Overview

Dataset statistics

Number of variables18
Number of observations3951
Missing cells2087
Missing cells (%)2.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory602.0 KiB
Average record size in memory156.0 B

Variable types

Numeric11
Categorical5
Text2

Dataset

Description부산광역시 상수도사업본부에서 상하수도 요금 계산 및 징수를 위해 운영하는 수용가정보시스템에 사용되는 검침정보 자료입니다.(사업소코드, 동코드, 관리번호, 가구수, 구경, 사용월수, 사용량 등)
Author부산광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15100354/fileData.do

Alerts

다량관리년도 has constant value ""Constant
사업소명 is highly overall correlated with 사업소코드 and 2 other fieldsHigh correlation
구명 is highly overall correlated with 사업소코드 and 3 other fieldsHigh correlation
연번 is highly overall correlated with 구분High correlation
사업소코드 is highly overall correlated with 사업소명 and 1 other fieldsHigh correlation
구코드 is highly overall correlated with 사업소명 and 1 other fieldsHigh correlation
동코드 is highly overall correlated with 구명High correlation
가구수 is highly overall correlated with 구경(mm) and 4 other fieldsHigh correlation
구경(mm) is highly overall correlated with 가구수 and 5 other fieldsHigh correlation
사용량 is highly overall correlated with 가구수 and 4 other fieldsHigh correlation
평균사용량 is highly overall correlated with 가구수 and 4 other fieldsHigh correlation
총사용금액 is highly overall correlated with 가구수 and 4 other fieldsHigh correlation
월평균사용금액 is highly overall correlated with 가구수 and 4 other fieldsHigh correlation
상수도업종 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
상수도업종 is highly imbalanced (52.5%)Imbalance
가구수 has 2087 (52.8%) missing valuesMissing
연번 has unique valuesUnique
가구수 has 158 (4.0%) zerosZeros

Reproduction

Analysis started2024-03-14 14:26:31.790584
Analysis finished2024-03-14 14:26:59.964520
Duration28.17 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct3951
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1976
Minimum1
Maximum3951
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:00.169984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile198.5
Q1988.5
median1976
Q32963.5
95-th percentile3753.5
Maximum3951
Range3950
Interquartile range (IQR)1975

Descriptive statistics

Standard deviation1140.6998
Coefficient of variation (CV)0.57727722
Kurtosis-1.2
Mean1976
Median Absolute Deviation (MAD)988
Skewness0
Sum7807176
Variance1301196
MonotonicityStrictly increasing
2024-03-14T23:27:00.626192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
< 0.1%
2626 1
 
< 0.1%
2628 1
 
< 0.1%
2629 1
 
< 0.1%
2630 1
 
< 0.1%
2631 1
 
< 0.1%
2632 1
 
< 0.1%
2633 1
 
< 0.1%
2634 1
 
< 0.1%
2635 1
 
< 0.1%
Other values (3941) 3941
99.7%
ValueCountFrequency (%)
1 1
< 0.1%
2 1
< 0.1%
3 1
< 0.1%
4 1
< 0.1%
5 1
< 0.1%
6 1
< 0.1%
7 1
< 0.1%
8 1
< 0.1%
9 1
< 0.1%
10 1
< 0.1%
ValueCountFrequency (%)
3951 1
< 0.1%
3950 1
< 0.1%
3949 1
< 0.1%
3948 1
< 0.1%
3947 1
< 0.1%
3946 1
< 0.1%
3945 1
< 0.1%
3944 1
< 0.1%
3943 1
< 0.1%
3942 1
< 0.1%

다량관리년도
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
2023
3951 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023
2nd row2023
3rd row2023
4th row2023
5th row2023

Common Values

ValueCountFrequency (%)
2023 3951
100.0%

Length

2024-03-14T23:27:01.041886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:27:01.347058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023 3951
100.0%
Distinct3079
Distinct (%)77.9%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
2024-03-14T23:27:02.666872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters23706
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2362 ?
Unique (%)59.8%

Sample

1st row*15*08
2nd row*51*19
3rd row*51*24
4th row*51*25
5th row*51*77
ValueCountFrequency (%)
93*61 5
 
0.1%
10*00 5
 
0.1%
30*38 5
 
0.1%
12*02 4
 
0.1%
12*24 4
 
0.1%
12*30 4
 
0.1%
00*92 4
 
0.1%
74*68 4
 
0.1%
13*59 4
 
0.1%
13*56 4
 
0.1%
Other values (3069) 3908
98.9%
2024-03-14T23:27:04.166872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
* 7902
33.3%
9 1860
 
