Overview

Dataset statistics

Number of variables11
Number of observations29
Missing cells56
Missing cells (%)17.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory96.6 B

Variable types

Categorical5
Text2
Numeric4

Dataset

Description2019년에 주한미군 공여구역주변지역 등 발전종합계획의 변경을 위해 실시하였던 용역 최종보고서입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15081559/fileData.do

Alerts

비고 is highly overall correlated with (총 사 업 비) 국비 and 4 other fieldsHigh correlation
사업기간 is highly overall correlated with (총 사 업 비) 국비 and 6 other fieldsHigh correlation
(총 사 업 비) 국비 is highly overall correlated with (총 사 업 비) 지방비 and 6 other fieldsHigh correlation
(총 사 업 비) 지방비 is highly overall correlated with (총 사 업 비) 국비 and 6 other fieldsHigh correlation
(2019년까지 투자실적) 국비 is highly overall correlated with (총 사 업 비) 국비 and 5 other fieldsHigh correlation
(2019년까지 투자실적) 지방비 is highly overall correlated with (총 사 업 비) 국비 and 5 other fieldsHigh correlation
구 분 is highly overall correlated with (총 사 업 비) 국비 and 5 other fieldsHigh correlation
사업기간.1 is highly overall correlated with (총 사 업 비) 국비 and 5 other fieldsHigh correlation
사업규모 has 2 (6.9%) missing valuesMissing
(총 사 업 비) 국비 has 7 (24.1%) missing valuesMissing
(총 사 업 비) 지방비 has 7 (24.1%) missing valuesMissing
(2019년까지 투자실적) 국비 has 20 (69.0%) missing valuesMissing
(2019년까지 투자실적) 지방비 has 20 (69.0%) missing valuesMissing

Reproduction

Analysis started2023-12-12 20:49:56.255630
Analysis finished2023-12-12 20:49:59.368174
Duration3.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

사업기간
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
2018 ~ 2022(금회 변경)
24 
2008 ~ 2017(완료)
3 
2008 ~ 2017
 
2

Length

Max length18
Median length18
Mean length17.206897
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2008 ~ 2017
2nd row2008 ~ 2017
3rd row2008 ~ 2017(완료)
4th row2008 ~ 2017(완료)
5th row2008 ~ 2017(완료)

Common Values

ValueCountFrequency (%)
2018 ~ 2022(금회 변경) 24
82.8%
2008 ~ 2017(완료) 3
 
10.3%
2008 ~ 2017 2
 
6.9%

Length

2023-12-13T05:49:59.482235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:49:59.637215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
29
26.1%
2018 24
21.6%
2022(금회 24
21.6%
변경 24
21.6%
2008 5
 
4.5%
2017(완료 3
 
2.7%
2017 2
 
1.8%

구 분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
동구
16 
대덕구
11 
총 계
2 

Length

Max length3
Median length2
Mean length2.4482759
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row총 계
2nd row총 계
3rd row동구
4th row동구
5th row대덕구

Common Values

ValueCountFrequency (%)
동구 16
55.2%
대덕구 11
37.9%
총 계 2
 
6.9%

Length

2023-12-13T05:49:59.771662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:49:59.891303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 16
51.6%
대덕구 11
35.5%
총 2
 
6.5%
계 2
 
6.5%

비고
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)20.7%
Missing0
Missing (%)0.0%
Memory size364.0 B
제외
12 
존치
6 
<NA>
5 
신규
2 
변경 (증액)
2 

Length

Max length7
Median length2
Mean length3.0344828
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제외 12
41.4%
존치 6
20.7%
<NA> 5
17.2%
신규 2
 
6.9%
변경 (증액) 2
 
6.9%
변경 (감액) 2
 
6.9%

Length

2023-12-13T05:50:00.023357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:50:00.156318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제외 12
36.4%
존치 6
18.2%
na 5
15.2%
변경 4
 
12.1%
신규 2
 
6.1%
증액 2
 
6.1%
감액 2
 
6.1%

사업명
Text

Distinct17
Distinct (%)58.6%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-13T05:50:00.347061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length17
Mean length15.172414
Min length7

Characters and Unicode

Total characters440
Distinct characters81
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)17.2%

Sample

1st row당초 14사업
2nd row변경 9개 사업
3rd row대청동세천1호선 도시계획도로사업
4th row추동 취수탑주변 레저단지조성사업
5th row신탄진동미집행도로 도시계획도로사업
ValueCountFrequency (%)
대청동 12
 
