Overview

Dataset statistics

Number of variables10
Number of observations7391
Missing cells15321
Missing cells (%)20.7%
Duplicate rows28
Duplicate rows (%)0.4%
Total size in memory584.8 KiB
Average record size in memory81.0 B

Variable types

Categorical5
Text4
Numeric1

Dataset

Description인천광역시 미추홀구 임대사업자 현황에 대한 데이터로 임대주택구분, 주소, 종류, 유형, 면적 등의 정보를 제공하고 있습니다.
Author인천광역시 미추홀구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15117528&srcSe=7661IVAWM27C61E190

Alerts

Dataset has 28 (0.4%) duplicate rowsDuplicates
임대주택구분 is highly overall correlated with 종류 and 1 other fieldsHigh correlation
종류 is highly overall correlated with 임대주택구분High correlation
유형 is highly overall correlated with 임대주택구분High correlation
사업자구분 is highly imbalanced (56.6%)Imbalance
동명 has 6085 (82.3%) missing valuesMissing
호명 has 2479 (33.5%) missing valuesMissing
실명 has 6727 (91.0%) missing valuesMissing

Reproduction

Analysis started2024-03-18 03:35:55.022195
Analysis finished2024-03-18 03:35:57.877539
Duration2.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

영업구분
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
등록영업중
5743 
전입영업중
1648 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록영업중 5743
77.7%
전입영업중 1648
 
22.3%

Length

2024-03-18T12:35:57.939306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:35:58.020339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록영업중 5743
77.7%
전입영업중 1648
 
22.3%

사업자구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
임대사업자
5787 
일반형임대사업자
1165 
매입임대사업자
 
289
허가건설임대사업자
 
125
주택건설업자
 
25

Length

Max length9
Median length5
Mean length5.622108
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대사업자 5787
78.3%
일반형임대사업자 1165
 
15.8%
매입임대사업자 289
 
3.9%
허가건설임대사업자 125
 
1.7%
주택건설업자 25
 
0.3%

Length

2024-03-18T12:35:58.134430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:35:58.235750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대사업자 5787
78.3%
일반형임대사업자 1165
 
15.8%
매입임대사업자 289
 
3.9%
허가건설임대사업자 125
 
1.7%
주택건설업자 25
 
0.3%

임대주택구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
민간매입임대주택
4753 
민간건설임대주택
2629 
<NA>
 
9

Length

Max length8
Median length8
Mean length7.9951292
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row민간매입임대주택
2nd row민간건설임대주택
3rd row민간건설임대주택
4th row민간건설임대주택
5th row민간건설임대주택

Common Values

ValueCountFrequency (%)
민간매입임대주택 4753
64.3%
민간건설임대주택 2629
35.6%
<NA> 9
 
0.1%

Length

2024-03-18T12:35:58.341473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:35:58.429480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간매입임대주택 4753
64.3%
민간건설임대주택 2629
35.6%
na 9
 
0.1%

지번주소
Text

Distinct3430
Distinct (%)46.5%
Missing9
Missing (%)0.1%
Memory size57.9 KiB
2024-03-18T12:35:58.642115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length61
Median length52
Mean length28.960851
Min length14

Characters and Unicode

Total characters213789
Distinct characters524
Distinct categories11 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2900 ?
Unique (%)39.3%

Sample

1st row인천광역시 부평구 청천동 385 월드메르디앙 부평
2nd row인천광역시미추홀구 숭의동 18
3rd row인천광역시미추홀구 숭의동 18
4th row인천광역시미추홀구 숭의동 18
5th row인천광역시미추홀구 숭의동 18
ValueCountFrequency (%)
인천광역시 5473
 
13.6%
미추홀구 2784
 
6.9%
372-18 1162
 
2.9%
미추홀구도화동 1153
 
2.9%
주안동 1057
 
2.6%
용현동 929
 
2.3%
숭의동 880
 
2.2%
서울특별시 837
 
2.1%
경기도 732
 
1.8%
남동구 421
 
1.0%
Other values (3890) 24844
61.7%
2024-03-18T12:35:59.028251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
34511
 
16.1%
동 11430
 
5.3%
1 9135
 
4.3%
시 7945
 
3.7%
구 7082
 
3.3%
- 6820
 
3.2%
2 6488
 
3.0%
인 6074
 
2.8%
천 6055
 
2.8%
역 6046
 
2.8%
Other values (514) 112203
52.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121532
56.8%
Decimal Number 44641
 
