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

Description부산광역시_해운대구_이륜자동차등록현황_20200114
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=3075710

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/구모 is highly overall correlated with 경형 and 3 other fieldsHigh correlation
경형 has 20 (47.6%) zerosZeros
소형 has 21 (50.0%) zerosZeros
중형 has 15 (35.7%) zerosZeros
대형 has 22 (52.4%) zerosZeros

Reproduction

Analysis started2023-12-10 17:02:14.189665
Analysis finished2023-12-10 17:02:16.926670
Duration2.74 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
2023-12-11T02:02:17.110586image/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%
2023-12-11T02:02:17.658405image/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

HIGH CORRELATION 

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

2023-12-11T02:02:17.886842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T02:02:18.105838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관용 21
50.0%
자가용 21
50.0%

경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.428571
Minimum0
Maximum64
Zeros20
Zeros (%)47.6%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:18.291968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.5
Q323.75
95-th percentile44.9
Maximum64
Range64
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation17.580675
Coefficient of variation (CV)1.3091992
Kurtosis0.38884645
Mean13.428571
Median Absolute Deviation (MAD)2.5
Skewness1.152642
Sum564
Variance309.08014
MonotonicityNot monotonic
2023-12-11T02:02:18.538322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 20
47.6%
4 2
 
4.8%
18 2
 
4.8%
2 1
 
2.4%
21 1
 
2.4%
50 1
 
2.4%
3 1
 
2.4%
26 1
 
2.4%
28 1
 
2.4%
27 1
 
2.4%
Other values (11) 11
26.2%
ValueCountFrequency (%)
0 20
47.6%
2 1
 
2.4%
3 1
 
2.4%
4 2
 
4.8%
11 1
 
2.4%
17 1
 
2.4%
18 2
 
4.8%
19 1
 
2.4%
21 1
 
2.4%
23 1
 
2.4%
ValueCountFrequency (%)
64 1
2.4%
50 1
2.4%
45 1
2.4%
43 1
2.4%
42 1
2.4%
38 1
2.4%
37 1
2.4%
28 1
2.4%
27 1
2.4%
26 1
2.4%

소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)47.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.738095
Minimum0
Maximum425
Zeros21
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:18.820334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.5
Q3145
95-th percentile345.15
Maximum425
Range425
Interquartile range (IQR)145

Descriptive statistics

Standard deviation114.93244
Coefficient of variation (CV)1.4596802
Kurtosis1.9862881
Mean78.738095
Median Absolute Deviation (MAD)1.5
Skewness1.6039765
Sum3307
Variance13209.466
MonotonicityNot monotonic
2023-12-11T02:02:19.017080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0 21
50.0%
3 2
 
4.8%
160 2
 
4.8%
84 1
 
2.4%
291 1
 
2.4%
127 1
 
2.4%
348 1
 
2.4%
425 1
 
2.4%
82 1
 
2.4%
72 1
 
2.4%
Other values (10) 10
23.8%
ValueCountFrequency (%)
0 21
50.0%
3 2
 
4.8%
24 1
 
2.4%
72 1
 
2.4%
76 1
 
2.4%
82 1
 
2.4%
84 1
 
2.4%
101 1
 
2.4%
103 1
 
2.4%
127 1
 
2.4%
ValueCountFrequency (%)
425 1
2.4%
386 1
2.4%
348 1
2.4%
291 1
2.4%
231 1
2.4%
169 1
2.4%
160 2
4.8%
158 1
2.4%
153 1
2.4%
151 1
2.4%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)57.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean187.40476
Minimum0
Maximum925
Zeros15
Zeros (%)35.7%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:19.215823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median23
Q3312
95-th percentile643.15
Maximum925
Range925
Interquartile range (IQR)312

Descriptive statistics

Standard deviation251.09944
Coefficient of variation (CV)1.3398776
Kurtosis0.67593555
Mean187.40476
Median Absolute Deviation (MAD)23
Skewness1.2483354
Sum7871
Variance63050.93
MonotonicityNot monotonic
2023-12-11T02:02:19.433530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 15
35.7%
1 4
 
