Overview

Dataset statistics

Number of variables8
Number of observations171
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.0 KiB
Average record size in memory65.8 B

Variable types

Numeric1
Categorical5
Text2

Dataset

Description인천광역시 남동구 공유재산 관리현황에 대한 데이터로 연번, 소유구분, 용도구분, 법정동, 소재지 지번, 지목, 면적 항목을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15075582&srcSe=7661IVAWM27C61E190

Alerts

용도구분 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
현황 is highly overall correlated with 소유구분 and 1 other fieldsHigh correlation
연번 is highly overall correlated with 소유구분 and 2 other fieldsHigh correlation
소유구분 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
법정동 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 09:07:07.707277
Analysis finished2024-01-28 09:07:08.252332
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct171
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean86
Minimum1
Maximum171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T18:07:08.311772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.5
Q143.5
median86
Q3128.5
95-th percentile162.5
Maximum171
Range170
Interquartile range (IQR)85

Descriptive statistics

Standard deviation49.507575
Coefficient of variation (CV)0.57566948
Kurtosis-1.2
Mean86
Median Absolute Deviation (MAD)43
Skewness0
Sum14706
Variance2451
MonotonicityStrictly increasing
2024-01-28T18:07:08.424507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
119 1
 
0.6%
111 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%
Other values (161) 161
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 (%)
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%
163 1
0.6%
162 1
0.6%

소유구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
시유지
94 
구유지
64 
사유지
13 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시유지 94
55.0%
구유지 64
37.4%
사유지 13
 
7.6%

Length

2024-01-28T18:07:08.533780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T18:07:08.621499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시유지 94
55.0%
구유지 64
37.4%
사유지 13
 
7.6%

용도구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
비축용
146 
활용
25 

Length

Max length3
Median length3
Mean length2.8538012
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비축용 146
85.4%
활용 25
 
14.6%

Length

2024-01-28T18:07:08.721373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T18:07:08.810119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비축용 146
85.4%
활용 25
 
14.6%

법정동
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
만수동
46 
구월동
26 
수산동
24 
간석동
19 
고잔동
17 
Other values (6)
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
만수동 46
26.9%
구월동 26
15.2%
수산동 24
14.0%
간석동 19
11.1%
고잔동 17
 
9.9%
운연동 13
 
7.6%
남촌동 13
 
7.6%
장수동 6
 
3.5%
논현동 3
 
1.8%
서창동 2
 
1.2%

Length

2024-01-28T18:07:08.902614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
만수동 46
26.9%
구월동 26
15.2%
수산동 24
14.0%
간석동 19
11.1%
고잔동 17
 
9.9%
운연동 13
 
7.6%
남촌동 13
 
7.6%
장수동 6
 
3.5%
논현동 3
 
1.8%
서창동 2
 
1.2%

지번
Text

Distinct169
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-01-28T18:07:09.169188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length5.245614
Min length2

Characters and Unicode

Total characters897
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique167 ?
Unique (%)97.7%

Sample

1st row301-2
2nd row349-472
3rd row349-546
4th row349-625
5th row349-717
ValueCountFrequency (%)
1-133 2
 
1.2%
621-4 2
 
1.2%
301-5 1
 
0.6%
44-5 1
 
0.6%
260-2 1
 
0.6%
653-11 1
 
0.6%
653-17 1
 
0.6%
35-5 1
 
0.6%
37-155 1
 
0.6%
49-18 1
 
0.6%
Other values (160) 160
93.0%
2024-01-28T18:07:09.548151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 177
19.7%
- 159
17.7%
3 90
10.0%
2 87
9.7%
5 67
 
7.5%
4 59
 
6.6%
6 58
 
6.5%
7 52
 
5.8%
9 52
 
5.8%
8 51
 
5.7%
Other values (3) 45
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 736
82.1%
Dash Punctuation 159
 
17.7%
Other Letter 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 177
24.0%
3 90
12.2%
2 87
11.8%
5 67
 
