Overview

Dataset statistics

Number of variables14
Number of observations120
Missing cells116
Missing cells (%)6.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory14.0 KiB
Average record size in memory119.1 B

Variable types

Numeric6
Categorical3
Text5

Dataset

Description경상남도 장례시설 현황입니다.
Author경상남도
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=3068678

Alerts

빈소수(개소) is highly overall correlated with 안치능력(구)High correlation
안치능력(구) is highly overall correlated with 빈소수(개소)High correlation
구분(공설_사설) is highly imbalanced (71.4%)Imbalance
참고 has 116 (96.7%) missing valuesMissing
연번 has unique valuesUnique
명칭 has unique valuesUnique
위치 has unique valuesUnique
전화번호 has unique valuesUnique
빈소 임대료(천원) has 7 (5.8%) zerosZeros
냉장시설 안치료(천원) has 2 (1.7%) zerosZeros

Reproduction

Analysis started2023-12-10 23:40:01.768974
Analysis finished2023-12-10 23:40:06.750550
Duration4.98 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60.5
Minimum1
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:40:06.821878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.95
Q130.75
median60.5
Q390.25
95-th percentile114.05
Maximum120
Range119
Interquartile range (IQR)59.5

Descriptive statistics

Standard deviation34.785054
Coefficient of variation (CV)0.57495957
Kurtosis-1.2
Mean60.5
Median Absolute Deviation (MAD)30
Skewness0
Sum7260
Variance1210
MonotonicityStrictly increasing
2023-12-11T08:40:06.992965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
0.8%
62 1
 
0.8%
90 1
 
0.8%
89 1
 
0.8%
88 1
 
0.8%
87 1
 
0.8%
86 1
 
0.8%
85 1
 
0.8%
84 1
 
0.8%
83 1
 
0.8%
Other values (110) 110
91.7%
ValueCountFrequency (%)
1 1
0.8%
2 1
0.8%
3 1
0.8%
4 1
0.8%
5 1
0.8%
6 1
0.8%
7 1
0.8%
8 1
0.8%
9 1
0.8%
10 1
0.8%
ValueCountFrequency (%)
120 1
0.8%
119 1
0.8%
118 1
0.8%
117 1
0.8%
116 1
0.8%
115 1
0.8%
114 1
0.8%
113 1
0.8%
112 1
0.8%
111 1
0.8%

구분(공설_사설)
Categorical

IMBALANCE 

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
사설
114 
공설
 
6

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row공설
2nd row공설
3rd row사설
4th row사설
5th row사설

Common Values

ValueCountFrequency (%)
사설 114
95.0%
공설 6
 
5.0%

Length

2023-12-11T08:40:07.124158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:40:07.213856image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사설 114
95.0%
공설 6
 
5.0%

명칭
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T08:40:07.403011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length14
Mean length8.425
Min length3

Characters and Unicode

Total characters1011
Distinct characters152
Distinct categories6 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st row창원시립상복공원 장례식장
2nd row마산의료원장례식장
3rd row창원파티마병원장례식장
4th row동창원장례식장
5th row행복한병원장례식장
ValueCountFrequency (%)
장례식장 25
 
17.0%
한솔병원 2
 
1.4%
양산장례식장 1
 
0.7%
창녕군장례식장 1
 
0.7%
소명요양병원장례식장 1
 
0.7%
새롬병원장례식장 1
 
0.7%
함안장례식장 1
 
0.7%
자굴산장례식장 1
 
0.7%
의령사랑병원장례식장 1
 
0.7%
부성장례식장 1
 
0.7%
Other values (112) 112
76.2%
2023-12-11T08:40:07.776343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 202
20.0%
례 100
 
9.9%
식 100
 
9.9%
원 88
 
8.7%
병 72
 
7.1%
27
 
2.7%
양 22
 
2.2%
산 15
 
1.5%
요 13
 
1.3%
문 12
 
1.2%
Other values (142) 360
35.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 977
96.6%
Space Separator 27
 
