Overview

Dataset statistics

Number of variables15
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.5 KiB
Average record size in memory128.3 B

Variable types

Categorical7
Text2
Numeric6

Alerts

ldgs_nm is highly overall correlated with rstrnt_la and 5 other fieldsHigh correlation
cty_nm is highly overall correlated with rstrnt_la and 4 other fieldsHigh correlation
base_ym is highly overall correlated with rstrnt_lo and 6 other fieldsHigh correlation
adstrd_nm is highly overall correlated with rstrnt_la and 5 other fieldsHigh correlation
ldgs_addr is highly overall correlated with rstrnt_la and 5 other fieldsHigh correlation
rstrnt_la is highly overall correlated with ldgs_nm and 3 other fieldsHigh correlation
rstrnt_lo is highly overall correlated with base_ym and 4 other fieldsHigh correlation
sccnt_sm_value is highly overall correlated with base_ymHigh correlation
jjinhbt_sales_price_rate is highly overall correlated with all_sales_price_rateHigh correlation
otsd_sales_price_rate is highly overall correlated with all_sales_price_rate and 1 other fieldsHigh correlation
all_sales_price_rate is highly overall correlated with jjinhbt_sales_price_rate and 2 other fieldsHigh correlation
lclas_nm is highly overall correlated with mlsfc_nmHigh correlation
mlsfc_nm is highly overall correlated with lclas_nmHigh correlation
base_ym is highly imbalanced (80.6%)Imbalance
otsd_sales_price_rate has 8 (8.0%) zerosZeros

Reproduction

Analysis started2023-12-10 09:48:37.964260
Analysis finished2023-12-10 09:48:46.463700
Duration8.5 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

base_ym
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
202307
97 
202309
 
3

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202307
2nd row202309
3rd row202307
4th row202307
5th row202307

Common Values

ValueCountFrequency (%)
202307 97
97.0%
202309 3
 
3.0%

Length

2023-12-10T18:48:46.569791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:46.723952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202307 97
97.0%
202309 3
 
3.0%

ldgs_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
롯데호텔 제주
55 
그랜드 조선 제주
13 
호텔 더 베이스(후렌드리호텔)
13 
스위트 호텔 제주
12 
서귀포 칼 호텔
 
4

Length

Max length16
Median length7
Mean length8.71
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row스위트 호텔 제주
2nd row씨사이드 아덴
3rd row스위트 호텔 제주
4th row스위트 호텔 제주
5th row스위트 호텔 제주

Common Values

ValueCountFrequency (%)
롯데호텔 제주 55
55.0%
그랜드 조선 제주 13
 
13.0%
호텔 더 베이스(후렌드리호텔) 13
 
13.0%
스위트 호텔 제주 12
 
12.0%
서귀포 칼 호텔 4
 
4.0%
씨사이드 아덴 3
 
3.0%

Length

2023-12-10T18:48:46.894288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:47.078214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주 80
33.1%
롯데호텔 55
22.7%
호텔 29
 
12.0%
그랜드 13
 
5.4%
조선 13
 
5.4%
더 13
 
5.4%
베이스(후렌드리호텔 13
 
5.4%
스위트 12
 
5.0%
서귀포 4
 
1.7%
칼 4
 
1.7%
Other values (2) 6
 
2.5%

ldgs_addr
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주특별자치도 제주시 도령로 83
55 
제주특별자치도 서귀포시 중문관광로72번길 60
13 
제주특별자치도 제주시 사장3길 33
13 
제주특별자치도 서귀포시 중문관광로72번길 67
12 
제주특별자치도 서귀포시 칠십리로 242
 
4

Length

Max length25
Median length18
Mean length20.12
Min length18

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row제주특별자치도 서귀포시 중문관광로72번길 67
2nd row제주특별자치도 서귀포시 중문관광로 124
3rd row제주특별자치도 서귀포시 중문관광로72번길 67
4th row제주특별자치도 서귀포시 중문관광로72번길 67
5th row제주특별자치도 서귀포시 중문관광로72번길 67

