Overview

Dataset statistics

Number of variables6
Number of observations22
Missing cells24
Missing cells (%)18.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.2 KiB
Average record size in memory58.0 B

Variable types

Text1
Categorical2
Numeric3

Dataset

Description서울특별시 중랑구 관내 연료별 자동차 등록현황입니다.데이터내용은 시군명, 승용차, 승합차, 화물차의 현황을 제공합니다. 참고해주시기 바랍니다. 감사합니다.
URLhttps://www.data.go.kr/data/15037579/fileData.do

Alerts

승합차 is highly overall correlated with 화물차 and 1 other fieldsHigh correlation
화물차 is highly overall correlated with 승합차 and 1 other fieldsHigh correlation
용도 is highly overall correlated with 특수차High correlation
특수차 is highly overall correlated with 승합차 and 2 other fieldsHigh correlation
승용차 has 3 (13.6%) missing valuesMissing
승합차 has 11 (50.0%) missing valuesMissing
화물차 has 10 (45.5%) missing valuesMissing

Reproduction

Analysis started2023-12-12 02:27:23.920844
Analysis finished2023-12-12 02:27:25.315663
Duration1.39 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연료
Text

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-12T11:27:25.478357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9.5
Mean length6.0454545
Min length2

Characters and Unicode

Total characters133
Distinct characters25
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st rowCNG
2nd rowCNG
3rd row경유
4th row경유
5th row기타연료
ValueCountFrequency (%)
cng 2
9.1%
경유 2
9.1%
기타연료 2
9.1%
lpg 2
9.1%
전기 2
9.1%
하이브리드(lpg+전기 2
9.1%
하이브리드(경유+전기 2
9.1%
하이브리드(휘발유+전기 2
9.1%
휘발유 2
9.1%
휘발유(무연 2
9.1%
Other values (2) 2
9.1%
2023-12-12T11:27:25.800905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 12
 
9.0%
기 10
 
7.5%
( 9
 
6.8%
) 9
 
6.8%
전 8
 
6.0%
휘 7
 
5.3%
발 7
 
5.3%
하 6
 
4.5%
드 6
 
4.5%
리 6
 
4.5%
Other values (15) 53
39.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
68.4%
Uppercase Letter 18
 
13.5%
Open Punctuation 9
 
6.8%
Close Punctuation 9
 
6.8%
Math Symbol 6
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 12
13.2%
기 10
11.0%
전 8
8.8%
휘 7
7.7%
발 7
7.7%
하 6
 
6.6%
드 6
 
6.6%
리 6
 
6.6%
브 6
 
6.6%
이 6
 
6.6%
Other values (7) 17
18.7%
Uppercase Letter
ValueCountFrequency (%)
G 6
33.3%
P 4
22.2%
L 4
22.2%
C 2
 
11.1%
N 2
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Math Symbol
ValueCountFrequency (%)
+ 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
68.4%
Common 24
 
18.0%
Latin 18
 
13.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 12
13.2%
기 10
11.0%
전 8
8.8%
휘 7
7.7%
발 7
7.7%
하 6
 
6.6%
드 6
 
6.6%
리 6
 
6.6%
브 6
 
6.6%
이 6
 
6.6%
Other values (7) 17
18.7%
Latin
ValueCountFrequency (%)
G 6
33.3%
P 4
22.2%
L 4
22.2%
C 2
 
11.1%
N 2
 
11.1%
Common
ValueCountFrequency (%)
( 9
37.5%
) 9
37.5%
+ 6
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
68.4%
ASCII 42
31.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 12
13.2%
기 10
11.0%
전 8
8.8%
휘 7
7.7%
발 7
7.7%
하 6
 
6.6%
드 6
 
6.6%
리 6
 
6.6%
브 6
 
6.6%
이 6
 
6.6%
Other values (7) 17
18.7%
ASCII
ValueCountFrequency (%)
( 9
21.4%
) 9
21.4%
+ 6
14.3%
G 6
14.3%
P 4
9.5%
L 4
9.5%
C 2
 
