Overview

Dataset statistics

Number of variables13
Number of observations2571
Missing cells5985
Missing cells (%)17.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory273.8 KiB
Average record size in memory109.1 B

Variable types

Text4
Numeric2
Categorical5
Boolean1
Unsupported1

Dataset

Description업체(시설)명,인허가번호,업종코드,업종명,지도점검일자,점검기관,점검기관명,지도점검구분,처분대상여부,점검사항,점검결과,소재지도로명주소,소재지주소
Author중구
URLhttps://data.seoul.go.kr/dataList/OA-10201/S/1/datasetView.do

Alerts

점검기관 has constant value ""Constant
점검기관명 has constant value ""Constant
업종명 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
업종코드 is highly overall correlated with 인허가번호 and 1 other fieldsHigh correlation
인허가번호 is highly overall correlated with 업종코드 and 1 other fieldsHigh correlation
업종코드 is highly imbalanced (70.4%)Imbalance
업종명 is highly imbalanced (51.5%)Imbalance
지도점검구분 is highly imbalanced (69.5%)Imbalance
처분대상여부 is highly imbalanced (95.6%)Imbalance
처분대상여부 has 1740 (67.7%) missing valuesMissing
점검사항 has 229 (8.9%) missing valuesMissing
점검결과 has 2571 (100.0%) missing valuesMissing
소재지도로명주소 has 1267 (49.3%) missing valuesMissing
소재지주소 has 178 (6.9%) missing valuesMissing
점검결과 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-05-11 05:43:59.855728
Analysis finished2024-05-11 05:44:03.122154
Duration3.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업체(시설)명
Text

Distinct1115
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
2024-05-11T14:44:03.363891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length5.9183197
Min length1

Characters and Unicode

Total characters15216
Distinct characters469
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique498 ?
Unique (%)19.4%

Sample

1st row에스에이명진지퍼
2nd row대동염색
3rd row일신염색
4th row그린염색
5th row대경
ValueCountFrequency (%)
의)제일의료재단제일병원 21
 
0.8%
동국염색 21
 
0.8%
세진염색 19
 
0.7%
현대염색 17
 
0.6%
그린염색 17
 
0.6%
보은상사 17
 
0.6%
미래섬유 15
 
0.6%
일신염색 14
 
0.5%
삼우염색 14
 
0.5%
대한염색 14
 
0.5%
Other values (1165) 2509
93.7%
2024-05-11T14:44:04.244864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 586
 
3.9%
( 523
 
3.4%
) 523
 
3.4%
사 457
 
3.0%
인 422
 
2.8%
쇄 319
 
2.1%
색 304
 
2.0%
스 285
 
1.9%
대 267
 
1.8%
문 254
 
1.7%
Other values (459) 11276
74.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13719
90.2%
Open Punctuation 529
 
3.5%
Close Punctuation 529
 
3.5%
Uppercase Letter 187
 
1.2%
Space Separator 107
 
0.7%
Lowercase Letter 50
 
0.3%
Other Punctuation 49
 
0.3%
Decimal Number 34
 
0.2%
Dash Punctuation 12
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 586
 
4.3%
사 457
 
3.3%
인 422
 
3.1%
쇄 319
 
2.3%
색 304
 
2.2%
스 285
 
2.1%
대 267
 
1.9%
문 254
 
1.9%
화 250
 
1.8%
원 243
 
1.8%
Other values (401) 10332
75.3%
Uppercase Letter
ValueCountFrequency (%)
P 40
21.4%
C 36
19.3%
T 16
 
8.6%
S 10
 
5.3%
L 10
 
5.3%
D 9
 
4.8%
I 7
 
3.7%
E 7
 
3.7%
O 6
 
3.2%
B 5
 
2.7%
Other values (12) 41
21.9%
Lowercase Letter
ValueCountFrequency (%)
w 10
20.0%
n 7
14.0%
i 6
12.0%
s 4
 
8.0%
e 3
 
6.0%
c 3
 
6.0%
o 3
 
6.0%
a 2
 
4.0%
d 2
 
4.0%
r 2
 
4.0%
Other values (7) 8
16.0%
Decimal Number
ValueCountFrequency (%)
1 8
23.5%
2 8
23.5%
7 6
17.6%
3 5
14.7%
5 3
 
