Overview

Dataset statistics

Number of variables49
Number of observations4339
Missing cells23237
Missing cells (%)10.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.8 MiB
Average record size in memory430.0 B

Variable types

Categorical13
Text9
Numeric27

Dataset

Description공공하수처리시설 찌꺼기 처리 현황(시도,구군,하수처리장명,찌꺼기발생량_합계(톤/년),찌꺼기발생량_자체,찌꺼기발생량_외부유입량,이송처리장,이송처리량,함수율(%),찌꺼기처리량_합계(톤/년),자체처리량_계,자체처리량_매립,자체처리량_소각,자체처리량_고화,자체처리량_건조,자체처리량_탄화,자체처리량_퇴비화,외부위탁처리량,수도권광역처리)
Author한국환경공단
URLhttps://www.data.go.kr/data/15065209/fileData.do

Alerts

자체찌꺼기처리량_소각 후 처리(2차)_복토재 has constant value ""Constant
자체찌꺼기처리량_소각 후 처리(2차)_시설(업체명) has constant value ""Constant
자체찌꺼기처리량_고화 후 처리(2차)_복토재 has constant value ""Constant
자체찌꺼기처리량_고화 후 처리(2차)_시설(업체명) has constant value ""Constant
자체찌꺼기처리량_건조 후 처리(2차)_매립 has constant value ""Constant
자체찌꺼기처리량_탄화 후 처리(2차)_소각 has constant value ""Constant
자체찌꺼기처리량_퇴비화_시설(업체명) has constant value ""Constant
자체찌꺼기처리량_소각 후 처리(2차)_소계 is highly imbalanced (99.5%)Imbalance
자체찌꺼기처리량_소각 후 처리(2차)_매립 is highly imbalanced (99.6%)Imbalance
자체찌꺼기처리량_소각 후 처리(2차)_제품원료 is highly imbalanced (99.7%)Imbalance
자체찌꺼기처리량_고화 is highly imbalanced (99.5%)Imbalance
자체찌꺼기처리량_고화 후 처리(2차)_소계 is highly imbalanced (99.7%)Imbalance
자체찌꺼기처리량_고화 후 처리(2차)_매립 is highly imbalanced (99.7%)Imbalance
자체찌꺼기처리량_건조 후 처리(2차)_소각 is highly imbalanced (99.7%)Imbalance
자체찌꺼기처리량_탄화 후 처리(2차)_시설(업체명) is highly imbalanced (98.7%)Imbalance
이송처리장 has 1960 (45.2%) missing valuesMissing
자체찌꺼기처리량_소각 후 처리(2차)_시설(업체명) has 4338 (> 99.9%) missing valuesMissing
자체찌꺼기처리량_고화 후 처리(2차)_시설(업체명) has 4338 (> 99.9%) missing valuesMissing
자체찌꺼기처리량_건조 후 처리(2차)_시설(업체명) has 4327 (99.7%) missing valuesMissing
자체찌꺼기처리량_퇴비화_시설(업체명) has 4338 (> 99.9%) missing valuesMissing
외부위탁처리량_시설(업체명) has 3910 (90.1%) missing valuesMissing
찌꺼기 발생량_외부유입량 is highly skewed (γ1 = 20.48580468)Skewed
이송처리량 is highly skewed (γ1 = 45.20275793)Skewed
자체찌꺼기처리량_합계 is highly skewed (γ1 = 22.27905975)Skewed
자체찌꺼기처리량_매립 is highly skewed (γ1 = 51.96301784)Skewed
자체찌꺼기처리량_소각 is highly skewed (γ1 = 29.37631002)Skewed
자체찌꺼기처리량_건조 is highly skewed (γ1 = 33.18798118)Skewed
자체찌꺼기처리량_건조 후 처리(2차)_소계 is highly skewed (γ1 = 35.59436877)Skewed
자체찌꺼기처리량_건조 후 처리(2차)_연료 is highly skewed (γ1 = 45.79905396)Skewed
자체찌꺼기처리량_건조 후 처리(2차)_제품원료 is highly skewed (γ1 = 55.50870286)Skewed
자체찌꺼기처리량_건조 후 처리(2차)_복토재 is highly skewed (γ1 = 62.44147643)Skewed
자체찌꺼기처리량_건조 후 처리(2차)_퇴비화 is highly skewed (γ1 = 37.16952102)Skewed
자체찌꺼기처리량_탄화 is highly skewed (γ1 = 41.81608908)Skewed
자체찌꺼기처리량_탄화 후 처리(2차)_소계 is highly skewed (γ1 = 45.26229791)Skewed
자체찌꺼기처리량_탄화 후 처리(2차)_연료 is highly skewed (γ1 = 45.26229791)Skewed
자체찌꺼기처리량_퇴비화 is highly skewed (γ1 = 36.88319485)Skewed
외부위탁처리량_매립 is highly skewed (γ1 = 34.98587508)Skewed
외부위탁처리량_소각 is highly skewed (γ1 = 39.76604757)Skewed
외부위탁처리량_연료 is highly skewed (γ1 = 25.49992064)Skewed
외부위탁처리량_제품원료 is highly skewed (γ1 = 22.21984374)Skewed
외부위탁처리량_복토재 is highly skewed (γ1 = 29.45176412)Skewed
수도권광역 위탁처리량 is highly skewed (γ1 = 28.34090582)Skewed
찌꺼기 발생량_합계 has 3491 (80.5%) zerosZeros
찌꺼기 발생량_자체 has 3584 (82.6%) zerosZeros
찌꺼기 발생량_외부유입량 has 4168 (96.1%) zerosZeros
이송처리량 has 2320 (53.5%) zerosZeros
함수율 has 3141 (72.4%) zerosZeros
찌꺼기처리량_합계 has 3730 (86.0%) zerosZeros
자체찌꺼기처리량_합계 has 4145 (95.5%) zerosZeros
자체찌꺼기처리량_매립 has 4313 (99.4%) zerosZeros
자체찌꺼기처리량_소각 has 4277 (98.6%) zerosZeros
자체찌꺼기처리량_건조 has 4263 (98.2%) zerosZeros
자체찌꺼기처리량_건조 후 처리(2차)_소계 has 4280 (98.6%) zerosZeros
자체찌꺼기처리량_건조 후 처리(2차)_연료 has 4300 (99.1%) zerosZeros
자체찌꺼기처리량_건조 후 처리(2차)_제품원료 has 4334 (99.9%) zerosZeros
자체찌꺼기처리량_건조 후 처리(2차)_복토재 has 4333 (99.9%) zerosZeros
자체찌꺼기처리량_건조 후 처리(2차)_퇴비화 has 4324 (99.7%) zerosZeros
자체찌꺼기처리량_탄화 has 4319 (99.5%) zerosZeros
자체찌꺼기처리량_탄화 후 처리(2차)_소계 has 4328 (99.7%) zerosZeros
자체찌꺼기처리량_탄화 후 처리(2차)_연료 has 4328 (99.7%) zerosZeros
자체찌꺼기처리량_퇴비화 has 4326 (99.7%) zerosZeros
외부위탁처리량_소계 has 3824 (88.1%) zerosZeros
외부위탁처리량_매립 has 4320 (99.6%) zerosZeros
외부위탁처리량_소각 has 4310 (99.3%) zerosZeros
외부위탁처리량_연료 has 4246 (97.9%) zerosZeros
외부위탁처리량_제품원료 has 4174 (96.2%) zerosZeros
외부위탁처리량_복토재 has 4269 (98.4%) zerosZeros
외부위탁처리량_퇴비화 has 4008 (92.4%) zerosZeros
수도권광역 위탁처리량 has 4299 (99.1%) zerosZeros

Reproduction

Analysis started2024-04-06 08:21:41.537884
Analysis finished2024-04-06 08:21:44.001619
Duration2.46 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도
Categorical

Distinct17
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
전라남도
954 
경상남도
643 
경상북도
504 
전라북도
494 
경기도
414 
Other values (12)
1330 

Length

Max length7
Median length4
Mean length3.8847661
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 954
22.0%
경상남도 643
14.8%
경상북도 504
11.6%
전라북도 494
11.4%
경기도 414
9.5%
충청남도 407
9.4%
강원도 387
8.9%
충청북도 353
 
8.1%
제주특별자치도 34
 
0.8%
부산광역시 30
 
0.7%
Other values (7) 119
 
2.7%

Length

2024-04-06T17:21:44.162029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전라남도 954
22.0%
경상남도 643
14.8%
경상북도 504
11.6%
전라북도 494
11.4%
경기도 414
9.5%
충청남도 407
9.4%
강원도 387
8.9%
충청북도 353
 
8.1%
제주특별자치도 34
 
0.8%
부산광역시 30
 
0.7%
Other values (7) 119
 
2.7%

구군
Text

Distinct175
Distinct (%)4.1%
Missing26
Missing (%)0.6%
Memory size34.0 KiB
2024-04-06T17:21:44.841225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length3.0018549
Min length2

Characters and Unicode

Total characters12947
Distinct characters120
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

Unique17 ?
Unique (%)0.4%

Sample

1st row성동구
2nd row강서구
3rd row강남구
4th row서구
5th row영도구
ValueCountFrequency (%)
충주시 67
 
1.6%
하동군 67
 
1.6%
남해군 65
 
1.5%
남원시 63
 
1.5%
김제시 58
 
1.3%
양평군 58
 
1.3%
순천시 57
 
1.3%
화순군 57
 
1.3%
강진군 55
 
1.3%
담양군 55
 
1.3%
Other values (165) 3711
86.0%
2024-04-06T17:21:45.845767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2632
20.3%
1663
 
12.8%
568
 
4.4%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (110) 5616
43.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12947
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2632
20.3%
1663
 
12.8%
568
 
4.4%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (110) 5616
43.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12947
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2632
20.3%
1663
 
12.8%
568
 
4.4%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (110) 5616
43.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12947
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2632
20.3%
1663
 
12.8%
568
 
4.4%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (110) 5616
43.4%
Distinct177
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
2024-04-06T17:21:46.398473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length3.0253515
Min length2

Characters and Unicode

Total characters13127
Distinct characters126
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

Unique18 ?
Unique (%)0.4%

Sample

1st row서울특별시
2nd row성동구
3rd row강서구
4th row강남구
5th row서구
ValueCountFrequency (%)
충주시 67
 
