Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory136.3 B

Variable types

Categorical8
Numeric6
Text1
Boolean1

Alerts

메틸메르캅탄 has constant value ""Constant
사용여부 has constant value ""Constant
사업코드 is highly overall correlated with 아황산가스 and 7 other fieldsHigh correlation
날짜 is highly overall correlated with 아황산가스 and 7 other fieldsHigh correlation
조사시작일 is highly overall correlated with 아황산가스 and 7 other fieldsHigh correlation
조사종료일 is highly overall correlated with 아황산가스 and 7 other fieldsHigh correlation
아황산가스 is highly overall correlated with 암모니아 and 5 other fieldsHigh correlation
이산화질소 is highly overall correlated with 날짜 and 5 other fieldsHigh correlation
일산화탄소 값 is highly overall correlated with 날짜 and 5 other fieldsHigh correlation
복합악취 인덱스 is highly overall correlated with 조사지점명High correlation
암모니아 is highly overall correlated with 아황산가스 and 2 other fieldsHigh correlation
황화수소 is highly overall correlated with 암모니아 and 1 other fieldsHigh correlation
조사구분 is highly overall correlated with 이산화질소 and 6 other fieldsHigh correlation
조사지점명 is highly overall correlated with 복합악취 인덱스 and 2 other fieldsHigh correlation
조사차수 is highly overall correlated with 아황산가스 and 7 other fieldsHigh correlation
복합악취 인덱스 has 2 (2.0%) zerosZeros
암모니아 has 58 (58.0%) zerosZeros
황화수소 has 79 (79.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:42:59.596920
Analysis finished2023-12-10 11:43:07.524318
Duration7.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018-01-19
20 
2018-02-01
14 
2018-01-17
10 
2018-01-30
9 
2018-01-23
7 
Other values (11)
40 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2018-01-01
2nd row2018-01-01
3rd row2018-01-01
4th row2018-01-01
5th row2018-01-01

Common Values

ValueCountFrequency (%)
2018-01-19 20
20.0%
2018-02-01 14
14.0%
2018-01-17 10
10.0%
2018-01-30 9
9.0%
2018-01-23 7
 
7.0%
2018-01-01 6
 
6.0%
2018-02-02 6
 
6.0%
2018-01-15 5
 
5.0%
2018-01-25 4
 
4.0%
2018-02-07 4
 
4.0%
Other values (6) 15
15.0%

Length

2023-12-10T20:43:07.612552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-01-19 20
20.0%
2018-02-01 14
14.0%
2018-01-17 10
10.0%
2018-01-30 9
9.0%
2018-01-23 7
 
7.0%
2018-01-01 6
 
6.0%
2018-02-02 6
 
6.0%
2018-01-15 5
 
5.0%
2018-01-25 4
 
4.0%
2018-02-07 4
 
4.0%
Other values (6) 15
15.0%

아황산가스
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040437873
Minimum0.003906553
Maximum0.0042806777
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:07.747985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.003906553
5-th percentile0.003906553
Q10.0039768323
median0.0040026888
Q30.0040576013
95-th percentile0.0042806777
Maximum0.0042806777
Range0.0003741247
Interquartile range (IQR)8.0768996 × 10-5

Descriptive statistics

Standard deviation0.000101184
Coefficient of variation (CV)0.025022089
Kurtosis0.9171534
Mean0.0040437873
Median Absolute Deviation (MAD)4.0833168 × 10-5
Skewness1.346916
Sum0.40437873
Variance1.0238203 × 10-8
MonotonicityNot monotonic
2023-12-10T20:43:08.028574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0040026888378151 20
20.0%
0.0039768322845645 14
14.0%
0.0040576012806038 10
10.0%
0.0042806776673958 9
9.0%
0.0039618556702834 7
 
7.0%
0.0039065529633166 6
 
6.0%
0.0040547189159873 6
 
6.0%
0.0040139038827824 5
 
5.0%
0.0039815858704745 4
 
4.0%
0.0040666528114775 4
 
4.0%
Other values (6) 15
15.0%
ValueCountFrequency (%)
0.0039065529633166 6
 
6.0%
0.0039618556702834 7
 
7.0%
0.0039768322845645 14
14.0%
0.0039815858704745 4
 
4.0%
0.0040026888378151 20
20.0%
0.0040041231261006 3
 
3.0%
0.0040139038827824 5
 
5.0%
0.0040380587393479 2
 
2.0%
0.0040547189159873 6
 
6.0%
0.0040576012806038 10
10.0%
ValueCountFrequency (%)
0.0042806776673958 9
9.0%
0.0042389680766563 2
 
