Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells776
Missing cells (%)0.6%
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 131 (1.3%) missing valuesMissing
2월사용량 has 121 (1.2%) missing valuesMissing
3월사용량 has 111 (1.1%) missing valuesMissing
4월사용량 has 104 (1.0%) missing valuesMissing
1월사용량 is highly skewed (γ1 = 30.67947536)Skewed
2월사용량 is highly skewed (γ1 = 31.35234603)Skewed
3월사용량 is highly skewed (γ1 = 31.5039583)Skewed
4월사용량 is highly skewed (γ1 = 30.78119042)Skewed
5월사용량 is highly skewed (γ1 = 31.46372161)Skewed
6월사용량 is highly skewed (γ1 = 30.4267333)Skewed
7월사용량 is highly skewed (γ1 = 30.69818109)Skewed
8월사용량 is highly skewed (γ1 = 29.88342719)Skewed
9월사용량 is highly skewed (γ1 = 31.75921132)Skewed
10월사용량 is highly skewed (γ1 = 31.72874098)Skewed
11월사용량 is highly skewed (γ1 = 31.03399109)Skewed
12월사용량 is highly skewed (γ1 = 30.50071944)Skewed
사용량합계 is highly skewed (γ1 = 31.23421377)Skewed
1월사용량 has 560 (5.6%) zerosZeros
2월사용량 has 642 (6.4%) zerosZeros
3월사용량 has 672 (6.7%) zerosZeros
4월사용량 has 662 (6.6%) zerosZeros
5월사용량 has 619 (6.2%) zerosZeros
6월사용량 has 572 (5.7%) zerosZeros
7월사용량 has 539 (5.4%) zerosZeros
8월사용량 has 533 (5.3%) zerosZeros
9월사용량 has 494 (4.9%) zerosZeros
10월사용량 has 539 (5.4%) zerosZeros
11월사용량 has 515 (5.1%) zerosZeros
12월사용량 has 554 (5.5%) zerosZeros

Reproduction

Analysis started2023-12-11 00:17:44.328522
Analysis finished2023-12-11 00:18:02.872501
Duration18.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주소
Text

Distinct9948
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:18:03.122215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length43
Mean length25.1958
Min length14

Characters and Unicode

Total characters251958
Distinct characters533
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9898 ?
Unique (%)99.0%

Sample

1st row경상남도 진주시 진성면 동부로1345번길 28-3
2nd row경상남도 진주시 지수면 방어산로 56-10
3rd row경상남도 진주시 금산면 갈전길63번길 6
4th row경상남도 진주시 진양호로239번길 6-7
5th row경상남도 진주시 일반성면 수목원로277번길 44-3
ValueCountFrequency (%)
경상남도 10000
 
20.1%
진주시 10000
 
20.1%
상대동 548
 
1.1%
문산읍 401
 
0.8%
금산면 373
 
0.7%
상봉동 334
 
0.7%
대곡면 332
 
0.7%
신안동 317
 
0.6%
명석면 272
 
0.5%
집현면 267
 
0.5%
Other values (5667) 27026
54.2%
2023-12-11T09:18:03.606307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
41765
 
16.6%
11859
 
4.7%
1 11847
 
4.7%
11256
 
4.5%
10801
 
4.3%
10747
 
4.3%
10454
 
4.1%
10283
 
4.1%
10051
 
4.0%
8449
 
3.4%
Other values (523) 114446
45.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141770
56.3%
Decimal Number 48591
 
19.3%
Space Separator 41765
 
16.6%
Open Punctuation 5577
 
2.2%
Close Punctuation 5414
 
2.1%
Dash Punctuation 5109
 
2.0%
Other Punctuation 3683
 
1.5%
Uppercase Letter 42
 
< 0.1%
Lowercase Letter 7
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11859
 
8.4%
11256
 
7.9%
10801
 
7.6%
10747
 
7.6%
10454
 
7.4%
10283
 
7.3%
10051
 
7.1%
8449
 
6.0%
7708
 
5.4%
6452
 
4.6%
Other values (479) 43710
30.8%
Uppercase Letter
ValueCountFrequency (%)
B 7
16.7%
A 5
11.9%
L 5
11.9%
P 3
 
