Overview

Dataset statistics

Number of variables17
Number of observations6495
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory913.5 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=CT08OSN005

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 4494 (69.2%) zerosZeros
METER_PER_IEM_CNT has 4494 (69.2%) zerosZeros

Reproduction

Analysis started2024-03-13 12:42:23.211395
Analysis finished2024-03-13 12:42:36.724950
Duration13.51 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%
Mean2012.7252
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:36.789131image/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 deviation2.8588182
Coefficient of variation (CV)0.0014203719
Kurtosis-1.2296509
Mean2012.7252
Median Absolute Deviation (MAD)2
Skewness-0.084329248
Sum13072650
Variance8.1728417
MonotonicityIncreasing
2024-03-13T21:42:36.948940image/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%
2014 660
10.2%
2009 630
9.7%
2010 630
9.7%
2011 630
9.7%
2012 630
9.7%
2013 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 660
10.2%
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 660
10.2%
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 size50.9 KiB
5차
1110 
6차
1110 
2차
1095 
3차
1095 
4차
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 (%)
5차 1110
17.1%
6차 1110
17.1%
2차 1095
16.9%
3차 1095
16.9%
4차 1095
16.9%
1차 990
15.2%

Length

2024-03-13T21:42:37.130788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:37.274166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5차 1110
17.1%
6차 1110
17.1%
2차 1095
16.9%
3차 1095
16.9%
4차 1095
16.9%
1차 990
15.2%

INVS_AREA_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
강화 여차리
1185 
안산 말부흥
885 
태안 백리포
885 
보령 석대도
885 
부안 변산
885 
Other values (2)
1770 

Length

Max length6
Median length6
Mean length5.8637413
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강화 여차리
2nd row강화 여차리
3rd row강화 여차리
4th row강화 여차리
5th row강화 여차리

Common Values

ValueCountFrequency (%)
강화 여차리 1185
18.2%
안산 말부흥 885
13.6%
태안 백리포 885
13.6%
보령 석대도 885
13.6%
부안 변산 885
13.6%
신안 임자도 885
13.6%
진도 하조도 885
13.6%

Length

2024-03-13T21:42:37.441241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:37.608202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강화 1185
 
9.1%
여차리 1185
 
9.1%
안산 885
 
6.8%
말부흥 885
 
6.8%
태안 885
 
6.8%
백리포 885
 
6.8%
보령 885
 
6.8%
석대도 885
 
6.8%
부안 885
 
6.8%
변산 885
 
6.8%
Other values (4) 3540
27.3%

DNF_SRC_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
국내기인
6495 

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 6495
100.0%

Length

2024-03-13T21:42:37.878195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:38.070811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내기인 6495
100.0%

QTMT_NM
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
플라스틱류
4763 
금속
866 
고무
 
433
의료 및 개인위생
 
433

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 4763
73.3%
금속 866
 
13.3%
고무 433
 
6.7%
의료 및 개인위생 433
 
6.7%

Length

2024-03-13T21:42:38.275185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:38.465645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
플라스틱류 4763
64.7%
금속 866
 
11.8%
고무 433
 
5.9%
의료 433
 
5.9%
433
 
5.9%
개인위생 433
 
5.9%

IEM_NM
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
비닐봉투, 비닐쇼핑백 등
 
433
6개들이 포장고리
 
433
장어/문어 통발
 
433
어망(2.5~50cm)
 
433
어망 (50cm 이상)
 
433
Other values (10)
4330 

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 433
 
6.7%
6개들이 포장고리 433
 
6.7%
장어/문어 통발 433
 
6.7%
어망(2.5~50cm) 433
 
6.7%
어망 (50cm 이상) 433
 
6.7%
밧줄/로프 (2.5~50cm) 433
 
6.7%
밧줄/로프 (50cm 이상) 433
 
6.7%
끈(플라스틱, 노끈) (2.5~50cm) 433
 
6.7%
끈(플라스틱, 노끈) (50cm 이상) 433
 
6.7%
낚시줄 433
 
6.7%
Other values (5) 2165
33.3%

Length

2024-03-13T21:42:38.645985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50cm 1299
 
