Overview

Dataset statistics

Number of variables3
Number of observations162
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory25.8 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description인천광역시 부평구 공유재산 구유지 현황 데이터입니다.(소재지,토지지목코드,면적)ex) 인천광역시 부평구 부평동 65-151,14-도로,1376
Author인천광역시 부평구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15089230&srcSe=7661IVAWM27C61E190

Alerts

토지지목코드 is highly imbalanced (87.3%)Imbalance
소재지 has unique valuesUnique

Reproduction

Analysis started2024-01-28 06:04:37.250199
Analysis finished2024-01-28 06:04:37.537954
Duration0.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

소재지
Text

UNIQUE 

Distinct162
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
2024-01-28T15:04:37.812508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length21
Mean length19.777778
Min length18

Characters and Unicode

Total characters3204
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

Unique162 ?
Unique (%)100.0%

Sample

1st row인천광역시 부평구 부평동 942
2nd row인천광역시 부평구 일신동 11-1
3rd row인천광역시 부평구 삼산동 167-83
4th row인천광역시 부평구 산곡동 100-107
5th row인천광역시 부평구 십정동 626
ValueCountFrequency (%)
인천광역시 162
24.9%
부평구 162
24.9%
삼산동 47
 
7.2%
갈산동 26
 
4.0%
산곡동 24
 
3.7%
부평동 16
 
2.5%
부개동 16
 
2.5%
청천동 11
 
1.7%
일신동 10
 
1.5%
십정동 9
 
1.4%
Other values (161) 168
25.8%
2024-01-28T15:04:38.218333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
651
20.3%
부 194
 
6.1%
평 178
 
5.6%
천 173
 
5.4%
구 165
 
5.1%
인 162
 
5.1%
광 162
 
5.1%
역 162
 
5.1%
시 162
 
5.1%
동 162
 
5.1%
Other values (21) 1033
32.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1785
55.7%
Decimal Number 655
 
20.4%
Space Separator 651
 
20.3%
Dash Punctuation 113
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
부 194
10.9%
평 178
10.0%
천 173
9.7%
구 165
9.2%
인 162
9.1%
광 162
9.1%
역 162
9.1%
시 162
9.1%
동 162
9.1%
산 103
5.8%
Other values (9) 162
9.1%
Decimal Number
ValueCountFrequency (%)
1 125
19.1%
4 100
15.3%
2 79
12.1%
5 62
9.5%
3 57
8.7%
6 52
7.9%
8 47
 
7.2%
7 46
 
7.0%
0 45
 
6.9%
9 42
 
6.4%
Space Separator
ValueCountFrequency (%)
651
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 113
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1785
55.7%
Common 1419
44.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
부 194
10.9%
평 178
10.0%
천 173
9.7%
구 165
9.2%
인 162
9.1%
광 162
9.1%
역 162
9.1%
시 162
9.1%
동 162
9.1%
산 103
5.8%
Other values (9) 162
9.1%
Common
ValueCountFrequency (%)
651
45.9%
1 125
 
8.8%
- 113
 
8.0%
4 100
 
7.0%
2 79
 
5.6%
5 62
 
4.4%
3 57
 
4.0%
6 52
 
3.7%
8 47
 
3.3%
7 46
 
3.2%
Other values (2) 87
 
6.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1785
55.7%
ASCII 1419
44.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
651
45.9%
1 125
 
8.8%
- 113
 
8.0%
4 100
 
7.0%
2 79
 
5.6%
5 62
 
4.4%
3 57
 
4.0%
6 52
 
3.7%
8 47
 
3.3%
7 46
 
3.2%
Other values (2) 87
 
6.1%
Hangul
ValueCountFrequency (%)
부 194
10.9%
평 178
10.0%
천 173
9.7%
구 165
9.2%
인 162
9.1%
광 162
9.1%
역 162
9.1%
시 162
9.1%
동 162
9.1%
산 103
5.8%
Other values (9) 162
9.1%

토지지목코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size1.4 KiB
14-도로
156 
18-구거
 