7.8%
0 1854
 
7.8%
1 1826
 
7.7%
3 1720
 
7.3%
2 1675
 
7.1%
8 1449
 
6.1%
5 1447
 
6.1%
7 1433
 
6.0%
4 1328
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 15804
66.7%
Other Punctuation 7902
33.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
9 1860
11.8%
0 1854
11.7%
1 1826
11.6%
3 1720
10.9%
2 1675
10.6%
8 1449
9.2%
5 1447
9.2%
7 1433
9.1%
4 1328
8.4%
6 1212
7.7%
Other Punctuation
ValueCountFrequency (%)
* 7902
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 23706
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
* 7902
33.3%
9 1860
 
7.8%
0 1854
 
7.8%
1 1826
 
7.7%
3 1720
 
7.3%
2 1675
 
7.1%
8 1449
 
6.1%
5 1447
 
6.1%
7 1433
 
6.0%
4 1328
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 23706
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
* 7902
33.3%
9 1860
 
7.8%
0 1854
 
7.8%
1 1826
 
7.7%
3 1720
 
7.3%
2 1675
 
7.1%
8 1449
 
6.1%
5 1447
 
6.1%
7 1433
 
6.0%
4 1328
 
5.6%

사업소코드
Real number (ℝ)

HIGH CORRELATION 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean296.51531
Minimum244
Maximum312
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:04.380490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum244
5-th percentile244
Q1302
median306
Q3308
95-th percentile312
Maximum312
Range68
Interquartile range (IQR)6

Descriptive statistics

Standard deviation23.382904
Coefficient of variation (CV)0.078859011
Kurtosis1.2119535
Mean296.51531
Median Absolute Deviation (MAD)3
Skewness-1.7649285
Sum1171532
Variance546.76021
MonotonicityNot monotonic
2024-03-14T23:27:04.734389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
244 646
16.4%
308 507
12.8%
307 492
12.5%
309 449
11.4%
304 447
11.3%
306 442
11.2%
311 281
7.1%
301 233
 
5.9%
312 209
 
5.3%
302 140
 
3.5%
ValueCountFrequency (%)
244 646
16.4%
301 233
 
5.9%
302 140
 
3.5%
303 105
 
2.7%
304 447
11.3%
306 442
11.2%
307 492
12.5%
308 507
12.8%
309 449
11.4%
311 281
7.1%
ValueCountFrequency (%)
312 209
5.3%
311 281
7.1%
309 449
11.4%
308 507
12.8%
307 492
12.5%
306 442
11.2%
304 447
11.3%
303 105
 
2.7%
302 140
 
3.5%
301 233
5.9%

사업소명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
동래통합사업소
646 
해운대사업소
507 
북부사업소
492 
사하사업소
449 
부산진 사업소
447 
Other values (6)
1410 

Length

Max length9
Median length5
Mean length5.9954442
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row북부사업소
2nd row해운대사업소
3rd row기장사업소
4th row해운대사업소
5th row동래통합사업소

Common Values

ValueCountFrequency (%)
동래통합사업소 646
16.4%
해운대사업소 507
12.8%
북부사업소 492
12.5%
사하사업소 449
11.4%
부산진 사업소 447
11.3%
남부사업소 442
11.2%
강서사업소 281
7.1%
중동부사업소 233
 
5.9%
기장사업소 209
 
5.3%
서부 사업소 140
 
3.5%

Length

2024-03-14T23:27:05.160100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
동래통합사업소 646
14.2%
사업소 587
12.9%
해운대사업소 507
11.2%
북부사업소 492
10.8%
사하사업소 449
9.9%
부산진 447
9.9%
남부사업소 442
9.7%
강서사업소 281
6.2%
중동부사업소 233
 
5.1%
기장사업소 209
 
4.6%
Other values (2) 245
 
5.4%

구코드
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean358.00304
Minimum110
Maximum710
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:05.504117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum110
5-th percentile140
Q1260
median350
Q3440
95-th percentile710
Maximum710
Range600
Interquartile range (IQR)180

Descriptive statistics

Standard deviation136.68345
Coefficient of variation (CV)0.38179411
Kurtosis0.34750183
Mean358.00304
Median Absolute Deviation (MAD)90
Skewness0.5901255
Sum1414470
Variance18682.366
MonotonicityNot monotonic
2024-03-14T23:27:05.868515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
350 507
12.8%
380 449
11.4%
230 447
11.3%
440 281
 
