Overview

Dataset statistics

Number of variables28
Number of observations100
Missing cells261
Missing cells (%)9.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.3 KiB
Average record size in memory238.3 B

Variable types

Text8
Categorical8
Numeric10
Unsupported2

Alerts

lclas_nm has constant value ""Constant
mlsfc_nm has constant value ""Constant
origin_nm has constant value ""Constant
updt_dt has constant value ""Constant
fclty_nm is highly imbalanced (80.6%)Imbalance
regist_dt is highly imbalanced (80.6%)Imbalance
buld_nm has 60 (60.0%) missing valuesMissing
hmpg_url has 100 (100.0%) missing valuesMissing
adit_dc has 100 (100.0%) missing valuesMissing
esntl_id has unique valuesUnique
bhf_nm has unique valuesUnique
fclty_road_nm_addr has unique valuesUnique
lnm_addr has unique valuesUnique
addr_eng_nm has unique valuesUnique
buld_manage_cd has unique valuesUnique
zip_no has unique valuesUnique
fclty_la has unique valuesUnique
fclty_lo has unique valuesUnique
hmpg_url is an unsupported type, check if it needs cleaning or further analysisUnsupported
adit_dc is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:43:24.834105
Analysis finished2023-12-10 09:43:26.253686
Duration1.42 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

esntl_id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:26.577367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters19
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st rowKCDTDAL21N000000001
2nd rowKCKDDPO20N000008661
3rd rowKCDTDAL21N000000003
4th rowKCDTDAL21N000000004
5th rowKCDTDAL21N000000005
ValueCountFrequency (%)
kcdtdal21n000000001 1
 
1.0%
kcdtdal21n000000063 1
 
1.0%
kcdtdal21n000000074 1
 
1.0%
kcdtdal21n000000073 1
 
1.0%
kcdtdal21n000000072 1
 
1.0%
kcdtdal21n000000071 1
 
1.0%
kcdtdal21n000000070 1
 
1.0%
kcdtdal21n000000069 1
 
1.0%
kcdtdal21n000000068 1
 
1.0%
kcdtdal21n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:43:27.330614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 714
37.6%
D 200
 
10.5%
2 119
 
6.3%
1 119
 
6.3%
K 103
 
5.4%
N 100
 
5.3%
C 100
 
5.3%
L 97
 
5.1%
A 97
 
5.1%
T 97
 
5.1%
Other values (9) 154
 
8.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1100
57.9%
Uppercase Letter 800
42.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 714
64.9%
2 119
 
10.8%
1 119
 
10.8%
6 26
 
2.4%
8 22
 
2.0%
3 20
 
1.8%
4 20
 
1.8%
5 20
 
1.8%
7 20
 
1.8%
9 20
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
D 200
25.0%
K 103
12.9%
N 100
12.5%
C 100
12.5%
L 97
12.1%
A 97
12.1%
T 97
12.1%
P 3
 
0.4%
O 3
 
0.4%

Most occurring scripts

ValueCountFrequency (%)
Common 1100
57.9%
Latin 800
42.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 714
64.9%
2 119
 
10.8%
1 119
 
10.8%
6 26
 
2.4%
8 22
 
2.0%
3 20
 
1.8%
4 20
 
1.8%
5 20
 
1.8%
7 20
 
1.8%
9 20
 
1.8%
Latin
ValueCountFrequency (%)
D 200
25.0%
K 103
12.9%
N 100
12.5%
C 100
12.5%
L 97
12.1%
A 97
12.1%
T 97
12.1%
P 3
 
0.4%
O 3
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 714
37.6%
D 200
 
10.5%
2 119
 
6.3%
1 119
 
6.3%
K 103
 
5.4%
N 100
 
5.3%
C 100
 
5.3%
L 97
 
5.1%
A 97
 
5.1%
T 97
 
5.1%
Other values (9) 154
 
8.1%

lclas_nm
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
편의시설 100
100.0%

Length

2023-12-10T18:43:27.690841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:27.974252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
편의시설 100
100.0%

mlsfc_nm
Categorical

CONSTANT 

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

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
편의시설_DT 100
100.0%

Length

2023-12-10T18:43:28.137732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:28.284372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
편의시설_dt 100
100.0%

fclty_nm
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
맥도날드
97 
롯데리아
 
3

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row맥도날드
2nd row롯데리아
3rd row맥도날드
4th row맥도날드
5th row맥도날드

Common Values

ValueCountFrequency (%)
맥도날드 97
97.0%
롯데리아 3
 
3.0%

Length

2023-12-10T18:43:28.472078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:28.645280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
맥도날드 97
97.0%
롯데리아 3
 
3.0%

bhf_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:29.118203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length6.54
Min length4

Characters and Unicode

Total characters654
Distinct characters133
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row등촌DT점
2nd row인천연희D/T
3rd row파리공원점
4th row양평SK점
5th row우장산DT점
ValueCountFrequency (%)
dt점 9
 
8.3%
등촌dt점 1
 
0.9%
하남시청dt점 1
 
0.9%
용인마북dt점 1
 
0.9%
수원화성dt점 1
 
0.9%
동수원gs 1
 
0.9%
북수원dt점 1
 
0.9%
수원정자dt점 1
 
0.9%
용인수지dt점 1
 
0.9%
양주덕계dt점 1
 
0.9%
Other values (91) 91
83.5%
2023-12-10T18:43:30.103122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 96
 
14.7%
D 94
 
14.4%
T 94
 
14.4%
천 15
 
2.3%
S 13
 
2.0%
인 12
 
1.8%
원 12
 
1.8%
수 11
 
1.7%
양 10
 
1.5%
산 10
 
1.5%
Other values (123) 287
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 426
65.1%
Uppercase Letter 216
33.0%
Space Separator 9
 
1.4%
Other Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 96
22.5%
천 15
 
3.5%
인 12
 
2.8%
원 12
 
2.8%
수 11
 
2.6%
양 10
 
2.3%
산 10
 
2.3%
동 8
 
1.9%
부 8
 
1.9%
포 7
 
1.6%
Other values (115) 237
55.6%
Uppercase Letter
ValueCountFrequency (%)
D 94
43.5%
T 94
43.5%
S 13
 
6.0%
K 7
 
3.2%
G 7
 
3.2%
L 1
 
0.5%
Space Separator
ValueCountFrequency (%)
9
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 426
65.1%
Latin 216
33.0%
Common 12
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 96
22.5%
천 15
 
3.5%
인 12
 
2.8%
원 12
 
2.8%
수 11
 
2.6%
양 10
 
2.3%
산 10
 
2.3%
동 8
 
1.9%
부 8
 
1.9%
포 7
 
1.6%
Other values (115) 237
55.6%
Latin
ValueCountFrequency (%)
D 94
43.5%
T 94
43.5%
S 13
 
6.0%
K 7
 
3.2%
G 7
 
3.2%
L 1
 
0.5%
Common
ValueCountFrequency (%)
9
75.0%
/ 3
 
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 426
65.1%
ASCII 228
34.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 96
22.5%
천 15
 
