Overview

Dataset statistics

Number of variables12
Number of observations7243
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory721.6 KiB
Average record size in memory102.0 B

Variable types

Numeric6
Categorical4
Text2

Dataset

Description수산물 수협 물류센터/공판장 입출고 정보는 수협이 운영하고 있는 물류센터나 공판장 기본정보 및 해당 물류센터 및 공판장에 입출고 되는 품목에 대한 정보 및 품목별 정보를 제공하는 목록입니다.
Author해양수산부
URLhttps://www.data.go.kr/data/15102797/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
물류센터-공판장 코드 is highly overall correlated with 물류센터-공판장명High correlation
상태가공분류코드 is highly overall correlated with 상태가공분류명High correlation
입고량 is highly overall correlated with 입고량(킬로그램)High correlation
출고량 is highly overall correlated with 출고량(킬로그램)High correlation
입고량(킬로그램) is highly overall correlated with 입고량High correlation
출고량(킬로그램) is highly overall correlated with 출고량High correlation
물류센터-공판장명 is highly overall correlated with 물류센터-공판장 코드High correlation
상태가공분류명 is highly overall correlated with 상태가공분류코드High correlation
상태가공분류코드 has 706 (9.7%) zerosZeros
입고량 has 6864 (94.8%) zerosZeros
출고량 has 5731 (79.1%) zerosZeros
입고량(킬로그램) has 6864 (94.8%) zerosZeros
출고량(킬로그램) has 5729 (79.1%) zerosZeros

Reproduction

Analysis started2024-04-20 18:24:38.369301
Analysis finished2024-04-20 18:24:50.135232
Duration11.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

물류센터-공판장 코드
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean180105.75
Minimum156
Maximum248010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size63.8 KiB
2024-04-21T03:24:50.245092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum156
5-th percentile108100
Q1130030
median200330
Q3218020
95-th percentile248010
Maximum248010
Range247854
Interquartile range (IQR)87990

Descriptive statistics

Standard deviation55402.55
Coefficient of variation (CV)0.30761122
Kurtosis1.8287905
Mean180105.75
Median Absolute Deviation (MAD)17690
Skewness-1.3905688
Sum1.304506 × 109
Variance3.0694426 × 109
MonotonicityNot monotonic
2024-04-21T03:24:50.488355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
200330 1502
20.7%
190010 1367
18.9%
218020 1265
17.5%
108100 1149
15.9%
248010 672
9.3%
130030 527
 
7.3%
218910 311
 
4.3%
218030 186
 
2.6%
156 140
 
1.9%
157 124
 
1.7%
ValueCountFrequency (%)
156 140
 
1.9%
157 124
 
1.7%
108100 1149
15.9%
130030 527
 
7.3%
190010 1367
18.9%
200330 1502
20.7%
218020 1265
17.5%
218030 186
 
2.6%
218910 311
 
4.3%
248010 672
9.3%
ValueCountFrequency (%)
248010 672
9.3%
218910 311
 
4.3%
218030 186
 
2.6%
218020 1265
17.5%
200330 1502
20.7%
190010 1367
18.9%
130030 527
 
7.3%
108100 1149
15.9%
157 124
 
1.7%
156 140
 
1.9%

물류센터-공판장명
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
광주공판장
1502 
감천항 물류센터
1367 
인천가공물류센터
1265 
외발산동 물류센터
1149 
전주공판장
672 
Other values (5)
1288 

Length

Max length14
Median length11
Mean length7.7143449
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(춘천) 강원물류센터
2nd row(춘천) 강원물류센터
3rd row(춘천) 강원물류센터
4th row(춘천) 강원물류센터
5th row외발산동 물류센터

Common Values

ValueCountFrequency (%)
광주공판장 1502
20.7%
감천항 물류센터 1367
18.9%
인천가공물류센터 1265
17.5%
외발산동 물류센터 1149
15.9%
전주공판장 672
9.3%
천안냉장 물류사업소 527
 
