Overview

Dataset statistics

Number of variables17
Number of observations7327
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 MiB
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=CT08OSN003

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 4265 (58.2%) zerosZeros
METER_PER_IEM_CNT has 4265 (58.2%) zerosZeros

Reproduction

Analysis started2024-03-13 12:51:48.339304
Analysis finished2024-03-13 12:52:02.828061
Duration14.49 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

INVS_YR
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.7193
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-03-13T21:52:02.914186image/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 deviation2.864096
Coefficient of variation (CV)0.0014229982
Kurtosis-1.2354283
Mean2012.7193
Median Absolute Deviation (MAD)2
Skewness-0.078459962
Sum14747194
Variance8.2030457
MonotonicityIncreasing
2024-03-13T21:52:03.078902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2015 816
11.1%
2016 816
11.1%
2017 816
11.1%
2009 714
9.7%
2010 714
9.7%
2011 714
9.7%
2012 714
9.7%
2013 714
9.7%
2014 714
9.7%
2008 595
8.1%
ValueCountFrequency (%)
2008 595
8.1%
2009 714
9.7%
2010 714
9.7%
2011 714
9.7%
2012 714
9.7%
2013 714
9.7%
2014 714
9.7%
2015 816
11.1%
2016 816
11.1%
2017 816
11.1%
ValueCountFrequency (%)
2017 816
11.1%
2016 816
11.1%
2015 816
11.1%
2014 714
9.7%
2013 714
9.7%
2012 714
9.7%
2011 714
9.7%
2010 714
9.7%
2009 714
9.7%
2008 595
8.1%

INVS_DSRT
Categorical

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
2차
1241 
3차
1241 
4차
1241 
5차
1241 
6차
1241 

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 1241
16.9%
3차 1241
16.9%
4차 1241
16.9%
5차 1241
16.9%
6차 1241
16.9%
1차 1122
15.3%

Length

2024-03-13T21:52:03.246589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:03.425029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2차 1241
16.9%
3차 1241
16.9%
4차 1241
16.9%
5차 1241
16.9%
6차 1241
16.9%
1차 1122
15.3%

INVS_AREA_NM
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
해남 묵동리
1003 
고흥 신흥
1003 
여수 반월
1003 
사천 아두도
1003 
통영 망일봉
1003 
Other values (3)
2312 

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 1003
13.7%
고흥 신흥 1003
13.7%
여수 반월 1003
13.7%
사천 아두도 1003
13.7%
통영 망일봉 1003
13.7%
마산 봉암 1003
13.7%
부산 해양대 1003
13.7%
제주 사계리 306
 
4.2%

Length

2024-03-13T21:52:03.614248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:03.796911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해남 1003
 
6.8%
묵동리 1003
 
6.8%
고흥 1003
 
6.8%
신흥 1003
 
6.8%
여수 1003
 
6.8%
반월 1003
 
6.8%
사천 1003
 
6.8%
아두도 1003
 
6.8%
통영 1003
 
6.8%
망일봉 1003
 
6.8%
Other values (6) 4624
31.6%

DNF_SRC_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
국내기인
7327 

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 7327
100.0%

Length

2024-03-13T21:52:04.045762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:04.253100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내기인 7327
100.0%

QTMT_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
플라스틱류
4741 
나무
2155 
금속
 
431

Length

Max length5
Median length5
Mean length3.9411765
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
플라스틱류 4741
64.7%
나무 2155
29.4%
금속 431
 
5.9%

Length

2024-03-13T21:52:04.436127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:04.618931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
플라스틱류 4741
64.7%
나무 2155
29.4%
금속 431
 
5.9%

IEM_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
비닐봉투, 비닐쇼핑백 등
 
431
음식물포장지(라면봉지, 과자봉지 등)
 
431
어망(2.5~50cm)
 
431
어망 (50cm 이상)
 
431
밧줄/로프 (2.5~50cm)
 
