Overview

Dataset statistics

Number of variables13
Number of observations2555
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory282.1 KiB
Average record size in memory113.1 B

Variable types

Categorical3
Numeric9
DateTime1

Dataset

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

Alerts

기관 명 has constant value ""Constant
모델명 has constant value ""Constant
데이터관측일시 is highly overall correlated with 시리얼High correlation
온도(℃) is highly overall correlated with 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 16:10:44.514817
Analysis finished2024-05-11 16:11:00.162749
Duration15.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관 명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
마포구
2555 

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 2555
100.0%

Length

2024-05-12T01:11:00.269984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:11:00.430145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
마포구 2555
100.0%

모델명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
AirGuard-K
2555 

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 2555
100.0%

Length

2024-05-12T01:11:00.594348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-12T01:11:00.752764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
airguard-k 2555
100.0%

시리얼
Categorical

HIGH CORRELATION 

Distinct29
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
V01G1613542
 
96
V01G1613615
 
96
V01G1613600
 
96
V01G1613601
 
96
V01G1613602
 
96
Other values (24)
2075 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
V01G1613542 96
 
3.8%
V01G1613615 96
 
3.8%
V01G1613600 96
 
3.8%
V01G1613601 96
 
3.8%
V01G1613602 96
 
3.8%
V01G1613603 96
 
3.8%
V01G1613604 96
 
3.8%
V01G1613605 96
 
3.8%
V01G1613606 96
 
3.8%
V01G1613609 96
 
3.8%
Other values (19) 1595
62.4%

Length

2024-05-12T01:11:00.919105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
v01g1613542 96
 
3.8%
v01g1613610 96
 
3.8%
v01g1613634 96
 
3.8%
v01g1613632 96
 
3.8%
v01g1613629 96
 
3.8%
v01g1613622 96
 
3.8%
v01g1613620 96
 
3.8%
v01g1613619 96
 
3.8%
v01g1613618 96
 
3.8%
v01g1613617 96
 
3.8%
Other values (19) 1595
62.4%

데이터관측일시
Real number (ℝ)

HIGH CORRELATION 

Distinct196
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.023893 × 1011
Minimum2.0230808 × 1011
Maximum2.0240204 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:01.145148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0230808 × 1011
5-th percentile2.0231011 × 1011
Q12.0240129 × 1011
median2.0240131 × 1011
Q32.0240202 × 1011
95-th percentile2.0240204 × 1011
Maximum2.0240204 × 1011
Range93961135
Interquartile range (IQR)729900

Descriptive statistics

Standard deviation31245666
Coefficient of variation (CV)0.00015438398
Kurtosis2.5311838
Mean2.023893 × 1011
Median Absolute Deviation (MAD)700300
Skewness-2.1276041
Sum5.1710466 × 1014
Variance9.7629167 × 1014
MonotonicityNot monotonic
2024-05-12T01:11:01.402741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
202311090355 75
 
2.9%
202312071235 68
 
2.7%
202310110850 64
 
2.5%
202401311535 63
 
2.5%
202401311450 62
 
2.4%
202310290350 51
 
2.0%
202310090845 51
 
2.0%
202308081020 38
 
1.5%
202401311355 23
 
0.9%
202401291155 23
 
0.9%
Other values (186) 2037
79.7%
ValueCountFrequency (%)
202308081020 38
1.5%
202310090845 51
2.0%
202310110850 64
2.5%
202310290350 51
2.0%
202311090355 75
2.9%
202312071235 68
2.7%
202401290850 3
 
0.1%
202401290855 20
 
0.8%
202401290945 1
 
< 0.1%
202401290950 4
 
0.2%
ValueCountFrequency (%)
202402042155 21
0.8%
202402042055 19
0.7%
202402042050 2
 
0.1%
202402041955 20
0.8%
202402041950 1
 
< 0.1%
202402041855 21
0.8%
202402041755 21
0.8%
202402041655 20
0.8%
202402041650 1
 
< 0.1%
202402041555 19
0.7%

온도(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct139
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1217.3648
Minimum1125
Maximum1269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:01.641809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1125
5-th percentile1141
Q11195
median1229
Q31244
95-th percentile1261
Maximum1269
Range144
Interquartile range (IQR)49

Descriptive statistics

Standard deviation36.666821
Coefficient of variation (CV)0.030119831
Kurtosis-0.27360172
Mean1217.3648
Median Absolute Deviation (MAD)18
Skewness-0.89061707
Sum3110367
Variance1344.4558
MonotonicityNot monotonic
2024-05-12T01:11:01.899412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1241 93
 
