Overview

Dataset statistics

Number of variables11
Number of observations62
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory93.1 B

Variable types

Categorical2
Text3
Boolean3
Numeric3

Alerts

위치명 has constant value ""Constant
지구지정일자 has constant value ""Constant
계획승인일자 has constant value ""Constant
면적 is highly overall correlated with 세대수 and 1 other fieldsHigh correlation
세대수 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
수용인구 is highly overall correlated with 면적 and 1 other fieldsHigh correlation
지구명 has unique valuesUnique
수용인구 has unique valuesUnique
세대수 has 1 (1.6%) zerosZeros

Reproduction

Analysis started2024-03-16 04:16:35.050702
Analysis finished2024-03-16 04:16:39.954892
Duration4.9 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

Distinct21
Distinct (%)33.9%
Missing0
Missing (%)0.0%
Memory size628.0 B
성남시
8 
시흥시
5 
부천시
5 
남양주시
5 
고양시
4 
Other values (16)
35 

Length

Max length4
Median length3
Mean length3.1290323
Min length3

Unique

Unique6 ?
Unique (%)9.7%

Sample

1st row수원시
2nd row수원시
3rd row성남시
4th row성남시
5th row성남시

Common Values

ValueCountFrequency (%)
성남시 8
12.9%
시흥시 5
 
8.1%
부천시 5
 
8.1%
남양주시 5
 
8.1%
고양시 4
 
6.5%
의왕시 4
 
6.5%
화성시 4
 
6.5%
과천시 3
 
4.8%
구리시 3
 
4.8%
의정부시 3
 
4.8%
Other values (11) 18
29.0%

Length

2024-03-16T04:16:40.175473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
성남시 8
12.9%
부천시 5
 
8.1%
남양주시 5
 
8.1%
시흥시 5
 
8.1%
고양시 4
 
6.5%
의왕시 4
 
6.5%
화성시 4
 
6.5%
의정부시 3
 
4.8%
광명시 3
 
4.8%
안산시 3
 
4.8%
Other values (11) 18
29.0%

지구명
Text

UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-03-16T04:16:40.786073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length2
Mean length2.7580645
Min length2

Characters and Unicode

Total characters171
Distinct characters95
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

Unique62 ?
Unique (%)100.0%

Sample

1st row당수
2nd row당수2
3rd row고등
4th row금토
5th row복정1
ValueCountFrequency (%)
다산 2
 
3.1%
당수 1
 
1.6%
갈매역세권 1
 
1.6%
장상 1
 
1.6%
신길2 1
 
1.6%
고천 1
 
1.6%
월암 1
 
1.6%
청계2 1
 
1.6%
대야미 1
 
1.6%
의왕군포안산 1
 
1.6%
Other values (53) 53
82.8%
2024-03-16T04:16:41.814149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
 
5.8%
정 5
 
2.9%
산 5
 
2.9%
장 5
 
2.9%
고 5
 
2.9%
안 4
 
2.3%
지 4
 
2.3%
현 4
 
2.3%
왕 4
 
2.3%
대 3
 
1.8%
Other values (85) 122
71.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 154
90.1%
Decimal Number 13
 
7.6%
Space Separator 2
 
1.2%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 5
 
3.2%
산 5
 
3.2%
장 5
 
3.2%
고 5
 
3.2%
안 4
 
2.6%
지 4
 
2.6%
현 4
 
2.6%
왕 4
 
2.6%
대 3
 
1.9%
역 3
 
1.9%
Other values (79) 112
72.7%
Decimal Number
ValueCountFrequency (%)
2 10
76.9%
3 2
 
15.4%
1 1
 
7.7%
Space Separator
ValueCountFrequency (%)
2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 154
90.1%
Common 17
 
9.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 5
 
3.2%
산 5
 
3.2%
장 5
 
3.2%
고 5
 
3.2%
안 4
 
2.6%
지 4
 
2.6%
현 4
 
2.6%
왕 4
 
2.6%
대 3
 
1.9%
역 3
 
1.9%
Other values (79) 112
72.7%
Common
ValueCountFrequency (%)
2 10
58.8%
3 2
 
11.8%
2
 
11.8%
1 1
 
5.9%
( 1
 
5.9%
) 1
 
5.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 154
90.1%
ASCII 17
 
9.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
58.8%
3 2
 
11.8%
2
 
11.8%
1 1
 
5.9%
( 1
 
5.9%
) 1
 
5.9%
Hangul
ValueCountFrequency (%)
정 5
 
3.2%
산 5
 
3.2%
장 5
 
3.2%
고 5
 
3.2%
안 4
 
2.6%
지 4
 
2.6%
현 4
 
2.6%
왕 4
 
2.6%
대 3
 
1.9%
역 3
 
1.9%
Other values (79) 112
72.7%

위치명
Boolean

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size194.0 B
False
62 
ValueCountFrequency (%)
False 62
100.0%
2024-03-16T04:16:42.148302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

