Overview

Dataset statistics

Number of variables13
Number of observations1109
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory122.5 KiB
Average record size in memory113.1 B

Variable types

Categorical3
Numeric9
DateTime1

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15978/S/1/datasetView.do

Alerts

기관 명 has constant value ""Constant
모델명 has constant value ""Constant
데이터관측일시 is highly overall correlated with 시리얼High correlation
온도(℃) is highly overall correlated with 습도(%) and 1 other fieldsHigh correlation
습도(%) is highly overall correlated with 온도(℃) and 1 other fieldsHigh correlation
미세먼지(㎍/㎥) is highly overall correlated with 초미세먼지(㎍/㎥)High correlation
소음(㏈) is highly overall correlated with 시리얼High correlation
이산화탄소(ppm) is highly overall correlated with 학습능률지수(%) and 1 other fieldsHigh correlation
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지(㎍/㎥)High correlation
학습능률지수(%) is highly overall correlated with 이산화탄소(ppm) and 1 other fieldsHigh correlation
시리얼 is highly overall correlated with 데이터관측일시 and 5 other fieldsHigh correlation

Reproduction

Analysis started2024-05-11 16:12:01.877368
Analysis finished2024-05-11 16:12:24.057388
Duration22.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
마포구
1109 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
마포구 1109
100.0%

Length

2024-05-12T01:12:24.164928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:12:24.324163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 1109
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
AirGuard-K
1109 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowAirGuard-K
2nd rowAirGuard-K
3rd rowAirGuard-K
4th rowAirGuard-K
5th rowAirGuard-K

Common Values

ValueCountFrequency (%)
AirGuard-K 1109
100.0%

Length

2024-05-12T01:12:24.486859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:12:24.644140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
airguard-k 1109
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
V01G1613539
 
42
V01G1613611
 
42
V01G1613634
 
42
V01G1613600
 
42
V01G1613601
 
42
Other values (24)
899 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV01G1613539
2nd rowV01G1613542
3rd rowV01G1613600
4th rowV01G1613601
5th rowV01G1613602

Common Values

ValueCountFrequency (%)
V01G1613539 42
 
3.8%
V01G1613611 42
 
3.8%
V01G1613634 42
 
3.8%
V01G1613600 42
 
3.8%
V01G1613601 42
 
3.8%
V01G1613602 42
 
3.8%
V01G1613603 42
 
3.8%
V01G1613604 42
 
3.8%
V01G1613605 42
 
3.8%
V01G1613606 42
 
3.8%
Other values (19) 689
62.1%

Length

2024-05-12T01:12:24.810133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v01g1613539 42
 
3.8%
v01g1613609 42
 
3.8%
v01g1613632 42
 
3.8%
v01g1613629 42
 
3.8%
v01g1613622 42
 
3.8%
v01g1613620 42
 
3.8%
v01g1613619 42
 
3.8%
v01g1613618 42
 
3.8%
v01g1613617 42
 
3.8%
v01g1613615 42
 
3.8%
Other values (19) 689
62.1%

데이터관측일시
Real number (ℝ)

HIGH CORRELATION 

Distinct90
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0239019 × 1011
Minimum2.0230808 × 1011
Maximum2.0240207 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:25.178014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230808 × 1011
5-th percentile2.0231011 × 1011
Q12.0240205 × 1011
median2.0240206 × 1011
Q32.0240207 × 1011
95-th percentile2.0240207 × 1011
Maximum2.0240207 × 1011
Range93991135
Interquartile range (IQR)19800

Descriptive statistics

Standard deviation30691578
Coefficient of variation (CV)0.00015164558
Kurtosis2.9261202
Mean2.0239019 × 1011
Median Absolute Deviation (MAD)9900
Skewness-2.2178839
Sum2.2445073 × 1014
Variance9.4197295 × 1014
MonotonicityNot monotonic
2024-05-12T01:12:25.433029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202401311450 42
 
3.8%
202401311535 42
 
3.8%
202311090355 27
 
2.4%
202310290350 26
 
2.3%
202310090845 26
 
2.3%
202312071235 26
 
2.3%
202310110850 23
 
2.1%
202402071955 21
 
1.9%
202402070855 21
 
1.9%
202402062055 21
 
1.9%
Other values (80) 834
75.2%
ValueCountFrequency (%)
202308081020 15
 
1.4%
202310090845 26
2.3%
202310110850 23
2.1%
202310290350 26
2.3%
202311090355 27
2.4%
202312071235 26
2.3%
202401311450 42
3.8%
202401311535 42
3.8%
202402050850 1
 
