Overview

Dataset statistics

Number of variables26
Number of observations100
Missing cells422
Missing cells (%)16.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.5 KiB
Average record size in memory220.3 B

Variable types

Text8
Categorical8
Numeric9
Unsupported1

Alerts

lclas_nm has constant value ""Constant
origin_nm has constant value ""Constant
updt_dt has constant value ""Constant
regist_dt has constant value ""Constant
mlsfc_nm is highly imbalanced (80.6%)Imbalance
tel_no is highly imbalanced (80.6%)Imbalance
hmpg_url is highly imbalanced (80.6%)Imbalance
legaldong_cd has 3 (3.0%) missing valuesMissing
road_nm_cd has 37 (37.0%) missing valuesMissing
fclty_road_nm_addr has 42 (42.0%) missing valuesMissing
addr_eng_nm has 42 (42.0%) missing valuesMissing
adstrd_cd has 42 (42.0%) missing valuesMissing
buld_nm has 69 (69.0%) missing valuesMissing
buld_manage_cd has 45 (45.0%) missing valuesMissing
zip_no has 42 (42.0%) missing valuesMissing
adit_dc has 100 (100.0%) missing valuesMissing
esntl_id has unique valuesUnique
lnm_addr has unique valuesUnique
fclty_la has unique valuesUnique
fclty_lo has unique valuesUnique
adit_dc is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 09:46:34.546141
Analysis finished2023-12-10 09:46:35.770695
Duration1.22 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
2023-12-10T18:46:36.038351image/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 rowKCPWRPO20N000000001
2nd rowKCPWRPO20N000010307
3rd rowKCPWRPO20N000000003
4th rowKCPWRPO20N000000004
5th rowKCPWRPO20N000000005
ValueCountFrequency (%)
kcpwrpo20n000000001 1
 
1.0%
kcpwrpo20n000000063 1
 
1.0%
kcpwrpo20n000000074 1
 
1.0%
kcpwrpo20n000000073 1
 
1.0%
kcpwrpo20n000000072 1
 
1.0%
kcpwrpo20n000000071 1
 
1.0%
kcpwrpo20n000000070 1
 
1.0%
kcpwrpo20n000000069 1
 
1.0%
kcpwrpo20n000000068 1
 
1.0%
kcpwrpo20n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:46:36.678705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 814
42.8%
P 200
 
10.5%
2 118
 
6.2%
K 100
 
5.3%
C 100
 
5.3%
N 100
 
5.3%
O 100
 
5.3%
R 100
 
5.3%
W 100
 
5.3%
1 24
 
1.3%
Other values (7) 144
 
7.6%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 814
74.0%
2 118
 
10.7%
1 24
 
2.2%
3 22
 
2.0%
7 21
 
1.9%
9 21
 
1.9%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
8 20
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
P 200
25.0%
K 100
12.5%
C 100
12.5%
N 100
12.5%
O 100
12.5%
R 100
12.5%
W 100
12.5%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 814
74.0%
2 118
 
10.7%
1 24
 
2.2%
3 22
 
2.0%
7 21
 
1.9%
9 21
 
1.9%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
8 20
 
1.8%
Latin
ValueCountFrequency (%)
P 200
25.0%
K 100
12.5%
C 100
12.5%
N 100
12.5%
O 100
12.5%
R 100
12.5%
W 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 814
42.8%
P 200
 
10.5%
2 118
 
6.2%
K 100
 
5.3%
C 100
 
5.3%
N 100
 
5.3%
O 100
 
5.3%
R 100
 
5.3%
W 100
 
5.3%
1 24
 
1.3%
Other values (7) 144
 
7.6%

lclas_nm
Categorical

CONSTANT 

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

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
자연 100
100.0%

Length

2023-12-10T18:46:36.959345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:37.154670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자연 100
100.0%

mlsfc_nm
Categorical

IMBALANCE 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자연_공원
2nd row자연_바다
3rd row자연_공원
4th row자연_공원
5th row자연_공원

Common Values

ValueCountFrequency (%)
자연_공원 97
97.0%
자연_바다 3
 
3.0%

Length

2023-12-10T18:46:37.320707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:37.509930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자연_공원 97
97.0%
자연_바다 3
 
3.0%

fclty_nm
Text

Distinct97
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:37.909448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.08
Min length2

Characters and Unicode

Total characters608
Distinct characters162
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

Unique96 ?
Unique (%)96.0%

Sample

1st row소사대공원
2nd row진산해수욕장
3rd row수주공원
4th row고향골공원
5th row활주로어린이공원
ValueCountFrequency (%)
근린공원 4
 
4.0%
소사대공원 1
 
1.0%
과기공원 1
 
1.0%
진해내수면환경생태공원 1
 
1.0%
남양체육공원 1
 
1.0%
서천체육공원 1
 
1.0%
밀양생태공원 1
 
1.0%
천안삼거리공원 1
 
1.0%
와룡공원 1
 
1.0%
봉담호수공원 1
 
1.0%
Other values (87) 87
87.0%
2023-12-10T18:46:38.636270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 90
 
14.8%
원 89
 
14.6%
체 21
 
3.5%
육 21
 
3.5%
산 13
 
2.1%
수 12
 
2.0%
린 11
 
1.8%
장 10
 
1.6%
이 10
 
1.6%
해 9
 
1.5%
Other values (152) 322
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 605
99.5%
Uppercase Letter 2
 
0.3%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 90
 
14.9%
원 89
 
14.7%
체 21
 
3.5%
육 21
 
3.5%
산 13
 
2.1%
수 12
 
2.0%
린 11
 
1.8%
장 10
 
1.7%
이 10
 
1.7%
해 9
 
1.5%
Other values (149) 319
52.7%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 605
99.5%
Latin 2
 
0.3%
Common 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 90
 
14.9%
원 89
 
14.7%
체 21
 
3.5%
육 21
 
3.5%
산 13
 
2.1%
수 12
 
2.0%
린 11
 
1.8%
장 10
 
1.7%
이 10
 
1.7%
해 9
 
1.5%
Other values (149) 319
52.7%
Latin
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%
Common
ValueCountFrequency (%)
2 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 605
99.5%
ASCII 3
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 90
 
14.9%
원 89
 
14.7%
체 21
 
3.5%
육 21
 
3.5%
산 13
 
2.1%
수 12
 
2.0%
린 11
 
1.8%
장 10
 
1.7%
이 10
 
1.7%
해 9
 
1.5%
Other values (149) 319
52.7%
ASCII
ValueCountFrequency (%)
I 1
33.3%
C 1
33.3%
2 1
33.3%

ctprvn_cd
Real number (ℝ)

