Overview

Dataset statistics

Number of variables10
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory927.7 KiB
Average record size in memory95.0 B

Variable types

Categorical2
Numeric7
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-20464/S/1/datasetView.do

Alerts

기관 명 has constant value ""Constant
모델명 has constant value ""Constant
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지(㎍/㎥)High correlation
미세먼지(㎍/㎥) is highly overall correlated with 초미세먼지(㎍/㎥)High correlation
총 유기 휘발성 화합물 is highly overall correlated with 이산화탄소High correlation
이산화탄소 is highly overall correlated with 총 유기 휘발성 화합물High correlation
초미세먼지(㎍/㎥) has 2022 (20.2%) zerosZeros
미세먼지(㎍/㎥) has 1886 (18.9%) zerosZeros

Reproduction

Analysis started2024-05-18 01:56:14.093659
Analysis finished2024-05-18 01:56:29.707580
Duration15.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
중랑구
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row중랑구
2nd row중랑구
3rd row중랑구
4th row중랑구
5th row중랑구

Common Values

ValueCountFrequency (%)
중랑구 10000
100.0%

Length

2024-05-18T10:56:29.890664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:56:30.183932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중랑구 10000
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
AN-EMD-TA100L
10000 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAN-EMD-TA100L
2nd rowAN-EMD-TA100L
3rd rowAN-EMD-TA100L
4th rowAN-EMD-TA100L
5th rowAN-EMD-TA100L

Common Values

ValueCountFrequency (%)
AN-EMD-TA100L 10000
100.0%

Length

2024-05-18T10:56:30.486357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:56:30.766162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
an-emd-ta100l 10000
100.0%

시리얼
Real number (ℝ)

Distinct291
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.7316
Minimum76
Maximum607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:31.095427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile91
Q1181
median312
Q3428
95-th percentile524
Maximum607
Range531
Interquartile range (IQR)247

Descriptive statistics

Standard deviation142.21373
Coefficient of variation (CV)0.45767385
Kurtosis-1.0182399
Mean310.7316
Median Absolute Deviation (MAD)125
Skewness0.085856439
Sum3107316
Variance20224.745
MonotonicityNot monotonic
2024-05-18T10:56:31.537586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
512 55
 
0.5%
335 51
 
0.5%
107 51
 
0.5%
348 51
 
0.5%
385 50
 
0.5%
307 50
 
0.5%
178 48
 
0.5%
181 48
 
0.5%
442 47
 
0.5%
603 47
 
0.5%
Other values (281) 9502
95.0%
ValueCountFrequency (%)
76 36
0.4%
77 34
0.3%
78 37
0.4%
79 39
0.4%
80 35
0.4%
82 38
0.4%
83 41
0.4%
84 34
0.3%
85 41
0.4%
86 45
0.4%
ValueCountFrequency (%)
607 28
0.3%
606 36
0.4%
605 39
0.4%
604 40
0.4%
603 47
0.5%
602 33
0.3%
601 40
0.4%
600 42
0.4%
599 40
0.4%
532 8
 
0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct120
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.7959
Minimum0
Maximum386
Zeros2022
Zeros (%)20.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:31.956449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median9
Q322
95-th percentile47
Maximum386
Range386
Interquartile range (IQR)20

Descriptive statistics

Standard deviation17.602955
Coefficient of variation (CV)1.1897184
Kurtosis42.179826
Mean14.7959
Median Absolute Deviation (MAD)9
Skewness3.5587651
Sum147959
Variance309.86403
MonotonicityNot monotonic
2024-05-18T10:56:32.363513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2022
20.2%
1 434
 
