Overview

Dataset statistics

Number of variables10
Number of observations43
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory89.1 B

Variable types

Numeric6
Categorical1
Text3

Dataset

Description경상남도 내 온천 현황으로, 시군명, 온천명, 소재지, 성분, 온도(도씨), 심도(미터), 온천원 보호지구면적(천제곱미터), 온천공 보호구역면적(천제곱미터), 적정양수량(톤_일) 데이터를 제공합니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3083233

Alerts

연번 is highly overall correlated with 시군High correlation
온천원 보호지구면적(천제곱미터) is highly overall correlated with 온천공 보호구역면적(천제곱미터) and 2 other fieldsHigh correlation
온천공 보호구역면적(천제곱미터) is highly overall correlated with 온천원 보호지구면적(천제곱미터)High correlation
적정양수량(톤_일) is highly overall correlated with 온천원 보호지구면적(천제곱미터)High correlation
시군 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
적정양수량(톤_일) has 1 (2.3%) missing valuesMissing
연번 has unique valuesUnique
온천명 has unique valuesUnique
소재지 has unique valuesUnique
온천원 보호지구면적(천제곱미터) has 34 (79.1%) zerosZeros
온천공 보호구역면적(천제곱미터) has 18 (41.9%) zerosZeros
적정양수량(톤_일) has 8 (18.6%) zerosZeros

Reproduction

Analysis started2023-12-10 23:37:00.235458
Analysis finished2023-12-10 23:37:03.559624
Duration3.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean22
Minimum1
Maximum43
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T08:37:03.621924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.1
Q111.5
median22
Q332.5
95-th percentile40.9
Maximum43
Range42
Interquartile range (IQR)21

Descriptive statistics

Standard deviation12.556539
Coefficient of variation (CV)0.57075176
Kurtosis-1.2
Mean22
Median Absolute Deviation (MAD)11
Skewness0
Sum946
Variance157.66667
MonotonicityStrictly increasing
2023-12-11T08:37:03.739510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
1 1
 
2.3%
2 1
 
2.3%
25 1
 
2.3%
26 1
 
2.3%
27 1
 
2.3%
28 1
 
2.3%
29 1
 
2.3%
30 1
 
2.3%
31 1
 
2.3%
32 1
 
2.3%
Other values (33) 33
76.7%
ValueCountFrequency (%)
1 1
2.3%
2 1
2.3%
3 1
2.3%
4 1
2.3%
5 1
2.3%
6 1
2.3%
7 1
2.3%
8 1
2.3%
9 1
2.3%
10 1
2.3%
ValueCountFrequency (%)
43 1
2.3%
42 1
2.3%
41 1
2.3%
40 1
2.3%
39 1
2.3%
38 1
2.3%
37 1
2.3%
36 1
2.3%
35 1
2.3%
34 1
2.3%

시군
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)32.6%
Missing0
Missing (%)0.0%
Memory size476.0 B
창원
11 
양산
7 
김해
5 
거제
4 
통영
3 
Other values (9)
13 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)11.6%

Sample

1st row거제
2nd row거제
3rd row거제
4th row거제
5th row거창

Common Values

ValueCountFrequency (%)
창원 11
25.6%
양산 7
16.3%
김해 5
11.6%
거제 4
 
9.3%
통영 3
 
7.0%
남해 2
 
4.7%
밀양 2
 
4.7%
진주 2
 
4.7%
창녕 2
 
4.7%
거창 1
 
2.3%
Other values (4) 4
 
9.3%

Length

2023-12-11T08:37:03.860931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원 11
25.6%
양산 7
16.3%
김해 5
11.6%
거제 4
 
9.3%
통영 3
 
7.0%
남해 2
 
4.7%
밀양 2
 
4.7%
진주 2
 
4.7%
창녕 2
 
4.7%
거창 1
 
2.3%
Other values (4) 4
 
9.3%

온천명
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-11T08:37:04.068090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.5116279
Min length2

Characters and Unicode

Total characters151
Distinct characters88
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row해수온천
2nd row계룡산
3rd row일운
4th row장목
5th row가조
ValueCountFrequency (%)
해수온천 1
 
2.1%
힐링스포렉스 1
 
2.1%
녹산 1
 
2.1%
진주윙스 1
 
2.1%
부곡 1
 
2.1%
초곡 1
 
2.1%
마금산 1
 
2.1%
소답 1
 
2.1%
곡안 1
 
2.1%
동산 1
 
2.1%
Other values (37) 37
78.7%
2023-12-11T08:37:04.376707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 7
 
