Overview

Dataset statistics

Number of variables15
Number of observations2845
Missing cells2774
Missing cells (%)6.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory347.4 KiB
Average record size in memory125.0 B

Variable types

Text6
Categorical2
Numeric5
Boolean1
DateTime1

Dataset

Description공공데이터 제공 표준데이터 속성정보(허용값, 표현형식/단위 등)는 [공공데이터 제공 표준] 전문을 참고하시기 바랍니다.(공공데이터포털>정보공유>자료실) 각 기관에서 등록한 표준데이터를 취합하여 제공하기 때문에 갱신주기는 개별 파일마다 다릅니다.(기관에서 등록한 데이터를 취합한 것으로 개별 파일별 갱신시점이 다름)
Author지방자치단체
URLhttps://www.data.go.kr/data/15034535/standard.do

Alerts

시도명 is highly overall correlated with 시군구코드 and 5 other fieldsHigh correlation
시군구명 is highly overall correlated with 시군구코드 and 6 other fieldsHigh correlation
시군구코드 is highly overall correlated with 위도 and 3 other fieldsHigh correlation
위도 is highly overall correlated with 시군구코드 and 3 other fieldsHigh correlation
경도 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
지정연도 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
제공기관코드 is highly overall correlated with 시군구코드 and 3 other fieldsHigh correlation
운영여부 is highly overall correlated with 시군구명High correlation
운영여부 is highly imbalanced (91.1%)Imbalance
소재지도로명주소 has 61 (2.1%) missing valuesMissing
소재지지번주소 has 1038 (36.5%) missing valuesMissing
여성안심지킴이집전화번호 has 858 (30.2%) missing valuesMissing
지정연도 has 817 (28.7%) missing valuesMissing

Reproduction

Analysis started2024-05-11 07:49:52.243621
Analysis finished2024-05-11 07:50:03.059313
Duration10.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

점포명
Text

Distinct2706
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
2024-05-11T16:50:03.643204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length9.5036907
Min length2

Characters and Unicode

Total characters27038
Distinct characters516
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

Unique2620 ?
Unique (%)92.1%

Sample

1st row미니스톱 방배본점
2nd row미니스톱 방배중앙점
3rd row미니스톱 방배현대점
4th rowGS25 방배E푸른점
5th rowCU 방배이수역점
ValueCountFrequency (%)
gs25 754
 
15.7%
세븐일레븐 489
 
10.2%
cu 420
 
8.7%
씨유 107
 
2.2%
미니스톱 87
 
1.8%
이마트24 49
 
1.0%
gs25시 18
 
0.4%
도서관(구립 12
 
0.2%
씨스페이스 10
 
0.2%
b 9
 
0.2%
Other values (2690) 2856
59.4%
2024-05-11T16:50:04.660376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 2209
 
8.2%
1981
 
7.3%
븐 1329
 
4.9%
2 1133
 
4.2%
5 1015
 
3.8%
S 1014
 
3.8%
G 1012
 
3.7%
일 747
 
2.8%
세 684
 
2.5%
레 674
 
2.5%
Other values (506) 15240
56.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19216
71.1%
Uppercase Letter 3197
 
11.8%
Decimal Number 2327
 
8.6%
Space Separator 1981
 
7.3%
Close Punctuation 105
 
0.4%
Open Punctuation 105
 
0.4%
Lowercase Letter 102
 
0.4%
Dash Punctuation 3
 
< 0.1%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 2209
 
11.5%
븐 1329
 
6.9%
일 747
 
3.9%
세 684
 
3.6%
레 674
 
3.5%
대 314
 
1.6%
로 298
 
1.6%
동 270
 
1.4%
스 263
 
1.4%
천 254
 
1.3%
Other values (460) 12174
63.4%
Uppercase Letter
ValueCountFrequency (%)
S 1014
31.7%
G 1012
31.7%
C 559
17.5%
U 553
17.3%
B 11
 
0.3%
D 6
 
0.2%
I 5
 
0.2%
P 5
 
0.2%
K 5
 
0.2%
T 4
 
0.1%
Other values (11) 23
 
0.7%
Decimal Number
ValueCountFrequency (%)
2 1133
48.7%
5 1015
43.6%
4 91
 
3.9%
1 45
 
1.9%
3 23
 
1.0%
6 8
 
0.3%
8 4
 
0.2%
9 3
 
0.1%
0 3
 
0.1%
7 2
 
0.1%
Lowercase Letter
ValueCountFrequency (%)
u 19
18.6%
c 19
18.6%
e 10
9.8%
s 9
8.8%
r 9
8.8%
t 9
8.8%
a 9
8.8%
m 9
8.8%
g 7
 
6.9%
k 2
 
2.0%
Space Separator
ValueCountFrequency (%)
1981
100.0%
Close Punctuation
ValueCountFrequency (%)
) 105
100.0%
Open Punctuation
ValueCountFrequency (%)
( 105
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19216
71.1%
Common 4523
 
