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 1552 (15.5%) zerosZeros
미세먼지(㎍/㎥) has 1284 (12.8%) zerosZeros
총 유기 휘발성 화합물 has 118 (1.2%) zerosZeros

Reproduction

Analysis started2024-05-18 01:55:46.901499
Analysis finished2024-05-18 01:56:03.149856
Duration16.25 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

2024-05-18T10:56:03.393762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:56:03.636100image/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

2024-05-18T10:56:03.812239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:56:03.971624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
an-emd-ta100l 10000
100.0%

시리얼
Real number (ℝ)

Distinct293
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean311.8771
Minimum76
Maximum607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:04.168266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile91
Q1182
median312
Q3430
95-th percentile526
Maximum607
Range531
Interquartile range (IQR)248

Descriptive statistics

Standard deviation142.878
Coefficient of variation (CV)0.45812275
Kurtosis-1.0323051
Mean311.8771
Median Absolute Deviation (MAD)125
Skewness0.086203109
Sum3118771
Variance20414.122
MonotonicityNot monotonic
2024-05-18T10:56:04.483551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
510 50
 
0.5%
102 48
 
0.5%
114 48
 
0.5%
127 48
 
0.5%
243 48
 
0.5%
88 47
 
0.5%
144 47
 
0.5%
215 47
 
0.5%
168 47
 
0.5%
515 47
 
0.5%
Other values (283) 9523
95.2%
ValueCountFrequency (%)
76 31
0.3%
77 42
0.4%
78 34
0.3%
79 43
0.4%
80 36
0.4%
82 35
0.4%
83 29
0.3%
84 32
0.3%
85 33
0.3%
86 34
0.3%
ValueCountFrequency (%)
607 42
0.4%
606 36
0.4%
605 40
0.4%
604 30
0.3%
603 46
0.5%
602 37
0.4%
601 44
0.4%
600 41
0.4%
599 35
0.4%
532 6
 
0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct89
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.0224
Minimum0
Maximum426
Zeros1552
Zeros (%)15.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:04.782881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q311
95-th percentile25
Maximum426
Range426
Interquartile range (IQR)9

Descriptive statistics

Standard deviation10.847371
Coefficient of variation (CV)1.3521354
Kurtosis278.32975
Mean8.0224
Median Absolute Deviation (MAD)4
Skewness9.8636657
Sum80224
Variance117.66546
MonotonicityNot monotonic
2024-05-18T10:56:05.040237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1552
15.5%
2 800
 
8.0%
1 757
 
7.6%
3 714
 
7.1%
4 672
 
6.7%
5 669
 
6.7%
7 514
 
5.1%
6 505
 
5.1%
8 486
 
4.9%
10 385
 
3.9%
Other values (79) 2946
29.5%
ValueCountFrequency (%)
0 1552
15.5%
1 757
7.6%
2 800
8.0%
3 714
7.1%
4 672
6.7%
5 669
6.7%
6 505
 
5.1%
7 514
 
5.1%
8 486
 
4.9%
9 373
 
3.7%
ValueCountFrequency (%)
426 1
< 0.1%
277 1
< 0.1%
209 1
< 0.1%
117 1
< 0.1%
115 1
< 0.1%
110 1
< 0.1%
99 1
< 0.1%
98 1
< 0.1%
93 1
< 0.1%
90 1
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct96
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.4719
Minimum0
Maximum484
Zeros1284
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:05.321469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median6
Q313
95-th percentile29
Maximum484
Range484
Interquartile range (IQR)11

Descriptive statistics

Standard deviation12.402959
Coefficient of variation (CV)1.3094478
Kurtosis264.94314
Mean9.4719
Median Absolute Deviation (MAD)5
Skewness9.5286118
Sum94719
Variance153.83339
MonotonicityNot monotonic
2024-05-18T10:56:05.769605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1284
 
12.8%
2 707
 
7.1%
1 672
 
6.7%
3 644
 
6.4%
4 618
 
6.2%
5 581
 
5.8%
6 575
 
5.8%
7 472
 
4.7%
9 442
 
4.4%
8 434
 
4.3%
Other values (86) 3571
35.7%
ValueCountFrequency (%)
0 1284
12.8%
1 672
6.7%
2 707
7.1%
3 644
6.4%
4 618
6.2%
5 581
5.8%
6 575
5.8%
7 472
 
4.7%
8 434
 
4.3%
9 442
 
4.4%
ValueCountFrequency (%)
484 1
< 0.1%
304 1
< 0.1%
233 1
< 0.1%
132 1
< 0.1%
128 1
< 0.1%
123 1
< 0.1%
122 1
< 0.1%
115 1
< 0.1%
113 1
< 0.1%
101 1
< 0.1%

온도(℃)
Real number (ℝ)

