Overview

Dataset statistics

Number of variables11
Number of observations174
Missing cells9
Missing cells (%)0.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory15.6 KiB
Average record size in memory91.8 B

Variable types

Numeric3
Categorical2
Text5
DateTime1

Dataset

Description연수구 관내 공동주택 현황(공동주택소재지, 관리사무소 연락처,주소 등)으로 공동주택명, 도로명주소, 관리사무소 전화번호 등의 항목을 제공합니다.
Author인천광역시 연수구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15065509&srcSe=7661IVAWM27C61E190

Alerts

의무관리대상 is highly overall correlated with 연번 and 3 other fieldsHigh correlation
동명 is highly overall correlated with 연번 and 1 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 (5.2%) missing valuesMissing
연번 has unique valuesUnique
도로명 주소 has unique valuesUnique

Reproduction

Analysis started2024-04-06 09:45:04.612164
Analysis finished2024-04-06 09:45:07.158485
Duration2.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.5
Minimum1
Maximum174
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-04-06T18:45:07.232085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile9.65
Q144.25
median87.5
Q3130.75
95-th percentile165.35
Maximum174
Range173
Interquartile range (IQR)86.5

Descriptive statistics

Standard deviation50.373604
Coefficient of variation (CV)0.57569833
Kurtosis-1.2
Mean87.5
Median Absolute Deviation (MAD)43.5
Skewness0
Sum15225
Variance2537.5
MonotonicityStrictly increasing
2024-04-06T18:45:07.377244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.6%
121 1
 
0.6%
113 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%
Other values (164) 164
94.3%
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 (%)
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%
167 1
0.6%
166 1
0.6%
165 1
0.6%

동명
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
청학동
21 
송도2동
19 
동춘1동
17 
송도1동
15 
송도4동
12 
Other values (12)
90 

Length

Max length5
Median length4
Mean length4.0057471
Min length3

Unique

Unique1 ?
Unique (%)0.6%

Sample

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

Common Values

ValueCountFrequency (%)
청학동 21
12.1%
송도2동 19
10.9%
동춘1동 17
9.8%
송도1동 15
 
8.6%
송도4동 12
 
6.9%
송도3동 11
 
6.3%
옥련 1동 11
 
6.3%
선학동 11
 
6.3%
연수1동 10
 
5.7%
옥련 2동 9
 
5.2%
Other values (7) 38
21.8%

Length

2024-04-06T18:45:07.524384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
청학동 21
10.8%
옥련 20
10.3%
송도2동 19
9.8%
동춘1동 17
 
8.8%
송도1동 15
 
7.7%
송도4동 13
 
6.7%
1동 11
 
5.7%
선학동 11
 
5.7%
송도3동 11
 
5.7%
연수1동 10
 
5.2%
Other values (7) 46
23.7%

지번
Text

Distinct162
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-06T18:45:07.905874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length7
Mean length4.0172414
Min length2

Characters and Unicode

Total characters699
Distinct characters26
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

Unique154 ?
Unique (%)88.5%

Sample

1st row939
2nd row940
3rd row917
4th row920-1
5th row922
ValueCountFrequency (%)
582-2 4
 
2.3%
535-2 3
 
1.7%
340 3
 
1.7%
102 2
 
1.1%
533 2
 
1.1%
532 2
 
1.1%
353 2
 
1.1%
633 2
 
1.1%
16-6 1
 
0.6%
18-10 1
 
0.6%
Other values (153) 153
87.4%
2024-04-06T18:45:08.448024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 105
15.0%
- 92
13.2%
3 87
12.4%
5 67
9.6%
9 63
9.0%
2 59
8.4%
4 53
7.6%
0 48
6.9%
6 40
 
5.7%
8 32
 
4.6%
Other values (16) 53
7.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 578
82.7%
Dash Punctuation 92
 
13.2%
Uppercase Letter 17
 
2.4%
Lowercase Letter 10
 
1.4%
Other Letter 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 105
18.2%
3 87
15.1%
5 67
11.6%
9 63
10.9%
2 59
10.2%
4 53
9.2%
0 48
8.3%
6 40
 
