Overview

Dataset statistics

Number of variables7
Number of observations31
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory62.1 B

Variable types

Numeric1
Text1
Categorical4
DateTime1

Dataset

Description2024년 광주광역시 동부소방서 관내 고층건축물 현황에 대한 데이터로 동부소방서 관내 안전센터별 고층건축물 현황에 대한 항목을 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/15055240/fileData.do

Alerts

지산119안전센터 has constant value ""Constant
데이터기준일자 has constant value ""Constant
계 is highly imbalanced (79.4%)Imbalance
대인119안전센터 is highly imbalanced (79.4%)Imbalance
용산119안전센터 is highly imbalanced (79.4%)Imbalance
연번 has unique valuesUnique
업종 has unique valuesUnique

Reproduction

Analysis started2024-03-14 15:11:09.917092
Analysis finished2024-03-14 15:11:11.129880
Duration1.21 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16
Minimum1
Maximum31
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size407.0 B
2024-03-15T00:11:11.315252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.5
Q18.5
median16
Q323.5
95-th percentile29.5
Maximum31
Range30
Interquartile range (IQR)15

Descriptive statistics

Standard deviation9.0921211
Coefficient of variation (CV)0.56825757
Kurtosis-1.2
Mean16
Median Absolute Deviation (MAD)8
Skewness0
Sum496
Variance82.666667
MonotonicityStrictly increasing
2024-03-15T00:11:11.718536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
1 1
 
3.2%
2 1
 
3.2%
31 1
 
3.2%
30 1
 
3.2%
29 1
 
3.2%
28 1
 
3.2%
27 1
 
3.2%
26 1
 
3.2%
25 1
 
3.2%
24 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
1 1
3.2%
2 1
3.2%
3 1
3.2%
4 1
3.2%
5 1
3.2%
6 1
3.2%
7 1
3.2%
8 1
3.2%
9 1
3.2%
10 1
3.2%
ValueCountFrequency (%)
31 1
3.2%
30 1
3.2%
29 1
3.2%
28 1
3.2%
27 1
3.2%
26 1
3.2%
25 1
3.2%
24 1
3.2%
23 1
3.2%
22 1
3.2%

업종
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size376.0 B
2024-03-15T00:11:12.407553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length5.4516129
Min length3

Characters and Unicode

Total characters169
Distinct characters81
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

Unique31 ?
Unique (%)100.0%

Sample

1st row공동주택(아파트)
2nd row공동주택(기숙사)
3rd row근린생활
4th row문화 및 집회시설
5th row종교시설
ValueCountFrequency (%)
및 6
 
13.3%
공동주택(아파트 1
 
2.2%
군사 1
 
2.2%
자동차 1
 
2.2%
동물 1
 
2.2%
식물 1
 
2.2%
관련 1
 
2.2%
분뇨 1
 
2.2%
쓰레기 1
 
2.2%
교정 1
 
2.2%
Other values (30) 30
66.7%
2024-03-15T00:11:13.892138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
설 17
 
10.1%
시 17
 
10.1%
14
 
8.3%
및 6
 
3.6%
동 5
 
3.0%
공 4
 
2.4%
물 4
 
2.4%
장 4
 
2.4%
관 3
 
1.8%
련 3
 
1.8%
Other values (71) 92
54.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
89.3%
Space Separator 14
 
8.3%
Open Punctuation 2
 
1.2%
Close Punctuation 2
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
설 17
 
11.3%
시 17
 
11.3%
및 6
 
4.0%
동 5
 
3.3%
공 4
 
2.6%
물 4
 
2.6%
장 4
 
2.6%
관 3
 
2.0%
련 3
 
2.0%
교 3
 
2.0%
Other values (68) 85
56.3%
Space Separator
ValueCountFrequency (%)
14
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
89.3%
Common 18
 
