Overview

Dataset statistics

Number of variables22
Number of observations100
Missing cells185
Missing cells (%)8.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory18.4 KiB
Average record size in memory188.3 B

Variable types

Text6
Categorical8
Numeric7
Unsupported1

Alerts

lclas has constant value ""Constant
file_name has constant value ""Constant
base_ymd has constant value ""Constant
mlsfc is highly imbalanced (80.6%)Imbalance
ctprvn_nm is highly imbalanced (80.6%)Imbalance
x_cd is highly imbalanced (60.5%)Imbalance
y_cd is highly imbalanced (86.0%)Imbalance
near_parking_lot_cnt has 42 (42.0%) missing valuesMissing
sum_prklt_capa has 42 (42.0%) missing valuesMissing
fyer_demand_forecast has 100 (100.0%) missing valuesMissing
id has unique valuesUnique
fclt_name has unique valuesUnique
rdnm_addr has unique valuesUnique
fyer_demand_forecast is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:51:52.936829
Analysis finished2023-12-10 09:51:53.764026
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

id
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:54.044614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters17
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 rowKCCVPIF21N000000002
2nd rowKCCVPIF21N000003119
3rd rowKCCVPIF21N000000004
4th rowKCCVPIF21N000000005
5th rowKCCVPIF21N000000006
ValueCountFrequency (%)
kccvpif21n000000002 1
 
1.0%
kccvpif21n000000064 1
 
1.0%
kccvpif21n000000075 1
 
1.0%
kccvpif21n000000074 1
 
1.0%
kccvpif21n000000073 1
 
1.0%
kccvpif21n000000072 1
 
1.0%
kccvpif21n000000071 1
 
1.0%
kccvpif21n000000070 1
 
1.0%
kccvpif21n000000069 1
 
1.0%
kccvpif21n000000068 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:51:54.678400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 711
37.4%
C 200
 
10.5%
1 127
 
6.7%
2 122
 
6.4%
K 100
 
5.3%
N 100
 
5.3%
F 100
 
5.3%
I 100
 
5.3%
P 100
 
5.3%
V 100
 
5.3%
Other values (7) 140
 
7.4%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 711
64.6%
1 127
 
11.5%
2 122
 
11.1%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
7 20
 
1.8%
8 20
 
1.8%
3 20
 
1.8%
9 20
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
C 200
25.0%
K 100
12.5%
N 100
12.5%
F 100
12.5%
I 100
12.5%
P 100
12.5%
V 100
12.5%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 711
64.6%
1 127
 
11.5%
2 122
 
11.1%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
7 20
 
1.8%
8 20
 
1.8%
3 20
 
1.8%
9 20
 
1.8%
Latin
ValueCountFrequency (%)
C 200
25.0%
K 100
12.5%
N 100
12.5%
F 100
12.5%
I 100
12.5%
P 100
12.5%
V 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 711
37.4%
C 200
 
10.5%
1 127
 
6.7%
2 122
 
6.4%
K 100
 
5.3%
N 100
 
5.3%
F 100
 
5.3%
I 100
 
5.3%
P 100
 
5.3%
V 100
 
5.3%
Other values (7) 140
 
7.4%

lclas
Categorical

CONSTANT 

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

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문화시설 100
100.0%

Length

2023-12-10T18:51:55.127547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:55.340638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화시설 100
100.0%

mlsfc
Categorical

IMBALANCE 

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

Length

Max length5
Median length3
Mean length3.06
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row박물관
2nd row공공도서관
3rd row박물관
4th row박물관
5th row박물관

Common Values

ValueCountFrequency (%)
박물관 97
97.0%
공공도서관 3
 
3.0%

Length

2023-12-10T18:51:55.552740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:55.728727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
박물관 97
97.0%
공공도서관 3
 
3.0%

fclt_name
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:56.058101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length13
Mean length7.97
Min length4

Characters and Unicode

Total characters797
Distinct characters211
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
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 row국립중앙박물관
2nd row김해지혜의바다
3rd row대한민국역사박물관
4th row국립한글박물관
5th row국립국악원 국악박물관
ValueCountFrequency (%)
국립중앙박물관 1
 
0.8%
국립관세박물관 1
 
0.8%
전기박물관 1
 
0.8%
인도박물관 1
 
0.8%
이화박물관 1
 
0.8%
이한열기념관 1
 
0.8%
유금와당박물관 1
 
0.8%
우석뮤지엄 1
 
0.8%
은행사박물관 1
 
0.8%
우리은행 1
 
0.8%
Other values (110) 110
91.7%
2023-12-10T18:51:56.752863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 98
 
12.3%
박 75
 
9.4%
물 75
 
9.4%
국 21
 
2.6%
20
 
2.5%
한 19
 
2.4%
사 18
 
2.3%
기 15
 
1.9%
서 14
 
1.8%
념 11
 
1.4%
Other values (201) 431
54.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 759
95.2%
Space Separator 20
 
