Overview

Dataset statistics

Number of variables6
Number of observations196
Missing cells8
Missing cells (%)0.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.5 KiB
Average record size in memory49.7 B

Variable types

Numeric1
Text3
Categorical2

Dataset

Description인천광역시 중구 관내 전문건설에 관한 정보입니다.파일명 인천광역시 중구_전문건설업현황내용 업체명, 영업소재지, 전화번호 등
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15038639&srcSe=7661IVAWM27C61E190

Alerts

데이터기준일자 has constant value ""Constant
전화번호 has 8 (4.1%) missing valuesMissing
순번 has unique valuesUnique

Reproduction

Analysis started2024-01-28 14:51:47.688466
Analysis finished2024-01-28 14:51:48.216124
Duration0.53 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

순번
Real number (ℝ)

UNIQUE 

Distinct196
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean98.5
Minimum1
Maximum196
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2024-01-28T23:51:48.276759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.75
Q149.75
median98.5
Q3147.25
95-th percentile186.25
Maximum196
Range195
Interquartile range (IQR)97.5

Descriptive statistics

Standard deviation56.72448
Coefficient of variation (CV)0.57588305
Kurtosis-1.2
Mean98.5
Median Absolute Deviation (MAD)49
Skewness0
Sum19306
Variance3217.6667
MonotonicityStrictly increasing
2024-01-28T23:51:48.398460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
125 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
129 1
 
0.5%
130 1
 
0.5%
131 1
 
0.5%
132 1
 
0.5%
133 1
 
0.5%
134 1
 
0.5%
Other values (186) 186
94.9%
ValueCountFrequency (%)
1 1
0.5%
2 1
0.5%
3 1
0.5%
4 1
0.5%
5 1
0.5%
6 1
0.5%
7 1
0.5%
8 1
0.5%
9 1
0.5%
10 1
0.5%
ValueCountFrequency (%)
196 1
0.5%
195 1
0.5%
194 1
0.5%
193 1
0.5%
192 1
0.5%
191 1
0.5%
190 1
0.5%
189 1
0.5%
188 1
0.5%
187 1
0.5%

업체명
Text

Distinct124
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T23:51:48.632127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length7.25
Min length3

Characters and Unicode

Total characters1421
Distinct characters175
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique81 ?
Unique (%)41.3%

Sample

1st row(유)이룸건설컨트럭션
2nd row(유)이룸건설컨트럭션
3rd row(주)99철거
4th row(주)가람종합건설
5th row(주)가람종합건설
ValueCountFrequency (%)
대한수중공사 7
 
3.6%
대주이엔티(주 6
 
3.1%
대주중공업(주 6
 
3.1%
인성개발(주 5
 
2.6%
우리건설(주 4
 
2.0%
경신플랜트(주 4
 
2.0%
주)대경건설 4
 
2.0%
주)지피건설 3
 
1.5%
주)우인이앤디 3
 
1.5%
주)창경 3
 
1.5%
Other values (114) 151
77.0%
2024-01-28T23:51:48.938721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 160
 
11.3%
( 132
 
9.3%
) 132
 
9.3%
설 57
 
4.0%
건 43
 
3.0%
대 35
 
2.5%
이 34
 
2.4%
경 31
 
2.2%
사 27
 
1.9%
공 25
 
1.8%
Other values (165) 745
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1141
80.3%
Open Punctuation 132
 
9.3%
Close Punctuation 132
 
9.3%
Uppercase Letter 12
 
0.8%
Decimal Number 2
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 160
 
14.0%
설 57
 
5.0%
건 43
 
3.8%
대 35
 
3.1%
이 34
 
3.0%
경 31
 
2.7%
사 27
 
2.4%
공 25
 
2.2%
발 23
 
2.0%
개 23
 
2.0%
Other values (156) 683
59.9%
Uppercase Letter
ValueCountFrequency (%)
G 4
33.3%
N 3
25.0%
E 3
25.0%
P 1
 
8.3%
L 1
 
8.3%
Open Punctuation
ValueCountFrequency (%)
( 132
100.0%
Close Punctuation
ValueCountFrequency (%)
) 132
100.0%
Decimal Number
ValueCountFrequency (%)
9 2
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1141
80.3%
Common 268
 
18.9%
Latin 12
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 160
 
14.0%
설 57
 
5.0%
건 43
 
3.8%
대 35
 
3.1%
이 34
 
3.0%
경 31
 
2.7%
사 27
 
2.4%
공 25
 
2.2%
발 23
 
2.0%
개 23
 
2.0%
Other values (156) 683
59.9%
Latin
ValueCountFrequency (%)
G 4
33.3%
N 3
25.0%
E 3
25.0%
P 1
 
8.3%
L 1
 
8.3%
Common
ValueCountFrequency (%)
( 132
49.3%
) 132
49.3%
9 2
 
0.7%
/ 2
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1141
80.3%
ASCII 280
 
