Overview

Dataset statistics

Number of variables6
Number of observations42
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory55.1 B

Variable types

Text1
Categorical1
Numeric4

Dataset

Description부산광역시해운대구_이륜차등록현황_20210114
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3075710

Alerts

경형 is highly overall correlated with 소형 and 2 other fieldsHigh correlation
소형 is highly overall correlated with 경형 and 2 other fieldsHigh correlation
중형 is highly overall correlated with 경형 and 2 other fieldsHigh correlation
대형 is highly overall correlated with 경형 and 2 other fieldsHigh correlation
경형 has 18 (42.9%) zerosZeros
소형 has 21 (50.0%) zerosZeros
중형 has 14 (33.3%) zerosZeros
대형 has 20 (47.6%) zerosZeros

Reproduction

Analysis started2023-12-10 17:02:37.268881
Analysis finished2023-12-10 17:02:40.324479
Duration3.06 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size468.0 B
2023-12-11T02:02:40.519185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.4285714
Min length2

Characters and Unicode

Total characters144
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
 
4.8%
재송1동 2
 
4.8%
중2동 2
 
4.8%
중1동 2
 
4.8%
좌동 2
 
4.8%
좌4동 2
 
4.8%
좌3동 2
 
4.8%
좌2동 2
 
4.8%
좌1동 2
 
4.8%
재송2동 2
 
4.8%
Other values (11) 22
52.4%
2023-12-11T02:02:41.076997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 40
27.8%
반 14
 
9.7%
1 12
 
8.3%
2 12
 
8.3%
송 12
 
8.3%
좌 10
 
6.9%
3 8
 
5.6%
여 8
 
5.6%
우 6
 
4.2%
4 4
 
2.8%
Other values (7) 18
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 108
75.0%
Decimal Number 36
 
25.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 40
37.0%
반 14
 
13.0%
송 12
 
11.1%
좌 10
 
9.3%
여 8
 
7.4%
우 6
 
5.6%
재 4
 
3.7%
중 4
 
3.7%
운 2
 
1.9%
대 2
 
1.9%
Other values (3) 6
 
5.6%
Decimal Number
ValueCountFrequency (%)
1 12
33.3%
2 12
33.3%
3 8
22.2%
4 4
 
11.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 108
75.0%
Common 36
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 40
37.0%
반 14
 
13.0%
송 12
 
11.1%
좌 10
 
9.3%
여 8
 
7.4%
우 6
 
5.6%
재 4
 
3.7%
중 4
 
3.7%
운 2
 
1.9%
대 2
 
1.9%
Other values (3) 6
 
5.6%
Common
ValueCountFrequency (%)
1 12
33.3%
2 12
33.3%
3 8
22.2%
4 4
 
11.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 108
75.0%
ASCII 36
 
25.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 40
37.0%
반 14
 
13.0%
송 12
 
11.1%
좌 10
 
9.3%
여 8
 
7.4%
우 6
 
5.6%
재 4
 
3.7%
중 4
 
3.7%
운 2
 
1.9%
대 2
 
1.9%
Other values (3) 6
 
5.6%
ASCII
ValueCountFrequency (%)
1 12
33.3%
2 12
33.3%
3 8
22.2%
4 4
 
11.1%

용도/구모
Categorical

Distinct2
Distinct (%)4.8%
Missing0
Missing (%)0.0%
Memory size468.0 B
관용
21 
자가용
21 

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 21
50.0%
자가용 21
50.0%

Length

2023-12-11T02:02:41.380548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:02:41.562367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관용 21
50.0%
자가용 21
50.0%

경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)52.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean27.952381
Minimum0
Maximum574
Zeros18
Zeros (%)42.9%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:41.743458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.5
Q324.75
95-th percentile54.85
Maximum574
Range574
Interquartile range (IQR)24.75

Descriptive statistics

Standard deviation88.109675
Coefficient of variation (CV)3.1521349
Kurtosis38.426115
Mean27.952381
Median Absolute Deviation (MAD)6.5
Skewness6.0813094
Sum1174
Variance7763.3148
MonotonicityNot monotonic
2023-12-11T02:02:41.959514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 18
42.9%
2 2
 
4.8%
18 2
 
4.8%
22 2
 
4.8%
57 1
 
2.4%
28 1
 
2.4%
574 1
 
2.4%
13 1
 
2.4%
55 1
 
2.4%
9 1
 
2.4%
Other values (12) 12
28.6%
ValueCountFrequency (%)
0 18
42.9%
2 2
 
4.8%
4 1
 
2.4%
9 1
 
2.4%
13 1
 
2.4%
14 1
 
2.4%
18 2
 
4.8%
20 1
 
2.4%
22 2
 
4.8%
23 1
 
2.4%
ValueCountFrequency (%)
574 1
2.4%
57 1
2.4%
55 1
2.4%
52 1
2.4%
48 1
2.4%
43 1
2.4%
42 1
2.4%
30 1
2.4%
29 1
2.4%
28 1
2.4%

소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.16667
Minimum0
Maximum3260
Zeros21
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:42.160739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q3150.75
95-th percentile372.85
Maximum3260
Range3260
Interquartile range (IQR)150.75

Descriptive statistics

Standard deviation503.45571
Coefficient of variation (CV)3.2446125
Kurtosis37.619775
Mean155.16667
Median Absolute Deviation (MAD)1.5
Skewness5.9967894
Sum6517
Variance253467.65
MonotonicityNot monotonic
2023-12-11T02:02:42.334384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 21
50.0%
79 2
 
