Overview

Dataset statistics

Number of variables6
Number of observations48
Missing cells96
Missing cells (%)33.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory53.8 B

Variable types

Categorical1
Text2
Numeric1
Unsupported2

Dataset

Description부산광역시_사상구_여행업등록현황_20230412
Author부산광역시 사상구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15025640

Alerts

Unnamed: 4 has 48 (100.0%) missing valuesMissing
Unnamed: 5 has 48 (100.0%) missing valuesMissing
Unnamed: 4 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-10 16:29:58.532651
Analysis finished2023-12-10 16:29:59.366243
Duration0.83 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

업종
Categorical

Distinct3
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size516.0 B
국내외여행업
22 
종합여행업
15 
국내여행업
11 

Length

Max length6
Median length5
Mean length5.4583333
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국내외여행업 22
45.8%
종합여행업 15
31.2%
국내여행업 11
22.9%

Length

2023-12-11T01:29:59.473058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T01:29:59.628307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국내외여행업 22
45.8%
종합여행업 15
31.2%
국내여행업 11
22.9%

상호
Text

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-11T01:29:59.900636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length20
Median length14
Mean length8.9583333
Min length4

Characters and Unicode

Total characters430
Distinct characters137
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)75.0%

Sample

1st row(주)모던고속관광
2nd row(주)경방레져투어
3rd row(주)명품고속관광
4th row제주무지개투어
5th row(주)트립플러스
ValueCountFrequency (%)
주식회사 13
 
19.1%
tour 3
 
4.4%
주)트립플러스 2
 
2.9%
에코웰 2
 
2.9%
트라벨라 2
 
2.9%
지구촌투어인터네셔날 2
 
2.9%
낙동강테마관광 2
 
2.9%
가자투어 2
 
2.9%
e-이편한여행 1
 
1.5%
주)모던고속관광 1
 
1.5%
Other values (38) 38
55.9%
2023-12-11T01:30:00.399806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 29
 
6.7%
어 22
 
5.1%
투 21
 
4.9%
20
 
4.7%
( 19
 
4.4%
) 19
 
4.4%
사 16
 
3.7%
회 13
 
3.0%
식 13
 
3.0%
T 7
 
1.6%
Other values (127) 251
58.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 317
73.7%
Uppercase Letter 34
 
7.9%
Space Separator 20
 
4.7%
Open Punctuation 19
 
4.4%
Close Punctuation 19
 
4.4%
Lowercase Letter 15
 
3.5%
Decimal Number 3
 
0.7%
Dash Punctuation 2
 
0.5%
Other Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 29
 
9.1%
어 22
 
6.9%
투 21
 
6.6%
사 16
 
5.0%
회 13
 
4.1%
식 13
 
4.1%
스 7
 
2.2%
이 6
 
1.9%
관 6
 
1.9%
광 6
 
1.9%
Other values (97) 178
56.2%
Uppercase Letter
ValueCountFrequency (%)
T 7
20.6%
R 6
17.6%
E 3
8.8%
O 3
8.8%
N 2
 
5.9%
H 2
 
5.9%
I 2
 
5.9%
A 2
 
5.9%
K 1
 
2.9%
V 1
 
2.9%
Other values (5) 5
14.7%
Lowercase Letter
ValueCountFrequency (%)
o 4
26.7%
r 3
20.0%
u 2
13.3%
e 2
13.3%
a 2
13.3%
n 1
 
6.7%
g 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
0 1
33.3%
5 1
33.3%
1 1
33.3%
Space Separator
ValueCountFrequency (%)
20
100.0%
Open Punctuation
ValueCountFrequency (%)
( 19
100.0%
Close Punctuation
ValueCountFrequency (%)
) 19
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
& 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 317
73.7%
Common 64
 
14.9%
Latin 49
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 29
 
9.1%
어 22
 
6.9%
투 21
 
6.6%
사 16
 
5.0%
회 13
 
4.1%
식 13
 
4.1%
스 7
 
2.2%
이 6
 
1.9%
관 6
 
1.9%
광 6
 
1.9%
Other values (97) 178
56.2%
Latin
ValueCountFrequency (%)
T 7
14.3%
R 6
 
12.2%
o 4
 
8.2%
E 3
 
6.1%
O 3
 
6.1%
r 3
 
6.1%
N 2
 
4.1%
u 2
 
4.1%
H 2
 
4.1%
I 2
 
4.1%
Other values (12) 15
30.6%
Common
ValueCountFrequency (%)
20
31.2%
( 19
29.7%
) 19
29.7%
- 2
 
3.1%
0 1
 
1.6%
& 1
 
1.6%
5 1
 
1.6%
1 1
 
1.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 317
73.7%
ASCII 113
 
26.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 29
 
9.1%
어 22
 
6.9%
투 21
 
6.6%
사 16
 
5.0%
회 13
 
4.1%
식 13
 
4.1%
스 7
 
2.2%
이 6
 
1.9%
관 6
 
1.9%
광 6
 
1.9%
Other values (97) 178
56.2%
ASCII
ValueCountFrequency (%)
20
17.7%
( 19
16.8%
) 19
16.8%
T 7
 
