Overview

Dataset statistics

Number of variables6
Number of observations24
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory56.5 B

Variable types

Categorical3
Text1
Numeric2

Dataset

Description대전광역시 2020년부터 2021년까지 전통시장별 전기화재 감지센서 구축현황입니다. 2022년 공공데이터 기업매칭지원사업으로 수행되었습니다.
Author대전광역시
URLhttps://www.data.go.kr/data/15111245/fileData.do

Alerts

구_명 is highly overall correlated with 비고High correlation
비고 is highly overall correlated with 상점_수(개소) and 3 other fieldsHigh correlation
설치연도 is highly overall correlated with 상점_수(개소) and 1 other fieldsHigh correlation
상점_수(개소) is highly overall correlated with 설치_수(채널) and 2 other fieldsHigh correlation
설치_수(채널) is highly overall correlated with 상점_수(개소) and 1 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 11:13:01.848855
Analysis finished2023-12-12 11:13:03.171008
Duration1.32 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

설치연도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
2021
14 
2020
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 14
58.3%
2020 10
41.7%

Length

2023-12-12T20:13:03.292966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:13:03.445284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 14
58.3%
2020 10
41.7%

구_명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)20.8%
Missing0
Missing (%)0.0%
Memory size324.0 B
중구
7 
동구
6 
대덕구
5 
서구
4 
유성구
2 

Length

Max length3
Median length2
Mean length2.2916667
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row동구
2nd row동구
3rd row동구
4th row동구
5th row중구

Common Values

ValueCountFrequency (%)
중구 7
29.2%
동구 6
25.0%
대덕구 5
20.8%
서구 4
16.7%
유성구 2
 
8.3%

Length

2023-12-12T20:13:03.637172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:13:03.828134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 7
29.2%
동구 6
25.0%
대덕구 5
20.8%
서구 4
16.7%
유성구 2
 
8.3%

시설_명
Text

Distinct22
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Memory size324.0 B
2023-12-12T20:13:04.111237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length6.25
Min length4

Characters and Unicode

Total characters150
Distinct characters49
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

Unique20 ?
Unique (%)83.3%

Sample

1st row역전지하상가
2nd row중앙시장내 주차타워
3rd row중앙시장
4th row신도꼼지락시장
5th row문창시장
ValueCountFrequency (%)
문창시장 2
 
8.0%
한민시장 2
 
8.0%
중리시장(2차 1
 
4.0%
역전지하상가 1
 
4.0%
오정농수산물도매시장(2차 1
 
4.0%
도마시장 1
 
4.0%
용두시장 1
 
4.0%
부사시장 1
 
4.0%
태평시장 1
 
4.0%
산성시장 1
 
4.0%
Other values (13) 13
52.0%
2023-12-12T20:13:04.642323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 23
 
15.3%
장 23
 
15.3%
차 9
 
6.0%
) 7
 
4.7%
( 7
 
4.7%
도 5
 
3.3%
중 5
 
3.3%
2 5
 
3.3%
산 3
 
2.0%
1 3
 
2.0%
Other values (39) 60
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 127
84.7%
Decimal Number 8
 
5.3%
Close Punctuation 7
 
4.7%
Open Punctuation 7
 
4.7%
Space Separator 1
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 23
18.1%
장 23
18.1%
차 9
 
7.1%
도 5
 
3.9%
중 5
 
3.9%
산 3
 
2.4%
앙 3
 
2.4%
물 2
 
1.6%
강 2
 
1.6%
수 2
 
1.6%
Other values (34) 50
39.4%
Decimal Number
ValueCountFrequency (%)
2 5
62.5%
1 3
37.5%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 127
84.7%
Common 23
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 23
18.1%
장 23
18.1%
차 9
 
7.1%
도 5
 
3.9%
중 5
 
3.9%
산 3
 
2.4%
앙 3
 
2.4%
물 2
 
1.6%
강 2
 
1.6%
수 2
 
1.6%
Other values (34) 50
39.4%
Common
ValueCountFrequency (%)
) 7
30.4%
( 7
30.4%
2 5
21.7%
1 3
13.0%
1
 
4.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 127
84.7%
ASCII 23
 
15.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 23
18.1%
장 23
18.1%
차 9
 
7.1%
도 5
 
3.9%
중 5
 
3.9%
산 3
 
2.4%
앙 3
 
2.4%
물 2
 
1.6%
강 2
 
1.6%
수 2
 
1.6%
Other values (34) 50
39.4%
ASCII
ValueCountFrequency (%)
) 7
30.4%
( 7
30.4%
2 5
21.7%
1 3
13.0%
1
 
4.3%

상점_수(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean77.958333
Minimum1
Maximum302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T20:13:04.867208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median49
Q3136.25
95-th percentile242.9
Maximum302
Range301
Interquartile range (IQR)135.25

