Overview

Dataset statistics

Number of variables11
Number of observations50
Missing cells104
Missing cells (%)18.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.7 KiB
Average record size in memory96.6 B

Variable types

Numeric6
Categorical1
Text3
DateTime1

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(천㎡) is highly overall correlated with 적정양수량(톤/일) and 1 other fieldsHigh correlation
적정양수량(톤/일) is highly overall correlated with 온천원 보호지구면적(천㎡)High correlation
시군 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
지구지정 일자 has 19 (38.0%) missing valuesMissing
온천원 보호지구면적(천㎡) has 41 (82.0%) missing valuesMissing
온천공 보호구역면적(천㎡) has 29 (58.0%) missing valuesMissing
적정양수량(톤/일) has 15 (30.0%) missing valuesMissing
연번 has unique valuesUnique
소재지 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:36:33.924095
Analysis finished2023-12-10 23:36:38.030452
Duration4.11 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean25.5
Minimum1
Maximum50
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T08:36:38.097245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.45
Q113.25
median25.5
Q337.75
95-th percentile47.55
Maximum50
Range49
Interquartile range (IQR)24.5

Descriptive statistics

Standard deviation14.57738
Coefficient of variation (CV)0.57166195
Kurtosis-1.2
Mean25.5
Median Absolute Deviation (MAD)12.5
Skewness0
Sum1275
Variance212.5
MonotonicityStrictly increasing
2023-12-11T08:36:38.218548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
2.0%
39 1
 
2.0%
29 1
 
2.0%
30 1
 
2.0%
31 1
 
2.0%
32 1
 
2.0%
33 1
 
2.0%
34 1
 
2.0%
35 1
 
2.0%
36 1
 
2.0%
Other values (40) 40
80.0%
ValueCountFrequency (%)
1 1
2.0%
2 1
2.0%
3 1
2.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
7 1
2.0%
8 1
2.0%
9 1
2.0%
10 1
2.0%
ValueCountFrequency (%)
50 1
2.0%
49 1
2.0%
48 1
2.0%
47 1
2.0%
46 1
2.0%
45 1
2.0%
44 1
2.0%
43 1
2.0%
42 1
2.0%
41 1
2.0%

시군
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)28.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
창원
12 
양산
8 
거제
6 
김해
5 
거창
4 
Other values (9)
15 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique5 ?
Unique (%)10.0%

Sample

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

Common Values

ValueCountFrequency (%)
창원 12
24.0%
양산 8
16.0%
거제 6
12.0%
김해 5
10.0%
거창 4
 
8.0%
밀양 3
 
6.0%
합천 3
 
6.0%
창녕 2
 
4.0%
하동 2
 
4.0%
남해 1
 
2.0%
Other values (4) 4
 
8.0%

Length

2023-12-11T08:36:38.334173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원 12
24.0%
양산 8
16.0%
거제 6
12.0%
김해 5
10.0%
거창 4
 
8.0%
밀양 3
 
6.0%
합천 3
 
6.0%
창녕 2
 
4.0%
하동 2
 
4.0%
남해 1
 
2.0%
Other values (4) 4
 
8.0%

온천명
Text

Distinct49
Distinct (%)98.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-11T08:36:38.527030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.28
Min length2

Characters and Unicode

Total characters164
Distinct characters85
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)96.0%

Sample

1st row해수온천
2nd row계룡산
3rd row일운
4th row하청
5th row구천
ValueCountFrequency (%)
수월 2
 
3.6%
장유 2
 
3.6%
인곡 1
 
1.8%
원동면 1
 
1.8%
용당 1
 
1.8%
남강 1
 
1.8%
워터피아 1
 
1.8%
부곡 1
 
1.8%
초곡 1
 
1.8%
마금산 1
 
1.8%
Other values (44) 44
78.6%
2023-12-11T08:36:38.852530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
18
 
11.0%
천 7
 
4.3%
산 7
 
4.3%
동 7
 
4.3%
곡 5
 
3.0%
수 4
 
2.4%
원 4
 
2.4%
장 4
 
2.4%
온 4
 
2.4%
해 3
 
1.8%
Other values (75) 101
61.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
86.6%
Space Separator 20
 