2.0%
0.0042323722586642 4
 
4.0%
0.004098181305223 1
 
1.0%
0.0040666528114775 4
 
4.0%
0.0040633336832198 3
 
3.0%
0.0040576012806038 10
10.0%
0.0040547189159873 6
6.0%
0.0040380587393479 2
 
2.0%
0.0040139038827824 5
5.0%

이산화질소
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013963134
Minimum0.013858103
Maximum0.01401119
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:08.250435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.013858103
5-th percentile0.013874954
Q10.013935372
median0.013977962
Q30.013991559
95-th percentile0.014003286
Maximum0.01401119
Range0.00015308665
Interquartile range (IQR)5.6187038 × 10-5

Descriptive statistics

Standard deviation3.8487388 × 10-5
Coefficient of variation (CV)0.0027563574
Kurtosis0.61811791
Mean0.013963134
Median Absolute Deviation (MAD)2.532373 × 10-5
Skewness-1.031684
Sum1.3963134
Variance1.481279 × 10-9
MonotonicityNot monotonic
2023-12-10T20:43:08.566643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0139915587113304 20
20.0%
0.0139901626334911 14
14.0%
0.0140032856610315 10
10.0%
0.0139313550043433 9
9.0%
0.0139643521016626 7
 
7.0%
0.0139416628318357 6
 
6.0%
0.0139353716728754 6
 
6.0%
0.013926999853497 5
 
5.0%
0.0139497637023574 4
 
4.0%
0.0139999220347991 4
 
4.0%
Other values (6) 15
15.0%
ValueCountFrequency (%)
0.0138581032940331 4
4.0%
0.0138749539456701 3
 
3.0%
0.013926999853497 5
5.0%
0.0139270561585288 2
 
2.0%
0.0139313550043433 9
9.0%
0.0139353716728754 6
6.0%
0.0139416628318357 6
6.0%
0.0139497637023574 4
4.0%
0.0139529796215993 3
 
3.0%
0.0139643521016626 7
7.0%
ValueCountFrequency (%)
0.0140111899411514 2
 
2.0%
0.0140032856610315 10
10.0%
0.0139999220347991 4
 
4.0%
0.0139915587113304 20
20.0%
0.0139901626334911 14
14.0%
0.0139657612284038 1
 
1.0%
0.0139643521016626 7
 
7.0%
0.0139529796215993 3
 
3.0%
0.0139497637023574 4
 
4.0%
0.0139416628318357 6
 
6.0%

일산화탄소 값
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.16504384
Minimum0.09984354
Maximum0.24995832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:08.710825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.09984354
5-th percentile0.11168172
Q10.14906556
median0.16122934
Q30.18309865
95-th percentile0.22425226
Maximum0.24995832
Range0.15011478
Interquartile range (IQR)0.034033091

Descriptive statistics

Standard deviation0.036619929
Coefficient of variation (CV)0.22188002
Kurtosis-0.25132198
Mean0.16504384
Median Absolute Deviation (MAD)0.019812446
Skewness0.38907728
Sum16.504384
Variance0.0013410192
MonotonicityNot monotonic
2023-12-10T20:43:08.840076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.1612293440396292 20
20.0%
0.1490655618974828 14
14.0%
0.2242522572783582 10
10.0%
0.1710086701253141 9
9.0%
0.1116817216696849 7
 
