Overview

Dataset statistics

Number of variables13
Number of observations3069
Missing cells0
Missing cells (%)0.0%
Duplicate rows392
Duplicate rows (%)12.8%
Total size in memory338.8 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 392 (12.8%) 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 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 16:11:35.972328
Analysis finished2024-05-11 16:11:51.455489
Duration15.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
마포구
3069 

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 3069
100.0%

Length

2024-05-12T01:11:51.567818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:11:51.731312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 3069
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
AirGuard-K
3069 

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 3069
100.0%

Length

2024-05-12T01:11:51.901504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:11:52.068741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
airguard-k 3069
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
V01G1613539
 
112
V01G1613611
 
112
V01G1613634
 
112
V01G1613600
 
112
V01G1613601
 
112
Other values (24)
2509 

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 112
 
3.6%
V01G1613611 112
 
3.6%
V01G1613634 112
 
3.6%
V01G1613600 112
 
3.6%
V01G1613601 112
 
3.6%
V01G1613602 112
 
3.6%
V01G1613603 112
 
3.6%
V01G1613604 112
 
3.6%
V01G1613606 112
 
3.6%
V01G1613605 112
 
3.6%
Other values (19) 1949
63.5%

Length

2024-05-12T01:11:52.240097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v01g1613539 112
 
3.6%
v01g1613609 112
 
3.6%
v01g1613629 112
 
3.6%
v01g1613622 112
 
3.6%
v01g1613620 112
 
3.6%
v01g1613619 112
 
3.6%
v01g1613618 112
 
3.6%
v01g1613617 112
 
3.6%
v01g1613615 112
 
3.6%
v01g1613542 112
 
3.6%
Other values (19) 1949
63.5%

데이터관측일시
Real number (ℝ)

HIGH CORRELATION 

Distinct209
Distinct (%)6.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0238661 × 1011
Minimum2.0230808 × 1011
Maximum2.0240121 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:52.471824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230808 × 1011
5-th percentile2.0231011 × 1011
Q12.0240115 × 1011
median2.0240117 × 1011
Q32.0240119 × 1011
95-th percentile2.0240121 × 1011
Maximum2.0240121 × 1011
Range93131135
Interquartile range (IQR)40395

Descriptive statistics

Standard deviation33308106
Coefficient of variation (CV)0.00016457663
Kurtosis1.4249031
Mean2.0238661 × 1011
Median Absolute Deviation (MAD)20000
Skewness-1.8500307
Sum6.211245 × 1014
Variance1.1094299 × 1015
MonotonicityNot monotonic
2024-05-12T01:11:52.872548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202401121625 112
 
3.6%
202312071235 96
 
3.1%
202311090355 95
 
3.1%
202310290350 92
 
3.0%
202310090845 79
 
2.6%
202310110850 77
 
2.5%
202308081020 54
 
1.8%
202401151555 44
 
1.4%
202401151455 44
 
1.4%
202401151955 44
 
1.4%
Other values (199) 2332
76.0%
ValueCountFrequency (%)
202308081020 54
1.8%
202310090845 79
2.6%
202310110850 77
2.5%
202310290350 92
3.0%
202311090355 95
3.1%
202312071235 96
3.1%
202401121625 112
3.6%
202401150850 8
 
0.3%
202401150855 36
 
1.2%
202401150950 2
 
0.1%
ValueCountFrequency (%)
202401212155 20
0.7%
202401212150 1
 
< 0.1%
202401212145 1
 
< 0.1%
202401212055 20
0.7%
202401212050 2
 
0.1%
202401211955 21
0.7%
202401211950 1
 
< 0.1%
202401211855 17
0.6%
202401211850 5
 
0.2%
202401211755 20
0.7%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct146
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1217.289
Minimum1120
Maximum1272
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:53.115535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1120
5-th percentile1139.4
Q11199
median1231
Q31242
95-th percentile1256
Maximum1272
Range152
Interquartile range (IQR)43

Descriptive statistics

Standard deviation35.753104
Coefficient of variation (CV)0.029371089
Kurtosis0.1186093
Mean1217.289
Median Absolute Deviation (MAD)16
Skewness-1.0672559
Sum3735860
Variance1278.2844
MonotonicityNot monotonic
2024-05-12T01:11:53.371184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1228 136
 