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.38
Minimum11
Maximum48
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:38.879134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11
5-th percentile11
Q128
median41
Q344
95-th percentile48
Maximum48
Range37
Interquartile range (IQR)16

Descriptive statistics

Standard deviation11.539813
Coefficient of variation (CV)0.32616768
Kurtosis-0.17388092
Mean35.38
Median Absolute Deviation (MAD)7
Skewness-0.92875358
Sum3538
Variance133.16727
MonotonicityNot monotonic
2023-12-10T18:46:39.096848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
41 22
22.0%
11 12
12.0%
28 11
11.0%
48 10
10.0%
29 8
 
8.0%
44 6
 
6.0%
47 5
 
5.0%
42 5
 
5.0%
46 4
 
4.0%
43 4
 
4.0%
Other values (5) 13
13.0%
ValueCountFrequency (%)
11 12
12.0%
26 4
 
4.0%
27 1
 
1.0%
28 11
11.0%
29 8
 
8.0%
30 2
 
2.0%
31 3
 
3.0%
41 22
22.0%
42 5
 
5.0%
43 4
 
4.0%
ValueCountFrequency (%)
48 10
10.0%
47 5
 
5.0%
46 4
 
4.0%
45 3
 
3.0%
44 6
 
6.0%
43 4
 
4.0%
42 5
 
5.0%
41 22
22.0%
31 3
 
3.0%
30 2
 
2.0%

ctprvn_nm
Categorical

Distinct15
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
경기도
22 
서울특별시
12 
인천광역시
11 
경상남도
10 
광주광역시
8 
Other values (10)
37 

Length

Max length5
Median length4
Mean length4.14
Min length3

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 22
22.0%
서울특별시 12
12.0%
인천광역시 11
11.0%
경상남도 10
10.0%
광주광역시 8
 
8.0%
충청남도 6
 
6.0%
경상북도 5
 
5.0%
강원도 5
 
5.0%
전라남도 4
 
4.0%
충청북도 4
 
4.0%
Other values (5) 13
13.0%

Length

2023-12-10T18:46:39.386092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 22
22.0%
서울특별시 12
12.0%
인천광역시 11
11.0%
경상남도 10
10.0%
광주광역시 8
 
8.0%
충청남도 6
 
6.0%
경상북도 5
 
5.0%
강원도 5
 
5.0%
전라남도 4
 
4.0%
충청북도 4
 
4.0%
Other values (5) 13
13.0%

signgu_cd
Real number (ℝ)

Distinct74
Distinct (%)74.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35736.95
Minimum11110
Maximum48820
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:39.655405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11500
Q128243
median41267.5
Q344202.5
95-th percentile48133.55
Maximum48820
Range37710
Interquartile range (IQR)15959.5

Descriptive statistics

Standard deviation11529.729
Coefficient of variation (CV)0.32262767
Kurtosis-0.22227103
Mean35736.95
Median Absolute Deviation (MAD)6855.5
Skewness-0.91396448
Sum3573695
Variance1.3293465 × 108
MonotonicityNot monotonic
2023-12-10T18:46:39.957474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28237 6
 
6.0%
29140 4
 
4.0%
41590 3
 
3.0%
48129 3
 
3.0%
41670 3
 
3.0%
41190 2
 
2.0%
41210 2
 
2.0%
41250 2
 
2.0%
48240 2
 
2.0%
44131 2
 
2.0%
Other values (64) 71
71.0%
ValueCountFrequency (%)
11110 1
1.0%
11200 1
1.0%
11230 1
1.0%
11470 1
1.0%
11500 2
2.0%
11560 1
1.0%
11620 1
1.0%
11680 1
1.0%
11710 2
2.0%
11740 1
1.0%
ValueCountFrequency (%)
48820 1
 
1.0%
48270 1
 
1.0%
48240 2
2.0%
48220 1
 
1.0%
48129 3
3.0%
48125 1
 
1.0%
48121 1
 
1.0%
47850 1
 
1.0%
47830 1
 
1.0%
47230 1
 
1.0%

signgu_nm
Text

Distinct69
Distinct (%)69.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:40.458151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.51
Min length2

Characters and Unicode

Total characters351
Distinct characters76
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

Unique51 ?
Unique (%)51.0%

Sample

1st row부천시
2nd row완도군
3rd row부천시
4th row계양구
5th row강서구
ValueCountFrequency (%)
서구 6
 
5.3%
부평구 6
 
5.3%
창원시 5
 
4.4%
남구 4
 
3.5%
진해구 3
 
2.6%
여주시 3
 
2.6%
화성시 3
 
2.6%
송파구 2
 
1.8%
북구 2
 
1.8%
고양시 2
 
1.8%
Other values (65) 78
68.4%
2023-12-10T18:46:41.248667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 54
 
15.4%
시 45
 
12.8%
군 17
 
4.8%
천 14
 
4.0%
14
 
4.0%
동 13
 
3.7%
양 12
 
3.4%
서 11
 
3.1%
남 11
 
3.1%
산 11
 
3.1%
Other values (66) 149
42.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 337
96.0%
Space Separator 14
 
4.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 54
 
16.0%
시 45
 
13.4%
군 17
 
5.0%
천 14
 
4.2%
동 13
 
3.9%
양 12
 
3.6%
서 11
 
3.3%
남 11
 
3.3%
산 11
 
3.3%
주 9
 
2.7%
Other values (65) 140
41.5%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 337
96.0%
Common 14
 
4.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 54
 
16.0%
시 45
 
13.4%
군 17
 
5.0%
천 14
 
4.2%
동 13
 
3.9%
양 12
 
3.6%
서 11
 
3.3%
남 11
 
3.3%
산 11
 
3.3%
주 9
 
2.7%
Other values (65) 140
41.5%
Common
ValueCountFrequency (%)
14
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 337
96.0%
ASCII 14
 
4.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 54
 
16.0%
시 45
 
13.4%
군 17
 
5.0%
천 14
 
4.2%
동 13
 
3.9%
양 12
 
3.6%
서 11
 
3.3%
남 11
 
3.3%
산 11
 
3.3%
주 9
 
2.7%
Other values (65) 140
41.5%
ASCII
ValueCountFrequency (%)
14
100.0%

legaldong_cd
Real number (ℝ)

MISSING 

Distinct90
Distinct (%)92.8%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean3.5417511 × 109
Minimum1.1110173 × 109
Maximum4.882034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:41.530393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110173 × 109
5-th percentile1.1500108 × 109
Q12.8237105 × 109
median4.1250102 × 109
Q34.413111 × 109
95-th percentile4.8147338 × 109
Maximum4.882034 × 109
Range3.7710167 × 109
Interquartile range (IQR)1.5894005 × 109

