Overview

Dataset statistics

Number of variables15
Number of observations44
Missing cells119
Missing cells (%)18.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory129.9 B

Variable types

Text3
Numeric5
Categorical6
DateTime1

Dataset

Description전북특별자치도 하수종말 처리장(처리장별,시설용량 (톤/일),처리량 (톤/일),처리방법, 분뇨, 축산, 침출수, 가동개시일, 사업비 등)
Author전북특별자치도
URLhttps://www.data.go.kr/data/15051310/fileData.do

Alerts

수계 is highly overall correlated with 축산 and 3 other fieldsHigh correlation
기타 is highly overall correlated with 시설용량 and 8 other fieldsHigh correlation
분류 is highly overall correlated with 축산 and 3 other fieldsHigh correlation
축산 is highly overall correlated with 시설용량 and 8 other fieldsHigh correlation
방류수소독방법 is highly overall correlated with 시설용량 and 3 other fieldsHigh correlation
운영방법 is highly overall correlated with 축산 and 3 other fieldsHigh correlation
시설용량 is highly overall correlated with 처리량 and 6 other fieldsHigh correlation
처리량 is highly overall correlated with 시설용량 and 6 other fieldsHigh correlation
분뇨 is highly overall correlated with 시설용량 and 5 other fieldsHigh correlation
침출수 is highly overall correlated with 시설용량 and 5 other fieldsHigh correlation
사업비 (백만원) is highly overall correlated with 시설용량 and 5 other fieldsHigh correlation
축산 is highly imbalanced (73.2%)Imbalance
기타 is highly imbalanced (73.2%)Imbalance
시설용량 has 1 (2.3%) missing valuesMissing
처리량 has 1 (2.3%) missing valuesMissing
처리방법 has 2 (4.5%) missing valuesMissing
분뇨 has 35 (79.5%) missing valuesMissing
침출수 has 35 (79.5%) missing valuesMissing
가동개시일 has 34 (77.3%) missing valuesMissing
사업비 (백만원) has 1 (2.3%) missing valuesMissing
지류 has 10 (22.7%) missing valuesMissing
처리장별 has unique valuesUnique

Reproduction

Analysis started2024-03-15 01:04:29.848487
Analysis finished2024-03-15 01:04:39.134690
Duration9.29 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

처리장별
Text

UNIQUE 

Distinct44
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size480.0 B
2024-03-15T10:04:40.336518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length17.5
Mean length16.113636
Min length12

Characters and Unicode

Total characters709
Distinct characters114
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

Unique44 ?
Unique (%)100.0%

Sample

1st row전주시 덕진구 송천동2가 1035
2nd row군산시 소룡동 1584
3rd row군산시 대야면 산월리 27-15
4th row군산시 옥서면 옥봉리 1809-1
5th row군산시 임피면 술산리 668-10
ValueCountFrequency (%)
군산시 6
 
3.6%
익산시 5
 
3.0%
김제시 4
 
2.4%
부안군 4
 
2.4%
고창군 4
 
2.4%
완주군 4
 
2.4%
무주군 4
 
2.4%
남원시 3
 
1.8%
임피면 2
 
1.2%
임실군 2
 
1.2%
Other values (127) 130
77.4%
2024-03-15T10:04:42.160667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
124
 
17.5%
1 47
 
6.6%
리 39
 
5.5%
군 30
 
4.2%
- 29
 
4.1%
면 27
 
3.8%
시 21
 
3.0%
산 20
 
2.8%
4 19
 
2.7%
8 17
 
2.4%
Other values (104) 336
47.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 377
53.2%
Decimal Number 179
25.2%
Space Separator 124
 
17.5%
Dash Punctuation 29
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 39
 
10.3%
군 30
 
8.0%
면 27
 
7.2%
시 21
 
5.6%
산 20
 
5.3%
안 10
 
2.7%
읍 10
 
2.7%
주 10
 
2.7%
창 9
 
2.4%
고 7
 
1.9%
Other values (92) 194
51.5%
Decimal Number
ValueCountFrequency (%)
1 47
26.3%
4 19
10.6%
8 17
 
