Overview

Dataset statistics

Number of variables16
Number of observations100
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory13.5 KiB
Average record size in memory138.3 B

Variable types

Categorical7
Text1
Numeric8

Alerts

anals_ym has constant value ""Constant
signgu_nm is highly overall correlated with otsd_sales_co_rate and 7 other fieldsHigh correlation
adstrd_nm is highly overall correlated with otsd_sales_price_rate and 3 other fieldsHigh correlation
area_nm is highly overall correlated with otsd_sales_price_rate and 4 other fieldsHigh correlation
mlsfc_nm is highly overall correlated with sclas_nmHigh correlation
sclas_nm is highly overall correlated with otsd_sales_price_rate and 8 other fieldsHigh correlation
gid_id is highly overall correlated with otsd_sales_price_rate and 8 other fieldsHigh correlation
jjinhbt_sales_price_rate is highly overall correlated with jjinhbt_sales_co_rate and 4 other fieldsHigh correlation
jjinhbt_sales_co_rate is highly overall correlated with jjinhbt_sales_price_rate and 3 other fieldsHigh correlation
otsd_sales_price_rate is highly overall correlated with jjinhbt_sales_price_rate and 7 other fieldsHigh correlation
otsd_sales_co_rate is highly overall correlated with jjinhbt_sales_price_rate and 9 other fieldsHigh correlation
all_sales_price_rate is highly overall correlated with jjinhbt_sales_price_rate and 7 other fieldsHigh correlation
all_sales_co_rate is highly overall correlated with jjinhbt_sales_price_rate and 7 other fieldsHigh correlation
vartion_rt is highly overall correlated with otsd_sales_co_rate and 4 other fieldsHigh correlation
rank_co is highly overall correlated with otsd_sales_co_rate and 1 other fieldsHigh correlation
signgu_nm is highly imbalanced (80.6%)Imbalance
adstrd_nm is highly imbalanced (78.7%)Imbalance
area_nm is highly imbalanced (84.9%)Imbalance
gid_id is highly imbalanced (80.1%)Imbalance
cmpnm_nm has unique valuesUnique
rank_co has unique valuesUnique
jjinhbt_sales_price_rate has 5 (5.0%) zerosZeros
jjinhbt_sales_co_rate has 5 (5.0%) zerosZeros
otsd_sales_price_rate has 18 (18.0%) zerosZeros
otsd_sales_co_rate has 5 (5.0%) zerosZeros
all_sales_price_rate has 5 (5.0%) zerosZeros
vartion_rt has 5 (5.0%) zerosZeros

Reproduction

Analysis started2023-12-10 10:19:39.344447
Analysis finished2023-12-10 10:19:51.829239
Duration12.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

anals_ym
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
202107
100 

Length

Max length6
Median length6
Mean length6
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row202107
2nd row202107
3rd row202107
4th row202107
5th row202107

Common Values

ValueCountFrequency (%)
202107 100
100.0%

Length

2023-12-10T19:19:51.929321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:52.083766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
202107 100
100.0%

cmpnm_nm
Text

UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
2023-12-10T19:19:52.423474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.17
Min length1

Characters and Unicode

Total characters717
Distinct characters245
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique100 ?
Unique (%)100.0%

Sample

1st row한국맥도날드(유)제주노형점
2nd row우리들골프장구내식당
3rd row봉크랑
4th row주식회사 파스쿠찌제주노형로터리점
5th row바르메
ValueCountFrequency (%)
노형점 4
 
3.3%
주식회사 3
 
2.5%
제주노형점 2
 
1.7%
제주점 2
 
1.7%
한국맥도날드(유)제주노형점 1
 
0.8%
뚜레쥬르 1
 
0.8%
푸동 1
 
0.8%
jeju 1
 
0.8%
주식회사위드제주(with 1
 
0.8%
서가앤쿡 1
 
0.8%
Other values (104) 104
86.0%
2023-12-10T19:19:53.159286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
주 41
 
5.7%
점 38
 
5.3%
제 32
 
4.5%
형 26
 
3.6%
노 26
 
3.6%
21
 
2.9%
이 21
 
2.9%
스 17
 
2.4%
리 12
 
1.7%
트 9
 
1.3%
Other values (235) 474
66.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 649
90.5%
Space Separator 21
 
