Overview

Dataset statistics

Number of variables11
Number of observations5781
Missing cells3886
Missing cells (%)6.1%
Duplicate rows976
Duplicate rows (%)16.9%
Total size in memory513.9 KiB
Average record size in memory91.0 B

Variable types

Categorical6
Text3
Numeric2

Dataset

Description등록년도,자치구,시설유형,시설주소,설치심도(m),양수량(톤/일),모터펌프종류,모터펌프출력(KW),자가발전기종류,자가발전기출력(KW),용도구분
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-15604/S/1/datasetView.do

Alerts

Dataset has 976 (16.9%) duplicate rowsDuplicates
자치구 is highly overall correlated with 자가발전기종류High correlation
자가발전기종류 is highly overall correlated with 자치구High correlation
모터펌프종류 is highly imbalanced (50.1%)Imbalance
자가발전기종류 is highly imbalanced (58.9%)Imbalance
용도구분 is highly imbalanced (82.4%)Imbalance
설치심도(m) has 77 (1.3%) missing valuesMissing
모터펌프출력(KW) has 67 (1.2%) missing valuesMissing
자가발전기출력(KW) has 3693 (63.9%) missing valuesMissing

Reproduction

Analysis started2024-04-17 16:32:28.039282
Analysis finished2024-04-17 16:32:29.868884
Duration1.83 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

등록년도
Categorical

Distinct3
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
2018
2435 
2019
2138 
2016
1208 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2018 2435
42.1%
2019 2138
37.0%
2016 1208
20.9%

Length

2024-04-18T01:32:29.916266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:32:29.991319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 2435
42.1%
2019 2138
37.0%
2016 1208
20.9%

자치구
Categorical

HIGH CORRELATION 

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
송파구
432 
은평구
425 
서초구
422 
동대문구
390 
강서구
 
311
Other values (20)
3801 

Length

Max length4
Median length3
Mean length3.1112264
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
송파구 432
 
7.5%
은평구 425
 
7.4%
서초구 422
 
7.3%
동대문구 390
 
6.7%
강서구 311
 
5.4%
동작구 294
 
5.1%
강남구 291
 
5.0%
노원구 282
 
4.9%
관악구 278
 
4.8%
금천구 269
 
4.7%
Other values (15) 2387
41.3%

Length

2024-04-18T01:32:30.084591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
송파구 432
 
7.5%
은평구 425
 
7.4%
서초구 422
 
7.3%
동대문구 390
 
6.7%
강서구 311
 
5.4%
동작구 294
 
5.1%
강남구 291
 
5.0%
노원구 282
 
4.9%
관악구 278
 
4.8%
금천구 269
 
4.7%
Other values (15) 2387
41.3%

시설유형
Categorical

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
민간지정
3524 
공공지정
1433 
정부지원
488 
지자체시설
 
330
자치구시설
 
6

Length

Max length5
Median length4
Mean length4.0581214
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
민간지정 3524
61.0%
공공지정 1433
24.8%
정부지원 488
 
8.4%
지자체시설 330
 
5.7%
자치구시설 6
 
0.1%

Length

2024-04-18T01:32:30.180532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:32:30.257438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간지정 3524
61.0%
공공지정 1433
24.8%
정부지원 488
 
8.4%
지자체시설 330
 
5.7%
자치구시설 6
 
0.1%

시설주소
Text

Distinct1936
Distinct (%)33.8%
Missing49
Missing (%)0.8%
Memory size45.3 KiB
2024-04-18T01:32:30.493464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length30
Mean length15.519539
Min length4

Characters and Unicode

Total characters88958
Distinct characters336
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique653 ?
Unique (%)11.4%

Sample

1st row남부순환로 74길 2
2nd row은행정로 5
3rd row신월로 185
4th row지양로 67
5th row신정로 7길 17
ValueCountFrequency (%)
서울특별시 302
 
2.0%
서초구 260
 
1.7%
강동구 201
 
1.3%
서대문구 147
 
1.0%
화곡동 125
 
0.8%
서울시 119
 
0.8%
서초동 114
 
0.7%
시흥동 97
 
0.6%
시흥대로 80
 
0.5%
양재동 78
 
0.5%
Other values (2253) 13753
90.0%
2024-04-18T01:32:30.863864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10172
 
11.4%
동 6617
 
7.4%
( 5251
 
5.9%
) 5221
 
5.9%
로 5099
 
5.7%
1 4398
 
4.9%
2 3225
 
3.6%
3 2653
 
3.0%
길 2450
 
2.8%
4 2131
 
2.4%
Other values (326) 41741
46.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 44949
50.5%
Decimal Number 21735
24.4%
Space Separator 10172
 
