Overview

Dataset statistics

Number of variables13
Number of observations270
Missing cells1
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory28.1 KiB
Average record size in memory106.5 B

Variable types

Numeric2
Categorical7
Text1
Boolean1
DateTime2

Dataset

Description전북특별자치도 낚시 어선업 어선 크기별 신고 현황(시도명, 시군구명, 어선명, 업종, 선형, 총톤수, 톤수별, 선질, 선외기 여부 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15055792/fileData.do

Alerts

시도명 has constant value ""Constant
선형 has constant value ""Constant
시군구명 is highly overall correlated with 연 번 and 1 other fieldsHigh correlation
출입항명 is highly overall correlated with 시군구명High correlation
연 번 is highly overall correlated with 시군구명High correlation
총톤수 is highly overall correlated with 톤수별 and 1 other fieldsHigh correlation
톤수별 is highly overall correlated with 총톤수 and 1 other fieldsHigh correlation
선외기 여부 is highly overall correlated with 총톤수 and 1 other fieldsHigh correlation
선질 is highly imbalanced (75.4%)Imbalance
출입항명 is highly imbalanced (63.7%)Imbalance
연 번 has unique valuesUnique

Reproduction

Analysis started2024-03-14 16:29:17.150278
Analysis finished2024-03-14 16:29:20.205142
Duration3.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct270
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean135.5
Minimum1
Maximum270
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-15T01:29:20.451339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile14.45
Q168.25
median135.5
Q3202.75
95-th percentile256.55
Maximum270
Range269
Interquartile range (IQR)134.5

Descriptive statistics

Standard deviation78.086491
Coefficient of variation (CV)0.57628406
Kurtosis-1.2
Mean135.5
Median Absolute Deviation (MAD)67.5
Skewness0
Sum36585
Variance6097.5
MonotonicityStrictly increasing
2024-03-15T01:29:20.903850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
187 1
 
0.4%
173 1
 
0.4%
174 1
 
0.4%
175 1
 
0.4%
176 1
 
0.4%
177 1
 
0.4%
178 1
 
0.4%
179 1
 
0.4%
180 1
 
0.4%
Other values (260) 260
96.3%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
270 1
0.4%
269 1
0.4%
268 1
0.4%
267 1
0.4%
266 1
0.4%
265 1
0.4%
264 1
0.4%
263 1
0.4%
262 1
0.4%
261 1
0.4%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
전북
270 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전북 270
100.0%

Length

2024-03-15T01:29:21.316577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:29:21.575469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북 270
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
군산시
207 
부안군
63 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
군산시 207
76.7%
부안군 63
 
23.3%

Length

2024-03-15T01:29:21.740701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:29:22.052004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
군산시 207
76.7%
부안군 63
 
23.3%

어선명
Text

Distinct251
Distinct (%)93.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
2024-03-15T01:29:23.294338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.9925926
Min length3

Characters and Unicode

Total characters1078
Distinct characters228
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique233 ?
Unique (%)86.3%

Sample

1st row강남호
2nd row새만금호
3rd row만복호
4th row이글스호
5th row3기성호
ValueCountFrequency (%)
만복호 3
 
1.1%
에이스호 2
 
0.7%
대영호 2
 
0.7%
신성호 2
 
0.7%
썬플라워호 2
 
0.7%
대경호 2
 
0.7%
용성호 2
 
0.7%
시애틀호 2
 
0.7%
넘버원호 2
 
0.7%
서해피싱호 2
 
0.7%
Other values (242) 250
92.3%
2024-03-15T01:29:25.136547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
호 268
24.9%
스 43
 
4.0%
이 27
 
2.5%
성 22
 
2.0%
리 22
 
2.0%
피 22
 
2.0%
싱 18
 
1.7%
대 17
 
1.6%
아 16
 
1.5%
타 14
 
1.3%
Other values (218) 609
56.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1041
96.6%
Decimal Number 36
 
3.3%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 268
25.7%
스 43
 
4.1%
이 27
 
2.6%
성 22
 
2.1%
리 22
 
2.1%
피 22
 
2.1%
싱 18
 
1.7%
대 17
 
1.6%
아 16
 
1.5%
타 14
 
1.3%
Other values (209) 572
54.9%
Decimal Number
ValueCountFrequency (%)
2 13
36.1%
1 9
25.0%
3 4
 
