Overview

Dataset statistics

Number of variables15
Number of observations43
Missing cells52
Missing cells (%)8.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory125.1 B

Variable types

Text6
Categorical8
Numeric1

Dataset

Description자동차관리법 및 자동차종합검사 시행등에 관한 규칙에 따라 한국교통안전공단(KOTSA)에서 관리하는 자동차검사 자료입니다.
Author한국교통안전공단
URLhttps://www.data.go.kr/data/15088040/fileData.do

Alerts

경로코드 is highly overall correlated with 이동거리(킬로미터) and 4 other fieldsHigh correlation
검사량 is highly overall correlated with 경로형식코드 and 2 other fieldsHigh correlation
경로형식코드 is highly overall correlated with 경로코드 and 2 other fieldsHigh correlation
이동거리(킬로미터) is highly overall correlated with 경로코드 and 2 other fieldsHigh correlation
사이트주소2 is highly overall correlated with 등록일시 and 1 other fieldsHigh correlation
업무시작일자 is highly overall correlated with 이동거리(킬로미터) and 1 other fieldsHigh correlation
등록일시 is highly overall correlated with 사이트주소2 and 1 other fieldsHigh correlation
검사소구분 is highly overall correlated with 이동거리(킬로미터) and 5 other fieldsHigh correlation
상위검사소코드 is highly overall correlated with 경로코드 and 1 other fieldsHigh correlation
사이트법정동코드 has 8 (18.6%) missing valuesMissing
사이트주소1 has 7 (16.3%) missing valuesMissing
전화번호 has 14 (32.6%) missing valuesMissing
각인일련번호 has 23 (53.5%) missing valuesMissing
검사소코드 has unique valuesUnique
이동거리(킬로미터) has 15 (34.9%) zerosZeros

Reproduction

Analysis started2023-12-12 23:49:13.707099
Analysis finished2023-12-12 23:49:14.886808
Duration1.18 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

검사소코드
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T08:49:15.021997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length18
Mean length11.255814
Min length9

Characters and Unicode

Total characters484
Distinct characters66
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row강동내압용기검사소
2nd row(구)춘천내압용기출장검사장
3rd row노원내압용기검사소
4th row양주내압용기출장검사장
5th row주례내압용기검사소
ValueCountFrequency (%)
강동내압용기검사소 1
 
2.3%
시흥내압용기출장검사장 1
 
2.3%
인천내압용기검사소 1
 
2.3%
현대장착내압용기출장검사장 1
 
2.3%
타타대우상용차장착내압용기출장검사장 1
 
2.3%
서수원내압용기검사소 1
 
2.3%
전주내압용기검사소 1
 
2.3%
구)창원내압용기출장검사장 1
 
2.3%
상암내압용기검사소 1
 
2.3%
동해내압용기출장검사장 1
 
2.3%
Other values (33) 33
76.7%
2023-12-13T08:49:15.313234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 60
12.4%
용 44
 
9.1%
내 43
 
8.9%
압 43
 
8.9%
기 43
 
8.9%
검 43
 
8.9%
사 43
 
8.9%
출 28
 
5.8%
소 15
 
3.1%
주 9
 
1.9%
Other values (56) 113
23.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 472
97.5%
Close Punctuation 6
 
1.2%
Open Punctuation 6
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 60
12.7%
용 44
9.3%
내 43
9.1%
압 43
9.1%
기 43
9.1%
검 43
9.1%
사 43
9.1%
출 28
 
5.9%
소 15
 
3.2%
주 9
 
1.9%
Other values (54) 101
21.4%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 472
97.5%
Common 12
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 60
12.7%
용 44
9.3%
내 43
9.1%
압 43
9.1%
기 43
9.1%
검 43
9.1%
사 43
9.1%
출 28
 
5.9%
소 15
 
3.2%
주 9
 
1.9%
Other values (54) 101
21.4%
Common
ValueCountFrequency (%)
) 6
50.0%
( 6
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 472
97.5%
ASCII 12
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 60
12.7%
용 44
9.3%
내 43
9.1%
압 43
9.1%
기 43
9.1%
검 43
9.1%
사 43
9.1%
출 28
 
5.9%
소 15
 
3.2%
주 9
 
1.9%
Other values (54) 101
21.4%
ASCII
ValueCountFrequency (%)
) 6
50.0%
( 6
50.0%