8.8%
6 2
 
5.9%
4 1
 
2.9%
8 1
 
2.9%
Other Punctuation
ValueCountFrequency (%)
. 23
46.9%
& 17
34.7%
, 5
 
10.2%
＆ 2
 
4.1%
# 2
 
4.1%
Open Punctuation
ValueCountFrequency (%)
( 523
98.9%
[ 6
 
1.1%
Close Punctuation
ValueCountFrequency (%)
) 523
98.9%
] 6
 
1.1%
Space Separator
ValueCountFrequency (%)
107
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13719
90.2%
Common 1260
 
8.3%
Latin 237
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 586
 
4.3%
사 457
 
3.3%
인 422
 
3.1%
쇄 319
 
2.3%
색 304
 
2.2%
스 285
 
2.1%
대 267
 
1.9%
문 254
 
1.9%
화 250
 
1.8%
원 243
 
1.8%
Other values (401) 10332
75.3%
Latin
ValueCountFrequency (%)
P 40
16.9%
C 36
15.2%
T 16
 
6.8%
S 10
 
4.2%
w 10
 
4.2%
L 10
 
4.2%
D 9
 
3.8%
I 7
 
3.0%
E 7
 
3.0%
n 7
 
3.0%
Other values (29) 85
35.9%
Common
ValueCountFrequency (%)
( 523
41.5%
) 523
41.5%
107
 
8.5%
. 23
 
1.8%
& 17
 
1.3%
- 12
 
1.0%
1 8
 
0.6%
2 8
 
0.6%
7 6
 
0.5%
[ 6
 
0.5%
Other values (9) 27
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13719
90.2%
ASCII 1495
 
9.8%
None 2
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 586
 
4.3%
사 457
 
3.3%
인 422
 
3.1%
쇄 319
 
2.3%
색 304
 
2.2%
스 285
 
2.1%
대 267
 
1.9%
문 254
 
1.9%
화 250
 
1.8%
원 243
 
1.8%
Other values (401) 10332
75.3%
ASCII
ValueCountFrequency (%)
( 523
35.0%
) 523
35.0%
107
 
7.2%
P 40
 
2.7%
C 36
 
2.4%
. 23
 
1.5%
& 17
 
1.1%
T 16
 
1.1%
- 12
 
0.8%
S 10
 
0.7%
Other values (47) 188
 
12.6%
None
ValueCountFrequency (%)
＆ 2
100.0%

인허가번호
Real number (ℝ)

HIGH CORRELATION 

Distinct1115
Distinct (%)43.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0100002 × 1017
Minimum3.0100002 × 1017
Maximum3.0100003 × 1017
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-11T14:44:04.445255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.0100002 × 1017
5-th percentile3.0100002 × 1017
Q13.0100002 × 1017
median3.0100002 × 1017
Q33.0100002 × 1017
95-th percentile3.0100002 × 1017
Maximum3.0100003 × 1017
Range4.0009 × 109
Interquartile range (IQR)798976

Descriptive statistics

Standard deviation3.1284065 × 108
Coefficient of variation (CV)1.0393376 × 10-9
Kurtosis14.974907
Mean3.0100002 × 1017
Median Absolute Deviation (MAD)400704
Skewness-1.1878395
Sum-8.9219426 × 1017
Variance9.7869272 × 1016
MonotonicityNot monotonic
2024-05-11T14:44:04.702842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
301000022200001325 21
 
0.8%
301000022200100219 19
 
0.7%
301000022199701121 19
 
0.7%
301000022200001597 17
 
0.7%
301000022200001273 17
 
0.7%
301000022199701152 17
 
0.7%
301000022199701123 14
 
0.5%
301000022200001352 14
 
0.5%
301000022200001279 14
 
0.5%
301000022200001282 13
 
0.5%
Other values (1105) 2406
93.6%
ValueCountFrequency (%)
301000021199900001 3
 
0.1%
301000021200100009 7
0.3%
301000021200100010 7
0.3%
301000021200100011 8
0.3%
301000021200100012 7
0.3%
301000021200100013 1
 
