Overview

Dataset statistics

Number of variables9
Number of observations317
Missing cells5
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory23.3 KiB
Average record size in memory75.4 B

Variable types

Numeric3
Text4
DateTime2

Dataset

Description인천광역시 서구관내에 위치한 공동주택 현황(아파트명, 소재지, 층수, 동수, 세대수, 사용검사일 등)입니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3078100&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
번호 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
동수(주택) is highly overall correlated with 번호 and 1 other fieldsHigh correlation
층수 has 4 (1.3%) missing valuesMissing
번호 has unique valuesUnique

Reproduction

Analysis started2024-03-18 02:11:57.428809
Analysis finished2024-03-18 02:11:59.842192
Duration2.41 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct317
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean159
Minimum1
Maximum317
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-18T11:11:59.902502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile16.8
Q180
median159
Q3238
95-th percentile301.2
Maximum317
Range316
Interquartile range (IQR)158

Descriptive statistics

Standard deviation91.654242
Coefficient of variation (CV)0.57644177
Kurtosis-1.2
Mean159
Median Absolute Deviation (MAD)79
Skewness0
Sum50403
Variance8400.5
MonotonicityStrictly increasing
2024-03-18T11:12:00.017001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
210 1
 
0.3%
217 1
 
0.3%
216 1
 
0.3%
215 1
 
0.3%
214 1
 
0.3%
213 1
 
0.3%
212 1
 
0.3%
211 1
 
0.3%
209 1
 
0.3%
Other values (307) 307
96.8%
ValueCountFrequency (%)
1 1
0.3%
2 1
0.3%
3 1
0.3%
4 1
0.3%
5 1
0.3%
6 1
0.3%
7 1
0.3%
8 1
0.3%
9 1
0.3%
10 1
0.3%
ValueCountFrequency (%)
317 1
0.3%
316 1
0.3%
315 1
0.3%
314 1
0.3%
313 1
0.3%
312 1
0.3%
311 1
0.3%
310 1
0.3%
309 1
0.3%
308 1
0.3%

아파트명
Text

Distinct299
Distinct (%)94.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-03-18T11:12:00.232340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length29
Median length19
Mean length9.0347003
Min length4

Characters and Unicode

Total characters2864
Distinct characters276
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique286 ?
Unique (%)90.2%

Sample

1st row덕산아파트
2nd row진주1단지아파트
3rd row석남1차아파트
4th row석남2차아파트
5th row황해아파트
ValueCountFrequency (%)
검단신도시 10
 
2.3%
더 6
 
1.4%
청라 6
 
1.4%
동진아파트 5
 
1.1%
우미린 5
 
1.1%
2단지 5
 
1.1%
1단지 5
 
1.1%
아파트 4
 
0.9%
루원시티 4
 
0.9%
푸르지오 4
 
0.9%
Other values (339) 384
87.7%
2024-03-18T11:12:00.558007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 239
 
8.3%
트 238
 
8.3%
파 232
 
8.1%
122
 
4.3%
차 65
 
2.3%
단 54
 
1.9%
이 51
 
1.8%
2 49
 
1.7%
검 44
 
1.5%
지 43
 
1.5%
Other values (266) 1727
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2514
87.8%
Space Separator 122
 
4.3%
Decimal Number 119
 
4.2%
Uppercase Letter 51
 
1.8%
Open Punctuation 17
 
0.6%
Close Punctuation 17
 
0.6%
Lowercase Letter 17
 
0.6%
Dash Punctuation 3
 
0.1%
Other Punctuation 3
 
0.1%
Final Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 239
 
9.5%
트 238
 
9.5%
파 232
 
9.2%
차 65
 
2.6%
단 54
 
2.1%
이 51
 
2.0%
검 44
 
1.8%
지 43
 
1.7%
스 41
 
1.6%
대 41
 
1.6%
Other values (219) 1466
58.3%
Uppercase Letter
ValueCountFrequency (%)
S 7
13.7%
A 6
11.8%
K 5
9.8%
L 5
9.8%
E 4
 
7.8%
I 3
 
5.9%
H 3
 
5.9%
B 3
 
5.9%
G 2
 
3.9%
P 2
 
3.9%
Other values (9) 11
21.6%
Decimal Number
ValueCountFrequency (%)
2 49
41.2%
1 36
30.3%
3 17
 
14.3%
0 5
 
4.2%
4 4
 
3.4%
6 3
 
2.5%
5 2
 
1.7%
8 1
 
0.8%
7 1
 
0.8%
9 1
 
0.8%
Lowercase Letter
ValueCountFrequency (%)
e 4
23.5%
a 2
11.8%
r 2
11.8%
k 2
11.8%
c 2
11.8%
d 1
 
