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
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 8 other fieldsHigh correlation
아황산가스 is highly overall correlated with 날짜 and 4 other fieldsHigh correlation
이산화질소 is highly overall correlated with 날짜 and 4 other fieldsHigh correlation
일산화탄소 값 is highly overall correlated with 날짜 and 4 other fieldsHigh correlation
복합악취 인덱스 is highly overall correlated with 암모니아High correlation
암모니아 is highly overall correlated with 복합악취 인덱스 and 3 other fieldsHigh correlation
황화수소 is highly overall correlated with 암모니아 and 2 other fieldsHigh correlation
사업코드 is highly overall correlated with 아황산가스 and 8 other fieldsHigh correlation
조사구분 is highly overall correlated with 황화수소 and 6 other fieldsHigh correlation
조사지점명 is highly overall correlated with 황화수소 and 1 other fieldsHigh correlation
조사차수 is highly overall correlated with 아황산가스 and 7 other fieldsHigh correlation
복합악취 인덱스 has 13 (13.0%) zerosZeros
암모니아 has 72 (72.0%) zerosZeros
황화수소 has 82 (82.0%) zerosZeros

Reproduction

Analysis started2023-12-10 11:42:41.891851
Analysis finished2023-12-10 11:42:51.318232
Duration9.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

날짜
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-03-25
8 
2019-03-11
8 
2019-02-18
7 
2019-01-01
 
6
2019-02-01
 
6
Other values (20)
65 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row2019-01-01
2nd row2019-01-01
3rd row2019-01-01
4th row2019-01-01
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-03-25 8
 
8.0%
2019-03-11 8
 
8.0%
2019-02-18 7
 
7.0%
2019-01-01 6
 
6.0%
2019-02-01 6
 
6.0%
2019-02-12 6
 
6.0%
2019-02-21 6
 
6.0%
2019-03-01 6
 
6.0%
2019-01-14 5
 
5.0%
2019-03-02 5
 
5.0%
Other values (15) 37
37.0%

Length

2023-12-10T20:42:51.444469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-03-25 8
 
8.0%
2019-03-11 8
 
8.0%
2019-02-18 7
 
7.0%
2019-01-01 6
 
6.0%
2019-02-01 6
 
6.0%
2019-02-12 6
 
6.0%
2019-02-21 6
 
6.0%
2019-03-01 6
 
6.0%
2019-01-14 5
 
5.0%
2019-03-02 5
 
5.0%
Other values (15) 37
37.0%

아황산가스
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0040081679
Minimum0.0039855284
Maximum0.004220775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:42:51.609840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.0039855284
5-th percentile0.0039855284
Q10.0039934627
median0.0039957154
Q30.0039976081
95-th percentile0.0040512082
Maximum0.004220775
Range0.00023524663
Interquartile range (IQR)4.1454398 × 10-6

Descriptive statistics

Standard deviation4.9872889 × 10-5
Coefficient of variation (CV)0.012442814
Kurtosis14.643467
Mean0.0040081679
Median Absolute Deviation (MAD)2.20097 × 10-6
Skewness3.9807388
Sum0.40081679
Variance2.487305 × 10-9
MonotonicityNot monotonic
2023-12-10T20:42:51.819643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0039934202539626 8
 
8.0%
0.0039940539799966 8
 
8.0%
0.0039949968024676 7
 
7.0%
0.0039964452610135 6
 
6.0%
0.0040027459175729 6
 
6.0%
0.0039892221094383 6
 
6.0%
0.0039855283751032 6
 
6.0%
0.0039957153568748 6
 
6.0%
0.0042207750050706 5
 
5.0%
0.0039974501585664 5
 
5.0%
Other values (15) 37
37.0%
ValueCountFrequency (%)
0.0039855283751032 6
6.0%
0.0039892221094383 6
6.0%
0.0039920105315708 2
 
2.0%
0.0039922971686886 2
 
2.0%
0.0039934202539626 8
8.0%
0.0039934627065416 3
 
3.0%
0.0039940539799966 8
8.0%
0.0039949141219305 3
 
3.0%
0.0039949968024676 7
7.0%
0.0039956117105836 3
 
3.0%
ValueCountFrequency (%)
0.0042207750050706 5
5.0%
0.004042283645935 3
3.0%
0.0040069729286298 3
3.0%
0.0040038958699262 2
 
