Overview

Dataset statistics

Number of variables9
Number of observations114
Missing cells144
Missing cells (%)14.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.5 KiB
Average record size in memory76.2 B

Variable types

Numeric3
Categorical1
Text4
DateTime1

Dataset

Description경상남도 김해시 폐기물 수집운반업체 현황(구분,업소명,등록일자,전화번호,주소,영업구역,위도,경도)에 대한 데이터입니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15033432

Alerts

연번 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번High correlation
전화번호 has 32 (28.1%) missing valuesMissing
영업구역 has 110 (96.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2024-03-13 00:13:31.196277
Analysis finished2024-03-13 00:13:32.645699
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct114
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.5
Minimum1
Maximum114
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T09:13:32.721296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.65
Q129.25
median57.5
Q385.75
95-th percentile108.35
Maximum114
Range113
Interquartile range (IQR)56.5

Descriptive statistics

Standard deviation33.052988
Coefficient of variation (CV)0.57483457
Kurtosis-1.2
Mean57.5
Median Absolute Deviation (MAD)28.5
Skewness0
Sum6555
Variance1092.5
MonotonicityStrictly increasing
2024-03-13T09:13:32.830921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.9%
87 1
 
0.9%
85 1
 
0.9%
84 1
 
0.9%
83 1
 
0.9%
82 1
 
0.9%
81 1
 
0.9%
80 1
 
0.9%
79 1
 
0.9%
78 1
 
0.9%
Other values (104) 104
91.2%
ValueCountFrequency (%)
1 1
0.9%
2 1
0.9%
3 1
0.9%
4 1
0.9%
5 1
0.9%
6 1
0.9%
7 1
0.9%
8 1
0.9%
9 1
0.9%
10 1
0.9%
ValueCountFrequency (%)
114 1
0.9%
113 1
0.9%
112 1
0.9%
111 1
0.9%
110 1
0.9%
109 1
0.9%
108 1
0.9%
107 1
0.9%
106 1
0.9%
105 1
0.9%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
사업장일반폐기물
84 
건설폐기물
26 
생활폐기물
 
4

Length

Max length8
Median length8
Mean length7.2105263
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row생활폐기물
2nd row생활폐기물
3rd row생활폐기물
4th row생활폐기물
5th row건설폐기물

Common Values

ValueCountFrequency (%)
사업장일반폐기물 84
73.7%
건설폐기물 26
 
22.8%
생활폐기물 4
 
3.5%

Length

2024-03-13T09:13:32.936166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-13T09:13:33.015761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사업장일반폐기물 84
73.7%
건설폐기물 26
 
22.8%
생활폐기물 4
 
3.5%

업소명
Text

Distinct105
Distinct (%)92.1%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-13T09:13:33.165524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length5.6315789
Min length3

Characters and Unicode

Total characters642
Distinct characters143
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique98 ?
Unique (%)86.0%

Sample

1st row㈜김해환경
2nd row김해시공영(유)
3rd row(유)김해공영
4th row㈜정우환경
5th row금광개발㈜
ValueCountFrequency (%)
㈜태창크린텍 3
 
2.5%
주식회사 3
 
2.5%
미래환경 3
 
2.5%
세영개발 2
 
1.7%
주)그린자원 2
 
1.7%
㈜금화로지스 2
 
1.7%
㈜대경오앤티 2
 
1.7%
세진환경산업㈜ 2
 
1.7%
㈜서안환경 1
 
0.8%
㈜김해환경 1
 
0.8%
Other values (97) 97
82.2%
2024-03-13T09:13:33.449018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 63
 
9.8%
경 39
 
6.1%
환 32
 
5.0%
산 30
 
4.7%
업 28
 
4.4%
원 16
 
2.5%
이 15
 
2.3%
대 12
 
1.9%
개 12
 
1.9%
주 12
 
1.9%
Other values (133) 383
59.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 559
87.1%
Other Symbol 63
 
9.8%
Open Punctuation 5
 
0.8%
Close Punctuation 5
 
0.8%
Uppercase Letter 5
 
0.8%
Space Separator 4
 
0.6%
Decimal Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
경 39
 
7.0%
환 32
 
5.7%
산 30
 
5.4%
업 28
 
5.0%
원 16
 
2.9%
이 15
 
2.7%
대 12
 
2.1%
개 12
 
2.1%
주 12
 
2.1%
발 11
 
2.0%
Other values (123) 352
63.0%
Uppercase Letter
ValueCountFrequency (%)
H 1
20.0%
C 1
20.0%
O 1
20.0%
N 1
20.0%
E 1
20.0%
Other Symbol
ValueCountFrequency (%)
㈜ 63
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 622
96.9%
Common 15
 
