Overview

Dataset statistics

Number of variables14
Number of observations317
Missing cells61
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.0 KiB
Average record size in memory119.4 B

Variable types

Numeric6
Categorical3
Text4
DateTime1

Dataset

Description경상남도 거제시 경로당 정보에 대한 현황을 제공합니다(면동, 경로당명칭, 소재지, 위도, 경도, 등록일자, 기준일자)에 대한 정보를 제공합니다.
Author경상남도 거제시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15037762

Alerts

개소수 has constant value ""Constant
기준일 has constant value ""Constant
회원수(총계) is highly overall correlated with 회원(남) and 1 other fieldsHigh correlation
회원(남) is highly overall correlated with 회원수(총계)High correlation
회원(여) is highly overall correlated with 회원수(총계)High correlation
위도 is highly overall correlated with 면동High correlation
경도 is highly overall correlated with 면동High correlation
면동 is highly overall correlated with 위도 and 1 other fieldsHigh correlation
전화번호 has 49 (15.5%) missing valuesMissing
위도 has 6 (1.9%) missing valuesMissing
경도 has 6 (1.9%) missing valuesMissing
회원(남) has 13 (4.1%) zerosZeros
회원(여) has 6 (1.9%) zerosZeros

Reproduction

Analysis started2023-12-11 00:36:03.043538
Analysis finished2023-12-11 00:36:07.587665
Duration4.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록번호
Real number (ℝ)

Distinct316
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean170.03785
Minimum1
Maximum350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T09:36:07.674308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile19.8
Q183
median166
Q3255
95-th percentile334.2
Maximum350
Range349
Interquartile range (IQR)172

Descriptive statistics

Standard deviation100.36294
Coefficient of variation (CV)0.59023881
Kurtosis-1.1528589
Mean170.03785
Median Absolute Deviation (MAD)86
Skewness0.10235306
Sum53902
Variance10072.72
MonotonicityIncreasing
2023-12-11T09:36:07.888084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
278 2
 
0.6%
1 1
 
0.3%
219 1
 
0.3%
225 1
 
0.3%
224 1
 
0.3%
223 1
 
0.3%
222 1
 
0.3%
221 1
 
0.3%
220 1
 
0.3%
218 1
 
0.3%
Other values (306) 306
96.5%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
6 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
11 1
0.3%
12 1
0.3%
ValueCountFrequency (%)
350 1
0.3%
349 1
0.3%
348 1
0.3%
347 1
0.3%
346 1
0.3%
345 1
0.3%
344 1
0.3%
343 1
0.3%
342 1
0.3%
341 1
0.3%

면동
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
사등면
32 
장목면
29 
연초면
28 
거제면
27 
하청면
27 
Other values (14)
174 

Length

Max length4
Median length3
Mean length3.0883281
Min length3

Unique

Unique1 ?
Unique (%)0.3%

Sample

1st row수양동
2nd row고현동
3rd row고현동
4th row상문동
5th row고현동

Common Values

ValueCountFrequency (%)
사등면 32
10.1%
장목면 29
 
9.1%
연초면 28
 
8.8%
거제면 27
 
8.5%
하청면 27
 
8.5%
둔덕면 21
 
6.6%
일운면 20
 
6.3%
동부면 20
 
6.3%
고현동 19
 
6.0%
옥포2동 15
 
4.7%
Other values (9) 79
24.9%

Length

2023-12-11T09:36:08.057178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사등면 32
10.1%
장목면 29
 
9.1%
연초면 28
 
8.8%
거제면 27
 
8.5%
하청면 27
 
8.5%
둔덕면 21
 
6.6%
일운면 20
 
6.3%
동부면 20
 
6.3%
고현동 19
 
6.0%
상문동 15
 
4.7%
Other values (8) 79
24.9%

시설명
Text

Distinct312
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T09:36:08.357044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length5
Mean length5.9432177
Min length4

Characters and Unicode

Total characters1884
Distinct characters236
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