Common Values

ValueCountFrequency (%)
제주특별자치도 제주시 도령로 83 55
55.0%
제주특별자치도 서귀포시 중문관광로72번길 60 13
 
13.0%
제주특별자치도 제주시 사장3길 33 13
 
13.0%
제주특별자치도 서귀포시 중문관광로72번길 67 12
 
12.0%
제주특별자치도 서귀포시 칠십리로 242 4
 
4.0%
제주특별자치도 서귀포시 중문관광로 124 3
 
3.0%

Length

2023-12-10T18:48:47.391190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:47.652518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주특별자치도 100
25.0%
제주시 68
17.0%
도령로 55
13.8%
83 55
13.8%
서귀포시 32
 
8.0%
중문관광로72번길 25
 
6.2%
60 13
 
3.2%
사장3길 13
 
3.2%
33 13
 
3.2%
67 12
 
3.0%
Other values (4) 14
 
3.5%

lclas_nm
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한식
66 
고기
10 
카페
9 
베이커리.디저트
8 
피자.치킨.햄버거
 
5

Length

Max length9
Median length2
Mean length2.87
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row고기
2nd row한식
3rd row한식
4th row고기
5th row카페

Common Values

ValueCountFrequency (%)
한식 66
66.0%
고기 10
 
10.0%
카페 9
 
9.0%
베이커리.디저트 8
 
8.0%
피자.치킨.햄버거 5
 
5.0%
일식.회 2
 
2.0%

Length

2023-12-10T18:48:48.001935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:48.208503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 66
66.0%
고기 10
 
10.0%
카페 9
 
9.0%
베이커리.디저트 8
 
8.0%
피자.치킨.햄버거 5
 
5.0%
일식.회 2
 
2.0%

mlsfc_nm
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)14.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
가정식
29 
단품요리 전문
17 
커피
11 
해장국
10 
돼지고기
8 
Other values (9)
25 

Length

Max length7
Median length4
Mean length3.51
Min length1

Unique

Unique3 ?
Unique (%)3.0%

Sample

1st row돼지고기
2nd row가정식
3rd row단품요리 전문
4th row돼지고기
5th row커피

Common Values

ValueCountFrequency (%)
가정식 29
29.0%
단품요리 전문 17
17.0%
커피 11
 
11.0%
해장국 10
 
10.0%
돼지고기 8
 
8.0%
면류 7
 
7.0%
베이커리 6
 
6.0%
양식 3
 
3.0%
분식 2
 
2.0%
회 2
 
2.0%
Other values (4) 5
 
5.0%

Length

2023-12-10T18:48:48.423188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
가정식 29
24.8%
단품요리 17
14.5%
전문 17
14.5%
커피 11
 
9.4%
해장국 10
 
8.5%
돼지고기 8
 
6.8%
면류 7
 
6.0%
베이커리 6
 
5.1%
양식 3
 
2.6%
분식 2
 
1.7%
Other values (5) 7
 
6.0%

rstrnt_nm
Text

Distinct81
Distinct (%)81.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:48:48.899278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length11
Mean length6.17
Min length1

Characters and Unicode

Total characters617
Distinct characters175
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)62.0%

Sample

1st row신우성흑돼지
2nd row형제도식당
3rd row망고하이
4th row돈이랑
5th row카페1950
ValueCountFrequency (%)
신우성흑돼지 2
 
2.0%
카페1950 2
 
2.0%
중문미향해장국본점 2
 
2.0%
고사리식당 2
 
2.0%
명륜진사갈비제주연동점 2
 
2.0%
시골길 2
 
2.0%
빽다방제주제원점 2
 
2.0%
어촌마당 2
 
2.0%
삼무국수 2
 
2.0%
자리돔 2
 
2.0%
Other values (71) 80
80.0%
2023-12-10T18:48:49.787335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
점 35
 
5.7%
주 26
 
4.2%
제 26
 
4.2%
국 21
 
3.4%
당 15
 
2.4%
식 13
 
2.1%
장 12
 
1.9%
리 12
 
1.9%
해 12
 
1.9%
중 10
 
1.6%
Other values (165) 435
70.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 581
94.2%
Decimal Number 16
 
2.6%
Uppercase Letter 14
 
2.3%
Open Punctuation 3
 
0.5%
Close Punctuation 3
 
0.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
점 35
 
6.0%
주 26
 
4.5%
제 26
 
4.5%
국 21
 
3.6%
당 15
 
2.6%
식 13
 
2.2%
장 12
 
2.1%
리 12
 
2.1%
해 12
 
2.1%
중 10
 
1.7%
Other values (151) 399
68.7%
Decimal Number
ValueCountFrequency (%)
2 4
25.0%
0 3
18.8%
4 2
12.5%
5 2
12.5%
9 2
12.5%
1 2
12.5%
6 1
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
D 4
28.6%
T 4
28.6%
C 2
14.3%
F 2
14.3%
K 2
14.3%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 581
94.2%
Common 22
 
