Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells1786
Missing cells (%)1.3%
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 259 (2.6%) missing valuesMissing
2월사용량 has 232 (2.3%) missing valuesMissing
3월사용량 has 226 (2.3%) missing valuesMissing
4월사용량 has 222 (2.2%) missing valuesMissing
5월사용량 has 198 (2.0%) missing valuesMissing
6월사용량 has 179 (1.8%) missing valuesMissing
7월사용량 has 156 (1.6%) missing valuesMissing
8월사용량 has 132 (1.3%) missing valuesMissing
1월사용량 is highly skewed (γ1 = 84.19008056)Skewed
2월사용량 is highly skewed (γ1 = 85.78432985)Skewed
3월사용량 is highly skewed (γ1 = 86.0413703)Skewed
4월사용량 is highly skewed (γ1 = 83.22831311)Skewed
5월사용량 is highly skewed (γ1 = 86.31187833)Skewed
6월사용량 is highly skewed (γ1 = 85.78871132)Skewed
7월사용량 is highly skewed (γ1 = 86.16489133)Skewed
8월사용량 is highly skewed (γ1 = 83.58781165)Skewed
9월사용량 is highly skewed (γ1 = 83.19106697)Skewed
10월사용량 is highly skewed (γ1 = 80.35529444)Skewed
11월사용량 is highly skewed (γ1 = 80.81720373)Skewed
12월사용량 is highly skewed (γ1 = 84.98271446)Skewed
사용량합계 is highly skewed (γ1 = 85.23933094)Skewed
1월사용량 has 387 (3.9%) zerosZeros
2월사용량 has 494 (4.9%) zerosZeros
3월사용량 has 555 (5.5%) zerosZeros
4월사용량 has 561 (5.6%) zerosZeros
5월사용량 has 594 (5.9%) zerosZeros
6월사용량 has 525 (5.2%) zerosZeros
7월사용량 has 486 (4.9%) zerosZeros
8월사용량 has 460 (4.6%) zerosZeros
9월사용량 has 476 (4.8%) zerosZeros
10월사용량 has 434 (4.3%) zerosZeros
11월사용량 has 446 (4.5%) zerosZeros
12월사용량 has 452 (4.5%) zerosZeros

Reproduction

Analysis started2023-12-11 00:15:09.014088
Analysis finished2023-12-11 00:15:32.718145
Duration23.7 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

주소
Text

Distinct9945
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-11T09:15:32.965483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length51
Median length44
Mean length25.3862
Min length14

Characters and Unicode

Total characters253862
Distinct characters491
Distinct categories9 ?
Distinct scripts4 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9892 ?
Unique (%)98.9%

Sample

1st row경상남동 진주시 강남로247번길 3
2nd row경상남동 진주시 향교로131번길 9-6
3rd row경상남동 진주시 진양호로625번길 3
4th row경상남동 진주시 진산로10번길 37-15
5th row경상남동 진주시 상봉대룡길 27-1, (22/4) (상봉동)
ValueCountFrequency (%)
경상남동 10000
 
20.0%
진주시 10000
 
20.0%
상대동 593
 
1.2%
문산읍 421
 
0.8%
신안동 380
 
0.8%
금산면 354
 
0.7%
상봉동 351
 
0.7%
대곡면 307
 
0.6%
집현면 271
 
0.5%
명석면 263
 
0.5%
Other values (5352) 27045
54.1%
2023-12-11T09:15:33.723195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
42065
 
16.6%
14654
 
5.8%
1 12198
 
4.8%
11997
 
4.7%
11334
 
4.5%
10885
 
4.3%
10709
 
4.2%
10437
 
4.1%
10049
 
4.0%
8576
 
3.4%
Other values (481) 110958
43.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 141818
55.9%
Decimal Number 49023
 
19.3%
Space Separator 42065
 
16.6%
Open Punctuation 5958
 
2.3%
Close Punctuation 5804
 
2.3%
Dash Punctuation 5065
 
2.0%
Other Punctuation 4111
 
1.6%
Uppercase Letter 16
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
14654
 
