Overview

Dataset statistics

Number of variables18
Number of observations10000
Missing cells34739
Missing cells (%)19.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 MiB
Average record size in memory162.0 B

Variable types

Numeric7
Categorical5
Boolean1
DateTime4
Text1

Dataset

Description해양수산부_어업경영체_현지조사결과 기타양식생산 기본정보는 현지조사접수번호,지청코드,현지조사번호,확정구분,일련번호,수산물품목코드,현재사육량,현재사육량단위코드,생산량,생산량단위코드,생산금액,수탁여부,현지조사지청코드,현지조사일자,현지조사완료코드,최초생성시점,최종변경시점,가공상태코드,어업구분,어업번호,계통판매비율,비계통판매비율,비계통판매코드,데이터기준일자를 제공합니다.
URLhttps://www.data.go.kr/data/15115880/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
현지조사접수번호 is highly overall correlated with 지청코드High correlation
지청코드 is highly overall correlated with 현지조사접수번호 and 1 other fieldsHigh correlation
생산량 is highly overall correlated with 생산금액 and 1 other fieldsHigh correlation
생산금액 is highly overall correlated with 생산량High correlation
계통판매비율 is highly overall correlated with 비계통판매비율High correlation
비계통판매비율 is highly overall correlated with 계통판매비율High correlation
지청코드명 is highly overall correlated with 지청코드High correlation
수탁여부 is highly overall correlated with 생산량High correlation
일련번호 is highly imbalanced (69.9%)Imbalance
수탁여부 is highly imbalanced (88.0%)Imbalance
현재사육량 has 7316 (73.2%) missing valuesMissing
생산량 has 1827 (18.3%) missing valuesMissing
생산금액 has 244 (2.4%) missing valuesMissing
수탁여부 has 4575 (45.8%) missing valuesMissing
현지조사일자 has 2578 (25.8%) missing valuesMissing
어업번호 has 5093 (50.9%) missing valuesMissing
계통판매비율 has 7778 (77.8%) missing valuesMissing
비계통판매비율 has 5328 (53.3%) missing valuesMissing
현재사육량 is highly skewed (γ1 = 42.99802908)Skewed
생산량 is highly skewed (γ1 = 90.16957294)Skewed
생산금액 is highly skewed (γ1 = 50.57767417)Skewed
현재사육량 has 849 (8.5%) zerosZeros
생산량 has 297 (3.0%) zerosZeros
생산금액 has 297 (3.0%) zerosZeros
계통판매비율 has 1001 (10.0%) zerosZeros
비계통판매비율 has 306 (3.1%) zerosZeros

Reproduction

Analysis started2023-12-12 07:18:38.483966
Analysis finished2023-12-12 07:20:32.102413
Duration1 minute and 53.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

현지조사접수번호
Real number (ℝ)

HIGH CORRELATION 

Distinct9660
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6628684 × 1016
Minimum1.1192357 × 1016
Maximum2.1192373 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:32.178075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1192357 × 1016
5-th percentile1.1192359 × 1016
Q11.1192363 × 1016
median1.1192367 × 1016
Q31.1192363 × 1017
95-th percentile1.1192367 × 1017
Maximum2.1192373 × 1017
Range2.0073137 × 1017
Interquartile range (IQR)1.0073127 × 1017

Descriptive statistics

Standard deviation5.076588 × 1016
Coefficient of variation (CV)0.89646936
Kurtosis-1.7319782
Mean5.6628684 × 1016
Median Absolute Deviation (MAD)7.9999002 × 109
Skewness0.27335441
Sum-5.5622258 × 1018
Variance2.5771746 × 1033
MonotonicityNot monotonic
2023-12-12T16:20:32.372564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11192363201501149 3
 