3.5%
인 12
 
2.8%
원 12
 
2.8%
수 11
 
2.6%
양 10
 
2.3%
산 10
 
2.3%
동 8
 
1.9%
부 8
 
1.9%
포 7
 
1.6%
Other values (115) 237
55.6%
ASCII
ValueCountFrequency (%)
D 94
41.2%
T 94
41.2%
S 13
 
5.7%
9
 
3.9%
K 7
 
3.1%
G 7
 
3.1%
/ 3
 
1.3%
L 1
 
0.4%

use_time_cn
Categorical

Distinct11
Distinct (%)11.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
매일 00:00 ~ 24:00
63 
매일 07:00 ~ 01:00
13 
매일 07:00 ~ 24:00
 
6
매일 07:00 ~ 02:00
 
4
매일 08:00 ~ 01:00
 
4
Other values (6)
10 

Length

Max length19
Median length16
Mean length16.09
Min length16

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row매일 00:00 ~ 24:00
2nd row오전 00:00 ~ 오후 24:00
3rd row매일 07:00 ~ 24:00
4th row매일 00:00 ~ 24:00
5th row매일 08:00 ~ 24:00

Common Values

ValueCountFrequency (%)
매일 00:00 ~ 24:00 63
63.0%
매일 07:00 ~ 01:00 13
 
13.0%
매일 07:00 ~ 24:00 6
 
6.0%
매일 07:00 ~ 02:00 4
 
4.0%
매일 08:00 ~ 01:00 4
 
4.0%
매일 08:30 ~ 24:00 3
 
3.0%
오전 00:00 ~ 오후 24:00 2
 
2.0%
매일 08:00 ~ 24:00 2
 
2.0%
매일 06:30 ~ 01:00 1
 
1.0%
오전 09:00 ~ 오후 23:00 1
 
1.0%

Length

2023-12-10T18:43:30.497161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100
24.8%
매일 97
24.1%
24:00 76
18.9%
00:00 66
16.4%
07:00 24
 
6.0%
01:00 18
 
4.5%
08:00 6
 
1.5%
02:00 4
 
1.0%
08:30 3
 
0.7%
오전 3
 
0.7%
Other values (4) 6
 
1.5%

ctprvn_cd
Real number (ℝ)

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.58
Minimum11
Maximum47
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:30.723556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q128
median41
Q341
95-th percentile42
Maximum47
Range36
Interquartile range (IQR)13

Descriptive statistics

Standard deviation12.662025
Coefficient of variation (CV)0.3886441
Kurtosis-0.8404452
Mean32.58
Median Absolute Deviation (MAD)0
Skewness-0.95773044
Sum3258
Variance160.32687
MonotonicityNot monotonic
2023-12-10T18:43:30.959108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
41 58
58.0%
11 23
 
23.0%
28 13
 
13.0%
44 3
 
3.0%
42 2
 
2.0%
47 1
 
1.0%
ValueCountFrequency (%)
11 23
 
23.0%
28 13
 
13.0%
41 58
58.0%
42 2
 
2.0%
44 3
 
3.0%
47 1
 
1.0%
ValueCountFrequency (%)
47 1
 
1.0%
44 3
 
3.0%
42 2
 
2.0%
41 58
58.0%
28 13
 
13.0%
11 23
 
23.0%

ctprvn_nm
Categorical

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
58 
서울특별시
23 
인천광역시
13 
충청남도
 
3
강원도
 
2

Length

Max length5
Median length3
Mean length3.76
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 58
58.0%
서울특별시 23
 
23.0%
인천광역시 13
 
13.0%
충청남도 3
 
3.0%
강원도 2
 
2.0%
경상북도 1
 
1.0%

Length

2023-12-10T18:43:31.204844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:31.455958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 58
58.0%
서울특별시 23
 
23.0%
인천광역시 13
 
13.0%
충청남도 3
 
3.0%
강원도 2
 
2.0%
경상북도 1
 
1.0%

signgu_cd
Real number (ℝ)

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32923.95
Minimum11290
Maximum47190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:31.720921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11290
5-th percentile11348.5
Q128183
median41161.5
Q341415
95-th percentile42110
Maximum47190
Range35900
Interquartile range (IQR)13232

Descriptive statistics

Standard deviation12582.382
Coefficient of variation (CV)0.38216502
Kurtosis-0.8543991
Mean32923.95
Median Absolute Deviation (MAD)408.5
Skewness-0.95088804
Sum3292395
Variance1.5831633 × 108
MonotonicityNot monotonic
2023-12-10T18:43:32.004114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
41463 4
 
4.0%
11470 4
 
4.0%
41150 4
 
4.0%
41590 4
 
4.0%
41190 4
 
4.0%
41570 3
 
3.0%
28185 3
 
3.0%
41360 3
 
3.0%
28200 3
 
3.0%
41220 3
 
3.0%
Other values (45) 65
65.0%
ValueCountFrequency (%)
11290 1
 
1.0%
11305 2
2.0%
11320 2
2.0%
11350 1
 
1.0%
11470 4
4.0%
11500 2
2.0%
11530 1
 
1.0%
11545 1
 
1.0%
11560 1
 
1.0%
11650 2
2.0%
ValueCountFrequency (%)
47190 1
 
1.0%
44270 1
 
1.0%
44133 1
 
1.0%
44131 1
 
1.0%
42110 2
2.0%
41650 1
 
1.0%
41630 2
2.0%
41590 4
4.0%
41570 3
3.0%
41500 1
 
1.0%

signgu_nm
Text

Distinct55
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:32.471351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.09
Min length2

Characters and Unicode

Total characters409
Distinct characters71
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)25.0%

Sample

1st row강서구
2nd row서구
3rd row양천구
4th row영등포구
5th row강서구
ValueCountFrequency (%)
수원시 8
 
6.4%
용인시 6
 
4.8%
의정부시 4
 
3.2%
화성시 4
 
3.2%
부천시 4
 
3.2%
고양시 4
 
3.2%
양천구 4
 
3.2%
기흥구 4
 
3.2%
남양주시 3
 
2.4%
연수구 3
 
2.4%
Other values (52) 81
64.8%
2023-12-10T18:43:33.177992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 64
 
15.6%
시 64
 
15.6%
25
 
6.1%
양 17
 
4.2%
천 16
 
3.9%
수 13
 
3.2%
남 12
 
2.9%
부 10
 
2.4%
동 10
 
2.4%
원 10
 
2.4%
Other values (61) 168
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 384
93.9%
Space Separator 25
 
6.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 64
 
16.7%
시 64
 
16.7%
양 17
 
4.4%
천 16
 
4.2%
수 13
 
3.4%
남 12
 
3.1%
부 10
 
2.6%
동 10
 
2.6%
원 10
 
2.6%
강 8
 
2.1%
Other values (60) 160
41.7%
Space Separator
ValueCountFrequency (%)
25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 384
93.9%
Common 25
 
6.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 64
 
16.7%
시 64
 
16.7%
양 17
 
4.4%
천 16
 
4.2%
수 13
 
3.4%
남 12
 
3.1%
부 10
 
2.6%
동 10
 
2.6%
원 10
 
2.6%
강 8
 
2.1%
Other values (60) 160
41.7%
Common
ValueCountFrequency (%)
25
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 384
93.9%
ASCII 25
 
