Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells28
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory143.3 B

Variable types

Text5
Categorical6
Numeric6

Alerts

ldgs_nm is highly overall correlated with fclty_la and 6 other fieldsHigh correlation
sclas_nm is highly overall correlated with fclty_tel_no and 3 other fieldsHigh correlation
ldgs_road_nm_addr is highly overall correlated with fclty_la and 6 other fieldsHigh correlation
lclas_nm is highly overall correlated with fclty_la and 8 other fieldsHigh correlation
ldgs_eng_road_nm_addr is highly overall correlated with fclty_la and 6 other fieldsHigh correlation
mlsfc_nm is highly overall correlated with fclty_la and 5 other fieldsHigh correlation
fclty_tel_no is highly overall correlated with sclas_nmHigh correlation
fclty_la is highly overall correlated with ldgs_la and 5 other fieldsHigh correlation
fclty_lo is highly overall correlated with ldgs_lo and 6 other fieldsHigh correlation
ldgs_la is highly overall correlated with fclty_la and 5 other fieldsHigh correlation
ldgs_lo is highly overall correlated with fclty_lo and 5 other fieldsHigh correlation
lclas_nm is highly imbalanced (61.1%)Imbalance
mlsfc_nm is highly imbalanced (60.3%)Imbalance
fclty_tel_no has 25 (25.0%) missing valuesMissing
fclty_road_nm_addr has 3 (3.0%) missing valuesMissing
esntl_id has unique valuesUnique
fclty_kwa_dstnc_km_value has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:39:21.550022
Analysis finished2023-12-10 09:39:29.944437
Duration8.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

esntl_id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:30.196989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters18
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowKCEFSPO20N000000001
2nd rowKCEFSPO20N000015950
3rd rowKCEFSPO20N000000003
4th rowKCEFSPO20N000000004
5th rowKCEFSPO20N000000005
ValueCountFrequency (%)
kcefspo20n000000001 1
 
1.0%
kcefspo20n000000063 1
 
1.0%
kcefspo20n000000074 1
 
1.0%
kcefspo20n000000073 1
 
1.0%
kcefspo20n000000072 1
 
1.0%
kcefspo20n000000071 1
 
1.0%
kcefspo20n000000070 1
 
1.0%
kcefspo20n000000069 1
 
1.0%
kcefspo20n000000068 1
 
1.0%
kcefspo20n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:39:30.815603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 809
42.6%
2 119
 
6.3%
K 100
 
5.3%
O 100
 
5.3%
C 100
 
5.3%
N 100
 
5.3%
P 100
 
5.3%
S 100
 
5.3%
F 100
 
5.3%
E 100
 
5.3%
Other values (8) 172
 
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
57.9%
Uppercase Letter 800
42.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 809
73.5%
2 119
 
10.8%
5 26
 
2.4%
1 25
 
2.3%
9 23
 
2.1%
4 20
 
1.8%
6 20
 
1.8%
7 20
 
1.8%
3 19
 
1.7%
8 19
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
K 100
12.5%
O 100
12.5%
C 100
12.5%
N 100
12.5%
P 100
12.5%
S 100
12.5%
F 100
12.5%
E 100
12.5%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
57.9%
Latin 800
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 809
73.5%
2 119
 
10.8%
5 26
 
2.4%
1 25
 
2.3%
9 23
 
2.1%
4 20
 
1.8%
6 20
 
1.8%
7 20
 
1.8%
3 19
 
1.7%
8 19
 
1.7%
Latin
ValueCountFrequency (%)
K 100
12.5%
O 100
12.5%
C 100
12.5%
N 100
12.5%
P 100
12.5%
S 100
12.5%
F 100
12.5%
E 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 809
42.6%
2 119
 
6.3%
K 100
 
5.3%
O 100
 
5.3%
C 100
 
5.3%
N 100
 
5.3%
P 100
 
5.3%
S 100
 
5.3%
F 100
 
5.3%
E 100
 
5.3%
Other values (8) 172
 
9.1%

lclas_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
문화,예술
83 
교육,학문
10 
가정,생활
 
5
서비스,산업
 
1
사회,공공기관
 
1

Length

Max length7
Median length5
Mean length5.03
Min length5

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row문화,예술
2nd row문화,예술
3rd row문화,예술
4th row문화,예술
5th row문화,예술

Common Values

ValueCountFrequency (%)
문화,예술 83
83.0%
교육,학문 10
 
10.0%
가정,생활 5
 
5.0%
서비스,산업 1
 
1.0%
사회,공공기관 1
 
1.0%

Length

2023-12-10T18:39:31.123743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:31.363539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화,예술 83
83.0%
교육,학문 10
 
10.0%
가정,생활 5
 
5.0%
서비스,산업 1
 
1.0%
사회,공공기관 1
 
1.0%

mlsfc_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
미술,공예
79 
교육단체
9 
문화시설
 
4
<NA>
 
2
미용
 
2
Other values (4)
 
4

Length

Max length8
Median length5
Mean length4.75
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row미술,공예
2nd row문화시설
3rd row미술,공예
4th row미술,공예
5th row미술,공예

Common Values

ValueCountFrequency (%)
미술,공예 79
79.0%
교육단체 9
 
9.0%
문화시설 4
 
4.0%
<NA> 2
 
2.0%
미용 2
 
2.0%
패션 1
 
1.0%
학원 1
 
1.0%
정보통신 1
 
1.0%
행정기관부속시설 1
 
1.0%

Length

2023-12-10T18:39:31.575891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:39:31.842182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미술,공예 79
79.0%
교육단체 9
 
9.0%
문화시설 4
 
4.0%
na 2
 
2.0%
미용 2
 
2.0%
패션 1
 
1.0%
학원 1
 
1.0%
정보통신 1
 
1.0%
행정기관부속시설 1
 
1.0%

sclas_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
31 
액자,표구
18 
화랑
16 
화방
10 
체험학습장
9 
Other values (8)
16 

Length

Max length12
Median length5
Mean length3.92
Min length2

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st row<NA>
2nd row전시관
3rd row<NA>
4th row액자,표구
5th row액자,표구

