Overview

Dataset statistics

Number of variables8
Number of observations216
Missing cells34
Missing cells (%)2.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.3 KiB
Average record size in memory67.6 B

Variable types

Numeric3
Text3
Categorical2

Dataset

Description경상남도 사천시 음식물폐기물 다량배출사업장에 관한 정보입니다.(상호명, 읍면동 소재지, 사업장구분, 사업장규모, 년배출량 예상, 월배출량 예상 )
Author경상남도 사천시
URLhttps://www.data.go.kr/data/15099935/fileData.do

Alerts

년배출량예상 is highly overall correlated with 월배출량 예상High correlation
월배출량 예상 is highly overall correlated with 년배출량예상High correlation
사업장구분 is highly imbalanced (54.5%)Imbalance
년배출량예상 has 17 (7.9%) missing valuesMissing
월배출량 예상 has 17 (7.9%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-12 01:13:48.590853
Analysis finished2023-12-12 01:13:50.742125
Duration2.15 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct216
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean109.66204
Minimum1
Maximum219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T10:13:50.851228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile11.75
Q155.75
median109.5
Q3163.25
95-th percentile208.25
Maximum219
Range218
Interquartile range (IQR)107.5

Descriptive statistics

Standard deviation63.328759
Coefficient of variation (CV)0.57749027
Kurtosis-1.1869244
Mean109.66204
Median Absolute Deviation (MAD)54
Skewness0.0091038234
Sum23687
Variance4010.5318
MonotonicityStrictly increasing
2023-12-12T10:13:51.043820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.5%
151 1
 
0.5%
140 1
 
0.5%
141 1
 
0.5%
142 1
 
0.5%
143 1
 
0.5%
144 1
 
0.5%
145 1
 
0.5%
146 1
 
0.5%
147 1
 
0.5%
Other values (206) 206
95.4%
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 (%)
219 1
0.5%
218 1
0.5%
217 1
0.5%
216 1
0.5%
215 1
0.5%
214 1
0.5%
213 1
0.5%
212 1
0.5%
211 1
0.5%
210 1
0.5%

상호명
Text

Distinct214
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T10:13:51.413619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length27
Median length23
Mean length8.0277778
Min length2

Characters and Unicode

Total characters1734
Distinct characters333
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

Unique212 ?
Unique (%)98.1%

Sample

1st row진주집
2nd row원조사천냉면
3rd row유나식당
4th row해변횟집
5th row대교횟집
ValueCountFrequency (%)
사천점 3
 
1.2%
에스앤케이항공주식회사㈜지희 2
 
0.8%
삼천포점 2
 
0.8%
부자손짜장 2
 
0.8%
호텔엘리너스 2
 
0.8%
파로스 2
 
0.8%
bhi사천점(풀무원푸드앤컬쳐 1
 
0.4%
경남국제외국인학교(㈜풀무원푸드앤컬처 1
 
0.4%
사천여자중학교 1
 
0.4%
㈜아스트(비원스토리 1
 
0.4%
Other values (227) 227
93.0%
2023-12-12T10:13:51.933285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 64
 
3.7%
사 48
 
2.8%
스 41
 
2.4%
삼 36
 
2.1%
리 34
 
2.0%
) 33
 
1.9%
( 33
 
1.9%
학 32
 
1.8%
교 32
 
1.8%
28
 
1.6%
Other values (323) 1353
78.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1579
91.1%
Close Punctuation 33
 
1.9%
Open Punctuation 33
 
1.9%
Uppercase Letter 30
 
1.7%
Space Separator 28
 
1.6%
Other Symbol 28
 
1.6%
Decimal Number 2
 
0.1%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 64
 
4.1%
사 48
 
3.0%
스 41
 
2.6%
삼 36
 
2.3%
리 34
 
2.2%
학 32
 
2.0%
교 32
 
2.0%
식 28
 
1.8%
원 27
 
1.7%
점 25
 
1.6%
Other values (303) 1212
76.8%
Uppercase Letter
ValueCountFrequency (%)
C 10
33.3%
B 3
 