1.5%
하동군 67
 
1.5%
남해군 65
 
1.5%
남원시 63
 
1.5%
양평군 58
 
1.3%
김제시 58
 
1.3%
순천시 57
 
1.3%
화순군 57
 
1.3%
담양군 55
 
1.3%
강진군 55
 
1.3%
Other values (167) 3737
86.1%
2024-04-06T17:21:47.180278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2632
20.1%
1689
 
12.9%
568
 
4.3%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (116) 5770
44.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 13127
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2632
20.1%
1689
 
12.9%
568
 
4.3%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (116) 5770
44.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 13127
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2632
20.1%
1689
 
12.9%
568
 
4.3%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (116) 5770
44.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 13127
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2632
20.1%
1689
 
12.9%
568
 
4.3%
521
 
4.0%
471
 
3.6%
354
 
2.7%
303
 
2.3%
290
 
2.2%
278
 
2.1%
251
 
1.9%
Other values (116) 5770
44.0%
Distinct3641
Distinct (%)83.9%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
2024-04-06T17:21:48.044126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length2
Mean length2.7900438
Min length2

Characters and Unicode

Total characters12106
Distinct characters429
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3227 ?
Unique (%)74.4%

Sample

1st row난지
2nd row중랑물재생센터
3rd row서남
4th row탄천
5th row중앙
ValueCountFrequency (%)
삼산 14
 
0.3%
평촌 12
 
0.3%
덕산 11
 
0.2%
용산 11
 
0.2%
신기 10
 
0.2%
지정면 10
 
0.2%
송지 10
 
0.2%
신촌 9
 
0.2%
동산 9
 
0.2%
황산 9
 
0.2%
Other values (3603) 4493
97.7%
2024-04-06T17:21:49.173892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
409
 
3.4%
406
 
3.4%
365
 
3.0%
332
 
2.7%
313
 
2.6%
261
 
2.2%
219
 
1.8%
205
 
1.7%
192
 
1.6%
187
 
1.5%
Other values (419) 9217
76.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11386
94.1%
Decimal Number 326
 
2.7%
Space Separator 261
 
2.2%
Close Punctuation 46
 
0.4%
Open Punctuation 46
 
0.4%
Dash Punctuation 16
 
0.1%
Uppercase Letter 14
 
0.1%
Other Punctuation 10
 
0.1%
Letter Number 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
409
 
3.6%
406
 
3.6%
365
 
3.2%
332
 
2.9%
313
 
2.7%
219
 
1.9%
205
 
1.8%
192
 
1.7%
187
 
1.6%
179
 
1.6%
Other values (398) 8579
75.3%
Decimal Number
ValueCountFrequency (%)
2 134
41.1%
1 116
35.6%
3 40
 
12.3%
4 13
 
4.0%
5 10
 
3.1%
0 6
 
1.8%
7 2
 
0.6%
9 2
 
0.6%
6 2
 
0.6%
8 1
 
0.3%
Other Punctuation
ValueCountFrequency (%)
· 6
60.0%
. 3
30.0%
/ 1
 
10.0%
Dash Punctuation
ValueCountFrequency (%)
- 15
93.8%
1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
B 8
57.1%
A 6
42.9%
Space Separator
ValueCountFrequency (%)
261
100.0%
Close Punctuation
ValueCountFrequency (%)
) 46
100.0%
Open Punctuation
ValueCountFrequency (%)
( 46
100.0%
Letter Number
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11386
94.1%
Common 705
 
5.8%
Latin 15
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
409
 
3.6%
406
 
3.6%
365
 
3.2%
332
 
2.9%
313
 
2.7%
219
 
1.9%
205
 
1.8%
192
 
1.7%
187
 
1.6%
179
 
1.6%
Other values (398) 8579
75.3%
Common
ValueCountFrequency (%)
261
37.0%
2 134
19.0%
1 116
16.5%
) 46
 
6.5%
( 46
 
6.5%
3 40
 
5.7%
- 15
 
2.1%
4 13
 
1.8%
5 10
 
1.4%
· 6
 
0.9%
Other values (8) 18
 
2.6%
Latin
ValueCountFrequency (%)
B 8
53.3%
A 6
40.0%
1
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11386
94.1%
ASCII 712
 
5.9%
None 7
 
0.1%
Number Forms 1
 
< 0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
409
 
3.6%
406
 
3.6%
365
 
3.2%
332
 
2.9%
313
 
2.7%
219
 
1.9%
205
 
1.8%
192
 
1.7%
187
 
1.6%
179
 
1.6%
Other values (398) 8579
75.3%
ASCII
ValueCountFrequency (%)
261
36.7%
2 134
18.8%
1 116
16.3%
) 46
 
6.5%
( 46
 
6.5%
3 40
 
5.6%
- 15
 
2.1%
4 13
 
1.8%
5 10
 
1.4%
B 8
 
1.1%
Other values (8) 23
 
3.2%
None
ValueCountFrequency (%)
· 6
85.7%
1
 
14.3%
Number Forms
ValueCountFrequency (%)
1
100.0%

찌꺼기 발생량_합계
Real number (ℝ)

ZEROS 

Distinct749
Distinct (%)17.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1043.3535
Minimum0
Maximum221134.6
Zeros3491
Zeros (%)80.5%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:50.018096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2777.18
Maximum221134.6
Range221134.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7676.985
Coefficient of variation (CV)7.3579901
Kurtosis304.29468
Mean1043.3535
Median Absolute Deviation (MAD)0
Skewness15.124194
Sum4527111
Variance58936099
MonotonicityNot monotonic
2024-04-06T17:21:50.355145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3491
80.5%
77.0 12
 
0.3%
371.0 9
 
0.2%
0.1 8
 
0.2%
5.0 7
 
0.2%
10.0 6
 
0.1%
70.0 6
 
0.1%
15.0 5
 
0.1%
1.0 4
 
0.1%
4.0 4
 
0.1%
Other values (739) 787
 
18.1%
ValueCountFrequency (%)
0.0 3491
80.5%
0.1 8
 
0.2%
0.3 2
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
1.0 4
 
0.1%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.7 1
 
< 0.1%
2.4 1
 
< 0.1%
ValueCountFrequency (%)
221134.6 1
< 0.1%
179521.1 1
< 0.1%
138383.3 1
< 0.1%
110318.2 1
< 0.1%
103899.5 1
< 0.1%
101236.1 1
< 0.1%
100553.1 1
< 0.1%
97780.9 1
< 0.1%
93557.7 1
< 0.1%
91257.1 1
< 0.1%

찌꺼기 발생량_자체
Real number (ℝ)

ZEROS 

Distinct698
Distinct (%)16.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1013.4619
Minimum0
Maximum221134.6
Zeros3584
Zeros (%)82.6%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:50.720795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2535.53
Maximum221134.6
Range221134.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7593.9606
Coefficient of variation (CV)7.4930897
Kurtosis313.93878
Mean1013.4619
Median Absolute Deviation (MAD)0
Skewness15.342786
Sum4397411
Variance57668237
MonotonicityNot monotonic
2024-04-06T17:21:50.991907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3584
82.6%
77.0 12
 
0.3%
371.0 9
 
0.2%
0.1 8
 
0.2%
70.0 6
 
0.1%
364.0 4
 
0.1%
6.8 2
 
< 0.1%
378.0 2
 
< 0.1%
82.0 2
 
< 0.1%
1113.0 2
 
< 0.1%
Other values (688) 708
 
16.3%
ValueCountFrequency (%)
0.0 3584
82.6%
0.1 8
 
0.2%
0.3 2
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
1.0 2
 
< 0.1%
1.2 1
 
< 0.1%
1.3 1
 
< 0.1%
1.7 1
 
< 0.1%
2.4 1
 
< 0.1%
ValueCountFrequency (%)
221134.6 1
< 0.1%
179521.1 1
< 0.1%
138383.3 1
< 0.1%
110318.2 1
< 0.1%
103899.5 1
< 0.1%
101236.1 1
< 0.1%
97780.9 1
< 0.1%
93557.7 1
< 0.1%
91257.1 1
< 0.1%
90115.6 1
< 0.1%

찌꺼기 발생량_외부유입량
Real number (ℝ)

SKEWED  ZEROS 

Distinct145
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.89168
Minimum0
Maximum11697
Zeros4168
Zeros (%)96.1%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:51.269666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11697
Range11697
Interquartile range (IQR)0

Descriptive statistics

Standard deviation402.79404
Coefficient of variation (CV)13.475122
Kurtosis475.45339
Mean29.89168
Median Absolute Deviation (MAD)0
Skewness20.485805
Sum129700
Variance162243.04
MonotonicityNot monotonic
2024-04-06T17:21:51.643006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4168
96.1%
5.0 7
 
0.2%
10.0 6
 
0.1%
15.0 4
 
0.1%
6.0 3
 
0.1%
1.0 3
 
0.1%
4.0 3
 
0.1%
21.0 2
 
< 0.1%
9.3 2
 
< 0.1%
23.4 2
 
< 0.1%
Other values (135) 139
 
3.2%
ValueCountFrequency (%)
0.0 4168
96.1%
0.2 1
 
< 0.1%
1.0 3
 
0.1%
2.6 1
 
< 0.1%
4.0 3
 
0.1%
4.5 1
 
< 0.1%
5.0 7
 
0.2%
5.2 1
 
< 0.1%
5.5 1
 
< 0.1%
6.0 3
 
0.1%
ValueCountFrequency (%)
11697.0 1
< 0.1%
10437.5 1
< 0.1%
10020.0 1
< 0.1%
8551.5 1
< 0.1%
8291.0 1
< 0.1%
5824.4 1
< 0.1%
5572.0 1
< 0.1%
5310.0 1
< 0.1%
5008.4 1
< 0.1%
4600.2 1
< 0.1%

이송처리장
Text

MISSING 

Distinct182
Distinct (%)7.7%
Missing1960
Missing (%)45.2%
Memory size34.0 KiB
2024-04-06T17:21:52.108403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length15
Mean length4.3850357
Min length1

Characters and Unicode

Total characters10432
Distinct characters171
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique49 ?
Unique (%)2.1%

Sample

1st row신라정화
2nd row신라정화
3rd row신라정화
4th row신라정화
5th row신라정화
ValueCountFrequency (%)
분뇨처리장 142
 
5.4%
하동 57
 
2.2%
화순읍 55
 
2.1%
김제 55
 
2.1%
함양공공하수처리장 53
 
2.0%
보성 49
 
1.9%
위생처리장 49
 
1.9%
옥천 46
 
1.7%
진안하수 46
 
1.7%
고흥하수 46
 
1.7%
Other values (182) 2032
77.3%
2024-04-06T17:21:52.887377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
758
 
