Overview

Dataset statistics

Number of variables30
Number of observations100
Missing cells457
Missing cells (%)15.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory24.9 KiB
Average record size in memory255.3 B

Variable types

Text6
Boolean1
Categorical14
Numeric9

Alerts

fclty_ncm_nm has constant value ""Constant
fclty_cl_cd has constant value ""Constant
fclty_cl_nm has constant value ""Constant
cmptnc_ctprvn_cd is highly imbalanced (86.1%)Imbalance
cmptnc_ctprvn_nm is highly imbalanced (86.1%)Imbalance
cmptnc_signgu_nm is highly imbalanced (51.4%)Imbalance
fclty_info_regist_de is highly imbalanced (56.2%)Imbalance
fclty_ncm_nm has 99 (99.0%) missing valuesMissing
zip_no has 7 (7.0%) missing valuesMissing
fclty_road_nm_addr has 4 (4.0%) missing valuesMissing
fclty_road_nm_detail_addr has 96 (96.0%) missing valuesMissing
fclty_addr has 10 (10.0%) missing valuesMissing
fclty_detail_addr has 91 (91.0%) missing valuesMissing
fclty_tel_no has 20 (20.0%) missing valuesMissing
fclty_totar_co has 21 (21.0%) missing valuesMissing
oper_clsbiz_de has 48 (48.0%) missing valuesMissing
safechk_de has 61 (61.0%) missing valuesMissing
fclty_totar_co has 3 (3.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:45:44.814380
Analysis finished2023-12-10 09:45:45.770292
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

fclty_nm
Text

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:45:46.102144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length5
Mean length5.74
Min length4

Characters and Unicode

Total characters574
Distinct characters167
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique91 ?
Unique (%)91.0%

Sample

1st row(주)한샘레포츠타운(공단점)
2nd row태권道장 휘영찬
3rd row뽀록 당구장
4th row안산 용인대 청룡 태권도장
5th row사동 용인대 올림픽 태권도장
ValueCountFrequency (%)
태양당구장 3
 
2.7%
그린당구장 2
 
1.8%
용인대 2
 
1.8%
태권도장 2
 
1.8%
국제당구장 2
 
1.8%
형제당구장 2
 
1.8%
혜원당구장 1
 
0.9%
현대당구장 1
 
0.9%
정인당구장 1
 
0.9%
하남검도관 1
 
0.9%
Other values (96) 96
85.0%
2023-12-10T18:45:47.089831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 66
 
11.5%
당 61
 
10.6%
구 61
 
10.6%
도 22
 
3.8%
태 16
 
2.8%
13
 
2.3%
권 12
 
2.1%
관 12
 
2.1%
스 11
 
1.9%
성 9
 
1.6%
Other values (157) 291
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 554
96.5%
Space Separator 13
 
2.3%
Close Punctuation 3
 
0.5%
Open Punctuation 3
 
0.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 66
 
11.9%
당 61
 
11.0%
구 61
 
11.0%
도 22
 
4.0%
태 16
 
2.9%
권 12
 
2.2%
관 12
 
2.2%
스 11
 
2.0%
성 9
 
1.6%
럽 7
 
1.3%
Other values (153) 277
50.0%
Space Separator
ValueCountFrequency (%)
13
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 550
95.8%
Common 20
 
3.5%
Han 4
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 66
 
12.0%
당 61
 
11.1%
구 61
 
11.1%
도 22
 
4.0%
태 16
 
2.9%
권 12
 
2.2%
관 12
 
2.2%
스 11
 
2.0%
성 9
 
1.6%
럽 7
 
1.3%
Other values (150) 273
49.6%
Common
ValueCountFrequency (%)
13
65.0%
) 3
 
15.0%
( 3
 
15.0%
& 1
 
5.0%
Han
ValueCountFrequency (%)
道 2
50.0%
心 1
25.0%
鄭 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 550
95.8%
ASCII 20
 
3.5%
CJK 4
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 66
 
12.0%
당 61
 
11.1%
구 61
 
11.1%
도 22
 
4.0%
태 16
 
2.9%
권 12
 
2.2%
관 12
 
2.2%
스 11
 
2.0%
성 9
 
1.6%
럽 7
 
1.3%
Other values (150) 273
49.6%
ASCII
ValueCountFrequency (%)
13
65.0%
) 3
 
15.0%
( 3
 
15.0%
& 1
 
5.0%
CJK
ValueCountFrequency (%)
道 2
50.0%
心 1
25.0%
鄭 1
25.0%

fclty_ncm_nm
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing99
Missing (%)99.0%
Memory size932.0 B
2023-12-10T18:45:47.481369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters5
Distinct characters5
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

Unique1 ?
Unique (%)100.0%

Sample

1st row중앙당구장
ValueCountFrequency (%)
중앙당구장 1
100.0%
2023-12-10T18:45:47.966994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
중 1
20.0%
앙 1
20.0%
당 1
20.0%
구 1
20.0%
장 1
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 5
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 1
20.0%
앙 1
20.0%
당 1
20.0%
구 1
20.0%
장 1
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 1
20.0%
앙 1
20.0%
당 1
20.0%
구 1
20.0%
장 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 5
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
중 1
20.0%
앙 1
20.0%
당 1
20.0%
구 1
20.0%
장 1
20.0%

fclty_cl_cd
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T18:45:48.156735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

fclty_cl_nm
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
신고업
100 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
신고업 100
100.0%

Length

2023-12-10T18:45:48.335486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:48.849041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신고업 100
100.0%

induty_cd
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
N11
63 
N08
21 
N10
8 
N14
 
4
N07
 
2
Other values (2)
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st rowN10
2nd rowN08
3rd rowN11
4th rowN08
5th rowN08

Common Values

ValueCountFrequency (%)
N11 63
63.0%
N08 21
 
21.0%
N10 8
 
8.0%
N14 4
 
4.0%
N07 2
 
2.0%
N13 1
 
1.0%
N09 1
 
1.0%

Length

2023-12-10T18:45:49.062945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:49.268519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n11 63
63.0%
n08 21
 
21.0%
n10 8
 
8.0%
n14 4
 
4.0%
n07 2
 
2.0%
n13 1
 
1.0%
n09 1
 
1.0%

induty_nm
Categorical

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
당구장업
63 
체육도장업
21 
체력단련장업
8 
무도학원업
 
