Overview

Dataset statistics

Number of variables8
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory70.4 B

Variable types

Categorical1
Text4
Boolean1
Numeric2

Dataset

Description경상남도 김해시 관내 음식물수거용기 판매소에 대한 자료로 지역, 업소명, 도로명주소, 지번주소, 연락처, 40리터 용기 판매여부, 위도, 경도 데이터로 구성되어 있습니다.
Author경상남도 김해시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15086014

Alerts

경도 is highly overall correlated with 지역High correlation
지역 is highly overall correlated with 경도High correlation
도로명주소 has unique valuesUnique
지번주소 has unique valuesUnique
연락처 has unique valuesUnique
위도 has unique valuesUnique
경도 has unique valuesUnique

Reproduction

Analysis started2023-12-11 00:43:52.117277
Analysis finished2023-12-11 00:43:53.046003
Duration0.93 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지역
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)43.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
삼안동
6 
내외동
4 
장유동
3 
동상동
3 
진영읍
2 
Other values (8)
12 

Length

Max length5
Median length3
Mean length3.0666667
Min length3

Unique

Unique4 ?
Unique (%)13.3%

Sample

1st row진영읍
2nd row진영읍
3rd row장유동
4th row장유동
5th row장유동

Common Values

ValueCountFrequency (%)
삼안동 6
20.0%
내외동 4
13.3%
장유동 3
10.0%
동상동 3
10.0%
진영읍 2
 
6.7%
진례면 2
 
6.7%
부원동 2
 
6.7%
회현동 2
 
6.7%
북부동 2
 
6.7%
생림면 1
 
3.3%
Other values (3) 3
10.0%

Length

2023-12-11T09:43:53.125517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
삼안동 6
20.0%
내외동 4
13.3%
장유동 3
10.0%
동상동 3
10.0%
진영읍 2
 
6.7%
진례면 2
 
6.7%
부원동 2
 
6.7%
회현동 2
 
6.7%
북부동 2
 
6.7%
생림면 1
 
3.3%
Other values (3) 3
10.0%

업소명
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T09:43:53.348751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length8
Mean length5.5333333
Min length3

Characters and Unicode

Total characters166
Distinct characters84
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row탑마트진영점
2nd row대가야식자재마트
3rd row탑마트관동점
4th row행복마트
5th row뉴월드마트장유점
ValueCountFrequency (%)
서원탑할인마트 2
 
6.7%
탑마트진영점 1
 
3.3%
탑마트김해점 1
 
3.3%
천지환경산업 1
 
3.3%
탑마트삼방점 1
 
3.3%
두배로마트 1
 
3.3%
동일마트 1
 
3.3%
금풍마트 1
 
3.3%
탑마트삼계점 1
 
3.3%
탑마트구산점 1
 
3.3%
Other values (19) 19
63.3%
2023-12-11T09:43:53.703248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
마 22
 
13.3%
트 22
 
13.3%
탑 10
 
6.0%
점 9
 
5.4%
동 4
 
2.4%
금 4
 
2.4%
산 3
 
1.8%
할 3
 
1.8%
인 3
 
1.8%
0 3
 
1.8%
Other values (74) 83
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
94.6%
Decimal Number 4
 
2.4%
Uppercase Letter 3
 
1.8%
Close Punctuation 1
 
0.6%
Open Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
마 22
 
14.0%
트 22
 
14.0%
탑 10
 
6.4%
점 9
 
5.7%
동 4
 
2.5%
금 4
 
2.5%
산 3
 
1.9%
할 3
 
1.9%
인 3
 
1.9%
삼 2
 
1.3%
Other values (67) 75
47.8%
Uppercase Letter
ValueCountFrequency (%)
K 1
33.3%
D 1
33.3%
C 1
33.3%
Decimal Number
ValueCountFrequency (%)
0 3
75.0%
1 1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
94.6%
Common 6
 
3.6%
Latin 3
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
마 22
 
14.0%
트 22
 
14.0%
탑 10
 
6.4%
점 9
 
5.7%
동 4
 
2.5%
금 4
 
2.5%
산 3
 
1.9%
할 3
 
1.9%
인 3
 
1.9%
삼 2
 
1.3%
Other values (67) 75
47.8%
Common
ValueCountFrequency (%)
0 3
50.0%
) 1
 
16.7%
( 1
 
16.7%
1 1
 
16.7%
Latin
ValueCountFrequency (%)
K 1
33.3%
D 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 157
94.6%
ASCII 9
 
