Overview

Dataset statistics

Number of variables14
Number of observations10000
Missing cells53346
Missing cells (%)38.1%
Duplicate rows134
Duplicate rows (%)1.3%
Total size in memory1.2 MiB
Average record size in memory130.0 B

Variable types

Text3
Unsupported1
Numeric9
Categorical1

Dataset

Description건축물명,건축물 위치,층수(지상_지하),연면적,조사년도,총발생량(톤),일평균발생량(톤/일),일평균이용현황(톤/일)_하천방류,일평균이용현황(톤/일)_도로청소,일평균이용현황(톤/일)_공원용수,일평균이용현황(톤/일)_화장실세척,일평균이용현황(톤/일)_건물용수,미사용_하수도방류(톤/일),일평균이용현황(톤/일)_기타건물용수
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15607/S/1/datasetView.do

Alerts

Dataset has 134 (1.3%) duplicate rowsDuplicates
총발생량(톤) is highly overall correlated with 일평균발생량(톤/일) and 2 other fieldsHigh correlation
일평균발생량(톤/일) is highly overall correlated with 총발생량(톤) and 2 other fieldsHigh correlation
일평균이용현황(톤/일)_하천방류 is highly overall correlated with 일평균이용현황(톤/일)_기타건물용수High correlation
일평균이용현황(톤/일)_도로청소 is highly overall correlated with 일평균이용현황(톤/일)_공원용수 and 1 other fieldsHigh correlation
일평균이용현황(톤/일)_공원용수 is highly overall correlated with 일평균이용현황(톤/일)_도로청소 and 2 other fieldsHigh correlation
일평균이용현황(톤/일)_화장실세척 is highly overall correlated with 일평균이용현황(톤/일)_공원용수 and 1 other fieldsHigh correlation
미사용_하수도방류(톤/일) is highly overall correlated with 총발생량(톤) and 1 other fieldsHigh correlation
일평균이용현황(톤/일)_기타건물용수 is highly overall correlated with 총발생량(톤) and 5 other fieldsHigh correlation
일평균이용현황(톤/일)_기타건물용수 is highly imbalanced (94.2%)Imbalance
건축물 위치 has 134 (1.3%) missing valuesMissing
층수(지상_지하) has 1880 (18.8%) missing valuesMissing
연면적 has 10000 (100.0%) missing valuesMissing
총발생량(톤) has 422 (4.2%) missing valuesMissing
일평균이용현황(톤/일)_하천방류 has 8651 (86.5%) missing valuesMissing
일평균이용현황(톤/일)_도로청소 has 8681 (86.8%) missing valuesMissing
일평균이용현황(톤/일)_공원용수 has 7908 (79.1%) missing valuesMissing
일평균이용현황(톤/일)_화장실세척 has 8106 (81.1%) missing valuesMissing
일평균이용현황(톤/일)_건물용수 has 7316 (73.2%) missing valuesMissing
미사용_하수도방류(톤/일) has 184 (1.8%) missing valuesMissing
일평균이용현황(톤/일)_도로청소 is highly skewed (γ1 = 33.79907082)Skewed
일평균이용현황(톤/일)_공원용수 is highly skewed (γ1 = 21.90508658)Skewed
연면적 is an unsupported type, check if it needs cleaning or further analysisUnsupported
총발생량(톤) has 550 (5.5%) zerosZeros
일평균발생량(톤/일) has 677 (6.8%) zerosZeros
일평균이용현황(톤/일)_하천방류 has 1249 (12.5%) zerosZeros
일평균이용현황(톤/일)_도로청소 has 1217 (12.2%) zerosZeros
일평균이용현황(톤/일)_공원용수 has 1684 (16.8%) zerosZeros
일평균이용현황(톤/일)_화장실세척 has 1780 (17.8%) zerosZeros
일평균이용현황(톤/일)_건물용수 has 2087 (20.9%) zerosZeros
미사용_하수도방류(톤/일) has 1129 (11.3%) zerosZeros

Reproduction

Analysis started2024-03-13 16:03:09.907780
Analysis finished2024-03-13 16:03:20.140810
Duration10.23 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Distinct2025
Distinct (%)20.3%
Missing4
Missing (%)< 0.1%
Memory size156.2 KiB
2024-03-14T01:03:20.312688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length47
Median length29
Mean length8.527411
Min length1

Characters and Unicode

Total characters85240
Distinct characters585
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique590 ?
Unique (%)5.9%

Sample

1st row상암월드시티
2nd row현대산업개발㈜, 잠실올림픽아이파크
3rd row(주)대명건설
4th row가산동 YS타워 신축공사현장
5th row신도림팰러티움
ValueCountFrequency (%)
아파트 140
 
1.1%
오피스텔 57
 
0.5%
신축공사 49
 
0.4%
서울대학교 45
 
0.4%
대림산업(주)광화문d타워 44
 
0.3%
관리사무소 44
 
0.3%
삼성생명 38
 
0.3%
36
 
0.3%
sk 34
 
0.3%
의과대학장 33
 
0.3%
Other values (2288) 12126
95.9%
2024-03-14T01:03:20.957450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2820
 
3.3%
2210
 
2.6%
) 1961
 
2.3%
( 1949
 
2.3%
1887
 
2.2%
1561
 
1.8%
1560
 
1.8%
1445
 
1.7%
1372
 
1.6%
1367
 
1.6%
Other values (575) 67108
78.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 73364
86.1%
Space Separator 2820
 
3.3%
Uppercase Letter 2565
 
3.0%
Close Punctuation 1962
 
2.3%
Open Punctuation 1950
 
2.3%
Decimal Number 1336
 
1.6%
Other Symbol 690
 
0.8%
Lowercase Letter 380
 
0.4%
Other Punctuation 83
 
0.1%
Dash Punctuation 75
 
0.1%
Other values (2) 15
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2210
 
