Overview

Dataset statistics

Number of variables9
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.3 KiB
Average record size in memory83.7 B

Variable types

Text2
Numeric7

Dataset

Description부산광역시해운대구_물가관리_20230711
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063783

Alerts

우동(센텀홈플러스) is highly overall correlated with 중동(이마트 해운대점) and 5 other fieldsHigh correlation
중동(이마트 해운대점) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
좌동(GS수퍼마켓) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
송정동 is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
반여2동(골목시장) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
반송동(탑마트) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
재송동(한마음시장) is highly overall correlated with 우동(센텀홈플러스) and 5 other fieldsHigh correlation
품 목 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:48:02.529363
Analysis finished2023-12-10 16:48:08.787703
Duration6.26 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품 목
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T01:48:08.937875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length3.7857143
Min length1

Characters and Unicode

Total characters106
Distinct characters53
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

Unique28 ?
Unique (%)100.0%

Sample

1st row사과
2nd row배
3rd row배추
4th row무
5th row대파
ValueCountFrequency (%)
사과 1
 
3.1%
배 1
 
3.1%
돼지갈비(외식 1
 
3.1%
맥주(외식 1
 
3.1%
소주(외식 1
 
3.1%
맥주(소매점 1
 
3.1%
소주(소매점 1
 
3.1%
식용유 1
 
3.1%
밀가루 1
 
3.1%
두부 1
 
3.1%
Other values (22) 22
68.8%
2023-12-11T01:48:09.344974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
) 8
 
7.5%
( 8
 
7.5%
고 6
 
5.7%
소 6
 
5.7%
식 5
 
4.7%
5
 
4.7%
기 5
 
4.7%
주 4
 
3.8%
외 4
 
3.8%
파 2
 
1.9%
Other values (43) 53
50.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 85
80.2%
Close Punctuation 8
 
7.5%
Open Punctuation 8
 
7.5%
Space Separator 5
 
4.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 6
 
7.1%
소 6
 
7.1%
식 5
 
5.9%
기 5
 
5.9%
주 4
 
4.7%
외 4
 
4.7%
파 2
 
2.4%
가 2
 
2.4%
맥 2
 
2.4%
갈 2
 
2.4%
Other values (40) 47
55.3%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 85
80.2%
Common 21
 
19.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 6
 
7.1%
소 6
 
7.1%
식 5
 
5.9%
기 5
 
5.9%
주 4
 
4.7%
외 4
 
4.7%
파 2
 
2.4%
가 2
 
2.4%
맥 2
 
2.4%
갈 2
 
2.4%
Other values (40) 47
55.3%
Common
ValueCountFrequency (%)
) 8
38.1%
( 8
38.1%
5
23.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 85
80.2%
ASCII 21
 
19.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
) 8
38.1%
( 8
38.1%
5
23.8%
Hangul
ValueCountFrequency (%)
고 6
 
7.1%
소 6
 
7.1%
식 5
 
5.9%
기 5
 
5.9%
주 4
 
4.7%
외 4
 
4.7%
파 2
 
2.4%
가 2
 
2.4%
맥 2
 
2.4%
갈 2
 
2.4%
Other values (40) 47
55.3%

규 격
Text

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T01:48:09.617013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length9.3928571
Min length2

Characters and Unicode

Total characters263
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

Unique16 ?
Unique (%)57.1%

Sample

1st row부사(1개 300g이상)3kg
2nd row신고 6㎏
3rd row1㎏
4th row1㎏
5th row1㎏(상품)
ValueCountFrequency (%)
1병 4
 
7.3%
500g 4
 
7.3%
200g 2
 
3.6%
시원소주 2
 
3.6%
정도 2
 
3.6%
1㎏ 2
 
3.6%
0.1㎏ 2
 
3.6%
360㎖ 2
 
3.6%
1마리(냉동 2
 
3.6%
상등육 2
 
3.6%
Other values (28) 31
56.4%
2023-12-11T01:48:10.162210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 29
 
