Overview

Dataset statistics

Number of variables14
Number of observations39
Missing cells2
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory121.4 B

Variable types

Numeric6
Text5
Categorical3

Dataset

Description광주광역시 상수도사업본부에서 운용중인 상수도 가압장에 대한 가압장명,위치,표고,급수가구,시설년도 정보 입니다.
Author광주광역시 상수도사업본부
URLhttps://www.data.go.kr/data/15099841/fileData.do

Alerts

데이터기준일 has constant value ""Constant
연번 is highly overall correlated with 표고(m)High correlation
표고(m) is highly overall correlated with 연번 and 1 other fieldsHigh correlation
양정(m) is highly overall correlated with 2차수압High correlation
1차수압 is highly overall correlated with 표고(m)High correlation
2차수압 is highly overall correlated with 양정(m)High correlation
시 설 개 요 is highly overall correlated with 관경High correlation
관경 is highly overall correlated with 시 설 개 요High correlation
계약용량 has 1 (2.6%) missing valuesMissing
2차수압 has 1 (2.6%) missing valuesMissing
연번 has unique valuesUnique
가압장명 has unique valuesUnique
위치 has unique valuesUnique

Reproduction

Analysis started2023-12-12 17:49:13.535995
Analysis finished2023-12-12 17:49:17.485043
Duration3.95 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T02:49:17.546336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2023-12-13T02:49:17.688085image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.6%
2 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
30 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
39 1
2.6%
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%

가압장명
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T02:49:17.901374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.3846154
Min length2

Characters and Unicode

Total characters93
Distinct characters57
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row내지
2nd row두암4단지
3rd row산수
4th row성촌
5th row신양파크
ValueCountFrequency (%)
내지 1
 
2.6%
월산 1
 
2.6%
진월택지 1
 
2.6%
진제 1
 
2.6%
각화 1
 
2.6%
동명 1
 
2.6%
부영 1
 
2.6%
산수도서관 1
 
2.6%
삼각 1
 
2.6%
진월 1
 
2.6%
Other values (29) 29
74.4%
2023-12-13T02:49:18.308260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 6
 
6.5%
지 5
 
5.4%
월 5
 
5.4%
동 3
 
3.2%
수 3
 
3.2%
곡 3
 
3.2%
진 3
 
3.2%
양 3
 
3.2%
정 3
 
3.2%
서 2
 
2.2%
Other values (47) 57
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 91
97.8%
Space Separator 1
 
1.1%
Decimal Number 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 6
 
6.6%
지 5
 
5.5%
월 5
 
5.5%
동 3
 
3.3%
수 3
 
3.3%
곡 3
 
3.3%
진 3
 
3.3%
양 3
 
3.3%
정 3
 
3.3%
서 2
 
2.2%
Other values (45) 55
60.4%
Space Separator
ValueCountFrequency (%)
1
100.0%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 91
97.8%
Common 2
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 6
 
6.6%
지 5
 
5.5%
월 5
 
5.5%
동 3
 
3.3%
수 3
 
3.3%
곡 3
 
3.3%
진 3
 
3.3%
양 3
 
3.3%
정 3
 
3.3%
서 2
 
2.2%
Other values (45) 55
60.4%
Common
ValueCountFrequency (%)
1
50.0%
4 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 91
97.8%
ASCII 2
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 6
 
6.6%
지 5
 
5.5%
월 5
 
5.5%
동 3
 
3.3%
수 3
 
3.3%
곡 3
 
3.3%
진 3
 
3.3%
양 3
 
3.3%
정 3
 
3.3%
서 2
 
2.2%
Other values (45) 55
60.4%
ASCII
ValueCountFrequency (%)
1
50.0%
4 1
50.0%

위치
Text

UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T02:49:18.567549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length31
Median length27
Mean length18.461538
Min length10

Characters and Unicode

Total characters720
Distinct characters76
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)100.0%

Sample

1st row 동구 내남동 68
2nd row 동구 산수동 568-2
3rd row 동구 밤실로 37-1 (동구 지산동 706-1)
4th row 동구 의재로 138 (동구 운림동 648-1)
5th row 동구 무등로 548 (동구 산수동 95-6)
ValueCountFrequency (%)
동구 20
 
