Overview

Dataset statistics

Number of variables11
Number of observations52
Missing cells106
Missing cells (%)18.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory96.5 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
소재지 has 2 (3.8%) missing valuesMissing
성분 has 1 (1.9%) missing valuesMissing
지구지정 일자 has 18 (34.6%) missing valuesMissing
온천원 보호지구면적(천㎡) has 42 (80.8%) missing valuesMissing
온천공 보호구역면적(천㎡) has 29 (55.8%) missing valuesMissing
적정양수량(톤/일) has 14 (26.9%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:36:51.641699
Analysis finished2023-12-10 23:36:55.711780
Duration4.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct52
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean26.5
Minimum1
Maximum52
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:36:55.787629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.55
Q113.75
median26.5
Q339.25
95-th percentile49.45
Maximum52
Range51
Interquartile range (IQR)25.5

Descriptive statistics

Standard deviation15.154757
Coefficient of variation (CV)0.57187763
Kurtosis-1.2
Mean26.5
Median Absolute Deviation (MAD)13
Skewness0
Sum1378
Variance229.66667
MonotonicityStrictly increasing
2023-12-11T08:36:55.924496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.9%
28 1
 
1.9%
30 1
 
1.9%
31 1
 
1.9%
32 1
 
1.9%
33 1
 
1.9%
34 1
 
1.9%
35 1
 
1.9%
36 1
 
1.9%
37 1
 
1.9%
Other values (42) 42
80.8%
ValueCountFrequency (%)
1 1
1.9%
2 1
1.9%
3 1
1.9%
4 1
1.9%
5 1
1.9%
6 1
1.9%
7 1
1.9%
8 1
1.9%
9 1
1.9%
10 1
1.9%
ValueCountFrequency (%)
52 1
1.9%
51 1
1.9%
50 1
1.9%
49 1
1.9%
48 1
1.9%
47 1
1.9%
46 1
1.9%
45 1
1.9%
44 1
1.9%
43 1
1.9%

시군
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size548.0 B
창원
14 
양산
7 
거제
6 
거창
4 
김해
4 
Other values (9)
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)5.8%

Sample

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

Common Values

ValueCountFrequency (%)
창원 14
26.9%
양산 7
13.5%
거제 6
11.5%
거창 4
 
7.7%
김해 4
 
7.7%
밀양 3
 
5.8%
합천 3
 
5.8%
남해 2
 
3.8%
진주 2
 
3.8%
창녕 2
 
3.8%
Other values (4) 5
 
9.6%

Length

2023-12-11T08:36:56.039745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원 14
26.9%
양산 7
13.5%
거제 6
11.5%
거창 4
 
7.7%
김해 4
 
7.7%
밀양 3
 
5.8%
합천 3
 
5.8%
남해 2
 
3.8%
진주 2
 
3.8%
창녕 2
 
3.8%
Other values (4) 5
 
9.6%

온천명
Text

Distinct51
Distinct (%)98.1%
Missing0
Missing (%)0.0%
Memory size548.0 B
2023-12-11T08:36:56.243124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.1153846
Min length2

Characters and Unicode

Total characters162
Distinct characters87
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

Unique50 ?
Unique (%)96.2%

Sample

1st row해수온천
2nd row계룡산
3rd row일운
4th row하청
5th row구천
ValueCountFrequency (%)
수월 2
 
3.6%
장유 2
 
3.6%
인곡 1
 
1.8%
해수온천 1
 
1.8%
진북황실 1
 
1.8%
상남 1
 
1.8%
진주윙스 1
 
1.8%
부곡 1
 
1.8%
초곡 1
 
1.8%
마금산 1
 
1.8%
Other values (44) 44
78.6%
2023-12-11T08:36:56.612271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
15
 
9.3%
산 7
 
4.3%
동 6
 
3.7%
천 6
 
3.7%
진 5
 
3.1%
해 4
 
2.5%
원 4
 
2.5%
수 4
 
2.5%
곡 4
 
2.5%
용 3
 
1.9%
Other values (77) 104
64.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 144
88.9%
Space Separator 16
 
