Overview

Dataset statistics

Number of variables11
Number of observations3043
Missing cells1907
Missing cells (%)5.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory267.6 KiB
Average record size in memory90.0 B

Variable types

Categorical3
Text3
Numeric2
DateTime3

Dataset

Description한국토지주택공사에서 건설한 전국 LH 건설임대 아파트의 단지정보(지역본부명, 단지코드, 단지명, 공급유형, 주소등)을 제공합니다.
URLhttps://www.data.go.kr/data/15080989/fileData.do

Alerts

임대유형 has constant value ""Constant
세대수 is highly overall correlated with 동수High correlation
동수 is highly overall correlated with 세대수High correlation
세대수 has 202 (6.6%) missing valuesMissing
동수 has 59 (1.9%) missing valuesMissing
준공일 has 1106 (36.3%) missing valuesMissing
입주지정시작일자 has 259 (8.5%) missing valuesMissing
입주지정종료일자 has 255 (8.4%) missing valuesMissing
단지코드 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:29:23.242384
Analysis finished2023-12-12 16:29:25.088739
Duration1.85 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역본부
Categorical

Distinct14
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
경기남부지역본부
656 
경기북부지역본부
305 
대구경북지역본부
300 
대전충남지역본부
282 
광주전남지역본부
272 
Other values (9)
1228 

Length

Max length8
Median length8
Mean length6.8964837
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기남부지역본부 656
21.6%
경기북부지역본부 305
10.0%
대구경북지역본부 300
9.9%
대전충남지역본부 282
9.3%
광주전남지역본부 272
8.9%
경남지역본부 206
 
6.8%
전북지사 182
 
6.0%
강원지사 180
 
5.9%
인천지역본부 160
 
5.3%
충북지사 158
 
5.2%
Other values (4) 342
11.2%

Length

2023-12-13T01:29:25.163760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기남부지역본부 656
21.6%
경기북부지역본부 305
10.0%
대구경북지역본부 300
9.9%
대전충남지역본부 282
9.3%
광주전남지역본부 272
8.9%
경남지역본부 206
 
6.8%
전북지사 182
 
6.0%
강원지사 180
 
5.9%
인천지역본부 160
 
5.3%
충북지사 158
 
5.2%
Other values (4) 342
11.2%

단지코드
Text

UNIQUE 

Distinct3043
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
2023-12-13T01:29:25.551471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.9635228
Min length3

Characters and Unicode

Total characters18147
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3043 ?
Unique (%)100.0%

Sample

1st rowC02487
2nd rowC02780
3rd rowC02170
4th rowC02690
5th rowC02693
ValueCountFrequency (%)
c02487 1
 
< 0.1%
c01822 1
 
< 0.1%
c01955 1
 
< 0.1%
c01627 1
 
< 0.1%
c01631 1
 
< 0.1%
c01632 1
 
< 0.1%
c01633 1
 
< 0.1%
c01638 1
 
< 0.1%
c01669 1
 
< 0.1%
c01688 1
 
< 0.1%
Other values (3033) 3033
99.7%
2023-12-13T01:29:26.072841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 5325
29.3%
C 2609
14.4%
1 1923
 
10.6%
2 1693
 
9.3%
3 934
 
5.1%
5 922
 
5.1%
7 900
 
5.0%
6 897
 
4.9%
4 891
 
4.9%
9 841
 
4.6%
Other values (4) 1212
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15154
83.5%
Uppercase Letter 2993
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 5325
35.1%
1 1923
 
12.7%
2 1693
 
11.2%
3 934
 
6.2%
5 922
 
6.1%
7 900
 
5.9%
6 897
 
5.9%
4 891
 
5.9%
9 841
 
5.5%
8 828
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
C 2609
87.2%
D 380
 
12.7%
B 2
 
0.1%
T 2
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
Common 15154
83.5%
Latin 2993
 
16.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 5325
35.1%
1 1923
 
12.7%
2 1693
 
11.2%
3 934
 
6.2%
5 922
 
6.1%
7 900
 
5.9%
6 897
 
5.9%
4 891
 
5.9%
9 841
 
5.5%
8 828
 
5.5%
Latin
ValueCountFrequency (%)
C 2609
87.2%
D 380
 
12.7%
B 2
 
0.1%
T 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 18147
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 5325
29.3%
C 2609
14.4%
1 1923
 
