Overview

Dataset statistics

Number of variables6
Number of observations36
Missing cells12
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.9 KiB
Average record size in memory54.7 B

Variable types

Numeric3
Text3

Dataset

Description인천광역시 미추홀구 용달업체에 관한 데이터이며 상호명, 도로명주소, 전화번호, 죄표값 등의 항목을 제공하고 있습니다.
Author인천광역시 미추홀구
URLhttps://www.data.go.kr/data/15087018/fileData.do

Alerts

전화번호 has 12 (33.3%) missing valuesMissing
연번 has unique valuesUnique
상호명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 02:20:02.547977
Analysis finished2023-12-12 02:20:03.999554
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18.5
Minimum1
Maximum36
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:20:04.073472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.75
Q19.75
median18.5
Q327.25
95-th percentile34.25
Maximum36
Range35
Interquartile range (IQR)17.5

Descriptive statistics

Standard deviation10.535654
Coefficient of variation (CV)0.5694948
Kurtosis-1.2
Mean18.5
Median Absolute Deviation (MAD)9
Skewness0
Sum666
Variance111
MonotonicityStrictly increasing
2023-12-12T11:20:04.218731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
1 1
 
2.8%
20 1
 
2.8%
22 1
 
2.8%
23 1
 
2.8%
24 1
 
2.8%
25 1
 
2.8%
26 1
 
2.8%
27 1
 
2.8%
28 1
 
2.8%
29 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
1 1
2.8%
2 1
2.8%
3 1
2.8%
4 1
2.8%
5 1
2.8%
6 1
2.8%
7 1
2.8%
8 1
2.8%
9 1
2.8%
10 1
2.8%
ValueCountFrequency (%)
36 1
2.8%
35 1
2.8%
34 1
2.8%
33 1
2.8%
32 1
2.8%
31 1
2.8%
30 1
2.8%
29 1
2.8%
28 1
2.8%
27 1
2.8%

상호명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T11:20:04.451173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length5.9166667
Min length2

Characters and Unicode

Total characters213
Distinct characters85
Distinct categories4 ?
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 (%)100.0%

Sample

1st row좋은아저씨용달
2nd row친절용달화물
3rd row화물용달
4th row인천용달이사
5th row인천용달
ValueCountFrequency (%)
좋은아저씨용달 1
 
2.7%
전국팔도화물배송서비스 1
 
2.7%
영호통운 1
 
2.7%
신명인테크 1
 
2.7%
현대용달yes 1
 
2.7%
정식용달화물 1
 
2.7%
이지패스트 1
 
2.7%
중앙탑차용달 1
 
2.7%
땡큐화물 1
 
2.7%
천순용달화물 1
 
2.7%
Other values (27) 27
73.0%
2023-12-12T11:20:04.839285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
용 28
 
13.1%
달 28
 
13.1%
물 19
 
8.9%
화 19
 
8.9%
인 10
 
4.7%
천 7
 
3.3%
스 4
 
1.9%
이 4
 
1.9%
영 3
 
1.4%
전 2
 
0.9%
Other values (75) 89
41.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 207
97.2%
Lowercase Letter 3
 
1.4%
Decimal Number 2
 
0.9%
Space Separator 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
용 28
 
13.5%
달 28
 
13.5%
물 19
 
9.2%
화 19
 
9.2%
인 10
 
4.8%
천 7
 
3.4%
스 4
 
1.9%
이 4
 
1.9%
영 3
 
1.4%
전 2
 
1.0%
Other values (69) 83
40.1%
Lowercase Letter
ValueCountFrequency (%)
y 1
33.3%
e 1
33.3%
s 1
33.3%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 207
97.2%
Latin 3
 
1.4%
Common 3
 
1.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
용 28
 
13.5%
달 28
 
13.5%
물 19
 
9.2%
화 19
 
9.2%
인 10
 
4.8%
천 7
 
3.4%
스 4
 
1.9%
이 4
 
1.9%
영 3
 
1.4%
전 2
 
1.0%
Other values (69) 83
40.1%
Latin
ValueCountFrequency (%)
y 1
33.3%
e 1
33.3%
s 1
33.3%
Common
ValueCountFrequency (%)
2 1
33.3%
4 1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 207
97.2%
ASCII 6
 
