Overview

Dataset statistics

Number of variables9
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory80.6 B

Variable types

Numeric4
Text4
Categorical1

Dataset

Description보령시 의무관리대상 공동주택 현황 (단지명, 주소, 준공년도, 동수, 세대수, 관리사무소 전화번호 및 팩스번호) 데이터
URLhttps://www.data.go.kr/data/15080857/fileData.do

Alerts

데이터기준일 has constant value ""Constant
구분 is highly overall correlated with 준공년도High correlation
준공년도 is highly overall correlated with 구분High correlation
동수 is highly overall correlated with 세대수 High correlation
세대수 is highly overall correlated with 동수High correlation
구분 has unique valuesUnique
단 지 명 has unique valuesUnique
세대수 has unique valuesUnique
전화번호 has unique valuesUnique
팩스(FAX) has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:26:24.008353
Analysis finished2023-12-12 11:26:27.251848
Duration3.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15
Minimum1
Maximum29
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T20:26:27.412157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.4
Q18
median15
Q322
95-th percentile27.6
Maximum29
Range28
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.5146932
Coefficient of variation (CV)0.56764621
Kurtosis-1.2
Mean15
Median Absolute Deviation (MAD)7
Skewness0
Sum435
Variance72.5
MonotonicityStrictly increasing
2023-12-12T20:26:27.659595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1 1
 
3.4%
2 1
 
3.4%
29 1
 
3.4%
28 1
 
3.4%
27 1
 
3.4%
26 1
 
3.4%
25 1
 
3.4%
24 1
 
3.4%
23 1
 
3.4%
22 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1 1
3.4%
2 1
3.4%
3 1
3.4%
4 1
3.4%
5 1
3.4%
6 1
3.4%
7 1
3.4%
8 1
3.4%
9 1
3.4%
10 1
3.4%
ValueCountFrequency (%)
29 1
3.4%
28 1
3.4%
27 1
3.4%
26 1
3.4%
25 1
3.4%
24 1
3.4%
23 1
3.4%
22 1
3.4%
21 1
3.4%
20 1
3.4%

단 지 명
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T20:26:28.084713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length7.5517241
Min length5

Characters and Unicode

Total characters219
Distinct characters82
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

Unique29 ?
Unique (%)100.0%

Sample

1st row유성1차아파트
2nd row명천주공1차아파트
3rd row동대주공아파트
4th row죽정현대아파트
5th row흥화아파트
ValueCountFrequency (%)
예미지 2
 
5.9%
금성백조 2
 
5.9%
유성1차아파트 1
 
2.9%
명천주공1차아파트 1
 
2.9%
시티프라디움 1
 
2.9%
2차 1
 
2.9%
1차 1
 
2.9%
e편한세상 1
 
2.9%
새미래에뜨젠아파트 1
 
2.9%
라온프라이빗 1
 
2.9%
Other values (22) 22
64.7%
2023-12-12T20:26:28.736125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
아 24
 
11.0%
파 23
 
10.5%
트 22
 
10.0%
차 7
 
3.2%
명 6
 
2.7%
대 6
 
2.7%
성 6
 
2.7%
5
 
2.3%
천 5
 
2.3%
주 5
 
2.3%
Other values (72) 110
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 203
92.7%
Decimal Number 8
 
3.7%
Space Separator 5
 
2.3%
Lowercase Letter 3
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
아 24
 
11.8%
파 23
 
11.3%
트 22
 
10.8%
차 7
 
3.4%
명 6
 
3.0%
대 6
 
3.0%
성 6
 
3.0%
천 5
 
2.5%
주 5
 
2.5%
공 5
 
2.5%
Other values (63) 94
46.3%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
3 2
25.0%
2 1
 
12.5%
4 1
 
12.5%
5 1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
e 1
33.3%
s 1
33.3%
k 1
33.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 203
92.7%
Common 13
 
