Overview

Dataset statistics

Number of variables10
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory91.3 B

Variable types

Categorical6
Numeric4

Alerts

액상슬러지처리량 has constant value ""Constant
협잡물함수율 has constant value ""Constant
액상슬러지함수율 has constant value ""Constant
권역 is highly overall correlated with 탈수슬러지처리량 and 3 other fieldsHigh correlation
하수처리시설명 is highly overall correlated with 탈수슬러지처리량 and 4 other fieldsHigh correlation
관리단 is highly overall correlated with 탈수슬러지처리량 and 3 other fieldsHigh correlation
탈수슬러지처리량 is highly overall correlated with 탈수슬러지함수율 and 3 other fieldsHigh correlation
탈수슬러지함수율 is highly overall correlated with 탈수슬러지처리량 and 1 other fieldsHigh correlation
협잡물처리량 is highly overall correlated with 권역 and 2 other fieldsHigh correlation
권역 is highly imbalanced (56.4%)Imbalance
관리단 is highly imbalanced (56.4%)Imbalance
탈수슬러지처리량 has 39 (39.0%) zerosZeros
탈수슬러지함수율 has 61 (61.0%) zerosZeros
협잡물처리량 has 91 (91.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:03:26.942578
Analysis finished2023-12-10 13:03:28.892968
Duration1.95 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

권역
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
90
91 
91
 
9

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row90
2nd row90
3rd row90
4th row90
5th row90

Common Values

ValueCountFrequency (%)
90 91
91.0%
91 9
 
9.0%

Length

2023-12-10T22:03:29.216795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:29.317309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
90 91
91.0%
91 9
 
9.0%

하수처리시설명
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
50001
52 
50002
26 
50003
13 
60001
9 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row50003
2nd row50001
3rd row50001
4th row50003
5th row50001

Common Values

ValueCountFrequency (%)
50001 52
52.0%
50002 26
26.0%
50003 13
 
13.0%
60001 9
 
9.0%

Length

2023-12-10T22:03:29.437385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:29.565527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
50001 52
52.0%
50002 26
26.0%
50003 13
 
13.0%
60001 9
 
9.0%

처리일자
Real number (ℝ)

Distinct30
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190416
Minimum20190401
Maximum20190430
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:29.681454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190401
5-th percentile20190402
Q120190409
median20190416
Q320190424
95-th percentile20190429
Maximum20190430
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.7572135
Coefficient of variation (CV)4.337312 × 10-7
Kurtosis-1.150563
Mean20190416
Median Absolute Deviation (MAD)7.5
Skewness-0.12129166
Sum2.0190416 × 109
Variance76.688788
MonotonicityNot monotonic
2023-12-10T22:03:29.805685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
20190429 5
 
5.0%
20190415 5
 
5.0%
20190422 5
 
5.0%
20190419 5
 
5.0%
20190424 5
 
5.0%
20190430 4
 
4.0%
20190425 4
 
4.0%
20190401 4
 
4.0%
20190417 4
 
4.0%
20190416 4
 
4.0%
Other values (20) 55
55.0%
ValueCountFrequency (%)
20190401 4
4.0%
20190402 3
3.0%
20190403 3
3.0%
20190404 4
4.0%
20190405 3
3.0%
20190406 2
2.0%
20190407 2
2.0%
20190408 2
2.0%
20190409 3
3.0%
20190410 4
4.0%
ValueCountFrequency (%)
20190430 4
4.0%
20190429 5
5.0%
20190428 2
 
2.0%
20190427 3
3.0%
20190426 3
3.0%
20190425 4
4.0%
20190424 5
5.0%
20190423 3
3.0%
20190422 5
5.0%
20190421 2
 
2.0%

관리단
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
500
91 
600
 
9

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row500
2nd row500
3rd row500
4th row500
5th row500

Common Values

ValueCountFrequency (%)
500 91
91.0%
600 9
 
9.0%

Length

2023-12-10T22:03:29.943518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:30.038074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500 91
91.0%
600 9
 
9.0%

탈수슬러지처리량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct57
Distinct (%)57.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3410.45
Minimum0
Maximum11264
Zeros39
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:30.134802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4715
Q35565
95-th percentile6954.5
Maximum11264
Range11264
Interquartile range (IQR)5565

Descriptive statistics

Standard deviation2981.2539
Coefficient of variation (CV)0.87415266
Kurtosis-0.87596746
Mean3410.45
Median Absolute Deviation (MAD)1550
Skewness0.14249652
Sum341045
Variance8887875
MonotonicityNot monotonic
2023-12-10T22:03:30.259933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 39
39.0%
5860 2
 
2.0%
5370 2
 
2.0%
5000 2
 
2.0%
5830 2
 
2.0%
5040 2
 
2.0%
5960 1
 
1.0%
4620 1
 
1.0%
5170 1
 
1.0%
5560 1
 
1.0%
Other values (47) 47
47.0%
ValueCountFrequency (%)
0 39
39.0%
3115 1
 
1.0%
3269 1
 
1.0%
3423 1
 
1.0%
3496 1
 
1.0%
3648 1
 
1.0%
4430 1
 
1.0%
4500 1
 
1.0%
4530 1
 
1.0%
4620 1
 
1.0%
ValueCountFrequency (%)
11264 1
1.0%
10427 1
1.0%
10255 1
1.0%
9258 1
1.0%
7040 1
1.0%
6950 1
1.0%
6540 1
1.0%
6390 1
1.0%
6360 1
1.0%
6270 1
1.0%

액상슬러지처리량
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T22:03:30.390019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:30.481008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