Unique307 ?
Unique (%)96.8%

Sample

1st row동산내경로당
2nd row성림마을경로당
3rd row신현경로당
4th row문동경로당
5th row서문경로당
ValueCountFrequency (%)
경로당 5
 
1.6%
롯데인벤스경로당 2
 
0.6%
서상경로당 2
 
0.6%
신촌경로당 2
 
0.6%
죽전경로당 2
 
0.6%
두모경로당 2
 
0.6%
봉송경로당 1
 
0.3%
항도경로당 1
 
0.3%
석천경로당 1
 
0.3%
서정경로당 1
 
0.3%
Other values (303) 303
94.1%
2023-12-11T09:36:08.724614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
경 304
 
16.1%
로 303
 
16.1%
당 303
 
16.1%
동 29
 
1.5%
포 28
 
1.5%
아 28
 
1.5%
산 26
 
1.4%
트 20
 
1.1%
차 19
 
1.0%
파 19
 
1.0%
Other values (226) 805
42.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1831
97.2%
Decimal Number 35
 
1.9%
Space Separator 5
 
0.3%
Uppercase Letter 5
 
0.3%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%
Other Punctuation 2
 
0.1%
Dash Punctuation 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 304
16.6%
로 303
16.5%
당 303
16.5%
동 29
 
1.6%
포 28
 
1.5%
아 28
 
1.5%
산 26
 
1.4%
트 20
 
1.1%
차 19
 
1.0%
파 19
 
1.0%
Other values (207) 752
41.1%
Decimal Number
ValueCountFrequency (%)
1 11
31.4%
2 8
22.9%
3 7
20.0%
6 3
 
8.6%
4 2
 
5.7%
5 2
 
5.7%
8 1
 
2.9%
7 1
 
2.9%
Uppercase Letter
ValueCountFrequency (%)
A 1
20.0%
H 1
20.0%
L 1
20.0%
K 1
20.0%
S 1
20.0%
Space Separator
ValueCountFrequency (%)
5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1831
97.2%
Common 47
 
2.5%
Latin 6
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
경 304
16.6%
로 303
16.5%
당 303
16.5%
동 29
 
1.6%
포 28
 
1.5%
아 28
 
1.5%
산 26
 
1.4%
트 20
 
1.1%
차 19
 
1.0%
파 19
 
1.0%
Other values (207) 752
41.1%
Common
ValueCountFrequency (%)
1 11
23.4%
2 8
17.0%
3 7
14.9%
5
10.6%
6 3
 
6.4%
( 2
 
4.3%
4 2
 
4.3%
5 2
 
4.3%
) 2
 
4.3%
. 2
 
4.3%
Other values (3) 3
 
6.4%
Latin
ValueCountFrequency (%)
e 1
16.7%
A 1
16.7%
H 1
16.7%
L 1
16.7%
K 1
16.7%
S 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1831
97.2%
ASCII 53
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
경 304
16.6%
로 303
16.5%
당 303
16.5%
동 29
 
1.6%
포 28
 
1.5%
아 28
 
1.5%
산 26
 
1.4%
트 20
 
1.1%
차 19
 
1.0%
파 19
 
1.0%
Other values (207) 752
41.1%
ASCII
ValueCountFrequency (%)
1 11
20.8%
2 8
15.1%
3 7
13.2%
5
9.4%
6 3
 
5.7%
( 2
 
3.8%
4 2
 
3.8%
5 2
 
3.8%
) 2
 
3.8%
. 2
 
3.8%
Other values (9) 9
17.0%

개소수
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
1
317 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 317
100.0%

Length

2023-12-11T09:36:08.871928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:36:08.956038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 317
100.0%

소 재 지
Text

Distinct313
Distinct (%)98.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T09:36:09.281867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length21.138801
Min length17

Characters and Unicode

Total characters6701
Distinct characters159
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique309 ?
Unique (%)97.5%