7.1%
M 3
 
7.1%
S 3
 
7.1%
F 2
 
4.8%
G 2
 
4.8%
T 2
 
4.8%
H 2
 
4.8%
Other values (8) 8
19.0%
Decimal Number
ValueCountFrequency (%)
1 11847
24.4%
2 6637
13.7%
3 5094
10.5%
4 4436
 
9.1%
5 4268
 
8.8%
6 3627
 
7.5%
7 3365
 
6.9%
9 3255
 
6.7%
0 3056
 
6.3%
8 3006
 
6.2%
Lowercase Letter
ValueCountFrequency (%)
s 2
28.6%
x 1
14.3%
k 1
14.3%
i 1
14.3%
o 1
14.3%
n 1
14.3%
Other Punctuation
ValueCountFrequency (%)
/ 1840
50.0%
, 1793
48.7%
. 45
 
1.2%
: 5
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 5531
99.2%
[ 46
 
0.8%
Close Punctuation
ValueCountFrequency (%)
) 5368
99.2%
] 46
 
0.8%
Space Separator
ValueCountFrequency (%)
41765
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5109
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141770
56.3%
Common 110139
43.7%
Latin 49
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11859
 
8.4%
11256
 
7.9%
10801
 
7.6%
10747
 
7.6%
10454
 
7.4%
10283
 
7.3%
10051
 
7.1%
8449
 
6.0%
7708
 
5.4%
6452
 
4.6%
Other values (479) 43710
30.8%
Latin
ValueCountFrequency (%)
B 7
14.3%
A 5
 
10.2%
L 5
 
10.2%
P 3
 
6.1%
M 3
 
6.1%
S 3
 
6.1%
F 2
 
4.1%
s 2
 
4.1%
G 2
 
4.1%
T 2
 
4.1%
Other values (14) 15
30.6%
Common
ValueCountFrequency (%)
41765
37.9%
1 11847
 
10.8%
2 6637
 
6.0%
( 5531
 
5.0%
) 5368
 
4.9%
- 5109
 
4.6%
3 5094
 
4.6%
4 4436
 
4.0%
5 4268
 
3.9%
6 3627
 
3.3%
Other values (10) 16457
 
14.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141770
56.3%
ASCII 110188
43.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
41765
37.9%
1 11847
 
10.8%
2 6637
 
6.0%
( 5531
 
5.0%
) 5368
 
4.9%
- 5109
 
4.6%
3 5094
 
4.6%
4 4436
 
4.0%
5 4268
 
3.9%
6 3627
 
3.3%
Other values (34) 16506
 
15.0%
Hangul
ValueCountFrequency (%)
11859
 
8.4%
11256
 
7.9%
10801
 
7.6%
10747
 
7.6%
10454
 
7.4%
10283
 
7.3%
10051
 
7.1%
8449
 
6.0%
7708
 
5.4%
6452
 
4.6%
Other values (479) 43710
30.8%

1월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct365
Distinct (%)3.7%
Missing131
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean59.085318
Minimum0
Maximum24875
Zeros560
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:03.745219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q337
95-th percentile116
Maximum24875
Range24875
Interquartile range (IQR)30

Descriptive statistics

Standard deviation535.58637
Coefficient of variation (CV)9.0646271
Kurtosis1126.1235
Mean59.085318
Median Absolute Deviation (MAD)12
Skewness30.679475
Sum583113
Variance286852.76
MonotonicityNot monotonic
2023-12-11T09:18:03.908162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 560
 
5.6%
5 318
 
3.2%
1 315
 
3.1%
2 313
 
3.1%
4 297
 
3.0%
10 294
 
2.9%
3 290
 
2.9%
6 277
 
2.8%
8 270
 
2.7%
9 245
 
2.5%
Other values (355) 6690
66.9%
ValueCountFrequency (%)
0 560
5.6%
1 315
3.1%
2 313
3.1%
3 290
2.9%
4 297
3.0%
5 318
3.2%
6 277
2.8%
7 243
2.4%
8 270
2.7%
9 245
2.5%
ValueCountFrequency (%)
24875 1
< 0.1%
21329 1
< 0.1%
19704 1
< 0.1%
16441 1
< 0.1%
16389 1
< 0.1%
10780 1
< 0.1%
8720 1
< 0.1%
8289 1
< 0.1%
7852 1
< 0.1%
7686 1
< 0.1%