면적
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)98.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1899.6935
Minimum41
Maximum12711
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-03-16T04:16:42.577232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile90.35
Q1514.75
median830.5
Q32263.75
95-th percentile7288.55
Maximum12711
Range12670
Interquartile range (IQR)1749

Descriptive statistics

Standard deviation2547.8157
Coefficient of variation (CV)1.3411719
Kurtosis6.2813809
Mean1899.6935
Median Absolute Deviation (MAD)697
Skewness2.4152726
Sum117781
Variance6491364.8
MonotonicityNot monotonic
2024-03-16T04:16:43.103646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
578 2
 
3.2%
968 1
 
1.6%
101 1
 
1.6%
543 1
 
1.6%
525 1
 
1.6%
265 1
 
1.6%
621 1
 
1.6%
5968 1
 
1.6%
1353 1
 
1.6%
1687 1
 
1.6%
Other values (51) 51
82.3%
ValueCountFrequency (%)
41 1
1.6%
48 1
1.6%
69 1
1.6%
90 1
1.6%
97 1
1.6%
101 1
1.6%
102 1
1.6%
112 1
1.6%
130 1
1.6%
139 1
1.6%
ValueCountFrequency (%)
12711 1
1.6%
10294 1
1.6%
7890 1
1.6%
7311 1
1.6%
6862 1
1.6%
5968 1
1.6%
4532 1
1.6%
4525 1
1.6%
4330 1
1.6%
3450 1
1.6%

세대수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)45.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.064516
Minimum0
Maximum70
Zeros1
Zeros (%)1.6%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-03-16T04:16:43.557935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q14
median6.5
Q314.75
95-th percentile40.75
Maximum70
Range70
Interquartile range (IQR)10.75

Descriptive statistics

Standard deviation14.038438
Coefficient of variation (CV)1.1636138
Kurtosis4.7839044
Mean12.064516
Median Absolute Deviation (MAD)5
Skewness2.0934618
Sum748
Variance197.07774
MonotonicityNot monotonic
2024-03-16T04:16:44.063485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4 10
16.1%
1 9
14.5%
10 4
 
6.5%
14 3
 
4.8%
6 3
 
4.8%
3 3
 
4.8%
5 3
 
4.8%
19 3
 
4.8%
33 2
 
3.2%
2 2
 
3.2%
Other values (18) 20
32.3%
ValueCountFrequency (%)
0 1
 
1.6%
1 9
14.5%
2 2
 
3.2%
3 3
 
4.8%
4 10
16.1%
5 3
 
4.8%
6 3
 
4.8%
7 2
 
3.2%
8 1
 
1.6%
9 2
 
3.2%
ValueCountFrequency (%)
70 1
 
1.6%
52 1
 
1.6%
46 1
 
1.6%
41 1
 
1.6%
36 1
 
1.6%
33 2
3.2%
31 1
 
1.6%
30 1
 
1.6%
19 3
4.8%
18 1
 
1.6%

수용인구
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct62
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28899.048
Minimum1052
Maximum167500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size690.0 B
2024-03-16T04:16:44.551993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1052
5-th percentile1982
Q18526.5
median16158.5
Q336143.5
95-th percentile97335.55
Maximum167500
Range166448
Interquartile range (IQR)27617

Descriptive statistics

Standard deviation33546.028
Coefficient of variation (CV)1.1608004
Kurtosis4.9539324
Mean28899.048
Median Absolute Deviation (MAD)11828
Skewness2.1075423
Sum1791741
Variance1.125336 × 109
MonotonicityNot monotonic
2024-03-16T04:16:45.050751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
18648 1
 