3.6%
Latin 14
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
점 35
 
6.0%
주 26
 
4.5%
제 26
 
4.5%
국 21
 
3.6%
당 15
 
2.6%
식 13
 
2.2%
장 12
 
2.1%
리 12
 
2.1%
해 12
 
2.1%
중 10
 
1.7%
Other values (151) 399
68.7%
Common
ValueCountFrequency (%)
2 4
18.2%
0 3
13.6%
( 3
13.6%
) 3
13.6%
4 2
9.1%
5 2
9.1%
9 2
9.1%
1 2
9.1%
6 1
 
4.5%
Latin
ValueCountFrequency (%)
D 4
28.6%
T 4
28.6%
C 2
14.3%
F 2
14.3%
K 2
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 581
94.2%
ASCII 36
 
5.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
점 35
 
6.0%
주 26
 
4.5%
제 26
 
4.5%
국 21
 
3.6%
당 15
 
2.6%
식 13
 
2.2%
장 12
 
2.1%
리 12
 
2.1%
해 12
 
2.1%
중 10
 
1.7%
Other values (151) 399
68.7%
ASCII
ValueCountFrequency (%)
2 4
11.1%
D 4
11.1%
T 4
11.1%
0 3
8.3%
( 3
8.3%
) 3
8.3%
4 2
 
5.6%
5 2
 
5.6%
9 2
 
5.6%
1 2
 
5.6%
Other values (4) 7
19.4%

cty_nm
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주시
68 
서귀포시
32 

Length

Max length4
Median length3
Mean length3.32
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 68
68.0%
서귀포시 32
32.0%

Length

2023-12-10T18:48:50.175168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:50.450059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 68
68.0%
서귀포시 32
32.0%

adstrd_nm
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)9.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
연동
44 
예래동
27 
노형동
21 
용담2동
 
2
송산동
 
2
Other values (4)
 
4

Length

Max length4
Median length3
Mean length2.58
Min length2

Unique

Unique4 ?
Unique (%)4.0%

Sample

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

Common Values

ValueCountFrequency (%)
연동 44
44.0%
예래동 27
27.0%
노형동 21
21.0%
용담2동 2
 
2.0%
송산동 2
 
2.0%
중문동 1
 
1.0%
도두동 1
 
1.0%
영천동 1
 
1.0%
동홍동 1
 
1.0%

Length

2023-12-10T18:48:50.717447image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T18:48:50.978907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연동 44
44.0%
예래동 27
27.0%
노형동 21
21.0%
용담2동 2
 
2.0%
송산동 2
 
2.0%
중문동 1
 
1.0%
도두동 1
 
1.0%
영천동 1
 
1.0%
동홍동 1
 
1.0%

rstrnt_addr
Text

Distinct78
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T18:48:51.562299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length25
Median length24
Mean length15.87
Min length12

Characters and Unicode

Total characters1587
Distinct characters65
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique62 ?
Unique (%)62.0%

Sample

1st row제주 서귀포시 중문관광로72번길 29-9
2nd row제주 서귀포시 일주서로 915
3rd row제주 서귀포시 일주서로 972
4th row제주 서귀포시 일주서로 953
5th row제주 서귀포시 중문관광로 90
ValueCountFrequency (%)
제주 100
24.8%
제주시 68
16.8%
서귀포시 32
 
7.9%
일주서로 15
 
3.7%
연동 8
 
2.0%
중문관광로72번길 6
 
1.5%
중문관광로 6
 
1.5%
신광로 5
 
1.2%
원노형로 5
 
1.2%
39 4
 
1.0%
Other values (106) 155
38.4%
2023-12-10T18:48:52.271082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
316
19.9%
주 183
 
11.5%
제 173
 
10.9%
시 100
 
6.3%
로 73
 
4.6%
1 58
 
3.7%
길 50
 
3.2%
서 47
 
3.0%
2 45
 
2.8%
9 40
 
2.5%
Other values (55) 502
31.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 937
59.0%
Space Separator 316
 
