Overview

Dataset statistics

Number of variables9
Number of observations2188
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory168.9 KiB
Average record size in memory79.1 B

Variable types

Categorical2
DateTime1
Numeric6

Dataset

Description전국 13개 중소형 항만에 설치된 오염물질 시설을 관리 운영 및 선박, 해양시설에서 발생한 오염물질(선저폐수, 폐윤활유, 슬러지 등)의 수거처리 정보
Author해양환경공단
URLhttps://www.data.go.kr/data/15003355/fileData.do

Alerts

폐연료유 is highly imbalanced (99.1%)Imbalance
회수유고상 is highly skewed (γ1 = 23.52686524)Skewed
회수유액상 is highly skewed (γ1 = 23.34446406)Skewed
기름걸레 등 has 1650 (75.4%) zerosZeros
선저폐수 has 634 (29.0%) zerosZeros
액상슬러지 has 1405 (64.2%) zerosZeros
폐윤활유 has 1227 (56.1%) zerosZeros
회수유고상 has 2171 (99.2%) zerosZeros
회수유액상 has 2180 (99.6%) zerosZeros

Reproduction

Analysis started2023-12-12 17:05:19.753826
Analysis finished2023-12-12 17:05:24.573767
Duration4.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업소
Categorical

Distinct13
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
목포사업소
224 
통영사업소
198 
옥계사업소
196 
서귀포사업소
181 
광양사업소
173 
Other values (8)
1216 

Length

Max length6
Median length5
Mean length5.0827239
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
목포사업소 224
10.2%
통영사업소 198
9.0%
옥계사업소 196
9.0%
서귀포사업소 181
8.3%
광양사업소 173
7.9%
속초사업소 169
7.7%
제주사업소 166
7.6%
군산사업소 162
7.4%
진해사업소 159
 
7.3%
마산사업소 157
 
7.2%
Other values (3) 403
18.4%

Length

2023-12-13T02:05:24.644421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
목포사업소 224
10.2%
통영사업소 198
9.0%
옥계사업소 196
9.0%
서귀포사업소 181
8.3%
광양사업소 173
7.9%
속초사업소 169
7.7%
제주사업소 166
7.6%
군산사업소 162
7.4%
진해사업소 159
 
7.3%
마산사업소 157
 
7.2%
Other values (3) 403
18.4%

작업일
Date

Distinct254
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
Minimum2022-01-03 00:00:00
Maximum2022-12-30 00:00:00
2023-12-13T02:05:24.771179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:24.912155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

기름걸레 등
Real number (ℝ)

ZEROS 

Distinct98
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean132.0457
Minimum0
Maximum5000
Zeros1650
Zeros (%)75.4%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2023-12-13T02:05:25.093320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile800
Maximum5000
Range5000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation421.39335
Coefficient of variation (CV)3.1912689
Kurtosis42.334951
Mean132.0457
Median Absolute Deviation (MAD)0
Skewness5.7497841
Sum288916
Variance177572.35
MonotonicityNot monotonic
2023-12-13T02:05:25.261987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1650
75.4%
100 58
 
2.7%
200 50
 
2.3%
300 48
 
2.2%
50 37
 
1.7%
400 31
 
1.4%
800 22
 
1.0%
600 21
 
1.0%
500 20
 
0.9%
1000 19
 
0.9%
Other values (88) 232
 
10.6%
ValueCountFrequency (%)
0 1650
75.4%
10 1
 
< 0.1%
20 12
 
0.5%
30 7
 
0.3%
40 13
 
0.6%
50 37
 
1.7%
60 1
 
< 0.1%
70 3
 
0.1%
80 8
 
0.4%
90 2
 
0.1%
ValueCountFrequency (%)
5000 1
< 0.1%
4700 1
< 0.1%
4500 1
< 0.1%
3850 1
< 0.1%
3700 1
< 0.1%
3600 1
< 0.1%
3500 1
< 0.1%
3400 2
0.1%
3320 1
< 0.1%
3290 1
< 0.1%