사이트법정동코드
Text

MISSING 

Distinct31
Distinct (%)88.6%
Missing8
Missing (%)18.6%
Memory size476.0 B
2023-12-13T08:49:15.696427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.7428571
Min length1

Characters and Unicode

Total characters341
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)80.0%

Sample

1st row1174011000
2nd row4211040025
3rd row1135010400
4th row
5th row2653010600
ValueCountFrequency (%)
3120012200 3
 
8.8%
4812112300 2
 
5.9%
4825032028 2
 
5.9%
1174011000 1
 
2.9%
4511311000 1
 
2.9%
4213011400 1
 
2.9%
4113510700 1
 
2.9%
4139011800 1
 
2.9%
2817010300 1
 
2.9%
4111313100 1
 
2.9%
Other values (20) 20
58.8%
2023-12-13T08:49:15.964961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 108
31.7%
1 88
25.8%
2 37
 
10.9%
4 33
 
9.7%
3 27
 
7.9%
5 15
 
4.4%
8 10
 
2.9%
6 10
 
2.9%
7 8
 
2.3%
9 4
 
1.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 340
99.7%
Space Separator 1
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 108
31.8%
1 88
25.9%
2 37
 
10.9%
4 33
 
9.7%
3 27
 
7.9%
5 15
 
4.4%
8 10
 
2.9%
6 10
 
2.9%
7 8
 
2.4%
9 4
 
1.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 341
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 108
31.7%
1 88
25.8%
2 37
 
10.9%
4 33
 
9.7%
3 27
 
7.9%
5 15
 
4.4%
8 10
 
2.9%
6 10
 
2.9%
7 8
 
2.3%
9 4
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 341
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 108
31.7%
1 88
25.8%
2 37
 
10.9%
4 33
 
9.7%
3 27
 
7.9%
5 15
 
4.4%
8 10
 
2.9%
6 10
 
2.9%
7 8
 
2.3%
9 4
 
1.2%

사이트주소1
Text

MISSING 

Distinct32
Distinct (%)88.9%
Missing7
Missing (%)16.3%
Memory size476.0 B
2023-12-13T08:49:16.225185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length25.75
Min length1

Characters and Unicode

Total characters927
Distinct characters128
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)77.8%

Sample

1st row서울특별시 강동구 아리수로 426 (강일동)
2nd row강원도 춘천시 남산면 보매기길 63(남산면)
3rd row서울특별시 노원구 공릉로62길 41 (하계동)
4th row
5th row부산광역시 사상구 학장로 256 (주례동)
ValueCountFrequency (%)
강원도 4
 
2.2%
경상남도 4
 
2.2%
경기도 4
 
2.2%
주촌면 4
 
2.2%
서울특별시 4
 
2.2%
울산광역시 3
 
1.6%
북구 3
 
1.6%
무룡로 3
 
1.6%
84 3
 
1.6%
전라남도 3
 
1.6%
Other values (127) 149
81.0%
2023-12-13T08:49:16.621844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
206
22.2%
시 37
 
4.0%
동 36
 
3.9%
) 35
 
3.8%
( 35
 
3.8%
로 31
 
3.3%
구 26
 
2.8%
도 21
 
2.3%
4 21
 
2.3%
남 17
 
1.8%
Other values (118) 462
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 526
56.7%
Space Separator 206
 
22.2%
Decimal Number 118
 
12.7%
Close Punctuation 35
 
3.8%
Open Punctuation 35
 
3.8%
Dash Punctuation 7
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 37
 
7.0%
동 36
 
6.8%
로 31
 
5.9%
구 26
 
4.9%
도 21
 
4.0%
남 17
 
3.2%
원 14
 
2.7%
대 12
 
2.3%
광 12
 
2.3%
산 12
 
2.3%
Other values (104) 308
58.6%
Decimal Number
ValueCountFrequency (%)
4 21
17.8%
2 16
13.6%
3 16
13.6%
1 15
12.7%
6 15
12.7%
5 9
7.6%
9 8
 
6.8%
8 7
 
5.9%
7 6
 
5.1%
0 5
 
4.2%
Space Separator
ValueCountFrequency (%)
206
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 526
56.7%
Common 401
43.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 37
 