9.9%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 7
 
4.9%
동 6
 
4.2%
천 6
 
4.2%
진 5
 
3.5%
해 4
 
2.8%
원 4
 
2.8%
수 4
 
2.8%
곡 4
 
2.8%
용 3
 
2.1%
온 3
 
2.1%
Other values (73) 98
68.1%
Space Separator
ValueCountFrequency (%)
15
93.8%
　 1
 
6.2%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 144
88.9%
Common 18
 
11.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 7
 
4.9%
동 6
 
4.2%
천 6
 
4.2%
진 5
 
3.5%
해 4
 
2.8%
원 4
 
2.8%
수 4
 
2.8%
곡 4
 
2.8%
용 3
 
2.1%
온 3
 
2.1%
Other values (73) 98
68.1%
Common
ValueCountFrequency (%)
15
83.3%
　 1
 
5.6%
( 1
 
5.6%
) 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 144
88.9%
ASCII 17
 
10.5%
None 1
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
15
88.2%
( 1
 
5.9%
) 1
 
5.9%
Hangul
ValueCountFrequency (%)
산 7
 
4.9%
동 6
 
4.2%
천 6
 
4.2%
진 5
 
3.5%
해 4
 
2.8%
원 4
 
2.8%
수 4
 
2.8%
곡 4
 
2.8%
용 3
 
2.1%
온 3
 
2.1%
Other values (73) 98
68.1%
None
ValueCountFrequency (%)
　 1
100.0%

소재지
Text

MISSING 

Distinct50
Distinct (%)100.0%
Missing2
Missing (%)3.8%
Memory size548.0 B
2023-12-11T08:36:56.924012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length16.26
Min length11

Characters and Unicode

Total characters813
Distinct characters115
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 7
 
3.7%
거제시 6
 
3.2%
김해시 4
 
2.1%
거창군 4
 
2.1%
진전면 3
 
1.6%
하북면 3
 
1.6%
가조면 3
 
1.6%
밀양시 3
 
1.6%
합천군 3
 
1.6%
Other values (132) 141
74.6%
2023-12-11T08:36:57.354305image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
158
 
19.4%
1 37
 
4.6%
시 36
 
4.4%
면 34
 
4.2%
리 25
 
3.1%
동 22
 
2.7%
2 22
 
2.7%
- 22
 
2.7%
산 20
 
2.5%
6 19
 
2.3%
Other values (105) 418
51.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 442
54.4%
Decimal Number 179
22.0%
Space Separator 158
 
19.4%
Dash Punctuation 22
 
2.7%
Close Punctuation 6
 
0.7%
Open Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 36
 
8.1%
면 34
 
7.7%
리 25
 
5.7%
동 22
 
5.0%
산 20
 
4.5%
창 18
 
4.1%
원 16
 
3.6%
군 14
 
3.2%
거 13
 
2.9%
양 12
 
2.7%
Other values (91) 232
52.5%
Decimal Number
ValueCountFrequency (%)
1 37
20.7%
2 22
12.3%
6 19
10.6%
0 17
9.5%
4 17
9.5%
5 16
8.9%
7 15
8.4%
3 14
 
7.8%
8 11
 
6.1%
9 11
 
6.1%
Space Separator
ValueCountFrequency (%)
158
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 442
54.4%
Common 371
45.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 36
 
8.1%
면 34
 
7.7%
리 25
 
5.7%
동 22
 
5.0%
산 20
 
4.5%
창 18
 
4.1%
원 16
 
3.6%
군 14
 
3.2%
거 13
 
2.9%
양 12
 
2.7%
Other values (91) 232
52.5%
Common
ValueCountFrequency (%)
158
42.6%
1 37
 
10.0%
2 22
 
5.9%
- 22
 
5.9%
6 19
 
5.1%
0 17
 
4.6%
4 17
 
4.6%
5 16
 
4.3%
7 15
 
4.0%
3 14
 
3.8%
Other values (4) 34
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 442
54.4%
ASCII 371
45.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
158
42.6%
1 37
 
