Overview

Dataset statistics

Number of variables20
Number of observations1427
Missing cells11044
Missing cells (%)38.7%
Duplicate rows1
Duplicate rows (%)0.1%
Total size in memory238.4 KiB
Average record size in memory171.1 B

Variable types

Categorical5
Text4
DateTime2
Unsupported3
Numeric6

Dataset

Description저수조 청소업관리 현황
Author행정안전부
URLhttps://data.gg.go.kr/portal/data/service/selectServicePage.do?&infId=9D3LNLA597YGE5656Y33147251&infSeq=1

Alerts

Dataset has 1 (0.1%) duplicate rowsDuplicates
업무구분명 is highly overall correlated with 도로명우편번호 and 7 other fieldsHigh correlation
업무구분정보 is highly overall correlated with 도로명우편번호 and 7 other fieldsHigh correlation
영업상태구분코드 is highly overall correlated with 소재지우편번호 and 3 other fieldsHigh correlation
시군명 is highly overall correlated with 도로명우편번호 and 7 other fieldsHigh correlation
영업상태명 is highly overall correlated with 영업상태구분코드 and 2 other fieldsHigh correlation
도로명우편번호 is highly overall correlated with 소재지우편번호 and 4 other fieldsHigh correlation
소재지우편번호 is highly overall correlated with 도로명우편번호 and 6 other fieldsHigh correlation
WGS84위도 is highly overall correlated with 소재지우편번호 and 1 other fieldsHigh correlation
WGS84경도 is highly overall correlated with 시군명High correlation
X좌표값 is highly overall correlated with 시군명 and 2 other fieldsHigh correlation
Y좌표값 is highly overall correlated with 도로명우편번호 and 4 other fieldsHigh correlation
영업상태구분코드 is highly imbalanced (65.3%)Imbalance
인허가취소일자 has 1427 (100.0%) missing valuesMissing
폐업일자 has 1121 (78.6%) missing valuesMissing
소재지시설전화번호 has 1181 (82.8%) missing valuesMissing
소재지면적정보 has 1427 (100.0%) missing valuesMissing
도로명우편번호 has 1178 (82.6%) missing valuesMissing
소재지도로명주소 has 64 (4.5%) missing valuesMissing
소재지우편번호 has 281 (19.7%) missing valuesMissing
WGS84위도 has 307 (21.5%) missing valuesMissing
WGS84경도 has 307 (21.5%) missing valuesMissing
업태구분명정보 has 1427 (100.0%) missing valuesMissing
X좌표값 has 1159 (81.2%) missing valuesMissing
Y좌표값 has 1159 (81.2%) missing valuesMissing
인허가취소일자 is an unsupported type, check if it needs cleaning or further analysisUnsupported
소재지면적정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported
업태구분명정보 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 22:24:23.949069
Analysis finished2023-12-10 22:24:28.307979
Duration4.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

HIGH CORRELATION 

Distinct31
Distinct (%)2.2%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
안산시
232 
수원시
159 
고양시
133 
성남시
102 
부천시
75 
Other values (26)
726 

Length

Max length4
Median length3
Mean length3.0903994
Min length3

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row가평군
2nd row가평군
3rd row가평군
4th row고양시
5th row고양시

Common Values

ValueCountFrequency (%)
안산시 232
16.3%
수원시 159
 
11.1%
고양시 133
 
9.3%
성남시 102
 
7.1%
부천시 75
 
5.3%
용인시 72
 
5.0%
남양주시 68
 
4.8%
시흥시 65
 
4.6%
평택시 61
 
4.3%
안양시 54
 
3.8%
Other values (21) 406
28.5%

Length

2023-12-11T07:24:28.366402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 232
16.3%
수원시 159
 
11.1%
고양시 133
 
9.3%
성남시 102
 
7.1%
부천시 75
 
5.3%
용인시 72
 
5.0%
남양주시 68
 
4.8%
시흥시 65
 
4.6%
평택시 61
 
4.3%
안양시 54
 
3.8%
Other values (21) 406
28.5%

사업장명
Text

Distinct1173
Distinct (%)82.2%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
2023-12-11T07:24:28.597094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length15
Mean length6.8759636
Min length2

Characters and Unicode

Total characters9812
Distinct characters387
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

Unique939 ?
Unique (%)65.8%

Sample

1st row(주)일진환경
2nd row청산환경건설
3rd row(주)코리아탑 시큐리티
4th row신아개발주식회사
5th row(주)원스-탑 그린뱅크
ValueCountFrequency (%)
주식회사 65
 
4.2%
주 12
 
0.8%
그린환경 7
 
0.4%
사회적협동조합 4
 
0.3%
아이토스 3
 
0.2%
좋은환경 3
 
0.2%
시티환경 3
 
0.2%
주)케이티씨 3
 
0.2%
협동조합 3
 
0.2%
c&s 3
 
0.2%
Other values (1201) 1457
93.2%
2023-12-11T07:24:28.950436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 799
 
8.1%
) 690
 
7.0%
( 683
 
7.0%
경 424
 
4.3%
환 375
 
3.8%
리 228
 
2.3%
스 196
 
2.0%
이 190
 
1.9%
사 181
 
1.8%
합 161
 
1.6%
Other values (377) 5885
60.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 8162
83.2%
Close Punctuation 690
 
7.0%
Open Punctuation 683
 
7.0%
Space Separator 137
 
1.4%
Uppercase Letter 90
 
0.9%
Decimal Number 25
 
0.3%
Other Punctuation 21
 
0.2%
Dash Punctuation 3
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 799
 
