Overview

Dataset statistics

Number of variables14
Number of observations419
Missing cells1003
Missing cells (%)17.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory48.0 KiB
Average record size in memory117.3 B

Variable types

Text4
DateTime2
Categorical3
Unsupported2
Numeric3

Dataset

Description휴게음식점(전통찻집) 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=4R8T0L7982Z1R3OTO1VI13436505&infSeq=1

Alerts

위생업태명 has constant value ""Constant
영업상태명 is highly overall correlated with 위생업종명High correlation
위생업종명 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with WGS84위도 and 1 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 위생업종명High correlation
폐업일자 has 125 (29.8%) missing valuesMissing
다중이용업소여부 has 419 (100.0%) missing valuesMissing
총시설규모(㎡) has 419 (100.0%) missing valuesMissing
소재지도로명주소 has 16 (3.8%) missing valuesMissing
소재지우편번호 has 8 (1.9%) missing valuesMissing
WGS84위도 has 8 (1.9%) missing valuesMissing
WGS84경도 has 8 (1.9%) missing valuesMissing
다중이용업소여부 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총시설규모(㎡) is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-10 21:12:01.022292
Analysis finished2024-05-10 21:12:06.774324
Duration5.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Text

Distinct55
Distinct (%)13.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-05-10T21:12:06.985423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.2649165
Min length3

Characters and Unicode

Total characters1368
Distinct characters39
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

Unique7 ?
Unique (%)1.7%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row고양시
5th row고양시
ValueCountFrequency (%)
성남시 53
 
12.6%
용인시 38
 
9.1%
고양시 35
 
8.4%
수원시 23
 
5.5%
파주시 21
 
5.0%
안산시 21
 
5.0%
화성시 19
 
4.5%
양평군 16
 
3.8%
부천시 16
 
3.8%
이천시 13
 
3.1%
Other values (21) 164
39.1%
2024-05-10T21:12:08.015412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 404
29.5%
양 87
 
6.4%
성 84
 
6.1%
84
 
6.1%
남 67
 
4.9%
주 66
 
4.8%
천 52
 
3.8%
안 46
 
3.4%
인 38
 
2.8%
용 38
 
2.8%
Other values (29) 402
29.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1284
93.9%
Space Separator 84
 
6.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 404
31.5%
양 87
 
6.8%
성 84
 
6.5%
남 67
 
5.2%
주 66
 
5.1%
천 52
 
4.0%
안 46
 
3.6%
인 38
 
3.0%
용 38
 
3.0%
고 35
 
2.7%
Other values (28) 367
28.6%
Space Separator
ValueCountFrequency (%)
84
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1284
93.9%
Common 84
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 404
31.5%
양 87
 
6.8%
성 84
 
6.5%
남 67
 
5.2%
주 66
 
5.1%
천 52
 
4.0%
안 46
 
3.6%
인 38
 
3.0%
용 38
 
3.0%
고 35
 
2.7%
Other values (28) 367
28.6%
Common
ValueCountFrequency (%)
84
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1284
93.9%
ASCII 84
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 404
31.5%
양 87
 
6.8%
성 84
 
6.5%
남 67
 
5.2%
주 66
 
5.1%
천 52
 
4.0%
안 46
 
3.6%
인 38
 
3.0%
용 38
 
3.0%
고 35
 
2.7%
Other values (28) 367
28.6%
ASCII
ValueCountFrequency (%)
84
100.0%

사업장명
Text

Distinct390
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-05-10T21:12:08.602131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length16
Mean length5.2840095
Min length1

Characters and Unicode

Total characters2214
Distinct characters410
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique362 ?
Unique (%)86.4%

Sample

1st row해후
2nd row차한잔의여유
3rd row다래헌청수
4th row꼽슬이네
5th row주식회사 명과정
ValueCountFrequency (%)
주식회사 6
 
