that the levels are dropped after recode"
1021 | ]
1022 | },
1023 | {
1024 | "cell_type": "code",
1025 | "execution_count": 43,
1026 | "metadata": {},
1027 | "outputs": [
1028 | {
1029 | "data": {
1030 | "text/plain": [
1031 | "3-element Vector{Int64}:\n",
1032 | " 3\n",
1033 | " 0\n",
1034 | " -1"
1035 | ]
1036 | },
1037 | "execution_count": 43,
1038 | "metadata": {},
1039 | "output_type": "execute_result"
1040 | }
1041 | ],
1042 | "source": [
1043 | "t = categorical([1,2,3], ordered=true)\n",
1044 | "levels(recode(t, 2=>0, 1=>-1)) # and if you introduce a new levels they are added at the end in the order of appearance"
1045 | ]
1046 | },
1047 | {
1048 | "cell_type": "code",
1049 | "execution_count": 44,
1050 | "metadata": {},
1051 | "outputs": [
1052 | {
1053 | "data": {
1054 | "text/plain": [
1055 | "3-element Vector{Int64}:\n",
1056 | " 100\n",
1057 | " 200\n",
1058 | " 300"
1059 | ]
1060 | },
1061 | "execution_count": 44,
1062 | "metadata": {},
1063 | "output_type": "execute_result"
1064 | }
1065 | ],
1066 | "source": [
1067 | "t = categorical([1,2,3,4,5], ordered=true) # when using default it becomes the last level\n",
1068 | "levels(recode(t, 300, [1,2]=>100, 3=>200))"
1069 | ]
1070 | },
1071 | {
1072 | "cell_type": "markdown",
1073 | "metadata": {},
1074 | "source": [
1075 | "### Comparisons"
1076 | ]
1077 | },
1078 | {
1079 | "cell_type": "code",
1080 | "execution_count": 45,
1081 | "metadata": {},
1082 | "outputs": [
1083 | {
1084 | "data": {
1085 | "text/plain": [
1086 | "4×4 Matrix{Bool}:\n",
1087 | " 1 1 1 1\n",
1088 | " 1 1 1 1\n",
1089 | " 1 1 1 1\n",
1090 | " 1 1 1 1"
1091 | ]
1092 | },
1093 | "execution_count": 45,
1094 | "metadata": {},
1095 | "output_type": "execute_result"
1096 | }
1097 | ],
1098 | "source": [
1099 | "x = categorical([1,2,3])\n",
1100 | "xs = [x, categorical(x), categorical(x, ordered=true), categorical(x, ordered=true)]\n",
1101 | "levels!(xs[2], [3,2,1])\n",
1102 | "levels!(xs[4], [2,3,1])\n",
1103 | "[a == b for a in xs, b in xs] # all are equal - comparison only by contents"
1104 | ]
1105 | },
1106 | {
1107 | "cell_type": "code",
1108 | "execution_count": 46,
1109 | "metadata": {},
1110 | "outputs": [
1111 | {
1112 | "data": {
1113 | "text/plain": [
1114 | "4×4 Matrix{Bool}:\n",
1115 | " 1 0 0 0\n",
1116 | " 0 1 0 0\n",
1117 | " 0 0 1 0\n",
1118 | " 0 0 0 1"
1119 | ]
1120 | },
1121 | "execution_count": 46,
1122 | "metadata": {},
1123 | "output_type": "execute_result"
1124 | }
1125 | ],
1126 | "source": [
1127 | "signature(x::CategoricalArray) = (x, levels(x), isordered(x)) # this is actually the full signature of CategoricalArray\n",
1128 | "# all are different, notice that x[1] and x[2] are unordered but have a different order of levels\n",
1129 | "[signature(a) == signature(b) for a in xs, b in xs]"
1130 | ]
1131 | },
1132 | {
1133 | "cell_type": "code",
1134 | "execution_count": 47,
1135 | "metadata": {},
1136 | "outputs": [
1137 | {
1138 | "ename": "LoadError",
1139 | "evalue": "ArgumentError: Unordered CategoricalValue objects cannot be tested for order using <. Use isless instead, or call the ordered! function on the parent array to change this",
1140 | "output_type": "error",
1141 | "traceback": [
1142 | "ArgumentError: Unordered CategoricalValue objects cannot be tested for order using <. Use isless instead, or call the ordered! function on the parent array to change this",
1143 | "",
1144 | "Stacktrace:",
1145 | " [1] <(x::CategoricalValue{Int64, UInt32}, y::CategoricalValue{Int64, UInt32})",
1146 | " @ CategoricalArrays C:\\Users\\bogum\\.julia\\packages\\CategoricalArrays\\tJ8hD\\src\\value.jl:166",
1147 | " [2] top-level scope",
1148 | " @ In[47]:1"
1149 | ]
1150 | }
1151 | ],
1152 | "source": [
1153 | "x[1] < x[2] # you cannot compare elements of unordered CategoricalArray"
1154 | ]
1155 | },
1156 | {
1157 | "cell_type": "code",