1.6%
15676 1
 
1.6%
14527 1
 
1.6%
10440 1
 
1.6%
8098 1
 
1.6%
4562 1
 
1.6%
11838 1
 
1.6%
97963 1
 
1.6%
19109 1
 
1.6%
25072 1
 
1.6%
Other values (52) 52
83.9%
ValueCountFrequency (%)
1052 1
1.6%
1410 1
1.6%
1610 1
1.6%
1980 1
1.6%
2020 1
1.6%
2222 1
1.6%
2619 1
1.6%
2950 1
1.6%
3064 1
1.6%
3556 1
1.6%
ValueCountFrequency (%)
167500 1
1.6%
130988 1
1.6%
103500 1
1.6%
97963 1
1.6%
85414 1
1.6%
77925 1
1.6%
75900 1
1.6%
71664 1
1.6%
71300 1
1.6%
47695 1
1.6%

사업기간
Text

Distinct56
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-03-16T04:16:45.676054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length19
Mean length17.596774
Min length6

Characters and Unicode

Total characters1091
Distinct characters17
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)82.3%

Sample

1st row‘17.03.28~’25.12.31
2nd row‘20.12.11~‘26.12.31
3rd row‘10.05.26~‘24.12.31
4th row‘18.08.07~‘25.12.31
5th row‘18.08.07~‘24.07.31
ValueCountFrequency (%)
‘19.10.15~‘28.12.31 3
 
4.8%
‘18.07.02~‘26.06.30 2
 
3.2%
25.~'34 2
 
3.2%
‘10.05.26~‘24.12.31 2
 
3.2%
‘18.08.07~‘25.12.31 2
 
3.2%
‘08.10.14~‘24.06.30 1
 
1.6%
‘16.12.28~‘25.12.31 1
 
1.6%
‘17.03.28~’25.12.31 1
 
1.6%
‘22.05.30..~‘29.12.31 1
 
1.6%
‘20.05.20~‘26.12.31 1
 
1.6%
Other values (46) 46
74.2%
2024-03-16T04:16:46.870500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 248
22.7%
1 147
13.5%
2 142
13.0%
0 119
10.9%
‘ 94
 
8.6%
3 86
 
7.9%
~ 62
 
5.7%
6 32
 
2.9%
5 30
 
2.7%
7 28
 
2.6%
Other values (7) 103
9.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 664
60.9%
Other Punctuation 257
 
23.6%
Initial Punctuation 94
 
8.6%
Math Symbol 62
 
5.7%
Final Punctuation 10
 
0.9%
Other Letter 4
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 147
22.1%
2 142
21.4%
0 119
17.9%
3 86
13.0%
6 32
 
4.8%
5 30
 
4.5%
7 28
 
4.2%
8 28
 
4.2%
4 27
 
4.1%
9 25
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 248
96.5%
' 9
 
3.5%
Other Letter
ValueCountFrequency (%)
미 2
50.0%
정 2
50.0%
Initial Punctuation
ValueCountFrequency (%)
‘ 94
100.0%
Math Symbol
ValueCountFrequency (%)
~ 62
100.0%
Final Punctuation
ValueCountFrequency (%)
’ 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1087
99.6%
Hangul 4
 
0.4%

Most frequent character per script

Common
ValueCountFrequency (%)
. 248
22.8%
1 147
13.5%
2 142
13.1%
0 119
10.9%
‘ 94
 
8.6%
3 86
 
7.9%
~ 62
 
5.7%
6 32
 
2.9%
5 30
 
2.8%
7 28
 
2.6%
Other values (5) 99
 
9.1%
Hangul
ValueCountFrequency (%)
미 2
50.0%
정 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 983
90.1%
Punctuation 104
 
9.5%
Hangul 4
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 248
25.2%
1 147
15.0%
2 142
14.4%
0 119
12.1%
3 86
 
8.7%
~ 62
 
6.3%
6 32
 
3.3%
5 30
 
3.1%
7 28
 
2.8%
8 28
 
2.8%
Other values (3) 61
 
6.2%
Punctuation
ValueCountFrequency (%)
‘ 94
90.4%
’ 10
 
9.6%
Hangul
ValueCountFrequency (%)
미 2
50.0%
정 2
50.0%

지구지정일자
Boolean

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size194.0 B
False
62 
ValueCountFrequency (%)
False 62
100.0%
2024-03-16T04:16:47.222055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

계획승인일자
Boolean

CONSTANT 

Distinct1
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size194.0 B
False
62 
ValueCountFrequency (%)
False 62
100.0%
2024-03-16T04:16:47.472759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

