Overview

Dataset statistics

Number of variables13
Number of observations295
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory32.4 KiB
Average record size in memory112.4 B

Variable types

Numeric8
Text3
DateTime1
Categorical1

Dataset

Description해운대구 20세대이상 공동주택관련 자료로 공동주택명칭, 공동주택 주소, 주택규모에 따른 수, 층수 및 동수 등의 관련자료입니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/3075582/fileData.do

Alerts

주택규모 60제곱미터초과-85제곱미터이하 is highly overall correlated with 동수 and 1 other fieldsHigh correlation
주택규모 85제곱미터초과-135제곱미터이하 is highly overall correlated with 주택규모 135제곱미터초과 and 2 other fieldsHigh correlation
주택규모 135제곱미터초과 is highly overall correlated with 주택규모 85제곱미터초과-135제곱미터이하High correlation
동수 is highly overall correlated with 주택규모 60제곱미터초과-85제곱미터이하 and 2 other fieldsHigh correlation
세대수 is highly overall correlated with 주택규모 60제곱미터초과-85제곱미터이하 and 3 other fieldsHigh correlation
구분 is highly overall correlated with 세대수High correlation
연번 has unique valuesUnique
주택규모 40제곱미터이하 has 262 (88.8%) zerosZeros
주택규모 40제곱미터초과-60제곱미터이하 has 125 (42.4%) zerosZeros
주택규모 60제곱미터초과-85제곱미터이하 has 98 (33.2%) zerosZeros
주택규모 85제곱미터초과-135제곱미터이하 has 191 (64.7%) zerosZeros
주택규모 135제곱미터초과 has 235 (79.7%) zerosZeros

Reproduction

Analysis started2024-03-16 04:13:42.086255
Analysis finished2024-03-16 04:13:53.861234
Duration11.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct295
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148
Minimum1
Maximum295
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:13:53.993051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.7
Q174.5
median148
Q3221.5
95-th percentile280.3
Maximum295
Range294
Interquartile range (IQR)147

Descriptive statistics

Standard deviation85.30338
Coefficient of variation (CV)0.57637419
Kurtosis-1.2
Mean148
Median Absolute Deviation (MAD)74
Skewness0
Sum43660
Variance7276.6667
MonotonicityStrictly increasing
2024-03-16T13:13:54.232325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.3%
204 1
 
0.3%
202 1
 
0.3%
201 1
 
0.3%
200 1
 
0.3%
199 1
 
0.3%
198 1
 
0.3%
197 1
 
0.3%
196 1
 
0.3%
195 1
 
0.3%
Other values (285) 285
96.6%
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 (%)
295 1
0.3%
294 1
0.3%
293 1
0.3%
292 1
0.3%
291 1
0.3%
290 1
0.3%
289 1
0.3%
288 1
0.3%
287 1
0.3%
286 1
0.3%

공동주택명
Text

Distinct292
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-16T13:13:54.525275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length17
Mean length7.0474576
Min length2

Characters and Unicode

Total characters2079
Distinct characters263
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

Unique289 ?
Unique (%)98.0%

Sample

1st row대림맨션
2nd row반여아파트
3rd row신혼서민아파트
4th row백조아파트
5th row온천맨션
ValueCountFrequency (%)
해운대 10
 
3.0%
센텀 4
 
1.2%
신동아아파트 2
 
0.6%
2차 2
 
0.6%
광하주택 2
 
0.6%
아파트 2
 
0.6%
경동아파트 2
 
0.6%
sk 2
 
0.6%
kcc 2
 
0.6%
스위첸 2
 
0.6%
Other values (298) 301
90.9%
2024-03-16T13:13:55.110662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 100
 
4.8%
파 91
 
4.4%
트 85
 
4.1%
대 84
 
4.0%
센 54
 
2.6%
텀 50
 
2.4%
운 49
 
2.4%
동 45
 
2.2%
해 43
 
2.1%
40
 
1.9%
Other values (253) 1438
69.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1884
90.6%
Decimal Number 55
 
