Overview

Dataset statistics

Number of variables7
Number of observations187
Missing cells188
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.1 KiB
Average record size in memory60.7 B

Variable types

Numeric4
Text3

Dataset

Description통영시 관내 음식물 쓰레기 다량 배출사업장에 대한 사업장, 주소, 전화번호, 예상배출물양, 면적, 급식인원에 대한 정보
Author경상남도 통영시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15034253

Alerts

면적(제곱미터) is highly overall correlated with 급식인원(명)High correlation
급식인원(명) is highly overall correlated with 면적(제곱미터)High correlation
전화번호 has 18 (9.6%) missing valuesMissing
면적(제곱미터) has 24 (12.8%) missing valuesMissing
급식인원(명) has 146 (78.1%) missing valuesMissing
연번 has unique valuesUnique
사업장 has unique valuesUnique

Reproduction

Analysis started2024-03-13 00:14:41.813381
Analysis finished2024-03-13 00:14:43.656990
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94
Minimum1
Maximum187
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:14:43.720241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10.3
Q147.5
median94
Q3140.5
95-th percentile177.7
Maximum187
Range186
Interquartile range (IQR)93

Descriptive statistics

Standard deviation54.126395
Coefficient of variation (CV)0.57581272
Kurtosis-1.2
Mean94
Median Absolute Deviation (MAD)47
Skewness0
Sum17578
Variance2929.6667
MonotonicityStrictly increasing
2024-03-13T09:14:43.838874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
130 1
 
0.5%
121 1
 
0.5%
122 1
 
0.5%
123 1
 
0.5%
124 1
 
0.5%
125 1
 
0.5%
126 1
 
0.5%
127 1
 
0.5%
128 1
 
0.5%
Other values (177) 177
94.7%
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 (%)
187 1
0.5%
186 1
0.5%
185 1
0.5%
184 1
0.5%
183 1
0.5%
182 1
0.5%
181 1
0.5%
180 1
0.5%
179 1
0.5%
178 1
0.5%

사업장
Text

UNIQUE 

Distinct187
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-13T09:14:44.011224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length23
Median length14
Mean length6.7807487
Min length2

Characters and Unicode

Total characters1268
Distinct characters300
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique187 ?
Unique (%)100.0%

Sample

1st row자연채한정식
2nd row수향
3rd row고성곱창
4th row산청한우갈비
5th row은혜갈비
ValueCountFrequency (%)
통영점 2
 
1.0%
자연채한정식 1
 
0.5%
주)우경기업 1
 
0.5%
하이브 1
 
0.5%
황금거북 1
 
0.5%
통영미가 1
 
0.5%
행복한화로구이 1
 
0.5%
수향귀빈정 1
 
0.5%
포르투나호텔 1
 
0.5%
벽방초등학교 1
 
0.5%
Other values (186) 186
94.4%
2024-03-13T09:14:44.257016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
영 53
 
4.2%
통 52
 
4.1%
식 33
 
2.6%
학 30
 
2.4%
교 28
 
2.2%
한 24
 
1.9%
등 24
 
1.9%
원 23
 
1.8%
점 22
 
1.7%
소 18
 
1.4%
Other values (290) 961
75.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1217
96.0%
Uppercase Letter 19
 
1.5%
Space Separator 10
 
0.8%
Close Punctuation 6
 
0.5%
Open Punctuation 6
 
0.5%
Decimal Number 6
 
0.5%
Other Symbol 2
 
0.2%
Other Punctuation 2
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
영 53
 
4.4%
통 52
 
4.3%
식 33
 
2.7%
학 30
 
2.5%
교 28
 
2.3%
한 24
 
2.0%
등 24
 
2.0%
원 23
 
1.9%
점 22
 
1.8%
소 18
 
1.5%
Other values (269) 910
74.8%
Uppercase Letter
ValueCountFrequency (%)
C 4
21.1%
B 3
15.8%
E 2
10.5%
U 2
10.5%
F 1
 