12.2%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
천 7
 
4.9%
산 7
 
4.9%
동 7
 
4.9%
곡 5
 
3.5%
수 4
 
2.8%
원 4
 
2.8%
장 4
 
2.8%
온 4
 
2.8%
해 3
 
2.1%
진 3
 
2.1%
Other values (71) 94
66.2%
Space Separator
ValueCountFrequency (%)
18
90.0%
　 2
 
10.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
86.6%
Common 22
 
13.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
천 7
 
4.9%
산 7
 
4.9%
동 7
 
4.9%
곡 5
 
3.5%
수 4
 
2.8%
원 4
 
2.8%
장 4
 
2.8%
온 4
 
2.8%
해 3
 
2.1%
진 3
 
2.1%
Other values (71) 94
66.2%
Common
ValueCountFrequency (%)
18
81.8%
　 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
86.6%
ASCII 20
 
12.2%
None 2
 
1.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
18
90.0%
( 1
 
5.0%
) 1
 
5.0%
Hangul
ValueCountFrequency (%)
천 7
 
4.9%
산 7
 
4.9%
동 7
 
4.9%
곡 5
 
3.5%
수 4
 
2.8%
원 4
 
2.8%
장 4
 
2.8%
온 4
 
2.8%
해 3
 
2.1%
진 3
 
2.1%
Other values (71) 94
66.2%
None
ValueCountFrequency (%)
　 2
100.0%

소재지
Text

UNIQUE 

Distinct50
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-11T08:36:39.109112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length16.38
Min length11

Characters and Unicode

Total characters819
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

Unique50 ?
Unique (%)100.0%

Sample

1st row거제시 수양로 570
2nd row거제시 거제중앙로 1779-1
3rd row거제시 일운면 거제대로 2190
4th row거제시 하청면 석포리 75-6
5th row거제시 동부면 구천리 144-1
ValueCountFrequency (%)
창원시 12
 
6.3%
양산시 8
 
4.2%
거제시 6
 
3.2%
김해시 5
 
2.6%
거창군 4
 
2.1%
하북면 4
 
2.1%
합천군 3
 
1.6%
밀양시 3
 
1.6%
진전면 3
 
1.6%
가조면 3
 
1.6%
Other values (131) 139
73.2%
2023-12-11T08:36:39.489669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
160
 
19.5%
1 37
 
4.5%
시 37
 
4.5%
면 34
 
4.2%
리 26
 
3.2%
2 24
 
2.9%
동 23
 
2.8%
- 23
 
2.8%
산 22
 
2.7%
창 18
 
2.2%
Other values (100) 415
50.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 446
54.5%
Decimal Number 178
 
21.7%
Space Separator 160
 
19.5%
Dash Punctuation 23
 
2.8%
Close Punctuation 6
 
0.7%
Open Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 37
 
8.3%
면 34
 
7.6%
리 26
 
5.8%
동 23
 
5.2%
산 22
 
4.9%
창 18
 
4.0%
원 17
 
3.8%
거 13
 
2.9%
군 13
 
2.9%
양 13
 
2.9%
Other values (86) 230
51.6%
Decimal Number
ValueCountFrequency (%)
1 37
20.8%
2 24
13.5%
4 17
9.6%
7 17
9.6%
6 17
9.6%
0 16
9.0%
5 15
8.4%
3 15
8.4%
8 12
 
6.7%
9 8
 
4.5%
Space Separator
ValueCountFrequency (%)
160
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 23
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 446
54.5%
Common 373
45.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 37
 
8.3%
면 34
 
7.6%
리 26
 
5.8%
동 23
 
5.2%
산 22
 
4.9%
창 18
 
4.0%
원 17
 
3.8%
거 13
 
2.9%
군 13
 
2.9%
양 13
 
2.9%
Other values (86) 230
51.6%
Common
ValueCountFrequency (%)
160
42.9%
1 37
 
9.9%
2 24
 
6.4%
- 23
 
6.2%
4 17
 
4.6%
7 17
 
4.6%
6 17
 
4.6%
0 16
 
4.3%
5 15
 
4.0%
3 15
 
4.0%
Other values (4) 32
 
8.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 446
54.5%
ASCII 373
45.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
160
42.9%
1 37
 
