Overview

Dataset statistics

Number of variables58
Number of observations100
Missing cells1269
Missing cells (%)21.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory47.3 KiB
Average record size in memory484.3 B

Variable types

Text10
Categorical25
Numeric12
Unsupported6
Boolean3
DateTime2

Alerts

faci_homepage has constant value ""Constant
open_yn has constant value ""Constant
th_ymd has constant value ""Constant
nation_yn has constant value ""Constant
cp_nm is highly imbalanced (59.6%)Imbalance
addr_cp_nm is highly imbalanced (54.6%)Imbalance
fmng_dept_nm is highly imbalanced (63.8%)Imbalance
cp_ymd is highly imbalanced (91.9%)Imbalance
ssm_dsn_yn is highly imbalanced (85.9%)Imbalance
del_yn is highly imbalanced (67.3%)Imbalance
faci_road_post has 14 (14.0%) missing valuesMissing
faci_road_addr1 has 12 (12.0%) missing valuesMissing
faci_road_addr2 has 95 (95.0%) missing valuesMissing
faci_post has 13 (13.0%) missing valuesMissing
faci_addr1 has 12 (12.0%) missing valuesMissing
faci_addr2 has 88 (88.0%) missing valuesMissing
faci_point_x has 8 (8.0%) missing valuesMissing
faci_point_y has 8 (8.0%) missing valuesMissing
faci_tel has 18 (18.0%) missing valuesMissing
faci_homepage has 99 (99.0%) missing valuesMissing
addr_cp_cd has 4 (4.0%) missing valuesMissing
addr_cpb_cd has 6 (6.0%) missing valuesMissing
addr_emd_cd has 82 (82.0%) missing valuesMissing
addr_emd_nm has 82 (82.0%) missing valuesMissing
addr_amd_cd has 100 (100.0%) missing valuesMissing
addr_amd_nm has 100 (100.0%) missing valuesMissing
fmng_cp_cd has 8 (8.0%) missing valuesMissing
fmng_cpb_cd has 8 (8.0%) missing valuesMissing
fmng_user_tel has 69 (69.0%) missing valuesMissing
stand_seat_cnt has 100 (100.0%) missing valuesMissing
stand_cpt_psn_cnt has 100 (100.0%) missing valuesMissing
tot_faci_area has 43 (43.0%) missing valuesMissing
life_gym_nm has 100 (100.0%) missing valuesMissing
use_asct_nm has 100 (100.0%) missing valuesMissing
addr_amd_cd is an unsupported type, check if it needs cleaning or further analysisUnsupported
addr_amd_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
stand_seat_cnt is an unsupported type, check if it needs cleaning or further analysisUnsupported
stand_cpt_psn_cnt is an unsupported type, check if it needs cleaning or further analysisUnsupported
life_gym_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
use_asct_nm is an unsupported type, check if it needs cleaning or further analysisUnsupported
tot_faci_area has 2 (2.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:52:14.786800
Analysis finished2023-12-10 09:52:16.648422
Duration1.86 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct95
Distinct (%)95.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:17.276770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length5
Mean length6.56
Min length3

Characters and Unicode

Total characters656
Distinct characters173
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(외립석입구)
2nd row우리 아이풀(舊 크레피아수영장)
3rd row(자산경로당)
4th row볼&큐 빌리어드클럽
5th row안산 힘찬 태권도
ValueCountFrequency (%)
스타당구장 3
 
2.1%
야외운동기구 3
 
2.1%
산청군 3
 
2.1%
산청읍 3
 
2.1%
운동기구 3
 
2.1%
길당구장 2
 
1.4%
국제당구장 2
 
1.4%
그린당구장 2
 
1.4%
안산 2
 
1.4%
대산검도관 1
 
0.7%
Other values (118) 118
83.1%
2023-12-10T18:52:18.224084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
66
 
10.1%
60
 
9.1%
59
 
9.0%
42
 
6.4%
15
 
2.3%
13
 
2.0%
13
 
2.0%
12
 
1.8%
11
 
1.7%
10
 
1.5%
Other values (163) 355
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 597
91.0%
Space Separator 42
 
6.4%
Close Punctuation 8
 
1.2%
Open Punctuation 8
 
1.2%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
66
 
11.1%
60
 
10.1%
59
 
9.9%
15
 
2.5%
13
 
2.2%
13
 
2.2%
12
 
2.0%
11
 
1.8%
10
 
1.7%
9
 
1.5%
Other values (159) 329
55.1%
Space Separator
ValueCountFrequency (%)
42
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 593
90.4%
Common 59
 
9.0%
Han 4
 
0.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
66
 
11.1%
60
 
10.1%
59
 
9.9%
15
 
2.5%
13
 
2.2%
13
 
2.2%
12
 
2.0%
11
 
1.9%
10
 
1.7%
9
 
1.5%
Other values (156) 325
54.8%
Common
ValueCountFrequency (%)
42
71.2%
) 8
 
13.6%
( 8
 
13.6%
& 1
 
1.7%
Han
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 593
90.4%
ASCII 59
 
9.0%
CJK 4
 
0.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
66
 
11.1%
60
 
10.1%
59
 
9.9%
15
 
2.5%
13
 
2.2%
13
 
2.2%
12
 
2.0%
11
 
1.9%
10
 
1.7%
9
 
1.5%
Other values (156) 325
54.8%
ASCII
ValueCountFrequency (%)
42
71.2%
) 8
 
13.6%
( 8
 
13.6%
& 1
 
1.7%
CJK
ValueCountFrequency (%)
2
50.0%
1
25.0%
1
25.0%

faci_gb_cd
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
N
82 
P
18 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowP
2nd rowN
3rd rowP
4th rowN
5th rowN

Common Values

ValueCountFrequency (%)
N 82
82.0%
P 18
 
18.0%

Length

2023-12-10T18:52:18.543750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:18.722957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 82
82.0%
p 18
 
18.0%

faci_gb_nm
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
신고
82 
공공
18 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공공
2nd row신고
3rd row공공
4th row신고
5th row신고

Common Values

ValueCountFrequency (%)
신고 82
82.0%
공공 18
 
18.0%

Length

2023-12-10T18:52:18.913268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:19.106012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
신고 82
82.0%
공공 18
 
18.0%

fcob_cd
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
N11
59 
P08
18 
N08
15 
N10
 
5
N07
 
2

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st rowP08
2nd rowN07
3rd rowP08
4th rowN11
5th rowN08

Common Values

ValueCountFrequency (%)
N11 59
59.0%
P08 18
 
18.0%
N08 15
 
15.0%
N10 5
 
5.0%
N07 2
 
2.0%
N13 1
 
1.0%

Length

2023-12-10T18:52:19.323176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:19.528261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n11 59
59.0%
p08 18
 
18.0%
n08 15
 
15.0%
n10 5
 
5.0%
n07 2
 
2.0%
n13 1
 
1.0%

fcob_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
당구장업
59 
간이운동장
18 
체육도장업
15 
체력단련장업
 
5
수영장업
 
2

Length

Max length6
Median length4
Mean length4.43
Min length4

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row간이운동장
2nd row수영장업
3rd row간이운동장
4th row당구장업
5th row체육도장업

Common Values

ValueCountFrequency (%)
당구장업 59
59.0%
간이운동장 18
 
18.0%
체육도장업 15
 
15.0%
체력단련장업 5
 
5.0%
수영장업 2
 
2.0%
무도장업 1
 
1.0%

Length

2023-12-10T18:52:19.816658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:20.106586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장업 59
59.0%
간이운동장 18
 
18.0%
체육도장업 15
 
15.0%
체력단련장업 5
 
5.0%
수영장업 2
 
2.0%
무도장업 1
 
1.0%

ftype_cd
Categorical

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
N1101
59 
P0801
18 
N0805
10 
N1001
 
5
N0804
 
4
Other values (3)
 
4

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st rowP0801
2nd rowN0701
3rd rowP0801
4th rowN1101
5th rowN0805

Common Values

ValueCountFrequency (%)
N1101 59
59.0%
P0801 18
 
18.0%
N0805 10
 
10.0%
N1001 5
 
5.0%
N0804 4
 
4.0%
N0701 2
 
2.0%
N1301 1
 
1.0%
N0803 1
 
1.0%

Length

2023-12-10T18:52:20.328642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:20.537122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n1101 59
59.0%
p0801 18
 
18.0%
n0805 10
 
10.0%
n1001 5
 
5.0%
n0804 4
 
4.0%
n0701 2
 
2.0%
n1301 1
 
1.0%
n0803 1
 
1.0%

ftype_nm
Categorical

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
당구장
59 
간이운동장
18 
태권도
10 
체력단련장
 
5
검도
 
4
Other values (3)
 
4

Length

Max length5
Median length3
Mean length3.39
Min length2

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row간이운동장
2nd row실내
3rd row간이운동장
4th row당구장
5th row태권도

Common Values

ValueCountFrequency (%)
당구장 59
59.0%
간이운동장 18
 
18.0%
태권도 10
 
10.0%
체력단련장 5
 
5.0%
검도 4
 
4.0%
실내 2
 
2.0%
무도장 1
 
1.0%
유도 1
 
1.0%

Length

2023-12-10T18:52:20.785713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:20.991662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당구장 59
59.0%
간이운동장 18
 