2.3%
Latin 5
 
0.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 63
 
10.1%
경 39
 
6.3%
환 32
 
5.1%
산 30
 
4.8%
업 28
 
4.5%
원 16
 
2.6%
이 15
 
2.4%
대 12
 
1.9%
개 12
 
1.9%
주 12
 
1.9%
Other values (124) 363
58.4%
Latin
ValueCountFrequency (%)
H 1
20.0%
C 1
20.0%
O 1
20.0%
N 1
20.0%
E 1
20.0%
Common
ValueCountFrequency (%)
( 5
33.3%
) 5
33.3%
4
26.7%
1 1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 559
87.1%
None 63
 
9.8%
ASCII 20
 
3.1%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 63
100.0%
Hangul
ValueCountFrequency (%)
경 39
 
7.0%
환 32
 
5.7%
산 30
 
5.4%
업 28
 
5.0%
원 16
 
2.9%
이 15
 
2.7%
대 12
 
2.1%
개 12
 
2.1%
주 12
 
2.1%
발 11
 
2.0%
Other values (123) 352
63.0%
ASCII
ValueCountFrequency (%)
( 5
25.0%
) 5
25.0%
4
20.0%
H 1
 
5.0%
C 1
 
5.0%
O 1
 
5.0%
N 1
 
5.0%
E 1
 
5.0%
1 1
 
5.0%

등록일자
Date

Distinct108
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
Minimum1987-09-05 00:00:00
Maximum2022-08-02 00:00:00
2024-03-13T09:13:33.571667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:33.709108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

전화번호
Text

MISSING 

Distinct73
Distinct (%)89.0%
Missing32
Missing (%)28.1%
Memory size1.0 KiB
2024-03-13T09:13:33.960532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters984
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

Unique66 ?
Unique (%)80.5%

Sample

1st row055-333-4800
2nd row055-334-6676
3rd row055-312-9721
4th row055-337-9511
5th row055-339-8643
ValueCountFrequency (%)
055-346-4932 3
 
3.7%
055-322-0772 3
 
3.7%
055-322-3274 2
 
2.4%
055-345-5141 2
 
2.4%
055-326-0814 2
 
2.4%
055-328-8572 2
 
2.4%
055-327-9867 2
 
2.4%
055-336-9720 1
 
1.2%
055-323-9779 1
 
1.2%
055-323-7084 1
 
1.2%
Other values (63) 63
76.8%
2024-03-13T09:13:34.306275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 204
20.7%
- 164
16.7%
3 147
14.9%
0 116
11.8%
2 78
 
7.9%
4 61
 
6.2%
1 54
 
5.5%
7 49
 
5.0%
8 41
 
4.2%
6 40
 
4.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 820
83.3%
Dash Punctuation 164
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 204
24.9%
3 147
17.9%
0 116
14.1%
2 78
 
9.5%
4 61
 
7.4%
1 54
 
6.6%
7 49
 
6.0%
8 41
 
5.0%
6 40
 
4.9%
9 30
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 164
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 984
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 204
20.7%
- 164
16.7%
3 147
14.9%
0 116
11.8%
2 78
 
7.9%
4 61
 
6.2%
1 54
 
5.5%
7 49
 
5.0%
8 41
 
4.2%
6 40
 
4.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 984
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 204
20.7%
- 164
16.7%
3 147
14.9%
0 116
11.8%
2 78
 
7.9%
4 61
 
6.2%
1 54
 
5.5%
7 49
 
5.0%
8 41
 
4.2%
6 40
 
4.1%

주소
Text

Distinct107
Distinct (%)93.9%
Missing0
Missing (%)0.0%
Memory size1.0 KiB
2024-03-13T09:13:34.521261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length39
Median length32
Mean length24.605263
Min length15

Characters and Unicode

Total characters2805
Distinct characters131
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

Unique101 ?
Unique (%)88.6%

Sample

1st row경상남도 김해시 전하로 43
2nd row경상남도 김해시 김해대로2385번길 8
3rd row경상남도 김해시 부곡로 71
4th row경상남도 김해시 김해대로2596번길 23-35
5th row경상남도 김해시 주촌면 김해대로1538번길 142
ValueCountFrequency (%)
김해시 113
20.5%
경상남도 88
 
16.0%
한림면 25
 
4.5%
김해대로 11
 
2.0%
생림면 8
 
1.5%
상동면 7
 
1.3%
주촌면 7
 
1.3%
호 7
 
1.3%
동 6
 
1.1%
진영읍 6
 
1.1%
Other values (218) 272
49.5%
2024-03-13T09:13:34.866201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
439
 
