Overview

Dataset statistics

Number of variables8
Number of observations1847
Missing cells0
Missing cells (%)0.0%
Duplicate rows30
Duplicate rows (%)1.6%
Total size in memory126.4 KiB
Average record size in memory70.1 B

Variable types

DateTime1
Categorical2
Numeric5

Dataset

Description수질오염사고 발생 시 재난관리자원방제비축센터의 방제물품 정보 및 재고수량을 시스템을 통해 제공하여 신속한 초동대응이 가능하도록 지원
URLhttps://www.data.go.kr/data/15065046/fileData.do

Alerts

Dataset has 30 (1.6%) duplicate rowsDuplicates
합계 is highly overall correlated with 수도권동부 and 4 other fieldsHigh correlation
수도권동부 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
충청권 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
대구경북 is highly overall correlated with 합계 and 4 other fieldsHigh correlation
광주전남제주 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
장비명 is highly overall correlated with 합계 and 3 other fieldsHigh correlation
본사 is highly overall correlated with 장비명High correlation
본사 is highly imbalanced (85.4%)Imbalance
수도권동부 has 324 (17.5%) zerosZeros
충청권 has 246 (13.3%) zerosZeros
대구경북 has 167 (9.0%) zerosZeros
광주전남제주 has 473 (25.6%) zerosZeros

Reproduction

Analysis started2023-12-12 04:29:30.968980
Analysis finished2023-12-12 04:29:35.121347
Duration4.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준일
Date

Distinct60
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
Minimum2018-06-14 00:00:00
Maximum2023-05-15 00:00:00
2023-12-12T13:29:35.187720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:35.320486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

장비명
Categorical

HIGH CORRELATION 

Distinct32
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
보조방제선
 
61
드론
 
61
고무보트(6인)
 
61
고무보트(2인)
 
61
RIB보트
 
61
Other values (27)
1542 

Length

Max length11
Median length9
Mean length5.9891716
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row보조방제선
2nd row방제바지선
3rd row고무보트(6인)
4th row고무보트(2인)
5th rowRIB보트

Common Values

ValueCountFrequency (%)
보조방제선 61
 
3.3%
드론 61
 
3.3%
고무보트(6인) 61
 
3.3%
고무보트(2인) 61
 
3.3%
RIB보트 61
 
3.3%
차량 총합 61
 
3.3%
기동방제차량 3.5t 61
 
3.3%
방제지원트럭 1t 61
 
3.3%
방제장비견인차량 61
 
3.3%
방제물품트레일러 61
 
3.3%
Other values (22) 1237
67.0%

Length

2023-12-12T13:29:35.480386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
유회수기 200
 
8.4%
총합 183
 
7.7%
보조방제선 61
 
2.6%
유처리제 61
 
2.6%
에어텐트 61
 
2.6%
동력분무기 61
 
2.6%
발전기 61
 
2.6%
오일펜스 61
 
2.6%
일회용작업복 61
 
2.6%
매트형유흡착재 61
 
2.6%
Other values (28) 1515
63.5%

합계
Real number (ℝ)

HIGH CORRELATION 

Distinct205
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean336.89442
Minimum0
Maximum8240
Zeros1
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T13:29:35.616897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median5
Q324
95-th percentile1076
Maximum8240
Range8240
Interquartile range (IQR)20

Descriptive statistics

Standard deviation1282.4049
Coefficient of variation (CV)3.8065484
Kurtosis23.436042
Mean336.89442
Median Absolute Deviation (MAD)3
Skewness4.9385649
Sum622244
Variance1644562.4
MonotonicityNot monotonic
2023-12-12T13:29:35.760760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.0 407
22.0%
1.0 180
 
9.7%
3.0 132
 
7.1%
2.0 115
 
6.2%
8.0 115
 
6.2%
5.0 104
 
5.6%
15.0 61
 
3.3%
7.0 52
 
2.8%
6.0 52
 
2.8%
12.0 43
 
2.3%
Other values (195) 586
31.7%
ValueCountFrequency (%)
0.0 1
 
0.1%
1.0 180
9.7%
2.0 115
 
6.2%
3.0 132
 
7.1%
4.0 407
22.0%
5.0 104
 
5.6%
6.0 52
 
2.8%
7.0 52
 
2.8%
8.0 115
 
6.2%
9.0 18
 
1.0%
ValueCountFrequency (%)
8240.0 1
 
0.1%
7900.0 1
 
0.1%
7860.0 1
 
0.1%
7740.0 1
 
0.1%
7540.0 3
0.2%
7500.0 1
 
0.1%
7460.0 1
 
0.1%
7420.0 1
 
0.1%
7380.0 4
0.2%
7280.0 4
0.2%

본사
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size14.6 KiB
0
1764 
2
 
43
32
 
22
3
 
12
1
 
6

Length

Max length2
Median length1
Mean length1.0119112
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 1764
95.5%
2 43
 