7.1%
260 264
 
6.7%
530 253
 
6.4%
290 252
 
6.4%
320 239
 
6.0%
470 216
 
5.5%
710 209
 
5.3%
Other values (6) 834
21.1%
ValueCountFrequency (%)
110 92
 
2.3%
140 140
 
3.5%
170 141
 
3.6%
200 105
 
2.7%
230 447
11.3%
260 264
6.7%
290 252
6.4%
320 239
6.0%
350 507
12.8%
380 449
11.4%
ValueCountFrequency (%)
710 209
5.3%
530 253
6.4%
500 190
 
4.8%
470 216
5.5%
440 281
7.1%
410 166
 
4.2%
380 449
11.4%
350 507
12.8%
320 239
6.0%
290 252
6.4%

구명
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
해운대구
507 
사하구
449 
부산진구
447 
강서구
281 
동래구
264 
Other values (11)
2003 

Length

Max length4
Median length3
Mean length3.022779
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사상구
2nd row해운대구
3rd row기장군
4th row해운대구
5th row연제구

Common Values

ValueCountFrequency (%)
해운대구 507
12.8%
사하구 449
11.4%
부산진구 447
11.3%
강서구 281
 
7.1%
동래구 264
 
6.7%
사상구 253
 
6.4%
남구 252
 
6.4%
북구 239
 
6.0%
연제구 216
 
5.5%
기장군 209
 
5.3%
Other values (6) 834
21.1%

Length

2024-03-14T23:27:06.293667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
해운대구 507
12.8%
사하구 449
11.4%
부산진구 447
11.3%
강서구 281
 
7.1%
동래구 264
 
6.7%
사상구 253
 
6.4%
남구 252
 
6.4%
북구 239
 
6.0%
연제구 216
 
5.5%
기장군 209
 
5.3%
Other values (6) 834
21.1%

동코드
Real number (ℝ)

HIGH CORRELATION 

Distinct62
Distinct (%)1.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean582.19894
Minimum250
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:06.681875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum250
5-th percentile310
Q1530
median572
Q3650
95-th percentile760
Maximum800
Range550
Interquartile range (IQR)120

Descriptive statistics

Standard deviation104.69284
Coefficient of variation (CV)0.17982314
Kurtosis2.7895852
Mean582.19894
Median Absolute Deviation (MAD)48
Skewness-1.0087686
Sum2300268
Variance10960.591
MonotonicityNot monotonic
2024-03-14T23:27:07.106762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
530 277
 
7.0%
560 265
 
6.7%
510 250
 
6.3%
520 203
 
5.1%
620 169
 
4.3%
680 161
 
4.1%
540 152
 
3.8%
610 147
 
3.7%
550 146
 
3.7%
660 141
 
3.6%
Other values (52) 2040
51.6%
ValueCountFrequency (%)
250 66
 
1.7%
253 30
 
0.8%
256 88
 
2.2%
310 19
 
0.5%
330 6
 
0.2%
510 250
6.3%
520 203
5.1%
521 9
 
0.2%
525 46
 
1.2%
530 277
7.0%
ValueCountFrequency (%)
800 36
0.9%
790 20
 
0.5%
780 30
 
0.8%
770 81
2.1%
762 8
 
0.2%
761 9
 
0.2%
760 33
0.8%
750 35
0.9%
740 45
1.1%
730 49
1.2%

동명
Text

Distinct211
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
2024-03-14T23:27:08.284362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3.6529992
Min length3

Characters and Unicode

Total characters14433
Distinct characters107
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)0.2%

Sample

1st row주례1동
2nd row우2동
3rd row장안읍
4th row송정동
5th row연산1동
ValueCountFrequency (%)
녹산동 160
 
4.0%
정관읍 88
 
2.2%
명지동 86
 
2.2%
중1동 80
 
2.0%
우2동 76
 
1.9%
기장읍 66
 
1.7%
신평2동 64
 
1.6%
우1동 58
 
1.5%
부전2동 58
 
1.5%
괘법동 57
 
1.4%
Other values (201) 3158
79.9%
2024-03-14T23:27:09.880089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3812
26.4%
1 1183
 
8.2%
2 933
 
6.5%
3 483
 
3.3%
327
 
2.3%
289
 
2.0%
274
 
1.9%
265
 
1.8%
239
 
1.7%
232
 
1.6%
Other values (97) 6396
44.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11565
80.1%
Decimal Number 2868
 
19.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3812
33.0%
327
 
2.8%
289
 
2.5%
274
 
2.4%
265
 
2.3%
239
 
2.1%
232
 
2.0%
221
 
1.9%
221
 
1.9%
220
 
1.9%
Other values (89) 5465
47.3%
Decimal Number
ValueCountFrequency (%)
1 1183
41.2%
2 933
32.5%
3 483
16.8%
4 136
 
