Overview

Dataset statistics

Number of variables5
Number of observations45
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory44.9 B

Variable types

Numeric2
Text2
Categorical1

Dataset

Description2015년 제·개정된 농축수산물 표준코드의 시장코드와 동일한 의미를 가지는 과거에 사용하던 시장코드를 나타낸 정보
Author농림수산식품교육문화정보원
URLhttps://www.data.go.kr/data/15045726/fileData.do

Alerts

업데이트일자 has constant value ""Constant
시장코드 has unique valuesUnique
시장명 has unique valuesUnique
구시장코드 has unique valuesUnique
구시장명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:38:31.962007
Analysis finished2023-12-12 12:38:32.751734
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시장코드
Real number (ℝ)

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1369892.1
Minimum1005601
Maximum3054001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T21:38:32.849373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1005601
5-th percentile1012301
Q11027301
median1048001
Q32006701
95-th percentile2838321
Maximum3054001
Range2048400
Interquartile range (IQR)979400

Descriptive statistics

Standard deviation604489.98
Coefficient of variation (CV)0.4412683
Kurtosis1.7915337
Mean1369892.1
Median Absolute Deviation (MAD)21700
Skewness1.6653853
Sum61645146
Variance3.6540813 × 1011
MonotonicityStrictly increasing
2023-12-12T21:38:33.027214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1005601 1
 
2.2%
2006901 1
 
2.2%
1051102 1
 
2.2%
1052601 1
 
2.2%
1054501 1
 
2.2%
1055001 1
 
2.2%
1056101 1
 
2.2%
1057901 1
 
2.2%
1061101 1
 
2.2%
1061901 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
1005601 1
2.2%
1007701 1
2.2%
1011901 1
2.2%
1013901 1
2.2%
1015201 1
2.2%
1016301 1
2.2%
1021401 1
2.2%
1021501 1
2.2%
1024201 1
2.2%
1025401 1
2.2%
ValueCountFrequency (%)
3054001 1
2.2%
3039601 1
2.2%
3033001 1
2.2%
2059601 1
2.2%
2058701 1
2.2%
2041901 1
2.2%
2039601 1
2.2%
2038901 1
2.2%
2038101 1
2.2%
2037801 1
2.2%

시장명
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-12T21:38:33.250365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length9.7111111
Min length6

Characters and Unicode

Total characters437
Distinct characters69
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row가락동농수산물시장
2nd row강서농수산물도매시장
3rd row구리농수산물도매시장
4th row안양농수산물도매시장
5th row안산농수산물도매시장
ValueCountFrequency (%)
가락동농수산물시장 1
 
2.2%
부산국제수산물도매시장 1
 
2.2%
창원팔용농산물도매시장 1
 
2.2%
진주농산물도매시장 1
 
2.2%
익산농수산물도매시장 1
 
2.2%
전주농수산물도매시장 1
 
2.2%
정읍농산물도매시장 1
 
2.2%
순천농산물도매시장 1
 
2.2%
광주각화농산물도매시장 1
 
2.2%
광주서부농수산물도매시장 1
 
2.2%
Other values (35) 35
77.8%
2023-12-12T21:38:33.632860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 49
11.2%
시 45
10.3%
장 45
10.3%
도 42
9.6%
매 42
9.6%
물 39
 
8.9%
농 37
 
8.5%
수 20
 
4.6%
주 8
 
1.8%
천 8
 
1.8%
Other values (59) 102
23.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 437
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 49
11.2%
시 45
10.3%
장 45
10.3%
도 42
9.6%
매 42
9.6%
물 39
 
8.9%
농 37
 
8.5%
수 20
 
4.6%
주 8
 
1.8%
천 8
 
1.8%
Other values (59) 102
23.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 437
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 49
11.2%
시 45
10.3%
장 45
10.3%
도 42
9.6%
매 42
9.6%
물 39
 
8.9%
농 37
 
8.5%
수 20
 
4.6%
주 8
 
1.8%
천 8
 
1.8%
Other values (59) 102
23.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 437
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 49
11.2%
시 45
10.3%
장 45
10.3%
도 42
9.6%
매 42
9.6%
물 39
 
8.9%
농 37
 
8.5%
수 20
 
4.6%
주 8
 
1.8%
천 8
 
1.8%
Other values (59) 102
23.3%

구시장코드
Real number (ℝ)

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean299110.78
Minimum110001
Maximum380401
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size537.0 B
2023-12-12T21:38:33.843904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110001
5-th percentile110005.6
Q1240001
median330101
Q3370101
95-th percentile380181
Maximum380401
Range270400
Interquartile range (IQR)130100

Descriptive statistics

Standard deviation82103.512
Coefficient of variation (CV)0.27449199
Kurtosis0.1791676
Mean299110.78
Median Absolute Deviation (MAD)40200
Skewness-1.0735631
Sum13459985
Variance6.7409866 × 109
MonotonicityNot monotonic
2023-12-12T21:38:34.022038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
110001 1
 
