Overview

Dataset statistics

Number of variables6
Number of observations37
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.0 KiB
Average record size in memory55.6 B

Variable types

Categorical1
Text1
Numeric4

Dataset

Description충청남도개발공사 행복주택(임대주택) 사업 추진과 관련하여 해당사업의 청약자 현황을 분석하여 세대별 통계자료 제공, 향후 충남도민들의 행복주택 청약신청시 활용가능 토록 정보제공
URLhttps://www.data.go.kr/data/15106738/fileData.do

Alerts

20대 is highly overall correlated with 30대 and 1 other fieldsHigh correlation
30대 is highly overall correlated with 20대 and 1 other fieldsHigh correlation
40대 is highly overall correlated with 20대 and 1 other fieldsHigh correlation
20대 has 7 (18.9%) zerosZeros
30대 has 8 (21.6%) zerosZeros
40대 has 9 (24.3%) zerosZeros
50대 이상 has 14 (37.8%) zerosZeros

Reproduction

Analysis started2023-12-12 23:56:44.246560
Analysis finished2023-12-12 23:56:45.700759
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업지구
Categorical

Distinct9
Distinct (%)24.3%
Missing0
Missing (%)0.0%
Memory size428.0 B
아산배방
7 
홍성내포
7 
예산주교
6 
아트빌리지
5 
천안남산
3 
Other values (4)
9 

Length

Max length5
Median length4
Mean length4.1081081
Min length3

Unique

Unique1 ?
Unique (%)2.7%

Sample

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

Common Values

ValueCountFrequency (%)
아산배방 7
18.9%
홍성내포 7
18.9%
예산주교 6
16.2%
아트빌리지 5
13.5%
천안남산 3
8.1%
서천군사 3
8.1%
당진채운 3
8.1%
매입형 2
 
5.4%
공주 덕성 1
 
2.7%

Length

2023-12-13T08:56:45.764783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:56:45.875007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
아산배방 7
18.4%
홍성내포 7
18.4%
예산주교 6
15.8%
아트빌리지 5
13.2%
천안남산 3
7.9%
서천군사 3
7.9%
당진채운 3
7.9%
매입형 2
 
5.3%
공주 1
 
2.6%
덕성 1
 
2.6%

구분
Text

Distinct27
Distinct (%)73.0%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-13T08:56:46.039425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length4.0540541
Min length3

Characters and Unicode

Total characters150
Distinct characters19
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)64.9%

Sample

1st row36㎡
2nd row44㎡
3rd row59㎡A
4th row59㎡B
5th row59㎡C
ValueCountFrequency (%)
59㎡ 5
 
13.5%
36㎡ 4
 
10.8%
44㎡ 4
 
10.8%
59b 1
 
2.7%
b형(140㎡ 1
 
2.7%
a3(120㎡ 1
 
2.7%
a2(140㎡ 1
 
2.7%
a1(130㎡ 1
 
2.7%
35㎡ 1
 
2.7%
73b 1
 
2.7%
Other values (17) 17
45.9%
2023-12-13T08:56:46.303445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㎡ 29
19.3%
4 21
14.0%
5 14
9.3%
9 13
8.7%
3 12
8.0%
A 11
 
7.3%
1 8
 
5.3%
B 8
 
5.3%
6 8
 
5.3%
( 5
 
3.3%
Other values (9) 21
14.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 84
56.0%
Other Symbol 29
 
19.3%
Uppercase Letter 23
 
15.3%
Open Punctuation 5
 
3.3%
Close Punctuation 5
 
3.3%
Dash Punctuation 2
 
1.3%
Other Letter 2
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 21
25.0%
5 14
16.7%
9 13
15.5%
3 12
14.3%
1 8
 
9.5%
6 8
 
9.5%
0 5
 
6.0%
2 2
 
2.4%
7 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
A 11
47.8%
B 8
34.8%
C 2
 
8.7%
D 1
 
4.3%
E 1
 
4.3%
Other Symbol
ValueCountFrequency (%)
㎡ 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Letter
ValueCountFrequency (%)
형 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 125
83.3%
Latin 23
 
15.3%
Hangul 2
 
1.3%

Most frequent character per script

Common
ValueCountFrequency (%)
㎡ 29
23.2%
4 21
16.8%
5 14
11.2%
9 13
10.4%
3 12
9.6%
1 8
 
