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 memory65.1 B

Variable types

Text1
Categorical1
Numeric4
DateTime1

Dataset

Description인천광역시 서구에 위치한 자동차 등록현황에 관한 데이터셋입니다. 인천광역시 서구에 위치한 자동차 등록현황의 연료별, 용도별에 관한 정보를 포함하고 있습니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15090737&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
승합 is highly overall correlated with 화물 and 1 other fieldsHigh correlation
화물 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
특수 is highly overall correlated with 승합 and 1 other fieldsHigh correlation
승용 has 4 (15.4%) zerosZeros
승합 has 14 (53.8%) zerosZeros
화물 has 12 (46.2%) zerosZeros
특수 has 20 (76.9%) zerosZeros

Reproduction

Analysis started2024-01-28 09:57:07.550727
Analysis finished2024-01-28 09:57:09.048227
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연료별
Text

Distinct14
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
2024-01-28T18:57:09.158959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length9.5
Mean length6.0769231
Min length2

Characters and Unicode

Total characters158
Distinct characters28
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 (%)7.7%

Sample

1st rowCNG
2nd rowCNG
3rd rowLNG
4th row경유
5th row경유
ValueCountFrequency (%)
cng 2
 
7.7%
경유 2
 
7.7%
기타연료 2
 
7.7%
수소 2
 
7.7%
엘피지 2
 
7.7%
전기 2
 
7.7%
하이브리드(lpg+전기 2
 
7.7%
하이브리드(경유+전기 2
 
7.7%
하이브리드(휘발유+전기 2
 
7.7%
휘발유 2
 
7.7%
Other values (4) 6
23.1%
2024-01-28T18:57:09.438499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 14
 
8.9%
) 11
 
7.0%
( 11
 
7.0%
기 11
 
7.0%
전 9
 
5.7%
발 8
 
5.1%
휘 8
 
5.1%
리 7
 
4.4%
+ 7
 
4.4%
드 7
 
4.4%
Other values (18) 65
41.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 111
70.3%
Uppercase Letter 18
 
11.4%
Close Punctuation 11
 
7.0%
Open Punctuation 11
 
7.0%
Math Symbol 7
 
4.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 14
12.6%
기 11
9.9%
전 9
 
8.1%
발 8
 
7.2%
휘 8
 
7.2%
리 7
 
6.3%
드 7
 
6.3%
하 7
 
6.3%
이 7
 
6.3%
브 7
 
6.3%
Other values (10) 26
23.4%
Uppercase Letter
ValueCountFrequency (%)
G 6
33.3%
N 4
22.2%
L 3
16.7%
C 3
16.7%
P 2
 
11.1%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Math Symbol
ValueCountFrequency (%)
+ 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 111
70.3%
Common 29
 
18.4%
Latin 18
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 14
12.6%
기 11
9.9%
전 9
 
8.1%
발 8
 
7.2%
휘 8
 
7.2%
리 7
 
6.3%
드 7
 
6.3%
하 7
 
6.3%
이 7
 
6.3%
브 7
 
6.3%
Other values (10) 26
23.4%
Latin
ValueCountFrequency (%)
G 6
33.3%
N 4
22.2%
L 3
16.7%
C 3
16.7%
P 2
 
11.1%
Common
ValueCountFrequency (%)
) 11
37.9%
( 11
37.9%
+ 7
24.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 111
70.3%
ASCII 47
29.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 14
12.6%
기 11
9.9%
전 9
 
8.1%
발 8
 
7.2%
휘 8
 
7.2%
리 7
 
6.3%
드 7
 
6.3%
하 7
 
6.3%
이 7
 
6.3%
브 7
 
6.3%
Other values (10) 26
23.4%
ASCII
ValueCountFrequency (%)
) 11
23.4%
( 11
23.4%
+ 7
14.9%
G 6
12.8%
N 4
 
