Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells1737
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 MiB
Average record size in memory133.0 B

Variable types

Text1
Numeric13

Dataset

Description진주시 지번별 2012년 1월~12월 상수도 사용량 에너지 관련 정보 제공(주소, 1월사용량, 2월사용량, 3월사용량, 4월사용량, 5월사용량, 6월사용량, 7월사용량, 8월사용량, 9월사용량, 10월사용량, 11월사용량, 12월사용량, 사용량합계)
Author경상남도 진주시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15025330

Alerts

1월사용량 is highly overall correlated with 2월사용량 and 11 other fieldsHigh correlation
2월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
3월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
4월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
5월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
6월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
7월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
8월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
9월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
10월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
11월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
12월사용량 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
사용량합계 is highly overall correlated with 1월사용량 and 11 other fieldsHigh correlation
1월사용량 has 274 (2.7%) missing valuesMissing
2월사용량 has 228 (2.3%) missing valuesMissing
3월사용량 has 225 (2.2%) missing valuesMissing
4월사용량 has 221 (2.2%) missing valuesMissing
5월사용량 has 213 (2.1%) missing valuesMissing
6월사용량 has 185 (1.8%) missing valuesMissing
7월사용량 has 171 (1.7%) missing valuesMissing
8월사용량 has 130 (1.3%) missing valuesMissing
1월사용량 is highly skewed (γ1 = 27.87073262)Skewed
2월사용량 is highly skewed (γ1 = 27.28948596)Skewed
3월사용량 is highly skewed (γ1 = 27.62684971)Skewed
4월사용량 is highly skewed (γ1 = 27.349113)Skewed
5월사용량 is highly skewed (γ1 = 27.19611081)Skewed
6월사용량 is highly skewed (γ1 = 27.56651397)Skewed
7월사용량 is highly skewed (γ1 = 27.06089951)Skewed
8월사용량 is highly skewed (γ1 = 28.40125515)Skewed
9월사용량 is highly skewed (γ1 = 27.59854901)Skewed
10월사용량 is highly skewed (γ1 = 27.74573206)Skewed
11월사용량 is highly skewed (γ1 = 26.65837963)Skewed
12월사용량 is highly skewed (γ1 = 27.21544309)Skewed
사용량합계 is highly skewed (γ1 = 27.68291541)Skewed
1월사용량 has 471 (4.7%) zerosZeros
2월사용량 has 578 (5.8%) zerosZeros
3월사용량 has 553 (5.5%) zerosZeros
4월사용량 has 570 (5.7%) zerosZeros
5월사용량 has 513 (5.1%) zerosZeros
6월사용량 has 483 (4.8%) zerosZeros
7월사용량 has 461 (4.6%) zerosZeros
8월사용량 has 442 (4.4%) zerosZeros
9월사용량 has 426 (4.3%) zerosZeros
10월사용량 has 440 (4.4%) zerosZeros
11월사용량 has 468 (4.7%) zerosZeros
12월사용량 has 511 (5.1%) zerosZeros

Reproduction

Analysis started2023-12-11 00:18:23.453681
Analysis finished2023-12-11 00:18:46.662058
Duration23.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주소
Text

Distinct9949
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:18:46.933148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length42
Mean length25.3792
Min length14

Characters and Unicode

Total characters253792
Distinct characters495
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9899 ?
Unique (%)99.0%

Sample

1st row경상남도 진주시 사봉면 동부로1769번길 9 (중촌마을)
2nd row경상남도 진주시 미천면 상미로 55-3
3rd row경상남도 진주시 촉석로 145, (9/5) (봉곡동) (옥외원격검침 2012)
4th row경상남도 진주시 동진로 169 (상대동) 도동치과
5th row경상남도 진주시 대곡면 오방로 108
ValueCountFrequency (%)
경상남도 10000
 
20.0%
진주시 10000
 
20.0%
상대동 590
 
1.2%
문산읍 438
 
0.9%
금산면 375
 
0.7%
상봉동 344
 
0.7%
신안동 339
 
0.7%
대곡면 320
 
0.6%
집현면 254
 
0.5%
명석면 233
 
0.5%
Other values (5523) 27160
54.3%
2023-12-11T09:18:47.456215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42098
 
