Overview

Dataset statistics

Number of variables10
Number of observations40
Missing cells25
Missing cells (%)6.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory88.3 B

Variable types

Text3
Numeric5
DateTime2

Dataset

Description대구도시공사에서 건립한 공영주택현황(사업지구명, 위치, 면적, 사업비, 건립규모(동,층,세대), 공사시작일, 공사종료일)을 제공합니다.
Author대구도시개발공사
URLhttps://www.data.go.kr/data/15053913/fileData.do

Alerts

면적(제곱미터) is highly overall correlated with 건립규모(동) and 1 other fieldsHigh correlation
사업비(백만원) is highly overall correlated with 건립규모(세대)High correlation
건립규모(동) is highly overall correlated with 면적(제곱미터) and 1 other fieldsHigh correlation
건립규모(세대) is highly overall correlated with 면적(제곱미터) and 2 other fieldsHigh correlation
사업지구 has 1 (2.5%) missing valuesMissing
위치 has 1 (2.5%) missing valuesMissing
면적(제곱미터) has 1 (2.5%) missing valuesMissing
사업비(백만원) has 10 (25.0%) missing valuesMissing
건립규모(동) has 1 (2.5%) missing valuesMissing
건립규모(층) has 1 (2.5%) missing valuesMissing
건립규모(세대) has 1 (2.5%) missing valuesMissing
공사시작일 has 1 (2.5%) missing valuesMissing
공사종료일 has 1 (2.5%) missing valuesMissing
비고 has 7 (17.5%) missing valuesMissing

Reproduction

Analysis started2023-12-23 07:42:13.501189
Analysis finished2023-12-23 07:42:32.905908
Duration19.4 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업지구
Text

MISSING 

Distinct37
Distinct (%)94.9%
Missing1
Missing (%)2.5%
Memory size452.0 B
2023-12-23T07:42:33.261758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.4871795
Min length4

Characters and Unicode

Total characters214
Distinct characters48
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

Unique36 ?
Unique (%)92.3%

Sample

1st row지산1단지구
2nd row지산2단지
3rd row지산3단지
4th row지산4단지
5th row지산5단지
ValueCountFrequency (%)
동서변지구 3
 
7.3%
죽곡4단지 1
 
2.4%
죽곡3단지 1
 
2.4%
장기지구 1
 
2.4%
학정지구 1
 
2.4%
대실역1단지 1
 
2.4%
대실역2단지 1
 
2.4%
죽곡1단지 1
 
2.4%
죽곡2단지 1
 
2.4%
지산2단지 1
 
2.4%
Other values (29) 29
70.7%
2023-12-23T07:42:34.585835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
지 42
19.6%
단 27
 
12.6%
2 8
 
3.7%
산 7
 
3.3%
구 7
 
3.3%
1 7
 
3.3%
동 6
 
2.8%
아 6
 
2.8%
청 6
 
2.8%
인 6
 
2.8%
Other values (38) 92
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 184
86.0%
Decimal Number 28
 
13.1%
Space Separator 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
지 42
22.8%
단 27
14.7%
산 7
 
3.8%
구 7
 
3.8%
동 6
 
3.3%
아 6
 
3.3%
청 6
 
3.3%
인 6
 
3.3%
람 6
 
3.3%
시 5
 
2.7%
Other values (32) 66
35.9%
Decimal Number
ValueCountFrequency (%)
2 8
28.6%
1 7
25.0%
4 5
17.9%
3 5
17.9%
5 3
 
10.7%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 184
86.0%
Common 30
 
14.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
지 42
22.8%
단 27
14.7%
산 7
 
3.8%
구 7
 
3.8%
동 6
 
3.3%
아 6
 
3.3%
청 6
 
3.3%
인 6
 
3.3%
람 6
 
3.3%
시 5
 
2.7%
Other values (32) 66
35.9%
Common
ValueCountFrequency (%)
2 8
26.7%
1 7
23.3%
4 5
16.7%
3 5
16.7%
5 3
 
10.0%
2
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 184
86.0%
ASCII 30
 
14.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
지 42
22.8%
단 27
14.7%
산 7
 