Descriptive statistics

Standard deviation1.1558332 × 109
Coefficient of variation (CV)0.32634512
Kurtosis-0.2881857
Mean3.5417511 × 109
Median Absolute Deviation (MAD)6.879035 × 108
Skewness-0.87530547
Sum3.4354986 × 1011
Variance1.3359504 × 1018
MonotonicityNot monotonic
2023-12-10T18:46:41.835615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2823710500 5
 
5.0%
1150010800 2
 
2.0%
4511114200 2
 
2.0%
1171010400 2
 
2.0%
4223012000 1
 
1.0%
4183035021 1
 
1.0%
4812512500 1
 
1.0%
4423025331 1
 
1.0%
4122011200 1
 
1.0%
2826010300 1
 
1.0%
Other values (80) 80
80.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
1111017300 1
1.0%
1120010700 1
1.0%
1123011000 1
1.0%
1147010200 1
1.0%
1150010800 2
2.0%
1156011000 1
1.0%
1162010100 1
1.0%
1168010600 1
1.0%
1171010400 2
2.0%
1174010600 1
1.0%
ValueCountFrequency (%)
4882034021 1
1.0%
4827035031 1
1.0%
4824032023 1
1.0%
4824011700 1
1.0%
4822011200 1
1.0%
4812914500 1
1.0%
4812914300 1
1.0%
4812913700 1
1.0%
4812512500 1
1.0%
4812110700 1
1.0%

legaldong_nm
Text

Distinct92
Distinct (%)92.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:42.439590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.64
Min length2

Characters and Unicode

Total characters364
Distinct characters121
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

Unique87 ?
Unique (%)87.0%

Sample

1st row소사본동
2nd row소안면
3rd row고강동
4th row계산동
5th row공항동
ValueCountFrequency (%)
삼산동 5
 
4.4%
공항동 2
 
1.8%
효자동3가 2
 
1.8%
능서면 2
 
1.8%
송파동 2
 
1.8%
고례리 1
 
0.9%
소사본동 1
 
0.9%
삼룡동 1
 
0.9%
검암동 1
 
0.9%
봉천동 1
 
0.9%
Other values (96) 96
84.2%
2023-12-10T18:46:43.295273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 72
 
19.8%
면 20
 
5.5%
리 15
 
4.1%
산 15
 
4.1%
14
 
3.8%
읍 11
 
3.0%
신 6
 
1.6%
삼 6
 
1.6%
성 6
 
1.6%
천 6
 
1.6%
Other values (111) 193
53.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 346
95.1%
Space Separator 14
 
3.8%
Decimal Number 4
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 72
 
20.8%
면 20
 
5.8%
리 15
 
4.3%
산 15
 
4.3%
읍 11
 
3.2%
신 6
 
1.7%
삼 6
 
1.7%
성 6
 
1.7%
천 6
 
1.7%
가 5
 
1.4%
Other values (108) 184
53.2%
Decimal Number
ValueCountFrequency (%)
3 3
75.0%
1 1
 
25.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 346
95.1%
Common 18
 
4.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 72
 
20.8%
면 20
 
5.8%
리 15
 
4.3%
산 15
 
4.3%
읍 11
 
3.2%
신 6
 
1.7%
삼 6
 
1.7%
성 6
 
1.7%
천 6
 
1.7%
가 5
 
1.4%
Other values (108) 184
53.2%
Common
ValueCountFrequency (%)
14
77.8%
3 3
 
16.7%
1 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 346
95.1%
ASCII 18
 
4.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 72
 
20.8%
면 20
 
5.8%
리 15
 
4.3%
산 15
 
4.3%
읍 11
 
3.2%
신 6
 
1.7%
삼 6
 
1.7%
성 6
 
1.7%
천 6
 
1.7%
가 5
 
1.4%
Other values (108) 184
53.2%
ASCII
ValueCountFrequency (%)
14
77.8%
3 3
 
16.7%
1 1
 
5.6%

road_nm_cd
Real number (ℝ)

MISSING 

Distinct63
Distinct (%)100.0%
Missing37
Missing (%)37.0%
Infinite0
Infinite (%)0.0%
Mean3.6511914 × 1011
Minimum1.111041 × 1011
Maximum4.8820302 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:43.608463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.111041 × 1011
5-th percentile1.1566312 × 1011
Q12.8677864 × 1011
median4.141032 × 1011
Q34.4680826 × 1011
95-th percentile4.8211248 × 1011
Maximum4.8820302 × 1011
Range3.7709892 × 1011
Interquartile range (IQR)1.6002962 × 1011

Descriptive statistics

Standard deviation1.1134798 × 1011
Coefficient of variation (CV)0.30496342
Kurtosis-0.019876158
Mean3.6511914 × 1011
Median Absolute Deviation (MAD)6.7190131 × 1010
Skewness-0.94896598
Sum2.3002506 × 1013
Variance1.2398373 × 1022
MonotonicityNot monotonic
2023-12-10T18:46:44.280995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
468903300014 1
 
1.0%
416703214045 1
 
1.0%
481293331065 1
 
1.0%
481294793465 1
 
1.0%
482402334003 1
 
1.0%
414103200016 1
 
1.0%
451113266056 1
 
1.0%
282374262592 1
 
1.0%
437503245005 1
 
1.0%
482403334037 1
 
1.0%
Other values (53) 53
53.0%
(Missing) 37
37.0%
ValueCountFrequency (%)
111104100213 1
1.0%
114703114014 1
1.0%
115004145353 1
1.0%
115603118020 1
1.0%
116203120004 1
1.0%
117104169006 1
1.0%
261704181260 1
1.0%
262903131031 1
1.0%
264702000010 1
1.0%
265004214072 1
1.0%
ValueCountFrequency (%)
488203019061 1
1.0%
482403334037 1
1.0%
482402334003 1
1.0%
482203333043 1
1.0%
481294793465 1
1.0%
481294793216 1
1.0%
481293331065 1
1.0%
481254787400 1
1.0%
478303320015 1
1.0%
471134712813 1
1.0%

fclty_road_nm_addr
Text

MISSING 

Distinct58
Distinct (%)100.0%
Missing42
Missing (%)42.0%
Memory size932.0 B
2023-12-10T18:46:44.824522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length24.448276
Min length19

Characters and Unicode

Total characters1418
Distinct characters179
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique58 ?
Unique (%)100.0%

