Overview

Dataset statistics

Number of variables10
Number of observations439
Missing cells438
Missing cells (%)10.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory37.0 KiB
Average record size in memory86.3 B

Variable types

Categorical3
Numeric6
Text1

Dataset

Description양주시 옥정지구 아파트 통반별 세대수 및 초,중,고 인구 현황
Author경기도 양주시
URLhttps://www.data.go.kr/data/15081162/fileData.do

Alerts

관리기관명 has constant value ""Constant
관리기관 전화번호 has constant value ""Constant
비고 has constant value ""Constant
통 is highly overall correlated with 단지명High correlation
초 is highly overall correlated with 중 and 1 other fieldsHigh correlation
중 is highly overall correlated with 초 and 1 other fieldsHigh correlation
고 is highly overall correlated with 초 and 1 other fieldsHigh correlation
단지명 is highly overall correlated with 통High correlation
비고 has 438 (99.8%) missing valuesMissing
초 has 52 (11.8%) zerosZeros
중 has 78 (17.8%) zerosZeros
고 has 87 (19.8%) zerosZeros

Reproduction

Analysis started2023-12-12 17:01:13.623179
Analysis finished2023-12-12 17:01:18.393139
Duration4.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

단지명
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
e편한세상옥정메트로포레
47 
옥정센트럴푸르지오
36 
세창리베하우스
34 
e편한세상옥정더퍼스트
34 
양주옥정LH3단지아파트
30 
Other values (14)
258 

Length

Max length12
Median length10
Mean length9.9658314
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row율정마을13단지
2nd row율정마을13단지
3rd row율정마을13단지
4th row율정마을13단지
5th row율정마을13단지

Common Values

ValueCountFrequency (%)
e편한세상옥정메트로포레 47
 
10.7%
옥정센트럴푸르지오 36
 
8.2%
세창리베하우스 34
 
7.7%
e편한세상옥정더퍼스트 34
 
7.7%
양주옥정LH3단지아파트 30
 
6.8%
e편한세상옥정에듀써밋 26
 
5.9%
율정마을7단지 26
 
5.9%
대방노블랜드더시그니처 26
 
5.9%
GS제이드웰아파트 21
 
4.8%
율정마을13단지 20
 
4.6%
Other values (9) 139
31.7%

Length

2023-12-13T02:01:18.475763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
e편한세상옥정메트로포레 47
 
10.7%
옥정센트럴푸르지오 36
 
8.2%
세창리베하우스 34
 
7.7%
e편한세상옥정더퍼스트 34
 
7.7%
양주옥정lh3단지아파트 30
 
6.8%
e편한세상옥정에듀써밋 26
 
5.9%
율정마을7단지 26
 
5.9%
대방노블랜드더시그니처 26
 
5.9%
gs제이드웰아파트 21
 
4.8%
율정마을13단지 20
 
4.6%
Other values (9) 139
31.7%

통
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20.94533
Minimum3
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-13T02:01:18.614279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile5
Q114
median21
Q329
95-th percentile35
Maximum36
Range33
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.3775073
Coefficient of variation (CV)0.44771351
Kurtosis-1.057036
Mean20.94533
Median Absolute Deviation (MAD)8
Skewness-0.20507819
Sum9195
Variance87.937644
MonotonicityIncreasing
2023-12-13T02:01:18.741957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
30 18
 
4.1%
21 18
 
4.1%
27 18
 
4.1%
28 17
 
3.9%
17 17
 
3.9%
16 17
 
3.9%
15 16
 
3.6%
29 16
 
3.6%
26 16
 
3.6%
31 16
 
3.6%
Other values (24) 270
61.5%
ValueCountFrequency (%)
3 10
2.3%
4 10
2.3%
5 10
2.3%
6 10
2.3%
7 6
1.4%
8 9
2.1%
9 10
2.3%
10 8
1.8%
11 9
2.1%
12 12
2.7%
ValueCountFrequency (%)
36 12
2.7%
35 12
2.7%
34 14
3.2%
33 15
3.4%
32 16
3.6%
31 16
3.6%
30 18
4.1%
29 16
3.6%
28 17
3.9%
27 18
4.1%

반
Real number (ℝ)

Distinct18
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.357631
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-13T02:01:18.864995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median7
Q310
95-th percentile15
Maximum18
Range17
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.3444322
Coefficient of variation (CV)0.59046617
Kurtosis-0.72722495
Mean7.357631
Median Absolute Deviation (MAD)3
Skewness0.3859308
Sum3230
Variance18.874091
MonotonicityNot monotonic
2023-12-13T02:01:18.995454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 34
 
