Overview

Dataset statistics

Number of variables10
Number of observations643
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory52.2 KiB
Average record size in memory83.2 B

Variable types

Numeric1
Categorical6
Text2
DateTime1

Dataset

Description부산광역시_식품방사능검사현황_20201231
Author부산광역시
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083358

Alerts

세슘검출량(Bq/kg) has constant value ""Constant
요오드검출량(Bq/kg) has constant value ""Constant
적부판정 has constant value ""Constant
연번 is highly overall correlated with 원산지High correlation
원산지 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
수입국 is highly overall correlated with 원산지High correlation
수입국 is highly imbalanced (60.7%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-30 09:11:29.195007
Analysis finished2024-03-30 09:11:32.636692
Duration3.44 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct643
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean322
Minimum1
Maximum643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size5.8 KiB
2024-03-30T09:11:32.919436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile33.1
Q1161.5
median322
Q3482.5
95-th percentile610.9
Maximum643
Range642
Interquartile range (IQR)321

Descriptive statistics

Standard deviation185.76239
Coefficient of variation (CV)0.57690184
Kurtosis-1.2
Mean322
Median Absolute Deviation (MAD)161
Skewness0
Sum207046
Variance34507.667
MonotonicityStrictly increasing
2024-03-30T09:11:33.662281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.2%
404 1
 
0.2%
426 1
 
0.2%
427 1
 
0.2%
428 1
 
0.2%
429 1
 
0.2%
430 1
 
0.2%
431 1
 
0.2%
432 1
 
0.2%
433 1
 
0.2%
Other values (633) 633
98.4%
ValueCountFrequency (%)
1 1
0.2%
2 1
0.2%
3 1
0.2%
4 1
0.2%
5 1
0.2%
6 1
0.2%
7 1
0.2%
8 1
0.2%
9 1
0.2%
10 1
0.2%
ValueCountFrequency (%)
643 1
0.2%
642 1
0.2%
641 1
0.2%
640 1
0.2%
639 1
0.2%
638 1
0.2%
637 1
0.2%
636 1
0.2%
635 1
0.2%
634 1
0.2%

분류
Categorical

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
가공식품
312 
수산물
265 
농산물
60 
축산물
 
6

Length

Max length4
Median length3
Mean length3.4852255
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row수산물
2nd row수산물
3rd row가공식품
4th row수산물
5th row농산물

Common Values

ValueCountFrequency (%)
가공식품 312
48.5%
수산물 265
41.2%
농산물 60
 
9.3%
축산물 6
 
0.9%

Length

2024-03-30T09:11:34.082065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:11:34.433208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
가공식품 312
48.5%
수산물 265
41.2%
농산물 60
 
9.3%
축산물 6
 
0.9%

제품명
Text

Distinct478
Distinct (%)74.3%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2024-03-30T09:11:34.972595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length6.2410575
Min length1

Characters and Unicode

Total characters4013
Distinct characters440
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique406 ?
Unique (%)63.1%

Sample

1st row 포항가자미
2nd row물메기
3rd row블독우스타소스
4th row생대구
5th row친환경골든키위
ValueCountFrequency (%)
고등어 16
 
2.1%
기꼬만혼쯔유(코이다시 9
 
1.2%
삼치 8
 
1.0%
오이오차녹차 7
 
0.9%
신주일미된장 7
 
0.9%
가자미 7
 
0.9%
농축쯔유 6
 
0.8%
동태 6
 
0.8%
친환경 6
 
0.8%
갈치 6
 
0.8%
Other values (524) 696
89.9%
2024-03-30T09:11:36.370172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
138
 
3.4%
스 110
 
2.7%
미 87
 
2.2%
어 83
 
2.1%
장 81
 
2.0%
기 71
 
1.8%
소 69
 
1.7%
오 69
 
1.7%
이 65
 
1.6%
리 59
 
1.5%
Other values (430) 3181
79.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3737
93.1%
Space Separator 138
 
3.4%
Close Punctuation 49
 
1.2%
Open Punctuation 49
 
1.2%
Decimal Number 27
 
0.7%
Other Punctuation 6
 
0.1%
Uppercase Letter 5
 
0.1%
Other Symbol 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 110
 
2.9%
미 87
 
2.3%
어 83
 
2.2%
장 81
 
2.2%
기 71
 
1.9%
소 69
 
1.8%
오 69
 
1.8%
이 65
 
1.7%
리 59
 
1.6%
시 56
 
1.5%
Other values (411) 2987
79.9%
Decimal Number
ValueCountFrequency (%)
0 9
33.3%
1 7
25.9%
2 5
18.5%
5 3
 