5.3%
A 1
 
5.3%
P 1
 
5.3%
L 1
 
5.3%
S 1
 
5.3%
D 1
 
5.3%
Other values (2) 2
10.5%
Decimal Number
ValueCountFrequency (%)
1 3
50.0%
0 2
33.3%
9 1
 
16.7%
Other Punctuation
ValueCountFrequency (%)
& 1
50.0%
. 1
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1219
96.1%
Common 30
 
2.4%
Latin 19
 
1.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
영 53
 
4.3%
통 52
 
4.3%
식 33
 
2.7%
학 30
 
2.5%
교 28
 
2.3%
한 24
 
2.0%
등 24
 
2.0%
원 23
 
1.9%
점 22
 
1.8%
소 18
 
1.5%
Other values (270) 912
74.8%
Latin
ValueCountFrequency (%)
C 4
21.1%
B 3
15.8%
E 2
10.5%
U 2
10.5%
F 1
 
5.3%
A 1
 
5.3%
P 1
 
5.3%
L 1
 
5.3%
S 1
 
5.3%
D 1
 
5.3%
Other values (2) 2
10.5%
Common
ValueCountFrequency (%)
10
33.3%
) 6
20.0%
( 6
20.0%
1 3
 
10.0%
0 2
 
6.7%
& 1
 
3.3%
. 1
 
3.3%
9 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1217
96.0%
ASCII 49
 
3.9%
None 2
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
영 53
 
4.4%
통 52
 
4.3%
식 33
 
2.7%
학 30
 
2.5%
교 28
 
2.3%
한 24
 
2.0%
등 24
 
2.0%
원 23
 
1.9%
점 22
 
1.8%
소 18
 
1.5%
Other values (269) 910
74.8%
ASCII
ValueCountFrequency (%)
10
20.4%
) 6
12.2%
( 6
12.2%
C 4
 
8.2%
1 3
 
6.1%
B 3
 
6.1%
0 2
 
4.1%
E 2
 
4.1%
U 2
 
4.1%
F 1
 
2.0%
Other values (10) 10
20.4%
None
ValueCountFrequency (%)
㈜ 2
100.0%

주소
Text

Distinct165
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2024-03-13T09:14:44.611709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length25
Mean length19.229947
Min length15

Characters and Unicode

Total characters3596
Distinct characters101
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

Unique151 ?
Unique (%)80.7%

Sample

1st row경상남도 통영시 봉수돌샘길 122
2nd row경상남도 통영시 항남3길 29
3rd row경상남도 통영시 무전3길 11-9
4th row경상남도 통영시 미수해안로 102
5th row경상남도 통영시 광도면 죽림4로 23-37
ValueCountFrequency (%)
경상남도 187
22.3%
통영시 187
22.3%
광도면 48
 
5.7%
용남면 19
 
2.3%
산양읍 13
 
1.6%
남해안대로 13
 
1.6%
죽림해안로 12
 
1.4%
죽림4로 11
 
1.3%
도남로 9
 
1.1%
미수해안로 8
 
1.0%
Other values (204) 330
39.4%
2024-03-13T09:14:45.019557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
650
18.1%
도 253
 
7.0%
남 234
 
6.5%
영 191
 
5.3%
통 190
 
5.3%
상 188
 
5.2%
경 187
 
5.2%
시 187
 
5.2%
1 126
 
3.5%
로 113
 
3.1%
Other values (91) 1277
35.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2268
63.1%
Space Separator 650
 
18.1%
Decimal Number 614
 
17.1%
Dash Punctuation 56
 
1.6%
Open Punctuation 3
 
0.1%
Close Punctuation 3
 
0.1%
Other Punctuation 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 253
11.2%
남 234
10.3%
영 191
 
8.4%
통 190
 
8.4%
상 188
 
8.3%
경 187
 
8.2%
시 187
 
8.2%
로 113
 
5.0%
길 73
 
3.2%
면 72
 
3.2%
Other values (76) 580
25.6%
Decimal Number
ValueCountFrequency (%)
1 126
20.5%
2 93
15.1%
5 82
13.4%
3 71
11.6%
4 58
9.4%
6 48
 