4
수영장업
 
2
Other values (2)
 
2

Length

Max length6
Median length4
Mean length4.43
Min length4

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row체력단련장업
2nd row체육도장업
3rd row당구장업
4th row체육도장업
5th row체육도장업

Common Values

ValueCountFrequency (%)
당구장업 63
63.0%
체육도장업 21
 
21.0%
체력단련장업 8
 
8.0%
무도학원업 4
 
4.0%
수영장업 2
 
2.0%
무도장업 1
 
1.0%
골프연습장업 1
 
1.0%

Length

2023-12-10T18:45:49.585501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:49.837645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장업 63
63.0%
체육도장업 21
 
21.0%
체력단련장업 8
 
8.0%
무도학원업 4
 
4.0%
수영장업 2
 
2.0%
무도장업 1
 
1.0%
골프연습장업 1
 
1.0%

fclty_ty_cd
Categorical

Distinct10
Distinct (%)10.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
N1101
63 
N0805
15 
N1001
8 
N0804
 
5
N1401
 
4
Other values (5)
 
5

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique5 ?
Unique (%)5.0%

Sample

1st rowN1001
2nd rowN0805
3rd rowN1101
4th rowN0805
5th rowN0805

Common Values

ValueCountFrequency (%)
N1101 63
63.0%
N0805 15
 
15.0%
N1001 8
 
8.0%
N0804 5
 
5.0%
N1401 4
 
4.0%
N0701 1
 
1.0%
N1301 1
 
1.0%
N0803 1
 
1.0%
N0901 1
 
1.0%
N0702 1
 
1.0%

Length

2023-12-10T18:45:50.044461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:50.254898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n1101 63
63.0%
n0805 15
 
15.0%
n1001 8
 
8.0%
n0804 5
 
5.0%
n1401 4
 
4.0%
n0701 1
 
1.0%
n1301 1
 
1.0%
n0803 1
 
1.0%
n0901 1
 
1.0%
n0702 1
 
1.0%

fclty_ty_nm
Categorical

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
당구장
63 
태권도
15 
체력단련장
8 
검도
 
5
무도학원
 
4
Other values (4)
 
5

Length

Max length5
Median length3
Mean length3.11
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row체력단련장
2nd row태권도
3rd row당구장
4th row태권도
5th row태권도

Common Values

ValueCountFrequency (%)
당구장 63
63.0%
태권도 15
 
15.0%
체력단련장 8
 
8.0%
검도 5
 
5.0%
무도학원 4
 
4.0%
실내 2
 
2.0%
무도장 1
 
1.0%
유도 1
 
1.0%
실외 1
 
1.0%

Length

2023-12-10T18:45:50.548394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:50.788742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장 63
63.0%
태권도 15
 
15.0%
체력단련장 8
 
8.0%
검도 5
 
5.0%
무도학원 4
 
4.0%
실내 2
 
2.0%
무도장 1
 
1.0%
유도 1
 
1.0%
실외 1
 
1.0%

cmptnc_ctprvn_cd
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
4100000000
96 
3000000000
 
1
1100000000
 
1
4800000000
 
1
5000000000
 
1

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row4100000000
2nd row3000000000
3rd row4100000000
4th row4100000000
5th row4100000000

Common Values

ValueCountFrequency (%)
4100000000 96
96.0%
3000000000 1
 
1.0%
1100000000 1
 
1.0%
4800000000 1
 
1.0%
5000000000 1
 
1.0%

Length

2023-12-10T18:45:51.034446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:51.278144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4100000000 96
96.0%
3000000000 1
 
1.0%
1100000000 1
 
1.0%
4800000000 1
 
1.0%
5000000000 1
 
1.0%

cmptnc_ctprvn_nm
Categorical

IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
96 
대전광역시
 
1
서울특별시
 
1
경상남도
 
1
제주특별자치도
 
1

Length

Max length7
Median length3
Mean length3.09
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row경기도
2nd row대전광역시
3rd row경기도
4th row경기도
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 96
96.0%
대전광역시 1
 
1.0%
서울특별시 1
 
1.0%
경상남도 1
 
1.0%
제주특별자치도 1
 
1.0%

Length

2023-12-10T18:45:51.546820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:45:51.788458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 96
96.0%
대전광역시 1
 
1.0%
서울특별시 1
 
1.0%
경상남도 1
 
1.0%
제주특별자치도 1
 
1.0%

cmptnc_signgu_cd
Real number (ℝ)

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.09704 × 109
Minimum1.132 × 109
Maximum5.013 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:45:51.986198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.132 × 109
5-th percentile4.113 × 109
Q14.113 × 109
median4.127 × 109
Q34.127 × 109
95-th percentile4.15 × 109
Maximum5.013 × 109
Range3.881 × 109
Interquartile range (IQR)14000000

Descriptive statistics

Standard deviation3.3912799 × 108
Coefficient of variation (CV)0.082773904
Kurtosis61.512962
Mean4.09704 × 109
Median Absolute Deviation (MAD)14000000
Skewness-6.9193263
Sum4.09704 × 1011
Variance1.150078 × 1017
MonotonicityNot monotonic
2023-12-10T18:45:52.290120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
4127000000 47
47.0%
4113000000 43
43.0%
4150000000 2
 
2.0%
3017000000 1
 
1.0%
4128000000 1
 
1.0%
1132000000 1
 
1.0%
4157000000 1
 
1.0%
4817000000 1
 
1.0%
4167000000 1
 
1.0%
4145000000 1
 
1.0%
ValueCountFrequency (%)
1132000000 1
 
1.0%
3017000000 1
 
1.0%
4113000000 43
43.0%
4127000000 47
47.0%
4128000000 1
 
1.0%
4145000000 1
 
1.0%
4150000000 2
 
2.0%
4157000000 1
 
1.0%
4167000000 1
 
1.0%
4817000000 1
 
1.0%
ValueCountFrequency (%)
5013000000 1
 
1.0%
4817000000 1
 
1.0%
4167000000 1
 
1.0%
4157000000 1
 
1.0%
4150000000 2
 
2.0%
4145000000 1
 
1.0%
4128000000 1
 
1.0%
4127000000 47
47.0%
4113000000 43
43.0%
3017000000 1
 
1.0%

cmptnc_signgu_nm
Categorical

IMBALANCE 

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안산시
47 
성남시
43 
이천시
 
2
서구
 
1
고양시
 
1
Other values (6)
6 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row안산시
2nd row서구
3rd row성남시
4th row안산시
5th row안산시