16.7%
Latin 3299
 
12.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 2209
 
11.5%
븐 1329
 
6.9%
일 747
 
3.9%
세 684
 
3.6%
레 674
 
3.5%
대 314
 
1.6%
로 298
 
1.6%
동 270
 
1.4%
스 263
 
1.4%
천 254
 
1.3%
Other values (460) 12174
63.4%
Latin
ValueCountFrequency (%)
S 1014
30.7%
G 1012
30.7%
C 559
16.9%
U 553
16.8%
u 19
 
0.6%
c 19
 
0.6%
B 11
 
0.3%
e 10
 
0.3%
s 9
 
0.3%
r 9
 
0.3%
Other values (21) 84
 
2.5%
Common
ValueCountFrequency (%)
1981
43.8%
2 1133
25.0%
5 1015
22.4%
) 105
 
2.3%
( 105
 
2.3%
4 91
 
2.0%
1 45
 
1.0%
3 23
 
0.5%
6 8
 
0.2%
8 4
 
0.1%
Other values (5) 13
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19216
71.1%
ASCII 7822
28.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 2209
 
11.5%
븐 1329
 
6.9%
일 747
 
3.9%
세 684
 
3.6%
레 674
 
3.5%
대 314
 
1.6%
로 298
 
1.6%
동 270
 
1.4%
스 263
 
1.4%
천 254
 
1.3%
Other values (460) 12174
63.4%
ASCII
ValueCountFrequency (%)
1981
25.3%
2 1133
14.5%
5 1015
13.0%
S 1014
13.0%
G 1012
12.9%
C 559
 
7.1%
U 553
 
7.1%
) 105
 
1.3%
( 105
 
1.3%
4 91
 
1.2%
Other values (36) 254
 
3.2%

시도명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
인천광역시
1332 
서울특별시
753 
경상남도
180 
대전광역시
177 
전라북도
138 
Other values (6)
265 

Length

Max length7
Median length5
Mean length4.7764499
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 1332
46.8%
서울특별시 753
26.5%
경상남도 180
 
6.3%
대전광역시 177
 
6.2%
전라북도 138
 
4.9%
경기도 107
 
3.8%
전라남도 83
 
2.9%
충청북도 34
 
1.2%
충청남도 19
 
0.7%
전북특별자치도 18
 
0.6%

Length

2024-05-11T16:50:04.997697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 1332
46.8%
서울특별시 753
26.5%
경상남도 180
 
6.3%
대전광역시 177
 
6.2%
전라북도 138
 
4.9%
경기도 107
 
3.8%
전라남도 83
 
2.9%
충청북도 34
 
1.2%
충청남도 19
 
0.7%
전북특별자치도 18
 
0.6%

시군구명
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
서구
283 
부평구
232 
남동구
212 
미추홀구
187 
연수구
181 
Other values (43)
1750 

Length

Max length4
Median length3
Mean length2.9275923
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 283
 
9.9%
부평구 232
 
8.2%
남동구 212
 
7.5%
미추홀구 187
 
6.6%
연수구 181
 
6.4%
창원시 180
 
6.3%
계양구 137
 
4.8%
중구 125
 
4.4%
고창군 120
 
4.2%
강남구 108
 
3.8%
Other values (38) 1080
38.0%

Length

2024-05-11T16:50:05.298830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서구 283
 
9.9%
부평구 232
 
8.2%
남동구 212
 
7.5%
미추홀구 187
 
6.6%
연수구 181
 
6.4%
창원시 180
 
6.3%
계양구 137
 
4.8%
중구 125
 
4.4%
고창군 120
 
4.2%
강남구 108
 
3.8%
Other values (38) 1080
38.0%

시군구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct56
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27410.921
Minimum11020
Maximum48129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2024-05-11T16:50:05.571655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11020
5-th percentile11260
Q111740
median28200
Q330200
95-th percentile48121
Maximum48129
Range37109
Interquartile range (IQR)18460

Descriptive statistics

Standard deviation11974.443
Coefficient of variation (CV)0.43684935
Kurtosis-0.83712346
Mean27410.921
Median Absolute Deviation (MAD)5160
Skewness0.19382935
Sum77984071
Variance1.4338729 × 108
MonotonicityNot monotonic
2024-05-11T16:50:05.866376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28260 237
 
8.3%
28237 232
 
8.2%
28200 212
 
7.5%
28177 187
 
6.6%
23040 181
 
6.4%
28245 137
 
4.8%
47800 120
 
4.2%
11680 108
 
3.8%
41570 83
 
2.9%
28110 73
 
2.6%
Other values (46) 1275
44.8%
ValueCountFrequency (%)
11020 25
0.9%
11110 25
0.9%
11170 23
0.8%
11200 15
 
0.5%
11215 15
 
0.5%
11230 39
1.4%
11260 28
1.0%
11290 30
1.1%
11305 25
0.9%
11350 27
0.9%
ValueCountFrequency (%)
48129 34
 
1.2%
48127 30
 
1.1%
48125 43
 
1.5%
48123 27
 
0.9%
48121 46
 
1.6%
47840 4
 
0.1%
47800 120
4.2%
46840 30
 
1.1%
46790 6
 
0.2%
46130 47
 
1.7%

소재지도로명주소
Text

MISSING 

Distinct2703
Distinct (%)97.1%
Missing61
Missing (%)2.1%
Memory size22.4 KiB
2024-05-11T16:50:06.572773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length71
Median length51
Mean length21.162356
Min length13