18.0%
태권도 10
 
10.0%
체력단련장 5
 
5.0%
검도 4
 
4.0%
실내 2
 
2.0%
무도장 1
 
1.0%
유도 1
 
1.0%

faci_stat
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
폐업
55 
정상운영
45 

Length

Max length4
Median length2
Mean length2.9
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
폐업 55
55.0%
정상운영 45
45.0%

Length

2023-12-10T18:52:21.682334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:21.878519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
폐업 55
55.0%
정상운영 45
45.0%

faci_road_post
Real number (ℝ)

MISSING 

Distinct67
Distinct (%)77.9%
Missing14
Missing (%)14.0%
Infinite0
Infinite (%)0.0%
Mean342651.48
Minimum1384
Maximum469101
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:22.115734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1384
5-th percentile13283.5
Q1333379
median425845.5
Q3461814.25
95-th percentile461870
Maximum469101
Range467717
Interquartile range (IQR)128435.25

Descriptive statistics

Standard deviation177927.67
Coefficient of variation (CV)0.51926718
Kurtosis-0.4106397
Mean342651.48
Median Absolute Deviation (MAD)35969.5
Skewness-1.2268106
Sum29468027
Variance3.1658254 × 1010
MonotonicityNot monotonic
2023-12-10T18:52:22.379796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425845 6
 
6.0%
461854 4
 
4.0%
461811 3
 
3.0%
461815 2
 
2.0%
461812 2
 
2.0%
461822 2
 
2.0%
426837 2
 
2.0%
461808 2
 
2.0%
425846 2
 
2.0%
461862 2
 
2.0%
Other values (57) 59
59.0%
(Missing) 14
 
14.0%
ValueCountFrequency (%)
1384 1
1.0%
10110 1
1.0%
10319 1
1.0%
11012 1
1.0%
13276 1
1.0%
13306 1
1.0%
13327 1
1.0%
15288 1
1.0%
15470 1
1.0%
15535 1
1.0%
ValueCountFrequency (%)
469101 1
 
1.0%
467803 1
 
1.0%
467800 1
 
1.0%
461873 1
 
1.0%
461872 1
 
1.0%
461864 1
 
1.0%
461862 2
2.0%
461854 4
4.0%
461831 1
 
1.0%
461827 1
 
1.0%

faci_road_addr1
Text

MISSING 

Distinct88
Distinct (%)100.0%
Missing12
Missing (%)12.0%
Memory size932.0 B
2023-12-10T18:52:22.988243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length26.977273
Min length11

Characters and Unicode

Total characters2374
Distinct characters145
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

Unique88 ?
Unique (%)100.0%

Sample

1st row대전광역시 서구 계백로 1186 (가수원동)
2nd row경상북도 김천시 자산3길 16 (성내동)
3rd row경기도 안산시 상록구 반석로 88, 4층 (본오동)
4th row경기도 안산시 상록구 각골로 127 (본오동)
5th row경기도 성남시 수정구 산성대로 403 (단대동,2층)
ValueCountFrequency (%)
경기도 74
 
14.1%
안산시 39
 
7.5%
수정구 30
 
5.7%
성남시 30
 
5.7%
단원구 28
 
5.4%
상록구 11
 
2.1%
원곡동 10
 
1.9%
태평동 9
 
1.7%
본오동 7
 
1.3%
신흥동 5
 
1.0%
Other values (221) 280
53.5%
2023-12-10T18:52:23.852935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
435
 
18.3%
90
 
3.8%
88
 
3.7%
86
 
3.6%
84
 
3.5%
1 80
 
3.4%
( 79
 
3.3%
) 79
 
3.3%
75
 
3.2%
74
 
3.1%
Other values (135) 1204
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1406
59.2%
Space Separator 435
 
18.3%
Decimal Number 326
 
13.7%
Open Punctuation 79
 
3.3%
Close Punctuation 79
 
3.3%
Other Punctuation 33
 
1.4%
Dash Punctuation 13
 
0.5%
Uppercase Letter 3
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
90
 
6.4%
88
 
6.3%
86
 
6.1%
84
 
6.0%
75
 
5.3%
74
 
5.3%
73
 
5.2%
62
 
4.4%
47
 
3.3%
46
 
3.3%
Other values (119) 681
48.4%
Decimal Number
ValueCountFrequency (%)
1 80
24.5%
2 46
14.1%
3 43
13.2%
4 34
10.4%
6 28
 
8.6%
5 27
 
8.3%
0 22
 
6.7%
8 20
 
6.1%
7 15
 
4.6%
9 11
 
3.4%
Space Separator
ValueCountFrequency (%)
435
100.0%
Open Punctuation
ValueCountFrequency (%)
( 79
100.0%
Close Punctuation
ValueCountFrequency (%)
) 79
100.0%
Other Punctuation
ValueCountFrequency (%)
, 33
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1406
59.2%
Common 965
40.6%
Latin 3
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
90
 
6.4%
88
 
6.3%
86
 
6.1%
84
 
6.0%
75
 
5.3%
74
 
5.3%
73
 
5.2%
62
 
4.4%
47
 
3.3%
46
 
3.3%
Other values (119) 681
48.4%
Common
ValueCountFrequency (%)
435
45.1%
1 80
 
8.3%
( 79
 
8.2%
) 79
 
8.2%
2 46
 
4.8%
3 43
 
4.5%
4 34
 
3.5%
, 33
 
3.4%
6 28
 
2.9%
5 27
 
2.8%
Other values (5) 81
 
8.4%
Latin
ValueCountFrequency (%)
B 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1406
59.2%
ASCII 968
40.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
435
44.9%
1 80
 
8.3%
( 79
 
8.2%
) 79
 
8.2%
2 46
 
4.8%
3 43
 
4.4%
4 34
 
3.5%
, 33
 
3.4%
6 28
 
2.9%
5 27
 
2.8%
Other values (6) 84
 
8.7%
Hangul
ValueCountFrequency (%)
90
 
6.4%
88
 
6.3%
86
 
6.1%
84
 
6.0%
75
 
5.3%
74
 
5.3%
73
 
5.2%
62
 
4.4%
47
 
3.3%
46
 
3.3%
Other values (119) 681
48.4%

faci_road_addr2
Text

MISSING 

Distinct4
Distinct (%)80.0%
Missing95
Missing (%)95.0%
Memory size932.0 B
2023-12-10T18:52:24.184025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length2
Mean length4.8
Min length2

Characters and Unicode

Total characters24
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

Unique3 ?
Unique (%)60.0%

Sample

1st row2층
2nd row3층
3rd row공설운동장 17호
4th row2층
5th row지1층,경원프라자
ValueCountFrequency (%)
2층 2
33.3%
3층 1
16.7%
공설운동장 1
16.7%
17호 1
16.7%
지1층,경원프라자 1
16.7%
2023-12-10T18:52:24.716008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
16.7%
2 2
 
8.3%
1 2
 
8.3%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
1
 
4.2%
3 1
 
4.2%
Other values (9) 9
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16
66.7%
Decimal Number 6
 
25.0%
Space Separator 1
 
4.2%
Other Punctuation 1
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
25.0%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3) 3
18.8%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
1 2
33.3%
3 1
16.7%
7 1
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16
66.7%
Common 8
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
25.0%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3) 3
18.8%
Common
ValueCountFrequency (%)
2 2
25.0%
1 2
25.0%
1
12.5%
3 1
12.5%
7 1
12.5%
, 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16
66.7%
ASCII 8
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4
25.0%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
1
 
6.2%
Other values (3) 3
18.8%
ASCII
ValueCountFrequency (%)
2 2
25.0%
1 2
25.0%
1
12.5%
3 1
12.5%
7 1
12.5%
, 1
12.5%

faci_post
Real number (ℝ)

MISSING 

Distinct68
Distinct (%)78.2%
Missing13
Missing (%)13.0%
Infinite0
Infinite (%)0.0%
Mean347286.32
Minimum1384
Maximum745883
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:24.973311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1384
5-th percentile13285
Q1363946
median425846
Q3461815
95-th percentile461872.7
Maximum745883
Range744499
Interquartile range (IQR)97869

Descriptive statistics

Standard deviation182096.27
Coefficient of variation (CV)0.52434045
Kurtosis-0.26785883
Mean347286.32
Median Absolute Deviation (MAD)35969
Skewness-1.079796
Sum30213910
Variance3.3159051 × 1010
MonotonicityNot monotonic
2023-12-10T18:52:25.264325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
425845 6
 
6.0%
461854 4
 
4.0%
461811 3
 
3.0%
461815 2
 
2.0%
461812 2
 
2.0%
461822 2
 
2.0%
426837 2
 
2.0%
461808 2
 
2.0%
425846 2
 
2.0%
461862 2
 
2.0%
Other values (58) 60
60.0%
(Missing) 13
 
13.0%
ValueCountFrequency (%)
1384 1
1.0%
10110 1
1.0%
10319 1
1.0%
11012 1
1.0%
13276 1
1.0%
13306 1
1.0%
13327 1
1.0%
15288 1
1.0%
15470 1
1.0%
15535 1
1.0%
ValueCountFrequency (%)
745883 1
 