2.7%
Uppercase Letter 3
 
0.3%
Decimal Number 2
 
0.2%
Other Symbol 1
 
0.1%
Lowercase Letter 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 202
20.7%
례 100
 
10.2%
식 100
 
10.2%
원 88
 
9.0%
병 72
 
7.4%
양 22
 
2.3%
산 15
 
1.5%
요 13
 
1.3%
문 12
 
1.2%
전 12
 
1.2%
Other values (134) 341
34.9%
Uppercase Letter
ValueCountFrequency (%)
C 1
33.3%
H 1
33.3%
M 1
33.3%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 978
96.7%
Common 29
 
2.9%
Latin 4
 
0.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 202
20.7%
례 100
 
10.2%
식 100
 
10.2%
원 88
 
9.0%
병 72
 
7.4%
양 22
 
2.2%
산 15
 
1.5%
요 13
 
1.3%
문 12
 
1.2%
전 12
 
1.2%
Other values (135) 342
35.0%
Latin
ValueCountFrequency (%)
C 1
25.0%
H 1
25.0%
e 1
25.0%
M 1
25.0%
Common
ValueCountFrequency (%)
27
93.1%
1 1
 
3.4%
2 1
 
3.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 977
96.6%
ASCII 33
 
3.3%
None 1
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 202
20.7%
례 100
 
10.2%
식 100
 
10.2%
원 88
 
9.0%
병 72
 
7.4%
양 22
 
2.3%
산 15
 
1.5%
요 13
 
1.3%
문 12
 
1.2%
전 12
 
1.2%
Other values (134) 341
34.9%
ASCII
ValueCountFrequency (%)
27
81.8%
C 1
 
3.0%
1 1
 
3.0%
2 1
 
3.0%
H 1
 
3.0%
e 1
 
3.0%
M 1
 
3.0%
None
ValueCountFrequency (%)
㈜ 1
100.0%

위치
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T08:40:08.146479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length23.5
Mean length15.9
Min length10

Characters and Unicode

Total characters1908
Distinct characters138
Distinct categories7 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique120 ?
Unique (%)100.0%

Sample

1st row창원시 성산구 공단로 474번길 160
2nd row창원시 마산합포구 3.15대로 231
3rd row창원시 의창구 창이대로 45
4th row창원시 의창구 동읍 의창대로 870-5(지하 101호)
5th row창원시 의창구 북면 동전리 292-1
ValueCountFrequency (%)
창원시 24
 
5.3%
김해시 16
 
3.6%
사천시 8
 
1.8%
밀양시 7
 
1.6%
마산회원구 7
 
1.6%
진주시 7
 
1.6%
양산시 7
 
1.6%
창녕군 7
 
1.6%
하동군 6
 
1.3%
통영시 6
 
1.3%
Other values (282) 354
78.8%
2023-12-11T08:40:08.606813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
329
 
17.2%
1 93
 
4.9%
로 85
 
4.5%
시 80
 
4.2%
창 49
 
2.6%
2 47
 
2.5%
3 45
 
2.4%
4 44
 
2.3%
5 43
 
2.3%
원 41
 
2.1%
Other values (128) 1052
55.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 1103
57.8%
Decimal Number 412
 
21.6%
Space Separator 329
 
17.2%
Dash Punctuation 36
 
1.9%
Close Punctuation 12
 
0.6%
Open Punctuation 12
 
0.6%
Other Punctuation 4
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 85
 
7.7%
시 80
 
7.3%
창 49
 
4.4%
원 41
 
3.7%
군 40
 
3.6%
해 39
 
3.5%
산 38
 
3.4%
동 37
 
3.4%
대 36
 
3.3%
읍 32
 
2.9%
Other values (112) 626
56.8%
Decimal Number
ValueCountFrequency (%)
1 93
22.6%
2 47
11.4%
3 45
10.9%
4 44
10.7%
5 43
10.4%
7 37
 