< 0.1%
11192367202000209 3
 
< 0.1%
111923602021004950 3
 
< 0.1%
11192362201400203 3
 
< 0.1%
111923672020000995 3
 
< 0.1%
11192363201501140 3
 
< 0.1%
11192360201400482 3
 
< 0.1%
111923672019002128 3
 
< 0.1%
11192360201501448 3
 
< 0.1%
11192367201603068 2
 
< 0.1%
Other values (9650) 9971
99.7%
ValueCountFrequency (%)
11192357201400548 1
< 0.1%
11192357201400559 1
< 0.1%
11192357201400707 1
< 0.1%
11192357201400743 1
< 0.1%
11192357201401033 1
< 0.1%
11192357201500070 1
< 0.1%
11192357201500108 1
< 0.1%
11192357201500304 1
< 0.1%
11192357201500530 1
< 0.1%
11192357201500585 1
< 0.1%
ValueCountFrequency (%)
211923732022002216 1
< 0.1%
211923732021000171 1
< 0.1%
211923732020001591 1
< 0.1%
211923732020001590 1
< 0.1%
211923732020001589 1
< 0.1%
211923672023004058 1
< 0.1%
211923672023000933 1
< 0.1%
211923672020002279 2
< 0.1%
211923672016003153 1
< 0.1%
211923652016000177 1
< 0.1%

지청코드
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1192363.8
Minimum1192357
Maximum1192373
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:32.525538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1192357
5-th percentile1192359
Q11192362
median1192363
Q31192367
95-th percentile1192367
Maximum1192373
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.0273839
Coefficient of variation (CV)2.5389768 × 10-6
Kurtosis-1.0778143
Mean1192363.8
Median Absolute Deviation (MAD)3
Skewness-0.23483026
Sum1.1923638 × 1010
Variance9.1650532
MonotonicityNot monotonic
2023-12-12T16:20:32.669614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
1192367 3943
39.4%
1192363 3344
33.4%
1192360 1302
 
13.0%
1192359 745
 
7.4%
1192358 206
 
2.1%
1192362 146
 
1.5%
1192364 87
 
0.9%
1192357 71
 
0.7%
1192361 55
 
0.5%
1192366 46
 
0.5%
Other values (2) 55
 
0.5%
ValueCountFrequency (%)
1192357 71
 
0.7%
1192358 206
 
2.1%
1192359 745
 
7.4%
1192360 1302
 
13.0%
1192361 55
 
0.5%
1192362 146
 
1.5%
1192363 3344
33.4%
1192364 87
 
0.9%
1192365 34
 
0.3%
1192366 46
 
0.5%
ValueCountFrequency (%)
1192373 21
 
0.2%
1192367 3943
39.4%
1192366 46
 
0.5%
1192365 34
 
0.3%
1192364 87
 
0.9%
1192363 3344
33.4%
1192362 146
 
1.5%
1192361 55
 
0.5%
1192360 1302
 
13.0%
1192359 745
 
7.4%

지청코드명
Categorical

HIGH CORRELATION 

Distinct12
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
대산지방해양수산청
3943 
목포지방해양수산청
3344 
마산지방해양수산청
1302 
여수지방해양수산청
745 
인천지방해양수산청
 
206
Other values (7)
460 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row목포지방해양수산청
2nd row여수지방해양수산청
3rd row마산지방해양수산청
4th row마산지방해양수산청
5th row대산지방해양수산청

Common Values

ValueCountFrequency (%)
대산지방해양수산청 3943
39.4%
목포지방해양수산청 3344
33.4%
마산지방해양수산청 1302
 
13.0%
여수지방해양수산청 745
 
7.4%
인천지방해양수산청 206
 
2.1%
군산지방해양수산청 146
 
1.5%
포항지방해양수산청 87
 
0.9%
부산지방해양수산청 71
 
0.7%
동해지방해양수산청 55
 
0.5%
울산지방해양수산청 46
 
0.5%
Other values (2) 55
 
0.5%

Length

2023-12-12T16:20:32.836902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대산지방해양수산청 3943
39.4%
목포지방해양수산청 3344
33.4%
마산지방해양수산청 1302
 
13.0%
여수지방해양수산청 745
 
7.4%
인천지방해양수산청 206
 
2.1%
군산지방해양수산청 146
 
1.5%
포항지방해양수산청 87
 
0.9%
부산지방해양수산청 71
 
0.7%
동해지방해양수산청 55
 
0.5%
울산지방해양수산청 46
 
0.5%
Other values (2) 55
 
0.5%

현지조사번호
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
6367 
2
2570 
3
1029 
4
 
