Overview

Dataset statistics

Number of variables7
Number of observations173
Missing cells33
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.9 KiB
Average record size in memory58.8 B

Variable types

Numeric2
Text2
Categorical2
DateTime1

Dataset

Description익산시 읍면동의 사용신고한 원룸 및 오피스텔 등의 유형별 사용 스인일 및 세대수 지번 주소등을 주소 등을 포함하고 있습니다.
Author전북특별자치도 익산시
URLhttps://www.data.go.kr/data/15080984/fileData.do

Alerts

세대수 is highly overall correlated with 주용도 and 1 other fieldsHigh correlation
주용도 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
기타용도 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
건물명 has 33 (19.1%) missing valuesMissing
순번 has unique valuesUnique
지번주소 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:08:33.089551
Analysis finished2024-04-06 08:08:35.117993
Duration2.03 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87
Minimum1
Maximum173
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:08:35.596599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.6
Q144
median87
Q3130
95-th percentile164.4
Maximum173
Range172
Interquartile range (IQR)86

Descriptive statistics

Standard deviation50.084928
Coefficient of variation (CV)0.57568883
Kurtosis-1.2
Mean87
Median Absolute Deviation (MAD)43
Skewness0
Sum15051
Variance2508.5
MonotonicityStrictly increasing
2024-04-06T17:08:35.849921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
120 1
 
0.6%
112 1
 
0.6%
113 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
Other values (163) 163
94.2%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%
167 1
0.6%
166 1
0.6%
165 1
0.6%
164 1
0.6%

건물명
Text

MISSING 

Distinct98
Distinct (%)70.0%
Missing33
Missing (%)19.1%
Memory size1.5 KiB
2024-04-06T17:08:36.606655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length23
Mean length12.607143
Min length1

Characters and Unicode

Total characters1765
Distinct characters177
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

Unique73 ?
Unique (%)52.1%

Sample

1st row김금성 다가구 주택
2nd row성일1
3rd row장경훈
4th row김광숙
5th row김광선
ValueCountFrequency (%)
공동주택 43
 
13.0%
신동 9
 
2.7%
다세대주택 9
 
2.7%
영등동 7
 
2.1%
남중동 7
 
2.1%
부송동 7
 
2.1%
마동 7
 
2.1%
노블리안 6
 
1.8%
6
 
1.8%
최수길 5
 
1.5%
Other values (147) 226
68.1%
2024-04-06T17:08:37.580785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
198
 
11.2%
동 111
 
6.3%
주 85
 
4.8%
택 80
 
4.5%
공 63
 
3.6%
) 54
 
3.1%
( 54
 
3.1%
- 50
 
2.8%
1 42
 
2.4%
신 34
 
1.9%
Other values (167) 994
56.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1178
66.7%
Decimal Number 220
 
12.5%
Space Separator 198
 
11.2%
Close Punctuation 54
 
3.1%
Open Punctuation 54
 
3.1%
Dash Punctuation 50
 
2.8%
Other Punctuation 7
 
0.4%
Uppercase Letter 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 111
 
9.4%
주 85
 
7.2%
택 80
 
6.8%
공 63
 
5.3%
신 34
 
2.9%
다 31
 
2.6%
대 30
 
2.5%
세 25
 
2.1%
리 21
 
1.8%
등 17
 
1.4%
Other values (148) 681
57.8%
Decimal Number
ValueCountFrequency (%)
1 42
19.1%
2 33
15.0%
8 32
14.5%
3 26
11.8%
6 20
9.1%
9 19
8.6%
7 16
 
7.3%
4 15
 
6.8%
5 12
 
5.5%
0 5
 
2.3%
Uppercase Letter
ValueCountFrequency (%)
K 1
25.0%
T 1
25.0%
B 1
25.0%
C 1
25.0%
Space Separator
ValueCountFrequency (%)
198
100.0%
Close Punctuation
ValueCountFrequency (%)
) 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 50
100.0%
Other Punctuation
ValueCountFrequency (%)
. 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1178
66.7%
Common 583
33.0%
Latin 4
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 111
 
