Overview

Dataset statistics

Number of variables11
Number of observations32
Missing cells12
Missing cells (%)3.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory95.1 B

Variable types

Text5
DateTime2
Numeric3
Categorical1

Dataset

Description인천광역시 동구 공동주택(아파트) 현황 데이터로, 아파트명, 지번주소, 전화번호, 팩스번호, 사업승인일, 사용승인일, 세대수, 층수, 동수, 난방방식, 승강기대수 등 항목을 제공합니다.
Author인천광역시
URLhttps://www.incheon.go.kr/data/DATA010201/view?docId=15044935

Alerts

세대수 is highly overall correlated with 동수 and 2 other fieldsHigh correlation
동수 is highly overall correlated with 세대수 and 2 other fieldsHigh correlation
승강기대수 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
난방방식 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
난방방식 is highly imbalanced (79.9%)Imbalance
팩스번호 has 12 (37.5%) missing valuesMissing
아파트명 has unique valuesUnique
지번주소 has unique valuesUnique
전화번호 has unique valuesUnique
승강기대수 has 8 (25.0%) zerosZeros

Reproduction

Analysis started2024-01-28 16:15:37.891945
Analysis finished2024-01-28 16:15:39.015580
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트명
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-01-29T01:15:39.151951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length5.46875
Min length2

Characters and Unicode

Total characters175
Distinct characters69
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row화수상가
2nd row삼익
3rd row송현1차
4th row삼두1차
5th row송현2차
ValueCountFrequency (%)
화수상가 1
 
3.0%
영풍 1
 
3.0%
브리즈힐 1
 
3.0%
송림파인앤유 1
 
3.0%
만석웰카운티 1
 
3.0%
괭이부리마을 1
 
3.0%
동산휴먼시아2단지 1
 
3.0%
동산휴먼시아1단지 1
 
3.0%
송림휴먼시아1단지 1
 
3.0%
풍림아이원 1
 
3.0%
Other values (23) 23
69.7%
2024-01-29T01:15:39.475098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
송 9
 
5.1%
차 9
 
5.1%
공 7
 
4.0%
2 7
 
4.0%
주 7
 
4.0%
림 6
 
3.4%
지 6
 
3.4%
1 6
 
3.4%
단 5
 
2.9%
석 5
 
2.9%
Other values (59) 108
61.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 156
89.1%
Decimal Number 14
 
8.0%
Close Punctuation 2
 
1.1%
Open Punctuation 2
 
1.1%
Space Separator 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
송 9
 
5.8%
차 9
 
5.8%
공 7
 
4.5%
주 7
 
4.5%
림 6
 
3.8%
지 6
 
3.8%
단 5
 
3.2%
석 5
 
3.2%
만 5
 
3.2%
아 4
 
2.6%
Other values (53) 93
59.6%
Decimal Number
ValueCountFrequency (%)
2 7
50.0%
1 6
42.9%
3 1
 
7.1%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 156
89.1%
Common 19
 
10.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
송 9
 
5.8%
차 9
 
5.8%
공 7
 
4.5%
주 7
 
4.5%
림 6
 
3.8%
지 6
 
3.8%
단 5
 
3.2%
석 5
 
3.2%
만 5
 
3.2%
아 4
 
2.6%
Other values (53) 93
59.6%
Common
ValueCountFrequency (%)
2 7
36.8%
1 6
31.6%
) 2
 
10.5%
( 2
 
10.5%
1
 
5.3%
3 1
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 156
89.1%
ASCII 19
 
10.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
송 9
 
5.8%
차 9
 
5.8%
공 7
 
4.5%
주 7
 
4.5%
림 6
 
3.8%
지 6
 
3.8%
단 5
 
3.2%
석 5
 
3.2%
만 5
 
3.2%
아 4
 
2.6%
Other values (53) 93
59.6%
ASCII
ValueCountFrequency (%)
2 7
36.8%
1 6
31.6%
) 2
 
10.5%
( 2
 
10.5%
1
 
5.3%
3 1
 
5.3%

지번주소
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-01-29T01:15:39.660096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length18
Mean length16.90625
Min length15

