Overview

Dataset statistics

Number of variables11
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory104.3 B

Variable types

Text1
Categorical5
Numeric5

Dataset

Description대전광역시 2021년 업무분야별 특별사법경찰 지명현황입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111049/fileData.do

Alerts

자치구_중구 is highly overall correlated with 자치구_서구 and 3 other fieldsHigh correlation
자치구_서구 is highly overall correlated with 자치구_중구 and 2 other fieldsHigh correlation
자치구_유성구 is highly overall correlated with 자치구_중구 and 2 other fieldsHigh correlation
자치구_대덕구 is highly overall correlated with 자치구_중구 and 2 other fieldsHigh correlation
대전광역시_본청_실과 is highly overall correlated with 자치구_중구High correlation
대전광역시_소방본부 is highly imbalanced (72.4%)Imbalance
대전광역시_건설관리본부 is highly imbalanced (72.4%)Imbalance
대전광역시_하천관리사업소 is highly imbalanced (72.4%)Imbalance
대전광역시_차량등록사업소 is highly imbalanced (72.4%)Imbalance
구분 has unique valuesUnique
자치구_동구 has 10 (47.6%) zerosZeros
자치구_중구 has 10 (47.6%) zerosZeros
자치구_서구 has 5 (23.8%) zerosZeros
자치구_유성구 has 8 (38.1%) zerosZeros
자치구_대덕구 has 7 (33.3%) zerosZeros

Reproduction

Analysis started2023-12-12 05:51:40.327032
Analysis finished2023-12-12 05:51:43.559228
Duration3.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T14:51:43.704819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length10
Mean length4.9047619
Min length2

Characters and Unicode

Total characters103
Distinct characters72
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

Unique21 ?
Unique (%)100.0%

Sample

1st row산림보호
2nd row식품
3rd row의약품
4th row공중위생
5th row계량기
ValueCountFrequency (%)
산림보호 1
 
4.5%
식품 1
 
4.5%
부동산거래주택법관련 1
 
4.5%
도로점용 1
 
4.5%
지방세 1
 
4.5%
주택건설 1
 
4.5%
소방관련 1
 
4.5%
자동차관련 1
 
4.5%
농약비료단속 1
 
4.5%
개발제한구역 1
 
4.5%
Other values (12) 12
54.5%
2023-12-12T14:51:44.116063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
관 4
 
3.9%
산 3
 
2.9%
지 3
 
2.9%
련 3
 
2.9%
차 3
 
2.9%
동 3
 
2.9%
경 2
 
1.9%
한 2
 
1.9%
제 2
 
1.9%
택 2
 
1.9%
Other values (62) 76
73.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 100
97.1%
Open Punctuation 1
 
1.0%
Space Separator 1
 
1.0%
Close Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
관 4
 
4.0%
산 3
 
3.0%
지 3
 
3.0%
련 3
 
3.0%
차 3
 
3.0%
동 3
 
3.0%
경 2
 
2.0%
한 2
 
2.0%
제 2
 
2.0%
택 2
 
2.0%
Other values (59) 73
73.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 100
97.1%
Common 3
 
2.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
관 4
 
4.0%
산 3
 
3.0%
지 3
 
3.0%
련 3
 
3.0%
차 3
 
3.0%
동 3
 
3.0%
경 2
 
2.0%
한 2
 
2.0%
제 2
 
2.0%
택 2
 
2.0%
Other values (59) 73
73.0%
Common
ValueCountFrequency (%)
( 1
33.3%
1
33.3%
) 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 100
97.1%
ASCII 3
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
관 4
 
4.0%
산 3
 
3.0%
지 3
 
3.0%
련 3
 
3.0%
차 3
 
3.0%
동 3
 
3.0%
경 2
 
2.0%
한 2
 
2.0%
제 2
 
2.0%
택 2
 
2.0%
Other values (59) 73
73.0%
ASCII
ValueCountFrequency (%)
( 1
33.3%
1
33.3%
) 1
33.3%

