Overview

Dataset statistics

Number of variables13
Number of observations770
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory85.1 KiB
Average record size in memory113.2 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 started2024-05-11 05:29:57.843440
Analysis finished2024-05-11 05:30:13.655488
Duration15.81 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
마포구
770 

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 770
100.0%

Length

2024-05-11T14:30:13.777509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:30:13.941311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 770
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
AirGuard-K
770 

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 770
100.0%

Length

2024-05-11T14:30:14.110924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:30:14.339721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
airguard-k 770
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
V01G1613539
 
28
V01G1613613
 
28
V01G1613601
 
28
V01G1613600
 
28
V01G1613603
 
28
Other values (24)
630 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowV01G1613539
2nd rowV01G1613602
3rd rowV01G1613601
4th rowV01G1613600
5th rowV01G1613603

Common Values

ValueCountFrequency (%)
V01G1613539 28
 
3.6%
V01G1613613 28
 
3.6%
V01G1613601 28
 
3.6%
V01G1613600 28
 
3.6%
V01G1613603 28
 
3.6%
V01G1613542 28
 
3.6%
V01G1613612 28
 
3.6%
V01G1613605 28
 
3.6%
V01G1613606 28
 
3.6%
V01G1613609 28
 
3.6%
Other values (19) 490
63.6%

Length

2024-05-11T14:30:14.618401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v01g1613539 28
 
3.6%
v01g1613604 28
 
3.6%
v01g1613634 28
 
3.6%
v01g1613632 28
 
3.6%
v01g1613629 28
 
3.6%
v01g1613622 28
 
3.6%
v01g1613620 28
 
3.6%
v01g1613619 28
 
3.6%
v01g1613618 28
 
3.6%
v01g1613615 28
 
3.6%
Other values (19) 490
63.6%

데이터관측일시
Real number (ℝ)

HIGH CORRELATION 

Distinct67
Distinct (%)8.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0231198 × 1011
Minimum2.0230808 × 1011
Maximum2.0231226 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:14.869109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230808 × 1011
5-th percentile2.0231011 × 1011
Q12.0231225 × 1011
median2.0231225 × 1011
Q32.0231226 × 1011
95-th percentile2.0231226 × 1011
Maximum2.0231226 × 1011
Range4181135
Interquartile range (IQR)10300

Descriptive statistics

Standard deviation766592.86
Coefficient of variation (CV)3.789162 × 10-6
Kurtosis10.065759
Mean2.0231198 × 1011
Median Absolute Deviation (MAD)9100
Skewness-3.1141141
Sum1.5578022 × 1014
Variance5.8766461 × 1011
MonotonicityNot monotonic
2024-05-11T14:30:15.169340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202311090355 27
 
3.5%
202312071235 26
 
3.4%
202310290350 25
 
3.2%
202312251555 23
 
3.0%
202312261455 23
 
3.0%
202312251855 23
 
3.0%
202312251955 22
 
2.9%
202312261955 22
 
2.9%
202312260955 22
 
2.9%
202312252155 22
 
2.9%
Other values (57) 535
69.5%
ValueCountFrequency (%)
202308081020 12
1.6%
202310090845 20
2.6%
202310110850 16
2.1%
202310290350 25
3.2%
202311090355 27
3.5%
202312071235 26
3.4%
202312250845 1
 
0.1%
202312250850 1
 
0.1%
202312250855 21
2.7%
202312250950 1
 
0.1%
ValueCountFrequency (%)
202312262155 19
2.5%
202312262150 4
 
0.5%
202312262055 22
2.9%
202312262050 1
 
0.1%
202312261955 22
2.9%
202312261950 1
 
0.1%
202312261855 17
2.2%
202312261850 5
 
0.6%
202312261845 1
 
0.1%
202312261755 15
1.9%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct128
Distinct (%)16.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1205.2273
Minimum1107
Maximum1265
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:15.422215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1107
5-th percentile1130.45
Q11185.25
median1211
Q31229
95-th percentile1252
Maximum1265
Range158
Interquartile range (IQR)43.75