3.6%
1211 87
 
3.4%
1228 85
 
3.3%
1252 77
 
3.0%
1232 74
 
2.9%
1244 69
 
2.7%
1265 65
 
2.5%
1224 65
 
2.5%
1229 59
 
2.3%
1240 56
 
2.2%
Other values (129) 1825
71.4%
ValueCountFrequency (%)
1125 1
 
< 0.1%
1127 1
 
< 0.1%
1128 2
 
0.1%
1129 2
 
0.1%
1131 3
 
0.1%
1132 1
 
< 0.1%
1133 3
 
0.1%
1134 12
0.5%
1135 9
0.4%
1136 15
0.6%
ValueCountFrequency (%)
1269 49
1.9%
1267 2
 
0.1%
1265 65
2.5%
1264 3
 
0.1%
1263 2
 
0.1%
1262 6
 
0.2%
1261 10
 
0.4%
1260 16
 
0.6%
1259 16
 
0.6%
1258 19
 
0.7%

습도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.989041
Minimum18
Maximum59
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:02.137405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum18
5-th percentile20
Q123
median26
Q332
95-th percentile39
Maximum59
Range41
Interquartile range (IQR)9

Descriptive statistics

Standard deviation7.3310705
Coefficient of variation (CV)0.26192646
Kurtosis3.1086895
Mean27.989041
Median Absolute Deviation (MAD)5
Skewness1.4029198
Sum71512
Variance53.744594
MonotonicityNot monotonic
2024-05-12T01:11:02.349424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
23 254
 
9.9%
22 231
 
9.0%
24 213
 
8.3%
21 170
 
6.7%
32 152
 
5.9%
25 143
 
5.6%
26 138
 
5.4%
36 119
 
4.7%
20 101
 
4.0%
28 100
 
3.9%
Other values (18) 934
36.6%
ValueCountFrequency (%)
18 61
 
2.4%
19 63
 
2.5%
20 101
 
4.0%
21 170
6.7%
22 231
9.0%
23 254
9.9%
24 213
8.3%
25 143
5.6%
26 138
5.4%
27 82
 
3.2%
ValueCountFrequency (%)
59 38
 
1.5%
45 53
2.1%
43 1
 
< 0.1%
42 4
 
0.2%
41 6
 
0.2%
40 9
 
0.4%
39 31
 
1.2%
38 79
3.1%
37 47
 
1.8%
36 119
4.7%

미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.102935
Minimum5
Maximum414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:02.582036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q110
median15
Q320
95-th percentile59
Maximum414
Range409
Interquartile range (IQR)10

Descriptive statistics

Standard deviation18.843708
Coefficient of variation (CV)0.98642996
Kurtosis83.494189
Mean19.102935
Median Absolute Deviation (MAD)5
Skewness5.6770806
Sum48808
Variance355.08533
MonotonicityNot monotonic
2024-05-12T01:11:02.850509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
17 482
18.9%
11 464
18.2%
5 330
12.9%
6 169
 
6.6%
15 143
 
5.6%
8 133
 
5.2%
20 92
 
3.6%
59 76
 
3.0%
28 67
 
2.6%
10 64
 
2.5%
Other values (55) 535
20.9%
ValueCountFrequency (%)
5 330
12.9%
6 169
 
6.6%
8 133
 
5.2%
10 64
 
2.5%
11 464
18.2%
13 61
 
2.4%
15 143
 
5.6%
17 482
18.9%
18 49
 
1.9%
20 92
 
3.6%
ValueCountFrequency (%)
414 1
 
< 0.1%
217 1
 
< 0.1%
142 1
 
< 0.1%
126 1
 
< 0.1%
118 1
 
< 0.1%
115 1
 
< 0.1%
113 1
 
< 0.1%
112 1
 
< 0.1%
105 3
0.1%
102 1
 
< 0.1%

소음(㏈)
Real number (ℝ)

HIGH CORRELATION 

Distinct48
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.032485
Minimum32
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:03.118288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32
5-th percentile33
Q139
median44
Q352
95-th percentile62
Maximum80
Range48
Interquartile range (IQR)13

Descriptive statistics

Standard deviation9.0620912
Coefficient of variation (CV)0.19686296
Kurtosis-0.23670365
Mean46.032485
Median Absolute Deviation (MAD)7
Skewness0.53134024
Sum117613
Variance82.121497
MonotonicityNot monotonic
2024-05-12T01:11:03.372257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
41 191
 