6.4%
6 8
 
6.4%
( 5
 
4.0%
0 5
 
4.0%
) 5
 
4.0%
Other values (3) 5
 
4.0%
Latin
ValueCountFrequency (%)
A 11
47.8%
B 8
34.8%
C 2
 
8.7%
D 1
 
4.3%
E 1
 
4.3%
Hangul
ValueCountFrequency (%)
형 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 119
79.3%
CJK Compat 29
 
19.3%
Hangul 2
 
1.3%

Most frequent character per block

CJK Compat
ValueCountFrequency (%)
㎡ 29
100.0%
ASCII
ValueCountFrequency (%)
4 21
17.6%
5 14
11.8%
9 13
10.9%
3 12
10.1%
A 11
9.2%
1 8
 
6.7%
B 8
 
6.7%
6 8
 
6.7%
( 5
 
4.2%
0 5
 
4.2%
Other values (7) 14
11.8%
Hangul
ValueCountFrequency (%)
형 2
100.0%

20대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.567568
Minimum0
Maximum119
Zeros7
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T08:56:46.413524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q313
95-th percentile64
Maximum119
Range119
Interquartile range (IQR)12

Descriptive statistics

Standard deviation25.077148
Coefficient of variation (CV)1.7214369
Kurtosis8.6145566
Mean14.567568
Median Absolute Deviation (MAD)5
Skewness2.808972
Sum539
Variance628.86336
MonotonicityNot monotonic
2023-12-13T08:56:46.505095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 7
18.9%
1 6
16.2%
5 4
 
10.8%
7 2
 
5.4%
4 2
 
5.4%
32 1
 
2.7%
42 1
 
2.7%
3 1
 
2.7%
10 1
 
2.7%
60 1
 
2.7%
Other values (11) 11
29.7%
ValueCountFrequency (%)
0 7
18.9%
1 6
16.2%
3 1
 
2.7%
4 2
 
5.4%
5 4
10.8%
6 1
 
2.7%
7 2
 
5.4%
8 1
 
2.7%
9 1
 
2.7%
10 1
 
2.7%
ValueCountFrequency (%)
119 1
2.7%
80 1
2.7%
60 1
2.7%
42 1
2.7%
39 1
2.7%
32 1
2.7%
25 1
2.7%
19 1
2.7%
14 1
2.7%
13 1
2.7%

30대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)56.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.945946
Minimum0
Maximum393
Zeros8
Zeros (%)21.6%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T08:56:46.599034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median5
Q330
95-th percentile210.8
Maximum393
Range393
Interquartile range (IQR)29

Descriptive statistics

Standard deviation85.4657
Coefficient of variation (CV)2.1395338
Kurtosis8.8610761
Mean39.945946
Median Absolute Deviation (MAD)5
Skewness2.9646966
Sum1478
Variance7304.3859
MonotonicityNot monotonic
2023-12-13T08:56:46.697553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 8
21.6%
3 4
 
10.8%
16 3
 
8.1%
1 3
 
8.1%
34 2
 
5.4%
2 2
 
5.4%
193 1
 
2.7%
13 1
 
2.7%
9 1
 
2.7%
393 1
 
2.7%
Other values (11) 11
29.7%
ValueCountFrequency (%)
0 8
21.6%
1 3
 
8.1%
2 2
 
5.4%
3 4
10.8%
4 1
 
2.7%
5 1
 
2.7%
7 1
 
2.7%
9 1
 
2.7%
13 1
 
2.7%
14 1
 
2.7%
ValueCountFrequency (%)
393 1
2.7%
274 1
2.7%
195 1
2.7%
193 1
2.7%
89 1
2.7%
45 1
2.7%
43 1
2.7%
34 2
5.4%
30 1
2.7%
29 1
2.7%

40대
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)43.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.6216216
Minimum0
Maximum81
Zeros9
Zeros (%)24.3%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T08:56:46.804820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q37
95-th percentile47
Maximum81
Range81
Interquartile range (IQR)6