7.0%
동 36
 
6.8%
로 31
 
5.9%
구 26
 
4.9%
도 21
 
4.0%
남 17
 
3.2%
원 14
 
2.7%
대 12
 
2.3%
광 12
 
2.3%
산 12
 
2.3%
Other values (104) 308
58.6%
Common
ValueCountFrequency (%)
206
51.4%
) 35
 
8.7%
( 35
 
8.7%
4 21
 
5.2%
2 16
 
4.0%
3 16
 
4.0%
1 15
 
3.7%
6 15
 
3.7%
5 9
 
2.2%
9 8
 
2.0%
Other values (4) 25
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 526
56.7%
ASCII 401
43.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
206
51.4%
) 35
 
8.7%
( 35
 
8.7%
4 21
 
5.2%
2 16
 
4.0%
3 16
 
4.0%
1 15
 
3.7%
6 15
 
3.7%
5 9
 
2.2%
9 8
 
2.0%
Other values (4) 25
 
6.2%
Hangul
ValueCountFrequency (%)
시 37
 
7.0%
동 36
 
6.8%
로 31
 
5.9%
구 26
 
4.9%
도 21
 
4.0%
남 17
 
3.2%
원 14
 
2.7%
대 12
 
2.3%
광 12
 
2.3%
산 12
 
2.3%
Other values (104) 308
58.6%

사이트주소2
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)18.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
<NA>
28 
9 
240-270
 
1
612-828
 
1
683-805
 
1
Other values (3)
3 

Length

Max length7
Median length4
Mean length3.744186
Min length1

Unique

Unique6 ?
Unique (%)14.0%

Sample

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

Common Values

ValueCountFrequency (%)
<NA> 28
65.1%
9
 
20.9%
240-270 1
 
2.3%
612-828 1
 
2.3%
683-805 1
 
2.3%
464-070 1
 
2.3%
641-808 1
 
2.3%
44247 1
 
2.3%

Length

2023-12-13T08:49:16.742412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:49:16.838713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
82.4%
240-270 1
 
2.9%
612-828 1
 
2.9%
683-805 1
 
2.9%
464-070 1
 
2.9%
641-808 1
 
2.9%
44247 1
 
2.9%

전화번호
Text

MISSING 

Distinct15
Distinct (%)51.7%
Missing14
Missing (%)32.6%
Memory size476.0 B
2023-12-13T08:49:16.971550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.862069
Min length11

Characters and Unicode

Total characters344
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)24.1%

Sample

1st row02-3426-3161
2nd row02-3426-3161
3rd row02-971-3161
4th row051-315-3161
5th row051-315-3161
ValueCountFrequency (%)
051-315-3161 5
17.2%
02-3426-3161 4
13.8%
042-935-3161 3
10.3%
062-672-3161 2
 
6.9%
02-373-3161 2
 
6.9%
031-293-3161 2
 
6.9%
032-833-3161 2
 
6.9%
051-781-3161 2
 
6.9%
02-971-3161 1
 
3.4%
063-672-3161 1
 
3.4%
Other values (5) 5
17.2%
2023-12-13T08:49:17.215140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 76
22.1%
3 60
17.4%
- 58
16.9%
6 40
11.6%
2 30
 
8.7%
0 29
 
8.4%
5 20
 
5.8%
7 11
 
3.2%
4 8
 
2.3%
9 6
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 286
83.1%
Dash Punctuation 58
 
16.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 76
26.6%
3 60
21.0%
6 40
14.0%
2 30
 
10.5%
0 29
 
10.1%
5 20
 
7.0%
7 11
 
3.8%
4 8
 
2.8%
9 6
 
2.1%
8 6
 
2.1%
Dash Punctuation
ValueCountFrequency (%)
- 58
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 344
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 76
22.1%
3 60
17.4%
- 58
16.9%
6 40
11.6%
2 30
 
8.7%
0 29
 
8.4%
5 20
 
5.8%
7 11
 
3.2%
4 8
 
2.3%
9 6
 
1.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 344
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 76
22.1%
3 60
17.4%
- 58
16.9%
6 40
11.6%
2 30
 