5.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
마 22
 
14.0%
트 22
 
14.0%
탑 10
 
6.4%
점 9
 
5.7%
동 4
 
2.5%
금 4
 
2.5%
산 3
 
1.9%
할 3
 
1.9%
인 3
 
1.9%
삼 2
 
1.3%
Other values (67) 75
47.8%
ASCII
ValueCountFrequency (%)
0 3
33.3%
K 1
 
11.1%
) 1
 
11.1%
( 1
 
11.1%
1 1
 
11.1%
D 1
 
11.1%
C 1
 
11.1%

도로명주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T09:43:53.950426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length19
Mean length17.5
Min length14

Characters and Unicode

Total characters525
Distinct characters58
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 (%)100.0%

Sample

1st row경상남도 김해시 진영읍 진영로221
2nd row경상남도 김해시 진영읍 김해대로464-35
3rd row경상남도 김해시 덕정로85
4th row경상남도 김해시 장유로334번길2
5th row경상남도 김해시 장유로312
ValueCountFrequency (%)
경상남도 30
30.9%
김해시 30
30.9%
진영읍 2
 
2.1%
진례면 2
 
2.1%
구산로50 1
 
1.0%
가락로83 1
 
1.0%
김해대로2301번길12 1
 
1.0%
분성로104 1
 
1.0%
평전로27 1
 
1.0%
금관대로1347번길6-15 1
 
1.0%
Other values (27) 27
27.8%
2023-12-11T09:43:54.442047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
12.8%
김 33
 
6.3%
해 33
 
6.3%
상 32
 
6.1%
경 30
 
5.7%
로 30
 
5.7%
남 30
 
5.7%
도 30
 
5.7%
시 30
 
5.7%
1 16
 
3.0%
Other values (48) 194
37.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 349
66.5%
Decimal Number 106
 
20.2%
Space Separator 67
 
12.8%
Dash Punctuation 3
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 33
9.5%
해 33
9.5%
상 32
9.2%
경 30
 
8.6%
로 30
 
8.6%
남 30
 
8.6%
도 30
 
8.6%
시 30
 
8.6%
번 11
 
3.2%
길 11
 
3.2%
Other values (36) 79
22.6%
Decimal Number
ValueCountFrequency (%)
1 16
15.1%
2 15
14.2%
3 15
14.2%
6 13
12.3%
5 13
12.3%
4 11
10.4%
7 7
6.6%
0 7
6.6%
8 6
 
5.7%
9 3
 
2.8%
Space Separator
ValueCountFrequency (%)
67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 349
66.5%
Common 176
33.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 33
9.5%
해 33
9.5%
상 32
9.2%
경 30
 
8.6%
로 30
 
8.6%
남 30
 
8.6%
도 30
 
8.6%
시 30
 
8.6%
번 11
 
3.2%
길 11
 
3.2%
Other values (36) 79
22.6%
Common
ValueCountFrequency (%)
67
38.1%
1 16
 
9.1%
2 15
 
8.5%
3 15
 
8.5%
6 13
 
7.4%
5 13
 
7.4%
4 11
 
6.2%
7 7
 
4.0%
0 7
 
4.0%
8 6
 
3.4%
Other values (2) 6
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 349
66.5%
ASCII 176
33.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
38.1%
1 16
 
9.1%
2 15
 
8.5%
3 15
 
8.5%
6 13
 
7.4%
5 13
 
7.4%
4 11
 
6.2%
7 7
 
4.0%
0 7
 
4.0%
8 6
 
3.4%
Other values (2) 6
 
3.4%
Hangul
ValueCountFrequency (%)
김 33
9.5%
해 33
9.5%
상 32
9.2%
경 30
 
8.6%
로 30
 
8.6%
남 30
 
8.6%
도 30
 
8.6%
시 30
 
8.6%
번 11
 
3.2%
길 11
 
3.2%
Other values (36) 79
22.6%

지번주소
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T09:43:54.693641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length17.466667
Min length15

Characters and Unicode

Total characters524
Distinct characters56
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 (%)100.0%

Sample

1st row경상남도 김해시 진영읍 여래리454-9
2nd row경상남도 김해시 진영읍 여래리473-2
3rd row경상남도 김해시 관동동590
4th row경상남도 김해시 신문동500-1
5th row경상남도 김해시 무계동278
ValueCountFrequency (%)
경상남도 30
30.9%
김해시 30
30.9%
진영읍 2
 
