Overview

Dataset statistics

Number of variables11
Number of observations63
Missing cells121
Missing cells (%)17.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.0 KiB
Average record size in memory97.1 B

Variable types

Text3
Numeric6
DateTime1
Categorical1

Dataset

Description충청남도 홍성군의 공동주택에 대한 데이터로 단지명, 법정동주소, 연면적, 동수, 세대수, 사용승인일 등을 제공합니다.
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=59&beforeMenuCd=DOM_000000201001001000&publicdatapk=15111615

Alerts

연면적 is highly overall correlated with 동수 and 5 other fieldsHigh correlation
동수 is highly overall correlated with 연면적 and 5 other fieldsHigh correlation
세대수 is highly overall correlated with 연면적 and 5 other fieldsHigh correlation
전용면적별 세대현황(60제곱미터 이하) is highly overall correlated with 연면적 and 3 other fieldsHigh correlation
전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하) is highly overall correlated with 연면적 and 4 other fieldsHigh correlation
전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하) is highly overall correlated with 연면적 and 4 other fieldsHigh correlation
전용면적별 세대현황(135제곱미터 초과) is highly overall correlated with 연면적 and 3 other fieldsHigh correlation
전용면적별 세대현황(135제곱미터 초과) is highly imbalanced (85.2%)Imbalance
관리사무소연락처 has 22 (34.9%) missing valuesMissing
전용면적별 세대현황(60제곱미터 이하) has 25 (39.7%) missing valuesMissing
전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하) has 18 (28.6%) missing valuesMissing
전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하) has 56 (88.9%) missing valuesMissing
단지명 has unique valuesUnique
법정동주소 has unique valuesUnique

Reproduction

Analysis started2024-03-13 11:52:54.448949
Analysis finished2024-03-13 11:52:59.900004
Duration5.45 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단지명
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2024-03-13T20:53:00.213908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length8.2698413
Min length4

Characters and Unicode

Total characters521
Distinct characters135
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique63 ?
Unique (%)100.0%

Sample

1st row홍양주택
2nd row연동주택
3rd row신진연립
4th row조양연립주택
5th row신천연립
ValueCountFrequency (%)
홍성 7
 
7.3%
아파트 3
 
3.1%
신천아파트 3
 
3.1%
거성아파트 3
 
3.1%
충남꿈비채홍성내포 2
 
2.1%
lh 2
 
2.1%
가동 2
 
2.1%
우주은하아파트 2
 
2.1%
내포 2
 
2.1%
b동 2
 
2.1%
Other values (68) 68
70.8%
2024-03-13T20:53:00.754294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 39
 
7.5%
트 37
 
7.1%
파 37
 
7.1%
33
 
6.3%
성 24
 
4.6%
홍 18
 
3.5%
동 12
 
2.3%
신 11
 
2.1%
주 10
 
1.9%
L 10
 
1.9%
Other values (125) 290
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 450
86.4%
Space Separator 33
 
6.3%
Uppercase Letter 24
 
4.6%
Decimal Number 14
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 39
 
8.7%
트 37
 
8.2%
파 37
 
8.2%
성 24
 
5.3%
홍 18
 
4.0%
동 12
 
2.7%
신 11
 
2.4%
주 10
 
2.2%
천 9
 
2.0%
스 8
 
1.8%
Other values (112) 245
54.4%
Uppercase Letter
ValueCountFrequency (%)
L 10
41.7%
B 6
25.0%
H 4
 
16.7%
R 2
 
8.3%
S 1
 
4.2%
A 1
 
4.2%
Decimal Number
ValueCountFrequency (%)
2 6
42.9%
1 4
28.6%
4 1
 
7.1%
8 1
 
7.1%
9 1
 
7.1%
3 1
 
7.1%
Space Separator
ValueCountFrequency (%)
33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 450
86.4%
Common 47
 
9.0%
Latin 24
 
4.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 39
 
8.7%
트 37
 
8.2%
파 37
 
8.2%
성 24
 
5.3%
홍 18
 
4.0%
동 12
 
2.7%
신 11
 
2.4%
주 10
 
2.2%
천 9
 
2.0%
스 8
 
1.8%
Other values (112) 245
54.4%
Common
ValueCountFrequency (%)
33
70.2%
2 6
 
12.8%
1 4
 
8.5%
4 1
 
2.1%
8 1
 
2.1%
9 1
 
2.1%
3 1
 
2.1%
Latin
ValueCountFrequency (%)
L 10
41.7%
B 6
25.0%
H 4
 
16.7%
R 2
 
8.3%
S 1
 
4.2%
A 1
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 450
86.4%
ASCII 71
 
