Overview

Dataset statistics

Number of variables10
Number of observations178
Missing cells10
Missing cells (%)0.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.6 KiB
Average record size in memory83.7 B

Variable types

Numeric3
Categorical2
Text4
DateTime1

Dataset

Description연수구 관내 공동주택 현황(공동주택소재지, 관리사무소 연락처,주소 등)으로 공동주택명, 도로명주소, 관리사무소 전화번호 등의 항목을 제공합니다.
Author인천광역시 연수구
URLhttps://www.data.go.kr/data/15065509/fileData.do

Alerts

동명 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
의무관리대상 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 동명 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 총세대수 and 1 other fieldsHigh correlation
총세대수 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
관리사무실 has 9 (5.1%) missing valuesMissing
도로명 주소 has unique valuesUnique

Reproduction

Analysis started2024-03-23 06:59:07.778681
Analysis finished2024-03-23 06:59:13.277727
Duration5.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION 

Distinct177
Distinct (%)100.0%
Missing1
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean89
Minimum1
Maximum177
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-23T06:59:13.516999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.8
Q145
median89
Q3133
95-th percentile168.2
Maximum177
Range176
Interquartile range (IQR)88

Descriptive statistics

Standard deviation51.239633
Coefficient of variation (CV)0.57572621
Kurtosis-1.2
Mean89
Median Absolute Deviation (MAD)44
Skewness0
Sum15753
Variance2625.5
MonotonicityStrictly increasing
2024-03-23T06:59:13.970876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
134 1
 
0.6%
114 1
 
0.6%
115 1
 
0.6%
116 1
 
0.6%
117 1
 
0.6%
118 1
 
0.6%
119 1
 
0.6%
120 1
 
0.6%
121 1
 
0.6%
Other values (167) 167
93.8%
ValueCountFrequency (%)
1 1
0.6%
2 1
0.6%
3 1
0.6%
4 1
0.6%
5 1
0.6%
6 1
0.6%
7 1
0.6%
8 1
0.6%
9 1
0.6%
10 1
0.6%
ValueCountFrequency (%)
177 1
0.6%
176 1
0.6%
175 1
0.6%
174 1
0.6%
173 1
0.6%
172 1
0.6%
171 1
0.6%
170 1
0.6%
169 1
0.6%
168 1
0.6%

동명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)9.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
청학동
21 
송도2동
19 
동춘1동
17 
송도4동
15 
송도1동
15 
Other values (12)
91 

Length

Max length5
Median length4
Mean length4
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

1st row동춘1동
2nd row동춘1동
3rd row동춘1동
4th row동춘1동
5th row동춘1동

Common Values

ValueCountFrequency (%)
청학동 21
11.8%
송도2동 19
10.7%
동춘1동 17
9.6%
송도4동 15
 
8.4%
송도1동 15
 
8.4%
선학동 12
 
6.7%
송도3동 11
 
6.2%
옥련 1동 11
 
6.2%
연수1동 10
 
5.6%
옥련 2동 9
 
5.1%
Other values (7) 38
21.3%

Length

2024-03-23T06:59:14.513526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청학동 21
10.6%
옥련 20
10.1%
송도2동 19
9.6%
동춘1동 17
 
8.6%
송도4동 16
 
8.1%
송도1동 15
 
7.6%
선학동 12
 
6.1%
1동 11
 
5.6%
송도3동 11
 
5.6%
연수1동 10
 
5.1%
Other values (7) 46
23.2%

공동주택명
Text

Distinct177
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-23T06:59:15.182863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length16
Mean length9.7977528
Min length3

Characters and Unicode

Total characters1744
Distinct characters191
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique176 ?
Unique (%)98.9%

Sample

1st row동춘 태평1차 아파트
2nd row동춘 대림3차 아파트
3rd row롯데 아파트
4th row연수대우3차 아파트
5th row풍림연수3차 아파트
ValueCountFrequency (%)
아파트 94
28.2%
송도 8
 