7.7%
2 34
 
7.7%
3 34
 
7.7%
4 34
 
7.7%
5 34
 
7.7%
6 34
 
7.7%
7 33
 
7.5%
8 33
 
7.5%
9 32
 
7.3%
10 29
 
6.6%
Other values (8) 108
24.6%
ValueCountFrequency (%)
1 34
7.7%
2 34
7.7%
3 34
7.7%
4 34
7.7%
5 34
7.7%
6 34
7.7%
7 33
7.5%
8 33
7.5%
9 32
7.3%
10 29
6.6%
ValueCountFrequency (%)
18 3
 
0.7%
17 6
 
1.4%
16 11
 
2.5%
15 12
 
2.7%
14 14
3.2%
13 15
3.4%
12 23
5.2%
11 24
5.5%
10 29
6.6%
9 32
7.3%

세대수
Real number (ℝ)

Distinct66
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46.261959
Minimum16
Maximum97
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-13T02:01:19.175948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16
5-th percentile29
Q138
median46
Q351.5
95-th percentile72.1
Maximum97
Range81
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation13.238666
Coefficient of variation (CV)0.28616743
Kurtosis2.0382398
Mean46.261959
Median Absolute Deviation (MAD)7
Skewness1.0329375
Sum20309
Variance175.26227
MonotonicityNot monotonic
2023-12-13T02:01:19.355325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
47 29
 
6.6%
48 29
 
6.6%
45 19
 
4.3%
38 18
 
4.1%
44 17
 
3.9%
46 16
 
3.6%
43 15
 
3.4%
49 14
 
3.2%
50 14
 
3.2%
39 14
 
3.2%
Other values (56) 254
57.9%
ValueCountFrequency (%)
16 1
 
0.2%
19 1
 
0.2%
20 2
0.5%
21 2
0.5%
22 1
 
0.2%
24 1
 
0.2%
25 1
 
0.2%
26 3
0.7%
27 4
0.9%
28 4
0.9%
ValueCountFrequency (%)
97 1
0.2%
95 2
0.5%
94 2
0.5%
89 1
0.2%
88 2
0.5%
86 1
0.2%
82 1
0.2%
81 1
0.2%
80 2
0.5%
78 1
0.2%

초
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct34
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.9476082
Minimum0
Maximum41
Zeros52
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-13T02:01:19.500298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median9
Q315
95-th percentile25
Maximum41
Range41
Interquartile range (IQR)11

Descriptive statistics

Standard deviation7.535274
Coefficient of variation (CV)0.75749606
Kurtosis0.28628932
Mean9.9476082
Median Absolute Deviation (MAD)5
Skewness0.70028472
Sum4367
Variance56.780354
MonotonicityNot monotonic
2023-12-13T02:01:19.648673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
0 52
 
11.8%
5 28
 
6.4%
8 25
 
5.7%
15 23
 
5.2%
16 22
 
5.0%
7 22
 
5.0%
14 21
 
4.8%
3 21
 
4.8%
11 20
 
4.6%
4 19
 
4.3%
Other values (24) 186
42.4%
ValueCountFrequency (%)
0 52
11.8%
1 18
 
4.1%
2 11
 
2.5%
3 21
4.8%
4 19
 
4.3%
5 28
6.4%
6 12
 
2.7%
7 22
5.0%
8 25
5.7%
9 19
 
4.3%
ValueCountFrequency (%)
41 1
 
0.2%
34 1
 
0.2%
33 1
 
0.2%
30 1
 
0.2%
29 1
 
0.2%
28 3
 
0.7%
27 2
 
0.5%
26 9
2.1%
25 6
1.4%
24 2
 
0.5%

중
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.8678815
Minimum0
Maximum16
Zeros78
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-13T02:01:19.798409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q36
95-th percentile10.1
Maximum16
Range16
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.3077801
Coefficient of variation (CV)0.85519167
Kurtosis0.37606
Mean3.8678815
Median Absolute Deviation (MAD)2
Skewness0.87076513
Sum1698
Variance10.941409
MonotonicityNot monotonic
2023-12-13T02:01:19.946537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 78
17.8%
1 52
11.8%
2 50
11.4%
3 48
10.9%
5 46
10.5%
4 44
10.0%
6 32
7.3%
7 31
 