10.3%
11997
 
8.5%
11334
 
8.0%
10885
 
7.7%
10709
 
7.6%
10437
 
7.4%
10049
 
7.1%
8576
 
6.0%
7772
 
5.5%
6545
 
4.6%
Other values (447) 38860
27.4%
Uppercase Letter
ValueCountFrequency (%)
B 3
18.8%
K 2
12.5%
G 2
12.5%
L 2
12.5%
A 1
 
6.2%
Q 1
 
6.2%
O 1
 
6.2%
S 1
 
6.2%
P 1
 
6.2%
C 1
 
6.2%
Decimal Number
ValueCountFrequency (%)
1 12198
24.9%
2 6629
13.5%
3 5151
10.5%
4 4451
 
9.1%
5 4416
 
9.0%
6 3652
 
7.4%
7 3347
 
6.8%
9 3191
 
6.5%
8 3008
 
6.1%
0 2980
 
6.1%
Other Punctuation
ValueCountFrequency (%)
/ 2040
49.6%
, 2007
48.8%
. 57
 
1.4%
: 4
 
0.1%
? 3
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 5924
99.4%
[ 34
 
0.6%
Close Punctuation
ValueCountFrequency (%)
) 5770
99.4%
] 34
 
0.6%
Math Symbol
ValueCountFrequency (%)
> 1
50.0%
< 1
50.0%
Space Separator
ValueCountFrequency (%)
42065
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 5065
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 141817
55.9%
Common 112028
44.1%
Latin 16
 
< 0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
14654
 
10.3%
11997
 
8.5%
11334
 
8.0%
10885
 
7.7%
10709
 
7.6%
10437
 
7.4%
10049
 
7.1%
8576
 
6.0%
7772
 
5.5%
6545
 
4.6%
Other values (446) 38859
27.4%
Common
ValueCountFrequency (%)
42065
37.5%
1 12198
 
10.9%
2 6629
 
5.9%
( 5924
 
5.3%
) 5770
 
5.2%
3 5151
 
4.6%
- 5065
 
4.5%
4 4451
 
4.0%
5 4416
 
3.9%
6 3652
 
3.3%
Other values (13) 16707
 
14.9%
Latin
ValueCountFrequency (%)
B 3
18.8%
K 2
12.5%
G 2
12.5%
L 2
12.5%
A 1
 
6.2%
Q 1
 
6.2%
O 1
 
6.2%
S 1
 
6.2%
P 1
 
6.2%
C 1
 
6.2%
Han
ValueCountFrequency (%)
1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 141817
55.9%
ASCII 112044
44.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
42065
37.5%
1 12198
 
10.9%
2 6629
 
5.9%
( 5924
 
5.3%
) 5770
 
5.1%
3 5151
 
4.6%
- 5065
 
4.5%
4 4451
 
4.0%
5 4416
 
3.9%
6 3652
 
3.3%
Other values (24) 16723
 
14.9%
Hangul
ValueCountFrequency (%)
14654
 
10.3%
11997
 
8.5%
11334
 
8.0%
10885
 
7.7%
10709
 
7.6%
10437
 
7.4%
10049
 
7.1%
8576
 
6.0%
7772
 
5.5%
6545
 
4.6%
Other values (446) 38859
27.4%
CJK
ValueCountFrequency (%)
1
100.0%

1월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct385
Distinct (%)4.0%
Missing259
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean69.197824
Minimum0
Maximum125820
Zeros387
Zeros (%)3.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:33.836979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median20
Q341
95-th percentile120
Maximum125820
Range125820
Interquartile range (IQR)32

Descriptive statistics

Standard deviation1351.8788
Coefficient of variation (CV)19.536435
Kurtosis7715.9222
Mean69.197824
Median Absolute Deviation (MAD)14
Skewness84.190081
Sum674056
Variance1827576.3
MonotonicityNot monotonic
2023-12-11T09:15:33.952187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 387
 
3.9%
10 339
 
3.4%
3 265
 
2.6%
6 259
 
2.6%
5 258
 
2.6%
4 257
 
2.6%
1 247
 
2.5%
15 245
 
2.5%
2 244
 
2.4%
7 233
 
2.3%
Other values (375) 7007
70.1%
(Missing) 259
 
2.6%
ValueCountFrequency (%)
0 387
3.9%
1 247
2.5%
2 244
2.4%
3 265
2.6%
4 257
2.6%
5 258
2.6%
6 259
2.6%
7 233
2.3%
8 225
2.2%
9 179
1.8%
ValueCountFrequency (%)
125820 1
< 0.1%
29140 1
< 0.1%
18960 1
< 0.1%
13371 1
< 0.1%
10096 1
< 0.1%
9001 1
< 0.1%
7263 1
< 0.1%
6126 1
< 0.1%
5595 1
< 0.1%
5539 1
< 0.1%