11.0%
28
 
10.6%
1 19
 
7.2%
5 10
 
3.8%
㎏ 10
 
3.8%
2 9
 
3.4%
g 9
 
3.4%
정 7
 
2.7%
) 7
 
2.7%
( 7
 
2.7%
Other values (59) 128
48.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107
40.7%
Decimal Number 78
29.7%
Space Separator 28
 
10.6%
Other Symbol 19
 
7.2%
Lowercase Letter 11
 
4.2%
Close Punctuation 7
 
2.7%
Open Punctuation 7
 
2.7%
Other Punctuation 5
 
1.9%
Dash Punctuation 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
정 7
 
6.5%
도 6
 
5.6%
리 5
 
4.7%
마 5
 
4.7%
등 5
 
4.7%
상 4
 
3.7%
동 4
 
3.7%
냉 4
 
3.7%
병 4
 
3.7%
부 3
 
2.8%
Other values (40) 60
56.1%
Decimal Number
ValueCountFrequency (%)
0 29
37.2%
1 19
24.4%
5 10
 
12.8%
2 9
 
11.5%
6 4
 
5.1%
3 4
 
5.1%
4 2
 
2.6%
8 1
 
1.3%
Other Symbol
ValueCountFrequency (%)
㎏ 10
52.6%
㎝ 5
26.3%
㎖ 4
 
21.1%
Lowercase Letter
ValueCountFrequency (%)
g 9
81.8%
k 1
 
9.1%
ℓ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
28
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Other Punctuation
ValueCountFrequency (%)
. 5
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 146
55.5%
Hangul 107
40.7%
Latin 10
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
정 7
 
6.5%
도 6
 
5.6%
리 5
 
4.7%
마 5
 
4.7%
등 5
 
4.7%
상 4
 
3.7%
동 4
 
3.7%
냉 4
 
3.7%
병 4
 
3.7%
부 3
 
2.8%
Other values (40) 60
56.1%
Common
ValueCountFrequency (%)
0 29
19.9%
28
19.2%
1 19
13.0%
5 10
 
6.8%
㎏ 10
 
6.8%
2 9
 
6.2%
) 7
 
4.8%
( 7
 
4.8%
. 5
 
3.4%
㎝ 5
 
3.4%
Other values (7) 17
11.6%
Latin
ValueCountFrequency (%)
g 9
90.0%
k 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 136
51.7%
Hangul 107
40.7%
CJK Compat 19
 
7.2%
Letterlike Symbols 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
21.3%
28
20.6%
1 19
14.0%
5 10
 
7.4%
2 9
 
6.6%
g 9
 
6.6%
) 7
 
5.1%
( 7
 
5.1%
. 5
 
3.7%
6 4
 
2.9%
Other values (5) 9
 
6.6%
CJK Compat
ValueCountFrequency (%)
㎏ 10
52.6%
㎝ 5
26.3%
㎖ 4
 
21.1%
Hangul
ValueCountFrequency (%)
정 7
 
6.5%
도 6
 
5.6%
리 5
 
4.7%
마 5
 
4.7%
등 5
 
4.7%
상 4
 
3.7%
동 4
 
3.7%
냉 4
 
3.7%
병 4
 
3.7%
부 3
 
2.8%
Other values (40) 60
56.1%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

우동(센텀홈플러스)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11585
Minimum690
Maximum69950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:10.372411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum690
5-th percentile1130
Q13447.5
median5000
Q311400
95-th percentile45015
Maximum69950
Range69260
Interquartile range (IQR)7952.5

Descriptive statistics

Standard deviation16007.696
Coefficient of variation (CV)1.3817605
Kurtosis6.6298026
Mean11585
Median Absolute Deviation (MAD)3000
Skewness2.5638032
Sum324380
Variance2.5624632 × 108
MonotonicityNot monotonic
2023-12-11T01:48:10.566371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12000 2
 
