Overview

Dataset statistics

Number of variables6
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory57.7 B

Variable types

Text1
Numeric4
DateTime1

Dataset

Description인천광역시 서구에서 등록된 행정동별 자동차 등록 대수에 대한 데이터로 행정동, 승용차, 승합차, 화물차, 특수차 등을 제공합니다.
Author인천광역시
URLhttps://www.incheon.go.kr/data/DATA010201/view?docId=15090846

Alerts

데이터기준일자 has constant value ""Constant
승용 is highly overall correlated with 승합 and 2 other fieldsHigh correlation
승합 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
화물 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
특수 is highly overall correlated with 승용 and 2 other fieldsHigh correlation
행정동 has unique valuesUnique
승용 has unique valuesUnique
승합 has unique valuesUnique
화물 has unique valuesUnique

Reproduction

Analysis started2024-01-28 15:24:28.703917
Analysis finished2024-01-28 15:24:29.973731
Duration1.27 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

행정동
Text

UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size316.0 B
2024-01-29T00:24:30.077784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.9130435
Min length3

Characters and Unicode

Total characters90
Distinct characters36
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

Unique23 ?
Unique (%)100.0%

Sample

1st row가정1동
2nd row가정2동
3rd row가정3동
4th row가좌1동
5th row가좌2동
ValueCountFrequency (%)
가정1동 1
 
4.3%
석남1동 1
 
4.3%
청라2동 1
 
4.3%
청라1동 1
 
4.3%
원당동 1
 
4.3%
오류왕길동 1
 
4.3%
연희동 1
 
4.3%
아라동 1
 
4.3%
신현원창동 1
 
4.3%
석남3동 1
 
4.3%
Other values (13) 13
56.5%
2024-01-29T00:24:30.353025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 23
25.6%
가 7
 
7.8%
1 4
 
4.4%
2 4
 
4.4%
3 4
 
4.4%
좌 4
 
4.4%
라 4
 
4.4%
정 3
 
3.3%
남 3
 
3.3%
석 3
 
3.3%
Other values (26) 31
34.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
85.6%
Decimal Number 13
 
14.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
정 3
 
3.9%
남 3
 
3.9%
석 3
 
3.9%
청 3
 
3.9%
원 2
 
2.6%
당 2
 
2.6%
Other values (22) 23
29.9%
Decimal Number
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
85.6%
Common 13
 
14.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
정 3
 
3.9%
남 3
 
3.9%
석 3
 
3.9%
청 3
 
3.9%
원 2
 
2.6%
당 2
 
2.6%
Other values (22) 23
29.9%
Common
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
85.6%
ASCII 13
 
14.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 23
29.9%
가 7
 
9.1%
좌 4
 
5.2%
라 4
 
5.2%
정 3
 
3.9%
남 3
 
3.9%
석 3
 
3.9%
청 3
 
3.9%
원 2
 
2.6%
당 2
 
2.6%
Other values (22) 23
29.9%
ASCII
ValueCountFrequency (%)
1 4
30.8%
2 4
30.8%
3 4
30.8%
4 1
 
7.7%

승용
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10545.478
Minimum2538
Maximum28908
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T00:24:30.454370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2538
5-th percentile3183.2
Q14965
median11373
Q313380.5
95-th percentile19082.4
Maximum28908
Range26370
Interquartile range (IQR)8415.5

Descriptive statistics

Standard deviation6214.0461
Coefficient of variation (CV)0.58926167
Kurtosis2.033393
Mean10545.478
Median Absolute Deviation (MAD)3160
Skewness1.0714405
Sum242546
Variance38614369
MonotonicityNot monotonic
2024-01-29T00:24:30.558378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
12803 1
 
4.3%
2538 1
 
4.3%
13902 1
 
4.3%
19272 1
 
4.3%
12387 1
 
4.3%
8480 1
 
4.3%
12092 1
 
4.3%
13759 1
 
4.3%
14035 1
 
4.3%
11373 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2538 1
4.3%
3134 1
4.3%
3626 1
4.3%
4022 1
4.3%
4318 1
4.3%
4514 1
4.3%
5416 1
4.3%
7762 1
4.3%
8213 1
4.3%
8480 1
4.3%
ValueCountFrequency (%)
28908 1
4.3%
19272 1
4.3%
17376 1
4.3%
14035 1
4.3%
13902 1
4.3%
13759 1
4.3%
13002 1
4.3%
12999 1
4.3%
12803 1
4.3%
12387 1
4.3%

승합
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean346.30435
Minimum68
Maximum917
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T00:24:30.640174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum68
5-th percentile116.2
Q1198.5
median268
Q3424
95-th percentile759.8
Maximum917
Range849
Interquartile range (IQR)225.5

Descriptive statistics

Standard deviation226.90435
Coefficient of variation (CV)0.65521658
Kurtosis0.61102426
Mean346.30435
Median Absolute Deviation (MAD)117
Skewness1.1577944
Sum7965
Variance51485.585
MonotonicityNot monotonic
2024-01-29T00:24:30.722064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
317 1
 