6.9%
8 32
 
5.5%
7 24
 
4.2%
Lowercase Letter
ValueCountFrequency (%)
a 2
20.0%
n 2
20.0%
u 1
10.0%
l 1
10.0%
p 1
10.0%
r 1
10.0%
e 1
10.0%
b 1
10.0%
Uppercase Letter
ValueCountFrequency (%)
B 6
35.3%
L 6
35.3%
J 3
17.6%
A 1
 
5.9%
F 1
 
5.9%
Dash Punctuation
ValueCountFrequency (%)
- 92
100.0%
Other Letter
ValueCountFrequency (%)
산 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 671
96.0%
Latin 27
 
3.9%
Hangul 1
 
0.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
B 6
22.2%
L 6
22.2%
J 3
11.1%
a 2
 
7.4%
n 2
 
7.4%
u 1
 
3.7%
l 1
 
3.7%
A 1
 
3.7%
p 1
 
3.7%
r 1
 
3.7%
Other values (3) 3
11.1%
Common
ValueCountFrequency (%)
1 105
15.6%
- 92
13.7%
3 87
13.0%
5 67
10.0%
9 63
9.4%
2 59
8.8%
4 53
7.9%
0 48
7.2%
6 40
 
6.0%
8 32
 
4.8%
Other values (2) 25
 
3.7%
Hangul
ValueCountFrequency (%)
산 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 698
99.9%
Hangul 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 105
15.0%
- 92
13.2%
3 87
12.5%
5 67
9.6%
9 63
9.0%
2 59
8.5%
4 53
7.6%
0 48
6.9%
6 40
 
5.7%
8 32
 
4.6%
Other values (15) 52
7.4%
Hangul
ValueCountFrequency (%)
산 1
100.0%

공동주택명
Text

Distinct173
Distinct (%)99.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-06T18:45:08.683682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length9.6724138
Min length3

Characters and Unicode

Total characters1683
Distinct characters186
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique172 ?
Unique (%)98.9%

Sample

1st row동춘 태평1차 아파트
2nd row동춘 대림3차 아파트
3rd row롯데 아파트
4th row연수대우3차 아파트
5th row풍림연수3차 아파트
ValueCountFrequency (%)
아파트 94
28.7%
송도 8
 
2.4%
더샾엑스포아파트 3
 
0.9%
송도더샵마스터뷰 3
 
0.9%
동춘 3
 
0.9%
임대 3
 
0.9%
송도더샵퍼스트파크 3
 
0.9%
연수 3
 
0.9%
아파트(영구임대 2
 
0.6%
연수시영1차 2
 
0.6%
Other values (188) 204
62.2%
2024-04-06T18:45:09.092950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
177
 
10.5%
트 117
 
7.0%
파 115
 
6.8%
아 113
 
6.7%
연 50
 
3.0%
수 48
 
2.9%
송 44
 
2.6%
도 43
 
2.6%
지 41
 
2.4%
차 39
 
2.3%
Other values (176) 896
53.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1376
81.8%
Space Separator 177
 
10.5%
Decimal Number 85
 
5.1%
Uppercase Letter 16
 
1.0%
Close Punctuation 13
 
0.8%
Open Punctuation 13
 
0.8%
Other Punctuation 2
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
트 117
 
8.5%
파 115
 
8.4%
아 113
 
8.2%
연 50
 
3.6%
수 48
 
3.5%
송 44
 
3.2%
도 43
 
3.1%
지 41
 
3.0%
차 39
 
2.8%
단 27
 
2.0%
Other values (151) 739
53.7%
Decimal Number
ValueCountFrequency (%)
1 29
34.1%
2 22
25.9%
3 16
18.8%
4 6
 
7.1%
5 5
 
5.9%
0 2
 
2.4%
6 2
 
2.4%
7 1
 
1.2%
8 1
 
1.2%
9 1
 
1.2%
Uppercase Letter
ValueCountFrequency (%)
K 3
18.8%
L 3
18.8%
B 3
18.8%
S 2
12.5%
E 1
 
