Overview

Dataset statistics

Number of variables6
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory55.2 B

Variable types

Text1
Categorical1
Numeric4

Dataset

Description부산광역시 해운대구 이륜차 등록 현황용도, 경,소,중,대형 등 분류 및 각 동별로 등록된 이륜차 현황을 확인할 수 있습니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/3075710/fileData.do

Alerts

경형 is highly overall correlated with 소형 and 3 other fieldsHigh correlation
소형 is highly overall correlated with 경형 and 3 other fieldsHigh correlation
중형 is highly overall correlated with 경형 and 3 other fieldsHigh correlation
대형 is highly overall correlated with 경형 and 3 other fieldsHigh correlation
용도(규모) is highly overall correlated with 경형 and 3 other fieldsHigh correlation
경형 has 17 (42.5%) zerosZeros
소형 has 16 (40.0%) zerosZeros
중형 has 16 (40.0%) zerosZeros
대형 has 20 (50.0%) zerosZeros

Reproduction

Analysis started2024-03-15 01:37:38.128916
Analysis finished2024-03-15 01:37:42.398980
Duration4.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

Distinct20
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size448.0 B
2024-03-15T10:37:43.213573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Characters and Unicode

Total characters140
Distinct characters17
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

Unique0 ?
Unique (%)0.0%

Sample

1st row반송1동
2nd row반송1동
3rd row반송2동
4th row반송2동
5th row반송3동
ValueCountFrequency (%)
반송1동 2
 
5.0%
반송2동 2
 
5.0%
중2동 2
 
5.0%
중1동 2
 
5.0%
좌4동 2
 
5.0%
좌3동 2
 
5.0%
좌2동 2
 
5.0%
좌1동 2
 
5.0%
재송2동 2
 
5.0%
재송1동 2
 
5.0%
Other values (10) 20
50.0%
2024-03-15T10:37:44.475774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 38
27.1%
반 14
 
10.0%
1 12
 
8.6%
2 12
 
8.6%
송 12
 
8.6%
3 8
 
5.7%
여 8
 
5.7%
좌 8
 
5.7%
우 6
 
4.3%
4 4
 
2.9%
Other values (7) 18
12.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
74.3%
Decimal Number 36
 
25.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 38
36.5%
반 14
 
13.5%
송 12
 
11.5%
여 8
 
7.7%
좌 8
 
7.7%
우 6
 
5.8%
재 4
 
3.8%
중 4
 
3.8%
운 2
 
1.9%
대 2
 
1.9%
Other values (3) 6
 
5.8%
Decimal Number
ValueCountFrequency (%)
1 12
33.3%
2 12
33.3%
3 8
22.2%
4 4
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
74.3%
Common 36
 
25.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 38
36.5%
반 14
 
13.5%
송 12
 
11.5%
여 8
 
7.7%
좌 8
 
7.7%
우 6
 
5.8%
재 4
 
3.8%
중 4
 
3.8%
운 2
 
1.9%
대 2
 
1.9%
Other values (3) 6
 
5.8%
Common
ValueCountFrequency (%)
1 12
33.3%
2 12
33.3%
3 8
22.2%
4 4
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
74.3%
ASCII 36
 
25.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 38
36.5%
반 14
 
13.5%
송 12
 
11.5%
여 8
 
7.7%
좌 8
 
7.7%
우 6
 
5.8%
재 4
 
3.8%
중 4
 
3.8%
운 2
 
1.9%
대 2
 
1.9%
Other values (3) 6
 
5.8%
ASCII
ValueCountFrequency (%)
1 12
33.3%
2 12
33.3%
3 8
22.2%
4 4
 
11.1%

용도(규모)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size448.0 B
관용
20 
자가용
20 

Length

Max length3
Median length2.5
Mean length2.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row관용
2nd row자가용
3rd row관용
4th row자가용
5th row관용

Common Values

ValueCountFrequency (%)
관용 20
50.0%
자가용 20
50.0%

Length

2024-03-15T10:37:44.977819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:37:45.324320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관용 20
50.0%
자가용 20
50.0%

경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)47.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.7
Minimum0
Maximum53
Zeros17
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size488.0 B
2024-03-15T10:37:45.652120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q321.25
95-th percentile40.25
Maximum53
Range53
Interquartile range (IQR)21.25

Descriptive statistics

Standard deviation15.764615
Coefficient of variation (CV)1.2413082
Kurtosis-0.2248698
Mean12.7
Median Absolute Deviation (MAD)2
Skewness0.96677487
Sum508
Variance248.52308
MonotonicityNot monotonic
2024-03-15T10:37:46.074284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 17
42.5%
40 2
 
5.0%
2 2
 
5.0%
17 2
 
5.0%
20 2
 
5.0%
1 2
 
5.0%
16 1
 
2.5%
45 1
 
2.5%
3 1
 
2.5%
27 1
 
2.5%
Other values (9) 9
22.5%
ValueCountFrequency (%)
0 17
42.5%
1 2
 
5.0%
2 2
 
5.0%
3 1
 
2.5%
14 1
 
2.5%
16 1
 
2.5%
17 2
 
5.0%
18 1
 
2.5%
20 2
 
5.0%
21 1
 
2.5%
ValueCountFrequency (%)
53 1
2.5%
45 1
2.5%
40 2
5.0%
37 1
2.5%
36 1
2.5%
33 1
2.5%
27 1
2.5%
23 1
2.5%
22 1
2.5%
21 1
2.5%

소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73.175
Minimum0
Maximum309
Zeros16
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2024-03-15T10:37:46.431927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3131.25
95-th percentile267.95
Maximum309
Range309
Interquartile range (IQR)131.25