10.0%
H 2
 
6.7%
E 2
 
6.7%
T 2
 
6.7%
Y 2
 
6.7%
M 2
 
6.7%
K 1
 
3.3%
W 1
 
3.3%
I 1
 
3.3%
Other values (4) 4
 
13.3%
Close Punctuation
ValueCountFrequency (%)
) 33
100.0%
Open Punctuation
ValueCountFrequency (%)
( 33
100.0%
Space Separator
ValueCountFrequency (%)
28
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 28
100.0%
Decimal Number
ValueCountFrequency (%)
2 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1607
92.7%
Common 97
 
5.6%
Latin 30
 
1.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 64
 
4.0%
사 48
 
3.0%
스 41
 
2.6%
삼 36
 
2.2%
리 34
 
2.1%
학 32
 
2.0%
교 32
 
2.0%
㈜ 28
 
1.7%
식 28
 
1.7%
원 27
 
1.7%
Other values (304) 1237
77.0%
Latin
ValueCountFrequency (%)
C 10
33.3%
B 3
 
10.0%
H 2
 
6.7%
E 2
 
6.7%
T 2
 
6.7%
Y 2
 
6.7%
M 2
 
6.7%
K 1
 
3.3%
W 1
 
3.3%
I 1
 
3.3%
Other values (4) 4
 
13.3%
Common
ValueCountFrequency (%)
) 33
34.0%
( 33
34.0%
28
28.9%
2 2
 
2.1%
. 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1579
91.1%
ASCII 127
 
7.3%
None 28
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 64
 
4.1%
사 48
 
3.0%
스 41
 
2.6%
삼 36
 
2.3%
리 34
 
2.2%
학 32
 
2.0%
교 32
 
2.0%
식 28
 
1.8%
원 27
 
1.7%
점 25
 
1.6%
Other values (303) 1212
76.8%
ASCII
ValueCountFrequency (%)
) 33
26.0%
( 33
26.0%
28
22.0%
C 10
 
7.9%
B 3
 
2.4%
H 2
 
1.6%
E 2
 
1.6%
T 2
 
1.6%
Y 2
 
1.6%
M 2
 
1.6%
Other values (9) 10
 
7.9%
None
ValueCountFrequency (%)
㈜ 28
100.0%

읍면동
Categorical

Distinct31
Distinct (%)14.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
사천읍
41 
사남면
35 
벌리동
15 
정동면
11 
대방동
 
10
Other values (26)
104 

Length

Max length4
Median length3
Mean length2.9814815
Min length2

Unique

Unique7 ?
Unique (%)3.2%

Sample

1st row사남면
2nd row사천읍
3rd row대방동
4th row용현면
5th row서포면

Common Values

ValueCountFrequency (%)
사천읍 41
19.0%
사남면 35
16.2%
벌리동 15
 
6.9%
정동면 11
 
5.1%
대방동 10
 
4.6%
용현면 10
 
4.6%
향촌동 10
 
4.6%
곤양면 9
 
4.2%
실안동 8
 
3.7%
서포면 7
 
3.2%
Other values (21) 60
27.8%

Length

2023-12-12T10:13:52.143139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
사천읍 42
19.4%
사남면 35
16.2%
벌리동 15
 
6.9%
정동면 12
 
5.6%
대방동 10
 
4.6%
용현면 10
 
4.6%
향촌동 10
 
4.6%
곤양면 9
 
4.2%
실안동 8
 
3.7%
서포면 7
 
3.2%
Other values (19) 58
26.9%

소재지
Text

Distinct206
Distinct (%)95.4%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T10:13:52.520584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length25
Mean length15.689815
Min length9

Characters and Unicode

Total characters3389
Distinct characters143
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique198 ?
Unique (%)91.7%

Sample

1st row사천시 사남면 사천대로 1589-23
2nd row사천시 사천읍 사천대로 1852
3rd row사천시 유람선길 70(대방동)
4th row사천시 용현면 선진공원길 516
5th row사천시 서포면 자구로 69-20
ValueCountFrequency (%)
사천시 213
27.1%
사천읍 42
 
5.3%
사남면 35
 
4.4%
진삼로 15
 
1.9%
사천대로 14
 
1.8%
정동면 12
 
1.5%
옥산로 11
 
1.4%
용현면 10
 
1.3%
곤양면 9
 
1.1%
서포면 7
 
0.9%
Other values (287) 419
53.2%
2023-12-12T10:13:53.388075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
585
17.3%
사 316
 
