Overview

Dataset statistics

Number of variables12
Number of observations55
Missing cells162
Missing cells (%)24.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.5 KiB
Average record size in memory102.4 B

Variable types

Numeric4
Categorical1
Text7

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 Unnamed: 11 and 1 other fieldsHigh correlation
온천공 보호구역면적(천㎡) is highly overall correlated with Unnamed: 11High correlation
Unnamed: 11 is highly overall correlated with 연번 and 2 other fieldsHigh correlation
시군 is highly overall correlated with 연번 and 1 other fieldsHigh correlation
소재지 has 2 (3.6%) missing valuesMissing
성분 has 1 (1.8%) missing valuesMissing
지구지정 일자 has 22 (40.0%) missing valuesMissing
온천원 보호지구면적(천㎡) has 46 (83.6%) missing valuesMissing
온천공 보호구역면적(천㎡) has 32 (58.2%) missing valuesMissing
적정양수량(톤/일) has 18 (32.7%) missing valuesMissing
Unnamed: 11 has 41 (74.5%) missing valuesMissing
연번 has unique valuesUnique

Reproduction

Analysis started2023-12-10 23:36:42.910992
Analysis finished2023-12-10 23:36:45.181397
Duration2.27 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct55
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28
Minimum1
Maximum55
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-11T08:36:45.263031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3.7
Q114.5
median28
Q341.5
95-th percentile52.3
Maximum55
Range54
Interquartile range (IQR)27

Descriptive statistics

Standard deviation16.02082
Coefficient of variation (CV)0.57217214
Kurtosis-1.2
Mean28
Median Absolute Deviation (MAD)14
Skewness0
Sum1540
Variance256.66667
MonotonicityStrictly increasing
2023-12-11T08:36:45.381746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
31 1
 
1.8%
32 1
 
1.8%
33 1
 
1.8%
34 1
 
1.8%
35 1
 
1.8%
36 1
 
1.8%
37 1
 
1.8%
38 1
 
1.8%
Other values (45) 45
81.8%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
55 1
1.8%
54 1
1.8%
53 1
1.8%
52 1
1.8%
51 1
1.8%
50 1
1.8%
49 1
1.8%
48 1
1.8%
47 1
1.8%
46 1
1.8%

시군
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)25.5%
Missing0
Missing (%)0.0%
Memory size572.0 B
창원
14 
양산
9 
거제
6 
김해
5 
거창
4 
Other values (9)
17 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique3 ?
Unique (%)5.5%

Sample

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

Common Values

ValueCountFrequency (%)
창원 14
25.5%
양산 9
16.4%
거제 6
10.9%
김해 5
 
9.1%
거창 4
 
7.3%
밀양 3
 
5.5%
합천 3
 
5.5%
남해 2
 
3.6%
진주 2
 
3.6%
창녕 2
 
3.6%
Other values (4) 5
 
9.1%

Length

2023-12-11T08:36:45.491948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
창원 14
25.5%
양산 9
16.4%
거제 6
10.9%
김해 5
 
9.1%
거창 4
 
7.3%
밀양 3
 
5.5%
합천 3
 
5.5%
남해 2
 
3.6%
진주 2
 
3.6%
창녕 2
 
3.6%
Other values (4) 5
 
9.1%

온천명
Text

Distinct54
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-11T08:36:45.694411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.1818182
Min length2

Characters and Unicode

Total characters175
Distinct characters92
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

Unique53 ?
Unique (%)96.4%

Sample

1st row해수온천
2nd row계룡산
3rd row일운
4th row하청
5th row구천
ValueCountFrequency (%)
수월 2
 
3.3%
장유 2
 
3.3%
진북황실 1
 
1.7%
상남 1
 
1.7%
남강 1
 
1.7%
워터피아 1
 
1.7%
진주윙스 1
 
1.7%
부곡 1
 
1.7%
초곡 1
 
1.7%
마금산 1
 
1.7%
Other values (48) 48
80.0%
2023-12-11T08:36:46.034203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
17
 
9.7%
동 8
 
4.6%
산 7
 
4.0%
천 6
 
3.4%
진 5
 
2.9%
원 5
 
2.9%
해 4
 
2.3%
용 4
 
2.3%
수 4
 
2.3%
곡 4
 
2.3%
Other values (82) 111
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 155
88.6%
Space Separator 18
 