9.0%
6 33
 
8.0%
9 26
 
6.3%
8 22
 
5.3%
0 22
 
5.3%
Other Punctuation
ValueCountFrequency (%)
. 3
75.0%
, 1
 
25.0%
Space Separator
ValueCountFrequency (%)
329
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 36
100.0%
Close Punctuation
ValueCountFrequency (%)
) 12
100.0%
Open Punctuation
ValueCountFrequency (%)
( 12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 1103
57.8%
Common 805
42.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 85
 
7.7%
시 80
 
7.3%
창 49
 
4.4%
원 41
 
3.7%
군 40
 
3.6%
해 39
 
3.5%
산 38
 
3.4%
동 37
 
3.4%
대 36
 
3.3%
읍 32
 
2.9%
Other values (112) 626
56.8%
Common
ValueCountFrequency (%)
329
40.9%
1 93
 
11.6%
2 47
 
5.8%
3 45
 
5.6%
4 44
 
5.5%
5 43
 
5.3%
7 37
 
4.6%
- 36
 
4.5%
6 33
 
4.1%
9 26
 
3.2%
Other values (6) 72
 
8.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 1103
57.8%
ASCII 805
42.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
329
40.9%
1 93
 
11.6%
2 47
 
5.8%
3 45
 
5.6%
4 44
 
5.5%
5 43
 
5.3%
7 37
 
4.6%
- 36
 
4.5%
6 33
 
4.1%
9 26
 
3.2%
Other values (6) 72
 
8.9%
Hangul
ValueCountFrequency (%)
로 85
 
7.7%
시 80
 
7.3%
창 49
 
4.4%
원 41
 
3.7%
군 40
 
3.6%
해 39
 
3.5%
산 38
 
3.4%
동 37
 
3.4%
대 36
 
3.3%
읍 32
 
2.9%
Other values (112) 626
56.8%

영업개시년월일
Text

Distinct118
Distinct (%)98.3%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T08:40:08.839626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters1200
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

Unique116 ?
Unique (%)96.7%

Sample

1st row2012-06-15
2nd row1997-05-22
3rd row2012-01-21
4th row2015-12-30
5th row2008-07-01
ValueCountFrequency (%)
1997-12-23 2
 
1.7%
2008-07-01 2
 
1.7%
2006-06-01 1
 
0.8%
1998-06-29 1
 
0.8%
2008-11-24 1
 
0.8%
2012-04-30 1
 
0.8%
2011-04-12 1
 
0.8%
2009-05-29 1
 
0.8%
2015-12-28 1
 
0.8%
2011-01-15 1
 
0.8%
Other values (108) 108
90.0%
2023-12-11T08:40:09.165840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 342
28.5%
- 240
20.0%
2 192
16.0%
1 178
14.8%
9 49
 
4.1%
5 40
 
3.3%
7 38
 
3.2%
6 38
 
3.2%
4 31
 
2.6%
3 26
 
2.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 960
80.0%
Dash Punctuation 240
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 342
35.6%
2 192
20.0%
1 178
18.5%
9 49
 
5.1%
5 40
 
4.2%
7 38
 
4.0%
6 38
 
4.0%
4 31
 
3.2%
3 26
 
2.7%
8 26
 
2.7%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1200
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 342
28.5%
- 240
20.0%
2 192
16.0%
1 178
14.8%
9 49
 
4.1%
5 40
 
3.3%
7 38
 
3.2%
6 38
 
3.2%
4 31
 
2.6%
3 26
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1200
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 342
28.5%
- 240
20.0%
2 192
16.0%
1 178
14.8%
9 49
 
4.1%
5 40
 
3.3%
7 38
 
3.2%
6 38
 
3.2%
4 31
 
2.6%
3 26
 
2.2%

종류(전문_병원)
Categorical

Distinct2
Distinct (%)1.7%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
병원
74 
전문
46 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전문
2nd row병원
3rd row병원
4th row병원
5th row병원