4.6%
7
 
4.6%
천 5
 
3.3%
진 4
 
2.6%
스 4
 
2.6%
동 4
 
2.6%
양 3
 
2.0%
한 3
 
2.0%
포 3
 
2.0%
터 3
 
2.0%
Other values (78) 108
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 143
94.7%
Space Separator 8
 
5.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 7
 
4.9%
천 5
 
3.5%
진 4
 
2.8%
스 4
 
2.8%
동 4
 
2.8%
양 3
 
2.1%
한 3
 
2.1%
포 3
 
2.1%
터 3
 
2.1%
곡 3
 
2.1%
Other values (76) 104
72.7%
Space Separator
ValueCountFrequency (%)
7
87.5%
　 1
 
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 143
94.7%
Common 8
 
5.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 7
 
4.9%
천 5
 
3.5%
진 4
 
2.8%
스 4
 
2.8%
동 4
 
2.8%
양 3
 
2.1%
한 3
 
2.1%
포 3
 
2.1%
터 3
 
2.1%
곡 3
 
2.1%
Other values (76) 104
72.7%
Common
ValueCountFrequency (%)
7
87.5%
　 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 143
94.7%
ASCII 7
 
4.6%
None 1
 
0.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 7
 
4.9%
천 5
 
3.5%
진 4
 
2.8%
스 4
 
2.8%
동 4
 
2.8%
양 3
 
2.1%
한 3
 
2.1%
포 3
 
2.1%
터 3
 
2.1%
곡 3
 
2.1%
Other values (76) 104
72.7%
ASCII
ValueCountFrequency (%)
7
100.0%
None
ValueCountFrequency (%)
　 1
100.0%

소재지
Text

UNIQUE 

Distinct43
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-11T08:37:04.639234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length17
Mean length15.581395
Min length11

Characters and Unicode

Total characters670
Distinct characters110
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)100.0%

Sample

1st row거제시 수양로 570
2nd row거제시 거제중앙로 1779-1
3rd row거제시 일운면 거제대로 2190
4th row거제시 장목면 산 126-6
5th row거창군 온천길 108-29(가조면)
ValueCountFrequency (%)
창원시 11
 
7.0%
양산시 7
 
4.5%
김해시 5
 
3.2%
거제시 4
 
2.5%
하북면 3
 
1.9%
장유로 3
 
1.9%
통영시 3
 
1.9%
용남면 2
 
1.3%
창녕군 2
 
1.3%
진전면 2
 
1.3%
Other values (111) 115
73.2%
2023-12-11T08:37:05.020914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
127
 
19.0%
시 35
 
5.2%
1 26
 
3.9%
면 26
 
3.9%
2 25
 
3.7%
동 20
 
3.0%
산 19
 
2.8%
리 18
 
2.7%
- 16
 
2.4%
원 16
 
2.4%
Other values (100) 342
51.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 370
55.2%
Decimal Number 147
 
21.9%
Space Separator 127
 
19.0%
Dash Punctuation 16
 
2.4%
Open Punctuation 5
 
0.7%
Close Punctuation 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 35
 
9.5%
면 26
 
7.0%
동 20
 
5.4%
산 19
 
5.1%
리 18
 
4.9%
원 16
 
4.3%
창 14
 
3.8%
로 11
 
3.0%
양 11
 
3.0%
거 8
 
2.2%
Other values (86) 192
51.9%
Decimal Number
ValueCountFrequency (%)
1 26
17.7%
2 25
17.0%
6 15
10.2%
7 14
9.5%
3 13
8.8%
5 12
8.2%
0 11
7.5%
4 11
7.5%
8 11
7.5%
9 9
 
6.1%
Space Separator
ValueCountFrequency (%)
127
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 5
100.0%
Close Punctuation
ValueCountFrequency (%)
) 5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 370
55.2%
Common 300
44.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 35
 
9.5%
면 26
 
7.0%
동 20
 
5.4%
산 19
 
5.1%
리 18
 
4.9%
원 16
 
4.3%
창 14
 
3.8%
로 11
 
3.0%
양 11
 
3.0%
거 8
 
2.2%
Other values (86) 192
51.9%
Common
ValueCountFrequency (%)
127
42.3%
1 26
 
8.7%
2 25
 
8.3%
- 16
 
5.3%
6 15
 
5.0%
7 14
 
4.7%
3 13
 
4.3%
5 12
 
4.0%
0 11
 
3.7%
4 11
 
3.7%
Other values (4) 30
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 370
55.2%
ASCII 300
44.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
127
42.3%
1 26
 