Common Values

ValueCountFrequency (%)
<NA> 31
31.0%
액자,표구 18
18.0%
화랑 16
16.0%
화방 10
 
10.0%
체험학습장 9
 
9.0%
수예,자수 6
 
6.0%
전시관 3
 
3.0%
목공예 2
 
2.0%
체형관리,다이어트,비만 1
 
1.0%
피부관리 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T18:39:32.253132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 31
31.0%
액자,표구 18
18.0%
화랑 16
16.0%
화방 10
 
10.0%
체험학습장 9
 
9.0%
수예,자수 6
 
6.0%
전시관 3
 
3.0%
목공예 2
 
2.0%
체형관리,다이어트,비만 1
 
1.0%
피부관리 1
 
1.0%
Other values (3) 3
 
3.0%

fclty_nm
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:32.674128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length16
Mean length6.78
Min length2

Characters and Unicode

Total characters678
Distinct characters212
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)93.0%

Sample

1st row반지마을 영등포점
2nd row애니메이션체험관
3rd row품프로젝트
4th row그림액자거울
5th row솔방울액자표구
ValueCountFrequency (%)
서울시민안전체험관 4
 
3.2%
애니메이션체험관 3
 
2.4%
삼분의일 2
 
1.6%
강남체험관 2
 
1.6%
김치체험관 2
 
1.6%
갤러리 2
 
1.6%
갤러리엠 1
 
0.8%
갤러리밈 1
 
0.8%
서원서예백화점 1
 
0.8%
갤러리브레송 1
 
0.8%
Other values (106) 106
84.8%
2023-12-10T18:39:33.739947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
체 26
 
3.8%
25
 
3.7%
험 25
 
3.7%
관 23
 
3.4%
리 20
 
2.9%
아 17
 
2.5%
방 16
 
2.4%
자 15
 
2.2%
트 15
 
2.2%
러 14
 
2.1%
Other values (202) 482
71.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 641
94.5%
Space Separator 25
 
3.7%
Uppercase Letter 6
 
0.9%
Decimal Number 6
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
체 26
 
4.1%
험 25
 
3.9%
관 23
 
3.6%
리 20
 
3.1%
아 17
 
2.7%
방 16
 
2.5%
자 15
 
2.3%
트 15
 
2.3%
러 14
 
2.2%
화 13
 
2.0%
Other values (191) 457
71.3%
Uppercase Letter
ValueCountFrequency (%)
K 2
33.3%
H 1
16.7%
Y 1
16.7%
E 1
16.7%
S 1
16.7%
Decimal Number
ValueCountFrequency (%)
8 2
33.3%
2 1
16.7%
4 1
16.7%
1 1
16.7%
9 1
16.7%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 641
94.5%
Common 31
 
4.6%
Latin 6
 
0.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
체 26
 
4.1%
험 25
 
3.9%
관 23
 
3.6%
리 20
 
3.1%
아 17
 
2.7%
방 16
 
2.5%
자 15
 
2.3%
트 15
 
2.3%
러 14
 
2.2%
화 13
 
2.0%
Other values (191) 457
71.3%
Common
ValueCountFrequency (%)
25
80.6%
8 2
 
6.5%
2 1
 
3.2%
4 1
 
3.2%
1 1
 
3.2%
9 1
 
3.2%
Latin
ValueCountFrequency (%)
K 2
33.3%
H 1
16.7%
Y 1
16.7%
E 1
16.7%
S 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 641
94.5%
ASCII 37
 
5.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
체 26
 
4.1%
험 25
 
3.9%
관 23
 
3.6%
리 20
 
3.1%
아 17
 
2.7%
방 16
 
2.5%
자 15
 
2.3%
트 15
 
2.3%
러 14
 
2.2%
화 13
 
2.0%
Other values (191) 457
71.3%
ASCII
ValueCountFrequency (%)
25
67.6%
K 2
 
5.4%
8 2
 
5.4%
H 1
 
2.7%
2 1
 
2.7%
4 1
 
2.7%
1 1
 
2.7%
9 1
 
2.7%
Y 1
 
2.7%
E 1
 
2.7%

fclty_tel_no
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct74
Distinct (%)98.7%
Missing25
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean6.604793 × 108
Minimum15990011
Maximum7.0762229 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:34.004559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15990011
5-th percentile23236405
Q127226630
median28413076
Q32.2274578 × 108
95-th percentile7.041201 × 109
Maximum7.0762229 × 109
Range7.0602329 × 109
Interquartile range (IQR)1.9551915 × 108

Descriptive statistics

Standard deviation1.7454485 × 109
Coefficient of variation (CV)2.6426998
Kurtosis10.050446
Mean6.604793 × 108
Median Absolute Deviation (MAD)5226025
Skewness3.3768197
Sum4.9535947 × 1010
Variance3.0465904 × 1018
MonotonicityNot monotonic
2023-12-10T18:39:34.228572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7043340758 2
 
2.0%
27338877 1
 
1.0%
27220999 1
 
1.0%
27364804 1
 
1.0%
222232533 1
 
1.0%
260215200 1
 
1.0%
27366669 1
 
1.0%
27359500 1
 
1.0%
27231661 1
 
1.0%
27272336 1
 
1.0%
Other values (64) 64
64.0%
(Missing) 25
 
25.0%
ValueCountFrequency (%)
15990011 1
1.0%
23187051 1
1.0%
23230666 1
1.0%
23230773 1
1.0%
23238819 1
1.0%
23239870 1
1.0%
23338976 1
1.0%
23367369 1
1.0%
23923553 1
1.0%
23926868 1
1.0%
ValueCountFrequency (%)
7076222888 1
1.0%
7050251004 1
1.0%
7043340758 2
2.0%
7040283987 1
1.0%
1092308767 1
1.0%
1092090906 1
1.0%
1088953368 1
1.0%
1085522846 1
1.0%
1065821516 1
1.0%
1053676433 1
1.0%

fclty_url
Text

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:34.761253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length37
Median length36
Mean length35.42
Min length34

Characters and Unicode

Total characters3542
Distinct characters23
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique93 ?
Unique (%)93.0%

Sample

1st rowhttp://place.map.kakao.com/1054487680
2nd rowhttp://place.map.kakao.com/12706646
3rd rowhttp://place.map.kakao.com/1268069100
4th rowhttp://place.map.kakao.com/20551427
5th rowhttp://place.map.kakao.com/20537320
ValueCountFrequency (%)
http://place.map.kakao.com/12706646 3
 