3.0%
1887
 
2.6%
1561
 
2.1%
1560
 
2.1%
1445
 
2.0%
1372
 
1.9%
1367
 
1.9%
1250
 
1.7%
1181
 
1.6%
1097
 
1.5%
Other values (508) 58434
79.6%
Uppercase Letter
ValueCountFrequency (%)
K 376
14.7%
S 312
12.2%
C 298
11.6%
T 218
 
8.5%
G 163
 
6.4%
I 142
 
5.5%
A 133
 
5.2%
L 133
 
5.2%
D 108
 
4.2%
E 100
 
3.9%
Other values (15) 582
22.7%
Lowercase Letter
ValueCountFrequency (%)
e 72
18.9%
s 52
13.7%
k 38
10.0%
w 36
9.5%
c 30
7.9%
n 29
7.6%
r 24
 
6.3%
t 24
 
6.3%
i 22
 
5.8%
o 20
 
5.3%
Other values (6) 33
8.7%
Decimal Number
ValueCountFrequency (%)
2 416
31.1%
1 364
27.2%
3 195
14.6%
5 83
 
6.2%
0 79
 
5.9%
4 61
 
4.6%
6 60
 
4.5%
8 43
 
3.2%
7 33
 
2.5%
9 2
 
0.1%
Other Punctuation
ValueCountFrequency (%)
, 48
57.8%
/ 14
 
16.9%
10
 
12.0%
. 6
 
7.2%
& 5
 
6.0%
Close Punctuation
ValueCountFrequency (%)
) 1961
99.9%
] 1
 
0.1%
Open Punctuation
ValueCountFrequency (%)
( 1949
99.9%
[ 1
 
0.1%
Other Symbol
ValueCountFrequency (%)
663
96.1%
27
 
3.9%
Math Symbol
ValueCountFrequency (%)
~ 7
70.0%
> 3
30.0%
Space Separator
ValueCountFrequency (%)
2820
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 75
100.0%
Letter Number
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 74027
86.8%
Common 8263
 
9.7%
Latin 2950
 
3.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2210
 
3.0%
1887
 
2.5%
1561
 
2.1%
1560
 
2.1%
1445
 
2.0%
1372
 
1.9%
1367
 
1.8%
1250
 
1.7%
1181
 
1.6%
1097
 
1.5%
Other values (509) 59097
79.8%
Latin
ValueCountFrequency (%)
K 376
 
12.7%
S 312
 
10.6%
C 298
 
10.1%
T 218
 
7.4%
G 163
 
5.5%
I 142
 
4.8%
A 133
 
4.5%
L 133
 
4.5%
D 108
 
3.7%
E 100
 
3.4%
Other values (32) 967
32.8%
Common
ValueCountFrequency (%)
2820
34.1%
) 1961
23.7%
( 1949
23.6%
2 416
 
5.0%
1 364
 
4.4%
3 195
 
2.4%
5 83
 
1.0%
0 79
 
1.0%
- 75
 
0.9%
4 61
 
0.7%
Other values (14) 260
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 73355
86.1%
ASCII 11171
 
13.1%
None 673
 
0.8%
Geometric Shapes 27
 
< 0.1%
Compat Jamo 9
 
< 0.1%
Number Forms 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2820
25.2%
) 1961
17.6%
( 1949
17.4%
2 416
 
3.7%
K 376
 
3.4%
1 364
 
3.3%
S 312
 
2.8%
C 298
 
2.7%
T 218
 
2.0%
3 195
 
1.7%
Other values (53) 2262
20.2%
Hangul
ValueCountFrequency (%)
2210
 
3.0%
1887
 
2.6%
1561
 
2.1%
1560
 
2.1%
1445
 
2.0%
1372
 
1.9%
1367
 
1.9%
1250
 
1.7%
1181
 
1.6%
1097
 
1.5%
Other values (507) 58425
79.6%
None
ValueCountFrequency (%)
663
98.5%
10
 
1.5%
Geometric Shapes
ValueCountFrequency (%)
27
100.0%
Compat Jamo
ValueCountFrequency (%)
9
100.0%
Number Forms
ValueCountFrequency (%)
5
100.0%

건축물 위치
Text

MISSING 

Distinct2703
Distinct (%)27.4%
Missing134
Missing (%)1.3%
Memory size156.2 KiB
2024-03-14T01:03:21.282862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length75
Median length47
Mean length17.24478
Min length2

Characters and Unicode

Total characters170137
Distinct characters433
Distinct categories12 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique896 ?
Unique (%)9.1%

Sample

1st row마포구 월드컵로34길 13
2nd row올림픽로43길 34(풍납동) 현대산업개발(주)
3rd row서울특별시 송파구 (문정동 641-3)
4th row가산동 345-40 가산동 YS타워 신축공사현장
5th row경인로 584(신도림동)
ValueCountFrequency (%)
서울특별시 1046
 
3.4%
863
 
2.8%
마포구 466
 
1.5%
가산동 309
 
1.0%
송파구 276
 
0.9%
양천구 257
 
0.8%
목동 236
 
0.8%
상암동 215
 
0.7%
강남구 205
 
0.7%
금천구 194
 
0.6%
Other values (2876) 26421
86.7%
2024-03-14T01:03:21.777404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
23340
 
13.7%
1 9389
 
5.5%
9245
 
5.4%
8954
 
5.3%
( 7263
 
4.3%
) 7258
 
4.3%
2 6524
 
3.8%
3 5272
 
3.1%
5 4265
 
2.5%
4 4125
 
2.4%
Other values (423) 84502
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 80860
47.5%
Decimal Number 45499
26.7%
Space Separator 23340
 
13.7%
Open Punctuation 7272
 
4.3%
Close Punctuation 7267
 
4.3%
Dash Punctuation 3579
 
2.1%
Other Punctuation 1880
 
1.1%
Uppercase Letter 416
 
0.2%
Other Symbol 12
 
< 0.1%
Lowercase Letter 6
 
< 0.1%
Other values (2) 6
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9245
 
