Overview

Dataset statistics

Number of variables4
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)1.6%
Total size in memory2.2 KiB
Average record size in memory36.1 B

Variable types

Numeric2
Categorical1
Text1

Dataset

Description국립농산물품질관리원에서 관리하는 농축산물 원산지표시 업태별 적발현황(연도, 업종, 거짓표시 적발실적, 미표시 적발실적)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220613000000002102

Alerts

Dataset has 1 (1.6%) duplicate rowsDuplicates

Reproduction

Analysis started2024-03-23 07:22:24.565629
Analysis finished2024-03-23 07:22:27.015629
Duration2.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct6
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2020.4355
Minimum2018
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-03-23T07:22:27.271856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2018
Q12019
median2020
Q32022
95-th percentile2023
Maximum2023
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7331954
Coefficient of variation (CV)0.00085783257
Kurtosis-1.2941004
Mean2020.4355
Median Absolute Deviation (MAD)1.5
Skewness0.053629018
Sum125267
Variance3.0039662
MonotonicityIncreasing
2024-03-23T07:22:27.757000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2018 11
17.7%
2019 11
17.7%
2020 10
16.1%
2021 10
16.1%
2022 10
16.1%
2023 10
16.1%
ValueCountFrequency (%)
2018 11
17.7%
2019 11
17.7%
2020 10
16.1%
2021 10
16.1%
2022 10
16.1%
2023 10
16.1%
ValueCountFrequency (%)
2023 10
16.1%
2022 10
16.1%
2021 10
16.1%
2020 10
16.1%
2019 11
17.7%
2018 11
17.7%

업종
Categorical

Distinct23
Distinct (%)37.1%
Missing0
Missing (%)0.0%
Memory size628.0 B
일반음식점
5 
통신판매업체
5 
식육판매업
5 
노점상
5 
휴게음식점
5 
Other values (18)
37 

Length

Max length14
Median length13
Mean length4.9354839
Min length2

Unique

Unique9 ?
Unique (%)14.5%

Sample

1st row일반음식점
2nd row식육판매업
3rd row가공업체
4th row통신판매업체
5th row집단급식소

Common Values

ValueCountFrequency (%)
일반음식점 5
 
8.1%
통신판매업체 5
 
8.1%
식육판매업 5
 
8.1%
노점상 5
 
8.1%
휴게음식점 5
 
8.1%
가공업체 5
 
8.1%
슈퍼 4
 
6.5%
식품유통업 4
 
6.5%
기타 3
 
4.8%
제과점영업 3
 
4.8%
Other values (13) 18
29.0%

Length

2024-03-23T07:22:28.157987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
기타 6
 
8.3%
일반음식점 5
 
6.9%
통신판매업체 5
 
6.9%
식육판매업 5
 
6.9%
노점상 5
 
6.9%
휴게음식점 5
 
6.9%
가공업체 5
 
6.9%
슈퍼 4
 
5.6%
식품유통업 4
 
5.6%
도매상 3
 
4.2%
Other values (19) 25
34.7%

거짓표시 적발실적(개소)
Text

Distinct48
Distinct (%)77.4%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-03-23T07:22:28.597136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.3709677
Min length1

Characters and Unicode

Total characters147
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)59.7%

Sample

1st row1633
2nd row244
3rd row197
4th row59
5th row37
ValueCountFrequency (%)
21 3
 
4.8%
19 3
 
4.8%
37 3
 
4.8%
35 2
 
3.2%
20 2
 
3.2%
15 2
 
3.2%
176 2
 
3.2%
30 2
 
3.2%
28 2
 
3.2%
29 2
 
3.2%
Other values (38) 39
62.9%
2024-03-23T07:22:29.619366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 36
24.5%
2 21
14.3%
3 17
11.6%
9 15
10.2%
7 13
 