3.0%
http://place.map.kakao.com/897890238 2
 
2.0%
http://place.map.kakao.com/229742217 2
 
2.0%
http://place.map.kakao.com/2073796550 1
 
1.0%
http://place.map.kakao.com/26933039 1
 
1.0%
http://place.map.kakao.com/1511830976 1
 
1.0%
http://place.map.kakao.com/502438956 1
 
1.0%
http://place.map.kakao.com/1562489804 1
 
1.0%
http://place.map.kakao.com/8684904 1
 
1.0%
http://place.map.kakao.com/12728195 1
 
1.0%
Other values (86) 86
86.0%
2023-12-10T18:39:35.437865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 400
 
11.3%
p 300
 
8.5%
/ 300
 
8.5%
. 300
 
8.5%
k 200
 
5.6%
t 200
 
5.6%
c 200
 
5.6%
o 200
 
5.6%
m 200
 
5.6%
1 128
 
3.6%
Other values (13) 1114
31.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2000
56.5%
Decimal Number 842
23.8%
Other Punctuation 700
 
19.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 400
20.0%
p 300
15.0%
k 200
10.0%
t 200
10.0%
c 200
10.0%
o 200
10.0%
m 200
10.0%
h 100
 
5.0%
e 100
 
5.0%
l 100
 
5.0%
Decimal Number
ValueCountFrequency (%)
1 128
15.2%
2 100
11.9%
8 86
10.2%
7 84
10.0%
5 81
9.6%
4 79
9.4%
0 76
9.0%
6 75
8.9%
9 69
8.2%
3 64
7.6%
Other Punctuation
ValueCountFrequency (%)
/ 300
42.9%
. 300
42.9%
: 100
 
14.3%

Most occurring scripts

ValueCountFrequency (%)
Latin 2000
56.5%
Common 1542
43.5%

Most frequent character per script

Common
ValueCountFrequency (%)
/ 300
19.5%
. 300
19.5%
1 128
8.3%
2 100
 
6.5%
: 100
 
6.5%
8 86
 
5.6%
7 84
 
5.4%
5 81
 
5.3%
4 79
 
5.1%
0 76
 
4.9%
Other values (3) 208
13.5%
Latin
ValueCountFrequency (%)
a 400
20.0%
p 300
15.0%
k 200
10.0%
t 200
10.0%
c 200
10.0%
o 200
10.0%
m 200
10.0%
h 100
 
5.0%
e 100
 
5.0%
l 100
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3542
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 400
 
11.3%
p 300
 
8.5%
/ 300
 
8.5%
. 300
 
8.5%
k 200
 
5.6%
t 200
 
5.6%
c 200
 
5.6%
o 200
 
5.6%
m 200
 
5.6%
1 128
 
3.6%
Other values (13) 1114
31.5%

fclty_road_nm_addr
Text

MISSING 

Distinct86
Distinct (%)88.7%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T18:39:35.913276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length19
Mean length15.226804
Min length11

Characters and Unicode

Total characters1477
Distinct characters86
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique78 ?
Unique (%)80.4%

Sample

1st row서울 영등포구 영신로34길 19
2nd row경기 파주시 탄현면 헤이리마을길 93-143
3rd row서울 영등포구 영등포로43길 17
4th row서울 영등포구 신길로 189
5th row서울 영등포구 영신로40길 9
ValueCountFrequency (%)
서울 93
23.8%
중구 31
 
7.9%
종로구 24
 
6.1%
광진구 14
 
3.6%
인사동길 9
 
2.3%
능동로 7
 
1.8%
마포구 7
 
1.8%
영등포구 6
 
1.5%
서대문구 6
 
1.5%
을지로 5
 
1.3%
Other values (131) 189
48.3%
2023-12-10T18:39:36.619832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
294
19.9%
서 99
 
6.7%
로 96
 
6.5%
구 93
 
6.3%
울 93
 
6.3%
1 70
 
4.7%
길 60
 
4.1%
3 55
 
3.7%
4 39
 
2.6%
동 38
 
2.6%
Other values (76) 540
36.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 857
58.0%
Decimal Number 307
 
20.8%
Space Separator 294
 
19.9%
Dash Punctuation 19
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 99
 
11.6%
로 96
 
11.2%
구 93
 
10.9%
울 93
 
10.9%
길 60
 
7.0%
동 38
 
4.4%
중 33
 
3.9%
종 24
 
2.8%
인 17
 
2.0%
사 17
 
2.0%
Other values (64) 287
33.5%
Decimal Number
ValueCountFrequency (%)
1 70
22.8%
3 55
17.9%
4 39
12.7%
2 36
11.7%
5 23
 
7.5%
9 20
 
6.5%
7 18
 
5.9%
0 17
 
5.5%
8 16
 
5.2%
6 13
 
4.2%
Space Separator
ValueCountFrequency (%)
294
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 857
58.0%
Common 620
42.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 99
 
11.6%
로 96
 
11.2%
구 93
 
10.9%
울 93
 
10.9%
길 60
 
7.0%
동 38
 
4.4%
중 33
 
3.9%
종 24
 
2.8%
인 17
 
2.0%
사 17
 
2.0%
Other values (64) 287
33.5%
Common
ValueCountFrequency (%)
294
47.4%
1 70
 
11.3%
3 55
 
8.9%
4 39
 
6.3%
2 36
 
5.8%
5 23
 
3.7%
9 20
 
3.2%
- 19
 
3.1%
7 18
 
2.9%
0 17
 
2.7%
Other values (2) 29
 
4.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 857
58.0%
ASCII 620
42.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
294
47.4%
1 70
 
11.3%
3 55
 
8.9%
4 39
 
6.3%
2 36
 
5.8%
5 23
 
3.7%
9 20
 
3.2%
- 19
 
3.1%
7 18
 
2.9%
0 17
 
2.7%
Other values (2) 29
 
4.7%
Hangul
ValueCountFrequency (%)
서 99
 
11.6%
로 96
 
11.2%
구 93
 
10.9%
울 93
 
10.9%
길 60
 
7.0%
동 38
 
4.4%
중 33
 
3.9%
종 24
 
2.8%
인 17
 
2.0%
사 17
 
2.0%
Other values (64) 287
33.5%

lnm_addr
Text

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:37.432626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length20
Mean length15.91
Min length11