8.7%
0 29
 
8.4%
5 20
 
5.8%
7 11
 
3.2%
4 8
 
2.3%
9 6
 
1.7%

경로형식코드
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.3%
Missing0
Missing (%)0.0%
Memory size476.0 B
단진로
32 
장진로
8 
단2진로
 
2
장2진로
 
1

Length

Max length4
Median length3
Mean length3.0697674
Min length3

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row장진로
2nd row단진로
3rd row장진로
4th row단진로
5th row장진로

Common Values

ValueCountFrequency (%)
단진로 32
74.4%
장진로 8
 
18.6%
단2진로 2
 
4.7%
장2진로 1
 
2.3%

Length

2023-12-13T08:49:17.319432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:49:17.410053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
단진로 32
74.4%
장진로 8
 
18.6%
단2진로 2
 
4.7%
장2진로 1
 
2.3%

경로코드
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
1진로
32 
2진로
11 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2진로
2nd row1진로
3rd row2진로
4th row1진로
5th row2진로

Common Values

ValueCountFrequency (%)
1진로 32
74.4%
2진로 11
 
25.6%

Length

2023-12-13T08:49:17.496738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:49:17.582991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
1진로 32
74.4%
2진로 11
 
25.6%

검사량
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
500
27 
1000
9 
<NA>
4 
250
 
2
1500
 
1

Length

Max length4
Median length3
Mean length3.3255814
Min length3

Unique

Unique1 ?
Unique (%)2.3%

Sample

1st row1000
2nd row500
3rd row1000
4th row500
5th row1000

Common Values

ValueCountFrequency (%)
500 27
62.8%
1000 9
 
20.9%
<NA> 4
 
9.3%
250 2
 
4.7%
1500 1
 
2.3%

Length

2023-12-13T08:49:17.726381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:49:17.853317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500 27
62.8%
1000 9
 
20.9%
na 4
 
9.3%
250 2
 
4.7%
1500 1
 
2.3%

업무시작일자
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Memory size476.0 B
2012-05-25
16 
2011-11-25
6 
2013-12-10
2 
2012-06-28
2 
2014-01-01
2 
Other values (15)
15 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique15 ?
Unique (%)34.9%

Sample

1st row2012-04-05
2nd row2012-06-28
3rd row2011-11-25
4th row2015-06-26
5th row2012-05-25

Common Values

ValueCountFrequency (%)
2012-05-25 16
37.2%
2011-11-25 6
 
14.0%
2013-12-10 2
 
4.7%
2012-06-28 2
 
4.7%
2014-01-01 2
 
4.7%
2012-10-08 1
 
2.3%
2015-06-26 1
 
2.3%
2014-12-31 1
 
2.3%
2014-12-29 1
 
2.3%
2013-01-01 1
 
2.3%
Other values (10) 10
23.3%

Length

2023-12-13T08:49:17.965618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2012-05-25 16
37.2%
2011-11-25 6
 
14.0%
2013-12-10 2
 
4.7%
2012-06-28 2
 
4.7%
2014-01-01 2
 
4.7%
2015-01-01 1
 
2.3%
2012-04-05 1
 
2.3%
2013-12-27 1
 
2.3%
2018-01-02 1
 
2.3%
2015-07-03 1
 
2.3%
Other values (10) 10
23.3%

이동거리(킬로미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct25
Distinct (%)58.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.186047
Minimum0
Maximum255
Zeros15
Zeros (%)34.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-13T08:49:18.095316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median28
Q369
95-th percentile99.1
Maximum255
Range255
Interquartile range (IQR)69

Descriptive statistics

Standard deviation48.911122
Coefficient of variation (CV)1.1594147
Kurtosis7.30601
Mean42.186047
Median Absolute Deviation (MAD)28
Skewness2.118134
Sum1814
Variance2392.2979
MonotonicityNot monotonic
2023-12-13T08:49:18.221387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0 15
34.9%
60 2
 
4.7%
23 2
 
4.7%
28 2
 
4.7%
85 2
 
4.7%
130 1
 
2.3%
26 1
 
2.3%
61 1
 
2.3%
255 1
 
2.3%
37 1
 
2.3%
Other values (15) 15
34.9%
ValueCountFrequency (%)
0 15
34.9%
17 1
 
2.3%
23 2
 
4.7%
25 1
 
2.3%
26 1
 
2.3%
28 2
 
4.7%
34 1
 
2.3%
37 1
 
2.3%
38 1
 
2.3%
40 1
 
2.3%
ValueCountFrequency (%)
255 1
2.3%
130 1
2.3%
100 1
2.3%
91 1
2.3%
89 1
2.3%
85 2
4.7%
81 1
2.3%
79 1
2.3%
78 1
2.3%
70 1
2.3%