6.2%
A 1
 
6.2%
I 1
 
6.2%
P 1
 
6.2%
R 1
 
6.2%
Other Punctuation
ValueCountFrequency (%)
' 1
50.0%
& 1
50.0%
Space Separator
ValueCountFrequency (%)
177
100.0%
Close Punctuation
ValueCountFrequency (%)
) 13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 13
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1376
81.8%
Common 290
 
17.2%
Latin 17
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
트 117
 
8.5%
파 115
 
8.4%
아 113
 
8.2%
연 50
 
3.6%
수 48
 
3.5%
송 44
 
3.2%
도 43
 
3.1%
지 41
 
3.0%
차 39
 
2.8%
단 27
 
2.0%
Other values (151) 739
53.7%
Common
ValueCountFrequency (%)
177
61.0%
1 29
 
10.0%
2 22
 
7.6%
3 16
 
5.5%
) 13
 
4.5%
( 13
 
4.5%
4 6
 
2.1%
5 5
 
1.7%
0 2
 
0.7%
6 2
 
0.7%
Other values (5) 5
 
1.7%
Latin
ValueCountFrequency (%)
K 3
17.6%
L 3
17.6%
B 3
17.6%
S 2
11.8%
e 1
 
5.9%
E 1
 
5.9%
A 1
 
5.9%
I 1
 
5.9%
P 1
 
5.9%
R 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1376
81.8%
ASCII 307
 
18.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
177
57.7%
1 29
 
9.4%
2 22
 
7.2%
3 16
 
5.2%
) 13
 
4.2%
( 13
 
4.2%
4 6
 
2.0%
5 5
 
1.6%
K 3
 
1.0%
L 3
 
1.0%
Other values (15) 20
 
6.5%
Hangul
ValueCountFrequency (%)
트 117
 
8.5%
파 115
 
8.4%
아 113
 
8.2%
연 50
 
3.6%
수 48
 
3.5%
송 44
 
3.2%
도 43
 
3.1%
지 41
 
3.0%
차 39
 
2.8%
단 27
 
2.0%
Other values (151) 739
53.7%

도로명 주소
Text

UNIQUE 

Distinct174
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-06T18:45:09.415290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length44
Median length35
Mean length25.488506
Min length14

Characters and Unicode

Total characters4435
Distinct characters210
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

Unique174 ?
Unique (%)100.0%

Sample

1st row인천 연수구 앵고개로 206번길 10 태평1차아파트
2nd row인천 연수구 먼우금로 83번길 49 대림3차아파트
3rd row인천 연수구 먼우금로 161번길 12 롯데아파트
4th row인천 연수구 동곡재로 117번길 22 연수3차대우아파트
5th row인천 연수구 먼우금로 149 풍림연수3차아파트
ValueCountFrequency (%)
연수구 174
 
18.2%
인천 166
 
17.4%
컨벤시아대로 16
 
1.7%
원인재로 16
 
1.7%
먼우금로 15
 
1.6%
선학로 8
 
0.8%
20 7
 
0.7%
해돋이로 7
 
0.7%
랜드마크로 6
 
0.6%
청학로 6
 
0.6%
Other values (356) 535
56.0%
2024-04-06T18:45:09.950738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
793
 
17.9%
인 192
 
4.3%
연 187
 
4.2%
수 185
 
4.2%
로 178
 
4.0%
구 176
 
4.0%
천 171
 
3.9%
1 168
 
3.8%
아 149
 
3.4%
트 130
 
2.9%
Other values (200) 2106
47.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2891
65.2%
Space Separator 793
 
17.9%
Decimal Number 712
 
16.1%
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%
Lowercase Letter 1
 
< 0.1%
Other Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 192
 
6.6%
연 187
 
6.5%
수 185
 
6.4%
로 178
 
6.2%
구 176
 
6.1%
천 171
 
5.9%
아 149
 
5.2%
트 130
 
4.5%
파 120
 
4.2%
번 92
 
3.2%
Other values (180) 1311
45.3%
Decimal Number
ValueCountFrequency (%)
1 168
23.6%
2 122
17.1%
3 77
10.8%
0 63
 
8.8%
4 56
 
7.9%
7 53
 
7.4%
5 48
 
6.7%
6 44
 
6.2%
8 44
 
6.2%
9 37
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
B 1
33.3%
D 1
33.3%
L 1
33.3%
Space Separator
ValueCountFrequency (%)
793
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%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2891
65.2%
Common 1540
34.7%
Latin 4
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 192
 
