Overview

Dataset statistics

Number of variables3
Number of observations431
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.7 KiB
Average record size in memory25.3 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description인천광역시 공유재산 시유지현황 데이터입니다.(소재지,공부지목,공부면적)ex) 인천광역시 부평구 부평동 산 8-15.임야.1598
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15089231&srcSe=7661IVAWM27C61E190

Alerts

공부지목 is highly imbalanced (79.1%)Imbalance
소재지 has unique valuesUnique

Reproduction

Analysis started2024-01-28 05:16:31.058029
Analysis finished2024-01-28 05:16:31.422842
Duration0.36 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지
Text

UNIQUE 

Distinct431
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
2024-01-28T14:16:31.700640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.540603
Min length16

Characters and Unicode

Total characters8422
Distinct characters31
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

Unique431 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 삼산동 206-1
2nd row인천광역시 부평구 갈산동 171-3
3rd row인천광역시 부평구 십정동 558-8
4th row인천광역시 부평구 십정동 527-12
5th row인천광역시 부평구 십정동 529-11
ValueCountFrequency (%)
인천광역시 431
24.7%
부평구 431
24.7%
부평동 86
 
4.9%
십정동 82
 
4.7%
청천동 70
 
4.0%
산곡동 47
 
2.7%
부개동 47
 
2.7%
갈산동 40
 
2.3%
삼산동 40
 
2.3%
산 18
 
1.0%
Other values (410) 450
25.8%
2024-01-28T14:16:32.149476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1742
20.7%
부 564
 
6.7%
평 517
 
6.1%
천 501
 
5.9%
구 437
 
5.2%
인 431
 
5.1%
동 431
 
5.1%
광 431
 
5.1%
역 431
 
5.1%
시 431
 
5.1%
Other values (21) 2506
29.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4759
56.5%
Space Separator 1742
 
20.7%
Decimal Number 1645
 
19.5%
Dash Punctuation 276
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 564
11.9%
평 517
10.9%
천 501
10.5%
구 437
9.2%
인 431
9.1%
동 431
9.1%
광 431
9.1%
역 431
9.1%
시 431
9.1%
산 151
 
3.2%
Other values (9) 434
9.1%
Decimal Number
ValueCountFrequency (%)
1 300
18.2%
2 199
12.1%
3 192
11.7%
4 181
11.0%
5 180
10.9%
7 134
8.1%
0 120
 
7.3%
9 116
 
7.1%
6 112
 
6.8%
8 111
 
6.7%
Space Separator
ValueCountFrequency (%)
1742
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4759
56.5%
Common 3663
43.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 564
11.9%
평 517
10.9%
천 501
10.5%
구 437
9.2%
인 431
9.1%
동 431
9.1%
광 431
9.1%
역 431
9.1%
시 431
9.1%
산 151
 
3.2%
Other values (9) 434
9.1%
Common
ValueCountFrequency (%)
1742
47.6%
1 300
 
8.2%
- 276
 
7.5%
2 199
 
5.4%
3 192
 
5.2%
4 181
 
4.9%
5 180
 
4.9%
7 134
 
3.7%
0 120
 
3.3%
9 116
 
3.2%
Other values (2) 223
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4759
56.5%
ASCII 3663
43.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1742
47.6%
1 300
 
8.2%
- 276
 
7.5%
2 199
 
5.4%
3 192
 
5.2%
4 181
 
4.9%
5 180
 
4.9%
7 134
 
3.7%
0 120
 
3.3%
9 116
 
3.2%
Other values (2) 223
 
6.1%
Hangul
ValueCountFrequency (%)
부 564
11.9%
평 517
10.9%
천 501
10.5%
구 437
9.2%
인 431
9.1%
동 431
9.1%
광 431
9.1%
역 431
9.1%
시 431
9.1%
산 151
 
3.2%
Other values (9) 434
9.1%

공부지목
Categorical

IMBALANCE 

Distinct6
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size3.5 KiB
도로
396 
구거
 
