Overview

Dataset statistics

Number of variables13
Number of observations3089
Missing cells0
Missing cells (%)0.0%
Duplicate rows390
Duplicate rows (%)12.6%
Total size in memory341.0 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
Dataset has 390 (12.6%) duplicate rowsDuplicates
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 started2024-05-11 16:10:05.944541
Analysis finished2024-05-11 16:10:29.532812
Duration23.59 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.3 KiB
마포구
3089 

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 3089
100.0%

Length

2024-05-12T01:10:29.724539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:10:30.026355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 3089
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.3 KiB
AirGuard-K
3089 

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 3089
100.0%

Length

2024-05-12T01:10:30.346023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:10:30.648888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
airguard-k 3089
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size24.3 KiB
V01G1613539
 
112
V01G1613610
 
112
V01G1613603
 
112
V01G1613602
 
112
V01G1613542
 
112
Other values (24)
2529 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV01G1613539
2nd rowV01G1613604
3rd rowV01G1613603
4th rowV01G1613602
5th rowV01G1613542

Common Values

ValueCountFrequency (%)
V01G1613539 112
 
3.6%
V01G1613610 112
 
3.6%
V01G1613603 112
 
3.6%
V01G1613602 112
 
3.6%
V01G1613542 112
 
3.6%
V01G1613601 112
 
3.6%
V01G1613605 112
 
3.6%
V01G1613600 112
 
3.6%
V01G1613606 112
 
3.6%
V01G1613615 112
 
3.6%
Other values (19) 1969
63.7%

Length

2024-05-12T01:10:30.969888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v01g1613539 112
 
3.6%
v01g1613613 112
 
3.6%
v01g1613632 112
 
3.6%
v01g1613634 112
 
3.6%
v01g1613617 112
 
3.6%
v01g1613618 112
 
3.6%
v01g1613619 112
 
3.6%
v01g1613620 112
 
3.6%
v01g1613622 112
 
3.6%
v01g1613629 112
 
3.6%
Other values (19) 1969
63.7%

데이터관측일시
Real number (ℝ)

HIGH CORRELATION 

Distinct212
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0238605 × 1011
Minimum2.0230808 × 1011
Maximum2.0240114 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:31.355343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230808 × 1011
5-th percentile2.0231011 × 1011
Q12.0240108 × 1011
median2.0240111 × 1011
Q32.0240113 × 1011
95-th percentile2.0240114 × 1011
Maximum2.0240114 × 1011
Range93061135
Interquartile range (IQR)49200

Descriptive statistics

Standard deviation33757544
Coefficient of variation (CV)0.00016679778
Kurtosis1.228704
Mean2.0238605 × 1011
Median Absolute Deviation (MAD)20200
Skewness-1.7962471
Sum6.2517052 × 1014
Variance1.1395718 × 1015
MonotonicityNot monotonic
2024-05-12T01:10:31.797578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202311090355 98
 
3.2%
202312071235 94
 
3.0%
202310290350 86
 
2.8%
202310110850 85
 
2.8%
202310090845 85
 
2.8%
202308081020 65
 
2.1%
202401121625 48
 
1.6%
202401131455 44
 
1.4%
202401132155 44
 
1.4%
202401131855 44
 
1.4%
Other values (202) 2396
77.6%
ValueCountFrequency (%)
202308081020 65
2.1%
202310090845 85
2.8%
202310110850 85
2.8%
202310290350 86
2.8%
202311090355 98
3.2%
202312071235 94
3.0%
202401080850 2
 
0.1%
202401080855 21
 
0.7%
202401080945 1
 
< 0.1%
202401080950 2
 
0.1%
ValueCountFrequency (%)
202401142155 18
0.6%
202401142150 4
 
0.1%
202401142055 21
0.7%
202401142045 1
 
< 0.1%
202401141955 20
0.6%
202401141950 2
 
0.1%
202401141855 22
0.7%
202401141755 19
0.6%
202401141750 3
 
0.1%
202401141655 22
0.7%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1215.7825
Minimum1123
Maximum1274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:32.195538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1123
5-th percentile1137
Q11195
median1229
Q31241
95-th percentile1255
Maximum1274
Range151
Interquartile range (IQR)46

Descriptive statistics

Standard deviation36.340395
Coefficient of variation (CV)0.029890541
Kurtosis-0.04681421
Mean1215.7825
Median Absolute Deviation (MAD)17
Skewness-1.0170477
Sum3755552
Variance1320.6243
MonotonicityNot monotonic
2024-05-12T01:10:32.638918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1228 121
 