Common Values

ValueCountFrequency (%)
안산시 47
47.0%
성남시 43
43.0%
이천시 2
 
2.0%
서구 1
 
1.0%
고양시 1
 
1.0%
도봉구 1
 
1.0%
김포시 1
 
1.0%
진주시 1
 
1.0%
여주시 1
 
1.0%
하남시 1
 
1.0%

Length

2023-12-10T18:45:52.608748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 47
47.0%
성남시 43
43.0%
이천시 2
 
2.0%
서구 1
 
1.0%
고양시 1
 
1.0%
도봉구 1
 
1.0%
김포시 1
 
1.0%
진주시 1
 
1.0%
여주시 1
 
1.0%
하남시 1
 
1.0%

zip_no
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)73.1%
Missing7
Missing (%)7.0%
Infinite0
Infinite (%)0.0%
Mean15332.656
Minimum1384
Maximum63629
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:45:52.879034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1384
5-th percentile13056.8
Q113306
median15217
Q315431
95-th percentile16328.2
Maximum63629
Range62245
Interquartile range (IQR)2125

Descriptive statistics

Standard deviation7052.0596
Coefficient of variation (CV)0.45993725
Kurtosis32.545234
Mean15332.656
Median Absolute Deviation (MAD)1864
Skewness5.3739289
Sum1425937
Variance49731545
MonotonicityNot monotonic
2023-12-10T18:45:53.156036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
13306 6
 
6.0%
15361 6
 
6.0%
13353 4
 
4.0%
13140 3
 
3.0%
13327 3
 
3.0%
15360 2
 
2.0%
15431 2
 
2.0%
15288 2
 
2.0%
13347 2
 
2.0%
13118 2
 
2.0%
Other values (58) 61
61.0%
(Missing) 7
 
7.0%
ValueCountFrequency (%)
1384 1
 
1.0%
10110 1
 
1.0%
10319 1
 
1.0%
12620 1
 
1.0%
12977 1
 
1.0%
13110 1
 
1.0%
13118 2
2.0%
13134 1
 
1.0%
13136 1
 
1.0%
13140 3
3.0%
ValueCountFrequency (%)
63629 1
1.0%
52731 1
1.0%
35381 1
1.0%
17374 1
1.0%
17353 1
1.0%
15645 1
1.0%
15632 1
1.0%
15629 1
1.0%
15581 1
1.0%
15577 1
1.0%

fclty_road_nm_addr
Text

MISSING 

Distinct95
Distinct (%)99.0%
Missing4
Missing (%)4.0%
Memory size932.0 B
2023-12-10T18:45:53.853619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length28.010417
Min length11

Characters and Unicode

Total characters2689
Distinct characters149
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

Unique94 ?
Unique (%)97.9%

Sample

1st row경기도 안산시 단원구 동산로 63 (원시동)
2nd row대전광역시 서구 관저동로 66 (관저동, 3층)
3rd row경기도 성남시 수정구 산성대로 403 (단대동,2층)
4th row경기도 안산시 상록구 중보로 22 (사동, 늘푸른아파트)
5th row경기도 안산시 상록구 본삼로 51 (본오동)
ValueCountFrequency (%)
경기도 91
 
15.4%
성남시 43
 
7.3%
수정구 43
 
7.3%
안산시 43
 
7.3%
단원구 24
 
4.1%
상록구 19
 
3.2%
태평동 13
 
2.2%
신흥동 6
 
1.0%
산성대로 6
 
1.0%
본오동 6
 
1.0%
Other values (225) 295
50.1%
2023-12-10T18:45:54.656149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
493
 
18.3%
동 104
 
3.9%
시 101
 
3.8%
( 95
 
3.5%
) 95
 
3.5%
도 93
 
3.5%
경 92
 
3.4%
기 91
 
3.4%
구 89
 
3.3%
로 84
 
3.1%
Other values (139) 1352
50.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1595
59.3%
Space Separator 493
 
18.3%
Decimal Number 353
 
13.1%
Open Punctuation 95
 
3.5%
Close Punctuation 95
 
3.5%
Other Punctuation 50
 
1.9%
Dash Punctuation 5
 
0.2%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 104
 
6.5%
시 101
 
6.3%
도 93
 
5.8%
경 92
 
5.8%
기 91
 
5.7%
구 89
 
5.6%
로 84
 
5.3%
산 65
 
4.1%
성 65
 
4.1%
정 58
 
3.6%
Other values (123) 753
47.2%
Decimal Number
ValueCountFrequency (%)
1 84
23.8%
2 50
14.2%
3 49
13.9%
4 38
10.8%
5 30
 
8.5%
6 26
 
7.4%
0 24
 
6.8%
7 19
 
5.4%
8 18
 
5.1%
9 15
 
4.2%
Space Separator
ValueCountFrequency (%)
493
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Other Punctuation
ValueCountFrequency (%)
, 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1595
59.3%
Common 1091
40.6%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 104
 
6.5%
시 101
 
6.3%
도 93
 
5.8%
경 92
 
5.8%
기 91
 
5.7%
구 89
 
5.6%
로 84
 
5.3%
산 65
 
4.1%
성 65
 
4.1%
정 58
 
3.6%
Other values (123) 753
47.2%
Common
ValueCountFrequency (%)
493
45.2%
( 95
 
8.7%
) 95
 
8.7%
1 84
 
7.7%
, 50
 
4.6%
2 50
 
4.6%
3 49
 
4.5%
4 38
 
3.5%
5 30
 
2.7%
6 26
 
2.4%
Other values (5) 81
 
7.4%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1595
59.3%
ASCII 1094
40.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
493
45.1%
( 95
 
8.7%
) 95
 
8.7%
1 84
 
7.7%
, 50
 
4.6%
2 50
 
4.6%
3 49
 
4.5%
4 38
 
3.5%
5 30
 
2.7%
6 26
 
2.4%
Other values (6) 84
 
7.7%
Hangul
ValueCountFrequency (%)
동 104
 
6.5%
시 101
 
6.3%
도 93
 
5.8%
경 92
 
5.8%
기 91
 
5.7%
구 89
 
5.6%
로 84
 
5.3%
산 65
 
4.1%
성 65
 
4.1%
정 58
 
3.6%
Other values (123) 753
47.2%

fclty_road_nm_detail_addr
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing96
Missing (%)96.0%
Memory size932.0 B
2023-12-10T18:45:54.960855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5.5
Mean length5.5
Min length2