13.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 39
 
8.7%
트 37
 
8.2%
파 37
 
8.2%
성 24
 
5.3%
홍 18
 
4.0%
동 12
 
2.7%
신 11
 
2.4%
주 10
 
2.2%
천 9
 
2.0%
스 8
 
1.8%
Other values (112) 245
54.4%
ASCII
ValueCountFrequency (%)
33
46.5%
L 10
 
14.1%
B 6
 
8.5%
2 6
 
8.5%
1 4
 
5.6%
H 4
 
5.6%
R 2
 
2.8%
S 1
 
1.4%
4 1
 
1.4%
8 1
 
1.4%
Other values (3) 3
 
4.2%

법정동주소
Text

UNIQUE 

Distinct63
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size636.0 B
2024-03-13T20:53:01.109289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length26
Mean length22.285714
Min length18

Characters and Unicode

Total characters1404
Distinct characters57
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

Unique63 ?
Unique (%)100.0%

Sample

1st row충청남도 홍성군 홍성읍 홍성천길 138-6
2nd row충청남도 홍성군 홍성읍 문화로80번길 53-5
3rd row충청남도 홍성군 광천읍 홍남로620번길 18
4th row충청남도 홍성군 홍성읍 문화로80번길 42
5th row충청남도 홍성군 광천읍 홍남로620번길 33
ValueCountFrequency (%)
충청남도 63
20.1%
홍성군 63
20.1%
홍성읍 34
 
10.8%
홍북읍 14
 
4.5%
광천읍 8
 
2.5%
충서로 6
 
1.9%
월계천길 5
 
1.6%
홍예로 5
 
1.6%
구항면 5
 
1.6%
문화로80번길 4
 
1.3%
Other values (86) 107
34.1%
2024-03-13T20:53:01.622109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
252
17.9%
홍 128
 
9.1%
성 98
 
7.0%
충 71
 
5.1%
남 68
 
4.8%
청 66
 
4.7%
도 65
 
4.6%
군 63
 
4.5%
읍 56
 
4.0%
로 52
 
3.7%
Other values (47) 485
34.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 885
63.0%
Space Separator 252
 
17.9%
Decimal Number 248
 
17.7%
Dash Punctuation 19
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
홍 128
14.5%
성 98
11.1%
충 71
 
8.0%
남 68
 
7.7%
청 66
 
7.5%
도 65
 
7.3%
군 63
 
7.1%
읍 56
 
6.3%
로 52
 
5.9%
길 36
 
4.1%
Other values (35) 182
20.6%
Decimal Number
ValueCountFrequency (%)
1 45
18.1%
2 39
15.7%
3 28
11.3%
0 27
10.9%
4 26
10.5%
8 21
8.5%
6 19
7.7%
5 19
7.7%
7 13
 
5.2%
9 11
 
4.4%
Space Separator
ValueCountFrequency (%)
252
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 885
63.0%
Common 519
37.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
홍 128
14.5%
성 98
11.1%
충 71
 
8.0%
남 68
 
7.7%
청 66
 
7.5%
도 65
 
7.3%
군 63
 
7.1%
읍 56
 
6.3%
로 52
 
5.9%
길 36
 
4.1%
Other values (35) 182
20.6%
Common
ValueCountFrequency (%)
252
48.6%
1 45
 
8.7%
2 39
 
7.5%
3 28
 
5.4%
0 27
 
5.2%
4 26
 
5.0%
8 21
 
4.0%
6 19
 
3.7%
5 19
 
3.7%
- 19
 
3.7%
Other values (2) 24
 
4.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 885
63.0%
ASCII 519
37.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
252
48.6%
1 45
 
8.7%
2 39
 
7.5%
3 28
 
5.4%
0 27
 
5.2%
4 26
 
5.0%
8 21
 
4.0%
6 19
 
3.7%
5 19
 
3.7%
- 19
 
3.7%
Other values (2) 24
 
4.6%
Hangul
ValueCountFrequency (%)
홍 128
14.5%
성 98
11.1%
충 71
 
8.0%
남 68
 
7.7%
청 66
 
7.5%
도 65
 
7.3%
군 63
 
7.1%
읍 56
 
6.3%
로 52
 
5.9%
길 36
 
4.1%
Other values (35) 182
20.6%

연면적
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45634.413
Minimum1575
Maximum311816
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-13T20:53:01.821287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1575
5-th percentile1691.7
Q13760
median14893
Q367800.5
95-th percentile148119.3
Maximum311816
Range310241
Interquartile range (IQR)64040.5

