Overview

Dataset statistics

Number of variables17
Number of observations6465
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory909.3 KiB
Average record size in memory144.0 B

Variable types

Numeric8
Categorical9

Dataset

DescriptionSample
Author(사)동아시아바다공동체오션
URLhttps://www.bigdata-coast.kr/gdsInfo/gdsInfoDetail.do?gdsCd=CT08OSN006

Alerts

DNF_SRC_NM has constant value ""Constant
CST_NM has constant value ""Constant
ADM_ZN_NM is highly overall correlated with STR_LA and 4 other fieldsHigh correlation
QTMT_CD is highly overall correlated with QTMT_NM and 2 other fieldsHigh correlation
IEM_NM is highly overall correlated with QTMT_NM and 2 other fieldsHigh correlation
INVS_AREA_NM is highly overall correlated with STR_LA and 4 other fieldsHigh correlation
IEM_CD is highly overall correlated with QTMT_NM and 2 other fieldsHigh correlation
QTMT_NM is highly overall correlated with IEM_NM and 2 other fieldsHigh correlation
INVS_YR is highly overall correlated with INVS_YMDHigh correlation
IEM_CNT is highly overall correlated with METER_PER_IEM_CNTHigh correlation
METER_PER_IEM_CNT is highly overall correlated with IEM_CNTHigh correlation
INVS_YMD is highly overall correlated with INVS_YRHigh correlation
STR_LA is highly overall correlated with STR_LO and 4 other fieldsHigh correlation
STR_LO is highly overall correlated with STR_LA and 4 other fieldsHigh correlation
END_LA is highly overall correlated with STR_LA and 4 other fieldsHigh correlation
END_LO is highly overall correlated with STR_LA and 4 other fieldsHigh correlation
IEM_CNT has 4159 (64.3%) zerosZeros
METER_PER_IEM_CNT has 4159 (64.3%) zerosZeros

Reproduction

Analysis started2024-03-13 12:51:06.184790
Analysis finished2024-03-13 12:51:21.352804
Duration15.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

INVS_YR
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.7193
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:21.429935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2008
5-th percentile2008
Q12010
median2013
Q32015
95-th percentile2017
Maximum2017
Range9
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.864122
Coefficient of variation (CV)0.0014230112
Kurtosis-1.2354315
Mean2012.7193
Median Absolute Deviation (MAD)2
Skewness-0.078462104
Sum13012230
Variance8.203195
MonotonicityIncreasing
2024-03-13T21:51:21.586421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2015 720
11.1%
2016 720
11.1%
2017 720
11.1%
2009 630
9.7%
2010 630
9.7%
2011 630
9.7%
2012 630
9.7%
2013 630
9.7%
2014 630
9.7%
2008 525
8.1%
ValueCountFrequency (%)
2008 525
8.1%
2009 630
9.7%
2010 630
9.7%
2011 630
9.7%
2012 630
9.7%
2013 630
9.7%
2014 630
9.7%
2015 720
11.1%
2016 720
11.1%
2017 720
11.1%
ValueCountFrequency (%)
2017 720
11.1%
2016 720
11.1%
2015 720
11.1%
2014 630
9.7%
2013 630
9.7%
2012 630
9.7%
2011 630
9.7%
2010 630
9.7%
2009 630
9.7%
2008 525
8.1%

INVS_DSRT
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
2차
1095 
3차
1095 
4차
1095 
5차
1095 
6차
1095 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2차
2nd row2차
3rd row2차
4th row2차
5th row2차

Common Values

ValueCountFrequency (%)
2차 1095
16.9%
3차 1095
16.9%
4차 1095
16.9%
5차 1095
16.9%
6차 1095
16.9%
1차 990
15.3%

Length

2024-03-13T21:51:21.751254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:21.931791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2차 1095
16.9%
3차 1095
16.9%
4차 1095
16.9%
5차 1095
16.9%
6차 1095
16.9%
1차 990
15.3%

INVS_AREA_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
해남 묵동리
885 
고흥 신흥
885 
여수 반월
885 
사천 아두도
885 
통영 망일봉
885 
Other values (3)
2040 

Length

Max length6
Median length6
Mean length5.5893271
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row해남 묵동리
2nd row해남 묵동리
3rd row해남 묵동리
4th row해남 묵동리
5th row해남 묵동리