10.0%
2 22
 
5.9%
- 22
 
5.9%
6 19
 
5.1%
0 17
 
4.6%
4 17
 
4.6%
5 16
 
4.3%
7 15
 
4.0%
3 14
 
3.8%
Other values (4) 34
 
9.2%
Hangul
ValueCountFrequency (%)
시 36
 
8.1%
면 34
 
7.7%
리 25
 
5.7%
동 22
 
5.0%
산 20
 
4.5%
창 18
 
4.1%
원 16
 
3.6%
군 14
 
3.2%
거 13
 
2.9%
양 12
 
2.7%
Other values (91) 232
52.5%

성분
Text

MISSING 

Distinct28
Distinct (%)54.9%
Missing1
Missing (%)1.9%
Memory size548.0 B
2023-12-11T08:36:57.508260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length12
Min length9

Characters and Unicode

Total characters612
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

Unique18 ?
Unique (%)35.3%

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 (%)
33
24.6%
약알 24
17.9%
알 15
11.2%
na-hco3 15
11.2%
na-so4 11
 
8.2%
na-ci 9
 
6.7%
ca-cl 6
 
4.5%
염화물 3
 
2.2%
중성 3
 
2.2%
황산염 2
 
1.5%
Other values (11) 13
 
9.7%
2023-12-11T08:36:57.762318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
19.8%
a 57
9.3%
C 56
9.2%
- 51
8.3%
, 50
8.2%
N 42
 
6.9%
알 39
 
6.4%
O 34
 
5.6%
약 24
 
3.9%
3 21
 
3.4%
Other values (17) 117
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 178
29.1%
Space Separator 121
19.8%
Other Letter 91
14.9%
Lowercase Letter 66
 
10.8%
Dash Punctuation 51
 
8.3%
Other Punctuation 50
 
8.2%
Decimal Number 35
 
5.7%
Open Punctuation 10
 
1.6%
Close Punctuation 10
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
알 39
42.9%
약 24
26.4%
염 6
 
6.6%
황 4
 
4.4%
화 3
 
3.3%
물 3
 
3.3%
중 3
 
3.3%
성 3
 
3.3%
산 3
 
3.3%
유 2
 
2.2%
Uppercase Letter
ValueCountFrequency (%)
C 56
31.5%
N 42
23.6%
O 34
19.1%
H 20
 
11.2%
S 14
 
7.9%
I 12
 
6.7%
Lowercase Letter
ValueCountFrequency (%)
a 57
86.4%
l 8
 
12.1%
o 1
 
1.5%
Decimal Number
ValueCountFrequency (%)
3 21
60.0%
4 14
40.0%
Space Separator
ValueCountFrequency (%)
121
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 51
100.0%
Other Punctuation
ValueCountFrequency (%)
, 50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 277
45.3%
Latin 244
39.9%
Hangul 91
 
14.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
알 39
42.9%
약 24
26.4%
염 6
 
6.6%
황 4
 
4.4%
화 3
 
3.3%
물 3
 
3.3%
중 3
 
3.3%
성 3
 
3.3%
산 3
 
3.3%
유 2
 
2.2%
Latin
ValueCountFrequency (%)
a 57
23.4%
C 56
23.0%
N 42
17.2%
O 34
13.9%
H 20
 
8.2%
S 14
 
5.7%
I 12
 
4.9%
l 8
 
3.3%
o 1
 
0.4%
Common
ValueCountFrequency (%)
121
43.7%
- 51
18.4%
, 50
18.1%
3 21
 
7.6%
4 14
 
5.1%
( 10
 
3.6%
) 10
 
3.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 521
85.1%
Hangul 91
 
14.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
23.2%
a 57
10.9%
C 56
10.7%
- 51
9.8%
, 50
9.6%
N 42
 
8.1%
O 34
 
6.5%
3 21
 
4.0%
H 20
 
3.8%
S 14
 
2.7%
Other values (6) 55
10.6%
Hangul
ValueCountFrequency (%)
알 39
42.9%
약 24
26.4%
염 6
 