3.9%
1232 115
 
3.7%
1252 113
 
3.7%
1211 111
 
3.6%
1244 107
 
3.5%
1241 95
 
3.1%
1265 86
 
2.8%
1231 84
 
2.7%
1236 75
 
2.4%
1229 74
 
2.4%
Other values (136) 2108
68.2%
ValueCountFrequency (%)
1123 2
 
0.1%
1124 1
 
< 0.1%
1125 4
 
0.1%
1126 3
 
0.1%
1127 9
0.3%
1128 8
0.3%
1129 11
0.4%
1130 12
0.4%
1131 15
0.5%
1132 11
0.4%
ValueCountFrequency (%)
1274 1
 
< 0.1%
1272 1
 
< 0.1%
1269 2
 
0.1%
1266 1
 
< 0.1%
1265 86
2.8%
1263 5
 
0.2%
1262 4
 
0.1%
1261 3
 
0.1%
1260 4
 
0.1%
1259 8
 
0.3%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.477825
Minimum14
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:33.052266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile20
Q123
median26
Q333
95-th percentile41.6
Maximum59
Range45
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.8471211
Coefficient of variation (CV)0.27555199
Kurtosis2.9860645
Mean28.477825
Median Absolute Deviation (MAD)5
Skewness1.4102761
Sum87968
Variance61.577309
MonotonicityNot monotonic
2024-05-12T01:10:33.445039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
23 297
 
9.6%
24 290
 
9.4%
22 256
 
8.3%
32 191
 
6.2%
25 179
 
5.8%
36 168
 
5.4%
21 166
 
5.4%
28 166
 
5.4%
33 143
 
4.6%
20 123
 
4.0%
Other values (24) 1110
35.9%
ValueCountFrequency (%)
14 2
 
0.1%
15 2
 
0.1%
16 15
 
0.5%
17 19
 
0.6%
18 52
 
1.7%
19 60
 
1.9%
20 123
4.0%
21 166
5.4%
22 256
8.3%
23 297
9.6%
ValueCountFrequency (%)
59 65
2.1%
54 1
 
< 0.1%
45 85
2.8%
44 1
 
< 0.1%
43 1
 
< 0.1%
42 2
 
0.1%
41 3
 
0.1%
40 4
 
0.1%
39 30
 
1.0%
38 114
3.7%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct53
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.372289
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:33.983848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median13
Q317
95-th percentile59
Maximum100
Range95
Interquartile range (IQR)9

Descriptive statistics

Standard deviation15.131097
Coefficient of variation (CV)0.8709904
Kurtosis5.448829
Mean17.372289
Median Absolute Deviation (MAD)4
Skewness2.2876559
Sum53663
Variance228.95009
MonotonicityNot monotonic
2024-05-12T01:10:34.416937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 621
20.1%
11 595
19.3%
5 410
13.3%
6 247
 
8.0%
15 190
 
6.2%
8 178
 
5.8%
13 119
 
3.9%
59 101
 
3.3%
10 72
 
2.3%
18 49
 
1.6%
Other values (43) 507
16.4%
ValueCountFrequency (%)
5 410
13.3%
6 247
 
8.0%
8 178
 
5.8%
10 72
 
2.3%
11 595
19.3%
13 119
 
3.9%
15 190
 
6.2%
17 621
20.1%
18 49
 
1.6%
20 34
 
1.1%
ValueCountFrequency (%)
100 2
 
0.1%
99 2
 
0.1%
96 3
0.1%
89 1
 
< 0.1%
84 4
0.1%
83 7
0.2%
81 1
 
< 0.1%
80 4
0.1%
79 3
0.1%
78 1
 
< 0.1%

소음(㏈)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.250567
Minimum32
Maximum79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:34.832621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile32
Q139
median44
Q352
95-th percentile65
Maximum79
Range47
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.4879946
Coefficient of variation (CV)0.20514332
Kurtosis-0.28896953
Mean46.250567
Median Absolute Deviation (MAD)7
Skewness0.56365297
Sum142868
Variance90.022041
MonotonicityNot monotonic
2024-05-12T01:10:35.271910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
39 239
 