7.3%
호남권 분산물류센터 311
 
4.3%
인천가공물류센터 분산물류팀 186
 
2.6%
경기북부 물류센터 140
 
1.9%
(춘천) 강원물류센터 124
 
1.7%

Length

2024-04-21T03:24:50.751624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:24:51.024315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
물류센터 2656
24.0%
광주공판장 1502
13.6%
인천가공물류센터 1451
13.1%
감천항 1367
12.4%
외발산동 1149
10.4%
전주공판장 672
 
6.1%
천안냉장 527
 
4.8%
물류사업소 527
 
4.8%
호남권 311
 
2.8%
분산물류센터 311
 
2.8%
Other values (4) 574
 
5.2%

기준일자
Categorical

Distinct31
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
2024-03-31
 
245
2024-03-29
 
245
2024-03-28
 
245
2024-03-27
 
245
2024-03-26
 
245
Other values (26)
6018 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-01
2nd row2024-03-01
3rd row2024-03-01
4th row2024-03-01
5th row2024-03-01

Common Values

ValueCountFrequency (%)
2024-03-31 245
 
3.4%
2024-03-29 245
 
3.4%
2024-03-28 245
 
3.4%
2024-03-27 245
 
3.4%
2024-03-26 245
 
3.4%
2024-03-30 245
 
3.4%
2024-03-25 243
 
3.4%
2024-03-24 243
 
3.4%
2024-03-23 243
 
3.4%
2024-03-22 243
 
3.4%
Other values (21) 4801
66.3%

Length

2024-04-21T03:24:51.319058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2024-03-31 245
 
3.4%
2024-03-28 245
 
3.4%
2024-03-27 245
 
3.4%
2024-03-26 245
 
3.4%
2024-03-30 245
 
3.4%
2024-03-29 245
 
3.4%
2024-03-25 243
 
3.4%
2024-03-24 243
 
3.4%
2024-03-23 243
 
3.4%
2024-03-22 243
 
3.4%
Other values (21) 4801
66.3%

수산물품목코드
Text

Distinct92
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
2024-04-21T03:24:52.296610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters43458
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row720199
2nd row614504
3rd row620899
4th row613102
5th row614502
ValueCountFrequency (%)
610399 273
 
3.8%
640599 239
 
3.3%
613102 237
 
3.3%
610899 211
 
2.9%
619601 211
 
2.9%
614501 211
 
2.9%
934110 196
 
2.7%
640301 183
 
2.5%
618199 183
 
2.5%
730399 180
 
2.5%
Other values (82) 5119
70.7%
2024-04-21T03:24:53.752125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8364
19.2%
9 7958
18.3%
0 7226
16.6%
6 6841
15.7%
3 3631
8.4%
4 2866
 
6.6%
2 2797
 
6.4%
5 1383
 
3.2%
8 1235
 
2.8%
7 822
 
1.9%
Other values (3) 335
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43123
99.2%
Uppercase Letter 335
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8364
19.4%
9 7958
18.5%
0 7226
16.8%
6 6841
15.9%
3 3631
8.4%
4 2866
 
6.6%
2 2797
 
6.5%
5 1383
 
3.2%
8 1235
 
2.9%
7 822
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
Z 186
55.5%
B 118
35.2%
A 31
 
9.3%

Most occurring scripts

ValueCountFrequency (%)
Common 43123
99.2%
Latin 335
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8364
19.4%
9 7958
18.5%
0 7226
16.8%
6 6841
15.9%
3 3631
8.4%
4 2866
 
6.6%
2 2797
 
6.5%
5 1383
 
3.2%
8 1235
 
2.9%
7 822
 
1.9%
Latin
ValueCountFrequency (%)
Z 186
55.5%
B 118
35.2%
A 31
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43458
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8364
19.2%
9 7958
18.3%
0 7226
16.6%
6 6841
15.7%
3 3631
8.4%
4 2866
 
6.6%
2 2797
 
6.4%
5 1383
 
3.2%
8 1235
 
2.8%
7 822
 
1.9%
Other values (3) 335
 
0.8%

수산물품목명
Text

Distinct92
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
2024-04-21T03:24:54.950048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length3.9899213
Min length2

Characters and Unicode

Total characters28899
Distinct characters111
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