2월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct396
Distinct (%)4.1%
Missing232
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean71.31767
Minimum0
Maximum137480
Zeros494
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:34.092625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q342
95-th percentile123
Maximum137480
Range137480
Interquartile range (IQR)34

Descriptive statistics

Standard deviation1465.1373
Coefficient of variation (CV)20.543819
Kurtosis7945.5822
Mean71.31767
Median Absolute Deviation (MAD)14
Skewness85.78433
Sum696631
Variance2146627.3
MonotonicityNot monotonic
2023-12-11T09:15:34.221154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 494
 
4.9%
10 287
 
2.9%
1 271
 
2.7%
2 265
 
2.6%
4 254
 
2.5%
3 253
 
2.5%
12 246
 
2.5%
15 240
 
2.4%
7 240
 
2.4%
6 234
 
2.3%
Other values (386) 6984
69.8%
(Missing) 232
 
2.3%
ValueCountFrequency (%)
0 494
4.9%
1 271
2.7%
2 265
2.6%
3 253
2.5%
4 254
2.5%
5 230
2.3%
6 234
2.3%
7 240
2.4%
8 223
2.2%
9 219
2.2%
ValueCountFrequency (%)
137480 1
< 0.1%
30320 1
< 0.1%
19000 1
< 0.1%
13169 1
< 0.1%
10096 1
< 0.1%
9015 1
< 0.1%
7027 1
< 0.1%
5595 1
< 0.1%
5463 1
< 0.1%
5419 1
< 0.1%

3월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct395
Distinct (%)4.0%
Missing226
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean72.827502
Minimum0
Maximum139540
Zeros555
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:34.341490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110
median21
Q344
95-th percentile126
Maximum139540
Range139540
Interquartile range (IQR)34

Descriptive statistics

Standard deviation1484.8854
Coefficient of variation (CV)20.389076
Kurtosis7985.8878
Mean72.827502
Median Absolute Deviation (MAD)14
Skewness86.04137
Sum711816
Variance2204884.7
MonotonicityNot monotonic
2023-12-11T09:15:34.466943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 555
 
5.5%
10 284
 
2.8%
7 227
 
2.3%
12 227
 
2.3%
11 226
 
2.3%
8 223
 
2.2%
6 222
 
2.2%
15 220
 
2.2%
13 219
 
2.2%
5 218
 
2.2%
Other values (385) 7153
71.5%
(Missing) 226
 
2.3%
ValueCountFrequency (%)
0 555
5.5%
1 187
 
1.9%
2 203
 
2.0%
3 209
 
2.1%
4 197
 
2.0%
5 218
 
2.2%
6 222
 
2.2%
7 227
2.3%
8 223
2.2%
9 194
 
1.9%
ValueCountFrequency (%)
139540 1
< 0.1%
29940 1
< 0.1%
19150 1
< 0.1%
13452 1
< 0.1%
9845 1
< 0.1%
8943 1
< 0.1%
7180 1
< 0.1%
6765 1
< 0.1%
6616 1
< 0.1%
6277 1
< 0.1%

4월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct370
Distinct (%)3.8%
Missing222
Missing (%)2.2%
Infinite0
Infinite (%)0.0%
Mean64.642463
Minimum0
Maximum123010
Zeros561
Zeros (%)5.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:34.709546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median18
Q338
95-th percentile113
Maximum123010
Range123010
Interquartile range (IQR)31

Descriptive statistics

Standard deviation1327.3241
Coefficient of variation (CV)20.533316
Kurtosis7567.6216
Mean64.642463
Median Absolute Deviation (MAD)13
Skewness83.228313
Sum632074
Variance1761789.4
MonotonicityNot monotonic
2023-12-11T09:15:34.876654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 561
 