< 0.1%
301000021200100014 2
 
0.1%
301000021200100015 7
0.3%
301000021200100016 7
0.3%
301000021200100017 1
 
< 0.1%
ValueCountFrequency (%)
301000025200800010 1
< 0.1%
301000025200600011 1
< 0.1%
301000025200600009 1
< 0.1%
301000022201600002 2
0.1%
301000022201500013 1
< 0.1%
301000022201500012 1
< 0.1%
301000022201500010 2
0.1%
301000022201500009 1
< 0.1%
301000022201500008 2
0.1%
301000022201500007 1
< 0.1%

업종코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
22
2321 
21
247 
25
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row22
2nd row22
3rd row22
4th row22
5th row22

Common Values

ValueCountFrequency (%)
22 2321
90.3%
21 247
 
9.6%
25 3
 
0.1%

Length

2024-05-11T14:44:04.881616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:05.006684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
22 2321
90.3%
21 247
 
9.6%
25 3
 
0.1%

업종명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
<NA>
1744 
폐수배출업소관리
804 
대기배출업소관리
 
20
기타수질오염원관리
 
3

Length

Max length9
Median length4
Mean length5.2878257
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1744
67.8%
폐수배출업소관리 804
31.3%
대기배출업소관리 20
 
0.8%
기타수질오염원관리 3
 
0.1%

Length

2024-05-11T14:44:05.170858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:05.314913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1744
67.8%
폐수배출업소관리 804
31.3%
대기배출업소관리 20
 
0.8%
기타수질오염원관리 3
 
0.1%

지도점검일자
Real number (ℝ)

Distinct446
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20129394
Minimum20100125
Maximum20161205
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size22.7 KiB
2024-05-11T14:44:05.530281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20100125
5-th percentile20100514
Q120111019
median20130703
Q320141112
95-th percentile20160928
Maximum20161205
Range61080
Interquartile range (IQR)30092.5

Descriptive statistics

Standard deviation20424.726
Coefficient of variation (CV)0.0010146717
Kurtosis-1.217925
Mean20129394
Median Absolute Deviation (MAD)19601
Skewness0.10075017
Sum5.1752672 × 1010
Variance4.1716944 × 108
MonotonicityDecreasing
2024-05-11T14:44:05.733419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20160928 187
 
7.3%
20120424 27
 
1.1%
20130620 21
 
0.8%
20111103 20
 
0.8%
20121121 15
 
0.6%
20101130 15
 
0.6%
20101129 14
 
0.5%
20110907 13
 
0.5%
20160324 12
 
0.5%
20100729 12
 
0.5%
Other values (436) 2235
86.9%
ValueCountFrequency (%)
20100125 1
 
< 0.1%
20100224 6
0.2%
20100226 1
 
< 0.1%
20100311 5
0.2%
20100312 5
0.2%
20100315 5
0.2%
20100316 6
0.2%
20100318 3
0.1%
20100319 5
0.2%
20100322 4
0.2%
ValueCountFrequency (%)
20161205 1
 
< 0.1%
20161108 1
 
< 0.1%
20161102 4
0.2%
20161028 3
0.1%
20161027 3
0.1%
20161021 4
0.2%
20161020 4
0.2%
20161017 3
0.1%
20161014 5
0.2%
20161012 3
0.1%

점검기관
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
3010000
2571 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3010000
2nd row3010000
3rd row3010000
4th row3010000
5th row3010000

Common Values

ValueCountFrequency (%)
3010000 2571
100.0%

Length

2024-05-11T14:44:05.920334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:06.042980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3010000 2571
100.0%

점검기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
서울특별시 중구
2571 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 중구
2nd row서울특별시 중구
3rd row서울특별시 중구
4th row서울특별시 중구
5th row서울특별시 중구

Common Values

ValueCountFrequency (%)
서울특별시 중구 2571
100.0%

Length

2024-05-11T14:44:06.208173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:06.337410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 2571
50.0%
중구 2571
50.0%

