Overview

Dataset statistics

Number of variables27
Number of observations100
Missing cells445
Missing cells (%)16.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory22.3 KiB
Average record size in memory228.3 B

Variable types

Text9
Categorical8
Numeric6
Unsupported3
Boolean1

Alerts

lclas_nm has constant value ""Constant
origin_nm has constant value ""Constant
dspsn_toilet_at has constant value ""Constant
updt_dt has constant value ""Constant
regist_dt has constant value ""Constant
mlsfc_nm is highly imbalanced (80.6%)Imbalance
ctprvn_cd is highly imbalanced (87.9%)Imbalance
ctprvn_nm is highly imbalanced (87.9%)Imbalance
legaldong_cd has 100 (100.0%) missing valuesMissing
road_nm_cd has 3 (3.0%) missing valuesMissing
addr_eng_nm has 3 (3.0%) missing valuesMissing
adstrd_cd has 3 (3.0%) missing valuesMissing
buld_nm has 35 (35.0%) missing valuesMissing
buld_manage_cd has 3 (3.0%) missing valuesMissing
tel_no has 97 (97.0%) missing valuesMissing
hmpg_url has 100 (100.0%) missing valuesMissing
adit_dc has 100 (100.0%) missing valuesMissing
esntl_id has unique valuesUnique
fclty_nm has unique valuesUnique
legaldong_cd is an unsupported type, check if it needs cleaning or further analysisUnsupported
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 09:53:34.819535
Analysis finished2023-12-10 09:53:36.151777
Duration1.33 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
2023-12-10T18:53:36.494330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length19
Mean length19
Min length19

Characters and Unicode

Total characters1900
Distinct characters16
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 rowKCDCOPO20N000000001
2nd rowKCDCOPO20N000002130
3rd rowKCDCOPO20N000000003
4th rowKCDCOPO20N000000004
5th rowKCDCOPO20N000000005
ValueCountFrequency (%)
kcdcopo20n000000001 1
 
1.0%
kcdcopo20n000000063 1
 
1.0%
kcdcopo20n000000074 1
 
1.0%
kcdcopo20n000000073 1
 
1.0%
kcdcopo20n000000072 1
 
1.0%
kcdcopo20n000000071 1
 
1.0%
kcdcopo20n000000070 1
 
1.0%
kcdcopo20n000000069 1
 
1.0%
kcdcopo20n000000068 1
 
1.0%
kcdcopo20n000000067 1
 
1.0%
Other values (90) 90
90.0%
2023-12-10T18:53:37.201683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 812
42.7%
C 200
 
10.5%
O 200
 
10.5%
2 122
 
6.4%
K 100
 
5.3%
D 100
 
5.3%
P 100
 
5.3%
N 100
 
5.3%
1 25
 
1.3%
3 22
 
1.2%
Other values (6) 119
 
6.3%

Most occurring categories

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

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 812
73.8%
2 122
 
11.1%
1 25
 
2.3%
3 22
 
2.0%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
7 20
 
1.8%
9 20
 
1.8%
8 19
 
1.7%
Uppercase Letter
ValueCountFrequency (%)
C 200
25.0%
O 200
25.0%
K 100
12.5%
D 100
12.5%
P 100
12.5%
N 100
12.5%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 812
73.8%
2 122
 
11.1%
1 25
 
2.3%
3 22
 
2.0%
4 20
 
1.8%
5 20
 
1.8%
6 20
 
1.8%
7 20
 
1.8%
9 20
 
1.8%
8 19
 
1.7%
Latin
ValueCountFrequency (%)
C 200
25.0%
O 200
25.0%
K 100
12.5%
D 100
12.5%
P 100
12.5%
N 100
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1900
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 812
42.7%
C 200
 
10.5%
O 200
 
10.5%
2 122
 
6.4%
K 100
 
5.3%
D 100
 
5.3%
P 100
 
5.3%
N 100
 
5.3%
1 25
 
1.3%
3 22
 
1.2%
Other values (6) 119
 
6.3%

lclas_nm
Categorical

CONSTANT 

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

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
무장애장소 100
100.0%

Length

2023-12-10T18:53:37.469786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:37.647603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무장애장소 100
100.0%

mlsfc_nm
Categorical

IMBALANCE 

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

Length

Max length9
Median length8
Mean length8.03
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row무장애장소_실외
2nd row무장애장소_나눔길
3rd row무장애장소_실외
4th row무장애장소_실외
5th row무장애장소_실외

Common Values

ValueCountFrequency (%)
무장애장소_실외 97
97.0%
무장애장소_나눔길 3
 
3.0%

Length

2023-12-10T18:53:37.840604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:38.044960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
무장애장소_실외 97
97.0%
무장애장소_나눔길 3
 
3.0%

fclty_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:38.430379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length7.7
Min length2

Characters and Unicode

Total characters770
Distinct characters215
Distinct categories7 ?
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 row갈산근린공원
2nd row안면도 자연휴양림 무장애나눔길
3rd row강서습지생태공원
4th row경복궁
5th row경의선책거리
ValueCountFrequency (%)
서울 6
 
3.9%
관악산 3
 
1.9%
덕수궁 3
 
1.9%
고려대학교 2
 
1.3%
본관 2
 
1.3%
무장애나눔길 2
 
1.3%
남산 2
 
1.3%
야외식물원 2
 
1.3%
남산서울타워 2
 
1.3%
명동 2
 
1.3%
Other values (129) 129
83.2%
2023-12-10T18:53:39.137006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
55
 
7.1%
원 29
 
3.8%
공 27
 
3.5%
서 25
 
3.2%
울 19
 
2.5%
산 17
 
2.2%
관 16
 
2.1%
동 15
 
1.9%
대 15
 
1.9%
문 14
 
1.8%
Other values (205) 538
69.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 681
88.4%
Space Separator 55
 
7.1%
Decimal Number 10
 
1.3%
Close Punctuation 9
 
1.2%
Open Punctuation 9
 
1.2%
Uppercase Letter 3
 
0.4%
Other Punctuation 3
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 29
 
4.3%
공 27
 
4.0%
서 25
 
3.7%
울 19
 
2.8%
산 17
 
2.5%
관 16
 
2.3%
동 15
 
2.2%
대 15
 
2.2%
문 14
 
2.1%
교 11
 
1.6%
Other values (192) 493
72.4%
Decimal Number
ValueCountFrequency (%)
9 3
30.0%
8 2
20.0%
4 2
20.0%
2 1
 
10.0%
5 1
 
10.0%
1 1
 
10.0%
Uppercase Letter
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%
Other Punctuation
ValueCountFrequency (%)
& 2
66.7%
. 1
33.3%
Space Separator
ValueCountFrequency (%)
55
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 681
88.4%
Common 86
 
