Overview

Dataset statistics

Number of variables9
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory78.4 B

Variable types

Numeric2
Text7

Dataset

Description샘플 데이터
Author한국해양과학기술원
URLhttps://www.bigdata-environment.kr/user/data_market/detail.do?id=f3328e00-5f03-11ec-ace8-ed82a0cc1285

Alerts

데이터_건수 is highly overall correlated with 유기물_농도(%)High correlation
유기물_농도(%) is highly overall correlated with 데이터_건수High correlation
데이터_건수 has unique valuesUnique
관리번호 has unique valuesUnique

Reproduction

Analysis started2024-04-17 09:41:11.246082
Analysis finished2024-04-17 09:41:11.977456
Duration0.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

데이터_건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean220.5
Minimum206
Maximum235
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T18:41:12.034515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum206
5-th percentile207.45
Q1213.25
median220.5
Q3227.75
95-th percentile233.55
Maximum235
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.039924755
Kurtosis-1.2
Mean220.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum6615
Variance77.5
MonotonicityStrictly increasing
2024-04-17T18:41:12.141400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
206 1
 
3.3%
222 1
 
3.3%
235 1
 
3.3%
234 1
 
3.3%
233 1
 
3.3%
232 1
 
3.3%
231 1
 
3.3%
230 1
 
3.3%
229 1
 
3.3%
228 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
206 1
3.3%
207 1
3.3%
208 1
3.3%
209 1
3.3%
210 1
3.3%
211 1
3.3%
212 1
3.3%
213 1
3.3%
214 1
3.3%
215 1
3.3%
ValueCountFrequency (%)
235 1
3.3%
234 1
3.3%
233 1
3.3%
232 1
3.3%
231 1
3.3%
230 1
3.3%
229 1
3.3%
228 1
3.3%
227 1
3.3%
226 1
3.3%

관리번호
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:12.303158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length2.8333333
Min length2

Characters and Unicode

Total characters85
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st rowN2
2nd rowN3
3rd rowN4
4th rowN5
5th rowN8
ValueCountFrequency (%)
n2 1
 
3.3%
n3 1
 
3.3%
n88 1
 
3.3%
n85 1
 
3.3%
n83 1
 
3.3%
n79 1
 
3.3%
n74 1
 
3.3%
n69 1
 
3.3%
n66 1
 
3.3%
n62 1
 
3.3%
Other values (20) 20
66.7%
2024-04-17T18:41:12.594056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 30
35.3%
2 8
 
9.4%
3 8
 
9.4%
5 7
 
8.2%
8 7
 
8.2%
4 5
 
5.9%
1 5
 
5.9%
7 5
 
5.9%
6 5
 
5.9%
9 4
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55
64.7%
Uppercase Letter 30
35.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 8
14.5%
3 8
14.5%
5 7
12.7%
8 7
12.7%
4 5
9.1%
1 5
9.1%
7 5
9.1%
6 5
9.1%
9 4
7.3%
0 1
 
1.8%
Uppercase Letter
ValueCountFrequency (%)
N 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55
64.7%
Latin 30
35.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 8
14.5%
3 8
14.5%
5 7
12.7%
8 7
12.7%
4 5
9.1%
1 5
9.1%
7 5
9.1%
6 5
9.1%
9 4
7.3%
0 1
 
1.8%
Latin
ValueCountFrequency (%)
N 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 85
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 30
35.3%
2 8
 
9.4%
3 8
 
9.4%
5 7
 
8.2%
8 7
 
8.2%
4 5
 
5.9%
1 5
 
5.9%
7 5
 
5.9%
6 5
 
5.9%
9 4
 
4.7%

위치(좌표계WGS84)위도_도
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:12.735734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length11.966667
Min length11

Characters and Unicode

Total characters359
Distinct characters14
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)36.7%

Sample

1st rowN 35.135167°
2nd rowN 35.125000°
3rd rowN 35.116667°
4th rowN 35.116667°
5th rowN 35.102300°
ValueCountFrequency (%)
n 30
50.0%
35.036667° 4
 