2.0%
0.0040027459175729 6
6.0%
0.0040026015745169 1
 
1.0%
0.0040015621212521 2
 
2.0%
0.0039979416167657 1
 
1.0%
0.0039978910369067 2
 
2.0%
0.003997513849479 2
 
2.0%

이산화질소
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.013970744
Minimum0.013953473
Maximum0.014129801
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:42:52.081256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.013953473
5-th percentile0.013955577
Q10.013960631
median0.013965977
Q30.013971722
95-th percentile0.013977363
Maximum0.014129801
Range0.00017632804
Interquartile range (IQR)1.1091345 × 10-5

Descriptive statistics

Standard deviation2.8831592 × 10-5
Coefficient of variation (CV)0.0020637121
Kurtosis26.744761
Mean0.013970744
Median Absolute Deviation (MAD)5.3884777 × 10-6
Skewness5.161173
Sum1.3970744
Variance8.3126072 × 10-10
MonotonicityNot monotonic
2023-12-10T20:42:52.298409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0139623182071993 8
 
8.0%
0.0139709001233038 8
 
8.0%
0.0139729150208775 7
 
7.0%
0.013971721851659 6
 
6.0%
0.0139682396967319 6
 
6.0%
0.0139585309587477 6
 
6.0%
0.0139555774714287 6
 
6.0%
0.013977362802156 6
 
6.0%
0.013963684317302 5
 
5.0%
0.0139615770769197 5
 
5.0%
Other values (15) 37
37.0%
ValueCountFrequency (%)
0.0139534728358048 3
 
3.0%
0.0139555774714287 6
6.0%
0.0139585309587477 6
6.0%
0.0139595151078382 3
 
3.0%
0.0139602510016713 3
 
3.0%
0.0139605458721767 2
 
2.0%
0.0139606305067057 4
4.0%
0.0139615770769197 5
5.0%
0.0139623182071993 8
8.0%
0.013963684317302 5
5.0%
ValueCountFrequency (%)
0.0141298008800054 3
 
3.0%
0.013977362802156 6
6.0%
0.0139752195649228 2
 
2.0%
0.0139741847295445 2
 
2.0%
0.0139729150208775 7
7.0%
0.0139723855859699 1
 
1.0%
0.013971721851659 6
6.0%
0.0139709001233038 8
8.0%
0.013970831570533 3
 
3.0%
0.0139682396967319 6
6.0%

일산화탄소 값
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.20458219
Minimum0.16063037
Maximum0.26037216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:42:52.515373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.16063037
5-th percentile0.16110157
Q10.18859207
median0.19993706
Q30.22402214
95-th percentile0.24370398
Maximum0.26037216
Range0.099741793
Interquartile range (IQR)0.035430071

Descriptive statistics

Standard deviation0.025070162
Coefficient of variation (CV)0.12254323
Kurtosis-0.54645904
Mean0.20458219
Median Absolute Deviation (MAD)0.018196851
Skewness0.032895367
Sum20.458219
Variance0.00062851304
MonotonicityNot monotonic
2023-12-10T20:42:52.716157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.2240221429091946 8
 
8.0%
0.2311576928346582 8
 
8.0%
0.1817402099085902 7
 
7.0%
0.1611015696768847 6
 
6.0%
0.196809389580128 6
 
6.0%
0.2437039771154288 6
 
6.0%
0.1885920716935342 6
 
6.0%
0.217402792159125 6
 
6.0%
0.1996956786366691 5
 
5.0%
0.1967473585236469 5
 
5.0%
Other values (15) 37
37.0%
ValueCountFrequency (%)
0.1606303692325529 4
4.0%
0.1611015696768847 6
6.0%
0.1713087011705292 3
3.0%
0.1749931653973781 2
 
