Overview

Dataset statistics

Number of variables4
Number of observations43
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory37.1 B

Variable types

Numeric2
Text1
DateTime1

Dataset

Description경상북도 김천시의 자전거보관소에 대한 데이터로 자전거보관소명(설치장소), 보관대수 등의 정보를 제공합니다.
Author경상북도 김천시
URLhttps://www.data.go.kr/data/15090258/fileData.do

Alerts

데이터 기준일자 has constant value ""Constant
연번 has unique valuesUnique
설치장소 has unique valuesUnique

Reproduction

Analysis started2024-04-17 19:03:10.215578
Analysis finished2024-04-17 19:03:10.730500
Duration0.51 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T04:03:10.784701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q111.5
median22
Q332.5
95-th percentile40.9
Maximum43
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.57075176
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)11
Skewness0
Sum946
Variance157.66667
MonotonicityStrictly increasing
2024-04-18T04:03:10.884126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 1
 
2.3%
2 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%
34 1
2.3%

설치장소
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2024-04-18T04:03:11.059031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length8.1395349
Min length4

Characters and Unicode

Total characters350
Distinct characters138
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row직지사 입구 버스정류장
2nd row성의고등학교
3rd row대신동행정복지센터
4th row김천시립문화회관
5th row김천시노인종합복지관
ValueCountFrequency (%)
앞 7
 
10.8%
근린공원 4
 
6.2%
김천 2
 
3.1%
입구 2
 
3.1%
lh 1
 
1.5%
동이산삼거리 1
 
1.5%
부곡초 1
 
1.5%
옆 1
 
1.5%
도로공사사거리 1
 
1.5%
농소면 1
 
1.5%
Other values (44) 44
67.7%
2024-04-18T04:03:11.323102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
6.3%
천 15
 
4.3%
김 15
 
4.3%
공 10
 
2.9%
학 10
 
2.9%
지 9
 
2.6%
원 9
 
2.6%
터 9
 
2.6%
앞 8
 
2.3%
센 8
 
2.3%
Other values (128) 235
67.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 312
89.1%
Space Separator 22
 
6.3%
Uppercase Letter 8
 
2.3%
Decimal Number 5
 
1.4%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 15
 
4.8%
김 15
 
4.8%
공 10
 
3.2%
학 10
 
3.2%
지 9
 
2.9%
원 9
 
2.9%
터 9
 
2.9%
앞 8
 
2.6%
센 8
 
2.6%
복 6
 
1.9%
Other values (112) 213
68.3%
Uppercase Letter
ValueCountFrequency (%)
H 1
12.5%
X 1
12.5%
T 1
12.5%
C 1
12.5%
G 1
12.5%
V 1
12.5%
L 1
12.5%
K 1
12.5%
Decimal Number
ValueCountFrequency (%)
1 2
40.0%
9 1
20.0%
7 1
20.0%
3 1
20.0%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 312
89.1%
Common 30
 
8.6%
Latin 8
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 15
 
4.8%
김 15
 
4.8%
공 10
 
3.2%
학 10
 
3.2%
지 9
 
2.9%
원 9
 
2.9%
터 9
 
2.9%
앞 8
 
2.6%
센 8
 
2.6%
복 6
 
1.9%
Other values (112) 213
68.3%
Common
ValueCountFrequency (%)
22
73.3%
1 2
 
6.7%
) 1
 
3.3%
( 1
 
3.3%
9 1
 
3.3%
- 1
 
3.3%
7 1
 
3.3%
3 1
 
3.3%
Latin
ValueCountFrequency (%)
H 1
12.5%
X 1
12.5%
T 1
12.5%
C 1
12.5%
G 1
12.5%
V 1
12.5%
L 1
12.5%
K 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 311
88.9%
ASCII 38
 
10.9%
Compat Jamo 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
57.9%
1 2
 
5.3%
H 1
 
2.6%
) 1
 
2.6%
X 1
 
2.6%
T 1
 
2.6%
( 1
 
2.6%
C 1
 
2.6%
G 1
 
2.6%
V 1
 
2.6%
Other values (6) 6
 
15.8%
Hangul
ValueCountFrequency (%)
천 15
 
4.8%
김 15
 
4.8%
공 10
 
3.2%
학 10
 
3.2%
지 9
 
2.9%
원 9
 
2.9%
터 9
 
2.9%
앞 8
 
2.6%
센 8
 
2.6%
복 6
 
1.9%
Other values (111) 212
68.2%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

보관대수
Real number (ℝ)

Distinct17
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.767442
Minimum7
Maximum80
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2024-04-18T04:03:11.433816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8
Q110
median10
Q324
95-th percentile49
Maximum80
Range73
Interquartile range (IQR)14

Descriptive statistics

Standard deviation15.004503
Coefficient of variation (CV)0.79949643
Kurtosis5.8762554
Mean18.767442
Median Absolute Deviation (MAD)2
Skewness2.2207003
Sum807
Variance225.13511
MonotonicityNot monotonic
2024-04-18T04:03:11.515851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
10 19
44.2%
30 4
 
9.3%
8 3
 
7.0%
20 3
 
7.0%
18 2
 
4.7%
50 1
 
2.3%
40 1
 
2.3%
12 1
 
2.3%
80 1
 
2.3%
7 1
 
2.3%
Other values (7) 7
 
16.3%
ValueCountFrequency (%)
7 1
 
2.3%
8 3
 
7.0%
9 1
 
2.3%
10 19
44.2%
12 1
 
2.3%
14 1
 
2.3%
17 1
 
2.3%
18 2
 
4.7%
20 3
 
7.0%
28 1
 
2.3%
ValueCountFrequency (%)
80 1
 
2.3%
52 1
 
2.3%
50 1
 
2.3%
40 1
 
2.3%
35 1
 
2.3%
33 1
 
2.3%
30 4
9.3%
28 1
 
2.3%
20 3
7.0%
18 2
4.7%

데이터 기준일자
Date

CONSTANT 

Distinct1
Distinct (%)2.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
Minimum2022-10-14 00:00:00
Maximum2022-10-14 00:00:00
2024-04-18T04:03:11.595625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:03:11.659852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-04-18T04:03:10.458495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:03:10.336475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:03:10.532897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T04:03:10.395622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T04:03:11.710402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번설치장소보관대수
연번1.0001.0000.000
설치장소1.0001.0001.000
보관대수0.0001.0001.000
2024-04-18T04:03:11.772150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번보관대수
연번1.0000.066
보관대수0.0661.000

Missing values

2024-04-18T04:03:10.634314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T04:03:10.705052image/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

연번설치장소보관대수데이터 기준일자
01직지사 입구 버스정류장102022-10-14
12성의고등학교332022-10-14
23대신동행정복지센터102022-10-14
34김천시립문화회관92022-10-14
45김천시노인종합복지관182022-10-14
56김천공용버스터미널102022-10-14
67하나로마트102022-10-14
78김천역전파출소302022-10-14
89김천세무서102022-10-14
910김천시립도서관172022-10-14
연번설치장소보관대수데이터 기준일자
3334한보아파트 앞102022-10-14
3435지좌동행정복지센터102022-10-14
3536녹색미래과학관 내302022-10-14
3637율곡동행정복지센터102022-10-14
3738구조공단앞 근린공원102022-10-14
3839CGV 앞 근린공원102022-10-14
3940동이산삼거리202022-10-14
4041LH 3단지 앞 근린공원202022-10-14
4142도로공사사거리402022-10-14
4243국립종자원삼거리 앞102022-10-14