Overview

Dataset statistics

Number of variables14
Number of observations187
Missing cells149
Missing cells (%)5.7%
Duplicate rows46
Duplicate rows (%)24.6%
Total size in memory21.1 KiB
Average record size in memory115.7 B

Variable types

Categorical7
Text4
Numeric3

Dataset

Description파주시 전주통신주에 대한 데이터로 파주시에 설치된 전주 및 통신주에 설치위치 정보(도로명주소, 위경도) 및 전주번호, 높이, 관리기관 정보 제공
Author경기도 파주시
URLhttps://www.data.go.kr/data/15066553/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
Dataset has 46 (24.6%) duplicate rowsDuplicates
관리기관명 is highly overall correlated with 높이 and 3 other fieldsHigh correlation
사업자 is highly overall correlated with 높이 and 3 other fieldsHigh correlation
지역 is highly overall correlated with 위도 and 4 other fieldsHigh correlation
종류 is highly overall correlated with 높이 and 3 other fieldsHigh correlation
높이 is highly overall correlated with 종류 and 2 other fieldsHigh correlation
위도 is highly overall correlated with 경도 and 1 other fieldsHigh correlation
경도 is highly overall correlated with 위도 and 2 other fieldsHigh correlation
데이터기준일자 is highly overall correlated with 경도High correlation
종류 is highly imbalanced (63.6%)Imbalance
사업자 is highly imbalanced (72.7%)Imbalance
관리기관명 is highly imbalanced (74.3%)Imbalance
도로명 has 114 (61.0%) missing valuesMissing
전주번호2 has 18 (9.6%) missing valuesMissing
높이 has 17 (9.1%) missing valuesMissing

Reproduction

Analysis started2023-12-12 10:52:51.378961
Analysis finished2023-12-12 10:52:55.460527
Duration4.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
경기도
187 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 187
100.0%

Length

2023-12-12T19:52:55.590125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:52:55.804920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 187
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
파주시
187 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
파주시 187
100.0%

Length

2023-12-12T19:52:55.991876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:52:56.161019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
파주시 187
100.0%

지번
Text

Distinct81
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T19:52:56.564979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.481283
Min length15

Characters and Unicode

Total characters3269
Distinct characters22
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

Unique37 ?
Unique (%)19.8%

Sample

1st row경기도 파주시 금릉동 210-6
2nd row경기도 파주시 금릉동 210-6
3rd row경기도 파주시 금촌동 962-29
4th row경기도 파주시 금촌동 962-29
5th row경기도 파주시 금촌동 962-29
ValueCountFrequency (%)
경기도 187
25.0%
파주시 187
25.0%
금촌동 185
24.7%
962-9 10
 
1.3%
803-8 10
 
1.3%
962-19 8
 
1.1%
962-29 7
 
0.9%
962-1 6
 
0.8%
962-8 6
 
0.8%
496-12 5
 
0.7%
Other values (75) 137
18.3%
2023-12-12T19:52:57.195557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
561
17.2%
경 187
 
5.7%
도 187
 
5.7%
파 187
 
5.7%
주 187
 
5.7%
시 187
 
5.7%
금 187
 
5.7%
동 187
 
5.7%
기 187
 
5.7%
촌 185
 
5.7%
Other values (12) 1027
31.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1683
51.5%
Decimal Number 844
25.8%
Space Separator 561
 
17.2%
Dash Punctuation 181
 
5.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 187
11.1%
도 187
11.1%
파 187
11.1%
주 187
11.1%
시 187
11.1%
금 187
11.1%
동 187
11.1%
기 187
11.1%
촌 185
11.0%
릉 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
9 181
21.4%
2 152
18.0%
1 107
12.7%
6 102
12.1%
3 77
9.1%
4 75
8.9%
8 57
 
6.8%
5 38
 
4.5%
0 29
 
3.4%
7 26
 
3.1%
Space Separator
ValueCountFrequency (%)
561
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 181
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1683
51.5%
Common 1586
48.5%