1.2%
찻집 4
 
0.8%
카페 4
 
0.8%
전통찻집 4
 
0.8%
미다원 3
 
0.6%
다시스 3
 
0.6%
다향 3
 
0.6%
지유명차 3
 
0.6%
다락방 2
 
0.4%
전통차전문 2
 
0.4%
Other values (427) 455
93.0%
2024-05-10T21:12:09.832881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
70
 
3.2%
다 70
 
3.2%
집 56
 
2.5%
차 54
 
2.4%
찻 52
 
2.3%
전 43
 
1.9%
원 42
 
1.9%
통 35
 
1.6%
이 32
 
1.4%
리 32
 
1.4%
Other values (400) 1728
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2045
92.4%
Space Separator 70
 
3.2%
Lowercase Letter 33
 
1.5%
Close Punctuation 22
 
1.0%
Open Punctuation 22
 
1.0%
Decimal Number 12
 
0.5%
Uppercase Letter 8
 
0.4%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
다 70
 
3.4%
집 56
 
2.7%
차 54
 
2.6%
찻 52
 
2.5%
전 43
 
2.1%
원 42
 
2.1%
통 35
 
1.7%
이 32
 
1.6%
리 32
 
1.6%
방 32
 
1.6%
Other values (368) 1597
78.1%
Lowercase Letter
ValueCountFrequency (%)
e 5
15.2%
a 5
15.2%
c 5
15.2%
n 3
9.1%
s 2
 
6.1%
g 2
 
6.1%
t 2
 
6.1%
i 2
 
6.1%
f 2
 
6.1%
b 1
 
3.0%
Other values (4) 4
12.1%
Uppercase Letter
ValueCountFrequency (%)
L 2
25.0%
M 1
12.5%
H 1
12.5%
E 1
12.5%
O 1
12.5%
A 1
12.5%
K 1
12.5%
Decimal Number
ValueCountFrequency (%)
1 4
33.3%
2 3
25.0%
0 2
16.7%
5 1
 
8.3%
6 1
 
8.3%
4 1
 
8.3%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
70
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2038
92.1%
Common 128
 
5.8%
Latin 41
 
1.9%
Han 7
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
다 70
 
3.4%
집 56
 
2.7%
차 54
 
2.6%
찻 52
 
2.6%
전 43
 
2.1%
원 42
 
2.1%
통 35
 
1.7%
이 32
 
1.6%
리 32
 
1.6%
방 32
 
1.6%
Other values (363) 1590
78.0%
Latin
ValueCountFrequency (%)
e 5
12.2%
a 5
12.2%
c 5
12.2%
n 3
 
7.3%
s 2
 
4.9%
L 2
 
4.9%
g 2
 
4.9%
t 2
 
4.9%
i 2
 
4.9%
f 2
 
4.9%
Other values (11) 11
26.8%
Common
ValueCountFrequency (%)
70
54.7%
) 22
 
17.2%
( 22
 
17.2%
1 4
 
3.1%
2 3
 
2.3%
0 2
 
1.6%
5 1
 
0.8%
6 1
 
0.8%
4 1
 
0.8%
, 1
 
0.8%
Han
ValueCountFrequency (%)
茶 3
42.9%
休 1
 
14.3%
秀 1
 
14.3%
與 1
 
14.3%
人 1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2038
92.1%
ASCII 169
 
7.6%
CJK 7
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
70
41.4%
) 22
 
13.0%
( 22
 
13.0%
e 5
 
3.0%
a 5
 
3.0%
c 5
 
3.0%
1 4
 
2.4%
n 3
 
1.8%
2 3
 
1.8%
0 2
 
1.2%
Other values (22) 28
 
16.6%
Hangul
ValueCountFrequency (%)
다 70
 
3.4%
집 56
 
2.7%
차 54
 
2.6%
찻 52
 
2.6%
전 43
 
2.1%
원 42
 
2.1%
통 35
 
1.7%
이 32
 
1.6%
리 32
 
1.6%
방 32
 
1.6%
Other values (363) 1590
78.0%
CJK
ValueCountFrequency (%)
茶 3
42.9%
休 1
 
14.3%
秀 1
 
14.3%
與 1
 
14.3%
人 1
 
14.3%

인허가일자
Date

Distinct381
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
Minimum1983-02-17 00:00:00
Maximum2024-03-12 00:00:00
2024-05-10T21:12:10.337068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:10.833798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