11.2%
Latin 3
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 29
 
4.3%
공 27
 
4.0%
서 25
 
3.7%
울 19
 
2.8%
산 17
 
2.5%
관 16
 
2.3%
동 15
 
2.2%
대 15
 
2.2%
문 14
 
2.1%
교 11
 
1.6%
Other values (192) 493
72.4%
Common
ValueCountFrequency (%)
55
64.0%
) 9
 
10.5%
( 9
 
10.5%
9 3
 
3.5%
8 2
 
2.3%
4 2
 
2.3%
& 2
 
2.3%
2 1
 
1.2%
5 1
 
1.2%
1 1
 
1.2%
Latin
ValueCountFrequency (%)
D 2
66.7%
P 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 681
88.4%
ASCII 89
 
11.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
55
61.8%
) 9
 
10.1%
( 9
 
10.1%
9 3
 
3.4%
D 2
 
2.2%
8 2
 
2.2%
4 2
 
2.2%
& 2
 
2.2%
2 1
 
1.1%
5 1
 
1.1%
Other values (3) 3
 
3.4%
Hangul
ValueCountFrequency (%)
원 29
 
4.3%
공 27
 
4.0%
서 25
 
3.7%
울 19
 
2.8%
산 17
 
2.5%
관 16
 
2.3%
동 15
 
2.2%
대 15
 
2.2%
문 14
 
2.1%
교 11
 
1.6%
Other values (192) 493
72.4%

ctprvn_cd
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11
97 
44
 
1
43
 
1
42
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row11
2nd row44
3rd row11
4th row11
5th row11

Common Values

ValueCountFrequency (%)
11 97
97.0%
44 1
 
1.0%
43 1
 
1.0%
42 1
 
1.0%

Length

2023-12-10T18:53:39.384683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:39.593912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11 97
97.0%
44 1
 
1.0%
43 1
 
1.0%
42 1
 
1.0%

ctprvn_nm
Categorical

IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
서울특별시
97 
충청남도
 
1
충청북도
 
1
강원도
 
1

Length

Max length5
Median length5
Mean length4.96
Min length3

Unique

Unique3 ?
Unique (%)3.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 97
97.0%
충청남도 1
 
1.0%
충청북도 1
 
1.0%
강원도 1
 
1.0%

Length

2023-12-10T18:53:39.849420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:40.056969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 97
97.0%
충청남도 1
 
1.0%
충청북도 1
 
1.0%
강원도 1
 
1.0%

signgu_cd
Real number (ℝ)

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12300.51
Minimum11110
Maximum44825
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:40.269265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11110
5-th percentile11110
Q111140
median11290
Q311560
95-th percentile11711.5
Maximum44825
Range33715
Interquartile range (IQR)420

Descriptive statistics

Standard deviation5534.7442
Coefficient of variation (CV)0.44996055
Kurtosis29.908351
Mean12300.51
Median Absolute Deviation (MAD)180
Skewness5.5882568
Sum1230051
Variance30633393
MonotonicityNot monotonic
2023-12-10T18:53:40.520693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
11110 22
22.0%
11140 15
15.0%
11440 6
 
6.0%
11170 6
 
6.0%
11650 5
 
5.0%
11470 4
 
4.0%
11590 4
 
4.0%
11710 4
 
4.0%
11620 4
 
4.0%
11560 3
 
3.0%
Other values (16) 27
27.0%
ValueCountFrequency (%)
11110 22
22.0%
11140 15
15.0%
11170 6
 
6.0%
11200 2
 
2.0%
11215 2
 
2.0%
11260 2
 
2.0%
11290 3
 
3.0%
11305 2
 
2.0%
11320 1
 
1.0%
11350 1
 
1.0%
ValueCountFrequency (%)
44825 1
 
1.0%
43111 1
 
1.0%
42800 1
 
1.0%
11740 2
 
2.0%
11710 4
4.0%
11680 2
 
2.0%
11650 5
5.0%
11620 4
4.0%
11590 4
4.0%
11560 3
3.0%

signgu_nm
Categorical

Distinct26
Distinct (%)26.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
종로구
22 
중구
15 
마포구
6 
용산구
6 
서초구
5 
Other values (21)
46 

Length

Max length7
Median length3
Mean length2.95
Min length2

Unique

Unique7 ?
Unique (%)7.0%

Sample

1st row양천구
2nd row태안군
3rd row강서구
4th row종로구
5th row마포구

Common Values

ValueCountFrequency (%)
종로구 22
22.0%
중구 15
15.0%
마포구 6
 
6.0%
용산구 6
 
6.0%
서초구 5
 
5.0%
양천구 4
 
4.0%
송파구 4
 
4.0%
관악구 4
 
4.0%
동작구 4
 
4.0%
성북구 3
 
3.0%
Other values (16) 27
27.0%

Length

2023-12-10T18:53:40.773802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
종로구 22
21.8%
중구 15
14.9%
마포구 6
 
5.9%
용산구 6
 
5.9%
서초구 5
 
5.0%
양천구 4
 
4.0%
송파구 4
 
4.0%
관악구 4
 
4.0%
동작구 4
 
4.0%
성북구 3
 
3.0%
Other values (17) 28
27.7%

legaldong_cd
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

legaldong_nm
Text

Distinct73
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:41.337006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.37
Min length2

Characters and Unicode

Total characters337
Distinct characters94
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

Unique53 ?
Unique (%)53.0%

Sample

1st row신정동
2nd row안면읍
3rd row방화동
4th row세종로
5th row동교동
ValueCountFrequency (%)
세종로 4
 
4.0%
정동 4
 
4.0%
반포동 3
 
3.0%
동숭동 3
 
3.0%
신림동 3
 
3.0%
용산동2가 2
 
2.0%
을지로7가 2
 
2.0%
신정동 2
 
2.0%
현저동 2
 
2.0%
서초동 2
 
2.0%
Other values (64) 74
73.3%
2023-12-10T18:53:42.169983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 91
27.0%
가 20
 
5.9%
신 16
 
4.7%
로 12
 
3.6%
정 8
 
2.4%
2 8
 
2.4%
산 7
 
2.1%
성 5
 
1.5%
세 4
 
1.2%
암 4
 
1.2%
Other values (84) 162
48.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
94.1%
Decimal Number 19
 
5.6%
Space Separator 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 91
28.7%
가 20
 
6.3%
신 16
 
5.0%
로 12
 
3.8%
정 8
 
2.5%
산 7
 
2.2%
성 5
 
1.6%
세 4
 
1.3%
암 4
 
1.3%
면 4
 
1.3%
Other values (77) 146
46.1%
Decimal Number
ValueCountFrequency (%)
2 8
42.1%
1 3
 
15.8%
3 2
 
10.5%
4 2
 
10.5%
5 2
 
10.5%
7 2
 
10.5%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
94.1%
Common 20
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 91
28.7%
가 20
 