2.3%
32 22
 
1.2%
3 12
 
0.6%
1 6
 
0.3%

Length

2023-12-12T13:29:35.985805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T13:29:36.132545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 1764
95.5%
2 43
 
2.3%
32 22
 
1.2%
3 12
 
0.6%
1 6
 
0.3%

수도권동부
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct73
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean97.847861
Minimum0
Maximum2500
Zeros324
Zeros (%)17.5%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T13:29:36.292648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q36
95-th percentile249.6
Maximum2500
Range2500
Interquartile range (IQR)5

Descriptive statistics

Standard deviation406.60596
Coefficient of variation (CV)4.1554916
Kurtosis27.138662
Mean97.847861
Median Absolute Deviation (MAD)1
Skewness5.2863972
Sum180725
Variance165328.41
MonotonicityNot monotonic
2023-12-12T13:29:36.485897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.0 712
38.5%
0.0 324
17.5%
2.0 140
 
7.6%
5.0 92
 
5.0%
4.0 79
 
4.3%
6.0 67
 
3.6%
9.0 43
 
2.3%
16.0 28
 
1.5%
2480.0 27
 
1.5%
13.0 19
 
1.0%
Other values (63) 316
17.1%
ValueCountFrequency (%)
0.0 324
17.5%
1.0 712
38.5%
2.0 140
 
7.6%
4.0 79
 
4.3%
5.0 92
 
5.0%
6.0 67
 
3.6%
7.0 18
 
1.0%
9.0 43
 
2.3%
10.0 18
 
1.0%
13.0 19
 
1.0%
ValueCountFrequency (%)
2500.0 11
0.6%
2480.0 27
1.5%
2420.0 4
 
0.2%
2380.0 3
 
0.2%
2080.0 1
 
0.1%
2000.0 1
 
0.1%
1700.0 1
 
0.1%
1520.0 1
 
0.1%
1480.0 11
0.6%
1420.0 1
 
0.1%

충청권
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.054142
Minimum0
Maximum1880
Zeros246
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T13:29:36.660955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q36
95-th percentile157
Maximum1880
Range1880
Interquartile range (IQR)5

Descriptive statistics

Standard deviation275.98331
Coefficient of variation (CV)3.9395716
Kurtosis23.953299
Mean70.054142
Median Absolute Deviation (MAD)1
Skewness5.0196121
Sum129390
Variance76166.786
MonotonicityNot monotonic
2023-12-12T13:29:36.836970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 756
40.9%
0 246
 
13.3%
2 177
 
9.6%
6 134
 
7.3%
4 105
 
5.7%
1540 52
 
2.8%
10 43
 
2.3%
11 22
 
1.2%
113 19
 
1.0%
9 18
 
1.0%
Other values (46) 275
 
14.9%
ValueCountFrequency (%)
0 246
 
13.3%
1 756
40.9%
2 177
 
9.6%
4 105
 
5.7%
5 5
 
0.3%
6 134
 
7.3%
9 18
 
1.0%
10 43
 
2.3%
11 22
 
1.2%
13 12
 
0.6%
ValueCountFrequency (%)
1880 1
 
0.1%
1560 1
 
0.1%
1540 52
2.8%
1500 7
 
0.4%
317 1
 
0.1%
280 1
 
0.1%
270 1
 
0.1%
269 1
 
0.1%
264 3
 
0.2%
261 1
 
0.1%

대구경북
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct94
Distinct (%)5.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100.57553
Minimum0
Maximum2180
Zeros167
Zeros (%)9.0%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T13:29:37.097010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q36
95-th percentile353
Maximum2180
Range2180
Interquartile range (IQR)5

Descriptive statistics

Standard deviation359.50717
Coefficient of variation (CV)3.5744994
Kurtosis23.120912
Mean100.57553
Median Absolute Deviation (MAD)1
Skewness4.8698652
Sum185763
Variance129245.4
MonotonicityNot monotonic
2023-12-12T13:29:37.284215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 572
31.0%
2 480
26.0%
0 167
 
9.0%
5 62
 
3.4%
9 61
 
3.3%
6 57
 
3.1%
4 43
 
2.3%
164 25
 
1.4%
7 21
 
1.1%
171 21
 
1.1%
Other values (84) 338
18.3%
ValueCountFrequency (%)
0 167
 
9.0%
1 572
31.0%
2 480
26.0%
3 18
 
1.0%
4 43
 
2.3%
5 62
 
3.4%
6 57
 
3.1%
7 21
 
1.1%
9 61
 
3.3%
34 1
 
0.1%
ValueCountFrequency (%)
2180 1
 
0.1%
2160 15
0.8%
2140 1
 
0.1%
2120 1
 
0.1%
2080 1
 
0.1%
2020 1
 
0.1%
2000 14
0.8%
1980 5
 
0.3%
1940 4
 
0.2%
1920 1
 
0.1%

광주전남제주
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct90
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean67.966432
Minimum0
Maximum1760
Zeros473
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size16.4 KiB
2023-12-12T13:29:37.486352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile372
Maximum1760
Range1760
Interquartile range (IQR)3

