Overview

Dataset statistics

Number of variables9
Number of observations55
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.2 KiB
Average record size in memory78.4 B

Variable types

Categorical3
Text2
Numeric4

Dataset

Description대전광역시 2019년부터 2020년까지 안전도시종합계획입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111013/fileData.do

Alerts

2019 목표 is highly overall correlated with 2020 목표 and 1 other fieldsHigh correlation
2020 목표 is highly overall correlated with 2019 목표 and 1 other fieldsHigh correlation
2020 추진실적 is highly overall correlated with 2019 목표 and 1 other fieldsHigh correlation
2020 추진실적 has 1 (1.8%) missing valuesMissing
과제명 has unique valuesUnique
2019 목표 has 36 (65.5%) zerosZeros
2020 목표 has 35 (63.6%) zerosZeros
2020 추진실적 has 32 (58.2%) zerosZeros
2020 달성률 has 40 (72.7%) zerosZeros

Reproduction

Analysis started2023-12-12 20:14:12.319865
Analysis finished2023-12-12 20:14:15.281057
Duration2.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

분야
Categorical

Distinct5
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
장소별
15 
기능별
14 
시기별
11 
안전의식제고
9 
소방구조
6 

Length

Max length6
Median length3
Mean length3.6
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row시기별
2nd row시기별
3rd row시기별
4th row시기별
5th row장소별

Common Values

ValueCountFrequency (%)
장소별 15
27.3%
기능별 14
25.5%
시기별 11
20.0%
안전의식제고 9
16.4%
소방구조 6
 
10.9%

Length

2023-12-13T05:14:15.363527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:14:15.482857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
장소별 15
27.3%
기능별 14
25.5%
시기별 11
20.0%
안전의식제고 9
16.4%
소방구조 6
 
10.9%

과제명
Text

UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-13T05:14:15.804616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length12
Mean length7.4363636
Min length2

Characters and Unicode

Total characters409
Distinct characters145
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique55 ?
Unique (%)100.0%

Sample

1st row국가안전대진단
2nd row황사
3rd row전세버스 교통사고
4th row폭설(한파)
5th row도로시설물
ValueCountFrequency (%)
등 4
 
4.1%
안전사고 4
 
4.1%
화재안전 2
 
2.1%
교통안전 2
 
2.1%
운영 2
 
2.1%
현장중심의 1
 
1.0%
대전 1
 
1.0%
안전한국훈련 1
 
1.0%
재난대응 1
 
1.0%
안전교육 1
 
1.0%
Other values (78) 78
80.4%
2023-12-13T05:14:16.308280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42
 
10.3%
전 21
 
5.1%
안 14
 
3.4%
사 13
 
3.2%
시 9
 
2.2%
화 9
 
2.2%
공 8
 
2.0%
대 8
 
2.0%
설 8
 
2.0%
교 7
 
1.7%
Other values (135) 270
66.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 355
86.8%
Space Separator 42
 
10.3%
Open Punctuation 3
 
0.7%
Close Punctuation 3
 
0.7%
Decimal Number 3
 
0.7%
Uppercase Letter 3
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 21
 
5.9%
안 14
 
3.9%
사 13
 
3.7%
시 9
 
2.5%
화 9
 
2.5%
공 8
 
2.3%
대 8
 
2.3%
설 8
 
2.3%
교 7
 
2.0%
방 6
 
1.7%
Other values (127) 252
71.0%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
B 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 2
66.7%
9 1
33.3%
Space Separator
ValueCountFrequency (%)
42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 355
86.8%
Common 51
 
12.5%
Latin 3
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 21
 
5.9%
안 14
 
3.9%
사 13
 
3.7%
시 9
 
2.5%
화 9
 
2.5%
공 8
 
2.3%
대 8
 
2.3%
설 8
 
2.3%
교 7
 
2.0%
방 6
 
1.7%
Other values (127) 252
71.0%
Common
ValueCountFrequency (%)
42
82.4%
( 3
 
5.9%
) 3
 
5.9%
1 2
 
3.9%
9 1
 
2.0%
Latin
ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
B 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 355
86.8%
ASCII 54
 
