Overview

Dataset statistics

Number of variables13
Number of observations32
Missing cells4
Missing cells (%)1.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory114.1 B

Variable types

Categorical2
Numeric6
Text5

Alerts

우수수입업소등록번호 is highly overall correlated with 시군명High correlation
허가일자 is highly overall correlated with 시군명High correlation
우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
시군명 is highly overall correlated with 우수수입업소등록번호 and 4 other fieldsHigh correlation
도로명주소 has 1 (3.1%) missing valuesMissing
우편번호 has 1 (3.1%) missing valuesMissing
WGS84위도 has 1 (3.1%) missing valuesMissing
WGS84경도 has 1 (3.1%) missing valuesMissing

Reproduction

Analysis started2023-12-10 22:33:19.617551
Analysis finished2023-12-10 22:33:24.224162
Duration4.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
이천시
9 
안양시
5 
성남시
4 
김포시
3 
화성시
3 
Other values (6)
8 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique4 ?
Unique (%)12.5%

Sample

1st row고양시
2nd row김포시
3rd row김포시
4th row김포시
5th row성남시

Common Values

ValueCountFrequency (%)
이천시 9
28.1%
안양시 5
15.6%
성남시 4
12.5%
김포시 3
 
9.4%
화성시 3
 
9.4%
평택시 2
 
6.2%
하남시 2
 
6.2%
고양시 1
 
3.1%
수원시 1
 
3.1%
용인시 1
 
3.1%

Length

2023-12-11T07:33:24.530248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
이천시 9
28.1%
안양시 5
15.6%
성남시 4
12.5%
김포시 3
 
9.4%
화성시 3
 
9.4%
평택시 2
 
6.2%
하남시 2
 
6.2%
고양시 1
 
3.1%
수원시 1
 
3.1%
용인시 1
 
3.1%

우수수입업소등록번호
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.3125
Minimum3
Maximum62
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:33:24.617637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile3
Q110
median22
Q343
95-th percentile52.45
Maximum62
Range59
Interquartile range (IQR)33

Descriptive statistics

Standard deviation17.937953
Coefficient of variation (CV)0.68172743
Kurtosis-1.3040896
Mean26.3125
Median Absolute Deviation (MAD)13
Skewness0.30667583
Sum842
Variance321.77016
MonotonicityNot monotonic
2023-12-11T07:33:24.711208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
10 8
25.0%
22 4
12.5%
3 3
 
9.4%
43 2
 
6.2%
35 1
 
3.1%
27 1
 
3.1%
62 1
 
3.1%
49 1
 
3.1%
38 1
 
3.1%
39 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
3 3
 
9.4%
8 1
 
3.1%
9 1
 
3.1%
10 8
25.0%
22 4
12.5%
27 1
 
3.1%
32 1
 
3.1%
33 1
 
3.1%
35 1
 
3.1%
38 1
 
3.1%
ValueCountFrequency (%)
62 1
3.1%
53 1
3.1%
52 1
3.1%
50 1
3.1%
49 1
3.1%
47 1
3.1%
45 1
3.1%
43 2
6.2%
39 1
3.1%
38 1
3.1%

허가일자
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20161275
Minimum20121019
Maximum20200311
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:33:24.815504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20121019
5-th percentile20130826
Q120140938
median20160218
Q320181215
95-th percentile20191046
Maximum20200311
Range79292
Interquartile range (IQR)40277

Descriptive statistics

Standard deviation22763.446
Coefficient of variation (CV)0.0011290678
Kurtosis-1.149881
Mean20161275
Median Absolute Deviation (MAD)20995
Skewness-0.081471576
Sum6.4516079 × 108
Variance5.1817446 × 108
MonotonicityNot monotonic
2023-12-11T07:33:24.949571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20160218 8
25.0%
20130826 4
12.5%
20190123 3
 
9.4%
20150521 2
 
6.2%
20181212 1
 
3.1%
20131127 1
 
3.1%
20190911 1
 
3.1%
20180801 1
 
3.1%
20141014 1
 
3.1%
20181217 1
 
3.1%
Other values (9) 9
28.1%
ValueCountFrequency (%)
20121019 1
 
3.1%
20130826 4
12.5%
20131127 1
 
3.1%
20131223 1
 
3.1%
20140709 1
 
3.1%
20141014 1
 
3.1%
20150521 2
 
6.2%
20160218 8
25.0%
20160830 1
 
3.1%
20170303 1
 
3.1%
ValueCountFrequency (%)
20200311 1
 
3.1%
20191210 1
 
3.1%
20190911 1
 
3.1%
20190123 3
9.4%
20181226 1
 
3.1%
20181217 1
 
3.1%
20181214 1
 
3.1%
20181212 1
 
3.1%
20180801 1
 
3.1%
20170303 1
 
3.1%

업소명
Text

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T07:33:25.121894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length9.40625
Min length5

Characters and Unicode

Total characters301
Distinct characters75
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st row(주)몽뜨레쎄코리아
2nd row(주)롯데아사히주류
3rd row(주)롯데아사히주류
4th row(주)롯데아사히주류
5th row(주)영인코퍼레이션
ValueCountFrequency (%)
씨제이프레시웨이주식회사 8
23.5%
주)오뚜기 4
 
11.8%
주)롯데아사히주류 3
 
8.8%
한국에스비식품(주 2
 
5.9%
주)동방실업 1
 
2.9%
삼도식품(주 1
 
2.9%
유통비유(유통bu 1
 
2.9%
대상(주 1
 
2.9%
주)골든피트 1
 
2.9%
주)산과들에 1
 
2.9%
Other values (11) 11
32.4%
2023-12-11T07:33:25.369303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 35
 
11.6%
) 23
 
7.6%
이 22
 
7.3%
( 22
 
7.3%
식 14
 
4.7%
사 13
 
4.3%
회 10
 
3.3%
씨 10
 
3.3%
레 10
 
3.3%
제 9
 
3.0%
Other values (65) 133
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 252
83.7%
Close Punctuation 23
 
7.6%
Open Punctuation 22
 
7.3%
Space Separator 2
 
0.7%
Uppercase Letter 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 35
 
13.9%
이 22
 
8.7%
식 14
 
5.6%
사 13
 
5.2%
회 10
 
4.0%
씨 10
 
4.0%
레 10
 
4.0%
제 9
 
3.6%
웨 8
 
3.2%
시 8
 
3.2%
Other values (60) 113
44.8%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
U 1
50.0%
Close Punctuation
ValueCountFrequency (%)
) 23
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 252
83.7%
Common 47
 