1.0%
469101 1
 
1.0%
467803 1
 
1.0%
467800 1
 
1.0%
461873 1
 
1.0%
461872 1
 
1.0%
461864 1
 
1.0%
461862 2
2.0%
461854 4
4.0%
461831 1
 
1.0%

faci_addr1
Text

MISSING 

Distinct88
Distinct (%)100.0%
Missing12
Missing (%)12.0%
Memory size932.0 B
2023-12-10T18:52:25.882352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length24
Mean length18.795455
Min length11

Characters and Unicode

Total characters1654
Distinct characters126
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

Unique88 ?
Unique (%)100.0%

Sample

1st row경상북도 예지리 680
2nd row경상북도 김천시 성내동 161-17
3rd row경기도 안산시 상록구 본오동 1118-10
4th row경기 성남시 수정구 산성대로 403
5th row경기도 안산시 상록구 샘골로 115
ValueCountFrequency (%)
경기 62
 
14.7%
안산시 39
 
9.3%
수정구 30
 
7.1%
성남시 30
 
7.1%
단원구 30
 
7.1%
경상북도 11
 
2.6%
경기도 10
 
2.4%
상록구 9
 
2.1%
6 3
 
0.7%
문경시 3
 
0.7%
Other values (167) 194
46.1%
2023-12-10T18:52:26.788297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
334
20.2%
89
 
5.4%
83
 
5.0%
72
 
4.4%
72
 
4.4%
1 66
 
4.0%
61
 
3.7%
52
 
3.1%
45
 
2.7%
42
 
2.5%
Other values (116) 738
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1019
61.6%
Space Separator 334
 
20.2%
Decimal Number 282
 
17.0%
Dash Punctuation 19
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
89
 
8.7%
83
 
8.1%
72
 
7.1%
72
 
7.1%
61
 
6.0%
52
 
5.1%
45
 
4.4%
42
 
4.1%
39
 
3.8%
37
 
3.6%
Other values (104) 427
41.9%
Decimal Number
ValueCountFrequency (%)
1 66
23.4%
3 36
12.8%
6 35
12.4%
2 29
10.3%
4 25
 
8.9%
5 25
 
8.9%
8 20
 
7.1%
9 17
 
6.0%
0 16
 
5.7%
7 13
 
4.6%
Space Separator
ValueCountFrequency (%)
334
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1019
61.6%
Common 635
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
89
 
8.7%
83
 
8.1%
72
 
7.1%
72
 
7.1%
61
 
6.0%
52
 
5.1%
45
 
4.4%
42
 
4.1%
39
 
3.8%
37
 
3.6%
Other values (104) 427
41.9%
Common
ValueCountFrequency (%)
334
52.6%
1 66
 
10.4%
3 36
 
5.7%
6 35
 
5.5%
2 29
 
4.6%
4 25
 
3.9%
5 25
 
3.9%
8 20
 
3.1%
- 19
 
3.0%
9 17
 
2.7%
Other values (2) 29
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1019
61.6%
ASCII 635
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
334
52.6%
1 66
 
10.4%
3 36
 
5.7%
6 35
 
5.5%
2 29
 
4.6%
4 25
 
3.9%
5 25
 
3.9%
8 20
 
3.1%
- 19
 
3.0%
9 17
 
2.7%
Other values (2) 29
 
4.6%
Hangul
ValueCountFrequency (%)
89
 
8.7%
83
 
8.1%
72
 
7.1%
72
 
7.1%
61
 
6.0%
52
 
5.1%
45
 
4.4%
42
 
4.1%
39
 
3.8%
37
 
3.6%
Other values (104) 427
41.9%

faci_addr2
Text

MISSING 

Distinct11
Distinct (%)91.7%
Missing88
Missing (%)88.0%
Memory size932.0 B
2023-12-10T18:52:27.098755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6.5
Mean length5.3333333
Min length2

Characters and Unicode

Total characters64
Distinct characters34
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

Unique10 ?
Unique (%)83.3%

Sample

1st row지하1층
2nd row자산경로당
3rd row, 501호
4th row모고마을 부근
5th row범학마을 부근
ValueCountFrequency (%)
부근 3
16.7%
3층 2
11.1%
2
11.1%
2층 2
11.1%
지하1층 1
 
5.6%
자산경로당 1
 
5.6%
501호 1
 
5.6%
모고마을 1
 
5.6%
범학마을 1
 
5.6%
청호마을 1
 
5.6%
Other values (3) 3
16.7%
2023-12-10T18:52:27.699535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
9.4%
6
 
9.4%
1 4
 
6.2%
3
 
4.7%
, 3
 
4.7%
3
 
4.7%
3
 
4.7%
3
 
4.7%
3
 
4.7%
2 2
 
3.1%
Other values (24) 28
43.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44
68.8%
Decimal Number 11
 
17.2%
Space Separator 6
 
9.4%
Other Punctuation 3
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
13.6%
3
 
6.8%
3
 
6.8%
3
 
6.8%
3
 
6.8%
3
 
6.8%
2
 
4.5%
2
 
4.5%
2
 
4.5%
1
 
2.3%
Other values (16) 16
36.4%
Decimal Number
ValueCountFrequency (%)
1 4
36.4%
2 2
18.2%
3 2
18.2%
7 1
 
9.1%
5 1
 
9.1%
0 1
 
9.1%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44
68.8%
Common 20
31.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
13.6%
3
 
6.8%
3
 
6.8%
3
 
6.8%
3
 
6.8%
3
 
6.8%
2
 
4.5%
2
 
4.5%
2
 
4.5%
1
 
2.3%
Other values (16) 16
36.4%
Common
ValueCountFrequency (%)
6
30.0%
1 4
20.0%
, 3
15.0%
2 2
 
10.0%
3 2
 
10.0%
7 1
 
5.0%
5 1
 
5.0%
0 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44
68.8%
ASCII 20
31.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
13.6%
3
 
6.8%
3
 
6.8%
3
 
6.8%
3
 
6.8%
3
 
6.8%
2
 
4.5%
2
 
4.5%
2
 
4.5%
1
 
2.3%
Other values (16) 16
36.4%
ASCII
ValueCountFrequency (%)
6
30.0%
1 4
20.0%
, 3
15.0%
2 2
 
10.0%
3 2
 
10.0%
7 1
 
5.0%
5 1
 
5.0%
0 1
 
5.0%

faci_point_x
Real number (ℝ)

MISSING 

Distinct92
Distinct (%)100.0%
Missing8
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean127.08882
Minimum126.43144
Maximum129.21053
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:27.919069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.43144
5-th percentile126.79072
Q1126.82383
median126.95267
Q3127.14637
95-th percentile128.0076
Maximum129.21053
Range2.7790895
Interquartile range (IQR)0.32253315

Descriptive statistics

Standard deviation0.42332011
Coefficient of variation (CV)0.0033308996
Kurtosis7.1153088
Mean127.08882
Median Absolute Deviation (MAD)0.17414085
Skewness2.312491
Sum11692.172
Variance0.17919991
MonotonicityNot monotonic
2023-12-10T18:52:28.366883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127.147019713 1
 
1.0%
127.143252358 1
 
1.0%
127.141262422 1
 
1.0%
126.794824349 1
 
1.0%
127.136926115 1
 
1.0%
127.127733757 1
 
1.0%
126.864439969 1
 
1.0%
127.126964942 1
 
1.0%
127.129429905 1
 
1.0%
127.127458287 1
 
1.0%
Other values (82) 82
82.0%
(Missing) 8
 
8.0%
ValueCountFrequency (%)
126.4314389443 1
1.0%
126.585213708 1
1.0%
126.719264453 1
1.0%
126.787515465 1
1.0%
126.789760367 1
1.0%
126.791502401 1
1.0%
126.7924493812 1
1.0%
126.792549905 1
1.0%
126.79264227 1
1.0%
126.794461355 1
1.0%
ValueCountFrequency (%)
129.210528481299 1
1.0%
128.1892312900023 1
1.0%
128.1879630940155 1
1.0%
128.18382300404443 1
1.0%
128.122092372718 1
1.0%
127.913929351 1
1.0%
127.8885301978 1
1.0%
127.8777549922 1
1.0%
127.8263680067 1
1.0%
127.641297155 1
1.0%

faci_point_y
Real number (ℝ)

MISSING 

Distinct91
Distinct (%)98.9%
Missing8
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean37.192603
Minimum35.14544
Maximum38.099404
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:28.743251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.14544
5-th percentile35.648715
Q137.300023
median37.330568
Q337.443057
95-th percentile37.45818
Maximum38.099404
Range2.9539643
Interquartile range (IQR)0.14303402

Descriptive statistics

Standard deviation0.54232748
Coefficient of variation (CV)0.014581595
Kurtosis5.7669507
Mean37.192603
Median Absolute Deviation (MAD)0.10646099
Skewness-2.4989396
Sum3421.7195
Variance0.29411909
MonotonicityNot monotonic
2023-12-10T18:52:28.989808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4488396406 2
 
