Overview

Dataset statistics

Number of variables5
Number of observations25
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory49.3 B

Variable types

Text1
Numeric4

Dataset

Description서울주택도시공사의 전세임대주택(신혼부부 2 유형)의 공급계획입니다 신혼부부 Ⅱ : 전년도 도시근로자 가구당 월평균소득의 100%이하인 자 (배우자가 소득이 있는 경우 120% 이하) ※ 지원한도액을 초과하는 전세주택은 전세금액을 입주자가 부담할 경우 지원가능. 단, 전세금은 호당 대출한도액의 250%이내로 제한하되 세대원수가 5인 이상일 경우 예외 가능
URLhttps://www.data.go.kr/data/15088463/fileData.do

Alerts

호수(2023년) is highly overall correlated with 호수(2022년) and 1 other fieldsHigh correlation
호수(2022년) is highly overall correlated with 호수(2023년) and 1 other fieldsHigh correlation
호수(2021년) is highly overall correlated with 호수(2023년) and 2 other fieldsHigh correlation
호수(2020년) is highly overall correlated with 호수(2021년)High correlation
지역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 14:25:49.634877
Analysis finished2023-12-12 14:25:51.708512
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Text

UNIQUE 

Distinct25
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size332.0 B
2023-12-12T23:25:51.878230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.08
Min length2

Characters and Unicode

Total characters77
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)100.0%

Sample

1st row강남구
2nd row강동구
3rd row강북구
4th row강서구
5th row관악구
ValueCountFrequency (%)
강남구 1
 
4.0%
서대문구 1
 
4.0%
중구 1
 
4.0%
종로구 1
 
4.0%
은평구 1
 
4.0%
용산구 1
 
4.0%
영등포구 1
 
4.0%
양천구 1
 
4.0%
송파구 1
 
4.0%
성북구 1
 
4.0%
Other values (15) 15
60.0%
2023-12-12T23:25:52.580087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
26
33.8%
4
 
5.2%
4
 
5.2%
3
 
3.9%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
2
 
2.6%
Other values (26) 28
36.4%

호수(2023년)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8
Minimum1
Maximum19
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T23:25:52.747860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median8
Q310
95-th percentile15.8
Maximum19
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.6992907
Coefficient of variation (CV)0.58741134
Kurtosis0.14256266
Mean8
Median Absolute Deviation (MAD)2
Skewness0.52632173
Sum200
Variance22.083333
MonotonicityNot monotonic
2023-12-12T23:25:52.893850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
8 4
16.0%
9 3
12.0%
6 3
12.0%
1 3
12.0%
10 2
8.0%
7 2
8.0%
3 2
8.0%
15 2
8.0%
4 1
 
4.0%
19 1
 
4.0%
Other values (2) 2
8.0%
ValueCountFrequency (%)
1 3
12.0%
3 2
8.0%
4 1
 
4.0%
6 3
12.0%
7 2
8.0%
8 4
16.0%
9 3
12.0%
10 2
8.0%
11 1
 
4.0%
15 2
8.0%
ValueCountFrequency (%)
19 1
 
4.0%
16 1
 
4.0%
15 2
8.0%
11 1
 
4.0%
10 2
8.0%
9 3
12.0%
8 4
16.0%
7 2
8.0%
6 3
12.0%
4 1
 
4.0%

호수(2022년)
Real number (ℝ)

HIGH CORRELATION 

Distinct8
Distinct (%)32.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T23:25:53.052000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q13
median3
Q35
95-th percentile7.8
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.1408721
Coefficient of variation (CV)0.53521802
Kurtosis1.4821154
Mean4
Median Absolute Deviation (MAD)1
Skewness1.1908264
Sum100
Variance4.5833333
MonotonicityNot monotonic
2023-12-12T23:25:53.201255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
3 10
40.0%
5 4
 
