Overview

Dataset statistics

Number of variables4
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory846.0 B
Average record size in memory40.3 B

Variable types

Numeric2
Text2

Dataset

Description서울특별시 용산구 민원1회방문상담제 현황(민원1회방문상담제에 대한 민원사무명, 처리기간, 처리부서)에 대한 데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15090582/fileData.do

Alerts

연번 has unique valuesUnique
민원사무명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 07:35:14.833743
Analysis finished2023-12-12 07:35:15.616089
Duration0.78 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T16:35:15.689079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation6.2048368
Coefficient of variation (CV)0.56407607
Kurtosis-1.2
Mean11
Median Absolute Deviation (MAD)5
Skewness0
Sum231
Variance38.5
MonotonicityStrictly increasing
2023-12-12T16:35:15.867102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 1
 
4.8%
2 1
 
4.8%
21 1
 
4.8%
20 1
 
4.8%
19 1
 
4.8%
18 1
 
4.8%
17 1
 
4.8%
16 1
 
4.8%
15 1
 
4.8%
14 1
 
4.8%
Other values (11) 11
52.4%
ValueCountFrequency (%)
1 1
4.8%
2 1
4.8%
3 1
4.8%
4 1
4.8%
5 1
4.8%
6 1
4.8%
7 1
4.8%
8 1
4.8%
9 1
4.8%
10 1
4.8%
ValueCountFrequency (%)
21 1
4.8%
20 1
4.8%
19 1
4.8%
18 1
4.8%
17 1
4.8%
16 1
4.8%
15 1
4.8%
14 1
4.8%
13 1
4.8%
12 1
4.8%

민원사무명
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:35:16.097240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12
Mean length9.8571429
Min length4

Characters and Unicode

Total characters207
Distinct characters63
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
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 row국(공)유재산 매수신청
2nd row공장등록신청
3rd row폐기물처리시설설치
4th row폐기물처리시설의 사용개시 신고
5th row준공인가전 사용허가신청
ValueCountFrequency (%)
허가신청 2
 
6.2%
국(공)유재산 1
 
3.1%
개발행위준공검사신청 1
 
3.1%
도로점용허가 1
 
3.1%
설치허가신청 1
 
3.1%
대기배출시설의 1
 
3.1%
폐수배출시설설치치허가신청 1
 
3.1%
액화석유가스의 1
 
3.1%
신청 1
 
3.1%
고압가스 1
 
3.1%
Other values (21) 21
65.6%
2023-12-12T16:35:16.492334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
신 15
 
7.2%
가 14
 
6.8%
청 14
 
6.8%
11
 
5.3%
설 10
 
4.8%
시 9
 
4.3%
사 9
 
4.3%
허 8
 
3.9%
인 6
 
2.9%
공 5
 
2.4%
Other values (53) 106
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 193
93.2%
Space Separator 12
 
5.8%
Open Punctuation 1
 
0.5%
Close Punctuation 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
신 15
 
7.8%
가 14
 
7.3%
청 14
 
7.3%
설 10
 
5.2%
시 9
 
4.7%
사 9
 
4.7%
허 8
 
4.1%
인 6
 
3.1%
공 5
 
2.6%
업 5
 
2.6%
Other values (49) 98
50.8%
Space Separator
ValueCountFrequency (%)
11
91.7%
  1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 193
93.2%
Common 14
 
6.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
신 15
 
7.8%
가 14
 
7.3%
청 14
 
7.3%
설 10
 
5.2%
시 9
 
4.7%
사 9
 
4.7%
허 8
 
4.1%
인 6
 
3.1%
공 5
 
2.6%
업 5
 
2.6%
Other values (49) 98
50.8%
Common
ValueCountFrequency (%)
11
78.6%
  1
 
7.1%
( 1
 
7.1%
) 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 193
93.2%
ASCII 13
 
6.3%
None 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
신 15
 
7.8%
가 14
 
7.3%
청 14
 
7.3%
설 10
 
5.2%
시 9
 
4.7%
사 9
 
4.7%
허 8
 
4.1%
인 6
 
3.1%
공 5
 
2.6%
업 5
 
2.6%
Other values (49) 98
50.8%
ASCII
ValueCountFrequency (%)
11
84.6%
( 1
 
7.7%
) 1
 
7.7%
None
ValueCountFrequency (%)
  1
100.0%

처리기간(일)
Real number (ℝ)

Distinct7
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17
Minimum5
Maximum60
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-12T16:35:16.628271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile5
Q17
median10
Q315
95-th percentile60
Maximum60
Range55
Interquartile range (IQR)8