9.3%
천 276
 
8.1%
시 217
 
6.4%
1 158
 
4.7%
로 132
 
3.9%
동 101
 
3.0%
2 96
 
2.8%
길 86
 
2.5%
면 83
 
2.4%
Other values (133) 1339
39.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1967
58.0%
Decimal Number 665
 
19.6%
Space Separator 585
 
17.3%
Close Punctuation 58
 
1.7%
Open Punctuation 56
 
1.7%
Dash Punctuation 41
 
1.2%
Other Punctuation 15
 
0.4%
Other Symbol 1
 
< 0.1%
Uppercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 316
16.1%
천 276
14.0%
시 217
 
11.0%
로 132
 
6.7%
동 101
 
5.1%
길 86
 
4.4%
면 83
 
4.2%
남 45
 
2.3%
읍 43
 
2.2%
대 31
 
1.6%
Other values (116) 637
32.4%
Decimal Number
ValueCountFrequency (%)
1 158
23.8%
2 96
14.4%
3 67
10.1%
4 65
9.8%
5 55
 
8.3%
6 52
 
7.8%
7 49
 
7.4%
0 47
 
7.1%
8 40
 
6.0%
9 36
 
5.4%
Space Separator
ValueCountFrequency (%)
585
100.0%
Close Punctuation
ValueCountFrequency (%)
) 58
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 41
100.0%
Other Punctuation
ValueCountFrequency (%)
, 15
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
A 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1968
58.1%
Common 1420
41.9%
Latin 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 316
16.1%
천 276
14.0%
시 217
 
11.0%
로 132
 
6.7%
동 101
 
5.1%
길 86
 
4.4%
면 83
 
4.2%
남 45
 
2.3%
읍 43
 
2.2%
대 31
 
1.6%
Other values (117) 638
32.4%
Common
ValueCountFrequency (%)
585
41.2%
1 158
 
11.1%
2 96
 
6.8%
3 67
 
4.7%
4 65
 
4.6%
) 58
 
4.1%
( 56
 
3.9%
5 55
 
3.9%
6 52
 
3.7%
7 49
 
3.5%
Other values (5) 179
 
12.6%
Latin
ValueCountFrequency (%)
A 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1967
58.0%
ASCII 1421
41.9%
None 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
585
41.2%
1 158
 
11.1%
2 96
 
6.8%
3 67
 
4.7%
4 65
 
4.6%
) 58
 
4.1%
( 56
 
3.9%
5 55
 
3.9%
6 52
 
3.7%
7 49
 
3.4%
Other values (6) 180
 
12.7%
Hangul
ValueCountFrequency (%)
사 316
16.1%
천 276
14.0%
시 217
 
11.0%
로 132
 
6.7%
동 101
 
5.1%
길 86
 
4.4%
면 83
 
4.2%
남 45
 
2.3%
읍 43
 
2.2%
대 31
 
1.6%
Other values (116) 637
32.4%
None
ValueCountFrequency (%)
㈜ 1
100.0%

사업장구분
Categorical

IMBALANCE 

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
일반음식점
140 
집단급식소
68 
관광숙박업
 
3
휴게음식점
 
2
대규모점포
 
2

Length

Max length9
Median length5
Mean length5.0185185
Min length5

Unique

Unique1 ?
Unique (%)0.5%

Sample

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

Common Values

ValueCountFrequency (%)
일반음식점 140
64.8%
집단급식소 68
31.5%
관광숙박업 3
 
1.4%
휴게음식점 2
 
0.9%
대규모점포 2
 
0.9%
집단급식소(위탁) 1
 
0.5%

Length

2023-12-12T10:13:53.618583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T10:13:53.784301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반음식점 140
64.8%
집단급식소 68
31.5%
관광숙박업 3
 
1.4%
휴게음식점 2
 
0.9%
대규모점포 2
 
0.9%
집단급식소(위탁 1
 
0.5%

사업장규모
Text

Distinct162
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
2023-12-12T10:13:54.274695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3657407
Min length3

Characters and Unicode

Total characters727
Distinct characters12
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique127 ?
Unique (%)58.8%

