Overview

Dataset statistics

Number of variables8
Number of observations22
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory74.8 B

Variable types

Categorical3
Text1
Numeric4

Dataset

Description서울특별시 중구 관내에 등록되어 있는 자동차에 대한 데이터로 연료별, 용도별, 차종별의 항목으로 처리하여 제공하고 있습니다.
Author서울특별시 중구
URLhttps://www.data.go.kr/data/15055281/fileData.do

Alerts

기관명 has constant value ""Constant
승 용 is highly overall correlated with 합 계High correlation
승 합 is highly overall correlated with 화 물 and 2 other fieldsHigh correlation
화 물 is highly overall correlated with 승 합 and 2 other fieldsHigh correlation
합 계 is highly overall correlated with 승 용 and 3 other fieldsHigh correlation
특 수 is highly overall correlated with 승 합 and 2 other fieldsHigh correlation
승 용 has 2 (9.1%) zerosZeros
승 합 has 13 (59.1%) zerosZeros
화 물 has 11 (50.0%) zerosZeros

Reproduction

Analysis started2024-03-14 11:33:24.170587
Analysis finished2024-03-14 11:33:29.164753
Duration4.99 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기관명
Categorical

CONSTANT 

Distinct1
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
서울특별시 중구
22 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시 중구
2nd row서울특별시 중구
3rd row서울특별시 중구
4th row서울특별시 중구
5th row서울특별시 중구

Common Values

ValueCountFrequency (%)
서울특별시 중구 22
100.0%

Length

2024-03-14T20:33:29.361367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:33:29.870566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 22
50.0%
중구 22
50.0%

연료별
Text

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Memory size304.0 B
2024-03-14T20:33:30.477514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length5.5454545
Min length2

Characters and Unicode

Total characters122
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 (%)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%
수소 2
9.1%
엘피지 2
9.1%
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%
2024-03-14T20:33:31.589930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
유 12
 
9.8%
기 9
 
7.4%
( 8
 
6.6%
) 8
 
6.6%
전 7
 
5.7%
휘 7
 
5.7%
발 7
 
5.7%
연 5
 
4.1%
브 5
 
4.1%
+ 5
 
4.1%
Other values (18) 49
40.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
75.4%
Uppercase Letter 9
 
7.4%
Open Punctuation 8
 
6.6%
Close Punctuation 8
 
6.6%
Math Symbol 5
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
유 12
13.0%
기 9
 
9.8%
전 7
 
7.6%
휘 7
 
7.6%
발 7
 
7.6%
연 5
 
5.4%
브 5
 
5.4%
드 5
 
5.4%
리 5
 
5.4%
하 5
 
5.4%
Other values (10) 25
27.2%
Uppercase Letter
ValueCountFrequency (%)
G 3
33.3%
C 2
22.2%
N 2
22.2%
L 1
 
11.1%
P 1
 
11.1%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 92
75.4%
Common 21
 
17.2%
Latin 9
 
7.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
유 12
13.0%
기 9
 
9.8%
전 7
 
7.6%
휘 7
 
7.6%
발 7
 
7.6%
연 5
 
5.4%
브 5
 
5.4%
드 5
 
5.4%
리 5
 
5.4%
하 5
 
5.4%
Other values (10) 25
27.2%
Latin
ValueCountFrequency (%)
G 3
33.3%
C 2
22.2%
N 2
22.2%
L 1
 
11.1%
P 1
 
11.1%
Common
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
+ 5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 92
75.4%
ASCII 30
 
24.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
유 12
13.0%
기 9
 
9.8%
전 7
 
7.6%
휘 7
 
7.6%
발 7
 
7.6%
연 5
 
5.4%
브 5
 
5.4%
드 5
 
5.4%
리 5
 
5.4%
하 5
 
5.4%
Other values (10) 25
27.2%
ASCII
ValueCountFrequency (%)
( 8
26.7%
) 8
26.7%
+ 5
16.7%
G 3
 
10.0%
C 2
 
6.7%
N 2
 
6.7%
L 1
 
3.3%
P 1
 
3.3%

용도별
Categorical

Distinct2
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size304.0 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

2024-03-14T20:33:31.899941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:33:32.085686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
비사업용 12
54.5%
사업용 10
45.5%