Descriptive statistics

Standard deviation33.913706
Coefficient of variation (CV)0.028138847
Kurtosis0.041732278
Mean1205.2273
Median Absolute Deviation (MAD)21
Skewness-0.76116152
Sum928025
Variance1150.1394
MonotonicityNot monotonic
2024-05-11T14:30:15.698960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1228 41
 
5.3%
1232 30
 
3.9%
1211 29
 
3.8%
1252 25
 
3.2%
1229 22
 
2.9%
1244 18
 
2.3%
1221 18
 
2.3%
1265 16
 
2.1%
1231 15
 
1.9%
1230 14
 
1.8%
Other values (118) 542
70.4%
ValueCountFrequency (%)
1107 1
 
0.1%
1108 2
0.3%
1109 1
 
0.1%
1111 1
 
0.1%
1113 1
 
0.1%
1114 1
 
0.1%
1116 1
 
0.1%
1117 1
 
0.1%
1119 1
 
0.1%
1123 3
0.4%
ValueCountFrequency (%)
1265 16
2.1%
1252 25
3.2%
1249 2
 
0.3%
1248 5
 
0.6%
1247 4
 
0.5%
1246 6
 
0.8%
1245 6
 
0.8%
1244 18
2.3%
1243 2
 
0.3%
1242 4
 
0.5%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.977922
Minimum17
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:15.937294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile18
Q122
median25
Q329
95-th percentile40.55
Maximum59
Range42
Interquartile range (IQR)7

Descriptive statistics

Standard deviation7.4137103
Coefficient of variation (CV)0.27480657
Kurtosis4.1695888
Mean26.977922
Median Absolute Deviation (MAD)4
Skewness1.6904423
Sum20773
Variance54.963101
MonotonicityNot monotonic
2024-05-11T14:30:16.147114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
21 72
 
9.4%
28 69
 
9.0%
25 67
 
8.7%
24 64
 
8.3%
18 52
 
6.8%
22 48
 
6.2%
29 44
 
5.7%
26 42
 
5.5%
20 40
 
5.2%
23 36
 
4.7%
Other values (19) 236
30.6%
ValueCountFrequency (%)
17 4
 
0.5%
18 52
6.8%
19 15
 
1.9%
20 40
5.2%
21 72
9.4%
22 48
6.2%
23 36
4.7%
24 64
8.3%
25 67
8.7%
26 42
5.5%
ValueCountFrequency (%)
59 12
 
1.6%
45 20
2.6%
43 2
 
0.3%
42 4
 
0.5%
41 1
 
0.1%
40 2
 
0.3%
39 4
 
0.5%
38 17
2.2%
37 8
 
1.0%
36 32
4.2%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.463636
Minimum5
Maximum81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:16.368046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q18
median11
Q317
95-th percentile56.55
Maximum81
Range76
Interquartile range (IQR)9

Descriptive statistics

Standard deviation13.686319
Coefficient of variation (CV)0.83130594
Kurtosis4.1369086
Mean16.463636
Median Absolute Deviation (MAD)6
Skewness2.087539
Sum12677
Variance187.31532
MonotonicityNot monotonic
2024-05-11T14:30:16.579844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
11 157
20.4%
17 152
19.7%
5 122
15.8%
6 63
8.2%
15 40
 
5.2%
8 37
 
4.8%
59 29
 
3.8%
10 21
 
2.7%
18 17
 
2.2%
13 15
 
1.9%
Other values (25) 117
15.2%
ValueCountFrequency (%)
5 122
15.8%
6 63
8.2%
8 37
 
4.8%
10 21
 
2.7%
11 157
20.4%
13 15
 
1.9%
15 40
 
5.2%
17 152
19.7%
18 17
 
2.2%
20 10
 
1.3%
ValueCountFrequency (%)
81 1
 
0.1%
79 1
 
0.1%
69 1
 
0.1%
64 3
 
0.4%
59 29
3.8%
57 4
 
0.5%
56 2
 
0.3%
54 1
 
0.1%
51 1
 
0.1%
49 3
 
0.4%

소음(㏈)
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.454545
Minimum32
Maximum68
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:16.832889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile32
Q135
median41
Q350
95-th percentile64.55
Maximum68
Range36
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.7149831
Coefficient of variation (CV)0.22356656
Kurtosis-0.48326352
Mean43.454545
Median Absolute Deviation (MAD)7
Skewness0.73385953
Sum33460
Variance94.380896
MonotonicityNot monotonic
2024-05-11T14:30:17.097651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
32 83
 
