Overview

Dataset statistics

Number of variables5
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory478.5 KiB
Average record size in memory49.0 B

Variable types

DateTime1
Categorical1
Text2
Numeric1

Dataset

Description김해도시개발공사 하수처리시설별에 대한 일자별 가동시간 현황을 조회하는 서비스로 기준연월일, 하수처리장구분명, 가동시간 등의 정보를 제공
Author김해시도시개발공사
URLhttps://www.data.go.kr/data/15096570/fileData.do

Alerts

가동시간 has 3744 (37.4%) zerosZeros

Reproduction

Analysis started2023-12-12 12:33:58.285893
Analysis finished2023-12-12 12:33:59.323275
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

기준연월일
Date

Distinct776
Distinct (%)7.8%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Minimum2011-03-13 00:00:00
Maximum2017-05-27 00:00:00
2023-12-12T21:33:59.432134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:33:59.621280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

하수처리장구분명
Categorical

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
안하 하수처리장
4736 
낙산마을 하수처리장
1068 
상동 공공하수처리시설
1016 
하사촌마을 하수처리장
808 
생철마을 하수처리장
662 
Other values (8)
1710 

Length

Max length13
Median length8
Mean length9.351
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row안하 하수처리장
2nd row하사촌마을 하수처리장
3rd row하사촌마을 하수처리장
4th row상동 공공하수처리시설
5th row안하 하수처리장

Common Values

ValueCountFrequency (%)
안하 하수처리장 4736
47.4%
낙산마을 하수처리장 1068
 
10.7%
상동 공공하수처리시설 1016
 
10.2%
하사촌마을 하수처리장 808
 
8.1%
생철마을 하수처리장 662
 
6.6%
독산마을 하수처리장 431
 
4.3%
신안,안양마을 하수처리장 364
 
3.6%
가달 하수처리장 231
 
2.3%
상동여차 마을하수처리장 208
 
2.1%
용산마을 하수처리장 203
 
2.0%
Other values (3) 273
 
2.7%

Length

2023-12-12T21:33:59.797399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
하수처리장 8776
43.9%
안하 4736
23.7%
낙산마을 1068
 
5.3%
상동 1016
 
5.1%
공공하수처리시설 1016
 
5.1%
하사촌마을 808
 
4.0%
생철마을 662
 
3.3%
독산마을 431
 
2.2%
신안,안양마을 364
 
1.8%
가달 231
 
1.2%
Other values (6) 892
 
4.5%

태그설명
Text

Distinct143
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:34:00.164098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length10
Mean length7.106
Min length3

Characters and Unicode

Total characters71060
Distinct characters145
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row드럼 스크린
2nd row유량조정조 펌프A
3rd row디켄트B
4th row총인유입펌프
5th row슬러지 농축설비
ValueCountFrequency (%)
교반기 1219
 
7.7%
공급펌프 900
 
5.7%
슬러지 757
 
4.8%
송풍기 530
 
3.3%
교대반응조 505
 
3.2%
naoh 466
 
2.9%
유량조정조 420
 
2.7%
잉여슬러지 380
 
2.4%
펌프a 356
 
2.2%
펌프b 354
 
2.2%
Other values (126) 9957
62.8%
2023-12-12T21:34:00.732438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5844
 
8.2%
프 5750
 
8.1%
펌 5614
 
7.9%
기 3161
 
4.4%
반 2631
 
3.7%
송 2317
 
3.3%
A 2211
 
3.1%
조 2114
 
3.0%
교 2051
 
2.9%
B 1863
 
2.6%
Other values (135) 37504
52.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 57687
81.2%
Uppercase Letter 6619
 
9.3%
Space Separator 5844
 
8.2%
Lowercase Letter 580
 
0.8%
Decimal Number 264
 
0.4%
Other Punctuation 66
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
프 5750
 
10.0%
펌 5614
 
9.7%
기 3161
 
5.5%
반 2631
 
4.6%
송 2317
 
4.0%
조 2114
 
3.7%
교 2051
 
3.6%
러 1437
 
2.5%
지 1437
 
2.5%
슬 1437
 
2.5%
Other values (116) 29738
51.6%
Uppercase Letter
ValueCountFrequency (%)
A 2211
33.4%
B 1863
28.1%
N 466
 
7.0%
O 466
 
7.0%
H 466
 
7.0%
U 295
 
4.5%
M 295
 
4.5%
L 295
 
4.5%
C 182
 
2.7%
D 47
 
0.7%
Lowercase Letter
ValueCountFrequency (%)
a 466
80.3%
u 38
 
6.6%
m 38
 
6.6%
l 38
 
6.6%
Decimal Number
ValueCountFrequency (%)
2 135
51.1%
1 129
48.9%
Space Separator
ValueCountFrequency (%)
5844
100.0%
Other Punctuation
ValueCountFrequency (%)
. 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 57687
81.2%
Latin 7199
 
