Overview

Dataset statistics

Number of variables3
Number of observations48
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory27.8 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description국토안전관리원에서 제공하는 데이터이며 민간시설물, 종별, 개수 통계현황의 CSV형식의 파일데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15047507/fileData.do

Reproduction

Analysis started2023-12-12 05:24:46.425612
Analysis finished2023-12-12 05:24:46.775891
Duration0.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설물종류별
Text

Distinct29
Distinct (%)60.4%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-12T14:24:46.914198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length4
Mean length4.7083333
Min length2

Characters and Unicode

Total characters226
Distinct characters60
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

Unique15 ?
Unique (%)31.2%

Sample

1st row공동주택
2nd row공동주택
3rd row대형건축물
4th row대형건축물
5th row대형건축물
ValueCountFrequency (%)
도로터널 3
 
6.0%
지하차도 3
 
6.0%
도로교량 3
 
6.0%
다중이용건축물 3
 
6.0%
대형건축물 3
 
6.0%
철도교량 2
 
4.0%
철도터널 2
 
4.0%
도로옹벽 2
 
4.0%
계류시설 2
 
4.0%
건축물옹벽 2
 
4.0%
Other values (21) 25
50.0%
2023-12-12T14:24:47.529935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 24
 
10.6%
물 11
 
4.9%
건 10
 
4.4%
축 10
 
4.4%
로 9
 
4.0%
철 8
 
3.5%
벽 7
 
3.1%
옹 7
 
3.1%
시 6
 
2.7%
지 6
 
2.7%
Other values (50) 128
56.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
97.8%
Space Separator 2
 
0.9%
Other Punctuation 1
 
0.4%
Open Punctuation 1
 
0.4%
Close Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 24
 
10.9%
물 11
 
5.0%
건 10
 
4.5%
축 10
 
4.5%
로 9
 
4.1%
철 8
 
3.6%
벽 7
 
3.2%
옹 7
 
3.2%
시 6
 
2.7%
지 6
 
2.7%
Other values (46) 123
55.7%
Space Separator
ValueCountFrequency (%)
2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
97.8%
Common 5
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 24
 
10.9%
물 11
 
5.0%
건 10
 
4.5%
축 10
 
4.5%
로 9
 
4.1%
철 8
 
3.6%
벽 7
 
3.2%
옹 7
 
3.2%
시 6
 
2.7%
지 6
 
2.7%
Other values (46) 123
55.7%
Common
ValueCountFrequency (%)
2
40.0%
, 1
20.0%
( 1
20.0%
) 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
97.8%
ASCII 5
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 24
 
10.9%
물 11
 
5.0%
건 10
 
4.5%
축 10
 
4.5%
로 9
 
4.1%
철 8
 
3.6%
벽 7
 
3.2%
옹 7
 
3.2%
시 6
 
2.7%
지 6
 
2.7%
Other values (46) 123
55.7%
ASCII
ValueCountFrequency (%)
2
40.0%
, 1
20.0%
( 1
20.0%
) 1
20.0%

종별
Categorical

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
2종
25 
1종
12 
3종
11 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2종
2nd row3종
3rd row1종
4th row2종
5th row3종

Common Values

ValueCountFrequency (%)
2종 25
52.1%
1종 12
25.0%
3종 11
22.9%

Length

2023-12-12T14:24:47.677748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T14:24:47.818111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2종 25
52.1%
1종 12
25.0%
3종 11
22.9%

개수
Real number (ℝ)

Distinct36
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1747.9583
Minimum1
Maximum63251
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-12T14:24:47.940730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median28
Q3237.75
95-th percentile3298.35
Maximum63251
Range63250
Interquartile range (IQR)231.75

Descriptive statistics

Standard deviation9117.7469
Coefficient of variation (CV)5.2162267
Kurtosis46.849932
Mean1747.9583
Median Absolute Deviation (MAD)27
Skewness6.8090946
Sum83902
Variance83133309
MonotonicityNot monotonic
2023-12-12T14:24:48.068084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
6 6
 
12.5%
1 4
 
8.3%
2 2
 
4.2%
7 2
 
4.2%
3 2
 
4.2%
4 2
 
4.2%
70 1
 
2.1%
26 1
 
2.1%
174 1
 
2.1%
5 1
 
2.1%
Other values (26) 26
54.2%
ValueCountFrequency (%)
1 4
8.3%
2 2
 
4.2%
3 2
 
4.2%
4 2
 
4.2%
5 1
 
2.1%
6 6
12.5%
7 2
 
4.2%
9 1
 
2.1%
12 1
 
2.1%
17 1
 
2.1%
ValueCountFrequency (%)
63251 1
2.1%
3779 1
2.1%
3403 1
2.1%
3104 1
2.1%
2936 1
2.1%
2597 1
2.1%
880 1
2.1%
833 1
2.1%
518 1
2.1%
439 1
2.1%

Interactions

2023-12-12T14:24:46.540292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T14:24:48.155168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설물종류별종별개수
시설물종류별1.0000.0000.000
종별0.0001.0000.000
개수0.0000.0001.000
2023-12-12T14:24:48.254798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개수종별
개수1.0000.000
종별0.0001.000

Missing values

2023-12-12T14:24:46.676805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T14:24:46.747582image/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

시설물종류별종별개수
0공동주택2종63251
1공동주택3종2936
2대형건축물1종3104
3대형건축물2종3403
4대형건축물3종880
5다중이용건축물1종6
6다중이용건축물2종3779
7다중이용건축물3종2597
8철도역시설1종3
9철도역시설2종136
시설물종류별종별개수
38지하차도2종26
39지하차도3종2
40철도터널1종70
41철도터널2종4
42도로터널1종174
43도로터널2종144
44도로터널3종1
45공공하수처리시설2종43
46공업용수도1종7
47지방상수도2종1