지도점검구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size20.2 KiB
정기
2267 
기타
 
151
합동
 
112
수시
 
27
<NA>
 
14

Length

Max length4
Median length2
Mean length2.0108907
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
정기 2267
88.2%
기타 151
 
5.9%
합동 112
 
4.4%
수시 27
 
1.1%
<NA> 14
 
0.5%

Length

2024-05-11T14:44:06.483490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T14:44:06.632160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정기 2267
88.2%
기타 151
 
5.9%
합동 112
 
4.4%
수시 27
 
1.1%
na 14
 
0.5%

처분대상여부
Boolean

IMBALANCE  MISSING 

Distinct2
Distinct (%)0.2%
Missing1740
Missing (%)67.7%
Memory size5.2 KiB
False
827 
True
 
4
(Missing)
1740 
ValueCountFrequency (%)
False 827
32.2%
True 4
 
0.2%
(Missing) 1740
67.7%
2024-05-11T14:44:06.777776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

점검사항
Text

MISSING 

Distinct161
Distinct (%)6.9%
Missing229
Missing (%)8.9%
Memory size20.2 KiB
2024-05-11T14:44:07.069590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length52
Median length35
Mean length16.13877
Min length1

Characters and Unicode

Total characters37797
Distinct characters113
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)2.5%

Sample

1st row배출시설 및 방지시설 적정 여부
2nd row배출시설 및 방지시설 적정운영 여부
3rd row배출시설 및 방지시설 적정 여부
4th row배출시설 및 방지시설 적정 여부
5th row배출시설 및 방지시설 적정 여부
ValueCountFrequency (%)
적정여부 1179
14.8%
위탁처리 1086
13.7%
및 878
11.0%
폐수보관및 774
 
9.7%
방지시설 516
 
6.5%
여부 383
 
4.8%
폐수보관 365
 
4.6%
폐수배출시설 266
 
3.3%
등 227
 
2.9%
폐수보관및위탁처리적정여부 190
 
2.4%
Other values (126) 2085
26.2%
2024-05-11T14:44:07.608569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5609
 
14.8%
부 2276
 
6.0%
여 2276
 
6.0%
정 2175
 
5.8%
및 2164
 
5.7%
적 1980
 
5.2%
수 1965
 
5.2%
폐 1881
 
5.0%
리 1628
 
4.3%
처 1617
 
4.3%
Other values (103) 14226
37.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32079
84.9%
Space Separator 5609
 
14.8%
Other Punctuation 92
 
0.2%
Decimal Number 9
 
< 0.1%
Dash Punctuation 5
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 2276
 
7.1%
여 2276
 
7.1%
정 2175
 
6.8%
및 2164
 
6.7%
적 1980
 
6.2%
수 1965
 
6.1%
폐 1881
 
5.9%
리 1628
 
5.1%
처 1617
 
5.0%
위 1616
 
5.0%
Other values (91) 12501
39.0%
Decimal Number
ValueCountFrequency (%)
0 3
33.3%
1 3
33.3%
8 1
 
11.1%
2 1
 
11.1%
3 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
, 88
95.7%
. 3
 
3.3%
: 1
 
1.1%
Space Separator
ValueCountFrequency (%)
5609
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32079
84.9%
Common 5718
 
15.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 2276
 
7.1%
여 2276
 
7.1%
정 2175
 
6.8%
및 2164
 
6.7%
적 1980
 
6.2%
수 1965
 
6.1%
폐 1881
 
5.9%
리 1628
 
5.1%
처 1617
 
5.0%
위 1616
 
5.0%
Other values (91) 12501
39.0%
Common
ValueCountFrequency (%)
5609
98.1%
, 88
 
1.5%
- 5
 
0.1%
0 3
 
0.1%
1 3
 
0.1%
. 3
 
0.1%
( 2
 
< 0.1%
) 1
 
< 0.1%
: 1
 
< 0.1%
8 1
 
< 0.1%
Other values (2) 2
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32079
84.9%
ASCII 5718
 