431
Other values (12)
5172 

Length

Max length22
Median length17
Mean length14.235294
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비닐봉투, 비닐쇼핑백 등
2nd row음식물포장지(라면봉지, 과자봉지 등)
3rd row어망(2.5~50cm)
4th row어망 (50cm 이상)
5th row밧줄/로프 (2.5~50cm)

Common Values

ValueCountFrequency (%)
비닐봉투, 비닐쇼핑백 등 431
 
5.9%
음식물포장지(라면봉지, 과자봉지 등) 431
 
5.9%
어망(2.5~50cm) 431
 
5.9%
어망 (50cm 이상) 431
 
5.9%
밧줄/로프 (2.5~50cm) 431
 
5.9%
밧줄/로프 (50cm 이상) 431
 
5.9%
끈(플라스틱, 노끈) (2.5~50cm) 431
 
5.9%
끈(플라스틱, 노끈) (50cm 이상) 431
 
5.9%
농업용폐비닐 (2.5~50cm) 431
 
5.9%
농업용폐비닐 (50cm 이상) 431
 
5.9%
Other values (7) 3017
41.2%

Length

2024-03-13T21:52:04.819076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50cm 2155
12.5%
이상 2155
12.5%
2.5~50cm 1724
 
10.0%
1293
 
7.5%
끈(플라스틱 862
 
5.0%
건축용목재 862
 
5.0%
밧줄/로프 862
 
5.0%
노끈 862
 
5.0%
농업용폐비닐 862
 
5.0%
나무 431
 
2.5%
Other values (12) 5172
30.0%

IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct180
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5770438
Minimum0
Maximum800
Zeros4265
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-03-13T21:52:05.088366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q34
95-th percentile34
Maximum800
Range800
Interquartile range (IQR)4

Descriptive statistics

Standard deviation35.332241
Coefficient of variation (CV)4.1193961
Kurtosis132.73558
Mean8.5770438
Median Absolute Deviation (MAD)0
Skewness9.8974985
Sum62844
Variance1248.3672
MonotonicityNot monotonic
2024-03-13T21:52:05.354110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4265
58.2%
1 492
 
6.7%
2 373
 
5.1%
3 282
 
3.8%
5 219
 
3.0%
4 178
 
2.4%
6 122
 
1.7%
7 115
 
1.6%
12 87
 
1.2%
10 84
 
1.1%
Other values (170) 1110
 
15.1%
ValueCountFrequency (%)
0 4265
58.2%
1 492
 
6.7%
2 373
 
5.1%
3 282
 
3.8%
4 178
 
2.4%
5 219
 
3.0%
6 122
 
1.7%
7 115
 
1.6%
8 81
 
1.1%
9 50
 
0.7%
ValueCountFrequency (%)
800 1
< 0.1%
620 1
< 0.1%
611 1
< 0.1%
590 1
< 0.1%
564 1
< 0.1%
540 1
< 0.1%
516 1
< 0.1%
507 1
< 0.1%
454 1
< 0.1%
450 1
< 0.1%

METER_PER_IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct180
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.085770438
Minimum0
Maximum8
Zeros4265
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-03-13T21:52:05.621420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.04
95-th percentile0.34
Maximum8
Range8
Interquartile range (IQR)0.04

Descriptive statistics

Standard deviation0.35332241
Coefficient of variation (CV)4.1193961
Kurtosis132.73558
Mean0.085770438
Median Absolute Deviation (MAD)0
Skewness9.8974985
Sum628.44
Variance0.12483672
MonotonicityNot monotonic
2024-03-13T21:52:05.846820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4265
58.2%
0.01 492
 
6.7%
0.02 373
 
5.1%
0.03 282
 
3.8%
0.05 219
 
3.0%
0.04 178
 
2.4%
0.06 122
 
1.7%
0.07 115
 
1.6%
0.12 87
 
1.2%
0.1 84
 
1.1%
Other values (170) 1110
 
15.1%
ValueCountFrequency (%)
0.0 4265
58.2%
0.01 492
 
6.7%
0.02 373
 
5.1%
0.03 282
 
3.8%
0.04 178
 
2.4%
0.05 219
 
3.0%
0.06 122
 
1.7%
0.07 115
 
1.6%
0.08 81
 
1.1%
0.09 50
 
0.7%
ValueCountFrequency (%)
8.0 1
< 0.1%
6.2 1
< 0.1%
6.11 1
< 0.1%
5.9 1
< 0.1%
5.64 1
< 0.1%
5.4 1
< 0.1%
5.16 1
< 0.1%
5.07 1
< 0.1%
4.54 1
< 0.1%
4.5 1
< 0.1%