10.6%
2 1693
 
9.3%
3 934
 
5.1%
5 922
 
5.1%
7 900
 
5.0%
6 897
 
4.9%
4 891
 
4.9%
9 841
 
4.6%
Other values (4) 1212
 
6.7%

단지명
Text

Distinct3020
Distinct (%)99.2%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
2023-12-13T01:29:26.351846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length29
Mean length9.2632271
Min length2

Characters and Unicode

Total characters28188
Distinct characters481
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2998 ?
Unique (%)98.5%

Sample

1st row원주태장7 행복주택
2nd row남원주역세권A-6블록 행복주택
3rd row춘천거두2 행복주택
4th row강릉유천 행복주택
5th row삼척당저 행복주택
ValueCountFrequency (%)
행복주택 160
 
3.6%
1단지 83
 
1.9%
2단지 41
 
0.9%
국민임대 27
 
0.6%
신혼희망타운 23
 
0.5%
화성동탄2 20
 
0.5%
3단지 18
 
0.4%
4단지 16
 
0.4%
행정중심복합도시 14
 
0.3%
성남판교 13
 
0.3%
Other values (3253) 3983
90.6%
2023-12-13T01:29:26.774450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1361
 
4.8%
( 926
 
3.3%
) 924
 
3.3%
1 897
 
3.2%
주 874
 
3.1%
2 776
 
2.8%
지 624
 
2.2%
산 608
 
2.2%
단 550
 
2.0%
천 473
 
1.7%
Other values (471) 20175
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 19905
70.6%
Decimal Number 3286
 
11.7%
Space Separator 1361
 
4.8%
Uppercase Letter 1213
 
4.3%
Open Punctuation 927
 
3.3%
Close Punctuation 925
 
3.3%
Dash Punctuation 376
 
1.3%
Other Punctuation 160
 
0.6%
Lowercase Letter 22
 
0.1%
Connector Punctuation 6
 
< 0.1%
Other values (2) 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 874
 
4.4%
지 624
 
3.1%
산 608
 
3.1%
단 550
 
2.8%
천 473
 
2.4%
대 456
 
2.3%
양 364
 
1.8%
동 333
 
1.7%
택 328
 
1.6%
임 323
 
1.6%
Other values (427) 14972
75.2%
Uppercase Letter
ValueCountFrequency (%)
A 356
29.3%
B 322
26.5%
L 280
23.1%
H 104
 
8.6%
F 34
 
2.8%
N 34
 
2.8%
S 33
 
2.7%
M 14
 
1.2%
C 13
 
1.1%
G 11
 
0.9%
Other values (5) 12
 
1.0%
Decimal Number
ValueCountFrequency (%)
1 897
27.3%
2 776
23.6%
3 414
12.6%
4 262
 
8.0%
5 243
 
7.4%
0 220
 
6.7%
6 173
 
5.3%
7 133
 
4.0%
8 91
 
2.8%
9 77
 
2.3%
Lowercase Letter
ValueCountFrequency (%)
a 8
36.4%
b 5
22.7%
e 5
22.7%
c 3
 
13.6%
l 1
 
4.5%
Other Punctuation
ValueCountFrequency (%)
, 134
83.8%
· 15
 
9.4%
. 8
 
5.0%
/ 3
 
1.9%
Open Punctuation
ValueCountFrequency (%)
( 926
99.9%
[ 1
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 924
99.9%
] 1
 
0.1%
Math Symbol
ValueCountFrequency (%)
+ 5
83.3%
~ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
1361
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 376
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 6
100.0%
Other Symbol
ValueCountFrequency (%)
㎡ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 19905
70.6%
Common 7048
 
25.0%
Latin 1235
 
4.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 874
 
4.4%
지 624
 
3.1%
산 608
 
3.1%
단 550
 
2.8%
천 473
 
2.4%
대 456
 
2.3%
양 364
 
1.8%
동 333
 
1.7%
택 328
 
1.6%
임 323
 
1.6%
Other values (427) 14972
75.2%
Common
ValueCountFrequency (%)
1361
19.3%
( 926
13.1%
) 924
13.1%
1 897
12.7%
2 776
11.0%
3 414
 