9.4%
주 85
 
7.2%
택 80
 
6.8%
공 63
 
5.3%
신 34
 
2.9%
다 31
 
2.6%
대 30
 
2.5%
세 25
 
2.1%
리 21
 
1.8%
등 17
 
1.4%
Other values (148) 681
57.8%
Common
ValueCountFrequency (%)
198
34.0%
) 54
 
9.3%
( 54
 
9.3%
- 50
 
8.6%
1 42
 
7.2%
2 33
 
5.7%
8 32
 
5.5%
3 26
 
4.5%
6 20
 
3.4%
9 19
 
3.3%
Other values (5) 55
 
9.4%
Latin
ValueCountFrequency (%)
K 1
25.0%
T 1
25.0%
B 1
25.0%
C 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1178
66.7%
ASCII 587
33.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
198
33.7%
) 54
 
9.2%
( 54
 
9.2%
- 50
 
8.5%
1 42
 
7.2%
2 33
 
5.6%
8 32
 
5.5%
3 26
 
4.4%
6 20
 
3.4%
9 19
 
3.2%
Other values (9) 59
 
10.1%
Hangul
ValueCountFrequency (%)
동 111
 
9.4%
주 85
 
7.2%
택 80
 
6.8%
공 63
 
5.3%
신 34
 
2.9%
다 31
 
2.6%
대 30
 
2.5%
세 25
 
2.1%
리 21
 
1.8%
등 17
 
1.4%
Other values (148) 681
57.8%

주용도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
공동주택
142 
단독주택
26 
업무시설
 
5

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row단독주택
2nd row단독주택
3rd row공동주택
4th row단독주택
5th row단독주택

Common Values

ValueCountFrequency (%)
공동주택 142
82.1%
단독주택 26
 
15.0%
업무시설 5
 
2.9%

Length

2024-04-06T17:08:37.958965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:08:38.197516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공동주택 142
82.1%
단독주택 26
 
15.0%
업무시설 5
 
2.9%

기타용도
Categorical

HIGH CORRELATION 

Distinct48
Distinct (%)27.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
다세대주택
52 
<NA>
23 
다세대
17 
연립주택
9 
다가구주택
7 
Other values (43)
65 

Length

Max length36
Median length33
Mean length7.716763
Min length2

Unique

Unique30 ?
Unique (%)17.3%

Sample

1st row다가구 주택
2nd row제1종근린생활시설(소매점)
3rd row<NA>
4th row다가구주택
5th row근린생활시설

Common Values

ValueCountFrequency (%)
다세대주택 52
30.1%
<NA> 23
13.3%
다세대 17
 
9.8%
연립주택 9
 
5.2%
다가구주택 7
 
4.0%
다세대주택 및 제1종근린생활시설 6
 
3.5%
제1종근린생활시설 공동주택 업무시설 4
 
2.3%
다세대(도시형생활주택) 4
 
2.3%
1~2종 근린생활시설 및 공동주택(아파트) 3
 
1.7%
다세대주택 (8세대) 2
 
1.2%
Other values (38) 46
26.6%

Length

2024-04-06T17:08:38.447392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
다세대주택 61
26.5%
na 23
 
10.0%
다세대 21
 
9.1%
및 14
 
6.1%
제1종근린생활시설 13
 
5.7%
연립주택 12
 
5.2%
다가구주택 7
 
3.0%
공동주택 4
 
1.7%
업무시설 4
 
1.7%
다세대(도시형생활주택 4
 
1.7%
Other values (43) 67
29.1%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.861272
Minimum3
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-04-06T17:08:38.704541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile7
Q18
median12
Q316
95-th percentile25.2
Maximum29
Range26
Interquartile range (IQR)8

Descriptive statistics

Standard deviation5.6920337
Coefficient of variation (CV)0.44257161
Kurtosis0.62838793
Mean12.861272
Median Absolute Deviation (MAD)4
Skewness0.97686351
Sum2225
Variance32.399247
MonotonicityNot monotonic
2024-04-06T17:08:38.964584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
8 61
35.3%
16 18
 
