Overview

Dataset statistics

Number of variables20
Number of observations22
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory183.0 B

Variable types

Categorical1
Numeric17
Text2

Alerts

댐높이(m) is highly overall correlated with 댐길이(m) and 7 other fieldsHigh correlation
댐길이(m) is highly overall correlated with 댐높이(m) and 7 other fieldsHigh correlation
댐체적(천m) is highly overall correlated with 댐높이(m) and 4 other fieldsHigh correlation
정상표고(ELm) is highly overall correlated with 계획홍수위(ELm) and 4 other fieldsHigh correlation
유역면적(km) is highly overall correlated with 댐길이(m) and 7 other fieldsHigh correlation
저수면적(km) is highly overall correlated with 댐길이(m) and 7 other fieldsHigh correlation
계획홍수위(ELm) is highly overall correlated with 정상표고(ELm) and 4 other fieldsHigh correlation
상시만수위(ELm) is highly overall correlated with 정상표고(ELm) and 4 other fieldsHigh correlation
홍수기제한수위(ELm) is highly overall correlated with 정상표고(ELm) and 4 other fieldsHigh correlation
홍수조절용량(백만m3) is highly overall correlated with 댐높이(m) and 8 other fieldsHigh correlation
저수위(ELm) is highly overall correlated with 정상표고(ELm) and 4 other fieldsHigh correlation
총저수용량(백만m3) is highly overall correlated with 댐높이(m) and 9 other fieldsHigh correlation
유효저수용량(백만m3) is highly overall correlated with 댐높이(m) and 10 other fieldsHigh correlation
연간용수공급계획량(백만m3) is highly overall correlated with 댐높이(m) and 9 other fieldsHigh correlation
월류정표고(ELm) is highly overall correlated with 정상표고(ELm) and 4 other fieldsHigh correlation
착공일 is highly overall correlated with 댐높이(m) and 8 other fieldsHigh correlation
준공일 is highly overall correlated with 댐높이(m) and 7 other fieldsHigh correlation
댐형식명 is highly overall correlated with 유효저수용량(백만m3) and 1 other fieldsHigh correlation
하천명 has 1 (4.5%) missing valuesMissing
댐체적(천m) has unique valuesUnique
유역면적(km) has unique valuesUnique
계획홍수위(ELm) has unique valuesUnique
총저수용량(백만m3) has unique valuesUnique
유효저수용량(백만m3) has unique valuesUnique
연간용수공급계획량(백만m3) has unique valuesUnique
월류정표고(ELm) has unique valuesUnique
댐명 has unique valuesUnique
댐높이(m) has 1 (4.5%) zerosZeros
댐길이(m) has 1 (4.5%) zerosZeros
댐체적(천m) has 1 (4.5%) zerosZeros
정상표고(ELm) has 1 (4.5%) zerosZeros
유역면적(km) has 1 (4.5%) zerosZeros
저수면적(km) has 1 (4.5%) zerosZeros
계획홍수위(ELm) has 1 (4.5%) zerosZeros
상시만수위(ELm) has 1 (4.5%) zerosZeros
홍수기제한수위(ELm) has 1 (4.5%) zerosZeros
홍수조절용량(백만m3) has 1 (4.5%) zerosZeros
저수위(ELm) has 1 (4.5%) zerosZeros
총저수용량(백만m3) has 1 (4.5%) zerosZeros
유효저수용량(백만m3) has 1 (4.5%) zerosZeros
연간용수공급계획량(백만m3) has 1 (4.5%) zerosZeros
월류정표고(ELm) has 1 (4.5%) zerosZeros

Reproduction

Analysis started2023-12-10 11:43:07.471298
Analysis finished2023-12-10 11:43:53.664919
Duration46.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

댐형식명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)22.7%
Missing0
Missing (%)0.0%
Memory size308.0 B
C.F.R.D
E.C.R.D
C.G.D
<NA>
C.G_E.C.R.D

Length

Max length11
Median length7
Mean length6.5909091
Min length4

Unique

Unique2 ?
Unique (%)9.1%

Sample

1st row<NA>
2nd rowC.F.R.D
3rd rowC.F.R.D
4th rowC.F.R.D
5th rowC.F.R.D

Common Values

ValueCountFrequency (%)
C.F.R.D 8
36.4%
E.C.R.D 7
31.8%
C.G.D 5
22.7%
<NA> 1
 
4.5%
C.G_E.C.R.D 1
 
4.5%

Length

2023-12-10T20:43:53.803322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T20:43:54.037610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
c.f.r.d 8
36.4%
e.c.r.d 7
31.8%
c.g.d 5
22.7%
na 1
 
4.5%
c.g_e.c.r.d 1
 
4.5%

댐높이(m)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)86.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.563636
Minimum0
Maximum123
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:54.273092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34.55
Q150.75
median61.25
Q380.5
95-th percentile99.78
Maximum123
Range123
Interquartile range (IQR)29.75

Descriptive statistics

Standard deviation26.10261
Coefficient of variation (CV)0.39812633
Kurtosis1.2259864
Mean65.563636
Median Absolute Deviation (MAD)11.5
Skewness-0.085294331
Sum1442.4
Variance681.34623
MonotonicityNot monotonic
2023-12-10T20:43:54.529480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
58.5 2
 
9.1%
64.0 2
 
9.1%
50.0 2
 
9.1%
96.0 1
 
4.5%
73.0 1
 
4.5%
99.9 1
 
4.5%
123.0 1
 
4.5%
58.0 1
 
4.5%
48.5 1
 
4.5%
83.0 1
 
4.5%
Other values (9) 9
40.9%
ValueCountFrequency (%)
0.0 1
4.5%
34.0 1
4.5%
45.0 1
4.5%
48.5 1
4.5%
50.0 2
9.1%
53.0 1
4.5%
55.5 1
4.5%
58.0 1
4.5%
58.5 2
9.1%
64.0 2
9.1%
ValueCountFrequency (%)
123.0 1
4.5%
99.9 1
4.5%
97.5 1
4.5%
96.0 1
4.5%
89.0 1
4.5%
83.0 1
4.5%
73.0 1
4.5%
72.0 1
4.5%
70.0 1
4.5%
64.0 2
9.1%

