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_20231005
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 16:51:20.293760
Analysis finished2023-12-10 16:51:28.584108
Duration8.29 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
2023-12-11T01:51:28.758631image/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%
2023-12-11T01:51:29.230769image/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
2023-12-11T01:51:29.472433image/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%
2023-12-11T01:51:30.002959image/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%
Mean11861.429
Minimum990
Maximum52900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:51:30.190481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1502
Q13915
median5000
Q312000
95-th percentile43867.5
Maximum52900
Range51910
Interquartile range (IQR)8085

Descriptive statistics

Standard deviation14340.921
Coefficient of variation (CV)1.2090382
Kurtosis2.5292975
Mean11861.429
Median Absolute Deviation (MAD)2600
Skewness1.9046642
Sum332120
Variance2.0566201 × 108
MonotonicityNot monotonic
2023-12-11T01:51:30.318936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
12000 2
 
7.1%
5000 2
 
7.1%
44900 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 (%)
990 1
3.6%
1390 1
3.6%
1710 1
3.6%
2290 1
3.6%
3200 1
3.6%
3490 1
3.6%
3690 1
3.6%
3990 1
3.6%
4400 1
3.6%
4600 1
3.6%
ValueCountFrequency (%)
52900 1
3.6%
44900 1
3.6%
41950 1
3.6%
37800 1
3.6%
19800 1
3.6%
14450 1
3.6%
12000 2
7.1%
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%
Mean13096.679
Minimum1380
Maximum54900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:51:30.432304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1380
5-th percentile1450.55
Q14852.5
median7490
Q315675
95-th percentile43717
Maximum54900
Range53520
Interquartile range (IQR)10822.5

Descriptive statistics

Standard deviation13741.723
Coefficient of variation (CV)1.0492525
Kurtosis3.8239315
Mean13096.679
Median Absolute Deviation (MAD)4980
Skewness1.9633969
Sum366707
Variance1.8883496 × 108
MonotonicityNot monotonic
2023-12-11T01:51:30.555758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
6000 2
 
7.1%
26000 1
 
3.6%
22860 1
 
3.6%
23000 1
 
3.6%
18000 1
 
3.6%
5000 1
 
3.6%
1550 1
 
3.6%
1380 1
 
3.6%
10200 1
 
3.6%
10690 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1380 1
3.6%
1397 1
3.6%
1550 1
3.6%
2220 1
3.6%
2800 1
3.6%
3980 1
3.6%
4530 1
3.6%
4960 1
3.6%
5000 1
3.6%
5480 1
3.6%
ValueCountFrequency (%)
54900 1
3.6%
51900 1
3.6%
28520 1
3.6%
26000 1
3.6%
23000 1
3.6%
22860 1
3.6%
18000 1
3.6%
14900 1
3.6%
14800 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%
Mean16733.214
Minimum1430
Maximum79800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:51:30.705752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile1882.5
Q14995
median6440
Q315200
95-th percentile66950
Maximum79800
Range78370
Interquartile range (IQR)10205

Descriptive statistics

Standard deviation22483.819
Coefficient of variation (CV)1.3436641
Kurtosis2.4407388
Mean16733.214
Median Absolute Deviation (MAD)3200
Skewness1.9323011
Sum468530
Variance5.0552211 × 108
MonotonicityNot monotonic
2023-12-11T01:51:30.836589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 2
 
7.1%
79800 1
 
3.6%
68000 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%
1980 1
3.6%
2490 1
3.6%
3720 1
3.6%
4580 1
3.6%
4980 1
3.6%
5000 2
7.1%
5400 1
3.6%
5450 1
3.6%
ValueCountFrequency (%)
79800 1
3.6%
68000 1
3.6%
65000 1
3.6%
59800 1
3.6%
33000 1
3.6%
18800 1
3.6%
15800 1
3.6%
15000 1
3.6%
10400 1
3.6%
9780 1
3.6%

송정동
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11772.429
Minimum698
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:51:30.956494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum698
5-th percentile1737.5
Q13495
median6940
Q313300
95-th percentile37490.5
Maximum65000
Range64302
Interquartile range (IQR)9805

Descriptive statistics

Standard deviation14067.554
Coefficient of variation (CV)1.1949577
Kurtosis7.6879946
Mean11772.429
Median Absolute Deviation (MAD)4700
Skewness2.608352
Sum329628
Variance1.9789608 × 108
MonotonicityNot monotonic
2023-12-11T01:51:31.085245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
5000 2
 
7.1%
12900 2
 
7.1%
9900 2
 
7.1%
21990 1
 
3.6%
3480 1
 
3.6%
15400 1
 
3.6%
22000 1
 
3.6%
4000 1
 
3.6%
1900 1
 
3.6%
1650 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
698 1
3.6%
1650 1
3.6%
1900 1
3.6%
1980 1
3.6%
2500 1
3.6%
2980 1
3.6%
3480 1
3.6%
3500 1
3.6%
3980 1
3.6%
4000 1
3.6%
ValueCountFrequency (%)
65000 1
3.6%
44900 1
3.6%
23730 1
3.6%
22000 1
3.6%
21990 1
3.6%
15400 1
3.6%
14500 1
3.6%
12900 2
7.1%
11960 1
3.6%
9900 2
7.1%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10421.429
Minimum1250
Maximum56000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:51:31.202630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1687.5
Q14000
median6250
Q310475
95-th percentile36150
Maximum56000
Range54750
Interquartile range (IQR)6475

