Overview

Dataset statistics

Number of variables8
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.1 KiB
Average record size in memory72.3 B

Variable types

Numeric6
Categorical2

Alerts

저수위(m) has constant value ""Constant
강우량(mm) is highly overall correlated with 유입량(ms) and 3 other fieldsHigh correlation
유입량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
방류량(ms) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수량(백만m3) is highly overall correlated with 강우량(mm) and 3 other fieldsHigh correlation
저수율 is highly overall correlated with 댐이름High correlation
댐이름 is highly overall correlated with 강우량(mm) and 4 other fieldsHigh correlation
강우량(mm) has 18 (18.0%) zerosZeros
유입량(ms) has 18 (18.0%) zerosZeros
방류량(ms) has 18 (18.0%) zerosZeros
저수량(백만m3) has 18 (18.0%) zerosZeros

Reproduction

Analysis started2023-12-10 12:52:41.125548
Analysis finished2023-12-10 12:52:45.464332
Duration4.34 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

일자/시간(t)
Real number (ℝ)

Distinct8
Distinct (%)8.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0191001 × 109
Minimum2.0191001 × 109
Maximum2.0191001 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:45.519080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.0191001 × 109
5-th percentile2.0191001 × 109
Q12.0191001 × 109
median2.0191001 × 109
Q32.0191001 × 109
95-th percentile2.0191001 × 109
Maximum2.0191001 × 109
Range7
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation1.9840526
Coefficient of variation (CV)9.8264201 × 10-10
Kurtosis-1.108822
Mean2.0191001 × 109
Median Absolute Deviation (MAD)2
Skewness0.05987088
Sum2.0191001 × 1011
Variance3.9364646
MonotonicityIncreasing
2023-12-10T21:52:45.667327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
2019100102 18
18.0%
2019100106 18
18.0%
2019100104 15
15.0%
2019100103 14
14.0%
2019100105 13
13.0%
2019100107 11
11.0%
2019100101 7
 
7.0%
2019100108 4
 
4.0%
ValueCountFrequency (%)
2019100101 7
 
7.0%
2019100102 18
18.0%
2019100103 14
14.0%
2019100104 15
15.0%
2019100105 13
13.0%
2019100106 18
18.0%
2019100107 11
11.0%
2019100108 4
 
4.0%
ValueCountFrequency (%)
2019100108 4
 
4.0%
2019100107 11
11.0%
2019100106 18
18.0%
2019100105 13
13.0%
2019100104 15
15.0%
2019100103 14
14.0%
2019100102 18
18.0%
2019100101 7
 
7.0%

저수위(m)
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

2023-12-10T21:52:45.798376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:52:45.901530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 100
100.0%

강우량(mm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)48.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.9591
Minimum0
Maximum99.507
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:46.016526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16.55
median17.1395
Q353.254
95-th percentile76.24825
Maximum99.507
Range99.507
Interquartile range (IQR)46.704

Descriptive statistics

Standard deviation28.456741
Coefficient of variation (CV)0.98265282
Kurtosis-0.40553583
Mean28.9591
Median Absolute Deviation (MAD)17.1395
Skewness0.83876104
Sum2895.91
Variance809.78613
MonotonicityNot monotonic
2023-12-10T21:52:46.156044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0.0 18
18.0%
53.254 7
 
7.0%
33.973 5
 
5.0%
11.398 5
 
5.0%
8.819 5
 
5.0%
17.16 5
 
5.0%
14.431 4
 
4.0%
74.242 4
 
4.0%
27.696 3
 
3.0%
27.654 2
 
2.0%
Other values (38) 42
42.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.995 1
 
1.0%
0.996 1
 
1.0%
0.997 2
 
2.0%
6.428 1
 
1.0%
6.458 1
 
1.0%
6.517 1
 
1.0%
6.561 1
 
1.0%
8.819 5
 
5.0%
9.795 1
 
1.0%
ValueCountFrequency (%)
99.507 1
 
1.0%
99.1 1
 
1.0%
98.082 1
 
1.0%
97.878 1
 
1.0%
76.443 1
 
1.0%
76.238 1
 
1.0%
76.135 1
 
1.0%
74.388 1
 
1.0%
74.242 4
4.0%
74.095 2
2.0%

유입량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)43.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean70.326
Minimum0
Maximum130.8
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:46.305395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q141.125
median80.65
Q3101
95-th percentile101.215
Maximum130.8
Range130.8
Interquartile range (IQR)59.875

Descriptive statistics

Standard deviation39.797568
Coefficient of variation (CV)0.5659012
Kurtosis-0.69747166
Mean70.326
Median Absolute Deviation (MAD)20.35
Skewness-0.8106791
Sum7032.6
Variance1583.8464
MonotonicityNot monotonic
2023-12-10T21:52:46.464721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 18
18.0%
101.0 12
 
