Overview

Dataset statistics

Number of variables6
Number of observations352
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.0 KiB
Average record size in memory49.4 B

Variable types

Categorical3
Text2
Numeric1

Dataset

Description신선농산물 수출검역단지현황 20200630
Author농림축산검역본부
URLhttps://data.mafra.go.kr/opendata/data/indexOpenDataDetail.do?data_id=20220721000000002286

Reproduction

Analysis started2023-12-11 03:33:59.738462
Analysis finished2023-12-11 03:34:00.597064
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명(지역본부사무소)
Categorical

Distinct20
Distinct (%)5.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
창원
75 
구미
63 
천안
56 
광주
21 
평택
20 
Other values (15)
117 

Length

Max length4
Median length2
Mean length2.2443182
Min length2

Unique

Unique2 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
창원 75
21.3%
구미 63
17.9%
천안 56
15.9%
광주 21
 
6.0%
평택 20
 
5.7%
대구 17
 
4.8%
호남지역 13
 
3.7%
의왕 12
 
3.4%
청주 12
 
3.4%
제주지역 11
 
3.1%
Other values (10) 52
14.8%

Length

2023-12-11T12:34:00.679871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원 75
21.3%
구미 63
17.9%
천안 56
15.9%
광주 21
 
6.0%
평택 20
 
5.7%
대구 17
 
4.8%
호남지역 13
 
3.7%
의왕 12
 
3.4%
청주 12
 
3.4%
제주지역 11
 
3.1%
Other values (10) 52
14.8%

품목
Categorical

Distinct11
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
배
117 
딸기
86 
포도
59 
사과
25 
파프리카
21 
Other values (6)
44 

Length

Max length4
Median length2
Mean length1.7670455
Min length1

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row감
2nd row감
3rd row감
4th row딸기
5th row딸기

Common Values

ValueCountFrequency (%)
배 117
33.2%
딸기 86
24.4%
포도 59
16.8%
사과 25
 
7.1%
파프리카 21
 
6.0%
감 20
 
5.7%
감귤 10
 
2.8%
복숭아 8
 
2.3%
참다래 3
 
0.9%
토마토 2
 
0.6%

Length

2023-12-11T12:34:00.833579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
배 117
33.2%
딸기 86
24.4%
포도 59
16.8%
사과 25
 
7.1%
파프리카 21
 
6.0%
감 20
 
5.7%
감귤 10
 
2.8%
복숭아 8
 
2.3%
참다래 3
 
0.9%
토마토 2
 
0.6%

국가
Categorical

Distinct13
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
대만
97 
태국
78 
베트남
60 
중국
38 
미국
27 
Other values (8)
52 

Length

Max length5
Median length2
Mean length2.3181818
Min length2

Unique

Unique3 ?
Unique (%)0.9%

Sample

1st row베트남
2nd row베트남
3rd row태국
4th row베트남
5th row태국

Common Values

ValueCountFrequency (%)
대만 97
27.6%
태국 78
22.2%
베트남 60
17.0%
중국 38
 
10.8%
미국 27
 
7.7%
캐나다 24
 
6.8%
호주 11
 
3.1%
뉴질랜드 8
 
2.3%
브라질 4
 
1.1%
아르헨티나 2
 
0.6%
Other values (3) 3
 
0.9%

Length

2023-12-11T12:34:00.998338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
대만 97
27.6%
태국 78
22.2%
베트남 60
17.0%
중국 38
 
10.8%
미국 27
 
7.7%
캐나다 24
 
6.8%
호주 11
 
3.1%
뉴질랜드 8
 
2.3%
브라질 4
 
1.1%
아르헨티나 2
 
0.6%
Other values (3) 3
 
0.9%

시/군명
Text

Distinct77
Distinct (%)21.9%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T12:34:01.299407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length4
Mean length2.6448864
Min length2

Characters and Unicode

Total characters931
Distinct characters75
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)6.8%

Sample

1st row김해
2nd row김해
3rd row김해
4th row김해
5th row김해
ValueCountFrequency (%)
진주시 38
 
10.6%
상주 38
 
10.6%
천안 26
 
7.2%
논산 13
 
3.6%
나주시 11
 
3.1%
평택시 11
 
3.1%
화성시 9
 
2.5%
김천 9
 
2.5%
서귀포시 8
 
2.2%
창원시 7
 
1.9%
Other values (69) 189
52.6%
2023-12-11T12:34:01.693738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 132
 