7.1%
9 14
 
3.2%
8 13
 
3.0%
Other values (7) 31
 
7.1%
ValueCountFrequency (%)
0 78
17.8%
1 52
11.8%
2 50
11.4%
3 48
10.9%
4 44
10.0%
5 46
10.5%
6 32
7.3%
7 31
 
7.1%
8 13
 
3.0%
9 14
 
3.2%
ValueCountFrequency (%)
16 1
 
0.2%
15 1
 
0.2%
14 2
 
0.5%
13 2
 
0.5%
12 6
 
1.4%
11 10
 
2.3%
10 9
 
2.1%
9 14
3.2%
8 13
3.0%
7 31
7.1%

고
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1230068
Minimum0
Maximum15
Zeros87
Zeros (%)19.8%
Negative0
Negative (%)0.0%
Memory size4.0 KiB
2023-12-13T02:01:20.093058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median3
Q35
95-th percentile8
Maximum15
Range15
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.6720939
Coefficient of variation (CV)0.85561578
Kurtosis1.1038676
Mean3.1230068
Median Absolute Deviation (MAD)2
Skewness0.97338489
Sum1371
Variance7.1400859
MonotonicityNot monotonic
2023-12-13T02:01:20.219661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 87
19.8%
4 66
15.0%
2 60
13.7%
1 56
12.8%
3 55
12.5%
5 46
10.5%
6 22
 
5.0%
7 17
 
3.9%
9 10
 
2.3%
8 9
 
2.1%
Other values (5) 11
 
2.5%
ValueCountFrequency (%)
0 87
19.8%
1 56
12.8%
2 60
13.7%
3 55
12.5%
4 66
15.0%
5 46
10.5%
6 22
 
5.0%
7 17
 
3.9%
8 9
 
2.1%
9 10
 
2.3%
ValueCountFrequency (%)
15 1
 
0.2%
13 1
 
0.2%
12 1
 
0.2%
11 3
 
0.7%
10 5
 
1.1%
9 10
 
2.3%
8 9
 
2.1%
7 17
 
3.9%
6 22
5.0%
5 46
10.5%

관리기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
회천4동
439 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row회천4동
2nd row회천4동
3rd row회천4동
4th row회천4동
5th row회천4동

Common Values

ValueCountFrequency (%)
회천4동 439
100.0%

Length

2023-12-13T02:01:20.378266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:01:20.488283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
회천4동 439
100.0%

관리기관 전화번호
Categorical

CONSTANT 

Distinct1
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size3.6 KiB
031-8082-7871
439 

Length

Max length13
Median length13
Mean length13
Min length13

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row031-8082-7871
2nd row031-8082-7871
3rd row031-8082-7871
4th row031-8082-7871
5th row031-8082-7871

Common Values

ValueCountFrequency (%)
031-8082-7871 439
100.0%

Length

2023-12-13T02:01:20.597358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:01:20.699142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
031-8082-7871 439
100.0%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing438
Missing (%)99.8%
Memory size3.6 KiB
2023-12-13T02:01:20.816995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length35
Mean length35
Min length35

Characters and Unicode

Total characters35
Distinct characters19
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

Unique1 ?
Unique (%)100.0%

Sample

1st row세대수 : 21.5.6 기준, 초중고 인구: 21.3.31 기준
ValueCountFrequency (%)
기준 2
25.0%
세대수 1
12.5%
1
12.5%
21.5.6 1
12.5%
초중고 1
12.5%
인구 1
12.5%
21.3.31 1
12.5%
2023-12-13T02:01:21.080427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7
20.0%
. 4
11.4%
1 3
 
8.6%
3 2
 
5.7%
: 2
 
5.7%
2 2
 
5.7%
기 2
 
5.7%
준 2
 
5.7%
초 1
 
2.9%
구 1
 
2.9%
Other values (9) 9
25.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12
34.3%
Decimal Number 9
25.7%
Space Separator 7
20.0%
Other Punctuation 7
20.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 2
16.7%
준 2
16.7%
초 1
8.3%
구 1
8.3%
인 1
8.3%
고 1
8.3%
중 1
8.3%
세 1
8.3%
대 1
8.3%
수 1
8.3%
Decimal Number
ValueCountFrequency (%)
1 3
33.3%
3 2
22.2%
2 2
22.2%
5 1
 
