Overview

Dataset statistics

Number of variables5
Number of observations217
Missing cells168
Missing cells (%)15.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory44.6 B

Variable types

Text1
Numeric4

Dataset

Description한국토지주택공사에서 관리중인 전국 공공임대주택(국민임대, 행복주택, 영구임대) 공급 호수 현황 자료로 국민임대, 영구임대, 행복주택, 총합계정보를 제공합니다.
URLhttps://www.data.go.kr/data/15060671/fileData.do

Alerts

국민임대 is highly overall correlated with 영구임대 and 2 other fieldsHigh correlation
영구임대 is highly overall correlated with 국민임대 and 1 other fieldsHigh correlation
행복주택 is highly overall correlated with 국민임대 and 1 other fieldsHigh correlation
총합계 is highly overall correlated with 국민임대 and 2 other fieldsHigh correlation
국민임대 has 21 (9.7%) missing valuesMissing
영구임대 has 66 (30.4%) missing valuesMissing
행복주택 has 81 (37.3%) missing valuesMissing

Reproduction

Analysis started2023-12-12 06:07:49.241482
Analysis finished2023-12-12 06:07:51.363208
Duration2.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

Distinct201
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T15:07:51.650175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0921659
Min length2

Characters and Unicode

Total characters671
Distinct characters133
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

Unique195 ?
Unique (%)89.9%

Sample

1st row강원특별자치도
2nd row강릉시
3rd row동해시
4th row삼척시
5th row속초시
ValueCountFrequency (%)
중구 4
 
1.8%
동구 4
 
1.8%
남구 4
 
1.8%
서구 4
 
1.8%
북구 4
 
1.8%
강서구 2
 
0.9%
강화군 1
 
0.5%
부평구 1
 
0.5%
미추홀구 1
 
0.5%
남동구 1
 
0.5%
Other values (191) 191
88.0%
2023-12-12T15:07:52.190970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 85
 
12.7%
군 77
 
11.5%
구 54
 
8.0%
천 22
 
3.3%
주 20
 
3.0%
산 17
 
2.5%
남 15
 
2.2%
도 14
 
2.1%
양 14
 
2.1%
성 13
 
1.9%
Other values (123) 340
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 671
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 85
 
12.7%
군 77
 
11.5%
구 54
 
8.0%
천 22
 
3.3%
주 20
 
3.0%
산 17
 
2.5%
남 15
 
2.2%
도 14
 
2.1%
양 14
 
2.1%
성 13
 
1.9%
Other values (123) 340
50.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 671
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 85
 
12.7%
군 77
 
11.5%
구 54
 
8.0%
천 22
 
3.3%
주 20
 
3.0%
산 17
 
2.5%
남 15
 
2.2%
도 14
 
2.1%
양 14
 
2.1%
성 13
 
1.9%
Other values (123) 340
50.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 671
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 85
 
12.7%
군 77
 
11.5%
구 54
 
8.0%
천 22
 
3.3%
주 20
 
3.0%
산 17
 
2.5%
남 15
 
2.2%
도 14
 
2.1%
양 14
 
2.1%
성 13
 
1.9%
Other values (123) 340
50.7%

국민임대
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct193
Distinct (%)98.5%
Missing21
Missing (%)9.7%
Infinite0
Infinite (%)0.0%
Mean5772.3929
Minimum2
Maximum232166
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T15:07:52.350505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile127.5
Q1465.75
median1753.5
Q35433
95-th percentile21665.75
Maximum232166
Range232164
Interquartile range (IQR)4967.25

Descriptive statistics

Standard deviation17782.893
Coefficient of variation (CV)3.0806797
Kurtosis136.09819
Mean5772.3929
Median Absolute Deviation (MAD)1390
Skewness10.827659
Sum1131389
Variance3.1623129 × 108
MonotonicityNot monotonic
2023-12-12T15:07:52.520761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
469 2
 
0.9%
1277 2
 
0.9%
2190 2
 
0.9%
1761 1
 
0.5%
2871 1
 
0.5%
6751 1
 
0.5%
90 1
 
0.5%
2011 1
 
0.5%
21218 1
 
0.5%
259 1
 
0.5%
Other values (183) 183
84.3%
(Missing) 21
 
9.7%
ValueCountFrequency (%)
2 1
0.5%
6 1
0.5%
16 1
0.5%
40 1
0.5%
80 1
0.5%
81 1
0.5%
90 1
0.5%
110 1
0.5%
111 1
0.5%
120 1
0.5%
ValueCountFrequency (%)
232166 1
0.5%
39791 1
0.5%
33229 1
0.5%
32338 1
0.5%
31944 1
0.5%
29405 1
0.5%
28829 1
0.5%
25165 1
0.5%
24762 1
0.5%
21785 1
0.5%

영구임대
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct131
Distinct (%)86.8%
Missing66
Missing (%)30.4%
Infinite0
Infinite (%)0.0%
Mean2206.053
Minimum6
Maximum33036
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T15:07:52.675344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile27.5
Q1190
median744
Q32249.5
95-th percentile7749
Maximum33036
Range33030
Interquartile range (IQR)2059.5

Descriptive statistics

Standard deviation4114.4033
Coefficient of variation (CV)1.8650519
Kurtosis26.155238
Mean2206.053
Median Absolute Deviation (MAD)672
Skewness4.4564931
Sum333114
Variance16928315
MonotonicityNot monotonic
2023-12-12T15:07:52.854002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 3
 