16.6%
1 12170
 
4.8%
11919
 
4.7%
11328
 
4.5%
10833
 
4.3%
10791
 
4.3%
10490
 
4.1%
10308
 
4.1%
10038
 
4.0%
8550
 
3.4%
Other values (485) 115267
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141969
55.9%
Decimal Number 48945
 
19.3%
Space Separator 42098
 
16.6%
Open Punctuation 5904
 
2.3%
Close Punctuation 5731
 
2.3%
Dash Punctuation 5094
 
2.0%
Other Punctuation 4001
 
1.6%
Uppercase Letter 43
 
< 0.1%
Math Symbol 6
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11919
 
8.4%
11328
 
8.0%
10833
 
7.6%
10791
 
7.6%
10490
 
7.4%
10308
 
7.3%
10038
 
7.1%
8550
 
6.0%
7819
 
5.5%
6560
 
4.6%
Other values (442) 43333
30.5%
Uppercase Letter
ValueCountFrequency (%)
S 7
16.3%
G 5
11.6%
L 4
9.3%
K 4
9.3%
A 3
 
7.0%
B 3
 
7.0%
T 3
 
7.0%
P 2
 
4.7%
O 2
 
4.7%
N 2
 
4.7%
Other values (7) 8
18.6%
Decimal Number
ValueCountFrequency (%)
1 12170
24.9%
2 6672
13.6%
3 5185
10.6%
4 4479
 
9.2%
5 4296
 
8.8%
6 3606
 
7.4%
7 3306
 
6.8%
9 3223
 
6.6%
0 3050
 
6.2%
8 2958
 
6.0%
Other Punctuation
ValueCountFrequency (%)
/ 2002
50.0%
, 1958
48.9%
. 36
 
0.9%
: 4
 
0.1%
& 1
 
< 0.1%
Math Symbol
ValueCountFrequency (%)
< 2
33.3%
> 2
33.3%
+ 1
16.7%
~ 1
16.7%
Open Punctuation
ValueCountFrequency (%)
( 5871
99.4%
[ 33
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 5698
99.4%
] 33
 
0.6%
Space Separator
ValueCountFrequency (%)
42098
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5094
100.0%
Lowercase Letter
ValueCountFrequency (%)
x 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141969
55.9%
Common 111779
44.0%
Latin 44
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11919
 
8.4%
11328
 
8.0%
10833
 
7.6%
10791
 
7.6%
10490
 
7.4%
10308
 
7.3%
10038
 
7.1%
8550
 
6.0%
7819
 
5.5%
6560
 
4.6%
Other values (442) 43333
30.5%
Common
ValueCountFrequency (%)
42098
37.7%
1 12170
 
10.9%
2 6672
 
6.0%
( 5871
 
5.3%
) 5698
 
5.1%
3 5185
 
4.6%
- 5094
 
4.6%
4 4479
 
4.0%
5 4296
 
3.8%
6 3606
 
3.2%
Other values (15) 16610
 
14.9%
Latin
ValueCountFrequency (%)
S 7
15.9%
G 5
11.4%
L 4
9.1%
K 4
9.1%
A 3
 
6.8%
B 3
 
6.8%
T 3
 
6.8%
P 2
 
4.5%
O 2
 
4.5%
N 2
 
4.5%
Other values (8) 9
20.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141969
55.9%
ASCII 111823
44.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42098
37.6%
1 12170
 
10.9%
2 6672
 
6.0%
( 5871
 
5.3%
) 5698
 
5.1%
3 5185
 
4.6%
- 5094
 
4.6%
4 4479
 
4.0%
5 4296
 
3.8%
6 3606
 
3.2%
Other values (33) 16654
 
14.9%
Hangul
ValueCountFrequency (%)
11919
 
8.4%
11328
 
8.0%
10833
 
7.6%
10791
 
7.6%
10490
 
7.4%
10308
 
7.3%
10038
 
7.1%
8550
 
6.0%
7819
 
5.5%
6560
 
4.6%
Other values (442) 43333
30.5%

1월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct384
Distinct (%)3.9%
Missing274
Missing (%)2.7%
Infinite0
Infinite (%)0.0%
Mean58.468024
Minimum0
Maximum21016
Zeros471
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:47.600224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median19
Q339
95-th percentile120
Maximum21016
Range21016
Interquartile range (IQR)31