9.4%
이상 1299
 
9.4%
2.5~50cm 866
 
6.2%
노끈 866
 
6.2%
밧줄/로프 866
 
6.2%
끈(플라스틱 866
 
6.2%
풍선 433
 
3.1%
스프링통발(그물포함 433
 
3.1%
낚싯바늘 433
 
3.1%
낚시추 433
 
3.1%
Other values (14) 6062
43.8%

IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9518091
Minimum0
Maximum846
Zeros4494
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:38.835759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31
95-th percentile17
Maximum846
Range846
Interquartile range (IQR)1

Descriptive statistics

Standard deviation22.004479
Coefficient of variation (CV)5.5682039
Kurtosis517.84175
Mean3.9518091
Median Absolute Deviation (MAD)0
Skewness18.805751
Sum25667
Variance484.19709
MonotonicityNot monotonic
2024-03-13T21:42:39.020032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4494
69.2%
1 461
 
7.1%
2 355
 
5.5%
3 200
 
3.1%
4 121
 
1.9%
5 105
 
1.6%
6 69
 
1.1%
10 58
 
0.9%
7 57
 
0.9%
8 50
 
0.8%
Other values (95) 525
 
8.1%
ValueCountFrequency (%)
0 4494
69.2%
1 461
 
7.1%
2 355
 
5.5%
3 200
 
3.1%
4 121
 
1.9%
5 105
 
1.6%
6 69
 
1.1%
7 57
 
0.9%
8 50
 
0.8%
9 26
 
0.4%
ValueCountFrequency (%)
846 1
< 0.1%
570 1
< 0.1%
500 1
< 0.1%
452 1
< 0.1%
450 1
< 0.1%
339 1
< 0.1%
300 1
< 0.1%
220 1
< 0.1%
213 1
< 0.1%
205 1
< 0.1%

METER_PER_IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct105
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.039518091
Minimum0
Maximum8.46
Zeros4494
Zeros (%)69.2%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:39.213597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.01
95-th percentile0.17
Maximum8.46
Range8.46
Interquartile range (IQR)0.01

Descriptive statistics

Standard deviation0.22004479
Coefficient of variation (CV)5.5682039
Kurtosis517.84175
Mean0.039518091
Median Absolute Deviation (MAD)0
Skewness18.805751
Sum256.67
Variance0.048419709
MonotonicityNot monotonic
2024-03-13T21:42:39.391932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4494
69.2%
0.01 461
 
7.1%
0.02 355
 
5.5%
0.03 200
 
3.1%
0.04 121
 
1.9%
0.05 105
 
1.6%
0.06 69
 
1.1%
0.1 58
 
0.9%
0.07 57
 
0.9%
0.08 50
 
0.8%
Other values (95) 525
 
8.1%
ValueCountFrequency (%)
0.0 4494
69.2%
0.01 461
 
7.1%
0.02 355
 
5.5%
0.03 200
 
3.1%
0.04 121
 
1.9%
0.05 105
 
1.6%
0.06 69
 
1.1%
0.07 57
 
0.9%
0.08 50
 
0.8%
0.09 26
 
0.4%
ValueCountFrequency (%)
8.46 1
< 0.1%
5.7 1
< 0.1%
5.0 1
< 0.1%
4.52 1
< 0.1%
4.5 1
< 0.1%
3.39 1
< 0.1%
3.0 1
< 0.1%
2.2 1
< 0.1%
2.13 1
< 0.1%
2.05 1
< 0.1%

ADM_ZN_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
전남
2070 
충남
1770 
인천
885 
경기
885 
전북
885 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전남 2070
31.9%
충남 1770
27.3%
인천 885
13.6%
경기 885
13.6%
전북 885
13.6%

Length

2024-03-13T21:42:39.566989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:39.698027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남 2070
31.9%
충남 1770
27.3%
인천 885
13.6%
경기 885
13.6%
전북 885
13.6%

QTMT_CD
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
PL
4763 
ME
866 
RB
 
433
MH
 
433

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 4763
73.3%
ME 866
 
13.3%
RB 433
 
6.7%
MH 433
 
6.7%

Length

2024-03-13T21:42:39.869674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:40.043390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pl 4763
73.3%
me 866
 