6.2%
R 6
 
5.3%
o 4
 
3.5%
E 3
 
2.7%
O 3
 
2.7%
r 3
 
2.7%
N 2
 
1.8%
Other values (20) 27
23.9%

우편번호
Real number (ℝ)

Distinct31
Distinct (%)64.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean46964
Minimum46901
Maximum47049
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size564.0 B
2023-12-11T01:30:00.567589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum46901
5-th percentile46905.2
Q146938
median46971.5
Q346986
95-th percentile47016.65
Maximum47049
Range148
Interquartile range (IQR)48

Descriptive statistics

Standard deviation35.619144
Coefficient of variation (CV)0.00075843505
Kurtosis-0.43392472
Mean46964
Median Absolute Deviation (MAD)24.5
Skewness-0.0058011149
Sum2254272
Variance1268.7234
MonotonicityNot monotonic
2023-12-11T01:30:00.725390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
46972 6
 
12.5%
46918 4
 
8.3%
46901 3
 
6.2%
46956 2
 
4.2%
46973 2
 
4.2%
46929 2
 
4.2%
46913 2
 
4.2%
46986 2
 
4.2%
46964 2
 
4.2%
46988 2
 
4.2%
Other values (21) 21
43.8%
ValueCountFrequency (%)
46901 3
6.2%
46913 2
4.2%
46918 4
8.3%
46926 1
 
2.1%
46929 2
4.2%
46941 1
 
2.1%
46943 1
 
2.1%
46945 1
 
2.1%
46948 1
 
2.1%
46956 2
4.2%
ValueCountFrequency (%)
47049 1
2.1%
47029 1
2.1%
47017 1
2.1%
47016 1
2.1%
47009 1
2.1%
47007 1
2.1%
47004 1
2.1%
47001 1
2.1%
46997 1
2.1%
46988 2
4.2%

소재지(도로명)
Text

Distinct42
Distinct (%)87.5%
Missing0
Missing (%)0.0%
Memory size516.0 B
2023-12-11T01:30:01.077305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length48
Median length39
Mean length32.666667
Min length22

Characters and Unicode

Total characters1568
Distinct characters114
Distinct categories7 ?
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 (%)75.0%

Sample

1st row부산광역시 사상구 광장로 34 (괘법동, 삼보빌딩 203호)
2nd row부산광역시 사상구 백양대로 955-16 (모라동)
3rd row부산광역시 사상구 새벽로 218, 2층 203호 (괘법동)
4th row부산광역시 사상구 사상로319번길 48 (덕포동, 선진금속)
5th row부산광역시 사상구 백양대로 943-28, 2층 (모라동)
ValueCountFrequency (%)
부산광역시 48
 
15.5%
사상구 48
 
15.5%
괘법동 15
 
4.9%
광장로 9
 
2.9%
주례동 7
 
2.3%
1층 7
 
2.3%
감전동 7
 
2.3%
모라동 7
 
2.3%
2층 7
 
2.3%
덕포동 6
 
1.9%
Other values (102) 148
47.9%
2023-12-11T01:30:01.585689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
261
 
16.6%
광 62
 
4.0%
상 58
 
3.7%
동 58
 
3.7%
산 57
 
3.6%
부 55
 
3.5%
사 52
 
3.3%
시 50
 
3.2%
역 48
 
3.1%
구 48
 
3.1%
Other values (104) 819
52.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 920
58.7%
Space Separator 261
 
16.6%
Decimal Number 242
 
15.4%
Close Punctuation 48
 
3.1%
Open Punctuation 48
 
3.1%
Other Punctuation 44
 
2.8%
Dash Punctuation 5
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
광 62
 
6.7%
상 58
 
6.3%
동 58
 
6.3%
산 57
 
6.2%
부 55
 
6.0%
사 52
 
5.7%
시 50
 
5.4%
역 48
 
5.2%
구 48
 
5.2%
로 48
 
5.2%
Other values (89) 384
41.7%
Decimal Number
ValueCountFrequency (%)
1 42
17.4%
3 39
16.1%
2 38
15.7%
0 27
11.2%
6 23
9.5%
5 19
7.9%
4 15
 
6.2%
9 14
 
5.8%
8 13
 
5.4%
7 12
 
5.0%
Space Separator
ValueCountFrequency (%)
261
100.0%
Close Punctuation
ValueCountFrequency (%)
) 48
100.0%
Open Punctuation
ValueCountFrequency (%)
( 48
100.0%
Other Punctuation
ValueCountFrequency (%)
, 44
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 920
58.7%
Common 648
41.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
광 62
 