2.6%
Space Separator 40
 
1.9%
Uppercase Letter 32
 
1.5%
Close Punctuation 30
 
1.4%
Open Punctuation 30
 
1.4%
Other Punctuation 5
 
0.2%
Lowercase Letter 2
 
0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 100
 
5.3%
파 91
 
4.8%
트 85
 
4.5%
대 84
 
4.5%
센 54
 
2.9%
텀 50
 
2.7%
운 49
 
2.6%
동 45
 
2.4%
해 43
 
2.3%
스 35
 
1.9%
Other values (224) 1248
66.2%
Uppercase Letter
ValueCountFrequency (%)
K 6
18.8%
C 5
15.6%
I 5
15.6%
S 4
12.5%
W 2
 
6.2%
E 2
 
6.2%
V 2
 
6.2%
R 1
 
3.1%
T 1
 
3.1%
L 1
 
3.1%
Other values (3) 3
9.4%
Decimal Number
ValueCountFrequency (%)
2 18
32.7%
1 15
27.3%
3 11
20.0%
0 4
 
7.3%
4 3
 
5.5%
5 2
 
3.6%
8 2
 
3.6%
Other Punctuation
ValueCountFrequency (%)
, 3
60.0%
' 1
 
20.0%
. 1
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
l 1
50.0%
h 1
50.0%
Space Separator
ValueCountFrequency (%)
40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 30
100.0%
Open Punctuation
ValueCountFrequency (%)
( 30
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1884
90.6%
Common 161
 
7.7%
Latin 34
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 100
 
5.3%
파 91
 
4.8%
트 85
 
4.5%
대 84
 
4.5%
센 54
 
2.9%
텀 50
 
2.7%
운 49
 
2.6%
동 45
 
2.4%
해 43
 
2.3%
스 35
 
1.9%
Other values (224) 1248
66.2%
Latin
ValueCountFrequency (%)
K 6
17.6%
C 5
14.7%
I 5
14.7%
S 4
11.8%
W 2
 
5.9%
E 2
 
5.9%
V 2
 
5.9%
R 1
 
2.9%
T 1
 
2.9%
L 1
 
2.9%
Other values (5) 5
14.7%
Common
ValueCountFrequency (%)
40
24.8%
) 30
18.6%
( 30
18.6%
2 18
11.2%
1 15
 
9.3%
3 11
 
6.8%
0 4
 
2.5%
, 3
 
1.9%
4 3
 
1.9%
5 2
 
1.2%
Other values (4) 5
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1884
90.6%
ASCII 195
 
9.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 100
 
5.3%
파 91
 
4.8%
트 85
 
4.5%
대 84
 
4.5%
센 54
 
2.9%
텀 50
 
2.7%
운 49
 
2.6%
동 45
 
2.4%
해 43
 
2.3%
스 35
 
1.9%
Other values (224) 1248
66.2%
ASCII
ValueCountFrequency (%)
40
20.5%
) 30
15.4%
( 30
15.4%
2 18
9.2%
1 15
 
7.7%
3 11
 
5.6%
K 6
 
3.1%
C 5
 
2.6%
I 5
 
2.6%
S 4
 
2.1%
Other values (19) 31
15.9%

주소
Text

Distinct292
Distinct (%)99.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-16T13:13:55.522115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length31
Mean length24.355932
Min length14

Characters and Unicode

Total characters7185
Distinct characters259
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

Unique290 ?
Unique (%)98.3%

Sample

1st row해운대해변로 302 (중동, 대림맨션)
2nd row삼어로 135 (반여동, 반여아파트)
3rd row좌동순환로 443 (중동, 신혼서민아파트)
4th row달맞이길 16-12 (중동, 백조맨션)
5th row해운대해변로 329-10 (중동, 온천맨션)
ValueCountFrequency (%)
재송동 52
 