2.0%
37.33089574 1
 
1.0%
37.3308691101 1
 
1.0%
37.4414692165 1
 
1.0%
37.4476316317 1
 
1.0%
37.3330237005 1
 
1.0%
37.4581274566 1
 
1.0%
37.4350840608 1
 
1.0%
37.4477941115 1
 
1.0%
37.4416300873 1
 
1.0%
Other values (81) 81
81.0%
(Missing) 8
 
8.0%
ValueCountFrequency (%)
35.1454398846 1
1.0%
35.3891397079 1
1.0%
35.4046813932 1
1.0%
35.4168467808 1
1.0%
35.4307559171 1
1.0%
35.8270459600073 1
1.0%
36.1207711973501 1
1.0%
36.298068364 1
1.0%
36.3048723145 1
1.0%
36.58543956870063 1
1.0%
ValueCountFrequency (%)
38.0994042341 1
1.0%
37.6792687208 1
1.0%
37.662069716124 1
1.0%
37.6164161706 1
1.0%
37.4582440509 1
1.0%
37.4581274566 1
1.0%
37.4563973595 1
1.0%
37.4550553221 1
1.0%
37.4515517864 1
1.0%
37.4508118639 1
1.0%

faci_tel
Text

MISSING 

Distinct77
Distinct (%)93.9%
Missing18
Missing (%)18.0%
Memory size932.0 B
2023-12-10T18:52:29.463178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters984
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

Unique75 ?
Unique (%)91.5%

Sample

1st row042-541-7944
2nd row054-421-2318
3rd row031-415-9970
4th row031-000-0000
5th row031-407-6785
ValueCountFrequency (%)
055-970-6433 4
 
4.9%
054-550-8884 3
 
3.7%
031-401-4064 1
 
1.2%
031-721-9546 1
 
1.2%
031-758-4464 1
 
1.2%
031-492-2760 1
 
1.2%
031-756-9078 1
 
1.2%
031-755-9670 1
 
1.2%
031-417-4146 1
 
1.2%
031-753-9586 1
 
1.2%
Other values (67) 67
81.7%
2023-12-10T18:52:30.106004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 164
16.7%
0 137
13.9%
3 127
12.9%
1 120
12.2%
4 105
10.7%
5 67
6.8%
7 65
 
6.6%
9 57
 
5.8%
8 57
 
5.8%
6 47
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 820
83.3%
Dash Punctuation 164
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 137
16.7%
3 127
15.5%
1 120
14.6%
4 105
12.8%
5 67
8.2%
7 65
7.9%
9 57
7.0%
8 57
7.0%
6 47
 
5.7%
2 38
 
4.6%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 984
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 164
16.7%
0 137
13.9%
3 127
12.9%
1 120
12.2%
4 105
10.7%
5 67
6.8%
7 65
 
6.6%
9 57
 
5.8%
8 57
 
5.8%
6 47
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 164
16.7%
0 137
13.9%
3 127
12.9%
1 120
12.2%
4 105
10.7%
5 67
6.8%
7 65
 
6.6%
9 57
 
5.8%
8 57
 
5.8%
6 47
 
4.8%

faci_homepage
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing99
Missing (%)99.0%
Memory size932.0 B
2023-12-10T18:52:30.420043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length15
Mean length15
Min length15

Characters and Unicode

Total characters15
Distinct characters11
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowWww.spkumdo.com
ValueCountFrequency (%)
www.spkumdo.com 1
100.0%
2023-12-10T18:52:30.937995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
w 2
13.3%
. 2
13.3%
m 2
13.3%
o 2
13.3%
W 1
6.7%
s 1
6.7%
p 1
6.7%
k 1
6.7%
u 1
6.7%
d 1
6.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 12
80.0%
Other Punctuation 2
 
13.3%
Uppercase Letter 1
 
6.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
w 2
16.7%
m 2
16.7%
o 2
16.7%
s 1
8.3%
p 1
8.3%
k 1
8.3%
u 1
8.3%
d 1
8.3%
c 1
8.3%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 13
86.7%
Common 2
 
13.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
w 2
15.4%
m 2
15.4%
o 2
15.4%
W 1
7.7%
s 1
7.7%
p 1
7.7%
k 1
7.7%
u 1
7.7%
d 1
7.7%
c 1
7.7%
Common
ValueCountFrequency (%)
. 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 15
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
w 2
13.3%
. 2
13.3%
m 2
13.3%
o 2
13.3%
W 1
6.7%
s 1
6.7%
p 1
6.7%
k 1
6.7%
u 1
6.7%
d 1
6.7%

cp_cd
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.144 × 109
Minimum1.1 × 109
Maximum4.8 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:31.154029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile4.1 × 109
Q14.1 × 109
median4.1 × 109
Q34.1 × 109
95-th percentile4.7 × 109
Maximum4.8 × 109
Range3.7 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.4137526 × 108
Coefficient of variation (CV)0.10650947
Kurtosis23.379363
Mean4.144 × 109
Median Absolute Deviation (MAD)0
Skewness-3.5755562
Sum4.144 × 1011
Variance1.9481212 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:31.434989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4100000000 78
78.0%
4700000000 12
 
12.0%
4800000000 4
 
4.0%
3000000000 2
 
2.0%
2900000000 1
 
1.0%
1100000000 1
 
1.0%
4600000000 1
 
1.0%
4400000000 1
 
1.0%
ValueCountFrequency (%)
1100000000 1
 
1.0%
2900000000 1
 
1.0%
3000000000 2
 
2.0%
4100000000 78
78.0%
4400000000 1
 
1.0%
4600000000 1
 
1.0%
4700000000 12
 
12.0%
4800000000 4
 
4.0%
ValueCountFrequency (%)
4800000000 4
 
4.0%
4700000000 12
 
12.0%
4600000000 1
 
1.0%
4400000000 1
 
1.0%
4100000000 78
78.0%
3000000000 2
 
2.0%
2900000000 1
 
1.0%
1100000000 1
 
1.0%

cp_nm
Categorical

IMBALANCE 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
78 
경상북도
12 
경상남도
 
4
대전광역시
 
2
광주광역시
 
1
Other values (3)
 
3

Length

Max length5
Median length3
Mean length3.26
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 78
78.0%
경상북도 12
 
12.0%
경상남도 4
 
4.0%
대전광역시 2
 
2.0%
광주광역시 1
 
1.0%
서울특별시 1
 
1.0%
전라남도 1
 
1.0%
충청남도 1
 
1.0%

Length

2023-12-10T18:52:31.789445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:32.023639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 78
78.0%
경상북도 12
 
12.0%
경상남도 4
 
4.0%
대전광역시 2
 
2.0%
광주광역시 1
 
1.0%
서울특별시 1
 
1.0%
전라남도 1
 
1.0%
충청남도 1
 
1.0%

cpb_cd
Real number (ℝ)

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.17006 × 109
Minimum1.132 × 109
Maximum4.886 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:32.344032image/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 percentile4.728 × 109
Maximum4.886 × 109
Range3.754 × 109
Interquartile range (IQR)14000000

Descriptive statistics

Standard deviation4.4505863 × 108
Coefficient of variation (CV)0.10672715
Kurtosis22.448277
Mean4.17006 × 109
Median Absolute Deviation (MAD)14000000
Skewness-3.446282
Sum4.17006 × 1011
Variance1.9807719 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:32.664047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4127000000 42
42.0%
4113000000 30
30.0%
4715000000 8
 
8.0%
4886000000 4
 
4.0%
4728000000 3
 
3.0%
3017000000 2
 
2.0%
4150000000 2
 
2.0%
4682000000 1
 
1.0%
4167000000 1
 
1.0%
4421000000 1
 
1.0%
Other values (6) 6
 
6.0%
ValueCountFrequency (%)
1132000000 1
 
1.0%
2920000000 1
 
1.0%
3017000000 2
 
2.0%
4113000000 30
30.0%
4127000000 42
42.0%
4128000000 1
 
1.0%
4150000000 2
 
2.0%
4157000000 1
 
1.0%
4167000000 1
 
1.0%
4180000000 1
 
1.0%
ValueCountFrequency (%)
4886000000 4
4.0%
4728000000 3
 
3.0%
4715000000 8
8.0%
4713000000 1
 
1.0%
4682000000 1
 
1.0%
4421000000 1
 
1.0%
4180000000 1
 
1.0%
4167000000 1
 
1.0%
4157000000 1
 
1.0%
4150000000 2
 
2.0%

cpb_nm
Categorical

Distinct16
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안산시
42 
성남시
30 
김천시
산청군
 
4
문경시
 
3
Other values (11)
13 

Length

Max length3
Median length3
Mean length2.98
Min length2

Unique

Unique9 ?
Unique (%)9.0%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 42
42.0%
성남시 30
30.0%
김천시 8
 
8.0%
산청군 4
 
4.0%
문경시 3
 
3.0%
서구 2
 
2.0%
이천시 2
 
2.0%
고양시 1
 
1.0%
연천군 1
 
1.0%
광산구 1
 
1.0%
Other values (6) 6
 
6.0%

Length

2023-12-10T18:52:32.930163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 42
42.0%
성남시 30
30.0%
김천시 8
 
8.0%
산청군 4
 
4.0%
문경시 3
 
3.0%
서구 2
 
2.0%
이천시 2
 
2.0%
고양시 1
 
1.0%
연천군 1
 
1.0%
광산구 1
 
1.0%
Other values (6) 6
 
6.0%

addr_cp_cd
Real number (ℝ)