2.1%
진례면 2
 
2.1%
구산동294-1 1
 
1.0%
서상동73-4 1
 
1.0%
봉황동21-12 1
 
1.0%
외동529-3 1
 
1.0%
외동706-1 1
 
1.0%
내동681-2 1
 
1.0%
Other values (27) 27
27.8%
2023-12-11T09:43:55.061587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
67
 
12.8%
상 35
 
6.7%
경 30
 
5.7%
남 30
 
5.7%
도 30
 
5.7%
김 30
 
5.7%
해 30
 
5.7%
시 30
 
5.7%
동 29
 
5.5%
- 25
 
4.8%
Other values (46) 188
35.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 315
60.1%
Decimal Number 117
 
22.3%
Space Separator 67
 
12.8%
Dash Punctuation 25
 
4.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
상 35
11.1%
경 30
9.5%
남 30
9.5%
도 30
9.5%
김 30
9.5%
해 30
9.5%
시 30
9.5%
동 29
9.2%
리 7
 
2.2%
삼 6
 
1.9%
Other values (34) 58
18.4%
Decimal Number
ValueCountFrequency (%)
1 22
18.8%
2 15
12.8%
8 12
10.3%
4 11
9.4%
5 11
9.4%
6 11
9.4%
7 10
8.5%
9 9
7.7%
3 9
7.7%
0 7
 
6.0%
Space Separator
ValueCountFrequency (%)
67
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 25
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 315
60.1%
Common 209
39.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
상 35
11.1%
경 30
9.5%
남 30
9.5%
도 30
9.5%
김 30
9.5%
해 30
9.5%
시 30
9.5%
동 29
9.2%
리 7
 
2.2%
삼 6
 
1.9%
Other values (34) 58
18.4%
Common
ValueCountFrequency (%)
67
32.1%
- 25
 
12.0%
1 22
 
10.5%
2 15
 
7.2%
8 12
 
5.7%
4 11
 
5.3%
5 11
 
5.3%
6 11
 
5.3%
7 10
 
4.8%
9 9
 
4.3%
Other values (2) 16
 
7.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 315
60.1%
ASCII 209
39.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
67
32.1%
- 25
 
12.0%
1 22
 
10.5%
2 15
 
7.2%
8 12
 
5.7%
4 11
 
5.3%
5 11
 
5.3%
6 11
 
5.3%
7 10
 
4.8%
9 9
 
4.3%
Other values (2) 16
 
7.7%
Hangul
ValueCountFrequency (%)
상 35
11.1%
경 30
9.5%
남 30
9.5%
도 30
9.5%
김 30
9.5%
해 30
9.5%
시 30
9.5%
동 29
9.2%
리 7
 
2.2%
삼 6
 
1.9%
Other values (34) 58
18.4%

연락처
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T09:43:55.282193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters360
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

Unique30 ?
Unique (%)100.0%

Sample

1st row055-345-0388
2nd row055-346-5580
3rd row055-337-6133
4th row055-313-4088
5th row055-314-2552
ValueCountFrequency (%)
055-345-0388 1
 
3.3%
055-346-5580 1
 
3.3%
055-328-3179 1
 
3.3%
055-324-6272 1
 
3.3%
055-335-7733 1
 
3.3%
055-325-7222 1
 
3.3%
055-321-8952 1
 
3.3%
055-313-4948 1
 
3.3%
055-333-3553 1
 
3.3%
055-325-6283 1
 
3.3%
Other values (20) 20
66.7%
2023-12-11T09:43:55.602567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 80
22.2%
3 68
18.9%
- 60
16.7%
0 46
12.8%
2 23
 
6.4%
1 16
 
4.4%
8 15
 
4.2%
6 15
 
4.2%
9 14
 
3.9%
7 12
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 300
83.3%
Dash Punctuation 60
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 80
26.7%
3 68
22.7%
0 46
15.3%
2 23
 
7.7%
1 16
 
5.3%
8 15
 
5.0%
6 15
 
5.0%
9 14
 
4.7%
7 12
 
4.0%
4 11
 
3.7%
Dash Punctuation
ValueCountFrequency (%)
- 60
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 80
22.2%
3 68
18.9%
- 60
16.7%
0 46
12.8%
2 23
 