Common Values

ValueCountFrequency (%)
병원 74
61.7%
전문 46
38.3%

Length

2023-12-11T08:40:09.277774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:40:09.352920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
병원 74
61.7%
전문 46
38.3%

운영형태(직영_임대)
Categorical

Distinct3
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
직영
61 
임대
58 
직업
 
1

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique1 ?
Unique (%)0.8%

Sample

1st row직영
2nd row직영
3rd row직영
4th row직영
5th row임대

Common Values

ValueCountFrequency (%)
직영 61
50.8%
임대 58
48.3%
직업 1
 
0.8%

Length

2023-12-11T08:40:09.440896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:40:09.522528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
직영 61
50.8%
임대 58
48.3%
직업 1
 
0.8%

전화번호
Text

UNIQUE 

Distinct120
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.1 KiB
2023-12-11T08:40:09.723352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters1440
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

Unique120 ?
Unique (%)100.0%

Sample

1st row055-712-0900
2nd row055-249-1000
3rd row055-270-1900
4th row055-244-4000
5th row055-255-0220
ValueCountFrequency (%)
055-712-0900 1
 
0.8%
055-249-1000 1
 
0.8%
055-536-4858 1
 
0.8%
055-533-8510 1
 
0.8%
055-589-5700 1
 
0.8%
055-586-6006 1
 
0.8%
055-584-5515 1
 
0.8%
055-573-2233 1
 
0.8%
055-572-3900 1
 
0.8%
055-573-4500 1
 
0.8%
Other values (110) 110
91.7%
2023-12-11T08:40:10.070837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 328
22.8%
0 243
16.9%
- 240
16.7%
4 184
12.8%
3 103
 
7.2%
2 75
 
5.2%
1 65
 
4.5%
8 61
 
4.2%
6 49
 
3.4%
9 47
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1200
83.3%
Dash Punctuation 240
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 328
27.3%
0 243
20.2%
4 184
15.3%
3 103
 
8.6%
2 75
 
6.2%
1 65
 
5.4%
8 61
 
5.1%
6 49
 
4.1%
9 47
 
3.9%
7 45
 
3.8%
Dash Punctuation
ValueCountFrequency (%)
- 240
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1440
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
5 328
22.8%
0 243
16.9%
- 240
16.7%
4 184
12.8%
3 103
 
7.2%
2 75
 
5.2%
1 65
 
4.5%
8 61
 
4.2%
6 49
 
3.4%
9 47
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1440
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 328
22.8%
0 243
16.9%
- 240
16.7%
4 184
12.8%
3 103
 
7.2%
2 75
 
5.2%
1 65
 
4.5%
8 61
 
4.2%
6 49
 
3.4%
9 47
 
3.3%

건물연면적(제곱미터)
Real number (ℝ)

Distinct117
Distinct (%)97.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3036.7833
Minimum38
Maximum139296
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:40:10.251297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum38
5-th percentile201.15
Q1639.75
median1084
Q31921.5
95-th percentile7451.5
Maximum139296
Range139258
Interquartile range (IQR)1281.75

Descriptive statistics

Standard deviation12797.991
Coefficient of variation (CV)4.2143248
Kurtosis110.51674
Mean3036.7833
Median Absolute Deviation (MAD)527
Skewness10.332617
Sum364414
Variance1.6378858 × 108
MonotonicityNot monotonic
2023-12-11T08:40:10.374493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
330 2
 
1.7%
495 2
 
1.7%
710 2
 
1.7%
5486 1
 
0.8%
1505 1
 
0.8%
597 1
 
0.8%
202 1
 
0.8%
1092 1
 
0.8%
861 1
 
0.8%
1320 1
 
0.8%
Other values (107) 107
89.2%
ValueCountFrequency (%)
38 1
0.8%
67 1
0.8%
105 1
0.8%
142 1
0.8%
151 1
0.8%
185 1
0.8%
202 1
0.8%
252 1
0.8%
266 1
0.8%
272 1
0.8%
ValueCountFrequency (%)
139296 1
0.8%
19667 1
0.8%
10600 1
0.8%
8215 1
0.8%
7936 1
0.8%
7499 1
0.8%
7449 1
0.8%
7130 1
0.8%
6340 1
0.8%
5486 1
0.8%