대전광역시_본청_실과
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)23.8%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
14 
1
3 
3
2 
5
 
1
16
 
1

Length

Max length2
Median length1
Mean length1.047619
Min length1

Unique

Unique2 ?
Unique (%)9.5%

Sample

1st row0
2nd row1
3rd row0
4th row0
5th row1

Common Values

ValueCountFrequency (%)
0 14
66.7%
1 3
 
14.3%
3 2
 
9.5%
5 1
 
4.8%
16 1
 
4.8%

Length

2023-12-12T14:51:44.259600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:51:44.366827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 14
66.7%
1 3
 
14.3%
3 2
 
9.5%
5 1
 
4.8%
16 1
 
4.8%

대전광역시_소방본부
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
20 
60
 
1

Length

Max length2
Median length1
Mean length1.047619
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
95.2%
60 1
 
4.8%

Length

2023-12-12T14:51:44.479102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:51:44.571368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
95.2%
60 1
 
4.8%

대전광역시_건설관리본부
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
20 
2
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
95.2%
2 1
 
4.8%

Length

2023-12-12T14:51:44.658103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:51:44.747802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
95.2%
2 1
 
4.8%

대전광역시_하천관리사업소
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
20 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
95.2%
5 1
 
4.8%

Length

2023-12-12T14:51:44.847364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:51:44.932543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
95.2%
5 1
 
4.8%

대전광역시_차량등록사업소
Categorical

IMBALANCE 

Distinct2
Distinct (%)9.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
0
20 
5
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)4.8%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 20
95.2%
5 1
 
4.8%

Length

2023-12-12T14:51:45.022443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:51:45.105263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 20
95.2%
5 1
 
4.8%

자치구_동구
Real number (ℝ)

ZEROS 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3333333
Minimum0
Maximum5
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T14:51:45.183565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile4
Maximum5
Range5
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.5599145
Coefficient of variation (CV)1.1699359
Kurtosis-0.23614582
Mean1.3333333
Median Absolute Deviation (MAD)1
Skewness0.86707282
Sum28
Variance2.4333333
MonotonicityNot monotonic
2023-12-12T14:51:45.272640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 10
47.6%
2 4
 
19.0%
3 3
 
14.3%
1 2
 
9.5%
5 1
 
4.8%
4 1
 
4.8%
ValueCountFrequency (%)
0 10
47.6%
1 2
 
9.5%
2 4
 
19.0%
3 3
 
14.3%
4 1
 
4.8%
5 1
 
4.8%
ValueCountFrequency (%)
5 1
 
4.8%
4 1
 
4.8%
3 3
 
14.3%
2 4
 
19.0%
1 2
 
9.5%
0 10
47.6%

자치구_중구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6666667
Minimum0
Maximum9
Zeros10
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T14:51:45.388721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile7
Maximum9
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.4562845
Coefficient of variation (CV)1.4737707
Kurtosis3.5699219
Mean1.6666667
Median Absolute Deviation (MAD)1
Skewness1.9258539
Sum35
Variance6.0333333
MonotonicityNot monotonic
2023-12-12T14:51:45.562896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 10
47.6%
2 3
 
14.3%
1 3
 
14.3%
3 2
 
9.5%
7 1
 
4.8%
9 1
 
4.8%
4 1
 
4.8%
ValueCountFrequency (%)
0 10
47.6%
1 3
 
14.3%
2 3
 
14.3%
3 2
 
9.5%
4 1
 
4.8%
7 1
 
4.8%
9 1
 
4.8%
ValueCountFrequency (%)
9 1
 
4.8%
7 1
 
4.8%
4 1
 
4.8%
3 2
 
9.5%
2 3
 
14.3%
1 3
 
14.3%
0 10
47.6%

자치구_서구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)38.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5714286
Minimum0
Maximum15
Zeros5
Zeros (%)23.8%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T14:51:45.673081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median2
Q33
95-th percentile7
Maximum15
Range15
Interquartile range (IQR)2

