Overview

Dataset statistics

Number of variables9
Number of observations81
Missing cells1
Missing cells (%)0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.2 KiB
Average record size in memory78.6 B

Variable types

Categorical2
Text2
Numeric5

Dataset

Description공공하수처리시설 찌꺼기처리시설 현황(시도, 구군, 찌꺼기처리시설명, 처리방식, 시설용량(톤/일), 연간처리량(톤/년), 일평균 가동시간, 연중가동일, 가동율(%))
Author한국환경공단
URLhttps://www.data.go.kr/data/15065216/fileData.do

Alerts

시설용량 is highly overall correlated with 연간처리량High correlation
연간처리량 is highly overall correlated with 시설용량High correlation
구군 has 1 (1.2%) missing valuesMissing
연간처리량 has unique valuesUnique

Reproduction

Analysis started2024-04-06 08:21:26.009826
Analysis finished2024-04-06 08:21:32.859173
Duration6.85 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct14
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Memory size780.0 B
경상남도
14 
경기도
12 
경상북도
12 
충청북도
8 
충청남도
8 
Other values (9)
27 

Length

Max length7
Median length4
Mean length3.9876543
Min length3

Unique

Unique3 ?
Unique (%)3.7%

Sample

1st row서울특별시
2nd row서울특별시
3rd row서울특별시
4th row서울특별시
5th row부산광역시

Common Values

ValueCountFrequency (%)
경상남도 14
17.3%
경기도 12
14.8%
경상북도 12
14.8%
충청북도 8
9.9%
충청남도 8
9.9%
전라북도 8
9.9%
전라남도 6
7.4%
서울특별시 4
 
4.9%
부산광역시 2
 
2.5%
울산광역시 2
 
2.5%
Other values (4) 5
 
6.2%

Length

2024-04-06T17:21:33.020782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경상남도 14
17.3%
경기도 12
14.8%
경상북도 12
14.8%
충청북도 8
9.9%
충청남도 8
9.9%
전라북도 8
9.9%
전라남도 6
7.4%
서울특별시 4
 
4.9%
부산광역시 2
 
2.5%
울산광역시 2
 
2.5%
Other values (4) 5
 
6.2%

구군
Text

MISSING 

Distinct72
Distinct (%)90.0%
Missing1
Missing (%)1.2%
Memory size780.0 B
2024-04-06T17:21:33.506485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.975
Min length2

Characters and Unicode

Total characters238
Distinct characters69
Distinct categories1 ?
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 (%)82.5%

Sample

1st row성동구
2nd row강서구
3rd row강남구
4th row해운대구
5th row강서구
ValueCountFrequency (%)
단양군 3
 
3.8%
창원시 3
 
3.8%
밀양시 2
 
2.5%
남구 2
 
2.5%
청주시 2
 
2.5%
강서구 2
 
2.5%
영암군 1
 
1.2%
안동시 1
 
1.2%
김천시 1
 
1.2%
경주시 1
 
1.2%
Other values (62) 62
77.5%
2024-04-06T17:21:34.538037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 48
20.2%
군 25
 
10.5%
구 11
 
4.6%
주 10
 
4.2%
양 9
 
3.8%
천 9
 
3.8%
안 8
 
3.4%
성 6
 
2.5%
창 6
 
2.5%
원 6
 
2.5%
Other values (59) 100
42.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 238
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 48
20.2%
군 25
 
10.5%
구 11
 
4.6%
주 10
 
4.2%
양 9
 
3.8%
천 9
 
3.8%
안 8
 
3.4%
성 6
 
2.5%
창 6
 
2.5%
원 6
 
2.5%
Other values (59) 100
42.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 238
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 48
20.2%
군 25
 
10.5%
구 11
 
4.6%
주 10
 
4.2%
양 9
 
3.8%
천 9
 
3.8%
안 8
 
3.4%
성 6
 
2.5%
창 6
 
2.5%
원 6
 
2.5%
Other values (59) 100
42.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 238
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 48
20.2%
군 25
 
