Overview

Dataset statistics

Number of variables4
Number of observations298
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory10.0 KiB
Average record size in memory34.4 B

Variable types

Categorical1
Text1
Numeric2

Dataset

Description부산교통공사_소음측정정보_20230630
Author부산교통공사
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15083212

Alerts

평균(Leq) is highly overall correlated with 최대(Lmax)High correlation
최대(Lmax) is highly overall correlated with 평균(Leq)High correlation

Reproduction

Analysis started2023-12-10 17:30:21.822180
Analysis finished2023-12-10 17:30:23.532374
Duration1.71 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

호선
Categorical

Distinct5
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2호선
84 
1호선(신차)
78 
1호선(구차)
78 
3호선
32 
4호선
26 

Length

Max length7
Median length7
Mean length5.0939597
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1호선(신차)
2nd row1호선(신차)
3rd row1호선(신차)
4th row1호선(신차)
5th row1호선(신차)

Common Values

ValueCountFrequency (%)
2호선 84
28.2%
1호선(신차) 78
26.2%
1호선(구차) 78
26.2%
3호선 32
 
10.7%
4호선 26
 
8.7%

Length

2023-12-11T02:30:23.761658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:30:24.130544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2호선 84
28.2%
1호선(신차 78
26.2%
1호선(구차 78
26.2%
3호선 32
 
10.7%
4호선 26
 
8.7%

구간
Text

Distinct222
Distinct (%)74.5%
Missing0
Missing (%)0.0%
Memory size2.5 KiB
2023-12-11T02:30:24.773520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12.5
Mean length5.9932886
Min length5

Characters and Unicode

Total characters1786
Distinct characters134
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique146 ?
Unique (%)49.0%

Sample

1st row노포→범어사
2nd row범어사→남산
3rd row남산→두실
4th row두실→구서
5th row구서→장전
ValueCountFrequency (%)
노포→범어사 2
 
0.7%
동대신→토성 2
 
0.7%
부산역→초량 2
 
0.7%
서대신→동대신 2
 
0.7%
괴정→대티 2
 
0.7%
사하→괴정 2
 
0.7%
당리→사하 2
 
0.7%
하단→당리 2
 
0.7%
신평→하단 2
 
0.7%
동매→신평 2
 
0.7%
Other values (212) 278
93.3%
2023-12-11T02:30:25.812555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
→ 298
 
16.7%
산 92
 
5.2%
대 90
 
5.0%
동 58
 
3.2%
부 52
 
2.9%
장 48
 
2.7%
포 40
 
2.2%
남 38
 
2.1%
서 36
 
2.0%
신 32
 
1.8%
Other values (124) 1002
56.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1488
83.3%
Math Symbol 298
 
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 92
 
6.2%
대 90
 
6.0%
동 58
 
3.9%
부 52
 
3.5%
장 48
 
3.2%
포 40
 
2.7%
남 38
 
2.6%
서 36
 
2.4%
신 32
 
2.2%
사 32
 
2.2%
Other values (123) 970
65.2%
Math Symbol
ValueCountFrequency (%)
→ 298
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1488
83.3%
Common 298
 
16.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 92
 
6.2%
대 90
 
6.0%
동 58
 
3.9%
부 52
 
3.5%
장 48
 
3.2%
포 40
 
2.7%
남 38
 
2.6%
서 36
 
2.4%
신 32
 
2.2%
사 32
 
2.2%
Other values (123) 970
65.2%
Common
ValueCountFrequency (%)
→ 298
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1488
83.3%
Arrows 298
 
16.7%

Most frequent character per block

Arrows
ValueCountFrequency (%)
→ 298
100.0%
Hangul
ValueCountFrequency (%)
산 92
 
6.2%
대 90
 
6.0%
동 58
 
3.9%
부 52
 
3.5%
장 48
 
3.2%
포 40
 
2.7%
남 38
 
2.6%
서 36
 
2.4%
신 32
 
2.2%
사 32
 
2.2%
Other values (123) 970
65.2%

평균(Leq)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.755034
Minimum57
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-11T02:30:26.175771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum57
5-th percentile61.85
Q167
median70
Q373
95-th percentile75
Maximum78
Range21
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.1750894
Coefficient of variation (CV)0.059853594
Kurtosis0.038629038
Mean69.755034
Median Absolute Deviation (MAD)3
Skewness-0.64732411
Sum20787
Variance17.431372
MonotonicityNot monotonic
2023-12-11T02:30:26.511870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
72 35
11.7%
70 34
11.4%
73 31
10.4%
69 30
10.1%
71 28
9.4%
74 20
 