선저폐수
Real number (ℝ)

ZEROS 

Distinct146
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2477.5366
Minimum0
Maximum22500
Zeros634
Zeros (%)29.0%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2023-12-13T02:05:25.407764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1600
Q34000
95-th percentile7500
Maximum22500
Range22500
Interquartile range (IQR)4000

Descriptive statistics

Standard deviation2880.7225
Coefficient of variation (CV)1.1627366
Kurtosis5.3928957
Mean2477.5366
Median Absolute Deviation (MAD)1600
Skewness1.8158374
Sum5420850
Variance8298562.3
MonotonicityNot monotonic
2023-12-13T02:05:25.566276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 634
29.0%
2000 121
 
5.5%
1000 114
 
5.2%
4000 88
 
4.0%
3000 85
 
3.9%
5000 67
 
3.1%
6000 47
 
2.1%
7000 46
 
2.1%
400 44
 
2.0%
1200 43
 
2.0%
Other values (136) 899
41.1%
ValueCountFrequency (%)
0 634
29.0%
100 6
 
0.3%
150 1
 
< 0.1%
200 40
 
1.8%
300 10
 
0.5%
350 1
 
< 0.1%
400 44
 
2.0%
500 14
 
0.6%
600 42
 
1.9%
700 9
 
0.4%
ValueCountFrequency (%)
22500 1
 
< 0.1%
22000 1
 
< 0.1%
21700 1
 
< 0.1%
20000 1
 
< 0.1%
18000 1
 
< 0.1%
17500 1
 
< 0.1%
16000 1
 
< 0.1%
15000 5
0.2%
14000 4
0.2%
13400 1
 
< 0.1%

액상슬러지
Real number (ℝ)

ZEROS 

Distinct104
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1164.6755
Minimum0
Maximum17000
Zeros1405
Zeros (%)64.2%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2023-12-13T02:05:25.736805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q31225
95-th percentile6000
Maximum17000
Range17000
Interquartile range (IQR)1225

Descriptive statistics

Standard deviation2282.4221
Coefficient of variation (CV)1.9597064
Kurtosis7.6446025
Mean1164.6755
Median Absolute Deviation (MAD)0
Skewness2.5630499
Sum2548310
Variance5209450.6
MonotonicityNot monotonic
2023-12-13T02:05:25.970865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1405
64.2%
6000 71
 
3.2%
2000 68
 
3.1%
1000 51
 
2.3%
3000 50
 
2.3%
4000 39
 
1.8%
600 36
 
1.6%
7000 34
 
1.6%
400 23
 
1.1%
1200 23
 
1.1%
Other values (94) 388
 
17.7%
ValueCountFrequency (%)
0 1405
64.2%
100 7
 
0.3%
200 21
 
1.0%
280 1
 
< 0.1%
300 13
 
0.6%
340 1
 
< 0.1%
400 23
 
1.1%
500 18
 
0.8%
600 36
 
1.6%
700 11
 
0.5%
ValueCountFrequency (%)
17000 1
 
< 0.1%
16000 1
 
< 0.1%
15000 1
 
< 0.1%
14000 3
0.1%
13200 1
 
< 0.1%
13000 1
 
< 0.1%
12600 1
 
< 0.1%
12000 5
0.2%
11600 3
0.1%
11400 1
 
< 0.1%

폐연료유
Categorical

IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size17.2 KiB
0
2185 
1200
 
1
1000
 
1
600
 
1

Length

Max length4
Median length1
Mean length1.0036563
Min length1

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 2185
99.9%
1200 1
 
< 0.1%
1000 1
 
< 0.1%
600 1
 
< 0.1%

Length

2023-12-13T02:05:26.152165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:05:26.293793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 2185
99.9%
1200 1
 
< 0.1%
1000 1
 
< 0.1%
600 1
 
< 0.1%

폐윤활유
Real number (ℝ)