2.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
용 28
 
13.5%
달 28
 
13.5%
물 19
 
9.2%
화 19
 
9.2%
인 10
 
4.8%
천 7
 
3.4%
스 4
 
1.9%
이 4
 
1.9%
영 3
 
1.4%
전 2
 
1.0%
Other values (69) 83
40.1%
ASCII
ValueCountFrequency (%)
y 1
16.7%
e 1
16.7%
s 1
16.7%
2 1
16.7%
4 1
16.7%
1
16.7%

도로명주소
Text

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T11:20:05.152030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length28
Mean length22.527778
Min length16

Characters and Unicode

Total characters811
Distinct characters80
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

Unique32 ?
Unique (%)88.9%

Sample

1st row인천광역시 미추홀구 매소홀로561번길 10-18
2nd row인천광역시 미추홀구 한나루로 562
3rd row인천광역시 미추홀구 승학길104번길 30 한신휴플러스
4th row인천광역시 미추홀구 낙섬중로 78-15
5th row인천광역시 미추홀구 경인로325번길 16
ValueCountFrequency (%)
인천광역시 36
23.1%
미추홀구 36
23.1%
16 3
 
1.9%
승학길 2
 
1.3%
40 2
 
1.3%
401호 2
 
1.3%
30 2
 
1.3%
낙섬중로 2
 
1.3%
석정로 2
 
1.3%
석정로433번길 2
 
1.3%
Other values (64) 67
42.9%
2023-12-12T11:20:05.624422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
120
 
14.8%
인 41
 
5.1%
홀 38
 
4.7%
미 37
 
4.6%
천 37
 
4.6%
추 37
 
4.6%
구 36
 
4.4%
시 36
 
4.4%
역 36
 
4.4%
광 36
 
4.4%
Other values (70) 357
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 520
64.1%
Decimal Number 159
 
19.6%
Space Separator 120
 
14.8%
Dash Punctuation 12
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
인 41
 
7.9%
홀 38
 
7.3%
미 37
 
7.1%
천 37
 
7.1%
추 37
 
7.1%
구 36
 
6.9%
시 36
 
6.9%
역 36
 
6.9%
광 36
 
6.9%
로 29
 
5.6%
Other values (58) 157
30.2%
Decimal Number
ValueCountFrequency (%)
1 29
18.2%
4 25
15.7%
2 21
13.2%
0 18
11.3%
3 17
10.7%
6 16
10.1%
5 14
8.8%
8 8
 
5.0%
7 6
 
3.8%
9 5
 
3.1%
Space Separator
ValueCountFrequency (%)
120
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 520
64.1%
Common 291
35.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
인 41
 
7.9%
홀 38
 
7.3%
미 37
 
7.1%
천 37
 
7.1%
추 37
 
7.1%
구 36
 
6.9%
시 36
 
6.9%
역 36
 
6.9%
광 36
 
6.9%
로 29
 
5.6%
Other values (58) 157
30.2%
Common
ValueCountFrequency (%)
120
41.2%
1 29
 
10.0%
4 25
 
8.6%
2 21
 
7.2%
0 18
 
6.2%
3 17
 
5.8%
6 16
 
5.5%
5 14
 
4.8%
- 12
 
4.1%
8 8
 
2.7%
Other values (2) 11
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 520
64.1%
ASCII 291
35.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
120
41.2%
1 29
 
10.0%
4 25
 
8.6%
2 21
 
7.2%
0 18
 
6.2%
3 17
 
5.8%
6 16
 
5.5%
5 14
 
4.8%
- 12
 
4.1%
8 8
 
2.7%
Other values (2) 11
 
3.8%
Hangul
ValueCountFrequency (%)
인 41
 
7.9%
홀 38
 
7.3%
미 37
 
7.1%
천 37
 
7.1%
추 37
 
7.1%
구 36
 
6.9%
시 36
 
6.9%
역 36
 
6.9%
광 36
 
6.9%
로 29
 
5.6%
Other values (58) 157
30.2%

전화번호
Text

MISSING 

Distinct24
Distinct (%)100.0%
Missing12
Missing (%)33.3%
Memory size420.0 B
2023-12-12T11:20:05.873096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length12.666667
Min length9