10.8%
33 61
 
7.9%
39 59
 
7.7%
41 54
 
7.0%
40 41
 
5.3%
35 41
 
5.3%
65 34
 
4.4%
45 30
 
3.9%
38 29
 
3.8%
50 27
 
3.5%
Other values (26) 311
40.4%
ValueCountFrequency (%)
32 83
10.8%
33 61
7.9%
34 21
 
2.7%
35 41
5.3%
36 15
 
1.9%
37 23
 
3.0%
38 29
 
3.8%
39 59
7.7%
40 41
5.3%
41 54
7.0%
ValueCountFrequency (%)
68 1
 
0.1%
66 4
 
0.5%
65 34
4.4%
64 6
 
0.8%
63 1
 
0.1%
62 7
 
0.9%
61 2
 
0.3%
60 22
2.9%
59 4
 
0.5%
58 10
 
1.3%

이산화탄소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct435
Distinct (%)56.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299.89481
Minimum-9999
Maximum1402
Zeros0
Zeros (%)0.0%
Negative27
Negative (%)3.5%
Memory size6.9 KiB
2024-05-11T14:30:17.297299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile272.25
Q1485
median619
Q3820
95-th percentile1104.55
Maximum1402
Range11401
Interquartile range (IQR)335

Descriptive statistics

Standard deviation1978.2391
Coefficient of variation (CV)6.5964433
Kurtosis22.987839
Mean299.89481
Median Absolute Deviation (MAD)165.5
Skewness-4.951724
Sum230919
Variance3913429.8
MonotonicityNot monotonic
2024-05-11T14:30:17.503930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
619 29
 
3.8%
-9999 27
 
3.5%
883 26
 
3.4%
820 25
 
3.2%
580 22
 
2.9%
429 17
 
2.2%
417 12
 
1.6%
468 6
 
0.8%
471 5
 
0.6%
639 4
 
0.5%
Other values (425) 597
77.5%
ValueCountFrequency (%)
-9999 27
3.5%
11 1
 
0.1%
216 1
 
0.1%
221 1
 
0.1%
229 2
 
0.3%
234 1
 
0.1%
237 1
 
0.1%
244 1
 
0.1%
248 1
 
0.1%
252 1
 
0.1%
ValueCountFrequency (%)
1402 1
 
0.1%
1384 1
 
0.1%
1369 1
 
0.1%
1317 1
 
0.1%
1316 3
0.4%
1312 1
 
0.1%
1305 1
 
0.1%
1293 1
 
0.1%
1287 2
0.3%
1268 2
0.3%
Distinct252
Distinct (%)32.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean240.73766
Minimum125
Maximum865
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:17.716695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1142
median216
Q3291
95-th percentile535.55
Maximum865
Range740
Interquartile range (IQR)149

Descriptive statistics

Standard deviation124.23376
Coefficient of variation (CV)0.51605453
Kurtosis3.9361278
Mean240.73766
Median Absolute Deviation (MAD)75
Skewness1.8048403
Sum185368
Variance15434.027
MonotonicityNot monotonic
2024-05-11T14:30:17.947791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 105
 
13.6%
337 28
 
3.6%
348 27
 
3.5%
216 27
 
3.5%
240 21
 
2.7%
126 21
 
2.7%
183 19
 
2.5%
140 16
 
2.1%
127 10
 
1.3%
163 9
 
1.2%
Other values (242) 487
63.2%
ValueCountFrequency (%)
125 105
13.6%
126 21
 
2.7%
127 10
 
1.3%
128 5
 
0.6%
129 3
 
0.4%
130 4
 
0.5%
131 3
 
0.4%
132 5
 
0.6%
133 1
 
0.1%
134 1
 
0.1%
ValueCountFrequency (%)
865 1
0.1%
786 1
0.1%
756 1
0.1%
705 1
0.1%
704 1
0.1%
699 1
0.1%
695 1
0.1%
691 1
0.1%
689 1
0.1%
680 1
0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct35
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.719481
Minimum3
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:18.297337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q15
median8
Q312
95-th percentile41.55
Maximum59
Range56
Interquartile range (IQR)7