7.0%
0.14141689794659 6
 
6.0%
0.1830986526505217 6
 
6.0%
0.2167183645609564 5
 
5.0%
0.1753587679622903 4
 
4.0%
0.0998435404563181 4
 
4.0%
Other values (6) 15
15.0%
ValueCountFrequency (%)
0.0998435404563181 4
 
4.0%
0.1116817216696849 7
 
7.0%
0.1177052658392487 4
 
4.0%
0.1282803731112165 3
 
3.0%
0.14141689794659 6
 
6.0%
0.1490655618974828 14
14.0%
0.1561576561746193 2
 
2.0%
0.1612293440396292 20
20.0%
0.1710086701253141 9
9.0%
0.1753587679622903 4
 
4.0%
ValueCountFrequency (%)
0.2499583242932095 3
 
3.0%
0.2242522572783582 10
10.0%
0.2167183645609564 5
 
5.0%
0.2071054320892666 1
 
1.0%
0.1865270206079089 2
 
2.0%
0.1830986526505217 6
 
6.0%
0.1753587679622903 4
 
4.0%
0.1710086701253141 9
9.0%
0.1612293440396292 20
20.0%
0.1561576561746193 2
 
2.0%

사업코드
Categorical

HIGH CORRELATION 

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
YS2009O003
20 
ME2000C002
12 
DG2012O001
7 
ND2017A004
6 
HG2017B011
6 
Other values (18)
49 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st rowME2000C002
2nd rowME2000C002
3rd rowME2000C002
4th rowME2000C002
5th rowME2000C002

Common Values

ValueCountFrequency (%)
YS2009O003 20
20.0%
ME2000C002 12
12.0%
DG2012O001 7
 
7.0%
ND2017A004 6
 
6.0%
HG2017B011 6
 
6.0%
ND2008B016 5
 
5.0%
GG2006D002 4
 
4.0%
YS2011D005 4
 
4.0%
ND2010B013 4
 
4.0%
ND2005B006 4
 
4.0%
Other values (13) 28
28.0%

Length

2023-12-10T20:43:09.060771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
ys2009o003 20
20.0%
me2000c002 12
12.0%
dg2012o001 7
 
7.0%
nd2017a004 6
 
6.0%
hg2017b011 6
 
6.0%
nd2008b016 5
 
5.0%
gg2006d002 4
 
4.0%
ys2011d005 4
 
4.0%
nd2010b013 4
 
4.0%
nd2005b006 4
 
4.0%
Other values (13) 28
28.0%

조사구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
IV29
61 
IV01
20 
IV20
19 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIV29
2nd rowIV29
3rd rowIV29
4th rowIV29
5th rowIV29

Common Values

ValueCountFrequency (%)
IV29 61
61.0%
IV01 20
 
20.0%
IV20 19
 
19.0%

Length

2023-12-10T20:43:09.245950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:09.425207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
iv29 61
61.0%
iv01 20
 
20.0%
iv20 19
 
19.0%

조사지점명
Categorical

HIGH CORRELATION 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
O-1
19 
O-2
15 
O-3
11 
O-4
9 
O-5
7 
Other values (21)
39 

Length

Max length4
Median length3
Mean length3.12
Min length3

Unique

Unique10 ?
Unique (%)10.0%

Sample

1st row1지점
2nd row4지점
3rd row6지점
4th row3지점
5th row2지점

Common Values

ValueCountFrequency (%)
O-1 19
19.0%
O-2 15
15.0%
O-3 11
11.0%
O-4 9
9.0%
O-5 7
 
7.0%
O-6 5
 
5.0%
O-7 5
 
5.0%
O-8 3
 
3.0%
2지점 2
 
2.0%
O-9 2
 
2.0%
Other values (16) 22
22.0%

Length

2023-12-10T20:43:09.615420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o-1 19
19.0%
o-2 15
15.0%
o-3 11
11.0%
o-4 9
9.0%
o-5 7
 
7.0%
o-6 5
 
5.0%
o-7 5
 
5.0%
o-8 3
 
3.0%
5지점 2
 
2.0%
o-11 2
 
2.0%
Other values (16) 22
22.0%

조사지점 주소
Text

Distinct80
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:43:10.240937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length28
Mean length15.89
Min length2

Characters and Unicode

Total characters1589
Distinct characters216
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)70.0%

Sample

1st rowOIP Tank(정문(보안과 옥상))
2nd rowOIP Tank(후문)
3rd rowOIP Tank(SEP 공장)
4th rowOIP Tank(육상출하과 옥상)
5th rowOIP Tank(동광화학사거리)
ValueCountFrequency (%)
가스포집공 12
 
3.6%
oip 12
 
3.6%
8개소(지정 8
 
2.4%
경기도 7
 
2.1%
감만동 6
 
1.8%
남구 6
 
1.8%
내 6
 
1.8%
인근 5
 
1.5%
율촌면 5
 
1.5%
조화리 5
 
1.5%
Other values (185) 258
78.2%
2023-12-10T20:43:11.101941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
237
 