6.6%
황 4
 
4.4%
화 3
 
3.3%
물 3
 
3.3%
중 3
 
3.3%
성 3
 
3.3%
산 3
 
3.3%
유 2
 
2.2%

온도(℃)
Real number (ℝ)

Distinct28
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.948077
Minimum25
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:36:57.883138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25
Q126
median27
Q330.525
95-th percentile37.9
Maximum78
Range53
Interquartile range (IQR)4.525

Descriptive statistics

Standard deviation8.5192168
Coefficient of variation (CV)0.28446624
Kurtosis21.460461
Mean29.948077
Median Absolute Deviation (MAD)1.55
Skewness4.3069277
Sum1557.3
Variance72.577055
MonotonicityNot monotonic
2023-12-11T08:36:58.006701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
26.0 12
23.1%
27.0 7
 
13.5%
25.0 5
 
9.6%
29.0 2
 
3.8%
28.0 2
 
3.8%
31.0 2
 
3.8%
32.9 1
 
1.9%
29.1 1
 
1.9%
29.5 1
 
1.9%
34.0 1
 
1.9%
Other values (18) 18
34.6%
ValueCountFrequency (%)
25.0 5
9.6%
25.7 1
 
1.9%
26.0 12
23.1%
26.6 1
 
1.9%
26.7 1
 
1.9%
27.0 7
13.5%
27.7 1
 
1.9%
27.8 1
 
1.9%
28.0 2
 
3.8%
28.8 1
 
1.9%
ValueCountFrequency (%)
78.0 1
1.9%
57.0 1
1.9%
39.0 1
1.9%
37.0 1
1.9%
36.0 1
1.9%
35.0 1
1.9%
34.0 1
1.9%
32.9 1
1.9%
32.0 1
1.9%
31.7 1
1.9%

심도(M)
Real number (ℝ)

Distinct45
Distinct (%)86.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean765.86538
Minimum185
Maximum1218
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:36:58.121473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum185
5-th percentile409.05
Q1635.75
median780
Q3968
95-th percentile1074.85
Maximum1218
Range1033
Interquartile range (IQR)332.25

Descriptive statistics

Standard deviation226.81668
Coefficient of variation (CV)0.29615737
Kurtosis-0.47208827
Mean765.86538
Median Absolute Deviation (MAD)180
Skewness-0.31336813
Sum39825
Variance51445.805
MonotonicityNot monotonic
2023-12-11T08:36:58.260184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
1000 4
 
7.7%
984 2
 
3.8%
850 2
 
3.8%
640 2
 
3.8%
690 2
 
3.8%
785 1
 
1.9%
185 1
 
1.9%
840 1
 
1.9%
423 1
 
1.9%
458 1
 
1.9%
Other values (35) 35
67.3%
ValueCountFrequency (%)
185 1
1.9%
374 1
1.9%
392 1
1.9%
423 1
1.9%
430 1
1.9%
458 1
1.9%
479 1
1.9%
484 1
1.9%
500 1
1.9%
529 1
1.9%
ValueCountFrequency (%)
1218 1
 
1.9%
1112 1
 
1.9%
1104 1
 
1.9%
1051 1
 
1.9%
1040 1
 
1.9%
1005 1
 
1.9%
1000 4
7.7%
984 2
3.8%
980 1
 
1.9%
964 1
 
1.9%

지구지정 일자
Date

MISSING 

Distinct33
Distinct (%)97.1%
Missing18
Missing (%)34.6%
Memory size548.0 B
Minimum1981-09-30 00:00:00
Maximum2019-01-03 00:00:00
2023-12-11T08:36:58.372785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:58.479862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)

온천원 보호지구면적(천㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct10
Distinct (%)100.0%
Missing42
Missing (%)80.8%
Infinite0
Infinite (%)0.0%
Mean1011
Minimum14
Maximum4819
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:36:58.607020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum14
5-th percentile14.45
Q1147.5
median310
Q31349
95-th percentile3492.85
Maximum4819
Range4805
Interquartile range (IQR)1201.5