4.4%
1231 125
 
4.1%
1252 124
 
4.0%
1211 117
 
3.8%
1232 113
 
3.7%
1241 105
 
3.4%
1244 93
 
3.0%
1265 79
 
2.6%
1240 75
 
2.4%
1235 74
 
2.4%
Other values (136) 2028
66.1%
ValueCountFrequency (%)
1120 1
 
< 0.1%
1121 2
 
0.1%
1122 4
 
0.1%
1123 3
 
0.1%
1124 4
 
0.1%
1125 2
 
0.1%
1126 14
0.5%
1127 4
 
0.1%
1128 12
0.4%
1129 11
0.4%
ValueCountFrequency (%)
1272 1
 
< 0.1%
1267 1
 
< 0.1%
1265 79
2.6%
1264 5
 
0.2%
1263 3
 
0.1%
1262 4
 
0.1%
1261 10
 
0.3%
1260 7
 
0.2%
1259 14
 
0.5%
1258 11
 
0.4%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.580971
Minimum16
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:53.620999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile18
Q123
median27
Q333
95-th percentile40
Maximum59
Range43
Interquartile range (IQR)10

Descriptive statistics

Standard deviation7.7727487
Coefficient of variation (CV)0.27195537
Kurtosis2.5137429
Mean28.580971
Median Absolute Deviation (MAD)5
Skewness1.1510296
Sum87715
Variance60.415622
MonotonicityNot monotonic
2024-05-12T01:11:53.849456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
24 217
 
7.1%
26 211
 
6.9%
28 203
 
6.6%
32 189
 
6.2%
23 164
 
5.3%
27 158
 
5.1%
33 158
 
5.1%
36 148
 
4.8%
25 147
 
4.8%
20 144
 
4.7%
Other values (24) 1330
43.3%
ValueCountFrequency (%)
16 48
 
1.6%
17 53
 
1.7%
18 88
2.9%
19 111
3.6%
20 144
4.7%
21 99
3.2%
22 101
3.3%
23 164
5.3%
24 217
7.1%
25 147
4.8%
ValueCountFrequency (%)
59 54
1.8%
51 1
 
< 0.1%
50 1
 
< 0.1%
46 1
 
< 0.1%
45 81
2.6%
44 1
 
< 0.1%
43 3
 
0.1%
42 7
 
0.2%
41 2
 
0.1%
40 7
 
0.2%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.396546
Minimum5
Maximum76
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:54.072100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median11
Q317
95-th percentile47
Maximum76
Range71
Interquartile range (IQR)9

Descriptive statistics

Standard deviation12.399536
Coefficient of variation (CV)0.80534532
Kurtosis5.3497876
Mean15.396546
Median Absolute Deviation (MAD)6
Skewness2.3153239
Sum47252
Variance153.7485
MonotonicityNot monotonic
2024-05-12T01:11:54.304875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
11 644
21.0%
17 628
20.5%
5 493
16.1%
6 224
 
7.3%
13 167
 
5.4%
15 161
 
5.2%
8 160
 
5.2%
59 99
 
3.2%
10 93
 
3.0%
18 47
 
1.5%
Other values (28) 353
11.5%
ValueCountFrequency (%)
5 493
16.1%
6 224
 
7.3%
8 160
 
5.2%
10 93
 
3.0%
11 644
21.0%
13 167
 
5.4%
15 161
 
5.2%
17 628
20.5%
18 47
 
1.5%
20 30
 
1.0%
ValueCountFrequency (%)
76 1
 
< 0.1%
69 1
 
< 0.1%
68 3
 
0.1%
66 3
 
0.1%
62 4
 
0.1%
61 1
 
< 0.1%
59 99
3.2%
57 20
 
0.7%
56 4
 
0.1%
54 11
 
0.4%

소음(㏈)
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.822418
Minimum32
Maximum82
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:54.563615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile33
Q139
median46
Q353
95-th percentile65
Maximum82
Range50
Interquartile range (IQR)14

Descriptive statistics

Standard deviation9.4820598
Coefficient of variation (CV)0.20251111
Kurtosis-0.12499609
Mean46.822418
Median Absolute Deviation (MAD)7
Skewness0.53344889
Sum143698
Variance89.909458
MonotonicityNot monotonic
2024-05-12T01:11:54.821917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
39 216
 