7.5%
39 186
 
7.3%
42 167
 
6.5%
51 160
 
6.3%
32 118
 
4.6%
40 117
 
4.6%
57 98
 
3.8%
50 96
 
3.8%
46 93
 
3.6%
54 91
 
3.6%
Other values (38) 1238
48.5%
ValueCountFrequency (%)
32 118
4.6%
33 69
 
2.7%
34 23
 
0.9%
35 80
3.1%
36 88
3.4%
37 65
 
2.5%
38 63
 
2.5%
39 186
7.3%
40 117
4.6%
41 191
7.5%
ValueCountFrequency (%)
80 1
 
< 0.1%
79 1
 
< 0.1%
78 1
 
< 0.1%
77 3
0.1%
76 5
0.2%
74 1
 
< 0.1%
73 1
 
< 0.1%
72 2
 
0.1%
71 4
0.2%
70 4
0.2%

이산화탄소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct826
Distinct (%)32.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean388.05793
Minimum-9999
Maximum3331
Zeros0
Zeros (%)0.0%
Negative96
Negative (%)3.8%
Memory size22.6 KiB
2024-05-12T01:11:03.623404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile348.1
Q1601
median748
Q3915
95-th percentile1242.3
Maximum3331
Range13330
Interquartile range (IQR)314

Descriptive statistics

Standard deviation2069.0946
Coefficient of variation (CV)5.3319221
Kurtosis20.934531
Mean388.05793
Median Absolute Deviation (MAD)154
Skewness-4.7400527
Sum991488
Variance4281152.6
MonotonicityNot monotonic
2024-05-12T01:11:04.026859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
883 119
 
4.7%
-9999 96
 
3.8%
619 93
 
3.6%
429 64
 
2.5%
1208 56
 
2.2%
580 54
 
2.1%
820 53
 
2.1%
417 39
 
1.5%
741 33
 
1.3%
597 22
 
0.9%
Other values (816) 1926
75.4%
ValueCountFrequency (%)
-9999 96
3.8%
260 1
 
< 0.1%
265 1
 
< 0.1%
266 3
 
0.1%
268 1
 
< 0.1%
269 1
 
< 0.1%
270 2
 
0.1%
271 1
 
< 0.1%
273 1
 
< 0.1%
277 2
 
0.1%
ValueCountFrequency (%)
3331 1
< 0.1%
2958 1
< 0.1%
2129 1
< 0.1%
2083 1
< 0.1%
1959 1
< 0.1%
1849 1
< 0.1%
1828 1
< 0.1%
1788 1
< 0.1%
1729 1
< 0.1%
1725 1
< 0.1%
Distinct336
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226.81761
Minimum125
Maximum2824
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:04.280359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum125
5-th percentile125
Q1152
median194
Q3266
95-th percentile380.3
Maximum2824
Range2699
Interquartile range (IQR)114

Descriptive statistics

Standard deviation128.37562
Coefficient of variation (CV)0.56598613
Kurtosis79.233333
Mean226.81761
Median Absolute Deviation (MAD)52
Skewness5.9341739
Sum579519
Variance16480.3
MonotonicityNot monotonic
2024-05-12T01:11:04.534965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
125 227
 
8.9%
183 83
 
3.2%
337 76
 
3.0%
216 73
 
2.9%
302 56
 
2.2%
240 54
 
2.1%
162 53
 
2.1%
140 53
 
2.1%
348 51
 
2.0%
152 34
 
1.3%
Other values (326) 1795
70.3%
ValueCountFrequency (%)
125 227
8.9%
126 29
 
1.1%
127 27
 
1.1%
128 17
 
0.7%
129 10
 
0.4%
130 13
 
0.5%
131 6
 
0.2%
132 14
 
0.5%
133 8
 
0.3%
134 7
 
0.3%
ValueCountFrequency (%)
2824 1
< 0.1%
1454 1
< 0.1%
1270 1
< 0.1%
1266 1
< 0.1%
1200 1
< 0.1%
1094 1
< 0.1%
1075 1
< 0.1%
1069 1
< 0.1%
1012 1
< 0.1%
979 1
< 0.1%

초미세먼지(㎍/㎥)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.587084
Minimum3
Maximum201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:04.785779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q17
median11
Q314
95-th percentile43
Maximum201
Range198
Interquartile range (IQR)7

