Overview

Dataset statistics

Number of variables7
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory64.0 B

Variable types

Categorical3
Text2
Numeric2

Dataset

Description경기도 평택시 내 지구별 주택공급 예정량 데이터로 지구명, 공급구분(민간/공공), 준공(예정), 수용인구수, 전체세대수, 담당부서 항목을 제공합니다. ※ 문의 : 미래첨단산업과(031-8024-3451), 도시개발과(031-8024-4011), 도시계획과(031-8024-3929)
URLhttps://www.data.go.kr/data/15114718/fileData.do

Alerts

시군구 has constant value ""Constant
수용인구수 is highly overall correlated with 전체세대수 and 1 other fieldsHigh correlation
전체세대수 is highly overall correlated with 수용인구수 and 1 other fieldsHigh correlation
담당부서 is highly overall correlated with 수용인구수 and 1 other fieldsHigh correlation
공급구분 is highly imbalanced (55.8%)Imbalance
지구명 has unique valuesUnique
수용인구수 has unique valuesUnique
전체세대수 has unique valuesUnique

Reproduction

Analysis started2023-12-12 22:31:14.643215
Analysis finished2023-12-12 22:31:15.397288
Duration0.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
평택시
22 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
평택시 22
100.0%

Length

2023-12-13T07:31:15.446868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:31:15.531277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
평택시 22
100.0%

지구명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T07:31:15.676818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length6
Mean length4.1818182
Min length2

Characters and Unicode

Total characters92
Distinct characters45
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row브레인시티 일반산업단지
2nd row지제세교지구
3rd row동삭세교지구
4th row수촌지구
5th row영신지구
ValueCountFrequency (%)
브레인시티 1
 
4.3%
소사3 1
 
4.3%
갈곶지구 1
 
4.3%
도곡2지구 1
 
4.3%
신촌지구 1
 
4.3%
가곡2 1
 
4.3%
현곡 1
 
4.3%
죽백1 1
 
4.3%
학현 1
 
4.3%
송화 1
 
4.3%
Other values (13) 13
56.5%
2023-12-13T07:31:15.960279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 15
 
16.3%
구 14
 
15.2%
2 4
 
4.3%
화 3
 
3.3%
송 3
 
3.3%
곡 3
 
3.3%
신 3
 
3.3%
교 2
 
2.2%
세 2
 
2.2%
가 2
 
2.2%
Other values (35) 41
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
92.4%
Decimal Number 6
 
6.5%
Space Separator 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 15
17.6%
구 14
 
16.5%
화 3
 
3.5%
송 3
 
3.5%
곡 3
 
3.5%
신 3
 
3.5%
교 2
 
2.4%
세 2
 
2.4%
가 2
 
2.4%
사 2
 
2.4%
Other values (31) 36
42.4%
Decimal Number
ValueCountFrequency (%)
2 4
66.7%
3 1
 
16.7%
1 1
 
16.7%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
92.4%
Common 7
 
7.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 15
17.6%
구 14
 
16.5%
화 3
 
3.5%
송 3
 
3.5%
곡 3
 
3.5%
신 3
 
3.5%
교 2
 
2.4%
세 2
 
2.4%
가 2
 
2.4%
사 2
 
2.4%
Other values (31) 36
42.4%
Common
ValueCountFrequency (%)
2 4
57.1%
3 1
 
14.3%
1 1
 
14.3%
1
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
92.4%
ASCII 7
 
7.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 15
17.6%
구 14
 
16.5%
화 3
 
3.5%
송 3
 
3.5%
곡 3
 
3.5%
신 3
 
3.5%
교 2
 
2.4%
세 2
 
2.4%
가 2
 
2.4%
사 2
 
2.4%
Other values (31) 36
42.4%
ASCII
ValueCountFrequency (%)
2 4
57.1%
3 1
 
14.3%
1 1
 
14.3%
1
 
14.3%

공급구분
Categorical

IMBALANCE 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
민간
19 
민간+공공
2 
공공
 
1

Length

Max length5
Median length2
Mean length2.2727273
Min length2

Unique

Unique1 ?
Unique (%)4.5%

Sample

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

Common Values

ValueCountFrequency (%)
민간 19
86.4%
민간+공공 2
 
9.1%
공공 1
 
4.5%

Length

2023-12-13T07:31:16.078263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:31:16.175402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간 19
86.4%
민간+공공 2
 
9.1%
공공 1
 
4.5%

준공(예정)
Text

Distinct14
Distinct (%)63.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-13T07:31:16.323395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length7
Mean length7.8181818
Min length4