댐길이(m)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean428.80909
Minimum0
Maximum1126
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:54.745176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile207.25
Q1300.75
median425
Q3510.75
95-th percentile609.53
Maximum1126
Range1126
Interquartile range (IQR)210

Descriptive statistics

Standard deviation210.45124
Coefficient of variation (CV)0.49078073
Kurtosis5.4706923
Mean428.80909
Median Absolute Deviation (MAD)100
Skewness1.3720944
Sum9433.8
Variance44289.724
MonotonicityNot monotonic
2023-12-10T20:43:54.960131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
472.0 2
 
9.1%
0.0 1
 
4.5%
274.0 1
 
4.5%
291.0 1
 
4.5%
515.0 1
 
4.5%
562.6 1
 
4.5%
530.0 1
 
4.5%
330.0 1
 
4.5%
205.0 1
 
4.5%
612.0 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0.0 1
4.5%
205.0 1
4.5%
250.0 1
4.5%
274.0 1
4.5%
282.0 1
4.5%
291.0 1
4.5%
330.0 1
4.5%
344.2 1
4.5%
390.0 1
4.5%
400.0 1
4.5%
ValueCountFrequency (%)
1126.0 1
4.5%
612.0 1
4.5%
562.6 1
4.5%
535.0 1
4.5%
530.0 1
4.5%
515.0 1
4.5%
498.0 1
4.5%
495.0 1
4.5%
472.0 2
9.1%
447.0 1
4.5%

댐체적(천m)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1901.8727
Minimum0
Maximum9591
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:55.183720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile11.29
Q1629.25
median1175
Q32201.75
95-th percentile4917.5
Maximum9591
Range9591
Interquartile range (IQR)1572.5

Descriptive statistics

Standard deviation2211.6949
Coefficient of variation (CV)1.1629037
Kurtosis6.2768253
Mean1901.8727
Median Absolute Deviation (MAD)856.5
Skewness2.2754649
Sum41841.2
Variance4891594.3
MonotonicityNot monotonic
2023-12-10T20:43:55.403888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
227.0 1
 
4.5%
1116.0 1
 
4.5%
3423.0 1
 
4.5%
4965.0 1
 
4.5%
9591.0 1
 
4.5%
1573.0 1
 
4.5%
675.0 1
 
4.5%
4015.0 1
 
4.5%
1234.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
1.2 1
4.5%
203.0 1
4.5%
227.0 1
4.5%
410.0 1
4.5%
614.0 1
4.5%
675.0 1
4.5%
877.0 1
4.5%
891.0 1
4.5%
902.0 1
4.5%
ValueCountFrequency (%)
9591.0 1
4.5%
4965.0 1
4.5%
4015.0 1
4.5%
3943.0 1
4.5%
3423.0 1
4.5%
2206.0 1
4.5%
2189.0 1
4.5%
1573.0 1
4.5%
1506.0 1
4.5%
1280.0 1
4.5%

하천명
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing1
Missing (%)4.5%
Memory size308.0 B
2023-12-10T20:43:55.680415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.7142857
Min length2

Characters and Unicode

Total characters57
Distinct characters30
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

Unique19 ?
Unique (%)90.5%

Sample

1st row금강
2nd row단장천
3rd row내성천
4th row탐진강
5th row부항천
ValueCountFrequency (%)
금강 2
 
9.5%
남한강 1
 
4.8%
고현천 1
 
4.8%
반변천 1
 
4.8%
이사천 1
 
4.8%
소양강 1
 
4.8%
보성강 1
 
4.8%
계천 1
 
4.8%
낙동강 1
 
4.8%
황강 1
 
4.8%
Other values (10) 10
47.6%
2023-12-10T20:43:56.239361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
21.1%
10
17.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
1
 
1.8%
Other values (20) 20
35.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
12
21.1%
10
17.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
1
 
1.8%
Other values (20) 20
35.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
12
21.1%
10
17.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
1
 
1.8%
Other values (20) 20
35.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
12
21.1%
10
17.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
2
 
3.5%
1
 
1.8%
Other values (20) 20
35.1%

정상표고(ELm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean158.85455
Minimum0
Maximum368.5
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:56.471294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile49.1
Q192.5
median168
Q3202.6
95-th percentile267.125
Maximum368.5
Range368.5
Interquartile range (IQR)110.1

Descriptive statistics

Standard deviation83.193021
Coefficient of variation (CV)0.52370564
Kurtosis0.7217518
Mean158.85455
Median Absolute Deviation (MAD)48.75
Skewness0.31989199
Sum3494.8
Variance6921.0788
MonotonicityNot monotonic
2023-12-10T20:43:56.693086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
168.0 2
 
9.1%
115.0 2
 
9.1%
0.0 1
 
4.5%
241.0 1
 
4.5%
79.0 1
 
4.5%
203.0 1
 
4.5%
184.0 1
 
4.5%
166.0 1
 
4.5%
83.0 1
 
4.5%
181.0 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
0.0 1
4.5%
49.0 1
4.5%
51.0 1
4.5%
79.0 1
4.5%
83.0 1
4.5%
85.0 1
4.5%
115.0 2
9.1%
147.5 1
4.5%
166.0 1
4.5%
168.0 2
9.1%
ValueCountFrequency (%)
368.5 1
4.5%
268.5 1
4.5%
241.0 1
4.5%
212.5 1
4.5%
208.4 1
4.5%
203.0 1
4.5%
201.4 1
4.5%
200.0 1
4.5%
184.0 1
4.5%
181.0 1
4.5%