Characters and Unicode

Total characters58916
Distinct characters450
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

Unique2625 ?
Unique (%)94.3%

Sample

1st row서울특별시 서초구 효령로 18
2nd row서울특별시 서초구 효령로34길 23-3
3rd row서울특별시 서초구 방배로 57
4th row서울특별시 서초구 서초대로27길 57
5th row서울특별시 서초구 동광로12가길 26
ValueCountFrequency (%)
인천광역시 1272
 
10.2%
서울특별시 752
 
6.0%
서구 280
 
2.2%
부평구 232
 
1.9%
남동구 212
 
1.7%
미추홀구 187
 
1.5%
경상남도 179
 
1.4%
창원시 179
 
1.4%
대전광역시 177
 
1.4%
전라북도 138
 
1.1%
Other values (3372) 8894
71.1%
2024-05-11T16:50:07.761399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
9718
 
16.5%
로 2619
 
4.4%
시 2612
 
4.4%
1 2529
 
4.3%
구 2447
 
4.2%
길 1504
 
2.6%
광 1491
 
2.5%
역 1465
 
2.5%
2 1464
 
2.5%
천 1435
 
2.4%
Other values (440) 31632
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 37167
63.1%
Decimal Number 10723
 
18.2%
Space Separator 9718
 
16.5%
Other Punctuation 381
 
0.6%
Dash Punctuation 345
 
0.6%
Open Punctuation 271
 
0.5%
Close Punctuation 270
 
0.5%
Uppercase Letter 36
 
0.1%
Lowercase Letter 3
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 2619
 
7.0%
시 2612
 
7.0%
구 2447
 
6.6%
길 1504
 
4.0%
광 1491
 
4.0%
역 1465
 
3.9%
천 1435
 
3.9%
인 1390
 
3.7%
서 1294
 
3.5%
동 891
 
2.4%
Other values (405) 20019
53.9%
Uppercase Letter
ValueCountFrequency (%)
B 10
27.8%
A 6
16.7%
C 4
 
11.1%
S 3
 
8.3%
K 2
 
5.6%
R 2
 
5.6%
U 2
 
5.6%
L 1
 
2.8%
F 1
 
2.8%
J 1
 
2.8%
Other values (4) 4
 
11.1%
Decimal Number
ValueCountFrequency (%)
1 2529
23.6%
2 1464
13.7%
3 1199
11.2%
4 985
 
9.2%
5 862
 
8.0%
6 837
 
7.8%
0 836
 
7.8%
7 742
 
6.9%
8 671
 
6.3%
9 598
 
5.6%
Other Punctuation
ValueCountFrequency (%)
, 368
96.6%
. 12
 
3.1%
· 1
 
0.3%
Lowercase Letter
ValueCountFrequency (%)
c 1
33.3%
e 1
33.3%
l 1
33.3%
Space Separator
ValueCountFrequency (%)
9718
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 345
100.0%
Open Punctuation
ValueCountFrequency (%)
( 271
100.0%
Close Punctuation
ValueCountFrequency (%)
) 270
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 37167
63.1%
Common 21710
36.8%
Latin 39
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 2619
 
7.0%
시 2612
 
7.0%
구 2447
 
6.6%
길 1504
 
4.0%
광 1491
 
4.0%
역 1465
 
3.9%
천 1435
 
3.9%
인 1390
 
3.7%
서 1294
 
3.5%
동 891
 
2.4%
Other values (405) 20019
53.9%
Common
ValueCountFrequency (%)
9718
44.8%
1 2529
 
11.6%
2 1464
 
6.7%
3 1199
 
5.5%
4 985
 
4.5%
5 862
 
4.0%
6 837
 
3.9%
0 836
 
3.9%
7 742
 
3.4%
8 671
 
3.1%
Other values (8) 1867
 
8.6%
Latin
ValueCountFrequency (%)
B 10
25.6%
A 6
15.4%
C 4
 
10.3%
S 3
 
7.7%
K 2
 
5.1%
R 2
 
5.1%
U 2
 
5.1%
c 1
 
2.6%
e 1
 
2.6%
L 1
 
2.6%
Other values (7) 7
17.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 37167
63.1%
ASCII 21748
36.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
9718
44.7%
1 2529
 
11.6%
2 1464
 
6.7%
3 1199
 
5.5%
4 985
 
4.5%
5 862
 
4.0%
6 837
 
3.8%
0 836
 
3.8%
7 742
 
3.4%
8 671
 
3.1%
Other values (24) 1905
 
8.8%
Hangul
ValueCountFrequency (%)
로 2619
 
7.0%
시 2612
 
7.0%
구 2447
 
6.6%
길 1504
 
4.0%
광 1491
 
4.0%
역 1465
 
3.9%
천 1435
 
3.9%
인 1390
 
3.7%
서 1294
 
3.5%
동 891
 
2.4%
Other values (405) 20019
53.9%
None
ValueCountFrequency (%)
· 1
100.0%

소재지지번주소
Text

MISSING 

Distinct1729
Distinct (%)95.7%
Missing1038
Missing (%)36.5%
Memory size22.4 KiB
2024-05-11T16:50:08.398086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length40
Mean length20.061428
Min length14