13.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42
77.8%
( 3
 
5.6%
) 3
 
5.6%
1 2
 
3.7%
9 1
 
1.9%
P 1
 
1.9%
E 1
 
1.9%
B 1
 
1.9%
Hangul
ValueCountFrequency (%)
전 21
 
5.9%
안 14
 
3.9%
사 13
 
3.7%
시 9
 
2.5%
화 9
 
2.5%
공 8
 
2.3%
대 8
 
2.3%
설 8
 
2.3%
교 7
 
2.0%
방 6
 
1.7%
Other values (127) 252
71.0%

관리지표
Text

Distinct30
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-13T05:14:16.569355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length19
Mean length11.654545
Min length4

Characters and Unicode

Total characters641
Distinct characters107
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

Unique27 ?
Unique (%)49.1%

Sample

1st row인명피해(사망) 제로화
2nd row인명피해(사망) 제로화
3rd row인명피해(사망) 제로화
4th row인명피해(사망) 제로화
5th row인명피해(사망) 제로화
ValueCountFrequency (%)
제로화 32
24.6%
인명피해(사망 25
19.2%
발생 4
 
3.1%
감소 3
 
2.3%
인명피해 3
 
2.3%
감축 3
 
2.3%
전년대비 2
 
1.5%
발생건수 2
 
1.5%
체험인원 2
 
1.5%
및 2
 
1.5%
Other values (46) 52
40.0%
2023-12-13T05:14:16.947143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
76
 
11.9%
인 37
 
5.8%
피 33
 
5.1%
해 33
 
5.1%
사 33
 
5.1%
화 33
 
5.1%
제 32
 
5.0%
로 32
 
5.0%
명 31
 
4.8%
( 29
 
4.5%
Other values (97) 272
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 494
77.1%
Space Separator 76
 
11.9%
Open Punctuation 29
 
4.5%
Close Punctuation 29
 
4.5%
Decimal Number 10
 
1.6%
Other Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 37
 
7.5%
피 33
 
6.7%
해 33
 
6.7%
사 33
 
6.7%
화 33
 
6.7%
제 32
 
6.5%
로 32
 
6.5%
명 31
 
6.3%
망 27
 
5.5%
생 11
 
2.2%
Other values (89) 192
38.9%
Decimal Number
ValueCountFrequency (%)
0 5
50.0%
1 4
40.0%
7 1
 
10.0%
Other Punctuation
ValueCountFrequency (%)
% 2
66.7%
, 1
33.3%
Space Separator
ValueCountFrequency (%)
76
100.0%
Open Punctuation
ValueCountFrequency (%)
( 29
100.0%
Close Punctuation
ValueCountFrequency (%)
) 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 494
77.1%
Common 147
 
22.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 37
 
7.5%
피 33
 
6.7%
해 33
 
6.7%
사 33
 
6.7%
화 33
 
6.7%
제 32
 
6.5%
로 32
 
6.5%
명 31
 
6.3%
망 27
 
5.5%
생 11
 
2.2%
Other values (89) 192
38.9%
Common
ValueCountFrequency (%)
76
51.7%
( 29
 
19.7%
) 29
 
19.7%
0 5
 
3.4%
1 4
 
2.7%
% 2
 
1.4%
7 1
 
0.7%
, 1
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 494
77.1%
ASCII 147
 
22.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
76
51.7%
( 29
 
19.7%
) 29
 
19.7%
0 5
 
3.4%
1 4
 
2.7%
% 2
 
1.4%
7 1
 
0.7%
, 1
 
0.7%
Hangul
ValueCountFrequency (%)
인 37
 
7.5%
피 33
 
6.7%
해 33
 
6.7%
사 33
 
6.7%
화 33
 
6.7%
제 32
 
6.5%
로 32
 
6.5%
명 31
 
6.3%
망 27
 
5.5%
생 11
 
2.2%
Other values (89) 192
38.9%

2019 목표
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9431.4818
Minimum0
Maximum377804
Zeros36
Zeros (%)65.5%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:14:17.089990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q328.5
95-th percentile26657.2
Maximum377804
Range377804
Interquartile range (IQR)28.5

Descriptive statistics

Standard deviation51746.745
Coefficient of variation (CV)5.4865976
Kurtosis50.007
Mean9431.4818
Median Absolute Deviation (MAD)0
Skewness6.9573944
Sum518731.5
Variance2.6777257 × 109
MonotonicityNot monotonic
2023-12-13T05:14:17.216626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 36
65.5%
480.0 1
 
