Overview

Dataset statistics

Number of variables11
Number of observations100
Missing cells17
Missing cells (%)1.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.3 KiB
Average record size in memory95.3 B

Variable types

Text4
Numeric6
Categorical1

Alerts

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
rprsntv_tel_no is highly overall correlated with ctprvn_cd and 4 other fieldsHigh correlation
rdnmadr_cd is highly overall correlated with ctprvn_cd and 3 other fieldsHigh correlation
fclty_crdnt_lo is highly overall correlated with ctprvn_nmHigh correlation
fclty_crdnt_la is highly overall correlated with rprsntv_tel_no and 1 other fieldsHigh correlation
ctprvn_nm is highly overall correlated with ctprvn_cd and 5 other fieldsHigh correlation
rprsntv_tel_no has 17 (17.0%) missing valuesMissing
fclty_nm has unique valuesUnique
rdnmadr_cd has unique valuesUnique
fclty_road_nm_addr has unique valuesUnique
fclty_crdnt_lo has unique valuesUnique
fclty_crdnt_la has unique valuesUnique
gid_cd has unique valuesUnique

Reproduction

Analysis started2023-12-10 09:38:49.879399
Analysis finished2023-12-10 09:39:04.863215
Duration14.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

fclty_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:05.162349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13.5
Mean length8.42
Min length2

Characters and Unicode

Total characters842
Distinct characters245
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
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 row금만검도관
2nd row경희엄지YG효자태권도 원곡지점
3rd row(주)어뮤즈
4th row(주)꿈자라다
5th row(주)다비드짐
ValueCountFrequency (%)
태권도 9
 
4.9%
커브스 6
 
3.3%
합기도 5
 
2.7%
경희대 3
 
1.6%
클럽 2
 
1.1%
탁구 2
 
1.1%
계명대 2
 
1.1%
북구 2
 
1.1%
야놀자 1
 
0.5%
망원점 1
 
0.5%
Other values (151) 151
82.1%
2023-12-10T18:39:05.817721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
84
 
10.0%
스 30
 
3.6%
도 29
 
3.4%
권 20
 
2.4%
태 20
 
2.4%
주 19
 
2.3%
) 17
 
2.0%
( 16
 
1.9%
장 15
 
1.8%
클 14
 
1.7%
Other values (235) 578
68.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 710
84.3%
Space Separator 84
 
10.0%
Close Punctuation 17
 
2.0%
Open Punctuation 16
 
1.9%
Other Punctuation 7
 
0.8%
Uppercase Letter 4
 
0.5%
Dash Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 30
 
4.2%
도 29
 
4.1%
권 20
 
2.8%
태 20
 
2.8%
주 19
 
2.7%
장 15
 
2.1%
클 14
 
2.0%
구 14
 
2.0%
럽 13
 
1.8%
터 11
 
1.5%
Other values (223) 525
73.9%
Other Punctuation
ValueCountFrequency (%)
& 4
57.1%
, 2
28.6%
* 1
 
14.3%
Uppercase Letter
ValueCountFrequency (%)
Y 2
50.0%
G 1
25.0%
T 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
84
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 710
84.3%
Common 126
 
15.0%
Latin 6
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 30
 
4.2%
도 29
 
4.1%
권 20
 
2.8%
태 20
 
2.8%
주 19
 
2.7%
장 15
 
2.1%
클 14
 
2.0%
구 14
 
2.0%
럽 13
 
1.8%
터 11
 
1.5%
Other values (223) 525
73.9%
Common
ValueCountFrequency (%)
84
66.7%
) 17
 
13.5%
( 16
 
12.7%
& 4
 
3.2%
, 2
 
1.6%
- 2
 
1.6%
* 1
 
0.8%
Latin
ValueCountFrequency (%)
Y 2
33.3%
G 1
16.7%
T 1
16.7%
y 1
16.7%
k 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 710
84.3%
ASCII 132
 
15.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
84
63.6%
) 17
 
12.9%
( 16
 
12.1%
& 4
 
3.0%
, 2
 
1.5%
Y 2
 
1.5%
- 2
 
1.5%
G 1
 
0.8%
T 1
 
0.8%
y 1
 
0.8%
Other values (2) 2
 
1.5%
Hangul
ValueCountFrequency (%)
스 30
 
4.2%
도 29
 
4.1%
권 20
 
2.8%
태 20
 
2.8%
주 19
 
2.7%
장 15
 
2.1%
클 14
 
2.0%
구 14
 
2.0%
럽 13
 
1.8%
터 11
 
1.5%
Other values (223) 525
73.9%

ctprvn_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.19
Minimum11
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:06.346502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q126
median41
Q344.25
95-th percentile48.1
Maximum50
Range39
Interquartile range (IQR)18.25