5.9%
Latin 3
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
아 24
 
11.8%
파 23
 
11.3%
트 22
 
10.8%
차 7
 
3.4%
명 6
 
3.0%
대 6
 
3.0%
성 6
 
3.0%
천 5
 
2.5%
주 5
 
2.5%
공 5
 
2.5%
Other values (63) 94
46.3%
Common
ValueCountFrequency (%)
5
38.5%
1 3
23.1%
3 2
 
15.4%
2 1
 
7.7%
4 1
 
7.7%
5 1
 
7.7%
Latin
ValueCountFrequency (%)
e 1
33.3%
s 1
33.3%
k 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 203
92.7%
ASCII 16
 
7.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
아 24
 
11.8%
파 23
 
11.3%
트 22
 
10.8%
차 7
 
3.4%
명 6
 
3.0%
대 6
 
3.0%
성 6
 
3.0%
천 5
 
2.5%
주 5
 
2.5%
공 5
 
2.5%
Other values (63) 94
46.3%
ASCII
ValueCountFrequency (%)
5
31.2%
1 3
18.8%
3 2
 
12.5%
e 1
 
6.2%
2 1
 
6.2%
s 1
 
6.2%
4 1
 
6.2%
5 1
 
6.2%
k 1
 
6.2%

주 소
Text

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T20:26:29.070316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length17
Min length15

Characters and Unicode

Total characters493
Distinct characters35
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

Unique26 ?
Unique (%)89.7%

Sample

1st row충청남도 보령시 죽정동 735-19
2nd row충청남도 보령시 명천동 436
3rd row충청남도 보령시 동대동 414
4th row충청남도 보령시 죽정동 666-6
5th row충청남도 보령시 대천동 503-17
ValueCountFrequency (%)
충청남도 29
25.0%
보령시 29
25.0%
동대동 8
 
6.9%
명천동 6
 
5.2%
죽정동 5
 
4.3%
대천동 4
 
3.4%
389 3
 
2.6%
명천로 2
 
1.7%
주공로 1
 
0.9%
1966 1
 
0.9%
Other values (28) 28
24.1%
2023-12-12T20:26:29.684516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
88
17.8%
동 33
 
6.7%
충 29
 
5.9%
남 29
 
5.9%
도 29
 
5.9%
보 29
 
5.9%
령 29
 
5.9%
시 29
 
5.9%
청 29
 
5.9%
1 21
 
4.3%
Other values (25) 148
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 290
58.8%
Decimal Number 107
 
21.7%
Space Separator 88
 
17.8%
Dash Punctuation 8
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 33
11.4%
충 29
10.0%
남 29
10.0%
도 29
10.0%
보 29
10.0%
령 29
10.0%
시 29
10.0%
청 29
10.0%
대 12
 
4.1%
천 12
 
4.1%
Other values (13) 30
10.3%
Decimal Number
ValueCountFrequency (%)
1 21
19.6%
3 15
14.0%
9 15
14.0%
6 13
12.1%
7 9
8.4%
4 9
8.4%
0 9
8.4%
8 9
8.4%
2 4
 
3.7%
5 3
 
2.8%
Space Separator
ValueCountFrequency (%)
88
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 290
58.8%
Common 203
41.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 33
11.4%
충 29
10.0%
남 29
10.0%
도 29
10.0%
보 29
10.0%
령 29
10.0%
시 29
10.0%
청 29
10.0%
대 12
 
4.1%
천 12
 
4.1%
Other values (13) 30
10.3%
Common
ValueCountFrequency (%)
88
43.3%
1 21
 
10.3%
3 15
 
7.4%
9 15
 
7.4%
6 13
 
6.4%
7 9
 
4.4%
4 9
 
4.4%
0 9
 
4.4%
8 9
 
4.4%
- 8
 
3.9%
Other values (2) 7
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 290
58.8%
ASCII 203
41.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
88
43.3%
1 21
 