7.1%
5000 2
 
7.1%
28400 1
 
3.6%
4620 1
 
3.6%
1710 1
 
3.6%
1390 1
 
3.6%
10800 1
 
3.6%
4600 1
 
3.6%
2290 1
 
3.6%
7490 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
690 1
3.6%
990 1
3.6%
1390 1
3.6%
1710 1
3.6%
2290 1
3.6%
2490 1
3.6%
3290 1
3.6%
3500 1
3.6%
3990 1
3.6%
4400 1
3.6%
ValueCountFrequency (%)
69950 1
3.6%
48900 1
3.6%
37800 1
3.6%
28400 1
3.6%
13200 1
3.6%
12000 2
7.1%
11200 1
3.6%
10990 1
3.6%
10800 1
3.6%
7490 1
3.6%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12245.75
Minimum1380
Maximum49900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:10.757845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1380
5-th percentile1449.9
Q14475
median7480
Q315630
95-th percentile39835
Maximum49900
Range48520
Interquartile range (IQR)11155

Descriptive statistics

Standard deviation12629.306
Coefficient of variation (CV)1.0313216
Kurtosis3.4069916
Mean12245.75
Median Absolute Deviation (MAD)5365
Skewness1.855627
Sum342881
Variance1.5949937 × 108
MonotonicityNot monotonic
2023-12-11T01:48:10.956435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 2
 
7.1%
20720 1
 
3.6%
25600 1
 
3.6%
23000 1
 
3.6%
14400 1
 
3.6%
1550 1
 
3.6%
1380 1
 
3.6%
16320 1
 
3.6%
10690 1
 
3.6%
4700 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1380 1
3.6%
1396 1
3.6%
1550 1
3.6%
1880 1
3.6%
2350 1
3.6%
3000 1
3.6%
3980 1
3.6%
4640 1
3.6%
4700 1
3.6%
4900 1
3.6%
ValueCountFrequency (%)
49900 1
3.6%
47500 1
3.6%
25600 1
3.6%
24835 1
3.6%
23000 1
3.6%
20720 1
3.6%
16320 1
3.6%
15400 1
3.6%
14400 1
3.6%
13800 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14570.536
Minimum1430
Maximum66330
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:11.170281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile1732.5
Q14535
median6262.5
Q315450
95-th percentile55130
Maximum66330
Range64900
Interquartile range (IQR)10915

Descriptive statistics

Standard deviation17882.819
Coefficient of variation (CV)1.2273275
Kurtosis2.5187813
Mean14570.536
Median Absolute Deviation (MAD)4057.5
Skewness1.8589794
Sum407975
Variance3.1979522 × 108
MonotonicityNot monotonic
2023-12-11T01:48:11.379154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 2
 
7.1%
39800 1
 
3.6%
58000 1
 
3.6%
15000 1
 
3.6%
9500 1
 
3.6%
1830 1
 
3.6%
1430 1
 
3.6%
9780 1
 
3.6%
5450 1
 
3.6%
4580 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1430 1
3.6%
1680 1
3.6%
1830 1
3.6%
1990 1
3.6%
2420 1
3.6%
3480 1
3.6%
4400 1
3.6%
4580 1
3.6%
4980 1
3.6%
5000 2
7.1%
ValueCountFrequency (%)
66330 1
3.6%
58000 1
3.6%
49800 1
3.6%
39800 1
3.6%
32500 1
3.6%
19800 1
3.6%
16800 1
3.6%
15000 1
3.6%
11900 1
3.6%
11800 1
3.6%

송정동
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11409.571
Minimum698
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:11.594039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum698
5-th percentile1695.5
Q13220
median5890
Q314600
95-th percentile37490.5
Maximum59800
Range59102
Interquartile range (IQR)11380

Descriptive statistics

Standard deviation13462.829
Coefficient of variation (CV)1.1799592
Kurtosis6.3053947
Mean11409.571
Median Absolute Deviation (MAD)4010
Skewness2.3967311
Sum319468
Variance1.8124777 × 108
MonotonicityNot monotonic
2023-12-11T01:48:11.781755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2500 2
 
