Overview

Dataset statistics

Number of variables13
Number of observations2658
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory293.4 KiB
Average record size in memory113.0 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 시리얼High correlation
습도(%) is highly overall correlated with 시리얼High 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 시리얼High correlation
초미세먼지(㎍/㎥) is highly overall correlated with 미세먼지(㎍/㎥)High correlation
학습능률지수(%) is highly overall correlated with 시리얼High correlation
시리얼 is highly overall correlated with 데이터관측일시 and 5 other fieldsHigh correlation

Reproduction

Analysis started2024-05-04 02:23:40.664617
Analysis finished2024-05-04 02:24:13.641892
Duration32.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
마포구
2658 

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 2658
100.0%

Length

2024-05-04T02:24:13.979917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:24:14.490166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 2658
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
AirGuard-K
2658 

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 2658
100.0%

Length

2024-05-04T02:24:14.855139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T02:24:15.131448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
airguard-k 2658
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
V01G1613602
 
98
V01G1613613
 
98
V01G1613600
 
98
V01G1613542
 
98
V01G1613539
 
98
Other values (24)
2168 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
V01G1613602 98
 
3.7%
V01G1613613 98
 
3.7%
V01G1613600 98
 
3.7%
V01G1613542 98
 
3.7%
V01G1613539 98
 
3.7%
V01G1613603 98
 
3.7%
V01G1613612 98
 
3.7%
V01G1613611 98
 
3.7%
V01G1613604 98
 
3.7%
V01G1613605 98
 
3.7%
Other values (19) 1678
63.1%

Length

2024-05-04T02:24:15.415998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v01g1613602 98
 
3.7%
v01g1613606 98
 
3.7%
v01g1613634 98
 
3.7%
v01g1613632 98
 
3.7%
v01g1613629 98
 
3.7%
v01g1613622 98
 
3.7%
v01g1613620 98
 
3.7%
v01g1613619 98
 
3.7%
v01g1613618 98
 
3.7%
v01g1613617 98
 
3.7%
Other values (19) 1678
63.1%

데이터관측일시
Real number (ℝ)

HIGH CORRELATION 

Distinct199
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0238747 × 1011
Minimum2.0230808 × 1011
Maximum2.0240128 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:15.817659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230808 × 1011
5-th percentile2.0231011 × 1011
Q12.0240122 × 1011
median2.0240124 × 1011
Q32.0240126 × 1011
95-th percentile2.0240128 × 1011
Maximum2.0240128 × 1011
Range93201135
Interquartile range (IQR)40195

Descriptive statistics

Standard deviation32568315
Coefficient of variation (CV)0.00016092061
Kurtosis1.768208
Mean2.0238747 × 1011
Median Absolute Deviation (MAD)20100
Skewness-1.9404923
Sum5.3794589 × 1014
Variance1.0606951 × 1015
MonotonicityNot monotonic
2024-05-04T02:24:16.271342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202312071235 85
 
3.2%
202311090355 84
 
3.2%
202310290350 72
 
2.7%
202310110850 67
 
2.5%
202310090845 57
 
2.1%
202308081020 39
 
1.5%
202401261055 23
 
0.9%
202401230855 23
 
0.9%
202401261855 23
 
0.9%
202401251055 23
 
0.9%
Other values (189) 2162
81.3%
ValueCountFrequency (%)
202308081020 39
1.5%
202310090845 57
2.1%
202310110850 67
2.5%
202310290350 72
2.7%
202311090355 84
3.2%
202312071235 85
3.2%
202401121625 12
 
0.5%
202401220850 1
 
< 0.1%
202401220855 21
 
0.8%
202401220950 4
 
0.2%
ValueCountFrequency (%)
202401282155 23
0.9%
202401282055 22
0.8%
202401282050 1
 
< 0.1%
202401281955 17
0.6%
202401281950 6
 
0.2%
202401281855 21
0.8%
202401281850 2
 
0.1%
202401281755 20
0.8%
202401281750 3
 
0.1%
202401281655 19
0.7%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct168
Distinct (%)6.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1212.2141
Minimum1093
Maximum1277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:16.684897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1093
5-th percentile1123
Q11194.25
median1228
Q31240
95-th percentile1252
Maximum1277
Range184
Interquartile range (IQR)45.75

