Overview

Dataset statistics

Number of variables6
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.2 KiB
Average record size in memory53.3 B

Variable types

Categorical3
DateTime1
Numeric2

Alerts

아파트 브랜드 has constant value ""Constant
잔류염소(ppm) is highly overall correlated with 산도(ppm)High correlation
산도(ppm) is highly overall correlated with 잔류염소(ppm)High correlation
음용수 수온(ºC) is highly imbalanced (50.0%)Imbalance
시간 has unique valuesUnique

Reproduction

Analysis started2023-12-10 13:18:23.283237
Analysis finished2023-12-10 13:18:25.123580
Duration1.84 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

아파트 브랜드
Categorical

CONSTANT 

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

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
centreville 100
100.0%

Length

2023-12-10T22:18:25.272732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:25.488770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
centreville 100
100.0%

시간
Date

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
Minimum2020-01-01 00:00:02
Maximum2020-01-01 01:39:00
2023-12-10T22:18:25.688879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:26.010119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

잔류염소(ppm)
Real number (ℝ)

HIGH CORRELATION 

Distinct13
Distinct (%)13.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.130701
Minimum0.1261
Maximum0.1337
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:18:26.238976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.1261
5-th percentile0.1272
Q10.1296
median0.1308
Q30.132
95-th percentile0.1332
Maximum0.1337
Range0.0076
Interquartile range (IQR)0.0024

Descriptive statistics

Standard deviation0.0017161441
Coefficient of variation (CV)0.013130306
Kurtosis-0.095429819
Mean0.130701
Median Absolute Deviation (MAD)0.0012
Skewness-0.54148613
Sum13.0701
Variance2.9451505 × 10-6
MonotonicityNot monotonic
2023-12-10T22:18:26.455131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.1308 19
19.0%
0.1296 17
17.0%
0.1325 17
17.0%
0.132 9
9.0%
0.1272 8
8.0%
0.1313 8
8.0%
0.1284 5
 
5.0%
0.1301 5
 
5.0%
0.1337 4
 
4.0%
0.1302 3
 
3.0%
Other values (3) 5
 
5.0%
ValueCountFrequency (%)
0.1261 1
 
1.0%
0.1272 8
8.0%
0.1284 5
 
5.0%
0.1296 17
17.0%
0.1301 5
 
5.0%
0.1302 3
 
3.0%
0.1308 19
19.0%
0.1313 8
8.0%
0.1314 2
 
2.0%
0.132 9
9.0%
ValueCountFrequency (%)
0.1337 4
 
4.0%
0.1332 2
 
2.0%
0.1325 17
17.0%
0.132 9
9.0%
0.1314 2
 
2.0%
0.1313 8
8.0%
0.1308 19
19.0%
0.1302 3
 
3.0%
0.1301 5
 
5.0%
0.1296 17
17.0%

산도(ppm)
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
6.95
61 
6.96
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row6.95
2nd row6.95
3rd row6.95
4th row6.95
5th row6.95

Common Values

ValueCountFrequency (%)
6.95 61
61.0%
6.96 39
39.0%

Length

2023-12-10T22:18:26.738242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:26.970852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
6.95 61
61.0%
6.96 39
39.0%

음용수 수온(ºC)
Categorical

IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
11.4
89 
11.5
11 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row11.4
2nd row11.4
3rd row11.4
4th row11.4
5th row11.4

Common Values

ValueCountFrequency (%)
11.4 89
89.0%
11.5 11
 
11.0%

Length

2023-12-10T22:18:27.138654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T22:18:27.301029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
11.4 89
89.0%
11.5 11
 
11.0%

탁도(ppm)
Real number (ℝ)

Distinct19
Distinct (%)19.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.038582
Minimum0.038
Maximum0.0398
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T22:18:27.493108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.038
5-th percentile0.038
Q10.0382
median0.0385
Q30.0389
95-th percentile0.039405
Maximum0.0398
Range0.0018
Interquartile range (IQR)0.0007