16.0%
4 3
 
12.0%
7 2
 
8.0%
2 2
 
8.0%
1 2
 
8.0%
10 1
 
4.0%
8 1
 
4.0%
ValueCountFrequency (%)
1 2
 
8.0%
2 2
 
8.0%
3 10
40.0%
4 3
 
12.0%
5 4
 
16.0%
7 2
 
8.0%
8 1
 
4.0%
10 1
 
4.0%
ValueCountFrequency (%)
10 1
 
4.0%
8 1
 
4.0%
7 2
 
8.0%
5 4
 
16.0%
4 3
 
12.0%
3 10
40.0%
2 2
 
8.0%
1 2
 
8.0%

호수(2021년)
Real number (ℝ)

HIGH CORRELATION 

Distinct7
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T23:25:53.332189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median4
Q35
95-th percentile6
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.7320508
Coefficient of variation (CV)0.4330127
Kurtosis0.081906017
Mean4
Median Absolute Deviation (MAD)1
Skewness0.05229622
Sum100
Variance3
MonotonicityNot monotonic
2023-12-12T23:25:53.474414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
4 7
28.0%
3 5
20.0%
6 4
16.0%
5 4
16.0%
1 3
12.0%
8 1
 
4.0%
2 1
 
4.0%
ValueCountFrequency (%)
1 3
12.0%
2 1
 
4.0%
3 5
20.0%
4 7
28.0%
5 4
16.0%
6 4
16.0%
8 1
 
4.0%
ValueCountFrequency (%)
8 1
 
4.0%
6 4
16.0%
5 4
16.0%
4 7
28.0%
3 5
20.0%
2 1
 
4.0%
1 3
12.0%

호수(2020년)
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)36.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size357.0 B
2023-12-12T23:25:53.621025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.2
Q13
median4
Q35
95-th percentile7.8
Maximum9
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.95789
Coefficient of variation (CV)0.48947251
Kurtosis0.94139887
Mean4
Median Absolute Deviation (MAD)1
Skewness0.86895752
Sum100
Variance3.8333333
MonotonicityNot monotonic
2023-12-12T23:25:53.765204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
4 8
32.0%
3 5
20.0%
5 3
 
12.0%
2 3
 
12.0%
1 2
 
8.0%
6 1
 
4.0%
8 1
 
4.0%
7 1
 
4.0%
9 1
 
4.0%
ValueCountFrequency (%)
1 2
 
8.0%
2 3
 
12.0%
3 5
20.0%
4 8
32.0%
5 3
 
12.0%
6 1
 
4.0%
7 1
 
4.0%
8 1
 
4.0%
9 1
 
4.0%
ValueCountFrequency (%)
9 1
 
4.0%
8 1
 
4.0%
7 1
 
4.0%
6 1
 
4.0%
5 3
 
12.0%
4 8
32.0%
3 5
20.0%
2 3
 
12.0%
1 2
 
8.0%

Interactions

2023-12-12T23:25:51.071459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:49.805555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.220677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.617115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:51.179387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:49.882933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.308672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.733558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:51.278290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.004148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.380388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.836872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:51.382907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.118917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.511486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:25:50.948492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:25:53.858342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역호수(2023년)호수(2022년)호수(2021년)호수(2020년)
지역1.0001.0001.0001.0001.000
호수(2023년)1.0001.0000.7280.6480.784
호수(2022년)1.0000.7281.0000.7550.583
호수(2021년)1.0000.6480.7551.0000.513
호수(2020년)1.0000.7840.5830.5131.000
2023-12-12T23:25:53.978441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호수(2023년)호수(2022년)호수(2021년)호수(2020년)
호수(2023년)1.0000.6950.5130.289
호수(2022년)0.6951.0000.6970.446
호수(2021년)0.5130.6971.0000.593
호수(2020년)0.2890.4460.5931.000

Missing values

2023-12-12T23:25:51.540050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:25:51.661376image/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

지역호수(2023년)호수(2022년)호수(2021년)호수(2020년)
0강남구8334
1강동구9766
2강북구4234
3강서구191055
4관악구11884
5광진구9364
6구로구6344
7금천구6441
8노원구10554
9도봉구7332
지역호수(2023년)호수(2022년)호수(2021년)호수(2020년)
15성동구1343
16성북구8447
17송파구16769
18양천구7255
19영등포구15311
20용산구3323
21은평구10544
22종로구1112
23중구1112
24중랑구15543