9.8%
경 424
 
5.2%
환 375
 
4.6%
리 228
 
2.8%
스 196
 
2.4%
이 190
 
2.3%
사 181
 
2.2%
합 161
 
2.0%
관 151
 
1.9%
종 138
 
1.7%
Other values (342) 5319
65.2%
Uppercase Letter
ValueCountFrequency (%)
S 20
22.2%
C 14
15.6%
M 8
 
8.9%
T 8
 
8.9%
E 6
 
6.7%
B 4
 
4.4%
D 4
 
4.4%
O 4
 
4.4%
K 4
 
4.4%
G 3
 
3.3%
Other values (9) 15
16.7%
Decimal Number
ValueCountFrequency (%)
1 11
44.0%
2 5
20.0%
5 2
 
8.0%
3 2
 
8.0%
6 2
 
8.0%
4 2
 
8.0%
9 1
 
4.0%
Other Punctuation
ValueCountFrequency (%)
& 10
47.6%
. 7
33.3%
/ 2
 
9.5%
, 2
 
9.5%
Close Punctuation
ValueCountFrequency (%)
) 690
100.0%
Open Punctuation
ValueCountFrequency (%)
( 683
100.0%
Space Separator
ValueCountFrequency (%)
137
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Lowercase Letter
ValueCountFrequency (%)
i 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 8162
83.2%
Common 1559
 
15.9%
Latin 91
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 799
 
9.8%
경 424
 
5.2%
환 375
 
4.6%
리 228
 
2.8%
스 196
 
2.4%
이 190
 
2.3%
사 181
 
2.2%
합 161
 
2.0%
관 151
 
1.9%
종 138
 
1.7%
Other values (342) 5319
65.2%
Latin
ValueCountFrequency (%)
S 20
22.0%
C 14
15.4%
M 8
 
8.8%
T 8
 
8.8%
E 6
 
6.6%
B 4
 
4.4%
D 4
 
4.4%
O 4
 
4.4%
K 4
 
4.4%
G 3
 
3.3%
Other values (10) 16
17.6%
Common
ValueCountFrequency (%)
) 690
44.3%
( 683
43.8%
137
 
8.8%
1 11
 
0.7%
& 10
 
0.6%
. 7
 
0.4%
2 5
 
0.3%
- 3
 
0.2%
5 2
 
0.1%
3 2
 
0.1%
Other values (5) 9
 
0.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 8162
83.2%
ASCII 1650
 
16.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 799
 
9.8%
경 424
 
5.2%
환 375
 
4.6%
리 228
 
2.8%
스 196
 
2.4%
이 190
 
2.3%
사 181
 
2.2%
합 161
 
2.0%
관 151
 
1.9%
종 138
 
1.7%
Other values (342) 5319
65.2%
ASCII
ValueCountFrequency (%)
) 690
41.8%
( 683
41.4%
137
 
8.3%
S 20
 
1.2%
C 14
 
0.8%
1 11
 
0.7%
& 10
 
0.6%
M 8
 
0.5%
T 8
 
0.5%
. 7
 
0.4%
Other values (25) 62
 
3.8%

인허가일자
Date

Distinct1082
Distinct (%)75.8%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
Minimum1994-11-28 00:00:00
Maximum2023-11-29 00:00:00
2023-12-11T07:24:29.277087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:29.386749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

인허가취소일자
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1427
Missing (%)100.0%
Memory size12.7 KiB

영업상태구분코드
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
<NA>
1155 
11
215 
2
 
29
5
 
24
1
 
3

Length

Max length4
Median length4
Mean length3.5788367
Min length1

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1155
80.9%
11 215
 
15.1%
2 29
 
2.0%
5 24
 
1.7%
1 3
 
0.2%
3 1
 
0.1%

Length

2023-12-11T07:24:29.490953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:29.596121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1155
80.9%
11 215
 
15.1%
2 29
 
2.0%
5 24
 
1.7%
1 3
 
0.2%
3 1
 
0.1%

영업상태명
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
운영중
903 
폐업 등
247 
정상
215 
폐업
 
29
제외사항
 
24
Other values (3)
 
9

Length

Max length4
Median length3
Mean length3.0203224
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

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

Common Values

ValueCountFrequency (%)
운영중 903
63.3%
폐업 등 247
 
17.3%
정상 215
 
15.1%
폐업 29
 
2.0%
제외사항 24
 
1.7%
휴업 등 5
 
0.4%
휴업 3
 
0.2%
재개업 1
 
0.1%

Length

2023-12-11T07:24:29.693298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:29.817853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
운영중 903
53.8%
폐업 276
 
16.4%
등 252
 
15.0%
정상 215
 
12.8%
제외사항 24
 
1.4%
휴업 8
 
0.5%
재개업 1
 
0.1%

폐업일자
Date

MISSING 

Distinct262
Distinct (%)85.6%
Missing1121
Missing (%)78.6%
Memory size11.3 KiB
Minimum2006-03-03 00:00:00
Maximum2023-12-01 00:00:00
2023-12-11T07:24:29.946028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:30.075093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

소재지시설전화번호
Text

MISSING 

Distinct237
Distinct (%)96.3%
Missing1181
Missing (%)82.8%
Memory size11.3 KiB
2023-12-11T07:24:30.283014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length10.556911
Min length8

Characters and Unicode

Total characters2597
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique230 ?
Unique (%)93.5%

