Overview

Dataset statistics

Number of variables25
Number of observations57
Missing cells125
Missing cells (%)8.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory11.8 KiB
Average record size in memory212.3 B

Variable types

Numeric9
Categorical5
Text6
Unsupported1
DateTime3
Boolean1

Alerts

city has constant value ""Constant
category has constant value ""Constant
apr_at has constant value ""Constant
last_load_dttm has constant value ""Constant
addr_jibun has 57 (100.0%) missing valuesMissing
site has 10 (17.5%) 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 00:30:42.493893
Analysis finished2024-04-18 00:30:42.846209
Duration0.35 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%
Mean320.61404
Minimum291
Maximum351
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:42.904319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum291
5-th percentile293.8
Q1305
median319
Q3337
95-th percentile348.2
Maximum351
Range60
Interquartile range (IQR)32

Descriptive statistics

Standard deviation18.310569
Coefficient of variation (CV)0.057110941
Kurtosis-1.3005411
Mean320.61404
Median Absolute Deviation (MAD)16
Skewness0.063290937
Sum18275
Variance335.27694
MonotonicityNot monotonic
2024-04-18T09:30:43.036851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
306 1
 
1.8%
317 1
 
1.8%
301 1
 
1.8%
302 1
 
1.8%
303 1
 
1.8%
304 1
 
1.8%
305 1
 
1.8%
308 1
 
1.8%
309 1
 
1.8%
310 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
291 1
1.8%
292 1
1.8%
293 1
1.8%
294 1
1.8%
295 1
1.8%
296 1
1.8%
297 1
1.8%
298 1
1.8%
299 1
1.8%
300 1
1.8%
ValueCountFrequency (%)
351 1
1.8%
350 1
1.8%
349 1
1.8%
348 1
1.8%
347 1
1.8%
346 1
1.8%
345 1
1.8%
344 1
1.8%
343 1
1.8%
342 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
2024-04-18T09:30:43.147712image/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
2024-04-18T09:30:43.255021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
3340000 10
17.5%
3290000 6
10.5%
3350000 6
10.5%
3300000 5
8.8%
3260000 4
 
7.0%
3390000 4
 
7.0%
3380000 4
 
7.0%
3330000 3
 
5.3%
3270000 3
 
5.3%
3280000 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

2024-04-18T09:30:43.370897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:43.453503image/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
2024-04-18T09:30:43.630593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length9
Min length6

Characters and Unicode

Total characters513
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 row반송 장례식장
2nd row해운대백병원 장례식장
3rd row성산현대요양병원장례식장
4th row정요양병원장례식장
5th row부산백병원장례식장
ValueCountFrequency (%)
장례식장 13
 
18.6%
반송 1
 
1.4%
갑을녹산장례식장 1
 
1.4%
기장병원장례식장 1
 
1.4%
동아대학교병원 1
 
1.4%
삼육부산병원 1
 
1.4%
부산대학교병원 1
 
1.4%
고신대학교병원 1
 
1.4%
성심 1
 
1.4%
아시아드장례식장 1
 
1.4%
Other values (48) 48
68.6%
2024-04-18T09:30:43.975541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
장 114
22.2%
례 58
 
11.3%
식 57
 
11.1%
원 43
 
8.4%
병 37
 
7.2%
13
 
2.5%
산 12
 
2.3%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
Other values (92) 156
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 494
96.3%
Space Separator 13
 
2.5%
Open Punctuation 2
 
0.4%
Close Punctuation 2
 
0.4%
Uppercase Letter 1
 
0.2%
Other Symbol 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
장 114
23.1%
례 58
 
11.7%
식 57
 
11.5%
원 43
 
8.7%
병 37
 
7.5%
산 12
 
2.4%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
민 4
 
0.8%
Other values (87) 146
29.6%
Space Separator
ValueCountFrequency (%)
13
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Uppercase Letter
ValueCountFrequency (%)
U 1
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 495
96.5%
Common 17
 
3.3%
Latin 1
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
장 114
23.0%
례 58
 
11.7%
식 57
 
11.5%
원 43
 
8.7%
병 37
 
7.5%
산 12
 
2.4%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
민 4
 
0.8%
Other values (88) 147
29.7%
Common
ValueCountFrequency (%)
13
76.5%
( 2
 
11.8%
) 2
 
11.8%
Latin
ValueCountFrequency (%)
U 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 494
96.3%
ASCII 18
 