15.6%
Latin 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 35
 
13.9%
이 22
 
8.7%
식 14
 
5.6%
사 13
 
5.2%
회 10
 
4.0%
씨 10
 
4.0%
레 10
 
4.0%
제 9
 
3.6%
웨 8
 
3.2%
시 8
 
3.2%
Other values (60) 113
44.8%
Common
ValueCountFrequency (%)
) 23
48.9%
( 22
46.8%
2
 
4.3%
Latin
ValueCountFrequency (%)
B 1
50.0%
U 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 252
83.7%
ASCII 49
 
16.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 35
 
13.9%
이 22
 
8.7%
식 14
 
5.6%
사 13
 
5.2%
회 10
 
4.0%
씨 10
 
4.0%
레 10
 
4.0%
제 9
 
3.6%
웨 8
 
3.2%
시 8
 
3.2%
Other values (60) 113
44.8%
ASCII
ValueCountFrequency (%)
) 23
46.9%
( 22
44.9%
2
 
4.1%
B 1
 
2.0%
U 1
 
2.0%

수출국가
Categorical

Distinct8
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
중국
16 
일본
3 
이탈리아
3 
미국
3 
태국
2 
Other values (3)
5 

Length

Max length4
Median length2
Mean length2.3125
Min length2

Unique

Unique1 ?
Unique (%)3.1%

Sample

1st row중국
2nd row일본
3rd row중국
4th row일본
5th row이탈리아

Common Values

ValueCountFrequency (%)
중국 16
50.0%
일본 3
 
9.4%
이탈리아 3
 
9.4%
미국 3
 
9.4%
태국 2
 
6.2%
필리핀 2
 
6.2%
베트남 2
 
6.2%
칠레 1
 
3.1%

Length

2023-12-11T07:33:25.478391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:33:25.574680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중국 16
50.0%
일본 3
 
9.4%
이탈리아 3
 
9.4%
미국 3
 
9.4%
태국 2
 
6.2%
필리핀 2
 
6.2%
베트남 2
 
6.2%
칠레 1
 
3.1%

수입제품제조회사명
Text

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T07:33:25.769860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length36
Mean length30.125
Min length8

Characters and Unicode

Total characters964
Distinct characters41
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)87.5%

Sample

1st rowJIANGSU LIANGFENG FOOD GROUP CO.,LTD.
2nd rowASAHI BREWERIES, LTD. HAKATA BREWERY
3rd rowBEIJING BEER ASAHI CO.,LTD
4th rowKIZAKURA CO., LTD
5th rowF.DIVELLA S.P.A
ValueCountFrequency (%)
food 10
 
7.3%
ltd 10
 
7.3%
co.,ltd 9
 
6.6%
co 9
 
6.6%
foods 9
 
6.6%
qingdao 4
 
2.9%
agricultural 3
 
2.2%
products 3
 
2.2%
s.p.a 2
 
1.5%
industry 2
 
1.5%
Other values (67) 76
55.5%
2023-12-11T07:33:26.080803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
106
 
11.0%
O 91
 
9.4%
A 70
 
7.3%
D 62
 
6.4%
I 58
 
6.0%
N 52
 
5.4%
C 48
 
5.0%
T 46
 
4.8%
S 44
 
4.6%
E 42
 
4.4%
Other values (31) 345
35.8%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 793
82.3%
Space Separator 106
 
11.0%
Other Punctuation 49
 
5.1%
Other Letter 8
 
0.8%
Open Punctuation 3
 
0.3%
Close Punctuation 3
 
0.3%
Dash Punctuation 1
 
0.1%
Decimal Number 1
 
0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
O 91
 
11.5%
A 70
 
8.8%
D 62
 
7.8%
I 58
 
7.3%
N 52
 
6.6%
C 48
 
6.1%
T 46
 
5.8%
S 44
 
5.5%
E 42
 
5.3%
L 42
 
5.3%
Other values (16) 238
30.0%
Other Letter
ValueCountFrequency (%)
오 1
12.5%
크 1
12.5%
필 1
12.5%
드 1
12.5%
캠 1
12.5%
브 1
12.5%
리 1
12.5%
아 1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 32
65.3%
, 17
34.7%
Space Separator
ValueCountFrequency (%)
106
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 793
82.3%
Common 163
 
16.9%
Hangul 8
 
0.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
O 91
 
11.5%
A 70
 
8.8%
D 62
 
7.8%
I 58
 
7.3%
N 52
 
6.6%
C 48
 
6.1%
T 46
 
5.8%
S 44
 
5.5%
E 42
 
5.3%
L 42
 
5.3%
Other values (16) 238
30.0%
Hangul
ValueCountFrequency (%)
오 1
12.5%
크 1
12.5%
필 1
12.5%
드 1
12.5%
캠 1
12.5%
브 1
12.5%
리 1
12.5%
아 1
12.5%
Common
ValueCountFrequency (%)
106
65.0%
. 32
 
19.6%
, 17
 
10.4%
( 3
 
1.8%
) 3
 
1.8%
- 1
 
0.6%
1 1
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 956
99.2%
Hangul 8
 
0.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
106
 
11.1%
O 91
 
9.5%
A 70
 
7.3%
D 62
 
6.5%
I 58
 
6.1%
N 52
 
5.4%
C 48
 
5.0%
T 46
 
4.8%
S 44
 
4.6%
E 42
 
4.4%
Other values (23) 337
35.3%
Hangul
ValueCountFrequency (%)
오 1
12.5%
크 1
12.5%
필 1
12.5%
드 1
12.5%
캠 1
12.5%
브 1
12.5%
리 1
12.5%
아 1
12.5%

품목수
Real number (ℝ)

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.25
Minimum1
Maximum92
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:33:26.175704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.55
Q17
median15
Q319
95-th percentile19.9
Maximum92
Range91
Interquartile range (IQR)12

Descriptive statistics

Standard deviation15.663807
Coefficient of variation (CV)1.0992145
Kurtosis20.567033
Mean14.25
Median Absolute Deviation (MAD)5
Skewness4.089156
Sum456
Variance245.35484
MonotonicityNot monotonic
2023-12-11T07:33:26.282322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
19 8
25.0%
16 4
12.5%
15 3
 