Descriptive statistics

Standard deviation18.422812
Coefficient of variation (CV)1.0836948
Kurtosis2.578661
Mean17
Median Absolute Deviation (MAD)3
Skewness2.0001213
Sum357
Variance339.4
MonotonicityNot monotonic
2023-12-12T16:35:16.755686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
7 7
33.3%
15 4
19.0%
10 3
14.3%
60 3
14.3%
5 2
 
9.5%
20 1
 
4.8%
8 1
 
4.8%
ValueCountFrequency (%)
5 2
 
9.5%
7 7
33.3%
8 1
 
4.8%
10 3
14.3%
15 4
19.0%
20 1
 
4.8%
60 3
14.3%
ValueCountFrequency (%)
60 3
14.3%
20 1
 
4.8%
15 4
19.0%
10 3
14.3%
8 1
 
4.8%
7 7
33.3%
5 2
 
9.5%

처리부서
Text

Distinct11
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-12T16:35:16.932987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length5.6190476
Min length3

Characters and Unicode

Total characters118
Distinct characters32
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)28.6%

Sample

1st row재무과
2nd row지역경제과
3rd row청소행정과
4th row청소행정과
5th row주택과
ValueCountFrequency (%)
도시계획과 6
24.0%
맑은환경과 5
20.0%
주택과 4
16.0%
청소행정과 2
 
8.0%
건축과 2
 
8.0%
재정비사업과 2
 
8.0%
재무과 1
 
4.0%
지역경제과 1
 
4.0%
건설관리과 1
 
4.0%
교통행정과 1
 
4.0%
2023-12-12T16:35:17.279964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
과 25
21.2%
획 6
 
5.1%
계 6
 
5.1%
경 6
 
5.1%
도 6
 
5.1%
시 6
 
5.1%
정 5
 
4.2%
맑 5
 
4.2%
환 5
 
4.2%
은 5
 
4.2%
Other values (22) 43
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
95.8%
Space Separator 5
 
4.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
과 25
22.1%
획 6
 
5.3%
계 6
 
5.3%
경 6
 
5.3%
도 6
 
5.3%
시 6
 
5.3%
정 5
 
4.4%
맑 5
 
4.4%
환 5
 
4.4%
은 5
 
4.4%
Other values (20) 38
33.6%
Space Separator
ValueCountFrequency (%)
  3
60.0%
2
40.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
95.8%
Common 5
 
4.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
과 25
22.1%
획 6
 
5.3%
계 6
 
5.3%
경 6
 
5.3%
도 6
 
5.3%
시 6
 
5.3%
정 5
 
4.4%
맑 5
 
4.4%
환 5
 
4.4%
은 5
 
4.4%
Other values (20) 38
33.6%
Common
ValueCountFrequency (%)
  3
60.0%
2
40.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
95.8%
None 3
 
2.5%
ASCII 2
 
1.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
과 25
22.1%
획 6
 
5.3%
계 6
 
5.3%
경 6
 
5.3%
도 6
 
5.3%
시 6
 
5.3%
정 5
 
4.4%
맑 5
 
4.4%
환 5
 
4.4%
은 5
 
4.4%
Other values (20) 38
33.6%
None
ValueCountFrequency (%)
  3
100.0%
ASCII
ValueCountFrequency (%)
2
100.0%

Interactions

2023-12-12T16:35:15.205479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:35:15.017632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:35:15.309191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:35:15.114869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:35:17.397486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번민원사무명처리기간(일)처리부서
연번1.0001.0000.3560.907
민원사무명1.0001.0001.0001.000
처리기간(일)0.3561.0001.0000.844
처리부서0.9071.0000.8441.000
2023-12-12T16:35:17.813219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번처리기간(일)
연번1.000-0.327
처리기간(일)-0.3271.000

Missing values

2023-12-12T16:35:15.445390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:35:15.560271image/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

연번민원사무명처리기간(일)처리부서
01국(공)유재산 매수신청20재무과
12공장등록신청7지역경제과
23폐기물처리시설설치10청소행정과
34폐기물처리시설의 사용개시 신고7청소행정과
45준공인가전 사용허가신청15주택과
56주택건설사업 계획 승인60주택과
67재개발사업시행인가신청60재정비사업과 주택과 도시계획과
78준공인가신청15재정비사업과 주택과 도시계획과
89도시계획시설사업실시계획인가신청60도시계획과
910개발행위허가15도시계획과
연번민원사무명처리기간(일)처리부서
1112사도개설 허가신청7도시계획과
1213건축허가7건축과
1314사용승인신청7건축과
1415석유판매업 등록7맑은환경과
1516고압가스 신청5맑은환경과
1617액화석유가스의 허가신청5맑은환경과
1718폐수배출시설설치치허가신청10맑은환경과
1819대기배출시설의 설치허가신청10맑은환경과
1920도로점용허가8건설관리과
2021자동차관리사업등록신청15교통행정과