Sample

1st row031-906-8939
2nd row031-913-2288
3rd row903-2396
4th row916-7685
5th row031-974-5544
ValueCountFrequency (%)
031-8086-5286 3
 
1.2%
031-906-8939 3
 
1.2%
484-4840 2
 
0.8%
031-245-9661 2
 
0.8%
031-511-1208 2
 
0.8%
02-734-1181 2
 
0.8%
031-401-4549 2
 
0.8%
411-0113 1
 
0.4%
417-1645 1
 
0.4%
031-414-3381 1
 
0.4%
Other values (230) 230
92.4%
2023-12-11T07:24:30.594876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 389
15.0%
0 329
12.7%
1 321
12.4%
3 308
11.9%
4 255
9.8%
2 189
7.3%
9 183
7.0%
5 177
6.8%
8 176
6.8%
7 143
 
5.5%
Other values (4) 127
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2203
84.8%
Dash Punctuation 389
 
15.0%
Space Separator 3
 
0.1%
Close Punctuation 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 329
14.9%
1 321
14.6%
3 308
14.0%
4 255
11.6%
2 189
8.6%
9 183
8.3%
5 177
8.0%
8 176
8.0%
7 143
6.5%
6 122
 
5.5%
Dash Punctuation
ValueCountFrequency (%)
- 389
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2597
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 389
15.0%
0 329
12.7%
1 321
12.4%
3 308
11.9%
4 255
9.8%
2 189
7.3%
9 183
7.0%
5 177
6.8%
8 176
6.8%
7 143
 
5.5%
Other values (4) 127
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2597
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 389
15.0%
0 329
12.7%
1 321
12.4%
3 308
11.9%
4 255
9.8%
2 189
7.3%
9 183
7.0%
5 177
6.8%
8 176
6.8%
7 143
 
5.5%
Other values (4) 127
 
4.9%

소재지면적정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1427
Missing (%)100.0%
Memory size12.7 KiB

도로명우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct200
Distinct (%)80.3%
Missing1178
Missing (%)82.6%
Infinite0
Infinite (%)0.0%
Mean14366.008
Minimum10012
Maximum18525
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T07:24:30.709561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10012
5-th percentile10338.8
Q112199
median15367
Q316229
95-th percentile17318.6
Maximum18525
Range8513
Interquartile range (IQR)4030

Descriptive statistics

Standard deviation2407.9252
Coefficient of variation (CV)0.16761269
Kurtosis-1.0137889
Mean14366.008
Median Absolute Deviation (MAD)1180
Skewness-0.57454242
Sum3577136
Variance5798103.8
MonotonicityNot monotonic
2023-12-11T07:24:30.813267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15431 9
 
0.6%
15361 6
 
0.4%
15461 5
 
0.4%
10420 5
 
0.4%
15459 4
 
0.3%
10507 3
 
0.2%
15402 3
 
0.2%
11624 2
 
0.1%
16943 2
 
0.1%
12248 2
 
0.1%
Other values (190) 208
 
14.6%
(Missing) 1178
82.6%
ValueCountFrequency (%)
10012 1
0.1%
10023 1
0.1%
10025 1
0.1%
10056 1
0.1%
10106 1
0.1%
10109 1
0.1%
10205 1
0.1%
10244 1
0.1%
10253 2
0.1%
10254 1
0.1%
ValueCountFrequency (%)
18525 1
0.1%
18469 1
0.1%
18401 1
0.1%
18340 1
0.1%
17914 1
0.1%
17899 1
0.1%
17827 1
0.1%
17789 1
0.1%
17601 1
0.1%
17586 1
0.1%

소재지도로명주소
Text

MISSING 

Distinct1215
Distinct (%)89.1%
Missing64
Missing (%)4.5%
Memory size11.3 KiB
2023-12-11T07:24:31.050263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length47
Mean length28.983859
Min length13

Characters and Unicode

Total characters39505
Distinct characters427
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1096 ?
Unique (%)80.4%

Sample

1st row경기도 가평군 가평읍 연인*길 **-**
2nd row경기도 가평군 가평읍 태평길 **
3rd row경기도 가평군 가평읍 연인*길 **
4th row경기도 고양시 일산동구 장백로 *** (장항동,우신프라자 ***)
5th row경기도 고양시 일산서구 강성로***번길 ** (대화동)
ValueCountFrequency (%)
1374
 
16.4%
경기도 1363
 
16.2%
호 368
 
4.4%
안산시 217
 
2.6%
층 164
 
2.0%
단원구 158
 
1.9%
수원시 154
 
1.8%
고양시 129
 
1.5%
성남시 100
 
1.2%
부천시 74
 
0.9%
Other values (1471) 4298
51.2%
2023-12-11T07:24:31.426890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7190
18.2%
* 6380
16.1%
시 1444
 
3.7%
경 1440
 
3.6%
기 1409
 
3.6%
도 1403
 
3.6%
동 1293
 
3.3%
로 1261
 
3.2%
( 1074
 
2.7%
) 1066
 
2.7%
Other values (417) 15545
39.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22702
57.5%
Space Separator 7190
 
