Overview

Dataset statistics

Number of variables11
Number of observations89
Missing cells47
Missing cells (%)4.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.1 KiB
Average record size in memory93.5 B

Variable types

Numeric4
Text5
Categorical1
DateTime1

Dataset

Description상주시 관내에 위치한 공동주택, 다세대주택, 연립주택 현황(단지명, 대지위치, 도로명주소, 세대수, 동수, 층수, 준공일자, 과리사무실 연락처, 우편번호) 데이터
Author경상북도 상주시
URLhttps://www.data.go.kr/data/15005339/fileData.do

Alerts

데이터기준일 has constant value ""Constant
세대수 is highly overall correlated with 동수High correlation
동수 is highly overall correlated with 세대수High correlation
관리사무실 has 44 (49.4%) missing valuesMissing
우편번호 has 3 (3.4%) missing valuesMissing
번호 has unique valuesUnique
단지명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:50:39.582198
Analysis finished2023-12-12 16:50:42.730153
Duration3.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45
Minimum1
Maximum89
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size933.0 B
2023-12-13T01:50:42.837569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile5.4
Q123
median45
Q367
95-th percentile84.6
Maximum89
Range88
Interquartile range (IQR)44

Descriptive statistics

Standard deviation25.836021
Coefficient of variation (CV)0.57413381
Kurtosis-1.2
Mean45
Median Absolute Deviation (MAD)22
Skewness0
Sum4005
Variance667.5
MonotonicityStrictly increasing
2023-12-13T01:50:43.008790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.1%
68 1
 
1.1%
66 1
 
1.1%
65 1
 
1.1%
64 1
 
1.1%
63 1
 
1.1%
62 1
 
1.1%
61 1
 
1.1%
60 1
 
1.1%
59 1
 
1.1%
Other values (79) 79
88.8%
ValueCountFrequency (%)
1 1
1.1%
2 1
1.1%
3 1
1.1%
4 1
1.1%
5 1
1.1%
6 1
1.1%
7 1
1.1%
8 1
1.1%
9 1
1.1%
10 1
1.1%
ValueCountFrequency (%)
89 1
1.1%
88 1
1.1%
87 1
1.1%
86 1
1.1%
85 1
1.1%
84 1
1.1%
83 1
1.1%
82 1
1.1%
81 1
1.1%
80 1
1.1%

단지명
Text

UNIQUE 

Distinct89
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-13T01:50:43.358854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length12
Mean length7.5842697
Min length4

Characters and Unicode

Total characters675
Distinct characters132
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

Unique89 ?
Unique (%)100.0%

Sample

1st row1주공아파트
2nd row동보아파트
3rd row세진아파트
4th row2주공아파트
5th row장미아파트
ValueCountFrequency (%)
102동 5
 
4.5%
냉림드림뷰 4
 
3.6%
성동드림뷰 3
 
2.7%
101동 3
 
2.7%
해오름 2
 
1.8%
프라임로즈 2
 
1.8%
태광패밀리 2
 
1.8%
명가드림파크 2
 
1.8%
103동 2
 
1.8%
2차 2
 
1.8%
Other values (82) 83
75.5%
2023-12-13T01:50:43.839814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
파 39
 
5.8%
아 36
 
5.3%
트 32
 
4.7%
동 32
 
4.7%
1 27
 
4.0%
림 25
 
3.7%
21
 
3.1%
드 17
 
2.5%
2 17
 
2.5%
0 15
 
2.2%
Other values (122) 414
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 543
80.4%
Decimal Number 70
 
10.4%
Space Separator 21
 
3.1%
Close Punctuation 14
 
2.1%
Open Punctuation 14
 
2.1%
Uppercase Letter 13
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
파 39
 
7.2%
아 36
 
6.6%
트 32
 
5.9%
동 32
 
5.9%
림 25
 
4.6%
드 17
 
3.1%
차 13
 
2.4%
지 13
 
2.4%
리 12
 
2.2%
주 11
 
2.0%
Other values (107) 313
57.6%
Decimal Number
ValueCountFrequency (%)
1 27
38.6%
2 17
24.3%
0 15
21.4%
3 5
 