20.9%
Space Separator 34511
 
16.1%
Dash Punctuation 6820
 
3.2%
Close Punctuation 2039
 
1.0%
Open Punctuation 2039
 
1.0%
Other Punctuation 1360
 
0.6%
Uppercase Letter 551
 
0.3%
Lowercase Letter 278
 
0.1%
Letter Number 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 11430
 
9.4%
시 7945
 
6.5%
구 7082
 
5.8%
인 6074
 
5.0%
천 6055
 
5.0%
역 6046
 
5.0%
광 5660
 
4.7%
미 4250
 
3.5%
홀 4134
 
3.4%
추 4134
 
3.4%
Other values (450) 58722
48.3%
Uppercase Letter
ValueCountFrequency (%)
S 63
11.4%
B 55
10.0%
K 53
9.6%
A 53
9.6%
J 50
9.1%
N 40
 
7.3%
I 35
 
6.4%
T 34
 
6.2%
E 23
 
4.2%
C 23
 
4.2%
Other values (13) 122
22.1%
Lowercase Letter
ValueCountFrequency (%)
e 221
79.5%
t 11
 
4.0%
s 9
 
3.2%
a 6
 
2.2%
y 5
 
1.8%
o 4
 
1.4%
k 4
 
1.4%
c 3
 
1.1%
i 3
 
1.1%
r 2
 
0.7%
Other values (8) 10
 
3.6%
Decimal Number
ValueCountFrequency (%)
1 9135
20.5%
2 6488
14.5%
3 5082
11.4%
0 4673
10.5%
8 3813
8.5%
7 3559
 
8.0%
5 3337
 
7.5%
6 3310
 
7.4%
4 2885
 
6.5%
9 2359
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 1349
99.2%
. 6
 
0.4%
& 3
 
0.2%
: 1
 
0.1%
# 1
 
0.1%
Letter Number
ValueCountFrequency (%)
Ⅱ 12
80.0%
Ⅲ 2
 
13.3%
Ⅰ 1
 
6.7%
Space Separator
ValueCountFrequency (%)
34511
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 6820
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2039
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2039
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121527
56.8%
Common 91413
42.8%
Latin 844
 
0.4%
Han 5
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 11430
 
9.4%
시 7945
 
6.5%
구 7082
 
5.8%
인 6074
 
5.0%
천 6055
 
5.0%
역 6046
 
5.0%
광 5660
 
4.7%
미 4250
 
3.5%
홀 4134
 
3.4%
추 4134
 
3.4%
Other values (445) 58717
48.3%
Latin
ValueCountFrequency (%)
e 221
26.2%
S 63
 
7.5%
B 55
 
6.5%
K 53
 
6.3%
A 53
 
6.3%
J 50
 
5.9%
N 40
 
4.7%
I 35
 
4.1%
T 34
 
4.0%
E 23
 
2.7%
Other values (34) 217
25.7%
Common
ValueCountFrequency (%)
34511
37.8%
1 9135
 
10.0%
- 6820
 
7.5%
2 6488
 
7.1%
3 5082
 
5.6%
0 4673
 
5.1%
8 3813
 
4.2%
7 3559
 
3.9%
5 3337
 
3.7%
6 3310
 
3.6%
Other values (10) 10685
 
11.7%
Han
ValueCountFrequency (%)
新 1
20.0%
名 1
20.0%
品 1
20.0%
家 1
20.0%
愛 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121527
56.8%
ASCII 92242
43.1%
Number Forms 15
 
< 0.1%
CJK 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
34511
37.4%
1 9135
 
9.9%
- 6820
 
7.4%
2 6488
 
7.0%
3 5082
 
5.5%
0 4673
 
5.1%
8 3813
 
4.1%
7 3559
 
3.9%
5 3337
 
3.6%
6 3310
 
3.6%
Other values (51) 11514
 
12.5%
Hangul
ValueCountFrequency (%)
동 11430
 
9.4%
시 7945
 
6.5%
구 7082
 
5.8%
인 6074
 
5.0%
천 6055
 
5.0%
역 6046
 
5.0%
광 5660
 
4.7%
미 4250
 
3.5%
홀 4134
 
3.4%
추 4134
 
3.4%
Other values (445) 58717
48.3%
Number Forms
ValueCountFrequency (%)
Ⅱ 12
80.0%
Ⅲ 2
 