19.9%
Decimal Number 311
 
19.6%
Dash Punctuation 17
 
1.1%
Open Punctuation 3
 
0.2%
Close Punctuation 3
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 183
19.5%
제 173
18.5%
시 100
10.7%
로 73
 
7.8%
길 50
 
5.3%
서 47
 
5.0%
귀 33
 
3.5%
포 32
 
3.4%
광 22
 
2.3%
연 19
 
2.0%
Other values (41) 205
21.9%
Decimal Number
ValueCountFrequency (%)
1 58
18.6%
2 45
14.5%
9 40
12.9%
7 35
11.3%
3 33
10.6%
5 27
8.7%
4 26
8.4%
6 17
 
5.5%
0 16
 
5.1%
8 14
 
4.5%
Space Separator
ValueCountFrequency (%)
316
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 17
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 937
59.0%
Common 650
41.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 183
19.5%
제 173
18.5%
시 100
10.7%
로 73
 
7.8%
길 50
 
5.3%
서 47
 
5.0%
귀 33
 
3.5%
포 32
 
3.4%
광 22
 
2.3%
연 19
 
2.0%
Other values (41) 205
21.9%
Common
ValueCountFrequency (%)
316
48.6%
1 58
 
8.9%
2 45
 
6.9%
9 40
 
6.2%
7 35
 
5.4%
3 33
 
5.1%
5 27
 
4.2%
4 26
 
4.0%
- 17
 
2.6%
6 17
 
2.6%
Other values (4) 36
 
5.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 937
59.0%
ASCII 650
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
316
48.6%
1 58
 
8.9%
2 45
 
6.9%
9 40
 
6.2%
7 35
 
5.4%
3 33
 
5.1%
5 27
 
4.2%
4 26
 
4.0%
- 17
 
2.6%
6 17
 
2.6%
Other values (4) 36
 
5.5%
Hangul
ValueCountFrequency (%)
주 183
19.5%
제 173
18.5%
시 100
10.7%
로 73
 
7.8%
길 50
 
5.3%
서 47
 
5.0%
귀 33
 
3.5%
포 32
 
3.4%
광 22
 
2.3%
연 19
 
2.0%
Other values (41) 205
21.9%

rstrnt_la
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33.413007
Minimum33.244215
Maximum33.498473
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:52.572757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum33.244215
5-th percentile33.245896
Q133.258285
median33.486443
Q333.488797
95-th percentile33.49382
Maximum33.498473
Range0.25425771
Interquartile range (IQR)0.23051139

Descriptive statistics

Standard deviation0.11038082
Coefficient of variation (CV)0.0033035284
Kurtosis-1.4079115
Mean33.413007
Median Absolute Deviation (MAD)0.0041494471
Skewness-0.78373122
Sum3341.3007
Variance0.012183925
MonotonicityNot monotonic
2023-12-10T18:48:52.934894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
33.249635813039 4
 
4.0%
33.250274655656 4
 
4.0%
33.258309876545 4
 
4.0%
33.484577209551 2
 
2.0%
33.490294948107 2
 
2.0%
33.482694518062 2
 
2.0%
33.488609193433 2
 
2.0%
33.48885304998 2
 
2.0%
33.48720627944 2
 
2.0%
33.490592767927 2
 
2.0%
Other values (66) 74
74.0%
ValueCountFrequency (%)
33.244215345627 1
 
1.0%
33.244429238368 1
 
1.0%
33.244794416718 1
 
1.0%
33.245388821254 1
 
1.0%
33.245896097872 2
2.0%
33.249635813039 4
4.0%
33.250269705394 1
 
1.0%
33.250274655656 4
4.0%
33.251364209747 1
 
1.0%
33.255288123184 1
 
1.0%
ValueCountFrequency (%)
33.498473050843 1
1.0%
33.495258697074 2
2.0%
33.493934671214 1
1.0%
33.493819940895 2
2.0%
33.493461566407 1
1.0%
33.49272350839 1
1.0%
33.492605144134 1
1.0%
33.491508058175 1
1.0%
33.491141669947 1
1.0%
33.490965153774 1
1.0%

rstrnt_lo
Real number (ℝ)