탈수슬러지함수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6513.3
Minimum0
Maximum42180
Zeros61
Zeros (%)61.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:30.569427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310917.5
95-th percentile23890.5
Maximum42180
Range42180
Interquartile range (IQR)10917.5

Descriptive statistics

Standard deviation9600.4945
Coefficient of variation (CV)1.4739832
Kurtosis1.8303463
Mean6513.3
Median Absolute Deviation (MAD)0
Skewness1.4923631
Sum651330
Variance92169495
MonotonicityNot monotonic
2023-12-10T22:03:30.676316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 61
61.0%
11930 5
 
5.0%
10530 3
 
3.0%
11940 3
 
3.0%
10520 2
 
2.0%
10570 2
 
2.0%
10540 2
 
2.0%
10510 2
 
2.0%
31650 1
 
1.0%
42180 1
 
1.0%
Other values (18) 18
 
18.0%
ValueCountFrequency (%)
0 61
61.0%
10510 2
 
2.0%
10520 2
 
2.0%
10530 3
 
3.0%
10540 2
 
2.0%
10550 1
 
1.0%
10560 1
 
1.0%
10570 2
 
2.0%
10580 1
 
1.0%
11930 5
 
5.0%
ValueCountFrequency (%)
42180 1
1.0%
35820 1
1.0%
31650 1
1.0%
31630 1
1.0%
23900 1
1.0%
23890 1
1.0%
23880 1
1.0%
21130 1
1.0%
21120 1
1.0%
21100 1
1.0%

협잡물처리량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.503
Minimum0
Maximum83.7
Zeros91
Zeros (%)91.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:30.773592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile83.4
Maximum83.7
Range83.7
Interquartile range (IQR)0

Descriptive statistics

Standard deviation23.978342
Coefficient of variation (CV)3.195834
Kurtosis6.5950441
Mean7.503
Median Absolute Deviation (MAD)0
Skewness2.9091897
Sum750.3
Variance574.9609
MonotonicityNot monotonic
2023-12-10T22:03:30.860739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
0.0 91
91.0%
83.5 3
 
3.0%
83.4 2
 
2.0%
83.3 2
 
2.0%
82.7 1
 
1.0%
83.7 1
 
1.0%
ValueCountFrequency (%)
0.0 91
91.0%
82.7 1
 
1.0%
83.3 2
 
2.0%
83.4 2
 
2.0%
83.5 3
 
3.0%
83.7 1
 
1.0%
ValueCountFrequency (%)
83.7 1
 
1.0%
83.5 3
 
3.0%
83.4 2
 
2.0%
83.3 2
 
2.0%
82.7 1
 
1.0%
0.0 91
91.0%

협잡물함수율
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T22:03:30.973950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:31.057961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

액상슬러지함수율
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
0
100 

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 (%)
0 100
100.0%

Length

2023-12-10T22:03:31.199035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:31.283612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

Interactions

2023-12-10T22:03:28.291575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.230456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.610621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.954435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:28.387289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.322662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.705278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:28.037021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:28.485002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.427242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.800413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:28.114285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:28.558173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.520298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:27.878484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:28.188742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:03:31.333687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역하수처리시설명처리일자관리단탈수슬러지처리량탈수슬러지함수율협잡물처리량
권역1.0001.0000.0000.9950.9910.0550.995
하수처리시설명1.0001.0000.0001.0000.9690.7061.000
처리일자0.0000.0001.0000.0000.0000.0000.000
관리단0.9951.0000.0001.0000.9910.0550.995
탈수슬러지처리량0.9910.9690.0000.9911.0000.5350.991
탈수슬러지함수율0.0550.7060.0000.0550.5351.0000.055
협잡물처리량0.9951.0000.0000.9950.9910.0551.000
2023-12-10T22:03:31.427471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역하수처리시설명관리단
권역1.0000.9900.938
하수처리시설명0.9901.0000.990
관리단0.9380.9901.000
2023-12-10T22:03:31.508480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리일자탈수슬러지처리량탈수슬러지함수율협잡물처리량권역하수처리시설명관리단
처리일자1.0000.0280.0620.1000.0000.0000.000
탈수슬러지처리량0.0281.000-0.8370.1850.8890.7490.889
탈수슬러지함수율0.062-0.8371.000-0.2410.0520.5650.052
협잡물처리량0.1000.185-0.2411.0000.9380.9900.938
권역0.0000.8890.0520.9381.0000.9900.938
하수처리시설명0.0000.7490.5650.9900.9901.0000.990
관리단0.0000.8890.0520.9380.9380.9901.000

Missing values

2023-12-10T22:03:28.670149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:03:28.824499image/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

권역하수처리시설명처리일자관리단탈수슬러지처리량액상슬러지처리량탈수슬러지함수율협잡물처리량협잡물함수율액상슬러지함수율
090500032019043050000358200.000
19050001201904255005010000.000
29050001201904015004530000.000
390500032019042950000179100.000
49050001201904275005850000.000
59050001201904265005180000.000
69050001201904025004900000.000
79050001201904015006390000.000
89050001201904235005730000.000
99050001201904305004780000.000
권역하수처리시설명처리일자관리단탈수슬러지처리량액상슬러지처리량탈수슬러지함수율협잡물처리량협잡물함수율액상슬러지함수율
909050001201904255005930000.000
9191600012019042460034230083.500
9291600012019041960092580083.400
9391600012019040660032690083.500
9491600012019042760036480083.300
9591600012019041660034960083.300
9691600012019041360031150083.500
97916000120190415600112640083.400
98916000120190422600102550082.700
99916000120190429600104270083.700