Overview

Dataset statistics

Number of variables5
Number of observations74
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.1 KiB
Average record size in memory42.8 B

Variable types

Numeric1
Text1
Categorical3

Dataset

Description기온과 수온의 상승에 따른 비브리오균 발생율 증가 등 수산물 식중독 발생 위험 증가에 대비하여 여름철 수산물 안정성을 검사한 결과
URLhttps://www.data.go.kr/data/15117865/fileData.do

Alerts

국내 및 수입 is highly imbalanced (89.7%)Imbalance
검사결과 is highly imbalanced (89.7%)Imbalance
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 13:23:50.210325
Analysis finished2023-12-12 13:23:50.734885
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct74
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.5
Minimum1
Maximum74
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size798.0 B
2023-12-12T22:23:50.799868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q119.25
median37.5
Q355.75
95-th percentile70.35
Maximum74
Range73
Interquartile range (IQR)36.5

Descriptive statistics

Standard deviation21.505813
Coefficient of variation (CV)0.57348835
Kurtosis-1.2
Mean37.5
Median Absolute Deviation (MAD)18.5
Skewness0
Sum2775
Variance462.5
MonotonicityStrictly increasing
2023-12-12T22:23:50.949434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.4%
57 1
 
1.4%
55 1
 
1.4%
54 1
 
1.4%
53 1
 
1.4%
52 1
 
1.4%
51 1
 
1.4%
50 1
 
1.4%
49 1
 
1.4%
48 1
 
1.4%
Other values (64) 64
86.5%
ValueCountFrequency (%)
1 1
1.4%
2 1
1.4%
3 1
1.4%
4 1
1.4%
5 1
1.4%
6 1
1.4%
7 1
1.4%
8 1
1.4%
9 1
1.4%
10 1
1.4%
ValueCountFrequency (%)
74 1
1.4%
73 1
1.4%
72 1
1.4%
71 1
1.4%
70 1
1.4%
69 1
1.4%
68 1
1.4%
67 1
1.4%
66 1
1.4%
65 1
1.4%

제품명
Text

Distinct60
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Memory size724.0 B
2023-12-12T22:23:51.191079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length4.2297297
Min length2

Characters and Unicode

Total characters313
Distinct characters112
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)67.6%

Sample

1st row광어(양식)
2nd row전복(양식)
3rd row참돔(양식)
4th row우럭(양식)
5th row돗돔(양식)
ValueCountFrequency (%)
생굴 5
 
6.3%
광어 4
 
5.1%
멍게 4
 
5.1%
전복 3
 
3.8%
우럭 3
 
3.8%
도다리 2
 
2.5%
참돔 2
 
2.5%
피꼬막 2
 
2.5%
생물고등어 2
 
2.5%
남해안생굴 2
 
2.5%
Other values (50) 50
63.3%
2023-12-12T22:23:51.637971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
생 12
 
3.8%
어 12
 
3.8%
) 11
 
3.5%
( 11
 
3.5%
10
 
3.2%
굴 8
 
2.6%
식 8
 
2.6%
양 8
 
2.6%
김 7
 
2.2%
다 7
 
2.2%
Other values (102) 219
70.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 280
89.5%
Close Punctuation 11
 
3.5%
Open Punctuation 11
 
3.5%
Space Separator 10
 
3.2%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
생 12
 
4.3%
어 12
 
4.3%
굴 8
 
2.9%
식 8
 
2.9%
양 8
 
2.9%
김 7
 
2.5%
다 7
 
2.5%
리 7
 
2.5%
우 7
 
2.5%
광 6
 
2.1%
Other values (98) 198
70.7%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 280
89.5%
Common 33
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
생 12
 
4.3%
어 12
 
4.3%
굴 8
 
2.9%
식 8
 
2.9%
양 8
 
2.9%
김 7
 
2.5%
다 7
 
2.5%
리 7
 
2.5%
우 7
 
2.5%
광 6
 
2.1%
Other values (98) 198
70.7%
Common
ValueCountFrequency (%)
) 11
33.3%
( 11
33.3%
10
30.3%
- 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 280
89.5%
ASCII 33
 