ZEROS 

Distinct140
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean570.0777
Minimum0
Maximum14000
Zeros1227
Zeros (%)56.1%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2023-12-13T02:05:26.442205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3600
95-th percentile2772
Maximum14000
Range14000
Interquartile range (IQR)600

Descriptive statistics

Standard deviation1142.6744
Coefficient of variation (CV)2.0044188
Kurtosis17.538711
Mean570.0777
Median Absolute Deviation (MAD)0
Skewness3.4332595
Sum1247330
Variance1305704.8
MonotonicityNot monotonic
2023-12-13T02:05:26.610651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 1227
56.1%
200 91
 
4.2%
400 80
 
3.7%
300 54
 
2.5%
1000 54
 
2.5%
600 45
 
2.1%
2000 44
 
2.0%
500 43
 
2.0%
100 38
 
1.7%
800 33
 
1.5%
Other values (130) 479
 
21.9%
ValueCountFrequency (%)
0 1227
56.1%
10 1
 
< 0.1%
20 1
 
< 0.1%
40 6
 
0.3%
50 7
 
0.3%
60 3
 
0.1%
70 1
 
< 0.1%
80 1
 
< 0.1%
100 38
 
1.7%
120 6
 
0.3%
ValueCountFrequency (%)
14000 1
 
< 0.1%
8000 2
 
0.1%
7000 3
0.1%
6800 1
 
< 0.1%
6600 3
0.1%
6500 2
 
0.1%
6250 1
 
< 0.1%
6200 1
 
< 0.1%
6000 6
0.3%
5700 1
 
< 0.1%

회수유고상
Real number (ℝ)

SKEWED  ZEROS 

Distinct17
Distinct (%)0.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1727605
Minimum0
Maximum5240
Zeros2171
Zeros (%)99.2%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2023-12-13T02:05:26.770489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5240
Range5240
Interquartile range (IQR)0

Descriptive statistics

Standard deviation167.96341
Coefficient of variation (CV)18.311108
Kurtosis610.86906
Mean9.1727605
Median Absolute Deviation (MAD)0
Skewness23.526865
Sum20070
Variance28211.707
MonotonicityNot monotonic
2023-12-13T02:05:26.926072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 2171
99.2%
160 2
 
0.1%
280 1
 
< 0.1%
360 1
 
< 0.1%
40 1
 
< 0.1%
20 1
 
< 0.1%
3430 1
 
< 0.1%
290 1
 
< 0.1%
2000 1
 
< 0.1%
520 1
 
< 0.1%
Other values (7) 7
 
0.3%
ValueCountFrequency (%)
0 2171
99.2%
20 1
 
< 0.1%
40 1
 
< 0.1%
160 2
 
0.1%
200 1
 
< 0.1%
280 1
 
< 0.1%
290 1
 
< 0.1%
320 1
 
< 0.1%
360 1
 
< 0.1%
400 1
 
< 0.1%
ValueCountFrequency (%)
5240 1
< 0.1%
3450 1
< 0.1%
3430 1
< 0.1%
2200 1
< 0.1%
2000 1
< 0.1%
1000 1
< 0.1%
520 1
< 0.1%
400 1
< 0.1%
360 1
< 0.1%
320 1
< 0.1%

회수유액상
Real number (ℝ)

SKEWED  ZEROS 

Distinct9
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.744059
Minimum0
Maximum12000
Zeros2180
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size19.4 KiB
2023-12-13T02:05:27.056740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum12000
Range12000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation392.86449
Coefficient of variation (CV)19.897859
Kurtosis595.52142
Mean19.744059
Median Absolute Deviation (MAD)0
Skewness23.344464
Sum43200
Variance154342.5
MonotonicityNot monotonic
2023-12-13T02:05:27.190311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 2180
99.6%
4000 1
 