3.8%
구 7
 
3.8%
동 6
 
3.3%
아 6
 
3.3%
청 6
 
3.3%
인 6
 
3.3%
람 6
 
3.3%
시 5
 
2.7%
Other values (32) 66
35.9%
ASCII
ValueCountFrequency (%)
2 8
26.7%
1 7
23.3%
4 5
16.7%
3 5
16.7%
5 3
 
10.0%
2
 
6.7%

위치
Text

MISSING 

Distinct38
Distinct (%)97.4%
Missing1
Missing (%)2.5%
Memory size452.0 B
2023-12-23T07:42:35.361714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length24
Mean length20.102564
Min length17

Characters and Unicode

Total characters784
Distinct characters66
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique37 ?
Unique (%)94.9%

Sample

1st row대구광역시 수성구 지산2동 124번지
2nd row대구광역시 수성구 지산2동 1234-1번지
3rd row대구광역시 수성구 지산1동 1288번지
4th row대구광역시 수성구 지산1동 1297-3번지
5th row대구광역시 수성구 지산1동 1297번지
ValueCountFrequency (%)
대구광역시 39
24.5%
수성구 15
 
9.4%
달서구 9
 
5.7%
달성군 9
 
5.7%
대실역남로 5
 
3.1%
상인동 5
 
3.1%
북구 4
 
2.5%
범물동 4
 
2.5%
지산1동 3
 
1.9%
매호동 3
 
1.9%
Other values (56) 63
39.6%
2023-12-23T07:42:37.284522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
15.3%
구 71
 
9.1%
대 46
 
5.9%
역 45
 
5.7%
시 41
 
5.2%
지 40
 
5.1%
광 39
 
5.0%
1 35
 
4.5%
번 31
 
4.0%
동 31
 
4.0%
Other values (56) 285
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 519
66.2%
Decimal Number 140
 
17.9%
Space Separator 120
 
15.3%
Dash Punctuation 4
 
0.5%
Uppercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 71
13.7%
대 46
 
8.9%
역 45
 
8.7%
시 41
 
7.9%
지 40
 
7.7%
광 39
 
7.5%
번 31
 
6.0%
동 31
 
6.0%
성 24
 
4.6%
달 18
 
3.5%
Other values (43) 133
25.6%
Decimal Number
ValueCountFrequency (%)
1 35
25.0%
2 23
16.4%
3 17
12.1%
5 14
 
10.0%
9 13
 
9.3%
8 12
 
8.6%
6 9
 
6.4%
4 8
 
5.7%
7 7
 
5.0%
0 2
 
1.4%
Space Separator
ValueCountFrequency (%)
120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 519
66.2%
Common 264
33.7%
Latin 1
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 71
13.7%
대 46
 
8.9%
역 45
 
8.7%
시 41
 
7.9%
지 40
 
7.7%
광 39
 
7.5%
번 31
 
6.0%
동 31
 
6.0%
성 24
 
4.6%
달 18
 
3.5%
Other values (43) 133
25.6%
Common
ValueCountFrequency (%)
120
45.5%
1 35
 
13.3%
2 23
 
8.7%
3 17
 
6.4%
5 14
 
5.3%
9 13
 
4.9%
8 12
 
4.5%
6 9
 
3.4%
4 8
 
3.0%
7 7
 
2.7%
Other values (2) 6
 
2.3%
Latin
ValueCountFrequency (%)
B 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 519
66.2%
ASCII 265
33.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
45.3%
1 35
 
13.2%
2 23
 
8.7%
3 17
 
6.4%
5 14
 
5.3%
9 13
 
4.9%
8 12
 
4.5%
6 9
 
3.4%
4 8
 
3.0%
7 7
 
2.6%
Other values (3) 7
 
2.6%
Hangul
ValueCountFrequency (%)
구 71
13.7%
대 46
 
8.9%
역 45
 
8.7%
시 41
 
7.9%
지 40
 
7.7%
광 39
 
7.5%
번 31
 
6.0%
동 31
 
6.0%
성 24
 
4.6%
달 18
 
3.5%
Other values (43) 133
25.6%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)100.0%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean28376.051
Minimum566
Maximum123304
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-23T07:42:38.066912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum566
5-th percentile810.6
Q114966
median22074
Q336894
95-th percentile60415.2
Maximum123304
Range122738
Interquartile range (IQR)21928