10.3%
3 15
 
7.4%
9 15
 
7.4%
6 13
 
6.4%
7 9
 
4.4%
4 9
 
4.4%
0 9
 
4.4%
8 9
 
4.4%
- 8
 
3.9%
Other values (2) 7
 
3.4%
Hangul
ValueCountFrequency (%)
동 33
11.4%
충 29
10.0%
남 29
10.0%
도 29
10.0%
보 29
10.0%
령 29
10.0%
시 29
10.0%
청 29
10.0%
대 12
 
4.1%
천 12
 
4.1%
Other values (13) 30
10.3%

준공년도
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)65.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003.9655
Minimum1989
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T20:26:29.925960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1989
5-th percentile1989.8
Q11996
median2004
Q32013
95-th percentile2019
Maximum2022
Range33
Interquartile range (IQR)17

Descriptive statistics

Standard deviation10.266042
Coefficient of variation (CV)0.0051228638
Kurtosis-1.2187367
Mean2003.9655
Median Absolute Deviation (MAD)8
Skewness0.25265595
Sum58115
Variance105.39163
MonotonicityIncreasing
2023-12-12T20:26:30.184996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1997 3
 
10.3%
2019 3
 
10.3%
1989 2
 
6.9%
2017 2
 
6.9%
1998 2
 
6.9%
2006 2
 
6.9%
1996 2
 
6.9%
1993 2
 
6.9%
2003 1
 
3.4%
2004 1
 
3.4%
Other values (9) 9
31.0%
ValueCountFrequency (%)
1989 2
6.9%
1991 1
 
3.4%
1992 1
 
3.4%
1993 2
6.9%
1996 2
6.9%
1997 3
10.3%
1998 2
6.9%
2003 1
 
3.4%
2004 1
 
3.4%
2005 1
 
3.4%
ValueCountFrequency (%)
2022 1
 
3.4%
2019 3
10.3%
2017 2
6.9%
2015 1
 
3.4%
2013 1
 
3.4%
2009 1
 
3.4%
2008 1
 
3.4%
2007 1
 
3.4%
2006 2
6.9%
2005 1
 
3.4%

동수
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)34.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.9655172
Minimum2
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T20:26:30.417655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q15
median6
Q39
95-th percentile10.6
Maximum24
Range22
Interquartile range (IQR)4

Descriptive statistics

Standard deviation4.1186229
Coefficient of variation (CV)0.59128744
Kurtosis9.9614089
Mean6.9655172
Median Absolute Deviation (MAD)2
Skewness2.5532053
Sum202
Variance16.963054
MonotonicityNot monotonic
2023-12-12T20:26:30.648986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 6
20.7%
9 5
17.2%
3 4
13.8%
5 4
13.8%
8 3
10.3%
4 2
 
6.9%
10 2
 
6.9%
24 1
 
3.4%
2 1
 
3.4%
11 1
 
3.4%
ValueCountFrequency (%)
2 1
 
3.4%
3 4
13.8%
4 2
 
6.9%
5 4
13.8%
6 6
20.7%
8 3
10.3%
9 5
17.2%
10 2
 
6.9%
11 1
 
3.4%
24 1
 
3.4%
ValueCountFrequency (%)
24 1
 
3.4%
11 1
 
3.4%
10 2
 
6.9%
9 5
17.2%
8 3
10.3%
6 6
20.7%
5 4
13.8%
4 2
 
6.9%
3 4
13.8%
2 1
 
3.4%

세대수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean446.68966
Minimum175
Maximum1130
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-12T20:26:30.894274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum175
5-th percentile206.6
Q1300
median420
Q3517
95-th percentile729.6
Maximum1130
Range955
Interquartile range (IQR)217

Descriptive statistics

Standard deviation207.87773
Coefficient of variation (CV)0.46537395
Kurtosis2.7471598
Mean446.68966
Median Absolute Deviation (MAD)120
Skewness1.3367564
Sum12954
Variance43213.15
MonotonicityNot monotonic
2023-12-12T20:26:31.155229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
330 1
 
