Overview

Dataset statistics

Number of variables9
Number of observations29
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory82.6 B

Variable types

Text3
Numeric6

Dataset

Description부산광역시해운대구_물가관리_20210924
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063783

Alerts

GS슈퍼마켓 대동점 is highly overall correlated with 이마트 해운대점 and 4 other fieldsHigh correlation
이마트 해운대점 is highly overall correlated with GS슈퍼마켓 대동점 and 4 other fieldsHigh correlation
농산물시장 is highly overall correlated with GS슈퍼마켓 대동점 and 4 other fieldsHigh correlation
반여2동시장 is highly overall correlated with GS슈퍼마켓 대동점 and 4 other fieldsHigh correlation
재송한마음시장 is highly overall correlated with GS슈퍼마켓 대동점 and 4 other fieldsHigh correlation
탑마트반송점 is highly overall correlated with GS슈퍼마켓 대동점 and 4 other fieldsHigh correlation
품목 has unique valuesUnique
탑마트반송점 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:50:18.724303
Analysis finished2023-12-10 16:50:24.721915
Duration6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-11T01:50:24.910066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.6896552
Min length1

Characters and Unicode

Total characters107
Distinct characters54
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)100.0%

Sample

1st row사과
2nd row배
3rd row밤
4th row대추
5th row배추
ValueCountFrequency (%)
사과 1
 
3.0%
고등어 1
 
3.0%
돼지갈비(외식 1
 
3.0%
맥주(외식 1
 
3.0%
소주(외식 1
 
3.0%
맥주(소매점 1
 
3.0%
소주(소매점 1
 
3.0%
식용유 1
 
3.0%
밀가루 1
 
3.0%
두부 1
 
3.0%
Other values (23) 23
69.7%
2023-12-11T01:50:25.262882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 8
 
7.5%
) 8
 
7.5%
소 6
 
5.6%
고 6
 
5.6%
식 5
 
4.7%
5
 
4.7%
기 5
 
4.7%
외 4
 
3.7%
주 4
 
3.7%
어 2
 
1.9%
Other values (44) 54
50.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 86
80.4%
Open Punctuation 8
 
7.5%
Close Punctuation 8
 
7.5%
Space Separator 5
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
소 6
 
7.0%
고 6
 
7.0%
식 5
 
5.8%
기 5
 
5.8%
외 4
 
4.7%
주 4
 
4.7%
어 2
 
2.3%
매 2
 
2.3%
갈 2
 
2.3%
가 2
 
2.3%
Other values (41) 48
55.8%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 86
80.4%
Common 21
 
19.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
소 6
 
7.0%
고 6
 
7.0%
식 5
 
5.8%
기 5
 
5.8%
외 4
 
4.7%
주 4
 
4.7%
어 2
 
2.3%
매 2
 
2.3%
갈 2
 
2.3%
가 2
 
2.3%
Other values (41) 48
55.8%
Common
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 86
80.4%
ASCII 21
 
19.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8
38.1%
) 8
38.1%
5
23.8%
Hangul
ValueCountFrequency (%)
소 6
 
7.0%
고 6
 
7.0%
식 5
 
5.8%
기 5
 
5.8%
외 4
 
4.7%
주 4
 
4.7%
어 2
 
2.3%
매 2
 
2.3%
갈 2
 
2.3%
가 2
 
2.3%
Other values (41) 48
55.8%

규 격(단위)
Text

Distinct24
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-11T01:50:25.515051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length24
Median length11
Mean length9.2068966
Min length2

Characters and Unicode

Total characters267
Distinct characters69
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)65.5%

Sample

1st row부사(300g이상)3kg
2nd row신고6㎏
3rd row1㎏(상품)
4th row1㎏(상품)
5th row1kg
ValueCountFrequency (%)
1병 4
 
7.0%
1마리(냉동 3
 
5.3%
500g 3
 
5.3%
1인분 2
 
3.5%
하이트 2
 
3.5%
백설표 2
 
3.5%
1㎏ 2
 
3.5%
등심상등육 2
 
3.5%
시원소주 2
 
3.5%
0.1㎏ 2
 
3.5%
Other values (30) 33
57.9%
2023-12-11T01:50:25.991185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
29
 
10.9%
0 25
 
9.4%
1 21
 
7.9%
5 10
 
3.7%
㎏ 10
 
3.7%
( 9
 
3.4%
) 9
 
3.4%
g 8
 
3.0%
2 7
 
2.6%
정 6
 
2.2%
Other values (59) 133
49.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
40.8%
Decimal Number 74
27.7%
Space Separator 29
 