11.1%
6 1
 
11.1%
Other Punctuation
ValueCountFrequency (%)
. 4
57.1%
: 2
28.6%
, 1
 
14.3%
Space Separator
ValueCountFrequency (%)
7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23
65.7%
Hangul 12
34.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 2
16.7%
준 2
16.7%
초 1
8.3%
구 1
8.3%
인 1
8.3%
고 1
8.3%
중 1
8.3%
세 1
8.3%
대 1
8.3%
수 1
8.3%
Common
ValueCountFrequency (%)
7
30.4%
. 4
17.4%
1 3
13.0%
3 2
 
8.7%
: 2
 
8.7%
2 2
 
8.7%
, 1
 
4.3%
5 1
 
4.3%
6 1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23
65.7%
Hangul 12
34.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7
30.4%
. 4
17.4%
1 3
13.0%
3 2
 
8.7%
: 2
 
8.7%
2 2
 
8.7%
, 1
 
4.3%
5 1
 
4.3%
6 1
 
4.3%
Hangul
ValueCountFrequency (%)
기 2
16.7%
준 2
16.7%
초 1
8.3%
구 1
8.3%
인 1
8.3%
고 1
8.3%
중 1
8.3%
세 1
8.3%
대 1
8.3%
수 1
8.3%

Interactions

2023-12-13T02:01:17.531987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:13.959521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.704079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.300494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.929656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.811528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.636113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.096101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.815774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.416790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.023087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.941273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.740232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.226963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.903762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.514060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.127605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.045200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.844007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.342953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.005183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.640754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.225007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.163534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.953360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.447361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.096618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.731270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.310871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.267854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:18.057076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:14.596183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.197396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:15.834213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:16.402696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:01:17.404036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:01:21.207309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
단지명통반세대수초중고
단지명1.0000.9810.0000.8030.6400.5890.567
통0.9811.0000.0000.7400.5100.5280.514
반0.0000.0001.0000.0000.0000.2780.074
세대수0.8030.7400.0001.0000.5520.3370.431
초0.6400.5100.0000.5521.0000.8180.755
중0.5890.5280.2780.3370.8181.0000.809
고0.5670.5140.0740.4310.7550.8091.000
2023-12-13T02:01:21.326787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
통반세대수초중고단지명
통1.0000.180-0.0440.2380.2510.2200.880
반0.1801.000-0.1690.0090.044-0.0010.000
세대수-0.044-0.1691.0000.1980.0930.1260.455
초0.2380.0090.1981.0000.7710.6470.297
중0.2510.0440.0930.7711.0000.7010.263
고0.220-0.0010.1260.6470.7011.0000.256
단지명0.8800.0000.4550.2970.2630.2561.000

Missing values

2023-12-13T02:01:18.192187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:01:18.339397image/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

단지명통반세대수초중고관리기관명관리기관 전화번호비고
0율정마을13단지31472074회천4동031-8082-7871세대수 : 21.5.6 기준, 초중고 인구: 21.3.31 기준
1율정마을13단지32431579회천4동031-8082-7871<NA>
2율정마을13단지33492076회천4동031-8082-7871<NA>
3율정마을13단지344510116회천4동031-8082-7871<NA>
4율정마을13단지3555638회천4동031-8082-7871<NA>
5율정마을13단지36472557회천4동031-8082-7871<NA>
6율정마을13단지37501542회천4동031-8082-7871<NA>
7율정마을13단지38482667회천4동031-8082-7871<NA>
8율정마을13단지39481525회천4동031-8082-7871<NA>
9율정마을13단지3105521106회천4동031-8082-7871<NA>
단지명통반세대수초중고관리기관명관리기관 전화번호비고
429양주옥정모아미래도파크뷰36347343회천4동031-8082-7871<NA>
430양주옥정모아미래도파크뷰36450403회천4동031-8082-7871<NA>
431양주옥정모아미래도파크뷰36541811회천4동031-8082-7871<NA>
432양주옥정모아미래도파크뷰36639823회천4동031-8082-7871<NA>
433양주옥정모아미래도파크뷰36744711회천4동031-8082-7871<NA>
434양주옥정모아미래도파크뷰36842512회천4동031-8082-7871<NA>
435양주옥정모아미래도파크뷰36956435회천4동031-8082-7871<NA>
436양주옥정모아미래도파크뷰361046523회천4동031-8082-7871<NA>
437양주옥정모아미래도파크뷰3611611212회천4동031-8082-7871<NA>
438양주옥정모아미래도파크뷰361247555회천4동031-8082-7871<NA>