Descriptive statistics

Standard deviation10.128011
Coefficient of variation (CV)0.86420305
Kurtosis4.0692701
Mean11.719481
Median Absolute Deviation (MAD)4
Skewness2.0634195
Sum9024
Variance102.5766
MonotonicityNot monotonic
2024-05-11T14:30:18.517362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
8 157
20.4%
12 152
19.7%
3 122
15.8%
4 63
8.2%
11 40
 
5.2%
5 37
 
4.8%
43 29
 
3.8%
7 21
 
2.7%
13 17
 
2.2%
9 15
 
1.9%
Other values (25) 117
15.2%
ValueCountFrequency (%)
3 122
15.8%
4 63
8.2%
5 37
 
4.8%
7 21
 
2.7%
8 157
20.4%
9 15
 
1.9%
11 40
 
5.2%
12 152
19.7%
13 17
 
2.2%
14 10
 
1.3%
ValueCountFrequency (%)
59 1
 
0.1%
58 1
 
0.1%
51 1
 
0.1%
47 3
 
0.4%
43 29
3.8%
42 4
 
0.5%
41 2
 
0.3%
39 1
 
0.1%
37 1
 
0.1%
36 3
 
0.4%

학습능률지수(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct47
Distinct (%)6.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean66.22987
Minimum46
Maximum159
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size6.9 KiB
2024-05-11T14:30:18.755359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46
5-th percentile50
Q159
median65
Q369
95-th percentile79
Maximum159
Range113
Interquartile range (IQR)10

Descriptive statistics

Standard deviation16.307924
Coefficient of variation (CV)0.24623216
Kurtosis15.449955
Mean66.22987
Median Absolute Deviation (MAD)5
Skewness3.6155124
Sum50997
Variance265.94839
MonotonicityNot monotonic
2024-05-11T14:30:18.994967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
67 54
 