9.4%
7 3
 
9.4%
3 3
 
9.4%
10 3
 
9.4%
8 2
 
6.2%
2 2
 
6.2%
1 2
 
6.2%
92 1
 
3.1%
ValueCountFrequency (%)
1 2
 
6.2%
2 2
 
6.2%
3 3
 
9.4%
7 3
 
9.4%
8 2
 
6.2%
10 3
 
9.4%
15 3
 
9.4%
16 4
12.5%
19 8
25.0%
21 1
 
3.1%
ValueCountFrequency (%)
92 1
 
3.1%
21 1
 
3.1%
19 8
25.0%
16 4
12.5%
15 3
 
9.4%
10 3
 
9.4%
8 2
 
6.2%
7 3
 
9.4%
3 3
 
9.4%
2 2
 
6.2%

품목명
Text

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T07:33:26.530962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length1024
Median length306
Mean length217.5
Min length12

Characters and Unicode

Total characters6960
Distinct characters116
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st rowBELGIAN SEASHELLS,FREE ROMANCE CHOCOLATE,BELGIAN ASSORTMENT,TRESOR DORE,MY LIKES,GOLDEN DREAM,DANISH COOKIE,BELGIAN P-4 SET
2nd rowASAHI SUPER DRY (5%),ASAHI SUPER DRY(5%),ASAHI SUPER DRY-DRY BLACK-(5.5%),CLEAR ASAHI PRIME RICH,YAMAHAI JIKOMI (14%),KINJIRUSHI (16%),KARAKUCHI IKKON (14%),JUNMAI KARAKUCHI IKKON (14%),GINJO NAMA CHOZO (15%),SHOFU (16%),JUNMAI (14%),HANA SHOFU (16%),HANA KIZAKURA JUNMAI GINJO,SEN NO YUME,JUNMAI DAIGINJO S
3rd rowASAHI SUPER DRY (5%),ASAHI SUPER DRY(5%),ASAHI SUPER DRY-DRY BLACK-(5.5%),CLEAR ASAHI PRIME RICH,YAMAHAI JIKOMI (14%),KINJIRUSHI (16%),KARAKUCHI IKKON (14%),JUNMAI KARAKUCHI IKKON (14%),GINJO NAMA CHOZO (15%),SHOFU (16%),JUNMAI (14%),HANA SHOFU (16%),HANA KIZAKURA JUNMAI GINJO,SEN NO YUME,JUNMAI DAIGINJO S
4th rowASAHI SUPER DRY (5%),ASAHI SUPER DRY(5%),ASAHI SUPER DRY-DRY BLACK-(5.5%),CLEAR ASAHI PRIME RICH,YAMAHAI JIKOMI (14%),KINJIRUSHI (16%),KARAKUCHI IKKON (14%),JUNMAI KARAKUCHI IKKON (14%),GINJO NAMA CHOZO (15%),SHOFU (16%),JUNMAI (14%),HANA SHOFU (16%),HANA KIZAKURA JUNMAI GINJO,SEN NO YUME,JUNMAI DAIGINJO S
5th rowDIVELLA FARINA,DIVELLA FARINA MANITOBA,DIVELLA FARINA NAPOLETANA,DIVELLA OTTIMINI BISCOTTI CLASSICI,DEVELLA OTTIMINI BISCOTTI CON RISO E MAIS,DEVELLA OTTIMINI BISCOTTI AL CACAO,DIVELLA GROTTOLI,DIVELLA OTTIMINI BISCOTTI ALLE MANDORLE,DIVELLA FETTUCCINE,DIVELLA LINGUINE,DIVELLA MAFALDINE,DIVELLA ACINI DI PEPE,DIVELLA CONCHIGLIONI,DIVELLA FARINA PIZZA SUPER,DIVELLA FUSILLI AVELLINESI,DIVELLA CAVATELLI,DIVELLA SAPGHETTI AL POMODORO E SPINACI,DIVELLA FUSILLI AL POMODORO E SPINACI,DIVELLA FARFALLE AL POMODORO E SPINACI,DIVELLA FUSILLONI,DIVELLA PENNE AL POMODORO E SPINACI,DIVELLA FUSILLI COL BUCO,DIVELLA FAGIOLINI,DIVELLA OTTIMINI AL CACAO,DIVELLA OTTIMINI INTEGRALI,DIVELLA OTTIMINI AL CACAO,DIVELLA GROTTOLI,DIVELLA OTTIMINI CLASSICI,DIVELLA ORECCHIETTE SVENTOLE,DIVELLA ORECCHIETTE,DIVELLA CASERECCE,DIVELLA TOFE,DIVELLA ROTELLE,DIVELLA GNOCCHI,DIVELLA PIPE RIGATE,DIVELLA SPAGHETTINI,LINGUINE,FETTUCCINE,DIVELLA CAPELLINI,DIVELLA CAPELLI D'ANGELO,DIVELLA SPAGHETTI RISTORANTE INTEGRALE,DIVELLA SPAGHETTI AL PEPERONCIN
ValueCountFrequency (%)
파인애플 32
 
4.0%
슬라이스,이츠웰 32
 
4.0%
sweet 18
 
2.3%
super 17
 
2.1%
프리미엄 16
 
2.0%
표고버섯 16
 
2.0%
청크,이츠웰 16
 
2.0%
커리,인델리 16
 
2.0%
spaghetti,fresco 12
 
1.5%
corn,ottogi 12
 
1.5%
Other values (268) 610
76.5%
2023-12-11T07:33:26.927219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
765
 
11.0%
I 437
 
6.3%
E 428
 
6.1%
, 374
 
5.4%
A 331
 
4.8%
O 319
 
4.6%
N 268
 
3.9%
R 264
 
3.8%
L 255
 
3.7%
S 244
 
3.5%
Other values (106) 3275
47.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 4203
60.4%
Other Letter 1401
 
20.1%
Space Separator 765
 
11.0%
Other Punctuation 422
 
6.1%
Decimal Number 73
 
1.0%
Open Punctuation 43
 
0.6%
Close Punctuation 43
 
0.6%
Dash Punctuation 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
이 152
 