9.1%
4 59
 
8.0%
6 58
 
7.9%
7 52
 
7.1%
9 52
 
7.1%
8 51
 
6.9%
0 43
 
5.8%
Dash Punctuation
ValueCountFrequency (%)
- 159
100.0%
Other Letter
ValueCountFrequency (%)
산 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 896
99.9%
Hangul 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 177
19.8%
- 159
17.7%
3 90
10.0%
2 87
9.7%
5 67
 
7.5%
4 59
 
6.6%
6 58
 
6.5%
7 52
 
5.8%
9 52
 
5.8%
8 51
 
5.7%
Other values (2) 44
 
4.9%
Hangul
ValueCountFrequency (%)
산 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 896
99.9%
Hangul 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 177
19.8%
- 159
17.7%
3 90
10.0%
2 87
9.7%
5 67
 
7.5%
4 59
 
6.6%
6 58
 
6.5%
7 52
 
5.8%
9 52
 
5.8%
8 51
 
5.7%
Other values (2) 44
 
4.9%
Hangul
ValueCountFrequency (%)
산 1
100.0%

지목
Categorical

Distinct8
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
대
62 
전
46 
답
24 
잡종지
17 
임야
11 
Other values (3)
11 

Length

Max length3
Median length1
Mean length1.3391813
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전
2nd row대
3rd row대
4th row대
5th row대

Common Values

ValueCountFrequency (%)
대 62
36.3%
전 46
26.9%
답 24
 
14.0%
잡종지 17
 
9.9%
임야 11
 
6.4%
도로 5
 
2.9%
묘지 4
 
2.3%
주차장 2
 
1.2%

Length

2024-01-28T18:07:09.683655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T18:07:09.791833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대 62
36.3%
전 46
26.9%
답 24
 
14.0%
잡종지 17
 
9.9%
임야 11
 
6.4%
도로 5
 
2.9%
묘지 4
 
2.3%
주차장 2
 
1.2%

면적(제곱미터)
Text

Distinct134
Distinct (%)78.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-01-28T18:07:10.062758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length2.6608187
Min length1

Characters and Unicode

Total characters455
Distinct characters12
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

Unique110 ?
Unique (%)64.3%

Sample

1st row674
2nd row17
3rd row1
4th row47
5th row34
ValueCountFrequency (%)
3 4
 
2.3%
1 4
 
2.3%
5 4
 
2.3%
35 3
 
1.8%
139 3
 
1.8%
21 3
 
1.8%
44 3
 
1.8%
11 3
 
1.8%
16 3
 
1.8%
28 3
 
1.8%
Other values (124) 138
80.7%
2024-01-28T18:07:10.451110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 97
21.3%
3 55
12.1%
2 41
9.0%
5 39
8.6%
4 39
8.6%
8 39
8.6%
7 38
 
8.4%
0 30
 
6.6%
6 27
 
5.9%
. 25
 
5.5%
Other values (2) 25
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 429
94.3%
Other Punctuation 26
 
5.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 97
22.6%
3 55
12.8%
2 41
9.6%
5 39
9.1%
4 39
9.1%
8 39
9.1%
7 38
 
8.9%
0 30
 
7.0%
6 27
 
6.3%
9 24
 
5.6%
Other Punctuation
ValueCountFrequency (%)
. 25
96.2%
, 1
 
3.8%

Most occurring scripts

ValueCountFrequency (%)
Common 455
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 97
21.3%
3 55
12.1%
2 41
9.0%
5 39
8.6%
4 39
8.6%
8 39
8.6%
7 38
 
8.4%
0 30
 
6.6%
6 27
 
5.9%
. 25
 
5.5%
Other values (2) 25
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 455
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 97
21.3%
3 55
12.1%
2 41
9.0%
5 39
8.6%
4 39
8.6%
8 39
8.6%
7 38
 