10.3%
Open Punctuation 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 8
 
5.2%
산 7
 
4.5%
천 6
 
3.9%
진 5
 
3.2%
원 5
 
3.2%
해 4
 
2.6%
용 4
 
2.6%
수 4
 
2.6%
곡 4
 
2.6%
장 4
 
2.6%
Other values (78) 104
67.1%
Space Separator
ValueCountFrequency (%)
17
94.4%
　 1
 
5.6%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 155
88.6%
Common 20
 
11.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 8
 
5.2%
산 7
 
4.5%
천 6
 
3.9%
진 5
 
3.2%
원 5
 
3.2%
해 4
 
2.6%
용 4
 
2.6%
수 4
 
2.6%
곡 4
 
2.6%
장 4
 
2.6%
Other values (78) 104
67.1%
Common
ValueCountFrequency (%)
17
85.0%
　 1
 
5.0%
( 1
 
5.0%
) 1
 
5.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 155
88.6%
ASCII 19
 
10.9%
None 1
 
0.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
17
89.5%
( 1
 
5.3%
) 1
 
5.3%
Hangul
ValueCountFrequency (%)
동 8
 
5.2%
산 7
 
4.5%
천 6
 
3.9%
진 5
 
3.2%
원 5
 
3.2%
해 4
 
2.6%
용 4
 
2.6%
수 4
 
2.6%
곡 4
 
2.6%
장 4
 
2.6%
Other values (78) 104
67.1%
None
ValueCountFrequency (%)
　 1
100.0%

소재지
Text

MISSING 

Distinct53
Distinct (%)100.0%
Missing2
Missing (%)3.6%
Memory size572.0 B
2023-12-11T08:36:46.300506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length19
Mean length16.132075
Min length9

Characters and Unicode

Total characters855
Distinct characters117
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

Unique53 ?
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
 
6.0%
양산시 8
 
4.0%
거제시 6
 
3.0%
김해시 5
 
2.5%
거창군 4
 
2.0%
하북면 4
 
2.0%
진전면 3
 
1.5%
합천군 3
 
1.5%
밀양시 3
 
1.5%
가조면 3
 
1.5%
Other values (138) 149
74.5%
2023-12-11T08:36:46.668124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
167
 
19.5%
시 38
 
4.4%
1 37
 
4.3%
면 36
 
4.2%
리 27
 
3.2%
동 25
 
2.9%
- 24
 
2.8%
2 24
 
2.8%
산 22
 
2.6%
6 20
 
2.3%
Other values (107) 435
50.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 464
54.3%
Decimal Number 188
22.0%
Space Separator 167
 
19.5%
Dash Punctuation 24
 
2.8%
Open Punctuation 6
 
0.7%
Close Punctuation 6
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 38
 
8.2%
면 36
 
7.8%
리 27
 
5.8%
동 25
 
5.4%
산 22
 
4.7%
창 18
 
3.9%
원 17
 
3.7%
군 14
 
3.0%
거 13
 
2.8%
양 13
 
2.8%
Other values (93) 241
51.9%
Decimal Number
ValueCountFrequency (%)
1 37
19.7%
2 24
12.8%
6 20
10.6%
4 18
9.6%
0 17
9.0%
3 16
8.5%
7 16
8.5%
5 16
8.5%
8 13
 
6.9%
9 11
 
5.9%
Space Separator
ValueCountFrequency (%)
167
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 24
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 464
54.3%
Common 391
45.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 38
 
8.2%
면 36
 
7.8%
리 27
 
5.8%
동 25
 
5.4%
산 22
 
4.7%
창 18
 
3.9%
원 17
 
3.7%
군 14
 
3.0%
거 13
 
2.8%
양 13
 
2.8%
Other values (93) 241
51.9%
Common
ValueCountFrequency (%)
167
42.7%
1 37
 
9.5%
- 24
 
6.1%
2 24
 
6.1%
6 20
 
5.1%
4 18
 
4.6%
0 17
 
4.3%
3 16
 
4.1%
7 16
 
4.1%
5 16
 
4.1%
Other values (4) 36
 
9.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 464
54.3%
ASCII 391
45.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
167
42.7%
1 37
 
