Overview

Dataset statistics

Number of variables9
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory77.7 B

Variable types

Text6
Categorical1
Numeric2

Alerts

시설용량 is highly overall correlated with 관로길이High correlation
관로길이 is highly overall correlated with 시설용량High correlation
시설명 has unique valuesUnique
시설약칭 has unique valuesUnique
시설용량 has 1 (2.8%) zerosZeros
관로길이 has 4 (11.1%) zerosZeros

Reproduction

Analysis started2023-12-10 12:45:31.465781
Analysis finished2023-12-10 12:45:32.563758
Duration1.1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시설명
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-10T21:45:32.724665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0833333
Min length5

Characters and Unicode

Total characters183
Distinct characters54
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

Unique36 ?
Unique (%)100.0%

Sample

1st row고령정수장
2nd row고산정수장
3rd row고양정수장
4th row공주정수장
5th row구미정수장
ValueCountFrequency (%)
고령정수장 1
 
2.8%
고산정수장 1
 
2.8%
천안정수장 1
 
2.8%
시흥정수장 1
 
2.8%
양산정수장 1
 
2.8%
와부정수장 1
 
2.8%
운문정수장 1
 
2.8%
일산정수장 1
 
2.8%
자인정수장 1
 
2.8%
청주정수장 1
 
2.8%
Other values (26) 26
72.2%
2023-12-10T21:45:33.127651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 37
20.2%
정 36
19.7%
장 35
19.1%
산 7
 
3.8%
주 4
 
2.2%
성 4
 
2.2%
고 3
 
1.6%
화 3
 
1.6%
양 3
 
1.6%
덕 2
 
1.1%
Other values (44) 49
26.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 183
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 37
20.2%
정 36
19.7%
장 35
19.1%
산 7
 
3.8%
주 4
 
2.2%
성 4
 
2.2%
고 3
 
1.6%
화 3
 
1.6%
양 3
 
1.6%
덕 2
 
1.1%
Other values (44) 49
26.8%

Most occurring scripts

ValueCountFrequency (%)
Hangul 183
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 37
20.2%
정 36
19.7%
장 35
19.1%
산 7
 
3.8%
주 4
 
2.2%
성 4
 
2.2%
고 3
 
1.6%
화 3
 
1.6%
양 3
 
1.6%
덕 2
 
1.1%
Other values (44) 49
26.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 183
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 37
20.2%
정 36
19.7%
장 35
19.1%
산 7
 
3.8%
주 4
 
2.2%
성 4
 
2.2%
고 3
 
1.6%
화 3
 
1.6%
양 3
 
1.6%
덕 2
 
1.1%
Other values (44) 49
26.8%

권역
Categorical

Distinct4
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
금강-섬진강권역
13 
한강권역
13 
낙동강권역
8 
0
2 

Length

Max length8
Median length5
Mean length5.5
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row낙동강권역
2nd row금강-섬진강권역
3rd row한강권역
4th row금강-섬진강권역
5th row낙동강권역

Common Values

ValueCountFrequency (%)
금강-섬진강권역 13
36.1%
한강권역 13
36.1%
낙동강권역 8
22.2%
0 2
 
5.6%

Length

2023-12-10T21:45:33.293986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T21:45:33.426868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금강-섬진강권역 13
36.1%
한강권역 13
36.1%
낙동강권역 8
22.2%
0 2
 
5.6%

사업단계명
Text

Distinct28
Distinct (%)77.8%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-10T21:45:33.612403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length10.888889
Min length1

Characters and Unicode

Total characters392
Distinct characters64
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)58.3%

Sample

1st row영남내륙권광역상수도사업
2nd row전주권계통광역상수도
3rd row수도권광역상수도 1단계
4th row충남중부권광역상수도
5th row구미권광역상수도 1단계
ValueCountFrequency (%)
수도권광역상수도 9
17.6%
1단계 3
 
5.9%
4단계 3
 
5.9%
충남중부권광역상수도 2
 
3.9%
6단계 2
 
3.9%
밀양댐계통광역상수도 2
 
3.9%
대청댐광역상수도 2
 
3.9%
1-2단계 2
 
3.9%
금호강계통광역상수도 2
 
3.9%
2단계 2
 
3.9%
Other values (22) 22
43.1%
2023-12-10T21:45:34.040646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 43
 