Descriptive statistics

Standard deviation0.00042554303
Coefficient of variation (CV)0.011029574
Kurtosis0.20539577
Mean0.038582
Median Absolute Deviation (MAD)0.0003
Skewness0.78522881
Sum3.8582
Variance1.8108687 × 10-7
MonotonicityNot monotonic
2023-12-10T22:18:27.753858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0.0384 12
12.0%
0.0382 12
12.0%
0.0386 11
11.0%
0.0389 10
10.0%
0.0385 9
9.0%
0.038 9
9.0%
0.0383 6
 
6.0%
0.0387 6
 
6.0%
0.0381 5
 
5.0%
0.0391 4
 
4.0%
Other values (9) 16
16.0%
ValueCountFrequency (%)
0.038 9
9.0%
0.0381 5
5.0%
0.0382 12
12.0%
0.0383 6
6.0%
0.0384 12
12.0%
0.0385 9
9.0%
0.0386 11
11.0%
0.0387 6
6.0%
0.0388 4
 
4.0%
0.0389 10
10.0%
ValueCountFrequency (%)
0.0398 1
 
1.0%
0.0397 1
 
1.0%
0.0396 2
 
2.0%
0.0395 1
 
1.0%
0.0394 1
 
1.0%
0.0393 2
 
2.0%
0.0392 3
 
3.0%
0.0391 4
 
4.0%
0.039 1
 
1.0%
0.0389 10
10.0%

Interactions

2023-12-10T22:18:24.232867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:23.639889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:24.449325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T22:18:24.016099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T22:18:27.932127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시간잔류염소(ppm)산도(ppm)음용수 수온(ºC)탁도(ppm)
시간1.0001.0001.0001.0001.000
잔류염소(ppm)1.0001.0000.7800.2950.316
산도(ppm)1.0000.7801.0000.5830.142
음용수 수온(ºC)1.0000.2950.5831.0000.000
탁도(ppm)1.0000.3160.1420.0001.000
2023-12-10T22:18:28.089360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산도(ppm)음용수 수온(ºC)
산도(ppm)1.0000.396
음용수 수온(ºC)0.3961.000
2023-12-10T22:18:28.225339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
잔류염소(ppm)탁도(ppm)산도(ppm)음용수 수온(ºC)
잔류염소(ppm)1.000-0.2240.7710.283
탁도(ppm)-0.2241.0000.1510.000
산도(ppm)0.7710.1511.0000.396
음용수 수온(ºC)0.2830.0000.3961.000

Missing values

2023-12-10T22:18:24.762747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T22:18:24.998453image/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

아파트 브랜드시간잔류염소(ppm)산도(ppm)음용수 수온(ºC)탁도(ppm)
0centreville2020-01-01 01:22:000.12966.9511.40.0393
1centreville2020-01-01 01:37:000.12726.9511.40.0396
2centreville2020-01-01 01:09:000.1326.9511.40.0384
3centreville2020-01-01 01:21:000.1326.9511.40.0396
4centreville2020-01-01 01:28:000.12966.9511.40.0384
5centreville2020-01-01 01:36:000.12726.9511.40.0382
6centreville2020-01-01 00:59:000.13086.9511.40.0383
7centreville2020-01-01 01:08:000.12966.9511.40.0384
8centreville2020-01-01 01:14:000.12966.9511.40.038
9centreville2020-01-01 01:20:020.12966.9511.40.0391
아파트 브랜드시간잔류염소(ppm)산도(ppm)음용수 수온(ºC)탁도(ppm)
90centreville2020-01-01 00:18:000.13016.9611.40.0385
91centreville2020-01-01 00:14:000.13256.9611.40.0389
92centreville2020-01-01 00:08:000.13256.9611.50.0384
93centreville2020-01-01 01:17:000.13086.9511.40.0386
94centreville2020-01-01 00:00:020.13376.9611.50.0388
95centreville2020-01-01 00:30:020.13016.9611.40.0384
96centreville2020-01-01 00:07:000.13146.9611.50.0387
97centreville2020-01-01 00:45:000.13086.9511.40.0386
98centreville2020-01-01 01:39:000.12726.9511.40.0382
99centreville2020-01-01 01:38:000.12616.9511.40.0392