Sample

1st row경상남도 거제시 수양동 수양로 382
2nd row경상남도 거제시 고현동 고현로 8길 20
3rd row경상남도 거제시 고현동 고현로8길 6-1
4th row경상남도 거제시 상문동 수양로 24-10
5th row경상남도 거제시 고현동 거제중앙로19길 37
ValueCountFrequency (%)
경상남도 317
19.6%
거제시 317
19.6%
사등면 32
 
2.0%
장목면 29
 
1.8%
연초면 28
 
1.7%
거제면 27
 
1.7%
하청면 27
 
1.7%
둔덕면 21
 
1.3%
일운면 20
 
1.2%
동부면 20
 
1.2%
Other values (443) 779
48.2%
2023-12-11T09:36:09.807998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1301
19.4%
거 379
 
5.7%
제 378
 
5.6%
상 339
 
5.1%
남 338
 
5.0%
도 321
 
4.8%
시 320
 
4.8%
경 317
 
4.7%
1 240
 
3.6%
길 218
 
3.3%
Other values (149) 2550
38.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4306
64.3%
Space Separator 1301
 
19.4%
Decimal Number 1010
 
15.1%
Dash Punctuation 80
 
1.2%
Close Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
거 379
 
8.8%
제 378
 
8.8%
상 339
 
7.9%
남 338
 
7.8%
도 321
 
7.5%
시 320
 
7.4%
경 317
 
7.4%
길 218
 
5.1%
면 215
 
5.0%
로 156
 
3.6%
Other values (135) 1325
30.8%
Decimal Number
ValueCountFrequency (%)
1 240
23.8%
2 175
17.3%
3 124
12.3%
5 99
9.8%
4 82
 
8.1%
6 72
 
7.1%
7 61
 
6.0%
8 55
 
5.4%
0 51
 
5.0%
9 51
 
5.0%
Space Separator
ValueCountFrequency (%)
1301
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 80
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4306
64.3%
Common 2395
35.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
거 379
 
8.8%
제 378
 
8.8%
상 339
 
7.9%
남 338
 
7.8%
도 321
 
7.5%
시 320
 
7.4%
경 317
 
7.4%
길 218
 
5.1%
면 215
 
5.0%
로 156
 
3.6%
Other values (135) 1325
30.8%
Common
ValueCountFrequency (%)
1301
54.3%
1 240
 
10.0%
2 175
 
7.3%
3 124
 
5.2%
5 99
 
4.1%
4 82
 
3.4%
- 80
 
3.3%
6 72
 
3.0%
7 61
 
2.5%
8 55
 
2.3%
Other values (4) 106
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4306
64.3%
ASCII 2395
35.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1301
54.3%
1 240
 
10.0%
2 175
 
7.3%
3 124
 
5.2%
5 99
 
4.1%
4 82
 
3.4%
- 80
 
3.3%
6 72
 
3.0%
7 61
 
2.5%
8 55
 
2.3%
Other values (4) 106
 
4.4%
Hangul
ValueCountFrequency (%)
거 379
 
8.8%
제 378
 
8.8%
상 339
 
7.9%
남 338
 
7.8%
도 321
 
7.5%
시 320
 
7.4%
경 317
 
7.4%
길 218
 
5.1%
면 215
 
5.0%
로 156
 
3.6%
Other values (135) 1325
30.8%

성명
Text

Distinct314
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2023-12-11T09:36:10.146816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0031546
Min length2

Characters and Unicode

Total characters952
Distinct characters172
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

Unique311 ?
Unique (%)98.1%

Sample

1st row이종규
2nd row김증자
3rd row손정목
4th row김대곤
5th row윤태근
ValueCountFrequency (%)
옥기영 2
 
0.6%
김정석 2
 
0.6%
정규오 2
 
0.6%
김영판 1
 
0.3%
배선각 1
 
0.3%
최문수 1
 
0.3%
김용조 1
 
0.3%
반풍년 1
 
0.3%
윤창환 1
 
0.3%
윤금자 1
 
0.3%
Other values (304) 304
95.9%
2023-12-11T09:36:10.575804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 77
 