9.5%
- 24
 
6.1%
2 24
 
6.1%
6 20
 
5.1%
4 18
 
4.6%
0 17
 
4.3%
3 16
 
4.1%
7 16
 
4.1%
5 16
 
4.1%
Other values (4) 36
 
9.2%
Hangul
ValueCountFrequency (%)
시 38
 
8.2%
면 36
 
7.8%
리 27
 
5.8%
동 25
 
5.4%
산 22
 
4.7%
창 18
 
3.9%
원 17
 
3.7%
군 14
 
3.0%
거 13
 
2.8%
양 13
 
2.8%
Other values (93) 241
51.9%

성분
Text

MISSING 

Distinct32
Distinct (%)59.3%
Missing1
Missing (%)1.8%
Memory size572.0 B
2023-12-11T08:36:46.852117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length18
Mean length11.944444
Min length9

Characters and Unicode

Total characters645
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

Unique23 ?
Unique (%)42.6%

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
22.3%
약알 29
20.9%
알 15
10.8%
na-hco3 14
10.1%
na-so4 11
 
7.9%
na-ci 10
 
7.2%
ca-cl 6
 
4.3%
ca-so4 3
 
2.2%
중성 3
 
2.2%
황산염 2
 
1.4%
Other values (12) 15
10.8%
2023-12-11T08:36:47.440486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
121
18.8%
a 62
9.6%
C 61
9.5%
, 54
8.4%
- 54
8.4%
N 44
 
6.8%
알 44
 
6.8%
O 33
 
5.1%
약 29
 
4.5%
3 20
 
3.1%
Other values (17) 123
19.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 188
29.1%
Space Separator 121
18.8%
Other Letter 98
15.2%
Lowercase Letter 71
 
11.0%
Other Punctuation 54
 
8.4%
Dash Punctuation 54
 
8.4%
Decimal Number 35
 
5.4%
Open Punctuation 12
 
1.9%
Close Punctuation 12
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
알 44
44.9%
약 29
29.6%
염 6
 
6.1%
성 3
 
3.1%
산 3
 
3.1%
황 3
 
3.1%
화 3
 
3.1%
중 3
 
3.1%
물 2
 
2.0%
유 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
C 61
32.4%
N 44
23.4%
O 33
17.6%
H 19
 
10.1%
I 16
 
8.5%
S 15
 
8.0%
Lowercase Letter
ValueCountFrequency (%)
a 62
87.3%
l 7
 
9.9%
o 2
 
2.8%
Decimal Number
ValueCountFrequency (%)
3 20
57.1%
4 15
42.9%
Space Separator
ValueCountFrequency (%)
121
100.0%
Other Punctuation
ValueCountFrequency (%)
, 54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 54
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 288
44.7%
Latin 259
40.2%
Hangul 98
 
15.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
알 44
44.9%
약 29
29.6%
염 6
 
6.1%
성 3
 
3.1%
산 3
 
3.1%
황 3
 
3.1%
화 3
 
3.1%
중 3
 
3.1%
물 2
 
2.0%
유 1
 
1.0%
Latin
ValueCountFrequency (%)
a 62
23.9%
C 61
23.6%
N 44
17.0%
O 33
12.7%
H 19
 
7.3%
I 16
 
6.2%
S 15
 
5.8%
l 7
 
2.7%
o 2
 
0.8%
Common
ValueCountFrequency (%)
121
42.0%
, 54
18.8%
- 54
18.8%
3 20
 
6.9%
4 15
 
5.2%
( 12
 
4.2%
) 12
 
4.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 547
84.8%
Hangul 98
 
15.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
121
22.1%
a 62
11.3%
C 61
11.2%
, 54
9.9%
- 54
9.9%
N 44
 
8.0%
O 33
 
6.0%
3 20
 
3.7%
H 19
 
3.5%
I 16
 
2.9%
Other values (6) 63
11.5%
Hangul
ValueCountFrequency (%)
알 44
44.9%
약 29
29.6%
염 6
 
6.1%
성 3
 
3.1%
산 3
 
3.1%
황 3
 
3.1%
화 3
 
3.1%
중 3
 
3.1%
물 2
 
2.0%
유 1
 
1.0%

온도(℃)
Real number (ℝ)