Distinct37
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.0331
Minimum0
Maximum36
Zeros16
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:06.155998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q118
median22
Q325
95-th percentile28
Maximum36
Range36
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.4586247
Coefficient of variation (CV)0.25952545
Kurtosis1.4543301
Mean21.0331
Median Absolute Deviation (MAD)3
Skewness-0.9664185
Sum210331
Variance29.796584
MonotonicityNot monotonic
2024-05-18T10:56:06.540762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
22 912
 
9.1%
23 875
 
8.8%
24 868
 
8.7%
25 821
 
8.2%
21 757
 
7.6%
20 681
 
6.8%
26 664
 
6.6%
19 621
 
6.2%
18 480
 
4.8%
27 470
 
4.7%
Other values (27) 2851
28.5%
ValueCountFrequency (%)
0 16
 
0.2%
1 36
0.4%
2 32
0.3%
3 33
0.3%
4 34
0.3%
5 34
0.3%
6 33
0.3%
7 56
0.6%
8 63
0.6%
9 61
0.6%
ValueCountFrequency (%)
36 1
 
< 0.1%
35 2
 
< 0.1%
34 8
 
0.1%
33 25
 
0.2%
32 25
 
0.2%
31 84
 
0.8%
30 161
 
1.6%
29 144
 
1.4%
28 291
2.9%
27 470
4.7%

습도(%)
Real number (ℝ)

Distinct59
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.1956
Minimum6
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:06.819612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile14
Q120
median25
Q331
95-th percentile42
Maximum76
Range70
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.7013512
Coefficient of variation (CV)0.33216842
Kurtosis0.68848405
Mean26.1956
Median Absolute Deviation (MAD)6
Skewness0.65282006
Sum261956
Variance75.713512
MonotonicityNot monotonic
2024-05-18T10:56:07.236982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
25 527
 
5.3%
24 516
 
5.2%
20 513
 
5.1%
22 510
 
5.1%
23 491
 
4.9%
26 474
 
4.7%
21 461
 
4.6%
27 423
 
4.2%
19 411
 
4.1%
28 372
 
3.7%
Other values (49) 5302
53.0%
ValueCountFrequency (%)
6 6
 
0.1%
7 14
 
0.1%
8 35
 
0.4%
9 51
 
0.5%
10 75
 
0.8%
11 72
 
0.7%
12 97
1.0%
13 108
1.1%
14 184
1.8%
15 199
2.0%
ValueCountFrequency (%)
76 2
 
< 0.1%
73 1
 
< 0.1%
69 1
 
< 0.1%
67 2
 
< 0.1%
62 3
 
< 0.1%
61 3
 
< 0.1%
59 1
 
< 0.1%
58 4
 
< 0.1%
57 5
0.1%
55 11
0.1%

총 유기 휘발성 화합물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct391
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54.0182
Minimum0
Maximum499
Zeros118
Zeros (%)1.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:07.627496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q119
median41
Q358
95-th percentile150.05
Maximum499
Range499
Interquartile range (IQR)39

Descriptive statistics

Standard deviation72.587343
Coefficient of variation (CV)1.3437572
Kurtosis19.368463
Mean54.0182
Median Absolute Deviation (MAD)20
Skewness4.1392579
Sum540182
Variance5268.9224
MonotonicityNot monotonic
2024-05-18T10:56:07.906285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
50 346
 
3.5%
51 311
 
3.1%
12 219
 
2.2%
52 212
 
2.1%
11 204
 
2.0%
53 187
 
1.9%
9 174
 
1.7%
58 173
 
1.7%
55 170
 
1.7%
56 163
 
1.6%
Other values (381) 7841
78.4%
ValueCountFrequency (%)
0 118
1.2%
1 60
 
0.6%
2 49
 
0.5%
3 70
0.7%
4 81
0.8%
5 106
1.1%
6 99
1.0%
7 114
1.1%
8 146
1.5%
9 174
1.7%
ValueCountFrequency (%)
499 63
0.6%
498 3
 
< 0.1%
497 2
 
< 0.1%
496 4
 
< 0.1%
495 5
 
0.1%
494 2
 
< 0.1%
493 1
 
< 0.1%
492 3
 
< 0.1%
490 4
 
< 0.1%
488 2
 
< 0.1%

이산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct1618
Distinct (%)16.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean735.9439
Minimum0
Maximum9665
Zeros34
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:08.165291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile400
Q1429
median540
Q3754
95-th percentile1855.05
Maximum9665
Range9665
Interquartile range (IQR)325

Descriptive statistics

Standard deviation575.08451
Coefficient of variation (CV)0.78142438
Kurtosis32.842524
Mean735.9439
Median Absolute Deviation (MAD)130
Skewness4.2624549
Sum7359439
Variance330722.19
MonotonicityNot monotonic
2024-05-18T10:56:08.511230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 871
 