8.7%
2 25
 
8.3%
- 16
 
5.3%
6 15
 
5.0%
7 14
 
4.7%
3 13
 
4.3%
5 12
 
4.0%
0 11
 
3.7%
4 11
 
3.7%
Other values (4) 30
 
10.0%
Hangul
ValueCountFrequency (%)
시 35
 
9.5%
면 26
 
7.0%
동 20
 
5.4%
산 19
 
5.1%
리 18
 
4.9%
원 16
 
4.3%
창 14
 
3.8%
로 11
 
3.0%
양 11
 
3.0%
거 8
 
2.2%
Other values (86) 192
51.9%

성분
Text

Distinct29
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Memory size476.0 B
2023-12-11T08:37:05.166440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length11.674419
Min length6

Characters and Unicode

Total characters502
Distinct characters28
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)48.8%

Sample

1st row약알, Ca-Cl
2nd row약알, Ca-Cl
3rd row약알, Na-CI
4th row중성, Ca(Na)-CI
5th row중성 , Na-HCO3
ValueCountFrequency (%)
19
18.3%
약알 19
18.3%
알 16
15.4%
na-hco3 10
9.6%
na-so4 8
7.7%
na-ci 7
 
6.7%
ca-cl 4
 
3.8%
중성 4
 
3.8%
ca-so4 3
 
2.9%
ca(na)-ci 3
 
2.9%
Other values (10) 11
10.6%
2023-12-11T08:37:05.448308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
16.9%
a 52
10.4%
C 50
10.0%
- 43
8.6%
, 42
8.4%
알 35
 
7.0%
N 35
 
7.0%
O 26
 
5.2%
약 19
 
3.8%
H 14
 
2.8%
Other values (18) 101
20.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 151
30.1%
Space Separator 85
16.9%
Other Letter 70
13.9%
Lowercase Letter 59
 
11.8%
Dash Punctuation 43
 
8.6%
Other Punctuation 42
 
8.4%
Decimal Number 26
 
5.2%
Open Punctuation 12
 
2.4%
Close Punctuation 12
 
2.4%
Other Number 2
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
알 35
50.0%
약 19
27.1%
성 4
 
5.7%
중 4
 
5.7%
황 2
 
2.9%
염 2
 
2.9%
산 1
 
1.4%
화 1
 
1.4%
물 1
 
1.4%
유 1
 
1.4%
Uppercase Letter
ValueCountFrequency (%)
C 50
33.1%
N 35
23.2%
O 26
17.2%
H 14
 
9.3%
I 13
 
8.6%
S 13
 
8.6%
Lowercase Letter
ValueCountFrequency (%)
a 52
88.1%
l 5
 
8.5%
o 2
 
3.4%
Decimal Number
ValueCountFrequency (%)
3 14
53.8%
4 12
46.2%
Other Number
ValueCountFrequency (%)
₃ 1
50.0%
₄ 1
50.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 43
100.0%
Other Punctuation
ValueCountFrequency (%)
, 42
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 222
44.2%
Latin 210
41.8%
Hangul 70
 
13.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
알 35
50.0%
약 19
27.1%
성 4
 
5.7%
중 4
 
5.7%
황 2
 
2.9%
염 2
 
2.9%
산 1
 
1.4%
화 1
 
1.4%
물 1
 
1.4%
유 1
 
1.4%
Common
ValueCountFrequency (%)
85
38.3%
- 43
19.4%
, 42
18.9%
3 14
 
6.3%
( 12
 
5.4%
4 12
 
5.4%
) 12
 
5.4%
₃ 1
 
0.5%
₄ 1
 
0.5%
Latin
ValueCountFrequency (%)
a 52
24.8%
C 50
23.8%
N 35
16.7%
O 26
12.4%
H 14
 
6.7%
I 13
 
6.2%
S 13
 
6.2%
l 5
 
2.4%
o 2
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 430
85.7%
Hangul 70
 
13.9%
None 2
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
19.8%
a 52
12.1%
C 50
11.6%
- 43
10.0%
, 42
9.8%
N 35
8.1%
O 26
 
6.0%
H 14
 
3.3%
3 14
 
3.3%
I 13
 
3.0%
Other values (6) 56
13.0%
Hangul
ValueCountFrequency (%)
알 35
50.0%
약 19
27.1%
성 4
 