영업상태명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
폐업 등
181 
폐업
113 
운영중
64 
영업
61 

Length

Max length4
Median length3
Mean length3.0167064
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row운영중
2nd row폐업 등
3rd row폐업
4th row영업
5th row영업

Common Values

ValueCountFrequency (%)
폐업 등 181
43.2%
폐업 113
27.0%
운영중 64
 
15.3%
영업 61
 
14.6%

Length

2024-05-10T21:12:11.381700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:12:11.791088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 294
49.0%
등 181
30.2%
운영중 64
 
10.7%
영업 61
 
10.2%

폐업일자
Date

MISSING 

Distinct278
Distinct (%)94.6%
Missing125
Missing (%)29.8%
Memory size3.4 KiB
Minimum1995-12-30 00:00:00
Maximum2024-04-29 00:00:00
2024-05-10T21:12:12.176035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:12.605660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

다중이용업소여부
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing419
Missing (%)100.0%
Memory size3.8 KiB

총시설규모(㎡)
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing419
Missing (%)100.0%
Memory size3.8 KiB

위생업종명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
휴게음식점
245 
<NA>
174 

Length

Max length5
Median length5
Mean length4.5847255
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row휴게음식점
2nd row휴게음식점
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
휴게음식점 245
58.5%
<NA> 174
41.5%

Length

2024-05-10T21:12:13.020362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:12:13.377570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
휴게음식점 245
58.5%
na 174
41.5%

위생업태명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
전통찻집
419 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전통찻집 419
100.0%

Length

2024-05-10T21:12:13.757829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-10T21:12:14.000999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전통찻집 419
100.0%

소재지도로명주소
Text

MISSING 

Distinct389
Distinct (%)96.5%
Missing16
Missing (%)3.8%
Memory size3.4 KiB
2024-05-10T21:12:14.477654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length68
Median length47
Mean length28.915633
Min length14

Characters and Unicode

Total characters11653
Distinct characters342
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

Unique376 ?
Unique (%)93.3%

Sample

1st row경기도 가평군 청평면 안대성길 26-1
2nd row경기도 가평군 조종면 대보간선로 547-6
3rd row경기도 가평군 청평면 경춘로 807-11
4th row경기도 고양시 일산서구 송포로 35, 대륙종합상가 2층 208호 (대화동)
5th row경기도 고양시 덕양구 으뜸로 130, 위프라임트윈타워 217호 (덕은동)
ValueCountFrequency (%)
경기도 403
 
16.0%
1층 99
 
3.9%
성남시 52
 
2.1%
분당구 41
 
1.6%
용인시 36
 
1.4%
고양시 34
 
1.4%
2층 26
 
1.0%
안산시 21
 
0.8%
수원시 21
 
0.8%
파주시 20
 
0.8%
Other values (1023) 1758
70.0%
2024-05-10T21:12:15.484715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2113
 
18.1%
1 545
 
4.7%
기 426
 
3.7%
도 422
 
3.6%
경 416
 
3.6%
시 399
 
3.4%
로 362
 
3.1%
동 307
 
2.6%
2 274
 
2.4%
, 272
 
2.3%
Other values (332) 6117
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6684
57.4%
Space Separator 2113
 
18.1%
Decimal Number 1958
 
16.8%
Other Punctuation 274
 
2.4%
Close Punctuation 234
 
2.0%
Open Punctuation 234
 
2.0%
Dash Punctuation 121
 
1.0%
Uppercase Letter 35
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 426
 
