Overview

Dataset statistics

Number of variables25
Number of observations57
Missing cells129
Missing cells (%)9.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.7 KiB
Average record size in memory210.3 B

Variable types

Numeric7
Categorical7
Text7
Unsupported1
DateTime3

Alerts

city has constant value ""Constant
category has constant value ""Constant
last_load_dttm has constant value ""Constant
apr_at is highly imbalanced (63.3%)Imbalance
addr_jibun has 57 (100.0%) missing valuesMissing
open_date has 5 (8.8%) missing valuesMissing
site has 9 (15.8%) missing valuesMissing
area has 5 (8.8%) missing valuesMissing
add_info has 53 (93.0%) missing valuesMissing
skey has unique valuesUnique
cmp_nm has unique valuesUnique
addr_road has unique valuesUnique
tel has unique valuesUnique
lat has unique valuesUnique
lng has unique valuesUnique
addr_jibun is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-04-18 00:30:24.254643
Analysis finished2024-04-18 00:30:24.628895
Duration0.37 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

skey
Real number (ℝ)

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean253.17544
Minimum207
Maximum287
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:24.689384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum207
5-th percentile209.8
Q1238
median254
Q3273
95-th percentile284.2
Maximum287
Range80
Interquartile range (IQR)35

Descriptive statistics

Standard deviation22.41422
Coefficient of variation (CV)0.088532362
Kurtosis-0.84122759
Mean253.17544
Median Absolute Deviation (MAD)18
Skewness-0.30297072
Sum14431
Variance502.39724
MonotonicityNot monotonic
2024-04-18T09:30:24.821626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
210 1
 
1.8%
256 1
 
1.8%
229 1
 
1.8%
230 1
 
1.8%
231 1
 
1.8%
232 1
 
1.8%
237 1
 
1.8%
241 1
 
1.8%
242 1
 
1.8%
243 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
207 1
1.8%
208 1
1.8%
209 1
1.8%
210 1
1.8%
224 1
1.8%
225 1
1.8%
226 1
1.8%
227 1
1.8%
228 1
1.8%
229 1
1.8%
ValueCountFrequency (%)
287 1
1.8%
286 1
1.8%
285 1
1.8%
284 1
1.8%
283 1
1.8%
282 1
1.8%
281 1
1.8%
280 1
1.8%
279 1
1.8%
278 1
1.8%

instt_code
Real number (ℝ)

Distinct16
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3325263.2
Minimum3250000
Maximum3400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:24.946307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3250000
5-th percentile3260000
Q13290000
median3330000
Q33350000
95-th percentile3390000
Maximum3400000
Range150000
Interquartile range (IQR)60000

Descriptive statistics

Standard deviation41880.155
Coefficient of variation (CV)0.012594538
Kurtosis-1.0135192
Mean3325263.2
Median Absolute Deviation (MAD)30000
Skewness0.039512207
Sum1.8954 × 108
Variance1.7539474 × 109
MonotonicityNot monotonic
2024-04-18T09:30:25.061359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3340000 10
17.5%
3350000 6
10.5%
3290000 6
10.5%
3300000 5
8.8%
3260000 4
 
7.0%
3390000 4
 
7.0%
3380000 4
 
7.0%
3280000 3
 
5.3%
3270000 3
 
5.3%
3330000 3
 
5.3%
Other values (6) 9
15.8%
ValueCountFrequency (%)
3250000 1
 
1.8%
3260000 4
 
7.0%
3270000 3
 
5.3%
3280000 3
 
5.3%
3290000 6
10.5%
3300000 5
8.8%
3310000 2
 
3.5%
3320000 2
 
3.5%
3330000 3
 
5.3%
3340000 10
17.5%
ValueCountFrequency (%)
3400000 2
 
3.5%
3390000 4
 
7.0%
3380000 4
 
7.0%
3370000 1
 
1.8%
3360000 1
 
1.8%
3350000 6
10.5%
3340000 10
17.5%
3330000 3
 
5.3%
3320000 2
 
3.5%
3310000 2
 
3.5%

city
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
부산광역시
57 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 57
100.0%

Length

2024-04-18T09:30:25.186341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:25.269390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 57
100.0%

cmp_nm
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:25.449728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length9.122807
Min length6

Characters and Unicode

Total characters520
Distinct characters102
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

Unique57 ?
Unique (%)100.0%

Sample

1st row인창병원 장례식장
2nd row동아대학교병원 장례식장
3rd row삼육부산병원 장례식장
4th row부산대학교병원 장례식장
5th row성심 장례식장
ValueCountFrequency (%)
장례식장 16
 
21.9%
인창병원 1
 
1.4%
좋은삼선병원장례식장 1
 
1.4%
강산병원장례식장 1
 
1.4%
동래한서병원장례식장 1
 
1.4%
침례병원장례식장 1
 
1.4%
행림병원장례식장 1
 
1.4%
금사장례식장 1
 
1.4%
고신대학교병원 1
 
1.4%
한중프라임장례식장 1
 
1.4%
Other values (48) 48
65.8%
2024-04-18T09:30:25.789245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 114
21.9%
례 58
 
11.2%
식 57
 
11.0%
원 44
 
8.5%
병 38
 
7.3%
18
 
3.5%
산 12
 
2.3%
동 8
 
1.5%
부 8
 
1.5%
대 7
 
1.3%
Other values (92) 156
30.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 496
95.4%
Space Separator 18
 
