Overview

Dataset statistics

Number of variables15
Number of observations108
Missing cells51
Missing cells (%)3.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.3 KiB
Average record size in memory126.2 B

Variable types

Categorical5
Text5
Numeric5

Dataset

Description경상북도 비상급수시설, 배수펌프장 등의 설치시군 시설명, 위치 규모 등의 현황(경상북도 재난대응용 배수펌프장의 시설명, 시설장소, 준공년도, 모터, 계약용량, 처리능력, 유지수용량 등의 현황입니다.)
Author경상북도
URLhttps://www.data.go.kr/data/15056378/fileData.do

Alerts

시군 is highly overall correlated with 계획 and 3 other fieldsHigh correlation
관리자 is highly overall correlated with 계획 and 4 other fieldsHigh correlation
주용도 is highly overall correlated with 배수유역 and 1 other fieldsHigh correlation
설치자 is highly overall correlated with 계획 and 3 other fieldsHigh correlation
배수유역 is highly overall correlated with 처리능력 and 2 other fieldsHigh correlation
처리능력 is highly overall correlated with 배수유역 and 1 other fieldsHigh correlation
유수지 is highly overall correlated with 배수유역 and 1 other fieldsHigh correlation
계획 is highly overall correlated with 시군 and 2 other fieldsHigh correlation
비고 is highly overall correlated with 시군 and 2 other fieldsHigh correlation
주용도 is highly imbalanced (92.4%)Imbalance
위치(읍면) has 42 (38.9%) missing valuesMissing
계획 has 6 (5.6%) missing valuesMissing
유수지 has 29 (26.9%) zerosZeros

Reproduction

Analysis started2023-12-12 20:02:19.865657
Analysis finished2023-12-12 20:02:23.980977
Duration4.12 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
구미시
21 
포항시
16 
김천시
15 
상주시
8 
영덕군
7 
Other values (14)
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique5 ?
Unique (%)4.6%

Sample

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

Common Values

ValueCountFrequency (%)
구미시 21
19.4%
포항시 16
14.8%
김천시 15
13.9%
상주시 8
 
7.4%
영덕군 7
 
6.5%
봉화군 7
 
6.5%
안동시 6
 
5.6%
경주시 5
 
4.6%
성주군 4
 
3.7%
예천군 4
 
3.7%
Other values (9) 15
13.9%

Length

2023-12-13T05:02:24.050371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구미시 21
19.4%
포항시 16
14.8%
김천시 15
13.9%
상주시 8
 
7.4%
영덕군 7
 
6.5%
봉화군 7
 
6.5%
안동시 6
 
5.6%
경주시 5
 
4.6%
예천군 4
 
3.7%
성주군 4
 
3.7%
Other values (9) 15
13.9%

펌프
Text

Distinct107
Distinct (%)99.1%
Missing0
Missing (%)0.0%
Memory size996.0 B
2023-12-13T05:02:24.337760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.2037037
Min length2

Characters and Unicode

Total characters238
Distinct characters107
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

Unique106 ?
Unique (%)98.1%

Sample

1st row형산
2nd row공단
3rd row해도
4th row연일
5th row대송
ValueCountFrequency (%)
금호 2
 
1.8%
송도 2
 
1.8%
간이 2
 
1.8%
황금 2
 
1.8%
유수지 1
 
0.9%
병성 1
 
0.9%
신촌 1
 
0.9%
금흔 1
 
0.9%
영덕 1
 
0.9%
위양 1
 
0.9%
Other values (97) 97
87.4%
2023-12-13T05:02:24.773916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 11
 
4.6%
금 8
 
3.4%
동 7
 
2.9%
성 7
 
2.9%
부 7
 
2.9%
원 6
 
2.5%
포 6
 
2.5%
신 6
 
2.5%
도 5
 
2.1%
곡 5
 
2.1%
Other values (97) 170
71.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 224
94.1%
Decimal Number 11
 
4.6%
Space Separator 3
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 11
 
4.9%
금 8
 
3.6%
동 7
 
3.1%
성 7
 
3.1%
부 7
 
3.1%
원 6
 
2.7%
포 6
 
2.7%
신 6
 
2.7%
도 5
 
2.2%
곡 5
 
2.2%
Other values (92) 156
69.6%
Decimal Number
ValueCountFrequency (%)
2 4
36.4%
1 4
36.4%
3 2
18.2%
4 1
 