3.5%
None 1
 
0.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
장 114
23.1%
례 58
 
11.7%
식 57
 
11.5%
원 43
 
8.7%
병 37
 
7.5%
산 12
 
2.4%
동 8
 
1.6%
부 8
 
1.6%
대 7
 
1.4%
민 4
 
0.8%
Other values (87) 146
29.6%
ASCII
ValueCountFrequency (%)
13
72.2%
( 2
 
11.1%
) 2
 
11.1%
U 1
 
5.6%
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

2024-04-18T09:30:44.097822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:44.181169image/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
2024-04-18T09:30:44.431750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length34
Median length25
Mean length20.789474
Min length10

Characters and Unicode

Total characters1185
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 row부산광역시 해운대구 반송로 832 (반송동)
2nd row부산광역시 해운대구 해운대로 875 (좌동, 해운대백병원 내)
3rd row부산광역시 동래구 시실로 12 (명륜동)
4th row부산광역시 영도구 절영로 32
5th row부산진구 복지로 75 (개금동) K동
ValueCountFrequency (%)
부산광역시 41
 
17.3%
사하구 10
 
4.2%
금정구 6
 
2.5%
부산시 6
 
2.5%
중앙대로 6
 
2.5%
부산진구 6
 
2.5%
동래구 5
 
2.1%
서구 4
 
1.7%
수영구 4
 
1.7%
사상구 4
 
1.7%
Other values (123) 145
61.2%
2024-04-18T09:30:44.842691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
180
 
15.2%
동 60
 
5.1%
구 59
 
5.0%
로 56
 
4.7%
산 56
 
4.7%
부 54
 
4.6%
시 48
 
4.1%
광 44
 
3.7%
역 41
 
3.5%
1 40
 
3.4%
Other values (100) 547
46.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 743
62.7%
Space Separator 180
 
15.2%
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 (%)
180
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
62.7%
Common 440
37.1%
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 (%)
180
40.9%
1 40
 
9.1%
( 40
 
9.1%
) 39
 
8.9%
2 30
 
6.8%
6 21
 
4.8%
5 18
 
4.1%
4 15
 
3.4%
3 14
 
3.2%
0 13
 
3.0%
Other values (5) 30
 
6.8%
Latin
ValueCountFrequency (%)
K 1
50.0%
A 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 743
62.7%
ASCII 442
37.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
180
40.7%
1 40
 
9.0%
( 40
 
9.0%
) 39
 
8.8%
2 30
 
6.8%
6 21
 
4.8%
5 18
 
4.1%
4 15
 
3.4%
3 14
 
3.2%
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

Distinct5
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
임대
31 
직영
23 
임대 남천사랑요양병원
 
1
임대 수영한서병원
 
1
사설
 
1

Length

Max length11
Median length2
Mean length2.2807018
Min length2

Unique

Unique3 ?
Unique (%)5.3%

Sample

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

Common Values

ValueCountFrequency (%)
임대 31
54.4%
직영 23
40.4%
임대 남천사랑요양병원 1
 
1.8%
임대 수영한서병원 1
 
1.8%
사설 1
 
1.8%

Length

2024-04-18T09:30:44.961973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:45.061201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
임대 33
55.9%
직영 23
39.0%
남천사랑요양병원 1
 
1.7%
수영한서병원 1
 
1.7%
사설 1
 
1.7%

ceo_nm
Text

Distinct54
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:45.260100image/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 row박환옥,김미경
2nd row㈜시민장례식장
3rd row이수향
4th row김조은
5th row문성훈
ValueCountFrequency (%)
전흥배 2
 
3.3%
문성훈 2
 
3.3%
박권수 2
 
3.3%
홍효석 1
 
1.7%
정성혜 1
 
1.7%
박현도 1
 
1.7%
김상태 1
 
1.7%
이명숙 1
 
1.7%
이영애 1
 
1.7%
정휘위 1
 
1.7%
Other values (47) 47
78.3%
2024-04-18T09:30:45.595435image/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%
영 5
 
2.7%
성 5
 
2.7%
경 4
 
2.1%
윤 4
 
2.1%
오 4
 
2.1%
옥 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%
Other Symbol 1
 
0.5%
Decimal Number 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%
영 5
 
2.8%
성 5
 
2.8%
경 4
 
2.2%
윤 4
 
2.2%
오 4
 
2.2%
옥 4
 
2.2%
Other values (72) 118
65.2%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Other Symbol
ValueCountFrequency (%)
㈜ 1
100.0%
Decimal Number
ValueCountFrequency (%)
1 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%
영 5
 