5.6%
1 322
 
3.2%
3 311
 
3.1%
2 301
 
3.0%
10 296
 
3.0%
7 264
 
2.6%
4 261
 
2.6%
5 258
 
2.6%
8 253
 
2.5%
12 247
 
2.5%
Other values (360) 6704
67.0%
ValueCountFrequency (%)
0 561
5.6%
1 322
3.2%
2 301
3.0%
3 311
3.1%
4 261
2.6%
5 258
2.6%
6 243
2.4%
7 264
2.6%
8 253
2.5%
9 225
2.2%
ValueCountFrequency (%)
123010 1
< 0.1%
28170 1
< 0.1%
26170 1
< 0.1%
11814 1
< 0.1%
8413 1
< 0.1%
8303 1
< 0.1%
6774 1
< 0.1%
6118 1
< 0.1%
5575 1
< 0.1%
4786 1
< 0.1%

5월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct374
Distinct (%)3.8%
Missing198
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean68.19292
Minimum0
Maximum138030
Zeros594
Zeros (%)5.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:35.035338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17
median19
Q339
95-th percentile119
Maximum138030
Range138030
Interquartile range (IQR)32

Descriptive statistics

Standard deviation1466.1905
Coefficient of variation (CV)21.500627
Kurtosis8022.3845
Mean68.19292
Median Absolute Deviation (MAD)14
Skewness86.311878
Sum668427
Variance2149714.7
MonotonicityNot monotonic
2023-12-11T09:15:35.177970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 594
 
5.9%
2 302
 
3.0%
10 302
 
3.0%
5 277
 
2.8%
3 274
 
2.7%
1 271
 
2.7%
4 262
 
2.6%
11 247
 
2.5%
7 243
 
2.4%
6 241
 
2.4%
Other values (364) 6789
67.9%
ValueCountFrequency (%)
0 594
5.9%
1 271
2.7%
2 302
3.0%
3 274
2.7%
4 262
2.6%
5 277
2.8%
6 241
2.4%
7 243
2.4%
8 234
 
2.3%
9 225
 
2.2%
ValueCountFrequency (%)
138030 1
< 0.1%
29730 1
< 0.1%
20560 1
< 0.1%
12976 1
< 0.1%
9287 1
< 0.1%
8936 1
< 0.1%
7290 1
< 0.1%
5993 1
< 0.1%
5803 1
< 0.1%
5234 1
< 0.1%

6월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct391
Distinct (%)4.0%
Missing179
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean69.797882
Minimum0
Maximum134110
Zeros525
Zeros (%)5.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:35.319891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18
median20
Q341
95-th percentile120
Maximum134110
Range134110
Interquartile range (IQR)33

Descriptive statistics

Standard deviation1426.6675
Coefficient of variation (CV)20.439982
Kurtosis7959.0608
Mean69.797882
Median Absolute Deviation (MAD)14
Skewness85.788711
Sum685485
Variance2035380.1
MonotonicityNot monotonic
2023-12-11T09:15:35.469448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 525
 
5.2%
10 266
 
2.7%
2 265
 
2.6%
5 264
 
2.6%
7 260
 
2.6%
3 256
 
2.6%
6 244
 
2.4%
1 238
 
2.4%
12 232
 
2.3%
4 231
 
2.3%
Other values (381) 7040
70.4%
ValueCountFrequency (%)
0 525
5.2%
1 238
2.4%
2 265
2.6%
3 256
2.6%
4 231
2.3%
5 264
2.6%
6 244
2.4%
7 260
2.6%
8 228
2.3%
9 217
2.2%
ValueCountFrequency (%)
134110 1
< 0.1%
29200 1
< 0.1%
19293 1
< 0.1%
13023 1
< 0.1%
9295 1
< 0.1%
8949 1
< 0.1%
7850 1
< 0.1%
6983 1
< 0.1%
5845 1
< 0.1%
5715 1
< 0.1%

7월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct381
Distinct (%)3.9%
Missing156
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean72.49807
Minimum0
Maximum138900
Zeros486
Zeros (%)4.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:35.638831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q19
median21
Q344
95-th percentile124
Maximum138900
Range138900
Interquartile range (IQR)35