7.7%
41 227
 
7.3%
42 170
 
5.5%
32 161
 
5.2%
51 153
 
5.0%
40 145
 
4.7%
52 120
 
3.9%
54 116
 
3.8%
60 109
 
3.5%
50 103
 
3.3%
Other values (37) 1546
50.0%
ValueCountFrequency (%)
32 161
5.2%
33 86
 
2.8%
34 29
 
0.9%
35 86
 
2.8%
36 90
 
2.9%
37 86
 
2.8%
38 95
 
3.1%
39 239
7.7%
40 145
4.7%
41 227
7.3%
ValueCountFrequency (%)
79 2
 
0.1%
77 1
 
< 0.1%
76 8
0.3%
75 4
 
0.1%
74 5
0.2%
73 2
 
0.1%
72 5
0.2%
71 5
0.2%
70 11
0.4%
69 8
0.3%

이산화탄소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct874
Distinct (%)28.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean396.87893
Minimum-9999
Maximum1751
Zeros0
Zeros (%)0.0%
Negative111
Negative (%)3.6%
Memory size27.3 KiB
2024-05-12T01:10:35.677657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile350.2
Q1580
median748
Q3925
95-th percentile1255.6
Maximum1751
Range11750
Interquartile range (IQR)345

Descriptive statistics

Standard deviation2023.2386
Coefficient of variation (CV)5.0978737
Kurtosis22.107535
Mean396.87893
Median Absolute Deviation (MAD)168
Skewness-4.862649
Sum1225959
Variance4093494.5
MonotonicityNot monotonic
2024-05-12T01:10:36.121890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
619 115
 
3.7%
-9999 111
 
3.6%
883 96
 
3.1%
820 88
 
2.8%
580 86
 
2.8%
429 86
 
2.8%
417 66
 
2.1%
430 42
 
1.4%
741 35
 
1.1%
710 27
 
0.9%
Other values (864) 2337
75.7%
ValueCountFrequency (%)
-9999 111
3.6%
103 1
 
< 0.1%
232 1
 
< 0.1%
234 2
 
0.1%
239 2
 
0.1%
250 1
 
< 0.1%
252 1
 
< 0.1%
270 1
 
< 0.1%
274 1
 
< 0.1%
276 1
 
< 0.1%
ValueCountFrequency (%)
1751 2
0.1%
1578 1
 
< 0.1%
1577 2
0.1%
1575 1
 
< 0.1%
1574 1
 
< 0.1%
1569 1
 
< 0.1%
1557 1
 
< 0.1%
1556 2
0.1%
1544 3
0.1%
1521 1
 
< 0.1%
Distinct367
Distinct (%)11.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean239.69764
Minimum125
Maximum1293
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:36.530518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1158
median216
Q3305
95-th percentile424
Maximum1293
Range1168
Interquartile range (IQR)147

Descriptive statistics

Standard deviation109.82822
Coefficient of variation (CV)0.45819486
Kurtosis9.9832893
Mean239.69764
Median Absolute Deviation (MAD)65
Skewness2.185808
Sum740426
Variance12062.239
MonotonicityNot monotonic
2024-05-12T01:10:36.965863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 220
 
7.1%
216 106
 
3.4%
337 102
 
3.3%
183 101
 
3.3%
240 88
 
2.8%
348 88
 
2.8%
140 73
 
2.4%
360 43
 
1.4%
126 40
 
1.3%
158 31
 
1.0%
Other values (357) 2197
71.1%
ValueCountFrequency (%)
125 220
7.1%
126 40
 
1.3%
127 26
 
0.8%
128 16
 
0.5%
129 11
 
0.4%
130 12
 
0.4%
131 10
 
0.3%
132 9
 
0.3%
133 11
 
0.4%
134 16
 
0.5%
ValueCountFrequency (%)
1293 1
< 0.1%
1188 2
0.1%
997 1
< 0.1%
883 1
< 0.1%
847 1
< 0.1%
830 2
0.1%
770 1
< 0.1%
753 2
0.1%
749 1
< 0.1%
744 1
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.386533
Minimum3
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:37.371775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median9
Q312
95-th percentile43
Maximum74
Range71
Interquartile range (IQR)7

Descriptive statistics

Standard deviation11.202165
Coefficient of variation (CV)0.9043826
Kurtosis5.4774623
Mean12.386533
Median Absolute Deviation (MAD)3
Skewness2.2802822
Sum38262
Variance125.4885
MonotonicityNot monotonic
2024-05-12T01:10:37.816477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
12 621
20.1%
8 595
19.3%
3 410
13.3%
4 247
 