5.9%
- 376
 
5.3%
4 262
 
3.7%
5 243
 
3.4%
0 220
 
3.1%
Other values (14) 649
9.2%
Latin
ValueCountFrequency (%)
A 356
28.8%
B 322
26.1%
L 280
22.7%
H 104
 
8.4%
F 34
 
2.8%
N 34
 
2.8%
S 33
 
2.7%
M 14
 
1.1%
C 13
 
1.1%
G 11
 
0.9%
Other values (10) 34
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 19905
70.6%
ASCII 8267
29.3%
None 15
 
0.1%
CJK Compat 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1361
16.5%
( 926
11.2%
) 924
11.2%
1 897
10.9%
2 776
9.4%
3 414
 
5.0%
- 376
 
4.5%
A 356
 
4.3%
B 322
 
3.9%
L 280
 
3.4%
Other values (32) 1635
19.8%
Hangul
ValueCountFrequency (%)
주 874
 
4.4%
지 624
 
3.1%
산 608
 
3.1%
단 550
 
2.8%
천 473
 
2.4%
대 456
 
2.3%
양 364
 
1.8%
동 333
 
1.7%
택 328
 
1.6%
임 323
 
1.6%
Other values (427) 14972
75.2%
None
ValueCountFrequency (%)
· 15
100.0%
CJK Compat
ValueCountFrequency (%)
㎡ 1
100.0%

세대수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct1171
Distinct (%)41.2%
Missing202
Missing (%)6.6%
Infinite0
Infinite (%)0.0%
Mean573.00598
Minimum1
Maximum3409
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-13T01:29:26.931616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile13
Q1182
median476
Q3828
95-th percentile1509
Maximum3409
Range3408
Interquartile range (IQR)646

Descriptive statistics

Standard deviation490.86212
Coefficient of variation (CV)0.85664397
Kurtosis2.4363169
Mean573.00598
Median Absolute Deviation (MAD)324
Skewness1.3244134
Sum1627910
Variance240945.62
MonotonicityNot monotonic
2023-12-13T01:29:27.112894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 38
 
1.2%
150 26
 
0.9%
100 21
 
0.7%
2 20
 
0.7%
120 18
 
0.6%
300 16
 
0.5%
40 15
 
0.5%
200 13
 
0.4%
20 13
 
0.4%
450 13
 
0.4%
Other values (1161) 2648
87.0%
(Missing) 202
 
6.6%
ValueCountFrequency (%)
1 38
1.2%
2 20
0.7%
3 9
 
0.3%
4 8
 
0.3%
5 9
 
0.3%
6 9
 
0.3%
7 7
 
0.2%
8 12
 
0.4%
9 7
 
0.2%
10 11
 
0.4%
ValueCountFrequency (%)
3409 1
< 0.1%
3321 1
< 0.1%
3156 1
< 0.1%
2840 1
< 0.1%
2742 1
< 0.1%
2699 1
< 0.1%
2693 1
< 0.1%
2610 1
< 0.1%
2596 1
< 0.1%
2572 1
< 0.1%

동수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct61
Distinct (%)2.0%
Missing59
Missing (%)1.9%
Infinite0
Infinite (%)0.0%
Mean8.2446381
Minimum1
Maximum113
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size26.9 KiB
2023-12-13T01:29:27.272179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile19
Maximum113
Range112
Interquartile range (IQR)6

Descriptive statistics

Standard deviation7.9042081
Coefficient of variation (CV)0.95870892
Kurtosis40.164006
Mean8.2446381
Median Absolute Deviation (MAD)3
Skewness4.7723762
Sum24602
Variance62.476505
MonotonicityNot monotonic
2023-12-13T01:29:27.427444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6 262
 
8.6%
5 258
 
8.5%
1 258
 
8.5%
8 248
 
8.1%
4 238
 
7.8%
7 224
 
7.4%
3 220
 
7.2%
2 205
 
6.7%
9 170
 
5.6%
10 162
 
5.3%
Other values (51) 739
24.3%
ValueCountFrequency (%)
1 258
8.5%
2 205
6.7%
3 220
7.2%
4 238
7.8%
5 258
8.5%
6 262
8.6%
7 224
7.4%
8 248
8.1%
9 170
5.6%
10 162
5.3%
ValueCountFrequency (%)
113 1
< 0.1%
106 1
< 0.1%
99 1
< 0.1%
85 1
< 0.1%
77 1
< 0.1%
76 1
< 0.1%
73 1
< 0.1%
72 1
< 0.1%
67 1
< 0.1%
64 1
< 0.1%

