Overview

Dataset statistics

Number of variables11
Number of observations10000
Missing cells15886
Missing cells (%)14.4%
Duplicate rows1
Duplicate rows (%)< 0.1%
Total size in memory1005.9 KiB
Average record size in memory103.0 B

Variable types

Categorical3
Text1
Numeric7

Dataset

Description고양시 바이오매스 악취 정보
Author고양시
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=40N2ILE9CI9755NV2HCQ26377202&infSeq=1

Alerts

설치장소명 has constant value ""Constant
디바이스명 has constant value ""Constant
보고형태 has constant value ""Constant
Dataset has 1 (< 0.1%) duplicate rowsDuplicates
온도측정값(℃) is highly overall correlated with 암모니아측정값(ppm) and 2 other fieldsHigh correlation
암모니아측정값(ppm) is highly overall correlated with 온도측정값(℃) and 1 other fieldsHigh correlation
휘발성유기화합물측정값(ppm) is highly overall correlated with 온도측정값(℃)High correlation
악취측정값(Odor Level( 0~ 1000)) is highly overall correlated with 온도측정값(℃) and 1 other fieldsHigh correlation
기압측정값(hPa) has 7943 (79.4%) missing valuesMissing
악취측정값(Odor Level( 0~ 1000)) has 7943 (79.4%) missing valuesMissing
풍향측정값(˚(degree)) has 1607 (16.1%) zerosZeros
습도측정값(%) has 182 (1.8%) zerosZeros
암모니아측정값(ppm) has 182 (1.8%) zerosZeros
휘발성유기화합물측정값(ppm) has 182 (1.8%) zerosZeros

Reproduction

Analysis started2023-12-10 22:21:50.860328
Analysis finished2023-12-10 22:21:56.564394
Duration5.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치장소명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
고양바이오메스
10000 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고양바이오메스
2nd row고양바이오메스
3rd row고양바이오메스
4th row고양바이오메스
5th row고양바이오메스

Common Values

ValueCountFrequency (%)
고양바이오메스 10000
100.0%

Length

2023-12-11T07:21:56.614512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:21:56.685808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
고양바이오메스 10000
100.0%

디바이스명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Goyangbiomes
10000 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowGoyangbiomes
2nd rowGoyangbiomes
3rd rowGoyangbiomes
4th rowGoyangbiomes
5th rowGoyangbiomes

Common Values

ValueCountFrequency (%)
Goyangbiomes 10000
100.0%

Length

2023-12-11T07:21:56.758764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:21:56.829376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
goyangbiomes 10000
100.0%

수집시간
Text

Distinct9998
Distinct (%)> 99.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T07:21:57.083355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length19.0009
Min length19

Characters and Unicode

Total characters190009
Distinct characters13
Distinct categories4 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9997 ?
Unique (%)> 99.9%

Sample

1st row2020-02-10 20:15:00
2nd row2020-02-05 12:09:00
3rd row2020-02-27 15:04:00
4th row2020-02-27 12:12:00
5th row2020-01-01 15:16:00
ValueCountFrequency (%)
2020-02-05 169
 
0.8%
2019-10-24 168
 
0.8%
2020-02-25 162
 
0.8%
2020-02-16 160
 
0.8%
2020-02-03 160
 
0.8%
2020-02-11 159
 
0.8%
2019-01-25 158
 
0.8%
2019-10-21 157
 
0.8%
2019-01-26 156
 
0.8%
2019-01-14 156
 
0.8%
Other values (1534) 18395
92.0%
2023-12-11T07:21:57.479628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 50189
26.4%
2 28618
15.1%
1 28618
15.1%
- 20000
 
10.5%
: 20000
 
10.5%
10000
 
5.3%
9 9216
 
4.9%
3 5262
 
2.8%
4 4799
 
2.5%
5 4615
 
2.4%
Other values (3) 8692
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 140009
73.7%
Dash Punctuation 20000
 
10.5%
Other Punctuation 20000
 
10.5%
Space Separator 10000
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50189
35.8%
2 28618
20.4%
1 28618
20.4%
9 9216
 