4.7%
5 74
 
2.6%
9 29
 
1.0%
6 25
 
0.9%
8 5
 
0.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11565
80.1%
Common 2868
 
19.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3812
33.0%
327
 
2.8%
289
 
2.5%
274
 
2.4%
265
 
2.3%
239
 
2.1%
232
 
2.0%
221
 
1.9%
221
 
1.9%
220
 
1.9%
Other values (89) 5465
47.3%
Common
ValueCountFrequency (%)
1 1183
41.2%
2 933
32.5%
3 483
16.8%
4 136
 
4.7%
5 74
 
2.6%
9 29
 
1.0%
6 25
 
0.9%
8 5
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11565
80.1%
ASCII 2868
 
19.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3812
33.0%
327
 
2.8%
289
 
2.5%
274
 
2.4%
265
 
2.3%
239
 
2.1%
232
 
2.0%
221
 
1.9%
221
 
1.9%
220
 
1.9%
Other values (89) 5465
47.3%
ASCII
ValueCountFrequency (%)
1 1183
41.2%
2 933
32.5%
3 483
16.8%
4 136
 
4.7%
5 74
 
2.6%
9 29
 
1.0%
6 25
 
0.9%
8 5
 
0.2%

상수도업종
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
일반용
2620 
가정용
1064 
욕탕용
 
205
공업용수
 
55
목욕장용(온천)
 
5

Length

Max length8
Median length3
Mean length3.0222728
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row가정용
2nd row일반용
3rd row일반용
4th row가정용
5th row가정용

Common Values

ValueCountFrequency (%)
일반용 2620
66.3%
가정용 1064
26.9%
욕탕용 205
 
5.2%
공업용수 55
 
1.4%
목욕장용(온천) 5
 
0.1%
영업용(온천) 2
 
0.1%

Length

2024-03-14T23:27:10.300472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:27:10.634780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
일반용 2620
66.3%
가정용 1064
26.9%
욕탕용 205
 
5.2%
공업용수 55
 
1.4%
목욕장용(온천 5
 
0.1%
영업용(온천 2
 
0.1%

가구수
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct799
Distinct (%)42.9%
Missing2087
Missing (%)52.8%
Infinite0
Infinite (%)0.0%
Mean408.94099
Minimum0
Maximum5235
Zeros158
Zeros (%)4.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:11.019715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q170
median267.5
Q3552.25
95-th percentile1319.55
Maximum5235
Range5235
Interquartile range (IQR)482.25

Descriptive statistics

Standard deviation471.37284
Coefficient of variation (CV)1.1526672
Kurtosis10.697861
Mean408.94099
Median Absolute Deviation (MAD)215.5
Skewness2.4577164
Sum762266
Variance222192.36
MonotonicityNot monotonic
2024-03-14T23:27:11.467456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 158
 
4.0%
1 17
 
0.4%
49 15
 
0.4%
48 14
 
0.4%
70 14
 
0.4%
50 13
 
0.3%
39 13
 
0.3%
67 11
 
0.3%
45 11
 
0.3%
69 10
 
0.3%
Other values (789) 1588
40.2%
(Missing) 2087
52.8%
ValueCountFrequency (%)
0 158
4.0%
1 17
 
0.4%
2 3
 
0.1%
3 3
 
0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 1
 
< 0.1%
7 1
 
< 0.1%
8 2
 
0.1%
10 2
 
0.1%
ValueCountFrequency (%)
5235 1
< 0.1%
3392 1
< 0.1%
3060 1
< 0.1%
3000 1
< 0.1%
2882 1
< 0.1%
2863 1
< 0.1%
2752 1
< 0.1%
2716 1
< 0.1%
2623 1
< 0.1%
2544 1
< 0.1%

구경(mm)
Real number (ℝ)

HIGH CORRELATION 

Distinct12
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78.535561
Minimum15
Maximum300
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:11.816712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum15
5-th percentile25
Q140
median50
Q3100
95-th percentile200
Maximum300
Range285
Interquartile range (IQR)60

Descriptive statistics

Standard deviation54.717907
Coefficient of variation (CV)0.69672778
Kurtosis2.1908602
Mean78.535561
Median Absolute Deviation (MAD)25
Skewness1.5145298
Sum310294
Variance2994.0493
MonotonicityNot monotonic
2024-03-14T23:27:12.150660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
50 903
22.9%
40 680
17.2%
80 594
15.0%
100 508
12.9%
150 425
10.8%
25 297
 