12.0%
남구 15
 
9.0%
북구 14
 
8.4%
산수동 4
 
2.4%
서구 3
 
1.8%
두암동 3
 
1.8%
진월동 3
 
1.8%
무등로 2
 
1.2%
노대동 2
 
1.2%
지산동 2
 
1.2%
Other values (92) 99
59.3%
2023-12-13T02:49:19.330992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
167
23.2%
동 59
 
8.2%
구 55
 
7.6%
1 37
 
5.1%
- 29
 
4.0%
3 25
 
3.5%
5 25
 
3.5%
2 20
 
2.8%
남 19
 
2.6%
7 18
 
2.5%
Other values (66) 266
36.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 302
41.9%
Decimal Number 190
26.4%
Space Separator 167
23.2%
Dash Punctuation 29
 
4.0%
Open Punctuation 16
 
2.2%
Close Punctuation 16
 
2.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 59
19.5%
구 55
18.2%
남 19
 
6.3%
산 16
 
5.3%
북 15
 
5.0%
로 14
 
4.6%
월 10
 
3.3%
길 9
 
3.0%
수 5
 
1.7%
서 5
 
1.7%
Other values (52) 95
31.5%
Decimal Number
ValueCountFrequency (%)
1 37
19.5%
3 25
13.2%
5 25
13.2%
2 20
10.5%
7 18
9.5%
6 17
8.9%
4 16
8.4%
8 14
 
7.4%
9 10
 
5.3%
0 8
 
4.2%
Space Separator
ValueCountFrequency (%)
167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 16
100.0%
Close Punctuation
ValueCountFrequency (%)
) 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 418
58.1%
Hangul 302
41.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 59
19.5%
구 55
18.2%
남 19
 
6.3%
산 16
 
5.3%
북 15
 
5.0%
로 14
 
4.6%
월 10
 
3.3%
길 9
 
3.0%
수 5
 
1.7%
서 5
 
1.7%
Other values (52) 95
31.5%
Common
ValueCountFrequency (%)
167
40.0%
1 37
 
8.9%
- 29
 
6.9%
3 25
 
6.0%
5 25
 
6.0%
2 20
 
4.8%
7 18
 
4.3%
6 17
 
4.1%
( 16
 
3.8%
) 16
 
3.8%
Other values (4) 48
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 418
58.1%
Hangul 302
41.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
167
40.0%
1 37
 
8.9%
- 29
 
6.9%
3 25
 
6.0%
5 25
 
6.0%
2 20
 
4.8%
7 18
 
4.3%
6 17
 
4.1%
( 16
 
3.8%
) 16
 
3.8%
Other values (4) 48
 
11.5%
Hangul
ValueCountFrequency (%)
동 59
19.5%
구 55
18.2%
남 19
 
6.3%
산 16
 
5.3%
북 15
 
5.0%
로 14
 
4.6%
월 10
 
3.3%
길 9
 
3.0%
수 5
 
1.7%
서 5
 
1.7%
Other values (52) 95
31.5%

시 설 개 요
Categorical

HIGH CORRELATION 

Distinct18
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Memory size444.0 B
인버터방식(그런포스)입형 / 펌프: 4HP(3kw)-3대
6 
인버터방식(그런포스)입형 / 펌프: 3HP(2.2kw)-3대
6 
인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-3대
5 
인버터방식(그런포스)입형 / 펌프: 2HP(1.5kw)-3대
3 
인버터방식(그런포스)입형 / 펌프: 1.5HP(1.1kw)-3대
3 
Other values (13)
16 

Length

Max length37
Median length35
Mean length32.871795
Min length27

Unique

Unique10 ?
Unique (%)25.6%

Sample

1st row인버터방식(그런포스)입형 / 펌프 : 4HP(3kw)-3대
2nd row인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-4대
3rd row인버터방식(그런포스)입형 / 펌프: 30HP(22kw)-4대
4th row인버터방식(그런포스)입형 / 펌프: 15HP(11kw)-3대
5th row인버터방식(윌로)입형 / 펌프: 15HP(11kw)-2대