9.5%
7 17
 
9.5%
2 16
 
8.9%
0 15
 
8.4%
3 14
 
7.8%
5 14
 
7.8%
6 13
 
7.3%
9 7
 
3.9%
Space Separator
ValueCountFrequency (%)
124
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 377
53.2%
Common 332
46.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 39
 
10.3%
군 30
 
8.0%
면 27
 
7.2%
시 21
 
5.6%
산 20
 
5.3%
안 10
 
2.7%
읍 10
 
2.7%
주 10
 
2.7%
창 9
 
2.4%
고 7
 
1.9%
Other values (92) 194
51.5%
Common
ValueCountFrequency (%)
124
37.3%
1 47
 
14.2%
- 29
 
8.7%
4 19
 
5.7%
8 17
 
5.1%
7 17
 
5.1%
2 16
 
4.8%
0 15
 
4.5%
3 14
 
4.2%
5 14
 
4.2%
Other values (2) 20
 
6.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 377
53.2%
ASCII 332
46.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
124
37.3%
1 47
 
14.2%
- 29
 
8.7%
4 19
 
5.7%
8 17
 
5.1%
7 17
 
5.1%
2 16
 
4.8%
0 15
 
4.5%
3 14
 
4.2%
5 14
 
4.2%
Other values (2) 20
 
6.0%
Hangul
ValueCountFrequency (%)
리 39
 
10.3%
군 30
 
8.0%
면 27
 
7.2%
시 21
 
5.6%
산 20
 
5.3%
안 10
 
2.7%
읍 10
 
2.7%
주 10
 
2.7%
창 9
 
2.4%
고 7
 
1.9%
Other values (92) 194
51.5%

시설용량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct31
Distinct (%)72.1%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean22713.953
Minimum500
Maximum403000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-15T10:04:42.522669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile555
Q1775
median1600
Q35550
95-th percentile95860
Maximum403000
Range402500
Interquartile range (IQR)4775

Descriptive statistics

Standard deviation68871.948
Coefficient of variation (CV)3.0321427
Kurtosis23.56659
Mean22713.953
Median Absolute Deviation (MAD)1000
Skewness4.6459198
Sum976700
Variance4.7433453 × 109
MonotonicityNot monotonic
2024-03-15T10:04:42.779461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
700 4
 
9.1%
600 3
 
6.8%
1000 3
 
6.8%
3000 2
 
4.5%
800 2
 
4.5%
550 2
 
4.5%
1200 2
 
4.5%
2000 2
 
4.5%
500 1
 
2.3%
3400 1
 
2.3%
Other values (21) 21
47.7%
ValueCountFrequency (%)
500 1
 
2.3%
550 2
4.5%
600 3
6.8%
700 4
9.1%
750 1
 
2.3%
800 2
4.5%
950 1
 
2.3%
1000 3
6.8%
1100 1
 
2.3%
1200 2
4.5%
ValueCountFrequency (%)
403000 1
2.3%
200000 1
2.3%
100000 1
2.3%
58600 1
2.3%
50000 1
2.3%
32000 1
2.3%
30000 1
2.3%
26000 1
2.3%
16000 1
2.3%
8000 1
2.3%

처리량
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)97.7%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean19277.93
Minimum278
Maximum389198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-15T10:04:43.037593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum278
5-th percentile331
Q1668
median1202
Q33787
95-th percentile82193
Maximum389198
Range388920
Interquartile range (IQR)3119

Descriptive statistics

Standard deviation64120.853
Coefficient of variation (CV)3.3261275
Kurtosis27.861823
Mean19277.93
Median Absolute Deviation (MAD)690
Skewness5.0654321
Sum828951
Variance4.1114838 × 109
MonotonicityNot monotonic
2024-03-15T10:04:43.351927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
792 2
 