7.1%
6 2
 
2.9%
5 2
 
2.9%
7 1
 
1.4%
4 1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
L 4
30.8%
H 4
30.8%
S 3
23.1%
G 2
15.4%
Space Separator
ValueCountFrequency (%)
21
100.0%
Close Punctuation
ValueCountFrequency (%)
) 14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 543
80.4%
Common 119
 
17.6%
Latin 13
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
파 39
 
7.2%
아 36
 
6.6%
트 32
 
5.9%
동 32
 
5.9%
림 25
 
4.6%
드 17
 
3.1%
차 13
 
2.4%
지 13
 
2.4%
리 12
 
2.2%
주 11
 
2.0%
Other values (107) 313
57.6%
Common
ValueCountFrequency (%)
1 27
22.7%
21
17.6%
2 17
14.3%
0 15
12.6%
) 14
11.8%
( 14
11.8%
3 5
 
4.2%
6 2
 
1.7%
5 2
 
1.7%
7 1
 
0.8%
Latin
ValueCountFrequency (%)
L 4
30.8%
H 4
30.8%
S 3
23.1%
G 2
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 543
80.4%
ASCII 132
 
19.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
파 39
 
7.2%
아 36
 
6.6%
트 32
 
5.9%
동 32
 
5.9%
림 25
 
4.6%
드 17
 
3.1%
차 13
 
2.4%
지 13
 
2.4%
리 12
 
2.2%
주 11
 
2.0%
Other values (107) 313
57.6%
ASCII
ValueCountFrequency (%)
1 27
20.5%
21
15.9%
2 17
12.9%
0 15
11.4%
) 14
10.6%
( 14
10.6%
3 5
 
3.8%
L 4
 
3.0%
H 4
 
3.0%
S 3
 
2.3%
Other values (5) 8
 
6.1%

대지위치
Text

Distinct88
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-13T01:50:44.164795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length17.662921
Min length15

Characters and Unicode

Total characters1572
Distinct characters47
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

Unique87 ?
Unique (%)97.8%

Sample

1st row경상북도 상주시 냉림동 172
2nd row경상북도 상주시 무양동 201-3
3rd row경상북도 상주시 함창 구향리 38-2
4th row경상북도 상주시 냉림동 119
5th row경상북도 상주시 무양동 284-1
ValueCountFrequency (%)
경상북도 89
24.6%
상주시 89
24.6%
냉림동 19
 
5.2%
낙양동 14
 
3.9%
남성동 12
 
3.3%
신봉동 12
 
3.3%
무양동 10
 
2.8%
성동동 8
 
2.2%
복룡동 4
 
1.1%
구향리 3
 
0.8%
Other values (95) 102
28.2%
2023-12-13T01:50:44.704449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
273
17.4%
상 178
11.3%
1 98
 
6.2%
동 93
 
5.9%
시 89
 
5.7%
경 89
 
5.7%
주 89
 
5.7%
도 89
 
5.7%
북 89
 
5.7%
- 66
 
4.2%
Other values (37) 419
26.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 906
57.6%
Decimal Number 327
 
20.8%
Space Separator 273
 
17.4%
Dash Punctuation 66
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 178
19.6%
동 93
10.3%
시 89
9.8%
경 89
9.8%
주 89
9.8%
도 89
9.8%
북 89
9.8%
양 24
 
2.6%
성 21
 
2.3%
림 19
 
2.1%
Other values (25) 126
13.9%
Decimal Number
ValueCountFrequency (%)
1 98
30.0%
2 46
14.1%
6 31
 
9.5%
9 26
 
8.0%
0 25
 
7.6%
5 25
 
7.6%
3 24
 
7.3%
8 19
 
5.8%
4 19
 
5.8%
7 14
 
4.3%
Space Separator
ValueCountFrequency (%)
273
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 906
57.6%
Common 666
42.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 178
19.6%
동 93
10.3%
시 89
9.8%
경 89
9.8%
주 89
9.8%
도 89
9.8%
북 89
9.8%
양 24
 