1158 | "execution_count": 48,
1159 | "metadata": {},
1160 | "outputs": [
1161 | {
1162 | "data": {
1163 | "text/plain": [
1164 | "true"
1165 | ]
1166 | },
1167 | "execution_count": 48,
1168 | "metadata": {},
1169 | "output_type": "execute_result"
1170 | }
1171 | ],
1172 | "source": [
1173 | "t[1] < t[2] # but you can do it for an ordered one"
1174 | ]
1175 | },
1176 | {
1177 | "cell_type": "code",
1178 | "execution_count": 49,
1179 | "metadata": {},
1180 | "outputs": [
1181 | {
1182 | "data": {
1183 | "text/plain": [
1184 | "true"
1185 | ]
1186 | },
1187 | "execution_count": 49,
1188 | "metadata": {},
1189 | "output_type": "execute_result"
1190 | }
1191 | ],
1192 | "source": [
1193 | "isless(x[1], x[2]) # isless works within the same CategoricalArray even if it is not ordered"
1194 | ]
1195 | },
1196 | {
1197 | "cell_type": "code",
1198 | "execution_count": 50,
1199 | "metadata": {},
1200 | "outputs": [
1201 | {
1202 | "data": {
1203 | "text/plain": [
1204 | "true"
1205 | ]
1206 | },
1207 | "execution_count": 50,
1208 | "metadata": {},
1209 | "output_type": "execute_result"
1210 | }
1211 | ],
1212 | "source": [
1213 | "y = deepcopy(x) # but not across categorical arrays\n",
1214 | "isless(x[1], y[2])"
1215 | ]
1216 | },
1217 | {
1218 | "cell_type": "code",
1219 | "execution_count": 51,
1220 | "metadata": {},
1221 | "outputs": [
1222 | {
1223 | "data": {
1224 | "text/plain": [
1225 | "true"
1226 | ]
1227 | },
1228 | "execution_count": 51,
1229 | "metadata": {},
1230 | "output_type": "execute_result"
1231 | }
1232 | ],
1233 | "source": [
1234 | "isless(unwrap(x[1]), unwrap(y[2])) # you can use get to make a comparison of the contents of CategoricalArray"
1235 | ]
1236 | },
1237 | {
1238 | "cell_type": "code",
1239 | "execution_count": 52,
1240 | "metadata": {},
1241 | "outputs": [
1242 | {
1243 | "data": {
1244 | "text/plain": [
1245 | "false"
1246 | ]
1247 | },
1248 | "execution_count": 52,
1249 | "metadata": {},
1250 | "output_type": "execute_result"
1251 | }
1252 | ],
1253 | "source": [
1254 | "x[1] == y[2] # equality tests works OK across CategoricalArrays"
1255 | ]
1256 | },
1257 | {
1258 | "cell_type": "markdown",
1259 | "metadata": {},
1260 | "source": [
1261 | "### Categorical columns in a DataFrame"
1262 | ]
1263 | },
1264 | {
1265 | "cell_type": "code",
1266 | "execution_count": 53,
1267 | "metadata": {},
1268 | "outputs": [
1269 | {
1270 | "data": {
1271 | "text/html": [
1272 | ""
1273 | ],
1274 | "text/latex": [
1275 | "\\begin{tabular}{r|ccc}\n",
1276 | "\t& x & y & z\\\\\n",
1277 | "\t\\hline\n",
1278 | "\t& Int64 & Char & String\\\\\n",
1279 | "\t\\hline\n",
1280 | "\t1 & 1 & a & a \\\\\n",
1281 | "\t2 & 2 & b & b \\\\\n",
1282 | "\t3 & 3 & c & c \\\\\n",
1283 | "\\end{tabular}\n"
1284 | ],
1285 | "text/plain": [
1286 | "\u001b[1m3×3 DataFrame\u001b[0m\n",
1287 | "\u001b[1m Row \u001b[0m│\u001b[1m x \u001b[0m\u001b[1m y \u001b[0m\u001b[1m z \u001b[0m\n",
1288 | " │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\u001b[90m String \u001b[0m\n",
1289 | "─────┼─────────────────────\n",
1290 | " 1 │ 1 a a\n",
1291 | " 2 │ 2 b b\n",
1292 | " 3 │ 3 c c"
1293 | ]
1294 | },
1295 | "execution_count": 53,
1296 | "metadata": {},
1297 | "output_type": "execute_result"
1298 | }
1299 | ],
1300 | "source": [
1301 | "df = DataFrame(x = 1:3, y = 'a':'c', z = [\"a\",\"b\",\"c\"])"
1302 | ]
1303 | },
1304 | {
1305 | "cell_type": "markdown",
1306 | "metadata": {},
1307 | "source": [
1308 | "Convert all `String` columns to categorical in-place"
1309 | ]
1310 | },
1311 | {
1312 | "cell_type": "code",
1313 | "execution_count": 54,
1314 | "metadata": {},
1315 | "outputs": [
1316 | {
1317 | "data": {
1318 | "text/html": [
1319 | ""
1320 | ],
1321 | "text/latex": [
1322 | "\\begin{tabular}{r|ccc}\n",
1323 | "\t& x & y & z\\\\\n",
1324 | "\t\\hline\n",
1325 | "\t& Int64 & Char & Cat…\\\\\n",