4.5%
우동 52
 
4.5%
중동 50
 
4.3%
좌동 43
 
3.7%
반여동 33
 
2.9%
반송동 22
 
1.9%
해운대로 17
 
1.5%
신반송로 17
 
1.5%
해운대해변로 15
 
1.3%
송정동 14
 
1.2%
Other values (603) 839
72.7%
2024-03-16T13:13:55.991770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
896
 
12.5%
동 387
 
5.4%
( 295
 
4.1%
) 294
 
4.1%
, 290
 
4.0%
로 278
 
3.9%
1 271
 
3.8%
아 187
 
2.6%
파 173
 
2.4%
2 170
 
2.4%
Other values (249) 3944
54.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 4211
58.6%
Decimal Number 1161
 
16.2%
Space Separator 896
 
12.5%
Open Punctuation 295
 
4.1%
Close Punctuation 294
 
4.1%
Other Punctuation 290
 
4.0%
Dash Punctuation 28
 
0.4%
Uppercase Letter 10
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 387
 
9.2%
로 278
 
6.6%
아 187
 
4.4%
파 173
 
4.1%
트 167
 
4.0%
길 167
 
4.0%
대 164
 
3.9%
송 163
 
3.9%
번 146
 
3.5%
해 133
 
3.2%
Other values (227) 2246
53.3%
Decimal Number
ValueCountFrequency (%)
1 271
23.3%
2 170
14.6%
3 134
11.5%
4 98
 
8.4%
7 94
 
8.1%
6 90
 
7.8%
8 82
 
7.1%
5 79
 
6.8%
9 79
 
6.8%
0 64
 
5.5%
Uppercase Letter
ValueCountFrequency (%)
C 3
30.0%
I 2
20.0%
K 1
 
10.0%
T 1
 
10.0%
L 1
 
10.0%
D 1
 
10.0%
S 1
 
10.0%
Space Separator
ValueCountFrequency (%)
896
100.0%
Open Punctuation
ValueCountFrequency (%)
( 295
100.0%
Close Punctuation
ValueCountFrequency (%)
) 294
100.0%
Other Punctuation
ValueCountFrequency (%)
, 290
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4211
58.6%
Common 2964
41.3%
Latin 10
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 387
 
9.2%
로 278
 
6.6%
아 187
 
4.4%
파 173
 
4.1%
트 167
 
4.0%
길 167
 
4.0%
대 164
 
3.9%
송 163
 
3.9%
번 146
 
3.5%
해 133
 
3.2%
Other values (227) 2246
53.3%
Common
ValueCountFrequency (%)
896
30.2%
( 295
 
10.0%
) 294
 
9.9%
, 290
 
9.8%
1 271
 
9.1%
2 170
 
5.7%
3 134
 
4.5%
4 98
 
3.3%
7 94
 
3.2%
6 90
 
3.0%
Other values (5) 332
 
11.2%
Latin
ValueCountFrequency (%)
C 3
30.0%
I 2
20.0%
K 1
 
10.0%
T 1
 
10.0%
L 1
 
10.0%
D 1
 
10.0%
S 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 4211
58.6%
ASCII 2974
41.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
896
30.1%
( 295
 
9.9%
) 294
 
9.9%
, 290
 
9.8%
1 271
 
9.1%
2 170
 
5.7%
3 134
 
4.5%
4 98
 
3.3%
7 94
 
3.2%
6 90
 
3.0%
Other values (12) 342
 
11.5%
Hangul
ValueCountFrequency (%)
동 387
 
9.2%
로 278
 
6.6%
아 187
 
4.4%
파 173
 
4.1%
트 167
 
4.0%
길 167
 
4.0%
대 164
 
3.9%
송 163
 
3.9%
번 146
 
3.5%
해 133
 
3.2%
Other values (227) 2246
53.3%

주택규모 40제곱미터이하
Real number (ℝ)

ZEROS 

Distinct28
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.128814
Minimum0
Maximum1710
Zeros262
Zeros (%)88.8%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:13:56.205607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile37.7
Maximum1710
Range1710
Interquartile range (IQR)0