6.7%
35.046667° 3
 
5.0%
35.065000° 3
 
5.0%
35.080000° 3
 
5.0%
35.116667° 2
 
3.3%
35.058333° 2
 
3.3%
35.075000° 2
 
3.3%
35.135167° 1
 
1.7%
35.079800° 1
 
1.7%
Other values (9) 9
 
15.0%
2024-04-17T18:41:12.967746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 66
18.4%
3 48
13.4%
5 43
12.0%
6 35
9.7%
N 30
8.4%
30
8.4%
. 30
8.4%
° 30
8.4%
7 15
 
4.2%
8 12
 
3.3%
Other values (4) 20
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 239
66.6%
Uppercase Letter 30
 
8.4%
Space Separator 30
 
8.4%
Other Punctuation 30
 
8.4%
Other Symbol 30
 
8.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 66
27.6%
3 48
20.1%
5 43
18.0%
6 35
14.6%
7 15
 
6.3%
8 12
 
5.0%
1 11
 
4.6%
4 4
 
1.7%
2 3
 
1.3%
9 2
 
0.8%
Uppercase Letter
ValueCountFrequency (%)
N 30
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Other Symbol
ValueCountFrequency (%)
° 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 329
91.6%
Latin 30
 
8.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 66
20.1%
3 48
14.6%
5 43
13.1%
6 35
10.6%
30
9.1%
. 30
9.1%
° 30
9.1%
7 15
 
4.6%
8 12
 
3.6%
1 11
 
3.3%
Other values (3) 9
 
2.7%
Latin
ValueCountFrequency (%)
N 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 329
91.6%
None 30
 
8.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 66
20.1%
3 48
14.6%
5 43
13.1%
6 35
10.6%
N 30
9.1%
30
9.1%
. 30
9.1%
7 15
 
4.6%
8 12
 
3.6%
1 11
 
3.3%
Other values (3) 9
 
2.7%
None
ValueCountFrequency (%)
° 30
100.0%

위치(좌표계WGS84)경도_도
Text

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:13.112094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters390
Distinct characters14
Distinct categories5 ?
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 (%)63.3%

Sample

1st rowE 128.961033°
2nd rowE 128.953333°
3rd rowE 128.941667°
4th rowE 128.954167°
5th rowE 128.895833°
ValueCountFrequency (%)
e 30
50.0%
128.861667° 3
 
5.0%
128.950000° 2
 
3.3%
128.901667° 2
 
3.3%
128.873333° 2
 
3.3%
128.925000° 2
 
3.3%
128.961033° 1
 
1.7%
128.942283° 1
 
1.7%
128.921667° 1
 
1.7%
128.931667° 1
 
1.7%
Other values (15) 15
25.0%
2024-04-17T18:41:13.370091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 49
12.6%
1 45
11.5%
2 37
9.5%
3 32
8.2%
E 30
7.7%
30
7.7%
. 30
7.7%
6 30
7.7%
° 30
7.7%
9 23
5.9%
Other values (4) 54
13.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 270
69.2%
Uppercase Letter 30
 
7.7%
Space Separator 30
 
7.7%
Other Punctuation 30
 
7.7%
Other Symbol 30
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
8 49
18.1%
1 45
16.7%
2 37
13.7%
3 32
11.9%
6 30
11.1%
9 23
8.5%
0 21
7.8%
7 16
 
5.9%
5 10
 
3.7%
4 7
 
2.6%
Uppercase Letter
ValueCountFrequency (%)
E 30
100.0%
Space Separator
ValueCountFrequency (%)
30
100.0%
Other Punctuation
ValueCountFrequency (%)
. 30
100.0%
Other Symbol
ValueCountFrequency (%)
° 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 360
92.3%
Latin 30
 
7.7%

Most frequent character per script

Common
ValueCountFrequency (%)
8 49
13.6%
1 45
12.5%
2 37
10.3%
3 32
8.9%
30
8.3%
. 30
8.3%
6 30
8.3%
° 30
8.3%
9 23
6.4%
0 21
5.8%
Other values (3) 33
9.2%
Latin
ValueCountFrequency (%)
E 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 360
92.3%
None 30
 
7.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 49
13.6%
1 45
12.5%
2 37
10.3%
3 32
8.9%
E 30
8.3%
30
8.3%
. 30
8.3%
6 30
8.3%
9 23
6.4%
0 21
5.8%
Other values (3) 33
9.2%
None
ValueCountFrequency (%)
° 30
100.0%