Descriptive statistics

Standard deviation12.416749
Coefficient of variation (CV)0.36316903
Kurtosis-0.79197498
Mean34.19
Median Absolute Deviation (MAD)9
Skewness-0.59921967
Sum3419
Variance154.17566
MonotonicityNot monotonic
2023-12-10T18:39:06.551304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
41 16
16.0%
11 14
14.0%
26 12
12.0%
27 9
9.0%
48 8
8.0%
29 6
 
6.0%
47 5
 
5.0%
50 5
 
5.0%
45 4
 
4.0%
44 4
 
4.0%
Other values (6) 17
17.0%
ValueCountFrequency (%)
11 14
14.0%
26 12
12.0%
27 9
9.0%
28 2
 
2.0%
29 6
 
6.0%
30 3
 
3.0%
31 1
 
1.0%
41 16
16.0%
42 4
 
4.0%
43 4
 
4.0%
ValueCountFrequency (%)
50 5
 
5.0%
48 8
8.0%
47 5
 
5.0%
46 3
 
3.0%
45 4
 
4.0%
44 4
 
4.0%
43 4
 
4.0%
42 4
 
4.0%
41 16
16.0%
31 1
 
1.0%

ctprvn_nm
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기
16 
서울
14 
부산
12 
대구
9 
경남
8 
Other values (11)
41 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row전북
2nd row경기
3rd row충남
4th row경기
5th row부산

Common Values

ValueCountFrequency (%)
경기 16
16.0%
서울 14
14.0%
부산 12
12.0%
대구 9
9.0%
경남 8
8.0%
광주 6
 
6.0%
경북 5
 
5.0%
제주 5
 
5.0%
전북 4
 
4.0%
충남 4
 
4.0%
Other values (6) 17
17.0%

Length

2023-12-10T18:39:06.756381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기 16
16.0%
서울 14
14.0%
부산 12
12.0%
대구 9
9.0%
경남 8
8.0%
광주 6
 
6.0%
경북 5
 
5.0%
제주 5
 
5.0%
전북 4
 
4.0%
충남 4
 
4.0%
Other values (6) 17
17.0%

signgu_cd
Real number (ℝ)

HIGH CORRELATION 

Distinct66
Distinct (%)66.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34485.7
Minimum11200
Maximum50130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:06.954640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11200
5-th percentile11380
Q126627.5
median41190
Q344896.25
95-th percentile48903.5
Maximum50130
Range38930
Interquartile range (IQR)18268.75

Descriptive statistics

Standard deviation12370.009
Coefficient of variation (CV)0.35869967
Kurtosis-0.79767844
Mean34485.7
Median Absolute Deviation (MAD)8920
Skewness-0.5983454
Sum3448570
Variance1.5301713 × 108
MonotonicityNot monotonic
2023-12-10T18:39:07.370149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
27230 4
 
4.0%
26320 4
 
4.0%
11410 4
 
4.0%
41190 3
 
3.0%
41280 3
 
3.0%
50110 3
 
3.0%
29140 3
 
3.0%
47110 3
 
3.0%
26350 3
 
3.0%
27290 2
 
2.0%
Other values (56) 68
68.0%
ValueCountFrequency (%)
11200 2
2.0%
11260 1
 
1.0%
11290 1
 
1.0%
11380 2
2.0%
11410 4
4.0%
11440 1
 
1.0%
11500 1
 
1.0%
11545 1
 
1.0%
11710 1
 
1.0%
26110 1
 
1.0%
ValueCountFrequency (%)
50130 2
2.0%
50110 3
3.0%
48840 1
 
1.0%
48330 1
 
1.0%
48310 2
2.0%
48250 1
 
1.0%
48170 1
 
1.0%
48120 2
2.0%
47190 2
2.0%
47110 3
3.0%

signgu_nm
Text

Distinct60
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:07.982229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.92
Min length2

Characters and Unicode

Total characters292
Distinct characters63
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)38.0%

Sample

1st row김제시
2nd row안산시
3rd row천안시
4th row성남시
5th row북구
ValueCountFrequency (%)
북구 10
 