5.9%
s 1
 
5.9%
w 1
 
5.9%
i 1
 
5.9%
v 1
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 1
33.3%
. 1
33.3%
' 1
33.3%
Space Separator
ValueCountFrequency (%)
122
100.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2514
87.8%
Common 282
 
9.8%
Latin 68
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 239
 
9.5%
트 238
 
9.5%
파 232
 
9.2%
차 65
 
2.6%
단 54
 
2.1%
이 51
 
2.0%
검 44
 
1.8%
지 43
 
1.7%
스 41
 
1.6%
대 41
 
1.6%
Other values (219) 1466
58.3%
Latin
ValueCountFrequency (%)
S 7
 
10.3%
A 6
 
8.8%
K 5
 
7.4%
L 5
 
7.4%
e 4
 
5.9%
E 4
 
5.9%
I 3
 
4.4%
H 3
 
4.4%
B 3
 
4.4%
a 2
 
2.9%
Other values (19) 26
38.2%
Common
ValueCountFrequency (%)
122
43.3%
2 49
17.4%
1 36
 
12.8%
( 17
 
6.0%
3 17
 
6.0%
) 17
 
6.0%
0 5
 
1.8%
4 4
 
1.4%
- 3
 
1.1%
6 3
 
1.1%
Other values (8) 9
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2514
87.8%
ASCII 349
 
12.2%
Punctuation 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 239
 
9.5%
트 238
 
9.5%
파 232
 
9.2%
차 65
 
2.6%
단 54
 
2.1%
이 51
 
2.0%
검 44
 
1.8%
지 43
 
1.7%
스 41
 
1.6%
대 41
 
1.6%
Other values (219) 1466
58.3%
ASCII
ValueCountFrequency (%)
122
35.0%
2 49
14.0%
1 36
 
10.3%
( 17
 
4.9%
3 17
 
4.9%
) 17
 
4.9%
S 7
 
2.0%
A 6
 
1.7%
K 5
 
1.4%
L 5
 
1.4%
Other values (36) 68
19.5%
Punctuation
ValueCountFrequency (%)
’ 1
100.0%

소재지(도로명)
Text

Distinct316
Distinct (%)99.7%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-03-18T11:12:00.802588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length42
Mean length32.082019
Min length12

Characters and Unicode

Total characters10170
Distinct characters260
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique315 ?
Unique (%)99.4%

Sample

1st row인천광역시 서구 건지로284번길 93 (가좌동, 덕산아파트)
2nd row인천광역시 서구 장고개로337번길 32 (가좌동, 진주아파트)
3rd row인천광역시 서구 신석로112번길 35 (석남동, 석남아파트)
4th row인천광역시 서구 길주로 104 (석남동, 석남아파트)
5th row인천광역시 서구 염곡로 318 (석남동, 황해아파트)
ValueCountFrequency (%)
서구 316
 
17.2%
인천광역시 298
 
16.2%
가좌동 43
 
2.3%
석남동 39
 
2.1%
마전동 35
 
1.9%
경서동 25
 
1.4%
가정동 23
 
1.2%
인천 21
 
1.1%
연희동 21
 
1.1%
당하동 15
 
0.8%
Other values (573) 1004
54.6%
2024-03-18T11:12:01.194844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1935
 
19.0%
서 377
 
3.7%
로 333
 
3.3%
인 330
 
3.2%
천 325
 
3.2%
동 320
 
3.1%
구 318
 
3.1%
광 313
 
3.1%
시 307
 
3.0%
역 302
 
3.0%
Other values (250) 5310
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6182
60.8%
Space Separator 1935
 
19.0%
Decimal Number 1189
 
11.7%
Open Punctuation 283
 
2.8%
Close Punctuation 282
 
2.8%
Other Punctuation 258
 
2.5%
Dash Punctuation 21
 
0.2%
Uppercase Letter 15
 
0.1%
Lowercase Letter 3
 
< 0.1%
Letter Number 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 377
 
6.1%
로 333
 
5.4%
인 330
 
5.3%
천 325
 
5.3%
동 320
 
5.2%
구 318
 
5.1%
광 313
 
5.1%
시 307
 
5.0%
역 302
 
4.9%
아 209
 
3.4%
Other values (220) 3048
49.3%
Uppercase Letter
ValueCountFrequency (%)
K 3
20.0%
C 2
13.3%
S 2
13.3%
A 1
 
