Overview

Dataset statistics

Number of variables11
Number of observations35
Missing cells53
Missing cells (%)13.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.2 KiB
Average record size in memory92.7 B

Variable types

Categorical3
Text6
DateTime1
Numeric1

Dataset

Description서울특별시 동작구 관내에 허가된 화물자동차 운송 주선사업체 목록입니다. 이 데이터에는 업종명, 대표자명(마스킹 처리),주선업유형, 허가일자, 우편번호, 주사무소 주소, 전화번호 등이 포함되어 있습니다.
Author서울특별시 동작구
URLhttps://www.data.go.kr/data/15080928/fileData.do

Alerts

시군명 has constant value ""Constant
업종명 has constant value ""Constant
비고 has constant value ""Constant
전화번호 has 19 (54.3%) missing valuesMissing
비고 has 34 (97.1%) missing valuesMissing
대표자명 has unique valuesUnique
허가일자 has unique valuesUnique

Reproduction

Analysis started2024-04-13 11:14:42.132484
Analysis finished2024-04-13 11:14:46.101358
Duration3.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
서울특별시 동작구
35 

Length

Max length9
Median length9
Mean length9
Min length9

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서울특별시 동작구 35
100.0%

Length

2024-04-13T20:14:46.237483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:14:46.414061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서울특별시 35
50.0%
동작구 35
50.0%

업종명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size408.0 B
화물주선업
35 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row화물주선업
2nd row화물주선업
3rd row화물주선업
4th row화물주선업
5th row화물주선업

Common Values

ValueCountFrequency (%)
화물주선업 35
100.0%

Length

2024-04-13T20:14:46.688767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:14:46.864470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
화물주선업 35
100.0%

업체명
Text

Distinct34
Distinct (%)97.1%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-04-13T20:14:47.509655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length6.9142857
Min length3

Characters and Unicode

Total characters242
Distinct characters90
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique33 ?
Unique (%)94.3%

Sample

1st row멀티퀵서비스
2nd row대양익스프레스
3rd row엘지익스프레스
4th row전국화물
5th row애드텐
ValueCountFrequency (%)
전국화물 2
 
5.1%
썬퀵종합물류 1
 
2.6%
통인익스프레스 1
 
2.6%
청담점 1
 
2.6%
주)농심 1
 
2.6%
충남이사 1
 
2.6%
스마트이사 1
 
2.6%
가가익스프레스 1
 
2.6%
주)삼라종합로지스틱스 1
 
2.6%
영구크린 1
 
2.6%
Other values (28) 28
71.8%
2024-04-13T20:14:48.401002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
스 32
 
13.2%
익 12
 
5.0%
프 12
 
5.0%
레 12
 
5.0%
물 8
 
3.3%
주 8
 
3.3%
이 6
 
2.5%
통 6
 
2.5%
) 6
 
2.5%
( 6
 
2.5%
Other values (80) 134
55.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 221
91.3%
Close Punctuation 6
 
2.5%
Open Punctuation 6
 
2.5%
Decimal Number 5
 
2.1%
Space Separator 4
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 32
 
14.5%
익 12
 
5.4%
프 12
 
5.4%
레 12
 
5.4%
물 8
 
3.6%
주 8
 
3.6%
이 6
 
2.7%
통 6
 
2.7%
종 5
 
2.3%
운 5
 
2.3%
Other values (73) 115
52.0%
Decimal Number
ValueCountFrequency (%)
2 2
40.0%
9 1
20.0%
3 1
20.0%
4 1
20.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 221
91.3%
Common 21
 
8.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
스 32
 
14.5%
익 12
 
5.4%
프 12
 
5.4%
레 12
 
5.4%
물 8
 
3.6%
주 8
 
3.6%
이 6
 
2.7%
통 6
 
2.7%
종 5
 
2.3%
운 5
 
2.3%
Other values (73) 115
52.0%
Common
ValueCountFrequency (%)
) 6
28.6%
( 6
28.6%
4
19.0%
2 2
 