1.4%
20 3
 
1.4%
447 3
 
1.4%
40 3
 
1.4%
30 3
 
1.4%
925 2
 
0.9%
260 2
 
0.9%
693 2
 
0.9%
200 2
 
0.9%
388 2
 
0.9%
Other values (121) 126
58.1%
(Missing) 66
30.4%
ValueCountFrequency (%)
6 1
 
0.5%
10 2
0.9%
18 1
 
0.5%
20 3
1.4%
27 1
 
0.5%
28 1
 
0.5%
30 3
1.4%
40 3
1.4%
48 1
 
0.5%
58 1
 
0.5%
ValueCountFrequency (%)
33036 1
0.5%
24316 1
0.5%
15667 1
0.5%
12356 1
0.5%
10816 1
0.5%
10383 1
0.5%
9670 1
0.5%
7834 1
0.5%
7664 1
0.5%
7370 1
0.5%

행복주택
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct119
Distinct (%)87.5%
Missing81
Missing (%)37.3%
Infinite0
Infinite (%)0.0%
Mean2071.7059
Minimum14
Maximum74484
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T15:07:53.333760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile37.5
Q1200
median691
Q32081.25
95-th percentile5963.25
Maximum74484
Range74470
Interquartile range (IQR)1881.25

Descriptive statistics

Standard deviation6643.7819
Coefficient of variation (CV)3.2069137
Kurtosis106.36783
Mean2071.7059
Median Absolute Deviation (MAD)596
Skewness9.8168485
Sum281752
Variance44139838
MonotonicityNot monotonic
2023-12-12T15:07:53.647728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
200 6
 
2.8%
40 4
 
1.8%
150 3
 
1.4%
80 2
 
0.9%
30 2
 
0.9%
324 2
 
0.9%
100 2
 
0.9%
90 2
 
0.9%
20 2
 
0.9%
2698 2
 
0.9%
Other values (109) 109
50.2%
(Missing) 81
37.3%
ValueCountFrequency (%)
14 1
 
0.5%
18 1
 
0.5%
20 2
0.9%
24 1
 
0.5%
30 2
0.9%
40 4
1.8%
42 1
 
0.5%
62 1
 
0.5%
70 1
 
0.5%
73 1
 
0.5%
ValueCountFrequency (%)
74484 1
0.5%
14715 1
0.5%
11347 1
0.5%
8473 1
0.5%
7722 1
0.5%
6605 1
0.5%
6024 1
0.5%
5943 1
0.5%
5849 1
0.5%
5575 1
0.5%

총합계
Real number (ℝ)

HIGH CORRELATION 

Distinct211
Distinct (%)97.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8047.2581
Minimum2
Maximum339686
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T15:07:54.112690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile150
Q1532
median2168
Q37770
95-th percentile32733.2
Maximum339686
Range339684
Interquartile range (IQR)7238

Descriptive statistics

Standard deviation24898.118
Coefficient of variation (CV)3.0939877
Kurtosis147.04842
Mean8047.2581
Median Absolute Deviation (MAD)1865
Skewness11.199672
Sum1746255
Variance6.1991627 × 108
MonotonicityNot monotonic
2023-12-12T15:07:54.340513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 3
 
1.4%
362 2
 
0.9%
842 2
 
0.9%
40 2
 
0.9%
532 2
 
0.9%
29566 1
 
0.5%
1702 1
 
0.5%
12958 1
 
0.5%
1578 1
 
0.5%
2168 1
 
0.5%
Other values (201) 201
92.6%
ValueCountFrequency (%)
2 1
 
0.5%
14 1
 
0.5%
36 1
 
0.5%
40 2
0.9%
80 1
 
0.5%
111 1
 
0.5%
124 1
 
0.5%
140 1
 
0.5%
150 3
1.4%
179 1
 
0.5%
ValueCountFrequency (%)
339686 1
0.5%
53660 1
0.5%
51782 1
0.5%
49595 1
0.5%
44672 1
0.5%
44399 1
0.5%
43227 1
0.5%
39173 1
0.5%
38866 1
0.5%
36385 1
0.5%

Interactions

2023-12-12T15:07:50.614912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.408475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.791705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.199277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.763884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.492679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.871939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.290971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.872811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.575272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.963742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.393970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.977416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:49.680583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.082220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:07:50.497614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:07:54.444589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국민임대영구임대행복주택총합계
국민임대1.0000.8510.9870.998
영구임대0.8511.0000.7860.884
행복주택0.9870.7861.0000.981
총합계0.9980.8840.9811.000
2023-12-12T15:07:54.551875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
국민임대영구임대행복주택총합계
국민임대1.0000.6410.7790.948
영구임대0.6411.0000.4850.766
행복주택0.7790.4851.0000.820
총합계0.9480.7660.8201.000

Missing values

2023-12-12T15:07:51.126841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:07:51.220877image/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.
2023-12-12T15:07:51.310960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분국민임대영구임대행복주택총합계
0강원특별자치도216265481245929566
1강릉시27908603764026
2동해시1054298<NA>1352
3삼척시8163601271303
4속초시803478<NA>1281
5양양군261<NA><NA>261
6영월군40100<NA>140
7원주시76851637122410546
8인제군2<NA><NA>2
9정선군63328<NA>661
구분국민임대영구임대행복주택총합계
207괴산군4241818460
208보은군800<NA><NA>800
209영동군389168200757
210옥천군610<NA>200810
211음성군5431<NA>3505781
212제천시32781153<NA>4431
213증평군2821358<NA>3179
214진천군23042185183040
215청주시142753189207219536
216충주시200615828454433