9.1%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 224
94.1%
Common 14
 
5.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 11
 
4.9%
금 8
 
3.6%
동 7
 
3.1%
성 7
 
3.1%
부 7
 
3.1%
원 6
 
2.7%
포 6
 
2.7%
신 6
 
2.7%
도 5
 
2.2%
곡 5
 
2.2%
Other values (92) 156
69.6%
Common
ValueCountFrequency (%)
2 4
28.6%
1 4
28.6%
3
21.4%
3 2
14.3%
4 1
 
7.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 224
94.1%
ASCII 14
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 11
 
4.9%
금 8
 
3.6%
동 7
 
3.1%
성 7
 
3.1%
부 7
 
3.1%
원 6
 
2.7%
포 6
 
2.7%
신 6
 
2.7%
도 5
 
2.2%
곡 5
 
2.2%
Other values (92) 156
69.6%
ASCII
ValueCountFrequency (%)
2 4
28.6%
1 4
28.6%
3
21.4%
3 2
14.3%
4 1
 
7.1%

위치(읍면)
Text

MISSING 

Distinct35
Distinct (%)53.0%
Missing42
Missing (%)38.9%
Memory size996.0 B
2023-12-13T05:02:24.986263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length2
Mean length2.0151515
Min length2

Characters and Unicode

Total characters133
Distinct characters53
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)34.8%

Sample

1st row연일
2nd row대송
3rd row구룡포
4th row강동
5th row강동
ValueCountFrequency (%)
봉화 7
 
10.6%
선산 6
 
9.1%
고아 4
 
6.1%
선남 3
 
4.5%
강구 3
 
4.5%
사벌 3
 
4.5%
해평 3
 
4.5%
영덕 3
 
4.5%
개령 3
 
4.5%
아포 3
 
4.5%
Other values (25) 28
42.4%
2023-12-13T05:02:25.671476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
선 9
 
6.8%
화 8
 
6.0%
봉 7
 
5.3%
산 7
 
5.3%
아 7
 
5.3%
강 6
 
4.5%
고 5
 
3.8%
포 5
 
3.8%
개 5
 
3.8%
남 4
 
3.0%
Other values (43) 70
52.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 133
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
선 9
 
6.8%
화 8
 
6.0%
봉 7
 
5.3%
산 7
 
5.3%
아 7
 
5.3%
강 6
 
4.5%
고 5
 
3.8%
포 5
 
3.8%
개 5
 
3.8%
남 4
 
3.0%
Other values (43) 70
52.6%

Most occurring scripts

ValueCountFrequency (%)
Hangul 133
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
선 9
 
6.8%
화 8
 
6.0%
봉 7
 
5.3%
산 7
 
5.3%
아 7
 
5.3%
강 6
 
4.5%
고 5
 
3.8%
포 5
 
3.8%
개 5
 
3.8%
남 4
 
3.0%
Other values (43) 70
52.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 133
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
선 9
 
6.8%
화 8
 
6.0%
봉 7
 
5.3%
산 7
 
5.3%
아 7
 
5.3%
강 6
 
4.5%
고 5
 
3.8%
포 5
 
3.8%
개 5
 
3.8%
남 4
 
3.0%
Other values (43) 70
52.6%

위치(리동)
Text

Distinct89
Distinct (%)83.2%
Missing1
Missing (%)0.9%
Memory size996.0 B
2023-12-13T05:02:25.961365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length2
Median length2
Mean length1.9626168
Min length1

Characters and Unicode

Total characters210
Distinct characters95
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique77 ?
Unique (%)72.0%

Sample

1st row상도
2nd row제철
3rd row송도
4th row생지
5th row제네
ValueCountFrequency (%)
공단 4
 
3.7%
내성 4
 
3.7%
송도 3
 
2.8%
원 3
 
2.8%
금곡 2
 
1.9%
죽도 2
 
1.9%
모암 2
 
1.9%
황금 2
 
1.9%
해저 2
 
1.9%
성건 2
 
1.9%
Other values (79) 81
75.7%
2023-12-13T05:02:26.341193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 12
 
5.7%
성 9
 
4.3%
내 7
 
3.3%
도 7
 
3.3%
금 7
 
3.3%
포 6
 
2.9%
상 5
 
2.4%
원 5
 
2.4%
곡 5
 
2.4%
공 4
 
1.9%
Other values (85) 143
68.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 210
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 12
 