Common Values

ValueCountFrequency (%)
인버터방식(그런포스)입형 / 펌프: 4HP(3kw)-3대 6
15.4%
인버터방식(그런포스)입형 / 펌프: 3HP(2.2kw)-3대 6
15.4%
인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-3대 5
12.8%
인버터방식(그런포스)입형 / 펌프: 2HP(1.5kw)-3대 3
 
7.7%
인버터방식(그런포스)입형 / 펌프: 1.5HP(1.1kw)-3대 3
 
7.7%
인버터방식(그런포스)입형 / 펌프: 5.5HP(4kw)-3대 2
 
5.1%
인버터방식(그런포스)입형 / 펌프 : 4HP(3kw)-3대 2
 
5.1%
인버터방식(그런포스)입형 / 펌프: 25HP(18.5kw)-3대 2
 
5.1%
인버터방식(그런포스)입형 / 펌프: 30HP(22kw)-4대 1
 
2.6%
입형(윌로) / 펌프: 40HP(30kw)-2대 1
 
2.6%
Other values (8) 8
20.5%

Length

2023-12-13T02:49:19.498043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
41
26.1%
펌프 38
24.2%
인버터방식(그런포스)입형 34
21.7%
4hp(3kw)-3대 8
 
5.1%
3hp(2.2kw)-3대 6
 
3.8%
7.5hp(5.5kw)-3대 5
 
3.2%
2hp(1.5kw)-3대 3
 
1.9%
1.5hp(1.1kw)-3대 3
 
1.9%
인버터방식(윌로)입형 3
 
1.9%
5.5hp(4kw)-3대 2
 
1.3%
Other values (13) 14
 
8.9%

계약용량
Text

MISSING 

Distinct21
Distinct (%)55.3%
Missing1
Missing (%)2.6%
Memory size444.0 B
2023-12-13T02:49:19.661679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length3.6578947
Min length3

Characters and Unicode

Total characters139
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)31.6%

Sample

1st row산)14
2nd row산)28
3rd row산)90
4th row산)23
5th row산)31
ValueCountFrequency (%)
산)12 5
12.8%
산)7 5
12.8%
산)14 3
 
7.7%
산)6 3
 
7.7%
산)31 2
 
5.1%
산)15 2
 
5.1%
산)5 2
 
5.1%
산)56 2
 
5.1%
산)4 2
 
5.1%
산)22 1
 
2.6%
Other values (12) 12
30.8%
2023-12-13T02:49:19.979866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 38
27.3%
) 38
27.3%
1 15
 
10.8%
2 9
 
6.5%
7 8
 
5.8%
5 8
 
5.8%
6 6
 
4.3%
4 5
 
3.6%
3 5
 
3.6%
0 3
 
2.2%
Other values (3) 4
 
2.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
44.6%
Other Letter 38
27.3%
Close Punctuation 38
27.3%
Space Separator 1
 
0.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
24.2%
2 9
14.5%
7 8
12.9%
5 8
12.9%
6 6
 
9.7%
4 5
 
8.1%
3 5
 
8.1%
0 3
 
4.8%
9 2
 
3.2%
8 1
 
1.6%
Other Letter
ValueCountFrequency (%)
산 38
100.0%
Close Punctuation
ValueCountFrequency (%)
) 38
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 101
72.7%
Hangul 38
 
27.3%

Most frequent character per script

Common
ValueCountFrequency (%)
) 38
37.6%
1 15
 
14.9%
2 9
 
8.9%
7 8
 
7.9%
5 8
 
7.9%
6 6
 
5.9%
4 5
 
5.0%
3 5
 
5.0%
0 3
 
3.0%
9 2
 
2.0%
Other values (2) 2
 
2.0%
Hangul
ValueCountFrequency (%)
산 38
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 101
72.7%
Hangul 38
 
27.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 38
100.0%
ASCII
ValueCountFrequency (%)
) 38
37.6%
1 15
 
14.9%
2 9
 
8.9%
7 8
 
7.9%
5 8
 
7.9%
6 6
 
5.9%
4 5
 
5.0%
3 5
 
5.0%
0 3
 
3.0%
9 2
 
2.0%
Other values (2) 2
 
2.0%

표고(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.225641
Minimum41
Maximum147.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T02:49:20.119168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum41
5-th percentile49.8
Q168
median72.2
Q387.5
95-th percentile102.8
Maximum147.3
Range106.3
Interquartile range (IQR)19.5