10.9%
Other Symbol 19
 
7.1%
Lowercase Letter 11
 
4.1%
Open Punctuation 9
 
3.4%
Close Punctuation 9
 
3.4%
Other Punctuation 6
 
2.2%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 6
 
5.5%
상 5
 
4.6%
등 5
 
4.6%
리 5
 
4.6%
마 5
 
4.6%
동 4
 
3.7%
냉 4
 
3.7%
도 4
 
3.7%
육 4
 
3.7%
병 4
 
3.7%
Other values (39) 63
57.8%
Decimal Number
ValueCountFrequency (%)
0 25
33.8%
1 21
28.4%
5 10
 
13.5%
2 7
 
9.5%
6 4
 
5.4%
3 4
 
5.4%
4 2
 
2.7%
8 1
 
1.4%
Other Symbol
ValueCountFrequency (%)
㎏ 10
52.6%
㎝ 5
26.3%
㎖ 4
 
21.1%
Lowercase Letter
ValueCountFrequency (%)
g 8
72.7%
k 2
 
18.2%
ℓ 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
. 5
83.3%
/ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
29
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 148
55.4%
Hangul 109
40.8%
Latin 10
 
3.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 6
 
5.5%
상 5
 
4.6%
등 5
 
4.6%
리 5
 
4.6%
마 5
 
4.6%
동 4
 
3.7%
냉 4
 
3.7%
도 4
 
3.7%
육 4
 
3.7%
병 4
 
3.7%
Other values (39) 63
57.8%
Common
ValueCountFrequency (%)
29
19.6%
0 25
16.9%
1 21
14.2%
5 10
 
6.8%
㎏ 10
 
6.8%
( 9
 
6.1%
) 9
 
6.1%
2 7
 
4.7%
㎝ 5
 
3.4%
. 5
 
3.4%
Other values (8) 18
12.2%
Latin
ValueCountFrequency (%)
g 8
80.0%
k 2
 
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 138
51.7%
Hangul 109
40.8%
CJK Compat 19
 
7.1%
Letterlike Symbols 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
29
21.0%
0 25
18.1%
1 21
15.2%
5 10
 
7.2%
( 9
 
6.5%
) 9
 
6.5%
g 8
 
5.8%
2 7
 
5.1%
. 5
 
3.6%
6 4
 
2.9%
Other values (6) 11
 
8.0%
CJK Compat
ValueCountFrequency (%)
㎏ 10
52.6%
㎝ 5
26.3%
㎖ 4
 
21.1%
Hangul
ValueCountFrequency (%)
정 6
 
5.5%
상 5
 
4.6%
등 5
 
4.6%
리 5
 
4.6%
마 5
 
4.6%
동 4
 
3.7%
냉 4
 
3.7%
도 4
 
3.7%
육 4
 
3.7%
병 4
 
3.7%
Other values (39) 63
57.8%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

GS슈퍼마켓 대동점
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)86.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15171.034
Minimum1300
Maximum63800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T01:50:26.179200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile1744
Q14000
median7730
Q317800
95-th percentile56000
Maximum63800
Range62500
Interquartile range (IQR)13800

Descriptive statistics

Standard deviation18034.489
Coefficient of variation (CV)1.1887448
Kurtosis1.9812832
Mean15171.034
Median Absolute Deviation (MAD)5070
Skewness1.7397811
Sum439960
Variance3.252428 × 108
MonotonicityNot monotonic
2023-12-11T01:50:26.358186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4980 2
 
6.9%
2000 2
 
6.9%
4000 2
 
6.9%
12800 2
 
6.9%
49100 1
 
3.4%
1990 1
 
3.4%
13800 1
 
3.4%
9500 1
 
3.4%
1580 1
 
3.4%
1300 1
 
3.4%
Other values (15) 15
51.7%
ValueCountFrequency (%)
1300 1
3.4%
1580 1
3.4%
1990 1
3.4%
2000 2
6.9%
3950 1
3.4%
3990 1
3.4%
4000 2
6.9%
4550 1
3.4%
4980 2
6.9%
6300 1
3.4%
ValueCountFrequency (%)
63800 1
3.4%
60000 1
3.4%
50000 1
3.4%
49100 1
3.4%
27900 1
3.4%
24800 1
3.4%
21330 1
3.4%
17800 1
3.4%
13800 1
3.4%
12800 2
6.9%