2.7%
성 5
 
2.7%
경 4
 
2.2%
윤 4
 
2.2%
오 4
 
2.2%
옥 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%
영 5
 
2.8%
성 5
 
2.8%
경 4
 
2.2%
윤 4
 
2.2%
오 4
 
2.2%
옥 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
2024-04-18T09:30:45.837598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12.017544
Min length12

Characters and Unicode

Total characters685
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

Unique57 ?
Unique (%)100.0%

Sample

1st row051-525-1024
2nd row051-711-4400
3rd row051-558-7996
4th row051-410-1777
5th row051-896-4444
ValueCountFrequency (%)
051-525-1024 1
 
1.8%
051-720-5421 1
 
1.8%
051-256-7070 1
 
1.8%
051-246-4114 1
 
1.8%
051-240-7161 1
 
1.8%
051-990-6444 1
 
1.8%
051-747-5600 1
 
1.8%
051-503-0770 1
 
1.8%
051-550-9991 1
 
1.8%
051-531-7100 1
 
1.8%
Other values (47) 47
82.5%
2024-04-18T09:30:46.168709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 120
17.5%
- 114
16.6%
1 112
16.4%
5 87
12.7%
4 76
11.1%
2 42
 
6.1%
7 37
 
5.4%
6 34
 
5.0%
9 25
 
3.6%
3 21
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 571
83.4%
Dash Punctuation 114
 
16.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 120
21.0%
1 112
19.6%
5 87
15.2%
4 76
13.3%
2 42
 
7.4%
7 37
 
6.5%
6 34
 
6.0%
9 25
 
4.4%
3 21
 
3.7%
8 17
 
3.0%
Dash Punctuation
ValueCountFrequency (%)
- 114
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 685
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 120
17.5%
- 114
16.6%
1 112
16.4%
5 87
12.7%
4 76
11.1%
2 42
 
6.1%
7 37
 
5.4%
6 34
 
5.0%
9 25
 
3.6%
3 21
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 685
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 120
17.5%
- 114
16.6%
1 112
16.4%
5 87
12.7%
4 76
11.1%
2 42
 
6.1%
7 37
 
5.4%
6 34
 
5.0%
9 25
 
3.6%
3 21
 
3.1%

open_date
Date

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum1984-05-01 00:00:00
Maximum2020-06-15 00:00:00
2024-04-18T09:30:46.310211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T09:30:46.438494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

site
Real number (ℝ)

MISSING 

Distinct47
Distinct (%)100.0%
Missing10
Missing (%)17.5%
Infinite0
Infinite (%)0.0%
Mean6404.4468
Minimum198
Maximum100375
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:46.564763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum198
5-th percentile410.8
Q1840
median1287
Q32222
95-th percentile33091.6
Maximum100375
Range100177
Interquartile range (IQR)1382

Descriptive statistics

Standard deviation18442.493
Coefficient of variation (CV)2.8796388
Kurtosis17.894742
Mean6404.4468
Median Absolute Deviation (MAD)633
Skewness4.1740827
Sum301009
Variance3.4012557 × 108
MonotonicityNot monotonic
2024-04-18T09:30:46.690251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
28081 1
 
1.8%
648 1
 
1.8%
1535 1
 
1.8%
496 1
 
1.8%
654 1
 
1.8%
2764 1
 
1.8%
2444 1
 
1.8%
530 1
 
1.8%
1064 1
 
1.8%
600 1
 
1.8%
Other values (37) 37
64.9%
(Missing) 10
 
17.5%
ValueCountFrequency (%)
198 1
1.8%
213 1
1.8%
400 1
1.8%
436 1
1.8%
496 1
1.8%
530 1
1.8%
600 1
1.8%
620 1
1.8%
648 1
1.8%
654 1
1.8%
ValueCountFrequency (%)
100375 1
1.8%
72968 1
1.8%
35239 1
1.8%
28081 1
1.8%
4513 1
1.8%
4090 1
1.8%
3957 1
1.8%
3719 1
1.8%
2922 1
1.8%
2764 1
1.8%

area
Real number (ℝ)