9.9%
2 24
 
6.4%
- 23
 
6.2%
4 17
 
4.6%
7 17
 
4.6%
6 17
 
4.6%
0 16
 
4.3%
5 15
 
4.0%
3 15
 
4.0%
Other values (4) 32
 
8.6%
Hangul
ValueCountFrequency (%)
시 37
 
8.3%
면 34
 
7.6%
리 26
 
5.8%
동 23
 
5.2%
산 22
 
4.9%
창 18
 
4.0%
원 17
 
3.8%
거 13
 
2.9%
군 13
 
2.9%
양 13
 
2.9%
Other values (86) 230
51.6%

성분
Text

Distinct30
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size532.0 B
2023-12-11T08:36:39.641173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length11.82
Min length6

Characters and Unicode

Total characters591
Distinct characters27
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)44.0%

Sample

1st row약알, Ca-Cl
2nd row약알, Ca-Cl
3rd row약알, Na-CI
4th row알, Ca(Na)-HCO3(CI)
5th row알, Na(Ca)-HCO3(SO4)
ValueCountFrequency (%)
31
23.7%
약알 26
19.8%
알 15
11.5%
na-hco3 12
 
9.2%
na-so4 11
 
8.4%
na-ci 10
 
7.6%
ca-cl 5
 
3.8%
중성 3
 
2.3%
황산염 2
 
1.5%
ca-so4 2
 
1.5%
Other values (11) 14
10.7%
2023-12-11T08:36:39.896512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
117
19.8%
a 57
9.6%
C 53
9.0%
, 50
8.5%
- 49
8.3%
N 42
 
7.1%
알 41
 
6.9%
O 30
 
5.1%
약 26
 
4.4%
3 17
 
2.9%
Other values (17) 109
18.4%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 170
28.8%
Space Separator 117
19.8%
Other Letter 90
15.2%
Lowercase Letter 64
 
10.8%
Other Punctuation 50
 
8.5%
Dash Punctuation 49
 
8.3%
Decimal Number 31
 
5.2%
Close Punctuation 10
 
1.7%
Open Punctuation 10
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
알 41
45.6%
약 26
28.9%
염 5
 
5.6%
성 3
 
3.3%
중 3
 
3.3%
산 3
 
3.3%
황 3
 
3.3%
화 2
 
2.2%
물 2
 
2.2%
유 1
 
1.1%
Uppercase Letter
ValueCountFrequency (%)
C 53
31.2%
N 42
24.7%
O 30
17.6%
H 16
 
9.4%
I 15
 
8.8%
S 14
 
8.2%
Lowercase Letter
ValueCountFrequency (%)
a 57
89.1%
l 6
 
9.4%
o 1
 
1.6%
Decimal Number
ValueCountFrequency (%)
3 17
54.8%
4 14
45.2%
Space Separator
ValueCountFrequency (%)
117
100.0%
Other Punctuation
ValueCountFrequency (%)
, 50
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 49
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 267
45.2%
Latin 234
39.6%
Hangul 90
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
알 41
45.6%
약 26
28.9%
염 5
 
5.6%
성 3
 
3.3%
중 3
 
3.3%
산 3
 
3.3%
황 3
 
3.3%
화 2
 
2.2%
물 2
 
2.2%
유 1
 
1.1%
Latin
ValueCountFrequency (%)
a 57
24.4%
C 53
22.6%
N 42
17.9%
O 30
12.8%
H 16
 
6.8%
I 15
 
6.4%
S 14
 
6.0%
l 6
 
2.6%
o 1
 
0.4%
Common
ValueCountFrequency (%)
117
43.8%
, 50
18.7%
- 49
18.4%
3 17
 
6.4%
4 14
 
5.2%
) 10
 
3.7%
( 10
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 501
84.8%
Hangul 90
 
15.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
117
23.4%
a 57
11.4%
C 53
10.6%
, 50
10.0%
- 49
9.8%
N 42
 
8.4%
O 30
 
6.0%
3 17
 
3.4%
H 16
 
3.2%
I 15
 
3.0%
Other values (6) 55
11.0%
Hangul
ValueCountFrequency (%)
알 41
45.6%
약 26
28.9%
염 5
 
5.6%
성 3
 
3.3%
중 3
 
3.3%
산 3
 
3.3%
황 3
 
3.3%
화 2
 
2.2%
물 2
 
2.2%
유 1
 
1.1%

온도(℃)
Real number (ℝ)

