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

Description행복도시내 기업연수시설, 노인휴양시설, 청소년수련시설, 민속마을, 도서관, 농업기술센터 등 토지이용계획입니다.
URLhttps://www.data.go.kr/data/15064075/fileData.do

Alerts

기정 is highly overall correlated with 변경High correlation
변경 is highly overall correlated with 기정High correlation
구분 has unique valuesUnique
기정 has unique valuesUnique
변경 has unique valuesUnique

Reproduction

Analysis started2023-12-13 00:07:52.011423
Analysis finished2023-12-13 00:07:52.560251
Duration0.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T09:07:52.654353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length18
Mean length13.102564
Min length3

Characters and Unicode

Total characters511
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
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 row주택용지/저밀
2nd row주택용지/중저밀
3rd row주택용지/중밀
4th row주택용지/중고밀
5th row주택용지/고밀(도심형)
ValueCountFrequency (%)
주택용지/저밀 1
 
2.6%
시설용지/공공기관 1
 
2.6%
시설용지/복지시설 1
 
2.6%
시설용지/문화시설 1
 
2.6%
시설용지/의료시설 1
 
2.6%
시설용지/체육시설 1
 
2.6%
시설용지/기타시설 1
 
2.6%
시설용지/공공기반시설/도로 1
 
2.6%
시설용지/공공기반시설/주차장 1
 
2.6%
시설용지/교육시설 1
 
2.6%
Other values (29) 29
74.4%
2023-12-13T09:07:52.913078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 49
 
9.6%
시 48
 
9.4%
용 47
 
9.2%
설 46
 
9.0%
지 45
 
8.8%
공 29
 
5.7%
업 18
 
3.5%
기 17
 
3.3%
반 13
 
2.5%
주 13
 
2.5%
Other values (71) 186
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 435
85.1%
Other Punctuation 49
 
9.6%
Decimal Number 10
 
2.0%
Close Punctuation 6
 
1.2%
Open Punctuation 6
 
1.2%
Dash Punctuation 5
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 48
 
11.0%
용 47
 
10.8%
설 46
 
10.6%
지 45
 
10.3%
공 29
 
6.7%
업 18
 
4.1%
기 17
 
3.9%
반 13
 
3.0%
주 13
 
3.0%
도 8
 
1.8%
Other values (63) 151
34.7%
Decimal Number
ValueCountFrequency (%)
5 4
40.0%
1 3
30.0%
2 2
20.0%
6 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
/ 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 435
85.1%
Common 76
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 48
 
11.0%
용 47
 
10.8%
설 46
 
10.6%
지 45
 
10.3%
공 29
 
6.7%
업 18
 
4.1%
기 17
 
3.9%
반 13
 
3.0%
주 13
 
3.0%
도 8
 
1.8%
Other values (63) 151
34.7%
Common
ValueCountFrequency (%)
/ 49
64.5%
) 6
 
7.9%
( 6
 
7.9%
- 5
 
6.6%
5 4
 
5.3%
1 3
 
3.9%
2 2
 
2.6%
6 1
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 435
85.1%
ASCII 76
 
14.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 49
64.5%
) 6
 
7.9%
( 6
 
7.9%
- 5
 
6.6%
5 4
 
5.3%
1 3
 
3.9%
2 2
 
2.6%
6 1
 
1.3%
Hangul
ValueCountFrequency (%)
시 48
 
11.0%
용 47
 
10.8%
설 46
 
10.6%
지 45
 
10.3%
공 29
 
6.7%
업 18
 
4.1%
기 17
 
3.9%
반 13
 
3.0%
주 13
 
3.0%
도 8
 
1.8%
Other values (63) 151
34.7%

기정
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1871961.6
Minimum1556
Maximum38448030
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T09:07:53.021592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1556
5-th percentile6996.07
Q182800.55
median306533.6
Q3810502.05
95-th percentile6255308.4
Maximum38448030
Range38446474
Interquartile range (IQR)727701.5

Descriptive statistics

Standard deviation6254136.9
Coefficient of variation (CV)3.3409536
Kurtosis32.855559
Mean1871961.6
Median Absolute Deviation (MAD)288729.6
Skewness5.576421
Sum73006502
Variance3.9114228 × 1013
MonotonicityNot monotonic
2023-12-13T09:07:53.138888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2986170.1 1
 