2.6%
성 21
 
2.3%
림 19
 
2.1%
Other values (25) 126
13.9%
Common
ValueCountFrequency (%)
273
41.0%
1 98
 
14.7%
- 66
 
9.9%
2 46
 
6.9%
6 31
 
4.7%
9 26
 
3.9%
0 25
 
3.8%
5 25
 
3.8%
3 24
 
3.6%
8 19
 
2.9%
Other values (2) 33
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 906
57.6%
ASCII 666
42.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
273
41.0%
1 98
 
14.7%
- 66
 
9.9%
2 46
 
6.9%
6 31
 
4.7%
9 26
 
3.9%
0 25
 
3.8%
5 25
 
3.8%
3 24
 
3.6%
8 19
 
2.9%
Other values (2) 33
 
5.0%
Hangul
ValueCountFrequency (%)
상 178
19.6%
동 93
10.3%
시 89
9.8%
경 89
9.8%
주 89
9.8%
도 89
9.8%
북 89
9.8%
양 24
 
2.6%
성 21
 
2.3%
림 19
 
2.1%
Other values (25) 126
13.9%

도로명주소
Text

Distinct82
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-13T01:50:45.064711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length33
Mean length29.730337
Min length16

Characters and Unicode

Total characters2646
Distinct characters149
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

Unique77 ?
Unique (%)86.5%

Sample

1st row경상북도 상주시 상산로 366(냉림동,냉림1주공아파트)
2nd row경상북도 상주시 동수3길 117(무양동,동보아파트)
3rd row경상북도 상주시 함창읍 구향2길 23(세진맨션)
4th row경상북도 상주시 상산로 384(냉림동,냉림2주공 아파트)
5th row경상북도 상주시 서문길 10 (무양동, 장미타운)
ValueCountFrequency (%)
경상북도 89
 
17.4%
상주시 89
 
17.4%
냉림동 13
 
2.5%
낙양동 12
 
2.3%
신봉동 11
 
2.1%
남성동 10
 
2.0%
무양동 8
 
1.6%
상산로 8
 
1.6%
성동동 8
 
1.6%
중앙로 7
 
1.4%
Other values (177) 257
50.2%
2023-12-13T01:50:45.768124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
423
 
16.0%
상 204
 
7.7%
동 114
 
4.3%
주 105
 
4.0%
경 95
 
3.6%
시 92
 
3.5%
도 89
 
3.4%
북 89
 
3.4%
) 85
 
3.2%
( 85
 
3.2%
Other values (139) 1265
47.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1653
62.5%
Space Separator 423
 
16.0%
Decimal Number 301
 
11.4%
Close Punctuation 85
 
3.2%
Open Punctuation 85
 
3.2%
Other Punctuation 75
 
2.8%
Dash Punctuation 18
 
0.7%
Uppercase Letter 6
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 204
 
12.3%
동 114
 
6.9%
주 105
 
6.4%
경 95
 
5.7%
시 92
 
5.6%
도 89
 
5.4%
북 89
 
5.4%
림 61
 
3.7%
길 51
 
3.1%
냉 43
 
2.6%
Other values (120) 710
43.0%
Decimal Number
ValueCountFrequency (%)
1 82
27.2%
2 50
16.6%
3 36
12.0%
7 23
 
7.6%
4 22
 
7.3%
8 22
 
7.3%
5 21
 
7.0%
6 20
 
6.6%
9 13
 
4.3%
0 12
 
4.0%
Uppercase Letter
ValueCountFrequency (%)
H 2
33.3%
L 2
33.3%
S 1
16.7%
G 1
16.7%
Space Separator
ValueCountFrequency (%)
423
100.0%
Close Punctuation
ValueCountFrequency (%)
) 85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 85
100.0%
Other Punctuation
ValueCountFrequency (%)
, 75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1653
62.5%
Common 987
37.3%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 204
 
12.3%
동 114
 
6.9%
주 105
 
6.4%
경 95
 
5.7%
시 92
 
5.6%
도 89
 
5.4%
북 89
 
5.4%
림 61
 
3.7%
길 51
 
3.1%
냉 43
 
2.6%
Other values (120) 710
43.0%
Common
ValueCountFrequency (%)
423
42.9%
) 85
 
8.6%
( 85
 
8.6%
1 82
 
8.3%
, 75
 
7.6%
2 50
 
5.1%
3 36
 
3.6%
7 23
 
2.3%
4 22
 
2.2%
8 22
 
2.2%
Other values (5) 84
 
8.5%
Latin
ValueCountFrequency (%)
H 2
33.3%
L 2
33.3%
S 1
16.7%
G 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1653
62.5%
ASCII 993
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
423
42.6%
) 85
 
