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 이륜차 등록 현황에 대한 데이터로 행정동별로 용도, 경형, 소형, 중형, 대형 등의 항목을 제공합니다.
Author부산광역시 동래구
URLhttps://www.data.go.kr/data/15026091/fileData.do

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 8 (30.8%) zerosZeros
경형 has 12 (46.2%) zerosZeros
소형 has 12 (46.2%) zerosZeros
중형 has 9 (34.6%) zerosZeros
대형 has 11 (42.3%) zerosZeros

Reproduction

Analysis started2023-12-23 07:45:47.161537
Analysis finished2023-12-23 07:46:01.726827
Duration14.57 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
2023-12-23T07:46:02.001041image/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%
2023-12-23T07:46:03.501871image/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

2023-12-23T07:46:04.285305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-23T07:46:04.819752image/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%
Mean352.07692
Minimum0
Maximum1348
Zeros8
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-23T07:46:05.226364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median216
Q3614
95-th percentile953.25
Maximum1348
Range1348
Interquartile range (IQR)614

Descriptive statistics

Standard deviation396.64008
Coefficient of variation (CV)1.1265722
Kurtosis-0.32196287
Mean352.07692
Median Absolute Deviation (MAD)216
Skewness0.75005146
Sum9154
Variance157323.35
MonotonicityNot monotonic
2023-12-23T07:46:05.938053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 8
30.8%
3 2
 
7.7%
60 1
 
3.8%
677 1
 
3.8%
1348 1
 
3.8%
620 1
 
3.8%
903 1
 
3.8%
830 1
 
3.8%
596 1
 
3.8%
970 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
0 8
30.8%
1 1
 
3.8%
3 2
 
7.7%
9 1
 
3.8%
60 1
 
3.8%
372 1
 
3.8%
411 1
 
3.8%
556 1
 
3.8%
576 1
 
3.8%
589 1
 
3.8%
ValueCountFrequency (%)
1348 1
3.8%
970 1
3.8%
903 1
3.8%
830 1
3.8%
677 1
3.8%
630 1
3.8%
620 1
3.8%
596 1
3.8%
589 1
3.8%
576 1
3.8%

경형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.576923
Minimum0
Maximum71
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-23T07:46:06.458969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q323.75
95-th percentile39.5
Maximum71
Range71
Interquartile range (IQR)23.75

Descriptive statistics

Standard deviation18.146455
Coefficient of variation (CV)1.2448756
Kurtosis2.0883394
Mean14.576923
Median Absolute Deviation (MAD)7
Skewness1.3470515
Sum379
Variance329.29385
MonotonicityNot monotonic
2023-12-23T07:46:07.130003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 12
46.2%
22 2
 
7.7%
34 1
 
3.8%
15 1
 
3.8%
1 1
 
3.8%
13 1
 
3.8%
16 1
 
3.8%
35 1
 
3.8%
29 1
 
3.8%
41 1
 
3.8%
Other values (4) 4
 
15.4%
ValueCountFrequency (%)
0 12
46.2%
1 1
 
3.8%
13 1
 
3.8%
15 1
 
3.8%
16 1
 
3.8%
22 2
 
7.7%
23 1
 
3.8%
24 1
 
3.8%
29 1
 
3.8%
33 1
 
3.8%
ValueCountFrequency (%)
71 1
3.8%
41 1
3.8%
35 1
3.8%
34 1
3.8%
33 1
3.8%
29 1
3.8%
24 1
3.8%
23 1
3.8%
22 2
7.7%
16 1
3.8%

소형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean82
Minimum0
Maximum329
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-23T07:46:07.765342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median53.5
Q3149.75
95-th percentile204.75
Maximum329
Range329
Interquartile range (IQR)149.75

Descriptive statistics

Standard deviation92.814654
Coefficient of variation (CV)1.131886
Kurtosis-0.0059872334
Mean82
Median Absolute Deviation (MAD)53.5
Skewness0.76911128
Sum2132
Variance8614.56
MonotonicityNot monotonic
2023-12-23T07:46:08.490391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
46.2%
137 1
 
3.8%
156 1
 
3.8%
1 1
 
3.8%
127 1
 
3.8%
106 1
 
3.8%
120 1
 
3.8%
115 1
 
3.8%
180 1
 
3.8%
198 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 12
46.2%
1 1
 
3.8%
106 1
 
3.8%
115 1
 
3.8%
120 1
 
3.8%
127 1
 
3.8%
137 1
 
3.8%
146 1
 
3.8%
151 1
 
3.8%
156 1
 
3.8%
ValueCountFrequency (%)
329 1
3.8%
207 1
3.8%
198 1
3.8%
180 1
3.8%
159 1
3.8%
156 1
3.8%
151 1
3.8%
146 1
3.8%
137 1
3.8%
127 1
3.8%