10.4%
19 14
 
8.1%
12 11
 
6.4%
14 10
 
5.8%
18 8
 
4.6%
15 8
 
4.6%
17 6
 
3.5%
7 5
 
2.9%
9 5
 
2.9%
Other values (12) 27
15.6%
ValueCountFrequency (%)
3 1
 
0.6%
4 1
 
0.6%
6 3
 
1.7%
7 5
 
2.9%
8 61
35.3%
9 5
 
2.9%
10 3
 
1.7%
11 4
 
2.3%
12 11
 
6.4%
13 2
 
1.2%
ValueCountFrequency (%)
29 5
 
2.9%
28 2
 
1.2%
27 2
 
1.2%
24 1
 
0.6%
21 1
 
0.6%
20 2
 
1.2%
19 14
8.1%
18 8
4.6%
17 6
 
3.5%
16 18
10.4%

사용승인일
Date

Distinct132
Distinct (%)76.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1998-02-22 00:00:00
Maximum2021-04-08 00:00:00
2024-04-06T17:08:39.352484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:39.715844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

지번주소
Text

UNIQUE 

Distinct173
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-06T17:08:40.239366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length20.485549
Min length16

Characters and Unicode

Total characters3544
Distinct characters57
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique173 ?
Unique (%)100.0%

Sample

1st row전북특별자치도 익산시 신동540-48
2nd row전북특별자치도 익산시 어양동628-4
3rd row전북특별자치도 익산시 신동358-1
4th row전북특별자치도 익산시 신동799-8
5th row전북특별자치도 익산시 부송동1105-3
ValueCountFrequency (%)
전북특별자치도 173
32.4%
익산시 173
32.4%
황등면 6
 
1.1%
함열읍 4
 
0.7%
마동 2
 
0.4%
영등동178-55 1
 
0.2%
황등리259-6 1
 
0.2%
여산면 1
 
0.2%
여산리389-3 1
 
0.2%
인화동1가77-23 1
 
0.2%
Other values (171) 171
32.0%
2024-04-06T17:08:41.151107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
361
 
10.2%
산 179
 
5.1%
전 173
 
4.9%
도 173
 
4.9%
시 173
 
4.9%
익 173
 
4.9%
북 173
 
4.9%
치 173
 
4.9%
자 173
 
4.9%
별 173
 
4.9%
Other values (47) 1620
45.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2256
63.7%
Decimal Number 760
 
21.4%
Space Separator 361
 
10.2%
Dash Punctuation 166
 
4.7%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 179
 
7.9%
전 173
 
7.7%
도 173
 
7.7%
시 173
 
7.7%
익 173
 
7.7%
북 173
 
7.7%
치 173
 
7.7%
자 173
 
7.7%
별 173
 
7.7%
특 173
 
7.7%
Other values (34) 520
23.0%
Decimal Number
ValueCountFrequency (%)
1 138
18.2%
3 92
12.1%
2 80
10.5%
6 77
10.1%
4 77
10.1%
7 72
9.5%
5 69
9.1%
8 67
8.8%
9 51
 
6.7%
0 37
 
4.9%
Space Separator
ValueCountFrequency (%)
361
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 166
100.0%
Other Punctuation
ValueCountFrequency (%)
' 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2256
63.7%
Common 1288
36.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 179
 
7.9%
전 173
 
7.7%
도 173
 
7.7%
시 173
 
7.7%
익 173
 
7.7%
북 173
 
7.7%
치 173
 
7.7%
자 173
 
7.7%
별 173
 
7.7%
특 173
 
7.7%
Other values (34) 520
23.0%
Common
ValueCountFrequency (%)
361
28.0%
- 166
12.9%
1 138
 
10.7%
3 92
 
7.1%
2 80
 
6.2%
6 77
 
6.0%
4 77
 
6.0%
7 72
 
5.6%
5 69
 
5.4%
8 67
 
5.2%
Other values (3) 89
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2256
63.7%
ASCII 1288
36.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
361
28.0%
- 166
12.9%
1 138
 