Sample

1st row경기도 부천시 소사로 107 (소사본동)
2nd row전라남도 완도군 소안면 소안남로 253
3rd row인천광역시 계양구 계산로41번길 5 (계산동)
4th row서울특별시 강서구 방화대로6길 24-3 (공항동)
5th row충청북도 괴산군 연풍면 새재로 1948
ValueCountFrequency (%)
경기도 12
 
3.9%
경상남도 8
 
2.6%
인천광역시 6
 
2.0%
서울특별시 6
 
2.0%
광주광역시 6
 
2.0%
충청남도 4
 
1.3%
서구 4
 
1.3%
창원시 4
 
1.3%
부평구 4
 
1.3%
부산광역시 4
 
1.3%
Other values (223) 247
81.0%
2023-12-10T18:46:45.475882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
247
 
17.4%
시 53
 
3.7%
동 52
 
3.7%
로 52
 
3.7%
1 42
 
3.0%
) 41
 
2.9%
( 41
 
2.9%
도 36
 
2.5%
구 33
 
2.3%
산 30
 
2.1%
Other values (169) 791
55.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 872
61.5%
Space Separator 247
 
17.4%
Decimal Number 203
 
14.3%
Close Punctuation 42
 
3.0%
Open Punctuation 42
 
3.0%
Dash Punctuation 10
 
0.7%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 53
 
6.1%
동 52
 
6.0%
로 52
 
6.0%
도 36
 
4.1%
구 33
 
3.8%
산 30
 
3.4%
광 30
 
3.4%
천 24
 
2.8%
경 23
 
2.6%
길 22
 
2.5%
Other values (151) 517
59.3%
Decimal Number
ValueCountFrequency (%)
1 42
20.7%
3 26
12.8%
2 25
12.3%
6 21
10.3%
5 18
8.9%
4 18
8.9%
0 16
 
7.9%
9 14
 
6.9%
8 12
 
5.9%
7 11
 
5.4%
Close Punctuation
ValueCountFrequency (%)
) 41
97.6%
] 1
 
2.4%
Open Punctuation
ValueCountFrequency (%)
( 41
97.6%
[ 1
 
2.4%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
* 1
50.0%
Space Separator
ValueCountFrequency (%)
247
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 872
61.5%
Common 546
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 53
 
6.1%
동 52
 
6.0%
로 52
 
6.0%
도 36
 
4.1%
구 33
 
3.8%
산 30
 
3.4%
광 30
 
3.4%
천 24
 
2.8%
경 23
 
2.6%
길 22
 
2.5%
Other values (151) 517
59.3%
Common
ValueCountFrequency (%)
247
45.2%
1 42
 
7.7%
) 41
 
7.5%
( 41
 
7.5%
3 26
 
4.8%
2 25
 
4.6%
6 21
 
3.8%
5 18
 
3.3%
4 18
 
3.3%
0 16
 
2.9%
Other values (8) 51
 
9.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 872
61.5%
ASCII 546
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
247
45.2%
1 42
 
7.7%
) 41
 
7.5%
( 41
 
7.5%
3 26
 
4.8%
2 25
 
4.6%
6 21
 
3.8%
5 18
 
3.3%
4 18
 
3.3%
0 16
 
2.9%
Other values (8) 51
 
9.3%
Hangul
ValueCountFrequency (%)
시 53
 
6.1%
동 52
 
6.0%
로 52
 
6.0%
도 36
 
4.1%
구 33
 
3.8%
산 30
 
3.4%
광 30
 
3.4%
천 24
 
2.8%
경 23
 
2.6%
길 22
 
2.5%
Other values (151) 517
59.3%

lnm_addr
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:46:45.896963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length30
Mean length21.83
Min length14

Characters and Unicode

Total characters2183
Distinct characters205
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 row경기도 부천시 소사본동 334-3 한울빛도서관
2nd row전라남도 완도군 소안면 진산리 29
3rd row경기도 부천시 고강동 190-11
4th row인천광역시 계양구 계산동 982-2
5th row서울 강서구 공항동 1341
ValueCountFrequency (%)
경기도 22
 
4.5%
경상남도 10
 
2.0%
인천광역시 6
 
1.2%
부평구 6
 
1.2%
서구 6
 
1.2%
충청남도 6
 
1.2%
광주광역시 6
 
1.2%
서울 6
 
1.2%
서울특별시 6
 
1.2%
인천 5
 
1.0%
Other values (346) 412
83.9%
2023-12-10T18:46:46.611594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
391
 
17.9%
1 92
 
4.2%
동 89
 
4.1%
시 72
 
3.3%
- 68
 
3.1%
도 64
 
2.9%
구 58
 
2.7%
2 51
 
2.3%
산 48
 
2.2%
경 40
 
1.8%
Other values (195) 1210
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1341
61.4%
Space Separator 391
 
17.9%
Decimal Number 381
 
17.5%
Dash Punctuation 68
 
3.1%
Uppercase Letter 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 89
 
6.6%
시 72
 
5.4%
도 64
 
4.8%
구 58
 
4.3%
산 48
 
3.6%
경 40
 
3.0%
광 38
 
2.8%
남 35
 
2.6%
리 33
 
2.5%
천 32
 
2.4%
Other values (181) 832
62.0%
Decimal Number
ValueCountFrequency (%)
1 92
24.1%
2 51
13.4%
3 39
10.2%
4 35
 
9.2%
5 30
 
7.9%
6 30
 
7.9%
8 28
 
7.3%
0 27
 
7.1%
9 27
 
7.1%
7 22
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%
Space Separator
ValueCountFrequency (%)
391
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1341
61.4%
Common 840
38.5%
Latin 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 89
 
6.6%
시 72
 
5.4%
도 64
 
4.8%
구 58
 
4.3%
산 48
 
3.6%
경 40
 
3.0%
광 38
 
2.8%
남 35
 
2.6%
리 33
 
2.5%
천 32
 
2.4%
Other values (181) 832
62.0%
Common
ValueCountFrequency (%)
391
46.5%
1 92
 
11.0%
- 68
 
8.1%
2 51
 
6.1%
3 39
 
4.6%
4 35
 
4.2%
5 30
 
3.6%
6 30
 
3.6%
8 28
 
3.3%
0 27
 
3.2%
Other values (2) 49
 
5.8%
Latin
ValueCountFrequency (%)
C 1
50.0%
I 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1341
61.4%
ASCII 842
38.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
391
46.4%
1 92
 