2.5%
Uppercase Letter 9
 
1.1%
Open Punctuation 4
 
0.5%
Close Punctuation 4
 
0.5%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 98
 
12.9%
박 75
 
9.9%
물 75
 
9.9%
국 21
 
2.8%
한 19
 
2.5%
사 18
 
2.4%
기 15
 
2.0%
서 14
 
1.8%
념 11
 
1.4%
문 9
 
1.2%
Other values (190) 404
53.2%
Uppercase Letter
ValueCountFrequency (%)
S 2
22.2%
E 2
22.2%
R 1
11.1%
U 1
11.1%
M 1
11.1%
P 1
11.1%
W 1
11.1%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Punctuation
ValueCountFrequency (%)
· 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 759
95.2%
Common 29
 
3.6%
Latin 9
 
1.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 98
 
12.9%
박 75
 
9.9%
물 75
 
9.9%
국 21
 
2.8%
한 19
 
2.5%
사 18
 
2.4%
기 15
 
2.0%
서 14
 
1.8%
념 11
 
1.4%
문 9
 
1.2%
Other values (190) 404
53.2%
Latin
ValueCountFrequency (%)
S 2
22.2%
E 2
22.2%
R 1
11.1%
U 1
11.1%
M 1
11.1%
P 1
11.1%
W 1
11.1%
Common
ValueCountFrequency (%)
20
69.0%
( 4
 
13.8%
) 4
 
13.8%
· 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 759
95.2%
ASCII 37
 
4.6%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 98
 
12.9%
박 75
 
9.9%
물 75
 
9.9%
국 21
 
2.8%
한 19
 
2.5%
사 18
 
2.4%
기 15
 
2.0%
서 14
 
1.8%
념 11
 
1.4%
문 9
 
1.2%
Other values (190) 404
53.2%
ASCII
ValueCountFrequency (%)
20
54.1%
( 4
 
10.8%
) 4
 
10.8%
S 2
 
5.4%
E 2
 
5.4%
R 1
 
2.7%
U 1
 
2.7%
M 1
 
2.7%
P 1
 
2.7%
W 1
 
2.7%
None
ValueCountFrequency (%)
· 1
100.0%

ctprvn_nm
Categorical

IMBALANCE 

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

Length

Max length5
Median length5
Mean length4.97
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 97
97.0%
경상남도 3
 
3.0%

Length

2023-12-10T18:51:57.015124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:51:57.192437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 97
97.0%
경상남도 3
 
3.0%

sgnr_nm
Categorical

Distinct23
Distinct (%)23.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
29 
중구
16 
강남구
8 
용산구
5 
송파구
5 
Other values (18)
37 

Length

Max length4
Median length3
Mean length2.91
Min length2

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row용산구
2nd row김해시
3rd row종로구
4th row용산구
5th row서초구

Common Values

ValueCountFrequency (%)
종로구 29
29.0%
중구 16
16.0%
강남구 8
 
8.0%
용산구 5
 
5.0%
송파구 5
 
5.0%
성북구 5
 
5.0%
서대문구 4
 
4.0%
마포구 4
 
4.0%
서초구 4
 
4.0%
강동구 2
 
2.0%
Other values (13) 18
18.0%

Length

2023-12-10T18:51:57.443918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 29
29.0%
중구 16
16.0%
강남구 8
 
8.0%
용산구 5
 
5.0%
송파구 5
 
5.0%
성북구 5
 
5.0%
서대문구 4
 
4.0%
마포구 4
 
4.0%
서초구 4
 
4.0%
성동구 2
 
2.0%
Other values (13) 18
18.0%

legaldong_cd
Real number (ℝ)

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2416529 × 109
Minimum1.1110119 × 109
Maximum4.825032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:57.743349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110119 × 109
5-th percentile1.1110129 × 109
Q11.1110184 × 109
median1.118512 × 109
Q31.1537608 × 109
95-th percentile1.1711611 × 109
Maximum4.825032 × 109
Range3.7140201 × 109
Interquartile range (IQR)42742475

Descriptive statistics

Standard deviation6.3280947 × 108
Coefficient of variation (CV)0.50965087
Kurtosis29.815821
Mean1.2416529 × 109
Median Absolute Deviation (MAD)7499550
Skewness5.58355
Sum1.2416529 × 1011
Variance4.0044783 × 1017
MonotonicityNot monotonic
2023-12-10T18:51:58.190598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1129010100 5
 