10.1%
Common 6174
 
8.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
프 5750
 
10.0%
펌 5614
 
9.7%
기 3161
 
5.5%
반 2631
 
4.6%
송 2317
 
4.0%
조 2114
 
3.7%
교 2051
 
3.6%
러 1437
 
2.5%
지 1437
 
2.5%
슬 1437
 
2.5%
Other values (116) 29738
51.6%
Latin
ValueCountFrequency (%)
A 2211
30.7%
B 1863
25.9%
N 466
 
6.5%
a 466
 
6.5%
O 466
 
6.5%
H 466
 
6.5%
U 295
 
4.1%
M 295
 
4.1%
L 295
 
4.1%
C 182
 
2.5%
Other values (5) 194
 
2.7%
Common
ValueCountFrequency (%)
5844
94.7%
2 135
 
2.2%
1 129
 
2.1%
. 66
 
1.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 57687
81.2%
ASCII 13373
 
18.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5844
43.7%
A 2211
 
16.5%
B 1863
 
13.9%
N 466
 
3.5%
a 466
 
3.5%
O 466
 
3.5%
H 466
 
3.5%
U 295
 
2.2%
M 295
 
2.2%
L 295
 
2.2%
Other values (9) 706
 
5.3%
Hangul
ValueCountFrequency (%)
프 5750
 
10.0%
펌 5614
 
9.7%
기 3161
 
5.5%
반 2631
 
4.6%
송 2317
 
4.0%
조 2114
 
3.7%
교 2051
 
3.6%
러 1437
 
2.5%
지 1437
 
2.5%
슬 1437
 
2.5%
Other values (116) 29738
51.6%

태그
Text

Distinct196
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-12T21:34:01.126360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length9
Mean length6.0801
Min length3

Characters and Unicode

Total characters60801
Distinct characters86
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowM-106B
2nd row유량조정조 펌프A
3rd row디켄트B
4th rowM-102B
5th rowM-502
ValueCountFrequency (%)
펌프a 356
 
3.1%
펌프b 354
 
3.1%
유량조정조 249
 
2.2%
슬러지 231
 
2.0%
송풍기a 219
 
1.9%
송풍기b 206
 
1.8%
부로와b 195
 
1.7%
저류조 195
 
1.7%
부로와a 173
 
1.5%
내부순환 165
 
1.4%
Other values (179) 9105
79.5%
2023-12-12T21:34:01.631977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 6291
 
10.3%
- 5697
 
9.4%
M 5655
 
9.3%
A 3872
 
6.4%
B 3818
 
6.3%
1 3177
 
5.2%
0 2979
 
4.9%
펌 2587
 
4.3%
프 2587
 
4.3%
1448
 
2.4%
Other values (76) 22690
37.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 21302
35.0%
Decimal Number 17227
28.3%
Uppercase Letter 15061
24.8%
Dash Punctuation 5697
 
9.4%
Space Separator 1448
 
2.4%
Other Punctuation 66
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
펌 2587
 
12.1%
프 2587
 
12.1%
송 1236
 
5.8%
기 1163
 
5.5%
조 840
 
3.9%
부 689
 
3.2%
풍 603
 
2.8%
수 549
 
2.6%
반 540
 
2.5%
중 531
 
2.5%
Other values (55) 9977
46.8%
Decimal Number
ValueCountFrequency (%)
2 6291
36.5%
1 3177
18.4%
0 2979
17.3%
4 964
 
5.6%
3 850
 
4.9%
9 782
 
4.5%
6 596
 
3.5%
8 595
 
3.5%
5 522
 
3.0%
7 471
 
2.7%
Uppercase Letter
ValueCountFrequency (%)
M 5655
37.5%
A 3872
25.7%
B 3818
25.4%
C 752
 
5.0%
S 538
 
3.6%
D 384
 
2.5%
P 32
 
0.2%
O 10
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 5697
100.0%
Space Separator
ValueCountFrequency (%)
1448
100.0%
Other Punctuation
ValueCountFrequency (%)
. 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 24438
40.2%
Hangul 21302
35.0%
Latin 15061
24.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
펌 2587
 
12.1%
프 2587
 
12.1%
송 1236
 
5.8%
기 1163
 
5.5%
조 840
 
3.9%
부 689
 
3.2%
풍 603
 
2.8%
수 549
 
2.6%
반 540
 
2.5%
중 531
 
2.5%
Other values (55) 9977
46.8%
Common
ValueCountFrequency (%)
2 6291
25.7%
- 5697
23.3%
1 3177
13.0%
0 2979
12.2%
1448
 