Sample

1st row493
2nd row263
3rd row260
4th row216
5th row210
ValueCountFrequency (%)
231 6
 
2.8%
200명 5
 
2.3%
225 4
 
1.9%
210 4
 
1.9%
212 4
 
1.9%
218 3
 
1.4%
216 3
 
1.4%
207 3
 
1.4%
228 3
 
1.4%
220 3
 
1.4%
Other values (152) 178
82.4%
2023-12-12T10:13:55.056463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 164
22.6%
0 113
15.5%
1 77
10.6%
3 72
9.9%
명 65
 
8.9%
4 53
 
7.3%
5 50
 
6.9%
8 39
 
5.4%
7 36
 
5.0%
6 34
 
4.7%
Other values (2) 24
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 661
90.9%
Other Letter 66
 
9.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 164
24.8%
0 113
17.1%
1 77
11.6%
3 72
10.9%
4 53
 
8.0%
5 50
 
7.6%
8 39
 
5.9%
7 36
 
5.4%
6 34
 
5.1%
9 23
 
3.5%
Other Letter
ValueCountFrequency (%)
명 65
98.5%
실 1
 
1.5%

Most occurring scripts

ValueCountFrequency (%)
Common 661
90.9%
Hangul 66
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 164
24.8%
0 113
17.1%
1 77
11.6%
3 72
10.9%
4 53
 
8.0%
5 50
 
7.6%
8 39
 
5.9%
7 36
 
5.4%
6 34
 
5.1%
9 23
 
3.5%
Hangul
ValueCountFrequency (%)
명 65
98.5%
실 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 661
90.9%
Hangul 66
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 164
24.8%
0 113
17.1%
1 77
11.6%
3 72
10.9%
4 53
 
8.0%
5 50
 
7.6%
8 39
 
5.9%
7 36
 
5.4%
6 34
 
5.1%
9 23
 
3.5%
Hangul
ValueCountFrequency (%)
명 65
98.5%
실 1
 
1.5%

년배출량예상
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct186
Distinct (%)93.5%
Missing17
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean9854.5327
Minimum80
Maximum70632
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T10:13:55.288526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum80
5-th percentile678
Q12756
median5024
Q312704
95-th percentile35020
Maximum70632
Range70552
Interquartile range (IQR)9948

Descriptive statistics

Standard deviation11863.873
Coefficient of variation (CV)1.2039001
Kurtosis6.7030465
Mean9854.5327
Median Absolute Deviation (MAD)3224
Skewness2.4146853
Sum1961052
Variance1.4075148 × 108
MonotonicityNot monotonic
2023-12-12T10:13:55.505735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1800 5
 
2.3%
22000 2
 
0.9%
3416 2
 
0.9%
14320 2
 
0.9%
2160 2
 
0.9%
3010 2
 
0.9%
2760 2
 
0.9%
4320 2
 
0.9%
4700 2
 
0.9%
120 2
 
0.9%
Other values (176) 176
81.5%
(Missing) 17
 
7.9%
ValueCountFrequency (%)
80 1
0.5%
100 1
0.5%
120 2
0.9%
150 1
0.5%
180 1
0.5%
200 1
0.5%
220 1
0.5%
300 1
0.5%
480 1
0.5%
700 1
0.5%
ValueCountFrequency (%)
70632 1
0.5%
61287 1
0.5%
53400 1
0.5%
50000 1
0.5%
47968 1
0.5%
45300 1
0.5%
44016 1
0.5%
43940 1
0.5%
43925 1
0.5%
39700 1
0.5%

월배출량 예상
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct181
Distinct (%)91.0%
Missing17
Missing (%)7.9%
Infinite0
Infinite (%)0.0%
Mean821.21106
Minimum7
Maximum5886
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2023-12-12T10:13:55.719355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile56.2
Q1229.5
median419
Q31058.5
95-th percentile2918.3
Maximum5886
Range5879
Interquartile range (IQR)829

Descriptive statistics

Standard deviation988.65175
Coefficient of variation (CV)1.2038948
Kurtosis6.7027437
Mean821.21106
Median Absolute Deviation (MAD)269
Skewness2.41462
Sum163421
Variance977432.28
MonotonicityNot monotonic
2023-12-12T10:13:55.926081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
150 6
 