5.0%
1114016700 4
 
4.0%
1165010800 3
 
3.0%
1168010700 3
 
3.0%
1117013500 2
 
2.0%
1111014300 2
 
2.0%
1111017100 2
 
2.0%
1111018300 2
 
2.0%
1111014000 2
 
2.0%
1111018400 2
 
2.0%
Other values (65) 73
73.0%
ValueCountFrequency (%)
1111011900 2
2.0%
1111012100 2
2.0%
1111012800 1
1.0%
1111012900 1
1.0%
1111013000 2
2.0%
1111013100 1
1.0%
1111013600 1
1.0%
1111013700 1
1.0%
1111014000 2
2.0%
1111014300 2
2.0%
ValueCountFrequency (%)
4825032025 1
1.0%
4817012700 1
1.0%
4817011900 1
1.0%
1174010700 1
1.0%
1174010200 1
1.0%
1171011100 2
2.0%
1171010700 1
1.0%
1171010200 1
1.0%
1171010100 1
1.0%
1168011200 1
1.0%

legalemd_nm
Text

Distinct75
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:51:58.716436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.42
Min length2

Characters and Unicode

Total characters342
Distinct characters101
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)57.0%

Sample

1st row용산동6가
2nd row주촌면 내삼리
3rd row세종로
4th row용산동6가
5th row서초동
ValueCountFrequency (%)
성북동 5
 
5.0%
정동 4
 
4.0%
서초동 3
 
3.0%
신사동 3
 
3.0%
와룡동 2
 
2.0%
용산동6가 2
 
2.0%
평창동 2
 
2.0%
부암동 2
 
2.0%
가회동 2
 
2.0%
홍지동 2
 
2.0%
Other values (66) 74
73.3%
2023-12-10T18:51:59.611524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 87
25.4%
가 26
 
7.6%
로 10
 
2.9%
1 9
 
2.6%
2 8
 
2.3%
신 7
 
2.0%
성 7
 
2.0%
정 7
 
2.0%
산 6
 
1.8%
사 5
 
1.5%
Other values (91) 170
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 319
93.3%
Decimal Number 22
 
6.4%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 87
27.3%
가 26
 
8.2%
로 10
 
3.1%
신 7
 
2.2%
성 7
 
2.2%
정 7
 
2.2%
산 6
 
1.9%
사 5
 
1.6%
문 5
 
1.6%
북 5
 
1.6%
Other values (85) 154
48.3%
Decimal Number
ValueCountFrequency (%)
1 9
40.9%
2 8
36.4%
3 2
 
9.1%
6 2
 
9.1%
5 1
 
4.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 319
93.3%
Common 23
 
6.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 87
27.3%
가 26
 
8.2%
로 10
 
3.1%
신 7
 
2.2%
성 7
 
2.2%
정 7
 
2.2%
산 6
 
1.9%
사 5
 
1.6%
문 5
 
1.6%
북 5
 
1.6%
Other values (85) 154
48.3%
Common
ValueCountFrequency (%)
1 9
39.1%
2 8
34.8%
3 2
 
8.7%
6 2
 
8.7%
1
 
4.3%
5 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 319
93.3%
ASCII 23
 
6.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 87
27.3%
가 26
 
8.2%
로 10
 
3.1%
신 7
 
2.2%
성 7
 
2.2%
정 7
 
2.2%
산 6
 
1.9%
사 5
 
1.6%
문 5
 
1.6%
북 5
 
1.6%
Other values (85) 154
48.3%
ASCII
ValueCountFrequency (%)
1 9
39.1%
2 8
34.8%
3 2
 
8.7%
6 2
 
8.7%
1
 
4.3%
5 1
 
4.3%

adstrd_cd
Real number (ℝ)

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2416991 × 109
Minimum1.111053 × 109
Maximum4.825032 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:51:59.899782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111053 × 109
5-th percentile1.111054 × 109
Q11.111064 × 109
median1.1185615 × 109
Q31.153822 × 109
95-th percentile1.1712202 × 109
Maximum4.825032 × 109
Range3.713979 × 109
Interquartile range (IQR)42758000

Descriptive statistics

Standard deviation6.3280814 × 108
Coefficient of variation (CV)0.50963081
Kurtosis29.8158
Mean1.2416991 × 109
Median Absolute Deviation (MAD)7507500
Skewness5.5835472
Sum1.2416991 × 1011
Variance4.0044614 × 1017
MonotonicityNot monotonic
2023-12-10T18:52:00.612368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111061500 9
 