10.8%
츠 112
 
8.0%
웰 104
 
7.4%
리 84
 
6.0%
인 64
 
4.6%
면 52
 
3.7%
당 52
 
3.7%
스 43
 
3.1%
파 40
 
2.9%
커 32
 
2.3%
Other values (65) 666
47.5%
Uppercase Letter
ValueCountFrequency (%)
I 437
 
10.4%
E 428
 
10.2%
A 331
 
7.9%
O 319
 
7.6%
N 268
 
6.4%
R 264
 
6.3%
L 255
 
6.1%
S 244
 
5.8%
T 235
 
5.6%
C 219
 
5.2%
Other values (16) 1203
28.6%
Decimal Number
ValueCountFrequency (%)
1 24
32.9%
5 19
26.0%
4 14
19.2%
6 11
15.1%
0 2
 
2.7%
3 2
 
2.7%
2 1
 
1.4%
Other Punctuation
ValueCountFrequency (%)
, 374
88.6%
% 33
 
7.8%
' 12
 
2.8%
. 3
 
0.7%
Space Separator
ValueCountFrequency (%)
765
100.0%
Open Punctuation
ValueCountFrequency (%)
( 43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 43
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 4203
60.4%
Hangul 1401
 
20.1%
Common 1356
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
이 152
 
10.8%
츠 112
 
8.0%
웰 104
 
7.4%
리 84
 
6.0%
인 64
 
4.6%
면 52
 
3.7%
당 52
 
3.7%
스 43
 
3.1%
파 40
 
2.9%
커 32
 
2.3%
Other values (65) 666
47.5%
Latin
ValueCountFrequency (%)
I 437
 
10.4%
E 428
 
10.2%
A 331
 
7.9%
O 319
 
7.6%
N 268
 
6.4%
R 264
 
6.3%
L 255
 
6.1%
S 244
 
5.8%
T 235
 
5.6%
C 219
 
5.2%
Other values (16) 1203
28.6%
Common
ValueCountFrequency (%)
765
56.4%
, 374
27.6%
( 43
 
3.2%
) 43
 
3.2%
% 33
 
2.4%
1 24
 
1.8%
5 19
 
1.4%
4 14
 
1.0%
' 12
 
0.9%
6 11
 
0.8%
Other values (5) 18
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 5559
79.9%
Hangul 1401
 
20.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
765
13.8%
I 437
 
7.9%
E 428
 
7.7%
, 374
 
6.7%
A 331
 
6.0%
O 319
 
5.7%
N 268
 
4.8%
R 264
 
4.7%
L 255
 
4.6%
S 244
 
4.4%
Other values (31) 1874
33.7%
Hangul
ValueCountFrequency (%)
이 152
 
10.8%
츠 112
 
8.0%
웰 104
 
7.4%
리 84
 
6.0%
인 64
 
4.6%
면 52
 
3.7%
당 52
 
3.7%
스 43
 
3.1%
파 40
 
2.9%
커 32
 
2.3%
Other values (65) 666
47.5%

도로명주소
Text

MISSING 

Distinct18
Distinct (%)58.1%
Missing1
Missing (%)3.1%
Memory size388.0 B
2023-12-11T07:33:27.167266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length25
Mean length20.064516
Min length15

Characters and Unicode

Total characters622
Distinct characters90
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

Unique14 ?
Unique (%)45.2%

Sample

1st row경기도 고양시 일산동구 중앙로 1227
2nd row경기도 김포시 하성면 월하로 586-55
3rd row경기도 김포시 하성면 월하로 586-55
4th row경기도 김포시 하성면 월하로 586-55
5th row경기도 성남시 중원구 둔촌대로 388
ValueCountFrequency (%)
경기도 31
20.4%
이천시 9
 
5.9%
마장면 8
 
5.3%
덕평로 8
 
5.3%
811 8
 
5.3%
안양시 5
 
3.3%
동안구 5
 
3.3%
흥안대로 4
 
2.6%
405 4
 
2.6%
586-55 3
 
2.0%
Other values (52) 67
44.1%
2023-12-11T07:33:27.444593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
19.5%
경 32
 
5.1%
시 32
 
5.1%
도 31
 
5.0%
기 31
 
5.0%
로 28
 
4.5%
1 27
 
4.3%
8 19
 
3.1%
5 15
 
2.4%
면 14
 
2.3%
Other values (80) 272
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 394
63.3%
Space Separator 121
 
19.5%
Decimal Number 102
 
16.4%
Dash Punctuation 5
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 32
 
8.1%
시 32
 
8.1%
도 31
 
7.9%
기 31
 
7.9%
로 28
 
7.1%
면 14
 
3.6%
안 14
 
3.6%
천 11
 
2.8%
구 11
 
2.8%
이 10
 
2.5%
Other values (68) 180
45.7%
Decimal Number
ValueCountFrequency (%)
1 27
26.5%
8 19
18.6%
5 15
14.7%
4 11
10.8%
6 8
 
7.8%
2 6
 
5.9%
0 5
 
4.9%
9 4
 
3.9%
3 4
 
3.9%
7 3
 
2.9%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 394
63.3%
Common 228
36.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 32
 
8.1%
시 32
 
8.1%
도 31
 
7.9%
기 31
 
7.9%
로 28
 
7.1%
면 14
 
3.6%
안 14
 
3.6%
천 11
 
2.8%
구 11
 
2.8%
이 10
 
2.5%
Other values (68) 180
45.7%
Common
ValueCountFrequency (%)
121
53.1%
1 27
 
11.8%
8 19
 
8.3%
5 15
 
6.6%
4 11
 
4.8%
6 8
 
3.5%
2 6
 
2.6%
0 5
 
2.2%
- 5
 
2.2%
9 4
 
1.8%
Other values (2) 7
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 394
63.3%
ASCII 228
36.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
53.1%
1 27
 
11.8%
8 19
 
8.3%
5 15
 
6.6%
4 11
 
4.8%
6 8
 
3.5%
2 6
 
2.6%
0 5
 
2.2%
- 5
 
2.2%
9 4
 
1.8%
Other values (2) 7
 
3.1%
Hangul
ValueCountFrequency (%)
경 32
 
8.1%
시 32
 
8.1%
도 31
 
7.9%
기 31
 
7.9%
로 28
 
7.1%
면 14
 
3.6%
안 14
 
3.6%
천 11
 
2.8%
구 11
 
2.8%
이 10
 
2.5%
Other values (68) 180
45.7%

지번주소
Text

Distinct19
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2023-12-11T07:33:27.660828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length60
Median length36
Mean length26.03125
Min length20

Characters and Unicode

Total characters833
Distinct characters116
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)46.9%