6.7%
I 1
 
6.7%
V 1
 
6.7%
H 1
 
6.7%
E 1
 
6.7%
L 1
 
6.7%
W 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
1 228
19.2%
2 177
14.9%
3 158
13.3%
4 117
9.8%
5 99
8.3%
7 91
 
7.7%
6 86
 
7.2%
9 80
 
6.7%
8 77
 
6.5%
0 76
 
6.4%
Other Punctuation
ValueCountFrequency (%)
, 256
99.2%
. 2
 
0.8%
Letter Number
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%
Space Separator
ValueCountFrequency (%)
1935
100.0%
Open Punctuation
ValueCountFrequency (%)
( 283
100.0%
Close Punctuation
ValueCountFrequency (%)
) 282
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 21
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6182
60.8%
Common 3968
39.0%
Latin 20
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 377
 
6.1%
로 333
 
5.4%
인 330
 
5.3%
천 325
 
5.3%
동 320
 
5.2%
구 318
 
5.1%
광 313
 
5.1%
시 307
 
5.0%
역 302
 
4.9%
아 209
 
3.4%
Other values (220) 3048
49.3%
Common
ValueCountFrequency (%)
1935
48.8%
( 283
 
7.1%
) 282
 
7.1%
, 256
 
6.5%
1 228
 
5.7%
2 177
 
4.5%
3 158
 
4.0%
4 117
 
2.9%
5 99
 
2.5%
7 91
 
2.3%
Other values (6) 342
 
8.6%
Latin
ValueCountFrequency (%)
K 3
15.0%
e 3
15.0%
C 2
10.0%
S 2
10.0%
A 1
 
5.0%
I 1
 
5.0%
V 1
 
5.0%
H 1
 
5.0%
E 1
 
5.0%
L 1
 
5.0%
Other values (4) 4
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6182
60.8%
ASCII 3986
39.2%
Number Forms 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1935
48.5%
( 283
 
7.1%
) 282
 
7.1%
, 256
 
6.4%
1 228
 
5.7%
2 177
 
4.4%
3 158
 
4.0%
4 117
 
2.9%
5 99
 
2.5%
7 91
 
2.3%
Other values (18) 360
 
9.0%
Hangul
ValueCountFrequency (%)
서 377
 
6.1%
로 333
 
5.4%
인 330
 
5.3%
천 325
 
5.3%
동 320
 
5.2%
구 318
 
5.1%
광 313
 
5.1%
시 307
 
5.0%
역 302
 
4.9%
아 209
 
3.4%
Other values (220) 3048
49.3%
Number Forms
ValueCountFrequency (%)
Ⅰ 1
50.0%
Ⅱ 1
50.0%

층수
Text

MISSING 

Distinct101
Distinct (%)32.3%
Missing4
Missing (%)1.3%
Memory size2.6 KiB
2024-03-18T11:12:01.486203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length3.456869
Min length1

Characters and Unicode

Total characters1082
Distinct characters18
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

Unique64 ?
Unique (%)20.4%

Sample

1st row5
2nd row14
3rd row5
4th row5
5th row5
ValueCountFrequency (%)
5 39
 
12.4%
6 36
 
11.5%
15 35
 
11.1%
20 12
 
3.8%
지하2층~지상25층 11
 
3.5%
18 9
 
2.9%
25 7
 
2.2%
10 7
 
2.2%
지하2층~지상29층 7
 
2.2%
14~15 6
 
1.9%
Other values (92) 145
46.2%
2024-03-18T11:12:01.805051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 184
17.0%
2 148
13.7%
5 142
13.1%
~ 102
9.4%
층 72
 
6.7%
지 70
 
6.5%
0 59
 
5.5%
6 50
 
4.6%
4 43
 
4.0%
3 37
 
3.4%
Other values (8) 175
16.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 746
68.9%
Other Letter 212
 
19.6%
Math Symbol 122
 
11.3%
Dash Punctuation 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 184
24.7%
2 148
19.8%
5 142
19.0%
0 59
 
7.9%
6 50
 
6.7%
4 43
 
5.8%
3 37
 
5.0%
8 35
 
4.7%
9 33
 
4.4%
7 15
 
2.0%
Other Letter
ValueCountFrequency (%)
층 72
34.0%
지 70
33.0%
하 36
17.0%
상 34
16.0%
Math Symbol
ValueCountFrequency (%)
~ 102
83.6%
～ 20
 
16.4%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 870
80.4%
Hangul 212
 
19.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 184
21.1%
2 148
17.0%
5 142
16.3%
~ 102
11.7%
0 59
 