MISSING 

Distinct8
Distinct (%)8.3%
Missing4
Missing (%)4.0%
Infinite0
Infinite (%)0.0%
Mean4.1458333 × 109
Minimum1.1 × 109
Maximum4.8 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:33.143847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1 × 109
5-th percentile4.1 × 109
Q14.1 × 109
median4.1 × 109
Q34.1 × 109
95-th percentile4.7 × 109
Maximum4.8 × 109
Range3.7 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4.5047733 × 108
Coefficient of variation (CV)0.10865785
Kurtosis22.451656
Mean4.1458333 × 109
Median Absolute Deviation (MAD)0
Skewness-3.5201884
Sum3.98 × 1011
Variance2.0292982 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:33.364134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
4100000000 74
74.0%
4700000000 12
 
12.0%
4800000000 4
 
4.0%
3000000000 2
 
2.0%
2900000000 1
 
1.0%
1100000000 1
 
1.0%
4600000000 1
 
1.0%
4400000000 1
 
1.0%
(Missing) 4
 
4.0%
ValueCountFrequency (%)
1100000000 1
 
1.0%
2900000000 1
 
1.0%
3000000000 2
 
2.0%
4100000000 74
74.0%
4400000000 1
 
1.0%
4600000000 1
 
1.0%
4700000000 12
 
12.0%
4800000000 4
 
4.0%
ValueCountFrequency (%)
4800000000 4
 
4.0%
4700000000 12
 
12.0%
4600000000 1
 
1.0%
4400000000 1
 
1.0%
4100000000 74
74.0%
3000000000 2
 
2.0%
2900000000 1
 
1.0%
1100000000 1
 
1.0%

addr_cp_nm
Categorical

IMBALANCE 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
74 
경상북도
12 
<NA>
 
4
경상남도
 
4
대전광역시
 
2
Other values (4)
 
4

Length

Max length5
Median length3
Mean length3.3
Min length3

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row경상북도
2nd row대전광역시
3rd row경상북도
4th row<NA>
5th row경기도

Common Values

ValueCountFrequency (%)
경기도 74
74.0%
경상북도 12
 
12.0%
<NA> 4
 
4.0%
경상남도 4
 
4.0%
대전광역시 2
 
2.0%
광주광역시 1
 
1.0%
서울특별시 1
 
1.0%
전라남도 1
 
1.0%
충청남도 1
 
1.0%

Length

2023-12-10T18:52:33.671546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:33.908413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 74
74.0%
경상북도 12
 
12.0%
na 4
 
4.0%
경상남도 4
 
4.0%
대전광역시 2
 
2.0%
광주광역시 1
 
1.0%
서울특별시 1
 
1.0%
전라남도 1
 
1.0%
충청남도 1
 
1.0%

addr_cpb_cd
Real number (ℝ)

MISSING 

Distinct16
Distinct (%)17.0%
Missing6
Missing (%)6.0%
Infinite0
Infinite (%)0.0%
Mean4.1724809 × 109
Minimum1.132 × 109
Maximum4.886 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:34.146445image/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 percentile4.728 × 109
Maximum4.886 × 109
Range3.754 × 109
Interquartile range (IQR)14000000

Descriptive statistics

Standard deviation4.5907474 × 108
Coefficient of variation (CV)0.11002441
Kurtosis21.086469
Mean4.1724809 × 109
Median Absolute Deviation (MAD)14000000
Skewness-3.3632341
Sum3.922132 × 1011
Variance2.1074962 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:34.390640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
4127000000 37
37.0%
4113000000 29
29.0%
4715000000 8
 
8.0%
4886000000 4
 
4.0%
4728000000 3
 
3.0%
3017000000 2
 
2.0%
4150000000 2
 
2.0%
4127100000 1
 
1.0%
4180000000 1
 
1.0%
1132000000 1
 
1.0%
Other values (6) 6
 
6.0%
(Missing) 6
 
6.0%
ValueCountFrequency (%)
1132000000 1
 
1.0%
2920000000 1
 
1.0%
3017000000 2
 
2.0%
4113000000 29
29.0%
4113100000 1
 
1.0%
4127000000 37
37.0%
4127100000 1
 
1.0%
4150000000 2
 
2.0%
4167000000 1
 
1.0%
4180000000 1
 
1.0%
ValueCountFrequency (%)
4886000000 4
4.0%
4728000000 3
 
3.0%
4715000000 8
8.0%
4713000000 1
 
1.0%
4682000000 1
 
1.0%
4421000000 1
 
1.0%
4180000000 1
 
1.0%
4167000000 1
 
1.0%
4150000000 2
 
2.0%
4127100000 1
 
1.0%

addr_cpb_nm
Categorical

Distinct17
Distinct (%)17.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안산시
37 
성남시
29 
김천시
<NA>
산청군
Other values (12)
16 

Length

Max length7
Median length3
Mean length3.12
Min length2

Unique

Unique9 ?
Unique (%)9.0%

Sample

1st row김천시
2nd row서구
3rd row김천시
4th row<NA>
5th row안산시 상록구

Common Values

ValueCountFrequency (%)
안산시 37
37.0%
성남시 29
29.0%
김천시 8
 
8.0%
<NA> 6
 
6.0%
산청군 4
 
4.0%
문경시 3
 
3.0%
서구 2
 
2.0%
이천시 2
 
2.0%
경주시 1
 
1.0%
안산시 상록구 1
 
1.0%
Other values (7) 7
 
7.0%

Length

2023-12-10T18:52:34.646367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 38
37.3%
성남시 30
29.4%
김천시 8
 
7.8%
na 6
 
5.9%
산청군 4
 
3.9%
문경시 3
 
2.9%
서구 2
 
2.0%
이천시 2
 
2.0%
해남군 1
 
1.0%
여주시 1
 
1.0%
Other values (7) 7
 
6.9%

addr_emd_cd
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)72.2%
Missing82
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean4.2791881 × 109
Minimum1.1320106 × 109
Maximum4.886034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:34.864891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1320106 × 109
5-th percentile3.6659453 × 109
Q14.1271104 × 109
median4.300525 × 109
Q34.728011 × 109
95-th percentile4.8860264 × 109
Maximum4.886034 × 109
Range3.7540234 × 109
Interquartile range (IQR)6.009006 × 108

Descriptive statistics

Standard deviation8.5325645 × 108
Coefficient of variation (CV)0.19939681
Kurtosis12.013329
Mean4.2791881 × 109
Median Absolute Deviation (MAD)3.009501 × 108
Skewness-3.1905075
Sum7.7025385 × 1010
Variance7.2804657 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:35.141251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
4127110400 3
 
3.0%
4728011000 3
 
3.0%
4113110200 2
 
2.0%
4715010400 1
 
1.0%
4128510200 1
 
1.0%
4886025024 1
 
1.0%
4886025032 1
 
1.0%
4886025021 1
 
1.0%
4180025021 1
 
1.0%
1132010600 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 82
82.0%
ValueCountFrequency (%)
1132010600 1
 
1.0%
4113110200 2
2.0%
4113110300 1
 
1.0%
4127110400 3
3.0%
4128510200 1
 
1.0%
4180025021 1
 
1.0%
4421025021 1
 
1.0%
4715010400 1
 
1.0%
4728011000 3
3.0%
4886025021 1
 
1.0%
ValueCountFrequency (%)
4886034024 1
 
1.0%
4886025032 1
 
1.0%
4886025024 1
 
1.0%
4886025021 1
 
1.0%
4728011000 3
3.0%
4715010400 1
 
1.0%
4421025021 1
 
1.0%
4180025021 1
 
1.0%
4128510200 1
 
1.0%
4127110400 3
3.0%

addr_emd_nm
Text

MISSING 

Distinct13
Distinct (%)72.2%
Missing82
Missing (%)82.0%
Memory size932.0 B
2023-12-10T18:52:35.432013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

Characters and Unicode

Total characters54
Distinct characters23
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

Unique10 ?
Unique (%)55.6%

Sample

1st row성내동
2nd row본오동
3rd row본오동
4th row모전동
5th row중산동
ValueCountFrequency (%)
본오동 3
16.7%
모전동 3
16.7%
태평동 2
11.1%
성내동 1
 
5.6%
중산동 1
 
5.6%
모고리 1
 
5.6%
범학리 1
 
5.6%
산청리 1
 
5.6%
차탄리 1
 
5.6%
방학동 1
 
5.6%
Other values (3) 3
16.7%
2023-12-10T18:52:36.037798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
22.2%
6
11.1%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (13) 14
25.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 54
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
22.2%
6
11.1%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (13) 14
25.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
22.2%
6
11.1%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (13) 14
25.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 54
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12
22.2%
6
11.1%
4
 
7.4%
3
 
5.6%
3
 
5.6%
3
 
5.6%
3
 
5.6%
2
 
3.7%
2
 
3.7%
2
 
3.7%
Other values (13) 14
25.9%

addr_amd_cd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

addr_amd_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

faci_mng_type
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
54 
자체운영
46 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 54
54.0%
자체운영 46
46.0%

Length

2023-12-10T18:52:36.276470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:36.882877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 54
54.0%
자체운영 46
46.0%

fmng_type_gb_cd
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
1
59 
<NA>
41 

Length

Max length4
Median length1
Mean length2.23
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row<NA>
3rd row1
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
1 59
59.0%
<NA> 41
41.0%

Length

2023-12-10T18:52:37.080597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:37.267533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 59
59.0%
na 41
41.0%

fmng_type_gb_nm
Categorical

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
지자체
59 
<NA>
41 

Length

Max length4
Median length3
Mean length3.41
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지자체 59
59.0%
<NA> 41
41.0%