7.1%
9900 2
 
7.1%
21990 1
 
3.6%
3480 1
 
3.6%
15400 1
 
3.6%
22000 1
 
3.6%
5000 1
 
3.6%
4000 1
 
3.6%
1900 1
 
3.6%
1650 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
698 1
3.6%
1650 1
3.6%
1780 1
3.6%
1900 1
3.6%
2500 2
7.1%
2980 1
3.6%
3300 1
3.6%
3480 1
3.6%
3980 1
3.6%
4000 1
3.6%
ValueCountFrequency (%)
59800 1
3.6%
44900 1
3.6%
23730 1
3.6%
22000 1
3.6%
21990 1
3.6%
15400 1
3.6%
14900 1
3.6%
14500 1
3.6%
12500 1
3.6%
11000 1
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10496.429
Minimum1250
Maximum56000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:11.977641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1687.5
Q14000
median5950
Q311925
95-th percentile36150
Maximum56000
Range54750
Interquartile range (IQR)7925

Descriptive statistics

Standard deviation12464.831
Coefficient of variation (CV)1.1875307
Kurtosis6.6318199
Mean10496.429
Median Absolute Deviation (MAD)3250
Skewness2.5034336
Sum293900
Variance1.5537202 × 108
MonotonicityNot monotonic
2023-12-11T01:48:12.138512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5000 3
 
10.7%
4000 3
 
10.7%
2000 2
 
7.1%
15000 2
 
7.1%
2500 1
 
3.6%
6500 1
 
3.6%
8000 1
 
3.6%
9000 1
 
3.6%
1850 1
 
3.6%
1600 1
 
3.6%
Other values (12) 12
42.9%
ValueCountFrequency (%)
1250 1
 
3.6%
1600 1
 
3.6%
1850 1
 
3.6%
2000 2
7.1%
2500 1
 
3.6%
4000 3
10.7%
4200 1
 
3.6%
5000 3
10.7%
5400 1
 
3.6%
6500 1
 
3.6%
ValueCountFrequency (%)
56000 1
3.6%
40000 1
3.6%
29000 1
3.6%
20000 1
3.6%
15000 2
7.1%
12000 1
3.6%
11900 1
3.6%
9000 1
3.6%
8500 1
3.6%
8200 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9243.2143
Minimum1400
Maximum45800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:12.323240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1400
5-th percentile1604.5
Q13995
median5425
Q311850
95-th percentile23755
Maximum45800
Range44400
Interquartile range (IQR)7855

Descriptive statistics

Standard deviation9409.0304
Coefficient of variation (CV)1.0179392
Kurtosis8.0119993
Mean9243.2143
Median Absolute Deviation (MAD)3600
Skewness2.5311687
Sum258810
Variance88529852
MonotonicityNot monotonic
2023-12-11T01:48:12.481284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4000 3
 
10.7%
14800 1
 
3.6%
6900 1
 
3.6%
11500 1
 
3.6%
11000 1
 
3.6%
1580 1
 
3.6%
1400 1
 
3.6%
8980 1
 
3.6%
5350 1
 
3.6%
4550 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1400 1
 
3.6%
1580 1
 
3.6%
1650 1
 
3.6%
1780 1
 
3.6%
2480 1
 
3.6%
3680 1
 
3.6%
3980 1
 
3.6%
4000 3
10.7%
4100 1
 
3.6%
4550 1
 
3.6%
ValueCountFrequency (%)
45800 1
3.6%
27500 1
3.6%
16800 1
3.6%
16500 1
3.6%
14900 1
3.6%
14800 1
3.6%
12900 1
3.6%
11500 1
3.6%
11000 1
3.6%
10000 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10092.143
Minimum1000
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:12.658997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1328
Q13000
median5050
Q313500
95-th percentile35275
Maximum50000
Range49000
Interquartile range (IQR)10500

Descriptive statistics

Standard deviation11868.83
Coefficient of variation (CV)1.1760466
Kurtosis5.6119192
Mean10092.143
Median Absolute Deviation (MAD)3610
Skewness2.3164781
Sum282580
Variance1.4086914 × 108
MonotonicityNot monotonic
2023-12-11T01:48:12.838031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 2
 