6.3%
신 16
 
5.0%
로 12
 
3.8%
정 8
 
2.5%
산 7
 
2.2%
성 5
 
1.6%
세 4
 
1.3%
암 4
 
1.3%
면 4
 
1.3%
Other values (77) 146
46.1%
Common
ValueCountFrequency (%)
2 8
40.0%
1 3
 
15.0%
3 2
 
10.0%
4 2
 
10.0%
5 2
 
10.0%
7 2
 
10.0%
1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
94.1%
ASCII 20
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 91
28.7%
가 20
 
6.3%
신 16
 
5.0%
로 12
 
3.8%
정 8
 
2.5%
산 7
 
2.2%
성 5
 
1.6%
세 4
 
1.3%
암 4
 
1.3%
면 4
 
1.3%
Other values (77) 146
46.1%
ASCII
ValueCountFrequency (%)
2 8
40.0%
1 3
 
15.0%
3 2
 
10.0%
4 2
 
10.0%
5 2
 
10.0%
7 2
 
10.0%
1
 
5.0%

road_nm_cd
Real number (ℝ)

MISSING 

Distinct79
Distinct (%)81.4%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1.2001066 × 1011
Minimum1.1110201 × 1011
Maximum4.4825226 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:42.470563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110201 × 1011
5-th percentile1.111031 × 1011
Q11.1140201 × 1011
median1.1260412 × 1011
Q31.1545415 × 1011
95-th percentile1.1710312 × 1011
Maximum4.4825226 × 1011
Range3.3715026 × 1011
Interquartile range (IQR)4.0521462 × 109

Descriptive statistics

Standard deviation4.6690015 × 1010
Coefficient of variation (CV)0.38904889
Kurtosis45.848686
Mean1.2001066 × 1011
Median Absolute Deviation (MAD)1.5010184 × 109
Skewness6.838144
Sum1.1641034 × 1013
Variance2.1799575 × 1021
MonotonicityNot monotonic
2023-12-10T18:53:42.740547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
111402005001 4
 
4.0%
111403101006 4
 
4.0%
111103005004 3
 
3.0%
116504163449 3
 
3.0%
117103123023 3
 
3.0%
114103000008 2
 
2.0%
112903005035 2
 
2.0%
111103100005 2
 
2.0%
115904160388 2
 
2.0%
111704103024 2
 
2.0%
Other values (69) 70
70.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
111102005001 1
 
1.0%
111103005004 3
3.0%
111103100002 1
 
1.0%
111103100004 1
 
1.0%
111103100005 2
2.0%
111103100007 2
2.0%
111103100010 1
 
1.0%
111103100013 1
 
1.0%
111103100014 1
 
1.0%
111103100019 1
 
1.0%
ValueCountFrequency (%)
448252264001 1
 
1.0%
431114520395 1
 
1.0%
117403016054 1
 
1.0%
117402000008 1
 
1.0%
117103123023 3
3.0%
117103123017 1
 
1.0%
116804166277 1
 
1.0%
116803122004 1
 
1.0%
116504163449 3
3.0%
116504163433 1
 
1.0%

fclty_road_nm_addr
Text

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:43.449202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length38
Median length29
Mean length24.34
Min length19

Characters and Unicode

Total characters2434
Distinct characters161
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

Unique79 ?
Unique (%)79.0%

Sample

1st row서울특별시 양천구 중앙로14나길 33-1 (신정동)
2nd row충청남도 태안군 안면읍 안면대로 3195-6
3rd row서울특별시 강서구 양천로27길 279-23 (방화동)
4th row서울특별시 종로구 사직로 161 (세종로)
5th row서울특별시 마포구 와우산로35길 50-4 (동교동)
ValueCountFrequency (%)
서울특별시 96
 
19.0%
종로구 22
 
4.3%
중구 15
 
3.0%
용산구 6
 
1.2%
마포구 6
 
1.2%
서초구 5
 
1.0%
세종대로 5
 
1.0%
세종로 4
 
0.8%
관악구 4
 
0.8%
지하 4
 
0.8%
Other values (256) 339
67.0%
2023-12-10T18:53:44.445743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
406
 
16.7%
로 120
 
4.9%
서 111
 
4.6%
동 106
 
4.4%
구 102
 
4.2%
울 98
 
4.0%
시 97
 
4.0%
별 96
 
3.9%
특 96
 
3.9%
) 95
 
3.9%
Other values (151) 1107
45.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1501
61.7%
Space Separator 406
 
16.7%
Decimal Number 324
 
13.3%
Close Punctuation 95
 
3.9%
Open Punctuation 95
 
3.9%
Dash Punctuation 11
 
0.5%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 120
 
8.0%
서 111
 
7.4%
동 106
 
7.1%
구 102
 
6.8%
울 98
 
6.5%
시 97
 
6.5%
별 96
 
6.4%
특 96
 
6.4%
길 38
 
2.5%
종 33
 
2.2%
Other values (135) 604
40.2%
Decimal Number
ValueCountFrequency (%)
1 73
22.5%
2 45
13.9%
3 36
11.1%
5 31
9.6%
0 30
9.3%
4 29
 
9.0%
7 24
 
7.4%
8 21
 
6.5%
9 18
 
5.6%
6 17
 
5.2%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
406
100.0%
Close Punctuation
ValueCountFrequency (%)
) 95
100.0%
Open Punctuation
ValueCountFrequency (%)
( 95
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1501
61.7%
Common 933
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 120
 
8.0%
서 111
 
7.4%
동 106
 
7.1%
구 102
 
6.8%
울 98
 
6.5%
시 97
 
6.5%
별 96
 
6.4%
특 96
 
6.4%
길 38
 
2.5%
종 33
 
2.2%
Other values (135) 604
40.2%
Common
ValueCountFrequency (%)
406
43.5%
) 95
 
10.2%
( 95
 
10.2%
1 73
 
7.8%
2 45
 
4.8%
3 36
 
3.9%
5 31
 
3.3%
0 30
 
3.2%
4 29
 
3.1%
7 24
 
2.6%
Other values (6) 69
 
7.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1501
61.7%
ASCII 933
38.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
406
43.5%
) 95
 
10.2%
( 95
 
10.2%
1 73
 
7.8%
2 45
 
4.8%
3 36
 
3.9%
5 31
 
3.3%
0 30
 
3.2%
4 29
 
3.1%
7 24
 
2.6%
Other values (6) 69
 
7.4%
Hangul
ValueCountFrequency (%)
로 120
 
8.0%
서 111
 
7.4%
동 106
 
7.1%
구 102
 
6.8%
울 98
 
6.5%
시 97
 
6.5%
별 96
 
6.4%
특 96
 
6.4%
길 38
 
2.5%
종 33
 
2.2%
Other values (135) 604
40.2%

lnm_addr
Text

Distinct89
Distinct (%)89.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:53:45.443406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length29
Mean length23.67
Min length14

