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_20220414
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 16:49:39.163940
Analysis finished2023-12-10 16:49:44.958854
Duration5.79 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
2023-12-11T01:49:45.116807image/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%
2023-12-11T01:49:45.508032image/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
2023-12-11T01:49:45.741166image/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%
2023-12-11T01:49:46.221693image/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%
Mean11048.571
Minimum1390
Maximum46750
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:46.359055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1390
5-th percentile1514.5
Q13165
median5000
Q311187.5
95-th percentile40315
Maximum46750
Range45360
Interquartile range (IQR)8022.5

Descriptive statistics

Standard deviation12894.996
Coefficient of variation (CV)1.1671189
Kurtosis2.3027925
Mean11048.571
Median Absolute Deviation (MAD)3415
Skewness1.7753822
Sum309360
Variance1.6628092 × 108
MonotonicityNot monotonic
2023-12-11T01:49:46.474808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10000 2
 
7.1%
5000 2
 
7.1%
8990 2
 
7.1%
22410 1
 
3.6%
5990 1
 
3.6%
1610 1
 
3.6%
1390 1
 
3.6%
7990 1
 
3.6%
4450 1
 
3.6%
1790 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1390 1
3.6%
1490 1
3.6%
1560 1
3.6%
1610 1
3.6%
1790 1
3.6%
2700 1
3.6%
2790 1
3.6%
3290 1
3.6%
3490 1
3.6%
3890 1
3.6%
ValueCountFrequency (%)
46750 1
3.6%
44900 1
3.6%
31800 1
3.6%
31450 1
3.6%
22410 1
3.6%
18900 1
3.6%
14750 1
3.6%
10000 2
7.1%
8990 2
7.1%
7990 1
3.6%

우동(농산물시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12398.571
Minimum990
Maximum64000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:46.893765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1500
Q13730
median6200
Q311850
95-th percentile54636
Maximum64000
Range63010
Interquartile range (IQR)8120

Descriptive statistics

Standard deviation16586.961
Coefficient of variation (CV)1.3378123
Kurtosis4.734006
Mean12398.571
Median Absolute Deviation (MAD)4125
Skewness2.3398653
Sum347160
Variance2.7512727 × 108
MonotonicityNot monotonic
2023-12-11T01:49:47.039100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
10000 3
 
10.7%
5000 2
 
7.1%
1500 2
 
7.1%
3980 2
 
7.1%
4900 2
 
7.1%
23190 1
 
3.6%
2980 1
 
3.6%
4000 1
 
3.6%
1850 1
 
3.6%
1650 1
 
3.6%
Other values (12) 12
42.9%
ValueCountFrequency (%)
990 1
3.6%
1500 2
7.1%
1650 1
3.6%
1850 1
3.6%
2300 1
3.6%
2980 1
3.6%
3980 2
7.1%
4000 1
3.6%
4900 2
7.1%
5000 2
7.1%
ValueCountFrequency (%)
64000 1
 
3.6%
58500 1
 
3.6%
47460 1
 
3.6%
23190 1
 
3.6%
14900 1
 
3.6%
14500 1
 
3.6%
12000 1
 
3.6%
11800 1
 
3.6%
10900 1
 
3.6%
10000 3
10.7%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10822.393
Minimum1280
Maximum58100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:47.168291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1371
Q13370
median5550
Q311100
95-th percentile42141.25
Maximum58100
Range56820
Interquartile range (IQR)7730

Descriptive statistics

Standard deviation13804.406
Coefficient of variation (CV)1.275541
Kurtosis6.4491407
Mean10822.393
Median Absolute Deviation (MAD)3765
Skewness2.5413563
Sum303027
Variance1.9056162 × 108
MonotonicityNot monotonic
2023-12-11T01:49:47.296903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4000 2
 
7.1%
13900 1
 
3.6%
21480 1
 
3.6%
16900 1
 
3.6%
11000 1
 
3.6%
1410 1
 
3.6%
1280 1
 
3.6%
8280 1
 
3.6%
5200 1
 
3.6%
5195 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1280 1
3.6%
1350 1
3.6%
1410 1
3.6%
1770 1
3.6%
1800 1
3.6%
1987 1
3.6%
3040 1
3.6%
3480 1
3.6%
3980 1
3.6%
4000 2
7.1%
ValueCountFrequency (%)
58100 1
3.6%
50900 1
3.6%
25875 1
3.6%
21480 1
3.6%
16900 1
3.6%
13900 1
3.6%
11400 1
3.6%
11000 1
3.6%
10880 1
3.6%
9980 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13686.429
Minimum1250
Maximum69670
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:47.448915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1570
Q14375
median6840
Q314050
95-th percentile54470
Maximum69670
Range68420
Interquartile range (IQR)9675