10.7%
3 92
 
7.1%
2 80
 
6.2%
6 77
 
6.0%
4 77
 
6.0%
7 72
 
5.6%
5 69
 
5.4%
8 67
 
5.2%
Other values (3) 89
 
6.9%
Hangul
ValueCountFrequency (%)
산 179
 
7.9%
전 173
 
7.7%
도 173
 
7.7%
시 173
 
7.7%
익 173
 
7.7%
북 173
 
7.7%
치 173
 
7.7%
자 173
 
7.7%
별 173
 
7.7%
특 173
 
7.7%
Other values (34) 520
23.0%

Interactions

2024-04-06T17:08:34.388907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:33.969593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:34.570145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:08:34.161876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:08:41.408420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번건물명주용도기타용도세대수
순번1.0000.9910.5820.8090.400
건물명0.9911.0001.0000.9980.998
주용도0.5821.0001.0001.0000.890
기타용도0.8090.9981.0001.0000.953
세대수0.4000.9980.8900.9531.000
2024-04-06T17:08:41.582960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기타용도주용도
기타용도1.0000.837
주용도0.8371.000
2024-04-06T17:08:41.743034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번세대수주용도기타용도
순번1.000-0.0960.4160.370
세대수-0.0961.0000.6050.626
주용도0.4160.6051.0000.837
기타용도0.3700.6260.8371.000

Missing values

2024-04-06T17:08:34.817560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:08:35.032262image/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

순번건물명주용도기타용도세대수사용승인일지번주소
01김금성 다가구 주택단독주택다가구 주택121999-11-10전북특별자치도 익산시 신동540-48
12<NA>단독주택제1종근린생활시설(소매점)92002-06-03전북특별자치도 익산시 어양동628-4
23<NA>공동주택<NA>192002-09-14전북특별자치도 익산시 신동358-1
34<NA>단독주택다가구주택192001-12-03전북특별자치도 익산시 신동799-8
45성일1단독주택근린생활시설112001-12-19전북특별자치도 익산시 부송동1105-3
56장경훈단독주택소매점 사무실92001-12-19전북특별자치도 익산시 부송동1105-4
67<NA>단독주택다가구주택182001-12-03전북특별자치도 익산시 신동767-27
78<NA>단독주택근생82001-12-31전북특별자치도 익산시 신동786-20
89김광숙단독주택<NA>152001-12-15전북특별자치도 익산시 어양동640-3
910김광선단독주택<NA>152001-12-17전북특별자치도 익산시 어양동640-4
순번건물명주용도기타용도세대수사용승인일지번주소
163164신동 근생및도시형생활주택및오피스텔업무시설제1종근린생활시설 공동주택 업무시설292020-02-06전북특별자치도 익산시 신동785-22
164165신동 근생및도시형생활주택및오피스텔업무시설제1종근린생활시설 공동주택 업무시설292020-02-06전북특별자치도 익산시 신동785-17
165166신동 근생및도시형생활주택및오피스텔업무시설제1종근린생활시설 공동주택 업무시설292020-02-06전북특별자치도 익산시 신동785-2
166167신동 근생및도시형생활주택및오피스텔업무시설제1종근린생활시설 공동주택 업무시설292020-02-06전북특별자치도 익산시 신동785-23
167168송학동 183-1 공동주택공동주택다세대주택62020-09-08전북특별자치도 익산시 송학동182-4
168169송학동 183-1 공동주택공동주택다세대주택62020-09-08전북특별자치도 익산시 송학동182-3
169170송학동 183-1 공동주택공동주택다세대주택62020-09-08전북특별자치도 익산시 송학동183-1
170171영등동 178-27 주상복합(아파트 및 근린생활시설)공동주택1~2종 근린생활시설 및 공동주택(아파트)152021-04-08전북특별자치도 익산시 영등동304-1
171172영등동 178-27 주상복합(아파트 및 근린생활시설)공동주택1~2종 근린생활시설 및 공동주택(아파트)152021-04-08전북특별자치도 익산시 영등동178-6
172173영등동 178-27 주상복합(아파트 및 근린생활시설)공동주택1~2종 근린생활시설 및 공동주택(아파트)152021-04-08전북특별자치도 익산시 영등동178-27