4.3%
2 427
 
4.3%
3 341
 
3.4%
5 334
 
3.3%
4 329
 
3.3%
6 315
 
3.1%
7 300
 
3.0%
8 279
 
2.8%
9 265
 
2.6%
Other values (110) 4954
49.5%
ValueCountFrequency (%)
0 2022
20.2%
1 434
 
4.3%
2 427
 
4.3%
3 341
 
3.4%
4 329
 
3.3%
5 334
 
3.3%
6 315
 
3.1%
7 300
 
3.0%
8 279
 
2.8%
9 265
 
2.6%
ValueCountFrequency (%)
386 1
< 0.1%
346 1
< 0.1%
278 1
< 0.1%
204 1
< 0.1%
193 1
< 0.1%
136 1
< 0.1%
135 1
< 0.1%
124 1
< 0.1%
123 1
< 0.1%
118 1
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct128
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.1806
Minimum0
Maximum426
Zeros1886
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:32.793944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median11
Q325
95-th percentile56
Maximum426
Range426
Interquartile range (IQR)23

Descriptive statistics

Standard deviation20.327088
Coefficient of variation (CV)1.1831419
Kurtosis32.613941
Mean17.1806
Median Absolute Deviation (MAD)10
Skewness3.1578076
Sum171806
Variance413.1905
MonotonicityNot monotonic
2024-05-18T10:56:33.186687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1886
 
18.9%
1 420
 
4.2%
2 391
 
3.9%
3 335
 
3.4%
6 307
 
3.1%
7 298
 
3.0%
4 285
 
2.9%
5 285
 
2.9%
8 246
 
2.5%
9 242
 
2.4%
Other values (118) 5305
53.0%
ValueCountFrequency (%)
0 1886
18.9%
1 420
 
4.2%
2 391
 
3.9%
3 335
 
3.4%
4 285
 
2.9%
5 285
 
2.9%
6 307
 
3.1%
7 298
 
3.0%
8 246
 
2.5%
9 242
 
2.4%
ValueCountFrequency (%)
426 1
< 0.1%
375 1
< 0.1%
283 1
< 0.1%
207 1
< 0.1%
202 1
< 0.1%
145 1
< 0.1%
141 1
< 0.1%
136 1
< 0.1%
135 1
< 0.1%
132 1
< 0.1%

온도(℃)
Real number (ℝ)

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.3517
Minimum0
Maximum36
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:33.758739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q120
median23
Q326
95-th percentile29
Maximum36
Range36
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9044482
Coefficient of variation (CV)0.21942171
Kurtosis1.4008443
Mean22.3517
Median Absolute Deviation (MAD)3
Skewness-0.87285229
Sum223517
Variance24.053612
MonotonicityNot monotonic
2024-05-18T10:56:34.093491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
24 962
 
9.6%
25 929
 
9.3%
23 923
 
9.2%
26 852
 
8.5%
22 788
 
7.9%
21 682
 
6.8%
20 651
 
6.5%
27 649
 
6.5%
19 506
 
5.1%
28 503
 
5.0%
Other values (27) 2555
25.6%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 8
 
0.1%
2 3
 
< 0.1%
3 11
 
0.1%
4 16
 
0.2%
5 20
 
0.2%
6 23
0.2%
7 35
0.4%
8 50
0.5%
9 43
0.4%
ValueCountFrequency (%)
36 1
 
< 0.1%
35 1
 
< 0.1%
34 27
 
0.3%
33 31
 
0.3%
32 40
 
0.4%
31 94
 
0.9%
30 196
 
2.0%
29 287
2.9%
28 503
5.0%
27 649
6.5%

습도(%)
Real number (ℝ)

Distinct72
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.9043
Minimum8
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:34.491741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile18
Q125
median31
Q338
95-th percentile49
Maximum81
Range73
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.6898158
Coefficient of variation (CV)0.30371504
Kurtosis1.2338983
Mean31.9043
Median Absolute Deviation (MAD)6
Skewness0.74636286
Sum319043
Variance93.892531
MonotonicityNot monotonic
2024-05-18T10:56:34.933747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 456
 
