Overview

Dataset statistics

Number of variables3
Number of observations72
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory26.8 B

Variable types

Text1
Categorical1
Numeric1

Dataset

Description국토안전관리원에서 제공하는 데이터이며 공공시설물종별현황인 시설물명, 개수, 종별의 CSV 형식의 파일데이터로 제공합니다.
URLhttps://www.data.go.kr/data/15047508/fileData.do

Reproduction

Analysis started2023-12-12 16:19:52.593003
Analysis finished2023-12-12 16:19:52.972814
Duration0.38 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설물종류별
Text

Distinct42
Distinct (%)58.3%
Missing0
Missing (%)0.0%
Memory size708.0 B
2023-12-13T01:19:53.435403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length4.6666667
Min length1

Characters and Unicode

Total characters336
Distinct characters73
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 (%)27.8%

Sample

1st row공동주택
2nd row공동주택
3rd row대형건축물
4th row대형건축물
5th row대형건축물
ValueCountFrequency (%)
및 4
 
5.0%
철도교량 3
 
3.8%
도로터널 3
 
3.8%
철도터널 3
 
3.8%
지하차도 3
 
3.8%
대형건축물 3
 
3.8%
도로교량 3
 
3.8%
다중이용건축물 3
 
3.8%
지하도상가 3
 
3.8%
계류시설 2
 
2.5%
Other values (35) 50
62.5%
2023-12-13T01:19:53.761187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
도 30
 
8.9%
수 13
 
3.9%
물 12
 
3.6%
용 11
 
3.3%
철 10
 
3.0%
축 10
 
3.0%
건 10
 
3.0%
지 9
 
2.7%
하 9
 
2.7%
로 9
 
2.7%
Other values (63) 213
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 324
96.4%
Space Separator 8
 
2.4%
Other Punctuation 2
 
0.6%
Open Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 30
 
9.3%
수 13
 
4.0%
물 12
 
3.7%
용 11
 
3.4%
철 10
 
3.1%
축 10
 
3.1%
건 10
 
3.1%
지 9
 
2.8%
하 9
 
2.8%
로 9
 
2.8%
Other values (59) 201
62.0%
Space Separator
ValueCountFrequency (%)
8
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 324
96.4%
Common 12
 
3.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 30
 
9.3%
수 13
 
4.0%
물 12
 
3.7%
용 11
 
3.4%
철 10
 
3.1%
축 10
 
3.1%
건 10
 
3.1%
지 9
 
2.8%
하 9
 
2.8%
로 9
 
2.8%
Other values (59) 201
62.0%
Common
ValueCountFrequency (%)
8
66.7%
, 2
 
16.7%
( 1
 
8.3%
) 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 324
96.4%
ASCII 12
 
3.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
도 30
 
9.3%
수 13
 
4.0%
물 12
 
3.7%
용 11
 
3.4%
철 10
 
3.1%
축 10
 
3.1%
건 10
 
3.1%
지 9
 
2.8%
하 9
 
2.8%
로 9
 
2.8%
Other values (59) 201
62.0%
ASCII
ValueCountFrequency (%)
8
66.7%
, 2
 
16.7%
( 1
 
8.3%
) 1
 
8.3%

종별
Categorical

Distinct3
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size708.0 B
2종
33 
1종
24 
3종
15 

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 33
45.8%
1종 24
33.3%
3종 15
20.8%

Length

2023-12-13T01:19:53.898613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:19:53.989666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2종 33
45.8%
1종 24
33.3%
3종 15
20.8%

개수
Real number (ℝ)

Distinct66
Distinct (%)91.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1075.6111
Minimum1
Maximum17515
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2023-12-13T01:19:54.093750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.65
Q137.75
median206
Q3940.75
95-th percentile4126.4
Maximum17515
Range17514
Interquartile range (IQR)903

Descriptive statistics

Standard deviation2564.8366
Coefficient of variation (CV)2.384539
Kurtosis25.744655
Mean1075.6111
Median Absolute Deviation (MAD)191
Skewness4.6789289
Sum77444
Variance6578386.9
MonotonicityNot monotonic
2023-12-13T01:19:54.213727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
39 2
 
2.8%
3 2
 
2.8%
1 2
 
2.8%
20 2
 
2.8%
6 2
 
2.8%
19 2
 
2.8%
1462 1
 
1.4%
112 1
 
1.4%
14 1
 
1.4%
108 1
 
1.4%
Other values (56) 56
77.8%
ValueCountFrequency (%)
1 2
2.8%
3 2
2.8%
6 2
2.8%
14 1
1.4%
16 1
1.4%
18 1
1.4%
19 2
2.8%
20 2
2.8%
21 1
1.4%
25 1
1.4%
ValueCountFrequency (%)
17515 1
1.4%
10321 1
1.4%
6484 1
1.4%
4309 1
1.4%
3977 1
1.4%
3830 1
1.4%
3552 1
1.4%
2971 1
1.4%
1619 1
1.4%
1578 1
1.4%

Interactions

2023-12-13T01:19:52.708475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:19:54.298777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설물종류별종별개수
시설물종류별1.0000.0000.000
종별0.0001.0000.353
개수0.0000.3531.000
2023-12-13T01:19:54.392749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
개수종별
개수1.0000.149
종별0.1491.000

Missing values

2023-12-13T01:19:52.850908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:19:52.936953image/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
0공동주택2종1462
1공동주택3종2971
2대형건축물1종165
3대형건축물2종242
4대형건축물3종4309
5다중이용건축물1종26
6다중이용건축물2종1316
7다중이용건축물3종10321
8철도역시설1종6
9철도역시설2종832
시설물종류별종별개수
62철도터널3종350
63도로터널1종697
64도로터널2종1619
65도로터널3종106
66공동구2종40
67공공하수처리시설2종694
68공업용수도1종63
69지방상수도1종429
70지방상수도2종997
71광역상수도1종49