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 memory55.3 B

Variable types

Text1
Categorical1
Numeric4

Dataset

Description부산광역시해운대구_이륜차등록현황_20220114
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 16 (40.0%) zerosZeros
소형 has 18 (45.0%) zerosZeros
중형 has 15 (37.5%) zerosZeros
대형 has 18 (45.0%) zerosZeros

Reproduction

Analysis started2023-12-10 17:02:29.785326
Analysis finished2023-12-10 17:02:32.607180
Duration2.82 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

Distinct20
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-11T02:02:32.796118image/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%
2023-12-11T02:02:33.222961image/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 및 규모
Categorical

Distinct2
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size452.0 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

2023-12-11T02:02:33.441696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:02:33.606748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관용 20
50.0%
자가용 20
50.0%

경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.55
Minimum0
Maximum579
Zeros16
Zeros (%)40.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T02:02:34.136712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median10.5
Q329.25
95-th percentile49.35
Maximum579
Range579
Interquartile range (IQR)29.25

Descriptive statistics

Standard deviation90.780363
Coefficient of variation (CV)3.0720935
Kurtosis36.897759
Mean29.55
Median Absolute Deviation (MAD)10.5
Skewness5.967961
Sum1182
Variance8241.0744
MonotonicityNot monotonic
2023-12-11T02:02:34.328310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 16
40.0%
17 3
 
7.5%
32 2
 
5.0%
49 2
 
5.0%
27 2
 
5.0%
44 1
 
2.5%
579 1
 
2.5%
12 1
 
2.5%
31 1
 
2.5%
9 1
 
2.5%
Other values (10) 10
25.0%
ValueCountFrequency (%)
0 16
40.0%
1 1
 
2.5%
2 1
 
2.5%
3 1
 
2.5%
9 1
 
2.5%
12 1
 
2.5%
17 3
 
7.5%
22 1
 
2.5%
25 1
 
2.5%
26 1
 
2.5%
ValueCountFrequency (%)
579 1
2.5%
56 1
2.5%
49 2
5.0%
46 1
2.5%
44 1
2.5%
32 2
5.0%
31 1
2.5%
30 1
2.5%
29 1
2.5%
27 2
5.0%

소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)55.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.2
Minimum0
Maximum2905
Zeros18
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T02:02:34.532994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18.5
Q3144.5
95-th percentile273.25
Maximum2905
Range2905
Interquartile range (IQR)144.5

Descriptive statistics

Standard deviation457.09524
Coefficient of variation (CV)3.1480389
Kurtosis36.50641
Mean145.2
Median Absolute Deviation (MAD)18.5
Skewness5.9253408
Sum5808
Variance208936.06
MonotonicityNot monotonic
2023-12-11T02:02:34.756047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 18
45.0%
2 2
 
5.0%
2905 1
 
2.5%
141 1
 
2.5%
259 1
 
2.5%
83 1
 
2.5%
76 1
 
2.5%
106 1
 
2.5%
68 1
 
2.5%
197 1
 
2.5%
Other values (12) 12
30.0%
ValueCountFrequency (%)
0 18
45.0%
2 2
 
5.0%
35 1
 
2.5%
67 1
 
2.5%
68 1
 
2.5%
76 1
 
2.5%
83 1
 
2.5%
106 1
 
2.5%
112 1
 
2.5%
126 1
 
2.5%
ValueCountFrequency (%)
2905 1
2.5%
316 1
2.5%
271 1
2.5%
262 1
2.5%
259 1
2.5%
197 1
2.5%
176 1
2.5%
164 1
2.5%
150 1
2.5%
146 1
2.5%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean395.6
Minimum0
Maximum7828
Zeros15
Zeros (%)37.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T02:02:34.974308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median86.5
Q3396.5
95-th percentile709.9
Maximum7828
Range7828
Interquartile range (IQR)396.5

Descriptive statistics

Standard deviation1229.5716
Coefficient of variation (CV)3.1081184
Kurtosis36.690898
Mean395.6
Median Absolute Deviation (MAD)86.5
Skewness5.9451153
Sum15824
Variance1511846.4
MonotonicityNot monotonic
2023-12-11T02:02:35.182096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 15
37.5%
70 1
 