15.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5609
98.1%
, 88
 
1.5%
- 5
 
0.1%
0 3
 
0.1%
1 3
 
0.1%
. 3
 
0.1%
( 2
 
< 0.1%
) 1
 
< 0.1%
: 1
 
< 0.1%
8 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
Hangul
ValueCountFrequency (%)
부 2276
 
7.1%
여 2276
 
7.1%
정 2175
 
6.8%
및 2164
 
6.7%
적 1980
 
6.2%
수 1965
 
6.1%
폐 1881
 
5.9%
리 1628
 
5.1%
처 1617
 
5.0%
위 1616
 
5.0%
Other values (91) 12501
39.0%

점검결과
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing2571
Missing (%)100.0%
Memory size22.7 KiB

소재지도로명주소
Text

MISSING 

Distinct753
Distinct (%)57.7%
Missing1267
Missing (%)49.3%
Memory size20.2 KiB
2024-05-11T14:44:07.986599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length42
Median length37.5
Mean length28.60046
Min length21

Characters and Unicode

Total characters37295
Distinct characters301
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique479 ?
Unique (%)36.7%

Sample

1st row서울특별시 중구 마장로15길 2-6 (황학동)
2nd row서울특별시 중구 을지로27길 32-6 (주교동)
3rd row서울특별시 중구 을지로27가길 9-10 (주교동)
4th row서울특별시 중구 을지로38길 19-7, 1층 (광희동1가)
5th row서울특별시 중구 을지로27가길 12 (주교동)
ValueCountFrequency (%)
서울특별시 1304
 
18.2%
중구 1304
 
18.2%
주교동 204
 
2.8%
2층 94
 
1.3%
을지로27가길 83
 
1.2%
인현동1가 82
 
1.1%
퇴계로 63
 
0.9%
충무로3가 63
 
0.9%
1층 61
 
0.9%
마른내로 60
 
0.8%
Other values (856) 3844
53.7%
2024-05-11T14:44:08.566574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6169
 
16.5%
로 1640
 
4.4%
서 1406
 
3.8%
) 1364
 
3.7%
( 1364
 
3.7%
시 1337
 
3.6%
울 1331
 
3.6%
중 1328
 
3.6%
구 1307
 
3.5%
별 1307
 
3.5%
Other values (291) 18742
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21114
56.6%
Space Separator 6169
 
16.5%
Decimal Number 5987
 
16.1%
Close Punctuation 1364
 
3.7%
Open Punctuation 1364
 
3.7%
Other Punctuation 768
 
2.1%
Dash Punctuation 434
 
1.2%
Uppercase Letter 78
 
0.2%
Lowercase Letter 15
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 1640
 
7.8%
서 1406
 
6.7%
시 1337
 
6.3%
울 1331
 
6.3%
중 1328
 
6.3%
구 1307
 
6.2%
별 1307
 
6.2%
특 1304
 
6.2%
동 1061
 
5.0%
가 911
 
4.3%
Other values (244) 8182
38.8%
Uppercase Letter
ValueCountFrequency (%)
T 8
 
10.3%
S 8
 
10.3%
E 8
 
10.3%
A 8
 
10.3%
C 6
 
7.7%
R 6
 
7.7%
B 4
 
5.1%
W 4
 
5.1%
I 3
 
3.8%
O 3
 
3.8%
Other values (12) 20
25.6%
Decimal Number
ValueCountFrequency (%)
1 1263
21.1%
2 1246
20.8%
3 894
14.9%
4 510
8.5%
5 474
 
7.9%
0 375
 
6.3%
6 365
 
6.1%
7 345
 
5.8%
8 309
 
5.2%
9 206
 
3.4%
Lowercase Letter
ValueCountFrequency (%)
o 5
33.3%
i 2
 
13.3%
d 2
 
13.3%
g 2
 
13.3%
n 1
 
6.7%
e 1
 
6.7%
r 1
 
6.7%
w 1
 
6.7%
Other Punctuation
ValueCountFrequency (%)
, 767
99.9%
& 1
 
0.1%
Space Separator
ValueCountFrequency (%)
6169
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1364
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1364
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 434
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21114
56.6%
Common 16088
43.1%
Latin 93
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 1640
 