4.8%
155 2
 
4.8%
223 1
 
2.4%
3260 1
 
2.4%
139 1
 
2.4%
332 1
 
2.4%
3 1
 
2.4%
73 1
 
2.4%
98 1
 
2.4%
Other values (10) 10
23.8%
ValueCountFrequency (%)
0 21
50.0%
3 1
 
2.4%
24 1
 
2.4%
68 1
 
2.4%
73 1
 
2.4%
79 2
 
4.8%
98 1
 
2.4%
102 1
 
2.4%
139 1
 
2.4%
141 1
 
2.4%
ValueCountFrequency (%)
3260 1
2.4%
416 1
2.4%
375 1
2.4%
332 1
2.4%
300 1
2.4%
223 1
2.4%
174 1
2.4%
167 1
2.4%
155 2
4.8%
154 1
2.4%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)64.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean387.2619
Minimum0
Maximum8048
Zeros14
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:42.494713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median69.5
Q3365.5
95-th percentile771.25
Maximum8048
Range8048
Interquartile range (IQR)365.5

Descriptive statistics

Standard deviation1237.621
Coefficient of variation (CV)3.1958243
Kurtosis38.226023
Mean387.2619
Median Absolute Deviation (MAD)69.5
Skewness6.0601567
Sum16265
Variance1531705.8
MonotonicityNot monotonic
2023-12-11T02:02:42.664388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 14
33.3%
1 3
 
7.1%
54 1
 
2.4%
550 1
 
2.4%
8048 1
 
2.4%
85 1
 
2.4%
382 1
 
2.4%
544 1
 
2.4%
2 1
 
2.4%
195 1
 
2.4%
Other values (17) 17
40.5%
ValueCountFrequency (%)
0 14
33.3%
1 3
 
7.1%
2 1
 
2.4%
3 1
 
2.4%
25 1
 
2.4%
54 1
 
2.4%
85 1
 
2.4%
110 1
 
2.4%
143 1
 
2.4%
195 1
 
2.4%
ValueCountFrequency (%)
8048 1
2.4%
919 1
2.4%
777 1
2.4%
662 1
2.4%
659 1
2.4%
566 1
2.4%
550 1
2.4%
544 1
2.4%
483 1
2.4%
382 1
2.4%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.238095
Minimum0
Maximum1075
Zeros20
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:42.851792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1
Q350.25
95-th percentile99.8
Maximum1075
Range1075
Interquartile range (IQR)50.25

Descriptive statistics

Standard deviation165.29542
Coefficient of variation (CV)3.2260259
Kurtosis38.323073
Mean51.238095
Median Absolute Deviation (MAD)1
Skewness6.0692443
Sum2152
Variance27322.576
MonotonicityNot monotonic
2023-12-11T02:02:43.027835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 20
47.6%
35 2
 
4.8%
1 2
 
4.8%
3 1
 
2.4%
86 1
 
2.4%
1075 1
 
2.4%
68 1
 
2.4%
84 1
 
2.4%
34 1
 
2.4%
64 1
 
2.4%
Other values (11) 11
26.2%
ValueCountFrequency (%)
0 20
47.6%
1 2
 
4.8%
3 1
 
2.4%
23 1
 
2.4%
34 1
 
2.4%
35 2
 
4.8%
37 1
 
2.4%
39 1
 
2.4%
41 1
 
2.4%
42 1
 
2.4%
ValueCountFrequency (%)
1075 1
2.4%
101 1
2.4%
100 1
2.4%
96 1
2.4%
86 1
2.4%
84 1
2.4%
69 1
2.4%
68 1
2.4%
65 1
2.4%
64 1
2.4%

Interactions

2023-12-11T02:02:39.407670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:37.566735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.178877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.787715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:39.545225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:37.732747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.336704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.938891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:39.698525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:37.865707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.503868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:39.100943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:39.852698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.026075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:38.646804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:39.262757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:02:43.149679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명용도/구모경형소형중형대형
행정동명1.0000.0000.1700.0000.3050.170
용도/구모0.0001.0000.0000.1490.0300.000
경형0.1700.0001.0001.0001.0000.671
소형0.0000.1491.0001.0000.9741.000
중형0.3050.0301.0000.9741.0001.000
대형0.1700.0000.6711.0001.0001.000
2023-12-11T02:02:43.298827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경형소형중형대형용도/구모
경형1.0000.9040.9240.9220.000
소형0.9041.0000.9370.9010.241
중형0.9240.9371.0000.8790.035
대형0.9220.9010.8791.0000.000
용도/구모0.0000.2410.0350.0001.000

Missing values

2023-12-11T02:02:40.043584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:02:40.253325image/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/구모경형소형중형대형
0반송1동관용00540
1반송1동자가용2515556639
2반송2동관용0000
3반송2동자가용4341691942
4반송3동관용0000
5반송3동자가용4791103
6반여1동관용0030
7반여1동자가용57300777100
8반여2동관용0000
9반여2동자가용2317448341
행정동명용도/구모경형소형중형대형
32좌4동관용0000
33좌4동자가용187919535
34좌동관용0000
35좌동자가용0320
36중1동관용9010
37중1동자가용5533254484
38중2동관용0000
39중2동자가용2213938268
40해운대구관용130851
41해운대구자가용574326080481075