7.1%
15000 2
 
7.1%
3900 2
 
7.1%
3000 2
 
7.1%
20000 1
 
3.6%
9000 1
 
3.6%
10000 1
 
3.6%
1800 1
 
3.6%
1380 1
 
3.6%
10800 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1000 1
3.6%
1300 1
3.6%
1380 1
3.6%
1500 1
3.6%
1600 1
3.6%
1800 1
3.6%
3000 2
7.1%
3800 1
3.6%
3900 2
7.1%
4000 2
7.1%
ValueCountFrequency (%)
50000 1
3.6%
43500 1
3.6%
20000 1
3.6%
19000 1
3.6%
17500 1
3.6%
15000 2
7.1%
13000 1
3.6%
10800 1
3.6%
10000 1
3.6%
9000 1
3.6%

Interactions

2023-12-11T01:48:07.590226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:02.873137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.586208image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.220402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.864111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:05.917533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.662019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:07.754107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:02.989667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.686445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.314876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.956096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.012282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.838181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:07.888362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.083886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.762769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.396192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:05.056455image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.103752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.946914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:08.012008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.189176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.857609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.483437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:05.139323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.214299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:07.072566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:08.156301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.277848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.954372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.573408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:05.247418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.318033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:07.205407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:08.247522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.397624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.063248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.674002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:05.715676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.426274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:07.325300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:08.372424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:03.488738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.137280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:04.772825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:05.817292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:06.534362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:07.449986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:48:12.933473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.000
규 격1.0001.0000.8980.8310.7370.5870.9800.8850.687
우동(센텀홈플러스)1.0000.8981.0000.9130.9880.9740.9500.9670.976
중동(이마트 해운대점)1.0000.8310.9131.0000.8580.9300.7480.9480.930
좌동(GS수퍼마켓)1.0000.7370.9880.8581.0000.9370.9280.9290.960
송정동1.0000.5870.9740.9300.9371.0000.8700.9890.996
반여2동(골목시장)1.0000.9800.9500.7480.9280.8701.0000.8660.892
반송동(탑마트)1.0000.8850.9670.9480.9290.9890.8661.0000.995
재송동(한마음시장)1.0000.6870.9760.9300.9600.9960.8920.9951.000
2023-12-11T01:48:13.077624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.9020.8860.9030.8200.8900.917
중동(이마트 해운대점)0.9021.0000.9580.9600.8680.9590.955
좌동(GS수퍼마켓)0.8860.9581.0000.9260.9370.9610.953
송정동0.9030.9600.9261.0000.8650.9320.940
반여2동(골목시장)0.8200.8680.9370.8651.0000.9010.900
반송동(탑마트)0.8900.9590.9610.9320.9011.0000.951
재송동(한마음시장)0.9170.9550.9530.9400.9000.9511.000

Missing values

2023-12-11T01:48:08.529846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:48:08.701660image/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 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
0사과부사(1개 300g이상)3kg28400207203980021990150001480020000
1배신고 6㎏37800256005800023730290001680019000
2배추1㎏690300034803300400039801600
3무1㎏2490188016802500200017801300
4대파1㎏(상품)3290464044001780250024803800
5소고기(국산)등심 상등육 500g69950475006633044900400002750043500
6소고기(수입)등심 상등육 500g11200248353250014500119001490015000
7돼지고기삼겹살 500g13200154001680014900120001290013000
8닭고기육계1㎏109909200119007900850086007500
9달 걀특란 10개3990398049803980420041003900
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏7490490054002980650045803900
19두부500g 판두부(포장두부 420g) 1모2290470045802500125045501500
20밀가루백설표 중력분1등2.5㎏46001069054505980540053504600
21식용유백설표옥수수기름1.8ℓ10800163209780125008200898010800
22소주(소매점)시원소주 360㎖ 1병1390138014301650160014001380
23맥주(소매점)하이트 500㎖ 1병1710155018301900185015801800
24소주(외식)시원소주 360㎖ 1병5000500050004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500050005000400040004000
26돼지갈비(외식)200g 정도120001440095002200090001100010000
27삼겹살(외식)200g 정도1200023000150001540080001150015000