Overview

Dataset statistics

Number of variables7
Number of observations10000
Missing cells11
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory634.8 KiB
Average record size in memory65.0 B

Variable types

Numeric1
Categorical4
Text2

Dataset

Description충청남도 보령시 도로명주소(건물번호)에 관련된 자료로 시군구명, 읍면동명, 도로명주소, 건축물용도, 형태, 건물표지판의 용도로 구성되어있습니다.
Author충청남도 보령시
URLhttps://www.data.go.kr/data/15041819/fileData.do

Alerts

시군구명 has constant value ""Constant
용도 is highly overall correlated with 형태High correlation
형태 is highly overall correlated with 용도High correlation
형태 is highly imbalanced (98.0%)Imbalance
용도 is highly imbalanced (97.2%)Imbalance
순번 has unique valuesUnique

Reproduction

Analysis started2024-01-14 13:26:24.463415
Analysis finished2024-01-14 13:26:25.804995
Duration1.34 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct10000
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17108.416
Minimum2
Maximum34337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-01-14T22:26:25.892981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile1714.9
Q18495.5
median17119.5
Q325686.25
95-th percentile32563.2
Maximum34337
Range34335
Interquartile range (IQR)17190.75

Descriptive statistics

Standard deviation9876.6448
Coefficient of variation (CV)0.57729745
Kurtosis-1.1970578
Mean17108.416
Median Absolute Deviation (MAD)8595
Skewness-0.0034987555
Sum1.7108416 × 108
Variance97548112
MonotonicityNot monotonic
2024-01-14T22:26:26.105303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
88 1
 
< 0.1%
13239 1
 
< 0.1%
23938 1
 
< 0.1%
14493 1
 
< 0.1%
4039 1
 
< 0.1%
21167 1
 
< 0.1%
11143 1
 
< 0.1%
4001 1
 
< 0.1%
7469 1
 
< 0.1%
27115 1
 
< 0.1%
Other values (9990) 9990
99.9%
ValueCountFrequency (%)
2 1
< 0.1%
15 1
< 0.1%
17 1
< 0.1%
19 1
< 0.1%
20 1
< 0.1%
21 1
< 0.1%
23 1
< 0.1%
24 1
< 0.1%
25 1
< 0.1%
26 1
< 0.1%
ValueCountFrequency (%)
34337 1
< 0.1%
34336 1
< 0.1%
34335 1
< 0.1%
34327 1
< 0.1%
34325 1
< 0.1%
34324 1
< 0.1%
34323 1
< 0.1%
34321 1
< 0.1%
34320 1
< 0.1%
34318 1
< 0.1%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
보령시
10000 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
보령시 10000
100.0%

Length

2024-01-14T22:26:26.302991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:26:26.431138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보령시 10000
100.0%

읍면동명
Categorical

Distinct21
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
웅천읍
1050 
대천동
966 
남포면
914 
오천면
838 
천북면
751 
Other values (16)
5481 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row청라면
2nd row신흑동
3rd row청소면
4th row오천면
5th row궁촌동

Common Values

ValueCountFrequency (%)
웅천읍 1050
10.5%
대천동 966
 
9.7%
남포면 914
 
9.1%
오천면 838
 
8.4%
천북면 751
 
7.5%
주교면 743
 
7.4%
청라면 698
 
7.0%
주산면 551
 
5.5%
동대동 542
 
5.4%
청소면 510
 
5.1%
Other values (11) 2437
24.4%

Length

2024-01-14T22:26:26.558888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
웅천읍 1050
10.5%
대천동 966
 
9.7%
남포면 914
 
9.1%
오천면 838
 
8.4%
천북면 751
 
7.5%
주교면 743
 
7.4%
청라면 698
 
7.0%
주산면 551
 
5.5%
동대동 542
 
5.4%
청소면 510
 
5.1%
Other values (11) 2437
24.4%

도로명주소
Text

Distinct727
Distinct (%)7.3%
Missing11
Missing (%)0.1%
Memory size156.2 KiB
2024-01-14T22:26:26.916748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.9982981
Min length2

Characters and Unicode

Total characters39939
Distinct characters309
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

Unique19 ?
Unique (%)0.2%

Sample

1st row가느실길
2nd row해수욕장3길
3rd row밧느락길
4th row원산도5길
5th row진등1길
ValueCountFrequency (%)
충서로 193
 