0.1%
202402050855 20
1.8%
ValueCountFrequency (%)
202402072155 18
1.6%
202402072150 2
 
0.2%
202402072145 1
 
0.1%
202402072055 18
1.6%
202402072050 3
 
0.3%
202402071955 21
1.9%
202402071855 21
1.9%
202402071755 20
1.8%
202402071750 1
 
0.1%
202402071655 21
1.9%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct121
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1219.7286
Minimum1137
Maximum1269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:25.685954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1137
5-th percentile1145
Q11201
median1231
Q31244
95-th percentile1265
Maximum1269
Range132
Interquartile range (IQR)43

Descriptive statistics

Standard deviation34.670383
Coefficient of variation (CV)0.028424671
Kurtosis-0.14454688
Mean1219.7286
Median Absolute Deviation (MAD)18
Skewness-0.94112157
Sum1352679
Variance1202.0355
MonotonicityNot monotonic
2024-05-12T01:12:25.950930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1244 54
 
4.9%
1224 44
 
4.0%
1252 38
 
3.4%
1232 36
 
3.2%
1269 35
 
3.2%
1229 34
 
3.1%
1211 30
 
2.7%
1228 29
 
2.6%
1243 29
 
2.6%
1241 28
 
2.5%
Other values (111) 752
67.8%
ValueCountFrequency (%)
1137 1
 
0.1%
1138 5
 
0.5%
1139 3
 
0.3%
1140 2
 
0.2%
1141 9
0.8%
1142 13
1.2%
1143 10
0.9%
1144 7
0.6%
1145 10
0.9%
1146 2
 
0.2%
ValueCountFrequency (%)
1269 35
3.2%
1265 23
2.1%
1259 5
 
0.5%
1258 5
 
0.5%
1257 4
 
0.4%
1256 11
 
1.0%
1255 9
 
0.8%
1254 6
 
0.5%
1253 6
 
0.5%
1252 38
3.4%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.559964
Minimum18
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:26.192816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile22
Q124
median27
Q335
95-th percentile40
Maximum59
Range41
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.2422785
Coefficient of variation (CV)0.24500295
Kurtosis2.1661894
Mean29.559964
Median Absolute Deviation (MAD)4
Skewness1.1942338
Sum32782
Variance52.450598
MonotonicityNot monotonic
2024-05-12T01:12:26.415080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
24 152
13.7%
23 99
 
8.9%
25 94
 
8.5%
26 92
 
8.3%
27 74
 
6.7%
38 72
 
6.5%
32 60
 
5.4%
22 49
 
4.4%
36 49
 
4.4%
28 45
 
4.1%
Other values (20) 323
29.1%
ValueCountFrequency (%)
18 35
 
3.2%
21 1
 
0.1%
22 49
 
4.4%
23 99
8.9%
24 152
13.7%
25 94
8.5%
26 92
8.3%
27 74
6.7%
28 45
 
4.1%
29 13
 
1.2%
ValueCountFrequency (%)
59 15
1.4%
52 1
 
0.1%
47 2
 
0.2%
46 1
 
0.1%
45 27
2.4%
44 1
 
0.1%
43 2
 
0.2%
42 3
 
0.3%
41 1
 
0.1%
40 7
 
0.6%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.494139
Minimum5
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:26.630364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median11
Q317
95-th percentile44.6
Maximum62
Range57
Interquartile range (IQR)9

Descriptive statistics

Standard deviation11.660635
Coefficient of variation (CV)0.75258361
Kurtosis5.3486328
Mean15.494139
Median Absolute Deviation (MAD)6
Skewness2.2203792
Sum17183
Variance135.97041
MonotonicityNot monotonic
2024-05-12T01:12:26.847349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
17 249
22.5%
11 241
21.7%
5 175
15.8%
6 75
 