Distinct29
Distinct (%)52.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean30.06
Minimum25
Maximum78
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-11T08:36:47.564736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile25
Q126
median27
Q330.55
95-th percentile39.3
Maximum78
Range53
Interquartile range (IQR)4.55

Descriptive statistics

Standard deviation8.4078579
Coefficient of variation (CV)0.27970252
Kurtosis21.000447
Mean30.06
Median Absolute Deviation (MAD)1.8
Skewness4.2116198
Sum1653.3
Variance70.692074
MonotonicityNot monotonic
2023-12-11T08:36:47.689980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
26.0 13
23.6%
27.0 7
 
12.7%
25.0 5
 
9.1%
31.0 2
 
3.6%
29.0 2
 
3.6%
30.0 2
 
3.6%
28.0 2
 
3.6%
32.9 1
 
1.8%
34.0 1
 
1.8%
29.5 1
 
1.8%
Other values (19) 19
34.5%
ValueCountFrequency (%)
25.0 5
 
9.1%
25.7 1
 
1.8%
26.0 13
23.6%
26.6 1
 
1.8%
26.7 1
 
1.8%
27.0 7
12.7%
27.7 1
 
1.8%
27.8 1
 
1.8%
28.0 2
 
3.6%
28.8 1
 
1.8%
ValueCountFrequency (%)
78.0 1
1.8%
57.0 1
1.8%
40.0 1
1.8%
39.0 1
1.8%
37.0 1
1.8%
36.0 1
1.8%
35.0 1
1.8%
34.0 1
1.8%
32.9 1
1.8%
32.0 1
1.8%

심도(M)
Text

Distinct49
Distinct (%)89.1%
Missing0
Missing (%)0.0%
Memory size572.0 B
2023-12-11T08:36:47.895554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.3454545
Min length3

Characters and Unicode

Total characters184
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique44 ?
Unique (%)80.0%

Sample

1st row785
2nd row662
3rd row900
4th row964
5th row850
ValueCountFrequency (%)
1,000 3
 
5.5%
640 2
 
3.6%
690 2
 
3.6%
984 2
 
3.6%
850 2
 
3.6%
1,005 1
 
1.8%
1,112 1
 
1.8%
392 1
 
1.8%
810 1
 
1.8%
185 1
 
1.8%
Other values (39) 39
70.9%
2023-12-11T08:36:48.238035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 44
23.9%
1 22
12.0%
4 18
9.8%
8 16
 
8.7%
6 16
 
8.7%
7 16
 
8.7%
9 14
 
7.6%
5 11
 
6.0%
, 9
 
4.9%
2 9
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 175
95.1%
Other Punctuation 9
 
4.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 44
25.1%
1 22
12.6%
4 18
10.3%
8 16
 
9.1%
6 16
 
9.1%
7 16
 
9.1%
9 14
 
8.0%
5 11
 
6.3%
2 9
 
5.1%
3 9
 
5.1%
Other Punctuation
ValueCountFrequency (%)
, 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 184
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 44
23.9%
1 22
12.0%
4 18
9.8%
8 16
 
8.7%
6 16
 
8.7%
7 16
 
8.7%
9 14
 
7.6%
5 11
 
6.0%
, 9
 
4.9%
2 9
 
4.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 184
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 44
23.9%
1 22
12.0%
4 18
9.8%
8 16
 
8.7%
6 16
 
8.7%
7 16
 
8.7%
9 14
 
7.6%
5 11
 
6.0%
, 9
 
4.9%
2 9
 
4.9%

지구지정 일자
Text

MISSING 

Distinct32
Distinct (%)97.0%
Missing22
Missing (%)40.0%
Memory size572.0 B
2023-12-11T08:36:48.448224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length9
Mean length8.7575758
Min length7

Characters and Unicode

Total characters289
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)93.9%

Sample

1st row01.08.27
2nd row02.07.22
3rd row08.12.11
4th row11.11.22
5th row87.02.24
ValueCountFrequency (%)
81.09.30 2
 
6.1%
08.12.11 1
 
3.0%
01.08.27 1
 
3.0%
04.10.27 1
 
3.0%
18.1.12 1
 
3.0%
11.05.11 1
 
3.0%
85.09.16 1
 
3.0%
92.09.29 1
 
3.0%
05.12.28 1
 
3.0%
04.11.26 1
 
3.0%
Other values (22) 22
66.7%
2023-12-11T08:36:48.815102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 66
22.8%
1 53
18.3%
0 50
17.3%
2 27
9.3%
25
 