2.0%
0.1817402099085902 7
7.0%
0.1885920716935342 6
6.0%
0.1904872862184941 1
 
1.0%
0.1953168490744645 2
 
2.0%
0.1960772615407651 3
3.0%
0.1967473585236469 5
5.0%
ValueCountFrequency (%)
0.2603721624544045 3
 
3.0%
0.2437039771154288 6
6.0%
0.2311576928346582 8
8.0%
0.2279511777688131 2
 
2.0%
0.2272073176267887 2
 
2.0%
0.2240221429091946 8
8.0%
0.2197388225816519 3
 
3.0%
0.217402792159125 6
6.0%
0.2173759623610379 1
 
1.0%
0.2136823050234177 3
 
3.0%

사업코드
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
ME2000C002
21 
DG2015D001
9 
HG2009J001
6 
ME2013C001
 
5
GG2015O005
 
5
Other values (20)
54 

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 (%)
ME2000C002 21
21.0%
DG2015D001 9
 
9.0%
HG2009J001 6
 
6.0%
ME2013C001 5
 
5.0%
GG2015O005 5
 
5.0%
ND2012A003 5
 
5.0%
GG2006D002 4
 
4.0%
HG2012J003 4
 
4.0%
HG2012A002 4
 
4.0%
YS2011D001 3
 
3.0%
Other values (15) 34
34.0%

Length

2023-12-10T20:42:52.927121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
me2000c002 21
21.0%
dg2015d001 9
 
9.0%
hg2009j001 6
 
6.0%
me2013c001 5
 
5.0%
gg2015o005 5
 
5.0%
nd2012a003 5
 
5.0%
gg2006d002 4
 
4.0%
hg2012j003 4
 
4.0%
hg2012a002 4
 
4.0%
hg2010d001 3
 
3.0%
Other values (15) 34
34.0%

조사구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
IV20
47 
IV29
35 
IV41
18 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowIV41
2nd rowIV41
3rd rowIV41
4th rowIV41
5th rowIV41

Common Values

ValueCountFrequency (%)
IV20 47
47.0%
IV29 35
35.0%
IV41 18
 
18.0%

Length

2023-12-10T20:42:53.151640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:53.320704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
iv20 47
47.0%
iv29 35
35.0%
iv41 18
 
18.0%

조사지점명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)12.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
O-1
26 
O-2
25 
O-3
19 
O-4
7 
O-5
4 
Other values (7)
19 

Length

Max length9
Median length3
Mean length4.08
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowO-3(모니터링)
2nd rowO-1(모니터링)
3rd rowO-4(모니터링)
4th rowO-5(모니터링)
5th rowO-6(모니터링)

Common Values

ValueCountFrequency (%)
O-1 26
26.0%
O-2 25
25.0%
O-3 19
19.0%
O-4 7
 
7.0%
O-5 4
 
4.0%
O-3(모니터링) 3
 
3.0%
O-1(모니터링) 3
 
3.0%
O-4(모니터링) 3
 
3.0%
O-5(모니터링) 3
 
3.0%
O-6(모니터링) 3
 
3.0%
Other values (2) 4
 
4.0%

Length

2023-12-10T20:42:53.510490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
o-1 26
26.0%
o-2 25
25.0%
o-3 19
19.0%
o-4 7
 
7.0%
o-5 4
 
4.0%
o-3(모니터링 3
 
3.0%
o-1(모니터링 3
 
3.0%
o-4(모니터링 3
 
3.0%
o-5(모니터링 3
 
3.0%
o-6(모니터링 3
 
3.0%
Other values (2) 4
 
4.0%

조사지점 주소
Text

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T20:42:54.033199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21.5
Mean length15.05
Min length2

Characters and Unicode

Total characters1505
Distinct characters171
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)62.0%

Sample

1st row육상출하과 옥상
2nd row정문 (보안과 옥상)
3rd row후문(87번 초소)
4th row대한유화정문
5th rowSEP 공정(95번 초소)
ValueCountFrequency (%)
사업지구 8
 
2.2%
경기도 7
 
1.9%
옥상 6
 
1.7%
평택시 6
 
1.7%
초소 6
 
1.7%
정문 6
 
1.7%
인근 6
 
1.7%
정온시설 6
 
1.7%
울산 5
 
1.4%
흥덕구 5
 
1.4%
Other values (182) 298
83.0%
2023-12-10T20:42:54.775556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
259
 