Common Values

ValueCountFrequency (%)
해남 묵동리 885
13.7%
고흥 신흥 885
13.7%
여수 반월 885
13.7%
사천 아두도 885
13.7%
통영 망일봉 885
13.7%
마산 봉암 885
13.7%
부산 해양대 885
13.7%
제주 사계리 270
 
4.2%

Length

2024-03-13T21:51:22.155853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:22.387209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해남 885
 
6.8%
묵동리 885
 
6.8%
고흥 885
 
6.8%
신흥 885
 
6.8%
여수 885
 
6.8%
반월 885
 
6.8%
사천 885
 
6.8%
아두도 885
 
6.8%
통영 885
 
6.8%
망일봉 885
 
6.8%
Other values (6) 4080
31.6%

DNF_SRC_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
국내기인
6465 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국내기인 6465
100.0%

Length

2024-03-13T21:51:22.613909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:22.772339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내기인 6465
100.0%

QTMT_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
플라스틱류
4741 
금속
862 
고무
 
431
의료 및 개인위생
 
431

Length

Max length9
Median length5
Mean length4.6666667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
플라스틱류 4741
73.3%
금속 862
 
13.3%
고무 431
 
6.7%
의료 및 개인위생 431
 
6.7%

Length

2024-03-13T21:51:22.926505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:23.119840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
플라스틱류 4741
64.7%
금속 862
 
11.8%
고무 431
 
5.9%
의료 431
 
5.9%
431
 
5.9%
개인위생 431
 
5.9%

IEM_NM
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
비닐봉투, 비닐쇼핑백 등
 
431
6개들이 포장고리
 
431
장어/문어 통발
 
431
어망(2.5~50cm)
 
431
어망 (50cm 이상)
 
431
Other values (10)
4310 

Length

Max length22
Median length15
Mean length11.133333
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비닐봉투, 비닐쇼핑백 등
2nd row6개들이 포장고리
3rd row장어/문어 통발
4th row어망(2.5~50cm)
5th row어망 (50cm 이상)

Common Values

ValueCountFrequency (%)
비닐봉투, 비닐쇼핑백 등 431
 
6.7%
6개들이 포장고리 431
 
6.7%
장어/문어 통발 431
 
6.7%
어망(2.5~50cm) 431
 
6.7%
어망 (50cm 이상) 431
 
6.7%
밧줄/로프 (2.5~50cm) 431
 
6.7%
밧줄/로프 (50cm 이상) 431
 
6.7%
끈(플라스틱, 노끈) (2.5~50cm) 431
 
6.7%
끈(플라스틱, 노끈) (50cm 이상) 431
 
6.7%
낚시줄 431
 
6.7%
Other values (5) 2155
33.3%

Length

2024-03-13T21:51:23.369982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50cm 1293
 
9.4%
이상 1293
 
9.4%
2.5~50cm 862
 
6.2%
노끈 862
 
6.2%
밧줄/로프 862
 
6.2%
끈(플라스틱 862
 
6.2%
풍선 431
 
3.1%
스프링통발(그물포함 431
 
3.1%
낚싯바늘 431
 
3.1%
낚시추 431
 
3.1%
Other values (14) 6034
43.8%

IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct136
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.9189482
Minimum0
Maximum800
Zeros4159
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:23.602334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile26
Maximum800
Range800
Interquartile range (IQR)2

Descriptive statistics

Standard deviation26.741826
Coefficient of variation (CV)4.518003
Kurtosis236.41798
Mean5.9189482
Median Absolute Deviation (MAD)0
Skewness12.573032
Sum38266
Variance715.12524
MonotonicityNot monotonic
2024-03-13T21:51:23.845567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4159
64.3%
1 500
 
7.7%
2 302
 
4.7%
3 208
 
3.2%
5 146
 
2.3%
4 129
 
2.0%
6 86
 
1.3%
7 78
 
1.2%
12 63
 
1.0%
10 54
 
0.8%
Other values (126) 740
 
11.4%
ValueCountFrequency (%)
0 4159
64.3%
1 500
 
7.7%
2 302
 
4.7%
3 208
 
3.2%
4 129
 
2.0%
5 146
 
2.3%
6 86
 
1.3%
7 78
 
1.2%
8 52
 
0.8%
9 37
 
0.6%
ValueCountFrequency (%)
800 1
< 0.1%
590 1
< 0.1%
454 1
< 0.1%
450 1
< 0.1%
448 1
< 0.1%
436 1
< 0.1%
431 1
< 0.1%
303 1
< 0.1%
300 1
< 0.1%
294 1
< 0.1%