17.1%
소하천정비사업 6
 
8.6%
설치사업 4
 
5.7%
회덕동(장동)~상서간 2
 
2.9%
도시계획도로사업 2
 
2.9%
생태공원조성사업 2
 
2.9%
신탄진동(갈전동 2
 
2.9%
조성사업 2
 
2.9%
역사공원 2
 
2.9%
6차산업화지구조성사업 2
 
2.9%
Other values (22) 34
48.6%
2023-12-13T05:50:00.769071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
48
 
10.9%
동 35
 
8.0%
업 31
 
7.0%
사 31
 
7.0%
대 15
 
3.4%
청 15
 
3.4%
도 13
 
3.0%
비 12
 
2.7%
정 10
 
2.3%
조 9
 
2.0%
Other values (71) 221
50.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 368
83.6%
Space Separator 48
 
10.9%
Open Punctuation 8
 
1.8%
Close Punctuation 8
 
1.8%
Decimal Number 6
 
1.4%
Math Symbol 2
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 35
 
9.5%
업 31
 
8.4%
사 31
 
8.4%
대 15
 
4.1%
청 15
 
4.1%
도 13
 
3.5%
비 12
 
3.3%
정 10
 
2.7%
조 9
 
2.4%
성 9
 
2.4%
Other values (63) 188
51.1%
Decimal Number
ValueCountFrequency (%)
1 2
33.3%
6 2
33.3%
9 1
16.7%
4 1
16.7%
Space Separator
ValueCountFrequency (%)
48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 368
83.6%
Common 72
 
16.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 35
 
9.5%
업 31
 
8.4%
사 31
 
8.4%
대 15
 
4.1%
청 15
 
4.1%
도 13
 
3.5%
비 12
 
3.3%
정 10
 
2.7%
조 9
 
2.4%
성 9
 
2.4%
Other values (63) 188
51.1%
Common
ValueCountFrequency (%)
48
66.7%
( 8
 
11.1%
) 8
 
11.1%
1 2
 
2.8%
~ 2
 
2.8%
6 2
 
2.8%
9 1
 
1.4%
4 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 368
83.6%
ASCII 72
 
16.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
48
66.7%
( 8
 
11.1%
) 8
 
11.1%
1 2
 
2.8%
~ 2
 
2.8%
6 2
 
2.8%
9 1
 
1.4%
4 1
 
1.4%
Hangul
ValueCountFrequency (%)
동 35
 
9.5%
업 31
 
8.4%
사 31
 
8.4%
대 15
 
4.1%
청 15
 
4.1%
도 13
 
3.5%
비 12
 
3.3%
정 10
 
2.7%
조 9
 
2.4%
성 9
 
2.4%
Other values (63) 188
51.1%

사업기간.1
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)44.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
2018~2020
9 
2018~2022
4 
<NA>
2 
2019~2022
2 
2018~2021
2 
Other values (8)
10 

Length

Max length10
Median length9
Mean length8.7241379
Min length4

Unique

Unique6 ?
Unique (%)20.7%

Sample

1st row<NA>
2nd row<NA>
3rd row2009~2013
4th row2008~2017
5th row2009~2017

Common Values

ValueCountFrequency (%)
2018~2020 9
31.0%
2018~2022 4
13.8%
<NA> 2
 
6.9%
2019~2022 2
 
6.9%
2018~2021 2
 
6.9%
20192021 2
 
6.9%
2018~2019 2
 
6.9%
2009~2013 1
 
3.4%
2008~2017 1
 
3.4%
2009~2017 1
 
3.4%
Other values (3) 3
 
10.3%

Length

2023-12-13T05:50:00.926293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2018~2020 9
31.0%
2018~2022 4
13.8%
2018~2021 3
 
10.3%
na 2
 
6.9%
2019~2022 2
 
6.9%
20192021 2
 
6.9%
2018~2019 2
 
6.9%
2009~2013 1
 
3.4%
2008~2017 1
 
3.4%
2009~2017 1
 
3.4%
Other values (2) 2
 
6.9%

사업규모
Text

MISSING 

Distinct18
Distinct (%)66.7%
Missing2
Missing (%)6.9%
Memory size364.0 B
2023-12-13T05:50:01.118631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length10.074074
Min length4

Characters and Unicode

Total characters272
Distinct characters35
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)33.3%

