Overview

Dataset statistics

Number of variables5
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory47.9 B

Variable types

Categorical1
Text1
Numeric3

Dataset

Description국립농산물품질관리원 원산지표시 지역별 적발현황(년도, 시도, 거짓표시 적발실적, 미표시 적발실적, 과태료)
Author국립농산물품질관리원
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20191011000000001215

Alerts

거짓표시 적발실적(개소) is highly overall correlated with 미표시 적발실적(개소) and 1 other fieldsHigh correlation
미표시 적발실적(개소) is highly overall correlated with 거짓표시 적발실적(개소) and 1 other fieldsHigh correlation
과태료(천원) is highly overall correlated with 거짓표시 적발실적(개소) and 1 other fieldsHigh correlation
과태료(천원) has unique valuesUnique

Reproduction

Analysis started2024-03-23 07:49:49.452949
Analysis finished2024-03-23 07:49:52.825686
Duration3.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2018
17 
2019
17 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 17
50.0%
2019 17
50.0%

Length

2024-03-23T07:49:53.038321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T07:49:53.345647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 17
50.0%
2019 17
50.0%

시도
Text

Distinct17
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size404.0 B
2024-03-23T07:49:53.645587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length2
Min length2

Characters and Unicode

Total characters68
Distinct characters21
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

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주
ValueCountFrequency (%)
서울 2
 
5.9%
강원 2
 
5.9%
경남 2
 
5.9%
경북 2
 
5.9%
전남 2
 
5.9%
전북 2
 
5.9%
충남 2
 
5.9%
충북 2
 
5.9%
경기 2
 
5.9%
부산 2
 
5.9%
Other values (7) 14
41.2%
2024-03-23T07:49:54.419172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 68
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 68
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
8.8%
6
 
8.8%
6
 
8.8%
6
 
8.8%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
4
 
5.9%
2
 
2.9%
Other values (11) 22
32.4%

거짓표시 적발실적(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.97059
Minimum7
Maximum390
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-23T07:49:54.741479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile20.3
Q146
median89.5
Q3144.5
95-th percentile235.7
Maximum390
Range383
Interquartile range (IQR)98.5

Descriptive statistics

Standard deviation79.38914
Coefficient of variation (CV)0.75629889
Kurtosis3.9049515
Mean104.97059
Median Absolute Deviation (MAD)48
Skewness1.6257031
Sum3569
Variance6302.6355
MonotonicityNot monotonic
2024-03-23T07:49:55.152224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
171 2
 
5.9%
263 1
 
2.9%
49 1
 
2.9%
54 1
 
2.9%
27 1
 
2.9%
21 1
 
2.9%
7 1
 
2.9%
143 1
 
2.9%
97 1
 
2.9%
45 1
 
2.9%
Other values (23) 23
67.6%
ValueCountFrequency (%)
7 1
2.9%
19 1
2.9%
21 1
2.9%
25 1
2.9%
27 1
2.9%
28 1
2.9%
41 1
2.9%
42 1
2.9%
45 1
2.9%
49 1
2.9%
ValueCountFrequency (%)
390 1
2.9%
263 1
2.9%
221 1
2.9%
176 1
2.9%
171 2
5.9%
161 1
2.9%
155 1
2.9%
145 1
2.9%
143 1
2.9%
136 1
2.9%

미표시 적발실적(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.088235
Minimum7
Maximum188
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-23T07:49:55.437586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile16.65
Q134.5
median60.5
Q389.25
95-th percentile164.55
Maximum188
Range181
Interquartile range (IQR)54.75

Descriptive statistics

Standard deviation44.700598
Coefficient of variation (CV)0.63777606
Kurtosis0.73192136
Mean70.088235
Median Absolute Deviation (MAD)26.5
Skewness0.98713829
Sum2383
Variance1998.1435
MonotonicityNot monotonic
2024-03-23T07:49:55.943142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
34 2
 
5.9%
80 2
 
5.9%
59 2
 
5.9%
69 1
 
2.9%
18 1
 
2.9%
110 1
 
2.9%
46 1
 
2.9%
51 1
 
2.9%
84 1
 
2.9%
121 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
7 1
2.9%
16 1
2.9%
17 1
2.9%
18 1
2.9%
26 1
2.9%
29 1
2.9%
31 1
2.9%
34 2
5.9%
36 1
2.9%
45 1
2.9%
ValueCountFrequency (%)
188 1
2.9%
173 1
2.9%
160 1
2.9%
121 1
2.9%
118 1
2.9%
110 1
2.9%
102 1
2.9%
101 1
2.9%
91 1
2.9%
84 1
2.9%

과태료(천원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17757.912
Minimum2450
Maximum52569
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-03-23T07:49:56.184424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2450
5-th percentile3631.8
Q17331.25
median14275
Q323791.5
95-th percentile46937.05
Maximum52569
Range50119
Interquartile range (IQR)16460.25

Descriptive statistics

Standard deviation13540.143
Coefficient of variation (CV)0.76248512
Kurtosis1.0297676
Mean17757.912
Median Absolute Deviation (MAD)7630
Skewness1.2318551
Sum603769
Variance1.8333549 × 108
MonotonicityNot monotonic
2024-03-23T07:49:56.438928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
24907 1
 
2.9%
26302 1
 
2.9%
4000 1
 
2.9%
10632 1
 
2.9%
6380 1
 
2.9%
2948 1
 
2.9%
2450 1
 
2.9%
37818 1
 
2.9%
24607 1
 
2.9%
4820 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
2450 1
2.9%
2948 1
2.9%
4000 1
2.9%
4080 1
2.9%
4369 1
2.9%
4820 1
2.9%
4950 1
2.9%
6380 1
2.9%
6910 1
2.9%
8595 1
2.9%
ValueCountFrequency (%)
52569 1
2.9%
52542 1
2.9%
43919 1
2.9%
37818 1
2.9%
35792 1
2.9%
28010 1
2.9%
26302 1
2.9%
24907 1
2.9%
24607 1
2.9%
21345 1
2.9%

Interactions

2024-03-23T07:49:51.257984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:49.712565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:50.472325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:51.519273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:49.979192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:50.757799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:51.997929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:50.229825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T07:49:51.015215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T07:49:56.673780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도시도거짓표시 적발실적(개소)미표시 적발실적(개소)과태료(천원)
년도1.0000.0000.6080.0000.000
시도0.0001.0000.2400.5180.404
거짓표시 적발실적(개소)0.6080.2401.0000.7600.809
미표시 적발실적(개소)0.0000.5180.7601.0000.852
과태료(천원)0.0000.4040.8090.8521.000
2024-03-23T07:49:56.998354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거짓표시 적발실적(개소)미표시 적발실적(개소)과태료(천원)년도
거짓표시 적발실적(개소)1.0000.8550.8330.409
미표시 적발실적(개소)0.8551.0000.9350.000
과태료(천원)0.8330.9351.0000.098
년도0.4090.0000.0981.000

Missing values

2024-03-23T07:49:52.321912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T07:49:52.702174image/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

년도시도거짓표시 적발실적(개소)미표시 적발실적(개소)과태료(천원)
02018서울2637824907
12018부산1366916454
22018대구1025617121
32018인천69458595
42018광주847212464
52018대전1154911423
62018울산41314080
72018세종25164369
82018경기39018852569
92018강원15516052542
년도시도거짓표시 적발실적(개소)미표시 적발실적(개소)과태료(천원)
242019세종772450
252019경기14312137818
262019강원498026302
272019충북978424607
282019충남775111808
292019전북805913786
302019전남954611110
312019경북1088017422
322019경남8111021067
332019제주19184950