Descriptive statistics

Standard deviation39.040145
Coefficient of variation (CV)0.032205652
Kurtosis0.49324496
Mean1212.2141
Median Absolute Deviation (MAD)17
Skewness-1.168844
Sum3222065
Variance1524.1329
MonotonicityNot monotonic
2024-05-04T02:24:17.252607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1228 105
 
4.0%
1211 98
 
3.7%
1232 88
 
3.3%
1241 85
 
3.2%
1252 84
 
3.2%
1265 67
 
2.5%
1236 65
 
2.4%
1244 65
 
2.4%
1229 64
 
2.4%
1240 63
 
2.4%
Other values (158) 1874
70.5%
ValueCountFrequency (%)
1093 1
 
< 0.1%
1095 1
 
< 0.1%
1099 1
 
< 0.1%
1102 4
0.2%
1103 2
 
0.1%
1104 5
0.2%
1105 4
0.2%
1106 5
0.2%
1107 5
0.2%
1108 5
0.2%
ValueCountFrequency (%)
1277 1
 
< 0.1%
1276 1
 
< 0.1%
1274 1
 
< 0.1%
1269 1
 
< 0.1%
1266 1
 
< 0.1%
1265 67
2.5%
1263 2
 
0.1%
1262 5
 
0.2%
1261 1
 
< 0.1%
1260 6
 
0.2%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean24.938676
Minimum13
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:17.987716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum13
5-th percentile15
Q119
median23
Q330
95-th percentile38
Maximum59
Range46
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.1833755
Coefficient of variation (CV)0.32813994
Kurtosis2.9845103
Mean24.938676
Median Absolute Deviation (MAD)5
Skewness1.3925297
Sum66287
Variance66.967634
MonotonicityNot monotonic
2024-05-04T02:24:18.412620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
22 182
 
6.8%
20 180
 
6.8%
23 175
 
6.6%
16 168
 
6.3%
18 162
 
6.1%
17 147
 
5.5%
32 143
 
5.4%
24 143
 
5.4%
21 140
 
5.3%
19 130
 
4.9%
Other values (19) 1088
40.9%
ValueCountFrequency (%)
13 14
 
0.5%
14 50
 
1.9%
15 74
2.8%
16 168
6.3%
17 147
5.5%
18 162
6.1%
19 130
4.9%
20 180
6.8%
21 140
5.3%
22 182
6.8%
ValueCountFrequency (%)
59 39
 
1.5%
45 57
 
2.1%
39 18
 
0.7%
38 71
2.7%
37 2
 
0.1%
36 81
3.0%
35 35
 
1.3%
34 37
 
1.4%
33 53
 
2.0%
32 143
5.4%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.357788
Minimum5
Maximum155
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:18.895069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median11
Q317
95-th percentile57
Maximum155
Range150
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.649116
Coefficient of variation (CV)0.83441086
Kurtosis7.8486362
Mean16.357788
Median Absolute Deviation (MAD)6
Skewness2.3967009
Sum43479
Variance186.29836
MonotonicityNot monotonic
2024-05-04T02:24:19.342109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
17 532
20.0%
11 526
19.8%
5 418
15.7%
6 191
 
7.2%
15 158
 
5.9%
8 151
 
5.7%
59 89
 
3.3%
13 78
 
2.9%
10 72
 
2.7%
25 44
 
1.7%
Other values (34) 399
15.0%
ValueCountFrequency (%)
5 418
15.7%
6 191
 
7.2%
8 151
 
5.7%
10 72
 
2.7%
11 526
19.8%
13 78
 
2.9%
15 158
 
5.9%
17 532
20.0%
18 42
 
1.6%
20 32
 
1.2%
ValueCountFrequency (%)
155 1
 
< 0.1%
89 1
 
< 0.1%
83 1
 
< 0.1%
81 2
0.1%
76 1
 
< 0.1%
74 1
 
< 0.1%
68 1
 
< 0.1%
66 2
0.1%
64 2
0.1%
62 4
0.2%

소음(㏈)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.67532
Minimum32
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:19.799947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile33
Q140
median45
Q353
95-th percentile65
Maximum80
Range48
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.3439444
Coefficient of variation (CV)0.20019026
Kurtosis-0.060344101
Mean46.67532
Median Absolute Deviation (MAD)6
Skewness0.63728738
Sum124063
Variance87.309297
MonotonicityNot monotonic
2024-05-04T02:24:20.241565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
41 210
 