13.3%
rb 433
 
6.7%
mh 433
 
6.7%

IEM_CD
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
PL_1_01
 
433
PL_1_07
 
433
PL_1_15
 
433
PL_1_16
 
433
PL_1_17
 
433
Other values (10)
4330 

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_01 433
 
6.7%
PL_1_07 433
 
6.7%
PL_1_15 433
 
6.7%
PL_1_16 433
 
6.7%
PL_1_17 433
 
6.7%
PL_1_20 433
 
6.7%
PL_1_21 433
 
6.7%
PL_1_22 433
 
6.7%
PL_1_23 433
 
6.7%
PL_1_30 433
 
6.7%
Other values (5) 2165
33.3%

Length

2024-03-13T21:42:40.233940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl_1_01 433
 
6.7%
pl_1_07 433
 
6.7%
pl_1_15 433
 
6.7%
pl_1_16 433
 
6.7%
pl_1_17 433
 
6.7%
pl_1_20 433
 
6.7%
pl_1_21 433
 
6.7%
pl_1_22 433
 
6.7%
pl_1_23 433
 
6.7%
pl_1_30 433
 
6.7%
Other values (5) 2165
33.3%

INVS_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct294
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20127962
Minimum20080329
Maximum20171207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:40.425278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20080329
5-th percentile20080926
Q120101001
median20130404
Q320151003
95-th percentile20170728
Maximum20171207
Range90878
Interquartile range (IQR)50002

Descriptive statistics

Standard deviation28548.647
Coefficient of variation (CV)0.0014183576
Kurtosis-1.224599
Mean20127962
Median Absolute Deviation (MAD)20721
Skewness-0.085672693
Sum1.3073111 × 1011
Variance8.1502526 × 108
MonotonicityNot monotonic
2024-03-13T21:42:41.056135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090926 75
 
1.2%
20080531 75
 
1.2%
20090531 75
 
1.2%
20080926 75
 
1.2%
20090331 60
 
0.9%
20080331 60
 
0.9%
20110326 60
 
0.9%
20140329 45
 
0.7%
20091128 45
 
0.7%
20120729 45
 
0.7%
Other values (284) 5880
90.5%
ValueCountFrequency (%)
20080329 45
0.7%
20080331 60
0.9%
20080528 15
 
0.2%
20080530 15
 
0.2%
20080531 75
1.2%
20080725 15
 
0.2%
20080730 30
 
0.5%
20080731 30
 
0.5%
20080801 15
 
0.2%
20080802 15
 
0.2%
ValueCountFrequency (%)
20171207 15
 
0.2%
20171202 30
0.5%
20171128 30
0.5%
20171125 45
0.7%
20171007 15
 
0.2%
20171006 30
0.5%
20170930 30
0.5%
20170929 15
 
0.2%
20170921 30
0.5%
20170812 15
 
0.2%

CST_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size50.9 KiB
서해안
6495 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서해안 6495
100.0%

Length

2024-03-13T21:42:41.241327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:42:41.389886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서해안 6495
100.0%

STR_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.208829
Minimum34.283875
Maximum37.609196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:41.490555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.283875
5-th percentile34.283875
Q135.140523
median36.244131
Q337.209391
95-th percentile37.609196
Maximum37.609196
Range3.325321
Interquartile range (IQR)2.068868

Descriptive statistics

Standard deviation1.1143459
Coefficient of variation (CV)0.03077553
Kurtosis-1.1090685
Mean36.208829
Median Absolute Deviation (MAD)0.96526
Skewness-0.35415003
Sum235176.34
Variance1.2417667
MonotonicityNot monotonic
2024-03-13T21:42:41.636468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37.609196 1185
18.2%
37.209391 885
13.6%
36.812857 885
13.6%
36.244131 885
13.6%
35.687127 885
13.6%
35.140523 885
13.6%
34.283875 885
13.6%
ValueCountFrequency (%)
34.283875 885
13.6%
35.140523 885
13.6%
35.687127 885
13.6%
36.244131 885
13.6%
36.812857 885
13.6%
37.209391 885
13.6%
37.609196 1185
18.2%
ValueCountFrequency (%)
37.609196 1185
18.2%
37.209391 885
13.6%
36.812857 885
13.6%
36.244131 885
13.6%
35.687127 885
13.6%
35.140523 885
13.6%
34.283875 885
13.6%

