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_20230117
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 16:48:25.783388
Analysis finished2023-12-10 16:48:31.582610
Duration5.8 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
2023-12-11T01:48:31.751342image/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%
2023-12-11T01:48:32.120852image/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
2023-12-11T01:48:32.336438image/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%
2023-12-11T01:48:32.772549image/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 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9397.1429
Minimum1260
Maximum54550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:32.905445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1260
5-th percentile1495
Q12692.5
median4800
Q310000
95-th percentile37585
Maximum54550
Range53290
Interquartile range (IQR)7307.5

Descriptive statistics

Standard deviation12745.302
Coefficient of variation (CV)1.3562954
Kurtosis6.8663223
Mean9397.1429
Median Absolute Deviation (MAD)2805
Skewness2.6245459
Sum263120
Variance1.6244272 × 108
MonotonicityNot monotonic
2023-12-11T01:48:33.046223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10000 2
 
7.1%
5000 2
 
7.1%
2800 2
 
7.1%
17900 1
 
3.6%
4620 1
 
3.6%
1710 1
 
3.6%
1390 1
 
3.6%
10800 1
 
3.6%
4600 1
 
3.6%
1990 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1260 1
3.6%
1390 1
3.6%
1690 1
3.6%
1710 1
3.6%
1990 1
3.6%
2000 1
3.6%
2370 1
3.6%
2800 2
7.1%
2990 1
3.6%
3590 1
3.6%
ValueCountFrequency (%)
54550 1
3.6%
44900 1
3.6%
24000 1
3.6%
17900 1
3.6%
17750 1
3.6%
10800 1
3.6%
10000 2
7.1%
8490 1
3.6%
6800 1
3.6%
5250 1
3.6%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11644.179
Minimum1327
Maximum53900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:33.172484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1327
5-th percentile1439.5
Q13547.5
median6877.5
Q314750
95-th percentile39193.75
Maximum53900
Range52573
Interquartile range (IQR)11202.5

Descriptive statistics

Standard deviation12970.676
Coefficient of variation (CV)1.1139193
Kurtosis4.5081164
Mean11644.179
Median Absolute Deviation (MAD)4455
Skewness2.0997949
Sum326037
Variance1.6823842 × 108
MonotonicityNot monotonic
2023-12-11T01:48:33.302400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1550 2
 
7.1%
5000 2
 
7.1%
4980 2
 
7.1%
18500 1
 
3.6%
53900 1
 
3.6%
23080 1
 
3.6%
14400 1
 
3.6%
1380 1
 
3.6%
10800 1
 
3.6%
9060 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1327 1
3.6%
1380 1
3.6%
1550 2
7.1%
1890 1
3.6%
3050 1
3.6%
3390 1
3.6%
3600 1
3.6%
3980 1
3.6%
4980 2
7.1%
5000 2
7.1%
ValueCountFrequency (%)
53900 1
3.6%
46500 1
3.6%
25625 1
3.6%
23080 1
3.6%
22560 1
3.6%
18500 1
3.6%
15800 1
3.6%
14400 1
3.6%
12000 1
3.6%
10800 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15301.786
Minimum1430
Maximum83000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:33.430048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile1882.5
Q14560
median6650
Q314950
95-th percentile59170
Maximum83000
Range81570
Interquartile range (IQR)10390

Descriptive statistics

Standard deviation20334.652
Coefficient of variation (CV)1.3289071
Kurtosis4.4163396
Mean15301.786
Median Absolute Deviation (MAD)3625
Skewness2.2058753
Sum428450
Variance4.1349807 × 108
MonotonicityNot monotonic
2023-12-11T01:48:33.567265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1980 2
 
7.1%
5000 2
 
7.1%
35000 1
 
3.6%
59800 1
 
3.6%
14600 1
 
3.6%
9500 1
 
3.6%
4500 1
 
3.6%
1830 1
 
3.6%
1430 1
 
3.6%
9780 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1430 1
3.6%
1830 1
3.6%
1980 2
7.1%
2770 1
3.6%
3280 1
3.6%
4500 1
3.6%
4580 1
3.6%
4900 1
3.6%
5000 2
7.1%
5450 1
3.6%
ValueCountFrequency (%)
83000 1
3.6%
59800 1
3.6%
58000 1
3.6%
35000 1
3.6%
32000 1
3.6%
19800 1
3.6%
16000 1
3.6%
14600 1
3.6%
11960 1
3.6%
9800 1
3.6%