Characters and Unicode

Total characters36251
Distinct characters282
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

Unique1653 ?
Unique (%)91.5%

Sample

1st row서울특별시 서초구 방배동 477-8
2nd row서울특별시 서초구 방배동 985-20
3rd row서울특별시 서초구 방배동 982-2
4th row서울특별시 서초구 방배동 850-3
5th row서울특별시 서초구 방배동 1457
ValueCountFrequency (%)
인천광역시 829
 
10.7%
서울특별시 414
 
5.4%
남동구 212
 
2.7%
미추홀구 187
 
2.4%
경상남도 180
 
2.3%
창원시 180
 
2.3%
서구 162
 
2.1%
전라북도 138
 
1.8%
고창군 120
 
1.6%
계양구 109
 
1.4%
Other values (2207) 5204
67.3%
2024-05-11T16:50:09.367880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5928
 
16.4%
동 2010
 
5.5%
1 1684
 
4.6%
구 1641
 
4.5%
시 1638
 
4.5%
- 1511
 
4.2%
광 960
 
2.6%
2 941
 
2.6%
역 935
 
2.6%
천 890
 
2.5%
Other values (272) 18113
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21111
58.2%
Decimal Number 7615
 
21.0%
Space Separator 5928
 
16.4%
Dash Punctuation 1511
 
4.2%
Other Punctuation 69
 
0.2%
Close Punctuation 7
 
< 0.1%
Open Punctuation 6
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Lowercase Letter 1
 
< 0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2010
 
9.5%
구 1641
 
7.8%
시 1638
 
7.8%
광 960
 
4.5%
역 935
 
4.4%
천 890
 
4.2%
인 838
 
4.0%
서 702
 
3.3%
남 565
 
2.7%
도 544
 
2.6%
Other values (253) 10388
49.2%
Decimal Number
ValueCountFrequency (%)
1 1684
22.1%
2 941
12.4%
3 807
10.6%
4 713
9.4%
6 677
8.9%
5 613
 
8.0%
0 580
 
7.6%
7 573
 
7.5%
9 526
 
6.9%
8 501
 
6.6%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
5928
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1511
100.0%
Other Punctuation
ValueCountFrequency (%)
, 69
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 21111
58.2%
Common 15137
41.8%
Latin 3
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2010
 
9.5%
구 1641
 
7.8%
시 1638
 
7.8%
광 960
 
4.5%
역 935
 
4.4%
천 890
 
4.2%
인 838
 
4.0%
서 702
 
3.3%
남 565
 
2.7%
도 544
 
2.6%
Other values (253) 10388
49.2%
Common
ValueCountFrequency (%)
5928
39.2%
1 1684
 
11.1%
- 1511
 
10.0%
2 941
 
6.2%
3 807
 
5.3%
4 713
 
4.7%
6 677
 
4.5%
5 613
 
4.0%
0 580
 
3.8%
7 573
 
3.8%
Other values (6) 1110
 
7.3%
Latin
ValueCountFrequency (%)
e 1
33.3%
B 1
33.3%
A 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 21111
58.2%
ASCII 15140
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5928
39.2%
1 1684
 
11.1%
- 1511
 
10.0%
2 941
 
6.2%
3 807
 
5.3%
4 713
 
4.7%
6 677
 
4.5%
5 613
 
4.0%
0 580
 
3.8%
7 573
 
3.8%
Other values (9) 1113
 
7.4%
Hangul
ValueCountFrequency (%)
동 2010
 
9.5%
구 1641
 
7.8%
시 1638
 
7.8%
광 960
 
4.5%
역 935
 
4.4%
천 890
 
4.2%
인 838
 
4.0%
서 702
 
3.3%
남 565
 
2.7%
도 544
 
2.6%
Other values (253) 10388
49.2%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct2736
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.092079
Minimum34.720677
Maximum37.782623
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2024-05-11T16:50:09.779783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.720677
5-th percentile35.193744
Q137.396162
median37.481748
Q337.534688
95-th percentile37.62051
Maximum37.782623
Range3.0619465
Interquartile range (IQR)0.13852626

Descriptive statistics

Standard deviation0.8254999
Coefficient of variation (CV)0.022255423
Kurtosis1.2131748
Mean37.092079
Median Absolute Deviation (MAD)0.062726789
Skewness-1.6674666
Sum105526.97
Variance0.68145008
MonotonicityNot monotonic
2024-05-11T16:50:10.075688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.36664055 3
 
