Overview

Dataset statistics

Number of variables5
Number of observations64
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory44.1 B

Variable types

Numeric2
Text3

Dataset

Description2022년 5월 18일 기준 부산광역시 남구 수영로 가로화단(신정시장입구~남구청일원) 외 63개소 현황(소재지, 시점, 종점, 수목합계 등) 자료 제공
URLhttps://www.data.go.kr/data/15047954/fileData.do

Alerts

연번 has unique valuesUnique
수목합계 has unique valuesUnique

Reproduction

Analysis started2023-12-12 15:08:13.147258
Analysis finished2023-12-12 15:08:14.105631
Duration0.96 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.5
Minimum1
Maximum64
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:08:14.218795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.15
Q116.75
median32.5
Q348.25
95-th percentile60.85
Maximum64
Range63
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation18.618987
Coefficient of variation (CV)0.5728919
Kurtosis-1.2
Mean32.5
Median Absolute Deviation (MAD)16
Skewness0
Sum2080
Variance346.66667
MonotonicityStrictly increasing
2023-12-13T00:08:14.398145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.6%
34 1
 
1.6%
36 1
 
1.6%
37 1
 
1.6%
38 1
 
1.6%
39 1
 
1.6%
40 1
 
1.6%
41 1
 
1.6%
42 1
 
1.6%
43 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
1 1
1.6%
2 1
1.6%
3 1
1.6%
4 1
1.6%
5 1
1.6%
6 1
1.6%
7 1
1.6%
8 1
1.6%
9 1
1.6%
10 1
1.6%
ValueCountFrequency (%)
64 1
1.6%
63 1
1.6%
62 1
1.6%
61 1
1.6%
60 1
1.6%
59 1
1.6%
58 1
1.6%
57 1
1.6%
56 1
1.6%
55 1
1.6%
Distinct32
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T00:08:14.624715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length13.171875
Min length12

Characters and Unicode

Total characters843
Distinct characters70
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)31.2%

Sample

1st row부산광역시 남구 수영로
2nd row부산광역시 남구 신선로
3rd row부산광역시 남구 신선로
4th row부산광역시 남구 황령대로(1차)
5th row부산광역시 남구 유엔평화로
ValueCountFrequency (%)
부산광역시 64
33.0%
남구 64
33.0%
우암로 9
 
4.6%
신선로 8
 
4.1%
수영로 8
 
4.1%
용호로 3
 
1.5%
용소로 3
 
1.5%
석포로 3
 
1.5%
분포로 3
 
1.5%
유엔로 2
 
1.0%
Other values (23) 27
13.9%
2023-12-13T00:08:15.011302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
135
16.0%
67
 
7.9%
65
 
7.7%
65
 
7.7%
64
 
7.6%
64
 
7.6%
64
 
7.6%
64
 
7.6%
64
 
7.6%
11
 
1.3%
Other values (60) 180
21.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 685
81.3%
Space Separator 135
 
16.0%
Decimal Number 17
 
2.0%
Close Punctuation 2
 
0.2%
Open Punctuation 2
 
0.2%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
67
9.8%
65
9.5%
65
9.5%
64
9.3%
64
9.3%
64
9.3%
64
9.3%
64
9.3%
11
 
1.6%
10
 
1.5%
Other values (47) 147
21.5%
Decimal Number
ValueCountFrequency (%)
2 4
23.5%
4 3
17.6%
0 3
17.6%
3 2
11.8%
7 2
11.8%
6 1
 
5.9%
1 1
 
5.9%
5 1
 
5.9%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
135
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 685
81.3%
Common 156
 
18.5%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
67
9.8%
65
9.5%
65
9.5%
64
9.3%
64
9.3%
64
9.3%
64
9.3%
64
9.3%
11
 
1.6%
10
 
1.5%
Other values (47) 147
21.5%
Common
ValueCountFrequency (%)
135
86.5%
2 4
 
2.6%
4 3
 
1.9%
0 3
 
1.9%
) 2
 
1.3%
( 2
 
1.3%
3 2
 
1.3%
7 2
 
1.3%
6 1
 
0.6%
1 1
 
0.6%
Latin
ValueCountFrequency (%)
G 1
50.0%
L 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 685
81.3%
ASCII 158
 
18.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
135
85.4%
2 4
 
2.5%
4 3
 
1.9%
0 3
 
1.9%
) 2
 
1.3%
( 2
 
1.3%
3 2
 
1.3%
7 2
 
1.3%
G 1
 
0.6%
L 1
 
0.6%
Other values (3) 3
 
1.9%
Hangul
ValueCountFrequency (%)
67
9.8%
65
9.5%
65
9.5%
64
9.3%
64
9.3%
64
9.3%
64
9.3%
64
9.3%
11
 
1.6%
10
 
1.5%
Other values (47) 147
21.5%

시점
Text

Distinct58
Distinct (%)90.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T00:08:15.324234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length20
Mean length16.34375
Min length12

Characters and Unicode

Total characters1046
Distinct characters141
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)81.2%