18.2%
Other Punctuation 7101
 
18.0%
Open Punctuation 1074
 
2.7%
Close Punctuation 1066
 
2.7%
Dash Punctuation 310
 
0.8%
Uppercase Letter 54
 
0.1%
Lowercase Letter 6
 
< 0.1%
Letter Number 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1444
 
6.4%
경 1440
 
6.3%
기 1409
 
6.2%
도 1403
 
6.2%
동 1293
 
5.7%
로 1261
 
5.6%
구 779
 
3.4%
길 646
 
2.8%
원 518
 
2.3%
번 501
 
2.2%
Other values (385) 12008
52.9%
Uppercase Letter
ValueCountFrequency (%)
B 15
27.8%
A 12
22.2%
I 3
 
5.6%
V 2
 
3.7%
C 2
 
3.7%
W 2
 
3.7%
K 2
 
3.7%
E 2
 
3.7%
M 2
 
3.7%
L 2
 
3.7%
Other values (7) 10
18.5%
Other Punctuation
ValueCountFrequency (%)
* 6380
89.8%
, 717
 
10.1%
. 2
 
< 0.1%
/ 1
 
< 0.1%
& 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
i 2
33.3%
n 2
33.3%
e 1
16.7%
t 1
16.7%
Space Separator
ValueCountFrequency (%)
7190
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1074
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1066
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 310
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22702
57.5%
Common 16742
42.4%
Latin 61
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1444
 
6.4%
경 1440
 
6.3%
기 1409
 
6.2%
도 1403
 
6.2%
동 1293
 
5.7%
로 1261
 
5.6%
구 779
 
3.4%
길 646
 
2.8%
원 518
 
2.3%
번 501
 
2.2%
Other values (385) 12008
52.9%
Latin
ValueCountFrequency (%)
B 15
24.6%
A 12
19.7%
I 3
 
4.9%
V 2
 
3.3%
C 2
 
3.3%
W 2
 
3.3%
i 2
 
3.3%
K 2
 
3.3%
n 2
 
3.3%
E 2
 
3.3%
Other values (12) 17
27.9%
Common
ValueCountFrequency (%)
7190
42.9%
* 6380
38.1%
( 1074
 
6.4%
) 1066
 
6.4%
, 717
 
4.3%
- 310
 
1.9%
. 2
 
< 0.1%
/ 1
 
< 0.1%
& 1
 
< 0.1%
~ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22702
57.5%
ASCII 16802
42.5%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7190
42.8%
* 6380
38.0%
( 1074
 
6.4%
) 1066
 
6.3%
, 717
 
4.3%
- 310
 
1.8%
B 15
 
0.1%
A 12
 
0.1%
I 3
 
< 0.1%
V 2
 
< 0.1%
Other values (21) 33
 
0.2%
Hangul
ValueCountFrequency (%)
시 1444
 
6.4%
경 1440
 
6.3%
기 1409
 
6.2%
도 1403
 
6.2%
동 1293
 
5.7%
로 1261
 
5.6%
구 779
 
3.4%
길 646
 
2.8%
원 518
 
2.3%
번 501
 
2.2%
Other values (385) 12008
52.9%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지지번주소
Text

Distinct1171
Distinct (%)82.4%
Missing6
Missing (%)0.4%
Memory size11.3 KiB
2023-12-11T07:24:31.678685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length44
Mean length25.759324
Min length7

Characters and Unicode

Total characters36604
Distinct characters389
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1019 ?
Unique (%)71.7%

Sample

1st row경기도 가평군 가평읍 읍내리 ***-**번지
2nd row경기도 가평군 가평읍 읍내리 ***번지
3rd row경기도 가평군 가평읍 읍내리 ***-*번지
4th row경기도 고양시 일산동구 장항동 ***-*번지 우신프라자 ***
5th row경기도 고양시 일산동구 중산동 ****-*번지 일산프라자 ***호
ValueCountFrequency (%)
경기도 1421
 
18.2%
번지 1130
 
14.5%
호 380
 
4.9%
306
 
3.9%
안산시 232
 
3.0%
단원구 170
 
2.2%
수원시 159
 
2.0%
고양시 133
 
1.7%
층 111
 
1.4%
성남시 101
 
1.3%
Other values (898) 3645
46.8%
2023-12-11T07:24:32.055292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 7475
20.4%
6560
17.9%
시 1481
 
4.0%
동 1455
 
4.0%
기 1452
 
4.0%
도 1450
 
4.0%
경 1432
 
3.9%
지 1271
 
3.5%
번 1133
 
3.1%
- 1132
 
3.1%
Other values (379) 11763
32.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21288
58.2%
Other Punctuation 7508
 
20.5%
Space Separator 6560
 
17.9%
Dash Punctuation 1132
 
3.1%
Uppercase Letter 72
 
0.2%
Open Punctuation 18
 
< 0.1%
Close Punctuation 17
 
< 0.1%
Lowercase Letter 7
 
< 0.1%
Math Symbol 1
 
< 0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 1481
 
7.0%
동 1455
 
6.8%
기 1452
 
6.8%
도 1450
 
6.8%
경 1432
 
6.7%
지 1271
 
6.0%
번 1133
 
5.3%
구 792
 
3.7%
원 488
 
2.3%
안 465
 
2.2%
Other values (346) 9869
46.4%
Uppercase Letter
ValueCountFrequency (%)
B 24
33.3%
A 15
20.8%
L 6
 
8.3%
I 3
 
4.2%
E 3
 
4.2%
C 3
 
4.2%
G 2
 
2.8%
W 2
 
2.8%
K 2
 
2.8%
V 2
 
2.8%
Other values (7) 10
13.9%
Other Punctuation
ValueCountFrequency (%)
* 7475
99.6%
, 27
 