6.4%
도 422
 
6.3%
경 416
 
6.2%
시 399
 
6.0%
로 362
 
5.4%
동 307
 
4.6%
구 205
 
3.1%
길 183
 
2.7%
층 175
 
2.6%
번 130
 
1.9%
Other values (301) 3659
54.7%
Uppercase Letter
ValueCountFrequency (%)
B 12
34.3%
A 7
20.0%
K 2
 
5.7%
I 2
 
5.7%
E 2
 
5.7%
C 1
 
2.9%
P 1
 
2.9%
S 1
 
2.9%
O 1
 
2.9%
D 1
 
2.9%
Other values (5) 5
14.3%
Decimal Number
ValueCountFrequency (%)
1 545
27.8%
2 274
14.0%
3 196
 
10.0%
0 172
 
8.8%
4 155
 
7.9%
5 153
 
7.8%
7 136
 
6.9%
6 124
 
6.3%
9 109
 
5.6%
8 94
 
4.8%
Other Punctuation
ValueCountFrequency (%)
, 272
99.3%
. 2
 
0.7%
Space Separator
ValueCountFrequency (%)
2113
100.0%
Close Punctuation
ValueCountFrequency (%)
) 234
100.0%
Open Punctuation
ValueCountFrequency (%)
( 234
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 121
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6684
57.4%
Common 4934
42.3%
Latin 35
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 426
 
6.4%
도 422
 
6.3%
경 416
 
6.2%
시 399
 
6.0%
로 362
 
5.4%
동 307
 
4.6%
구 205
 
3.1%
길 183
 
2.7%
층 175
 
2.6%
번 130
 
1.9%
Other values (301) 3659
54.7%
Common
ValueCountFrequency (%)
2113
42.8%
1 545
 
11.0%
2 274
 
5.6%
, 272
 
5.5%
) 234
 
4.7%
( 234
 
4.7%
3 196
 
4.0%
0 172
 
3.5%
4 155
 
3.1%
5 153
 
3.1%
Other values (6) 586
 
11.9%
Latin
ValueCountFrequency (%)
B 12
34.3%
A 7
20.0%
K 2
 
5.7%
I 2
 
5.7%
E 2
 
5.7%
C 1
 
2.9%
P 1
 
2.9%
S 1
 
2.9%
O 1
 
2.9%
D 1
 
2.9%
Other values (5) 5
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6684
57.4%
ASCII 4969
42.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2113
42.5%
1 545
 
11.0%
2 274
 
5.5%
, 272
 
5.5%
) 234
 
4.7%
( 234
 
4.7%
3 196
 
3.9%
0 172
 
3.5%
4 155
 
3.1%
5 153
 
3.1%
Other values (21) 621
 
12.5%
Hangul
ValueCountFrequency (%)
기 426
 
6.4%
도 422
 
6.3%
경 416
 
6.2%
시 399
 
6.0%
로 362
 
5.4%
동 307
 
4.6%
구 205
 
3.1%
길 183
 
2.7%
층 175
 
2.6%
번 130
 
1.9%
Other values (301) 3659
54.7%

소재지지번주소
Text

Distinct417
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size3.4 KiB
2024-05-10T21:12:16.084996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length43
Mean length27.348449
Min length15

Characters and Unicode

Total characters11459
Distinct characters327
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

Unique415 ?
Unique (%)99.0%

Sample

1st row경기도 가평군 청평면 대성리 356-10번지
2nd row경기도 가평군 조종면 대보리 97-11번지
3rd row경기도 가평군 청평면 청평리 445-3번지
4th row경기도 고양시 일산서구 대화동 1464-7 대륙종합상가 208호
5th row경기도 고양시 덕양구 덕은동 0 위프라임트윈타워 217호
ValueCountFrequency (%)
경기도 419
 
16.9%
1층 81
 
3.3%
성남시 53
 
2.1%
분당구 41
 
1.7%
용인시 38
 
1.5%
고양시 35
 
1.4%
2층 30
 
1.2%
수원시 23
 
0.9%
파주시 21
 
0.8%
안산시 21
 
0.8%
Other values (1009) 1718
69.3%
2024-05-10T21:12:17.192712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2138
 