6.8%
6 50
 
5.7%
4 43
 
4.9%
3 37
 
4.3%
8 35
 
4.0%
9 33
 
3.8%
Other values (4) 37
 
4.3%
Hangul
ValueCountFrequency (%)
층 72
34.0%
지 70
33.0%
하 36
17.0%
상 34
16.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 850
78.6%
Hangul 212
 
19.6%
None 20
 
1.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 184
21.6%
2 148
17.4%
5 142
16.7%
~ 102
12.0%
0 59
 
6.9%
6 50
 
5.9%
4 43
 
5.1%
3 37
 
4.4%
8 35
 
4.1%
9 33
 
3.9%
Other values (3) 17
 
2.0%
Hangul
ValueCountFrequency (%)
층 72
34.0%
지 70
33.0%
하 36
17.0%
상 34
16.0%
None
ValueCountFrequency (%)
～ 20
100.0%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.1041009
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-18T11:12:01.911122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median5
Q39
95-th percentile19
Maximum39
Range38
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.9479719
Coefficient of variation (CV)0.83725892
Kurtosis5.4632143
Mean7.1041009
Median Absolute Deviation (MAD)3
Skewness1.9369035
Sum2252
Variance35.378369
MonotonicityNot monotonic
2024-03-18T11:12:02.038881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5 42
13.2%
1 40
12.6%
4 28
8.8%
7 28
8.8%
3 26
8.2%
2 25
7.9%
6 20
 
6.3%
8 19
 
6.0%
11 16
 
5.0%
9 12
 
3.8%
Other values (19) 61
19.2%
ValueCountFrequency (%)
1 40
12.6%
2 25
7.9%
3 26
8.2%
4 28
8.8%
5 42
13.2%
6 20
6.3%
7 28
8.8%
8 19
6.0%
9 12
 
3.8%
10 11
 
3.5%
ValueCountFrequency (%)
39 1
0.3%
37 1
0.3%
33 1
0.3%
27 1
0.3%
26 1
0.3%
24 1
0.3%
23 2
0.6%
22 1
0.3%
21 2
0.6%
20 2
0.6%

동수(주택)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)7.9%
Missing1
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean6.1392405
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.9 KiB
2024-03-18T11:12:02.152107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5
Q38
95-th percentile16
Maximum36
Range35
Interquartile range (IQR)6

Descriptive statistics

Standard deviation5.1485468
Coefficient of variation (CV)0.83862928
Kurtosis6.8031631
Mean6.1392405
Median Absolute Deviation (MAD)3
Skewness2.0609109
Sum1940
Variance26.507535
MonotonicityNot monotonic
2024-03-18T11:12:02.248167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1 44
13.9%
4 41
12.9%
2 38
12.0%
6 33
10.4%
5 32
10.1%
3 23
7.3%
7 18
 
5.7%
8 15
 
4.7%
10 15
 
4.7%
9 9
 
2.8%
Other values (15) 48
15.1%
ValueCountFrequency (%)
1 44
13.9%
2 38
12.0%
3 23
7.3%
4 41
12.9%
5 32
10.1%
6 33
10.4%
7 18
5.7%
8 15
 
4.7%
9 9
 
2.8%
10 15
 
4.7%
ValueCountFrequency (%)
36 1
 
0.3%
35 1
 
0.3%
25 1
 
0.3%
23 1
 
0.3%
22 1
 
0.3%
21 1
 
0.3%
20 1
 
0.3%
19 3
0.9%
17 3
0.9%
16 5
1.6%

세대수
Text

Distinct250
Distinct (%)78.9%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
2024-03-18T11:12:02.581844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.1198738
Min length2

Characters and Unicode

Total characters989
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

Unique204 ?
Unique (%)64.4%

Sample

1st row120
2nd row686
3rd row30
4th row30
5th row90
ValueCountFrequency (%)
299 5
 
1.6%
252 5
 
1.6%
192 5
 
1.6%
132 4
 
1.3%
120 4
 
1.3%
30 4
 
1.3%
160 4
 
1.3%
150 3
 
0.9%
510 3
 
0.9%
174 3
 
0.9%
Other values (236) 277
87.4%
2024-03-18T11:12:03.032268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 155
15.7%
2 123
12.4%
4 105
10.6%
0 101
10.2%
3 88
8.9%
5 81
8.2%
9 77
7.8%
6 77
7.8%
8 76
7.7%
7 73
7.4%
Other values (2) 33
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 956
96.7%
Space Separator 22
 