4.8%
N 2
 
4.8%

용도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size308.0 B
비사업용
12 
사업용
10 

Length

Max length4
Median length4
Mean length3.5454545
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row비사업용
2nd row사업용
3rd row비사업용
4th row사업용
5th row비사업용

Common Values

ValueCountFrequency (%)
비사업용 12
54.5%
사업용 10
45.5%

Length

2023-12-12T11:27:25.935701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:27:26.045408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 12
54.5%
사업용 10
45.5%

승용차
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)89.5%
Missing3
Missing (%)13.6%
Infinite0
Infinite (%)0.0%
Mean5126.3684
Minimum2
Maximum36139
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:27:26.150239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q139
median297
Q34719.5
95-th percentile25970.8
Maximum36139
Range36137
Interquartile range (IQR)4680.5

Descriptive statistics

Standard deviation10123.556
Coefficient of variation (CV)1.9748007
Kurtosis4.6615629
Mean5126.3684
Median Absolute Deviation (MAD)283
Skewness2.2873378
Sum97401
Variance1.0248638 × 108
MonotonicityNot monotonic
2023-12-12T11:27:26.282438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
2 2
 
9.1%
37 2
 
9.1%
91 1
 
4.5%
484 1
 
4.5%
36139 1
 
4.5%
18342 1
 
4.5%
45 1
 
4.5%
4939 1
 
4.5%
14 1
 
4.5%
24841 1
 
4.5%
Other values (7) 7
31.8%
(Missing) 3
13.6%
ValueCountFrequency (%)
2 2
9.1%
14 1
4.5%
37 2
9.1%
41 1
4.5%
45 1
4.5%
57 1
4.5%
91 1
4.5%
297 1
4.5%
484 1
4.5%
576 1
4.5%
ValueCountFrequency (%)
36139 1
4.5%
24841 1
4.5%
18342 1
4.5%
6219 1
4.5%
4939 1
4.5%
4500 1
4.5%
738 1
4.5%
576 1
4.5%
484 1
4.5%
297 1
4.5%

승합차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)90.9%
Missing11
Missing (%)50.0%
Infinite0
Infinite (%)0.0%
Mean349.72727
Minimum2
Maximum2715
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:27:26.399509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2
Q115
median51
Q3223.5
95-th percentile1611.5
Maximum2715
Range2713
Interquartile range (IQR)208.5

Descriptive statistics

Standard deviation800.19824
Coefficient of variation (CV)2.2880636
Kurtosis9.8652777
Mean349.72727
Median Absolute Deviation (MAD)49
Skewness3.0974828
Sum3847
Variance640317.22
MonotonicityNot monotonic
2023-12-12T11:27:26.510400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
2 2
 
9.1%
296 1
 
4.5%
2715 1
 
4.5%
151 1
 
4.5%
51 1
 
4.5%
508 1
 
4.5%
13 1
 
4.5%
74 1
 
4.5%
18 1
 
4.5%
17 1
 
4.5%
(Missing) 11
50.0%
ValueCountFrequency (%)
2 2
9.1%
13 1
4.5%
17 1
4.5%
18 1
4.5%
51 1
4.5%
74 1
4.5%
151 1
4.5%
296 1
4.5%
508 1
4.5%
2715 1
4.5%
ValueCountFrequency (%)
2715 1
4.5%
508 1
4.5%
296 1
4.5%
151 1
4.5%
74 1
4.5%
51 1
4.5%
18 1
4.5%
17 1
4.5%
13 1
4.5%
2 2
9.1%

화물차
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct12
Distinct (%)100.0%
Missing10
Missing (%)45.5%
Infinite0
Infinite (%)0.0%
Mean1229.5833
Minimum1
Maximum10709
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-12T11:27:26.645861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.3
Q122.25
median150
Q3421.5
95-th percentile6089.55
Maximum10709
Range10708
Interquartile range (IQR)399.25

Descriptive statistics

Standard deviation3055.1
Coefficient of variation (CV)2.4846628
Kurtosis10.601707
Mean1229.5833
Median Absolute Deviation (MAD)136.5
Skewness3.2133742
Sum14755
Variance9333636.1
MonotonicityNot monotonic
2023-12-12T11:27:26.783293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
7 1
 
