Overview

Dataset statistics

Number of variables17
Number of observations7361
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=CT08OSN002

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 4814 (65.4%) zerosZeros
METER_PER_IEM_CNT has 4814 (65.4%) zerosZeros

Reproduction

Analysis started2024-03-13 12:46:22.344179
Analysis finished2024-03-13 12:46:35.740658
Duration13.4 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%
Mean2012.7252
Minimum2008
Maximum2017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2024-03-13T21:46:35.828834image/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 deviation2.8587923
Coefficient of variation (CV)0.001420359
Kurtosis-1.2296482
Mean2012.7252
Median Absolute Deviation (MAD)2
Skewness-0.084326956
Sum14815670
Variance8.1726937
MonotonicityIncreasing
2024-03-13T21:46:35.987536image/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%
2014 748
10.2%
2009 714
9.7%
2010 714
9.7%
2011 714
9.7%
2012 714
9.7%
2013 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 748
10.2%
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 748
10.2%
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 size57.6 KiB
5차
1258 
6차
1258 
2차
1241 
3차
1241 
4차
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 (%)
5차 1258
17.1%
6차 1258
17.1%
2차 1241
16.9%
3차 1241
16.9%
4차 1241
16.9%
1차 1122
15.2%

Length

2024-03-13T21:46:36.162463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:36.317504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
5차 1258
17.1%
6차 1258
17.1%
2차 1241
16.9%
3차 1241
16.9%
4차 1241
16.9%
1차 1122
15.2%

INVS_AREA_NM
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
강화 여차리
1343 
안산 말부흥
1003 
태안 백리포
1003 
보령 석대도
1003 
부안 변산
1003 
Other values (2)
2006 

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 1343
18.2%
안산 말부흥 1003
13.6%
태안 백리포 1003
13.6%
보령 석대도 1003
13.6%
부안 변산 1003
13.6%
신안 임자도 1003
13.6%
진도 하조도 1003
13.6%

Length

2024-03-13T21:46:36.483828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:36.679027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
강화 1343
 
9.1%
여차리 1343
 
9.1%
안산 1003
 
6.8%
말부흥 1003
 
6.8%
태안 1003
 
6.8%
백리포 1003
 
6.8%
보령 1003
 
6.8%
석대도 1003
 
6.8%
부안 1003
 
6.8%
변산 1003
 
6.8%
Other values (4) 4012
27.3%

DNF_SRC_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
국내기인
7361 

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 7361
100.0%

Length

2024-03-13T21:46:36.870688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:37.025298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내기인 7361
100.0%

QTMT_NM
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
플라스틱류
4763 
나무
2165 
금속
 
433

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 4763
64.7%
나무 2165
29.4%
금속 433
 
5.9%

Length

2024-03-13T21:46:37.176909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:37.322836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
플라스틱류 4763
64.7%
나무 2165
29.4%
금속 433
 
5.9%

IEM_NM
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
비닐봉투, 비닐쇼핑백 등
 
433
음식물포장지(라면봉지, 과자봉지 등)
 
433
어망(2.5~50cm)
 
433
어망 (50cm 이상)
 
433
밧줄/로프 (2.5~50cm)
 
433
Other values (12)
5196 

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 433
 
5.9%
음식물포장지(라면봉지, 과자봉지 등) 433
 
5.9%
어망(2.5~50cm) 433
 
5.9%
어망 (50cm 이상) 433
 
5.9%
밧줄/로프 (2.5~50cm) 433
 
5.9%
밧줄/로프 (50cm 이상) 433
 
5.9%
끈(플라스틱, 노끈) (2.5~50cm) 433
 
5.9%
끈(플라스틱, 노끈) (50cm 이상) 433
 
5.9%
농업용폐비닐 (2.5~50cm) 433
 
5.9%
농업용폐비닐 (50cm 이상) 433
 
5.9%
Other values (7) 3031
41.2%

Length

2024-03-13T21:46:37.530122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
50cm 2165
12.5%
이상 2165
12.5%
2.5~50cm 1732
 