Characters and Unicode

Total characters22
Distinct characters19
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

Unique4 ?
Unique (%)100.0%

Sample

1st row3층
2nd row공설운동장 17호
3rd row2층
4th row지1층,경원프라자
ValueCountFrequency (%)
3층 1
20.0%
공설운동장 1
20.0%
17호 1
20.0%
2층 1
20.0%
지1층,경원프라자 1
20.0%
2023-12-10T18:45:55.561577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
층 3
 
13.6%
1 2
 
9.1%
3 1
 
4.5%
2 1
 
4.5%
라 1
 
4.5%
프 1
 
4.5%
원 1
 
4.5%
경 1
 
4.5%
, 1
 
4.5%
지 1
 
4.5%
Other values (9) 9
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
68.2%
Decimal Number 5
 
22.7%
Other Punctuation 1
 
4.5%
Space Separator 1
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
층 3
20.0%
라 1
 
6.7%
프 1
 
6.7%
원 1
 
6.7%
경 1
 
6.7%
지 1
 
6.7%
호 1
 
6.7%
장 1
 
6.7%
동 1
 
6.7%
운 1
 
6.7%
Other values (3) 3
20.0%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
3 1
20.0%
2 1
20.0%
7 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15
68.2%
Common 7
31.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
층 3
20.0%
라 1
 
6.7%
프 1
 
6.7%
원 1
 
6.7%
경 1
 
6.7%
지 1
 
6.7%
호 1
 
6.7%
장 1
 
6.7%
동 1
 
6.7%
운 1
 
6.7%
Other values (3) 3
20.0%
Common
ValueCountFrequency (%)
1 2
28.6%
3 1
14.3%
2 1
14.3%
, 1
14.3%
7 1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15
68.2%
ASCII 7
31.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
층 3
20.0%
라 1
 
6.7%
프 1
 
6.7%
원 1
 
6.7%
경 1
 
6.7%
지 1
 
6.7%
호 1
 
6.7%
장 1
 
6.7%
동 1
 
6.7%
운 1
 
6.7%
Other values (3) 3
20.0%
ASCII
ValueCountFrequency (%)
1 2
28.6%
3 1
14.3%
2 1
14.3%
, 1
14.3%
7 1
14.3%
1
14.3%

fclty_addr
Text

MISSING 

Distinct88
Distinct (%)97.8%
Missing10
Missing (%)10.0%
Memory size932.0 B
2023-12-10T18:45:56.293720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length19.244444
Min length13

Characters and Unicode

Total characters1732
Distinct characters123
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

Unique86 ?
Unique (%)95.6%

Sample

1st row대전 서구 관저동로 66
2nd row경기 성남시 수정구 산성대로 403
3rd row경기도 안산시 상록구 샘골로 115
4th row경기도 안산시 상록구 사동 1533 늘푸른아파트
5th row서울특별시 도봉구 방학동 669-2
ValueCountFrequency (%)
경기 75
16.7%
성남시 43
 
9.6%
수정구 43
 
9.6%
안산시 40
 
8.9%
단원구 24
 
5.3%
상록구 16
 
3.6%
경기도 11
 
2.4%
산성대로 6
 
1.3%
고잔1길 5
 
1.1%
성남대로 4
 
0.9%
Other values (153) 183
40.7%
2023-12-10T18:45:57.348803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
360
20.8%
시 94
 
5.4%
경 88
 
5.1%
기 86
 
5.0%
구 85
 
4.9%
로 70
 
4.0%
1 69
 
4.0%
성 62
 
3.6%
산 55
 
3.2%
정 55
 
3.2%
Other values (113) 708
40.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1090
62.9%
Space Separator 360
 
20.8%
Decimal Number 275
 
15.9%
Dash Punctuation 7
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 94
 
8.6%
경 88
 
8.1%
기 86
 
7.9%
구 85
 
7.8%
로 70
 
6.4%
성 62
 
5.7%
산 55
 
5.0%
정 55
 
5.0%
남 55
 
5.0%
수 53
 
4.9%
Other values (101) 387
35.5%
Decimal Number
ValueCountFrequency (%)
1 69
25.1%
3 35
12.7%
5 28
10.2%
4 27
 
9.8%
6 26
 
9.5%
2 26
 
9.5%
9 19
 
6.9%
8 16
 
5.8%
7 15
 
5.5%
0 14
 
5.1%
Space Separator
ValueCountFrequency (%)
360
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1090
62.9%
Common 642
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 94
 
8.6%
경 88
 
8.1%
기 86
 
7.9%
구 85
 
7.8%
로 70
 
6.4%
성 62
 
5.7%
산 55
 
5.0%
정 55
 
5.0%
남 55
 
5.0%
수 53
 
4.9%
Other values (101) 387
35.5%
Common
ValueCountFrequency (%)
360
56.1%
1 69
 
10.7%
3 35
 
5.5%
5 28
 
4.4%
4 27
 
4.2%
6 26
 
4.0%
2 26
 
4.0%
9 19
 
3.0%
8 16
 
2.5%
7 15
 
2.3%
Other values (2) 21
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1090
62.9%
ASCII 642
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
360
56.1%
1 69
 
10.7%
3 35
 
5.5%
5 28
 
4.4%
4 27
 
4.2%
6 26
 
4.0%
2 26
 
4.0%
9 19
 
3.0%
8 16
 
2.5%
7 15
 
2.3%
Other values (2) 21
 
3.3%
Hangul
ValueCountFrequency (%)
시 94
 
8.6%
경 88
 
8.1%
기 86
 
7.9%
구 85
 
7.8%
로 70
 
6.4%
성 62
 
5.7%
산 55
 
5.0%
정 55
 
5.0%
남 55
 
5.0%
수 53
 
4.9%
Other values (101) 387
35.5%

fclty_detail_addr
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing91
Missing (%)91.0%
Memory size932.0 B
2023-12-10T18:45:57.749201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length6
Mean length6.1111111
Min length2

Characters and Unicode

Total characters55
Distinct characters27
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

Unique9 ?
Unique (%)100.0%

Sample

1st row207호
2nd row, 501호
3rd row3층
4th row공설운동장 17호
5th row2층
ValueCountFrequency (%)
4
26.7%
2층 2
13.3%
207호 1
 
6.7%
501호 1
 
6.7%
3층 1
 
6.7%
공설운동장 1
 
6.7%
17호 1
 
6.7%
지1층,경원프라자 1
 
6.7%
302호 1
 
6.7%
신영빌딩 1
 
6.7%
2023-12-10T18:45:58.186627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
10.9%
2 5
 