14.2%
주 116
 
12.5%
천 63
 
6.8%
군 56
 
6.0%
진 40
 
4.3%
안 39
 
4.2%
산 39
 
4.2%
상 38
 
4.1%
성 30
 
3.2%
양 20
 
2.1%
Other values (65) 358
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 924
99.2%
Space Separator 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 132
 
14.3%
주 116
 
12.6%
천 63
 
6.8%
군 56
 
6.1%
진 40
 
4.3%
안 39
 
4.2%
산 39
 
4.2%
상 38
 
4.1%
성 30
 
3.2%
양 20
 
2.2%
Other values (64) 351
38.0%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 924
99.2%
Common 7
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 132
 
14.3%
주 116
 
12.6%
천 63
 
6.8%
군 56
 
6.1%
진 40
 
4.3%
안 39
 
4.2%
산 39
 
4.2%
상 38
 
4.1%
성 30
 
3.2%
양 20
 
2.2%
Other values (64) 351
38.0%
Common
ValueCountFrequency (%)
7
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 924
99.2%
ASCII 7
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 132
 
14.3%
주 116
 
12.6%
천 63
 
6.8%
군 56
 
6.1%
진 40
 
4.3%
안 39
 
4.2%
산 39
 
4.2%
상 38
 
4.1%
성 30
 
3.2%
양 20
 
2.2%
Other values (64) 351
38.0%
ASCII
ValueCountFrequency (%)
7
100.0%

단지/선과장명
Text

Distinct256
Distinct (%)72.7%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
2023-12-11T12:34:01.942072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length22
Mean length8.3380682
Min length2

Characters and Unicode

Total characters2935
Distinct characters234
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

Unique190 ?
Unique (%)54.0%

Sample

1st row경남단감원예농협 APC
2nd row진영농협 APC
3rd row진영농협 APC
4th row한림농협딸기수출농단
5th row한림농협딸기수출농단
ValueCountFrequency (%)
apc 8
 
2.0%
한국배영농조합법인 7
 
1.7%
농업회사법인 7
 
1.7%
포도수출협의회 5
 
1.2%
서상주농협 5
 
1.2%
천안포도 5
 
1.2%
산지유통센터 4
 
1.0%
영천친환경영농조합법인 4
 
1.0%
새김천농협 4
 
1.0%
참배수출영농조합법인 4
 
1.0%
Other values (271) 356
87.0%
2023-12-11T12:34:02.317836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
농 260
 
8.9%
협 146
 
5.0%
조 107
 
3.6%
합 96
 
3.3%
수 93
 
3.2%
영 91
 
3.1%
인 77
 
2.6%
법 74
 
2.5%
산 73
 
2.5%
출 71
 
2.4%
Other values (224) 1847
62.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2758
94.0%
Space Separator 63
 
2.1%
Uppercase Letter 45
 
1.5%
Other Punctuation 24
 
0.8%
Open Punctuation 22
 
0.7%
Close Punctuation 22
 
0.7%
Decimal Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
농 260
 
9.4%
협 146
 
5.3%
조 107
 
3.9%
합 96
 
3.5%
수 93
 
3.4%
영 91
 
3.3%
인 77
 
2.8%
법 74
 
2.7%
산 73
 
2.6%
출 71
 
2.6%
Other values (213) 1670
60.6%
Other Punctuation
ValueCountFrequency (%)
/ 16
66.7%
. 4
 
16.7%
, 2
 
8.3%
? 2
 
8.3%
Uppercase Letter
ValueCountFrequency (%)
C 15
33.3%
P 15
33.3%
A 15
33.3%
Space Separator
ValueCountFrequency (%)
63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2758
94.0%
Common 132
 
4.5%
Latin 45
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
농 260
 
9.4%
협 146
 
5.3%
조 107
 
3.9%
합 96
 
3.5%
수 93
 
3.4%
영 91
 
3.3%
인 77
 
2.8%
법 74
 
2.7%
산 73
 
2.6%
출 71
 
2.6%
Other values (213) 1670
60.6%
Common
ValueCountFrequency (%)
63
47.7%
( 22
 
16.7%
) 22
 
16.7%
/ 16
 
12.1%
. 4
 
3.0%
, 2
 
1.5%
? 2
 
1.5%
2 1
 
0.8%
Latin
ValueCountFrequency (%)
C 15
33.3%
P 15
33.3%
A 15
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2758
94.0%
ASCII 177
 