3.4%
300 1
 
3.4%
420 1
 
3.4%
599 1
 
3.4%
517 1
 
3.4%
480 1
 
3.4%
677 1
 
3.4%
711 1
 
3.4%
294 1
 
3.4%
457 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
175 1
3.4%
205 1
3.4%
209 1
3.4%
216 1
3.4%
250 1
3.4%
267 1
3.4%
294 1
3.4%
300 1
3.4%
324 1
3.4%
330 1
3.4%
ValueCountFrequency (%)
1130 1
3.4%
742 1
3.4%
711 1
3.4%
687 1
3.4%
677 1
3.4%
623 1
3.4%
599 1
3.4%
517 1
3.4%
490 1
3.4%
480 1
3.4%

전화번호
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T20:26:31.556988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters348
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

Unique29 ?
Unique (%)100.0%

Sample

1st row041-935-0392
2nd row041-935-3215
3rd row041-934-7581
4th row041-936-2240
5th row041-936-1047
ValueCountFrequency (%)
041-935-0392 1
 
3.4%
041-931-9661 1
 
3.4%
041-931-9387 1
 
3.4%
041-933-1101 1
 
3.4%
041-931-0996 1
 
3.4%
041-936-9906 1
 
3.4%
041-933-2091 1
 
3.4%
041-936-7883 1
 
3.4%
041-932-6865 1
 
3.4%
041-934-0067 1
 
3.4%
Other values (19) 19
65.5%
2023-12-12T20:26:32.137387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58
16.7%
1 48
13.8%
0 45
12.9%
4 41
11.8%
9 41
11.8%
3 41
11.8%
6 27
7.8%
5 13
 
3.7%
2 13
 
3.7%
8 12
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290
83.3%
Dash Punctuation 58
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48
16.6%
0 45
15.5%
4 41
14.1%
9 41
14.1%
3 41
14.1%
6 27
9.3%
5 13
 
4.5%
2 13
 
4.5%
8 12
 
4.1%
7 9
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58
16.7%
1 48
13.8%
0 45
12.9%
4 41
11.8%
9 41
11.8%
3 41
11.8%
6 27
7.8%
5 13
 
3.7%
2 13
 
3.7%
8 12
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58
16.7%
1 48
13.8%
0 45
12.9%
4 41
11.8%
9 41
11.8%
3 41
11.8%
6 27
7.8%
5 13
 
3.7%
2 13
 
3.7%
8 12
 
3.4%

팩스(FAX)
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-12T20:26:32.528235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters348
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

Unique29 ?
Unique (%)100.0%

Sample

1st row041-935-0393
2nd row041-935-3215
3rd row041-934-7582
4th row041-936-2241
5th row041-934-1047
ValueCountFrequency (%)
041-935-0393 1
 
3.4%
041-931-9662 1
 
3.4%
041-931-9386 1
 
3.4%
041-933-1102 1
 
3.4%
041-931-0997 1
 
3.4%
041-936-9905 1
 
3.4%
041-934-2092 1
 
3.4%
041-934-7884 1
 
3.4%
041-932-6864 1
 
3.4%
041-934-6644 1
 
3.4%
Other values (19) 19
65.5%
2023-12-12T20:26:33.619218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 58
16.7%
1 48
13.8%
4 47
13.5%
9 42
12.1%
3 42
12.1%
0 41
11.8%
6 22
 
6.3%
2 17
 
4.9%
5 12
 
3.4%
8 10
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 290
83.3%
Dash Punctuation 58
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 48
16.6%
4 47
16.2%
9 42
14.5%
3 42
14.5%
0 41
14.1%
6 22
7.6%
2 17
 
5.9%
5 12
 
4.1%
8 10
 
3.4%
7 9
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 348
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 58
16.7%
1 48
13.8%
4 47
13.5%
9 42
12.1%
3 42
12.1%
0 41
11.8%
6 22
 
6.3%
2 17
 
4.9%
5 12
 
3.4%
8 10
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 348
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 58
16.7%
1 48
13.8%
4 47
13.5%
9 42
12.1%
3 42
12.1%
0 41
11.8%
6 22
 