Descriptive statistics

Standard deviation64953.426
Coefficient of variation (CV)1.4233431
Kurtosis5.2030627
Mean45634.413
Median Absolute Deviation (MAD)13033
Skewness2.1676987
Sum2874968
Variance4.2189476 × 109
MonotonicityNot monotonic
2024-03-13T20:53:01.984819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1575 2
 
3.2%
7128 1
 
1.6%
83641 1
 
1.6%
68186 1
 
1.6%
5978 1
 
1.6%
72747 1
 
1.6%
5691 1
 
1.6%
79299 1
 
1.6%
70290 1
 
1.6%
37822 1
 
1.6%
Other values (52) 52
82.5%
ValueCountFrequency (%)
1575 2
3.2%
1659 1
1.6%
1684 1
1.6%
1761 1
1.6%
1860 1
1.6%
1904 1
1.6%
1914 1
1.6%
2334 1
1.6%
2523 1
1.6%
2848 1
1.6%
ValueCountFrequency (%)
311816 1
1.6%
264570 1
1.6%
190741 1
1.6%
148184 1
1.6%
147537 1
1.6%
143879 1
1.6%
141973 1
1.6%
136842 1
1.6%
117080 1
1.6%
101923 1
1.6%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)27.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3015873
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-13T20:53:02.128922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median3
Q38
95-th percentile14.9
Maximum28
Range27
Interquartile range (IQR)7

Descriptive statistics

Standard deviation5.7097396
Coefficient of variation (CV)1.0769868
Kurtosis3.8313072
Mean5.3015873
Median Absolute Deviation (MAD)2
Skewness1.8465631
Sum334
Variance32.601126
MonotonicityNot monotonic
2024-03-13T20:53:02.314210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
1 21
33.3%
2 7
 
11.1%
3 7
 
11.1%
4 5
 
7.9%
5 3
 
4.8%
13 3
 
4.8%
8 3
 
4.8%
12 3
 
4.8%
6 2
 
3.2%
9 2
 
3.2%
Other values (7) 7
 
11.1%
ValueCountFrequency (%)
1 21
33.3%
2 7
 
11.1%
3 7
 
11.1%
4 5
 
7.9%
5 3
 
4.8%
6 2
 
3.2%
7 1
 
1.6%
8 3
 
4.8%
9 2
 
3.2%
11 1
 
1.6%
ValueCountFrequency (%)
28 1
 
1.6%
23 1
 
1.6%
16 1
 
1.6%
15 1
 
1.6%
14 1
 
1.6%
13 3
4.8%
12 3
4.8%
11 1
 
1.6%
9 2
3.2%
8 3
4.8%

세대수
Real number (ℝ)

HIGH CORRELATION 

Distinct54
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean382.50794
Minimum20
Maximum2127
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-13T20:53:02.487999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile24
Q143.5
median182
Q3614
95-th percentile1253.6
Maximum2127
Range2107
Interquartile range (IQR)570.5

Descriptive statistics

Standard deviation459.81433
Coefficient of variation (CV)1.202104
Kurtosis2.8298813
Mean382.50794
Median Absolute Deviation (MAD)152
Skewness1.6632569
Sum24098
Variance211429.22
MonotonicityNot monotonic
2024-03-13T20:53:02.681507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
30 4
 
6.3%
24 3
 
4.8%
21 2
 
3.2%
684 2
 
3.2%
36 2
 
3.2%
60 2
 
3.2%
915 1
 
1.6%
430 1
 
1.6%
468 1
 
1.6%
716 1
 
1.6%
Other values (44) 44
69.8%
ValueCountFrequency (%)
20 1
 
1.6%
21 2
3.2%
24 3
4.8%
29 1
 
1.6%
30 4
6.3%
32 1
 
1.6%
36 2
3.2%
40 1
 
1.6%
42 1
 
1.6%
45 1
 
1.6%
ValueCountFrequency (%)
2127 1
1.6%
1660 1
1.6%
1400 1
1.6%
1260 1
1.6%
1196 1
1.6%
996 1
1.6%
990 1
1.6%
938 1
1.6%
915 1
1.6%
885 1
1.6%