Descriptive statistics

Standard deviation17312.925
Coefficient of variation (CV)1.2649702
Kurtosis5.0187111
Mean13686.429
Median Absolute Deviation (MAD)3640
Skewness2.3082241
Sum383220
Variance2.9973736 × 108
MonotonicityNot monotonic
2023-12-11T01:49:47.607919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4000 2
 
7.1%
29000 1
 
3.6%
39000 1
 
3.6%
13800 1
 
3.6%
9500 1
 
3.6%
1830 1
 
3.6%
1430 1
 
3.6%
8860 1
 
3.6%
4500 1
 
3.6%
4550 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1250 1
3.6%
1430 1
3.6%
1830 1
3.6%
2240 1
3.6%
2660 1
3.6%
4000 2
7.1%
4500 1
3.6%
4550 1
3.6%
4780 1
3.6%
4980 1
3.6%
ValueCountFrequency (%)
69670 1
3.6%
62800 1
3.6%
39000 1
3.6%
29000 1
3.6%
24670 1
3.6%
19800 1
3.6%
14800 1
3.6%
13800 1
3.6%
11000 1
3.6%
9960 1
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9998.2143
Minimum1250
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:47.791696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1250
5-th percentile1467.5
Q14000
median5100
Q310850
95-th percentile34625
Maximum58000
Range56750
Interquartile range (IQR)6850

Descriptive statistics

Standard deviation12622.478
Coefficient of variation (CV)1.2624732
Kurtosis8.4466461
Mean9998.2143
Median Absolute Deviation (MAD)3100
Skewness2.8146161
Sum279950
Variance1.5932694 × 108
MonotonicityNot monotonic
2023-12-11T01:49:47.937350image/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 (%)
1250 1
 
3.6%
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%
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%
11900 1
3.6%
10500 1
3.6%
9000 1
3.6%
8000 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11765
Minimum1380
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:48.052669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1380
5-th percentile1552.5
Q13862.5
median5940
Q310125
95-th percentile50122.5
Maximum58000
Range56620
Interquartile range (IQR)6262.5

Descriptive statistics

Standard deviation15428.102
Coefficient of variation (CV)1.3113559
Kurtosis3.8573987
Mean11765
Median Absolute Deviation (MAD)3510
Skewness2.1927856
Sum329420
Variance2.3802634 × 108
MonotonicityNot monotonic
2023-12-11T01:49:48.177119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
9000 2
 
7.1%
4000 2
 
7.1%
10000 2
 
7.1%
45800 1
 
3.6%
16000 1
 
3.6%
1650 1
 
3.6%
1500 1
 
3.6%
6980 1
 
3.6%
4680 1
 
3.6%
4550 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1380 1
3.6%
1500 1
3.6%
1650 1
3.6%
1780 1
3.6%
1980 1
3.6%
2980 1
3.6%
3750 1
3.6%
3900 1
3.6%
3980 1
3.6%
4000 2
7.1%
ValueCountFrequency (%)
58000 1
3.6%
52450 1
3.6%
45800 1
3.6%
29800 1
3.6%
16000 1
3.6%
12400 1
3.6%
10500 1
3.6%
10000 2
7.1%
9000 2
7.1%
7480 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10052.5
Minimum600
Maximum53000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:48.308977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile1187.5
Q12150
median4550
Q39750
95-th percentile42300
Maximum53000
Range52400
Interquartile range (IQR)7600

Descriptive statistics

Standard deviation13341.753
Coefficient of variation (CV)1.3272074
Kurtosis5.5713613
Mean10052.5
Median Absolute Deviation (MAD)3125
Skewness2.4082884
Sum281470
Variance1.7800236 × 108
MonotonicityNot monotonic
2023-12-11T01:49:48.459004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
9000 3
 