10.0%
서구 5
 
5.0%
서대문구 4
 
4.0%
부천시 3
 
3.0%
고양시 3
 
3.0%
포항시 3
 
3.0%
제주시 3
 
3.0%
해운대구 3
 
3.0%
은평구 2
 
2.0%
구미시 2
 
2.0%
Other values (50) 62
62.0%
2023-12-10T18:39:08.607154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 49
16.8%
구 46
 
15.8%
서 14
 
4.8%
성 11
 
3.8%
주 11
 
3.8%
북 11
 
3.8%
군 9
 
3.1%
천 7
 
2.4%
포 7
 
2.4%
대 7
 
2.4%
Other values (53) 120
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 292
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 49
16.8%
구 46
 
15.8%
서 14
 
4.8%
성 11
 
3.8%
주 11
 
3.8%
북 11
 
3.8%
군 9
 
3.1%
천 7
 
2.4%
포 7
 
2.4%
대 7
 
2.4%
Other values (53) 120
41.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 292
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 49
16.8%
구 46
 
15.8%
서 14
 
4.8%
성 11
 
3.8%
주 11
 
3.8%
북 11
 
3.8%
군 9
 
3.1%
천 7
 
2.4%
포 7
 
2.4%
대 7
 
2.4%
Other values (53) 120
41.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 292
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 49
16.8%
구 46
 
15.8%
서 14
 
4.8%
성 11
 
3.8%
주 11
 
3.8%
북 11
 
3.8%
군 9
 
3.1%
천 7
 
2.4%
포 7
 
2.4%
대 7
 
2.4%
Other values (53) 120
41.1%

rprsntv_tel_no
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct83
Distinct (%)100.0%
Missing17
Missing (%)17.0%
Infinite0
Infinite (%)0.0%
Mean4.1193823 × 108
Minimum22557656
Maximum5.4793393 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:08.839011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum22557656
5-th percentile23327265
Q11.2487455 × 108
median3.3532577 × 108
Q35.412526 × 108
95-th percentile6.3519285 × 108
Maximum5.4793393 × 109
Range5.4567816 × 109
Interquartile range (IQR)4.1637806 × 108

Descriptive statistics

Standard deviation6.0504387 × 108
Coefficient of variation (CV)1.4687733
Kurtosis61.496831
Mean4.1193823 × 108
Median Absolute Deviation (MAD)2.0761142 × 108
Skewness7.2801154
Sum3.4190873 × 1010
Variance3.6607809 × 1017
MonotonicityNot monotonic
2023-12-10T18:39:09.026568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
23381700 1
 
1.0%
23323630 1
 
1.0%
337459679 1
 
1.0%
23359978 1
 
1.0%
539568021 1
 
1.0%
647214939 1
 
1.0%
533557953 1
 
1.0%
539420050 1
 
1.0%
23077982 1
 
1.0%
515452600 1
 
1.0%
Other values (73) 73
73.0%
(Missing) 17
 
17.0%
ValueCountFrequency (%)
22557656 1
1.0%
22583873 1
1.0%
23077982 1
1.0%
23197330 1
1.0%
23323630 1
1.0%
23359978 1
1.0%
23381700 1
1.0%
23758333 1
1.0%
23798815 1
1.0%
23931330 1
1.0%
ValueCountFrequency (%)
5479339279 1
1.0%
647531228 1
1.0%
647214939 1
1.0%
647028716 1
1.0%
635454525 1
1.0%
632837788 1
1.0%
632722500 1
1.0%
623832067 1
1.0%
623823960 1
1.0%
623760985 1
1.0%

rdnmadr_cd
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.4488087 × 1011
Minimum1.1200301 × 1011
Maximum5.0130335 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:09.231540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1200301 × 1011
5-th percentile1.1380311 × 1011
Q12.6665422 × 1011
median4.1190309 × 1011
Q34.4896826 × 1011
95-th percentile4.8903739 × 1011
Maximum5.0130335 × 1011
Range3.8930035 × 1011
Interquartile range (IQR)1.8231405 × 1011

Descriptive statistics

Standard deviation1.2369451 × 1011
Coefficient of variation (CV)0.35865866
Kurtosis-0.7968785
Mean3.4488087 × 1011
Median Absolute Deviation (MAD)8.9201756 × 1010
Skewness-0.59868514
Sum3.4488087 × 1013
Variance1.5300333 × 1022
MonotonicityNot monotonic
2023-12-10T18:39:09.473495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
452104616250 1
 