Descriptive statistics

Standard deviation20.20647
Coefficient of variation (CV)0.26508757
Kurtosis2.7569346
Mean76.225641
Median Absolute Deviation (MAD)12.8
Skewness1.1112432
Sum2972.8
Variance408.30143
MonotonicityNot monotonic
2023-12-13T02:49:20.257264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
100.0 3
 
7.7%
70.0 3
 
7.7%
72.0 2
 
5.1%
76.0 2
 
5.1%
69.7 2
 
5.1%
88.0 2
 
5.1%
55.0 2
 
5.1%
48.0 1
 
2.6%
52.3 1
 
2.6%
75.0 1
 
2.6%
Other values (20) 20
51.3%
ValueCountFrequency (%)
41.0 1
2.6%
48.0 1
2.6%
50.0 1
2.6%
52.3 1
2.6%
53.5 1
2.6%
54.0 1
2.6%
55.0 2
5.1%
65.0 1
2.6%
66.3 1
2.6%
69.7 2
5.1%
ValueCountFrequency (%)
147.3 1
 
2.6%
110.0 1
 
2.6%
102.0 1
 
2.6%
100.0 3
7.7%
98.6 1
 
2.6%
91.0 1
 
2.6%
88.0 2
5.1%
87.0 1
 
2.6%
85.0 1
 
2.6%
78.0 1
 
2.6%

급수지역정점표고
Text

Distinct32
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T02:49:20.457796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length5
Mean length3.2564103
Min length2

Characters and Unicode

Total characters127
Distinct characters20
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)64.1%

Sample

1st row160
2nd row110
3rd row132
4th row130
5th row120
ValueCountFrequency (%)
95 2
 
5.0%
89 2
 
5.0%
133 2
 
5.0%
120 2
 
5.0%
132 2
 
5.0%
93 2
 
5.0%
104 2
 
5.0%
78 1
 
2.5%
550 1
 
2.5%
64 1
 
2.5%
Other values (23) 23
57.5%
2023-12-13T02:49:20.845828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
17.3%
0 18
14.2%
9 14
11.0%
3 13
10.2%
6 8
 
6.3%
5 8
 
6.3%
8 8
 
6.3%
. 7
 
5.5%
7 6
 
4.7%
2 6
 
4.7%
Other values (10) 17
13.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 107
84.3%
Other Punctuation 9
 
7.1%
Lowercase Letter 6
 
4.7%
Other Letter 4
 
3.1%
Space Separator 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 22
20.6%
0 18
16.8%
9 14
13.1%
3 13
12.1%
6 8
 
7.5%
5 8
 
7.5%
8 8
 
7.5%
7 6
 
5.6%
2 6
 
5.6%
4 4
 
3.7%
Other Letter
ValueCountFrequency (%)
금 1
25.0%
광 1
25.0%
한 1
25.0%
국 1
25.0%
Lowercase Letter
ValueCountFrequency (%)
a 2
33.3%
p 2
33.3%
t 2
33.3%
Other Punctuation
ValueCountFrequency (%)
. 7
77.8%
: 2
 
22.2%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117
92.1%
Latin 6
 
4.7%
Hangul 4
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 22
18.8%
0 18
15.4%
9 14
12.0%
3 13
11.1%
6 8
 
6.8%
5 8
 
6.8%
8 8
 
6.8%
. 7
 
6.0%
7 6
 
5.1%
2 6
 
5.1%
Other values (3) 7
 
6.0%
Hangul
ValueCountFrequency (%)
금 1
25.0%
광 1
25.0%
한 1
25.0%
국 1
25.0%
Latin
ValueCountFrequency (%)
a 2
33.3%
p 2
33.3%
t 2
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123
96.9%
Hangul 4
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 22
17.9%
0 18
14.6%
9 14
11.4%
3 13
10.6%
6 8
 