STR_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.34373
Minimum126.0732
Maximum126.61523
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:41.787332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.0732
5-th percentile126.0732
Q1126.11558
median126.3812
Q3126.5328
95-th percentile126.61523
Maximum126.61523
Range0.542031
Interquartile range (IQR)0.417226

Descriptive statistics

Standard deviation0.20327096
Coefficient of variation (CV)0.0016088726
Kurtosis-1.6424705
Mean126.34373
Median Absolute Deviation (MAD)0.225884
Skewness-0.098000641
Sum820602.55
Variance0.041319085
MonotonicityNot monotonic
2024-03-13T21:42:41.955832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
126.381198 1185
18.2%
126.615229 885
13.6%
126.155314 885
13.6%
126.520114 885
13.6%
126.532802 885
13.6%
126.115576 885
13.6%
126.073198 885
13.6%
ValueCountFrequency (%)
126.073198 885
13.6%
126.115576 885
13.6%
126.155314 885
13.6%
126.381198 1185
18.2%
126.520114 885
13.6%
126.532802 885
13.6%
126.615229 885
13.6%
ValueCountFrequency (%)
126.615229 885
13.6%
126.532802 885
13.6%
126.520114 885
13.6%
126.381198 1185
18.2%
126.155314 885
13.6%
126.115576 885
13.6%
126.073198 885
13.6%

END_LA
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.208324
Minimum34.283149
Maximum37.608776
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:42.100893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.283149
5-th percentile34.283149
Q135.139151
median36.244157
Q337.208837
95-th percentile37.608776
Maximum37.608776
Range3.325627
Interquartile range (IQR)2.069686

Descriptive statistics

Standard deviation1.1146136
Coefficient of variation (CV)0.030783352
Kurtosis-1.1097752
Mean36.208324
Median Absolute Deviation (MAD)0.96468
Skewness-0.35431581
Sum235173.07
Variance1.2423635
MonotonicityNot monotonic
2024-03-13T21:42:42.245285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37.608776 1185
18.2%
37.208837 885
13.6%
36.813211 885
13.6%
36.244157 885
13.6%
35.68626 885
13.6%
35.139151 885
13.6%
34.283149 885
13.6%
ValueCountFrequency (%)
34.283149 885
13.6%
35.139151 885
13.6%
35.68626 885
13.6%
36.244157 885
13.6%
36.813211 885
13.6%
37.208837 885
13.6%
37.608776 1185
18.2%
ValueCountFrequency (%)
37.608776 1185
18.2%
37.208837 885
13.6%
36.813211 885
13.6%
36.244157 885
13.6%
35.68626 885
13.6%
35.139151 885
13.6%
34.283149 885
13.6%

END_LO
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.34393
Minimum126.07261
Maximum126.61493
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size57.2 KiB
2024-03-13T21:42:42.446587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.07261
5-th percentile126.07261
Q1126.11579
median126.38206
Q3126.53276
95-th percentile126.61493
Maximum126.61493
Range0.542324
Interquartile range (IQR)0.416978

Descriptive statistics

Standard deviation0.20343625
Coefficient of variation (CV)0.0016101782
Kurtosis-1.6437162
Mean126.34393
Median Absolute Deviation (MAD)0.226739
Skewness-0.10177211
Sum820603.85
Variance0.041386307
MonotonicityNot monotonic
2024-03-13T21:42:42.620797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
126.382063 1185
18.2%
126.614933 885
13.6%
126.155324 885
13.6%
126.521135 885
13.6%
126.532763 885
13.6%
126.115785 885
13.6%
126.072609 885
13.6%
ValueCountFrequency (%)
126.072609 885
13.6%
126.115785 885
13.6%
126.155324 885
13.6%
126.382063 1185
18.2%
126.521135 885
13.6%
126.532763 885
13.6%
126.614933 885
13.6%
ValueCountFrequency (%)
126.614933 885
13.6%
126.532763 885
13.6%
126.521135 885
13.6%
126.382063 1185
18.2%
126.155324 885
13.6%
126.115785 885
13.6%
126.072609 885
13.6%