위치(좌표계WGS84)위도_도분
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:13.513534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters360
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)36.7%

Sample

1st rowN 35° 8.110'
2nd rowN 35° 7.500'
3rd rowN 35° 7.000'
4th rowN 35° 7.000'
5th rowN 35° 6.138'
ValueCountFrequency (%)
n 30
33.3%
35° 30
33.3%
2.200 4
 
4.4%
2.800 3
 
3.3%
3.900 3
 
3.3%
4.800 3
 
3.3%
7.000 2
 
2.2%
3.500 2
 
2.2%
4.500 2
 
2.2%
8.110 1
 
1.1%
Other values (10) 10
 
11.1%
2024-04-17T18:41:13.756917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60
16.7%
0 48
13.3%
3 39
10.8%
5 39
10.8%
N 30
8.3%
° 30
8.3%
. 30
8.3%
' 30
8.3%
2 13
 
3.6%
8 11
 
3.1%
Other values (5) 30
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 180
50.0%
Space Separator 60
 
16.7%
Other Punctuation 60
 
16.7%
Uppercase Letter 30
 
8.3%
Other Symbol 30
 
8.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 48
26.7%
3 39
21.7%
5 39
21.7%
2 13
 
7.2%
8 11
 
6.1%
4 11
 
6.1%
1 8
 
4.4%
9 4
 
2.2%
7 4
 
2.2%
6 3
 
1.7%
Other Punctuation
ValueCountFrequency (%)
. 30
50.0%
' 30
50.0%
Space Separator
ValueCountFrequency (%)
60
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 30
100.0%
Other Symbol
ValueCountFrequency (%)
° 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 330
91.7%
Latin 30
 
8.3%

Most frequent character per script

Common
ValueCountFrequency (%)
60
18.2%
0 48
14.5%
3 39
11.8%
5 39
11.8%
° 30
9.1%
. 30
9.1%
' 30
9.1%
2 13
 
3.9%
8 11
 
3.3%
4 11
 
3.3%
Other values (4) 19
 
5.8%
Latin
ValueCountFrequency (%)
N 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 330
91.7%
None 30
 
8.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
60
18.2%
0 48
14.5%
3 39
11.8%
5 39
11.8%
N 30
9.1%
. 30
9.1%
' 30
9.1%
2 13
 
3.9%
8 11
 
3.3%
4 11
 
3.3%
Other values (4) 19
 
5.8%
None
ValueCountFrequency (%)
° 30
100.0%

위치(좌표계WGS84)경도_도분
Text

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:13.912514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

Characters and Unicode

Total characters420
Distinct characters15
Distinct categories5 ?
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 (%)63.3%

Sample

1st rowE 128° 57.662'
2nd rowE 128° 57.200'
3rd rowE 128° 56.500'
4th rowE 128° 57.250'
5th rowE 128° 53.750'
ValueCountFrequency (%)
e 30
33.3%
128° 30
33.3%
51.700 3
 
3.3%
57.000 2
 
2.2%
54.100 2
 
2.2%
52.400 2
 
2.2%
55.500 2
 
2.2%
57.662 1
 
1.1%
56.537 1
 
1.1%
55.300 1
 
1.1%
Other values (16) 16
17.8%
2024-04-17T18:41:14.175635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
60
14.3%
0 50
11.9%
5 43
10.2%
1 42
10.0%
2 36
8.6%
8 31
7.4%
E 30
7.1%
° 30
7.1%
. 30
7.1%
' 30
7.1%
Other values (5) 38
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 240
57.1%
Space Separator 60
 
14.3%
Other Punctuation 60
 
14.3%
Uppercase Letter 30
 
7.1%
Other Symbol 30
 
7.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 50
20.8%
5 43
17.9%
1 42
17.5%
2 36
15.0%
8 31
12.9%
7 13
 
5.4%
6 8
 
3.3%
4 6
 
2.5%
3 6
 
2.5%
9 5
 
2.1%
Other Punctuation
ValueCountFrequency (%)
. 30
50.0%
' 30
50.0%
Space Separator
ValueCountFrequency (%)
60
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 30
100.0%
Other Symbol
ValueCountFrequency (%)
° 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 390
92.9%
Latin 30
 