2월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct372
Distinct (%)3.8%
Missing121
Missing (%)1.2%
Infinite0
Infinite (%)0.0%
Mean58.739549
Minimum0
Maximum26144
Zeros642
Zeros (%)6.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:04.088180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q336
95-th percentile115.1
Maximum26144
Range26144
Interquartile range (IQR)29

Descriptive statistics

Standard deviation539.65104
Coefficient of variation (CV)9.187184
Kurtosis1179.3171
Mean58.739549
Median Absolute Deviation (MAD)13
Skewness31.352346
Sum580288
Variance291223.25
MonotonicityNot monotonic
2023-12-11T09:18:04.247849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 642
 
6.4%
1 344
 
3.4%
4 327
 
3.3%
2 315
 
3.1%
5 298
 
3.0%
3 290
 
2.9%
10 286
 
2.9%
8 274
 
2.7%
7 259
 
2.6%
11 253
 
2.5%
Other values (362) 6591
65.9%
ValueCountFrequency (%)
0 642
6.4%
1 344
3.4%
2 315
3.1%
3 290
2.9%
4 327
3.3%
5 298
3.0%
6 252
 
2.5%
7 259
2.6%
8 274
2.7%
9 234
 
2.3%
ValueCountFrequency (%)
26144 1
< 0.1%
20883 1
< 0.1%
19141 1
< 0.1%
17454 1
< 0.1%
16489 1
< 0.1%
11839 1
< 0.1%
7907 1
< 0.1%
7732 1
< 0.1%
7508 1
< 0.1%
7210 1
< 0.1%

3월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct357
Distinct (%)3.6%
Missing111
Missing (%)1.1%
Infinite0
Infinite (%)0.0%
Mean56.059864
Minimum0
Maximum25047
Zeros672
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:04.398643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median16
Q334
95-th percentile110
Maximum25047
Range25047
Interquartile range (IQR)28

Descriptive statistics

Standard deviation522.40074
Coefficient of variation (CV)9.318623
Kurtosis1185.1693
Mean56.059864
Median Absolute Deviation (MAD)12
Skewness31.503958
Sum554376
Variance272902.53
MonotonicityNot monotonic
2023-12-11T09:18:04.563123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 672
 
6.7%
2 367
 
3.7%
1 350
 
3.5%
3 333
 
3.3%
4 330
 
3.3%
10 309
 
3.1%
7 297
 
3.0%
5 293
 
2.9%
6 276
 
2.8%
8 272
 
2.7%
Other values (347) 6390
63.9%
ValueCountFrequency (%)
0 672
6.7%
1 350
3.5%
2 367
3.7%
3 333
3.3%
4 330
3.3%
5 293
2.9%
6 276
2.8%
7 297
3.0%
8 272
2.7%
9 215
 
2.1%
ValueCountFrequency (%)
25047 1
< 0.1%
19933 1
< 0.1%
19160 1
< 0.1%
18317 1
< 0.1%
15086 1
< 0.1%
10660 1
< 0.1%
7851 1
< 0.1%
7512 1
< 0.1%
7280 1
< 0.1%
7187 1
< 0.1%

4월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct341
Distinct (%)3.4%
Missing104
Missing (%)1.0%
Infinite0
Infinite (%)0.0%
Mean52.966148
Minimum0
Maximum22481
Zeros662
Zeros (%)6.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:04.998464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median16
Q333
95-th percentile101
Maximum22481
Range22481
Interquartile range (IQR)26

Descriptive statistics

Standard deviation485.29794
Coefficient of variation (CV)9.1624171
Kurtosis1127.0229
Mean52.966148
Median Absolute Deviation (MAD)11
Skewness30.78119
Sum524153
Variance235514.09
MonotonicityNot monotonic
2023-12-11T09:18:05.149934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 662
 
6.6%
10 344
 
3.4%
1 319
 
3.2%
2 311
 
3.1%
4 308
 
3.1%
5 302
 
3.0%
8 281
 
2.8%
7 273
 
2.7%
6 273
 
2.7%
12 271
 
2.7%
Other values (331) 6552
65.5%
ValueCountFrequency (%)
0 662
6.6%
1 319
3.2%
2 311
3.1%
3 260
 