7.3%
748
 
7.2%
728
 
7.0%
719
 
6.9%
710
 
6.8%
611
 
5.9%
284
 
2.7%
270
 
2.6%
253
 
2.4%
248
 
2.4%
Other values (161) 5103
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 10134
97.1%
Space Separator 270
 
2.6%
Other Punctuation 17
 
0.2%
Decimal Number 5
 
< 0.1%
Dash Punctuation 2
 
< 0.1%
Uppercase Letter 2
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
758
 
7.5%
748
 
7.4%
728
 
7.2%
719
 
7.1%
710
 
7.0%
611
 
6.0%
284
 
2.8%
253
 
2.5%
248
 
2.4%
248
 
2.4%
Other values (152) 4827
47.6%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
0 2
40.0%
Uppercase Letter
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
270
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 17
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 10134
97.1%
Common 296
 
2.8%
Latin 2
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
758
 
7.5%
748
 
7.4%
728
 
7.2%
719
 
7.1%
710
 
7.0%
611
 
6.0%
284
 
2.8%
253
 
2.5%
248
 
2.4%
248
 
2.4%
Other values (152) 4827
47.6%
Common
ValueCountFrequency (%)
270
91.2%
/ 17
 
5.7%
1 3
 
1.0%
0 2
 
0.7%
- 2
 
0.7%
( 1
 
0.3%
) 1
 
0.3%
Latin
ValueCountFrequency (%)
B 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 10134
97.1%
ASCII 298
 
2.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
758
 
7.5%
748
 
7.4%
728
 
7.2%
719
 
7.1%
710
 
7.0%
611
 
6.0%
284
 
2.8%
253
 
2.5%
248
 
2.4%
248
 
2.4%
Other values (152) 4827
47.6%
ASCII
ValueCountFrequency (%)
270
90.6%
/ 17
 
5.7%
1 3
 
1.0%
0 2
 
0.7%
- 2
 
0.7%
( 1
 
0.3%
) 1
 
0.3%
B 1
 
0.3%
A 1
 
0.3%

이송처리량
Real number (ℝ)

SKEWED  ZEROS 

Distinct1035
Distinct (%)23.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean241.021
Minimum0
Maximum238014
Zeros2320
Zeros (%)53.5%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:53.162909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q342.4
95-th percentile549.45
Maximum238014
Range238014
Interquartile range (IQR)42.4

Descriptive statistics

Standard deviation5024.6405
Coefficient of variation (CV)20.847315
Kurtosis2082.1016
Mean241.021
Median Absolute Deviation (MAD)0
Skewness45.202758
Sum1045790.1
Variance25247012
MonotonicityNot monotonic
2024-04-06T17:21:53.459094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2320
53.5%
5.0 49
 
1.1%
8.0 42
 
1.0%
20.0 33
 
0.8%
7.0 29
 
0.7%
4.0 28
 
0.6%
14.0 26
 
0.6%
15.0 23
 
0.5%
10.0 21
 
0.5%
6.0 20
 
0.5%
Other values (1025) 1748
40.3%
ValueCountFrequency (%)
0.0 2320
53.5%
0.1 7
 
0.2%
0.3 2
 
< 0.1%
0.5 1
 
< 0.1%
0.6 2
 
< 0.1%
0.7 1
 
< 0.1%
0.9 5
 
0.1%
1.0 4
 
0.1%
1.1 1
 
< 0.1%
1.2 2
 
< 0.1%
ValueCountFrequency (%)
238014.0 1
< 0.1%
225183.0 1
< 0.1%
28765.0 1
< 0.1%
23174.5 1
< 0.1%
10911.6 1
< 0.1%
9362.6 1
< 0.1%
7101.6 1
< 0.1%
6701.1 1
< 0.1%
5008.4 1
< 0.1%
4791.5 1
< 0.1%

함수율
Real number (ℝ)

ZEROS 

Distinct113
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.202835
Minimum0
Maximum99.9
Zeros3141
Zeros (%)72.4%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:53.729007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q379.6
95-th percentile86.29
Maximum99.9
Range99.9
Interquartile range (IQR)79.6

Descriptive statistics

Standard deviation37.881531
Coefficient of variation (CV)1.6326251
Kurtosis-0.84997269
Mean23.202835
Median Absolute Deviation (MAD)0
Skewness1.0446901
Sum100677.1
Variance1435.0104
MonotonicityNot monotonic
2024-04-06T17:21:53.990848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3141
72.4%
99.0 126
 
2.9%
80.0 104
 
2.4%
83.2 53
 
1.2%
82.4 52
 
1.2%
81.4 49
 
1.1%
81.1 47
 
1.1%
82.1 45
 
1.0%
80.6 44
 
1.0%
81.9 37
 
0.9%
Other values (103) 641
 
14.8%
ValueCountFrequency (%)
0.0 3141
72.4%
0.1 5
 
0.1%
22.7 1
 
< 0.1%
61.7 1
 
< 0.1%
62.5 1
 
< 0.1%
63.2 2
 
< 0.1%
67.3 3
 
0.1%
71.6 1
 
< 0.1%
71.8 1
 
< 0.1%
72.2 1
 
< 0.1%
ValueCountFrequency (%)
99.9 22
 
0.5%
99.8 30
 
0.7%
99.0 126
2.9%
98.0 36
 
0.8%
90.0 1
 
< 0.1%
88.2 1
 
< 0.1%
88.0 1
 
< 0.1%
86.1 2
 
< 0.1%
86.0 2
 
< 0.1%
85.9 2
 
< 0.1%

찌꺼기처리량_합계
Real number (ℝ)

ZEROS 

Distinct596
Distinct (%)13.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean955.20104
Minimum0
Maximum221134.6
Zeros3730
Zeros (%)86.0%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:54.486444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2388.04
Maximum221134.6
Range221134.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation7359.1291
Coefficient of variation (CV)7.7042725
Kurtosis349.95301
Mean955.20104
Median Absolute Deviation (MAD)0
Skewness16.215918
Sum4144617.3
Variance54156781
MonotonicityNot monotonic
2024-04-06T17:21:54.735735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3730
86.0%
20.8 2
 
< 0.1%
16.1 2
 
< 0.1%
482.0 2
 
< 0.1%
21.5 2
 
< 0.1%
5118.7 2
 
< 0.1%
6128.6 2
 
< 0.1%
378.0 2
 
< 0.1%
17.6 2
 
< 0.1%
6.8 2
 
< 0.1%
Other values (586) 591
 
13.6%
ValueCountFrequency (%)
0.0 3730
86.0%
0.1 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
1.0 1
 
< 0.1%
1.7 1
 
< 0.1%
2.4 1
 
< 0.1%
2.9 1
 
< 0.1%
4.5 1
 
< 0.1%
ValueCountFrequency (%)
221134.6 1
< 0.1%
179521.1 1
< 0.1%
138383.3 1
< 0.1%
110318.2 1
< 0.1%
103899.5 1
< 0.1%
101236.1 1
< 0.1%
100553.1 1
< 0.1%
93557.7 1
< 0.1%
91257.1 1
< 0.1%
85453.0 1
< 0.1%

자체찌꺼기처리량_합계
Real number (ℝ)

SKEWED  ZEROS 

Distinct194
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean348.94985
Minimum0
Maximum157308.1
Zeros4145
Zeros (%)95.5%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:54.989604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum157308.1
Range157308.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation4445.9649
Coefficient of variation (CV)12.740985
Kurtosis596.57056
Mean348.94985
Median Absolute Deviation (MAD)0
Skewness22.27906
Sum1514093.4
Variance19766604
MonotonicityNot monotonic
2024-04-06T17:21:55.289591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4145
95.5%
378.0 2
 
< 0.1%
96819.0 1
 
< 0.1%
1695.8 1
 
< 0.1%
610.5 1
 
< 0.1%
3779.8 1
 
< 0.1%
6591.0 1
 
< 0.1%
646.1 1
 
< 0.1%
125.0 1
 
< 0.1%
84.0 1
 
< 0.1%
Other values (184) 184
 
4.2%
ValueCountFrequency (%)
0.0 4145
95.5%
8.0 1
 
< 0.1%
10.8 1
 
< 0.1%
11.0 1
 
< 0.1%
16.0 1
 
< 0.1%
24.0 1
 
< 0.1%
28.0 1
 
< 0.1%
36.7 1
 
< 0.1%
44.0 1
 
< 0.1%
48.0 1
 
< 0.1%
ValueCountFrequency (%)
157308.1 1
< 0.1%
101236.1 1
< 0.1%
100553.1 1
< 0.1%
96819.0 1
< 0.1%
88005.0 1
< 0.1%
76686.5 1
< 0.1%
61160.3 1
< 0.1%
39399.7 1
< 0.1%
37862.5 1
< 0.1%
37532.8 1
< 0.1%

자체찌꺼기처리량_매립
Real number (ℝ)

SKEWED  ZEROS 

Distinct26
Distinct (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.5122609
Minimum0
Maximum11095
Zeros4313
Zeros (%)99.4%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:55.557069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum11095
Range11095
Interquartile range (IQR)0

Descriptive statistics

Standard deviation185.65114
Coefficient of variation (CV)33.679672
Kurtosis2984.5573
Mean5.5122609
Median Absolute Deviation (MAD)0
Skewness51.963018
Sum23917.7
Variance34466.345
MonotonicityNot monotonic
2024-04-06T17:21:55.780007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 4313
99.4%
378.0 2
 
< 0.1%
16.0 1
 
< 0.1%
149.9 1
 
< 0.1%
326.5 1
 
< 0.1%
511.3 1
 
< 0.1%
16.7 1
 
< 0.1%
2893.0 1
 
< 0.1%
52.0 1
 
< 0.1%
8.0 1
 
< 0.1%
Other values (16) 16
 
0.4%
ValueCountFrequency (%)
0.0 4313
99.4%
8.0 1
 
< 0.1%
16.0 1
 
< 0.1%
16.7 1
 
< 0.1%
24.0 1
 
< 0.1%
28.0 1
 
< 0.1%
44.0 1
 
< 0.1%
48.0 1
 
< 0.1%
52.0 1
 
< 0.1%
63.0 1
 
< 0.1%
ValueCountFrequency (%)
11095.0 1
< 0.1%
3620.0 1
< 0.1%
2893.0 1
< 0.1%
1800.0 1
< 0.1%
674.0 1
< 0.1%
555.7 1
< 0.1%
511.3 1
< 0.1%
506.0 1
< 0.1%
378.0 2
< 0.1%
326.5 1
< 0.1%