유역면적(km)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1045.9545
Minimum0
Maximum6648
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:56.868946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile33.035
Q189.475
median354.5
Q31273.25
95-th percentile3178.95
Maximum6648
Range6648
Interquartile range (IQR)1183.775

Descriptive statistics

Standard deviation1558.5434
Coefficient of variation (CV)1.490068
Kurtosis7.5414552
Mean1045.9545
Median Absolute Deviation (MAD)338.2
Skewness2.5301308
Sum23011
Variance2429057.5
MonotonicityNot monotonic
2023-12-10T20:43:57.030085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
41.3 1
 
4.5%
163.6 1
 
4.5%
1361.0 1
 
4.5%
134.6 1
 
4.5%
2703.0 1
 
4.5%
1010.0 1
 
4.5%
209.0 1
 
4.5%
1584.0 1
 
4.5%
3204.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
32.6 1
4.5%
41.3 1
4.5%
59.0 1
4.5%
82.0 1
4.5%
87.5 1
4.5%
95.4 1
4.5%
134.6 1
4.5%
163.6 1
4.5%
193.0 1
4.5%
ValueCountFrequency (%)
6648.0 1
4.5%
3204.0 1
4.5%
2703.0 1
4.5%
2285.0 1
4.5%
1584.0 1
4.5%
1361.0 1
4.5%
1010.0 1
4.5%
930.0 1
4.5%
925.0 1
4.5%
763.0 1
4.5%

저수면적(km)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.642273
Minimum0
Maximum97
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:57.190627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1.5015
Q12.775
median10.35
Q331.8
95-th percentile72.66
Maximum97
Range97
Interquartile range (IQR)29.025

Descriptive statistics

Standard deviation27.218049
Coefficient of variation (CV)1.151245
Kurtosis1.366016
Mean23.642273
Median Absolute Deviation (MAD)9.6
Skewness1.417172
Sum520.13
Variance740.82217
MonotonicityNot monotonic
2023-12-10T20:43:57.353316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
5.8 2
 
9.1%
0.0 1
 
4.5%
36.2 1
 
4.5%
26.4 1
 
4.5%
7.8 1
 
4.5%
70.0 1
 
4.5%
33.0 1
 
4.5%
51.5 1
 
4.5%
72.8 1
 
4.5%
25.0 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0.0 1
4.5%
1.5 1
4.5%
1.53 1
4.5%
2.2 1
4.5%
2.5 1
4.5%
2.7 1
4.5%
3.0 1
4.5%
5.8 2
9.1%
7.8 1
4.5%
10.3 1
4.5%
ValueCountFrequency (%)
97.0 1
4.5%
72.8 1
4.5%
70.0 1
4.5%
51.5 1
4.5%
36.2 1
4.5%
33.0 1
4.5%
28.2 1
4.5%
26.5 1
4.5%
26.4 1
4.5%
25.0 1
4.5%

계획홍수위(ELm)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean155.57273
Minimum0
Maximum364.9
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:57.558211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile43.91
Q189.725
median164.35
Q3198.45
95-th percentile264.15
Maximum364.9
Range364.9
Interquartile range (IQR)108.725

Descriptive statistics

Standard deviation83.158358
Coefficient of variation (CV)0.53453044
Kurtosis0.67466819
Mean155.57273
Median Absolute Deviation (MAD)49.55
Skewness0.33036152
Sum3422.6
Variance6915.3126
MonotonicityNot monotonic
2023-12-10T20:43:57.739426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
364.9 1
 
4.5%
75.5 1
 
4.5%
164.7 1
 
4.5%
111.1 1
 
4.5%
198.0 1
 
4.5%
110.5 1
 
4.5%
180.0 1
 
4.5%
161.7 1
 
4.5%
80.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
43.8 1
4.5%
46.0 1
4.5%
75.5 1
4.5%
80.0 1
4.5%
82.8 1
4.5%
110.5 1
4.5%
111.1 1
4.5%
145.0 1
4.5%
161.7 1
4.5%
ValueCountFrequency (%)
364.9 1
4.5%
265.5 1
4.5%
238.5 1
4.5%
210.2 1
4.5%
205.1 1
4.5%
198.6 1
4.5%
198.0 1
4.5%
197.7 1
4.5%
180.0 1
4.5%
179.0 1
4.5%

상시만수위(ELm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.29091
Minimum0
Maximum364
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:57.913312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.01
Q188.625
median162
Q3196.125
95-th percentile262.125
Maximum364
Range364
Interquartile range (IQR)107.5

Descriptive statistics

Standard deviation83.271077
Coefficient of variation (CV)0.54322254
Kurtosis0.69594316
Mean153.29091
Median Absolute Deviation (MAD)49.35
Skewness0.351597
Sum3372.4
Variance6934.0723
MonotonicityNot monotonic
2023-12-10T20:43:58.073792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
108.5 2
 
9.1%
0.0 1
 
4.5%
263.5 1
 
4.5%
74.0 1
 
4.5%
163.0 1
 
4.5%
193.5 1
 
4.5%
180.0 1
 
4.5%
160.0 1
 
4.5%
76.5 1
 
4.5%
176.0 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0.0 1
4.5%
41.0 1
4.5%
41.2 1
4.5%
74.0 1
4.5%
76.5 1
4.5%
82.0 1
4.5%
108.5 2
9.1%
141.0 1
4.5%
160.0 1
4.5%
161.0 1
4.5%
ValueCountFrequency (%)
364.0 1
4.5%
263.5 1
4.5%
236.0 1
4.5%
207.2 1
4.5%
204.0 1
4.5%
196.5 1
4.5%
195.0 1
4.5%
193.5 1
4.5%
180.0 1
4.5%
176.0 1
4.5%