9.5%
9 1
 
4.8%
3 1
 
4.8%
4 1
 
4.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 221
91.3%
ASCII 21
 
8.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
스 32
 
14.5%
익 12
 
5.4%
프 12
 
5.4%
레 12
 
5.4%
물 8
 
3.6%
주 8
 
3.6%
이 6
 
2.7%
통 6
 
2.7%
종 5
 
2.3%
운 5
 
2.3%
Other values (73) 115
52.0%
ASCII
ValueCountFrequency (%)
) 6
28.6%
( 6
28.6%
4
19.0%
2 2
 
9.5%
9 1
 
4.8%
3 1
 
4.8%
4 1
 
4.8%

대표자명
Text

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-04-13T20:14:49.315248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.9714286
Min length2

Characters and Unicode

Total characters104
Distinct characters49
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 (%)100.0%

Sample

1st row송*상
2nd row윤*미
3rd row허*례
4th row이*숙
5th row김*복
ValueCountFrequency (%)
송*상 1
 
2.9%
김*자 1
 
2.9%
최*휴 1
 
2.9%
원*혜 1
 
2.9%
김*태 1
 
2.9%
현*화 1
 
2.9%
황*원 1
 
2.9%
강*명 1
 
2.9%
이*석 1
 
2.9%
박*진 1
 
2.9%
Other values (25) 25
71.4%
2024-04-13T20:14:50.525027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 35
33.7%
김 7
 
6.7%
수 3
 
2.9%
황 3
 
2.9%
송 2
 
1.9%
남 2
 
1.9%
원 2
 
1.9%
박 2
 
1.9%
봉 2
 
1.9%
조 2
 
1.9%
Other values (39) 44
42.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
66.3%
Other Punctuation 35
33.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 7
 
10.1%
수 3
 
4.3%
황 3
 
4.3%
송 2
 
2.9%
남 2
 
2.9%
원 2
 
2.9%
박 2
 
2.9%
봉 2
 
2.9%
조 2
 
2.9%
석 2
 
2.9%
Other values (38) 42
60.9%
Other Punctuation
ValueCountFrequency (%)
* 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
66.3%
Common 35
33.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 7
 
10.1%
수 3
 
4.3%
황 3
 
4.3%
송 2
 
2.9%
남 2
 
2.9%
원 2
 
2.9%
박 2
 
2.9%
봉 2
 
2.9%
조 2
 
2.9%
석 2
 
2.9%
Other values (38) 42
60.9%
Common
ValueCountFrequency (%)
* 35
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
66.3%
ASCII 35
33.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 35
100.0%
Hangul
ValueCountFrequency (%)
김 7
 
10.1%
수 3
 
4.3%
황 3
 
4.3%
송 2
 
2.9%
남 2
 
2.9%
원 2
 
2.9%
박 2
 
2.9%
봉 2
 
2.9%
조 2
 
2.9%
석 2
 
2.9%
Other values (38) 42
60.9%

주선업유형
Categorical

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size408.0 B
이사
20 
일반
12 
일반/이사
3 

Length

Max length5
Median length2
Mean length2.2571429
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row일반
2nd row이사
3rd row이사
4th row이사
5th row일반/이사

Common Values

ValueCountFrequency (%)
이사 20
57.1%
일반 12
34.3%
일반/이사 3
 
8.6%

Length

2024-04-13T20:14:50.948659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-13T20:14:51.282881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
이사 20
57.1%
일반 12
34.3%
일반/이사 3
 
8.6%

허가일자
Date

UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size408.0 B
Minimum1990-11-30 00:00:00
Maximum2020-08-07 00:00:00
2024-04-13T20:14:51.623842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-13T20:14:52.015263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)

우편번호
Real number (ℝ)

Distinct24
Distinct (%)68.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6996.8286
Minimum6902
Maximum7073
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size443.0 B
2024-04-13T20:14:52.377928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6902
5-th percentile6912.4
Q16968.5
median7006
Q37022
95-th percentile7071
Maximum7073
Range171
Interquartile range (IQR)53.5