Descriptive statistics

Standard deviation1487.6742
Coefficient of variation (CV)1.4714878
Kurtosis5.2175733
Mean1011
Median Absolute Deviation (MAD)295.5
Skewness2.1919575
Sum10110
Variance2213174.4
MonotonicityNot monotonic
2023-12-11T08:36:58.696913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
1010 1
 
1.9%
1462 1
 
1.9%
215 1
 
1.9%
146 1
 
1.9%
4819 1
 
1.9%
152 1
 
1.9%
14 1
 
1.9%
15 1
 
1.9%
405 1
 
1.9%
1872 1
 
1.9%
(Missing) 42
80.8%
ValueCountFrequency (%)
14 1
1.9%
15 1
1.9%
146 1
1.9%
152 1
1.9%
215 1
1.9%
405 1
1.9%
1010 1
1.9%
1462 1
1.9%
1872 1
1.9%
4819 1
1.9%
ValueCountFrequency (%)
4819 1
1.9%
1872 1
1.9%
1462 1
1.9%
1010 1
1.9%
405 1
1.9%
215 1
1.9%
152 1
1.9%
146 1
1.9%
15 1
1.9%
14 1
1.9%

온천공 보호구역면적(천㎡)
Real number (ℝ)

MISSING 

Distinct18
Distinct (%)78.3%
Missing29
Missing (%)55.8%
Infinite0
Infinite (%)0.0%
Mean14.130435
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:36:58.793916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.1
Q15
median10
Q322
95-th percentile30.8
Maximum40
Range39
Interquartile range (IQR)17

Descriptive statistics

Standard deviation11.294827
Coefficient of variation (CV)0.79932624
Kurtosis-0.53584399
Mean14.130435
Median Absolute Deviation (MAD)8
Skewness0.69040112
Sum325
Variance127.57312
MonotonicityNot monotonic
2023-12-11T08:36:58.899714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
8 2
 
3.8%
28 2
 
3.8%
1 2
 
3.8%
2 2
 
3.8%
5 2
 
3.8%
9 1
 
1.9%
6 1
 
1.9%
13 1
 
1.9%
4 1
 
1.9%
10 1
 
1.9%
Other values (8) 8
 
15.4%
(Missing) 29
55.8%
ValueCountFrequency (%)
1 2
3.8%
2 2
3.8%
4 1
1.9%
5 2
3.8%
6 1
1.9%
8 2
3.8%
9 1
1.9%
10 1
1.9%
13 1
1.9%
16 1
1.9%
ValueCountFrequency (%)
40 1
1.9%
31 1
1.9%
29 1
1.9%
28 2
3.8%
23 1
1.9%
21 1
1.9%
18 1
1.9%
17 1
1.9%
16 1
1.9%
13 1
1.9%

적정양수량(톤/일)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct35
Distinct (%)92.1%
Missing14
Missing (%)26.9%
Infinite0
Infinite (%)0.0%
Mean876.23684
Minimum210
Maximum5468
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size600.0 B
2023-12-11T08:36:59.012262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum210
5-th percentile265.7
Q1321
median431
Q31026.75
95-th percentile2835
Maximum5468
Range5258
Interquartile range (IQR)705.75

Descriptive statistics

Standard deviation1067.8053
Coefficient of variation (CV)1.2186264
Kurtosis9.3618391
Mean876.23684
Median Absolute Deviation (MAD)121
Skewness2.8726681
Sum33297
Variance1140208.2
MonotonicityNot monotonic
2023-12-11T08:36:59.115761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
310 3
 