4.5%
389198 1
 
2.3%
2624 1
 
2.3%
2716 1
 
2.3%
421 1
 
2.3%
2978 1
 
2.3%
432 1
 
2.3%
657 1
 
2.3%
1137 1
 
2.3%
1334 1
 
2.3%
Other values (32) 32
72.7%
ValueCountFrequency (%)
278 1
2.3%
301 1
2.3%
321 1
2.3%
421 1
2.3%
432 1
2.3%
512 1
2.3%
520 1
2.3%
529 1
2.3%
540 1
2.3%
610 1
2.3%
ValueCountFrequency (%)
389198 1
2.3%
159090 1
2.3%
87058 1
2.3%
38408 1
2.3%
37066 1
2.3%
22039 1
2.3%
20363 1
2.3%
15458 1
2.3%
12455 1
2.3%
9033 1
2.3%

처리방법
Text

MISSING 

Distinct22
Distinct (%)52.4%
Missing2
Missing (%)4.5%
Memory size480.0 B
2024-03-15T10:04:44.001832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length10
Mean length6.7142857
Min length3

Characters and Unicode

Total characters282
Distinct characters63
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique14 ?
Unique (%)33.3%

Sample

1st rowCNR CSBR
2nd row4 stage BNR
3rd rowKIDEA
4th rowKIDEA
5th rowKIDEA
ValueCountFrequency (%)
kidea 11
22.9%
kidea공법 4
 
8.3%
선회와류식sbr 3
 
6.2%
bcs 2
 
4.2%
fluidyne 2
 
4.2%
symbio 2
 
4.2%
mle+응집침전 2
 
4.2%
sbr(2a2o 2
 
4.2%
bcs공법 2
 
4.2%
sbr 2
 
4.2%
Other values (16) 16
33.3%
2024-03-15T10:04:44.798518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 21
 
7.4%
A 20
 
7.1%
I 20
 
7.1%
D 18
 
6.4%
S 17
 
6.0%
B 17
 
6.0%
K 16
 
5.7%
R 14
 
5.0%
M 9
 
3.2%
법 7
 
2.5%
Other values (53) 123
43.6%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 180
63.8%
Other Letter 57
 
20.2%
Lowercase Letter 23
 
8.2%
Space Separator 6
 
2.1%
Math Symbol 5
 
1.8%
Decimal Number 5
 
1.8%
Open Punctuation 3
 
1.1%
Close Punctuation 3
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
법 7
 
12.3%
공 7
 
12.3%
전 3
 
5.3%
집 3
 
5.3%
응 3
 
5.3%
침 3
 
5.3%
식 3
 
5.3%
류 3
 
5.3%
와 3
 
5.3%
회 3
 
5.3%
Other values (14) 19
33.3%
Uppercase Letter
ValueCountFrequency (%)
E 21
11.7%
A 20
11.1%
I 20
11.1%
D 18
10.0%
S 17
9.4%
B 17
9.4%
K 16
8.9%
R 14
7.8%
M 9
5.0%
C 7
 
3.9%
Other values (7) 21
11.7%
Lowercase Letter
ValueCountFrequency (%)
e 3
13.0%
i 3
13.0%
o 2
 
8.7%
y 2
 
8.7%
s 2
 
8.7%
b 1
 
4.3%
g 1
 
4.3%
a 1
 
4.3%
t 1
 
4.3%
h 1
 
4.3%
Other values (6) 6
26.1%
Decimal Number
ValueCountFrequency (%)
2 4
80.0%
4 1
 
20.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Math Symbol
ValueCountFrequency (%)
+ 5
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 203
72.0%
Hangul 57
 
20.2%
Common 22
 
7.8%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 21
10.3%
A 20
9.9%
I 20
9.9%
D 18
8.9%
S 17
 
