Overview

Dataset statistics

Number of variables17
Number of observations100
Missing cells902
Missing cells (%)53.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory146.3 B

Variable types

Text5
Categorical3
Numeric2
Unsupported7

Alerts

signgu_nm is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
locplc_dc is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
ctprvn_cd is highly overall correlated with signgu_cd and 3 other fieldsHigh correlation
signgu_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
fclty_road_nm_addr has 65 (65.0%) missing valuesMissing
lnm_addr has 65 (65.0%) missing valuesMissing
addr_eng_nm has 100 (100.0%) missing valuesMissing
adstrd_cd has 100 (100.0%) missing valuesMissing
buld_nm has 100 (100.0%) missing valuesMissing
buld_manage_cd has 100 (100.0%) missing valuesMissing
fclty_la has 100 (100.0%) missing valuesMissing
fclty_lo has 100 (100.0%) missing valuesMissing
tel_no has 72 (72.0%) missing valuesMissing
zip_no has 100 (100.0%) missing valuesMissing
esntl_id has unique valuesUnique
grp_nm has unique valuesUnique
addr_eng_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
adstrd_cd is an unsupported type, check if it needs cleaning or further analysisUnsupported
buld_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
buld_manage_cd is an unsupported type, check if it needs cleaning or further analysisUnsupported
fclty_la is an unsupported type, check if it needs cleaning or further analysisUnsupported
fclty_lo is an unsupported type, check if it needs cleaning or further analysisUnsupported
zip_no is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 10:09:24.338793
Analysis finished2023-12-10 10:09:27.093879
Duration2.76 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
2023-12-10T19:09:27.368697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters17
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 rowKCCBSPO20N000000001
2nd rowKCCBSPO20N000021849
3rd rowKCCBSPO20N000000003
4th rowKCCBSPO20N000000004
5th rowKCCBSPO20N000000005
ValueCountFrequency (%)
kccbspo20n000000001 1
 
1.0%
kccbspo20n000000063 1
 
1.0%
kccbspo20n000000074 1
 
1.0%
kccbspo20n000000073 1
 
1.0%
kccbspo20n000000072 1
 
1.0%
kccbspo20n000000071 1
 
1.0%
kccbspo20n000000070 1
 
1.0%
kccbspo20n000000069 1
 
1.0%
kccbspo20n000000068 1
 
1.0%
kccbspo20n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T19:09:28.001137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 809
42.6%
C 200
 
10.5%
2 121
 
6.4%
K 100
 
5.3%
O 100
 
5.3%
N 100
 
5.3%
P 100
 
5.3%
S 100
 
5.3%
B 100
 
5.3%
1 25
 
1.3%
Other values (7) 145
 
7.6%

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%
2 121
 
11.0%
1 25
 
2.3%
5 22
 
2.0%
8 22
 
2.0%
4 21
 
1.9%
9 21
 
1.9%
6 20
 
1.8%
7 20
 
1.8%
3 19
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 200
25.0%
K 100
12.5%
O 100
12.5%
N 100
12.5%
P 100
12.5%
S 100
12.5%
B 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%
2 121
 
11.0%
1 25
 
2.3%
5 22
 
2.0%
8 22
 
2.0%
4 21
 
1.9%
9 21
 
1.9%
6 20
 
1.8%
7 20
 
1.8%
3 19
 
1.7%
Latin
ValueCountFrequency (%)
C 200
25.0%
K 100
12.5%
O 100
12.5%
N 100
12.5%
P 100
12.5%
S 100
12.5%
B 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 809
42.6%
C 200
 
10.5%
2 121
 
6.4%
K 100
 
5.3%
O 100
 
5.3%
N 100
 
5.3%
P 100
 
5.3%
S 100
 
5.3%
B 100
 
5.3%
1 25
 
1.3%
Other values (7) 145
 
7.6%

grp_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:09:28.347826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length18
Mean length9.51
Min length2

Characters and Unicode

Total characters951
Distinct characters259
Distinct categories8 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row프라미스 컴퍼니
2nd row충북.세종 가야금연구회 세종지부
3rd row한국무용협회 인천광역시지회
4th row(사)대한민국심바람문화예술협회
5th row(사)연예협인천지회
ValueCountFrequency (%)
극단 6
 