6.6%
3 5262
 
3.8%
4 4799
 
3.4%
5 4615
 
3.3%
6 2965
 
2.1%
7 2904
 
2.1%
8 2823
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 20000
100.0%
Other Punctuation
ValueCountFrequency (%)
: 20000
100.0%
Space Separator
ValueCountFrequency (%)
10000
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 190009
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 50189
26.4%
2 28618
15.1%
1 28618
15.1%
- 20000
 
10.5%
: 20000
 
10.5%
10000
 
5.3%
9 9216
 
4.9%
3 5262
 
2.8%
4 4799
 
2.5%
5 4615
 
2.4%
Other values (3) 8692
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 190009
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 50189
26.4%
2 28618
15.1%
1 28618
15.1%
- 20000
 
10.5%
: 20000
 
10.5%
10000
 
5.3%
9 9216
 
4.9%
3 5262
 
2.8%
4 4799
 
2.5%
5 4615
 
2.4%
Other values (3) 8692
 
4.6%

보고형태
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
report
10000 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowreport
2nd rowreport
3rd rowreport
4th rowreport
5th rowreport

Common Values

ValueCountFrequency (%)
report 10000
100.0%

Length

2023-12-11T07:21:57.587033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:21:57.654474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
report 10000
100.0%

온도측정값(℃)
Real number (ℝ)

HIGH CORRELATION 

Distinct356
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-173.4254
Minimum-9999
Maximum24.1
Zeros34
Zeros (%)0.3%
Negative2504
Negative (%)25.0%
Memory size166.0 KiB
2023-12-11T07:21:57.731929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile-6.9
Q1-0.1
median4.7
Q311.1
95-th percentile19
Maximum24.1
Range10023.1
Interquartile range (IQR)11.2

Descriptive statistics

Standard deviation1326.5854
Coefficient of variation (CV)-7.6493142
Kurtosis50.90679
Mean-173.4254
Median Absolute Deviation (MAD)5.5
Skewness-7.2728816
Sum-1734254
Variance1759828.8
MonotonicityNot monotonic
2023-12-11T07:21:57.838872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
-9999.0 179
 
1.8%
1.6 90
 
0.9%
6.5 88
 
0.9%
3.2 84
 
0.8%
2.0 82
 
0.8%
4.0 80
 
0.8%
6.3 79
 
0.8%
2.4 77
 
0.8%
5.5 75
 
0.8%
0.5 74
 
0.7%
Other values (346) 9092
90.9%
ValueCountFrequency (%)
-9999.0 179
1.8%
-11.4 3
 
< 0.1%
-11.3 2
 
< 0.1%
-11.2 6
 
0.1%
-11.1 5
 
0.1%
-10.9 2
 
< 0.1%
-10.8 3
 
< 0.1%
-10.7 4
 
< 0.1%
-10.6 4
 
< 0.1%
-10.5 9
 
0.1%
ValueCountFrequency (%)
24.1 1
 
< 0.1%
24.0 2
 
< 0.1%
23.9 4
 
< 0.1%
23.8 4
 
< 0.1%
23.7 7
0.1%
23.6 6
0.1%
23.5 10
0.1%
23.4 3
 
< 0.1%
23.3 6
0.1%
23.2 10
0.1%

풍향측정값(˚(degree))
Real number (ℝ)

ZEROS 

Distinct2795
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean176.23453
Minimum0
Maximum359.9
Zeros1607
Zeros (%)16.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:21:57.940055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q118.2
median180.2
Q3314.4
95-th percentile350
Maximum359.9
Range359.9
Interquartile range (IQR)296.2

Descriptive statistics

Standard deviation136.3044
Coefficient of variation (CV)0.77342616
Kurtosis-1.6479833
Mean176.23453
Median Absolute Deviation (MAD)141.8
Skewness-0.095602458
Sum1762345.3
Variance18578.888
MonotonicityNot monotonic
2023-12-11T07:21:58.046901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1607
 