13.3%
Ⅰ 1
 
6.7%
CJK
ValueCountFrequency (%)
新 1
20.0%
名 1
20.0%
品 1
20.0%
家 1
20.0%
愛 1
20.0%

동명
Text

MISSING 

Distinct178
Distinct (%)13.6%
Missing6085
Missing (%)82.3%
Memory size57.9 KiB
2024-03-18T12:35:59.221519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length3.3139357
Min length1

Characters and Unicode

Total characters4328
Distinct characters104
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)7.5%

Sample

1st row102동
2nd row101
3rd row101
4th row101
5th row101
ValueCountFrequency (%)
102동 122
 
9.1%
1동 121
 
9.1%
101동 94
 
7.0%
103 76
 
5.7%
주건축물제1동 63
 
4.7%
2동 57
 
4.3%
a동 55
 
4.1%
101 50
 
3.7%
b동 43
 
3.2%
b 41
 
3.1%
Other values (173) 615
46.0%
2024-03-18T12:35:59.520188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 1014
23.4%
1 988
22.8%
0 613
14.2%
2 313
 
7.2%
3 171
 
4.0%
B 100
 
2.3%
A 86
 
2.0%
4 79
 
1.8%
제 73
 
1.7%
축 67
 
1.5%
Other values (94) 824
19.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2342
54.1%
Other Letter 1732
40.0%
Uppercase Letter 217
 
5.0%
Space Separator 31
 
0.7%
Other Punctuation 2
 
< 0.1%
Open Punctuation 2
 
< 0.1%
Close Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 1014
58.5%
제 73
 
4.2%
축 67
 
3.9%
물 67
 
3.9%
건 67
 
3.9%
주 67
 
3.9%
가 37
 
2.1%
나 33
 
1.9%
르 20
 
1.2%
스 17
 
1.0%
Other values (74) 270
 
15.6%
Decimal Number
ValueCountFrequency (%)
1 988
42.2%
0 613
26.2%
2 313
 
13.4%
3 171
 
7.3%
4 79
 
3.4%
5 58
 
2.5%
6 41
 
1.8%
8 39
 
1.7%
7 25
 
1.1%
9 15
 
0.6%
Uppercase Letter
ValueCountFrequency (%)
B 100
46.1%
A 86
39.6%
C 28
 
12.9%
F 1
 
0.5%
E 1
 
0.5%
D 1
 
0.5%
Space Separator
ValueCountFrequency (%)
31
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2379
55.0%
Hangul 1732
40.0%
Latin 217
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 1014
58.5%
제 73
 
4.2%
축 67
 
3.9%
물 67
 
3.9%
건 67
 
3.9%
주 67
 
3.9%
가 37
 
2.1%
나 33
 
1.9%
르 20
 
1.2%
스 17
 
1.0%
Other values (74) 270
 
15.6%
Common
ValueCountFrequency (%)
1 988
41.5%
0 613
25.8%
2 313
 
13.2%
3 171
 
7.2%
4 79
 
3.3%
5 58
 
2.4%
6 41
 
1.7%
8 39
 
1.6%
31
 
1.3%
7 25
 
1.1%
Other values (4) 21
 
0.9%
Latin
ValueCountFrequency (%)
B 100
46.1%
A 86
39.6%
C 28
 
12.9%
F 1
 
0.5%
E 1
 
0.5%
D 1
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2596
60.0%
Hangul 1732
40.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 1014
58.5%
제 73
 
4.2%
축 67
 
3.9%
물 67
 
3.9%
건 67
 
3.9%
주 67
 
3.9%
가 37
 
2.1%
나 33
 
1.9%
르 20
 
1.2%
스 17
 
1.0%
Other values (74) 270
 
15.6%
ASCII
ValueCountFrequency (%)
1 988
38.1%
0 613
23.6%
2 313
 
12.1%
3 171
 
6.6%
B 100
 
3.9%
A 86
 
3.3%
4 79
 
3.0%
5 58
 
2.2%
6 41
 
1.6%
8 39
 
1.5%
Other values (10) 108
 
4.2%

호명
Text

MISSING 

Distinct1132
Distinct (%)23.0%
Missing2479
Missing (%)33.5%
Memory size57.9 KiB
2024-03-18T12:35:59.831334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length3
Mean length3.637215
Min length1