10.9%
- 68
 
8.1%
2 51
 
6.1%
3 39
 
4.6%
4 35
 
4.2%
5 30
 
3.6%
6 30
 
3.6%
8 28
 
3.3%
0 27
 
3.2%
Other values (4) 51
 
6.1%
Hangul
ValueCountFrequency (%)
동 89
 
6.6%
시 72
 
5.4%
도 64
 
4.8%
구 58
 
4.3%
산 48
 
3.6%
경 40
 
3.0%
광 38
 
2.8%
남 35
 
2.6%
리 33
 
2.5%
천 32
 
2.4%
Other values (181) 832
62.0%

addr_eng_nm
Text

MISSING 

Distinct58
Distinct (%)100.0%
Missing42
Missing (%)42.0%
Memory size932.0 B
2023-12-10T18:46:47.136267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length78
Median length58.5
Mean length49.896552
Min length31

Characters and Unicode

Total characters2894
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

Unique58 ?
Unique (%)100.0%

Sample

1st row107, Sosa-ro, Bucheon-si, Gyeonggi-do
2nd row253, Soannam-ro, Soan-myeon, Wando-gun, Jeollanam-do
3rd row5, Gyesan-ro 41beon-gil, Gyeyang-gu, Incheon
4th row24-3, Banghwa-daero 6-gil, Gangseo-gu, Seoul
5th row1948, Saejae-ro, Yeonpung-myeon, Goesan-gun, Chungcheongbuk-do
ValueCountFrequency (%)
gyeonggi-do 12
 
4.3%
gyeongsangnam-do 8
 
2.9%
incheon 6
 
2.2%
gwangju 6
 
2.2%
seoul 6
 
2.2%
seo-gu 4
 
1.4%
changwon-si 4
 
1.4%
busan 4
 
1.4%
bupyeong-gu 4
 
1.4%
chungcheongnam-do 4
 
1.4%
Other values (198) 220
79.1%
2023-12-10T18:46:48.104527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
n 294
 
10.2%
o 293
 
10.1%
g 221
 
7.6%
220
 
7.6%
- 202
 
7.0%
, 201
 
6.9%
e 186
 
6.4%
a 147
 
5.1%
u 124
 
4.3%
i 76
 
2.6%
Other values (42) 930
32.1%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1871
64.7%
Space Separator 220
 
7.6%
Dash Punctuation 202
 
7.0%
Uppercase Letter 202
 
7.0%
Other Punctuation 201
 
6.9%
Decimal Number 198
 
6.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
n 294
15.7%
o 293
15.7%
g 221
11.8%
e 186
9.9%
a 147
7.9%
u 124
 
6.6%
i 76
 
4.1%
s 73
 
3.9%
y 60
 
3.2%
r 56
 
3.0%
Other values (11) 341
18.2%
Uppercase Letter
ValueCountFrequency (%)
G 52
25.7%
S 31
15.3%
B 18
 
8.9%
C 16
 
7.9%
Y 16
 
7.9%
J 15
 
7.4%
D 13
 
6.4%
I 8
 
4.0%
H 8
 
4.0%
N 6
 
3.0%
Other values (8) 19
 
9.4%
Decimal Number
ValueCountFrequency (%)
1 41
20.7%
2 25
12.6%
3 23
11.6%
6 21
10.6%
5 18
9.1%
4 18
9.1%
0 16
 
8.1%
9 14
 
7.1%
8 12
 
6.1%
7 10
 
5.1%
Space Separator
ValueCountFrequency (%)
220
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 202
100.0%
Other Punctuation
ValueCountFrequency (%)
, 201
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2073
71.6%
Common 821
 
28.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
n 294
14.2%
o 293
14.1%
g 221
10.7%
e 186
 
9.0%
a 147
 
7.1%
u 124
 
6.0%
i 76
 
3.7%
s 73
 
3.5%
y 60
 
2.9%
r 56
 
2.7%
Other values (29) 543
26.2%
Common
ValueCountFrequency (%)
220
26.8%
- 202
24.6%
, 201
24.5%
1 41
 
5.0%
2 25
 
3.0%
3 23
 
2.8%
6 21
 
2.6%
5 18
 
2.2%
4 18
 
2.2%
0 16
 
1.9%
Other values (3) 36
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2894
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
n 294
 
10.2%
o 293
 
10.1%
g 221
 
7.6%
220
 
7.6%
- 202
 
7.0%
, 201
 
6.9%
e 186
 
6.4%
a 147
 
5.1%
u 124
 
4.3%
i 76
 
2.6%
Other values (42) 930
32.1%

adstrd_cd
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)94.8%
Missing42
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean3.6097711 × 109
Minimum1.1110173 × 109
Maximum4.882034 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:48.584420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110173 × 109
5-th percentile1.155111 × 109
Q12.8239104 × 109
median4.1250102 × 109
Q34.4895935 × 109
95-th percentile4.8223113 × 109
Maximum4.882034 × 109
Range3.7710167 × 109
Interquartile range (IQR)1.6656831 × 109

Descriptive statistics

Standard deviation1.139409 × 109
Coefficient of variation (CV)0.31564578
Kurtosis-0.19752434
Mean3.6097711 × 109
Median Absolute Deviation (MAD)6.8790415 × 108
Skewness-0.87373621
Sum2.0936672 × 1011
Variance1.2982529 × 1018
MonotonicityNot monotonic
2023-12-10T18:46:48.891562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2823710500 3
 
3.0%
4511114200 2
 
2.0%
4812512500 1
 
1.0%
4167034023 1
 
1.0%
4812914300 1
 
1.0%
4812914500 1
 
1.0%
4824011700 1
 
1.0%
4141010400 1
 
1.0%
2823710300 1
 
1.0%
4824032023 1
 
1.0%
Other values (45) 45
45.0%
(Missing) 42
42.0%
ValueCountFrequency (%)
1111017300 1
1.0%
1147010200 1
1.0%
1150010800 1
1.0%
1156011000 1
1.0%
1162010100 1
1.0%
1171010400 1
1.0%
2617010400 1
1.0%
2629010700 1
1.0%
2647010200 1
1.0%
2650010300 1
1.0%
ValueCountFrequency (%)
4882034021 1
1.0%
4824032023 1
1.0%
4824011700 1
1.0%
4822011200 1
1.0%
4812914500 1
1.0%
4812914300 1
1.0%
4812913700 1
1.0%
4812512500 1
1.0%
4783025324 1
1.0%
4711333025 1
1.0%

buld_nm
Text

MISSING 

Distinct31
Distinct (%)100.0%
Missing69
Missing (%)69.0%
Memory size932.0 B
2023-12-10T18:46:49.292781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length8
Min length3