Descriptive statistics

Standard deviation121.36285
Coefficient of variation (CV)7.524599
Kurtosis147.6721
Mean16.128814
Median Absolute Deviation (MAD)0
Skewness11.632657
Sum4758
Variance14728.943
MonotonicityNot monotonic
2024-03-16T13:13:56.420658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 262
88.8%
32 2
 
0.7%
18 2
 
0.7%
1 2
 
0.7%
20 2
 
0.7%
26 2
 
0.7%
50 2
 
0.7%
2 1
 
0.3%
35 1
 
0.3%
220 1
 
0.3%
Other values (18) 18
 
6.1%
ValueCountFrequency (%)
0 262
88.8%
1 2
 
0.7%
2 1
 
0.3%
7 1
 
0.3%
8 1
 
0.3%
15 1
 
0.3%
18 2
 
0.7%
20 2
 
0.7%
24 1
 
0.3%
25 1
 
0.3%
ValueCountFrequency (%)
1710 1
0.3%
1050 1
0.3%
462 1
0.3%
220 1
0.3%
208 1
0.3%
125 1
0.3%
111 1
0.3%
100 1
0.3%
90 1
0.3%
87 1
0.3%

주택규모 40제곱미터초과-60제곱미터이하
Real number (ℝ)

ZEROS 

Distinct136
Distinct (%)46.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.74237
Minimum0
Maximum1596
Zeros125
Zeros (%)42.4%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:13:56.635669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24
Q3180
95-th percentile574.2
Maximum1596
Range1596
Interquartile range (IQR)180

Descriptive statistics

Standard deviation212.70545
Coefficient of variation (CV)1.6394447
Kurtosis9.754382
Mean129.74237
Median Absolute Deviation (MAD)24
Skewness2.6365019
Sum38274
Variance45243.607
MonotonicityNot monotonic
2024-03-16T13:13:56.955291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 125
42.4%
180 4
 
1.4%
2 3
 
1.0%
24 3
 
1.0%
152 3
 
1.0%
110 2
 
0.7%
71 2
 
0.7%
144 2
 
0.7%
130 2
 
0.7%
75 2
 
0.7%
Other values (126) 147
49.8%
ValueCountFrequency (%)
0 125
42.4%
1 2
 
0.7%
2 3
 
1.0%
4 1
 
0.3%
6 2
 
0.7%
8 1
 
0.3%
9 1
 
0.3%
10 2
 
0.7%
13 1
 
0.3%
14 1
 
0.3%
ValueCountFrequency (%)
1596 1
0.3%
1000 1
0.3%
928 1
0.3%
890 1
0.3%
832 1
0.3%
824 1
0.3%
764 1
0.3%
749 1
0.3%
728 1
0.3%
660 1
0.3%

주택규모 60제곱미터초과-85제곱미터이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct157
Distinct (%)53.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.85085
Minimum0
Maximum1104
Zeros98
Zeros (%)33.2%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:13:57.416225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median70
Q3200
95-th percentile592.1
Maximum1104
Range1104
Interquartile range (IQR)200

Descriptive statistics

Standard deviation203.65079
Coefficient of variation (CV)1.3962949
Kurtosis4.7398851
Mean145.85085
Median Absolute Deviation (MAD)70
Skewness2.086516
Sum43026
Variance41473.644
MonotonicityNot monotonic
2024-03-16T13:13:57.864955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 98
33.2%
108 4
 
1.4%
60 3
 
1.0%
180 3
 
1.0%
224 2
 
0.7%
147 2
 
0.7%
36 2
 
0.7%
45 2
 
0.7%
24 2
 
0.7%
6 2
 
0.7%
Other values (147) 175
59.3%
ValueCountFrequency (%)
0 98
33.2%
1 1
 
0.3%
2 1
 
0.3%
4 1
 
0.3%
5 1
 
0.3%
6 2
 
0.7%
7 1
 
0.3%
10 1
 
0.3%
12 1
 
0.3%
15 1
 
0.3%
ValueCountFrequency (%)
1104 1
0.3%
1042 1
0.3%
1000 1
0.3%
908 1
0.3%
802 1
0.3%
800 1
0.3%
774 1
0.3%
736 1
0.3%
714 1
0.3%
676 1
0.3%