2.9%
Lowercase Letter 14
 
2.0%
Uppercase Letter 13
 
1.8%
Open Punctuation 7
 
1.0%
Close Punctuation 7
 
1.0%
Decimal Number 6
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
주 41
 
6.3%
점 38
 
5.9%
제 32
 
4.9%
형 26
 
4.0%
노 26
 
4.0%
이 21
 
3.2%
스 17
 
2.6%
리 12
 
1.8%
트 9
 
1.4%
국 9
 
1.4%
Other values (207) 418
64.4%
Lowercase Letter
ValueCountFrequency (%)
j 2
14.3%
e 2
14.3%
a 2
14.3%
w 1
7.1%
i 1
7.1%
t 1
7.1%
h 1
7.1%
u 1
7.1%
b 1
7.1%
y 1
7.1%
Uppercase Letter
ValueCountFrequency (%)
L 2
15.4%
T 2
15.4%
C 2
15.4%
B 1
7.7%
G 1
7.7%
V 1
7.7%
E 1
7.7%
A 1
7.7%
M 1
7.7%
D 1
7.7%
Decimal Number
ValueCountFrequency (%)
9 2
33.3%
8 2
33.3%
3 1
16.7%
1 1
16.7%
Space Separator
ValueCountFrequency (%)
21
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 649
90.5%
Common 41
 
5.7%
Latin 27
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
주 41
 
6.3%
점 38
 
5.9%
제 32
 
4.9%
형 26
 
4.0%
노 26
 
4.0%
이 21
 
3.2%
스 17
 
2.6%
리 12
 
1.8%
트 9
 
1.4%
국 9
 
1.4%
Other values (207) 418
64.4%
Latin
ValueCountFrequency (%)
j 2
 
7.4%
e 2
 
7.4%
L 2
 
7.4%
T 2
 
7.4%
a 2
 
7.4%
C 2
 
7.4%
w 1
 
3.7%
i 1
 
3.7%
t 1
 
3.7%
h 1
 
3.7%
Other values (11) 11
40.7%
Common
ValueCountFrequency (%)
21
51.2%
( 7
 
17.1%
) 7
 
17.1%
9 2
 
4.9%
8 2
 
4.9%
3 1
 
2.4%
1 1
 
2.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 649
90.5%
ASCII 68
 
9.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
주 41
 
6.3%
점 38
 
5.9%
제 32
 
4.9%
형 26
 
4.0%
노 26
 
4.0%
이 21
 
3.2%
스 17
 
2.6%
리 12
 
1.8%
트 9
 
1.4%
국 9
 
1.4%
Other values (207) 418
64.4%
ASCII
ValueCountFrequency (%)
21
30.9%
( 7
 
10.3%
) 7
 
10.3%
j 2
 
2.9%
9 2
 
2.9%
8 2
 
2.9%
e 2
 
2.9%
L 2
 
2.9%
T 2
 
2.9%
a 2
 
2.9%
Other values (18) 19
27.9%

signgu_nm
Categorical

HIGH CORRELATION  IMBALANCE 

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

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 97
97.0%
서귀포시 3
 
3.0%

Length

2023-12-10T19:19:53.394450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:53.554154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
제주시 97
97.0%
서귀포시 3
 
3.0%

adstrd_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
노형동
93 
일도2동
 
3
안덕면
 
2
영천동
 
1
한경면
 
1

Length

Max length4
Median length3
Mean length3.03
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

1st row노형동
2nd row영천동
3rd row노형동
4th row노형동
5th row노형동

Common Values

ValueCountFrequency (%)
노형동 93
93.0%
일도2동 3
 
3.0%
안덕면 2
 
2.0%
영천동 1
 
1.0%
한경면 1
 
1.0%

Length

2023-12-10T19:19:53.728069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:53.903791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
노형동 93
93.0%
일도2동 3
 
3.0%
안덕면 2
 
2.0%
영천동 1
 
1.0%
한경면 1
 
1.0%

area_nm
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)4.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
제주시 동지역
96 
안덕면
 
