Overview

Dataset statistics

Number of variables3
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory730.0 B
Average record size in memory31.7 B

Variable types

Numeric2
Text1

Dataset

Description2021년 12월 31일 기준 광주광역시 서부소방서 관내의 다중이용업소 업종별 현황 등을 나타내는 파일 데이터 입니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15045514/fileData.do

Alerts

연번 has unique valuesUnique
업종별 has unique valuesUnique
계 has 5 (21.7%) zerosZeros

Reproduction

Analysis started2023-12-12 07:15:55.644069
Analysis finished2023-12-12 07:15:56.285905
Duration0.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12
Minimum1
Maximum23
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T16:15:56.358297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.1
Q16.5
median12
Q317.5
95-th percentile21.9
Maximum23
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.56519417
Kurtosis-1.2
Mean12
Median Absolute Deviation (MAD)6
Skewness0
Sum276
Variance46
MonotonicityStrictly increasing
2023-12-12T16:15:56.507343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1 1
 
4.3%
2 1
 
4.3%
23 1
 
4.3%
22 1
 
4.3%
21 1
 
4.3%
20 1
 
4.3%
19 1
 
4.3%
18 1
 
4.3%
17 1
 
4.3%
16 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1 1
4.3%
2 1
4.3%
3 1
4.3%
4 1
4.3%
5 1
4.3%
6 1
4.3%
7 1
4.3%
8 1
4.3%
9 1
4.3%
10 1
4.3%
ValueCountFrequency (%)
23 1
4.3%
22 1
4.3%
21 1
4.3%
20 1
4.3%
19 1
4.3%
18 1
4.3%
17 1
4.3%
16 1
4.3%
15 1
4.3%
14 1
4.3%

업종별
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2023-12-12T16:15:56.763451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length6.3478261
Min length2

Characters and Unicode

Total characters146
Distinct characters81
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

Unique23 ?
Unique (%)100.0%

Sample

1st row휴게음식점
2nd row제과점
3rd row일반음식점
4th row유흥주점
5th row단란주점
ValueCountFrequency (%)
휴게음식점 1
 
3.8%
제과점 1
 
3.8%
수명방업 1
 
3.8%
화상대화방 1
 
3.8%
및 1
 
3.8%
전화방 1
 
3.8%
고시원업 1
 
3.8%
안마시술소 1
 
3.8%
골프연습장 1
 
3.8%
권총사격장 1
 
3.8%
Other values (16) 16
61.5%
2023-12-12T16:15:57.192127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
업 10
 
6.8%
장 6
 
4.1%
방 5
 
3.4%
점 5
 
3.4%
화 4
 
2.7%
게 4
 
2.7%
원 4
 
2.7%
상 4
 
2.7%
임 3
 
2.1%
3
 
2.1%
Other values (71) 98
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 132
90.4%
Space Separator 3
 
2.1%
Open Punctuation 3
 
2.1%
Close Punctuation 3
 
2.1%
Decimal Number 3
 
2.1%
Uppercase Letter 2
 
1.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
업 10
 
7.6%
장 6
 
4.5%
방 5
 
3.8%
점 5
 
3.8%
화 4
 
3.0%
게 4
 
3.0%
원 4
 
3.0%
상 4
 
3.0%
임 3
 
2.3%
인 3
 
2.3%
Other values (64) 84
63.6%
Decimal Number
ValueCountFrequency (%)
0 2
66.7%
1 1
33.3%
Uppercase Letter
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 132
90.4%
Common 12
 
8.2%
Latin 2
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
업 10
 
7.6%
장 6
 
4.5%
방 5
 
3.8%
점 5
 
3.8%
화 4
 
3.0%
게 4
 
3.0%
원 4
 
3.0%
상 4
 
3.0%
임 3
 
2.3%
인 3
 
2.3%
Other values (64) 84
63.6%
Common
ValueCountFrequency (%)
3
25.0%
( 3
25.0%
) 3
25.0%
0 2
16.7%
1 1
 
8.3%
Latin
ValueCountFrequency (%)
P 1
50.0%
C 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 132
90.4%
ASCII 14
 
9.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
업 10
 
7.6%
장 6
 
4.5%
방 5
 
3.8%
점 5
 
3.8%
화 4
 
3.0%
게 4
 
3.0%
원 4
 
3.0%
상 4
 
3.0%
임 3
 
2.3%
인 3
 
2.3%
Other values (64) 84
63.6%
ASCII
ValueCountFrequency (%)
3
21.4%
( 3
21.4%
) 3
21.4%
0 2
14.3%
1 1
 
7.1%
P 1
 
7.1%
C 1
 
7.1%

계
Real number (ℝ)

ZEROS 

Distinct14
Distinct (%)60.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.913043
Minimum0
Maximum334
Zeros5
Zeros (%)21.7%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-12T16:15:57.311062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median4
Q348.5
95-th percentile227.9
Maximum334
Range334
Interquartile range (IQR)47.5

Descriptive statistics

Standard deviation89.609716
Coefficient of variation (CV)1.9517268
Kurtosis4.8310724
Mean45.913043
Median Absolute Deviation (MAD)4
Skewness2.3432671
Sum1056
Variance8029.9012
MonotonicityNot monotonic
2023-12-12T16:15:57.442011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 5
21.7%
1 4
17.4%
58 2
 
8.7%
2 2
 
8.7%
334 1
 
4.3%
218 1
 
4.3%
70 1
 
4.3%
4 1
 
4.3%
5 1
 
4.3%
17 1
 
4.3%
Other values (4) 4
17.4%
ValueCountFrequency (%)
0 5
21.7%
1 4
17.4%
2 2
 
8.7%
4 1
 
4.3%
5 1
 
4.3%
7 1
 
4.3%
9 1
 
4.3%
17 1
 
4.3%
39 1
 
4.3%
58 2
 
8.7%
ValueCountFrequency (%)
334 1
4.3%
229 1
4.3%
218 1
4.3%
70 1
4.3%
58 2
8.7%
39 1
4.3%
17 1
4.3%
9 1
4.3%
7 1
4.3%
5 1
4.3%

Interactions

2023-12-12T16:15:55.928620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:15:55.744542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:15:56.016148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:15:55.825883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:15:57.529339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종별계
연번1.0001.0000.162
업종별1.0001.0001.000
계0.1621.0001.000
2023-12-12T16:15:57.625107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번계
연번1.000-0.317
계-0.3171.000

Missing values

2023-12-12T16:15:56.141302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:15:56.252135image/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

연번업종별계
01휴게음식점58
12제과점2
23일반음식점334
34유흥주점218
45단란주점70
56영화상영관4
67비디오물감상실업0
78비디오물소극장업0
89학원1
910목욕장업(수용인원100인 이상)0
연번업종별계
1314복합유통게임제공업2
1415노래연습장229
1516산후조리원1
1617권총사격장0
1718골프연습장39
1819안마시술소7
1920고시원업9
2021전화방 및 화상대화방1
2122수명방업1
2223콜라텍업0