2.4%
송도더샵마스터뷰 3
 
0.9%
동춘 3
 
0.9%
더샾엑스포아파트 3
 
0.9%
임대 3
 
0.9%
연수 3
 
0.9%
송도더샵퍼스트파크 3
 
0.9%
송도자이하버뷰 2
 
0.6%
14단지 2
 
0.6%
Other values (193) 209
62.8%
2024-03-23T06:59:16.355527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
178
 
10.2%
트 119
 
6.8%
파 115
 
6.6%
아 113
 
6.5%
연 51
 
2.9%
수 49
 
2.8%
송 47
 
2.7%
도 46
 
2.6%
지 41
 
2.4%
차 40
 
2.3%
Other values (181) 945
54.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1415
81.1%
Space Separator 178
 
10.2%
Decimal Number 92
 
5.3%
Uppercase Letter 23
 
1.3%
Open Punctuation 16
 
0.9%
Close Punctuation 16
 
0.9%
Other Punctuation 2
 
0.1%
Lowercase Letter 1
 
0.1%
Dash Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 119
 
8.4%
파 115
 
8.1%
아 113
 
8.0%
연 51
 
3.6%
수 49
 
3.5%
송 47
 
3.3%
도 46
 
3.3%
지 41
 
2.9%
차 40
 
2.8%
단 27
 
1.9%
Other values (154) 767
54.2%
Decimal Number
ValueCountFrequency (%)
1 33
35.9%
2 22
23.9%
3 17
18.5%
4 7
 
7.6%
5 5
 
5.4%
9 2
 
2.2%
0 2
 
2.2%
6 2
 
2.2%
7 1
 
1.1%
8 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
B 6
26.1%
L 5
21.7%
K 3
13.0%
A 2
 
8.7%
S 2
 
8.7%
E 1
 
4.3%
F 1
 
4.3%
I 1
 
4.3%
P 1
 
4.3%
R 1
 
4.3%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
178
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1415
81.1%
Common 305
 
17.5%
Latin 24
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 119
 
8.4%
파 115
 
8.1%
아 113
 
8.0%
연 51
 
3.6%
수 49
 
3.5%
송 47
 
3.3%
도 46
 
3.3%
지 41
 
2.9%
차 40
 
2.8%
단 27
 
1.9%
Other values (154) 767
54.2%
Common
ValueCountFrequency (%)
178
58.4%
1 33
 
10.8%
2 22
 
7.2%
3 17
 
5.6%
( 16
 
5.2%
) 16
 
5.2%
4 7
 
2.3%
5 5
 
1.6%
9 2
 
0.7%
0 2
 
0.7%
Other values (6) 7
 
2.3%
Latin
ValueCountFrequency (%)
B 6
25.0%
L 5
20.8%
K 3
12.5%
A 2
 
8.3%
S 2
 
8.3%
e 1
 
4.2%
E 1
 
4.2%
F 1
 
4.2%
I 1
 
4.2%
P 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1415
81.1%
ASCII 329
 
18.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
178
54.1%
1 33
 
10.0%
2 22
 
6.7%
3 17
 
5.2%
( 16
 
4.9%
) 16
 
4.9%
4 7
 
2.1%
B 6
 
1.8%
L 5
 
1.5%
5 5
 
1.5%
Other values (17) 24
 
7.3%
Hangul
ValueCountFrequency (%)
트 119
 
8.4%
파 115
 
8.1%
아 113
 
8.0%
연 51
 
3.6%
수 49
 
3.5%
송 47
 
3.3%
도 46
 
3.3%
지 41
 
2.9%
차 40
 
2.8%
단 27
 
1.9%
Other values (154) 767
54.2%

도로명 주소
Text

UNIQUE 

Distinct178
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-23T06:59:17.001332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length37
Mean length28.157303
Min length16

Characters and Unicode

Total characters5012
Distinct characters211
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique178 ?
Unique (%)100.0%