10.7%
4000 2
 
7.1%
4100 2
 
7.1%
20000 1
 
3.6%
5000 1
 
3.6%
1720 1
 
3.6%
1350 1
 
3.6%
8700 1
 
3.6%
1500 1
 
3.6%
1100 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
600 1
3.6%
1100 1
3.6%
1350 1
3.6%
1500 1
3.6%
1600 1
3.6%
1720 1
3.6%
2000 1
3.6%
2200 1
3.6%
2700 1
3.6%
3800 1
3.6%
ValueCountFrequency (%)
53000 1
 
3.6%
50000 1
 
3.6%
28000 1
 
3.6%
20000 1
 
3.6%
17500 1
 
3.6%
12500 1
 
3.6%
12000 1
 
3.6%
9000 3
10.7%
8700 1
 
3.6%
7000 1
 
3.6%

Interactions

2023-12-11T01:49:44.013108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:39.490879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.165136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.974795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.769384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.567995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.355747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:44.090299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:39.571280image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.280258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.077942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.879273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.669767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.440404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:44.182082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:39.679769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.379574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.180699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.968604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.772077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.536686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:44.288345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:39.778306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.489265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.290756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.089439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.881379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.625727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:44.394094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:39.867889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.604789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.415888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.208359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.991516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.735925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:44.479565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:39.952515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.720698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.535112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.334646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.122879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.833393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:44.566011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.056622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:40.852937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:41.661800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:42.445385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.255253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:43.914479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:49:48.561857image/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 격1.0001.0000.9270.9220.2180.8360.9190.7300.922
우동(센텀홈플러스)1.0000.9271.0000.9360.9370.9520.8110.7820.919
우동(농산물시장)1.0000.9220.9361.0000.9100.9580.9060.9620.996
중동(이마트 해운대점)1.0000.2180.9370.9101.0000.9570.8950.9180.898
좌동(GS수퍼마켓)1.0000.8360.9520.9580.9571.0000.9240.9070.943
반여2동(골목시장)1.0000.9190.8110.9060.8950.9241.0000.9420.922
반송동(탑마트)1.0000.7300.7820.9620.9180.9070.9421.0000.939
재송동(한마음시장)1.0000.9220.9190.9960.8980.9430.9220.9391.000
2023-12-11T01:49:48.711267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)우동(농산물시장)중동(이마트 해운대점)좌동(GS수퍼마켓)반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.9120.9300.9240.8960.9190.958
우동(농산물시장)0.9121.0000.9050.9140.8700.9360.931
중동(이마트 해운대점)0.9300.9051.0000.9270.8720.9560.932
좌동(GS수퍼마켓)0.9240.9140.9271.0000.9220.9230.935
반여2동(골목시장)0.8960.8700.8720.9221.0000.8660.909
반송동(탑마트)0.9190.9360.9560.9230.8661.0000.946
재송동(한마음시장)0.9580.9310.9320.9350.9090.9461.000

Missing values

2023-12-11T01:49:44.721261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:49:44.902447image/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)3kg22410231901390029000105002980020000
1배신고 6㎏31800474602148039000192005800028000
2배추1㎏3990500018005980500039802000
3무1㎏149015001350125015001380600
4대파1㎏(상품)3290150030405960200017802700
5소고기(국산)등심 상등육 500g46750640005810069670425005245050000
6소고기(수입)등심 상등육 500g31450145002587524670119001240012500
7돼지고기삼겹살 500g14750149001140014800139001050012000
8닭고기육계1㎏8990798079809960670074807000
9달 걀특란 10개3890398039804780520037503800
품 목규 격우동(센텀홈플러스)우동(농산물시장)중동(이마트 해운대점)좌동(GS수퍼마켓)반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏8990740059008600650049804100
19두부500g 판두부(포장두부 420g) 1모1790230051954550125045501500
20밀가루백설표 중력분1등2.5㎏4450398052004500440046804100
21식용유백설표옥수수기름1.8ℓ79901090082808860630069808700
22소주(소매점)시원소주 360㎖ 1병1390165012801430145015001350
23맥주(소매점)하이트 500㎖ 1병1610185014101830165016501720
24소주(외식)시원소주 360㎖ 1병5000400040004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500040004000400040004000
26돼지갈비(외식)200g 정도1000010000110009500800090009000
27삼겹살(외식)200g 정도100001000016900138009000160009000