8.0%
11 190
 
6.2%
5 178
 
5.8%
9 119
 
3.9%
43 101
 
3.3%
7 72
 
2.3%
13 49
 
1.6%
Other values (39) 507
16.4%
ValueCountFrequency (%)
3 410
13.3%
4 247
 
8.0%
5 178
 
5.8%
7 72
 
2.3%
8 595
19.3%
9 119
 
3.9%
11 190
 
6.2%
12 621
20.1%
13 49
 
1.6%
14 34
 
1.1%
ValueCountFrequency (%)
74 2
 
0.1%
73 2
 
0.1%
71 3
0.1%
65 1
 
< 0.1%
62 4
0.1%
61 7
0.2%
59 5
0.2%
58 3
0.1%
57 1
 
< 0.1%
56 2
 
0.1%

학습능률지수(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct55
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.410813
Minimum43
Maximum166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.3 KiB
2024-05-12T01:10:38.240414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile52
Q159
median64
Q369
95-th percentile79
Maximum166
Range123
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.614356
Coefficient of variation (CV)0.25017546
Kurtosis15.276733
Mean66.410813
Median Absolute Deviation (MAD)5
Skewness3.7257203
Sum205143
Variance276.03681
MonotonicityNot monotonic
2024-05-12T01:10:38.685021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 229
 
7.4%
64 200
 
6.5%
70 193
 
6.2%
67 176
 
5.7%
63 161
 
5.2%
66 146
 
4.7%
65 146
 
4.7%
61 135
 
4.4%
72 129
 
4.2%
60 125
 
4.0%
Other values (45) 1449
46.9%
ValueCountFrequency (%)
43 1
 
< 0.1%
44 6
 
0.2%
46 7
 
0.2%
47 8
 
0.3%
48 11
 
0.4%
49 17
 
0.6%
50 25
0.8%
51 52
1.7%
52 33
1.1%
53 49
1.6%
ValueCountFrequency (%)
166 1
 
< 0.1%
150 3
 
0.1%
149 2
 
0.1%
148 5
 
0.2%
147 14
0.5%
146 25
0.8%
145 23
0.7%
144 21
0.7%
143 3
 
0.1%
142 1
 
< 0.1%
Distinct451
Distinct (%)14.6%
Missing0
Missing (%)0.0%
Memory size24.3 KiB
Minimum2024-01-08 08:59:33
Maximum2024-01-14 21:59:21
2024-05-12T01:10:39.076141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:39.627450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-12T01:10:26.234642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:07.305291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:09.435263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:11.883488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:14.185144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:16.487124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:18.960317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:21.347566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:23.702371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:26.495275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:07.459949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:09.703609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:12.136168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:14.436721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:16.743233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:19.220058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:21.605743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:23.966931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:26.775236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:07.636428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:09.986411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:12.407701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:14.709422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:17.018587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:19.502361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:21.884439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:24.248733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:27.029034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:07.788140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:10.250099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:12.652428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:14.954289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:17.267400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:19.757826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:22.135428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:24.504898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:27.280989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:07.984637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:10.511401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:12.897201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:15.199332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:17.654700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:20.011821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:22.388277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:24.759379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:27.541039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:08.242131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:10.780831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:13.149724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:15.450990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:17.911823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:20.274523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:22.645470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:25.022338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:27.807078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:08.503906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:11.056566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:13.408166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:15.711025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:18.173298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:20.538462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:22.911888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:25.290631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:28.069982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:08.902854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:11.324594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:13.662190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:15.963711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:18.430007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:20.800984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:23.166135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:25.555953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:28.343758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:09.171579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:11.608047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:13.925579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:16.229023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:18.696509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:21.074555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:23.438273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:25.827796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T01:10:39.899882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)
시리얼1.0001.0000.9380.9290.7310.8790.7050.5990.7310.839
데이터관측일시1.0001.0000.5660.7330.4180.5550.0000.1580.4180.201
온도(℃)0.9380.5661.0000.6470.5130.6680.4260.2540.5130.549
습도(%)0.9290.7330.6471.0000.4090.5900.4510.7930.4090.387
미세먼지(㎍/㎥)0.7310.4180.5130.4091.0000.6150.1430.3311.0000.303
소음(㏈)0.8790.5550.6680.5900.6151.0000.4750.2380.6150.273
이산화탄소(ppm)0.7050.0000.4260.4510.1430.4751.0000.2060.1430.236
휘발성유기화합물(ppb)0.5990.1580.2540.7930.3310.2380.2061.0000.3310.146
초미세먼지(㎍/㎥)0.7310.4180.5130.4091.0000.6150.1430.3311.0000.303
학습능률지수(%)0.8390.2010.5490.3870.3030.2730.2360.1460.3031.000
2024-05-12T01:10:40.228430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)시리얼
데이터관측일시1.000-0.155-0.306-0.034-0.1320.322-0.144-0.034-0.2000.996
온도(℃)-0.1551.000-0.330-0.1600.2020.3000.243-0.1600.0500.696
습도(%)-0.306-0.3301.0000.047-0.071-0.308-0.0370.0470.1280.698
미세먼지(㎍/㎥)-0.034-0.1600.0471.0000.178-0.077-0.0041.000-0.0240.359
소음(㏈)-0.1320.202-0.0710.1781.000-0.0170.0720.178-0.1360.554
이산화탄소(ppm)0.3220.300-0.308-0.077-0.0171.0000.348-0.077-0.4790.824
휘발성유기화합물(ppb)-0.1440.243-0.037-0.0040.0720.3481.000-0.004-0.1000.272
초미세먼지(㎍/㎥)-0.034-0.1600.0471.0000.178-0.077-0.0041.000-0.0240.359
학습능률지수(%)-0.2000.0500.128-0.024-0.136-0.479-0.100-0.0241.0000.541
시리얼0.9960.6960.6980.3590.5540.8240.2720.3590.5411.000

