Overview

Dataset statistics

Number of variables3
Number of observations68
Missing cells1
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 KiB
Average record size in memory26.9 B

Variable types

Text2
Numeric1

Dataset

Description2016년 기준 보건환경연구원식약품 분석항목 및 수수료 정보 제공(검체명(과자류, 식빵, 빵류, 떡류 등), 시험항목, 수수료)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15045394/fileData.do

Alerts

수수료 has 1 (1.5%) missing valuesMissing

Reproduction

Analysis started2024-03-15 00:52:56.943770
Analysis finished2024-03-15 00:52:58.040706
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검 체 명
Text

Distinct49
Distinct (%)72.1%
Missing0
Missing (%)0.0%
Memory size672.0 B
2024-03-15T09:52:58.780680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length14
Mean length7.7058824
Min length5

Characters and Unicode

Total characters524
Distinct characters111
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)52.9%

Sample

1st row(1)과자류
2nd row(2)식빵
3rd row(3)빵류
4th row(4)떡류(앙금류)
5th row(5)크림빵
ValueCountFrequency (%)
28)얼음류 3
 
4.2%
42)소금 3
 
4.2%
41)즉석섭취 3
 
4.2%
37)벌꿀 3
 
4.2%
23)과실,채소류음료 3
 
4.2%
43)수산물 3
 
4.2%
48)기타 2
 
2.8%
13)어육소세지 2
 
2.8%
22)다류 2
 
2.8%
21)면류 2
 
2.8%
Other values (43) 46
63.9%
2024-03-15T09:53:00.184966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 68
 
13.0%
) 68
 
13.0%
류 26
 
5.0%
2 25
 
4.8%
3 24
 
4.6%
4 21
 
4.0%
1 21
 
4.0%
식 12
 
2.3%
품 10
 
1.9%
기 9
 
1.7%
Other values (101) 240
45.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 246
46.9%
Decimal Number 125
23.9%
Open Punctuation 68
 
13.0%
Close Punctuation 68
 
13.0%
Other Punctuation 9
 
1.7%
Space Separator 4
 
0.8%
Uppercase Letter 4
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
류 26
 
10.6%
식 12
 
4.9%
품 10
 
4.1%
기 9
 
3.7%
소 9
 
3.7%
가 6
 
2.4%
음 6
 
2.4%
수 5
 
2.0%
육 5
 
2.0%
공 5
 
2.0%
Other values (85) 153
62.2%
Decimal Number
ValueCountFrequency (%)
2 25
20.0%
3 24
19.2%
4 21
16.8%
1 21
16.8%
8 9
 
7.2%
7 7
 
5.6%
6 5
 
4.0%
5 5
 
4.0%
9 4
 
3.2%
0 4
 
3.2%
Uppercase Letter
ValueCountFrequency (%)
P 3
75.0%
E 1
 
25.0%
Open Punctuation
ValueCountFrequency (%)
( 68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 68
100.0%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 274
52.3%
Hangul 246
46.9%
Latin 4
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
류 26
 
10.6%
식 12
 
4.9%
품 10
 
4.1%
기 9
 
3.7%
소 9
 
3.7%
가 6
 
2.4%
음 6
 
2.4%
수 5
 
2.0%
육 5
 
2.0%
공 5
 
2.0%
Other values (85) 153
62.2%
Common
ValueCountFrequency (%)
( 68
24.8%
) 68
24.8%
2 25
 
9.1%
3 24
 
8.8%
4 21
 
7.7%
1 21
 
7.7%
, 9
 
3.3%
8 9
 
3.3%
7 7
 
2.6%
6 5
 
1.8%
Other values (4) 17
 
6.2%
Latin
ValueCountFrequency (%)
P 3
75.0%
E 1
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 278
53.1%
Hangul 246
46.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 68
24.5%
) 68
24.5%
2 25
 
9.0%
3 24
 
8.6%
4 21
 
7.6%
1 21
 
7.6%
, 9
 
3.2%
8 9
 
3.2%
7 7
 
2.5%
6 5
 
1.8%
Other values (6) 21
 
7.6%
Hangul
ValueCountFrequency (%)
류 26
 
10.6%
식 12
 
4.9%
품 10
 
4.1%
기 9
 
3.7%
소 9
 
3.7%
가 6
 
2.4%
음 6
 
2.4%
수 5
 
2.0%
육 5
 
2.0%
공 5
 
2.0%
Other values (85) 153
62.2%

시 험 항 목
Text

Distinct65
Distinct (%)95.6%
Missing0
Missing (%)0.0%
Memory size672.0 B
2024-03-15T09:53:01.117982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length114
Median length49
Mean length38.735294
Min length10