17.2%
시 48
 
3.2%
1 41
 
2.7%
구 34
 
2.3%
동 33
 
2.2%
도 29
 
1.9%
7 26
 
1.7%
산 24
 
1.6%
정 24
 
1.6%
리 23
 
1.5%
Other values (161) 964
64.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 997
66.2%
Space Separator 259
 
17.2%
Decimal Number 174
 
11.6%
Close Punctuation 20
 
1.3%
Open Punctuation 20
 
1.3%
Dash Punctuation 19
 
1.3%
Uppercase Letter 13
 
0.9%
Other Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 48
 
4.8%
구 34
 
3.4%
동 33
 
3.3%
도 29
 
2.9%
산 24
 
2.4%
정 24
 
2.4%
리 23
 
2.3%
마 20
 
2.0%
지 19
 
1.9%
남 18
 
1.8%
Other values (141) 725
72.7%
Decimal Number
ValueCountFrequency (%)
1 41
23.6%
7 26
14.9%
2 21
12.1%
3 16
 
9.2%
5 15
 
8.6%
8 14
 
8.0%
0 13
 
7.5%
9 11
 
6.3%
4 9
 
5.2%
6 8
 
4.6%
Uppercase Letter
ValueCountFrequency (%)
N 3
23.1%
S 3
23.1%
E 3
23.1%
P 3
23.1%
B 1
 
7.7%
Space Separator
ValueCountFrequency (%)
259
100.0%
Close Punctuation
ValueCountFrequency (%)
) 20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 20
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%
Other Punctuation
ValueCountFrequency (%)
\ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 997
66.2%
Common 495
32.9%
Latin 13
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 48
 
4.8%
구 34
 
3.4%
동 33
 
3.3%
도 29
 
2.9%
산 24
 
2.4%
정 24
 
2.4%
리 23
 
2.3%
마 20
 
2.0%
지 19
 
1.9%
남 18
 
1.8%
Other values (141) 725
72.7%
Common
ValueCountFrequency (%)
259
52.3%
1 41
 
8.3%
7 26
 
5.3%
2 21
 
4.2%
) 20
 
4.0%
( 20
 
4.0%
- 19
 
3.8%
3 16
 
3.2%
5 15
 
3.0%
8 14
 
2.8%
Other values (5) 44
 
8.9%
Latin
ValueCountFrequency (%)
N 3
23.1%
S 3
23.1%
E 3
23.1%
P 3
23.1%
B 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 997
66.2%
ASCII 508
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
259
51.0%
1 41
 
8.1%
7 26
 
5.1%
2 21
 
4.1%
) 20
 
3.9%
( 20
 
3.9%
- 19
 
3.7%
3 16
 
3.1%
5 15
 
3.0%
8 14
 
2.8%
Other values (10) 57
 
11.2%
Hangul
ValueCountFrequency (%)
시 48
 
4.8%
구 34
 
3.4%
동 33
 
3.3%
도 29
 
2.9%
산 24
 
2.4%
정 24
 
2.4%
리 23
 
2.3%
마 20
 
2.0%
지 19
 
1.9%
남 18
 
1.8%
Other values (141) 725
72.7%

조사차수
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019년_1분기
36 
2019년 1분기
15 
2019년 3월
9 
2019년_02월
7 
2019년 1월
6 
Other values (6)
27 

Length

Max length10
Median length9
Mean length8.4
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019년 1월
2nd row2019년 1월
3rd row2019년 1월
4th row2019년 1월
5th row2019년 1월

Common Values

ValueCountFrequency (%)
2019년_1분기 36
36.0%
2019년 1분기 15
15.0%
2019년 3월 9
 
9.0%
2019년_02월 7
 
7.0%
2019년 1월 6
 
6.0%
2019년 2월 6
 
6.0%
2019_1분기 6
 
6.0%
4차 5
 
5.0%
2019년_03월 5
 
5.0%
2019년_01월 3
 
3.0%

Length

2023-12-10T20:42:55.035030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019년_1분기 36
26.5%
2019년 36
26.5%
1분기 15
11.0%
3월 9
 