9.1%
호 5
 
9.1%
, 5
 
9.1%
1 4
 
7.3%
0 4
 
7.3%
층 4
 
7.3%
3 2
 
3.6%
7 2
 
3.6%
프 1
 
1.8%
Other values (17) 17
30.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 24
43.6%
Decimal Number 18
32.7%
Space Separator 6
 
10.9%
Other Punctuation 5
 
9.1%
Open Punctuation 1
 
1.8%
Close Punctuation 1
 
1.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 5
20.8%
층 4
16.7%
프 1
 
4.2%
딩 1
 
4.2%
빌 1
 
4.2%
영 1
 
4.2%
신 1
 
4.2%
자 1
 
4.2%
라 1
 
4.2%
동 1
 
4.2%
Other values (7) 7
29.2%
Decimal Number
ValueCountFrequency (%)
2 5
27.8%
1 4
22.2%
0 4
22.2%
3 2
 
11.1%
7 2
 
11.1%
5 1
 
5.6%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 31
56.4%
Hangul 24
43.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 5
20.8%
층 4
16.7%
프 1
 
4.2%
딩 1
 
4.2%
빌 1
 
4.2%
영 1
 
4.2%
신 1
 
4.2%
자 1
 
4.2%
라 1
 
4.2%
동 1
 
4.2%
Other values (7) 7
29.2%
Common
ValueCountFrequency (%)
6
19.4%
2 5
16.1%
, 5
16.1%
1 4
12.9%
0 4
12.9%
3 2
 
6.5%
7 2
 
6.5%
( 1
 
3.2%
5 1
 
3.2%
) 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31
56.4%
Hangul 24
43.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6
19.4%
2 5
16.1%
, 5
16.1%
1 4
12.9%
0 4
12.9%
3 2
 
6.5%
7 2
 
6.5%
( 1
 
3.2%
5 1
 
3.2%
) 1
 
3.2%
Hangul
ValueCountFrequency (%)
호 5
20.8%
층 4
16.7%
프 1
 
4.2%
딩 1
 
4.2%
빌 1
 
4.2%
영 1
 
4.2%
신 1
 
4.2%
자 1
 
4.2%
라 1
 
4.2%
동 1
 
4.2%
Other values (7) 7
29.2%

fclty_crdnt_lo
Real number (ℝ)

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.00748
Minimum126.58521
Maximum128.11316
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:45:58.455142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.58521
5-th percentile126.79697
Q1126.83845
median126.96666
Q3127.14071
95-th percentile127.20968
Maximum128.11316
Range1.5279456
Interquartile range (IQR)0.30225952

Descriptive statistics

Standard deviation0.21593472
Coefficient of variation (CV)0.0017001732
Kurtosis6.0067071
Mean127.00748
Median Absolute Deviation (MAD)0.16120608
Skewness1.6099181
Sum12700.748
Variance0.046627803
MonotonicityNot monotonic
2023-12-10T18:45:58.755116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.787515465 2
 
2.0%
126.839561727 2
 
2.0%
127.145336629 1
 
1.0%
126.863550107 1
 
1.0%
126.90066704 1
 
1.0%
126.847251063 1
 
1.0%
127.202769818 1
 
1.0%
127.167415058 1
 
1.0%
127.127993857 1
 
1.0%
127.133780631 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
126.585213708 1
1.0%
126.719264453 1
1.0%
126.787515465 2
2.0%
126.7924493812 1
1.0%
126.797203704 1
1.0%
126.7972696479 1
1.0%
126.797269648 1
1.0%
126.800502924 1
1.0%
126.804047498 1
1.0%
126.811302831 1
1.0%
ValueCountFrequency (%)
128.11315927686408 1
1.0%
127.641297155 1
1.0%
127.449535099 1
1.0%
127.445540136 1
1.0%
127.340993802 1
1.0%
127.202769818 1
1.0%
127.167415058 1
1.0%
127.16399718 1
1.0%
127.162415335 1
1.0%
127.161826145 1
1.0%

fclty_crdnt_la
Real number (ℝ)

Distinct96
Distinct (%)96.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.311481
Minimum33.322872
Maximum37.679269
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:45:59.065777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.322872
5-th percentile37.279769
Q137.311769
median37.341482
Q337.443944
95-th percentile37.458446
Maximum37.679269
Range4.356397
Interquartile range (IQR)0.13217484

Descriptive statistics

Standard deviation0.47720977
Coefficient of variation (CV)0.012789891
Kurtosis53.526991
Mean37.311481
Median Absolute Deviation (MAD)0.095546877
Skewness-6.9708284
Sum3731.1481
Variance0.22772916
MonotonicityNot monotonic
2023-12-10T18:45:59.313923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.3144312985 2
 
2.0%
37.4488396406 2
 
2.0%
37.3179561616 2
 
2.0%
37.3234461322 2
 
2.0%
37.30568875757079 1
 
1.0%
37.327191803 1
 
1.0%
37.4481184807 1
 
1.0%
37.4481069076 1
 
1.0%
37.3045942125 1
 
1.0%
37.3292112192 1
 
1.0%
Other values (86) 86
86.0%
ValueCountFrequency (%)
33.3228717542 1
1.0%
35.21299805922235 1
1.0%
36.298068364 1
1.0%
37.2427612322 1
1.0%
37.2778691869 1
1.0%
37.2798694683 1
1.0%
37.28345521 1
1.0%
37.2884647267 1
1.0%
37.2903747487 1
1.0%
37.2903883545 1
1.0%
ValueCountFrequency (%)
37.6792687208 1
1.0%
37.662069716124 1
1.0%
37.6164161706 1
1.0%
37.5385463143 1
1.0%
37.4622793926 1
1.0%
37.4582440509 1
1.0%
37.4581274566 1
1.0%
37.4581242793 1
1.0%
37.4563973595 1
1.0%
37.4557170027 1
1.0%

fclty_tel_no
Real number (ℝ)

MISSING 

Distinct80
Distinct (%)100.0%
Missing20
Missing (%)20.0%
Infinite0
Infinite (%)0.0%
Mean3.1621195 × 108
Minimum3.1402062 × 108
Maximum3.1983641 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:45:59.596343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.1402062 × 108
5-th percentile3.1405066 × 108
Q13.1434258 × 108
median3.1727113 × 108
Q33.1752935 × 108
95-th percentile3.1761277 × 108
Maximum3.1983641 × 108
Range5815793
Interquartile range (IQR)3186774.2