2.5%
1 1
 
2.5%
7828 1
 
2.5%
85 1
 
2.5%
416 1
 
2.5%
560 1
 
2.5%
197 1
 
2.5%
202 1
 
2.5%
390 1
 
2.5%
Other values (16) 16
40.0%
ValueCountFrequency (%)
0 15
37.5%
1 1
 
2.5%
2 1
 
2.5%
12 1
 
2.5%
70 1
 
2.5%
85 1
 
2.5%
88 1
 
2.5%
169 1
 
2.5%
197 1
 
2.5%
202 1
 
2.5%
ValueCountFrequency (%)
7828 1
2.5%
765 1
2.5%
707 1
2.5%
667 1
2.5%
612 1
2.5%
560 1
2.5%
548 1
2.5%
545 1
2.5%
489 1
2.5%
416 1
2.5%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.5
Minimum0
Maximum1189
Zeros18
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T02:02:35.401304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q362.75
95-th percentile104.15
Maximum1189
Range1189
Interquartile range (IQR)62.75

Descriptive statistics

Standard deviation187.13837
Coefficient of variation (CV)3.1451827
Kurtosis36.452367
Mean59.5
Median Absolute Deviation (MAD)2
Skewness5.918393
Sum2380
Variance35020.769
MonotonicityNot monotonic
2023-12-11T02:02:35.637352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 18
45.0%
1 2
 
5.0%
34 2
 
5.0%
90 1
 
2.5%
1189 1
 
2.5%
85 1
 
2.5%
101 1
 
2.5%
41 1
 
2.5%
37 1
 
2.5%
66 1
 
2.5%
Other values (11) 11
27.5%
ValueCountFrequency (%)
0 18
45.0%
1 2
 
5.0%
3 1
 
2.5%
22 1
 
2.5%
34 2
 
5.0%
37 1
 
2.5%
40 1
 
2.5%
41 1
 
2.5%
47 1
 
2.5%
48 1
 
2.5%
ValueCountFrequency (%)
1189 1
2.5%
126 1
2.5%
103 1
2.5%
102 1
2.5%
101 1
2.5%
90 1
2.5%
85 1
2.5%
83 1
2.5%
66 1
2.5%
65 1
2.5%

Interactions

2023-12-11T02:02:31.804387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.069942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.576865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:31.208527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:31.936967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.187535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.740555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:31.352578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:32.062019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.308424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.871111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:31.517646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:32.201114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:30.444437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:31.036133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:31.680602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:02:35.803062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명용도 및 규모경형소형중형대형
행정동명1.0000.0000.1930.2750.1930.275
용도 및 규모0.0001.0000.0000.0320.0000.032
경형0.1930.0001.0001.0000.6691.000
소형0.2750.0321.0001.0001.0000.935
중형0.1930.0000.6691.0001.0001.000
대형0.2750.0321.0000.9351.0001.000
2023-12-11T02:02:36.006892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경형소형중형대형용도 및 규모
경형1.0000.9420.9090.9170.000
소형0.9421.0000.9660.9170.037
중형0.9090.9661.0000.8940.000
대형0.9170.9170.8941.0000.037
용도 및 규모0.0000.0370.0000.0371.000

Missing values

2023-12-11T02:02:32.379049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:02:32.541191image/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 및 규모경형소형중형대형
0반송1동관용00700
1반송1동자가용2715054834
2반송2동관용0000
3반송2동자가용4626270748
4반송3동관용0000
5반송3동자가용367883
6반여1동관용0020
7반여1동자가용56271765126
8반여2동관용0000
9반여2동자가용1716448947
행정동명용도 및 규모경형소형중형대형
30좌3동관용0000
31좌3동자가용177620237
32좌4동관용0000
33좌4동자가용278319741
34중1동관용9000
35중1동자가용49259560101
36중2동관용0000
37중2동자가용3114141685
38해운대구관용122851
39해운대구자가용579290578281189