1.0%
415903210120 1
 
1.0%
302002166002 1
 
1.0%
451404608304 1
 
1.0%
467903291008 1
 
1.0%
114403113018 1
 
1.0%
421303219012 1
 
1.0%
263204196378 1
 
1.0%
272303007005 1
 
1.0%
501103349085 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
112003005001 1
1.0%
112004109422 1
1.0%
112603005027 1
1.0%
112904121456 1
1.0%
113803005053 1
1.0%
113803111007 1
1.0%
114103000008 1
1.0%
114103112010 1
1.0%
114103112019 1
1.0%
114104136396 1
1.0%
ValueCountFrequency (%)
501303350069 1
1.0%
501303349234 1
1.0%
501104849184 1
1.0%
501104848129 1
1.0%
501103349085 1
1.0%
488402343003 1
1.0%
483304814456 1
1.0%
483104811295 1
1.0%
483103337014 1
1.0%
482504805224 1
1.0%

fclty_road_nm_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:09.849948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length55
Median length41
Mean length31.13
Min length14

Characters and Unicode

Total characters3113
Distinct characters298
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row전라북도 김제시 도작9길 14금만검도관(신풍동)
2nd row경기도 안산시 단원구 라성안길 17303호(원곡동)
3rd row충청남도 천안시 서북구 불당25로 146605호(불당동)
4th row경기도 성남시 분당구 야탑로 32삼부빌딜 4층(야탑동)
5th row부산광역시 북구 덕천로281번길 122층 다비드짐(만덕동)
ValueCountFrequency (%)
경기도 16
 
3.0%
서울특별시 11
 
2.1%
부산광역시 11
 
2.1%
북구 10
 
1.9%
대구광역시 9
 
1.7%
경상남도 8
 
1.5%
광주광역시 6
 
1.1%
서구 5
 
0.9%
제주특별자치도 5
 
0.9%
2층 5
 
0.9%
Other values (397) 450
84.0%
2023-12-10T18:39:10.445925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
437
 
14.0%
동 116
 
3.7%
시 99
 
3.2%
1 98
 
3.1%
로 89
 
2.9%
) 85
 
2.7%
( 85
 
2.7%
구 76
 
2.4%
2 76
 
2.4%
층 71
 
2.3%
Other values (288) 1881
60.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1992
64.0%
Decimal Number 483
 
15.5%
Space Separator 437
 
14.0%
Close Punctuation 86
 
2.8%
Open Punctuation 86
 
2.8%
Other Punctuation 14
 
0.4%
Dash Punctuation 9
 
0.3%
Uppercase Letter 3
 
0.1%
Lowercase Letter 2
 
0.1%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 116
 
5.8%
시 99
 
5.0%
로 89
 
4.5%
구 76
 
3.8%
층 71
 
3.6%
도 69
 
3.5%
호 40
 
2.0%
산 40
 
2.0%
광 39
 
2.0%
대 39
 
2.0%
Other values (266) 1314
66.0%
Decimal Number
ValueCountFrequency (%)
1 98
20.3%
2 76
15.7%
3 69
14.3%
0 56
11.6%
4 47
9.7%
5 35
 
7.2%
6 34
 
7.0%
7 29
 
6.0%
8 23
 
4.8%
9 16
 
3.3%
Close Punctuation
ValueCountFrequency (%)
) 85
98.8%
] 1
 
1.2%
Open Punctuation
ValueCountFrequency (%)
( 85
98.8%
[ 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
B 2
66.7%
F 1
33.3%
Lowercase Letter
ValueCountFrequency (%)
y 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
437
100.0%
Other Punctuation
ValueCountFrequency (%)
, 14
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1992
64.0%
Common 1116
35.8%
Latin 5
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 116
 
5.8%
시 99
 
5.0%
로 89
 
4.5%
구 76
 
3.8%
층 71
 
3.6%
도 69
 
3.5%
호 40
 
2.0%
산 40
 
2.0%
광 39
 
2.0%
대 39
 
2.0%
Other values (266) 1314
66.0%
Common
ValueCountFrequency (%)
437
39.2%
1 98
 
8.8%
) 85
 
7.6%
( 85
 
7.6%
2 76
 
6.8%
3 69
 
6.2%
0 56
 
5.0%
4 47
 
4.2%
5 35
 
3.1%
6 34
 
3.0%
Other values (8) 94
 
8.4%
Latin
ValueCountFrequency (%)
B 2
40.0%
F 1
20.0%
y 1
20.0%
k 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1992
64.0%
ASCII 1121
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
437
39.0%
1 98
 