7.1%

Most frequent character per script

Common
ValueCountFrequency (%)
60
15.4%
0 50
12.8%
5 43
11.0%
1 42
10.8%
2 36
9.2%
8 31
7.9%
° 30
7.7%
. 30
7.7%
' 30
7.7%
7 13
 
3.3%
Other values (4) 25
6.4%
Latin
ValueCountFrequency (%)
E 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 390
92.9%
None 30
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
60
15.4%
0 50
12.8%
5 43
11.0%
1 42
10.8%
2 36
9.2%
8 31
7.9%
E 30
7.7%
. 30
7.7%
' 30
7.7%
7 13
 
3.3%
Other values (4) 25
6.4%
None
ValueCountFrequency (%)
° 30
100.0%

위치(좌표계WGS84)위도_도분초
Text

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:14.335121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length16
Mean length15.833333
Min length15

Characters and Unicode

Total characters475
Distinct characters15
Distinct categories5 ?
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 (%)40.0%

Sample

1st rowN 35° 8' 6.600"
2nd rowN 35° 7' 30.000"
3rd rowN 35° 7' 0.000"
4th rowN 35° 7' 0.000"
5th rowN 35° 6' 8.280"
ValueCountFrequency (%)
n 30
25.0%
35° 30
25.0%
2 8
 
6.7%
4 7
 
5.8%
48.000 6
 
5.0%
3 6
 
5.0%
30.000 5
 
4.2%
12.000 4
 
3.3%
54.000 3
 
2.5%
7 3
 
2.5%
Other values (14) 18
15.0%
2024-04-17T18:41:14.582140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
91
19.2%
0 84
17.7%
3 41
8.6%
5 38
8.0%
N 30
 
6.3%
° 30
 
6.3%
' 30
 
6.3%
. 30
 
6.3%
" 30
 
6.3%
4 20
 
4.2%
Other values (5) 51
10.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 234
49.3%
Space Separator 91
 
19.2%
Other Punctuation 90
 
18.9%
Uppercase Letter 30
 
6.3%
Other Symbol 30
 
6.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 84
35.9%
3 41
17.5%
5 38
16.2%
4 20
 
8.5%
2 18
 
7.7%
8 13
 
5.6%
6 8
 
3.4%
1 6
 
2.6%
7 6
 
2.6%
Other Punctuation
ValueCountFrequency (%)
' 30
33.3%
. 30
33.3%
" 30
33.3%
Space Separator
ValueCountFrequency (%)
91
100.0%
Uppercase Letter
ValueCountFrequency (%)
N 30
100.0%
Other Symbol
ValueCountFrequency (%)
° 30
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 445
93.7%
Latin 30
 
6.3%

Most frequent character per script

Common
ValueCountFrequency (%)
91
20.4%
0 84
18.9%
3 41
9.2%
5 38
8.5%
° 30
 
6.7%
' 30
 
6.7%
. 30
 
6.7%
" 30
 
6.7%
4 20
 
4.5%
2 18
 
4.0%
Other values (4) 33
 
7.4%
Latin
ValueCountFrequency (%)
N 30
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 445
93.7%
None 30
 
6.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
91
20.4%
0 84
18.9%
3 41
9.2%
5 38
8.5%
N 30
 
6.7%
' 30
 
6.7%
. 30
 
6.7%
" 30
 
6.7%
4 20
 
4.5%
2 18
 
4.0%
Other values (4) 33
 
7.4%
None
ValueCountFrequency (%)
° 30
100.0%

위치(좌표계WGS84)경도_도분초
Text

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2024-04-17T18:41:14.723157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length18
Median length18
Mean length17.7
Min length16

Characters and Unicode

Total characters531
Distinct characters16
Distinct categories5 ?
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 (%)63.3%

Sample

1st rowE 128° 57' 39.720"
2nd rowE 128° 57' 12.000"
3rd rowE 128° 56' 30.000"
4th rowE 128° 57' 15.000"
5th rowE 128° 53' 45.000"
ValueCountFrequency (%)
128° 30
25.2%
e 29
24.4%
57 6
 