자체찌꺼기처리량_소각
Real number (ℝ)

SKEWED  ZEROS 

Distinct63
Distinct (%)1.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.28313
Minimum0
Maximum100553.1
Zeros4277
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:21:56.034996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum100553.1
Range100553.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2447.6541
Coefficient of variation (CV)19.694179
Kurtosis991.90635
Mean124.28313
Median Absolute Deviation (MAD)0
Skewness29.37631
Sum539264.5
Variance5991010.8
MonotonicityNot monotonic
2024-04-06T17:21:56.379186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4277
98.6%
57924.0 1
 
< 0.1%
220.9 1
 
< 0.1%
213.3 1
 
< 0.1%
2889.7 1
 
< 0.1%
3952.1 1
 
< 0.1%
37862.5 1
 
< 0.1%
1256.3 1
 
< 0.1%
480.5 1
 
< 0.1%
66.1 1
 
< 0.1%
Other values (53) 53
 
1.2%
ValueCountFrequency (%)
0.0 4277
98.6%
10.8 1
 
< 0.1%
66.1 1
 
< 0.1%
84.0 1
 
< 0.1%
102.5 1
 
< 0.1%
125.0 1
 
< 0.1%
133.2 1
 
< 0.1%
213.3 1
 
< 0.1%
220.9 1
 
< 0.1%
226.7 1
 
< 0.1%
ValueCountFrequency (%)
100553.1 1
< 0.1%
76686.5 1
< 0.1%
57924.0 1
< 0.1%
39362.0 1
< 0.1%
37862.5 1
< 0.1%
37532.8 1
< 0.1%
25672.2 1
< 0.1%
24988.9 1
< 0.1%
22706.5 1
< 0.1%
8983.6 1
< 0.1%
Distinct4
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0.0
4336 
2558.8
 
1
2893.1
 
1
16.7
 
1

Length

Max length6
Median length3
Mean length3.0016133
Min length3

Unique

Unique3 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4336
99.9%
2558.8 1
 
< 0.1%
2893.1 1
 
< 0.1%
16.7 1
 
< 0.1%

Length

2024-04-06T17:21:56.719611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:21:56.940351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4336
99.9%
2558.8 1
 
< 0.1%
2893.1 1
 
< 0.1%
16.7 1
 
< 0.1%
Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0.0
4337 
2893.1
 
1
16.7
 
1

Length

Max length6
Median length3
Mean length3.0009219
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4337
> 99.9%
2893.1 1
 
< 0.1%
16.7 1
 
< 0.1%

Length

2024-04-06T17:21:57.210843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:21:57.443549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4337
> 99.9%
2893.1 1
 
< 0.1%
16.7 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0.0
4338 
2558.8
 
1

Length

Max length6
Median length3
Mean length3.0006914
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4338
> 99.9%
2558.8 1
 
< 0.1%

Length

2024-04-06T17:21:57.682616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:21:57.957394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4338
> 99.9%
2558.8 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0
4339 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4339
100.0%

Length

2024-04-06T17:21:58.265156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:21:58.512528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4339
100.0%
Distinct1
Distinct (%)100.0%
Missing4338
Missing (%)> 99.9%
Memory size34.0 KiB
2024-04-06T17:21:58.785212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters4
Distinct characters4
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row㈜에이지
ValueCountFrequency (%)
㈜에이지 1
100.0%
2024-04-06T17:21:59.435483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 3
75.0%
Other Symbol 1
 
25.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 4
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 3
75.0%
None 1
 
25.0%

Most frequent character per block

None
ValueCountFrequency (%)
1
100.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

자체찌꺼기처리량_고화
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0.0
4335 
10801.8
 
1
469.2
 
1
3779.8
 
1
455.5
 
1

Length

Max length7
Median length3
Mean length3.0025351
Min length3

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4335
99.9%
10801.8 1
 
< 0.1%
469.2 1
 
< 0.1%
3779.8 1
 
< 0.1%
455.5 1
 
< 0.1%

Length

2024-04-06T17:21:59.872311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:00.157669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4335
99.9%
10801.8 1
 
< 0.1%
469.2 1
 
< 0.1%
3779.8 1
 
< 0.1%
455.5 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0.0
4338 
3779.8
 
1

Length

Max length6
Median length3
Mean length3.0006914
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4338
> 99.9%
3779.8 1
 
< 0.1%

Length

2024-04-06T17:22:00.424122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:00.637800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4338
> 99.9%
3779.8 1
 
< 0.1%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0.0
4338 
3779.8
 
1

Length

Max length6
Median length3
Mean length3.0006914
Min length3

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0.0
2nd row0.0
3rd row0.0
4th row0.0
5th row0.0

Common Values

ValueCountFrequency (%)
0.0 4338
> 99.9%
3779.8 1
 
< 0.1%

Length

2024-04-06T17:22:00.869211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:01.117840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0.0 4338
> 99.9%
3779.8 1
 
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0
4339 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4339
100.0%

Length

2024-04-06T17:22:01.460102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:01.663487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4339
100.0%
Distinct1
Distinct (%)100.0%
Missing4338
Missing (%)> 99.9%
Memory size34.0 KiB
2024-04-06T17:22:01.870135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters8
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row(주)남부지엔씨
ValueCountFrequency (%)
주)남부지엔씨 1
100.0%
2024-04-06T17:22:02.385088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 1
12.5%
1
12.5%
) 1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
75.0%
Open Punctuation 1
 
12.5%
Close Punctuation 1
 
12.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
75.0%
Common 2
 
25.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
75.0%
ASCII 2
 
25.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

자체찌꺼기처리량_건조
Real number (ℝ)

SKEWED  ZEROS 

Distinct77
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean188.14623
Minimum0
Maximum157308.1
Zeros4263
Zeros (%)98.2%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:03.124929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum157308.1
Range157308.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3346.103
Coefficient of variation (CV)17.784587
Kurtosis1345.8251
Mean188.14623
Median Absolute Deviation (MAD)0
Skewness33.187981
Sum816366.5
Variance11196405
MonotonicityNot monotonic
2024-04-06T17:22:03.406249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4263
98.2%
38895.0 1
 
< 0.1%
4512.3 1
 
< 0.1%
119.3 1
 
< 0.1%
23046.8 1
 
< 0.1%
352.2 1
 
< 0.1%
415.5 1
 
< 0.1%
1104.9 1
 
< 0.1%
2520.7 1
 
< 0.1%
5293.4 1
 
< 0.1%
Other values (67) 67
 
1.5%
ValueCountFrequency (%)
0.0 4263
98.2%
65.6 1
 
< 0.1%
80.8 1
 
< 0.1%
83.0 1
 
< 0.1%
88.1 1
 
< 0.1%
94.4 1
 
< 0.1%
119.3 1
 
< 0.1%
143.4 1
 
< 0.1%
150.3 1
 
< 0.1%
177.1 1
 
< 0.1%
ValueCountFrequency (%)
157308.1 1
< 0.1%
99896.3 1
< 0.1%
61160.3 1
< 0.1%
48643.0 1
< 0.1%
38895.0 1
< 0.1%
28219.5 1
< 0.1%
23174.5 1
< 0.1%
23046.8 1
< 0.1%
22846.0 1
< 0.1%
22607.4 1
< 0.1%
Distinct60
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean58.020466
Minimum0
Maximum59798.6
Zeros4280
Zeros (%)98.6%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:03.711967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum59798.6
Range59798.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1327.4939
Coefficient of variation (CV)22.879753
Kurtosis1424.3253
Mean58.020466
Median Absolute Deviation (MAD)0
Skewness35.594369
Sum251750.8
Variance1762240.2
MonotonicityNot monotonic
2024-04-06T17:22:04.024670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4280
98.6%
71.7 1
 
< 0.1%
129.2 1
 
< 0.1%
42.2 1
 
< 0.1%
108.5 1
 
< 0.1%
137.9 1
 
< 0.1%
231.0 1
 
< 0.1%
141.7 1
 
< 0.1%
126.2 1
 
< 0.1%
69.3 1
 
< 0.1%
Other values (50) 50
 
1.2%
ValueCountFrequency (%)
0.0 4280
98.6%
13.7 1
 
< 0.1%
42.2 1
 
< 0.1%
51.2 1
 
< 0.1%
53.4 1
 
< 0.1%
65.6 1
 
< 0.1%
68.9 1
 
< 0.1%
69.0 1
 
< 0.1%
69.3 1
 
< 0.1%
71.0 1
 
< 0.1%
ValueCountFrequency (%)
59798.6 1
< 0.1%
49402.8 1
< 0.1%
23174.5 1
< 0.1%
19630.4 1
< 0.1%
16574.4 1
< 0.1%
10803.3 1
< 0.1%
9151.5 1
< 0.1%
8373.3 1
< 0.1%
6858.1 1
< 0.1%
5621.3 1
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0
4339 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4339
100.0%

Length

2024-04-06T17:22:04.386284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:04.643172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4339
100.0%
Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0
4338 
1609
 
1

Length

Max length4
Median length1
Mean length1.0006914
Min length1

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4338
> 99.9%
1609 1
 
< 0.1%

Length

2024-04-06T17:22:04.848134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:05.095922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4338
> 99.9%
1609 1
 
< 0.1%
Distinct40
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.635215
Minimum0
Maximum49402.8
Zeros4300
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:05.313048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum49402.8
Range49402.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation877.8449
Coefficient of variation (CV)28.654765
Kurtosis2404.4139
Mean30.635215
Median Absolute Deviation (MAD)0
Skewness45.799054
Sum132926.2
Variance770611.67
MonotonicityNot monotonic
2024-04-06T17:22:05.639708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.0 4300
99.1%
1781.7 1
 
< 0.1%
504.5 1
 
< 0.1%
3241.4 1
 
< 0.1%
168.0 1
 
< 0.1%
71.7 1
 
< 0.1%
71.0 1
 
< 0.1%
129.2 1
 
< 0.1%
42.2 1
 
< 0.1%
108.5 1
 
< 0.1%
Other values (30) 30
 
0.7%
ValueCountFrequency (%)
0.0 4300
99.1%
13.7 1
 
< 0.1%
42.2 1
 
< 0.1%
51.2 1
 
< 0.1%
53.4 1
 
< 0.1%
68.9 1
 
< 0.1%
69.0 1
 
< 0.1%
69.3 1
 
< 0.1%
71.0 1
 
< 0.1%
71.7 1
 
< 0.1%
ValueCountFrequency (%)
49402.8 1
< 0.1%
21295.8 1
< 0.1%
12649.4 1
< 0.1%
10803.3 1
< 0.1%
8253.0 1
< 0.1%
6858.1 1
< 0.1%
5621.3 1
< 0.1%
3241.4 1
< 0.1%
2209.5 1
< 0.1%
2149.4 1
< 0.1%
Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.856119
Minimum0
Maximum38502.8
Zeros4334
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:05.848462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum38502.8
Range38502.8
Interquartile range (IQR)0

