Overview

Dataset statistics

Number of variables12
Number of observations284
Missing cells12
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory27.9 KiB
Average record size in memory100.5 B

Variable types

Numeric4
Text4
Categorical2
DateTime1
Boolean1

Dataset

Description대구광역시 수성구_공동주택 현황_20180517
Author대구광역시 수성구
URLhttp://data.daegu.go.kr/open/data/dataView.do?dataSetId=15038448&dataSetDetailId=15038448183be57f05ea3_201909091321&provdMethod=FILE

Alerts

데이터기준일자 has constant value ""Constant
연번 is highly overall correlated with 층수 and 1 other fieldsHigh correlation
동수 is highly overall correlated with 세대수High correlation
층수 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 동수High correlation
의무관리여부 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
전화번호 has 12 (4.2%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-04-21 09:01:45.011737
Analysis finished2024-04-21 09:01:48.620354
Duration3.61 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct284
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean142.5
Minimum1
Maximum284
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T18:01:48.742544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15.15
Q171.75
median142.5
Q3213.25
95-th percentile269.85
Maximum284
Range283
Interquartile range (IQR)141.5

Descriptive statistics

Standard deviation82.127949
Coefficient of variation (CV)0.57633648
Kurtosis-1.2
Mean142.5
Median Absolute Deviation (MAD)71
Skewness0
Sum40470
Variance6745
MonotonicityStrictly increasing
2024-04-21T18:01:48.981491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.4%
189 1
 
0.4%
195 1
 
0.4%
194 1
 
0.4%
193 1
 
0.4%
192 1
 
0.4%
191 1
 
0.4%
190 1
 
0.4%
188 1
 
0.4%
197 1
 
0.4%
Other values (274) 274
96.5%
ValueCountFrequency (%)
1 1
0.4%
2 1
0.4%
3 1
0.4%
4 1
0.4%
5 1
0.4%
6 1
0.4%
7 1
0.4%
8 1
0.4%
9 1
0.4%
10 1
0.4%
ValueCountFrequency (%)
284 1
0.4%
283 1
0.4%
282 1
0.4%
281 1
0.4%
280 1
0.4%
279 1
0.4%
278 1
0.4%
277 1
0.4%
276 1
0.4%
275 1
0.4%

아파트명
Text

Distinct280
Distinct (%)98.6%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-21T18:01:49.800000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length13
Mean length6.693662
Min length2

Characters and Unicode

Total characters1901
Distinct characters240
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique276 ?
Unique (%)97.2%

Sample

1st row삼풍아파트
2nd row한도맨션
3rd row삼일아파트
4th row수성아파트
5th row삼익아파트
ValueCountFrequency (%)
범어 7
 
2.3%
경북아파트 2
 
0.7%
한도맨션 2
 
0.7%
수성아파트 2
 
0.7%
삼성맨션 2
 
0.7%
태왕아너스 2
 
0.7%
우방유쉘 2
 
0.7%
수성롯데캐슬 2
 
0.7%
대림e-편한세상 1
 
0.3%
리버아크로파크 1
 
0.3%
Other values (279) 279
92.4%
2024-04-21T18:01:50.856325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
성 84
 
4.4%
지 65
 
3.4%
파 59
 
3.1%
아 54
 
2.8%
운 49
 
2.6%
타 48
 
2.5%
트 47
 
2.5%
맨 46
 
2.4%
션 46
 
2.4%
범 40
 
2.1%
Other values (230) 1363
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1762
92.7%
Decimal Number 52
 
2.7%
Open Punctuation 22
 
1.2%
Close Punctuation 22
 
1.2%
Space Separator 18
 
0.9%
Uppercase Letter 17
 
0.9%
Lowercase Letter 3
 
0.2%
Dash Punctuation 3
 
0.2%
Math Symbol 1
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
성 84
 
4.8%
지 65
 
3.7%
파 59
 
3.3%
아 54
 
3.1%
운 49
 
2.8%
타 48
 
2.7%
트 47
 
2.7%
맨 46
 
2.6%
션 46
 
2.6%
범 40
 
2.3%
Other values (207) 1224
69.5%
Uppercase Letter
ValueCountFrequency (%)
S 5
29.4%
K 4
23.5%
A 2
 