1326 | "\t\\hline\n",
1327 | "\t1 & 1 & a & a \\\\\n",
1328 | "\t2 & 2 & b & b \\\\\n",
1329 | "\t3 & 3 & c & c \\\\\n",
1330 | "\\end{tabular}\n"
1331 | ],
1332 | "text/plain": [
1333 | "\u001b[1m3×3 DataFrame\u001b[0m\n",
1334 | "\u001b[1m Row \u001b[0m│\u001b[1m x \u001b[0m\u001b[1m y \u001b[0m\u001b[1m z \u001b[0m\n",
1335 | " │\u001b[90m Int64 \u001b[0m\u001b[90m Char \u001b[0m\u001b[90m Cat… \u001b[0m\n",
1336 | "─────┼───────────────────\n",
1337 | " 1 │ 1 a a\n",
1338 | " 2 │ 2 b b\n",
1339 | " 3 │ 3 c c"
1340 | ]
1341 | },
1342 | "execution_count": 54,
1343 | "metadata": {},
1344 | "output_type": "execute_result"
1345 | }
1346 | ],
1347 | "source": [
1348 | "transform!(df, names(df, String) => categorical, renamecols=false)"
1349 | ]
1350 | },
1351 | {
1352 | "cell_type": "code",
1353 | "execution_count": 55,
1354 | "metadata": {},
1355 | "outputs": [
1356 | {
1357 | "data": {
1358 | "text/html": [
1359 | "1 | x | 2.0 | 1 | 2.0 | 3 | 0 | Int64 |
2 | y | | a | | c | 0 | Char |
3 | z | | a | | c | 0 | CategoricalValue{String, UInt32} |
"
1360 | ],
1361 | "text/latex": [
1362 | "\\begin{tabular}{r|ccccccc}\n",
1363 | "\t& variable & mean & min & median & max & nmissing & eltype\\\\\n",
1364 | "\t\\hline\n",
1365 | "\t& Symbol & Union… & Any & Union… & Any & Int64 & DataType\\\\\n",
1366 | "\t\\hline\n",
1367 | "\t1 & x & 2.0 & 1 & 2.0 & 3 & 0 & Int64 \\\\\n",
1368 | "\t2 & y & & a & & c & 0 & Char \\\\\n",
1369 | "\t3 & z & & a & & c & 0 & CategoricalValue\\{String, UInt32\\} \\\\\n",
1370 | "\\end{tabular}\n"
1371 | ],
1372 | "text/plain": [
1373 | "\u001b[1m3×7 DataFrame\u001b[0m\n",
1374 | "\u001b[1m Row \u001b[0m│\u001b[1m variable \u001b[0m\u001b[1m mean \u001b[0m\u001b[1m min \u001b[0m\u001b[1m median \u001b[0m\u001b[1m max \u001b[0m\u001b[1m nmissing \u001b[0m\u001b[1m eltype \u001b[0m ⋯\n",
1375 | " │\u001b[90m Symbol \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Union… \u001b[0m\u001b[90m Any \u001b[0m\u001b[90m Int64 \u001b[0m\u001b[90m DataType \u001b[0m ⋯\n",
1376 | "─────┼──────────────────────────────────────────────────────────────────────────\n",
1377 | " 1 │ x 2.0 1 2.0 3 0 Int64 ⋯\n",
1378 | " 2 │ y \u001b[90m \u001b[0m a \u001b[90m \u001b[0m c 0 Char\n",
1379 | " 3 │ z \u001b[90m \u001b[0m a \u001b[90m \u001b[0m c 0 CategoricalValue{String,\n",
1380 | "\u001b[36m 1 column omitted\u001b[0m"
1381 | ]
1382 | },
1383 | "execution_count": 55,
1384 | "metadata": {},
1385 | "output_type": "execute_result"
1386 | }
1387 | ],
1388 | "source": [
1389 | "describe(df)"
1390 | ]
1391 | }
1392 | ],
1393 | "metadata": {
1394 | "@webio": {
1395 | "lastCommId": null,
1396 | "lastKernelId": null
1397 | },
1398 | "kernelspec": {
1399 | "display_name": "Julia 1.9.0",
1400 | "language": "julia",
1401 | "name": "julia-1.9"
1402 | },
1403 | "language_info": {
1404 | "file_extension": ".jl",
1405 | "mimetype": "application/julia",
1406 | "name": "julia",
1407 | "version": "1.9.0"
1408 | }
1409 | },
1410 | "nbformat": 4,
1411 | "nbformat_minor": 1
1412 | }
1413 |
--------------------------------------------------------------------------------
/LICENSE:
--------------------------------------------------------------------------------
1 | The MIT License (MIT)
2 |
3 | Copyright (c) 2017-18 Bogumił Kamiński
4 |
5 | Permission is hereby granted, free of charge, to any person obtaining a copy
6 | of this software and associated documentation files (the "Software"), to deal
7 | in the Software without restriction, including without limitation the rights
8 | to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
9 | copies of the Software, and to permit persons to whom the Software is
10 | furnished to do so, subject to the following conditions:
11 |
12 | The above copyright notice and this permission notice shall be included in all
13 | copies or substantial portions of the Software.
