Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells9
Missing cells (%)2.7%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory100.4 B

Variable types

Numeric8
Text3

Dataset

Description부산광역시해운대구_물가관리_20200826
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063783

Alerts

탑마트 반송점 is highly overall correlated with 이마트 중동점 and 5 other fieldsHigh correlation
이마트 중동점 is highly overall correlated with 탑마트 반송점 and 5 other fieldsHigh correlation
지에스슈퍼 좌동점 is highly overall correlated with 탑마트 반송점 and 5 other fieldsHigh correlation
반여2동시장 is highly overall correlated with 탑마트 반송점 and 5 other fieldsHigh correlation
탑마트 반여점 is highly overall correlated with 탑마트 반송점 and 5 other fieldsHigh correlation
세이브존 is highly overall correlated with 탑마트 반송점 and 5 other fieldsHigh correlation
농수산물마트 is highly overall correlated with 탑마트 반송점 and 5 other fieldsHigh correlation
제품명 has 6 (20.0%) missing valuesMissing
이마트 중동점 has 2 (6.7%) missing valuesMissing
반여2동시장 has 1 (3.3%) missing valuesMissing
연번 has unique valuesUnique
품목 has unique valuesUnique
세이브존 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:51:02.096929
Analysis finished2023-12-10 16:51:12.674938
Duration10.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:12.783741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-11T01:51:12.981692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

품목
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T01:51:13.338124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.6333333
Min length1

Characters and Unicode

Total characters79
Distinct characters57
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row쌀
2nd row밀가루
3rd row라면
4th row설탕
5th row식용유
ValueCountFrequency (%)
쌀 1
 
3.3%
밀가루 1
 
3.3%
배 1
 
3.3%
사과 1
 
3.3%
밀감 1
 
3.3%
양파 1
 
3.3%
대파 1
 
3.3%
무 1
 
3.3%
배추 1
 
3.3%
사이다 1
 
3.3%
Other values (20) 20
66.7%
2023-12-11T01:51:13.832710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 4
 
5.1%
기 4
 
5.1%
장 2
 
2.5%
부 2
 
2.5%
소 2
 
2.5%
밀 2
 
2.5%
지 2
 
2.5%
사 2
 
2.5%
피 2
 
2.5%
커 2
 
2.5%
Other values (47) 55
69.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 77
97.5%
Decimal Number 2
 
2.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 4
 
5.2%
기 4
 
5.2%
장 2
 
2.6%
부 2
 
2.6%
소 2
 
2.6%
밀 2
 
2.6%
지 2
 
2.6%
사 2
 
2.6%
피 2
 
2.6%
커 2
 
2.6%
Other values (45) 53
68.8%
Decimal Number
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 77
97.5%
Common 2
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 4
 
5.2%
기 4
 
5.2%
장 2
 
2.6%
부 2
 
2.6%
소 2
 
2.6%
밀 2
 
2.6%
지 2
 
2.6%
사 2
 
2.6%
피 2
 
2.6%
커 2
 
2.6%
Other values (45) 53
68.8%
Common
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 77
97.5%
ASCII 2
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 4
 
5.2%
기 4
 
5.2%
장 2
 
2.6%
부 2
 
2.6%
소 2
 
2.6%
밀 2
 
2.6%
지 2
 
2.6%
사 2
 
2.6%
피 2
 
2.6%
커 2
 
2.6%
Other values (45) 53
68.8%
ASCII
ValueCountFrequency (%)
2 1
50.0%
4 1
50.0%

단위
Text

Distinct22
Distinct (%)73.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T01:51:14.082948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1
Min length3

Characters and Unicode

Total characters123
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row20kg
2nd row3kg
3rd row120g
4th row1kg
5th row1.8ℓ
ValueCountFrequency (%)
1.0kg 3
 
10.0%
500g 3
 
10.0%
500㎖ 2
 
6.7%
2.0kg 2
 
6.7%
100g 2
 
6.7%
1.5ℓ 2
 
6.7%
3.0kg 1
 
3.3%
20kg 1
 
3.3%
300g 1
 
3.3%
1.2kg 1
 
3.3%
Other values (12) 12
40.0%
2023-12-11T01:51:14.459849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 32
26.0%
g 21
17.1%
1 12
 
9.8%
. 11
 
8.9%
k 10
 
8.1%
5 8
 
6.5%
2 7
 
5.7%
3 5
 
4.1%
㎖ 4
 
3.3%
ℓ 4
 
3.3%
Other values (6) 9
 
7.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
58.5%
Lowercase Letter 36
29.3%
Other Punctuation 11
 
8.9%
Other Symbol 4
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 32
44.4%
1 12
 