6.4%
1 16
 
4.4%
8 15
 
4.2%
6 15
 
4.2%
9 14
 
3.9%
7 12
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 80
22.2%
3 68
18.9%
- 60
16.7%
0 46
12.8%
2 23
 
6.4%
1 16
 
4.4%
8 15
 
4.2%
6 15
 
4.2%
9 14
 
3.9%
7 12
 
3.3%

40리터용기 판매여부
Boolean

Distinct2
Distinct (%)6.7%
Missing0
Missing (%)0.0%
Memory size162.0 B
True
21 
False
9 
ValueCountFrequency (%)
True 21
70.0%
False 9
30.0%
2023-12-11T09:43:55.711040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

위도
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.243475
Minimum35.179944
Maximum35.308986
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:43:55.806773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.179944
5-th percentile35.197742
Q135.231834
median35.239363
Q335.248838
95-th percentile35.303512
Maximum35.308986
Range0.12904259
Interquartile range (IQR)0.017003924

Descriptive statistics

Standard deviation0.029412407
Coefficient of variation (CV)0.00083454899
Kurtosis1.192405
Mean35.243475
Median Absolute Deviation (MAD)0.0093219728
Skewness0.61818886
Sum1057.3043
Variance0.00086508968
MonotonicityNot monotonic
2023-12-11T09:43:55.934542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
35.3026923317 1
 
3.3%
35.227235071 1
 
3.3%
35.2324103388 1
 
3.3%
35.2364276565 1
 
3.3%
35.244139318 1
 
3.3%
35.2488410675 1
 
3.3%
35.2518743756 1
 
3.3%
35.2507395433 1
 
3.3%
35.2220721265 1
 
3.3%
35.2597545642 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
35.1799436684 1
3.3%
35.1968631946 1
3.3%
35.198815292 1
3.3%
35.2220721265 1
3.3%
35.227235071 1
3.3%
35.2287844978 1
3.3%
35.2305541049 1
3.3%
35.2316424917 1
3.3%
35.2324103388 1
3.3%
35.2340686067 1
3.3%
ValueCountFrequency (%)
35.3089862593 1
3.3%
35.3041820966 1
3.3%
35.3026923317 1
3.3%
35.2998986425 1
3.3%
35.2597545642 1
3.3%
35.2518743756 1
3.3%
35.2507395433 1
3.3%
35.2488410675 1
3.3%
35.2488303056 1
3.3%
35.2485404962 1
3.3%

경도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean128.8628
Minimum128.73691
Maximum128.98109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T09:43:56.064039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.73691
5-th percentile128.74463
Q1128.85334
median128.87783
Q3128.89509
95-th percentile128.92511
Maximum128.98109
Range0.24418294
Interquartile range (IQR)0.041751848

Descriptive statistics

Standard deviation0.057872515
Coefficient of variation (CV)0.00044910179
Kurtosis0.45202858
Mean128.8628
Median Absolute Deviation (MAD)0.023111514
Skewness-0.76345354
Sum3865.8841
Variance0.003349228
MonotonicityNot monotonic
2023-12-11T09:43:56.191126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
128.7369090115 1
 
3.3%
128.8786071891 1
 
3.3%
128.8968450203 1
 
3.3%
128.9164564287 1
 
3.3%
128.9074675448 1
 
3.3%
128.910333012 1
 
3.3%
128.9097186435 1
 
3.3%
128.9095960603 1
 
3.3%
128.8595149752 1
 
3.3%
128.8665291699 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
128.7369090115 1
3.3%
128.7409250529 1
3.3%
128.7491559408 1
3.3%
128.7674561962 1
3.3%
128.7944697078 1
3.3%
128.8153650923 1
3.3%
128.8161142453 1
3.3%
128.8519552559 1
3.3%
128.857491114 1
3.3%
128.8582752871 1
3.3%
ValueCountFrequency (%)
128.9810919512 1
3.3%
128.9321864196 1
3.3%
128.9164564287 1
3.3%
128.910333012 1
3.3%
128.9097186435 1
3.3%
128.9095960603 1
3.3%
128.9074675448 1
3.3%
128.8968450203 1
3.3%
128.8898292148 1
3.3%
128.8873007421 1
3.3%

