Overview

Dataset statistics

Number of variables8
Number of observations24
Missing cells23
Missing cells (%)12.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory72.5 B

Variable types

Categorical1
Text4
Numeric3

Dataset

Description대전광역시 특별사법경찰 수사활동 현황 입니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15072680/fileData.do

Alerts

검찰송치 is highly overall correlated with 행정처분High correlation
행정처분 is highly overall correlated with 검찰송치 and 1 other fieldsHigh correlation
점검분야 is highly overall correlated with 행정처분High correlation
행정처분 has 5 (20.8%) missing valuesMissing
비고 has 18 (75.0%) missing valuesMissing
점검업체수 has 2 (8.3%) zerosZeros
검찰송치 has 5 (20.8%) zerosZeros
행정처분 has 4 (16.7%) zerosZeros

Reproduction

Analysis started2023-12-12 16:47:22.865905
Analysis finished2023-12-12 16:47:24.682938
Duration1.82 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

점검분야
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
환경
7 
원산지표시
6 
위생
4 
공중위생
2 
축산
2 
Other values (3)
3 

Length

Max length5
Median length2
Mean length3.0833333
Min length2

Unique

Unique3 ?
Unique (%)12.5%

Sample

1st row식품
2nd row위생
3rd row위생
4th row위생
5th row위생

Common Values

ValueCountFrequency (%)
환경 7
29.2%
원산지표시 6
25.0%
위생 4
16.7%
공중위생 2
 
8.3%
축산 2
 
8.3%
식품 1
 
4.2%
의약품 1
 
4.2%
청소년보호 1
 
4.2%

Length

2023-12-13T01:47:24.770191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:47:24.950857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
환경 7
29.2%
원산지표시 6
25.0%
위생 4
16.7%
공중위생 2
 
8.3%
축산 2
 
8.3%
식품 1
 
4.2%
의약품 1
 
4.2%
청소년보호 1
 
4.2%

단속, 수사명
Text

Distinct21
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T01:47:25.225903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17
Mean length12.416667
Min length7

Characters and Unicode

Total characters298
Distinct characters96
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

Unique19 ?
Unique (%)79.2%

Sample

1st row설명절 성수식품 기획수사
2nd row모범업소 기획수사
3rd row학교급식납품업체 기획수사
4th row겨울철 성수식품제조업체 기획수사
5th row겨울철식중독예방기획수사
ValueCountFrequency (%)
기획수사 12
21.4%
한우유전자검사 3
 
5.4%
원산지 3
 
5.4%
시,구 2
 
3.6%
합동단속 2
 
3.6%
겨울철 2
 
3.6%
기획단속 2
 
3.6%
대기오염사업장 1
 
1.8%
미세먼지대응기획수사 1
 
1.8%
비산먼지발생사업장 1
 
1.8%
Other values (27) 27
48.2%
2023-12-13T01:47:25.674602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
32
 
10.7%
기 20
 
6.7%
사 20
 
6.7%
수 17
 
5.7%
획 17
 
5.7%
업 10
 
3.4%
단 7
 
2.3%
속 6
 
2.0%
산 6
 
2.0%
지 6
 
2.0%
Other values (86) 157
52.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 262
87.9%
Space Separator 32
 
10.7%
Other Punctuation 2
 
0.7%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 20
 
7.6%
사 20
 
7.6%
수 17
 
6.5%
획 17
 
6.5%
업 10
 
3.8%
단 7
 
2.7%
속 6
 
2.3%
산 6
 
2.3%
지 6
 
2.3%
원 5
 
1.9%
Other values (82) 148
56.5%
Space Separator
ValueCountFrequency (%)
32
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 262
87.9%
Common 36
 
12.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 20
 
7.6%
사 20
 
7.6%
수 17
 
6.5%
획 17
 
6.5%
업 10
 
3.8%
단 7
 
2.7%
속 6
 
2.3%
산 6
 
2.3%
지 6
 
2.3%
원 5
 
1.9%
Other values (82) 148
56.5%
Common
ValueCountFrequency (%)
32
88.9%
, 2
 
5.6%
( 1
 
2.8%
) 1
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 262
87.9%
ASCII 36
 