16.7%
5 8
 
11.1%
2 7
 
9.7%
3 5
 
6.9%
6 4
 
5.6%
7 1
 
1.4%
8 1
 
1.4%
4 1
 
1.4%
9 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
g 21
58.3%
k 10
27.8%
ℓ 4
 
11.1%
m 1
 
2.8%
Other Punctuation
ValueCountFrequency (%)
. 11
100.0%
Other Symbol
ValueCountFrequency (%)
㎖ 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 91
74.0%
Latin 32
 
26.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 32
35.2%
1 12
 
13.2%
. 11
 
12.1%
5 8
 
8.8%
2 7
 
7.7%
3 5
 
5.5%
㎖ 4
 
4.4%
ℓ 4
 
4.4%
6 4
 
4.4%
7 1
 
1.1%
Other values (3) 3
 
3.3%
Latin
ValueCountFrequency (%)
g 21
65.6%
k 10
31.2%
m 1
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 115
93.5%
CJK Compat 4
 
3.3%
Letterlike Symbols 4
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 32
27.8%
g 21
18.3%
1 12
 
10.4%
. 11
 
9.6%
k 10
 
8.7%
5 8
 
7.0%
2 7
 
6.1%
3 5
 
4.3%
6 4
 
3.5%
7 1
 
0.9%
Other values (4) 4
 
3.5%
CJK Compat
ValueCountFrequency (%)
㎖ 4
100.0%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 4
100.0%

제품명
Text

MISSING 

Distinct23
Distinct (%)95.8%
Missing6
Missing (%)20.0%
Memory size372.0 B
2023-12-11T01:51:14.678986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.6666667
Min length1

Characters and Unicode

Total characters88
Distinct characters74
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)91.7%

Sample

1st row정미포장
2nd row중력분
3rd row신라면
4th row제일제당
5th row해표
ValueCountFrequency (%)
동서맥심 2
 
8.0%
신라면 1
 
4.0%
정미포장 1
 
4.0%
시원 1
 
4.0%
1포기 1
 
4.0%
통배추 1
 
4.0%
칠성 1
 
4.0%
참그린 1
 
4.0%
닭 1
 
4.0%
삼겹살 1
 
4.0%
Other values (14) 14
56.0%
2023-12-11T01:51:15.046158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
오 3
 
3.4%
동 2
 
2.3%
우 2
 
2.3%
포 2
 
2.3%
원 2
 
2.3%
1 2
 
2.3%
표 2
 
2.3%
서 2
 
2.3%
제 2
 
2.3%
라 2
 
2.3%
Other values (64) 67
76.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 83
94.3%
Decimal Number 2
 
2.3%
Uppercase Letter 2
 
2.3%
Space Separator 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
오 3
 
3.6%
동 2
 
2.4%
우 2
 
2.4%
포 2
 
2.4%
원 2
 
2.4%
표 2
 
2.4%
서 2
 
2.4%
제 2
 
2.4%
라 2
 
2.4%
심 2
 
2.4%
Other values (60) 62
74.7%
Uppercase Letter
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%
Decimal Number
ValueCountFrequency (%)
1 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 83
94.3%
Common 3
 
3.4%
Latin 2
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
오 3
 
3.6%
동 2
 
2.4%
우 2
 
2.4%
포 2
 
2.4%
원 2
 
2.4%
표 2
 
2.4%
서 2
 
2.4%
제 2
 
2.4%
라 2
 
2.4%
심 2
 
2.4%
Other values (60) 62
74.7%
Common
ValueCountFrequency (%)
1 2
66.7%
1
33.3%
Latin
ValueCountFrequency (%)
L 1
50.0%
G 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 83
94.3%
ASCII 5
 
5.7%

Most frequent character per block

Hangul
ValueCountFrequency (%)
오 3
 
3.6%
동 2
 
2.4%
우 2
 
2.4%
포 2
 
2.4%
원 2
 
2.4%
표 2
 
2.4%
서 2
 
2.4%
제 2
 
2.4%
라 2
 
2.4%
심 2
 
2.4%
Other values (60) 62
74.7%
ASCII
ValueCountFrequency (%)
1 2
40.0%
1
20.0%
L 1
20.0%
G 1
20.0%

탑마트 반송점
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10723.867
Minimum676
Maximum69000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:15.230678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum676
5-th percentile1284.5
Q11830
median3090
Q37870
95-th percentile61640
Maximum69000
Range68324
Interquartile range (IQR)6040