10.5%
구 11
 
4.6%
주 10
 
4.2%
양 9
 
3.8%
천 9
 
3.8%
안 8
 
3.4%
성 6
 
2.5%
창 6
 
2.5%
원 6
 
2.5%
Other values (59) 100
42.0%

찌꺼기처리시설명
Text

Distinct71
Distinct (%)87.7%
Missing0
Missing (%)0.0%
Memory size780.0 B
2024-04-06T17:21:34.936160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length13
Mean length9.5432099
Min length2

Characters and Unicode

Total characters773
Distinct characters103
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique66 ?
Unique (%)81.5%

Sample

1st row난지물재생센터
2nd row중랑물재생센터
3rd row서남물재생센터
4th row탄천물재생센터
5th row하수찌꺼기 건조시설(해운대)
ValueCountFrequency (%)
하수슬러지 12
 
10.1%
하수슬러지처리시설 8
 
6.7%
자원화시설 6
 
5.0%
처리시설 6
 
5.0%
건조시설 5
 
4.2%
하수찌꺼기 4
 
3.4%
소각시설 4
 
3.4%
하수슬러지자원화시설 2
 
1.7%
하수찌꺼기처리시설 2
 
1.7%
슬러지처리시설 2
 
1.7%
Other values (66) 68
57.1%
2024-04-06T17:21:35.897503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
시 77
 
10.0%
설 65
 
8.4%
지 51
 
6.6%
수 49
 
6.3%
슬 48
 
6.2%
러 48
 
6.2%
하 46
 
6.0%
38
 
4.9%
리 37
 
4.8%
처 36
 
4.7%
Other values (93) 278
36.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 725
93.8%
Space Separator 38
 
4.9%
Decimal Number 4
 
0.5%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%
Other Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 77
 
10.6%
설 65
 
9.0%
지 51
 
7.0%
수 49
 
6.8%
슬 48
 
6.6%
러 48
 
6.6%
하 46
 
6.3%
리 37
 
5.1%
처 36
 
5.0%
원 19
 
2.6%
Other values (85) 249
34.3%
Decimal Number
ValueCountFrequency (%)
1 1
25.0%
2 1
25.0%
3 1
25.0%
4 1
25.0%
Space Separator
ValueCountFrequency (%)
38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 725
93.8%
Common 48
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 77
 
10.6%
설 65
 
9.0%
지 51
 
7.0%
수 49
 
6.8%
슬 48
 
6.6%
러 48
 
6.6%
하 46
 
6.3%
리 37
 
5.1%
처 36
 
5.0%
원 19
 
2.6%
Other values (85) 249
34.3%
Common
ValueCountFrequency (%)
38
79.2%
) 2
 
4.2%
( 2
 
4.2%
, 2
 
4.2%
1 1
 
2.1%
2 1
 
2.1%
3 1
 
2.1%
4 1
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 725
93.8%
ASCII 48
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
시 77
 
10.6%
설 65
 
9.0%
지 51
 
7.0%
수 49
 
6.8%
슬 48
 
6.6%
러 48
 
6.6%
하 46
 
6.3%
리 37
 
5.1%
처 36
 
5.0%
원 19
 
2.6%
Other values (85) 249
34.3%
ASCII
ValueCountFrequency (%)
38
79.2%
) 2
 
4.2%
( 2
 
4.2%
, 2
 
4.2%
1 1
 
2.1%
2 1
 
2.1%
3 1
 
2.1%
4 1
 
2.1%

처리방식
Categorical

Distinct22
Distinct (%)27.2%
Missing0
Missing (%)0.0%
Memory size780.0 B
건조
24 
소각
12 
부숙화
10 
탄화
8 
건조연료화
6 
Other values (17)
21 

Length

Max length16
Median length2
Mean length3.3950617
Min length2

Unique

Unique15 ?
Unique (%)18.5%

Sample

1st row건조, 소각
2nd row건조
3rd row건조,소각
4th row건조
5th row건조

Common Values

ValueCountFrequency (%)
건조 24
29.6%
소각 12
14.8%
부숙화 10
12.3%
탄화 8
 
9.9%
건조연료화 6
 
7.4%
원심탈수 4
 
4.9%
고화 2
 
2.5%
건조 후 소각 1
 
1.2%
건조,소각 1
 
1.2%
간접 건조방식 1
 
1.2%
Other values (12) 12
14.8%

Length

2024-04-06T17:21:36.464802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
건조 26
28.3%
소각 14
15.2%
부숙화 10
 