7.8%
서 1406
 
6.7%
시 1337
 
6.3%
울 1331
 
6.3%
중 1328
 
6.3%
구 1307
 
6.2%
별 1307
 
6.2%
특 1304
 
6.2%
동 1061
 
5.0%
가 911
 
4.3%
Other values (244) 8182
38.8%
Latin
ValueCountFrequency (%)
T 8
 
8.6%
S 8
 
8.6%
E 8
 
8.6%
A 8
 
8.6%
C 6
 
6.5%
R 6
 
6.5%
o 5
 
5.4%
B 4
 
4.3%
W 4
 
4.3%
I 3
 
3.2%
Other values (20) 33
35.5%
Common
ValueCountFrequency (%)
6169
38.3%
) 1364
 
8.5%
( 1364
 
8.5%
1 1263
 
7.9%
2 1246
 
7.7%
3 894
 
5.6%
, 767
 
4.8%
4 510
 
3.2%
5 474
 
2.9%
- 434
 
2.7%
Other values (7) 1603
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21114
56.6%
ASCII 16181
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6169
38.1%
) 1364
 
8.4%
( 1364
 
8.4%
1 1263
 
7.8%
2 1246
 
7.7%
3 894
 
5.5%
, 767
 
4.7%
4 510
 
3.2%
5 474
 
2.9%
- 434
 
2.7%
Other values (37) 1696
 
10.5%
Hangul
ValueCountFrequency (%)
로 1640
 
7.8%
서 1406
 
6.7%
시 1337
 
6.3%
울 1331
 
6.3%
중 1328
 
6.3%
구 1307
 
6.2%
별 1307
 
6.2%
특 1304
 
6.2%
동 1061
 
5.0%
가 911
 
4.3%
Other values (244) 8182
38.8%

소재지주소
Text

MISSING 

Distinct905
Distinct (%)37.8%
Missing178
Missing (%)6.9%
Memory size20.2 KiB
2024-05-11T14:44:09.004057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length35
Mean length23.521939
Min length13

Characters and Unicode

Total characters56288
Distinct characters175
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique306 ?
Unique (%)12.8%

Sample

1st row서울특별시 중구 황학동 1154번지
2nd row서울특별시 중구 주교동 281번지
3rd row서울특별시 중구 주교동 289번지
4th row서울특별시 중구 광희동1가 53번지
5th row서울특별시 중구 주교동 272-6번지
ValueCountFrequency (%)
서울특별시 2393
22.7%
중구 2393
22.7%
주교동 383
 
3.6%
2층 234
 
2.2%
인현동1가 199
 
1.9%
충무로3가 177
 
1.7%
초동 163
 
1.5%
필동3가 162
 
1.5%
필동2가 133
 
1.3%
3층 119
 
1.1%
Other values (938) 4186
39.7%
2024-05-11T14:44:09.597049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10547
18.7%
지 2662
 
4.7%
1 2474
 
4.4%
시 2439
 
4.3%
중 2409
 
4.3%
별 2405
 
4.3%
서 2401
 
4.3%
울 2399
 
4.3%
구 2396
 
4.3%
특 2393
 
4.3%
Other values (165) 23763
42.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 32211
57.2%
Decimal Number 11055
 
19.6%
Space Separator 10547
 
18.7%
Dash Punctuation 1893
 
3.4%
Close Punctuation 253
 
0.4%
Open Punctuation 251
 
0.4%
Other Punctuation 62
 
0.1%
Uppercase Letter 11
 
< 0.1%
Math Symbol 5
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 2662
 
8.3%
시 2439
 
7.6%
중 2409
 
7.5%
별 2405
 
7.5%
서 2401
 
7.5%
울 2399
 
7.4%
구 2396
 
7.4%
특 2393
 
7.4%
번 2387
 
7.4%
동 1768
 
5.5%
Other values (144) 8552
26.5%
Decimal Number
ValueCountFrequency (%)
1 2474
22.4%
2 2271
20.5%
3 1585
14.3%
4 936
 
