Overview

Dataset statistics

Number of variables11
Number of observations30
Missing cells4
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.9 KiB
Average record size in memory99.4 B

Variable types

Numeric7
Text4

Dataset

Description부산광역시해운대구_물가관리_20210321
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 4 other fieldsHigh correlation
이마트 중동점 is highly overall correlated with 탑마트 반송점 and 4 other fieldsHigh correlation
지에스슈퍼 좌동점 is highly overall correlated with 탑마트 반송점 and 4 other fieldsHigh correlation
반여2동시장 is highly overall correlated with 탑마트 반송점 and 4 other fieldsHigh correlation
탑마트 반여점 is highly overall correlated with 탑마트 반송점 and 4 other fieldsHigh correlation
농수산물마트 is highly overall correlated with 탑마트 반송점 and 4 other fieldsHigh correlation
제품명 has 4 (13.3%) missing valuesMissing
연번 has unique valuesUnique
품목 has unique valuesUnique
지에스슈퍼 좌동점 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:50:45.467136
Analysis finished2023-12-10 16:50:53.833752
Duration8.37 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
2023-12-11T01:50:53.905475image/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
2023-12-11T01:50:54.098517image/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
2023-12-11T01:50:54.365000image/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%
2023-12-11T01:50:54.767863image/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
2023-12-11T01:50:54.989869image/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%
2023-12-11T01:50:55.390427image/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 

Distinct25
Distinct (%)96.2%
Missing4
Missing (%)13.3%
Memory size372.0 B
2023-12-11T01:50:55.649838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length7
Mean length4.1538462
Min length1

Characters and Unicode

Total characters108
Distinct characters84
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

Unique24 ?
Unique (%)92.3%

Sample

1st row정미포장
2nd row중력분
3rd row신라면
4th row제일제당
5th row해표
ValueCountFrequency (%)
동서맥심 2
 
6.5%
신라면 1
 
3.2%
정미포장 1
 
3.2%
하이트 1
 
3.2%
잎없는것 1
 
3.2%
1개 1
 
3.2%
것 1
 
3.2%
씻은 1
 
3.2%
없으며 1
 
3.2%
잎이 1
 
3.2%
Other values (20) 20
64.5%
2023-12-11T01:50:56.607471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
4.6%
1 3
 
2.8%
오 3
 
2.8%
심 2
 
1.9%
정 2
 
1.9%
포 2
 
1.9%
잎 2
 
1.9%
이 2
 
1.9%
없 2
 
1.9%
우 2
 
1.9%
Other values (74) 83
76.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 94
87.0%
Space Separator 5
 
4.6%
Decimal Number 5
 
4.6%
Lowercase Letter 2
 
1.9%
Uppercase Letter 2
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
오 3
 
3.2%
심 2
 
2.1%
정 2
 
2.1%
포 2
 
2.1%
잎 2
 
2.1%
이 2
 
2.1%
없 2
 
2.1%
우 2
 
2.1%
맥 2
 
2.1%
것 2
 
2.1%
Other values (66) 73
77.7%
Decimal Number
ValueCountFrequency (%)
1 3
60.0%
3 1
 
20.0%
0 1
 
20.0%
Lowercase Letter
ValueCountFrequency (%)
c 1
50.0%
m 1
50.0%
Uppercase Letter
ValueCountFrequency (%)
G 1
50.0%
L 1
50.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 94
87.0%
Common 10
 
9.3%
Latin 4
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
오 3
 
3.2%
심 2
 
2.1%
정 2
 
2.1%
포 2
 
2.1%
잎 2
 
2.1%
이 2
 
2.1%
없 2
 
2.1%
우 2
 
2.1%
맥 2
 
2.1%
것 2
 
2.1%
Other values (66) 73
77.7%
Common
ValueCountFrequency (%)
5
50.0%
1 3
30.0%
3 1
 
10.0%
0 1
 
10.0%
Latin
ValueCountFrequency (%)
c 1
25.0%
m 1
25.0%
G 1
25.0%
L 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 94
87.0%
ASCII 14
 
13.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5
35.7%
1 3
21.4%
3 1
 
7.1%
0 1
 
7.1%
c 1
 
7.1%
m 1
 
7.1%
G 1
 
7.1%
L 1
 
7.1%
Hangul
ValueCountFrequency (%)
오 3
 
3.2%
심 2
 
2.1%
정 2
 
2.1%
포 2
 
2.1%
잎 2
 
2.1%
이 2
 
2.1%
없 2
 
2.1%
우 2
 
2.1%
맥 2
 
2.1%
것 2
 
2.1%
Other values (66) 73
77.7%

탑마트 반송점
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10243.533
Minimum796
Maximum49000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:50:56.786923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum796
5-th percentile1124
Q12470
median4975
Q36762.5
95-th percentile47090
Maximum49000
Range48204
Interquartile range (IQR)4292.5