8.4%
B 17
 
8.4%
K 16
 
7.9%
R 14
 
6.9%
M 9
 
4.4%
C 7
 
3.4%
Other values (23) 44
21.7%
Hangul
ValueCountFrequency (%)
법 7
 
12.3%
공 7
 
12.3%
전 3
 
5.3%
집 3
 
5.3%
응 3
 
5.3%
침 3
 
5.3%
식 3
 
5.3%
류 3
 
5.3%
와 3
 
5.3%
회 3
 
5.3%
Other values (14) 19
33.3%
Common
ValueCountFrequency (%)
6
27.3%
+ 5
22.7%
2 4
18.2%
( 3
13.6%
) 3
13.6%
4 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 225
79.8%
Hangul 57
 
20.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 21
 
9.3%
A 20
 
8.9%
I 20
 
8.9%
D 18
 
8.0%
S 17
 
7.6%
B 17
 
7.6%
K 16
 
7.1%
R 14
 
6.2%
M 9
 
4.0%
C 7
 
3.1%
Other values (29) 66
29.3%
Hangul
ValueCountFrequency (%)
법 7
 
12.3%
공 7
 
12.3%
전 3
 
5.3%
집 3
 
5.3%
응 3
 
5.3%
침 3
 
5.3%
식 3
 
5.3%
류 3
 
5.3%
와 3
 
5.3%
회 3
 
5.3%
Other values (14) 19
33.3%

분뇨
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing35
Missing (%)79.5%
Infinite0
Infinite (%)0.0%
Mean65.222222
Minimum9
Maximum162
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-15T10:04:45.148328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9
5-th percentile12.6
Q119
median61
Q3103
95-th percentile142.4
Maximum162
Range153
Interquartile range (IQR)84

Descriptive statistics

Standard deviation53.415302
Coefficient of variation (CV)0.81897396
Kurtosis-0.67620096
Mean65.222222
Median Absolute Deviation (MAD)42
Skewness0.6379809
Sum587
Variance2853.1944
MonotonicityNot monotonic
2024-03-15T10:04:45.336927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
162 1
 
2.3%
103 1
 
2.3%
113 1
 
2.3%
61 1
 
2.3%
19 1
 
2.3%
21 1
 
2.3%
18 1
 
2.3%
9 1
 
2.3%
81 1
 
2.3%
(Missing) 35
79.5%
ValueCountFrequency (%)
9 1
2.3%
18 1
2.3%
19 1
2.3%
21 1
2.3%
61 1
2.3%
81 1
2.3%
103 1
2.3%
113 1
2.3%
162 1
2.3%
ValueCountFrequency (%)
162 1
2.3%
113 1
2.3%
103 1
2.3%
81 1
2.3%
61 1
2.3%
21 1
2.3%
19 1
2.3%
18 1
2.3%
9 1
2.3%

축산
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size480.0 B
<NA>
40 
162
 
1
69
 
1
37
 
1
93
 
1

Length

Max length4
Median length4
Mean length3.8409091
Min length2

Unique

Unique4 ?
Unique (%)9.1%

Sample

1st row<NA>
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 40
90.9%
162 1
 
2.3%
69 1
 
2.3%
37 1
 
2.3%
93 1
 
2.3%

Length

2024-03-15T10:04:45.575532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:04:45.844262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
90.9%
162 1
 
2.3%
69 1
 
2.3%
37 1
 
2.3%
93 1
 
2.3%

침출수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing35
Missing (%)79.5%
Infinite0
Infinite (%)0.0%
Mean66.333333
Minimum10
Maximum190
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-15T10:04:46.197652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile12
Q117
median59
Q394
95-th percentile163.6
Maximum190
Range180
Interquartile range (IQR)77

Descriptive statistics

Standard deviation60.404884
Coefficient of variation (CV)0.91062639
Kurtosis0.87359582
Mean66.333333
Median Absolute Deviation (MAD)42
Skewness1.1762869
Sum597
Variance3648.75
MonotonicityNot monotonic
2024-03-15T10:04:46.403503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
190 1
 