빈소수(개소)
Real number (ℝ)

HIGH CORRELATION 

Distinct10
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9916667
Minimum1
Maximum10
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:40:10.489273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q35
95-th percentile7.05
Maximum10
Range9
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8538891
Coefficient of variation (CV)0.46443986
Kurtosis1.2539836
Mean3.9916667
Median Absolute Deviation (MAD)1
Skewness1.1229053
Sum479
Variance3.4369048
MonotonicityNot monotonic
2023-12-11T08:40:10.587481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
3 39
32.5%
4 27
22.5%
2 16
13.3%
5 12
 
10.0%
7 8
 
6.7%
6 8
 
6.7%
1 4
 
3.3%
10 2
 
1.7%
9 2
 
1.7%
8 2
 
1.7%
ValueCountFrequency (%)
1 4
 
3.3%
2 16
13.3%
3 39
32.5%
4 27
22.5%
5 12
 
10.0%
6 8
 
6.7%
7 8
 
6.7%
8 2
 
1.7%
9 2
 
1.7%
10 2
 
1.7%
ValueCountFrequency (%)
10 2
 
1.7%
9 2
 
1.7%
8 2
 
1.7%
7 8
 
6.7%
6 8
 
6.7%
5 12
 
10.0%
4 27
22.5%
3 39
32.5%
2 16
13.3%
1 4
 
3.3%

안치능력(구)
Real number (ℝ)

HIGH CORRELATION 

Distinct14
Distinct (%)11.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3583333
Minimum2
Maximum20
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:40:10.690467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile2.95
Q14
median6
Q38
95-th percentile12
Maximum20
Range18
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.0918593
Coefficient of variation (CV)0.48626883
Kurtosis4.4806877
Mean6.3583333
Median Absolute Deviation (MAD)2
Skewness1.7754222
Sum763
Variance9.5595938
MonotonicityNot monotonic
2023-12-11T08:40:10.811646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
6 44
36.7%
4 33
27.5%
8 19
15.8%
2 6
 
5.0%
12 5
 
4.2%
10 3
 
2.5%
14 2
 
1.7%
3 2
 
1.7%
16 1
 
0.8%
9 1
 
0.8%
Other values (4) 4
 
3.3%
ValueCountFrequency (%)
2 6
 
5.0%
3 2
 
1.7%
4 33
27.5%
5 1
 
0.8%
6 44
36.7%
8 19
15.8%
9 1
 
0.8%
10 3
 
2.5%
11 1
 
0.8%
12 5
 
4.2%
ValueCountFrequency (%)
20 1
 
0.8%
18 1
 
0.8%
16 1
 
0.8%
14 2
 
1.7%
12 5
 
4.2%
11 1
 
0.8%
10 3
 
2.5%
9 1
 
0.8%
8 19
15.8%
6 44
36.7%

빈소 임대료(천원)
Real number (ℝ)

ZEROS 

Distinct49
Distinct (%)40.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean372.71667
Minimum0
Maximum1560
Zeros7
Zeros (%)5.8%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:40:10.961037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1235
median350
Q3500
95-th percentile860.5
Maximum1560
Range1560
Interquartile range (IQR)265

Descriptive statistics

Standard deviation267.7513
Coefficient of variation (CV)0.71837759
Kurtosis3.1108397
Mean372.71667
Median Absolute Deviation (MAD)150
Skewness1.3389363
Sum44726
Variance71690.759
MonotonicityNot monotonic
2023-12-11T08:40:11.111500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
400 10
 