주택규모 85제곱미터초과-135제곱미터이하
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct86
Distinct (%)29.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.450847
Minimum0
Maximum1622
Zeros191
Zeros (%)64.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:13:58.098881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q351.5
95-th percentile321.2
Maximum1622
Range1622
Interquartile range (IQR)51.5

Descriptive statistics

Standard deviation181.91669
Coefficient of variation (CV)2.5821789
Kurtosis28.361416
Mean70.450847
Median Absolute Deviation (MAD)0
Skewness4.746813
Sum20783
Variance33093.684
MonotonicityNot monotonic
2024-03-16T13:13:58.353365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 191
64.7%
60 4
 
1.4%
30 4
 
1.4%
40 3
 
1.0%
91 2
 
0.7%
194 2
 
0.7%
186 2
 
0.7%
92 2
 
0.7%
20 2
 
0.7%
80 2
 
0.7%
Other values (76) 81
27.5%
ValueCountFrequency (%)
0 191
64.7%
2 1
 
0.3%
4 1
 
0.3%
8 1
 
0.3%
12 1
 
0.3%
14 1
 
0.3%
20 2
 
0.7%
22 1
 
0.3%
24 1
 
0.3%
27 1
 
0.3%
ValueCountFrequency (%)
1622 1
0.3%
1136 1
0.3%
1092 1
0.3%
976 1
0.3%
915 1
0.3%
814 1
0.3%
570 1
0.3%
552 1
0.3%
486 1
0.3%
432 1
0.3%

주택규모 135제곱미터초과
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct50
Distinct (%)16.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.976271
Minimum0
Maximum882
Zeros235
Zeros (%)79.7%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:13:58.662047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile161.3
Maximum882
Range882
Interquartile range (IQR)0

Descriptive statistics

Standard deviation96.185847
Coefficient of variation (CV)3.4381225
Kurtosis35.351197
Mean27.976271
Median Absolute Deviation (MAD)0
Skewness5.4427048
Sum8253
Variance9251.7171
MonotonicityNot monotonic
2024-03-16T13:13:58.895230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 235
79.7%
80 3
 
1.0%
50 3
 
1.0%
30 2
 
0.7%
88 2
 
0.7%
100 2
 
0.7%
42 2
 
0.7%
46 2
 
0.7%
6 2
 
0.7%
35 2
 
0.7%
Other values (40) 40
 
13.6%
ValueCountFrequency (%)
0 235
79.7%
2 1
 
0.3%
5 1
 
0.3%
6 2
 
0.7%
14 1
 
0.3%
16 1
 
0.3%
18 1
 
0.3%
20 1
 
0.3%
24 1
 
0.3%
26 1
 
0.3%
ValueCountFrequency (%)
882 1
0.3%
696 1
0.3%
574 1
0.3%
536 1
0.3%
402 1
0.3%
390 1
0.3%
290 1
0.3%
272 1
0.3%
270 1
0.3%
267 1
0.3%

층수
Text

Distinct97
Distinct (%)32.9%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
2024-03-16T13:13:59.324661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length2.6067797
Min length1

Characters and Unicode

Total characters769
Distinct characters16
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique59 ?
Unique (%)20.0%

Sample

1st row6
2nd row4
3rd row6
4th row5
5th row5
ValueCountFrequency (%)
5 38
 
12.6%
15 25
 
8.3%
25 22
 
7.3%
24 19
 
6.3%
6 18
 
6.0%
20 13
 
4.3%
지1/14 7
 
2.3%
19 7
 
2.3%
10 6
 
2.0%
14 5
 
1.7%
Other values (87) 141
46.8%
2024-03-16T13:14:00.059680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 133
17.3%
1 120
15.6%
5 110
14.3%
/ 65
8.5%
4 61
7.9%
지 56
7.3%
3 55
7.2%
0 38
 