Descriptive statistics

Standard deviation13.016485
Coefficient of variation (CV)0.95800431
Kurtosis23.14547
Mean13.587084
Median Absolute Deviation (MAD)3
Skewness3.3190057
Sum34715
Variance169.42889
MonotonicityNot monotonic
2024-05-12T01:11:05.048381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12 482
18.9%
8 464
18.2%
3 330
12.9%
4 169
 
6.6%
11 143
 
5.6%
5 133
 
5.2%
14 92
 
3.6%
43 76
 
3.0%
20 67
 
2.6%
7 64
 
2.5%
Other values (50) 535
20.9%
ValueCountFrequency (%)
3 330
12.9%
4 169
 
6.6%
5 133
 
5.2%
7 64
 
2.5%
8 464
18.2%
9 61
 
2.4%
11 143
 
5.6%
12 482
18.9%
13 49
 
1.9%
14 92
 
3.6%
ValueCountFrequency (%)
201 1
 
< 0.1%
123 1
 
< 0.1%
93 1
 
< 0.1%
87 1
 
< 0.1%
83 1
 
< 0.1%
82 1
 
< 0.1%
81 2
0.1%
78 3
0.1%
77 1
 
< 0.1%
71 1
 
< 0.1%

학습능률지수(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct46
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.986301
Minimum43
Maximum148
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.6 KiB
2024-05-12T01:11:05.312529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum43
5-th percentile51
Q158
median63
Q369
95-th percentile79
Maximum148
Range105
Interquartile range (IQR)11

Descriptive statistics

Standard deviation16.949768
Coefficient of variation (CV)0.25686798
Kurtosis14.383243
Mean65.986301
Median Absolute Deviation (MAD)5
Skewness3.6182036
Sum168595
Variance287.29464
MonotonicityNot monotonic
2024-05-12T01:11:05.569335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
62 192
 