Descriptive statistics

Standard deviation14959.946
Coefficient of variation (CV)1.4604283
Kurtosis2.9099945
Mean10243.533
Median Absolute Deviation (MAD)2435
Skewness2.0979277
Sum307306
Variance2.238 × 108
MonotonicityNot monotonic
2023-12-11T01:50:56.955878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5780 2
 
6.7%
48800 1
 
3.3%
3750 1
 
3.3%
6900 1
 
3.3%
49000 1
 
3.3%
45000 1
 
3.3%
5900 1
 
3.3%
5580 1
 
3.3%
980 1
 
3.3%
3980 1
 
3.3%
Other values (19) 19
63.3%
ValueCountFrequency (%)
796 1
3.3%
980 1
3.3%
1300 1
3.3%
1440 1
3.3%
1550 1
3.3%
1630 1
3.3%
2180 1
3.3%
2400 1
3.3%
2680 1
3.3%
3180 1
3.3%
ValueCountFrequency (%)
49000 1
3.3%
48800 1
3.3%
45000 1
3.3%
44550 1
3.3%
16900 1
3.3%
9900 1
3.3%
7980 1
3.3%
6900 1
3.3%
6350 1
3.3%
5900 1
3.3%

이마트 중동점
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9816.0333
Minimum676
Maximum58900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:50:57.120496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum676
5-th percentile1289
Q12482.5
median4400
Q37260
95-th percentile49205
Maximum58900
Range58224
Interquartile range (IQR)4777.5

Descriptive statistics

Standard deviation15290.147
Coefficient of variation (CV)1.5576707
Kurtosis5.6827754
Mean9816.0333
Median Absolute Deviation (MAD)2270
Skewness2.5373616
Sum294481
Variance2.337886 × 108
MonotonicityNot monotonic
2023-12-11T01:50:57.300869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5480 3
 
10.0%
58900 1
 
3.3%
1410 1
 
3.3%
39800 1
 
3.3%
23600 1
 
3.3%
5220 1
 
3.3%
3060 1
 
3.3%
7475 1
 
3.3%
1980 1
 
3.3%
2580 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
676 1
3.3%
1190 1
3.3%
1410 1
3.3%
1550 1
3.3%
1680 1
3.3%
1980 1
3.3%
2280 1
3.3%
2480 1
3.3%
2490 1
3.3%
2550 1
3.3%
ValueCountFrequency (%)
58900 1
3.3%
56900 1
3.3%
39800 1
3.3%
23600 1
3.3%
17900 1
3.3%
8580 1
3.3%
7475 1
3.3%
7380 1
3.3%
6900 1
3.3%
6480 1
3.3%

지에스슈퍼 좌동점
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12734.5
Minimum690
Maximum66335
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:50:57.490974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum690
5-th percentile1258
Q12682.5
median4840
Q39512.5
95-th percentile60290
Maximum66335
Range65645
Interquartile range (IQR)6830

Descriptive statistics

Standard deviation19273.5
Coefficient of variation (CV)1.513487
Kurtosis3.2300797
Mean12734.5
Median Absolute Deviation (MAD)3050
Skewness2.1473695
Sum382035
Variance3.7146782 × 108
MonotonicityNot monotonic
2023-12-11T01:50:57.695323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
63800 1
 
3.3%
4480 1
 
3.3%
5900 1
 
3.3%
56000 1
 
3.3%
53000 1
 
3.3%
8800 1
 
3.3%
2990 1
 
3.3%
12450 1
 
3.3%
1280 1
 
3.3%
3980 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
690 1
3.3%
1240 1
3.3%
1280 1
3.3%
1560 1
3.3%
1650 1
3.3%
1880 1
3.3%
2480 1
3.3%
2590 1
3.3%
2960 1
3.3%
2990 1
3.3%
ValueCountFrequency (%)
66335 1
3.3%
63800 1
3.3%
56000 1
3.3%
53000 1
3.3%
16900 1
3.3%
13900 1
3.3%
12450 1
3.3%
9750 1
3.3%
8800 1
3.3%
7980 1
3.3%

반여2동시장
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9208
Minimum750
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:50:57.872932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum750
5-th percentile1445
Q12172.5
median3500
Q36725
95-th percentile36550
Maximum59800
Range59050
Interquartile range (IQR)4552.5

Descriptive statistics

Standard deviation13745.089
Coefficient of variation (CV)1.4927334
Kurtosis6.0508072
Mean9208
Median Absolute Deviation (MAD)1500
Skewness2.4763345
Sum276240
Variance1.8892747 × 108
MonotonicityNot monotonic
2023-12-11T01:50:58.087700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2800 2
 
