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

Categorical4
Numeric6

Alerts

협잡물함수율 has constant value ""Constant
액상슬러지함수율 has constant value ""Constant
권역 is highly overall correlated with 하수처리시설명 and 2 other fieldsHigh correlation
관리단 is highly overall correlated with 하수처리시설명 and 4 other fieldsHigh correlation
하수처리시설명 is highly overall correlated with 협잡물처리량 and 2 other fieldsHigh correlation
탈수슬러지처리량 is highly overall correlated with 탈수슬러지함수율 and 1 other fieldsHigh correlation
액상슬러지처리량 is highly overall correlated with 관리단High correlation
탈수슬러지함수율 is highly overall correlated with 탈수슬러지처리량High correlation
협잡물처리량 is highly overall correlated with 하수처리시설명 and 2 other fieldsHigh correlation
관리단 is highly imbalanced (53.4%)Imbalance
탈수슬러지처리량 has 32 (32.0%) zerosZeros
액상슬러지처리량 has 94 (94.0%) zerosZeros
탈수슬러지함수율 has 68 (68.0%) zerosZeros
협잡물처리량 has 77 (77.0%) zerosZeros

Reproduction

Analysis started2023-12-10 13:03:36.931428
Analysis finished2023-12-10 13:03:40.514179
Duration3.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

권역
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
90
75 
91
25 

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 75
75.0%
91 25
 
25.0%

Length

2023-12-10T22:03:40.566851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:40.649118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
90 75
75.0%
91 25
 
25.0%

하수처리시설명
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52401.46
Minimum40001
Maximum70001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:40.727828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum40001
5-th percentile50001
Q150001
median50002
Q350003
95-th percentile60001
Maximum70001
Range30000
Interquartile range (IQR)2

Descriptive statistics

Standard deviation4739.4532
Coefficient of variation (CV)0.09044506
Kurtosis1.1352198
Mean52401.46
Median Absolute Deviation (MAD)1
Skewness1.2201454
Sum5240146
Variance22462417
MonotonicityNot monotonic
2023-12-10T22:03:40.822345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
50001 43
43.0%
60001 23
23.0%
50002 18
18.0%
50003 14
 
14.0%
70001 1
 
1.0%
40001 1
 
1.0%
ValueCountFrequency (%)
40001 1
 
1.0%
50001 43
43.0%
50002 18
18.0%
50003 14
 
14.0%
60001 23
23.0%
70001 1
 
1.0%
ValueCountFrequency (%)
70001 1
 
1.0%
60001 23
23.0%
50003 14
 
14.0%
50002 18
18.0%
50001 43
43.0%
40001 1
 
1.0%

처리일자
Real number (ℝ)

Distinct28
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20190215
Minimum20190201
Maximum20190228
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:40.939933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20190201
5-th percentile20190202
Q120190208
median20190215
Q320190222
95-th percentile20190228
Maximum20190228
Range27
Interquartile range (IQR)14

Descriptive statistics

Standard deviation8.1786307
Coefficient of variation (CV)4.0507892 × 10-7
Kurtosis-1.1000885
Mean20190215
Median Absolute Deviation (MAD)7
Skewness-0.10272856
Sum2.0190215 × 109
Variance66.89
MonotonicityNot monotonic
2023-12-10T22:03:41.080774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
20190228 7
 
7.0%
20190201 5
 
5.0%
20190214 5
 
5.0%
20190212 5
 
5.0%
20190218 5
 
5.0%
20190222 5
 
5.0%
20190208 4
 
4.0%
20190213 4
 
4.0%
20190219 4
 
4.0%
20190206 4
 
4.0%
Other values (18) 52
52.0%
ValueCountFrequency (%)
20190201 5
5.0%
20190202 2
 
2.0%
20190203 2
 
2.0%
20190204 4
4.0%
20190205 1
 
1.0%
20190206 4
4.0%
20190207 4
4.0%
20190208 4
4.0%
20190209 2
 
2.0%
20190210 2
 
2.0%
ValueCountFrequency (%)
20190228 7
7.0%
20190227 4
4.0%
20190226 4
4.0%
20190225 3
3.0%
20190224 1
 
1.0%
20190223 3
3.0%
20190222 5
5.0%
20190221 4
4.0%
20190220 4
4.0%
20190219 4
4.0%