4.5%
10709 1
 
4.5%
2310 1
 
4.5%
113 1
 
4.5%
20 1
 
4.5%
774 1
 
4.5%
304 1
 
4.5%
210 1
 
4.5%
187 1
 
4.5%
1 1
 
4.5%
Other values (2) 2
 
9.1%
(Missing) 10
45.5%
ValueCountFrequency (%)
1 1
4.5%
7 1
4.5%
20 1
4.5%
23 1
4.5%
97 1
4.5%
113 1
4.5%
187 1
4.5%
210 1
4.5%
304 1
4.5%
774 1
4.5%
ValueCountFrequency (%)
10709 1
4.5%
2310 1
4.5%
774 1
4.5%
304 1
4.5%
210 1
4.5%
187 1
4.5%
113 1
4.5%
97 1
4.5%
23 1
4.5%
20 1
4.5%

특수차
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)27.3%
Missing0
Missing (%)0.0%
Memory size308.0 B
<NA>
16 
1
2 
177
 
1
327
 
1
30
 
1

Length

Max length4
Median length4
Mean length3.4090909
Min length1

Unique

Unique4 ?
Unique (%)18.2%

Sample

1st row<NA>
2nd row<NA>
3rd row177
4th row327
5th row30

Common Values

ValueCountFrequency (%)
<NA> 16
72.7%
1 2
 
9.1%
177 1
 
4.5%
327 1
 
4.5%
30 1
 
4.5%
3 1
 
4.5%

Length

2023-12-12T11:27:26.947529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T11:27:27.092381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 16
72.7%
1 2
 
9.1%
177 1
 
4.5%
327 1
 
4.5%
30 1
 
4.5%
3 1
 
4.5%

Interactions

2023-12-12T11:27:24.720707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.155752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.425496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.808665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.244132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.541791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.901953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.334696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:27:24.637997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:27:27.210666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료용도승용차승합차화물차특수차
연료1.0000.0000.0000.0000.0000.859
용도0.0001.0000.0000.0000.1421.000
승용차0.0000.0001.0000.7340.5970.647
승합차0.0000.0000.7341.0000.9031.000
화물차0.0000.1420.5970.9031.0001.000
특수차0.8591.0000.6471.0001.0001.000
2023-12-12T11:27:27.343110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
용도특수차
용도1.0000.500
특수차0.5001.000
2023-12-12T11:27:27.449675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용차승합차화물차용도특수차
승용차1.0000.2070.0730.0000.000
승합차0.2071.0000.6690.0000.707
화물차0.0730.6691.0000.1840.707
용도0.0000.0000.1841.0000.500
특수차0.0000.7070.7070.5001.000

Missing values

2023-12-12T11:27:25.029126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:27:25.147549image/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.
2023-12-12T11:27:25.249933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연료용도승용차승합차화물차특수차
0CNG비사업용1427<NA>
1CNG사업용<NA>296<NA><NA>
2경유비사업용24841271510709177
3경유사업용2971512310327
4기타연료비사업용25111330
5기타연료사업용<NA><NA>20<NA>
6수소비사업용57<NA><NA><NA>
7LPG비사업용62195087743
8LPG사업용450013304<NA>
9전기비사업용73822101
연료용도승용차승합차화물차특수차
12하이브리드(LPG+전기)사업용<NA><NA><NA><NA>
13하이브리드(경유+전기)비사업용91<NA><NA><NA>
14하이브리드(경유+전기)사업용2<NA><NA><NA>
15하이브리드(휘발유+전기)비사업용4939<NA>1<NA>
16하이브리드(휘발유+전기)사업용45<NA><NA><NA>
17휘발유비사업용183421897<NA>
18휘발유사업용37<NA><NA><NA>
19휘발유(무연)비사업용3613917231
20휘발유(무연)사업용484<NA><NA><NA>
21휘발유(유연)비사업용37<NA><NA><NA>