6.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 64
 
16.7%
시 64
 
16.7%
양 17
 
4.4%
천 16
 
4.2%
수 13
 
3.4%
남 12
 
3.1%
부 10
 
2.6%
동 10
 
2.6%
원 10
 
2.6%
강 8
 
2.1%
Other values (60) 160
41.7%
ASCII
ValueCountFrequency (%)
25
100.0%

legaldong_cd
Real number (ℝ)

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2924067 × 109
Minimum1.1290135 × 109
Maximum4.7190118 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:33.468955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1290135 × 109
5-th percentile1.1348605 × 109
Q12.8183102 × 109
median4.1161606 × 109
Q34.1415104 × 109
95-th percentile4.211012 × 109
Maximum4.7190118 × 109
Range3.5899983 × 109
Interquartile range (IQR)1.3232002 × 109

Descriptive statistics

Standard deviation1.2582389 × 109
Coefficient of variation (CV)0.38216389
Kurtosis-0.8543999
Mean3.2924067 × 109
Median Absolute Deviation (MAD)40857362
Skewness-0.9508877
Sum3.2924067 × 1011
Variance1.5831652 × 1018
MonotonicityNot monotonic
2023-12-10T18:43:33.785125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2826010700 2
 
2.0%
1147010300 2
 
2.0%
2820010100 2
 
2.0%
4136025321 1
 
1.0%
4111514100 1
 
1.0%
4146311300 1
 
1.0%
4111514000 1
 
1.0%
4111710300 1
 
1.0%
4111113500 1
 
1.0%
4111113000 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
1129013500 1
1.0%
1130510100 1
1.0%
1130510200 1
1.0%
1132010500 1
1.0%
1132010800 1
1.0%
1135010500 1
1.0%
1147010100 1
1.0%
1147010200 1
1.0%
1147010300 2
2.0%
1150010200 1
1.0%
ValueCountFrequency (%)
4719011800 1
1.0%
4427010100 1
1.0%
4413310400 1
1.0%
4413110700 1
1.0%
4211012300 1
1.0%
4211012000 1
1.0%
4165010500 1
1.0%
4163034027 1
1.0%
4163011600 1
1.0%
4159025940 1
1.0%

legaldong_nm
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:34.377870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3
Min length2

Characters and Unicode

Total characters300
Distinct characters115
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique94 ?
Unique (%)94.0%

Sample

1st row등촌동
2nd row심곡동
3rd row목동
4th row양평동3가
5th row화곡동
ValueCountFrequency (%)
심곡동 2
 
2.0%
신월동 2
 
2.0%
구월동 2
 
2.0%
설운동 1
 
1.0%
평내동 1
 
1.0%
송도동 1
 
1.0%
마북동 1
 
1.0%
우만동 1
 
1.0%
이의동 1
 
1.0%
송죽동 1
 
1.0%
Other values (87) 87
87.0%
2023-12-10T18:43:35.205972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 95
31.7%
신 6
 
2.0%
가 5
 
1.7%
정 5
 
1.7%
읍 5
 
1.7%
월 5
 
1.7%
산 5
 
1.7%
계 5
 
1.7%
흥 4
 
1.3%
중 4
 
1.3%
Other values (105) 161
53.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 298
99.3%
Decimal Number 2
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 95
31.9%
신 6
 
2.0%
가 5
 
1.7%
정 5
 
1.7%
읍 5
 
1.7%
월 5
 
1.7%
산 5
 
1.7%
계 5
 
1.7%
흥 4
 
1.3%
중 4
 
1.3%
Other values (104) 159
53.4%
Decimal Number
ValueCountFrequency (%)
3 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 298
99.3%
Common 2
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 95
31.9%
신 6
 
2.0%
가 5
 
1.7%
정 5
 
1.7%
읍 5
 
1.7%
월 5
 
1.7%
산 5
 
1.7%
계 5
 
1.7%
흥 4
 
1.3%
중 4
 
1.3%
Other values (104) 159
53.4%
Common
ValueCountFrequency (%)
3 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 298
99.3%
ASCII 2
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 95
31.9%
신 6
 
2.0%
가 5
 
1.7%
정 5
 
1.7%
읍 5
 
1.7%
월 5
 
1.7%
산 5
 
1.7%
계 5
 
1.7%
흥 4
 
1.3%
중 4
 
1.3%
Other values (104) 159
53.4%
ASCII
ValueCountFrequency (%)
3 2
100.0%

road_nm_cd
Real number (ℝ)

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2924224 × 1011
Minimum1.1290301 × 1011
Maximum4.7190202 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:35.558435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1290301 × 1011
5-th percentile1.13488 × 1011
Q12.8183229 × 1011
median4.1161761 × 1011
Q34.1415276 × 1011
95-th percentile4.2110302 × 1011
Maximum4.7190202 × 1011
Range3.5899901 × 1011
Interquartile range (IQR)1.3232047 × 1011

Descriptive statistics

Standard deviation1.258238 × 1011
Coefficient of variation (CV)0.38216178
Kurtosis-0.85439869
Mean3.2924224 × 1011
Median Absolute Deviation (MAD)4.0855955 × 109
Skewness-0.95088862
Sum3.2924224 × 1013
Variance1.5831629 × 1022
MonotonicityNot monotonic
2023-12-10T18:43:35.852704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
412813000136 2
 
2.0%
117402000006 2
 
2.0%
113203005039 2
 
2.0%
282603156046 2
 
2.0%
414103012052 2
 
2.0%
415903012007 2
 
2.0%
114702000003 2
 
2.0%
413702012013 2
 
2.0%
411903000028 2
 
2.0%
412202012013 2
 
2.0%
Other values (79) 80
80.0%
ValueCountFrequency (%)
112903005036 1
1.0%
113053005041 1
1.0%
113053107018 1
1.0%
113203005039 2
2.0%
113503000001 1
1.0%
114702000003 2
2.0%
114703114001 1
1.0%
114703114003 1
1.0%
115003115001 1
1.0%
115003115008 1
1.0%
ValueCountFrequency (%)
471902018004 1
1.0%
442703000117 1
1.0%
441332250001 1
1.0%
441312249005 1
1.0%
421103218049 1
1.0%
421103013022 1
1.0%
416503000136 1
1.0%
416303215066 1
1.0%
416301000140 1
1.0%
415903210114 1
1.0%

fclty_road_nm_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:36.454549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length22.4
Min length16

Characters and Unicode

Total characters2240
Distinct characters182
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

Unique100 ?
Unique (%)100.0%

Sample

1st row서울특별시 강서구 양천로 546(등촌동)
2nd row인천광역시 서구 서곶로 289
3rd row서울특별시 양천구 목동서로 45(목동)
4th row서울특별시 영등포구 선유로 195(양평동3가)
5th row서울특별시 강서구 강서로 216(화곡동)
ValueCountFrequency (%)
경기도 58
 
13.5%
서울특별시 23
 
5.3%
인천광역시 13
 
3.0%
수원시 8
 
1.9%
용인시 6
 
1.4%
경수대로 5
 
1.2%
경기대로 5
 
1.2%
화성시 4
 
0.9%
고양시 4
 
0.9%
양천구 4
 
0.9%
Other values (240) 300
69.8%
2023-12-10T18:43:37.178020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
330
 