6.6%
연 187
 
6.5%
수 185
 
6.4%
로 178
 
6.2%
구 176
 
6.1%
천 171
 
5.9%
아 149
 
5.2%
트 130
 
4.5%
파 120
 
4.2%
번 92
 
3.2%
Other values (180) 1311
45.3%
Common
ValueCountFrequency (%)
793
51.5%
1 168
 
10.9%
2 122
 
7.9%
3 77
 
5.0%
0 63
 
4.1%
4 56
 
3.6%
7 53
 
3.4%
5 48
 
3.1%
6 44
 
2.9%
8 44
 
2.9%
Other values (6) 72
 
4.7%
Latin
ValueCountFrequency (%)
B 1
25.0%
D 1
25.0%
L 1
25.0%
e 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2891
65.2%
ASCII 1544
34.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
793
51.4%
1 168
 
10.9%
2 122
 
7.9%
3 77
 
5.0%
0 63
 
4.1%
4 56
 
3.6%
7 53
 
3.4%
5 48
 
3.1%
6 44
 
2.8%
8 44
 
2.8%
Other values (10) 76
 
4.9%
Hangul
ValueCountFrequency (%)
인 192
 
6.6%
연 187
 
6.5%
수 185
 
6.4%
로 178
 
6.2%
구 176
 
6.1%
천 171
 
5.9%
아 149
 
5.2%
트 130
 
4.5%
파 120
 
4.2%
번 92
 
3.2%
Other values (180) 1311
45.3%

관리사무실
Text

MISSING 

Distinct161
Distinct (%)97.6%
Missing9
Missing (%)5.2%
Memory size1.5 KiB
2024-04-06T18:45:10.278913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length12.012121
Min length12

Characters and Unicode

Total characters1982
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

Unique158 ?
Unique (%)95.8%

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%
032-833-9930 1
 
0.6%
032-851-0644 1
 
0.6%
032-858-2235 1
 
0.6%
032-811-7071 1
 
0.6%
032-851-0106 1
 
0.6%
032-858-1800 1
 
0.6%
032-851-7923 1
 
0.6%
Other values (151) 151
91.5%
2024-04-06T18:45:10.688548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 330
16.6%
3 311
15.7%
2 268
13.5%
8 251
12.7%
0 235
11.9%
1 202
10.2%
5 88
 
4.4%
4 76
 
3.8%
9 74
 
3.7%
7 74
 
3.7%
Other values (2) 73
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1651
83.3%
Dash Punctuation 330
 
16.6%
Math Symbol 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 311
18.8%
2 268
16.2%
8 251
15.2%
0 235
14.2%
1 202
12.2%
5 88
 
5.3%
4 76
 
4.6%
9 74
 
4.5%
7 74
 
4.5%
6 72
 
4.4%
Dash Punctuation
ValueCountFrequency (%)
- 330
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1982
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 330
16.6%
3 311
15.7%
2 268
13.5%
8 251
12.7%
0 235
11.9%
1 202
10.2%
5 88
 
4.4%
4 76
 
3.8%
9 74
 
3.7%
7 74
 
3.7%
Other values (2) 73
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1982
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 330
16.6%
3 311
15.7%
2 268
13.5%
8 251
12.7%
0 235
11.9%
1 202
10.2%
5 88
 
4.4%
4 76
 
3.8%
9 74
 
3.7%
7 74
 
3.7%
Other values (2) 73
 
3.7%

사용검사일
Date

Distinct152
Distinct (%)87.4%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
Minimum1983-07-13 00:00:00
Maximum2022-12-30 00:00:00
2024-04-06T18:45:10.874019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:11.102356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

층수
Text

Distinct101
Distinct (%)58.0%
Missing0
Missing (%)0.0%
Memory size1.5 KiB
2024-04-06T18:45:11.364328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.3333333
Min length1