Characters and Unicode

Total characters2634
Distinct characters209
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)91.2%

Sample

1st row산가(8600 유탕처리), 세균수(13000), 빙과(세균수 13000, 대장균군 13000)
2nd row타르색소(33800), 허용외인공감미료(22200), 보존료(43000)
3rd row보존료 (43000)
4th row보존료(앙금류 43000)
5th row보존료 (43000), 황색포도상구균(15000), 살모넬라(15000)
ValueCountFrequency (%)
타르색소(33800 18
 
6.5%
보존료(43000 15
 
5.5%
세균수(13000 14
 
5.1%
대장균군(13000 13
 
4.7%
납(76700 9
 
3.3%
산가(8600 8
 
2.9%
카드뮴(76700 6
 
2.2%
제외 5
 
1.8%
멸균 5
 
1.8%
살균 5
 
1.8%
Other values (125) 177
64.4%
2024-03-15T09:53:02.741412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 479
18.2%
207
 
7.9%
( 189
 
7.2%
) 188
 
7.1%
, 124
 
4.7%
3 114
 
4.3%
8 75
 
2.8%
6 70
 
2.7%
균 64
 
2.4%
1 53
 
2.0%
Other values (199) 1071
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1003
38.1%
Decimal Number 908
34.5%
Space Separator 207
 
7.9%
Open Punctuation 190
 
7.2%
Close Punctuation 189
 
7.2%
Other Punctuation 128
 
4.9%
Uppercase Letter 6
 
0.2%
Math Symbol 2
 
0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
균 64
 
6.4%
소 31
 
3.1%
산 27
 
2.7%
료 26
 
2.6%
장 26
 
2.6%
수 25
 
2.5%
색 24
 
2.4%
타 23
 
2.3%
대 22
 
2.2%
르 22
 
2.2%
Other values (175) 713
71.1%
Decimal Number
ValueCountFrequency (%)
0 479
52.8%
3 114
 
12.6%
8 75
 
8.3%
6 70
 
7.7%
1 53
 
5.8%
7 48
 
5.3%
2 26
 
2.9%
4 22
 
2.4%
5 18
 
2.0%
9 3
 
0.3%
Uppercase Letter
ValueCountFrequency (%)
H 3
50.0%
O 1
 
16.7%
F 1
 
16.7%
M 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
, 124
96.9%
: 3
 
2.3%
% 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 189
99.5%
{ 1
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 188
99.5%
} 1
 
0.5%
Space Separator
ValueCountFrequency (%)
207
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Lowercase Letter
ValueCountFrequency (%)
p 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1624
61.7%
Hangul 1003
38.1%
Latin 7
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
균 64
 
6.4%
소 31
 
3.1%
산 27
 
2.7%
료 26
 
2.6%
장 26
 
2.6%
수 25
 
2.5%
색 24
 
2.4%
타 23
 
2.3%
대 22
 
2.2%
르 22
 
2.2%
Other values (175) 713
71.1%
Common
ValueCountFrequency (%)
0 479
29.5%
207
12.7%
( 189
 
11.6%
) 188
 
11.6%
, 124
 
7.6%
3 114
 
7.0%
8 75
 
4.6%
6 70
 
4.3%
1 53
 
3.3%
7 48
 
3.0%
Other values (9) 77
 
4.7%
Latin
ValueCountFrequency (%)
H 3
42.9%
p 1
 
14.3%
O 1
 
14.3%
F 1
 
14.3%
M 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1631
61.9%
Hangul 1003
38.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 479
29.4%
207
12.7%
( 189
 
11.6%
) 188
 
11.5%
, 124
 
7.6%
3 114
 
7.0%
8 75
 
4.6%
6 70
 
4.3%
1 53
 
3.2%
7 48
 
2.9%
Other values (14) 84
 
5.2%
Hangul
ValueCountFrequency (%)
균 64
 
6.4%
소 31
 
3.1%
산 27
 
2.7%
료 26
 
2.6%
장 26
 
2.6%
수 25
 
2.5%
색 24
 
2.4%
타 23
 
2.3%
대 22
 
2.2%
르 22
 
2.2%
Other values (175) 713
71.1%

수수료
Real number (ℝ)