3.5%
Close Punctuation 2
 
0.4%
Open Punctuation 2
 
0.4%
Other Symbol 1
 
0.2%
Uppercase Letter 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 114
23.0%
례 58
 
11.7%
식 57
 
11.5%
원 44
 
8.9%
병 38
 
7.7%
산 12
 
2.4%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
민 4
 
0.8%
Other values (87) 146
29.4%
Space Separator
ValueCountFrequency (%)
18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 497
95.6%
Common 22
 
4.2%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 114
22.9%
례 58
 
11.7%
식 57
 
11.5%
원 44
 
8.9%
병 38
 
7.6%
산 12
 
2.4%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
민 4
 
0.8%
Other values (88) 147
29.6%
Common
ValueCountFrequency (%)
18
81.8%
) 2
 
9.1%
( 2
 
9.1%
Latin
ValueCountFrequency (%)
U 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 496
95.4%
ASCII 23
 
4.4%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 114
23.0%
례 58
 
11.7%
식 57
 
11.5%
원 44
 
8.9%
병 38
 
7.7%
산 12
 
2.4%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
민 4
 
0.8%
Other values (87) 146
29.4%
ASCII
ValueCountFrequency (%)
18
78.3%
) 2
 
8.7%
( 2
 
8.7%
U 1
 
4.3%
None
ValueCountFrequency (%)
㈜ 1
100.0%

category
Categorical

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
사설
57 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사설 57
100.0%

Length

2024-04-18T09:30:25.914529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:25.997741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사설 57
100.0%

addr_road
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:26.240790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length24
Mean length20.842105
Min length10

Characters and Unicode

Total characters1188
Distinct characters110
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row동구 중앙대로 281
2nd row부산광역시 서구 대신공원로 26(동대신동3가)
3rd row부산광역시 서구 대티로 170(서대신동2가)
4th row부산광역시 서구 구덕로179(아미동 1가)
5th row부산광역시 해운대구 좌동순환로 462 (중동)
ValueCountFrequency (%)
부산광역시 41
 
17.2%
사하구 10
 
4.2%
금정구 6
 
2.5%
중앙대로 6
 
2.5%
부산시 6
 
2.5%
부산진구 6
 
2.5%
동래구 5
 
2.1%
사상구 4
 
1.7%
서구 4
 
1.7%
수영구 4
 
1.7%
Other values (122) 146
61.3%
2024-04-18T09:30:26.653342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
183
 
15.4%
동 60
 
5.1%
구 59
 
5.0%
로 56
 
4.7%
산 56
 
4.7%
부 54
 
4.5%
시 48
 
4.0%
광 44
 
3.7%
역 41
 
3.5%
1 40
 
3.4%
Other values (100) 547
46.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 743
62.5%
Space Separator 183
 
15.4%
Decimal Number 178
 
15.0%
Open Punctuation 40
 
3.4%
Close Punctuation 39
 
3.3%
Dash Punctuation 2
 
0.2%
Uppercase Letter 2
 
0.2%
Other Punctuation 1
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 60
 
8.1%
구 59
 
7.9%
로 56
 
7.5%
산 56
 
7.5%
부 54
 
7.3%
시 48
 
6.5%
광 44
 
5.9%
역 41
 
5.5%
대 31
 
4.2%
사 18
 
2.4%
Other values (83) 276
37.1%
Decimal Number
ValueCountFrequency (%)
1 40
22.5%
2 30
16.9%
6 21
11.8%
5 18
10.1%
4 15
 
8.4%
3 14
 
7.9%
0 13
 
7.3%
7 12
 
6.7%
9 9
 
5.1%
8 6
 
3.4%
Uppercase Letter
ValueCountFrequency (%)
K 1
50.0%
A 1
50.0%
Space Separator
ValueCountFrequency (%)
183
100.0%
Open Punctuation
ValueCountFrequency (%)
( 40
100.0%
Close Punctuation
ValueCountFrequency (%)
) 39
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Other Punctuation
ValueCountFrequency (%)
, 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 743
62.5%
Common 443
37.3%
Latin 2
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 60
 
8.1%
구 59
 
7.9%
로 56
 
7.5%
산 56
 
7.5%
부 54
 
7.3%
시 48
 
6.5%
광 44
 
5.9%
역 41
 
5.5%
대 31
 
4.2%
사 18
 
2.4%
Other values (83) 276
37.1%
Common
ValueCountFrequency (%)
183
41.3%
1 40
 
9.0%
( 40
 
9.0%
) 39
 
8.8%
2 30
 
6.8%
6 21
 
4.7%
5 18
 
4.1%
4 15
 
3.4%
3 14
 
3.2%
0 13
 
2.9%
Other values (5) 30
 
6.8%
Latin
ValueCountFrequency (%)
K 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 743
62.5%
ASCII 445
37.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
183
41.1%
1 40
 
9.0%
( 40
 
9.0%
) 39
 
8.8%
2 30
 
6.7%
6 21
 
4.7%
5 18
 
4.0%
4 15
 
3.4%
3 14
 
3.1%
0 13
 
2.9%
Other values (7) 32
 
7.2%
Hangul
ValueCountFrequency (%)
동 60
 
8.1%
구 59
 
7.9%
로 56
 
7.5%
산 56
 
7.5%
부 54
 
7.3%
시 48
 
6.5%
광 44
 
5.9%
역 41
 
5.5%
대 31
 
4.2%
사 18
 
2.4%
Other values (83) 276
37.1%

addr_jibun
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing57
Missing (%)100.0%
Memory size645.0 B

operation
Categorical

Distinct6
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
임대
30 
직영
23 
임대 남천사랑요양병원
 