Descriptive statistics

Standard deviation21830.422
Coefficient of variation (CV)0.76932559
Kurtosis8.5410022
Mean28376.051
Median Absolute Deviation (MAD)8467
Skewness2.3367791
Sum1106666
Variance4.7656734 × 108
MonotonicityNot monotonic
2023-12-23T07:42:38.730002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
38248 1
 
2.5%
17802 1
 
2.5%
22074 1
 
2.5%
46545 1
 
2.5%
53830 1
 
2.5%
44601 1
 
2.5%
34048 1
 
2.5%
65214 1
 
2.5%
13243 1
 
2.5%
32094 1
 
2.5%
Other values (29) 29
72.5%
ValueCountFrequency (%)
566 1
2.5%
663 1
2.5%
827 1
2.5%
13243 1
2.5%
13607 1
2.5%
14265 1
2.5%
14303 1
2.5%
14351 1
2.5%
14425 1
2.5%
14732 1
2.5%
ValueCountFrequency (%)
123304 1
2.5%
65214 1
2.5%
59882 1
2.5%
53830 1
2.5%
46545 1
2.5%
44601 1
2.5%
43285 1
2.5%
41111 1
2.5%
38248 1
2.5%
37206 1
2.5%

사업비(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)96.7%
Missing10
Missing (%)25.0%
Infinite0
Infinite (%)0.0%
Mean40383.7
Minimum1006
Maximum161232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-23T07:42:39.187892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1006
5-th percentile1605
Q113988.25
median21814
Q345293.25
95-th percentile132621.3
Maximum161232
Range160226
Interquartile range (IQR)31305

Descriptive statistics

Standard deviation43856.162
Coefficient of variation (CV)1.0859867
Kurtosis1.3496766
Mean40383.7
Median Absolute Deviation (MAD)11248.5
Skewness1.5433121
Sum1211511
Variance1.923363 × 109
MonotonicityNot monotonic
2023-12-23T07:42:39.922193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1605 2
 
5.0%
24865 1
 
2.5%
1797 1
 
2.5%
132705 1
 
2.5%
1006 1
 
2.5%
13890 1
 
2.5%
13030 1
 
2.5%
161232 1
 
2.5%
88480 1
 
2.5%
132519 1
 
2.5%
Other values (19) 19
47.5%
(Missing) 10
25.0%
ValueCountFrequency (%)
1006 1
2.5%
1605 2
5.0%
1797 1
2.5%
9802 1
2.5%
11329 1
2.5%
13030 1
2.5%
13890 1
2.5%
14283 1
2.5%
15023 1
2.5%
18653 1
2.5%
ValueCountFrequency (%)
161232 1
2.5%
132705 1
2.5%
132519 1
2.5%
108280 1
2.5%
97033 1
2.5%
88480 1
2.5%
55352 1
2.5%
47970 1
2.5%
37263 1
2.5%
34351 1
2.5%

건립규모(동)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct13
Distinct (%)33.3%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean6.025641
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-23T07:42:40.446337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13.5
median5
Q38
95-th percentile12.1
Maximum14
Range13
Interquartile range (IQR)4.5

Descriptive statistics

Standard deviation3.421966
Coefficient of variation (CV)0.56790075
Kurtosis-0.29246753
Mean6.025641
Median Absolute Deviation (MAD)2
Skewness0.69553517
Sum235
Variance11.709852
MonotonicityNot monotonic
2023-12-23T07:42:41.018326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 7
17.5%
4 6
15.0%
5 6
15.0%
7 5
12.5%
8 3
7.5%
1 3
7.5%
12 2
 
5.0%
11 2
 
5.0%
6 1
 
2.5%
10 1
 
2.5%
Other values (3) 3
7.5%
ValueCountFrequency (%)
1 3
7.5%
3 7
17.5%
4 6
15.0%
5 6
15.0%
6 1
 