8.7%
8 14
 
4.8%
5 11
 
3.8%
9 9
 
3.1%
7 9
 
3.1%
4 9
 
3.1%
Other values (3) 16
 
5.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 196
67.8%
Other Punctuation 66
 
22.8%
Space Separator 27
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 53
27.0%
0 50
25.5%
2 27
13.8%
8 14
 
7.1%
5 11
 
5.6%
9 9
 
4.6%
7 9
 
4.6%
4 9
 
4.6%
3 8
 
4.1%
6 6
 
3.1%
Space Separator
ValueCountFrequency (%)
25
92.6%
　 2
 
7.4%
Other Punctuation
ValueCountFrequency (%)
. 66
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 289
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 66
22.8%
1 53
18.3%
0 50
17.3%
2 27
9.3%
25
 
8.7%
8 14
 
4.8%
5 11
 
3.8%
9 9
 
3.1%
7 9
 
3.1%
4 9
 
3.1%
Other values (3) 16
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 287
99.3%
None 2
 
0.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 66
23.0%
1 53
18.5%
0 50
17.4%
2 27
9.4%
25
 
8.7%
8 14
 
4.9%
5 11
 
3.8%
9 9
 
3.1%
7 9
 
3.1%
4 9
 
3.1%
Other values (2) 14
 
4.9%
None
ValueCountFrequency (%)
　 2
100.0%

온천원 보호지구면적(천㎡)
Text

MISSING 

Distinct9
Distinct (%)100.0%
Missing46
Missing (%)83.6%
Memory size572.0 B
2023-12-11T08:36:48.986629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.6666667
Min length2

Characters and Unicode

Total characters33
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique9 ?
Unique (%)100.0%

Sample

1st row1,010
2nd row1,462
3rd row215
4th row4,819
5th row152
ValueCountFrequency (%)
1,010 1
11.1%
1,462 1
11.1%
215 1
11.1%
4,819 1
11.1%
152 1
11.1%
14 1
11.1%
15 1
11.1%
405 1
11.1%
1,872 1
11.1%
2023-12-11T08:36:49.284847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 9
27.3%
, 4
12.1%
4 4
12.1%
2 4
12.1%
5 4
12.1%
0 3
 
9.1%
8 2
 
6.1%
6 1
 
3.0%
9 1
 
3.0%
7 1
 
3.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 29
87.9%
Other Punctuation 4
 
12.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 9
31.0%
4 4
13.8%
2 4
13.8%
5 4
13.8%
0 3
 
10.3%
8 2
 
6.9%
6 1
 
3.4%
9 1
 
3.4%
7 1
 
3.4%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 33
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 9
27.3%
, 4
12.1%
4 4
12.1%
2 4
12.1%
5 4
12.1%
0 3
 
9.1%
8 2
 
6.1%
6 1
 
3.0%
9 1
 
3.0%
7 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 33
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 9
27.3%
, 4
12.1%
4 4
12.1%
2 4
12.1%
5 4
12.1%
0 3
 
9.1%
8 2
 
6.1%
6 1
 
3.0%
9 1
 
3.0%
7 1
 
3.0%

온천공 보호구역면적(천㎡)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)78.3%
Missing32
Missing (%)58.2%
Infinite0
Infinite (%)0.0%
Mean14.130435
Minimum1
Maximum40
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-11T08:36:49.413505image/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
2023-12-11T08:36:49.524572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
8 2
 
3.6%
28 2
 
3.6%
1 2
 
3.6%
2 2
 
3.6%
5 2
 
3.6%
9 1
 
1.8%
6 1
 
1.8%
13 1
 
1.8%
4 1
 
1.8%
10 1
 
1.8%
Other values (8) 8
 
14.5%
(Missing) 32
58.2%
ValueCountFrequency (%)
1 2
3.6%
2 2
3.6%
4 1
1.8%
5 2
3.6%
6 1
1.8%
8 2
3.6%
9 1
1.8%
10 1
1.8%
13 1
1.8%
16 1
1.8%
ValueCountFrequency (%)
40 1
1.8%
31 1
1.8%
29 1
1.8%
28 2
3.6%
23 1
1.8%
21 1
1.8%
18 1
1.8%
17 1
1.8%
16 1
1.8%
13 1
1.8%