2.6%
4 308
3.1%
5 302
3.0%
6 273
2.7%
7 273
2.7%
8 281
2.8%
9 259
 
2.6%
ValueCountFrequency (%)
22481 1
< 0.1%
19043 1
< 0.1%
17001 1
< 0.1%
16018 1
< 0.1%
15919 1
< 0.1%
10248 1
< 0.1%
7362 1
< 0.1%
6788 1
< 0.1%
6706 1
< 0.1%
6636 1
< 0.1%

5월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct365
Distinct (%)3.7%
Missing92
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean58.643117
Minimum0
Maximum26066
Zeros619
Zeros (%)6.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:05.317875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q16
median16
Q336
95-th percentile116
Maximum26066
Range26066
Interquartile range (IQR)30

Descriptive statistics

Standard deviation546.49348
Coefficient of variation (CV)9.3189707
Kurtosis1200.9885
Mean58.643117
Median Absolute Deviation (MAD)12
Skewness31.463722
Sum581036
Variance298655.13
MonotonicityNot monotonic
2023-12-11T09:18:05.484018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 619
 
6.2%
1 354
 
3.5%
2 347
 
3.5%
7 327
 
3.3%
10 318
 
3.2%
5 314
 
3.1%
3 313
 
3.1%
4 290
 
2.9%
12 267
 
2.7%
6 249
 
2.5%
Other values (355) 6510
65.1%
ValueCountFrequency (%)
0 619
6.2%
1 354
3.5%
2 347
3.5%
3 313
3.1%
4 290
2.9%
5 314
3.1%
6 249
2.5%
7 327
3.3%
8 248
2.5%
9 246
 
2.5%
ValueCountFrequency (%)
26066 1
< 0.1%
24020 1
< 0.1%
17345 1
< 0.1%
17265 1
< 0.1%
15245 1
< 0.1%
11451 1
< 0.1%
9214 1
< 0.1%
8220 1
< 0.1%
8067 1
< 0.1%
7506 1
< 0.1%

6월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct365
Distinct (%)3.7%
Missing76
Missing (%)0.8%
Infinite0
Infinite (%)0.0%
Mean59.185611
Minimum0
Maximum25306
Zeros572
Zeros (%)5.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:05.650320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median17
Q337
95-th percentile117
Maximum25306
Range25306
Interquartile range (IQR)30

Descriptive statistics

Standard deviation532.78229
Coefficient of variation (CV)9.0018889
Kurtosis1116.2473
Mean59.185611
Median Absolute Deviation (MAD)12
Skewness30.426733
Sum587358
Variance283856.97
MonotonicityNot monotonic
2023-12-11T09:18:05.796627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 572
 
5.7%
4 310
 
3.1%
1 301
 
3.0%
10 301
 
3.0%
2 298
 
3.0%
5 296
 
3.0%
3 292
 
2.9%
7 283
 
2.8%
6 276
 
2.8%
15 248
 
2.5%
Other values (355) 6747
67.5%
ValueCountFrequency (%)
0 572
5.7%
1 301
3.0%
2 298
3.0%
3 292
2.9%
4 310
3.1%
5 296
3.0%
6 276
2.8%
7 283
2.8%
8 244
2.4%
9 236
2.4%
ValueCountFrequency (%)
25306 1
< 0.1%
20255 1
< 0.1%
18867 1
< 0.1%
17667 1
< 0.1%
15472 1
< 0.1%
11757 1
< 0.1%
8080 1
< 0.1%
8065 1
< 0.1%
7836 1
< 0.1%
7182 1
< 0.1%

7월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct371
Distinct (%)3.7%
Missing51
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean64.181827
Minimum0
Maximum27909
Zeros539
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:05.945773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median19
Q340
95-th percentile129
Maximum27909
Range27909
Interquartile range (IQR)32

Descriptive statistics

Standard deviation574.40596
Coefficient of variation (CV)8.9496667
Kurtosis1153.77
Mean64.181827
Median Absolute Deviation (MAD)14
Skewness30.698181
Sum638545
Variance329942.21
MonotonicityNot monotonic
2023-12-11T09:18:06.183893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 539
 