9.0%
1114052000 6
 
6.0%
1129052500 5
 
5.0%
1111055000 4
 
4.0%
1114055000 4
 
4.0%
1111054000 4
 
4.0%
1168054500 3
 
3.0%
1111065000 3
 
3.0%
1114058000 3
 
3.0%
1111056000 3
 
3.0%
Other values (49) 56
56.0%
ValueCountFrequency (%)
1111053000 2
 
2.0%
1111054000 4
4.0%
1111055000 4
4.0%
1111056000 3
 
3.0%
1111060000 2
 
2.0%
1111061500 9
9.0%
1111064000 2
 
2.0%
1111065000 3
 
3.0%
1114052000 6
6.0%
1114054000 2
 
2.0%
ValueCountFrequency (%)
4825032000 1
1.0%
4817072000 1
1.0%
4817067300 1
1.0%
1174058000 1
1.0%
1174055000 1
1.0%
1171071000 1
1.0%
1171068000 1
1.0%
1171063100 1
1.0%
1171056600 2
2.0%
1168070000 1
1.0%

adstrd_nm
Text

Distinct59
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:01.069586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length3
Mean length3.94
Min length2

Characters and Unicode

Total characters394
Distinct characters87
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique42 ?
Unique (%)42.0%

Sample

1st row서빙고동
2nd row주촌면
3rd row종로1.2.3.4가동
4th row서빙고동
5th row서초3동
ValueCountFrequency (%)
종로1.2.3.4가동 9
 
9.0%
소공동 6
 
6.0%
성북동 5
 
5.0%
삼청동 4
 
4.0%
부암동 4
 
4.0%
명동 4
 
4.0%
압구정동 3
 
3.0%
장충동 3
 
3.0%
평창동 3
 
3.0%
혜화동 3
 
3.0%
Other values (49) 56
56.0%
2023-12-10T18:52:01.688983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 99
25.1%
. 27
 
6.9%
1 18
 
4.6%
2 17
 
4.3%
가 14
 
3.6%
3 11
 
2.8%
종 9
 
2.3%
4 9
 
2.3%
로 9
 
2.3%
성 8
 
2.0%
Other values (77) 173
43.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 310
78.7%
Decimal Number 57
 
14.5%
Other Punctuation 27
 
6.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 99
31.9%
가 14
 
4.5%
종 9
 
2.9%
로 9
 
2.9%
성 8
 
2.6%
공 7
 
2.3%
암 6
 
1.9%
소 6
 
1.9%
서 6
 
1.9%
화 6
 
1.9%
Other values (71) 140
45.2%
Decimal Number
ValueCountFrequency (%)
1 18
31.6%
2 17
29.8%
3 11
19.3%
4 9
15.8%
6 2
 
3.5%
Other Punctuation
ValueCountFrequency (%)
. 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 310
78.7%
Common 84
 
21.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 99
31.9%
가 14
 
4.5%
종 9
 
2.9%
로 9
 
2.9%
성 8
 
2.6%
공 7
 
2.3%
암 6
 
1.9%
소 6
 
1.9%
서 6
 
1.9%
화 6
 
1.9%
Other values (71) 140
45.2%
Common
ValueCountFrequency (%)
. 27
32.1%
1 18
21.4%
2 17
20.2%
3 11
13.1%
4 9
 
10.7%
6 2
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 310
78.7%
ASCII 84
 
21.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 99
31.9%
가 14
 
4.5%
종 9
 
2.9%
로 9
 
2.9%
성 8
 
2.6%
공 7
 
2.3%
암 6
 
1.9%
소 6
 
1.9%
서 6
 
1.9%
화 6
 
1.9%
Other values (71) 140
45.2%
ASCII
ValueCountFrequency (%)
. 27
32.1%
1 18
21.4%
2 17
20.2%
3 11
13.1%
4 9
 
10.7%
6 2
 
2.4%

rdnmaddr_cd
Real number (ℝ)

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3242054 × 1011
Minimum1.1110201 × 1011
Maximum4.82505 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:02.090255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110201 × 1011
5-th percentile1.111031 × 1011
Q11.111041 × 1011
median1.120781 × 1011
Q31.1575338 × 1011
95-th percentile2.8797365 × 1011
Maximum4.82505 × 1011
Range3.7140299 × 1011
Interquartile range (IQR)4.6492781 × 109

Descriptive statistics

Standard deviation7.8996654 × 1010
Coefficient of variation (CV)0.5965589
Kurtosis14.727424
Mean1.3242054 × 1011
Median Absolute Deviation (MAD)9.7500201 × 108
Skewness4.0079383
Sum1.3242054 × 1013
Variance6.2404713 × 1021
MonotonicityNot monotonic
2023-12-10T18:52:02.411833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111703102004 2
 