등록일시
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)39.5%
Missing0
Missing (%)0.0%
Memory size476.0 B
2014-12-08
12 
2015-08-20
4 
2015-08-27
3 
2016-01-26
3 
2017-04-19
3 
Other values (12)
18 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique6 ?
Unique (%)14.0%

Sample

1st row2014-12-08
2nd row2019-01-03
3rd row2014-12-08
4th row2019-04-01
5th row2016-01-26

Common Values

ValueCountFrequency (%)
2014-12-08 12
27.9%
2015-08-20 4
 
9.3%
2015-08-27 3
 
7.0%
2016-01-26 3
 
7.0%
2017-04-19 3
 
7.0%
2015-01-08 2
 
4.7%
2020-04-01 2
 
4.7%
2019-01-21 2
 
4.7%
2014-12-29 2
 
4.7%
2019-01-03 2
 
4.7%
Other values (7) 8
18.6%

Length

2023-12-13T08:49:18.371604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2014-12-08 12
27.9%
2015-08-20 4
 
9.3%
2015-08-27 3
 
7.0%
2016-01-26 3
 
7.0%
2017-04-19 3
 
7.0%
2019-08-29 2
 
4.7%
2019-01-03 2
 
4.7%
2014-12-29 2
 
4.7%
2019-01-21 2
 
4.7%
2020-04-01 2
 
4.7%
Other values (7) 8
18.6%

검사소명
Text

Distinct39
Distinct (%)90.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-13T08:49:18.573668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.8139535
Min length2

Characters and Unicode

Total characters121
Distinct characters59
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)83.7%

Sample

1st row강동
2nd row(구)춘천
3rd row노원
4th row양주
5th row주례
ValueCountFrequency (%)
울산 3
 
7.0%
김포 2
 
4.7%
광주 2
 
4.7%
춘천 1
 
2.3%
서수원 1
 
2.3%
여수 1
 
2.3%
강동 1
 
2.3%
상암 1
 
2.3%
인천 1
 
2.3%
현대장착 1
 
2.3%
Other values (29) 29
67.4%
2023-12-13T08:49:18.929958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 9
 
7.4%
산 5
 
4.1%
천 5
 
4.1%
원 5
 
4.1%
해 4
 
3.3%
김 4
 
3.3%
포 4
 
3.3%
착 4
 
3.3%
장 4
 
3.3%
( 4
 
3.3%
Other values (49) 73
60.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
93.4%
Open Punctuation 4
 
3.3%
Close Punctuation 4
 
3.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 9
 
8.0%
산 5
 
4.4%
천 5
 
4.4%
원 5
 
4.4%
해 4
 
3.5%
김 4
 
3.5%
포 4
 
3.5%
착 4
 
3.5%
장 4
 
3.5%
구 4
 
3.5%
Other values (47) 65
57.5%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
93.4%
Common 8
 
6.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 9
 
8.0%
산 5
 
4.4%
천 5
 
4.4%
원 5
 
4.4%
해 4
 
3.5%
김 4
 
3.5%
포 4
 
3.5%
착 4
 
3.5%
장 4
 
3.5%
구 4
 
3.5%
Other values (47) 65
57.5%
Common
ValueCountFrequency (%)
( 4
50.0%
) 4
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
93.4%
ASCII 8
 
6.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 9
 
8.0%
산 5
 
4.4%
천 5
 
4.4%
원 5
 
4.4%
해 4
 
3.5%
김 4
 
3.5%
포 4
 
3.5%
착 4
 
3.5%
장 4
 
3.5%
구 4
 
3.5%
Other values (47) 65
57.5%
ASCII
ValueCountFrequency (%)
( 4
50.0%
) 4
50.0%

검사소구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)4.7%
Missing0
Missing (%)0.0%
Memory size476.0 B
내압용기출장검사소
28 
내압용기검사소
15 