Missing values

2024-05-12T01:10:28.729872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T01:10:29.294205image/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-KV01G16135392024010808551185216514561954652024-01-08 08:59:33
1마포구AirGuard-KV01G1613604202401080855118233173648313012712024-01-08 08:59:34
2마포구AirGuard-KV01G1613603202401080855112525454952526533472024-01-08 08:59:34
3마포구AirGuard-KV01G16136022024010808551128284941-9999181361342024-01-08 08:59:34
4마포구AirGuard-KV01G16135422024010808501216165543302063602024-01-08 08:59:34
5마포구AirGuard-KV01G1613601202401080855112328404161917329462024-01-08 08:59:34
6마포구AirGuard-KV01G1613605202401080855118633173656912512702024-01-08 08:59:34
7마포구AirGuard-KV01G1613600202401080855114432253952113218592024-01-08 08:59:34
8마포구AirGuard-KV01G16136062024010808551157375412391263582024-01-08 08:59:35
9마포구AirGuard-KV01G16136152024010808551225215435102033762024-01-08 08:59:35
기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
3079마포구AirGuard-KV01G1613617202401142150122825173580319512702024-01-14 21:59:20
3080마포구AirGuard-KV01G16136152024011421551230266327932044712024-01-14 21:59:20
3081마포구AirGuard-KV01G16136142023102903501252366328203484672024-01-14 21:59:20
3082마포구AirGuard-KV01G16136342024011421551251235326181253672024-01-14 21:59:21
3083마포구AirGuard-KV01G1613632202401142155124922175874119112622024-01-14 21:59:21
3084마포구AirGuard-KV01G1613630202311090355121132153961933711722024-01-14 21:59:21
3085마포구AirGuard-KV01G16136292024011421501237256486901464692024-01-14 21:59:21
3086마포구AirGuard-KV01G161362820231009084512324511605802408792024-01-14 21:59:21
3087마포구AirGuard-KV01G16136222024011421501253221733113117412602024-01-14 21:59:21
3088마포구AirGuard-KV01G1613637202312071235122828596588321643622024-01-14 21:59:21

Duplicate rows

Most frequently occurring

기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자# duplicates
0마포구AirGuard-KV01G1613539202401130855118324175944912512632024-01-13 08:59:222
1마포구AirGuard-KV01G16135392024011309551197235495441453612024-01-13 09:59:212
2마포구AirGuard-KV01G161353920240113105512052311516422098672024-01-13 10:59:222
3마포구AirGuard-KV01G161353920240113115512082411527612268642024-01-13 11:59:212
4마포구AirGuard-KV01G161353920240113125512112413528802419612024-01-13 12:59:222
5마포구AirGuard-KV01G1613539202401131355120229155384322311612024-01-13 13:59:212
6마포구AirGuard-KV01G16135392024011314551218245251115031138542024-01-13 14:59:212
7마포구AirGuard-KV01G1613539202401131555121925115212883728532024-01-13 15:59:212
8마포구AirGuard-KV01G16135392024011316551218241751126138512542024-01-13 16:59:202
9마포구AirGuard-KV01G1613539202401131755121724115111893698562024-01-13 17:59:212