8.7%
) 85
 
7.6%
( 85
 
7.6%
2 76
 
6.8%
3 69
 
6.2%
0 56
 
5.0%
4 47
 
4.2%
5 35
 
3.1%
6 34
 
3.0%
Other values (12) 99
 
8.8%
Hangul
ValueCountFrequency (%)
동 116
 
5.8%
시 99
 
5.0%
로 89
 
4.5%
구 76
 
3.8%
층 71
 
3.6%
도 69
 
3.5%
호 40
 
2.0%
산 40
 
2.0%
광 39
 
2.0%
대 39
 
2.0%
Other values (266) 1314
66.0%

fclty_crdnt_lo
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.66397
Minimum126.29169
Maximum129.4226
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:11.054804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.29169
5-th percentile126.53652
Q1126.89228
median127.13798
Q3128.58323
95-th percentile129.16751
Maximum129.4226
Range3.1309088
Interquartile range (IQR)1.6909471

Descriptive statistics

Standard deviation0.95713303
Coefficient of variation (CV)0.0074972841
Kurtosis-1.3606974
Mean127.66397
Median Absolute Deviation (MAD)0.5025201
Skewness0.4719447
Sum12766.397
Variance0.91610363
MonotonicityNot monotonic
2023-12-10T18:39:12.290519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.8980745 1
 
1.0%
126.9163674 1
 
1.0%
127.33314 1
 
1.0%
126.9588116 1
 
1.0%
126.9911633 1
 
1.0%
126.9049244 1
 
1.0%
127.9641115 1
 
1.0%
129.0085046 1
 
1.0%
128.6276666 1
 
1.0%
126.5365733 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.2916948 1
1.0%
126.3391735 1
1.0%
126.4254379 1
1.0%
126.5315138 1
1.0%
126.5354662 1
1.0%
126.5365733 1
1.0%
126.5746714 1
1.0%
126.5757182 1
1.0%
126.6513368 1
1.0%
126.7148037 1
1.0%
ValueCountFrequency (%)
129.4226036 1
1.0%
129.4038166 1
1.0%
129.3565423 1
1.0%
129.3405448 1
1.0%
129.175297 1
1.0%
129.1670953 1
1.0%
129.1572069 1
1.0%
129.1442096 1
1.0%
129.1021007 1
1.0%
129.058892 1
1.0%

fclty_crdnt_la
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.214617
Minimum33.258164
Maximum37.865216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:39:13.397749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.258164
5-th percentile34.699014
Q135.209815
median36.017574
Q337.501366
95-th percentile37.673011
Maximum37.865216
Range4.6070522
Interquartile range (IQR)2.2915516

Descriptive statistics

Standard deviation1.1951718
Coefficient of variation (CV)0.03300247
Kurtosis-0.66118888
Mean36.214617
Median Absolute Deviation (MAD)0.909897
Skewness-0.37417664
Sum3621.4617
Variance1.4284357
MonotonicityNot monotonic
2023-12-10T18:39:13.796346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.8029579 1
 
1.0%
37.1288208 1
 
1.0%
36.3482504 1
 
1.0%
35.9271476 1
 
1.0%
35.0614145 1
 
1.0%
37.5613848 1
 
1.0%
37.3373337 1
 
1.0%
35.2233492 1
 
1.0%
35.8917545 1
 
1.0%
33.5109423 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
33.2581635 1
1.0%
33.4060009 1
1.0%
33.4953248 1
1.0%
33.5109423 1
1.0%
33.5144419 1
1.0%
34.7613603 1
1.0%
34.8073337 1
1.0%
34.8417908 1
1.0%
34.8611639 1
1.0%
34.8745611 1
1.0%
ValueCountFrequency (%)
37.8652157 1
1.0%
37.7834462 1
1.0%
37.720236 1
1.0%
37.7035429 1
1.0%
37.6792802 1
1.0%
37.6726814 1
1.0%
37.6247495 1
1.0%
37.6229606 1
1.0%
37.6050767 1
1.0%
37.6046396 1
1.0%

gid_cd
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:39:14.272947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters16
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

Unique100 ?
Unique (%)100.0%

Sample

1st row다마456564
2nd row다사381256
3rd row다바650685
4th row다사666344
5th row마라397916
ValueCountFrequency (%)
다마456564 1
 
