Overview

Dataset statistics

Number of variables7
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory66.1 B

Variable types

Text1
Categorical1
Numeric5

Dataset

Description부산광역시_동래구_이륜차등록현황_20221214
Author부산광역시 동래구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15026091

Alerts

계 is highly overall correlated with 경형 and 4 other fieldsHigh correlation
경형 is highly overall correlated with 계 and 4 other fieldsHigh correlation
소형 is highly overall correlated with 계 and 4 other fieldsHigh correlation
중형 is highly overall correlated with 계 and 4 other fieldsHigh correlation
대형 is highly overall correlated with 계 and 4 other fieldsHigh correlation
용도(규모) is highly overall correlated with 계 and 4 other fieldsHigh correlation
계 has 9 (34.6%) zerosZeros
경형 has 12 (46.2%) zerosZeros
소형 has 12 (46.2%) zerosZeros
중형 has 10 (38.5%) zerosZeros
대형 has 12 (46.2%) zerosZeros

Reproduction

Analysis started2023-12-10 16:18:46.777008
Analysis finished2023-12-10 16:18:50.122575
Duration3.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동명
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-11T01:18:50.247611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.7692308
Min length3

Characters and Unicode

Total characters98
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 row명륜동
2nd row명륜동
3rd row명장1동
4th row명장1동
5th row명장2동
ValueCountFrequency (%)
명륜동 2
 
7.7%
명장1동 2
 
7.7%
명장2동 2
 
7.7%
복산동 2
 
7.7%
사직1동 2
 
7.7%
사직2동 2
 
7.7%
사직3동 2
 
7.7%
수민동 2
 
7.7%
안락1동 2
 
7.7%
안락2동 2
 
7.7%
Other values (3) 6
23.1%
2023-12-11T01:18:50.525016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 26
26.5%
1 8
 
8.2%
2 8
 
8.2%
명 6
 
6.1%
직 6
 
6.1%
온 6
 
6.1%
사 6
 
6.1%
천 6
 
6.1%
3 4
 
4.1%
안 4
 
4.1%
Other values (7) 18
18.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 78
79.6%
Decimal Number 20
 
20.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 26
33.3%
명 6
 
7.7%
직 6
 
7.7%
온 6
 
7.7%
사 6
 
7.7%
천 6
 
7.7%
안 4
 
5.1%
락 4
 
5.1%
장 4
 
5.1%
산 2
 
2.6%
Other values (4) 8
 
10.3%
Decimal Number
ValueCountFrequency (%)
1 8
40.0%
2 8
40.0%
3 4
20.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 78
79.6%
Common 20
 
20.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 26
33.3%
명 6
 
7.7%
직 6
 
7.7%
온 6
 
7.7%
사 6
 
7.7%
천 6
 
7.7%
안 4
 
5.1%
락 4
 
5.1%
장 4
 
5.1%
산 2
 
2.6%
Other values (4) 8
 
10.3%
Common
ValueCountFrequency (%)
1 8
40.0%
2 8
40.0%
3 4
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 78
79.6%
ASCII 20
 
20.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 26
33.3%
명 6
 
7.7%
직 6
 
7.7%
온 6
 
7.7%
사 6
 
7.7%
천 6
 
7.7%
안 4
 
5.1%
락 4
 
5.1%
장 4
 
5.1%
산 2
 
2.6%
Other values (4) 8
 
10.3%
ASCII
ValueCountFrequency (%)
1 8
40.0%
2 8
40.0%
3 4
20.0%

용도(규모)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
관용
13 
자가용
13 

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 13
50.0%
자가용 13
50.0%

Length

2023-12-11T01:18:50.651014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:18:50.736852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
관용 13
50.0%
자가용 13
50.0%

계
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.07692
Minimum0
Maximum1329
Zeros9
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:50.819239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median212
Q3610.5
95-th percentile879.25
Maximum1329
Range1329
Interquartile range (IQR)610.5

Descriptive statistics

Standard deviation387.88781
Coefficient of variation (CV)1.1208139
Kurtosis-0.33838424
Mean346.07692
Median Absolute Deviation (MAD)212
Skewness0.72670058
Sum8998
Variance150456.95
MonotonicityNot monotonic
2023-12-11T01:18:50.946507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9
34.6%
61 1
 