11.0%
도 43
 
11.0%
광 35
 
8.9%
역 33
 
8.4%
상 33
 
8.4%
계 28
 
7.1%
권 20
 
5.1%
단 17
 
4.3%
16
 
4.1%
통 12
 
3.1%
Other values (54) 112
28.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 352
89.8%
Decimal Number 19
 
4.8%
Space Separator 16
 
4.1%
Dash Punctuation 2
 
0.5%
Connector Punctuation 1
 
0.3%
Close Punctuation 1
 
0.3%
Open Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 43
12.2%
도 43
12.2%
광 35
9.9%
역 33
9.4%
상 33
9.4%
계 28
 
8.0%
권 20
 
5.7%
단 17
 
4.8%
통 12
 
3.4%
댐 10
 
2.8%
Other values (42) 78
22.2%
Decimal Number
ValueCountFrequency (%)
1 7
36.8%
2 4
21.1%
4 3
15.8%
6 2
 
10.5%
5 1
 
5.3%
3 1
 
5.3%
0 1
 
5.3%
Space Separator
ValueCountFrequency (%)
16
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 352
89.8%
Common 40
 
10.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 43
12.2%
도 43
12.2%
광 35
9.9%
역 33
9.4%
상 33
9.4%
계 28
 
8.0%
권 20
 
5.7%
단 17
 
4.8%
통 12
 
3.4%
댐 10
 
2.8%
Other values (42) 78
22.2%
Common
ValueCountFrequency (%)
16
40.0%
1 7
17.5%
2 4
 
10.0%
4 3
 
7.5%
6 2
 
5.0%
- 2
 
5.0%
5 1
 
2.5%
_ 1
 
2.5%
) 1
 
2.5%
( 1
 
2.5%
Other values (2) 2
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 352
89.8%
ASCII 40
 
10.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 43
12.2%
도 43
12.2%
광 35
9.9%
역 33
9.4%
상 33
9.4%
계 28
 
8.0%
권 20
 
5.7%
단 17
 
4.8%
통 12
 
3.4%
댐 10
 
2.8%
Other values (42) 78
22.2%
ASCII
ValueCountFrequency (%)
16
40.0%
1 7
17.5%
2 4
 
10.0%
4 3
 
7.5%
6 2
 
5.0%
- 2
 
5.0%
5 1
 
2.5%
_ 1
 
2.5%
) 1
 
2.5%
( 1
 
2.5%
Other values (2) 2
 
5.0%

사업명
Text

Distinct24
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-10T21:45:34.246117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length8
Mean length8.1666667
Min length1

Characters and Unicode

Total characters294
Distinct characters52
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)52.8%

Sample

1st row영남내륙권광역상수도사업
2nd row전주권광역상수도
3rd row수도권광역상수도
4th row충남중부권광역상수도
5th row구미권광역상수도
ValueCountFrequency (%)
수도권광역상수도 9
25.0%
대청댐광역상수도 2
 
5.6%
금호강계통광역상수도 2
 
5.6%
충남중부권광역상수도 2
 
5.6%
밀양댐광역상수도 2
 
5.6%
일산광역상수도 1
 
2.8%
영남내륙권광역상수도사업 1
 
2.8%
원주권광역상수도 1
 
2.8%
태백권광역상수도 1
 
2.8%
주암댐광역상수도 1
 
2.8%
Other values (14) 14
38.9%
2023-12-10T21:45:34.584415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
수 44
15.0%
도 44
15.0%
상 34
11.6%
광 33
11.2%
역 33
11.2%
권 20
 
6.8%
댐 9
 
3.1%
남 7
 
2.4%
부 5
 
1.7%
주 4
 
1.4%
Other values (42) 61
20.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 293
99.7%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
수 44
15.0%
도 44
15.0%
상 34
11.6%
광 33
11.3%
역 33
11.3%
권 20
 
6.8%
댐 9
 
3.1%
남 7
 
2.4%
부 5
 
1.7%
주 4
 
1.4%
Other values (41) 60
20.5%
Decimal Number
ValueCountFrequency (%)
0 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 293
99.7%
Common 1
 