Most frequent character per script

Common
ValueCountFrequency (%)
561
35.4%
9 181
 
11.4%
- 181
 
11.4%
2 152
 
9.6%
1 107
 
6.7%
6 102
 
6.4%
3 77
 
4.9%
4 75
 
4.7%
8 57
 
3.6%
5 38
 
2.4%
Other values (2) 55
 
3.5%
Hangul
ValueCountFrequency (%)
경 187
11.1%
도 187
11.1%
파 187
11.1%
주 187
11.1%
시 187
11.1%
금 187
11.1%
동 187
11.1%
기 187
11.1%
촌 185
11.0%
릉 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1683
51.5%
ASCII 1586
48.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
561
35.4%
9 181
 
11.4%
- 181
 
11.4%
2 152
 
9.6%
1 107
 
6.7%
6 102
 
6.4%
3 77
 
4.9%
4 75
 
4.7%
8 57
 
3.6%
5 38
 
2.4%
Other values (2) 55
 
3.5%
Hangul
ValueCountFrequency (%)
경 187
11.1%
도 187
11.1%
파 187
11.1%
주 187
11.1%
시 187
11.1%
금 187
11.1%
동 187
11.1%
기 187
11.1%
촌 185
11.0%
릉 2
 
0.1%

도로명
Text

MISSING 

Distinct47
Distinct (%)64.4%
Missing114
Missing (%)61.0%
Memory size1.6 KiB
2023-12-12T19:52:57.493770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length8.2191781
Min length5

Characters and Unicode

Total characters600
Distinct characters39
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

Unique30 ?
Unique (%)41.1%

Sample

1st row경기도 파주시 황골로 40
2nd row경기도 파주시 황골로 40
3rd row경기도 파주시 황골로 40
4th row경기도 파주시 황골로 40
5th row경기도 파주시 황골로 40
ValueCountFrequency (%)
경기도 13
 
7.6%
파주시 13
 
7.6%
동산3길 12
 
7.0%
황골로 10
 
5.8%
한마음2길 9
 
5.2%
번영로 9
 
5.2%
40 7
 
4.1%
16 6
 
3.5%
11 6
 
3.5%
동산길 6
 
3.5%
Other values (37) 81
47.1%
2023-12-12T19:52:57.977456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
99
16.5%
길 44
 
7.3%
1 39
 
6.5%
2 36
 
6.0%
산 29
 
4.8%
동 29
 
4.8%
로 29
 
4.8%
3 24
 
4.0%
4 21
 
3.5%
시 15
 
2.5%
Other values (29) 235
39.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 324
54.0%
Decimal Number 173
28.8%
Space Separator 99
 
16.5%
Dash Punctuation 4
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
길 44
 
13.6%
산 29
 
9.0%
동 29
 
9.0%
로 29
 
9.0%
시 15
 
4.6%
기 13
 
4.0%
경 13
 
4.0%
주 13
 
4.0%
파 13
 
4.0%
도 13
 
4.0%
Other values (17) 113
34.9%
Decimal Number
ValueCountFrequency (%)
1 39
22.5%
2 36
20.8%
3 24
13.9%
4 21
12.1%
0 14
 
8.1%
6 12
 
6.9%
8 10
 
5.8%
5 10
 
5.8%
9 4
 
2.3%
7 3
 
1.7%
Space Separator
ValueCountFrequency (%)
99
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 324
54.0%
Common 276
46.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
길 44
 
13.6%
산 29
 
9.0%
동 29
 
9.0%
로 29
 
9.0%
시 15
 
4.6%
기 13
 
4.0%
경 13
 
4.0%
주 13
 
4.0%
파 13
 
4.0%
도 13
 
4.0%
Other values (17) 113
34.9%
Common
ValueCountFrequency (%)
99
35.9%
1 39
 
14.1%
2 36
 
13.0%
3 24
 
8.7%
4 21
 
7.6%
0 14
 
5.1%
6 12
 
4.3%
8 10
 
3.6%
5 10
 
3.6%
9 4
 
1.4%
Other values (2) 7
 
2.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 324
54.0%
ASCII 276
46.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
99
35.9%
1 39
 