0.1%
37.522166 3
 
0.1%
35.43872865 2
 
0.1%
35.33858688 2
 
0.1%
35.463928 2
 
0.1%
35.46959899 2
 
0.1%
35.50663023 2
 
0.1%
35.36344815 2
 
0.1%
35.36923237 2
 
0.1%
35.34092366 2
 
0.1%
Other values (2726) 2823
99.2%
ValueCountFrequency (%)
34.7206769 1
< 0.1%
34.72270136 1
< 0.1%
34.7242358 1
< 0.1%
34.7251883 1
< 0.1%
34.72604035 1
< 0.1%
34.73167591 1
< 0.1%
34.7340872 1
< 0.1%
34.7341691 1
< 0.1%
34.7373934 1
< 0.1%
34.7380387 1
< 0.1%
ValueCountFrequency (%)
37.78262342 1
< 0.1%
37.78180654 1
< 0.1%
37.77296602 1
< 0.1%
37.7692319 1
< 0.1%
37.76104012 1
< 0.1%
37.7539554 1
< 0.1%
37.753713 1
< 0.1%
37.75094902 1
< 0.1%
37.74849025 1
< 0.1%
37.74822611 1
< 0.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct2734
Distinct (%)96.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.96387
Minimum126.29441
Maximum128.82083
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2024-05-11T16:50:10.361594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.29441
5-th percentile126.5573
Q1126.67587
median126.73474
Q3127.04152
95-th percentile128.56441
Maximum128.82083
Range2.5264191
Interquartile range (IQR)0.3656519

Descriptive statistics

Standard deviation0.5072674
Coefficient of variation (CV)0.0039953683
Kurtosis4.7589451
Mean126.96387
Median Absolute Deviation (MAD)0.1148412
Skewness2.2612362
Sum361212.2
Variance0.25732022
MonotonicityNot monotonic
2024-05-11T16:50:10.633545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6472802 3
 
0.1%
126.743057 3
 
0.1%
126.7102068 2
 
0.1%
126.595873 2
 
0.1%
126.6386178 2
 
0.1%
126.5409618 2
 
0.1%
126.5408388 2
 
0.1%
126.4825339 2
 
0.1%
126.4902705 2
 
0.1%
126.6472736 2
 
0.1%
Other values (2724) 2823
99.2%
ValueCountFrequency (%)
126.2944079 1
< 0.1%
126.334208 1
< 0.1%
126.3350777 1
< 0.1%
126.369038 1
< 0.1%
126.372307 1
< 0.1%
126.372447 1
< 0.1%
126.3725398 1
< 0.1%
126.373472 1
< 0.1%
126.376205 1
< 0.1%
126.3762841 1
< 0.1%
ValueCountFrequency (%)
128.820827 1
< 0.1%
128.8195965 1
< 0.1%
128.8187261 1
< 0.1%
128.8154966 1
< 0.1%
128.8131443 1
< 0.1%
128.8066118 1
< 0.1%
128.8058554 1
< 0.1%
128.8057173 1
< 0.1%
128.8034174 1
< 0.1%
128.7922004 1
< 0.1%

여성안심지킴이집전화번호
Text

MISSING 

Distinct1561
Distinct (%)78.6%
Missing858
Missing (%)30.2%
Memory size22.4 KiB
2024-05-11T16:50:11.350106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.864117
Min length9

Characters and Unicode

Total characters23574
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1520 ?
Unique (%)76.5%

Sample

1st row02-581-2209
2nd row02-587-9989
3rd row02-587-2683
4th row02-593-2218
5th row02-537-4764
ValueCountFrequency (%)
000-0000-0000 180
 
9.1%
032-000-0000 107
 
5.4%
042-251-4514 32
 
1.6%
02-351-6234 27
 
1.4%
1644-5425 14
 
0.7%
1577-9621 11
 
0.6%
080-855-5525 9
 
0.5%
1577-0711 8
 
0.4%
1577-8007 6
 
0.3%
061-000-0000 6
 
0.3%
Other values (1551) 1587
79.9%
2024-05-11T16:50:12.405467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5481
23.3%
- 3934
16.7%
2 2892
12.3%
3 2130
 
9.0%
5 1633
 
6.9%
4 1469
 
6.2%
1 1328
 
5.6%
6 1305
 
5.5%
8 1277
 
5.4%
7 1221
 
5.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 19640
83.3%
Dash Punctuation 3934
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5481
27.9%
2 2892
14.7%
3 2130
 
10.8%
5 1633
 
8.3%
4 1469
 
7.5%
1 1328
 
6.8%
6 1305
 
6.6%
8 1277
 
6.5%
7 1221
 
6.2%
9 904
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 3934
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23574
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5481
23.3%
- 3934
16.7%
2 2892
12.3%
3 2130
 
9.0%
5 1633
 
6.9%
4 1469
 
6.2%
1 1328
 
5.6%
6 1305
 
5.5%
8 1277
 
5.4%
7 1221
 
5.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23574
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5481
23.3%
- 3934
16.7%
2 2892
12.3%
3 2130
 
9.0%
5 1633
 
6.9%
4 1469
 
6.2%
1 1328
 
5.6%
6 1305
 
5.5%
8 1277
 
5.4%
7 1221
 
5.2%

관할경찰서명
Text

Distinct130
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
2024-05-11T16:50:13.036896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length7
Mean length6.4949033
Min length4

Characters and Unicode

Total characters18478
Distinct characters134
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)0.7%

Sample

1st row방배경찰서
2nd row방배경찰서
3rd row방배경찰서
4th row방배경찰서
5th row방배경찰서
ValueCountFrequency (%)
인천서부경찰서 237
 