Characters and Unicode

Total characters248
Distinct characters114
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

Unique31 ?
Unique (%)100.0%

Sample

1st row한울빛도서관
2nd row다솔경로당
3rd row연풍레포츠공원
4th row부평역사박물관
5th row금호2동주민센터
ValueCountFrequency (%)
공중화장실 3
 
7.3%
화장실 2
 
4.9%
동면 1
 
2.4%
진해야외공연장 1
 
2.4%
산본ic체육공원 1
 
2.4%
내수면양식연구센터 1
 
2.4%
성현드림공원공중화장실 1
 
2.4%
마산음악관 1
 
2.4%
능서레포츠공원 1
 
2.4%
우정어린이공원 1
 
2.4%
Other values (28) 28
68.3%
2023-12-10T18:46:49.957906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
공 19
 
7.7%
원 13
 
5.2%
10
 
4.0%
장 8
 
3.2%
화 8
 
3.2%
실 7
 
2.8%
중 6
 
2.4%
관 5
 
2.0%
산 5
 
2.0%
린 5
 
2.0%
Other values (104) 162
65.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 234
94.4%
Space Separator 10
 
4.0%
Uppercase Letter 2
 
0.8%
Decimal Number 2
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
공 19
 
8.1%
원 13
 
5.6%
장 8
 
3.4%
화 8
 
3.4%
실 7
 
3.0%
중 6
 
2.6%
관 5
 
2.1%
산 5
 
2.1%
린 5
 
2.1%
육 5
 
2.1%
Other values (99) 153
65.4%
Uppercase Letter
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%
Decimal Number
ValueCountFrequency (%)
7 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 234
94.4%
Common 12
 
4.8%
Latin 2
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
공 19
 
8.1%
원 13
 
5.6%
장 8
 
3.4%
화 8
 
3.4%
실 7
 
3.0%
중 6
 
2.6%
관 5
 
2.1%
산 5
 
2.1%
린 5
 
2.1%
육 5
 
2.1%
Other values (99) 153
65.4%
Common
ValueCountFrequency (%)
10
83.3%
7 1
 
8.3%
2 1
 
8.3%
Latin
ValueCountFrequency (%)
I 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 234
94.4%
ASCII 14
 
5.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
공 19
 
8.1%
원 13
 
5.6%
장 8
 
3.4%
화 8
 
3.4%
실 7
 
3.0%
중 6
 
2.6%
관 5
 
2.1%
산 5
 
2.1%
린 5
 
2.1%
육 5
 
2.1%
Other values (99) 153
65.4%
ASCII
ValueCountFrequency (%)
10
71.4%
I 1
 
7.1%
C 1
 
7.1%
7 1
 
7.1%
2 1
 
7.1%

buld_manage_cd
Real number (ℝ)

MISSING 

Distinct55
Distinct (%)100.0%
Missing45
Missing (%)45.0%
Infinite0
Infinite (%)0.0%
Mean3.6296072 × 1024
Minimum1.1110173 × 1024
Maximum4.882034 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:50.230336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110173 × 1024
5-th percentile1.1542109 × 1024
Q12.8677614 × 1024
median4.1410104 × 1024
Q34.5111142 × 1024
95-th percentile4.8226114 × 1024
Maximum4.882034 × 1024
Range3.7710167 × 1024
Interquartile range (IQR)1.6433528 × 1024

Descriptive statistics

Standard deviation1.1587389 × 1024
Coefficient of variation (CV)0.31924637
Kurtosis-0.17233734
Mean3.6296072 × 1024
Median Absolute Deviation (MAD)6.719039 × 1023
Skewness-0.92117491
Sum1.996284 × 1026
Variance1.3426758 × 1048
MonotonicityNot monotonic
2023-12-10T18:46:50.508814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4.68903602510029e+24 1
 
1.0%
4.1730350231093294e+24 1
 
1.0%
4.81291430010584e+24 1
 
1.0%
4.8129145002000097e+24 1
 
1.0%
4.8240117001068095e+24 1
 
1.0%
4.1410104001105696e+24 1
 
1.0%
4.5111142001152306e+24 1
 
1.0%
2.82371030010179e+24 1
 
1.0%
4.82403202310652e+24 1
 
1.0%
1.11101730020002e+24 1
 
1.0%
Other values (45) 45
45.0%
(Missing) 45
45.0%
ValueCountFrequency (%)
1.11101730020002e+24 1
1.0%
1.14701020010404e+24 1
1.0%
1.15001080011361e+24 1
1.0%
1.15601100010008e+24 1
1.0%
1.16201010011712e+24 1
1.0%
1.17101040010095e+24 1
1.0%
2.61701040011403e+24 1
1.0%
2.62901070020028e+24 1
1.0%
2.64701020011e+24 1
1.0%
2.65001030010181e+24 1
1.0%
ValueCountFrequency (%)
4.88203402110118e+24 1
1.0%
4.82403202310652e+24 1
1.0%
4.8240117001068095e+24 1
1.0%
4.82201120010462e+24 1
1.0%
4.8129145002000097e+24 1
1.0%
4.81291430010584e+24 1
1.0%
4.8129137001057697e+24 1
1.0%
4.81251250010068e+24 1
1.0%
4.78302502410331e+24 1
1.0%
4.7113330251009e+24 1
1.0%

tel_no
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
97 
-
 
3

Length

Max length4
Median length4
Mean length3.91
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 97
97.0%
- 3
 
3.0%

Length

2023-12-10T18:46:50.780273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:50.948824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
97.0%
3
 
3.0%

zip_no
Real number (ℝ)

MISSING 

Distinct57
Distinct (%)98.3%
Missing42
Missing (%)42.0%
Infinite0
Infinite (%)0.0%
Mean33862.397
Minimum3063
Maximum62250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:51.127913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3063
5-th percentile7598.65
Q114344
median32418
Q352329
95-th percentile61920.35
Maximum62250
Range59187
Interquartile range (IQR)37985

Descriptive statistics

Standard deviation19568.725
Coefficient of variation (CV)0.57788956
Kurtosis-1.5571852
Mean33862.397
Median Absolute Deviation (MAD)19254.5
Skewness0.0539729
Sum1964019
Variance3.8293501 × 108
MonotonicityNot monotonic
2023-12-10T18:46:51.373907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21327 2
 