14.9%
( 47
 
3.0%
) 47
 
3.0%
시 45
 
2.8%
지 39
 
2.5%
동 39
 
2.5%
1 32
 
2.0%
공 26
 
1.6%
가 22
 
1.4%
8 22
 
1.4%
Other values (206) 1033
65.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 959
60.4%
Space Separator 237
 
14.9%
Decimal Number 165
 
10.4%
Uppercase Letter 73
 
4.6%
Open Punctuation 47
 
3.0%
Close Punctuation 47
 
3.0%
Lowercase Letter 41
 
2.6%
Dash Punctuation 16
 
1.0%
Other Punctuation 4
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 45
 
4.7%
지 39
 
4.1%
동 39
 
4.1%
공 26
 
2.7%
가 22
 
2.3%
포 20
 
2.1%
산 20
 
2.1%
구 20
 
2.1%
마 19
 
2.0%
을 18
 
1.9%
Other values (173) 691
72.1%
Uppercase Letter
ValueCountFrequency (%)
T 14
19.2%
P 14
19.2%
I 12
16.4%
O 12
16.4%
N 4
 
5.5%
B 3
 
4.1%
L 3
 
4.1%
A 3
 
4.1%
M 2
 
2.7%
V 2
 
2.7%
Other values (2) 4
 
5.5%
Decimal Number
ValueCountFrequency (%)
1 32
19.4%
8 22
13.3%
2 20
12.1%
4 18
10.9%
5 15
9.1%
3 15
9.1%
0 13
7.9%
9 11
 
6.7%
6 10
 
6.1%
7 9
 
5.5%
Lowercase Letter
ValueCountFrequency (%)
k 12
29.3%
a 12
29.3%
n 12
29.3%
t 2
 
4.9%
p 2
 
4.9%
e 1
 
2.4%
Space Separator
ValueCountFrequency (%)
237
100.0%
Open Punctuation
ValueCountFrequency (%)
( 47
100.0%
Close Punctuation
ValueCountFrequency (%)
) 47
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 959
60.4%
Common 516
32.5%
Latin 114
 
7.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 45
 
4.7%
지 39
 
4.1%
동 39
 
4.1%
공 26
 
2.7%
가 22
 
2.3%
포 20
 
2.1%
산 20
 
2.1%
구 20
 
2.1%
마 19
 
2.0%
을 18
 
1.9%
Other values (173) 691
72.1%
Latin
ValueCountFrequency (%)
T 14
12.3%
P 14
12.3%
k 12
10.5%
a 12
10.5%
I 12
10.5%
O 12
10.5%
n 12
10.5%
N 4
 
3.5%
B 3
 
2.6%
L 3
 
2.6%
Other values (8) 16
14.0%
Common
ValueCountFrequency (%)
237
45.9%
( 47
 
9.1%
) 47
 
9.1%
1 32
 
6.2%
8 22
 
4.3%
2 20
 
3.9%
4 18
 
3.5%
- 16
 
3.1%
5 15
 
2.9%
3 15
 
2.9%
Other values (5) 47
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 959
60.4%
ASCII 630
39.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
237
37.6%
( 47
 
7.5%
) 47
 
7.5%
1 32
 
5.1%
8 22
 
3.5%
2 20
 
3.2%
4 18
 
2.9%
- 16
 
2.5%
5 15
 
2.4%
3 15
 
2.4%
Other values (23) 161
25.6%
Hangul
ValueCountFrequency (%)
시 45
 
4.7%
지 39
 
4.1%
동 39
 
4.1%
공 26
 
2.7%
가 22
 
2.3%
포 20
 
2.1%
산 20
 
2.1%
구 20
 
2.1%
마 19
 
2.0%
을 18
 
1.9%
Other values (173) 691
72.1%

조사차수
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018_1분기(1월)
25 
2018_1분기
19 
2018_1월
16 
2차
12 
2018년_1분기
7 
Other values (5)
21 

Length

Max length12
Median length8
Mean length7.72
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018_1월
2nd row2018_1월
3rd row2018_1월
4th row2018_1월
5th row2018_1월