0.4%
. 4
 
0.1%
& 1
 
< 0.1%
/ 1
 
< 0.1%
Lowercase Letter
ValueCountFrequency (%)
n 2
28.6%
i 2
28.6%
e 1
14.3%
t 1
14.3%
a 1
14.3%
Space Separator
ValueCountFrequency (%)
6560
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1132
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%
Letter Number
ValueCountFrequency (%)
Ⅱ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21288
58.2%
Common 15236
41.6%
Latin 80
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 1481
 
7.0%
동 1455
 
6.8%
기 1452
 
6.8%
도 1450
 
6.8%
경 1432
 
6.7%
지 1271
 
6.0%
번 1133
 
5.3%
구 792
 
3.7%
원 488
 
2.3%
안 465
 
2.2%
Other values (346) 9869
46.4%
Latin
ValueCountFrequency (%)
B 24
30.0%
A 15
18.8%
L 6
 
7.5%
I 3
 
3.8%
E 3
 
3.8%
C 3
 
3.8%
G 2
 
2.5%
n 2
 
2.5%
i 2
 
2.5%
W 2
 
2.5%
Other values (13) 18
22.5%
Common
ValueCountFrequency (%)
* 7475
49.1%
6560
43.1%
- 1132
 
7.4%
, 27
 
0.2%
( 18
 
0.1%
) 17
 
0.1%
. 4
 
< 0.1%
& 1
 
< 0.1%
~ 1
 
< 0.1%
/ 1
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21288
58.2%
ASCII 15315
41.8%
Number Forms 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 7475
48.8%
6560
42.8%
- 1132
 
7.4%
, 27
 
0.2%
B 24
 
0.2%
( 18
 
0.1%
) 17
 
0.1%
A 15
 
0.1%
L 6
 
< 0.1%
. 4
 
< 0.1%
Other values (22) 37
 
0.2%
Hangul
ValueCountFrequency (%)
시 1481
 
7.0%
동 1455
 
6.8%
기 1452
 
6.8%
도 1450
 
6.8%
경 1432
 
6.7%
지 1271
 
6.0%
번 1133
 
5.3%
구 792
 
3.7%
원 488
 
2.3%
안 465
 
2.2%
Other values (346) 9869
46.4%
Number Forms
ValueCountFrequency (%)
Ⅱ 1
100.0%

소재지우편번호
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct782
Distinct (%)68.2%
Missing281
Missing (%)19.7%
Infinite0
Infinite (%)0.0%
Mean14454.976
Minimum10037
Maximum18625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T07:24:32.175190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10037
5-th percentile10420
Q112441
median14748
Q316347.25
95-th percentile18118
Maximum18625
Range8588
Interquartile range (IQR)3906.25

Descriptive statistics

Standard deviation2368.7573
Coefficient of variation (CV)0.1638714
Kurtosis-1.0267501
Mean14454.976
Median Absolute Deviation (MAD)1796.5
Skewness-0.17723944
Sum16565403
Variance5611011.1
MonotonicityNot monotonic
2023-12-11T07:24:32.308387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15431 12
 
0.8%
15461 9
 
0.6%
15361 9
 
0.6%
13504 9
 
0.6%
14543 8
 
0.6%
11932 6
 
0.4%
14998 6
 
0.4%
16039 5
 
0.4%
15464 5
 
0.4%
15002 5
 
0.4%
Other values (772) 1072
75.1%
(Missing) 281
 
19.7%
ValueCountFrequency (%)
10037 1
0.1%
10050 1
0.1%
10080 1
0.1%
10092 1
0.1%
10105 1
0.1%
10106 2
0.1%
10108 1
0.1%
10109 2
0.1%
10110 1
0.1%
10117 1
0.1%
ValueCountFrequency (%)
18625 1
0.1%
18589 1
0.1%
18584 1
0.1%
18575 1
0.1%
18549 1
0.1%
18541 1
0.1%
18537 1
0.1%
18533 1
0.1%
18516 1
0.1%
18455 2
0.1%

WGS84위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1013
Distinct (%)90.4%
Missing307
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean37.414893
Minimum36.957461
Maximum38.001019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T07:24:32.447096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.957461
5-th percentile37.067109
Q137.29159
median37.380009
Q337.53673
95-th percentile37.749573
Maximum38.001019
Range1.043558
Interquartile range (IQR)0.24514052

Descriptive statistics

Standard deviation0.19920153
Coefficient of variation (CV)0.0053241241
Kurtosis-0.16229491
Mean37.414893
Median Absolute Deviation (MAD)0.11012706
Skewness0.23813071
Sum41904.68
Variance0.03968125
MonotonicityNot monotonic
2023-12-11T07:24:32.555083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3138544185 4
 
0.3%
37.3239975466 4
 
0.3%
37.3183708813 4
 
0.3%
37.2112132145 3
 
0.2%
37.3147864287 3
 
0.2%
37.3453574542 3
 
0.2%
37.3027171163 3
 
0.2%
37.3347248053 3
 
0.2%
37.3361222457 3
 
0.2%
37.307285412 3
 
0.2%
Other values (1003) 1087
76.2%
(Missing) 307
 
21.5%
ValueCountFrequency (%)
36.9574614357 2
0.1%
36.9631358353 1
0.1%
36.9683316661 1
0.1%
36.972222512 1
0.1%
36.9774451945 1
0.1%
36.9847923243 1
0.1%
36.9858629484 1
0.1%
36.9863672621 2
0.1%
36.9873304398 1
0.1%
36.988046749 1
0.1%
ValueCountFrequency (%)
38.0010193865 1
0.1%
37.9820372955 1
0.1%
37.9254455006 1
0.1%
37.9150082524 1
0.1%
37.9110330679 1
0.1%
37.9035837671 1
0.1%
37.9034303369 1
0.1%
37.9026640375 2
0.1%
37.896102422 1
0.1%
37.8940120303 1
0.1%