2
서귀포시 동지역
 
1
한경면
 
1

Length

Max length8
Median length7
Mean length6.89
Min length3

Unique

Unique2 ?
Unique (%)2.0%

Sample

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

Common Values

ValueCountFrequency (%)
제주시 동지역 96
96.0%
안덕면 2
 
2.0%
서귀포시 동지역 1
 
1.0%
한경면 1
 
1.0%

Length

2023-12-10T19:19:54.422871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:54.588614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동지역 97
49.2%
제주시 96
48.7%
안덕면 2
 
1.0%
서귀포시 1
 
0.5%
한경면 1
 
0.5%

mlsfc_nm
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)7.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
한식
58 
아시아음식
10 
간식
9 
음료
8 
양식
7 
Other values (2)
8 

Length

Max length7
Median length2
Mean length2.58
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row패스트푸드
2nd row한식
3rd row한식
4th row음료
5th row한식

Common Values

ValueCountFrequency (%)
한식 58
58.0%
아시아음식 10
 
10.0%
간식 9
 
9.0%
음료 8
 
8.0%
양식 7
 
7.0%
패스트푸드 6
 
6.0%
주점및주류판매 2
 
2.0%

Length

2023-12-10T19:19:54.731596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:54.907102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
한식 58
58.0%
아시아음식 10
 
10.0%
간식 9
 
9.0%
음료 8
 
8.0%
양식 7
 
7.0%
패스트푸드 6
 
6.0%
주점및주류판매 2
 
2.0%

sclas_nm
Categorical

HIGH CORRELATION 

Distinct20
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
단품요리 전문
34 
가정식
22 
일식
6 
베이커리
5 
커피
5 
Other values (15)
28 

Length

Max length9
Median length8
Mean length4.48
Min length1

Unique

Unique8 ?
Unique (%)8.0%

Sample

1st row햄버거
2nd row구내식당/푸드코트
3rd row가정식
4th row커피
5th row가정식

Common Values

ValueCountFrequency (%)
단품요리 전문 34
34.0%
가정식 22
22.0%
일식 6
 
6.0%
베이커리 5
 
5.0%
커피 5
 
5.0%
양식 5
 
5.0%
햄버거 4
 
4.0%
중식 3
 
3.0%
패밀리 레스토랑 2
 
2.0%
분식 2
 
2.0%
Other values (10) 12
 
12.0%

Length

2023-12-10T19:19:55.153947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
단품요리 34
25.0%
전문 34
25.0%
가정식 22
16.2%
일식 6
 
4.4%
베이커리 5
 
3.7%
커피 5
 
3.7%
양식 5
 
3.7%
햄버거 4
 
2.9%
중식 3
 
2.2%
주스 2
 
1.5%
Other values (12) 16
11.8%

jjinhbt_sales_price_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct82
Distinct (%)82.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.7119
Minimum0
Maximum27.06
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:55.370180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0095
Q10.2575
median0.795
Q31.635
95-th percentile4.2735
Maximum27.06
Range27.06
Interquartile range (IQR)1.3775

Descriptive statistics

Standard deviation3.8908369
Coefficient of variation (CV)2.2728178
Kurtosis28.692718
Mean1.7119
Median Absolute Deviation (MAD)0.63
Skewness5.190476
Sum171.19
Variance15.138612
MonotonicityNot monotonic
2023-12-10T19:19:55.607593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.13 4
 
4.0%
0.19 3
 
3.0%
0.29 2
 
2.0%
0.17 2
 
2.0%
0.35 2
 
2.0%
0.64 2
 
2.0%
0.41 2
 
2.0%
0.07 2
 
2.0%
1.59 2
 
2.0%
Other values (72) 74
74.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.01 1
 
1.0%
0.04 1
 
1.0%
0.07 2
 
2.0%
0.08 1
 
1.0%
0.11 1
 
1.0%
0.13 4
4.0%
0.15 1
 
1.0%
0.16 1
 
1.0%
0.17 2
 
2.0%
ValueCountFrequency (%)
27.06 1
1.0%
23.28 1
1.0%
16.51 1
1.0%
7.56 1
1.0%
4.91 1
1.0%
4.24 1
1.0%
3.74 1
1.0%
2.72 1
1.0%
2.68 1
1.0%
2.52 1
1.0%

jjinhbt_sales_co_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct70
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.918
Minimum0
Maximum101.76
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:55.874584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0285
Q10.39
median1
Q31.9825
95-th percentile8.9555
Maximum101.76
Range101.76
Interquartile range (IQR)1.5925

