Overview

Dataset statistics

Number of variables7
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory64.3 B

Variable types

Numeric1
Text1
Categorical5

Dataset

Description광역버스정보시스템(MPBIS)의 목포시 버스노선에 대하여 노선식별자, 노선명칭, 노선서브식별자, 노선유형, 노선방향, 노선상태, 국토교통부권역코드식별자를 제공하고 있습니다
Author전라남도 목포시
URLhttps://www.data.go.kr/data/15066981/fileData.do

Alerts

노선유형 has constant value ""Constant
국토교통부권역코드식별자 has constant value ""Constant
노선방향 is highly imbalanced (61.6%)Imbalance
노선식별자 has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:28:57.430210
Analysis finished2023-12-12 16:28:57.947123
Duration0.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

노선식별자
Real number (ℝ)

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2400005 × 108
Minimum3.24 × 108
Maximum3.2400022 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-13T01:28:58.052156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3.24 × 108
5-th percentile3.24 × 108
Q13.2400001 × 108
median3.2400003 × 108
Q33.2400011 × 108
95-th percentile3.2400013 × 108
Maximum3.2400022 × 108
Range215
Interquartile range (IQR)93.75

Descriptive statistics

Standard deviation52.375952
Coefficient of variation (CV)1.6165415 × 10-7
Kurtosis1.1117596
Mean3.2400005 × 108
Median Absolute Deviation (MAD)15
Skewness1.3496583
Sum1.2960002 × 1010
Variance2743.2404
MonotonicityStrictly increasing
2023-12-13T01:28:58.242458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
324000001 1
 
2.5%
324000027 1
 
2.5%
324000029 1
 
2.5%
324000031 1
 
2.5%
324000032 1
 
2.5%
324000036 1
 
2.5%
324000037 1
 
2.5%
324000038 1
 
2.5%
324000105 1
 
2.5%
324000107 1
 
2.5%
Other values (30) 30
75.0%
ValueCountFrequency (%)
324000001 1
2.5%
324000002 1
2.5%
324000003 1
2.5%
324000004 1
2.5%
324000005 1
2.5%
324000006 1
2.5%
324000007 1
2.5%
324000008 1
2.5%
324000010 1
2.5%
324000011 1
2.5%
ValueCountFrequency (%)
324000216 1
2.5%
324000131 1
2.5%
324000125 1
2.5%
324000124 1
2.5%
324000123 1
2.5%
324000118 1
2.5%
324000117 1
2.5%
324000116 1
2.5%
324000110 1
2.5%
324000107 1
2.5%

노선명칭
Text

Distinct27
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-13T01:28:58.447458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length2.825
Min length1

Characters and Unicode

Total characters113
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)40.0%

Sample

1st row1
2nd row1
3rd row01월 01일
4th row01월 02일
5th row2
ValueCountFrequency (%)
01월 4
 
8.9%
112 3
 
6.7%
800 3
 
6.7%
108 2
 
4.4%
01일 2
 
4.4%
15 2
 
4.4%
300 2
 
4.4%
1 2
 
4.4%
10 2
 
4.4%
900 2
 
4.4%
Other values (18) 21
46.7%
2023-12-13T01:28:58.821236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 36
31.9%
1 26
23.0%
2 11
 
9.7%
3 9
 
8.0%
8 5
 
4.4%
5 5
 
4.4%
월 5
 
4.4%
5
 
4.4%
일 5
 
4.4%
9 3
 
2.7%
Other values (2) 3
 
2.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 98
86.7%
Other Letter 10
 
8.8%
Space Separator 5
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 36
36.7%
1 26
26.5%
2 11
 
11.2%
3 9
 
9.2%
8 5
 
5.1%
5 5
 
5.1%
9 3
 
3.1%
6 2
 
2.0%
7 1
 
1.0%
Other Letter
ValueCountFrequency (%)
월 5
50.0%
일 5
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 103
91.2%
Hangul 10
 
8.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 36
35.0%
1 26
25.2%
2 11
 
10.7%
3 9
 
8.7%
8 5
 
4.9%
5 5
 
4.9%
5
 
4.9%
9 3
 
2.9%
6 2
 
1.9%
7 1
 
1.0%
Hangul
ValueCountFrequency (%)
월 5
50.0%
일 5
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 103
91.2%
Hangul 10
 
8.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 36
35.0%
1 26
25.2%
2 11
 
10.7%
3 9
 
8.7%
8 5
 
4.9%
5 5
 
4.9%
5
 
4.9%
9 3
 
2.9%
6 2
 
1.9%
7 1
 
1.0%
Hangul
ValueCountFrequency (%)
월 5
50.0%
일 5
50.0%

노선서브식별자
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
0
35 
A
5 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd rowA
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 35
87.5%
A 5
 
12.5%

Length

2023-12-13T01:28:58.960196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:59.060012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 35
87.5%
a 5
 
12.5%

노선유형
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
13
40 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row13
2nd row13
3rd row13
4th row13
5th row13

Common Values

ValueCountFrequency (%)
13 40
100.0%

Length

2023-12-13T01:28:59.173387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:59.283518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
13 40
100.0%

노선방향
Categorical

IMBALANCE 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
3
37 
1
 
3

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row3
2nd row3
3rd row3
4th row3
5th row3

Common Values

ValueCountFrequency (%)
3 37
92.5%
1 3
 
7.5%

Length

2023-12-13T01:28:59.381209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:59.479185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
3 37
92.5%
1 3
 
7.5%

노선상태
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
1
30 
0
10 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row1
3rd row0
4th row1
5th row0

Common Values

ValueCountFrequency (%)
1 30
75.0%
0 10
 
25.0%

Length

2023-12-13T01:28:59.598217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:59.702719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 30
75.0%
0 10
 
25.0%

국토교통부권역코드식별자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
324
40 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row324
2nd row324
3rd row324
4th row324
5th row324

Common Values

ValueCountFrequency (%)
324 40
100.0%

Length

2023-12-13T01:28:59.818287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:28:59.951097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
324 40
100.0%

Interactions

2023-12-13T01:28:57.651632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:29:00.035367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선식별자노선명칭노선서브식별자노선방향노선상태
노선식별자1.0000.0000.3750.7110.000
노선명칭0.0001.0000.0000.6420.000
노선서브식별자0.3750.0001.0000.0000.000
노선방향0.7110.6420.0001.0000.000
노선상태0.0000.0000.0000.0001.000
2023-12-13T01:29:00.161975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선방향노선상태노선서브식별자
노선방향1.0000.0000.000
노선상태0.0001.0000.000
노선서브식별자0.0000.0001.000
2023-12-13T01:29:00.274456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
노선식별자노선서브식별자노선방향노선상태
노선식별자1.0000.2540.4920.000
노선서브식별자0.2541.0000.0000.000
노선방향0.4920.0001.0000.000
노선상태0.0000.0000.0001.000

Missing values

2023-12-13T01:28:57.760103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:28:57.890796image/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

노선식별자노선명칭노선서브식별자노선유형노선방향노선상태국토교통부권역코드식별자
0324000001101331324
13240000021A1331324
232400000301월 01일01330324
332400000401월 02일01331324
4324000005201330324
5324000006301331324
6324000007601330324
7324000008701331324
83240000101001330324
93240000111301331324
노선식별자노선명칭노선서브식별자노선유형노선방향노선상태국토교통부권역코드식별자
30324000107601331324
313240001101001331324
3232400011680001330324
33324000117901331324
3432400011890001331324
3532400012350001331324
3632400012410801331324
37324000125112A1331324
38324000131900A1331324
3932400021680001311324