0.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
수 44
15.0%
도 44
15.0%
상 34
11.6%
광 33
11.3%
역 33
11.3%
권 20
 
6.8%
댐 9
 
3.1%
남 7
 
2.4%
부 5
 
1.7%
주 4
 
1.4%
Other values (41) 60
20.5%
Common
ValueCountFrequency (%)
0 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 293
99.7%
ASCII 1
 
0.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
수 44
15.0%
도 44
15.0%
상 34
11.6%
광 33
11.3%
역 33
11.3%
권 20
 
6.8%
댐 9
 
3.1%
남 7
 
2.4%
부 5
 
1.7%
주 4
 
1.4%
Other values (41) 60
20.5%
ASCII
ValueCountFrequency (%)
0 1
100.0%

시설약칭
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-10T21:45:34.847193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length5
Mean length5.0833333
Min length5

Characters and Unicode

Total characters183
Distinct characters55
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

Unique36 ?
Unique (%)100.0%

Sample

1st row고령(정)
2nd row고산(정)
3rd row고양(정)
4th row공주(정)
5th row구미(정)
ValueCountFrequency (%)
고령(정 1
 
2.8%
고산(정 1
 
2.8%
천안(정 1
 
2.8%
시흥(정 1
 
2.8%
양산(정 1
 
2.8%
와부(정 1
 
2.8%
운문(정 1
 
2.8%
일산(정 1
 
2.8%
자인(정 1
 
2.8%
청주(정 1
 
2.8%
Other values (26) 26
72.2%
2023-12-10T21:45:35.230067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
정 36
19.7%
( 35
19.1%
) 35
19.1%
산 7
 
3.8%
성 4
 
2.2%
주 4
 
2.2%
고 3
 
1.6%
화 3
 
1.6%
양 3
 
1.6%
지 2
 
1.1%
Other values (45) 51
27.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 113
61.7%
Open Punctuation 35
 
19.1%
Close Punctuation 35
 
19.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 36
31.9%
산 7
 
6.2%
성 4
 
3.5%
주 4
 
3.5%
고 3
 
2.7%
화 3
 
2.7%
양 3
 
2.7%
지 2
 
1.8%
수 2
 
1.8%
령 2
 
1.8%
Other values (43) 47
41.6%
Open Punctuation
ValueCountFrequency (%)
( 35
100.0%
Close Punctuation
ValueCountFrequency (%)
) 35
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 113
61.7%
Common 70
38.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 36
31.9%
산 7
 
6.2%
성 4
 
3.5%
주 4
 
3.5%
고 3
 
2.7%
화 3
 
2.7%
양 3
 
2.7%
지 2
 
1.8%
수 2
 
1.8%
령 2
 
1.8%
Other values (43) 47
41.6%
Common
ValueCountFrequency (%)
( 35
50.0%
) 35
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
61.7%
ASCII 70
38.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
정 36
31.9%
산 7
 
6.2%
성 4
 
3.5%
주 4
 
3.5%
고 3
 
2.7%
화 3
 
2.7%
양 3
 
2.7%
지 2
 
1.8%
수 2
 
1.8%
령 2
 
1.8%
Other values (43) 47
41.6%
ASCII
ValueCountFrequency (%)
( 35
50.0%
) 35
50.0%

소재지
Text

Distinct34
Distinct (%)94.4%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-10T21:45:35.480810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length32
Median length8
Mean length10.416667
Min length7

Characters and Unicode

Total characters375
Distinct characters80
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

Unique32 ?
Unique (%)88.9%

Sample

1st row경북 고령군 다산면 노곡리 746
2nd row전라북도 완주군
3rd row경기도 고양시 일산동구
4th row충청남도 공주시
5th row경상북도 구미시
ValueCountFrequency (%)
경기도 9
 
9.5%
경상북도 4
 
4.2%
충청남도 4
 
4.2%
전라남도 4
 
4.2%
전라북도 4
 
4.2%
경상남도 3
 
3.2%
고양시 3
 
3.2%
강원도 2
 
2.1%
충청북도 2
 
2.1%
충남 2
 
2.1%
Other values (55) 58
61.1%
2023-12-10T21:45:35.853795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
59
 