Sample

1st row9.3㎞
2nd row44,000㎡
3rd row3.3㎞
4th rowL=0.5㎞B=5m 광장2개소
5th rowL=0.5㎞B=5m 광장2개소
ValueCountFrequency (%)
l=1.4㎞ 2
 
4.9%
교량2기 2
 
4.9%
l=1.7km 2
 
4.9%
a=23,400㎡ 2
 
4.9%
대지면적405㎡ 2
 
4.9%
교량1기 2
 
4.9%
b=5m 2
 
4.9%
l=2km 2
 
4.9%
건축면적608㎡ 2
 
4.9%
l=0.8㎞ 2
 
4.9%
Other values (15) 21
51.2%
2023-12-13T05:50:01.440638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 31
 
11.4%
= 28
 
10.3%
m 15
 
5.5%
2 15
 
5.5%
14
 
5.1%
L 14
 
5.1%
. 12
 
4.4%
㎡ 11
 
4.0%
4 11
 
4.0%
1 10
 
3.7%
Other values (25) 111
40.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 97
35.7%
Other Letter 42
15.4%
Math Symbol 30
 
11.0%
Uppercase Letter 28
 
10.3%
Lowercase Letter 22
 
8.1%
Other Symbol 20
 
7.4%
Other Punctuation 19
 
7.0%
Space Separator 14
 
5.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 6
14.3%
량 6
14.3%
교 6
14.3%
적 4
9.5%
면 4
9.5%
지 2
 
4.8%
대 2
 
4.8%
광 2
 
4.8%
축 2
 
4.8%
건 2
 
4.8%
Other values (3) 6
14.3%
Decimal Number
ValueCountFrequency (%)
0 31
32.0%
2 15
15.5%
4 11
 
11.3%
1 10
 
10.3%
5 10
 
10.3%
3 9
 
9.3%
8 4
 
4.1%
6 3
 
3.1%
9 2
 
2.1%
7 2
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
L 14
50.0%
B 8
28.6%
A 6
21.4%
Math Symbol
ValueCountFrequency (%)
= 28
93.3%
~ 2
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
m 15
68.2%
k 7
31.8%
Other Punctuation
ValueCountFrequency (%)
. 12
63.2%
, 7
36.8%
Other Symbol
ValueCountFrequency (%)
㎡ 11
55.0%
㎞ 9
45.0%
Space Separator
ValueCountFrequency (%)
14
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 180
66.2%
Latin 50
 
18.4%
Hangul 42
 
15.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 31
17.2%
= 28
15.6%
2 15
8.3%
14
7.8%
. 12
 
6.7%
㎡ 11
 
6.1%
4 11
 
6.1%
1 10
 
5.6%
5 10
 
5.6%
3 9
 
5.0%
Other values (7) 29
16.1%
Hangul
ValueCountFrequency (%)
기 6
14.3%
량 6
14.3%
교 6
14.3%
적 4
9.5%
면 4
9.5%
지 2
 
4.8%
대 2
 
4.8%
광 2
 
4.8%
축 2
 
4.8%
건 2
 
4.8%
Other values (3) 6
14.3%
Latin
ValueCountFrequency (%)
m 15
30.0%
L 14
28.0%
B 8
16.0%
k 7
14.0%
A 6
 
12.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 210
77.2%
Hangul 42
 
15.4%
CJK Compat 20
 
7.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 31
14.8%
= 28
13.3%
m 15
 
7.1%
2 15
 
7.1%
14
 
6.7%
L 14
 
6.7%
. 12
 
5.7%
4 11
 
5.2%
1 10
 
4.8%
5 10
 
4.8%
Other values (10) 50
23.8%
CJK Compat
ValueCountFrequency (%)
㎡ 11
55.0%
㎞ 9
45.0%
Hangul
ValueCountFrequency (%)
기 6
14.3%
량 6
14.3%
교 6
14.3%
적 4
9.5%
면 4
9.5%
지 2
 
4.8%
대 2
 
4.8%
광 2
 
4.8%
축 2
 
4.8%
건 2
 
4.8%
Other values (3) 6
14.3%

구분
Categorical

Distinct3
Distinct (%)10.3%
Missing0
Missing (%)0.0%
Memory size364.0 B
당초
13 
변경
13 
<NA>
3 

Length

Max length4
Median length2
Mean length2.2068966
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
당초 13
44.8%
변경 13
44.8%
<NA> 3
 