관리단
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
500
75 
600
23 
700
 
1
400
 
1

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
500 75
75.0%
600 23
 
23.0%
700 1
 
1.0%
400 1
 
1.0%

Length

2023-12-10T22:03:41.221116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:41.311363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
500 75
75.0%
600 23
 
23.0%
700 1
 
1.0%
400 1
 
1.0%

탈수슬러지처리량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10521.83
Minimum0
Maximum653610
Zeros32
Zeros (%)32.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:41.439307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3315.5
Q35920
95-th percentile9673.05
Maximum653610
Range653610
Interquartile range (IQR)5920

Descriptive statistics

Standard deviation65248.078
Coefficient of variation (CV)6.2012101
Kurtosis98.184446
Mean10521.83
Median Absolute Deviation (MAD)3184.5
Skewness9.8703502
Sum1052183
Variance4.2573117 × 109
MonotonicityNot monotonic
2023-12-10T22:03:41.570855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 32
32.0%
4580 3
 
3.0%
6870 3
 
3.0%
2561 2
 
2.0%
2752 2
 
2.0%
5920 2
 
2.0%
2615 2
 
2.0%
2523 1
 
1.0%
2923 1
 
1.0%
4320 1
 
1.0%
Other values (51) 51
51.0%
ValueCountFrequency (%)
0 32
32.0%
2485 1
 
1.0%
2508 1
 
1.0%
2523 1
 
1.0%
2561 2
 
2.0%
2600 1
 
1.0%
2615 2
 
2.0%
2692 1
 
1.0%
2752 2
 
2.0%
2791 1
 
1.0%
ValueCountFrequency (%)
653610 1
1.0%
57500 1
1.0%
10841 1
1.0%
10342 1
1.0%
10339 1
1.0%
9638 1
1.0%
8792 1
1.0%
7040 1
1.0%
6910 1
1.0%
6900 1
1.0%

액상슬러지처리량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean236.7
Minimum0
Maximum12780
Zeros94
Zeros (%)94.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:41.677505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1648.5
Maximum12780
Range12780
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1355.7599
Coefficient of variation (CV)5.7277563
Kurtosis75.801647
Mean236.7
Median Absolute Deviation (MAD)0
Skewness8.3208935
Sum23670
Variance1838085
MonotonicityNot monotonic
2023-12-10T22:03:41.773477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 94
94.0%
2460 1
 
1.0%
1630 1
 
1.0%
2490 1
 
1.0%
2000 1
 
1.0%
2310 1
 
1.0%
12780 1
 
1.0%
ValueCountFrequency (%)
0 94
94.0%
1630 1
 
1.0%
2000 1
 
1.0%
2310 1
 
1.0%
2460 1
 
1.0%
2490 1
 
1.0%
12780 1
 
1.0%
ValueCountFrequency (%)
12780 1
 
1.0%
2490 1
 
1.0%
2460 1
 
1.0%
2310 1
 
1.0%
2000 1
 
1.0%
1630 1
 
1.0%
0 94
94.0%

탈수슬러지함수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)31.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5953.4
Minimum0
Maximum39010
Zeros68
Zeros (%)68.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:41.876354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q310535
95-th percentile26001
Maximum39010
Range39010
Interquartile range (IQR)10535

Descriptive statistics

Standard deviation9710.1155
Coefficient of variation (CV)1.6310202
Kurtosis0.91219055
Mean5953.4
Median Absolute Deviation (MAD)0
Skewness1.4216827
Sum595340
Variance94286344
MonotonicityNot monotonic
2023-12-10T22:03:42.032976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 68
68.0%
10510 2
 
2.0%
11950 2
 
2.0%
21060 1
 
1.0%
10530 1
 
1.0%
10550 1
 
1.0%
21020 1
 
1.0%
13020 1
 
1.0%
26000 1
 
1.0%
21010 1
 
1.0%
Other values (21) 21
 
21.0%
ValueCountFrequency (%)
0 68
68.0%
5970 1
 
1.0%
10480 1
 
1.0%
10490 1
 
1.0%
10510 2
 
2.0%
10520 1
 
1.0%
10530 1
 
1.0%
10550 1
 
1.0%
10560 1
 
1.0%
11950 2
 
2.0%
ValueCountFrequency (%)
39010 1
1.0%
31760 1
1.0%
31550 1
1.0%
26050 1
1.0%
26020 1
1.0%
26000 1
1.0%
23830 1
1.0%
21130 1
1.0%
21090 1
1.0%
21080 1
1.0%