MISSING 

Distinct51
Distinct (%)98.1%
Missing5
Missing (%)8.8%
Infinite0
Infinite (%)0.0%
Mean4729.8846
Minimum198
Maximum60138
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:46.809252image/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 deviation11605.394
Coefficient of variation (CV)2.4536314
Kurtosis19.821295
Mean4729.8846
Median Absolute Deviation (MAD)1262
Skewness4.4686397
Sum245954
Variance1.3468516 × 108
MonotonicityNot monotonic
2024-04-18T09:30:46.932532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
60138 2
 
3.5%
3510 1
 
1.8%
1535 1
 
1.8%
496 1
 
1.8%
654 1
 
1.8%
2764 1
 
1.8%
249 1
 
1.8%
4333 1
 
1.8%
595 1
 
1.8%
800 1
 
1.8%
Other values (41) 41
71.9%
(Missing) 5
 
8.8%
ValueCountFrequency (%)
198 1
1.8%
249 1
1.8%
400 1
1.8%
461 1
1.8%
480 1
1.8%
496 1
1.8%
562 1
1.8%
565 1
1.8%
595 1
1.8%
648 1
1.8%
ValueCountFrequency (%)
60138 2
3.5%
20609 1
1.8%
7538 1
1.8%
6630 1
1.8%
6312 1
1.8%
4881 1
1.8%
4713 1
1.8%
4333 1
1.8%
4234 1
1.8%
3649 1
1.8%

vacan_num
Real number (ℝ)

Distinct12
Distinct (%)21.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.1052632
Minimum1
Maximum18
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:47.044282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q13
median4
Q37
95-th percentile9.2
Maximum18
Range17
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.9861837
Coefficient of variation (CV)0.58492259
Kurtosis5.0571453
Mean5.1052632
Median Absolute Deviation (MAD)1
Skewness1.762644
Sum291
Variance8.9172932
MonotonicityNot monotonic
2024-04-18T09:30:47.150920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
3 18
31.6%
5 7
 
12.3%
4 7
 
12.3%
7 6
 
10.5%
9 4
 
7.0%
6 4
 
7.0%
8 3
 
5.3%
2 3
 
5.3%
1 2
 
3.5%
10 1
 
1.8%
Other values (2) 2
 
3.5%
ValueCountFrequency (%)
1 2
 
3.5%
2 3
 
5.3%
3 18
31.6%
4 7
 
12.3%
5 7
 
12.3%
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%
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 7
 
12.3%
4 7
 
12.3%
3 18
31.6%

anchor
Real number (ℝ)

Distinct10
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6491228
Minimum3
Maximum34
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:47.246336image/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 deviation5.2047068
Coefficient of variation (CV)0.60176123
Kurtosis9.3751584
Mean8.6491228
Median Absolute Deviation (MAD)2
Skewness2.4762742
Sum493
Variance27.088972
MonotonicityNot monotonic
2024-04-18T09:30:47.344386image/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 6
10.5%
16 2
 
3.5%
12 2
 
3.5%
3 1
 
1.8%
22 1
 
1.8%
34 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 2
 
3.5%
14 6
10.5%
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 6
10.5%
12 2
 
3.5%
10 7
12.3%
8 13
22.8%
6 12
21.1%
4 12
21.1%
3 1
 
1.8%

anchor_cost
Real number (ℝ)

Distinct10
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean93593.86
Minimum100
Maximum150000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:47.440132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100000
median100000
Q3100000
95-th percentile150000
Maximum150000
Range149900
Interquartile range (IQR)0

Descriptive statistics

Standard deviation41817.992
Coefficient of variation (CV)0.44680273
Kurtosis0.99458925
Mean93593.86
Median Absolute Deviation (MAD)0
Skewness-1.0210369
Sum5334850
Variance1.7487445 × 109
MonotonicityNot monotonic
2024-04-18T09:30:47.535369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
100000 33
57.9%
150000 10
 