10.3%

Length

2023-12-13T05:50:01.580879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:50:01.682255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
당초 13
44.8%
변경 13
44.8%
na 3
 
10.3%

(총 사 업 비) 국비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct17
Distinct (%)77.3%
Missing7
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean45.166818
Minimum2.9
Maximum265.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T05:50:01.798626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.9
5-th percentile3.5
Q15.7575
median20
Q340.75
95-th percentile255.818
Maximum265.95
Range263.05
Interquartile range (IQR)34.9925

Descriptive statistics

Standard deviation73.733596
Coefficient of variation (CV)1.6324727
Kurtosis6.7637642
Mean45.166818
Median Absolute Deviation (MAD)15.84
Skewness2.7338126
Sum993.67
Variance5436.6433
MonotonicityNot monotonic
2023-12-13T05:50:01.918662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
20.0 2
 
6.9%
34.0 2
 
6.9%
3.5 2
 
6.9%
265.95 2
 
6.9%
50.0 2
 
6.9%
63.31 1
 
3.4%
38.5 1
 
3.4%
5.0 1
 
3.4%
10.6 1
 
3.4%
2.9 1
 
3.4%
Other values (7) 7
24.1%
(Missing) 7
24.1%
ValueCountFrequency (%)
2.9 1
3.4%
3.5 2
6.9%
4.82 1
3.4%
5.0 1
3.4%
5.01 1
3.4%
8.0 1
3.4%
9.13 1
3.4%
10.6 1
3.4%
18.5 1
3.4%
20.0 2
6.9%
ValueCountFrequency (%)
265.95 2
6.9%
63.31 1
3.4%
50.0 2
6.9%
41.0 1
3.4%
40.0 1
3.4%
38.5 1
3.4%
34.0 2
6.9%
20.0 2
6.9%
18.5 1
3.4%
10.6 1
3.4%

(총 사 업 비) 지방비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)81.8%
Missing7
Missing (%)24.1%
Infinite0
Infinite (%)0.0%
Mean44.905909
Minimum2.14
Maximum265.95
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T05:50:02.038798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.14
5-th percentile2.93
Q15.75
median20
Q340.75
95-th percentile253.0915
Maximum265.95
Range263.81
Interquartile range (IQR)35

Descriptive statistics

Standard deviation73.402896
Coefficient of variation (CV)1.6345933
Kurtosis6.7336266
Mean44.905909
Median Absolute Deviation (MAD)16.5
Skewness2.7258184
Sum987.93
Variance5387.9852
MonotonicityNot monotonic
2023-12-13T05:50:02.186243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
20.0 2
 
6.9%
34.0 2
 
6.9%
50.0 2
 
6.9%
3.5 2
 
6.9%
2.9 1
 
3.4%
18.5 1
 
3.4%
2.14 1
 
3.4%
8.0 1
 
3.4%
40.0 1
 
3.4%
265.95 1
 
3.4%
Other values (8) 8
27.6%
(Missing) 7
24.1%
ValueCountFrequency (%)
2.14 1
3.4%
2.9 1
3.4%
3.5 2
6.9%
4.82 1
3.4%
5.0 1
3.4%
8.0 1
3.4%
9.13 1
3.4%
10.6 1
3.4%
18.5 1
3.4%
20.0 2
6.9%
ValueCountFrequency (%)
265.95 1
3.4%
263.08 1
3.4%
63.31 1
3.4%
50.0 2
6.9%
41.0 1
3.4%
40.0 1
3.4%
38.5 1
3.4%
34.0 2
6.9%
20.0 2
6.9%
18.5 1
3.4%

(2019년까지 투자실적) 국비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)77.8%
Missing20
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean30.306667
Minimum3.34
Maximum85.72
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T05:50:02.312998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.34
5-th percentile3.344
Q19.13
median12.25
Q341
95-th percentile85.72
Maximum85.72
Range82.38
Interquartile range (IQR)31.87

Descriptive statistics

Standard deviation33.384597
Coefficient of variation (CV)1.1015595
Kurtosis-0.18270892
Mean30.306667
Median Absolute Deviation (MAD)8.9
Skewness1.2092675
Sum272.76
Variance1114.5313
MonotonicityNot monotonic
2023-12-13T05:50:02.415617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
85.72 2
 