Descriptive statistics

Standard deviation45.874821
Coefficient of variation (CV)0.0065565163
Kurtosis-0.34861365
Mean6996.8286
Median Absolute Deviation (MAD)23
Skewness-0.40894028
Sum244889
Variance2104.4992
MonotonicityNot monotonic
2024-04-13T20:14:52.772974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
6937 3
 
8.6%
7006 3
 
8.6%
7007 3
 
8.6%
6988 2
 
5.7%
7013 2
 
5.7%
7012 2
 
5.7%
7029 2
 
5.7%
7071 2
 
5.7%
6997 1
 
2.9%
7005 1
 
2.9%
Other values (14) 14
40.0%
ValueCountFrequency (%)
6902 1
 
2.9%
6904 1
 
2.9%
6916 1
 
2.9%
6937 3
8.6%
6947 1
 
2.9%
6950 1
 
2.9%
6968 1
 
2.9%
6969 1
 
2.9%
6988 2
5.7%
6990 1
 
2.9%
ValueCountFrequency (%)
7073 1
2.9%
7071 2
5.7%
7057 1
2.9%
7047 1
2.9%
7043 1
2.9%
7030 1
2.9%
7029 2
5.7%
7015 1
2.9%
7013 2
5.7%
7012 2
5.7%

주사무소 주소
Text

Distinct30
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-04-13T20:14:53.650144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length20
Mean length18.114286
Min length13

Characters and Unicode

Total characters634
Distinct characters46
Distinct categories4 ?
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 (%)80.0%

Sample

1st row서울특별시 동작구 등용로 38
2nd row서울특별시 동작구 사당로27길 19
3rd row서울특별시 동작구 사당로 165
4th row서울특별시 동작구 상도로41가길 21
5th row서울특별시 동작구 사당동
ValueCountFrequency (%)
서울특별시 35
25.7%
동작구 35
25.7%
사당동 4
 
2.9%
등용로 3
 
2.2%
38 3
 
2.2%
사당로 3
 
2.2%
사당로27길 3
 
2.2%
21 2
 
1.5%
23 1
 
0.7%
보라매로5가길 1
 
0.7%
Other values (46) 46
33.8%
2024-04-13T20:14:54.787998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
101
15.9%
동 43
 
6.8%
작 39
 
6.2%
서 35
 
5.5%
특 35
 
5.5%
별 35
 
5.5%
시 35
 
5.5%
구 35
 
5.5%
울 35
 
5.5%
로 30
 
4.7%
Other values (36) 211
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 424
66.9%
Decimal Number 106
 
16.7%
Space Separator 101
 
15.9%
Dash Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 43
10.1%
작 39
9.2%
서 35
8.3%
특 35
8.3%
별 35
8.3%
시 35
8.3%
구 35
8.3%
울 35
8.3%
로 30
 
7.1%
길 21
 
5.0%
Other values (25) 81
19.1%
Decimal Number
ValueCountFrequency (%)
1 24
22.6%
2 20
18.9%
5 13
12.3%
3 12
11.3%
8 9
 
8.5%
7 8
 
7.5%
6 8
 
7.5%
9 7
 
6.6%
4 5
 
4.7%
Space Separator
ValueCountFrequency (%)
101
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 424
66.9%
Common 210
33.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 43
10.1%
작 39
9.2%
서 35
8.3%
특 35
8.3%
별 35
8.3%
시 35
8.3%
구 35
8.3%
울 35
8.3%
로 30
 
7.1%
길 21
 
5.0%
Other values (25) 81
19.1%
Common
ValueCountFrequency (%)
101
48.1%
1 24
 
11.4%
2 20
 
9.5%
5 13
 
6.2%
3 12
 
5.7%
8 9
 
4.3%
7 8
 
3.8%
6 8
 
3.8%
9 7
 
3.3%
4 5
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 424
66.9%
ASCII 210
33.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
101
48.1%
1 24
 