6.6%
2019년_02월 7
 
5.1%
1월 6
 
4.4%
2월 6
 
4.4%
2019_1분기 6
 
4.4%
4차 5
 
3.7%
2019년_03월 5
 
3.7%
Other values (2) 5
 
3.7%

조사시작일
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-03-25
8 
2019-03-11
8 
2019-02-18
7 
2019-01-01
 
6
2019-02-01
 
6
Other values (20)
65 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row2019-01-01
2nd row2019-01-01
3rd row2019-01-01
4th row2019-01-01
5th row2019-01-01

Common Values

ValueCountFrequency (%)
2019-03-25 8
 
8.0%
2019-03-11 8
 
8.0%
2019-02-18 7
 
7.0%
2019-01-01 6
 
6.0%
2019-02-01 6
 
6.0%
2019-02-12 6
 
6.0%
2019-02-21 6
 
6.0%
2019-03-01 6
 
6.0%
2019-01-14 5
 
5.0%
2019-03-02 5
 
5.0%
Other values (15) 37
37.0%

Length

2023-12-10T20:42:55.242874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-03-25 8
 
8.0%
2019-03-11 8
 
8.0%
2019-02-18 7
 
7.0%
2019-01-01 6
 
6.0%
2019-02-01 6
 
6.0%
2019-02-12 6
 
6.0%
2019-02-21 6
 
6.0%
2019-03-01 6
 
6.0%
2019-01-14 5
 
5.0%
2019-03-02 5
 
5.0%
Other values (15) 37
37.0%

조사종료일
Categorical

HIGH CORRELATION 

Distinct24
Distinct (%)24.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2019-02-28
9 
2019-03-11
8 
2019-03-25
8 
2019-02-18
7 
2019-01-31
 
6
Other values (19)
62 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row2019-01-31
2nd row2019-01-31
3rd row2019-01-31
4th row2019-01-31
5th row2019-01-31

Common Values

ValueCountFrequency (%)
2019-02-28 9
 
9.0%
2019-03-11 8
 
8.0%
2019-03-25 8
 
8.0%
2019-02-18 7
 
7.0%
2019-01-31 6
 
6.0%
2019-02-21 6
 
6.0%
2019-03-31 6
 
6.0%
2019-02-12 6
 
6.0%
2019-01-16 5
 
5.0%
2019-03-02 5
 
5.0%
Other values (14) 34
34.0%

Length

2023-12-10T20:42:55.459126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2019-02-28 9
 
9.0%
2019-03-25 8
 
8.0%
2019-03-11 8
 
8.0%
2019-02-18 7
 
7.0%
2019-01-31 6
 
6.0%
2019-02-21 6
 
6.0%
2019-03-31 6
 
6.0%
2019-02-12 6
 
6.0%
2019-01-16 5
 
5.0%
2019-03-02 5
 
5.0%
Other values (14) 34
34.0%

복합악취 인덱스
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.397
Minimum0
Maximum6.7
Zeros13
Zeros (%)13.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:42:55.632324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.3
median3
Q33
95-th percentile4
Maximum6.7
Range6.7
Interquartile range (IQR)2.7

Descriptive statistics

Standard deviation1.6322789
Coefficient of variation (CV)0.68096741
Kurtosis-0.62668644
Mean2.397
Median Absolute Deviation (MAD)1
Skewness-0.20829587
Sum239.7
Variance2.6643343
MonotonicityNot monotonic
2023-12-10T20:42:55.837145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
3.0 44
44.0%
4.0 20
20.0%
0.0 13
 