10.0%
1299
 
7.5%
끈(플라스틱 866
 
5.0%
건축용목재 866
 
5.0%
밧줄/로프 866
 
5.0%
노끈 866
 
5.0%
농업용폐비닐 866
 
5.0%
나무 433
 
2.5%
Other values (12) 5196
30.0%

IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.8464882
Minimum0
Maximum846
Zeros4814
Zeros (%)65.4%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2024-03-13T21:46:37.780151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q32
95-th percentile20
Maximum846
Range846
Interquartile range (IQR)2

Descriptive statistics

Standard deviation24.359983
Coefficient of variation (CV)5.0263163
Kurtosis357.27816
Mean4.8464882
Median Absolute Deviation (MAD)0
Skewness15.717701
Sum35675
Variance593.40877
MonotonicityNot monotonic
2024-03-13T21:46:38.033436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4814
65.4%
1 451
 
6.1%
2 412
 
5.6%
3 273
 
3.7%
4 182
 
2.5%
5 149
 
2.0%
6 120
 
1.6%
7 96
 
1.3%
10 75
 
1.0%
8 68
 
0.9%
Other values (116) 721
 
9.8%
ValueCountFrequency (%)
0 4814
65.4%
1 451
 
6.1%
2 412
 
5.6%
3 273
 
3.7%
4 182
 
2.5%
5 149
 
2.0%
6 120
 
1.6%
7 96
 
1.3%
8 68
 
0.9%
9 37
 
0.5%
ValueCountFrequency (%)
846 1
< 0.1%
570 1
< 0.1%
500 1
< 0.1%
483 1
< 0.1%
452 1
< 0.1%
450 1
< 0.1%
434 1
< 0.1%
425 1
< 0.1%
340 1
< 0.1%
339 1
< 0.1%

METER_PER_IEM_CNT
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct126
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.048464882
Minimum0
Maximum8.46
Zeros4814
Zeros (%)65.4%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2024-03-13T21:46:38.271485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30.02
95-th percentile0.2
Maximum8.46
Range8.46
Interquartile range (IQR)0.02

Descriptive statistics

Standard deviation0.24359983
Coefficient of variation (CV)5.0263163
Kurtosis357.27816
Mean0.048464882
Median Absolute Deviation (MAD)0
Skewness15.717701
Sum356.75
Variance0.059340877
MonotonicityNot monotonic
2024-03-13T21:46:38.448871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4814
65.4%
0.01 451
 
6.1%
0.02 412
 
5.6%
0.03 273
 
3.7%
0.04 182
 
2.5%
0.05 149
 
2.0%
0.06 120
 
1.6%
0.07 96
 
1.3%
0.1 75
 
1.0%
0.08 68
 
0.9%
Other values (116) 721
 
9.8%
ValueCountFrequency (%)
0.0 4814
65.4%
0.01 451
 
6.1%
0.02 412
 
5.6%
0.03 273
 
3.7%
0.04 182
 
2.5%
0.05 149
 
2.0%
0.06 120
 
1.6%
0.07 96
 
1.3%
0.08 68
 
0.9%
0.09 37
 
0.5%
ValueCountFrequency (%)
8.46 1
< 0.1%
5.7 1
< 0.1%
5.0 1
< 0.1%
4.83 1
< 0.1%
4.52 1
< 0.1%
4.5 1
< 0.1%
4.34 1
< 0.1%
4.25 1
< 0.1%
3.4 1
< 0.1%
3.39 1
< 0.1%

ADM_ZN_NM
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
전남
2346 
충남
2006 
인천
1003 
경기
1003 
전북
1003 

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 2346
31.9%
충남 2006
27.3%
인천 1003
13.6%
경기 1003
13.6%
전북 1003
13.6%

Length

2024-03-13T21:46:38.618630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:38.753717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전남 2346
31.9%
충남 2006
27.3%
인천 1003
13.6%
경기 1003
13.6%
전북 1003
13.6%

QTMT_CD
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
PL
4763 
WD
2165 
ME
 