ADM_ZN_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
전남
3009 
경남
3009 
부산
1003 
제주
306 

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 3009
41.1%
경남 3009
41.1%
부산 1003
 
13.7%
제주 306
 
4.2%

Length

2024-03-13T21:52:06.080427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:06.248610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남 3009
41.1%
경남 3009
41.1%
부산 1003
 
13.7%
제주 306
 
4.2%

QTMT_CD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
PL
4741 
WD
2155 
ME
 
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
64.7%
WD 2155
29.4%
ME 431
 
5.9%

Length

2024-03-13T21:52:06.481190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:06.713749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pl 4741
64.7%
wd 2155
29.4%
me 431
 
5.9%

IEM_CD
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
PL_1_01
 
431
PL_1_05
 
431
PL_1_16
 
431
PL_1_17
 
431
PL_1_20
 
431
Other values (12)
5172 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPL_1_01
2nd rowPL_1_05
3rd rowPL_1_16
4th rowPL_1_17
5th rowPL_1_20

Common Values

ValueCountFrequency (%)
PL_1_01 431
 
5.9%
PL_1_05 431
 
5.9%
PL_1_16 431
 
5.9%
PL_1_17 431
 
5.9%
PL_1_20 431
 
5.9%
PL_1_21 431
 
5.9%
PL_1_22 431
 
5.9%
PL_1_23 431
 
5.9%
PL_1_27 431
 
5.9%
PL_1_28 431
 
5.9%
Other values (7) 3017
41.2%

Length

2024-03-13T21:52:06.878978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl_1_01 431
 
5.9%
pl_1_28 431
 
5.9%
wd_1_05 431
 
5.9%
wd_1_04 431
 
5.9%
wd_1_03 431
 
5.9%
wd_1_02 431
 
5.9%
wd_1_01 431
 
5.9%
pl_1_30 431
 
5.9%
pl_1_27 431
 
5.9%
pl_1_05 431
 
5.9%
Other values (7) 3017
41.2%

INVS_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct320
Distinct (%)4.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20127905
Minimum20080327
Maximum20171206
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.5 KiB
2024-03-13T21:52:07.115195image/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 deviation28598.027
Coefficient of variation (CV)0.0014208149
Kurtosis-1.2303727
Mean20127905
Median Absolute Deviation (MAD)20710
Skewness-0.080113501
Sum1.4747716 × 1011
Variance8.1784715 × 108
MonotonicityNot monotonic
2024-03-13T21:52:07.327910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20100731 85
 
1.2%
20161127 68
 
0.9%
20160327 51
 
0.7%
20131006 51
 
0.7%
20090530 51
 
0.7%
20090329 51
 
0.7%
20090531 51
 
0.7%
20111127 51
 
0.7%
20110529 51
 
0.7%
20100530 51
 
0.7%
Other values (310) 6766
92.3%
ValueCountFrequency (%)
20080327 17
 
0.2%
20080328 34
0.5%
20080329 51
0.7%
20080404 17
 
0.2%
20080522 17
 
0.2%
20080530 51
0.7%
20080531 51
0.7%
20080726 51
0.7%
20080728 17
 
0.2%
20080730 17
 
0.2%
ValueCountFrequency (%)
20171206 17
0.2%
20171203 17
0.2%
20171128 17
0.2%
20171126 34
0.5%
20171125 34
0.5%
20171123 17
0.2%
20171002 17
0.2%
20171001 17
0.2%
20170930 17
0.2%
20170929 34
0.5%