14.7%
동 112
 
5.0%
시 102
 
4.6%
로 101
 
4.5%
( 92
 
4.1%
) 92
 
4.1%
도 75
 
3.3%
경 74
 
3.3%
기 69
 
3.1%
구 69
 
3.1%
Other values (172) 1124
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1408
62.9%
Space Separator 330
 
14.7%
Decimal Number 316
 
14.1%
Open Punctuation 92
 
4.1%
Close Punctuation 92
 
4.1%
Dash Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 112
 
8.0%
시 102
 
7.2%
로 101
 
7.2%
도 75
 
5.3%
경 74
 
5.3%
기 69
 
4.9%
구 69
 
4.9%
서 41
 
2.9%
대 33
 
2.3%
천 33
 
2.3%
Other values (158) 699
49.6%
Decimal Number
ValueCountFrequency (%)
1 55
17.4%
6 34
10.8%
9 33
10.4%
2 32
10.1%
7 31
9.8%
5 31
9.8%
3 29
9.2%
4 28
8.9%
0 23
7.3%
8 20
 
6.3%
Space Separator
ValueCountFrequency (%)
330
100.0%
Open Punctuation
ValueCountFrequency (%)
( 92
100.0%
Close Punctuation
ValueCountFrequency (%)
) 92
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1408
62.9%
Common 832
37.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 112
 
8.0%
시 102
 
7.2%
로 101
 
7.2%
도 75
 
5.3%
경 74
 
5.3%
기 69
 
4.9%
구 69
 
4.9%
서 41
 
2.9%
대 33
 
2.3%
천 33
 
2.3%
Other values (158) 699
49.6%
Common
ValueCountFrequency (%)
330
39.7%
( 92
 
11.1%
) 92
 
11.1%
1 55
 
6.6%
6 34
 
4.1%
9 33
 
4.0%
2 32
 
3.8%
7 31
 
3.7%
5 31
 
3.7%
3 29
 
3.5%
Other values (4) 73
 
8.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1408
62.9%
ASCII 832
37.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
330
39.7%
( 92
 
11.1%
) 92
 
11.1%
1 55
 
6.6%
6 34
 
4.1%
9 33
 
4.0%
2 32
 
3.8%
7 31
 
3.7%
5 31
 
3.7%
3 29
 
3.5%
Other values (4) 73
 
8.8%
Hangul
ValueCountFrequency (%)
동 112
 
8.0%
시 102
 
7.2%
로 101
 
7.2%
도 75
 
5.3%
경 74
 
5.3%
기 69
 
4.9%
구 69
 
4.9%
서 41
 
2.9%
대 33
 
2.3%
천 33
 
2.3%
Other values (158) 699
49.6%

lnm_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:37.920257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length29
Mean length22.11
Min length16

Characters and Unicode

Total characters2211
Distinct characters182
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row서울특별시 강서구 등촌동 630
2nd row인천광역시 서구 심곡동 248-1
3rd row서울특별시 양천구 목동 908-25 맥도날드파리공원점
4th row서울특별시 영등포구 양평동3가 80-2 SK양평복합빌딩
5th row서울특별시 강서구 화곡동 1026 우장산 맥도날드
ValueCountFrequency (%)
경기도 58
 
12.0%
서울특별시 23
 
4.8%
맥도날드 22
 
4.5%
인천광역시 13
 
2.7%
수원시 8
 
1.7%
용인시 6
 
1.2%
의정부시 4
 
0.8%
화성시 4
 
0.8%
부천시 4
 
0.8%
고양시 4
 
0.8%
Other values (289) 338
69.8%
2023-12-10T18:43:38.940997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
384
 
17.4%
동 109
 
4.9%
시 103
 
4.7%
도 99
 
4.5%
- 82
 
3.7%
1 77
 
3.5%
구 66
 
3.0%
기 64
 
2.9%
2 60
 
2.7%
경 59
 
2.7%
Other values (172) 1108
50.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1320
59.7%
Decimal Number 407
 
18.4%
Space Separator 384
 
17.4%
Dash Punctuation 82
 
3.7%
Uppercase Letter 18
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 109
 
8.3%
시 103
 
7.8%
도 99
 
7.5%
구 66
 
5.0%
기 64
 
4.8%
경 59
 
4.5%
천 34
 
2.6%
서 32
 
2.4%
드 28
 
2.1%
맥 28
 
2.1%
Other values (155) 698
52.9%
Decimal Number
ValueCountFrequency (%)
1 77
18.9%
2 60
14.7%
5 50
12.3%
0 41
10.1%
3 39
9.6%
4 39
9.6%
7 32
7.9%
6 28
 
6.9%
8 22
 
5.4%
9 19
 
4.7%
Uppercase Letter
ValueCountFrequency (%)
T 7
38.9%
D 7
38.9%
S 2
 
11.1%
G 1
 
5.6%
K 1
 
5.6%
Space Separator
ValueCountFrequency (%)
384
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1320
59.7%
Common 873
39.5%
Latin 18
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 109
 
8.3%
시 103
 
7.8%
도 99
 
7.5%
구 66
 
5.0%
기 64
 
4.8%
경 59
 
4.5%
천 34
 
2.6%
서 32
 
2.4%
드 28
 
2.1%
맥 28
 
2.1%
Other values (155) 698
52.9%
Common
ValueCountFrequency (%)
384
44.0%
- 82
 
9.4%
1 77
 
8.8%
2 60
 
6.9%
5 50
 
5.7%
0 41
 
4.7%
3 39
 
4.5%
4 39
 
4.5%
7 32
 
3.7%
6 28
 
3.2%
Other values (2) 41
 
4.7%
Latin
ValueCountFrequency (%)
T 7
38.9%
D 7
38.9%
S 2
 
11.1%
G 1
 
5.6%
K 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1320
59.7%
ASCII 891
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
384
43.1%
- 82
 
9.2%
1 77
 
8.6%
2 60
 
6.7%
5 50
 
5.6%
0 41
 
4.6%
3 39
 
4.4%
4 39
 
4.4%
7 32
 
3.6%
6 28
 
3.1%
Other values (7) 59
 
6.6%
Hangul
ValueCountFrequency (%)
동 109
 
8.3%
시 103
 
7.8%
도 99
 
7.5%
구 66
 
5.0%
기 64
 
4.8%
경 59
 
4.5%
천 34
 
2.6%
서 32
 
2.4%
드 28
 
2.1%
맥 28
 
2.1%
Other values (155) 698
52.9%

addr_eng_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:43:39.504114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length52
Mean length43.61
Min length31

Characters and Unicode

Total characters4361
Distinct characters52
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row546, Yangcheon-ro, Gangseo-gu, Seoul
2nd row289, Seogot-ro, Seo-gu, Incheon
3rd row45, Mokdongseo-ro, Yangcheon-gu, Seoul
4th row195, Seonyu-ro, Yeongdeungpo-gu, Seoul
5th row216, Gangseo-ro, Gangseo-gu, Seoul
ValueCountFrequency (%)
gyeonggi-do 58
 