Characters and Unicode

Total characters541
Distinct characters26
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row인천광역시 동구 화수동 287-106
2nd row인천광역시 동구 송림동 228-1
3rd row인천광역시 동구 송현동 1-57
4th row인천광역시 동구 송현동 66-51
5th row인천광역시 동구 송현동 1-9
ValueCountFrequency (%)
인천광역시 32
25.0%
동구 32
25.0%
송림동 11
 
8.6%
송현동 11
 
8.6%
만석동 6
 
4.7%
화수동 4
 
3.1%
346 1
 
0.8%
37-10 1
 
0.8%
127 1
 
0.8%
126 1
 
0.8%
Other values (28) 28
21.9%
2024-01-29T01:15:39.966063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
96
17.7%
동 64
11.8%
인 32
 
5.9%
천 32
 
5.9%
광 32
 
5.9%
역 32
 
5.9%
시 32
 
5.9%
구 32
 
5.9%
송 22
 
4.1%
1 21
 
3.9%
Other values (16) 146
27.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 320
59.1%
Decimal Number 107
 
19.8%
Space Separator 96
 
17.7%
Dash Punctuation 18
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 64
20.0%
인 32
10.0%
천 32
10.0%
광 32
10.0%
역 32
10.0%
시 32
10.0%
구 32
10.0%
송 22
 
6.9%
림 11
 
3.4%
현 11
 
3.4%
Other values (4) 20
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 21
19.6%
6 18
16.8%
2 16
15.0%
3 16
15.0%
4 11
10.3%
7 8
 
7.5%
5 7
 
6.5%
9 5
 
4.7%
8 3
 
2.8%
0 2
 
1.9%
Space Separator
ValueCountFrequency (%)
96
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 320
59.1%
Common 221
40.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 64
20.0%
인 32
10.0%
천 32
10.0%
광 32
10.0%
역 32
10.0%
시 32
10.0%
구 32
10.0%
송 22
 
6.9%
림 11
 
3.4%
현 11
 
3.4%
Other values (4) 20
 
6.2%
Common
ValueCountFrequency (%)
96
43.4%
1 21
 
9.5%
6 18
 
8.1%
- 18
 
8.1%
2 16
 
7.2%
3 16
 
7.2%
4 11
 
5.0%
7 8
 
3.6%
5 7
 
3.2%
9 5
 
2.3%
Other values (2) 5
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 320
59.1%
ASCII 221
40.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
96
43.4%
1 21
 
9.5%
6 18
 
8.1%
- 18
 
8.1%
2 16
 
7.2%
3 16
 
7.2%
4 11
 
5.0%
7 8
 
3.6%
5 7
 
3.2%
9 5
 
2.3%
Other values (2) 5
 
2.3%
Hangul
ValueCountFrequency (%)
동 64
20.0%
인 32
10.0%
천 32
10.0%
광 32
10.0%
역 32
10.0%
시 32
10.0%
구 32
10.0%
송 22
 
6.9%
림 11
 
3.4%
현 11
 
3.4%
Other values (4) 20
 
6.2%

전화번호
Text

UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-01-29T01:15:40.139573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters384
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)100.0%

Sample

1st row032-764-7704
2nd row032-773-3937
3rd row032-761-3360
4th row032-762-4075
5th row032-765-2330
ValueCountFrequency (%)
032-764-7704 1
 
3.1%
032-773-3937 1
 
3.1%
032-764-8324 1
 
3.1%
032-772-0794 1
 
3.1%
032-764-8717 1
 
3.1%
032-772-3603 1
 
3.1%
032-765-9144 1
 
3.1%
032-765-9063 1
 
3.1%
032-764-5577 1
 
3.1%
032-765-0452 1
 
3.1%
Other values (22) 22
68.8%
2024-01-29T01:15:40.414730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 64
16.7%
7 63
16.4%
3 55
14.3%
2 48
12.5%
0 47
12.2%
6 35
9.1%
4 19
 
4.9%
5 15
 
3.9%
1 14
 
3.6%
8 13
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 320
83.3%
Dash Punctuation 64
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
7 63
19.7%
3 55
17.2%
2 48
15.0%
0 47
14.7%
6 35
10.9%
4 19
 
5.9%
5 15
 
4.7%
1 14
 
4.4%
8 13
 
4.1%
9 11
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 64
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 64
16.7%
7 63
16.4%
3 55
14.3%
2 48
12.5%
0 47
12.2%
6 35
9.1%
4 19
 