5.4%
5 287
 
2.9%
10 286
 
2.9%
1 264
 
2.6%
6 262
 
2.6%
2 262
 
2.6%
7 256
 
2.6%
8 255
 
2.5%
4 251
 
2.5%
3 250
 
2.5%
Other values (361) 7037
70.4%
ValueCountFrequency (%)
0 539
5.4%
1 264
2.6%
2 262
2.6%
3 250
2.5%
4 251
2.5%
5 287
2.9%
6 262
2.6%
7 256
2.6%
8 255
2.5%
9 221
2.2%
ValueCountFrequency (%)
27909 1
< 0.1%
23504 1
< 0.1%
18826 1
< 0.1%
17186 1
< 0.1%
17018 1
< 0.1%
11775 1
< 0.1%
9951 1
< 0.1%
8907 1
< 0.1%
8198 1
< 0.1%
7471 1
< 0.1%

8월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct379
Distinct (%)3.8%
Missing39
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean65.048389
Minimum0
Maximum28367
Zeros533
Zeros (%)5.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:06.345124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median19
Q340
95-th percentile124
Maximum28367
Range28367
Interquartile range (IQR)32

Descriptive statistics

Standard deviation584.49271
Coefficient of variation (CV)8.9855064
Kurtosis1098.8286
Mean65.048389
Median Absolute Deviation (MAD)14
Skewness29.883427
Sum647947
Variance341631.73
MonotonicityNot monotonic
2023-12-11T09:18:06.498992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 533
 
5.3%
5 300
 
3.0%
10 289
 
2.9%
2 282
 
2.8%
1 279
 
2.8%
6 267
 
2.7%
4 261
 
2.6%
11 247
 
2.5%
8 246
 
2.5%
7 243
 
2.4%
Other values (369) 7014
70.1%
ValueCountFrequency (%)
0 533
5.3%
1 279
2.8%
2 282
2.8%
3 239
2.4%
4 261
2.6%
5 300
3.0%
6 267
2.7%
7 243
2.4%
8 246
2.5%
9 233
2.3%
ValueCountFrequency (%)
28367 1
< 0.1%
21973 1
< 0.1%
19481 1
< 0.1%
18589 1
< 0.1%
17277 1
< 0.1%
12099 1
< 0.1%
9242 1
< 0.1%
8190 1
< 0.1%
8106 1
< 0.1%
7592 1
< 0.1%

9월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct385
Distinct (%)3.9%
Missing27
Missing (%)0.3%
Infinite0
Infinite (%)0.0%
Mean67.962298
Minimum0
Maximum30708
Zeros494
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:06.668592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median21
Q343
95-th percentile131
Maximum30708
Range30708
Interquartile range (IQR)34

Descriptive statistics

Standard deviation628.2329
Coefficient of variation (CV)9.2438443
Kurtosis1222.6209
Mean67.962298
Median Absolute Deviation (MAD)15
Skewness31.759211
Sum677788
Variance394676.58
MonotonicityNot monotonic
2023-12-11T09:18:06.840236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 494
 
4.9%
10 265
 
2.6%
5 252
 
2.5%
8 244
 
2.4%
3 243
 
2.4%
6 242
 
2.4%
4 238
 
2.4%
2 237
 
2.4%
1 231
 
2.3%
9 230
 
2.3%
Other values (375) 7297
73.0%
ValueCountFrequency (%)
0 494
4.9%
1 231
2.3%
2 237
2.4%
3 243
2.4%
4 238
2.4%
5 252
2.5%
6 242
2.4%
7 220
2.2%
8 244
2.4%
9 230
2.3%
ValueCountFrequency (%)
30708 1
< 0.1%
26644 1
< 0.1%
20935 1
< 0.1%
20026 1
< 0.1%
17911 1
< 0.1%
13001 1
< 0.1%
9924 1
< 0.1%
8831 1
< 0.1%
8764 1
< 0.1%
7854 1
< 0.1%

10월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct382
Distinct (%)3.8%
Missing14
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean66.673843
Minimum0
Maximum29563
Zeros539
Zeros (%)5.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:07.015026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q342
95-th percentile131
Maximum29563
Range29563
Interquartile range (IQR)34