7.5%
200 190
 
4.8%
32 117
 
3.0%
20 100
 
2.5%
250 60
 
1.5%
Other values (2) 77
 
1.9%
ValueCountFrequency (%)
15 47
 
1.2%
20 100
 
2.5%
25 297
 
7.5%
32 117
 
3.0%
40 680
17.2%
50 903
22.9%
80 594
15.0%
100 508
12.9%
150 425
10.8%
200 190
 
4.8%
ValueCountFrequency (%)
300 30
 
0.8%
250 60
 
1.5%
200 190
 
4.8%
150 425
10.8%
100 508
12.9%
80 594
15.0%
50 903
22.9%
40 680
17.2%
32 117
 
3.0%
25 297
 
7.5%

사용월수
Real number (ℝ)

Distinct11
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.972918
Minimum1
Maximum13
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:12.488838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile12
Q112
median12
Q312
95-th percentile12
Maximum13
Range12
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.47149164
Coefficient of variation (CV)0.039379843
Kurtosis355.78122
Mean11.972918
Median Absolute Deviation (MAD)0
Skewness-17.81883
Sum47305
Variance0.22230437
MonotonicityNot monotonic
2024-03-14T23:27:12.841676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
12 3910
99.0%
13 16
 
0.4%
9 8
 
0.2%
10 5
 
0.1%
2 3
 
0.1%
8 2
 
0.1%
5 2
 
0.1%
1 2
 
0.1%
4 1
 
< 0.1%
11 1
 
< 0.1%
ValueCountFrequency (%)
1 2
 
0.1%
2 3
 
0.1%
4 1
 
< 0.1%
5 2
 
0.1%
6 1
 
< 0.1%
8 2
 
0.1%
9 8
 
0.2%
10 5
 
0.1%
11 1
 
< 0.1%
12 3910
99.0%
ValueCountFrequency (%)
13 16
 
0.4%
12 3910
99.0%
11 1
 
< 0.1%
10 5
 
0.1%
9 8
 
0.2%
8 2
 
0.1%
6 1
 
< 0.1%
5 2
 
0.1%
4 1
 
< 0.1%
2 3
 
0.1%

사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct3659
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean52659.571
Minimum621
Maximum4273935
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:13.230832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum621
5-th percentile6342.5
Q18284
median15583
Q356466.5
95-th percentile199229
Maximum4273935
Range4273314
Interquartile range (IQR)48182.5

Descriptive statistics

Standard deviation124491.52
Coefficient of variation (CV)2.3640816
Kurtosis455.44007
Mean52659.571
Median Absolute Deviation (MAD)8911
Skewness16.723483
Sum2.0805796 × 108
Variance1.549814 × 1010
MonotonicityNot monotonic
2024-03-14T23:27:13.664100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7533 5
 
0.1%
6976 4
 
0.1%
6085 4
 
0.1%
7029 4
 
0.1%
6614 4
 
0.1%
6309 3
 
0.1%
7586 3
 
0.1%
6447 3
 
0.1%
7248 3
 
0.1%
7431 3
 
0.1%
Other values (3649) 3915
99.1%
ValueCountFrequency (%)
621 1
< 0.1%
1234 1
< 0.1%
2231 1
< 0.1%
3940 1
< 0.1%
4033 1
< 0.1%
5819 1
< 0.1%
5999 1
< 0.1%
6012 1
< 0.1%
6014 1
< 0.1%
6022 1
< 0.1%
ValueCountFrequency (%)
4273935 1
< 0.1%
2790040 1
< 0.1%
2704090 1
< 0.1%
1326312 1
< 0.1%
1252254 1
< 0.1%
1237732 1
< 0.1%
1179349 1
< 0.1%
854348 1
< 0.1%
778622 1
< 0.1%
721094 1
< 0.1%

평균사용량
Real number (ℝ)

HIGH CORRELATION 

Distinct2444
Distinct (%)61.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4395.7628
Minimum501
Maximum356161
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:14.073560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum501
5-th percentile530
Q1690
median1307
Q34717.5
95-th percentile16602
Maximum356161
Range355660
Interquartile range (IQR)4027.5

Descriptive statistics

Standard deviation10374.213
Coefficient of variation (CV)2.3600484
Kurtosis455.40329
Mean4395.7628
Median Absolute Deviation (MAD)749
Skewness16.721593
Sum17367659
Variance1.076243 × 108
MonotonicityNot monotonic
2024-03-14T23:27:14.702161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
555 15
 