7.9%
39 188
 
7.1%
42 186
 
7.0%
40 137
 
5.2%
51 125
 
4.7%
32 102
 
3.8%
50 98
 
3.7%
43 96
 
3.6%
46 91
 
3.4%
54 90
 
3.4%
Other values (39) 1335
50.2%
ValueCountFrequency (%)
32 102
3.8%
33 43
 
1.6%
34 22
 
0.8%
35 62
 
2.3%
36 79
 
3.0%
37 90
3.4%
38 63
 
2.4%
39 188
7.1%
40 137
5.2%
41 210
7.9%
ValueCountFrequency (%)
80 2
 
0.1%
79 1
 
< 0.1%
78 2
 
0.1%
77 3
0.1%
76 7
0.3%
75 3
0.1%
74 5
0.2%
73 3
0.1%
72 5
0.2%
71 7
0.3%

이산화탄소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct824
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean350.79496
Minimum-9999
Maximum2178
Zeros0
Zeros (%)0.0%
Negative98
Negative (%)3.7%
Memory size23.5 KiB
2024-05-04T02:24:20.651187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile329
Q1560
median710
Q3883
95-th percentile1187.3
Maximum2178
Range12177
Interquartile range (IQR)323

Descriptive statistics

Standard deviation2039.996
Coefficient of variation (CV)5.8153516
Kurtosis21.495692
Mean350.79496
Median Absolute Deviation (MAD)169
Skewness-4.8031238
Sum932413
Variance4161583.7
MonotonicityNot monotonic
2024-05-04T02:24:21.119111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
619 105
 
4.0%
-9999 98
 
3.7%
883 85
 
3.2%
820 76
 
2.9%
429 69
 
2.6%
580 61
 
2.3%
417 41
 
1.5%
741 34
 
1.3%
597 25
 
0.9%
710 25
 
0.9%
Other values (814) 2039
76.7%
ValueCountFrequency (%)
-9999 98
3.7%
191 1
 
< 0.1%
199 1
 
< 0.1%
205 1
 
< 0.1%
226 1
 
< 0.1%
234 1
 
< 0.1%
249 1
 
< 0.1%
251 1
 
< 0.1%
253 1
 
< 0.1%
264 1
 
< 0.1%
ValueCountFrequency (%)
2178 1
< 0.1%
2143 1
< 0.1%
2054 1
< 0.1%
2022 1
< 0.1%
1928 1
< 0.1%
1838 1
< 0.1%
1740 1
< 0.1%
1710 1
< 0.1%
1708 1
< 0.1%
1702 1
< 0.1%
Distinct322
Distinct (%)12.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean227.84199
Minimum125
Maximum1489
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:21.533833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1156
median209
Q3284
95-th percentile366
Maximum1489
Range1364
Interquartile range (IQR)128

Descriptive statistics

Standard deviation93.621974
Coefficient of variation (CV)0.41090747
Kurtosis15.928289
Mean227.84199
Median Absolute Deviation (MAD)60
Skewness2.2866883
Sum605604
Variance8765.074
MonotonicityNot monotonic
2024-05-04T02:24:21.963573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 204
 
7.7%
216 94
 
3.5%
337 88
 
3.3%
183 84
 
3.2%
348 76
 
2.9%
240 62
 
2.3%
140 47
 
1.8%
152 35
 
1.3%
126 34
 
1.3%
201 29
 
1.1%
Other values (312) 1905
71.7%
ValueCountFrequency (%)
125 204
7.7%
126 34
 
1.3%
127 18
 
0.7%
128 13
 
0.5%
129 12
 
0.5%
130 10
 
0.4%
131 6
 
0.2%
132 11
 
0.4%
133 11
 
0.4%
134 11
 
0.4%
ValueCountFrequency (%)
1489 1
 
< 0.1%
843 1
 
< 0.1%
741 1
 
< 0.1%
682 17
0.6%
620 1
 
< 0.1%
601 1
 
< 0.1%
598 1
 
< 0.1%
566 1
 
< 0.1%
551 1
 
< 0.1%
547 1
 
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.620391
Minimum3
Maximum98
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:22.433485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median8
Q312
95-th percentile42
Maximum98
Range95
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.032352
Coefficient of variation (CV)0.86334031
Kurtosis6.012731
Mean11.620391
Median Absolute Deviation (MAD)4
Skewness2.255759
Sum30887
Variance100.64809
MonotonicityNot monotonic
2024-05-04T02:24:22.863613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
12 532
20.0%
8 526
19.8%
3 418
15.7%
4 191
 