13.3%
seoul 23
 
5.3%
incheon 13
 
3.0%
suwon-si 8
 
1.8%
yongin-si 6
 
1.4%
gyeongsu-daero 5
 
1.1%
gyeonggi-daero 5
 
1.1%
giheung-gu 4
 
0.9%
hoguk-ro 4
 
0.9%
goyang-si 4
 
0.9%
Other values (242) 306
70.2%
2023-12-10T18:43:40.564130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 473
 
10.8%
g 373
 
8.6%
n 366
 
8.4%
336
 
7.7%
, 330
 
7.6%
- 296
 
6.8%
e 268
 
6.1%
u 187
 
4.3%
a 166
 
3.8%
i 165
 
3.8%
Other values (42) 1401
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2753
63.1%
Space Separator 336
 
7.7%
Uppercase Letter 332
 
7.6%
Other Punctuation 330
 
7.6%
Decimal Number 314
 
7.2%
Dash Punctuation 296
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 473
17.2%
g 373
13.5%
n 366
13.3%
e 268
9.7%
u 187
 
6.8%
a 166
 
6.0%
i 165
 
6.0%
d 117
 
4.2%
y 112
 
4.1%
s 108
 
3.9%
Other values (12) 418
15.2%
Uppercase Letter
ValueCountFrequency (%)
G 119
35.8%
S 57
17.2%
Y 25
 
7.5%
D 19
 
5.7%
I 18
 
5.4%
H 16
 
4.8%
B 15
 
4.5%
J 12
 
3.6%
C 10
 
3.0%
N 10
 
3.0%
Other values (7) 31
 
9.3%
Decimal Number
ValueCountFrequency (%)
1 55
17.5%
6 34
10.8%
9 33
10.5%
2 32
10.2%
7 31
9.9%
5 31
9.9%
4 28
8.9%
3 27
8.6%
0 23
7.3%
8 20
 
6.4%
Space Separator
ValueCountFrequency (%)
336
100.0%
Other Punctuation
ValueCountFrequency (%)
, 330
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 296
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 3085
70.7%
Common 1276
29.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 473
15.3%
g 373
12.1%
n 366
11.9%
e 268
 
8.7%
u 187
 
6.1%
a 166
 
5.4%
i 165
 
5.3%
G 119
 
3.9%
d 117
 
3.8%
y 112
 
3.6%
Other values (29) 739
24.0%
Common
ValueCountFrequency (%)
336
26.3%
, 330
25.9%
- 296
23.2%
1 55
 
4.3%
6 34
 
2.7%
9 33
 
2.6%
2 32
 
2.5%
7 31
 
2.4%
5 31
 
2.4%
4 28
 
2.2%
Other values (3) 70
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4361
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 473
 
10.8%
g 373
 
8.6%
n 366
 
8.4%
336
 
7.7%
, 330
 
7.6%
- 296
 
6.8%
e 268
 
6.1%
u 187
 
4.3%
a 166
 
3.8%
i 165
 
3.8%
Other values (42) 1401
32.1%

adstrd_cd
Real number (ℝ)

Distinct98
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2924082 × 109
Minimum1.1290135 × 109
Maximum4.719066 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:40.939930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1290135 × 109
5-th percentile1.1348605 × 109
Q12.8183102 × 109
median4.1161606 × 109
Q34.1415104 × 109
95-th percentile4.211012 × 109
Maximum4.719066 × 109
Range3.5900525 × 109
Interquartile range (IQR)1.3232002 × 109

Descriptive statistics

Standard deviation1.2582397 × 109
Coefficient of variation (CV)0.38216395
Kurtosis-0.85439782
Mean3.2924082 × 109
Median Absolute Deviation (MAD)40857362
Skewness-0.95088707
Sum3.2924082 × 1011
Variance1.5831672 × 1018
MonotonicityNot monotonic
2023-12-10T18:43:41.244910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2820010100 2
 
2.0%
1147010300 2
 
2.0%
1150010200 1
 
1.0%
4136025321 1
 
1.0%
4111514100 1
 
1.0%
4146311300 1
 
1.0%
4111514000 1
 
1.0%
4111710300 1
 
1.0%
4111113500 1
 
1.0%
4111113000 1
 
1.0%
Other values (88) 88
88.0%
ValueCountFrequency (%)
1129013500 1
1.0%
1130510100 1
1.0%
1130510200 1
1.0%
1132010500 1
1.0%
1132010800 1
1.0%
1135010500 1
1.0%
1147010100 1
1.0%
1147010200 1
1.0%
1147010300 2
2.0%
1150010200 1
1.0%
ValueCountFrequency (%)
4719066000 1
1.0%
4427010100 1
1.0%
4413310400 1
1.0%
4413110700 1
1.0%
4211012300 1
1.0%
4211012000 1
1.0%
4165010500 1
1.0%
4163034027 1
1.0%
4163011600 1
1.0%
4159025940 1
1.0%

buld_nm
Text

MISSING 

Distinct31
Distinct (%)77.5%
Missing60
Missing (%)60.0%
Memory size932.0 B
2023-12-10T18:43:41.648926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.325
Min length4

Characters and Unicode

Total characters293
Distinct characters80
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)72.5%

Sample

1st row롯데리아
2nd row맥도날드파리공원점
3rd rowSK양평복합빌딩
4th row우장산 맥도날드
5th row맥도널드신정점
ValueCountFrequency (%)
맥도날드 22
39.3%
한국맥도날드 2
 
3.6%
한국맥도날드유한회사 2
 
3.6%
남부주유소 2
 
3.6%
우장산 1
 
1.8%
맥도널드신정점 1
 
1.8%
연희dt점 1
 
1.8%
천안두정점 1
 
1.8%
동두천 1
 
1.8%
동문주유소 1
 
1.8%
Other values (22) 22
39.3%
2023-12-10T18:43:42.337972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 30
 
10.2%
드 29
 
9.9%
맥 28
 
9.6%
날 27
 
9.2%
16
 
5.5%
유 10
 
3.4%
점 9
 
3.1%
주 9
 
3.1%
소 8
 
2.7%
T 7
 
2.4%
Other values (70) 120
41.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 259
88.4%
Uppercase Letter 18
 
6.1%
Space Separator 16
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 30
 
11.6%
드 29
 
11.2%
맥 28
 
10.8%
날 27
 
10.4%
유 10
 
3.9%
점 9
 
3.5%
주 9
 
3.5%
소 8
 
3.1%
한 6
 
2.3%
국 5
 
1.9%
Other values (64) 98
37.8%
Uppercase Letter
ValueCountFrequency (%)
T 7
38.9%
D 7
38.9%
S 2
 
11.1%
G 1
 
5.6%
K 1
 
5.6%
Space Separator
ValueCountFrequency (%)
16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 259
88.4%
Latin 18
 
6.1%
Common 16
 
5.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 30
 
11.6%
드 29
 
11.2%
맥 28
 
10.8%
날 27
 
10.4%
유 10
 
3.9%
점 9
 
3.5%
주 9
 
3.5%
소 8
 
3.1%
한 6
 
2.3%
국 5
 
1.9%
Other values (64) 98
37.8%
Latin
ValueCountFrequency (%)
T 7
38.9%
D 7
38.9%
S 2
 