2.0%
111404103276 2
 
2.0%
111104100259 2
 
2.0%
117103123023 2
 
2.0%
111104100174 2
 
2.0%
112903107008 2
 
2.0%
116803005086 2
 
2.0%
111103005004 2
 
2.0%
111104100113 2
 
2.0%
111403101021 2
 
2.0%
Other values (78) 80
80.0%
ValueCountFrequency (%)
111102005001 2
2.0%
111103005004 2
2.0%
111103005005 1
1.0%
111103100002 1
1.0%
111103100003 1
1.0%
111103100004 1
1.0%
111103100009 1
1.0%
111103100010 1
1.0%
111104100063 1
1.0%
111104100102 1
1.0%
ValueCountFrequency (%)
482505000000 1
1.0%
481705000000 1
1.0%
481703000000 1
1.0%
471303000104 1
1.0%
412813192088 1
1.0%
281403150010 1
1.0%
117404172195 1
1.0%
117403123023 1
1.0%
117104169340 1
1.0%
117103123023 2
2.0%

rdnm_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:03.051368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length20
Mean length15.2
Min length12

Characters and Unicode

Total characters1520
Distinct characters151
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

Unique100 ?
Unique (%)100.0%

Sample

1st row서울 용산구 서빙고로 137
2nd row경상남도 김해시 주촌면 서부로1541번길 8
3rd row서울 종로구 세종대로 198
4th row서울 용산구 서빙고로 139
5th row서울 서초구 남부순환로 2364
ValueCountFrequency (%)
서울 97
24.2%
종로구 29
 
7.2%
중구 16
 
4.0%
강남구 8
 
2.0%
송파구 5
 
1.2%
용산구 5
 
1.2%
성북구 5
 
1.2%
세종대로 4
 
1.0%
26 4
 
1.0%
서초구 4
 
1.0%
Other values (178) 224
55.9%
2023-12-10T18:52:04.032639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
301
19.8%
로 120
 
7.9%
서 113
 
7.4%
구 98
 
6.4%
울 97
 
6.4%
1 70
 
4.6%
길 46
 
3.0%
2 38
 
2.5%
5 34
 
2.2%
종 33
 
2.2%
Other values (141) 570
37.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 907
59.7%
Decimal Number 302
 
19.9%
Space Separator 301
 
19.8%
Dash Punctuation 10
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 120
 
13.2%
서 113
 
12.5%
구 98
 
10.8%
울 97
 
10.7%
길 46
 
5.1%
종 33
 
3.6%
대 20
 
2.2%
중 18
 
2.0%
북 17
 
1.9%
남 15
 
1.7%
Other values (129) 330
36.4%
Decimal Number
ValueCountFrequency (%)
1 70
23.2%
2 38
12.6%
5 34
11.3%
3 28
 
9.3%
7 25
 
8.3%
4 24
 
7.9%
6 23
 
7.6%
9 22
 
7.3%
0 20
 
6.6%
8 18
 
6.0%
Space Separator
ValueCountFrequency (%)
301
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 907
59.7%
Common 613
40.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 120
 
13.2%
서 113
 
12.5%
구 98
 
10.8%
울 97
 
10.7%
길 46
 
5.1%
종 33
 
3.6%
대 20
 
2.2%
중 18
 
2.0%
북 17
 
1.9%
남 15
 
1.7%
Other values (129) 330
36.4%
Common
ValueCountFrequency (%)
301
49.1%
1 70
 
11.4%
2 38
 
6.2%
5 34
 
5.5%
3 28
 
4.6%
7 25
 
4.1%
4 24
 
3.9%
6 23
 
3.8%
9 22
 
3.6%
0 20
 
3.3%
Other values (2) 28
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 907
59.7%
ASCII 613
40.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
301
49.1%
1 70
 
11.4%
2 38
 
6.2%
5 34
 
5.5%
3 28
 
4.6%
7 25
 
4.1%
4 24
 
3.9%
6 23
 
3.8%
9 22
 
3.6%
0 20
 
3.3%
Other values (2) 28
 
4.6%
Hangul
ValueCountFrequency (%)
로 120
 
13.2%
서 113
 
12.5%
구 98
 
10.8%
울 97
 
10.7%
길 46
 
5.1%
종 33
 
3.6%
대 20
 
2.2%
중 18
 
2.0%
북 17
 
1.9%
남 15
 
1.7%
Other values (129) 330
36.4%

zip_cd
Real number (ℝ)

Distinct88
Distinct (%)88.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5747.96
Minimum1007
Maximum52786
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:04.466645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1007
5-th percentile2822
Q13078.75
median4368
Q35517.5
95-th percentile7683.75
Maximum52786
Range51779
Interquartile range (IQR)2438.75

Descriptive statistics

Standard deviation8332.7507
Coefficient of variation (CV)1.4496884
Kurtosis27.835091
Mean5747.96
Median Absolute Deviation (MAD)1236.5
Skewness5.3136586
Sum574796
Variance69434734
MonotonicityNot monotonic
2023-12-10T18:52:04.884179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4516 4
 