4.9%
6 32
 
4.2%
9 26
 
3.4%
Other values (6) 73
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 616
80.1%
Other Punctuation 65
 
8.5%
Other Letter 57
 
7.4%
Dash Punctuation 20
 
2.6%
Space Separator 6
 
0.8%
Math Symbol 5
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 133
21.6%
1 120
19.5%
5 110
17.9%
4 61
9.9%
3 55
8.9%
0 38
 
6.2%
6 32
 
5.2%
9 26
 
4.2%
7 21
 
3.4%
8 20
 
3.2%
Other Letter
ValueCountFrequency (%)
지 56
98.2%
하 1
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 65
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 20
100.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
~ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 712
92.6%
Hangul 57
 
7.4%

Most frequent character per script

Common
ValueCountFrequency (%)
2 133
18.7%
1 120
16.9%
5 110
15.4%
/ 65
9.1%
4 61
8.6%
3 55
7.7%
0 38
 
5.3%
6 32
 
4.5%
9 26
 
3.7%
7 21
 
2.9%
Other values (4) 51
 
7.2%
Hangul
ValueCountFrequency (%)
지 56
98.2%
하 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 712
92.6%
Hangul 57
 
7.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 133
18.7%
1 120
16.9%
5 110
15.4%
/ 65
9.1%
4 61
8.6%
3 55
7.7%
0 38
 
5.3%
6 32
 
4.5%
9 26
 
3.7%
7 21
 
2.9%
Other values (4) 51
 
7.2%
Hangul
ValueCountFrequency (%)
지 56
98.2%
하 1
 
1.8%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3694915
Minimum1
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:14:00.238421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q36
95-th percentile13
Maximum25
Range24
Interquartile range (IQR)5

Descriptive statistics

Standard deviation4.4203939
Coefficient of variation (CV)1.0116495
Kurtosis3.1114319
Mean4.3694915
Median Absolute Deviation (MAD)2
Skewness1.7269291
Sum1289
Variance19.539882
MonotonicityNot monotonic
2024-03-16T13:14:00.450078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 106
35.9%
2 40
 
13.6%
3 32
 
10.8%
4 23
 
7.8%
6 16
 
5.4%
7 15
 
5.1%
9 10
 
3.4%
8 9
 
3.1%
11 8
 
2.7%
12 7
 
2.4%
Other values (11) 29
 
9.8%
ValueCountFrequency (%)
1 106
35.9%
2 40
 
13.6%
3 32
 
10.8%
4 23
 
7.8%
5 5
 
1.7%
6 16
 
5.4%
7 15
 
5.1%
8 9
 
3.1%
9 10
 
3.4%
10 6
 
2.0%
ValueCountFrequency (%)
25 1
 
0.3%
21 2
 
0.7%
20 1
 
0.3%
19 2
 
0.7%
17 1
 
0.3%
16 1
 
0.3%
15 2
 
0.7%
14 3
1.0%
13 5
1.7%
12 7
2.4%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct215
Distinct (%)72.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.09831
Minimum20
Maximum2752
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2024-03-16T13:14:00.667639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile29.7
Q189
median228
Q3554
95-th percentile1275.4
Maximum2752
Range2732
Interquartile range (IQR)465

Descriptive statistics

Standard deviation444.73832
Coefficient of variation (CV)1.1256397
Kurtosis5.1221477
Mean395.09831
Median Absolute Deviation (MAD)179
Skewness2.0318643
Sum116554
Variance197792.18
MonotonicityNot monotonic
2024-03-16T13:14:00.936229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 5
 