Interactions

2024-03-13T21:42:34.939949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:26.375023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.449093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:28.634174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.775774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.966061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:32.618752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.794576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:35.088691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:26.503400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.565949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:28.795605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.919488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:31.134627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:32.761697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.922617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:35.249707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:26.649667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.692600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:28.923796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.043771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:31.288956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:32.891407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:34.097569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:35.425073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:26.772077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.814869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.059430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.156510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:31.431262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.042862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:34.257959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:35.606490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:26.881416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.958581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.174338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.283638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:31.576637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.226073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:34.404204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:35.756296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.010473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:28.102624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.324237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.436505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:32.159433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.389395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:34.535284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:35.889556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.163151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:28.243493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.464483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.611650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:32.322355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.525902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:34.663934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:36.070746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:27.319955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:28.439674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:29.612509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:30.772089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:32.480656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:33.667840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:42:34.767759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:42:42.756241image/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_YR1.0000.0000.0710.0000.0000.0000.0000.0440.0000.0001.0000.0710.0910.0710.091
INVS_DSRT0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0920.0000.0000.0000.000
INVS_AREA_NM0.0710.0001.0000.0000.0000.0570.0570.9590.0000.0000.0841.0001.0001.0001.000
QTMT_NM0.0000.0000.0001.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.000
IEM_NM0.0000.0000.0001.0001.0000.1520.1520.0001.0001.0000.0000.0000.0000.0000.000
IEM_CNT0.0000.0000.0570.0000.1521.0001.0000.0820.0000.1520.0000.0570.0630.0570.063
METER_PER_IEM_CNT0.0000.0000.0570.0000.1521.0001.0000.0820.0000.1520.0000.0570.0630.0570.063
ADM_ZN_NM0.0440.0000.9590.0000.0000.0820.0821.0000.0000.0000.0840.9590.9890.9590.989
QTMT_CD0.0000.0000.0001.0001.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.000
IEM_CD0.0000.0000.0001.0001.0000.1520.1520.0001.0001.0000.0000.0000.0000.0000.000
INVS_YMD1.0000.0920.0840.0000.0000.0000.0000.0840.0000.0001.0000.0840.1380.0840.138
STR_LA0.0710.0001.0000.0000.0000.0570.0570.9590.0000.0000.0841.0001.0001.0001.000
STR_LO0.0910.0001.0000.0000.0000.0630.0630.9890.0000.0000.1381.0001.0001.0001.000
END_LA0.0710.0001.0000.0000.0000.0570.0570.9590.0000.0000.0841.0001.0001.0001.000
END_LO0.0910.0001.0000.0000.0000.0630.0630.9890.0000.0000.1381.0001.0001.0001.000
2024-03-13T21:42:42.962175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ADM_ZN_NMQTMT_CDIEM_NMINVS_AREA_NMIEM_CDQTMT_NMINVS_DSRT
ADM_ZN_NM1.0000.0000.0000.9540.0000.0000.000
QTMT_CD0.0001.0000.9990.0000.9991.0000.000
IEM_NM0.0000.9991.0000.0001.0000.9990.000
INVS_AREA_NM0.9540.0000.0001.0000.0000.0000.000
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
2024-03-13T21:42:43.170568image/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_YR1.000-0.086-0.0860.9940.0750.0000.0750.0000.0430.0360.0000.0000.0230.0000.000
IEM_CNT-0.0861.0001.000-0.085-0.037-0.037-0.037-0.0370.0000.0300.0000.0660.0500.0000.066
METER_PER_IEM_CNT-0.0861.0001.000-0.085-0.037-0.037-0.037-0.0370.0000.0300.0000.0660.0500.0000.066
INVS_YMD0.994-0.085-0.0851.0000.072-0.0020.072-0.0020.0450.0420.0000.0000.0290.0000.000
STR_LA0.075-0.037-0.0370.0721.0000.5231.0000.5230.0001.0000.0000.0000.9540.0000.000
STR_LO0.000-0.037-0.037-0.0020.5231.0000.5231.0000.0001.0000.0000.0000.8500.0000.000
END_LA0.075-0.037-0.0370.0721.0000.5231.0000.5230.0001.0000.0000.0000.9540.0000.000
END_LO0.000-0.037-0.037-0.0020.5231.0000.5231.0000.0001.0000.0000.0000.8500.0000.000
INVS_DSRT0.0430.0000.0000.0450.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
INVS_AREA_NM0.0360.0300.0300.0421.0001.0001.0001.0000.0001.0000.0000.0000.9540.0000.000
QTMT_NM0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_NM0.0000.0660.0660.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000
ADM_ZN_NM0.0230.0500.0500.0290.9540.8500.9540.8500.0000.9540.0000.0001.0000.0000.000
QTMT_CD0.0000.0000.0000.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_CD0.0000.0660.0660.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000