Descriptive statistics

Standard deviation94.002424
Coefficient of variation (CV)1.2846249
Kurtosis0.20130101
Mean73.175
Median Absolute Deviation (MAD)4
Skewness1.1133858
Sum2927
Variance8836.4558
MonotonicityNot monotonic
2024-03-15T10:37:46.828087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 16
40.0%
1 2
 
5.0%
58 1
 
2.5%
3 1
 
2.5%
128 1
 
2.5%
2 1
 
2.5%
267 1
 
2.5%
84 1
 
2.5%
79 1
 
2.5%
110 1
 
2.5%
Other values (14) 14
35.0%
ValueCountFrequency (%)
0 16
40.0%
1 2
 
5.0%
2 1
 
2.5%
3 1
 
2.5%
5 1
 
2.5%
58 1
 
2.5%
59 1
 
2.5%
65 1
 
2.5%
79 1
 
2.5%
84 1
 
2.5%
ValueCountFrequency (%)
309 1
2.5%
286 1
2.5%
267 1
2.5%
265 1
2.5%
188 1
2.5%
186 1
2.5%
159 1
2.5%
154 1
2.5%
148 1
2.5%
141 1
2.5%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean203.95
Minimum0
Maximum751
Zeros16
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2024-03-15T10:37:47.192776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36
Q3361
95-th percentile672.2
Maximum751
Range751
Interquartile range (IQR)361

Descriptive statistics

Standard deviation249.53556
Coefficient of variation (CV)1.2235134
Kurtosis-0.71412921
Mean203.95
Median Absolute Deviation (MAD)36
Skewness0.83311594
Sum8158
Variance62267.997
MonotonicityNot monotonic
2024-03-15T10:37:47.591615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 16
40.0%
1 3
 
7.5%
78 1
 
2.5%
425 1
 
2.5%
576 1
 
2.5%
208 1
 
2.5%
215 1
 
2.5%
403 1
 
2.5%
250 1
 
2.5%
670 1
 
2.5%
Other values (13) 13
32.5%
ValueCountFrequency (%)
0 16
40.0%
1 3
 
7.5%
2 1
 
2.5%
70 1
 
2.5%
78 1
 
2.5%
208 1
 
2.5%
215 1
 
2.5%
250 1
 
2.5%
288 1
 
2.5%
301 1
 
2.5%
ValueCountFrequency (%)
751 1
2.5%
714 1
2.5%
670 1
2.5%
617 1
2.5%
576 1
2.5%
550 1
2.5%
548 1
2.5%
495 1
2.5%
425 1
2.5%
403 1
2.5%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)42.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38.65
Minimum0
Maximum157
Zeros20
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size488.0 B
2024-03-15T10:37:48.019706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.5
Q356.5
95-th percentile156
Maximum157
Range157
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation51.982024
Coefficient of variation (CV)1.3449424
Kurtosis0.23079627
Mean38.65
Median Absolute Deviation (MAD)0.5
Skewness1.1752292
Sum1546
Variance2702.1308
MonotonicityNot monotonic
2024-03-15T10:37:48.405431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 20
50.0%
53 3
 
7.5%
37 2
 
5.0%
156 2
 
5.0%
1 1
 
2.5%
101 1
 
2.5%
42 1
 
2.5%
95 1
 
2.5%
93 1
 
2.5%
157 1
 
2.5%
Other values (7) 7
 
17.5%
ValueCountFrequency (%)
0 20
50.0%
1 1
 
2.5%
3 1
 
2.5%
37 2
 
5.0%
42 1
 
2.5%
46 1
 
2.5%
53 3
 
7.5%
54 1
 
2.5%
64 1
 
2.5%
88 1
 
2.5%
ValueCountFrequency (%)
157 1
2.5%
156 2
5.0%
152 1
2.5%
105 1
2.5%
101 1
2.5%
95 1
2.5%
93 1
2.5%
88 1
2.5%
64 1
2.5%
54 1
2.5%

Interactions

2024-03-15T10:37:41.203220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:38.483052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:39.292059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:40.201278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:41.418228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:38.724466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:39.496757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:40.391768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:41.592653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:38.970313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:39.749202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:40.647128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:41.770563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:39.122838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:40.017049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:37:40.894387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:37:48.758633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명용도(규모)경형소형중형대형
행정동명1.0000.0000.0000.0000.0000.000
용도(규모)0.0001.0000.8480.9000.9800.817
경형0.0000.8481.0000.8990.8390.746
소형0.0000.9000.8991.0000.8060.830
중형0.0000.9800.8390.8061.0000.828
대형0.0000.8170.7460.8300.8281.000
2024-03-15T10:37:49.038687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경형소형중형대형용도(규모)
경형1.0000.8770.8690.8800.793
소형0.8771.0000.9420.8940.847
중형0.8690.9421.0000.8980.777
대형0.8800.8940.8981.0000.826
용도(규모)0.7930.8470.7770.8261.000

Missing values

2024-03-15T10:37:41.989381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:37:42.259629image/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

행정동명용도(규모)경형소형중형대형
0반송1동관용00780
1반송1동자가용2314855046
2반송2동관용0000
3반송2동자가용4026571453
4반송3동관용0000
5반송3동자가용259703
6반여1동관용0520
7반여1동자가용53286751152
8반여2동관용0000
9반여2동자가용1715449554
행정동명용도(규모)경형소형중형대형
30좌3동관용0000
31좌3동자가용147921542
32좌4동관용0000
33좌4동자가용278420853
34중1동관용3000
35중1동자가용45267576156
36중2동관용0200
37중2동자가용40128425101
38해운대구관용0000
39해운대구자가용0310