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
64.7%
WD 2165
29.4%
ME 433
 
5.9%

Length

2024-03-13T21:46:38.914739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:39.055794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
pl 4763
64.7%
wd 2165
29.4%
me 433
 
5.9%

IEM_CD
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
PL_1_01
 
433
PL_1_05
 
433
PL_1_16
 
433
PL_1_17
 
433
PL_1_20
 
433
Other values (12)
5196 

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_01 433
 
5.9%
PL_1_05 433
 
5.9%
PL_1_16 433
 
5.9%
PL_1_17 433
 
5.9%
PL_1_20 433
 
5.9%
PL_1_21 433
 
5.9%
PL_1_22 433
 
5.9%
PL_1_23 433
 
5.9%
PL_1_27 433
 
5.9%
PL_1_28 433
 
5.9%
Other values (7) 3031
41.2%

Length

2024-03-13T21:46:39.175196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
pl_1_01 433
 
5.9%
pl_1_28 433
 
5.9%
wd_1_05 433
 
5.9%
wd_1_04 433
 
5.9%
wd_1_03 433
 
5.9%
wd_1_02 433
 
5.9%
wd_1_01 433
 
5.9%
pl_1_30 433
 
5.9%
pl_1_27 433
 
5.9%
pl_1_05 433
 
5.9%
Other values (7) 3031
41.2%

INVS_YMD
Real number (ℝ)

HIGH CORRELATION 

Distinct294
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20127962
Minimum20080329
Maximum20171207
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size64.8 KiB
2024-03-13T21:46:39.324759image/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 deviation28548.389
Coefficient of variation (CV)0.0014183447
Kurtosis-1.2245967
Mean20127962
Median Absolute Deviation (MAD)20721
Skewness-0.085670365
Sum1.4816193 × 1011
Variance8.150105 × 108
MonotonicityNot monotonic
2024-03-13T21:46:39.474204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20090926 85
 
1.2%
20080531 85
 
1.2%
20090531 85
 
1.2%
20080926 85
 
1.2%
20090331 68
 
0.9%
20080331 68
 
0.9%
20110326 68
 
0.9%
20140329 51
 
0.7%
20091128 51
 
0.7%
20120729 51
 
0.7%
Other values (284) 6664
90.5%
ValueCountFrequency (%)
20080329 51
0.7%
20080331 68
0.9%
20080528 17
 