8.2%
인천남동경찰서 210
 
7.3%
인천미추홀경찰서 187
 
6.5%
인천연수경찰서 181
 
6.3%
인천계양경찰서 137
 
4.8%
고창경찰서 120
 
4.2%
인천삼산경찰서 116
 
4.0%
인천부평경찰서 116
 
4.0%
인천중부경찰서 107
 
3.7%
서울강남경찰서 75
 
2.6%
Other values (121) 1389
48.3%
2024-05-11T16:50:13.974724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 3267
17.7%
경 2454
13.3%
찰 2453
13.3%
천 1340
 
7.3%
인 1293
 
7.0%
부 660
 
3.6%
대 383
 
2.1%
울 374
 
2.0%
동 358
 
1.9%
남 332
 
1.8%
Other values (124) 5564
30.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18435
99.8%
Space Separator 30
 
0.2%
Decimal Number 13
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 3267
17.7%
경 2454
13.3%
찰 2453
13.3%
천 1340
 
7.3%
인 1293
 
7.0%
부 660
 
3.6%
대 383
 
2.1%
울 374
 
2.0%
동 358
 
1.9%
남 332
 
1.8%
Other values (118) 5521
29.9%
Decimal Number
ValueCountFrequency (%)
1 5
38.5%
4 3
23.1%
5 2
 
15.4%
2 2
 
15.4%
6 1
 
7.7%
Space Separator
ValueCountFrequency (%)
30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18435
99.8%
Common 43
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 3267
17.7%
경 2454
13.3%
찰 2453
13.3%
천 1340
 
7.3%
인 1293
 
7.0%
부 660
 
3.6%
대 383
 
2.1%
울 374
 
2.0%
동 358
 
1.9%
남 332
 
1.8%
Other values (118) 5521
29.9%
Common
ValueCountFrequency (%)
30
69.8%
1 5
 
11.6%
4 3
 
7.0%
5 2
 
4.7%
2 2
 
4.7%
6 1
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18435
99.8%
ASCII 43
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 3267
17.7%
경 2454
13.3%
찰 2453
13.3%
천 1340
 
7.3%
인 1293
 
7.0%
부 660
 
3.6%
대 383
 
2.1%
울 374
 
2.0%
동 358
 
1.9%
남 332
 
1.8%
Other values (118) 5521
29.9%
ASCII
ValueCountFrequency (%)
30
69.8%
1 5
 
11.6%
4 3
 
7.0%
5 2
 
4.7%
2 2
 
4.7%
6 1
 
2.3%

지정연도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)0.5%
Missing817
Missing (%)28.7%
Infinite0
Infinite (%)0.0%
Mean2018.7761
Minimum2014
Maximum2023
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2024-05-11T16:50:14.238403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2015
Q12018
median2018
Q32020
95-th percentile2023
Maximum2023
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.2138244
Coefficient of variation (CV)0.0010966171
Kurtosis-0.32574448
Mean2018.7761
Median Absolute Deviation (MAD)1
Skewness0.084412518
Sum4094078
Variance4.9010187
MonotonicityNot monotonic
2024-05-11T16:50:14.481042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2018 620
21.8%
2020 256
 
9.0%
2017 252
 
8.9%
2019 216
 
7.6%
2022 179
 
6.3%
2021 139
 
4.9%
2023 132
 
4.6%
2016 96
 
3.4%
2014 89
 
3.1%
2015 49
 
1.7%
(Missing) 817
28.7%
ValueCountFrequency (%)
2014 89
 
3.1%
2015 49
 
1.7%
2016 96
 
3.4%
2017 252
8.9%
2018 620
21.8%
2019 216
 
7.6%
2020 256
9.0%
2021 139
 
4.9%
2022 179
 
6.3%
2023 132
 
4.6%
ValueCountFrequency (%)
2023 132
 
4.6%
2022 179
 
6.3%
2021 139
 
4.9%
2020 256
9.0%
2019 216
 
7.6%
2018 620
21.8%
2017 252
8.9%
2016 96
 
3.4%
2015 49
 
1.7%
2014 89
 
3.1%

운영여부
Boolean

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size2.9 KiB
True
2813 
False
 
32
ValueCountFrequency (%)
True 2813
98.9%
False 32
 
1.1%
2024-05-11T16:50:14.681916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

데이터기준일자
Date

Distinct47
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
Minimum2021-11-01 00:00:00
Maximum2024-03-31 00:00:00
2024-05-11T16:50:14.911218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:50:15.298250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)

제공기관코드
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3719895.1
Minimum3000000
Maximum5670000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size25.1 KiB
2024-05-11T16:50:15.649133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3000000
5-th percentile3060000
Q13240000
median3530000
Q33650000
95-th percentile5670000
Maximum5670000
Range2670000
Interquartile range (IQR)410000

Descriptive statistics

Standard deviation681497.4
Coefficient of variation (CV)0.18320339
Kurtosis2.0121106
Mean3719895.1
Median Absolute Deviation (MAD)130000
Skewness1.6895935
Sum1.0583102 × 1010
Variance4.6443871 × 1011
MonotonicityNot monotonic
2024-05-11T16:50:16.021377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3560000 237
 