METER_PER_IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct136
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.059189482
Minimum0
Maximum8
Zeros4159
Zeros (%)64.3%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:24.090407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.26
Maximum8
Range8
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.26741826
Coefficient of variation (CV)4.518003
Kurtosis236.41798
Mean0.059189482
Median Absolute Deviation (MAD)0
Skewness12.573032
Sum382.66
Variance0.071512524
MonotonicityNot monotonic
2024-03-13T21:51:24.343569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4159
64.3%
0.01 500
 
7.7%
0.02 302
 
4.7%
0.03 208
 
3.2%
0.05 146
 
2.3%
0.04 129
 
2.0%
0.06 86
 
1.3%
0.07 78
 
1.2%
0.12 63
 
1.0%
0.1 54
 
0.8%
Other values (126) 740
 
11.4%
ValueCountFrequency (%)
0.0 4159
64.3%
0.01 500
 
7.7%
0.02 302
 
4.7%
0.03 208
 
3.2%
0.04 129
 
2.0%
0.05 146
 
2.3%
0.06 86
 
1.3%
0.07 78
 
1.2%
0.08 52
 
0.8%
0.09 37
 
0.6%
ValueCountFrequency (%)
8.0 1
< 0.1%
5.9 1
< 0.1%
4.54 1
< 0.1%
4.5 1
< 0.1%
4.48 1
< 0.1%
4.36 1
< 0.1%
4.31 1
< 0.1%
3.03 1
< 0.1%
3.0 1
< 0.1%
2.94 1
< 0.1%

ADM_ZN_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
전남
2655 
경남
2655 
부산
885 
제주
270 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전남 2655
41.1%
경남 2655
41.1%
부산 885
 
13.7%
제주 270
 
4.2%

Length

2024-03-13T21:51:24.595575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:24.794699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남 2655
41.1%
경남 2655
41.1%
부산 885
 
13.7%
제주 270
 
4.2%

QTMT_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
PL
4741 
ME
862 
RB
 
431
MH
 
431

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
PL 4741
73.3%
ME 862
 
13.3%
RB 431
 
6.7%
MH 431
 
6.7%

Length

2024-03-13T21:51:24.979272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:25.137564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pl 4741
73.3%
me 862
 
13.3%
rb 431
 
6.7%
mh 431
 
6.7%

IEM_CD
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
PL_1_01
 
431
PL_1_07
 
431
PL_1_15
 
431
PL_1_16
 
431
PL_1_17
 
431
Other values (10)
4310 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPL_1_01
2nd rowPL_1_07
3rd rowPL_1_15
4th rowPL_1_16
5th rowPL_1_17

Common Values

ValueCountFrequency (%)
PL_1_01 431
 
6.7%
PL_1_07 431
 
6.7%
PL_1_15 431
 
6.7%
PL_1_16 431
 
6.7%
PL_1_17 431
 
6.7%
PL_1_20 431
 
6.7%
PL_1_21 431
 
6.7%
PL_1_22 431
 
6.7%
PL_1_23 431
 
6.7%
PL_1_30 431
 
6.7%
Other values (5) 2155
33.3%

Length

2024-03-13T21:51:25.334335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl_1_01 431
 
6.7%
pl_1_07 431
 
6.7%
pl_1_15 431
 
6.7%
pl_1_16 431
 
6.7%
pl_1_17 431
 
6.7%
pl_1_20 431
 
6.7%
pl_1_21 431
 
6.7%
pl_1_22 431
 
6.7%
pl_1_23 431
 
6.7%
pl_1_30 431
 
6.7%
Other values (5) 2155
33.3%

INVS_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct320
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20127905
Minimum20080327
Maximum20171206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:25.552392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080327
5-th percentile20080927
Q120101002
median20130419
Q320151010
95-th percentile20170727
Maximum20171206
Range90879
Interquartile range (IQR)50008