Descriptive statistics

Standard deviation630.81658
Coefficient of variation (CV)45.52621
Kurtosis3268.745
Mean13.856119
Median Absolute Deviation (MAD)0
Skewness55.508703
Sum60121.7
Variance397929.56
MonotonicityNot monotonic
2024-04-06T17:22:06.043514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 4334
99.9%
38502.8 1
 
< 0.1%
1301.7 1
 
< 0.1%
14921.5 1
 
< 0.1%
1012.1 1
 
< 0.1%
4383.6 1
 
< 0.1%
ValueCountFrequency (%)
0.0 4334
99.9%
1012.1 1
 
< 0.1%
1301.7 1
 
< 0.1%
4383.6 1
 
< 0.1%
14921.5 1
 
< 0.1%
38502.8 1
 
< 0.1%
ValueCountFrequency (%)
38502.8 1
 
< 0.1%
14921.5 1
 
< 0.1%
4383.6 1
 
< 0.1%
1301.7 1
 
< 0.1%
1012.1 1
 
< 0.1%
0.0 4334
99.9%
Distinct7
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.7412537
Minimum0
Maximum19630.4
Zeros4333
Zeros (%)99.9%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:06.269747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum19630.4
Range19630.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation304.16188
Coefficient of variation (CV)52.978302
Kurtosis4003.7425
Mean5.7412537
Median Absolute Deviation (MAD)0
Skewness62.441476
Sum24911.3
Variance92514.447
MonotonicityNot monotonic
2024-04-06T17:22:06.495183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0.0 4333
99.9%
3925.0 1
 
< 0.1%
372.9 1
 
< 0.1%
186.4 1
 
< 0.1%
19630.4 1
 
< 0.1%
65.6 1
 
< 0.1%
731.0 1
 
< 0.1%
ValueCountFrequency (%)
0.0 4333
99.9%
65.6 1
 
< 0.1%
186.4 1
 
< 0.1%
372.9 1
 
< 0.1%
731.0 1
 
< 0.1%
3925.0 1
 
< 0.1%
19630.4 1
 
< 0.1%
ValueCountFrequency (%)
19630.4 1
 
< 0.1%
3925.0 1
 
< 0.1%
731.0 1
 
< 0.1%
372.9 1
 
< 0.1%
186.4 1
 
< 0.1%
65.6 1
 
< 0.1%
0.0 4333
99.9%
Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.4170546
Minimum0
Maximum9151.5
Zeros4324
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:06.702153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum9151.5
Range9151.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation208.60609
Coefficient of variation (CV)28.125192
Kurtosis1496.7958
Mean7.4170546
Median Absolute Deviation (MAD)0
Skewness37.169521
Sum32182.6
Variance43516.499
MonotonicityNot monotonic
2024-04-06T17:22:06.919853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0.0 4324
99.7%
9151.5 1
 
< 0.1%
218.2 1
 
< 0.1%
177.1 1
 
< 0.1%
1880.0 1
 
< 0.1%
291.7 1
 
< 0.1%
292.4 1
 
< 0.1%
4257.2 1
 
< 0.1%
403.0 1
 
< 0.1%
885.3 1
 
< 0.1%
Other values (6) 6
 
0.1%
ValueCountFrequency (%)
0.0 4324
99.7%
174.7 1
 
< 0.1%
177.1 1
 
< 0.1%
218.2 1
 
< 0.1%
291.7 1
 
< 0.1%
292.4 1
 
< 0.1%
403.0 1
 
< 0.1%
453.7 1
 
< 0.1%
885.3 1
 
< 0.1%
1275.0 1
 
< 0.1%
ValueCountFrequency (%)
9151.5 1
< 0.1%
8373.3 1
< 0.1%
4257.2 1
< 0.1%
2873.4 1
< 0.1%
1880.0 1
< 0.1%
1476.1 1
< 0.1%
1275.0 1
< 0.1%
885.3 1
< 0.1%
453.7 1
< 0.1%
403.0 1
< 0.1%
Distinct9
Distinct (%)75.0%
Missing4327
Missing (%)99.7%
Memory size34.0 KiB
2024-04-06T17:22:07.204553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length12.5
Mean length8.75
Min length1

Characters and Unicode

Total characters105
Distinct characters57
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)66.7%

Sample

1st row
2nd row해운대사업소(자체소각)
3rd row태안화력발전소 청산이엔씨 푸른
4th row한국서부발전/에코리더
5th row한국서부발전
ValueCountFrequency (%)
한국서부발전 4
25.0%
1
 
6.2%
해운대사업소(자체소각 1
 
6.2%
태안화력발전소 1
 
6.2%
청산이엔씨 1
 
6.2%
푸른 1
 
6.2%
한국서부발전/에코리더 1
 
6.2%
중부발전 1
 
6.2%
㈜진에너텍 1
 
6.2%
산우리 1
 
6.2%
Other values (3) 3
18.8%
2024-04-06T17:22:07.767445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8
 
7.6%
8
 
7.6%
6
 
5.7%
6
 
5.7%
5
 
4.8%
5
 
4.8%
5
 
4.8%
3
 
2.9%
) 2
 
1.9%
2
 
1.9%
Other values (47) 55
52.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 93
88.6%
Space Separator 6
 
5.7%
Close Punctuation 2
 
1.9%
Open Punctuation 2
 
1.9%
Other Symbol 1
 
1.0%
Other Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
8
 
8.6%
8
 
8.6%
6
 
6.5%
5
 
5.4%
5
 
5.4%
5
 
5.4%
3
 
3.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (42) 47
50.5%
Space Separator
ValueCountFrequency (%)
6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%
Other Punctuation
ValueCountFrequency (%)
/ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
89.5%
Common 11
 
10.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
8
 
8.5%
8
 
8.5%
6
 
6.4%
5
 
5.3%
5
 
5.3%
5
 
5.3%
3
 
3.2%
2
 
2.1%
2
 
2.1%
2
 
2.1%
Other values (43) 48
51.1%
Common
ValueCountFrequency (%)
6
54.5%
) 2
 
18.2%
( 2
 
18.2%
/ 1
 
9.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 93
88.6%
ASCII 11
 
10.5%
None 1
 
1.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
8
 
8.6%
8
 
8.6%
6
 
6.5%
5
 
5.4%
5
 
5.4%
5
 
5.4%
3
 
3.2%
2
 
2.2%
2
 
2.2%
2
 
2.2%
Other values (42) 47
50.5%
ASCII
ValueCountFrequency (%)
6
54.5%
) 2
 
18.2%
( 2
 
18.2%
/ 1
 
9.1%
None
ValueCountFrequency (%)
1
100.0%

자체찌꺼기처리량_탄화
Real number (ℝ)

SKEWED  ZEROS 

Distinct21
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.140793
Minimum0
Maximum39399.7
Zeros4319
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:08.035875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum39399.7
Range39399.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation754.90946
Coefficient of variation (CV)32.622455
Kurtosis1941.9337
Mean23.140793
Median Absolute Deviation (MAD)0
Skewness41.816089
Sum100407.9
Variance569888.3
MonotonicityNot monotonic
2024-04-06T17:22:08.314868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 4319
99.5%
39399.7 1
 
< 0.1%
149.2 1
 
< 0.1%
270.1 1
 
< 0.1%
238.3 1
 
< 0.1%
354.1 1
 
< 0.1%
297.7 1
 
< 0.1%
1446.7 1
 
< 0.1%
12098.6 1
 
< 0.1%
16067.3 1
 
< 0.1%
Other values (11) 11
 
0.3%
ValueCountFrequency (%)
0.0 4319
99.5%
72.8 1
 
< 0.1%
100.3 1
 
< 0.1%
105.7 1
 
< 0.1%
149.2 1
 
< 0.1%
159.4 1
 
< 0.1%
219.9 1
 
< 0.1%
222.5 1
 
< 0.1%
238.3 1
 
< 0.1%
270.1 1
 
< 0.1%
ValueCountFrequency (%)
39399.7 1
< 0.1%
22261.0 1
< 0.1%
16067.3 1
< 0.1%
12098.6 1
< 0.1%
3461.6 1
< 0.1%
2669.8 1
< 0.1%
1446.7 1
< 0.1%
521.3 1
< 0.1%
354.1 1
< 0.1%
297.7 1
< 0.1%
Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2289237
Minimum0
Maximum4383
Zeros4328
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:08.562972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4383
Range4383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation81.896418
Coefficient of variation (CV)36.742584
Kurtosis2207.2603
Mean2.2289237
Median Absolute Deviation (MAD)0
Skewness45.262298
Sum9671.3
Variance6707.0232
MonotonicityNot monotonic
2024-04-06T17:22:08.812989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 4328
99.7%
4383.0 1
 
< 0.1%
314.2 1
 
< 0.1%
2788.0 1
 
< 0.1%
1135.9 1
 
< 0.1%
855.4 1
 
< 0.1%
102.3 1
 
< 0.1%
16.8 1
 
< 0.1%
19.1 1
 
< 0.1%
21.0 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0.0 4328
99.7%
10.6 1
 
< 0.1%
16.8 1
 
< 0.1%
19.1 1
 
< 0.1%
21.0 1
 
< 0.1%
25.0 1
 
< 0.1%
102.3 1
 
< 0.1%
314.2 1
 
< 0.1%
855.4 1
 
< 0.1%
1135.9 1
 
< 0.1%
ValueCountFrequency (%)
4383.0 1
< 0.1%
2788.0 1
< 0.1%
1135.9 1
< 0.1%
855.4 1
< 0.1%
314.2 1
< 0.1%
102.3 1
< 0.1%
25.0 1
< 0.1%
21.0 1
< 0.1%
19.1 1
< 0.1%
16.8 1
< 0.1%
Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
0
4339 

Length

Max length1
Median length1
Mean length1
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 4339
100.0%

Length

2024-04-06T17:22:09.043645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:09.225326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 4339
100.0%
Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2289237
Minimum0
Maximum4383
Zeros4328
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:09.487521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum4383
Range4383
Interquartile range (IQR)0