2.0%
51714 1
 
1.0%
12642 1
 
1.0%
51657 1
 
1.0%
51630 1
 
1.0%
52555 1
 
1.0%
15800 1
 
1.0%
54955 1
 
1.0%
21367 1
 
1.0%
52534 1
 
1.0%
Other values (47) 47
47.0%
(Missing) 42
42.0%
ValueCountFrequency (%)
3063 1
1.0%
5668 1
1.0%
7336 1
1.0%
7645 1
1.0%
7999 1
1.0%
8723 1
1.0%
10808 1
1.0%
11344 1
1.0%
11346 1
1.0%
12616 1
1.0%
ValueCountFrequency (%)
62250 1
1.0%
62029 1
1.0%
62013 1
1.0%
61904 1
1.0%
61509 1
1.0%
61220 1
1.0%
59569 1
1.0%
59166 1
1.0%
58672 1
1.0%
54958 1
1.0%

hmpg_url
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
<NA>
97 
-
 
3

Length

Max length4
Median length4
Mean length3.91
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 97
97.0%
- 3
 
3.0%

Length

2023-12-10T18:46:51.643623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:51.829304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 97
97.0%
3
 
3.0%

fclty_la
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.572648
Minimum34.133992
Maximum38.10066
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:52.044506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34.133992
5-th percentile34.970878
Q135.429651
median37.035759
Q337.513186
95-th percentile37.873741
Maximum38.10066
Range3.9666679
Interquartile range (IQR)2.0835345

Descriptive statistics

Standard deviation1.0887393
Coefficient of variation (CV)0.029769222
Kurtosis-1.3575859
Mean36.572648
Median Absolute Deviation (MAD)0.6351552
Skewness-0.44671244
Sum3657.2648
Variance1.1853532
MonotonicityNot monotonic
2023-12-10T18:46:52.334826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.4672613 1
 
1.0%
37.2189535 1
 
1.0%
37.0866748 1
 
1.0%
37.5580589 1
 
1.0%
37.491462 1
 
1.0%
37.4584352 1
 
1.0%
35.163443 1
 
1.0%
37.436969 1
 
1.0%
34.9729156 1
 
1.0%
35.4860463 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
34.1339923 1
1.0%
34.4644137 1
1.0%
34.8178091 1
1.0%
34.8577526 1
1.0%
34.932161 1
1.0%
34.9729156 1
1.0%
34.9942585 1
1.0%
35.0606426 1
1.0%
35.1168279 1
1.0%
35.1219055 1
1.0%
ValueCountFrequency (%)
38.1006602 1
1.0%
37.9944594 1
1.0%
37.8977993 1
1.0%
37.8947418 1
1.0%
37.8924217 1
1.0%
37.8727573 1
1.0%
37.6938415 1
1.0%
37.6786415 1
1.0%
37.6631876 1
1.0%
37.604218 1
1.0%

fclty_lo
Real number (ℝ)

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.48704
Minimum126.41898
Maximum129.37175
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:46:52.598051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.41898
5-th percentile126.69265
Q1126.86839
median127.10006
Q3128.05453
95-th percentile129.16228
Maximum129.37175
Range2.9527736
Interquartile range (IQR)1.1861405

Descriptive statistics

Standard deviation0.84211322
Coefficient of variation (CV)0.0066054812
Kurtosis-0.50765985
Mean127.48704
Median Absolute Deviation (MAD)0.3558717
Skewness0.94075111
Sum12748.704
Variance0.70915467
MonotonicityNot monotonic
2023-12-10T18:46:52.791443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.7967643 1
 
1.0%
126.9509069 1
 
1.0%
127.0575584 1
 
1.0%
126.6749923 1
 
1.0%
126.9521517 1
 
1.0%
126.7015124 1
 
1.0%
128.6591582 1
 
1.0%
129.1681919 1
 
1.0%
127.5748004 1
 
1.0%
128.9316147 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
126.4189783 1
1.0%
126.4418917 1
1.0%
126.5590737 1
1.0%
126.6402027 1
1.0%
126.6749923 1
1.0%
126.6935825 1
1.0%
126.7015124 1
1.0%
126.7194412 1
1.0%
126.7364086 1
1.0%
126.7378639 1
1.0%
ValueCountFrequency (%)
129.3717519 1
1.0%
129.306315 1
1.0%
129.2611484 1
1.0%
129.2444856 1
1.0%
129.1681919 1
1.0%
129.1619654 1
1.0%
129.1309795 1
1.0%
129.1228726 1
1.0%
129.0738221 1
1.0%
129.0509311 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

2023-12-10T18:46:53.010904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:53.175032image/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
20201201120000
100 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20201201120000 100
100.0%

Length

2023-12-10T18:46:53.321458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:53.483594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201201120000 100
100.0%

regist_dt
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
20201201120000
100 

Length

Max length14
Median length14
Mean length14
Min length14

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
20201201120000 100
100.0%

Length

2023-12-10T18:46:53.660943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:46:53.828608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20201201120000 100
100.0%