홍수기제한수위(ELm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean152.28636
Minimum0
Maximum362
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:58.222716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile41.01
Q186.375
median160.85
Q3195.75
95-th percentile260.225
Maximum362
Range362
Interquartile range (IQR)109.375

Descriptive statistics

Standard deviation82.894909
Coefficient of variation (CV)0.54433573
Kurtosis0.68362313
Mean152.28636
Median Absolute Deviation (MAD)49.35
Skewness0.35859841
Sum3350.3
Variance6871.566
MonotonicityNot monotonic
2023-12-10T20:43:58.383516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
108.5 2
 
9.1%
0.0 1
 
4.5%
261.5 1
 
4.5%
74.0 1
 
4.5%
161.7 1
 
4.5%
190.3 1
 
4.5%
178.2 1
 
4.5%
160.0 1
 
4.5%
76.5 1
 
4.5%
176.0 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0.0 1
4.5%
41.0 1
4.5%
41.2 1
4.5%
74.0 1
4.5%
76.5 1
4.5%
79.0 1
4.5%
108.5 2
9.1%
138.0 1
4.5%
156.7 1
4.5%
160.0 1
4.5%
ValueCountFrequency (%)
362.0 1
4.5%
261.5 1
4.5%
236.0 1
4.5%
207.2 1
4.5%
204.0 1
4.5%
196.5 1
4.5%
193.5 1
4.5%
190.3 1
4.5%
178.2 1
4.5%
176.0 1
4.5%

홍수조절용량(백만m3)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)95.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean104.34955
Minimum0
Maximum616
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:58.526172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile3.1195
Q18.325
median26
Q3102.5
95-th percentile488.49
Maximum616
Range616
Interquartile range (IQR)94.175

Descriptive statistics

Standard deviation166.20115
Coefficient of variation (CV)1.5927348
Kurtosis4.4331774
Mean104.34955
Median Absolute Deviation (MAD)24.45
Skewness2.2023901
Sum2295.69
Variance27622.823
MonotonicityNot monotonic
2023-12-10T20:43:58.680799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
80.0 2
 
9.1%
0.0 1
 
4.5%
137.0 1
 
4.5%
10.0 1
 
4.5%
20.0 1
 
4.5%
500.0 1
 
4.5%
60.0 1
 
4.5%
9.5 1
 
4.5%
110.0 1
 
4.5%
250.0 1
 
4.5%
Other values (11) 11
50.0%
ValueCountFrequency (%)
0.0 1
4.5%
3.1 1
4.5%
3.49 1
4.5%
4.2 1
4.5%
6.0 1
4.5%
8.0 1
4.5%
9.3 1
4.5%
9.5 1
4.5%
10.0 1
4.5%
12.3 1
4.5%
ValueCountFrequency (%)
616.0 1
4.5%
500.0 1
4.5%
269.8 1
4.5%
250.0 1
4.5%
137.0 1
4.5%
110.0 1
4.5%
80.0 2
9.1%
75.0 1
4.5%
60.0 1
4.5%
32.0 1
4.5%

저수위(ELm)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean124.86545
Minimum0
Maximum333
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:58.831425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile23.45
Q160
median136
Q3158.635
95-th percentile227.475
Maximum333
Range333
Interquartile range (IQR)98.635

Descriptive statistics

Standard deviation77.127126
Coefficient of variation (CV)0.61768185
Kurtosis1.1304767
Mean124.86545
Median Absolute Deviation (MAD)48
Skewness0.67079381
Sum2747.04
Variance5948.5935
MonotonicityNot monotonic
2023-12-10T20:43:59.015455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
150.0 2
 
9.1%
60.0 2
 
9.1%
0.0 1
 
4.5%
208.0 1
 
4.5%
50.0 1
 
4.5%
137.0 1
 
4.5%
85.0 1
 
4.5%
160.0 1
 
4.5%
130.0 1
 
4.5%
140.0 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
0.0 1
4.5%
23.0 1
4.5%
32.0 1
4.5%
50.0 1
4.5%
55.0 1
4.5%
60.0 2
9.1%
85.0 1
4.5%
110.0 1
4.5%
130.0 1
4.5%
135.0 1
4.5%
ValueCountFrequency (%)
333.0 1
4.5%
228.5 1
4.5%
208.0 1
4.5%
181.0 1
4.5%
165.0 1
4.5%
160.0 1
4.5%
154.54 1
4.5%
150.0 2
9.1%
140.0 1
4.5%
137.0 1
4.5%

총저수용량(백만m3)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean587.40955
Minimum0
Maximum2900
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:59.243153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile22.3995
Q159.125
median220.5
Q3741.25
95-th percentile2687
Maximum2900
Range2900
Interquartile range (IQR)682.125

Descriptive statistics

Standard deviation830.67655
Coefficient of variation (CV)1.4141353
Kurtosis3.4638617
Mean587.40955
Median Absolute Deviation (MAD)195.495
Skewness2.005528
Sum12923.01
Variance690023.53
MonotonicityNot monotonic
2023-12-10T20:43:59.452754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
27.9 1
 
4.5%
116.9 1
 
4.5%
595.0 1
 
4.5%
250.0 1
 
4.5%
2900.0 1
 
4.5%
457.0 1
 
4.5%
86.9 1
 
4.5%
1248.0 1
 
4.5%
1490.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
22.11 1
4.5%
27.9 1
4.5%
48.7 1
4.5%
50.3 1
4.5%
54.3 1
4.5%
73.6 1
4.5%
86.9 1
4.5%
116.9 1
4.5%
181.1 1
4.5%
ValueCountFrequency (%)
2900.0 1
4.5%
2750.0 1
4.5%
1490.0 1
4.5%
1248.0 1
4.5%
815.0 1
4.5%
790.0 1
4.5%
595.0 1
4.5%
466.0 1
4.5%
457.0 1
4.5%
309.2 1
4.5%