3
05-임야
 
1
08-대
 
1
17-하천
 
1

Length

Max length5
Median length5
Mean length4.9938272
Min length4

Unique

Unique3 ?
Unique (%)1.9%

Sample

1st row14-도로
2nd row18-구거
3rd row14-도로
4th row14-도로
5th row14-도로

Common Values

ValueCountFrequency (%)
14-도로 156
96.3%
18-구거 3
 
1.9%
05-임야 1
 
0.6%
08-대 1
 
0.6%
17-하천 1
 
0.6%

Length

2024-01-28T15:04:38.327395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T15:04:38.421077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
14-도로 156
96.3%
18-구거 3
 
1.9%
05-임야 1
 
0.6%
08-대 1
 
0.6%
17-하천 1
 
0.6%

면적
Real number (ℝ)

Distinct161
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2669.5549
Minimum1011.4
Maximum13233.9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.6 KiB
2024-01-28T15:04:38.530131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1011.4
5-th percentile1045.045
Q11260.175
median1999.85
Q32953.725
95-th percentile7385.775
Maximum13233.9
Range12222.5
Interquartile range (IQR)1693.55

Descriptive statistics

Standard deviation2208.5989
Coefficient of variation (CV)0.82732851
Kurtosis7.2592184
Mean2669.5549
Median Absolute Deviation (MAD)798.7
Skewness2.5405325
Sum432467.9
Variance4877909.1
MonotonicityIncreasing
2024-01-28T15:04:38.906631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1059.0 2
 
1.2%
1011.4 1
 
0.6%
2716.0 1
 
0.6%
2508.5 1
 
0.6%
2510.3 1
 
0.6%
2536.0 1
 
0.6%
2557.7 1
 
0.6%
2577.7 1
 
0.6%
2637.0 1
 
0.6%
2650.1 1
 
0.6%
Other values (151) 151
93.2%
ValueCountFrequency (%)
1011.4 1
0.6%
1012.0 1
0.6%
1013.0 1
0.6%
1025.0 1
0.6%
1027.3 1
0.6%
1028.0 1
0.6%
1029.0 1
0.6%
1033.0 1
0.6%
1045.0 1
0.6%
1045.9 1
0.6%
ValueCountFrequency (%)
13233.9 1
0.6%
12485.9 1
0.6%
11660.2 1
0.6%
10120.7 1
0.6%
8885.3 1
0.6%
8518.0 1
0.6%
7976.6 1
0.6%
7916.0 1
0.6%
7391.0 1
0.6%
7286.5 1
0.6%

Interactions

2024-01-28T15:04:37.342688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T15:04:38.978722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
토지지목코드면적
토지지목코드1.0000.000
면적0.0001.000
2024-01-28T15:04:39.043720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적토지지목코드
면적1.0000.000
토지지목코드0.0001.000

Missing values

2024-01-28T15:04:37.443183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T15:04:37.511273image/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

소재지토지지목코드면적
0인천광역시 부평구 부평동 94214-도로1011.4
1인천광역시 부평구 일신동 11-118-구거1012.0
2인천광역시 부평구 삼산동 167-8314-도로1013.0
3인천광역시 부평구 산곡동 100-10714-도로1025.0
4인천광역시 부평구 십정동 62614-도로1027.3
5인천광역시 부평구 부평동 665-39414-도로1028.0
6인천광역시 부평구 부개동 12-12914-도로1029.0
7인천광역시 부평구 산곡동 185-1614-도로1033.0
8인천광역시 부평구 산곡동 82-10614-도로1045.0
9인천광역시 부평구 삼산동 454-314-도로1045.9
소재지토지지목코드면적
152인천광역시 부평구 갈산동 40914-도로7286.5
153인천광역시 부평구 부평동 738-3214-도로7391.0
154인천광역시 부평구 일신동 30-614-도로7916.0
155인천광역시 부평구 부개동 511-114-도로7976.6
156인천광역시 부평구 산곡동 317-214-도로8518.0
157인천광역시 부평구 삼산동 395-814-도로8885.3
158인천광역시 부평구 삼산동 395-514-도로10120.7
159인천광역시 부평구 삼산동 48214-도로11660.2
160인천광역시 부평구 부개동 51614-도로12485.9
161인천광역시 부평구 십정동 60614-도로13233.9