Descriptive statistics

Standard deviation18644.055
Coefficient of variation (CV)1.7385571
Kurtosis5.443953
Mean10723.867
Median Absolute Deviation (MAD)1685
Skewness2.5615644
Sum321716
Variance3.4760078 × 108
MonotonicityNot monotonic
2023-12-11T01:51:15.426848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2980 2
 
6.7%
63800 1
 
3.3%
2350 1
 
3.3%
5900 1
 
3.3%
69000 1
 
3.3%
59000 1
 
3.3%
7900 1
 
3.3%
1980 1
 
3.3%
2280 1
 
3.3%
1580 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
676 1
3.3%
1190 1
3.3%
1400 1
3.3%
1410 1
3.3%
1550 1
3.3%
1580 1
3.3%
1630 1
3.3%
1780 1
3.3%
1980 1
3.3%
2280 1
3.3%
ValueCountFrequency (%)
69000 1
3.3%
63800 1
3.3%
59000 1
3.3%
19900 1
3.3%
17900 1
3.3%
9900 1
3.3%
7980 1
3.3%
7900 1
3.3%
7780 1
3.3%
6350 1
3.3%

이마트 중동점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)89.3%
Missing2
Missing (%)6.7%
Infinite0
Infinite (%)0.0%
Mean9281.5714
Minimum1190
Maximum48990
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:15.620446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1190
5-th percentile1390.5
Q12180
median4407
Q38237.5
95-th percentile43840.5
Maximum48990
Range47800
Interquartile range (IQR)6057.5

Descriptive statistics

Standard deviation13190.694
Coefficient of variation (CV)1.4211703
Kurtosis4.7540203
Mean9281.5714
Median Absolute Deviation (MAD)2677
Skewness2.3523853
Sum259884
Variance1.739944 × 108
MonotonicityNot monotonic
2023-12-11T01:51:15.800161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2180 3
 
10.0%
5480 2
 
6.7%
48170 1
 
3.3%
3990 1
 
3.3%
35800 1
 
3.3%
14850 1
 
3.3%
11800 1
 
3.3%
1380 1
 
3.3%
2980 1
 
3.3%
1880 1
 
3.3%
Other values (15) 15
50.0%
(Missing) 2
 
6.7%
ValueCountFrequency (%)
1190 1
 
3.3%
1380 1
 
3.3%
1410 1
 
3.3%
1500 1
 
3.3%
1580 1
 
3.3%
1880 1
 
3.3%
2180 3
10.0%
2550 1
 
3.3%
2780 1
 
3.3%
2980 1
 
3.3%
ValueCountFrequency (%)
48990 1
3.3%
48170 1
3.3%
35800 1
3.3%
17540 1
3.3%
14850 1
3.3%
11800 1
3.3%
10900 1
3.3%
7350 1
3.3%
7250 1
3.3%
5500 1
3.3%

지에스슈퍼 좌동점
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10638
Minimum690
Maximum54000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:15.988592image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum690
5-th percentile1025.5
Q12330
median4380
Q38780
95-th percentile47695
Maximum54000
Range53310
Interquartile range (IQR)6450

Descriptive statistics

Standard deviation15421.993
Coefficient of variation (CV)1.4497079
Kurtosis3.056554
Mean10638
Median Absolute Deviation (MAD)2660
Skewness2.0941808
Sum319140
Variance2.3783787 × 108
MonotonicityNot monotonic
2023-12-11T01:51:16.169347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
1980 2
 
6.7%
2480 2
 
6.7%
3980 2
 
6.7%
49900 1
 
3.3%
1560 1
 
3.3%
4980 1
 
3.3%
45000 1
 
3.3%
43300 1
 
3.3%
9980 1
 
3.3%
2580 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
690 1
3.3%
850 1
3.3%
1240 1
3.3%
1560 1
3.3%
1880 1
3.3%
1980 2
6.7%
2280 1
3.3%
2480 2
6.7%
2580 1
3.3%
3020 1
3.3%
ValueCountFrequency (%)
54000 1
3.3%
49900 1
3.3%
45000 1
3.3%
43300 1
3.3%
15800 1
3.3%
12900 1
3.3%
9980 1
3.3%
8980 1
3.3%
8180 1
3.3%
7430 1
3.3%

반여2동시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)89.7%
Missing1
Missing (%)3.3%
Infinite0
Infinite (%)0.0%
Mean8155.1724
Minimum750
Maximum52800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:16.330178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum750
5-th percentile850
Q11580
median2900
Q36000
95-th percentile31500
Maximum52800
Range52050
Interquartile range (IQR)4420