8.1%
정 49
 
5.1%
이 39
 
4.1%
옥 28
 
2.9%
자 21
 
2.2%
윤 20
 
2.1%
박 20
 
2.1%
영 19
 
2.0%
수 17
 
1.8%
상 17
 
1.8%
Other values (162) 645
67.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 951
99.9%
Space Separator 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 77
 
8.1%
정 49
 
5.2%
이 39
 
4.1%
옥 28
 
2.9%
자 21
 
2.2%
윤 20
 
2.1%
박 20
 
2.1%
영 19
 
2.0%
수 17
 
1.8%
상 17
 
1.8%
Other values (161) 644
67.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 951
99.9%
Common 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 77
 
8.1%
정 49
 
5.2%
이 39
 
4.1%
옥 28
 
2.9%
자 21
 
2.2%
윤 20
 
2.1%
박 20
 
2.1%
영 19
 
2.0%
수 17
 
1.8%
상 17
 
1.8%
Other values (161) 644
67.7%
Common
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 951
99.9%
ASCII 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 77
 
8.1%
정 49
 
5.2%
이 39
 
4.1%
옥 28
 
2.9%
자 21
 
2.2%
윤 20
 
2.1%
박 20
 
2.1%
영 19
 
2.0%
수 17
 
1.8%
상 17
 
1.8%
Other values (161) 644
67.7%
ASCII
ValueCountFrequency (%)
1
100.0%

회원수(총계)
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.419558
Minimum0
Maximum105
Zeros1
Zeros (%)0.3%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T09:36:10.736483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17
Q122
median29
Q341
95-th percentile60.2
Maximum105
Range105
Interquartile range (IQR)19

Descriptive statistics

Standard deviation15.429423
Coefficient of variation (CV)0.46168841
Kurtosis2.8767674
Mean33.419558
Median Absolute Deviation (MAD)8
Skewness1.4608909
Sum10594
Variance238.06708
MonotonicityNot monotonic
2023-12-11T09:36:10.881258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 27
 
8.5%
22 17
 
5.4%
28 17
 
5.4%
25 15
 
4.7%
27 11
 
3.5%
30 11
 
3.5%
24 11
 
3.5%
23 11
 
3.5%
21 10
 
3.2%
33 10
 
3.2%
Other values (56) 177
55.8%
ValueCountFrequency (%)
0 1
 
0.3%
10 1
 
0.3%
11 1
 
0.3%
12 2
 
0.6%
13 2
 
0.6%
14 1
 
0.3%
15 4
1.3%
16 2
 
0.6%
17 5
1.6%
18 4
1.3%
ValueCountFrequency (%)
105 1
0.3%
99 1
0.3%
87 1
0.3%
85 1
0.3%
82 1
0.3%
81 1
0.3%
80 1
0.3%
77 2
0.6%
69 1
0.3%
68 1
0.3%

회원(남)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)10.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.589905
Minimum0
Maximum58
Zeros13
Zeros (%)4.1%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T09:36:11.027421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.8
Q17
median10
Q315
95-th percentile25
Maximum58
Range58
Interquartile range (IQR)8

Descriptive statistics

Standard deviation7.7383411
Coefficient of variation (CV)0.6676794
Kurtosis6.7782188
Mean11.589905
Median Absolute Deviation (MAD)4
Skewness1.8743108
Sum3674
Variance59.881923
MonotonicityNot monotonic
2023-12-11T09:36:11.141879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
10 38
 
12.0%
9 23
 
7.3%
11 20
 
6.3%
8 20
 
6.3%
6 18
 
5.7%
7 18
 
5.7%
13 18
 
5.7%
5 16
 
5.0%
14 15
 
4.7%
4 14
 
4.4%
Other values (24) 117
36.9%
ValueCountFrequency (%)
0 13
4.1%
1 3
 
0.9%
2 3
 
0.9%
3 7
 
2.2%
4 14
4.4%
5 16
5.0%
6 18
5.7%
7 18
5.7%
8 20
6.3%
9 23
7.3%
ValueCountFrequency (%)
58 1
 