Characters and Unicode

Total characters17866
Distinct characters32
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique660 ?
Unique (%)13.4%

Sample

1st row404
2nd row1205
3rd row1006
4th row2006
5th row1906
ValueCountFrequency (%)
302 151
 
3.1%
301 130
 
2.6%
402 125
 
2.5%
201 121
 
2.4%
202 118
 
2.4%
401 115
 
2.3%
502 102
 
2.1%
501 97
 
2.0%
303 95
 
1.9%
403 80
 
1.6%
Other values (1106) 3805
77.0%
2024-03-18T12:36:00.284706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4586
25.7%
1 3245
18.2%
2 2301
12.9%
3 1788
 
10.0%
4 1421
 
8.0%
5 1072
 
6.0%
호 838
 
4.7%
6 705
 
3.9%
7 536
 
3.0%
8 479
 
2.7%
Other values (22) 895
 
5.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16540
92.6%
Other Letter 1174
 
6.6%
Uppercase Letter 90
 
0.5%
Dash Punctuation 35
 
0.2%
Space Separator 27
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
호 838
71.4%
동 189
 
16.1%
층 37
 
3.2%
지 26
 
2.2%
오 20
 
1.7%
텔 15
 
1.3%
스 15
 
1.3%
피 15
 
1.3%
하 5
 
0.4%
티 5
 
0.4%
Other values (5) 9
 
0.8%
Decimal Number
ValueCountFrequency (%)
0 4586
27.7%
1 3245
19.6%
2 2301
13.9%
3 1788
 
10.8%
4 1421
 
8.6%
5 1072
 
6.5%
6 705
 
4.3%
7 536
 
3.2%
8 479
 
2.9%
9 407
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
B 53
58.9%
A 32
35.6%
E 2
 
2.2%
O 2
 
2.2%
D 1
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Space Separator
ValueCountFrequency (%)
27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16602
92.9%
Hangul 1174
 
6.6%
Latin 90
 
0.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
호 838
71.4%
동 189
 
16.1%
층 37
 
3.2%
지 26
 
2.2%
오 20
 
1.7%
텔 15
 
1.3%
스 15
 
1.3%
피 15
 
1.3%
하 5
 
0.4%
티 5
 
0.4%
Other values (5) 9
 
0.8%
Common
ValueCountFrequency (%)
0 4586
27.6%
1 3245
19.5%
2 2301
13.9%
3 1788
 
10.8%
4 1421
 
8.6%
5 1072
 
6.5%
6 705
 
4.2%
7 536
 
3.2%
8 479
 
2.9%
9 407
 
2.5%
Other values (2) 62
 
0.4%
Latin
ValueCountFrequency (%)
B 53
58.9%
A 32
35.6%
E 2
 
2.2%
O 2
 
2.2%
D 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16692
93.4%
Hangul 1174
 
6.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 4586
27.5%
1 3245
19.4%
2 2301
13.8%
3 1788
 
10.7%
4 1421
 
8.5%
5 1072
 
6.4%
6 705
 
4.2%
7 536
 
3.2%
8 479
 
2.9%
9 407
 
2.4%
Other values (7) 152
 
0.9%
Hangul
ValueCountFrequency (%)
호 838
71.4%
동 189
 
16.1%
층 37
 
3.2%
지 26
 
2.2%
오 20
 
1.7%
텔 15
 
1.3%
스 15
 
1.3%
피 15
 
1.3%
하 5
 
0.4%
티 5
 
0.4%
Other values (5) 9
 
0.8%

실명
Text

MISSING 

Distinct82
Distinct (%)12.3%
Missing6727
Missing (%)91.0%
Memory size57.9 KiB
2024-03-18T12:36:00.508141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.0873494
Min length1

Characters and Unicode

Total characters2050
Distinct characters17
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)4.8%

Sample

1st row304
2nd row303
3rd row205
4th row204
5th row302
ValueCountFrequency (%)
201 46
 
6.7%
202 45
 
6.6%
302 41
 
6.0%
301 40
 
5.8%
203 36
 
5.3%
303 35
 
5.1%
204 32
 
4.7%
304 29
 
4.2%
401 24
 
3.5%
402 24
 
3.5%
Other values (70) 333
48.6%
2024-03-18T12:36:00.836204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 635
31.0%
2 372
18.1%
3 318
15.5%
1 243
 