CST_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.4 KiB
남해안
7327 

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 7327
100.0%

Length

2024-03-13T21:52:07.552031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:52:07.718598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남해안 7327
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 size64.5 KiB
2024-03-13T21:52:07.884448image/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 deviation0.41432459
Coefficient of variation (CV)0.011919905
Kurtosis5.2945804
Mean34.759051
Median Absolute Deviation (MAD)0.231089
Skewness-2.0202561
Sum254679.56
Variance0.17166487
MonotonicityNot monotonic
2024-03-13T21:52:08.074235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
34.353639 1003
13.7%
34.584309 1003
13.7%
34.80759 1003
13.7%
34.901437 1003
13.7%
34.844393 1003
13.7%
35.2154 1003
13.7%
35.075482 1003
13.7%
33.222112 306
 
4.2%
ValueCountFrequency (%)
33.222112 306
 
4.2%
34.353639 1003
13.7%
34.584309 1003
13.7%
34.80759 1003
13.7%
34.844393 1003
13.7%
34.901437 1003
13.7%
35.075482 1003
13.7%
35.2154 1003
13.7%
ValueCountFrequency (%)
35.2154 1003
13.7%
35.075482 1003
13.7%
34.901437 1003
13.7%
34.844393 1003
13.7%
34.80759 1003
13.7%
34.584309 1003
13.7%
34.353639 1003
13.7%
33.222112 306
 
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 size64.5 KiB
2024-03-13T21:52:08.257174image/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 deviation0.85899563
Coefficient of variation (CV)0.0067180123
Kurtosis-1.163287
Mean127.86455
Median Absolute Deviation (MAD)0.565308
Skewness-0.22937739
Sum936863.58
Variance0.7378735
MonotonicityNot monotonic
2024-03-13T21:52:08.427901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
126.611089 1003
13.7%
127.145063 1003
13.7%
127.556183 1003
13.7%
128.058667 1003
13.7%
128.443972 1003
13.7%
128.623975 1003
13.7%
129.091375 1003
13.7%
126.296288 306
 
4.2%
ValueCountFrequency (%)
126.296288 306
 
4.2%
126.611089 1003
13.7%
127.145063 1003
13.7%
127.556183 1003
13.7%
128.058667 1003
13.7%
128.443972 1003
13.7%
128.623975 1003
13.7%
129.091375 1003
13.7%
ValueCountFrequency (%)
129.091375 1003
13.7%
128.623975 1003
13.7%
128.443972 1003
13.7%
128.058667 1003
13.7%
127.556183 1003
13.7%
127.145063 1003
13.7%
126.611089 1003
13.7%
126.296288 306
 
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 size64.5 KiB
2024-03-13T21:52:08.604573image/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 deviation0.41408735
Coefficient of variation (CV)0.011913086
Kurtosis5.2913467
Mean34.759032
Median Absolute Deviation (MAD)0.230994
Skewness-2.0194725
Sum254679.43
Variance0.17146833
MonotonicityNot monotonic
2024-03-13T21:52:09.323640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
34.352882 1003
13.7%
34.586191 1003
13.7%
34.807212 1003
13.7%
34.901096 1003
13.7%
34.843857 1003
13.7%
35.215688 1003
13.7%
35.074851 1003
13.7%
33.223216 306
 
4.2%
ValueCountFrequency (%)
33.223216 306
 
4.2%
34.352882 1003
13.7%
34.586191 1003
13.7%
34.807212 1003
13.7%
34.843857 1003
13.7%
34.901096 1003
13.7%
35.074851 1003
13.7%
35.215688 1003
13.7%
ValueCountFrequency (%)
35.215688 1003
13.7%
35.074851 1003
13.7%
34.901096 1003
13.7%
34.843857 1003
13.7%
34.807212 1003
13.7%
34.586191 1003
13.7%
34.352882 1003
13.7%
33.223216 306
 