2.3%
94 1
 
2.3%
60 1
 
2.3%
17 1
 
2.3%
124 1
 
2.3%
28 1
 
2.3%
10 1
 
2.3%
15 1
 
2.3%
59 1
 
2.3%
(Missing) 35
79.5%
ValueCountFrequency (%)
10 1
2.3%
15 1
2.3%
17 1
2.3%
28 1
2.3%
59 1
2.3%
60 1
2.3%
94 1
2.3%
124 1
2.3%
190 1
2.3%
ValueCountFrequency (%)
190 1
2.3%
124 1
2.3%
94 1
2.3%
60 1
2.3%
59 1
2.3%
28 1
2.3%
17 1
2.3%
15 1
2.3%
10 1
2.3%

기타
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size480.0 B
<NA>
40 
47256
 
1
395
 
1
10381
 
1
20
 
1

Length

Max length5
Median length4
Mean length3.9772727
Min length2

Unique

Unique4 ?
Unique (%)9.1%

Sample

1st row47256
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 40
90.9%
47256 1
 
2.3%
395 1
 
2.3%
10381 1
 
2.3%
20 1
 
2.3%

Length

2024-03-15T10:04:46.630167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:04:46.829948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
90.9%
47256 1
 
2.3%
395 1
 
2.3%
10381 1
 
2.3%
20 1
 
2.3%

가동개시일
Date

MISSING 

Distinct6
Distinct (%)60.0%
Missing34
Missing (%)77.3%
Memory size480.0 B
Minimum1990-03-01 00:00:00
Maximum2010-05-01 00:00:00
2024-03-15T10:04:47.059602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:47.290788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)

사업비 (백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct43
Distinct (%)100.0%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean26938.279
Minimum4636
Maximum219593
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size524.0 B
2024-03-15T10:04:47.524704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4636
5-th percentile5846.9
Q18120
median11302
Q322725
95-th percentile70421.3
Maximum219593
Range214957
Interquartile range (IQR)14605

Descriptive statistics

Standard deviation45395.591
Coefficient of variation (CV)1.6851704
Kurtosis13.867577
Mean26938.279
Median Absolute Deviation (MAD)4406
Skewness3.6937201
Sum1158346
Variance2.0607596 × 109
MonotonicityNot monotonic
2024-03-15T10:04:47.854919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
215151 1
 
2.3%
219593 1
 
2.3%
8049 1
 
2.3%
13000 1
 
2.3%
4636 1
 
2.3%
16807 1
 
2.3%
7916 1
 
2.3%
8351 1
 
2.3%
9385 1
 
2.3%
8700 1
 
2.3%
Other values (33) 33
75.0%
ValueCountFrequency (%)
4636 1
2.3%
5500 1
2.3%
5765 1
2.3%
6584 1
2.3%
6716 1
2.3%
6896 1
2.3%
6927 1
2.3%
7024 1
2.3%
7870 1
2.3%
7916 1
2.3%
ValueCountFrequency (%)
219593 1
2.3%
215151 1
2.3%
71101 1
2.3%
64304 1
2.3%
62050 1
2.3%
43243 1
2.3%
33003 1
2.3%
30043 1
2.3%
28664 1
2.3%
25861 1
2.3%

운영방법
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size480.0 B
민간
28 
공기업
8 
자체
6 
<NA>
 
2

Length

Max length4
Median length2
Mean length2.2727273
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
민간 28
63.6%
공기업 8
 
18.2%
자체 6
 
13.6%
<NA> 2
 
4.5%

Length

2024-03-15T10:04:48.230332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:04:48.715074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
민간 28
63.6%
공기업 8
 
18.2%
자체 6
 
13.6%
na 2
 
4.5%

방류수소독방법
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)15.9%
Missing0
Missing (%)0.0%
Memory size480.0 B
자외선
29 
UV
5 
UV소독
4 
염소 자외선
 