1.9%
홍보로 124
 
1.2%
토정로 120
 
1.2%
대해로 87
 
0.9%
중앙로 82
 
0.8%
만수로 81
 
0.8%
성주산로 78
 
0.8%
충청수영로 77
 
0.8%
대청로 77
 
0.8%
봉덕삼현길 71
 
0.7%
Other values (717) 8999
90.1%
2024-01-14T22:26:27.428111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
길 7550
 
18.9%
로 2487
 
6.2%
1 1082
 
2.7%
2 928
 
2.3%
대 853
 
2.1%
산 831
 
2.1%
장 673
 
1.7%
도 625
 
1.6%
천 583
 
1.5%
동 536
 
1.3%
Other values (299) 23791
59.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 36918
92.4%
Decimal Number 3021
 
7.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 7550
 
20.5%
로 2487
 
6.7%
대 853
 
2.3%
산 831
 
2.3%
장 673
 
1.8%
도 625
 
1.7%
천 583
 
1.6%
동 536
 
1.5%
성 462
 
1.3%
원 461
 
1.2%
Other values (289) 21857
59.2%
Decimal Number
ValueCountFrequency (%)
1 1082
35.8%
2 928
30.7%
3 428
 
14.2%
4 218
 
7.2%
5 107
 
3.5%
6 82
 
2.7%
7 71
 
2.4%
9 36
 
1.2%
8 36
 
1.2%
0 33
 
1.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 36918
92.4%
Common 3021
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 7550
 
20.5%
로 2487
 
6.7%
대 853
 
2.3%
산 831
 
2.3%
장 673
 
1.8%
도 625
 
1.7%
천 583
 
1.6%
동 536
 
1.5%
성 462
 
1.3%
원 461
 
1.2%
Other values (289) 21857
59.2%
Common
ValueCountFrequency (%)
1 1082
35.8%
2 928
30.7%
3 428
 
14.2%
4 218
 
7.2%
5 107
 
3.5%
6 82
 
2.7%
7 71
 
2.4%
9 36
 
1.2%
8 36
 
1.2%
0 33
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 36918
92.4%
ASCII 3021
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
길 7550
 
20.5%
로 2487
 
6.7%
대 853
 
2.3%
산 831
 
2.3%
장 673
 
1.8%
도 625
 
1.7%
천 583
 
1.6%
동 536
 
1.5%
성 462
 
1.3%
원 461
 
1.2%
Other values (289) 21857
59.2%
ASCII
ValueCountFrequency (%)
1 1082
35.8%
2 928
30.7%
3 428
 
14.2%
4 218
 
7.2%
5 107
 
3.5%
6 82
 
2.7%
7 71
 
2.4%
9 36
 
1.2%
8 36
 
1.2%
0 33
 
1.1%

건축물용도
Text

Distinct154
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-01-14T22:26:27.803812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length4
Mean length4.1988
Min length2

Characters and Unicode

Total characters41988
Distinct characters195
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)0.4%

Sample

1st row기타창고시설
2nd row노래연습장
3rd row단독주택
4th row소매점
5th row단독주택
ValueCountFrequency (%)
단독주택 7717
77.2%
일반음식점 194
 
1.9%
창고 124
 
1.2%
기타판매및영업시설 121
 
1.2%
기타창고시설 116
 
1.2%
사무소 116
 
1.2%
소매점 115
 
1.1%
일반공장 101
 
1.0%
축사 98
 
1.0%
기타관광숙박시설 83
 
0.8%
Other values (146) 1217
 
12.2%
2024-01-14T22:26:28.460683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 7844
18.7%
택 7821
18.6%
단 7723
18.4%
독 7717
18.4%
시 682
 
1.6%
기 674
 
1.6%
타 662
 
1.6%
설 660
 
1.6%
점 465
 
1.1%
반 411
 
1.0%
Other values (185) 7329
17.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41870
99.7%
Decimal Number 53
 
0.1%
Open Punctuation 28
 
0.1%
Close Punctuation 28
 
0.1%
Other Punctuation 7
 
< 0.1%
Space Separator 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 7844
18.7%
택 7821
18.7%
단 7723
18.4%
독 7717
18.4%
시 682
 