6.9%
12.25 2
 
6.9%
20.0 1
 
3.4%
9.13 1
 
3.4%
41.0 1
 
3.4%
3.34 1
 
3.4%
3.35 1
 
3.4%
(Missing) 20
69.0%
ValueCountFrequency (%)
3.34 1
3.4%
3.35 1
3.4%
9.13 1
3.4%
12.25 2
6.9%
20.0 1
3.4%
41.0 1
3.4%
85.72 2
6.9%
ValueCountFrequency (%)
85.72 2
6.9%
41.0 1
3.4%
20.0 1
3.4%
12.25 2
6.9%
9.13 1
3.4%
3.35 1
3.4%
3.34 1
3.4%

(2019년까지 투자실적) 지방비
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)77.8%
Missing20
Missing (%)69.0%
Infinite0
Infinite (%)0.0%
Mean29.455556
Minimum1.42
Maximum83.81
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T05:50:02.526308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.42
5-th percentile1.424
Q19.13
median12.25
Q341
95-th percentile83.81
Maximum83.81
Range82.39
Interquartile range (IQR)31.87

Descriptive statistics

Standard deviation32.992627
Coefficient of variation (CV)1.1200816
Kurtosis-0.26183553
Mean29.455556
Median Absolute Deviation (MAD)10.82
Skewness1.1600902
Sum265.1
Variance1088.5135
MonotonicityNot monotonic
2023-12-13T05:50:02.685022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
83.81 2
 
6.9%
12.25 2
 
6.9%
20.0 1
 
3.4%
9.13 1
 
3.4%
41.0 1
 
3.4%
1.43 1
 
3.4%
1.42 1
 
3.4%
(Missing) 20
69.0%
ValueCountFrequency (%)
1.42 1
3.4%
1.43 1
3.4%
9.13 1
3.4%
12.25 2
6.9%
20.0 1
3.4%
41.0 1
3.4%
83.81 2
6.9%
ValueCountFrequency (%)
83.81 2
6.9%
41.0 1
3.4%
20.0 1
3.4%
12.25 2
6.9%
9.13 1
3.4%
1.43 1
3.4%
1.42 1
3.4%

Interactions

2023-12-13T05:49:58.392599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:56.967129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.495165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.944147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:58.492547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.073472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.620565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:58.060261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:58.620959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.235611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.733535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:58.188046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:58.740139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.382300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:57.836398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:49:58.292064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:50:02.803507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업기간구 분비고사업명사업기간.1사업규모구분(총 사 업 비) 국비(총 사 업 비) 지방비(2019년까지 투자실적) 국비(2019년까지 투자실적) 지방비
사업기간1.0000.932NaN1.0001.0001.0000.0000.6330.6330.7900.790
구 분0.9321.0000.5101.0000.6031.0000.0000.6490.6490.7580.758
비고NaN0.5101.0001.0000.9161.0000.0000.7360.7360.0000.000
사업명1.0001.0001.0001.0000.9571.0000.0000.9740.9741.0001.000
사업기간.11.0000.6030.9160.9571.0000.9720.0001.0001.0001.0001.000
사업규모1.0001.0001.0001.0000.9721.0000.0001.0001.0001.0001.000
구분0.0000.0000.0000.0000.0000.0001.0000.2730.2730.0000.000
(총 사 업 비) 국비0.6330.6490.7360.9741.0001.0000.2731.0001.0000.8050.805
(총 사 업 비) 지방비0.6330.6490.7360.9741.0001.0000.2731.0001.0000.8050.805
(2019년까지 투자실적) 국비0.7900.7580.0001.0001.0001.0000.0000.8050.8051.0001.000
(2019년까지 투자실적) 지방비0.7900.7580.0001.0001.0001.0000.0000.8050.8051.0001.000
2023-12-13T05:50:02.956213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업기간.1비고구분구 분사업기간
사업기간.11.0000.7290.0000.3480.775
비고0.7291.0000.0000.5711.000
구분0.0000.0001.0000.0000.000
구 분0.3480.5710.0001.0000.680
사업기간0.7751.0000.0000.6801.000
2023-12-13T05:50:03.106566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
(총 사 업 비) 국비(총 사 업 비) 지방비(2019년까지 투자실적) 국비(2019년까지 투자실적) 지방비사업기간구 분비고사업기간.1구분
(총 사 업 비) 국비1.0000.9830.9450.9620.6330.6530.6710.6860.142
(총 사 업 비) 지방비0.9831.0000.9410.9580.6330.6530.6710.6860.142
(2019년까지 투자실적) 국비0.9450.9411.0000.9830.6380.5960.0001.0000.000
(2019년까지 투자실적) 지방비0.9620.9580.9831.0000.6380.5960.0001.0000.000
사업기간0.6330.6330.6380.6381.0000.6801.0000.7750.000
구 분0.6530.6530.5960.5960.6801.0000.5710.3480.000
비고0.6710.6710.0000.0001.0000.5711.0000.7290.000
사업기간.10.6860.6861.0001.0000.7750.3480.7291.0000.000
구분0.1420.1420.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-13T05:49:58.890305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:49:59.052267image/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.
2023-12-13T05:49:59.228611image/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