Descriptive statistics

Standard deviation12094.636
Coefficient of variation (CV)1.4830632
Kurtosis6.1927672
Mean8155.1724
Median Absolute Deviation (MAD)1650
Skewness2.4341679
Sum236500
Variance1.4628022 × 108
MonotonicityNot monotonic
2023-12-11T01:51:16.545738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
750 2
 
6.7%
1500 2
 
6.7%
1000 2
 
6.7%
52800 1
 
3.3%
3500 1
 
3.3%
3000 1
 
3.3%
30000 1
 
3.3%
20000 1
 
3.3%
2000 1
 
3.3%
2300 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
750 2
6.7%
1000 2
6.7%
1250 1
3.3%
1500 2
6.7%
1580 1
3.3%
1700 1
3.3%
2000 1
3.3%
2190 1
3.3%
2300 1
3.3%
2400 1
3.3%
ValueCountFrequency (%)
52800 1
3.3%
32500 1
3.3%
30000 1
3.3%
20000 1
3.3%
18500 1
3.3%
13500 1
3.3%
12800 1
3.3%
6000 1
3.3%
5000 1
3.3%
4900 1
3.3%

탑마트 반여점
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10573.2
Minimum676
Maximum59000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:16.757749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum676
5-th percentile1289
Q12055
median3790
Q37762.5
95-th percentile47560
Maximum59000
Range58324
Interquartile range (IQR)5707.5

Descriptive statistics

Standard deviation16191.015
Coefficient of variation (CV)1.5313259
Kurtosis3.0990794
Mean10573.2
Median Absolute Deviation (MAD)2085
Skewness2.0867143
Sum317196
Variance2.6214897 × 108
MonotonicityNot monotonic
2023-12-11T01:51:17.273211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2980 2
 
6.7%
45800 1
 
3.3%
2550 1
 
3.3%
4800 1
 
3.3%
49000 1
 
3.3%
59000 1
 
3.3%
7900 1
 
3.3%
1980 1
 
3.3%
2280 1
 
3.3%
1680 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
676 1
3.3%
1190 1
3.3%
1410 1
3.3%
1550 1
3.3%
1630 1
3.3%
1680 1
3.3%
1780 1
3.3%
1980 1
3.3%
2280 1
3.3%
2350 1
3.3%
ValueCountFrequency (%)
59000 1
3.3%
49000 1
3.3%
45800 1
3.3%
42800 1
3.3%
24700 1
3.3%
10900 1
3.3%
7980 1
3.3%
7900 1
3.3%
7350 1
3.3%
5850 1
3.3%

세이브존
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8423.5333
Minimum676
Maximum51800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:17.473761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum676
5-th percentile1080
Q11797.5
median3880
Q37845
95-th percentile35045
Maximum51800
Range51124
Interquartile range (IQR)6047.5

Descriptive statistics

Standard deviation11922.602
Coefficient of variation (CV)1.415392
Kurtosis6.552441
Mean8423.5333
Median Absolute Deviation (MAD)2390
Skewness2.5662678
Sum252706
Variance1.4214843 × 108
MonotonicityNot monotonic
2023-12-11T01:51:17.668035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
51800 1
 
3.3%
3780 1
 
3.3%
5000 1
 
3.3%
29600 1
 
3.3%
11980 1
 
3.3%
7960 1
 
3.3%
1580 1
 
3.3%
1780 1
 
3.3%
990 1
 
3.3%
3980 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
676 1
3.3%
990 1
3.3%
1190 1
3.3%
1430 1
3.3%
1550 1
3.3%
1580 1
3.3%
1700 1
3.3%
1780 1
3.3%
1850 1
3.3%
1980 1
3.3%
ValueCountFrequency (%)
51800 1
3.3%
39500 1
3.3%
29600 1
3.3%
17200 1
3.3%
15050 1
3.3%
11980 1
3.3%
9100 1
3.3%
7960 1
3.3%
7500 1
3.3%
7400 1
3.3%

농수산물마트
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8190.3333
Minimum700
Maximum55800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:51:17.848250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile1222.5
Q12397.5
median3700
Q38125
95-th percentile33695
Maximum55800
Range55100
Interquartile range (IQR)5727.5

Descriptive statistics

Standard deviation12411.078
Coefficient of variation (CV)1.5153324
Kurtosis9.1866694
Mean8190.3333
Median Absolute Deviation (MAD)2150
Skewness3.0197534
Sum245710
Variance1.5403484 × 108
MonotonicityNot monotonic
2023-12-11T01:51:18.036187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1750 2
 