11.1%
0 3
 
8.3%
7 3
 
8.3%
5 2
 
5.6%
6 1
 
2.8%
8 1
 
2.8%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1041
96.6%
Common 37
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 268
25.7%
스 43
 
4.1%
이 27
 
2.6%
성 22
 
2.1%
리 22
 
2.1%
피 22
 
2.1%
싱 18
 
1.7%
대 17
 
1.6%
아 16
 
1.5%
타 14
 
1.3%
Other values (209) 572
54.9%
Common
ValueCountFrequency (%)
2 13
35.1%
1 9
24.3%
3 4
 
10.8%
0 3
 
8.1%
7 3
 
8.1%
5 2
 
5.4%
6 1
 
2.7%
8 1
 
2.7%
1
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1041
96.6%
ASCII 37
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
호 268
25.7%
스 43
 
4.1%
이 27
 
2.6%
성 22
 
2.1%
리 22
 
2.1%
피 22
 
2.1%
싱 18
 
1.7%
대 17
 
1.6%
아 16
 
1.5%
타 14
 
1.3%
Other values (209) 572
54.9%
ASCII
ValueCountFrequency (%)
2 13
35.1%
1 9
24.3%
3 4
 
10.8%
0 3
 
8.1%
7 3
 
8.1%
5 2
 
5.4%
6 1
 
2.7%
8 1
 
2.7%
1
 
2.7%

업종
Categorical

Distinct13
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
연안복합어업
107 
연안자망어업
57 
바닥식양식어업
46 
수하식양식어업
27 
마을어업
12 
Other values (8)
21 

Length

Max length9
Median length6
Mean length6.1962963
Min length2

Unique

Unique2 ?
Unique (%)0.7%

Sample

1st row바닥식양식어업
2nd row마을어업
3rd row연안복합어업
4th row연안복합어업
5th row바닥식양식어업

Common Values

ValueCountFrequency (%)
연안복합어업 107
39.6%
연안자망어업 57
21.1%
바닥식양식어업 46
17.0%
수하식양식어업 27
 
10.0%
마을어업 12
 
4.4%
연안개량안강망어업 5
 
1.9%
연안조망어업 4
 
1.5%
연안통발어업 3
 
1.1%
장망류어업 3
 
1.1%
가두리양식어업 2
 
0.7%
Other values (3) 4
 
1.5%

Length

2024-03-15T01:29:25.532297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
연안복합어업 107
39.6%
연안자망어업 57
21.1%
바닥식양식어업 46
17.0%
수하식양식어업 27
 
10.0%
마을어업 12
 
4.4%
연안개량안강망어업 5
 
1.9%
연안조망어업 4
 
1.5%
연안통발어업 3
 
1.1%
장망류어업 3
 
1.1%
가두리양식어업 2
 
0.7%
Other values (3) 4
 
1.5%

선형
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
어선
270 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
어선 270
100.0%

Length

2024-03-15T01:29:25.781209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:29:25.995562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
어선 270
100.0%

총톤수
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3832222
Minimum1.01
Maximum9.77
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2024-03-15T01:29:26.245132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.01
5-th percentile2.578
Q14.9575
median7.93
Q39.77
95-th percentile9.77
Maximum9.77
Range8.76
Interquartile range (IQR)4.8125

Descriptive statistics

Standard deviation2.6513029
Coefficient of variation (CV)0.35909835
Kurtosis-0.7488448
Mean7.3832222
Median Absolute Deviation (MAD)1.84
Skewness-0.7720516
Sum1993.47
Variance7.0294071
MonotonicityNot monotonic
2024-03-15T01:29:26.570326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9.77 112
41.5%
7.93 36
 
13.3%
6.67 12
 
4.4%
8.55 10
 
3.7%
7.31 7
 
2.6%
3.0 6
 
2.2%
9.16 5
 
1.9%
2.99 4
 
1.5%
4.81 3
 
1.1%
4.5 3
 
1.1%
Other values (63) 72
26.7%
ValueCountFrequency (%)
1.01 1
0.4%
1.17 1
0.4%
1.23 1
0.4%
1.35 1
0.4%
1.42 1
0.4%
1.61 1
0.4%
1.77 1
0.4%
1.81 1
0.4%
1.92 2
0.7%
1.93 1
0.4%
ValueCountFrequency (%)
9.77 112
41.5%
9.16 5
 