7.5%
63 170
 
6.7%
64 143
 
5.6%
70 136
 
5.3%
61 119
 
4.7%
58 118
 
4.6%
67 117
 
4.6%
72 113
 
4.4%
57 104
 
4.1%
69 102
 
4.0%
Other values (36) 1241
48.6%
ValueCountFrequency (%)
43 1
 
< 0.1%
44 2
 
0.1%
45 4
 
0.2%
46 8
 
0.3%
47 54
2.1%
48 11
 
0.4%
49 11
 
0.4%
50 22
0.9%
51 37
1.4%
52 27
1.1%
ValueCountFrequency (%)
148 3
 
0.1%
147 11
 
0.4%
146 11
 
0.4%
145 15
0.6%
144 33
1.3%
143 15
0.6%
142 4
 
0.2%
138 1
 
< 0.1%
137 3
 
0.1%
82 1
 
< 0.1%
Distinct433
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Memory size20.1 KiB
Minimum2024-01-29 08:58:45
Maximum2024-02-04 21:58:33
2024-05-12T01:11:05.811016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:11:06.052084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-05-12T01:10:58.041753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:45.728350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:47.197713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:48.739518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:50.300256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:51.880045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:53.348040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:54.997264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:56.472748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:58.198514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:45.883209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:47.361743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:48.892524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:50.469587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:52.038360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:53.510893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:55.154623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:56.641668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:58.370333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:46.053695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:47.538146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:49.059940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:50.650156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:52.212536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:53.685501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:55.326546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:56.824308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:58.520638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:46.206030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:47.695594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:49.203743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:50.814349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:52.362574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:53.842823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:55.477654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:56.985253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:58.701849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:46.381907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:47.881640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:49.377439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:51.003156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:52.539341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:54.023245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:55.655613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:57.171000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:58.858557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:46.540457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:48.046873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:49.663293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:51.171067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:52.694083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:54.186598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:55.815705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:57.339604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:59.023704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:46.703585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:48.222158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:49.823467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:51.346797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:52.858520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:54.351804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:55.979618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:57.518323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:59.182348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:46.862325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:48.386462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:49.976075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:51.513629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:53.014828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:54.514981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:56.135630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:57.689424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:59.358942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:47.038083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:48.572091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:50.148061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:51.705838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:53.191391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:54.695361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:56.312009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-12T01:10:57.872000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-12T01:11:06.228381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)
시리얼1.0001.0000.9210.9430.6240.8720.6900.6190.7090.901
데이터관측일시1.0001.0000.4330.5000.2640.4920.1310.0970.2870.482
온도(℃)0.9210.4331.0000.6400.2760.6170.3390.1590.3900.600
습도(%)0.9430.5000.6401.0000.2600.5530.4200.2920.3390.619
미세먼지(㎍/㎥)0.6240.2640.2760.2601.0000.3590.0000.2330.9430.323
소음(㏈)0.8720.4920.6170.5530.3591.0000.3060.0920.4270.437
이산화탄소(ppm)0.6900.1310.3390.4200.0000.3061.0000.3540.0610.306
휘발성유기화합물(ppb)0.6190.0970.1590.2920.2330.0920.3541.0000.1680.189
초미세먼지(㎍/㎥)0.7090.2870.3900.3390.9430.4270.0610.1681.0000.265
학습능률지수(%)0.9010.4820.6000.6190.3230.4370.3060.1890.2651.000
2024-05-12T01:11:06.457452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)시리얼
데이터관측일시1.000-0.039-0.045-0.027-0.1630.239-0.098-0.027-0.1640.995
온도(℃)-0.0391.000-0.488-0.1750.2300.3450.225-0.175-0.0690.648
습도(%)-0.045-0.4881.0000.030-0.104-0.3170.0640.0300.1340.749
미세먼지(㎍/㎥)-0.027-0.1750.0301.0000.193-0.0600.0041.000-0.1290.331
소음(㏈)-0.1630.230-0.1040.1931.000-0.0400.0940.193-0.0900.542
이산화탄소(ppm)0.2390.345-0.317-0.060-0.0401.0000.347-0.060-0.5120.689
휘발성유기화합물(ppb)-0.0980.2250.0640.0040.0940.3471.0000.004-0.0650.328
초미세먼지(㎍/㎥)-0.027-0.1750.0301.0000.193-0.0600.0041.000-0.1290.385
학습능률지수(%)-0.164-0.0690.134-0.129-0.090-0.512-0.065-0.1291.0000.670
시리얼0.9950.6480.7490.3310.5420.6890.3280.3850.6701.000

Missing values

2024-05-12T01:10:59.580019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-12T01:10:59.897122image/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-KV01G16135422024012908551230245586051963712024-01-29 08:58:45
1마포구AirGuard-KV01G1613539202401290855116825325155023823552024-01-29 08:58:45
2마포구AirGuard-KV01G161360020240129085511383310386551257592024-01-29 08:58:45
3마포구AirGuard-KV01G16136012024012908551128306417201794462024-01-29 08:58:45
4마포구AirGuard-KV01G16136022024012908551148324542-9999125331442024-01-29 08:58:45
5마포구AirGuard-KV01G1613603202401290855113940745278542354472024-01-29 08:58:45
6마포구AirGuard-KV01G1613604202401290855117630173562712512532024-01-29 08:58:45
7마포구AirGuard-KV01G161360520240129085511802711366761258642024-01-29 08:58:46
8마포구AirGuard-KV01G16136062024012908551175396466702784572024-01-29 08:58:46
9마포구AirGuard-KV01G161360920240129085511992211367071578612024-01-29 08:58:46
기관 명모델명시리얼데이터관측일시온도(℃)습도(%)미세먼지(㎍/㎥)소음(㏈)이산화탄소(ppm)휘발성유기화합물(ppb)초미세먼지(㎍/㎥)학습능률지수(%)등록일자
2545마포구AirGuard-KV01G161361720240204215512322653310491953672024-02-04 21:58:32
2546마포구AirGuard-KV01G16136182024020421551251241833100321413602024-02-04 21:58:32
2547마포구AirGuard-KV01G1613619202402042155125224173290820212622024-02-04 21:58:32
2548마포구AirGuard-KV01G1613620202402042155123633254161868218742024-02-04 21:58:32
2549마포구AirGuard-KV01G16136342024020421551259248337591255632024-02-04 21:58:33
2550마포구AirGuard-KV01G1613630202311090355121132153961933711722024-02-04 21:58:33
2551마포구AirGuard-KV01G16136292024020421551230245626171423712024-02-04 21:58:33
2552마포구AirGuard-KV01G16136222024020421551257231733137718912552024-02-04 21:58:33
2553마포구AirGuard-KV01G1613637202312071235122828596588321643622024-02-04 21:58:33
2554마포구AirGuard-KV01G1613632202401311535126918285788316220472024-02-04 21:58:33