Descriptive statistics

Standard deviation1474.0211
Coefficient of variation (CV)20.331867
Kurtosis8017.1876
Mean72.49807
Median Absolute Deviation (MAD)15
Skewness86.164891
Sum713671
Variance2172738.3
MonotonicityNot monotonic
2023-12-11T09:15:35.833893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 486
 
4.9%
20 257
 
2.6%
10 255
 
2.5%
5 247
 
2.5%
3 247
 
2.5%
6 238
 
2.4%
4 233
 
2.3%
7 230
 
2.3%
15 228
 
2.3%
13 228
 
2.3%
Other values (371) 7195
72.0%
ValueCountFrequency (%)
0 486
4.9%
1 226
2.3%
2 202
2.0%
3 247
2.5%
4 233
2.3%
5 247
2.5%
6 238
2.4%
7 230
2.3%
8 210
2.1%
9 208
2.1%
ValueCountFrequency (%)
138900 1
< 0.1%
30020 1
< 0.1%
18486 1
< 0.1%
14545 1
< 0.1%
10414 1
< 0.1%
9274 1
< 0.1%
8463 1
< 0.1%
6274 1
< 0.1%
5941 1
< 0.1%
5772 1
< 0.1%

8월사용량
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct393
Distinct (%)4.0%
Missing132
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean72.750203
Minimum0
Maximum128300
Zeros460
Zeros (%)4.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:36.058344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median23
Q346
95-th percentile126
Maximum128300
Range128300
Interquartile range (IQR)36

Descriptive statistics

Standard deviation1377.9929
Coefficient of variation (CV)18.941431
Kurtosis7639.4494
Mean72.750203
Median Absolute Deviation (MAD)16
Skewness83.587812
Sum717899
Variance1898864.5
MonotonicityNot monotonic
2023-12-11T09:15:36.290408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 460
 
4.6%
10 270
 
2.7%
2 242
 
2.4%
5 240
 
2.4%
8 233
 
2.3%
1 230
 
2.3%
20 225
 
2.2%
4 223
 
2.2%
12 221
 
2.2%
3 220
 
2.2%
Other values (383) 7304
73.0%
ValueCountFrequency (%)
0 460
4.6%
1 230
2.3%
2 242
2.4%
3 220
2.2%
4 223
2.2%
5 240
2.4%
6 186
1.9%
7 216
2.2%
8 233
2.3%
9 192
1.9%
ValueCountFrequency (%)
128300 1
< 0.1%
33140 1
< 0.1%
18622 1
< 0.1%
14348 1
< 0.1%
10271 1
< 0.1%
9247 1
< 0.1%
8880 1
< 0.1%
6482 1
< 0.1%
5862 1
< 0.1%
5588 1
< 0.1%

9월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct396
Distinct (%)4.0%
Missing87
Missing (%)0.9%
Infinite0
Infinite (%)0.0%
Mean75.055483
Minimum0
Maximum129670
Zeros476
Zeros (%)4.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:36.475266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median24
Q349
95-th percentile129.4
Maximum129670
Range129670
Interquartile range (IQR)39

Descriptive statistics

Standard deviation1392.5848
Coefficient of variation (CV)18.554071
Kurtosis7603.8916
Mean75.055483
Median Absolute Deviation (MAD)17
Skewness83.191067
Sum744025
Variance1939292.3
MonotonicityNot monotonic
2023-12-11T09:15:36.644210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 476
 
4.8%
4 230
 
2.3%
10 228
 
2.3%
15 216
 
2.2%
7 214
 
2.1%
1 213
 
2.1%
5 213
 
2.1%
20 212
 
2.1%
2 211
 
2.1%
8 211
 
2.1%
Other values (386) 7489
74.9%
ValueCountFrequency (%)
0 476
4.8%
1 213
2.1%
2 211
2.1%
3 195
1.9%
4 230
2.3%
5 213
2.1%
6 202
2.0%
7 214
2.1%
8 211
2.1%
9 187
 
1.9%
ValueCountFrequency (%)
129670 1
< 0.1%
32230 1
< 0.1%
19922 1
< 0.1%
15896 1
< 0.1%
10834 1
< 0.1%
10155 1
< 0.1%
9262 1
< 0.1%
7389 1
< 0.1%
6930 1
< 0.1%
6553 1
< 0.1%