11.4%
Open Punctuation 5251
 
5.9%
Close Punctuation 5221
 
5.9%
Dash Punctuation 1156
 
1.3%
Other Punctuation 423
 
0.5%
Lowercase Letter 30
 
< 0.1%
Uppercase Letter 21
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 6617
 
14.7%
로 5099
 
11.3%
길 2450
 
5.5%
서 1299
 
2.9%
대 1105
 
2.5%
구 991
 
2.2%
시 859
 
1.9%
산 817
 
1.8%
신 664
 
1.5%
가 497
 
1.1%
Other values (299) 24551
54.6%
Decimal Number
ValueCountFrequency (%)
1 4398
20.2%
2 3225
14.8%
3 2653
12.2%
4 2131
9.8%
5 1865
8.6%
6 1707
 
7.9%
7 1580
 
7.3%
0 1515
 
7.0%
9 1392
 
6.4%
8 1269
 
5.8%
Uppercase Letter
ValueCountFrequency (%)
C 10
47.6%
T 5
23.8%
B 2
 
9.5%
A 2
 
9.5%
L 2
 
9.5%
Lowercase Letter
ValueCountFrequency (%)
e 10
33.3%
c 5
16.7%
r 5
16.7%
n 5
16.7%
t 5
16.7%
Other Punctuation
ValueCountFrequency (%)
, 406
96.0%
. 12
 
2.8%
？ 5
 
1.2%
Space Separator
ValueCountFrequency (%)
10172
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5251
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5221
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1156
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 44949
50.5%
Common 43958
49.4%
Latin 51
 
0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 6617
 
14.7%
로 5099
 
11.3%
길 2450
 
5.5%
서 1299
 
2.9%
대 1105
 
2.5%
구 991
 
2.2%
시 859
 
1.9%
산 817
 
1.8%
신 664
 
1.5%
가 497
 
1.1%
Other values (299) 24551
54.6%
Common
ValueCountFrequency (%)
10172
23.1%
( 5251
11.9%
) 5221
11.9%
1 4398
10.0%
2 3225
 
7.3%
3 2653
 
6.0%
4 2131
 
4.8%
5 1865
 
4.2%
6 1707
 
3.9%
7 1580
 
3.6%
Other values (7) 5755
13.1%
Latin
ValueCountFrequency (%)
C 10
19.6%
e 10
19.6%
T 5
9.8%
c 5
9.8%
r 5
9.8%
n 5
9.8%
t 5
9.8%
B 2
 
3.9%
A 2
 
3.9%
L 2
 
3.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 44949
50.5%
ASCII 44004
49.5%
None 5
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10172
23.1%
( 5251
11.9%
) 5221
11.9%
1 4398
10.0%
2 3225
 
7.3%
3 2653
 
6.0%
4 2131
 
4.8%
5 1865
 
4.2%
6 1707
 
3.9%
7 1580
 
3.6%
Other values (16) 5801
13.2%
Hangul
ValueCountFrequency (%)
동 6617
 
14.7%
로 5099
 
11.3%
길 2450
 
5.5%
서 1299
 
2.9%
대 1105
 
2.5%
구 991
 
2.2%
시 859
 
1.9%
산 817
 
1.8%
신 664
 
1.5%
가 497
 
1.1%
Other values (299) 24551
54.6%
None
ValueCountFrequency (%)
？ 5
100.0%

설치심도(m)
Real number (ℝ)

MISSING 

Distinct143
Distinct (%)2.5%
Missing77
Missing (%)1.3%
Infinite0
Infinite (%)0.0%
Mean109.99419
Minimum0
Maximum1015
Zeros38
Zeros (%)0.7%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-04-18T01:32:30.974919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13
Q170
median100
Q3140
95-th percentile210
Maximum1015
Range1015
Interquartile range (IQR)70

Descriptive statistics

Standard deviation87.617902
Coefficient of variation (CV)0.79656848
Kurtosis25.052719
Mean109.99419
Median Absolute Deviation (MAD)30
Skewness3.8213803
Sum627406.84
Variance7676.8968
MonotonicityNot monotonic
2024-04-18T01:32:31.124192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100.0 1574
27.2%
150.0 669
 