11.1%
7 2
 
7.4%
3 1
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
B 1
20.0%
S 1
20.0%
T 1
20.0%
O 1
20.0%
P 1
20.0%
Other Punctuation
ValueCountFrequency (%)
& 3
50.0%
. 2
33.3%
/ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
138
100.0%
Close Punctuation
ValueCountFrequency (%)
) 49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 49
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3738
93.1%
Common 270
 
6.7%
Latin 5
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 110
 
2.9%
미 87
 
2.3%
어 83
 
2.2%
장 81
 
2.2%
기 71
 
1.9%
소 69
 
1.8%
오 69
 
1.8%
이 65
 
1.7%
리 59
 
1.6%
시 56
 
1.5%
Other values (412) 2988
79.9%
Common
ValueCountFrequency (%)
138
51.1%
) 49
 
18.1%
( 49
 
18.1%
0 9
 
3.3%
1 7
 
2.6%
2 5
 
1.9%
5 3
 
1.1%
& 3
 
1.1%
. 2
 
0.7%
7 2
 
0.7%
Other values (3) 3
 
1.1%
Latin
ValueCountFrequency (%)
B 1
20.0%
S 1
20.0%
T 1
20.0%
O 1
20.0%
P 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3737
93.1%
ASCII 275
 
6.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
138
50.2%
) 49
 
17.8%
( 49
 
17.8%
0 9
 
3.3%
1 7
 
2.5%
2 5
 
1.8%
5 3
 
1.1%
& 3
 
1.1%
. 2
 
0.7%
7 2
 
0.7%
Other values (8) 8
 
2.9%
Hangul
ValueCountFrequency (%)
스 110
 
2.9%
미 87
 
2.3%
어 83
 
2.2%
장 81
 
2.2%
기 71
 
1.9%
소 69
 
1.8%
오 69
 
1.8%
이 65
 
1.7%
리 59
 
1.6%
시 56
 
1.5%
Other values (411) 2987
79.9%
None
ValueCountFrequency (%)
㈜ 1
100.0%

품목(또는 식품유형)
Text

Distinct174
Distinct (%)27.1%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
2024-03-30T09:11:37.171119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length3.6236392
Min length1

Characters and Unicode

Total characters2330
Distinct characters196
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)14.6%

Sample

1st row가자미
2nd row메기
3rd row소스
4th row대구
5th row키위
ValueCountFrequency (%)
소스 99
 
15.2%
기타수산물가공품 75
 
11.5%
혼합장 34
 
5.2%
액상차 20
 
3.1%
카레 19
 
2.9%
가자미 15
 
2.3%
고등어 12
 
1.8%
갈치 11
 
1.7%
오징어 11
 
1.7%
다시마 10
 
1.5%
Other values (163) 344
52.9%
2024-03-30T09:11:38.514834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
가 121
 
5.2%
기 117
 
5.0%
품 110
 
4.7%
소 108
 
4.6%
산 107
 
4.6%
스 99
 
4.2%
공 98
 
4.2%
물 94
 
4.0%
타 94
 
4.0%
수 91
 
3.9%
Other values (186) 1291
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2286
98.1%
Open Punctuation 15
 
0.6%
Close Punctuation 15
 
0.6%
Space Separator 9
 
0.4%
Other Punctuation 5
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
가 121
 
5.3%
기 117
 
5.1%
품 110
 
4.8%
소 108
 
4.7%
산 107
 
4.7%
스 99
 
4.3%
공 98
 
4.3%
물 94
 
4.1%
타 94
 
4.1%
수 91
 
4.0%
Other values (181) 1247
54.5%
Other Punctuation
ValueCountFrequency (%)
. 4
80.0%
· 1
 
20.0%
Open Punctuation
ValueCountFrequency (%)
( 15
100.0%
Close Punctuation
ValueCountFrequency (%)
) 15
100.0%
Space Separator
ValueCountFrequency (%)
9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2286
98.1%
Common 44
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
가 121
 
5.3%
기 117
 
5.1%
품 110
 
4.8%
소 108
 
4.7%
산 107
 
4.7%
스 99
 
4.3%
공 98
 
4.3%
물 94
 
4.1%
타 94
 
4.1%
수 91
 
4.0%
Other values (181) 1247
54.5%
Common
ValueCountFrequency (%)
( 15
34.1%
) 15
34.1%
9
20.5%
. 4
 