사업기간구 분비고사업명사업기간.1사업규모구분(총 사 업 비) 국비(총 사 업 비) 지방비(2019년까지 투자실적) 국비(2019년까지 투자실적) 지방비
02008 ~ 2017총 계<NA>당초 14사업<NA><NA>당초265.95265.9585.7283.81
12008 ~ 2017총 계<NA>변경 9개 사업<NA><NA>변경265.95263.0885.7283.81
22008 ~ 2017(완료)동구<NA>대청동세천1호선 도시계획도로사업2009~20139.3㎞<NA>20.020.020.020.0
32008 ~ 2017(완료)동구<NA>추동 취수탑주변 레저단지조성사업2008~201744,000㎡<NA>9.139.139.139.13
42008 ~ 2017(완료)대덕구<NA>신탄진동미집행도로 도시계획도로사업2009~20173.3㎞<NA>41.041.041.041.0
52018 ~ 2022(금회 변경)동구존치대청동 인도교 설치사업2018~2022L=0.5㎞B=5m 광장2개소당초50.050.0<NA><NA>
62018 ~ 2022(금회 변경)동구존치대청동 인도교 설치사업2018~2022L=0.5㎞B=5m 광장2개소변경50.050.0<NA><NA>
72018 ~ 2022(금회 변경)동구신규대청동자연취락지구 도시기반시설정비2019~2022L=5.2㎞당초<NA><NA><NA><NA>
82018 ~ 2022(금회 변경)동구신규대청동자연취락지구 도시기반시설정비2019~2022B=4~10m변경63.3163.31<NA><NA>
92018 ~ 2022(금회 변경)동구제외대청동 생태문화관광공원조성사업2018~2021A=42,000㎡당초38.538.5<NA><NA>
사업기간구 분비고사업명사업기간.1사업규모구분(총 사 업 비) 국비(총 사 업 비) 지방비(2019년까지 투자실적) 국비(2019년까지 투자실적) 지방비
192018 ~ 2022(금회 변경)대덕구변경 (증액)회덕동(장동)~상서간 도로개설사업2018~2020L=1.7km B=15m당초20.020.012.2512.25
202018 ~ 2022(금회 변경)대덕구변경 (증액)회덕동(장동)~상서간 도로개설사업2018~2021L=1.7km B=10m변경40.040.012.2512.25
212018 ~ 2022(금회 변경)대덕구변경 (감액)회덕동(장동)임도 설치사업2018~2019L=2km B=5m당초8.08.03.341.43
222018 ~ 2022(금회 변경)대덕구변경 (감액)회덕동(장동)임도 설치사업2019~2020L=2km B=5m변경5.012.143.351.42
232018 ~ 2022(금회 변경)대덕구존치회덕동 역사공원 조성사업2018~2019건축면적608㎡ 대지면적405㎡당초3.53.5<NA><NA>
242018 ~ 2022(금회 변경)대덕구존치회덕동 역사공원 조성사업2019~2021건축면적608㎡ 대지면적405㎡변경3.53.5<NA><NA>
252018 ~ 2022(금회 변경)대덕구존치신탄진동(갈전동) 생태공원조성사업2018~2022A=23,400㎡당초34.034.0<NA><NA>
262018 ~ 2022(금회 변경)대덕구존치신탄진동(갈전동) 생태공원조성사업2018~2022A=23,400㎡변경34.034.0<NA><NA>
272018 ~ 2022(금회 변경)대덕구제외회덕동(장동) 소하천정비사업사업2018~2020L=2.9km당초18.518.5<NA><NA>
282018 ~ 2022(금회 변경)대덕구제외회덕동(장동) 소하천정비사업사업2018~2020B=6~12m변경<NA><NA><NA><NA>