6.8%
15 57
 
5.1%
8 55
 
5.0%
20 40
 
3.6%
28 38
 
3.4%
59 27
 
2.4%
10 25
 
2.3%
Other values (24) 127
11.5%
ValueCountFrequency (%)
5 175
15.8%
6 75
 
6.8%
8 55
 
5.0%
10 25
 
2.3%
11 241
21.7%
13 22
 
2.0%
15 57
 
5.1%
17 249
22.5%
18 7
 
0.6%
20 40
 
3.6%
ValueCountFrequency (%)
62 1
 
0.1%
61 2
 
0.2%
59 27
2.4%
57 7
 
0.6%
56 4
 
0.4%
54 2
 
0.2%
52 2
 
0.2%
49 3
 
0.3%
47 1
 
0.1%
45 7
 
0.6%

소음(㏈)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.551849
Minimum32
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:27.086250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile33
Q139
median46
Q353
95-th percentile60
Maximum80
Range48
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.8750995
Coefficient of variation (CV)0.19064978
Kurtosis-0.28152517
Mean46.551849
Median Absolute Deviation (MAD)7
Skewness0.41802799
Sum51626
Variance78.767391
MonotonicityNot monotonic
2024-05-12T01:12:27.331950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
39 94
 
8.5%
41 78
 
7.0%
51 70
 
6.3%
42 53
 
4.8%
40 52
 
4.7%
54 50
 
4.5%
57 49
 
4.4%
38 48
 
4.3%
48 48
 
4.3%
32 46
 
4.1%
Other values (34) 521
47.0%
ValueCountFrequency (%)
32 46
4.1%
33 19
 
1.7%
34 10
 
0.9%
35 20
 
1.8%
36 38
3.4%
37 25
 
2.3%
38 48
4.3%
39 94
8.5%
40 52
4.7%
41 78
7.0%
ValueCountFrequency (%)
80 1
 
0.1%
79 1
 
0.1%
78 1
 
0.1%
77 1
 
0.1%
76 1
 
0.1%
75 1
 
0.1%
73 1
 
0.1%
69 1
 
0.1%
67 3
0.3%
66 1
 
0.1%

이산화탄소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct508
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean317.09739
Minimum-9999
Maximum1465
Zeros0
Zeros (%)0.0%
Negative42
Negative (%)3.8%
Memory size9.9 KiB
2024-05-12T01:12:27.572404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile313.8
Q1537
median699
Q3875
95-th percentile1208
Maximum1465
Range11464
Interquartile range (IQR)338

Descriptive statistics

Standard deviation2060.027
Coefficient of variation (CV)6.4965121
Kurtosis20.965037
Mean317.09739
Median Absolute Deviation (MAD)166
Skewness-4.7538241
Sum351661
Variance4243711.3
MonotonicityNot monotonic
2024-05-12T01:12:27.825600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
883 62
 
5.6%
-9999 42
 
3.8%
1208 35
 
3.2%
619 32
 
2.9%
820 27
 
2.4%
580 26
 
2.3%
429 23
 
2.1%
417 16
 
1.4%
741 16
 
1.4%
710 15
 
1.4%
Other values (498) 815
73.5%
ValueCountFrequency (%)
-9999 42
3.8%
202 1
 
0.1%
206 1
 
0.1%
212 1
 
0.1%
264 1
 
0.1%
268 1
 
0.1%
270 1
 
0.1%
274 1
 
0.1%
277 1
 
0.1%
280 1
 
0.1%
ValueCountFrequency (%)
1465 1
0.1%
1428 1
0.1%
1421 1
0.1%
1408 1
0.1%
1403 1
0.1%
1379 1
0.1%
1367 1
0.1%
1334 1
0.1%
1332 1
0.1%
1318 1
0.1%
Distinct264
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.83228
Minimum125
Maximum737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:28.070160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1146
median184
Q3278
95-th percentile394
Maximum737
Range612
Interquartile range (IQR)132

Descriptive statistics

Standard deviation95.90474
Coefficient of variation (CV)0.43626322
Kurtosis3.0863323
Mean219.83228
Median Absolute Deviation (MAD)53
Skewness1.5038889
Sum243794
Variance9197.7191
MonotonicityNot monotonic
2024-05-12T01:12:28.313551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 93
 
8.4%
162 40
 
3.6%
302 36
 
3.2%
183 32
 
2.9%
126 29
 
2.6%
348 27
 
2.4%
337 27
 
2.4%
240 26
 
2.3%
216 26
 
2.3%
140 21
 
1.9%
Other values (254) 752
67.8%
ValueCountFrequency (%)
125 93
8.4%
126 29
 
2.6%
127 17
 
1.5%
128 10
 
0.9%
129 5
 
0.5%
130 7
 
0.6%
131 12
 
1.1%
132 9
 
0.8%
133 13
 
1.2%
134 6
 
0.5%
ValueCountFrequency (%)
737 1
 
0.1%
682 4
0.4%
647 1
 
0.1%
621 1
 
0.1%
601 1
 
0.1%
569 1
 
0.1%
549 1
 
0.1%
534 1
 
0.1%
528 1
 
0.1%
520 1
 
0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.982867
Minimum3
Maximum45
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:28.545269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median8
Q312
95-th percentile32.6
Maximum45
Range42
Interquartile range (IQR)7

