Overview

Dataset statistics

Number of variables9
Number of observations21
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.7 KiB
Average record size in memory85.3 B

Variable types

Text2
Numeric7

Dataset

Description부산광역시해운대구_물가관리_20211210
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063783

Alerts

센텀홈플러스 is highly overall correlated with 반여2동시장 and 2 other fieldsHigh correlation
농산물시장 is highly overall correlated with GS슈퍼마켓 and 3 other fieldsHigh correlation
이마트해운대점 is highly overall correlated with 탑마트반송점 and 1 other fieldsHigh correlation
GS슈퍼마켓 is highly overall correlated with 농산물시장 and 3 other fieldsHigh correlation
반여2동시장 is highly overall correlated with 센텀홈플러스 and 4 other fieldsHigh correlation
탑마트반송점 is highly overall correlated with 센텀홈플러스 and 5 other fieldsHigh correlation
재송한마음시장 is highly overall correlated with 센텀홈플러스 and 5 other fieldsHigh correlation
품 목 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:50:01.397512
Analysis finished2023-12-10 16:50:10.973232
Duration9.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품 목
Text

UNIQUE 

Distinct21
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T01:50:11.200381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length4.2380952
Min length1

Characters and Unicode

Total characters89
Distinct characters45
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

Unique21 ?
Unique (%)100.0%

Sample

1st row배추
2nd row무
3rd row대파
4th row쪽파
5th row소고기(국산)
ValueCountFrequency (%)
배추 1
 
4.2%
무 1
 
4.2%
돼지갈비(외식 1
 
4.2%
맥주(외식 1
 
4.2%
소주(외식 1
 
4.2%
식용유 1
 
4.2%
밀가루 1
 
4.2%
두부 1
 
4.2%
고춧가루(국산 1
 
4.2%
깐마늘(국산 1
 
4.2%
Other values (14) 14
58.3%
2023-12-11T01:50:11.782259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
( 8
 
9.0%
) 8
 
9.0%
식 5
 
5.6%
고 5
 
5.6%
외 4
 
4.5%
4
 
4.5%
국 4
 
4.5%
산 4
 
4.5%
기 3
 
3.4%
지 2
 
2.2%
Other values (35) 42
47.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 69
77.5%
Open Punctuation 8
 
9.0%
Close Punctuation 8
 
9.0%
Space Separator 4
 
4.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
식 5
 
7.2%
고 5
 
7.2%
외 4
 
5.8%
국 4
 
5.8%
산 4
 
5.8%
기 3
 
4.3%
지 2
 
2.9%
루 2
 
2.9%
갈 2
 
2.9%
주 2
 
2.9%
Other values (32) 36
52.2%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 69
77.5%
Common 20
 
22.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
식 5
 
7.2%
고 5
 
7.2%
외 4
 
5.8%
국 4
 
5.8%
산 4
 
5.8%
기 3
 
4.3%
지 2
 
2.9%
루 2
 
2.9%
갈 2
 
2.9%
주 2
 
2.9%
Other values (32) 36
52.2%
Common
ValueCountFrequency (%)
( 8
40.0%
) 8
40.0%
4
20.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 69
77.5%
ASCII 20
 
22.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
( 8
40.0%
) 8
40.0%
4
20.0%
Hangul
ValueCountFrequency (%)
식 5
 
7.2%
고 5
 
7.2%
외 4
 
5.8%
국 4
 
5.8%
산 4
 
5.8%
기 3
 
4.3%
지 2
 
2.9%
루 2
 
2.9%
갈 2
 
2.9%
주 2
 
2.9%
Other values (32) 36
52.2%

규 격(단위)
Text

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Memory size300.0 B
2023-12-11T01:50:12.132267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length12
Mean length8.9047619
Min length2

Characters and Unicode

Total characters187
Distinct characters60
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)81.0%

Sample

1st row1포기(상품)
2nd row1개(상품)
3rd row1㎏(1단)
4th row1㎏(1단)
5th row등심 상등육 500g
ValueCountFrequency (%)
200g 2
 
4.8%
500g 2
 
4.8%
정도 2
 
4.8%
1마리(중품 2
 
4.8%
1병 2
 
4.8%
1㎏ 2
 
4.8%
1㎏(1단 2
 
4.8%
백설표 2
 
4.8%
500㎖ 1
 
2.4%
하이트 1
 
2.4%
Other values (24) 24
57.1%
2023-12-11T01:50:12.675493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
11.8%
0 18
 