중형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)69.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219.03846
Minimum0
Maximum807
Zeros9
Zeros (%)34.6%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-23T07:46:09.316842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median133
Q3373.75
95-th percentile617.75
Maximum807
Range807
Interquartile range (IQR)373.75

Descriptive statistics

Standard deviation245.61246
Coefficient of variation (CV)1.1213211
Kurtosis-0.50252987
Mean219.03846
Median Absolute Deviation (MAD)133
Skewness0.72767545
Sum5695
Variance60325.478
MonotonicityNot monotonic
2023-12-23T07:46:10.059674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0 9
34.6%
60 1
 
3.8%
421 1
 
3.8%
807 1
 
3.8%
353 1
 
3.8%
608 1
 
3.8%
494 1
 
3.8%
376 1
 
3.8%
621 1
 
3.8%
2 1
 
3.8%
Other values (8) 8
30.8%
ValueCountFrequency (%)
0 9
34.6%
1 1
 
3.8%
2 1
 
3.8%
9 1
 
3.8%
60 1
 
3.8%
206 1
 
3.8%
262 1
 
3.8%
335 1
 
3.8%
353 1
 
3.8%
361 1
 
3.8%
ValueCountFrequency (%)
807 1
3.8%
621 1
3.8%
608 1
3.8%
494 1
3.8%
421 1
3.8%
412 1
3.8%
376 1
3.8%
367 1
3.8%
361 1
3.8%
353 1
3.8%

대형
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean36.461538
Minimum0
Maximum141
Zeros11
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-23T07:46:10.951320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median15.5
Q365.25
95-th percentile111.5
Maximum141
Range141
Interquartile range (IQR)65.25

Descriptive statistics

Standard deviation44.459627
Coefficient of variation (CV)1.2193569
Kurtosis-0.38040202
Mean36.461538
Median Absolute Deviation (MAD)15.5
Skewness0.91941561
Sum948
Variance1976.6585
MonotonicityNot monotonic
2023-12-23T07:46:11.653922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 11
42.3%
47 2
 
7.7%
1 2
 
7.7%
30 2
 
7.7%
70 1
 
3.8%
40 1
 
3.8%
78 1
 
3.8%
110 1
 
3.8%
51 1
 
3.8%
96 1
 
3.8%
Other values (3) 3
 
11.5%
ValueCountFrequency (%)
0 11
42.3%
1 2
 
7.7%
30 2
 
7.7%
40 1
 
3.8%
47 2
 
7.7%
51 1
 
3.8%
70 1
 
3.8%
78 1
 
3.8%
94 1
 
3.8%
96 1
 
3.8%
ValueCountFrequency (%)
141 1
3.8%
112 1
3.8%
110 1
3.8%
96 1
3.8%
94 1
3.8%
78 1
3.8%
70 1
3.8%
51 1
3.8%
47 2
7.7%
40 1
3.8%

Interactions

2023-12-23T07:45:58.420629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:47.809308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:50.005525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:53.254103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:55.476393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:59.016222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:48.147229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:50.646187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:53.747139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:55.864526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:59.404661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:48.518680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:51.470712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:54.394700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:56.293594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:59.785793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:49.047426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:51.892894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:54.671510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:57.134206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:46:00.368565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:49.471027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:52.801456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:55.049751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-23T07:45:57.674867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-23T07:46:12.384735image/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
계0.0001.0001.0000.9320.9420.9930.932
경형0.0001.0000.9321.0000.8950.8990.818
소형0.0001.0000.9420.8951.0000.8970.846
중형0.0001.0000.9930.8990.8971.0000.960
대형0.0001.0000.9320.8180.8460.9601.000
2023-12-23T07:46:13.221738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
계경형소형중형대형용도(규모)
계1.0000.9100.9350.9860.9310.866
경형0.9101.0000.9120.9050.9520.890
소형0.9350.9121.0000.9160.9110.913
중형0.9860.9050.9161.0000.9260.866
대형0.9310.9520.9110.9261.0000.866
용도(규모)0.8660.8900.9130.8660.8661.000

Missing values

2023-12-23T07:46:00.835759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-23T07:46:01.428844image/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
0명륜동관용6000600
1명륜동자가용5763413733570
2명장1동관용90090
3명장1동자가용6302215641240
4명장2동관용10100
5명장2동자가용5561512736747
6복산동관용31011
7복산동자가용4111310626230
8사직1동관용00000
9사직1동자가용3721612020630
행정동명용도(규모)계경형소형중형대형
16안락1동관용00000
17안락1동자가용5962314637651
18안락2동관용00000
19안락2동자가용8303320749496
20온천1동관용00000
21온천1동자가용90324159608112
22온천2동관용00000
23온천2동자가용6202215135394
24온천3동관용00000
25온천3동자가용134871329807141