Descriptive statistics

Standard deviation256.54594
Coefficient of variation (CV)3.7745978
Kurtosis23.974753
Mean67.966432
Median Absolute Deviation (MAD)1
Skewness4.8510746
Sum125534
Variance65815.821
MonotonicityNot monotonic
2023-12-12T13:29:37.677287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 633
34.3%
0 473
25.6%
2 175
 
9.5%
3 126
 
6.8%
4 78
 
4.2%
5 37
 
2.0%
71 15
 
0.8%
547 14
 
0.8%
330 13
 
0.7%
70 11
 
0.6%
Other values (80) 272
14.7%
ValueCountFrequency (%)
0 473
25.6%
1 633
34.3%
2 175
 
9.5%
3 126
 
6.8%
4 78
 
4.2%
5 37
 
2.0%
6 6
 
0.3%
7 9
 
0.5%
8 2
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
1760 5
0.3%
1740 5
0.3%
1680 5
0.3%
1660 1
 
0.1%
1580 3
 
0.2%
1560 2
 
0.1%
1460 1
 
0.1%
1380 4
0.2%
1240 8
0.4%
1220 5
0.3%

Interactions

2023-12-12T13:29:34.328320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.362990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.799221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.566984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:33.307016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:34.447894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.443157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.918697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.701972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:33.430008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:34.545529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.529817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.048071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.838035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:33.556624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:34.666913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.619208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.185413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.999554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:34.049870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:34.778477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:31.700353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:32.430976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:33.134266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T13:29:34.183547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T13:29:37.813833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기준일장비명합계본사수도권동부충청권대구경북광주전남제주
기준일1.0000.0000.0000.0000.0000.0000.0000.000
장비명0.0001.0000.8630.8270.8120.8250.8720.822
합계0.0000.8631.0000.4350.8870.7380.9130.947
본사0.0000.8270.4351.0000.2530.0000.2820.570
수도권동부0.0000.8120.8870.2531.0000.7880.7400.844
충청권0.0000.8250.7380.0000.7881.0000.6940.835
대구경북0.0000.8720.9130.2820.7400.6941.0000.856
광주전남제주0.0000.8220.9470.5700.8440.8350.8561.000
2023-12-12T13:29:37.971025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
장비명본사
장비명1.0000.568
본사0.5681.000
2023-12-12T13:29:38.101337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
합계수도권동부충청권대구경북광주전남제주장비명본사
합계1.0000.9400.9500.8390.8980.5980.313
수도권동부0.9401.0000.9420.7230.8160.4380.157
충청권0.9500.9421.0000.7570.8340.5640.000
대구경북0.8390.7230.7571.0000.8650.6140.195
광주전남제주0.8980.8160.8340.8651.0000.4770.374
장비명0.5980.4380.5640.6140.4771.0000.568
본사0.3130.1570.0000.1950.3740.5681.000

Missing values

2023-12-12T13:29:34.923886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T13:29:35.070421image/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

기준일장비명합계본사수도권동부충청권대구경북광주전남제주
02023-05-15보조방제선15.004.0452
12023-05-15방제바지선1.000.0100
22023-05-15고무보트(6인)4.001.0120
32023-05-15고무보트(2인)4.001.0111
42023-05-15RIB보트4.001.0111
52023-05-15차량 총합30.0010.0974
62023-05-15기동방제차량 3.5t5.001.0121
72023-05-15방제지원트럭 1t4.001.0111
82023-05-15방제장비견인차량5.001.0121
92023-05-15방제물품트레일러13.007.0600
기준일장비명합계본사수도권동부충청권대구경북광주전남제주
18372018-10-15드론7.031.0111
18382018-10-15에어텐트1.000.0010
18392018-10-15동력분무기8.002.0222
18402018-10-15발전기4.001.0111
18412018-10-15오일펜스7160.002500.0154020001120
18422018-10-15일회용작업복567.0089.042264172
18432018-10-15유처리제140.0013.037855
18442018-10-15매트형유흡착재553.00105.014826238
18452018-10-15롤형유흡착재440.00110.011617242
18462018-10-15붐형유흡착재411.00148.08815916

Duplicate rows

Most frequently occurring

기준일장비명합계본사수도권동부충청권대구경북광주전남제주# duplicates
02020-08-15FRP선3.001.01102
12020-08-15RIB보트4.001.01112
22020-08-15고무보트(2인)4.001.01112
32020-08-15고무보트(6인)4.001.01202
42020-08-15기동방제선2.000.00202
52020-08-15기동방제차량 3.5t5.001.01212
62020-08-15대형 유회수기8.002.02222
72020-08-15동력분무기8.002.02222
82020-08-15드론7.021.01212
92020-08-15롤형유흡착재404.00115.0110168112