9.6%
1 18
 
9.6%
) 8
 
4.3%
( 8
 
4.3%
㎏ 7
 
3.7%
2 5
 
2.7%
g 5
 
2.7%
정 5
 
2.7%
5 4
 
2.1%
Other values (50) 87
46.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 76
40.6%
Decimal Number 52
27.8%
Space Separator 22
 
11.8%
Other Symbol 12
 
6.4%
Close Punctuation 8
 
4.3%
Open Punctuation 8
 
4.3%
Lowercase Letter 6
 
3.2%
Other Punctuation 3
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 5
 
6.6%
품 4
 
5.3%
도 4
 
5.3%
포 3
 
3.9%
중 3
 
3.9%
마 3
 
3.9%
등 3
 
3.9%
리 3
 
3.9%
상 3
 
3.9%
수 2
 
2.6%
Other values (33) 43
56.6%
Decimal Number
ValueCountFrequency (%)
0 18
34.6%
1 18
34.6%
2 5
 
9.6%
5 4
 
7.7%
6 2
 
3.8%
3 2
 
3.8%
4 2
 
3.8%
8 1
 
1.9%
Other Symbol
ValueCountFrequency (%)
㎏ 7
58.3%
㎝ 3
25.0%
㎖ 2
 
16.7%
Lowercase Letter
ValueCountFrequency (%)
g 5
83.3%
ℓ 1
 
16.7%
Space Separator
ValueCountFrequency (%)
22
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Other Punctuation
ValueCountFrequency (%)
. 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 106
56.7%
Hangul 76
40.6%
Latin 5
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 5
 
6.6%
품 4
 
5.3%
도 4
 
5.3%
포 3
 
3.9%
중 3
 
3.9%
마 3
 
3.9%
등 3
 
3.9%
리 3
 
3.9%
상 3
 
3.9%
수 2
 
2.6%
Other values (33) 43
56.6%
Common
ValueCountFrequency (%)
22
20.8%
0 18
17.0%
1 18
17.0%
) 8
 
7.5%
( 8
 
7.5%
㎏ 7
 
6.6%
2 5
 
4.7%
5 4
 
3.8%
. 3
 
2.8%
㎝ 3
 
2.8%
Other values (6) 10
9.4%
Latin
ValueCountFrequency (%)
g 5
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98
52.4%
Hangul 76
40.6%
CJK Compat 12
 
6.4%
Letterlike Symbols 1
 
0.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
22
22.4%
0 18
18.4%
1 18
18.4%
) 8
 
8.2%
( 8
 
8.2%
2 5
 
5.1%
g 5
 
5.1%
5 4
 
4.1%
. 3
 
3.1%
6 2
 
2.0%
Other values (3) 5
 
5.1%
CJK Compat
ValueCountFrequency (%)
㎏ 7
58.3%
㎝ 3
25.0%
㎖ 2
 
16.7%
Hangul
ValueCountFrequency (%)
정 5
 
6.6%
품 4
 
5.3%
도 4
 
5.3%
포 3
 
3.9%
중 3
 
3.9%
마 3
 
3.9%
등 3
 
3.9%
리 3
 
3.9%
상 3
 
3.9%
수 2
 
2.6%
Other values (33) 43
56.6%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

센텀홈플러스
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9960.4762
Minimum1450
Maximum54900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:12.921206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1450
5-th percentile1590
Q13990
median4300
Q38990
95-th percentile47650
Maximum54900
Range53450
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation14131.213
Coefficient of variation (CV)1.4187287
Kurtosis6.8633214
Mean9960.4762
Median Absolute Deviation (MAD)2710
Skewness2.7521618
Sum209170
Variance1.9969118 × 108
MonotonicityNot monotonic
2023-12-11T01:50:13.117313image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1590 2
 
9.5%
4000 2
 
9.5%
4290 1
 
4.8%
1450 1
 
4.8%
11000 1
 
4.8%
7690 1
 
4.8%
4450 1
 
4.8%
8990 1
 
4.8%
11900 1
 
4.8%
54900 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
1450 1
4.8%
1590 2
9.5%
3060 1
4.8%
3490 1
4.8%
3990 1
4.8%
4000 2
9.5%
4190 1
4.8%
4290 1
4.8%
4300 1
4.8%
4450 1
4.8%
ValueCountFrequency (%)
54900 1
4.8%
47650 1
4.8%
11900 1
4.8%
11000 1
4.8%
10200 1
4.8%
8990 1
4.8%
8450 1
4.8%
7990 1
4.8%
7690 1
4.8%
4450 1
4.8%