6.5%
5 8
 
6.5%
8 8
 
6.5%
. 7
 
5.7%
7 6
 
4.9%
2 6
 
4.9%
Other values (6) 13
10.6%
Hangul
ValueCountFrequency (%)
금 1
25.0%
광 1
25.0%
한 1
25.0%
국 1
25.0%

양정(m)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean45.248718
Minimum8.7
Maximum90
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T02:49:21.016211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.7
5-th percentile19.94
Q134.5
median45
Q351
95-th percentile75
Maximum90
Range81.3
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation17.899719
Coefficient of variation (CV)0.3955851
Kurtosis0.029731887
Mean45.248718
Median Absolute Deviation (MAD)10
Skewness0.32655581
Sum1764.7
Variance320.39993
MonotonicityNot monotonic
2023-12-13T02:49:21.151417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
35.0 3
 
7.7%
50.0 3
 
7.7%
75.0 3
 
7.7%
51.0 3
 
7.7%
45.0 3
 
7.7%
60.0 2
 
5.1%
40.0 2
 
5.1%
31.0 2
 
5.1%
48.0 2
 
5.1%
20.0 2
 
5.1%
Other values (13) 14
35.9%
ValueCountFrequency (%)
8.7 1
 
2.6%
19.4 1
 
2.6%
20.0 2
5.1%
21.6 1
 
2.6%
22.0 1
 
2.6%
31.0 2
5.1%
32.0 1
 
2.6%
34.0 1
 
2.6%
35.0 3
7.7%
36.0 1
 
2.6%
ValueCountFrequency (%)
90.0 1
 
2.6%
75.0 3
7.7%
70.0 1
 
2.6%
65.0 1
 
2.6%
64.0 1
 
2.6%
60.0 2
5.1%
51.0 3
7.7%
50.0 3
7.7%
48.0 2
5.1%
46.0 2
5.1%

급수가구
Text

Distinct36
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
2023-12-13T02:49:21.345386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.2564103
Min length1

Characters and Unicode

Total characters88
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

Unique33 ?
Unique (%)84.6%

Sample

1st row70
2nd row144
3rd row521
4th row202
5th row5
ValueCountFrequency (%)
8 2
 
5.1%
5 2
 
5.1%
1 2
 
5.1%
69 1
 
2.6%
154 1
 
2.6%
57 1
 
2.6%
165 1
 
2.6%
255 1
 
2.6%
67 1
 
2.6%
164 1
 
2.6%
Other values (26) 26
66.7%
2023-12-13T02:49:21.712641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15
17.0%
5 15
17.0%
2 12
13.6%
4 9
10.2%
7 9
10.2%
6 8
9.1%
3 7
8.0%
9 5
 
5.7%
8 4
 
4.5%
0 3
 
3.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 87
98.9%
Other Punctuation 1
 
1.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
17.2%
5 15
17.2%
2 12
13.8%
4 9
10.3%
7 9
10.3%
6 8
9.2%
3 7
8.0%
9 5
 
5.7%
8 4
 
4.6%
0 3
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 88
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
17.0%
5 15
17.0%
2 12
13.6%
4 9
10.2%
7 9
10.2%
6 8
9.1%
3 7
8.0%
9 5
 
5.7%
8 4
 
4.5%
0 3
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 88
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
17.0%
5 15
17.0%
2 12
13.6%
4 9
10.2%
7 9
10.2%
6 8
9.1%
3 7
8.0%
9 5
 
5.7%
8 4
 
4.5%
0 3
 
3.4%

시설년도
Real number (ℝ)

Distinct23
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2007.0256
Minimum1985
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T02:49:21.846563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1985
5-th percentile1993
Q11998
median2008
Q32015
95-th percentile2019.1
Maximum2021
Range36
Interquartile range (IQR)17

Descriptive statistics

Standard deviation9.6749464
Coefficient of variation (CV)0.0048205395
Kurtosis-0.98063767
Mean2007.0256
Median Absolute Deviation (MAD)9
Skewness-0.34649466
Sum78274
Variance93.604588
MonotonicityNot monotonic
2023-12-13T02:49:21.976149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2018 4
 