1
임대 수영한서병원
 
1
사설
 
1

Length

Max length11
Median length2
Mean length2.2807018
Min length2

Unique

Unique4 ?
Unique (%)7.0%

Sample

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

Common Values

ValueCountFrequency (%)
임대 30
52.6%
직영 23
40.4%
임대 남천사랑요양병원 1
 
1.8%
임대 수영한서병원 1
 
1.8%
사설 1
 
1.8%
민간 1
 
1.8%

Length

2024-04-18T09:30:26.786643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:26.877242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대 32
54.2%
직영 23
39.0%
남천사랑요양병원 1
 
1.7%
수영한서병원 1
 
1.7%
사설 1
 
1.7%
민간 1
 
1.7%

ceo_nm
Text

Distinct54
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:27.101456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length3
Mean length3.2982456
Min length3

Characters and Unicode

Total characters188
Distinct characters86
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique51 ?
Unique (%)89.5%

Sample

1st row김상태
2nd row정휘위
3rd row김외태
4th row이정주
5th row박철균
ValueCountFrequency (%)
박권수 2
 
3.3%
문성훈 2
 
3.3%
전흥배 2
 
3.3%
최범영 1
 
1.7%
김재명 1
 
1.7%
김상태 1
 
1.7%
㈜시민장례식장 1
 
1.7%
장효진 1
 
1.7%
윤병일 1
 
1.7%
박광열 1
 
1.7%
Other values (47) 47
78.3%
2024-04-18T09:30:27.438833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
박 11
 
5.9%
김 11
 
5.9%
이 9
 
4.8%
정 6
 
3.2%
성 5
 
2.7%
영 5
 
2.7%
오 4
 
2.1%
윤 4
 
2.1%
현 4
 
2.1%
경 4
 
2.1%
Other values (76) 125
66.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 181
96.3%
Space Separator 3
 
1.6%
Other Punctuation 2
 
1.1%
Decimal Number 1
 
0.5%
Other Symbol 1
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
박 11
 
6.1%
김 11
 
6.1%
이 9
 
5.0%
정 6
 
3.3%
성 5
 
2.8%
영 5
 
2.8%
오 4
 
2.2%
윤 4
 
2.2%
현 4
 
2.2%
경 4
 
2.2%
Other values (72) 118
65.2%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Decimal Number
ValueCountFrequency (%)
1 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 182
96.8%
Common 6
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
박 11
 
6.0%
김 11
 
6.0%
이 9
 
4.9%
정 6
 
3.3%
성 5
 
2.7%
영 5
 
2.7%
오 4
 
2.2%
윤 4
 
2.2%
현 4
 
2.2%
경 4
 
2.2%
Other values (73) 119
65.4%
Common
ValueCountFrequency (%)
3
50.0%
, 2
33.3%
1 1
 
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 181
96.3%
ASCII 6
 
3.2%
None 1
 
0.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
박 11
 
6.1%
김 11
 
6.1%
이 9
 
5.0%
정 6
 
3.3%
성 5
 
2.8%
영 5
 
2.8%
오 4
 
2.2%
윤 4
 
2.2%
현 4
 
2.2%
경 4
 
2.2%
Other values (72) 118
65.2%
ASCII
ValueCountFrequency (%)
3
50.0%
, 2
33.3%
1 1
 
16.7%
None
ValueCountFrequency (%)
㈜ 1
100.0%

tel
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:27.671938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.052632
Min length12

Characters and Unicode

Total characters687
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)100.0%

Sample

1st row051-464-5858
2nd row051-256-7070
3rd row051-246-4114
4th row051-240-7161
5th row051-747-5600
ValueCountFrequency (%)
051-464-5858 1
 
1.8%
051-527-7711 1
 
1.8%
051-582-1041 1
 
1.8%
051-580-1335 1
 
1.8%
051-582-1024 1
 
1.8%
051-977-4444 1
 
1.8%
051-990-6444 1
 
1.8%
051-305-4000 1
 
1.8%
051-911-4444 1
 
1.8%
051-465-1024 1
 
1.8%
Other values (47) 47
82.5%
2024-04-18T09:30:28.011543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 119
17.3%
- 114
16.6%
1 113
16.4%
5 88
12.8%
4 76
11.1%
2 42
 
6.1%
7 35
 
5.1%
6 33
 
4.8%
9 25
 
3.6%
3 21
 
3.1%
Other values (2) 21
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 570
83.0%
Dash Punctuation 114
 
16.6%
Space Separator 3
 
0.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 119
20.9%
1 113
19.8%
5 88
15.4%
4 76
13.3%
2 42
 
7.4%
7 35
 
6.1%
6 33
 
5.8%
9 25
 
4.4%
3 21
 
3.7%
8 18
 
3.2%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%
Space Separator
ValueCountFrequency (%)
3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 687
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 119
17.3%
- 114
16.6%
1 113
16.4%
5 88
12.8%
4 76
11.1%
2 42
 
6.1%
7 35
 
5.1%
6 33
 
4.8%
9 25
 
3.6%
3 21
 
3.1%
Other values (2) 21
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 687
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 119
17.3%
- 114
16.6%
1 113
16.4%
5 88
12.8%
4 76
11.1%
2 42
 