21
임야
 
7
하천
 
4
답
 
2

Length

Max length2
Median length2
Mean length1.9930394
Min length1

Unique

Unique1 ?
Unique (%)0.2%

Sample

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

Common Values

ValueCountFrequency (%)
도로 396
91.9%
구거 21
 
4.9%
임야 7
 
1.6%
하천 4
 
0.9%
답 2
 
0.5%
대 1
 
0.2%

Length

2024-01-28T14:16:32.259012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:16:32.356372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도로 396
91.9%
구거 21
 
4.9%
임야 7
 
1.6%
하천 4
 
0.9%
답 2
 
0.5%
대 1
 
0.2%

공부면적
Real number (ℝ)

Distinct426
Distinct (%)98.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7000.549
Minimum1034
Maximum241605.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size3.9 KiB
2024-01-28T14:16:32.469136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1034
5-th percentile1140.05
Q11765.45
median3909.3
Q37913.5
95-th percentile20346.55
Maximum241605.9
Range240571.9
Interquartile range (IQR)6148.05

Descriptive statistics

Standard deviation13693.872
Coefficient of variation (CV)1.956114
Kurtosis201.87759
Mean7000.549
Median Absolute Deviation (MAD)2425.3
Skewness12.296183
Sum3017236.6
Variance1.8752212 × 108
MonotonicityIncreasing
2024-01-28T14:16:32.596452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1293.0 2
 
0.5%
18560.3 2
 
0.5%
2512.0 2
 
0.5%
2555.0 2
 
0.5%
4198.0 2
 
0.5%
6172.5 1
 
0.2%
6649.6 1
 
0.2%
6613.9 1
 
0.2%
6602.1 1
 
0.2%
6548.8 1
 
0.2%
Other values (416) 416
96.5%
ValueCountFrequency (%)
1034.0 1
0.2%
1037.5 1
0.2%
1045.3 1
0.2%
1045.5 1
0.2%
1052.5 1
0.2%
1053.7 1
0.2%
1054.0 1
0.2%
1055.6 1
0.2%
1067.0 1
0.2%
1071.7 1
0.2%
ValueCountFrequency (%)
241605.9 1
0.2%
62316.6 1
0.2%
56302.2 1
0.2%
52057.8 1
0.2%
43460.2 1
0.2%
42114.4 1
0.2%
32268.7 1
0.2%
31639.0 1
0.2%
31457.6 1
0.2%
30489.8 1
0.2%

Interactions

2024-01-28T14:16:31.162414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T14:16:32.670559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공부지목공부면적
공부지목1.0000.000
공부면적0.0001.000
2024-01-28T14:16:32.735873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
공부면적공부지목
공부면적1.0000.000
공부지목0.0001.000

Missing values

2024-01-28T14:16:31.294150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T14:16:31.386689image/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

소재지공부지목공부면적
0인천광역시 부평구 삼산동 206-1도로1034.0
1인천광역시 부평구 갈산동 171-3도로1037.5
2인천광역시 부평구 십정동 558-8도로1045.3
3인천광역시 부평구 십정동 527-12도로1045.5
4인천광역시 부평구 십정동 529-11도로1052.5
5인천광역시 부평구 갈산동 451-1도로1053.7
6인천광역시 부평구 부평동 650-20도로1054.0
7인천광역시 부평구 십정동 527-3도로1055.6
8인천광역시 부평구 산곡동 산 98-24도로1067.0
9인천광역시 부평구 청천동 190-16도로1071.7
소재지공부지목공부면적
421인천광역시 부평구 갈산동 187도로30489.8
422인천광역시 부평구 십정동 335도로31457.6
423인천광역시 부평구 부평동 431-40도로31639.0
424인천광역시 부평구 청천동 410도로32268.7
425인천광역시 부평구 갈산동 404-12구거42114.4
426인천광역시 부평구 부평동 589도로43460.2
427인천광역시 부평구 청천동 112-1도로52057.8
428인천광역시 부평구 부평동 686도로56302.2
429인천광역시 부평구 청천동 111도로62316.6
430인천광역시 부평구 부평동 126-10도로241605.9