6.7%
3000 2
 
6.7%
55800 1
 
3.3%
2480 1
 
3.3%
20000 1
 
3.3%
10000 1
 
3.3%
7000 1
 
3.3%
1500 1
 
3.3%
1200 1
 
3.3%
4000 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
700 1
3.3%
1200 1
3.3%
1250 1
3.3%
1500 1
3.3%
1650 1
3.3%
1750 2
6.7%
2380 1
3.3%
2450 1
3.3%
2480 1
3.3%
2750 1
3.3%
ValueCountFrequency (%)
55800 1
3.3%
44900 1
3.3%
20000 1
3.3%
17900 1
3.3%
10000 1
3.3%
8450 1
3.3%
8350 1
3.3%
8250 1
3.3%
7750 1
3.3%
7000 1
3.3%

Interactions

2023-12-11T01:51:10.914550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:02.654535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.823347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.950476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.887749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:07.454782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.627928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.743808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.048698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:02.817354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.993488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.070374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:06.042572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:07.610079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.757159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.884190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.186376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:02.977509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.134972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.192360image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:06.197935image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:07.783710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.918707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:10.050889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.307553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.123425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.274068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.284033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:06.309192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:07.929554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.057966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:10.194178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.427084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.251304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.413769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.375150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:06.428399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.080865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.195785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:10.353419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.565361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.378989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.555213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.477008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:06.586829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.230807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.327049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:10.500780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.746590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.533040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.704974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.623197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:07.156055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.372433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.458310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:10.652440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:11.898721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:03.676796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:04.819661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:05.756948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:07.309092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:08.494744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:09.592817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:10.791655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:51:18.195419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번품목단위제품명탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점세이브존농수산물마트
연번1.0001.0000.8110.9280.0000.6120.6530.2320.0000.5840.356
품목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
단위0.8111.0001.0000.9670.9250.9480.9390.7620.9560.9660.758
제품명0.9281.0000.9671.0000.7410.8210.8811.0000.7410.9440.930
탑마트 반송점0.0001.0000.9250.7411.0000.7900.8590.8190.9430.8910.948
이마트 중동점0.6121.0000.9480.8210.7901.0000.9750.8340.9870.9370.816
지에스슈퍼 좌동점0.6531.0000.9390.8810.8590.9751.0000.8770.9920.9360.838
반여2동시장0.2321.0000.7621.0000.8190.8340.8771.0000.8650.9790.929
탑마트 반여점0.0001.0000.9560.7410.9430.9870.9920.8651.0000.9350.855
세이브존0.5841.0000.9660.9440.8910.9370.9360.9790.9351.0000.953
농수산물마트0.3561.0000.7580.9300.9480.8160.8380.9290.8550.9531.000
2023-12-11T01:51:18.481037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점세이브존농수산물마트
연번1.0000.105-0.0550.081-0.0560.003-0.010-0.064
탑마트 반송점0.1051.0000.9060.9530.8270.9380.9710.928
이마트 중동점-0.0550.9061.0000.9570.7710.9510.9280.969
지에스슈퍼 좌동점0.0810.9530.9571.0000.8250.9660.9560.948
반여2동시장-0.0560.8270.7710.8251.0000.7900.8630.803
탑마트 반여점0.0030.9380.9510.9660.7901.0000.9380.960
세이브존-0.0100.9710.9280.9560.8630.9381.0000.950
농수산물마트-0.0640.9280.9690.9480.8030.9600.9501.000

Missing values

2023-12-11T01:51:12.071394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:51:12.294402image/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.
2023-12-11T01:51:12.520745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

연번품목단위제품명탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점세이브존농수산물마트
01쌀20kg정미포장63800489904990052800458005180055800
12밀가루3kg중력분4220<NA>47804380548043504550
23라면120g신라면676<NA>690750676676700
34설탕1kg제일제당1630158018802400163018501750
45식용유1.8ℓ해표7980548081804900798075008350
56두부420g풀무원1400482439501500540017003750
67참기름320㎖오뚜기5650725072805000565072505800
78간장0.9ℓ오복왕표63505480680012800585091007750
89분말커피175g동서맥심778073507430750735074008250
910커피크림500g동서맥심2400255024802900235032402750
연번품목단위제품명탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점세이브존농수산물마트
2021부엌용세제1.2kg참그린3200490049004000298029803650
2122사이다1.5ℓ칠성2450218025802300245024502450
2223배추2.0kg통배추 1포기2980327039802000250039804000
2324무2.0kg<NA>158018801980100016809901200
2425대파1.0kg<NA>2280298019801500228017803000
2526양파1.0kg잎없는것1980138024801000198015801500
2627밀감100g<NA>7900118009980<NA>790079607000
2728사과300g<NA>59000148504330020000590001198010000
2829배600g<NA>69000358004500030000490002960020000
2930고등어500g<NA>5900399049803000480050003000