8.5%
0 844
 
7.6%
5 781
 
7.1%
8 649
 
5.9%
6 563
 
5.1%
9 491
 
4.4%
7 461
 
4.2%
Uppercase Letter
ValueCountFrequency (%)
B 5
45.5%
S 3
27.3%
J 2
 
18.2%
G 1
 
9.1%
Close Punctuation
ValueCountFrequency (%)
) 251
99.2%
] 2
 
0.8%
Space Separator
ValueCountFrequency (%)
10547
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1893
100.0%
Open Punctuation
ValueCountFrequency (%)
( 251
100.0%
Other Punctuation
ValueCountFrequency (%)
, 62
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 32211
57.2%
Common 24066
42.8%
Latin 11
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 2662
 
8.3%
시 2439
 
7.6%
중 2409
 
7.5%
별 2405
 
7.5%
서 2401
 
7.5%
울 2399
 
7.4%
구 2396
 
7.4%
특 2393
 
7.4%
번 2387
 
7.4%
동 1768
 
5.5%
Other values (144) 8552
26.5%
Common
ValueCountFrequency (%)
10547
43.8%
1 2474
 
10.3%
2 2271
 
9.4%
- 1893
 
7.9%
3 1585
 
6.6%
4 936
 
3.9%
0 844
 
3.5%
5 781
 
3.2%
8 649
 
2.7%
6 563
 
2.3%
Other values (7) 1523
 
6.3%
Latin
ValueCountFrequency (%)
B 5
45.5%
S 3
27.3%
J 2
 
18.2%
G 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 32211
57.2%
ASCII 24077
42.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10547
43.8%
1 2474
 
10.3%
2 2271
 
9.4%
- 1893
 
7.9%
3 1585
 
6.6%
4 936
 
3.9%
0 844
 
3.5%
5 781
 
3.2%
8 649
 
2.7%
6 563
 
2.3%
Other values (11) 1534
 
6.4%
Hangul
ValueCountFrequency (%)
지 2662
 
8.3%
시 2439
 
7.6%
중 2409
 
7.5%
별 2405
 
7.5%
서 2401
 
7.5%
울 2399
 
7.4%
구 2396
 
7.4%
특 2393
 
7.4%
번 2387
 
7.4%
동 1768
 
5.5%
Other values (144) 8552
26.5%

Interactions

2024-05-11T14:44:02.296580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:01.889352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:02.447911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T14:44:02.094642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T14:44:09.729667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호업종코드업종명지도점검일자지도점검구분처분대상여부
인허가번호1.0001.0001.0000.5250.0460.000
업종코드1.0001.0001.0000.5240.0470.000
업종명1.0001.0001.0000.2870.0000.000
지도점검일자0.5250.5240.2871.0000.3970.399
지도점검구분0.0460.0470.0000.3971.0000.000
처분대상여부0.0000.0000.0000.3990.0001.000
2024-05-11T14:44:09.867480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지도점검구분처분대상여부업종명업종코드
지도점검구분1.0000.0000.0000.044
처분대상여부0.0001.0000.0000.000
업종명0.0000.0001.0001.000
업종코드0.0440.0001.0001.000
2024-05-11T14:44:10.017481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인허가번호지도점검일자업종코드업종명지도점검구분처분대상여부
인허가번호1.000-0.1581.0001.0000.0440.000
지도점검일자-0.1581.0000.3900.2750.1850.267
업종코드1.0000.3901.0001.0000.0440.000
업종명1.0000.2751.0001.0000.0000.000
지도점검구분0.0440.1850.0440.0001.0000.000
처분대상여부0.0000.2670.0000.0000.0001.000

Missing values

2024-05-11T14:44:02.610973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T14:44:02.830796image/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.
2024-05-11T14:44:03.024198image/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