11.4%
2 20
 
9.5%
5 13
 
6.2%
3 12
 
5.7%
8 9
 
4.3%
7 8
 
3.8%
6 8
 
3.8%
9 7
 
3.3%
4 5
 
2.4%
Hangul
ValueCountFrequency (%)
동 43
10.1%
작 39
9.2%
서 35
8.3%
특 35
8.3%
별 35
8.3%
시 35
8.3%
구 35
8.3%
울 35
8.3%
로 30
 
7.1%
길 21
 
5.0%
Other values (25) 81
19.1%

주사무소 상세주소
Text

Distinct27
Distinct (%)77.1%
Missing0
Missing (%)0.0%
Memory size408.0 B
2024-04-13T20:14:55.469883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length31
Mean length12.085714
Min length5

Characters and Unicode

Total characters423
Distinct characters65
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)62.9%

Sample

1st row상가동 비1, A-1305호
2nd row (사당동)
3rd row201동 상가 지하층 비101호 (사당동, 대아아파트)
4th row(상도1동)
5th row1004-1
ValueCountFrequency (%)
사당동 14
 
17.7%
1층 8
 
10.1%
상도동 5
 
6.3%
상가동 4
 
5.1%
신대방동 3
 
3.8%
상가 2
 
2.5%
상도1동 2
 
2.5%
602호 1
 
1.3%
상도동래미안 1
 
1.3%
1차아파트 1
 
1.3%
Other values (38) 38
48.1%
2024-04-13T20:14:56.416513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
49
 
11.6%
동 37
 
8.7%
1 34
 
8.0%
( 31
 
7.3%
) 31
 
7.3%
상 17
 
4.0%
사 16
 
3.8%
당 16
 
3.8%
0 15
 
3.5%
층 14
 
3.3%
Other values (55) 163
38.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 210
49.6%
Decimal Number 80
 
18.9%
Space Separator 49
 
11.6%
Open Punctuation 31
 
7.3%
Close Punctuation 31
 
7.3%
Other Punctuation 10
 
2.4%
Dash Punctuation 7
 
1.7%
Uppercase Letter 5
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 37
17.6%
상 17
 
8.1%
사 16
 
7.6%
당 16
 
7.6%
층 14
 
6.7%
호 13
 
6.2%
도 9
 
4.3%
가 8
 
3.8%
아 8
 
3.8%
대 7
 
3.3%
Other values (37) 65
31.0%
Decimal Number
ValueCountFrequency (%)
1 34
42.5%
0 15
18.8%
2 9
 
11.2%
3 6
 
7.5%
6 4
 
5.0%
4 4
 
5.0%
5 3
 
3.8%
9 3
 
3.8%
8 2
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
C 2
40.0%
B 2
40.0%
A 1
20.0%
Other Punctuation
ValueCountFrequency (%)
, 9
90.0%
， 1
 
10.0%
Space Separator
ValueCountFrequency (%)
49
100.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 210
49.6%
Common 208
49.2%
Latin 5
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 37
17.6%
상 17
 
8.1%
사 16
 
7.6%
당 16
 
7.6%
층 14
 
6.7%
호 13
 
6.2%
도 9
 
4.3%
가 8
 
3.8%
아 8
 
3.8%
대 7
 
3.3%
Other values (37) 65
31.0%
Common
ValueCountFrequency (%)
49
23.6%
1 34
16.3%
( 31
14.9%
) 31
14.9%
0 15
 
7.2%
, 9
 
4.3%
2 9
 
4.3%
- 7
 
3.4%
3 6
 
2.9%
6 4
 
1.9%
Other values (5) 13
 
6.2%
Latin
ValueCountFrequency (%)
C 2
40.0%
B 2
40.0%
A 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 212
50.1%
Hangul 210
49.6%
None 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
49
23.1%
1 34
16.0%
( 31
14.6%
) 31
14.6%
0 15
 