Distinct25
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.258
Minimum25
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T08:36:40.007097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25
Q126
median27
Q330.575
95-th percentile39.55
Maximum78
Range53
Interquartile range (IQR)4.575

Descriptive statistics

Standard deviation8.7582519
Coefficient of variation (CV)0.28945244
Kurtosis19.286993
Mean30.258
Median Absolute Deviation (MAD)1.55
Skewness4.0571316
Sum1512.9
Variance76.706976
MonotonicityNot monotonic
2023-12-11T08:36:40.116189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
26.0 12
24.0%
27.0 7
14.0%
25.0 4
 
8.0%
30.0 3
 
6.0%
31.0 2
 
4.0%
28.0 2
 
4.0%
29.0 2
 
4.0%
29.5 1
 
2.0%
32.0 1
 
2.0%
34.0 1
 
2.0%
Other values (15) 15
30.0%
ValueCountFrequency (%)
25.0 4
 
8.0%
25.7 1
 
2.0%
26.0 12
24.0%
26.6 1
 
2.0%
26.7 1
 
2.0%
27.0 7
14.0%
27.8 1
 
2.0%
28.0 2
 
4.0%
28.8 1
 
2.0%
29.0 2
 
4.0%
ValueCountFrequency (%)
78.0 1
2.0%
57.0 1
2.0%
40.0 1
2.0%
39.0 1
2.0%
37.0 1
2.0%
36.0 1
2.0%
35.0 1
2.0%
34.0 1
2.0%
32.0 1
2.0%
31.7 1
2.0%

심도(M)
Real number (ℝ)

Distinct45
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean738.94
Minimum174
Maximum1218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T08:36:40.245729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum174
5-th percentile382.1
Q1608
median767.5
Q3917.5
95-th percentile1046.05
Maximum1218
Range1044
Interquartile range (IQR)309.5

Descriptive statistics

Standard deviation235.00746
Coefficient of variation (CV)0.31803321
Kurtosis-0.23851346
Mean738.94
Median Absolute Deviation (MAD)160
Skewness-0.38437924
Sum36947
Variance55228.507
MonotonicityNot monotonic
2023-12-11T08:36:40.616640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1000 2
 
4.0%
984 2
 
4.0%
850 2
 
4.0%
640 2
 
4.0%
690 2
 
4.0%
392 1
 
2.0%
810 1
 
2.0%
185 1
 
2.0%
840 1
 
2.0%
423 1
 
2.0%
Other values (35) 35
70.0%
ValueCountFrequency (%)
174 1
2.0%
185 1
2.0%
374 1
2.0%
392 1
2.0%
423 1
2.0%
430 1
2.0%
458 1
2.0%
479 1
2.0%
484 1
2.0%
500 1
2.0%
ValueCountFrequency (%)
1218 1
2.0%
1104 1
2.0%
1051 1
2.0%
1040 1
2.0%
1005 1
2.0%
1000 2
4.0%
984 2
4.0%
976 1
2.0%
964 1
2.0%
960 1
2.0%

지구지정 일자
Date

MISSING 

Distinct30
Distinct (%)96.8%
Missing19
Missing (%)38.0%
Memory size532.0 B
Minimum1981-09-30 00:00:00
Maximum2014-06-13 00:00:00
2023-12-11T08:36:40.744943image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:40.864111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)

온천원 보호지구면적(천㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing41
Missing (%)82.0%
Infinite0
Infinite (%)0.0%
Mean1107.1111
Minimum14
Maximum4819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T08:36:40.951835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.4
Q1152
median405
Q31462
95-th percentile3640.2
Maximum4819
Range4805
Interquartile range (IQR)1310

Descriptive statistics

Standard deviation1544.6362
Coefficient of variation (CV)1.3951953
Kurtosis4.5925504
Mean1107.1111
Median Absolute Deviation (MAD)391
Skewness2.0538959
Sum9964
Variance2385901.1
MonotonicityNot monotonic
2023-12-11T08:36:41.055285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
1010 1
 