14 |
15 | THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
16 | IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
17 | FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
18 | AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
19 | LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
20 | OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
21 | SOFTWARE.
22 |
--------------------------------------------------------------------------------
/Manifest.toml:
--------------------------------------------------------------------------------
1 | # This file is machine-generated - editing it directly is not advised
2 |
3 | julia_version = "1.9.0"
4 | manifest_format = "2.0"
5 | project_hash = "5a686dfaa1c76c393e25dc0e8ea240febe155185"
6 |
7 | [[deps.AbstractFFTs]]
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11 | version = "1.2.1"
12 |
13 | [[deps.Adapt]]
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1324 | uuid = "12413925-8142-5f55-bb0e-6d7ca50bb09b"
1325 | version = "0.4.0+1"
1326 |
1327 | [[deps.Xorg_xcb_util_jll]]
1328 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxcb_jll"]
1329 | git-tree-sha1 = "e7fd7b2881fa2eaa72717420894d3938177862d1"
1330 | uuid = "2def613f-5ad1-5310-b15b-b15d46f528f5"
1331 | version = "0.4.0+1"
1332 |
1333 | [[deps.Xorg_xcb_util_keysyms_jll]]
1334 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
1335 | git-tree-sha1 = "d1151e2c45a544f32441a567d1690e701ec89b00"
1336 | uuid = "975044d2-76e6-5fbe-bf08-97ce7c6574c7"
1337 | version = "0.4.0+1"
1338 |
1339 | [[deps.Xorg_xcb_util_renderutil_jll]]
1340 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
1341 | git-tree-sha1 = "dfd7a8f38d4613b6a575253b3174dd991ca6183e"
1342 | uuid = "0d47668e-0667-5a69-a72c-f761630bfb7e"
1343 | version = "0.3.9+1"
1344 |
1345 | [[deps.Xorg_xcb_util_wm_jll]]
1346 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xcb_util_jll"]
1347 | git-tree-sha1 = "e78d10aab01a4a154142c5006ed44fd9e8e31b67"
1348 | uuid = "c22f9ab0-d5fe-5066-847c-f4bb1cd4e361"
1349 | version = "0.4.1+1"
1350 |
1351 | [[deps.Xorg_xkbcomp_jll]]
1352 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_libxkbfile_jll"]
1353 | git-tree-sha1 = "4bcbf660f6c2e714f87e960a171b119d06ee163b"
1354 | uuid = "35661453-b289-5fab-8a00-3d9160c6a3a4"
1355 | version = "1.4.2+4"
1356 |
1357 | [[deps.Xorg_xkeyboard_config_jll]]
1358 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Xorg_xkbcomp_jll"]
1359 | git-tree-sha1 = "5c8424f8a67c3f2209646d4425f3d415fee5931d"
1360 | uuid = "33bec58e-1273-512f-9401-5d533626f822"
1361 | version = "2.27.0+4"
1362 |
1363 | [[deps.Xorg_xtrans_jll]]
1364 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1365 | git-tree-sha1 = "79c31e7844f6ecf779705fbc12146eb190b7d845"
1366 | uuid = "c5fb5394-a638-5e4d-96e5-b29de1b5cf10"
1367 | version = "1.4.0+3"
1368 |
1369 | [[deps.ZMQ]]
1370 | deps = ["FileWatching", "Sockets", "ZeroMQ_jll"]
1371 | git-tree-sha1 = "356d2bdcc0bce90aabee1d1c0f6d6f301eda8f77"
1372 | uuid = "c2297ded-f4af-51ae-bb23-16f91089e4e1"
1373 | version = "1.2.2"
1374 |
1375 | [[deps.ZeroMQ_jll]]
1376 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "libsodium_jll"]
1377 | git-tree-sha1 = "fe5c65a526f066fb3000da137d5785d9649a8a47"
1378 | uuid = "8f1865be-045e-5c20-9c9f-bfbfb0764568"
1379 | version = "4.3.4+0"
1380 |
1381 | [[deps.ZipFile]]
1382 | deps = ["Libdl", "Printf", "Zlib_jll"]
1383 | git-tree-sha1 = "f492b7fe1698e623024e873244f10d89c95c340a"