10.9%
탄화 9
 
9.8%
건조연료화 6
 
6.5%
원심탈수 4
 
4.3%
고화 2
 
2.2%
건조방식 2
 
2.2%
직접가열 1
 
1.1%
벨트 1
 
1.1%
Other values (17) 17
18.5%

시설용량
Real number (ℝ)

HIGH CORRELATION 

Distinct36
Distinct (%)44.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean120.30864
Minimum5
Maximum650
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-04-06T17:21:36.814783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10
Q120
median50
Q3180
95-th percentile400
Maximum650
Range645
Interquartile range (IQR)160

Descriptive statistics

Standard deviation142.57828
Coefficient of variation (CV)1.1851042
Kurtosis3.1210704
Mean120.30864
Median Absolute Deviation (MAD)40
Skewness1.8019701
Sum9745
Variance20328.566
MonotonicityNot monotonic
2024-04-06T17:21:37.058906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
10 7
 
8.6%
30 7
 
8.6%
300 5
 
6.2%
20 5
 
6.2%
15 5
 
6.2%
40 5
 
6.2%
200 4
 
4.9%
50 4
 
4.9%
100 3
 
3.7%
90 3
 
3.7%
Other values (26) 33
40.7%
ValueCountFrequency (%)
5 2
 
2.5%
8 1
 
1.2%
10 7
8.6%
15 5
6.2%
16 1
 
1.2%
20 5
6.2%
25 1
 
1.2%
30 7
8.6%
35 1
 
1.2%
40 5
6.2%
ValueCountFrequency (%)
650 1
 
1.2%
585 1
 
1.2%
550 1
 
1.2%
435 1
 
1.2%
400 1
 
1.2%
360 1
 
1.2%
330 1
 
1.2%
300 5
6.2%
288 1
 
1.2%
250 1
 
1.2%

연간처리량
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct81
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25830.535
Minimum5.8
Maximum157308.1
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-04-06T17:21:37.367004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5.8
5-th percentile992.4
Q13411
median10471
Q333752.5
95-th percentile89429.8
Maximum157308.1
Range157302.3
Interquartile range (IQR)30341.5

Descriptive statistics

Standard deviation33368.572
Coefficient of variation (CV)1.2918266
Kurtosis3.3121329
Mean25830.535
Median Absolute Deviation (MAD)8591
Skewness1.8601048
Sum2092273.3
Variance1.1134616 × 109
MonotonicityNot monotonic
2024-04-06T17:21:38.030341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
84416.4 1
 
1.2%
5.8 1
 
1.2%
8736.0 1
 
1.2%
74277.3 1
 
1.2%
8080.9 1
 
1.2%
13341.1 1
 
1.2%
23777.0 1
 
1.2%
36128.1 1
 
1.2%
1628.0 1
 
1.2%
7548.6 1
 
1.2%
Other values (71) 71
87.7%
ValueCountFrequency (%)
5.8 1
1.2%
46.0 1
1.2%
120.6 1
1.2%
455.0 1
1.2%
992.4 1
1.2%
1108.7 1
1.2%
1476.1 1
1.2%
1628.0 1
1.2%
1633.9 1
1.2%
1880.0 1
1.2%
ValueCountFrequency (%)
157308.1 1
1.2%
133791.9 1
1.2%
105600.0 1
1.2%
100553.1 1
1.2%
89429.8 1
1.2%
85453.0 1
1.2%
84999.8 1
1.2%
84416.4 1
1.2%
76686.5 1
1.2%
74277.3 1
1.2%

일평균가동시간
Real number (ℝ)

Distinct13
Distinct (%)16.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21.740741
Minimum7
Maximum24
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-04-06T17:21:38.306494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile13
Q121
median24
Q324
95-th percentile24
Maximum24
Range17
Interquartile range (IQR)3