5.7%
성 9
 
4.3%
내 7
 
3.3%
도 7
 
3.3%
금 7
 
3.3%
포 6
 
2.9%
상 5
 
2.4%
원 5
 
2.4%
곡 5
 
2.4%
공 4
 
1.9%
Other values (85) 143
68.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 210
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 12
 
5.7%
성 9
 
4.3%
내 7
 
3.3%
도 7
 
3.3%
금 7
 
3.3%
포 6
 
2.9%
상 5
 
2.4%
원 5
 
2.4%
곡 5
 
2.4%
공 4
 
1.9%
Other values (85) 143
68.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 210
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 12
 
5.7%
성 9
 
4.3%
내 7
 
3.3%
도 7
 
3.3%
금 7
 
3.3%
포 6
 
2.9%
상 5
 
2.4%
원 5
 
2.4%
곡 5
 
2.4%
공 4
 
1.9%
Other values (85) 143
68.1%

주용도
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size996.0 B
내수
107 
배수
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.9%

Sample

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

Common Values

ValueCountFrequency (%)
내수 107
99.1%
배수 1
 
0.9%

Length

2023-12-13T05:02:26.485718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:02:26.579777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내수 107
99.1%
배수 1
 
0.9%

배수유역
Real number (ℝ)

HIGH CORRELATION 

Distinct82
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean127.34102
Minimum0.13
Maximum818
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T05:02:26.697292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.13
5-th percentile3.405
Q121.75
median61.5
Q3119
95-th percentile585.915
Maximum818
Range817.87
Interquartile range (IQR)97.25

Descriptive statistics

Standard deviation182.85158
Coefficient of variation (CV)1.4359205
Kurtosis5.6287108
Mean127.34102
Median Absolute Deviation (MAD)46.4
Skewness2.46059
Sum13752.83
Variance33434.699
MonotonicityNot monotonic
2023-12-13T05:02:26.837098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
119.0 4
 
3.7%
50.0 4
 
3.7%
13.0 3
 
2.8%
10.0 3
 
2.8%
110.0 3
 
2.8%
20.0 2
 
1.9%
816.0 2
 
1.9%
66.0 2
 
1.9%
15.0 2
 
1.9%
34.0 2
 
1.9%
Other values (72) 81
75.0%
ValueCountFrequency (%)
0.13 1
0.9%
0.25 1
0.9%
1.2 1
0.9%
2.0 1
0.9%
3.0 1
0.9%
3.3 1
0.9%
3.6 1
0.9%
6.0 2
1.9%
8.0 1
0.9%
9.0 1
0.9%
ValueCountFrequency (%)
818.0 1
0.9%
816.0 2
1.9%
676.0 1
0.9%
606.0 1
0.9%
589.1 1
0.9%
580.0 1
0.9%
575.0 1
0.9%
501.0 1
0.9%
383.0 1
0.9%
365.0 1
0.9%

모타(HPX대)
Text

Distinct91
Distinct (%)85.0%
Missing1
Missing (%)0.9%
Memory size996.0 B
2023-12-13T05:02:27.147827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25
Mean length11.214953
Min length7

Characters and Unicode

Total characters1200
Distinct characters23
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

Unique79 ?
Unique (%)73.8%

Sample

1st row1250HP×3대 1100HP×2대
2nd row215HP×5대. 500HP×3대
3rd row500HP×6대
4th row473HP×6대
5th row200HP×4대
ValueCountFrequency (%)
50hp×2대 5
 
3.4%
100hp×2대 5
 
3.4%
150hp×2대 4
 
2.7%
100hp×1대 4
 
2.7%
55hp×1대 4
 
2.7%
75hp×1대 4
 
2.7%
50hp×3대 3
 
2.0%
200hp×2대 3
 
2.0%
200hp×3대 3
 
2.0%
120hp×2대 3
 
2.0%
Other values (88) 111
74.5%
2023-12-13T05:02:27.571347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
대 146
12.2%
H 145
12.1%
P 145
12.1%
× 144
12.0%
0 139
11.6%
1 97
8.1%
2 96
8.0%
5 67
5.6%
3 59
4.9%
45
 
3.8%
Other values (13) 117
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 546
45.5%
Uppercase Letter 290
24.2%
Other Letter 157
 
13.1%
Math Symbol 144
 
12.0%
Space Separator 45
 
3.8%
Lowercase Letter 10
 
0.8%
Close Punctuation 3
 
0.2%
Open Punctuation 3
 
0.2%
Other Punctuation 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 139
25.5%
1 97
17.8%
2 96
17.6%
5 67
12.3%
3 59
10.8%
4 38
 