12.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
32
88.9%
, 2
 
5.6%
( 1
 
2.8%
) 1
 
2.8%
Hangul
ValueCountFrequency (%)
기 20
 
7.6%
사 20
 
7.6%
수 17
 
6.5%
획 17
 
6.5%
업 10
 
3.8%
단 7
 
2.7%
속 6
 
2.3%
산 6
 
2.3%
지 6
 
2.3%
원 5
 
1.9%
Other values (82) 148
56.5%

기간
Text

Distinct19
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T01:47:25.924529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length16
Mean length16
Min length16

Characters and Unicode

Total characters384
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)66.7%

Sample

1st row2019-01-02~02-28
2nd row2019-03-04~04-30
3rd row2019-04-01~06-30
4th row2019-10-14~11-29
5th row2019-11-01~12-30
ValueCountFrequency (%)
2019-01-02~02-28 3
 
12.5%
2019-03-04~04-30 3
 
12.5%
2019-05-01~06-28 2
 
8.3%
2019-05-20~05-24 1
 
4.2%
2019-11-01~12-20 1
 
4.2%
2019-09-02~10-31 1
 
4.2%
2019-07-01~08-30 1
 
4.2%
2018-11-01~12-30 1
 
4.2%
2019-07-28~10-07 1
 
4.2%
2019-11-01~12-31 1
 
4.2%
Other values (9) 9
37.5%
2023-12-13T01:47:26.316321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 90
23.4%
- 72
18.8%
1 69
18.0%
2 51
13.3%
9 24
 
6.2%
~ 24
 
6.2%
3 16
 
4.2%
8 12
 
3.1%
4 10
 
2.6%
5 6
 
1.6%
Other values (2) 10
 
2.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 288
75.0%
Dash Punctuation 72
 
18.8%
Math Symbol 24
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 90
31.2%
1 69
24.0%
2 51
17.7%
9 24
 
8.3%
3 16
 
5.6%
8 12
 
4.2%
4 10
 
3.5%
5 6
 
2.1%
6 5
 
1.7%
7 5
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 72
100.0%
Math Symbol
ValueCountFrequency (%)
~ 24
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 384
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 90
23.4%
- 72
18.8%
1 69
18.0%
2 51
13.3%
9 24
 
6.2%
~ 24
 
6.2%
3 16
 
4.2%
8 12
 
3.1%
4 10
 
2.6%
5 6
 
1.6%
Other values (2) 10
 
2.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 384
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 90
23.4%
- 72
18.8%
1 69
18.0%
2 51
13.3%
9 24
 
6.2%
~ 24
 
6.2%
3 16
 
4.2%
8 12
 
3.1%
4 10
 
2.6%
5 6
 
1.6%
Other values (2) 10
 
2.6%

점검업체수
Real number (ℝ)

ZEROS 

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.416667
Minimum0
Maximum70
Zeros2
Zeros (%)8.3%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T01:47:26.447273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.15
Q124.75
median45.5
Q352.25
95-th percentile55.85
Maximum70
Range70
Interquartile range (IQR)27.5

Descriptive statistics

Standard deviation20.491603
Coefficient of variation (CV)0.54765977
Kurtosis-0.68103624
Mean37.416667
Median Absolute Deviation (MAD)9.5
Skewness-0.6924554
Sum898
Variance419.9058
MonotonicityNot monotonic
2023-12-13T01:47:26.574303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
50 3
 
12.5%
0 2
 
8.3%
55 2
 
8.3%
30 1
 
4.2%
26 1
 
4.2%
56 1
 
4.2%
54 1
 
4.2%
70 1
 
4.2%
43 1
 
4.2%
40 1
 
4.2%
Other values (10) 10
41.7%
ValueCountFrequency (%)
0 2
8.3%
1 1
4.2%
7 1
4.2%
15 1
4.2%
24 1
4.2%
25 1
4.2%
26 1
4.2%
30 1
4.2%
40 1
4.2%
43 1
4.2%
ValueCountFrequency (%)
70 1
 