4.2%
인천광역시지회 3
 
2.1%
사단법인 3
 
2.1%
주식회사 2
 
1.4%
사)한국문인협회 2
 
1.4%
인천지회 2
 
1.4%
한울소리 2
 
1.4%
국가무형문화재 2
 
1.4%
아템파우제앙상블 1
 
0.7%
쉐마미술관 1
 
0.7%
Other values (119) 119
83.2%
2023-12-10T19:09:29.096118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
43
 
4.5%
회 37
 
3.9%
인 26
 
2.7%
사 23
 
2.4%
단 21
 
2.2%
) 21
 
2.2%
( 21
 
2.2%
국 18
 
1.9%
한 18
 
1.9%
연 17
 
1.8%
Other values (249) 706
74.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 796
83.7%
Space Separator 43
 
4.5%
Lowercase Letter 29
 
3.0%
Uppercase Letter 23
 
2.4%
Close Punctuation 21
 
2.2%
Open Punctuation 21
 
2.2%
Other Punctuation 14
 
1.5%
Decimal Number 4
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 37
 
4.6%
인 26
 
3.3%
사 23
 
2.9%
단 21
 
2.6%
국 18
 
2.3%
한 18
 
2.3%
연 17
 
2.1%
극 16
 
2.0%
소 16
 
2.0%
천 15
 
1.9%
Other values (209) 589
74.0%
Uppercase Letter
ValueCountFrequency (%)
T 3
13.0%
M 2
 
8.7%
O 2
 
8.7%
R 2
 
8.7%
B 2
 
8.7%
S 1
 
4.3%
J 1
 
4.3%
C 1
 
4.3%
K 1
 
4.3%
H 1
 
4.3%
Other values (7) 7
30.4%
Lowercase Letter
ValueCountFrequency (%)
e 5
17.2%
r 4
13.8%
a 4
13.8%
t 3
10.3%
c 2
 
6.9%
n 2
 
6.9%
u 2
 
6.9%
k 1
 
3.4%
m 1
 
3.4%
o 1
 
3.4%
Other values (4) 4
13.8%
Other Punctuation
ValueCountFrequency (%)
. 8
57.1%
" 4
28.6%
' 2
 
14.3%
Decimal Number
ValueCountFrequency (%)
1 2
50.0%
2 1
25.0%
5 1
25.0%
Space Separator
ValueCountFrequency (%)
43
100.0%
Close Punctuation
ValueCountFrequency (%)
) 21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 21
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 795
83.6%
Common 103
 
10.8%
Latin 52
 
5.5%
Han 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 37
 
4.7%
인 26
 
3.3%
사 23
 
2.9%
단 21
 
2.6%
국 18
 
2.3%
한 18
 
2.3%
연 17
 
2.1%
극 16
 
2.0%
소 16
 
2.0%
천 15
 
1.9%
Other values (208) 588
74.0%
Latin
ValueCountFrequency (%)
e 5
 
9.6%
r 4
 
7.7%
a 4
 
7.7%
t 3
 
5.8%
T 3
 
5.8%
M 2
 
3.8%
O 2
 
3.8%
c 2
 
3.8%
n 2
 
3.8%
u 2
 
3.8%
Other values (21) 23
44.2%
Common
ValueCountFrequency (%)
43
41.7%
) 21
20.4%
( 21
20.4%
. 8
 
7.8%
" 4
 
3.9%
' 2
 
1.9%
1 2
 
1.9%
2 1
 
1.0%
5 1
 
1.0%
Han
ValueCountFrequency (%)
律 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 795
83.6%
ASCII 155
 
16.3%
CJK Compat Ideographs 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
43
27.7%
) 21
13.5%
( 21
13.5%
. 8
 
5.2%
e 5
 
3.2%
r 4
 
2.6%
a 4
 
2.6%
" 4
 
2.6%
t 3
 
1.9%
T 3
 
1.9%
Other values (30) 39
25.2%
Hangul
ValueCountFrequency (%)
회 37
 
4.7%
인 26
 
3.3%
사 23
 
2.9%
단 21
 
2.6%
국 18
 
2.3%
한 18
 
2.3%
연 17
 
2.1%
극 16
 
2.0%
소 16
 
2.0%
천 15
 
1.9%
Other values (208) 588
74.0%
CJK Compat Ideographs
ValueCountFrequency (%)
律 1
100.0%

locplc_dc
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)25.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
인천 미추홀구
58 
충북 청주시 청원구
13 
제주 이도동
 