유효저수용량(백만m3)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean416.84091
Minimum0
Maximum1900
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:59.617665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile18.226
Q149.4
median190.5
Q3527.25
95-th percentile1749.55
Maximum1900
Range1900
Interquartile range (IQR)477.85

Descriptive statistics

Standard deviation538.1704
Coefficient of variation (CV)1.2910691
Kurtosis3.1032613
Mean416.84091
Median Absolute Deviation (MAD)163.6
Skewness1.8834539
Sum9170.5
Variance289627.38
MonotonicityNot monotonic
2023-12-10T20:43:59.771607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
24.8 1
 
4.5%
108.7 1
 
4.5%
424.0 1
 
4.5%
210.0 1
 
4.5%
1900.0 1
 
4.5%
352.0 1
 
4.5%
73.4 1
 
4.5%
1000.0 1
 
4.5%
790.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
17.88 1
4.5%
24.8 1
4.5%
35.6 1
4.5%
40.1 1
4.5%
42.6 1
4.5%
69.8 1
4.5%
73.4 1
4.5%
108.7 1
4.5%
160.4 1
4.5%
ValueCountFrequency (%)
1900.0 1
4.5%
1789.0 1
4.5%
1000.0 1
4.5%
790.0 1
4.5%
672.52 1
4.5%
560.0 1
4.5%
429.0 1
4.5%
424.0 1
4.5%
352.0 1
4.5%
299.7 1
4.5%

연간용수공급계획량(백만m3)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean535.18045
Minimum0
Maximum3380
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:43:59.956785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile15.1415
Q146.975
median211
Q3597.15
95-th percentile1627.2
Maximum3380
Range3380
Interquartile range (IQR)550.175

Descriptive statistics

Standard deviation784.33815
Coefficient of variation (CV)1.4655583
Kurtosis7.9085078
Mean535.18045
Median Absolute Deviation (MAD)193.415
Skewness2.5830375
Sum11773.97
Variance615186.34
MonotonicityNot monotonic
2023-12-10T20:44:00.154516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
20.3 1
 
4.5%
106.6 1
 
4.5%
591.6 1
 
4.5%
218.7 1
 
4.5%
1213.0 1
 
4.5%
270.1 1
 
4.5%
119.5 1
 
4.5%
926.0 1
 
4.5%
1649.0 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
14.87 1
4.5%
20.3 1
4.5%
35.1 1
4.5%
36.3 1
4.5%
38.3 1
4.5%
73.0 1
4.5%
106.6 1
4.5%
119.5 1
4.5%
127.8 1
4.5%
ValueCountFrequency (%)
3380.0 1
4.5%
1649.0 1
4.5%
1213.0 1
4.5%
1143.2 1
4.5%
926.0 1
4.5%
599.0 1
4.5%
591.6 1
4.5%
573.3 1
4.5%
435.0 1
4.5%
270.1 1
4.5%

월류정표고(ELm)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean145.62727
Minimum0
Maximum360
Zeros1
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:44:00.314648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile29.61
Q177.875
median152.2
Q3192.15
95-th percentile251.885
Maximum360
Range360
Interquartile range (IQR)114.275

Descriptive statistics

Standard deviation83.444439
Coefficient of variation (CV)0.57300008
Kurtosis0.72054912
Mean145.62727
Median Absolute Deviation (MAD)46.15
Skewness0.4403766
Sum3203.8
Variance6962.9745
MonotonicityNot monotonic
2023-12-10T20:44:00.474516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 1
 
4.5%
360.0 1
 
4.5%
64.0 1
 
4.5%
151.4 1
 
4.5%
108.5 1
 
4.5%
185.5 1
 
4.5%
98.5 1
 
4.5%
167.0 1
 
4.5%
151.0 1
 
4.5%
64.5 1
 
4.5%
Other values (12) 12
54.5%
ValueCountFrequency (%)
0.0 1
4.5%
29.0 1
4.5%
41.2 1
4.5%
64.0 1
4.5%
64.5 1
4.5%
71.0 1
4.5%
98.5 1
4.5%
108.5 1
4.5%
126.0 1
4.5%
151.0 1
4.5%
ValueCountFrequency (%)
360.0 1
4.5%
252.8 1
4.5%
234.5 1
4.5%
199.5 1
4.5%
197.2 1
4.5%
192.7 1
4.5%
190.5 1
4.5%
185.5 1
4.5%
167.0 1
4.5%
166.0 1
4.5%

착공일
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19895230
Minimum19610801
Maximum20151230
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:44:00.667086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19610801
5-th percentile19672401
Q119825526
median19900151
Q320008426
95-th percentile20099656
Maximum20151230
Range540429
Interquartile range (IQR)182900

Descriptive statistics

Standard deviation141588.18
Coefficient of variation (CV)0.00711669
Kurtosis-0.4385854
Mean19895230
Median Absolute Deviation (MAD)105500
Skewness-0.11626166
Sum4.3769505 × 108
Variance2.0047213 × 1010
MonotonicityNot monotonic
2023-12-10T20:44:00.853170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20020801 2
 
9.1%
19840901 2
 
9.1%
20151230 1
 
4.5%
20100101 1
 
4.5%
19901101 1
 
4.5%
19841201 1
 
4.5%
19670401 1
 
4.5%
19900101 1
 
4.5%
19710401 1
 
4.5%
19750301 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
19610801 1
4.5%
19670401 1
4.5%
19710401 1
4.5%
19750301 1
4.5%
19780601 1
4.5%
19820401 1
4.5%
19840901 2
9.1%
19841201 1
4.5%
19871101 1
4.5%
19900101 1
4.5%
ValueCountFrequency (%)
20151230 1
4.5%
20100101 1
4.5%
20091201 1
4.5%
20020801 2
9.1%
20011201 1
4.5%
20000101 1
4.5%
19960201 1
4.5%
19901101 1
4.5%
19901001 1
4.5%
19900201 1
4.5%

