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_20220317
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063783

Alerts

우동 (센텀홈플러스) is highly overall correlated with 우동 (농산물시장) and 5 other fieldsHigh correlation
우동 (농산물시장) is highly overall correlated with 우동 (센텀홈플러스) 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
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 16:49:50.255209
Analysis finished2023-12-10 16:49:55.681200
Duration5.43 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
2023-12-11T01:49:55.850938image/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%
2023-12-11T01:49:56.228447image/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
2023-12-11T01:49:56.430883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length22
Median length12
Mean length9.4285714
Min length2

Characters and Unicode

Total characters264
Distinct characters70
Distinct categories10 ?
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
 
7.1%
500g 4
 
7.1%
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 (29) 32
57.1%
2023-12-11T01:49:56.807974image/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 (60) 129
48.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 107
40.5%
Decimal Number 78
29.5%
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%
Control 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%
냉 4
 
3.7%
병 4
 
3.7%
동 4
 
3.7%
이 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%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 147
55.7%
Hangul 107
40.5%
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%
냉 4
 
3.7%
병 4
 
3.7%
동 4
 
3.7%
이 3
 
2.8%
Other values (40) 60
56.1%
Common
ValueCountFrequency (%)
0 29
19.7%
28
19.0%
1 19
12.9%
5 10
 
6.8%
㎏ 10
 
6.8%
2 9
 
6.1%
) 7
 
4.8%
( 7
 
4.8%
. 5
 
3.4%
㎝ 5
 
3.4%
Other values (8) 18
12.2%
Latin
ValueCountFrequency (%)
g 9
90.0%
k 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 137
51.9%
Hangul 107
40.5%
CJK Compat 19
 
7.2%
Letterlike Symbols 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 29
21.2%
28
20.4%
1 19
13.9%
5 10
 
7.3%
2 9
 
6.6%
g 9
 
6.6%
) 7
 
5.1%
( 7
 
5.1%
. 5
 
3.6%
6 4
 
2.9%
Other values (6) 10
 
7.3%
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%
냉 4
 
3.7%
병 4
 
3.7%
동 4
 
3.7%
이 3
 
2.8%
Other values (40) 60
56.1%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

우동 (센텀홈플러스)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10238.571
Minimum1260
Maximum49900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:56.928398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1260
5-th percentile1284
Q13165
median4880
Q310862.5
95-th percentile40217.5
Maximum49900
Range48640
Interquartile range (IQR)7697.5

Descriptive statistics

Standard deviation12777.619
Coefficient of variation (CV)1.2479885
Kurtosis3.9215528
Mean10238.571
Median Absolute Deviation (MAD)3100
Skewness2.1016283
Sum286680
Variance1.6326755 × 108
MonotonicityNot monotonic
2023-12-11T01:49:57.050307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3490 2
 
7.1%
1790 2
 
7.1%
10000 2
 
7.1%
5000 2
 
7.1%
23400 1
 
3.6%
49900 1
 
3.6%
1530 1
 
3.6%
1310 1
 
3.6%
7990 1
 
3.6%
4450 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1260 1
3.6%
1270 1
3.6%
1310 1
3.6%
1530 1
3.6%
1790 2
7.1%
2790 1
3.6%
3290 1
3.6%
3490 2
7.1%
3890 1
3.6%
4000 1
3.6%
ValueCountFrequency (%)
49900 1
3.6%
44750 1
3.6%
31800 1
3.6%
23400 1
3.6%
18900 1
3.6%
15450 1
3.6%
13450 1
3.6%
10000 2
7.1%
7990 1
3.6%
5990 1
3.6%

우동 (농산물시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12326.071
Minimum990
Maximum69000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:57.219694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1535
Q13550
median6300
Q310225
95-th percentile52946
Maximum69000
Range68010
Interquartile range (IQR)6675

Descriptive statistics

Standard deviation16936.958
Coefficient of variation (CV)1.3740759
Kurtosis5.3916307
Mean12326.071
Median Absolute Deviation (MAD)3750
Skewness2.4435023
Sum345130
Variance2.8686054 × 108
MonotonicityNot monotonic
2023-12-11T01:49:57.510634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
10000 3
 