8.3%
350 9
 
7.5%
100 8
 
6.7%
0 7
 
5.8%
250 7
 
5.8%
300 7
 
5.8%
500 7
 
5.8%
600 6
 
5.0%
240 5
 
4.2%
480 3
 
2.5%
Other values (39) 51
42.5%
ValueCountFrequency (%)
0 7
5.8%
20 1
 
0.8%
27 1
 
0.8%
33 1
 
0.8%
40 1
 
0.8%
50 2
 
1.7%
100 8
6.7%
120 2
 
1.7%
150 1
 
0.8%
180 2
 
1.7%
ValueCountFrequency (%)
1560 1
0.8%
1200 1
0.8%
1100 1
0.8%
1000 1
0.8%
900 1
0.8%
870 1
0.8%
860 1
0.8%
850 2
1.7%
800 2
1.7%
750 1
0.8%

냉장시설 안치료(천원)
Real number (ℝ)

ZEROS 

Distinct18
Distinct (%)15.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean76.766667
Minimum0
Maximum150
Zeros2
Zeros (%)1.7%
Negative0
Negative (%)0.0%
Memory size1.2 KiB
2023-12-11T08:40:11.246131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile47.6
Q160
median80
Q3100
95-th percentile120
Maximum150
Range150
Interquartile range (IQR)40

Descriptive statistics

Standard deviation26.8584
Coefficient of variation (CV)0.34987061
Kurtosis0.94169252
Mean76.766667
Median Absolute Deviation (MAD)20
Skewness0.032232813
Sum9212
Variance721.37367
MonotonicityNot monotonic
2023-12-11T08:40:11.373016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
100 31
25.8%
60 24
20.0%
80 18
15.0%
50 13
10.8%
70 10
 
8.3%
120 3
 
2.5%
48 3
 
2.5%
96 3
 
2.5%
0 2
 
1.7%
90 2
 
1.7%
Other values (8) 11
 
9.2%
ValueCountFrequency (%)
0 2
 
1.7%
10 1
 
0.8%
15 1
 
0.8%
40 2
 
1.7%
48 3
 
2.5%
50 13
10.8%
60 24
20.0%
61 1
 
0.8%
70 10
8.3%
72 1
 
0.8%
ValueCountFrequency (%)
150 2
 
1.7%
144 2
 
1.7%
120 3
 
2.5%
100 31
25.8%
96 3
 
2.5%
90 2
 
1.7%
84 1
 
0.8%
80 18
15.0%
72 1
 
0.8%
70 10
 
8.3%

참고
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing116
Missing (%)96.7%
Memory size1.1 KiB
2023-12-11T08:40:11.524552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length36
Median length27.5
Mean length18.25
Min length9

Characters and Unicode

Total characters73
Distinct characters19
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)100.0%

Sample

1st row빈소임대료: 대-850, 소-600
2nd row빈소임대료: 대 1,000, 중 800, 소 600, 최소 200
3rd row빈소임대로: 무료
4th row빈소임대료: 무료
ValueCountFrequency (%)
빈소임대료 3
18.8%
무료 2
12.5%
대-850 1
 
6.2%
소-600 1
 
6.2%
대 1
 
6.2%
1,000 1
 
6.2%
중 1
 
6.2%
800 1
 
6.2%
소 1
 
6.2%
600 1
 
6.2%
Other values (3) 3
18.8%
2023-12-11T08:40:11.789662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12
16.4%
12
16.4%
소 7
9.6%
대 6
8.2%
료 5
6.8%
, 5
6.8%
빈 4
 
5.5%
임 4
 
5.5%
: 4
 
5.5%
6 2
 
2.7%
Other values (9) 12
16.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
42.5%
Decimal Number 19
26.0%
Space Separator 12
 
16.4%
Other Punctuation 9
 
12.3%
Dash Punctuation 2
 
2.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 7
22.6%
대 6
19.4%
료 5
16.1%
빈 4
12.9%
임 4
12.9%
무 2
 