관리사무소연락처
Text

MISSING 

Distinct39
Distinct (%)95.1%
Missing22
Missing (%)34.9%
Memory size636.0 B
2024-03-13T20:53:02.938553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters492
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

Unique37 ?
Unique (%)90.2%

Sample

1st row041-633-0843
2nd row041-634-4505
3rd row041-632-0140
4th row041-633-8580
5th row041-632-0140
ValueCountFrequency (%)
041-631-2983 2
 
4.9%
041-632-0140 2
 
4.9%
041-631-2208 1
 
2.4%
041-634-6161 1
 
2.4%
041-631-9891 1
 
2.4%
041-634-2340 1
 
2.4%
041-631-3585 1
 
2.4%
041-634-0916 1
 
2.4%
041-634-9801 1
 
2.4%
041-641-6009 1
 
2.4%
Other values (29) 29
70.7%
2024-03-13T20:53:03.309272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 82
16.7%
4 78
15.9%
1 73
14.8%
3 64
13.0%
0 62
12.6%
6 57
11.6%
8 20
 
4.1%
2 19
 
3.9%
9 18
 
3.7%
5 17
 
3.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 410
83.3%
Dash Punctuation 82
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 78
19.0%
1 73
17.8%
3 64
15.6%
0 62
15.1%
6 57
13.9%
8 20
 
4.9%
2 19
 
4.6%
9 18
 
4.4%
5 17
 
4.1%
7 2
 
0.5%
Dash Punctuation
ValueCountFrequency (%)
- 82
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 492
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 82
16.7%
4 78
15.9%
1 73
14.8%
3 64
13.0%
0 62
12.6%
6 57
11.6%
8 20
 
4.1%
2 19
 
3.9%
9 18
 
3.7%
5 17
 
3.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 492
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 82
16.7%
4 78
15.9%
1 73
14.8%
3 64
13.0%
0 62
12.6%
6 57
11.6%
8 20
 
4.1%
2 19
 
3.9%
9 18
 
3.7%
5 17
 
3.5%

사용승인일
Date

Distinct59
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size636.0 B
Minimum1985-01-01 00:00:00
Maximum2023-07-19 00:00:00
2024-03-13T20:53:03.483881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:53:03.632533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전용면적별 세대현황(60제곱미터 이하)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct36
Distinct (%)94.7%
Missing25
Missing (%)39.7%
Infinite0
Infinite (%)0.0%
Mean266.73684
Minimum6
Maximum1400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-13T20:53:03.778117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile18
Q157.25
median190
Q3348.5
95-th percentile816.9
Maximum1400
Range1394
Interquartile range (IQR)291.25

Descriptive statistics

Standard deviation297.82978
Coefficient of variation (CV)1.1165678
Kurtosis4.7151963
Mean266.73684
Median Absolute Deviation (MAD)136.5
Skewness1.9881617
Sum10136
Variance88702.578
MonotonicityNot monotonic
2024-03-13T20:53:03.944492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
220 2
 
3.2%
18 2
 
3.2%
314 1
 
1.6%
468 1
 
1.6%
659 1
 
1.6%
250 1
 
1.6%
284 1
 
1.6%
249 1
 
1.6%
163 1
 
1.6%
418 1
 
1.6%
Other values (26) 26
41.3%
(Missing) 25
39.7%
ValueCountFrequency (%)
6 1
1.6%
18 2
3.2%
24 1
1.6%
28 1
1.6%
30 1
1.6%
31 1
1.6%
45 1
1.6%
50 1
1.6%
57 1
1.6%
58 1
1.6%
ValueCountFrequency (%)
1400 1
1.6%
822 1
1.6%
816 1
1.6%
778 1
1.6%
659 1
1.6%
518 1
1.6%
468 1
1.6%
418 1
1.6%
412 1
1.6%
360 1
1.6%

전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)82.2%
Missing18
Missing (%)28.6%
Infinite0
Infinite (%)0.0%
Mean296
Minimum20
Maximum1907
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-13T20:53:04.106728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile21.6
Q132
median106
Q3368
95-th percentile1187.4
Maximum1907
Range1887
Interquartile range (IQR)336