Length

2023-12-10T18:52:37.467602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:37.724297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지자체 59
59.0%
na 41
41.0%

fmng_cp_cd
Real number (ℝ)

MISSING 

Distinct7
Distinct (%)7.6%
Missing8
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean4.1804348 × 109
Minimum2.9 × 109
Maximum4.8 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:37.956621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9 × 109
5-th percentile4.1 × 109
Q14.1 × 109
median4.1 × 109
Q34.1 × 109
95-th percentile4.7 × 109
Maximum4.8 × 109
Range1.9 × 109
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.2958153 × 108
Coefficient of variation (CV)0.078839055
Kurtosis4.9281305
Mean4.1804348 × 109
Median Absolute Deviation (MAD)0
Skewness-0.84941982
Sum3.846 × 1011
Variance1.0862398 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:38.180901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4100000000 71
71.0%
4700000000 12
 
12.0%
4800000000 4
 
4.0%
3000000000 2
 
2.0%
2900000000 1
 
1.0%
4600000000 1
 
1.0%
4400000000 1
 
1.0%
(Missing) 8
 
8.0%
ValueCountFrequency (%)
2900000000 1
 
1.0%
3000000000 2
 
2.0%
4100000000 71
71.0%
4400000000 1
 
1.0%
4600000000 1
 
1.0%
4700000000 12
 
12.0%
4800000000 4
 
4.0%
ValueCountFrequency (%)
4800000000 4
 
4.0%
4700000000 12
 
12.0%
4600000000 1
 
1.0%
4400000000 1
 
1.0%
4100000000 71
71.0%
3000000000 2
 
2.0%
2900000000 1
 
1.0%

fmng_cp_nm
Categorical

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
71 
경상북도
12 
<NA>
경상남도
 
4
대전광역시
 
2
Other values (3)
 
3

Length

Max length5
Median length3
Mean length3.32
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 71
71.0%
경상북도 12
 
12.0%
<NA> 8
 
8.0%
경상남도 4
 
4.0%
대전광역시 2
 
2.0%
광주광역시 1
 
1.0%
전라남도 1
 
1.0%
충청남도 1
 
1.0%

Length

2023-12-10T18:52:38.474617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:38.767205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 71
71.0%
경상북도 12
 
12.0%
na 8
 
8.0%
경상남도 4
 
4.0%
대전광역시 2
 
2.0%
광주광역시 1
 
1.0%
전라남도 1
 
1.0%
충청남도 1
 
1.0%

fmng_cpb_cd
Real number (ℝ)

MISSING 

Distinct14
Distinct (%)15.2%
Missing8
Missing (%)8.0%
Infinite0
Infinite (%)0.0%
Mean4.2063804 × 109
Minimum2.92 × 109
Maximum4.886 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:38.969588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.92 × 109
5-th percentile4.113 × 109
Q14.113 × 109
median4.127 × 109
Q34.12725 × 109
95-th percentile4.728 × 109
Maximum4.886 × 109
Range1.966 × 109
Interquartile range (IQR)14250000

Descriptive statistics

Standard deviation3.3550241 × 108
Coefficient of variation (CV)0.079760357
Kurtosis4.7493456
Mean4.2063804 × 109
Median Absolute Deviation (MAD)14000000
Skewness-0.75989479
Sum3.86987 × 1011
Variance1.1256186 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:39.264715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
4127000000 39
39.0%
4113000000 27
27.0%
4715000000 8
 
8.0%
4886000000 4
 
4.0%
4728000000 3
 
3.0%
3017000000 2
 
2.0%
4150000000 2
 
2.0%
4128000000 1
 
1.0%
4180000000 1
 
1.0%
2920000000 1
 
1.0%
Other values (4) 4
 
4.0%
(Missing) 8
 
8.0%
ValueCountFrequency (%)
2920000000 1
 
1.0%
3017000000 2
 
2.0%
4113000000 27
27.0%
4127000000 39
39.0%
4128000000 1
 
1.0%
4150000000 2
 
2.0%
4157000000 1
 
1.0%
4180000000 1
 
1.0%
4421000000 1
 
1.0%
4682000000 1
 
1.0%
ValueCountFrequency (%)
4886000000 4
4.0%
4728000000 3
 
3.0%
4715000000 8
8.0%
4713000000 1
 
1.0%
4682000000 1
 
1.0%
4421000000 1
 
1.0%
4180000000 1
 
1.0%
4157000000 1
 
1.0%
4150000000 2
 
2.0%
4128000000 1
 
1.0%

fmng_cpb_nm
Categorical

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
안산시
39 
성남시
27 
김천시
<NA>
산청군
Other values (10)
14 

Length

Max length4
Median length3
Mean length3.06
Min length2

Unique

Unique7 ?
Unique (%)7.0%

Sample

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

Common Values

ValueCountFrequency (%)
안산시 39
39.0%
성남시 27
27.0%
김천시 8
 
8.0%
<NA> 8
 
8.0%
산청군 4
 
4.0%
문경시 3
 
3.0%
서구 2
 
2.0%
이천시 2
 
2.0%
고양시 1
 
1.0%
연천군 1
 
1.0%
Other values (5) 5
 
5.0%

Length

2023-12-10T18:52:39.539324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
안산시 39
39.0%
성남시 27
27.0%
김천시 8
 
8.0%
na 8
 
8.0%
산청군 4
 
4.0%
문경시 3
 
3.0%
서구 2
 
2.0%
이천시 2
 
2.0%
고양시 1
 
1.0%
연천군 1
 
1.0%
Other values (5) 5
 
5.0%

fmng_dept_nm
Categorical

IMBALANCE 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
83 
스포츠산업과
 
8
점촌5동
 
3
문화관광과
 
3
체육진흥팀
 
1
Other values (2)
 
2

Length

Max length6
Median length4
Mean length4.2
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 83
83.0%
스포츠산업과 8
 
8.0%
점촌5동 3
 
3.0%
문화관광과 3
 
3.0%
체육진흥팀 1
 
1.0%
문화체육팀 1
 
1.0%
황남동 1
 
1.0%

Length

2023-12-10T18:52:39.798479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:40.023293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 83
83.0%
스포츠산업과 8
 
8.0%
점촌5동 3
 
3.0%
문화관광과 3
 
3.0%
체육진흥팀 1
 
1.0%
문화체육팀 1
 
1.0%
황남동 1
 
1.0%

fmng_user_tel
Text

MISSING 

Distinct27
Distinct (%)87.1%
Missing69
Missing (%)69.0%
Memory size932.0 B
2023-12-10T18:52:40.361396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.419355
Min length8

Characters and Unicode

Total characters354
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

Unique25 ?
Unique (%)80.6%

Sample

1st row054-420-8033
2nd row042-541-7944
3rd row054-420-8077
4th row042-542-9091
5th row054-420-8031
ValueCountFrequency (%)
055-970-6414 3
 
9.7%
054-550-8884 3
 
9.7%
6483-2186 1
 
3.2%
054-420-8033 1
 
3.2%
031-721-6928 1
 
3.2%
031-756-9078 1
 
3.2%
734-5945 1
 
3.2%
041-681-9934 1
 
3.2%
031-983-6413 1
 
3.2%
638-0434 1
 
3.2%
Other values (17) 17
54.8%
2023-12-10T18:52:41.102199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 57
16.1%
0 55
15.5%
4 49
13.8%
5 34
9.6%
9 28
7.9%
8 27
7.6%
3 26
7.3%
2 24
6.8%
1 19
 
5.4%
6 18
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 297
83.9%
Dash Punctuation 57
 
16.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 55
18.5%
4 49
16.5%
5 34
11.4%
9 28
9.4%
8 27
9.1%
3 26
8.8%
2 24
8.1%
1 19
 
6.4%
6 18
 
6.1%
7 17
 
5.7%
Dash Punctuation
ValueCountFrequency (%)
- 57
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 354
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 57
16.1%
0 55
15.5%
4 49
13.8%
5 34
9.6%
9 28
7.9%
8 27
7.6%
3 26
7.3%
2 24
6.8%
1 19
 
5.4%
6 18
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 354
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 57
16.1%
0 55
15.5%
4 49
13.8%
5 34
9.6%
9 28
7.9%
8 27
7.6%
3 26
7.3%
2 24
6.8%
1 19
 
5.4%
6 18
 
5.1%

inout_gbn
Categorical

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
67 
실내
28 
실내외
 
3
없음
 
2

Length

Max length4
Median length4
Mean length3.37
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 67
67.0%
실내 28
28.0%
실내외 3
 
3.0%
없음 2
 
2.0%

Length

2023-12-10T18:52:41.390924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:41.641420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 67
67.0%
실내 28
28.0%
실내외 3
 
3.0%
없음 2
 
2.0%

stand_seat_cnt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

stand_cpt_psn_cnt
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

tot_faci_area
Real number (ℝ)

MISSING  ZEROS 

Distinct51
Distinct (%)89.5%
Missing43
Missing (%)43.0%
Infinite0
Infinite (%)0.0%
Mean200.21053
Minimum0
Maximum1371
Zeros2
Zeros (%)2.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:41.875292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile78.8
Q1126
median153
Q3199
95-th percentile504.6
Maximum1371
Range1371
Interquartile range (IQR)73