10.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
생 12
 
4.3%
어 12
 
4.3%
굴 8
 
2.9%
식 8
 
2.9%
양 8
 
2.9%
김 7
 
2.5%
다 7
 
2.5%
리 7
 
2.5%
우 7
 
2.5%
광 6
 
2.1%
Other values (98) 198
70.7%
ASCII
ValueCountFrequency (%)
) 11
33.3%
( 11
33.3%
10
30.3%
- 1
 
3.0%

식품군
Categorical

Distinct25
Distinct (%)33.8%
Missing0
Missing (%)0.0%
Memory size724.0 B
패류
20 
김
7 
해양어류
6 
멍게
5 
넙치
4 
Other values (20)
32 

Length

Max length6
Median length2
Mean length2.3648649
Min length1

Unique

Unique12 ?
Unique (%)16.2%

Sample

1st row넙치
2nd row패류
3rd row참돔
4th row검정우럭
5th row돔

Common Values

ValueCountFrequency (%)
패류 20
27.0%
김 7
 
9.5%
해양어류 6
 
8.1%
멍게 5
 
6.8%
넙치 4
 
5.4%
두족류 4
 
5.4%
참돔 3
 
4.1%
검정우럭 3
 
4.1%
멸치 2
 
2.7%
고등어 2
 
2.7%
Other values (15) 18
24.3%

Length

2023-12-12T22:23:51.767769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
패류 20
27.0%
김 7
 
9.5%
해양어류 6
 
8.1%
멍게 5
 
6.8%
넙치 4
 
5.4%
두족류 4
 
5.4%
참돔 3
 
4.1%
검정우럭 3
 
4.1%
새우 2
 
2.7%
미역 2
 
2.7%
Other values (15) 18
24.3%

국내 및 수입
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
국내
73 
국외
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
국내 73
98.6%
국외 1
 
1.4%

Length

2023-12-12T22:23:51.902248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:23:52.019288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내 73
98.6%
국외 1
 
1.4%

검사결과
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.7%
Missing0
Missing (%)0.0%
Memory size724.0 B
적합
73 
부적합
 
1

Length

Max length3
Median length2
Mean length2.0135135
Min length2

Unique

Unique1 ?
Unique (%)1.4%

Sample

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

Common Values

ValueCountFrequency (%)
적합 73
98.6%
부적합 1
 
1.4%

Length

2023-12-12T22:23:52.147320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T22:23:52.267508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
적합 73
98.6%
부적합 1
 
1.4%

Interactions

2023-12-12T22:23:50.512222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T22:23:52.358351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번제품명식품군국내 및 수입검사결과
연번1.0000.1750.0000.0000.000
제품명0.1751.0000.9911.0001.000
식품군0.0000.9911.0000.5730.000
국내 및 수입0.0001.0000.5731.0000.000
검사결과0.0001.0000.0000.0001.000
2023-12-12T22:23:52.486021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
식품군국내 및 수입검사결과
식품군1.0000.4080.000
국내 및 수입0.4081.0000.000
검사결과0.0000.0001.000
2023-12-12T22:23:52.602761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번식품군국내 및 수입검사결과
연번1.0000.0000.0000.000
식품군0.0001.0000.4080.000
국내 및 수입0.0000.4081.0000.000
검사결과0.0000.0000.0001.000

Missing values

2023-12-12T22:23:50.618094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T22:23:50.700724image/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

연번제품명식품군국내 및 수입검사결과
01광어(양식)넙치국내적합
12전복(양식)패류국내적합
23참돔(양식)참돔국내적합
34우럭(양식)검정우럭국내적합
45돗돔(양식)돔국내적합
56도다리(양식)해양어류국내적합
67멍게(양식)멍게국내적합
78키조개(양식)패류국내적합
89골뱅이(흑고동)패류국내적합
910은대구해양어류국내적합
연번제품명식품군국내 및 수입검사결과
6465절단 동태수산물국내적합
6566생물고등어고등어국내적합
6667국산 참치(해동)삼치국내적합
6768바지락살패류국내적합
6869생광어해양어류국내적합
6970국산참조기김국내적합
7071곱창돌김김국내적합
7172오징어두족류국내적합
7273주꾸미두족류국내적합
7374냉동새우살(흰다리새우-자숙)새우국외적합