10월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct401
Distinct (%)4.0%
Missing49
Missing (%)0.5%
Infinite0
Infinite (%)0.0%
Mean75.700533
Minimum0
Maximum121430
Zeros434
Zeros (%)4.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:36.820104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q111
median25
Q349
95-th percentile130
Maximum121430
Range121430
Interquartile range (IQR)38

Descriptive statistics

Standard deviation1322.3287
Coefficient of variation (CV)17.467891
Kurtosis7191.0858
Mean75.700533
Median Absolute Deviation (MAD)16
Skewness80.355294
Sum753296
Variance1748553.1
MonotonicityNot monotonic
2023-12-11T09:15:37.001281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 434
 
4.3%
10 241
 
2.4%
15 226
 
2.3%
12 221
 
2.2%
3 220
 
2.2%
20 216
 
2.2%
6 215
 
2.1%
5 205
 
2.1%
8 202
 
2.0%
13 196
 
2.0%
Other values (391) 7575
75.8%
ValueCountFrequency (%)
0 434
4.3%
1 177
1.8%
2 171
 
1.7%
3 220
2.2%
4 171
 
1.7%
5 205
2.1%
6 215
2.1%
7 182
1.8%
8 202
2.0%
9 167
 
1.7%
ValueCountFrequency (%)
121430 1
< 0.1%
35610 1
< 0.1%
19829 1
< 0.1%
15627 1
< 0.1%
10563 1
< 0.1%
9260 1
< 0.1%
9130 1
< 0.1%
7415 1
< 0.1%
6453 1
< 0.1%
6239 1
< 0.1%

11월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct392
Distinct (%)3.9%
Missing45
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean69.878252
Minimum0
Maximum105280
Zeros446
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:37.187879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q110
median23
Q346
95-th percentile127
Maximum105280
Range105280
Interquartile range (IQR)36

Descriptive statistics

Standard deviation1141.5502
Coefficient of variation (CV)16.336273
Kurtosis7291.755
Mean69.878252
Median Absolute Deviation (MAD)16
Skewness80.817204
Sum695638
Variance1303136.8
MonotonicityNot monotonic
2023-12-11T09:15:37.357246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 446
 
4.5%
10 310
 
3.1%
20 241
 
2.4%
2 236
 
2.4%
15 230
 
2.3%
3 228
 
2.3%
5 225
 
2.2%
1 218
 
2.2%
7 217
 
2.2%
13 214
 
2.1%
Other values (382) 7390
73.9%
ValueCountFrequency (%)
0 446
4.5%
1 218
2.2%
2 236
2.4%
3 228
2.3%
4 205
2.1%
5 225
2.2%
6 203
2.0%
7 217
2.2%
8 203
2.0%
9 165
 
1.7%
ValueCountFrequency (%)
105280 1
< 0.1%
27340 1
< 0.1%
17115 1
< 0.1%
14522 1
< 0.1%
9347 1
< 0.1%
8449 1
< 0.1%
8302 1
< 0.1%
6636 1
< 0.1%
6189 1
< 0.1%
5911 1
< 0.1%

12월사용량
Real number (ℝ)

HIGH CORRELATION  SKEWED  ZEROS 

Distinct395
Distinct (%)4.0%
Missing1
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean67.493649
Minimum0
Maximum109780
Zeros452
Zeros (%)4.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:37.846032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1
Q18
median20
Q343
95-th percentile122
Maximum109780
Range109780
Interquartile range (IQR)35

Descriptive statistics

Standard deviation1163.467
Coefficient of variation (CV)17.23817
Kurtosis7924.1138
Mean67.493649
Median Absolute Deviation (MAD)14
Skewness84.982714
Sum674869
Variance1353655.4
MonotonicityNot monotonic
2023-12-11T09:15:38.020867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 452
 