Characters and Unicode

Total characters1591
Distinct characters88
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique80 ?
Unique (%)80.0%

Sample

1st row서울 영등포구 영등포동4가 146-5
2nd row경기 파주시 탄현면 법흥리 1652-86
3rd row서울 영등포구 영등포동5가 41-6
4th row서울 영등포구 신길동 226-113
5th row서울 영등포구 영등포동6가 121-5
ValueCountFrequency (%)
서울 94
23.3%
중구 31
 
7.7%
종로구 24
 
6.0%
광진구 15
 
3.7%
관훈동 11
 
2.7%
충무로2가 9
 
2.2%
화양동 7
 
1.7%
마포구 7
 
1.7%
서대문구 6
 
1.5%
영등포구 6
 
1.5%
Other values (133) 193
47.9%
2023-12-10T18:39:38.400919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
303
19.0%
서 105
 
6.6%
울 94
 
5.9%
구 94
 
5.9%
1 89
 
5.6%
동 79
 
5.0%
- 77
 
4.8%
2 66
 
4.1%
3 44
 
2.8%
로 43
 
2.7%
Other values (78) 597
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 817
51.4%
Decimal Number 394
24.8%
Space Separator 303
 
19.0%
Dash Punctuation 77
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 105
 
12.9%
울 94
 
11.5%
구 94
 
11.5%
동 79
 
9.7%
로 43
 
5.3%
중 31
 
3.8%
가 26
 
3.2%
종 24
 
2.9%
포 17
 
2.1%
진 15
 
1.8%
Other values (66) 289
35.4%
Decimal Number
ValueCountFrequency (%)
1 89
22.6%
2 66
16.8%
3 44
11.2%
4 38
9.6%
8 37
9.4%
5 35
 
8.9%
6 26
 
6.6%
7 22
 
5.6%
0 19
 
4.8%
9 18
 
4.6%
Space Separator
ValueCountFrequency (%)
303
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 77
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 817
51.4%
Common 774
48.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 105
 
12.9%
울 94
 
11.5%
구 94
 
11.5%
동 79
 
9.7%
로 43
 
5.3%
중 31
 
3.8%
가 26
 
3.2%
종 24
 
2.9%
포 17
 
2.1%
진 15
 
1.8%
Other values (66) 289
35.4%
Common
ValueCountFrequency (%)
303
39.1%
1 89
 
11.5%
- 77
 
9.9%
2 66
 
8.5%
3 44
 
5.7%
4 38
 
4.9%
8 37
 
4.8%
5 35
 
4.5%
6 26
 
3.4%
7 22
 
2.8%
Other values (2) 37
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 817
51.4%
ASCII 774
48.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
303
39.1%
1 89
 
11.5%
- 77
 
9.9%
2 66
 
8.5%
3 44
 
5.7%
4 38
 
4.9%
8 37
 
4.8%
5 35
 
4.5%
6 26
 
3.4%
7 22
 
2.8%
Other values (2) 37
 
4.8%
Hangul
ValueCountFrequency (%)
서 105
 
12.9%
울 94
 
11.5%
구 94
 
11.5%
동 79
 
9.7%
로 43
 
5.3%
중 31
 
3.8%
가 26
 
3.2%
종 24
 
2.9%
포 17
 
2.1%
진 15
 
1.8%
Other values (66) 289
35.4%

fclty_la
Real number (ℝ)

HIGH CORRELATION 

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.546068
Minimum35.346714
Maximum37.790858
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:38.769233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.346714
5-th percentile37.519318
Q137.553283
median37.562802
Q337.572149
95-th percentile37.584087
Maximum37.790858
Range2.4441443
Interquartile range (IQR)0.018865925

Descriptive statistics

Standard deviation0.22772388
Coefficient of variation (CV)0.0060651858
Kurtosis90.336787
Mean37.546068
Median Absolute Deviation (MAD)0.00954285
Skewness-9.234405
Sum3754.6068
Variance0.051858164
MonotonicityNot monotonic
2023-12-10T18:39:39.075158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.790858 3
 
3.0%
37.4903214 2
 
2.0%
37.7381075 2
 
2.0%
37.519821 1
 
1.0%
37.5728133 1
 
1.0%
37.5720059 1
 
1.0%
37.5675552 1
 
1.0%
37.5666584 1
 
1.0%
37.5735448 1
 
1.0%
37.5669423 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
35.3467137 1
1.0%
37.4903214 2
2.0%
37.5090581 1
1.0%
37.5097641 1
1.0%
37.519821 1
1.0%
37.5208332 1
1.0%
37.5213445 1
1.0%
37.5214097 1
1.0%
37.5242473 1
1.0%
37.5265095 1
1.0%
ValueCountFrequency (%)
37.790858 3
3.0%
37.7381075 2
2.0%
37.5759809 1
 
1.0%
37.5754746 1
 
1.0%
37.5752715 1
 
1.0%
37.5748097 1
 
1.0%
37.5741033 1
 
1.0%
37.5740295 1
 
1.0%
37.5740096 1
 
1.0%
37.5739883 1
 
1.0%

fclty_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.00189
Minimum126.69852
Maximum129.03345
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:39.349507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.69852
5-th percentile126.8997
Q1126.98291
median126.98745
Q3127.00042
95-th percentile127.07714
Maximum129.03345
Range2.3349249
Interquartile range (IQR)0.01750745

Descriptive statistics

Standard deviation0.21706392
Coefficient of variation (CV)0.0017091393
Kurtosis79.389267
Mean127.00189
Median Absolute Deviation (MAD)0.0127045
Skewness8.354949
Sum12700.189
Variance0.047116747
MonotonicityNot monotonic
2023-12-10T18:39:39.610701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.6985244 3
 
3.0%
127.0316655 2
 
2.0%
127.0525652 2
 
2.0%
126.9925794 2
 
2.0%
126.9844915 1
 
1.0%
126.9864985 1
 
1.0%
126.9939421 1
 
1.0%
126.9848402 1
 
1.0%
126.9849004 1
 
1.0%
126.9841562 1
 
1.0%
Other values (85) 85
85.0%
ValueCountFrequency (%)
126.6985244 3
3.0%
126.8460669 1
 