1384 | uuid = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
1385 | version = "0.10.1"
1386 |
1387 | [[deps.Zlib_jll]]
1388 | deps = ["Libdl"]
1389 | uuid = "83775a58-1f1d-513f-b197-d71354ab007a"
1390 | version = "1.2.13+0"
1391 |
1392 | [[deps.Zstd_jll]]
1393 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1394 | git-tree-sha1 = "e45044cd873ded54b6a5bac0eb5c971392cf1927"
1395 | uuid = "3161d3a3-bdf6-5164-811a-617609db77b4"
1396 | version = "1.5.2+0"
1397 |
1398 | [[deps.fzf_jll]]
1399 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1400 | git-tree-sha1 = "868e669ccb12ba16eaf50cb2957ee2ff61261c56"
1401 | uuid = "214eeab7-80f7-51ab-84ad-2988db7cef09"
1402 | version = "0.29.0+0"
1403 |
1404 | [[deps.libaom_jll]]
1405 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1406 | git-tree-sha1 = "3a2ea60308f0996d26f1e5354e10c24e9ef905d4"
1407 | uuid = "a4ae2306-e953-59d6-aa16-d00cac43593b"
1408 | version = "3.4.0+0"
1409 |
1410 | [[deps.libass_jll]]
1411 | deps = ["Artifacts", "Bzip2_jll", "FreeType2_jll", "FriBidi_jll", "HarfBuzz_jll", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
1412 | git-tree-sha1 = "5982a94fcba20f02f42ace44b9894ee2b140fe47"
1413 | uuid = "0ac62f75-1d6f-5e53-bd7c-93b484bb37c0"
1414 | version = "0.15.1+0"
1415 |
1416 | [[deps.libblastrampoline_jll]]
1417 | deps = ["Artifacts", "Libdl"]
1418 | uuid = "8e850b90-86db-534c-a0d3-1478176c7d93"
1419 | version = "5.4.0+0"
1420 |
1421 | [[deps.libfdk_aac_jll]]
1422 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1423 | git-tree-sha1 = "daacc84a041563f965be61859a36e17c4e4fcd55"
1424 | uuid = "f638f0a6-7fb0-5443-88ba-1cc74229b280"
1425 | version = "2.0.2+0"
1426 |
1427 | [[deps.libpng_jll]]
1428 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Zlib_jll"]
1429 | git-tree-sha1 = "94d180a6d2b5e55e447e2d27a29ed04fe79eb30c"
1430 | uuid = "b53b4c65-9356-5827-b1ea-8c7a1a84506f"
1431 | version = "1.6.38+0"
1432 |
1433 | [[deps.libsodium_jll]]
1434 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1435 | git-tree-sha1 = "848ab3d00fe39d6fbc2a8641048f8f272af1c51e"
1436 | uuid = "a9144af2-ca23-56d9-984f-0d03f7b5ccf8"
1437 | version = "1.0.20+0"
1438 |
1439 | [[deps.libvorbis_jll]]
1440 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Ogg_jll", "Pkg"]
1441 | git-tree-sha1 = "b910cb81ef3fe6e78bf6acee440bda86fd6ae00c"
1442 | uuid = "f27f6e37-5d2b-51aa-960f-b287f2bc3b7a"
1443 | version = "1.3.7+1"
1444 |
1445 | [[deps.nghttp2_jll]]
1446 | deps = ["Artifacts", "Libdl"]
1447 | uuid = "8e850ede-7688-5339-a07c-302acd2aaf8d"
1448 | version = "1.48.0+0"
1449 |
1450 | [[deps.p7zip_jll]]
1451 | deps = ["Artifacts", "Libdl"]
1452 | uuid = "3f19e933-33d8-53b3-aaab-bd5110c3b7a0"
1453 | version = "17.4.0+0"
1454 |
1455 | [[deps.x264_jll]]
1456 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1457 | git-tree-sha1 = "4fea590b89e6ec504593146bf8b988b2c00922b2"
1458 | uuid = "1270edf5-f2f9-52d2-97e9-ab00b5d0237a"
1459 | version = "2021.5.5+0"
1460 |
1461 | [[deps.x265_jll]]
1462 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg"]
1463 | git-tree-sha1 = "ee567a171cce03570d77ad3a43e90218e38937a9"
1464 | uuid = "dfaa095f-4041-5dcd-9319-2fabd8486b76"
1465 | version = "3.5.0+0"
1466 |
1467 | [[deps.xkbcommon_jll]]
1468 | deps = ["Artifacts", "JLLWrappers", "Libdl", "Pkg", "Wayland_jll", "Wayland_protocols_jll", "Xorg_libxcb_jll", "Xorg_xkeyboard_config_jll"]
1469 | git-tree-sha1 = "9ebfc140cc56e8c2156a15ceac2f0302e327ac0a"