11.4%
8954
 
11.1%
2657
 
3.3%
2523
 
3.1%
2470
 
3.1%
2038
 
2.5%
2006
 
2.5%
1587
 
2.0%
1491
 
1.8%
1279
 
1.6%
Other values (383) 46610
57.6%
Uppercase Letter
ValueCountFrequency (%)
B 84
20.2%
C 72
17.3%
A 52
12.5%
S 46
11.1%
G 36
8.7%
K 35
8.4%
D 29
 
7.0%
L 15
 
3.6%
N 13
 
3.1%
J 12
 
2.9%
Other values (5) 22
 
5.3%
Decimal Number
ValueCountFrequency (%)
1 9389
20.6%
2 6524
14.3%
3 5272
11.6%
5 4265
9.4%
4 4125
9.1%
6 3981
8.7%
0 3302
 
7.3%
7 3120
 
6.9%
9 2829
 
6.2%
8 2692
 
5.9%
Other Punctuation
ValueCountFrequency (%)
, 1813
96.4%
/ 48
 
2.6%
. 19
 
1.0%
Open Punctuation
ValueCountFrequency (%)
( 7263
99.9%
[ 9
 
0.1%
Close Punctuation
ValueCountFrequency (%)
) 7258
99.9%
] 9
 
0.1%
Math Symbol
ValueCountFrequency (%)
~ 4
80.0%
+ 1
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
k 3
50.0%
s 3
50.0%
Space Separator
ValueCountFrequency (%)
23340
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3579
100.0%
Other Symbol
ValueCountFrequency (%)
12
100.0%
Modifier Symbol
ValueCountFrequency (%)
` 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88843
52.2%
Hangul 80872
47.5%
Latin 422
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9245
 
11.4%
8954
 
11.1%
2657
 
3.3%
2523
 
3.1%
2470
 
3.1%
2038
 
2.5%
2006
 
2.5%
1587
 
2.0%
1491
 
1.8%
1279
 
1.6%
Other values (384) 46622
57.6%
Common
ValueCountFrequency (%)
23340
26.3%
1 9389
10.6%
( 7263
 
8.2%
) 7258
 
8.2%
2 6524
 
7.3%
3 5272
 
5.9%
5 4265
 
4.8%
4 4125
 
4.6%
6 3981
 
4.5%
- 3579
 
4.0%
Other values (12) 13847
15.6%
Latin
ValueCountFrequency (%)
B 84
19.9%
C 72
17.1%
A 52
12.3%
S 46
10.9%
G 36
8.5%
K 35
8.3%
D 29
 
6.9%
L 15
 
3.6%
N 13
 
3.1%
J 12
 
2.8%
Other values (7) 28
 
6.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 89265
52.5%
Hangul 80860
47.5%
None 12
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
23340
26.1%
1 9389
10.5%
( 7263
 
8.1%
) 7258
 
8.1%
2 6524
 
7.3%
3 5272
 
5.9%
5 4265
 
4.8%
4 4125
 
4.6%
6 3981
 
4.5%
- 3579
 
4.0%
Other values (29) 14269
16.0%
Hangul
ValueCountFrequency (%)
9245
 
11.4%
8954
 
11.1%
2657
 
3.3%
2523
 
3.1%
2470
 
3.1%
2038
 
2.5%
2006
 
2.5%
1587
 
2.0%
1491
 
1.8%
1279
 
1.6%
Other values (383) 46610
57.6%
None
ValueCountFrequency (%)
12
100.0%

층수(지상_지하)
Text

MISSING 

Distinct838
Distinct (%)10.3%
Missing1880
Missing (%)18.8%
Memory size156.2 KiB
2024-03-14T01:03:22.077263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length4
Mean length3.962931
Min length1

Characters and Unicode

Total characters32179
Distinct characters54
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique294 ?
Unique (%)3.6%

Sample

1st row15/5
2nd row35/3
3rd row/
4th row26/5
5th row/
ValueCountFrequency (%)
487
 
6.0%
15/3 194
 
2.4%
15/5 162
 
2.0%
15/4 158
 
1.9%
5/2 134
 
1.6%
6/2 116
 
1.4%
20/5 114
 
1.4%
20/3 113
 
1.4%
12/5 112
 
1.4%
8/4 111
 
1.4%
Other values (740) 6467
79.2%
2024-03-14T01:03:22.512885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 7526
23.4%
1 4122
12.8%
2 3689
11.5%
5 2877
 
8.9%
3 2544
 
7.9%
4 2383
 
7.4%
6 1550
 
4.8%
7 1262
 
3.9%
0 1155
 
3.6%
( 982
 
3.1%
Other values (44) 4089
12.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 21250
66.0%
Other Punctuation 7567
 
23.5%
Other Letter 1169
 
3.6%
Open Punctuation 982
 
3.1%
Close Punctuation 981
 
3.0%
Math Symbol 115
 
0.4%
Space Separator 75
 
0.2%
Dash Punctuation 28
 
0.1%
Uppercase Letter 10
 
< 0.1%
Lowercase Letter 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
619
53.0%
112
 
9.6%
102
 
8.7%
58
 
5.0%
45
 
3.8%
44
 
3.8%
40
 
3.4%
19
 
1.6%
18
 
1.5%
11
 
0.9%
Other values (22) 101
 
8.6%
Decimal Number
ValueCountFrequency (%)
1 4122
19.4%
2 3689
17.4%
5 2877
13.5%
3 2544
12.0%
4 2383
11.2%
6 1550
 
7.3%
7 1262
 
5.9%
0 1155
 
5.4%
8 944
 
4.4%
9 724
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 7526
99.5%
? 20
 
0.3%
, 11
 
0.1%
. 10
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
B 5
50.0%
A 5
50.0%
Open Punctuation
ValueCountFrequency (%)
( 982
100.0%
Close Punctuation
ValueCountFrequency (%)
) 981
100.0%
Math Symbol
ValueCountFrequency (%)
~ 115
100.0%
Space Separator
ValueCountFrequency (%)
75
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 28
100.0%
Lowercase Letter
ValueCountFrequency (%)
m 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 30998
96.3%
Hangul 1169
 