5.0%
55 6
 
5.0%
56 5
 
4.2%
42.000 4
 
3.4%
30.000 4
 
3.4%
51 4
 
3.4%
0.000 3
 
2.5%
6.000 3
 
2.5%
Other values (17) 25
21.0%
2024-04-17T18:41:14.977187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
89
16.8%
0 83
15.6%
2 46
8.7%
5 43
8.1%
1 39
7.3%
8 32
 
6.0%
° 30
 
5.6%
' 30
 
5.6%
. 30
 
5.6%
" 30
 
5.6%
Other values (6) 79
14.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 293
55.2%
Other Punctuation 90
 
16.9%
Space Separator 89
 
16.8%
Other Symbol 30
 
5.6%
Uppercase Letter 29
 
5.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 83
28.3%
2 46
15.7%
5 43
14.7%
1 39
13.3%
8 32
 
10.9%
4 18
 
6.1%
6 12
 
4.1%
3 9
 
3.1%
7 7
 
2.4%
9 4
 
1.4%
Other Punctuation
ValueCountFrequency (%)
' 30
33.3%
. 30
33.3%
" 30
33.3%
Space Separator
ValueCountFrequency (%)
89
100.0%
Other Symbol
ValueCountFrequency (%)
° 30
100.0%
Uppercase Letter
ValueCountFrequency (%)
E 29
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 502
94.5%
Latin 29
 
5.5%

Most frequent character per script

Common
ValueCountFrequency (%)
89
17.7%
0 83
16.5%
2 46
9.2%
5 43
8.6%
1 39
7.8%
8 32
 
6.4%
° 30
 
6.0%
' 30
 
6.0%
. 30
 
6.0%
" 30
 
6.0%
Other values (5) 50
10.0%
Latin
ValueCountFrequency (%)
E 29
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 501
94.4%
None 30
 
5.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
89
17.8%
0 83
16.6%
2 46
9.2%
5 43
8.6%
1 39
7.8%
8 32
 
6.4%
' 30
 
6.0%
. 30
 
6.0%
" 30
 
6.0%
E 29
 
5.8%
Other values (5) 50
10.0%
None
ValueCountFrequency (%)
° 30
100.0%

유기물_농도(%)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.37363333
Minimum0.026
Maximum1.172
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2024-04-17T18:41:15.083849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.026
5-th percentile0.03245
Q10.0565
median0.2205
Q30.6095
95-th percentile1.1567
Maximum1.172
Range1.146
Interquartile range (IQR)0.553

Descriptive statistics

Standard deviation0.37913063
Coefficient of variation (CV)1.0147131
Kurtosis-0.35279938
Mean0.37363333
Median Absolute Deviation (MAD)0.1865
Skewness0.9336101
Sum11.209
Variance0.14374003
MonotonicityNot monotonic
2024-04-17T18:41:15.184475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1.172 2
 
6.7%
0.221 2
 
6.7%
0.034 2
 
6.7%
0.501 1
 
3.3%
0.117 1
 
3.3%
0.048 1
 
3.3%
0.084 1
 
3.3%
0.055 1
 
3.3%
0.061 1
 
3.3%
0.032 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
0.026 1
3.3%
0.032 1
3.3%
0.033 1
3.3%
0.034 2
6.7%
0.037 1
3.3%
0.048 1
3.3%
0.055 1
3.3%
0.061 1
3.3%
0.063 1
3.3%
0.084 1
3.3%
ValueCountFrequency (%)
1.172 2
6.7%
1.138 1
3.3%
0.883 1
3.3%
0.855 1
3.3%
0.727 1
3.3%
0.724 1
3.3%
0.615 1
3.3%
0.593 1
3.3%
0.501 1
3.3%
0.485 1
3.3%

Interactions

2024-04-17T18:41:11.669694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:41:11.532071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:41:11.736654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-17T18:41:11.598484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-17T18:41:15.259021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초유기물_농도(%)
데이터_건수1.0001.0000.8110.6180.8110.6180.8130.6180.485
관리번호1.0001.0001.0001.0001.0001.0001.0001.0001.000
위치(좌표계WGS84)위도_도0.8111.0001.0000.9581.0000.9581.0000.9580.866
위치(좌표계WGS84)경도_도0.6181.0000.9581.0000.9581.0000.9391.0000.863
위치(좌표계WGS84)위도_도분0.8111.0001.0000.9581.0000.9581.0000.9580.866
위치(좌표계WGS84)경도_도분0.6181.0000.9581.0000.9581.0000.9391.0000.863
위치(좌표계WGS84)위도_도분초0.8131.0001.0000.9391.0000.9391.0000.9390.839
위치(좌표계WGS84)경도_도분초0.6181.0000.9581.0000.9581.0000.9391.0000.863
유기물_농도(%)0.4851.0000.8660.8630.8660.8630.8390.8631.000
2024-04-17T18:41:15.393073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터_건수유기물_농도(%)
데이터_건수1.000-0.659
유기물_농도(%)-0.6591.000