14.1%
2 36
 
13.0%
3 24
 
8.7%
4 21
 
7.6%
0 14
 
5.1%
6 12
 
4.3%
8 10
 
3.6%
5 10
 
3.6%
9 4
 
1.4%
Other values (2) 7
 
2.5%
Hangul
ValueCountFrequency (%)
길 44
 
13.6%
산 29
 
9.0%
동 29
 
9.0%
로 29
 
9.0%
시 15
 
4.6%
기 13
 
4.0%
경 13
 
4.0%
주 13
 
4.0%
파 13
 
4.0%
도 13
 
4.0%
Other values (17) 113
34.9%

종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
전주
174 
통신주
 
13

Length

Max length3
Median length2
Mean length2.0695187
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전주 174
93.0%
통신주 13
 
7.0%

Length

2023-12-12T19:52:58.180422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:52:58.339886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전주 174
93.0%
통신주 13
 
7.0%

사업자
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
한전
174 
LG유플러스
 
7
KT
 
6

Length

Max length6
Median length2
Mean length2.1497326
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한전 174
93.0%
LG유플러스 7
 
3.7%
KT 6
 
3.2%

Length

2023-12-12T19:52:58.522910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:52:58.765416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한전 174
93.0%
lg유플러스 7
 
3.7%
kt 6
 
3.2%

전주번호
Text

Distinct141
Distinct (%)75.4%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T19:52:59.397999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length8
Mean length8.0695187
Min length3

Characters and Unicode

Total characters1509
Distinct characters24
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

Unique95 ?
Unique (%)50.8%

Sample

1st row9036H182
2nd row9036H082
3rd row9036B912
4th row9036B901
5th row9036B911
ValueCountFrequency (%)
9037s331 2
 
1.1%
9036e441 2
 
1.1%
9036e461 2
 
1.1%
l-9037r851-01 2
 
1.1%
9036e413 2
 
1.1%
9036e411 2
 
1.1%
9036e422 2
 
1.1%
9036e421 2
 
1.1%
l9036e434-01 2
 
1.1%
9036e434 2
 
1.1%
Other values (131) 167
89.3%
2023-12-12T19:53:00.216358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 244
16.2%
9 220
14.6%
0 202
13.4%
6 162
10.7%
1 124
8.2%
2 97
 
6.4%
B 90
 
6.0%
7 80
 
5.3%
4 80
 
5.3%
E 41
 
2.7%
Other values (14) 169
11.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1283
85.0%
Uppercase Letter 193
 
12.8%
Dash Punctuation 12
 
0.8%
Other Punctuation 12
 
0.8%
Math Symbol 8
 
0.5%
Other Letter 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 244
19.0%
9 220
17.1%
0 202
15.7%
6 162
12.6%
1 124
9.7%
2 97
 
7.6%
7 80
 
6.2%
4 80
 
6.2%
8 38
 
3.0%
5 36
 
2.8%
Uppercase Letter
ValueCountFrequency (%)
B 90
46.6%
E 41
21.2%
S 29
 
15.0%
R 17
 
8.8%
L 7
 
3.6%
F 3
 
1.6%
A 3
 
1.6%
H 2
 
1.0%
W 1
 
0.5%
Other Punctuation
ValueCountFrequency (%)
. 8
66.7%
* 4
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 12
100.0%
Math Symbol
ValueCountFrequency (%)
+ 8
100.0%
Other Letter
ValueCountFrequency (%)
좌 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1315
87.1%
Latin 193
 
12.8%
Hangul 1
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
3 244
18.6%
9 220
16.7%
0 202
15.4%
6 162
12.3%
1 124
9.4%
2 97
 
7.4%
7 80
 
6.1%
4 80
 
6.1%
8 38
 
2.9%
5 36
 
2.7%
Other values (4) 32
 
2.4%
Latin
ValueCountFrequency (%)
B 90
46.6%
E 41
21.2%
S 29
 
15.0%
R 17
 
8.8%
L 7
 
3.6%
F 3
 
1.6%
A 3
 
1.6%
H 2
 
1.0%
W 1
 
0.5%
Hangul
ValueCountFrequency (%)
좌 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1508
99.9%
Hangul 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 244
16.2%
9 220
14.6%
0 202
13.4%
6 162
10.7%
1 124
8.2%
2 97
 