2.0%
1462 1
 
2.0%
215 1
 
2.0%
4819 1
 
2.0%
152 1
 
2.0%
14 1
 
2.0%
15 1
 
2.0%
405 1
 
2.0%
1872 1
 
2.0%
(Missing) 41
82.0%
ValueCountFrequency (%)
14 1
2.0%
15 1
2.0%
152 1
2.0%
215 1
2.0%
405 1
2.0%
1010 1
2.0%
1462 1
2.0%
1872 1
2.0%
4819 1
2.0%
ValueCountFrequency (%)
4819 1
2.0%
1872 1
2.0%
1462 1
2.0%
1010 1
2.0%
405 1
2.0%
215 1
2.0%
152 1
2.0%
15 1
2.0%
14 1
2.0%

온천공 보호구역면적(천㎡)
Real number (ℝ)

MISSING 

Distinct17
Distinct (%)81.0%
Missing29
Missing (%)58.0%
Infinite0
Infinite (%)0.0%
Mean14.761905
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T08:36:41.153479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q15
median13
Q323
95-th percentile31
Maximum40
Range39
Interquartile range (IQR)18

Descriptive statistics

Standard deviation11.614236
Coefficient of variation (CV)0.78677082
Kurtosis-0.7525441
Mean14.761905
Median Absolute Deviation (MAD)9
Skewness0.55375304
Sum310
Variance134.89048
MonotonicityNot monotonic
2023-12-11T08:36:41.261470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
8 2
 
4.0%
28 2
 
4.0%
1 2
 
4.0%
2 2
 
4.0%
5 1
 
2.0%
6 1
 
2.0%
13 1
 
2.0%
4 1
 
2.0%
9 1
 
2.0%
17 1
 
2.0%
Other values (7) 7
 
14.0%
(Missing) 29
58.0%
ValueCountFrequency (%)
1 2
4.0%
2 2
4.0%
4 1
2.0%
5 1
2.0%
6 1
2.0%
8 2
4.0%
9 1
2.0%
13 1
2.0%
16 1
2.0%
17 1
2.0%
ValueCountFrequency (%)
40 1
2.0%
31 1
2.0%
29 1
2.0%
28 2
4.0%
23 1
2.0%
21 1
2.0%
18 1
2.0%
17 1
2.0%
16 1
2.0%
13 1
2.0%

적정양수량(톤/일)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)97.1%
Missing15
Missing (%)30.0%
Infinite0
Infinite (%)0.0%
Mean895.62857
Minimum210
Maximum5468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size582.0 B
2023-12-11T08:36:41.369420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile265.4
Q1327
median442
Q31025.5
95-th percentile2970
Maximum5468
Range5258
Interquartile range (IQR)698.5

Descriptive statistics

Standard deviation1102.5024
Coefficient of variation (CV)1.2309817
Kurtosis8.7463614
Mean895.62857
Median Absolute Deviation (MAD)132
Skewness2.8040761
Sum31347
Variance1215511.6
MonotonicityNot monotonic
2023-12-11T08:36:41.487324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
400 2
 
4.0%
374 1
 
2.0%
800 1
 
2.0%
3600 1
 
2.0%
1112 1
 
2.0%
350 1
 
2.0%
324 1
 
2.0%
2122 1
 
2.0%
1028 1
 
2.0%
303 1
 
2.0%
Other values (24) 24
48.0%
(Missing) 15
30.0%
ValueCountFrequency (%)
210 1
2.0%
264 1
2.0%
266 1
2.0%
300 1
2.0%
301 1
2.0%
303 1
2.0%
310 1
2.0%
320 1
2.0%
324 1
2.0%
330 1
2.0%
ValueCountFrequency (%)
5468 1
2.0%
3600 1
2.0%
2700 1
2.0%
2122 1
2.0%
1842 1
2.0%
1411 1
2.0%
1410 1
2.0%
1112 1
2.0%
1028 1
2.0%
1023 1
2.0%