13.0%
0.3 7
 
7.0%
0.2 7
 
7.0%
0.4 3
 
3.0%
6.7 2
 
2.0%
0.5 1
 
1.0%
2.1 1
 
1.0%
2.5 1
 
1.0%
ValueCountFrequency (%)
0.0 13
 
13.0%
0.2 7
 
7.0%
0.3 7
 
7.0%
0.4 3
 
3.0%
0.5 1
 
1.0%
2.1 1
 
1.0%
2.5 1
 
1.0%
3.0 44
44.0%
4.0 20
20.0%
4.5 1
 
1.0%
ValueCountFrequency (%)
6.7 2
 
2.0%
4.5 1
 
1.0%
4.0 20
20.0%
3.0 44
44.0%
2.5 1
 
1.0%
2.1 1
 
1.0%
0.5 1
 
1.0%
0.4 3
 
3.0%
0.3 7
 
7.0%
0.2 7
 
7.0%

암모니아
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.019743
Minimum0
Maximum0.499
Zeros72
Zeros (%)72.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:42:56.051941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.0066
95-th percentile0.13
Maximum0.499
Range0.499
Interquartile range (IQR)0.0066

Descriptive statistics

Standard deviation0.064370003
Coefficient of variation (CV)3.2603962
Kurtosis32.620695
Mean0.019743
Median Absolute Deviation (MAD)0
Skewness5.1847402
Sum1.9743
Variance0.0041434972
MonotonicityNot monotonic
2023-12-10T20:42:56.395861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 72
72.0%
0.13 2
 
2.0%
0.0284 1
 
1.0%
0.0138 1
 
1.0%
0.094 1
 
1.0%
0.213 1
 
1.0%
0.499 1
 
1.0%
0.231 1
 
1.0%
0.0077 1
 
1.0%
0.0061 1
 
1.0%
Other values (18) 18
 
18.0%
ValueCountFrequency (%)
0.0 72
72.0%
0.0061 1
 
1.0%
0.0063 1
 
1.0%
0.0064 1
 
1.0%
0.0072 1
 
1.0%
0.0077 1
 
1.0%
0.0079 1
 
1.0%
0.0092 1
 
1.0%
0.0104 1
 
1.0%
0.0113 1
 
1.0%
ValueCountFrequency (%)
0.499 1
1.0%
0.231 1
1.0%
0.213 1
1.0%
0.17 1
1.0%
0.13 2
2.0%
0.12 1
1.0%
0.1 1
1.0%
0.094 1
1.0%
0.03 1
1.0%
0.0284 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

2023-12-10T20:42:56.601953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:42:56.767171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

황화수소
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.000819
Minimum0
Maximum0.0086
Zeros82
Zeros (%)82.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T20:42:56.901561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0.00571
Maximum0.0086
Range0.0086
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.0019095557
Coefficient of variation (CV)2.3315699
Kurtosis4.4913557
Mean0.000819
Median Absolute Deviation (MAD)0
Skewness2.3179526
Sum0.0819
Variance3.646403 × 10-6
MonotonicityNot monotonic
2023-12-10T20:42:57.067880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0.0 82
82.0%
0.0025 2
 
2.0%
0.0038 2
 
2.0%
0.0044 1
 
1.0%
0.0067 1
 
1.0%
0.0028 1
 
1.0%
0.0041 1
 
1.0%
0.0057 1
 
1.0%
0.0061 1
 
1.0%
0.0046 1
 
1.0%
Other values (7) 7
 
7.0%
ValueCountFrequency (%)
0.0 82
82.0%
0.0025 2
 
2.0%
0.0026 1
 
1.0%
0.0027 1
 
1.0%
0.0028 1
 
1.0%
0.0029 1
 
1.0%
0.0038 2
 
2.0%
0.0041 1
 
1.0%
0.0044 1
 
1.0%
0.0046 1
 
1.0%
ValueCountFrequency (%)
0.0086 1
1.0%
0.0069 1
1.0%
0.0067 1
1.0%
0.0061 1
1.0%
0.0059 1
1.0%
0.0057 1
1.0%
0.0053 1
1.0%
0.0046 1
1.0%
0.0044 1
1.0%
0.0041 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%
2023-12-10T20:42:57.215484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Interactions