4.2%
56 1
 
4.2%
55 2
8.3%
54 1
 
4.2%
53 1
 
4.2%
52 1
 
4.2%
51 1
 
4.2%
50 3
12.5%
46 1
 
4.2%
45 1
 
4.2%

위반내용
Text

Distinct20
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-13T01:47:26.830243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length23.5
Mean length17.958333
Min length6

Characters and Unicode

Total characters431
Distinct characters106
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

Unique18 ?
Unique (%)75.0%

Sample

1st row유통기한경과, 영업자 준수사항 등
2nd row지하수 수질검사여부, 영업자 준수사항 등
3rd row허위표시 유통, 판매, 무신고영업 등
4th row부정,불량식품 원료 사용, 제조, 가공행위 등
5th row지하수 수질검사여부 등
ValueCountFrequency (%)
등 12
 
11.4%
및 7
 
6.7%
원산지 6
 
5.7%
거짓표시 6
 
5.7%
미신고 4
 
3.8%
혼동표시 3
 
2.9%
한우 3
 
2.9%
영업자 2
 
1.9%
영업 2
 
1.9%
수질검사여부 2
 
1.9%
Other values (54) 58
55.2%
2023-12-13T01:47:27.279402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
86
 
20.0%
시 17
 
3.9%
, 15
 
3.5%
표 13
 
3.0%
등 12
 
2.8%
지 11
 
2.6%
위 11
 
2.6%
기 9
 
2.1%
산 9
 
2.1%
행 8
 
1.9%
Other values (96) 240
55.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 328
76.1%
Space Separator 86
 
20.0%
Other Punctuation 15
 
3.5%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 17
 
5.2%
표 13
 
4.0%
등 12
 
3.7%
지 11
 
3.4%
위 11
 
3.4%
기 9
 
2.7%
산 9
 
2.7%
행 8
 
2.4%
및 7
 
2.1%
미 7
 
2.1%
Other values (92) 224
68.3%
Space Separator
ValueCountFrequency (%)
86
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 328
76.1%
Common 103
 
23.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 17
 
5.2%
표 13
 
4.0%
등 12
 
3.7%
지 11
 
3.4%
위 11
 
3.4%
기 9
 
2.7%
산 9
 
2.7%
행 8
 
2.4%
및 7
 
2.1%
미 7
 
2.1%
Other values (92) 224
68.3%
Common
ValueCountFrequency (%)
86
83.5%
, 15
 
14.6%
( 1
 
1.0%
) 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 328
76.1%
ASCII 103
 
23.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
86
83.5%
, 15
 
14.6%
( 1
 
1.0%
) 1
 
1.0%
Hangul
ValueCountFrequency (%)
시 17
 
5.2%
표 13
 
4.0%
등 12
 
3.7%
지 11
 
3.4%
위 11
 
3.4%
기 9
 
2.7%
산 9
 
2.7%
행 8
 
2.4%
및 7
 
2.1%
미 7
 
2.1%
Other values (92) 224
68.3%

검찰송치
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)37.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3333333
Minimum0
Maximum11
Zeros5
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T01:47:27.435004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q36
95-th percentile9.85
Maximum11
Range11
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.2925036
Coefficient of variation (CV)0.75980851
Kurtosis-0.72301534
Mean4.3333333
Median Absolute Deviation (MAD)2.5
Skewness0.21232364
Sum104
Variance10.84058
MonotonicityNot monotonic
2023-12-13T01:47:27.578943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
6 6
25.0%
0 5
20.8%
4 4
16.7%
7 2
 
8.3%
1 2
 
8.3%
3 2
 
8.3%
9 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
ValueCountFrequency (%)
0 5
20.8%
1 2
 
8.3%
3 2
 
8.3%
4 4
16.7%
6 6
25.0%
7 2
 
8.3%
9 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
ValueCountFrequency (%)
11 1
 