이마트 해운대점
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)96.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12468.759
Minimum1280
Maximum59400
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T01:50:26.540723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1426
Q14000
median5685
Q311400
95-th percentile46436
Maximum59400
Range58120
Interquartile range (IQR)7400

Descriptive statistics

Standard deviation15010.131
Coefficient of variation (CV)1.2038192
Kurtosis3.6003501
Mean12468.759
Median Absolute Deviation (MAD)4235
Skewness2.0222166
Sum361594
Variance2.2530403 × 108
MonotonicityNot monotonic
2023-12-11T01:50:26.753068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
4000 2
 
6.9%
30020 1
 
3.4%
11060 1
 
3.4%
10000 1
 
3.4%
9000 1
 
3.4%
1410 1
 
3.4%
1280 1
 
3.4%
7480 1
 
3.4%
4750 1
 
3.4%
3486 1
 
3.4%
Other values (18) 18
62.1%
ValueCountFrequency (%)
1280 1
3.4%
1410 1
3.4%
1450 1
3.4%
1640 1
3.4%
1890 1
3.4%
1933 1
3.4%
3486 1
3.4%
4000 2
6.9%
4680 1
3.4%
4750 1
3.4%
ValueCountFrequency (%)
59400 1
3.4%
51900 1
3.4%
38240 1
3.4%
30020 1
3.4%
27000 1
3.4%
19400 1
3.4%
14000 1
3.4%
11400 1
3.4%
11060 1
3.4%
10750 1
3.4%

센텀 홈플러스
Text

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Memory size364.0 B
2023-12-11T01:50:27.026148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.2758621
Min length4

Characters and Unicode

Total characters153
Distinct characters15
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)86.2%

Sample

1st row35600
2nd row34100
3rd row7700
4th row14970
5th row2790
ValueCountFrequency (%)
4000 2
 
6.9%
7490 2
 
6.9%
3890 1
 
3.4%
35600 1
 
3.4%
3840 1
 
3.4%
10000 1
 
3.4%
판매안함 1
 
3.4%
1310 1
 
3.4%
7690 1
 
3.4%
3490 1
 
3.4%
Other values (17) 17
58.6%
2023-12-11T01:50:27.572052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 41
26.8%
28
18.3%
9 14
 
9.2%
1 13
 
8.5%
4 12
 
7.8%
7 10
 
6.5%
5 10
 
6.5%
3 9
 
5.9%
8 5
 
3.3%
6 4
 
2.6%
Other values (5) 7
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
79.1%
Space Separator 28
 
18.3%
Other Letter 4
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 41
33.9%
9 14
 
11.6%
1 13
 
10.7%
4 12
 
9.9%
7 10
 
8.3%
5 10
 
8.3%
3 9
 
7.4%
8 5
 
4.1%
6 4
 
3.3%
2 3
 
2.5%
Other Letter
ValueCountFrequency (%)
판 1
25.0%
매 1
25.0%
안 1
25.0%
함 1
25.0%
Space Separator
ValueCountFrequency (%)
28
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 149
97.4%
Hangul 4
 
2.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 41
27.5%
28
18.8%
9 14
 
9.4%
1 13
 
8.7%
4 12
 
8.1%
7 10
 
6.7%
5 10
 
6.7%
3 9
 
6.0%
8 5
 
3.4%
6 4
 
2.7%
Hangul
ValueCountFrequency (%)
판 1
25.0%
매 1
25.0%
안 1
25.0%
함 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 149
97.4%
Hangul 4
 
2.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 41
27.5%
28
18.8%
9 14
 
9.4%
1 13
 
8.7%
4 12
 
8.1%
7 10
 
6.7%
5 10
 
6.7%
3 9
 
6.0%
8 5
 
3.4%
6 4
 
2.7%
Hangul
ValueCountFrequency (%)
판 1
25.0%
매 1
25.0%
안 1
25.0%
함 1
25.0%

농산물시장
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)82.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12560.414
Minimum990
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T01:50:27.761338image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1500
Q13950
median7950
Q312900
95-th percentile51000
Maximum59800
Range58810
Interquartile range (IQR)8950

Descriptive statistics

Standard deviation15583.798
Coefficient of variation (CV)1.2407074
Kurtosis3.7402876
Mean12560.414
Median Absolute Deviation (MAD)4158
Skewness2.1225082
Sum364252
Variance2.4285476 × 108
MonotonicityNot monotonic
2023-12-11T01:50:27.906543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10000 2
 