8.5%
L 3
 
6.4%
C 3
 
6.4%
P 2
 
4.3%

용도별
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
비사업용
13 
사업용
13 

Length

Max length4
Median length3.5
Mean length3.5
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
비사업용 13
50.0%
사업용 13
50.0%

Length

2024-01-28T18:57:09.543341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T18:57:09.615264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 13
50.0%
사업용 13
50.0%

승용
Real number (ℝ)

ZEROS 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9351.8846
Minimum0
Maximum88133
Zeros4
Zeros (%)15.4%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T18:57:09.688964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.25
median137
Q31798.75
95-th percentile64884
Maximum88133
Range88133
Interquartile range (IQR)1795.5

Descriptive statistics

Standard deviation23033.088
Coefficient of variation (CV)2.4629355
Kurtosis6.5035733
Mean9351.8846
Median Absolute Deviation (MAD)137
Skewness2.704091
Sum243149
Variance5.3052316 × 108
MonotonicityNot monotonic
2024-01-28T18:57:09.778845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 4
 
15.4%
35 1
 
3.8%
1 1
 
3.8%
117 1
 
3.8%
444 1
 
3.8%
88133 1
 
3.8%
71 1
 
3.8%
50289 1
 
3.8%
19 1
 
3.8%
13559 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
0 4
15.4%
1 1
 
3.8%
2 1
 
3.8%
3 1
 
3.8%
4 1
 
3.8%
6 1
 
3.8%
19 1
 
3.8%
35 1
 
3.8%
71 1
 
3.8%
117 1
 
3.8%
ValueCountFrequency (%)
88133 1
3.8%
69749 1
3.8%
50289 1
3.8%
13559 1
3.8%
13156 1
3.8%
3598 1
3.8%
2193 1
3.8%
616 1
3.8%
444 1
3.8%
390 1
3.8%

승합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean310.76923
Minimum0
Maximum5841
Zeros14
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T18:57:09.867733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q337
95-th percentile725.25
Maximum5841
Range5841
Interquartile range (IQR)37

Descriptive statistics

Standard deviation1147.2827
Coefficient of variation (CV)3.6917514
Kurtosis24.073303
Mean310.76923
Median Absolute Deviation (MAD)0
Skewness4.8401856
Sum8080
Variance1316257.7
MonotonicityNot monotonic
2024-01-28T18:57:09.947160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 14
53.8%
6 1
 
3.8%
693 1
 
3.8%
5841 1
 
3.8%
490 1
 
3.8%
183 1
 
3.8%
736 1
 
3.8%
4 1
 
3.8%
3 1
 
3.8%
46 1
 
3.8%
Other values (3) 3
 
11.5%
ValueCountFrequency (%)
0 14
53.8%
3 1
 
3.8%
4 1
 
3.8%
6 1
 
3.8%
16 1
 
3.8%
19 1
 
3.8%
43 1
 
3.8%
46 1
 
3.8%
183 1
 
3.8%
490 1
 
3.8%
ValueCountFrequency (%)
5841 1
3.8%
736 1
3.8%
693 1
3.8%
490 1
3.8%
183 1
3.8%
46 1
3.8%
43 1
3.8%
19 1
3.8%
16 1
3.8%
6 1
3.8%

화물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1484.6154
Minimum0
Maximum30289
Zeros12
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T18:57:10.046834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q3299.5
95-th percentile3709
Maximum30289
Range30289
Interquartile range (IQR)299.5

Descriptive statistics

Standard deviation5944.1075
Coefficient of variation (CV)4.003803
Kurtosis24.661214
Mean1484.6154
Median Absolute Deviation (MAD)2
Skewness4.923685
Sum38600
Variance35332414
MonotonicityNot monotonic
2024-01-28T18:57:10.139483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 12
46.2%
16 1
 
