Overview

Dataset statistics

Number of variables4
Number of observations37
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 KiB
Average record size in memory37.6 B

Variable types

Numeric1
Categorical2
Text1

Dataset

Description자동차튜닝종합지원포털(TS튜닝알리고)내 튜닝작업을 희망하는 지역에 대한 정보로, 어느지역에서 튜닝수요가 많은지 알수 있는 데이터입니다.
URLhttps://www.data.go.kr/data/15103268/fileData.do

Alerts

번호 is highly overall correlated with 등록건수High correlation
대분류 is highly overall correlated with 등록건수High correlation
등록건수 is highly overall correlated with 번호 and 1 other fieldsHigh correlation
번호 has unique valuesUnique

Reproduction

Analysis started2023-12-12 11:45:33.762305
Analysis finished2023-12-12 11:45:34.195452
Duration0.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

번호
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct37
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19
Minimum1
Maximum37
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size465.0 B
2023-12-12T20:45:34.269961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.8
Q110
median19
Q328
95-th percentile35.2
Maximum37
Range36
Interquartile range (IQR)18

Descriptive statistics

Standard deviation10.824355
Coefficient of variation (CV)0.56970291
Kurtosis-1.2
Mean19
Median Absolute Deviation (MAD)9
Skewness0
Sum703
Variance117.16667
MonotonicityStrictly increasing
2023-12-12T20:45:34.400552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1 1
 
2.7%
29 1
 
2.7%
22 1
 
2.7%
23 1
 
2.7%
24 1
 
2.7%
25 1
 
2.7%
26 1
 
2.7%
27 1
 
2.7%
28 1
 
2.7%
30 1
 
2.7%
Other values (27) 27
73.0%
ValueCountFrequency (%)
1 1
2.7%
2 1
2.7%
3 1
2.7%
4 1
2.7%
5 1
2.7%
6 1
2.7%
7 1
2.7%
8 1
2.7%
9 1
2.7%
10 1
2.7%
ValueCountFrequency (%)
37 1
2.7%
36 1
2.7%
35 1
2.7%
34 1
2.7%
33 1
2.7%
32 1
2.7%
31 1
2.7%
30 1
2.7%
29 1
2.7%
28 1
2.7%

대분류
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)40.5%
Missing0
Missing (%)0.0%
Memory size428.0 B
경기도
10 
서울특별시
5 
충청남도
3 
경상북도
3 
광주광역시
2 
Other values (10)
14 

Length

Max length7
Median length5
Mean length4.2972973
Min length3

Unique

Unique6 ?
Unique (%)16.2%

Sample

1st row제주특별자치도
2nd row경기도
3rd row경기도
4th row경기도
5th row대전광역시

Common Values

ValueCountFrequency (%)
경기도 10
27.0%
서울특별시 5
13.5%
충청남도 3
 
8.1%
경상북도 3
 
8.1%
광주광역시 2
 
5.4%
대구광역시 2
 
5.4%
충청북도 2
 
5.4%
인천광역시 2
 
5.4%
부산광역시 2
 
5.4%
제주특별자치도 1
 
2.7%
Other values (5) 5
13.5%

Length

2023-12-12T20:45:34.560217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 10
27.0%
서울특별시 5
13.5%
충청남도 3
 
8.1%
경상북도 3
 
8.1%
광주광역시 2
 
5.4%
대구광역시 2
 
5.4%
충청북도 2
 
5.4%
인천광역시 2
 
5.4%
부산광역시 2
 
5.4%
제주특별자치도 1
 
2.7%
Other values (5) 5
13.5%

소분류
Text

Distinct36
Distinct (%)97.3%
Missing0
Missing (%)0.0%
Memory size428.0 B
2023-12-12T20:45:34.785026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.9189189
Min length2

Characters and Unicode

Total characters145
Distinct characters55
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