6.9%
4000 2
 
6.9%
2000 2
 
6.9%
12000 2
 
6.9%
12900 2
 
6.9%
30000 1
 
3.4%
2500 1
 
3.4%
5000 1
 
3.4%
1650 1
 
3.4%
1400 1
 
3.4%
Other values (14) 14
48.3%
ValueCountFrequency (%)
990 1
3.4%
1400 1
3.4%
1650 1
3.4%
2000 2
6.9%
2500 1
3.4%
3792 1
3.4%
3950 1
3.4%
3980 1
3.4%
4000 2
6.9%
4500 1
3.4%
ValueCountFrequency (%)
59800 1
3.4%
53000 1
3.4%
48000 1
3.4%
30000 1
3.4%
20000 1
3.4%
14000 1
3.4%
12900 2
6.9%
12000 2
6.9%
10000 2
6.9%
7980 1
3.4%

반여2동시장
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)79.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14163.207
Minimum1450
Maximum70000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T01:50:28.466458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1450
5-th percentile1710
Q14000
median6500
Q312500
95-th percentile55880
Maximum70000
Range68550
Interquartile range (IQR)8500

Descriptive statistics

Standard deviation18383.97
Coefficient of variation (CV)1.298009
Kurtosis3.0643431
Mean14163.207
Median Absolute Deviation (MAD)3500
Skewness1.997539
Sum410733
Variance3.3797035 × 108
MonotonicityNot monotonic
2023-12-11T01:50:28.640677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
4000 3
 
10.3%
10000 2
 
6.9%
8000 2
 
6.9%
5000 2
 
6.9%
38000 2
 
6.9%
70000 1
 
3.4%
2000 1
 
3.4%
7000 1
 
3.4%
1650 1
 
3.4%
1450 1
 
3.4%
Other values (13) 13
44.8%
ValueCountFrequency (%)
1450 1
 
3.4%
1650 1
 
3.4%
1800 1
 
3.4%
2000 1
 
3.4%
2083 1
 
3.4%
4000 3
10.3%
4400 1
 
3.4%
5000 2
6.9%
5200 1
 
3.4%
5350 1
 
3.4%
ValueCountFrequency (%)
70000 1
3.4%
59800 1
3.4%
50000 1
3.4%
38000 2
6.9%
18000 1
3.4%
15000 1
3.4%
12500 1
3.4%
10000 2
6.9%
8500 1
3.4%
8000 2
6.9%

재송한마음시장
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)93.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11635.862
Minimum1000
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T01:50:28.845495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1180
Q12500
median6500
Q313000
95-th percentile49600
Maximum60000
Range59000
Interquartile range (IQR)10500

Descriptive statistics

Standard deviation15469.643
Coefficient of variation (CV)1.3294798
Kurtosis4.8746442
Mean11635.862
Median Absolute Deviation (MAD)4900
Skewness2.2814974
Sum337440
Variance2.3930987 × 108
MonotonicityNot monotonic
2023-12-11T01:50:29.013425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
8000 2
 
6.9%
3700 2
 
6.9%
21000 1
 
3.4%
60000 1
 
3.4%
12000 1
 
3.4%
7000 1
 
3.4%
4000 1
 
3.4%
3000 1
 
3.4%
1540 1
 
3.4%
1300 1
 
3.4%
Other values (17) 17
58.6%
ValueCountFrequency (%)
1000 1
3.4%
1100 1
3.4%
1300 1
3.4%
1500 1
3.4%
1540 1
3.4%
1600 1
3.4%
2000 1
3.4%
2500 1
3.4%
3000 1
3.4%
3400 1
3.4%
ValueCountFrequency (%)
60000 1
3.4%
58000 1
3.4%
37000 1
3.4%
23000 1
3.4%
21000 1
3.4%
19000 1
3.4%
14000 1
3.4%
13000 1
3.4%
12000 1
3.4%
9000 1
3.4%

탑마트반송점
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13090
Minimum1300
Maximum53800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-11T01:50:29.216436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile1872
Q14000
median5980
Q314500
95-th percentile44900
Maximum53800
Range52500
Interquartile range (IQR)10500

Descriptive statistics

Standard deviation14579.502
Coefficient of variation (CV)1.1137893
Kurtosis2.0083172
Mean13090
Median Absolute Deviation (MAD)3000
Skewness1.6845487
Sum379610
Variance2.1256186 × 108
MonotonicityNot monotonic
2023-12-11T01:50:29.430416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
36500 1
 