30
5
 
4

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 6367
63.7%
2 2570
25.7%
3 1029
 
10.3%
4 30
 
0.3%
5 4
 
< 0.1%

Length

2023-12-12T16:20:32.969190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:20:33.104363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 6367
63.7%
2 2570
25.7%
3 1029
 
10.3%
4 30
 
0.3%
5 4
 
< 0.1%

확정구분
Categorical

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
4
4476 
2
3399 
1
2125 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row4
2nd row4
3rd row4
4th row4
5th row4

Common Values

ValueCountFrequency (%)
4 4476
44.8%
2 3399
34.0%
1 2125
21.2%

Length

2023-12-12T16:20:33.254285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:20:33.397498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4 4476
44.8%
2 3399
34.0%
1 2125
21.2%

일련번호
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
1
8528 
2
1289 
3
 
150
4
 
24
5
 
9

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 8528
85.3%
2 1289
 
12.9%
3 150
 
1.5%
4 24
 
0.2%
5 9
 
0.1%

Length

2023-12-12T16:20:33.517249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:20:33.648315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 8528
85.3%
2 1289
 
12.9%
3 150
 
1.5%
4 24
 
0.2%
5 9
 
0.1%

현재사육량
Real number (ℝ)

MISSING  SKEWED  ZEROS 

Distinct239
Distinct (%)8.9%
Missing7316
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean44097.242
Minimum0
Maximum32000000
Zeros849
Zeros (%)8.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:33.798602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2500
Q310000
95-th percentile80000
Maximum32000000
Range32000000
Interquartile range (IQR)10000

Descriptive statistics

Standard deviation663798.75
Coefficient of variation (CV)15.053067
Kurtosis2022.608
Mean44097.242
Median Absolute Deviation (MAD)2500
Skewness42.998029
Sum1.18357 × 108
Variance4.4062878 × 1011
MonotonicityNot monotonic
2023-12-12T16:20:33.956470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 849
 
8.5%
5000.0 166
 
1.7%
10000.0 149
 
1.5%
3000.0 121
 
1.2%
2000.0 120
 
1.2%
1000.0 91
 
0.9%
50000.0 64
 
0.6%
4000.0 63
 
0.6%
30000.0 63
 
0.6%
15000.0 57
 
0.6%
Other values (229) 941
 
9.4%
(Missing) 7316
73.2%
ValueCountFrequency (%)
0.0 849
8.5%
0.02 1
 
< 0.1%
0.05 1
 
< 0.1%
0.5 2
 
< 0.1%
1.0 1
 
< 0.1%
1.5 1
 
< 0.1%
2.0 2
 
< 0.1%
3.0 2
 
< 0.1%
5.0 2
 
< 0.1%
8.0 1
 
< 0.1%
ValueCountFrequency (%)
32000000.0 1
 
< 0.1%
10000000.0 1
 
< 0.1%
2250000.0 1
 
< 0.1%
2000000.0 1
 
< 0.1%
1890000.0 12
0.1%
1000000.0 1
 
< 0.1%
800000.0 2
 
< 0.1%
700000.0 2
 
< 0.1%
602533.0 1
 
< 0.1%
600000.0 2
 
< 0.1%

생산량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1566
Distinct (%)19.2%
Missing1827
Missing (%)18.3%
Infinite0
Infinite (%)0.0%
Mean164503.18
Minimum0
Maximum1.07232 × 109
Zeros297
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:34.122223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile30
Q1400
median1340
Q35357
95-th percentile100000
Maximum1.07232 × 109
Range1.07232 × 109
Interquartile range (IQR)4957

Descriptive statistics

Standard deviation11871480
Coefficient of variation (CV)72.165658
Kurtosis8144.0972
Mean164503.18
Median Absolute Deviation (MAD)1215
Skewness90.169573
Sum1.3444845 × 109
Variance1.4093204 × 1014
MonotonicityNot monotonic
2023-12-12T16:20:34.257485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000.0 310
 