7.0%
62 51
 
6.6%
72 49
 
6.4%
64 49
 
6.4%
69 46
 
6.0%
65 44
 
5.7%
70 40
 
5.2%
68 40
 
5.2%
66 37
 
4.8%
58 30
 
3.9%
Other values (37) 330
42.9%
ValueCountFrequency (%)
46 3
 
0.4%
47 7
 
0.9%
48 12
1.6%
49 11
1.4%
50 11
1.4%
51 14
1.8%
52 18
2.3%
53 21
2.7%
54 11
1.4%
55 12
1.6%
ValueCountFrequency (%)
159 2
0.3%
154 1
0.1%
152 1
0.1%
151 2
0.3%
150 1
0.1%
149 2
0.3%
148 1
0.1%
147 2
0.3%
144 1
0.1%
137 1
0.1%
Distinct143
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size6.1 KiB
Minimum2023-12-25 08:59:57
Maximum2023-12-26 22:00:06
2024-05-11T14:30:19.559346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:19.822209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-11T14:30:11.602351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:58.717726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:00.077983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:01.886189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:03.280198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:04.816326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:06.359127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.849431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.669200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:11.760935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:58.863230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:00.230102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.024797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:03.436769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:04.949821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:06.570014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.033700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.842793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:11.931538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.019891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:00.434479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.180274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:03.625650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:05.110569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:06.744652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.244063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.058108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:12.094542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.155697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:00.616567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.313309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:03.803374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:05.276570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:06.857613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.421646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.213952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:12.277369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.334860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:00.844046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.483766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:03.997114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:05.404944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.003946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.661202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.376117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:12.441155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.480458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:01.295192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.648385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:04.174837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:05.535524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.131148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:08.925392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.560230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:12.601150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.602100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:01.439701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.797989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:04.355670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:05.728290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.333630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.113619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:10.722695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:12.784206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.782439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:01.623765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:02.982776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:04.545956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:05.943125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.511482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.313818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:11.268062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:12.932483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:29:59.934668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:01.750929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:03.130106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:04.689240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:06.139393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:07.686256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:09.493428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:30:11.437828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:30:20.059186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)
시리얼1.0001.0000.9250.9570.7520.9280.6850.8350.7520.853
데이터관측일시1.0001.0000.7600.7700.3350.6750.0000.6730.3350.352
온도(℃)0.9250.7601.0000.7230.5500.6950.4470.6490.5500.656
습도(%)0.9570.7700.7231.0000.4470.7330.2230.5930.4440.500
미세먼지(㎍/㎥)0.7520.3350.5500.4471.0000.6470.0000.5171.0000.284
소음(㏈)0.9280.6750.6950.7330.6471.0000.2280.6580.6460.610
이산화탄소(ppm)0.6850.0000.4470.2230.0000.2281.0000.0250.0000.113
휘발성유기화합물(ppb)0.8350.6730.6490.5930.5170.6580.0251.0000.5110.244
초미세먼지(㎍/㎥)0.7520.3350.5500.4441.0000.6460.0000.5111.0000.278
학습능률지수(%)0.8530.3520.6560.5000.2840.6100.1130.2440.2781.000
2024-05-11T14:30:20.307019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)시리얼
데이터관측일시1.000-0.040-0.222-0.1020.1840.4300.271-0.102-0.1610.984
온도(℃)-0.0401.000-0.210-0.1950.1100.3720.211-0.1950.4360.656
습도(%)-0.222-0.2101.0000.1180.355-0.1080.4200.118-0.0900.788
미세먼지(㎍/㎥)-0.102-0.1950.1181.0000.141-0.140-0.0451.000-0.0660.376
소음(㏈)0.1840.1100.3550.1411.0000.1060.2720.141-0.0060.661
이산화탄소(ppm)0.4300.372-0.108-0.1400.1061.0000.354-0.140-0.2670.768
휘발성유기화합물(ppb)0.2710.2110.420-0.0450.2720.3541.000-0.0450.1400.477
초미세먼지(㎍/㎥)-0.102-0.1950.1181.0000.141-0.140-0.0451.000-0.0660.376
학습능률지수(%)-0.1610.436-0.090-0.066-0.006-0.2670.140-0.0661.0000.578
시리얼0.9840.6560.7880.3760.6610.7680.4770.3760.5781.000

Missing values

2024-05-11T14:30:13.188796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:30:13.528933image/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-KV01G161353920231225085511572711524931928482023-12-25 08:59:57
1마포구AirGuard-KV01G16136022023122508551127284440-9999125321352023-12-25 08:59:57
2마포구AirGuard-KV01G1613601202312250855113026234170117417522023-12-25 08:59:57
3마포구AirGuard-KV01G161360020231225085511552811376251608492023-12-25 08:59:57
4마포구AirGuard-KV01G1613603202312250855110842234749223917602023-12-25 08:59:57
5마포구AirGuard-KV01G1613542202312250855120721175335519212692023-12-25 08:59:57
6마포구AirGuard-KV01G161361220231225085512192011327251558572023-12-25 08:59:58
7마포구AirGuard-KV01G161360520231225085511802411356191388662023-12-25 08:59:58
8마포구AirGuard-KV01G161360620231225085511532911423041488512023-12-25 08:59:58
9마포구AirGuard-KV01G161360920231225085512002011357041738612023-12-25 08:59:58
기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
760마포구AirGuard-KV01G161361820231226215012362211338843328692023-12-26 22:00:04
761마포구AirGuard-KV01G16136192023122621551244215337873283682023-12-26 22:00:04
762마포구AirGuard-KV01G1613620202312262155121935154054768911682023-12-26 22:00:04
763마포구AirGuard-KV01G161362220231226215512452153312322783632023-12-26 22:00:04
764마포구AirGuard-KV01G1613630202311090355121132153961933711722023-12-26 22:00:05
765마포구AirGuard-KV01G161363420231226215012002710347182997682023-12-26 22:00:05
766마포구AirGuard-KV01G161362820231009084512324511605802408792023-12-26 22:00:05
767마포구AirGuard-KV01G161362920231226215512272511467612348702023-12-26 22:00:05
768마포구AirGuard-KV01G161363220231226215511972713357322909582023-12-26 22:00:05
769마포구AirGuard-KV01G1613637202312071235122828596588321643622023-12-26 22:00:06