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 1048 (10.5%) zerosZeros
미세먼지(㎍/㎥) has 966 (9.7%) zerosZeros

Reproduction

Analysis started2024-05-18 01:56:42.387318
Analysis finished2024-05-18 01:56:57.146893
Duration14.76 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

2024-05-18T10:56:57.341717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:56:57.665793image/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

2024-05-18T10:56:57.959631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-18T10:56:58.219808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
an-emd-ta100l 10000
100.0%

시리얼
Real number (ℝ)

Distinct287
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean309.3378
Minimum76
Maximum607
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:58.539316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum76
5-th percentile90
Q1181
median310.5
Q3425
95-th percentile524
Maximum607
Range531
Interquartile range (IQR)244

Descriptive statistics

Standard deviation142.47633
Coefficient of variation (CV)0.46058494
Kurtosis-1.0111943
Mean309.3378
Median Absolute Deviation (MAD)124.5
Skewness0.096703992
Sum3093378
Variance20299.505
MonotonicityNot monotonic
2024-05-18T10:56:58.999270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
107 56
 
0.6%
131 52
 
0.5%
404 51
 
0.5%
264 49
 
0.5%
365 48
 
0.5%
121 48
 
0.5%
252 48
 
0.5%
440 48
 
0.5%
361 48
 
0.5%
80 48
 
0.5%
Other values (277) 9504
95.0%
ValueCountFrequency (%)
76 46
0.5%
77 40
0.4%
78 37
0.4%
79 33
0.3%
80 48
0.5%
82 33
0.3%
83 34
0.3%
84 34
0.3%
85 36
0.4%
86 43
0.4%
ValueCountFrequency (%)
607 36
0.4%
606 39
0.4%
605 37
0.4%
604 41
0.4%
603 44
0.4%
602 36
0.4%
601 45
0.4%
600 34
0.3%
599 32
0.3%
532 4
 
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct142
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22.1289
Minimum0
Maximum240
Zeros1048
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:56:59.669570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15
median13
Q335
95-th percentile68
Maximum240
Range240
Interquartile range (IQR)30

Descriptive statistics

Standard deviation23.516215
Coefficient of variation (CV)1.0626925
Kurtosis3.7348507
Mean22.1289
Median Absolute Deviation (MAD)11
Skewness1.624207
Sum221289
Variance553.01239
MonotonicityNot monotonic
2024-05-18T10:57:00.101036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1048
 
10.5%
4 407
 
4.1%
2 391
 
3.9%
6 383
 
3.8%
5 362
 
3.6%
3 348
 
3.5%
7 337
 
3.4%
8 310
 
3.1%
1 297
 
3.0%
9 279
 
2.8%
Other values (132) 5838
58.4%
ValueCountFrequency (%)
0 1048
10.5%
1 297
 
3.0%
2 391
 
3.9%
3 348
 
3.5%
4 407
 
4.1%
5 362
 
3.6%
6 383
 
3.8%
7 337
 
3.4%
8 310
 
3.1%
9 279
 
2.8%
ValueCountFrequency (%)
240 1
< 0.1%
204 1
< 0.1%
193 1
< 0.1%
172 1
< 0.1%
168 1
< 0.1%
161 1
< 0.1%
152 1
< 0.1%
146 2
< 0.1%
145 1
< 0.1%
141 1
< 0.1%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct168
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.1872
Minimum0
Maximum281
Zeros966
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:57:00.546752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q15.75
median15
Q340
95-th percentile83
Maximum281
Range281
Interquartile range (IQR)34.25

Descriptive statistics

Standard deviation28.213142
Coefficient of variation (CV)1.0773638
Kurtosis3.5965514
Mean26.1872
Median Absolute Deviation (MAD)13
Skewness1.6527989
Sum261872
Variance795.98135
MonotonicityNot monotonic
2024-05-18T10:57:00.975524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 966
 
9.7%
5 361
 
3.6%
2 342
 
3.4%
7 330
 
3.3%
8 323
 
3.2%
4 313
 
3.1%
6 308
 
3.1%
9 301
 
3.0%
3 284
 
2.8%
10 254
 
2.5%
Other values (158) 6218
62.2%
ValueCountFrequency (%)
0 966
9.7%
1 234
 
2.3%
2 342
 
3.4%
3 284
 
2.8%
4 313
 
3.1%
5 361
 
3.6%
6 308
 
3.1%
7 330
 
3.3%
8 323
 
3.2%
9 301
 
3.0%
ValueCountFrequency (%)
281 1
< 0.1%
257 1
< 0.1%
208 1
< 0.1%
185 1
< 0.1%
182 1
< 0.1%
173 1
< 0.1%
172 1
< 0.1%
171 1
< 0.1%
170 1
< 0.1%
162 2
< 0.1%

온도(℃)
Real number (ℝ)

Distinct38
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.1607
Minimum0
Maximum38
Zeros15
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:57:01.369936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11
Q118
median22
Q325
95-th percentile29
Maximum38
Range38
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.3665776
Coefficient of variation (CV)0.25361059
Kurtosis1.5620304
Mean21.1607
Median Absolute Deviation (MAD)3
Skewness-0.8888973
Sum211607
Variance28.800156
MonotonicityNot monotonic
2024-05-18T10:57:01.777504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
22 935
 