Sample

1st row경기도 고양시 일산동구 장항동 849번지 동양메이져타워 506호
2nd row경기도 김포시 하성면 원산리 670-8번지 나동 1층
3rd row경기도 김포시 하성면 원산리 670-8번지 나동 1층
4th row경기도 김포시 하성면 원산리 670-8번지 나동 1층
5th row경기도 성남시 중원구 상대원동 5442-1번지 크란츠테크노 B114
ValueCountFrequency (%)
경기도 32
 
17.4%
이천시 9
 
4.9%
마장면 8
 
4.3%
덕평리 8
 
4.3%
508번지 8
 
4.3%
1층 5
 
2.7%
안양시 5
 
2.7%
동안구 5
 
2.7%
성남시 4
 
2.2%
160-4번지 4
 
2.2%
Other values (70) 96
52.2%
2023-12-11T07:33:28.011182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
152
 
18.2%
1 33
 
4.0%
기 33
 
4.0%
시 32
 
3.8%
경 32
 
3.8%
도 32
 
3.8%
지 31
 
3.7%
번 31
 
3.7%
동 29
 
3.5%
0 19
 
2.3%
Other values (106) 409
49.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 514
61.7%
Space Separator 152
 
18.2%
Decimal Number 144
 
17.3%
Dash Punctuation 17
 
2.0%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Uppercase Letter 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 33
 
6.4%
시 32
 
6.2%
경 32
 
6.2%
도 32
 
6.2%
지 31
 
6.0%
번 31
 
6.0%
동 29
 
5.6%
리 17
 
3.3%
평 14
 
2.7%
면 14
 
2.7%
Other values (90) 249
48.4%
Decimal Number
ValueCountFrequency (%)
1 33
22.9%
0 19
13.2%
8 18
12.5%
5 16
11.1%
6 16
11.1%
4 15
10.4%
2 13
 
9.0%
7 6
 
4.2%
9 4
 
2.8%
3 4
 
2.8%
Space Separator
ValueCountFrequency (%)
152
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 514
61.7%
Common 318
38.2%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 33
 
6.4%
시 32
 
6.2%
경 32
 
6.2%
도 32
 
6.2%
지 31
 
6.0%
번 31
 
6.0%
동 29
 
5.6%
리 17
 
3.3%
평 14
 
2.7%
면 14
 
2.7%
Other values (90) 249
48.4%
Common
ValueCountFrequency (%)
152
47.8%
1 33
 
10.4%
0 19
 
6.0%
8 18
 
5.7%
- 17
 
5.3%
5 16
 
5.0%
6 16
 
5.0%
4 15
 
4.7%
2 13
 
4.1%
7 6
 
1.9%
Other values (5) 13
 
4.1%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 514
61.7%
ASCII 319
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
152
47.6%
1 33
 
10.3%
0 19
 
6.0%
8 18
 
5.6%
- 17
 
5.3%
5 16
 
5.0%
6 16
 
5.0%
4 15
 
4.7%
2 13
 
4.1%
7 6
 
1.9%
Other values (6) 14
 
4.4%
Hangul
ValueCountFrequency (%)
기 33
 
6.4%
시 32
 
6.2%
경 32
 
6.2%
도 32
 
6.2%
지 31
 
6.0%
번 31
 
6.0%
동 29
 
5.6%
리 17
 
3.3%
평 14
 
2.7%
면 14
 
2.7%
Other values (90) 249
48.4%

우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)58.1%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean15128.226
Minimum10011
Maximum18633
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:33:28.132409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10011
5-th percentile10011
Q113261
median16388
Q317389
95-th percentile18358
Maximum18633
Range8622
Interquartile range (IQR)4128

Descriptive statistics

Standard deviation2840.7641
Coefficient of variation (CV)0.18777906
Kurtosis-1.0502528
Mean15128.226
Median Absolute Deviation (MAD)2245
Skewness-0.53769209
Sum468975
Variance8069940.4
MonotonicityNot monotonic
2023-12-11T07:33:28.221807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
17389 8
25.0%
14060 4
12.5%
10011 3
 
9.4%
18358 2
 
6.2%
10403 1
 
3.1%
17966 1
 
3.1%
18633 1
 
3.1%
12913 1
 
3.1%
12985 1
 
3.1%
11154 1
 
3.1%
Other values (8) 8
25.0%
ValueCountFrequency (%)
10011 3
9.4%
10403 1
 
3.1%
11154 1
 
3.1%
12913 1
 
3.1%
12985 1
 
3.1%
13119 1
 
3.1%
13403 1
 
3.1%
13477 1
 
3.1%
14048 1
 
3.1%
14060 4
12.5%
ValueCountFrequency (%)
18633 1
 
3.1%
18358 2
 
6.2%
17966 1
 
3.1%
17958 1
 
3.1%
17396 1
 
3.1%
17389 8
25.0%
17031 1
 
3.1%
16388 1
 
3.1%
14060 4
12.5%
14048 1
 
3.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)58.1%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean37.357719
Minimum36.948447
Maximum37.877381
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:33:28.321303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.948447
5-th percentile37.023223
Q137.231262
median37.341347
Q337.444213
95-th percentile37.709076
Maximum37.877381
Range0.92893435
Interquartile range (IQR)0.21295137

Descriptive statistics

Standard deviation0.21743498
Coefficient of variation (CV)0.0058203494
Kurtosis0.13887814
Mean37.357719
Median Absolute Deviation (MAD)0.11008503
Skewness0.47424544
Sum1158.0893
Variance0.04727797
MonotonicityNot monotonic
2023-12-11T07:33:28.424506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
37.231261832 8
25.0%
37.3906256591 4
12.5%
37.709076431 3
 
9.4%
37.2150943703 2
 
6.2%
37.6558477325 1
 
3.1%
36.9484470713 1
 
3.1%
37.0798319008 1
 
3.1%
37.5609350078 1
 
3.1%
37.5334667179 1
 
3.1%
37.8773814226 1
 
3.1%
Other values (8) 8
25.0%
ValueCountFrequency (%)
36.9484470713 1
 
3.1%
36.9666145903 1
 
3.1%
37.0798319008 1
 
3.1%
37.2118210121 1
 
3.1%
37.2150943703 2
 
6.2%
37.231261832 8
25.0%
37.2727961742 1
 
3.1%
37.3413468617 1
 
3.1%
37.3906256591 4
12.5%
37.390812691 1
 
3.1%
ValueCountFrequency (%)
37.8773814226 1
 
3.1%
37.709076431 3
9.4%
37.6558477325 1
 
3.1%
37.5609350078 1
 
3.1%
37.5334667179 1
 
3.1%
37.4557663743 1
 
3.1%
37.4326600274 1
 
3.1%
37.3915524384 1
 
3.1%
37.390812691 1
 
3.1%
37.3906256591 4
12.5%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)58.1%
Missing1
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean127.08395
Minimum126.62071
Maximum127.39431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-11T07:33:28.530269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62071
5-th percentile126.62071
Q1126.94682
median127.08956
Q3127.36515
95-th percentile127.36515
Maximum127.39431
Range0.77359626
Interquartile range (IQR)0.41833303