17.5%
100 6
 
10.5%
72000 2
 
3.5%
50000 1
 
1.8%
70000 1
 
1.8%
94100 1
 
1.8%
96000 1
 
1.8%
80000 1
 
1.8%
150 1
 
1.8%
ValueCountFrequency (%)
100 6
 
10.5%
150 1
 
1.8%
50000 1
 
1.8%
70000 1
 
1.8%
72000 2
 
3.5%
80000 1
 
1.8%
94100 1
 
1.8%
96000 1
 
1.8%
100000 33
57.9%
150000 10
 
17.5%
ValueCountFrequency (%)
150000 10
 
17.5%
100000 33
57.9%
96000 1
 
1.8%
94100 1
 
1.8%
80000 1
 
1.8%
72000 2
 
3.5%
70000 1
 
1.8%
50000 1
 
1.8%
150 1
 
1.8%
100 6
 
10.5%

exes
Categorical

Distinct10
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size588.0 B
<NA>
19 
250000
12 
300000
7 
200000
6 
200
5 
Other values (5)
8 

Length

Max length38
Median length7
Mean length5.5964912
Min length3

Unique

Unique4 ?
Unique (%)7.0%

Sample

1st row200000
2nd row250000
3rd row<NA>
4th row250000
5th row200

Common Values

ValueCountFrequency (%)
<NA> 19
33.3%
250000 12
21.1%
300000 7
 
12.3%
200000 6
 
10.5%
200 5
 
8.8%
300,000 4
 
7.0%
100 1
 
1.8%
병사 200,000원, 외인사 300,000원, 야간 400,000원 1
 
1.8%
340000 1
 
1.8%
300 1
 
1.8%

Length

2024-04-18T09:30:47.664675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-18T09:30:47.791323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 19
30.6%
250000 12
19.4%
300000 7
 
11.3%
200000 6
 
9.7%
200 5
 
8.1%
300,000 4
 
6.5%
100 1
 
1.6%
병사 1
 
1.6%
200,000원 1
 
1.6%
외인사 1
 
1.6%
Other values (5) 5
 
8.1%

rent
Text

Distinct54
Distinct (%)94.7%
Missing0
Missing (%)0.0%
Memory size588.0 B
2024-04-18T09:30:48.003593image/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 row250000
2nd row1500000
3rd row500000
4th row450000~800000
5th row375~890
ValueCountFrequency (%)
250000 2
 
2.8%
300,000원 2
 
2.8%
500000 2
 
2.8%
300000~400000 2
 
2.8%
400000~500000 2
 
2.8%
350000~750000 1
 
1.4%
600000 1
 
1.4%
450000~550000 1
 
1.4%
240,000원 1
 
1.4%
특실 1
 
1.4%
Other values (56) 56
78.9%
2024-04-18T09:30:48.330627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 377
51.8%
5 51
 
7.0%
~ 43
 
5.9%
, 39
 
5.4%
32
 
4.4%
3 29
 
4.0%
2 25
 
3.4%
8 24
 
3.3%
4 20
 
2.7%
1 17
 
2.3%
Other values (17) 71
 
9.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 573
78.7%
Math Symbol 43
 
5.9%
Other Punctuation 43
 
5.9%
Other Letter 33
 
4.5%
Space Separator 32
 
4.4%
Close Punctuation 2
 
0.3%
Open Punctuation 2
 
0.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 377
65.8%
5 51
 
8.9%
3 29
 
5.1%
2 25
 
4.4%
8 24
 
4.2%
4 20
 
3.5%
1 17
 
3.0%
6 15
 
2.6%
7 10
 
1.7%
9 5
 
0.9%
Other Letter
ValueCountFrequency (%)
원 7
21.2%
호 7
21.2%
실 5
15.2%
특 4
12.1%
준 2
 
6.1%
기 2
 
6.1%
간 2
 
6.1%
시 2
 
6.1%
일 1
 
3.0%
반 1
 
3.0%
Other Punctuation
ValueCountFrequency (%)
, 39
90.7%
/ 2
 
4.7%
: 2
 
4.7%
Math Symbol
ValueCountFrequency (%)
~ 43
100.0%
Space Separator
ValueCountFrequency (%)
32
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open 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 51
 