WGS84경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1013
Distinct (%)90.4%
Missing307
Missing (%)21.5%
Infinite0
Infinite (%)0.0%
Mean126.99751
Minimum126.58305
Maximum127.71417
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T07:24:32.665358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58305
5-th percentile126.75199
Q1126.82882
median127.00259
Q3127.12719
95-th percentile127.36903
Maximum127.71417
Range1.1311214
Interquartile range (IQR)0.2983744

Descriptive statistics

Standard deviation0.19413185
Coefficient of variation (CV)0.0015286272
Kurtosis0.6180238
Mean126.99751
Median Absolute Deviation (MAD)0.15115428
Skewness0.78224431
Sum142237.21
Variance0.037687175
MonotonicityNot monotonic
2023-12-11T07:24:32.777374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8293500999 4
 
0.3%
126.788142049 4
 
0.3%
126.8390610787 4
 
0.3%
127.0363667892 3
 
0.2%
126.8283938409 3
 
0.2%
126.73153564 3
 
0.2%
126.8364110348 3
 
0.2%
126.7265416247 3
 
0.2%
126.8080988348 3
 
0.2%
126.8490883536 3
 
0.2%
Other values (1003) 1087
76.2%
(Missing) 307
 
21.5%
ValueCountFrequency (%)
126.5830489319 1
0.1%
126.6128265615 1
0.1%
126.6665253754 1
0.1%
126.6914131078 1
0.1%
126.6964092552 1
0.1%
126.7057154427 1
0.1%
126.7060788009 1
0.1%
126.7066358146 1
0.1%
126.7070665743 1
0.1%
126.7091865594 1
0.1%
ValueCountFrequency (%)
127.7141703278 1
 
0.1%
127.6406399272 1
 
0.1%
127.6341545345 1
 
0.1%
127.6324935725 1
 
0.1%
127.6312905685 1
 
0.1%
127.630500527 1
 
0.1%
127.6299638451 1
 
0.1%
127.6290374543 1
 
0.1%
127.6282696004 1
 
0.1%
127.6164271659 3
0.2%

업태구분명정보
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing1427
Missing (%)100.0%
Memory size12.7 KiB

X좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct249
Distinct (%)92.9%
Missing1159
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean196135.91
Minimum161181.72
Maximum256491.66
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T07:24:32.888616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum161181.72
5-th percentile178771.85
Q1183209.89
median188595.99
Q3205348.41
95-th percentile225230.88
Maximum256491.66
Range95309.939
Interquartile range (IQR)22138.525

Descriptive statistics

Standard deviation17117.902
Coefficient of variation (CV)0.087275715
Kurtosis0.95763922
Mean196135.91
Median Absolute Deviation (MAD)9453.8392
Skewness1.0203215
Sum52564425
Variance2.9302258 × 108
MonotonicityNot monotonic
2023-12-11T07:24:33.025156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
181108.608529259 3
 
0.2%
181034.70168025 3
 
0.2%
184718.323201476 3
 
0.2%
180555.458077737 2
 
0.1%
218938.408266357 2
 
0.1%
181258.71259879 2
 
0.1%
182923.824124947 2
 
0.1%
181022.224075507 2
 
0.1%
184448.21 2
 
0.1%
184803.757629819 2
 
0.1%
Other values (239) 245
 
17.2%
(Missing) 1159
81.2%
ValueCountFrequency (%)
161181.722215233 1
0.1%
161888.203769045 1
0.1%
167080.701583619 1
0.1%
167894.010935815 1
0.1%
174346.438182627 1
0.1%
174882.281777347 1
0.1%
174953.053370205 1
0.1%
176035.535074099 1
0.1%
176717.500710357 1
0.1%
177289.000360864 1
0.1%
ValueCountFrequency (%)
256491.661612981 1
0.1%
255699.772760242 1
0.1%
252171.11518754 1
0.1%
245929.822384599 1
0.1%
243465.78361712 1
0.1%
242718.653458807 1
0.1%
242669.922155297 1
0.1%
240617.874997809 1
0.1%
240440.69522239 1
0.1%
235479.583978839 1
0.1%

Y좌표값
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct249
Distinct (%)92.9%
Missing1159
Missing (%)81.2%
Infinite0
Infinite (%)0.0%
Mean434015.32
Minimum385198.47
Maximum472565.64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size12.7 KiB
2023-12-11T07:24:33.147329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum385198.47
5-th percentile414394.09
Q1421673.87
median425100.28
Q3456761.57
95-th percentile468593.44
Maximum472565.64
Range87367.167
Interquartile range (IQR)35087.702

Descriptive statistics

Standard deviation19917.961
Coefficient of variation (CV)0.0458923
Kurtosis-0.55491917
Mean434015.32
Median Absolute Deviation (MAD)6846.6922
Skewness0.35303404
Sum1.1631611 × 108
Variance3.9672518 × 108
MonotonicityNot monotonic
2023-12-11T07:24:33.277438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
424698.61951891 3
 