Descriptive statistics

Standard deviation14.060339
Coefficient of variation (CV)3.5886521
Kurtosis41.855917
Mean3.918
Median Absolute Deviation (MAD)0.71
Skewness6.4035896
Sum391.8
Variance197.69313
MonotonicityNot monotonic
2023-12-10T19:19:56.134736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 5
 
5.0%
0.45 4
 
4.0%
0.39 3
 
3.0%
1.23 3
 
3.0%
0.05 3
 
3.0%
0.03 3
 
3.0%
1.31 2
 
2.0%
0.58 2
 
2.0%
0.63 2
 
2.0%
1.0 2
 
2.0%
Other values (60) 71
71.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.03 3
3.0%
0.05 3
3.0%
0.08 1
 
1.0%
0.1 2
 
2.0%
0.13 1
 
1.0%
0.18 2
 
2.0%
0.26 2
 
2.0%
0.29 2
 
2.0%
0.32 1
 
1.0%
ValueCountFrequency (%)
101.76 1
1.0%
95.95 1
1.0%
27.13 1
1.0%
10.82 1
1.0%
10.01 1
1.0%
8.9 1
1.0%
8.35 1
1.0%
8.22 1
1.0%
8.06 1
1.0%
6.75 1
1.0%

otsd_sales_price_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct18
Distinct (%)18.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.1175
Minimum0
Maximum7.98
Zeros18
Zeros (%)18.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:56.351822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.01
median0.02
Q30.04
95-th percentile0.1715
Maximum7.98
Range7.98
Interquartile range (IQR)0.03

Descriptive statistics

Standard deviation0.79745222
Coefficient of variation (CV)6.7868274
Kurtosis98.32542
Mean0.1175
Median Absolute Deviation (MAD)0.01
Skewness9.8787683
Sum11.75
Variance0.63593005
MonotonicityNot monotonic
2023-12-10T19:19:56.578811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
0.01 31
31.0%
0.0 18
18.0%
0.02 15
15.0%
0.04 7
 
7.0%
0.05 6
 
6.0%
0.03 6
 
6.0%
0.07 3
 
3.0%
0.06 3
 
3.0%
0.08 2
 
2.0%
0.2 1
 
1.0%
Other values (8) 8
 
8.0%
ValueCountFrequency (%)
0.0 18
18.0%
0.01 31
31.0%
0.02 15
15.0%
0.03 6
 
6.0%
0.04 7
 
7.0%
0.05 6
 
6.0%
0.06 3
 
3.0%
0.07 3
 
3.0%
0.08 2
 
2.0%
0.09 1
 
1.0%
ValueCountFrequency (%)
7.98 1
1.0%
0.54 1
1.0%
0.3 1
1.0%
0.29 1
1.0%
0.2 1
1.0%
0.17 1
1.0%
0.16 1
1.0%
0.1 1
1.0%
0.09 1
1.0%
0.08 2
2.0%

otsd_sales_co_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct40
Distinct (%)40.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.6221
Minimum0
Maximum36.61
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:56.797926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0095
Q10.0275
median0.08
Q30.2025
95-th percentile0.6415
Maximum36.61
Range36.61
Interquartile range (IQR)0.175

Descriptive statistics

Standard deviation3.7332187
Coefficient of variation (CV)6.0009945
Kurtosis89.728826
Mean0.6221
Median Absolute Deviation (MAD)0.06
Skewness9.3051919
Sum62.21
Variance13.936922
MonotonicityNot monotonic
2023-12-10T19:19:57.017934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
0.02 10
 
10.0%
0.01 10
 
10.0%
0.04 7
 
7.0%
0.03 6
 
6.0%
0.07 5
 
5.0%
0.08 5
 
5.0%
0.0 5
 
5.0%
0.1 5
 
5.0%
0.11 4
 
4.0%
0.15 4
 
4.0%
Other values (30) 39
39.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.01 10
10.0%
0.02 10
10.0%
0.03 6
6.0%
0.04 7
7.0%
0.05 2
 