6.4%
B 90
 
6.0%
7 80
 
5.3%
4 80
 
5.3%
E 41
 
2.7%
Other values (13) 168
11.1%
Hangul
ValueCountFrequency (%)
좌 1
100.0%

지역
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)3.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
금택간
78 
장미간
57 
종점간
37 
<NA>
12 
시장
 
2

Length

Max length4
Median length3
Mean length3.0534759
Min length2

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
금택간 78
41.7%
장미간 57
30.5%
종점간 37
19.8%
<NA> 12
 
6.4%
시장 2
 
1.1%
서패간 1
 
0.5%

Length

2023-12-12T19:53:00.541396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:53:00.783922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금택간 78
41.7%
장미간 57
30.5%
종점간 37
19.8%
na 12
 
6.4%
시장 2
 
1.1%
서패간 1
 
0.5%

전주번호2
Text

MISSING 

Distinct126
Distinct (%)74.6%
Missing18
Missing (%)9.6%
Memory size1.6 KiB
2023-12-12T19:53:01.305070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length11
Mean length6.6745562
Min length2

Characters and Unicode

Total characters1128
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique85 ?
Unique (%)50.3%

Sample

1st row51-L2-R3
2nd row51-L2-R4
3rd row73
4th row73-AL1
5th row72
ValueCountFrequency (%)
84-l8 4
 
2.4%
61-l4 2
 
1.2%
73-al6-r3 2
 
1.2%
61-l4-r4 2
 
1.2%
85-r6-l3 2
 
1.2%
64 2
 
1.2%
64-r1 2
 
1.2%
64-r2 2
 
1.2%
64-r2-r1 2
 
1.2%
73-al7 2
 
1.2%
Other values (116) 147
87.0%
2023-12-12T19:53:02.191436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 256
22.7%
L 162
14.4%
3 99
 
8.8%
1 98
 
8.7%
R 91
 
8.1%
4 80
 
7.1%
7 77
 
6.8%
6 69
 
6.1%
2 65
 
5.8%
8 55
 
4.9%
Other values (5) 76
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 605
53.6%
Uppercase Letter 267
23.7%
Dash Punctuation 256
22.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 99
16.4%
1 98
16.2%
4 80
13.2%
7 77
12.7%
6 69
11.4%
2 65
10.7%
8 55
9.1%
5 35
 
5.8%
9 14
 
2.3%
0 13
 
2.1%
Uppercase Letter
ValueCountFrequency (%)
L 162
60.7%
R 91
34.1%
A 11
 
4.1%
H 3
 
1.1%
Dash Punctuation
ValueCountFrequency (%)
- 256
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 861
76.3%
Latin 267
 
23.7%

Most frequent character per script

Common
ValueCountFrequency (%)
- 256
29.7%
3 99
 
11.5%
1 98
 
11.4%
4 80
 
9.3%
7 77
 
8.9%
6 69
 
8.0%
2 65
 
7.5%
8 55
 
6.4%
5 35
 
4.1%
9 14
 
1.6%
Latin
ValueCountFrequency (%)
L 162
60.7%
R 91
34.1%
A 11
 
4.1%
H 3
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1128
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 256
22.7%
L 162
14.4%
3 99
 
8.8%
1 98
 
8.7%
R 91
 
8.1%
4 80
 
7.1%
7 77
 
6.8%
6 69
 
6.1%
2 65
 
5.8%
8 55
 
4.9%
Other values (5) 76
 
6.7%

높이
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct6
Distinct (%)3.5%
Missing17
Missing (%)9.1%
Infinite0
Infinite (%)0.0%
Mean14.188235
Minimum8
Maximum16
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T19:53:02.481981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8
5-th percentile10
Q114
median16
Q316
95-th percentile16
Maximum16
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation2.3127847
Coefficient of variation (CV)0.16300722
Kurtosis0.42255989
Mean14.188235
Median Absolute Deviation (MAD)0
Skewness-1.1817468
Sum2412
Variance5.3489732
MonotonicityNot monotonic
2023-12-12T19:53:02.773295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
16 86
46.0%
14 42
22.5%
12 18
 
