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_20230411
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 16:48:15.411270
Analysis finished2023-12-10 16:48:20.828962
Duration5.42 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
2023-12-11T01:48:20.973140image/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%
2023-12-11T01:48:21.388618image/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
2023-12-11T01:48:21.633106image/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%
2023-12-11T01:48:22.025581image/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%
Mean9591.4286
Minimum1390
Maximum44950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:22.174793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1390
5-th percentile1776.5
Q13447.5
median5000
Q310000
95-th percentile39678
Maximum44950
Range43560
Interquartile range (IQR)6552.5

Descriptive statistics

Standard deviation11671.648
Coefficient of variation (CV)1.2168832
Kurtosis4.9471502
Mean9591.4286
Median Absolute Deviation (MAD)2860
Skewness2.3577941
Sum268560
Variance1.3622738 × 108
MonotonicityNot monotonic
2023-12-11T01:48:22.289976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10000 2
 
7.1%
5000 2
 
7.1%
29980 1
 
3.6%
4620 1
 
3.6%
1710 1
 
3.6%
1390 1
 
3.6%
8600 1
 
3.6%
4600 1
 
3.6%
2290 1
 
3.6%
5520 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1390 1
3.6%
1710 1
3.6%
1900 1
3.6%
1990 1
3.6%
2290 1
3.6%
3190 1
3.6%
3290 1
3.6%
3500 1
3.6%
3990 1
3.6%
4290 1
3.6%
ValueCountFrequency (%)
44950 1
3.6%
44900 1
3.6%
29980 1
3.6%
20800 1
3.6%
10960 1
3.6%
10450 1
3.6%
10000 2
7.1%
8600 1
3.6%
8540 1
3.6%
7250 1
3.6%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11604.643
Minimum1380
Maximum49900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:22.405747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1380
5-th percentile1439.5
Q13697.5
median6900
Q314500
95-th percentile39450
Maximum49900
Range48520
Interquartile range (IQR)10802.5

Descriptive statistics

Standard deviation12503.349
Coefficient of variation (CV)1.0774437
Kurtosis3.9059432
Mean11604.643
Median Absolute Deviation (MAD)4760
Skewness1.9883447
Sum324930
Variance1.5633374 × 108
MonotonicityNot monotonic
2023-12-11T01:48:22.532988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
5000 2
 
7.1%
1380 2
 
7.1%
14160 1
 
3.6%
14800 1
 
3.6%
23080 1
 
3.6%
14400 1
 
3.6%
1550 1
 
3.6%
11800 1
 
3.6%
9060 1
 
3.6%
4700 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1380 2
7.1%
1550 1
3.6%
2280 1
3.6%
2350 1
3.6%
2660 1
3.6%
3450 1
3.6%
3780 1
3.6%
3980 1
3.6%
4580 1
3.6%
4700 1
3.6%
ValueCountFrequency (%)
49900 1
3.6%
46800 1
3.6%
25800 1
3.6%
23700 1
3.6%
23080 1
3.6%
14900 1
3.6%
14800 1
3.6%
14400 1
3.6%
14160 1
3.6%
11900 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15072.857
Minimum1430
Maximum66500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:22.642955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile1837
Q14555
median6600
Q315700
95-th percentile57930
Maximum66500
Range65070
Interquartile range (IQR)11145

Descriptive statistics

Standard deviation18488.158
Coefficient of variation (CV)1.2265861
Kurtosis2.3568818
Mean15072.857
Median Absolute Deviation (MAD)3895
Skewness1.8464877
Sum422040
Variance3.4181198 × 108
MonotonicityNot monotonic
2023-12-11T01:48:22.753544image/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%
1830 1
3.6%
1850 1
3.6%
1990 1
3.6%
3550 1
3.6%
3980 1
3.6%
4480 1
3.6%
4580 1
3.6%
4940 1
3.6%
5000 2
7.1%
ValueCountFrequency (%)
66500 1
3.6%
58000 1
3.6%
57800 1
3.6%
39800 1
3.6%
33000 1
3.6%
19800 1
3.6%
17800 1
3.6%
15000 1
3.6%
12800 1
3.6%
12250 1
3.6%

송정동
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11604.286
Minimum800
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:23.124812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile1604.5
Q13220
median5400
Q314050
95-th percentile39685
Maximum59800
Range59000
Interquartile range (IQR)10830

Descriptive statistics

Standard deviation13808.441
Coefficient of variation (CV)1.1899432
Kurtosis5.3594588
Mean11604.286
Median Absolute Deviation (MAD)3785
Skewness2.2458181
Sum324920
Variance1.9067306 × 108
MonotonicityNot monotonic
2023-12-11T01:48:23.254367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
2980 2
 
7.1%
23190 1
 
3.6%
59800 1
 
3.6%
15400 1
 
3.6%
22000 1
 
3.6%
5000 1
 
3.6%
4000 1
 
3.6%
1800 1
 
3.6%
1650 1
 
3.6%
12900 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
800 1
3.6%
1580 1
3.6%
1650 1
3.6%
1800 1
3.6%
2500 1
3.6%
2980 2
7.1%
3300 1
3.6%
3480 1
3.6%
3500 1
3.6%
3980 1
3.6%
ValueCountFrequency (%)
59800 1
3.6%
44900 1
3.6%
30000 1
3.6%
23190 1
3.6%
22000 1
3.6%
15400 1
3.6%
14500 1
3.6%
13900 1
3.6%
12900 1
3.6%
12800 1
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10407.143
Minimum1000
Maximum56000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:23.362845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1372.5
Q13712.5
median6750
Q311925
95-th percentile36150
Maximum56000
Range55000
Interquartile range (IQR)8212.5