Descriptive statistics

Standard deviation0.2414096
Coefficient of variation (CV)0.0018996073
Kurtosis-0.75640938
Mean127.08395
Median Absolute Deviation (MAD)0.14735001
Skewness-0.3790624
Sum3939.6024
Variance0.058278595
MonotonicityNot monotonic
2023-12-11T07:33:28.645084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
127.3651499177 8
25.0%
126.9698549937 4
12.5%
126.6207107417 3
 
9.4%
127.0173406388 2
 
6.2%
126.7748505682 1
 
3.1%
126.8765260364 1
 
3.1%
126.9440015995 1
 
3.1%
127.1927704069 1
 
3.1%
127.1844282042 1
 
3.1%
127.1991815997 1
 
3.1%
Other values (8) 8
25.0%
ValueCountFrequency (%)
126.6207107417 3
9.4%
126.7748505682 1
 
3.1%
126.8470125641 1
 
3.1%
126.8765260364 1
 
3.1%
126.942213483 1
 
3.1%
126.9440015995 1
 
3.1%
126.9496321803 1
 
3.1%
126.9698549937 4
12.5%
127.0173406388 2
6.2%
127.0895634969 1
 
3.1%
ValueCountFrequency (%)
127.3943070064 1
 
3.1%
127.3651499177 8
25.0%
127.2235394968 1
 
3.1%
127.1991815997 1
 
3.1%
127.1927704069 1
 
3.1%
127.1844282042 1
 
3.1%
127.1591934176 1
 
3.1%
127.1277236338 1
 
3.1%
127.0895634969 1
 
3.1%
127.0173406388 2
 
6.2%

Interactions

2023-12-11T07:33:23.424602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.450114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.878875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.297405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.680400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.057846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.506587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.552455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.948363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.361686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.742801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.116970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.602355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.624398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.024134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.436365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.818379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.187275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.670967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.683137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.092896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.490962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.875204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.242725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.734404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.741505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.155928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.548687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.931765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.298672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.803385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:21.803489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.220166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.610110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:22.988470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:33:23.355045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:33:28.757910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명우수수입업소등록번호허가일자업소명수출국가수입제품제조회사명품목수품목명도로명주소지번주소우편번호WGS84위도WGS84경도
시군명1.0000.8560.8251.0000.5440.6850.5461.0001.0001.0000.9840.9630.956
우수수입업소등록번호0.8561.0000.9501.0000.3840.9730.7201.0001.0001.0000.8210.8750.927
허가일자0.8250.9501.0001.0000.0000.0000.8241.0001.0001.0000.7610.8410.959
업소명1.0001.0001.0001.0000.0000.8891.0001.0001.0001.0001.0001.0001.000
수출국가0.5440.3840.0000.0001.0001.0000.7150.0000.0000.0000.0000.4670.000
수입제품제조회사명0.6850.9730.0000.8891.0001.0000.9690.8890.8710.8890.0000.9120.860
품목수0.5460.7200.8241.0000.7150.9691.0001.0001.0001.0000.4890.6300.637
품목명1.0001.0001.0001.0000.0000.8891.0001.0001.0001.0001.0001.0001.000
도로명주소1.0001.0001.0001.0000.0000.8711.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0000.0000.8891.0001.0001.0001.0001.0001.0001.000
우편번호0.9840.8210.7611.0000.0000.0000.4891.0001.0001.0001.0000.9220.836
WGS84위도0.9630.8750.8411.0000.4670.9120.6301.0001.0001.0000.9221.0000.929
WGS84경도0.9560.9270.9591.0000.0000.8600.6371.0001.0001.0000.8360.9291.000
2023-12-11T07:33:28.892086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수출국가시군명
수출국가1.0000.252
시군명0.2521.000
2023-12-11T07:33:28.998169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우수수입업소등록번호허가일자품목수우편번호WGS84위도WGS84경도시군명수출국가
우수수입업소등록번호1.0000.133-0.3840.056-0.048-0.1440.5840.168
허가일자0.1331.000-0.148-0.3720.301-0.3190.5910.015
품목수-0.384-0.1481.0000.094-0.1000.3520.2980.349
우편번호0.056-0.3720.0941.000-0.9860.3640.8810.247
WGS84위도-0.0480.301-0.100-0.9861.000-0.3240.8250.207
WGS84경도-0.144-0.3190.3520.364-0.3241.0000.8160.000
시군명0.5840.5910.2980.8810.8250.8161.0000.252
수출국가0.1680.0150.3490.2470.2070.0000.2521.000