7.0%
7 22
 
4.0%
6 15
 
2.7%
8 10
 
1.8%
9 3
 
0.5%
Other Letter
ValueCountFrequency (%)
대 146
93.0%
기 5
 
3.2%
비 3
 
1.9%
예 3
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
H 145
50.0%
P 145
50.0%
Lowercase Letter
ValueCountFrequency (%)
m 6
60.0%
x 4
40.0%
Math Symbol
ValueCountFrequency (%)
× 144
100.0%
Space Separator
ValueCountFrequency (%)
45
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 743
61.9%
Latin 300
25.0%
Hangul 157
 
13.1%

Most frequent character per script

Common
ValueCountFrequency (%)
× 144
19.4%
0 139
18.7%
1 97
13.1%
2 96
12.9%
5 67
9.0%
3 59
7.9%
45
 
6.1%
4 38
 
5.1%
7 22
 
3.0%
6 15
 
2.0%
Other values (5) 21
 
2.8%
Hangul
ValueCountFrequency (%)
대 146
93.0%
기 5
 
3.2%
비 3
 
1.9%
예 3
 
1.9%
Latin
ValueCountFrequency (%)
H 145
48.3%
P 145
48.3%
m 6
 
2.0%
x 4
 
1.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 899
74.9%
Hangul 157
 
13.1%
None 144
 
12.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
대 146
93.0%
기 5
 
3.2%
비 3
 
1.9%
예 3
 
1.9%
ASCII
ValueCountFrequency (%)
H 145
16.1%
P 145
16.1%
0 139
15.5%
1 97
10.8%
2 96
10.7%
5 67
7.5%
3 59
6.6%
45
 
5.0%
4 38
 
4.2%
7 22
 
2.4%
Other values (8) 46
 
5.1%
None
ValueCountFrequency (%)
× 144
100.0%

펌프(mmX대)
Text

Distinct72
Distinct (%)67.3%
Missing1
Missing (%)0.9%
Memory size996.0 B
2023-12-13T05:02:27.814509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length35
Median length27
Mean length11.766355
Min length8

Characters and Unicode

Total characters1259
Distinct characters21
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)48.6%

Sample

1st row1500mm×5대
2nd row900mm×5대 1650mm×3대
3rd row1500mm×6대
4th row1350mm×6대
5th row1200mm×4대
ValueCountFrequency (%)
600mm×1대 11
 
7.6%
800mm×3대 7
 
4.8%
800mm×2대 7
 
4.8%
500mm×1대 7
 
4.8%
900mm×2대 6
 
4.1%
500mm×2대 6
 
4.1%
1000mm×2대 6
 
4.1%
700mm×2대 5
 
3.4%
200mm×2대 4
 
2.8%
1350mm×3대 4
 
2.8%
Other values (47) 82
56.6%
2023-12-13T05:02:28.383190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
m 288
22.9%
0 274
21.8%
대 148
11.8%
× 141
11.2%
1 88
 
7.0%
2 78
 
6.2%
3 51
 
4.1%
5 51
 
4.1%
38
 
3.0%
4 25
 
2.0%
Other values (11) 77
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 626
49.7%
Lowercase Letter 292
23.2%
Other Letter 154
 
12.2%
Math Symbol 141
 
11.2%
Space Separator 38
 
3.0%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%
Uppercase Letter 2
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 274
43.8%
1 88
 
14.1%
2 78
 
12.5%
3 51
 
8.1%
5 51
 
8.1%
4 25
 
4.0%
6 20
 
3.2%
8 17
 
2.7%
7 11
 
1.8%
9 11
 
1.8%
Other Letter
ValueCountFrequency (%)
대 148
96.1%
예 3
 
1.9%
비 3
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
m 288
98.6%
x 4
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
H 1
50.0%
P 1
50.0%
Math Symbol
ValueCountFrequency (%)
× 141
100.0%
Space Separator
ValueCountFrequency (%)
38
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 811
64.4%
Latin 294
 
23.4%
Hangul 154
 
12.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 274
33.8%
× 141
17.4%
1 88
 