Interactions

2023-12-11T09:43:52.616079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:52.448745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:52.700731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:43:52.534473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:43:56.272965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역업소명도로명주소지번주소연락처40리터용기 판매여부위도경도
지역1.0000.8811.0001.0001.0000.1860.8040.875
업소명0.8811.0001.0001.0001.0001.0000.9650.000
도로명주소1.0001.0001.0001.0001.0001.0001.0001.000
지번주소1.0001.0001.0001.0001.0001.0001.0001.000
연락처1.0001.0001.0001.0001.0001.0001.0001.000
40리터용기 판매여부0.1861.0001.0001.0001.0001.0000.2240.423
위도0.8040.9651.0001.0001.0000.2241.0000.743
경도0.8750.0001.0001.0001.0000.4230.7431.000
2023-12-11T09:43:56.368086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지역40리터용기 판매여부
지역1.0000.066
40리터용기 판매여부0.0661.000
2023-12-11T09:43:56.438897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
위도경도지역40리터용기 판매여부
위도1.0000.1190.4530.201
경도0.1191.0000.5800.379
지역0.4530.5801.0000.066
40리터용기 판매여부0.2010.3790.0661.000

Missing values

2023-12-11T09:43:52.819208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:43:52.989970image/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

지역업소명도로명주소지번주소연락처40리터용기 판매여부위도경도
0진영읍탑마트진영점경상남도 김해시 진영읍 진영로221경상남도 김해시 진영읍 여래리454-9055-345-0388Y35.302692128.736909
1진영읍대가야식자재마트경상남도 김해시 진영읍 김해대로464-35경상남도 김해시 진영읍 여래리473-2055-346-5580Y35.304182128.740925
2장유동탑마트관동점경상남도 김해시 덕정로85경상남도 김해시 관동동590055-337-6133N35.179944128.79447
3장유동행복마트경상남도 김해시 장유로334번길2경상남도 김해시 신문동500-1055-313-4088Y35.196863128.816114
4장유동뉴월드마트장유점경상남도 김해시 장유로312경상남도 김해시 무계동278055-314-2552Y35.198815128.815365
5진례면정진농산경상남도 김해시 진례면 송현로180경상남도 김해시 진례면 송현리732-2055-345-6895Y35.245822128.767456
6진례면1000냥부터경상남도 김해시 진례면 송현로8경상남도 김해시 진례면 송정리240-4055-346-7961Y35.24883128.749156
7생림면탑훼미리마트경상남도 김해시 생림면 나전로174경상남도 김해시 생림면 나전리596-6055-323-3311Y35.299899128.877062
8상동면천사할인마트경상남도 김해시 상동면 상동로575경상남도 김해시 상동면 대감리651-3055-339-3008Y35.308986128.932186
9대동면서원탑할인마트경상남도 김해시 대동면 대동로616경상남도 김해시 대동면 초정리146-3055-335-3012N35.242032128.981092
지역업소명도로명주소지번주소연락처40리터용기 판매여부위도경도
20내외동뉴홀마트경상남도 김해시 평전로106경상남도 김해시 내동187-1055-335-3100N35.239538128.857491
21북부동탑마트구산점경상남도 김해시 구산로50경상남도 김해시 구산동294-1055-325-6283N35.24854128.874134
22북부동탑마트삼계점경상남도 김해시 삼계중앙로12경상남도 김해시 삼계동1492-3055-333-3553N35.259755128.866529
23칠산서부동금풍마트경상남도 김해시 흥동로99경상남도 김해시 흥동55-12055-313-4948Y35.222072128.859515
24삼안동동일마트경상남도 김해시 삼안로255번길4경상남도 김해시 삼방동665-7055-321-8952Y35.25074128.909596
25삼안동두배로마트경상남도 김해시 삼안로268번길4경상남도 김해시 삼방동692055-325-7222Y35.251874128.909719
26삼안동서원탑할인마트경상남도 김해시 삼안로235번길3경상남도 김해시 삼방동688-1055-335-7733N35.248841128.910333
27삼안동탑마트삼방점경상남도 김해시 활천로255번길16경상남도 김해시 삼방동178-1055-324-6272Y35.244139128.907468
28삼안동천지환경산업경상남도 김해시 분성로645경상남도 김해시 안동248-10055-328-3179Y35.236428128.916456
29삼안동한양쇼핑경상남도 김해시 활천로36번길36경상남도 김해시 삼정동594-1055-327-0880Y35.23241128.896845