10.3%
1998 3
 
7.7%
1993 3
 
7.7%
2019 2
 
5.1%
1996 2
 
5.1%
2003 2
 
5.1%
2013 2
 
5.1%
2008 2
 
5.1%
2014 2
 
5.1%
2006 2
 
5.1%
Other values (13) 15
38.5%
ValueCountFrequency (%)
1985 1
 
2.6%
1993 3
7.7%
1994 1
 
2.6%
1995 1
 
2.6%
1996 2
5.1%
1998 3
7.7%
2000 1
 
2.6%
2003 2
5.1%
2004 1
 
2.6%
2005 1
 
2.6%
ValueCountFrequency (%)
2021 1
 
2.6%
2020 1
 
2.6%
2019 2
5.1%
2018 4
10.3%
2017 1
 
2.6%
2015 2
5.1%
2014 2
5.1%
2013 2
5.1%
2012 2
5.1%
2009 1
 
2.6%

관경
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)35.9%
Missing0
Missing (%)0.0%
Memory size444.0 B
100/100
10 
80/80
7 
150/150
7 
250/250
2 
200/200
2 
Other values (9)
11 

Length

Max length10
Median length7
Mean length6.6153846
Min length5

Unique

Unique7 ?
Unique (%)17.9%

Sample

1st row100/100
2nd row150/150/80
3rd row300/250
4th row250/250
5th row100/100

Common Values

ValueCountFrequency (%)
100/100 10
25.6%
80/80 7
17.9%
150/150 7
17.9%
250/250 2
 
5.1%
200/200 2
 
5.1%
100/80 2
 
5.1%
350/350 2
 
5.1%
150/150/80 1
 
2.6%
300/250 1
 
2.6%
50/50 1
 
2.6%
Other values (4) 4
 
10.3%

Length

2023-12-13T02:49:22.115681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100/100 10
25.6%
80/80 7
17.9%
150/150 7
17.9%
250/250 2
 
5.1%
200/200 2
 
5.1%
100/80 2
 
5.1%
350/350 2
 
5.1%
150/150/80 1
 
2.6%
300/250 1
 
2.6%
50/50 1
 
2.6%
Other values (4) 4
 
10.3%

1차수압
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6405128
Minimum0.38
Maximum5.7
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T02:49:22.251845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.38
5-th percentile1.16
Q12
median2.5
Q33.15
95-th percentile4.73
Maximum5.7
Range5.32
Interquartile range (IQR)1.15

Descriptive statistics

Standard deviation1.1662399
Coefficient of variation (CV)0.44167175
Kurtosis0.45729527
Mean2.6405128
Median Absolute Deviation (MAD)0.5
Skewness0.6512988
Sum102.98
Variance1.3601155
MonotonicityNot monotonic
2023-12-13T02:49:22.375148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2.1 4
 
10.3%
2.4 3
 
7.7%
2.0 3
 
7.7%
1.2 3
 
7.7%
2.5 2
 
5.1%
2.7 2
 
5.1%
3.0 2
 
5.1%
1.9 2
 
5.1%
3.3 2
 
5.1%
2.8 2
 
5.1%
Other values (14) 14
35.9%
ValueCountFrequency (%)
0.38 1
 
2.6%
0.8 1
 
2.6%
1.2 3
7.7%
1.4 1
 
2.6%
1.8 1
 
2.6%
1.9 2
5.1%
2.0 3
7.7%
2.1 4
10.3%
2.4 3
7.7%
2.5 2
5.1%
ValueCountFrequency (%)
5.7 1
2.6%
5.0 1
2.6%
4.7 1
2.6%
4.6 1
2.6%
4.5 1
2.6%
3.9 1
2.6%
3.7 1
2.6%
3.4 1
2.6%
3.3 2
5.1%
3.0 2
5.1%

2차수압
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)55.3%
Missing1
Missing (%)2.6%
Infinite0
Infinite (%)0.0%
Mean5.0131579
Minimum2.5
Maximum8.3
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T02:49:22.483660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2.5
5-th percentile3.34
Q14
median4.6
Q35.65
95-th percentile7.575
Maximum8.3
Range5.8
Interquartile range (IQR)1.65