Descriptive statistics

Standard deviation609.5759
Coefficient of variation (CV)9.1426543
Kurtosis1231.0569
Mean66.673843
Median Absolute Deviation (MAD)14
Skewness31.728741
Sum665805
Variance371582.78
MonotonicityNot monotonic
2023-12-11T09:18:07.180678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 539
 
5.4%
10 293
 
2.9%
2 277
 
2.8%
1 277
 
2.8%
7 262
 
2.6%
5 257
 
2.6%
6 247
 
2.5%
4 246
 
2.5%
8 239
 
2.4%
3 227
 
2.3%
Other values (372) 7122
71.2%
ValueCountFrequency (%)
0 539
5.4%
1 277
2.8%
2 277
2.8%
3 227
2.3%
4 246
2.5%
5 257
2.6%
6 247
2.5%
7 262
2.6%
8 239
2.4%
9 219
2.2%
ValueCountFrequency (%)
29563 1
< 0.1%
27033 1
< 0.1%
18901 1
< 0.1%
18740 1
< 0.1%
18090 1
< 0.1%
12254 1
< 0.1%
9072 1
< 0.1%
8893 1
< 0.1%
8399 1
< 0.1%
8256 1
< 0.1%

11월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct364
Distinct (%)3.6%
Missing10
Missing (%)0.1%
Infinite0
Infinite (%)0.0%
Mean62.488388
Minimum0
Maximum27911
Zeros515
Zeros (%)5.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:07.338795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q340
95-th percentile122
Maximum27911
Range27911
Interquartile range (IQR)32

Descriptive statistics

Standard deviation541.93662
Coefficient of variation (CV)8.6725972
Kurtosis1200.6637
Mean62.488388
Median Absolute Deviation (MAD)14
Skewness31.033991
Sum624259
Variance293695.31
MonotonicityNot monotonic
2023-12-11T09:18:07.479267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 515
 
5.1%
10 277
 
2.8%
1 270
 
2.7%
5 267
 
2.7%
3 256
 
2.6%
6 249
 
2.5%
12 248
 
2.5%
7 247
 
2.5%
2 243
 
2.4%
15 242
 
2.4%
Other values (354) 7176
71.8%
ValueCountFrequency (%)
0 515
5.1%
1 270
2.7%
2 243
2.4%
3 256
2.6%
4 236
2.4%
5 267
2.7%
6 249
2.5%
7 247
2.5%
8 242
2.4%
9 223
2.2%
ValueCountFrequency (%)
27911 1
< 0.1%
18865 1
< 0.1%
18188 1
< 0.1%
17355 1
< 0.1%
16234 1
< 0.1%
11661 1
< 0.1%
8425 1
< 0.1%
7868 1
< 0.1%
7563 1
< 0.1%
7139 1
< 0.1%

12월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct377
Distinct (%)3.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean62.0282
Minimum0
Maximum26794
Zeros554
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:07.612635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median18
Q338
95-th percentile123
Maximum26794
Range26794
Interquartile range (IQR)31

Descriptive statistics

Standard deviation563.07314
Coefficient of variation (CV)9.0776959
Kurtosis1127.3261
Mean62.0282
Median Absolute Deviation (MAD)13
Skewness30.500719
Sum620282
Variance317051.36
MonotonicityNot monotonic
2023-12-11T09:18:07.758025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 554
 
5.5%
1 318
 
3.2%
2 310
 
3.1%
8 284
 
2.8%
4 284
 
2.8%
3 282
 
2.8%
6 275
 
2.8%
10 274
 
2.7%
5 271
 
2.7%
7 262
 
2.6%
Other values (367) 6886
68.9%
ValueCountFrequency (%)
0 554
5.5%
1 318
3.2%
2 310
3.1%
3 282
2.8%
4 284
2.8%
5 271
2.7%
6 275
2.8%
7 262
2.6%
8 284
2.8%
9 256
2.6%
ValueCountFrequency (%)
26794 1
< 0.1%
22523 1
< 0.1%
19197 1
< 0.1%
18329 1
< 0.1%
16573 1
< 0.1%
11604 1
< 0.1%
8646 1
< 0.1%
8219 1
< 0.1%
7816 1
< 0.1%
7631 1
< 0.1%