0.2%
424985.683162494 3
 
0.2%
423657.325483107 3
 
0.2%
465725.147077495 2
 
0.1%
414475.290886411 2
 
0.1%
424761.274007584 2
 
0.1%
426028.995440808 2
 
0.1%
425347.954335905 2
 
0.1%
423576.22 2
 
0.1%
423567.160477284 2
 
0.1%
Other values (239) 245
 
17.2%
(Missing) 1159
81.2%
ValueCountFrequency (%)
385198.470511118 1
0.1%
385889.75012944 1
0.1%
387553.277410345 1
0.1%
387759.335224252 1
0.1%
389153.855358563 1
0.1%
389238.307880895 1
0.1%
389465.178035941 1
0.1%
391844.443393126 1
0.1%
396841.610858772 1
0.1%
410066.066492533 1
0.1%
ValueCountFrequency (%)
472565.637951553 1
0.1%
472456.975575134 1
0.1%
472188.68 1
0.1%
471891.909569186 1
0.1%
470995.977317913 1
0.1%
470854.91496945 1
0.1%
470736.627841584 1
0.1%
470318.600784268 1
0.1%
470287.952077264 1
0.1%
470263.664631551 1
0.1%

업무구분정보
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
<NA>
1155 
31
272 

Length

Max length4
Median length4
Mean length3.6187807
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1155
80.9%
31 272
 
19.1%

Length

2023-12-11T07:24:33.384551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:33.470689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1155
80.9%
31 272
 
19.1%

업무구분명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size11.3 KiB
<NA>
1155 
저수조청소업
272 

Length

Max length6
Median length4
Mean length4.3812193
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 1155
80.9%
저수조청소업 272
 
19.1%

Length

2023-12-11T07:24:33.569587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T07:24:33.659809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 1155
80.9%
저수조청소업 272
 
19.1%

Interactions

2023-12-11T07:24:27.321836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.014915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.456981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.927033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.383239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.862052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.389975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.082996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.535713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.998721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.467935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.940357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.464180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.166054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.621564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.078814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.564123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.031594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.523528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.248232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.696357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.155986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.652408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.096905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.583146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.320999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.775837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.249284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.733765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.185399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.652102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.392018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:25.858238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.314918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:26.794641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T07:24:27.256137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T07:24:33.720436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명영업상태구분코드영업상태명도로명우편번호소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값
시군명1.0000.6060.4190.9930.9930.9750.9430.9510.947
영업상태구분코드0.6061.0001.0000.5650.912NaNNaN0.4690.473
영업상태명0.4191.0001.0000.5650.1610.1050.1660.4690.473
도로명우편번호0.9930.5650.5651.0001.000NaNNaN0.8920.930
소재지우편번호0.9930.9120.1611.0001.0000.9390.8850.8780.868
WGS84위도0.975NaN0.105NaN0.9391.0000.683NaNNaN
WGS84경도0.943NaN0.166NaN0.8850.6831.000NaNNaN
X좌표값0.9510.4690.4690.8920.878NaNNaN1.0000.689
Y좌표값0.9470.4730.4730.9300.868NaNNaN0.6891.000
2023-12-11T07:24:33.818339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업무구분명업무구분정보영업상태구분코드시군명영업상태명
업무구분명1.0001.0001.0001.0001.000
업무구분정보1.0001.0001.0001.0001.000
영업상태구분코드1.0001.0001.0000.3321.000
시군명1.0001.0000.3321.0000.175
영업상태명1.0001.0001.0000.1751.000
2023-12-11T07:24:34.142856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
도로명우편번호소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값시군명영업상태구분코드영업상태명업무구분정보업무구분명
도로명우편번호1.0000.986NaNNaN0.434-0.9400.9300.2660.2661.0001.000
소재지우편번호0.9861.000-0.9340.1060.270-0.9290.9300.5650.0821.0001.000
WGS84위도NaN-0.9341.000-0.153NaNNaN0.8260.0000.0620.0000.000
WGS84경도NaN0.106-0.1531.000NaNNaN0.7050.0000.0990.0000.000
X좌표값0.4340.270NaNNaN1.000-0.3220.7420.2120.2121.0001.000
Y좌표값-0.940-0.929NaNNaN-0.3221.0000.7250.2140.2141.0001.000
시군명0.9300.9300.8260.7050.7420.7251.0000.3320.1751.0001.000
영업상태구분코드0.2660.5650.0000.0000.2120.2140.3321.0001.0001.0001.000
영업상태명0.2660.0820.0620.0990.2120.2140.1751.0001.0001.0001.000
업무구분정보1.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000
업무구분명1.0001.0000.0000.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2023-12-11T07:24:27.759618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T07:24:27.986099image/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.
2023-12-11T07:24:28.171746image/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