16.1%
335.1 15
 
0.1%
325.6 14
 
0.1%
350.1 13
 
0.1%
323.1 13
 
0.1%
315.0 12
 
0.1%
3.4 12
 
0.1%
336.5 12
 
0.1%
352.2 11
 
0.1%
336.8 11
 
0.1%
Other values (2785) 8280
82.8%
ValueCountFrequency (%)
0.0 1607
16.1%
0.1 6
 
0.1%
0.2 7
 
0.1%
0.3 8
 
0.1%
0.4 4
 
< 0.1%
0.5 6
 
0.1%
0.6 10
 
0.1%
0.7 7
 
0.1%
0.8 8
 
0.1%
0.9 5
 
0.1%
ValueCountFrequency (%)
359.9 8
0.1%
359.8 7
0.1%
359.7 2
 
< 0.1%
359.6 4
< 0.1%
359.5 5
0.1%
359.4 3
 
< 0.1%
359.3 2
 
< 0.1%
359.2 3
 
< 0.1%
359.1 1
 
< 0.1%
359.0 3
 
< 0.1%

기압측정값(hPa)
Real number (ℝ)

MISSING 

Distinct129
Distinct (%)6.3%
Missing7943
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean922.99081
Minimum916.3
Maximum929.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:21:58.350979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum916.3
5-th percentile917.3
Q1921.1
median923.3
Q3925.3
95-th percentile928.2
Maximum929.2
Range12.9
Interquartile range (IQR)4.2

Descriptive statistics

Standard deviation3.2052999
Coefficient of variation (CV)0.0034727322
Kurtosis-0.75988235
Mean922.99081
Median Absolute Deviation (MAD)2.1
Skewness-0.23975771
Sum1898592.1
Variance10.273948
MonotonicityNot monotonic
2023-12-11T07:21:58.459418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
925.2 56
 
0.6%
923.6 52
 
0.5%
923.3 37
 
0.4%
925.1 35
 
0.4%
923.5 34
 
0.3%
923.9 34
 
0.3%
923.4 33
 
0.3%
917.0 33
 
0.3%
925.3 33
 
0.3%
922.1 32
 
0.3%
Other values (119) 1678
 
16.8%
(Missing) 7943
79.4%
ValueCountFrequency (%)
916.3 3
 
< 0.1%
916.4 3
 
< 0.1%
916.5 7
 
0.1%
916.6 1
 
< 0.1%
916.7 2
 
< 0.1%
916.8 2
 
< 0.1%
916.9 9
 
0.1%
917.0 33
0.3%
917.1 21
0.2%
917.2 15
0.1%
ValueCountFrequency (%)
929.2 4
 
< 0.1%
929.1 4
 
< 0.1%
929.0 3
 
< 0.1%
928.9 2
 
< 0.1%
928.8 11
0.1%
928.7 20
0.2%
928.6 16
0.2%
928.5 17
0.2%
928.4 9
0.1%
928.3 8
 
0.1%

습도측정값(%)
Real number (ℝ)

ZEROS 

Distinct799
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.93934
Minimum0
Maximum99
Zeros182
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:21:58.567715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.2
Q147.975
median60.8
Q377.4
95-th percentile92.2
Maximum99
Range99
Interquartile range (IQR)29.425

Descriptive statistics

Standard deviation20.502874
Coefficient of variation (CV)0.33644726
Kurtosis-0.039274629
Mean60.93934
Median Absolute Deviation (MAD)14.7
Skewness-0.38722127
Sum609393.4
Variance420.36785
MonotonicityNot monotonic
2023-12-11T07:21:58.670687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 182
 