9.3%
23 909
 
9.1%
25 829
 
8.3%
24 823
 
8.2%
21 803
 
8.0%
20 725
 
7.2%
19 623
 
6.2%
26 606
 
6.1%
18 564
 
5.6%
27 393
 
3.9%
Other values (28) 2790
27.9%
ValueCountFrequency (%)
0 15
 
0.1%
1 24
 
0.2%
2 21
 
0.2%
3 45
0.4%
4 38
0.4%
5 26
0.3%
6 37
0.4%
7 43
0.4%
8 48
0.5%
9 60
0.6%
ValueCountFrequency (%)
38 1
 
< 0.1%
37 1
 
< 0.1%
35 7
 
0.1%
34 31
 
0.3%
33 30
 
0.3%
32 41
 
0.4%
31 75
 
0.8%
30 143
1.4%
29 199
2.0%
28 308
3.1%

습도(%)
Real number (ℝ)

Distinct65
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5289
Minimum4
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:57:02.035912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile13
Q120
median25
Q330
95-th percentile41
Maximum76
Range72
Interquartile range (IQR)10

Descriptive statistics

Standard deviation8.5523142
Coefficient of variation (CV)0.3350052
Kurtosis1.1039268
Mean25.5289
Median Absolute Deviation (MAD)5
Skewness0.70049339
Sum255289
Variance73.142079
MonotonicityNot monotonic
2024-05-18T10:57:02.387437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21 533
 
5.3%
25 518
 
5.2%
23 512
 
5.1%
26 502
 
5.0%
24 488
 
4.9%
20 468
 
4.7%
27 467
 
4.7%
22 466
 
4.7%
19 433
 
4.3%
18 415
 
4.2%
Other values (55) 5198
52.0%
ValueCountFrequency (%)
4 1
 
< 0.1%
5 9
 
0.1%
6 9
 
0.1%
7 27
 
0.3%
8 37
 
0.4%
9 67
0.7%
10 49
 
0.5%
11 73
0.7%
12 133
1.3%
13 136
1.4%
ValueCountFrequency (%)
76 2
< 0.1%
75 1
 
< 0.1%
74 1
 
< 0.1%
70 1
 
< 0.1%
65 1
 
< 0.1%
64 3
< 0.1%
62 2
< 0.1%
61 2
< 0.1%
60 3
< 0.1%
59 3
< 0.1%

총 유기 휘발성 화합물
Real number (ℝ)

HIGH CORRELATION 

Distinct417
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.4729
Minimum0
Maximum499
Zeros88
Zeros (%)0.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:57:02.680744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10
Q133
median52
Q366
95-th percentile219.15
Maximum499
Range499
Interquartile range (IQR)33

Descriptive statistics

Standard deviation83.734337
Coefficient of variation (CV)1.2052806
Kurtosis14.309741
Mean69.4729
Median Absolute Deviation (MAD)16
Skewness3.6846581
Sum694729
Variance7011.4392
MonotonicityNot monotonic
2024-05-18T10:57:03.168479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
51 511
 
5.1%
50 473
 
4.7%
52 283
 
2.8%
55 273
 
2.7%
54 266
 
2.7%
53 254
 
2.5%
56 244
 
2.4%
57 232
 
2.3%
58 189
 
1.9%
59 182
 
1.8%
Other values (407) 7093
70.9%
ValueCountFrequency (%)
0 88
0.9%
1 27
 
0.3%
2 25
 
0.2%
3 22
 
0.2%
4 29
 
0.3%
5 34
 
0.3%
6 38
0.4%
7 41
0.4%
8 66
0.7%
9 82
0.8%
ValueCountFrequency (%)
499 133
1.3%
498 3
 
< 0.1%
497 5
 
0.1%
496 3
 
< 0.1%
495 3
 
< 0.1%
494 1
 
< 0.1%
493 2
 
< 0.1%
491 3
 
< 0.1%
490 2
 
< 0.1%
489 2
 
< 0.1%

이산화탄소
Real number (ℝ)

HIGH CORRELATION 

Distinct1913
Distinct (%)19.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean898.3757
Minimum0
Maximum9700
Zeros41
Zeros (%)0.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-18T10:57:03.588712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile402
Q1528
median681
Q3955
95-th percentile2261.05
Maximum9700
Range9700
Interquartile range (IQR)427

Descriptive statistics

Standard deviation715.71598
Coefficient of variation (CV)0.7966778
Kurtosis27.610957
Mean898.3757
Median Absolute Deviation (MAD)179
Skewness4.1236086
Sum8983757
Variance512249.36
MonotonicityNot monotonic
2024-05-18T10:57:04.031574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
400 376
 