7.0%
42 203
 
6.6%
41 191
 
6.2%
49 166
 
5.4%
32 142
 
4.6%
50 134
 
4.4%
54 132
 
4.3%
40 129
 
4.2%
51 126
 
4.1%
52 123
 
4.0%
Other values (39) 1507
49.1%
ValueCountFrequency (%)
32 142
4.6%
33 85
 
2.8%
34 20
 
0.7%
35 79
 
2.6%
36 106
3.5%
37 72
 
2.3%
38 56
 
1.8%
39 216
7.0%
40 129
4.2%
41 191
6.2%
ValueCountFrequency (%)
82 2
 
0.1%
80 1
 
< 0.1%
79 3
 
0.1%
78 2
 
0.1%
77 1
 
< 0.1%
76 9
0.3%
75 1
 
< 0.1%
74 6
0.2%
73 6
0.2%
72 6
0.2%

이산화탄소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct831
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean366.24112
Minimum-9999
Maximum1906
Zeros0
Zeros (%)0.0%
Negative108
Negative (%)3.5%
Memory size27.1 KiB
2024-05-12T01:11:55.072130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile337.4
Q1534
median709
Q3883
95-th percentile1190.2
Maximum1906
Range11905
Interquartile range (IQR)349

Descriptive statistics

Standard deviation1994.9396
Coefficient of variation (CV)5.4470662
Kurtosis22.708076
Mean366.24112
Median Absolute Deviation (MAD)174
Skewness-4.9245963
Sum1123994
Variance3979784.1
MonotonicityNot monotonic
2024-05-12T01:11:55.331923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
619 113
 
3.7%
-9999 108
 
3.5%
430 100
 
3.3%
883 96
 
3.1%
820 93
 
3.0%
429 79
 
2.6%
580 79
 
2.6%
417 57
 
1.9%
617 29
 
0.9%
741 25
 
0.8%
Other values (821) 2290
74.6%
ValueCountFrequency (%)
-9999 108
3.5%
4 1
 
< 0.1%
60 1
 
< 0.1%
111 1
 
< 0.1%
224 1
 
< 0.1%
255 2
 
0.1%
261 1
 
< 0.1%
265 2
 
0.1%
270 2
 
0.1%
273 1
 
< 0.1%
ValueCountFrequency (%)
1906 1
< 0.1%
1612 1
< 0.1%
1607 1
< 0.1%
1564 1
< 0.1%
1562 1
< 0.1%
1544 2
0.1%
1537 1
< 0.1%
1534 1
< 0.1%
1521 1
< 0.1%
1512 1
< 0.1%
Distinct325
Distinct (%)10.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.99935
Minimum125
Maximum737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:55.579194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1152
median201
Q3289
95-th percentile369
Maximum737
Range612
Interquartile range (IQR)137

Descriptive statistics

Standard deviation92.707152
Coefficient of variation (CV)0.40840272
Kurtosis2.6511554
Mean226.99935
Median Absolute Deviation (MAD)59
Skewness1.289504
Sum696661
Variance8594.616
MonotonicityNot monotonic
2024-05-12T01:11:55.821664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 239
 
7.8%
216 102
 
3.3%
183 102
 
3.3%
360 101
 
3.3%
337 99
 
3.2%
348 96
 
3.1%
240 83
 
2.7%
140 67
 
2.2%
126 41
 
1.3%
147 35
 
1.1%
Other values (315) 2104
68.6%
ValueCountFrequency (%)
125 239
7.8%
126 41
 
1.3%
127 27
 
0.9%
128 23
 
0.7%
129 12
 
0.4%
130 11
 
0.4%
131 8
 
0.3%
132 25
 
0.8%
133 13
 
0.4%
134 15
 
0.5%
ValueCountFrequency (%)
737 1
 
< 0.1%
682 15
0.5%
665 2
 
0.1%
618 2
 
0.1%
575 1
 
< 0.1%
569 1
 
< 0.1%
564 1
 
< 0.1%
551 1
 
< 0.1%
550 1
 
< 0.1%
548 2
 
0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.913978
Minimum3
Maximum56
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:56.057162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median8
Q312
95-th percentile34
Maximum56
Range53
Interquartile range (IQR)7

Descriptive statistics

Standard deviation9.1649254
Coefficient of variation (CV)0.83974193
Kurtosis5.2920141
Mean10.913978
Median Absolute Deviation (MAD)4
Skewness2.2899524
Sum33495
Variance83.995857
MonotonicityNot monotonic
2024-05-12T01:11:56.288867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
8 644
21.0%
12 628
20.5%
3 493
16.1%
4 224
 