Unique1 ?
Unique (%)< 0.1%

Sample

1st row기타논우렁이류
2nd row북어
3rd row기타꼬막류
4th row민대구
5th row코다리
ValueCountFrequency (%)
기타갈치류 273
 
3.8%
기타오징어류 239
 
3.3%
민대구 237
 
3.3%
기타고등어류 211
 
2.9%
참조기 211
 
2.9%
명태 211
 
2.9%
오징어포 196
 
2.7%
낙지 183
 
2.5%
기타아귀류 183
 
2.5%
기타민물새우류 180
 
2.5%
Other values (82) 5119
70.7%
2024-04-21T03:24:56.550592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 3506
 
12.1%
류 3444
 
11.9%
타 3202
 
11.1%
어 1905
 
6.6%
치 893
 
3.1%
민 724
 
2.5%
오 704
 
2.4%
징 642
 
2.2%
대 566
 
2.0%
우 531
 
1.8%
Other values (101) 12782
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 28843
99.8%
Close Punctuation 28
 
0.1%
Open Punctuation 28
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 3506
 
12.2%
류 3444
 
11.9%
타 3202
 
11.1%
어 1905
 
6.6%
치 893
 
3.1%
민 724
 
2.5%
오 704
 
2.4%
징 642
 
2.2%
대 566
 
2.0%
우 531
 
1.8%
Other values (99) 12726
44.1%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 28843
99.8%
Common 56
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 3506
 
12.2%
류 3444
 
11.9%
타 3202
 
11.1%
어 1905
 
6.6%
치 893
 
3.1%
민 724
 
2.5%
오 704
 
2.4%
징 642
 
2.2%
대 566
 
2.0%
우 531
 
1.8%
Other values (99) 12726
44.1%
Common
ValueCountFrequency (%)
) 28
50.0%
( 28
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 28843
99.8%
ASCII 56
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 3506
 
12.2%
류 3444
 
11.9%
타 3202
 
11.1%
어 1905
 
6.6%
치 893
 
3.1%
민 724
 
2.5%
오 704
 
2.4%
징 642
 
2.2%
대 566
 
2.0%
우 531
 
1.8%
Other values (99) 12726
44.1%
ASCII
ValueCountFrequency (%)
) 28
50.0%
( 28
50.0%

상태가공분류코드
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.859036
Minimum0
Maximum42
Zeros706
Zeros (%)9.7%
Negative0
Negative (%)0.0%
Memory size63.8 KiB
2024-04-21T03:24:56.910506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q130
median30
Q330
95-th percentile40
Maximum42
Range42
Interquartile range (IQR)0

Descriptive statistics

Standard deviation10.3736
Coefficient of variation (CV)0.35945761
Kurtosis3.1171517
Mean28.859036
Median Absolute Deviation (MAD)0
Skewness-1.8646168
Sum209026
Variance107.61158
MonotonicityNot monotonic
2024-04-21T03:24:57.178914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
30 4977
68.7%
40 1324
 
18.3%
0 706
 
9.7%
20 118
 
1.6%
42 62
 
0.9%
32 56
 
0.8%
ValueCountFrequency (%)
0 706
 
9.7%
20 118
 
1.6%
30 4977
68.7%
32 56
 
0.8%
40 1324
 
18.3%
42 62
 
0.9%
ValueCountFrequency (%)
42 62
 
0.9%
40 1324
 
18.3%
32 56
 
0.8%
30 4977
68.7%
20 118
 
1.6%
0 706
 
9.7%

상태가공분류명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
(냉동)
4977 
(건)
1324 
<NA>
706 
(냉장/신선)
 
118
(건)(탈각/탈피)
 
62

Length

Max length11
Median length4
Mean length3.9715587
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row(냉동)
2nd row(건)
3rd row(냉동)
4th row(냉동)
5th row(건)

Common Values

ValueCountFrequency (%)
(냉동) 4977
68.7%
(건) 1324
 
18.3%
<NA> 706
 
9.7%
(냉장/신선) 118
 
1.6%
(건)(탈각/탈피) 62
 
0.9%
(냉동)(탈각/탈피) 56
 
0.8%

Length

2024-04-21T03:24:57.505020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:24:57.871195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
냉동 4977
68.7%
건 1324
 