시행자
Categorical

Distinct17
Distinct (%)27.4%
Missing0
Missing (%)0.0%
Memory size628.0 B
LH
43 
GH
 
4
경기도,LH GH, 안산도공
 
1
경기도, LH
 
1
경기도, LH, GH, 고양도공
 
1
Other values (12)
12 

Length

Max length17
Median length2
Mean length4.4032258
Min length2

Unique

Unique15 ?
Unique (%)24.2%

Sample

1st rowLH
2nd rowLH
3rd rowLH
4th row경기도,LH, 성남시,GH
5th rowLH

Common Values

ValueCountFrequency (%)
LH 43
69.4%
GH 4
 
6.5%
경기도,LH GH, 안산도공 1
 
1.6%
경기도, LH 1
 
1.6%
경기도, LH, GH, 고양도공 1
 
1.6%
경기도, LH, GH, 과천도공 1
 
1.6%
LH, 대우건설(주) 컨소시엄 1
 
1.6%
LH, 의왕시 1
 
1.6%
LH, 안산도공 1
 
1.6%
경기도,LH, 성남시,GH 1
 
1.6%
Other values (7) 7
 
11.3%

Length

2024-03-16T04:16:48.189649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
lh 54
62.8%
gh 8
 
9.3%
경기도 5
 
5.8%
경기도,lh 3
 
3.5%
부천도공 2
 
2.3%
컨소시엄 2
 
2.3%
안산도공 2
 
2.3%
과천도공 1
 
1.2%
대우건설(주 1
 
1.2%
고양도공 1
 
1.2%
Other values (7) 7
 
8.1%

추진현황
Text

Distinct45
Distinct (%)72.6%
Missing0
Missing (%)0.0%
Memory size628.0 B
2024-03-16T04:16:48.777004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length12.370968
Min length5

Characters and Unicode

Total characters767
Distinct characters38
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

Unique41 ?
Unique (%)66.1%

Sample

1st row공사 80%, 보상 99%
2nd row보상 60%
3rd row공사 96%, 보상 99%
4th row공사 35%, 보상 100%
5th row공사 30%, 보상 98%
ValueCountFrequency (%)
보상 45
23.0%
공사 34
17.3%
99 22
11.2%
100 18
 
9.2%
지구계획 8
 
4.1%
수립중 8
 
4.1%
지구지정 6
 
3.1%
추진중 6
 
3.1%
0 6
 
3.1%
95 4
 
2.0%
Other values (30) 39
19.9%
2024-03-16T04:16:49.886895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
190
24.8%
% 79
10.3%
9 54
 
7.0%
0 49
 
6.4%
상 45
 
5.9%
보 45
 
5.9%
, 35
 
4.6%
사 35
 
4.6%
공 34
 
4.4%
1 24
 
3.1%
Other values (28) 177
23.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 295
38.5%
Space Separator 190
24.8%
Decimal Number 166
21.6%
Other Punctuation 116
 
15.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 45
15.3%
보 45
15.3%
사 35
11.9%
공 34
11.5%
지 24
8.1%
중 19
6.4%
구 17
 
5.8%
립 11
 
3.7%
수 11
 
3.7%
획 11
 
3.7%
Other values (14) 43
14.6%
Decimal Number
ValueCountFrequency (%)
9 54
32.5%
0 49
29.5%
1 24
14.5%
5 8
 
4.8%
6 7
 
4.2%
8 7
 
4.2%
4 6
 
3.6%
3 5
 
3.0%
2 3
 
1.8%
7 3
 
1.8%
Other Punctuation
ValueCountFrequency (%)
% 79
68.1%
, 35
30.2%
. 2
 
1.7%
Space Separator
ValueCountFrequency (%)
190
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 472
61.5%
Hangul 295
38.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 45
15.3%
보 45
15.3%
사 35
11.9%
공 34
11.5%
지 24
8.1%
중 19
6.4%
구 17
 
5.8%
립 11
 
3.7%
수 11
 
3.7%
획 11
 
3.7%
Other values (14) 43
14.6%
Common
ValueCountFrequency (%)
190
40.3%
% 79
16.7%
9 54
 
11.4%
0 49
 
10.4%
, 35
 
7.4%
1 24
 
5.1%
5 8
 
1.7%
6 7
 
1.5%
8 7
 
1.5%
4 6
 
1.3%
Other values (4) 13
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 472
61.5%
Hangul 295
38.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
190
40.3%
% 79
16.7%
9 54
 