7.3%
~ 43
 
6.2%
, 39
 
5.6%
32
 
4.6%
3 29
 
4.2%
2 25
 
3.6%
8 24
 
3.5%
4 20
 
2.9%
1 17
 
2.4%
Other values (7) 38
 
5.5%
Hangul
ValueCountFrequency (%)
원 7
21.2%
호 7
21.2%
실 5
15.2%
특 4
12.1%
준 2
 
6.1%
기 2
 
6.1%
간 2
 
6.1%
시 2
 
6.1%
일 1
 
3.0%
반 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 51
 
7.3%
~ 43
 
6.2%
, 39
 
5.6%
32
 
4.6%
3 29
 
4.2%
2 25
 
3.6%
8 24
 
3.5%
4 20
 
2.9%
1 17
 
2.4%
Other values (7) 38
 
5.5%
Hangul
ValueCountFrequency (%)
원 7
21.2%
호 7
21.2%
실 5
15.2%
특 4
12.1%
준 2
 
6.1%
기 2
 
6.1%
간 2
 
6.1%
시 2
 
6.1%
일 1
 
3.0%
반 1
 
3.0%

add_info
Text

MISSING 

Distinct3
Distinct (%)75.0%
Missing53
Missing (%)93.0%
Memory size588.0 B
2024-04-18T09:30:48.462933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length9
Mean length5.5
Min length2

Characters and Unicode

Total characters22
Distinct characters18
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 row안치료 수급자: 3,750
4th row별도안내
ValueCountFrequency (%)
없음 2
33.3%
안치료 1
16.7%
수급자 1
16.7%
3,750 1
16.7%
별도안내 1
16.7%
2024-04-18T09:30:48.703540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
없 2
 
9.1%
안 2
 
9.1%
2
 
9.1%
음 2
 
9.1%
, 1
 
4.5%
도 1
 
4.5%
별 1
 
4.5%
0 1
 
4.5%
5 1
 
4.5%
7 1
 
4.5%
Other values (8) 8
36.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14
63.6%
Decimal Number 4
 
18.2%
Space Separator 2
 
9.1%
Other Punctuation 2
 
9.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
없 2
14.3%
안 2
14.3%
음 2
14.3%
도 1
7.1%
별 1
7.1%
자 1
7.1%
급 1
7.1%
수 1
7.1%
료 1
7.1%
치 1
7.1%
Decimal Number
ValueCountFrequency (%)
0 1
25.0%
5 1
25.0%
7 1
25.0%
3 1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 1
50.0%
: 1
50.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14
63.6%
Common 8
36.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
없 2
14.3%
안 2
14.3%
음 2
14.3%
도 1
7.1%
별 1
7.1%
자 1
7.1%
급 1
7.1%
수 1
7.1%
료 1
7.1%
치 1
7.1%
Common
ValueCountFrequency (%)
2
25.0%
, 1
12.5%
0 1
12.5%
5 1
12.5%
7 1
12.5%
: 1
12.5%
3 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14
63.6%
ASCII 8
36.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
없 2
14.3%
안 2
14.3%
음 2
14.3%
도 1
7.1%
별 1
7.1%
자 1
7.1%
급 1
7.1%
수 1
7.1%
료 1
7.1%
치 1
7.1%
ASCII
ValueCountFrequency (%)
2
25.0%
, 1
12.5%
0 1
12.5%
5 1
12.5%
7 1
12.5%
: 1
12.5%
3 1
12.5%

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 사상구
4 
Other values (11)
26 

Length

Max length10
Median length9
Mean length8.9473684
Min length8

Unique

Unique3 ?
Unique (%)5.3%

Sample

1st row부산광역시 해운대구
2nd row부산광역시 해운대구
3rd row부산광역시 동래구
4th row부산광역시 영도구
5th row부산광역시 부산진구

Common Values

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

Length

2024-04-18T09:30:48.830450image/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%
사상구 4
 
3.5%
수영구 4
 
3.5%
서구 4
 
3.5%
동구 3
 
2.6%
해운대구 3
 
2.6%
Other values (7) 12
 
10.5%

data_day
Date

Distinct4
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum2020-12-31 00:00:00
Maximum2021-01-20 00:00:00
2024-04-18T09:30:48.925829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T09:30:49.016158image/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%
Mean35.15219
Minimum35.055893
Maximum35.321843
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:49.132730image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum35.055893
5-th percentile35.07412
Q135.100809
median35.150149
Q335.197243
95-th percentile35.255477
Maximum35.321843
Range0.2659497
Interquartile range (IQR)0.096434

Descriptive statistics

Standard deviation0.059514321
Coefficient of variation (CV)0.0016930473
Kurtosis-0.17665595
Mean35.15219
Median Absolute Deviation (MAD)0.04909401
Skewness0.56144696
Sum2003.6748
Variance0.0035419545
MonotonicityNot monotonic
2024-04-18T09:30:49.274292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
35.22775285 1
 