송정동
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9891.7857
Minimum1000
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:33.719045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1487
Q12860
median6000
Q310000
95-th percentile34175
Maximum59800
Range58800
Interquartile range (IQR)7140

Descriptive statistics

Standard deviation12883.239
Coefficient of variation (CV)1.302418
Kurtosis9.5342604
Mean9891.7857
Median Absolute Deviation (MAD)3900
Skewness3.0022151
Sum276970
Variance1.6597786 × 108
MonotonicityNot monotonic
2023-12-11T01:48:33.879569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
9900 3
 
10.7%
15000 2
 
7.1%
10000 2
 
7.1%
2500 2
 
7.1%
2980 1
 
3.6%
5000 1
 
3.6%
4000 1
 
3.6%
1800 1
 
3.6%
1580 1
 
3.6%
12900 1
 
3.6%
Other values (13) 13
46.4%
ValueCountFrequency (%)
1000 1
3.6%
1480 1
3.6%
1500 1
3.6%
1580 1
3.6%
1800 1
3.6%
2500 2
7.1%
2980 1
3.6%
3350 1
3.6%
3980 1
3.6%
4000 1
3.6%
ValueCountFrequency (%)
59800 1
 
3.6%
44500 1
 
3.6%
15000 2
7.1%
14500 1
 
3.6%
12900 1
 
3.6%
10000 2
7.1%
9900 3
10.7%
9600 1
 
3.6%
7500 1
 
3.6%
6200 1
 
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10551.786
Minimum1500
Maximum54800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:34.006519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1687.5
Q13800
median6350
Q310850
95-th percentile34400
Maximum54800
Range53300
Interquartile range (IQR)7050

Descriptive statistics

Standard deviation12090.519
Coefficient of variation (CV)1.1458268
Kurtosis6.7623117
Mean10551.786
Median Absolute Deviation (MAD)3750
Skewness2.4909141
Sum295450
Variance1.4618064 × 108
MonotonicityNot monotonic
2023-12-11T01:48:34.152031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
10000 2
 
7.1%
2000 2
 
7.1%
9000 2
 
7.1%
4000 2
 
7.1%
5000 2
 
7.1%
6000 1
 
3.6%
1850 1
 
3.6%
1600 1
 
3.6%
8200 1
 
3.6%
5400 1
 
3.6%
Other values (13) 13
46.4%
ValueCountFrequency (%)
1500 1
3.6%
1600 1
3.6%
1850 1
3.6%
2000 2
7.1%
2500 1
3.6%
3200 1
3.6%
4000 2
7.1%
4200 1
3.6%
5000 2
7.1%
5400 1
3.6%
ValueCountFrequency (%)
54800 1
3.6%
40000 1
3.6%
24000 1
3.6%
20000 1
3.6%
19200 1
3.6%
13900 1
3.6%
11900 1
3.6%
10500 1
3.6%
10000 2
7.1%
9000 2
7.1%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10894.643
Minimum980
Maximum54800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:34.263913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum980
5-th percentile1463
Q13620
median5415
Q311350
95-th percentile39235
Maximum54800
Range53820
Interquartile range (IQR)7730

Descriptive statistics

Standard deviation13155.242
Coefficient of variation (CV)1.2074964
Kurtosis4.461043
Mean10894.643
Median Absolute Deviation (MAD)3435
Skewness2.1823471
Sum305050
Variance1.730604 × 108
MonotonicityNot monotonic
2023-12-11T01:48:34.364984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 3
 
10.7%
2480 2
 
7.1%
1980 2
 
7.1%
25000 1
 
3.6%
54800 1
 
3.6%
11000 1
 
3.6%
9500 1
 
3.6%
1580 1
 
3.6%
1400 1
 
3.6%
8480 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
980 1
 
3.6%
1400 1
 
3.6%
1580 1
 
3.6%
1980 2
7.1%
2480 2
7.1%
4000 3
10.7%
4050 1
 
3.6%
4550 1
 
3.6%
5280 1
 
3.6%
5350 1
 
3.6%
ValueCountFrequency (%)
54800 1
3.6%
39900 1
3.6%
38000 1
3.6%
25000 1
3.6%
16000 1
3.6%
14900 1
3.6%
12400 1
3.6%
11000 1
3.6%
10000 1
3.6%
9500 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9824.2857
Minimum1100
Maximum51000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:34.461008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1100
5-th percentile1198
Q12650
median4800
Q313125
95-th percentile32650
Maximum51000
Range49900
Interquartile range (IQR)10475