10.7%
9900 2
 
7.1%
5000 2
 
7.1%
55900 1
 
3.6%
4000 1
 
3.6%
1750 1
 
3.6%
1600 1
 
3.6%
10900 1
 
3.6%
5200 1
 
3.6%
2300 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
990 1
3.6%
1500 1
3.6%
1600 1
3.6%
1750 1
3.6%
1980 1
3.6%
2300 1
3.6%
2500 1
3.6%
3900 1
3.6%
3980 1
3.6%
4000 1
3.6%
ValueCountFrequency (%)
69000 1
 
3.6%
55900 1
 
3.6%
47460 1
 
3.6%
23190 1
 
3.6%
14500 1
 
3.6%
14400 1
 
3.6%
10900 1
 
3.6%
10000 3
10.7%
9900 2
7.1%
7980 1
 
3.6%

중동 (이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10678.393
Minimum1280
Maximum59500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:57.710758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1525.5
Q13452.5
median5377.5
Q311175
95-th percentile41642.5
Maximum59500
Range58220
Interquartile range (IQR)7722.5

Descriptive statistics

Standard deviation14025.137
Coefficient of variation (CV)1.3134127
Kurtosis6.7076775
Mean10678.393
Median Absolute Deviation (MAD)3592.5
Skewness2.5941885
Sum298995
Variance1.9670446 × 108
MonotonicityNot monotonic
2023-12-11T01:49:57.841161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4000 2
 
7.1%
3980 2
 
7.1%
15960 1
 
3.6%
50900 1
 
3.6%
10000 1
 
3.6%
9000 1
 
3.6%
1410 1
 
3.6%
1280 1
 
3.6%
8780 1
 
3.6%
5100 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1280 1
3.6%
1410 1
3.6%
1740 1
3.6%
1770 1
3.6%
1800 1
3.6%
1880 1
3.6%
3040 1
3.6%
3590 1
3.6%
3980 2
7.1%
4000 2
7.1%
ValueCountFrequency (%)
59500 1
3.6%
50900 1
3.6%
24450 1
3.6%
23900 1
3.6%
15960 1
3.6%
12800 1
3.6%
12000 1
3.6%
10900 1
3.6%
10000 1
3.6%
9000 1
3.6%

좌동 (GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13326.786
Minimum1300
Maximum66340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:57.974867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile1457
Q14360
median5930
Q313850
95-th percentile53820
Maximum66340
Range65040
Interquartile range (IQR)9490

Descriptive statistics

Standard deviation16914.008
Coefficient of variation (CV)1.2691739
Kurtosis4.5966596
Mean13326.786
Median Absolute Deviation (MAD)3905
Skewness2.2316575
Sum373150
Variance2.8608368 × 108
MonotonicityNot monotonic
2023-12-11T01:49:58.089440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4980 2
 
7.1%
4000 2
 
7.1%
29000 1
 
3.6%
61800 1
 
3.6%
13800 1
 
3.6%
9500 1
 
3.6%
1660 1
 
3.6%
1300 1
 
3.6%
8860 1
 
3.6%
4500 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1300 1
3.6%
1380 1
3.6%
1600 1
3.6%
1660 1
3.6%
1990 1
3.6%
4000 2
7.1%
4480 1
3.6%
4500 1
3.6%
4550 1
3.6%
4900 1
3.6%
ValueCountFrequency (%)
66340 1
3.6%
61800 1
3.6%
39000 1
3.6%
29000 1
3.6%
24670 1
3.6%
19800 1
3.6%
14000 1
3.6%
13800 1
3.6%
11800 1
3.6%
9800 1
3.6%

반여2동 (골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10400
Minimum1450
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:58.197757image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1450
5-th percentile1552.5
Q14300
median5750
Q312050
95-th percentile34625
Maximum58000
Range56550
Interquartile range (IQR)7750

Descriptive statistics

Standard deviation12512.268
Coefficient of variation (CV)1.2031027
Kurtosis8.4324407
Mean10400
Median Absolute Deviation (MAD)3500
Skewness2.7998174
Sum291200
Variance1.5655685 × 108
MonotonicityNot monotonic
2023-12-11T01:49:58.337541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
5000 5
17.9%
2000 2
 