11.1%
G 1
 
5.6%
K 1
 
5.6%
Common
ValueCountFrequency (%)
16
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 259
88.4%
ASCII 34
 
11.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 30
 
11.6%
드 29
 
11.2%
맥 28
 
10.8%
날 27
 
10.4%
유 10
 
3.9%
점 9
 
3.5%
주 9
 
3.5%
소 8
 
3.1%
한 6
 
2.3%
국 5
 
1.9%
Other values (64) 98
37.8%
ASCII
ValueCountFrequency (%)
16
47.1%
T 7
20.6%
D 7
20.6%
S 2
 
5.9%
G 1
 
2.9%
K 1
 
2.9%

buld_manage_cd
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2924277 × 1024
Minimum1.1290135 × 1024
Maximum4.7190118 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:42.625778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1290135 × 1024
5-th percentile1.1348605 × 1024
Q12.8181353 × 1024
median4.1161606 × 1024
Q34.1415104 × 1024
95-th percentile4.211012 × 1024
Maximum4.7190118 × 1024
Range3.5899983 × 1024
Interquartile range (IQR)1.3233751 × 1024

Descriptive statistics

Standard deviation1.2582602 × 1024
Coefficient of variation (CV)0.3821679
Kurtosis-0.85445665
Mean3.2924277 × 1024
Median Absolute Deviation (MAD)4.0857362 × 1022
Skewness-0.95086334
Sum3.2924277 × 1026
Variance1.5832187 × 1048
MonotonicityNot monotonic
2023-12-10T18:43:42.923401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.1500102001063e+24 1
 
1.0%
2.81851060010008e+24 1
 
1.0%
4.1115141001036306e+24 1
 
1.0%
4.14631130010552e+24 1
 
1.0%
4.1115140001049e+24 1
 
1.0%
4.11171030010422e+24 1
 
1.0%
4.1111135001045105e+24 1
 
1.0%
4.11111300010076e+24 1
 
1.0%
4.1465101001004305e+24 1
 
1.0%
4.1630116001049506e+24 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1.1290135001001e+24 1
1.0%
1.13051010010682e+24 1
1.0%
1.13051020010106e+24 1
1.0%
1.132010500107e+24 1
1.0%
1.1320108001062e+24 1
1.0%
1.13501050011022e+24 1
1.0%
1.14701010010323e+24 1
1.0%
1.14701020010908e+24 1
1.0%
1.14701030010199e+24 1
1.0%
1.14701030010525e+24 1
1.0%
ValueCountFrequency (%)
4.71901180010742e+24 1
1.0%
4.42701010010423e+24 1
1.0%
4.4133104001062e+24 1
1.0%
4.41311070010442e+24 1
1.0%
4.21101230010418e+24 1
1.0%
4.21101200010702e+24 1
1.0%
4.1650105001001297e+24 1
1.0%
4.1630340271025e+24 1
1.0%
4.1630116001049506e+24 1
1.0%
4.1590259401029695e+24 1
1.0%

tel_no
Real number (ℝ)

Distinct99
Distinct (%)100.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean6.8978632 × 109
Minimum3.256117 × 108
Maximum7.0720916 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:43.222064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.256117 × 108
5-th percentile7.0701705 × 109
Q17.0701719 × 109
median7.0720905 × 109
Q37.0720906 × 109
95-th percentile7.0720915 × 109
Maximum7.0720916 × 109
Range6.7464799 × 109
Interquartile range (IQR)1918656

Descriptive statistics

Standard deviation1.0114936 × 109
Coefficient of variation (CV)0.14663869
Kurtosis35.107589
Mean6.8978632 × 109
Median Absolute Deviation (MAD)1110
Skewness-5.9496424
Sum6.8288845 × 1011
Variance1.0231193 × 1018
MonotonicityNot monotonic
2023-12-10T18:43:43.494240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7070176526 1
 
1.0%
7072090962 1
 
1.0%
7072090042 1
 
1.0%
7070171447 1
 
1.0%
7070170670 1
 
1.0%
7070176491 1
 
1.0%
7070171416 1
 
1.0%
7072090532 1
 
1.0%
7070176474 1
 
1.0%
7070172427 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
325611695 1
1.0%
544640033 1
1.0%
3180033200 1
1.0%
7070170301 1
1.0%
7070170462 1
1.0%
7070170493 1
1.0%
7070170522 1
1.0%
7070170532 1
1.0%
7070170536 1
1.0%
7070170560 1
1.0%
ValueCountFrequency (%)
7072091632 1
1.0%
7072091618 1
1.0%
7072091599 1
1.0%
7072091595 1
1.0%
7072091555 1
1.0%
7072091549 1
1.0%
7072091524 1
1.0%
7072091495 1
1.0%
7072091068 1
1.0%
7072091062 1
1.0%

zip_no
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14409.94
Minimum1201
Maximum39362
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:43.830880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1201
5-th percentile2741.55
Q110119.5
median14611.5
Q317928.25
95-th percentile24306.35
Maximum39362
Range38161
Interquartile range (IQR)7808.75

Descriptive statistics

Standard deviation6948.3597
Coefficient of variation (CV)0.48219213
Kurtosis1.1490303
Mean14409.94
Median Absolute Deviation (MAD)3904
Skewness0.53621949
Sum1440994
Variance48279702
MonotonicityNot monotonic
2023-12-10T18:43:44.480281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7550 1
 
1.0%
21998 1
 
1.0%
16487 1
 
1.0%
16908 1
 
1.0%
16235 1
 
1.0%
16229 1
 
1.0%
16303 1
 
1.0%
16314 1
 
1.0%
16831 1
 
1.0%
11440 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
1201 1
1.0%
1231 1
1.0%
1349 1
1.0%
1437 1
1.0%
1631 1
1.0%
2800 1
1.0%
5307 1
1.0%
5372 1
1.0%
5573 1
1.0%
5784 1
1.0%
ValueCountFrequency (%)
39362 1
1.0%
31769 1
1.0%
31126 1
1.0%
31099 1
1.0%
24446 1
1.0%
24299 1
1.0%
22730 1
1.0%
22727 1
1.0%
22333 1
1.0%
22235 1
1.0%

hmpg_url
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

fclty_la
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.441792
Minimum36.070761
Maximum37.892165
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:44.768633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.070761
5-th percentile37.004966
Q137.300666
median37.485083
Q337.603437
95-th percentile37.760089
Maximum37.892165
Range1.8214042
Interquartile range (IQR)0.30277085

Descriptive statistics

Standard deviation0.25808503
Coefficient of variation (CV)0.0068929668
Kurtosis7.2958502
Mean37.441792
Median Absolute Deviation (MAD)0.15586198
Skewness-1.778437
Sum3744.1792
Variance0.066607883
MonotonicityNot monotonic
2023-12-10T18:43:45.200098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.5582961015697 1
 