Common Values

ValueCountFrequency (%)
2018_1분기(1월) 25
25.0%
2018_1분기 19
19.0%
2018_1월 16
16.0%
2차 12
12.0%
2018년_1분기 7
 
7.0%
1차 6
 
6.0%
2018_2월 6
 
6.0%
2018년_상반기 4
 
4.0%
2018년_01월 3
 
3.0%
4차 2
 
2.0%

Length

2023-12-10T20:43:11.405265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:11.693108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018_1분기(1월 25
25.0%
2018_1분기 19
19.0%
2018_1월 16
16.0%
2차 12
12.0%
2018년_1분기 7
 
7.0%
1차 6
 
6.0%
2018_2월 6
 
6.0%
2018년_상반기 4
 
4.0%
2018년_01월 3
 
3.0%
4차 2
 
2.0%

조사시작일
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018-01-19
20 
2018-02-01
14 
2018-01-17
10 
2018-01-30
9 
2018-01-23
7 
Other values (11)
40 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2018-01-01
2nd row2018-01-01
3rd row2018-01-01
4th row2018-01-01
5th row2018-01-01

Common Values

ValueCountFrequency (%)
2018-01-19 20
20.0%
2018-02-01 14
14.0%
2018-01-17 10
10.0%
2018-01-30 9
9.0%
2018-01-23 7
 
7.0%
2018-01-01 6
 
6.0%
2018-02-02 6
 
6.0%
2018-01-15 5
 
5.0%
2018-01-25 4
 
4.0%
2018-02-07 4
 
4.0%
Other values (6) 15
15.0%

Length

2023-12-10T20:43:11.997395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-01-19 20
20.0%
2018-02-01 14
14.0%
2018-01-17 10
10.0%
2018-01-30 9
9.0%
2018-01-23 7
 
7.0%
2018-01-01 6
 
6.0%
2018-02-02 6
 
6.0%
2018-01-15 5
 
5.0%
2018-01-25 4
 
4.0%
2018-02-07 4
 
4.0%
Other values (6) 15
15.0%

조사종료일
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2018-01-19
20 
2018-02-01
14 
2018-01-17
13 
2018-01-30
9 
2018-01-23
7 
Other values (11)
37 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row2018-01-01
2nd row2018-01-01
3rd row2018-01-01
4th row2018-01-01
5th row2018-01-01

Common Values

ValueCountFrequency (%)
2018-01-19 20
20.0%
2018-02-01 14
14.0%
2018-01-17 13
13.0%
2018-01-30 9
9.0%
2018-01-23 7
 
7.0%
2018-01-01 6
 
6.0%
2018-02-02 6
 
6.0%
2018-01-25 4
 
4.0%
2018-02-07 4
 
4.0%
2018-02-09 4
 
4.0%
Other values (6) 13
13.0%

Length

2023-12-10T20:43:12.307777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018-01-19 20
20.0%
2018-02-01 14
14.0%
2018-01-17 13
13.0%
2018-01-30 9
9.0%
2018-01-23 7
 
7.0%
2018-01-01 6
 
6.0%
2018-02-02 6
 
6.0%
2018-01-25 4
 
4.0%
2018-02-07 4
 
4.0%
2018-02-09 4
 
4.0%
Other values (6) 13
13.0%

복합악취 인덱스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.899943
Minimum0
Maximum208
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:12.549584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.22295
Q13
median3
Q34
95-th percentile173
Maximum208
Range208
Interquartile range (IQR)1

Descriptive statistics

Standard deviation52.584703
Coefficient of variation (CV)2.4011342
Kurtosis5.416869
Mean21.899943
Median Absolute Deviation (MAD)1
Skewness2.6166742
Sum2189.9943
Variance2765.151
MonotonicityNot monotonic
2023-12-10T20:43:12.817376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
3.0 48
48.0%
4.0 15
 
15.0%
173.0 3
 
3.0%
208.0 3
 
3.0%
144.0 3
 
3.0%
4.4 3
 
3.0%
0.0 2
 
2.0%
5.0 2
 
2.0%
120.0 2
 
2.0%
10.0 2
 
2.0%
Other values (16) 17
 
17.0%
ValueCountFrequency (%)
0.0 2
2.0%
0.217 1
1.0%
0.22 1
1.0%
0.222 1
1.0%
0.223 1
1.0%
0.226 2
2.0%
0.2339999999999999 1
1.0%
0.238 1
1.0%
0.65 1
1.0%
0.674 1
1.0%
ValueCountFrequency (%)
208.0 3
3.0%
173.0 3
3.0%
144.0 3
3.0%
120.0 2
2.0%
100.0 1
 