7.1%
4000 2
 
7.1%
10500 1
 
3.6%
6500 1
 
3.6%
9000 1
 
3.6%
8000 1
 
3.6%
1650 1
 
3.6%
1450 1
 
3.6%
6300 1
 
3.6%
Other values (12) 12
42.9%
ValueCountFrequency (%)
1450 1
 
3.6%
1500 1
 
3.6%
1650 1
 
3.6%
2000 2
 
7.1%
4000 2
 
7.1%
4400 1
 
3.6%
5000 5
17.9%
5200 1
 
3.6%
6300 1
 
3.6%
6500 1
 
3.6%
ValueCountFrequency (%)
58000 1
3.6%
42500 1
3.6%
20000 1
3.6%
19200 1
3.6%
15000 1
3.6%
13900 1
3.6%
12500 1
3.6%
11900 1
3.6%
10500 1
3.6%
9000 1
3.6%

반송동 (탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12021.786
Minimum1300
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:58.460346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1300
5-th percentile1401
Q13812.5
median5440
Q310125
95-th percentile54222.5
Maximum58000
Range56700
Interquartile range (IQR)6312.5

Descriptive statistics

Standard deviation16350.766
Coefficient of variation (CV)1.3600946
Kurtosis3.7249294
Mean12021.786
Median Absolute Deviation (MAD)2960
Skewness2.2041238
Sum336610
Variance2.6734753 × 108
MonotonicityNot monotonic
2023-12-11T01:49:58.604039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4000 2
 
7.1%
10000 2
 
7.1%
29800 1
 
3.6%
53800 1
 
3.6%
16000 1
 
3.6%
9000 1
 
3.6%
1440 1
 
3.6%
1300 1
 
3.6%
6980 1
 
3.6%
4680 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1300 1
3.6%
1380 1
3.6%
1440 1
3.6%
2180 1
3.6%
2780 1
3.6%
2980 1
3.6%
3550 1
3.6%
3900 1
3.6%
3980 1
3.6%
4000 2
7.1%
ValueCountFrequency (%)
58000 1
3.6%
54450 1
3.6%
53800 1
3.6%
29800 1
3.6%
16000 1
3.6%
12400 1
3.6%
10500 1
3.6%
10000 2
7.1%
9000 1
3.6%
7080 1
3.6%

재송동 (한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9759.6429
Minimum700
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:58.785873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile1187.5
Q12325
median4000
Q39500
95-th percentile41050
Maximum55000
Range54300
Interquartile range (IQR)7175

Descriptive statistics

Standard deviation13307.468
Coefficient of variation (CV)1.36352
Kurtosis5.9784294
Mean9759.6429
Median Absolute Deviation (MAD)2575
Skewness2.4885532
Sum273270
Variance1.7708871 × 108
MonotonicityNot monotonic
2023-12-11T01:49:58.902312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4000 5
17.9%
9000 2
 
7.1%
1500 2
 
7.1%
8000 2
 
7.1%
17000 1
 
3.6%
30000 1
 
3.6%
1520 1
 
3.6%
1350 1
 
3.6%
1100 1
 
3.6%
55000 1
 
3.6%
Other values (11) 11
39.3%
ValueCountFrequency (%)
700 1
 
3.6%
1100 1
 
3.6%
1350 1
 
3.6%
1500 2
 
7.1%
1520 1
 
3.6%
1800 1
 
3.6%
2500 1
 
3.6%
2800 1
 
3.6%
3500 1
 
3.6%
4000 5
17.9%
ValueCountFrequency (%)
55000 1
3.6%
47000 1
3.6%
30000 1
3.6%
17000 1
3.6%
16500 1
3.6%
12000 1
3.6%
11000 1
3.6%
9000 2
7.1%
8000 2
7.1%
6500 1
3.6%