Descriptive statistics

Standard deviation86.750757
Coefficient of variation (CV)1.1127836
Kurtosis0.55741736
Mean77.958333
Median Absolute Deviation (MAD)48
Skewness1.1065233
Sum1871
Variance7525.6938
MonotonicityNot monotonic
2023-12-12T20:13:05.059961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
1 7
29.2%
159 1
 
4.2%
41 1
 
4.2%
48 1
 
4.2%
155 1
 
4.2%
302 1
 
4.2%
37 1
 
4.2%
180 1
 
4.2%
52 1
 
4.2%
126 1
 
4.2%
Other values (8) 8
33.3%
ValueCountFrequency (%)
1 7
29.2%
5 1
 
4.2%
8 1
 
4.2%
37 1
 
4.2%
41 1
 
4.2%
48 1
 
4.2%
50 1
 
4.2%
51 1
 
4.2%
52 1
 
4.2%
123 1
 
4.2%
ValueCountFrequency (%)
302 1
4.2%
254 1
4.2%
180 1
4.2%
159 1
4.2%
155 1
4.2%
137 1
4.2%
136 1
4.2%
126 1
4.2%
123 1
4.2%
52 1
4.2%

설치_수(채널)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)95.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean161.5
Minimum10
Maximum577
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size348.0 B
2023-12-12T20:13:05.237983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile14.35
Q133
median96
Q3253.75
95-th percentile487.7
Maximum577
Range567
Interquartile range (IQR)220.75

Descriptive statistics

Standard deviation157.497
Coefficient of variation (CV)0.97521361
Kurtosis1.2019647
Mean161.5
Median Absolute Deviation (MAD)73
Skewness1.309184
Sum3876
Variance24805.304
MonotonicityNot monotonic
2023-12-12T20:13:05.433818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
94 2
 
8.3%
333 1
 
4.2%
34 1
 
4.2%
90 1
 
4.2%
298 1
 
4.2%
577 1
 
4.2%
42 1
 
4.2%
329 1
 
4.2%
224 1
 
4.2%
130 1
 
4.2%
Other values (13) 13
54.2%
ValueCountFrequency (%)
10 1
4.2%
13 1
4.2%
22 1
4.2%
24 1
4.2%
29 1
4.2%
30 1
4.2%
34 1
4.2%
42 1
4.2%
86 1
4.2%
90 1
4.2%
ValueCountFrequency (%)
577 1
4.2%
515 1
4.2%
333 1
4.2%
329 1
4.2%
298 1
4.2%
256 1
4.2%
253 1
4.2%
224 1
4.2%
175 1
4.2%
130 1
4.2%

비고
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size324.0 B
공용시설포함
12 
<NA>
12 

Length

Max length6
Median length5
Mean length5
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
공용시설포함 12
50.0%
<NA> 12
50.0%

Length

2023-12-12T20:13:05.681518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:13:05.849458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
공용시설포함 12
50.0%
na 12
50.0%

Interactions

2023-12-12T20:13:02.528329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:13:02.234946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:13:02.678020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T20:13:02.392587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:13:05.949808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치연도구_명시설_명상점_수(개소)설치_수(채널)
설치연도1.0000.2030.0000.8310.197
구_명0.2031.0001.0000.0000.000
시설_명0.0001.0001.0000.8080.907
상점_수(개소)0.8310.0000.8081.0000.949
설치_수(채널)0.1970.0000.9070.9491.000
2023-12-12T20:13:06.125081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구_명비고설치연도
구_명1.0001.0000.215
비고1.0001.0001.000
설치연도0.2151.0001.000
2023-12-12T20:13:06.289854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
상점_수(개소)설치_수(채널)설치연도구_명비고
상점_수(개소)1.0000.9400.5700.0001.000
설치_수(채널)0.9401.0000.0540.0001.000
설치연도0.5700.0541.0000.2151.000
구_명0.0000.0000.2151.0001.000
비고1.0001.0001.0001.0001.000

Missing values

2023-12-12T20:13:02.905068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:13:03.097183image/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

설치연도구_명시설_명상점_수(개소)설치_수(채널)비고
02021동구역전지하상가159333공용시설포함
12021동구중앙시장내 주차타워134공용시설포함
22021동구중앙시장110공용시설포함
32021동구신도꼼지락시장124공용시설포함
42021중구문창시장8120공용시설포함
52021중구태평시장(2차)113공용시설포함
62021서구도마큰시장130공용시설포함
72021서구한민시장129공용시설포함
82021유성구송강시장(1차)5186<NA>
92021유성구송강시장(2차)122공용시설포함
설치연도구_명시설_명상점_수(개소)설치_수(채널)비고
142020동구역전시장136253<NA>
152020동구중앙시장2차123130<NA>
162020중구산성시장4194<NA>
172020중구태평시장126224<NA>
182020중구부사시장5294<NA>
192020중구문창시장180329<NA>
202020중구용두시장3742<NA>
212020서구도마시장302577<NA>
222020서구한민시장155298<NA>
232020대덕구법동시장4890<NA>