6.1%
7 35
 
5.1%
6 33
 
4.8%
9 25
 
3.6%
3 21
 
3.1%
Other values (2) 21
 
3.1%

open_date
Date

MISSING 

Distinct51
Distinct (%)98.1%
Missing5
Missing (%)8.8%
Memory size588.0 B
Minimum1984-05-01 00:00:00
Maximum2019-10-29 00:00:00
2024-04-18T09:30:28.148910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T09:30:28.278628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

site
Text

MISSING 

Distinct48
Distinct (%)100.0%
Missing9
Missing (%)15.8%
Memory size588.0 B
2024-04-18T09:30:28.484587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length3.75
Min length2

Characters and Unicode

Total characters180
Distinct characters13
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique48 ?
Unique (%)100.0%

Sample

1st row1535
2nd row496
3rd row654
4th row2444
5th row1144
ValueCountFrequency (%)
1535 1
 
2.1%
496 1
 
2.1%
620 1
 
2.1%
1750 1
 
2.1%
880 1
 
2.1%
1145.9 1
 
2.1%
2764 1
 
2.1%
1508 1
 
2.1%
2922 1
 
2.1%
병원 1
 
2.1%
Other values (38) 38
79.2%
2024-04-18T09:30:28.820162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 33
18.3%
2 21
11.7%
0 21
11.7%
4 19
10.6%
5 16
8.9%
9 15
8.3%
6 15
8.3%
8 15
8.3%
3 11
 
6.1%
7 11
 
6.1%
Other values (3) 3
 
1.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 177
98.3%
Other Letter 2
 
1.1%
Other Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 33
18.6%
2 21
11.9%
0 21
11.9%
4 19
10.7%
5 16
9.0%
9 15
8.5%
6 15
8.5%
8 15
8.5%
3 11
 
6.2%
7 11
 
6.2%
Other Letter
ValueCountFrequency (%)
병 1
50.0%
원 1
50.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178
98.9%
Hangul 2
 
1.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 33
18.5%
2 21
11.8%
0 21
11.8%
4 19
10.7%
5 16
9.0%
9 15
8.4%
6 15
8.4%
8 15
8.4%
3 11
 
6.2%
7 11
 
6.2%
Hangul
ValueCountFrequency (%)
병 1
50.0%
원 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 178
98.9%
Hangul 2
 
1.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 33
18.5%
2 21
11.8%
0 21
11.8%
4 19
10.7%
5 16
9.0%
9 15
8.4%
6 15
8.4%
8 15
8.4%
3 11
 
6.2%
7 11
 
6.2%
Hangul
ValueCountFrequency (%)
병 1
50.0%
원 1
50.0%

area
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)98.1%
Missing5
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean4739.6312
Minimum198
Maximum60138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:28.964311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile433.55
Q1724.75
median1948.5
Q33327
95-th percentile13419.95
Maximum60138
Range59940
Interquartile range (IQR)2602.25

Descriptive statistics

Standard deviation11603.625
Coefficient of variation (CV)2.4482126
Kurtosis19.819057
Mean4739.6312
Median Absolute Deviation (MAD)1262
Skewness4.4681535
Sum246460.82
Variance1.3464411 × 108
MonotonicityNot monotonic
2024-04-18T09:30:29.096820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60138.0 2
 
3.5%
3313.0 1
 
1.8%
807.0 1
 
1.8%
1750.0 1
 
1.8%
480.0 1
 
1.8%
931.82 1
 
1.8%
2764.0 1
 
1.8%
2383.0 1
 
1.8%
6630.0 1
 
1.8%
730.0 1
 
1.8%
Other values (41) 41
71.9%
(Missing) 5
 
8.8%
ValueCountFrequency (%)
198.0 1
1.8%
249.0 1
1.8%
400.0 1
1.8%
461.0 1
1.8%
480.0 1
1.8%
496.0 1
1.8%
562.0 1
1.8%
565.0 1
1.8%
595.0 1
1.8%
648.0 1
1.8%
ValueCountFrequency (%)
60138.0 2
3.5%
20609.0 1
1.8%
7538.0 1
1.8%
6630.0 1
1.8%
6312.0 1
1.8%
4881.0 1
1.8%
4713.0 1
1.8%
4333.0 1
1.8%
4234.0 1
1.8%
3649.0 1
1.8%

vacan_num
Real number (ℝ)

Distinct13
Distinct (%)22.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3333333
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:29.202210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q37
95-th percentile10.4
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation3.2859731
Coefficient of variation (CV)0.61611995
Kurtosis4.2375964
Mean5.3333333
Median Absolute Deviation (MAD)1
Skewness1.8058725
Sum304
Variance10.797619
MonotonicityNot monotonic
2024-04-18T09:30:29.301673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
3 17
29.8%
4 8
14.0%
7 6
 
10.5%
5 6
 
10.5%
9 4
 
7.0%
6 4
 
7.0%
2 4
 
7.0%
8 3
 
5.3%
16 1
 
1.8%
1 1
 
1.8%
Other values (3) 3
 
5.3%
ValueCountFrequency (%)
1 1
 
1.8%
2 4
 
7.0%
3 17
29.8%
4 8
14.0%
5 6
 
10.5%
6 4
 
7.0%
7 6
 
10.5%
8 3
 
5.3%
9 4
 
7.0%
10 1
 
1.8%
ValueCountFrequency (%)
18 1
 
1.8%
16 1
 
1.8%
12 1
 
1.8%
10 1
 
1.8%
9 4
7.0%
8 3
 
5.3%
7 6
10.5%
6 4
7.0%
5 6
10.5%
4 8
14.0%

anchor
Real number (ℝ)