8.3%
3540000 232
 
8.2%
3530000 212
 
7.5%
3510500 187
 
6.6%
3520000 181
 
6.4%
5670000 180
 
6.3%
3550000 137
 
4.8%
3220000 108
 
3.8%
4090000 83
 
2.9%
3490000 73
 
2.6%
Other values (44) 1215
42.7%
ValueCountFrequency (%)
3000000 25
0.9%
3010000 25
0.9%
3020000 23
0.8%
3030000 15
 
0.5%
3040000 15
 
0.5%
3050000 39
1.4%
3060000 28
1.0%
3070000 30
1.1%
3080000 25
0.9%
3100000 27
0.9%
ValueCountFrequency (%)
5670000 180
6.3%
5210000 4
 
0.1%
4950000 30
 
1.1%
4900000 6
 
0.2%
4810000 47
 
1.7%
4781000 60
 
2.1%
4780000 60
 
2.1%
4681000 18
 
0.6%
4680000 18
 
0.6%
4600000 19
 
0.7%

제공기관명
Text

Distinct54
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size22.4 KiB
2024-05-11T16:50:16.497820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length8.7673111
Min length7

Characters and Unicode

Total characters24943
Distinct characters75
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 서초구
2nd row서울특별시 서초구
3rd row서울특별시 서초구
4th row서울특별시 서초구
5th row서울특별시 서초구
ValueCountFrequency (%)
인천광역시 1332
23.4%
서울특별시 753
 
13.2%
서구 283
 
5.0%
부평구 232
 
4.1%
남동구 212
 
3.7%
미추홀구 187
 
3.3%
연수구 181
 
3.2%
경상남도 180
 
3.2%
창원시 180
 
3.2%
대전광역시 177
 
3.1%
Other values (49) 1973
34.7%
2024-05-11T16:50:17.308358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2845
 
11.4%
시 2632
 
10.6%
구 2254
 
9.0%
광 1524
 
6.1%
역 1509
 
6.0%
천 1383
 
5.5%
인 1332
 
5.3%
서 1122
 
4.5%
별 831
 
3.3%
특 831
 
3.3%
Other values (65) 8680
34.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22098
88.6%
Space Separator 2845
 
11.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2632
 
11.9%
구 2254
 
10.2%
광 1524
 
6.9%
역 1509
 
6.8%
천 1383
 
6.3%
인 1332
 
6.0%
서 1122
 
5.1%
별 831
 
3.8%
특 831
 
3.8%
울 753
 
3.4%
Other values (64) 7927
35.9%
Space Separator
ValueCountFrequency (%)
2845
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22098
88.6%
Common 2845
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2632
 
11.9%
구 2254
 
10.2%
광 1524
 
6.9%
역 1509
 
6.8%
천 1383
 
6.3%
인 1332
 
6.0%
서 1122
 
5.1%
별 831
 
3.8%
특 831
 
3.8%
울 753
 
3.4%
Other values (64) 7927
35.9%
Common
ValueCountFrequency (%)
2845
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22098
88.6%
ASCII 2845
 
11.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2845
100.0%
Hangul
ValueCountFrequency (%)
시 2632
 
11.9%
구 2254
 
10.2%
광 1524
 
6.9%
역 1509
 
6.8%
천 1383
 
6.3%
인 1332
 
6.0%
서 1122
 
5.1%
별 831
 
3.8%
특 831
 
3.8%
울 753
 
3.4%
Other values (64) 7927
35.9%

Interactions

2024-05-11T16:50:00.270322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:55.223954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:56.547063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:57.826007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:59.094904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:50:00.476220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:55.550740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:56.904060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:58.080616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:59.337453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:50:00.749445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:55.891450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:57.081315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:58.378642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:59.610762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:50:01.004321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:56.094222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:57.302558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:58.727208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:59.843263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:50:01.209406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:56.305518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:57.598561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:49:58.908574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T16:50:00.037355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T16:50:17.629374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명시군구코드위도경도지정연도운영여부데이터기준일자제공기관코드제공기관명
시도명1.0000.9950.9270.9560.9170.8090.2170.9890.9621.000
시군구명0.9951.0000.9930.9880.9790.9320.6720.9990.9991.000
시군구코드0.9270.9931.0000.8180.8210.7570.2190.9960.8931.000
위도0.9560.9880.8181.0000.8520.7270.2640.9800.9650.993
경도0.9170.9790.8210.8521.0000.8640.1800.9630.8800.994
지정연도0.8090.9320.7570.7270.8641.0000.1050.9380.7870.941
운영여부0.2170.6720.2190.2640.1800.1051.0000.6460.0750.671
데이터기준일자0.9890.9990.9960.9800.9630.9380.6461.0000.9881.000
제공기관코드0.9620.9990.8930.9650.8800.7870.0750.9881.0001.000
제공기관명1.0001.0001.0000.9930.9940.9410.6711.0001.0001.000
2024-05-11T16:50:17.953051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명운영여부시군구명
시도명1.0000.2080.937
운영여부0.2081.0000.541
시군구명0.9370.5411.000
2024-05-11T16:50:18.156176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구코드위도경도지정연도제공기관코드시도명시군구명운영여부
시군구코드1.000-0.521-0.0220.2180.9920.8010.9240.235
위도-0.5211.000-0.144-0.147-0.5410.8250.8900.202
경도-0.022-0.1441.000-0.215-0.0050.7490.8430.179
지정연도0.218-0.147-0.2151.0000.2120.5280.7220.104
제공기관코드0.992-0.541-0.0050.2121.0000.8450.9860.057
시도명0.8010.8250.7490.5280.8451.0000.9370.208
시군구명0.9240.8900.8430.7220.9860.9371.0000.541
운영여부0.2350.2020.1790.1040.0570.2080.5411.000