0.3%
52 1
 
0.3%
41 2
 
0.6%
34 1
 
0.3%
32 3
0.9%
31 1
 
0.3%
30 1
 
0.3%
28 2
 
0.6%
25 6
1.9%
24 3
0.9%

회원(여)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)15.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.829653
Minimum0
Maximum69
Zeros6
Zeros (%)1.9%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T09:36:11.264205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile8
Q115
median20
Q327
95-th percentile40
Maximum69
Range69
Interquartile range (IQR)12

Descriptive statistics

Standard deviation10.496353
Coefficient of variation (CV)0.48083003
Kurtosis2.2634174
Mean21.829653
Median Absolute Deviation (MAD)6
Skewness1.0726709
Sum6920
Variance110.17342
MonotonicityNot monotonic
2023-12-11T09:36:11.401743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20 27
 
8.5%
17 20
 
6.3%
15 19
 
6.0%
14 13
 
4.1%
18 13
 
4.1%
19 13
 
4.1%
12 13
 
4.1%
25 12
 
3.8%
22 12
 
3.8%
23 11
 
3.5%
Other values (40) 164
51.7%
ValueCountFrequency (%)
0 6
1.9%
5 2
 
0.6%
7 1
 
0.3%
8 8
2.5%
9 4
 
1.3%
10 7
2.2%
11 9
2.8%
12 13
4.1%
13 10
3.2%
14 13
4.1%
ValueCountFrequency (%)
69 1
0.3%
64 1
0.3%
60 1
0.3%
54 1
0.3%
53 1
0.3%
51 1
0.3%
50 1
0.3%
49 1
0.3%
48 1
0.3%
45 1
0.3%

전화번호
Text

MISSING 

Distinct266
Distinct (%)99.3%
Missing49
Missing (%)15.5%
Memory size2.6 KiB
2023-12-11T09:36:11.647893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters3216
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

Unique264 ?
Unique (%)98.5%

Sample

1st row055-635-7111
2nd row055-636-0023
3rd row055-635-3336
4th row055-636-5105
5th row055-635-8775
ValueCountFrequency (%)
055-635-8874 2
 
0.7%
055-635-0938 2
 
0.7%
055-632-1222 1
 
0.4%
055-636-8804 1
 
0.4%
055-635-7111 1
 
0.4%
055-632-4577 1
 
0.4%
055-632-8891 1
 
0.4%
055-632-6766 1
 
0.4%
055-636-5636 1
 
0.4%
055-632-6661 1
 
0.4%
Other values (256) 256
95.5%
2023-12-11T09:36:12.019323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 726
22.6%
- 536
16.7%
6 421
13.1%
0 400
12.4%
3 350
10.9%
8 159
 
4.9%
1 155
 
4.8%
2 152
 
4.7%
7 131
 
4.1%
4 102
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2680
83.3%
Dash Punctuation 536
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 726
27.1%
6 421
15.7%
0 400
14.9%
3 350
13.1%
8 159
 
5.9%
1 155
 
5.8%
2 152
 
5.7%
7 131
 
4.9%
4 102
 
3.8%
9 84
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 536
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3216
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 726
22.6%
- 536
16.7%
6 421
13.1%
0 400
12.4%
3 350
10.9%
8 159
 
4.9%
1 155
 
4.8%
2 152
 
4.7%
7 131
 
4.1%
4 102
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3216
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 726
22.6%
- 536
16.7%
6 421
13.1%
0 400
12.4%
3 350
10.9%
8 159
 
4.9%
1 155
 
4.8%
2 152
 
4.7%
7 131
 
4.1%
4 102
 
3.2%

개소정보(설립일)
Date

Distinct252
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum1935-01-01 00:00:00
Maximum2019-06-18 00:00:00
2023-12-11T09:36:12.185489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:12.332336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