HIGH CORRELATION 

Distinct76
Distinct (%)76.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean126.47075
Minimum126.40456
Maximum126.59057
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:53.165910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum126.40456
5-th percentile126.40792
Q1126.41486
median126.4849
Q3126.4923
95-th percentile126.50011
Maximum126.59057
Range0.18600587
Interquartile range (IQR)0.077442987

Descriptive statistics

Standard deviation0.042104566
Coefficient of variation (CV)0.00033291939
Kurtosis0.4396419
Mean126.47075
Median Absolute Deviation (MAD)0.0081371473
Skewness0.038746793
Sum12647.075
Variance0.0017727945
MonotonicityNot monotonic
2023-12-10T18:48:53.444144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
126.411415843216 4
 
4.0%
126.414140512525 4
 
4.0%
126.407953610466 4
 
4.0%
126.492298803953 2
 
2.0%
126.491704751278 2
 
2.0%
126.485036782399 2
 
2.0%
126.484331241924 2
 
2.0%
126.494720984608 2
 
2.0%
126.489789289828 2
 
2.0%
126.490989424409 2
 
2.0%
Other values (66) 74
74.0%
ValueCountFrequency (%)
126.404562659884 1
 
1.0%
126.406072307333 1
 
1.0%
126.406903334433 2
2.0%
126.407218853339 1
 
1.0%
126.407953610466 4
4.0%
126.408189869169 2
2.0%
126.409091408813 2
2.0%
126.411415843216 4
4.0%
126.412974947964 2
2.0%
126.412994713637 1
 
1.0%
ValueCountFrequency (%)
126.590568531114 1
1.0%
126.587212653361 1
1.0%
126.58206314256 1
1.0%
126.574297102929 1
1.0%
126.500283152246 1
1.0%
126.500095671011 1
1.0%
126.497311282323 1
1.0%
126.497108066358 1
1.0%
126.497086821717 1
1.0%
126.496334662433 1
1.0%

sccnt_sm_value
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)65.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean404.59
Minimum84
Maximum7403
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:53.715087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum84
5-th percentile93
Q1129
median228
Q3360.5
95-th percentile1069
Maximum7403
Range7319
Interquartile range (IQR)231.5

Descriptive statistics

Standard deviation787.15863
Coefficient of variation (CV)1.9455711
Kurtosis64.53107
Mean404.59
Median Absolute Deviation (MAD)104
Skewness7.4791981
Sum40459
Variance619618.71
MonotonicityNot monotonic
2023-12-10T18:48:54.337384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
117 5
 
5.0%
124 3
 
3.0%
200 3
 
3.0%
230 3
 
3.0%
129 3
 
3.0%
273 3
 
3.0%
116 2
 
2.0%
178 2
 
2.0%
844 2
 
2.0%
92 2
 
2.0%
Other values (55) 72
72.0%
ValueCountFrequency (%)
84 1
 
1.0%
85 1
 
1.0%
92 2
 
2.0%
93 2
 
2.0%
95 1
 
1.0%
101 1
 
1.0%
107 2
 
2.0%
109 1
 
1.0%
116 2
 
2.0%
117 5
5.0%
ValueCountFrequency (%)
7403 1
1.0%
2368 1
1.0%
1812 1
1.0%
1328 1
1.0%
1069 2
2.0%
897 1
1.0%
844 2
2.0%
769 1
1.0%
707 1
1.0%
612 2
2.0%

jjinhbt_sales_price_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)61.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.7198
Minimum0
Maximum5.77
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:54.617245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.04
Q10.1975
median0.41
Q30.8675
95-th percentile2.652
Maximum5.77
Range5.77
Interquartile range (IQR)0.67

Descriptive statistics

Standard deviation0.93004071
Coefficient of variation (CV)1.2920821
Kurtosis10.470632
Mean0.7198
Median Absolute Deviation (MAD)0.325
Skewness2.8998007
Sum71.98
Variance0.86497572
MonotonicityNot monotonic
2023-12-10T18:48:54.876570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.05 10
 
10.0%
0.3 5
 
5.0%
0.53 3
 
3.0%
0.24 3
 
3.0%
0.26 3
 
3.0%
0.73 2
 
2.0%
0.01 2
 
2.0%
0.47 2
 
2.0%
0.49 2
 
2.0%
0.33 2
 
2.0%
Other values (51) 66
66.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.01 2
 