1.0%
126.8486892 1
 
1.0%
126.9023848 1
 
1.0%
126.9032602 1
 
1.0%
126.9064754 1
 
1.0%
126.9070869 1
 
1.0%
126.9102585 1
 
1.0%
126.910662 1
 
1.0%
126.9274735 1
 
1.0%
ValueCountFrequency (%)
129.0334493 1
1.0%
127.0784053 1
1.0%
127.0780519 1
1.0%
127.0771982 1
1.0%
127.0771754 1
1.0%
127.0771356 1
1.0%
127.0770258 1
1.0%
127.0767332 1
1.0%
127.0739189 1
1.0%
127.0737239 1
1.0%

ldgs_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로 쉴
55 
YAJA 건대점
16 
신촌 림
13 
영등포 라이프스타일 F HOTEL
6 
화곡 호텔타워
 
2
Other values (8)
8 

Length

Max length18
Median length4
Mean length5.81
Min length4

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row영등포 라이프스타일 F HOTEL
2nd row파주 헤이리애펜션
3rd row영등포 라이프스타일 F HOTEL
4th row영등포 라이프스타일 F HOTEL
5th row영등포 라이프스타일 F HOTEL

Common Values

ValueCountFrequency (%)
종로 쉴 55
55.0%
YAJA 건대점 16
 
16.0%
신촌 림 13
 
13.0%
영등포 라이프스타일 F HOTEL 6
 
6.0%
화곡 호텔타워 2
 
2.0%
파주 헤이리애펜션 1
 
1.0%
파주 마리별펜션 1
 
1.0%
강남 렉시 1
 
1.0%
의정부 버스 1
 
1.0%
파주 행복한家 펜션 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T18:39:39.861149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로 55
25.7%
쉴 55
25.7%
yaja 16
 
7.5%
건대점 16
 
7.5%
신촌 13
 
6.1%
림 13
 
6.1%
영등포 6
 
2.8%
라이프스타일 6
 
2.8%
f 6
 
2.8%
hotel 6
 
2.8%
Other values (17) 22
 
10.3%

ldgs_road_nm_addr
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시 종로구 수표로18길 10 (관수동)
55 
서울특별시 성동구 동일로 217 (송정동)
16 
서울특별시 서대문구 연세로4길 16 (창천동)
13 
서울특별시 영등포구 경인로108길 8 (영등포동1가)
6 
서울특별시 강서구 곰달래로 72 (화곡동)
 
2
Other values (8)
8 

Length

Max length29
Median length25
Mean length24.83
Min length19

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row서울특별시 영등포구 경인로108길 8 (영등포동1가)
2nd row경기도 파주시 탄현면 헤이리로133번길 30-11
3rd row서울특별시 영등포구 경인로108길 8 (영등포동1가)
4th row서울특별시 영등포구 경인로108길 8 (영등포동1가)
5th row서울특별시 영등포구 경인로108길 8 (영등포동1가)

Common Values

ValueCountFrequency (%)
서울특별시 종로구 수표로18길 10 (관수동) 55
55.0%
서울특별시 성동구 동일로 217 (송정동) 16
 
16.0%
서울특별시 서대문구 연세로4길 16 (창천동) 13
 
13.0%
서울특별시 영등포구 경인로108길 8 (영등포동1가) 6
 
6.0%
서울특별시 강서구 곰달래로 72 (화곡동) 2
 
2.0%
경기도 파주시 탄현면 헤이리로133번길 30-11 1
 
1.0%
경기도 파주시 탄현면 매봉길 125 1
 
1.0%
서울특별시 강남구 테헤란로16길 11 (역삼동) 1
 
1.0%
경기도 의정부시 신흥로232번길 32 (의정부동) 1
 
1.0%
경기도 파주시 탄현면 헤이리로133번길 29 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T18:39:40.181082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 94
18.8%
수표로18길 55
11.0%
10 55
11.0%
관수동 55
11.0%
종로구 55
11.0%
성동구 16
 
3.2%
동일로 16
 
3.2%
217 16
 
3.2%
송정동 16
 
3.2%
16 13
 
2.6%
Other values (37) 109
21.8%

ldgs_eng_road_nm_addr
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
10, Supyo-ro 18-gil, Jongno-gu, Seoul
55 
217, Dongil-ro, Seongdong-gu, Seoul
16 
16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul
13 
8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul
6 
72, Gomdallae-ro, Gangseo-gu, Seoul
 
2
Other values (8)
8 

Length

Max length65
Median length37
Mean length38.72
Min length35

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul
2nd row30-11, Heyri-ro 133beon-gil, Tanhyeon-myeon, Paju-si, Gyeonggi-do
3rd row8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul
4th row8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul
5th row8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul

Common Values

ValueCountFrequency (%)
10, Supyo-ro 18-gil, Jongno-gu, Seoul 55
55.0%
217, Dongil-ro, Seongdong-gu, Seoul 16
 
16.0%
16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul 13
 
13.0%
8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul 6
 
6.0%
72, Gomdallae-ro, Gangseo-gu, Seoul 2
 
2.0%
30-11, Heyri-ro 133beon-gil, Tanhyeon-myeon, Paju-si, Gyeonggi-do 1
 
1.0%
125, Maebong-gil, Tanhyeon-myeon, Paju-si, Gyeonggi-do 1
 
1.0%
11, Teheran-ro 16-gil, Gangnam-gu, Seoul 1
 
1.0%
32, Sinheung-ro 232beon-gil, Uijeongbu-si, Gyeonggi-do 1
 
1.0%
29, Heyri-ro 133beon-gil, Tanhyeon-myeon, Paju-si, Gyeonggi-do 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T18:39:40.380710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
seoul 94
19.4%
10 55
11.4%
18-gil 55
11.4%
jongno-gu 55
11.4%
supyo-ro 55
11.4%
217 16
 
3.3%
dongil-ro 16
 
3.3%
seongdong-gu 16
 
3.3%
4-gil 14
 
2.9%
seodaemun-gu 13
 
2.7%
Other values (35) 95
19.6%

ldgs_la
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.547199
Minimum35.345486
Maximum37.796961
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:40.574611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.345486
5-th percentile37.517121
Q137.551322
median37.569206
Q337.569206
95-th percentile37.577554
Maximum37.796961
Range2.4514749
Interquartile range (IQR)0.0178846