3.8%
571 1
 
3.8%
1329 1
 
3.8%
636 1
 
3.8%
850 1
 
3.8%
847 1
 
3.8%
585 1
 
3.8%
889 1
 
3.8%
683 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
0 9
34.6%
1 1
 
3.8%
3 1
 
3.8%
9 1
 
3.8%
61 1
 
3.8%
363 1
 
3.8%
408 1
 
3.8%
564 1
 
3.8%
571 1
 
3.8%
580 1
 
3.8%
ValueCountFrequency (%)
1329 1
3.8%
889 1
3.8%
850 1
3.8%
847 1
3.8%
683 1
3.8%
636 1
3.8%
619 1
3.8%
585 1
3.8%
580 1
3.8%
571 1
3.8%

경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.076923
Minimum0
Maximum74
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:51.333931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8
Q330.25
95-th percentile40
Maximum74
Range74
Interquartile range (IQR)30.25

Descriptive statistics

Standard deviation19.139327
Coefficient of variation (CV)1.1904845
Kurtosis1.5317319
Mean16.076923
Median Absolute Deviation (MAD)8
Skewness1.1500709
Sum418
Variance366.31385
MonotonicityNot monotonic
2023-12-11T01:18:51.448781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 12
46.2%
31 2
 
7.7%
34 2
 
7.7%
28 2
 
7.7%
42 1
 
3.8%
26 1
 
3.8%
22 1
 
3.8%
1 1
 
3.8%
15 1
 
3.8%
19 1
 
3.8%
Other values (2) 2
 
7.7%
ValueCountFrequency (%)
0 12
46.2%
1 1
 
3.8%
15 1
 
3.8%
19 1
 
3.8%
22 1
 
3.8%
26 1
 
3.8%
28 2
 
7.7%
31 2
 
7.7%
33 1
 
3.8%
34 2
 
7.7%
ValueCountFrequency (%)
74 1
3.8%
42 1
3.8%
34 2
7.7%
33 1
3.8%
31 2
7.7%
28 2
7.7%
26 1
3.8%
22 1
3.8%
19 1
3.8%
15 1
3.8%

소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean81.5
Minimum0
Maximum326
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:51.582198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median54.5
Q3145
95-th percentile207.25
Maximum326
Range326
Interquartile range (IQR)145

Descriptive statistics

Standard deviation92.436032
Coefficient of variation (CV)1.1341844
Kurtosis-0.033569389
Mean81.5
Median Absolute Deviation (MAD)54.5
Skewness0.7725613
Sum2119
Variance8544.42
MonotonicityNot monotonic
2023-12-11T01:18:51.730079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
46.2%
139 1
 
3.8%
146 1
 
3.8%
1 1
 
3.8%
126 1
 
3.8%
108 1
 
3.8%
117 1
 
3.8%
110 1
 
3.8%
193 1
 
3.8%
188 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 12
46.2%
1 1
 
3.8%
108 1
 
3.8%
110 1
 
3.8%
117 1
 
3.8%
126 1
 
3.8%
139 1
 
3.8%
142 1
 
3.8%
146 1
 
3.8%
154 1
 
3.8%
ValueCountFrequency (%)
326 1
3.8%
212 1
3.8%
193 1
3.8%
188 1
3.8%
157 1
3.8%
154 1
3.8%
146 1
3.8%
142 1
3.8%
139 1
3.8%
126 1
3.8%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)65.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean215.57692
Minimum0
Maximum808
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:51.887312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median126.5
Q3378
95-th percentile585.75
Maximum808
Range808
Interquartile range (IQR)378

Descriptive statistics

Standard deviation241.48825
Coefficient of variation (CV)1.1201953
Kurtosis-0.45829223
Mean215.57692
Median Absolute Deviation (MAD)126.5
Skewness0.72492336
Sum5605
Variance58316.574
MonotonicityNot monotonic
2023-12-11T01:18:52.017173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 10
38.5%
61 1
 