10.9%
2 78
 
9.6%
3 51
 
6.3%
5 51
 
6.3%
38
 
4.7%
4 25
 
3.1%
6 20
 
2.5%
8 17
 
2.1%
Other values (4) 28
 
3.5%
Latin
ValueCountFrequency (%)
m 288
98.0%
x 4
 
1.4%
H 1
 
0.3%
P 1
 
0.3%
Hangul
ValueCountFrequency (%)
대 148
96.1%
예 3
 
1.9%
비 3
 
1.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 964
76.6%
Hangul 154
 
12.2%
None 141
 
11.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
m 288
29.9%
0 274
28.4%
1 88
 
9.1%
2 78
 
8.1%
3 51
 
5.3%
5 51
 
5.3%
38
 
3.9%
4 25
 
2.6%
6 20
 
2.1%
8 17
 
1.8%
Other values (7) 34
 
3.5%
Hangul
ValueCountFrequency (%)
대 148
96.1%
예 3
 
1.9%
비 3
 
1.9%
None
ValueCountFrequency (%)
× 141
100.0%

처리능력
Real number (ℝ)

HIGH CORRELATION 

Distinct84
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean440.62963
Minimum2
Maximum3363
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T05:02:28.552340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile9.35
Q169
median222
Q3569.25
95-th percentile1615.6
Maximum3363
Range3361
Interquartile range (IQR)500.25

Descriptive statistics

Standard deviation604.83826
Coefficient of variation (CV)1.3726682
Kurtosis7.4078047
Mean440.62963
Median Absolute Deviation (MAD)193
Skewness2.4942839
Sum47588
Variance365829.32
MonotonicityNot monotonic
2023-12-13T05:02:28.721150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
90 3
 
2.8%
216 3
 
2.8%
7 3
 
2.8%
180 3
 
2.8%
360 2
 
1.9%
660 2
 
1.9%
14 2
 
1.9%
18 2
 
1.9%
80 2
 
1.9%
21 2
 
1.9%
Other values (74) 84
77.8%
ValueCountFrequency (%)
2 1
 
0.9%
3 1
 
0.9%
7 3
2.8%
9 1
 
0.9%
10 2
1.9%
13 1
 
0.9%
14 2
1.9%
15 1
 
0.9%
16 1
 
0.9%
18 2
1.9%
ValueCountFrequency (%)
3363 1
0.9%
2975 1
0.9%
2130 1
0.9%
2040 1
0.9%
1920 1
0.9%
1624 1
0.9%
1600 1
0.9%
1482 1
0.9%
1400 1
0.9%
1260 1
0.9%

유수지
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct71
Distinct (%)65.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16725.176
Minimum0
Maximum436000
Zeros29
Zeros (%)26.9%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T05:02:28.899787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median749
Q34985
95-th percentile81900
Maximum436000
Range436000
Interquartile range (IQR)4985

Descriptive statistics

Standard deviation65031.58
Coefficient of variation (CV)3.8882449
Kurtosis32.027128
Mean16725.176
Median Absolute Deviation (MAD)749
Skewness5.4893893
Sum1806319
Variance4.2291065 × 109
MonotonicityNot monotonic
2023-12-13T05:02:29.084846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 29
26.9%
510 2
 
1.9%
436000 2
 
1.9%
3000 2
 
1.9%
870 2
 
1.9%
200 2
 
1.9%
180 2
 
1.9%
1200 2
 
1.9%
10000 2
 
1.9%
5000 2
 
1.9%
Other values (61) 61
56.5%
ValueCountFrequency (%)
0 29
26.9%
25 1
 
0.9%
35 1
 
0.9%
42 1
 
0.9%
64 1
 
0.9%
66 1
 
0.9%
150 1
 
0.9%
179 1
 
0.9%
180 2
 
1.9%
200 2
 
1.9%
ValueCountFrequency (%)
436000 2
1.9%
180000 1
0.9%
165667 1
0.9%
163000 1
0.9%
105000 1
0.9%
39000 1
0.9%
29750 1
0.9%
24000 1
0.9%
22300 1
0.9%
18400 1
0.9%

계획
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct7
Distinct (%)6.9%
Missing6
Missing (%)5.6%
Infinite0
Infinite (%)0.0%
Mean31.078431
Minimum5
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T05:02:29.203043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile10.5
Q120
median20
Q330
95-th percentile100
Maximum100
Range95
Interquartile range (IQR)10

Descriptive statistics

Standard deviation22.978977
Coefficient of variation (CV)0.73938665
Kurtosis3.643585
Mean31.078431
Median Absolute Deviation (MAD)0
Skewness2.1207998
Sum3170
Variance528.03339
MonotonicityNot monotonic
2023-12-13T05:02:29.329770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
20 56
51.9%
30 22
 