5.8%
400 2
 
3.8%
374 1
 
1.9%
800 1
 
1.9%
3600 1
 
1.9%
1112 1
 
1.9%
350 1
 
1.9%
324 1
 
1.9%
301 1
 
1.9%
2122 1
 
1.9%
Other values (25) 25
48.1%
(Missing) 14
26.9%
ValueCountFrequency (%)
210 1
 
1.9%
264 1
 
1.9%
266 1
 
1.9%
300 1
 
1.9%
301 1
 
1.9%
303 1
 
1.9%
310 3
5.8%
320 1
 
1.9%
324 1
 
1.9%
330 1
 
1.9%
ValueCountFrequency (%)
5468 1
1.9%
3600 1
1.9%
2700 1
1.9%
2122 1
1.9%
1842 1
1.9%
1411 1
1.9%
1410 1
1.9%
1330 1
1.9%
1112 1
1.9%
1028 1
1.9%

Interactions

2023-12-11T08:36:54.539237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.057612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.527252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.998284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.519316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.979835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.616790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.132690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.612265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.068315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.586838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.070004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.692898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.209218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.680306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.152142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.662152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.157951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.768595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.279461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.756038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.226500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.749746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.264909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:55.109069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.353823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.832271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.323129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.822975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.334404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:55.200901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.439403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:52.920467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.439849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:53.896346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:54.430191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:36:59.198397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군온천명소재지성분온도(℃)심도(M)지구지정 일자온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)
연번1.0000.9291.0001.0000.6910.0000.4080.9630.9680.6870.298
시군0.9291.0001.0001.0000.0000.0000.0000.9531.0000.5960.516
온천명1.0001.0001.0001.0001.0001.0000.9661.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
성분0.6910.0001.0001.0001.0000.3100.0000.9581.0000.8610.000
온도(℃)0.0000.0001.0001.0000.3101.0000.1160.0000.4980.0000.698
심도(M)0.4080.0000.9661.0000.0000.1161.0001.0000.6280.2080.000
지구지정 일자0.9630.9531.0001.0000.9580.0001.0001.0000.2571.0000.000
온천원 보호지구면적(천㎡)0.9681.0001.0001.0001.0000.4980.6280.2571.000NaN0.513
온천공 보호구역면적(천㎡)0.6870.5961.0001.0000.8610.0000.2081.000NaN1.0000.000
적정양수량(톤/일)0.2980.5161.0001.0000.0000.6980.0000.0000.5130.0001.000
2023-12-11T08:36:59.311698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번온도(℃)심도(M)온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)시군
연번1.000-0.045-0.057-0.139-0.492-0.0740.695
온도(℃)-0.0451.0000.0220.261-0.0160.2220.000
심도(M)-0.0570.0221.0000.067-0.278-0.4460.000
온천원 보호지구면적(천㎡)-0.1390.2610.0671.000NaN0.5270.707
온천공 보호구역면적(천㎡)-0.492-0.016-0.278NaN1.0000.2600.267
적정양수량(톤/일)-0.0740.222-0.4460.5270.2601.0000.255
시군0.6950.0000.0000.7070.2670.2551.000

Missing values

2023-12-11T08:36:55.329395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:36:55.491203image/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.
2023-12-11T08:36:55.624403image/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)
4243창원용원창원시 용원동 산18염화물, Ca-Cl26.6870<NA><NA><NA>301
4344창원진동리창원시 진동면 진동리 11번지외 4필지약알, Na-HCO334.01051<NA><NA><NA>324
4445창원진해용원<NA>염화물, Ca-Cl32.91000<NA><NA><NA><NA>
4546창원가음정<NA>유황 , Na-HCO329.1710<NA><NA><NA><NA>
4647통영산양통영시 산양읍 신전리 926유황 , Na-HCO332.0637<NA><NA><NA><NA>
4748하동화개온천리조트하동군 화개면 화개로 265약알 , Na-CI27.06402000-11-07<NA>13350
4849하동한려하동군 금남면 경춘로 243-31알 , Na-CI26.08632005-10-18405<NA>1112
4950합천가야합천군 가야면 대전리 501-3알 , Na-HCO327.06321995-05-111872<NA>3600
5051합천쌍책합천군 쌍책면 하신리 106-1약알 , Na-HCO325.0700<NA><NA><NA>800
5152합천청덕합천군 청덕면 대부리 1435탄산염 , Ca-HCO325.09202005-08-09<NA>6374