적정양수량(톤/일)
Text

MISSING 

Distinct34
Distinct (%)91.9%
Missing18
Missing (%)32.7%
Memory size572.0 B
2023-12-11T08:36:49.715603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length3.5405405
Min length3

Characters and Unicode

Total characters131
Distinct characters10
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)83.8%

Sample

1st row481
2nd row341
3rd row443
4th row340
5th row1,842
ValueCountFrequency (%)
310 2
 
5.4%
400 2
 
5.4%
450 2
 
5.4%
2,700 1
 
2.7%
210 1
 
2.7%
480 1
 
2.7%
264 1
 
2.7%
266 1
 
2.7%
303 1
 
2.7%
800 1
 
2.7%
Other values (24) 24
64.9%
2023-12-11T08:36:50.104035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29
22.1%
4 21
16.0%
1 19
14.5%
3 17
13.0%
2 16
12.2%
, 10
 
7.6%
8 6
 
4.6%
5 5
 
3.8%
6 5
 
3.8%
7 3
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
92.4%
Other Punctuation 10
 
7.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 29
24.0%
4 21
17.4%
1 19
15.7%
3 17
14.0%
2 16
13.2%
8 6
 
5.0%
5 5
 
4.1%
6 5
 
4.1%
7 3
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 131
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 29
22.1%
4 21
16.0%
1 19
14.5%
3 17
13.0%
2 16
12.2%
, 10
 
7.6%
8 6
 
4.6%
5 5
 
3.8%
6 5
 
3.8%
7 3
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
22.1%
4 21
16.0%
1 19
14.5%
3 17
13.0%
2 16
12.2%
, 10
 
7.6%
8 6
 
4.6%
5 5
 
3.8%
6 5
 
3.8%
7 3
 
2.3%

Unnamed: 11
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct14
Distinct (%)100.0%
Missing41
Missing (%)74.5%
Infinite0
Infinite (%)0.0%
Mean7.5
Minimum1
Maximum14
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size627.0 B
2023-12-11T08:36:50.221343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1.65
Q14.25
median7.5
Q310.75
95-th percentile13.35
Maximum14
Range13
Interquartile range (IQR)6.5

Descriptive statistics

Standard deviation4.1833001
Coefficient of variation (CV)0.55777335
Kurtosis-1.2
Mean7.5
Median Absolute Deviation (MAD)3.5
Skewness0
Sum105
Variance17.5
MonotonicityStrictly increasing
2023-12-11T08:36:50.328559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
1 1
 
1.8%
2 1
 
1.8%
3 1
 
1.8%
4 1
 
1.8%
5 1
 
1.8%
6 1
 
1.8%
7 1
 
1.8%
8 1
 
1.8%
9 1
 
1.8%
10 1
 
1.8%
Other values (4) 4
 
7.3%
(Missing) 41
74.5%
ValueCountFrequency (%)
1 1
1.8%
2 1
1.8%
3 1
1.8%
4 1
1.8%
5 1
1.8%
6 1
1.8%
7 1
1.8%
8 1
1.8%
9 1
1.8%
10 1
1.8%
ValueCountFrequency (%)
14 1
1.8%
13 1
1.8%
12 1
1.8%
11 1
1.8%
10 1
1.8%
9 1
1.8%
8 1
1.8%
7 1
1.8%
6 1
1.8%
5 1
1.8%