19.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 160
 
14.0%
설 57
 
5.0%
건 43
 
3.8%
대 35
 
3.1%
이 34
 
3.0%
경 31
 
2.7%
사 27
 
2.4%
공 25
 
2.2%
발 23
 
2.0%
개 23
 
2.0%
Other values (156) 683
59.9%
ASCII
ValueCountFrequency (%)
( 132
47.1%
) 132
47.1%
G 4
 
1.4%
N 3
 
1.1%
E 3
 
1.1%
9 2
 
0.7%
/ 2
 
0.7%
P 1
 
0.4%
L 1
 
0.4%

업종
Categorical

Distinct14
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
가스난방공사업
33 
지반조성ㆍ포장공사업
26 
상ㆍ하수도설비공사업
20 
조경식재ㆍ시설물공사업
18 
금속ㆍ창호ㆍ지붕ㆍ건축물조립공사업
17 
Other values (9)
82 

Length

Max length17
Median length13
Mean length9.994898
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row구조물해체ㆍ비계공사업
2nd row철근ㆍ콘크리트공사업
3rd row구조물해체ㆍ비계공사업
4th row지반조성ㆍ포장공사업
5th row상ㆍ하수도설비공사업

Common Values

ValueCountFrequency (%)
가스난방공사업 33
16.8%
지반조성ㆍ포장공사업 26
13.3%
상ㆍ하수도설비공사업 20
10.2%
조경식재ㆍ시설물공사업 18
9.2%
금속ㆍ창호ㆍ지붕ㆍ건축물조립공사업 17
8.7%
구조물해체ㆍ비계공사업 15
7.7%
실내건축공사업 14
7.1%
도장ㆍ습식ㆍ방수ㆍ석공사업 13
 
6.6%
기계가스설비공사업 11
 
5.6%
수중ㆍ준설공사업 9
 
4.6%
Other values (4) 20
10.2%

Length

2024-01-28T23:51:49.058198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가스난방공사업 33
16.8%
지반조성ㆍ포장공사업 26
13.3%
상ㆍ하수도설비공사업 20
10.2%
조경식재ㆍ시설물공사업 18
9.2%
금속ㆍ창호ㆍ지붕ㆍ건축물조립공사업 17
8.7%
구조물해체ㆍ비계공사업 15
7.7%
실내건축공사업 14
7.1%
도장ㆍ습식ㆍ방수ㆍ석공사업 13
 
6.6%
기계가스설비공사업 11
 
5.6%
수중ㆍ준설공사업 9
 
4.6%
Other values (4) 20
10.2%

영업소재지(도로명주소)
Text

Distinct123
Distinct (%)62.8%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2024-01-28T23:51:49.258361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length53
Median length36.5
Mean length26.30102
Min length15

Characters and Unicode

Total characters5155
Distinct characters130
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique79 ?
Unique (%)40.3%

Sample

1st row인천광역시 중구 하늘달빛로 94 608호 (중산동)
2nd row인천광역시 중구 하늘달빛로 94 608호 (중산동)
3rd row인천광역시 중구 운중로 137-83(중산동)
4th row인천광역시 중구 축항대로86번길 22
5th row인천광역시 중구 축항대로86번길 22
ValueCountFrequency (%)
중구 196
 
18.4%
인천광역시 189
 
17.7%
2층 26
 
2.4%
제물량로 19
 
1.8%
1층 17
 
1.6%
4 15
 
1.4%
항동7가 15
 
1.4%
사동 14
 
1.3%
중산동 13
 
1.2%
도원동 13
 
1.2%
Other values (229) 548
51.5%
2024-01-28T23:51:49.608406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
869
 
16.9%
중 221
 
4.3%
인 207
 
4.0%
구 198
 
3.8%
천 197
 
3.8%
1 196
 
3.8%
광 192
 
3.7%
로 192
 
3.7%
시 191
 
3.7%
역 189
 
3.7%
Other values (120) 2503
48.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2928
56.8%
Decimal Number 966
 
18.7%
Space Separator 869
 
16.9%
Close Punctuation 138
 
2.7%
Open Punctuation 138
 
2.7%
Dash Punctuation 55
 
1.1%
Other Punctuation 54
 
1.0%
Uppercase Letter 5
 
0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 221
 
7.5%
인 207
 
7.1%
구 198
 
6.8%
천 197
 
6.7%
광 192
 
6.6%
로 192
 
6.6%
시 191
 
6.5%
역 189
 
6.5%
동 144
 
4.9%
길 91
 
3.1%
Other values (101) 1106
37.8%
Decimal Number
ValueCountFrequency (%)
1 196
20.3%
2 183
18.9%
3 110
11.4%
4 103
10.7%
0 87
9.0%
6 71
 