Missing values

2024-03-13T21:42:36.306635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:42:36.603561image/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
020082차강화 여차리국내기인플라스틱류비닐봉투, 비닐쇼핑백 등30.03인천PLPL_1_0120080331서해안37.609196126.38119837.608776126.382063
120082차강화 여차리국내기인플라스틱류6개들이 포장고리00.0인천PLPL_1_0720080331서해안37.609196126.38119837.608776126.382063
220082차강화 여차리국내기인플라스틱류장어/문어 통발00.0인천PLPL_1_1520080331서해안37.609196126.38119837.608776126.382063
320082차강화 여차리국내기인플라스틱류어망(2.5~50cm)00.0인천PLPL_1_1620080331서해안37.609196126.38119837.608776126.382063
420082차강화 여차리국내기인플라스틱류어망 (50cm 이상)00.0인천PLPL_1_1720080331서해안37.609196126.38119837.608776126.382063
520082차강화 여차리국내기인플라스틱류밧줄/로프 (2.5~50cm)40.04인천PLPL_1_2020080331서해안37.609196126.38119837.608776126.382063
620082차강화 여차리국내기인플라스틱류밧줄/로프 (50cm 이상)220.22인천PLPL_1_2120080331서해안37.609196126.38119837.608776126.382063
720082차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (2.5~50cm)130.13인천PLPL_1_2220080331서해안37.609196126.38119837.608776126.382063
820082차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (50cm 이상)90.09인천PLPL_1_2320080331서해안37.609196126.38119837.608776126.382063
920082차강화 여차리국내기인플라스틱류낚시줄00.0인천PLPL_1_3020080331서해안37.609196126.38119837.608776126.382063
INVS_YRINVS_DSRTINVS_AREA_NMDNF_SRC_NMQTMT_NMIEM_NMIEM_CNTMETER_PER_IEM_CNTADM_ZN_NMQTMT_CDIEM_CDINVS_YMDCST_NMSTR_LASTR_LOEND_LAEND_LO
648520176차강화 여차리국내기인플라스틱류밧줄/로프 (2.5~50cm)20.02전남PLPL_1_2020171128서해안37.609196126.38119837.608776126.382063
648620176차강화 여차리국내기인플라스틱류밧줄/로프 (50cm 이상)40.04전남PLPL_1_2120171128서해안37.609196126.38119837.608776126.382063
648720176차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (2.5~50cm)20.02전남PLPL_1_2220171128서해안37.609196126.38119837.608776126.382063
648820176차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (50cm 이상)10.01전남PLPL_1_2320171128서해안37.609196126.38119837.608776126.382063
648920176차강화 여차리국내기인플라스틱류낚시줄00.0전남PLPL_1_3020171128서해안37.609196126.38119837.608776126.382063
649020176차강화 여차리국내기인플라스틱류가짜미끼, 형광찌130.13전남PLPL_1_3120171128서해안37.609196126.38119837.608776126.382063
649120176차강화 여차리국내기인금속납 낚시추, 낚싯바늘00.0전남MEME_1_0620171128서해안37.609196126.38119837.608776126.382063
649220176차강화 여차리국내기인금속스프링통발(그물포함)00.0전남MEME_1_0720171128서해안37.609196126.38119837.608776126.382063
649320176차강화 여차리국내기인고무풍선10.01전남RBRB_1_0220171128서해안37.609196126.38119837.608776126.382063
649420176차강화 여차리국내기인의료 및 개인위생주사기00.0전남MHMH_1_0220171128서해안37.609196126.38119837.608776126.382063