4.6%
27 444
 
4.4%
31 443
 
4.4%
26 439
 
4.4%
25 428
 
4.3%
24 422
 
4.2%
28 417
 
4.2%
29 414
 
4.1%
32 398
 
4.0%
34 369
 
3.7%
Other values (62) 5770
57.7%
ValueCountFrequency (%)
8 2
 
< 0.1%
9 3
 
< 0.1%
10 11
 
0.1%
11 25
 
0.2%
12 43
0.4%
13 44
0.4%
14 45
0.4%
15 59
0.6%
16 62
0.6%
17 80
0.8%
ValueCountFrequency (%)
81 3
< 0.1%
80 4
< 0.1%
78 1
 
< 0.1%
77 1
 
< 0.1%
76 6
0.1%
75 2
 
< 0.1%
74 2
 
< 0.1%
73 1
 
< 0.1%
72 4
< 0.1%
71 5
0.1%

총 유기 휘발성 화합물
Real number (ℝ)

HIGH CORRELATION 

Distinct425
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.9843
Minimum0
Maximum499
Zeros96
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:35.401432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q141
median54
Q369
95-th percentile238.05
Maximum499
Range499
Interquartile range (IQR)28

Descriptive statistics

Standard deviation84.007765
Coefficient of variation (CV)1.135481
Kurtosis13.584893
Mean73.9843
Median Absolute Deviation (MAD)14
Skewness3.5884218
Sum739843
Variance7057.3046
MonotonicityNot monotonic
2024-05-18T10:56:35.764393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 507
 
5.1%
50 462
 
4.6%
52 322
 
3.2%
53 295
 
2.9%
57 271
 
2.7%
55 265
 
2.6%
56 258
 
2.6%
54 241
 
2.4%
58 239
 
2.4%
59 210
 
2.1%
Other values (415) 6930
69.3%
ValueCountFrequency (%)
0 96
1.0%
1 30
 
0.3%
2 27
 
0.3%
3 22
 
0.2%
4 16
 
0.2%
5 26
 
0.3%
6 26
 
0.3%
7 32
 
0.3%
8 34
 
0.3%
9 38
 
0.4%
ValueCountFrequency (%)
499 145
1.5%
498 1
 
< 0.1%
497 2
 
< 0.1%
496 3
 
< 0.1%
495 2
 
< 0.1%
493 1
 
< 0.1%
492 4
 
< 0.1%
491 1
 
< 0.1%
490 1
 
< 0.1%
489 2
 
< 0.1%

이산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct1859
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean848.8131
Minimum0
Maximum9700
Zeros37
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:36.310637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile401
Q1484
median639
Q3906
95-th percentile2150.1
Maximum9700
Range9700
Interquartile range (IQR)422

Descriptive statistics

Standard deviation654.81358
Coefficient of variation (CV)0.77144613
Kurtosis22.00986
Mean848.8131
Median Absolute Deviation (MAD)182
Skewness3.6302119
Sum8488131
Variance428780.83
MonotonicityNot monotonic
2024-05-18T10:56:36.744628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 427
 