Interactions

2023-12-11T08:36:44.474130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.454343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.780669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.083928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.554048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.535324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.851645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.174532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.628897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.622518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.922331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.290381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.700462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:43.706796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.002467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:36:44.388803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:36:50.419414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번시군온천명소재지성분온도(℃)심도(M)지구지정 일자온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)Unnamed: 11
연번1.0000.9451.0001.0000.7860.3560.5341.0001.0000.5940.9230.906
시군0.9451.0001.0001.0000.8070.0810.5690.9571.0000.5960.854NaN
온천명1.0001.0001.0001.0001.0001.0000.9971.0001.0001.0001.0001.000
소재지1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
성분0.7860.8071.0001.0001.0000.4410.9790.9691.0000.9000.1520.371
온도(℃)0.3560.0811.0001.0000.4411.0000.9340.0001.0000.0000.9680.000
심도(M)0.5340.5690.9971.0000.9790.9341.0000.9751.0001.0000.9791.000
지구지정 일자1.0000.9571.0001.0000.9690.0000.9751.0001.0001.0000.9701.000
온천원 보호지구면적(천㎡)1.0001.0001.0001.0001.0001.0001.0001.0001.000NaN1.0001.000
온천공 보호구역면적(천㎡)0.5940.5961.0001.0000.9000.0001.0001.000NaN1.0000.9631.000
적정양수량(톤/일)0.9230.8541.0001.0000.1520.9680.9790.9701.0000.9631.0000.878
Unnamed: 110.906NaN1.0001.0000.3710.0001.0001.0001.0001.0000.8781.000
2023-12-11T08:36:50.547275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번온도(℃)온천공 보호구역면적(천㎡)Unnamed: 11시군
연번1.000-0.044-0.4921.0000.751
온도(℃)-0.0441.000-0.016-0.0420.000
온천공 보호구역면적(천㎡)-0.492-0.0161.000-0.6000.267
Unnamed: 111.000-0.042-0.6001.0001.000
시군0.7510.0000.2671.0001.000

Missing values

2023-12-11T08:36:44.807775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:36:44.976051image/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.
2023-12-11T08:36:45.092150image/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)Unnamed: 11
01거제해수온천거제시 수양로 570약알, Ca-Cl31.078501.08.27<NA>17481<NA>
12거제계룡산거제시 거제중앙로 1779-1약알, Ca-Cl29.066202.07.22<NA>2341<NA>
23거제일운거제시 일운면 거제대로 2190약알, Na-CI37.090008.12.11<NA>23443<NA>
34거제하청거제시 하청면 석포리 75-6알, Ca(Na)-HCO3(CI)27.8964<NA><NA><NA><NA><NA>
45거제구천거제시 동부면 구천리 144-1알, Na(Ca)-HCO3(SO4)27.085011.11.22<NA>21340<NA>
56거제장목거제시 장목면 산 126-6중성, Ca(Na)-CI30.6910<NA><NA><NA><NA><NA>
67거창가조거창군 온천길 108-29(가조면)중성 , Na-HCO327.050087.02.241,010<NA>1,842<NA>
78거창수월거창군 가조면 수월리 450-1약알 , Na-SO426.0640<NA><NA><NA><NA><NA>
89거창일부거창군 가조면 일부리 99-12알 , Na-HCO327.0570<NA><NA><NA><NA><NA>
910거창수월거창군 가조면 수월리 산83-71약알 , Na-SO426.0850<NA><NA><NA><NA><NA>
연번시군온천명소재지성분온도(℃)심도(M)지구지정 일자온천원 보호지구면적(천㎡)온천공 보호구역면적(천㎡)적정양수량(톤/일)Unnamed: 11
4546창원용원창원시 용원동 산18염화물, Ca-Cl26.6870<NA><NA><NA>30111
4647창원진동리창원시 진동면 진동리 11번지외 4필지약알, Na-HCO334.01,051<NA><NA><NA>32412
4748창원진해용원<NA>염화, Ca-Cl32.91,000<NA><NA><NA><NA>13
4849창원가음정<NA>약알, Na-HCO329.1710<NA><NA><NA><NA>14
4950통영산양통영시 산양읍 신전리 926유황 , Na-HCO332.0637<NA><NA><NA><NA><NA>
5051하동화개온천리조트하동군 화개면 화개로 265약알 , Na-CI27.064000.11.07<NA>13350<NA>
5152하동한려하동군 금남면 경춘로 243-31알 , Na-CI26.086305.10.18405<NA>1,112<NA>
5253합천가야합천군 가야면 대전리 501-3알 , Na-HCO327.063295.05.111,872<NA>3,600<NA>
5354합천쌍책합천군 쌍책면 하신리 106-1약알 , Na-HCO325.0700<NA><NA><NA>800<NA>
5455합천청덕합천군 청덕면 대부리 1435탄산염 , Ca-HCO325.092005.08.09<NA>6374<NA>