11.4%
0 49
 
10.4%
, 35
 
7.4%
1 24
 
5.1%
5 8
 
1.7%
6 7
 
1.5%
8 7
 
1.5%
4 6
 
1.3%
Other values (4) 13
 
2.8%
Hangul
ValueCountFrequency (%)
상 45
15.3%
보 45
15.3%
사 35
11.9%
공 34
11.5%
지 24
8.1%
중 19
6.4%
구 17
 
5.8%
립 11
 
3.7%
수 11
 
3.7%
획 11
 
3.7%
Other values (14) 43
14.6%

Interactions

2024-03-16T04:16:38.112752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:36.273826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:37.185541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:38.385443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:36.620176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:37.488232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:38.712034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:36.893932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T04:16:37.840836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T04:16:50.179286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명지구명면적세대수수용인구사업기간시행자추진현황
시군명1.0001.0000.3950.5640.7340.7800.0000.000
지구명1.0001.0001.0001.0001.0001.0001.0001.000
면적0.3951.0001.0000.9480.9520.0000.7650.000
세대수0.5641.0000.9481.0001.0000.0000.6400.000
수용인구0.7341.0000.9521.0001.0000.0000.6410.000
사업기간0.7801.0000.0000.0000.0001.0000.0000.848
시행자0.0001.0000.7650.6400.6410.0001.0000.987
추진현황0.0001.0000.0000.0000.0000.8480.9871.000
2024-03-16T04:16:50.597932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군명시행자
시군명1.0000.000
시행자0.0001.000
2024-03-16T04:16:50.933854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적세대수수용인구시군명시행자
면적1.0000.9900.9930.1220.396
세대수0.9901.0000.9950.1970.282
수용인구0.9930.9951.0000.2180.303
시군명0.1220.1970.2181.0000.000
시행자0.3960.2820.3030.0001.000

Missing values

2024-03-16T04:16:39.117260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T04:16:39.706232image/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

시군명지구명위치명면적세대수수용인구사업기간지구지정일자계획승인일자시행자추진현황
0수원시당수N968818648‘17.03.28~’25.12.31NNLH공사 80%, 보상 99%
1수원시당수2N685512613‘20.12.11~‘26.12.31NNLH보상 60%
2성남시고등N56949857‘10.05.26~‘24.12.31NNLH공사 96%, 보상 99%
3성남시금토N58349242‘18.08.07~‘25.12.31NN경기도,LH, 성남시,GH공사 35%, 보상 100%
4성남시복정1N578410149‘18.08.07~‘24.07.31NNLH공사 30%, 보상 98%
5성남시복정2N9012619‘18.08.07~‘25.12.31NNLH공사 7%, 보상 100%
6성남시서현N24835710‘19.05.03~‘24.12.31NNLH지구계획수립중, 보상 48%
7성남시신촌N6911410‘19.07.19~‘26.06.30.NNLH공사 24%, 보상 100%
8성남시낙생N578410688‘19.12.23~‘27.06.30.NNLH, 성남도공보상 100%
9성남시상대원N9713064‘16.11.30~미정NN성남시사업추진방향 전환 검토중
시군명지구명위치명면적세대수수용인구사업기간지구지정일자계획승인일자시행자추진현황
52오산시세교3N4330317130025.~'35.NNLH지구지정 추진중
53고양시창릉N78903685414‘20.03.06~‘29.12.31NN경기도, LH, GH, 고양도공공사 1%, 보상 99%
54고양시탄현N42236288‘20.03.06~‘25.12.31NNLH보상 95%
55남양주시다산 진건N27141847054‘09.12.03~‘24.06.30NNGH공사 99%, 보상 100%
56남양주시다산 지금N20361435716‘10.07.14~‘25.12.31NNGH공사 99%, 보상 100%
57남양주시진접2N12921023715‘18.07.10~’25.06.30NNLH공사 20%, 보상 99%
58남양주시왕숙N1029452130988‘19.10.15~‘28.12.31NN경기도, LH공사 0%, 보상 99 %
59남양주시왕숙2N23931536286‘19.10.15~‘28.12.31NN경기도, LH, 남양주도공공사 0% ,보상 99 %
60의정부시고산N13021025266‘08.10.24~‘24.12.31NNLH공사 95%, 보상 100%
61의정부시우정N51248744‘19.07.19~‘27.06.30.NNLH공사 0%, 보상 96.7%