Descriptive statistics

Standard deviation4.1041984
Coefficient of variation (CV)0.18877914
Kurtosis4.4315168
Mean21.740741
Median Absolute Deviation (MAD)0
Skewness-2.1897149
Sum1761
Variance16.844444
MonotonicityNot monotonic
2024-04-06T17:21:38.629023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
24 49
60.5%
23 7
 
8.6%
18 5
 
6.2%
19 4
 
4.9%
22 4
 
4.9%
20 3
 
3.7%
13 2
 
2.5%
7 2
 
2.5%
21 1
 
1.2%
14 1
 
1.2%
Other values (3) 3
 
3.7%
ValueCountFrequency (%)
7 2
 
2.5%
8 1
 
1.2%
12 1
 
1.2%
13 2
 
2.5%
14 1
 
1.2%
15 1
 
1.2%
18 5
6.2%
19 4
4.9%
20 3
3.7%
21 1
 
1.2%
ValueCountFrequency (%)
24 49
60.5%
23 7
 
8.6%
22 4
 
4.9%
21 1
 
1.2%
20 3
 
3.7%
19 4
 
4.9%
18 5
 
6.2%
15 1
 
1.2%
14 1
 
1.2%
13 2
 
2.5%

연중 가동일
Real number (ℝ)

Distinct37
Distinct (%)45.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.12346
Minimum72
Maximum330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-04-06T17:21:38.932604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum72
5-th percentile189
Q1291
median318
Q3330
95-th percentile330
Maximum330
Range258
Interquartile range (IQR)39

Descriptive statistics

Standard deviation51.505432
Coefficient of variation (CV)0.17393229
Kurtosis5.0695823
Mean296.12346
Median Absolute Deviation (MAD)12
Skewness-2.1489782
Sum23986
Variance2652.8096
MonotonicityNot monotonic
2024-04-06T17:21:39.242950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
330 34
42.0%
300 6
 
7.4%
321 2
 
2.5%
329 2
 
2.5%
301 2
 
2.5%
250 2
 
2.5%
279 2
 
2.5%
313 2
 
2.5%
249 1
 
1.2%
257 1
 
1.2%
Other values (27) 27
33.3%
ValueCountFrequency (%)
72 1
1.2%
129 1
1.2%
154 1
1.2%
184 1
1.2%
189 1
1.2%
200 1
1.2%
210 1
1.2%
225 1
1.2%
228 1
1.2%
240 1
1.2%
ValueCountFrequency (%)
330 34
42.0%
329 2
 
2.5%
325 1
 
1.2%
323 1
 
1.2%
321 2
 
2.5%
318 1
 
1.2%
317 1
 
1.2%
313 2
 
2.5%
312 1
 
1.2%
310 1
 
1.2%

가동율
Real number (ℝ)

Distinct77
Distinct (%)95.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.37284
Minimum0.2
Maximum118.8
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size861.0 B
2024-04-06T17:21:39.499253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.2
5-th percentile21
Q154.5
median73.1
Q386.6
95-th percentile103.8
Maximum118.8
Range118.6
Interquartile range (IQR)32.1

Descriptive statistics

Standard deviation25.645638
Coefficient of variation (CV)0.36967837
Kurtosis0.56171375
Mean69.37284
Median Absolute Deviation (MAD)14
Skewness-0.77283483
Sum5619.2
Variance657.69875
MonotonicityNot monotonic
2024-04-06T17:21:39.823397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
75.0 2
 
2.5%
68.4 2
 
2.5%
59.1 2
 
2.5%
73.0 2
 
2.5%
80.3 1
 
1.2%
80.9 1
 
1.2%
80.1 1
 
1.2%
92.6 1
 
1.2%
54.3 1
 
1.2%
83.9 1
 
1.2%
Other values (67) 67
82.7%
ValueCountFrequency (%)
0.2 1
1.2%
0.3 1
1.2%
0.4 1
1.2%
15.2 1
1.2%
21.0 1
1.2%
21.4 1
1.2%
32.8 1
1.2%
34.9 1
1.2%
40.2 1
1.2%
40.4 1
1.2%
ValueCountFrequency (%)
118.8 1
1.2%
114.8 1
1.2%
108.2 1
1.2%
106.7 1
1.2%
103.8 1
1.2%
101.7 1
1.2%
100.3 1
1.2%
97.1 1
1.2%
96.6 1
1.2%
96.5 1
1.2%