1.0%
10.0 2
2.0%
7.0 1
 
1.0%
6.7 1
 
1.0%
6.0 1
 
1.0%
5.0 2
2.0%

암모니아
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)33.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.09142
Minimum0
Maximum1.55
Zeros58
Zeros (%)58.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:13.033229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04475
95-th percentile0.6805
Maximum1.55
Range1.55
Interquartile range (IQR)0.04475

Descriptive statistics

Standard deviation0.26815772
Coefficient of variation (CV)2.9332501
Kurtosis15.996106
Mean0.09142
Median Absolute Deviation (MAD)0
Skewness3.9430021
Sum9.142
Variance0.071908565
MonotonicityNot monotonic
2023-12-10T20:43:13.650802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0.0 58
58.0%
0.06 4
 
4.0%
0.01 3
 
3.0%
0.03 3
 
3.0%
0.011 2
 
2.0%
0.05 2
 
2.0%
0.017 2
 
2.0%
0.021 1
 
1.0%
0.1 1
 
1.0%
0.099 1
 
1.0%
Other values (23) 23
 
23.0%
ValueCountFrequency (%)
0.0 58
58.0%
0.009 1
 
1.0%
0.01 3
 
3.0%
0.011 2
 
2.0%
0.017 2
 
2.0%
0.018 1
 
1.0%
0.02 1
 
1.0%
0.021 1
 
1.0%
0.0233333329999999 1
 
1.0%
0.0279999999999999 1
 
1.0%
ValueCountFrequency (%)
1.55 1
1.0%
1.3 1
1.0%
1.28 1
1.0%
0.8 1
1.0%
0.69 1
1.0%
0.68 1
1.0%
0.49 1
1.0%
0.4 1
1.0%
0.29 1
1.0%
0.27 1
1.0%

메틸메르캅탄
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 100
100.0%

Length

2023-12-10T20:43:13.895532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:14.048339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

황화수소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.720222
Minimum0
Maximum17.64
Zeros79
Zeros (%)79.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:43:14.186127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile4.7275
Maximum17.64
Range17.64
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6556181
Coefficient of variation (CV)3.6872215
Kurtosis23.600452
Mean0.720222
Median Absolute Deviation (MAD)0
Skewness4.6414202
Sum72.0222
Variance7.0523074
MonotonicityNot monotonic
2023-12-10T20:43:14.392700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 79
79.0%
0.008 3
 
3.0%
0.01 1
 
1.0%
0.022 1
 
1.0%
5.63 1
 
1.0%
8.61 1
 
1.0%
17.64 1
 
1.0%
3.19 1
 
1.0%
3.13 1
 
1.0%
0.015 1
 
1.0%
Other values (10) 10
 
10.0%
ValueCountFrequency (%)
0.0 79
79.0%
0.0032 1
 
1.0%
0.008 3
 
3.0%
0.009 1
 
1.0%
0.01 1
 
1.0%
0.0139999999999999 1
 
1.0%
0.015 1
 
1.0%
0.017 1
 
1.0%
0.018 1
 
1.0%
0.022 1
 
1.0%
ValueCountFrequency (%)
17.64 1
1.0%
14.34 1
1.0%
8.61 1
1.0%
7.65 1
1.0%
5.63 1
1.0%
4.68 1
1.0%
4.6 1
1.0%
3.19 1
1.0%
3.13 1
1.0%
2.42 1
1.0%