Descriptive statistics

Standard deviation8.6214343
Coefficient of variation (CV)0.78498938
Kurtosis5.3300351
Mean10.982867
Median Absolute Deviation (MAD)4
Skewness2.2023495
Sum12180
Variance74.329129
MonotonicityNot monotonic
2024-05-12T01:12:28.756751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
12 249
22.5%
8 241
21.7%
3 175
15.8%
4 75
 
6.8%
11 57
 
5.1%
5 55
 
5.0%
14 40
 
3.6%
20 38
 
3.4%
43 27
 
2.4%
7 25
 
2.3%
Other values (22) 127
11.5%
ValueCountFrequency (%)
3 175
15.8%
4 75
 
6.8%
5 55
 
5.0%
7 25
 
2.3%
8 241
21.7%
9 22
 
2.0%
11 57
 
5.1%
12 249
22.5%
13 7
 
0.6%
14 40
 
3.6%
ValueCountFrequency (%)
45 3
 
0.3%
43 27
2.4%
42 7
 
0.6%
41 4
 
0.4%
39 2
 
0.2%
38 2
 
0.2%
36 3
 
0.3%
34 1
 
0.1%
33 7
 
0.6%
32 1
 
0.1%

학습능률지수(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct41
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.61046
Minimum47
Maximum152
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size9.9 KiB
2024-05-12T01:12:29.128874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum47
5-th percentile53
Q161
median65
Q370
95-th percentile79
Maximum152
Range105
Interquartile range (IQR)9

Descriptive statistics

Standard deviation16.604763
Coefficient of variation (CV)0.24559459
Kurtosis14.358347
Mean67.61046
Median Absolute Deviation (MAD)4
Skewness3.598566
Sum74980
Variance275.71816
MonotonicityNot monotonic
2024-05-12T01:12:29.365164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
63 83
 
7.5%
64 83
 
7.5%
67 74
 
6.7%
62 71
 
6.4%
65 64
 
5.8%
72 63
 
5.7%
69 61
 
5.5%
70 58
 
5.2%
71 56
 
5.0%
61 48
 
4.3%
Other values (31) 448
40.4%
ValueCountFrequency (%)
47 35
3.2%
50 1
 
0.1%
51 7
 
0.6%
52 12
 
1.1%
53 12
 
1.1%
54 19
1.7%
55 28
2.5%
56 30
2.7%
57 22
2.0%
58 28
2.5%
ValueCountFrequency (%)
152 1
 