Descriptive statistics

Standard deviation28598.287
Coefficient of variation (CV)0.0014208278
Kurtosis-1.2303754
Mean20127905
Median Absolute Deviation (MAD)20710
Skewness-0.080115689
Sum1.301269 × 1011
Variance8.1786204 × 108
MonotonicityNot monotonic
2024-03-13T21:51:25.791356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20100731 75
 
1.2%
20161127 60
 
0.9%
20160327 45
 
0.7%
20131006 45
 
0.7%
20090530 45
 
0.7%
20090329 45
 
0.7%
20090531 45
 
0.7%
20111127 45
 
0.7%
20110529 45
 
0.7%
20100530 45
 
0.7%
Other values (310) 5970
92.3%
ValueCountFrequency (%)
20080327 15
 
0.2%
20080328 30
0.5%
20080329 45
0.7%
20080404 15
 
0.2%
20080522 15
 
0.2%
20080530 45
0.7%
20080531 45
0.7%
20080726 45
0.7%
20080728 15
 
0.2%
20080730 15
 
0.2%
ValueCountFrequency (%)
20171206 15
0.2%
20171203 15
0.2%
20171128 15
0.2%
20171126 30
0.5%
20171125 30
0.5%
20171123 15
0.2%
20171002 15
0.2%
20171001 15
0.2%
20170930 15
0.2%
20170929 30
0.5%

CST_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.6 KiB
남해안
6465 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남해안 6465
100.0%

Length

2024-03-13T21:51:25.983446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:51:26.118044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남해안 6465
100.0%

STR_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.759051
Minimum33.222112
Maximum35.2154
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:26.237479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.222112
5-th percentile34.353639
Q134.584309
median34.844393
Q335.075482
95-th percentile35.2154
Maximum35.2154
Range1.993288
Interquartile range (IQR)0.491173

Descriptive statistics

Standard deviation0.41432836
Coefficient of variation (CV)0.011920014
Kurtosis5.2951715
Mean34.759051
Median Absolute Deviation (MAD)0.231089
Skewness-2.0203113
Sum224717.26
Variance0.17166799
MonotonicityNot monotonic
2024-03-13T21:51:26.418777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
34.353639 885
13.7%
34.584309 885
13.7%
34.80759 885
13.7%
34.901437 885
13.7%
34.844393 885
13.7%
35.2154 885
13.7%
35.075482 885
13.7%
33.222112 270
 
4.2%
ValueCountFrequency (%)
33.222112 270
 
4.2%
34.353639 885
13.7%
34.584309 885
13.7%
34.80759 885
13.7%
34.844393 885
13.7%
34.901437 885
13.7%
35.075482 885
13.7%
35.2154 885
13.7%
ValueCountFrequency (%)
35.2154 885
13.7%
35.075482 885
13.7%
34.901437 885
13.7%
34.844393 885
13.7%
34.80759 885
13.7%
34.584309 885
13.7%
34.353639 885
13.7%
33.222112 270
 
4.2%

STR_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.86455
Minimum126.29629
Maximum129.09137
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:26.615711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.29629
5-th percentile126.61109
Q1127.14506
median128.05867
Q3128.62398
95-th percentile129.09137
Maximum129.09137
Range2.795087
Interquartile range (IQR)1.478912

Descriptive statistics

Standard deviation0.85900345
Coefficient of variation (CV)0.0067180734
Kurtosis-1.1632837
Mean127.86455
Median Absolute Deviation (MAD)0.565308
Skewness-0.22938365
Sum826644.33
Variance0.73788693
MonotonicityNot monotonic
2024-03-13T21:51:26.777527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
126.611089 885
13.7%
127.145063 885
13.7%
127.556183 885
13.7%
128.058667 885
13.7%
128.443972 885
13.7%
128.623975 885
13.7%
129.091375 885
13.7%
126.296288 270
 
4.2%
ValueCountFrequency (%)
126.296288 270
 
4.2%
126.611089 885
13.7%
127.145063 885
13.7%
127.556183 885
13.7%
128.058667 885
13.7%
128.443972 885
13.7%
128.623975 885
13.7%
129.091375 885
13.7%
ValueCountFrequency (%)
129.091375 885
13.7%
128.623975 885
13.7%
128.443972 885
13.7%
128.058667 885
13.7%
127.556183 885
13.7%
127.145063 885
13.7%
126.611089 885
13.7%
126.296288 270
 