18.7%
1 624
 
5.4%
기 438
 
3.8%
도 429
 
3.7%
경 421
 
3.7%
시 412
 
3.6%
지 410
 
3.6%
동 401
 
3.5%
- 332
 
2.9%
번 324
 
2.8%
Other values (317) 5530
48.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6599
57.6%
Decimal Number 2256
 
19.7%
Space Separator 2138
 
18.7%
Dash Punctuation 332
 
2.9%
Uppercase Letter 41
 
0.4%
Open Punctuation 31
 
0.3%
Close Punctuation 31
 
0.3%
Other Punctuation 31
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 438
 
6.6%
도 429
 
6.5%
경 421
 
6.4%
시 412
 
6.2%
지 410
 
6.2%
동 401
 
6.1%
번 324
 
4.9%
구 209
 
3.2%
층 170
 
2.6%
호 129
 
2.0%
Other values (281) 3256
49.3%
Uppercase Letter
ValueCountFrequency (%)
B 12
29.3%
A 4
 
9.8%
K 3
 
7.3%
E 3
 
7.3%
I 2
 
4.9%
O 2
 
4.9%
C 2
 
4.9%
P 2
 
4.9%
Y 1
 
2.4%
T 1
 
2.4%
Other values (9) 9
22.0%
Decimal Number
ValueCountFrequency (%)
1 624
27.7%
2 312
13.8%
0 214
 
9.5%
3 200
 
8.9%
4 189
 
8.4%
5 179
 
7.9%
6 144
 
6.4%
7 142
 
6.3%
8 137
 
6.1%
9 115
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 28
90.3%
. 2
 
6.5%
@ 1
 
3.2%
Space Separator
ValueCountFrequency (%)
2138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 332
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6599
57.6%
Common 4819
42.1%
Latin 41
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 438
 
6.6%
도 429
 
6.5%
경 421
 
6.4%
시 412
 
6.2%
지 410
 
6.2%
동 401
 
6.1%
번 324
 
4.9%
구 209
 
3.2%
층 170
 
2.6%
호 129
 
2.0%
Other values (281) 3256
49.3%
Latin
ValueCountFrequency (%)
B 12
29.3%
A 4
 
9.8%
K 3
 
7.3%
E 3
 
7.3%
I 2
 
4.9%
O 2
 
4.9%
C 2
 
4.9%
P 2
 
4.9%
Y 1
 
2.4%
T 1
 
2.4%
Other values (9) 9
22.0%
Common
ValueCountFrequency (%)
2138
44.4%
1 624
 
12.9%
- 332
 
6.9%
2 312
 
6.5%
0 214
 
4.4%
3 200
 
4.2%
4 189
 
3.9%
5 179
 
3.7%
6 144
 
3.0%
7 142
 
2.9%
Other values (7) 345
 
7.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6599
57.6%
ASCII 4860
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2138
44.0%
1 624
 
12.8%
- 332
 
6.8%
2 312
 
6.4%
0 214
 
4.4%
3 200
 
4.1%
4 189
 
3.9%
5 179
 
3.7%
6 144
 
3.0%
7 142
 
2.9%
Other values (26) 386
 
7.9%
Hangul
ValueCountFrequency (%)
기 438
 
6.6%
도 429
 
6.5%
경 421
 
6.4%
시 412
 
6.2%
지 410
 
6.2%
동 401
 
6.1%
번 324
 
4.9%
구 209
 
3.2%
층 170
 
2.6%
호 129
 
2.0%
Other values (281) 3256
49.3%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct328
Distinct (%)79.8%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean14077.557
Minimum10013
Maximum18602
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-05-10T21:12:17.535682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10013
5-th percentile10358
Q111931
median13616
Q316463
95-th percentile18125.5
Maximum18602
Range8589
Interquartile range (IQR)4532