5.7%
중 4
 
5.7%
황 2
 
2.9%
염 2
 
2.9%
산 1
 
1.4%
화 1
 
1.4%
물 1
 
1.4%
유 1
 
1.4%
None
ValueCountFrequency (%)
₃ 1
50.0%
₄ 1
50.0%

온도(도씨)
Real number (ℝ)

Distinct29
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.425581
Minimum25
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T08:37:05.585572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25.7
Q126
median28.1
Q332.45
95-th percentile44.41
Maximum78
Range53
Interquartile range (IQR)6.45

Descriptive statistics

Standard deviation9.5372726
Coefficient of variation (CV)0.30348755
Kurtosis14.014482
Mean31.425581
Median Absolute Deviation (MAD)2.1
Skewness3.4112012
Sum1351.3
Variance90.959568
MonotonicityNot monotonic
2023-12-11T08:37:05.695478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
26.0 10
23.3%
27.0 3
 
7.0%
29.0 2
 
4.7%
25.0 2
 
4.7%
25.7 2
 
4.7%
31.0 1
 
2.3%
31.7 1
 
2.3%
57.0 1
 
2.3%
29.5 1
 
2.3%
26.7 1
 
2.3%
Other values (19) 19
44.2%
ValueCountFrequency (%)
25.0 2
 
4.7%
25.7 2
 
4.7%
26.0 10
23.3%
26.7 1
 
2.3%
27.0 3
 
7.0%
27.1 1
 
2.3%
27.7 1
 
2.3%
28.0 1
 
2.3%
28.1 1
 
2.3%
29.0 2
 
4.7%
ValueCountFrequency (%)
78.0 1
2.3%
57.0 1
2.3%
44.9 1
2.3%
40.0 1
2.3%
39.0 1
2.3%
37.0 1
2.3%
36.0 1
2.3%
35.0 1
2.3%
34.5 1
2.3%
34.0 1
2.3%

심도(미터)
Real number (ℝ)

Distinct37
Distinct (%)86.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean756.89465
Minimum174
Maximum1218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T08:37:05.816336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum174
5-th percentile395.1
Q1610
median775
Q3992
95-th percentile1075.3
Maximum1218
Range1044
Interquartile range (IQR)382

Descriptive statistics

Standard deviation250.56879
Coefficient of variation (CV)0.33104844
Kurtosis-0.39779312
Mean756.89465
Median Absolute Deviation (MAD)209
Skewness-0.42827902
Sum32546.47
Variance62784.719
MonotonicityNot monotonic
2023-12-11T08:37:05.926801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
1000.0 5
 
11.6%
984.0 2
 
4.7%
900.0 2
 
4.7%
185.0 1
 
2.3%
840.0 1
 
2.3%
423.0 1
 
2.3%
458.0 1
 
2.3%
1005.0 1
 
2.3%
684.0 1
 
2.3%
1218.0 1
 
2.3%
Other values (27) 27
62.8%
ValueCountFrequency (%)
174.0 1
2.3%
185.0 1
2.3%
392.0 1
2.3%
423.0 1
2.3%
430.0 1
2.3%
458.0 1
2.3%
479.0 1
2.3%
484.0 1
2.3%
500.0 1
2.3%
529.0 1
2.3%
ValueCountFrequency (%)
1218.0 1
 
2.3%
1112.0 1
 
2.3%
1078.0 1
 
2.3%
1051.0 1
 
2.3%
1040.0 1
 
2.3%
1005.0 1
 
2.3%
1000.0 5
11.6%
984.0 2
 
4.7%
910.0 1
 
2.3%
900.0 2
 
4.7%

온천원 보호지구면적(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)23.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean231.72093
Minimum0
Maximum4819
Zeros34
Zeros (%)79.1%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T08:37:06.021367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1416.8
Maximum4819
Range4819
Interquartile range (IQR)0

Descriptive statistics

Standard deviation813.71571
Coefficient of variation (CV)3.5116194
Kurtosis25.142985
Mean231.72093
Median Absolute Deviation (MAD)0
Skewness4.7662127
Sum9964
Variance662133.25
MonotonicityNot monotonic
2023-12-11T08:37:06.119028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0 34
79.1%
1010 1
 