4.3%
68 1
 
4.3%
323 1
 
4.3%
324 1
 
4.3%
246 1
 
4.3%
250 1
 
4.3%
519 1
 
4.3%
665 1
 
4.3%
186 1
 
4.3%
704 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
68 1
4.3%
114 1
4.3%
136 1
4.3%
144 1
4.3%
151 1
4.3%
186 1
4.3%
211 1
4.3%
213 1
4.3%
224 1
4.3%
246 1
4.3%
ValueCountFrequency (%)
917 1
4.3%
766 1
4.3%
704 1
4.3%
665 1
4.3%
519 1
4.3%
426 1
4.3%
422 1
4.3%
371 1
4.3%
324 1
4.3%
323 1
4.3%

화물
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1646.8696
Minimum329
Maximum3849
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T00:24:30.819880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum329
5-th percentile480.7
Q1912
median1278
Q32445
95-th percentile3390.4
Maximum3849
Range3520
Interquartile range (IQR)1533

Descriptive statistics

Standard deviation1049.9839
Coefficient of variation (CV)0.63756349
Kurtosis-0.61461886
Mean1646.8696
Median Absolute Deviation (MAD)605
Skewness0.78584883
Sum37878
Variance1102466.2
MonotonicityNot monotonic
2024-01-29T00:24:30.911563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1466 1
 
4.3%
329 1
 
4.3%
1086 1
 
4.3%
1666 1
 
4.3%
1159 1
 
4.3%
988 1
 
4.3%
3391 1
 
4.3%
2592 1
 
4.3%
842 1
 
4.3%
3385 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
329 1
4.3%
464 1
4.3%
631 1
4.3%
643 1
4.3%
673 1
4.3%
842 1
4.3%
982 1
4.3%
988 1
4.3%
1086 1
4.3%
1128 1
4.3%
ValueCountFrequency (%)
3849 1
4.3%
3391 1
4.3%
3385 1
4.3%
3089 1
4.3%
2776 1
4.3%
2592 1
4.3%
2298 1
4.3%
1687 1
4.3%
1666 1
4.3%
1476 1
4.3%

특수
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57
Minimum9
Maximum227
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2024-01-29T00:24:31.002326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile9.8
Q124.5
median46
Q363.5
95-th percentile151
Maximum227
Range218
Interquartile range (IQR)39

Descriptive statistics

Standard deviation51.021386
Coefficient of variation (CV)0.89511203
Kurtosis5.0115032
Mean57
Median Absolute Deviation (MAD)21
Skewness2.0907953
Sum1311
Variance2603.1818
MonotonicityNot monotonic
2024-01-29T00:24:31.083110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
22 2
 
8.7%
9 2
 
8.7%
60 2
 
8.7%
52 2
 
8.7%
55 1
 
4.3%
83 1
 
4.3%
33 1
 
4.3%
227 1
 
4.3%
20 1
 
4.3%
87 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
9 2
8.7%
17 1
4.3%
20 1
4.3%
22 2
8.7%
27 1
4.3%
30 1
4.3%
31 1
4.3%
32 1
4.3%
33 1
4.3%
46 1
4.3%
ValueCountFrequency (%)
227 1
4.3%
155 1
4.3%
115 1
4.3%
87 1
4.3%
83 1
4.3%
67 1
4.3%
60 2
8.7%
55 1
4.3%
52 2
8.7%
46 1
4.3%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size316.0 B
Minimum2022-09-01 00:00:00
Maximum2022-09-01 00:00:00
2024-01-29T00:24:31.165069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:31.242180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-29T00:24:29.602496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:28.866447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.110167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.352253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.666941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:28.926655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.170569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.418111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.721993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:28.982386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.229695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.476262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.786577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.047873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.294827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T00:24:29.543945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T00:24:31.304310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
행정동승용승합화물특수
행정동1.0001.0001.0001.0001.000
승용1.0001.0000.8220.7830.617
승합1.0000.8221.0000.9530.767
화물1.0000.7830.9531.0000.876
특수1.0000.6170.7670.8761.000
2024-01-29T00:24:31.386233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수
승용1.0000.7530.6390.672
승합0.7531.0000.9430.854
화물0.6390.9431.0000.856
특수0.6720.8540.8561.000

Missing values

2024-01-29T00:24:29.861787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T00:24:29.941592image/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

행정동승용승합화물특수데이터기준일자
0가정1동128033171466462022-09-01
1가정2동25386832992022-09-01
2가정3동313411446492022-09-01
3가좌1동289087663849672022-09-01
4가좌2동7762213982302022-09-01
5가좌3동54162241128272022-09-01
6가좌4동3626136643172022-09-01
7검단동1299942622981152022-09-01
8검암경서동1737691730891552022-09-01
9당하동130023711687322022-09-01
행정동승용승합화물특수데이터기준일자
13석남2동43182111476522022-09-01
14석남3동4022151673202022-09-01
15신현원창동1137370433852272022-09-01
16아라동14035186842332022-09-01
17연희동137596652592832022-09-01
18오류왕길동120925193391602022-09-01
19원당동8480250988222022-09-01
20청라1동123872461159602022-09-01
21청라2동192723241666552022-09-01
22청라3동139023231086522022-09-01