9.6%
10 15
 
8.0%
8 7
 
3.7%
13 2
 
1.1%
(Missing) 17
 
9.1%
ValueCountFrequency (%)
8 7
 
3.7%
10 15
 
8.0%
12 18
 
9.6%
13 2
 
1.1%
14 42
22.5%
16 86
46.0%
ValueCountFrequency (%)
16 86
46.0%
14 42
22.5%
13 2
 
1.1%
12 18
 
9.6%
10 15
 
8.0%
8 7
 
3.7%

위도
Real number (ℝ)

HIGH CORRELATION 

Distinct131
Distinct (%)70.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37.758313
Minimum37.749461
Maximum37.763274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T19:53:03.098495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum37.749461
5-th percentile37.75546
Q137.756953
median37.758262
Q337.759343
95-th percentile37.761862
Maximum37.763274
Range0.013813
Interquartile range (IQR)0.00239

Descriptive statistics

Standard deviation0.0020363657
Coefficient of variation (CV)5.3931586 × 10-5
Kurtosis1.4700002
Mean37.758313
Median Absolute Deviation (MAD)0.001184
Skewness0.010089114
Sum7060.8045
Variance4.1467852 × 10-6
MonotonicityNot monotonic
2023-12-12T19:53:03.391475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
37.756356 6
 
3.2%
37.761862 4
 
2.1%
37.755734 4
 
2.1%
37.756451 3
 
1.6%
37.754301 2
 
1.1%
37.757611 2
 
1.1%
37.759173 2
 
1.1%
37.759343 2
 
1.1%
37.755505 2
 
1.1%
37.755892 2
 
1.1%
Other values (121) 158
84.5%
ValueCountFrequency (%)
37.749461 1
0.5%
37.754301 2
1.1%
37.755062 1
0.5%
37.7552 1
0.5%
37.755262 1
0.5%
37.755348 1
0.5%
37.755399 1
0.5%
37.755449 1
0.5%
37.755452 1
0.5%
37.755478 1
0.5%
ValueCountFrequency (%)
37.763274 1
 
0.5%
37.763206 1
 
0.5%
37.763201 1
 
0.5%
37.763135 1
 
0.5%
37.763017 1
 
0.5%
37.762936 1
 
0.5%
37.762874 1
 
0.5%
37.762447 1
 
0.5%
37.761862 4
2.1%
37.761854 2
1.1%

경도
Real number (ℝ)

HIGH CORRELATION 

Distinct134
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.77155
Minimum126.76321
Maximum126.78021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T19:53:03.674977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.76321
5-th percentile126.76623
Q1126.76937
median126.77095
Q3126.77366
95-th percentile126.7763
Maximum126.78021
Range0.0170072
Interquartile range (IQR)0.0042895

Descriptive statistics

Standard deviation0.0032138691
Coefficient of variation (CV)2.535166 × 10-5
Kurtosis-0.32850764
Mean126.77155
Median Absolute Deviation (MAD)0.0019848
Skewness0.23188789
Sum23706.279
Variance1.0328955 × 10-5
MonotonicityNot monotonic
2023-12-12T19:53:03.977674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.776278 6
 
3.2%
126.776174 4
 
2.1%
126.767437 4
 
2.1%
126.773233 2
 
1.1%
126.770206 2
 
1.1%
126.7707701 2
 
1.1%
126.776336 2
 
1.1%
126.776393 2
 
1.1%
126.776209 2
 
1.1%
126.776301 2
 
1.1%
Other values (124) 159
85.0%
ValueCountFrequency (%)
126.763206 1
0.5%
126.765413 2
1.1%
126.765526 1
0.5%
126.765535 1
0.5%
126.765649 1
0.5%
126.765659 1
0.5%
126.765824 1
0.5%
126.766142 1
0.5%
126.766164 1
0.5%
126.766377 1
0.5%
ValueCountFrequency (%)
126.7802132 1
 
0.5%
126.7802016 1
 
0.5%
126.779616 1
 
0.5%
126.777019 1
 
0.5%
126.776393 2
 
1.1%
126.776336 2
 
1.1%
126.776301 2
 
1.1%
126.776287 2
 
1.1%
126.776278 6
3.2%
126.776209 2
 
1.1%

관리기관명
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
한전
175 
LG유플러스
 
7
KT
 
5

Length

Max length6
Median length2
Mean length2.1497326
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
한전 175
93.6%
LG유플러스 7
 