6.7%
75 17
 
5.7%
67 15
 
5.0%
66 14
 
4.7%
68 14
 
4.7%
Other values (11) 60
20.1%
ValueCountFrequency (%)
57 2
 
0.7%
59 1
 
0.3%
60 2
 
0.7%
61 10
3.4%
62 11
3.7%
63 5
 
1.7%
64 10
3.4%
65 6
 
2.0%
66 14
4.7%
67 15
5.0%
ValueCountFrequency (%)
78 3
 
1.0%
77 6
 
2.0%
76 4
 
1.3%
75 17
5.7%
74 20
6.7%
73 31
10.4%
72 35
11.7%
71 28
9.4%
70 34
11.4%
69 30
10.1%

최대(Lmax)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.731544
Minimum61
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.7 KiB
2023-12-11T02:30:26.790297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum61
5-th percentile68
Q174
median77
Q380
95-th percentile85
Maximum88
Range27
Interquartile range (IQR)6

Descriptive statistics

Standard deviation4.9904027
Coefficient of variation (CV)0.065037174
Kurtosis-0.15520956
Mean76.731544
Median Absolute Deviation (MAD)3
Skewness-0.03838388
Sum22866
Variance24.904119
MonotonicityNot monotonic
2023-12-11T02:30:27.171480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
76 27
 
9.1%
78 27
 
9.1%
75 26
 
8.7%
79 23
 
7.7%
74 22
 
7.4%
73 21
 
7.0%
77 21
 
7.0%
80 14
 
4.7%
81 12
 
4.0%
84 12
 
4.0%
Other values (15) 93
31.2%
ValueCountFrequency (%)
61 1
 
0.3%
63 1
 
0.3%
66 2
 
0.7%
67 5
 
1.7%
68 7
 
2.3%
69 10
3.4%
70 10
3.4%
71 5
 
1.7%
72 11
3.7%
73 21
7.0%
ValueCountFrequency (%)
88 3
 
1.0%
87 4
 
1.3%
86 4
 
1.3%
85 12
4.0%
84 12
4.0%
83 7
 
2.3%
82 11
3.7%
81 12
4.0%
80 14
4.7%
79 23
7.7%

Interactions

2023-12-11T02:30:22.765841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:30:22.410016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:30:22.954875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:30:22.581138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:30:27.411659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
호선평균(Leq)최대(Lmax)
호선1.0000.7840.734
평균(Leq)0.7841.0000.922
최대(Lmax)0.7340.9221.000
2023-12-11T02:30:27.698054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균(Leq)최대(Lmax)호선
평균(Leq)1.0000.8930.437
최대(Lmax)0.8931.0000.389
호선0.4370.3891.000

Missing values

2023-12-11T02:30:23.230520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:30:23.449375image/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

호선구간평균(Leq)최대(Lmax)
01호선(신차)노포→범어사6272
11호선(신차)범어사→남산6167
21호선(신차)남산→두실6271
31호선(신차)두실→구서6269
41호선(신차)구서→장전6169
51호선(신차)장전→부산대6473
61호선(신차)부산대→온천장6067
71호선(신차)온천장→명륜6170
81호선(신차)명륜동→동래6168
91호선(신차)동래→교대6167
호선구간평균(Leq)최대(Lmax)
2884호선낙민→충렬사6877
2894호선충렬사→명장7077
2904호선명장→서동6771
2914호선서동→금사6776
2924호선금사→반여농산물시장7079
2934호선반여농산물시장→석대6669
2944호선석대→영산대6877
2954호선영산대→동부산대학7177
2964호선동부산대학→고촌6775
2974호선고촌→안평6976