4.9%
5 15
 
3.9%
1 14
 
3.6%
8 13
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 64
16.7%
7 63
16.4%
3 55
14.3%
2 48
12.5%
0 47
12.2%
6 35
9.1%
4 19
 
4.9%
5 15
 
3.9%
1 14
 
3.6%
8 13
 
3.4%

팩스번호
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing12
Missing (%)37.5%
Memory size388.0 B
2024-01-29T01:15:40.569668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters240
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row032-772-4163
2nd row032-239-3361
3rd row032-762-4075
4th row032-251-2330
5th row032-763-8616
ValueCountFrequency (%)
032-772-4163 1
 
5.0%
032-239-3361 1
 
5.0%
032-764-8326 1
 
5.0%
032-764-8718 1
 
5.0%
032-765-9145 1
 
5.0%
032-765-9064 1
 
5.0%
032-764-5578 1
 
5.0%
032-765-0454 1
 
5.0%
032-777-9062 1
 
5.0%
032-777-9082 1
 
5.0%
Other values (10) 10
50.0%
2024-01-29T01:15:40.806948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 40
16.7%
2 34
14.2%
7 33
13.8%
3 31
12.9%
0 28
11.7%
6 24
10.0%
4 15
 
6.2%
5 10
 
4.2%
9 9
 
3.8%
1 8
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 200
83.3%
Dash Punctuation 40
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 34
17.0%
7 33
16.5%
3 31
15.5%
0 28
14.0%
6 24
12.0%
4 15
7.5%
5 10
 
5.0%
9 9
 
4.5%
1 8
 
4.0%
8 8
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 40
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 240
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 40
16.7%
2 34
14.2%
7 33
13.8%
3 31
12.9%
0 28
11.7%
6 24
10.0%
4 15
 
6.2%
5 10
 
4.2%
9 9
 
3.8%
1 8
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 240
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 40
16.7%
2 34
14.2%
7 33
13.8%
3 31
12.9%
0 28
11.7%
6 24
10.0%
4 15
 
6.2%
5 10
 
4.2%
9 9
 
3.8%
1 8
 
3.3%

사업승인일
Date

Distinct28
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum1978-08-02 00:00:00
Maximum2017-12-29 00:00:00
2024-01-29T01:15:40.909606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:41.003523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)

사용승인일
Date

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size388.0 B
Minimum1979-08-01 00:00:00
Maximum2022-08-22 00:00:00
2024-01-29T01:15:41.089463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:41.186739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean564.625
Minimum30
Maximum2711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-29T01:15:41.283778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum30
5-th percentile81
Q1122.25
median356.5
Q3647
95-th percentile1898.15
Maximum2711
Range2681
Interquartile range (IQR)524.75

Descriptive statistics

Standard deviation655.38025
Coefficient of variation (CV)1.1607354
Kurtosis4.8551747
Mean564.625
Median Absolute Deviation (MAD)237
Skewness2.1820756
Sum18068
Variance429523.27
MonotonicityNot monotonic
2024-01-29T01:15:41.374918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
98 2
 
6.2%
264 2
 
6.2%
30 1
 
3.1%
365 1
 
3.1%
2562 1
 
3.1%
920 1
 
3.1%
232 1
 
3.1%
178 1
 
3.1%
863 1
 
3.1%
310 1
 
3.1%
Other values (20) 20
62.5%
ValueCountFrequency (%)
30 1
3.1%
70 1
3.1%
90 1
3.1%
94 1
3.1%
98 2
6.2%
100 1
3.1%
114 1
3.1%
125 1
3.1%
178 1
3.1%
232 1
3.1%
ValueCountFrequency (%)
2711 1
3.1%
2562 1
3.1%
1355 1
3.1%
1273 1
3.1%
1140 1
3.1%
1011 1
3.1%
920 1
3.1%
863 1
3.1%
575 1
3.1%
560 1
3.1%

층수
Text

Distinct21
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Memory size388.0 B
2024-01-29T01:15:41.476177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.96875
Min length1

Characters and Unicode

Total characters95
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)50.0%

Sample

1st row4
2nd row12
3rd row5
4th row12
5th row5
ValueCountFrequency (%)
5 7
21.9%
12 3
 