8.6%
( 85
 
8.6%
1 82
 
8.3%
, 75
 
7.6%
2 50
 
5.0%
3 36
 
3.6%
7 23
 
2.3%
4 22
 
2.2%
8 22
 
2.2%
Other values (9) 90
 
9.1%
Hangul
ValueCountFrequency (%)
상 204
 
12.3%
동 114
 
6.9%
주 105
 
6.4%
경 95
 
5.7%
시 92
 
5.6%
도 89
 
5.4%
북 89
 
5.4%
림 61
 
3.7%
길 51
 
3.1%
냉 43
 
2.6%
Other values (120) 710
43.0%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)60.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean121.58427
Minimum10
Maximum737
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size933.0 B
2023-12-13T01:50:45.986871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14
Q118
median49
Q3168
95-th percentile461.2
Maximum737
Range727
Interquartile range (IQR)150

Descriptive statistics

Standard deviation154.71916
Coefficient of variation (CV)1.2725261
Kurtosis2.511792
Mean121.58427
Median Absolute Deviation (MAD)33
Skewness1.7199561
Sum10821
Variance23938.018
MonotonicityNot monotonic
2023-12-13T01:50:46.198888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18 12
 
13.5%
19 8
 
9.0%
16 6
 
6.7%
10 4
 
4.5%
210 3
 
3.4%
36 2
 
2.2%
375 2
 
2.2%
49 2
 
2.2%
50 2
 
2.2%
14 2
 
2.2%
Other values (44) 46
51.7%
ValueCountFrequency (%)
10 4
 
4.5%
14 2
 
2.2%
15 2
 
2.2%
16 6
6.7%
18 12
13.5%
19 8
9.0%
20 1
 
1.1%
24 1
 
1.1%
30 1
 
1.1%
32 1
 
1.1%
ValueCountFrequency (%)
737 1
1.1%
498 1
1.1%
496 1
1.1%
478 1
1.1%
468 1
1.1%
451 1
1.1%
450 1
1.1%
387 1
1.1%
375 2
2.2%
346 1
1.1%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct9
Distinct (%)10.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.011236
Minimum1
Maximum9
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size933.0 B
2023-12-13T01:50:46.340215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median1
Q32
95-th percentile6
Maximum9
Range8
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.8058892
Coefficient of variation (CV)0.89790024
Kurtosis4.2908573
Mean2.011236
Median Absolute Deviation (MAD)0
Skewness2.1501693
Sum179
Variance3.261236
MonotonicityNot monotonic
2023-12-13T01:50:46.464328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1 56
62.9%
2 13
 
14.6%
3 6
 
6.7%
4 5
 
5.6%
5 3
 
3.4%
6 2
 
2.2%
8 2
 
2.2%
7 1
 
1.1%
9 1
 
1.1%
ValueCountFrequency (%)
1 56
62.9%
2 13
 
14.6%
3 6
 
6.7%
4 5
 
5.6%
5 3
 
3.4%
6 2
 
2.2%
7 1
 
1.1%
8 2
 
2.2%
9 1
 
1.1%
ValueCountFrequency (%)
9 1
 
1.1%
8 2
 
2.2%
7 1
 
1.1%
6 2
 
2.2%
5 3
 
3.4%
4 5
 
5.6%
3 6
 
6.7%
2 13
 
14.6%
1 56
62.9%

층수
Categorical

Distinct12
Distinct (%)13.5%
Missing0
Missing (%)0.0%
Memory size844.0 B
15
25 
10
18 
5
16 
9
6 
6
5 
Other values (7)
19 