11.8%
T 1
 
5.9%
N 1
 
5.9%
X 1
 
5.9%
B 1
 
5.9%
I 1
 
5.9%
O 1
 
5.9%
Decimal Number
ValueCountFrequency (%)
2 16
30.8%
1 15
28.8%
3 12
23.1%
5 5
 
9.6%
6 2
 
3.8%
4 1
 
1.9%
0 1
 
1.9%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 22
100.0%
Space Separator
ValueCountFrequency (%)
18
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Math Symbol
ValueCountFrequency (%)
= 1
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1760
92.6%
Common 119
 
6.3%
Latin 20
 
1.1%
Han 2
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
성 84
 
4.8%
지 65
 
3.7%
파 59
 
3.4%
아 54
 
3.1%
운 49
 
2.8%
타 48
 
2.7%
트 47
 
2.7%
맨 46
 
2.6%
션 46
 
2.6%
범 40
 
2.3%
Other values (205) 1222
69.4%
Common
ValueCountFrequency (%)
( 22
18.5%
) 22
18.5%
18
15.1%
2 16
13.4%
1 15
12.6%
3 12
10.1%
5 5
 
4.2%
- 3
 
2.5%
6 2
 
1.7%
= 1
 
0.8%
Other values (3) 3
 
2.5%
Latin
ValueCountFrequency (%)
S 5
25.0%
K 4
20.0%
e 3
15.0%
A 2
 
10.0%
T 1
 
5.0%
N 1
 
5.0%
X 1
 
5.0%
B 1
 
5.0%
I 1
 
5.0%
O 1
 
5.0%
Han
ValueCountFrequency (%)
靑 1
50.0%
宮 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1760
92.6%
ASCII 139
 
7.3%
CJK 2
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
성 84
 
4.8%
지 65
 
3.7%
파 59
 
3.4%
아 54
 
3.1%
운 49
 
2.8%
타 48
 
2.7%
트 47
 
2.7%
맨 46
 
2.6%
션 46
 
2.6%
범 40
 
2.3%
Other values (205) 1222
69.4%
ASCII
ValueCountFrequency (%)
( 22
15.8%
) 22
15.8%
18
12.9%
2 16
11.5%
1 15
10.8%
3 12
8.6%
5 5
 
3.6%
S 5
 
3.6%
K 4
 
2.9%
e 3
 
2.2%
Other values (13) 17
12.2%
CJK
ValueCountFrequency (%)
靑 1
50.0%
宮 1
50.0%

지번주소
Text

Distinct282
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-21T18:01:52.068915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length21
Mean length18.739437
Min length15

Characters and Unicode

Total characters5322
Distinct characters44
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique280 ?
Unique (%)98.6%

Sample

1st row대구광역시 수성구 두산동 929-1
2nd row대구광역시 수성구 수성4가 985-77
3rd row대구광역시 수성구 범어3동 13-9
4th row대구광역시 수성구 황금2동 665
5th row대구광역시 수성구 수성1가 649-55
ValueCountFrequency (%)
대구광역시 284
25.2%
수성구 284
25.2%
신매동 23
 
2.0%
수성1가 18
 
1.6%
범어1동 16
 
1.4%
범어4동 16
 
1.4%
지산2동 15
 
1.3%
만촌3동 15
 
1.3%
매호동 15
 
1.3%
파동 14
 
1.2%
Other values (299) 425
37.8%
2024-04-21T18:01:53.668857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
841
15.8%
구 568
 
10.7%
수 327
 
6.1%
성 324
 
6.1%
시 296
 
5.6%
1 287
 
5.4%
대 284
 
5.3%
광 284
 
5.3%
역 284
 
5.3%
동 246
 
4.6%
Other values (34) 1581
29.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3101
58.3%
Decimal Number 1244
23.4%
Space Separator 841
 
15.8%
Dash Punctuation 134
 
2.5%
Other Punctuation 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 568
18.3%
수 327
10.5%
성 324
10.4%
시 296
9.5%
대 284
9.2%
광 284
9.2%
역 284
9.2%
동 246
7.9%
범 77
 
2.5%
어 60
 
1.9%
Other values (21) 351
11.3%
Decimal Number
ValueCountFrequency (%)
1 287
23.1%
2 171
13.7%
3 142
11.4%
0 121
9.7%
4 111
 
8.9%
6 90
 
7.2%
5 88
 
7.1%
7 83
 
6.7%
9 80
 
6.4%
8 71
 
5.7%
Space Separator
ValueCountFrequency (%)
841
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 134
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3101
58.3%
Common 2221
41.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 568
18.3%
수 327
10.5%
성 324
10.4%
시 296
9.5%
대 284
9.2%
광 284
9.2%
역 284
9.2%
동 246
7.9%
범 77
 