2.0%
0.06 3
 
3.0%
0.07 5
5.0%
0.08 5
5.0%
0.09 1
 
1.0%
ValueCountFrequency (%)
36.61 1
1.0%
7.72 1
1.0%
3.05 1
1.0%
2.33 1
1.0%
0.67 1
1.0%
0.64 1
1.0%
0.56 1
1.0%
0.53 1
1.0%
0.51 1
1.0%
0.48 2
2.0%

all_sales_price_rate
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)29.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.2043
Minimum0
Maximum7.69
Zeros5
Zeros (%)5.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:57.249978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.0095
Q10.02
median0.065
Q30.1225
95-th percentile0.38
Maximum7.69
Range7.69
Interquartile range (IQR)0.1025

Descriptive statistics

Standard deviation0.80318464
Coefficient of variation (CV)3.9313981
Kurtosis78.229751
Mean0.2043
Median Absolute Deviation (MAD)0.045
Skewness8.5086744
Sum20.43
Variance0.64510557
MonotonicityNot monotonic
2023-12-10T19:19:57.447132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.01 12
 
12.0%
0.04 10
 
10.0%
0.02 9
 
9.0%
0.03 7
 
7.0%
0.1 7
 
7.0%
0.0 5
 
5.0%
0.11 5
 
5.0%
0.06 5
 
5.0%
0.09 4
 
4.0%
0.07 4
 
4.0%
Other values (19) 32
32.0%
ValueCountFrequency (%)
0.0 5
5.0%
0.01 12
12.0%
0.02 9
9.0%
0.03 7
7.0%
0.04 10
10.0%
0.05 2
 
2.0%
0.06 5
5.0%
0.07 4
 
4.0%
0.08 4
 
4.0%
0.09 4
 
4.0%
ValueCountFrequency (%)
7.69 1
1.0%
1.98 1
1.0%
1.46 1
1.0%
1.19 1
1.0%
0.57 1
1.0%
0.37 1
1.0%
0.31 1
1.0%
0.27 2
2.0%
0.22 1
1.0%
0.2 2
2.0%

all_sales_co_rate
Real number (ℝ)

HIGH CORRELATION 

Distinct63
Distinct (%)63.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.3156
Minimum0
Maximum33.73
Zeros1
Zeros (%)1.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:57.654886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.01
Q10.12
median0.26
Q30.5825
95-th percentile2.245
Maximum33.73
Range33.73
Interquartile range (IQR)0.4625

Descriptive statistics

Standard deviation4.737398
Coefficient of variation (CV)3.600941
Kurtosis32.457804
Mean1.3156
Median Absolute Deviation (MAD)0.19
Skewness5.636642
Sum131.56
Variance22.44294
MonotonicityNot monotonic
2023-12-10T19:19:57.918544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.01 5
 
5.0%
0.22 4
 
4.0%
0.14 3
 
3.0%
0.34 3
 
3.0%
0.09 3
 
3.0%
0.07 3
 
3.0%
0.02 3
 
3.0%
0.15 3
 
3.0%
0.21 3
 
3.0%
0.66 3
 
3.0%
Other values (53) 67
67.0%
ValueCountFrequency (%)
0.0 1
 
1.0%
0.01 5
5.0%
0.02 3
3.0%
0.03 2
 
2.0%
0.04 1
 
1.0%
0.05 1
 
1.0%
0.06 1
 
1.0%
0.07 3
3.0%
0.08 1
 
1.0%
0.09 3
3.0%
ValueCountFrequency (%)
33.73 1
1.0%
26.56 1
1.0%
21.22 1
1.0%
7.33 1
1.0%
2.53 1
1.0%
2.23 1
1.0%
2.14 1
1.0%
2.07 1
1.0%
2.03 1
1.0%
1.83 1
1.0%

vartion_rt
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct47
Distinct (%)47.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.269
Minimum-10.82
Maximum0.72
Zeros5
Zeros (%)5.0%
Negative63
Negative (%)63.0%
Memory size1.0 KiB
2023-12-10T19:19:58.142439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10.82
5-th percentile-0.8835
Q1-0.125
median-0.035
Q30.0125
95-th percentile0.1015
Maximum0.72
Range11.54
Interquartile range (IQR)0.1375

Descriptive statistics

Standard deviation1.2343055
Coefficient of variation (CV)-4.5884963
Kurtosis56.409382
Mean-0.269
Median Absolute Deviation (MAD)0.06
Skewness-7.0771052
Sum-26.9
Variance1.5235101
MonotonicityNot monotonic
2023-12-10T19:19:58.381916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=47)
ValueCountFrequency (%)
0.01 7
 