3.6%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
619
53.0%
112
 
9.6%
102
 
8.7%
58
 
5.0%
45
 
3.8%
44
 
3.8%
40
 
3.4%
19
 
1.6%
18
 
1.5%
11
 
0.9%
Other values (22) 101
 
8.6%
Common
ValueCountFrequency (%)
/ 7526
24.3%
1 4122
13.3%
2 3689
11.9%
5 2877
 
9.3%
3 2544
 
8.2%
4 2383
 
7.7%
6 1550
 
5.0%
7 1262
 
4.1%
0 1155
 
3.7%
( 982
 
3.2%
Other values (9) 2908
 
9.4%
Latin
ValueCountFrequency (%)
B 5
41.7%
A 5
41.7%
m 2
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 31010
96.4%
Hangul 1169
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 7526
24.3%
1 4122
13.3%
2 3689
11.9%
5 2877
 
9.3%
3 2544
 
8.2%
4 2383
 
7.7%
6 1550
 
5.0%
7 1262
 
4.1%
0 1155
 
3.7%
( 982
 
3.2%
Other values (12) 2920
 
9.4%
Hangul
ValueCountFrequency (%)
619
53.0%
112
 
9.6%
102
 
8.7%
58
 
5.0%
45
 
3.8%
44
 
3.8%
40
 
3.4%
19
 
1.6%
18
 
1.5%
11
 
0.9%
Other values (22) 101
 
8.6%

연면적
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing10000
Missing (%)100.0%
Memory size166.0 KiB

조사년도
Real number (ℝ)

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.3285
Minimum2014
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:22.627563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2014
5-th percentile2014
Q12016
median2018
Q32021
95-th percentile2022
Maximum2022
Range8
Interquartile range (IQR)5

Descriptive statistics

Standard deviation2.5110592
Coefficient of variation (CV)0.0012441281
Kurtosis-1.1803685
Mean2018.3285
Median Absolute Deviation (MAD)2
Skewness-0.12820632
Sum20183285
Variance6.3054183
MonotonicityNot monotonic
2024-03-14T01:03:22.728979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
2021 1321
13.2%
2020 1255
12.6%
2016 1252
12.5%
2022 1244
12.4%
2018 1161
11.6%
2019 1075
10.8%
2017 1065
10.7%
2015 838
8.4%
2014 789
7.9%
ValueCountFrequency (%)
2014 789
7.9%
2015 838
8.4%
2016 1252
12.5%
2017 1065
10.7%
2018 1161
11.6%
2019 1075
10.8%
2020 1255
12.6%
2021 1321
13.2%
2022 1244
12.4%
ValueCountFrequency (%)
2022 1244
12.4%
2021 1321
13.2%
2020 1255
12.6%
2019 1075
10.8%
2018 1161
11.6%
2017 1065
10.7%
2016 1252
12.5%
2015 838
8.4%
2014 789
7.9%

총발생량(톤)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct5280
Distinct (%)55.1%
Missing422
Missing (%)4.2%
Infinite0
Infinite (%)0.0%
Mean9500.6736
Minimum0
Maximum841451
Zeros550
Zeros (%)5.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:22.850557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1586.44
median2384.64
Q38640
95-th percentile36020.25
Maximum841451
Range841451
Interquartile range (IQR)8053.56

Descriptive statistics

Standard deviation27901.55
Coefficient of variation (CV)2.9367971
Kurtosis345.18888
Mean9500.6736
Median Absolute Deviation (MAD)2259.73
Skewness14.794888
Sum90997451
Variance7.7849651 × 108
MonotonicityNot monotonic
2024-03-14T01:03:22.964553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 550
 
5.5%
2160.0 47
 
0.5%
1800.0 44
 
0.4%
720.0 37
 
0.4%
552.0 33
 
0.3%
368.0 29
 
0.3%
1440.0 27
 
0.3%
184.0 26
 
0.3%
1.0 26
 
0.3%
4500.0 25
 
0.2%
Other values (5270) 8734
87.3%
(Missing) 422
 
4.2%
ValueCountFrequency (%)
0.0 550
5.5%
0.18 1
 
< 0.1%
1.0 26
 
0.3%
1.81 2
 
< 0.1%
1.84 2
 
< 0.1%
2.0 10
 
0.1%
2.01 1
 
< 0.1%
2.51 2
 
< 0.1%
2.9 3
 
< 0.1%
2.99 1
 
< 0.1%
ValueCountFrequency (%)
841451.0 1
< 0.1%
818301.0 1
< 0.1%
796354.0 1
< 0.1%
652834.0 1
< 0.1%
644000.0 1
< 0.1%
637000.0 1
< 0.1%
576865.0 1
< 0.1%
499008.0 1
< 0.1%
422611.2 1
< 0.1%
254198.0 1
< 0.1%

일평균발생량(톤/일)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct3662
Distinct (%)36.8%
Missing60
Missing (%)0.6%
Infinite0
Infinite (%)0.0%
Mean52.319659
Minimum0
Maximum4573.1
Zeros677
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:23.078222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.29
median13.54
Q348.0775
95-th percentile198.75
Maximum4573.1
Range4573.1
Interquartile range (IQR)44.7875

Descriptive statistics

Standard deviation148.04825
Coefficient of variation (CV)2.8296869
Kurtosis347.00474
Mean52.319659
Median Absolute Deviation (MAD)12.75
Skewness14.482165
Sum520057.41
Variance21918.286
MonotonicityNot monotonic
2024-03-14T01:03:23.188965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 677
 