2.5%
7 5
12.5%
8 3
7.5%
9 1
 
2.5%
10 1
 
2.5%
11 2
 
5.0%
ValueCountFrequency (%)
14 1
 
2.5%
13 1
 
2.5%
12 2
 
5.0%
11 2
 
5.0%
10 1
 
2.5%
9 1
 
2.5%
8 3
7.5%
7 5
12.5%
6 1
 
2.5%
5 6
15.0%

건립규모(층)
Real number (ℝ)

MISSING 

Distinct11
Distinct (%)28.2%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean15.820513
Minimum4
Maximum25
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-23T07:42:41.797431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4
5-th percentile4
Q115
median15
Q320
95-th percentile24.1
Maximum25
Range21
Interquartile range (IQR)5

Descriptive statistics

Standard deviation5.4380327
Coefficient of variation (CV)0.34373302
Kurtosis0.49745744
Mean15.820513
Median Absolute Deviation (MAD)3
Skewness-0.69264887
Sum617
Variance29.5722
MonotonicityNot monotonic
2023-12-23T07:42:42.437720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
15 19
47.5%
20 8
20.0%
4 3
 
7.5%
25 2
 
5.0%
5 1
 
2.5%
6 1
 
2.5%
12 1
 
2.5%
19 1
 
2.5%
24 1
 
2.5%
23 1
 
2.5%
ValueCountFrequency (%)
4 3
 
7.5%
5 1
 
2.5%
6 1
 
2.5%
12 1
 
2.5%
15 19
47.5%
19 1
 
2.5%
20 8
20.0%
21 1
 
2.5%
23 1
 
2.5%
24 1
 
2.5%
ValueCountFrequency (%)
25 2
 
5.0%
24 1
 
2.5%
23 1
 
2.5%
21 1
 
2.5%
20 8
20.0%
19 1
 
2.5%
15 19
47.5%
12 1
 
2.5%
6 1
 
2.5%
5 1
 
2.5%

건립규모(세대)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)97.4%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean715.61538
Minimum9
Maximum2092
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-23T07:42:43.234449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12
Q1497
median732
Q3919
95-th percentile1236.8
Maximum2092
Range2083
Interquartile range (IQR)422

Descriptive statistics

Standard deviation400.26725
Coefficient of variation (CV)0.55933294
Kurtosis2.5512799
Mean715.61538
Median Absolute Deviation (MAD)226
Skewness0.83585051
Sum27909
Variance160213.87
MonotonicityNot monotonic
2023-12-23T07:42:44.133039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
12 2
 
5.0%
700 1
 
2.5%
544 1
 
2.5%
524 1
 
2.5%
775 1
 
2.5%
1160 1
 
2.5%
943 1
 
2.5%
769 1
 
2.5%
1316 1
 
2.5%
214 1
 
2.5%
Other values (28) 28
70.0%
ValueCountFrequency (%)
9 1
2.5%
12 2
5.0%
214 1
2.5%
304 1
2.5%
356 1
2.5%
387 1
2.5%
415 1
2.5%
430 1
2.5%
488 1
2.5%
506 1
2.5%
ValueCountFrequency (%)
2092 1
2.5%
1316 1
2.5%
1228 1
2.5%
1200 1
2.5%
1160 1
2.5%
1150 1
2.5%
1076 1
2.5%
1000 1
2.5%
990 1
2.5%
943 1
2.5%

공사시작일
Date

MISSING 

Distinct23
Distinct (%)59.0%
Missing1
Missing (%)2.5%
Memory size452.0 B
Minimum1989-02-21 00:00:00
Maximum2022-09-19 00:00:00
2023-12-23T07:42:45.016549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:45.448787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)

공사종료일
Date

MISSING 

Distinct27
Distinct (%)69.2%
Missing1
Missing (%)2.5%
Memory size452.0 B
Minimum1989-10-18 00:00:00
Maximum2023-09-07 00:00:00
2023-12-23T07:42:46.119234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:47.132038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)

비고
Text

MISSING 

Distinct28
Distinct (%)84.8%
Missing7
Missing (%)17.5%
Memory size452.0 B
2023-12-23T07:42:47.877270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length16
Mean length11.333333
Min length4