3
세종 고운동
 
2
대구 북구
 
2
Other values (20)
22 

Length

Max length10
Median length7
Mean length7.04
Min length5

Unique

Unique18 ?
Unique (%)18.0%

Sample

1st row인천 미추홀구
2nd row세종 새롬동
3rd row인천 미추홀구
4th row인천 미추홀구
5th row인천 미추홀구

Common Values

ValueCountFrequency (%)
인천 미추홀구 58
58.0%
충북 청주시 청원구 13
 
13.0%
제주 이도동 3
 
3.0%
세종 고운동 2
 
2.0%
대구 북구 2
 
2.0%
서울 중구 2
 
2.0%
서울 종로구 2
 
2.0%
충남 제주시 1
 
1.0%
경북 포항시 1
 
1.0%
서울 성북구 1
 
1.0%
Other values (15) 15
 
15.0%

Length

2023-12-10T19:09:29.361877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천 58
27.2%
미추홀구 58
27.2%
충북 13
 
6.1%
청주시 13
 
6.1%
청원구 13
 
6.1%
서울 7
 
3.3%
제주 5
 
2.3%
이도동 3
 
1.4%
세종 3
 
1.4%
북구 3
 
1.4%
Other values (28) 37
17.4%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.41
Minimum11
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:29.578802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q123
median23
Q329.5
95-th percentile39
Maximum39
Range28
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation6.5104345
Coefficient of variation (CV)0.25621545
Kurtosis0.47960387
Mean25.41
Median Absolute Deviation (MAD)0
Skewness0.33873366
Sum2541
Variance42.385758
MonotonicityNot monotonic
2023-12-10T19:09:29.789033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
23 58
58.0%
33 13
 
13.0%
39 7
 
7.0%
11 6
 
6.0%
29 3
 
3.0%
21 3
 
3.0%
22 2
 
2.0%
24 2
 
2.0%
31 2
 
2.0%
37 1
 
1.0%
Other values (3) 3
 
3.0%
ValueCountFrequency (%)
11 6
 
6.0%
21 3
 
3.0%
22 2
 
2.0%
23 58
58.0%
24 2
 
2.0%
25 1
 
1.0%
29 3
 
3.0%
31 2
 
2.0%
33 13
 
13.0%
35 1
 
1.0%
ValueCountFrequency (%)
39 7
 
7.0%
38 1
 
1.0%
37 1
 
1.0%
35 1
 
1.0%
33 13
 
13.0%
31 2
 
2.0%
29 3
 
3.0%
25 1
 
1.0%
24 2
 
2.0%
23 58
58.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
인천광역시
58 
충청북도
13 
제주특별자치도
7 
서울특별시
6 
세종특별자치시
 
3
Other values (8)
13 

Length

Max length7
Median length5
Mean length5
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row인천광역시
2nd row세종특별자치시
3rd row인천광역시
4th row인천광역시
5th row인천광역시

Common Values

ValueCountFrequency (%)
인천광역시 58
58.0%
충청북도 13
 
13.0%
제주특별자치도 7
 
7.0%
서울특별시 6
 
6.0%
세종특별자치시 3
 
3.0%
부산광역시 3
 
3.0%
대구광역시 2
 
2.0%
광주광역시 2
 
2.0%
경기도 2
 
2.0%
경상북도 1
 
1.0%
Other values (3) 3
 
3.0%

Length

2023-12-10T19:09:30.070611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
인천광역시 58
58.0%
충청북도 13
 
13.0%
제주특별자치도 7
 
7.0%
서울특별시 6
 
6.0%
세종특별자치시 3
 
3.0%
부산광역시 3
 
3.0%
대구광역시 2
 
2.0%
광주광역시 2
 
2.0%
경기도 2
 
2.0%
경상북도 1
 
1.0%
Other values (3) 3
 
3.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25481.92
Minimum11010
Maximum39010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:09:30.302564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11010
5-th percentile11146.5
Q123090
median23090
Q329555
95-th percentile39010
Maximum39010
Range28000
Interquartile range (IQR)6465

Descriptive statistics

Standard deviation6494.9883
Coefficient of variation (CV)0.25488614
Kurtosis0.49081128
Mean25481.92
Median Absolute Deviation (MAD)0
Skewness0.32635075
Sum2548192
Variance42184874
MonotonicityNot monotonic
2023-12-10T19:09:30.557138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
23090 58
58.0%
33044 13
 