Characters and Unicode

Total characters2367
Distinct characters220
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)79.0%

Sample

1st row서울특별시 양천구 신정동 177
2nd row충청남도 태안군 안면읍 승언리 135
3rd row서울특별시 강서구 방화동 47
4th row서울특별시 종로구 세종로 1-1
5th row서울특별시 마포구 동교동 190-1
ValueCountFrequency (%)
서울특별시 95
 
19.6%
종로구 22
 
4.5%
중구 15
 
3.1%
용산구 6
 
1.2%
마포구 6
 
1.2%
서초구 5
 
1.0%
양천구 4
 
0.8%
동작구 4
 
0.8%
관악구 4
 
0.8%
정동 4
 
0.8%
Other values (249) 320
66.0%
2023-12-10T18:53:46.378456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
385
 
16.3%
서 119
 
5.0%
울 105
 
4.4%
구 104
 
4.4%
동 104
 
4.4%
1 104
 
4.4%
시 100
 
4.2%
별 96
 
4.1%
특 95
 
4.0%
- 68
 
2.9%
Other values (210) 1087
45.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1540
65.1%
Space Separator 385
 
16.3%
Decimal Number 350
 
14.8%
Dash Punctuation 68
 
2.9%
Lowercase Letter 11
 
0.5%
Uppercase Letter 6
 
0.3%
Other Punctuation 3
 
0.1%
Open Punctuation 2
 
0.1%
Close Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 119
 
7.7%
울 105
 
6.8%
구 104
 
6.8%
동 104
 
6.8%
시 100
 
6.5%
별 96
 
6.2%
특 95
 
6.2%
로 35
 
2.3%
산 29
 
1.9%
종 27
 
1.8%
Other values (184) 726
47.1%
Decimal Number
ValueCountFrequency (%)
1 104
29.7%
2 53
15.1%
5 33
 
9.4%
0 30
 
8.6%
4 27
 
7.7%
3 26
 
7.4%
6 21
 
6.0%
7 20
 
5.7%
9 19
 
5.4%
8 17
 
4.9%
Lowercase Letter
ValueCountFrequency (%)
o 4
36.4%
d 2
18.2%
g 2
18.2%
m 1
 
9.1%
p 1
 
9.1%
a 1
 
9.1%
Uppercase Letter
ValueCountFrequency (%)
N 2
33.3%
Y 2
33.3%
T 2
33.3%
Other Punctuation
ValueCountFrequency (%)
& 1
33.3%
; 1
33.3%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
385
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 68
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1540
65.1%
Common 810
34.2%
Latin 17
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 119
 
7.7%
울 105
 
6.8%
구 104
 
6.8%
동 104
 
6.8%
시 100
 
6.5%
별 96
 
6.2%
특 95
 
6.2%
로 35
 
2.3%
산 29
 
1.9%
종 27
 
1.8%
Other values (184) 726
47.1%
Common
ValueCountFrequency (%)
385
47.5%
1 104
 
12.8%
- 68
 
8.4%
2 53
 
6.5%
5 33
 
4.1%
0 30
 
3.7%
4 27
 
3.3%
3 26
 
3.2%
6 21
 
2.6%
7 20
 
2.5%
Other values (7) 43
 
5.3%
Latin
ValueCountFrequency (%)
o 4
23.5%
d 2
11.8%
g 2
11.8%
N 2
11.8%
Y 2
11.8%
T 2
11.8%
m 1
 
5.9%
p 1
 
5.9%
a 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1540
65.1%
ASCII 827
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
385
46.6%
1 104
 
12.6%
- 68
 
8.2%
2 53
 
6.4%
5 33
 
4.0%
0 30
 
3.6%
4 27
 
3.3%
3 26
 
3.1%
6 21
 
2.5%
7 20
 
2.4%
Other values (16) 60
 
7.3%
Hangul
ValueCountFrequency (%)
서 119
 
7.7%
울 105
 
6.8%
구 104
 
6.8%
동 104
 
6.8%
시 100
 
6.5%
별 96
 
6.2%
특 95
 
6.2%
로 35
 
2.3%
산 29
 
1.9%
종 27
 
1.8%
Other values (184) 726
47.1%

addr_eng_nm
Text

MISSING 

Distinct86
Distinct (%)88.7%
Missing3
Missing (%)3.0%
Memory size932.0 B
2023-12-10T18:53:46.972425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length74
Median length45
Mean length36.701031
Min length27

Characters and Unicode

Total characters3560
Distinct characters51
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

Unique76 ?
Unique (%)78.4%

Sample

1st row33-1, Jungang-ro 14na-gil, Yangcheon-gu, Seoul
2nd row3195-6, Anmyeon-daero, Anmyeon-eup, Taean-gun, Chungcheongnam-do
3rd row279-23, Yangcheon-ro 27-gil, Gangseo-gu, Seoul
4th row161, Sajik-ro, Jongno-gu, Seoul
5th row50-4, Wausan-ro 35-gil, Mapo-gu, Seoul
ValueCountFrequency (%)
seoul 95
 
22.5%
jongno-gu 22
 
5.2%
jung-gu 15
 
3.5%
yongsan-gu 6
 
1.4%
sejong-daero 6
 
1.4%
mapo-gu 6
 
1.4%
seocho-gu 5
 
1.2%
dongjak-gu 4
 
0.9%
eulji-ro 4
 
0.9%
songpa-gu 4
 
0.9%
Other values (192) 256
60.5%
2023-12-10T18:53:47.744633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 362
 
10.2%
326
 
9.2%
, 294
 
8.3%
g 287
 
8.1%
u 268
 
7.5%
n 254
 
7.1%
- 232
 
6.5%
e 198
 
5.6%
l 150
 
4.2%
a 147
 
4.1%
Other values (41) 1042
29.3%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 2114
59.4%
Space Separator 326
 
9.2%
Uppercase Letter 298
 
8.4%
Other Punctuation 295
 
8.3%
Decimal Number 295
 
8.3%
Dash Punctuation 232
 
6.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 362
17.1%
g 287
13.6%
u 268
12.7%
n 254
12.0%
e 198
9.4%
l 150
7.1%
a 147
7.0%
r 89
 
4.2%
i 66
 
3.1%
d 40
 
1.9%
Other values (10) 253
12.0%
Uppercase Letter
ValueCountFrequency (%)
S 142
47.7%
J 42
 
14.1%
G 21
 
7.0%
Y 18
 
6.0%
N 12
 
4.0%
M 10
 
3.4%
D 10
 
3.4%
B 8
 
2.7%
C 8
 
2.7%
T 6
 
2.0%
Other values (7) 21
 
7.0%
Decimal Number
ValueCountFrequency (%)
1 67
22.7%
2 35
11.9%
3 33
11.2%
5 29
9.8%
0 28
9.5%
4 26
 