4.2%

END_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.759032
Minimum33.223216
Maximum35.215688
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:26.980554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.223216
5-th percentile34.352882
Q134.586191
median34.843857
Q335.074851
95-th percentile35.215688
Maximum35.215688
Range1.992472
Interquartile range (IQR)0.48866

Descriptive statistics

Standard deviation0.41409112
Coefficient of variation (CV)0.011913195
Kurtosis5.2919376
Mean34.759032
Median Absolute Deviation (MAD)0.230994
Skewness-2.0195276
Sum224717.14
Variance0.17147145
MonotonicityNot monotonic
2024-03-13T21:51:27.204413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
34.352882 885
13.7%
34.586191 885
13.7%
34.807212 885
13.7%
34.901096 885
13.7%
34.843857 885
13.7%
35.215688 885
13.7%
35.074851 885
13.7%
33.223216 270
 
4.2%
ValueCountFrequency (%)
33.223216 270
 
4.2%
34.352882 885
13.7%
34.586191 885
13.7%
34.807212 885
13.7%
34.843857 885
13.7%
34.901096 885
13.7%
35.074851 885
13.7%
35.215688 885
13.7%
ValueCountFrequency (%)
35.215688 885
13.7%
35.074851 885
13.7%
34.901096 885
13.7%
34.843857 885
13.7%
34.807212 885
13.7%
34.586191 885
13.7%
34.352882 885
13.7%
33.223216 270
 
4.2%

END_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.86457
Minimum126.29709
Maximum129.09091
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.0 KiB
2024-03-13T21:51:27.389241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.29709
5-th percentile126.61001
Q1127.14585
median128.05941
Q3128.62372
95-th percentile129.09091
Maximum129.09091
Range2.793819
Interquartile range (IQR)1.477861

Descriptive statistics

Standard deviation0.85887435
Coefficient of variation (CV)0.0067170626
Kurtosis-1.1623771
Mean127.86457
Median Absolute Deviation (MAD)0.564304
Skewness-0.23061617
Sum826644.47
Variance0.73766515
MonotonicityNot monotonic
2024-03-13T21:51:27.551330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
126.610013 885
13.7%
127.145855 885
13.7%
127.556956 885
13.7%
128.059412 885
13.7%
128.443373 885
13.7%
128.623716 885
13.7%
129.090912 885
13.7%
126.297093 270
 
4.2%
ValueCountFrequency (%)
126.297093 270
 
4.2%
126.610013 885
13.7%
127.145855 885
13.7%
127.556956 885
13.7%
128.059412 885
13.7%
128.443373 885
13.7%
128.623716 885
13.7%
129.090912 885
13.7%
ValueCountFrequency (%)
129.090912 885
13.7%
128.623716 885
13.7%
128.443373 885
13.7%
128.059412 885
13.7%
127.556956 885
13.7%
127.145855 885
13.7%
126.610013 885
13.7%
126.297093 270
 