Sample

1st row인천광역시 연수구 앵고개로 206번길 10 태평1차아파트
2nd row인천광역시 연수구 먼우금로 83번길 49 대림3차아파트
3rd row인천광역시 연수구 먼우금로 161번길 12 롯데아파트
4th row인천광역시 연수구 동곡재로 117번길 22 연수3차대우아파트
5th row인천광역시 연수구 먼우금로 149 풍림연수3차아파트
ValueCountFrequency (%)
연수구 178
 
18.3%
인천광역시 170
 
17.5%
컨벤시아대로 16
 
1.6%
원인재로 16
 
1.6%
먼우금로 15
 
1.5%
선학로 8
 
0.8%
20 7
 
0.7%
해돋이로 7
 
0.7%
청학로 6
 
0.6%
새말로 6
 
0.6%
Other values (359) 543
55.9%
2024-03-23T06:59:17.992572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
805
 
16.1%
인 197
 
3.9%
시 194
 
3.9%
연 191
 
3.8%
수 189
 
3.8%
로 182
 
3.6%
구 180
 
3.6%
천 176
 
3.5%
광 171
 
3.4%
역 170
 
3.4%
Other values (201) 2557
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3443
68.7%
Space Separator 805
 
16.1%
Decimal Number 725
 
14.5%
Open Punctuation 14
 
0.3%
Close Punctuation 13
 
0.3%
Dash Punctuation 4
 
0.1%
Math Symbol 3
 
0.1%
Uppercase Letter 3
 
0.1%
Other Punctuation 1
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 197
 
5.7%
시 194
 
5.6%
연 191
 
5.5%
수 189
 
5.5%
로 182
 
5.3%
구 180
 
5.2%
천 176
 
5.1%
광 171
 
5.0%
역 170
 
4.9%
아 151
 
4.4%
Other values (181) 1642
47.7%
Decimal Number
ValueCountFrequency (%)
1 169
23.3%
2 125
17.2%
3 79
10.9%
0 63
 
8.7%
4 59
 
8.1%
7 54
 
7.4%
5 48
 
6.6%
6 45
 
6.2%
8 45
 
6.2%
9 38
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
D 1
33.3%
L 1
33.3%
B 1
33.3%
Space Separator
ValueCountFrequency (%)
805
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3443
68.7%
Common 1565
31.2%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 197
 
5.7%
시 194
 
5.6%
연 191
 
5.5%
수 189
 
5.5%
로 182
 
5.3%
구 180
 
5.2%
천 176
 
5.1%
광 171
 
5.0%
역 170
 
4.9%
아 151
 
4.4%
Other values (181) 1642
47.7%
Common
ValueCountFrequency (%)
805
51.4%
1 169
 
10.8%
2 125
 
8.0%
3 79
 
5.0%
0 63
 
4.0%
4 59
 
3.8%
7 54
 
3.5%
5 48
 
3.1%
6 45
 
2.9%
8 45
 
2.9%
Other values (6) 73
 
4.7%
Latin
ValueCountFrequency (%)
D 1
25.0%
L 1
25.0%
e 1
25.0%
B 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3443
68.7%
ASCII 1569
31.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
805
51.3%
1 169
 
10.8%
2 125
 
8.0%
3 79
 
5.0%
0 63
 
4.0%
4 59
 
3.8%
7 54
 
3.4%
5 48
 
3.1%
6 45
 
2.9%
8 45
 
2.9%
Other values (10) 77
 
4.9%
Hangul
ValueCountFrequency (%)
인 197
 
5.7%
시 194
 
5.6%
연 191
 
5.5%
수 189
 
5.5%
로 182
 
5.3%
구 180
 
5.2%
천 176
 
5.1%
광 171
 
5.0%
역 170
 
4.9%
아 151
 
4.4%
Other values (181) 1642
47.7%

관리사무실
Text

MISSING 

Distinct165
Distinct (%)97.6%
Missing9
Missing (%)5.1%
Memory size1.5 KiB
2024-03-23T06:59:18.750546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.011834
Min length12