Characters and Unicode

Total characters304
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

Unique24 ?
Unique (%)100.0%

Sample

1st row0507-1385-2479
2nd row1588-7924
3rd row0507-1402-2482
4th row0507-1344-1744
5th row070-8018-1822
ValueCountFrequency (%)
0507-1385-2479 1
 
4.2%
1588-7924 1
 
4.2%
032-265-6030 1
 
4.2%
032-861-2424 1
 
4.2%
032-264-5030 1
 
4.2%
070-7433-8808 1
 
4.2%
070-8225-4950 1
 
4.2%
070-4561-4679 1
 
4.2%
032-264-6030 1
 
4.2%
070-4587-3844 1
 
4.2%
Other values (14) 14
58.3%
2023-12-12T11:20:06.363748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 56
18.4%
- 46
15.1%
4 30
9.9%
8 28
9.2%
2 26
8.6%
7 25
8.2%
3 24
7.9%
5 21
 
6.9%
1 20
 
6.6%
6 15
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 258
84.9%
Dash Punctuation 46
 
15.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56
21.7%
4 30
11.6%
8 28
10.9%
2 26
10.1%
7 25
9.7%
3 24
9.3%
5 21
 
8.1%
1 20
 
7.8%
6 15
 
5.8%
9 13
 
5.0%
Dash Punctuation
ValueCountFrequency (%)
- 46
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 304
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56
18.4%
- 46
15.1%
4 30
9.9%
8 28
9.2%
2 26
8.6%
7 25
8.2%
3 24
7.9%
5 21
 
6.9%
1 20
 
6.6%
6 15
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 304
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56
18.4%
- 46
15.1%
4 30
9.9%
8 28
9.2%
2 26
8.6%
7 25
8.2%
3 24
7.9%
5 21
 
6.9%
1 20
 
6.6%
6 15
 
4.9%

위도
Real number (ℝ)

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.45561
Minimum37.438385
Maximum37.468725
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:20:06.562523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.438385
5-th percentile37.440248
Q137.448666
median37.458774
Q337.462144
95-th percentile37.468413
Maximum37.468725
Range0.03033999
Interquartile range (IQR)0.013478155

Descriptive statistics

Standard deviation0.0095050914
Coefficient of variation (CV)0.0002537695
Kurtosis-1.0878664
Mean37.45561
Median Absolute Deviation (MAD)0.006432795
Skewness-0.40259594
Sum1348.4019
Variance9.0346762 × 10-5
MonotonicityNot monotonic
2023-12-12T11:20:06.750108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
37.45952759 2
 
5.6%
37.46122978 2
 
5.6%
37.43890937 1
 
2.8%
37.44903448 1
 
2.8%
37.45832304 1
 
2.8%
37.44932931 1
 
2.8%
37.43838496 1
 
2.8%
37.46853031 1
 
2.8%
37.46178163 1
 
2.8%
37.46170958 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
37.43838496 1
2.8%
37.43890937 1
2.8%
37.44069363 1
2.8%
37.44131738 1
2.8%
37.44170089 1
2.8%
37.44276115 1
2.8%
37.44366578 1
2.8%
37.4437066 1
2.8%
37.44756126 1
2.8%
37.44903448 1
2.8%
ValueCountFrequency (%)
37.46872495 1
2.8%
37.46853031 1
2.8%
37.46837436 1
2.8%
37.46746073 1
2.8%
37.46710903 1
2.8%
37.46682149 1
2.8%
37.46396882 1
2.8%
37.46341946 1
2.8%
37.46323243 1
2.8%
37.46178163 1
2.8%

경도
Real number (ℝ)

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.66945
Minimum126.63588
Maximum126.69354
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T11:20:06.935955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.63588
5-th percentile126.63955
Q1126.6606
median126.67623
Q3126.68108
95-th percentile126.68749
Maximum126.69354
Range0.0576633
Interquartile range (IQR)0.02048175

Descriptive statistics

Standard deviation0.016171455
Coefficient of variation (CV)0.00012766658
Kurtosis-0.64697463
Mean126.66945
Median Absolute Deviation (MAD)0.00873545
Skewness-0.66942749
Sum4560.1
Variance0.00026151595
MonotonicityNot monotonic
2023-12-12T11:20:07.114267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
126.6767911 2
 