업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
0에스에이명진지퍼30100002220000127522폐수배출업소관리201612053010000서울특별시 중구정기N배출시설 및 방지시설 적정 여부<NA>서울특별시 중구 마장로15길 2-6 (황학동)서울특별시 중구 황학동 1154번지
1대동염색30100002220000127822폐수배출업소관리201611083010000서울특별시 중구정기N배출시설 및 방지시설 적정운영 여부<NA>서울특별시 중구 을지로27길 32-6 (주교동)서울특별시 중구 주교동 281번지
2일신염색30100002220000135222폐수배출업소관리201611023010000서울특별시 중구정기N배출시설 및 방지시설 적정 여부<NA>서울특별시 중구 을지로27가길 9-10 (주교동)서울특별시 중구 주교동 289번지
3그린염색30100002220000159722폐수배출업소관리201611023010000서울특별시 중구정기N배출시설 및 방지시설 적정 여부<NA>서울특별시 중구 을지로38길 19-7, 1층 (광희동1가)서울특별시 중구 광희동1가 53번지
4대경30100002220000161522폐수배출업소관리201611023010000서울특별시 중구정기N배출시설 및 방지시설 적정 여부<NA>서울특별시 중구 을지로27가길 12 (주교동)서울특별시 중구 주교동 272-6번지
5미래염색30100002220010021922폐수배출업소관리201611023010000서울특별시 중구정기N배출시설 및 방지시설 적정 여부<NA>서울특별시 중구 을지로42길 6-6 (을지로6가)서울특별시 중구 을지로6가 33번지
6그린염색30100002220000159722폐수배출업소관리201610283010000서울특별시 중구정기N폐수배출시설 및 방지시설 적정운영여부<NA>서울특별시 중구 을지로38길 19-7, 1층 (광희동1가)서울특별시 중구 광희동1가 53번지
7에스에이명진지퍼30100002220000127522폐수배출업소관리201610283010000서울특별시 중구정기N폐수배출시설 및 방지시설 적정운영여부<NA>서울특별시 중구 마장로15길 2-6 (황학동)서울특별시 중구 황학동 1154번지
8미래염색30100002220010021922폐수배출업소관리201610283010000서울특별시 중구정기N폐수배출시설 및 방지시설 적정운영여부<NA>서울특별시 중구 을지로42길 6-6 (을지로6가)서울특별시 중구 을지로6가 33번지
9삼일문화사30100002220050005122폐수배출업소관리201610273010000서울특별시 중구정기N폐수배출시설 및 방지시설 적정운영 여부<NA>서울특별시 중구 퇴계로50가길 10 (묵정동)서울특별시 중구 묵정동 24-10번지
업체(시설)명인허가번호업종코드업종명지도점검일자점검기관점검기관명지도점검구분처분대상여부점검사항점검결과소재지도로명주소소재지주소
2561우창교정30100002220040001622폐수배출업소관리201003113010000서울특별시 중구정기N폐수보관및위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 131-6번지
2562하이큐30100002220010021622폐수배출업소관리201003113010000서울특별시 중구정기N폐수보관및위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 12-1번지
2563서울석유(주)세화주유소30100002219970122422폐수배출업소관리201002263010000서울특별시 중구수시N개선명령이행보고사항확인<NA>서울특별시 중구 왕십리로 403 (신당동, 외1필지)서울특별시 중구 신당동 741번지
2564성조문화사30100002219970084522폐수배출업소관리201002243010000서울특별시 중구정기N폐수위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 108-3번지
2565우리그래픽30100002220050008422폐수배출업소관리201002243010000서울특별시 중구정기N폐수위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 148-8번지
2566범일인쇄30100002219950060622폐수배출업소관리201002243010000서울특별시 중구정기N폐수위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 140번지
2567뿌리인쇄30100002220020010622폐수배출업소관리201002243010000서울특별시 중구정기N폐수위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 148-1번지
2568프린팅카페30100002220060002422폐수배출업소관리201002243010000서울특별시 중구정기N폐수위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 135번지
2569진성문화30100002220090002322폐수배출업소관리201002243010000서울특별시 중구정기N폐수위탁처리 적정여부<NA><NA>서울특별시 중구 충무로4가 149-7번지 3층2호
2570서울석유(주)세화주유소30100002219970122422폐수배출업소관리201001253010000서울특별시 중구수시N개선명령이행여부 확인<NA>서울특별시 중구 왕십리로 403 (신당동, 외1필지)서울특별시 중구 신당동 741번지