2
<NA>
 
2
Other values (2)
 
2

Length

Max length6
Median length3
Mean length3.1136364
Min length2

Unique

Unique2 ?
Unique (%)4.5%

Sample

1st row염소 자외선
2nd rowUV
3rd row자외선
4th row자외선
5th row자외선

Common Values

ValueCountFrequency (%)
자외선 29
65.9%
UV 5
 
11.4%
UV소독 4
 
9.1%
염소 자외선 2
 
4.5%
<NA> 2
 
4.5%
염소 1
 
2.3%
기타 1
 
2.3%

Length

2024-03-15T10:04:49.138834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:04:49.590461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자외선 31
67.4%
uv 5
 
10.9%
uv소독 4
 
8.7%
염소 3
 
6.5%
na 2
 
4.3%
기타 1
 
2.2%

지류
Text

MISSING 

Distinct24
Distinct (%)70.6%
Missing10
Missing (%)22.7%
Memory size480.0 B
2024-03-15T10:04:50.481466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.9117647
Min length2

Characters and Unicode

Total characters99
Distinct characters41
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

Unique18 ?
Unique (%)52.9%

Sample

1st row전주천
2nd row고척천
3rd row만경강
4th row탑천
5th row만경강
ValueCountFrequency (%)
만경강 4
 
11.8%
원평천 3
 
8.8%
남대천 3
 
8.8%
람천 2
 
5.9%
고척천 2
 
5.9%
탑천 2
 
5.9%
전주천 1
 
2.9%
삼천 1
 
2.9%
계화조류지 1
 
2.9%
하장천 1
 
2.9%
Other values (14) 14
41.2%
2024-03-15T10:04:52.001433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
천 29
29.3%
경 5
 
5.1%
강 5
 
5.1%
평 4
 
4.0%
만 4
 
4.0%
대 3
 
3.0%
원 3
 
3.0%
남 3
 
3.0%
장 3
 
3.0%
산 3
 
3.0%
Other values (31) 37
37.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 99
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 29
29.3%
경 5
 
5.1%
강 5
 
5.1%
평 4
 
4.0%
만 4
 
4.0%
대 3
 
3.0%
원 3
 
3.0%
남 3
 
3.0%
장 3
 
3.0%
산 3
 
3.0%
Other values (31) 37
37.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 29
29.3%
경 5
 
5.1%
강 5
 
5.1%
평 4
 
4.0%
만 4
 
4.0%
대 3
 
3.0%
원 3
 
3.0%
남 3
 
3.0%
장 3
 
3.0%
산 3
 
3.0%
Other values (31) 37
37.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 99
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
천 29
29.3%
경 5
 
5.1%
강 5
 
5.1%
평 4
 
4.0%
만 4
 
4.0%
대 3
 
3.0%
원 3
 
3.0%
남 3
 
3.0%
장 3
 
3.0%
산 3
 
3.0%
Other values (31) 37
37.4%

분류
Categorical

HIGH CORRELATION 

Distinct16
Distinct (%)36.4%
Missing0
Missing (%)0.0%
Memory size480.0 B
만경강
14 
동진강
6 
<NA>
4 
금강
4 
남대천
3 
Other values (11)
13 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique9 ?
Unique (%)20.5%

Sample

1st row만경강
2nd row<NA>
3rd row만경강
4th row만경강
5th row만경강

Common Values

ValueCountFrequency (%)
만경강 14
31.8%
동진강 6
13.6%
<NA> 4
 
9.1%
금강 4
 
9.1%
남대천 3
 
6.8%
섬진강 2
 
4.5%
낙동강 2
 
4.5%
진안천 1
 
2.3%
방화천 1
 
2.3%
구량천 1
 
2.3%
Other values (6) 6
13.6%

Length

2024-03-15T10:04:52.500058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
만경강 14
31.8%
동진강 6
13.6%
na 4
 
9.1%
금강 4
 
9.1%
남대천 3
 
6.8%
섬진강 2
 
4.5%
낙동강 2
 
4.5%
진안천 1
 
2.3%
방화천 1
 
2.3%
구량천 1
 
2.3%
Other values (6) 6
13.6%

수계
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)11.4%
Missing0
Missing (%)0.0%
Memory size480.0 B
서해
15 
금강
15 
연안(서해)
8 
섬진강
4 
낙동강
2 