Descriptive statistics

Standard deviation195.68759
Coefficient of variation (CV)0.97740911
Kurtosis23.536969
Mean200.21053
Median Absolute Deviation (MAD)43
Skewness4.3699808
Sum11412
Variance38293.633
MonotonicityNot monotonic
2023-12-10T18:52:42.135870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
127 2
 
2.0%
141 2
 
2.0%
132 2
 
2.0%
157 2
 
2.0%
0 2
 
2.0%
110 2
 
2.0%
146 1
 
1.0%
183 1
 
1.0%
206 1
 
1.0%
148 1
 
1.0%
Other values (41) 41
41.0%
(Missing) 43
43.0%
ValueCountFrequency (%)
0 2
2.0%
74 1
1.0%
80 1
1.0%
94 1
1.0%
97 1
1.0%
98 1
1.0%
99 1
1.0%
100 1
1.0%
105 1
1.0%
110 2
2.0%
ValueCountFrequency (%)
1371 1
1.0%
707 1
1.0%
531 1
1.0%
498 1
1.0%
339 1
1.0%
285 1
1.0%
283 1
1.0%
276 1
1.0%
254 1
1.0%
251 1
1.0%

open_yn
Categorical

CONSTANT 

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

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
100
100.0%

Length

2023-12-10T18:52:42.425335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:42.636135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

life_gym_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

use_asct_nm
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

base_ymd
Categorical

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20161231
39 
27 
20161107
20160929
 
1
20030219
 
1
Other values (23)
23 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique25 ?
Unique (%)25.0%

Sample

1st row
2nd row19990428
3rd row
4th row20030219
5th row20030214

Common Values

ValueCountFrequency (%)
20161231 39
39.0%
27
27.0%
20161107 9
 
9.0%
20160929 1
 
1.0%
20030219 1
 
1.0%
20030214 1
 
1.0%
19950811 1
 
1.0%
20021212 1
 
1.0%
20140627 1
 
1.0%
20030210 1
 
1.0%
Other values (18) 18
18.0%

Length

2023-12-10T18:52:42.890851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20161231 39
53.4%
20161107 9
 
12.3%
19960214 1
 
1.4%
19980224 1
 
1.4%
20030204 1
 
1.4%
19961024 1
 
1.4%
20021011 1
 
1.4%
20010314 1
 
1.4%
19991217 1
 
1.4%
20020904 1
 
1.4%
Other values (17) 17
23.3%
Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:43.502787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
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

Unique81 ?
Unique (%)81.0%

Sample

1st row
2nd row20091013
3rd row
4th row20030219
5th row20030214
ValueCountFrequency (%)
19930327 1
 
1.2%
19940126 1
 
1.2%
20020904 1
 
1.2%
19940415 1
 
1.2%
20030922 1
 
1.2%
19941010 1
 
1.2%
20000802 1
 
1.2%
19951128 1
 
1.2%
20010921 1
 
1.2%
20020409 1
 
1.2%
Other values (71) 71
87.7%
2023-12-10T18:52:44.322492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 178
22.2%
152
19.0%
1 122
15.2%
2 107
13.4%
9 98
12.2%
7 30
 
3.8%
8 28
 
3.5%
3 27
 
3.4%
5 24
 
3.0%
4 21
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 648
81.0%
Space Separator 152
 
19.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 178
27.5%
1 122
18.8%
2 107
16.5%
9 98
15.1%
7 30
 
4.6%
8 28
 
4.3%
3 27
 
4.2%
5 24
 
3.7%
4 21
 
3.2%
6 13
 
2.0%
Space Separator
ValueCountFrequency (%)
152
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 800
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 178
22.2%
152
19.0%
1 122
15.2%
2 107
13.4%
9 98
12.2%
7 30
 
3.8%
8 28
 
3.5%
3 27
 
3.4%
5 24
 
3.0%
4 21
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 800
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 178
22.2%
152
19.0%
1 122
15.2%
2 107
13.4%
9 98
12.2%
7 30
 
3.8%
8 28
 
3.5%
3 27
 
3.4%
5 24
 
3.0%
4 21
 
2.6%

cp_ymd
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
99 
20160929
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
99
99.0%
20160929 1
 
1.0%

Length

2023-12-10T18:52:44.599246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:44.860945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20160929 1
100.0%

th_ymd
Categorical

CONSTANT 

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

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
100
100.0%

Length

2023-12-10T18:52:45.054045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:45.227356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
No values found.

sdwn_ymd
Categorical

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
45 
20170517
14 
20191105
20191210
 
4
20171201
 
4
Other values (16)
26 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique11 ?
Unique (%)11.0%

Sample

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

Common Values

ValueCountFrequency (%)
45
45.0%
20170517 14
 
14.0%
20191105 7
 
7.0%
20191210 4
 
4.0%
20171201 4
 
4.0%
20201223 4
 
4.0%
20211028 4
 
4.0%
20191209 3
 
3.0%
20170106 2
 
2.0%
20190716 2
 
2.0%
Other values (11) 11
 
11.0%

Length

2023-12-10T18:52:45.431336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
20170517 14
25.5%
20191105 7
12.7%
20191210 4
 
7.3%
20171201 4
 
7.3%
20201223 4
 
7.3%
20211028 4
 
7.3%
20191209 3
 
5.5%
20170106 2
 
3.6%
20190716 2
 
3.6%
20170920 1
 
1.8%
Other values (10) 10
18.2%

nation_yn
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T18:52:45.648265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

ssm_dsn_yn
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
98 
N
 
2

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd rowN
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
98
98.0%
N 2
 
2.0%

Length

2023-12-10T18:52:45.834169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:46.018290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
n 2
100.0%
Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
True
79 
False
21 
ValueCountFrequency (%)
True 79
79.0%
False 21
 
21.0%
2023-12-10T18:52:46.200559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

data_from_gb_cd
Categorical

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
75 
SFMS
19 
태블릿
 
6

Length

Max length4
Median length4
Mean length3.94
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 75
75.0%
SFMS 19
 
19.0%
태블릿 6
 
6.0%

Length

2023-12-10T18:52:46.377990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:46.533149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 75
75.0%
sfms 19
 
19.0%
태블릿 6
 
6.0%

del_yn
Boolean

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
94 
True
 
6
ValueCountFrequency (%)
False 94
94.0%
True 6
 
6.0%
2023-12-10T18:52:46.684250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

reg_dt
Date

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2016-11-07 00:00:00
Maximum2021-09-01 00:00:00
2023-12-10T18:52:46.823049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:47.006165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)

upd_dt
Date

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2016-12-31 00:00:00
Maximum2021-10-28 00:00:00
2023-12-10T18:52:47.208006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:52:47.419113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)