Descriptive statistics

Standard deviation1652014.6
Coefficient of variation (CV)0.0052243901
Kurtosis-1.3855977
Mean3.1621195 × 108
Median Absolute Deviation (MAD)503474.5
Skewness-0.090582605
Sum2.5296956 × 1010
Variance2.7291521 × 1012
MonotonicityNot monotonic
2023-12-10T18:45:59.928652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
317474242 1
 
1.0%
314028034 1
 
1.0%
317954433 1
 
1.0%
317488989 1
 
1.0%
317529173 1
 
1.0%
317535800 1
 
1.0%
317594784 1
 
1.0%
314842358 1
 
1.0%
314394757 1
 
1.0%
314397767 1
 
1.0%
Other values (70) 70
70.0%
(Missing) 20
 
20.0%
ValueCountFrequency (%)
314020620 1
1.0%
314028034 1
1.0%
314033399 1
1.0%
314039688 1
1.0%
314051238 1
1.0%
314056492 1
1.0%
314076785 1
1.0%
314100814 1
1.0%
314107717 1
1.0%
314110329 1
1.0%
ValueCountFrequency (%)
319836413 1
1.0%
319755503 1
1.0%
318865640 1
1.0%
317954433 1
1.0%
317594784 1
1.0%
317584464 1
1.0%
317581887 1
1.0%
317581695 1
1.0%
317580502 1
1.0%
317574381 1
1.0%

fclty_totar_co
Real number (ℝ)

MISSING  ZEROS 

Distinct64
Distinct (%)81.0%
Missing21
Missing (%)21.0%
Infinite0
Infinite (%)0.0%
Mean197.48646
Minimum0
Maximum911
Zeros3
Zeros (%)3.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:00.276873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile73.596
Q1131
median154
Q3202.5
95-th percentile531.4
Maximum911
Range911
Interquartile range (IQR)71.5

Descriptive statistics

Standard deviation154.40904
Coefficient of variation (CV)0.78187156
Kurtosis9.0340269
Mean197.48646
Median Absolute Deviation (MAD)39
Skewness2.7938956
Sum15601.43
Variance23842.153
MonotonicityNot monotonic
2023-12-10T18:46:00.599578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
130.0 3
 
3.0%
132.0 3
 
3.0%
157.0 3
 
3.0%
0.0 3
 
3.0%
110.0 2
 
2.0%
150.0 2
 
2.0%
146.0 2
 
2.0%
199.0 2
 
2.0%
154.0 2
 
2.0%
141.0 2
 
2.0%
Other values (54) 55
55.0%
(Missing) 21
 
21.0%
ValueCountFrequency (%)
0.0 3
3.0%
69.96 1
 
1.0%
74.0 1
 
1.0%
80.0 1
 
1.0%
94.0 1
 
1.0%
96.57 1
 
1.0%
97.0 1
 
1.0%
99.0 1
 
1.0%
100.0 1
 
1.0%
105.0 1
 
1.0%
ValueCountFrequency (%)
911.0 1
1.0%
800.0 1
1.0%
707.0 1
1.0%
535.0 1
1.0%
531.0 1
1.0%
498.0 1
1.0%
339.0 1
1.0%
305.0 1
1.0%
284.86 1
1.0%
283.0 1
1.0%

oper_state_cd
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
99
52 
0
47 
3
 
1

Length

Max length2
Median length2
Mean length1.52
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
99 52
52.0%
0 47
47.0%
3 1
 
1.0%

Length

2023-12-10T18:46:00.909799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:01.088793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 52
52.0%
0 47
47.0%
3 1
 
1.0%

oper_state_nm
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
폐업
52 
정상운영
47 
휴업
 
1

Length

Max length4
Median length2
Mean length2.94
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 52
52.0%
정상운영 47
47.0%
휴업 1
 
1.0%

Length

2023-12-10T18:46:01.305886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:01.609846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 52
52.0%
정상운영 47
47.0%
휴업 1
 
1.0%

oper_clsbiz_de
Real number (ℝ)

MISSING 

Distinct19
Distinct (%)36.5%
Missing48
Missing (%)48.0%
Infinite0
Infinite (%)0.0%
Mean20179166
Minimum20150901
Maximum20210414
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:01.965408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20150901
5-th percentile20170108
Q120170517
median20170628
Q320191105
95-th percentile20201223
Maximum20210414
Range59513
Interquartile range (IQR)20588

Descriptive statistics

Standard deviation14142.943
Coefficient of variation (CV)0.00070086857
Kurtosis-0.51382684
Mean20179166
Median Absolute Deviation (MAD)522.5
Skewness0.85217374
Sum1.0493167 × 109
Variance2.0002285 × 108
MonotonicityNot monotonic
2023-12-10T18:46:02.288669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
20170517 19
 
19.0%
20171201 7
 
7.0%
20201223 5
 
5.0%
20191105 3
 
3.0%
20170106 2
 
2.0%
20170110 2
 
2.0%
20190716 2
 
2.0%
20170526 1
 
1.0%
20210414 1
 
1.0%
20210331 1
 
1.0%
Other values (9) 9
 
9.0%
(Missing) 48
48.0%
ValueCountFrequency (%)
20150901 1
 
1.0%
20170106 2
 
2.0%
20170110 2
 
2.0%
20170330 1
 
1.0%
20170517 19
19.0%
20170526 1
 
1.0%
20170731 1
 
1.0%
20170920 1
 
1.0%
20171201 7
 
7.0%
20180530 1
 
1.0%
ValueCountFrequency (%)
20210414 1
 
1.0%
20210331 1
 
1.0%
20201223 5
5.0%
20201019 1
 
1.0%
20200803 1
 
1.0%
20200518 1
 
1.0%
20200110 1
 
1.0%
20191105 3
3.0%
20190716 2
 
2.0%
20180530 1
 
1.0%

fclty_info_updt_de
Real number (ℝ)

Distinct42
Distinct (%)42.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20195093
Minimum20161231
Maximum20210901
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:02.640873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20161231
5-th percentile20161231
Q120190128
median20200619
Q320200619
95-th percentile20210415
Maximum20210901
Range49670
Interquartile range (IQR)10491.25