2.3%
1462 1
 
2.3%
215 1
 
2.3%
4819 1
 
2.3%
152 1
 
2.3%
14 1
 
2.3%
15 1
 
2.3%
405 1
 
2.3%
1872 1
 
2.3%
ValueCountFrequency (%)
0 34
79.1%
14 1
 
2.3%
15 1
 
2.3%
152 1
 
2.3%
215 1
 
2.3%
405 1
 
2.3%
1010 1
 
2.3%
1462 1
 
2.3%
1872 1
 
2.3%
4819 1
 
2.3%
ValueCountFrequency (%)
4819 1
 
2.3%
1872 1
 
2.3%
1462 1
 
2.3%
1010 1
 
2.3%
405 1
 
2.3%
215 1
 
2.3%
152 1
 
2.3%
15 1
 
2.3%
14 1
 
2.3%
0 34
79.1%

온천공 보호구역면적(천제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct20
Distinct (%)46.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.555814
Minimum0
Maximum31
Zeros18
Zeros (%)41.9%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T08:37:06.213664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q38.75
95-th percentile28
Maximum31
Range31
Interquartile range (IQR)8.75

Descriptive statistics

Standard deviation9.231192
Coefficient of variation (CV)1.4080924
Kurtosis1.242036
Mean6.555814
Median Absolute Deviation (MAD)2
Skewness1.5175199
Sum281.9
Variance85.214906
MonotonicityNot monotonic
2023-12-11T08:37:06.322177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
0.0 18
41.9%
1.0 2
 
4.7%
9.0 2
 
4.7%
28.0 2
 
4.7%
8.0 2
 
4.7%
2.0 2
 
4.7%
5.0 2
 
4.7%
17.0 1
 
2.3%
5.5 1
 
2.3%
7.6 1
 
2.3%
Other values (10) 10
23.3%
ValueCountFrequency (%)
0.0 18
41.9%
0.6 1
 
2.3%
1.0 2
 
4.7%
2.0 2
 
4.7%
4.0 1
 
2.3%
5.0 2
 
4.7%
5.5 1
 
2.3%
5.7 1
 
2.3%
7.6 1
 
2.3%
8.0 2
 
4.7%
ValueCountFrequency (%)
31.0 1
2.3%
29.0 1
2.3%
28.0 2
4.7%
23.0 1
2.3%
18.0 1
2.3%
17.0 1
2.3%
16.0 1
2.3%
10.0 1
2.3%
9.0 2
4.7%
8.5 1
2.3%

적정양수량(톤_일)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct32
Distinct (%)76.2%
Missing1
Missing (%)2.3%
Infinite0
Infinite (%)0.0%
Mean757.35714
Minimum0
Maximum5468
Zeros8
Zeros (%)18.6%
Negative0
Negative (%)0.0%
Memory size519.0 B
2023-12-11T08:37:06.420370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1300.75
median400
Q3904.75
95-th percentile2671.1
Maximum5468
Range5468
Interquartile range (IQR)604

Descriptive statistics

Standard deviation1066.4643
Coefficient of variation (CV)1.4081392
Kurtosis9.4401609
Mean757.35714
Median Absolute Deviation (MAD)135
Skewness2.8427382
Sum31809
Variance1137346.1
MonotonicityNot monotonic
2023-12-11T08:37:06.520999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 8
 
18.6%
310 2
 
4.7%
400 2
 
4.7%
450 2
 
4.7%
481 1
 
2.3%
2700 1
 
2.3%
264 1
 
2.3%
266 1
 
2.3%
303 1
 
2.3%
1028 1
 
2.3%
Other values (22) 22
51.2%
ValueCountFrequency (%)
0 8
18.6%
264 1
 
2.3%
266 1
 
2.3%
300 1
 
2.3%
303 1
 
2.3%
308 1
 
2.3%
310 2
 
4.7%
312 1
 
2.3%
320 1
 
2.3%
324 1
 
2.3%
ValueCountFrequency (%)
5468 1
2.3%
3600 1
2.3%
2700 1
2.3%
2122 1
2.3%
1842 1
2.3%
1411 1
2.3%
1410 1
2.3%
1380 1
2.3%
1112 1
2.3%
1028 1
2.3%