Missing values

2024-04-17T18:41:11.825070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-17T18:41:11.932112image/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

데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초유기물_농도(%)
0206N2N 35.135167°E 128.961033°N 35° 8.110'E 128° 57.662'N 35° 8' 6.600"E 128° 57' 39.720"0.501
1207N3N 35.125000°E 128.953333°N 35° 7.500'E 128° 57.200'N 35° 7' 30.000"E 128° 57' 12.000"0.381
2208N4N 35.116667°E 128.941667°N 35° 7.000'E 128° 56.500'N 35° 7' 0.000"E 128° 56' 30.000"1.172
3209N5N 35.116667°E 128.954167°N 35° 7.000'E 128° 57.250'N 35° 7' 0.000"E 128° 57' 15.000"0.037
4210N8N 35.102300°E 128.895833°N 35° 6.138'E 128° 53.750'N 35° 6' 8.280"E 128° 53' 45.000"0.221
5211N13N 35.106850°E 128.934017°N 35° 6.411'E 128° 56.041'N 35° 6' 24.660"E 128° 56' 2.460"0.593
6212N17N 35.088333°E 128.928333°N 35° 5.300'E 128° 55.700'N 35° 5' 18.000"E 128° 55' 42.000"1.172
7213N18N 35.100217°E 128.949850°N 35° 6.013'E 128° 56.991'N 35° 6' 0.780"E 128° 56' 59.460"0.178
8214N22N 35.085667°E 128.946017°N 35° 5.140'E 128° 56.761'N 35° 5' 8.400"E 128° 56' 45.660"1.138
9215N23N 35.080833°E 128.838333°N 35° 4.850'E 128° 50.300'N 35° 4' 51.000"E 128° 50' 18.000"0.724
데이터_건수관리번호위치(좌표계WGS84)위도_도위치(좌표계WGS84)경도_도위치(좌표계WGS84)위도_도분위치(좌표계WGS84)경도_도분위치(좌표계WGS84)위도_도분초위치(좌표계WGS84)경도_도분초유기물_농도(%)
20226N57N 35.058333°E 128.873333°N 35° 3.500'E 128° 52.400'N 35° 3' 30.000"E 128° 52' 24.000"0.034
21227N62N 35.058333°E 128.925000°N 35° 3.500'E 128° 55.500'N 35° 3' 30.000"E 128° 55' 30.000"0.026
22228N66N 35.046667°E 128.851667°N 35° 2.800'E 128° 51.100'N 35° 2' 48.000"E 128° 51' 6.000"0.22
23229N69N 35.046667°E 128.881667°N 35° 2.800'E 128° 52.900'N 35° 2' 48.000"E 128° 52' 54.000"0.033
24230N74N 35.046667°E 128.931667°N 35° 2.800'E 128° 55.900'N 35° 2' 48.000"E 128° 55' 54.000"0.032
25231N79N 35.036667°E 128.861667°N 35° 2.200'E 128° 51.700'N 35° 2' 12.000"E 128° 51' 42.000"0.061
26232N83N 35.036667°E 128.901667°N 35° 2.200'E 128° 54.100'N 35° 2' 12.000"E 128° 54' 6.000"0.034
27233N85N 35.036667°E 128.921667°N 35° 2.200'E 128° 55.300'N 35° 2' 12.000"E 128° 55' 18.000"0.055
28234N88N 35.036667°E 128.950000°N 35° 2.200'E 128° 57.000'N 35° 2' 12.000"E 128° 57' 0.000"0.084
29235N90N 35.040900°E 128.963650°N 35° 2.454'E 128° 57.819'N 35° 2' 27.240"E 128° 57' 49.140"0.048