1.8%
35.1986548 1
 
1.8%
35.1195459068 1
 
1.8%
35.1120656235 1
 
1.8%
35.1010549897 1
 
1.8%
35.0810780662 1
 
1.8%
35.16266428 1
 
1.8%
35.195499 1
 
1.8%
35.204725 1
 
1.8%
35.197243 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%
35.0810780662 1
1.8%
35.087107 1
1.8%
35.090536 1
1.8%
35.090594 1
1.8%
ValueCountFrequency (%)
35.3218426987 1
1.8%
35.2672611234 1
1.8%
35.2595887 1
1.8%
35.2544487796 1
1.8%
35.2384778286 1
1.8%
35.2358117647 1
1.8%
35.22775285 1
1.8%
35.217415 1
1.8%
35.2161406896 1
1.8%
35.2155261742 1
1.8%

lng
Real number (ℝ)

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean129.05284
Minimum128.85329
Maximum129.24433
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size645.0 B
2024-04-18T09:30:49.401885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum128.85329
5-th percentile128.97177
Q1129.00781
median129.04988
Q3129.09359
95-th percentile129.17366
Maximum129.24433
Range0.3910437
Interquartile range (IQR)0.085784919

Descriptive statistics

Standard deviation0.06833183
Coefficient of variation (CV)0.00052948721
Kurtosis1.1312419
Mean129.05284
Median Absolute Deviation (MAD)0.043712749
Skewness0.26732905
Sum7356.0121
Variance0.004669239
MonotonicityNot monotonic
2024-04-18T09:30:49.540096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
129.1470716 1
 
1.8%
128.9905558 1
 
1.8%
129.0180241527 1
 
1.8%
129.0109762825 1
 
1.8%
129.0192223191 1
 
1.8%
129.0145127995 1
 
1.8%
129.1715332 1
 
1.8%
129.068609 1
 
1.8%
129.080273 1
 
1.8%
129.096305 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 (%)
129.2443306989 1
1.8%
129.2167551174 1
1.8%
129.1821691 1
1.8%
129.1715332 1
1.8%
129.1470716 1
1.8%
129.1155231774 1
1.8%
129.1152464055 1
1.8%
129.1128819238 1
1.8%
129.1106007076 1
1.8%
129.1103289362 1
1.8%

apr_at
Boolean

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size189.0 B
False
57 
ValueCountFrequency (%)
False 57
100.0%
2024-04-18T09:30:49.650216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