2.5%
어 60
 
1.9%
Other values (21) 351
11.3%
Common
ValueCountFrequency (%)
841
37.9%
1 287
 
12.9%
2 171
 
7.7%
3 142
 
6.4%
- 134
 
6.0%
0 121
 
5.4%
4 111
 
5.0%
6 90
 
4.1%
5 88
 
4.0%
7 83
 
3.7%
Other values (3) 153
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3101
58.3%
ASCII 2221
41.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
841
37.9%
1 287
 
12.9%
2 171
 
7.7%
3 142
 
6.4%
- 134
 
6.0%
0 121
 
5.4%
4 111
 
5.0%
6 90
 
4.1%
5 88
 
4.0%
7 83
 
3.7%
Other values (3) 153
 
6.9%
Hangul
ValueCountFrequency (%)
구 568
18.3%
수 327
10.5%
성 324
10.4%
시 296
9.5%
대 284
9.2%
광 284
9.2%
역 284
9.2%
동 246
7.9%
범 77
 
2.5%
어 60
 
1.9%
Other values (21) 351
11.3%

도로명주소
Text

Distinct282
Distinct (%)99.3%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2024-04-21T18:01:54.720659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length21
Mean length18.461268
Min length10

Characters and Unicode

Total characters5243
Distinct characters64
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique280 ?
Unique (%)98.6%

Sample

1st row대구광역시 수성구 용학로 22
2nd row대구광역시 수성구 달구벌대로 2367
3rd row대구광역시 수성구 동대구로73길 10-10
4th row대구광역시 수성구 동대구로 204
5th row대구광역시 수성구 신천동로 278
ValueCountFrequency (%)
대구광역시 284
25.0%
수성구 284
25.0%
달구벌대로 25
 
2.2%
동대구로 15
 
1.3%
청호로 11
 
1.0%
용학로 10
 
0.9%
파동로 9
 
0.8%
신천동로 9
 
0.8%
수성로 8
 
0.7%
천을로 7
 
0.6%
Other values (281) 473
41.7%
2024-04-21T18:01:56.188072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
855
16.3%
구 653
12.5%
대 369
 
7.0%
수 314
 
6.0%
성 301
 
5.7%
시 287
 
5.5%
광 284
 
5.4%
역 284
 
5.4%
로 283
 
5.4%
1 148
 
2.8%
Other values (54) 1465
27.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3413
65.1%
Decimal Number 964
 
18.4%
Space Separator 855
 
16.3%
Dash Punctuation 11
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 653
19.1%
대 369
10.8%
수 314
9.2%
성 301
8.8%
시 287
8.4%
광 284
8.3%
역 284
8.3%
로 283
8.3%
길 116
 
3.4%
달 59
 
1.7%
Other values (42) 463
13.6%
Decimal Number
ValueCountFrequency (%)
1 148
15.4%
3 143
14.8%
2 134
13.9%
4 102
10.6%
0 95
9.9%
6 89
9.2%
5 88
9.1%
7 64
6.6%
9 56
 
5.8%
8 45
 
4.7%
Space Separator
ValueCountFrequency (%)
855
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 3413
65.1%
Common 1830
34.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 653
19.1%
대 369
10.8%
수 314
9.2%
성 301
8.8%
시 287
8.4%
광 284
8.3%
역 284
8.3%
로 283
8.3%
길 116
 
3.4%
달 59
 
1.7%
Other values (42) 463
13.6%
Common
ValueCountFrequency (%)
855
46.7%
1 148
 
8.1%
3 143
 
7.8%
2 134
 
7.3%
4 102
 
5.6%
0 95
 
5.2%
6 89
 
4.9%
5 88
 
4.8%
7 64
 
3.5%
9 56
 
3.1%
Other values (2) 56
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3413
65.1%
ASCII 1830
34.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
855
46.7%
1 148
 
8.1%
3 143
 
7.8%
2 134
 
7.3%
4 102
 
5.6%
0 95
 
5.2%
6 89
 
4.9%
5 88
 
4.8%
7 64
 
3.5%
9 56
 
3.1%
Other values (2) 56
 
3.1%
Hangul
ValueCountFrequency (%)
구 653
19.1%
대 369
10.8%
수 314
9.2%
성 301
8.8%
시 287
8.4%
광 284
8.3%
역 284
8.3%
로 283
8.3%
길 116
 