승 용
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.9545
Minimum0
Maximum13881
Zeros2
Zeros (%)9.1%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T20:33:32.248485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.1
Q16.75
median204
Q3986.25
95-th percentile12044.05
Maximum13881
Range13881
Interquartile range (IQR)979.5

Descriptive statistics

Standard deviation4250.5356
Coefficient of variation (CV)2.1053152
Kurtosis3.5183335
Mean2018.9545
Median Absolute Deviation (MAD)199.5
Skewness2.2079029
Sum44417
Variance18067053
MonotonicityNot monotonic
2024-03-14T20:33:32.452212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
12 2
 
9.1%
266 2
 
9.1%
0 2
 
9.1%
171 1
 
4.5%
237 1
 
4.5%
13881 1
 
4.5%
257 1
 
4.5%
12116 1
 
4.5%
101 1
 
4.5%
3463 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
0 2
9.1%
2 1
4.5%
3 1
4.5%
4 1
4.5%
5 1
4.5%
12 2
9.1%
56 1
4.5%
101 1
4.5%
171 1
4.5%
237 1
4.5%
ValueCountFrequency (%)
13881 1
4.5%
12116 1
4.5%
10677 1
4.5%
3463 1
4.5%
1323 1
4.5%
1190 1
4.5%
375 1
4.5%
266 2
9.1%
257 1
4.5%
237 1
4.5%

승 합
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)40.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean131.54545
Minimum0
Maximum1829
Zeros13
Zeros (%)59.1%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T20:33:32.648123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q319
95-th percentile592.3
Maximum1829
Range1829
Interquartile range (IQR)19

Descriptive statistics

Standard deviation403.25033
Coefficient of variation (CV)3.0654828
Kurtosis16.566744
Mean131.54545
Median Absolute Deviation (MAD)0
Skewness3.9658158
Sum2894
Variance162610.83
MonotonicityNot monotonic
2024-03-14T20:33:32.862528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 13
59.1%
4 2
 
9.1%
1 1
 
4.5%
1829 1
 
4.5%
218 1
 
4.5%
24 1
 
4.5%
612 1
 
4.5%
41 1
 
4.5%
161 1
 
4.5%
ValueCountFrequency (%)
0 13
59.1%
1 1
 
4.5%
4 2
 
9.1%
24 1
 
4.5%
41 1
 
4.5%
161 1
 
4.5%
218 1
 
4.5%
612 1
 
4.5%
1829 1
 
4.5%
ValueCountFrequency (%)
1829 1
 
4.5%
612 1
 
4.5%
218 1
 
4.5%
161 1
 
4.5%
41 1
 
4.5%
24 1
 
4.5%
4 2
 
9.1%
1 1
 
4.5%
0 13
59.1%

화 물
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)54.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean305.31818
Minimum0
Maximum4325
Zeros11
Zeros (%)50.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T20:33:33.151002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7
Q386
95-th percentile979.65
Maximum4325
Range4325
Interquartile range (IQR)86

Descriptive statistics

Standard deviation931.22834
Coefficient of variation (CV)3.0500258
Kurtosis18.562819
Mean305.31818
Median Absolute Deviation (MAD)7
Skewness4.2091175
Sum6717
Variance867186.23
MonotonicityNot monotonic
2024-03-14T20:33:33.341666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 11
50.0%
14 1
 
4.5%
4325 1
 
4.5%
998 1
 
4.5%
104 1
 
4.5%
90 1
 
4.5%
364 1
 
4.5%
47 1
 
4.5%
631 1
 
4.5%
74 1
 
4.5%
Other values (2) 2
 
9.1%
ValueCountFrequency (%)
0 11
50.0%
14 1
 
4.5%
25 1
 
4.5%
45 1
 
4.5%
47 1
 
4.5%
74 1
 
4.5%
90 1
 
4.5%
104 1
 
4.5%
364 1
 
4.5%
631 1
 
4.5%
ValueCountFrequency (%)
4325 1
4.5%
998 1
4.5%
631 1
4.5%
364 1
4.5%
104 1
4.5%
90 1
4.5%
74 1
4.5%
47 1
4.5%
45 1
4.5%
25 1
4.5%

특 수
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size304.0 B
0
17 
1
2 
268
 