7.3%
5 63
 
6.5%
7 58
 
6.0%
8 55
 
5.7%
9 40
 
4.1%
Other Punctuation
ValueCountFrequency (%)
, 45
83.3%
， 9
 
16.7%
Uppercase Letter
ValueCountFrequency (%)
A 3
60.0%
B 2
40.0%
Space Separator
ValueCountFrequency (%)
869
100.0%
Close Punctuation
ValueCountFrequency (%)
) 138
100.0%
Open Punctuation
ValueCountFrequency (%)
( 138
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 55
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2928
56.8%
Common 2220
43.1%
Latin 7
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 221
 
7.5%
인 207
 
7.1%
구 198
 
6.8%
천 197
 
6.7%
광 192
 
6.6%
로 192
 
6.6%
시 191
 
6.5%
역 189
 
6.5%
동 144
 
4.9%
길 91
 
3.1%
Other values (101) 1106
37.8%
Common
ValueCountFrequency (%)
869
39.1%
1 196
 
8.8%
2 183
 
8.2%
) 138
 
6.2%
( 138
 
6.2%
3 110
 
5.0%
4 103
 
4.6%
0 87
 
3.9%
6 71
 
3.2%
5 63
 
2.8%
Other values (6) 262
 
11.8%
Latin
ValueCountFrequency (%)
A 3
42.9%
B 2
28.6%
b 2
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2928
56.8%
ASCII 2218
43.0%
None 9
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
869
39.2%
1 196
 
8.8%
2 183
 
8.3%
) 138
 
6.2%
( 138
 
6.2%
3 110
 
5.0%
4 103
 
4.6%
0 87
 
3.9%
6 71
 
3.2%
5 63
 
2.8%
Other values (8) 260
 
11.7%
Hangul
ValueCountFrequency (%)
중 221
 
7.5%
인 207
 
7.1%
구 198
 
6.8%
천 197
 
6.7%
광 192
 
6.6%
로 192
 
6.6%
시 191
 
6.5%
역 189
 
6.5%
동 144
 
4.9%
길 91
 
3.1%
Other values (101) 1106
37.8%
None
ValueCountFrequency (%)
， 9
100.0%

전화번호
Text

MISSING 

Distinct115
Distinct (%)61.2%
Missing8
Missing (%)4.1%
Memory size1.7 KiB
2024-01-28T23:51:49.818435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.101064
Min length9

Characters and Unicode

Total characters2275
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

Unique73 ?
Unique (%)38.8%

Sample

1st row031-411-3108
2nd row031-411-3108
3rd row032-751-4444
4th row032-715-6100
5th row032-715-6100
ValueCountFrequency (%)
032-888-5311 7
 
3.7%
070-7015-1335 6
 
3.2%
070-7015-1312 6
 
3.2%
032-886-0701 5
 
2.7%
032-763-7779 4
 
2.1%
032-888-0389 4
 
2.1%
070-5035-0392 4
 
2.1%
032-588-0803 4
 
2.1%
032-764-6566 3
 
1.6%
032-765-4589 3
 
1.6%
Other values (105) 142
75.5%
2024-01-28T23:51:50.141517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 375
16.5%
0 333
14.6%
3 292
12.8%
2 257
11.3%
8 230
10.1%
7 197
8.7%
1 154
6.8%
5 131
 
5.8%
6 120
 
5.3%
4 115
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1900
83.5%
Dash Punctuation 375
 
16.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 333
17.5%
3 292
15.4%
2 257
13.5%
8 230
12.1%
7 197
10.4%
1 154
8.1%
5 131
 
6.9%
6 120
 
6.3%
4 115
 
6.1%
9 71
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 375
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2275
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 375
16.5%
0 333
14.6%
3 292
12.8%
2 257
11.3%
8 230
10.1%
7 197
8.7%
1 154
6.8%
5 131
 
5.8%
6 120
 
5.3%
4 115
 
5.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2275
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 375
16.5%
0 333
14.6%
3 292
12.8%
2 257
11.3%
8 230
10.1%
7 197
8.7%
1 154
6.8%
5 131
 
5.8%
6 120
 
5.3%
4 115
 
5.1%

데이터기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-07-31
196 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-07-31
2nd row2023-07-31
3rd row2023-07-31
4th row2023-07-31
5th row2023-07-31

Common Values

ValueCountFrequency (%)
2023-07-31 196
100.0%

Length

2024-01-28T23:51:50.263896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T23:51:50.343977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-07-31 196
100.0%

Interactions

2024-01-28T23:51:47.926472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T23:51:50.392751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종
순번1.0000.162
업종0.1621.000
2024-01-28T23:51:50.463600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순번업종
순번1.0000.088
업종0.0881.000

Missing values

2024-01-28T23:51:48.042628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T23:51:48.168303image/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

순번업체명업종영업소재지(도로명주소)전화번호데이터기준일자
01(유)이룸건설컨트럭션구조물해체ㆍ비계공사업인천광역시 중구 하늘달빛로 94 608호 (중산동)031-411-31082023-07-31
12(유)이룸건설컨트럭션철근ㆍ콘크리트공사업인천광역시 중구 하늘달빛로 94 608호 (중산동)031-411-31082023-07-31
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