Descriptive statistics

Standard deviation3.4142558
Coefficient of variation (CV)1.3277662
Kurtosis8.7343526
Mean2.5714286
Median Absolute Deviation (MAD)1
Skewness2.7053749
Sum54
Variance11.657143
MonotonicityNot monotonic
2023-12-12T14:51:45.783023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2 6
28.6%
0 5
23.8%
1 4
19.0%
3 2
 
9.5%
15 1
 
4.8%
4 1
 
4.8%
7 1
 
4.8%
6 1
 
4.8%
ValueCountFrequency (%)
0 5
23.8%
1 4
19.0%
2 6
28.6%
3 2
 
9.5%
4 1
 
4.8%
6 1
 
4.8%
7 1
 
4.8%
15 1
 
4.8%
ValueCountFrequency (%)
15 1
 
4.8%
7 1
 
4.8%
6 1
 
4.8%
4 1
 
4.8%
3 2
 
9.5%
2 6
28.6%
1 4
19.0%
0 5
23.8%

자치구_유성구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.1428571
Minimum0
Maximum10
Zeros8
Zeros (%)38.1%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T14:51:45.916846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q32
95-th percentile9
Maximum10
Range10
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.8685487
Coefficient of variation (CV)1.338656
Kurtosis2.7621807
Mean2.1428571
Median Absolute Deviation (MAD)1
Skewness1.7803107
Sum45
Variance8.2285714
MonotonicityNot monotonic
2023-12-12T14:51:46.032451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 8
38.1%
2 5
23.8%
1 3
 
14.3%
4 2
 
9.5%
9 1
 
4.8%
10 1
 
4.8%
5 1
 
4.8%
ValueCountFrequency (%)
0 8
38.1%
1 3
 
14.3%
2 5
23.8%
4 2
 
9.5%
5 1
 
4.8%
9 1
 
4.8%
10 1
 
4.8%
ValueCountFrequency (%)
10 1
 
4.8%
9 1
 
4.8%
5 1
 
4.8%
4 2
 
9.5%
2 5
23.8%
1 3
 
14.3%
0 8
38.1%

자치구_대덕구
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)28.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6190476
Minimum0
Maximum5
Zeros7
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T14:51:46.164881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q33
95-th percentile5
Maximum5
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6575944
Coefficient of variation (CV)1.0238083
Kurtosis-0.34808275
Mean1.6190476
Median Absolute Deviation (MAD)1
Skewness0.82466593
Sum34
Variance2.747619
MonotonicityNot monotonic
2023-12-12T14:51:46.278811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 7
33.3%
1 5
23.8%
3 3
14.3%
2 3
14.3%
5 2
 