1
102
 
1
34
 
1

Length

Max length3
Median length1
Mean length1.2272727
Min length1

Unique

Unique3 ?
Unique (%)13.6%

Sample

1st row0
2nd row0
3rd row268
4th row102
5th row34

Common Values

ValueCountFrequency (%)
0 17
77.3%
1 2
 
9.1%
268 1
 
4.5%
102 1
 
4.5%
34 1
 
4.5%

Length

2024-03-14T20:33:33.630359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T20:33:34.001796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 17
77.3%
1 2
 
9.1%
268 1
 
4.5%
102 1
 
4.5%
34 1
 
4.5%

합 계
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2474.2727
Minimum2
Maximum17099
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size326.0 B
2024-03-14T20:33:34.333680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3.05
Q129
median204
Q31865.25
95-th percentile13973.75
Maximum17099
Range17097
Interquartile range (IQR)1836.25

Descriptive statistics

Standard deviation5014.2476
Coefficient of variation (CV)2.0265541
Kurtosis3.8635276
Mean2474.2727
Median Absolute Deviation (MAD)196
Skewness2.2480077
Sum54434
Variance25142679
MonotonicityNot monotonic
2024-03-14T20:33:34.567730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12 2
 
9.1%
20 1
 
4.5%
4 1
 
4.5%
237 1
 
4.5%
14067 1
 
4.5%
257 1
 
4.5%
12202 1
 
4.5%
101 1
 
4.5%
3463 1
 
4.5%
3 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
2 1
4.5%
3 1
4.5%
4 1
4.5%
12 2
9.1%
20 1
4.5%
56 1
4.5%
90 1
4.5%
101 1
4.5%
166 1
4.5%
171 1
4.5%
ValueCountFrequency (%)
17099 1
4.5%
14067 1
4.5%
12202 1
4.5%
3463 1
4.5%
2167 1
4.5%
1959 1
4.5%
1584 1
4.5%
449 1
4.5%
313 1
4.5%
257 1
4.5%

Interactions

2024-03-14T20:33:27.483747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:24.487178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:25.508571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:26.518652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:27.748031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:24.746336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:25.766523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:26.763520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:28.006305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:25.007764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:26.024802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:27.011527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:28.247964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:25.246699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:26.261101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T20:33:27.236776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T20:33:34.734507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연료별용도별승 용승 합화 물특 수합 계
연료별1.0000.0000.0000.0000.0000.0000.000
용도별0.0001.0000.0520.0000.0000.1850.460
승 용0.0000.0521.0000.5100.5100.6721.000
승 합0.0000.0000.5101.0000.9750.8890.751
화 물0.0000.0000.5100.9751.0000.8890.751
특 수0.0000.1850.6720.8890.8891.0000.731
합 계0.0000.4601.0000.7510.7510.7311.000
2024-03-14T20:33:34.914222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
특 수용도별
특 수1.0000.186
용도별0.1861.000
2024-03-14T20:33:35.057216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
승 용승 합화 물합 계용도별특 수
승 용1.0000.4320.4150.9370.0000.293
승 합0.4321.0000.7120.5520.0000.860
화 물0.4150.7121.0000.6030.0000.860
합 계0.9370.5520.6031.0000.2800.575
용도별0.0000.0000.0000.2801.0000.186
특 수0.2930.8600.8600.5750.1861.000

Missing values

2024-03-14T20:33:28.600860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T20:33:29.010303image/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

기관명연료별용도별승 용승 합화 물특 수합 계
0서울특별시 중구CNG비사업용5114020
1서울특별시 중구CNG사업용04004
2서울특별시 중구경유비사업용106771829432526817099
3서울특별시 중구경유사업용2662189981021584
4서울특별시 중구기타연료비사업용42410434166
5서울특별시 중구기타연료사업용0090090
6서울특별시 중구수소비사업용5600056
7서울특별시 중구수소사업용20002
8서울특별시 중구엘피지비사업용119061236412167
9서울특별시 중구엘피지사업용2660470313
기관명연료별용도별승 용승 합화 물특 수합 계
12서울특별시 중구하이브리드(LPG+전기)비사업용1200012
13서울특별시 중구하이브리드(경유+전기)비사업용171000171
14서울특별시 중구하이브리드(경유+전기)사업용30003
15서울특별시 중구하이브리드(휘발유+전기)비사업용34630003463
16서울특별시 중구하이브리드(휘발유+전기)사업용101000101
17서울특별시 중구휘발유비사업용121164145012202
18서울특별시 중구휘발유사업용257000257
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