Descriptive statistics

Standard deviation1.4050432
Coefficient of variation (CV)0.28027109
Kurtosis-0.1933336
Mean5.0131579
Median Absolute Deviation (MAD)0.85
Skewness0.61570585
Sum190.5
Variance1.9741465
MonotonicityNot monotonic
2023-12-13T02:49:22.591730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
6.5 4
 
10.3%
4.5 4
 
10.3%
3.5 3
 
7.7%
4.6 3
 
7.7%
4.0 3
 
7.7%
5.5 3
 
7.7%
5.0 3
 
7.7%
3.8 2
 
5.1%
4.4 1
 
2.6%
5.1 1
 
2.6%
Other values (11) 11
28.2%
ValueCountFrequency (%)
2.5 1
 
2.6%
3.0 1
 
2.6%
3.4 1
 
2.6%
3.5 3
7.7%
3.8 2
5.1%
3.9 1
 
2.6%
4.0 3
7.7%
4.4 1
 
2.6%
4.5 4
10.3%
4.6 3
7.7%
ValueCountFrequency (%)
8.3 1
 
2.6%
8.0 1
 
2.6%
7.5 1
 
2.6%
7.2 1
 
2.6%
6.8 1
 
2.6%
6.5 4
10.3%
5.7 1
 
2.6%
5.5 3
7.7%
5.3 1
 
2.6%
5.1 1
 
2.6%

데이터기준일
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
2022-10-28
39 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-10-28
2nd row2022-10-28
3rd row2022-10-28
4th row2022-10-28
5th row2022-10-28

Common Values

ValueCountFrequency (%)
2022-10-28 39
100.0%

Length

2023-12-13T02:49:22.754619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T02:49:22.859024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-10-28 39
100.0%

Interactions

2023-12-13T02:49:16.511235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.104954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.614332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.130186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.575235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.074564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.610819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.194610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.700472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.215908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.665659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.154222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.695721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.276119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.774864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.286072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.743325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.224791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.841299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.364428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.857366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.359734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.837308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.297790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.947538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.451210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.954379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.434660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.919866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.368509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:17.018074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:14.535858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.055574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:15.500462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.000781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T02:49:16.432481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T02:49:22.951018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번가압장명위치시 설 개 요계약용량표고(m)급수지역정점표고양정(m)급수가구시설년도관경1차수압2차수압
연번1.0001.0001.0000.0000.0000.2940.7760.7080.8020.4740.5170.6480.338
가압장명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
위치1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시 설 개 요0.0001.0001.0001.0000.8950.0000.7850.0000.9540.0000.9130.2760.583
계약용량0.0001.0001.0000.8951.0000.0000.9430.6100.9720.6680.9560.4740.745
표고(m)0.2941.0001.0000.0000.0001.0000.9430.4560.0000.7590.2970.6090.666
급수지역정점표고0.7761.0001.0000.7850.9430.9431.0000.7650.9270.9810.9310.7880.871
양정(m)0.7081.0001.0000.0000.6100.4560.7651.0000.4110.3340.0000.0000.742
급수가구0.8021.0001.0000.9540.9720.0000.9270.4111.0000.8550.9680.8530.955
시설년도0.4741.0001.0000.0000.6680.7590.9810.3340.8551.0000.4680.5080.515
관경0.5171.0001.0000.9130.9560.2970.9310.0000.9680.4681.0000.4280.130
1차수압0.6481.0001.0000.2760.4740.6090.7880.0000.8530.5080.4281.0000.378
2차수압0.3381.0001.0000.5830.7450.6660.8710.7420.9550.5150.1300.3781.000
2023-12-13T02:49:23.088919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
관경시 설 개 요
관경1.0000.553
시 설 개 요0.5531.000
2023-12-13T02:49:23.170068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번표고(m)양정(m)시설년도1차수압2차수압시 설 개 요관경
연번1.000-0.677-0.2340.1240.225-0.0590.0000.239
표고(m)-0.6771.0000.271-0.132-0.570-0.0400.0000.065
양정(m)-0.2340.2711.000-0.352-0.1390.5710.0000.000
시설년도0.124-0.132-0.3521.0000.296-0.3230.1960.341
1차수압0.225-0.570-0.1390.2961.0000.2210.0000.139
2차수압-0.059-0.0400.571-0.3230.2211.0000.2030.000
시 설 개 요0.0000.0000.0000.1960.0000.2031.0000.553
관경0.2390.0650.0000.3410.1390.0000.5531.000