2.6%
2627564.8 1
 
2.6%
112034.1 1
 
2.6%
306533.6 1
 
2.6%
109442.5 1
 
2.6%
1221608.5 1
 
2.6%
340193.4 1
 
2.6%
8668044.7 1
 
2.6%
336303.2 1
 
2.6%
1556.0 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1556.0 1
2.6%
5090.5 1
2.6%
7207.8 1
2.6%
9360.3 1
2.6%
10384.0 1
2.6%
17804.0 1
2.6%
40128.0 1
2.6%
42642.0 1
2.6%
45304.0 1
2.6%
63829.1 1
2.6%
ValueCountFrequency (%)
38448030.4 1
2.6%
8668044.7 1
2.6%
5987226.6 1
2.6%
2986170.1 1
2.6%
2922016.3 1
2.6%
2627564.8 1
2.6%
1484535.9 1
2.6%
1426033.9 1
2.6%
1221608.5 1
2.6%
904607.8 1
2.6%

변경
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1871961.6
Minimum1556
Maximum38422495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T09:07:53.254781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1556
5-th percentile6996.07
Q182799.05
median305462.5
Q3810501.05
95-th percentile6255471.4
Maximum38422495
Range38420939
Interquartile range (IQR)727702

Descriptive statistics

Standard deviation6250223.4
Coefficient of variation (CV)3.338863
Kurtosis32.84537
Mean1871961.6
Median Absolute Deviation (MAD)287658.5
Skewness5.5753604
Sum73006502
Variance3.9065292 × 1013
MonotonicityNot monotonic
2023-12-13T09:07:53.362945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
2985341.1 1
 
2.6%
2627564.8 1
 
2.6%
112034.1 1
 
2.6%
305462.5 1
 
2.6%
109437.5 1
 
2.6%
1221487.5 1
 
2.6%
340193.4 1
 
2.6%
8673238.6 1
 
2.6%
336297.2 1
 
2.6%
1556.0 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1556.0 1
2.6%
5090.5 1
2.6%
7207.8 1
2.6%
9360.3 1
2.6%
10384.0 1
2.6%
17804.0 1
2.6%
40128.0 1
2.6%
45304.0 1
2.6%
46735.0 1
2.6%
63826.1 1
2.6%
ValueCountFrequency (%)
38422494.6 1
2.6%
8673238.6 1
2.6%
5986830.6 1
2.6%
2985341.1 1
2.6%
2922003.3 1
2.6%
2627564.8 1
2.6%
1484513.9 1
2.6%
1426032.9 1
2.6%
1221487.5 1
2.6%
904607.8 1
2.6%

Interactions

2023-12-13T09:07:52.282900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:07:52.112420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:07:52.374077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T09:07:52.202717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T09:07:53.433901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분기정변경
구분1.0001.0001.000
기정1.0001.0001.000
변경1.0001.0001.000
2023-12-13T09:07:53.494989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
기정변경
기정1.0001.000
변경1.0001.000

Missing values

2023-12-13T09:07:52.482444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T09:07:52.538918image/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

구분기정변경
0주택용지/저밀2986170.12985341.1
1주택용지/중저밀2627564.82627564.8
2주택용지/중밀5987226.65986830.6
3주택용지/중고밀124226.0124226.0
4주택용지/고밀(도심형)716396.3716394.3
5주택용지/특화주거용지42642.046735.0
6주택용지/주거형용도혼합(5-1생활권)585701.0585701.0
7주택용지/주거형용도혼합(6-2생활권)10384.010384.0
8상업업무용지/상업업무용지1426033.91426032.9
9상업업무용지/특정업무시설용지9360.39360.3
구분기정변경
29시설용지/공공기반시설/방송통신시설1556.01556.0
30시설용지/공공기반시설/자동차정류장109253.0109253.0
31시설용지/공공기반시설/농산물도매시장40128.040128.0
32시설용지/공공기반시설/자동차운전면허시험장17804.017804.0
33시설용지/공공기반시설/자동차검사소7207.87207.8
34시설용지/공공기반시설/농업관련시설5090.55090.5
35시설용지/공공기반시설/종교용지145922.0145922.0
36시설용지/공공기반시설/주유소63829.163826.1
37시설용지/공공기반시설/교통광장320383.3320383.3
38시설용지/공공기반시설/기반시설680454.2680498.2