Missing values

2023-12-11T07:33:23.915991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:33:24.059544image/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.
2023-12-11T07:33:24.171058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시군명우수수입업소등록번호허가일자업소명수출국가수입제품제조회사명품목수품목명도로명주소지번주소우편번호WGS84위도WGS84경도
0고양시3520181212(주)몽뜨레쎄코리아중국JIANGSU LIANGFENG FOOD GROUP CO.,LTD.8BELGIAN SEASHELLS,FREE ROMANCE CHOCOLATE,BELGIAN ASSORTMENT,TRESOR DORE,MY LIKES,GOLDEN DREAM,DANISH COOKIE,BELGIAN P-4 SET경기도 고양시 일산동구 중앙로 1227경기도 고양시 일산동구 장항동 849번지 동양메이져타워 506호1040337.655848126.774851
1김포시320190123(주)롯데아사히주류일본ASAHI BREWERIES, LTD. HAKATA BREWERY15ASAHI SUPER DRY (5%),ASAHI SUPER DRY(5%),ASAHI SUPER DRY-DRY BLACK-(5.5%),CLEAR ASAHI PRIME RICH,YAMAHAI JIKOMI (14%),KINJIRUSHI (16%),KARAKUCHI IKKON (14%),JUNMAI KARAKUCHI IKKON (14%),GINJO NAMA CHOZO (15%),SHOFU (16%),JUNMAI (14%),HANA SHOFU (16%),HANA KIZAKURA JUNMAI GINJO,SEN NO YUME,JUNMAI DAIGINJO S경기도 김포시 하성면 월하로 586-55경기도 김포시 하성면 원산리 670-8번지 나동 1층1001137.709076126.620711
2김포시320190123(주)롯데아사히주류중국BEIJING BEER ASAHI CO.,LTD15ASAHI SUPER DRY (5%),ASAHI SUPER DRY(5%),ASAHI SUPER DRY-DRY BLACK-(5.5%),CLEAR ASAHI PRIME RICH,YAMAHAI JIKOMI (14%),KINJIRUSHI (16%),KARAKUCHI IKKON (14%),JUNMAI KARAKUCHI IKKON (14%),GINJO NAMA CHOZO (15%),SHOFU (16%),JUNMAI (14%),HANA SHOFU (16%),HANA KIZAKURA JUNMAI GINJO,SEN NO YUME,JUNMAI DAIGINJO S경기도 김포시 하성면 월하로 586-55경기도 김포시 하성면 원산리 670-8번지 나동 1층1001137.709076126.620711
3김포시320190123(주)롯데아사히주류일본KIZAKURA CO., LTD15ASAHI SUPER DRY (5%),ASAHI SUPER DRY(5%),ASAHI SUPER DRY-DRY BLACK-(5.5%),CLEAR ASAHI PRIME RICH,YAMAHAI JIKOMI (14%),KINJIRUSHI (16%),KARAKUCHI IKKON (14%),JUNMAI KARAKUCHI IKKON (14%),GINJO NAMA CHOZO (15%),SHOFU (16%),JUNMAI (14%),HANA SHOFU (16%),HANA KIZAKURA JUNMAI GINJO,SEN NO YUME,JUNMAI DAIGINJO S경기도 김포시 하성면 월하로 586-55경기도 김포시 하성면 원산리 670-8번지 나동 1층1001137.709076126.620711
4성남시3220131223(주)영인코퍼레이션이탈리아F.DIVELLA S.P.A92DIVELLA FARINA,DIVELLA FARINA MANITOBA,DIVELLA FARINA NAPOLETANA,DIVELLA OTTIMINI BISCOTTI CLASSICI,DEVELLA OTTIMINI BISCOTTI CON RISO E MAIS,DEVELLA OTTIMINI BISCOTTI AL CACAO,DIVELLA GROTTOLI,DIVELLA OTTIMINI BISCOTTI ALLE MANDORLE,DIVELLA FETTUCCINE,DIVELLA LINGUINE,DIVELLA MAFALDINE,DIVELLA ACINI DI PEPE,DIVELLA CONCHIGLIONI,DIVELLA FARINA PIZZA SUPER,DIVELLA FUSILLI AVELLINESI,DIVELLA CAVATELLI,DIVELLA SAPGHETTI AL POMODORO E SPINACI,DIVELLA FUSILLI AL POMODORO E SPINACI,DIVELLA FARFALLE AL POMODORO E SPINACI,DIVELLA FUSILLONI,DIVELLA PENNE AL POMODORO E SPINACI,DIVELLA FUSILLI COL BUCO,DIVELLA FAGIOLINI,DIVELLA OTTIMINI AL CACAO,DIVELLA OTTIMINI INTEGRALI,DIVELLA OTTIMINI AL CACAO,DIVELLA GROTTOLI,DIVELLA OTTIMINI CLASSICI,DIVELLA ORECCHIETTE SVENTOLE,DIVELLA ORECCHIETTE,DIVELLA CASERECCE,DIVELLA TOFE,DIVELLA ROTELLE,DIVELLA GNOCCHI,DIVELLA PIPE RIGATE,DIVELLA SPAGHETTINI,LINGUINE,FETTUCCINE,DIVELLA CAPELLINI,DIVELLA CAPELLI D'ANGELO,DIVELLA SPAGHETTI RISTORANTE INTEGRALE,DIVELLA SPAGHETTI AL PEPERONCIN경기도 성남시 중원구 둔촌대로 388경기도 성남시 중원구 상대원동 5442-1번지 크란츠테크노 B1141340337.43266127.159193
5성남시820181226(주)조이푸드중국WEIHAI JK FOODS CO., LTD.2FROZEN RED BEAN BREAD,FROZEN CREAM BREAD<NA>경기도 성남시 중원구 사기막골로105-25(상대원동, 중원구 상대원동 144-5 중앙인더스피아2차 611호)<NA><NA><NA>
6성남시4720200311주)에스에이치에스미국FRENCH GOURMET INC21RAISIN CUSTARD ROLLS,APRICOT ALMOND MEDALLIONS,COCONUT CREAM POCKETS,MAPLE WALNUT COMBS,WILD BLUEBERRY POCKETS,APPLE LATTICE,BUTTER CROISSANTS,RAISIN CUSTARD ROLLS,COCONUT CREAM POCKETS,PAIN AU CHOCOLAT,RASPBERRY LEAVES,WHEAT CROISSANTS,RASPBERRY LEAVES,BING CHERRY BURST,GUAVA BURST,CINNAMON ROLLS,LEMON CREAM CHEESE PILLOWS,ALMOND BEAR CLAWS,BUTTER PUFF PASTRY FULL SHEETS,APPLE RAISIN WALNUT STRUDELS,TRADITIONAL CROISSANT CURVED경기도 성남시 분당구 판교공원로3길 16경기도 성남시 분당구 판교동 614-6번지 2층1347737.391552127.089563