준공일
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)90.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19985671
Minimum19651231
Maximum20181231
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size330.0 B
2023-12-10T20:44:01.032644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum19651231
5-th percentile19733196
Q119898731
median20010880
Q320136231
95-th percentile20151231
Maximum20181231
Range530000
Interquartile range (IQR)237500

Descriptive statistics

Standard deviation153815.44
Coefficient of variation (CV)0.0076962862
Kurtosis-0.57841999
Mean19985671
Median Absolute Deviation (MAD)125000
Skewness-0.57017914
Sum4.3968476 × 108
Variance2.3659191 × 1010
MonotonicityNot monotonic
2023-12-10T20:44:01.230315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
20151231 2
 
9.1%
19921231 2
 
9.1%
20151230 1
 
4.5%
20000630 1
 
4.5%
19931231 1
 
4.5%
19731231 1
 
4.5%
20021130 1
 
4.5%
19770531 1
 
4.5%
19810630 1
 
4.5%
19891231 1
 
4.5%
Other values (10) 10
45.5%
ValueCountFrequency (%)
19651231 1
4.5%
19731231 1
4.5%
19770531 1
4.5%
19810630 1
4.5%
19861031 1
4.5%
19891231 1
4.5%
19921231 2
9.1%
19931231 1
4.5%
19961231 1
4.5%
20000630 1
4.5%
ValueCountFrequency (%)
20181231 1
4.5%
20151231 2
9.1%
20151230 1
4.5%
20151118 1
4.5%
20141231 1
4.5%
20121231 1
4.5%
20071231 1
4.5%
20061231 1
4.5%
20031231 1
4.5%
20021130 1
4.5%

댐명
Text

UNIQUE 

Distinct22
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size308.0 B
2023-12-10T20:44:01.495127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.6363636
Min length2

Characters and Unicode

Total characters58
Distinct characters40
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

Unique22 ?
Unique (%)100.0%

Sample

1st row남강(구)
2nd row용담
3rd row밀양
4th row영주
5th row장흥
ValueCountFrequency (%)
남강(구 1
 
4.5%
용담 1
 
4.5%
임하 1
 
4.5%
주암(조 1
 
4.5%
소양강 1
 
4.5%
주암(본 1
 
4.5%
횡성 1
 
4.5%
안동 1
 
4.5%
대청 1
 
4.5%
합천 1
 
4.5%
Other values (12) 12
54.5%
2023-12-10T20:44:01.956407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
 
6.9%
4
 
6.9%
( 3
 
5.2%
) 3
 
5.2%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
2
 
3.4%
Other values (30) 32
55.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 52
89.7%
Open Punctuation 3
 
5.2%
Close Punctuation 3
 
5.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
 
7.7%
4
 
7.7%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
Other values (28) 28
53.8%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 52
89.7%
Common 6
 
10.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
 
7.7%
4
 
7.7%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
Other values (28) 28
53.8%
Common
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 52
89.7%
ASCII 6
 
10.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4
 
7.7%
4
 
7.7%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
2
 
3.8%
Other values (28) 28
53.8%
ASCII
ValueCountFrequency (%)
( 3
50.0%
) 3
50.0%