임대유형
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
건설임대
3043 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
건설임대 3043
100.0%

Length

2023-12-13T01:29:27.582400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:29:27.703581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
건설임대 3043
100.0%

주택유형
Categorical

Distinct43
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size23.9 KiB
국민임대
822 
공공분양
820 
공공임대(5년)
205 
공공임대(10년)
183 
행복주택+임대상가
129 
Other values (38)
884 

Length

Max length23
Median length4
Mean length6.3943477
Min length2

Unique

Unique9 ?
Unique (%)0.3%

Sample

1st row행복주택+임대상가
2nd row행복주택+임대상가
3rd row행복주택
4th row행복주택
5th row행복주택

Common Values

ValueCountFrequency (%)
국민임대 822
27.0%
공공분양 820
26.9%
공공임대(5년) 205
 
6.7%
공공임대(10년) 183
 
6.0%
행복주택+임대상가 129
 
4.2%
영구임대+임대상가 119
 
3.9%
공공분양+공공임대(10년) 118
 
3.9%
행복주택 114
 
3.7%
공공분양+공공임대(5년) 106
 
3.5%
분양단지 79
 
2.6%
Other values (33) 348
11.4%

Length

2023-12-13T01:29:27.831170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
국민임대 822
27.0%
공공분양 820
26.9%
공공임대(5년 205
 
6.7%
공공임대(10년 183
 
6.0%
행복주택+임대상가 129
 
4.2%
영구임대+임대상가 119
 
3.9%
공공분양+공공임대(10년 118
 
3.9%
행복주택 114
 
3.7%
공공분양+공공임대(5년 106
 
3.5%
분양단지 79
 
2.6%
Other values (33) 348
11.4%

주소
Text

Distinct2717
Distinct (%)90.1%
Missing26
Missing (%)0.9%
Memory size23.9 KiB
2023-12-13T01:29:28.224819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length79
Median length58
Mean length28.106729
Min length1

Characters and Unicode

Total characters84798
Distinct characters528
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2652 ?
Unique (%)87.9%

Sample

1st row강원특별자치도 원주시 보수골1길 15(태장동)
2nd row강원특별자치도 원주시 마재2로 34(무실동)
3rd row강원특별자치도 춘천시 동내면 거두택지길 70(춘천거두2지구행복주택) 춘천거두2 행복주택
4th row강원특별자치도 강릉시 사임당로 188(유천동)
5th row강원특별자치도 삼척시 대학로 29(당저동)
ValueCountFrequency (%)
경기도 777
 
5.3%
강원특별자치도 177
 
1.2%
충청남도 148
 
1.0%
전라북도 143
 
1.0%
전라남도 140
 
1.0%
경상남도 136
 
0.9%
충청북도 134
 
0.9%
경상북도 127
 
0.9%
경기 122
 
0.8%
광주광역시 101
 
0.7%
Other values (5847) 12624
86.3%
2023-12-13T01:29:29.193146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13910
 
16.4%
시 2931
 
3.5%
동 2589
 
3.1%
1 2403
 
2.8%
( 2251
 
2.7%
) 2250
 
2.7%
로 2025
 
2.4%
도 2005
 
2.4%
아 1530
 
1.8%
2 1520
 
1.8%
Other values (518) 51384
60.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53978
63.7%
Space Separator 13910
 
16.4%
Decimal Number 10505
 
12.4%
Open Punctuation 2269
 
2.7%
Close Punctuation 2268
 
2.7%
Other Punctuation 981
 
1.2%
Dash Punctuation 440
 
0.5%
Uppercase Letter 431
 
0.5%
Lowercase Letter 13
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2931
 
5.4%
동 2589
 
4.8%
로 2025
 
3.8%
도 2005
 
3.7%
아 1530
 
2.8%
구 1445
 
2.7%
주 1365
 
2.5%
경 1350
 
2.5%
파 1150
 
2.1%
지 1102
 
2.0%
Other values (471) 36486
67.6%
Uppercase Letter
ValueCountFrequency (%)
L 169
39.2%
H 150
34.8%
A 39
 