0.4%
520 15
 
0.4%
627 14
 
0.4%
659 12
 
0.3%
530 11
 
0.3%
551 10
 
0.3%
503 10
 
0.3%
593 10
 
0.3%
589 10
 
0.3%
522 10
 
0.3%
Other values (2434) 3834
97.0%
ValueCountFrequency (%)
501 3
 
0.1%
502 7
0.2%
503 10
0.3%
504 9
0.2%
505 6
0.2%
506 3
 
0.1%
507 7
0.2%
508 9
0.2%
509 4
 
0.1%
510 7
0.2%
ValueCountFrequency (%)
356161 1
< 0.1%
232503 1
< 0.1%
225340 1
< 0.1%
110526 1
< 0.1%
104354 1
< 0.1%
103144 1
< 0.1%
98279 1
< 0.1%
71195 1
< 0.1%
64885 1
< 0.1%
60091 1
< 0.1%

총사용금액
Real number (ℝ)

HIGH CORRELATION 

Distinct3883
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41845855
Minimum0
Maximum3.5245134 × 109
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:15.130211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5856570
Q19432305
median17087120
Q345327130
95-th percentile1.4365839 × 108
Maximum3.5245134 × 109
Range3.5245134 × 109
Interquartile range (IQR)35894825

Descriptive statistics

Standard deviation89298426
Coefficient of variation (CV)2.133985
Kurtosis645.74715
Mean41845855
Median Absolute Deviation (MAD)9584720
Skewness19.408973
Sum1.6533297 × 1011
Variance7.9742088 × 1015
MonotonicityNot monotonic
2024-03-14T23:27:15.579151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9801380 3
 
0.1%
8065200 3
 
0.1%
8535220 2
 
0.1%
8587060 2
 
0.1%
13988400 2
 
0.1%
9351840 2
 
0.1%
11164630 2
 
0.1%
7736400 2
 
0.1%
7740000 2
 
0.1%
9060570 2
 
0.1%
Other values (3873) 3929
99.4%
ValueCountFrequency (%)
0 1
< 0.1%
195840 1
< 0.1%
801930 1
< 0.1%
1593220 1
< 0.1%
2934000 1
< 0.1%
2943230 1
< 0.1%
3329340 1
< 0.1%
3890160 1
< 0.1%
3924180 1
< 0.1%
3957840 1
< 0.1%
ValueCountFrequency (%)
3524513450 1
< 0.1%
1665209820 1
< 0.1%
1645895560 1
< 0.1%
904921970 1
< 0.1%
864140120 1
< 0.1%
641090250 1
< 0.1%
583526140 1
< 0.1%
577615260 1
< 0.1%
536828160 1
< 0.1%
534955870 1
< 0.1%

월평균사용금액
Real number (ℝ)

HIGH CORRELATION 

Distinct3862
Distinct (%)97.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3492691.4
Minimum0
Maximum2.9370945 × 108
Zeros1
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size34.9 KiB
2024-03-14T23:27:15.999497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile491585
Q1788600
median1428580
Q33778750
95-th percentile11971530
Maximum2.9370945 × 108
Range2.9370945 × 108
Interquartile range (IQR)2990150

Descriptive statistics

Standard deviation7440157.2
Coefficient of variation (CV)2.1302074
Kurtosis646.16924
Mean3492691.4
Median Absolute Deviation (MAD)803130
Skewness19.417275
Sum1.3799624 × 1010
Variance5.5355939 × 1013
MonotonicityNot monotonic
2024-03-14T23:27:16.448285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
672100 3
 
0.1%
816780 3
 
0.1%
693200 3
 
0.1%
852690 2
 
0.1%
2211500 2
 
0.1%
692530 2
 
0.1%
652300 2
 
0.1%
677130 2
 
0.1%
626700 2
 
0.1%
644700 2
 
0.1%
Other values (3852) 3928
99.4%
ValueCountFrequency (%)
0 1
< 0.1%
16320 1
< 0.1%
244500 1
< 0.1%
277440 1
< 0.1%
324180 1
< 0.1%
327010 1
< 0.1%
329820 1
< 0.1%
331140 1
< 0.1%
340260 1
< 0.1%
342300 1
< 0.1%
ValueCountFrequency (%)
293709450 1
< 0.1%
138767480 1
< 0.1%
137157960 1
< 0.1%
75410160 1
< 0.1%
72011670 1
< 0.1%
53424180 1
< 0.1%
48627170 1
< 0.1%
48134600 1
< 0.1%
44735680 1
< 0.1%
44579650 1
< 0.1%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size31.0 KiB
다량
2259 
비다량
1692 

Length

Max length3
Median length2
Mean length2.428246
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row다량
2nd row다량
3rd row다량
4th row다량
5th row다량