MISSING 

Distinct39
Distinct (%)58.2%
Missing1
Missing (%)1.5%
Infinite0
Infinite (%)0.0%
Mean113264.22
Minimum17200
Maximum398002
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size740.0 B
2024-03-15T09:53:03.194931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17200
5-th percentile25440
Q169250
median85400
Q3148000
95-th percentile243360
Maximum398002
Range380802
Interquartile range (IQR)78750

Descriptive statistics

Standard deviation83963.219
Coefficient of variation (CV)0.74130397
Kurtosis4.0280053
Mean113264.22
Median Absolute Deviation (MAD)37600
Skewness1.859097
Sum7588703
Variance7.0498222 × 109
MonotonicityNot monotonic
2024-03-15T09:53:03.664751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
76800 7
 
10.3%
148000 3
 
4.4%
43000 3
 
4.4%
93300 3
 
4.4%
163000 3
 
4.4%
81800 3
 
4.4%
189200 3
 
4.4%
222400 3
 
4.4%
26000 2
 
2.9%
51000 2
 
2.9%
Other values (29) 35
51.5%
ValueCountFrequency (%)
17200 2
2.9%
21600 1
 
1.5%
25200 1
 
1.5%
26000 2
2.9%
33800 1
 
1.5%
36000 1
 
1.5%
42400 1
 
1.5%
43000 3
4.4%
51000 2
2.9%
59800 1
 
1.5%
ValueCountFrequency (%)
398002 1
 
1.5%
398001 1
 
1.5%
398000 1
 
1.5%
249000 1
 
1.5%
230200 1
 
1.5%
222400 3
4.4%
189200 3
4.4%
163000 3
4.4%
153400 1
 
1.5%
148000 3
4.4%

Interactions

2024-03-15T09:52:57.422178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T09:53:03.941556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검 체 명시 험 항 목수수료
검 체 명1.0000.9130.998
시 험 항 목0.9131.0001.000
수수료0.9981.0001.000

Missing values

2024-03-15T09:52:57.714398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T09:52:57.942654image/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

검 체 명시 험 항 목수수료
0(1)과자류산가(8600 유탕처리), 세균수(13000), 빙과(세균수 13000, 대장균군 13000)21600
1(2)식빵타르색소(33800), 허용외인공감미료(22200), 보존료(43000)99000
2(3)빵류보존료 (43000)43000
3(4)떡류(앙금류)보존료(앙금류 43000)43000
4(5)크림빵보존료 (43000), 황색포도상구균(15000), 살모넬라(15000)73000
5(6)캔디류허용외인공감미료(22200), 허용외타르색소(33800), 세균수(13000)69000
6(7)초콜릿류허용외타르색소(33800)33800
7(8)쨈, 마말레이드보존료(43000), 타르색소(33800 기타잼류제외)76800
8(9)포도당포도당당량(8600), 인공감미료(22200), 납(76700)107500
9(10)엿류포도당당량(8600), 인공감미료(22200), 납(76700)107500
검 체 명시 험 항 목수수료
58(42)소금수은(86300), 페로시안화이온(30000)398002
59(43)수산물총수은(86300 어류 생물로 심해성다랑어새치류 제외 연체류 및 패류)163000
60(43)수산물메틸수은(70000 어류 생물로 심해성 다랑어새치류에 한함), 납(76700 어류연체류패류)163000
61(43)수산물카드뮴(76700 연체류패류)163000
62(44)농산물납(76700), 카드뮴(76700)153400
63(45)규격 외 식품이물(8600), 타르색소(33800), 보존료(43000)85400
64(46)PE,PP용기납및카드뮴(40000), 중금속(36000), 과망간산칼륨소비량(8700), 증발잔류물(8600)93300
65(47)잔류농약다종농약다성분시험법: 577800(전항목), 단성분시험(농약1종): 8140093300
66(48)기타아황산염(8600), 삭카린나트륨(22200), 세균발육시험(12000)93300
67(48)기타기생충및그알(37800), 말라카이트그린(40000), 벤조피렌(86000)<NA>