Distinct10
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6140351
Minimum3
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:29.404643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum3
5-th percentile4
Q16
median8
Q310
95-th percentile16
Maximum34
Range31
Interquartile range (IQR)4

Descriptive statistics

Standard deviation5.1746442
Coefficient of variation (CV)0.60072244
Kurtosis9.7232124
Mean8.6140351
Median Absolute Deviation (MAD)2
Skewness2.5253288
Sum491
Variance26.776942
MonotonicityNot monotonic
2024-04-18T09:30:29.495015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
8 13
22.8%
4 12
21.1%
6 12
21.1%
10 7
12.3%
14 5
 
8.8%
12 3
 
5.3%
16 2
 
3.5%
22 1
 
1.8%
34 1
 
1.8%
3 1
 
1.8%
ValueCountFrequency (%)
3 1
 
1.8%
4 12
21.1%
6 12
21.1%
8 13
22.8%
10 7
12.3%
12 3
 
5.3%
14 5
 
8.8%
16 2
 
3.5%
22 1
 
1.8%
34 1
 
1.8%
ValueCountFrequency (%)
34 1
 
1.8%
22 1
 
1.8%
16 2
 
3.5%
14 5
 
8.8%
12 3
 
5.3%
10 7
12.3%
8 13
22.8%
6 12
21.1%
4 12
21.1%
3 1
 
1.8%

anchor_cost
Categorical

Distinct11
Distinct (%)19.3%
Missing0
Missing (%)0.0%
Memory size588.0 B
100000
34 
150000
11 
72000
 
2
100,000
 
2
80000
 
2
Other values (6)
6 

Length

Max length21
Median length6
Mean length6.0526316
Min length1

Unique

Unique6 ?
Unique (%)10.5%

Sample

1st row100000
2nd row100000
3rd row150000
4th row일반: 92500, 수급자: 3,750
5th row100000

Common Values

ValueCountFrequency (%)
100000 34
59.6%
150000 11
 
19.3%
72000 2
 
3.5%
100,000 2
 
3.5%
80000 2
 
3.5%
일반: 92500, 수급자: 3,750 1
 
1.8%
6 1
 
1.8%
50,000 1
 
1.8%
96000 1
 
1.8%
100 1
 
1.8%

Length

2024-04-18T09:30:29.615819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
100000 34
56.7%
150000 11
 
18.3%
72000 2
 
3.3%
100,000 2
 
3.3%
80000 2
 
3.3%
일반 1
 
1.7%
92500 1
 
1.7%
수급자 1
 
1.7%
3,750 1
 
1.7%
6 1
 
1.7%
Other values (4) 4
 
6.7%

exes
Categorical

Distinct8
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
19 
250000
16 
300000
7 
200000
7 
300,000
4 
Other values (3)
4 

Length

Max length50
Median length6
Mean length6.122807
Min length3

Unique

Unique2 ?
Unique (%)3.5%

Sample

1st row250000
2nd row300000
3rd row250000
4th row일반(병사) 200,000원, 특수(외인사) 300,000원, 특수(야간) 400,000원
5th row300000

Common Values

ValueCountFrequency (%)
<NA> 19
33.3%
250000 16
28.1%
300000 7
 
12.3%
200000 7
 
12.3%
300,000 4
 
7.0%
340000 2
 
3.5%
일반(병사) 200,000원, 특수(외인사) 300,000원, 특수(야간) 400,000원 1
 
1.8%
100 1
 
1.8%

Length

2024-04-18T09:30:29.755070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:29.861232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
30.6%
250000 16
25.8%
300000 7
 
11.3%
200000 7
 
11.3%
300,000 4
 
6.5%
340000 2
 
3.2%
일반(병사 1
 
1.6%
200,000원 1
 
1.6%
특수(외인사 1
 
1.6%
300,000원 1
 
1.6%
Other values (3) 3
 
4.8%

rent
Text

Distinct54
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:30.069483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length30
Median length26
Mean length12.77193
Min length3

Characters and Unicode

Total characters728
Distinct characters27
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

Unique51 ?
Unique (%)89.5%

Sample

1st row180000~880000
2nd row6호실 300,000원, 특2호실 600,000원, 특
3rd row일반: 240,000원, 특실: 480,000원
4th row특1호 350,000원, 2호 200,000원, 3호
5th row600000
ValueCountFrequency (%)
400000~500000 2
 
2.8%
300,000원 2
 
2.8%
300000~400000 2
 
2.8%
250000 2
 
2.8%
500000 2
 
2.8%
250,000~350,000 1
 
1.4%
240,000~600,000 1
 
1.4%
450000 1
 
1.4%
200~750 1
 
1.4%
550000~650000 1
 
1.4%
Other values (56) 56
78.9%
2024-04-18T09:30:30.413397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 377
51.8%
5 53
 
7.3%
~ 43
 
5.9%
, 40
 
5.5%
32
 
4.4%
2 27
 
3.7%
3 26
 
3.6%
8 23
 
3.2%
4 22
 
3.0%
1 16
 
2.2%
Other values (17) 69
 
9.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 572
78.6%
Other Punctuation 44
 
6.0%
Math Symbol 43
 
5.9%
Other Letter 33
 
4.5%
Space Separator 32
 
4.4%
Open Punctuation 2
 
0.3%
Close Punctuation 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 377
65.9%
5 53
 