15.7%
도 33
 
8.8%
시 25
 
6.7%
경 18
 
4.8%
남 17
 
4.5%
북 12
 
3.2%
구 12
 
3.2%
군 11
 
2.9%
산 10
 
2.7%
충 9
 
2.4%
Other values (70) 169
45.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 302
80.5%
Space Separator 59
 
15.7%
Decimal Number 13
 
3.5%
Dash Punctuation 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 33
 
10.9%
시 25
 
8.3%
경 18
 
6.0%
남 17
 
5.6%
북 12
 
4.0%
구 12
 
4.0%
군 11
 
3.6%
산 10
 
3.3%
충 9
 
3.0%
양 9
 
3.0%
Other values (60) 146
48.3%
Decimal Number
ValueCountFrequency (%)
8 3
23.1%
6 3
23.1%
1 2
15.4%
4 1
 
7.7%
7 1
 
7.7%
0 1
 
7.7%
2 1
 
7.7%
3 1
 
7.7%
Space Separator
ValueCountFrequency (%)
59
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 302
80.5%
Common 73
 
19.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
도 33
 
10.9%
시 25
 
8.3%
경 18
 
6.0%
남 17
 
5.6%
북 12
 
4.0%
구 12
 
4.0%
군 11
 
3.6%
산 10
 
3.3%
충 9
 
3.0%
양 9
 
3.0%
Other values (60) 146
48.3%
Common
ValueCountFrequency (%)
59
80.8%
8 3
 
4.1%
6 3
 
4.1%
1 2
 
2.7%
4 1
 
1.4%
7 1
 
1.4%
0 1
 
1.4%
2 1
 
1.4%
- 1
 
1.4%
3 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 302
80.5%
ASCII 73
 
19.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
59
80.8%
8 3
 
4.1%
6 3
 
4.1%
1 2
 
2.7%
4 1
 
1.4%
7 1
 
1.4%
0 1
 
1.4%
2 1
 
1.4%
- 1
 
1.4%
3 1
 
1.4%
Hangul
ValueCountFrequency (%)
도 33
 
10.9%
시 25
 
8.3%
경 18
 
6.0%
남 17
 
5.6%
북 12
 
4.0%
구 12
 
4.0%
군 11
 
3.6%
산 10
 
3.3%
충 9
 
3.0%
양 9
 
3.0%
Other values (60) 146
48.3%

시설용량
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)88.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean222594.44
Minimum0
Maximum916000
Zeros1
Zeros (%)2.8%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-10T21:45:35.994810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile13100
Q167975
median170000
Q3265625
95-th percentile721500
Maximum916000
Range916000
Interquartile range (IQR)197650

Descriptive statistics

Standard deviation225035.16
Coefficient of variation (CV)1.0109649
Kurtosis2.3241841
Mean222594.44
Median Absolute Deviation (MAD)104050
Skewness1.6196999
Sum8013400
Variance5.0640825 × 1010
MonotonicityNot monotonic
2023-12-10T21:45:36.115544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
70000 2
 
5.6%
30000 2
 
5.6%
200000 2
 
5.6%
250000 2
 
5.6%
596000 1
 
2.8%
80000 1
 
2.8%
215000 1
 
2.8%
16000 1
 
2.8%
40000 1
 
2.8%
414000 1
 
2.8%
Other values (22) 22
61.1%
ValueCountFrequency (%)
0 1
2.8%
4400 1
2.8%
16000 1
2.8%
30000 2
5.6%
40000 1
2.8%
45000 1
2.8%
52000 1
2.8%
61900 1
2.8%
70000 2
5.6%
80000 1
2.8%
ValueCountFrequency (%)
916000 1
2.8%
786000 1
2.8%
700000 1
2.8%
596000 1
2.8%
450000 1
2.8%
414000 1
2.8%
350000 1
2.8%
325000 1
2.8%
282500 1
2.8%
260000 1
2.8%

관로길이
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)72.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean175.025
Minimum0
Maximum459
Zeros4
Zeros (%)11.1%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-10T21:45:36.229509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q187
median141.4
Q3278.9
95-th percentile378.1
Maximum459
Range459
Interquartile range (IQR)191.9