last_load_dttm
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size588.0 B
Minimum2021-05-01 05:50:03
Maximum2021-05-01 05:50:03
2024-04-18T09:30:49.723003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-18T09:30:49.804795image/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
03063330000부산광역시반송 장례식장사설부산광역시 해운대구 반송로 832 (반송동)<NA>임대박환옥,김미경051-525-10242009-06-16114466434100000200000250000<NA>부산광역시 해운대구2020-12-3135.227753129.147072N2021-05-01 05:50:03
13073330000부산광역시해운대백병원 장례식장사설부산광역시 해운대구 해운대로 875 (좌동, 해운대백병원 내)<NA>임대㈜시민장례식장051-711-44002011-03-102808142349141000002500001500000<NA>부산광역시 해운대구2020-12-3135.17326129.182169N2021-05-01 05:50:03
23123300000부산광역시성산현대요양병원장례식장사설부산광역시 동래구 시실로 12 (명륜동)<NA>임대이수향051-558-79962017-03-15<NA><NA>36150000<NA>500000<NA>부산광역시 동래구2020-12-3135.217415129.085505N2021-05-01 05:50:03
33153280000부산광역시정요양병원장례식장사설부산광역시 영도구 절영로 32<NA>직영김조은051-410-17772013-01-02949122158100000250000450000~800000<NA>부산광역시 영도구2020-12-3135.091698129.039072N2021-05-01 05:50:03
43233290000부산광역시부산백병원장례식장사설부산진구 복지로 75 (개금동) K동<NA>임대문성훈051-896-44442019-06-2416122684714100200375~890<NA>부산광역시 부산진구2020-12-3135.146453129.020571N2021-05-01 05:50:03
53243290000부산광역시동의의료원장례식장사설부산진구 양정로 62 (양정동) A동<NA>임대정상오 외 1051-850-85771994-09-301287124658100200160~650<NA>부산광역시 부산진구2020-12-3135.169949129.075921N2021-05-01 05:50:03
63313390000부산광역시부산보훈병원장례식장사설부산광역시 사상구 백양대로 420<NA>직영김덕남051-601-67852005-05-201200241991450000300,000214,000~728,000<NA>부산광역시 사상구2020-12-3135.152895129.008248N2021-05-01 05:50:03
73323390000부산광역시삼신전문장례식장사설부산광역시 사상구 대동로 261<NA>직영안영선051-323-00442005-11-1522662700910100000300,000210,000~800,000<NA>부산광역시 사상구2020-12-3135.150876128.985175N2021-05-01 05:50:03
83333310000부산광역시부산성모병원 장례식장사설부산광역시 남구 용호로 232번길25-14<NA>직영김준현051-933-74802006-05-19352396013851672000250000408,000~1,500,000(1실/24시간기준)없음부산광역시 남구2020-12-3135.110511129.109192N2021-05-01 05:50:03
93343310000부산광역시대연장례식장사설부산광역시 남구 수영로164<NA>직영박창렬051-711-44482019-10-23<NA>6013836100000200000305,000~650,000(1실/24시간기준)없음부산광역시 남구2020-12-3135.134468129.084599N2021-05-01 05:50:03
skeyinstt_codecitycmp_nmcategoryaddr_roadaddr_jibunoperationceo_nmtelopen_datesiteareavacan_numanchoranchor_costexesrentadd_infogugundata_daylatlngapr_atlast_load_dttm
473213290000부산광역시(주)시민장례식장사설부산진구 자유평화로 31 (범천동)<NA>직영문성훈051-636-44442013-12-194090206091834100200186~3,800<NA>부산광역시 부산진구2020-12-3135.141614129.063007N2021-05-01 05:50:03
483223290000부산광역시(주)수장례식장사설부산진구 동평로 401 (양정동)<NA>임대고범찬070-4015-32672013-11-151654168268100300300~800<NA>부산광역시 부산진구2020-12-3135.174272129.069034N2021-05-01 05:50:03
493293390000부산광역시부산전문장례식장사설부산광역시 사상구 낙동대로 1056<NA>직영박경옥051-312-44442002-07-02217833131214100000300,000295,000~889,000<NA>부산광역시 사상구2020-12-3135.154585128.97352N2021-05-01 05:50:03
503303390000부산광역시좋은삼선병원장례식장사설부산광역시 사상구 가야대로 326<NA>직영이민태051-310-92921995-06-011701336956100000300,000150,000~600,000<NA>부산광역시 사상구2020-12-3135.150149129.007808N2021-05-01 05:50:03
513353340000부산광역시괴정병원장례식장사설부산광역시 사하구 낙동대로249번길11(괴정동)<NA>임대전흥배051-293-43822011-01-01859471334150000<NA>350000~750000<NA>부산광역시 사하구2020-12-3135.098886128.990361N2021-05-01 05:50:03
523363340000부산광역시구평예일요양병원장례식장사설부산광역시 사하구 사하로 55(구평동)<NA>임대정성혜051-262-10242013-07-101002211226100000<NA>450000~550000<NA>부산광역시 사하구2020-12-3135.087107128.993421N2021-05-01 05:50:03
533373340000부산광역시동산병원장례식장사설부산광역시 사하구 낙동대로216번길10(괴정동)<NA>임대홍효석051-201-09092000-01-1543656536100000<NA>550000~650000<NA>부산광역시 사하구2020-12-3135.100809128.993335N2021-05-01 05:50:03
543383340000부산광역시경희병원장례식장사설부산광역시 사하구 다대로 321(장림동)<NA>임대허종실051-266-41112000-04-06213631226150000<NA>400000~500000<NA>부산광역시 사하구2020-12-3135.073531128.976831N2021-05-01 05:50:03
553493270000부산광역시인창병원 장례식장사설동구 중앙대로 281<NA>임대김상태051-464-58582004-02-09<NA>2770912100000250000180000~880000<NA>부산광역시 동구2021-01-0835.1218129.042949N2021-05-01 05:50:03
563503270000부산광역시청십자병원 장례식장사설동구 중앙대로 364<NA>직영류정건051-469-10112007-04-01<NA>46134100000300000250000~390,000<NA>부산광역시 동구2021-01-0835.127801129.04838N2021-05-01 05:50:03