9.3%
2 27
 
4.7%
3 26
 
4.5%
8 23
 
4.0%
4 22
 
3.8%
1 16
 
2.8%
6 14
 
2.4%
7 10
 
1.7%
9 4
 
0.7%
Other Letter
ValueCountFrequency (%)
호 7
21.2%
원 7
21.2%
실 5
15.2%
특 4
12.1%
시 2
 
6.1%
간 2
 
6.1%
기 2
 
6.1%
준 2
 
6.1%
반 1
 
3.0%
일 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 40
90.9%
: 2
 
4.5%
/ 2
 
4.5%
Math Symbol
ValueCountFrequency (%)
~ 43
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 695
95.5%
Hangul 33
 
4.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 377
54.2%
5 53
 
7.6%
~ 43
 
6.2%
, 40
 
5.8%
32
 
4.6%
2 27
 
3.9%
3 26
 
3.7%
8 23
 
3.3%
4 22
 
3.2%
1 16
 
2.3%
Other values (7) 36
 
5.2%
Hangul
ValueCountFrequency (%)
호 7
21.2%
원 7
21.2%
실 5
15.2%
특 4
12.1%
시 2
 
6.1%
간 2
 
6.1%
기 2
 
6.1%
준 2
 
6.1%
반 1
 
3.0%
일 1
 
3.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 695
95.5%
Hangul 33
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 377
54.2%
5 53
 
7.6%
~ 43
 
6.2%
, 40
 
5.8%
32
 
4.6%
2 27
 
3.9%
3 26
 
3.7%
8 23
 
3.3%
4 22
 
3.2%
1 16
 
2.3%
Other values (7) 36
 
5.2%
Hangul
ValueCountFrequency (%)
호 7
21.2%
원 7
21.2%
실 5
15.2%
특 4
12.1%
시 2
 
6.1%
간 2
 
6.1%
기 2
 
6.1%
준 2
 
6.1%
반 1
 
3.0%
일 1
 
3.0%

add_info
Text

MISSING 

Distinct3
Distinct (%)75.0%
Missing53
Missing (%)93.0%
Memory size588.0 B
2024-04-18T09:30:30.548205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5.5
Mean length3.75
Min length2

Characters and Unicode

Total characters15
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)50.0%

Sample

1st row없음
2nd row없음
3rd row별도안내
4th row관리비 3~5
ValueCountFrequency (%)
없음 2
40.0%
별도안내 1
20.0%
관리비 1
20.0%
3~5 1
20.0%
2024-04-18T09:30:30.771669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
없 2
13.3%
음 2
13.3%
별 1
 
6.7%
도 1
 
6.7%
안 1
 
6.7%
내 1
 
6.7%
관 1
 
6.7%
리 1
 
6.7%
비 1
 
6.7%
1
 
6.7%
Other values (3) 3
20.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11
73.3%
Decimal Number 2
 
13.3%
Space Separator 1
 
6.7%
Math Symbol 1
 
6.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
없 2
18.2%
음 2
18.2%
별 1
9.1%
도 1
9.1%
안 1
9.1%
내 1
9.1%
관 1
9.1%
리 1
9.1%
비 1
9.1%
Decimal Number
ValueCountFrequency (%)
3 1
50.0%
5 1
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Math Symbol
ValueCountFrequency (%)
~ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11
73.3%
Common 4
 
26.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
없 2
18.2%
음 2
18.2%
별 1
9.1%
도 1
9.1%
안 1
9.1%
내 1
9.1%
관 1
9.1%
리 1
9.1%
비 1
9.1%
Common
ValueCountFrequency (%)
1
25.0%
3 1
25.0%
~ 1
25.0%
5 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11
73.3%
ASCII 4
 
26.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
없 2
18.2%
음 2
18.2%
별 1
9.1%
도 1
9.1%
안 1
9.1%
내 1
9.1%
관 1
9.1%
리 1
9.1%
비 1
9.1%
ASCII
ValueCountFrequency (%)
1
25.0%
3 1
25.0%
~ 1
25.0%
5 1
25.0%

gugun
Categorical

Distinct16
Distinct (%)28.1%
Missing0
Missing (%)0.0%
Memory size588.0 B
부산광역시 사하구
10 
부산광역시 부산진구
6 
부산광역시 금정구
6 
부산광역시 동래구
5 
부산광역시 서구
4 
Other values (11)
26 

Length

Max length10
Median length9
Mean length8.9473684
Min length8

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st row부산광역시 동구
2nd row부산광역시 서구
3rd row부산광역시 서구
4th row부산광역시 서구
5th row부산광역시 해운대구

Common Values

ValueCountFrequency (%)
부산광역시 사하구 10
17.5%
부산광역시 부산진구 6
10.5%
부산광역시 금정구 6
10.5%
부산광역시 동래구 5
8.8%
부산광역시 서구 4
 
7.0%
부산광역시 사상구 4
 
7.0%
부산광역시 수영구 4
 
7.0%
부산광역시 동구 3
 
5.3%
부산광역시 해운대구 3
 
5.3%
부산광역시 영도구 3
 
5.3%
Other values (6) 9
15.8%

Length

2024-04-18T09:30:30.891454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
부산광역시 57
50.0%
사하구 10
 