1.0%
마라399914 1
 
1.0%
다마511701 1
 
1.0%
다라536741 1
 
1.0%
다사474515 1
 
1.0%
라사411265 1
 
1.0%
마라372930 1
 
1.0%
마마017667 1
 
1.0%
다다105025 1
 
1.0%
라마980663 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:39:15.011012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 75
 
9.4%
5 67
 
8.4%
6 67
 
8.4%
9 62
 
7.8%
8 59
 
7.4%
다 58
 
7.2%
1 58
 
7.2%
3 55
 
6.9%
0 53
 
6.6%
7 52
 
6.5%
Other values (6) 194
24.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 600
75.0%
Other Letter 200
 
25.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 75
12.5%
5 67
11.2%
6 67
11.2%
9 62
10.3%
8 59
9.8%
1 58
9.7%
3 55
9.2%
0 53
8.8%
7 52
8.7%
2 52
8.7%
Other Letter
ValueCountFrequency (%)
다 58
29.0%
마 47
23.5%
라 45
22.5%
사 35
17.5%
바 11
 
5.5%
나 4
 
2.0%

Most occurring scripts

ValueCountFrequency (%)
Common 600
75.0%
Hangul 200
 
25.0%

Most frequent character per script

Common
ValueCountFrequency (%)
4 75
12.5%
5 67
11.2%
6 67
11.2%
9 62
10.3%
8 59
9.8%
1 58
9.7%
3 55
9.2%
0 53
8.8%
7 52
8.7%
2 52
8.7%
Hangul
ValueCountFrequency (%)
다 58
29.0%
마 47
23.5%
라 45
22.5%
사 35
17.5%
바 11
 
5.5%
나 4
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 600
75.0%
Hangul 200
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 75
12.5%
5 67
11.2%
6 67
11.2%
9 62
10.3%
8 59
9.8%
1 58
9.7%
3 55
9.2%
0 53
8.8%
7 52
8.7%
2 52
8.7%
Hangul
ValueCountFrequency (%)
다 58
29.0%
마 47
23.5%
라 45
22.5%
사 35
17.5%
바 11
 
5.5%
나 4
 
2.0%

Interactions

2023-12-10T18:39:02.868543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:52.929840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:54.877628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:59.788277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:00.906295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:02.053069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:03.221102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:53.196895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:55.519260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:00.134176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:01.131881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:02.199407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:03.677164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:53.487863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:56.216641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:00.333397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:01.344310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:02.351792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:03.970129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:53.694150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:57.274952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:00.463802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:01.601365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:02.470202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:04.182827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:53.925135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:58.285425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:00.634557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:01.762294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:02.623368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:04.343929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:54.337198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:38:59.046960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:00.783584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:01.913467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:39:02.748557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:39:15.177366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
fclty_nmctprvn_cdctprvn_nmsigngu_cdsigngu_nmrprsntv_tel_nordnmadr_cdfclty_road_nm_addrfclty_crdnt_lofclty_crdnt_lagid_cd
fclty_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
ctprvn_cd1.0001.0001.0001.0000.9620.3451.0001.0000.6990.7511.000
ctprvn_nm1.0001.0001.0000.9960.9810.8420.9961.0000.8800.9861.000
signgu_cd1.0001.0000.9961.0000.9620.3351.0001.0000.6860.7501.000
signgu_nm1.0000.9620.9810.9621.0000.6120.9611.0000.9860.9791.000
rprsntv_tel_no1.0000.3450.8420.3350.6121.0000.3401.0000.5300.5671.000
rdnmadr_cd1.0001.0000.9961.0000.9610.3401.0001.0000.6900.7511.000
fclty_road_nm_addr1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
fclty_crdnt_lo1.0000.6990.8800.6860.9860.5300.6901.0001.0000.6961.000
fclty_crdnt_la1.0000.7510.9860.7500.9790.5670.7511.0000.6961.0001.000
gid_cd1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T18:39:15.371552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ctprvn_cdsigngu_cdrprsntv_tel_nordnmadr_cdfclty_crdnt_lofclty_crdnt_lactprvn_nm
ctprvn_cd1.0000.9950.5180.995-0.080-0.3260.950
signgu_cd0.9951.0000.5161.000-0.081-0.3280.943
rprsntv_tel_no0.5180.5161.0000.5140.093-0.6000.638
rdnmadr_cd0.9951.0000.5141.000-0.082-0.3280.943
fclty_crdnt_lo-0.080-0.0810.093-0.0821.000-0.2620.583
fclty_crdnt_la-0.326-0.328-0.600-0.328-0.2621.0000.773
ctprvn_nm0.9500.9430.6380.9430.5830.7731.000

Missing values

2023-12-10T18:39:04.534521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:39:04.764199image/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.