11.6%
120.0 375
 
6.5%
200.0 262
 
4.5%
70.0 231
 
4.0%
80.0 226
 
3.9%
20.0 188
 
3.3%
30.0 145
 
2.5%
50.0 140
 
2.4%
130.0 109
 
1.9%
Other values (133) 1785
30.9%
ValueCountFrequency (%)
0.0 38
0.7%
2.7 5
 
0.1%
2.8 1
 
< 0.1%
3.0 5
 
0.1%
4.0 5
 
0.1%
5.0 5
 
0.1%
6.0 10
 
0.2%
7.0 13
 
0.2%
8.0 9
 
0.2%
9.0 2
 
< 0.1%
ValueCountFrequency (%)
1015.0 5
 
0.1%
750.0 11
 
0.2%
700.0 25
0.4%
520.0 4
 
0.1%
500.0 23
0.4%
400.0 41
0.7%
380.0 5
 
0.1%
350.0 5
 
0.1%
330.0 5
 
0.1%
310.0 5
 
0.1%

양수량(톤/일)
Real number (ℝ)

Distinct241
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean153.88205
Minimum1
Maximum8000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size50.9 KiB
2024-04-18T01:32:31.233511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile36
Q160
median85
Q3116
95-th percentile400
Maximum8000
Range7999
Interquartile range (IQR)56

Descriptive statistics

Standard deviation398.24457
Coefficient of variation (CV)2.5879859
Kurtosis206.79254
Mean153.88205
Median Absolute Deviation (MAD)25
Skewness12.827025
Sum889592.13
Variance158598.74
MonotonicityNot monotonic
2024-04-18T01:32:31.343214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
80.0 378
 
6.5%
50.0 371
 
6.4%
100.0 356
 
6.2%
90.0 338
 
5.8%
60.0 233
 
4.0%
70.0 204
 
3.5%
150.0 127
 
2.2%
30.0 127
 
2.2%
85.0 117
 
2.0%
72.0 114
 
2.0%
Other values (231) 3416
59.1%
ValueCountFrequency (%)
1.0 10
0.2%
1.6 2
 
< 0.1%
2.74 1
 
< 0.1%
3.0 5
0.1%
5.918 1
 
< 0.1%
7.0 4
 
0.1%
10.0 5
0.1%
11.0 12
0.2%
16.438 1
 
< 0.1%
20.0 10
0.2%
ValueCountFrequency (%)
8000.0 5
0.1%
6580.0 4
0.1%
5000.0 5
0.1%
4500.0 4
0.1%
2400.0 2
 
< 0.1%
2300.0 4
0.1%
2240.0 5
0.1%
1929.0 4
0.1%
1900.0 4
0.1%
1808.219 2
 
< 0.1%

모터펌프종류
Categorical

IMBALANCE 

Distinct25
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
수중모터
3010 
수중펌프
1230 
수중
586 
'
 
198
전동와권
 
189
Other values (20)
568 

Length

Max length5
Median length4
Mean length3.6270541
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
수중모터 3010
52.1%
수중펌프 1230
21.3%
수중 586
 
10.1%
' 198
 
3.4%
전동와권 189
 
3.3%
<NA> 112
 
1.9%
와권펌프 89
 
1.5%
〃 62
 
1.1%
와권 59
 
1.0%
지상모터 48
 
0.8%
Other values (15) 198
 
3.4%

Length

2024-04-18T01:32:31.463638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
수중모터 3019
52.2%
수중펌프 1230
21.3%
수중 586
 
10.1%
198
 
3.4%
전동와권 189
 
3.3%
na 112
 
1.9%
와권펌프 89
 
1.5%
〃 62
 
1.1%
와권 59
 
1.0%
지상모터 48
 
0.8%
Other values (14) 189
 
3.3%

모터펌프출력(KW)
Text

MISSING 

Distinct84
Distinct (%)1.5%
Missing67
Missing (%)1.2%
Memory size45.3 KiB
2024-04-18T01:32:31.633324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.5145257
Min length1

Characters and Unicode

Total characters14368
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7 ?
Unique (%)0.1%

Sample

1st row1.5
2nd row1.5
3rd row1.5
4th row1.5
5th row2.25
ValueCountFrequency (%)
1.5 1003
17.6%
2 778
13.6%
1 607
10.6%
0.75 496
 
8.7%
3 395
 
6.9%
2.25 348
 
6.1%
3.75 215
 
3.8%
5 204
 
3.6%
7.5 194
 
3.4%
3.7 152
 
2.7%
Other values (74) 1322
23.1%
2024-04-18T01:32:31.910091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 3444
24.0%
5 3192
22.2%
2 2032
14.1%
1 1948
13.6%
7 1198
 