7.3%
9 167
 
5.4%
11 161
 
5.2%
5 160
 
5.2%
43 99
 
3.2%
7 93
 
3.0%
13 47
 
1.5%
Other values (26) 353
11.5%
ValueCountFrequency (%)
3 493
16.1%
4 224
 
7.3%
5 160
 
5.2%
7 93
 
3.0%
8 644
21.0%
9 167
 
5.4%
11 161
 
5.2%
12 628
20.5%
13 47
 
1.5%
14 30
 
1.0%
ValueCountFrequency (%)
56 1
 
< 0.1%
51 1
 
< 0.1%
50 3
 
0.1%
48 3
 
0.1%
45 5
 
0.2%
43 99
3.2%
42 20
 
0.7%
41 4
 
0.1%
39 11
 
0.4%
38 3
 
0.1%

학습능률지수(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct59
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.37276
Minimum39
Maximum163
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size27.1 KiB
2024-05-12T01:11:56.684379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum39
5-th percentile52
Q159
median63
Q369
95-th percentile79
Maximum163
Range124
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.708596
Coefficient of variation (CV)0.25173875
Kurtosis15.812218
Mean66.37276
Median Absolute Deviation (MAD)5
Skewness3.786107
Sum203698
Variance279.17717
MonotonicityNot monotonic
2024-05-12T01:11:56.939198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
62 269
 
8.8%
64 213
 
6.9%
67 209
 
6.8%
63 174
 
5.7%
61 154
 
5.0%
70 148
 
4.8%
58 146
 
4.8%
59 128
 
4.2%
72 124
 
4.0%
56 116
 
3.8%
Other values (49) 1388
45.2%
ValueCountFrequency (%)
39 1
 
< 0.1%
41 1
 
< 0.1%
42 1
 
< 0.1%
44 1
 
< 0.1%
45 3
 
0.1%
46 5
 
0.2%
47 15
0.5%
48 5
 
0.2%
49 11
0.4%
50 24
0.8%
ValueCountFrequency (%)
163 1
 
< 0.1%
160 1
 
< 0.1%
157 4
 
0.1%
156 2
 
0.1%
154 3
 
0.1%
151 4
 
0.1%
150 2
 
0.1%
149 9
0.3%
148 15
0.5%
147 4
 
0.1%
Distinct454
Distinct (%)14.8%
Missing0
Missing (%)0.0%
Memory size24.1 KiB
Minimum2024-01-15 08:59:17
Maximum2024-01-21 21:59:06
2024-05-12T01:11:57.179023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:57.414428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-12T01:11:49.449585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:37.362777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.814606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.336514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:41.756642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:43.408204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:44.865785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:46.370461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.035597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:49.610544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:37.515818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.982136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.490603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:42.057230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:43.567549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:45.029660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:46.535435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.190121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:49.788258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:37.688429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:39.158954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.656544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:42.232550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:43.739986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:45.205838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:46.721616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.356937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:49.942560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:37.840839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:39.316690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.802076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:42.386867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:43.894000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:45.365568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:46.878093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.504316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:50.112952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.005939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:39.490081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.960860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:42.552358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:44.059016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:45.535197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:47.049493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.662344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:50.273492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.165365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:39.655157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:41.113729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:42.711977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:44.213288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:45.699676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:47.211925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.817033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:50.443123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.329365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:39.828786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:41.272370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:42.880835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:44.380268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:45.864604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:47.395610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:48.976978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:50.615526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.498507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.004142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:41.434483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:43.051699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:44.548302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:46.036578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:47.562160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:49.141412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:50.772483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:38.650111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:40.164757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:41.580844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:43.217206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:44.701757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:46.192757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:47.866456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:49.286402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T01:11:57.589575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)
시리얼1.0001.0000.9140.9180.7610.8760.6860.7120.7570.881
데이터관측일시1.0001.0000.4870.4750.5070.5630.1130.4840.4960.289
온도(℃)0.9140.4871.0000.7510.5490.6190.4850.4570.5480.545
습도(%)0.9180.4750.7511.0000.5200.6830.5560.4960.5190.544
미세먼지(㎍/㎥)0.7610.5070.5490.5201.0000.6580.1980.5291.0000.396
소음(㏈)0.8760.5630.6190.6830.6581.0000.3600.4730.6520.425
이산화탄소(ppm)0.6860.1130.4850.5560.1980.3601.0000.5000.1910.584
휘발성유기화합물(ppb)0.7120.4840.4570.4960.5290.4730.5001.0000.5230.365
초미세먼지(㎍/㎥)0.7570.4960.5480.5191.0000.6520.1910.5231.0000.394
학습능률지수(%)0.8810.2890.5450.5440.3960.4250.5840.3650.3941.000
2024-05-12T01:11:57.818601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)시리얼
데이터관측일시1.000-0.040-0.135-0.052-0.0760.370-0.061-0.052-0.2900.996
온도(℃)-0.0401.000-0.301-0.1630.2050.3120.266-0.163-0.0190.631
습도(%)-0.135-0.3011.0000.038-0.116-0.1960.0880.0380.1610.640
미세먼지(㎍/㎥)-0.052-0.1630.0381.0000.179-0.0160.0271.000-0.0510.389
소음(㏈)-0.0760.205-0.1160.1791.000-0.0350.0590.179-0.0540.549
이산화탄소(ppm)0.3700.312-0.196-0.016-0.0351.0000.422-0.016-0.5190.809
휘발성유기화합물(ppb)-0.0610.2660.0880.0270.0590.4221.0000.027-0.0940.342
초미세먼지(㎍/㎥)-0.052-0.1630.0381.0000.179-0.0160.0271.000-0.0510.384
학습능률지수(%)-0.290-0.0190.161-0.051-0.054-0.519-0.094-0.0511.0000.614
시리얼0.9960.6310.6400.3890.5490.8090.3420.3840.6141.000