9.5%
43 2
 
4.8%
136 1
 
2.4%
343 1
 
2.4%
502 1
 
2.4%
2 1
 
2.4%
198 1
 
2.4%
192 1
 
2.4%
313 1
 
2.4%
Other values (14) 14
33.3%
ValueCountFrequency (%)
0 15
35.7%
1 4
 
9.5%
2 1
 
2.4%
3 1
 
2.4%
43 2
 
4.8%
124 1
 
2.4%
136 1
 
2.4%
192 1
 
2.4%
195 1
 
2.4%
198 1
 
2.4%
ValueCountFrequency (%)
925 1
2.4%
740 1
2.4%
644 1
2.4%
627 1
2.4%
560 1
2.4%
548 1
2.4%
502 1
2.4%
461 1
2.4%
349 1
2.4%
343 1
2.4%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.642857
Minimum0
Maximum91
Zeros22
Zeros (%)52.4%
Negative0
Negative (%)0.0%
Memory size510.0 B
2023-12-11T02:02:19.643726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q339.25
95-th percentile79.5
Maximum91
Range91
Interquartile range (IQR)39.25

Descriptive statistics

Standard deviation28.842568
Coefficient of variation (CV)1.3326599
Kurtosis-0.25767878
Mean21.642857
Median Absolute Deviation (MAD)0
Skewness1.0283915
Sum909
Variance831.89373
MonotonicityNot monotonic
2023-12-11T02:02:19.889169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 22
52.4%
27 1
 
2.4%
55 1
 
2.4%
65 1
 
2.4%
33 1
 
2.4%
30 1
 
2.4%
66 1
 
2.4%
32 1
 
2.4%
50 1
 
2.4%
91 1
 
2.4%
Other values (11) 11
26.2%
ValueCountFrequency (%)
0 22
52.4%
1 1
 
2.4%
4 1
 
2.4%
20 1
 
2.4%
21 1
 
2.4%
27 1
 
2.4%
30 1
 
2.4%
32 1
 
2.4%
33 1
 
2.4%
37 1
 
2.4%
ValueCountFrequency (%)
91 1
2.4%
85 1
2.4%
80 1
2.4%
70 1
2.4%
66 1
2.4%
65 1
2.4%
58 1
2.4%
55 1
2.4%
50 1
2.4%
44 1
2.4%

Interactions

2023-12-11T02:02:16.148476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:14.588366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.188725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.664553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:16.282975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:14.778594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.315421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.798983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:16.393522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:14.918787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.427703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.919509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:16.524545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.053699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:15.555154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T02:02:16.028512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T02:02:20.057527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명용도/구모경형소형중형대형
행정동명1.0000.0000.0000.0000.0000.000
용도/구모0.0001.0000.8070.9650.8540.807
경형0.0000.8071.0000.8260.9230.971
소형0.0000.9650.8261.0000.9040.898
중형0.0000.8540.9230.9041.0000.950
대형0.0000.8070.9710.8980.9501.000
2023-12-11T02:02:20.247899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경형소형중형대형용도/구모
경형1.0000.9030.9070.9020.754
소형0.9031.0000.9320.8950.771
중형0.9070.9321.0000.8580.804
대형0.9020.8950.8581.0000.754
용도/구모0.7540.7710.8040.7541.000

Missing values

2023-12-11T02:02:16.715772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T02:02:16.867777image/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
0반송1동관용00430
1반송1동자가용3715854827
2반송2동관용0000
3반송2동자가용4542592540
4반송3동관용0000
5반송3동자가용4841244
6반여1동관용0030
7반여1동자가용4229174085
8반여2동관용0000
9반여2동자가용2316946137
행정동명용도/구모경형소형중형대형
32좌4동관용0000
33좌4동자가용188219833
34좌동관용0000
35좌동자가용0320
36중1동관용3010
37중1동자가용5034850265
38중2동관용0000
39중2동자가용2112734355
40해운대구관용0000
41해운대구자가용0310