Descriptive statistics

Standard deviation12487.369
Coefficient of variation (CV)1.1998845
Kurtosis6.64947
Mean10407.143
Median Absolute Deviation (MAD)4000
Skewness2.5054358
Sum291400
Variance1.5593439 × 108
MonotonicityNot monotonic
2023-12-11T01:48:23.457495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4000 2
 
7.1%
5000 2
 
7.1%
7000 2
 
7.1%
15000 1
 
3.6%
56000 1
 
3.6%
8000 1
 
3.6%
9000 1
 
3.6%
1850 1
 
3.6%
1600 1
 
3.6%
7950 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1000 1
3.6%
1250 1
3.6%
1600 1
3.6%
1850 1
3.6%
2000 1
3.6%
2500 1
3.6%
3000 1
3.6%
3950 1
3.6%
4000 2
7.1%
5000 2
7.1%
ValueCountFrequency (%)
56000 1
3.6%
40000 1
3.6%
29000 1
3.6%
20000 1
3.6%
15000 1
3.6%
14000 1
3.6%
12000 1
3.6%
11900 1
3.6%
9000 1
3.6%
8000 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9077.1429
Minimum1400
Maximum45800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:23.556162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1400
5-th percentile1580
Q13682.5
median5475
Q311600
95-th percentile23755
Maximum45800
Range44400
Interquartile range (IQR)7917.5

Descriptive statistics

Standard deviation9529.6111
Coefficient of variation (CV)1.049847
Kurtosis7.6727939
Mean9077.1429
Median Absolute Deviation (MAD)3510
Skewness2.4878529
Sum254160
Variance90813488
MonotonicityNot monotonic
2023-12-11T01:48:23.666923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 3
 
10.7%
16800 2
 
7.1%
1580 2
 
7.1%
45800 1
 
3.6%
11500 1
 
3.6%
11000 1
 
3.6%
1400 1
 
3.6%
8480 1
 
3.6%
5350 1
 
3.6%
4550 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1400 1
 
3.6%
1580 2
7.1%
1750 1
 
3.6%
2180 1
 
3.6%
2280 1
 
3.6%
3480 1
 
3.6%
3750 1
 
3.6%
4000 3
10.7%
4550 1
 
3.6%
4580 1
 
3.6%
ValueCountFrequency (%)
45800 1
3.6%
27500 1
3.6%
16800 2
7.1%
16500 1
3.6%
14900 1
3.6%
11900 1
3.6%
11500 1
3.6%
11000 1
3.6%
10000 1
3.6%
8480 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
2023-12-11T01:48:23.809227image/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
2023-12-11T01:48:23.932158image/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

2023-12-11T01:48:19.953829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:15.740437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.617002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.288509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.084405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.712165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.343244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:20.041758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:15.825693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.702138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.372189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.172885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.812437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.422348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:20.132416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:15.917162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.791154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.498696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.262745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.891441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.516761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:20.218765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.282237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.895533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.610642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.333259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.981004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.601880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:20.330717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.360899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.010234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.745162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.412086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.069147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.706055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:20.425492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.445940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.120759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.856688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.528847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.151032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.797429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:20.519210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:16.540374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.203195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:17.973323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:18.616239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.256907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:19.872285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:48:24.019365image/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 격1.0001.0000.9220.0000.3880.8250.9380.8850.687
우동(센텀홈플러스)1.0000.9221.0000.9390.9010.8810.8640.8870.948
중동(이마트 해운대점)1.0000.0000.9391.0000.8350.8250.7450.8860.927
좌동(GS수퍼마켓)1.0000.3880.9010.8351.0000.9670.9520.8270.903
송정동1.0000.8250.8810.8250.9671.0000.9800.8720.926
반여2동(골목시장)1.0000.9380.8640.7450.9520.9801.0000.8800.904
반송동(탑마트)1.0000.8850.8870.8860.8270.8720.8801.0000.986
재송동(한마음시장)1.0000.6870.9480.9270.9030.9260.9040.9861.000
2023-12-11T01:48:24.150365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.9160.9390.9270.8930.9300.946
중동(이마트 해운대점)0.9161.0000.9360.9350.8910.9420.939
좌동(GS수퍼마켓)0.9390.9361.0000.9340.9490.9690.956
송정동0.9270.9350.9341.0000.8970.9270.952
반여2동(골목시장)0.8930.8910.9490.8971.0000.9350.932
반송동(탑마트)0.9300.9420.9690.9270.9351.0000.956
재송동(한마음시장)0.9460.9390.9560.9520.9320.9561.000

Missing values

2023-12-11T01:48:20.639083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:48:20.774870image/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)3kg29980141603980023190150001680020000
1배신고 6㎏20800237005800030000290001680019000
2배추1㎏3290235039803300300034801600
3무1㎏1990228018501580100015801300
4대파1㎏(상품)4290345049402980250021803800
5소고기(국산)등심 상등육 500g44950468006650044900400002750043500
6소고기(수입)등심 상등육 500g10960258003300014500119001490015000
7돼지고기삼겹살 500g10450119001780013900120001190013000
8닭고기육계1㎏85408800128007500750056007500
9달 걀특란 10개3990398044803980395037503900
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏5520479054002980650045803900
19두부500g 판두부(포장두부 420g) 1모2290470045802500125045501500
20밀가루백설표 중력분1등2.5㎏4600906054504980540053504600
21식용유백설표옥수수기름1.8ℓ8600118009780129007950848010800
22소주(소매점)시원소주 360㎖ 1병1390138014301650160014001380
23맥주(소매점)하이트 500㎖ 1병1710155018301800185015801800
24소주(외식)시원소주 360㎖ 1병5000500050004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500050005000400040004000
26돼지갈비(외식)200g 정도100001440095002200090001100010000
27삼겹살(외식)200g 정도1000023080150001540080001150015000