시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값업무구분정보업무구분명
0가평군(주)일진환경20100303<NA><NA>운영중<NA><NA><NA><NA>경기도 가평군 가평읍 연인*길 **-**경기도 가평군 가평읍 읍내리 ***-**번지1241837.829383127.512477<NA><NA><NA><NA><NA>
1가평군청산환경건설20170831<NA><NA>운영중<NA><NA><NA><NA>경기도 가평군 가평읍 태평길 **경기도 가평군 가평읍 읍내리 ***번지1241937.82776127.516272<NA><NA><NA><NA><NA>
2가평군(주)코리아탑 시큐리티20101102<NA><NA>운영중<NA><NA><NA><NA>경기도 가평군 가평읍 연인*길 **경기도 가평군 가평읍 읍내리 ***-*번지1241837.830032127.511886<NA><NA><NA><NA><NA>
3고양시신아개발주식회사20060315<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산동구 장백로 *** (장항동,우신프라자 ***)경기도 고양시 일산동구 장항동 ***-*번지 우신프라자 ***1041437.651274126.777162<NA><NA><NA><NA><NA>
4고양시(주)원스-탑 그린뱅크20110718<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산서구 강성로***번길 ** (대화동)경기도 고양시 일산동구 중산동 ****-*번지 일산프라자 ***호1037737.6763126.751926<NA><NA><NA><NA><NA>
5고양시대승종합관리(주)20110217<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 덕양구 중앙로 ***, ***호 (행신동,삼일프라자)경기도 고양시 덕양구 행신동 ***-*번지 **통*반 삼일프라자 ***호1049537.6235126.836222<NA><NA><NA><NA><NA>
6고양시광성그린환경(주)20060407<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산서구 성저로**번길 ** (대화동)경기도 고양시 일산서구 대화동 ****-**번지1036837.683505126.754009<NA><NA><NA><NA><NA>
7고양시지유환경20180110<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산동구 중산로 ***, 해태쇼핑타운 ***호 (중산동)경기도 고양시 일산동구 중산동 ****-*번지 해태쇼핑타운1033637.6937126.780142<NA><NA><NA><NA><NA>
8고양시신한환경20020504<NA><NA>운영중<NA><NA><NA><NA><NA>경기도 고양시 덕양구 성사동 ***-*번지 형제주택지층<NA><NA><NA><NA><NA><NA><NA><NA>
9고양시대청환경20090706<NA><NA>운영중<NA><NA><NA><NA>경기도 고양시 일산동구 강송로 *** (마두동,백마프라자 ***호)경기도 고양시 일산동구 마두동 ***-*번지 백마프라자 ***호1041637.654722126.783806<NA><NA><NA><NA><NA>
시군명사업장명인허가일자인허가취소일자영업상태구분코드영업상태명폐업일자소재지시설전화번호소재지면적정보도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도업태구분명정보X좌표값Y좌표값업무구분정보업무구분명
1417화성시세종환경(주)20180710<NA><NA>운영중<NA><NA><NA><NA>경기도 화성시 배양남길 **-* (배양동)경기도 화성시 배양동 **-**번지1834437.220714126.99235<NA><NA><NA><NA><NA>
1418화성시(주)에스테크윈2023-07-05<NA>11정상<NA>031-381-8493<NA>18525경기도 화성시 비봉면 주석로***번길 **-**경기도 화성시 비봉면 청요리 ***-*<NA><NA><NA><NA>190566.13396410066.06649331저수조청소업
1419화성시(주)우리비엠씨2023-11-08<NA>11정상<NA>031-898-8021<NA>18401경기도 화성시 효행로 ***, 비젼월드 (진안동)경기도 화성시 진안동 ***-* 비젼월드<NA><NA><NA><NA>203164.820809412147.45899931저수조청소업
1420화성시티알엠주식회사2023-10-04<NA>11정상<NA>031-8086-5286<NA>18340경기도 화성시 배양길 **-*, *호 (배양동)경기도 화성시 배양동 **-**<NA><NA><NA><NA>199486.684508413415.96539931저수조청소업
1421화성시(주)베스트환경산업2023-06-01<NA>11정상<NA>031-8014-2479<NA>18469경기도 화성시 동탄첨단산업*로 **-**, 동탄비즈타워 *층 ***호 (영천동)경기도 화성시 영천동 ***-*<NA><NA><NA><NA>208176.492853412642.30813831저수조청소업
1422화성시현대건물관리20110415<NA><NA>폐업 등20110415<NA><NA><NA>경기도 화성시 동탄지성로***번길 **-*경기도 화성시 반월동 ***-*번지1838537.22809127.051042<NA><NA><NA><NA><NA>
1423화성시(주)지코20080708<NA><NA>폐업 등20080708<NA><NA><NA>경기도 화성시 작현길 *-*경기도 화성시 송산동 **-**번지1835837.20656127.017891<NA><NA><NA><NA><NA>
1424화성시제일크리닝상사20150708<NA><NA>폐업 등20150708<NA><NA><NA>경기도 화성시 동탄공원로 **-**, 지층 ***호 (능동, 동탄 푸른마을 신일해피트리)경기도 화성시 능동 ****번지1842937.201375127.054231<NA><NA><NA><NA><NA>
1425화성시(주)효성테크20120710<NA><NA>폐업 등20120710<NA><NA><NA>경기도 화성시 비봉면 비봉로 ***경기도 화성시 비봉면 양노리 ***번지 파크빌 ***호1828437.235618126.866041<NA><NA><NA><NA><NA>
1426화성시(주)청도비엠20080717<NA><NA>휴업 등20090101<NA><NA><NA><NA>경기도 화성시 동탄면 오산리 ***-**번지<NA>37.19114127.086<NA><NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

시군명사업장명인허가일자영업상태구분코드영업상태명폐업일자소재지시설전화번호도로명우편번호소재지도로명주소소재지지번주소소재지우편번호WGS84위도WGS84경도X좌표값Y좌표값업무구분정보업무구분명# duplicates
0안성시명인테크주식회사20160204<NA>폐업 등20180112<NA><NA>경기도 안성시 미양면 법전길 **경기도 안성시 미양면 개정리 ***-*번지1760236.957461127.19811<NA><NA><NA><NA>2