Descriptive statistics

Standard deviation2536.5951
Coefficient of variation (CV)0.18018716
Kurtosis-1.2098901
Mean14077.557
Median Absolute Deviation (MAD)2249
Skewness0.12422715
Sum5785876
Variance6434314.6
MonotonicityNot monotonic
2024-05-10T21:12:17.881981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12636 6
 
1.4%
15865 5
 
1.2%
10859 4
 
1.0%
17384 4
 
1.0%
13837 4
 
1.0%
17075 3
 
0.7%
10858 3
 
0.7%
14542 3
 
0.7%
14709 3
 
0.7%
13627 3
 
0.7%
Other values (318) 373
89.0%
(Missing) 8
 
1.9%
ValueCountFrequency (%)
10013 1
0.2%
10019 1
0.2%
10035 1
0.2%
10059 1
0.2%
10079 1
0.2%
10097 1
0.2%
10109 1
0.2%
10118 1
0.2%
10129 1
0.2%
10219 2
0.5%
ValueCountFrequency (%)
18602 1
0.2%
18594 1
0.2%
18593 1
0.2%
18584 2
0.5%
18583 1
0.2%
18556 1
0.2%
18554 1
0.2%
18547 1
0.2%
18476 1
0.2%
18466 1
0.2%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct383
Distinct (%)93.2%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean37.442358
Minimum36.979729
Maximum38.069458
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-05-10T21:12:18.306707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.979729
5-th percentile37.066956
Q137.298477
median37.395803
Q337.622683
95-th percentile37.81129
Maximum38.069458
Range1.089729
Interquartile range (IQR)0.32420622

Descriptive statistics

Standard deviation0.22153209
Coefficient of variation (CV)0.0059166169
Kurtosis-0.37861175
Mean37.442358
Median Absolute Deviation (MAD)0.12086803
Skewness0.2715878
Sum15388.809
Variance0.049076466
MonotonicityNot monotonic
2024-05-10T21:12:18.791876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4834455807 3
 
0.7%
37.2565731109 3
 
0.7%
37.4012708935 3
 
0.7%
37.297420524 3
 
0.7%
37.5061562339 3
 
0.7%
37.4012858766 2
 
0.5%
37.2629162665 2
 
0.5%
37.5159323285 2
 
0.5%
37.3001409008 2
 
0.5%
37.6696305734 2
 
0.5%
Other values (373) 386
92.1%
(Missing) 8
 
1.9%
ValueCountFrequency (%)
36.9797294668 1
0.2%
36.9894344679 1
0.2%
36.9932323084 1
0.2%
36.9936725113 1
0.2%
36.9980571769 1
0.2%
36.9997542744 1
0.2%
37.0009340506 1
0.2%
37.0014750949 1
0.2%
37.004821284 1
0.2%
37.005028783 1
0.2%
ValueCountFrequency (%)
38.0694584401 1
0.2%
38.0378803276 1
0.2%
37.9576545896 1
0.2%
37.9247423362 1
0.2%
37.9245451432 1
0.2%
37.9146909259 1
0.2%
37.9081162039 1
0.2%
37.9061311346 1
0.2%
37.9010906827 1
0.2%
37.90035489 1
0.2%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct383
Distinct (%)93.2%
Missing8
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean127.04438
Minimum126.59025
Maximum127.65995
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.8 KiB
2024-05-10T21:12:19.197078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.59025
5-th percentile126.72998
Q1126.86046
median127.05764
Q3127.14109
95-th percentile127.46861
Maximum127.65995
Range1.0697029
Interquartile range (IQR)0.28062824

Descriptive statistics

Standard deviation0.22389481
Coefficient of variation (CV)0.0017623354
Kurtosis0.24774539
Mean127.04438
Median Absolute Deviation (MAD)0.12526422
Skewness0.56547673
Sum52215.242
Variance0.050128888
MonotonicityNot monotonic
2024-05-10T21:12:19.778230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7801961513 3
 