Descriptive statistics

Standard deviation126.80505
Coefficient of variation (CV)0.7244968
Kurtosis-0.79539257
Mean175.025
Median Absolute Deviation (MAD)89.75
Skewness0.48770494
Sum6300.9
Variance16079.521
MonotonicityNot monotonic
2023-12-10T21:45:36.353956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0.0 4
 
11.1%
278.9 2
 
5.6%
100.0 2
 
5.6%
349.2 2
 
5.6%
90.0 2
 
5.6%
236.8 2
 
5.6%
78.0 2
 
5.6%
378.1 2
 
5.6%
70.1 1
 
2.8%
57.3 1
 
2.8%
Other values (16) 16
44.4%
ValueCountFrequency (%)
0.0 4
11.1%
35.4 1
 
2.8%
57.3 1
 
2.8%
70.1 1
 
2.8%
78.0 2
5.6%
90.0 2
5.6%
99.0 1
 
2.8%
100.0 2
5.6%
102.1 1
 
2.8%
104.3 1
 
2.8%
ValueCountFrequency (%)
459.0 1
2.8%
378.1 2
5.6%
355.1 1
2.8%
349.2 2
5.6%
344.0 1
2.8%
279.6 1
2.8%
278.9 2
5.6%
245.0 1
2.8%
236.8 2
5.6%
198.0 1
2.8%

취수원
Text

Distinct22
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-10T21:45:36.529317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length3
Mean length3.75
Min length1

Characters and Unicode

Total characters135
Distinct characters53
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

Unique16 ?
Unique (%)44.4%

Sample

1st row0
2nd row용담댐
3rd row팔당댐
4th row대청조정지댐
5th row낙동강 표류수
ValueCountFrequency (%)
팔당댐 8
19.5%
0 4
 
9.8%
대청조정지댐 2
 
4.9%
밀양댐 2
 
4.9%
대청댐 2
 
4.9%
운문댐 2
 
4.9%
한강 1
 
2.4%
횡성댐 1
 
2.4%
주암댐 1
 
2.4%
형산강 1
 
2.4%
Other values (17) 17
41.5%
2023-12-10T21:45:36.889065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
댐 30
22.2%
팔 8
 
5.9%
당 8
 
5.9%
6
 
4.4%
강 5
 
3.7%
대 4
 
3.0%
0 4
 
3.0%
. 4
 
3.0%
수 4
 
3.0%
청 4
 
3.0%
Other values (43) 58
43.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 121
89.6%
Space Separator 6
 
4.4%
Decimal Number 4
 
3.0%
Other Punctuation 4
 
3.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
댐 30
24.8%
팔 8
 
6.6%
당 8
 
6.6%
강 5
 
4.1%
대 4
 
3.3%
수 4
 
3.3%
청 4
 
3.3%
동 4
 
3.3%
장 3
 
2.5%
취 2
 
1.7%
Other values (40) 49
40.5%
Space Separator
ValueCountFrequency (%)
6
100.0%
Decimal Number
ValueCountFrequency (%)
0 4
100.0%
Other Punctuation
ValueCountFrequency (%)
. 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 121
89.6%
Common 14
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
댐 30
24.8%
팔 8
 
6.6%
당 8
 
6.6%
강 5
 
4.1%
대 4
 
3.3%
수 4
 
3.3%
청 4
 
3.3%
동 4
 
3.3%
장 3
 
2.5%
취 2
 
1.7%
Other values (40) 49
40.5%
Common
ValueCountFrequency (%)
6
42.9%
0 4
28.6%
. 4
28.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 121
89.6%
ASCII 14
 
10.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
댐 30
24.8%
팔 8
 
6.6%
당 8
 
6.6%
강 5
 
4.1%
대 4
 
3.3%
수 4
 
3.3%
청 4
 
3.3%
동 4
 
3.3%
장 3
 
2.5%
취 2
 
1.7%
Other values (40) 49
40.5%
ASCII
ValueCountFrequency (%)
6
42.9%
0 4
28.6%
. 4
28.6%