사용여부
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
100 
ValueCountFrequency (%)
True 100
100.0%
2023-12-10T20:43:14.605285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-10T20:43:06.270225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:01.157949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:02.071035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:03.435111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.382456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:05.303222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:06.379388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:01.298285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:02.253781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:03.590014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.532192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:05.498746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:06.510090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:01.453156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:02.414496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:03.751761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.688889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:05.678548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:06.670825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:01.617652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:02.561710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:03.902481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.856460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:05.835559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:06.809202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:01.758944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:02.703443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.042172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.992895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:05.971281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:06.975473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:01.899718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:03.274765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:04.211029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:05.165648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:06.122397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:43:14.717736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜아황산가스이산화질소일산화탄소 값사업코드조사구분조사지점명조사지점 주소조사차수조사시작일조사종료일복합악취 인덱스암모니아황화수소
날짜1.0001.0001.0001.0000.9970.9090.0000.9960.9551.0000.9990.0000.0000.000
아황산가스1.0001.0000.9690.8940.9900.5720.0000.9630.8561.0001.0000.0000.6300.435
이산화질소1.0000.9691.0000.9090.9960.7080.0000.9940.8931.0001.0000.0000.0000.000
일산화탄소 값1.0000.8940.9091.0000.9900.9040.0000.9650.8881.0000.9880.0000.4760.443
사업코드0.9970.9900.9960.9901.0001.0000.0001.0000.9910.9970.9970.0000.0000.000
조사구분0.9090.5720.7080.9041.0001.0000.0001.0000.8970.9090.8880.0000.0000.000
조사지점명0.0000.0000.0000.0000.0000.0001.0000.0000.0000.0000.0000.9670.8650.923
조사지점 주소0.9960.9630.9940.9651.0001.0000.0001.0000.9800.9960.9960.0000.0000.000
조사차수0.9550.8560.8930.8880.9910.8970.0000.9801.0000.9550.9610.0000.4530.425
조사시작일1.0001.0001.0001.0000.9970.9090.0000.9960.9551.0000.9990.0000.0000.000
조사종료일0.9991.0001.0000.9880.9970.8880.0000.9960.9610.9991.0000.0000.0000.000
복합악취 인덱스0.0000.0000.0000.0000.0000.0000.9670.0000.0000.0000.0001.0000.0000.000
암모니아0.0000.6300.0000.4760.0000.0000.8650.0000.4530.0000.0000.0001.0000.862
황화수소0.0000.4350.0000.4430.0000.0000.9230.0000.4250.0000.0000.0000.8621.000
2023-12-10T20:43:14.957763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사차수사업코드조사구분날짜조사시작일조사지점명조사종료일
조사차수1.0000.8780.8160.7760.7760.0000.796
사업코드0.8781.0000.8910.9280.9280.0000.928
조사구분0.8160.8911.0000.7560.7560.0000.719
날짜0.7760.9280.7561.0001.0000.0000.942
조사시작일0.7760.9280.7561.0001.0000.0000.942
조사지점명0.0000.0000.0000.0000.0001.0000.000
조사종료일0.7960.9280.7190.9420.9420.0001.000
2023-12-10T20:43:15.161597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아황산가스이산화질소일산화탄소 값복합악취 인덱스암모니아황화수소날짜사업코드조사구분조사지점명조사차수조사시작일조사종료일
아황산가스1.000-0.1190.4620.250-0.501-0.4560.9560.8590.4260.0000.6300.9560.926
이산화질소-0.1191.000-0.0480.0480.0060.0750.9560.8910.5800.0000.7010.9560.922
일산화탄소 값0.462-0.0481.0000.132-0.421-0.2250.9610.8680.6230.0000.6720.9610.916
복합악취 인덱스0.2500.0480.1321.000-0.106-0.0920.0000.0000.0000.7620.0000.0000.000
암모니아-0.5010.006-0.421-0.1061.0000.7360.0000.0000.0000.5160.2310.0000.000
황화수소-0.4560.075-0.225-0.0920.7361.0000.0000.0000.0000.6410.2250.0000.000
날짜0.9560.9560.9610.0000.0000.0001.0000.9280.7560.0000.7761.0000.942
사업코드0.8590.8910.8680.0000.0000.0000.9281.0000.8910.0000.8780.9280.928
조사구분0.4260.5800.6230.0000.0000.0000.7560.8911.0000.0000.8160.7560.719
조사지점명0.0000.0000.0000.7620.5160.6410.0000.0000.0001.0000.0000.0000.000
조사차수0.6300.7010.6720.0000.2310.2250.7760.8780.8160.0001.0000.7760.796
조사시작일0.9560.9560.9610.0000.0000.0001.0000.9280.7560.0000.7761.0000.942
조사종료일0.9260.9220.9160.0000.0000.0000.9420.9280.7190.0000.7960.9421.000