2.2%
110003 1
 
2.2%
380101 1
 
2.2%
380401 1
 
2.2%
350301 1
 
2.2%
350101 1
 
2.2%
350402 1
 
2.2%
360301 1
 
2.2%
240001 1
 
2.2%
240004 1
 
2.2%
Other values (35) 35
77.8%
ValueCountFrequency (%)
110001 1
2.2%
110003 1
2.2%
110005 1
2.2%
110008 1
2.2%
210001 1
2.2%
210005 1
2.2%
210009 1
2.2%
220001 1
2.2%
220003 1
2.2%
230001 1
2.2%
ValueCountFrequency (%)
380401 1
2.2%
380303 1
2.2%
380201 1
2.2%
380101 1
2.2%
371501 1
2.2%
370701 1
2.2%
370401 1
2.2%
370301 1
2.2%
370203 1
2.2%
370201 1
2.2%

구시장명
Text

UNIQUE 

Distinct45
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size492.0 B
2023-12-12T21:38:34.308683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length6
Mean length6.7333333
Min length6

Characters and Unicode

Total characters303
Distinct characters68
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique45 ?
Unique (%)100.0%

Sample

1st row서울가락도매시장
2nd row서울강서도매시장
3rd row구리도매시장
4th row안양도매시장
5th row안산도매시장
ValueCountFrequency (%)
서울가락도매시장 1
 
2.2%
부산국제수산물도매시장 1
 
2.2%
창원팔용도매시장 1
 
2.2%
진주도매시장 1
 
2.2%
익산도매시장 1
 
2.2%
전주도매시장 1
 
2.2%
정읍도매시장 1
 
2.2%
순천도매시장 1
 
2.2%
광주각화도매시장 1
 
2.2%
광주서부도매시장 1
 
2.2%
Other values (35) 35
77.8%
2023-12-12T21:38:35.120305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 45
14.9%
장 45
14.9%
도 34
 
11.2%
매 34
 
11.2%
산 17
 
5.6%
주 8
 
2.6%
천 8
 
2.6%
서 5
 
1.7%
수 5
 
1.7%
부 5
 
1.7%
Other values (58) 97
32.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 303
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 45
14.9%
장 45
14.9%
도 34
 
11.2%
매 34
 
11.2%
산 17
 
5.6%
주 8
 
2.6%
천 8
 
2.6%
서 5
 
1.7%
수 5
 
1.7%
부 5
 
1.7%
Other values (58) 97
32.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 303
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 45
14.9%
장 45
14.9%
도 34
 
11.2%
매 34
 
11.2%
산 17
 
5.6%
주 8
 
2.6%
천 8
 
2.6%
서 5
 
1.7%
수 5
 
1.7%
부 5
 
1.7%
Other values (58) 97
32.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 303
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 45
14.9%
장 45
14.9%
도 34
 
11.2%
매 34
 
11.2%
산 17
 
5.6%
주 8
 
2.6%
천 8
 
2.6%
서 5
 
1.7%
수 5
 
1.7%
부 5
 
1.7%
Other values (58) 97
32.0%

업데이트일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size492.0 B
2015-12-15
45 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2015-12-15
2nd row2015-12-15
3rd row2015-12-15
4th row2015-12-15
5th row2015-12-15

Common Values

ValueCountFrequency (%)
2015-12-15 45
100.0%

Length

2023-12-12T21:38:35.274206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:38:35.418825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2015-12-15 45
100.0%

Interactions

2023-12-12T21:38:32.342038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:38:32.148204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:38:32.463837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:38:32.240645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:38:35.505321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장코드시장명구시장코드구시장명
시장코드1.0001.0000.6231.000
시장명1.0001.0001.0001.000
구시장코드0.6231.0001.0001.000
구시장명1.0001.0001.0001.000
2023-12-12T21:38:35.619690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시장코드구시장코드
시장코드1.0000.357
구시장코드0.3571.000

Missing values

2023-12-12T21:38:32.602082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:38:32.714054image/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

시장코드시장명구시장코드구시장명업데이트일자
01005601가락동농수산물시장110001서울가락도매시장2015-12-15
11007701강서농수산물도매시장110008서울강서도매시장2015-12-15
21011901구리농수산물도매시장311201구리도매시장2015-12-15
31013901안양농수산물도매시장310401안양도매시장2015-12-15
41015201안산농수산물도매시장310901안산도매시장2015-12-15
51016301수원농수산물도매시장310101수원도매시장2015-12-15
61021401인천삼산농산물도매시장230003인천삼산도매시장2015-12-15
71021501인천구월농축산물도매시장230001인천구월도매시장2015-12-15
81024201춘천농수산물도매시장320101춘천도매시장2015-12-15
91025401강릉농산물도매시장320301강릉도매시장2015-12-15
시장코드시장명구시장코드구시장명업데이트일자
352037801포항수산물도매시장370102포항수산시장2015-12-15
362038101경주농산물도매시장370201경주농산시장2015-12-15
372038901영천농산물도매시장370701영천농산시장2015-12-15
382039601김천농산물도매시장370301김천농산시장2015-12-15
392041901대구한약재도매시장220003대구한약재시장2015-12-15
402058701목포농산물도매시장360101목포농산시장2015-12-15
412059601여수농산물도매시장360201여수농산시장2015-12-15
423033001논산민영농산물도매시장340201논산민영시장2015-12-15
433039601김천민영시장370203김천민영시장2015-12-15
443054001군산민영시장350201군산민영시장2015-12-15