Interactions

2023-12-10T21:45:32.056789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:45:31.907079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:45:32.186024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T21:45:31.975178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T21:45:37.009452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설명권역사업단계명사업명시설약칭소재지시설용량관로길이취수원
시설명1.0001.0001.0001.0001.0001.0001.0001.0001.000
권역1.0001.0001.0001.0001.0001.0000.4630.6550.881
사업단계명1.0001.0001.0001.0001.0000.9770.0000.9501.000
사업명1.0001.0001.0001.0001.0000.9900.0000.0000.999
시설약칭1.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0000.9770.9901.0001.0000.9450.7540.987
시설용량1.0000.4630.0000.0001.0000.9451.0000.4100.000
관로길이1.0000.6550.9500.0001.0000.7540.4101.0000.664
취수원1.0000.8811.0000.9991.0000.9870.0000.6641.000
2023-12-10T21:45:37.122042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설용량관로길이권역
시설용량1.0000.5670.278
관로길이0.5671.0000.403
권역0.2780.4031.000

Missing values

2023-12-10T21:45:32.371151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T21:45:32.511974image/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

시설명권역사업단계명사업명시설약칭소재지시설용량관로길이취수원
0고령정수장낙동강권역영남내륙권광역상수도사업영남내륙권광역상수도사업고령(정)경북 고령군 다산면 노곡리 74644000.00
1고산정수장금강-섬진강권역전주권계통광역상수도전주권광역상수도고산(정)전라북도 완주군700000181.0용담댐
2고양정수장한강권역수도권광역상수도 1단계수도권광역상수도고양(정)경기도 고양시 일산동구350000278.9팔당댐
3공주정수장금강-섬진강권역충남중부권광역상수도충남중부권광역상수도공주(정)충청남도 공주시30000100.0대청조정지댐
4구미정수장낙동강권역구미권광역상수도 1단계구미권광역상수도구미(정)경상북도 구미시200000136.0낙동강 표류수
5금산정수장0금산권관리단지방상수도금산(정)충남 금산군 금산읍 아인리 620-8번지 아인택지개발지구내00.00
6덕소정수장한강권역수도권광역상수도 6단계수도권광역상수도덕소(정)경기도 남양주시450000349.2팔당댐
7덕정정수장금강-섬진강권역전남남부권광역상수도(1단계)사업전남남부권광역상수도덕정(정)전라남도 장흥군150000355.1장흥댐
8동화정수장금강-섬진강권역동화댐계통광역상수도동화댐광역상수도동화(정)전라북도 장수군52000155.0동화댐
9밀양정수장낙동강권역밀양댐계통광역상수도밀양댐광역상수도밀양(정)경상남도 밀양시7000090.0밀양댐
시설명권역사업단계명사업명시설약칭소재지시설용량관로길이취수원
26천안정수장금강-섬진강권역대청댐광역상수도 1-2단계대청댐광역상수도천안(정)충청남도 천안시 동남구414000236.8대청댐
27청주정수장금강-섬진강권역대청댐광역상수도 1-2단계대청댐광역상수도청주(정)충청북도 청주시 흥덕구596000236.8대청댐
28충주정수장한강권역충주댐계통광역상수도충주권광역상수도충주(정)충청북도 충주시250000245.0충주댐
29파주정수장한강권역수도권광역상수도 1단계수도권광역상수도파주(정)경기도 고양시 덕양구222000278.9팔당댐
30평림정수장금강-섬진강권역전남서부권광역상수도사업전남서부권광역상수도평림(정)전라남도 장성군30000102.1평림댐
31학야정수장낙동강권역포항권광역상수도포항권광역상수도학야(정)경상북도 포항시 북구6190099.0임하댐. 영천댐. 형산강
32화성정수장한강권역수도권광역상수도 5단계수도권광역상수도화성(정)화성시 매송면 천천리 18번지 일원2600000.00
33화순정수장금강-섬진강권역주암댐계통광역상수도 2단계주암댐광역상수도화순(정)전라남도 화순군100000146.8주암댐
34황지정수장한강권역태백권광역상수도_광동태백권광역상수도황지(정)강원도 태백시7000057.3광동댐
35대산산업용수센터000대산산업용수센터충남 서산시 대산읍 평신1로 3861190000.00