Descriptive statistics

Standard deviation466.82278
Coefficient of variation (CV)7.9842407
Kurtosis941.09844
Mean58.468024
Median Absolute Deviation (MAD)13
Skewness27.870733
Sum568660
Variance217923.5
MonotonicityNot monotonic
2023-12-11T09:18:47.720892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 471
 
4.7%
1 293
 
2.9%
10 293
 
2.9%
2 286
 
2.9%
5 273
 
2.7%
4 269
 
2.7%
8 265
 
2.6%
3 265
 
2.6%
12 253
 
2.5%
7 244
 
2.4%
Other values (374) 6814
68.1%
(Missing) 274
 
2.7%
ValueCountFrequency (%)
0 471
4.7%
1 293
2.9%
2 286
2.9%
3 265
2.6%
4 269
2.7%
5 273
2.7%
6 230
2.3%
7 244
2.4%
8 265
2.6%
9 217
2.2%
ValueCountFrequency (%)
21016 1
< 0.1%
17868 1
< 0.1%
15440 1
< 0.1%
12122 1
< 0.1%
10911 1
< 0.1%
10560 1
< 0.1%
10160 1
< 0.1%
10105 1
< 0.1%
8872 1
< 0.1%
8027 1
< 0.1%

2월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct390
Distinct (%)4.0%
Missing228
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean58.76054
Minimum0
Maximum21804
Zeros578
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:47.841586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median19
Q340
95-th percentile121
Maximum21804
Range21804
Interquartile range (IQR)33

Descriptive statistics

Standard deviation466.18513
Coefficient of variation (CV)7.9336427
Kurtosis917.63961
Mean58.76054
Median Absolute Deviation (MAD)14
Skewness27.289486
Sum574208
Variance217328.57
MonotonicityNot monotonic
2023-12-11T09:18:47.961278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 578
 
5.8%
10 315
 
3.1%
5 296
 
3.0%
2 280
 
2.8%
1 278
 
2.8%
3 277
 
2.8%
4 257
 
2.6%
7 247
 
2.5%
20 247
 
2.5%
15 244
 
2.4%
Other values (380) 6753
67.5%
ValueCountFrequency (%)
0 578
5.8%
1 278
2.8%
2 280
2.8%
3 277
2.8%
4 257
2.6%
5 296
3.0%
6 242
2.4%
7 247
2.5%
8 227
 
2.3%
9 208
 
2.1%
ValueCountFrequency (%)
21804 1
< 0.1%
16529 1
< 0.1%
13834 1
< 0.1%
12598 1
< 0.1%
10829 1
< 0.1%
10683 1
< 0.1%
9951 1
< 0.1%
9449 1
< 0.1%
8942 1
< 0.1%
8322 1
< 0.1%

3월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct393
Distinct (%)4.0%
Missing225
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean59.368389
Minimum0
Maximum21850
Zeros553
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:48.320120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19
median19
Q340.5
95-th percentile122
Maximum21850
Range21850
Interquartile range (IQR)31.5

Descriptive statistics

Standard deviation465.69222
Coefficient of variation (CV)7.8441107
Kurtosis938.92451
Mean59.368389
Median Absolute Deviation (MAD)13
Skewness27.62685
Sum580326
Variance216869.24
MonotonicityNot monotonic
2023-12-11T09:18:48.443580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 553
 
5.5%
10 276
 
2.8%
13 258
 
2.6%
5 257
 
2.6%
7 256
 
2.6%
3 246
 
2.5%
11 244
 
2.4%
2 236
 
2.4%
12 234
 
2.3%
20 234
 
2.3%
Other values (383) 6981
69.8%
ValueCountFrequency (%)
0 553
5.5%
1 214
 
2.1%
2 236
2.4%
3 246
2.5%
4 234
2.3%
5 257
2.6%
6 226
2.3%
7 256
2.6%
8 218
 
2.2%
9 204
 
2.0%
ValueCountFrequency (%)
21850 1
< 0.1%
16766 1
< 0.1%
14300 1
< 0.1%
12664 1
< 0.1%
11023 1
< 0.1%
10064 1
< 0.1%
9355 1
< 0.1%
9191 1
< 0.1%
8957 1
< 0.1%
8444 1
< 0.1%

4월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct366
Distinct (%)3.7%
Missing221
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean54.452807
Minimum0
Maximum19594
Zeros570
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:48.557021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q337
95-th percentile112
Maximum19594
Range19594
Interquartile range (IQR)30