13.0%
39010 7
 
7.0%
29010 3
 
3.0%
11010 2
 
2.0%
22050 2
 
2.0%
11020 2
 
2.0%
21140 1
 
1.0%
21090 1
 
1.0%
35010 1
 
1.0%
Other values (10) 10
 
10.0%
ValueCountFrequency (%)
11010 2
 
2.0%
11020 2
 
2.0%
11080 1
 
1.0%
11150 1
 
1.0%
21090 1
 
1.0%
21110 1
 
1.0%
21140 1
 
1.0%
22050 2
 
2.0%
23090 58
58.0%
24010 1
 
1.0%
ValueCountFrequency (%)
39010 7
7.0%
38070 1
 
1.0%
37010 1
 
1.0%
35010 1
 
1.0%
33044 13
13.0%
31200 1
 
1.0%
31190 1
 
1.0%
29010 3
 
3.0%
25040 1
 
1.0%
24040 1
 
1.0%

signgu_nm
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
미추홀구
58 
청주시 청원구
13 
제주시
7 
북구
 
3
세종특별자치시
 
3
Other values (14)
16 

Length

Max length7
Median length4
Mean length4.17
Min length2

Unique

Unique12 ?
Unique (%)12.0%

Sample

1st row미추홀구
2nd row세종특별자치시
3rd row미추홀구
4th row미추홀구
5th row미추홀구

Common Values

ValueCountFrequency (%)
미추홀구 58
58.0%
청주시 청원구 13
 
13.0%
제주시 7
 
7.0%
북구 3
 
3.0%
세종특별자치시 3
 
3.0%
종로구 2
 
2.0%
중구 2
 
2.0%
유성구 1
 
1.0%
포항시 1
 
1.0%
성북구 1
 
1.0%
Other values (9) 9
 
9.0%

Length

2023-12-10T19:09:30.781793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
미추홀구 58
51.3%
청원구 13
 
11.5%
청주시 13
 
11.5%
제주시 7
 
6.2%
북구 3
 
2.7%
세종특별자치시 3
 
2.7%
종로구 2
 
1.8%
중구 2
 
1.8%
파주시 1
 
0.9%
해운대구 1
 
0.9%
Other values (10) 10
 
8.8%

fclty_road_nm_addr
Text

MISSING 

Distinct33
Distinct (%)94.3%
Missing65
Missing (%)65.0%
Memory size932.0 B
2023-12-10T19:09:31.176003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length16.314286
Min length12

Characters and Unicode

Total characters571
Distinct characters104
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

Unique31 ?
Unique (%)88.6%

Sample

1st row전북 전주시 완산구 고사평1길 16
2nd row인천 미추홀구 인주대로 314-1
3rd row서울 영등포구 선유동2로 46
4th row서울 성북구 종암로21길 127
5th row인천 미추홀구 수봉안길 78
ValueCountFrequency (%)
서울 15
 
10.2%
인천 6
 
4.1%
미추홀구 4
 
2.7%
중구 4
 
2.7%
부산 3
 
2.0%
제주특별자치도 3
 
2.0%
7 3
 
2.0%
종로구 3
 
2.0%
청주시 3
 
2.0%
충북 3
 
2.0%
Other values (85) 100
68.0%
2023-12-10T19:09:31.901421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
19.6%
로 33
 
5.8%
구 32
 
5.6%
2 23
 
4.0%
1 22
 
3.9%
서 19
 
3.3%
울 15
 
2.6%
6 13
 
2.3%
길 12
 
2.1%
주 11
 
1.9%
Other values (94) 279
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 343
60.1%
Space Separator 112
 
19.6%
Decimal Number 109
 
19.1%
Dash Punctuation 7
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 33
 
9.6%
구 32
 
9.3%
서 19
 
5.5%
울 15
 
4.4%
길 12
 
3.5%
주 11
 
3.2%
시 9
 
2.6%
동 9
 
2.6%
인 8
 
2.3%
북 8
 
2.3%
Other values (82) 187
54.5%
Decimal Number
ValueCountFrequency (%)
2 23
21.1%
1 22
20.2%
6 13
11.9%
0 10
9.2%
5 10
9.2%
3 9
 
8.3%
7 7
 
6.4%
9 6
 
5.5%
8 5
 
4.6%
4 4
 
3.7%
Space Separator
ValueCountFrequency (%)
112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 343
60.1%
Common 228
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 33
 