9.4%
4 2
 
6.2%
18~25 2
 
6.2%
14~23 2
 
6.2%
16~21 1
 
3.1%
8~25 1
 
3.1%
20~29 1
 
3.1%
21~22 1
 
3.1%
22~26 1
 
3.1%
Other values (11) 11
34.4%
2024-01-29T01:15:41.948448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 26
27.4%
1 20
21.1%
5 14
14.7%
~ 14
14.7%
4 7
 
7.4%
3 5
 
5.3%
8 4
 
4.2%
6 2
 
2.1%
7 1
 
1.1%
0 1
 
1.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 81
85.3%
Math Symbol 14
 
14.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 26
32.1%
1 20
24.7%
5 14
17.3%
4 7
 
8.6%
3 5
 
6.2%
8 4
 
4.9%
6 2
 
2.5%
7 1
 
1.2%
0 1
 
1.2%
9 1
 
1.2%
Math Symbol
ValueCountFrequency (%)
~ 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 95
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 26
27.4%
1 20
21.1%
5 14
14.7%
~ 14
14.7%
4 7
 
7.4%
3 5
 
5.3%
8 4
 
4.2%
6 2
 
2.1%
7 1
 
1.1%
0 1
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 95
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 26
27.4%
1 20
21.1%
5 14
14.7%
~ 14
14.7%
4 7
 
7.4%
3 5
 
5.3%
8 4
 
4.2%
6 2
 
2.1%
7 1
 
1.1%
0 1
 
1.1%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)46.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.4375
Minimum1
Maximum27
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-29T01:15:42.039725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q310.25
95-th percentile17.25
Maximum27
Range26
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation6.1745445
Coefficient of variation (CV)0.95915255
Kurtosis2.8971728
Mean6.4375
Median Absolute Deviation (MAD)2
Skewness1.6517142
Sum206
Variance38.125
MonotonicityNot monotonic
2024-01-29T01:15:42.128424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
3 7
21.9%
2 6
18.8%
1 4
12.5%
11 3
9.4%
5 2
 
6.2%
14 1
 
3.1%
10 1
 
3.1%
8 1
 
3.1%
4 1
 
3.1%
15 1
 
3.1%
Other values (5) 5
15.6%
ValueCountFrequency (%)
1 4
12.5%
2 6
18.8%
3 7
21.9%
4 1
 
3.1%
5 2
 
6.2%
7 1
 
3.1%
8 1
 
3.1%
9 1
 
3.1%
10 1
 
3.1%
11 3
9.4%
ValueCountFrequency (%)
27 1
 
3.1%
20 1
 
3.1%
15 1
 
3.1%
14 1
 
3.1%
12 1
 
3.1%
11 3
9.4%
10 1
 
3.1%
9 1
 
3.1%
8 1
 
3.1%
7 1
 
3.1%

난방방식
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
개별
31 
중앙
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)3.1%

Sample

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

Common Values

ValueCountFrequency (%)
개별 31
96.9%
중앙 1
 
3.1%

Length

2024-01-29T01:15:42.220270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:15:42.309135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개별 31
96.9%
중앙 1
 
3.1%

승강기대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)62.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.59375
Minimum0
Maximum59
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2024-01-29T01:15:42.383204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.75
median6.5
Q313.5
95-th percentile43.8
Maximum59
Range59
Interquartile range (IQR)12.75

Descriptive statistics

Standard deviation15.487475
Coefficient of variation (CV)1.3358469
Kurtosis3.4566417
Mean11.59375
Median Absolute Deviation (MAD)6
Skewness1.9230798
Sum371
Variance239.8619
MonotonicityNot monotonic
2024-01-29T01:15:42.467935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 8
25.0%
7 3
 
9.4%
6 2
 
6.2%
9 2
 
6.2%
2 2
 
6.2%
8 1
 
3.1%
57 1
 
3.1%
24 1
 
3.1%
5 1
 
3.1%
18 1
 
3.1%
Other values (10) 10
31.2%
ValueCountFrequency (%)
0 8
25.0%
1 1
 
3.1%
2 2
 
6.2%
3 1
 
3.1%
4 1
 
3.1%
5 1
 
3.1%
6 2
 
6.2%
7 3
 
9.4%
8 1
 
3.1%
9 2
 
6.2%
ValueCountFrequency (%)
59 1
3.1%
57 1
3.1%
33 1
3.1%
30 1
3.1%
29 1
3.1%
24 1
3.1%
23 1
3.1%
18 1
3.1%
12 1
3.1%
10 1
3.1%