< 0.1%
3000 1
 
< 0.1%
12000 1
 
< 0.1%
6000 1
 
< 0.1%
9000 1
 
< 0.1%
400 1
 
< 0.1%
1800 1
 
< 0.1%
7000 1
 
< 0.1%
ValueCountFrequency (%)
0 2180
99.6%
400 1
 
< 0.1%
1800 1
 
< 0.1%
3000 1
 
< 0.1%
4000 1
 
< 0.1%
6000 1
 
< 0.1%
7000 1
 
< 0.1%
9000 1
 
< 0.1%
12000 1
 
< 0.1%
ValueCountFrequency (%)
12000 1
 
< 0.1%
9000 1
 
< 0.1%
7000 1
 
< 0.1%
6000 1
 
< 0.1%
4000 1
 
< 0.1%
3000 1
 
< 0.1%
1800 1
 
< 0.1%
400 1
 
< 0.1%
0 2180
99.6%

Interactions

2023-12-13T02:05:23.814469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:20.445702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.250005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.905282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.569171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.094531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.901337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:20.585204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.363498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.009623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.660426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.170150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.978966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:20.708564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.477177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.108080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.756922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.245333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:24.075337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:20.850309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.608958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.215421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.844995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.566185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:24.165623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:20.995938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.712396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.348596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.927141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.650275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:24.253067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.115548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:21.797198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:22.445225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.006309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:05:23.728931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:05:27.284505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업소기름걸레 등선저폐수액상슬러지폐연료유폐윤활유회수유고상회수유액상
사업소1.0000.2380.3450.3500.0000.2410.0000.026
기름걸레 등0.2381.0000.0000.2000.0000.0870.0000.000
선저폐수0.3450.0001.0000.1400.0000.2640.0000.000
액상슬러지0.3500.2000.1401.0000.0000.0800.0000.000
폐연료유0.0000.0000.0000.0001.0000.0000.0000.000
폐윤활유0.2410.0870.2640.0800.0001.0000.1330.000
회수유고상0.0000.0000.0000.0000.0000.1331.0000.661
회수유액상0.0260.0000.0000.0000.0000.0000.6611.000
2023-12-13T02:05:27.454904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업소폐연료유
사업소1.0000.000
폐연료유0.0001.000
2023-12-13T02:05:27.567656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기름걸레 등선저폐수액상슬러지폐윤활유회수유고상회수유액상사업소폐연료유
기름걸레 등1.000-0.0640.0550.060-0.0250.0030.1010.000
선저폐수-0.0641.000-0.295-0.172-0.064-0.0560.1500.000
액상슬러지0.055-0.2951.000-0.087-0.032-0.0190.1530.000
폐윤활유0.060-0.172-0.0871.0000.011-0.0050.1140.000
회수유고상-0.025-0.064-0.0320.0111.0000.1670.0000.000
회수유액상0.003-0.056-0.019-0.0050.1671.0000.0110.000
사업소0.1010.1500.1530.1140.0000.0111.0000.000
폐연료유0.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T02:05:24.386868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:05:24.517920image/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

사업소작업일기름걸레 등선저폐수액상슬러지폐연료유폐윤활유회수유고상회수유액상
0광양사업소2022-01-04040000090000
1광양사업소2022-01-06600230034000000
2광양사업소2022-01-07013000020000
3광양사업소2022-01-10800260034000000
4광양사업소2022-01-12030000020000
5광양사업소2022-01-13030002000000
6광양사업소2022-01-180032000000
7광양사업소2022-01-19006000000
8광양사업소2022-01-2130040004000000
9광양사업소2022-01-24500000000
사업소작업일기름걸레 등선저폐수액상슬러지폐연료유폐윤활유회수유고상회수유액상
2178평택사업소2022-11-290350040000000
2179평택사업소2022-12-020200000000
2180평택사업소2022-12-040200000000
2181평택사업소2022-12-060727000000
2182평택사업소2022-12-070708000000
2183평택사업소2022-12-120719000000
2184평택사업소2022-12-130713000000
2185평택사업소2022-12-140722000000
2186평택사업소2022-12-200713000000
2187평택사업소2022-12-210700000000