Sample

1st row부산광역시 남구 신정시장입구
2nd row부산광역시 남구 산업인력공단
3rd row부산광역시 남구 49호광장
4th row부산광역시 남구 대남교차로
5th row부산광역시 남구 유엔탑
ValueCountFrequency (%)
부산광역시 64
29.6%
남구 64
29.6%
일원 6
 
2.8%
3
 
1.4%
감만현대아파트 2
 
0.9%
대연동 2
 
0.9%
우암로259 2
 
0.9%
동천삼거리 2
 
0.9%
대남교차로 2
 
0.9%
유엔탑 2
 
0.9%
Other values (63) 67
31.0%
2023-12-13T00:08:15.753901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
154
 
14.7%
69
 
6.6%
67
 
6.4%
67
 
6.4%
67
 
6.4%
66
 
6.3%
66
 
6.3%
65
 
6.2%
20
 
1.9%
15
 
1.4%
Other values (131) 390
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 809
77.3%
Space Separator 154
 
14.7%
Decimal Number 67
 
6.4%
Uppercase Letter 7
 
0.7%
Dash Punctuation 5
 
0.5%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
69
 
8.5%
67
 
8.3%
67
 
8.3%
67
 
8.3%
66
 
8.2%
66
 
8.2%
65
 
8.0%
20
 
2.5%
15
 
1.9%
15
 
1.9%
Other values (112) 292
36.1%
Decimal Number
ValueCountFrequency (%)
1 12
17.9%
5 10
14.9%
2 10
14.9%
6 9
13.4%
4 7
10.4%
0 6
9.0%
9 5
7.5%
8 3
 
4.5%
3 3
 
4.5%
7 2
 
3.0%
Uppercase Letter
ValueCountFrequency (%)
L 2
28.6%
G 2
28.6%
N 1
14.3%
T 1
14.3%
B 1
14.3%
Space Separator
ValueCountFrequency (%)
154
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 809
77.3%
Common 230
 
22.0%
Latin 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
69
 
8.5%
67
 
8.3%
67
 
8.3%
67
 
8.3%
66
 
8.2%
66
 
8.2%
65
 
8.0%
20
 
2.5%
15
 
1.9%
15
 
1.9%
Other values (112) 292
36.1%
Common
ValueCountFrequency (%)
154
67.0%
1 12
 
5.2%
5 10
 
4.3%
2 10
 
4.3%
6 9
 
3.9%
4 7
 
3.0%
0 6
 
2.6%
- 5
 
2.2%
9 5
 
2.2%
8 3
 
1.3%
Other values (4) 9
 
3.9%
Latin
ValueCountFrequency (%)
L 2
28.6%
G 2
28.6%
N 1
14.3%
T 1
14.3%
B 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 809
77.3%
ASCII 237
 
22.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
154
65.0%
1 12
 
5.1%
5 10
 
4.2%
2 10
 
4.2%
6 9
 
3.8%
4 7
 
3.0%
0 6
 
2.5%
- 5
 
2.1%
9 5
 
2.1%
8 3
 
1.3%
Other values (9) 16
 
6.8%
Hangul
ValueCountFrequency (%)
69
 
8.5%
67
 
8.3%
67
 
8.3%
67
 
8.3%
66
 
8.2%
66
 
8.2%
65
 
8.0%
20
 
2.5%
15
 
1.9%
15
 
1.9%
Other values (112) 292
36.1%

종점
Text

Distinct62
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Memory size644.0 B
2023-12-13T00:08:16.022636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length21
Mean length15.96875
Min length13

Characters and Unicode

Total characters1022
Distinct characters149
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique60 ?
Unique (%)93.8%

Sample

1st row부산광역시 남구 남구청일원
2nd row부산광역시 남구 삼성아파트
3rd row부산광역시 남구 영남제분 앞
4th row부산광역시 남구 맥도날드
5th row부산광역시 남구 문화회관 앞 터널입구
ValueCountFrequency (%)
부산광역시 64
29.6%
남구 64
29.6%
일원 6
 
2.8%
4
 
1.9%
맥도날드 2
 
0.9%
용호지구대 2
 
0.9%
대연동 2
 
0.9%
입구 2
 
0.9%
평화공원 2
 
0.9%
신선로447번길 1
 
0.5%
Other values (67) 67
31.0%
2023-12-13T00:08:16.483069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
155
15.2%
74
 
7.2%
67
 
6.6%
66
 
6.5%
66
 
6.5%
65
 
6.4%
65
 
6.4%
64
 
6.3%
15
 
1.5%
14
 
1.4%
Other values (139) 371
36.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 798
78.1%
Space Separator 155
 