3.1%
0.0 297
 
3.0%
3000.0 269
 
2.7%
2000.0 258
 
2.6%
500.0 257
 
2.6%
5000.0 241
 
2.4%
10000.0 178
 
1.8%
300.0 171
 
1.7%
100.0 161
 
1.6%
200.0 143
 
1.4%
Other values (1556) 5888
58.9%
(Missing) 1827
 
18.3%
ValueCountFrequency (%)
0.0 297
3.0%
0.15 1
 
< 0.1%
0.195 1
 
< 0.1%
0.235 1
 
< 0.1%
0.36 1
 
< 0.1%
0.4 3
 
< 0.1%
0.434 1
 
< 0.1%
0.6 1
 
< 0.1%
0.63 1
 
< 0.1%
0.7 3
 
< 0.1%
ValueCountFrequency (%)
1072320000.0 1
 
< 0.1%
40000000.0 1
 
< 0.1%
10000000.0 3
< 0.1%
5100000.0 1
 
< 0.1%
4130000.0 1
 
< 0.1%
3000000.0 3
< 0.1%
2016000.0 1
 
< 0.1%
2000000.0 1
 
< 0.1%
1626016.0 1
 
< 0.1%
1531680.0 1
 
< 0.1%

생산금액
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct1403
Distinct (%)14.4%
Missing244
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean188780.23
Minimum0
Maximum4.2 × 108
Zeros297
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:34.398455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile50
Q1200
median500
Q33701
95-th percentile29821.75
Maximum4.2 × 108
Range4.2 × 108
Interquartile range (IQR)3501

Descriptive statistics

Standard deviation6186061
Coefficient of variation (CV)32.768584
Kurtosis2925.427
Mean188780.23
Median Absolute Deviation (MAD)401
Skewness50.577674
Sum1.84174 × 109
Variance3.8267351 × 1013
MonotonicityNot monotonic
2023-12-12T16:20:34.537038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 474
 
4.7%
300 434
 
4.3%
150 383
 
3.8%
1000 356
 
3.6%
500 305
 
3.0%
0 297
 
3.0%
3000 228
 
2.3%
10000 214
 
2.1%
5000 207
 
2.1%
120 206
 
2.1%
Other values (1393) 6652
66.5%
(Missing) 244
 
2.4%
ValueCountFrequency (%)
0 297
3.0%
5 3
 
< 0.1%
6 2
 
< 0.1%
7 1
 
< 0.1%
10 5
 
0.1%
12 1
 
< 0.1%
14 5
 
0.1%
15 8
 
0.1%
17 1
 
< 0.1%
19 1
 
< 0.1%
ValueCountFrequency (%)
420000000 1
< 0.1%
300000000 1
< 0.1%
200000000 1
< 0.1%
163000000 1
< 0.1%
130000000 1
< 0.1%
75000000 1
< 0.1%
70852300 1
< 0.1%
62300000 1
< 0.1%
55000000 1
< 0.1%
40000000 1
< 0.1%

수탁여부
Boolean

HIGH CORRELATION  IMBALANCE  MISSING 

Distinct2
Distinct (%)< 0.1%
Missing4575
Missing (%)45.8%
Memory size97.7 KiB
False
5337 
True
 
88
(Missing)
4575 
ValueCountFrequency (%)
False 5337
53.4%
True 88
 
0.9%
(Missing) 4575
45.8%
2023-12-12T16:20:34.637227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

현지조사일자
Date

MISSING 

Distinct1412
Distinct (%)19.0%
Missing2578
Missing (%)25.8%
Memory size156.2 KiB
Minimum2015-07-01 00:00:00
Maximum2023-08-16 00:00:00
2023-12-12T16:20:35.608257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:35.807229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최초생성시점
Date

Distinct1631
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2015-07-01 00:00:00
Maximum2023-08-16 00:00:00
2023-12-12T16:20:36.012517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:36.185497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

최종변경시점
Date

Distinct1638
Distinct (%)16.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2015-07-01 00:00:00
Maximum2023-08-16 00:00:00
2023-12-12T16:20:36.356450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:36.579165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

어업구분
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
5013 
11
4564 
12
 
358
43
 
64
42
 
1

Length

Max length4
Median length4
Mean length3.0026
Min length2

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row11
3rd row<NA>
4th row11
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 5013
50.1%
11 4564
45.6%
12 358
 