8.8%
8 21
 
7.1%
7 21
 
7.1%
9 18
 
6.1%
6 17
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 294
99.7%
. 1
 
0.3%
Space Separator
ValueCountFrequency (%)
326
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 232
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 2412
67.8%
Common 1148
32.2%

Most frequent character per script

Latin
ValueCountFrequency (%)
o 362
15.0%
g 287
11.9%
u 268
11.1%
n 254
10.5%
e 198
8.2%
l 150
 
6.2%
a 147
 
6.1%
S 142
 
5.9%
r 89
 
3.7%
i 66
 
2.7%
Other values (27) 449
18.6%
Common
ValueCountFrequency (%)
326
28.4%
, 294
25.6%
- 232
20.2%
1 67
 
5.8%
2 35
 
3.0%
3 33
 
2.9%
5 29
 
2.5%
0 28
 
2.4%
4 26
 
2.3%
8 21
 
1.8%
Other values (4) 57
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3560
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
o 362
 
10.2%
326
 
9.2%
, 294
 
8.3%
g 287
 
8.1%
u 268
 
7.5%
n 254
 
7.1%
- 232
 
6.5%
e 198
 
5.6%
l 150
 
4.2%
a 147
 
4.1%
Other values (41) 1042
29.3%

adstrd_cd
Real number (ℝ)

MISSING 

Distinct71
Distinct (%)73.2%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1.2000856 × 109
Minimum1.1110113 × 109
Maximum4.4825253 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:48.042971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110113 × 109
5-th percentile1.1110119 × 109
Q11.1140112 × 109
median1.1260105 × 109
Q31.1545102 × 109
95-th percentile1.1710103 × 109
Maximum4.4825253 × 109
Range3.371514 × 109
Interquartile range (IQR)40499000

Descriptive statistics

Standard deviation4.6690236 × 108
Coefficient of variation (CV)0.38905753
Kurtosis45.848756
Mean1.2000856 × 109
Median Absolute Deviation (MAD)14998400
Skewness6.8381502
Sum1.1640831 × 1011
Variance2.1799782 × 1017
MonotonicityNot monotonic
2023-12-10T18:53:48.327536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1111011900 4
 
4.0%
1114016700 4
 
4.0%
1111016800 3
 
3.0%
1165010700 3
 
3.0%
1114014900 2
 
2.0%
1117012900 2
 
2.0%
1165010800 2
 
2.0%
1141010900 2
 
2.0%
1144012500 2
 
2.0%
1171010200 2
 
2.0%
Other values (61) 71
71.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
1111011300 1
 
1.0%
1111011600 1
 
1.0%
1111011900 4
4.0%
1111012100 2
2.0%
1111013000 1
 
1.0%
1111013500 1
 
1.0%
1111013600 1
 
1.0%
1111014000 2
2.0%
1111014800 1
 
1.0%
1111016800 3
3.0%
ValueCountFrequency (%)
4482525321 1
1.0%
4311132021 1
1.0%
1174010900 1
1.0%
1174010500 1
1.0%
1171010800 1
1.0%
1171010200 2
2.0%
1171010100 1
1.0%
1168010700 1
1.0%
1168010500 1
1.0%
1165010800 2
2.0%

buld_nm
Text

MISSING 

Distinct57
Distinct (%)87.7%
Missing35
Missing (%)35.0%
Memory size932.0 B
2023-12-10T18:53:48.884601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length7.2
Min length2

Characters and Unicode

Total characters468
Distinct characters176
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique50 ?
Unique (%)76.9%

Sample

1st row영등포경찰서
2nd row서울역사박물관
3rd row충청북도산림환경연구소
4th row경희궁,시립미술관
5th row종근당빌딩
ValueCountFrequency (%)
한강공원 3
 
3.7%
안내센터 3
 
3.7%
반포 3
 
3.7%
1호선 2
 
2.5%
ytn서울타워 2
 
2.5%
하나금융그룹 2
 
2.5%
명동사옥 2
 
2.5%
롯데월드타워앤드롯데월드몰 2
 
2.5%
덕수궁 2
 
2.5%
고려대학교안암캠퍼스 2
 
2.5%
Other values (57) 58
71.6%
2023-12-10T18:53:49.727022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
16
 
3.4%
원 12
 
2.6%
관 11
 
2.4%
사 11
 
2.4%
공 11
 
2.4%
대 11
 
2.4%
서 10
 
2.1%
울 8
 
1.7%
터 8
 
1.7%
센 7
 
1.5%
Other values (166) 363
77.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 422
90.2%
Space Separator 16
 
3.4%
Lowercase Letter 11
 
2.4%
Uppercase Letter 6
 
1.3%
Decimal Number 6
 
1.3%
Other Punctuation 3
 
0.6%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
원 12
 
2.8%
관 11
 
2.6%
사 11
 
2.6%
공 11
 
2.6%
대 11
 
2.6%
서 10
 
2.4%
울 8
 
1.9%
터 8
 
1.9%
센 7
 
1.7%
드 7
 
1.7%
Other values (146) 326
77.3%
Lowercase Letter
ValueCountFrequency (%)
o 4
36.4%
d 2
18.2%
g 2
18.2%
a 1
 
9.1%
m 1
 
9.1%
p 1
 
9.1%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
5 1
16.7%
9 1
16.7%
2 1
16.7%
6 1
16.7%
Uppercase Letter
ValueCountFrequency (%)
N 2
33.3%
T 2
33.3%
Y 2
33.3%
Other Punctuation
ValueCountFrequency (%)
; 1
33.3%
& 1
33.3%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 422
90.2%
Common 29
 
6.2%
Latin 17
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
원 12
 
2.8%
관 11
 
2.6%
사 11
 
2.6%
공 11
 
2.6%
대 11
 
2.6%
서 10
 
2.4%
울 8
 
1.9%
터 8
 
1.9%
센 7
 
1.7%
드 7
 
1.7%
Other values (146) 326
77.3%
Common
ValueCountFrequency (%)
16
55.2%
1 2
 
6.9%
) 2
 
6.9%
( 2
 
6.9%
; 1
 
3.4%
& 1
 
3.4%
5 1
 
3.4%
9 1
 
3.4%
2 1
 
3.4%
6 1
 
3.4%
Latin
ValueCountFrequency (%)
o 4
23.5%
N 2
11.8%
T 2
11.8%
Y 2
11.8%
d 2
11.8%
g 2
11.8%
a 1
 