Descriptive statistics

Standard deviation11536.4
Coefficient of variation (CV)1.1742736
Kurtosis6.4530939
Mean9824.2857
Median Absolute Deviation (MAD)3560
Skewness2.4157429
Sum275080
Variance1.3308851 × 108
MonotonicityNot monotonic
2023-12-11T01:48:34.561707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
15000 3
 
10.7%
4000 3
 
10.7%
1100 2
 
7.1%
16000 1
 
3.6%
51000 1
 
3.6%
10000 1
 
3.6%
1800 1
 
3.6%
1380 1
 
3.6%
10800 1
 
3.6%
4600 1
 
3.6%
Other values (13) 13
46.4%
ValueCountFrequency (%)
1100 2
7.1%
1380 1
 
3.6%
1500 1
 
3.6%
1700 1
 
3.6%
1800 1
 
3.6%
2500 1
 
3.6%
2700 1
 
3.6%
3000 1
 
3.6%
3400 1
 
3.6%
4000 3
10.7%
ValueCountFrequency (%)
51000 1
 
3.6%
40000 1
 
3.6%
19000 1
 
3.6%
16000 1
 
3.6%
15000 3
10.7%
12500 1
 
3.6%
12000 1
 
3.6%
10800 1
 
3.6%
10000 1
 
3.6%
9000 1
 
3.6%

Interactions

2023-12-11T01:48:30.694732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.088245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.767667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.529081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.277543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.075284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.761608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.783659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.187436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.849362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.645685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.394955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.187772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.860930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.871408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.287164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.949445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.757132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.494918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.296144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.945768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.964125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.406405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.102583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.869003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.671920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.400409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.049355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:31.045409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.491862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.196363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.963572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.780269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.503432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.133152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:31.123370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.574111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.292081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.070874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.883061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.588643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.511148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:31.210355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:26.669622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:27.397663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.171660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:28.980956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:29.675668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:30.594226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:48:34.632327image/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 격1.0001.0000.8310.7990.8260.7930.7230.9170.708
우동(센텀홈플러스)1.0000.8311.0000.8800.9780.9100.8490.9210.988
중동(이마트 해운대점)1.0000.7990.8801.0000.9160.9180.9410.9790.885
좌동(GS수퍼마켓)1.0000.8260.9780.9161.0000.9230.8530.9460.935
송정동1.0000.7930.9100.9180.9231.0000.8780.8940.912
반여2동(골목시장)1.0000.7230.8490.9410.8530.8781.0000.9670.883
반송동(탑마트)1.0000.9170.9210.9790.9460.8940.9671.0000.934
재송동(한마음시장)1.0000.7080.9880.8850.9350.9120.8830.9341.000
2023-12-11T01:48:34.735894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.8680.8700.8910.7530.8270.878
중동(이마트 해운대점)0.8681.0000.9150.9410.8230.9360.929
좌동(GS수퍼마켓)0.8700.9151.0000.8950.8420.9380.947
송정동0.8910.9410.8951.0000.8210.9280.950
반여2동(골목시장)0.7530.8230.8420.8211.0000.9130.860
반송동(탑마트)0.8270.9360.9380.9280.9131.0000.961
재송동(한마음시장)0.8780.9290.9470.9500.8600.9611.000

Missing values

2023-12-11T01:48:31.353812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:48:31.524289image/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)3kg17900185003500015000105002500016000
1배신고 6㎏24000225605800015000192003800019000
2배추1㎏2990132732802500200024801700
3무1㎏169015501980150015009801100
4대파1㎏(상품)3590339077301480250024803000
5소고기(국산)등심 상등육 500g54550465008300044500400003990040000
6소고기(수입)등심 상등육 500g17750256253200014500119001490015000
7돼지고기삼겹살 500g680015800160009900139001240012500
8닭고기육계1㎏84908580119607500670054808000
9달 걀특란 10개3890398054803980420040504000
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏49803600490033502400052803400
19두부500g 판두부(포장두부 420g) 1모1990519545802500320045501500
20밀가루백설표 중력분1등2.5㎏4600906054506200540053504600
21식용유백설표옥수수기름1.8ℓ10800108009780129008200848010800
22소주(소매점)시원소주 360㎖ 1병1390138014301580160014001380
23맥주(소매점)하이트 500㎖ 1병1710155018301800185015801800
24소주(외식)시원소주 360㎖ 1병5000500045004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500050005000400040004000
26돼지갈비(외식)200g 정도10000144009500100009000950010000
27삼겹살(외식)200g 정도1000023080146001000090001100015000