Descriptive statistics

Standard deviation17.664074
Coefficient of variation (CV)1.8358729
Kurtosis8.0867171
Mean9.6216216
Median Absolute Deviation (MAD)2
Skewness2.7858503
Sum356
Variance312.01952
MonotonicityNot monotonic
2023-12-13T08:56:46.926843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 9
24.3%
1 6
16.2%
2 5
13.5%
6 2
 
5.4%
5 2
 
5.4%
7 2
 
5.4%
3 2
 
5.4%
18 1
 
2.7%
31 1
 
2.7%
16 1
 
2.7%
Other values (6) 6
16.2%
ValueCountFrequency (%)
0 9
24.3%
1 6
16.2%
2 5
13.5%
3 2
 
5.4%
5 2
 
5.4%
6 2
 
5.4%
7 2
 
5.4%
11 1
 
2.7%
15 1
 
2.7%
16 1
 
2.7%
ValueCountFrequency (%)
81 1
2.7%
59 1
2.7%
44 1
2.7%
31 1
2.7%
23 1
2.7%
18 1
2.7%
16 1
2.7%
15 1
2.7%
11 1
2.7%
7 2
5.4%

50대 이상
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)35.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2432432
Minimum0
Maximum27
Zeros14
Zeros (%)37.8%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-13T08:56:47.040426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q34
95-th percentile16.4
Maximum27
Range27
Interquartile range (IQR)4

Descriptive statistics

Standard deviation6.5845164
Coefficient of variation (CV)1.551765
Kurtosis3.8017665
Mean4.2432432
Median Absolute Deviation (MAD)1
Skewness2.0105101
Sum157
Variance43.355856
MonotonicityNot monotonic
2023-12-13T08:56:47.130378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 14
37.8%
1 6
16.2%
4 3
 
8.1%
2 3
 
8.1%
3 2
 
5.4%
12 2
 
5.4%
22 1
 
2.7%
27 1
 
2.7%
15 1
 
2.7%
8 1
 
2.7%
Other values (3) 3
 
8.1%
ValueCountFrequency (%)
0 14
37.8%
1 6
16.2%
2 3
 
8.1%
3 2
 
5.4%
4 3
 
8.1%
6 1
 
2.7%
8 1
 
2.7%
11 1
 
2.7%
12 2
 
5.4%
14 1
 
2.7%
ValueCountFrequency (%)
27 1
 
2.7%
22 1
 
2.7%
15 1
 
2.7%
14 1
 
2.7%
12 2
5.4%
11 1
 
2.7%
8 1
 
2.7%
6 1
 
2.7%
4 3
8.1%
3 2
5.4%

Interactions

2023-12-13T08:56:45.295453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.448382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.712724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.988721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:45.362380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.522912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.781213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:45.056133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:45.437436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.590223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.853112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:45.127959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:45.511173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.655420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:44.925277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:56:45.217739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:56:47.211473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업지구구분20대30대40대50대 이상
사업지구1.0000.0000.2250.0000.0000.286
구분0.0001.0000.0000.0000.0000.000
20대0.2250.0001.0000.9180.9870.652
30대0.0000.0000.9181.0000.9450.000
40대0.0000.0000.9870.9451.0000.535
50대 이상0.2860.0000.6520.0000.5351.000
2023-12-13T08:56:47.317210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
20대30대40대50대 이상사업지구
20대1.0000.8710.8200.2270.079
30대0.8711.0000.8640.0320.000
40대0.8200.8641.0000.2120.000
50대 이상0.2270.0320.2121.0000.123
사업지구0.0790.0000.0000.1231.000

Missing values

2023-12-13T08:56:45.590952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:56:45.668174image/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

사업지구구분20대30대40대50대 이상
0아산배방36㎡32341522
1아산배방44㎡253073
2아산배방59㎡A802745912
3아산배방59㎡B1193938112
4아산배방59㎡C51370
5아산배방59㎡D51660
6아산배방59㎡E39193444
7홍성내포36A13324
8홍성내포36B6913
9홍성내포44A1100
사업지구구분20대30대40대50대 이상
27당진채운44㎡1001
28당진채운59㎡1943162
29매입형59㎡60195310
30매입형73B102950
31공주 덕성35㎡33211
32아트빌리지A1(130㎡)0116
33아트빌리지A2(140㎡)0031
34아트빌리지A3(120㎡)0011
35아트빌리지B형(140㎡)0004
36아트빌리지C형(140㎡)0001