4.2%

Interactions

2024-03-13T21:51:19.110075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:09.134420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:10.445230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:12.210067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:13.621821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:14.902067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:16.381650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:17.711472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:19.259387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:09.291954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:10.625262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:12.351266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:13.775750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:15.062342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:16.527596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:17.929350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:19.395769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:09.491295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:10.825570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:12.499771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:13.966336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:15.263972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:16.709986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:18.094823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:19.554755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:09.661662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:10.992731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:12.673416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:14.143524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:15.521280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:16.881840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:18.258032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:19.711135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:09.793527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:11.140761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:12.829942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:14.276769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:15.703599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:17.041328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:18.405171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:19.868787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:09.973415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:11.299049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:13.036525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:14.459360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:15.877721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:17.192599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:18.583702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:20.032246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:10.118072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:11.900549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:13.233284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:14.617156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:16.041443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:17.344430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:18.774127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:20.195801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:10.298563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:12.055723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:13.470309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:14.772057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:16.199628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:17.512899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:18.968112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:51:27.706067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INVS_YRINVS_DSRTINVS_AREA_NMQTMT_NMIEM_NMIEM_CNTMETER_PER_IEM_CNTADM_ZN_NMQTMT_CDIEM_CDINVS_YMDSTR_LASTR_LOEND_LAEND_LO
INVS_YR1.0000.0000.2070.0000.0000.0500.0400.2510.0000.0001.0000.2440.2070.2440.207
INVS_DSRT0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0840.0000.0000.0000.000
INVS_AREA_NM0.2070.0001.0000.0000.0000.0910.0901.0000.0000.0000.2211.0001.0001.0001.000
QTMT_NM0.0000.0000.0001.0001.0000.0440.0440.0001.0001.0000.0000.0000.0000.0000.000
IEM_NM0.0000.0000.0001.0001.0000.1980.1980.0001.0001.0000.0000.0000.0000.0000.000
IEM_CNT0.0500.0000.0910.0440.1981.0001.0000.0300.0440.1980.0510.0660.0910.0660.091
METER_PER_IEM_CNT0.0400.0000.0900.0440.1981.0001.0000.0280.0440.1980.0450.0650.0900.0650.090
ADM_ZN_NM0.2510.0001.0000.0000.0000.0300.0281.0000.0000.0000.2770.9681.0000.9681.000
QTMT_CD0.0000.0000.0001.0001.0000.0440.0440.0001.0001.0000.0000.0000.0000.0000.000
IEM_CD0.0000.0000.0001.0001.0000.1980.1980.0001.0001.0000.0000.0000.0000.0000.000
INVS_YMD1.0000.0840.2210.0000.0000.0510.0450.2770.0000.0001.0000.2380.2210.2380.221
STR_LA0.2440.0001.0000.0000.0000.0660.0650.9680.0000.0000.2381.0001.0001.0001.000
STR_LO0.2070.0001.0000.0000.0000.0910.0901.0000.0000.0000.2211.0001.0001.0001.000
END_LA0.2440.0001.0000.0000.0000.0660.0650.9680.0000.0000.2381.0001.0001.0001.000
END_LO0.2070.0001.0000.0000.0000.0910.0901.0000.0000.0000.2211.0001.0001.0001.000
2024-03-13T21:51:27.954962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_ZN_NMQTMT_CDIEM_NMINVS_AREA_NMIEM_CDQTMT_NMINVS_DSRT
ADM_ZN_NM1.0000.0000.0001.0000.0000.0000.000
QTMT_CD0.0001.0000.9990.0000.9991.0000.000
IEM_NM0.0000.9991.0000.0001.0000.9990.000
INVS_AREA_NM1.0000.0000.0001.0000.0000.0000.000
IEM_CD0.0000.9991.0000.0001.0000.9990.000
QTMT_NM0.0001.0000.9990.0000.9991.0000.000
INVS_DSRT0.0000.0000.0000.0000.0000.0001.000
2024-03-13T21:51:28.116457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
INVS_YRIEM_CNTMETER_PER_IEM_CNTINVS_YMDSTR_LASTR_LOEND_LAEND_LOINVS_DSRTINVS_AREA_NMQTMT_NMIEM_NMADM_ZN_NMQTMT_CDIEM_CD
INVS_YR1.000-0.071-0.0710.994-0.085-0.085-0.085-0.0850.0420.1050.0000.0000.1660.0000.000
IEM_CNT-0.0711.0001.000-0.0700.0220.0170.0220.0170.0000.0490.0290.0920.0200.0290.092
METER_PER_IEM_CNT-0.0711.0001.000-0.0700.0220.0170.0220.0170.0000.0490.0290.0920.0200.0290.092
INVS_YMD0.994-0.070-0.0701.000-0.087-0.085-0.087-0.0850.0450.1070.0000.0000.1690.0000.000
STR_LA-0.0850.0220.022-0.0871.0000.9371.0000.9370.0001.0000.0000.0000.8820.0000.000
STR_LO-0.0850.0170.017-0.0850.9371.0000.9371.0000.0001.0000.0000.0001.0000.0000.000
END_LA-0.0850.0220.022-0.0871.0000.9371.0000.9370.0001.0000.0000.0000.8820.0000.000
END_LO-0.0850.0170.017-0.0850.9371.0000.9371.0000.0001.0000.0000.0001.0000.0000.000
INVS_DSRT0.0420.0000.0000.0450.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
INVS_AREA_NM0.1050.0490.0490.1071.0001.0001.0001.0000.0001.0000.0000.0001.0000.0000.000
QTMT_NM0.0000.0290.0290.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_NM0.0000.0920.0920.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000
ADM_ZN_NM0.1660.0200.0200.1690.8821.0000.8821.0000.0001.0000.0000.0001.0000.0000.000
QTMT_CD0.0000.0290.0290.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_CD0.0000.0920.0920.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000