Descriptive statistics

Standard deviation0.22797089
Coefficient of variation (CV)0.0060715819
Kurtosis90.334166
Mean37.547199
Median Absolute Deviation (MAD)0
Skewness-9.2330296
Sum3754.7199
Variance0.051970729
MonotonicityNot monotonic
2023-12-10T18:39:40.745347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
37.5692061 55
55.0%
37.5513215 16
 
16.0%
37.5569476 13
 
13.0%
37.5171206 6
 
6.0%
37.5299617 2
 
2.0%
37.796961 1
 
1.0%
37.7942859 1
 
1.0%
37.4987984 1
 
1.0%
37.7367536 1
 
1.0%
37.7963343 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
35.3454861 1
 
1.0%
37.4846709 1
 
1.0%
37.4987984 1
 
1.0%
37.5171206 6
 
6.0%
37.5299617 2
 
2.0%
37.5513215 16
 
16.0%
37.5569476 13
 
13.0%
37.5692061 55
55.0%
37.7361717 1
 
1.0%
37.7367536 1
 
1.0%
ValueCountFrequency (%)
37.796961 1
 
1.0%
37.7963343 1
 
1.0%
37.7942859 1
 
1.0%
37.7367536 1
 
1.0%
37.7361717 1
 
1.0%
37.5692061 55
55.0%
37.5569476 13
 
13.0%
37.5513215 16
 
16.0%
37.5299617 2
 
2.0%
37.5171206 6
 
6.0%

ldgs_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.00169
Minimum126.68907
Maximum129.03806
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:41.000796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.68907
5-th percentile126.90768
Q1126.99011
median126.99011
Q3126.99011
95-th percentile127.06888
Maximum129.03806
Range2.3489866
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.21725715
Coefficient of variation (CV)0.0017106634
Kurtosis79.88571
Mean127.00169
Median Absolute Deviation (MAD)0
Skewness8.3870068
Sum12700.169
Variance0.047200669
MonotonicityNot monotonic
2023-12-10T18:39:41.235492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
126.9901068 55
55.0%
127.0688826 16
 
16.0%
126.938025 13
 
13.0%
126.9111999 6
 
6.0%
126.8408227 2
 
2.0%
126.6939318 1
 
1.0%
126.6890733 1
 
1.0%
127.033984 1
 
1.0%
127.045257 1
 
1.0%
126.6937198 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
126.6890733 1
 
1.0%
126.6937198 1
 
1.0%
126.6939318 1
 
1.0%
126.8408227 2
 
2.0%
126.9111999 6
 
6.0%
126.938025 13
 
13.0%
126.9901068 55
55.0%
127.0292073 1
 
1.0%
127.033984 1
 
1.0%
127.0450434 1
 
1.0%
ValueCountFrequency (%)
129.0380599 1
 
1.0%
127.0688826 16
 
16.0%
127.045257 1
 
1.0%
127.0450434 1
 
1.0%
127.033984 1
 
1.0%
127.0292073 1
 
1.0%
126.9901068 55
55.0%
126.938025 13
 
13.0%
126.9111999 6
 
6.0%
126.8408227 2
 
2.0%

fclty_kwa_dstnc_km_value
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7347696
Minimum0.19172
Maximum0.99949
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:41.624725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.19172
5-th percentile0.419428
Q10.62557
median0.744585
Q30.8682375
95-th percentile0.963694
Maximum0.99949
Range0.80777
Interquartile range (IQR)0.2426675

Descriptive statistics

Standard deviation0.1782202
Coefficient of variation (CV)0.2425525
Kurtosis-0.12711243
Mean0.7347696
Median Absolute Deviation (MAD)0.123285
Skewness-0.66822542
Sum73.47696
Variance0.031762441
MonotonicityNot monotonic
2023-12-10T18:39:41.861906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.7619 1
 
1.0%
0.54056 1
 
1.0%
0.71992 1
 
1.0%
0.55755 1
 
1.0%
0.36721 1
 
1.0%
0.44104 1
 
1.0%
0.66947 1
 
1.0%
0.52339 1
 
1.0%
0.81442 1
 
1.0%
0.51238 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
0.19172 1
1.0%
0.33217 1
1.0%
0.341 1
1.0%
0.36721 1
1.0%
0.39317 1
1.0%
0.42081 1
1.0%
0.43989 1
1.0%
0.44104 1
1.0%
0.46621 1
1.0%
0.46828 1
1.0%
ValueCountFrequency (%)
0.99949 1
1.0%
0.99934 1
1.0%
0.98483 1
1.0%
0.9828 1
1.0%
0.96453 1
1.0%
0.96365 1
1.0%
0.95922 1
1.0%
0.94613 1
1.0%
0.94569 1
1.0%
0.94253 1
1.0%