8.8%
부산진구 6
 
5.3%
금정구 6
 
5.3%
동래구 5
 
4.4%
서구 4
 
3.5%
사상구 4
 
3.5%
수영구 4
 
3.5%
영도구 3
 
2.6%
동구 3
 
2.6%
Other values (7) 12
 
10.5%

data_day
Date

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum2020-07-31 00:00:00
Maximum2020-09-06 00:00:00
2024-04-18T09:30:30.999382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T09:30:31.105710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=4)

lat
Real number (ℝ)

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean35.152065
Minimum35.055893
Maximum35.320842
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:31.232554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.055893
5-th percentile35.07412
Q135.100746
median35.150149
Q335.197243
95-th percentile35.255455
Maximum35.320842
Range0.264949
Interquartile range (IQR)0.096497

Descriptive statistics

Standard deviation0.059442388
Coefficient of variation (CV)0.001691007
Kurtosis-0.18823294
Mean35.152065
Median Absolute Deviation (MAD)0.04934
Skewness0.56158674
Sum2003.6677
Variance0.0035333975
MonotonicityNot monotonic
2024-04-18T09:30:31.362730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.1217999178 1
 
1.8%
35.1416293 1
 
1.8%
35.238154 1
 
1.8%
35.267355 1
 
1.8%
35.254421 1
 
1.8%
35.215521 1
 
1.8%
35.0810817524 1
 
1.8%
35.1986548 1
 
1.8%
35.2595887 1
 
1.8%
35.107652 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
35.055893 1
1.8%
35.068597 1
1.8%
35.073531 1
1.8%
35.07426787 1
1.8%
35.0801589068 1
1.8%
35.080944 1
1.8%
35.0810817524 1
1.8%
35.087107 1
1.8%
35.090536 1
1.8%
35.090594 1
1.8%
ValueCountFrequency (%)
35.320842 1
1.8%
35.267355 1
1.8%
35.2595887 1
1.8%
35.254421 1
1.8%
35.238154 1
1.8%
35.235854 1
1.8%
35.2277528472 1
1.8%
35.217415 1
1.8%
35.216186 1
1.8%
35.215521 1
1.8%

lng
Real number (ℝ)

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.0527
Minimum128.85329
Maximum129.24355
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:31.499185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.85329
5-th percentile128.97177
Q1129.00781
median129.04838
Q3129.09413
95-th percentile129.17366
Maximum129.24355
Range0.390265
Interquartile range (IQR)0.086325

Descriptive statistics

Standard deviation0.068291473
Coefficient of variation (CV)0.00052917509
Kurtosis1.1192984
Mean129.0527
Median Absolute Deviation (MAD)0.045753087
Skewness0.26755924
Sum7356.0037
Variance0.0046637252
MonotonicityNot monotonic
2024-04-18T09:30:31.631072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.0429490464 1
 
1.8%
129.061012 1
 
1.8%
129.091741 1
 
1.8%
129.094133 1
 
1.8%
129.091025 1
 
1.8%
129.1152448 1
 
1.8%
129.014511454 1
 
1.8%
128.9905558 1
 
1.8%
129.0138139 1
 
1.8%
129.032984 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
128.853287 1
1.8%
128.9619094416 1
1.8%
128.970898 1
1.8%
128.971985 1
1.8%
128.97352 1
1.8%
128.976831 1
1.8%
128.977147 1
1.8%
128.977244 1
1.8%
128.985175 1
1.8%
128.990361 1
1.8%
ValueCountFrequency (%)
129.243552 1
1.8%
129.216441 1
1.8%
129.1821691294 1
1.8%
129.1715331876 1
1.8%
129.1470716189 1
1.8%
129.115504 1
1.8%
129.1152448 1
1.8%
129.113186 1
1.8%
129.110312 1
1.8%
129.109969 1
1.8%

apr_at
Categorical

IMBALANCE 

Distinct2
Distinct (%)3.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
53 
 
4

Length

Max length4
Median length4
Mean length3.7894737
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row<NA>
3rd row<NA>
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
<NA> 53
93.0%
4
 
7.0%

Length

2024-04-18T09:30:31.769451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:31.862891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 53
100.0%

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum2021-01-05 15:27:10
Maximum2021-01-05 15:27:10
2024-04-18T09:30:31.933803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T09:30:32.021867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Sample