Descriptive statistics

Standard deviation12909.684
Coefficient of variation (CV)0.00063924859
Kurtosis1.6189396
Mean20195093
Median Absolute Deviation (MAD)355.5
Skewness-1.4927106
Sum2.0195093 × 109
Variance1.6665995 × 108
MonotonicityNot monotonic
2023-12-10T18:46:02.996911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
20200619 42
42.0%
20161231 8
 
8.0%
20201224 5
 
5.0%
20210608 3
 
3.0%
20200924 3
 
3.0%
20190116 2
 
2.0%
20181228 2
 
2.0%
20190124 1
 
1.0%
20210123 1
 
1.0%
20190102 1
 
1.0%
Other values (32) 32
32.0%
ValueCountFrequency (%)
20161231 8
8.0%
20170226 1
 
1.0%
20171011 1
 
1.0%
20180209 1
 
1.0%
20180405 1
 
1.0%
20180920 1
 
1.0%
20181227 1
 
1.0%
20181228 2
 
2.0%
20181231 1
 
1.0%
20190102 1
 
1.0%
ValueCountFrequency (%)
20210901 1
 
1.0%
20210608 3
3.0%
20210424 1
 
1.0%
20210415 1
 
1.0%
20210324 1
 
1.0%
20210319 1
 
1.0%
20210225 1
 
1.0%
20210218 1
 
1.0%
20210123 1
 
1.0%
20201224 5
5.0%

safechk_gnrlz_grad_cd
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
99
61 
1
38 
2
 
1

Length

Max length2
Median length2
Mean length1.61
Min length1

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row99
2nd row99
3rd row99
4th row1
5th row1

Common Values

ValueCountFrequency (%)
99 61
61.0%
1 38
38.0%
2 1
 
1.0%

Length

2023-12-10T18:46:03.465814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:03.930217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
99 61
61.0%
1 38
38.0%
2 1
 
1.0%

safechk_gnrlz_grad_nm
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
없음
61 
양호
38 
주의
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row없음
2nd row없음
3rd row없음
4th row양호
5th row양호

Common Values

ValueCountFrequency (%)
없음 61
61.0%
양호 38
38.0%
주의 1
 
1.0%

Length

2023-12-10T18:46:04.201830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:04.922585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
없음 61
61.0%
양호 38
38.0%
주의 1
 
1.0%

safechk_de
Real number (ℝ)

MISSING 

Distinct28
Distinct (%)71.8%
Missing61
Missing (%)61.0%
Infinite0
Infinite (%)0.0%
Mean20201019
Minimum20200716
Maximum20201231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:05.271329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20200716
5-th percentile20200724
Q120201014
median20201023
Q320201102
95-th percentile20201204
Maximum20201231
Range515
Interquartile range (IQR)88

Descriptive statistics

Standard deviation125.43492
Coefficient of variation (CV)6.2093363 × 10-6
Kurtosis1.6213073
Mean20201019
Median Absolute Deviation (MAD)15
Skewness-1.1544878
Sum7.8783973 × 108
Variance15733.919
MonotonicityNot monotonic
2023-12-10T18:46:05.757135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20201029 4
 
4.0%
20201019 3
 
3.0%
20201130 2
 
2.0%
20201023 2
 
2.0%
20201116 2
 
2.0%
20201231 2
 
2.0%
20201028 2
 
2.0%
20201022 2
 
2.0%
20201020 1
 
1.0%
20201006 1
 
1.0%
Other values (18) 18
 
18.0%
(Missing) 61
61.0%
ValueCountFrequency (%)
20200716 1
1.0%
20200720 1
1.0%
20200724 1
1.0%
20200727 1
1.0%
20200805 1
1.0%
20201005 1
1.0%
20201006 1
1.0%
20201007 1
1.0%
20201008 1
1.0%
20201012 1
1.0%
ValueCountFrequency (%)
20201231 2
2.0%
20201201 1
 
1.0%
20201130 2
2.0%
20201117 1
 
1.0%
20201116 2
2.0%
20201104 1
 
1.0%
20201103 1
 
1.0%
20201102 1
 
1.0%
20201029 4
4.0%
20201028 2
2.0%

safechk_othbc_de
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
61 
20210131
39 

Length

Max length8
Median length4
Mean length5.56
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row20210131
5th row20210131

Common Values

ValueCountFrequency (%)
<NA> 61
61.0%
20210131 39
39.0%

Length

2023-12-10T18:46:06.147689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:06.356095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 61
61.0%
20210131 39
39.0%

fclty_info_regist_de
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20161231
78 
20161107
20 
20210901
 
1
20191129
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row20161231
2nd row20161231
3rd row20161107
4th row20161231
5th row20161231

Common Values

ValueCountFrequency (%)
20161231 78
78.0%
20161107 20
 
20.0%
20210901 1
 
1.0%
20191129 1
 
1.0%

Length

2023-12-10T18:46:06.521650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:06.758772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20161231 78
78.0%
20161107 20
 