Unique35 ?
Unique (%)94.6%

Sample

1st row제주시
2nd row수원시 팔달구
3rd row평택시
4th row광명시
5th row중구
ValueCountFrequency (%)
수원시 3
 
6.4%
남구 2
 
4.3%
천안시 2
 
4.3%
북구 2
 
4.3%
계양구 1
 
2.1%
분당구 1
 
2.1%
구로구 1
 
2.1%
익산시 1
 
2.1%
경산시 1
 
2.1%
강남구 1
 
2.1%
Other values (32) 32
68.1%
2023-12-12T20:45:35.195893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
구 27
18.6%
시 21
 
14.5%
10
 
6.9%
원 5
 
3.4%
남 5
 
3.4%
산 4
 
2.8%
북 4
 
2.8%
안 4
 
2.8%
수 4
 
2.8%
천 3
 
2.1%
Other values (45) 58
40.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 135
93.1%
Space Separator 10
 
6.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
구 27
20.0%
시 21
 
15.6%
원 5
 
3.7%
남 5
 
3.7%
산 4
 
3.0%
북 4
 
3.0%
안 4
 
3.0%
수 4
 
3.0%
천 3
 
2.2%
강 3
 
2.2%
Other values (44) 55
40.7%
Space Separator
ValueCountFrequency (%)
10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 135
93.1%
Common 10
 
6.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
구 27
20.0%
시 21
 
15.6%
원 5
 
3.7%
남 5
 
3.7%
산 4
 
3.0%
북 4
 
3.0%
안 4
 
3.0%
수 4
 
3.0%
천 3
 
2.2%
강 3
 
2.2%
Other values (44) 55
40.7%
Common
ValueCountFrequency (%)
10
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 135
93.1%
ASCII 10
 
6.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
구 27
20.0%
시 21
 
15.6%
원 5
 
3.7%
남 5
 
3.7%
산 4
 
3.0%
북 4
 
3.0%
안 4
 
3.0%
수 4
 
3.0%
천 3
 
2.2%
강 3
 
2.2%
Other values (44) 55
40.7%
ASCII
ValueCountFrequency (%)
10
100.0%

등록건수
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)8.1%
Missing0
Missing (%)0.0%
Memory size428.0 B
1
29 
2
7 
3
 
1

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique1 ?
Unique (%)2.7%

Sample

1st row3
2nd row2
3rd row2
4th row2
5th row2

Common Values

ValueCountFrequency (%)
1 29
78.4%
2 7
 
18.9%
3 1
 
2.7%

Length

2023-12-12T20:45:35.332055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T20:45:35.485804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1 29
78.4%
2 7
 
18.9%
3 1
 
2.7%

Interactions

2023-12-12T20:45:33.922935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T20:45:35.563406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호대분류소분류등록건수
번호1.0000.5460.9160.777
대분류0.5461.0000.3100.922
소분류0.9160.3101.0000.687
등록건수0.7770.9220.6871.000
2023-12-12T20:45:35.720521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록건수대분류
등록건수1.0000.559
대분류0.5591.000
2023-12-12T20:45:35.814250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
번호대분류등록건수
번호1.0000.1750.583
대분류0.1751.0000.559
등록건수0.5830.5591.000

Missing values

2023-12-12T20:45:34.046777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T20:45:34.145938image/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

번호대분류소분류등록건수
01제주특별자치도제주시3
12경기도수원시 팔달구2
23경기도평택시2
34경기도광명시2
45대전광역시중구2
56충청남도천안시 동남구2
67경기도수원시 장안구2
78광주광역시남구2
89충청남도천안시 서북구1
910경기도김포시1
번호대분류소분류등록건수
2728서울특별시구로구1
2829전라북도익산시1
2930부산광역시사상구1
3031부산광역시부산진구1
3132충청북도청주시 흥덕구1
3233서울특별시관악구1
3334경상북도김천시1
3435세종특별자치시세종시1
3536경상북도포항시 북구1
3637서울특별시강북구1