Interactions

2023-12-10T20:43:50.159060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:08.392244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.445757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.169500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.580517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.317177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.020669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.391205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.804288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.442600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.681248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.791378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.497905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.928113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.475676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.692796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:47.728769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:50.282024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:08.583035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.624526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.312025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.693436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.674760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.165122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.521714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.939710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.544242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.781139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.914359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.647118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.067335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.607293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.870469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:47.881998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:50.417598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:08.674379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.790102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.464185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.806306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.884834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.297745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.668850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:27.389015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.676923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.877677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.029754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.788912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.206444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.710644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.072083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:48.022367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:50.573022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:08.763123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.931086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.584598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.931471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.020164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.437557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.813400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:27.513676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.831376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.001814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.161015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.930746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.347067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.831281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.230674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:48.164761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:50.723914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:09.217424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.112135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.722352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.071237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.146845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.576623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.959880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:27.648144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.966989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.119003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.316246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.115987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.512371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.985502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.465087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:48.323320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:50.872343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:09.374198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.292453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.862639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.201420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.273784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.718211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:25.089700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:27.775299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:30.113257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.227775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.457046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.251137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.684702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.184921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.634134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:48.445987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:51.015223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:09.509119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.438465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.009293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.324373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.406985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:22.841229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:25.212293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:27.890107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:30.231249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.336651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.584016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.373761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:39.832636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.318338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:45.783355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:48.589062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:51.171821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:09.681226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:12.604218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.176567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.462241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.554510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-10T20:43:25.359057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:28.039548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:30.367457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.464545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:34.723880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.521099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-10T20:43:45.983868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
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2023-12-10T20:43:28.305174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:30.648140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.663474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.370299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.804762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.294447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:42.883422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.364592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.021791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:51.638598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.240446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.086963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.627760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.857358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:20.991566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.434203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:25.764961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:28.427728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:30.771586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.781049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.495254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:37.941494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.446014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.053503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.591182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.166314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:51.783986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.389869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.238665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.762194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:17.980628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.159279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.573328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:25.895568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:28.564865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:30.873485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:32.909623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.623712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.070791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.592408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.219156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.738071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.307992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:51.922281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.542248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.378899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:15.915162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.527663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.317440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.716696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.049413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:28.703505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.012560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.009194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.768968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.209308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.761669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.381776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:46.912093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.460672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:52.060248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.747852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.539774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.063036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.686627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.469387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:23.874767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.229712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:28.864981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.151470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.131280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:35.906948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.360152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:40.913973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:43.984193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:47.103260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.595590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:52.221684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:10.933396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.696439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.207972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.837846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.614516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.021102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.385010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.017745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.292330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.264118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.075770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.518903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.060573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.140180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:47.271159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.714326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:52.364844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.104774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:13.860401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.350589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:18.974505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.752518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.155179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.532542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.167290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.427122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.479677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.203331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.670810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.216422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.318107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:47.420535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:49.869557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:52.496040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:11.279818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:14.010094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:16.476282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:19.113559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:21.889719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:24.273541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:26.674164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:29.312705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:31.551962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:33.659890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:36.342189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:38.801669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:41.355711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:44.496526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:47.576512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T20:43:50.015501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T20:44:02.532532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐형식명댐높이(m)댐길이(m)댐체적(천m)하천명정상표고(ELm)유역면적(km)저수면적(km)계획홍수위(ELm)상시만수위(ELm)홍수기제한수위(ELm)홍수조절용량(백만m3)저수위(ELm)총저수용량(백만m3)유효저수용량(백만m3)연간용수공급계획량(백만m3)월류정표고(ELm)착공일준공일댐명
댐형식명1.0000.0000.0000.2520.0000.5470.5450.3860.4000.4060.4060.3730.0000.6430.7090.7060.0000.8660.7051.000
댐높이(m)0.0001.0000.8920.7871.0000.2440.7700.5540.0000.0000.0000.6670.0000.7240.6810.7410.0000.0000.8251.000
댐길이(m)0.0000.8921.0000.0001.0000.4210.5750.7170.4230.5250.5250.0000.1520.5280.4590.5060.0000.0000.4211.000
댐체적(천m)0.2520.7870.0001.0000.8740.0000.3780.2780.0000.0000.0000.6540.0000.4860.8250.4190.0000.0000.4311.000
하천명0.0001.0001.0000.8741.0000.0000.7420.6560.0000.6390.6390.0000.9100.0000.0000.0000.7570.7970.9211.000
정상표고(ELm)0.5470.2440.4210.0000.0001.0000.0000.0000.9990.9960.9960.6160.8790.0000.4750.0000.9880.6340.0001.000
유역면적(km)0.5450.7700.5750.3780.7420.0001.0000.8960.0000.5720.5720.9540.0000.9520.8050.9540.2720.4470.7341.000
저수면적(km)0.3860.5540.7170.2780.6560.0000.8961.0000.0000.1480.1480.8050.3690.8570.9590.9240.0000.7570.7101.000
계획홍수위(ELm)0.4000.0000.4230.0000.0000.9990.0000.0001.0000.9980.9980.7200.8600.0000.4390.0000.9930.4730.0001.000
상시만수위(ELm)0.4060.0000.5250.0000.6390.9960.5720.1480.9981.0001.0000.8610.8750.0000.4180.5250.9860.2710.0001.000
홍수기제한수위(ELm)0.4060.0000.5250.0000.6390.9960.5720.1480.9981.0001.0000.8610.8750.0000.4180.5250.9860.2710.0001.000
홍수조절용량(백만m3)0.3730.6670.0000.6540.0000.6160.9540.8050.7200.8610.8611.0000.0000.9180.8380.9580.7590.6060.7551.000
저수위(ELm)0.0000.0000.1520.0000.9100.8790.0000.3690.8600.8750.8750.0001.0000.0000.3700.1810.8910.0000.0541.000
총저수용량(백만m3)0.6430.7240.5280.4860.0000.0000.9520.8570.0000.0000.0000.9180.0001.0000.9470.9800.0000.7660.8181.000
유효저수용량(백만m3)0.7090.6810.4590.8250.0000.4750.8050.9590.4390.4180.4180.8380.3700.9471.0000.9300.4740.7490.7891.000
연간용수공급계획량(백만m3)0.7060.7410.5060.4190.0000.0000.9540.9240.0000.5250.5250.9580.1810.9800.9301.0000.0000.7750.8461.000
월류정표고(ELm)0.0000.0000.0000.0000.7570.9880.2720.0000.9930.9860.9860.7590.8910.0000.4740.0001.0000.0000.0001.000
착공일0.8660.0000.0000.0000.7970.6340.4470.7570.4730.2710.2710.6060.0000.7660.7490.7750.0001.0000.8551.000
준공일0.7050.8250.4210.4310.9210.0000.7340.7100.0000.0000.0000.7550.0540.8180.7890.8460.0000.8551.0001.000
댐명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-10T20:44:02.881213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
댐높이(m)댐길이(m)댐체적(천m)정상표고(ELm)유역면적(km)저수면적(km)계획홍수위(ELm)상시만수위(ELm)홍수기제한수위(ELm)홍수조절용량(백만m3)저수위(ELm)총저수용량(백만m3)유효저수용량(백만m3)연간용수공급계획량(백만m3)월류정표고(ELm)착공일준공일댐형식명
댐높이(m)1.0000.5940.6010.3320.4450.4660.3330.3190.3300.5300.2410.6470.6430.6000.317-0.546-0.5190.000
댐길이(m)0.5941.0000.8090.0180.5860.5440.024-0.020-0.0070.641-0.0750.6280.6290.639-0.020-0.465-0.3760.000
댐체적(천m)0.6010.8091.0000.0570.4670.4540.0630.0200.0400.471-0.0290.5610.5570.5140.005-0.459-0.4360.095
정상표고(ELm)0.3320.0180.0571.000-0.156-0.1710.9980.9940.992-0.1270.974-0.076-0.049-0.0670.9940.1600.2470.193
유역면적(km)0.4450.5860.467-0.1561.0000.964-0.174-0.186-0.1790.930-0.1510.9400.9350.958-0.196-0.785-0.7020.342
저수면적(km)0.4660.5440.454-0.1710.9641.000-0.190-0.191-0.1830.925-0.1610.9600.9580.964-0.197-0.820-0.7550.216
계획홍수위(ELm)0.3330.0240.0630.998-0.174-0.1901.0000.9950.994-0.1400.977-0.092-0.065-0.0820.9940.1720.2560.090
상시만수위(ELm)0.319-0.0200.0200.994-0.186-0.1910.9951.0000.999-0.1620.976-0.098-0.069-0.0900.9990.1540.2340.095
홍수기제한수위(ELm)0.330-0.0070.0400.992-0.179-0.1830.9940.9991.000-0.1580.975-0.088-0.058-0.0820.9960.1350.2120.095
홍수조절용량(백만m3)0.5300.6410.471-0.1270.9300.925-0.140-0.162-0.1581.000-0.1250.9210.9090.931-0.164-0.727-0.6520.203
저수위(ELm)0.241-0.075-0.0290.974-0.151-0.1610.9770.9760.975-0.1251.000-0.093-0.065-0.0740.9740.1460.2260.000
총저수용량(백만m3)0.6470.6280.561-0.0760.9400.960-0.092-0.098-0.0880.921-0.0931.0000.9970.986-0.106-0.849-0.7940.432
유효저수용량(백만m3)0.6430.6290.557-0.0490.9350.958-0.065-0.069-0.0580.909-0.0650.9971.0000.983-0.076-0.854-0.7980.514
연간용수공급계획량(백만m3)0.6000.6390.514-0.0670.9580.964-0.082-0.090-0.0820.931-0.0740.9860.9831.000-0.097-0.815-0.7420.495
월류정표고(ELm)0.317-0.0200.0050.994-0.196-0.1970.9940.9990.996-0.1640.974-0.106-0.076-0.0971.0000.1660.2490.000
착공일-0.546-0.465-0.4590.160-0.785-0.8200.1720.1540.135-0.7270.146-0.849-0.854-0.8150.1661.0000.9750.606
준공일-0.519-0.376-0.4360.247-0.702-0.7550.2560.2340.212-0.6520.226-0.794-0.798-0.7420.2490.9751.0000.424
댐형식명0.0000.0000.0950.1930.3420.2160.0900.0950.0950.2030.0000.4320.5140.4950.0000.6060.4241.000