Missing values

2023-12-10T20:43:07.185802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:43:07.421815image/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

날짜아황산가스이산화질소일산화탄소 값사업코드조사구분조사지점명조사지점 주소조사차수조사시작일조사종료일복합악취 인덱스암모니아메틸메르캅탄황화수소사용여부
02018-01-010.0039070.0139420.141417ME2000C002IV291지점OIP Tank(정문(보안과 옥상))2018_1월2018-01-012018-01-010.220.2900.01Y
12018-01-010.0039070.0139420.141417ME2000C002IV294지점OIP Tank(후문)2018_1월2018-01-012018-01-010.650.27014.34Y
22018-01-010.0039070.0139420.141417ME2000C002IV296지점OIP Tank(SEP 공장)2018_1월2018-01-012018-01-010.2341.2802.42Y
32018-01-010.0039070.0139420.141417ME2000C002IV293지점OIP Tank(육상출하과 옥상)2018_1월2018-01-012018-01-010.2260.807.65Y
42018-01-010.0039070.0139420.141417ME2000C002IV292지점OIP Tank(동광화학사거리)2018_1월2018-01-012018-01-010.2170.0304.68Y
52018-01-010.0039070.0139420.141417ME2000C002IV295지점OIP Tank(대한 유화정문)2018_1월2018-01-012018-01-010.2230.4904.6Y
62018-01-150.0040140.0139270.216718HG2003B002IV20O-4안산시 단원구 초지동 737 (고잔그린빌아파트 1710동 앞)2018_1월2018-01-152018-01-176.00.11666700.0032Y
72018-01-150.0040140.0139270.216718ND2018B001IV01O-2김해시 한림면 명동리 80 모업마을1차2018-01-152018-01-1510.00.000.0Y
82018-01-150.0040140.0139270.216718HG2003B002IV20O-7안산시 단원구 성곡동 611-4 (MTV 사업부지 5공구 내)2018_1월2018-01-152018-01-173.3333330.000.0Y
92018-01-150.0040140.0139270.216718HG2003B002IV20O-3시흥시 정왕동 2205-18 (MTV 사업부지 3공구 내)2018_1월2018-01-152018-01-174.00.02333300.0Y
날짜아황산가스이산화질소일산화탄소 값사업코드조사구분조사지점명조사지점 주소조사차수조사시작일조사종료일복합악취 인덱스암모니아메틸메르캅탄황화수소사용여부
902018-02-070.0042320.0138580.117705YS2011D005IV29O-3주거지2018년_상반기2018-02-072018-02-074.40.02100.0Y
912018-02-070.0042320.0138580.117705YS2011D005IV29O-2공생재활원 본관2018년_상반기2018-02-072018-02-074.40.01100.0Y
922018-02-070.0042320.0138580.117705YS2011D005IV29O-1주거시설2018년_상반기2018-02-072018-02-074.40.01700.0Y
932018-02-070.0042320.0138580.117705YS2011D005IV29O-4고하마을회관2018년_상반기2018-02-072018-02-076.70.00900.0Y
942018-02-080.0039820.013950.175359ME2014D006IV20O-2매립공사 진행지점 인근 정온시설2018년_1분기2018-02-082018-02-093.00.000.0Y
952018-02-080.0039820.013950.175359ME2014D006IV20O-1매립공사 진행지점 인근 정온시설2018년_1분기2018-02-082018-02-094.00.000.0Y
962018-02-080.0039820.013950.175359HG2008A006IV20O-2경기도 오산시 가장동 산1-3번지(공동주택 A-4 예정지)2018_1분기2018-02-082018-02-083.00.000.0Y
972018-02-080.0039820.013950.175359HG2008A006IV20O-1경기도 오산시 가장동 314번지 육성농장2018_1분기2018-02-082018-02-083.00.000.0Y
982018-02-090.0040380.0139270.186527ME2013C006IV29O-1세종천연가스 발전소 내2018_1분기2018-02-092018-02-093.00.01700.0Y
992018-02-090.0040380.0139270.186527ME2013C006IV29O-2세종특별자치시 누리로 59 (한솔동 316-6) 613동 옆 공터2018_1분기2018-02-092018-02-093.00.01100.0Y