2.0%
0.03 1
 
1.0%
0.04 2
 
2.0%
0.05 10
10.0%
0.06 2
 
2.0%
0.09 1
 
1.0%
0.13 1
 
1.0%
0.14 1
 
1.0%
0.16 1
 
1.0%
ValueCountFrequency (%)
5.77 1
1.0%
3.86 2
2.0%
3.43 1
1.0%
2.69 1
1.0%
2.65 1
1.0%
2.31 1
1.0%
1.88 1
1.0%
1.86 1
1.0%
1.67 1
1.0%
1.64 1
1.0%

otsd_sales_price_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct45
Distinct (%)45.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.3417
Minimum0
Maximum4.39
Zeros8
Zeros (%)8.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:55.108901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.0275
median0.105
Q30.4325
95-th percentile1.3725
Maximum4.39
Range4.39
Interquartile range (IQR)0.405

Descriptive statistics

Standard deviation0.59218659
Coefficient of variation (CV)1.73306
Kurtosis22.403451
Mean0.3417
Median Absolute Deviation (MAD)0.095
Skewness4.0226679
Sum34.17
Variance0.35068496
MonotonicityNot monotonic
2023-12-10T18:48:55.343077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=45)
ValueCountFrequency (%)
0.01 11
 
11.0%
0.0 8
 
8.0%
0.04 7
 
7.0%
0.02 6
 
6.0%
0.1 5
 
5.0%
0.09 4
 
4.0%
0.12 3
 
3.0%
0.08 3
 
3.0%
0.15 3
 
3.0%
0.21 2
 
2.0%
Other values (35) 48
48.0%
ValueCountFrequency (%)
0.0 8
8.0%
0.01 11
11.0%
0.02 6
6.0%
0.03 1
 
1.0%
0.04 7
7.0%
0.05 1
 
1.0%
0.06 2
 
2.0%
0.07 2
 
2.0%
0.08 3
 
3.0%
0.09 4
 
4.0%
ValueCountFrequency (%)
4.39 1
1.0%
2.17 1
1.0%
1.72 2
2.0%
1.61 1
1.0%
1.36 1
1.0%
1.03 1
1.0%
1.02 1
1.0%
0.97 2
2.0%
0.91 1
1.0%
0.88 1
1.0%

all_sales_price_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct49
Distinct (%)49.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.4024
Minimum0
Maximum4.55
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T18:48:55.645813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.105
median0.21
Q30.53
95-th percentile1.234
Maximum4.55
Range4.55
Interquartile range (IQR)0.425

Descriptive statistics

Standard deviation0.56993021
Coefficient of variation (CV)1.4163276
Kurtosis28.194467
Mean0.4024
Median Absolute Deviation (MAD)0.17
Skewness4.4139583
Sum40.24
Variance0.32482044
MonotonicityNot monotonic
2023-12-10T18:48:55.916612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=49)
ValueCountFrequency (%)
0.01 7
 
7.0%
0.13 5
 
5.0%
0.21 5
 
5.0%
0.12 5
 
5.0%
0.7 4
 
4.0%
0.18 3
 
3.0%
0.53 3
 
3.0%
0.08 3
 
3.0%
0.03 3
 
3.0%
0.02 3
 
3.0%
Other values (39) 59
59.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.01 7
7.0%
0.02 3
3.0%
0.03 3
3.0%
0.04 2
 
2.0%
0.05 2
 
2.0%
0.06 3
3.0%
0.08 3
3.0%
0.09 1
 
1.0%
0.11 1
 
1.0%
ValueCountFrequency (%)
4.55 1
1.0%
1.92 1
1.0%
1.51 1
1.0%
1.5 2
2.0%
1.22 1
1.0%
1.21 1
1.0%
0.99 1
1.0%
0.95 1
1.0%
0.93 2
2.0%
0.85 1
1.0%