15.7%
1 143
 
5.1%
김 142
 
5.1%
해 142
 
5.1%
시 115
 
4.1%
로 111
 
4.0%
2 105
 
3.7%
상 103
 
3.7%
남 90
 
3.2%
경 89
 
3.2%
Other values (121) 1326
47.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1577
56.2%
Decimal Number 593
 
21.1%
Space Separator 439
 
15.7%
Other Punctuation 82
 
2.9%
Dash Punctuation 44
 
1.6%
Open Punctuation 35
 
1.2%
Close Punctuation 35
 
1.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 142
 
9.0%
해 142
 
9.0%
시 115
 
7.3%
로 111
 
7.0%
상 103
 
6.5%
남 90
 
5.7%
경 89
 
5.6%
도 88
 
5.6%
동 67
 
4.2%
번 59
 
3.7%
Other values (105) 571
36.2%
Decimal Number
ValueCountFrequency (%)
1 143
24.1%
2 105
17.7%
5 63
10.6%
3 53
 
8.9%
4 52
 
8.8%
0 49
 
8.3%
7 33
 
5.6%
8 33
 
5.6%
9 32
 
5.4%
6 30
 
5.1%
Other Punctuation
ValueCountFrequency (%)
* 45
54.9%
, 37
45.1%
Space Separator
ValueCountFrequency (%)
439
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 44
100.0%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1577
56.2%
Common 1228
43.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 142
 
9.0%
해 142
 
9.0%
시 115
 
7.3%
로 111
 
7.0%
상 103
 
6.5%
남 90
 
5.7%
경 89
 
5.6%
도 88
 
5.6%
동 67
 
4.2%
번 59
 
3.7%
Other values (105) 571
36.2%
Common
ValueCountFrequency (%)
439
35.7%
1 143
 
11.6%
2 105
 
8.6%
5 63
 
5.1%
3 53
 
4.3%
4 52
 
4.2%
0 49
 
4.0%
* 45
 
3.7%
- 44
 
3.6%
, 37
 
3.0%
Other values (6) 198
16.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1577
56.2%
ASCII 1228
43.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
439
35.7%
1 143
 
11.6%
2 105
 
8.6%
5 63
 
5.1%
3 53
 
4.3%
4 52
 
4.2%
0 49
 
4.0%
* 45
 
3.7%
- 44
 
3.6%
, 37
 
3.0%
Other values (6) 198
16.1%
Hangul
ValueCountFrequency (%)
김 142
 
9.0%
해 142
 
9.0%
시 115
 
7.3%
로 111
 
7.0%
상 103
 
6.5%
남 90
 
5.7%
경 89
 
5.6%
도 88
 
5.6%
동 67
 
4.2%
번 59
 
3.7%
Other values (105) 571
36.2%

영업구역
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing110
Missing (%)96.5%
Memory size1.0 KiB
2024-03-13T09:13:35.072296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length33
Median length21.5
Mean length21.5
Min length10

Characters and Unicode

Total characters86
Distinct characters36
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

Unique4 ?
Unique (%)100.0%

Sample

1st row주촌면, 진례면, 내외면, 칠산서부동
2nd row동상동, 회현동, 부원동, 활천동, 북부동
3rd row장유1, 2, 3동
4th row생림면, 상동면, 대동면, 삼안동, 불암동, 진영읍, 한림면
ValueCountFrequency (%)
주촌면 1
 
5.3%
2 1
 
5.3%
진영읍 1
 
5.3%
불암동 1
 
5.3%
삼안동 1
 
5.3%
대동면 1
 
5.3%
상동면 1
 
5.3%
생림면 1
 
5.3%
3동 1
 
5.3%
장유1 1
 
5.3%
Other values (9) 9
47.4%
2024-03-13T09:13:35.334149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 15
17.4%
15
17.4%
동 12
14.0%
면 7
 
8.1%
부 3
 
3.5%
진 2
 
2.3%
림 2
 
2.3%
상 2
 
2.3%
주 1
 
1.2%
2 1
 
1.2%
Other values (26) 26
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 53
61.6%
Other Punctuation 15
 
17.4%
Space Separator 15
 
17.4%
Decimal Number 3
 
3.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 12
22.6%
면 7
 
13.2%
부 3
 
5.7%
진 2
 
3.8%
림 2
 
3.8%
상 2
 
3.8%
주 1
 
1.9%
생 1
 
1.9%
대 1
 
1.9%
안 1
 
1.9%
Other values (21) 21
39.6%
Decimal Number
ValueCountFrequency (%)
2 1
33.3%
3 1
33.3%
1 1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Space Separator
ValueCountFrequency (%)
15
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 53
61.6%
Common 33
38.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 12
22.6%
면 7
 
