Overview

Dataset statistics

Number of variables2
Number of observations234
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory17.6 B

Variable types

Text1
Numeric1

Dataset

Description서울시농수산식품공사에서 제공하는 자동응답서비스(ARS)를 통해 농수산물 가격정보 조회시 사용합니다. 공공데이터로 등록된 해당 품목코드를 사용하면 더 쉽고 빠르게 원하는 품목의 가격정보를 안내 받을 수 있습니다.
URLhttps://www.data.go.kr/data/15106406/fileData.do

Alerts

품목(품종)명 has unique valuesUnique
코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:05:28.352027
Analysis finished2023-12-12 14:05:28.712887
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목(품종)명
Text

UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size2.0 KiB
2023-12-12T23:05:28.980702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.4786325
Min length1

Characters and Unicode

Total characters1048
Distinct characters234
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique234 ?
Unique (%)100.0%

Sample

1st row가시오이
2nd row가지
3rd row감 대봉시
4th row감 말랭이
5th row감귤
ValueCountFrequency (%)
수입 25
 
7.5%
복숭아 24
 
7.2%
포도 7
 
2.1%
사과 7
 
2.1%
감귤 4
 
1.2%
배 4
 
1.2%
자두 3
 
0.9%
건고추 3
 
0.9%
오이 3
 
0.9%
국산 3
 
0.9%
Other values (228) 249
75.0%
2023-12-12T23:05:29.403988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
98
 
9.4%
수 34
 
3.2%
아 32
 
3.1%
입 26
 
2.5%
복 25
 
2.4%
숭 24
 
2.3%
추 23
 
2.2%
고 19
 
1.8%
도 19
 
1.8%
리 18
 
1.7%
Other values (224) 730
69.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 928
88.5%
Space Separator 98
 
9.4%
Close Punctuation 11
 
1.0%
Open Punctuation 11
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 34
 
3.7%
아 32
 
3.4%
입 26
 
2.8%
복 25
 
2.7%
숭 24
 
2.6%
추 23
 
2.5%
고 19
 
2.0%
도 19
 
2.0%
리 18
 
1.9%
마 16
 
1.7%
Other values (221) 692
74.6%
Space Separator
ValueCountFrequency (%)
98
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 928
88.5%
Common 120
 
11.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 34
 
3.7%
아 32
 
3.4%
입 26
 
2.8%
복 25
 
2.7%
숭 24
 
2.6%
추 23
 
2.5%
고 19
 
2.0%
도 19
 
2.0%
리 18
 
1.9%
마 16
 
1.7%
Other values (221) 692
74.6%
Common
ValueCountFrequency (%)
98
81.7%
) 11
 
9.2%
( 11
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 928
88.5%
ASCII 120
 
11.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
98
81.7%
) 11
 
9.2%
( 11
 
9.2%
Hangul
ValueCountFrequency (%)
수 34
 
3.7%
아 32
 
3.4%
입 26
 
2.8%
복 25
 
2.7%
숭 24
 
2.6%
추 23
 
2.5%
고 19
 
2.0%
도 19
 
2.0%
리 18
 
1.9%
마 16
 
1.7%
Other values (221) 692
74.6%

코드
Real number (ℝ)

UNIQUE 

Distinct234
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18181.667
Minimum12101
Maximum29201
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.2 KiB
2023-12-12T23:05:29.561404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12101
5-th percentile12301.65
Q113226
median17301
Q321676
95-th percentile26437.95
Maximum29201
Range17100
Interquartile range (IQR)8450

Descriptive statistics

Standard deviation4898.2217
Coefficient of variation (CV)0.26940444
Kurtosis-1.0764076
Mean18181.667
Median Absolute Deviation (MAD)4248.5
Skewness0.40938087
Sum4254510
Variance23992576
MonotonicityNot monotonic
2023-12-12T23:05:29.714872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20602 1
 
0.4%
16201 1
 
0.4%
16301 1
 
0.4%
19701 1
 
0.4%
21751 1
 
0.4%
25541 1
 
0.4%
19401 1
 
0.4%
16601 1
 
0.4%
14208 1
 
0.4%
14251 1
 
0.4%
Other values (224) 224
95.7%
ValueCountFrequency (%)
12101 1
0.4%
12102 1
0.4%
12103 1
0.4%
12104 1
0.4%
12105 1
0.4%
12106 1
0.4%
12107 1
0.4%
12201 1
0.4%
12202 1
0.4%
12203 1
0.4%
ValueCountFrequency (%)
29201 1
0.4%
28901 1
0.4%
28201 1
0.4%
28101 1
0.4%
27902 1
0.4%
27901 1
0.4%
27802 1
0.4%
27801 1
0.4%
26761 1
0.4%
26701 1
0.4%

Interactions

2023-12-12T23:05:28.479843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Missing values

2023-12-12T23:05:28.618370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:05:28.685228image/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

품목(품종)명코드
0가시오이20602
1가지20801
2감 대봉시14206
3감 말랭이14207
4감귤12301
5감귤 비가림14205
6감귤 온주12302
7감귤 하우스12303
8감자 대지18401
9감자 수미18402
품목(품종)명코드
224풋고추 청양21203
225풋옥수수27801
226피잣14501
227호두14301
228호두 수입14302
229호박고구마18302
230호박꼬지26401
231호박꼬지 수입26402
232홍고추21301
233홍자두13001