Interactions

2023-12-10T18:39:28.008807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:23.356626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.189725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.046487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.961544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.080997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.245662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:23.509730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.326182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.200423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:26.122108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.262940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.464602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:23.647535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.459074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.401576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:26.300438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.383700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.645493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:23.770341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.600065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.543436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:26.475266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.500314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.796486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:23.897918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.719808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.668486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:26.636698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.628304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:28.944805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.035002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:24.898517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:25.802781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:26.917074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:27.768353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:39:42.097613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
esntl_idlclas_nmmlsfc_nmsclas_nmfclty_nmfclty_tel_nofclty_urlfclty_road_nm_addrlnm_addrfclty_lafclty_loldgs_nmldgs_road_nm_addrldgs_eng_road_nm_addrldgs_laldgs_lofclty_kwa_dstnc_km_value
esntl_id1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
lclas_nm1.0001.0001.0001.0001.0000.2381.0001.0001.0000.7130.7130.7930.7930.7930.7270.7090.497
mlsfc_nm1.0001.0001.0001.0001.0000.0001.0001.0001.0000.7760.7950.7430.7430.7430.7900.8150.493
sclas_nm1.0001.0001.0001.0001.0000.6191.0000.9971.0000.0000.6890.6070.6070.6070.4640.6700.696
fclty_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0000.917
fclty_tel_no1.0000.2380.0000.6191.0001.0001.0000.8620.8620.5650.0000.6160.6160.6160.5600.0000.000
fclty_url1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.0000.0000.0001.0001.0000.917
fclty_road_nm_addr1.0001.0001.0000.9971.0000.8621.0001.0001.0001.0001.0000.0000.0000.0001.0001.0000.991
lnm_addr1.0001.0001.0001.0001.0000.8621.0001.0001.0001.0001.0000.0000.0000.0001.0001.0000.987
fclty_la1.0000.7130.7760.0001.0000.5651.0001.0001.0001.0000.9520.9510.9510.9510.9930.9700.304
fclty_lo1.0000.7130.7950.6891.0000.0001.0001.0001.0000.9521.0000.9610.9610.9610.9440.9890.349
ldgs_nm1.0000.7930.7430.6070.0000.6160.0000.0000.0000.9510.9611.0001.0001.0001.0001.0000.258
ldgs_road_nm_addr1.0000.7930.7430.6070.0000.6160.0000.0000.0000.9510.9611.0001.0001.0001.0001.0000.258
ldgs_eng_road_nm_addr1.0000.7930.7430.6070.0000.6160.0000.0000.0000.9510.9611.0001.0001.0001.0001.0000.258
ldgs_la1.0000.7270.7900.4641.0000.5601.0001.0001.0000.9930.9441.0001.0001.0001.0000.9580.257
ldgs_lo1.0000.7090.8150.6701.0000.0001.0001.0001.0000.9700.9891.0001.0001.0000.9581.0000.342
fclty_kwa_dstnc_km_value1.0000.4970.4930.6960.9170.0000.9170.9910.9870.3040.3490.2580.2580.2580.2570.3421.000
2023-12-10T18:39:42.478151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ldgs_nmsclas_nmldgs_road_nm_addrlclas_nmldgs_eng_road_nm_addrmlsfc_nm
ldgs_nm1.0000.2981.0000.5631.0000.464
sclas_nm0.2981.0000.2980.9360.2980.951
ldgs_road_nm_addr1.0000.2981.0000.5631.0000.464
lclas_nm0.5630.9360.5631.0000.5630.984
ldgs_eng_road_nm_addr1.0000.2981.0000.5631.0000.464
mlsfc_nm0.4640.9510.4640.9840.4641.000
2023-12-10T18:39:42.737080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_tel_nofclty_lafclty_loldgs_laldgs_lofclty_kwa_dstnc_km_valuelclas_nmmlsfc_nmsclas_nmldgs_nmldgs_road_nm_addrldgs_eng_road_nm_addr
fclty_tel_no1.000-0.2000.354-0.0480.1490.0220.1960.0000.5590.4700.4700.470
fclty_la-0.2001.000-0.1540.903-0.085-0.2070.6930.6700.0000.8710.8710.871
fclty_lo0.354-0.1541.000-0.0870.9090.0330.6940.6980.5000.8910.8910.891
ldgs_la-0.0480.903-0.0871.000-0.096-0.1380.7110.6910.3300.9470.9470.947
ldgs_lo0.149-0.0850.909-0.0961.000-0.0420.6880.7260.4850.9470.9470.947
fclty_kwa_dstnc_km_value0.022-0.2070.033-0.138-0.0421.0000.2200.2570.3720.1010.1010.101
lclas_nm0.1960.6930.6940.7110.6880.2201.0000.9840.9360.5630.5630.563
mlsfc_nm0.0000.6700.6980.6910.7260.2570.9841.0000.9510.4640.4640.464
sclas_nm0.5590.0000.5000.3300.4850.3720.9360.9511.0000.2980.2980.298
ldgs_nm0.4700.8710.8910.9470.9470.1010.5630.4640.2981.0001.0001.000
ldgs_road_nm_addr0.4700.8710.8910.9470.9470.1010.5630.4640.2981.0001.0001.000
ldgs_eng_road_nm_addr0.4700.8710.8910.9470.9470.1010.5630.4640.2981.0001.0001.000

Missing values

2023-12-10T18:39:29.205140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:29.600133image/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.
2023-12-10T18:39:29.819834image/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