13.2%
부 3
 
5.7%
진 2
 
3.8%
림 2
 
3.8%
상 2
 
3.8%
주 1
 
1.9%
생 1
 
1.9%
대 1
 
1.9%
안 1
 
1.9%
Other values (21) 21
39.6%
Common
ValueCountFrequency (%)
, 15
45.5%
15
45.5%
2 1
 
3.0%
3 1
 
3.0%
1 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 53
61.6%
ASCII 33
38.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 15
45.5%
15
45.5%
2 1
 
3.0%
3 1
 
3.0%
1 1
 
3.0%
Hangul
ValueCountFrequency (%)
동 12
22.6%
면 7
 
13.2%
부 3
 
5.7%
진 2
 
3.8%
림 2
 
3.8%
상 2
 
3.8%
주 1
 
1.9%
생 1
 
1.9%
대 1
 
1.9%
안 1
 
1.9%
Other values (21) 21
39.6%

위도
Real number (ℝ)

Distinct101
Distinct (%)89.4%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean35.26385
Minimum35.171892
Maximum35.38418
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T09:13:35.453953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.171892
5-th percentile35.196592
Q135.22882
median35.258731
Q335.299895
95-th percentile35.325868
Maximum35.38418
Range0.21228752
Interquartile range (IQR)0.07107482

Descriptive statistics

Standard deviation0.044260271
Coefficient of variation (CV)0.0012551174
Kurtosis-0.70804925
Mean35.26385
Median Absolute Deviation (MAD)0.03411751
Skewness0.18490571
Sum3984.8151
Variance0.0019589716
MonotonicityNot monotonic
2024-03-13T09:13:35.573412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.28997851 3
 
2.6%
35.30274642 3
 
2.6%
35.3168349 3
 
2.6%
35.26866643 2
 
1.8%
35.23313979 2
 
1.8%
35.32467647 2
 
1.8%
35.17189248 2
 
1.8%
35.22959386 2
 
1.8%
35.23094837 2
 
1.8%
35.29665854 1
 
0.9%
Other values (91) 91
79.8%
ValueCountFrequency (%)
35.17189248 2
1.8%
35.17929108 1
0.9%
35.1912 1
0.9%
35.19157 1
0.9%
35.19374701 1
0.9%
35.19848857 1
0.9%
35.20799039 1
0.9%
35.21009824 1
0.9%
35.21225422 1
0.9%
35.21366773 1
0.9%
ValueCountFrequency (%)
35.38418 1
0.9%
35.35877017 1
0.9%
35.34603527 1
0.9%
35.34314018 1
0.9%
35.32941251 1
0.9%
35.32738101 1
0.9%
35.32485937 1
0.9%
35.32467647 2
1.8%
35.3215945 1
0.9%
35.32075517 1
0.9%

경도
Real number (ℝ)

Distinct101
Distinct (%)89.4%
Missing1
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean128.84495
Minimum128.7479
Maximum128.9783
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2024-03-13T09:13:35.732738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.7479
5-th percentile128.75893
Q1128.8037
median128.85288
Q3128.8837
95-th percentile128.91423
Maximum128.9783
Range0.2304
Interquartile range (IQR)0.08

Descriptive statistics

Standard deviation0.052231134
Coefficient of variation (CV)0.00040537975
Kurtosis-0.52830729
Mean128.84495
Median Absolute Deviation (MAD)0.0416208
Skewness0.047981845
Sum14559.479
Variance0.0027280913
MonotonicityNot monotonic
2024-03-13T09:13:35.901743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
128.8106839 3
 
2.6%
128.7896558 3
 
2.6%
128.7501357 3
 
2.6%
128.8414438 2
 
1.8%
128.8597308 2
 
1.8%
128.7702588 2
 
1.8%
128.8203542 2
 
1.8%
128.9034493 2
 
1.8%
128.9067381 2
 
1.8%
128.8701555 1
 
0.9%
Other values (91) 91
79.8%
ValueCountFrequency (%)
128.7479 1
 
0.9%
128.7501357 3
2.6%
128.751077 1
 
0.9%
128.7514831 1
 
0.9%
128.7639 1
 
0.9%
128.764532 1
 
0.9%
128.7663493 1
 
0.9%
128.769127 1
 
0.9%
128.7702588 2
1.8%
128.7726692 1
 
0.9%
ValueCountFrequency (%)
128.9783 1
0.9%
128.9600574 1
0.9%
128.9597457 1
0.9%
128.9576 1
0.9%
128.9193462 1
0.9%
128.916911 1
0.9%
128.912449 1
0.9%
128.9122096 1
0.9%
128.9106 1
0.9%
128.9103586 1
0.9%