3.4%
달 59
 
1.7%
Other values (42) 463
13.6%

행정동
Categorical

Distinct24
Distinct (%)8.5%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
고산3동
27 
고산1동
22 
범어4동
21 
범어1동
20 
만촌3동
20 
Other values (19)
174 

Length

Max length6
Median length4
Mean length3.8556338
Min length2

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row두산동
2nd row수성4가
3rd row범어3동
4th row황금2동
5th row수성1가

Common Values

ValueCountFrequency (%)
고산3동 27
 
9.5%
고산1동 22
 
7.7%
범어4동 21
 
7.4%
범어1동 20
 
7.0%
만촌3동 20
 
7.0%
수성1가 18
 
6.3%
고산2동 16
 
5.6%
지산2동 15
 
5.3%
수성4가 14
 
4.9%
파동 14
 
4.9%
Other values (14) 97
34.2%

Length

2024-04-21T18:01:56.630678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
고산3동 27
 
9.5%
고산1동 22
 
7.7%
범어4동 21
 
7.4%
범어1동 20
 
7.0%
만촌3동 20
 
7.0%
수성1가 18
 
6.3%
고산2동 16
 
5.6%
지산2동 15
 
5.3%
수성4가 14
 
4.9%
파동 14
 
4.9%
Other values (14) 97
34.2%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1232394
Minimum1
Maximum67
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T18:01:56.980329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median3
Q35
95-th percentile9
Maximum67
Range66
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.7587326
Coefficient of variation (CV)1.1541247
Kurtosis108.09534
Mean4.1232394
Median Absolute Deviation (MAD)2
Skewness8.5669093
Sum1171
Variance22.645536
MonotonicityNot monotonic
2024-04-21T18:01:57.346306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
1 69
24.3%
2 42
14.8%
4 41
14.4%
3 33
11.6%
5 32
11.3%
6 29
10.2%
8 12
 
4.2%
7 7
 
2.5%
9 7
 
2.5%
12 3
 
1.1%
Other values (6) 9
 
3.2%
ValueCountFrequency (%)
1 69
24.3%
2 42
14.8%
3 33
11.6%
4 41
14.4%
5 32
11.3%
6 29
10.2%
7 7
 
2.5%
8 12
 
4.2%
9 7
 
2.5%
10 2
 
0.7%
ValueCountFrequency (%)
67 1
 
0.4%
21 1
 
0.4%
15 2
 
0.7%
14 2
 
0.7%
12 3
 
1.1%
11 1
 
0.4%
10 2
 
0.7%
9 7
2.5%
8 12
4.2%
7 7
2.5%

층수
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.200704
Minimum3
Maximum57
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T18:01:57.725370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q111.75
median15
Q320
95-th percentile29.85
Maximum57
Range54
Interquartile range (IQR)8.25

Descriptive statistics

Standard deviation8.2478308
Coefficient of variation (CV)0.50910323
Kurtosis3.0928349
Mean16.200704
Median Absolute Deviation (MAD)5
Skewness0.94065713
Sum4601
Variance68.026713
MonotonicityNot monotonic
2024-04-21T18:01:58.122213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
15 59
20.8%
5 45
15.8%
20 39
13.7%
18 19
 
6.7%
17 10
 
3.5%
22 9
 
3.2%
23 9
 
3.2%
12 8
 
2.8%
25 8
 
2.8%
10 7
 
2.5%
Other values (26) 71
25.0%
ValueCountFrequency (%)
3 3
 
1.1%
4 6
 
2.1%
5 45
15.8%
6 1
 
0.4%
7 1
 
0.4%
8 3
 
1.1%
9 2
 
0.7%
10 7
 
2.5%
11 3
 
1.1%
12 8
 
2.8%
ValueCountFrequency (%)
57 1
 
0.4%
54 1
 
0.4%
42 2
0.7%
36 3
1.1%
35 1
 
0.4%
33 1
 
0.4%
32 1
 
0.4%
31 1
 
0.4%
30 4
1.4%
29 2
0.7%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct217
Distinct (%)76.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean357.91197
Minimum20
Maximum4256
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.6 KiB
2024-04-21T18:01:58.521210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile39
Q1120.75
median261
Q3461.25
95-th percentile950.4
Maximum4256
Range4236
Interquartile range (IQR)340.5