3.8%
401 64
 
0.6%
402 54
 
0.5%
403 52
 
0.5%
0 41
 
0.4%
404 31
 
0.3%
407 30
 
0.3%
601 30
 
0.3%
405 30
 
0.3%
607 28
 
0.3%
Other values (1903) 9264
92.6%
ValueCountFrequency (%)
0 41
0.4%
202 1
 
< 0.1%
351 1
 
< 0.1%
363 1
 
< 0.1%
373 1
 
< 0.1%
378 1
 
< 0.1%
379 1
 
< 0.1%
383 1
 
< 0.1%
385 1
 
< 0.1%
386 1
 
< 0.1%
ValueCountFrequency (%)
9700 3
< 0.1%
9271 1
 
< 0.1%
8741 1
 
< 0.1%
8638 1
 
< 0.1%
8235 1
 
< 0.1%
7953 1
 
< 0.1%
7671 1
 
< 0.1%
7585 1
 
< 0.1%
7514 1
 
< 0.1%
7312 1
 
< 0.1%

등록일시
Date

Distinct4739
Distinct (%)47.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-01-02 00:22:18
Maximum2023-01-08 23:22:57
2024-05-18T10:57:04.477911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:57:04.927556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-18T10:56:54.975208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:44.779378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:46.480267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:48.068476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:49.612736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:51.459573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:53.077735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:55.233579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:45.036279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:46.731003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:48.261252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:49.878546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:51.718153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:53.317657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:55.447407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:45.296228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:46.899621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:48.424766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:50.142190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:51.981262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:53.575530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:55.655041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:45.474071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:47.075239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:48.671135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:50.385686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:52.239253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:53.840199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:55.883697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:45.749327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:47.265353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:48.939783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:50.656900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:52.514247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:54.184876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:56.217654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:46.043196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:47.553034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:49.205377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:50.926230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:52.734342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:54.456210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:56.390288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:46.299906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:47.810165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:49.409065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:51.190784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:52.910237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-18T10:56:54.716827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-18T10:57:05.203389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소
시리얼1.0000.2030.1460.4440.3050.2290.183
초미세먼지(㎍/㎥)0.2031.0000.9420.3200.5840.1540.164
미세먼지(㎍/㎥)0.1460.9421.0000.2330.3820.0970.091
온도(℃)0.4440.3200.2331.0000.4760.2290.273
습도(%)0.3050.5840.3820.4761.0000.2930.235
총 유기 휘발성 화합물0.2290.1540.0970.2290.2931.0000.660
이산화탄소0.1830.1640.0910.2730.2350.6601.000
2024-05-18T10:57:05.508520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소
시리얼1.0000.0560.048-0.144-0.009-0.147-0.022
초미세먼지(㎍/㎥)0.0561.0000.998-0.1330.085-0.0170.039
미세먼지(㎍/㎥)0.0480.9981.000-0.1440.089-0.0190.034
온도(℃)-0.144-0.133-0.1441.000-0.3610.2360.255
습도(%)-0.0090.0850.089-0.3611.0000.2830.262
총 유기 휘발성 화합물-0.147-0.017-0.0190.2360.2831.0000.669
이산화탄소-0.0220.0390.0340.2550.2620.6691.000

Missing values

2024-05-18T10:56:56.715738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-18T10:56:57.027562image/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
4950중랑구AN-EMD-TA100L8222282029820432023-01-02 19:22:20
11080중랑구AN-EMD-TA100L143782026365752023-01-03 18:22:26
33999중랑구AN-EMD-TA100L24667252514311522023-01-07 09:22:36
12266중랑구AN-EMD-TA100L40418202017536732023-01-03 22:22:42
6748중랑구AN-EMD-TA100L51067193404002023-01-03 04:22:49
12273중랑구AN-EMD-TA100L4079102216545752023-01-03 22:22:42
40935중랑구AN-EMD-TA100L30020223415746032023-01-08 12:22:41
24756중랑구AN-EMD-TA100L104992338859542023-01-05 22:22:26
35970중랑구AN-EMD-TA100L7823262523496612023-01-07 17:22:26
26392중랑구AN-EMD-TA100L29315182332535292023-01-06 04:23:38
기관 명모델명시리얼초미세먼지(㎍/㎥)미세먼지(㎍/㎥)온도(℃)습도(%)총 유기 휘발성 화합물이산화탄소등록일시
2370중랑구AN-EMD-TA100L200343011125282023-01-02 09:22:28
13186중랑구AN-EMD-TA100L209222421666322023-01-04 04:22:29
28703중랑구AN-EMD-TA100L1357385143044252023-01-06 13:22:29
24124중랑구AN-EMD-TA100L37561691418866082023-01-05 19:22:43
21631중랑구AN-EMD-TA100L31121252417497892023-01-05 10:22:38
11087중랑구AN-EMD-TA100L1561721826244952023-01-03 18:22:27
7542중랑구AN-EMD-TA100L60012131125174612023-01-03 04:24:25
5865중랑구AN-EMD-TA100L310562315555332023-01-02 22:22:35
26937중랑구AN-EMD-TA100L338222320225414042023-01-06 06:22:41
40347중랑구AN-EMD-TA100L1445468162956502023-01-08 10:22:32