1.0%
37.3853440537704 1
 
1.0%
37.2706790614092 1
 
1.0%
37.3025739054371 1
 
1.0%
37.2856915084451 1
 
1.0%
37.294941344411 1
 
1.0%
37.3052002760815 1
 
1.0%
37.2903307533159 1
 
1.0%
37.3303521259748 1
 
1.0%
37.8248129262252 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
36.0707609 1
1.0%
36.8105986332762 1
1.0%
36.8256144825341 1
1.0%
36.8973082257926 1
1.0%
36.9962719997358 1
1.0%
37.0054235094194 1
1.0%
37.0697174148586 1
1.0%
37.1218109698539 1
1.0%
37.1399444725336 1
1.0%
37.1785643751881 1
1.0%
ValueCountFrequency (%)
37.8921651397291 1
1.0%
37.8808807060345 1
1.0%
37.8591591597452 1
1.0%
37.8457965690017 1
1.0%
37.8248129262252 1
1.0%
37.7566820927745 1
1.0%
37.7554924377846 1
1.0%
37.7309572679961 1
1.0%
37.7219886936366 1
1.0%
37.7201209561645 1
1.0%

fclty_lo
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.98716
Minimum126.62699
Maximum128.34884
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:43:45.540145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.62699
5-th percentile126.67601
Q1126.83408
median127.02387
Q3127.08412
95-th percentile127.2382
Maximum128.34884
Range1.721857
Interquartile range (IQR)0.2500439

Descriptive statistics

Standard deviation0.24486087
Coefficient of variation (CV)0.0019282332
Kurtosis9.7801469
Mean126.98716
Median Absolute Deviation (MAD)0.1195006
Skewness2.1462947
Sum12698.716
Variance0.059956847
MonotonicityNot monotonic
2023-12-10T18:43:45.959517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.859767021184 1
 
1.0%
126.644851862481 1
 
1.0%
127.03496560759 1
 
1.0%
127.106588324208 1
 
1.0%
127.028044530161 1
 
1.0%
127.048290514334 1
 
1.0%
127.002725971136 1
 
1.0%
127.001579189 1
 
1.0%
127.10221921208 1
 
1.0%
127.050837907394 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.626987615157 1
1.0%
126.634494059237 1
1.0%
126.644851862481 1
1.0%
126.654224530201 1
1.0%
126.670672724569 1
1.0%
126.676295798168 1
1.0%
126.6764993 1
1.0%
126.677061122042 1
1.0%
126.688907442366 1
1.0%
126.693832043782 1
1.0%
ValueCountFrequency (%)
128.3488446 1
1.0%
127.749485364148 1
1.0%
127.731061560382 1
1.0%
127.446507255101 1
1.0%
127.302343114984 1
1.0%
127.234818992662 1
1.0%
127.214970781947 1
1.0%
127.189729607885 1
1.0%
127.174561834976 1
1.0%
127.164303728796 1
1.0%

origin_nm
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문화정보원 100
100.0%

Length

2023-12-10T18:43:46.200660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:46.418088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화정보원 100
100.0%

adit_dc
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

updt_dt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210903145959
100 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210903145959
2nd row20210903145959
3rd row20210903145959
4th row20210903145959
5th row20210903145959

Common Values

ValueCountFrequency (%)
20210903145959 100
100.0%

Length

2023-12-10T18:43:46.662190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:46.832799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210903145959 100
100.0%

regist_dt
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20210903145959
97 
20201201120000
 
3

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20210903145959
2nd row20201201120000
3rd row20210903145959
4th row20210903145959
5th row20210903145959

Common Values

ValueCountFrequency (%)
20210903145959 97
97.0%
20201201120000 3
 
3.0%

Length

2023-12-10T18:43:47.037319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:43:47.206945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20210903145959 97
97.0%
20201201120000 3
 