Sample

fclty_nmctprvn_cdctprvn_nmsigngu_cdsigngu_nmrprsntv_tel_nordnmadr_cdfclty_road_nm_addrfclty_crdnt_lofclty_crdnt_lagid_cd
0금만검도관45전북45210김제시635454525452104616250전라북도 김제시 도작9길 14금만검도관(신풍동)126.89807535.802958다마456564
1경희엄지YG효자태권도 원곡지점41경기41270안산시314859695412734373072경기도 안산시 단원구 라성안길 17303호(원곡동)126.80234437.32776다사381256
2(주)어뮤즈44충남44130천안시25566742441333351004충청남도 천안시 서북구 불당25로 146605호(불당동)127.10773636.814184다바650685
3(주)꿈자라다41경기41130성남시317191441411353180041경기도 성남시 분당구 야탑로 32삼부빌딜 4층(야탑동)127.12261537.408783다사666344
4(주)다비드짐26부산26320북구519882114263204196123부산광역시 북구 덕천로281번길 122층 다비드짐(만덕동)129.03509935.210195마라397916
5(주)엠엔제이 정석볼링장44충남44825태안군26740880448253264037충남 태안군 태안읍 환동로 31-14정석볼링장126.29169536.749485나바921619
6(주)애플스포츠43충북43130충주시438541919431303238030충청북도 충주시 삼원로 751층,2층(봉방동)127.92058836.973604라바374862
7yk휘트니스48경남48120창원시552925001481274790356경상남도 창원시 마산회원구 합성서9길 93층 yk휘트니스(합성동)128.58172335.238406라라984942
8(사)광주광역시 거점스포츠클럽29광주29140서구623760985291403009011광주광역시 서구 금화로 240월드컵경기장 203호(풍암동)126.87489235.133723다라430822
9(유)라온볼링센터45전북45110전주시632722500451133267021전라북도 전주시 덕진구 매봉로 40지하1층(금암동)127.14184835.841802다마676606
fclty_nmctprvn_cdctprvn_nmsigngu_cdsigngu_nmrprsntv_tel_nordnmadr_cdfclty_road_nm_addrfclty_crdnt_lofclty_crdnt_lagid_cd
90커브스 신안클럽48경남48170진주시557473030481703332056경상남도 진주시 진양호로 2825층, 커브스 신안클럽(신안동)128.0644535.178491라라513871
91커브스 좌동클럽26부산26350해운대구517049001263503133039부산광역시 해운대구 좌동순환로 176장산빌딩3층 커브스 좌동클럽(좌동)129.17529735.177565마라525882
92커브스 진영클럽48경남48250김해시553435330482504805224경상남도 김해시 진영읍 김해대로361번길 16나경타운 303호128.72989535.308227마마118021
93커브스 학동클럽46전남46130여수시616852330461303282025전라남도 여수시 무선로 64층 401호(학동)127.65637734.76136라라143407
94터미널 볼링센터48경남48840남해군558634438488402343003경상남도 남해군 남해읍 남해대로 28353층 310호127.89848534.841791라라364497
95플러그 구리센터41경기41310구리시315233033413102196001경기도 구리시 건원대로 44503호(인창동)127.14048837.605077다사682562
96합기도 월드도장30대전30140중구422723198301403010004대전광역시 중구 대종로 1352층(호동)127.44973636.304086다바954118
97훌륭한 태권스쿨29광주29140서구623832067291404280296광주광역시 서구 염화로57번길 3금호타운 1차 상가 2층 훌륭한 태권스쿨(화정동, 금호타운(1차))126.87223235.139354다라428828
98커브스 홍제클럽11서울11410서대문구23931330114104136396서울특별시 서대문구 통일로39가길 305층(홍제동)126.94285937.589312다사508545
99계명대 도복사랑 태권도47경북47190구미시5479339279471903308014경상북도 구미시 문장로 111도량 롯데캐슬 상가 2층(도량동, 도량 롯데캐슬 골드파크)128.33045336.142806라마747942