1.7%
48 5
 
1.7%
180 4
 
1.4%
35 4
 
1.4%
90 4
 
1.4%
298 4
 
1.4%
466 3
 
1.0%
32 3
 
1.0%
152 3
 
1.0%
24 3
 
1.0%
Other values (205) 257
87.1%
ValueCountFrequency (%)
20 3
1.0%
21 1
 
0.3%
22 3
1.0%
24 3
1.0%
26 1
 
0.3%
27 1
 
0.3%
28 2
0.7%
29 1
 
0.3%
30 3
1.0%
31 1
 
0.3%
ValueCountFrequency (%)
2752 1
0.3%
2369 1
0.3%
2290 1
0.3%
1848 1
0.3%
1788 1
0.3%
1721 1
0.3%
1710 1
0.3%
1680 1
0.3%
1631 1
0.3%
1564 1
0.3%

사용검사일
Date

Distinct278
Distinct (%)94.2%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
Minimum1975-10-27 00:00:00
Maximum2023-01-20 00:00:00
2024-03-16T13:14:01.175881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:14:01.468215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size2.4 KiB
의무
167 
비의무
122 
임대
 
6

Length

Max length3
Median length2
Mean length2.4135593
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의무 167
56.6%
비의무 122
41.4%
임대 6
 
2.0%

Length

2024-03-16T13:14:01.755058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:14:01.995118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의무 167
56.6%
비의무 122
41.4%
임대 6
 
2.0%

Interactions

2024-03-16T13:13:52.226743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:42.922051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:44.566782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:46.625504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:47.985938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:49.340433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.686051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.525130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.311186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:43.073428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:44.770760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:46.776626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:48.221836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:49.503601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.868910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.605751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.423683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:43.278396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:45.377120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:46.954282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:48.487513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:49.681545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.983479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.688852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.560765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:43.541982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:45.584421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:47.109164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:48.666957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:49.839822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.078449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.797332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.664502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:43.728302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:45.762502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:47.279633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:48.802585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.003242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.165584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.887156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.777838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:43.996413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:46.038565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:47.465914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:48.948471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.196363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.257062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.969591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.898609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:44.186811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:46.349535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:47.617825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:49.088585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.366145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.330262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.066286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:53.040766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:44.408936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:46.490559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:47.793684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:49.223564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:50.506119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:51.424535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:13:52.153295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:14:02.145562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주택규모 40제곱미터이하주택규모 40제곱미터초과-60제곱미터이하주택규모 60제곱미터초과-85제곱미터이하주택규모 85제곱미터초과-135제곱미터이하주택규모 135제곱미터초과층수동수세대수구분
연번1.0000.0000.4400.2830.3480.2020.8460.4740.3670.484
주택규모 40제곱미터이하0.0001.0000.0000.0000.0000.0000.6820.0000.3450.548
주택규모 40제곱미터초과-60제곱미터이하0.4400.0001.0000.1840.6440.0000.0000.7230.6020.485
주택규모 60제곱미터초과-85제곱미터이하0.2830.0000.1841.0000.6840.2550.7580.7450.7060.567
주택규모 85제곱미터초과-135제곱미터이하0.3480.0000.6440.6841.0000.8510.9480.7730.8380.304
주택규모 135제곱미터초과0.2020.0000.0000.2550.8511.0000.9810.4620.6030.000
층수0.8460.6820.0000.7580.9480.9811.0000.0000.8630.864
동수0.4740.0000.7230.7450.7730.4620.0001.0000.7590.563
세대수0.3670.3450.6020.7060.8380.6030.8630.7591.0000.812
구분0.4840.5480.4850.5670.3040.0000.8640.5630.8121.000
2024-03-16T13:14:02.374779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번주택규모 40제곱미터이하주택규모 40제곱미터초과-60제곱미터이하주택규모 60제곱미터초과-85제곱미터이하주택규모 85제곱미터초과-135제곱미터이하주택규모 135제곱미터초과동수세대수구분
연번1.0000.052-0.204-0.0020.0800.114-0.0250.1020.329
주택규모 40제곱미터이하0.0521.000-0.054-0.215-0.105-0.155-0.049-0.0740.488
주택규모 40제곱미터초과-60제곱미터이하-0.204-0.0541.0000.0930.147-0.0160.4710.4800.349
주택규모 60제곱미터초과-85제곱미터이하-0.002-0.2150.0931.0000.4540.0620.5050.5850.405
주택규모 85제곱미터초과-135제곱미터이하0.080-0.1050.1470.4541.0000.5090.5080.5520.200
주택규모 135제곱미터초과0.114-0.155-0.0160.0620.5091.0000.2970.3270.000
동수-0.025-0.0490.4710.5050.5080.2971.0000.8160.402
세대수0.102-0.0740.4800.5850.5520.3270.8161.0000.514
구분0.3290.4880.3490.4050.2000.0000.4020.5141.000