Descriptive statistics

Standard deviation421.45748
Coefficient of variation (CV)1.4238428
Kurtosis4.5141809
Mean296
Median Absolute Deviation (MAD)77
Skewness2.1060473
Sum13320
Variance177626.41
MonotonicityNot monotonic
2024-03-13T20:53:04.284207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
30 3
 
4.8%
32 3
 
4.8%
36 2
 
3.2%
24 2
 
3.2%
21 2
 
3.2%
40 2
 
3.2%
182 1
 
1.6%
831 1
 
1.6%
418 1
 
1.6%
394 1
 
1.6%
Other values (27) 27
42.9%
(Missing) 18
28.6%
ValueCountFrequency (%)
20 1
 
1.6%
21 2
3.2%
24 2
3.2%
26 1
 
1.6%
29 1
 
1.6%
30 3
4.8%
32 3
4.8%
36 2
3.2%
40 2
3.2%
42 1
 
1.6%
ValueCountFrequency (%)
1907 1
1.6%
1346 1
1.6%
1260 1
1.6%
897 1
1.6%
885 1
1.6%
831 1
1.6%
827 1
1.6%
689 1
1.6%
569 1
1.6%
418 1
1.6%

전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)85.7%
Missing56
Missing (%)88.9%
Infinite0
Infinite (%)0.0%
Mean89
Minimum4
Maximum348
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size699.0 B
2024-03-13T20:53:04.438304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile5.2
Q111.5
median15
Q3116.5
95-th percentile291.9
Maximum348
Range344
Interquartile range (IQR)105

Descriptive statistics

Standard deviation127.33944
Coefficient of variation (CV)1.4307802
Kurtosis2.909789
Mean89
Median Absolute Deviation (MAD)11
Skewness1.783891
Sum623
Variance16215.333
MonotonicityNot monotonic
2024-03-13T20:53:04.622013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
15 2
 
3.2%
4 1
 
1.6%
72 1
 
1.6%
8 1
 
1.6%
161 1
 
1.6%
348 1
 
1.6%
(Missing) 56
88.9%
ValueCountFrequency (%)
4 1
1.6%
8 1
1.6%
15 2
3.2%
72 1
1.6%
161 1
1.6%
348 1
1.6%
ValueCountFrequency (%)
348 1
1.6%
161 1
1.6%
72 1
1.6%
15 2
3.2%
8 1
1.6%
4 1
1.6%

전용면적별 세대현황(135제곱미터 초과)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size636.0 B
<NA>
61 
12
 
1
7
 
1

Length

Max length4
Median length4
Mean length3.9206349
Min length1

Unique

Unique2 ?
Unique (%)3.2%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 61
96.8%
12 1
 
1.6%
7 1
 
1.6%

Length

2024-03-13T20:53:04.802894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T20:53:04.964250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 61
96.8%
12 1
 
1.6%
7 1
 
1.6%

Interactions

2024-03-13T20:52:58.305637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.020492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.753232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.430450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.031074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.621970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.435023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.123907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.850581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.529198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.137231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.755476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.531824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.220871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.957173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.623229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.237920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.876447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.624605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.327200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.066066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.707294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.327426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.973541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.730715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.474302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.188345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.817741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.421624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.083672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.946517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:55.634701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.317882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:56.923321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:57.535996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T20:52:58.201569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T20:53:05.062591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단지명법정동주소연면적동수세대수관리사무소연락처사용승인일전용면적별 세대현황(60제곱미터 이하)전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)전용면적별 세대현황(135제곱미터 초과)
단지명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
법정동주소1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0000.000
연면적1.0001.0001.0000.9600.9181.0000.9270.9050.9821.000NaN
동수1.0001.0000.9601.0000.8731.0000.9940.6790.9671.000NaN
세대수1.0001.0000.9180.8731.0001.0000.0000.8380.9711.0000.000
관리사무소연락처1.0001.0001.0001.0001.0001.0000.9921.0001.0001.000NaN
사용승인일1.0001.0000.9270.9940.0000.9921.0000.0001.0001.0000.000
전용면적별 세대현황(60제곱미터 이하)1.0001.0000.9050.6790.8381.0000.0001.0000.646NaNNaN
전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)1.0001.0000.9820.9670.9711.0001.0000.6461.0001.000NaN
전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)1.0001.0001.0001.0001.0001.0001.000NaN1.0001.000NaN
전용면적별 세대현황(135제곱미터 초과)0.0000.000NaNNaN0.000NaN0.000NaNNaNNaN1.000
2024-03-13T20:53:05.239172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연면적동수세대수전용면적별 세대현황(60제곱미터 이하)전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)전용면적별 세대현황(135제곱미터 초과)
연면적1.0000.8480.9760.6780.9690.9911.000
동수0.8481.0000.8680.7300.7910.5981.000
세대수0.9760.8681.0000.7630.9580.9911.000
전용면적별 세대현황(60제곱미터 이하)0.6780.7300.7631.0000.4841.000NaN
전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)0.9690.7910.9580.4841.0000.9911.000
전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)0.9910.5980.9911.0000.9911.000NaN
전용면적별 세대현황(135제곱미터 초과)1.0001.0001.000NaN1.000NaN1.000