Interactions

2023-12-11T01:49:54.797962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:50.605768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.346853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.984189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.605108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.189020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.856814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.876512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:50.698682image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.448841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.070369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.700176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.268277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.973195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.978123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:50.808899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.541250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.162605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.784953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.349193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.077045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:55.080808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:50.910223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.620231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.248328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.858197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.446321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.424197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:55.170766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.005214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.709600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.333801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.935772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.548066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.516016image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:55.254922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.155414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.807880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.411231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.018445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.650307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.604264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:55.351317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.251130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:51.893780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:52.506517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.108302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:53.771953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:54.702400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:49:59.007164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품 목규 격우동 (센텀홈플러스)우동 (농산물시장)중동 (이마트 해운대점)좌동 (GS수퍼마켓)반여2동 (골목시장)반송동 (탑마트)재송동 (한마음시장)
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.000
규 격1.0001.0000.9260.9070.8310.8640.8930.5770.934
우동\n(센텀홈플러스)1.0000.9261.0000.9790.9710.9470.9140.8620.964
우동\n(농산물시장)1.0000.9070.9791.0000.9940.9060.9620.8930.982
중동\n(이마트 해운대점)1.0000.8310.9710.9941.0000.9080.9630.8310.952
좌동\n(GS수퍼마켓)1.0000.8640.9470.9060.9081.0000.7950.8550.982
반여2동\n(골목시장)1.0000.8930.9140.9620.9630.7951.0000.6180.887
반송동\n(탑마트)1.0000.5770.8620.8930.8310.8550.6181.0000.872
재송동\n(한마음시장)1.0000.9340.9640.9820.9520.9820.8870.8721.000
2023-12-11T01:49:59.144087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동 (센텀홈플러스)우동 (농산물시장)중동 (이마트 해운대점)좌동 (GS수퍼마켓)반여2동 (골목시장)반송동 (탑마트)재송동 (한마음시장)
우동\n(센텀홈플러스)1.0000.9610.9420.8970.8010.9110.976
우동\n(농산물시장)0.9611.0000.9360.9120.8070.9480.972
중동\n(이마트 해운대점)0.9420.9361.0000.9160.8940.9370.935
좌동\n(GS수퍼마켓)0.8970.9120.9161.0000.8840.8990.912
반여2동\n(골목시장)0.8010.8070.8940.8841.0000.8610.809
반송동\n(탑마트)0.9110.9480.9370.8990.8611.0000.943
재송동\n(한마음시장)0.9760.9720.9350.9120.8090.9431.000

Missing values

2023-12-11T01:49:55.461395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:49:55.617613image/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)우동 (농산물시장)중동 (이마트 해운대점)좌동 (GS수퍼마켓)반여2동 (골목시장)반송동 (탑마트)재송동 (한마음시장)
0사과부사 (1개 300g이상)3kg23400231901596029000105002980017000
1배신고 6㎏31800474602390039000192005800030000
2배추1㎏3490500018004980500039801800
3무1㎏179015001880160015001380700
4대파1㎏(상품)3290250030405960200021802800
5소고기(국산)등심 상등육 500g44750690005950066340425005445047000
6소고기(수입)등심 상등육 500g15450145002445024670119001240011000
7돼지고기삼겹살 500g13450144001090014000139001050012000
8닭고기육계1㎏5940798064809800670070806500
9달 걀특란 10개3890398039804480520035503500
품 목규 격우동 (센텀홈플러스)우동 (농산물시장)중동 (이마트 해운대점)좌동 (GS수퍼마켓)반여2동 (골목시장)반송동 (탑마트)재송동 (한마음시장)
18고춧가루0.1㎏4760740060008600650049804000
19두부500g 판두부(포장두부 420g) 1모17902300519545501250045501500
20밀가루백설표 중력분1등2.5㎏4450520051004500440046804000
21식용유백설표옥수수기름1.8ℓ79901090087808860630069808000
22소주(소매점)시원소주 360㎖ 1병1310160012801300145013001350
23맥주(소매점)하이트 500㎖ 1병1530175014101660165014401520
24소주(외식)시원소주 360㎖ 1병5000400040004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500040004000400040004000
26돼지갈비(외식)200g 정도100001000090009500800090009000
27삼겹살(외식)200g 정도100001000010000138009000160009000