Missing values

2023-12-10T20:43:53.055684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T20:43:53.490729image/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

댐형식명댐높이(m)댐길이(m)댐체적(천m)하천명정상표고(ELm)유역면적(km)저수면적(km)계획홍수위(ELm)상시만수위(ELm)홍수기제한수위(ELm)홍수조절용량(백만m3)저수위(ELm)총저수용량(백만m3)유효저수용량(백만m3)연간용수공급계획량(백만m3)월류정표고(ELm)착공일준공일댐명
0<NA>0.00.00.0<NA>0.00.00.00.00.00.00.00.00.00.00.00.02015123020151230남강(구)
1C.F.R.D70.0498.02206.0금강268.5930.036.2265.5263.5261.5137.0228.5815.0672.521143.2252.81990100120061231용담
2C.F.R.D89.0535.03943.0단장천212.595.42.2210.2207.2207.26.0150.073.669.873.0199.52001120120151118밀양
3C.F.R.D55.5400.01.2내성천168.0500.010.4164.0161.0156.775.0135.0181.1160.4203.3153.02009120120181231영주
4C.F.R.D53.0403.01506.0탐진강85.0193.010.382.882.079.08.055.0191.0171.0127.871.01996020120071231장흥
5C.F.R.D64.0472.02189.0부항천201.482.02.5198.6195.0193.512.3165.054.342.636.3190.52002080120141231김천부항
6C.F.R.D50.0282.0614.0직소천49.059.03.043.841.241.29.323.050.335.635.141.21990020119961231부안
7C.F.R.D45.0390.0877.0위천208.487.52.7205.1204.0204.03.1181.048.740.138.3197.22000010120121231군위
8C.F.R.D34.01126.01280.0남강51.02285.028.246.041.041.0269.832.0309.2299.7573.329.01987110120031231남강
9C.G.D64.0344.2410.0섬진강200.0763.026.5197.7196.5196.532.0154.54466.0429.0435.0192.71961080119651231섬진강
댐형식명댐높이(m)댐길이(m)댐체적(천m)하천명정상표고(ELm)유역면적(km)저수면적(km)계획홍수위(ELm)상시만수위(ELm)홍수기제한수위(ELm)홍수조절용량(백만m3)저수위(ELm)총저수용량(백만m3)유효저수용량(백만m3)연간용수공급계획량(백만m3)월류정표고(ELm)착공일준공일댐명
12C.G.D58.5274.0227.0보현천368.541.31.53364.9364.0362.04.2333.027.924.820.3360.02002080120151231성덕
13C.G.D96.0472.0891.0황강181.0925.025.0179.0176.0176.080.0140.0790.0560.0599.0166.01982040119891231합천
14C.G_E.C.R.D72.0495.01234.0금강83.03204.072.880.076.576.5250.060.01490.0790.01649.064.51975030119810630대청
15E.C.R.D83.0612.04015.0낙동강166.01584.051.5161.7160.0160.0110.0130.01248.01000.0926.0151.01971040119770531안동
16E.C.R.D48.5205.0675.0계천184.0209.05.8180.0180.0178.29.5160.086.973.4119.5167.01990010120021130횡성
17E.C.R.D58.0330.01573.0보성강115.01010.033.0110.5108.5108.560.085.0457.0352.0270.198.51984090119921231주암(본)
18E.C.R.D123.0530.09591.0소양강203.02703.070.0198.0193.5190.3500.0150.02900.01900.01213.0185.51967040119731231소양강
19E.C.R.D99.9562.64965.0이사천115.0134.67.8111.1108.5108.520.060.0250.0210.0218.7108.51984090119921231주암(조)
20E.C.R.D73.0515.03423.0반변천168.01361.026.4164.7163.0161.780.0137.0595.0424.0591.6151.41984120119931231임하
21E.C.R.D50.0291.01116.0웅천천79.0163.65.875.574.074.010.050.0116.9108.7106.664.01990110120000630보령