Interactions

2023-12-11T08:36:37.247088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:34.434527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.229021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.736445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.244984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.749482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.313285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:34.527614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.300572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.825622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.336080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.830081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.385500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:34.611187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.368149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.899841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.415362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.910538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.459373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:34.691239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.442707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.974567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.502883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.000126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.532970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:34.800426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.519460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.061822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.593160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.078249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.614061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.157970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:35.616178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.165285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:36.669410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:37.166364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:36:41.567001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군온천명소재지성분온도(℃)심도(M)지구지정 일자온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)
연번1.0000.9421.0001.0000.7690.0000.4661.0000.5730.5780.310
시군0.9421.0001.0001.0000.7230.2550.0000.9641.0000.4810.474
온천명1.0001.0001.0001.0001.0001.0000.9771.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
성분0.7690.7231.0001.0001.0000.5260.0000.9681.0000.8600.000
온도(℃)0.0000.2551.0001.0000.5261.0000.8450.0000.4360.0000.714
심도(M)0.4660.0000.9771.0000.0000.8451.0000.0000.7050.0000.308
지구지정 일자1.0000.9641.0001.0000.9680.0000.0001.0000.6201.0000.000
온천원 보호지구면적(천㎡)0.5731.0001.0001.0001.0000.4360.7050.6201.000NaN0.318
온천공 보호구역면적(천㎡)0.5780.4811.0001.0000.8600.0000.0001.000NaN1.0000.000
적정양수량(톤/일)0.3100.4741.0001.0000.0000.7140.3080.0000.3180.0001.000
2023-12-11T08:36:41.695990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번온도(℃)심도(M)온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)시군
연번1.000-0.146-0.040-0.167-0.489-0.0620.730
온도(℃)-0.1461.000-0.0050.2680.0370.2310.095
심도(M)-0.040-0.0051.000-0.017-0.316-0.4070.000
온천원 보호지구면적(천㎡)-0.1670.268-0.0171.000NaN0.5000.707
온천공 보호구역면적(천㎡)-0.4890.037-0.316NaN1.0000.2350.143
적정양수량(톤/일)-0.0620.231-0.4070.5000.2351.0000.227
시군0.7300.0950.0000.7070.1430.2271.000

Missing values

2023-12-11T08:36:37.712864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:36:37.855870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2023-12-11T08:36:37.957543image/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

연번시군온천명소재지성분온도(℃)심도(M)지구지정 일자온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)
01거제해수온천거제시 수양로 570약알, Ca-Cl31.07852001-08-27<NA>17481
12거제계룡산거제시 거제중앙로 1779-1약알, Ca-Cl29.06622002-07-22<NA>2341
23거제일운거제시 일운면 거제대로 2190약알, Na-CI37.09002008-12-11<NA>23443
34거제하청거제시 하청면 석포리 75-6알, Ca(Na)-HCO3(CI)27.8964<NA><NA><NA><NA>
45거제구천거제시 동부면 구천리 144-1알, Na(Ca)-HCO3(SO4)27.08502011-11-22<NA>21340
56거제장목거제시 장목면 산 126-6중성, Ca(Na)-CI30.6910<NA><NA><NA><NA>
67거창가조거창군 온천길 108-29(가조면)중성 , Na-HCO327.05001987-02-241010<NA>1842
78거창수월거창군 가조면 수월리 450-1약알 , Na-SO426.0640<NA><NA><NA><NA>
89거창일부거창군 가조면 일부리 99-12알 , Na-HCO327.0570<NA><NA><NA><NA>
910거창수월거창군 가조면 수월리 산83-71약알 , Na-SO426.0850<NA><NA><NA><NA>
연번시군온천명소재지성분온도(℃)심도(M)지구지정 일자온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)
4041창원녹산창원시 안골동 366-1약알, Na-SO430.512182005-12-05<NA>2303
4142창원석전창원시 석전동 275-21약알 , Ca-Cl26.04302006-02-21<NA>41028
4243창원용원창원시 용원동 산18염화물, Ca-Cl26.6870<NA><NA><NA>301
4344창원진동리창원시 진동면 진동리 11번지외 4필지약알, Na-HCO334.01051<NA><NA><NA>324
4445통영산양통영시 산양읍 신전리 926유황 , Na-HCO332.0637<NA><NA><NA><NA>
4546하동화개온천리조트하동군 화개면 화개로 265약알 , Na-CI27.06402000-11-07<NA>13350
4647하동한려하동군 금남면 경춘로 243-31알 , Na-CI26.08632005-10-18405<NA>1112
4748합천가야합천군 가야면 대전리 501-3알 , Na-HCO327.06321995-05-111872<NA>3600
4849합천쌍책합천군 쌍책면 하신리 106-1약알 , Na-HCO325.0700<NA><NA><NA>800
4950합천청덕합천군 청덕면 대부리 1435탄산염 , Ca-HCO325.09202005-08-09<NA>6374