5.9%
m 1
 
5.9%
p 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 422
90.2%
ASCII 46
 
9.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
16
34.8%
o 4
 
8.7%
N 2
 
4.3%
T 2
 
4.3%
1 2
 
4.3%
Y 2
 
4.3%
) 2
 
4.3%
( 2
 
4.3%
d 2
 
4.3%
g 2
 
4.3%
Other values (10) 10
21.7%
Hangul
ValueCountFrequency (%)
원 12
 
2.8%
관 11
 
2.6%
사 11
 
2.6%
공 11
 
2.6%
대 11
 
2.6%
서 10
 
2.4%
울 8
 
1.9%
터 8
 
1.9%
센 7
 
1.7%
드 7
 
1.7%
Other values (146) 326
77.3%

buld_manage_cd
Real number (ℝ)

MISSING 

Distinct82
Distinct (%)84.5%
Missing3
Missing (%)3.0%
Infinite0
Infinite (%)0.0%
Mean1.2007032 × 1024
Minimum1.1110115 × 1024
Maximum4.4825253 × 1024
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:50.039642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.1110115 × 1024
5-th percentile1.1110119 × 1024
Q11.1140112 × 1024
median1.1260105 × 1024
Q31.1545102 × 1024
95-th percentile1.1710103 × 1024
Maximum4.4825253 × 1024
Range3.3715138 × 1024
Interquartile range (IQR)4.0499 × 1022

Descriptive statistics

Standard deviation4.7108081 × 1023
Coefficient of variation (CV)0.39233744
Kurtosis45.769751
Mean1.2007032 × 1024
Median Absolute Deviation (MAD)1.49984 × 1022
Skewness6.8341618
Sum1.1646821 × 1026
Variance2.2191713 × 1047
MonotonicityNot monotonic
2023-12-10T18:53:50.328703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1.11101190010001e+24 3
 
3.0%
1.11401670010005e+24 3
 
3.0%
1.16501070010115e+24 3
 
3.0%
1.11101680010001e+24 2
 
2.0%
1.14101090010101e+24 2
 
2.0%
1.17101020010029e+24 2
 
2.0%
1.11401050010181e+24 2
 
2.0%
1.11701020020001e+24 2
 
2.0%
1.15901090010385e+24 2
 
2.0%
1.11401490010002e+24 2
 
2.0%
Other values (72) 74
74.0%
(Missing) 3
 
3.0%
ValueCountFrequency (%)
1.11101150010001e+24 1
 
1.0%
1.1110116001011e+24 1
 
1.0%
1.11101160010148e+24 1
 
1.0%
1.11101190010001e+24 3
3.0%
1.11101210010001e+24 1
 
1.0%
1.11101210010002e+24 1
 
1.0%
1.11101300010002e+24 1
 
1.0%
1.11101350010045e+24 1
 
1.0%
1.11101360010043e+24 1
 
1.0%
1.11101400010105e+24 1
 
1.0%
ValueCountFrequency (%)
4.48252532110142e+24 1
1.0%
4.37103202100016e+24 1
1.0%
1.17401090010522e+24 1
1.0%
1.17401050010003e+24 1
1.0%
1.17101080010409e+24 1
1.0%
1.17101020010029e+24 2
2.0%
1.1710101001004e+24 1
1.0%
1.16801070010649e+24 1
1.0%
1.16801050010073e+24 1
1.0%
1.16501080020141e+24 1
1.0%

tel_no
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing97
Missing (%)97.0%
Memory size932.0 B
2023-12-10T18:53:50.620464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.333333
Min length11

Characters and Unicode

Total characters34
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3 ?
Unique (%)100.0%

Sample

1st row02-880-3675
2nd row02-749-4500
3rd row02-3153-5800
ValueCountFrequency (%)
02-880-3675 1
33.3%
02-749-4500 1
33.3%
02-3153-5800 1
33.3%
2023-12-10T18:53:51.229477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8
23.5%
- 6
17.6%
5 4
11.8%
2 3
 
8.8%
8 3
 
8.8%
3 3
 
8.8%
7 2
 
5.9%
4 2
 
5.9%
6 1
 
2.9%
9 1
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 28
82.4%
Dash Punctuation 6
 
17.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8
28.6%
5 4
14.3%
2 3
 
10.7%
8 3
 
10.7%
3 3
 
10.7%
7 2
 
7.1%
4 2
 
7.1%
6 1
 
3.6%
9 1
 
3.6%
1 1
 
3.6%
Dash Punctuation
ValueCountFrequency (%)
- 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 34
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8
23.5%
- 6
17.6%
5 4
11.8%
2 3
 
8.8%
8 3
 
8.8%
3 3
 
8.8%
7 2
 
5.9%
4 2
 
5.9%
6 1
 
2.9%
9 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 34
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8
23.5%
- 6
17.6%
5 4
11.8%
2 3
 
8.8%
8 3
 
8.8%
3 3
 
8.8%
7 2
 
5.9%
4 2
 
5.9%
6 1
 
2.9%
9 1
 
2.9%

zip_no
Text

Distinct82
Distinct (%)82.8%
Missing1
Missing (%)1.0%
Memory size932.0 B
2023-12-10T18:53:51.717983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length5
Mean length5
Min length5

Characters and Unicode

Total characters495
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique70 ?
Unique (%)70.7%

Sample

1st row08104
2nd row32166
3rd row07518
4th row03045
5th row04052
ValueCountFrequency (%)
04519 4
 
4.0%
04340 3
 
3.0%
03045 3
 
3.0%
06500 3
 
3.0%
04538 2
 
2.0%
02841 2
 
2.0%
07062 2
 
2.0%
03087 2
 
2.0%
05551 2
 
2.0%
03732 2
 
2.0%
Other values (72) 74
74.7%
2023-12-10T18:53:52.447511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 141
28.5%
3 51
 
10.3%
5 49
 
9.9%
4 47
 
9.5%
1 39
 
7.9%
2 38
 
7.7%
9 33
 
6.7%
7 33
 
6.7%
8 32
 
6.5%
6 31
 
6.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 494
99.8%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 141
28.5%
3 51
 
10.3%
5 49
 
9.9%
4 47
 
9.5%
1 39
 
7.9%
2 38
 
7.7%
9 33
 
6.7%
7 33
 
6.7%
8 32
 
6.5%
6 31
 
6.3%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 495
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 141
28.5%
3 51
 
10.3%
5 49
 
9.9%
4 47
 
9.5%
1 39
 
7.9%
2 38
 
7.7%
9 33
 
6.7%
7 33
 
6.7%
8 32
 
6.5%
6 31
 
6.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 495
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 141
28.5%
3 51
 
10.3%
5 49
 
9.9%
4 47
 
9.5%
1 39
 
7.9%
2 38
 
7.7%
9 33
 
6.7%
7 33
 
6.7%
8 32
 
6.5%
6 31
 
6.3%

hmpg_url
Unsupported

MISSING  REJECTED  UNSUPPORTED 

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

fclty_la
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.537305
Minimum36.49656
Maximum38.098909
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:52.749849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36.49656
5-th percentile37.468368
Q137.520012
median37.559862
Q337.576218
95-th percentile37.603096
Maximum38.098909
Range1.6023483
Interquartile range (IQR)0.056206725