9.6%
구 32
 
9.3%
서 19
 
5.5%
울 15
 
4.4%
길 12
 
3.5%
주 11
 
3.2%
시 9
 
2.6%
동 9
 
2.6%
인 8
 
2.3%
북 8
 
2.3%
Other values (82) 187
54.5%
Common
ValueCountFrequency (%)
112
49.1%
2 23
 
10.1%
1 22
 
9.6%
6 13
 
5.7%
0 10
 
4.4%
5 10
 
4.4%
3 9
 
3.9%
7 7
 
3.1%
- 7
 
3.1%
9 6
 
2.6%
Other values (2) 9
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 343
60.1%
ASCII 228
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
49.1%
2 23
 
10.1%
1 22
 
9.6%
6 13
 
5.7%
0 10
 
4.4%
5 10
 
4.4%
3 9
 
3.9%
7 7
 
3.1%
- 7
 
3.1%
9 6
 
2.6%
Other values (2) 9
 
3.9%
Hangul
ValueCountFrequency (%)
로 33
 
9.6%
구 32
 
9.3%
서 19
 
5.5%
울 15
 
4.4%
길 12
 
3.5%
주 11
 
3.2%
시 9
 
2.6%
동 9
 
2.6%
인 8
 
2.3%
북 8
 
2.3%
Other values (82) 187
54.5%

lnm_addr
Text

MISSING 

Distinct34
Distinct (%)97.1%
Missing65
Missing (%)65.0%
Memory size932.0 B
2023-12-10T19:09:32.338988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length17.057143
Min length13

Characters and Unicode

Total characters597
Distinct characters97
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

Unique33 ?
Unique (%)94.3%

Sample

1st row전북 전주시 완산구 서신동 943-12
2nd row인천 미추홀구 주안동 722-26
3rd row서울 영등포구 당산동5가 7-2
4th row서울 성북구 종암동 130
5th row인천 미추홀구 숭의동 7-4
ValueCountFrequency (%)
서울 15
 
10.2%
인천 6
 
4.1%
중구 4
 
2.7%
미추홀구 4
 
2.7%
부산 3
 
2.0%
종로구 3
 
2.0%
성북구 3
 
2.0%
청주시 3
 
2.0%
제주특별자치도 3
 
2.0%
충북 3
 
2.0%
Other values (86) 100
68.0%
2023-12-10T19:09:33.115164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
112
18.8%
동 34
 
5.7%
구 32
 
5.4%
1 27
 
4.5%
- 26
 
4.4%
2 21
 
3.5%
3 21
 
3.5%
서 18
 
3.0%
울 15
 
2.5%
4 15
 
2.5%
Other values (87) 276
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 318
53.3%
Decimal Number 141
23.6%
Space Separator 112
 
18.8%
Dash Punctuation 26
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 34
 
10.7%
구 32
 
10.1%
서 18
 
5.7%
울 15
 
4.7%
주 11
 
3.5%
천 9
 
2.8%
시 9
 
2.8%
북 8
 
2.5%
가 7
 
2.2%
인 7
 
2.2%
Other values (75) 168
52.8%
Decimal Number
ValueCountFrequency (%)
1 27
19.1%
2 21
14.9%
3 21
14.9%
4 15
10.6%
5 13
9.2%
9 11
7.8%
6 10
 
7.1%
0 9
 
6.4%
8 8
 
5.7%
7 6
 
4.3%
Space Separator
ValueCountFrequency (%)
112
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 26
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 318
53.3%
Common 279
46.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 34
 
10.7%
구 32
 
10.1%
서 18
 
5.7%
울 15
 
4.7%
주 11
 
3.5%
천 9
 
2.8%
시 9
 
2.8%
북 8
 
2.5%
가 7
 
2.2%
인 7
 
2.2%
Other values (75) 168
52.8%
Common
ValueCountFrequency (%)
112
40.1%
1 27
 
9.7%
- 26
 
9.3%
2 21
 
7.5%
3 21
 
7.5%
4 15
 
5.4%
5 13
 
4.7%
9 11
 
3.9%
6 10
 
3.6%
0 9
 
3.2%
Other values (2) 14
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 318
53.3%
ASCII 279
46.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
112
40.1%
1 27
 