Characters and Unicode

Total characters2030
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique162 ?
Unique (%)95.9%

Sample

1st row032-811-7071
2nd row032-813-2366
3rd row032-813-4184
4th row032-813-8550
5th row032-812-5450
ValueCountFrequency (%)
032-833-0812 3
 
1.8%
032-812-8998 2
 
1.2%
032-811-3030 2
 
1.2%
032-834-5008 1
 
0.6%
032-831-2268 1
 
0.6%
032-851-7923 1
 
0.6%
032-858-3191 1
 
0.6%
032-811-7071 1
 
0.6%
032-831-9350 1
 
0.6%
032-858-3400 1
 
0.6%
Other values (155) 155
91.7%
2024-03-23T06:59:19.941447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 338
16.7%
3 321
15.8%
2 275
13.5%
8 258
12.7%
0 242
11.9%
1 204
10.0%
5 91
 
4.5%
4 78
 
3.8%
9 75
 
3.7%
7 75
 
3.7%
Other values (2) 73
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1691
83.3%
Dash Punctuation 338
 
16.7%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 321
19.0%
2 275
16.3%
8 258
15.3%
0 242
14.3%
1 204
12.1%
5 91
 
5.4%
4 78
 
4.6%
9 75
 
4.4%
7 75
 
4.4%
6 72
 
4.3%
Dash Punctuation
ValueCountFrequency (%)
- 338
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2030
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 338
16.7%
3 321
15.8%
2 275
13.5%
8 258
12.7%
0 242
11.9%
1 204
10.0%
5 91
 
4.5%
4 78
 
3.8%
9 75
 
3.7%
7 75
 
3.7%
Other values (2) 73
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2030
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 338
16.7%
3 321
15.8%
2 275
13.5%
8 258
12.7%
0 242
11.9%
1 204
10.0%
5 91
 
4.5%
4 78
 
3.8%
9 75
 
3.7%
7 75
 
3.7%
Other values (2) 73
 
3.6%

사용검사일
Date

Distinct156
Distinct (%)87.6%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1983-07-13 00:00:00
Maximum2023-10-24 00:00:00
2024-03-23T06:59:20.741384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:21.408401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

층수
Text

Distinct104
Distinct (%)58.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-03-23T06:59:22.123108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.3539326
Min length1

Characters and Unicode

Total characters597
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique82 ?
Unique (%)46.1%

Sample

1st row6
2nd row6
3rd row5
4th row5∼6
5th row5
ValueCountFrequency (%)
15 22
 
12.4%
5 17
 
9.6%
6 13
 
7.3%
10~15 5
 
2.8%
10~20 3
 
1.7%
12∼15 3
 
1.7%
18 3
 
1.7%
15~25 2
 
1.1%
23~32 2
 
1.1%
15~42 2
 
1.1%
Other values (94) 106
59.6%
2024-03-23T06:59:23.097702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 119
19.9%
5 76
12.7%
2 76
12.7%
3 63
10.6%
~ 57
9.5%
4 45
 
7.5%
∼ 37
 
6.2%
6 35
 
5.9%
0 33
 
5.5%
8 21
 
3.5%
Other values (5) 35
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 500
83.8%
Math Symbol 94
 
15.7%
Other Letter 1
 
0.2%
Other Punctuation 1
 
0.2%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 119
23.8%
5 76
15.2%
2 76
15.2%
3 63
12.6%
4 45
 
9.0%
6 35
 
7.0%
0 33
 
6.6%
8 21
 
4.2%
9 18
 
3.6%
7 14
 
2.8%
Math Symbol
ValueCountFrequency (%)
~ 57
60.6%
∼ 37
39.4%
Other Letter
ValueCountFrequency (%)
지 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 596
99.8%
Hangul 1
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 119
20.0%
5 76
12.8%
2 76
12.8%
3 63
10.6%
~ 57
9.6%
4 45
 