1.9%
8.55 10
 
3.7%
7.93 36
 
13.3%
7.31 7
 
2.6%
6.67 12
 
4.4%
6.61 1
 
0.4%
6.57 2
 
0.7%
6.55 1
 
0.4%
6.5 1
 
0.4%

톤수별
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
9~10톤 미만
117 
7~8톤 미만
43 
4~5톤 미만
31 
6~7톤 미만
25 
2~3톤 미만
14 
Other values (5)
40 

Length

Max length8
Median length7
Mean length7.3888889
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row9~10톤 미만
2nd row7~8톤 미만
3rd row2~3톤 미만
4th row9~10톤 미만
5th row3~4톤미만

Common Values

ValueCountFrequency (%)
9~10톤 미만 117
43.3%
7~8톤 미만 43
 
15.9%
4~5톤 미만 31
 
11.5%
6~7톤 미만 25
 
9.3%
2~3톤 미만 14
 
5.2%
3~4톤미만 12
 
4.4%
1~2톤 미만 11
 
4.1%
8~9톤 미만 10
 
3.7%
3~4톤 미만 4
 
1.5%
5~6톤 미만 3
 
1.1%

Length

2024-03-15T01:29:26.939071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:29:27.209888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미만 258
48.9%
9~10톤 117
22.2%
7~8톤 43
 
8.1%
4~5톤 31
 
5.9%
6~7톤 25
 
4.7%
2~3톤 14
 
2.7%
3~4톤미만 12
 
2.3%
1~2톤 11
 
2.1%
8~9톤 10
 
1.9%
3~4톤 4
 
0.8%

선질
Categorical

IMBALANCE 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
FRP
259 
알류미늄합금
 
11

Length

Max length6
Median length3
Mean length3.1222222
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
FRP 259
95.9%
알류미늄합금 11
 
4.1%

Length

2024-03-15T01:29:27.586203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T01:29:27.920203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
frp 259
95.9%
알류미늄합금 11
 
4.1%

선외기 여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing1
Missing (%)0.4%
Memory size668.0 B
False
180 
True
89 
(Missing)
 
1
ValueCountFrequency (%)
False 180
66.7%
True 89
33.0%
(Missing) 1
 
0.4%
2024-03-15T01:29:28.186762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

출입항명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct24
Distinct (%)8.9%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
군산시 옥도면
207 
격포항
 
18
변산면 격포항
 
8
위도면 위도항, 격포항
 
5
위도면
 
4
Other values (19)
28 

Length

Max length14
Median length7
Mean length6.7592593
Min length2

Unique

Unique13 ?
Unique (%)4.8%

Sample

1st row군산시 옥도면
2nd row군산시 옥도면
3rd row군산시 옥도면
4th row군산시 옥도면
5th row군산시 옥도면

Common Values

ValueCountFrequency (%)
군산시 옥도면 207
76.7%
격포항 18
 
6.7%
변산면 격포항 8
 
3.0%
위도면 위도항, 격포항 5
 
1.9%
위도면 4
 
1.5%
부안군 변산면 격포항 4
 
1.5%
가력항 3
 
1.1%
부안군 변산면 가력항 2
 
0.7%
위도항 2
 
0.7%
위도항, 격포항 2
 
0.7%
Other values (14) 15
 
5.6%

Length

2024-03-15T01:29:28.557470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
군산시 207
40.0%
옥도면 207
40.0%
격포항 38
 
7.3%
변산면 17
 
3.3%
위도항 11
 
2.1%
위도면 10
 
1.9%
가력항 8
 
1.5%
부안군 7
 
1.4%
진서면 3
 
0.6%
왕포항 3
 
0.6%
Other values (7) 7
 
1.4%

시작일
Date

Distinct136
Distinct (%)50.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2017-02-10 00:00:00
Maximum2019-12-09 00:00:00
2024-03-15T01:29:28.943073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:29:29.354689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