Interactions

2024-03-13T09:13:32.175972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:31.503030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:31.996962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:32.235727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:31.565573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:32.059934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:32.294544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:31.627194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:13:32.117970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:13:36.011406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분전화번호영업구역위도경도
연번1.0000.8170.876NaN0.3730.000
구분0.8171.0000.873NaN0.3550.000
전화번호0.8760.8731.0001.0000.9990.998
영업구역NaNNaN1.0001.0001.0001.000
위도0.3730.3550.9991.0001.0000.601
경도0.0000.0000.9981.0000.6011.000
2024-03-13T09:13:36.150914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번위도경도구분
연번1.000-0.0820.0350.693
위도-0.0821.000-0.2060.219
경도0.035-0.2061.0000.000
구분0.6930.2190.0001.000

Missing values

2024-03-13T09:13:32.378704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:13:32.503335image/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.
2024-03-13T09:13:32.593776image/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

연번구분업소명등록일자전화번호주소영업구역위도경도
01생활폐기물㈜김해환경1989-09-22055-333-4800경상남도 김해시 전하로 43주촌면, 진례면, 내외면, 칠산서부동35.22119128.852915
12생활폐기물김해시공영(유)1989-09-22055-334-6676경상남도 김해시 김해대로2385번길 8동상동, 회현동, 부원동, 활천동, 북부동35.228049128.888052
23생활폐기물(유)김해공영1987-09-05055-312-9721경상남도 김해시 부곡로 71장유1, 2, 3동35.210098128.799575
34생활폐기물㈜정우환경2012-07-13055-337-9511경상남도 김해시 김해대로2596번길 23-35생림면, 상동면, 대동면, 삼안동, 불암동, 진영읍, 한림면35.227076128.91221
45건설폐기물금광개발㈜1995-10-25055-339-8643경상남도 김해시 주촌면 김해대로1538번길 142<NA>35.266142128.842836
56건설폐기물남도산업㈜1997-10-24055-343-0059경상남도 김해시 한림면 안곡로333번길 82<NA>35.285076128.840811
67건설폐기물㈜중앙환경2004-10-27055-343-7755경상남도 김해시 한림면 안하로 178<NA>35.314892128.822766
78건설폐기물㈜성창산업2007-03-16055-339-0416경상남도 김해시 김해대로2453번길 3 (삼정동)<NA>35.228043128.895083
89건설폐기물㈜경부이엔티2007-11-08055-326-9123경상남도 김해시 생림면 나전로 76<NA>35.293526128.872002
910건설폐기물㈜태창크린텍2008-12-09055-322-3273경상남도 김해시 진영읍 본산로219번길 10<NA>35.316835128.750136
연번구분업소명등록일자전화번호주소영업구역위도경도
104105사업장일반폐기물에이스환경2020-01-07<NA>김해시 김해대로 2349, ***동 ****호<NA>35.22683128.8837
105106사업장일반폐기물㈜전국환경2022-01-28055-328-8572김해시 상동면 상동로 122-126, 1층<NA>35.2953128.8882
106107사업장일반폐기물이노산업개발2022-01-28<NA>김해시 계동로 233, 마루애빌딩1 ****호<NA>35.1912128.8037
107108사업장일반폐기물대진환경자원2022-02-14<NA>김해시 김해대로2576번길 56-8<NA>35.22419128.9106
108109사업장일반폐기물㈜에이치씨리사이클2022-02-18055-343-9685김해시 진례면 고모로134번길 46<NA>35.23816128.7728
109110사업장일반폐기물대호자원2022-02-28<NA>김해시 생림면 안양로274번안길 3<NA>35.38418128.8539
110111사업장일반폐기물성주상사2022-03-04<NA>김해시 봉황대길 44-1(44-3)<NA>35.22882128.8785
111112사업장일반폐기물김해시의사회의료폐기물공동운영기구2022-03-11055-323-0413김해시 생림면 인제로 607-20<NA>35.27886128.8871
112113사업장일반폐기물담덕기업2022-03-28<NA>김해시 대동면 동북로227번길 8-2<NA>35.29466128.9783
113114사업장일반폐기물유성건기2022-08-02<NA>김해시 진례면 서부로 857-4<NA>35.22934128.7639