Missing values

2023-12-13T02:49:17.138037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T02:49:17.327768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-13T02:49:17.432232image/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

연번가압장명위치시 설 개 요계약용량표고(m)급수지역정점표고양정(m)급수가구시설년도관경1차수압2차수압데이터기준일
01내지동구 내남동 68인버터방식(그런포스)입형 / 펌프 : 4HP(3kw)-3대산)14100.016090.0702004100/1002.18.02022-10-28
12두암4단지동구 산수동 568-2인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-4대산)2877.011048.01441993150/150/801.25.52022-10-28
23산수동구 밤실로 37-1 (동구 지산동 706-1)인버터방식(그런포스)입형 / 펌프: 30HP(22kw)-4대산)9072.013260.05211985300/2503.45.72022-10-28
34성촌동구 의재로 138 (동구 운림동 648-1)인버터방식(그런포스)입형 / 펌프: 15HP(11kw)-3대산)2376.013065.02021996250/2502.77.52022-10-28
45신양파크동구 무등로 548 (동구 산수동 95-6)인버터방식(윌로)입형 / 펌프: 15HP(11kw)-2대산)31100.012050.052003100/1002.16.52022-10-28
56용산동구 용산동 386인버터방식(그런포스)입형 / 펌프: 3HP(2.2kw)-3대산)1498.6107.38.71202080/803.03.02022-10-28
67용연동구 선교동 257인버터방식(그런포스)입형 / 펌프 : 4HP(3kw)-3대산)12110.018075.01342000100/1001.26.82022-10-28
78월남동구 월남동 642인버터방식(그런포스)입형 / 펌프: 25HP(18.5kw)-3대산)1491.093.575.010272018200/2002.44.62022-10-28
89잣고개동구 무등로 548 (동구 산수동 95-5)인버터방식(그런포스)입형 / 펌프: 3HP(2.2kw)-3대산)31100.012050.0282003100/1002.16.52022-10-28
910증심사동구 운림동 74-10인버터방식(그런포스)입형 / 펌프: 1.5HP(1.1kw)-3대산) 5147.327019.41202150/500.382.52022-10-28
연번가압장명위치시 설 개 요계약용량표고(m)급수지역정점표고양정(m)급수가구시설년도관경1차수압2차수압데이터기준일
2930석곡북구 망월동 1124-5인버터방식(그런포스)입형 / 펌프: 4HP(3kw)-3대산)1270.013364.0692013100/803.78.32022-10-28
3031신흥북구 군왕로182번길 9 (북구 두암동 594-5)인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-3대산)3570.587.645.05491994200/1501.83.52022-10-28
3132양산북구 양산동 361-10인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-3대산)1550.06836.0622018150/1504.65.52022-10-28
3233양지북구 양지길 3 (북구 일곡동 753-55)인버터방식(그런포스)입형 / 펌프: 3HP(2.2kw)-3대산)773.591.731.01521993150/1501.94.62022-10-28
3334운정북구 문흥동 65-1인버터방식(그런포스)입형 / 펌프: 25HP(18.5kw)-3대산)7075.09370.01,3431995350/3501.95.32022-10-28
3435일곡북구 일곡택지로 37입형(윌로) / 펌프: 40HP(30kw)-2대<NA>52.37835.07371996350/3505.7<NA>2022-10-28
3536일곡교정북구 일곡동 산36-1인버터방식(그런포스)입형 / 펌프: 30HP(25kw)-3대산)5655.09340.022014200/2002.16.52022-10-28
3637풍향북구 두암동 285인버터방식(그런포스)입형 / 펌프: 7.5HP(5.5kw)-3대산)769.78950.01662017150/1502.74.52022-10-28
3738가정광산구 두정동 385-2인버터방식(그런포스)입형 / 펌프: 3HP(2.2kw)-3대산)670.09546.017201280/802.05.12022-10-28
3839운수광산구 운수동 산233-2인버터방식(그런포스)입형 / 펌프: 1.5HP(1.1kw)-3대산)448.09551.08201380/804.76.52022-10-28