0.2%
20080530 17
 
0.2%
20080531 85
1.2%
20080725 17
 
0.2%
20080730 34
 
0.5%
20080731 34
 
0.5%
20080801 17
 
0.2%
20080802 17
 
0.2%
ValueCountFrequency (%)
20171207 17
 
0.2%
20171202 34
0.5%
20171128 34
0.5%
20171125 51
0.7%
20171007 17
 
0.2%
20171006 34
0.5%
20170930 34
0.5%
20170929 17
 
0.2%
20170921 34
0.5%
20170812 17
 
0.2%

CST_NM
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.6 KiB
서해안
7361 

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 7361
100.0%

Length

2024-03-13T21:46:39.687133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T21:46:39.804312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서해안 7361
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 size64.8 KiB
2024-03-13T21:46:39.901141image/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 deviation1.1143358
Coefficient of variation (CV)0.030775251
Kurtosis-1.1090767
Mean36.208829
Median Absolute Deviation (MAD)0.96526
Skewness-0.35414041
Sum266533.19
Variance1.2417442
MonotonicityNot monotonic
2024-03-13T21:46:40.025922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37.609196 1343
18.2%
37.209391 1003
13.6%
36.812857 1003
13.6%
36.244131 1003
13.6%
35.687127 1003
13.6%
35.140523 1003
13.6%
34.283875 1003
13.6%
ValueCountFrequency (%)
34.283875 1003
13.6%
35.140523 1003
13.6%
35.687127 1003
13.6%
36.244131 1003
13.6%
36.812857 1003
13.6%
37.209391 1003
13.6%
37.609196 1343
18.2%
ValueCountFrequency (%)
37.609196 1343
18.2%
37.209391 1003
13.6%
36.812857 1003
13.6%
36.244131 1003
13.6%
35.687127 1003
13.6%
35.140523 1003
13.6%
34.283875 1003
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 size64.8 KiB
2024-03-13T21:46:40.160777image/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 deviation0.20326912
Coefficient of variation (CV)0.001608858
Kurtosis-1.6424304
Mean126.34373
Median Absolute Deviation (MAD)0.225884
Skewness-0.097997978
Sum930016.22
Variance0.041318336
MonotonicityNot monotonic
2024-03-13T21:46:40.352571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
126.381198 1343
18.2%
126.615229 1003
13.6%
126.155314 1003
13.6%
126.520114 1003
13.6%
126.532802 1003
13.6%
126.115576 1003
13.6%
126.073198 1003
13.6%
ValueCountFrequency (%)
126.073198 1003
13.6%
126.115576 1003
13.6%
126.155314 1003
13.6%
126.381198 1343
18.2%
126.520114 1003
13.6%
126.532802 1003
13.6%
126.615229 1003
13.6%
ValueCountFrequency (%)
126.615229 1003
13.6%
126.532802 1003
13.6%
126.520114 1003
13.6%
126.381198 1343
18.2%
126.155314 1003
13.6%
126.115576 1003
13.6%
126.073198 1003
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 size64.8 KiB
2024-03-13T21:46:40.492931image/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 deviation1.1146035
Coefficient of variation (CV)0.030783073
Kurtosis-1.1097834
Mean36.208324
Median Absolute Deviation (MAD)0.96468
Skewness-0.35430618
Sum266529.48
Variance1.242341
MonotonicityNot monotonic
2024-03-13T21:46:40.623976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
37.608776 1343
18.2%
37.208837 1003
13.6%
36.813211 1003
13.6%
36.244157 1003
13.6%
35.68626 1003
13.6%
35.139151 1003
13.6%
34.283149 1003
13.6%
ValueCountFrequency (%)
34.283149 1003
13.6%
35.139151 1003
13.6%
35.68626 1003
13.6%
36.244157 1003
13.6%
36.813211 1003
13.6%
37.208837 1003
13.6%
37.608776 1343
18.2%
ValueCountFrequency (%)
37.608776 1343
18.2%
37.208837 1003
13.6%
36.813211 1003
13.6%
36.244157 1003
13.6%
35.68626 1003
13.6%
35.139151 1003
13.6%
34.283149 1003
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 size64.8 KiB
2024-03-13T21:46:40.765444image/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 deviation0.2034344
Coefficient of variation (CV)0.0016101636
Kurtosis-1.643676
Mean126.34393
Median Absolute Deviation (MAD)0.226739
Skewness-0.10176934
Sum930017.7
Variance0.041385557
MonotonicityNot monotonic
2024-03-13T21:46:40.902035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
126.382063 1343
18.2%
126.614933 1003
13.6%
126.155324 1003
13.6%
126.521135 1003
13.6%
126.532763 1003
13.6%
126.115785 1003
13.6%
126.072609 1003
13.6%
ValueCountFrequency (%)
126.072609 1003
13.6%
126.115785 1003
13.6%
126.155324 1003
13.6%
126.382063 1343
18.2%
126.521135 1003
13.6%
126.532763 1003
13.6%
126.614933 1003
13.6%
ValueCountFrequency (%)
126.614933 1003
13.6%
126.532763 1003
13.6%
126.521135 1003
13.6%
126.382063 1343
18.2%
126.155324 1003
13.6%
126.115785 1003
13.6%
126.072609 1003
13.6%