Characters and Unicode

Total characters374
Distinct characters65
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

Unique24 ?
Unique (%)72.7%

Sample

1st row일반분양 330장기임대 370
2nd row일반분양
3rd row장기임대
4th row일반분양
5th row영구임대
ValueCountFrequency (%)
청아람 4
 
7.5%
일반분양 3
 
5.7%
영구임대(용지 3
 
5.7%
근로복지(장미아파트 2
 
3.8%
영구임대(비둘기 2
 
3.8%
리슈빌 2
 
3.8%
45일반분양 2
 
3.8%
푸르지오 2
 
3.8%
370 2
 
3.8%
용산파크)근로복지 2
 
3.8%
Other values (29) 29
54.7%
2023-12-23T07:42:49.449056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 22
 
5.9%
( 22
 
5.9%
20
 
5.3%
반 19
 
5.1%
일 19
 
5.1%
양 19
 
5.1%
분 19
 
5.1%
지 18
 
4.8%
대 12
 
3.2%
임 12
 
3.2%
Other values (55) 192
51.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 273
73.0%
Decimal Number 34
 
9.1%
Close Punctuation 25
 
6.7%
Open Punctuation 22
 
5.9%
Space Separator 20
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
반 19
 
7.0%
일 19
 
7.0%
양 19
 
7.0%
분 19
 
7.0%
지 18
 
6.6%
대 12
 
4.4%
임 12
 
4.4%
아 11
 
4.0%
청 9
 
3.3%
공 8
 
2.9%
Other values (43) 127
46.5%
Decimal Number
ValueCountFrequency (%)
3 7
20.6%
4 6
17.6%
5 5
14.7%
0 5
14.7%
2 4
11.8%
1 4
11.8%
7 2
 
5.9%
8 1
 
2.9%
Close Punctuation
ValueCountFrequency (%)
) 22
88.0%
] 3
 
12.0%
Open Punctuation
ValueCountFrequency (%)
( 22
100.0%
Space Separator
ValueCountFrequency (%)
20
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 273
73.0%
Common 101
 
27.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
반 19
 
7.0%
일 19
 
7.0%
양 19
 
7.0%
분 19
 
7.0%
지 18
 
6.6%
대 12
 
4.4%
임 12
 
4.4%
아 11
 
4.0%
청 9
 
3.3%
공 8
 
2.9%
Other values (43) 127
46.5%
Common
ValueCountFrequency (%)
) 22
21.8%
( 22
21.8%
20
19.8%
3 7
 
6.9%
4 6
 
5.9%
5 5
 
5.0%
0 5
 
5.0%
2 4
 
4.0%
1 4
 
4.0%
] 3
 
3.0%
Other values (2) 3
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 273
73.0%
ASCII 101
 
27.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 22
21.8%
( 22
21.8%
20
19.8%
3 7
 