농산물시장
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9549.0476
Minimum1000
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:13.260618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1500
Q12500
median4950
Q37950
95-th percentile52000
Maximum55000
Range54000
Interquartile range (IQR)5450

Descriptive statistics

Standard deviation14907.46
Coefficient of variation (CV)1.5611463
Kurtosis6.8086498
Mean9549.0476
Median Absolute Deviation (MAD)2950
Skewness2.7755574
Sum200530
Variance2.2223237 × 108
MonotonicityNot monotonic
2023-12-11T01:50:13.406972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
2000 2
 
9.5%
10000 2
 
9.5%
1500 2
 
9.5%
3300 1
 
4.8%
1000 1
 
4.8%
5000 1
 
4.8%
4000 1
 
4.8%
7950 1
 
4.8%
4550 1
 
4.8%
7500 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
1000 1
4.8%
1500 2
9.5%
2000 2
9.5%
2500 1
4.8%
3000 1
4.8%
3300 1
4.8%
4000 1
4.8%
4550 1
4.8%
4950 1
4.8%
4980 1
4.8%
ValueCountFrequency (%)
55000 1
4.8%
52000 1
4.8%
10000 2
9.5%
9900 1
4.8%
7950 1
4.8%
7900 1
4.8%
7500 1
4.8%
5000 1
4.8%
4980 1
4.8%
4950 1
4.8%

이마트해운대점
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9864
Minimum2
Maximum59670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:13.547958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile4
Q12000
median4000
Q39800
95-th percentile53900
Maximum59670
Range59668
Interquartile range (IQR)7800

Descriptive statistics

Standard deviation16229.71
Coefficient of variation (CV)1.6453477
Kurtosis6.2017456
Mean9864
Median Absolute Deviation (MAD)3987
Skewness2.6178201
Sum207144
Variance2.6340349 × 108
MonotonicityNot monotonic
2023-12-11T01:50:13.717188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4000 2
 
9.5%
2000 1
 
4.8%
53900 1
 
4.8%
15830 1
 
4.8%
12 1
 
4.8%
7 1
 
4.8%
5930 1
 
4.8%
3486 1
 
4.8%
5900 1
 
4.8%
13500 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
2 1
4.8%
4 1
4.8%
7 1
4.8%
12 1
4.8%
13 1
4.8%
2000 1
4.8%
3280 1
4.8%
3486 1
4.8%
3890 1
4.8%
4000 2
9.5%
ValueCountFrequency (%)
59670 1
4.8%
53900 1
4.8%
15830 1
4.8%
13500 1
4.8%
9960 1
4.8%
9800 1
4.8%
6980 1
4.8%
5930 1
4.8%
5900 1
4.8%
4980 1
4.8%

GS슈퍼마켓
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)90.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12272.286
Minimum1500
Maximum69670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:13.967155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile2480
Q14500
median5490
Q39500
95-th percentile59800
Maximum69670
Range68170
Interquartile range (IQR)5000

Descriptive statistics

Standard deviation17942.144
Coefficient of variation (CV)1.4620051
Kurtosis6.9307083
Mean12272.286
Median Absolute Deviation (MAD)2910
Skewness2.7711558
Sum257718
Variance3.2192054 × 108
MonotonicityNot monotonic
2023-12-11T01:50:14.173111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
4000 2
 
9.5%
8800 2
 
9.5%
2833 1
 
4.8%
1500 1
 
4.8%
13800 1
 
4.8%
9500 1
 
4.8%
10800 1
 
4.8%
4500 1
 
4.8%
4550 1
 
4.8%
6435 1
 
4.8%
Other values (9) 9
42.9%
ValueCountFrequency (%)
1500 1
4.8%
2480 1
4.8%
2833 1
4.8%
4000 2
9.5%
4500 1
4.8%
4550 1
4.8%
4680 1
4.8%
4900 1
4.8%
4980 1
4.8%
5490 1
4.8%
ValueCountFrequency (%)
69670 1
4.8%
59800 1
4.8%
17800 1
4.8%
13800 1
4.8%
10800 1
4.8%
9500 1
4.8%
8800 2
9.5%
8400 1
4.8%
6435 1
4.8%
5490 1
4.8%