Descriptive statistics

Standard deviation81.896418
Coefficient of variation (CV)36.742584
Kurtosis2207.2603
Mean2.2289237
Median Absolute Deviation (MAD)0
Skewness45.262298
Sum9671.3
Variance6707.0232
MonotonicityNot monotonic
2024-04-06T17:22:09.680234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0.0 4328
99.7%
4383.0 1
 
< 0.1%
314.2 1
 
< 0.1%
2788.0 1
 
< 0.1%
1135.9 1
 
< 0.1%
855.4 1
 
< 0.1%
102.3 1
 
< 0.1%
16.8 1
 
< 0.1%
19.1 1
 
< 0.1%
21.0 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
ValueCountFrequency (%)
0.0 4328
99.7%
10.6 1
 
< 0.1%
16.8 1
 
< 0.1%
19.1 1
 
< 0.1%
21.0 1
 
< 0.1%
25.0 1
 
< 0.1%
102.3 1
 
< 0.1%
314.2 1
 
< 0.1%
855.4 1
 
< 0.1%
1135.9 1
 
< 0.1%
ValueCountFrequency (%)
4383.0 1
< 0.1%
2788.0 1
< 0.1%
1135.9 1
< 0.1%
855.4 1
< 0.1%
314.2 1
< 0.1%
102.3 1
< 0.1%
25.0 1
< 0.1%
21.0 1
< 0.1%
19.1 1
< 0.1%
16.8 1
< 0.1%
Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size34.0 KiB
<NA>
4328 
한국남동발전(삼천포화력발전소)
 
7
김제하수찌꺼지처리시설
 
2
삼천포화력발전소
 
1
한국남동발전 삼천포발전본부
 
1

Length

Max length16
Median length4
Mean length4.0258124
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 4328
99.7%
한국남동발전(삼천포화력발전소) 7
 
0.2%
김제하수찌꺼지처리시설 2
 
< 0.1%
삼천포화력발전소 1
 
< 0.1%
한국남동발전 삼천포발전본부 1
 
< 0.1%

Length

2024-04-06T17:22:09.912426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:22:10.148756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 4328
99.7%
한국남동발전(삼천포화력발전소 7
 
0.2%
김제하수찌꺼지처리시설 2
 
< 0.1%
삼천포화력발전소 1
 
< 0.1%
한국남동발전 1
 
< 0.1%
삼천포발전본부 1
 
< 0.1%

자체찌꺼기처리량_퇴비화
Real number (ℝ)

SKEWED  ZEROS 

Distinct14
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2937313
Minimum0
Maximum5595.6
Zeros4326
Zeros (%)99.7%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:10.405767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5595.6
Range5595.6
Interquartile range (IQR)0

Descriptive statistics

Standard deviation122.31414
Coefficient of variation (CV)28.486679
Kurtosis1477.0629
Mean4.2937313
Median Absolute Deviation (MAD)0
Skewness36.883195
Sum18630.5
Variance14960.749
MonotonicityNot monotonic
2024-04-06T17:22:10.609520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0.0 4326
99.7%
1339.8 1
 
< 0.1%
119.5 1
 
< 0.1%
4329.7 1
 
< 0.1%
992.4 1
 
< 0.1%
3186.8 1
 
< 0.1%
5595.6 1
 
< 0.1%
36.7 1
 
< 0.1%
611.9 1
 
< 0.1%
348.5 1
 
< 0.1%
Other values (4) 4
 
0.1%
ValueCountFrequency (%)
0.0 4326
99.7%
11.0 1
 
< 0.1%
36.7 1
 
< 0.1%
119.5 1
 
< 0.1%
348.5 1
 
< 0.1%
504.9 1
 
< 0.1%
611.9 1
 
< 0.1%
694.6 1
 
< 0.1%
859.1 1
 
< 0.1%
992.4 1
 
< 0.1%
ValueCountFrequency (%)
5595.6 1
< 0.1%
4329.7 1
< 0.1%
3186.8 1
< 0.1%
1339.8 1
< 0.1%
992.4 1
< 0.1%
859.1 1
< 0.1%
694.6 1
< 0.1%
611.9 1
< 0.1%
504.9 1
< 0.1%
348.5 1
< 0.1%
Distinct1
Distinct (%)100.0%
Missing4338
Missing (%)> 99.9%
Memory size34.0 KiB
2024-04-06T17:22:10.858642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters14
Distinct characters12
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st row(주)건양기술공사건축사무소
ValueCountFrequency (%)
주)건양기술공사건축사무소 1
100.0%
2024-04-06T17:22:11.416621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2
14.3%
2
14.3%
( 1
7.1%
1
7.1%
) 1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
1
7.1%
Other values (2) 2
14.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 12
85.7%
Open Punctuation 1
 
7.1%
Close Punctuation 1
 
7.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 12
85.7%
Common 2
 
14.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 12
85.7%
ASCII 2
 
14.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
2
16.7%
2
16.7%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
1
8.3%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

외부위탁처리량_소계
Real number (ℝ)

ZEROS 

Distinct504
Distinct (%)11.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525.47518
Minimum0
Maximum91257.1
Zeros3824
Zeros (%)88.1%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:11.703667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1407.51
Maximum91257.1
Range91257.1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3608.1935
Coefficient of variation (CV)6.8665346
Kurtosis230.2357
Mean525.47518
Median Absolute Deviation (MAD)0
Skewness13.025398
Sum2280036.8
Variance13019060
MonotonicityNot monotonic
2024-04-06T17:22:12.034111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 3824
88.1%
17.6 2
 
< 0.1%
1471.6 2
 
< 0.1%
4.9 2
 
< 0.1%
20.8 2
 
< 0.1%
6.8 2
 
< 0.1%
482.0 2
 
< 0.1%
82.0 2
 
< 0.1%
16.1 2
 
< 0.1%
129.4 2
 
< 0.1%
Other values (494) 497
 
11.5%
ValueCountFrequency (%)
0.0 3824
88.1%
0.1 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
1.0 1
 
< 0.1%
1.7 1
 
< 0.1%
2.4 1
 
< 0.1%
2.9 2
 
< 0.1%
3.2 1
 
< 0.1%
ValueCountFrequency (%)
91257.1 1
< 0.1%
85453.0 1
< 0.1%
62543.8 1
< 0.1%
52132.1 1
< 0.1%
47780.7 1
< 0.1%
42739.2 1
< 0.1%
39851.5 1
< 0.1%
37982.1 1
< 0.1%
35558.1 1
< 0.1%
35550.2 1
< 0.1%

외부위탁처리량_매립
Real number (ℝ)

SKEWED  ZEROS 

Distinct20
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5555428
Minimum0
Maximum5319.3
Zeros4320
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:12.300708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5319.3
Range5319.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation126.79974
Coefficient of variation (CV)27.834167
Kurtosis1335.4417
Mean4.5555428
Median Absolute Deviation (MAD)0
Skewness34.985875
Sum19766.5
Variance16078.173
MonotonicityNot monotonic
2024-04-06T17:22:12.545135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 4320
99.6%
19.6 1
 
< 0.1%
35.1 1
 
< 0.1%
10.5 1
 
< 0.1%
11.7 1
 
< 0.1%
242.4 1
 
< 0.1%
2403.3 1
 
< 0.1%
5319.3 1
 
< 0.1%
8.1 1
 
< 0.1%
20.7 1
 
< 0.1%
Other values (10) 10
 
0.2%
ValueCountFrequency (%)
0.0 4320
99.6%
8.1 1
 
< 0.1%
10.5 1
 
< 0.1%
11.7 1
 
< 0.1%
17.2 1
 
< 0.1%
19.6 1
 
< 0.1%
20.1 1
 
< 0.1%
20.7 1
 
< 0.1%
31.0 1
 
< 0.1%
35.1 1
 
< 0.1%
ValueCountFrequency (%)
5319.3 1
< 0.1%
5008.4 1
< 0.1%
2403.3 1
< 0.1%
2295.0 1
< 0.1%
1868.7 1
< 0.1%
918.1 1
< 0.1%
902.0 1
< 0.1%
350.3 1
< 0.1%
285.0 1
< 0.1%
242.4 1
< 0.1%

외부위탁처리량_소각
Real number (ℝ)

SKEWED  ZEROS 

Distinct30
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean39.557847
Minimum0
Maximum54218.3
Zeros4310
Zeros (%)99.3%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:12.785138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum54218.3
Range54218.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1076.9609
Coefficient of variation (CV)27.224961
Kurtosis1774.8325
Mean39.557847
Median Absolute Deviation (MAD)0
Skewness39.766048
Sum171641.5
Variance1159844.7
MonotonicityNot monotonic
2024-04-06T17:22:13.010648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 4310
99.3%
43.0 1
 
< 0.1%
129.2 1
 
< 0.1%
1802.6 1
 
< 0.1%
19.0 1
 
< 0.1%
5235.0 1
 
< 0.1%
0.9 1
 
< 0.1%
39.1 1
 
< 0.1%
2001.5 1
 
< 0.1%
33.2 1
 
< 0.1%
Other values (20) 20
 
0.5%
ValueCountFrequency (%)
0.0 4310
99.3%
0.9 1
 
< 0.1%
17.0 1
 
< 0.1%
19.0 1
 
< 0.1%
23.0 1
 
< 0.1%
26.0 1
 
< 0.1%
33.2 1
 
< 0.1%
39.1 1
 
< 0.1%
43.0 1
 
< 0.1%
129.2 1
 
< 0.1%
ValueCountFrequency (%)
54218.3 1
< 0.1%
35146.1 1
< 0.1%
18230.5 1
< 0.1%
18228.7 1
< 0.1%
7962.1 1
< 0.1%
5296.8 1
< 0.1%
5235.0 1
< 0.1%
4187.4 1
< 0.1%
3887.7 1
< 0.1%
3679.8 1
< 0.1%

외부위탁처리량_연료
Real number (ℝ)

SKEWED  ZEROS 

Distinct93
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79.463794
Minimum0
Maximum53805.4
Zeros4246
Zeros (%)97.9%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:13.336746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum53805.4
Range53805.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1333.4527
Coefficient of variation (CV)16.780632
Kurtosis800.7399
Mean79.463794
Median Absolute Deviation (MAD)0
Skewness25.499921
Sum344793.4
Variance1778096
MonotonicityNot monotonic
2024-04-06T17:22:13.797631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4246
97.9%
16.1 2
 