Characters and Unicode

Total characters580
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

Unique80 ?
Unique (%)46.0%

Sample

1st row6
2nd row6
3rd row5
4th row5∼6
5th row5
ValueCountFrequency (%)
15 22
 
12.6%
5 17
 
9.8%
6 13
 
7.5%
10~15 5
 
2.9%
12∼15 3
 
1.7%
18 3
 
1.7%
10~20 3
 
1.7%
18∼25 2
 
1.1%
43 2
 
1.1%
29~34 2
 
1.1%
Other values (91) 102
58.6%
2024-04-06T18:45:12.075079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 118
20.3%
5 75
12.9%
2 75
12.9%
3 59
10.2%
~ 54
9.3%
4 42
 
7.2%
∼ 37
 
6.4%
6 35
 
6.0%
0 33
 
5.7%
8 19
 
3.3%
Other values (5) 33
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 486
83.8%
Math Symbol 91
 
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 (%)
1 118
24.3%
5 75
15.4%
2 75
15.4%
3 59
12.1%
4 42
 
8.6%
6 35
 
7.2%
0 33
 
6.8%
8 19
 
3.9%
9 16
 
3.3%
7 14
 
2.9%
Math Symbol
ValueCountFrequency (%)
~ 54
59.3%
∼ 37
40.7%
Other Letter
ValueCountFrequency (%)
지 1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 579
99.8%
Hangul 1
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 118
20.4%
5 75
13.0%
2 75
13.0%
3 59
10.2%
~ 54
9.3%
4 42
 
7.3%
∼ 37
 
6.4%
6 35
 
6.0%
0 33
 
5.7%
8 19
 
3.3%
Other values (4) 32
 
5.5%
Hangul
ValueCountFrequency (%)
지 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 542
93.4%
Math Operators 37
 
6.4%
Hangul 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 118
21.8%
5 75
13.8%
2 75
13.8%
3 59
10.9%
~ 54
10.0%
4 42
 
7.7%
6 35
 
6.5%
0 33
 
6.1%
8 19
 
3.5%
9 16
 
3.0%
Other values (3) 16
 
3.0%
Math Operators
ValueCountFrequency (%)
∼ 37
100.0%
Hangul
ValueCountFrequency (%)
지 1
100.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)13.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.7873563
Minimum1
Maximum41
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-04-06T18:45:12.248659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median6
Q310
95-th percentile18.35
Maximum41
Range40
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.9268821
Coefficient of variation (CV)0.7610904
Kurtosis6.1955795
Mean7.7873563
Median Absolute Deviation (MAD)3
Skewness1.9701886
Sum1355
Variance35.127932
MonotonicityNot monotonic
2024-04-06T18:45:12.401228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
6 22
12.6%
3 19
10.9%
5 18
10.3%
2 17
9.8%
9 13
 
7.5%
7 12
 
6.9%
8 11
 
6.3%
4 10
 
5.7%
11 7
 
4.0%
1 7
 
4.0%
Other values (13) 38
21.8%
ValueCountFrequency (%)
1 7
 
4.0%
2 17
9.8%
3 19
10.9%
4 10
5.7%
5 18
10.3%
6 22
12.6%
7 12
6.9%
8 11
6.3%
9 13
7.5%
10 5
 
2.9%
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
2.3%
16 5
2.9%
15 4
2.3%
14 4
2.3%

총세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct161
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean678.60345
Minimum24
Maximum3100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.7 KiB
2024-04-06T18:45:12.558779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24
5-th percentile78.5
Q1314
median543
Q3951
95-th percentile1728.55
Maximum3100
Range3076
Interquartile range (IQR)637

Descriptive statistics

Standard deviation537.09158
Coefficient of variation (CV)0.79146603
Kurtosis3.6462821
Mean678.60345
Median Absolute Deviation (MAD)253
Skewness1.6574797
Sum118077
Variance288467.36
MonotonicityNot monotonic
2024-04-06T18:45:12.700130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
300 4
 
2.3%
390 3
 
1.7%
220 2
 
1.1%
420 2
 
1.1%
540 2
 
1.1%
504 2
 
1.1%
1200 2
 
1.1%
1180 2
 
1.1%
344 2
 
1.1%
45 2
 
1.1%
Other values (151) 151
86.8%
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>
24 