Interactions

2024-03-13T21:46:33.737248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:25.445719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:26.624790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.688262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:28.981659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.046171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:31.300648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.444641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:34.257368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:25.562453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:26.760490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.803666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.116955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.192283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:31.428363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.575394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:34.383047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:25.680571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:26.889174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.927966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.244515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.312799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:31.570029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.719123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:34.517475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:25.814700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.024468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:28.082668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.377291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.450781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:31.706965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.870458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:34.621061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:25.950107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.156447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:28.218504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.519592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.597030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:31.852921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.999896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:34.754827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:26.089501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.310979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:28.349483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.652162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.742517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.003500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:33.150514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:34.877538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:26.265865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.444022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:28.504797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.772261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:30.915802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.171298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:33.398025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:35.029057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:26.448871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:27.562075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:28.769386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:29.923217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:31.152009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:32.315831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T21:46:33.568055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T21:46:41.012693image/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_YR1.0000.0000.0750.0000.0000.0260.0260.0490.0000.0001.0000.0750.0940.0750.094
INVS_DSRT0.0001.0000.0000.0000.0000.0000.0000.0000.0000.0000.0950.0000.0000.0000.000
INVS_AREA_NM0.0750.0001.0000.0000.0000.0870.0870.9590.0000.0000.0881.0001.0001.0001.000
QTMT_NM0.0000.0000.0001.0001.0000.0530.0530.0001.0001.0000.0000.0000.0000.0000.000
IEM_NM0.0000.0000.0001.0001.0000.1320.1320.0001.0001.0000.0000.0000.0000.0000.000
IEM_CNT0.0260.0000.0870.0530.1321.0001.0000.1160.0530.1320.0280.0870.0960.0870.096
METER_PER_IEM_CNT0.0260.0000.0870.0530.1321.0001.0000.1160.0530.1320.0280.0870.0960.0870.096
ADM_ZN_NM0.0490.0000.9590.0000.0000.1160.1161.0000.0000.0000.0900.9590.9890.9590.989
QTMT_CD0.0000.0000.0001.0001.0000.0530.0530.0001.0001.0000.0000.0000.0000.0000.000
IEM_CD0.0000.0000.0001.0001.0000.1320.1320.0001.0001.0000.0000.0000.0000.0000.000
INVS_YMD1.0000.0950.0880.0000.0000.0280.0280.0900.0000.0001.0000.0880.1410.0880.141
STR_LA0.0750.0001.0000.0000.0000.0870.0870.9590.0000.0000.0881.0001.0001.0001.000
STR_LO0.0940.0001.0000.0000.0000.0960.0960.9890.0000.0000.1411.0001.0001.0001.000
END_LA0.0750.0001.0000.0000.0000.0870.0870.9590.0000.0000.0881.0001.0001.0001.000
END_LO0.0940.0001.0000.0000.0000.0960.0960.9890.0000.0000.1411.0001.0001.0001.000
2024-03-13T21:46:41.640328image/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
2024-03-13T21:46:41.817631image/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_YR1.000-0.117-0.1170.9940.0750.0000.0750.0000.0450.0380.0000.0000.0260.0000.000
IEM_CNT-0.1171.0001.000-0.117-0.005-0.025-0.005-0.0250.0000.0460.0330.0550.0710.0330.055
METER_PER_IEM_CNT-0.1171.0001.000-0.117-0.005-0.025-0.005-0.0250.0000.0460.0330.0550.0710.0330.055
INVS_YMD0.994-0.117-0.1171.0000.072-0.0020.072-0.0020.0470.0440.0000.0000.0310.0000.000
STR_LA0.075-0.005-0.0050.0721.0000.5231.0000.5230.0001.0000.0000.0000.9540.0000.000
STR_LO0.000-0.025-0.025-0.0020.5231.0000.5231.0000.0001.0000.0000.0000.8500.0000.000
END_LA0.075-0.005-0.0050.0721.0000.5231.0000.5230.0001.0000.0000.0000.9540.0000.000
END_LO0.000-0.025-0.025-0.0020.5231.0000.5231.0000.0001.0000.0000.0000.8500.0000.000
INVS_DSRT0.0450.0000.0000.0470.0000.0000.0000.0001.0000.0000.0000.0000.0000.0000.000
INVS_AREA_NM0.0380.0460.0460.0441.0001.0001.0001.0000.0001.0000.0000.0000.9540.0000.000
QTMT_NM0.0000.0330.0330.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_NM0.0000.0550.0550.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000
ADM_ZN_NM0.0260.0710.0710.0310.9540.8500.9540.8500.0000.9540.0000.0001.0000.0000.000
QTMT_CD0.0000.0330.0330.0000.0000.0000.0000.0000.0000.0001.0000.9990.0001.0000.999
IEM_CD0.0000.0550.0550.0000.0000.0000.0000.0000.0000.0000.9991.0000.0000.9991.000