1470 | uuid = "d8fb68d0-12a3-5cfd-a85a-d49703b185fd"
1471 | version = "1.4.1+0"
1472 |
--------------------------------------------------------------------------------
/Project.toml:
--------------------------------------------------------------------------------
1 | [deps]
2 | Arrow = "69666777-d1a9-59fb-9406-91d4454c9d45"
3 | BenchmarkTools = "6e4b80f9-dd63-53aa-95a3-0cdb28fa8baf"
4 | CSV = "336ed68f-0bac-5ca0-87d4-7b16caf5d00b"
5 | CategoricalArrays = "324d7699-5711-5eae-9e2f-1d82baa6b597"
6 | Chain = "8be319e6-bccf-4806-a6f7-6fae938471bc"
7 | CodecZlib = "944b1d66-785c-5afd-91f1-9de20f533193"
8 | DataFrames = "a93c6f00-e57d-5684-b7b6-d8193f3e46c0"
9 | DataFramesMeta = "1313f7d8-7da2-5740-9ea0-a2ca25f37964"
10 | FileIO = "5789e2e9-d7fb-5bc7-8068-2c6fae9b9549"
11 | FreqTables = "da1fdf0e-e0ff-5433-a45f-9bb5ff651cb1"
12 | IJulia = "7073ff75-c697-5162-941a-fcdaad2a7d2a"
13 | JDF = "babc3d20-cd49-4f60-a736-a8f9c08892d3"
14 | JLSO = "9da8a3cd-07a3-59c0-a743-3fdc52c30d11"
15 | JSONTables = "b9914132-a727-11e9-1322-f18e41205b0b"
16 | NamedArrays = "86f7a689-2022-50b4-a561-43c23ac3c673"
17 | PooledArrays = "2dfb63ee-cc39-5dd5-95bd-886bf059d720"
18 | Random = "9a3f8284-a2c9-5f02-9a11-845980a1fd5c"
19 | Statistics = "10745b16-79ce-11e8-11f9-7d13ad32a3b2"
20 | StatsPlots = "f3b207a7-027a-5e70-b257-86293d7955fd"
21 | Tables = "bd369af6-aec1-5ad0-b16a-f7cc5008161c"
22 | ZipFile = "a5390f91-8eb1-5f08-bee0-b1d1ffed6cea"
23 |
--------------------------------------------------------------------------------
/README.md:
--------------------------------------------------------------------------------
1 | # An Introduction to DataFrames.jl
2 |
3 | [Bogumił Kamiński](http://bogumilkaminski.pl/about/), February 13, 2023
4 |
5 | **The tutorial is for DataFrames.jl 1.5.0**
6 |
7 | A brief introduction to basic usage of [DataFrames](https://github.com/JuliaData/DataFrames.jl).
8 |
9 | The tutorial contains a specification of the project environment version under
10 | which it should be run. In order to prepare this environment, before using the
11 | tutorial notebooks, while in the project folder run the following command in the
12 | command line:
13 |
14 | ```
15 | julia -e 'using Pkg; Pkg.activate("."); Pkg.instantiate()'
16 | ```
17 |
18 | Tested under Julia 1.9.0. The project dependencies are the following:
19 |
20 | ```
21 | [69666777] Arrow v2.4.3
22 | [6e4b80f9] BenchmarkTools v1.3.2
23 | [336ed68f] CSV v0.10.9
24 | [324d7699] CategoricalArrays v0.10.7
25 | [8be319e6] Chain v0.5.0
26 | [944b1d66] CodecZlib v0.7.1
27 | [a93c6f00] DataFrames v1.5.0
28 | [1313f7d8] DataFramesMeta v0.13.0
29 | [5789e2e9] FileIO v1.16.0
30 | [da1fdf0e] FreqTables v0.4.5
31 | [7073ff75] IJulia v1.24.0
32 | [babc3d20] JDF v0.5.1
33 | [9da8a3cd] JLSO v2.7.0
34 | [b9914132] JSONTables v1.0.3
35 | [86f7a689] NamedArrays v0.9.6
36 | [2dfb63ee] PooledArrays v1.4.2
37 | [f3b207a7] StatsPlots v0.15.4
38 | [bd369af6] Tables v1.10.0
39 | [a5390f91] ZipFile v0.10.1
40 | [9a3f8284] Random
41 | [10745b16] Statistics v1.9.0
42 | ```
43 |
44 | I will try to keep the material up to date as the packages evolve.
45 |
46 | This tutorial covers
47 | [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl)
48 | and [CategoricalArrays.jl](https://github.com/JuliaData/CategoricalArrays.jl),
49 | as they constitute the core of [DataFrames.jl](https://github.com/JuliaData/DataFrames.jl)
50 | along with selected file reading and writing packages.
