Overview

Dataset statistics

Number of variables3
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.1 KiB
Average record size in memory29.4 B

Variable types

Text1
Numeric2

Dataset

Description한국토지주택공사에서 개발 조성중인 남양주왕숙 지구 토지이용계획표로 전체 토지이용계획의 면적, 구성비 및 주택건설용지, 단독주택, 공동주택등 부분별 면적, 구성비 정보 제공
Author한국토지주택공사
URLhttps://www.data.go.kr/data/15087757/fileData.do

Alerts

면적(제곱미터) is highly overall correlated with 구성비(퍼센트)High correlation
구성비(퍼센트) is highly overall correlated with 면적(제곱미터) High correlation
남양주왕숙 지구 토지이용계획표 has unique valuesUnique
면적(제곱미터) has unique valuesUnique
구성비(퍼센트) has 4 (10.3%) zerosZeros

Reproduction

Analysis started2023-12-12 23:41:26.146473
Analysis finished2023-12-12 23:41:26.761868
Duration0.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

남양주왕숙 지구 토지이용계획표
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T08:41:26.906698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length8.8717949
Min length3

Characters and Unicode

Total characters346
Distinct characters77
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row단독주택
2nd row공동주택 연립
3rd row공동주택 아파트
4th row근린생활시설
5th row주거유형복합용지
ValueCountFrequency (%)
도시지원시설 12
 
16.9%
공원녹지 6
 
8.5%
철도 5
 
7.0%
도로 5
 
7.0%
및 4
 
5.6%
공동주택 2
 
2.8%
보행자도로 1
 
1.4%
자원순환센터 1
 
1.4%
수도용지 1
 
1.4%
공공공지 1
 
1.4%
Other values (33) 33
46.5%
2023-12-13T08:41:27.233068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 38
 
11.0%
시 33
 
9.5%
32
 
9.2%
도 25
 
7.2%
설 22
 
6.4%
원 21
 
6.1%
공 17
 
4.9%
용 16
 
4.6%
합 7
 
2.0%
주 7
 
2.0%
Other values (67) 128
37.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 307
88.7%
Space Separator 32
 
9.2%
Open Punctuation 2
 
0.6%
Close Punctuation 2
 
0.6%
Decimal Number 2
 
0.6%
Uppercase Letter 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 38
 
12.4%
시 33
 
10.7%
도 25
 
8.1%
설 22
 
7.2%
원 21
 
6.8%
공 17
 
5.5%
용 16
 
5.2%
합 7
 
2.3%
주 7
 
2.3%
녹 7
 
2.3%
Other values (61) 114
37.1%
Decimal Number
ValueCountFrequency (%)
0 1
50.0%
1 1
50.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
M 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 307
88.7%
Common 38
 
11.0%
Latin 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 38
 
12.4%
시 33
 
10.7%
도 25
 
8.1%
설 22
 
7.2%
원 21
 
6.8%
공 17
 
5.5%
용 16
 
5.2%
합 7
 
2.3%
주 7
 
2.3%
녹 7
 
2.3%
Other values (61) 114
37.1%
Common
ValueCountFrequency (%)
32
84.2%
( 2
 
5.3%
) 2
 
5.3%
0 1
 
2.6%
1 1
 
2.6%
Latin
ValueCountFrequency (%)
M 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 307
88.7%
ASCII 39
 
11.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 38
 
12.4%
시 33
 
10.7%
도 25
 
8.1%
설 22
 
7.2%
원 21
 
6.8%
공 17
 
5.5%
용 16
 
5.2%
합 7
 
2.3%
주 7
 
2.3%
녹 7
 
2.3%
Other values (61) 114
37.1%
ASCII
ValueCountFrequency (%)
32
82.1%
( 2
 
5.1%
) 2
 
5.1%
M 1
 
2.6%
0 1
 
2.6%
1 1
 
2.6%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean226181.77
Minimum1202
Maximum1881307
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T08:41:27.358836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1202
5-th percentile7170.4
Q119381
median59245
Q3187041
95-th percentile1342544.4
Maximum1881307
Range1880105
Interquartile range (IQR)167660