1.8%
1.2 1
 
1.8%
1.0 1
 
1.8%
97.4 1
 
1.8%
83.9 1
 
1.8%
377804.0 1
 
1.8%
52.0 1
 
1.8%
49788.0 1
 
1.8%
62836.0 1
 
1.8%
Other values (10) 10
 
18.2%
ValueCountFrequency (%)
0.0 36
65.5%
1.0 1
 
1.8%
1.2 1
 
1.8%
3.0 1
 
1.8%
13.0 1
 
1.8%
20.0 1
 
1.8%
37.0 1
 
1.8%
52.0 1
 
1.8%
83.9 1
 
1.8%
97.4 1
 
1.8%
ValueCountFrequency (%)
377804.0 1
1.8%
62836.0 1
1.8%
49788.0 1
1.8%
16744.0 1
1.8%
6365.0 1
1.8%
1584.0 1
1.8%
1261.0 1
1.8%
978.0 1
1.8%
583.0 1
1.8%
480.0 1
1.8%

2020 목표
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)38.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10203.442
Minimum0
Maximum380000
Zeros35
Zeros (%)63.6%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:14:17.368694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q392
95-th percentile35510
Maximum380000
Range380000
Interquartile range (IQR)92

Descriptive statistics

Standard deviation52114.635
Coefficient of variation (CV)5.1075545
Kurtosis49.327455
Mean10203.442
Median Absolute Deviation (MAD)0
Skewness6.8911358
Sum561189.3
Variance2.7159352 × 109
MonotonicityNot monotonic
2023-12-13T05:14:17.510016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 35
63.6%
8300.0 1
 
1.8%
0.2 1
 
1.8%
1.1 1
 
1.8%
100.0 1
 
1.8%
84.0 1
 
1.8%
380000.0 1
 
1.8%
65.0 1
 
1.8%
50000.0 1
 
1.8%
65000.0 1
 
1.8%
Other values (11) 11
 
20.0%
ValueCountFrequency (%)
0.0 35
63.6%
0.2 1
 
1.8%
1.1 1
 
1.8%
19.0 1
 
1.8%
33.0 1
 
1.8%
65.0 1
 
1.8%
84.0 1
 
1.8%
100.0 1
 
1.8%
148.0 1
 
1.8%
500.0 1
 
1.8%
ValueCountFrequency (%)
380000.0 1
1.8%
65000.0 1
1.8%
50000.0 1
1.8%
29300.0 1
1.8%
17000.0 1
1.8%
8300.0 1
1.8%
6300.0 1
1.8%
1424.0 1
1.8%
1387.0 1
1.8%
965.0 1
1.8%

2020 추진실적
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct23
Distinct (%)42.6%
Missing1
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean1669.6706
Minimum0
Maximum36655
Zeros32
Zeros (%)58.2%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-13T05:14:17.636256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q365.15
95-th percentile9528.45
Maximum36655
Range36655
Interquartile range (IQR)65.15

Descriptive statistics

Standard deviation5680.3571
Coefficient of variation (CV)3.4020826
Kurtosis28.269812
Mean1669.6706
Median Absolute Deviation (MAD)0
Skewness4.9952216
Sum90162.21
Variance32266457
MonotonicityNot monotonic
2023-12-13T05:14:17.782245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.0 32
58.2%
10430.0 1
 
1.8%
0.11 1
 
1.8%
0.7 1
 
1.8%
1.0 1
 
1.8%
98.9 1
 
1.8%
84.3 1
 
1.8%
36655.0 1
 
1.8%
65.2 1
 
1.8%
4442.0 1
 
1.8%
Other values (13) 13
23.6%
ValueCountFrequency (%)
0.0 32
58.2%
0.11 1
 
1.8%
0.7 1
 
1.8%
1.0 1
 
1.8%
2.0 1
 
1.8%
6.0 1
 
1.8%
24.0 1
 
1.8%
36.0 1
 
1.8%
65.0 1
 
1.8%
65.2 1
 
1.8%
ValueCountFrequency (%)
36655.0 1
1.8%
15961.0 1
1.8%
10430.0 1
1.8%
9043.0 1
1.8%
6129.0 1
1.8%
4442.0 1
1.8%
3159.0 1
1.8%
1372.0 1
1.8%
1266.0 1
1.8%
883.0 1
1.8%