7.1%
, 9
 
4.2%
2 9
 
4.2%
- 7
 
3.3%
3 6
 
2.8%
6 4
 
1.9%
Other values (7) 17
 
8.0%
Hangul
ValueCountFrequency (%)
동 37
17.6%
상 17
 
8.1%
사 16
 
7.6%
당 16
 
7.6%
층 14
 
6.7%
호 13
 
6.2%
도 9
 
4.3%
가 8
 
3.8%
아 8
 
3.8%
대 7
 
3.3%
Other values (37) 65
31.0%
None
ValueCountFrequency (%)
， 1
100.0%

전화번호
Text

MISSING 

Distinct16
Distinct (%)100.0%
Missing19
Missing (%)54.3%
Memory size408.0 B
2024-04-13T20:14:57.076971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length11.0625
Min length11

Characters and Unicode

Total characters177
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 (%)100.0%

Sample

1st row02-522-2424
2nd row02-824-2424
3rd row02-588-2424
4th row02-596-2424
5th row02-445-1336
ValueCountFrequency (%)
02-522-2424 1
 
6.2%
02-824-2424 1
 
6.2%
02-588-2424 1
 
6.2%
02-596-2424 1
 
6.2%
02-445-1336 1
 
6.2%
02-822-6999 1
 
6.2%
02-821-0111 1
 
6.2%
02-504-0725 1
 
6.2%
02-2634-1058 1
 
6.2%
02-848-8000 1
 
6.2%
Other values (6) 6
37.5%
2024-04-13T20:14:58.054705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 35
19.8%
- 32
18.1%
0 26
14.7%
4 20
11.3%
5 18
10.2%
8 12
 
6.8%
1 12
 
6.8%
3 8
 
4.5%
9 6
 
3.4%
6 6
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 145
81.9%
Dash Punctuation 32
 
18.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 35
24.1%
0 26
17.9%
4 20
13.8%
5 18
12.4%
8 12
 
8.3%
1 12
 
8.3%
3 8
 
5.5%
9 6
 
4.1%
6 6
 
4.1%
7 2
 
1.4%
Dash Punctuation
ValueCountFrequency (%)
- 32
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 177
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 35
19.8%
- 32
18.1%
0 26
14.7%
4 20
11.3%
5 18
10.2%
8 12
 
6.8%
1 12
 
6.8%
3 8
 
4.5%
9 6
 
3.4%
6 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 177
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 35
19.8%
- 32
18.1%
0 26
14.7%
4 20
11.3%
5 18
10.2%
8 12
 
6.8%
1 12
 
6.8%
3 8
 
4.5%
9 6
 
3.4%
6 6
 
3.4%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing34
Missing (%)97.1%
Memory size408.0 B
2024-04-13T20:14:58.578971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters7
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

Unique1 ?
Unique (%)100.0%

Sample

1st row02-3486-2424
ValueCountFrequency (%)
02-3486-2424 1
100.0%
2024-04-13T20:14:59.221234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 3
25.0%
4 3
25.0%
- 2
16.7%
0 1
 
8.3%
3 1
 
8.3%
8 1
 
8.3%
6 1
 
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10
83.3%
Dash Punctuation 2
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 3
30.0%
4 3
30.0%
0 1
 
10.0%
3 1
 
10.0%
8 1
 
10.0%
6 1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 3
25.0%
4 3
25.0%
- 2
16.7%
0 1
 
8.3%
3 1
 
8.3%
8 1
 
8.3%
6 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 3
25.0%
4 3
25.0%
- 2
16.7%
0 1
 
8.3%
3 1
 
8.3%
8 1
 
8.3%
6 1
 
8.3%

Interactions

2024-04-13T20:14:44.900499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-13T20:14:59.480110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업체명대표자명주선업유형허가일자우편번호주사무소 주소주사무소 상세주소전화번호
업체명1.0001.0000.8411.0000.0000.9690.9521.000
대표자명1.0001.0001.0001.0001.0001.0001.0001.000
주선업유형0.8411.0001.0001.0000.2520.0000.7831.000
허가일자1.0001.0001.0001.0001.0001.0001.0001.000
우편번호0.0001.0000.2521.0001.0001.0000.9801.000
주사무소 주소0.9691.0000.0001.0001.0001.0000.0001.000
주사무소 상세주소0.9521.0000.7831.0000.9800.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.0001.0001.000
2024-04-13T20:14:59.989064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호주선업유형
우편번호1.0000.000
주선업유형0.0001.000