만료일
Date

Distinct197
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
Minimum2019-12-31 00:00:00
Maximum2022-12-02 00:00:00
2024-03-15T01:29:29.956577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:29:30.377799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2024-03-15T01:29:19.191476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:29:18.499377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:29:19.453445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T01:29:18.860928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T01:29:30.594136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 번시군구명업종총톤수톤수별선질선외기 여부출입항명
연 번1.0000.9940.2130.2750.3350.2650.0000.729
시군구명0.9941.0000.1510.2690.4210.2540.1111.000
업종0.2130.1511.0000.3220.4290.0000.0390.000
총톤수0.2750.2690.3221.0000.9890.0000.7850.686
톤수별0.3350.4210.4290.9891.0000.0000.7860.691
선질0.2650.2540.0000.0000.0001.0000.0000.487
선외기 여부0.0000.1110.0390.7850.7860.0001.0000.394
출입항명0.7291.0000.0000.6860.6910.4870.3941.000
2024-03-15T01:29:30.894421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
톤수별시군구명업종출입항명선외기 여부선질
톤수별1.0000.3180.1920.3250.6120.000
시군구명0.3181.0000.1370.9580.0710.164
업종0.1920.1371.0000.0000.0330.000
출입항명0.3250.9580.0001.0000.2990.371
선외기 여부0.6120.0710.0330.2991.0000.000
선질0.0000.1640.0000.3710.0001.000
2024-03-15T01:29:31.226698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 번총톤수시군구명업종톤수별선질선외기 여부출입항명
연 번1.000-0.0990.9190.0880.1080.2000.0000.358
총톤수-0.0991.0000.2030.1370.8090.0000.6110.321
시군구명0.9190.2031.0000.1370.3180.1640.0710.958
업종0.0880.1370.1371.0000.1920.0000.0330.000
톤수별0.1080.8090.3180.1921.0000.0000.6120.325
선질0.2000.0000.1640.0000.0001.0000.0000.371
선외기 여부0.0000.6110.0710.0330.6120.0001.0000.299
출입항명0.3580.3210.9580.0000.3250.3710.2991.000

Missing values

2024-03-15T01:29:19.739105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T01:29:20.076037image/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

연 번시도명시군구명어선명업종선형총톤수톤수별선질선외기 여부출입항명시작일만료일
01전북군산시강남호바닥식양식어업어선9.779~10톤 미만FRPN군산시 옥도면2019-12-092020-12-20
12전북군산시새만금호마을어업어선7.937~8톤 미만FRPN군산시 옥도면2019-11-212020-05-13
23전북군산시만복호연안복합어업어선2.992~3톤 미만FRPN군산시 옥도면2019-10-252021-12-31
34전북군산시이글스호연안복합어업어선9.779~10톤 미만FRPN군산시 옥도면2019-10-222020-10-22
45전북군산시3기성호바닥식양식어업어선3.633~4톤미만FRPY군산시 옥도면2019-10-042021-09-27
56전북군산시다온호연안복합어업어선9.779~10톤 미만FRPN군산시 옥도면2019-10-022022-10-02
67전북군산시드래곤호연안자망어업어선8.558~9톤 미만FRPN군산시 옥도면2019-10-022020-10-02
78전북군산시뉴반석호연안복합어업어선9.779~10톤 미만FRPN군산시 옥도면2019-09-272020-09-27
89전북군산시챔피언호연안복합어업어선9.779~10톤 미만FRPN군산시 옥도면2019-09-242020-09-24
910전북군산시이지스호바닥식양식어업어선7.317~8톤 미만FRPN군산시 옥도면2019-09-232020-05-06
연 번시도명시군구명어선명업종선형총톤수톤수별선질선외기 여부출입항명시작일만료일
260261전북부안군용이호연안복합어업어선1.611~2톤 미만FRPY진서면2018-07-062021-07-06
261262전북부안군대영호연안자망어업어선7.317~8톤 미만FRPN위도면2018-04-092021-04-09
262263전북부안군대박3호연안복합어업어선9.779~10톤 미만알류미늄합금N격포항2018-06-052021-06-05
263264전북부안군창영호연안자망어업어선7.937~8톤 미만FRPN위도면2018-06-052020-03-14
264265전북부안군블루스카이호연안복합어업어선9.779~10톤 미만알류미늄합금Y격포항2018-06-052021-06-05
265266전북부안군남부스타호바닥식양식어업어선7.937~8톤 미만FRPY격포항2018-04-272020-04-26
266267전북부안군최길1호바닥식양식어업어선1.351~2톤 미만FRPY왕포항2018-04-012020-03-11
267268전북부안군7순복호수하식양식어업어선4.924~5톤 미만FRPN위도항2018-03-072021-03-07
268269전북부안군해오름호바닥식양식어업어선9.779~10톤 미만FRPN격포항2018-01-012019-12-31
269270전북부안군영덕호바닥식양식어업어선2.732~3톤 미만FRPY곰소항2017-09-012020-09-01