Interactions

2024-04-06T17:21:31.370019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:27.053622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:28.274979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:29.491210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:30.515677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:31.563824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:27.229918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:28.481529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:29.662824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:30.682744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:31.780515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:27.468503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:28.675621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:29.870193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:30.881376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:31.955257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:27.847494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:28.917958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:30.072593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:31.056650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:32.138587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:28.088305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:29.175225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:30.296334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:21:31.214712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:21:40.117829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도구군찌꺼기처리시설명처리방식시설용량연간처리량일평균가동시간연중 가동일가동율
시도1.0000.9870.0000.8720.6560.5360.0000.5070.000
구군0.9871.0000.0000.9610.0000.0000.0000.0000.000
찌꺼기처리시설명0.0000.0001.0000.0000.8570.9680.0000.0000.920
처리방식0.8720.9610.0001.0000.6630.4900.4970.5810.668
시설용량0.6560.0000.8570.6631.0000.9660.0000.0000.000
연간처리량0.5360.0000.9680.4900.9661.0000.0000.0000.000
일평균가동시간0.0000.0000.0000.4970.0000.0001.0000.4610.619
연중 가동일0.5070.0000.0000.5810.0000.0000.4611.0000.366
가동율0.0000.0000.9200.6680.0000.0000.6190.3661.000
2024-04-06T17:21:40.442114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도처리방식
시도1.0000.457
처리방식0.4571.000
2024-04-06T17:21:40.679559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설용량연간처리량일평균가동시간연중 가동일가동율시도처리방식
시설용량1.0000.8930.0590.2010.0490.3330.293
연간처리량0.8931.0000.1020.3430.3770.2470.185
일평균가동시간0.0590.1021.0000.1790.1020.0000.201
연중 가동일0.2010.3430.1791.0000.1660.2290.237
가동율0.0490.3770.1020.1661.0000.0000.286
시도0.3330.2470.0000.2290.0001.0000.457
처리방식0.2930.1850.2010.2370.2860.4571.000

Missing values

2024-04-06T17:21:32.460758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:21:32.759965image/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

시도구군찌꺼기처리시설명처리방식시설용량연간처리량일평균가동시간연중 가동일가동율
0서울특별시<NA>난지물재생센터건조, 소각30084416.42132187.7
1서울특별시성동구중랑물재생센터건조650157308.12333073.3
2서울특별시강서구서남물재생센터건조,소각43589429.82030168.4
3서울특별시강남구탄천물재생센터건조20022846.01822850.1
4부산광역시해운대구하수찌꺼기 건조시설(해운대)건조485381.42422549.8
5부산광역시강서구하수찌꺼기 건조시설(하수자원)건조55061683.62427640.6
6광주광역시서구하수슬러지처리시설간접 건조방식33084999.81929387.9
7대전광역시유성구대전광역시환경에너지종합타운스팀건조방식30061160.32431764.3
8울산광역시남구울산하수슬러지처리시설1,2호기소각30071710.22433072.4
9울산광역시남구울산하수슬러지처리시설3,4호기소각2009925.1247268.9
시도구군찌꺼기처리시설명처리방식시설용량연간처리량일평균가동시간연중 가동일가동율
71경상남도김해시하수슬러지 자원화처리시설직접가열 회전로상식 탄화10030922.42433093.7
72경상남도밀양시하수찌꺼기처리시설부숙화303186.82430434.9
73경상남도밀양시밀양시 폐기물 소각장소각51930.024325118.8
74경상남도거제시하수슬러지 자원화시설부숙화307060.92333071.3
75경상남도양산시양산슬러지처리시설건조12028056.72333070.9
76경상남도의령군의령하수찌꺼기처리시설탄화152093.22431244.7
77경상남도창녕군남지슬러지처리시설부숙화102411.62433073.1
78경상남도고성군부숙시설부숙화151880.02431040.4
79경상남도거창군건조시설건조연료화153296.12430173.0
80제주특별자치도제주시광역하수슬러지자원화시설재활용(복토재 등으로 활용)7019630.42433085.0