Missing values

2024-03-13T21:51:20.827885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:51:21.206289image/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

INVS_YRINVS_DSRTINVS_AREA_NMDNF_SRC_NMQTMT_NMIEM_NMIEM_CNTMETER_PER_IEM_CNTADM_ZN_NMQTMT_CDIEM_CDINVS_YMDCST_NMSTR_LASTR_LOEND_LAEND_LO
020082차해남 묵동리국내기인플라스틱류비닐봉투, 비닐쇼핑백 등00.0전남PLPL_1_0120080404남해안34.353639126.61108934.352882126.610013
120082차해남 묵동리국내기인플라스틱류6개들이 포장고리00.0전남PLPL_1_0720080404남해안34.353639126.61108934.352882126.610013
220082차해남 묵동리국내기인플라스틱류장어/문어 통발10.01전남PLPL_1_1520080404남해안34.353639126.61108934.352882126.610013
320082차해남 묵동리국내기인플라스틱류어망(2.5~50cm)00.0전남PLPL_1_1620080404남해안34.353639126.61108934.352882126.610013
420082차해남 묵동리국내기인플라스틱류어망 (50cm 이상)130.13전남PLPL_1_1720080404남해안34.353639126.61108934.352882126.610013
520082차해남 묵동리국내기인플라스틱류밧줄/로프 (2.5~50cm)110.11전남PLPL_1_2020080404남해안34.353639126.61108934.352882126.610013
620082차해남 묵동리국내기인플라스틱류밧줄/로프 (50cm 이상)140.14전남PLPL_1_2120080404남해안34.353639126.61108934.352882126.610013
720082차해남 묵동리국내기인플라스틱류끈(플라스틱, 노끈) (2.5~50cm)70.07전남PLPL_1_2220080404남해안34.353639126.61108934.352882126.610013
820082차해남 묵동리국내기인플라스틱류끈(플라스틱, 노끈) (50cm 이상)30.03전남PLPL_1_2320080404남해안34.353639126.61108934.352882126.610013
920082차해남 묵동리국내기인플라스틱류낚시줄00.0전남PLPL_1_3020080404남해안34.353639126.61108934.352882126.610013
INVS_YRINVS_DSRTINVS_AREA_NMDNF_SRC_NMQTMT_NMIEM_NMIEM_CNTMETER_PER_IEM_CNTADM_ZN_NMQTMT_CDIEM_CDINVS_YMDCST_NMSTR_LASTR_LOEND_LAEND_LO
645520176차제주 사계리국내기인플라스틱류밧줄/로프 (2.5~50cm)00.0제주PLPL_1_2020171206남해안33.222112126.29628833.223216126.297093
645620176차제주 사계리국내기인플라스틱류밧줄/로프 (50cm 이상)00.0제주PLPL_1_2120171206남해안33.222112126.29628833.223216126.297093
645720176차제주 사계리국내기인플라스틱류끈(플라스틱, 노끈) (2.5~50cm)10.01제주PLPL_1_2220171206남해안33.222112126.29628833.223216126.297093
645820176차제주 사계리국내기인플라스틱류끈(플라스틱, 노끈) (50cm 이상)00.0제주PLPL_1_2320171206남해안33.222112126.29628833.223216126.297093
645920176차제주 사계리국내기인플라스틱류낚시줄00.0제주PLPL_1_3020171206남해안33.222112126.29628833.223216126.297093
646020176차제주 사계리국내기인플라스틱류가짜미끼, 형광찌00.0제주PLPL_1_3120171206남해안33.222112126.29628833.223216126.297093
646120176차제주 사계리국내기인금속납 낚시추, 낚싯바늘00.0제주MEME_1_0620171206남해안33.222112126.29628833.223216126.297093
646220176차제주 사계리국내기인금속스프링통발(그물포함)00.0제주MEME_1_0720171206남해안33.222112126.29628833.223216126.297093
646320176차제주 사계리국내기인고무풍선00.0제주RBRB_1_0220171206남해안33.222112126.29628833.223216126.297093
646420176차제주 사계리국내기인의료 및 개인위생주사기00.0제주MHMH_1_0220171206남해안33.222112126.29628833.223216126.297093