Descriptive statistics

Standard deviation0.15648294
Coefficient of variation (CV)0.0041687314
Kurtosis32.054625
Mean37.537305
Median Absolute Deviation (MAD)0.0212327
Skewness-4.5688102
Sum3753.7305
Variance0.024486911
MonotonicityNot monotonic
2023-12-10T18:53:53.039443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.507678 2
 
2.0%
37.5066954 1
 
1.0%
37.5651434 1
 
1.0%
37.4929729 1
 
1.0%
37.5549251 1
 
1.0%
37.5076836 1
 
1.0%
37.5171534 1
 
1.0%
37.5089677 1
 
1.0%
37.5560137 1
 
1.0%
37.524649 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
36.4965604 1
1.0%
36.6260981 1
1.0%
37.4342219 1
1.0%
37.4584718 1
1.0%
37.458569 1
1.0%
37.4688835 1
1.0%
37.4711317 1
1.0%
37.4759904 1
1.0%
37.4787114 1
1.0%
37.4929339 1
1.0%
ValueCountFrequency (%)
38.0989087 1
1.0%
37.696987 1
1.0%
37.6725633 1
1.0%
37.6484481 1
1.0%
37.6233877 1
1.0%
37.6020279 1
1.0%
37.5990952 1
1.0%
37.5984058 1
1.0%
37.5924871 1
1.0%
37.5898997 1
1.0%

fclty_lo
Real number (ℝ)

Distinct99
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.99385
Minimum126.35502
Maximum128.02563
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:53:53.293989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.35502
5-th percentile126.87014
Q1126.95645
median126.9812
Q3127.01
95-th percentile127.11419
Maximum128.02563
Range1.6706087
Interquartile range (IQR)0.05355405

Descriptive statistics

Standard deviation0.15331432
Coefficient of variation (CV)0.0012072579
Kurtosis26.275902
Mean126.99385
Median Absolute Deviation (MAD)0.02833035
Skewness3.2439995
Sum12699.385
Variance0.023505281
MonotonicityNot monotonic
2023-12-10T18:53:53.572277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.9926825 2
 
2.0%
126.8701381 1
 
1.0%
126.9850038 1
 
1.0%
126.9231615 1
 
1.0%
126.9798934 1
 
1.0%
126.9926797 1
 
1.0%
126.9186112 1
 
1.0%
126.8912917 1
 
1.0%
126.9716025 1
 
1.0%
126.8773231 1
 
1.0%
Other values (89) 89
89.0%
ValueCountFrequency (%)
126.3550176 1
1.0%
126.8128383 1
1.0%
126.8300634 1
1.0%
126.8537657 1
1.0%
126.870085 1
1.0%
126.8701381 1
1.0%
126.8773231 1
1.0%
126.8897676 1
1.0%
126.8912917 1
1.0%
126.8919733 1
1.0%
ValueCountFrequency (%)
128.0256263 1
1.0%
127.6678269 1
1.0%
127.1568388 1
1.0%
127.1189829 1
1.0%
127.1148327 1
1.0%
127.1141593 1
1.0%
127.1042833 1
1.0%
127.1025918 1
1.0%
127.0979006 1
1.0%
127.0957822 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

2023-12-10T18:53:53.852548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:54.015728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문화정보원 100
100.0%

dspsn_toilet_at
Boolean

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size232.0 B
False
100 
ValueCountFrequency (%)
False 100
100.0%
2023-12-10T18:53:54.126283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

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

2023-12-10T18:53:54.286259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:54.437865image/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