7.2%
11 158
 
5.9%
5 151
 
5.7%
43 89
 
3.3%
9 78
 
2.9%
7 72
 
2.7%
18 44
 
1.7%
Other values (32) 399
15.0%
ValueCountFrequency (%)
3 418
15.7%
4 191
 
7.2%
5 151
 
5.7%
7 72
 
2.7%
8 526
19.8%
9 78
 
2.9%
11 158
 
5.9%
12 532
20.0%
13 42
 
1.6%
14 32
 
1.2%
ValueCountFrequency (%)
98 1
 
< 0.1%
65 1
 
< 0.1%
61 1
 
< 0.1%
59 2
 
0.1%
56 1
 
< 0.1%
54 1
 
< 0.1%
50 1
 
< 0.1%
48 2
 
0.1%
47 2
 
0.1%
45 6
0.2%

학습능률지수(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.524454
Minimum40
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size23.5 KiB
2024-05-04T02:24:23.410057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40
5-th percentile48
Q157
median62
Q368
95-th percentile79
Maximum159
Range119
Interquartile range (IQR)11

Descriptive statistics

Standard deviation16.732716
Coefficient of variation (CV)0.25932363
Kurtosis12.964929
Mean64.524454
Median Absolute Deviation (MAD)5
Skewness3.3085747
Sum171506
Variance279.98378
MonotonicityNot monotonic
2024-05-04T02:24:23.731434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 219
 