Interactions

2023-12-11T08:37:02.879509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.581810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.974868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.593041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.996052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.413558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.972325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.644055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.036613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.658964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.058765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.477401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:03.051918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.712497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.101613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.726229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.128794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.546143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:03.128140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.780016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.167635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.792402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.196242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.616713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:03.204306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.843875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.235370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.859162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.265029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.684104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:03.290233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:00.911165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.528286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:01.931303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.340023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:37:02.784083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:37:06.601093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군온천명소재지성분온도(도씨)심도(미터)온천원 보호지구면적(천제곱미터)온천공 보호구역면적(천제곱미터)적정양수량(톤_일)
연번1.0000.9091.0001.0000.7510.0000.0830.0000.0000.206
시군0.9091.0001.0001.0000.8660.0000.0000.9180.0000.811
온천명1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
성분0.7510.8661.0001.0001.0000.3700.0001.0000.8730.624
온도(도씨)0.0000.0001.0001.0000.3701.0000.6660.7060.0000.643
심도(미터)0.0830.0001.0001.0000.0000.6661.0000.3650.0000.398
온천원 보호지구면적(천제곱미터)0.0000.9181.0001.0001.0000.7060.3651.0000.0000.868
온천공 보호구역면적(천제곱미터)0.0000.0001.0001.0000.8730.0000.0000.0001.0000.000
적정양수량(톤_일)0.2060.8111.0001.0000.6240.6430.3980.8680.0001.000
2023-12-11T08:37:06.713298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번온도(도씨)심도(미터)온천원 보호지구면적(천제곱미터)온천공 보호구역면적(천제곱미터)적정양수량(톤_일)시군
연번1.000-0.0890.1260.160-0.1920.1580.655
온도(도씨)-0.0891.000-0.0150.220-0.3420.0220.000
심도(미터)0.126-0.0151.000-0.2240.130-0.2160.000
온천원 보호지구면적(천제곱미터)0.1600.220-0.2241.000-0.5340.6610.682
온천공 보호구역면적(천제곱미터)-0.192-0.3420.130-0.5341.0000.0380.000
적정양수량(톤_일)0.1580.022-0.2160.6610.0381.0000.387
시군0.6550.0000.0000.6820.0000.3871.000

Missing values

2023-12-11T08:37:03.390930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:37:03.511453image/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

연번시군온천명소재지성분온도(도씨)심도(미터)온천원 보호지구면적(천제곱미터)온천공 보호구역면적(천제곱미터)적정양수량(톤_일)
01거제해수온천거제시 수양로 570약알, Ca-Cl31.0785.0017.0481
12거제계룡산거제시 거제중앙로 1779-1약알, Ca-Cl29.0662.002.0341
23거제일운거제시 일운면 거제대로 2190약알, Na-CI37.0900.0023.0443
34거제장목거제시 장목면 산 126-6중성, Ca(Na)-CI30.6910.000.00
45거창가조거창군 온천길 108-29(가조면)중성 , Na-HCO327.0500.010100.01842
56김해주촌김해시 주촌면 서부로1637번길 416약알 , Na-SO426.0484.000.00
67김해장유관동김해시 관동동 472-2약알, Na(Ca)-CI40.0174.000.00
78김해장유 워터파크김해시 장유로 555(신문동)중성, Na(Ca)-CI28.0647.0029.01023
89김해한림 신천김해시 장유로 1164알, Na(Ca)-HCO₃(SO₄)27.1761.000.00
910김해한양에스쇼핑센터김해시 장유로 270알, Ca(Na)-CI28.1710.005.7312
연번시군온천명소재지성분온도(도씨)심도(미터)온천원 보호지구면적(천제곱미터)온천공 보호구역면적(천제곱미터)적정양수량(톤_일)
3334창원녹산창원시 안골동 366-1약알, Na-SO430.51218.002.0303
3435창원석전창원시 석전동 275-21약알 , Ca-Cl26.0430.004.01028
3536창원진동리창원시 진동면 진동리 11번지외 4필지약알, Na-HCO334.01051.000.0324
3637창원오렌지타운창원시 용원동 1342염화물, Ca-Cl32.91000.000.6308
3738창원유산창원시 구산면 유산리 123알, Na-HCO326.01078.007.6550
3839통영산양통영시 산양읍 신전리 926유황 , Na-HCO332.0637.000.0<NA>
3940통영용남통영시 용남면 동달리 1026약알, Ca-HCO325.7750.005.5337
4041통영원평통영시 용남면 원평리 산58-7중성, Ca(Na)-CI44.9900.000.00
4142하동한려하동군 금남면 경춘로 243-31알 , Na-CI26.0863.04050.01112
4243합천가야합천군 가야면 대전리 501-3알 , Na-HCO327.0632.018720.03600