Descriptive statistics

Standard deviation390.9354
Coefficient of variation (CV)1.0922669
Kurtosis38.233982
Mean357.91197
Median Absolute Deviation (MAD)160
Skewness4.7009398
Sum101647
Variance152830.49
MonotonicityNot monotonic
2024-04-21T18:01:58.936795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
40 6
 
2.1%
299 4
 
1.4%
120 4
 
1.4%
80 3
 
1.1%
145 3
 
1.1%
260 3
 
1.1%
179 3
 
1.1%
455 3
 
1.1%
30 3
 
1.1%
209 2
 
0.7%
Other values (207) 250
88.0%
ValueCountFrequency (%)
20 1
 
0.4%
21 1
 
0.4%
28 1
 
0.4%
30 3
1.1%
33 1
 
0.4%
34 2
0.7%
35 1
 
0.4%
36 2
0.7%
38 2
0.7%
39 2
0.7%
ValueCountFrequency (%)
4256 1
0.4%
2646 1
0.4%
1498 1
0.4%
1494 1
0.4%
1411 1
0.4%
1224 1
0.4%
1200 1
0.4%
1095 1
0.4%
1076 1
0.4%
1032 1
0.4%

준공
Date

Distinct255
Distinct (%)89.8%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
Minimum1973-10-18 00:00:00
Maximum2018-04-25 00:00:00
2024-04-21T18:01:59.313451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:59.734794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

의무관리여부
Boolean

HIGH CORRELATION 

Distinct2
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size412.0 B
True
190 
False
94 
ValueCountFrequency (%)
True 190
66.9%
False 94
33.1%
2024-04-21T18:02:00.294295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

전화번호
Text

MISSING 

Distinct272
Distinct (%)100.0%
Missing12
Missing (%)4.2%
Memory size2.3 KiB
2024-04-21T18:02:01.169294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.003676
Min length12

Characters and Unicode

Total characters3265
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique272 ?
Unique (%)100.0%

Sample

1st row053-763-9973
2nd row053-755-7449
3rd row053-756-9192
4th row053-746-8788
5th row053-762-3410
ValueCountFrequency (%)
053-751-3221 1
 
0.4%
053-763-9973 1
 
0.4%
053-761-8893 1
 
0.4%
053-741-1486 1
 
0.4%
053-765-6604 1
 
0.4%
053-762-4326 1
 
0.4%
053-755-3277 1
 
0.4%
053-751-0130 1
 
0.4%
053-945-0272 1
 
0.4%
053-756-9258 1
 
0.4%
Other values (262) 262
96.3%
2024-04-21T18:02:02.496552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 544
16.7%
5 482
14.8%
3 423
13.0%
0 422
12.9%
7 354
10.8%
4 194
 
5.9%
6 188
 
5.8%
1 186
 
5.7%
9 175
 
5.4%
8 149
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2721
83.3%
Dash Punctuation 544
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 482
17.7%
3 423
15.5%
0 422
15.5%
7 354
13.0%
4 194
7.1%
6 188
 
6.9%
1 186
 
6.8%
9 175
 
6.4%
8 149
 
5.5%
2 148
 
5.4%
Dash Punctuation
ValueCountFrequency (%)
- 544
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 3265
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 544
16.7%
5 482
14.8%
3 423
13.0%
0 422
12.9%
7 354
10.8%
4 194
 
5.9%
6 188
 
5.8%
1 186
 
5.7%
9 175
 
5.4%
8 149
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 3265
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 544
16.7%
5 482
14.8%
3 423
13.0%
0 422
12.9%
7 354
10.8%
4 194
 
5.9%
6 188
 
5.8%
1 186
 
5.7%
9 175
 
5.4%
8 149
 
4.6%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.3 KiB
2018-05-17
284 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018-05-17
2nd row2018-05-17
3rd row2018-05-17
4th row2018-05-17
5th row2018-05-17

Common Values

ValueCountFrequency (%)
2018-05-17 284
100.0%

Length

2024-04-21T18:02:02.896679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T18:02:03.187181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018-05-17 284
100.0%