5.9%
4 964
 
3.9%
3 850
 
3.5%
9 782
 
3.2%
6 596
 
2.4%
8 595
 
2.4%
Other values (3) 1059
 
4.3%
Latin
ValueCountFrequency (%)
M 5655
37.5%
A 3872
25.7%
B 3818
25.4%
C 752
 
5.0%
S 538
 
3.6%
D 384
 
2.5%
P 32
 
0.2%
O 10
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 39499
65.0%
Hangul 21302
35.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 6291
15.9%
- 5697
14.4%
M 5655
14.3%
A 3872
9.8%
B 3818
9.7%
1 3177
8.0%
0 2979
7.5%
1448
 
3.7%
4 964
 
2.4%
3 850
 
2.2%
Other values (11) 4748
12.0%
Hangul
ValueCountFrequency (%)
펌 2587
 
12.1%
프 2587
 
12.1%
송 1236
 
5.8%
기 1163
 
5.5%
조 840
 
3.9%
부 689
 
3.2%
풍 603
 
2.8%
수 549
 
2.6%
반 540
 
2.5%
중 531
 
2.5%
Other values (55) 9977
46.8%

가동시간
Real number (ℝ)

ZEROS 

Distinct1090
Distinct (%)10.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.7418
Minimum0
Maximum24736
Zeros3744
Zeros (%)37.4%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-12T21:34:01.838333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median100
Q3795.25
95-th percentile1440
Maximum24736
Range24736
Interquartile range (IQR)795.25

Descriptive statistics

Standard deviation699.66761
Coefficient of variation (CV)1.5874773
Kurtosis277.27935
Mean440.7418
Median Absolute Deviation (MAD)100
Skewness9.7522513
Sum4407418
Variance489534.77
MonotonicityNot monotonic
2023-12-12T21:34:02.032207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 3744
37.4%
1440 1678
16.8%
720 78
 
0.8%
120 71
 
0.7%
719 67
 
0.7%
600 54
 
0.5%
60 51
 
0.5%
240 49
 
0.5%
2 44
 
0.4%
4 43
 
0.4%
Other values (1080) 4121
41.2%
ValueCountFrequency (%)
0 3744
37.4%
1 28
 
0.3%
2 44
 
0.4%
3 36
 
0.4%
4 43
 
0.4%
5 27
 
0.3%
6 28
 
0.3%
7 22
 
0.2%
8 15
 
0.1%
9 17
 
0.2%
ValueCountFrequency (%)
24736 1
 
< 0.1%
23222 1
 
< 0.1%
11710 1
 
< 0.1%
10406 1
 
< 0.1%
8984 3
< 0.1%
8052 1
 
< 0.1%
7796 1
 
< 0.1%
7077 1
 
< 0.1%
5600 1
 
< 0.1%
4815 1
 
< 0.1%

Interactions

2023-12-12T21:33:58.649514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:34:02.150771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하수처리장구분명가동시간
하수처리장구분명1.0000.269
가동시간0.2691.000
2023-12-12T21:34:02.257788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
가동시간하수처리장구분명
가동시간1.0000.136
하수처리장구분명0.1361.000

Missing values

2023-12-12T21:33:59.137941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:33:59.265411image/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

기준연월일하수처리장구분명태그설명태그가동시간
608532017-01-11안하 하수처리장드럼 스크린M-106B1440
556332016-12-13하사촌마을 하수처리장유량조정조 펌프A유량조정조 펌프A73
286542016-03-05하사촌마을 하수처리장디켄트B디켄트B0
886722017-05-01상동 공공하수처리시설총인유입펌프M-102B614
106362015-08-22안하 하수처리장슬러지 농축설비M-502520
581142016-12-31상동여차 마을하수처리장백학1.중계펌프A백학1.중계펌프A753
840892017-04-13생철마을 하수처리장교반기교반기902
542422016-12-04독산마을 하수처리장원수이송펌프A원수이송펌프A736
172052015-12-01안하 하수처리장슬러지 농축설비M-502415
639282017-01-24상동 공공하수처리시설유입펌프MS-104B134
기준연월일하수처리장구분명태그설명태그가동시간
417812016-09-08낙산마을 하수처리장송풍기 증설C송풍기 증설C1440
683292017-02-11상동 공공하수처리시설PAC주입펌프M-106B0
435022016-09-20낙산마을 하수처리장슬러지 교반기슬러지 교반기1440
580292016-12-31독산마을 하수처리장교반기A교반기A0
203822016-01-08낙산마을 하수처리장감속기B감속기B1440
664782017-02-03안하 하수처리장슬러지 반송펌프M-208A572
173332015-12-03안하 하수처리장바닥배수펌프M-217B1
666542017-02-04상동여차 마을하수처리장백학1.중계펌프A백학1.중계펌프A275
56692015-06-05안하 하수처리장처리수 흡입펌프M-211B1152
184942015-12-20안하 하수처리장교대반응조 교반기M-204B1191