7.6%
∼ 37
 
6.2%
6 35
 
5.9%
0 33
 
5.5%
8 21
 
3.5%
Other values (4) 34
 
5.7%
Hangul
ValueCountFrequency (%)
지 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 559
93.6%
Math Operators 37
 
6.2%
Hangul 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 119
21.3%
5 76
13.6%
2 76
13.6%
3 63
11.3%
~ 57
10.2%
4 45
 
8.1%
6 35
 
6.3%
0 33
 
5.9%
8 21
 
3.8%
9 18
 
3.2%
Other values (3) 16
 
2.9%
Math Operators
ValueCountFrequency (%)
∼ 37
100.0%
Hangul
ValueCountFrequency (%)
지 1
100.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)12.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.752809
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-23T06:59:23.485151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q39.75
95-th percentile18.15
Maximum41
Range40
Interquartile range (IQR)5.75

Descriptive statistics

Standard deviation5.8701214
Coefficient of variation (CV)0.75716059
Kurtosis6.384336
Mean7.752809
Median Absolute Deviation (MAD)3
Skewness1.9972598
Sum1380
Variance34.458325
MonotonicityNot monotonic
2024-03-23T06:59:23.869061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 22
12.4%
5 19
10.7%
3 19
10.7%
2 17
9.6%
9 13
 
7.3%
8 13
 
7.3%
7 12
 
6.7%
4 11
 
6.2%
11 7
 
3.9%
1 7
 
3.9%
Other values (13) 38
21.3%
ValueCountFrequency (%)
1 7
 
3.9%
2 17
9.6%
3 19
10.7%
4 11
6.2%
5 19
10.7%
6 22
12.4%
7 12
6.7%
8 13
7.3%
9 13
7.3%
10 5
 
2.8%
ValueCountFrequency (%)
41 1
 
0.6%
30 1
 
0.6%
25 2
 
1.1%
23 1
 
0.6%
20 3
1.7%
19 1
 
0.6%
18 4
2.2%
16 5
2.8%
15 4
2.2%
14 4
2.2%

총세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct165
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean679.01124
Minimum24
Maximum3100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-03-23T06:59:24.365502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile80.5
Q1322.5
median547
Q3951
95-th percentile1713.95
Maximum3100
Range3076
Interquartile range (IQR)628.5

Descriptive statistics

Standard deviation532.62826
Coefficient of variation (CV)0.78441744
Kurtosis3.7110719
Mean679.01124
Median Absolute Deviation (MAD)252
Skewness1.6597094
Sum120864
Variance283692.86
MonotonicityNot monotonic
2024-03-23T06:59:25.154017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 4
 
2.2%
390 3
 
1.7%
504 2
 
1.1%
45 2
 
1.1%
344 2
 
1.1%
1180 2
 
1.1%
420 2
 
1.1%
540 2
 
1.1%
1200 2
 
1.1%
220 2
 
1.1%
Other values (155) 155
87.1%
ValueCountFrequency (%)
24 1
0.6%
30 1
0.6%
32 1
0.6%
40 1
0.6%
45 2
1.1%
50 1
0.6%
60 1
0.6%
72 1
0.6%
82 1
0.6%
97 1
0.6%
ValueCountFrequency (%)
3100 1
0.6%
2708 1
0.6%
2610 1
0.6%
2230 1
0.6%
2100 1
0.6%
2044 1
0.6%
1834 1
0.6%
1820 1
0.6%
1776 1
0.6%
1703 1
0.6%

의무관리대상
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
○
150 
<NA>
28 

Length

Max length4
Median length1
Mean length1.4719101
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row○
2nd row○
3rd row○
4th row○
5th row○

Common Values

ValueCountFrequency (%)
○ 150
84.3%
<NA> 28
 
15.7%

Length

2024-03-23T06:59:25.608079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-23T06:59:25.992146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
○ 150
84.3%
na 28
 