4.2%
10 1
 
4.2%
9 1
 
4.2%
7 2
 
8.3%
6 6
25.0%
4 4
16.7%
3 2
 
8.3%
1 2
 
8.3%
0 5
20.8%

행정처분
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct10
Distinct (%)52.6%
Missing5
Missing (%)20.8%
Infinite0
Infinite (%)0.0%
Mean4.8421053
Minimum0
Maximum11
Zeros4
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-13T01:47:27.735054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.5
median6
Q36.5
95-th percentile10.1
Maximum11
Range11
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.4199278
Coefficient of variation (CV)0.70628945
Kurtosis-0.82071827
Mean4.8421053
Median Absolute Deviation (MAD)2
Skewness0.010552338
Sum92
Variance11.695906
MonotonicityNot monotonic
2023-12-13T01:47:27.861638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 5
20.8%
0 4
16.7%
4 3
12.5%
7 1
 
4.2%
9 1
 
4.2%
3 1
 
4.2%
8 1
 
4.2%
10 1
 
4.2%
2 1
 
4.2%
11 1
 
4.2%
(Missing) 5
20.8%
ValueCountFrequency (%)
0 4
16.7%
2 1
 
4.2%
3 1
 
4.2%
4 3
12.5%
6 5
20.8%
7 1
 
4.2%
8 1
 
4.2%
9 1
 
4.2%
10 1
 
4.2%
11 1
 
4.2%
ValueCountFrequency (%)
11 1
 
4.2%
10 1
 
4.2%
9 1
 
4.2%
8 1
 
4.2%
7 1
 
4.2%
6 5
20.8%
4 3
12.5%
3 1
 
4.2%
2 1
 
4.2%
0 4
16.7%

비고
Text

MISSING 

Distinct6
Distinct (%)100.0%
Missing18
Missing (%)75.0%
Memory size324.0 B
2023-12-13T01:47:28.037894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length7
Mean length7.6666667
Min length7

Characters and Unicode

Total characters46
Distinct characters19
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

Unique6 ?
Unique (%)100.0%

Sample

1st row검찰합동 수사 3건
2nd row2018년 사건
3rd row수사중(6건)
4th row수사중(4건)
5th row수사중(2건)
ValueCountFrequency (%)
검찰합동 1
11.1%
수사 1
11.1%
3건 1
11.1%
2018년 1
11.1%
사건 1
11.1%
수사중(6건 1
11.1%
수사중(4건 1
11.1%
수사중(2건 1
11.1%
2018년사건 1
11.1%
2023-12-13T01:47:28.368591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 6
13.0%
건 6
13.0%
수 4
 
8.7%
2 3
 
6.5%
) 3
 
6.5%
3
 
6.5%
( 3
 
6.5%
중 3
 
6.5%
년 2
 
4.3%
0 2
 
4.3%
Other values (9) 11
23.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25
54.3%
Decimal Number 12
26.1%
Close Punctuation 3
 
6.5%
Space Separator 3
 
6.5%
Open Punctuation 3
 
6.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 6
24.0%
건 6
24.0%
수 4
16.0%
중 3
12.0%
년 2
 
8.0%
검 1
 
4.0%
찰 1
 
4.0%
동 1
 
4.0%
합 1
 
4.0%
Decimal Number
ValueCountFrequency (%)
2 3
25.0%
0 2
16.7%
1 2
16.7%
8 2
16.7%
6 1
 
8.3%
3 1
 
8.3%
4 1
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25
54.3%
Common 21
45.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3
14.3%
) 3
14.3%
3
14.3%
( 3
14.3%
0 2
9.5%
1 2
9.5%
8 2
9.5%
6 1
 
4.8%
3 1
 
4.8%
4 1
 
4.8%
Hangul
ValueCountFrequency (%)
사 6
24.0%
건 6
24.0%
수 4
16.0%
중 3
12.0%
년 2
 
8.0%
검 1
 
4.0%
찰 1
 
4.0%
동 1
 
4.0%
합 1
 
4.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25
54.3%
ASCII 21
45.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 6
24.0%
건 6
24.0%
수 4
16.0%
중 3
12.0%
년 2
 
8.0%
검 1
 
4.0%
찰 1
 
4.0%
동 1
 
4.0%
합 1
 
4.0%
ASCII
ValueCountFrequency (%)
2 3
14.3%
) 3
14.3%
3
14.3%
( 3
14.3%
0 2
9.5%
1 2
9.5%
8 2
9.5%
6 1
 