0.1%
151 1
 
0.1%
148 1
 
0.1%
145 3
 
0.3%
144 21
1.9%
143 12
1.1%
142 3
 
0.3%
85 1
 
0.1%
81 1
 
0.1%
80 1
 
0.1%
Distinct189
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size8.8 KiB
Minimum2024-02-05 08:58:30
Maximum2024-02-07 21:58:27
2024-05-12T01:12:29.606602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:29.849649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-12T01:12:21.613223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:02.799524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:05.098714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:07.440962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:09.842432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:12.144546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:14.442915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:16.793095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:19.289325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:21.865507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:03.052501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:05.360547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:07.691643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:10.097636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:12.399457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:14.704861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:17.053560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:19.537921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:22.127092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:03.314479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:05.623363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:08.091401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:10.363398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:12.664646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:14.971839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:17.461870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:19.817013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:22.374292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:03.564753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:05.879356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:08.335497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:10.611445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:12.911745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:15.228142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:17.716644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:20.115437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:22.630332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:03.820708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:06.140532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:08.585775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:10.866737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:13.170322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:15.490030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:17.977872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:20.368927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:22.882565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:04.078208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:06.403617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:08.837708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:11.121646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:13.422687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:15.753251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:18.240192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:20.617477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:23.095080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:04.340222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:06.671230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:09.095637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:11.385414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:13.686715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:16.016876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:18.511734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:20.875529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:23.261360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:04.606009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:06.939132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:09.356556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:11.649083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:13.949699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:16.287975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:18.778854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:21.132730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:23.405494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:04.849475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:07.187870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:09.595608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:11.893855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:14.194293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:16.536077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:19.029393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:12:21.370133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T01:12:30.015601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)
시리얼1.0001.0000.9290.9420.8120.8830.4670.7510.8020.953
데이터관측일시1.0001.0000.3760.4970.5260.4960.0350.5830.5000.335
온도(℃)0.9290.3761.0000.8290.6600.6570.3880.5150.6490.831
습도(%)0.9420.4970.8291.0000.7060.7610.1510.5120.6920.858
미세먼지(㎍/㎥)0.8120.5260.6600.7061.0000.6950.0000.5770.9990.548
소음(㏈)0.8830.4960.6570.7610.6951.0000.0630.4560.6970.657
이산화탄소(ppm)0.4670.0350.3880.1510.0000.0631.0000.0000.0000.067
휘발성유기화합물(ppb)0.7510.5830.5150.5120.5770.4560.0001.0000.5440.500
초미세먼지(㎍/㎥)0.8020.5000.6490.6920.9990.6970.0000.5441.0000.548
학습능률지수(%)0.9530.3350.8310.8580.5480.6570.0670.5000.5481.000
2024-05-12T01:12:30.250050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)시리얼
데이터관측일시1.000-0.125-0.204-0.065-0.1860.1540.080-0.065-0.1200.988
온도(℃)-0.1251.000-0.639-0.0430.2990.2830.182-0.043-0.1250.667
습도(%)-0.204-0.6391.000-0.083-0.220-0.3410.083-0.0830.1410.705
미세먼지(㎍/㎥)-0.065-0.043-0.0831.0000.2580.1110.0651.000-0.1640.447
소음(㏈)-0.1860.299-0.2200.2581.0000.0840.0690.258-0.2020.559
이산화탄소(ppm)0.1540.283-0.3410.1110.0841.0000.4270.111-0.5560.746
휘발성유기화합물(ppb)0.0800.1820.0830.0650.0690.4271.0000.065-0.1310.376
초미세먼지(㎍/㎥)-0.065-0.043-0.0831.0000.2580.1110.0651.000-0.1640.433
학습능률지수(%)-0.120-0.1250.141-0.164-0.202-0.556-0.131-0.1641.0000.806
시리얼0.9880.6670.7050.4470.5590.7460.3760.4330.8061.000

Missing values

2024-05-12T01:12:23.619366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T01:12:23.935011image/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 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
0마포구AirGuard-KV01G1613539202401311450120229155384322311612024-02-05 08:58:30
1마포구AirGuard-KV01G161354220240205085512252611543131488742024-02-05 08:58:30
2마포구AirGuard-KV01G1613600202402050855117138173975612512582024-02-05 08:58:30
3마포구AirGuard-KV01G1613601202402050855113838174163912512562024-02-05 08:58:30
4마포구AirGuard-KV01G16136022024020508551142391141-999912781442024-02-05 08:58:30
5마포구AirGuard-KV01G1613603202402050855115147395459423228572024-02-05 08:58:30
6마포구AirGuard-KV01G16136042024020508551179356366111254612024-02-05 08:58:30
7마포구AirGuard-KV01G161360520240205085511893011397841588582024-02-05 08:58:30
8마포구AirGuard-KV01G1613606202402050850116936174031612512642024-02-05 08:58:31
9마포구AirGuard-KV01G1613609202402050855121027173569312512662024-02-05 08:58:31
기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
1099마포구AirGuard-KV01G16136192024020721551252225337452643652024-02-07 21:58:26
1100마포구AirGuard-KV01G1613620202402072155122434495565862136732024-02-07 21:58:26
1101마포구AirGuard-KV01G161362220240207215512582263312092324582024-02-07 21:58:26
1102마포구AirGuard-KV01G161362820231009084512324511605802408792024-02-07 21:58:26
1103마포구AirGuard-KV01G1613629202402072155124324174765622412662024-02-07 21:58:26
1104마포구AirGuard-KV01G1613630202311090355121132153961933711722024-02-07 21:58:26
1105마포구AirGuard-KV01G1613632202401311535126918285788316220472024-02-07 21:58:27
1106마포구AirGuard-KV01G161363420240207215512552211336421268662024-02-07 21:58:27
1107마포구AirGuard-KV01G16136352023101108501265388514291835642024-02-07 21:58:27
1108마포구AirGuard-KV01G1613637202312071235122828596588321643622024-02-07 21:58:27