7.8%
8 40
 
6.5%
7 38
 
6.2%
9 33
 
5.4%
0 25
 
4.1%
Space Separator
ValueCountFrequency (%)
650
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2268
63.1%
Common 1328
36.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 253
11.2%
남 234
10.3%
영 191
 
8.4%
통 190
 
8.4%
상 188
 
8.3%
경 187
 
8.2%
시 187
 
8.2%
로 113
 
5.0%
길 73
 
3.2%
면 72
 
3.2%
Other values (76) 580
25.6%
Common
ValueCountFrequency (%)
650
48.9%
1 126
 
9.5%
2 93
 
7.0%
5 82
 
6.2%
3 71
 
5.3%
4 58
 
4.4%
- 56
 
4.2%
6 48
 
3.6%
8 40
 
3.0%
7 38
 
2.9%
Other values (5) 66
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2268
63.1%
ASCII 1328
36.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
650
48.9%
1 126
 
9.5%
2 93
 
7.0%
5 82
 
6.2%
3 71
 
5.3%
4 58
 
4.4%
- 56
 
4.2%
6 48
 
3.6%
8 40
 
3.0%
7 38
 
2.9%
Other values (5) 66
 
5.0%
Hangul
ValueCountFrequency (%)
도 253
11.2%
남 234
10.3%
영 191
 
8.4%
통 190
 
8.4%
상 188
 
8.3%
경 187
 
8.2%
시 187
 
8.2%
로 113
 
5.0%
길 73
 
3.2%
면 72
 
3.2%
Other values (76) 580
25.6%

전화번호
Text

MISSING 

Distinct166
Distinct (%)98.2%
Missing18
Missing (%)9.6%
Memory size1.6 KiB
2024-03-13T09:14:45.214717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.005917
Min length12

Characters and Unicode

Total characters2029
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

Unique163 ?
Unique (%)96.4%

Sample

1st row055-645-3839
2nd row055-645-3052
3rd row055-645-4011
4th row055-643-4737
5th row055-644-7621
ValueCountFrequency (%)
055-648-0701 2
 
1.2%
055-644-2700 2
 
1.2%
055-640-5000 2
 
1.2%
055-644-0096 1
 
0.6%
055-650-7290 1
 
0.6%
055-648-8700 1
 
0.6%
055-725-0000 1
 
0.6%
055-649-3310 1
 
0.6%
055-648-2789 1
 
0.6%
055-648-1580 1
 
0.6%
Other values (156) 156
92.3%
2024-03-13T09:14:45.771215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 427
21.0%
- 338
16.7%
0 305
15.0%
6 244
12.0%
4 217
10.7%
2 100
 
4.9%
8 98
 
4.8%
1 83
 
4.1%
3 81
 
4.0%
9 78
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1691
83.3%
Dash Punctuation 338
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 427
25.3%
0 305
18.0%
6 244
14.4%
4 217
12.8%
2 100
 
5.9%
8 98
 
5.8%
1 83
 
4.9%
3 81
 
4.8%
9 78
 
4.6%
7 58
 
3.4%
Dash Punctuation
ValueCountFrequency (%)
- 338
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2029
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 427
21.0%
- 338
16.7%
0 305
15.0%
6 244
12.0%
4 217
10.7%
2 100
 
4.9%
8 98
 
4.8%
1 83
 
4.1%
3 81
 
4.0%
9 78
 
3.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2029
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 427
21.0%
- 338
16.7%
0 305
15.0%
6 244
12.0%
4 217
10.7%
2 100
 
4.9%
8 98
 
4.8%
1 83
 
4.1%
3 81
 
4.0%
9 78
 
3.8%

예상배출물양(kg)
Real number (ℝ)

Distinct133
Distinct (%)71.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12997.369
Minimum120
Maximum173639
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:14:45.886864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile1200
Q12330
median8600
Q315391
95-th percentile40063.1
Maximum173639
Range173519
Interquartile range (IQR)13061