9.7%
- 26
 
9.3%
2 21
 
7.5%
3 21
 
7.5%
4 15
 
5.4%
5 13
 
4.7%
9 11
 
3.9%
6 10
 
3.6%
0 9
 
3.2%
Other values (2) 14
 
5.0%
Hangul
ValueCountFrequency (%)
동 34
 
10.7%
구 32
 
10.1%
서 18
 
5.7%
울 15
 
4.7%
주 11
 
3.5%
천 9
 
2.8%
시 9
 
2.8%
북 8
 
2.5%
가 7
 
2.2%
인 7
 
2.2%
Other values (75) 168
52.8%

addr_eng_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

adstrd_cd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

buld_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

buld_manage_cd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

fclty_la
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

fclty_lo
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

tel_no
Text

MISSING 

Distinct27
Distinct (%)96.4%
Missing72
Missing (%)72.0%
Memory size932.0 B
2023-12-10T19:09:33.516111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length11.892857
Min length9

Characters and Unicode

Total characters333
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)92.9%

Sample

1st row063-274-9912(팩스)
2nd row032-888-3000
3rd row02-6949-2016
4th row032-862-9683
5th row043-211-0752
ValueCountFrequency (%)
051-319-6435 2
 
7.1%
064-722-5254 1
 
3.6%
02-744-8046 1
 
3.6%
02-744-8066 1
 
3.6%
070-5030-5200 1
 
3.6%
031-881-0531 1
 
3.6%
02-720-1161 1
 
3.6%
02-2051-8288 1
 
3.6%
02-984-7567 1
 
3.6%
02-563-4020 1
 
3.6%
Other values (17) 17
60.7%
2023-12-10T19:09:34.386141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 55
16.5%
0 54
16.2%
2 38
11.4%
3 35
10.5%
5 23
6.9%
1 23
6.9%
6 23
6.9%
4 23
6.9%
7 21
 
6.3%
8 18
 
5.4%
Other values (5) 20
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 274
82.3%
Dash Punctuation 55
 
16.5%
Other Letter 2
 
0.6%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 54
19.7%
2 38
13.9%
3 35
12.8%
5 23
8.4%
1 23
8.4%
6 23
8.4%
4 23
8.4%
7 21
 
7.7%
8 18
 
6.6%
9 16
 
5.8%
Other Letter
ValueCountFrequency (%)
팩 1
50.0%
스 1
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 331
99.4%
Hangul 2
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
- 55
16.6%
0 54
16.3%
2 38
11.5%
3 35
10.6%
5 23
6.9%
1 23
6.9%
6 23
6.9%
4 23
6.9%
7 21
 
6.3%
8 18
 
5.4%
Other values (3) 18
 
5.4%
Hangul
ValueCountFrequency (%)
팩 1
50.0%
스 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 331
99.4%
Hangul 2
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 55
16.6%
0 54
16.3%
2 38
11.5%
3 35
10.6%
5 23
6.9%
1 23
6.9%
6 23
6.9%
4 23
6.9%
7 21
 
6.3%
8 18
 
5.4%
Other values (3) 18
 
5.4%
Hangul
ValueCountFrequency (%)
팩 1
50.0%
스 1
50.0%

zip_no
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing100
Missing (%)100.0%
Memory size1.0 KiB

Interactions

2023-12-10T19:09:25.812191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:25.486012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:25.991608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:09:25.659846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:09:34.665549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
esntl_idgrp_nmlocplc_dcctprvn_cdctprvn_nmsigngu_cdsigngu_nmfclty_road_nm_addrlnm_addrtel_no
esntl_id1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
grp_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
locplc_dc1.0001.0001.0001.0001.0001.0001.0000.0001.0001.000
ctprvn_cd1.0001.0001.0001.0001.0001.0000.9961.0001.0001.000
ctprvn_nm1.0001.0001.0001.0001.0001.0000.9951.0001.0001.000
signgu_cd1.0001.0001.0001.0001.0001.0000.9971.0001.0001.000
signgu_nm1.0001.0001.0000.9960.9950.9971.0000.0001.0001.000
fclty_road_nm_addr1.0001.0000.0001.0001.0001.0000.0001.0001.0001.000
lnm_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
tel_no1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T19:09:34.958262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
signgu_nmlocplc_dcctprvn_nm
signgu_nm1.0000.9620.932
locplc_dc0.9621.0000.928
ctprvn_nm0.9320.9281.000
2023-12-10T19:09:35.148282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_cdsigngu_cdlocplc_dcctprvn_nmsigngu_nm
ctprvn_cd1.0001.0000.8980.9670.919
signgu_cd1.0001.0000.9030.9720.926
locplc_dc0.8980.9031.0000.9280.962
ctprvn_nm0.9670.9720.9281.0000.932
signgu_nm0.9190.9260.9620.9321.000