6.8%
1.0 118
 
1.2%
2.0 112
 
1.1%
3.0 97
 
1.0%
5.0 83
 
0.8%
6.0 66
 
0.7%
4.0 62
 
0.6%
8.0 61
 
0.6%
10.0 54
 
0.5%
9.0 54
 
0.5%
Other values (3652) 8556
85.6%
(Missing) 60
 
0.6%
ValueCountFrequency (%)
0.0 677
6.8%
0.01 29
 
0.3%
0.02 30
 
0.3%
0.03 31
 
0.3%
0.04 11
 
0.1%
0.05 9
 
0.1%
0.06 16
 
0.2%
0.07 22
 
0.2%
0.08 9
 
0.1%
0.09 9
 
0.1%
ValueCountFrequency (%)
4573.1 1
< 0.1%
4521.0 1
< 0.1%
4328.0 1
< 0.1%
3587.0 1
< 0.1%
3500.0 1
< 0.1%
3187.1 1
< 0.1%
2712.0 1
< 0.1%
2296.8 1
< 0.1%
1440.0 1
< 0.1%
1381.51 1
< 0.1%

일평균이용현황(톤/일)_하천방류
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct51
Distinct (%)3.8%
Missing8651
Missing (%)86.5%
Infinite0
Infinite (%)0.0%
Mean18.048169
Minimum0
Maximum1000
Zeros1249
Zeros (%)12.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:23.347426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile30
Maximum1000
Range1000
Interquartile range (IQR)0

Descriptive statistics

Standard deviation86.61764
Coefficient of variation (CV)4.7992481
Kurtosis32.750652
Mean18.048169
Median Absolute Deviation (MAD)0
Skewness5.46245
Sum24346.98
Variance7502.6156
MonotonicityNot monotonic
2024-03-14T01:03:23.494290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1249
 
12.5%
254.0 13
 
0.1%
14.0 10
 
0.1%
477.7 9
 
0.1%
30.0 6
 
0.1%
508.0 5
 
0.1%
500.0 3
 
< 0.1%
250.0 3
 
< 0.1%
479.0 3
 
< 0.1%
210.0 2
 
< 0.1%
Other values (41) 46
 
0.5%
(Missing) 8651
86.5%
ValueCountFrequency (%)
0.0 1249
12.5%
1.0 1
 
< 0.1%
1.17 1
 
< 0.1%
2.0 2
 
< 0.1%
2.8 1
 
< 0.1%
4.14 1
 
< 0.1%
4.6 1
 
< 0.1%
4.66 1
 
< 0.1%
5.47 1
 
< 0.1%
8.0 1
 
< 0.1%
ValueCountFrequency (%)
1000.0 1
 
< 0.1%
616.9 1
 
< 0.1%
546.0 2
 
< 0.1%
537.0 1
 
< 0.1%
525.4 1
 
< 0.1%
508.29 1
 
< 0.1%
508.0 5
0.1%
500.0 3
< 0.1%
498.0 2
 
< 0.1%
479.0 3
< 0.1%

일평균이용현황(톤/일)_도로청소
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct26
Distinct (%)2.0%
Missing8681
Missing (%)86.8%
Infinite0
Infinite (%)0.0%
Mean2.0577559
Minimum0
Maximum1285
Zeros1217
Zeros (%)12.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:23.611235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1.5
Maximum1285
Range1285
Interquartile range (IQR)0

Descriptive statistics

Standard deviation36.250875
Coefficient of variation (CV)17.616704
Kurtosis1193.0101
Mean2.0577559
Median Absolute Deviation (MAD)0
Skewness33.799071
Sum2714.18
Variance1314.126
MonotonicityNot monotonic
2024-03-14T01:03:23.731541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 1217
 
12.2%
1.0 25
 
0.2%
2.0 11
 
0.1%
50.0 9
 
0.1%
8.3 6
 
0.1%
19.0 5
 
0.1%
3.0 5
 
0.1%
1.5 5
 
0.1%
10.0 5
 
0.1%
1.2 5
 
0.1%
Other values (16) 26
 
0.3%
(Missing) 8681
86.8%
ValueCountFrequency (%)
0.0 1217
12.2%
0.4 1
 
< 0.1%
0.5 3
 
< 0.1%
1.0 25
 
0.2%
1.2 5
 
0.1%
1.5 5
 
0.1%
2.0 11
 
0.1%
2.38 1
 
< 0.1%
3.0 5
 
0.1%
4.0 1
 
< 0.1%
ValueCountFrequency (%)
1285.0 1
 
< 0.1%
117.6 1
 
< 0.1%
117.0 3
 
< 0.1%
50.0 9
0.1%
45.0 1
 
< 0.1%
40.0 1
 
< 0.1%
30.0 1
 
< 0.1%
25.0 1
 
< 0.1%
20.0 1
 
< 0.1%
19.0 5
0.1%

일평균이용현황(톤/일)_공원용수
Real number (ℝ)

HIGH CORRELATION  MISSING  SKEWED  ZEROS 

Distinct87
Distinct (%)4.2%
Missing7908
Missing (%)79.1%
Infinite0
Infinite (%)0.0%
Mean4.036544
Minimum0
Maximum774.4
Zeros1684
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:23.855874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile21
Maximum774.4
Range774.4
Interquartile range (IQR)0

Descriptive statistics

Standard deviation22.645337
Coefficient of variation (CV)5.6100806
Kurtosis675.54782
Mean4.036544
Median Absolute Deviation (MAD)0
Skewness21.905087
Sum8444.45
Variance512.81129
MonotonicityNot monotonic
2024-03-14T01:03:23.987317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1684
 