5.6%
126.6610971 2
 
5.6%
126.6849522 1
 
2.8%
126.6358757 1
 
2.8%
126.6483923 1
 
2.8%
126.6809258 1
 
2.8%
126.680814 1
 
2.8%
126.6674813 1
 
2.8%
126.659117 1
 
2.8%
126.6779868 1
 
2.8%
Other values (24) 24
66.7%
ValueCountFrequency (%)
126.6358757 1
2.8%
126.6372818 1
2.8%
126.6403122 1
2.8%
126.6428888 1
2.8%
126.6449375 1
2.8%
126.6483923 1
2.8%
126.6521201 1
2.8%
126.654354 1
2.8%
126.659117 1
2.8%
126.6610971 2
5.6%
ValueCountFrequency (%)
126.693539 1
2.8%
126.6914194 1
2.8%
126.6861775 1
2.8%
126.6858385 1
2.8%
126.6849522 1
2.8%
126.684544 1
2.8%
126.6841424 1
2.8%
126.6821749 1
2.8%
126.6812456 1
2.8%
126.6810299 1
2.8%

Interactions

2023-12-12T11:20:03.487181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:02.836720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:03.195492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:03.607446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:02.937089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:03.283498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:03.709748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:03.088582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T11:20:03.375407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T11:20:07.227353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번상호명도로명주소전화번호위도경도
연번1.0001.0000.8291.0000.0000.297
상호명1.0001.0001.0001.0001.0001.000
도로명주소0.8291.0001.0001.0001.0001.000
전화번호1.0001.0001.0001.0001.0001.000
위도0.0001.0001.0001.0001.0000.536
경도0.2971.0001.0001.0000.5361.000
2023-12-12T11:20:07.363966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도
연번1.0000.359-0.140
위도0.3591.000-0.084
경도-0.140-0.0841.000

Missing values

2023-12-12T11:20:03.823167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T11:20:03.950258image/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

연번상호명도로명주소전화번호위도경도
01좋은아저씨용달인천광역시 미추홀구 매소홀로561번길 10-18<NA>37.438909126.684952
12친절용달화물인천광역시 미추홀구 한나루로 562<NA>37.456438126.670862
23화물용달인천광역시 미추홀구 승학길104번길 30 한신휴플러스<NA>37.443707126.681246
34인천용달이사인천광역시 미추홀구 낙섬중로 78-15<NA>37.454547126.640312
45인천용달인천광역시 미추홀구 경인로325번길 160507-1385-247937.459528126.676791
56인천종합용달화물서비스인천광역시 미추홀구 경인로471번길 30<NA>37.460134126.691419
67전국24시콜화물인천광역시 미추홀구 석정로282번길 39-3 도화프라자1588-792437.466821126.66762
78인천용달의달인인천광역시 미추홀구 주안동로 40 두산아파트2010507-1402-248237.461778126.686177
89인주용달인천광역시 미추홀구 경인로325번길 160507-1344-174437.459528126.676791
910인천용달이사변강쇠인천광역시 미추홀구 승학길 28<NA>37.440694126.679651
연번상호명도로명주소전화번호위도경도
2627영호통운인천광역시 미추홀구 수봉안길 16070-4587-384437.461782126.659117
2728천순용달화물인천광역시 미추홀구 낙섬중로 18032-264-603037.449034126.635876
2829승영인천광역시 미추홀구 주안서로 34070-4561-467937.46171126.677987
2930신한용달화물인천광역시 미추홀구 석정로433번길 43-24 가동 401호070-8225-495037.468725126.684142
3031용달화물인천광역시 미추홀구 독정이로45번길 25-8070-7433-880837.459226126.65212
3132대원화물인천광역시 미추홀구 승학길 93 3층032-264-503037.443666126.679677
3233치수용달화물인천광역시 미추홀구 석정로240번길 20-12<NA>37.467461126.661929
3334개별용달인천광역시 미추홀구 석정로433번길 41032-861-242437.468374126.684544
3435영택용달화물인천광역시 미추홀구 석정로 360032-265-603037.467109126.675674
3536인천용달화물인천광역시 미추홀구 수봉로67번길 45-11070-8749-892437.46123126.661097