Interactions

2024-04-21T18:01:47.608006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:45.906379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.467426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:47.055976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:47.745413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.044248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.613832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:47.193552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:47.898263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.198021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.772271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:47.346149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:48.030970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.331307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:46.912304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T18:01:47.475458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T18:02:03.363811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번행정동동수층수세대수의무관리여부
연번1.0000.6520.0000.6670.2750.740
행정동0.6521.0000.6660.6370.5410.338
동수0.0000.6661.0000.0000.8250.155
층수0.6670.6370.0001.0000.4260.650
세대수0.2750.5410.8250.4261.0000.578
의무관리여부0.7400.3380.1550.6500.5781.000
2024-04-21T18:02:03.622946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동의무관리여부
행정동1.0000.257
의무관리여부0.2571.000
2024-04-21T18:02:03.867260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번동수층수세대수행정동의무관리여부
연번1.0000.0910.7670.2520.2950.572
동수0.0911.0000.1410.8220.3820.188
층수0.7670.1411.0000.4180.2960.648
세대수0.2520.8220.4181.0000.2400.417
행정동0.2950.3820.2960.2401.0000.257
의무관리여부0.5720.1880.6480.4170.2571.000

Missing values

2024-04-21T18:01:48.226653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T18:01:48.511338image/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

연번아파트명지번주소도로명주소행정동동수층수세대수준공의무관리여부전화번호데이터기준일자
01삼풍아파트대구광역시 수성구 두산동 929-1대구광역시 수성구 용학로 22두산동63721973-10-18N053-763-99732018-05-17
12한도맨션대구광역시 수성구 수성4가 985-77대구광역시 수성구 달구벌대로 2367수성4가18281976-11-29N053-755-74492018-05-17
23삼일아파트대구광역시 수성구 범어3동 13-9대구광역시 수성구 동대구로73길 10-10범어3동15301978-03-10N053-756-91922018-05-17
34수성아파트대구광역시 수성구 황금2동 665대구광역시 수성구 동대구로 204황금2동24391978-11-10N053-746-87882018-05-17
45삼익아파트대구광역시 수성구 수성1가 649-55대구광역시 수성구 신천동로 278수성1가1121001978-12-14N053-762-34102018-05-17
56목화아파트대구광역시 수성구 범어2동 50-2대구광역시 수성구 동대구로74안길 6범어2동451101978-12-15N053-743-85362018-05-17
67범어청구빌라맨션대구광역시 수성구 범어3동 476대구광역시 수성구 상록로11길 5범어3동1101001979-03-19N053-755-17842018-05-17
78아진아파트대구광역시 수성구 만촌3동 1025-5대구광역시 수성구 청호로94길 10만촌3동15381979-04-06N053-755-78232018-05-17
89에덴맨션(범어)대구광역시 수성구 범어3동 475-4대구광역시 수성구 상록로11길 35범어3동24481979-04-14N053-751-32212018-05-17
910삼일맨션대구광역시 수성구 범어3동 31-10대구광역시 수성구 동대구로73길 7범어3동25651979-05-17N053-754-39932018-05-17
연번아파트명지번주소도로명주소행정동동수층수세대수준공의무관리여부전화번호데이터기준일자
274275이편한세상 범어대구광역시 수성구 범어동 2240대구광역시 수성구 들안로78길 45범어3동10298422015-09-21Y053-746-67672018-05-17
275276만촌3차화성파크드림대구광역시 수성구 만촌동 860-1대구광역시 수성구 교학로 13만촌3동5224102016-04-21Y053-945-47002018-05-17
276277만촌 신동아패밀리에대구광역시 수성구 만촌동 1480대구광역시 수성구 화랑로34길 173만촌1동213922016-08-19N053-942-22992018-05-17
277278수성아이파크대구광역시 수성구 파동 1000대구광역시 수성구 파동로22길 15파동6154552016-10-17Y053-761-54482018-05-17
278279만촌역 태왕아너스대구광역시 수성구 만촌동 1481대구광역시 수성구 달구벌대로522길 17만촌3동115692016-10-26N053-754-39392018-05-17
279280범어 라온프라이빗대구광역시 수성구 범어동 2248대구광역시 수성구 동대구로55길 11범어1동3201752016-11-11Y053-781-06642018-05-17
280281브라운스톤 범어대구광역시 수성구 범어동 175-1대구광역시 수성구 동대구로 354범어2동1361802017-06-30Y053-945-45002018-05-17
281282마크펠리스 범어대구광역시 수성구 범어동 177-3대구광역시 수성구 동대구로 336범어2동1361602017-10-26Y053-795-40012018-05-17
282283범어효성해링턴플레이스대구광역시 수성구 범어동 75-2대구광역시 수성구 청호로 433범어4동2201792018-02-08Y053-744-22552018-05-17
283284현대힐스테이트 황금동대구광역시 수성구 황금동 240대구광역시 수성구 청수로 274황금1동8367822018-04-25Y053-219-93002018-05-17