20.4%
50 8
 
7.4%
100 7
 
6.5%
10 4
 
3.7%
80 3
 
2.8%
5 2
 
1.9%
(Missing) 6
 
5.6%
ValueCountFrequency (%)
5 2
 
1.9%
10 4
 
3.7%
20 56
51.9%
30 22
 
20.4%
50 8
 
7.4%
80 3
 
2.8%
100 7
 
6.5%
ValueCountFrequency (%)
100 7
 
6.5%
80 3
 
2.8%
50 8
 
7.4%
30 22
 
20.4%
20 56
51.9%
10 4
 
3.7%
5 2
 
1.9%

설치자
Categorical

HIGH CORRELATION 

Distinct19
Distinct (%)17.6%
Missing0
Missing (%)0.0%
Memory size996.0 B
구미시장
21 
포항시장
16 
김천시장
15 
상주시장
8 
영덕군수
7 
Other values (14)
41 

Length

Max length4
Median length4
Mean length3.9907407
Min length3

Unique

Unique5 ?
Unique (%)4.6%

Sample

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

Common Values

ValueCountFrequency (%)
구미시장 21
19.4%
포항시장 16
14.8%
김천시장 15
13.9%
상주시장 8
 
7.4%
영덕군수 7
 
6.5%
봉화군수 7
 
6.5%
안동시장 6
 
5.6%
경주시장 5
 
4.6%
성주군수 4
 
3.7%
예천군수 4
 
3.7%
Other values (9) 15
13.9%

Length

2023-12-13T05:02:29.456173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
구미시장 21
19.4%
포항시장 16
14.8%
김천시장 15
13.9%
상주시장 8
 
7.4%
영덕군수 7
 
6.5%
봉화군수 7
 
6.5%
안동시장 6
 
5.6%
경주시장 5
 
4.6%
예천군수 4
 
3.7%
성주군수 4
 
3.7%
Other values (9) 15
13.9%

관리자
Categorical

HIGH CORRELATION 

Distinct22
Distinct (%)20.4%
Missing0
Missing (%)0.0%
Memory size996.0 B
포항시장
16 
구미시장
16 
(주)티에스케이워터
8 
영덕군수
7 
김천시
7 
Other values (17)
54 

Length

Max length10
Median length4
Mean length4.4537037
Min length3

Unique

Unique6 ?
Unique (%)5.6%

Sample

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

Common Values

ValueCountFrequency (%)
포항시장 16
14.8%
구미시장 16
14.8%
(주)티에스케이워터 8
 
7.4%
영덕군수 7
 
6.5%
김천시 7
 
6.5%
봉화군수 7
 
6.5%
상주시장 7
 
6.5%
안동시장 6
 
5.6%
구미시설공단 5
 
4.6%
경주시장 5
 
4.6%
Other values (12) 24
22.2%

Length

2023-12-13T05:02:29.616045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
포항시장 16
14.8%
구미시장 16
14.8%
주)티에스케이워터 8
 
7.4%
영덕군수 7
 
6.5%
김천시 7
 
6.5%
봉화군수 7
 
6.5%
상주시장 7
 
6.5%
안동시장 6
 
5.6%
구미시설공단 5
 
4.6%
경주시장 5
 
4.6%
Other values (12) 24
22.2%

설치
Real number (ℝ)

Distinct28
Distinct (%)25.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2005.6389
Minimum1973
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.1 KiB
2023-12-13T05:02:29.765947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1973
5-th percentile1991.35
Q12001
median2006
Q32012
95-th percentile2019
Maximum2020
Range47
Interquartile range (IQR)11

Descriptive statistics

Standard deviation8.4868874
Coefficient of variation (CV)0.0042315132
Kurtosis0.85120728
Mean2005.6389
Median Absolute Deviation (MAD)6
Skewness-0.57509001
Sum216609
Variance72.027259
MonotonicityNot monotonic
2023-12-13T05:02:29.918107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2004 9
 