51 |
52 | In the last [extras](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/13_extras.ipynb)
53 | part mentions *selected* functionalities of *selected* useful packages that I find useful for data manipulation, currently those are:
54 | [FreqTables.jl](https://github.com/nalimilan/FreqTables.jl),
55 | [DataFramesMeta.jl](https://github.com/JuliaStats/DataFramesMeta.jl)
56 | [StatsPlots.jl](https://github.com/JuliaPlots/StatsPlots.jl).
57 |
58 | # TOC
59 |
60 | | File | Topic |
61 | |-------------------------------------------------------------------------------------------------------------------|-----------------------------------|
62 | | [01_constructors.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/01_constructors.ipynb) | Creating DataFrame and conversion |
63 | | [02_basicinfo.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/02_basicinfo.ipynb) | Getting summary information |
64 | | [03_missingvalues.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/03_missingvalues.ipynb) | Handling missing values |
65 | | [04_loadsave.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/04_loadsave.ipynb) | Loading and saving DataFrames |
66 | | [05_columns.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/05_columns.ipynb) | Working with columns of DataFrame |
67 | | [06_rows.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/06_rows.ipynb) | Working with row of DataFrame |
68 | | [07_factors.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/07_factors.ipynb) | Working with categorical data |
69 | | [08_joins.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/08_joins.ipynb) | Joining DataFrames |
70 | | [09_reshaping.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/09_reshaping.ipynb) | Reshaping DataFrames |
71 | | [10_transforms.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/10_transforms.ipynb) | Transforming DataFrames |
72 | | [11_performance.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/11_performance.ipynb) | Performance tips |
73 | | [12_pitfalls.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/12_pitfalls.ipynb) | Possible pitfalls |
74 | | [13_extras.ipynb](https://github.com/bkamins/Julia-DataFrames-Tutorial/blob/master/13_extras.ipynb) | Additional interesting packages |
75 |
76 | Changelog:
77 |
78 | | Date | Changes |
79 | | ---------- | ------------------------------------------------------------ |
80 | | 2017-12-05 | Initial release |
81 | | 2017-12-06 | Added description of `insert!`, `merge!`, `empty!`, `categorical!`, `delete!`, `DataFrames.index` |
82 | | 2017-12-09 | Added performance tips |
83 | | 2017-12-10 | Added pitfalls |
84 | | 2017-12-18 | Added additional worthwhile packages: *FreqTables* and *DataFramesMeta* |
85 | | 2017-12-29 | Added description of `filter` and `filter!` |
86 | | 2017-12-31 | Added description of conversion to `Matrix` |
87 | | 2018-04-06 | Added example of extracting a row from a `DataFrame` |
88 | | 2018-04-21 | Major update of whole tutorial |
89 | | 2018-05-01 | Added `byrow!` example |
90 | | 2018-05-13 | Added `StatPlots` package to extras |
91 | | 2018-05-23 | Improved comments in sections 1 do 5 by [Jane Herriman](https://github.com/xorJane) |
92 | | 2018-07-25 | Update to 0.11.7 release |
93 | | 2018-08-25 | Update to Julia 1.0 release: sections 1 to 10 |
94 | | 2018-08-29 | Update to Julia 1.0 release: sections 11, 12 and 13 |
95 | | 2018-09-05 | Update to Julia 1.0 release: FreqTables section |
96 | | 2018-09-10 | Added CSVFiles section to chapter on load/save |
97 | | 2018-09-26 | Updated to DataFrames 0.14.0 |
98 | | 2018-10-04 | Updated to DataFrames 0.14.1, added `haskey` and `repeat` |
99 | | 2018-12-08 | Updated to DataFrames 0.15.2 |
100 | | 2019-01-03 | Updated to DataFrames 0.16.0, added serialization instructions |
101 | | 2019-01-18 | Updated to DataFrames 0.17.0, added `passmissing` |
102 | | 2019-01-27 | Added Feather.jl file read/write |
103 | | 2019-01-30 | Renamed StatPlots.jl to StatsPlots.jl and added Tables.jl|
104 | | 2019-02-08 | Added `groupvars` and `groupindices` functions|
105 | | 2019-04-27 | Updated to DataFrames 0.18.0, dropped JLD2.jl |