Length

Max length5
Median length2
Mean length1.6966292
Min length1

Unique

Unique1 ?
Unique (%)1.1%

Sample

1st row5
2nd row5
3rd row5
4th row5
5th row5

Common Values

ValueCountFrequency (%)
15 25
28.1%
10 18
20.2%
5 16
18.0%
9 6
 
6.7%
6 5
 
5.6%
14 4
 
4.5%
18 4
 
4.5%
7 3
 
3.4%
11 3
 
3.4%
13 2
 
2.2%
Other values (2) 3
 
3.4%

Length

2023-12-13T01:50:46.632226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
15 25
28.1%
10 18
20.2%
5 16
18.0%
9 6
 
6.7%
6 5
 
5.6%
14 4
 
4.5%
18 4
 
4.5%
7 3
 
3.4%
11 3
 
3.4%
13 2
 
2.2%
Other values (2) 3
 
3.4%

준공일자
Text

Distinct80
Distinct (%)89.9%
Missing0
Missing (%)0.0%
Memory size844.0 B
2023-12-13T01:50:46.918593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9213483
Min length3

Characters and Unicode

Total characters883
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique71 ?
Unique (%)79.8%

Sample

1st row1984-12-07
2nd row1988-12-29
3rd row1989-06-02
4th row1989-10-14
5th row1989-12-06
ValueCountFrequency (%)
2011-12-02 2
 
2.2%
2015-12-01 2
 
2.2%
2011-06-29 2
 
2.2%
2014-04-03 2
 
2.2%
1991-07-29 2
 
2.2%
2013-12-17 2
 
2.2%
2013-08-01 2
 
2.2%
1995-09-01 2
 
2.2%
2016-06-16 2
 
2.2%
1984-12-07 1
 
1.1%
Other values (70) 70
78.7%
2023-12-13T01:50:47.390531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 185
21.0%
1 176
19.9%
- 176
19.9%
2 128
14.5%
9 88
10.0%
5 26
 
2.9%
7 24
 
2.7%
4 22
 
2.5%
3 22
 
2.5%
8 17
 
1.9%
Other values (4) 19
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 704
79.7%
Dash Punctuation 176
 
19.9%
Other Letter 3
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 185
26.3%
1 176
25.0%
2 128
18.2%
9 88
12.5%
5 26
 
3.7%
7 24
 
3.4%
4 22
 
3.1%
3 22
 
3.1%
8 17
 
2.4%
6 16
 
2.3%
Other Letter
ValueCountFrequency (%)
미 1
33.3%
준 1
33.3%
공 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 176
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 880
99.7%
Hangul 3
 
0.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 185
21.0%
1 176
20.0%
- 176
20.0%
2 128
14.5%
9 88
10.0%
5 26
 
3.0%
7 24
 
2.7%
4 22
 
2.5%
3 22
 
2.5%
8 17
 
1.9%
Hangul
ValueCountFrequency (%)
미 1
33.3%
준 1
33.3%
공 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 880
99.7%
Hangul 3
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 185
21.0%
1 176
20.0%
- 176
20.0%
2 128
14.5%
9 88
10.0%
5 26
 
3.0%
7 24
 
2.7%
4 22
 
2.5%
3 22
 
2.5%
8 17
 
1.9%
Hangul
ValueCountFrequency (%)
미 1
33.3%
준 1
33.3%
공 1
33.3%

관리사무실
Text

MISSING 

Distinct44
Distinct (%)97.8%
Missing44
Missing (%)49.4%
Memory size844.0 B
2023-12-13T01:50:47.668160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters540
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

Unique43 ?
Unique (%)95.6%

Sample

1st row054-533-4188
2nd row054-535-4427
3rd row054-541-4105
4th row054-535-6692
5th row054-533-6411
ValueCountFrequency (%)
054-536-7388 2
 
4.4%
054-535-1504 1
 
2.2%
054-533-0852 1
 
2.2%
054-533-4188 1
 
2.2%
054-531-0661 1
 
2.2%
054-531-2181 1
 
2.2%
054-535-0601 1
 
2.2%
054-534-0147 1
 
2.2%
054-536-6432 1
 
2.2%
054-534-6325 1
 
2.2%
Other values (34) 34
75.6%
2023-12-13T01:50:48.034108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 117
21.7%
- 90
16.7%
4 69
12.8%
0 68
12.6%
3 65
12.0%
1 42
 