16.8%
1.0 29
 
0.3%
10.0 27
 
0.3%
20.0 25
 
0.2%
2.0 21
 
0.2%
6.0 20
 
0.2%
5.0 19
 
0.2%
8.0 16
 
0.2%
3.0 16
 
0.2%
30.0 15
 
0.1%
Other values (77) 220
 
2.2%
(Missing) 7908
79.1%
ValueCountFrequency (%)
0.0 1684
16.8%
0.04 1
 
< 0.1%
0.4 8
 
0.1%
0.42 2
 
< 0.1%
0.5 13
 
0.1%
0.7 7
 
0.1%
0.71 1
 
< 0.1%
0.8 1
 
< 0.1%
0.82 1
 
< 0.1%
0.87 1
 
< 0.1%
ValueCountFrequency (%)
774.4 1
 
< 0.1%
360.0 1
 
< 0.1%
220.0 1
 
< 0.1%
156.0 1
 
< 0.1%
100.0 1
 
< 0.1%
82.56 14
0.1%
81.0 1
 
< 0.1%
80.0 4
 
< 0.1%
78.0 2
 
< 0.1%
72.3 6
0.1%

일평균이용현황(톤/일)_화장실세척
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct56
Distinct (%)3.0%
Missing8106
Missing (%)81.1%
Infinite0
Infinite (%)0.0%
Mean2.989773
Minimum0
Maximum506
Zeros1780
Zeros (%)17.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:24.113194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile6.35
Maximum506
Range506
Interquartile range (IQR)0

Descriptive statistics

Standard deviation24.412095
Coefficient of variation (CV)8.1652002
Kurtosis196.4493
Mean2.989773
Median Absolute Deviation (MAD)0
Skewness13.027868
Sum5662.63
Variance595.95037
MonotonicityNot monotonic
2024-03-14T01:03:24.273715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1780
 
17.8%
30.0 13
 
0.1%
10.0 9
 
0.1%
20.0 8
 
0.1%
22.0 6
 
0.1%
8.0 5
 
0.1%
3.0 5
 
0.1%
200.0 4
 
< 0.1%
2.0 4
 
< 0.1%
15.0 3
 
< 0.1%
Other values (46) 57
 
0.6%
(Missing) 8106
81.1%
ValueCountFrequency (%)
0.0 1780
17.8%
1.5 2
 
< 0.1%
2.0 4
 
< 0.1%
3.0 5
 
0.1%
3.9 1
 
< 0.1%
4.0 1
 
< 0.1%
4.8 1
 
< 0.1%
5.0 3
 
< 0.1%
6.0 2
 
< 0.1%
7.0 2
 
< 0.1%
ValueCountFrequency (%)
506.0 1
 
< 0.1%
382.5 1
 
< 0.1%
345.0 1
 
< 0.1%
320.0 1
 
< 0.1%
300.0 1
 
< 0.1%
277.0 1
 
< 0.1%
260.6 1
 
< 0.1%
200.0 4
< 0.1%
176.6 1
 
< 0.1%
110.0 1
 
< 0.1%

일평균이용현황(톤/일)_건물용수
Real number (ℝ)

MISSING  ZEROS 

Distinct258
Distinct (%)9.6%
Missing7316
Missing (%)73.2%
Infinite0
Infinite (%)0.0%
Mean12.438621
Minimum0
Maximum1300
Zeros2087
Zeros (%)20.9%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-03-14T01:03:24.400491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile56
Maximum1300
Range1300
Interquartile range (IQR)0

Descriptive statistics

Standard deviation60.570702
Coefficient of variation (CV)4.8695671
Kurtosis152.67926
Mean12.438621
Median Absolute Deviation (MAD)0
Skewness10.81452
Sum33385.26
Variance3668.81
MonotonicityNot monotonic
2024-03-14T01:03:24.525388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 2087
 
20.9%
20.0 23
 
0.2%
5.0 18
 
0.2%
1.0 16
 
0.2%
2.0 15
 
0.1%
12.0 14
 
0.1%
3.0 13
 
0.1%
30.0 11
 
0.1%
15.0 9
 
0.1%
8.0 9
 
0.1%
Other values (248) 469
 
4.7%
(Missing) 7316
73.2%
ValueCountFrequency (%)
0.0 2087
20.9%
0.02 1
 
< 0.1%
0.07 3
 
< 0.1%
0.1 3
 
< 0.1%
0.2 1
 
< 0.1%
0.3 1
 
< 0.1%
0.5 3
 
< 0.1%
0.66 1
 
< 0.1%
0.69 1
 
< 0.1%
0.73 1
 
< 0.1%
ValueCountFrequency (%)
1300.0 1
< 0.1%
860.51 1
< 0.1%
834.55 1
< 0.1%
789.01 1
< 0.1%
774.4 1
< 0.1%
700.0 2
< 0.1%
655.57 1
< 0.1%
610.49 1
< 0.1%
567.6 1
< 0.1%
555.0 1
< 0.1%

미사용_하수도방류(톤/일)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct3396
Distinct (%)34.6%
Missing184
Missing (%)1.8%
Infinite0
Infinite (%)0.0%
Mean43.10948
Minimum-21.28
Maximum4573.1
Zeros1129
Zeros (%)11.3%
Negative62
Negative (%)0.6%
Memory size166.0 KiB
2024-03-14T01:03:24.652962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-21.28
5-th percentile0
Q11.98
median10.315
Q340.45
95-th percentile164.0725
Maximum4573.1
Range4594.38
Interquartile range (IQR)38.47

Descriptive statistics

Standard deviation136.51459
Coefficient of variation (CV)3.1666953
Kurtosis479.24326
Mean43.10948
Median Absolute Deviation (MAD)10.285
Skewness17.639518
Sum423162.66
Variance18636.233
MonotonicityNot monotonic
2024-03-14T01:03:24.775406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 1129
 
11.3%
1.0 125
 
1.2%
3.0 107
 
1.1%
2.0 105
 
1.1%
4.0 76
 
0.8%
5.0 73
 
0.7%
6.0 66
 
0.7%
9.0 64
 
0.6%
8.0 63
 
0.6%
12.0 51
 
0.5%
Other values (3386) 7957
79.6%
(Missing) 184
 
1.8%
ValueCountFrequency (%)
-21.28 1
 
< 0.1%
-20.0 1
 
< 0.1%
-14.79 1
 
< 0.1%
-13.0 1
 
< 0.1%
-12.9 1
 
< 0.1%
-7.9 1
 
< 0.1%
-6.25 3
< 0.1%
-6.0 1
 
< 0.1%
-4.0 1
 
< 0.1%
-3.95 1
 
< 0.1%
ValueCountFrequency (%)
4573.1 1
< 0.1%
4521.0 1
< 0.1%
4328.0 1
< 0.1%
3577.0 1
< 0.1%
3500.0 1
< 0.1%
3187.1 1
< 0.1%
2712.0 1
< 0.1%
1381.51 1
< 0.1%
1366.19 2
< 0.1%
1356.41 1
< 0.1%