Length

Max length6
Median length2
Mean length2.8636364
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서해 15
34.1%
금강 15
34.1%
연안(서해) 8
18.2%
섬진강 4
 
9.1%
낙동강 2
 
4.5%

Length

2024-03-15T10:04:52.986494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T10:04:53.428979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서해 15
34.1%
금강 15
34.1%
연안(서해 8
18.2%
섬진강 4
 
9.1%
낙동강 2
 
4.5%

Interactions

2024-03-15T10:04:36.358391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:31.204408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:32.413658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:33.804044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:34.994913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:36.619194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:31.376434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:32.696031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:34.073731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:35.256357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:36.877874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:31.590793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:33.016898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:34.350698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:35.522215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:37.128831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:31.903185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:33.294810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:34.508013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:35.777012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:37.305333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:32.168480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:33.556989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:34.750789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T10:04:36.089931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T10:04:53.947821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
처리장별시설용량처리량처리방법분뇨축산침출수기타가동개시일사업비 (백만원)운영방법방류수소독방법지류분류수계
처리장별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
시설용량1.0001.0001.0000.8870.8871.0000.8781.0001.0000.9420.0000.7321.0000.0000.000
처리량1.0001.0001.0000.9370.697NaN0.8761.0001.0000.7240.0000.8411.0000.0000.000
처리방법1.0000.8870.9371.0000.5691.0000.4871.0001.0000.8590.9370.9090.9760.9390.917
분뇨1.0000.8870.6970.5691.0001.0001.0001.0000.0000.8530.0000.5811.0000.0000.000
축산1.0001.000NaN1.0001.0001.0000.000NaNNaN1.0001.0001.0001.0001.0001.000
침출수1.0000.8780.8760.4871.0000.0001.0001.0000.0000.6950.6950.4841.0000.9270.749
기타1.0001.0001.0001.0001.000NaN1.0001.000NaN1.000NaN1.0001.0001.0001.000
가동개시일1.0001.0001.0001.0000.000NaN0.000NaN1.0001.0001.0001.0001.0001.0001.000
사업비 (백만원)1.0000.9420.7240.8590.8531.0000.6951.0001.0001.0000.0000.5610.9440.0000.000
운영방법1.0000.0000.0000.9370.0001.0000.695NaN1.0000.0001.0000.6211.0000.9810.719
방류수소독방법1.0000.7320.8410.9090.5811.0000.4841.0001.0000.5610.6211.0000.9240.7240.623
지류1.0001.0001.0000.9761.0001.0001.0001.0001.0000.9441.0000.9241.0000.9920.954
분류1.0000.0000.0000.9390.0001.0000.9271.0001.0000.0000.9810.7240.9921.0000.972
수계1.0000.0000.0000.9170.0001.0000.7491.0001.0000.0000.7190.6230.9540.9721.000
2024-03-15T10:04:54.278853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
수계기타분류축산방류수소독방법운영방법
수계1.0001.0000.6511.0000.4730.689
기타1.0001.0001.000NaN1.0001.000
분류0.6511.0001.0001.0000.3680.689
축산1.000NaN1.0001.0001.0001.000
방류수소독방법0.4731.0000.3681.0001.0000.302
운영방법0.6891.0000.6891.0000.3021.000
2024-03-15T10:04:54.553713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시설용량처리량분뇨침출수사업비 (백만원)축산기타운영방법방류수소독방법분류수계
시설용량1.0000.9720.8670.8170.8271.0001.0000.0000.5940.0000.000
처리량0.9721.0000.8000.8170.8081.0001.0000.0000.6840.0000.000
분뇨0.8670.8001.0000.7140.6001.0001.0000.0000.0000.0000.000
침출수0.8170.8170.7141.0000.7671.0001.0000.0000.0000.2640.000
사업비 (백만원)0.8270.8080.6000.7671.0001.0001.0000.0000.4180.0000.000
축산1.0001.0001.0001.0001.0001.000NaN1.0001.0001.0001.000
기타1.0001.0001.0001.0001.000NaN1.0001.0001.0001.0001.000
운영방법0.0000.0000.0000.0000.0001.0001.0001.0000.3020.6890.689
방류수소독방법0.5940.6840.0000.0000.4181.0001.0000.3021.0000.3680.473
분류0.0000.0000.0000.2640.0001.0001.0000.6890.3681.0000.651
수계0.0000.0000.0000.0000.0001.0001.0000.6890.4730.6511.000