Descriptive statistics

Standard deviation437.1302
Coefficient of variation (CV)8.0276889
Kurtosis910.24658
Mean54.452807
Median Absolute Deviation (MAD)12
Skewness27.349113
Sum532494
Variance191082.81
MonotonicityNot monotonic
2023-12-11T09:18:48.687331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 570
 
5.7%
1 317
 
3.2%
10 317
 
3.2%
2 311
 
3.1%
5 300
 
3.0%
4 284
 
2.8%
3 275
 
2.8%
6 260
 
2.6%
8 259
 
2.6%
12 244
 
2.4%
Other values (356) 6642
66.4%
ValueCountFrequency (%)
0 570
5.7%
1 317
3.2%
2 311
3.1%
3 275
2.8%
4 284
2.8%
5 300
3.0%
6 260
2.6%
7 242
2.4%
8 259
2.6%
9 228
 
2.3%
ValueCountFrequency (%)
19594 1
< 0.1%
16872 1
< 0.1%
13236 1
< 0.1%
11995 1
< 0.1%
10129 1
< 0.1%
9722 1
< 0.1%
9668 1
< 0.1%
8884 1
< 0.1%
8175 1
< 0.1%
6814 1
< 0.1%

5월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct392
Distinct (%)4.0%
Missing213
Missing (%)2.1%
Infinite0
Infinite (%)0.0%
Mean58.898028
Minimum0
Maximum22298
Zeros513
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:48.810949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median18
Q339
95-th percentile123
Maximum22298
Range22298
Interquartile range (IQR)32

Descriptive statistics

Standard deviation470.94533
Coefficient of variation (CV)7.9959439
Kurtosis918.34141
Mean58.898028
Median Absolute Deviation (MAD)13
Skewness27.196111
Sum576435
Variance221789.5
MonotonicityNot monotonic
2023-12-11T09:18:48.930823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 513
 
5.1%
2 307
 
3.1%
3 306
 
3.1%
10 296
 
3.0%
5 294
 
2.9%
4 285
 
2.9%
1 280
 
2.8%
7 268
 
2.7%
13 262
 
2.6%
6 256
 
2.6%
Other values (382) 6720
67.2%
ValueCountFrequency (%)
0 513
5.1%
1 280
2.8%
2 307
3.1%
3 306
3.1%
4 285
2.9%
5 294
2.9%
6 256
2.6%
7 268
2.7%
8 237
2.4%
9 214
2.1%
ValueCountFrequency (%)
22298 1
< 0.1%
15259 1
< 0.1%
14966 1
< 0.1%
13341 1
< 0.1%
10388 1
< 0.1%
10128 1
< 0.1%
9807 1
< 0.1%
9350 1
< 0.1%
9264 1
< 0.1%
8920 1
< 0.1%

6월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct387
Distinct (%)3.9%
Missing185
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean59.347427
Minimum0
Maximum21969
Zeros483
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:49.049855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median19
Q340
95-th percentile123
Maximum21969
Range21969
Interquartile range (IQR)32

Descriptive statistics

Standard deviation478.5569
Coefficient of variation (CV)8.0636504
Kurtosis927.02909
Mean59.347427
Median Absolute Deviation (MAD)13
Skewness27.566514
Sum582495
Variance229016.71
MonotonicityNot monotonic
2023-12-11T09:18:49.162336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 483
 
4.8%
5 319
 
3.2%
10 294
 
2.9%
2 283
 
2.8%
3 266
 
2.7%
6 261
 
2.6%
8 258
 
2.6%
4 254
 
2.5%
1 251
 
2.5%
15 250
 
2.5%
Other values (377) 6896
69.0%
ValueCountFrequency (%)
0 483
4.8%
1 251
2.5%
2 283
2.8%
3 266
2.7%
4 254
2.5%
5 319
3.2%
6 261
2.6%
7 235
2.4%
8 258
2.6%
9 233
2.3%
ValueCountFrequency (%)
21969 1
< 0.1%
17555 1
< 0.1%
14709 1
< 0.1%
14371 1
< 0.1%
10786 1
< 0.1%
10107 1
< 0.1%
10006 1
< 0.1%
9820 1
< 0.1%
8904 1
< 0.1%
8310 1
< 0.1%