위도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct307
Distinct (%)98.7%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean34.886889
Minimum34.714529
Maximum35.029753
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T09:36:12.476339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.714529
5-th percentile34.789671
Q134.850696
median34.886518
Q334.92
95-th percentile34.991568
Maximum35.029753
Range0.31522406
Interquartile range (IQR)0.06930397

Descriptive statistics

Standard deviation0.060900592
Coefficient of variation (CV)0.0017456584
Kurtosis0.24680619
Mean34.886889
Median Absolute Deviation (MAD)0.03424173
Skewness-0.097687619
Sum10849.823
Variance0.0037088821
MonotonicityNot monotonic
2023-12-11T09:36:12.613911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
34.87615028 2
 
0.6%
34.91369475 2
 
0.6%
34.82553033 2
 
0.6%
34.91020488 2
 
0.6%
34.89881242 1
 
0.3%
34.93503371 1
 
0.3%
34.99148777 1
 
0.3%
34.91090185 1
 
0.3%
34.98217703 1
 
0.3%
34.89992649 1
 
0.3%
Other values (297) 297
93.7%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
34.71452921 1
0.3%
34.71485716 1
0.3%
34.72545699 1
0.3%
34.72894983 1
0.3%
34.73274779 1
0.3%
34.73687369 1
0.3%
34.73798293 1
0.3%
34.73975612 1
0.3%
34.74139883 1
0.3%
34.76269195 1
0.3%
ValueCountFrequency (%)
35.02975327 1
0.3%
35.02903987 1
0.3%
35.02745927 1
0.3%
35.02092568 1
0.3%
35.01436784 1
0.3%
35.00954352 1
0.3%
35.00940238 1
0.3%
35.00901896 1
0.3%
35.00662799 1
0.3%
35.00639308 1
0.3%

경도
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct307
Distinct (%)98.7%
Missing6
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean128.63021
Minimum128.46506
Maximum128.73866
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2023-12-11T09:36:12.854849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.46506
5-th percentile128.50742
Q1128.58975
median128.63776
Q3128.68621
95-th percentile128.72391
Maximum128.73866
Range0.273599
Interquartile range (IQR)0.09645035

Descriptive statistics

Standard deviation0.068159497
Coefficient of variation (CV)0.00052988717
Kurtosis-0.56239173
Mean128.63021
Median Absolute Deviation (MAD)0.0484042
Skewness-0.53541887
Sum40003.995
Variance0.0046457171
MonotonicityNot monotonic
2023-12-11T09:36:13.226325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.7348002 2
 
0.6%
128.6543877 2
 
0.6%
128.6055704 2
 
0.6%
128.6440578 2
 
0.6%
128.6948366 1
 
0.3%
128.6568367 1
 
0.3%
128.6451152 1
 
0.3%
128.6182169 1
 
0.3%
128.6631142 1
 
0.3%
128.6861627 1
 
0.3%
Other values (297) 297
93.7%
(Missing) 6
 
1.9%
ValueCountFrequency (%)
128.465064 1
0.3%
128.472862 1
0.3%
128.4764886 1
0.3%
128.4783711 1
0.3%
128.4797251 1
0.3%
128.4797511 1
0.3%
128.4805137 1
0.3%
128.4809108 1
0.3%
128.4815401 1
0.3%
128.4895079 1
0.3%
ValueCountFrequency (%)
128.738663 1
0.3%
128.7362959 1
0.3%
128.7348002 2
0.6%
128.7342142 1
0.3%
128.7341492 1
0.3%
128.7325245 1
0.3%
128.7324638 1
0.3%
128.7313095 1
0.3%
128.7312088 1
0.3%
128.730901 1
0.3%

기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2019-08-01
317 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019-08-01
2nd row2019-08-01
3rd row2019-08-01
4th row2019-08-01
5th row2019-08-01