3.0%

Sample

esntl_idlclas_nmmlsfc_nmfclty_nmbhf_nmuse_time_cnctprvn_cdctprvn_nmsigngu_cdsigngu_nmlegaldong_cdlegaldong_nmroad_nm_cdfclty_road_nm_addrlnm_addraddr_eng_nmadstrd_cdbuld_nmbuld_manage_cdtel_nozip_nohmpg_urlfclty_lafclty_loorigin_nmadit_dcupdt_dtregist_dt
0KCDTDAL21N000000001편의시설편의시설_DT맥도날드등촌DT점매일 00:00 ~ 24:0011서울특별시11500강서구1150010200등촌동115003115008서울특별시 강서구 양천로 546(등촌동)서울특별시 강서구 등촌동 630546, Yangcheon-ro, Gangseo-gu, Seoul1150010200<NA>115001020010630000002890570701765267550<NA>37.558296126.859767문화정보원<NA>2021090314595920210903145959
1KCKDDPO20N000008661편의시설편의시설_DT롯데리아인천연희D/T오전 00:00 ~ 오후 24:0028인천광역시28260서구2826010700심곡동282603156046인천광역시 서구 서곶로 289인천광역시 서구 심곡동 248-1289, Seogot-ro, Seo-gu, Incheon2826053000롯데리아282601070010248000101234732561169522727<NA>37.543333126.676499문화정보원<NA>2021090314595920201201120000
2KCDTDAL21N000000003편의시설편의시설_DT맥도날드파리공원점매일 07:00 ~ 24:0011서울특별시11470양천구1147010200목동114703114003서울특별시 양천구 목동서로 45(목동)서울특별시 양천구 목동 908-25 맥도날드파리공원점45, Mokdongseo-ro, Yangcheon-gu, Seoul1147010200맥도날드파리공원점114701020010908002501635170701705227984<NA>37.537195126.881018문화정보원<NA>2021090314595920210903145959
3KCDTDAL21N000000004편의시설편의시설_DT맥도날드양평SK점매일 00:00 ~ 24:0011서울특별시11560영등포구1156012700양평동3가115603005074서울특별시 영등포구 선유로 195(양평동3가)서울특별시 영등포구 양평동3가 80-2 SK양평복합빌딩195, Seonyu-ro, Yeongdeungpo-gu, Seoul1156012700SK양평복합빌딩115601270010080000202003670701765057211<NA>37.531048126.893507문화정보원<NA>2021090314595920210903145959
4KCDTDAL21N000000005편의시설편의시설_DT맥도날드우장산DT점매일 08:00 ~ 24:0011서울특별시11500강서구1150010300화곡동115003115001서울특별시 강서구 강서로 216(화곡동)서울특별시 강서구 화곡동 1026 우장산 맥도날드216, Gangseo-ro, Gangseo-gu, Seoul1150010300우장산 맥도날드115001030011026000002942470720905427698<NA>37.545103126.83743문화정보원<NA>2021090314595920210903145959
5KCDTDAL21N000000006편의시설편의시설_DT맥도날드양천구청DT점매일 06:30 ~ 01:0011서울특별시11470양천구1147010100신정동114703114001서울특별시 양천구 목동동로 71(신정동)서울특별시 양천구 신정동 323-20 맥도널드신정점71, Mokdongdong-ro, Yangcheon-gu, Seoul1147010100맥도널드신정점114701010010323002000052970701705368093<NA>37.515514126.862973문화정보원<NA>2021090314595920210903145959
6KCDTDAL21N000000007편의시설편의시설_DT맥도날드신월DT점매일 00:00 ~ 24:0011서울특별시11470양천구1147010300신월동114702000003서울특별시 양천구 남부순환로 404(신월동)서울특별시 양천구 신월동 199-3 한국맥도날드 신월점404, Nambusunhwan-ro, Yangcheon-gu, Seoul1147010300한국맥도날드 신월점114701030010199000301249870701768537915<NA>37.531423126.830878문화정보원<NA>2021090314595920210903145959
7KCKDDPO20N000008662편의시설편의시설_DT롯데리아구미오태D/T오전 09:00 ~ 오후 23:0047경상북도47190구미시4719011800오태동471902018004경상북도 구미시 금오대로 292경상북도 구미시 오태동 742-14292, Geumo-daero, Gumi-si, Gyeongsangbuk-do4719066000<NA>471901180010742001404418554464003339362<NA>36.070761128.348845문화정보원<NA>2021090314595920201201120000
8KCDTDAL21N000000009편의시설편의시설_DT맥도날드신월남부DT점매일 07:00 ~ 24:0011서울특별시11470양천구1147010300신월동114702000003서울특별시 양천구 남부순환로 553(신월동)서울특별시 양천구 신월동 525-1 남부주유소553, Nambusunhwan-ro, Yangcheon-gu, Seoul1147010300남부주유소114701030010525000101098470701769178032<NA>37.519729126.838723문화정보원<NA>2021090314595920210903145959
9KCDTDAL21N000000010편의시설편의시설_DT맥도날드고척DT점매일 07:00 ~ 02:0011서울특별시11530구로구1153010600고척동115303000028서울특별시 구로구 경인로 393(고척동)서울특별시 구로구 고척동 73-20 맥도날드 고척DT393, Gyeongin-ro, Guro-gu, Seoul1153010600맥도날드 고척DT115301060010073002000000170720905588227<NA>37.497239126.863514문화정보원<NA>2021090314595920210903145959
esntl_idlclas_nmmlsfc_nmfclty_nmbhf_nmuse_time_cnctprvn_cdctprvn_nmsigngu_cdsigngu_nmlegaldong_cdlegaldong_nmroad_nm_cdfclty_road_nm_addrlnm_addraddr_eng_nmadstrd_cdbuld_nmbuld_manage_cdtel_nozip_nohmpg_urlfclty_lafclty_loorigin_nmadit_dcupdt_dtregist_dt
90KCDTDAL21N000000091편의시설편의시설_DT맥도날드화성향남DT점매일 00:00 ~ 24:0041경기도41590화성시4159025940향남읍415903210114경기도 화성시 향남읍 행정남로 120경기도 화성시 향남읍 방축리 297-75120, Haengjeongnam-ro, Hyangnam-eup, Hwaseong-si, Gyeonggi-do4159025940<NA>4159025940102970075009500707209066018608<NA>37.121811126.918735문화정보원<NA>2021090314595920210903145959
91KCDTDAL21N000000092편의시설편의시설_DT맥도날드이천DT점매일 00:00 ~ 24:0041경기도41500이천시4150010300중리동415002012002경기도 이천시 경충대로 2578(중리동)경기도 이천시 중리동 461-32578, Gyeongchung-daero, Icheon-si, Gyeonggi-do4150010300<NA>4150010300104610003000001707209053817377<NA>37.275161127.446507문화정보원<NA>2021090314595920210903145959
92KCDTDAL21N000000093편의시설편의시설_DT맥도날드평택서정DT점매일 07:00 ~ 01:0041경기도41220평택시4122010100서정동412202012013경기도 평택시 경기대로 1395(서정동)경기도 평택시 서정동 779-51395, Gyeonggi-daero, Pyeongtaek-si, Gyeonggi-do4122010100<NA>4122010100107790005006224707209149517773<NA>37.069717127.063972문화정보원<NA>2021090314595920210903145959
93KCDTDAL21N000000094편의시설편의시설_DT맥도날드평택세교DT점매일 00:00 ~ 24:0041경기도41220평택시4122012000세교동412202012013경기도 평택시 경기대로 570(세교동)경기도 평택시 세교동 277-10570, Gyeonggi-daero, Pyeongtaek-si, Gyeonggi-do4122012000<NA>4122012000102770010013251707017030117842<NA>37.005424127.084471문화정보원<NA>2021090314595920210903145959
94KCDTDAL21N000000095편의시설편의시설_DT맥도날드평택GS매일 00:00 ~ 24:0041경기도41220평택시4122012400용이동412202012006경기도 평택시 서동대로 3929(용이동)경기도 평택시 용이동 470-53929, Seodong-daero, Pyeongtaek-si, Gyeonggi-do4122012400<NA>4122012400104700005213781707017649517867<NA>36.996272127.142774문화정보원<NA>2021090314595920210903145959
95KCDTDAL21N000000096편의시설편의시설_DT맥도날드춘천퇴계DT점매일 00:00 ~ 24:0042강원도42110춘천시4211012300퇴계동421103013022강원도 춘천시 영서로 2173(퇴계동)강원도 춘천시 퇴계동 418-202173, Yeongseo-ro, Chuncheon-si, Gangwon-do4211012300<NA>4211012300104180020000001707209106224446<NA>37.859159127.731062문화정보원<NA>2021090314595920210903145959
96KCDTDAL21N000000097편의시설편의시설_DT맥도날드충남당진DT점매일 00:00 ~ 24:0044충청남도44270당진시4427010100읍내동442703000117충청남도 당진시 서해로 5754(읍내동)충청남도 당진시 읍내동 423-25754, Seohae-ro, Dangjin-si, Chungcheongnam-do4427010100<NA>4427010100104230002000001707209061931769<NA>36.897308126.626988문화정보원<NA>2021090314595920210903145959
97KCDTDAL21N000000098편의시설편의시설_DT맥도날드춘천후평DT점매일 00:00 ~ 24:0042강원도42110춘천시4211012000후평동421103218049강원도 춘천시 후석로 334(후평동)강원도 춘천시 후평동 702-5334, Huseok-ro, Chuncheon-si, Gangwon-do4211012000<NA>4211012000107020005031117707209066224299<NA>37.880881127.749485문화정보원<NA>2021090314595920210903145959
98KCDTDAL21N000000099편의시설편의시설_DT맥도날드천안두정DT점매일 00:00 ~ 24:0044충청남도44133천안시 서북구4413310400두정동441332250001충청남도 천안시 서북구 동서대로 49(두정동)충청남도 천안시 서북구 두정동 620 맥도날드 천안두정점49, Dongseo-daero, Seobuk-gu, Cheonan-si, Chungcheongnam-do4413310400맥도날드 천안두정점4413310400106200000000001707017058531099<NA>36.825614127.128648문화정보원<NA>2021090314595920210903145959
99KCDTDAL21N000000100편의시설편의시설_DT맥도날드천안원성DT점매일 00:00 ~ 24:0044충청남도44131천안시 동남구4413110700원성동441312249005충청남도 천안시 동남구 천안대로 699(원성동)충청남도 천안시 동남구 원성동 442-1 맥도날드 천안원성점699, Cheonan-daero, Dongnam-gu, Cheonan-si, Chungcheongnam-do4413110700맥도날드 천안원성점4413110700104420001015999707017699331126<NA>36.810599127.164304문화정보원<NA>2021090314595920210903145959