3.8%
411 1
 
3.8%
808 1
 
3.8%
380 1
 
3.8%
570 1
 
3.8%
498 1
 
3.8%
370 1
 
3.8%
591 1
 
3.8%
353 1
 
3.8%
Other values (7) 7
26.9%
ValueCountFrequency (%)
0 10
38.5%
1 1
 
3.8%
9 1
 
3.8%
61 1
 
3.8%
192 1
 
3.8%
254 1
 
3.8%
331 1
 
3.8%
353 1
 
3.8%
370 1
 
3.8%
372 1
 
3.8%
ValueCountFrequency (%)
808 1
3.8%
591 1
3.8%
570 1
3.8%
498 1
3.8%
411 1
3.8%
404 1
3.8%
380 1
3.8%
372 1
3.8%
370 1
3.8%
353 1
3.8%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean32.923077
Minimum0
Maximum121
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-11T01:18:52.151943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median16
Q363
95-th percentile100.25
Maximum121
Range121
Interquartile range (IQR)63

Descriptive statistics

Standard deviation38.643937
Coefficient of variation (CV)1.1737644
Kurtosis-0.59903753
Mean32.923077
Median Absolute Deviation (MAD)16
Skewness0.78639477
Sum856
Variance1493.3538
MonotonicityNot monotonic
2023-12-11T01:18:52.294671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
46.2%
68 1
 
3.8%
43 1
 
3.8%
44 1
 
3.8%
1 1
 
3.8%
31 1
 
3.8%
35 1
 
3.8%
75 1
 
3.8%
48 1
 
3.8%
76 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 12
46.2%
1 1
 
3.8%
31 1
 
3.8%
35 1
 
3.8%
43 1
 
3.8%
44 1
 
3.8%
45 1
 
3.8%
48 1
 
3.8%
68 1
 
3.8%
74 1
 
3.8%
ValueCountFrequency (%)
121 1
3.8%
103 1
3.8%
92 1
3.8%
76 1
3.8%
75 1
3.8%
74 1
3.8%
68 1
3.8%
48 1
3.8%
45 1
3.8%
44 1
3.8%

Interactions

2023-12-11T01:18:49.375021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.071545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.688440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.257445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.847331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.465490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.191335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.818370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.370632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.956703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.577019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.345136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.961056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.500085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.086777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.665475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.446872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.055855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.630229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.171193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.765602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:47.558016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.149824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:48.736620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:18:49.285608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:18:52.398506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동명용도(규모)계경형소형중형대형
행정동명1.0000.0000.0000.0000.0000.0000.000
용도(규모)0.0001.0001.0001.0001.0001.0001.000
계0.0001.0001.0000.8650.8691.0000.871
경형0.0001.0000.8651.0000.9720.8680.931
소형0.0001.0000.8690.9721.0000.9420.905
중형0.0001.0001.0000.8680.9421.0000.974
대형0.0001.0000.8710.9310.9050.9741.000
2023-12-11T01:18:52.533111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계경형소형중형대형용도(규모)
계1.0000.9200.9460.9860.9360.890
경형0.9201.0000.9180.9120.9810.913
소형0.9460.9181.0000.9280.9300.913
중형0.9860.9120.9281.0000.9350.866
대형0.9360.9810.9300.9351.0000.866
용도(규모)0.8900.9130.9130.8660.8661.000

Missing values

2023-12-11T01:18:49.917811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:18:50.077075image/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)계경형소형중형대형
0명륜동관용6100610
1명륜동자가용5804213933168
2명장1동관용90090
3명장1동자가용6192614640443
4명장2동관용10100
5명장2동자가용5642212637244
6복산동관용31011
7복산동자가용4081510825431
8사직1동관용00000
9사직1동자가용3631911719235
행정동명용도(규모)계경형소형중형대형
16안락1동관용00000
17안락1동자가용5852814237045
18안락2동관용00000
19안락2동자가용84734212498103
20온천1동관용00000
21온천1동자가용8503115757092
22온천2동관용00000
23온천2동자가용6362815438074
24온천3동관용00000
25온천3동자가용132974326808121