Length

Max length4
Median length1
Mean length1.4137931
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
○ 150
86.2%
<NA> 24
 
13.8%

Length

2024-04-06T18:45:12.836996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T18:45:13.050923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
○ 150
86.2%
na 24
 
13.8%

Interactions

2024-04-06T18:45:06.482402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:05.749698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:06.093892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:06.601157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:05.843042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:06.256904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:06.761159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:05.975814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T18:45:06.369742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T18:45:13.177534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동명동수총세대수
연번1.0000.9460.2870.564
동명0.9461.0000.5210.660
동수0.2870.5211.0000.648
총세대수0.5640.6600.6481.000
2024-04-06T18:45:13.315833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
의무관리대상동명
의무관리대상1.0001.000
동명1.0001.000
2024-04-06T18:45:13.425456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수총세대수동명의무관리대상
연번1.000-0.0740.2080.7561.000
동수-0.0741.0000.6960.2301.000
총세대수0.2080.6961.0000.3191.000
동명0.7560.2300.3191.0001.000
의무관리대상1.0001.0001.0001.0001.000

Missing values

2024-04-06T18:45:06.924642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T18:45:07.089165image/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

연번동명지번공동주택명도로명 주소관리사무실사용검사일층수동수총세대수의무관리대상
01동춘1동939동춘 태평1차 아파트인천 연수구 앵고개로 206번길 10 태평1차아파트032-811-70711992-12-0165192○
12동춘1동940동춘 대림3차 아파트인천 연수구 먼우금로 83번길 49 대림3차아파트032-813-23661993-07-1068408○
23동춘1동917롯데 아파트인천 연수구 먼우금로 161번길 12 롯데아파트032-813-41841993-08-30513320○
34동춘1동920-1연수대우3차 아파트인천 연수구 동곡재로 117번길 22 연수3차대우아파트032-813-85501993-12-115∼611344○
45동춘1동922풍림연수3차 아파트인천 연수구 먼우금로 149 풍림연수3차아파트032-812-54501993-12-17514440○
56동춘1동938연수건영 아파트인천 연수구 먼우금로 83번길 12 건영아파트032-815-49911994-04-27530970○
67동춘1동921동춘마을 아파트인천 연수구 먼우금로 123 동춘마을아파트032-816-12631994-07-13518930○
78동춘1동919연수하나2차 아파트인천 연수구 앵고개로 205번길 41 하나아파트032-816-92851994-09-2969264○
89동춘1동961동춘태평2차 아파트인천 연수구 청능대로 38 태평2차아파트032-818-40271995-11-2215∼162198○
910동춘1동918조흥 아파트인천 연수구 먼우금로 141번길 62 조흥아파트032-816-93121997-02-205397<NA>
연번동명지번공동주택명도로명 주소관리사무실사용검사일층수동수총세대수의무관리대상
164165송도4동397-8힐스테이트 레이크 송도2차인천 연수구 아카데미로 446032-858-99512020-01-3117~439889○
165166송도5동310-1송도오션파크베르디움인천 연수구 랜드마크로 110032-858-88862020-02-1431~49101530○
166167송도5동308-1송도더샵마리나베이인천 연수구 랜드마크로 160032-851-80612020-07-138~38253100○
167168송도5동311송도랜드마크시티센트럴더샵인천 연수구 랜드마크로 68032-858-73772020-07-1646~4982230○
168169송도1동10-30송도SK뷰센트럴인천 연수구 하모니로188번길 17032-833-17432020-10-1533~363299○
169170송도4동114더샵송도프라임뷰25BL인천 연수구 인천타워대로231번길 117032-833-99302021-10-2916~194164○
170171송도2동15-10송도더프라우3단지인천 연수구 컨벤시아대로 42번길 20032-832-91152012-07-27203180○
171172송도4동109더샵송도프라임뷰20BL인천 연수구 인천타워대로231번길 97032-831-22682022-07-2929~375662○
172173송도4동92더샵센트럴파크3차 E5BL인천 연수구 인천타워대로180번길 11<NA>2022-12-30402351○
173174송도5동312-1 312-4호반써밋송도인천 연수구 랜드마크로 20<NA>2022-02-1440-4971820○