9.5%
4 1
 
4.8%
ValueCountFrequency (%)
0 7
33.3%
1 5
23.8%
2 3
14.3%
3 3
14.3%
4 1
 
4.8%
5 2
 
9.5%
ValueCountFrequency (%)
5 2
 
9.5%
4 1
 
4.8%
3 3
14.3%
2 3
14.3%
1 5
23.8%
0 7
33.3%

Interactions

2023-12-12T14:51:42.794497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:40.826829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.249277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.631600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.372087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.878832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:40.902246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.320682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.757899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.462080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.955607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:40.980326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.401687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.098386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.534794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:43.042651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.067630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.480054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.190033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.626697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:43.138208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.148538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:41.555903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.287122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T14:51:42.708052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:51:46.371428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분대전광역시_본청_실과대전광역시_소방본부대전광역시_건설관리본부대전광역시_하천관리사업소대전광역시_차량등록사업소자치구_동구자치구_중구자치구_서구자치구_유성구자치구_대덕구
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
대전광역시_본청_실과1.0001.0000.0000.0000.0000.0000.4740.7090.7990.5960.299
대전광역시_소방본부1.0000.0001.0000.0000.0000.0000.0000.0000.0000.0000.000
대전광역시_건설관리본부1.0000.0000.0001.0000.0000.0000.0000.1320.0000.1320.000
대전광역시_하천관리사업소1.0000.0000.0000.0001.0000.0000.3960.0000.0000.0000.000
대전광역시_차량등록사업소1.0000.0000.0000.0000.0001.0000.3960.0000.0000.0000.000
자치구_동구1.0000.4740.0000.0000.3960.3961.0000.6630.5520.6000.807
자치구_중구1.0000.7090.0000.1320.0000.0000.6631.0000.7990.9400.648
자치구_서구1.0000.7990.0000.0000.0000.0000.5520.7991.0000.7600.486
자치구_유성구1.0000.5960.0000.1320.0000.0000.6000.9400.7601.0000.438
자치구_대덕구1.0000.2990.0000.0000.0000.0000.8070.6480.4860.4381.000
2023-12-12T14:51:46.532602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대전광역시_건설관리본부대전광역시_소방본부대전광역시_하천관리사업소대전광역시_차량등록사업소대전광역시_본청_실과
대전광역시_건설관리본부1.0000.0000.0000.0000.000
대전광역시_소방본부0.0001.0000.0000.0000.000
대전광역시_하천관리사업소0.0000.0001.0000.0000.000
대전광역시_차량등록사업소0.0000.0000.0001.0000.000
대전광역시_본청_실과0.0000.0000.0000.0001.000
2023-12-12T14:51:46.681782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치구_동구자치구_중구자치구_서구자치구_유성구자치구_대덕구대전광역시_본청_실과대전광역시_소방본부대전광역시_건설관리본부대전광역시_하천관리사업소대전광역시_차량등록사업소
자치구_동구1.0000.2870.3870.4020.2930.3140.0000.0000.2290.229
자치구_중구0.2871.0000.5590.7060.6300.5110.0000.0000.0000.000
자치구_서구0.3870.5591.0000.5550.6640.4260.0000.0000.0000.000
자치구_유성구0.4020.7060.5551.0000.5780.4450.0000.0000.0000.000
자치구_대덕구0.2930.6300.6640.5781.0000.1610.0000.0000.0000.000
대전광역시_본청_실과0.3140.5110.4260.4450.1611.0000.0000.0000.0000.000
대전광역시_소방본부0.0000.0000.0000.0000.0000.0001.0000.0000.0000.000
대전광역시_건설관리본부0.0000.0000.0000.0000.0000.0000.0001.0000.0000.000
대전광역시_하천관리사업소0.2290.0000.0000.0000.0000.0000.0000.0001.0000.000
대전광역시_차량등록사업소0.2290.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2023-12-12T14:51:43.290104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:51:43.480472image/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

구분대전광역시_본청_실과대전광역시_소방본부대전광역시_건설관리본부대전광역시_하천관리사업소대전광역시_차량등록사업소자치구_동구자치구_중구자치구_서구자치구_유성구자치구_대덕구
0산림보호0000023323
1식품10000571595
2의약품0000023224
3공중위생0000010401
4계량기1000010121
5환경50000397101
6자동차이전등록0000530000
7차량운행제한0020002010
8청소년보호1000021201
9원산지 표시0000020113
구분대전광역시_본청_실과대전광역시_소방본부대전광역시_건설관리본부대전광역시_하천관리사업소대전광역시_차량등록사업소자치구_동구자치구_중구자치구_서구자치구_유성구자치구_대덕구
11관광지도0000000100
12개발제한구역0000001252
13농약비료단속0000000010
14자동차관련0000004645
15소방관련06000000000
16주택건설0000000100
17지방세3000002222
18도로점용0000001001
19부동산거래주택법관련3000042243
20기타(민생사법경찰과)16000000302