Descriptive statistics

Standard deviation20345.071
Coefficient of variation (CV)1.5653223
Kurtosis38.296966
Mean12997.369
Median Absolute Deviation (MAD)6595
Skewness5.472197
Sum2430508
Variance4.1392193 × 108
MonotonicityNot monotonic
2024-03-13T09:14:46.009739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1600 23
 
12.3%
14400 5
 
2.7%
2000 4
 
2.1%
8640 4
 
2.1%
5760 4
 
2.1%
17280 3
 
1.6%
7680 3
 
1.6%
11520 3
 
1.6%
600 2
 
1.1%
9500 2
 
1.1%
Other values (123) 134
71.7%
ValueCountFrequency (%)
120 1
0.5%
226 1
0.5%
240 1
0.5%
418 1
0.5%
500 1
0.5%
600 2
1.1%
667 1
0.5%
720 1
0.5%
1200 2
1.1%
1400 1
0.5%
ValueCountFrequency (%)
173639 1
0.5%
167640 1
0.5%
82001 1
0.5%
58900 1
0.5%
57600 1
0.5%
48700 1
0.5%
43268 1
0.5%
42050 1
0.5%
40580 1
0.5%
40133 1
0.5%

면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct162
Distinct (%)99.4%
Missing24
Missing (%)12.8%
Infinite0
Infinite (%)0.0%
Mean456.63202
Minimum19.25
Maximum19957.43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:14:46.127121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19.25
5-th percentile201.606
Q1218.255
median250
Q3328
95-th percentile669.441
Maximum19957.43
Range19938.18
Interquartile range (IQR)109.745

Descriptive statistics

Standard deviation1605.5253
Coefficient of variation (CV)3.5160156
Kurtosis137.21195
Mean456.63202
Median Absolute Deviation (MAD)40.02
Skewness11.441504
Sum74431.02
Variance2577711.5
MonotonicityNot monotonic
2024-03-13T09:14:46.249757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
249.83 2
 
1.1%
361.2 1
 
0.5%
240.16 1
 
0.5%
289.72 1
 
0.5%
206.74 1
 
0.5%
306.9 1
 
0.5%
208.87 1
 
0.5%
215.52 1
 
0.5%
288.7 1
 
0.5%
235.63 1
 
0.5%
Other values (152) 152
81.3%
(Missing) 24
 
12.8%
ValueCountFrequency (%)
19.25 1
0.5%
20.79 1
0.5%
31.0 1
0.5%
60.8 1
0.5%
165.7 1
0.5%
181.7 1
0.5%
195.0 1
0.5%
201.0 1
0.5%
201.6 1
0.5%
201.66 1
0.5%
ValueCountFrequency (%)
19957.43 1
0.5%
5909.0 1
0.5%
1108.57 1
0.5%
965.25 1
0.5%
795.9 1
0.5%
781.48 1
0.5%
712.88 1
0.5%
700.0 1
0.5%
678.12 1
0.5%
591.33 1
0.5%

급식인원(명)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)92.7%
Missing146
Missing (%)78.1%
Infinite0
Infinite (%)0.0%
Mean476.60976
Minimum100
Maximum1500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2024-03-13T09:14:46.357935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile123
Q1213
median374
Q3650
95-th percentile1220
Maximum1500
Range1400
Interquartile range (IQR)437

Descriptive statistics

Standard deviation346.33581
Coefficient of variation (CV)0.72666538
Kurtosis1.5052163
Mean476.60976
Median Absolute Deviation (MAD)174
Skewness1.3494048
Sum19541
Variance119948.49
MonotonicityNot monotonic
2024-03-13T09:14:46.492457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
200 3
 