0.7%
127.1175081433 3
 
0.7%
127.1086502502 3
 
0.7%
127.659948866 3
 
0.7%
126.7563767746 3
 
0.7%
127.1104579421 2
 
0.5%
126.9420720782 2
 
0.5%
126.7572529215 2
 
0.5%
127.6545498438 2
 
0.5%
126.7323768636 2
 
0.5%
Other values (373) 386
92.1%
(Missing) 8
 
1.9%
ValueCountFrequency (%)
126.5902459861 1
0.2%
126.6092789289 1
0.2%
126.6240981846 1
0.2%
126.6447147584 1
0.2%
126.6560145899 1
0.2%
126.6708932658 1
0.2%
126.6882320737 1
0.2%
126.689309094 1
0.2%
126.6904344189 1
0.2%
126.6951497381 1
0.2%
ValueCountFrequency (%)
127.659948866 3
0.7%
127.6572227621 1
 
0.2%
127.6545498438 2
0.5%
127.6445977835 1
 
0.2%
127.635805545 1
 
0.2%
127.6350809464 1
 
0.2%
127.6336796798 1
 
0.2%
127.6256824846 1
 
0.2%
127.593654565 1
 
0.2%
127.5789398544 1
 
0.2%

Interactions

2024-05-10T21:12:04.538065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:02.694160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:03.530518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:04.787948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:02.933947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:03.790604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:05.046816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:03.218474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-10T21:12:04.243736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-10T21:12:20.083366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태명소재지우편번호WGS84위도WGS84경도
시군명1.0000.6970.9940.9680.960
영업상태명0.6971.0000.2890.1610.193
소재지우편번호0.9940.2891.0000.9170.862
WGS84위도0.9680.1610.9171.0000.609
WGS84경도0.9600.1930.8620.6091.000
2024-05-10T21:12:20.540937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업상태명위생업종명
영업상태명1.0001.000
위생업종명1.0001.000
2024-05-10T21:12:20.798860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지우편번호WGS84위도WGS84경도영업상태명위생업종명
소재지우편번호1.000-0.9010.1860.1761.000
WGS84위도-0.9011.000-0.2640.0961.000
WGS84경도0.186-0.2641.0000.1151.000
영업상태명0.1760.0960.1151.0001.000
위생업종명1.0001.0001.0001.0001.000

Missing values

2024-05-10T21:12:05.414101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-10T21:12:06.122057image/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.
2024-05-10T21:12:06.544978image/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