4.5%
2 314
 
3.1%
10 298
 
3.0%
5 274
 
2.7%
4 263
 
2.6%
3 261
 
2.6%
1 256
 
2.6%
6 243
 
2.4%
7 241
 
2.4%
15 227
 
2.3%
Other values (385) 7170
71.7%
ValueCountFrequency (%)
0 452
4.5%
1 256
2.6%
2 314
3.1%
3 261
2.6%
4 263
2.6%
5 274
2.7%
6 243
2.4%
7 241
2.4%
8 205
2.1%
9 219
2.2%
ValueCountFrequency (%)
109780 1
< 0.1%
17880 1
< 0.1%
17308 1
< 0.1%
13903 1
< 0.1%
9671 1
< 0.1%
9088 1
< 0.1%
8445 1
< 0.1%
6353 1
< 0.1%
6020 1
< 0.1%
5641 1
< 0.1%

사용량합계
Real number (ℝ)

HIGH CORRELATION  SKEWED 

Distinct1681
Distinct (%)16.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean836.7887
Minimum1
Maximum1531350
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-11T09:15:38.166587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q1120
median258
Q3514
95-th percentile1398.1
Maximum1531350
Range1531349
Interquartile range (IQR)394

Descriptive statistics

Standard deviation16242.863
Coefficient of variation (CV)19.41095
Kurtosis7913.4661
Mean836.7887
Median Absolute Deviation (MAD)169
Skewness85.239331
Sum8367887
Variance2.6383061 × 108
MonotonicityNot monotonic
2023-12-11T09:15:38.348224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 53
 
0.5%
2 38
 
0.4%
73 34
 
0.3%
4 34
 
0.3%
151 31
 
0.3%
36 31
 
0.3%
78 29
 
0.3%
49 28
 
0.3%
84 28
 
0.3%
127 28
 
0.3%
Other values (1671) 9666
96.7%
ValueCountFrequency (%)
1 53
0.5%
2 38
0.4%
3 28
0.3%
4 34
0.3%
5 19
 
0.2%
6 20
 
0.2%
7 17
 
0.2%
8 22
0.2%
9 15
 
0.1%
10 24
0.2%
ValueCountFrequency (%)
1531350 1
< 0.1%
350720 1
< 0.1%
236415 1
< 0.1%
166646 1
< 0.1%
117230 1
< 0.1%
109522 1
< 0.1%
95866 1
< 0.1%
73761 1
< 0.1%
72165 1
< 0.1%
69231 1
< 0.1%