1.6%
220 2
 
1.1%
467 1
 
0.5%
868 1
 
0.5%
330 1
 
0.5%
463 1
 
0.5%
490 1
 
0.5%
1091 1
 
0.5%
100 1
 
0.5%
172 1
 
0.5%
Other values (28) 28
 
15.0%
(Missing) 146
78.1%
ValueCountFrequency (%)
100 1
 
0.5%
120 1
 
0.5%
123 1
 
0.5%
127 1
 
0.5%
130 1
 
0.5%
167 1
 
0.5%
172 1
 
0.5%
200 3
1.6%
213 1
 
0.5%
220 2
1.1%
ValueCountFrequency (%)
1500 1
0.5%
1360 1
0.5%
1220 1
0.5%
1091 1
0.5%
870 1
0.5%
868 1
0.5%
710 1
0.5%
700 1
0.5%
680 1
0.5%
660 1
0.5%

Interactions

2024-03-13T09:14:43.090689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.108286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.444715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.751337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:43.171885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.222296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.516223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.846691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:43.261740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.304942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.585584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.926601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:43.338729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.377593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:42.663409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-13T09:14:43.002604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-13T09:14:46.599948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번예상배출물양(kg)면적(제곱미터)급식인원(명)
연번1.0000.2380.1780.602
예상배출물양(kg)0.2381.0000.8130.598
면적(제곱미터)0.1780.8131.000NaN
급식인원(명)0.6020.598NaN1.000
2024-03-13T09:14:46.686442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번예상배출물양(kg)면적(제곱미터)급식인원(명)
연번1.0000.0800.0650.196
예상배출물양(kg)0.0801.0000.1620.264
면적(제곱미터)0.0650.1621.0000.694
급식인원(명)0.1960.2640.6941.000

Missing values

2024-03-13T09:14:43.427680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-13T09:14:43.519521image/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.
2024-03-13T09:14:43.614008image/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

연번사업장주소전화번호예상배출물양(kg)면적(제곱미터)급식인원(명)
01자연채한정식경상남도 통영시 봉수돌샘길 122055-645-38396720265.73<NA>
12수향경상남도 통영시 항남3길 29055-645-305218720209.98<NA>
23고성곱창경상남도 통영시 무전3길 11-9055-645-40111600231.97<NA>
34산청한우갈비경상남도 통영시 미수해안로 102055-643-47379200240.0<NA>
45은혜갈비경상남도 통영시 광도면 죽림4로 23-37055-644-76218600335.0<NA>
56국일관식당경상남도 통영시 항남5길 25055-645-41417200233.0<NA>
67독도횟집경상남도 통영시 미수해안로 104055-643-145517280282.0<NA>
78형제갈비경상남도 통영시 정동2길 42055-644-789414400327.79<NA>
89향토집경상남도 통영시 무전5길 37-41055-645-480817000208.7<NA>
910엘리제CAFE&PUB경상남도 통영시 큰발개1길 33055-640-81341600483.0<NA>
연번사업장주소전화번호예상배출물양(kg)면적(제곱미터)급식인원(명)
177178㈜이마트 통영점경상남도 통영시 광도면 죽림4로 9055-650-123417363919957.43<NA>
178179롯데쇼핑(주)롯데마트통영점경상남도 통영시 무전대로 65055-640-250058900315.0<NA>
179180통영수산업협동조합경상남도 통영시 동호안길 33055-646-1221268805909.0<NA>
180181금호리조트㈜통영마리나경상남도 통영시 큰발개1길 33055-646-7001167640272.0<NA>
181182CLUB E.S 통영리조트경상남도 통영시 산양읍 척포길 628-113055-644-00966000201.0<NA>
182183통영한산마리나호텔엔리조트경상남도 통영시 산양읍 삼칭이해안길 820055-648-3222576031.0<NA>
183184(주)아워홈 스탠포드호텔 앤 리조트 통영점경상남도 통영시 도남로 347<NA>5894268.0<NA>
184185(주)통영베이콘도호텔경상남도 통영시 도남로 257-93055-648-88648515<NA><NA>
185186통영비치캐슬호텔경상남도 통영시 평인일주로 486-1055-644-270010800<NA><NA>
186187통영관광호텔경상남도 통영시 항북길 12-9055-641-10002800<NA><NA>