2.8%
285 3
 
1.4%
175 2
 
0.9%
1193 2
 
0.9%
345 2
 
0.9%
251 2
 
0.9%
392 2
 
0.9%
180 2
 
0.9%
230 2
 
0.9%
360 2
 
0.9%
Other values (171) 174
80.6%
(Missing) 17
 
7.9%
ValueCountFrequency (%)
7 1
0.5%
8 1
0.5%
10 2
0.9%
13 1
0.5%
15 1
0.5%
17 1
0.5%
18 1
0.5%
25 1
0.5%
40 1
0.5%
58 1
0.5%
ValueCountFrequency (%)
5886 1
0.5%
5107 1
0.5%
4450 1
0.5%
4167 1
0.5%
3997 1
0.5%
3775 1
0.5%
3668 1
0.5%
3662 1
0.5%
3660 1
0.5%
3308 1
0.5%

Interactions

2023-12-12T10:13:49.919394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:49.167813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:49.536370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:50.038190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:49.282248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:49.659245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:50.173294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:49.410674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:13:49.789298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:13:56.079336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번읍면동사업장구분년배출량예상월배출량 예상
연번1.0000.4360.6720.4040.404
읍면동0.4361.0000.3430.0000.000
사업장구분0.6720.3431.0000.4290.429
년배출량예상0.4040.0000.4291.0001.000
월배출량 예상0.4040.0000.4291.0001.000
2023-12-12T10:13:56.227079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
사업장구분읍면동
사업장구분1.0000.146
읍면동0.1461.000
2023-12-12T10:13:56.350269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번년배출량예상월배출량 예상읍면동사업장구분
연번1.0000.1710.1710.1560.425
년배출량예상0.1711.0001.0000.0000.240
월배출량 예상0.1711.0001.0000.0000.240
읍면동0.1560.0000.0001.0000.146
사업장구분0.4250.2400.2400.1461.000

Missing values

2023-12-12T10:13:50.348459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:13:50.548774image/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.
2023-12-12T10:13:50.683038image/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

연번상호명읍면동소재지사업장구분사업장규모년배출량예상월배출량 예상
01진주집사남면사천시 사남면 사천대로 1589-23일반음식점4931270106
12원조사천냉면사천읍사천시 사천읍 사천대로 1852일반음식점2631805150
23유나식당대방동사천시 유람선길 70(대방동)일반음식점260<NA><NA>
34해변횟집용현면사천시 용현면 선진공원길 516일반음식점21615013
45대교횟집서포면사천시 서포면 자구로 69-20일반음식점2101700142
56홍천뚝배기사천읍사천시 사천읍 옥산로 54일반음식점2404608384
67목장원가든사천읍사천시 사천읍 옥산로 65-1일반음식점2073760313
78벌떡새우남양동사천시 해안관광로 375일반음식점2526976581
89자연횟집곤양면사천시 곤양면 질매섬길 67일반음식점23112010
910한일식당정동면사천시 정동면 진삼로 1396-11일반음식점2083900325
연번상호명읍면동소재지사업장구분사업장규모년배출량예상월배출량 예상
206210삼천포맛집 조선생삼계탕신벽동사천시 진삼로 261(신벽동)일반음식점2752232186
207211풍경추어탕용현면사천시 용현면 온정1길 39일반음식점2641230103
208212사천왕새우용현면사천시 용현면 석양길 376일반음식점2283240270
209213빨간도깨비 샤브샤브손칼국수노룡동사천시 미룡길 8(노룡동)일반음식점3291980165
210214고집남사천집사천읍사천시 사천읍 사주길 64일반음식점2412740228
211215란이식당대방동사천시 삼천포대교로 310일반음식점2512500208
212216에프앤에스(주)(한국경남태양유전)사남면사남면 외국기업로 82집단급식소1000명154421287
213217웰빙팜아르떼 레스토랑실안동사천시 해안관광로 109-10, 49동301호일반음식점424336902808
214218착한낙지 사천점사천읍사천읍 선인길 7일반음식점366<NA><NA>
215219하나병원사천읍사천시 사천읍 진삼로 1468-8집단급식소440명1600133