15.7%

Interactions

2024-03-23T06:59:11.211962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:09.015766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:09.900064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:11.537442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:09.282812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:10.282973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:11.855659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:09.563392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-23T06:59:10.935887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-23T06:59:26.196624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명동수총세대수
연번1.0000.9520.2450.446
동명0.9521.0000.5240.648
동수0.2450.5241.0000.646
총세대수0.4460.6480.6461.000
2024-03-23T06:59:26.453738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
동명의무관리대상
동명1.0001.000
의무관리대상1.0001.000
2024-03-23T06:59:26.720843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수총세대수동명의무관리대상
연번1.000-0.0790.2080.7821.000
동수-0.0791.0000.6940.2321.000
총세대수0.2080.6941.0000.3101.000
동명0.7820.2320.3101.0001.000
의무관리대상1.0001.0001.0001.0001.000

Missing values

2024-03-23T06:59:12.314312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-23T06:59:12.797654image/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.
2024-03-23T06:59:13.107736image/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

연번동명공동주택명도로명 주소관리사무실사용검사일층수동수총세대수의무관리대상
01동춘1동동춘 태평1차 아파트인천광역시 연수구 앵고개로 206번길 10 태평1차아파트032-811-70711992-12-0165192○
12동춘1동동춘 대림3차 아파트인천광역시 연수구 먼우금로 83번길 49 대림3차아파트032-813-23661993-07-1068408○
23동춘1동롯데 아파트인천광역시 연수구 먼우금로 161번길 12 롯데아파트032-813-41841993-08-30513320○
34동춘1동연수대우3차 아파트인천광역시 연수구 동곡재로 117번길 22 연수3차대우아파트032-813-85501993-12-115∼611344○
45동춘1동풍림연수3차 아파트인천광역시 연수구 먼우금로 149 풍림연수3차아파트032-812-54501993-12-17514440○
56동춘1동연수건영 아파트인천광역시 연수구 먼우금로 83번길 12 건영아파트032-815-49911994-04-27530970○
67동춘1동동춘마을 아파트인천광역시 연수구 먼우금로 123 동춘마을아파트032-816-12631994-07-13518930○
78동춘1동연수하나2차 아파트인천광역시 연수구 앵고개로 205번길 41 하나아파트032-816-92851994-09-2969264○
89동춘1동동춘태평2차 아파트인천광역시 연수구 청능대로 38 태평2차아파트032-818-40271995-11-2215∼162198○
910동춘1동조흥 아파트인천광역시 연수구 먼우금로 141번길 62 조흥아파트032-816-93121997-02-205397<NA>
연번동명공동주택명도로명 주소관리사무실사용검사일층수동수총세대수의무관리대상
168168송도5동송도랜드마크시티센트럴더샵인천광역시 연수구 랜드마크로 68032-858-73772020-07-1646~4982230○
169169송도1동송도SK뷰센트럴인천광역시 연수구 하모니로188번길 17032-833-17432020-10-1533~363299○
170170송도4동더샵송도프라임뷰25BL인천광역시 연수구 인천타워대로231번길 117032-833-99302021-10-2916~194164○
171171송도2동송도더프라우3단지인천광역시 연수구 컨벤시아대로 42번길 20032-832-91152012-07-27203180○
172172송도4동더샵송도프라임뷰20BL인천광역시 연수구 인천타워대로231번길 97032-831-22682022-07-2929~375662○
173173송도4동더샵센트럴파크3차 E5BL인천광역시 연수구 인천타워대로180번길 11<NA>2022-12-30402351○
174174송도5동호반써밋송도인천광역시 연수구 랜드마크로 20<NA>2022-02-1440-4971820○
175175송도4동더샵송도센터니얼(F19-1)인천광역시 연수구 컨벤시아대로274번길 62032-834-50082023-04-1018~395342<NA>
176176송도4동디에트르송도시그니처뷰(B1BL)인천광역시 연수구 인천타워대로 393032-858-07312023-09-2643~484578<NA>
177177송도4동힐스테이트레이크송도3차(A14BL)인천광역시 연수구 아카데미로 442032-833-38492023-10-2435~4981100<NA>