시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
0가평군해후20160111운영중<NA><NA><NA>휴게음식점전통찻집경기도 가평군 청평면 안대성길 26-1경기도 가평군 청평면 대성리 356-10번지1245737.696791127.380249
1가평군차한잔의여유20150427폐업 등20170321<NA><NA>휴게음식점전통찻집경기도 가평군 조종면 대보간선로 547-6경기도 가평군 조종면 대보리 97-11번지1243937.797528127.384664
2가평군다래헌청수20080929폐업20090526<NA><NA><NA>전통찻집경기도 가평군 청평면 경춘로 807-11경기도 가평군 청평면 청평리 445-3번지1245137.737888127.417023
3고양시꼽슬이네2023-05-26영업<NA><NA><NA><NA>전통찻집경기도 고양시 일산서구 송포로 35, 대륙종합상가 2층 208호 (대화동)경기도 고양시 일산서구 대화동 1464-7 대륙종합상가 208호1021937.669631126.732377
4고양시주식회사 명과정2023-08-04영업<NA><NA><NA><NA>전통찻집경기도 고양시 덕양구 으뜸로 130, 위프라임트윈타워 217호 (덕은동)경기도 고양시 덕양구 덕은동 0 위프라임트윈타워 217호1054437.579442126.870657
5고양시향기로운 힐링하우스20220127영업<NA><NA><NA><NA>전통찻집경기도 고양시 덕양구 꽃마을로 68, 로데오프라자 5층 506호 (향동동)경기도 고양시 덕양구 향동동 522 로데오프라자1054637.600961126.893767
6고양시지장수찻집2023-10-27영업<NA><NA><NA><NA>전통찻집경기도 고양시 일산서구 덕이로 30-18, 1층 101호 (덕이동)경기도 고양시 일산서구 덕이동 449 1층 101호1023737.690468126.756151
7고양시뜰안에차20141112운영중<NA><NA><NA>휴게음식점전통찻집경기도 고양시 일산동구 일산로428번길 14-14, 1층 전부호 (정발산동)경기도 고양시 일산동구 정발산동 1219-3번지 1층 전부호1035837.672654126.780232
8고양시생생한방카페20161205운영중<NA><NA><NA>휴게음식점전통찻집경기도 고양시 덕양구 원당로59번길 49-18, 2(일부)층 (주교동)경기도 고양시 덕양구 주교동 595-8번지 2(일부)1046037.658796126.831395
9고양시쿤밍20180409운영중<NA><NA><NA>휴게음식점전통찻집경기도 고양시 일산서구 일현로 97-11, B137(일부)호 (탄현동, 일산 위브더제니스)경기도 고양시 일산서구 탄현동 1640번지 일산 위브더제니스, B137(일부)1024237.695229126.763578
시군명사업장명인허가일자영업상태명폐업일자다중이용업소여부총시설규모(㎡)위생업종명위생업태명소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도
409화성시봉숭아 학당20060110폐업 등20060406<NA><NA>휴게음식점전통찻집경기도 화성시 봉담읍 세자로406번길 13경기도 화성시 봉담읍 수기리 7-42번지1832637.202167126.985003
410화성시다울터20111229폐업 등20160526<NA><NA>휴게음식점전통찻집경기도 화성시 장안면 신촌길 32-4경기도 화성시 장안면 독정리 530번지1858337.073558126.851609
411화성시미소다방20110909폐업 등20180105<NA><NA>휴게음식점전통찻집경기도 화성시 향남읍 평1길 11, 1층 (평리상가)경기도 화성시 향남읍 평리 85-10번지 평리상가1859337.132433126.909391
412화성시임자영의무심천20120802폐업 등20130718<NA><NA>휴게음식점전통찻집경기도 화성시 송산면 공룡로 122, 1층경기도 화성시 송산면 쌍정리 1255-1번지 1층1854737.227071126.727586
413화성시해원20140217폐업 등20150414<NA><NA>휴게음식점전통찻집경기도 화성시 병점중앙로 87 (병점동, 외 15필지 태안병점 V-TOWN II 107호 전부)경기도 화성시 병점동 292번지 외 15필지 태안병점 V-TOWN II 107호 전부1841437.206286127.041896
414화성시종로강남한의원식품사업부전통한방다전동탄점20151202폐업 등20160705<NA><NA>휴게음식점전통찻집경기도 화성시 동탄중심상가2길 7, 103호 (반송동)경기도 화성시 반송동 88-11번지 103호1845337.205897127.072946
415화성시붉은못허브팜 동탄점20160615폐업 등20170915<NA><NA>휴게음식점전통찻집경기도 화성시 동탄순환대로 706, 109호 (영천동, 서건에듀프라자2)경기도 화성시 영천동 산 27-56번지 109호1846637.207812127.110598
416화성시차마시는 뜰 화인규방20130122영업<NA><NA><NA><NA>전통찻집경기도 화성시 세자로475번길 5 (안녕동, (1층))경기도 화성시 안녕동 180-118 (1층)1832437.206264126.986068
417화성시하내찻집20060727폐업20181004<NA><NA><NA>전통찻집경기도 화성시 서신면 당성로 102-20경기도 화성시 서신면 전곡리 508-111855437.183655126.690434
418화성시초당전통찻집20020109폐업20050711<NA><NA><NA>전통찻집<NA>경기도 화성시 안녕동 444 -3,5<NA><NA><NA>