Interactions

2023-12-10T18:48:44.957539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:39.494827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:40.416394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:41.789125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:42.789540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:43.855457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:45.144088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:39.632366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:40.611775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:41.968097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:42.949280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:44.097758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:45.313883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:39.780248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:40.774174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:42.162028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:43.131924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:44.346066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:45.493713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:39.930849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:40.964717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:42.337134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:43.319199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:44.500227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:45.648520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:40.104163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:41.151489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:42.494678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:43.502792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:44.678994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:45.777797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:40.230912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:41.629333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:42.646961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:43.696634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T18:48:44.808415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T18:48:56.107652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
base_ymldgs_nmldgs_addrlclas_nmmlsfc_nmrstrnt_nmcty_nmadstrd_nmrstrnt_addrrstrnt_larstrnt_losccnt_sm_valuejjinhbt_sales_price_rateotsd_sales_price_rateall_sales_price_rate
base_ym1.0001.0001.0000.3950.0001.0000.2590.5521.0000.2590.7680.4500.2580.7340.736
ldgs_nm1.0001.0001.0000.2900.2540.0001.0000.8650.0001.0000.9530.3100.0000.6290.579
ldgs_addr1.0001.0001.0000.2900.2540.0001.0000.8650.0001.0000.9530.3100.0000.6290.579
lclas_nm0.3950.2900.2901.0000.9781.0000.2870.5690.7890.2870.4010.1360.4750.4560.504
mlsfc_nm0.0000.2540.2540.9781.0001.0000.4060.6550.9550.4060.5090.1510.6970.0000.312
rstrnt_nm1.0000.0000.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
cty_nm0.2591.0001.0000.2870.4061.0001.0001.0001.0000.9991.0000.0130.0000.4360.274
adstrd_nm0.5520.8650.8650.5690.6551.0001.0001.0001.0001.0000.9430.2610.4010.0000.000
rstrnt_addr1.0000.0000.0000.7890.9551.0001.0001.0001.0001.0001.0001.0000.9990.9910.993
rstrnt_la0.2591.0001.0000.2870.4061.0000.9991.0001.0001.0001.0000.0130.0000.4360.274
rstrnt_lo0.7680.9530.9530.4010.5091.0001.0000.9431.0001.0001.0000.0000.3720.0000.000
sccnt_sm_value0.4500.3100.3100.1360.1511.0000.0130.2611.0000.0130.0001.0000.3990.6670.626
jjinhbt_sales_price_rate0.2580.0000.0000.4750.6971.0000.0000.4010.9990.0000.3720.3991.0000.3100.445
otsd_sales_price_rate0.7340.6290.6290.4560.0001.0000.4360.0000.9910.4360.0000.6670.3101.0000.991
all_sales_price_rate0.7360.5790.5790.5040.3121.0000.2740.0000.9930.2740.0000.6260.4450.9911.000
2023-12-10T18:48:56.414238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
mlsfc_nmldgs_nmcty_nmbase_ymadstrd_nmldgs_addrlclas_nm
mlsfc_nm1.0000.1160.2960.0000.3360.1160.888
ldgs_nm0.1161.0000.9790.9790.6371.0000.106
cty_nm0.2960.9791.0000.1660.9640.9790.201
base_ym0.0000.9790.1661.0000.5350.9790.278
adstrd_nm0.3360.6370.9640.5351.0000.6370.318
ldgs_addr0.1161.0000.9790.9790.6371.0000.106
lclas_nm0.8880.1060.2010.2780.3180.1061.000
2023-12-10T18:48:56.664105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
rstrnt_larstrnt_losccnt_sm_valuejjinhbt_sales_price_rateotsd_sales_price_rateall_sales_price_ratebase_ymldgs_nmldgs_addrlclas_nmmlsfc_nmcty_nmadstrd_nm
rstrnt_la1.0000.4120.113-0.065-0.403-0.3290.1660.9790.9790.2010.2960.9770.964
rstrnt_lo0.4121.0000.0340.021-0.442-0.3890.5620.6820.6820.1530.2650.9790.782
sccnt_sm_value0.1130.0341.000-0.1460.2660.1950.5390.2130.2130.0890.0650.0000.148
jjinhbt_sales_price_rate-0.0650.021-0.1461.0000.2670.5490.1860.0000.0000.2840.3830.0000.206
otsd_sales_price_rate-0.403-0.4420.2660.2671.0000.9180.5320.2700.2700.1780.0000.3070.000
all_sales_price_rate-0.329-0.3890.1950.5490.9181.0000.5340.2410.2410.2010.1470.1920.000
base_ym0.1660.5620.5390.1860.5320.5341.0000.9790.9790.2780.0000.1660.535
ldgs_nm0.9790.6820.2130.0000.2700.2410.9791.0001.0000.1060.1160.9790.637
ldgs_addr0.9790.6820.2130.0000.2700.2410.9791.0001.0000.1060.1160.9790.637
lclas_nm0.2010.1530.0890.2840.1780.2010.2780.1060.1061.0000.8880.2010.318
mlsfc_nm0.2960.2650.0650.3830.0000.1470.0000.1160.1160.8881.0000.2960.336
cty_nm0.9770.9790.0000.0000.3070.1920.1660.9790.9790.2010.2961.0000.964
adstrd_nm0.9640.7820.1480.2060.0000.0000.5350.6370.6370.3180.3360.9641.000