1.8%
52.9 34
 
0.3%
51.7 32
 
0.3%
50.2 31
 
0.3%
53.0 30
 
0.3%
55.3 29
 
0.3%
51.3 28
 
0.3%
53.2 28
 
0.3%
49.7 28
 
0.3%
87.1 28
 
0.3%
Other values (789) 9550
95.5%
ValueCountFrequency (%)
0.0 182
1.8%
13.6 1
 
< 0.1%
15.5 1
 
< 0.1%
15.6 1
 
< 0.1%
15.7 1
 
< 0.1%
15.8 1
 
< 0.1%
16.2 1
 
< 0.1%
16.4 1
 
< 0.1%
16.8 1
 
< 0.1%
17.0 1
 
< 0.1%
ValueCountFrequency (%)
99.0 6
0.1%
98.9 1
 
< 0.1%
98.8 2
 
< 0.1%
98.7 4
< 0.1%
98.6 2
 
< 0.1%
98.5 3
< 0.1%
98.4 3
< 0.1%
98.3 2
 
< 0.1%
98.2 3
< 0.1%
98.1 1
 
< 0.1%

암모니아측정값(ppm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct114
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-84.294402
Minimum-9999
Maximum25.5
Zeros182
Zeros (%)1.8%
Negative87
Negative (%)0.9%
Memory size166.0 KiB
2023-12-11T07:21:58.772639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.67
Q10.82
median0.85
Q31.02
95-th percentile21
Maximum25.5
Range10024.5
Interquartile range (IQR)0.2

Descriptive statistics

Standard deviation928.89535
Coefficient of variation (CV)-11.019656
Kurtosis109.99839
Mean-84.294402
Median Absolute Deviation (MAD)0.07
Skewness-10.581683
Sum-842944.02
Variance862846.57
MonotonicityNot monotonic
2023-12-11T07:21:58.874346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.84 966
 
9.7%
0.85 921
 
9.2%
21.0 805
 
8.1%
0.83 692
 
6.9%
0.86 440
 
4.4%
0.82 392
 
3.9%
1.77 346
 
3.5%
1.78 282
 
2.8%
0.8 273
 
2.7%
1.76 241
 
2.4%
Other values (104) 4642
46.4%
ValueCountFrequency (%)
-9999.0 87
0.9%
0.0 182
1.8%
0.03 2
 
< 0.1%
0.04 1
 
< 0.1%
0.06 1
 
< 0.1%
0.13 1
 
< 0.1%
0.14 1
 
< 0.1%
0.2 1
 
< 0.1%
0.24 1
 
< 0.1%
0.33 1
 
< 0.1%
ValueCountFrequency (%)
25.5 1
 
< 0.1%
25.0 14
0.1%
24.88 4
 
< 0.1%
24.87 2
 
< 0.1%
24.77 4
 
< 0.1%
24.75 2
 
< 0.1%
24.66 4
 
< 0.1%
24.62 1
 
< 0.1%
24.55 1
 
< 0.1%
24.5 2
 
< 0.1%

휘발성유기화합물측정값(ppm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct2634
Distinct (%)26.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-73.13487
Minimum-9999
Maximum124.55
Zeros182
Zeros (%)1.8%
Negative87
Negative (%)0.9%
Memory size166.0 KiB
2023-12-11T07:21:58.975384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-9999
5-th percentile0.2
Q13.16
median7.65
Q322.275
95-th percentile43.27
Maximum124.55
Range10123.55
Interquartile range (IQR)19.115

Descriptive statistics

Standard deviation930.02666
Coefficient of variation (CV)-12.716597
Kurtosis109.95672
Mean-73.13487
Median Absolute Deviation (MAD)6.305
Skewness-10.578699
Sum-731348.7
Variance864949.59
MonotonicityNot monotonic
2023-12-11T07:21:59.080697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 182
 
1.8%
-9999.0 87
 
0.9%
0.07 23
 
0.2%
5.99 20
 
0.2%
3.96 19
 
0.2%
1.73 18
 
0.2%
7.49 18
 
0.2%
0.08 18
 
0.2%
2.83 17
 
0.2%
0.22 17
 
0.2%
Other values (2624) 9581
95.8%
ValueCountFrequency (%)
-9999.0 87
0.9%
0.0 182
1.8%
0.01 4
 
< 0.1%
0.02 9
 
0.1%
0.03 16
 
0.2%
0.04 15
 
0.1%
0.05 13
 
0.1%
0.06 11
 
0.1%
0.07 23
 
0.2%
0.08 18
 
0.2%
ValueCountFrequency (%)
124.55 1
< 0.1%
124.12 1
< 0.1%
98.77 1
< 0.1%
98.22 1
< 0.1%
93.93 1
< 0.1%
88.04 1
< 0.1%
73.59 1
< 0.1%
73.16 2
< 0.1%
73.01 1
< 0.1%
72.39 1
< 0.1%