Descriptive statistics

Standard deviation450958.72
Coefficient of variation (CV)1.993789
Kurtosis8.4220005
Mean226181.77
Median Absolute Deviation (MAD)50229
Skewness2.9812648
Sum8821089
Variance2.0336377 × 1011
MonotonicityNot monotonic
2023-12-13T08:41:27.497603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
181480 1
 
2.6%
18603 1
 
2.6%
15801 1
 
2.6%
6823 1
 
2.6%
678476 1
 
2.6%
335609 1
 
2.6%
27178 1
 
2.6%
7609 1
 
2.6%
9016 1
 
2.6%
7526 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1202 1
2.6%
6823 1
2.6%
7209 1
2.6%
7526 1
2.6%
7609 1
2.6%
9016 1
2.6%
11137 1
2.6%
15801 1
2.6%
15829 1
2.6%
18603 1
2.6%
ValueCountFrequency (%)
1881307 1
2.6%
1841643 1
2.6%
1287089 1
2.6%
678476 1
2.6%
451093 1
2.6%
335609 1
2.6%
298156 1
2.6%
224784 1
2.6%
224049 1
2.6%
192602 1
2.6%

구성비(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5641026
Minimum0
Maximum21.7
Zeros4
Zeros (%)10.3%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T08:41:27.659493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.2
median0.6
Q32.15
95-th percentile15.54
Maximum21.7
Range21.7
Interquartile range (IQR)1.95

Descriptive statistics

Standard deviation5.2265762
Coefficient of variation (CV)2.0383647
Kurtosis8.3933304
Mean2.5641026
Median Absolute Deviation (MAD)0.5
Skewness2.9778924
Sum100
Variance27.317099
MonotonicityNot monotonic
2023-12-13T08:41:27.791200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.2 5
12.8%
0.1 4
 
10.3%
0.3 4
 
10.3%
0.0 4
 
10.3%
0.6 2
 
5.1%
0.8 2
 
5.1%
2.6 2
 
5.1%
0.7 2
 
5.1%
0.9 1
 
2.6%
2.2 1
 
2.6%
Other values (12) 12
30.8%
ValueCountFrequency (%)
0.0 4
10.3%
0.1 4
10.3%
0.2 5
12.8%
0.3 4
10.3%
0.5 1
 
2.6%
0.6 2
 
5.1%
0.7 2
 
5.1%
0.8 2
 
5.1%
0.9 1
 
2.6%
1.0 1
 
2.6%
ValueCountFrequency (%)
21.7 1
2.6%
21.3 1
2.6%
14.9 1
2.6%
7.8 1
2.6%
5.2 1
2.6%
3.9 1
2.6%
3.4 1
2.6%
2.6 2
5.1%
2.2 1
2.6%
2.1 1
2.6%

Interactions

2023-12-13T08:41:26.449092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:26.253849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:26.539108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:41:26.347249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:41:27.876054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
남양주왕숙 지구 토지이용계획표면적(제곱미터)구성비(퍼센트)
남양주왕숙 지구 토지이용계획표1.0001.0001.000
면적(제곱미터)1.0001.0001.000
구성비(퍼센트)1.0001.0001.000
2023-12-13T08:41:27.968457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)구성비(퍼센트)
면적(제곱미터)1.0000.924
구성비(퍼센트)0.9241.000

Missing values

2023-12-13T08:41:26.644455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:41:26.722997image/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

남양주왕숙 지구 토지이용계획표면적(제곱미터)구성비(퍼센트)
0단독주택1814802.1
1공동주택 연립186030.2
2공동주택 아파트188130721.7
3근린생활시설551940.6
4주거유형복합용지706640.8
5주상복합용지2247842.6
6상업용지1266401.5
7도시지원시설 자족복합용지855791.0
8도시지원시설 자족시설용지2240492.6
9도시지원시설 업무시설용지1344841.6
남양주왕숙 지구 토지이용계획표면적(제곱미터)구성비(퍼센트)
29전기공급설비75260.1
30수도용지213770.2
31저류지1501860.0
32종교용지158290.2
33주차장597600.7
34주유소용지218610.3
35도로 및 철도 도로128708914.9
36도로 및 철도 보행자도로265840.3
37도로 및 철도 교통광장1926022.2
38도로 및 철도 철도732130.8