Missing values

2024-03-13T21:46:35.245706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T21:46:35.570003image/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 여차리국내기인플라스틱류음식물포장지(라면봉지, 과자봉지 등)2302.3인천PLPL_1_0520080331서해안37.609196126.38119837.608776126.382063
220082차강화 여차리국내기인플라스틱류어망(2.5~50cm)00.0인천PLPL_1_1620080331서해안37.609196126.38119837.608776126.382063
320082차강화 여차리국내기인플라스틱류어망 (50cm 이상)00.0인천PLPL_1_1720080331서해안37.609196126.38119837.608776126.382063
420082차강화 여차리국내기인플라스틱류밧줄/로프 (2.5~50cm)40.04인천PLPL_1_2020080331서해안37.609196126.38119837.608776126.382063
520082차강화 여차리국내기인플라스틱류밧줄/로프 (50cm 이상)220.22인천PLPL_1_2120080331서해안37.609196126.38119837.608776126.382063
620082차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (2.5~50cm)130.13인천PLPL_1_2220080331서해안37.609196126.38119837.608776126.382063
720082차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (50cm 이상)90.09인천PLPL_1_2320080331서해안37.609196126.38119837.608776126.382063
820082차강화 여차리국내기인플라스틱류농업용폐비닐 (2.5~50cm)4344.34인천PLPL_1_2720080331서해안37.609196126.38119837.608776126.382063
920082차강화 여차리국내기인플라스틱류농업용폐비닐 (50cm 이상)30.03인천PLPL_1_2820080331서해안37.609196126.38119837.608776126.382063
INVS_YRINVS_DSRTINVS_AREA_NMDNF_SRC_NMQTMT_NMIEM_NMIEM_CNTMETER_PER_IEM_CNTADM_ZN_NMQTMT_CDIEM_CDINVS_YMDCST_NMSTR_LASTR_LOEND_LAEND_LO
735120176차강화 여차리국내기인플라스틱류끈(플라스틱, 노끈) (50cm 이상)10.01전남PLPL_1_2320171128서해안37.609196126.38119837.608776126.382063
735220176차강화 여차리국내기인플라스틱류농업용폐비닐 (2.5~50cm)00.0전남PLPL_1_2720171128서해안37.609196126.38119837.608776126.382063
735320176차강화 여차리국내기인플라스틱류농업용폐비닐 (50cm 이상)00.0전남PLPL_1_2820171128서해안37.609196126.38119837.608776126.382063
735420176차강화 여차리국내기인플라스틱류낚시줄00.0전남PLPL_1_3020171128서해안37.609196126.38119837.608776126.382063
735520176차강화 여차리국내기인나무나무 팔레트00.0전남WDWD_1_0120171128서해안37.609196126.38119837.608776126.382063
735620176차강화 여차리국내기인나무대형나무포장상자00.0전남WDWD_1_0220171128서해안37.609196126.38119837.608776126.382063
735720176차강화 여차리국내기인나무건축용목재 (2.5~50cm)30.03전남WDWD_1_0320171128서해안37.609196126.38119837.608776126.382063
735820176차강화 여차리국내기인나무건축용목재 (50cm 이상)20.02전남WDWD_1_0420171128서해안37.609196126.38119837.608776126.382063
735920176차강화 여차리국내기인나무어업용목재(폐목선, 양식장시설 등)70.07전남WDWD_1_0520171128서해안37.609196126.38119837.608776126.382063
736020176차강화 여차리국내기인금속스프링통발(그물포함)00.0전남MEME_1_0720171128서해안37.609196126.38119837.608776126.382063