18.3%
na 706
 
9.7%
냉장/신선 118
 
1.6%
건)(탈각/탈피 62
 
0.9%
냉동)(탈각/탈피 56
 
0.8%

입고량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct218
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.00359
Minimum0
Maximum15545
Zeros6864
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size63.8 KiB
2024-04-21T03:24:58.253537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.9
Maximum15545
Range15545
Interquartile range (IQR)0

Descriptive statistics

Standard deviation514.00855
Coefficient of variation (CV)11.173227
Kurtosis413.84196
Mean46.00359
Median Absolute Deviation (MAD)0
Skewness18.385323
Sum333204
Variance264204.79
MonotonicityNot monotonic
2024-04-21T03:24:58.696507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6864
94.8%
50 17
 
0.2%
200 15
 
0.2%
20 10
 
0.1%
10 9
 
0.1%
400 8
 
0.1%
100 8
 
0.1%
30 8
 
0.1%
40 7
 
0.1%
150 7
 
0.1%
Other values (208) 290
 
4.0%
ValueCountFrequency (%)
0 6864
94.8%
1 6
 
0.1%
2 4
 
0.1%
4 1
 
< 0.1%
5 4
 
0.1%
6 1
 
< 0.1%
7 4
 
0.1%
8 1
 
< 0.1%
9 3
 
< 0.1%
10 9
 
0.1%
ValueCountFrequency (%)
15545 1
< 0.1%
14279 1
< 0.1%
13094 1
< 0.1%
12925 1
< 0.1%
11657 1
< 0.1%
10114 1
< 0.1%
8478 1
< 0.1%
8048 1
< 0.1%
7820 1
< 0.1%
6482 1
< 0.1%

출고량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct388
Distinct (%)5.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47.034792
Minimum0
Maximum7806
Zeros5731
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size63.8 KiB
2024-04-21T03:24:59.119717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile124.8
Maximum7806
Range7806
Interquartile range (IQR)0

Descriptive statistics

Standard deviation320.56201
Coefficient of variation (CV)6.8154231
Kurtosis208.65524
Mean47.034792
Median Absolute Deviation (MAD)0
Skewness12.882928
Sum340673
Variance102760
MonotonicityNot monotonic
2024-04-21T03:24:59.576570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5731
79.1%
10 127
 
1.8%
1 88
 
1.2%
2 84
 
1.2%
3 68
 
0.9%
5 57
 
0.8%
20 41
 
0.6%
6 39
 
0.5%
15 37
 
0.5%
30 37
 
0.5%
Other values (378) 934
 
12.9%
ValueCountFrequency (%)
0 5731
79.1%
1 88
 
1.2%
2 84
 
1.2%
3 68
 
0.9%
4 32
 
0.4%
5 57
 
0.8%
6 39
 
0.5%
7 17
 
0.2%
8 16
 
0.2%
9 10
 
0.1%
ValueCountFrequency (%)
7806 1
< 0.1%
7472 1
< 0.1%
6153 1
< 0.1%
5473 1
< 0.1%
5440 1
< 0.1%
5400 1
< 0.1%
5270 1
< 0.1%
5081 1
< 0.1%
5014 1
< 0.1%
4958 1
< 0.1%

입고량(킬로그램)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct268
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean411.49054
Minimum0
Maximum129057
Zeros6864
Zeros (%)94.8%
Negative0
Negative (%)0.0%
Memory size63.8 KiB
2024-04-21T03:25:00.009762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile84
Maximum129057
Range129057
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4447.8657
Coefficient of variation (CV)10.809157
Kurtosis329.21245
Mean411.49054
Median Absolute Deviation (MAD)0
Skewness16.756561
Sum2980426
Variance19783509
MonotonicityNot monotonic
2024-04-21T03:25:00.455935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6864
94.8%
1000 12
 