9.0%
B 36
 
8.4%
N 7
 
1.6%
F 7
 
1.6%
T 4
 
0.9%
P 4
 
0.9%
S 2
 
0.5%
I 2
 
0.5%
Other values (9) 11
 
2.6%
Decimal Number
ValueCountFrequency (%)
1 2403
22.9%
2 1520
14.5%
3 1146
10.9%
5 913
 
8.7%
4 906
 
8.6%
0 884
 
8.4%
6 809
 
7.7%
7 687
 
6.5%
8 643
 
6.1%
9 594
 
5.7%
Lowercase Letter
ValueCountFrequency (%)
e 7
53.8%
h 2
 
15.4%
o 2
 
15.4%
l 1
 
7.7%
m 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 971
99.0%
. 4
 
0.4%
/ 4
 
0.4%
: 2
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 2251
99.2%
[ 18
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 2250
99.2%
] 18
 
0.8%
Math Symbol
ValueCountFrequency (%)
∼ 1
50.0%
~ 1
50.0%
Space Separator
ValueCountFrequency (%)
13910
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 440
100.0%
Other Symbol
ValueCountFrequency (%)
ⓐ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53978
63.7%
Common 30376
35.8%
Latin 444
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2931
 
5.4%
동 2589
 
4.8%
로 2025
 
3.8%
도 2005
 
3.7%
아 1530
 
2.8%
구 1445
 
2.7%
주 1365
 
2.5%
경 1350
 
2.5%
파 1150
 
2.1%
지 1102
 
2.0%
Other values (471) 36486
67.6%
Latin
ValueCountFrequency (%)
L 169
38.1%
H 150
33.8%
A 39
 
8.8%
B 36
 
8.1%
N 7
 
1.6%
F 7
 
1.6%
e 7
 
1.6%
T 4
 
0.9%
P 4
 
0.9%
h 2
 
0.5%
Other values (14) 19
 
4.3%
Common
ValueCountFrequency (%)
13910
45.8%
1 2403
 
7.9%
( 2251
 
7.4%
) 2250
 
7.4%
2 1520
 
5.0%
3 1146
 
3.8%
, 971
 
3.2%
5 913
 
3.0%
4 906
 
3.0%
0 884
 
2.9%
Other values (13) 3222
 
10.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53978
63.7%
ASCII 30818
36.3%
Math Operators 1
 
< 0.1%
Enclosed Alphanum 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13910
45.1%
1 2403
 
7.8%
( 2251
 
7.3%
) 2250
 
7.3%
2 1520
 
4.9%
3 1146
 
3.7%
, 971
 
3.2%
5 913
 
3.0%
4 906
 
2.9%
0 884
 
2.9%
Other values (35) 3664
 
11.9%
Hangul
ValueCountFrequency (%)
시 2931
 
5.4%
동 2589
 
4.8%
로 2025
 
3.8%
도 2005
 
3.7%
아 1530
 
2.8%
구 1445
 
2.7%
주 1365
 
2.5%
경 1350
 
2.5%
파 1150
 
2.1%
지 1102
 
2.0%
Other values (471) 36486
67.6%
Math Operators
ValueCountFrequency (%)
∼ 1
100.0%
Enclosed Alphanum
ValueCountFrequency (%)
ⓐ 1
100.0%

준공일
Date

MISSING 

Distinct1488
Distinct (%)76.8%
Missing1106
Missing (%)36.3%
Memory size23.9 KiB
Minimum1958-06-15 00:00:00
Maximum2025-11-30 00:00:00
2023-12-13T01:29:29.374708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:29:29.532792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입주지정시작일자
Date

MISSING 

Distinct1768
Distinct (%)63.5%
Missing259
Missing (%)8.5%
Memory size23.9 KiB
Minimum1974-10-01 00:00:00
Maximum2025-12-31 00:00:00
2023-12-13T01:29:29.659270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:29:29.817441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

입주지정종료일자
Date

MISSING 

Distinct1929
Distinct (%)69.2%
Missing255
Missing (%)8.4%
Memory size23.9 KiB
Minimum1974-10-31 00:00:00
Maximum2026-03-31 00:00:00
2023-12-13T01:29:29.985827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:29:30.143880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