3.6%
43 64
 
0.6%
42 1
 
< 0.1%

Length

2023-12-12T16:20:36.760617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:20:36.881265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 5013
50.1%
11 4564
45.6%
12 358
 
3.6%
43 64
 
0.6%
42 1
 
< 0.1%

어업번호
Text

MISSING 

Distinct2506
Distinct (%)51.1%
Missing5093
Missing (%)50.9%
Memory size156.2 KiB
2023-12-12T16:20:37.159051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length19.342368
Min length19

Characters and Unicode

Total characters94913
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1812 ?
Unique (%)36.9%

Sample

1st row1111100491000010520
2nd row1111600533000001395
3rd row1111100499004911918
4th row4110100543000000552
5th row1111100499004911821
ValueCountFrequency (%)
1114200462000000224 69
 
1.4%
1114200462000000186 47
 
1.0%
1114200462000000222 46
 
0.9%
1114200462000000225 43
 
0.9%
1114200462000000324 38
 
0.8%
1114200462000000313 36
 
0.7%
1114200462000000644 30
 
0.6%
1114200462000000082 27
 
0.6%
1114200358000000225 26
 
0.5%
1114200462000000122 25
 
0.5%
Other values (2496) 4520
92.1%
2023-12-12T16:20:37.646458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 34971
36.8%
1 24077
25.4%
4 8247
 
8.7%
2 6623
 
7.0%
9 4664
 
4.9%
6 4254
 
4.5%
5 4044
 
4.3%
3 3655
 
3.9%
8 2434
 
2.6%
7 1941
 
2.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 94910
> 99.9%
Dash Punctuation 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 34971
36.8%
1 24077
25.4%
4 8247
 
8.7%
2 6623
 
7.0%
9 4664
 
4.9%
6 4254
 
4.5%
5 4044
 
4.3%
3 3655
 
3.9%
8 2434
 
2.6%
7 1941
 
2.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94913
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 34971
36.8%
1 24077
25.4%
4 8247
 
8.7%
2 6623
 
7.0%
9 4664
 
4.9%
6 4254
 
4.5%
5 4044
 
4.3%
3 3655
 
3.9%
8 2434
 
2.6%
7 1941
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94913
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 34971
36.8%
1 24077
25.4%
4 8247
 
8.7%
2 6623
 
7.0%
9 4664
 
4.9%
6 4254
 
4.5%
5 4044
 
4.3%
3 3655
 
3.9%
8 2434
 
2.6%
7 1941
 
2.0%

계통판매비율
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)0.7%
Missing7778
Missing (%)77.8%
Infinite0
Infinite (%)0.0%
Mean49.717372
Minimum0
Maximum100
Zeros1001
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:37.795281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median50
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)100

Descriptive statistics

Standard deviation48.314604
Coefficient of variation (CV)0.97178517
Kurtosis-1.9452601
Mean49.717372
Median Absolute Deviation (MAD)50
Skewness0.015849349
Sum110472
Variance2334.301
MonotonicityNot monotonic
2023-12-12T16:20:37.940154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
100 1004
 
10.0%
0 1001
 
10.0%
50 49
 
0.5%
10 33
 
0.3%
30 24
 
0.2%
70 23
 
0.2%
20 21
 
0.2%
90 17
 
0.2%
80 14
 
0.1%
40 13
 
0.1%
Other values (6) 23
 
0.2%
(Missing) 7778
77.8%
ValueCountFrequency (%)
0 1001
10.0%
3 1
 
< 0.1%
5 6
 
0.1%
10 33
 
0.3%
15 1
 
< 0.1%
20 21
 
0.2%
30 24
 
0.2%
40 13
 
0.1%
50 49
 
0.5%
60 3
 
< 0.1%
ValueCountFrequency (%)
100 1004
10.0%
99 1
 
< 0.1%
95 11
 
0.1%
90 17
 
0.2%
80 14
 
0.1%
70 23
 
0.2%
60 3
 
< 0.1%
50 49
 
0.5%
40 13
 
0.1%
30 24
 
0.2%

비계통판매비율
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct16
Distinct (%)0.3%
Missing5328
Missing (%)53.3%
Infinite0
Infinite (%)0.0%
Mean91.294521
Minimum0
Maximum100
Zeros306
Zeros (%)3.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T16:20:38.061094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1100
median100
Q3100
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)0