0.2%
600 6
 
0.1%
4000 6
 
0.1%
800 6
 
0.1%
120 6
 
0.1%
20 5
 
0.1%
500 5
 
0.1%
2000 5
 
0.1%
3000 5
 
0.1%
Other values (258) 323
 
4.5%
ValueCountFrequency (%)
0 6864
94.8%
8 1
 
< 0.1%
11 1
 
< 0.1%
12 2
 
< 0.1%
20 5
 
0.1%
24 2
 
< 0.1%
30 1
 
< 0.1%
50 1
 
< 0.1%
75 1
 
< 0.1%
80 1
 
< 0.1%
ValueCountFrequency (%)
129057 1
 
< 0.1%
107520 1
 
< 0.1%
107006 1
 
< 0.1%
81599 1
 
< 0.1%
80500 2
< 0.1%
80270 1
 
< 0.1%
74920 1
 
< 0.1%
72450 1
 
< 0.1%
72220 1
 
< 0.1%
69875 3
< 0.1%

출고량(킬로그램)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct654
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean523.87809
Minimum0
Maximum133615
Zeros5729
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size63.8 KiB
2024-04-21T03:25:00.870180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1427.9
Maximum133615
Range133615
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3973.3158
Coefficient of variation (CV)7.5844283
Kurtosis361.45455
Mean523.87809
Median Absolute Deviation (MAD)0
Skewness16.493942
Sum3794449
Variance15787239
MonotonicityNot monotonic
2024-04-21T03:25:01.325774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 5729
79.1%
100 62
 
0.9%
20 52
 
0.7%
60 38
 
0.5%
30 36
 
0.5%
40 33
 
0.5%
150 32
 
0.4%
45 31
 
0.4%
200 26
 
0.4%
50 24
 
0.3%
Other values (644) 1180
 
16.3%
ValueCountFrequency (%)
0 5729
79.1%
2 4
 
0.1%
4 2
 
< 0.1%
5 10
 
0.1%
6 3
 
< 0.1%
7 1
 
< 0.1%
8 2
 
< 0.1%
9 3
 
< 0.1%
10 23
 
0.3%
12 6
 
0.1%
ValueCountFrequency (%)
133615 1
 
< 0.1%
89992 1
 
< 0.1%
80500 3
< 0.1%
74920 1
 
< 0.1%
72450 2
< 0.1%
57498 1
 
< 0.1%
54642 1
 
< 0.1%
54400 1
 
< 0.1%
54000 1
 
< 0.1%
50742 1
 
< 0.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size56.7 KiB
2024-03-31
7243 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2024-03-31
2nd row2024-03-31
3rd row2024-03-31
4th row2024-03-31
5th row2024-03-31

Common Values

ValueCountFrequency (%)
2024-03-31 7243
100.0%

Length

2024-04-21T03:25:01.972121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T03:25:02.141287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2024-03-31 7243
100.0%

