Overview

Dataset statistics

Number of variables5
Number of observations487
Missing cells0
Missing cells (%)0.0%
Duplicate rows1
Duplicate rows (%)0.2%
Total size in memory20.6 KiB
Average record size in memory43.3 B

Variable types

Text1
Categorical1
Numeric3

Dataset

Description정책보증상품(주택분양보증, 주택임대보증, 주상복합주택분양보증, 임대보증금보증, 주택구입자금보증)의 현황 제공
URLhttps://www.data.go.kr/data/15003708/fileData.do

Alerts

Dataset has 1 (0.2%) duplicate rowsDuplicates
건수 is highly overall correlated with 금액(억원) and 1 other fieldsHigh correlation
금액(억원) is highly overall correlated with 건수 and 1 other fieldsHigh correlation
세대수(천세대) is highly overall correlated with 건수 and 1 other fieldsHigh correlation
건수 has 78 (16.0%) zerosZeros
금액(억원) has 74 (15.2%) zerosZeros
세대수(천세대) has 92 (18.9%) zerosZeros

Reproduction

Analysis started2023-12-12 18:15:35.310757
Analysis finished2023-12-12 18:15:36.790558
Duration1.48 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Text

Distinct104
Distinct (%)21.4%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
2023-12-13T03:15:37.059948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length6.4969199
Min length4

Characters and Unicode

Total characters3164
Distinct characters13
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

Unique0 ?
Unique (%)0.0%

Sample

1st row1999이전
2nd row1999이전
3rd row1999이전
4th row1999이전
5th row1999이전
ValueCountFrequency (%)
2017-01 10
 
2.1%
2016-01 10
 
2.1%
2018-01 10
 
2.1%
2018-12 5
 
1.0%
2019-01 5
 
1.0%
2019-11 5
 
1.0%
2019-05 5
 
1.0%
2019-04 5
 
1.0%
2000 5
 
1.0%
2019-02 5
 
1.0%
Other values (94) 422
86.7%
2023-12-13T03:15:37.795949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 940
29.7%
2 757
23.9%
1 496
15.7%
- 402
12.7%
9 112
 
3.5%
6 100
 
3.2%
7 97
 
3.1%
8 97
 
3.1%
3 63
 
2.0%
5 45
 
1.4%
Other values (3) 55
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2752
87.0%
Dash Punctuation 402
 
12.7%
Other Letter 10
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 940
34.2%
2 757
27.5%
1 496
18.0%
9 112
 
4.1%
6 100
 
3.6%
7 97
 
3.5%
8 97
 
3.5%
3 63
 
2.3%
5 45
 
1.6%
4 45
 
1.6%
Other Letter
ValueCountFrequency (%)
이 5
50.0%
전 5
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 402
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3154
99.7%
Hangul 10
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 940
29.8%
2 757
24.0%
1 496
15.7%
- 402
12.7%
9 112
 
3.6%
6 100
 
3.2%
7 97
 
3.1%
8 97
 
3.1%
3 63
 
2.0%
5 45
 
1.4%
Hangul
ValueCountFrequency (%)
이 5
50.0%
전 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3154
99.7%
Hangul 10
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 940
29.8%
2 757
24.0%
1 496
15.7%
- 402
12.7%
9 112
 
3.6%
6 100
 
3.2%
7 97
 
3.1%
8 97
 
3.1%
3 63
 
2.0%
5 45
 
1.4%
Hangul
ValueCountFrequency (%)
이 5
50.0%
전 5
50.0%

구분
Categorical

Distinct5
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size3.9 KiB
주택분양보증
107 
주상복합주택분양보증
107 
주택구입자금보증
107 
주택임대보증
89 
임대보증금보증
77 

Length

Max length10
Median length8
Mean length7.476386
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row주택분양보증
2nd row주택임대보증
3rd row주상복합주택분양보증
4th row임대보증금보증
5th row주택구입자금보증

Common Values

ValueCountFrequency (%)
주택분양보증 107
22.0%
주상복합주택분양보증 107
22.0%
주택구입자금보증 107
22.0%
주택임대보증 89
18.3%
임대보증금보증 77
15.8%

Length

2023-12-13T03:15:38.049434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T03:15:38.237335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주택분양보증 107
22.0%
주상복합주택분양보증 107
22.0%
주택구입자금보증 107
22.0%
주택임대보증 89
18.3%
임대보증금보증 77
15.8%

건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct227
Distinct (%)46.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3182.7495
Minimum0
Maximum218203
Zeros78
Zeros (%)16.0%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-13T03:15:38.456525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median39
Q3343
95-th percentile14693.7
Maximum218203
Range218203
Interquartile range (IQR)334

Descriptive statistics

Standard deviation12747.84
Coefficient of variation (CV)4.0052918
Kurtosis186.97018
Mean3182.7495
Median Absolute Deviation (MAD)39
Skewness12.300229
Sum1549999
Variance1.6250743 × 108
MonotonicityNot monotonic
2023-12-13T03:15:38.680227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 78
 