3.8%
21 1
 
3.8%
3 1
 
3.8%
30289 1
 
3.8%
4509 1
 
3.8%
709 1
 
3.8%
474 1
 
3.8%
1 1
 
3.8%
1309 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 12
46.2%
1 1
 
3.8%
3 1
 
3.8%
16 1
 
3.8%
21 1
 
3.8%
41 1
 
3.8%
180 1
 
3.8%
277 1
 
3.8%
307 1
 
3.8%
464 1
 
3.8%
ValueCountFrequency (%)
30289 1
3.8%
4509 1
3.8%
1309 1
3.8%
709 1
3.8%
474 1
3.8%
464 1
3.8%
307 1
3.8%
277 1
3.8%
180 1
3.8%
41 1
3.8%

특수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)23.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.461538
Minimum0
Maximum683
Zeros20
Zeros (%)76.9%
Negative0
Negative (%)0.0%
Memory size366.0 B
2024-01-28T18:57:10.218470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile396
Maximum683
Range683
Interquartile range (IQR)0

Descriptive statistics

Standard deviation161.50374
Coefficient of variation (CV)3.2005315
Kurtosis11.228247
Mean50.461538
Median Absolute Deviation (MAD)0
Skewness3.4176035
Sum1312
Variance26083.458
MonotonicityNot monotonic
2024-01-28T18:57:10.296938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0 20
76.9%
1 2
 
7.7%
484 1
 
3.8%
683 1
 
3.8%
132 1
 
3.8%
11 1
 
3.8%
ValueCountFrequency (%)
0 20
76.9%
1 2
 
7.7%
11 1
 
3.8%
132 1
 
3.8%
484 1
 
3.8%
683 1
 
3.8%
ValueCountFrequency (%)
683 1
 
3.8%
484 1
 
3.8%
132 1
 
3.8%
11 1
 
3.8%
1 2
 
7.7%
0 20
76.9%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Memory size340.0 B
Minimum2022-09-01 00:00:00
Maximum2022-09-01 00:00:00
2024-01-28T18:57:10.377023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:10.448894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-28T18:57:08.591060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:07.738945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.019407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.293504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.669310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:07.814150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.083492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.371738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.751530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:07.889131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.153193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.436500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.824954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:07.949590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.226388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T18:57:08.507295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T18:57:10.506869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료별용도별승용승합화물특수
연료별1.0000.0000.0000.0000.0000.000
용도별0.0001.0000.2580.0000.0000.066
승용0.0000.2581.0000.7350.6710.540
승합0.0000.0000.7351.0000.9310.640
화물0.0000.0000.6710.9311.0001.000
특수0.0000.0660.5400.6401.0001.000
2024-01-28T18:57:10.585635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승용승합화물특수용도별
승용1.0000.2880.2630.1900.285
승합0.2881.0000.7750.7580.000
화물0.2630.7751.0000.6890.000
특수0.1900.7580.6891.0000.000
용도별0.2850.0000.0000.0001.000

Missing values

2024-01-28T18:57:08.925163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T18:57:09.014601image/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

연료별용도별승용승합화물특수데이터기준일자
0CNG비사업용3561602022-09-01
1CNG사업용06932112022-09-01
2LNG비사업용00302022-09-01
3경유비사업용697495841302894842022-09-01
4경유사업용36949045096832022-09-01
5기타연료비사업용21837091322022-09-01
6기타연료사업용0047402022-09-01
7수소비사업용2380002022-09-01
8수소사업용30102022-09-01
9엘피지비사업용131567361309112022-09-01
연료별용도별승용승합화물특수데이터기준일자
16하이브리드(경유+전기)비사업용6160002022-09-01
17하이브리드(경유+전기)사업용60002022-09-01
18하이브리드(휘발유+전기)비사업용135590002022-09-01
19하이브리드(휘발유+전기)사업용190002022-09-01
20휘발유비사업용502891627712022-09-01
21휘발유사업용710002022-09-01
22휘발유(무연)비사업용88133434102022-09-01
23휘발유(무연)사업용4440002022-09-01
24휘발유(유연)비사업용1170002022-09-01
25휘발유(유연)사업용10002022-09-01