15.2%
Decimal Number 54
 
5.3%
Uppercase Letter 5
 
0.5%
Dash Punctuation 3
 
0.3%
Open Punctuation 2
 
0.2%
Close Punctuation 2
 
0.2%
Lowercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
74
 
9.3%
67
 
8.4%
66
 
8.3%
66
 
8.3%
65
 
8.1%
65
 
8.1%
64
 
8.0%
15
 
1.9%
14
 
1.8%
11
 
1.4%
Other values (118) 291
36.5%
Decimal Number
ValueCountFrequency (%)
6 8
14.8%
1 8
14.8%
4 7
13.0%
2 7
13.0%
5 6
11.1%
7 5
9.3%
8 5
9.3%
9 3
 
5.6%
0 3
 
5.6%
3 2
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
G 2
40.0%
T 1
20.0%
K 1
20.0%
L 1
20.0%
Lowercase Letter
ValueCountFrequency (%)
s 1
50.0%
k 1
50.0%
Space Separator
ValueCountFrequency (%)
155
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 798
78.1%
Common 217
 
21.2%
Latin 7
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
74
 
9.3%
67
 
8.4%
66
 
8.3%
66
 
8.3%
65
 
8.1%
65
 
8.1%
64
 
8.0%
15
 
1.9%
14
 
1.8%
11
 
1.4%
Other values (118) 291
36.5%
Common
ValueCountFrequency (%)
155
71.4%
6 8
 
3.7%
1 8
 
3.7%
4 7
 
3.2%
2 7
 
3.2%
5 6
 
2.8%
7 5
 
2.3%
8 5
 
2.3%
- 3
 
1.4%
9 3
 
1.4%
Other values (5) 10
 
4.6%
Latin
ValueCountFrequency (%)
G 2
28.6%
T 1
14.3%
K 1
14.3%
s 1
14.3%
k 1
14.3%
L 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 798
78.1%
ASCII 224
 
21.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
155
69.2%
6 8
 
3.6%
1 8
 
3.6%
4 7
 
3.1%
2 7
 
3.1%
5 6
 
2.7%
7 5
 
2.2%
8 5
 
2.2%
- 3
 
1.3%
9 3
 
1.3%
Other values (11) 17
 
7.6%
Hangul
ValueCountFrequency (%)
74
 
9.3%
67
 
8.4%
66
 
8.3%
66
 
8.3%
65
 
8.1%
65
 
8.1%
64
 
8.0%
15
 
1.9%
14
 
1.8%
11
 
1.4%
Other values (118) 291
36.5%

수목합계
Real number (ℝ)

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3038.2969
Minimum75
Maximum13580
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2023-12-13T00:08:16.647555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum75
5-th percentile354.95
Q1776
median1988
Q34835.75
95-th percentile8862.45
Maximum13580
Range13505
Interquartile range (IQR)4059.75

Descriptive statistics

Standard deviation2964.6873
Coefficient of variation (CV)0.97577274
Kurtosis1.7664631
Mean3038.2969
Median Absolute Deviation (MAD)1420.5
Skewness1.4027164
Sum194451
Variance8789370.6
MonotonicityNot monotonic
2023-12-13T00:08:16.809396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2531 1
 
1.6%
1000 1
 
1.6%
6875 1
 
1.6%
75 1
 
1.6%
5560 1
 
1.6%
3036 1
 
1.6%
530 1
 
1.6%
7686 1
 
1.6%
410 1
 
1.6%
796 1
 
1.6%
Other values (54) 54
84.4%
ValueCountFrequency (%)
75 1
1.6%
297 1
1.6%
313 1
1.6%
350 1
1.6%
383 1
1.6%
410 1
1.6%
420 1
1.6%
530 1
1.6%
550 1
1.6%
557 1
1.6%
ValueCountFrequency (%)
13580 1
1.6%
10670 1
1.6%
9526 1
1.6%
9027 1
1.6%
7930 1
1.6%
7686 1
1.6%
7170 1
1.6%
6875 1
1.6%
6500 1
1.6%
6225 1
1.6%

Interactions

2023-12-13T00:08:13.728693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:13.558262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:13.818178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:08:13.634259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:08:16.925295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번노선별(소재지)시점종점수목합계
연번1.0000.3620.8250.8690.315
노선별(소재지)0.3621.0000.9650.8330.845
시점0.8250.9651.0000.9850.000
종점0.8690.8330.9851.0000.876
수목합계0.3150.8450.0000.8761.000
2023-12-13T00:08:17.046038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번수목합계
연번1.000-0.024
수목합계-0.0241.000

Missing values

2023-12-13T00:08:13.930175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:08:14.048334image/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

연번노선별(소재지)시점종점수목합계
01부산광역시 남구 수영로부산광역시 남구 신정시장입구부산광역시 남구 남구청일원2531
12부산광역시 남구 신선로부산광역시 남구 산업인력공단부산광역시 남구 삼성아파트992
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34부산광역시 남구 황령대로(1차)부산광역시 남구 대남교차로부산광역시 남구 맥도날드4448
45부산광역시 남구 유엔평화로부산광역시 남구 유엔탑부산광역시 남구 문화회관 앞 터널입구1250
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6162부산광역시 남구 우암로부산광역시 남구 포시즌모터스부산광역시 남구 농협사료2024
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