8.4%
0 30
 
6.6%
6 27
 
5.9%
. 25
 
5.5%
Other values (2) 25
 
5.5%

현황
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)12.3%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
잔여지
85 
잔여지(만부지구)
17 
대부중
14 
기타(녹지)
13 
대부중(일부)
11 
Other values (16)
31 

Length

Max length12
Median length3
Mean length4.8888889
Min length2

Unique

Unique10 ?
Unique (%)5.8%

Sample

1st row잔여지
2nd row기타(무허가주택)
3rd row기타(사잇길)
4th row잔여지
5th row잔여지

Common Values

ValueCountFrequency (%)
잔여지 85
49.7%
잔여지(만부지구) 17
 
9.9%
대부중 14
 
8.2%
기타(녹지) 13
 
7.6%
대부중(일부) 11
 
6.4%
기타(통행로) 7
 
4.1%
기타(옹벽위 임야) 4
 
2.3%
기타(임야) 3
 
1.8%
기타(사잇길) 3
 
1.8%
기타 2
 
1.2%
Other values (11) 12
 
7.0%

Length

2024-01-28T18:07:10.571908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
잔여지 85
47.8%
잔여지(만부지구 17
 
9.6%
대부중 14
 
7.9%
기타(녹지 13
 
7.3%
대부중(일부 11
 
6.2%
기타(통행로 7
 
3.9%
기타(옹벽위 4
 
2.2%
임야 4
 
2.2%
기타(사잇길 3
 
1.7%
기타(임야 3
 
1.7%
Other values (15) 17
 
9.6%

Interactions

2024-01-28T18:07:08.016485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T18:07:10.632279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소유구분용도구분법정동지목현황
연번1.0000.8670.8320.8640.5360.779
소유구분0.8671.0000.4780.5940.2600.900
용도구분0.8320.4781.0000.4530.3881.000
법정동0.8640.5940.4531.0000.5450.731
지목0.5360.2600.3880.5451.0000.446
현황0.7790.9001.0000.7310.4461.000
2024-01-28T18:07:10.715714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도구분지목법정동소유구분현황
용도구분1.0000.2860.4230.7310.942
지목0.2861.0000.2910.1670.192
법정동0.4230.2911.0000.4160.348
소유구분0.7310.1670.4161.0000.640
현황0.9420.1920.3480.6401.000
2024-01-28T18:07:10.798315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번소유구분용도구분법정동지목현황
연번1.0000.7920.6650.5900.2830.409
소유구분0.7921.0000.7310.4160.1670.640
용도구분0.6650.7311.0000.4230.2860.942
법정동0.5900.4160.4231.0000.2910.348
지목0.2830.1670.2860.2911.0000.192
현황0.4090.6400.9420.3480.1921.000

Missing values

2024-01-28T18:07:08.115354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T18:07:08.212628image/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

연번소유구분용도구분법정동지번지목면적(제곱미터)현황
01시유지비축용구월동301-2전674잔여지
12시유지비축용구월동349-472대17기타(무허가주택)
23시유지비축용구월동349-546대1기타(사잇길)
34시유지비축용구월동349-625대47잔여지
45시유지비축용구월동349-717대34잔여지
56시유지비축용구월동349-756대5기타(사잇길)
67시유지비축용구월동349-782잡종지124잔여지
78시유지비축용구월동349-821대127잔여지
89시유지비축용구월동349-823대80잔여지
910시유지비축용구월동349-954대5잔여지
연번소유구분용도구분법정동지번지목면적(제곱미터)현황
161162구유지활용서창동275-11잡종지118대부중(일부)
162163구유지활용남촌동329-8대55대부중(일부)
163164구유지활용운연동259-7임야9대부중
164165구유지활용고잔동510-7잡종지78대부중
165166구유지활용고잔동511-2잡종지30대부중
166167구유지활용고잔동511-3잡종지87대부중
167168구유지활용고잔동742-15임야187.8대부중
168169구유지활용고잔동742-16임야10.7대부중
169170구유지활용고잔동742-17임야117.2대부중
170171구유지활용고잔동761잡종지1470.6대부중(일부)