9.1%
· 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2286
98.1%
ASCII 43
 
1.8%
None 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
가 121
 
5.3%
기 117
 
5.1%
품 110
 
4.8%
소 108
 
4.7%
산 107
 
4.7%
스 99
 
4.3%
공 98
 
4.3%
물 94
 
4.1%
타 94
 
4.1%
수 91
 
4.0%
Other values (181) 1247
54.5%
ASCII
ValueCountFrequency (%)
( 15
34.9%
) 15
34.9%
9
20.9%
. 4
 
9.3%
None
ValueCountFrequency (%)
· 1
100.0%

수거일
Date

Distinct96
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
Minimum2019-12-04 00:00:00
Maximum2020-12-04 00:00:00
2024-03-30T09:11:39.007379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T09:11:39.514956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

원산지
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
국내
274 
국외
255 
국산
96 
수입
 
18

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국내
2nd row국내
3rd row수입
4th row국내
5th row국내

Common Values

ValueCountFrequency (%)
국내 274
42.6%
국외 255
39.7%
국산 96
 
14.9%
수입 18
 
2.8%

Length

2024-03-30T09:11:39.949836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:11:40.259404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내 274
42.6%
국외 255
39.7%
국산 96
 
14.9%
수입 18
 
2.8%

수입국
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct26
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
대한민국
366 
일본
195 
미국
 
16
러시아
 
15
중국
 
11
Other values (21)
40 

Length

Max length5
Median length4
Mean length3.251944
Min length2

Unique

Unique12 ?
Unique (%)1.9%

Sample

1st row대한민국
2nd row대한민국
3rd row일본
4th row대한민국
5th row대한민국

Common Values

ValueCountFrequency (%)
대한민국 366
56.9%
일본 195
30.3%
미국 16
 
2.5%
러시아 15
 
2.3%
중국 11
 
1.7%
노르웨이 6
 
0.9%
베트남 5
 
0.8%
페루 4
 
0.6%
포르투갈 3
 
0.5%
말레이시아 2
 
0.3%
Other values (16) 20
 
3.1%

Length

2024-03-30T09:11:40.741949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대한민국 366
56.9%
일본 195
30.3%
미국 16
 
2.5%
러시아 15
 
2.3%
중국 11
 
1.7%
노르웨이 6
 
0.9%
베트남 5
 
0.8%
페루 4
 
0.6%
포르투갈 3
 
0.5%
원양산 2
 
0.3%
Other values (16) 20
 
3.1%

세슘검출량(Bq/kg)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
643 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 643
100.0%

Length

2024-03-30T09:11:41.322315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:11:41.660820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 643
100.0%

요오드검출량(Bq/kg)
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
0
643 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 643
100.0%

Length

2024-03-30T09:11:42.107990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:11:42.470897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 643
100.0%

적부판정
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size5.2 KiB
적합
643 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
적합 643
100.0%

Length

2024-03-30T09:11:42.805077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T09:11:43.126621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 643
100.0%

Interactions

2024-03-30T09:11:30.920141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T09:11:43.292521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류수거일원산지수입국
연번1.0000.4120.9950.7070.416
분류0.4121.0000.9300.5980.574
수거일0.9950.9301.0000.9410.601
원산지0.7070.5980.9411.0000.819
수입국0.4160.5740.6010.8191.000
2024-03-30T09:11:43.549354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분류수입국원산지
분류1.0000.3320.269
수입국0.3321.0000.576
원산지0.2690.5761.000
2024-03-30T09:11:43.809696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번분류원산지수입국
연번1.0000.2570.5070.161
분류0.2571.0000.2690.332
원산지0.5070.2691.0000.576
수입국0.1610.3320.5761.000

Missing values

2024-03-30T09:11:31.448856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T09:11:32.448519image/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

연번분류제품명품목(또는 식품유형)수거일원산지수입국세슘검출량(Bq/kg)요오드검출량(Bq/kg)적부판정
01수산물포항가자미가자미2019-12-04국내대한민국00적합
12수산물물메기메기2019-12-04국내대한민국00적합
23가공식품블독우스타소스소스2019-12-04수입일본00적합
34수산물생대구대구2019-12-11국내대한민국00적합
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