11.9%
4 208
 
10.1%
5 66
 
3.2%
호 38
 
1.9%
6 37
 
1.8%
층 28
 
1.4%
B 25
 
1.2%
Other values (7) 80
 
3.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1888
92.1%
Other Letter 112
 
5.5%
Uppercase Letter 25
 
1.2%
Space Separator 21
 
1.0%
Lowercase Letter 3
 
0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 635
33.6%
2 372
19.7%
3 318
16.8%
1 243
 
12.9%
4 208
 
11.0%
5 66
 
3.5%
6 37
 
2.0%
7 9
 
0.5%
Other Letter
ValueCountFrequency (%)
호 38
33.9%
층 28
25.0%
지 21
18.8%
하 21
18.8%
실 4
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
B 25
100.0%
Space Separator
ValueCountFrequency (%)
21
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1910
93.2%
Hangul 112
 
5.5%
Latin 28
 
1.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 635
33.2%
2 372
19.5%
3 318
16.6%
1 243
 
12.7%
4 208
 
10.9%
5 66
 
3.5%
6 37
 
1.9%
21
 
1.1%
7 9
 
0.5%
, 1
 
0.1%
Hangul
ValueCountFrequency (%)
호 38
33.9%
층 28
25.0%
지 21
18.8%
하 21
18.8%
실 4
 
3.6%
Latin
ValueCountFrequency (%)
B 25
89.3%
b 3
 
10.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1938
94.5%
Hangul 112
 
5.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 635
32.8%
2 372
19.2%
3 318
16.4%
1 243
 
12.5%
4 208
 
10.7%
5 66
 
3.4%
6 37
 
1.9%
B 25
 
1.3%
21
 
1.1%
7 9
 
0.5%
Other values (2) 4
 
0.2%
Hangul
ValueCountFrequency (%)
호 38
33.9%
층 28
25.0%
지 21
18.8%
하 21
18.8%
실 4
 
3.6%

종류
Categorical

HIGH CORRELATION 

Distinct10
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
장기일반민간임대주택(10년)
2247 
장기일반민간임대주택(8년)
2213 
공공지원민간임대주택(10년)
1157 
단기민간임대주택
1024 
준공공임대
414 
Other values (5)
336 

Length

Max length15
Median length14
Mean length12.714924
Min length4

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row장기일반민간임대주택(10년)
2nd row장기일반민간임대주택(10년)
3rd row장기일반민간임대주택(10년)
4th row장기일반민간임대주택(10년)
5th row장기일반민간임대주택(10년)

Common Values

ValueCountFrequency (%)
장기일반민간임대주택(10년) 2247
30.4%
장기일반민간임대주택(8년) 2213
29.9%
공공지원민간임대주택(10년) 1157
15.7%
단기민간임대주택 1024
13.9%
준공공임대 414
 
5.6%
단기임대 179
 
2.4%
매입임대주택 141
 
1.9%
<NA> 9
 
0.1%
5년임대주택(민간) 6
 
0.1%
공공지원민간임대주택(8년) 1
 
< 0.1%

Length

2024-03-18T12:36:00.946763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:36:01.042989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장기일반민간임대주택(10년 2247
30.4%
장기일반민간임대주택(8년 2213
29.9%
공공지원민간임대주택(10년 1157
15.7%
단기민간임대주택 1024
13.9%
준공공임대 414
 
5.6%
단기임대 179
 
2.4%
매입임대주택 141
 
1.9%
na 9
 
0.1%
5년임대주택(민간 6
 
0.1%
공공지원민간임대주택(8년 1
 
< 0.1%

유형
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size57.9 KiB
준주택(오피스텔)
2397 
아파트
2350 
다세대주택
1486 
다가구주택
619 
연립주택
241 
Other values (4)
298 

Length

Max length12
Median length9
Mean length5.6829928
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row준주택(오피스텔)
2nd row아파트
3rd row아파트
4th row아파트
5th row아파트

Common Values

ValueCountFrequency (%)
준주택(오피스텔) 2397
32.4%
아파트 2350
31.8%
다세대주택 1486
20.1%
다가구주택 619
 
8.4%
연립주택 241
 
3.3%
단독주택 125
 
1.7%
도시형생활주택 105
 
1.4%
아파트(도시형생활주택) 48
 
0.6%
<NA> 20
 
0.3%

Length

2024-03-18T12:36:01.214314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T12:36:01.339831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
준주택(오피스텔 2397
32.4%
아파트 2350
31.8%
다세대주택 1486
20.1%
다가구주택 619
 