Sample

faci_nmfaci_gb_cdfaci_gb_nmfcob_cdfcob_nmftype_cdftype_nmfaci_statfaci_road_postfaci_road_addr1faci_road_addr2faci_postfaci_addr1faci_addr2faci_point_xfaci_point_yfaci_telfaci_homepagecp_cdcp_nmcpb_cdcpb_nmaddr_cp_cdaddr_cp_nmaddr_cpb_cdaddr_cpb_nmaddr_emd_cdaddr_emd_nmaddr_amd_cdaddr_amd_nmfaci_mng_typefmng_type_gb_cdfmng_type_gb_nmfmng_cp_cdfmng_cp_nmfmng_cpb_cdfmng_cpb_nmfmng_dept_nmfmng_user_telinout_gbnstand_seat_cntstand_cpt_psn_cnttot_faci_areaopen_ynlife_gym_nmuse_asct_nmbase_ymdfaci_reg_ymdcp_ymdth_ymdsdwn_ymdnation_ynssm_dsn_ynatnm_chk_yndata_from_gb_cddel_ynreg_dtupd_dt
0(외립석입구)P공공P08간이운동장P0801간이운동장정상운영<NA><NA><NA><NA>경상북도 예지리 680<NA><NA><NA><NA><NA>4700000000경상북도4715000000김천시4700000000경상북도4715000000김천시<NA><NA><NA><NA>자체운영1지자체4700000000경상북도4715000000김천시스포츠산업과054-420-8033<NA><NA><NA><NA><NA><NA>NN<NA>N2020-02-102020-02-10
1우리 아이풀(舊 크레피아수영장)N신고N07수영장업N0701실내정상운영302800대전광역시 서구 계백로 1186 (가수원동)<NA>302800<NA>지하1층127.35522636.304872042-541-7944<NA>3000000000대전광역시3017000000서구3000000000대전광역시3017000000서구<NA><NA><NA><NA>자체운영<NA><NA>3000000000대전광역시3017000000서구<NA>042-541-7944실내<NA><NA>1371<NA><NA>1999042820091013NNN<NA>N2016-12-312018-09-19
2(자산경로당)P공공P08간이운동장P0801간이운동장정상운영39588경상북도 김천시 자산3길 16 (성내동)<NA>39588경상북도 김천시 성내동 161-17자산경로당128.12209236.120771054-421-2318<NA>4700000000경상북도4715000000김천시4700000000경상북도4715000000김천시4715010400성내동<NA><NA>자체운영1지자체4700000000경상북도4715000000김천시스포츠산업과054-420-8077<NA><NA><NA><NA><NA><NA>NNSFMSN2020-02-102021-04-29
3볼&큐 빌리어드클럽N신고N11당구장업N1101당구장정상운영426811경기도 안산시 상록구 반석로 88, 4층 (본오동)<NA>426811<NA><NA>126.86946837.296411031-415-9970<NA>4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA><NA>자체운영<NA><NA>4100000000경기도4127000000안산시<NA><NA>실내<NA><NA>189<NA><NA>2003021920030219NY태블릿N2016-12-312019-01-14
4안산 힘찬 태권도N신고N08체육도장업N0805태권도정상운영15535경기도 안산시 상록구 각골로 127 (본오동)2층15535경기도 안산시 상록구 본오동 1118-10<NA>126.872737.300895<NA><NA>4100000000경기도4127000000안산시4100000000경기도4127100000안산시 상록구4127110400본오동<NA><NA>자체운영<NA><NA>4100000000경기도4127000000안산시<NA><NA>실내<NA><NA>171<NA><NA>2003021420030214NYSFMSN2016-12-312019-03-13
5뽀록 당구장N신고N11당구장업N1101당구장정상운영461801경기도 성남시 수정구 산성대로 403 (단대동,2층)<NA>461801경기 성남시 수정구 산성대로 403<NA>127.15793637.448107031-000-0000<NA>4100000000경기도4113000000성남시4100000000경기도4113000000성남시<NA><NA><NA><NA>자체운영<NA><NA>4100000000경기도4113000000성남시<NA><NA>실내<NA><NA>74<NA><NA>20070411NY<NA>N2016-11-072020-07-27
6안산 용인대 청룡 태권도장N신고N08체육도장업N0805태권도정상운영<NA><NA><NA><NA>경기도 안산시 상록구 샘골로 115<NA>126.86269637.294169<NA><NA>4100000000경기도4127000000안산시4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA>4100000000경기도4127000000안산시<NA><NA><NA><NA><NA>127<NA><NA>1995081119950811NY<NA>N2016-12-312016-12-31
7태권道장 휘영찬N신고N08체육도장업N0805태권도정상운영302812대전광역시 서구 관저동로 66 (관저동, 3층)<NA>302812대전 서구 관저동로 66<NA>127.34099436.298068<NA><NA>3000000000대전광역시3017000000서구3000000000대전광역시3017000000서구<NA><NA><NA><NA>자체운영<NA><NA>3000000000대전광역시3017000000서구<NA>042-542-9091실내<NA><NA>285<NA><NA>2002121220021212NY<NA>N2016-12-312018-09-20
8신리 봉계초등학교 (강당뒤)P공공P08간이운동장P0801간이운동장정상운영<NA><NA><NA><NA>경상북도 신리 472<NA><NA><NA><NA><NA>4700000000경상북도4715000000김천시4700000000경상북도4715000000김천시<NA><NA><NA><NA>자체운영1지자체4700000000경상북도4715000000김천시스포츠산업과054-420-8031<NA><NA><NA><NA><NA><NA>NN<NA>N2020-02-102020-02-10
9성당뒤P공공P08간이운동장P0801간이운동장정상운영<NA><NA><NA><NA>경상북도 교리 589-4<NA><NA><NA><NA><NA>4700000000경상북도4715000000김천시4700000000경상북도4715000000김천시<NA><NA><NA><NA>자체운영1지자체4700000000경상북도4715000000김천시스포츠산업과054-420-8092<NA><NA><NA><NA><NA><NA>NNSFMSY2020-02-102020-02-10
faci_nmfaci_gb_cdfaci_gb_nmfcob_cdfcob_nmftype_cdftype_nmfaci_statfaci_road_postfaci_road_addr1faci_road_addr2faci_postfaci_addr1faci_addr2faci_point_xfaci_point_yfaci_telfaci_homepagecp_cdcp_nmcpb_cdcpb_nmaddr_cp_cdaddr_cp_nmaddr_cpb_cdaddr_cpb_nmaddr_emd_cdaddr_emd_nmaddr_amd_cdaddr_amd_nmfaci_mng_typefmng_type_gb_cdfmng_type_gb_nmfmng_cp_cdfmng_cp_nmfmng_cpb_cdfmng_cpb_nmfmng_dept_nmfmng_user_telinout_gbnstand_seat_cntstand_cpt_psn_cnttot_faci_areaopen_ynlife_gym_nmuse_asct_nmbase_ymdfaci_reg_ymdcp_ymdth_ymdsdwn_ymdnation_ynssm_dsn_ynatnm_chk_yndata_from_gb_cddel_ynreg_dtupd_dt
90영동당구장N신고N11당구장업N1101당구장폐업425715경기도 안산시 단원구 화랑로 402 (고잔동)<NA>425715경기 안산시 단원구 화랑로 402<NA>126.83242137.320754031-411-8081<NA>4100000000경기도4127000000안산시4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA>4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA><NA>201612311997101020201223NY<NA>N2016-12-312020-12-24
91예술당구장N신고N11당구장업N1101당구장폐업425805경기도 안산시 단원구 예술대학로 147-1 (고잔동,201호)<NA>425805경기 안산시 단원구 예술대학로 147-1<NA>126.83838537.330079<NA><NA>4100000000경기도4127000000안산시4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA>4100000000경기도4127000000안산시<NA><NA><NA><NA><NA>98<NA><NA>200302042010053120191211NY<NA>N2016-12-312020-06-19
92오성당구장N신고N11당구장업N1101당구장폐업461811경기도 성남시 수정구 공원로339번길 16 (신흥동)<NA>461811경기 성남시 수정구 공원로339번길 16<NA>127.14891137.442723031-731-9444<NA>4100000000경기도4113000000성남시4100000000경기도4113000000성남시<NA><NA><NA><NA><NA>1지자체4100000000경기도4113000000성남시<NA><NA><NA><NA><NA>80<NA><NA>201612311994071820170517NY<NA>N2016-12-312020-06-19
93우성당구장N신고N11당구장업N1101당구장폐업425831경기도 안산시 단원구 선부로 185 (선부동)<NA>425831경기 안산시 단원구 선부로 185<NA>126.81569937.341983031-481-9367<NA>4100000000경기도4127000000안산시4100000000경기도4127000000안산시<NA><NA><NA><NA><NA>1지자체4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA><NA>201612311998050720191210NY<NA>N2016-12-312020-06-19
94월드당구장N신고N11당구장업N1101당구장정상운영13306경기도 성남시 수정구 성남대로 1334 (태평동)지1층,경원프라자13306경기도 성남시 수정구 태평동 5113-9 경원프라자지1층,경원프라자127.1271337.44884031-721-3441<NA>4100000000경기도4113000000성남시4100000000경기도4113100000성남시 수정구4113110200태평동<NA><NA>자체운영<NA><NA><NA><NA><NA><NA><NA><NA>실내<NA><NA>283<NA><NA>2016110720150702NY<NA>N2016-11-072021-02-18
95은행당구장N신고N11당구장업N1101당구장폐업461815경기도 성남시 수정구 산성대로 529 (양지동)<NA>461815경기 성남시 수정구 산성대로 529<NA>127.16399737.458244031-742-8364<NA>4100000000경기도4113000000성남시4100000000경기도4113000000성남시<NA><NA><NA><NA><NA>1지자체4100000000경기도4113000000성남시<NA><NA><NA><NA><NA>100<NA><NA>201612311990081420170517NY<NA>N2016-12-312020-06-19
96이천검도관N신고N08체육도장업N0804검도정상운영467803경기도 이천시 증신로 181 (증포동)<NA>467803경기 이천시 증신로 181<NA>127.44953537.290577031-634-0012<NA>4100000000경기도4150000000이천시4100000000경기도4150000000이천시<NA><NA><NA><NA>자체운영<NA><NA>4100000000경기도4150000000이천시<NA><NA>실내<NA><NA>224<NA><NA>1993050320050927NNY<NA>N2016-12-312018-02-09
97자유당구장N신고N11당구장업N1101당구장폐업425801경기도 안산시 단원구 화랑로 358 (고잔동,자유센타 315호)<NA>425801경기 안산시 단원구 화랑로 358<NA>126.82749237.320999031-482-9400<NA>4100000000경기도4127000000안산시4100000000경기도4127000000안산시<NA><NA><NA><NA><NA><NA><NA>4100000000경기도4127000000안산시<NA><NA><NA><NA><NA>117<NA><NA>201612311999052920201223NY<NA>N2016-12-312020-12-24
98잔디당구장N신고N11당구장업N1101당구장정상운영461816경기도 성남시 수정구 논골로 8 (양지동,2층)<NA>461816경기 성남시 수정구 논골로 8<NA>127.16075337.455055<NA><NA>4100000000경기도4113000000성남시4100000000경기도4113000000성남시<NA><NA><NA><NA>자체운영<NA><NA>4100000000경기도4113000000성남시<NA><NA>실내<NA><NA>150<NA><NA>20110705NY<NA>N2016-11-072020-07-27
99정인당구장N신고N11당구장업N1101당구장폐업426837경기도 안산시 상록구 예술광장1로 94, B102호 (성포동)<NA>426837경기 안산시 상록구 예술광장1로 94<NA>126.84704937.326465031-414-6477<NA>4100000000경기도4127000000안산시4100000000경기도4127000000안산시<NA><NA><NA><NA><NA>1지자체4100000000경기도4127000000안산시<NA><NA><NA><NA><NA>110<NA><NA>201612311997112120171201NY<NA>N2016-12-312020-06-19