Descriptive statistics

Standard deviation26.751594
Coefficient of variation (CV)0.29302519
Kurtosis6.8621121
Mean91.294521
Median Absolute Deviation (MAD)0
Skewness-2.9302508
Sum426528
Variance715.64777
MonotonicityNot monotonic
2023-12-12T16:20:38.190155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
100 4149
41.5%
0 306
 
3.1%
50 49
 
0.5%
90 33
 
0.3%
70 24
 
0.2%
30 23
 
0.2%
80 21
 
0.2%
10 17
 
0.2%
20 14
 
0.1%
60 13
 
0.1%
Other values (6) 23
 
0.2%
(Missing) 5328
53.3%
ValueCountFrequency (%)
0 306
3.1%
1 1
 
< 0.1%
5 11
 
0.1%
10 17
 
0.2%
20 14
 
0.1%
30 23
 
0.2%
40 3
 
< 0.1%
50 49
 
0.5%
60 13
 
0.1%
70 24
 
0.2%
ValueCountFrequency (%)
100 4149
41.5%
97 1
 
< 0.1%
95 6
 
0.1%
90 33
 
0.3%
85 1
 
< 0.1%
80 21
 
0.2%
70 24
 
0.2%
60 13
 
0.1%
50 49
 
0.5%
40 3
 
< 0.1%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2023-08-18 00:00:00
Maximum2023-08-18 00:00:00
2023-12-12T16:20:38.303279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:38.416230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-12T16:20:25.340603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:18:41.451812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:42.627635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.812785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:59.653036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:09.083735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:21.286631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:30.931116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:03.753568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.153503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:58.981803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:08.357568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:20.554896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:24.799492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:31.031683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:15.042288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.279648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:59.104279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:08.468471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:20.662772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:24.890801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:31.126733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:16.987002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.381710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:59.226802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:08.602232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:20.795189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:24.986341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:31.206848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:25.902570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.476958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:59.322831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:08.720020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:20.900836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:25.073992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:31.302922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:35.044687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.578087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:59.413191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:08.834020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:21.001456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:25.164567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:31.386242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:38.099134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:54.689317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:19:59.520837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:08.937594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:21.128190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:20:25.241640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:20:38.512770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현지조사접수번호지청코드지청코드명현지조사번호확정구분일련번호현재사육량생산량생산금액수탁여부어업구분계통판매비율비계통판매비율
현지조사접수번호1.0000.2350.3420.5390.9430.0360.0000.0000.0000.0000.1430.0000.100
지청코드0.2351.0001.0000.0980.3610.2040.1550.0000.0000.1890.2540.4690.369
지청코드명0.3421.0001.0000.1530.6210.2390.2680.0000.1190.2010.4060.4770.388
현지조사번호0.5390.0980.1531.0000.5430.0490.0000.0000.0000.0290.1120.1490.130
확정구분0.9430.3610.6210.5431.0000.0700.0570.0000.0000.0290.1030.3160.145
일련번호0.0360.2040.2390.0490.0701.0000.0120.0000.0000.0180.0000.1700.228
현재사육량0.0000.1550.2680.0000.0570.0121.000NaN0.0000.0000.000NaN0.000
생산량0.0000.0000.0000.0000.0000.000NaN1.0000.000NaN0.0000.000NaN
생산금액0.0000.0000.1190.0000.0000.0000.0000.0001.0000.0000.0000.0000.000
수탁여부0.0000.1890.2010.0290.0290.0180.000NaN0.0001.0000.0260.1690.000
어업구분0.1430.2540.4060.1120.1030.0000.0000.0000.0000.0261.0000.2400.061
계통판매비율0.0000.4690.4770.1490.3160.170NaN0.0000.0000.1690.2401.0000.998
비계통판매비율0.1000.3690.3880.1300.1450.2280.000NaN0.0000.0000.0610.9981.000
2023-12-12T16:20:38.697692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
어업구분현지조사번호수탁여부일련번호지청코드명확정구분
어업구분1.0000.0920.0430.0000.1980.097
현지조사번호0.0921.0000.0360.0180.0850.484
수탁여부0.0430.0361.0000.0220.1560.048
일련번호0.0000.0180.0221.0000.1340.052
지청코드명0.1980.0850.1560.1341.0000.351
확정구분0.0970.4840.0480.0520.3511.000
2023-12-12T16:20:38.843758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
현지조사접수번호지청코드현재사육량생산량생산금액계통판매비율비계통판매비율지청코드명현지조사번호확정구분일련번호수탁여부어업구분
현지조사접수번호1.0000.9360.247-0.269-0.276-0.0800.3470.1810.1840.1840.0000.1720.144
지청코드0.9361.0000.245-0.296-0.307-0.0650.3571.0000.0570.2490.1250.1420.115
현재사육량0.2470.2451.0000.3470.3190.2080.0950.1250.0000.0170.0090.0000.000
생산량-0.269-0.2960.3471.0000.8020.363-0.0150.0000.0000.0000.0001.0000.000
생산금액-0.276-0.3070.3190.8021.0000.377-0.0400.0470.0000.0000.0000.0000.000
계통판매비율-0.080-0.0650.2080.3630.3771.000-0.8330.2240.0620.2040.0730.1290.144
비계통판매비율0.3470.3570.095-0.015-0.040-0.8331.0000.1710.0530.0810.0920.0000.036
지청코드명0.1811.0000.1250.0000.0470.2240.1711.0000.0850.3510.1340.1560.198
현지조사번호0.1840.0570.0000.0000.0000.0620.0530.0851.0000.4840.0180.0360.092
확정구분0.1840.2490.0170.0000.0000.2040.0810.3510.4841.0000.0520.0480.097
일련번호0.0000.1250.0090.0000.0000.0730.0920.1340.0180.0521.0000.0220.000
수탁여부0.1720.1420.0001.0000.0000.1290.0000.1560.0360.0480.0221.0000.043
어업구분0.1440.1150.0000.0000.0000.1440.0360.1980.0920.0970.0000.0431.000