일평균이용현황(톤/일)_기타건물용수
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct17
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
<NA>
9744 
x
 
141
미방류
 
21
우이천
 
14
성북천
 
14
Other values (12)
 
66

Length

Max length6
Median length4
Mean length3.9492
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 9744
97.4%
x 141
 
1.4%
미방류 21
 
0.2%
우이천 14
 
0.1%
성북천 14
 
0.1%
석촌호수 12
 
0.1%
탄천 10
 
0.1%
중랑천 10
 
0.1%
고덕천 9
 
0.1%
도림천 6
 
0.1%
Other values (7) 19
 
0.2%

Length

2024-03-14T01:03:24.883170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 9744
97.4%
x 141
 
1.4%
미방류 21
 
0.2%
우이천 14
 
0.1%
성북천 14
 
0.1%
석촌호수 12
 
0.1%
탄천 10
 
0.1%
중랑천 10
 
0.1%
고덕천 9
 
0.1%
도림천 6
 
0.1%
Other values (7) 19
 
0.2%

Interactions

2024-03-14T01:03:18.645740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:11.394152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.395256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.186590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.970081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.103631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.998612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.987972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.815277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.745552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:11.508353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.492382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.284483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.062504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.206524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.118304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.094534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.920979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.865356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:11.622156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.571723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.388588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.137159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.309571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.211749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.176899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.014362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.984437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:11.714440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.646964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.463482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.232955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.396135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.315160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.261758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.105151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:19.078176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:11.854407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.731815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.538566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.314492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.487301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.403402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.364334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.195346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:19.164716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:11.959123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.821749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.626537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.741147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.583877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.519444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.451286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.283166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:19.257684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.065163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.929995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.723051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.825469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.688733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.653705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.547515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.374166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:19.337618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.157089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.007284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.803457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:14.930313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.803079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.798644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.632835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.469246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:19.442337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:12.286881image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.089689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:13.893698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.013588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:15.917094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:16.897001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:17.721123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T01:03:18.561512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T01:03:24.962274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사년도총발생량(톤)일평균발생량(톤/일)일평균이용현황(톤/일)_하천방류일평균이용현황(톤/일)_도로청소일평균이용현황(톤/일)_공원용수일평균이용현황(톤/일)_화장실세척일평균이용현황(톤/일)_건물용수미사용_하수도방류(톤/일)일평균이용현황(톤/일)_기타건물용수
조사년도1.0000.0000.0000.5290.2720.2210.6940.1400.0000.701
총발생량(톤)0.0001.0001.0000.6210.4730.8620.5830.5660.9870.728
일평균발생량(톤/일)0.0001.0001.0000.6200.4730.8630.5830.7170.9890.777
일평균이용현황(톤/일)_하천방류0.5290.6210.6201.0000.0000.0000.0000.0000.0000.864
일평균이용현황(톤/일)_도로청소0.2720.4730.4730.0001.0000.0000.0000.0000.000NaN
일평균이용현황(톤/일)_공원용수0.2210.8620.8630.0000.0001.0001.0000.7680.0000.886
일평균이용현황(톤/일)_화장실세척0.6940.5830.5830.0000.0001.0001.0000.6870.0000.764
일평균이용현황(톤/일)_건물용수0.1400.5660.7170.0000.0000.7680.6871.0000.0000.000
미사용_하수도방류(톤/일)0.0000.9870.9890.0000.0000.0000.0000.0001.0000.000
일평균이용현황(톤/일)_기타건물용수0.7010.7280.7770.864NaN0.8860.7640.0000.0001.000
2024-03-14T01:03:25.105394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조사년도총발생량(톤)일평균발생량(톤/일)일평균이용현황(톤/일)_하천방류일평균이용현황(톤/일)_도로청소일평균이용현황(톤/일)_공원용수일평균이용현황(톤/일)_화장실세척일평균이용현황(톤/일)_건물용수미사용_하수도방류(톤/일)일평균이용현황(톤/일)_기타건물용수
조사년도1.0000.0590.0490.101-0.1590.182-0.270-0.3990.0680.421
총발생량(톤)0.0591.0000.9990.3010.2050.3220.1830.2780.8480.529
일평균발생량(톤/일)0.0490.9991.0000.3000.2070.3230.1790.2930.8530.712
일평균이용현황(톤/일)_하천방류0.1010.3010.3001.0000.1230.304-0.0200.006-0.3170.631
일평균이용현황(톤/일)_도로청소-0.1590.2050.2070.1231.0000.6160.4990.141-0.0401.000
일평균이용현황(톤/일)_공원용수0.1820.3220.3230.3040.6161.0000.5450.2470.0640.687
일평균이용현황(톤/일)_화장실세척-0.2700.1830.179-0.0200.4990.5451.0000.129-0.0750.530
일평균이용현황(톤/일)_건물용수-0.3990.2780.2930.0060.1410.2470.1291.000-0.1560.000
미사용_하수도방류(톤/일)0.0680.8480.853-0.317-0.0400.064-0.075-0.1561.0000.000
일평균이용현황(톤/일)_기타건물용수0.4210.5290.7120.6311.0000.6870.5300.0000.0001.000

Missing values

2024-03-14T01:03:19.565677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T01:03:19.762268image/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.
2024-03-14T01:03:20.010784image/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