skeyinstt_codecitycmp_nmcategoryaddr_roadaddr_jibunoperationceo_nmtelopen_datesiteareavacan_numanchoranchor_costexesrentadd_infogugundata_daylatlngapr_atlast_load_dttm
02103270000부산광역시인창병원 장례식장사설동구 중앙대로 281<NA>임대김상태051-464-58582004-02-09<NA>2770.0912100000250000180000~880000<NA>부산광역시 동구2020-09-0635.1218129.0429492021-01-05 15:27:10
12383260000부산광역시동아대학교병원 장례식장사설부산광역시 서구 대신공원로 26(동대신동3가)<NA>직영정휘위051-256-70701997-09-0115351535.06161000003000006호실 300,000원, 특2호실 600,000원, 특<NA>부산광역시 서구2020-07-3135.119542129.018028<NA>2021-01-05 15:27:10
22393260000부산광역시삼육부산병원 장례식장사설부산광역시 서구 대티로 170(서대신동2가)<NA>임대김외태051-246-41142007-03-08496496.038150000250000일반: 240,000원, 특실: 480,000원<NA>부산광역시 서구2020-07-3135.112066129.01098<NA>2021-01-05 15:27:10
32403260000부산광역시부산대학교병원 장례식장사설부산광역시 서구 구덕로179(아미동 1가)<NA>직영이정주051-240-71612019-06-20654654.0412일반: 92500, 수급자: 3,750일반(병사) 200,000원, 특수(외인사) 300,000원, 특수(야간) 400,000원특1호 350,000원, 2호 200,000원, 3호<NA>부산광역시 서구2020-07-3135.100746129.018709<NA>2021-01-05 15:27:10
42463330000부산광역시성심 장례식장사설부산광역시 해운대구 좌동순환로 462 (중동)<NA>직영박철균051-747-56002014-01-022444249.024100000300000600000<NA>부산광역시 해운대구2020-08-3135.162664129.171533<NA>2021-01-05 15:27:10
52473330000부산광역시반송 장례식장사설부산광역시 해운대구 반송로 832 (반송동)<NA>임대박환옥,김미경051-525-10242009-06-161144664.034100000200000250000<NA>부산광역시 해운대구2020-08-3135.227753129.147072<NA>2021-01-05 15:27:10
62503310000부산광역시부산성모병원 장례식장사설부산광역시 남구 용호로 232번길25-14<NA>직영김준현051-933-74802006-05-193523960138.0161672000250000408,000~1,500,000(1실/24시간기준)없음부산광역시 남구2020-07-3135.110511129.109192<NA>2021-01-05 15:27:10
72513310000부산광역시대연장례식장사설부산광역시 남구 수영로164<NA>직영박창렬051-711-44482019-10-23<NA>60138.0366200000305,000~650,000(1실/24시간기준)없음부산광역시 남구2020-07-3135.134468129.084599<NA>2021-01-05 15:27:10
82543390000부산광역시부산보훈병원장례식장사설부산광역시 사상구 백양대로 420<NA>직영김덕남051-601-67852005-05-2012002419.091450,000300,000214,000~728,000<NA>부산광역시 사상구2020-07-3135.152895129.008248<NA>2021-01-05 15:27:10
92553390000부산광역시삼신전문장례식장사설부산광역시 사상구 대동로 261<NA>직영안영선051-323-00442005-11-1522662700.0910100,000300,000210,000~800,000<NA>부산광역시 사상구2020-07-3135.150876128.985175<NA>2021-01-05 15:27:10
skeyinstt_codecitycmp_nmcategoryaddr_roadaddr_jibunoperationceo_nmtelopen_datesiteareavacan_numanchoranchor_costexesrentadd_infogugundata_daylatlngapr_atlast_load_dttm
472673400000부산광역시동남권원자력의학원 장례식장사설부산광역시 기장군 장안읍 좌동길 40<NA>직영김동원051-720-54212010-07-16729682675.081072000250000240,000~600,000<NA>부산광역시 기장군2020-07-3135.320842129.243552<NA>2021-01-05 15:27:10
482683400000부산광역시기장병원장례식장사설부산광역시 기장군 기장읍 대청로72번길 6<NA>임대박현도051-724-10241997-05-01648648.05670000300000250,000~350,000<NA>부산광역시 기장군2020-07-3135.235854129.216441<NA>2021-01-05 15:27:10
492693340000부산광역시서부산시민장례식장사설부산광역시 사하구 장림번영로 59(장림동)<NA>직영박일정, 박준길051-264-04442019-10-2915147538.043150000<NA>500000~750000<NA>부산광역시 사하구2020-07-3135.080944128.971985<NA>2021-01-05 15:27:10
502703340000부산광역시우리원병원장례식장사설부산광역시 사하구 다대로 565(다대동)<NA>임대김재명051-264-35352016-01-128921020.034100000<NA>400000~730000<NA>부산광역시 사하구2020-07-3135.055893128.970898<NA>2021-01-05 15:27:10
512713340000부산광역시중앙U병원장례식장사설부산광역시 사하구 감천로 59(감천동)<NA>임대전흥배051-201-04682009-06-0510814881.048150000<NA>300000~400000<NA>부산광역시 사하구2020-07-3135.090594128.9991<NA>2021-01-05 15:27:10
522723340000부산광역시사하구민장례식장사설부산광역시 사하구 하신중앙로17번길 25(장림동)<NA>직영최범영051-715-44412019-01-047212381.058150000<NA>400000~500000<NA>부산광역시 사하구2020-07-3135.080159128.961909<NA>2021-01-05 15:27:10
532833300000부산광역시대동병원장례식장사설부산광역시 동래구 충렬대로 187 (명륜동)<NA>임대이준오051-550-9991<NA><NA><NA>68100000<NA>500000<NA>부산광역시 동래구2020-07-3135.204725129.080273<NA>2021-01-05 15:27:10
542843300000부산광역시동래봉생병원장례식장사설부산광역시 동래구 안연로 109번길 26 (안락동<NA>임대박권수051-531-7100<NA><NA><NA>36100000<NA>250000<NA>부산광역시 동래구2020-07-3135.197243129.096305<NA>2021-01-05 15:27:10
552853300000부산광역시프라임장례원사설부산광역시 동래구 미남로 146번길 11 (온천동)<NA>임대박이숙051-506-1022<NA><NA><NA>56100000<NA>300000<NA>부산광역시 동래구2020-07-3135.206771129.071195<NA>2021-01-05 15:27:10
562863300000부산광역시성산현대요양병원장례식장사설부산광역시 동래구 시실로 12 (명륜동)<NA>임대이수향051-558-7996<NA><NA><NA>36150000<NA>500000<NA>부산광역시 동래구2020-07-3135.217415129.085505<NA>2021-01-05 15:27:10