7.0%
-0.05 7
 
7.0%
0.02 6
 
6.0%
0.0 5
 
5.0%
-0.03 5
 
5.0%
-0.02 4
 
4.0%
-0.17 4
 
4.0%
-0.01 4
 
4.0%
0.03 3
 
3.0%
-0.1 3
 
3.0%
Other values (37) 52
52.0%
ValueCountFrequency (%)
-10.82 1
1.0%
-4.54 1
1.0%
-3.71 1
1.0%
-1.03 1
1.0%
-0.95 1
1.0%
-0.88 1
1.0%
-0.75 1
1.0%
-0.68 1
1.0%
-0.62 2
2.0%
-0.49 1
1.0%
ValueCountFrequency (%)
0.72 1
 
1.0%
0.59 1
 
1.0%
0.29 1
 
1.0%
0.27 1
 
1.0%
0.13 1
 
1.0%
0.1 1
 
1.0%
0.09 1
 
1.0%
0.08 3
3.0%
0.07 2
2.0%
0.06 2
2.0%

rank_co
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct100
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8827.53
Minimum28
Maximum16780
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-10T19:19:58.590766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum28
5-th percentile512.65
Q12887.25
median10004
Q313813
95-th percentile16567.4
Maximum16780
Range16752
Interquartile range (IQR)10925.75

Descriptive statistics

Standard deviation5683.9041
Coefficient of variation (CV)0.64388386
Kurtosis-1.4552471
Mean8827.53
Median Absolute Deviation (MAD)5349.5
Skewness-0.18527291
Sum882753
Variance32306766
MonotonicityNot monotonic
2023-12-10T19:19:58.822346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
16767 1
 
1.0%
16241 1
 
1.0%
15351 1
 
1.0%
7942 1
 
1.0%
45 1
 
1.0%
12793 1
 
1.0%
15560 1
 
1.0%
15168 1
 
1.0%
117 1
 
1.0%
15062 1
 
1.0%
Other values (90) 90
90.0%
ValueCountFrequency (%)
28 1
1.0%
45 1
1.0%
117 1
1.0%
132 1
1.0%
392 1
1.0%
519 1
1.0%
598 1
1.0%
683 1
1.0%
692 1
1.0%
734 1
1.0%
ValueCountFrequency (%)
16780 1
1.0%
16767 1
1.0%
16759 1
1.0%
16612 1
1.0%
16594 1
1.0%
16566 1
1.0%
16509 1
1.0%
16464 1
1.0%
16409 1
1.0%
16402 1
1.0%

gid_id
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct6
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Memory size932.0 B
다나05a99b
93 
다다10b02a
 
3
다나15a80a
 
1
나나92a73b
 
1
나나79a85b
 
1

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique4 ?
Unique (%)4.0%

Sample

1st row다나05a99b
2nd row다나15a80a
3rd row다나05a99b
4th row다나05a99b
5th row다나05a99b

Common Values

ValueCountFrequency (%)
다나05a99b 93
93.0%
다다10b02a 3
 
3.0%
다나15a80a 1
 
1.0%
나나92a73b 1
 
1.0%
나나79a85b 1
 
1.0%
나나87a80a 1
 
1.0%

Length

2023-12-10T19:19:59.100009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-10T19:19:59.378378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
다나05a99b 93
93.0%
다다10b02a 3
 