Missing values

2024-03-15T10:04:37.722416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T10:04:38.336154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-03-15T10:04:38.810131image/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

처리장별시설용량처리량처리방법분뇨축산침출수기타가동개시일사업비 (백만원)운영방법방류수소독방법지류분류수계
0전주시 덕진구 송천동2가 1035403000389198CNR CSBR162<NA>190472561990-03-01215151민간염소 자외선전주천만경강서해
1군산시 소룡동 15842000001590904 stage BNR103<NA>94<NA>2010-03-01219593민간UV<NA><NA>서해
2군산시 대야면 산월리 27-1519001487KIDEA<NA><NA><NA><NA>2008-06-0112753민간자외선고척천만경강서해
3군산시 옥서면 옥봉리 1809-11600823KIDEA<NA><NA><NA><NA>2008-06-0111634민간자외선만경강만경강서해
4군산시 임피면 술산리 668-10950792KIDEA<NA><NA><NA><NA>2008-06-0110003민간자외선탑천만경강서해
5군산시 임피면 미원리 778550278KIDEA<NA><NA><NA><NA>2008-06-016927민간자외선만경강만경강서해
6군산시 회현면 대정리 762-1550301KIDEA<NA><NA><NA><NA>2008-06-018191민간자외선고척천만경강서해
7익산시 금강동 1091-410000087058MLE+응집침전113<NA>60395<NA>64304민간염소목천포천만경강금강
8익산시 황등면 신기리 118-163000020363KIDEA<NA><NA><NA><NA><NA>33003민간UV탑천만경강금강
9익산시 용안면 덕용리 1062-460004596FLUIDYNE<NA><NA>17<NA><NA>28664민간UV산북천금강금강
처리장별시설용량처리량처리방법분뇨축산침출수기타가동개시일사업비 (백만원)운영방법방류수소독방법지류분류수계
34임실군 오수면 용정리 312117001568산화구+symbio993<NA><NA><NA>25861자체UV소독<NA>오수천섬진강
35순창군 유등면 창신리 843번지51002101생물여과81<NA><NA><NA><NA>7024민간UV소독경천섬진강섬진강
36고창군 고창읍 죽림리 8711600012455SYMBIO<NA><NA>59<NA><NA>30043민간자외선<NA>고창천연안(서해)
37고창군 아산면 삼인리 70-3750520금호MBR<NA><NA><NA><NA><NA>10417민간기타<NA>삼인천연안(서해)
38고창군 대산면 덕천리 715700529NPR<NA><NA><NA><NA><NA>14691민간자외선<NA>대산천연안(서해)
39고창군 흥덕면 석교리 615700610SMMIAR<NA><NA><NA><NA><NA>6896민간자외선<NA>갈곡천연안(서해)
40부안군 부안읍 신운리 48880009033Fluidyne SBR공법<NA><NA><NA><NA><NA>22950민간자외선하장천동진강서해
41부안군 계화면 창북리 1467-115001202KIDEA공법<NA><NA><NA><NA><NA>11302민간자외선계화조류지<NA>서해
42부안군 진서면 곰소리 850<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>서해
43부안군 변산면 격포리 270-2633001926<NA><NA><NA><NA><NA><NA>19200<NA><NA>상동두천<NA>서해