Common Values

ValueCountFrequency (%)
다량 2259
57.2%
비다량 1692
42.8%

Length

2024-03-14T23:27:16.862389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T23:27:17.180206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다량 2259
57.2%
비다량 1692
42.8%

Interactions

2024-03-14T23:26:55.893000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:33.477354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:35.688389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:37.951317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:40.028236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:42.452796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:44.727441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:46.627796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:48.939897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:50.901246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:53.041254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:56.096518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:33.762797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:35.870526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:38.134895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:40.204770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:42.739844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:44.904598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:46.810660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:49.114267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:51.090844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:53.324322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:56.309887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:34.030205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:36.040745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:38.306093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:40.373541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:43.005516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:45.070063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:47.062596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:49.360633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:51.266334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:53.600550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:56.581959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:34.207270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:36.257611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:38.565307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:40.537269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:43.285060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:45.237628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:47.418048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:49.526682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:51.444711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:53.875955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:56.824582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:34.375968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:36.455563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:38.795921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:40.697001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:43.503188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:45.395775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:47.576642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:49.682326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:51.661613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:54.146834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:57.092321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:34.544501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:36.616566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:38.957605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:40.855045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:43.658238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:45.556227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:47.786637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:49.845112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:51.856936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:54.398022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:57.356263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:34.713866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:36.779518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:39.117951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:41.106180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:43.814543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:45.792804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:48.054996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:49.999307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:52.025120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:54.669538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:57.634060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:34.885128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:37.153302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:39.284351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:41.366031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:43.990529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:45.958107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:48.268608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:50.157279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:52.197257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:54.938465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:57.896586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:35.127194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:37.314506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:39.442390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:41.624105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:44.160002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:46.111398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:48.422745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:50.305964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:52.360454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:55.234605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:58.366129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:35.321214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:37.579453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:39.622633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:41.898151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:44.317040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:46.286607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:48.593758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:50.562598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:52.542123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:55.498705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:58.646670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:35.506360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:37.778414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:39.846928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:42.181665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:44.511881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:46.460245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:48.771016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:50.736273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:52.761137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T23:26:55.715764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T23:27:17.404296image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드사업소명구코드구명동코드상수도업종가구수구경(mm)사용월수사용량평균사용량총사용금액월평균사용금액구분
연번1.0000.3120.4700.4570.5700.2560.3010.1590.3200.0760.0000.0000.0000.0000.734
사업소코드0.3121.0001.0001.0001.0000.4440.1680.1910.0870.0000.0530.0510.0310.0310.107
사업소명0.4701.0001.0000.9561.0000.7440.2650.2000.1590.0820.0630.0620.0000.0000.104
구코드0.4571.0000.9561.0001.0000.8610.2040.1960.1310.0720.0280.0260.0100.0100.119
구명0.5701.0001.0001.0001.0000.8940.2880.2760.1890.0610.0190.0000.0000.0000.125
동코드0.2560.4440.7440.8610.8941.0000.1580.1620.0800.1300.0000.0000.0000.0000.065
상수도업종0.3010.1680.2650.2040.2880.1581.0000.5660.5200.0000.3370.3410.0000.0000.739
가구수0.1590.1910.2000.1960.2760.1620.5661.0000.5910.0350.9550.9590.8800.8800.478
구경(mm)0.3200.0870.1590.1310.1890.0800.5200.5911.0000.0000.3360.3380.3160.3160.602
사용월수0.0760.0000.0820.0720.0610.1300.0000.0350.0001.0000.0000.0000.0000.0000.000
사용량0.0000.0530.0630.0280.0190.0000.3370.9550.3360.0001.0001.0000.7670.7670.089
평균사용량0.0000.0510.0620.0260.0000.0000.3410.9590.3380.0001.0001.0000.7690.7690.087
총사용금액0.0000.0310.0000.0100.0000.0000.0000.8800.3160.0000.7670.7691.0001.0000.050
월평균사용금액0.0000.0310.0000.0100.0000.0000.0000.8800.3160.0000.7670.7691.0001.0000.050
구분0.7340.1070.1040.1190.1250.0650.7390.4780.6020.0000.0890.0870.0500.0501.000
2024-03-14T23:27:17.755904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분사업소명구명상수도업종
구분1.0000.1000.0980.548
사업소명0.1001.0000.9990.139
구명0.0980.9991.0000.142
상수도업종0.5480.1390.1421.000
2024-03-14T23:27:18.035329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번사업소코드구코드동코드가구수구경(mm)사용월수사용량평균사용량총사용금액월평균사용금액사업소명구명상수도업종구분
연번1.0000.0000.2030.077-0.278-0.324-0.014-0.427-0.427-0.395-0.3950.2210.2640.1630.575
사업소코드0.0001.0000.492-0.3870.1900.032-0.0310.0610.0630.0820.0840.9990.9980.0970.061
구코드0.2030.4921.0000.0370.140-0.004-0.0150.0440.0460.0570.0590.8700.9990.1140.089
동코드0.077-0.3870.0371.000-0.0340.0180.0140.0200.019-0.002-0.0030.4740.5470.0880.047
가구수-0.2780.1900.140-0.0341.0000.826-0.0560.9240.9260.8540.8560.0950.0990.2810.359
구경(mm)-0.3240.032-0.0040.0180.8261.000-0.0080.7740.7740.7210.7230.0790.0870.3420.648
사용월수-0.014-0.031-0.0150.014-0.056-0.0081.0000.031-0.0100.022-0.0240.0370.0250.0000.000
사용량-0.4270.0610.0440.0200.9240.7740.0311.0000.9980.9380.9360.0320.0090.1280.064
평균사용량-0.4270.0630.0460.0190.9260.774-0.0100.9981.0000.9360.9390.0310.0000.1290.062
총사용금액-0.3950.0820.057-0.0020.8540.7210.0220.9380.9361.0000.9970.0000.0000.0000.061
월평균사용금액-0.3950.0840.059-0.0030.8560.723-0.0240.9360.9390.9971.0000.0000.0000.0000.061
사업소명0.2210.9990.8700.4740.0950.0790.0370.0320.0310.0000.0001.0000.9990.1390.100
구명0.2640.9980.9990.5470.0990.0870.0250.0090.0000.0000.0000.9991.0000.1420.098
상수도업종0.1630.0970.1140.0880.2810.3420.0000.1280.1290.0000.0000.1390.1421.0000.548
구분0.5750.0610.0890.0470.3590.6480.0000.0640.0620.0610.0610.1000.0980.5481.000