20.0%
20210901 1
 
1.0%
20191129 1
 
1.0%

Sample

fclty_nmfclty_ncm_nmfclty_cl_cdfclty_cl_nminduty_cdinduty_nmfclty_ty_cdfclty_ty_nmcmptnc_ctprvn_cdcmptnc_ctprvn_nmcmptnc_signgu_cdcmptnc_signgu_nmzip_nofclty_road_nm_addrfclty_road_nm_detail_addrfclty_addrfclty_detail_addrfclty_crdnt_lofclty_crdnt_lafclty_tel_nofclty_totar_cooper_state_cdoper_state_nmoper_clsbiz_defclty_info_updt_desafechk_gnrlz_grad_cdsafechk_gnrlz_grad_nmsafechk_desafechk_othbc_defclty_info_regist_de
0(주)한샘레포츠타운(공단점)<NA>N신고업N10체력단련장업N1001체력단련장4100000000경기도4127000000안산시15433경기도 안산시 단원구 동산로 63 (원시동)<NA><NA>207호126.7972737.314431314923392911.00정상운영<NA>2017022699없음<NA><NA>20161231
1태권道장 휘영찬<NA>N신고업N08체육도장업N0805태권도3000000000대전광역시3017000000서구35381대전광역시 서구 관저동로 66 (관저동, 3층)<NA>대전 서구 관저동로 66<NA>127.34099436.298068<NA>284.860정상운영<NA>2018092099없음<NA><NA>20161231
2뽀록 당구장<NA>N신고업N11당구장업N1101당구장4100000000경기도4113000000성남시13147경기도 성남시 수정구 산성대로 403 (단대동,2층)<NA>경기 성남시 수정구 산성대로 403<NA>127.15793637.448107<NA>74.00정상운영<NA>2018123199없음<NA><NA>20161107
3안산 용인대 청룡 태권도장<NA>N신고업N08체육도장업N0805태권도4100000000경기도4127000000안산시<NA><NA><NA>경기도 안산시 상록구 샘골로 115<NA>126.86269637.294169<NA>127.00정상운영<NA>201612311양호202012312021013120161231
4사동 용인대 올림픽 태권도장<NA>N신고업N08체육도장업N0805태권도4100000000경기도4127000000안산시15500경기도 안산시 상록구 중보로 22 (사동, 늘푸른아파트)<NA>경기도 안산시 상록구 사동 1533 늘푸른아파트<NA>126.85142737.305689<NA>240.00정상운영<NA>201911281양호202010212021013120161231
5빌포츠 클럽<NA>N신고업N11당구장업N1101당구장4100000000경기도4127000000안산시15537경기도 안산시 상록구 본삼로 51 (본오동)<NA><NA>, 501호126.86587337.301075314076785531.00정상운영<NA>201901031양호202010282021013120161231
6늘푸른 수영장<NA>N신고업N07수영장업N0701실내4100000000경기도4128000000고양시10319경기도 고양시 일산동구 약산길 2 (중산동)<NA><NA><NA>126.79244937.679269319755503707.099폐업202008032020080499없음<NA><NA>20161107
7道心성인태권도<NA>N신고업N08체육도장업N0805태권도1100000000서울특별시1132000000도봉구1384서울특별시 도봉구 방학로 175(방학동)<NA>서울특별시 도봉구 방학동 669-2<NA>127.03264837.66207<NA>165.30정상운영<NA>2021090199없음<NA><NA>20210901
8당구생각<NA>N신고업N11당구장업N1101당구장4100000000경기도4113000000성남시<NA>경기도 성남시 수정구 희망로 547 (신흥동, 2층)<NA>경기 성남시 수정구 희망로 547<NA>127.15009537.451552317456120157.099폐업201709202020061999없음<NA><NA>20161107
9당구회관<NA>N신고업N11당구장업N1101당구장4100000000경기도4113000000성남시<NA>경기도 성남시 수정구 희망로 535 (신흥동,외1)<NA>경기 성남시 수정구 희망로 535<NA>127.15095337.450812317416338149.099폐업201701062020061999없음<NA><NA>20161231
fclty_nmfclty_ncm_nmfclty_cl_cdfclty_cl_nminduty_cdinduty_nmfclty_ty_cdfclty_ty_nmcmptnc_ctprvn_cdcmptnc_ctprvn_nmcmptnc_signgu_cdcmptnc_signgu_nmzip_nofclty_road_nm_addrfclty_road_nm_detail_addrfclty_addrfclty_detail_addrfclty_crdnt_lofclty_crdnt_lafclty_tel_nofclty_totar_cooper_state_cdoper_state_nmoper_clsbiz_defclty_info_updt_desafechk_gnrlz_grad_cdsafechk_gnrlz_grad_nmsafechk_desafechk_othbc_defclty_info_regist_de
90선부태권도장<NA>N신고업N08체육도장업N0805태권도4100000000경기도4127000000안산시15365경기도 안산시 단원구 선부광장서로1길 5, 바동 201, 202호 (선부동, 동국연립)<NA>경기 안산시 단원구 선부광장서로1길 5<NA>126.80404737.333483<NA><NA>0정상운영<NA>202008051양호202011302021013120161231
91스카이당구장<NA>N신고업N11당구장업N1101당구장4100000000경기도4127000000안산시15632경기도 안산시 상록구 선진4길 44, 4층 (사동)<NA>경기 안산시 상록구 선진4길 44<NA>126.85208737.279869314170954154.099폐업201712012020061999없음<NA><NA>20161231
92승리태권도장<NA>N신고업N08체육도장업N0805태권도4100000000경기도4127000000안산시15629경기도 안산시 상록구 평안로2길 11 (사동)<NA><NA>, 201호126.84892237.283455<NA>164.00정상운영<NA>201710111양호202011162021013120161231
93신성태권도장<NA>N신고업N08체육도장업N0805태권도4100000000경기도4127000000안산시15325경기도 안산시 상록구 안산대학로 116, 2층 (일동)<NA><NA><NA>126.87053637.310108<NA>281.099폐업20210414202104151양호202010192021013120161231
94신세기당구장<NA>N신고업N11당구장업N1101당구장4100000000경기도4127000000안산시15361경기도 안산시 단원구 고잔2길 59 (고잔동)<NA>경기 안산시 단원구 고잔2길 59<NA>126.83772137.317745314868819<NA>99폐업202012232020122499없음<NA><NA>20161231
95신호동체육관<NA>N신고업N08체육도장업N0805태권도4100000000경기도4127000000안산시<NA><NA><NA>경기도 안산시 상록구 도매시장로1길 10<NA>126.85809637.310054<NA>157.00정상운영<NA>201612311양호202012312021013120161231
96아우디당구장<NA>N신고업N11당구장업N1101당구장4100000000경기도4113000000성남시13327경기도 성남시 수정구 탄리로 61 (수진동)<NA>경기 성남시 수정구 탄리로 61<NA>127.13623937.441549317543598161.00정상운영<NA>2019012199없음<NA><NA>20161231
97아트당구클럽<NA>N신고업N11당구장업N1101당구장4100000000경기도4127000000안산시15258경기도 안산시 상록구 광덕산안길 14, 2층 (월피동)<NA>경기 안산시 상록구 광덕산안길 14<NA>126.84438237.334877314803535229.00정상운영<NA>201612311양호202010192021013120161231
98안산무도학원<NA>N신고업N14무도학원업N1401무도학원4100000000경기도4127000000안산시15361경기도 안산시 단원구 중앙대로 907, 1동 3층호 (고잔동,안산종합상가)<NA>경기 안산시 단원구 중앙대로 907<NA>126.83771837.31711314827101<NA>0정상운영<NA>2021031999없음<NA><NA>20161231
99알까기당구장<NA>N신고업N11당구장업N1101당구장4100000000경기도4113000000성남시13354경기도 성남시 수정구 산성대로 311 (신흥동)<NA>경기 성남시 수정구 산성대로 311<NA>127.15117637.442623317433527150.099폐업201705172020061999없음<NA><NA>20161231