7.8%
6 25
 
4.6%
8 17
 
3.1%
7 16
 
3.0%
2 16
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 450
83.3%
Dash Punctuation 90
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 117
26.0%
4 69
15.3%
0 68
15.1%
3 65
14.4%
1 42
 
9.3%
6 25
 
5.6%
8 17
 
3.8%
7 16
 
3.6%
2 16
 
3.6%
9 15
 
3.3%
Dash Punctuation
ValueCountFrequency (%)
- 90
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 540
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 117
21.7%
- 90
16.7%
4 69
12.8%
0 68
12.6%
3 65
12.0%
1 42
 
7.8%
6 25
 
4.6%
8 17
 
3.1%
7 16
 
3.0%
2 16
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 540
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 117
21.7%
- 90
16.7%
4 69
12.8%
0 68
12.6%
3 65
12.0%
1 42
 
7.8%
6 25
 
4.6%
8 17
 
3.1%
7 16
 
3.0%
2 16
 
3.0%

우편번호
Real number (ℝ)

MISSING 

Distinct44
Distinct (%)51.2%
Missing3
Missing (%)3.4%
Infinite0
Infinite (%)0.0%
Mean37195.616
Minimum37114
Maximum37240
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size933.0 B
2023-12-13T01:50:48.235200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37114
5-th percentile37167
Q137176
median37202
Q337215
95-th percentile37232.75
Maximum37240
Range126
Interquartile range (IQR)39

Descriptive statistics

Standard deviation27.882598
Coefficient of variation (CV)0.00074962054
Kurtosis1.1866029
Mean37195.616
Median Absolute Deviation (MAD)18.5
Skewness-0.89053677
Sum3198823
Variance777.43926
MonotonicityNot monotonic
2023-12-13T01:50:48.408267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
37232 7
 
7.9%
37208 5
 
5.6%
37202 4
 
4.5%
37176 4
 
4.5%
37168 4
 
4.5%
37204 3
 
3.4%
37215 3
 
3.4%
37218 3
 
3.4%
37197 3
 
3.4%
37179 2
 
2.2%
Other values (34) 48
53.9%
(Missing) 3
 
3.4%
ValueCountFrequency (%)
37114 2
2.2%
37115 1
 
1.1%
37117 1
 
1.1%
37167 2
2.2%
37168 4
4.5%
37169 2
2.2%
37170 1
 
1.1%
37171 1
 
1.1%
37172 2
2.2%
37173 1
 
1.1%
ValueCountFrequency (%)
37240 1
 
1.1%
37235 1
 
1.1%
37234 2
 
2.2%
37233 1
 
1.1%
37232 7
7.9%
37221 1
 
1.1%
37220 2
 
2.2%
37219 1
 
1.1%
37218 3
3.4%
37216 2
 
2.2%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size844.0 B
Minimum2021-10-14 00:00:00
Maximum2021-10-14 00:00:00
2023-12-13T01:50:48.532295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:48.645817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-13T01:50:41.895781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:40.466825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.064782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.417840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.980952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:40.814683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.143108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.541950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:42.089818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:40.890933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.225455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.685166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:42.206128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:40.981098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.325174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:50:41.797372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:50:48.771524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호단지명대지위치도로명주소세대수동수층수준공일자관리사무실우편번호
번호1.0001.0001.0000.9720.4230.4550.6211.0001.0000.390
단지명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대지위치1.0001.0001.0000.9960.0000.8911.0001.0000.9961.000
도로명주소0.9721.0000.9961.0001.0001.0001.0000.9881.0001.000
세대수0.4231.0000.0001.0001.0000.7620.3210.0001.0000.258
동수0.4551.0000.8911.0000.7621.0000.0000.9831.0000.203
층수0.6211.0001.0001.0000.3210.0001.0000.9831.0000.374
준공일자1.0001.0001.0000.9880.0000.9830.9831.0000.9840.998
관리사무실1.0001.0000.9961.0001.0001.0001.0000.9841.0000.986
우편번호0.3901.0001.0001.0000.2580.2030.3740.9980.9861.000
2023-12-13T01:50:48.944102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호세대수동수우편번호층수
번호1.000-0.226-0.227-0.1240.305
세대수-0.2261.0000.781-0.1180.133
동수-0.2270.7811.000-0.1350.000
우편번호-0.124-0.118-0.1351.0000.170
층수0.3050.1330.0000.1701.000