Characters and Unicode

Total characters172
Distinct characters25
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

Unique7 ?
Unique (%)31.8%

Sample

1st row2024
2nd row2026-12
3rd row2024-12
4th row2028-12
5th row2024-12
ValueCountFrequency (%)
미정(주택건설사업계획 3
12.0%
승인 3
12.0%
2024 2
 
8.0%
2026-12 2
 
8.0%
2024-12 2
 
8.0%
2030-12 2
 
8.0%
2026-02 2
 
8.0%
2027-12 2
 
8.0%
2028-12 1
 
4.0%
2024-06 1
 
4.0%
Other values (5) 5
20.0%
2023-12-13T07:31:16.588281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 49
28.5%
0 28
16.3%
- 17
 
9.9%
1 12
 
7.0%
4 6
 
3.5%
6 5
 
2.9%
3 4
 
2.3%
미 3
 
1.7%
정 3
 
1.7%
) 3
 
1.7%
Other values (15) 42
24.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 110
64.0%
Other Letter 36
 
20.9%
Dash Punctuation 17
 
9.9%
Close Punctuation 3
 
1.7%
Space Separator 3
 
1.7%
Open Punctuation 3
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
미 3
8.3%
정 3
8.3%
인 3
8.3%
승 3
8.3%
획 3
8.3%
계 3
8.3%
업 3
8.3%
사 3
8.3%
설 3
8.3%
건 3
8.3%
Other values (2) 6
16.7%
Decimal Number
ValueCountFrequency (%)
2 49
44.5%
0 28
25.5%
1 12
 
10.9%
4 6
 
5.5%
6 5
 
4.5%
3 4
 
3.6%
7 2
 
1.8%
8 2
 
1.8%
5 2
 
1.8%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 136
79.1%
Hangul 36
 
20.9%

Most frequent character per script

Common
ValueCountFrequency (%)
2 49
36.0%
0 28
20.6%
- 17
 
12.5%
1 12
 
8.8%
4 6
 
4.4%
6 5
 
3.7%
3 4
 
2.9%
) 3
 
2.2%
3
 
2.2%
( 3
 
2.2%
Other values (3) 6
 
4.4%
Hangul
ValueCountFrequency (%)
미 3
8.3%
정 3
8.3%
인 3
8.3%
승 3
8.3%
획 3
8.3%
계 3
8.3%
업 3
8.3%
사 3
8.3%
설 3
8.3%
건 3
8.3%
Other values (2) 6
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
79.1%
Hangul 36
 
20.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 49
36.0%
0 28
20.6%
- 17
 
12.5%
1 12
 
8.8%
4 6
 
4.4%
6 5
 
3.7%
3 4
 
2.9%
) 3
 
2.2%
3
 
2.2%
( 3
 
2.2%
Other values (3) 6
 
4.4%
Hangul
ValueCountFrequency (%)
미 3
8.3%
정 3
8.3%
인 3
8.3%
승 3
8.3%
획 3
8.3%
계 3
8.3%
업 3
8.3%
사 3
8.3%
설 3
8.3%
건 3
8.3%
Other values (2) 6
16.7%

수용인구수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11882.091
Minimum2168
Maximum53277
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T07:31:16.705693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2168
5-th percentile2312.6
Q14178.75
median9314
Q313043
95-th percentile39696.1
Maximum53277
Range51109
Interquartile range (IQR)8864.25

Descriptive statistics

Standard deviation12530.74
Coefficient of variation (CV)1.0545905
Kurtosis6.0056996
Mean11882.091
Median Absolute Deviation (MAD)5049.5
Skewness2.3970982
Sum261406
Variance1.5701944 × 108
MonotonicityNot monotonic
2023-12-13T07:31:16.805653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
40831 1
 
4.5%
53277 1
 
4.5%
2590 1
 
4.5%
5739 1
 
4.5%
4436 1
 
4.5%
11976 1
 
4.5%
5490 1
 
4.5%
18133 1
 
4.5%
9998 1
 
4.5%
2168 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
2168 1
4.5%
2298 1
4.5%
2590 1
4.5%
2780 1
4.5%
2999 1
4.5%
4093 1
4.5%
4436 1
4.5%
5490 1
4.5%
5739 1
4.5%
7668 1
4.5%
ValueCountFrequency (%)
53277 1
4.5%
40831 1
4.5%
18133 1
4.5%
16448 1
4.5%
15911 1
4.5%
13146 1
4.5%
12734 1
4.5%
11976 1
4.5%
10063 1
4.5%
9998 1
4.5%