악취측정값(Odor Level( 0~ 1000))
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)0.9%
Missing7943
Missing (%)79.4%
Infinite0
Infinite (%)0.0%
Mean23.733082
Minimum23.61
Maximum23.79
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T07:21:59.166091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum23.61
5-th percentile23.68
Q123.7
median23.72
Q323.77
95-th percentile23.79
Maximum23.79
Range0.18
Interquartile range (IQR)0.07

Descriptive statistics

Standard deviation0.039544212
Coefficient of variation (CV)0.0016662063
Kurtosis-1.1178717
Mean23.733082
Median Absolute Deviation (MAD)0.02
Skewness0.1171515
Sum48818.95
Variance0.0015637447
MonotonicityNot monotonic
2023-12-11T07:21:59.251951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
23.7 603
 
6.0%
23.79 358
 
3.6%
23.71 182
 
1.8%
23.78 147
 
1.5%
23.77 114
 
1.1%
23.72 113
 
1.1%
23.76 111
 
1.1%
23.73 101
 
1.0%
23.74 99
 
1.0%
23.68 79
 
0.8%
Other values (9) 150
 
1.5%
(Missing) 7943
79.4%
ValueCountFrequency (%)
23.61 1
 
< 0.1%
23.62 4
 
< 0.1%
23.63 6
 
0.1%
23.64 2
 
< 0.1%
23.65 10
 
0.1%
23.66 16
 
0.2%
23.67 41
 
0.4%
23.68 79
 
0.8%
23.69 4
 
< 0.1%
23.7 603
6.0%
ValueCountFrequency (%)
23.79 358
3.6%
23.78 147
 
1.5%
23.77 114
 
1.1%
23.76 111
 
1.1%
23.75 66
 
0.7%
23.74 99
 
1.0%
23.73 101
 
1.0%
23.72 113
 
1.1%
23.71 182
 
1.8%
23.7 603
6.0%

Interactions

2023-12-11T07:21:55.625890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:51.850971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.416575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.199023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.855740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.447156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.040179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.710462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:51.926263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.494077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.293614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.932897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.543996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.135389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.806648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.011489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.572697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.391687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.025518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.625232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.228691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.889014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.099424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.661556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.475632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.122150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.711548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.308317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.997050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.175282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.742740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.565001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.196678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.788602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.388345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:56.085345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.255218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.827716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.676759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.276147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.863864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.465389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:56.165124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.330154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:52.910797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:53.764909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.351491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:54.941407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:21:55.545573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:21:59.320272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도측정값(℃)풍향측정값(˚(degree))기압측정값(hPa)습도측정값(%)암모니아측정값(ppm)휘발성유기화합물측정값(ppm)악취측정값(Odor Level( 0~ 1000))
온도측정값(℃)1.000NaNNaNNaNNaNNaNNaN
풍향측정값(˚(degree))NaN1.0000.3100.453NaNNaN0.532
기압측정값(hPa)NaN0.3101.0000.500NaNNaN0.554
습도측정값(%)NaN0.4530.5001.000NaNNaN0.481
암모니아측정값(ppm)NaNNaNNaNNaN1.000NaNNaN
휘발성유기화합물측정값(ppm)NaNNaNNaNNaNNaN1.000NaN
악취측정값(Odor Level( 0~ 1000))NaN0.5320.5540.481NaNNaN1.000
2023-12-11T07:21:59.411603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
온도측정값(℃)풍향측정값(˚(degree))기압측정값(hPa)습도측정값(%)암모니아측정값(ppm)휘발성유기화합물측정값(ppm)악취측정값(Odor Level( 0~ 1000))
온도측정값(℃)1.0000.017-0.4580.0510.6020.5450.917
풍향측정값(˚(degree))0.0171.0000.013-0.2580.064-0.2360.251
기압측정값(hPa)-0.4580.0131.000-0.249-0.356-0.293-0.327
습도측정값(%)0.051-0.258-0.2491.0000.0110.284-0.372
암모니아측정값(ppm)0.6020.064-0.3560.0111.0000.4670.935
휘발성유기화합물측정값(ppm)0.545-0.236-0.2930.2840.4671.0000.069
악취측정값(Odor Level( 0~ 1000))0.9170.251-0.327-0.3720.9350.0691.000