7월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct403
Distinct (%)4.1%
Missing171
Missing (%)1.7%
Infinite0
Infinite (%)0.0%
Mean64.178553
Minimum0
Maximum22525
Zeros461
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:49.308014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median21
Q343
95-th percentile131.6
Maximum22525
Range22525
Interquartile range (IQR)34

Descriptive statistics

Standard deviation504.71526
Coefficient of variation (CV)7.8642355
Kurtosis890.73359
Mean64.178553
Median Absolute Deviation (MAD)15
Skewness27.0609
Sum630811
Variance254737.49
MonotonicityNot monotonic
2023-12-11T09:18:49.439287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 461
 
4.6%
10 296
 
3.0%
4 251
 
2.5%
5 244
 
2.4%
3 238
 
2.4%
7 238
 
2.4%
2 236
 
2.4%
20 231
 
2.3%
6 231
 
2.3%
14 223
 
2.2%
Other values (393) 7180
71.8%
ValueCountFrequency (%)
0 461
4.6%
1 216
2.2%
2 236
2.4%
3 238
2.4%
4 251
2.5%
5 244
2.4%
6 231
2.3%
7 238
2.4%
8 212
2.1%
9 213
2.1%
ValueCountFrequency (%)
22525 1
< 0.1%
19204 1
< 0.1%
15617 1
< 0.1%
13467 1
< 0.1%
11553 1
< 0.1%
11105 1
< 0.1%
10603 1
< 0.1%
9848 1
< 0.1%
9834 1
< 0.1%
9833 1
< 0.1%

8월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct397
Distinct (%)4.0%
Missing130
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean64.229483
Minimum0
Maximum25113
Zeros442
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:49.560078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median21
Q344
95-th percentile134
Maximum25113
Range25113
Interquartile range (IQR)35

Descriptive statistics

Standard deviation516.60105
Coefficient of variation (CV)8.0430516
Kurtosis996.65769
Mean64.229483
Median Absolute Deviation (MAD)15
Skewness28.401255
Sum633945
Variance266876.64
MonotonicityNot monotonic
2023-12-11T09:18:49.684796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 442
 
4.4%
10 278
 
2.8%
5 252
 
2.5%
1 249
 
2.5%
2 246
 
2.5%
4 244
 
2.4%
7 239
 
2.4%
3 239
 
2.4%
8 234
 
2.3%
6 232
 
2.3%
Other values (387) 7215
72.2%
ValueCountFrequency (%)
0 442
4.4%
1 249
2.5%
2 246
2.5%
3 239
2.4%
4 244
2.4%
5 252
2.5%
6 232
2.3%
7 239
2.4%
8 234
2.3%
9 202
2.0%
ValueCountFrequency (%)
25113 1
< 0.1%
17276 1
< 0.1%
17036 1
< 0.1%
14201 1
< 0.1%
12161 1
< 0.1%
11612 1
< 0.1%
10657 1
< 0.1%
10111 1
< 0.1%
10030 1
< 0.1%
9932 1
< 0.1%

9월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct413
Distinct (%)4.2%
Missing59
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean68.810482
Minimum0
Maximum23999
Zeros426
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:49.804651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median24
Q349
95-th percentile142
Maximum23999
Range23999
Interquartile range (IQR)39

Descriptive statistics

Standard deviation546.73752
Coefficient of variation (CV)7.9455557
Kurtosis904.55193
Mean68.810482
Median Absolute Deviation (MAD)16
Skewness27.598549
Sum684045
Variance298921.91
MonotonicityNot monotonic
2023-12-11T09:18:49.939334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 426
 
4.3%
10 242
 
2.4%
5 236
 
2.4%
20 221
 
2.2%
4 215
 
2.1%
12 211
 
2.1%
18 206
 
2.1%
6 205
 
2.1%
8 203
 
2.0%
2 202
 
2.0%
Other values (403) 7574
75.7%
ValueCountFrequency (%)
0 426
4.3%
1 196
2.0%
2 202
2.0%
3 185
1.8%
4 215
2.1%
5 236
2.4%
6 205
2.1%
7 194
1.9%
8 203
2.0%
9 196
2.0%
ValueCountFrequency (%)
23999 1
< 0.1%
20600 1
< 0.1%
18088 1
< 0.1%
15507 1
< 0.1%
12960 1
< 0.1%
12530 1
< 0.1%
12261 1
< 0.1%
11694 1
< 0.1%
10593 1
< 0.1%
10447 1
< 0.1%