Missing values

2024-03-14T23:26:59.059252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T23:26:59.733191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

연번다량관리년도고객번호사업소코드사업소명구코드구명동코드동명상수도업종가구수구경(mm)사용월수사용량평균사용량총사용금액월평균사용금액구분
012023*15*08307북부사업소530사상구650주례1동가정용49315012976248135715099205959160다량
122023*51*19308해운대사업소350해운대구520우2동일반용<NA>5012178451487234458501953820다량
232023*51*24312기장사업소710기장군253장안읍일반용<NA>100128768773071163357109694640다량
342023*51*25308해운대사업소350해운대구560송정동가정용8891501214257211881854229607118580다량
452023*51*77244동래통합사업소470연제구650연산1동가정용1598200123235462696223654748019712290다량
562023*51*95311강서사업소440강서구560녹산동일반용<NA>801298160818013026480010855400다량
672023*51*06304부산진 사업소230부산진구530범전동일반용020012693235776919115907659290다량
782023*51*28304부산진 사업소230부산진구680당감2동일반용<NA>80127979666491058406808820050다량
892023*51*77244동래통합사업소470연제구660연산2동일반용<NA>8012124991041163356401361300다량
9102023*51*78307북부사업소320북구543화명3동일반용<NA>5012172521437226571601888090다량
연번다량관리년도고객번호사업소코드사업소명구코드구명동코드동명상수도업종가구수구경(mm)사용월수사용량평균사용량총사용금액월평균사용금액구분
394139422023*32*04244동래통합사업소260동래구570온천3동일반용<NA>501263425287610400634200비다량
394239432023*32*13244동래통합사업소260동래구570온천3동일반용0401211438953149245401243710비다량
394339442023*33*27244동래통합사업소260동래구570온천3동일반용39321266185514470720372560비다량
394439452023*33*36244동래통합사업소260동래구570온천3동일반용<NA>2012852971011055570921290비다량
394539462023*33*38244동래통합사업소260동래구570온천3동일반용54401290077506145740512140비다량
394639472023*34*22244동래통합사업소260동래구580사직1동일반용<NA>50129895824128723201072690비다량
394739482023*34*44244동래통합사업소260동래구580사직1동일반용45151274046175371860447650비다량
394839492023*34*55244동래통합사업소260동래구580사직1동일반용<NA>501211024918143739201197820비다량
394939502023*34*41244동래통합사업소260동래구580사직1동일반용644012111009258284380690360비다량
395039512023*35*29309사하사업소380사하구572신평2동공업용수<NA>100125819848498729700727470다량