esntl_idlclas_nmmlsfc_nmsclas_nmfclty_nmfclty_tel_nofclty_urlfclty_road_nm_addrlnm_addrfclty_lafclty_loldgs_nmldgs_road_nm_addrldgs_eng_road_nm_addrldgs_laldgs_lofclty_kwa_dstnc_km_value
0KCEFSPO20N000000001문화,예술미술,공예<NA>반지마을 영등포점<NA>http://place.map.kakao.com/1054487680서울 영등포구 영신로34길 19서울 영등포구 영등포동4가 146-537.519821126.90326영등포 라이프스타일 F HOTEL서울특별시 영등포구 경인로108길 8 (영등포동1가)8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul37.517121126.91120.7619
1KCEFSPO20N000015950문화,예술문화시설전시관애니메이션체험관<NA>http://place.map.kakao.com/12706646경기 파주시 탄현면 헤이리마을길 93-143경기 파주시 탄현면 법흥리 1652-8637.790858126.698524파주 헤이리애펜션경기도 파주시 탄현면 헤이리로133번길 30-1130-11, Heyri-ro 133beon-gil, Tanhyeon-myeon, Paju-si, Gyeonggi-do37.796961126.6939320.78954
2KCEFSPO20N000000003문화,예술미술,공예<NA>품프로젝트<NA>http://place.map.kakao.com/1268069100서울 영등포구 영등포로43길 17서울 영등포구 영등포동5가 41-637.520833126.907087영등포 라이프스타일 F HOTEL서울특별시 영등포구 경인로108길 8 (영등포동1가)8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul37.517121126.91120.54954
3KCEFSPO20N000000004문화,예술미술,공예액자,표구그림액자거울28477300http://place.map.kakao.com/20551427서울 영등포구 신길로 189서울 영등포구 신길동 226-11337.509058126.910662영등포 라이프스타일 F HOTEL서울특별시 영등포구 경인로108길 8 (영등포동1가)8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul37.517121126.91120.89777
4KCEFSPO20N000000005문화,예술미술,공예액자,표구솔방울액자표구<NA>http://place.map.kakao.com/20537320서울 영등포구 영신로40길 9서울 영등포구 영등포동6가 121-537.52141126.902385영등포 라이프스타일 F HOTEL서울특별시 영등포구 경인로108길 8 (영등포동1가)8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul37.517121126.91120.91206
5KCEFSPO20N000000006문화,예술미술,공예액자,표구신길액자28413076http://place.map.kakao.com/26825517서울 영등포구 신길로47길 1서울 영등포구 신길동 225-2637.509764126.910258영등포 라이프스타일 F HOTEL서울특별시 영등포구 경인로108길 8 (영등포동1가)8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul37.517121126.91120.82221
6KCEFSPO20N000000007문화,예술미술,공예화방미술닷컴220685662http://place.map.kakao.com/27105747서울 영등포구 영중로20길 9서울 영등포구 영등포동5가 125-137.521344126.906475영등포 라이프스타일 F HOTEL서울특별시 영등포구 경인로108길 8 (영등포동1가)8, Gyeongin-ro 108-gil, Yeongdeungpo-gu, Seoul37.517121126.91120.62786
7KCEFSPO20N000015951문화,예술문화시설전시관애니메이션체험관<NA>http://place.map.kakao.com/12706646경기 파주시 탄현면 헤이리마을길 93-143경기 파주시 탄현면 법흥리 1652-8637.790858126.698524파주 마리별펜션경기도 파주시 탄현면 매봉길 125125, Maebong-gil, Tanhyeon-myeon, Paju-si, Gyeonggi-do37.794286126.6890730.91376
8KCEFSPO20N000000009가정,생활<NA><NA>삼분의일 강남체험관7043340758http://place.map.kakao.com/897890238서울 강남구 강남대로 302서울 강남구 역삼동 837-1037.490321127.031666강남 렉시서울특별시 강남구 테헤란로16길 11 (역삼동)11, Teheran-ro 16-gil, Gangnam-gu, Seoul37.498798127.0339840.96453
9KCEFSPO20N000000010교육,학문교육단체체험학습장김치체험관<NA>http://place.map.kakao.com/229742217<NA>경기 의정부시 의정부동 15-737.738107127.052565의정부 버스경기도 의정부시 신흥로232번길 32 (의정부동)32, Sinheung-ro 232beon-gil, Uijeongbu-si, Gyeonggi-do37.736754127.0452570.66005
esntl_idlclas_nmmlsfc_nmsclas_nmfclty_nmfclty_tel_nofclty_urlfclty_road_nm_addrlnm_addrfclty_lafclty_loldgs_nmldgs_road_nm_addrldgs_eng_road_nm_addrldgs_laldgs_lofclty_kwa_dstnc_km_value
90KCEFSPO20N000000091문화,예술미술,공예목공예오목수공방7076222888http://place.map.kakao.com/764991145서울 서대문구 신촌로3길 29서울 서대문구 창천동 28837.559278126.929381신촌 림서울특별시 서대문구 연세로4길 16 (창천동)16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul37.556948126.9380250.80484
91KCEFSPO20N000000092문화,예술미술,공예액자,표구삼성액자표구23367369http://place.map.kakao.com/1091448102서울 서대문구 성산로 379서울 서대문구 연희동 341-137.563247126.931002신촌 림서울특별시 서대문구 연세로4길 16 (창천동)16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul37.556948126.9380250.9348
92KCEFSPO20N000000093문화,예술미술,공예액자,표구하나화방23239870http://place.map.kakao.com/10866670서울 마포구 와우산로 133-1서울 마포구 서교동 337-2937.553803126.928354신촌 림서울특별시 서대문구 연세로4길 16 (창천동)16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul37.556948126.9380250.92145
93KCEFSPO20N000000094문화,예술미술,공예화방덕수표구사23926868http://place.map.kakao.com/16997295서울 서대문구 신촌로 143서울 서대문구 대현동 104-4737.556793126.941788신촌 림서울특별시 서대문구 연세로4길 16 (창천동)16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul37.556948126.9380250.33217
94KCEFSPO20N000000095문화,예술미술,공예화방삼성화방23338976http://place.map.kakao.com/8357742서울 마포구 와우산로 134서울 마포구 창전동 6-12937.553493126.928273신촌 림서울특별시 서대문구 연세로4길 16 (창천동)16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul37.556948126.9380250.94161
95KCEFSPO20N000000096문화,예술미술,공예화방세일화방23230773http://place.map.kakao.com/15905678서울 마포구 와우산로 125서울 마포구 서교동 338-5237.553593126.927508신촌 림서울특별시 서대문구 연세로4길 16 (창천동)16, Yonsei-ro 4-gil, Seodaemun-gu, Seoul37.556948126.9380250.99934
96KCEFSPO20N000000097교육,학문교육단체체험학습장김치체험관<NA>http://place.map.kakao.com/229742217<NA>경기 의정부시 의정부동 15-737.738107127.052565의정부 엘리경기도 의정부시 신흥로222번길 27 (의정부동)27, Sinheung-ro 222beon-gil, Uijeongbu-si, Gyeonggi-do37.736172127.0450430.69558
97KCEFSPO20N000000098사회,공공기관행정기관부속시설<NA>양산시 시민안전체험관553925547http://place.map.kakao.com/602123655경남 양산시 양산대로 849경남 양산시 북부동 53335.346714129.033449양산 북부동 미노스경상남도 양산시 북안남4길 6 (북부동)6, Bugannam 4-gil, Yangsan-si, Gyeongsangnam-do35.345486129.038060.43989
98KCEFSPO20N000000099교육,학문교육단체체험학습장대한적십자사 재난안전체험관<NA>http://place.map.kakao.com/1459439111서울 양천구 중앙로 345서울 양천구 신월동 472-137.52651126.848689화곡 호텔타워서울특별시 강서구 곰달래로 72 (화곡동)72, Gomdallae-ro, Gangseo-gu, Seoul37.529962126.8408230.79282
99KCEFSPO20N000000100문화,예술미술,공예<NA>노아벨리아1065821516http://place.map.kakao.com/779485914서울 양천구 오목로15길 14서울 양천구 신월동 480-837.524247126.846067화곡 호텔타워서울특별시 강서구 곰달래로 72 (화곡동)72, Gomdallae-ro, Gangseo-gu, Seoul37.529962126.8408230.78589