2.2%
Other Punctuation 11
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 155
16.2%
2 123
12.9%
4 105
11.0%
0 101
10.6%
3 88
9.2%
5 81
8.5%
9 77
8.1%
6 77
8.1%
8 76
7.9%
7 73
7.6%
Space Separator
ValueCountFrequency (%)
22
100.0%
Other Punctuation
ValueCountFrequency (%)
, 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 989
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 155
15.7%
2 123
12.4%
4 105
10.6%
0 101
10.2%
3 88
8.9%
5 81
8.2%
9 77
7.8%
6 77
7.8%
8 76
7.7%
7 73
7.4%
Other values (2) 33
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 989
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 155
15.7%
2 123
12.4%
4 105
10.6%
0 101
10.2%
3 88
8.9%
5 81
8.2%
9 77
7.8%
6 77
7.8%
8 76
7.7%
7 73
7.4%
Other values (2) 33
 
3.3%

사용검사일
Date

Distinct268
Distinct (%)84.5%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum1982-02-27 00:00:00
Maximum2022-08-19 00:00:00
2024-03-18T11:12:03.148093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:12:03.257249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size2.6 KiB
Minimum2023-12-01 00:00:00
Maximum2023-12-01 00:00:00
2024-03-18T11:12:03.647796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:12:03.717601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T11:11:59.371126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:58.777049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:59.103493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:59.444584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:58.893750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:59.198072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:59.522769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:58.991381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:11:59.289121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:12:03.775435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동수동수(주택)
번호1.0000.3870.375
동수0.3871.0000.929
동수(주택)0.3750.9291.000
2024-03-18T11:12:03.842057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호동수동수(주택)
번호1.0000.5390.518
동수0.5391.0000.960
동수(주택)0.5180.9601.000

Missing values

2024-03-18T11:11:59.619333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:11:59.722534image/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.
2024-03-18T11:11:59.805199image/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

번호아파트명소재지(도로명)층수동수동수(주택)세대수사용검사일데이터기준일자
01덕산아파트인천광역시 서구 건지로284번길 93 (가좌동, 덕산아파트)5321201982-02-272023-12-01
12진주1단지아파트인천광역시 서구 장고개로337번길 32 (가좌동, 진주아파트)1411106861983-03-012023-12-01
23석남1차아파트인천광역시 서구 신석로112번길 35 (석남동, 석남아파트)511301983-04-092023-12-01
34석남2차아파트인천광역시 서구 길주로 104 (석남동, 석남아파트)511301983-04-092023-12-01
45황해아파트인천광역시 서구 염곡로 318 (석남동, 황해아파트)522901983-09-052023-12-01
56한샘아파트인천광역시 서구 원적로 31 (가좌동, 한샘아파트)5441601983-12-192023-12-01
67동진아파트인천광역시 서구 석남로109번길 18 (석남동, 동진아파트)533601983-12-202023-12-01
78금성아파트인천광역시 서구 가정로261번길 11 (석남동, 금성아파트)511301984-05-092023-12-01
89아주아파트인천광역시 서구 건지로318번길 4 (가좌동, 아주아파트)622961984-05-192023-12-01
910세우아파트인천광역시 서구 건지로318번길 26 (가좌동, 세우아파트)5551801984-06-272023-12-01
번호아파트명소재지(도로명)층수동수동수(주택)세대수사용검사일데이터기준일자
307308루원 린스트라우스 더 린시티인천 서구 서곶로 45지하3층~지상47층751,4122022-08-192023-12-01
308309검암역 로열파크씨티 푸르지오 1단지인천 서구 한들로 33지하2층~지상40층18132,3792022-08-192023-12-01
309310검암역 로열파크씨티 푸르지오 2단지인천 서구 한들로 73지하2층~지상40층17122,4262022-08-192023-12-01
310311우미린 파크뷰 1단지인천 서구 이음2로 89지하2층~지상25층543702022-08-192023-12-01
311312우미린 파크뷰 2단지인천 서구 이음2로 90지하2층~지상25층988102022-08-192023-12-01
312313우미린 리버포레인천 서구 이음3로 221지하2층~지상29층1087652022-08-192023-12-01
313314가재울역 트루엘 에코시티인천 서구 열우물로240번길 20지하3층~지상29층11101,2182022-08-192023-12-01
314315예미지 더 시그니스인천시 서구 원당동 검단신도시 AB3-2블록지하2층~지상25층10101,1722022-08-192023-12-01
315316인천가정2 A-1BL행복주택아파트인천 서구 봉수대로 700<NA>655102022-08-192023-12-01
316317인천가정2 A-3BL행복주택아파트인천 서구 가정동 58-21<NA>322462022-08-192023-12-01