106 | | 2019-04-30 | Updated handling of missing values description |
107 | | 2019-07-16 | Updated to DataFrames 0.19.0 |
108 | | 2019-08-14 | Added JSONTables.jl and `Tables.columnindex` |
109 | | 2019-08-16 | Added Project.toml and Manifest.toml |
110 | | 2019-08-26 | Update to Julia 1.2 and DataFrames 0.19.3 |
111 | | 2019-08-29 | Add example how to compress/decompress CSV file using CodecZlib |
112 | | 2019-08-30 | Add examples of JLSO.jl and ZipFile.jl by [xiaodaigh](https://github.com/xiaodaigh) |
113 | | 2019-11-03 | Add examples of JDF.jl by [xiaodaigh](https://github.com/xiaodaigh) |
114 | | 2019-12-08 | Updated to DataFrames 0.20.0 |
115 | | 2020-05-06 | Updated to DataFrames 0.21.0 (except load/save and extras) |
116 | | 2020-11-20 | Updated to DataFrames 0.22.0 (except DataFramesMeta.jl which does not work yet) |
117 | | 2020-11-26 | Updated to DataFramesMeta.jl 0.6; update by @pdeffebach |
118 | | 2021-05-15 | Updated to DataFrames.jl 1.1.1 |
119 | | 2021-05-15 | Updated to DataFrames.jl 1.2 and DataFramesMeta.jl 0.8, added Chain.jl instead of Pipe.jl |
120 | | 2021-12-12 | Updated to DataFrames.jl 1.3 |
121 | | 2022-10-05 | Updated to DataFrames.jl 1.4 |
122 | | 2023-02-13 | Updated to DataFrames.jl 1.5 |
123 |
124 | # Core functions summary
125 |
126 | 1. Constructors: `DataFrame`, `DataFrame!`, `Tables.rowtable`, `Tables.columntable`, `Matrix`, `eachcol`, `eachrow`, `Tables.namedtupleiterator`, `empty`, `empty!`
127 | 2. Getting summary: `size`, `nrow`, `ncol`, `describe`, `names`, `eltypes`, `first`, `last`, `getindex`, `setindex!`, `@view`, `isapprox`, `metadata`, `metadata!`, `colmetadata`, `colmetadata!`
128 | 3. Handling missing: `missing` (singleton instance of `Missing`), `ismissing`, `nonmissingtype`, `skipmissing`, `replace`, `replace!`, `coalesce`, `allowmissing`, `disallowmissing`, `allowmissing!`, `completecases`, `dropmissing`, `dropmissing!`, `disallowmissing`, `disallowmissing!`, `passmissing`
129 | 4. Loading and saving: `CSV` (package), `CSVFiles` (package), `Serialization` (module), `CSV.read`, `CSV.write`, `save`, `load`, `serialize`, `deserialize`, `Arrow.write`, `Arrow.Table` (from Arrow.jl package), `JSONTables` (package), `arraytable`, `objecttable`, `jsontable`, `CodecZlib` (module), `GzipCompressorStream`, `GzipDecompressorStream`, `JDF.jl` (package), `JDF.save`, `JDF.load`, `JLSO.jl` (package), `JLSO.save`, `JLSO.load`, `ZipFile.jl` (package), `ZipFile.reader`, `ZipFile.writer`, `ZipFile.addfile`
130 | 5. Working with columns: `rename`, `rename!`, `hcat`, `insertcols!`, `categorical!`, `columnindex`, `hasproperty`, `select`, `select!`, `transform`, `transform!`, `combine`, `Not`, `All`, `Between`, `ByRow`, `AsTable`
131 | 6. Working with rows: `sort!`, `sort`, `issorted`, `append!`, `vcat`, `push!`, `view`, `filter`, `filter!`, `deleteat!`, `unique`, `nonunique`, `unique!`, `allunique`, `repeat`, `parent`, `parentindices`, `flatten`, `@chain` (from `Chain.jl` package), `only`, `subset`, `subset!`, `shuffle`, `prepend!`, `pushfirst!`, `insert!`, `keepat!`
132 | 7. Working with categorical: `categorical`, `cut`, `isordered`, `ordered!`, `levels`, `unique`, `levels!`, `droplevels!`, `unwrap`, `recode`, `recode!`
133 | 8. Joining: `innerjoin`, `leftjoin`, `leftjoin!`, `rightjoin`, `outerjoin`, `semijoin`, `antijoin`, `crossjoin`
134 | 9. Reshaping: `stack`, `unstack`
135 | 10. Transforming: `groupby`, `mapcols`, `parent`, `groupcols`, `valuecols`, `groupindices`, `keys` (for `GroupedDataFrame`), `combine`, `select`, `select!`, `transform`, `transform!`, `@chain` (from `Chain.jl` package)
136 | 11. Extras:
137 | * [FreqTables](https://github.com/nalimilan/FreqTables.jl): `freqtable`, `prop`, `Name`
138 | * [DataFramesMeta](https://github.com/JuliaStats/DataFramesMeta.jl): `@with`, `@subset`, `@select`, `@transform`, `@orderby`, `@by`, `@combine`, `@eachrow`, `@newcol`, `^`, `$`
139 | * [StatsPlots](https://github.com/JuliaPlots/StatsPlots.jl): `@df`, `plot`, `density`, `histogram`,`boxplot`, `violin`
140 |
--------------------------------------------------------------------------------