3.7%
KT 5
 
2.7%

Length

2023-12-12T19:53:04.250146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:53:04.511729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한전 175
93.6%
lg유플러스 7
 
3.7%
kt 5
 
2.7%

데이터기준일자
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2020-09-22
95 
2020-09-21
45 
2020-09-17
24 
2020-09-16
23 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020-09-16
2nd row2020-09-16
3rd row2020-09-16
4th row2020-09-16
5th row2020-09-16

Common Values

ValueCountFrequency (%)
2020-09-22 95
50.8%
2020-09-21 45
24.1%
2020-09-17 24
 
12.8%
2020-09-16 23
 
12.3%

Length

2023-12-12T19:53:04.731573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:53:04.956886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020-09-22 95
50.8%
2020-09-21 45
24.1%
2020-09-17 24
 
12.8%
2020-09-16 23
 
12.3%

Interactions

2023-12-12T19:52:53.442412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:52.341348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:52.923890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:53.641342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:52.550237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:53.097326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:53.836417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:52.720802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:52:53.262075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:53:05.133889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지번도로명종류사업자지역높이위도경도관리기관명데이터기준일자
지번1.0001.0000.6500.8290.9840.9080.9910.9940.8100.994
도로명1.0001.0000.6090.5841.0000.9440.9961.0000.4690.993
종류0.6500.6091.0001.0001.0001.0000.2160.5070.7170.214
사업자0.8290.5841.0001.0001.0001.0000.3570.4290.9990.087
지역0.9841.0001.0001.0001.0000.2040.8220.9621.0000.442
높이0.9080.9441.0001.0000.2041.0000.3310.4201.0000.152
위도0.9910.9960.2160.3570.8220.3311.0000.8360.3890.769
경도0.9941.0000.5070.4290.9620.4200.8361.0000.4360.781
관리기관명0.8100.4690.7170.9991.0001.0000.3890.4361.0000.113
데이터기준일자0.9940.9930.2140.0870.4420.1520.7690.7810.1131.000
2023-12-12T19:53:05.423161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관리기관명사업자지역데이터기준일자종류
관리기관명1.0000.9560.9910.1060.955
사업자0.9561.0000.9910.0810.997
지역0.9910.9911.0000.3740.991
데이터기준일자0.1060.0810.3741.0000.141
종류0.9550.9970.9910.1411.000
2023-12-12T19:53:05.658069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
높이위도경도종류사업자지역관리기관명데이터기준일자
높이1.0000.0060.0680.9880.9880.1670.9880.097
위도0.0061.000-0.7690.1590.2390.6860.2650.431
경도0.068-0.7691.0000.3820.2800.7200.2860.588
종류0.9880.1590.3821.0000.9970.9910.9550.141
사업자0.9880.2390.2800.9971.0000.9910.9560.081
지역0.1670.6860.7200.9910.9911.0000.9910.374
관리기관명0.9880.2650.2860.9550.9560.9911.0000.106
데이터기준일자0.0970.4310.5880.1410.0810.3740.1061.000

Missing values

2023-12-12T19:52:54.757146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:52:55.107188image/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.
2023-12-12T19:52:55.345183image/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