8.3%
0 1109
 
7.7%
3 1063
 
7.4%
6 98
 
0.7%
8 96
 
0.7%
4 93
 
0.6%
Other values (4) 95
 
0.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10835
75.4%
Other Punctuation 3461
 
24.1%
Uppercase Letter 72
 
0.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 3192
29.5%
2 2032
18.8%
1 1948
18.0%
7 1198
 
11.1%
0 1109
 
10.2%
3 1063
 
9.8%
6 98
 
0.9%
8 96
 
0.9%
4 93
 
0.9%
9 6
 
0.1%
Other Punctuation
ValueCountFrequency (%)
. 3444
99.5%
* 17
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
H 36
50.0%
P 36
50.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14296
99.5%
Latin 72
 
0.5%

Most frequent character per script

Common
ValueCountFrequency (%)
. 3444
24.1%
5 3192
22.3%
2 2032
14.2%
1 1948
13.6%
7 1198
 
8.4%
0 1109
 
7.8%
3 1063
 
7.4%
6 98
 
0.7%
8 96
 
0.7%
4 93
 
0.7%
Other values (2) 23
 
0.2%
Latin
ValueCountFrequency (%)
H 36
50.0%
P 36
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14368
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 3444
24.0%
5 3192
22.2%
2 2032
14.1%
1 1948
13.6%
7 1198
 
8.3%
0 1109
 
7.7%
3 1063
 
7.4%
6 98
 
0.7%
8 96
 
0.7%
4 93
 
0.6%
Other values (4) 95
 
0.7%

자가발전기종류
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct42
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
<NA>
3657 
0
656 
디젤엔진
416 
디 젤 엔 진
 
220
디젤발전기
 
158
Other values (37)
674 

Length

Max length13
Median length4
Mean length3.7733956
Min length1

Unique

Unique4 ?
Unique (%)0.1%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-

Common Values

ValueCountFrequency (%)
<NA> 3657
63.3%
0 656
 
11.3%
디젤엔진 416
 
7.2%
디 젤 엔 진 220
 
3.8%
디젤발전기 158
 
2.7%
디젤 134
 
2.3%
- 78
 
1.3%
대형원동기 57
 
1.0%
가솔린엔진 47
 
0.8%
디이젤엔진 45
 
0.8%
Other values (32) 313
 
5.4%

Length

2024-04-18T01:32:32.014906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
na 3657
56.6%
0 656
 
10.2%
디젤엔진 416
 
6.4%
디 220
 
3.4%
젤 220
 
3.4%
엔 220
 
3.4%
진 220
 
3.4%
디젤발전기 158
 
2.4%
디젤 134
 
2.1%
82
 
1.3%
Other values (34) 480
 
7.4%

자가발전기출력(KW)
Text

MISSING 

Distinct92
Distinct (%)4.4%
Missing3693
Missing (%)63.9%
Memory size45.3 KiB
2024-04-18T01:32:32.156444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length13
Mean length2.197318
Min length1

Characters and Unicode

Total characters4588
Distinct characters21
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)0.5%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
0 668
31.9%
20 321
15.3%
10 133
 
6.3%
78
 
3.7%
13 60
 
2.9%
500 53
 
2.5%
5 46
 
2.2%
26 38
 
1.8%
300 31
 
1.5%
1500 27
 
1.3%
Other values (79) 642
30.6%
2024-04-18T01:32:32.419584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2023
44.1%
2 673
 
14.7%
1 541
 
11.8%
5 460
 
10.0%
3 192
 
4.2%
6 107
 
2.3%
7 89
 
1.9%
- 78
 
1.7%
) 77
 
1.7%
( 77
 
1.7%
Other values (11) 271
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 4247
92.6%
Dash Punctuation 78
 
1.7%
Close Punctuation 77
 
1.7%
Open Punctuation 77
 
1.7%
Other Punctuation 62
 
1.4%
Other Letter 27
 
0.6%
Space Separator 19
 
0.4%
Math Symbol 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2023
47.6%
2 673
 