1.6%
기 674
 
1.6%
타 662
 
1.6%
설 660
 
1.6%
점 465
 
1.1%
반 411
 
1.0%
Other values (179) 7211
17.2%
Decimal Number
ValueCountFrequency (%)
1 28
52.8%
2 25
47.2%
Open Punctuation
ValueCountFrequency (%)
( 28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 28
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41870
99.7%
Common 118
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 7844
18.7%
택 7821
18.7%
단 7723
18.4%
독 7717
18.4%
시 682
 
1.6%
기 674
 
1.6%
타 662
 
1.6%
설 660
 
1.6%
점 465
 
1.1%
반 411
 
1.0%
Other values (179) 7211
17.2%
Common
ValueCountFrequency (%)
1 28
23.7%
( 28
23.7%
) 28
23.7%
2 25
21.2%
. 7
 
5.9%
2
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41870
99.7%
ASCII 118
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 7844
18.7%
택 7821
18.7%
단 7723
18.4%
독 7717
18.4%
시 682
 
1.6%
기 674
 
1.6%
타 662
 
1.6%
설 660
 
1.6%
점 465
 
1.1%
반 411
 
1.0%
Other values (179) 7211
17.2%
ASCII
ValueCountFrequency (%)
1 28
23.7%
( 28
23.7%
) 28
23.7%
2 25
21.2%
. 7
 
5.9%
2
 
1.7%

형태
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
표준형
9981 
비표준형
 
19

Length

Max length4
Median length3
Mean length3.0019
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
표준형 9981
99.8%
비표준형 19
 
0.2%

Length

2024-01-14T22:26:28.673631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:26:28.846743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
표준형 9981
99.8%
비표준형 19
 
0.2%

용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
일반용(오각형)
9932 
관공서용
 
47
자율형
 
19
일반용(4각형)
 
1
문화재 관광용
 
1

Length

Max length8
Median length8
Mean length7.9716
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row일반용(오각형)
2nd row일반용(오각형)
3rd row일반용(오각형)
4th row일반용(오각형)
5th row일반용(오각형)

Common Values

ValueCountFrequency (%)
일반용(오각형) 9932
99.3%
관공서용 47
 
0.5%
자율형 19
 
0.2%
일반용(4각형) 1
 
< 0.1%
문화재 관광용 1
 
< 0.1%

Length

2024-01-14T22:26:29.009317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-14T22:26:29.203302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반용(오각형 9932
99.3%
관공서용 47
 
0.5%
자율형 19
 
0.2%
일반용(4각형 1
 
< 0.1%
문화재 1
 
< 0.1%
관광용 1
 
< 0.1%

Interactions

2024-01-14T22:26:25.303039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-14T22:26:29.679724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번읍면동명형태용도
순번1.0000.5790.0190.048
읍면동명0.5791.0000.0270.044
형태0.0190.0271.0001.000
용도0.0480.0441.0001.000
2024-01-14T22:26:29.800123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도읍면동명형태
용도1.0000.0211.000
읍면동명0.0211.0000.024
형태1.0000.0241.000
2024-01-14T22:26:29.921626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번읍면동명형태용도
순번1.0000.2550.0150.020
읍면동명0.2551.0000.0240.021
형태0.0150.0241.0001.000
용도0.0200.0211.0001.000

Missing values

2024-01-14T22:26:25.498700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-14T22:26:25.716949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

순번시군구명읍면동명도로명주소건축물용도형태용도
8788보령시청라면가느실길기타창고시설표준형일반용(오각형)
3179831799보령시신흑동해수욕장3길노래연습장표준형일반용(오각형)
1027710278보령시청소면밧느락길단독주택표준형일반용(오각형)
2221922220보령시오천면원산도5길소매점표준형일반용(오각형)
2676826769보령시궁촌동진등1길단독주택표준형일반용(오각형)
74067407보령시미산면도흥길단독주택표준형일반용(오각형)
23292330보령시대천동구시5길단독주택표준형일반용(오각형)
2493124932보령시웅천읍정굴길단독주택표준형일반용(오각형)
3005730058보령시신흑동통나무마을1길단독주택표준형일반용(오각형)
2224922250보령시오천면원산도6길단독주택표준형일반용(오각형)
순번시군구명읍면동명도로명주소건축물용도형태용도
3180031801보령시신흑동해수욕장3길공중화장실표준형일반용(오각형)
2339723398보령시남포면읍내냇둑길단독주택표준형일반용(오각형)
1024510246보령시주포면밖강술길단독주택표준형일반용(오각형)
38243825보령시남포면남포역전길단독주택표준형일반용(오각형)
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