4.3%
401 155
 
1.6%
402 114
 
1.1%
403 74
 
0.7%
405 66
 
0.7%
404 63
 
0.6%
408 42
 
0.4%
406 42
 
0.4%
407 41
 
0.4%
410 40
 
0.4%
Other values (1849) 8936
89.4%
ValueCountFrequency (%)
0 37
 
0.4%
267 1
 
< 0.1%
320 1
 
< 0.1%
362 1
 
< 0.1%
371 1
 
< 0.1%
380 1
 
< 0.1%
397 1
 
< 0.1%
400 427
4.3%
401 155
 
1.6%
402 114
 
1.1%
ValueCountFrequency (%)
9700 2
< 0.1%
8536 1
< 0.1%
8073 1
< 0.1%
6685 1
< 0.1%
6530 1
< 0.1%
6349 1
< 0.1%
6340 1
< 0.1%
6271 1
< 0.1%
5990 1
< 0.1%
5968 1
< 0.1%
Distinct4767
Distinct (%)47.7%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-09 00:22:28
Maximum2023-01-15 23:23:06
2024-05-18T10:56:37.170435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:37.614230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-18T10:56:27.281771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:16.418072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:18.033449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:19.661673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:21.260148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:23.154530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:25.370428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:27.448756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:16.663145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:18.266991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:19.913883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:21.521445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:23.599262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:25.575227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:27.669080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:16.858475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:18.551590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:20.161191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:21.815869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:23.926419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:25.927271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:28.126065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:17.042437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:18.812305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:20.314467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:22.070873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:24.201107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:26.196915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:28.395312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:17.251577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:19.059025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:20.487969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:22.345529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:24.508537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:26.479551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:28.667083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:17.523493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:19.253286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:20.751291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:22.620945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:24.800463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:26.771146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:28.925158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:17.786980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:19.430504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:21.004586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:22.890493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:25.083574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:27.050656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T10:56:37.884716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소
시리얼1.0000.1490.1480.4540.3020.2100.128
초미세먼지(㎍/㎥)0.1491.0000.9720.2690.0630.1050.115
미세먼지(㎍/㎥)0.1480.9721.0000.2610.0850.1160.106
온도(℃)0.4540.2690.2611.0000.5880.2150.174
습도(%)0.3020.0630.0850.5881.0000.1750.143
총 유기 휘발성 화합물0.2100.1050.1160.2150.1751.0000.549
이산화탄소0.1280.1150.1060.1740.1430.5491.000
2024-05-18T10:56:38.177616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소
시리얼1.0000.0510.045-0.1580.013-0.148-0.048
초미세먼지(㎍/㎥)0.0511.0000.998-0.149-0.276-0.0720.069
미세먼지(㎍/㎥)0.0450.9981.000-0.155-0.274-0.0690.072
온도(℃)-0.158-0.149-0.1551.000-0.3930.2140.253
습도(%)0.013-0.276-0.274-0.3931.0000.1630.159
총 유기 휘발성 화합물-0.148-0.072-0.0690.2140.1631.0000.686
이산화탄소-0.0480.0690.0720.2530.1590.6861.000

Missing values

2024-05-18T10:56:29.256688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T10:56:29.536048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

기관 명모델명시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소등록일시
35122중랑구AN-EMD-TA100L2840023307610012023-01-14 11:22:49
28612중랑구AN-EMD-TA100L5154525356611902023-01-13 10:23:04
32665중랑구AN-EMD-TA100L52491024399916152023-01-14 01:59:35
38881중랑구AN-EMD-TA100L603001839244072023-01-15 01:59:27
7462중랑구AN-EMD-TA100L242671937444002023-01-10 04:23:41
13372중랑구AN-EMD-TA100L25421252530296142023-01-11 04:22:42
2215중랑구AN-EMD-TA100L37810121741424482023-01-09 08:22:48
15912중랑구AN-EMD-TA100L463002033327192023-01-11 11:22:59
26414중랑구AN-EMD-TA100L41437411438334282023-01-13 04:22:56
4423중랑구AN-EMD-TA100L42934403214244882023-01-09 16:22:52
기관 명모델명시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소등록일시
35678중랑구AN-EMD-TA100L344001532627872023-01-14 13:22:53
3761중랑구AN-EMD-TA100L2171416254915524262023-01-09 14:22:39
34821중랑구AN-EMD-TA100L200342929587472023-01-14 10:22:43
20263중랑구AN-EMD-TA100L52752582421506282023-01-12 04:23:33
37560중랑구AN-EMD-TA100L473241441294152023-01-14 20:23:02
43532중랑구AN-EMD-TA100L8613152132524282023-01-15 20:22:38
424중랑구AN-EMD-TA100L39239442318464762023-01-09 01:59:09
37531중랑구AN-EMD-TA100L429113421597972023-01-14 20:22:58
30848중랑구AN-EMD-TA100L6051124455511182023-01-13 18:23:07
4824중랑구AN-EMD-TA100L171131525269411182023-01-09 18:22:36