15.8%
1 541
 
12.7%
5 460
 
10.8%
3 192
 
4.5%
6 107
 
2.5%
7 89
 
2.1%
4 64
 
1.5%
8 58
 
1.4%
9 40
 
0.9%
Other Punctuation
ValueCountFrequency (%)
. 28
45.2%
* 17
27.4%
, 17
27.4%
Other Letter
ValueCountFrequency (%)
이 9
33.3%
동 9
33.3%
용 9
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 78
100.0%
Close Punctuation
ValueCountFrequency (%)
) 77
100.0%
Open Punctuation
ValueCountFrequency (%)
( 77
100.0%
Space Separator
ValueCountFrequency (%)
19
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 4561
99.4%
Hangul 27
 
0.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2023
44.4%
2 673
 
14.8%
1 541
 
11.9%
5 460
 
10.1%
3 192
 
4.2%
6 107
 
2.3%
7 89
 
2.0%
- 78
 
1.7%
) 77
 
1.7%
( 77
 
1.7%
Other values (8) 244
 
5.3%
Hangul
ValueCountFrequency (%)
이 9
33.3%
동 9
33.3%
용 9
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 4561
99.4%
Hangul 27
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2023
44.4%
2 673
 
14.8%
1 541
 
11.9%
5 460
 
10.1%
3 192
 
4.2%
6 107
 
2.3%
7 89
 
2.0%
- 78
 
1.7%
) 77
 
1.7%
( 77
 
1.7%
Other values (8) 244
 
5.3%
Hangul
ValueCountFrequency (%)
이 9
33.3%
동 9
33.3%
용 9
33.3%

용도구분
Categorical

IMBALANCE 

Distinct9
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size45.3 KiB
생활용수
5117 
음용수
634 
분수대 및 성북천유지용수
 
9
공업용수
 
7
농업용수
 
6
Other values (4)
 
8

Length

Max length14
Median length4
Mean length3.9109151
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

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

Common Values

ValueCountFrequency (%)
생활용수 5117
88.5%
음용수 634
 
11.0%
분수대 및 성북천유지용수 9
 
0.2%
공업용수 7
 
0.1%
농업용수 6
 
0.1%
성북천유지용수 3
 
0.1%
분수대 및 정릉천 유지용수 3
 
0.1%
유출수 1
 
< 0.1%
음용용수 1
 
< 0.1%

Length

2024-04-18T01:32:32.525456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T01:32:32.612754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
생활용수 5117
88.1%
음용수 634
 
10.9%
분수대 12
 
0.2%
및 12
 
0.2%
성북천유지용수 12
 
0.2%
공업용수 7
 
0.1%
농업용수 6
 
0.1%
정릉천 3
 
0.1%
유지용수 3
 
0.1%
유출수 1
 
< 0.1%

Interactions

2024-04-18T01:32:29.156645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:29.010467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:29.230451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T01:32:29.079737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-18T01:32:32.691179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등록년도자치구시설유형설치심도(m)양수량(톤/일)모터펌프종류모터펌프출력(KW)자가발전기종류자가발전기출력(KW)용도구분
등록년도1.0000.0000.3180.0000.0000.0950.0000.6640.6480.000
자치구0.0001.0000.3930.4760.3940.8730.8800.9370.9340.313
시설유형0.3180.3931.0000.0570.0700.1770.4890.7610.9020.160
설치심도(m)0.0000.4760.0571.0000.5400.2130.8980.5820.8070.113
양수량(톤/일)0.0000.3940.0700.5401.0000.0000.9460.2590.8000.259
모터펌프종류0.0950.8730.1770.2130.0001.0000.8900.7770.5060.455
모터펌프출력(KW)0.0000.8800.4890.8980.9460.8901.0000.8900.9650.611
자가발전기종류0.6640.9370.7610.5820.2590.7770.8901.0000.9760.272
자가발전기출력(KW)0.6480.9340.9020.8070.8000.5060.9650.9761.0000.619
용도구분0.0000.3130.1600.1130.2590.4550.6110.2720.6191.000
2024-04-18T01:32:32.793569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
모터펌프종류자가발전기종류자치구용도구분시설유형등록년도
모터펌프종류1.0000.2910.4230.1900.0860.043
자가발전기종류0.2911.0000.5160.1280.4700.426
자치구0.4230.5161.0000.1250.1800.000
용도구분0.1900.1280.1251.0000.0920.000
시설유형0.0860.4700.1800.0921.0000.253
등록년도0.0430.4260.0000.0000.2531.000
2024-04-18T01:32:32.877432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
설치심도(m)양수량(톤/일)등록년도자치구시설유형모터펌프종류자가발전기종류용도구분
설치심도(m)1.0000.1110.0000.2030.0330.0820.2510.037
양수량(톤/일)0.1111.0000.0000.1790.0450.0000.1060.139
등록년도0.0000.0001.0000.0000.2530.0430.4260.000
자치구0.2030.1790.0001.0000.1800.4230.5160.125
시설유형0.0330.0450.2530.1801.0000.0860.4700.092
모터펌프종류0.0820.0000.0430.4230.0861.0000.2910.190
자가발전기종류0.2510.1060.4260.5160.4700.2911.0000.128
용도구분0.0370.1390.0000.1250.0920.1900.1281.000