< 0.1%
70.0 1
 
< 0.1%
133.5 1
 
< 0.1%
114.9 1
 
< 0.1%
32.9 1
 
< 0.1%
37.7 1
 
< 0.1%
60.9 1
 
< 0.1%
68.2 1
 
< 0.1%
233.9 1
 
< 0.1%
Other values (83) 83
 
1.9%
ValueCountFrequency (%)
0.0 4246
97.9%
15.7 1
 
< 0.1%
16.1 2
 
< 0.1%
20.8 1
 
< 0.1%
21.9 1
 
< 0.1%
23.7 1
 
< 0.1%
24.0 1
 
< 0.1%
24.6 1
 
< 0.1%
29.0 1
 
< 0.1%
29.7 1
 
< 0.1%
ValueCountFrequency (%)
53805.4 1
< 0.1%
31861.2 1
< 0.1%
28305.4 1
< 0.1%
23567.5 1
< 0.1%
22072.8 1
< 0.1%
21883.6 1
< 0.1%
16699.1 1
< 0.1%
15771.9 1
< 0.1%
15573.1 1
< 0.1%
13059.2 1
< 0.1%

외부위탁처리량_제품원료
Real number (ℝ)

SKEWED  ZEROS 

Distinct164
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean194.58331
Minimum0
Maximum85453
Zeros4174
Zeros (%)96.2%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:14.060029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum85453
Range85453
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2103.3583
Coefficient of variation (CV)10.809551
Kurtosis710.75598
Mean194.58331
Median Absolute Deviation (MAD)0
Skewness22.219844
Sum844297
Variance4424116.3
MonotonicityNot monotonic
2024-04-06T17:22:14.326175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4174
96.2%
365.8 2
 
< 0.1%
372.4 2
 
< 0.1%
262.8 1
 
< 0.1%
166.6 1
 
< 0.1%
244.7 1
 
< 0.1%
27.7 1
 
< 0.1%
41.3 1
 
< 0.1%
6.8 1
 
< 0.1%
1263.4 1
 
< 0.1%
Other values (154) 154
 
3.5%
ValueCountFrequency (%)
0.0 4174
96.2%
6.8 1
 
< 0.1%
20.2 1
 
< 0.1%
21.5 1
 
< 0.1%
24.3 1
 
< 0.1%
27.7 1
 
< 0.1%
39.4 1
 
< 0.1%
41.3 1
 
< 0.1%
43.9 1
 
< 0.1%
56.6 1
 
< 0.1%
ValueCountFrequency (%)
85453.0 1
< 0.1%
36136.8 1
< 0.1%
35079.2 1
< 0.1%
34076.0 1
< 0.1%
29762.7 1
< 0.1%
27252.3 1
< 0.1%
24211.0 1
< 0.1%
23930.1 1
< 0.1%
21324.4 1
< 0.1%
20943.8 1
< 0.1%

외부위탁처리량_복토재
Real number (ℝ)

SKEWED  ZEROS 

Distinct71
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.09811
Minimum0
Maximum28237.4
Zeros4269
Zeros (%)98.4%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:15.026559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum28237.4
Range28237.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation658.34931
Coefficient of variation (CV)15.275596
Kurtosis1090.5531
Mean43.09811
Median Absolute Deviation (MAD)0
Skewness29.451764
Sum187002.7
Variance433423.82
MonotonicityNot monotonic
2024-04-06T17:22:15.277971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4269
98.4%
5590.9 1
 
< 0.1%
1046.7 1
 
< 0.1%
114.3 1
 
< 0.1%
853.6 1
 
< 0.1%
3757.7 1
 
< 0.1%
474.2 1
 
< 0.1%
474.5 1
 
< 0.1%
370.5 1
 
< 0.1%
691.0 1
 
< 0.1%
Other values (61) 61
 
1.4%
ValueCountFrequency (%)
0.0 4269
98.4%
5.2 1
 
< 0.1%
6.1 1
 
< 0.1%
6.9 1
 
< 0.1%
15.3 1
 
< 0.1%
16.4 1
 
< 0.1%
19.5 1
 
< 0.1%
19.7 1
 
< 0.1%
30.2 1
 
< 0.1%
34.6 1
 
< 0.1%
ValueCountFrequency (%)
28237.4 1
< 0.1%
22130.0 1
< 0.1%
8260.9 1
< 0.1%
7870.6 1
< 0.1%
7061.8 1
< 0.1%
6252.1 1
< 0.1%
6122.9 1
< 0.1%
5796.8 1
< 0.1%
5590.9 1
< 0.1%
5567.3 1
< 0.1%

외부위탁처리량_퇴비화
Real number (ℝ)

ZEROS 

Distinct325
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean164.21657
Minimum0
Maximum39724
Zeros4008
Zeros (%)92.4%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:15.543880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile268.02
Maximum39724
Range39724
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1411.4855
Coefficient of variation (CV)8.5952685
Kurtosis377.98598
Mean164.21657
Median Absolute Deviation (MAD)0
Skewness17.21529
Sum712535.7
Variance1992291.3
MonotonicityNot monotonic
2024-04-06T17:22:15.936634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 4008
92.4%
4.9 2
 
< 0.1%
82.0 2
 
< 0.1%
129.4 2
 
< 0.1%
198.0 2
 
< 0.1%
21.6 2
 
< 0.1%
17.6 2
 
< 0.1%
2.9 2
 
< 0.1%
3888.0 1
 
< 0.1%
4892.0 1
 
< 0.1%
Other values (315) 315
 
7.3%
ValueCountFrequency (%)
0.0 4008
92.4%
0.1 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 1
 
< 0.1%
0.7 1
 
< 0.1%
1.0 1
 
< 0.1%
1.7 1
 
< 0.1%
2.4 1
 
< 0.1%
2.9 2
 
< 0.1%
3.2 1
 
< 0.1%
ValueCountFrequency (%)
39724.0 1
< 0.1%
36701.6 1
< 0.1%
33229.6 1
< 0.1%
23641.6 1
< 0.1%
23179.5 1
< 0.1%
22299.4 1
< 0.1%
15267.0 1
< 0.1%
14699.9 1
< 0.1%
14379.9 1
< 0.1%
14339.0 1
< 0.1%
Distinct226
Distinct (%)52.7%
Missing3910
Missing (%)90.1%
Memory size34.0 KiB
2024-04-06T17:22:16.474857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length209
Median length124
Mean length21.869464
Min length2

Characters and Unicode

Total characters9382
Distinct characters262
Distinct categories11 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)40.6%

Sample

1st row진들농산 엔아이티
2nd row지엔티
3rd row성원환경 광동산업 에덴녹화 대부산업
4th row한일현대시멘트㈜영월공장 신기산업㈜ 아세아시멘트㈜제천 농업회사법인㈜ 도담바이오 한농영농조합법인
5th row한일시멘트 아세아시멘트 한일현대시멘트 성신양회
ValueCountFrequency (%)
송원농장 51
 
3.6%
푸른농장 51
 
3.6%
자연농원 49
 
3.4%
㈜다나에너지솔루션 49
 
3.4%
청산농원 48
 
3.4%
성지농장 35
 
2.5%
동남농장 30
 
2.1%
한율농장 28
 
2.0%
진들농산 28
 
2.0%
농업회사법인 27
 
1.9%
Other values (433) 1028
72.2%
2024-04-06T17:22:17.557663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1282
 
13.7%
687
 
7.3%
405
 
4.3%
207
 
2.2%
202
 
2.2%
) 200
 
2.1%
( 199
 
2.1%
196
 
2.1%
194
 
2.1%
174
 
1.9%
Other values (252) 5636
60.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 7305
77.9%
Space Separator 1282
 
13.7%
Close Punctuation 203
 
2.2%
Other Symbol 202
 
2.2%
Open Punctuation 202
 
2.2%
Decimal Number 66
 
0.7%
Uppercase Letter 62
 
0.7%
Other Punctuation 48
 
0.5%
Lowercase Letter 5
 
0.1%
Dash Punctuation 5
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
687
 
9.4%
405
 
5.5%
207
 
2.8%
196
 
2.7%
194
 
2.7%
174
 
2.4%
150
 
2.1%
148
 
2.0%
141
 
1.9%
139
 
1.9%
Other values (221) 4864
66.6%
Decimal Number
ValueCountFrequency (%)
2 9
13.6%
4 9
13.6%
8 9
13.6%
3 8
12.1%
5 6
9.1%
7 6
9.1%
1 6
9.1%
0 5
7.6%
9 4
6.1%
6 4
6.1%
Uppercase Letter
ValueCountFrequency (%)
M 16
25.8%
K 11
17.7%
I 10
16.1%
R 8
12.9%
F 5
 
8.1%
A 5
 
8.1%
N 3
 
4.8%
P 3
 
4.8%
E 1
 
1.6%
Other Punctuation
ValueCountFrequency (%)
. 28
58.3%
: 16
33.3%
/ 4
 
8.3%
Close Punctuation
ValueCountFrequency (%)
) 200
98.5%
] 3
 
1.5%
Open Punctuation
ValueCountFrequency (%)
( 199
98.5%
[ 3
 
1.5%
Space Separator
ValueCountFrequency (%)
1282
100.0%
Other Symbol
ValueCountFrequency (%)
202
100.0%
Lowercase Letter
ValueCountFrequency (%)
n 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5
100.0%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 7507
80.0%
Common 1808
 
19.3%
Latin 67
 
0.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
687
 
9.2%
405
 
5.4%
207
 
2.8%
202
 
2.7%
196
 
2.6%
194
 
2.6%
174
 
2.3%
150
 
2.0%
148
 
2.0%
141
 
1.9%
Other values (222) 5003
66.6%
Common
ValueCountFrequency (%)
1282
70.9%
) 200
 
11.1%
( 199
 
11.0%
. 28
 
1.5%
: 16
 
0.9%
2 9
 
0.5%
4 9
 
0.5%
8 9
 
0.5%
3 8
 
0.4%
5 6
 
0.3%
Other values (10) 42
 
2.3%
Latin
ValueCountFrequency (%)
M 16
23.9%
K 11
16.4%
I 10
14.9%
R 8
11.9%
n 5
 