Missing values

2023-12-13T01:50:42.350424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:50:42.533637image/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.
2023-12-13T01:50:42.655065image/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

번호단지명대지위치도로명주소세대수동수층수준공일자관리사무실우편번호데이터기준일
011주공아파트경상북도 상주시 냉림동 172경상북도 상주시 상산로 366(냉림동,냉림1주공아파트)130451984-12-07054-533-4188371712021-10-14
12동보아파트경상북도 상주시 무양동 201-3경상북도 상주시 동수3길 117(무양동,동보아파트)20151988-12-29054-535-4427371692021-10-14
23세진아파트경상북도 상주시 함창 구향리 38-2경상북도 상주시 함창읍 구향2길 23(세진맨션)60251989-06-02054-541-4105371152021-10-14
342주공아파트경상북도 상주시 냉림동 119경상북도 상주시 상산로 384(냉림동,냉림2주공 아파트)290751989-10-14054-535-6692371732021-10-14
45장미아파트경상북도 상주시 무양동 284-1경상북도 상주시 서문길 10 (무양동, 장미타운)16151989-12-06<NA>371842021-10-14
56대림맨션경상북도 상주시 서문동 140경상북도 상주시 문무3길 7(서문동,대림맨션)15151990-01-29<NA>371852021-10-14
67그린맨션경상북도 상주시 남성동 13-80경상북도 상주시 중앙로 178-14(남성동,그린맨션)60251990-03-17054-533-6411371972021-10-14
78파크맨션경상북도 상주시 냉림동 160경상북도 상주시 냉림4길 48-2 (냉림동, 남일파크맨션)30151990-07-21054-533-7464371722021-10-14
89백합맨션경상북도 상주시 성동동 626-1경상북도 상주시 성동로 24-8 (성동동, 백합맨션)132361990-09-14054-534-9293372132021-10-14
910동보고층아파트경상북도 상주시 무양동 159-1경상북도 상주시 동수3길 90 (무양동, 동보고층아파트)168291991-07-29054-533-3831371692021-10-14
번호단지명대지위치도로명주소세대수동수층수준공일자관리사무실우편번호데이터기준일
7980명가드림파크 101동경상북도 상주시 남성동 180-3경상북도 상주시 왕산로 51 (남성동, 명가드림파크)161102014-04-03<NA>372152021-10-14
8081무양다미아S주상복합경상북도 상주시 무양동 281-4경상북도 상주시 삼백로 88 (무양동)901152014-12-19<NA>371842021-10-14
8182무양 지엘리베라움경상북도 상주시 무양동 315경상북도 상주시 봉양1길 126 (무양동)3435182015-10-05054-536-8951371682021-10-14
8283태광 패밀리2차경상북도 상주시 낙양동 111경상북도 상주시 상서문2길 33 (낙양동)491182015-10-22<NA>372002021-10-14
8384새빛힐즈(101동)경상북도 상주시 냉림동 212-7경상북도 상주시 냉림1길 63-9(냉림동)181102015-12-01<NA>371762021-10-14
8485새빛힐즈(102동)경상북도 상주시 냉림동 212-12경상북도 상주시 냉림1길 63-7(냉림동)181102015-12-01<NA>371762021-10-14
8586함창LH천년나무1단지(임대)경상북도 상주시 함창읍 구향리 608경상북도 상주시 함창읍 함령길 1383163152016-06-16054-541-1551371142021-10-14
8687함창LH천년나무2단지(분양)경상북도 상주시 함창읍 구향리 609경상북도 상주시 함창읍 구향4길 152103182016-06-16054-541-3773371142021-10-14
8788드림힐즈경상북도 상주시 남성동 159-3경상북도 상주시 왕산로 128241142017-05-10<NA><NA>2021-10-14
8889삼백아파트(구 동양아파트)경상북도 상주시 냉림동 136-1경상북도 상주시 냉림4길 11221055미준공054-531-2334371752021-10-14