Length

Max length9
Median length9
Mean length8.3023256
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row내압용기검사소
2nd row내압용기출장검사소
3rd row내압용기검사소
4th row내압용기출장검사소
5th row내압용기검사소

Common Values

ValueCountFrequency (%)
내압용기출장검사소 28
65.1%
내압용기검사소 15
34.9%

Length

2023-12-13T08:49:19.056721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:49:19.183399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내압용기출장검사소 28
65.1%
내압용기검사소 15
34.9%

상위검사소코드
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)48.8%
Missing0
Missing (%)0.0%
Memory size476.0 B
서울본부
4 
부산본부
4 
주례내압용기검사소
4 
강동내압용기검사소
4 
광주내압용기검사소
3 
Other values (16)
24 

Length

Max length10
Median length9
Mean length7.4883721
Min length4

Unique

Unique9 ?
Unique (%)20.9%

Sample

1st row서울본부
2nd row강동내압용기검사소
3rd row서울본부
4th row노원내압용기검사소
5th row부산본부

Common Values

ValueCountFrequency (%)
서울본부 4
 
9.3%
부산본부 4
 
9.3%
주례내압용기검사소 4
 
9.3%
강동내압용기검사소 4
 
9.3%
광주내압용기검사소 3
 
7.0%
전주내압용기검사소 3
 
7.0%
신탄진내압용기검사소 2
 
4.7%
인천본부 2
 
4.7%
서수원내압용기검사소 2
 
4.7%
노원내압용기검사소 2
 
4.7%
Other values (11) 13
30.2%

Length

2023-12-13T08:49:19.307796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울본부 4
 
9.3%
주례내압용기검사소 4
 
9.3%
강동내압용기검사소 4
 
9.3%
부산본부 4
 
9.3%
광주내압용기검사소 3
 
7.0%
전주내압용기검사소 3
 
7.0%
노원내압용기검사소 2
 
4.7%
수성내압용기검사소 2
 
4.7%
창원내압용기검사소 2
 
4.7%
서수원내압용기검사소 2
 
4.7%
Other values (11) 13
30.2%

각인일련번호
Text

MISSING 

Distinct17
Distinct (%)85.0%
Missing23
Missing (%)53.5%
Memory size476.0 B
2023-12-13T08:49:19.448742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length1
Mean length1.4
Min length1

Characters and Unicode

Total characters28
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)80.0%

Sample

1st row3
2nd row4
3rd row12
4th row
5th row11
ValueCountFrequency (%)
4 1
 
6.2%
12 1
 
6.2%
11 1
 
6.2%
14 1
 
6.2%
10 1
 
6.2%
2 1
 
6.2%
8 1
 
6.2%
7 1
 
6.2%
15 1
 
6.2%
5 1
 
6.2%
Other values (6) 6
37.5%
2023-12-13T08:49:19.731018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9
32.1%
4
14.3%
4 2
 
7.1%
2 2
 
7.1%
7 2
 
7.1%
5 2
 
7.1%
6 2
 
7.1%
3 2
 
7.1%
0 1
 
3.6%
8 1
 
3.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 24
85.7%
Space Separator 4
 
14.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
37.5%
4 2
 
8.3%
2 2
 
8.3%
7 2
 
8.3%
5 2
 
8.3%
6 2
 
8.3%
3 2
 
8.3%
0 1
 
4.2%
8 1
 
4.2%
9 1
 
4.2%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 28
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
32.1%
4
14.3%
4 2
 
7.1%
2 2
 
7.1%
7 2
 
7.1%
5 2
 
7.1%
6 2
 
7.1%
3 2
 
7.1%
0 1
 
3.6%
8 1
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 28
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
32.1%
4
14.3%
4 2
 