Interactions

2024-04-21T03:24:48.293529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:40.357744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:41.940696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:43.517424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:45.212182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:46.778608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:48.460561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:40.611155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:42.196982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:43.789022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:45.455765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:47.019697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:48.619832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:40.862217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:42.443374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:44.049617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:45.653158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:47.282948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:48.848144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:41.138422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:42.715193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:44.335196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:45.931184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:47.573863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:49.072915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:41.413288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:42.984296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:44.627453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:46.219972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:47.755219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:49.297696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:41.678335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:43.247968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:44.917000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:46.497136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T03:24:47.919060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T03:25:02.259762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물류센터-공판장 코드물류센터-공판장명기준일자수산물품목코드수산물품목명상태가공분류코드상태가공분류명입고량출고량입고량(킬로그램)출고량(킬로그램)
물류센터-공판장 코드1.0001.0000.0000.8350.8350.2270.2150.0740.1430.0900.116
물류센터-공판장명1.0001.0000.0000.8580.8580.2990.4010.0620.1150.0690.105
기준일자0.0000.0001.0000.0000.0000.0000.0000.0080.0460.0500.059
수산물품목코드0.8350.8580.0001.0001.0000.9940.9800.0290.1340.0000.000
수산물품목명0.8350.8580.0001.0001.0000.9940.9800.0290.1340.0000.000
상태가공분류코드0.2270.2990.0000.9940.9941.0001.0000.0980.1170.0910.110
상태가공분류명0.2150.4010.0000.9800.9801.0001.0000.1110.1030.0790.059
입고량0.0740.0620.0080.0290.0290.0980.1111.0000.4780.7540.487
출고량0.1430.1150.0460.1340.1340.1170.1030.4781.0000.7460.825
입고량(킬로그램)0.0900.0690.0500.0000.0000.0910.0790.7540.7461.0000.760
출고량(킬로그램)0.1160.1050.0590.0000.0000.1100.0590.4870.8250.7601.000
2024-04-21T03:25:02.481084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물류센터-공판장명상태가공분류명기준일자
물류센터-공판장명1.0000.1780.000
상태가공분류명0.1781.0000.000
기준일자0.0000.0001.000
2024-04-21T03:25:02.643467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
물류센터-공판장 코드상태가공분류코드입고량출고량입고량(킬로그램)출고량(킬로그램)물류센터-공판장명기준일자상태가공분류명
물류센터-공판장 코드1.0000.010-0.093-0.075-0.094-0.0761.0000.0000.147
상태가공분류코드0.0101.0000.0150.0300.0150.0270.1820.0001.000
입고량-0.0930.0151.0000.3521.0000.3510.0190.0030.046
출고량-0.0750.0300.3521.0000.3520.9980.0520.0170.059
입고량(킬로그램)-0.0940.0151.0000.3521.0000.3510.0310.0190.045
출고량(킬로그램)-0.0760.0270.3510.9980.3511.0000.0500.0230.036
물류센터-공판장명1.0000.1820.0190.0520.0310.0501.0000.0000.178
기준일자0.0000.0000.0030.0170.0190.0230.0001.0000.000
상태가공분류명0.1471.0000.0460.0590.0450.0360.1780.0001.000

Missing values

2024-04-21T03:24:49.597605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T03:24:49.982541image/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

물류센터-공판장 코드물류센터-공판장명기준일자수산물품목코드수산물품목명상태가공분류코드상태가공분류명입고량출고량입고량(킬로그램)출고량(킬로그램)데이터기준일자
0157(춘천) 강원물류센터2024-03-01720199기타논우렁이류30(냉동)00002024-03-31
1157(춘천) 강원물류센터2024-03-01614504북어40(건)00002024-03-31
2157(춘천) 강원물류센터2024-03-01620899기타꼬막류30(냉동)00002024-03-31
3157(춘천) 강원물류센터2024-03-01613102민대구30(냉동)00002024-03-31
4108100외발산동 물류센터2024-03-01614502코다리40(건)00002024-03-31
5108100외발산동 물류센터2024-03-01622099기타홍합류30(냉동)00002024-03-31
6108100외발산동 물류센터2024-03-01617799기타성대류30(냉동)01001002024-03-31
7108100외발산동 물류센터2024-03-01730399기타민물새우류30(냉동)00002024-03-31
8108100외발산동 물류센터2024-03-01612302임연수어30(냉동)01001802024-03-31
9108100외발산동 물류센터2024-03-01730200민물게류30(냉동)01501502024-03-31
물류센터-공판장 코드물류센터-공판장명기준일자수산물품목코드수산물품목명상태가공분류코드상태가공분류명입고량출고량입고량(킬로그램)출고량(킬로그램)데이터기준일자
7233248010전주공판장2024-03-31730200민물게류30(냉동)00002024-03-31
7234248010전주공판장2024-03-31614502코다리40(건)00002024-03-31
7235248010전주공판장2024-03-31614501명태30(냉동)00002024-03-31
7236248010전주공판장2024-03-3161B399기타홍어류20(냉장/신선)00002024-03-31
7237248010전주공판장2024-03-31621001바지락30(냉동)00002024-03-31
7238248010전주공판장2024-03-31616099기타병어류30(냉동)00002024-03-31
7239248010전주공판장2024-03-31619603부세30(냉동)00002024-03-31
7240248010전주공판장2024-03-31613102민대구30(냉동)00002024-03-31
7241248010전주공판장2024-03-31610899기타고등어류30(냉동)00002024-03-31
7242248010전주공판장2024-03-31619601참조기30(냉동)00002024-03-31