Missing values

2024-05-12T01:11:51.010447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T01:11:51.327275image/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-KV01G1613539202401150855120229155384322311612024-01-15 08:59:17
1마포구AirGuard-KV01G1613542202401150855121318565530916541582024-01-15 08:59:17
2마포구AirGuard-KV01G1613600202401150855116131354070716625632024-01-15 08:59:17
3마포구AirGuard-KV01G1613601202401150855112232254161712518552024-01-15 08:59:17
4마포구AirGuard-KV01G16136022024011508551126321041-999912771442024-01-15 08:59:17
5마포구AirGuard-KV01G1613603202401150850115235575761920142512024-01-15 08:59:17
6마포구AirGuard-KV01G1613604202401150855117732303761712522612024-01-15 08:59:17
7마포구AirGuard-KV01G1613539202401150855120229155384322311612024-01-15 08:59:17
8마포구AirGuard-KV01G1613603202401150850115235575761920142512024-01-15 08:59:17
9마포구AirGuard-KV01G16136022024011508551126321041-999912771442024-01-15 08:59:17
기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
3059마포구AirGuard-KV01G16136292024012121551230245626171423712024-01-21 21:59:05
3060마포구AirGuard-KV01G1613630202311090355121132153961933711722024-01-21 21:59:05
3061마포구AirGuard-KV01G1613632202401212155124825323467122523642024-01-21 21:59:05
3062마포구AirGuard-KV01G161362020240112162512313313494303609772024-01-21 21:59:05
3063마포구AirGuard-KV01G16136222024012121551241221750107215712632024-01-21 21:59:05
3064마포구AirGuard-KV01G1613625202308081020124459175241714012702024-01-21 21:59:05
3065마포구AirGuard-KV01G161362820231009084512324511605802408792024-01-21 21:59:05
3066마포구AirGuard-KV01G16136352023101108501265388514291835642024-01-21 21:59:06
3067마포구AirGuard-KV01G1613637202312071235122828596588321643622024-01-21 21:59:06
3068마포구AirGuard-KV01G16136342024012121551259258327101265632024-01-21 21:59:06

Duplicate rows

Most frequently occurring

기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자# duplicates
0마포구AirGuard-KV01G1613539202401150855120229155384322311612024-01-15 08:59:172
1마포구AirGuard-KV01G1613539202401150950119823175549516612592024-01-15 09:59:172
2마포구AirGuard-KV01G1613539202401151055120522275450816719682024-01-15 10:59:162
3마포구AirGuard-KV01G16135392024011511551210215535051643682024-01-15 11:59:162
4마포구AirGuard-KV01G161353920240115125512102010544911477592024-01-15 12:59:162
5마포구AirGuard-KV01G16135392024011513551213205545131653582024-01-15 13:59:162
6마포구AirGuard-KV01G161353920240115145512152011525081648572024-01-15 14:59:162
7마포구AirGuard-KV01G1613539202401151555121519175250817012572024-01-15 15:59:172
8마포구AirGuard-KV01G16135392024011516551213196525071874592024-01-15 16:59:162
9마포구AirGuard-KV01G161353920240115175512111911515101818592024-01-15 17:59:162