Missing values

2024-03-13T20:52:59.091737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T20:52:59.293449image/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.
2024-03-13T20:52:59.785887image/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

단지명법정동주소연면적동수세대수관리사무소연락처사용승인일전용면적별 세대현황(60제곱미터 이하)전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)전용면적별 세대현황(135제곱미터 초과)
0홍양주택충청남도 홍성군 홍성읍 홍성천길 138-61659221<NA>1985-01-01<NA>21<NA><NA>
1연동주택충청남도 홍성군 홍성읍 문화로80번길 53-51575124<NA>1985-02-18<NA>24<NA><NA>
2신진연립충청남도 홍성군 광천읍 홍남로620번길 181575124<NA>1985-03-13<NA>24<NA><NA>
3조양연립주택충청남도 홍성군 홍성읍 문화로80번길 421860230<NA>1985-12-31<NA>30<NA><NA>
4신천연립충청남도 홍성군 광천읍 홍남로620번길 331761221<NA>1987-02-02<NA>21<NA><NA>
5선광연립충청남도 홍성군 홍성읍 홍장북로441번길 163209257<NA>1987-08-1857<NA><NA><NA>
6신천주택충청남도 홍성군 홍성읍 교동길 92523230<NA>1988-04-09<NA>30<NA><NA>
7신천아파트 가동충청남도 홍성군 홍성읍 월계천길 2902848129<NA>1989-07-11<NA>29<NA><NA>
8백조연립주택충청남도 홍성군 홍성읍 내포로 722958336<NA>1989-11-236264<NA>
9미주아파트충청남도 홍성군 홍성읍 조양로232번길 20-38136398<NA>1990-01-115840<NA><NA>
단지명법정동주소연면적동수세대수관리사무소연락처사용승인일전용면적별 세대현황(60제곱미터 이하)전용면적별 세대현황(60제곱미터 초과 85제곱미터 이하)전용면적별 세대현황(85제곱미터 초과 135제곱미터 이하)전용면적별 세대현황(135제곱미터 초과)
53한울마을 모아엘가충청남도 홍성군 홍북읍 홍학리 88190741151260041-631-89362016-04-08<NA>1260<NA><NA>
54내포 상록아파트충청남도 홍성군 홍북읍 홍학로 256499916497041-634-29852016-07-2541879<NA><NA>
55홍성남장천년나무4단지충청남도 홍성군 홍성읍 남장중로 37288444518041-631-64042016-11-30518<NA><NA><NA>
56홍성남장 이안아파트충청남도 홍성군 홍성읍 문화로72번길 50516968394041-632-29842017-05-30<NA>394<NA><NA>
57충남꿈비채홍성내포 RL8BL충청남도 홍성군 홍북읍 홍예로 2885830145041-631-29832022-10-2645<NA><NA><NA>
58충남꿈비채홍성내포 RL9BL충청남도 홍성군 홍북읍 홍예로 2903799130041-631-29832022-10-2630<NA><NA><NA>
59한울마을LH2단지1BL아파트충청남도 홍성군 홍북읍 홍학로 124101923141400041-634-61612022-11-081400<NA><NA><NA>
60한울마을LH2단지2BL충청남도 홍성군 홍북읍 홍예로 16394508121196041-631-22082022-11-08778418<NA><NA>
61가람마을 LH 1단지충청남도 홍성군 홍북읍 홍예로 164674155822041-634-23402023-07-18822<NA><NA><NA>
62대방엘리움 2차 더센트럴충청남도 홍성군 홍북읍 자경로 1814818413831041-631-55112023-07-19<NA>831<NA><NA>