10월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct412
Distinct (%)4.1%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean68.19609
Minimum0
Maximum23034
Zeros440
Zeros (%)4.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:50.083831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median24
Q348
95-th percentile143.3
Maximum23034
Range23034
Interquartile range (IQR)38

Descriptive statistics

Standard deviation536.06042
Coefficient of variation (CV)7.8605741
Kurtosis916.8518
Mean68.19609
Median Absolute Deviation (MAD)16
Skewness27.745732
Sum680256
Variance287360.78
MonotonicityNot monotonic
2023-12-11T09:18:50.201722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 440
 
4.4%
20 251
 
2.5%
10 246
 
2.5%
2 229
 
2.3%
7 224
 
2.2%
4 219
 
2.2%
15 218
 
2.2%
5 217
 
2.2%
1 215
 
2.1%
11 215
 
2.1%
Other values (402) 7501
75.0%
ValueCountFrequency (%)
0 440
4.4%
1 215
2.1%
2 229
2.3%
3 195
1.9%
4 219
2.2%
5 217
2.2%
6 202
2.0%
7 224
2.2%
8 180
1.8%
9 191
1.9%
ValueCountFrequency (%)
23034 1
< 0.1%
21345 1
< 0.1%
17587 1
< 0.1%
15539 1
< 0.1%
12993 1
< 0.1%
12260 1
< 0.1%
12087 1
< 0.1%
11213 1
< 0.1%
10327 1
< 0.1%
8849 1
< 0.1%

11월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct407
Distinct (%)4.1%
Missing6
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean63.231539
Minimum0
Maximum20299
Zeros468
Zeros (%)4.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:50.320676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median22
Q344
95-th percentile131.35
Maximum20299
Range20299
Interquartile range (IQR)34

Descriptive statistics

Standard deviation481.7037
Coefficient of variation (CV)7.6180923
Kurtosis841.67083
Mean63.231539
Median Absolute Deviation (MAD)15
Skewness26.65838
Sum631936
Variance232038.46
MonotonicityNot monotonic
2023-12-11T09:18:50.457030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 468
 
4.7%
10 274
 
2.7%
1 246
 
2.5%
2 244
 
2.4%
4 234
 
2.3%
20 232
 
2.3%
5 228
 
2.3%
12 219
 
2.2%
6 212
 
2.1%
16 211
 
2.1%
Other values (397) 7426
74.3%
ValueCountFrequency (%)
0 468
4.7%
1 246
2.5%
2 244
2.4%
3 209
2.1%
4 234
2.3%
5 228
2.3%
6 212
2.1%
7 202
2.0%
8 208
2.1%
9 209
2.1%
ValueCountFrequency (%)
20299 1
< 0.1%
17930 1
< 0.1%
14623 1
< 0.1%
14580 1
< 0.1%
13541 1
< 0.1%
11067 1
< 0.1%
10251 1
< 0.1%
9852 1
< 0.1%
9593 1
< 0.1%
8373 1
< 0.1%

12월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct399
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.2098
Minimum0
Maximum21130
Zeros511
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:50.590556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q342
95-th percentile130
Maximum21130
Range21130
Interquartile range (IQR)34

Descriptive statistics

Standard deviation497.48314
Coefficient of variation (CV)7.9968612
Kurtosis880.22419
Mean62.2098
Median Absolute Deviation (MAD)14
Skewness27.215443
Sum622098
Variance247489.47
MonotonicityNot monotonic
2023-12-11T09:18:50.729222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 511
 
5.1%
3 302
 
3.0%
1 283
 
2.8%
10 280
 
2.8%
4 273
 
2.7%
2 268
 
2.7%
5 260
 
2.6%
15 257
 
2.6%
6 249
 
2.5%
7 237
 
2.4%
Other values (389) 7080
70.8%
ValueCountFrequency (%)
0 511
5.1%
1 283
2.8%
2 268
2.7%
3 302
3.0%
4 273
2.7%
5 260
2.6%
6 249
2.5%
7 237
2.4%
8 236
2.4%
9 223
2.2%
ValueCountFrequency (%)
21130 1
< 0.1%
19141 1
< 0.1%
16584 1
< 0.1%
13355 1
< 0.1%
13077 1
< 0.1%
12247 1
< 0.1%
11261 1
< 0.1%
10425 1
< 0.1%
9822 1
< 0.1%
8285 1
< 0.1%