8.8%
5 11
 
7.5%
6 11
 
7.5%
4 9
 
6.1%
0 7
 
4.8%
8 6
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 146
99.3%
Other Punctuation 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 36
24.7%
2 21
14.4%
3 17
11.6%
9 15
10.3%
7 13
 
8.9%
5 11
 
7.5%
6 11
 
7.5%
4 9
 
6.2%
0 7
 
4.8%
8 6
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 36
24.5%
2 21
14.3%
3 17
11.6%
9 15
10.2%
7 13
 
8.8%
5 11
 
7.5%
6 11
 
7.5%
4 9
 
6.1%
0 7
 
4.8%
8 6
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 36
24.5%
2 21
14.3%
3 17
11.6%
9 15
10.2%
7 13
 
8.8%
5 11
 
7.5%
6 11
 
7.5%
4 9
 
6.1%
0 7
 
4.8%
8 6
 
4.1%

미표시 적발실적(개소)
Real number (ℝ)

Distinct51
Distinct (%)82.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean140.32258
Minimum12
Maximum1008
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-03-23T07:22:30.221230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12
5-th percentile20
Q132
median60
Q3141.25
95-th percentile649.55
Maximum1008
Range996
Interquartile range (IQR)109.25

Descriptive statistics

Standard deviation209.45822
Coefficient of variation (CV)1.4926908
Kurtosis6.6980663
Mean140.32258
Median Absolute Deviation (MAD)36
Skewness2.6538731
Sum8700
Variance43872.747
MonotonicityNot monotonic
2024-03-23T07:22:30.736108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33 3
 
4.8%
24 3
 
4.8%
39 2
 
3.2%
152 2
 
3.2%
36 2
 
3.2%
108 2
 
3.2%
23 2
 
3.2%
32 2
 
3.2%
20 2
 
3.2%
652 1
 
1.6%
Other values (41) 41
66.1%
ValueCountFrequency (%)
12 1
 
1.6%
16 1
 
1.6%
19 1
 
1.6%
20 2
3.2%
21 1
 
1.6%
23 2
3.2%
24 3
4.8%
27 1
 
1.6%
28 1
 
1.6%
29 1
 
1.6%
ValueCountFrequency (%)
1008 1
1.6%
806 1
1.6%
746 1
1.6%
652 1
1.6%
603 1
1.6%
559 1
1.6%
296 1
1.6%
234 1
1.6%
233 1
1.6%
212 1
1.6%

Interactions

2024-03-23T07:22:25.584365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:22:24.985708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:22:25.887407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:22:25.343113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:22:31.101769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도업종거짓표시 적발실적(개소)미표시 적발실적(개소)
연도1.0000.0000.6220.000
업종0.0001.0000.9030.822
거짓표시 적발실적(개소)0.6220.9031.0000.993
미표시 적발실적(개소)0.0000.8220.9931.000
2024-03-23T07:22:31.605540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도미표시 적발실적(개소)업종
연도1.000-0.0080.000
미표시 적발실적(개소)-0.0081.0000.448
업종0.0000.4481.000

Missing values

2024-03-23T07:22:26.427556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:22:26.843378image/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

연도업종거짓표시 적발실적(개소)미표시 적발실적(개소)
02018일반음식점1633652
12018식육판매업244152
22018가공업체197212
32018통신판매업체5929
42018집단급식소3733
52018노점상3762
62018슈퍼2851
72018휴게음식점2828
82018식품유통업2723
92018도매상2123
연도업종거짓표시 적발실적(개소)미표시 적발실적(개소)
522023일반음식점업11111008
532023기타 음식료품 제조업191152
542023축산물 소매업123112
552023제과점업35108
562023기타 휴게음식점업4153
572023중개사이트5332
582023슈퍼마켓3736
592023노점 및 유사이동 소매업1952
602023기타 식용 농축산물 도매업2336
612023제과점업35108

Duplicate rows

Most frequently occurring

연도업종거짓표시 적발실적(개소)미표시 적발실적(개소)# duplicates
02023제과점업351082