12.0%
101.2 6
 
6.0%
98.0 5
 
5.0%
66.8 5
 
5.0%
100.6 4
 
4.0%
101.1 4
 
4.0%
80.4 4
 
4.0%
17.6 4
 
4.0%
100.9 2
 
2.0%
Other values (33) 36
36.0%
ValueCountFrequency (%)
0.0 18
18.0%
17.6 4
 
4.0%
40.5 1
 
1.0%
40.7 1
 
1.0%
40.9 1
 
1.0%
41.2 1
 
1.0%
66.2 1
 
1.0%
66.7 1
 
1.0%
66.8 5
 
5.0%
67.0 1
 
1.0%
ValueCountFrequency (%)
130.8 1
 
1.0%
130.6 2
 
2.0%
130.4 1
 
1.0%
101.5 1
 
1.0%
101.2 6
6.0%
101.1 4
 
4.0%
101.0 12
12.0%
100.9 2
 
2.0%
100.6 4
 
4.0%
98.6 1
 
1.0%

방류량(ms)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct77
Distinct (%)77.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean94.75592
Minimum0
Maximum391.627
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:46.611878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.904
median94.935
Q3125.00675
95-th percentile237.25545
Maximum391.627
Range391.627
Interquartile range (IQR)108.10275

Descriptive statistics

Standard deviation88.135536
Coefficient of variation (CV)0.93013224
Kurtosis1.9351176
Mean94.75592
Median Absolute Deviation (MAD)63.9615
Skewness1.2478862
Sum9475.592
Variance7767.8727
MonotonicityNot monotonic
2023-12-10T21:52:47.018515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
18.0%
158.6 3
 
3.0%
21.3 3
 
3.0%
32.661 2
 
2.0%
15.997 2
 
2.0%
98.116 1
 
1.0%
81.522 1
 
1.0%
110.502 1
 
1.0%
233.666 1
 
1.0%
30.761 1
 
1.0%
Other values (67) 67
67.0%
ValueCountFrequency (%)
0.0 18
18.0%
9.939 1
 
1.0%
9.972 1
 
1.0%
15.941 1
 
1.0%
15.969 1
 
1.0%
15.997 2
 
2.0%
16.883 1
 
1.0%
16.911 1
 
1.0%
18.828 1
 
1.0%
20.994 1
 
1.0%
ValueCountFrequency (%)
391.627 1
1.0%
375.276 1
1.0%
353.099 1
1.0%
348.934 1
1.0%
305.455 1
1.0%
233.666 1
1.0%
232.117 1
1.0%
205.787 1
1.0%
205.777 1
1.0%
202.292 1
1.0%

저수량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean96.94952
Minimum0
Maximum466.21
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:47.173205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q112.8
median94.87
Q3125.00675
95-th percentile281.3698
Maximum466.21
Range466.21
Interquartile range (IQR)112.20675

Descriptive statistics

Standard deviation98.629634
Coefficient of variation (CV)1.0173298
Kurtosis3.8984987
Mean96.94952
Median Absolute Deviation (MAD)63.7625
Skewness1.7096431
Sum9694.952
Variance9727.8046
MonotonicityNot monotonic
2023-12-10T21:52:47.336987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 18
 
18.0%
21.3 6
 
6.0%
0.33 4
 
4.0%
12.8 4
 
4.0%
158.6 3
 
3.0%
154.447 1
 
1.0%
448.183 1
 
1.0%
81.522 1
 
1.0%
110.502 1
 
1.0%
281.86 1
 
1.0%
Other values (60) 60
60.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.33 4
 
4.0%
12.8 4
 
4.0%
21.3 6
 
6.0%
21.333 1
 
1.0%
27.778 1
 
1.0%
29.653 1
 
1.0%
30.65 1
 
1.0%
31.075 1
 
1.0%
64.075 1
 
1.0%
ValueCountFrequency (%)
466.21 1
1.0%
448.183 1
1.0%
431.832 1
1.0%
405.49 1
1.0%
281.86 1
1.0%
281.344 1
1.0%
232.117 1
1.0%
227.517 1
1.0%
219.488 1
1.0%
211.032 1
1.0%

저수율
Real number (ℝ)

HIGH CORRELATION 

Distinct52
Distinct (%)52.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34.1807
Minimum1.69
Maximum134.09
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T21:52:47.495015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1.69
5-th percentile1.73
Q18.775
median30.3
Q347.0625
95-th percentile86.05
Maximum134.09
Range132.4
Interquartile range (IQR)38.2875

Descriptive statistics

Standard deviation31.881849
Coefficient of variation (CV)0.93274417
Kurtosis1.9826376
Mean34.1807
Median Absolute Deviation (MAD)17.49
Skewness1.4260619
Sum3418.07
Variance1016.4523
MonotonicityNot monotonic
2023-12-10T21:52:47.649658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
32.58 7
 