단위
Categorical

Distinct7
Distinct (%)12.7%
Missing0
Missing (%)0.0%
Memory size572.0 B
명
39 
건
8 
%
4 
개소
 
1
개동
 
1
Other values (2)
 
2

Length

Max length4
Median length1
Mean length1.0909091
Min length1

Unique

Unique4 ?
Unique (%)7.3%

Sample

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

Common Values

ValueCountFrequency (%)
명 39
70.9%
건 8
 
14.5%
% 4
 
7.3%
개소 1
 
1.8%
개동 1
 
1.8%
회 1
 
1.8%
ha/건 1
 
1.8%

Length

2023-12-13T05:14:17.958565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:14:18.087991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
명 39
70.9%
건 8
 
14.5%
4
 
7.3%
개소 1
 
1.8%
개동 1
 
1.8%
회 1
 
1.8%
ha/건 1
 
1.8%

2020 달성률
Real number (ℝ)

ZEROS 

Distinct16
Distinct (%)29.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-7.9163636
Minimum-600
Maximum200
Zeros40
Zeros (%)72.7%
Negative6
Negative (%)10.9%
Memory size627.0 B
2023-12-13T05:14:18.217610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-600
5-th percentile-37.85
Q10
median0
Q30
95-th percentile27.4
Maximum200
Range800
Interquartile range (IQR)0

Descriptive statistics

Standard deviation87.822561
Coefficient of variation (CV)-11.093801
Kurtosis40.265978
Mean-7.9163636
Median Absolute Deviation (MAD)0
Skewness-5.6462717
Sum-435.4
Variance7712.8021
MonotonicityNot monotonic
2023-12-13T05:14:18.352815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 40
72.7%
2.8 1
 
1.8%
-55.0 1
 
1.8%
-100.0 1
 
1.8%
-1.1 1
 
1.8%
0.3 1
 
1.8%
200.0 1
 
1.8%
40.0 1
 
1.8%
9.3 1
 
1.8%
20.4 1
 
1.8%
Other values (6) 6
 
10.9%
ValueCountFrequency (%)
-600.0 1
 
1.8%
-100.0 1
 
1.8%
-55.0 1
 
1.8%
-30.5 1
 
1.8%
-10.8 1
 
1.8%
-1.1 1
 
1.8%
0.0 40
72.7%
0.3 1
 
1.8%
2.8 1
 
1.8%
9.3 1
 
1.8%
ValueCountFrequency (%)
200.0 1
 
1.8%
56.1 1
 
1.8%
40.0 1
 
1.8%
22.0 1
 
1.8%
20.4 1
 
1.8%
11.1 1
 
1.8%
9.3 1
 
1.8%
2.8 1
 
1.8%
0.3 1
 
1.8%
0.0 40
72.7%

담당부서
Categorical

Distinct24
Distinct (%)43.6%
Missing0
Missing (%)0.0%
Memory size572.0 B
재난관리과
11 
예방안전과
8 
안전정책과
4 
기반산업과
4 
공공교통정책과
3 
Other values (19)
25 

Length

Max length9
Median length5
Mean length5.6545455
Min length5

Unique

Unique13 ?
Unique (%)23.6%

Sample

1st row재난관리과
2nd row미세먼지대응과
3rd row운송주차과
4th row재난관리과
5th row건설관리본부

Common Values

ValueCountFrequency (%)
재난관리과 11
20.0%
예방안전과 8
14.5%
안전정책과 4
 
7.3%
기반산업과 4
 
7.3%
공공교통정책과 3
 
5.5%
관광마케팅과 2
 
3.6%
성인지정책 담당관 2
 
3.6%
미세먼지대응과 2
 
3.6%
화재대응조사과 2
 
3.6%
건설관리본부 2
 
3.6%
Other values (14) 15
27.3%

Length

2023-12-13T05:14:18.496057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
재난관리과 11
19.0%
예방안전과 8
13.8%
안전정책과 4
 