Missing values

2024-04-13T20:14:45.286353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-13T20:14:45.705236image/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.
2024-04-13T20:14:46.019619image/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

시군명업종명업체명대표자명주선업유형허가일자우편번호주사무소 주소주사무소 상세주소전화번호비고
0서울특별시 동작구화물주선업멀티퀵서비스송*상일반1991-06-306937서울특별시 동작구 등용로 38상가동 비1, A-1305호<NA><NA>
1서울특별시 동작구화물주선업대양익스프레스윤*미이사2000-05-017007서울특별시 동작구 사당로27길 19(사당동)02-522-242402-3486-2424
2서울특별시 동작구화물주선업엘지익스프레스허*례이사1999-12-176988서울특별시 동작구 사당로 165201동 상가 지하층 비101호 (사당동, 대아아파트)<NA><NA>
3서울특별시 동작구화물주선업전국화물이*숙이사1998-07-036968서울특별시 동작구 상도로41가길 21(상도1동)02-824-2424<NA>
4서울특별시 동작구화물주선업애드텐김*복일반/이사1992-12-297013서울특별시 동작구 사당동1004-102-588-2424<NA>
5서울특별시 동작구화물주선업우주통운황*남이사1999-06-087013서울특별시 동작구 사당로28길 141층 (사당동)<NA><NA>
6서울특별시 동작구화물주선업풀하우스하*주이사1990-11-306988서울특별시 동작구 사당로9가길 82상가동 제1층 제103호 (사당동, 경남아너스빌 아파트)<NA><NA>
7서울특별시 동작구화물주선업연세익스프레스윤*석이사1995-06-057006서울특별시 동작구 사당동162-12202-596-2424<NA>
8서울특별시 동작구화물주선업동작익스프레스조*관이사1999-09-206937서울특별시 동작구 등용로 38상가동 B1층 C-93호 (상도동, 상도동래미안(1차)아파트)<NA><NA>
9서울특별시 동작구화물주선업한일익스프레스채*자이사1997-04-117007서울특별시 동작구 사당로27길 921층 (사당동)02-445-1336<NA>
시군명업종명업체명대표자명주선업유형허가일자우편번호주사무소 주소주사무소 상세주소전화번호비고
25서울특별시 동작구화물주선업썬퀵종합물류강*명일반/이사2003-05-286947서울특별시 동작구 여의대방로36길 98(대방동)<NA><NA>
26서울특별시 동작구화물주선업엘유케이종합물류박*진일반2000-04-127006서울특별시 동작구 사당동152-2 4층 401호<NA><NA>
27서울특별시 동작구화물주선업(주)삼라종합로지스틱스김*송일반2013-01-037071서울특별시 동작구 보라매로5가길 166층 602호 (신대방동，보라매아카데미)<NA><NA>
28서울특별시 동작구화물주선업스마트이사신*봉이사2013-08-287029서울특별시 동작구 사당로2자길 2-15(사당동)<NA><NA>
29서울특별시 동작구화물주선업충남이사김*호이사1992-12-227015서울특별시 동작구 사당로28가길 21층 (사당동)<NA><NA>
30서울특별시 동작구화물주선업(주)농심박*일반2014-05-157057서울특별시 동작구 여의대방로 112(신대방동)<NA><NA>
31서울특별시 동작구화물주선업통인익스프레스 청담점서*륜이사2002-04-247007서울특별시 동작구 사당로27길 56(사당동)<NA><NA>
32서울특별시 동작구화물주선업해와달화물정보(주)백*순일반2020-08-076990서울특별시 동작구 동작대로29길 115204호 (사당동, 사당우성상가3단지)02-533-4911<NA>
33서울특별시 동작구화물주선업전국화물배*준일반2003-10-047030서울특별시 동작구 사당로 164305호 (사당동)<NA><NA>
34서울특별시 동작구화물주선업한성물류남*우일반1993-01-117005서울특별시 동작구 사당로 253-3302호 (사당동)<NA><NA>