8.4%
연립주택 241
 
3.3%
단독주택 125
 
1.7%
도시형생활주택 105
 
1.4%
아파트(도시형생활주택 48
 
0.6%
na 20
 
0.3%

면적
Real number (ℝ)

Distinct2114
Distinct (%)28.7%
Missing21
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean41.692179
Minimum0
Maximum335.99
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.1 KiB
2024-03-18T12:36:01.463820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile17.47
Q124.3736
median38.8886
Q359.4393
95-th percentile74.9352
Maximum335.99
Range335.99
Interquartile range (IQR)35.0657

Descriptive statistics

Standard deviation19.60638
Coefficient of variation (CV)0.47026518
Kurtosis11.042383
Mean41.692179
Median Absolute Deviation (MAD)16.9186
Skewness1.3519916
Sum307271.36
Variance384.41013
MonotonicityNot monotonic
2024-03-18T12:36:01.573559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
59.9561 521
 
7.0%
59.4393 221
 
3.0%
59.9925 208
 
2.8%
21.6777 184
 
2.5%
74.9352 179
 
2.4%
37.89 75
 
1.0%
26.6542 72
 
1.0%
39.95 67
 
0.9%
39.98 58
 
0.8%
58.674 52
 
0.7%
Other values (2104) 5733
77.6%
ValueCountFrequency (%)
0.0 3
 
< 0.1%
1.0 1
 
< 0.1%
9.3 1
 
< 0.1%
10.0 33
0.4%
11.55 2
 
< 0.1%
11.64 1
 
< 0.1%
11.88 3
 
< 0.1%
11.946 1
 
< 0.1%
12.22 12
 
0.2%
12.26 1
 
< 0.1%
ValueCountFrequency (%)
335.99 1
 
< 0.1%
290.82 1
 
< 0.1%
174.09 4
0.1%
151.95 1
 
< 0.1%
147.358 1
 
< 0.1%
144.5839 1
 
< 0.1%
142.1275 1
 
< 0.1%
126.527 1
 
< 0.1%
125.11 1
 
< 0.1%
124.86 1
 
< 0.1%

Interactions

2024-03-18T12:35:57.385697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T12:36:01.653835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
영업구분사업자구분임대주택구분실명종류유형면적
영업구분1.0000.2230.3610.0420.3140.2860.065
사업자구분0.2231.0000.1270.0000.6520.3190.139
임대주택구분0.3610.1271.0000.0000.5980.7970.461
실명0.0420.0000.0001.0000.0000.5140.870
종류0.3140.6520.5980.0001.0000.5300.327
유형0.2860.3190.7970.5140.5301.0000.525
면적0.0650.1390.4610.8700.3270.5251.000
2024-03-18T12:36:01.748940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업자구분유형영업구분임대주택구분종류
사업자구분1.0000.2020.2720.1550.450
유형0.2021.0000.2150.6190.294
영업구분0.2720.2151.0000.2350.313
임대주택구분0.1550.6190.2351.0000.602
종류0.4500.2940.3130.6021.000
2024-03-18T12:36:02.067541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적영업구분사업자구분임대주택구분종류유형
면적1.0000.0490.0850.3470.1660.199
영업구분0.0491.0000.2720.2350.3130.215
사업자구분0.0850.2721.0000.1550.4500.202
임대주택구분0.3470.2350.1551.0000.6020.619
종류0.1660.3130.4500.6021.0000.294
유형0.1990.2150.2020.6190.2941.000

Missing values

2024-03-18T12:35:57.538763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T12:35:57.658703image/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.
2024-03-18T12:35:57.795158image/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