6.9%
기반산업과 4
 
6.9%
공공교통정책과 3
 
5.2%
미세먼지대응과 2
 
3.4%
건설도로과 2
 
3.4%
건설관리본부 2
 
3.4%
화재대응조사과 2
 
3.4%
담당관 2
 
3.4%
Other values (16) 18
31.0%

Interactions

2023-12-13T05:14:14.209018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:12.794534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.236365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.690337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:14.326897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:12.924361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.342824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.843301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:14.450179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.020827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.446841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.957476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:14.560548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.132570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:13.571457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:14:14.083497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:14:18.595549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
분야과제명관리지표2019 목표2020 목표2020 추진실적단위2020 달성률담당부서
분야1.0001.0000.8740.3510.3510.3590.5610.1660.682
과제명1.0001.0001.0001.0001.0001.0001.0001.0001.000
관리지표0.8741.0001.0000.7190.7190.8391.0000.9440.887
2019 목표0.3511.0000.7191.0001.0000.8810.0000.0000.000
2020 목표0.3511.0000.7191.0001.0000.8810.0000.0000.000
2020 추진실적0.3591.0000.8390.8810.8811.0000.5630.0000.000
단위0.5611.0001.0000.0000.0000.5631.0000.4450.772
2020 달성률0.1661.0000.9440.0000.0000.0000.4451.0000.798
담당부서0.6821.0000.8870.0000.0000.0000.7720.7981.000
2023-12-13T05:14:18.710522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
담당부서분야단위
담당부서1.0000.3120.366
분야0.3121.0000.393
단위0.3660.3931.000
2023-12-13T05:14:18.792685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2019 목표2020 목표2020 추진실적2020 달성률분야단위담당부서
2019 목표1.0000.7840.8540.0790.2760.0000.000
2020 목표0.7841.0000.9450.2260.2760.0000.000
2020 추진실적0.8540.9451.0000.1820.1350.3950.000
2020 달성률0.0790.2260.1821.0000.0860.2150.369
분야0.2760.2760.1350.0861.0000.3930.312
단위0.0000.0000.3950.2150.3931.0000.366
담당부서0.0000.0000.0000.3690.3120.3661.000

Missing values

2023-12-13T05:14:15.060229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:14:15.216982image/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

분야과제명관리지표2019 목표2020 목표2020 추진실적단위2020 달성률담당부서
0시기별국가안전대진단인명피해(사망) 제로화0.00.00.0명0.0재난관리과
1시기별황사인명피해(사망) 제로화0.00.00.0명0.0미세먼지대응과
2시기별전세버스 교통사고인명피해(사망) 제로화0.00.00.0명0.0운송주차과
3시기별폭설(한파)인명피해(사망) 제로화0.00.00.0명0.0재난관리과
4장소별도로시설물인명피해(사망) 제로화0.00.00.0명0.0건설관리본부
5장소별공공대형공사장인명피해(사망) 제로화0.00.00.0명0.0건설관리본부
6장소별지반침하(싱크홀)인명피해(사망) 제로화0.00.00.0명0.0건설도로과
7장소별원자력시설인명피해(사망) 제로화0.00.00.0명0.0안전정책과
8장소별유원시설인명피해 발생 제로화0.00.00.0명0.0관광마케팅과
9장소별승강기사고발생 제로화0.00.00.0건0.0기반산업과
분야과제명관리지표2019 목표2020 목표2020 추진실적단위2020 달성률담당부서
45안전의식제고공공부문 산재 제로화인명피해(사망) 제로화1.00.01.0명-100.0안전정책과
46기능별학교폭력피해응답률 감소1.21.10.7%0.0교육 청소년과
47시기별산불발생건수 및 피해면적 저감0.00.20.11ha/건-55.0공원녹지과
48시기별풍수해인명피해(사망) 제로화0.00.00.0명0.0재난관리과
49시기별가뭄농작물 피해 제로화0.00.00.0건0.0재난관리과
50시기별물놀이인명피해(사망) 제로화0.00.00.0명0.0재난관리과
51시기별전력수급정전고장 발생건수0.00.00.0건0.0기반산업과
52시기별폭염인명피해(사망) 제로화0.00.00.0명0.0재난관리과
53시기별가축전염병가축전염병 발생 제로화0.00.00.0건0.0농생명정책과
54장소별기타 유원시설(키즈카페 등)인명피해 발생 제로화0.00.00.0명0.0관광마케팅과