3.4%
36000 1
 
3.4%
14500 1
 
3.4%
8000 1
 
3.4%
4000 1
 
3.4%
3500 1
 
3.4%
1800 1
 
3.4%
1300 1
 
3.4%
6480 1
 
3.4%
4220 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
1300 1
3.4%
1800 1
3.4%
1980 1
3.4%
2980 1
3.4%
3280 1
3.4%
3500 1
3.4%
3650 1
3.4%
4000 1
3.4%
4220 1
3.4%
4350 1
3.4%
ValueCountFrequency (%)
53800 1
3.4%
50500 1
3.4%
36500 1
3.4%
36000 1
3.4%
26900 1
3.4%
24950 1
3.4%
20900 1
3.4%
14500 1
3.4%
13400 1
3.4%
12800 1
3.4%

Interactions

2023-12-11T01:50:23.731136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:19.308959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:20.448206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.513841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.324257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.048127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.862901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:19.523133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:20.706683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.675069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.463405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.145840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:24.010479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:19.686480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:20.860703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.809595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.581678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.256587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:24.127733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:19.884981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.015885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.937922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.712654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.371556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:24.263990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:20.110984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.188610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.062322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.820773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.511036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:24.366331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:20.296710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:21.359603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.199152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:22.939078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:23.622204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:50:29.584185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목규 격(단위)GS슈퍼마켓 대동점이마트 해운대점센텀 홈플러스농산물시장반여2동시장재송한마음시장탑마트반송점
품목1.0001.0001.0001.0001.0001.0001.0001.0001.000
규 격(단위)1.0001.0000.8650.9660.9690.9480.9170.9690.831
GS슈퍼마켓 대동점1.0000.8651.0000.8931.0000.8710.9680.8650.970
이마트 해운대점1.0000.9660.8931.0001.0000.9910.9520.9240.897
센텀 홈플러스1.0000.9691.0001.0001.0000.9871.0001.0001.000
농산물시장1.0000.9480.8710.9910.9871.0000.9240.8960.824
반여2동시장1.0000.9170.9680.9521.0000.9241.0000.8130.954
재송한마음시장1.0000.9690.8650.9241.0000.8960.8131.0000.923
탑마트반송점1.0000.8310.9700.8971.0000.8240.9540.9231.000
2023-12-11T01:50:29.797737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
GS슈퍼마켓 대동점이마트 해운대점농산물시장반여2동시장재송한마음시장탑마트반송점
GS슈퍼마켓 대동점1.0000.9470.9540.9220.9280.956
이마트 해운대점0.9471.0000.9570.9160.9700.972
농산물시장0.9540.9571.0000.9140.9550.938
반여2동시장0.9220.9160.9141.0000.8930.932
재송한마음시장0.9280.9700.9550.8931.0000.945
탑마트반송점0.9560.9720.9380.9320.9451.000

Missing values

2023-12-11T01:50:24.491787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:50:24.648062image/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

품목규 격(단위)GS슈퍼마켓 대동점이마트 해운대점센텀 홈플러스농산물시장반여2동시장재송한마음시장탑마트반송점
0사과부사(300g이상)3kg49100300203560030000380002100036500
1배신고6㎏50000382403410048000380003700036000
2밤1㎏(상품)800010750770014000800080007980
3대추1㎏(상품)27900194001497020000180002300024950
4배추1kg4980189027904000850016004500
5무1㎏2000145016902000180010001980
6소고기(국산)등심상등육 500g60000594005595053000700005800050500
7소고기(수입)등심상등육 500g(호주)21330270001975012000125001400020900
8돼지고기정육 500g17800114001225012000100001300013400
9닭고기육계 1㎏12800528074907980550065004900
품목규 격(단위)GS슈퍼마켓 대동점이마트 해운대점센텀 홈플러스농산물시장반여2동시장재송한마음시장탑마트반송점
19고춧가루0.1㎏7730568574906000650037005480
20두부500g 판두부/ (포장두부 420g) 1모4550348617902000208315004350
21밀가루백설표 중력분1등 2.5㎏3950475034903792440034004220
22식용유백설표 옥수수기름 1.8ℓ8180748076907950535066006480
23소주(소매점)시원소주 360㎖ 1병1300128013101400145013001300
24맥주(소매점)하이트 500㎖ 1병15801410판매안함1650165015401800
25소주(외식)시원소주 360㎖ 1병4000400040004000400030003500
26맥주(외식)하이트 500㎖ 1병4000400040005000400040004000
27돼지갈비(외식)1인분950090001000010000700070008000
28삼겹살(외식)1인분1380010000153801000080001200014500