4.8%
3 1
 
4.8%
4 1
 
4.8%

Interactions

2023-12-13T01:47:24.003140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:23.192704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:23.435845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:24.101418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:23.278802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:23.823898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:24.228442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:23.357045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:47:23.909052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:47:28.481762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
점검분야단속, 수사명기간점검업체수위반내용검찰송치행정처분비고
점검분야1.0001.0000.0000.3721.0000.5990.9061.000
단속, 수사명1.0001.0000.7130.0001.0000.9531.0001.000
기간0.0000.7131.0000.4810.4570.0000.5891.000
점검업체수0.3720.0000.4811.0000.0000.0000.0001.000
위반내용1.0001.0000.4570.0001.0000.8751.0001.000
검찰송치0.5990.9530.0000.0000.8751.0000.9731.000
행정처분0.9061.0000.5890.0001.0000.9731.0001.000
비고1.0001.0001.0001.0001.0001.0001.0001.000
2023-12-13T01:47:28.618759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
점검업체수검찰송치행정처분점검분야
점검업체수1.0000.2220.1050.178
검찰송치0.2221.0000.8480.343
행정처분0.1050.8481.0000.693
점검분야0.1780.3430.6931.000

Missing values

2023-12-13T01:47:24.346091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:47:24.489582image/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.
2023-12-13T01:47:24.615067image/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

점검분야단속, 수사명기간점검업체수위반내용검찰송치행정처분비고
0식품설명절 성수식품 기획수사2019-01-02~02-2830유통기한경과, 영업자 준수사항 등66<NA>
1위생모범업소 기획수사2019-03-04~04-3051지하수 수질검사여부, 영업자 준수사항 등66<NA>
2위생학교급식납품업체 기획수사2019-04-01~06-3045허위표시 유통, 판매, 무신고영업 등66검찰합동 수사 3건
3위생겨울철 성수식품제조업체 기획수사2019-10-14~11-2946부정,불량식품 원료 사용, 제조, 가공행위 등44<NA>
4위생겨울철식중독예방기획수사2019-11-01~12-3052지하수 수질검사여부 등66<NA>
5공중위생미용업소기획단속2019-01-02~02-2853미신고 영업, 무면허 미용행위77<NA>
6공중위생민원신고 조사2019-06-18~07-161미신고 영업1<NA><NA>
7의약품의약품 판매업소 기획단속2019-05-01~07-3150사용기간 경과 의약품 판매목적 저장, 진열49<NA>
8청소년보호신학기 청소년 유해물질 등 기획수사2019-03-04~04-3025청소년유해 매체물 미표시33<NA>
9원산지표시한우유전자검사2019-01-21~01-3115한우 원산지 거짓표시00<NA>
점검분야단속, 수사명기간점검업체수위반내용검찰송치행정처분비고
14원산지표시원산지 시,구 합동단속2019-11-01~12-3140원산지 거짓표시 및 혼동표시3<NA><NA>
15축산추석명절 축산물 기획수사2019-07-28~10-0755식품 및 축산물제조일자, 위조 및 기준규격위반 행위 등98<NA>
16축산식육포장가공업 기획수사2018-11-01~12-300표시기준위반(허위표시), 기준 및 규격판매금지 등4<NA>2018년 사건
17환경미세먼지대응기획수사2019-01-02~02-2843무허가 대기오염물질 배출시설설치 등1010<NA>
18환경비산먼지발생사업장 기획단속2019-03-04~04-3070비산먼지 발생 억제, 조치 미이행, 미신고66<NA>
19환경폐기물배출업체 기획수사2019-05-01~06-2854폐기물처리 미신고 등62수사중(6건)
20환경생활주변 대기오염사업장 기획수사2019-07-01~08-3050자동차샌딩작업, 산업기계 불법도장행위44수사중(4건)
21환경산업단지주변 악취배출시설 기획수사2019-09-02~10-3156공단주변 악취 유발행위 등04<NA>
22환경겨울철 환경오염관리실태 기획수사2019-11-01~12-2055무허가 배출시설 및 방지시설 비정상가동행위 등00수사중(2건)
23환경운수장비 및 환경오염 기획수사2018-01-01~12-300악취, 대기배출시설 설치 신고 이행 여부 등11112018년사건