8.7%
401 215
 
2.1%
402 143
 
1.4%
403 113
 
1.1%
404 108
 
1.1%
405 75
 
0.8%
406 73
 
0.7%
410 60
 
0.6%
407 57
 
0.6%
408 56
 
0.6%
Other values (1608) 8229
82.3%
ValueCountFrequency (%)
0 34
 
0.3%
320 1
 
< 0.1%
369 1
 
< 0.1%
383 2
 
< 0.1%
385 1
 
< 0.1%
387 1
 
< 0.1%
388 2
 
< 0.1%
394 1
 
< 0.1%
400 871
8.7%
401 215
 
2.1%
ValueCountFrequency (%)
9665 1
< 0.1%
9385 1
< 0.1%
9296 1
< 0.1%
8496 1
< 0.1%
7780 1
< 0.1%
6899 1
< 0.1%
5585 1
< 0.1%
5432 1
< 0.1%
5377 1
< 0.1%
5346 1
< 0.1%

등록일시
Date

Distinct4829
Distinct (%)48.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-16 00:22:38
Maximum2023-01-22 23:23:17
2024-05-18T10:56:08.792329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:09.372888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-18T10:56:00.286060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:49.162107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:51.198890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:53.128902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:54.752079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:56.606078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:58.641556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:00.555391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:49.520527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:51.463241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:53.393158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:55.015249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:56.865472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:58.813409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:00.815721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:49.817892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:51.698756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:53.582094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:55.274394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:57.119991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:58.973895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:01.084598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:50.098816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:51.944798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:53.751591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:55.608320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:57.366330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:59.141989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:01.376848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:50.383877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:52.217637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:53.958384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:55.793025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:57.842259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:59.328805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:01.620550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:50.658359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:52.501961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:54.227072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:56.065950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:58.106202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:59.675955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:01.892769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:50.924328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:52.772236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:54.488813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:56.335169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:58.373564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:55:59.977555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T10:56:09.555650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소
시리얼1.0000.0690.0650.4740.3210.2540.119
초미세먼지(㎍/㎥)0.0691.0001.0000.0730.1570.0540.132
미세먼지(㎍/㎥)0.0651.0001.0000.0800.1560.0470.127
온도(℃)0.4740.0730.0801.0000.4750.2600.168
습도(%)0.3210.1570.1560.4751.0000.2690.186
총 유기 휘발성 화합물0.2540.0540.0470.2600.2691.0000.553
이산화탄소0.1190.1320.1270.1680.1860.5531.000
2024-05-18T10:56:09.758988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소
시리얼1.0000.0920.084-0.155-0.010-0.138-0.036
초미세먼지(㎍/㎥)0.0921.0000.993-0.173-0.022-0.0940.003
미세먼지(㎍/㎥)0.0840.9931.000-0.185-0.011-0.0930.006
온도(℃)-0.155-0.173-0.1851.000-0.3640.2720.226
습도(%)-0.010-0.022-0.011-0.3641.0000.2530.251
총 유기 휘발성 화합물-0.138-0.094-0.0930.2720.2531.0000.635
이산화탄소-0.0360.0030.0060.2260.2510.6351.000

Missing values

2024-05-18T10:56:02.259326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T10:56:02.909941image/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
27533중랑구AN-EMD-TA100L40321242022604962023-01-20 06:23:05
1132중랑구AN-EMD-TA100L2708102326204002023-01-16 04:23:53
29468중랑구AN-EMD-TA100L440792420569832023-01-20 13:23:09
10912중랑구AN-EMD-TA100L4391721236235922023-01-17 16:23:06
33980중랑구AN-EMD-TA100L115002128264322023-01-21 07:22:47
41998중랑구AN-EMD-TA100L392552215304552023-01-22 14:23:06
2766중랑구AN-EMD-TA100L362561838147742023-01-16 10:22:58
26111중랑구AN-EMD-TA100L192582232506372023-01-20 01:59:41
18109중랑구AN-EMD-TA100L275442429506962023-01-18 19:22:54
15118중랑구AN-EMD-TA100L39616182614304472023-01-18 08:23:03
기관 명모델명시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소등록일시
28014중랑구AN-EMD-TA100L31110122522595002023-01-20 08:22:59
9346중랑구AN-EMD-TA100L1459121130518352023-01-17 11:22:45
1156중랑구AN-EMD-TA100L308012229184002023-01-16 04:23:55
15583중랑구AN-EMD-TA100L246572524559532023-01-18 10:22:52
28008중랑구AN-EMD-TA100L302673024809482023-01-20 08:22:59
36191중랑구AN-EMD-TA100L4628101416175132023-01-21 15:23:09
38353중랑구AN-EMD-TA100L226342217515132023-01-22 00:22:56
23021중랑구AN-EMD-TA100L4662325315513952023-01-19 13:23:11
3796중랑구AN-EMD-TA100L22645252327417122023-01-16 14:22:49
22722중랑구AN-EMD-TA100L4392834237498112023-01-19 12:23:08