Interactions

2023-12-11T09:15:30.333225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:12.876640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.274005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.514612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.904589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.200199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.390333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.159331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.663208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.099135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.529282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.383382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.795661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.457303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:12.954206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.362967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.614135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.020062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.288661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.506759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.274914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.757192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.194404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.624997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.478887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.907990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.593243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.044279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.454692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.707228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.135885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.384962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.633735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.389188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.869956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.397866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.742697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.600011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.009982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.720356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.132696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.561440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.801522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.240217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.472040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.072961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.485926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.975953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.496603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.847329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.721514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.104483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.853179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.215357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.652139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.918969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.344792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.563313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.169868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.589364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.068246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.601319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.940202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.838697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.198225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.991225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.300902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.740493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.010947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.435750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.653944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.278846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.716589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.171822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.700018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:26.039297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.972706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.327584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.134631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.386037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.831051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.101864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.532326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.743332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.377612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.829221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.325567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.788722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:26.146507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.082194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.490767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.284074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.472321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:14.924216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.223894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.623478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.824473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.488443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.925032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.449299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:24.901066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:26.571761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.188447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.591960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.446659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.558748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.009299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.327842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.731466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.926455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.607857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.105757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.565269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.004377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:26.685400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.293216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.708120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.561373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.648784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.104487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.421869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.827991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.017395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.744166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.199677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.693050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.112863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:26.873663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.387558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.828420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.687505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.734815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.202926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.557617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:17.916165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.107655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.864113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.324130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.789474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.225113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.020216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.478832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:29.941844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.834815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.831242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.301433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.666706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.000093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.197540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:20.965714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.418450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.888590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.313834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.141912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.564715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.061880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:31.966157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:13.912794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:15.391050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:16.772180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:18.092899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:19.290198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:21.052162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:22.530026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:23.986628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:25.420847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:27.241245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:28.655531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:15:30.182210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:15:38.497097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
1월사용량1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
2월사용량0.9961.0001.0001.0001.0001.0000.9960.9960.9960.9960.9960.8420.996
3월사용량0.9961.0001.0001.0001.0001.0000.9960.9960.9960.9960.9960.8420.996
4월사용량0.8631.0001.0001.0001.0001.0000.8630.8630.8630.8630.8630.9950.863
5월사용량0.9961.0001.0001.0001.0001.0000.9960.9960.9960.9960.9960.8420.996
6월사용량0.9961.0001.0001.0001.0001.0000.9960.9960.9960.9960.9960.8420.996
7월사용량1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
8월사용량1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
9월사용량1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
10월사용량1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
11월사용량1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
12월사용량1.0000.8420.8420.9950.8420.8421.0001.0001.0001.0001.0001.0001.000
사용량합계1.0000.9960.9960.8630.9960.9961.0001.0001.0001.0001.0001.0001.000
2023-12-11T09:15:38.635735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
1월사용량1.0000.9150.8460.8400.8560.8590.8530.8490.8330.8340.8300.8310.914
2월사용량0.9151.0000.8720.8410.8450.8450.8380.8320.8140.8130.8110.8130.907
3월사용량0.8460.8721.0000.8360.8240.8180.8110.8010.7860.7870.7810.7800.886
4월사용량0.8400.8410.8361.0000.9030.8730.8520.8360.8200.8160.8130.8110.907
5월사용량0.8560.8450.8240.9031.0000.9260.8980.8750.8560.8480.8420.8420.928
6월사용량0.8590.8450.8180.8730.9261.0000.9360.9050.8820.8740.8630.8610.938
7월사용량0.8530.8380.8110.8520.8980.9361.0000.9330.9030.8940.8830.8790.939
8월사용량0.8490.8320.8010.8360.8750.9050.9331.0000.9340.9160.9000.8910.941
9월사용량0.8330.8140.7860.8200.8560.8820.9030.9341.0000.9390.9120.8950.933
10월사용량0.8340.8130.7870.8160.8480.8740.8940.9160.9391.0000.9350.9120.933
11월사용량0.8300.8110.7810.8130.8420.8630.8830.9000.9120.9351.0000.9350.927
12월사용량0.8310.8130.7800.8110.8420.8610.8790.8910.8950.9120.9351.0000.916
사용량합계0.9140.9070.8860.9070.9280.9380.9390.9410.9330.9330.9270.9161.000

Missing values

2023-12-11T09:15:32.134593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:15:32.337909image/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.
2023-12-11T09:15:32.532857image/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
15752경상남동 진주시 강남로247번길 310108171510111620151615163
39405경상남동 진주시 향교로131번길 9-6363249446056737477647261698
36975경상남동 진주시 진양호로625번길 3324448344346535156665648577
11799경상남동 진주시 진산로10번길 37-1561214108820781377120
29267경상남동 진주시 상봉대룡길 27-1, (22/4) (상봉동)272729224130283239282627356
38751경상남동 진주시 창렬로43번길 8, (13/1 (인사동)544552425153576272595359659
25658경상남동 진주시 돗골로117번길 18 (상대동)192237242023212225242133291
29269경상남동 진주시 의곡길 36-1192835192523264336392934356
46949경상남동 진주시 가호로 13 (가좌동)21983013328824791862199223232811355430082526184330897
10768경상남동 진주시 망경남길24번길 7-151151187679881712109
주소1월사용량2월사용량3월사용량4월사용량5월사용량6월사용량7월사용량8월사용량9월사용량10월사용량11월사용량12월사용량사용량합계
2940경상남동 진주시 내동면 산유로456번길 54-1700211154535229
12824경상남동 진주시 진양호로 409-5 (신안동)105855582817131314131
25961경상남동 진주시 북장대로69번길 6-3, (7/2) (상봉동)201718162823301224393534296
20886경상남동 진주시 일반성면 수목원로277번길 12-6101325171921202323201814223
28327경상남동 진주시 비봉로97번길 15-8282432272521273236392521337
5740경상남동 진주시 월아산로 2083, 동명모터펌프 (초전동)230295346516257
44180경상남동 진주시 도동로223번길 19 (하대동)91911321061111039511415910690941292
41267경상남동 진주시 도동로263번길 10-5 (하대동)535594779563617175676963843
32283경상남동 진주시 창렬로 199263226343037273653504529425
32850경상남동 진주시 모덕로197번길 4-2428637311113153840455329440