반여2동시장
Real number (ℝ)

HIGH CORRELATION 

Distinct16
Distinct (%)76.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10202.381
Minimum1250
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:14.370909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1500
Q14400
median5200
Q38000
95-th percentile45000
Maximum59800
Range58550
Interquartile range (IQR)3600

Descriptive statistics

Standard deviation14526.833
Coefficient of variation (CV)1.4238669
Kurtosis7.9304632
Mean10202.381
Median Absolute Deviation (MAD)1300
Skewness2.8970164
Sum214250
Variance2.1102887 × 108
MonotonicityNot monotonic
2023-12-11T01:50:14.567589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
5000 4
19.0%
8000 2
 
9.5%
4000 2
 
9.5%
59800 1
 
4.8%
7000 1
 
4.8%
5350 1
 
4.8%
4400 1
 
4.8%
1250 1
 
4.8%
6500 1
 
4.8%
12000 1
 
4.8%
Other values (6) 6
28.6%
ValueCountFrequency (%)
1250 1
 
4.8%
1500 1
 
4.8%
2500 1
 
4.8%
4000 2
9.5%
4400 1
 
4.8%
5000 4
19.0%
5200 1
 
4.8%
5350 1
 
4.8%
6000 1
 
4.8%
6500 1
 
4.8%
ValueCountFrequency (%)
59800 1
4.8%
45000 1
4.8%
13750 1
4.8%
12000 1
4.8%
8000 2
9.5%
7000 1
4.8%
6500 1
4.8%
6000 1
4.8%
5350 1
4.8%
5200 1
4.8%

탑마트반송점
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)95.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11176.19
Minimum1980
Maximum55800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:14.758543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1980
5-th percentile2580
Q14000
median5200
Q311800
95-th percentile54900
Maximum55800
Range53820
Interquartile range (IQR)7800

Descriptive statistics

Standard deviation15170.399
Coefficient of variation (CV)1.3573855
Kurtosis6.2551815
Mean11176.19
Median Absolute Deviation (MAD)1720
Skewness2.6552157
Sum234700
Variance2.3014099 × 108
MonotonicityNot monotonic
2023-12-11T01:50:14.954524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
4000 2
 
9.5%
3480 1
 
4.8%
55800 1
 
4.8%
16000 1
 
4.8%
9000 1
 
4.8%
6480 1
 
4.8%
4220 1
 
4.8%
4350 1
 
4.8%
4980 1
 
4.8%
12800 1
 
4.8%
Other values (10) 10
47.6%
ValueCountFrequency (%)
1980 1
4.8%
2580 1
4.8%
3450 1
4.8%
3480 1
4.8%
4000 2
9.5%
4220 1
4.8%
4350 1
4.8%
4900 1
4.8%
4980 1
4.8%
5200 1
4.8%
ValueCountFrequency (%)
55800 1
4.8%
54900 1
4.8%
16000 1
4.8%
12900 1
4.8%
12800 1
4.8%
11800 1
4.8%
9000 1
4.8%
6480 1
4.8%
5980 1
4.8%
5900 1
4.8%

재송한마음시장
Real number (ℝ)

HIGH CORRELATION 

Distinct18
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10647.619
Minimum1500
Maximum57000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size321.0 B
2023-12-11T01:50:15.112655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1800
Q13500
median4200
Q310000
95-th percentile50000
Maximum57000
Range55500
Interquartile range (IQR)6500

Descriptive statistics

Standard deviation14803.906
Coefficient of variation (CV)1.3903489
Kurtosis6.4108361
Mean10647.619
Median Absolute Deviation (MAD)2400
Skewness2.6500225
Sum223600
Variance2.1915562 × 108
MonotonicityNot monotonic
2023-12-11T01:50:15.302678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
4000 3
 
14.3%
10000 2
 
9.5%
2800 1
 
4.8%
57000 1
 
4.8%
14000 1
 
4.8%
12000 1
 
4.8%
7600 1
 
4.8%
4200 1
 
4.8%
1500 1
 
4.8%
3700 1
 
4.8%
Other values (8) 8
38.1%
ValueCountFrequency (%)
1500 1
 
4.8%
1800 1
 
4.8%
2000 1
 
4.8%
2800 1
 
4.8%
3000 1
 
4.8%
3500 1
 
4.8%
3700 1
 
4.8%
4000 3
14.3%
4200 1
 
4.8%
6000 1
 
4.8%
ValueCountFrequency (%)
57000 1
4.8%
50000 1
4.8%
14000 1
4.8%
13500 1
4.8%
12000 1
4.8%
10000 2
9.5%
9000 1
4.8%
7600 1
4.8%
6000 1
4.8%
4200 1
4.8%