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 size64.5 KiB
2024-03-13T21:52:09.500814image/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 deviation0.85886654
Coefficient of variation (CV)0.0067170015
Kurtosis-1.1623805
Mean127.86457
Median Absolute Deviation (MAD)0.564304
Skewness-0.23060987
Sum936863.74
Variance0.73765173
MonotonicityNot monotonic
2024-03-13T21:52:09.650013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
126.610013 1003
13.7%
127.145855 1003
13.7%
127.556956 1003
13.7%
128.059412 1003
13.7%
128.443373 1003
13.7%
128.623716 1003
13.7%
129.090912 1003
13.7%
126.297093 306
 
4.2%
ValueCountFrequency (%)
126.297093 306
 
4.2%
126.610013 1003
13.7%
127.145855 1003
13.7%
127.556956 1003
13.7%
128.059412 1003
13.7%
128.443373 1003
13.7%
128.623716 1003
13.7%
129.090912 1003
13.7%
ValueCountFrequency (%)
129.090912 1003
13.7%
128.623716 1003
13.7%
128.443373 1003
13.7%
128.059412 1003
13.7%
127.556956 1003
13.7%
127.145855 1003
13.7%
126.610013 1003
13.7%
126.297093 306
 
4.2%

Interactions

2024-03-13T21:52:00.911999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:51.279320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:52.368070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:53.726481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.189750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:56.476730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:57.859663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:59.078139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:01.058539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:51.384459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:52.545267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:53.878567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.336132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:56.616933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.014854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:59.217949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:01.220371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:51.521704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:52.719500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:54.054197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.505254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:56.775910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.194649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:59.914324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:01.372469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:51.649526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:52.862629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:54.215251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.650749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:56.939001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.348643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:00.097344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:01.519416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:51.780128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:53.008159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:54.409748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.812673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:57.163566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.466585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:00.240331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:01.683373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:51.921122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:53.170360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:54.604374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.993260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:57.380491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.635766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:00.425878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:01.889413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:52.048645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:53.351751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:54.821442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:56.140971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:57.553888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.779043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:00.605843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:02.085181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:52.221593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:53.564296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:55.028158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:56.314361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:57.716859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:51:58.948633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:52:00.757655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:52:09.813064image/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_YR1.0000.0000.2090.0000.0000.0440.0400.2520.0000.0001.0000.2460.2090.2460.209
INVS_DSRT0.0001.0000.0000.0000.0000.0000.0050.0000.0000.0000.0880.0000.0000.0000.000
INVS_AREA_NM0.2090.0001.0000.0000.0000.1410.1431.0000.0000.0000.2231.0001.0001.0001.000
QTMT_NM0.0000.0000.0001.0001.0000.1070.1140.0001.0001.0000.0000.0000.0000.0000.000
IEM_NM0.0000.0000.0001.0001.0000.2060.2110.0001.0001.0000.0000.0000.0000.0000.000
IEM_CNT0.0440.0000.1410.1070.2061.0001.0000.0650.1070.2060.0470.1030.1410.1030.141
METER_PER_IEM_CNT0.0400.0050.1430.1140.2111.0001.0000.0670.1140.2110.0420.1060.1430.1060.143
ADM_ZN_NM0.2520.0001.0000.0000.0000.0650.0671.0000.0000.0000.2780.9681.0000.9681.000
QTMT_CD0.0000.0000.0001.0001.0000.1070.1140.0001.0001.0000.0000.0000.0000.0000.000
IEM_CD0.0000.0000.0001.0001.0000.2060.2110.0001.0001.0000.0000.0000.0000.0000.000
INVS_YMD1.0000.0880.2230.0000.0000.0470.0420.2780.0000.0001.0000.2390.2230.2390.223
STR_LA0.2460.0001.0000.0000.0000.1030.1060.9680.0000.0000.2391.0001.0001.0001.000
STR_LO0.2090.0001.0000.0000.0000.1410.1431.0000.0000.0000.2231.0001.0001.0001.000
END_LA0.2460.0001.0000.0000.0000.1030.1060.9680.0000.0000.2391.0001.0001.0001.000
END_LO0.2090.0001.0000.0000.0000.1410.1431.0000.0000.0000.2231.0001.0001.0001.000
2024-03-13T21:52:10.091738image/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
2024-03-13T21:52:10.282490image/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_YR1.000-0.111-0.1110.994-0.085-0.085-0.085-0.0850.0440.1060.0000.0000.1670.0000.000
IEM_CNT-0.1111.0001.000-0.1110.0730.0610.0730.0610.0000.0700.0480.0840.0410.0480.084
METER_PER_IEM_CNT-0.1111.0001.000-0.1110.0730.0610.0730.0610.0000.0700.0480.0840.0410.0480.084
INVS_YMD0.994-0.111-0.1111.000-0.087-0.085-0.087-0.0850.0460.1080.0000.0000.1690.0000.000
STR_LA-0.0850.0730.073-0.0871.0000.9371.0000.9370.0001.0000.0000.0000.8820.0000.000
STR_LO-0.0850.0610.061-0.0850.9371.0000.9371.0000.0001.0000.0000.0001.0000.0000.000
END_LA-0.0850.0730.073-0.0871.0000.9371.0000.9370.0001.0000.0000.0000.8820.0000.000
END_LO-0.0850.0610.061-0.0850.9371.0000.9371.0000.0001.0000.0000.0001.0000.0000.000
INVS_DSRT0.0440.0000.0000.0460.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
INVS_AREA_NM0.1060.0700.0700.1081.0001.0001.0001.0000.0001.0000.0000.0001.0000.0000.000
QTMT_NM0.0000.0480.0480.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_NM0.0000.0840.0840.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000
ADM_ZN_NM0.1670.0410.0410.1690.8821.0000.8821.0000.0001.0000.0000.0001.0000.0000.000
QTMT_CD0.0000.0480.0480.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_CD0.0000.0840.0840.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000