사용량합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1632
Distinct (%)16.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean728.495
Minimum1
Maximum321171
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:18:07.908584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile15
Q199
median224
Q3457
95-th percentile1381.15
Maximum321171
Range321170
Interquartile range (IQR)358

Descriptive statistics

Standard deviation6606.9424
Coefficient of variation (CV)9.0693037
Kurtosis1180.4981
Mean728.495
Median Absolute Deviation (MAD)150
Skewness31.234214
Sum7284950
Variance43651688
MonotonicityNot monotonic
2023-12-11T09:18:08.079200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 81
 
0.8%
2 53
 
0.5%
3 37
 
0.4%
50 37
 
0.4%
113 35
 
0.4%
4 35
 
0.4%
76 35
 
0.4%
5 33
 
0.3%
190 33
 
0.3%
78 33
 
0.3%
Other values (1622) 9588
95.9%
ValueCountFrequency (%)
1 81
0.8%
2 53
0.5%
3 37
0.4%
4 35
0.4%
5 33
0.3%
6 26
 
0.3%
7 30
 
0.3%
8 29
 
0.3%
9 24
 
0.2%
10 29
 
0.3%
ValueCountFrequency (%)
321171 1
< 0.1%
266005 1
< 0.1%
219866 1
< 0.1%
219185 1
< 0.1%
198785 1
< 0.1%
139129 1
< 0.1%
102039 1
< 0.1%
95689 1
< 0.1%
94619 1
< 0.1%
89087 1
< 0.1%

Interactions

2023-12-11T09:18:01.350609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.114276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.312136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.257179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.239818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.224956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.203181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.656165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.723064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.678659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.692626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.879985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.260845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.437619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.418690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.382170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.328690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.311775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.295858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.314050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.736104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.795801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.756050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.767596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.957052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.342076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.514572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.488038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.455802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.398644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.382617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.366580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.432173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.817562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.867031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.831446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.901490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.036632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.442110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.593776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.557799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.527662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.474802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.461430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.441206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.516227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.900281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.938158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.901779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.999020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.116289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.530786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.668587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.630739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.596850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.541353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.548592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.509260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.596394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.974741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.012532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.982604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.076355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.199677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.607064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.747014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.701970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.670680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.610201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.639074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.585126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.985484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.075444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.091954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.053921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.158975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.542463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.692380image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.822271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.772053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.746253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.681625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.711392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.660667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.071096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.175031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.166082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.135473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.265218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.621008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.771582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.898299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.850841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.820247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.750153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.802522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.733484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.157557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.252889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.235562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.213148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.356989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.717496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.859412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.976483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.920853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.893149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.821805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.877894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.806857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.235735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.335963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.310412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.287642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.454994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.809963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.943483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:02.061112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:48.994328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.967420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.915904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.948662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.880991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.326004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.420091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.384510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.370998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.542297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.894728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.032412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:02.165112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.065717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.041926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.013561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.018101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.957147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.411258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.497864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.462271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.458226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.626405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:59.974874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.114206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:02.262686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.145349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.111189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.088236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.084558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.045667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.489885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.574373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.535100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.537370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.710051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.069369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.193008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:02.351120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:49.223723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:50.183304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:51.164642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:52.154234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:53.119396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:54.567806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:55.647900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:56.606361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:57.612442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:17:58.791474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:00.158436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:18:01.265807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:18:08.190130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
1월사용량1.0000.9500.9590.9690.9470.9950.9660.9640.9900.9460.9430.9930.972
2월사용량0.9501.0000.9880.9900.9830.9620.9870.9850.9370.9220.9860.9590.988
3월사용량0.9590.9881.0000.9920.9900.9520.9860.9860.9550.9360.9940.9460.990
4월사용량0.9690.9900.9921.0000.9880.9710.9940.9950.9630.9680.9890.9650.998
5월사용량0.9470.9830.9900.9881.0000.9660.9900.9850.9620.9600.9900.9420.987
6월사용량0.9950.9620.9520.9710.9661.0000.9840.9700.9930.9730.9590.9990.974
7월사용량0.9660.9870.9860.9940.9900.9841.0000.9990.9720.9690.9950.9740.998
8월사용량0.9640.9850.9860.9950.9850.9700.9991.0000.9860.9690.9970.9750.999
9월사용량0.9900.9370.9550.9630.9620.9930.9720.9861.0000.9550.9830.9950.981
10월사용량0.9460.9220.9360.9680.9600.9730.9690.9690.9551.0000.9580.9590.962
11월사용량0.9430.9860.9940.9890.9900.9590.9950.9970.9830.9581.0000.9640.996
12월사용량0.9930.9590.9460.9650.9420.9990.9740.9750.9950.9590.9641.0000.981
사용량합계0.9720.9880.9900.9980.9870.9740.9980.9990.9810.9620.9960.9811.000
2023-12-11T09:18:08.350142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
1월사용량1.0000.9070.8940.8880.8830.8750.8640.8520.8410.8360.8300.8300.915
2월사용량0.9071.0000.9210.8970.8840.8690.8560.8420.8290.8220.8170.8190.917
3월사용량0.8940.9211.0000.9210.9040.8860.8700.8530.8420.8340.8270.8280.925
4월사용량0.8880.8970.9211.0000.9360.9130.8900.8740.8600.8510.8480.8430.933
5월사용량0.8830.8840.9040.9361.0000.9430.9190.8980.8810.8720.8640.8660.944
6월사용량0.8750.8690.8860.9130.9431.0000.9390.9130.8970.8840.8750.8750.948
7월사용량0.8640.8560.8700.8900.9190.9391.0000.9380.9170.9010.8860.8830.949
8월사용량0.8520.8420.8530.8740.8980.9130.9381.0000.9370.9160.9000.8930.945
9월사용량0.8410.8290.8420.8600.8810.8970.9170.9371.0000.9400.9160.9060.940
10월사용량0.8360.8220.8340.8510.8720.8840.9010.9160.9401.0000.9380.9230.937
11월사용량0.8300.8170.8270.8480.8640.8750.8860.9000.9160.9381.0000.9400.930
12월사용량0.8300.8190.8280.8430.8660.8750.8830.8930.9060.9230.9401.0000.924
사용량합계0.9150.9170.9250.9330.9440.9480.9490.9450.9400.9370.9300.9241.000