4.0%
4383 2
 
2.0%
4519 2
 
2.0%
3015 2
 
2.0%
5540 2
 
2.0%
2822 2
 
2.0%
2837 2
 
2.0%
3022 2
 
2.0%
3052 2
 
2.0%
3055 2
 
2.0%
Other values (78) 78
78.0%
ValueCountFrequency (%)
1007 1
1.0%
1376 1
1.0%
1805 1
1.0%
2570 1
1.0%
2822 2
2.0%
2837 2
2.0%
2880 1
1.0%
3004 1
1.0%
3007 1
1.0%
3011 1
1.0%
ValueCountFrequency (%)
52786 1
1.0%
52669 1
1.0%
50877 1
1.0%
8772 1
1.0%
8344 1
1.0%
7649 1
1.0%
7525 1
1.0%
7333 1
1.0%
7233 1
1.0%
6762 1
1.0%

grid_cd
Text

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:52:05.424697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters800
Distinct characters14
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

Unique98 ?
Unique (%)98.0%

Sample

1st row다사538473
2nd row마라202942
3rd row다사539528
4th row다사540469
5th row다사566421
ValueCountFrequency (%)
다사545535 2
 
2.0%
다사544511 1
 
1.0%
다사549558 1
 
1.0%
다사563512 1
 
1.0%
다사539487 1
 
1.0%
다사483513 1
 
1.0%
다사582430 1
 
1.0%
다사574428 1
 
1.0%
다사533519 1
 
1.0%
다사499507 1
 
1.0%
Other values (89) 89
89.0%
2023-12-10T18:52:06.248620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 186
23.2%
다 97
12.1%
사 97
12.1%
4 87
10.9%
2 61
 
7.6%
6 49
 
6.1%
3 47
 
5.9%
8 41
 
5.1%
1 34
 
4.2%
0 33
 
4.1%
Other values (4) 68
 
8.5%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 186
31.0%
4 87
14.5%
2 61
 
10.2%
6 49
 
8.2%
3 47
 
7.8%
8 41
 
6.8%
1 34
 
5.7%
0 33
 
5.5%
9 31
 
5.2%
7 31
 
5.2%
Other Letter
ValueCountFrequency (%)
다 97
48.5%
사 97
48.5%
라 5
 
2.5%
마 1
 
0.5%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
5 186
31.0%
4 87
14.5%
2 61
 
10.2%
6 49
 
8.2%
3 47
 
7.8%
8 41
 
6.8%
1 34
 
5.7%
0 33
 
5.5%
9 31
 
5.2%
7 31
 
5.2%
Hangul
ValueCountFrequency (%)
다 97
48.5%
사 97
48.5%
라 5
 
2.5%
마 1
 
0.5%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 186
31.0%
4 87
14.5%
2 61
 
10.2%
6 49
 
8.2%
3 47
 
7.8%
8 41
 
6.8%
1 34
 
5.7%
0 33
 
5.5%
9 31
 
5.2%
7 31
 
5.2%
Hangul
ValueCountFrequency (%)
다 97
48.5%
사 97
48.5%
라 5
 
2.5%
마 1
 
0.5%

x_cd
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
38
88 
37
9 
35
 
3

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row38
2nd row35
3rd row38
4th row38
5th row37

Common Values

ValueCountFrequency (%)
38 88
88.0%
37 9
 
9.0%
35 3
 
3.0%

Length

2023-12-10T18:52:06.529810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:06.732217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
38 88
88.0%
37 9
 
9.0%
35 3
 
3.0%

y_cd
Categorical

IMBALANCE 

Distinct3
Distinct (%)3.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
127
97 
128
 
2
129
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

1st row127
2nd row129
3rd row127
4th row127
5th row127

Common Values

ValueCountFrequency (%)
127 97
97.0%
128 2
 
2.0%
129 1
 
1.0%

Length

2023-12-10T18:52:06.932410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:07.130094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
127 97
97.0%
128 2
 
2.0%
129 1
 
1.0%

sum_fyer_usr_cnt
Real number (ℝ)

Distinct97
Distinct (%)98.0%
Missing1
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean185701.63
Minimum0
Maximum3354161
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:07.340652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile749.9
Q13847.5
median20000
Q3102890
95-th percentile1018991.7
Maximum3354161
Range3354161
Interquartile range (IQR)99042.5

Descriptive statistics

Standard deviation505114.03
Coefficient of variation (CV)2.7200302
Kurtosis21.995548
Mean185701.63
Median Absolute Deviation (MAD)18400
Skewness4.4802245
Sum18384461
Variance2.5514019 × 1011
MonotonicityNot monotonic
2023-12-10T18:52:07.645459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1767 2
 