Missing values

2024-03-13T21:52:02.307327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:52:02.679159image/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 묵동리국내기인플라스틱류음식물포장지(라면봉지, 과자봉지 등)90.09전남PLPL_1_0520080404남해안34.353639126.61108934.352882126.610013
220082차해남 묵동리국내기인플라스틱류어망(2.5~50cm)00.0전남PLPL_1_1620080404남해안34.353639126.61108934.352882126.610013
320082차해남 묵동리국내기인플라스틱류어망 (50cm 이상)130.13전남PLPL_1_1720080404남해안34.353639126.61108934.352882126.610013
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INVS_YRINVS_DSRTINVS_AREA_NMDNF_SRC_NMQTMT_NMIEM_NMIEM_CNTMETER_PER_IEM_CNTADM_ZN_NMQTMT_CDIEM_CDINVS_YMDCST_NMSTR_LASTR_LOEND_LAEND_LO
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731820176차제주 사계리국내기인플라스틱류농업용폐비닐 (2.5~50cm)00.0제주PLPL_1_2720171206남해안33.222112126.29628833.223216126.297093
731920176차제주 사계리국내기인플라스틱류농업용폐비닐 (50cm 이상)00.0제주PLPL_1_2820171206남해안33.222112126.29628833.223216126.297093
732020176차제주 사계리국내기인플라스틱류낚시줄00.0제주PLPL_1_3020171206남해안33.222112126.29628833.223216126.297093
732120176차제주 사계리국내기인나무나무 팔레트00.0제주WDWD_1_0120171206남해안33.222112126.29628833.223216126.297093
732220176차제주 사계리국내기인나무대형나무포장상자00.0제주WDWD_1_0220171206남해안33.222112126.29628833.223216126.297093
732320176차제주 사계리국내기인나무건축용목재 (2.5~50cm)20.02제주WDWD_1_0320171206남해안33.222112126.29628833.223216126.297093
732420176차제주 사계리국내기인나무건축용목재 (50cm 이상)00.0제주WDWD_1_0420171206남해안33.222112126.29628833.223216126.297093
732520176차제주 사계리국내기인나무어업용목재(폐목선, 양식장시설 등)00.0제주WDWD_1_0520171206남해안33.222112126.29628833.223216126.297093
732620176차제주 사계리국내기인금속스프링통발(그물포함)00.0제주MEME_1_0720171206남해안33.222112126.29628833.223216126.297093