7.0%
86.05 6
 
6.0%
33.03 5
 
5.0%
39.88 5
 
5.0%
1.73 5
 
5.0%
38.02 5
 
5.0%
62.56 4
 
4.0%
28.02 4
 
4.0%
80.85 4
 
4.0%
18.34 4
 
4.0%
Other values (42) 51
51.0%
ValueCountFrequency (%)
1.69 1
 
1.0%
1.72 1
 
1.0%
1.73 5
5.0%
1.74 1
 
1.0%
1.82 1
 
1.0%
1.83 1
 
1.0%
1.84 1
 
1.0%
1.85 1
 
1.0%
4.85 1
 
1.0%
4.86 1
 
1.0%
ValueCountFrequency (%)
134.09 1
 
1.0%
134.08 2
 
2.0%
134.07 1
 
1.0%
86.05 6
6.0%
80.85 4
4.0%
62.56 4
4.0%
47.79 3
3.0%
47.78 1
 
1.0%
47.07 3
3.0%
47.06 2
 
2.0%

댐이름
Categorical

HIGH CORRELATION 

Distinct21
Distinct (%)21.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
죽산보
8 
구미보
7 
강정고령보
7 
낙단보
 
6
안동보
 
6
Other values (16)
66 

Length

Max length5
Median length3
Mean length3.36
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row강정고령보
2nd row상주보
3rd row죽산보
4th row백제보
5th row단양수중보

Common Values

ValueCountFrequency (%)
죽산보 8
 
8.0%
구미보 7
 
7.0%
강정고령보 7
 
7.0%
낙단보 6
 
6.0%
안동보 6
 
6.0%
여주보 5
 
5.0%
달성보 5
 
5.0%
상주보 5
 
5.0%
강천보 5
 
5.0%
이포보 4
 
4.0%
Other values (11) 42
42.0%

Length

2023-12-10T21:52:47.812047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
죽산보 8
 
8.0%
강정고령보 7
 
7.0%
구미보 7
 
7.0%
낙단보 6
 
6.0%
안동보 6
 
6.0%
여주보 5
 
5.0%
달성보 5
 
5.0%
상주보 5
 
5.0%
강천보 5
 
5.0%
수하보 4
 
4.0%
Other values (11) 42
42.0%

Interactions

2023-12-10T21:52:44.594460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:41.663739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.276299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.865269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.436360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.049327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.709502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:41.752162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.395351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.955498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.550229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.149295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.811332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:41.849461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.482546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.040375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.639248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.241845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.923241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:41.928759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.581590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.133610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.725374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.321528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:45.045875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.014265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.693008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.239367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.840496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.409792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:45.129285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.162689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:42.779932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.348582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:43.938187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:52:44.483534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:52:47.897762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.0000.0000.0000.0000.0000.0000.000
강우량(mm)0.0001.0000.8050.7980.9330.9321.000
유입량(ms)0.0000.8051.0000.6570.6870.8261.000
방류량(ms)0.0000.7980.6571.0000.9240.5370.928
저수량(백만m3)0.0000.9330.6870.9241.0000.7950.954
저수율0.0000.9320.8260.5370.7951.0001.000
댐이름0.0001.0001.0000.9280.9541.0001.000
2023-12-10T21:52:48.001811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
일자/시간(t)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
일자/시간(t)1.000-0.065-0.020-0.026-0.0060.0370.000
강우량(mm)-0.0651.0000.6090.7810.799-0.3330.927
유입량(ms)-0.0200.6091.0000.5120.5230.0840.922
방류량(ms)-0.0260.7810.5121.0000.990-0.3790.655
저수량(백만m3)-0.0060.7990.5230.9901.000-0.3820.749
저수율0.037-0.3330.084-0.379-0.3821.0000.927
댐이름0.0000.9270.9220.6550.7490.9271.000

Missing values

2023-12-10T21:52:45.282166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:52:45.417515image/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

일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
02019100101074.09580.2195.169154.44718.33강정고령보
12019100101027.696101.183.62383.62347.07상주보
22019100101017.20167.032.66121.31.74죽산보
3201910010109.79540.592.2592.251.82백제보
42019100101046.986130.415.9410.33134.07단양수중보
52019100101011.398101.2110.805110.80533.03여주보
62019100101054.74893.5205.787205.78713.64달성보
7201910010206.42871.720.99412.85.71승촌보
8201910010200.00.00.00.080.85수하보
9201910010200.00.00.00.047.79쾌쾌보
일자/시간(t)저수위(m)강우량(mm)유입량(ms)방류량(ms)저수량(백만m3)저수율댐이름
902019100107011.398101.2110.972110.97233.03여주보
912019100107033.97398.0140.667140.66739.88낙단보
922019100107074.38880.6202.292161.5718.35강정고령보
932019100107027.654100.981.5781.5747.06상주보
942019100107017.1666.821.321.31.73죽산보
952019100107053.254101.0111.95111.9532.58구미보
96201910010800.00.00.00.062.56구담보
972019100108053.254101.0112.038112.03832.58구미보
98201910010808.819101.0110.542110.54238.02강천보
992019100108016.99666.218.82864.31.69죽산보