Descriptive statistics

Standard deviation12388.289
Coefficient of variation (CV)1.1887323
Kurtosis6.9478443
Mean10421.429
Median Absolute Deviation (MAD)3000
Skewness2.5735802
Sum291800
Variance1.5346971 × 108
MonotonicityNot monotonic
2023-12-11T01:51:31.312979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5000 3
 
10.7%
2000 2
 
7.1%
4000 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%
8200 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1250 1
 
3.6%
1600 1
 
3.6%
1850 1
 
3.6%
2000 2
7.1%
3000 1
 
3.6%
4000 2
7.1%
4200 1
 
3.6%
5000 3
10.7%
5400 1
 
3.6%
6000 1
 
3.6%
ValueCountFrequency (%)
56000 1
3.6%
40000 1
3.6%
29000 1
3.6%
20000 1
3.6%
15000 1
3.6%
12000 1
3.6%
11900 1
3.6%
10000 1
3.6%
9000 1
3.6%
8900 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10666.071
Minimum1390
Maximum51800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:51:31.436471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1390
5-th percentile1463
Q13975
median5650
Q311850
95-th percentile29930
Maximum51800
Range50410
Interquartile range (IQR)7875

Descriptive statistics

Standard deviation11625.126
Coefficient of variation (CV)1.0899163
Kurtosis4.979508
Mean10666.071
Median Absolute Deviation (MAD)3000
Skewness2.150624
Sum298650
Variance1.3514354 × 108
MonotonicityNot monotonic
2023-12-11T01:51:31.564825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2980 2
 
7.1%
4000 2
 
7.1%
29800 1
 
3.6%
51800 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%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1390 1
3.6%
1400 1
3.6%
1580 1
3.6%
2980 2
7.1%
3580 1
3.6%
3900 1
3.6%
4000 2
7.1%
4500 1
3.6%
4550 1
3.6%
4580 1
3.6%
ValueCountFrequency (%)
51800 1
3.6%
30000 1
3.6%
29800 1
3.6%
28800 1
3.6%
18500 1
3.6%
16400 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
2023-12-11T01:51:31.682930image/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
2023-12-11T01:51:31.801436image/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

2023-12-11T01:51:27.149917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:20.708414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.724584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:23.020719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.174702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:25.274885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.308478image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:27.300843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:20.847849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.886201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:23.201896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.314611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:25.414697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.443798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:27.412558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.005152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:22.073010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:23.350531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.465402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:25.572551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.569424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:27.839906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.168011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:22.309392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:23.507388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.647337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:25.739080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.713675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:27.944180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.297274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:22.469390image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:23.658400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.811183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:25.895838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.824995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:28.055954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.438558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:22.657317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:23.822177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.959732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.071792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.929463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:28.162121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:21.586137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:22.859155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:24.015543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:25.120019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:26.195912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:51:27.028562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:51:31.888831image/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 격1.0001.0000.8970.0000.8970.0000.9800.8850.687
우동(센텀홈플러스)1.0000.8971.0000.9230.9950.8410.9650.8490.843
중동(이마트 해운대점)1.0000.0000.9231.0000.9280.8870.8660.7690.809
좌동(GS수퍼마켓)1.0000.8970.9950.9281.0000.8340.9660.8420.849
송정동1.0000.0000.8410.8870.8341.0000.8640.9600.986
반여2동(골목시장)1.0000.9800.9650.8660.9660.8641.0000.8920.905
반송동(탑마트)1.0000.8850.8490.7690.8420.9600.8921.0000.990
재송동(한마음시장)1.0000.6870.8430.8090.8490.9860.9050.9901.000
2023-12-11T01:51:32.042284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.8600.8660.8510.8330.8700.867
중동(이마트 해운대점)0.8601.0000.9050.9420.8710.9450.936
좌동(GS수퍼마켓)0.8660.9051.0000.9090.9170.9330.899
송정동0.8510.9420.9091.0000.8640.9350.922
반여2동(골목시장)0.8330.8710.9170.8641.0000.9030.888
반송동(탑마트)0.8700.9450.9330.9350.9031.0000.923
재송동(한마음시장)0.8670.9360.8990.9220.8880.9231.000

Missing values

2023-12-11T01:51:28.318291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:51:28.516088image/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)3kg44900260007980021990150002980020000
1배신고 6㎏37800228606800023730290002880019000
2배추1㎏5450496067505000600059801600
3무1㎏3690280037203500200029801300
4대파1㎏(상품)3990453055101980300029803800
5소고기(국산)등심 상등육 500g41950519006500044900400003000043500
6소고기(수입)등심 상등육 500g19800285203300014500119001640015000
7돼지고기삼겹살 500g14450149001880012900120001290013000
8닭고기육계1㎏109908980104007900890054007500
9달 걀특란 10개3490398049803980420039003900
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏7490600054002980650045803900
19두부500g 판두부(포장두부 420g) 1모2290548045802500125045501500
20밀가루백설표 중력분1등2.5㎏46001069054508500540053504600
21식용유백설표옥수수기름1.8ℓ10800102009780129008200898010800
22소주(소매점)시원소주 360㎖ 1병1390138014301650160014001380
23맥주(소매점)하이트 500㎖ 1병1710155018301900185015801800
24소주(외식)시원소주 360㎖ 1병5000500050004000400040004000
25맥주(외식)하이트 500㎖ 1병5000600050005000400040004000
26돼지갈비(외식)200g 정도120001800095002200090001100010000
27삼겹살(외식)200g 정도1200023000150001540080001150015000