7성남시5320170303주식회사가야김치중국QINGDAO GUXIANG AGRICULTURAL PRODUCT CO.,LTD1KAYA KIM CHI경기도 성남시 수정구 복정로32번길 4경기도 성남시 수정구 복정동 696-12번지 1층(일부)1311937.455766127.127724
8수원시5220191210주식회사메이저코리아중국QINGDAO GUXIANG AGRICULTURAL PRODUCT CO.,LTD7NONGCHON KIMCHI,BUZA KIMCHI,DABOK POGI KIMCHI,DABOK MINI MAT KIMCHI,NONGCHON KKAKDUGI KIMCHI,NONGCHON WHITE KIMCHI,NONGCHON CHONGGAK KIMCHI경기도 수원시 권선구 금곡로102번길 49-24경기도 수원시 권선구 금곡동 1089번지 건우프라자 금곡동1638837.272796126.942213
9안양시4520181214(주)씨알중국ZHUCHENG FUWEI FOOD CO., LTD.1FROZEN ROASTED ONION 50경기도 안양시 동안구 시민대로 167경기도 안양시 동안구 비산동 1107-1번지 안양벤처텔 비산동1404837.390813126.949632
시군명우수수입업소등록번호허가일자업소명수출국가수입제품제조회사명품목수품목명도로명주소지번주소우편번호WGS84위도WGS84경도
22이천시1020160218씨제이프레시웨이주식회사필리핀POLARIS PINEAPPLE CANNERY19이츠웰 스위트콘(SWEET CORN),FROZEN BLUEBERRY,인델리 마크니풍 커리,이츠웰 자른당면,이츠웰 찰당면,이츠웰 중화당면,이츠웰 납작당면,이츠웰 양송이 슬라이스,이츠웰 양송이홀,이츠웰 후르츠 칵테일,IT'S WELL 파인애플 슬라이스,이츠웰 파인애플 청크,이츠웰 프리미엄 파인애플 청크,이츠웰 프리미엄 파인애플 슬라이스,이츠웰 표고버섯 슬라이스,이츠웰 표고버섯 홀,인델리 데미 커리,인델리 빈달루 커리,인델리 파니르 커리경기도 이천시 마장면 덕평로 811경기도 이천시 마장면 덕평리 508번지1738937.231262127.36515
23이천시1020160218씨제이프레시웨이주식회사중국ZHANGZHOU GANGCHANG CANNED FOODS CO., LTD. FUJIAN19이츠웰 스위트콘(SWEET CORN),FROZEN BLUEBERRY,인델리 마크니풍 커리,이츠웰 자른당면,이츠웰 찰당면,이츠웰 중화당면,이츠웰 납작당면,이츠웰 양송이 슬라이스,이츠웰 양송이홀,이츠웰 후르츠 칵테일,IT'S WELL 파인애플 슬라이스,이츠웰 파인애플 청크,이츠웰 프리미엄 파인애플 청크,이츠웰 프리미엄 파인애플 슬라이스,이츠웰 표고버섯 슬라이스,이츠웰 표고버섯 홀,인델리 데미 커리,인델리 빈달루 커리,인델리 파니르 커리경기도 이천시 마장면 덕평로 811경기도 이천시 마장면 덕평리 508번지1738937.231262127.36515
24평택시5020160830(주)동방실업중국QINGDAO DEESHENG FOOD CO.,LTD8HATSAL KIMCHI,YOUJIN KIMCHI,OSAEK KIMCHI,MI DO KIMCHI,HAEARAM KIMCHI,CABBAGE KIMCHI,HANKYUL KIMCHI,DONGBANG KIMCHIDA경기도 평택시 포승읍 직산동길 66경기도 평택시 포승읍 신영리 428번지1796636.948447126.876526
25평택시3920181217제이엔씨무역(주)중국LINYI AJIN FOODS CO LTD7GO HYANG JIN KIMCHI,SINJEONG KKAKDUGI KIMCHI,SINJEONG BACK KIMCHI,CHONGGAK KIMCHI,KIMGANE MYOUNGIN KIMCHI,KIMCHI GAJOK,SANYA KIMCHI경기도 평택시 포승읍 포승공단순환로 49경기도 평택시 포승읍 만호리 641번지1795836.966615126.847013
26포천시3820141014(주)산과들에중국CHENGDE SHENLI FOOD CO., LTD.3YOONSEO'S SWEET CHESTNUT KERNEL,YOONHOO'S SWEET CHESTNUT KERNEL,YOONHOO'S UNIQUE CHESTNUT경기도 포천시 군내면 용정경제로1길 48-48경기도 포천시 군내면 용정리 485-1번지1115437.877381127.199182
27하남시4920180801(주)골든피트베트남HUNG HAU AGRICULTURAL CORP.,-FACTORY NO.110NEW PTO SHRIMP ROLL,MENBOSHA,SHRIMP POTATO ROLL,BREADED SHRIMP,NET PTO SHRIMP ROLL,SHRIMP POTATO ROLL,SCALLOP GRADANG,NET SCALLOP ROLL,SWIMMING CRAB GRADANG,BREADED SHRIMP BURGER PATTY경기도 하남시 초일로 175경기도 하남시 초일동 218번지 1층1298537.533467127.184428
28하남시6220190911대상(주) 유통비유(유통BU)베트남NHA TRANG SEAPRODUCT COMPANY(NHA TRANG SEAFOODS)3베스트코 왕새우튀김,베스트코 왕새우튀김 (20G),베스트코 왕새우튀김 (45G)경기도 하남시 미사강변남로 99경기도 하남시 망월동 1121번지 더 오페라2 2층1291337.560935127.19277
29화성시2720131127삼도식품(주)중국QINGDAO DONGXUAN FOODS CO.,LTD.7RED PEPPER SEASONING,RED PEPPER SEASONING A,RED PEPPER SEASONING POWDER,RED PEPPER SEASONING FOR KIMCHI-NP,RED PEPPER SEASONING FOR KIMCHI-JW,MIXED SEASONINGS,RED PEPPER SEASONING FOR KIMCHI-SP경기도 화성시 양감면 은행나무로 238경기도 화성시 양감면 신왕리 698-6번지1863337.079832126.944002
30화성시4320150521한국에스비식품(주)중국ANQIU HUATAO FOOD CO.,LTD10쫄깃한 당면,요리가 맛있는 당면,REAL PRICE 납작당면,REAL PRICE 자른당면,365 ILPOOM DANGMYUN,쫄깃한 당면,쫄깃쫄깃 고향당면,ILPOOM DANGMYUN,요리가 맛있는 당면,홍수계 넓은당면경기도 화성시 황계남길 118경기도 화성시 송산동 125-37번지1835837.215094127.017341
31화성시4320150521한국에스비식품(주)중국SISHUI LIFENG FOOD PRODUCTS CO LTD10쫄깃한 당면,요리가 맛있는 당면,REAL PRICE 납작당면,REAL PRICE 자른당면,365 ILPOOM DANGMYUN,쫄깃한 당면,쫄깃쫄깃 고향당면,ILPOOM DANGMYUN,요리가 맛있는 당면,홍수계 넓은당면경기도 화성시 황계남길 118경기도 화성시 송산동 125-37번지1835837.215094127.017341