2.0%
5000 2
 
2.0%
3354161 1
 
1.0%
18229 1
 
1.0%
2379672 1
 
1.0%
22253 1
 
1.0%
77844 1
 
1.0%
4500 1
 
1.0%
24000 1
 
1.0%
3807 1
 
1.0%
Other values (87) 87
87.0%
ValueCountFrequency (%)
0 1
1.0%
100 1
1.0%
476 1
1.0%
600 1
1.0%
614 1
1.0%
765 1
1.0%
800 1
1.0%
1340 1
1.0%
1399 1
1.0%
1453 1
1.0%
ValueCountFrequency (%)
3354161 1
1.0%
2413596 1
1.0%
2379672 1
1.0%
1058981 1
1.0%
1058886 1
1.0%
1014559 1
1.0%
788010 1
1.0%
776570 1
1.0%
499367 1
1.0%
349147 1
1.0%

near_parking_lot_cnt
Real number (ℝ)

MISSING 

Distinct13
Distinct (%)22.4%
Missing42
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean7.862069
Minimum2
Maximum38
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:07.872302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q12
median4
Q310
95-th percentile28.3
Maximum38
Range36
Interquartile range (IQR)8

Descriptive statistics

Standard deviation8.215194
Coefficient of variation (CV)1.044915
Kurtosis4.0145152
Mean7.862069
Median Absolute Deviation (MAD)2
Skewness2.0599571
Sum456
Variance67.489413
MonotonicityNot monotonic
2023-12-10T18:52:08.079201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2 19
19.0%
4 11
 
11.0%
10 7
 
7.0%
6 7
 
7.0%
8 4
 
4.0%
12 2
 
2.0%
18 2
 
2.0%
28 1
 
1.0%
38 1
 
1.0%
20 1
 
1.0%
Other values (3) 3
 
3.0%
(Missing) 42
42.0%
ValueCountFrequency (%)
2 19
19.0%
4 11
11.0%
6 7
 
7.0%
8 4
 
4.0%
10 7
 
7.0%
12 2
 
2.0%
18 2
 
2.0%
20 1
 
1.0%
22 1
 
1.0%
28 1
 
1.0%
ValueCountFrequency (%)
38 1
 
1.0%
32 1
 
1.0%
30 1
 
1.0%
28 1
 
1.0%
22 1
 
1.0%
20 1
 
1.0%
18 2
 
2.0%
12 2
 
2.0%
10 7
7.0%
8 4
4.0%

sum_prklt_capa
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)87.9%
Missing42
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean641.96552
Minimum16
Maximum3430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:52:08.333313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile17.7
Q199
median240
Q3763.5
95-th percentile2697
Maximum3430
Range3414
Interquartile range (IQR)664.5

Descriptive statistics

Standard deviation898.42017
Coefficient of variation (CV)1.3994835
Kurtosis2.0650348
Mean641.96552
Median Absolute Deviation (MAD)204
Skewness1.7893611
Sum37234
Variance807158.81
MonotonicityNot monotonic
2023-12-10T18:52:08.641117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16 3
 
3.0%
36 3
 
3.0%
34 2
 
2.0%
130 2
 
2.0%
240 2
 
2.0%
260 1
 
1.0%
80 1
 
1.0%
392 1
 
1.0%
312 1
 
1.0%
3430 1
 
1.0%
Other values (41) 41
41.0%
(Missing) 42
42.0%
ValueCountFrequency (%)
16 3
3.0%
18 1
 
1.0%
22 1
 
1.0%
28 1
 
1.0%
34 2
2.0%
36 3
3.0%
74 1
 
1.0%
80 1
 
1.0%
82 1
 
1.0%
92 1
 
1.0%
ValueCountFrequency (%)
3430 1
1.0%
3024 1
1.0%
2748 1
1.0%
2688 1
1.0%
2680 1
1.0%
2668 1
1.0%
2004 1
1.0%
1930 1
1.0%
1670 1
1.0%
1490 1
1.0%

fyer_demand_forecast
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

file_name
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021
100 

Length

Max length39
Median length39
Mean length39
Min length39

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowKC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021
2nd rowKC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021
3rd rowKC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021
4th rowKC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021
5th rowKC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021

Common Values

ValueCountFrequency (%)
KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_2021 100
100.0%

Length

2023-12-10T18:52:08.934534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:09.150932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
kc_606_ctlstt_vs_prklt_invt_frcsta_2021 100
100.0%

base_ymd
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20200101
100 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row20200101
2nd row20200101
3rd row20200101
4th row20200101
5th row20200101

Common Values

ValueCountFrequency (%)
20200101 100
100.0%

Length

2023-12-10T18:52:09.358896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:52:09.597635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20200101 100
100.0%