시도명시군구명지번도로명종류사업자전주번호지역전주번호2높이위도경도관리기관명데이터기준일자
0경기도파주시경기도 파주시 금릉동 210-6<NA>전주한전9036H182장미간51-L2-R31637.754301126.780202한전2020-09-16
1경기도파주시경기도 파주시 금릉동 210-6<NA>전주한전9036H082장미간51-L2-R41637.754301126.780213한전2020-09-16
2경기도파주시경기도 파주시 금촌동 962-29경기도 파주시 황골로 40전주한전9036B912장미간731437.755348126.773559한전2020-09-16
3경기도파주시경기도 파주시 금촌동 962-29경기도 파주시 황골로 40전주한전9036B901장미간73-AL11437.755062126.773555한전2020-09-16
4경기도파주시경기도 파주시 금촌동 962-29경기도 파주시 황골로 40전주한전9036B911장미간721437.755262126.773768한전2020-09-16
5경기도파주시경기도 파주시 금촌동 962-29경기도 파주시 황골로 40전주한전9036B913장미간74<NA>37.755449126.773683한전2020-09-16
6경기도파주시경기도 파주시 금촌동 962-29경기도 파주시 황골로 40전주한전9036B922장미간751437.755945126.773528한전2020-09-16
7경기도파주시경기도 파주시 금촌동 953-2경기도 파주시 후곡로 15전주한전9036B921장미간761637.756008126.773699한전2020-09-16
8경기도파주시경기도 파주시 금촌동 962-29경기도 파주시 황골로 40전주한전9036B932장미간77<NA>37.756299126.773534한전2020-09-16
9경기도파주시경기도 파주시 금촌동 962-27<NA>전주한전9036B931장미간781637.756485126.77352한전2020-09-16
시도명시군구명지번도로명종류사업자전주번호지역전주번호2높이위도경도관리기관명데이터기준일자
177경기도파주시경기도 파주시 금촌동 934-1<NA>전주한전9037S112금택간61-L31637.759637126.769262한전2020-09-22
178경기도파주시경기도 파주시 금촌동 962-8<NA>전주한전9037S214금택간61-L41637.759928126.769649한전2020-09-22
179경기도파주시경기도 파주시 금촌동 934-15동산3길 13전주한전9037S213금택간61-L4-R11437.759775126.769742한전2020-09-22
180경기도파주시경기도 파주시 금촌동 934-16동산3길 11전주한전9037S301금택간61-L4-R21437.759619126.76985한전2020-09-22
181경기도파주시경기도 파주시 금촌동 962-7동산3길 16전주한전9037S302금택간61-L4-R31437.759446126.770031한전2020-09-22
182경기도파주시경기도 파주시 금촌동 935-40동산3길 8통신주KT2F1<NA><NA><NA>37.759472126.770077KT2020-09-22
183경기도파주시경기도 파주시 금촌동 932-28동산3길 25전주한전9037S222금택간61-L61637.760216126.769426한전2020-09-22
184경기도파주시경기도 파주시 금촌동 932-15<NA>전주한전9037S122금택간61-L6-L11637.760522126.769209한전2020-09-22
185경기도파주시경기도 파주시 금촌동 962-3<NA>전주한전9037S231종점간99-L91637.760839126.769638한전2020-09-22
186경기도파주시경기도 파주시 금촌동 962-3<NA>전주한전9037S331종점간99-L81637.760939126.769896한전2020-09-22

Duplicate rows

Most frequently occurring

시도명시군구명지번도로명종류사업자전주번호지역전주번호2높이위도경도관리기관명데이터기준일자# duplicates
0경기도파주시경기도 파주시 금촌동 496-1아리랑로 11통신주LG유플러스L-9037R851-01<NA><NA>837.761862126.767437LG유플러스2020-09-222
1경기도파주시경기도 파주시 금촌동 774-14금정로 22전주한전9036E441종점간73-AL6-R11337.757224126.776287한전2020-09-222
2경기도파주시경기도 파주시 금촌동 789-11<NA>전주한전9036E411장미간64-R11637.755734126.776174한전2020-09-222
3경기도파주시경기도 파주시 금촌동 789-11<NA>전주한전9036E422장미간64-R21637.755734126.776174한전2020-09-222
4경기도파주시경기도 파주시 금촌동 803-14<NA>전주한전9036E421장미간64-R2-R11637.755892126.776393한전2020-09-222
5경기도파주시경기도 파주시 금촌동 803-15<NA>전주한전9036E433종점간73-AL71437.756451126.776209한전2020-09-222
6경기도파주시경기도 파주시 금촌동 803-7<NA>전주한전9036E371종점간84-L91637.758612126.775719한전2020-09-222
7경기도파주시경기도 파주시 금촌동 803-8<NA>전주한전9036E413장미간641437.755505126.776336한전2020-09-222
8경기도파주시경기도 파주시 금촌동 803-8<NA>전주한전9036E431종점간73-AL61437.756952126.776301한전2020-09-222
9경기도파주시경기도 파주시 금촌동 803-8<NA>전주한전9036E434장미간64-R31637.756356126.776278한전2020-09-222