Missing values

2023-12-11T07:21:56.277273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:21:56.411534image/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.
2023-12-11T07:21:56.516240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

설치장소명디바이스명수집시간보고형태온도측정값(℃)풍향측정값(˚(degree))기압측정값(hPa)습도측정값(%)암모니아측정값(ppm)휘발성유기화합물측정값(ppm)악취측정값(Odor Level( 0~ 1000))
24654고양바이오메스Goyangbiomes2020-02-10 20:15:00report3.60.0<NA>57.00.864.1<NA>
27875고양바이오메스Goyangbiomes2020-02-05 12:09:00report-6.0350.7<NA>39.70.810.07<NA>
535고양바이오메스Goyangbiomes2020-02-27 15:04:00report11.2341.4<NA>40.50.792.14<NA>
706고양바이오메스Goyangbiomes2020-02-27 12:12:00report9.6321.6<NA>49.00.81.73<NA>
35099고양바이오메스Goyangbiomes2020-01-01 15:16:00report-9999.00.0<NA>0.00.00.0<NA>
72912고양바이오메스Goyangbiomes2019-10-22 03:08:00report12.60.0<NA>89.81.7948.96<NA>
83046고양바이오메스Goyangbiomes2019-01-26 01:55:00report-6.6142.9925.353.40.6511.3623.7
21991고양바이오메스Goyangbiomes2020-02-12 16:44:00report9.2358.8<NA>95.70.876.71<NA>
10734고양바이오메스Goyangbiomes2020-02-20 12:47:00report9.399.4<NA>57.40.784.15<NA>
69820고양바이오메스Goyangbiomes2019-10-24 06:45:00report13.20.0<NA>88.61.842.43<NA>
설치장소명디바이스명수집시간보고형태온도측정값(℃)풍향측정값(˚(degree))기압측정값(hPa)습도측정값(%)암모니아측정값(ppm)휘발성유기화합물측정값(ppm)악취측정값(Odor Level( 0~ 1000))
30928고양바이오메스Goyangbiomes2020-02-03 09:09:00report0.20.0<NA>65.30.833.57<NA>
27842고양바이오메스Goyangbiomes2020-02-05 12:42:00report-5.5287.7<NA>39.20.810.09<NA>
46606고양바이오메스Goyangbiomes2019-12-04 17:08:00report5.5355.4<NA>45.221.06.73<NA>
17942고양바이오메스Goyangbiomes2020-02-15 12:21:00report15.3151.4<NA>49.20.773.84<NA>
63435고양바이오메스Goyangbiomes2019-10-31 14:59:00report19.9175.5<NA>67.51.0430.66<NA>
18669고양바이오메스Goyangbiomes2020-02-15 00:11:00report5.71.0<NA>90.20.874.36<NA>
56661고양바이오메스Goyangbiomes2019-11-16 16:56:00report11.90.0<NA>62.80.9823.54<NA>
51068고양바이오메스Goyangbiomes2019-11-26 17:57:00report9.4333.1<NA>71.70.9314.02<NA>
1322고양바이오메스Goyangbiomes2020-02-27 01:55:00report2.5131.8<NA>84.70.853.48<NA>
65109고양바이오메스Goyangbiomes2019-10-29 11:31:00report17.0316.9<NA>54.91.0421.48<NA>

Duplicate rows

Most frequently occurring

설치장소명디바이스명수집시간보고형태온도측정값(℃)풍향측정값(˚(degree))기압측정값(hPa)습도측정값(%)암모니아측정값(ppm)휘발성유기화합물측정값(ppm)악취측정값(Odor Level( 0~ 1000))# duplicates
0고양바이오메스Goyangbiomes2019-12-204 204:204:00report0.00.0<NA>0.00.00.0<NA>3