Sample

idlclasmlsfcfclt_namectprvn_nmsgnr_nmlegaldong_cdlegalemd_nmadstrd_cdadstrd_nmrdnmaddr_cdrdnm_addrzip_cdgrid_cdx_cdy_cdsum_fyer_usr_cntnear_parking_lot_cntsum_prklt_capafyer_demand_forecastfile_namebase_ymd
0KCCVPIF21N000000002문화시설박물관국립중앙박물관서울특별시용산구1117013500용산동6가1117069000서빙고동111703102004서울 용산구 서빙고로 1374383다사538473381273354161<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
1KCCVPIF21N000003119문화시설공공도서관김해지혜의바다경상남도김해시4825032025주촌면 내삼리4825032000주촌면482505000000경상남도 김해시 주촌면 서부로1541번길 850877마라2029423512979160<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
2KCCVPIF21N000000004문화시설박물관대한민국역사박물관서울특별시종로구1111011900세종로1111061500종로1.2.3.4가동111102005001서울 종로구 세종대로 1983141다사539528381271058886102748<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
3KCCVPIF21N000000005문화시설박물관국립한글박물관서울특별시용산구1117013500용산동6가1117069000서빙고동111703102004서울 용산구 서빙고로 1394383다사54046938127776570<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
4KCCVPIF21N000000006문화시설박물관국립국악원 국악박물관서울특별시서초구1165010800서초동1165053000서초3동116502000003서울 서초구 남부순환로 23646757다사5664213712741218<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
5KCCVPIF21N000000007문화시설박물관국립극장 공연예술박물관서울특별시중구1114014400장충동2가1114058000장충동111403101021서울 중구 장충단로 594621다사5575043812783734168<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
6KCCVPIF21N000000008문화시설박물관한국영화박물관서울특별시마포구1144012700상암동1144074000상암동114403113019서울 마포구 월드컵북로 4003925다사4615363812778337<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
7KCCVPIF21N000003120문화시설공공도서관진주시립서부도서관경상남도진주시4817012700이현동4817072000이현동481703000000경상남도 진주시 평거로 24952669라라51188435128324668<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
8KCCVPIF21N000000010문화시설박물관석조전 대한제국역사관서울특별시중구1114016700정동1114052000소공동111402005001서울 중구 세종대로 994519다사536519381272413596281930<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
9KCCVPIF21N000000011문화시설박물관육군박물관서울특별시노원구1135010300공릉동1135060000공릉2동113503005046서울 노원구 화랑로 5741805다사6475833812763800<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
idlclasmlsfcfclt_namectprvn_nmsgnr_nmlegaldong_cdlegalemd_nmadstrd_cdadstrd_nmrdnmaddr_cdrdnm_addrzip_cdgrid_cdx_cdy_cdsum_fyer_usr_cntnear_parking_lot_cntsum_prklt_capafyer_demand_forecastfile_namebase_ymd
90KCCVPIF21N000000092문화시설박물관혜곡최순우기념관서울특별시성북구1129010100성북동1129052500성북동112904121327서울 성북구 성북로15길 92880다사5585493812715553<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
91KCCVPIF21N000000093문화시설박물관호림박물관(신림본관)서울특별시관악구1162010200신림동1162076500미성동116204160194서울 관악구 남부순환로152길 538772다사485425371272633282<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
92KCCVPIF21N000000094문화시설박물관호림박물관(신사분관)서울특별시강남구1168010700신사동1168054500압구정동116802122001서울 강남구 도산대로 3176021다사5904713812741674120<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
93KCCVPIF21N000000095문화시설박물관화정박물관서울특별시종로구1111018300평창동1111056000평창동412813192088서울 종로구 평창8길 33011다사528562381273513<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
94KCCVPIF21N000000096문화시설박물관평강성서유물박물관서울특별시구로구1153010800오류동1153078000오류2동115304148425서울 구로구 오류로8라길 448344다사420438371271767<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
95KCCVPIF21N000000097문화시설박물관간송박물관서울특별시성북구1129010100성북동1129052500성북동112903107008서울 성북구 성북로 102-112837다사5555513812740035<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
96KCCVPIF21N000000098문화시설박물관꼭두박물관(휴관)서울특별시종로구1111017100명륜2가1111065000혜화동111104100174서울 종로구 성균관로4길 213074다사55754038127<NA>216<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
97KCCVPIF21N000000099문화시설박물관쇳대박물관(휴관)서울특별시종로구1111016800동숭동1111064000이화동111104100245서울 종로구 이화장길 1003088다사562534381276000<NA><NA><NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
98KCCVPIF21N000000100문화시설박물관서울올림픽기념관서울특별시송파구1171011100방이동1171056600오륜동117103123023서울 송파구 올림픽로 4245540다사6604683812702178<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101
99KCCVPIF21N000000101문화시설박물관안중근의사기념관서울특별시중구1114011800남대문로5가1114054000회현동111403101018서울 중구 소월로 914636다사5415053812712809210136<NA>KC_606_CTLSTT_VS_PRKLT_INVT_FRCSTA_202120200101