Interactions

2024-01-29T01:15:38.609470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.237435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.415708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.676059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.294987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.474160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.752280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.354414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:15:38.542778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:15:42.536549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
아파트명지번주소전화번호팩스번호사업승인일사용승인일세대수층수동수난방방식승강기대수
아파트명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
팩스번호1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사업승인일1.0001.0001.0001.0001.0001.0000.9650.9340.9941.0000.967
사용승인일1.0001.0001.0001.0001.0001.0001.0000.9551.0001.0001.000
세대수1.0001.0001.0001.0000.9651.0001.0000.9230.8121.0000.847
층수1.0001.0001.0001.0000.9340.9550.9231.0000.9281.0000.985
동수1.0001.0001.0001.0000.9941.0000.8120.9281.0000.7570.788
난방방식1.0001.0001.0001.0001.0001.0001.0001.0000.7571.0000.000
승강기대수1.0001.0001.0001.0000.9671.0000.8470.9850.7880.0001.000
2024-01-29T01:15:42.634607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세대수동수승강기대수난방방식
세대수1.0000.8900.7320.931
동수0.8901.0000.5890.516
승강기대수0.7320.5891.0000.000
난방방식0.9310.5160.0001.000

Missing values

2024-01-29T01:15:38.846767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:15:38.965489image/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

아파트명지번주소전화번호팩스번호사업승인일사용승인일세대수층수동수난방방식승강기대수
0화수상가인천광역시 동구 화수동 287-106032-764-7704<NA>1978-08-021979-10-273041개별0
1삼익인천광역시 동구 송림동 228-1032-773-3937032-772-41631978-08-021979-08-01264122개별2
2송현1차인천광역시 동구 송현동 1-57032-761-3360032-239-33611982-01-301982-12-14500511개별0
3삼두1차인천광역시 동구 송현동 66-51032-762-4075032-762-40751983-09-291984-10-18264122개별4
4송현2차인천광역시 동구 송현동 1-9032-765-2330032-251-23301983-04-281984-04-18400511개별0
5만석1차인천광역시 동구 만석동 43-15032-764-3083<NA>1984-12-261985-11-0912553개별0
6삼부인천광역시 동구 송현동 12-4032-762-8806<NA>1986-09-171985-07-129052개별0
7만석2차인천광역시 동구 만석동 71032-766-4338<NA>1986-12-301987-10-1610052개별0
8삼두2차인천광역시 동구 송현동 66-24032-763-8616032-763-86161986-10-241987-10-17432123개별6
9미륭인천광역시 동구 화수동 2-7032-764-0759<NA>1987-12-291990-06-25560514개별0
아파트명지번주소전화번호팩스번호사업승인일사용승인일세대수층수동수난방방식승강기대수
22솔빛마을주공2차(2단지)인천광역시 동구 송현동 166032-777-9061032-777-90621999-04-022003-12-2535415~233개별8
23송림 풍림아이원인천광역시 동구 송림동 339032-765-0452032-765-04541999-12-302009-08-0713551420개별33
24송림휴먼시아1단지인천광역시 동구 송림동 341032-764-5577032-764-55782003-12-222009-07-2910112511개별23
25동산휴먼시아1단지인천광역시 동구 송림동 342032-765-9063032-765-90642004-12-302010-09-2031018~253개별6
26동산휴먼시아2단지인천광역시 동구 송림동 343032-765-9144032-765-91452004-12-302010-11-1986318~259개별18
27괭이부리마을인천광역시 동구 만석동 126032-772-3603<NA>2012-08-232013-11-149842개별2
28만석웰카운티인천광역시 동구 만석동 127032-764-8717032-764-87182009-04-272014-06-2317822~263개별5
29송림파인앤유인천광역시 동구 송림동 346032-772-0794<NA>2009-04-272020-11-0623221~223개별9
30브리즈힐인천광역시 동구 송림동 37-10032-764-8324032-764-83262017-12-292021-06-3092020~297개별24
31동인천역파크푸르지오인천광역시 동구 송림동 253-5032-765-4108032-763-41092009-10-012022-08-22256223~4812개별57