협잡물처리량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19.16
Minimum0
Maximum83.5
Zeros77
Zeros (%)77.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:03:42.142922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

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

Descriptive statistics

Standard deviation35.23404
Coefficient of variation (CV)1.8389374
Kurtosis-0.30909765
Mean19.16
Median Absolute Deviation (MAD)0
Skewness1.3028461
Sum1916
Variance1241.4376
MonotonicityNot monotonic
2023-12-10T22:03:42.227418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0.0 77
77.0%
83.4 9
 
9.0%
83.5 7
 
7.0%
83.3 3
 
3.0%
82.6 1
 
1.0%
82.9 1
 
1.0%
83.0 1
 
1.0%
82.5 1
 
1.0%
ValueCountFrequency (%)
0.0 77
77.0%
82.5 1
 
1.0%
82.6 1
 
1.0%
82.9 1
 
1.0%
83.0 1
 
1.0%
83.3 3
 
3.0%
83.4 9
 
9.0%
83.5 7
 
7.0%
ValueCountFrequency (%)
83.5 7
 
7.0%
83.4 9
 
9.0%
83.3 3
 
3.0%
83.0 1
 
1.0%
82.9 1
 
1.0%
82.6 1
 
1.0%
82.5 1
 
1.0%
0.0 77
77.0%

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

2023-12-10T22:03:42.327377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:42.400799image/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

2023-12-10T22:03:42.480393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:03:42.559301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

Interactions

2023-12-10T22:03:39.635320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.176903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.612268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.113523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.654191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.144255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.725995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.242475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.683851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.199661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.738007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.223770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.796852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.312283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.753650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.287400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.813813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.303884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.868734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.386331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.864056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.366207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.902488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.384543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.945744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.456321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.966003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.460468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.984770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.464964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:40.263010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:37.541461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.047518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:38.571940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.072203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:03:39.546705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:03:42.621074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역하수처리시설명처리일자관리단탈수슬러지처리량액상슬러지처리량탈수슬러지함수율협잡물처리량
권역1.0001.0000.0001.0000.0000.1000.2890.992
하수처리시설명1.0001.0000.0001.0001.0000.9420.0001.000
처리일자0.0000.0001.0000.0000.0000.0000.0000.000
관리단1.0001.0000.0001.0001.0000.6730.0001.000
탈수슬러지처리량0.0001.0000.0001.0001.0001.0000.0000.000
액상슬러지처리량0.1000.9420.0000.6731.0001.0000.0000.000
탈수슬러지함수율0.2890.0000.0000.0000.0000.0001.0000.257
협잡물처리량0.9921.0000.0001.0000.0000.0000.2571.000
2023-12-10T22:03:42.722194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
권역관리단
권역1.0000.990
관리단0.9901.000
2023-12-10T22:03:42.843966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하수처리시설명처리일자탈수슬러지처리량액상슬러지처리량탈수슬러지함수율협잡물처리량권역관리단
하수처리시설명1.000-0.071-0.435-0.1290.2540.7410.9901.000
처리일자-0.0711.0000.0420.2220.026-0.1430.0000.000
탈수슬러지처리량-0.4350.0421.0000.229-0.8020.0170.0000.990
액상슬러지처리량-0.1290.2220.2291.000-0.169-0.1370.1640.699
탈수슬러지함수율0.2540.026-0.802-0.1691.000-0.3620.2770.000
협잡물처리량0.741-0.1430.017-0.137-0.3621.0000.9180.990
권역0.9900.0000.0000.1640.2770.9181.0000.990
관리단1.0000.0000.9900.6990.0000.9900.9901.000

Missing values

2023-12-10T22:03:40.354169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:03:40.467282image/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
09050002201902075000059700.000
19050001201902195005420000.000
29050001201902195004880000.000
39050001201902205006270000.000
49050001201902205004580000.000
59050001201902215006750000.000
69050001201902245006620000.000
790500032019022850000194900.000
890500022019022350000210800.000
990500022019022650000104900.000
권역하수처리시설명처리일자관리단탈수슬러지처리량액상슬러지처리량탈수슬러지함수율협잡물처리량협잡물함수율액상슬러지함수율
9091600012019022660029610083.500
9191600012019022860087920083.000
9291600012019022360025230083.400
9391600012019022160025610083.400
9491600012019021960024850083.400
9591600012019020360027520083.400
9691600012019021760025610083.400
97916000120190218600103390082.500
9891600012019020260026150083.500
9991400012019022840057500000.000