영업구분사업자구분임대주택구분지번주소동명호명실명종류유형면적
0등록영업중임대사업자민간매입임대주택인천광역시 부평구 청천동 385 월드메르디앙 부평102동404<NA>장기일반민간임대주택(10년)준주택(오피스텔)60.89
1등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011205<NA>장기일반민간임대주택(10년)아파트39.9578
2등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011006<NA>장기일반민간임대주택(10년)아파트39.9578
3등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181012006<NA>장기일반민간임대주택(10년)아파트39.9578
4등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011906<NA>장기일반민간임대주택(10년)아파트39.9578
5등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011806<NA>장기일반민간임대주택(10년)아파트39.9578
6등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011706<NA>장기일반민간임대주택(10년)아파트39.9578
7등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011606<NA>장기일반민간임대주택(10년)아파트39.9578
8등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011506<NA>장기일반민간임대주택(10년)아파트39.9578
9등록영업중임대사업자민간건설임대주택인천광역시미추홀구 숭의동 181011406<NA>장기일반민간임대주택(10년)아파트39.9578
영업구분사업자구분임대주택구분지번주소동명호명실명종류유형면적
7381전입영업중일반형임대사업자민간매입임대주택인천광역시 서구 검암동 512-2 (검암동, 풍림아이원3차아파트)403동204호<NA>준공공임대아파트59.8
7382전입영업중일반형임대사업자민간매입임대주택인천광역시 미추홀구 용현동 492-30 (용현동)<NA><NA><NA>매입임대주택단독주택76.03
7383전입영업중일반형임대사업자민간매입임대주택인천광역시 미추홀구 용현동 492-30 (용현동)<NA><NA><NA>매입임대주택단독주택48.59
7384전입영업중일반형임대사업자민간매입임대주택인천광역시 연수구 송도동 192-8 (송도동, 베르디움 더퍼스트)107동403<NA>준공공임대아파트63.94
7385전입영업중일반형임대사업자민간매입임대주택인천광역시 연수구 송도동 190-4 (송도동, 송도 더샵 센트럴시티)202동605<NA>장기일반민간임대주택(8년)아파트59.99
7386전입영업중일반형임대사업자민간매입임대주택인천광역시 연수구 송도동 190-4 (송도동, 송도 더샵 센트럴시티)205동401<NA>장기일반민간임대주택(8년)아파트59.99
7387전입영업중일반형임대사업자민간매입임대주택인천광역시 연수구 동춘동 943 (동춘동, 동남아파트)103동111호<NA>장기일반민간임대주택(8년)아파트52.14
7388전입영업중매입임대사업자민간매입임대주택인천광역시 연수구 청학동 534-13 (청학동, 청학빌라)<NA>202호<NA>장기일반민간임대주택(8년)다세대주택47.16
7389전입영업중매입임대사업자민간매입임대주택인천광역시 연수구 청학동 451 (청학동, 시대아파트)106동1301호<NA>장기일반민간임대주택(8년)아파트49.585
7390전입영업중매입임대사업자민간매입임대주택인천광역시 서구 청라동 156-1 청라센트럴에일린의뜰씨동2409<NA>장기일반민간임대주택(8년)준주택(오피스텔)45.4494

Duplicate rows

Most frequently occurring

영업구분사업자구분임대주택구분지번주소동명호명실명종류유형면적# duplicates
6등록영업중일반형임대사업자민간매입임대주택인천광역시 남동구 구월동<NA><NA><NA>장기일반민간임대주택(8년)다세대주택59.875
14등록영업중임대사업자<NA><NA><NA><NA><NA><NA><NA><NA>5
0등록영업중매입임대사업자민간매입임대주택경기도 시흥시 시흥시 49-4<NA><NA><NA>매입임대주택다가구주택10.04
1등록영업중일반형임대사업자민간건설임대주택인천광역시 미추홀구 관교동<NA><NA><NA>장기일반민간임대주택(8년)다세대주택32.534
15전입영업중일반형임대사업자민간매입임대주택서울특별시 강서구 마곡동<NA><NA><NA>준공공임대준주택(오피스텔)24.02124
2등록영업중일반형임대사업자민간건설임대주택인천광역시 미추홀구 관교동<NA><NA><NA>장기일반민간임대주택(8년)다세대주택32.743
3등록영업중일반형임대사업자민간건설임대주택인천광역시 미추홀구 관교동<NA><NA><NA>장기일반민간임대주택(8년)다세대주택36.992
4등록영업중일반형임대사업자민간건설임대주택인천광역시 미추홀구 숭의동 119-25 (숭의동)<NA><NA><NA>준공공임대다가구주택35.912
5등록영업중일반형임대사업자민간건설임대주택인천광역시 미추홀구 숭의동 119-25 (숭의동)<NA><NA><NA>준공공임대다가구주택66.862
7등록영업중임대사업자민간매입임대주택경기도 고양시 덕양구 고양동 1132-1 그린파크그린파크 102동501<NA>장기일반민간임대주택(10년)다세대주택56.772