사용량합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1684
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean729.7709
Minimum1
Maximum264631
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:50.850727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile21
Q1114
median247
Q3500
95-th percentile1490
Maximum264631
Range264630
Interquartile range (IQR)386

Descriptive statistics

Standard deviation5799.97
Coefficient of variation (CV)7.9476587
Kurtosis927.15281
Mean729.7709
Median Absolute Deviation (MAD)161.5
Skewness27.682915
Sum7297709
Variance33639652
MonotonicityNot monotonic
2023-12-11T09:18:50.972425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 52
 
0.5%
128 37
 
0.4%
92 37
 
0.4%
2 33
 
0.3%
5 33
 
0.3%
181 32
 
0.3%
19 31
 
0.3%
136 31
 
0.3%
129 30
 
0.3%
69 30
 
0.3%
Other values (1674) 9654
96.5%
ValueCountFrequency (%)
1 52
0.5%
2 33
0.3%
3 28
0.3%
4 26
0.3%
5 33
0.3%
6 19
 
0.2%
7 22
0.2%
8 25
0.2%
9 22
0.2%
10 14
 
0.1%
ValueCountFrequency (%)
264631 1
< 0.1%
216345 1
< 0.1%
185682 1
< 0.1%
160493 1
< 0.1%
139097 1
< 0.1%
134286 1
< 0.1%
124774 1
< 0.1%
123443 1
< 0.1%
113215 1
< 0.1%
96999 1
< 0.1%

Interactions

2023-12-11T09:18:44.843861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:28.896070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.112408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.367444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.764729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.269528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.826048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.038165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.241477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.647823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.926608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.470749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.777965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.925125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:28.982391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.193798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.470335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.868528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.380484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.905899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.127197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.367257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.748354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.027544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.586596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.864410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.007990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.066362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.287866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.584556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.974124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.491378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.979038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.235047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.484575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.838917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.113063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.685775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.944114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.090461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.151462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.390081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.670758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.124820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.604148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.058613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.321975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.578593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.917534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.204807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.778310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.033893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.183852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.251466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.500176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.821799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.230458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.713349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.162923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.414109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.681031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.014295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.298085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.877274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.121370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.268322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.343684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.594429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.908648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.330662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.811843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.241217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.493109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.771309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.100645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.375594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.970249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.186781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.376828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.426566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.669961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.009276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.423977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.907872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.321637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.571891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.869731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.196970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.455728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.050828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.255569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.471619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.532821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.756767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.120066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.541674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.994497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.398364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.695102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.001209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.274531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.538833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.143157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.324440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.561482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.633123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.848356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.228703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.663063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.357698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.485096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.781664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.146352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.357176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.627586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.266655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.401273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.647787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.728301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.953731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.314273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.774710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.446115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.568761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.874060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.239070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.440742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.765239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.372056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.493848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.734443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.820023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.046820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.408767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:33.889495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.542274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.706057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:37.956929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.334692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.563582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:41.899672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.465357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.569237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.871231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:29.939380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.164388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.544512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.048373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.655395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.817473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.056621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.442494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.700879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.279416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.580630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.681710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:45.984925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:30.026630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:31.253106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:32.651777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:34.153229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:35.741567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:36.928199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:38.144313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:39.534746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:40.806822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:42.380335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:43.672957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:44.769932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:18:51.060068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
1월사용량1.0000.9930.9920.9880.9450.9590.9960.9220.9680.9910.9350.9820.963
2월사용량0.9931.0000.9930.9940.9580.9730.9910.9510.9700.9810.9520.9850.980
3월사용량0.9920.9931.0000.9890.9300.9620.9960.9350.9620.9860.9420.9850.975
4월사용량0.9880.9940.9891.0000.9390.9760.9830.9290.9480.9840.9420.9810.966
5월사용량0.9450.9580.9300.9391.0000.9870.9370.9950.9690.9030.9430.9000.956
6월사용량0.9590.9730.9620.9760.9871.0000.9640.9880.9730.9310.9510.9450.990
7월사용량0.9960.9910.9960.9830.9370.9641.0000.9280.9720.9820.9450.9850.973
8월사용량0.9220.9510.9350.9290.9950.9880.9281.0000.9760.9220.9600.9240.969
9월사용량0.9680.9700.9620.9480.9690.9730.9720.9761.0000.9650.9940.9480.998
10월사용량0.9910.9810.9860.9840.9030.9310.9820.9220.9651.0000.9430.9920.963
11월사용량0.9350.9520.9420.9420.9430.9510.9450.9600.9940.9431.0000.9560.993
12월사용량0.9820.9850.9850.9810.9000.9450.9850.9240.9480.9920.9561.0000.967
사용량합계0.9630.9800.9750.9660.9560.9900.9730.9690.9980.9630.9930.9671.000
2023-12-11T09:18:51.437419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
1월사용량1.0000.8940.8440.8570.8640.8620.8550.8490.8410.8290.8280.8310.912
2월사용량0.8941.0000.8610.8530.8470.8380.8280.8200.8070.7950.7920.8000.899
3월사용량0.8440.8611.0000.8740.8470.8310.8170.8060.7950.7830.7810.7840.894
4월사용량0.8570.8530.8741.0000.9130.8860.8650.8520.8370.8230.8220.8250.921
5월사용량0.8640.8470.8470.9131.0000.9340.9090.8900.8710.8530.8510.8520.937
6월사용량0.8620.8380.8310.8860.9341.0000.9340.9100.8900.8710.8650.8650.940
7월사용량0.8550.8280.8170.8650.9090.9341.0000.9400.9160.8920.8820.8770.942
8월사용량0.8490.8200.8060.8520.8900.9100.9401.0000.9390.9100.8950.8860.941
9월사용량0.8410.8070.7950.8370.8710.8900.9160.9391.0000.9390.9070.8920.934
10월사용량0.8290.7950.7830.8230.8530.8710.8920.9100.9391.0000.9260.9020.925
11월사용량0.8280.7920.7810.8220.8510.8650.8820.8950.9070.9261.0000.9320.921
12월사용량0.8310.8000.7840.8250.8520.8650.8770.8860.8920.9020.9321.0000.916
사용량합계0.9120.8990.8940.9210.9370.9400.9420.9410.9340.9250.9210.9161.000