3.0%
다나15a80a 1
 
1.0%
나나92a73b 1
 
1.0%
나나79a85b 1
 
1.0%
나나87a80a 1
 
1.0%

Interactions

2023-12-10T19:19:49.994561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:40.727132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.932954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.131703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.926749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.144071image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.396628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.751582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.148600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.009106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.058023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.287012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.056839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.291078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.588626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.883127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.299496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.138449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.165852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.459098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.196518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.435716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.759574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.022170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.475726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.260362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.300965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.653094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.347463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.599817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.946921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.198010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.646312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.406850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.474602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:43.826108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.523860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.754935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.102222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.349540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.802010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.549118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.647606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.071205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.706070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:46.932272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.267270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.529990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:50.956467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.688791image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.790771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.269112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.853441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.096184image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.429014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.699672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:51.108140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:41.810561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:42.941504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:44.426182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:45.993949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:47.243103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:48.576021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-10T19:19:49.841293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-10T19:19:59.665720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
cmpnm_nmsigngu_nmadstrd_nmarea_nmmlsfc_nmsclas_nmjjinhbt_sales_price_ratejjinhbt_sales_co_rateotsd_sales_price_rateotsd_sales_co_rateall_sales_price_rateall_sales_co_ratevartion_rtrank_cogid_id
cmpnm_nm1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
signgu_nm1.0001.0001.0001.0000.0000.9030.0000.0000.3960.3500.7620.4500.4510.3121.000
adstrd_nm1.0001.0001.0001.0000.0000.8480.3320.4240.5700.5270.5010.7930.8320.3191.000
area_nm1.0001.0001.0001.0000.0000.8900.0000.0000.8850.4770.7350.4270.4290.0821.000
mlsfc_nm1.0000.0000.0000.0001.0001.0000.4840.5140.2280.3910.4040.4510.4880.2290.000
sclas_nm1.0000.9030.8480.8901.0001.0000.6260.6811.0000.8750.9060.8540.8880.0000.822
jjinhbt_sales_price_rate1.0000.0000.3320.0000.4840.6261.0000.9530.0000.9330.9170.9090.8180.0000.459
jjinhbt_sales_co_rate1.0000.0000.4240.0000.5140.6810.9531.0000.0000.4770.8280.8350.6880.0000.500
otsd_sales_price_rate1.0000.3960.5700.8850.2281.0000.0000.0001.0001.0001.0001.0001.0000.0001.000
otsd_sales_co_rate1.0000.3500.5270.4770.3910.8750.9330.4771.0001.0001.0001.0001.0000.0000.933
all_sales_price_rate1.0000.7620.5010.7350.4040.9060.9170.8281.0001.0001.0000.9040.9040.0000.755
all_sales_co_rate1.0000.4500.7930.4270.4510.8540.9090.8351.0001.0000.9041.0000.9930.0000.682
vartion_rt1.0000.4510.8320.4290.4880.8880.8180.6881.0001.0000.9040.9931.0000.5490.715
rank_co1.0000.3120.3190.0820.2290.0000.0000.0000.0000.0000.0000.0000.5491.0000.223
gid_id1.0001.0001.0001.0000.0000.8220.4590.5001.0000.9330.7550.6820.7150.2231.000
2023-12-10T19:20:00.017491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
signgu_nmadstrd_nmarea_nmmlsfc_nmsclas_nmgid_id
signgu_nm1.0000.9850.9900.0000.6920.979
adstrd_nm0.9851.0000.9950.0000.4970.995
area_nm0.9900.9951.0000.0000.5710.990
mlsfc_nm0.0000.0000.0001.0000.9270.000
sclas_nm0.6920.4970.5710.9271.0000.517
gid_id0.9790.9950.9900.0000.5171.000
2023-12-10T19:20:00.232609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
jjinhbt_sales_price_ratejjinhbt_sales_co_rateotsd_sales_price_rateotsd_sales_co_rateall_sales_price_rateall_sales_co_ratevartion_rtrank_cosigngu_nmadstrd_nmarea_nmmlsfc_nmsclas_nmgid_id
jjinhbt_sales_price_rate1.0000.7510.6590.5420.9450.710-0.2180.2130.0000.2300.0000.3110.3200.179
jjinhbt_sales_co_rate0.7511.0000.3890.7550.6510.956-0.4460.4410.0000.3550.0000.3730.3510.341
otsd_sales_price_rate0.6590.3891.0000.6740.8380.521-0.2190.2130.2590.6780.6850.2370.9040.979
otsd_sales_co_rate0.5420.7550.6741.0000.6200.893-0.5220.5150.5560.4600.4710.2790.6620.678
all_sales_price_rate0.9450.6510.8380.6201.0000.682-0.2380.2330.5480.4260.3730.2830.5960.587
all_sales_co_rate0.7100.9560.5210.8930.6821.000-0.4980.4910.5380.4150.3590.3050.5040.541
vartion_rt-0.218-0.446-0.219-0.522-0.238-0.4981.000-0.9990.5390.4620.3600.3350.5520.578
rank_co0.2130.4410.2130.5150.2330.491-0.9991.0000.2270.1310.0360.1120.0000.112
signgu_nm0.0000.0000.2590.5560.5480.5380.5390.2271.0000.9850.9900.0000.6920.979
adstrd_nm0.2300.3550.6780.4600.4260.4150.4620.1310.9851.0000.9950.0000.4970.995
area_nm0.0000.0000.6850.4710.3730.3590.3600.0360.9900.9951.0000.0000.5710.990
mlsfc_nm0.3110.3730.2370.2790.2830.3050.3350.1120.0000.0000.0001.0000.9270.000
sclas_nm0.3200.3510.9040.6620.5960.5040.5520.0000.6920.4970.5710.9271.0000.517
gid_id0.1790.3410.9790.6780.5870.5410.5780.1120.9790.9950.9900.0000.5171.000