6.7%
2000 2
 
6.7%
5000 2
 
6.7%
3000 2
 
6.7%
1400 1
 
3.3%
36000 1
 
3.3%
30000 1
 
3.3%
1500 1
 
3.3%
4000 1
 
3.3%
6500 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
750 1
3.3%
1400 1
3.3%
1500 1
3.3%
1650 1
3.3%
1700 1
3.3%
2000 2
6.7%
2100 1
3.3%
2390 1
3.3%
2400 1
3.3%
2800 2
6.7%
ValueCountFrequency (%)
59800 1
3.3%
37000 1
3.3%
36000 1
3.3%
30000 1
3.3%
18500 1
3.3%
13500 1
3.3%
7850 1
3.3%
6800 1
3.3%
6500 1
3.3%
5000 2
6.7%

탑마트 반여점
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11177.533
Minimum676
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:50:58.380332image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum676
5-th percentile1090
Q12445
median4740
Q36687.5
95-th percentile51605
Maximum59800
Range59124
Interquartile range (IQR)4242.5

Descriptive statistics

Standard deviation16728.014
Coefficient of variation (CV)1.4965747
Kurtosis3.0770035
Mean11177.533
Median Absolute Deviation (MAD)2150
Skewness2.0888673
Sum335326
Variance2.7982645 × 108
MonotonicityNot monotonic
2023-12-11T01:50:58.584231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
5900 2
 
6.7%
3980 2
 
6.7%
59800 1
 
3.3%
1440 1
 
3.3%
6800 1
 
3.3%
53000 1
 
3.3%
41000 1
 
3.3%
3480 1
 
3.3%
4900 1
 
3.3%
1000 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
676 1
3.3%
1000 1
3.3%
1200 1
3.3%
1440 1
3.3%
1550 1
3.3%
1630 1
3.3%
1880 1
3.3%
2200 1
3.3%
3180 1
3.3%
3480 1
3.3%
ValueCountFrequency (%)
59800 1
3.3%
53000 1
3.3%
49900 1
3.3%
41000 1
3.3%
26300 1
3.3%
12900 1
3.3%
6980 1
3.3%
6800 1
3.3%
6350 1
3.3%
5980 1
3.3%

홈플러스 센텀점
Text

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-11T01:50:58.836108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length5.3333333
Min length4

Characters and Unicode

Total characters160
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)76.7%

Sample

1st row59900
2nd row5700
3rd row680
4th row1790
5th row7700
ValueCountFrequency (%)
3290 3
 
10.0%
15900 2
 
6.7%
7490 2
 
6.7%
50350 1
 
3.3%
59900 1
 
3.3%
3890 1
 
3.3%
70000 1
 
3.3%
33000 1
 
3.3%
9990 1
 
3.3%
5390 1
 
3.3%
Other values (16) 16
53.3%
2023-12-11T01:50:59.323675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 42
26.2%
29
18.1%
9 22
13.8%
5 13
 
8.1%
3 11
 
6.9%
1 11
 
6.9%
7 8
 
5.0%
2 7
 
4.4%
8 6
 
3.8%
4 4
 
2.5%
Other values (4) 7
 
4.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
80.0%
Space Separator 29
 
18.1%
Open Punctuation 1
 
0.6%
Math Symbol 1
 
0.6%
Close Punctuation 1
 
0.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 42
32.8%
9 22
17.2%
5 13
 
10.2%
3 11
 
8.6%
1 11
 
8.6%
7 8
 
6.2%
2 7
 
5.5%
8 6
 
4.7%
4 4
 
3.1%
6 4
 
3.1%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 160
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 42
26.2%
29
18.1%
9 22
13.8%
5 13
 
8.1%
3 11
 
6.9%
1 11
 
6.9%
7 8
 
5.0%
2 7
 
4.4%
8 6
 
3.8%
4 4
 
2.5%
Other values (4) 7
 
4.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 160
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 42
26.2%
29
18.1%
9 22
13.8%
5 13
 
8.1%
3 11
 
6.9%
1 11
 
6.9%
7 8
 
5.0%
2 7
 
4.4%
8 6
 
3.8%
4 4
 
2.5%
Other values (4) 7
 
4.4%

농수산물마트
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9545
Minimum770
Maximum69800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-11T01:50:59.554497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum770
5-th percentile1485
Q12630
median3940
Q38150
95-th percentile40950
Maximum69800
Range69030
Interquartile range (IQR)5520

Descriptive statistics

Standard deviation15460.121
Coefficient of variation (CV)1.6197089
Kurtosis9.4424656
Mean9545
Median Absolute Deviation (MAD)2065
Skewness3.0599651
Sum286350
Variance2.3901534 × 108
MonotonicityNot monotonic
2023-12-11T01:50:59.778913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1750 2
 