2023-12-10T18:53:54.608617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:53:54.795033image/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_nmdspsn_toilet_atadit_dcupdt_dtregist_dt
0KCDCOPO20N000000001무장애장소무장애장소_실외갈산근린공원11서울특별시11470양천구<NA>신정동114704142362서울특별시 양천구 중앙로14나길 33-1 (신정동)서울특별시 양천구 신정동 17733-1, Jungang-ro 14na-gil, Yangcheon-gu, Seoul1147010100<NA>1147010100101770013000586<NA>08104<NA>37.506695126.870138문화정보원N<NA>2020120112000020201201120000
1KCDCOPO20N000002130무장애장소무장애장소_나눔길안면도 자연휴양림 무장애나눔길44충청남도44825태안군<NA>안면읍448252264001충청남도 태안군 안면읍 안면대로 3195-6충청남도 태안군 안면읍 승언리 1353195-6, Anmyeon-daero, Anmyeon-eup, Taean-gun, Chungcheongnam-do4482525321<NA>4482525321101420000002653<NA>32166<NA>36.49656126.355018문화정보원N<NA>2020120112000020201201120000
2KCDCOPO20N000000003무장애장소무장애장소_실외강서습지생태공원11서울특별시11500강서구<NA>방화동115004145401서울특별시 강서구 양천로27길 279-23 (방화동)서울특별시 강서구 방화동 47279-23, Yangcheon-ro 27-gil, Gangseo-gu, Seoul1150010900<NA>1150010900100260000000001<NA>07518<NA>37.588899126.812838문화정보원N<NA>2020120112000020201201120000
3KCDCOPO20N000000004무장애장소무장애장소_실외경복궁11서울특별시11110종로구<NA>세종로111103100005서울특별시 종로구 사직로 161 (세종로)서울특별시 종로구 세종로 1-1161, Sajik-ro, Jongno-gu, Seoul1111011900<NA>1111011900100010057031464<NA>03045<NA>37.578822126.976993문화정보원N<NA>2020120112000020201201120000
4KCDCOPO20N000000005무장애장소무장애장소_실외경의선책거리11서울특별시11440마포구<NA>동교동114404139475서울특별시 마포구 와우산로35길 50-4 (동교동)서울특별시 마포구 동교동 190-150-4, Wausan-ro 35-gil, Mapo-gu, Seoul1144012100<NA>1144012100101900001000001<NA>04052<NA>37.556524126.928376문화정보원N<NA>2020120112000020201201120000
5KCDCOPO20N000000006무장애장소무장애장소_실외경찰혼11서울특별시11560영등포구<NA>당산동3가115602005008서울특별시 영등포구 국회대로 608 (당산동3가)서울특별시 영등포구 당산동3가 2-11 영등포경찰서608, Gukhoe-daero, Yeongdeungpo-gu, Seoul1156011300영등포경찰서1156011300100020011028928<NA>07258<NA>37.526341126.900643문화정보원N<NA>2020120112000020201201120000
6KCDCOPO20N000000007무장애장소무장애장소_실외경희궁11서울특별시11110종로구<NA>신문로2가111103005004서울특별시 종로구 새문안로 55 (신문로2가)서울특별시 종로구 신문로2가 2-1 서울역사박물관55, Saemunan-ro, Jongno-gu, Seoul1111012100서울역사박물관1111012100100020001028158<NA>03177<NA>37.570442126.968508문화정보원N<NA>2020120112000020201201120000
7KCDCOPO20N000002131무장애장소무장애장소_나눔길미동산수목원 무장애 나눔길43충청북도43111청주시 상당구<NA>미원면431114520395충청북도 청주시 상당구 미원면 수목원길 51충청북도 청주시 상당구 미원면 미원리 20 충청북도산림환경연구소51, Sumogwon-gil, Miwon-myeon, Sangdang-gu, Cheongju-si, Chungcheongbuk-do4311132021충청북도산림환경연구소4371032021000160000062284<NA>28199<NA>36.626098127.667827문화정보원N<NA>2020120112000020201201120000
8KCDCOPO20N000000009무장애장소무장애장소_실외경희궁공원11서울특별시11110종로구<NA>신문로2가111103005004서울특별시 종로구 새문안로 45 (신문로2가)서울특별시 종로구 신문로2가 1-126 경희궁,시립미술관45, Saemunan-ro, Jongno-gu, Seoul1111012100경희궁,시립미술관1111012100100010126028567<NA>03177<NA>37.570759126.969105문화정보원N<NA>2020120112000020201201120000
9KCDCOPO20N000000010무장애장소무장애장소_실외고촌홀(제약박물관)11서울특별시11410서대문구<NA>충정로3가114103112011서울특별시 서대문구 충정로 8 (충정로3가)서울특별시 서대문구 충정로3가 368 종근당빌딩8, Chungjeong-ro, Seodaemun-gu, Seoul1141010200종근당빌딩1141010200103680002030454<NA>03742<NA>37.559746126.963139문화정보원N<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_nmdspsn_toilet_atadit_dcupdt_dtregist_dt
90KCDCOPO20N000000091무장애장소무장애장소_실외삼청공원11서울특별시11110종로구<NA>삼청동111103100004서울특별시 종로구 북촌로 134-3 (삼청동)서울특별시 종로구 삼청동 산2-2 삼청공원134-3, Bukchon-ro, Jongno-gu, Seoul1111014000삼청공원1111014000200020001026142<NA>03050<NA>37.587985126.98407문화정보원N<NA>2020120112000020201201120000
91KCDCOPO20N000000092무장애장소무장애장소_실외삼청동길11서울특별시11110종로구<NA>삼청동111103100007서울특별시 종로구 삼청로 107 (삼청동)서울특별시 종로구 삼청동 105-1 삼청동주민센터107, Samcheong-ro, Jongno-gu, Seoul1111014000삼청동주민센터1111014000101050001026465<NA>03049<NA>37.584994126.981837문화정보원N<NA>2020120112000020201201120000
92KCDCOPO20N000000093무장애장소무장애장소_실외상동교회11서울특별시11140중구<NA>남창동111403101001서울특별시 중구 남대문로 30 (남창동)서울특별시 중구 남창동 1-1 good&amp;good상동교회30, Namdaemun-ro, Jung-gu, Seoul1114011200good&amp;good상동교회1114011200100010002020725<NA>04529<NA>37.560824126.979029문화정보원N<NA>2020120112000020201201120000
93KCDCOPO20N000000094무장애장소무장애장소_실외새남터기념성당11서울특별시11170용산구<NA>이촌동111703102008서울특별시 용산구 이촌로 80-8 (이촌동)서울특별시 용산구 이촌동 199-1 새남터천주교회80-8, Ichon-ro, Yongsan-gu, Seoul1117012900새남터천주교회1117012900101990001010910<NA>04374<NA>37.524959126.956719문화정보원N<NA>2020120112000020201201120000
94KCDCOPO20N000000095무장애장소무장애장소_실외생명보험교육문화센터11서울특별시11110종로구<NA>도렴동111104100160서울특별시 종로구 새문안로5길 31 (도렴동)서울특별시 종로구 도렴동 65 센터포인트광화문31, Saemunan-ro 5-gil, Jongno-gu, Seoul1111011600센터포인트광화문1111011600101100001028756<NA>03173<NA>37.572959126.974327문화정보원N<NA>2020120112000020201201120000
95KCDCOPO20N000000096무장애장소무장애장소_실외서대문독립공원11서울특별시11410서대문구<NA>현저동114103000008서울특별시 서대문구 통일로 251 (현저동)서울특별시 서대문구 현저동 101251, Tongil-ro, Seodaemun-gu, Seoul1141010900<NA>1141010900101010000030617<NA>03732<NA>37.576655126.955645문화정보원N<NA>2020120112000020201201120000
96KCDCOPO20N000000097무장애장소무장애장소_실외서서울호수공원11서울특별시11470양천구<NA>신월동114704142055서울특별시 양천구 남부순환로64길 20 (신월동)서울특별시 양천구 신월동 149-20 공원관리사무소20, Nambusunhwan-ro 64-gil, Yangcheon-gu, Seoul1147010300공원관리사무소1147010300101490020000001<NA>07916<NA>37.527417126.830063문화정보원N<NA>2020120112000020201201120000
97KCDCOPO20N000000098무장애장소무장애장소_실외서울 경교장11서울특별시11110종로구<NA>평동111103005004서울특별시 종로구 새문안로 29 (평동)서울특별시 종로구 평동 108-1 강북삼성병원29, Saemunan-ro, Jongno-gu, Seoul1111017700강북삼성병원1111017700100470000020681<NA>03181<NA>37.56839126.967716문화정보원N<NA>2020120112000020201201120000
98KCDCOPO20N000000099무장애장소무장애장소_실외서울 고려대학교 본관11서울특별시11290성북구<NA>안암동5가112903005035서울특별시 성북구 안암로 145 (안암동5가)서울특별시 성북구 안암동5가 1-2 고려대학교안암캠퍼스145, Anam-ro, Seongbuk-gu, Seoul1129012500고려대학교안암캠퍼스1129012500100010002039525<NA>02841<NA>37.589466127.032271문화정보원N<NA>2020120112000020201201120000
99KCDCOPO20N000000100무장애장소무장애장소_실외서울 고려대학교 중앙도서관11서울특별시11290성북구<NA>안암동5가112903005035서울특별시 성북구 안암로 145 (안암동5가)서울특별시 성북구 안암동5가 1-2 고려대학교안암캠퍼스145, Anam-ro, Seongbuk-gu, Seoul1129012500고려대학교안암캠퍼스1129012500100010002039525<NA>02841<NA>37.5899127.031788문화정보원N<NA>2020120112000020201201120000