8.3%
2001 8
 
7.4%
2000 6
 
5.6%
2013 6
 
5.6%
2019 6
 
5.6%
2010 6
 
5.6%
2007 6
 
5.6%
2011 6
 
5.6%
1994 5
 
4.6%
2012 5
 
4.6%
Other values (18) 45
41.7%
ValueCountFrequency (%)
1973 1
 
0.9%
1990 1
 
0.9%
1991 4
3.7%
1992 3
2.8%
1993 1
 
0.9%
1994 5
4.6%
1995 1
 
0.9%
1998 2
 
1.9%
1999 2
 
1.9%
2000 6
5.6%
ValueCountFrequency (%)
2020 3
2.8%
2019 6
5.6%
2017 2
 
1.9%
2016 2
 
1.9%
2014 5
4.6%
2013 6
5.6%
2012 5
4.6%
2011 6
5.6%
2010 6
5.6%
2009 3
2.8%

비고
Categorical

HIGH CORRELATION 

Distinct44
Distinct (%)40.7%
Missing0
Missing (%)0.0%
Memory size996.0 B
낙동강(국가)
13 
감천(국가)
8 
감천(국가하천)
8 
내성천
8 
오십천(지방하천)
7 
Other values (39)
64 

Length

Max length11
Median length9
Mean length6.712963
Min length2

Unique

Unique26 ?
Unique (%)24.1%

Sample

1st row형산강(국가)
2nd row구무천(소하천)
3rd row형산강(국가)
4th row형산강(국가)
5th row칠성천(지방2급)

Common Values

ValueCountFrequency (%)
낙동강(국가) 13
 
12.0%
감천(국가) 8
 
7.4%
감천(국가하천) 8
 
7.4%
내성천 8
 
7.4%
오십천(지방하천) 7
 
6.5%
형산강(국가하천) 6
 
5.6%
형산강(국가) 4
 
3.7%
직지사천(지방하천) 4
 
3.7%
내수 4
 
3.7%
광평천 3
 
2.8%
Other values (34) 43
39.8%

Length

2023-12-13T05:02:30.089408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
낙동강(국가 13
 
11.0%
감천(국가 8
 
6.8%
감천(국가하천 8
 
6.8%
내성천 8
 
6.8%
오십천(지방하천 7
 
5.9%
형산강(국가하천 6
 
5.1%
천(지방하천 5
 
4.2%
직지사천(지방하천 4
 
3.4%
형산강(국가 4
 
3.4%
내수 4
 
3.4%
Other values (38) 51
43.2%

Interactions

2023-12-13T05:02:23.061486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.093866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.624405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.102249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.566449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:23.172517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.191636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.730631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.194949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.674787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:23.273821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.298758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.848278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.305040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.786986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:23.347456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.416314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.933729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.399498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.877283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:23.430809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:21.518800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.015361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.483246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:02:22.971857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:02:30.190710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군위치(읍면)위치(리동)주용도배수유역모타(HPX대)펌프(mmX대)처리능력유수지계획설치자관리자설치비고
시군1.0001.0001.0000.0000.7600.9860.9570.6530.4330.8661.0001.0000.5270.989
위치(읍면)1.0001.0000.999NaN0.9570.9850.9560.8591.0000.9631.0001.0000.8250.980
위치(리동)1.0000.9991.0001.0000.8760.9840.9910.9260.8210.9781.0001.0000.8480.992
주용도0.000NaN1.0001.0000.5211.0001.0000.4270.0000.0000.0001.0000.0000.000
배수유역0.7600.9570.8760.5211.0000.8710.9060.8640.8530.0000.7600.8230.2310.654
모타(HPX대)0.9860.9850.9841.0000.8711.0000.9960.9971.0000.9790.9860.9890.8510.986
펌프(mmX대)0.9570.9560.9911.0000.9060.9961.0000.9860.7260.8490.9570.9600.8330.891
처리능력0.6530.8590.9260.4270.8640.9970.9861.0000.7500.0000.6530.6930.0000.761
유수지0.4331.0000.8210.0000.8531.0000.7260.7501.0000.0000.4330.5930.2120.468
계획0.8660.9630.9780.0000.0000.9790.8490.0000.0001.0000.8660.9160.4550.858
설치자1.0001.0001.0000.0000.7600.9860.9570.6530.4330.8661.0001.0000.5270.989
관리자1.0001.0001.0001.0000.8230.9890.9600.6930.5930.9161.0001.0000.6220.986
설치0.5270.8250.8480.0000.2310.8510.8330.0000.2120.4550.5270.6221.0000.526
비고0.9890.9800.9920.0000.6540.9860.8910.7610.4680.8580.9890.9860.5261.000
2023-12-13T05:02:30.352164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군관리자주용도비고설치자
시군1.0000.9830.0000.7271.000
관리자0.9831.0000.9010.6890.983
주용도0.0000.9011.0000.0000.000
비고0.7270.6890.0001.0000.727
설치자1.0000.9830.0000.7271.000
2023-12-13T05:02:30.459884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
배수유역처리능력유수지계획설치시군주용도설치자관리자비고
배수유역1.0000.6620.6180.1000.0380.4000.5050.4000.4650.236
처리능력0.6621.0000.7320.0840.0490.3040.4120.3040.3270.315
유수지0.6180.7321.0000.1000.0180.2120.0000.2120.3100.179
계획0.1000.0840.1001.0000.4080.5880.0000.5880.6640.459
설치0.0380.0490.0180.4081.0000.1830.0000.1830.2650.000
시군0.4000.3040.2120.5880.1831.0000.0001.0000.9830.727
주용도0.5050.4120.0000.0000.0000.0001.0000.0000.9010.000
설치자0.4000.3040.2120.5880.1831.0000.0001.0000.9830.727
관리자0.4650.3270.3100.6640.2650.9830.9010.9831.0000.689
비고0.2360.3150.1790.4590.0000.7270.0000.7270.6891.000