2023-12-10T20:42:49.582435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.085307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.016616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.959731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.809193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.694140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.715676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.285317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.156063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.107043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.939739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.841777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.884597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.457025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.318732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.269172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.109674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.010177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.027567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.611093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.481035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.417862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.252819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.155293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.185822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.775225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.646089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.561752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.399596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.309519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:50.320628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:45.906765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:46.805989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:47.680842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:48.542868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:42:49.449419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:42:57.322701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
날짜아황산가스이산화질소일산화탄소 값사업코드조사구분조사지점명조사지점 주소조사차수조사시작일조사종료일복합악취 인덱스암모니아황화수소
날짜1.0001.0001.0001.0000.9980.9880.0000.9800.9861.0001.0000.7730.8250.000
아황산가스1.0001.0000.0000.7070.8900.5170.0000.0000.8671.0001.0000.0000.0000.000
이산화질소1.0000.0001.0000.5250.7950.7120.2020.0000.8271.0000.9990.0000.0000.487
일산화탄소 값1.0000.7070.5251.0000.9790.6500.0000.8490.8271.0001.0000.3410.1140.000
사업코드0.9980.8900.7950.9791.0000.9750.0001.0000.9520.9980.9910.7960.9040.000
조사구분0.9880.5170.7120.6500.9751.0000.9081.0000.8120.9880.9950.8210.3020.922
조사지점명0.0000.0000.2020.0000.0000.9081.0000.9960.0000.0000.0000.7520.0000.880
조사지점 주소0.9800.0000.0000.8491.0001.0000.9961.0000.0000.9800.9740.9961.0000.000
조사차수0.9860.8670.8270.8270.9520.8120.0000.0001.0000.9860.9820.5820.6080.532
조사시작일1.0001.0001.0001.0000.9980.9880.0000.9800.9861.0001.0000.7730.8250.000
조사종료일1.0001.0000.9991.0000.9910.9950.0000.9740.9821.0001.0000.7990.8710.000
복합악취 인덱스0.7730.0000.0000.3410.7960.8210.7520.9960.5820.7730.7991.0000.0000.272
암모니아0.8250.0000.0000.1140.9040.3020.0001.0000.6080.8250.8710.0001.0000.000
황화수소0.0000.0000.4870.0000.0000.9220.8800.0000.5320.0000.0000.2720.0001.000
2023-12-10T20:42:57.555851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사차수사업코드조사구분날짜조사시작일조사지점명조사종료일
조사차수1.0000.6850.6640.8360.8360.0000.816
사업코드0.6851.0000.8140.8620.8620.0000.858
조사구분0.6640.8141.0000.8520.8520.6340.811
날짜0.8360.8620.8521.0001.0000.0000.993
조사시작일0.8360.8620.8521.0001.0000.0000.993
조사지점명0.0000.0000.6340.0000.0001.0000.000
조사종료일0.8160.8580.8110.9930.9930.0001.000
2023-12-10T20:42:57.735867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아황산가스이산화질소일산화탄소 값복합악취 인덱스암모니아황화수소날짜사업코드조사구분조사지점명조사차수조사시작일조사종료일
아황산가스1.0000.417-0.4430.130-0.085-0.1820.8790.6500.2130.0000.7480.8790.885
이산화질소0.4171.000-0.330-0.0500.0110.1470.8790.5230.3680.0810.6860.8790.853
일산화탄소 값-0.443-0.3301.0000.134-0.212-0.2890.9130.7710.4790.0000.5390.9130.919
복합악취 인덱스0.130-0.0500.1341.000-0.509-0.4670.4290.4540.4980.3810.3350.4290.413
암모니아-0.0850.011-0.212-0.5091.0000.6370.4910.6140.1270.0000.3570.4910.501
황화수소-0.1820.147-0.289-0.4670.6371.0000.0000.0000.6540.6210.2690.0000.000
날짜0.8790.8790.9130.4290.4910.0001.0000.8620.8520.0000.8361.0000.993
사업코드0.6500.5230.7710.4540.6140.0000.8621.0000.8140.0000.6850.8620.858
조사구분0.2130.3680.4790.4980.1270.6540.8520.8141.0000.6340.6640.8520.811
조사지점명0.0000.0810.0000.3810.0000.6210.0000.0000.6341.0000.0000.0000.000
조사차수0.7480.6860.5390.3350.3570.2690.8360.6850.6640.0001.0000.8360.816
조사시작일0.8790.8790.9130.4290.4910.0001.0000.8620.8520.0000.8361.0000.993
조사종료일0.8850.8530.9190.4130.5010.0000.9930.8580.8110.0000.8160.9931.000