6.5%
중 1
 
3.2%
최 1
 
3.2%
로 1
 
3.2%
Decimal Number
ValueCountFrequency (%)
0 12
63.2%
6 2
 
10.5%
8 2
 
10.5%
1 1
 
5.3%
2 1
 
5.3%
5 1
 
5.3%
Other Punctuation
ValueCountFrequency (%)
, 5
55.6%
: 4
44.4%
Space Separator
ValueCountFrequency (%)
12
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 42
57.5%
Hangul 31
42.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
28.6%
12
28.6%
, 5
11.9%
: 4
 
9.5%
6 2
 
4.8%
8 2
 
4.8%
- 2
 
4.8%
1 1
 
2.4%
2 1
 
2.4%
5 1
 
2.4%
Hangul
ValueCountFrequency (%)
소 7
22.6%
대 6
19.4%
료 5
16.1%
빈 4
12.9%
임 4
12.9%
무 2
 
6.5%
중 1
 
3.2%
최 1
 
3.2%
로 1
 
3.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 42
57.5%
Hangul 31
42.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12
28.6%
12
28.6%
, 5
11.9%
: 4
 
9.5%
6 2
 
4.8%
8 2
 
4.8%
- 2
 
4.8%
1 1
 
2.4%
2 1
 
2.4%
5 1
 
2.4%
Hangul
ValueCountFrequency (%)
소 7
22.6%
대 6
19.4%
료 5
16.1%
빈 4
12.9%
임 4
12.9%
무 2
 
6.5%
중 1
 
3.2%
최 1
 
3.2%
로 1
 
3.2%

Interactions

2023-12-11T08:40:05.445274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.285709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.882669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.573803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.155982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.846497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:05.857365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.372617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.974653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.675460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.279952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.949760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:05.978664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.499339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.090217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.785644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.406043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:05.038570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:06.083261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.580273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.199455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.862214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.510937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:05.126651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:06.196239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.686061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.313517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.972857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.611118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:05.232788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:06.317231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:02.786309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:03.427455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.062036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:04.727728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:40:05.327365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:40:11.890299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번구분(공설_사설)종류(전문_병원)운영형태(직영_임대)건물연면적(제곱미터)빈소수(개소)안치능력(구)빈소 임대료(천원)냉장시설 안치료(천원)참고
연번1.0000.3760.4080.3730.0420.0000.2280.5030.341NaN
구분(공설_사설)0.3761.0000.2300.0000.0000.4610.4570.1450.226NaN
종류(전문_병원)0.4080.2301.0000.2800.0270.0000.0000.1480.1991.000
운영형태(직영_임대)0.3730.0000.2801.0000.0000.0000.0000.0000.000NaN
건물연면적(제곱미터)0.0420.0000.0270.0001.0000.0000.4790.0000.0001.000
빈소수(개소)0.0000.4610.0000.0000.0001.0000.8880.5280.2281.000
안치능력(구)0.2280.4570.0000.0000.4790.8881.0000.4160.2911.000
빈소 임대료(천원)0.5030.1450.1480.0000.0000.5280.4161.0000.0001.000
냉장시설 안치료(천원)0.3410.2260.1990.0000.0000.2280.2910.0001.0001.000
참고NaNNaN1.000NaN1.0001.0001.0001.0001.0001.000
2023-12-11T08:40:12.020207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
종류(전문_병원)운영형태(직영_임대)구분(공설_사설)
종류(전문_병원)1.0000.4510.147
운영형태(직영_임대)0.4511.0000.000
구분(공설_사설)0.1470.0001.000
2023-12-11T08:40:12.111832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번건물연면적(제곱미터)빈소수(개소)안치능력(구)빈소 임대료(천원)냉장시설 안치료(천원)구분(공설_사설)종류(전문_병원)운영형태(직영_임대)
연번1.000-0.192-0.303-0.327-0.209-0.0930.2780.3010.233
건물연면적(제곱미터)-0.1921.0000.4820.4390.132-0.0310.0000.0430.000
빈소수(개소)-0.3030.4821.0000.7320.2160.0220.3410.0000.000
안치능력(구)-0.3270.4390.7321.0000.2310.0500.3390.0000.000
빈소 임대료(천원)-0.2090.1320.2160.2311.000-0.0060.1390.1410.000
냉장시설 안치료(천원)-0.093-0.0310.0220.050-0.0061.0000.0880.1510.128
구분(공설_사설)0.2780.0000.3410.3390.1390.0881.0000.1470.000
종류(전문_병원)0.3010.0430.0000.0000.1410.1510.1471.0000.451
운영형태(직영_임대)0.2330.0000.0000.0000.0000.1280.0000.4511.000