Missing values

2023-12-10T18:48:45.981616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T18:48:46.338448image/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

base_ymldgs_nmldgs_addrlclas_nmmlsfc_nmrstrnt_nmcty_nmadstrd_nmrstrnt_addrrstrnt_larstrnt_losccnt_sm_valuejjinhbt_sales_price_rateotsd_sales_price_rateall_sales_price_rate
0202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67고기돼지고기신우성흑돼지서귀포시예래동제주 서귀포시 중문관광로72번길 29-933.249636126.4114161160.730.970.93
1202309씨사이드 아덴제주특별자치도 서귀포시 중문관광로 124한식가정식형제도식당서귀포시예래동제주 서귀포시 일주서로 91533.25786126.412995840.00.430.42
2202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67한식단품요리 전문망고하이서귀포시예래동제주 서귀포시 일주서로 97233.258233126.4069031220.040.280.25
3202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67고기돼지고기돈이랑서귀포시예래동제주 서귀포시 일주서로 95333.257702126.4090915620.191.721.5
4202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67카페커피카페1950서귀포시예래동제주 서귀포시 중문관광로 9033.250275126.4141411070.30.090.12
5202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67한식해장국쉬멍해장국중문점서귀포시예래동제주 서귀포시 천제연로 8533.255288126.414898950.270.120.14
6202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67한식가정식덤장중문점서귀포시예래동제주 서귀포시 천제연로 1733.257709126.408193450.920.660.7
7202309씨사이드 아덴제주특별자치도 서귀포시 중문관광로 124카페커피마노커피하우스서귀포시중문동제주 서귀포시 천제연로188번길 6-633.251364126.4239611570.690.240.31
8202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67한식가정식제주오성서귀포시예래동제주 서귀포시 중문관광로 2733.255647126.414737070.462.171.92
9202307스위트 호텔 제주제주특별자치도 서귀포시 중문관광로72번길 67피자.치킨.햄버거양식KFC서귀포중문DT점서귀포시예래동제주 서귀포시 중문관광로 9033.250275126.4141412300.930.230.33
base_ymldgs_nmldgs_addrlclas_nmmlsfc_nmrstrnt_nmcty_nmadstrd_nmrstrnt_addrrstrnt_larstrnt_losccnt_sm_valuejjinhbt_sales_price_rateotsd_sales_price_rateall_sales_price_rate
90202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33한식면류삼무국수제주시연동제주 제주시 연동 271-1033.490593126.4909894180.530.530.53
91202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33고기돼지고기돈가제주시연동제주 제주시 문송길 3633.486439126.4970874710.660.020.12
92202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33한식해장국은희네해장국2호점제주시연동제주 제주시 연북로 17833.481401126.5002833181.860.210.46
93202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33한식가정식어촌마당제주시연동제주 제주시 연동 273-4533.486248126.4918181240.050.00.01
94202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33카페커피빽다방제주제원점제주시연동제주 제주시 신광로 5633.487206126.4897893070.20.010.04
95202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33한식단품요리 전문시골길제주시연동제주 제주시 연동13길 933.488853126.4947212220.740.10.19
96202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33베이커리.디저트커피배스킨라빈스신제주점제주시노형동제주 제주시 도령로 5233.488609126.484331930.670.040.13
97202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33한식면류신제주보말칼국수제주본점제주시연동제주 제주시 선덕로5길 1933.487908126.5000962280.240.120.14
98202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33베이커리.디저트베이커리파리바게뜨제주연동2호점제주시노형동제주 제주시 원노형남2길 3533.482695126.4850371171.170.040.21
99202307호텔 더 베이스(후렌드리호텔)제주특별자치도 제주시 사장3길 33한식가정식고사리식당제주시연동제주 제주시 삼무로3길 3933.490295126.4917052000.050.020.03