건축물명건축물 위치층수(지상_지하)연면적조사년도총발생량(톤)일평균발생량(톤/일)일평균이용현황(톤/일)_하천방류일평균이용현황(톤/일)_도로청소일평균이용현황(톤/일)_공원용수일평균이용현황(톤/일)_화장실세척일평균이용현황(톤/일)_건물용수미사용_하수도방류(톤/일)일평균이용현황(톤/일)_기타건물용수
13254상암월드시티마포구 월드컵로34길 1315/5<NA>2014<NA><NA><NA><NA><NA><NA><NA><NA><NA>
5188현대산업개발㈜, 잠실올림픽아이파크올림픽로43길 34(풍납동) 현대산업개발(주)35/3<NA>202025102.0137.9<NA><NA><NA><NA><NA>138.0<NA>
10271(주)대명건설서울특별시 송파구 (문정동 641-3)<NA><NA>20162133.011.0<NA><NA><NA><NA><NA>11.0<NA>
3203가산동 YS타워 신축공사현장가산동 345-40 가산동 YS타워 신축공사현장/<NA>20212941.029.71<NA><NA><NA><NA><NA>29.71<NA>
6771신도림팰러티움경인로 584(신도림동)26/5<NA>2019993.65.4<NA><NA><NA><NA><NA>5.4<NA>
379한화 꿈에그린아파트마포대로10길 22/<NA>2022900.04.89<NA><NA><NA><NA><NA>4.89<NA>
6273알로프트 서울명동호텔남대문로 5620/5<NA>20191637.68.9<NA><NA><NA><NA><NA>8.9<NA>
14183(주)케이씨씨웰츠밸리금천구 가산동 470-8,가산디지털1로 205 (가산동)14/3<NA>20143510.7219.08<NA><NA><NA><NA><NA>19.08<NA>
5766서울대 교수아파트관악로1(대학동)5/2<NA>20191592.88.8<NA><NA><NA><NA><NA>8.8<NA>
3206에이스 가산포휴가산디지털1로 225 (가산동 517-4)20/4<NA>202191948.0508.0<NA><NA><NA><NA><NA>508.0<NA>
건축물명건축물 위치층수(지상_지하)연면적조사년도총발생량(톤)일평균발생량(톤/일)일평균이용현황(톤/일)_하천방류일평균이용현황(톤/일)_도로청소일평균이용현황(톤/일)_공원용수일평균이용현황(톤/일)_화장실세척일평균이용현황(톤/일)_건물용수미사용_하수도방류(톤/일)일평균이용현황(톤/일)_기타건물용수
13814교보생명빌딩영등포로96(당산동2가25)7 / 5<NA>2014<NA>163.0<NA><NA><NA><NA>65.098.0<NA>
4232우리금융상암센타월드컵북로60길 17 (상암동)13/5<NA>202017520.096.26<NA><NA><NA><NA><NA>96.26<NA>
6290우림필유(아)이촌로29길 21-7 (한강로3가 91 ) 우림필유12/1<NA>2019250.241.4<NA><NA><NA><NA><NA>1.4<NA>
14032백석예술대학교방배동490-17/4<NA>2014<NA>3.0<NA><NA><NA><NA><NA>3.0<NA>
7473휴먼타워관리사무소강남대로 605(잠원동 20-9)14/5<NA>2018720.03.910.00.00.00.00.03.91<NA>
12741메가빌딩노원구 노해로 459(상계동)14/4<NA>20151748.09.5<NA><NA><NA><NA><NA>9.5<NA>
1625삼익아파트1단지독산로50길 89(시흥동 5-13 ,삼익아파트 )25/4<NA>202221091.92114.63<NA><NA><NA><NA><NA>114.63<NA>
12441가든파이브웍스서울특별시 송파구 충민로 52(문정동 289)<NA><NA>20159413.051.0<NA><NA><NA><NA><NA>51.0<NA>
5780일성트루엘신림로23길 16(신림동 1523)11/2<NA>20196624.636.6<NA><NA><NA><NA><NA>36.6<NA>
10293GS한강에클라트이촌로 1 (한강로3가, GS한강에클라트)26/6<NA>201633970.0184.0<NA><NA><NA><NA><NA>184.0<NA>

Duplicate rows

Most frequently occurring

건축물명건축물 위치층수(지상_지하)조사년도총발생량(톤)일평균발생량(톤/일)일평균이용현황(톤/일)_하천방류일평균이용현황(톤/일)_도로청소일평균이용현황(톤/일)_공원용수일평균이용현황(톤/일)_화장실세척일평균이용현황(톤/일)_건물용수미사용_하수도방류(톤/일)일평균이용현황(톤/일)_기타건물용수# duplicates
32근린생활시설 신축공사(㈜호유엠케이)대치동 953-22번지<NA>20180.00.00.00.00.00.00.00.0<NA>7
89아파트 관리사무소숭인동길 21(숭인동 766)<NA>20160.00.0<NA><NA><NA><NA><NA>0.0<NA>6
103자일개발주식회사돈화문로 26(묘동 56)<NA>20150.00.0<NA><NA><NA><NA><NA>0.0<NA>6
33김한중종로41길 36(종로6가 189)8/220150.00.0<NA><NA><NA><NA><NA>0.0<NA>5
77소나무동호로 38길 24(종로5가 321-27)5/220150.00.0<NA><NA><NA><NA><NA>0.0<NA>5
28골든팰리스통일로16길 4-1(무악동 66-3, 골든팰리스)8/420160.00.0<NA><NA><NA><NA><NA>0.0<NA>4
30국립현대미술관서울특별시 종로구 (소격동 ,서울관 )<NA>20160.00.0<NA><NA><NA><NA><NA>0.0<NA>4
78소나무동호로 38길 24(종로5가 321-27)5/220160.00.0<NA><NA><NA><NA><NA>0.0<NA>4
1(주)단성사돈화문로 26(묘동 56)9/420150.00.0<NA><NA><NA><NA><NA>0.0<NA>3
27골든팰리스통일로16길 4-1(무악동 66-3, 골든팰리스)8/420150.00.0<NA><NA><NA><NA><NA>0.0<NA>3