16.0%
11 11
 
2.3%
2 9
 
1.8%
55 8
 
1.6%
14 8
 
1.6%
1 7
 
1.4%
9 6
 
1.2%
6 6
 
1.2%
18 6
 
1.2%
17 6
 
1.2%
Other values (217) 342
70.2%
ValueCountFrequency (%)
0 78
16.0%
1 7
 
1.4%
2 9
 
1.8%
3 3
 
0.6%
4 5
 
1.0%
5 5
 
1.0%
6 6
 
1.2%
7 3
 
0.6%
8 4
 
0.8%
9 6
 
1.2%
ValueCountFrequency (%)
218203 1
0.2%
128513 1
0.2%
72153 1
0.2%
26716 1
0.2%
24895 1
0.2%
21558 1
0.2%
19227 1
0.2%
19134 1
0.2%
18869 1
0.2%
17964 1
0.2%

금액(억원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct410
Distinct (%)84.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32387.148
Minimum-895
Maximum811294
Zeros74
Zeros (%)15.2%
Negative3
Negative (%)0.6%
Memory size4.4 KiB
2023-12-13T03:15:39.226588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-895
5-th percentile0
Q12249.5
median12637
Q329616
95-th percentile95680.1
Maximum811294
Range812189
Interquartile range (IQR)27366.5

Descriptive statistics

Standard deviation80846.78
Coefficient of variation (CV)2.4962612
Kurtosis43.069916
Mean32387.148
Median Absolute Deviation (MAD)11827
Skewness5.9997296
Sum15772541
Variance6.5362018 × 109
MonotonicityNot monotonic
2023-12-13T03:15:39.469633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 74
 
15.2%
386664 2
 
0.4%
8564 2
 
0.4%
3197 2
 
0.4%
23318 2
 
0.4%
12211 1
 
0.2%
2885 1
 
0.2%
24915 1
 
0.2%
15977 1
 
0.2%
14313 1
 
0.2%
Other values (400) 400
82.1%
ValueCountFrequency (%)
-895 1
 
0.2%
-25 1
 
0.2%
-1 1
 
0.2%
0 74
15.2%
14 1
 
0.2%
128 1
 
0.2%
132 1
 
0.2%
136 1
 
0.2%
161 1
 
0.2%
179 1
 
0.2%
ValueCountFrequency (%)
811294 1
0.2%
720734 1
0.2%
700980 1
0.2%
475662 1
0.2%
396622 1
0.2%
390431 1
0.2%
387143 1
0.2%
386664 2
0.4%
340091 1
0.2%
329175 1
0.2%

세대수(천세대)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct66
Distinct (%)13.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.149897
Minimum0
Maximum2357
Zeros92
Zeros (%)18.9%
Negative0
Negative (%)0.0%
Memory size4.4 KiB
2023-12-13T03:15:39.679062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median9
Q316
95-th percentile126.3
Maximum2357
Range2357
Interquartile range (IQR)15

Descriptive statistics

Standard deviation122.04162
Coefficient of variation (CV)4.6670018
Kurtosis279.89315
Mean26.149897
Median Absolute Deviation (MAD)8
Skewness15.355307
Sum12735
Variance14894.156
MonotonicityNot monotonic
2023-12-13T03:15:39.868107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 92
18.9%
1 48
 
9.9%
2 29
 
6.0%
11 26
 
5.3%
10 21
 
4.3%
9 19
 
3.9%
12 18
 
3.7%
3 16
 
3.3%
15 14
 
2.9%
14 13
 
2.7%
Other values (56) 191
39.2%
ValueCountFrequency (%)
0 92
18.9%
1 48
9.9%
2 29
 
6.0%
3 16
 
3.3%
4 9
 
1.8%
5 6
 
1.2%
6 11
 
2.3%
7 12
 
2.5%
8 12
 
2.5%
9 19
 
3.9%
ValueCountFrequency (%)
2357 1
0.2%
890 1
0.2%
389 1
0.2%
364 1
0.2%
299 1
0.2%
280 1
0.2%
237 1
0.2%
233 1
0.2%
229 1
0.2%
218 1
0.2%

Interactions

2023-12-13T03:15:36.239451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:35.499657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:35.854389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:36.348828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:35.603912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:35.991230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:36.482198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:35.735941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:15:36.110102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:15:39.989784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분건수금액(억원)세대수(천세대)
구분1.0000.0850.2730.120
건수0.0851.0000.4410.000
금액(억원)0.2730.4411.0000.873
세대수(천세대)0.1200.0000.8731.000
2023-12-13T03:15:40.129327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건수금액(억원)세대수(천세대)구분
건수1.0000.8050.8020.031
금액(억원)0.8051.0000.8950.170
세대수(천세대)0.8020.8951.0000.098
구분0.0310.1700.0981.000

Missing values

2023-12-13T03:15:36.625955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:15:36.747813image/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

연도구분건수금액(억원)세대수(천세대)
01999이전주택분양보증30117207342357
11999이전주택임대보증66418715890
21999이전주상복합주택분양보증000
31999이전임대보증금보증000
41999이전주택구입자금보증000
52000주택분양보증526209476299
62000주택임대보증14319499114
72000주상복합주택분양보증000
82000임대보증금보증000
92000주택구입자금보증000
연도구분건수금액(억원)세대수(천세대)
4772023-03주택구입자금보증112202478911
4782023-04주택분양보증27316049
4792023-04주상복합주택분양보증812540
4802023-04주택구입자금보증9276274649
4812023-05주택분양보증25329177
4822023-05주상복합주택분양보증1331801
4832023-05주택구입자금보증8641210239
4842023-06주택분양보증376266715
4852023-06주상복합주택분양보증530671
4862023-06주택구입자금보증8990274779

Duplicate rows

Most frequently occurring

연도구분건수금액(억원)세대수(천세대)# duplicates
02018-01주택임대보증0002