6.7%
3000 2
 
6.7%
69800 1
 
3.3%
3580 1
 
3.3%
25000 1
 
3.3%
16000 1
 
3.3%
2500 1
 
3.3%
4500 1
 
3.3%
4980 1
 
3.3%
2000 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
770 1
3.3%
1350 1
3.3%
1650 1
3.3%
1750 2
6.7%
2000 1
3.3%
2500 1
3.3%
2590 1
3.3%
2750 1
3.3%
2800 1
3.3%
3000 2
6.7%
ValueCountFrequency (%)
69800 1
3.3%
54000 1
3.3%
25000 1
3.3%
21500 1
3.3%
16000 1
3.3%
8450 1
3.3%
8350 1
3.3%
8250 1
3.3%
7850 1
3.3%
6980 1
3.3%

Interactions

2023-12-11T01:50:52.528414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:46.315251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.259005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.203497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.213500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:50.319021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.519579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:52.651888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:46.447726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.392396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.336349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.369524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:50.506641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.705412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:52.818367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:46.565658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.485821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.515938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.506755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:50.648144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.851961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:53.015471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:46.696249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.612590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.642072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.649815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:50.790834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.992587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:53.119870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:46.841031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.785070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.780009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.796127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.040046image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:52.116072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:53.226753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:46.975508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.940896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.920988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.996903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.204250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:52.268958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:53.367402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:47.115175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:48.066084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:49.058785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:50.161560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:51.362023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:52.405677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:51:00.006041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번품목단위제품명탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점홈플러스 센텀점농수산물마트
연번1.0001.0000.8110.9470.6460.4010.5900.5820.3940.6930.474
품목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
단위0.8111.0001.0000.9720.8650.9500.9390.9580.9780.9630.928
제품명0.9471.0000.9721.0000.9520.7390.8680.8691.0000.9800.910
탑마트 반송점0.6461.0000.8650.9521.0000.9220.8370.9840.8881.0000.848
이마트 중동점0.4011.0000.9500.7390.9221.0000.9920.9620.9010.9520.971
지에스슈퍼 좌동점0.5901.0000.9390.8680.8370.9921.0000.9430.8760.9570.962
반여2동시장0.5821.0000.9580.8690.9840.9620.9431.0000.9540.8870.991
탑마트 반여점0.3941.0000.9781.0000.8880.9010.8760.9541.0000.9150.894
홈플러스 센텀점0.6931.0000.9630.9801.0000.9520.9570.8870.9151.0001.000
농수산물마트0.4741.0000.9280.9100.8480.9710.9620.9910.8941.0001.000
2023-12-11T01:51:00.245120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점농수산물마트
연번1.0000.1370.0590.124-0.0370.017-0.050
탑마트 반송점0.1371.0000.9390.9290.8360.9550.897
이마트 중동점0.0590.9391.0000.9600.9000.9530.934
지에스슈퍼 좌동점0.1240.9290.9601.0000.8970.9420.904
반여2동시장-0.0370.8360.9000.8971.0000.8900.933
탑마트 반여점0.0170.9550.9530.9420.8901.0000.891
농수산물마트-0.0500.8970.9340.9040.9330.8911.000

Missing values

2023-12-11T01:50:53.527508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:50:53.763216image/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

연번품목단위제품명탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점홈플러스 센텀점농수산물마트
01쌀20kg정미포장48800589006380059800598005990069800
12밀가루3kg중력분4220410047804400548057004550
23라면120g신라면796676690750676680770
34설탕1kg제일제당1630168018802400163017901750
45식용유1.8ℓ해표7980648076004850598077008350
56두부420g풀무원4550470045502100458064004300
67참기름320㎖오뚜기5650858079805000698084905800
78간장0.9ℓ오복왕표63505480680068006350159007850
89분말커피175g동서맥심5780738074307850590055508250
910커피크림500g동서맥심2400255024802900421032902750
연번품목단위제품명탑마트 반송점이마트 중동점지에스슈퍼 좌동점반여2동시장탑마트 반여점홈플러스 센텀점농수산물마트
2021부엌용세제1.2kg참그린320024904900400039808910(1+1)3450
2122사이다1.5ℓ칠성2680248025902800220026802800
2223배추2.0kg통배추 1포기3980258039803000398032903000
2324무2.0kg잎이 없으며 씻은 것 1개980198012801500100019502000
2425대파1.0kg<NA>55807475124505000490074904980
2526양파1.0kg잎없는것5780306029902000348053904500
2627밀감100g<NA>5900522088002000590099902500
2728사과300g<NA>45000236005300030000410003300016000
2829배600g<NA>49000398005600036000530007000025000
2930고등어500g30cm정도6900548059003000680039903000