6.9%
4 6
 
5.9%
5 5
 
5.0%
0 5
 
5.0%
2 4
 
4.0%
1 4
 
4.0%
] 3
 
3.0%
Other values (2) 3
 
3.0%
Hangul
ValueCountFrequency (%)
반 19
 
7.0%
일 19
 
7.0%
양 19
 
7.0%
분 19
 
7.0%
지 18
 
6.6%
대 12
 
4.4%
임 12
 
4.4%
아 11
 
4.0%
청 9
 
3.3%
공 8
 
2.9%
Other values (43) 127
46.5%

Interactions

2023-12-23T07:42:28.142442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:15.678082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:18.549188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:21.466997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:25.133477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:28.574856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:16.134859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:19.009911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:21.979883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:25.702803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:29.025991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:16.774776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:19.490339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:22.760166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:26.369633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:29.508770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:17.316014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:19.931600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:23.144014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:26.905990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:29.954738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:18.088181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:20.706882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:24.416744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:42:27.418801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:42:49.866782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업지구위치면적(제곱미터)사업비(백만원)건립규모(동)건립규모(층)건립규모(세대)공사시작일공사종료일비고
사업지구1.0000.9820.0000.0000.0001.0000.9291.0001.0000.898
위치0.9821.0000.0001.0000.8461.0000.9520.8250.9331.000
면적(제곱미터)0.0000.0001.0000.7880.8960.4030.7960.6680.0000.966
사업비(백만원)0.0001.0000.7881.0000.7380.5760.9170.0000.0001.000
건립규모(동)0.0000.8460.8960.7381.0000.4700.7370.7620.7290.898
건립규모(층)1.0001.0000.4030.5760.4701.0000.6720.9440.9960.771
건립규모(세대)0.9290.9520.7960.9170.7370.6721.0000.4520.0000.515
공사시작일1.0000.8250.6680.0000.7620.9440.4521.0001.0001.000
공사종료일1.0000.9330.0000.0000.7290.9960.0001.0001.0000.965
비고0.8981.0000.9661.0000.8980.7710.5151.0000.9651.000
2023-12-23T07:42:50.536316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
면적(제곱미터)사업비(백만원)건립규모(동)건립규모(층)건립규모(세대)
면적(제곱미터)1.0000.4510.9030.3340.836
사업비(백만원)0.4511.0000.4860.4100.505
건립규모(동)0.9030.4861.0000.4250.703
건립규모(층)0.3340.4100.4251.0000.130
건립규모(세대)0.8360.5050.7030.1301.000

Missing values

2023-12-23T07:42:30.608391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:42:31.493098image/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.
2023-12-23T07:42:32.280263image/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

사업지구위치면적(제곱미터)사업비(백만원)건립규모(동)건립규모(층)건립규모(세대)공사시작일공사종료일비고
0지산1단지구대구광역시 수성구 지산2동 124번지3824898021257001989-02-211989-10-18일반분양 330장기임대 370
1지산2단지대구광역시 수성구 지산2동 1234-1번지17802248654156941989-11-231991-11-08일반분양
2지산3단지대구광역시 수성구 지산1동 1288번지16065113293156301989-11-231991-11-08장기임대
3지산4단지대구광역시 수성구 지산1동 1297-3번지14265<NA>3154881989-11-231991-11-08일반분양
4지산5단지대구광역시 수성구 지산1동 1297번지1233041428351510761989-11-231991-11-08영구임대
5범물1단지대구광역시 수성구 범물동 1261번지24437213425610001990-09-121992-04-15근로복지
6범물2단지대구광역시 수성구 범물동 1283번지22016479705129901990-12-221992-07-20영구임대(용지])
7범물3단지대구광역시 수성구 범물동 1285번지25184<NA>71511501990-12-221992-07-25영구임대(용지])
8범물4단지대구광역시 수성구 범물동 1287번지17253<NA>3155061990-12-221992-06-30영구임대(용지])
9상인1단지대구광역시 달서구 상인동 1563번지372065535281520921991-12-301993-09-20영구임대(비둘기)
사업지구위치면적(제곱미터)사업비(백만원)건립규모(동)건립규모(층)건립규모(세대)공사시작일공사종료일비고
30죽곡3단지대구광역시 달성군 대실역남로 3334902<NA>11205972009-09-252012-02-09청아람 리슈빌 3단지일반분양
31죽곡4단지대구광역시 달성군 대실역남로 35죽곡2택지개발지구 B2블럭14425<NA>4203042009-05-252011-10-12청아람 리슈빌 4단지일반분양
32달성2차대구광역시 달성군 대실역남로 355988213030132012282008-10-152010-11-03청아람공공임대
33과학마을 청아람대구광역시 달성군 구지면 응암리43285138909208952014-07-152016-10-26<NA>
34죽곡청아람 5단지대구광역시 달성군 대실역남로 353054810067207422015-04-012017-07-27<NA>
35수성알파시티청아람대구광역시 수성구 알파시티2로 964111113270511258442018-08-242021-04-02<NA>
36원대동청아람더영대구광역시 서구 원대동1가 645번지827160514122022-03-102023-02-02<NA>
37송현동청아람더영대구광역시 달서구 송현동 984-21번지56616051492022-05-022023-02-15<NA>
38인동촌청아람더영대구광역시 서구 비산동 68-21번지663179714122022-09-192023-09-07<NA>
39<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>