Interactions

2023-12-11T01:50:09.353401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:01.764852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.644678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:03.684735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.477703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:06.089491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:08.093812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:09.504771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:01.879499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.757680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:03.773778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.577697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:06.481588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:08.274324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:09.645776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:01.994622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.863407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:03.898497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.671691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:06.921445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:08.475053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:09.825697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.108283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.969256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.003218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.780362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:07.226739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:08.662854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:10.009651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.229951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:03.083432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.108111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.959363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:07.537695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:08.811157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:10.185679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.370787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:03.514863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.242267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:05.299727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:07.745323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:08.972782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:10.349590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:02.509810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:03.603264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:04.368722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:05.782290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:07.916199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:09.125051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:50:15.456511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품 목규 격(단위)센텀홈플러스농산물시장이마트해운대점GS슈퍼마켓반여2동시장탑마트반송점재송한마음시장
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.000
규 격(단위)1.0001.0000.9191.0000.0000.9620.8010.4660.667
센텀홈플러스1.0000.9191.0000.7420.8690.9000.8200.8720.876
농산물시장1.0001.0000.7421.0000.6660.7900.7260.7990.825
이마트해운대점1.0000.0000.8690.6661.0000.7000.6910.9510.662
GS슈퍼마켓1.0000.9620.9000.7900.7001.0000.9950.7560.991
반여2동시장1.0000.8010.8200.7260.6910.9951.0000.6660.981
탑마트반송점1.0000.4660.8720.7990.9510.7560.6661.0000.807
재송한마음시장1.0000.6670.8760.8250.6620.9910.9810.8071.000
2023-12-11T01:50:16.091521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
센텀홈플러스농산물시장이마트해운대점GS슈퍼마켓반여2동시장탑마트반송점재송한마음시장
센텀홈플러스1.0000.4360.3090.4170.6390.5460.676
농산물시장0.4361.0000.2260.7750.7500.6140.690
이마트해운대점0.3090.2261.0000.3150.4730.6210.537
GS슈퍼마켓0.4170.7750.3151.0000.8310.7330.702
반여2동시장0.6390.7500.4730.8311.0000.8240.824
탑마트반송점0.5460.6140.6210.7330.8241.0000.826
재송한마음시장0.6760.6900.5370.7020.8240.8261.000

Missing values

2023-12-11T01:50:10.597351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:50:10.877554image/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

품 목규 격(단위)센텀홈플러스농산물시장이마트해운대점GS슈퍼마켓반여2동시장탑마트반송점재송한마음시장
0배추1포기(상품)4290330020002833500034802800
1무1개(상품)1590150022480150019801800
2대파1㎏(1단)3990150032805490250025803500
3쪽파1㎏(1단)84502000996084008000598010000
4소고기(국산)등심 상등육 500g47650550005967069670450005490050000
5돼지고기(국산)삼겹살 500g1020099001317800137501290013500
6닭고기육계 1㎏7990495069808800600052006000
7달 걀특란 10개4300498044680520034504000
8명 태40㎝정도 1마리(냉동)3060250049804900500059002000
9고등어30㎝ 1마리(중품)3490300038904980500049003000
품 목규 격(단위)센텀홈플러스농산물시장이마트해운대점GS슈퍼마켓반여2동시장탑마트반송점재송한마음시장
11쌀정미포장미 20㎏54900520005390059800598005580057000
12깐마늘(국산)1㎏11900100013500150050001280010000
13고춧가루(국산)0.1㎏8990750059006435650049803700
14두부(포장두부 420g) 1모1590200034864550125043501500
15밀가루백설표 중력분1등 2.5㎏4450455059304500440042204200
16식용유백설표 옥수수기름 1.8ℓ76907950710800535064807600
17소주(외식)시원소주 360㎖ 1병4000400040004000400040004000
18맥주(외식)하이트 500㎖ 1병4000500040004000400040004000
19돼지갈비(외식)200g 정도11000100001295007000900012000
20삼겹살(외식)200g 정도145010000158301380080001600014000