Common Values

ValueCountFrequency (%)
2019-08-01 317
100.0%

Length

2023-12-11T09:36:13.496631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:36:13.705195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019-08-01 317
100.0%

Interactions

2023-12-11T09:36:06.267637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:03.611427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.147027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.604917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.116252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.728773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.371794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:03.702019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.232748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.686611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.200660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.855938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.464237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:03.819194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.305518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.778397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.282614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.942729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.559512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:03.898344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.383553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.873180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.353237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.019396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.661367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:03.993732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.463526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.963161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.443821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.098914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:07.058770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.069327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:04.536994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.037632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:05.591148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:36:06.179727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:36:13.838415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록번호면동회원수(총계)회원(남)회원(여)위도경도
등록번호1.0000.6720.4340.2240.5470.5570.626
면동0.6721.0000.2450.4170.2480.8970.872
회원수(총계)0.4340.2451.0000.7420.9550.3110.308
회원(남)0.2240.4170.7421.0000.6620.1510.168
회원(여)0.5470.2480.9550.6621.0000.4140.381
위도0.5570.8970.3110.1510.4141.0000.529
경도0.6260.8720.3080.1680.3810.5291.000
2023-12-11T09:36:13.998979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록번호회원수(총계)회원(남)회원(여)위도경도면동
등록번호1.000-0.482-0.293-0.4590.0450.1930.320
회원수(총계)-0.4821.0000.7600.871-0.043-0.1520.096
회원(남)-0.2930.7601.0000.414-0.036-0.2400.174
회원(여)-0.4590.8710.4141.000-0.040-0.0900.093
위도0.045-0.043-0.036-0.0401.0000.2220.610
경도0.193-0.152-0.240-0.0900.2221.0000.560
면동0.3200.0960.1740.0930.6100.5601.000

Missing values

2023-12-11T09:36:07.187488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:36:07.385150image/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.
2023-12-11T09:36:07.522391image/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

등록번호면동시설명개소수소 재 지성명회원수(총계)회원(남)회원(여)전화번호개소정보(설립일)위도경도기준일
01수양동동산내경로당1경상남도 거제시 수양동 수양로 382이종규21210055-635-71112000-09-0734.888325128.646742019-08-01
12고현동성림마을경로당1경상남도 거제시 고현동 고현로 8길 20김증자30030055-636-00232001-09-0134.885066128.6237952019-08-01
23고현동신현경로당1경상남도 거제시 고현동 고현로8길 6-1손정목58580055-635-33361999-02-0134.886037128.6231152019-08-01
34상문동문동경로당1경상남도 거제시 상문동 수양로 24-10김대곤27720055-636-51051992-01-0134.861491128.6455612019-08-01
46고현동서문경로당1경상남도 거제시 고현동 거제중앙로19길 37윤태근351025055-635-87751994-07-0134.884069128.6208882019-08-01
58장평동와치경로당1경상남도 거제시 장평동 장평3로 7길 57천경숙22517055-635-55581991-01-0134.892651128.605862019-08-01
69고현동화인경로당1경상남도 거제시 고현동 서문3길 8천세호000055-637-09011991-06-0134.893036128.6308652019-08-01
710상문동삼거경로당1경상남도 거제시 상문동 거제중앙로 1274윤봉권532132055-637-36631992-01-0134.842942128.6483062019-08-01
811장평동장평4경로당1경상남도 거제시 장평동 장평1로 143황갑연30327055-636-09721992-03-0134.891464128.6045692019-08-01
912장평동장평경로당1경상남도 거제시 장평동 장평로 71김형부281315055-632-43452008-05-0134.890917128.609452019-08-01
등록번호면동시설명개소수소 재 지성명회원수(총계)회원(남)회원(여)전화번호개소정보(설립일)위도경도기준일
307341고현동고려3차경로당1경상남도 거제시 고현로13길 12이명덕20020<NA>2017-06-1434.889001128.6265542019-08-01
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