10.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
설 17
 
11.3%
시 17
 
11.3%
및 6
 
4.0%
동 5
 
3.3%
공 4
 
2.6%
물 4
 
2.6%
장 4
 
2.6%
관 3
 
2.0%
련 3
 
2.0%
교 3
 
2.0%
Other values (68) 85
56.3%
Common
ValueCountFrequency (%)
14
77.8%
( 2
 
11.1%
) 2
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
89.3%
ASCII 18
 
10.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
설 17
 
11.3%
시 17
 
11.3%
및 6
 
4.0%
동 5
 
3.3%
공 4
 
2.6%
물 4
 
2.6%
장 4
 
2.6%
관 3
 
2.0%
련 3
 
2.0%
교 3
 
2.0%
Other values (68) 85
56.3%
ASCII
ValueCountFrequency (%)
14
77.8%
( 2
 
11.1%
) 2
 
11.1%

계
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size376.0 B
0
30 
2단지
 
1

Length

Max length3
Median length1
Mean length1.0645161
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row2단지
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
2단지 1
 
3.2%

Length

2024-03-15T00:11:14.421632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:11:15.012993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
2단지 1
 
3.2%

대인119안전센터
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size376.0 B
0
30 
1단지(15개동)
 
1

Length

Max length9
Median length1
Mean length1.2580645
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row1단지(15개동)
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
1단지(15개동) 1
 
3.2%

Length

2024-03-15T00:11:15.399824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:11:15.738559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
1단지(15개동 1
 
3.2%

용산119안전센터
Categorical

IMBALANCE 

Distinct2
Distinct (%)6.5%
Missing0
Missing (%)0.0%
Memory size376.0 B
0
30 
1단지(8개동)
 
1

Length

Max length8
Median length1
Mean length1.2258065
Min length1

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row1단지(8개동)
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 30
96.8%
1단지(8개동) 1
 
3.2%

Length

2024-03-15T00:11:16.098826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:11:16.430585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 30
96.8%
1단지(8개동 1
 
3.2%

지산119안전센터
Categorical

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
0
31 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
0 31
100.0%

Length

2024-03-15T00:11:16.761129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T00:11:17.062716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 31
100.0%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size376.0 B
Minimum2024-02-07 00:00:00
Maximum2024-02-07 00:00:00
2024-03-15T00:11:17.321722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T00:11:17.621055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T00:11:10.252286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T00:11:17.850955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번업종계대인119안전센터용산119안전센터
연번1.0001.0000.2230.2230.223
업종1.0001.0001.0001.0001.000
계0.2231.0001.0000.6570.657
대인119안전센터0.2231.0000.6571.0000.657
용산119안전센터0.2231.0000.6570.6571.000
2024-03-15T00:11:18.112261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대인119안전센터용산119안전센터계
대인119안전센터1.0000.4550.455
용산119안전센터0.4551.0000.455
계0.4550.4551.000
2024-03-15T00:11:18.268055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번계대인119안전센터용산119안전센터
연번1.0000.0000.0000.000
계0.0001.0000.4550.455
대인119안전센터0.0000.4551.0000.455
용산119안전센터0.0000.4550.4551.000

Missing values

2024-03-15T00:11:10.594326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T00:11:10.979062image/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

연번업종계대인119안전센터용산119안전센터지산119안전센터데이터기준일자
01공동주택(아파트)2단지1단지(15개동)1단지(8개동)02024-02-07
12공동주택(기숙사)00002024-02-07
23근린생활00002024-02-07
34문화 및 집회시설00002024-02-07
45종교시설00002024-02-07
56판매시설00002024-02-07
67운수시설00002024-02-07
78의료시설00002024-02-07
89교육연구시설00002024-02-07
910노유자시설00002024-02-07
연번업종계대인119안전센터용산119안전센터지산119안전센터데이터기준일자
2122교정 및 군사00002024-02-07
2223방송통신시설00002024-02-07
2324발전시설00002024-02-07
2425묘지관련시설00002024-02-07
2526관광휴게시설00002024-02-07
2627장례식장00002024-02-07
2728지하가00002024-02-07
2829지하구00002024-02-07
2930문화재00002024-02-07
3031복합건축물00002024-02-07