Missing values

2023-12-10T19:09:26.209462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:09:26.720664image/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.
2023-12-10T19:09:26.973440image/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_idgrp_nmlocplc_dcctprvn_cdctprvn_nmsigngu_cdsigngu_nmfclty_road_nm_addrlnm_addraddr_eng_nmadstrd_cdbuld_nmbuld_manage_cdfclty_lafclty_lotel_nozip_no
0KCCBSPO20N000000001프라미스 컴퍼니인천 미추홀구23인천광역시23090미추홀구전북 전주시 완산구 고사평1길 16전북 전주시 완산구 서신동 943-12<NA><NA><NA><NA><NA><NA>063-274-9912(팩스)<NA>
1KCCBSPO20N000021849충북.세종 가야금연구회 세종지부세종 새롬동29세종특별자치시29010세종특별자치시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2KCCBSPO20N000000003한국무용협회 인천광역시지회인천 미추홀구23인천광역시23090미추홀구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3KCCBSPO20N000000004(사)대한민국심바람문화예술협회인천 미추홀구23인천광역시23090미추홀구인천 미추홀구 인주대로 314-1인천 미추홀구 주안동 722-26<NA><NA><NA><NA><NA><NA>032-888-3000<NA>
4KCCBSPO20N000000005(사)연예협인천지회인천 미추홀구23인천광역시23090미추홀구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
5KCCBSPO20N000000006(사)한국문인협회 인천광역시지회인천 미추홀구23인천광역시23090미추홀구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
6KCCBSPO20N000000007(사)한국예술문화단체총연합회 인천광역시연합회인천 미추홀구23인천광역시23090미추홀구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
7KCCBSPO20N000021850한국전통가무악연구원세종 고운동29세종특별자치시29010세종특별자치시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
8KCCBSPO20N00000000915분연극제X인천인천 미추홀구23인천광역시23090미추홀구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
9KCCBSPO20N000000010극단 경험과 상상인천 미추홀구23인천광역시23090미추홀구서울 영등포구 선유동2로 46서울 영등포구 당산동5가 7-2<NA><NA><NA><NA><NA><NA>02-6949-2016<NA>
esntl_idgrp_nmlocplc_dcctprvn_cdctprvn_nmsigngu_cdsigngu_nmfclty_road_nm_addrlnm_addraddr_eng_nmadstrd_cdbuld_nmbuld_manage_cdfclty_lafclty_lotel_nozip_no
90KCCBSPO20N000000091페미씨어터서울 종로구11서울특별시11010종로구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
91KCCBSPO20N000000092한국미술관경기 용인시31경기도31190용인시서울 종로구 인사동길 12서울 종로구 인사동 43<NA><NA><NA><NA><NA><NA>02-720-1161<NA>
92KCCBSPO20N000000093한국문화예술통경남 김해시38경상남도38070김해시경기 여주시 세종로 338경기 여주시 교동 454-4<NA><NA><NA><NA><NA><NA>031-881-0531<NA>
93KCCBSPO20N000000094Craker서울 중구11서울특별시11020중구서울 송파구 충민로 10서울 송파구 문정동 628<NA><NA><NA><NA><NA><NA>070-5030-5200<NA>
94KCCBSPO20N000000095(사)한국무용협회서울 종로구11서울특별시11010종로구서울 양천구 목동서로 225서울 양천구 목동 923-6<NA><NA><NA><NA><NA><NA>02-744-8066<NA>
95KCCBSPO20N000000096(사)한국문인협회서울 양천구11서울특별시11150양천구서울 양천구 목동서로 225서울 양천구 목1동 923-6<NA><NA><NA><NA><NA><NA>02-744-8046<NA>
96KCCBSPO20N000000097(사)한국문화공동체 B.O.K대구 북구22대구광역시22050북구대구 북구 대동로 33대구 북구 산격동 1265-9<NA><NA><NA><NA><NA><NA>053-959-3065<NA>
97KCCBSPO20N000000098포엠만경전북 전주시35전라북도35010전주시<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
98KCCBSPO20N000000099폼앤동백부산 해운대구21부산광역시21090해운대구<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
99KCCBSPO20N000000100풍장국악마을부산 수영구21부산광역시21140수영구부산 수영구 연수로 320-1부산 수영구 망미동 802-35<NA><NA><NA><NA><NA><NA><NA><NA>