6.7%
상 58
 
6.3%
동 58
 
6.3%
산 57
 
6.2%
부 55
 
6.0%
사 52
 
5.7%
시 50
 
5.4%
역 48
 
5.2%
구 48
 
5.2%
로 48
 
5.2%
Other values (89) 384
41.7%
Common
ValueCountFrequency (%)
261
40.3%
) 48
 
7.4%
( 48
 
7.4%
, 44
 
6.8%
1 42
 
6.5%
3 39
 
6.0%
2 38
 
5.9%
0 27
 
4.2%
6 23
 
3.5%
5 19
 
2.9%
Other values (5) 59
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 920
58.7%
ASCII 648
41.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
261
40.3%
) 48
 
7.4%
( 48
 
7.4%
, 44
 
6.8%
1 42
 
6.5%
3 39
 
6.0%
2 38
 
5.9%
0 27
 
4.2%
6 23
 
3.5%
5 19
 
2.9%
Other values (5) 59
 
9.1%
Hangul
ValueCountFrequency (%)
광 62
 
6.7%
상 58
 
6.3%
동 58
 
6.3%
산 57
 
6.2%
부 55
 
6.0%
사 52
 
5.7%
시 50
 
5.4%
역 48
 
5.2%
구 48
 
5.2%
로 48
 
5.2%
Other values (89) 384
41.7%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing48
Missing (%)100.0%
Memory size564.0 B

Interactions

2023-12-11T01:29:58.938313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:30:01.707464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
업종상호우편번호소재지(도로명)
업종1.0000.0000.5180.000
상호0.0001.0001.0001.000
우편번호0.5181.0001.0001.000
소재지(도로명)0.0001.0001.0001.000
2023-12-11T01:30:01.832105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호업종
우편번호1.0000.246
업종0.2461.000

Missing values

2023-12-11T01:29:59.131338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:29:59.298374image/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

업종상호우편번호소재지(도로명)Unnamed: 4Unnamed: 5
0국내여행업(주)모던고속관광46971부산광역시 사상구 광장로 34 (괘법동, 삼보빌딩 203호)<NA><NA>
1국내여행업(주)경방레져투어46926부산광역시 사상구 백양대로 955-16 (모라동)<NA><NA>
2국내여행업(주)명품고속관광46973부산광역시 사상구 새벽로 218, 2층 203호 (괘법동)<NA><NA>
3국내여행업제주무지개투어46948부산광역시 사상구 사상로319번길 48 (덕포동, 선진금속)<NA><NA>
4국내여행업(주)트립플러스46929부산광역시 사상구 백양대로 943-28, 2층 (모라동)<NA><NA>
5국내여행업낙동강테마관광 주식회사46901부산광역시 사상구 낙동대로1530번길 14, 1층 (삼락동)<NA><NA>
6국내여행업가자투어46972부산광역시 사상구 광장로 62, 5층 599호 (괘법동)<NA><NA>
7국내여행업주식회사 에코웰46918부산광역시 사상구 모라로 22, 부산벤처타워 1301호 (모라동)<NA><NA>
8국내여행업주식회사 블루맥파이47007부산광역시 사상구 가야대로 350, 3층 (주례동, 노다지편의점)<NA><NA>
9국내여행업트라벨라 주식회사46918부산광역시 사상구 모라로 22, 부산벤처타워 1306호 (모라동)<NA><NA>
업종상호우편번호소재지(도로명)Unnamed: 4Unnamed: 5
38종합여행업(주)클럽투어46975부산광역시 사상구 광장로86번길 27, 1층 (괘법동)<NA><NA>
39종합여행업(주)허브플랫폼47001부산광역시 사상구 학감대로222번길 83 (주례동)<NA><NA>
40종합여행업케이비스투어 주식회사46964부산광역시 사상구 광장로81번길 55, 소르젠떼호텔 9층 (괘법동)<NA><NA>
41종합여행업리트레버(RETRAVER)46943부산광역시 사상구 사상로 403, 2층 (덕포동)<NA><NA>
42종합여행업모닝투어(MORNING TOUR)46913부산광역시 사상구 사상로309번길 61, 동아조각방전 (삼락동)<NA><NA>
43종합여행업주식회사 세폰그룹46972부산광역시 사상구 광장로 62, 제이엠빌딩 506호 (괘법동)<NA><NA>
44종합여행업해피투어46913부산광역시 사상구 삼덕로46번길 26 (삼락동)<NA><NA>
45종합여행업한-베투어46968부산광역시 사상구 광장로81번길 30, 2층 (괘법동)<NA><NA>
46종합여행업e-이편한여행46972부산광역시 사상구 광장로 62, 제이엠빌딩 509호 (괘법동)<NA><NA>
47종합여행업Korea Tango Tour46988부산광역시 사상구 새벽로 131, 부산산업용재유통상가 6동 313호 (감전동)<NA><NA>