전체세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4633.2273
Minimum867
Maximum20388
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-13T07:31:16.915731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum867
5-th percentile922.85
Q11606.25
median3567
Q35016.5
95-th percentile15878.05
Maximum20388
Range19521
Interquartile range (IQR)3410.25

Descriptive statistics

Standard deviation4871.1808
Coefficient of variation (CV)1.051358
Kurtosis5.6977552
Mean4633.2273
Median Absolute Deviation (MAD)1927.5
Skewness2.3609888
Sum101931
Variance23728402
MonotonicityNot monotonic
2023-12-13T07:31:17.119982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
16332 1
 
4.5%
20388 1
 
4.5%
996 1
 
4.5%
2207 1
 
4.5%
1706 1
 
4.5%
4606 1
 
4.5%
2196 1
 
4.5%
7253 1
 
4.5%
3999 1
 
4.5%
867 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
867 1
4.5%
919 1
4.5%
996 1
4.5%
1112 1
4.5%
1154 1
4.5%
1573 1
4.5%
1706 1
4.5%
2196 1
4.5%
2207 1
4.5%
3066 1
4.5%
ValueCountFrequency (%)
20388 1
4.5%
16332 1
4.5%
7253 1
4.5%
6325 1
4.5%
6119 1
4.5%
5056 1
4.5%
4898 1
4.5%
4606 1
4.5%
4025 1
4.5%
3999 1
4.5%

담당부서
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Memory size308.0 B
도시개발과
17 
도시계획과
4 
미래첨단산업과
 
1

Length

Max length7
Median length5
Mean length5.0909091
Min length5

Unique

Unique1 ?
Unique (%)4.5%

Sample

1st row미래첨단산업과
2nd row도시개발과
3rd row도시개발과
4th row도시개발과
5th row도시개발과

Common Values

ValueCountFrequency (%)
도시개발과 17
77.3%
도시계획과 4
 
18.2%
미래첨단산업과 1
 
4.5%

Length

2023-12-13T07:31:17.323216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:31:17.480945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
도시개발과 17
77.3%
도시계획과 4
 
18.2%
미래첨단산업과 1
 
4.5%

Interactions

2023-12-13T07:31:15.039296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:14.887590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:15.130772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:31:14.955984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:31:17.571645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지구명공급구분준공(예정)수용인구수전체세대수담당부서
지구명1.0001.0001.0001.0001.0001.000
공급구분1.0001.0001.0000.8000.8000.000
준공(예정)1.0001.0001.0000.6990.6990.669
수용인구수1.0000.8000.6991.0001.0000.923
전체세대수1.0000.8000.6991.0001.0000.923
담당부서1.0000.0000.6690.9230.9231.000
2023-12-13T07:31:17.731453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담당부서공급구분
담당부서1.0000.000
공급구분0.0001.000
2023-12-13T07:31:17.819098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수용인구수전체세대수공급구분담당부서
수용인구수1.0001.0000.2450.599
전체세대수1.0001.0000.2450.599
공급구분0.2450.2451.0000.000
담당부서0.5990.5990.0001.000

Missing values

2023-12-13T07:31:15.254875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:31:15.356648image/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

시군구지구명공급구분준공(예정)수용인구수전체세대수담당부서
0평택시브레인시티 일반산업단지민간20244083116332미래첨단산업과
1평택시지제세교지구민간2026-12164486325도시개발과
2평택시동삭세교지구민간2024-1229991154도시개발과
3평택시수촌지구민간2028-1298183746도시개발과
4평택시영신지구민간2024-12131465056도시개발과
5평택시모산영신지구민간2024-06159116119도시개발과
6평택시송화2지구민간2030-12100634025도시개발과
7평택시가재지구민간2026-02127344898도시개발과
8평택시구룡지구민간2030-1276683066도시개발과
9평택시송북지구민간2026-022298919도시개발과
시군구지구명공급구분준공(예정)수용인구수전체세대수담당부서
12평택시화양민간2025-085327720388도시개발과
13평택시송화민간2024-1027801112도시개발과
14평택시학현공공2023-102168867도시개발과
15평택시죽백1민간2026-1299983999도시개발과
16평택시현곡민간+공공2027-12181337253도시개발과
17평택시가곡2민간+공공2027-1254902196도시개발과
18평택시신촌지구민간2024119764606도시계획과
19평택시도곡2지구민간미정(주택건설사업계획 승인)44361706도시계획과
20평택시갈곶지구민간미정(주택건설사업계획 승인)57392207도시계획과
21평택시비전지구민간미정(주택건설사업계획 승인)2590996도시계획과