8.2%
64 147
 
5.5%
67 141
 
5.3%
61 135
 
5.1%
63 127
 
4.8%
70 115
 
4.3%
59 111
 
4.2%
72 104
 
3.9%
58 101
 
3.8%
60 97
 
3.6%
Other values (56) 1361
51.2%
ValueCountFrequency (%)
40 7
 
0.3%
41 4
 
0.2%
42 7
 
0.3%
43 10
 
0.4%
44 13
 
0.5%
45 17
 
0.6%
46 33
1.2%
47 21
0.8%
48 44
1.7%
49 51
1.9%
ValueCountFrequency (%)
159 1
 
< 0.1%
156 1
 
< 0.1%
154 1
 
< 0.1%
153 1
 
< 0.1%
152 1
 
< 0.1%
149 2
 
0.1%
148 1
 
< 0.1%
147 1
 
< 0.1%
145 2
 
0.1%
144 21
0.8%
Distinct451
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size20.9 KiB
Minimum2024-01-22 08:59:01
Maximum2024-01-28 21:58:49
2024-05-04T02:24:24.110070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:24.518218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-04T02:24:08.603354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:43.629562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:46.263515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:49.620150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:52.645982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:56.432037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:59.816660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:02.686523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:05.788162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:08.911117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:43.899408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:46.547956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:49.882867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:53.136287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:56.740744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:00.111387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:03.140836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:06.099656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:09.309725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:44.207793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:47.069532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:50.122412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:53.535161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:57.250951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:00.410212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:03.466414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:06.398500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:09.695660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:44.461600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:47.408866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:50.358445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:53.873994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:57.616074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:00.683005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:03.759899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:06.681234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:09.997909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:44.745818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:47.809210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:50.791826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:54.300531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:57.991812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:01.090247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:04.077088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:06.986904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:10.349484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:45.063823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:48.164544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:51.120827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:54.662510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:58.319976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:01.463548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:04.439927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:07.296303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:10.747061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:45.399664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:48.540460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:51.476487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:55.101442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:58.747020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:01.744193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:04.864948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:07.703477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:11.254669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:45.683304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:48.901384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:51.802741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:55.573647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:59.111536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:02.096855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:05.178328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:07.992894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:11.601641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:45.958287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:49.275538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:52.109494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:56.041271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:23:59.477468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:02.388640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:05.496815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T02:24:08.330972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T02:24:24.810860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)
시리얼1.0001.0000.9120.9460.7390.8720.5830.5870.7440.895
데이터관측일시1.0001.0000.5240.6220.3110.5290.2790.1210.4910.354
온도(℃)0.9120.5241.0000.6590.3910.5970.3430.2480.4080.604
습도(%)0.9460.6220.6591.0000.3050.5650.2940.2920.4530.610
미세먼지(㎍/㎥)0.7390.3110.3910.3051.0000.4420.1090.3870.9340.487
소음(㏈)0.8720.5290.5970.5650.4421.0000.2300.1890.4630.435
이산화탄소(ppm)0.5830.2790.3430.2940.1090.2301.0000.2850.1520.371
휘발성유기화합물(ppb)0.5870.1210.2480.2920.3870.1890.2851.0000.3030.198
초미세먼지(㎍/㎥)0.7440.4910.4080.4530.9340.4630.1520.3031.0000.367
학습능률지수(%)0.8950.3540.6040.6100.4870.4350.3710.1980.3671.000
2024-05-04T02:24:25.149883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)시리얼
데이터관측일시1.000-0.104-0.240-0.029-0.0880.3320.081-0.029-0.2830.995
온도(℃)-0.1041.000-0.248-0.2040.2250.2870.211-0.2040.1330.626
습도(%)-0.240-0.2481.0000.133-0.138-0.2360.0110.1330.2640.759
미세먼지(㎍/㎥)-0.029-0.2040.1331.0000.165-0.091-0.0211.000-0.0150.412
소음(㏈)-0.0880.225-0.1380.1651.000-0.0190.0300.165-0.0860.542
이산화탄소(ppm)0.3320.287-0.236-0.091-0.0191.0000.439-0.091-0.4460.777
휘발성유기화합물(ppb)0.0810.2110.011-0.0210.0300.4391.000-0.021-0.1070.287
초미세먼지(㎍/㎥)-0.029-0.2040.1331.0000.165-0.091-0.0211.000-0.0150.407
학습능률지수(%)-0.2830.1330.264-0.015-0.086-0.446-0.107-0.0151.0000.640
시리얼0.9950.6260.7590.4120.5420.7770.2870.4070.6401.000

Missing values

2024-05-04T02:24:12.492286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T02:24:13.319575image/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-KV01G16136022024012208551148324542-9999125331442024-01-22 08:59:01
1마포구AirGuard-KV01G1613601202401220850114430154161012611492024-01-22 08:59:01
2마포구AirGuard-KV01G161360020240122085511383611395391258592024-01-22 08:59:01
3마포구AirGuard-KV01G1613542202401220855121619285029613820592024-01-22 08:59:01
4마포구AirGuard-KV01G1613539202401220855117725175343513112552024-01-22 08:59:01
5마포구AirGuard-KV01G1613603202401220855113227544847014539512024-01-22 08:59:01
6마포구AirGuard-KV01G16136122024012208551226245336561373752024-01-22 08:59:02
7마포구AirGuard-KV01G1613611202401220855124222183362013913712024-01-22 08:59:02
8마포구AirGuard-KV01G1613604202401220855118336173656112512692024-01-22 08:59:02
9마포구AirGuard-KV01G161360520240122085511893011397841588582024-01-22 08:59:02
기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
2648마포구AirGuard-KV01G1613617202401282155122924174174116712712024-01-28 21:58:47
2649마포구AirGuard-KV01G1613632202401282155123920153669121411622024-01-28 21:58:48
2650마포구AirGuard-KV01G1613618202401282155124120223388728616582024-01-28 21:58:48
2651마포구AirGuard-KV01G161361920240128215512462011327932588582024-01-28 21:58:48
2652마포구AirGuard-KV01G1613620202401282155123336284650539220742024-01-28 21:58:48
2653마포구AirGuard-KV01G161362220240128215512501963312532454512024-01-28 21:58:48
2654마포구AirGuard-KV01G161363420240128215512412011467031258602024-01-28 21:58:48
2655마포구AirGuard-KV01G161362820231009084512324511605802408792024-01-28 21:58:48
2656마포구AirGuard-KV01G16136292024012821551236238468623335672024-01-28 21:58:48
2657마포구AirGuard-KV01G1613637202312071235122828596588321643622024-01-28 21:58:49