Missing values

2023-12-10T20:42:50.869131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:42:51.184213image/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
02019-01-010.0039960.0139720.161102ME2000C002IV41O-3(모니터링)육상출하과 옥상2019년 1월2019-01-012019-01-310.30.028400.0044Y
12019-01-010.0039960.0139720.161102ME2000C002IV41O-1(모니터링)정문 (보안과 옥상)2019년 1월2019-01-012019-01-310.20.006300.0029Y
22019-01-010.0039960.0139720.161102ME2000C002IV41O-4(모니터링)후문(87번 초소)2019년 1월2019-01-012019-01-310.40.013600.0069Y
32019-01-010.0039960.0139720.161102ME2000C002IV41O-5(모니터링)대한유화정문2019년 1월2019-01-012019-01-310.20.007200.0025Y
42019-01-010.0039960.0139720.161102ME2000C002IV41O-6(모니터링)SEP 공정(95번 초소)2019년 1월2019-01-012019-01-310.30.017600.0053Y
52019-01-010.0039960.0139720.161102ME2000C002IV41O-2(모니터링)동광화학 사거리2019년 1월2019-01-012019-01-310.50.020300.0086Y
62019-01-040.0040420.0139710.171309DG2015D001IV20O-1죽천1동 노인정 앞2019년_01월2019-01-042019-01-043.00.000.0Y
72019-01-040.0040420.0139710.171309DG2015D001IV20O-2마을 내부2019년_01월2019-01-042019-01-043.00.000.0Y
82019-01-040.0040420.0139710.171309DG2015D001IV20O-3용한2리 마을회관2019년_01월2019-01-042019-01-043.00.000.0Y
92019-01-140.0039970.0139620.196747GG2015O005IV20O-4충청북도 청주시 흥덕구 오송읍 오송1길 1544차2019-01-142019-01-160.00.100.0Y
날짜아황산가스이산화질소일산화탄소 값사업코드조사구분조사지점명조사지점 주소조사차수조사시작일조사종료일복합악취 인덱스암모니아메틸메르캅탄황화수소사용여부
902019-03-220.0039980.0139750.195317ND2012D005IV29O-1경남 창원시 진해구 연도동 67 웅천초교 연도분교장2019년_1분기2019-03-222019-03-224.00.000.0Y
912019-03-250.0039930.0139620.224022ME2005M002IV29O-2원포마을2019년_1분기2019-03-252019-03-253.00.000.0Y
922019-03-250.0039930.0139620.224022ME2005M002IV29O-1개인하수처리시설B 방류구2019년_1분기2019-03-252019-03-253.00.000.0Y
932019-03-250.0039930.0139620.224022ND2007B012IV29O-3거제 연초면 한내리 신전마을 입구2019_1분기2019-03-252019-03-253.00.000.0Y
942019-03-250.0039930.0139620.224022ND2007B012IV29O-2거제 연초면 한내리 냉정마을2019_1분기2019-03-252019-03-253.00.000.0Y
952019-03-250.0039930.0139620.224022ND2007B012IV29O-1사업지구내2019_1분기2019-03-252019-03-253.00.000.0Y
962019-03-250.0039930.0139620.224022HG2010D001IV29O-2사업지구 주변 정온시설2019년_1분기2019-03-252019-03-253.00.23100.0Y
972019-03-250.0039930.0139620.224022HG2010D001IV29O-3사업지구 주변2019년_1분기2019-03-252019-03-253.00.49900.0Y
982019-03-250.0039930.0139620.224022HG2010D001IV29O-1사업지구 주변 정온시설2019년_1분기2019-03-252019-03-253.00.21300.0Y
992019-03-290.0039980.0139720.217376ME1992O001IV29O-3대전광역시 유성구 대동 323번지 (해맑음센터)2019년 1분기2019-03-292019-03-293.00.09400.0Y