Missing values

2023-12-13T05:02:23.581477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:02:23.771049image/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.
2023-12-13T05:02:23.907362image/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

시군펌프위치(읍면)위치(리동)주용도배수유역모타(HPX대)펌프(mmX대)처리능력유수지계획설치자관리자설치비고
0포항시형산<NA>상도내수606.01250HP×3대 1100HP×2대1500mm×5대213010500020포항시장포항시장1993형산강(국가)
1포항시공단<NA>제철내수580.0215HP×5대. 500HP×3대900mm×5대 1650mm×3대162418000020포항시장포항시장1994구무천(소하천)
2포항시해도<NA>송도내수223.0500HP×6대1500mm×6대2040173120포항시장포항시장2002형산강(국가)
3포항시연일연일생지내수818.0473HP×6대1350mm×6대14821000020포항시장포항시장2001형산강(국가)
4포항시대송대송제네내수85.0200HP×4대1200mm×4대564523620포항시장포항시장1994칠성천(지방2급)
5포항시장성<NA>장성내수22.020HP×1대 25HP×1대 87HP×2대200mm×1대 300mm×1대 400mm×1대 500mm×1대7031320포항시장포항시장1994내수
6포항시송도<NA>송도내수43.050HP×3대800mm×3대27021820포항시장포항시장2002형산강(국가)
7포항시대도<NA>상대내수80.020HP×1대 30HP×1대 47HP×1대200mm×1대 300mm×1대 150mm×1대2615020포항시장포항시장1990내수
8포항시눌태리구룡포눌태내수56.0160HP×3대 90HP×1대900mm×3대 600mm×1대340220020포항시장포항시장2006눌태(소하천)
9포항시송도 내항<NA>송도내수8.050HP×1대 40HP×1대600mm×1대 450mm×1대6018020포항시장포항시장2006바다
시군펌프위치(읍면)위치(리동)주용도배수유역모타(HPX대)펌프(mmX대)처리능력유수지계획설치자관리자설치비고
98봉화군서부1봉화해저내수178.0120HP×3대1000mm×3대360030봉화군수봉화군수2019가계천
99봉화군동부1봉화삼계내수10.030HP×1대(예비 1대)400mm×1대(예비1대)20030봉화군수봉화군수2019내성천
100봉화군동부2봉화내성내수119.0150HP×2대1000mm×2대240030봉화군수봉화군수2019내성천
101봉화군동부3봉화내성내수119.0150HP×2대1000mm×2대240030봉화군수봉화군수2019내성천
102봉화군동부4봉화내성내수89.0120HP×2대900mm×2대180030봉화군수봉화군수2019내성천
103봉화군내성봉화내성내수20.030HP×1대(예비1대)400mm×1대(예비1대)15030봉화군수봉화군수2001내성천
104봉화군해저봉화해저내수20.030HP×1대(예비1대)400mm×1대(예비1대)32030봉화군수봉화군수2003내성천
105울진군읍내울진읍내내수61.0400HP×3대 250HP×1대1400mm×4대790132020울진군수울진군수1995울진, 남대천
106울진군후포후포삼율내수311.050HP×3대500mm×3대90116020울진군수울진군수2001동해
107울진군산포근남산포내수62.0425HP×4대1350mm×4대9302230020울진군수울진군수2019왕피천