7.1%
2 2
 
7.1%
7 2
 
7.1%
5 2
 
7.1%
6 2
 
7.1%
3 2
 
7.1%
0 1
 
3.6%
8 1
 
3.6%

Interactions

2023-12-13T08:49:14.457278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:49:19.829093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
검사소코드사이트법정동코드사이트주소1사이트주소2전화번호경로형식코드경로코드검사량업무시작일자이동거리(킬로미터)등록일시검사소명검사소구분상위검사소코드각인일련번호
검사소코드1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
사이트법정동코드1.0001.0001.0000.3290.9191.0001.0001.0000.9210.8440.0000.9920.2550.8701.000
사이트주소11.0001.0001.0000.9110.9821.0001.0001.0000.9790.9120.7590.9730.8970.9471.000
사이트주소21.0000.3290.9111.0000.0000.0000.0000.0000.8840.5850.9450.0000.6520.4540.964
전화번호1.0000.9190.9820.0001.0000.4120.0000.0000.8320.0000.7990.8670.0000.7360.849
경로형식코드1.0001.0001.0000.0000.4121.0001.0000.9710.0000.2740.7090.9740.9430.8401.000
경로코드1.0001.0001.0000.0000.0001.0001.0000.9940.0000.7950.6070.7900.9160.9421.000
검사량1.0001.0001.0000.0000.0000.9710.9941.0000.0000.4850.4890.9780.9060.8141.000
업무시작일자1.0000.9210.9790.8840.8320.0000.0000.0001.0000.8850.9170.0000.2310.6830.827
이동거리(킬로미터)1.0000.8440.9120.5850.0000.2740.7950.4850.8851.0000.8150.7610.9480.2570.000
등록일시1.0000.0000.7590.9450.7990.7090.6070.4890.9170.8151.0000.0000.5100.8730.640
검사소명1.0000.9920.9730.0000.8670.9740.7900.9780.0000.7610.0001.0000.0000.0000.925
검사소구분1.0000.2550.8970.6520.0000.9430.9160.9060.2310.9480.5100.0001.0000.9510.000
상위검사소코드1.0000.8700.9470.4540.7360.8400.9420.8140.6830.2570.8730.0000.9511.0000.846
각인일련번호1.0001.0001.0000.9640.8491.0001.0001.0000.8270.0000.6400.9250.0000.8461.000
2023-12-13T08:49:19.958069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
경로코드검사량사이트주소2업무시작일자검사소구분등록일시경로형식코드상위검사소코드
경로코드1.0000.9060.0000.0000.7380.4310.9750.665
검사량0.9061.0000.0000.0000.7020.2080.7700.415
사이트주소20.0000.0001.0000.5000.5380.6370.0000.000
업무시작일자0.0000.0000.5001.0000.0900.5400.0000.214
검사소구분0.7380.7020.5380.0901.0000.3570.7640.677
등록일시0.4310.2080.6370.5400.3571.0000.3840.432
경로형식코드0.9750.7700.0000.0000.7640.3841.0000.468
상위검사소코드0.6650.4150.0000.2140.6770.4320.4681.000
2023-12-13T08:49:20.063426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이동거리(킬로미터)사이트주소2경로형식코드경로코드검사량업무시작일자등록일시검사소구분상위검사소코드
이동거리(킬로미터)1.0000.3360.1660.5680.3180.5200.4610.7560.000
사이트주소20.3361.0000.0000.0000.0000.5000.6370.5380.000
경로형식코드0.1660.0001.0000.9750.7700.0000.3840.7640.468
경로코드0.5680.0000.9751.0000.9060.0000.4310.7380.665
검사량0.3180.0000.7700.9061.0000.0000.2080.7020.415
업무시작일자0.5200.5000.0000.0000.0001.0000.5400.0900.214
등록일시0.4610.6370.3840.4310.2080.5401.0000.3570.432
검사소구분0.7560.5380.7640.7380.7020.0900.3571.0000.677
상위검사소코드0.0000.0000.4680.6650.4150.2140.4320.6771.000

Missing values

2023-12-13T08:49:14.554746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:49:14.715002image/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.
2023-12-13T08:49:14.820570image/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

검사소코드사이트법정동코드사이트주소1사이트주소2전화번호경로형식코드경로코드검사량업무시작일자이동거리(킬로미터)등록일시검사소명검사소구분상위검사소코드각인일련번호
0강동내압용기검사소1174011000서울특별시 강동구 아리수로 426 (강일동)<NA>02-3426-3161장진로2진로10002012-04-0502014-12-08강동내압용기검사소서울본부3
1(구)춘천내압용기출장검사장4211040025강원도 춘천시 남산면 보매기길 63(남산면)02-3426-3161단진로1진로5002012-06-28852019-01-03(구)춘천내압용기출장검사소강동내압용기검사소<NA>
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