6.3%
2 17
 
4.9%
5 12
 
3.4%
8 10
 
2.9%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-05-01
29 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-05-01
2nd row2023-05-01
3rd row2023-05-01
4th row2023-05-01
5th row2023-05-01

Common Values

ValueCountFrequency (%)
2023-05-01 29
100.0%

Length

2023-12-12T20:26:33.863755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:26:34.044179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-05-01 29
100.0%

Interactions

2023-12-12T20:26:26.322812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:24.510124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.137770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.772730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:26.465080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:24.688324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.303961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.906354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:26.622767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:24.853444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.471665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:26.047487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:26.758099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.000865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:25.630503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:26:26.182119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:26:34.148524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분단 지 명주 소준공년도동수세대수전화번호팩스(FAX)
구분1.0001.0000.9420.8950.5190.0001.0001.000
단 지 명1.0001.0001.0001.0001.0001.0001.0001.000
주 소0.9421.0001.0001.0000.0000.9271.0001.000
준공년도0.8951.0001.0001.0000.2930.5101.0001.000
동수0.5191.0000.0000.2931.0000.7241.0001.000
세대수0.0001.0000.9270.5100.7241.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.000
팩스(FAX)1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-12T20:26:34.346719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분준공년도동수세대수
구분1.0000.9980.0320.148
준공년도0.9981.0000.0390.157
동수0.0320.0391.0000.749
세대수0.1480.1570.7491.000

Missing values

2023-12-12T20:26:26.935721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:26:27.153618image/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

구분단 지 명주 소준공년도동수세대수전화번호팩스(FAX)데이터기준일
01유성1차아파트충청남도 보령시 죽정동 735-1919899330041-935-0392041-935-03932023-05-01
12명천주공1차아파트충청남도 보령시 명천동 43619899300041-935-3215041-935-32152023-05-01
23동대주공아파트충청남도 보령시 동대동 4141991241130041-934-7581041-934-75822023-05-01
34죽정현대아파트충청남도 보령시 죽정동 666-619924333041-936-2240041-936-22412023-05-01
45흥화아파트충청남도 보령시 대천동 503-1719936463041-936-1047041-934-10472023-05-01
56태영아파트충청남도 보령시 대천동 288-3619932209041-936-3033041-936-30322023-05-01
67유성산호아파트충청남도 보령시 신흑동 1634-619963358041-934-9718041-934-97112023-05-01
78명천주공3차아파트충청남도 보령시 명천동 389199610687041-936-4681041-932-30702023-05-01
89명천주공4차아파트충청남도 보령시 명천동 38919975490041-936-4463041-931-03952023-05-01
910죽정대우아파트충청남도 보령시 죽정동 74319976466041-936-7676041-936-76752023-05-01
구분단 지 명주 소준공년도동수세대수전화번호팩스(FAX)데이터기준일
1920코아루아파트충청남도 보령시 명천동 101020089623041-935-1395041-935-13942023-05-01
2021휴먼시아 3단지충청남도 보령시 동대동 196620096267041-934-0067041-934-66442023-05-01
2122한성필하우스충청남도 보령시 동대동 197020136457041-932-6865041-932-68642023-05-01
2223라온프라이빗충청남도 보령시 대천동 148220153294041-936-7883041-934-78842023-05-01
2324새미래에뜨젠아파트충청남도 보령시 동대동 197920179711041-933-2091041-934-20922023-05-01
2425e편한세상충청남도 보령시 동대동 198120179677041-936-9906041-936-99052023-05-01
2526금성백조 예미지 1차충청남도 보령시 주공로 100201910480041-931-0996041-931-09972023-05-01
2627금성백조 예미지 2차충청남도 보령시 명천로 9420198517041-933-1101041-933-11022023-05-01
2728시티프라디움충청남도 보령시 명천로 10620198599041-931-9387041-931-93862023-05-01
2829명천대원칸타빌아파트충청남도 보령시 진등1길 2420225420041-931-8805041-931-88062023-05-01