Missing values

2023-12-11T09:18:46.159796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:18:46.346456image/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.
2023-12-11T09:18:46.514857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

주소1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
18210경상남도 진주시 사봉면 동부로1769번길 9 (중촌마을)1510129151417151721199173
16758경상남도 진주시 미천면 상미로 55-31111119811141721181512158
38079경상남도 진주시 촉석로 145, (9/5) (봉곡동) (옥외원격검침 2012)403839334443434748664059540
31123경상남도 진주시 동진로 169 (상대동) 도동치과282827271838283028362832348
6560경상남도 진주시 대곡면 오방로 108135111156886560
26815경상남도 진주시 선학산길 45-2, (4/3) (상대동)272223182423302322252018275
22783경상남도 진주시 명석면 나불로289번길 6-4201325101518212024232212223
11048경상남도 진주시 집현면 덕오리 471-1000186101158671714102
7728경상남도 진주시 금곡면 월아산로480번길 41-4222237156987871
3907경상남도 진주시 진주대로1134번길 15-524221380253335
주소1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
45125경상남도 진주시 촉석로 217-114013395546965807795101104951108
8483경상남도 진주시 진성면 동부로 1602번길 1071127345669109678
45865경상남도 진주시 논개길 57, (7/3) 고천장여관 (장대동)105102102729390120861081221141391253
43285경상남도 진주시 돗골로 73, 김홍규 (상평동)787771748384989692474613859
48879경상남도 진주시 돗골로117번길 15 (상평동) (옥외원격검침 2014)14811284150712311368132416171604164916861683155217986
16772경상남도 진주시 천수로138번길 9-7240383774745734158
17917경상남도 진주시 미천면 오방로409번길 39-3 (정성마을)121316161213141014251312170
31603경상남도 진주시 문산읍 월아산로1094번길 14-10212215411921403448243043358
1283경상남도 진주시 미천면 오방로7111220100010029
8313경상남도 진주시 의병로58번길 3-1, (18/3) (상봉동)1111610281312101176