Missing values

2023-12-11T08:40:06.464169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:40:06.676944image/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

연번구분(공설_사설)명칭위치영업개시년월일종류(전문_병원)운영형태(직영_임대)전화번호건물연면적(제곱미터)빈소수(개소)안치능력(구)빈소 임대료(천원)냉장시설 안치료(천원)참고
01공설창원시립상복공원 장례식장창원시 성산구 공단로 474번길 1602012-06-15전문직영055-712-09005486101600<NA>
12공설마산의료원장례식장창원시 마산합포구 3.15대로 2311997-05-22병원직영055-249-100015409819248<NA>
23사설창원파티마병원장례식장창원시 의창구 창이대로 452012-01-21병원직영055-270-1900115271250060<NA>
34사설동창원장례식장창원시 의창구 동읍 의창대로 870-5(지하 101호)2015-12-30병원직영055-244-40008294487060<NA>
45사설행복한병원장례식장창원시 의창구 북면 동전리 292-12008-07-01병원임대055-255-02208063960060<NA>
56사설근로복지공단 창원병원장례식장창원시 성산구 창원대로 721(중앙동, 창원병원)2012-09-16병원임대055-287-11009844886048<NA>
67사설창원경상대학교병원장례식장창원시 성산구 삼정자로 92016-05-18병원임대055-214-19003907814156084<NA>
78사설신마산전문장례식장창원시 마산합포구 문화동 15길102015-07-27전문임대055-245-444410765625080<NA>
89사설태봉병원장례식장창원시 마산합포구 진동면 동전고개로21997-06-20전문임대055-271-79906481225060<NA>
910사설MH연세병원장례식장창원시 마산합포구 3.15대로 762010-06-17병원임대055-223-1000531371030060<NA>
연번구분(공설_사설)명칭위치영업개시년월일종류(전문_병원)운영형태(직영_임대)전화번호건물연면적(제곱미터)빈소수(개소)안치능력(구)빈소 임대료(천원)냉장시설 안치료(천원)참고
110111사설산청경호장례식장산청군 산청읍 중앙로 93-82016-09-09전문직영055-973-12347722385080<NA>
111112사설산청장례식장산청군 산청읍 웅석봉로 133번길 146-12016-12-06전문직영055-974-450099946100080빈소임대료: 대 1,000, 중 800, 소 600, 최소 200
112113사설함양장례식장함양군 함양읍 고운로 2842007-12-17전문직영055-964-200012964660060<NA>
113114사설함양성심병원장례식장함양군 함양읍 용평1길 52008-01-01병원임대055-962-99992522435060<NA>
114115사설서경병원장례식장거창군 거창읍 송정리 791997-04-23병원직영055-940-524511473525050<NA>
115116사설거창적십자병원장례식장거창군 거창읍 상림리 74-12004-03-13병원직영055-949-338411803430050<NA>
116117사설거창장례식장거창군 거창읍 장팔리 17-22005-11-10전문직영055-944-44445605650070<NA>
117118사설합천장례식장합천군 대양면 동부로 64-22007-02-16전문직영055-932-700079363860080<NA>
118119사설합천추모공원 장례식장합천군 합천읍 합천호수로 16132007-08-22전문직영055-933-4444122636060빈소임대로: 무료
119120사설합천고려병원 장례식장합천군 대양면 대야로 737-202007-09-17병원직영055-931-4464139296440100빈소임대료: 무료