Missing values

2023-12-11T09:18:02.459387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:18:02.610380image/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.
2023-12-11T09:18:02.758050image/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
33107경상남도 진주시 진성면 동부로1345번길 28-3162121192025222025223292335
14988경상남도 진주시 지수면 방어산로 56-101091010691461412910119
15662경상남도 진주시 금산면 갈전길63번길 615777981614129129125
25722경상남도 진주시 진양호로239번길 6-715141329923232421181818225
14302경상남도 진주시 일반성면 수목원로277번길 44-388788810111511109113
1294경상남도 진주시 진양호로555번길 10-1, 진주땅콩 (장대동)0110101010106
44757경상남도 진주시 대신로231번길 19-1 (상대동)403561525778899484757861804
34344경상남도 진주시 진주대로931번길 14-1402323242827353238333126360
41328경상남도 진주시 평거로 154, (20/3) 두피클리닉 (신안동)464531324648516256556047579
9970경상남도 진주시 봉래길27번길 6-7768678981012375
주소1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
3632경상남도 진주시 이반성면 길성길 139-421112232333225
41457경상남도 진주시 모덕로 53, (2/1)거성스포츠 (상대동)636361616573333635354021586
14380경상남도 진주시 향교로131번길 10-469420678101410127113
16867경상남도 진주시 명석면 남성리 58484655510128174016136
41655경상남도 진주시 봉수대길5번길 7454140374249765659535147596
57경상남도 진주시 진성면 일사로499번길 22-41000000000001
10331경상남도 진주시 진주대로1164번길 20 (상봉동) 102호5453555515146678
15749경상남도 진주시 사봉면 지사로 144-1716561110101189101812126
44419경상남도 진주시 강남로177번길 23 (칠암동)1085476811491100587286112771
48140경상남도 진주시 동진로107번길 3, (13/1) (상대동)10712084116120108150104971021061051319