6.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
농 260
 
9.4%
협 146
 
5.3%
조 107
 
3.9%
합 96
 
3.5%
수 93
 
3.4%
영 91
 
3.3%
인 77
 
2.8%
법 74
 
2.7%
산 73
 
2.6%
출 71
 
2.6%
Other values (213) 1670
60.6%
ASCII
ValueCountFrequency (%)
63
35.6%
( 22
 
12.4%
) 22
 
12.4%
/ 16
 
9.0%
C 15
 
8.5%
P 15
 
8.5%
A 15
 
8.5%
. 4
 
2.3%
, 2
 
1.1%
? 2
 
1.1%

농가수
Real number (ℝ)

Distinct66
Distinct (%)18.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.880682
Minimum1
Maximum304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.2 KiB
2023-12-11T12:34:02.469384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median11.5
Q321
95-th percentile67.45
Maximum304
Range303
Interquartile range (IQR)15

Descriptive statistics

Standard deviation30.244163
Coefficient of variation (CV)1.521284
Kurtosis37.140531
Mean19.880682
Median Absolute Deviation (MAD)6.5
Skewness5.2310438
Sum6998
Variance914.70937
MonotonicityNot monotonic
2023-12-11T12:34:02.605759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 20
 
5.7%
4 20
 
5.7%
7 20
 
5.7%
5 19
 
5.4%
10 19
 
5.4%
1 19
 
5.4%
8 15
 
4.3%
18 15
 
4.3%
9 13
 
3.7%
17 13
 
3.7%
Other values (56) 179
50.9%
ValueCountFrequency (%)
1 19
5.4%
2 12
3.4%
3 11
3.1%
4 20
5.7%
5 19
5.4%
6 20
5.7%
7 20
5.7%
8 15
4.3%
9 13
3.7%
10 19
5.4%
ValueCountFrequency (%)
304 1
0.3%
259 1
0.3%
181 1
0.3%
174 1
0.3%
149 1
0.3%
103 1
0.3%
102 1
0.3%
96 1
0.3%
93 1
0.3%
90 1
0.3%

Interactions

2023-12-11T12:34:00.077600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T12:34:02.701168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명(지역본부사무소)품목국가시/군명농가수
기관명(지역본부사무소)1.0000.8030.4271.0000.000
품목0.8031.0000.7310.9600.000
국가0.4270.7311.0000.0000.073
시/군명1.0000.9600.0001.0000.000
농가수0.0000.0000.0730.0001.000
2023-12-11T12:34:02.800035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기관명(지역본부사무소)품목국가
기관명(지역본부사무소)1.0000.4430.156
품목0.4431.0000.400
국가0.1560.4001.000
2023-12-11T12:34:02.889826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
농가수기관명(지역본부사무소)품목국가
농가수1.0000.0000.0000.031
기관명(지역본부사무소)0.0001.0000.4430.156
품목0.0000.4431.0000.400
국가0.0310.1560.4001.000

Missing values

2023-12-11T12:34:00.445219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T12:34:00.549642image/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

기관명(지역본부사무소)품목국가시/군명단지/선과장명농가수
0김해공항감베트남김해경남단감원예농협 APC9
1김해공항감베트남김해진영농협 APC10
2김해공항감태국김해진영농협 APC5
3김해공항딸기베트남김해한림농협딸기수출농단4
4김해공항딸기태국김해한림농협딸기수출농단31
5김해공항토마토베트남부산부산경남화훼농협 수출선별장3
6김해공항파프리카중국부산김해 부경 파프리카수출선별장8
7대구배태국영천영천친환경영농조합법인1
8대구사과대만영덕농산물산지집하장68
9대구사과대만청송청송사과유통센터 현동 APC15
기관명(지역본부사무소)품목국가시/군명단지/선과장명농가수
342제주지역감귤미국제주시조천농협35
343제주지역감귤미국서귀포시서귀포농협35
344제주지역감귤미국서귀포시중문농협24
345제주지역감귤미국서귀포시감협28
346제주지역감귤미국서귀포시성산농협5
347제주지역감귤미국서귀포시위미농협6
348제주지역감귤태국서귀포시감협11
349제주지역감귤태국서귀포시중문농협17
350제주지역감귤태국서귀포시위미농협6
351제주지역참다래대만제주시한라골드19