Missing values

2024-05-11T16:50:01.525774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T16:50:01.947803image/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.
2024-05-11T16:50:02.840129image/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

점포명시도명시군구명시군구코드소재지도로명주소소재지지번주소위도경도여성안심지킴이집전화번호관할경찰서명지정연도운영여부데이터기준일자제공기관코드제공기관명
0미니스톱 방배본점서울특별시서초구11650서울특별시 서초구 효령로 18서울특별시 서초구 방배동 477-837.477143126.98706402-581-2209방배경찰서2015Y2023-07-053210000서울특별시 서초구
1미니스톱 방배중앙점서울특별시서초구11650서울특별시 서초구 효령로34길 23-3서울특별시 서초구 방배동 985-2037.480739126.99976502-587-9989방배경찰서2015Y2023-07-053210000서울특별시 서초구
2미니스톱 방배현대점서울특별시서초구11650서울특별시 서초구 방배로 57서울특별시 서초구 방배동 982-237.479658126.99843502-587-2683방배경찰서2015Y2023-07-053210000서울특별시 서초구
3GS25 방배E푸른점서울특별시서초구11650서울특별시 서초구 서초대로27길 57서울특별시 서초구 방배동 850-337.490473126.99342402-593-2218방배경찰서2015Y2023-07-053210000서울특별시 서초구
4CU 방배이수역점서울특별시서초구11650서울특별시 서초구 동광로12가길 26서울특별시 서초구 방배동 145737.48741126.98352402-537-4764방배경찰서2015Y2023-07-053210000서울특별시 서초구
5세븐일레븐 반포사평로점서울특별시서초구11650서울특별시 서초구 사평대로6길 7서울특별시 서초구 방배동 727-337.497719126.99093102-535-3955방배경찰서2015Y2023-07-053210000서울특별시 서초구
6CU 방배롯데캐슬점서울특별시서초구11650서울특별시 서초구 방배중앙로29길 25서울특별시 서초구 방배동 763-137.496401126.98402202-532-3305방배경찰서2015Y2023-07-053210000서울특별시 서초구
7미니스톱 방배홈타운점서울특별시서초구11650서울특별시 서초구 동광로 9서울특별시 서초구 방배동 213337.491024126.98404502-594-3842방배경찰서2015Y2023-07-053210000서울특별시 서초구
8세븐일레븐 서초19호점서울특별시서초구11650서울특별시 서초구 사임당로17길 113서울특별시 서초구 서초동 1668-1237.493325127.01878202-3472-2483서초경찰서2015Y2023-07-053210000서울특별시 서초구
9GS25 서초아남점서울특별시서초구11650서울특별시 서초구 서초대로64길 55서울특별시 서초구 서초동 1635-637.493229127.021552080-855-5525서초경찰서2015Y2023-07-053210000서울특별시 서초구
점포명시도명시군구명시군구코드소재지도로명주소소재지지번주소위도경도여성안심지킴이집전화번호관할경찰서명지정연도운영여부데이터기준일자제공기관코드제공기관명
2835세븐일레븐 거평타운점서울특별시강남구11680서울특별시 강남구 봉은사로 129<NA>37.505917127.028308<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2836세븐일레븐 논현12호점서울특별시강남구11680서울특별시 강남구 강남대로128길 48<NA>37.511153127.025632<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2837세븐일레븐 논현13호점서울특별시강남구11680서울특별시 강남구 학동로43길 14<NA>37.517034127.037509<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2838세븐일레븐 논현보람점서울특별시강남구11680서울특별시 강남구 논현로132길 15<NA>37.515219127.031807<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2839세븐일레븐 논현제일점서울특별시강남구11680서울특별시 강남구 선릉로 641<NA>37.513777127.042287<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2840세븐일레븐 논현한바위점서울특별시강남구11680서울특별시 강남구 강남대로118길 30<NA>37.506859127.025768<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2841세븐일레븐 논현힐탑점서울특별시강남구11680서울특별시 강남구 논현로 639<NA>37.510531127.032098<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2842세븐일레븐 뉴강남센터점서울특별시강남구11680서울특별시 강남구 강남대로96길 9<NA>37.499896127.027723<NA>서울강남경찰서<NA>Y2024-01-163220000서울특별시 강남구
2843세븐일레븐 대치10호점서울특별시강남구11680서울특별시 강남구 역삼로64길 25<NA>37.499439127.053564<NA>서울수서경찰서<NA>Y2024-01-163220000서울특별시 강남구
2844CU 부평형제점인천광역시부평구28237인천광역시 부평구 광장로30번길 78<NA>37.490076126.728754032-527-6110인천부평경찰서2018Y2024-03-313540000인천광역시 부평구