Missing values

2023-12-12T16:20:31.533383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:20:31.754942image/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.
2023-12-12T16:20:31.976901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

현지조사접수번호지청코드지청코드명현지조사번호확정구분일련번호현재사육량생산량생산금액수탁여부현지조사일자최초생성시점최종변경시점어업구분어업번호계통판매비율비계통판매비율데이터기준일자
3101119236320150052311192363목포지방해양수산청141<NA>4000.012000N2015-08-212015-08-192015-08-21<NA><NA><NA><NA>2023-08-18
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191031119236020220014261192360마산지방해양수산청14110800.06090.06700<NA>2022-02-232022-02-232022-02-23111111600533000001395100<NA>2023-08-18
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12907111923632014027781192363목포지방해양수산청311<NA><NA>140N2023-07-062023-06-222023-07-06111111100499004911821<NA>1002023-08-18
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31593111923602017016751192360마산지방해양수산청1210.0192.050N2020-06-262020-07-022020-07-02<NA><NA><NA><NA>2023-08-18
현지조사접수번호지청코드지청코드명현지조사번호확정구분일련번호현재사육량생산량생산금액수탁여부현지조사일자최초생성시점최종변경시점어업구분어업번호계통판매비율비계통판매비율데이터기준일자
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21968111923632015032771192363목포지방해양수산청121<NA>5000.0400N<NA>2019-07-292019-07-29<NA><NA><NA><NA>2023-08-18
21402111923672016005621192367대산지방해양수산청221<NA><NA>315N2022-02-282021-02-232022-02-28<NA><NA>100<NA>2023-08-18
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52281119236020170022511192360마산지방해양수산청1410.0315.0126N2017-07-262017-07-312017-07-31<NA><NA><NA><NA>2023-08-18
20942111923602015026411192360마산지방해양수산청321225000.0240000.012000<NA>2022-02-162022-02-192022-02-19111111600543000000362<NA>1002023-08-18