Sample

esntl_idlclas_nmmlsfc_nmfclty_nmctprvn_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
0KCPWRPO20N000000001자연자연_공원소사대공원41경기도41190부천시4119011000소사본동411903012096경기도 부천시 소사로 107 (소사본동)경기도 부천시 소사본동 334-3 한울빛도서관107, Sosa-ro, Bucheon-si, Gyeonggi-do4119011000한울빛도서관4119710100103370001047049<NA>14761<NA>37.467261126.796764문화정보원<NA>2020120112000020201201120000
1KCPWRPO20N000010307자연자연_바다진산해수욕장46전라남도46890완도군<NA>소안면468903300014전라남도 완도군 소안면 소안남로 253전라남도 완도군 소안면 진산리 29253, Soannam-ro, Soan-myeon, Wando-gun, Jeollanam-do4689036025<NA>4689036025100290000000001-59166-34.133992126.640203문화정보원<NA>2020120112000020201201120000
2KCPWRPO20N000000003자연자연_공원수주공원41경기도41190부천시4119012100고강동<NA><NA>경기도 부천시 고강동 190-11<NA><NA><NA><NA><NA><NA><NA>37.530025126.812598문화정보원<NA>2020120112000020201201120000
3KCPWRPO20N000000004자연자연_공원고향골공원28인천광역시28245계양구2824510200계산동282454265045인천광역시 계양구 계산로41번길 5 (계산동)인천광역시 계양구 계산동 982-25, Gyesan-ro 41beon-gil, Gyeyang-gu, Incheon2824510200<NA>2824510200109820002181365<NA>21054<NA>37.540579126.719441문화정보원<NA>2020120112000020201201120000
4KCPWRPO20N000000005자연자연_공원활주로어린이공원11서울특별시11500강서구1150010800공항동<NA><NA>서울 강서구 공항동 1341<NA><NA><NA><NA><NA><NA><NA>37.554357126.816002문화정보원<NA>2020120112000020201201120000
5KCPWRPO20N000000006자연자연_공원다솔공원11서울특별시11500강서구1150010800공항동115004145353서울특별시 강서구 방화대로6길 24-3 (공항동)서울특별시 강서구 공항동 1361-1 다솔경로당24-3, Banghwa-daero 6-gil, Gangseo-gu, Seoul1150010800다솔경로당1150010800113610001007925<NA>7645<NA>37.554565126.818369문화정보원<NA>2020120112000020201201120000
6KCPWRPO20N000000007자연자연_공원연풍레포츠공원43충청북도43760괴산군4376033025연풍면437603000165충청북도 괴산군 연풍면 새재로 1948충청북도 괴산군 연풍면 원풍리 199 연풍레포츠공원1948, Saejae-ro, Yeonpung-myeon, Goesan-gun, Chungcheongbuk-do4376033025연풍레포츠공원4376033025101990000013532<NA>28012<NA>36.8145128.026918문화정보원<NA>2020120112000020201201120000
7KCPWRPO20N000010308자연자연_바다간월도해수욕장44충청남도44210서산시<NA>부석면442104562016충청남도 서산시 부석면 간월달발길 2충청남도 서산시 부석면 간월도리 1622, Ganwoldalbal-gil, Buseok-myeon, Seosan-si, Chungcheongnam-do4421032034<NA>4421032034101620000075976-32023-36.610762126.418978문화정보원<NA>2020120112000020201201120000
8KCPWRPO20N000000009자연자연_공원유수지체육공원28인천광역시28237부평구2823710500삼산동282373154056인천광역시 부평구 체육관로 161 (삼산동)인천광역시 부평구 삼산동 450-1161, Cheyukgwan-ro, Bupyeong-gu, Incheon2823710500<NA><NA><NA>21325<NA>37.514219126.742278문화정보원<NA>2020120112000020201201120000
9KCPWRPO20N000000010자연자연_공원박물관공원28인천광역시28237부평구2823710500삼산동282373154006인천광역시 부평구 굴포로 151 (삼산동)인천광역시 부평구 삼산동 451-1 부평역사박물관151, Gulpo-ro, Bupyeong-gu, Incheon2823710500부평역사박물관2823710500104510001110414<NA>21327<NA>37.512207126.737864문화정보원<NA>2020120112000020201201120000
esntl_idlclas_nmmlsfc_nmfclty_nmctprvn_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
90KCPWRPO20N000000091자연자연_공원수레울놀이공원41경기도41800연천군4180025021연천읍 차탄리<NA><NA>경기도 연천군 연천읍 차탄리 624<NA><NA><NA><NA><NA><NA><NA>38.10066127.077265문화정보원<NA>2020120112000020201201120000
91KCPWRPO20N000000092자연자연_공원어울림근린공원41경기도41250동두천시4125010300생연동412503189031경기도 동두천시 중앙로 183 (생연동)경기도 동두천시 생연동 714-6183, Jungang-ro, Dongducheon-si, Gyeonggi-do4125010300<NA>4125010300107140006000001<NA>11344<NA>37.897799127.051996문화정보원<NA>2020120112000020201201120000
92KCPWRPO20N000000093자연자연_공원근린공원41경기도41250동두천시4125010200지행동412503189002경기도 동두천시 거북마루로 64 (지행동, 대방 노블랜드 7단지 아파트)경기도 동두천시 지행동 692-1 대방 노블랜드 7단지 아파트64, Geobungmaru-ro, Dongducheon-si, Gyeonggi-do4125010200대방 노블랜드 7단지 아파트4125010200106920001008709<NA>11346<NA>37.894742127.052719문화정보원<NA>2020120112000020201201120000
93KCPWRPO20N000000094자연자연_공원화랑체육공원31울산광역시31710울주군3171037029두서면317103173054울산광역시 울주군 두서면 서하천전로 76울산광역시 울주군 두서면 서하리 100 화랑체육공원76, Seohacheonjeon-ro, Duseo-myeon, Ulju-gun, Ulsan3171037029화랑체육공원3171037029101000000000001<NA>44912<NA>35.627959129.161965문화정보원<NA>2020120112000020201201120000
94KCPWRPO20N000000095자연자연_공원느티나무공원41경기도41590화성시4159011600진안동<NA><NA>경기도 화성시 진안동 920<NA><NA><NA><NA><NA><NA><NA>37.218951127.036025문화정보원<NA>2020120112000020201201120000
95KCPWRPO20N000000096자연자연_공원근린공원41경기도41590화성시4159011800능동415903012002경기도 화성시 동탄원천로 315-17 (능동)경기도 화성시 능동 1084 시립능동어린이집315-17, Dongtanwoncheon-ro, Hwaseong-si, Gyeonggi-do4159011800시립능동어린이집4159011800110840000000001<NA>18432<NA>37.21343127.059983문화정보원<NA>2020120112000020201201120000
96KCPWRPO20N000000097자연자연_공원파란마음공원41경기도41210광명시4121010100광명동412104361049경기도 광명시 광복로21번길 7-1 (광명동)경기도 광명시 광명동 10-9 노인정7-1, Gwangbok-ro 21beon-gil, Gwangmyeong-si, Gyeonggi-do4121010100노인정4121010100100100009000001<NA>14205<NA>37.487864126.861485문화정보원<NA>2020120112000020201201120000
97KCPWRPO20N000000098자연자연_공원이문둔치공원11서울특별시11230동대문구1123011000이문동<NA><NA>서울 동대문구 이문동 352-2<NA><NA><NA><NA><NA><NA><NA>37.604218127.071665문화정보원<NA>2020120112000020201201120000
98KCPWRPO20N000000099자연자연_공원철산공원41경기도41210광명시4121010200철산동<NA><NA>경기도 광명시 철산동 산 80-1<NA><NA><NA><NA><NA><NA><NA>37.471771126.870831문화정보원<NA>2020120112000020201201120000
99KCPWRPO20N000000100자연자연_공원뒷말공원41경기도41173안양시 동안구4117310400호계동411734349010경기도 안양시 동안구 갈산로86번길 54 (호계동)경기도 안양시 동안구 호계동 1080-1 평촌선교교회54, Galsan-ro 86beon-gil, Dongan-gu, Anyang-si, Gyeonggi-do4117310400평촌선교교회4117310400110800001004812<NA>14107<NA>37.379757126.961036문화정보원<NA>2020120112000020201201120000