Missing values

2024-03-16T13:13:53.476784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:13:53.758996image/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

연번공동주택명주소주택규모 40제곱미터이하주택규모 40제곱미터초과-60제곱미터이하주택규모 60제곱미터초과-85제곱미터이하주택규모 85제곱미터초과-135제곱미터이하주택규모 135제곱미터초과층수동수세대수사용검사일구분
01대림맨션해운대해변로 302 (중동, 대림맨션)32600061381975-12-30비의무
12반여아파트삼어로 135 (반여동, 반여아파트)0152000471521975-10-27비의무
23신혼서민아파트좌동순환로 443 (중동, 신혼서민아파트)18400061221975-12-17비의무
34백조아파트달맞이길 16-12 (중동, 백조맨션)123100051341976-08-04비의무
45온천맨션해운대해변로 329-10 (중동, 온천맨션)00300051301976-05-21비의무
56대림비치해운대해변로 295 (중동, 대림비치아파트)02971001011001977-08-18비의무
67우일아파트우동1로38번길 11 (우동, 우일맨션)153440051531978-11-21비의무
78중동맨션좌동순환로 387 (중동, 중동맨션)00490051491978-07-01비의무
89스카이아파트달맞이길117번라길 22 (중동, 스카이파크빌라)00332061351991-04-18비의무
910보훈아파트(반여보훈)재반로242번길 41 (반여동, 보훈아파트)0300000563001979-08-14의무
연번공동주택명주소주택규모 40제곱미터이하주택규모 40제곱미터초과-60제곱미터이하주택규모 60제곱미터초과-85제곱미터이하주택규모 85제곱미터초과-135제곱미터이하주택규모 135제곱미터초과층수동수세대수사용검사일구분
285286해운대센텀미진이지비아 아파트해운대로349번길 17(우동, 해운대센텀미진이지비아 아파트)0018400지4/3421842020-06-05의무
286287롯데캐슬스타중동2로34번길 15(중동, 해운대롯데캐슬스타아파트)00736920지4/4948282020-09-02의무
287288후스좌동순환로468번나길 8(중동, 후스)26000051262020-12-24비의무
288289해피투모로우송정중앙로9번길 16(송정동, 해피투모로우)039000141392021-03-18비의무
289290해운대경동리인뷰1차달맞이길23(중동, 해운대경동리인뷰1차아파트)00259390지1/4922982021-05-27의무
290291쌍용더플래티넘 해운대중동2로24번길 24(중동, 쌍용더플래티넘)0015200지4/2021522022-02-11의무
291292센텀마티안해운대로 469번길 181(우동, 센텀마티안아파트)0019900지3/3021992021-08-26의무
292293센텀 KCC 스위첸반여로 42(반여동, 센텀KCC스위첸아파트)3524535440지3/2886382022-08-30의무
293294해운대센트럴푸르지오해운대로 650(우동, 해운대센트럴푸르지오)0054800지6/4935482023-01-20의무
294295협성 루에나 센텀재송1로 5(재송동, 협성 루에나 센텀)0152000지1/2011522023-01-16의무