Missing values

2024-04-18T01:32:29.570868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-18T01:32:29.697258image/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.
2024-04-18T01:32:29.802158image/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

등록년도자치구시설유형시설주소설치심도(m)양수량(톤/일)모터펌프종류모터펌프출력(KW)자가발전기종류자가발전기출력(KW)용도구분
02019양천구민간지정남부순환로 74길 2100.086.7수중펌프1.5--생활용수
12019양천구민간지정은행정로 590.087.6수중펌프1.5--생활용수
22019양천구민간지정신월로 185100.081.0수중펌프1.5--생활용수
32019양천구민간지정지양로 67130.082.0수중펌프1.5--생활용수
42019양천구민간지정신정로 7길 17200.060.0수중펌프2.25--생활용수
52019양천구민간지정신정로 146100.068.0수중펌프0.75--생활용수
62019양천구민간지정남부순환로 311100.067.0수중펌프0.75--생활용수
72019양천구민간지정안양천로 667100.065.0수중펌프0.75--생활용수
82019양천구민간지정등촌로 22070.077.0수중펌프1.5--생활용수
92019양천구민간지정목동중앙로 143, 2동 (목동,우성아파트)100.090.0수중펌프2.25--음용수
등록년도자치구시설유형시설주소설치심도(m)양수량(톤/일)모터펌프종류모터펌프출력(KW)자가발전기종류자가발전기출력(KW)용도구분
57712016송파구민간지정장지동 686-6(장지동)150.0130.0수중모터2<NA><NA>생활용수
57722016송파구민간지정양산로4길 9(거여동)100.060.0수중모터1<NA><NA>생활용수
57732016송파구민간지정송파대로28길 11 (가락동)100.080.0수중모터2<NA><NA>생활용수
57742016송파구민간지정올림픽로34길 21 (방이동)100.050.0수중모터1<NA><NA>생활용수
57752016송파구민간지정충민로6길 17 (장지동)100.050.0'1<NA><NA>생활용수
57762016송파구민간지정동남로 160 (문정동)8.030.0'0.5<NA><NA>음용수
57772016송파구민간지정동남로18길 40 (가락동)125.050.0'1<NA><NA>생활용수
57782016송파구민간지정오금로 396 (가락동)80.060.0수중모터2<NA><NA>생활용수
57792016송파구민간지정오금로 296 (가락동)70.066.0'2<NA><NA>생활용수
57802016송파구민간지정백제고분로22길 9 (삼전동)127.070.0'2<NA><NA>생활용수

Duplicate rows

Most frequently occurring

등록년도자치구시설유형시설주소설치심도(m)양수량(톤/일)모터펌프종류모터펌프출력(KW)자가발전기종류자가발전기출력(KW)용도구분# duplicates
2382018관악구민간지정봉천로23길92(보라매동)12.036.0지상모터0.5<NA><NA>음용수4
5382018동작구공공지정여의대방로20길 33(신대방동)400.0184.0수중펌프3.7<NA><NA>생활용수4
7352018송파구민간지정올림픽로 424 (방이동)100.0130.0'3.75<NA><NA>생활용수4
9642019송파구민간지정올림픽로 424 (방이동)100.0130.0'3.75<NA><NA>생활용수4
52016서초구공공지정서울특별시 서초구 신반포로16길 30 (반포동)150.0300.0수중7.5<NA><NA>생활용수3
72016송파구민간지정올림픽로 424 (방이동)120.0130.0'7.5<NA><NA>생활용수3
6522018서초구공공지정서울특별시 서초구 신반포로16길 30 (반포동)150.0300.0수중7.5<NA><NA>생활용수3
6532018서초구공공지정신반포로16길 30 (반포동)150.0300.0수중7.5<NA><NA>생활용수3
7172018성북구민간지정한천로76길 42 (석관동)10.032.0육상펌프0.15<NA><NA>생활용수3
7362018송파구민간지정올림픽로 424 (방이동)120.0130.0'7.5<NA><NA>생활용수3