Interactions

2023-12-13T01:29:24.417343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:29:24.198878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:29:24.516600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:29:24.287725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:29:30.270160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역본부세대수동수주택유형
지역본부1.0000.2180.1120.337
세대수0.2181.0000.7090.333
동수0.1120.7091.0000.044
주택유형0.3370.3330.0441.000
2023-12-13T01:29:30.392675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
주택유형지역본부
주택유형1.0000.104
지역본부0.1041.000
2023-12-13T01:29:30.489324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수동수지역본부주택유형
세대수1.0000.6160.0890.120
동수0.6161.0000.0450.015
지역본부0.0890.0451.0000.104
주택유형0.1200.0150.1041.000

Missing values

2023-12-13T01:29:24.679531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:29:24.877412image/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.
2023-12-13T01:29:25.012323image/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

지역본부단지코드단지명세대수동수임대유형주택유형주소준공일입주지정시작일자입주지정종료일자
0강원지사C02487원주태장7 행복주택2613건설임대행복주택+임대상가강원특별자치도 원주시 보수골1길 15(태장동)2020-12-202021-12-032022-01-16
1강원지사C02780남원주역세권A-6블록 행복주택4383건설임대행복주택+임대상가강원특별자치도 원주시 마재2로 34(무실동)2023-09-012023-09-012023-10-15
2강원지사C02170춘천거두2 행복주택9603건설임대행복주택강원특별자치도 춘천시 동내면 거두택지길 70(춘천거두2지구행복주택) 춘천거두2 행복주택2018-02-232018-02-232018-03-25
3강원지사C02690강릉유천 행복주택1961건설임대행복주택강원특별자치도 강릉시 사임당로 188(유천동)2023-08-012023-11-302024-01-13
4강원지사C02693삼척당저 행복주택1271건설임대행복주택강원특별자치도 삼척시 대학로 29(당저동)2023-09-012023-12-012023-12-31
5강원지사C02710춘천후평 행복주택2121건설임대행복주택강원특별자치도 춘천시 삭주로145번길 15(후평동)2024-01-102024-01-012024-01-31
6강원지사C02711강릉교동 행복주택1802건설임대행복주택강원특별자치도 강릉시 강릉대로313번길 29(포남동)2024-08-012024-11-012024-11-30
7강원지사C02686원주태장8 행복주택6966건설임대영구임대+행복주택+임대상가강원특별자치도 원주시 소일마을2길 60(태장동)2023-09-012023-09-222023-11-20
8강원지사C00041강릉입암36015건설임대영구임대+임대상가강원특별자치도 강릉시 강변로 510 (입암3아파트)1994-12-041994-12-191995-01-18
9강원지사C00297원주명륜211619건설임대영구임대+임대상가강원특별자치도 원주시 예술관길 31 (명륜2단지아파트)1992-08-041992-08-311992-11-04
지역본부단지코드단지명세대수동수임대유형주택유형주소준공일입주지정시작일자입주지정종료일자
3033충북지사C01484청주운천4627건설임대공공분양충청북도 청주시 흥덕구 1순환로501번길 15 (신봉동)<NA>1990-01-011990-01-31
3034충북지사C01502충주교현8414건설임대공공분양충북 충주시 교현동 충북 충주시 교현동 559-1<NA>1980-01-011980-01-01
3035충북지사C01503충주남산25024건설임대공공분양충북 충주시 교현1동 1061 남산주공아파트<NA>1990-03-011990-03-31
3036충북지사C01504충주남산217311건설임대공공분양충북 충주시 교현1동 1083<NA>1985-03-011985-04-01
3037충북지사C01505충주남산31998건설임대공공분양충북 충주시 교현1동 1060<NA>1986-03-011986-04-01
3038충북지사C01506충주연수(1)86018건설임대공공분양충청북도 충주시 예성로 353 (연수동)<NA>1990-01-011990-02-01
3039충북지사C01508충주연수499011건설임대공공분양충청북도 충주시 예성로 401 (연수동)<NA>1994-03-011994-04-01
3040충북지사C01511충주용산8115건설임대공공분양충청북도 충주시 거룡2길 17 (용산동)<NA>1980-03-011980-04-01
3041충북지사C01554하소(2)7808건설임대공공분양충청북도 제천시 하소로 88 (하소동)<NA>1995-10-131995-11-12
3042충북지사C02669충주호암A-1블록<NA>7건설임대공공분양2021-09-172021-09-302021-11-29