7.5%
F 5
 
7.5%
A 5
 
7.5%
N 3
 
4.5%
P 3
 
4.5%
E 1
 
1.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 7305
77.9%
ASCII 1875
 
20.0%
None 202
 
2.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1282
68.4%
) 200
 
10.7%
( 199
 
10.6%
. 28
 
1.5%
: 16
 
0.9%
M 16
 
0.9%
K 11
 
0.6%
I 10
 
0.5%
2 9
 
0.5%
4 9
 
0.5%
Other values (20) 95
 
5.1%
Hangul
ValueCountFrequency (%)
687
 
9.4%
405
 
5.5%
207
 
2.8%
196
 
2.7%
194
 
2.7%
174
 
2.4%
150
 
2.1%
148
 
2.0%
141
 
1.9%
139
 
1.9%
Other values (221) 4864
66.6%
None
ValueCountFrequency (%)
202
100.0%

수도권광역 위탁처리량
Real number (ℝ)

SKEWED  ZEROS 

Distinct41
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.776008
Minimum0
Maximum58286.5
Zeros4299
Zeros (%)99.1%
Negative0
Negative (%)0.0%
Memory size38.3 KiB
2024-04-06T17:22:17.967068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum58286.5
Range58286.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1610.7055
Coefficient of variation (CV)19.940394
Kurtosis883.64413
Mean80.776008
Median Absolute Deviation (MAD)0
Skewness28.340906
Sum350487.1
Variance2594372.1
MonotonicityNot monotonic
2024-04-06T17:22:18.362252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
0.0 4299
99.1%
34050.6 1
 
< 0.1%
5243.9 1
 
< 0.1%
11349.6 1
 
< 0.1%
2404.5 1
 
< 0.1%
431.1 1
 
< 0.1%
1098.8 1
 
< 0.1%
7452.4 1
 
< 0.1%
205.3 1
 
< 0.1%
6406.5 1
 
< 0.1%
Other values (31) 31
 
0.7%
ValueCountFrequency (%)
0.0 4299
99.1%
88.3 1
 
< 0.1%
103.3 1
 
< 0.1%
205.3 1
 
< 0.1%
239.4 1
 
< 0.1%
422.1 1
 
< 0.1%
431.1 1
 
< 0.1%
458.6 1
 
< 0.1%
683.3 1
 
< 0.1%
989.8 1
 
< 0.1%
ValueCountFrequency (%)
58286.5 1
< 0.1%
51922.0 1
< 0.1%
48054.6 1
< 0.1%
34050.6 1
< 0.1%
23456.9 1
< 0.1%
19555.0 1
< 0.1%
14841.5 1
< 0.1%
11349.6 1
< 0.1%
9224.4 1
< 0.1%
7452.4 1
< 0.1%

Sample

시도구군행정구역명하수처리장명찌꺼기 발생량_합계찌꺼기 발생량_자체찌꺼기 발생량_외부유입량이송처리장이송처리량함수율찌꺼기처리량_합계자체찌꺼기처리량_합계자체찌꺼기처리량_매립자체찌꺼기처리량_소각자체찌꺼기처리량_소각 후 처리(2차)_소계자체찌꺼기처리량_소각 후 처리(2차)_매립자체찌꺼기처리량_소각 후 처리(2차)_제품원료자체찌꺼기처리량_소각 후 처리(2차)_복토재자체찌꺼기처리량_소각 후 처리(2차)_시설(업체명)자체찌꺼기처리량_고화자체찌꺼기처리량_고화 후 처리(2차)_소계자체찌꺼기처리량_고화 후 처리(2차)_매립자체찌꺼기처리량_고화 후 처리(2차)_복토재자체찌꺼기처리량_고화 후 처리(2차)_시설(업체명)자체찌꺼기처리량_건조자체찌꺼기처리량_건조 후 처리(2차)_소계자체찌꺼기처리량_건조 후 처리(2차)_매립자체찌꺼기처리량_건조 후 처리(2차)_소각자체찌꺼기처리량_건조 후 처리(2차)_연료자체찌꺼기처리량_건조 후 처리(2차)_제품원료자체찌꺼기처리량_건조 후 처리(2차)_복토재자체찌꺼기처리량_건조 후 처리(2차)_퇴비화자체찌꺼기처리량_건조 후 처리(2차)_시설(업체명)자체찌꺼기처리량_탄화자체찌꺼기처리량_탄화 후 처리(2차)_소계자체찌꺼기처리량_탄화 후 처리(2차)_소각자체찌꺼기처리량_탄화 후 처리(2차)_연료자체찌꺼기처리량_탄화 후 처리(2차)_시설(업체명)자체찌꺼기처리량_퇴비화자체찌꺼기처리량_퇴비화_시설(업체명)외부위탁처리량_소계외부위탁처리량_매립외부위탁처리량_소각외부위탁처리량_연료외부위탁처리량_제품원료외부위탁처리량_복토재외부위탁처리량_퇴비화외부위탁처리량_시설(업체명)수도권광역 위탁처리량
0서울특별시<NA>서울특별시난지138383.3138383.30.0<NA>0.078.4138383.396819.00.057924.00.00.00.00<NA>0.00.00.00<NA>38895.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>7513.70.00.00.00.05590.91922.8진들농산 엔아이티34050.6
1서울특별시성동구성동구중랑물재생센터221134.6221134.60.0<NA>0.078.7221134.6157308.10.00.00.00.00.00<NA>0.00.00.00<NA>157308.10.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>15771.90.00.015771.90.00.00.0지엔티48054.6
2서울특별시강서구강서구서남179521.1179521.10.0<NA>0.076.9179521.188005.00.039362.00.00.00.00<NA>0.00.00.00<NA>48643.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>33229.60.00.00.00.00.033229.6성원환경 광동산업 에덴녹화 대부산업58286.5
3서울특별시강남구강남구탄천110318.2110318.20.0<NA>0.079.2110318.222846.00.00.00.00.00.00<NA>0.00.00.00<NA>22846.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>35550.20.00.00.020283.20.015267.0한일현대시멘트㈜영월공장 신기산업㈜ 아세아시멘트㈜제천 농업회사법인㈜ 도담바이오 한농영농조합법인51922.0
4부산광역시서구서구중앙8062.18062.10.0<NA>0.079.58062.10.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.00.00.000.0<NA>0.0<NA>8062.10.00.00.08062.10.00.0한일시멘트 아세아시멘트 한일현대시멘트 성신양회0.0
5부산광역시영도구영도구영도5479.85479.80.0<NA>0.078.75479.80.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>5479.80.00.05479.80.00.00.0성신양회(주)단양공장 한일시멘트(주)단양공장0.0
6부산광역시동래구동래구수영52370.152370.10.0<NA>0.078.652370.122607.40.00.00.00.00.00<NA>0.00.00.00<NA>22607.40.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>29762.70.00.00.029762.70.00.0아세아시멘트 한일시멘트단양공장 한일현대시멘트영월공장0.0
7부산광역시남구남구남부40269.840269.80.0<NA>0.080.840269.816702.30.00.00.00.00.00<NA>0.00.00.00<NA>16702.30.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>23567.50.00.023567.50.00.00.0아세아시멘트㈜ 한일시멘트단양공장 한일현대시멘트영월공장 성신양회0.0
8부산광역시해운대구해운대구동부14789.114789.10.0<NA>0.072.214789.10.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>14789.10.00.00.014789.10.00.0한일시멘트0.0
9부산광역시해운대구해운대구해운대11046.511046.50.0<NA>0.080.011046.55381.40.00.00.00.00.00<NA>0.00.00.00<NA>5381.41609.0016090.00.00.00.0해운대사업소(자체소각)0.00.000.0<NA>0.0<NA>5665.10.00.00.05665.10.00.0성신양회 한일시멘트단양공장 한일현대시멘트영월공장0.0
시도구군행정구역명하수처리장명찌꺼기 발생량_합계찌꺼기 발생량_자체찌꺼기 발생량_외부유입량이송처리장이송처리량함수율찌꺼기처리량_합계자체찌꺼기처리량_합계자체찌꺼기처리량_매립자체찌꺼기처리량_소각자체찌꺼기처리량_소각 후 처리(2차)_소계자체찌꺼기처리량_소각 후 처리(2차)_매립자체찌꺼기처리량_소각 후 처리(2차)_제품원료자체찌꺼기처리량_소각 후 처리(2차)_복토재자체찌꺼기처리량_소각 후 처리(2차)_시설(업체명)자체찌꺼기처리량_고화자체찌꺼기처리량_고화 후 처리(2차)_소계자체찌꺼기처리량_고화 후 처리(2차)_매립자체찌꺼기처리량_고화 후 처리(2차)_복토재자체찌꺼기처리량_고화 후 처리(2차)_시설(업체명)자체찌꺼기처리량_건조자체찌꺼기처리량_건조 후 처리(2차)_소계자체찌꺼기처리량_건조 후 처리(2차)_매립자체찌꺼기처리량_건조 후 처리(2차)_소각자체찌꺼기처리량_건조 후 처리(2차)_연료자체찌꺼기처리량_건조 후 처리(2차)_제품원료자체찌꺼기처리량_건조 후 처리(2차)_복토재자체찌꺼기처리량_건조 후 처리(2차)_퇴비화자체찌꺼기처리량_건조 후 처리(2차)_시설(업체명)자체찌꺼기처리량_탄화자체찌꺼기처리량_탄화 후 처리(2차)_소계자체찌꺼기처리량_탄화 후 처리(2차)_소각자체찌꺼기처리량_탄화 후 처리(2차)_연료자체찌꺼기처리량_탄화 후 처리(2차)_시설(업체명)자체찌꺼기처리량_퇴비화자체찌꺼기처리량_퇴비화_시설(업체명)외부위탁처리량_소계외부위탁처리량_매립외부위탁처리량_소각외부위탁처리량_연료외부위탁처리량_제품원료외부위탁처리량_복토재외부위탁처리량_퇴비화외부위탁처리량_시설(업체명)수도권광역 위탁처리량
4329제주특별자치도서귀포시서귀포시세화1리0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4330제주특별자치도서귀포시서귀포시수망리0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4331제주특별자치도서귀포시서귀포시무릉 좌기동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4332제주특별자치도서귀포시서귀포시신흥 고수동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4333제주특별자치도서귀포시서귀포시남원 서의동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4334제주특별자치도서귀포시서귀포시위미 대성동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4335제주특별자치도서귀포시서귀포시의귀 월산동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4336제주특별자치도서귀포시서귀포시가시 두리동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4337제주특별자치도서귀포시서귀포시가시 역지동0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0
4338제주특별자치도서귀포시서귀포시광평리0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00<NA>0.00.00.00<NA>0.00.0000.00.00.00.0<NA>0.00.000.0<NA>0.0<NA>0.00.00.00.00.00.00.0<NA>0.0