Missing values

2023-12-10T19:19:51.327275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-10T19:19:51.685766image/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

anals_ymcmpnm_nmsigngu_nmadstrd_nmarea_nmmlsfc_nmsclas_nmjjinhbt_sales_price_ratejjinhbt_sales_co_rateotsd_sales_price_rateotsd_sales_co_rateall_sales_price_rateall_sales_co_ratevartion_rtrank_cogid_id
0202107한국맥도날드(유)제주노형점제주시노형동제주시 동지역패스트푸드햄버거27.06101.760.547.721.9826.56-4.5416767다나05a99b
1202107우리들골프장구내식당서귀포시영천동서귀포시 동지역한식구내식당/푸드코트0.00.00.00.010.00.010.013579다나15a80a
2202107봉크랑제주시노형동제주시 동지역한식가정식0.160.450.00.040.010.130.022280다나05a99b
3202107주식회사 파스쿠찌제주노형로터리점제주시노형동제주시 동지역음료커피2.478.90.040.480.172.140.012891다나05a99b
4202107바르메제주시노형동제주시 동지역한식가정식1.861.340.010.030.110.29-0.029111다나05a99b
5202107버거킹제주이마트점제주시노형동제주시 동지역패스트푸드햄버거7.5627.130.172.330.577.33-1.0316612다나05a99b
6202107노형근고기제주시노형동제주시 동지역한식가정식0.110.130.010.020.020.050.04531다나05a99b
7202107더온서귀포시안덕면안덕면한식단품요리 전문0.00.00.00.010.00.010.013506나나92a73b
8202107미스터킴팝제주시노형동제주시 동지역한식가정식0.562.020.010.120.040.5-0.1714707다나05a99b
9202107애월연어 노형점제주시노형동제주시 동지역양식양식1.021.230.030.130.090.36-0.0410717다나05a99b
anals_ymcmpnm_nmsigngu_nmadstrd_nmarea_nmmlsfc_nmsclas_nmjjinhbt_sales_price_ratejjinhbt_sales_co_rateotsd_sales_price_rateotsd_sales_co_rateall_sales_price_rateall_sales_co_ratevartion_rtrank_cogid_id
90202107금땡이양꼬치제주시노형동제주시 동지역아시아음식중식1.560.740.050.080.130.210.013948다나05a99b
91202107에그셀런트 제주노형점제주시노형동제주시 동지역한식단품요리 전문0.170.660.010.110.020.22-0.1714672다나05a99b
92202107Baby Cafe 어놀제주시노형동제주시 동지역한식단품요리 전문0.320.660.00.020.020.15-0.0912886다나05a99b
93202107활주로제주시노형동제주시 동지역한식가정식0.560.630.010.030.040.15-0.1113600다나05a99b
94202107막둥이횟집제주시노형동제주시 동지역아시아음식일식1.280.580.040.070.110.18-0.018087다나05a99b
95202107노형집밥제주시노형동제주시 동지역한식가정식0.510.470.010.020.030.11-0.017174다나05a99b
96202107부자국수제주시노형동제주시 동지역한식단품요리 전문0.150.450.020.150.020.22-0.0511241다나05a99b
97202107지중해참치제주시노형동제주시 동지역아시아음식일식1.40.450.020.020.10.10.022312다나05a99b
98202107밥이보약제주시노형동제주시 동지역한식가정식0.220.450.00.00.010.09-0.039506다나05a99b
99202107우마담풍하제주시노형동제주시 동지역한식단품요리 전문1.770.390.080.070.170.14-0.039397다나05a99b