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_20220610
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
좌동(GS수퍼마켓) has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:49:12.996309
Analysis finished2023-12-10 16:49:20.927101
Duration7.93 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
2023-12-11T01:49:21.126477image/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%
2023-12-11T01:49:21.615133image/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
2023-12-11T01:49:21.942514image/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%
2023-12-11T01:49:22.499049image/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 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11023.571
Minimum1390
Maximum54550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:22.687440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1390
5-th percentile1577.5
Q13080
median5000
Q310987.5
95-th percentile42265
Maximum54550
Range53160
Interquartile range (IQR)7907.5

Descriptive statistics

Standard deviation13543.058
Coefficient of variation (CV)1.2285545
Kurtosis4.5079028
Mean11023.571
Median Absolute Deviation (MAD)3415
Skewness2.1925819
Sum308660
Variance1.8341442 × 108
MonotonicityNot monotonic
2023-12-11T01:49:22.821477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3990 2
 
7.1%
10000 2
 
7.1%
5000 2
 
7.1%
8990 2
 
7.1%
23900 1
 
3.6%
47900 1
 
3.6%
1610 1
 
3.6%
1390 1
 
3.6%
8780 1
 
3.6%
4600 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1390 1
3.6%
1560 1
3.6%
1610 1
3.6%
1790 1
3.6%
2190 1
3.6%
2700 1
3.6%
2750 1
3.6%
3190 1
3.6%
3890 1
3.6%
3990 2
7.1%
ValueCountFrequency (%)
54550 1
3.6%
47900 1
3.6%
31800 1
3.6%
23900 1
3.6%
18900 1
3.6%
17450 1
3.6%
13950 1
3.6%
10000 2
7.1%
8990 2
7.1%
8780 1
3.6%

우동(농산물시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11783.214
Minimum990
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:22.971162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1203
Q13200
median6495
Q310375
95-th percentile46599
Maximum60000
Range59010
Interquartile range (IQR)7175

Descriptive statistics

Standard deviation14970.472
Coefficient of variation (CV)1.2704914
Kurtosis4.3362709
Mean11783.214
Median Absolute Deviation (MAD)3850
Skewness2.2105928
Sum329930
Variance2.2411504 × 108
MonotonicityNot monotonic
2023-12-11T01:49:23.118104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
10000 4
 
14.3%
23190 1
 
3.6%
45000 1
 
3.6%
5000 1
 
3.6%
4000 1
 
3.6%
1780 1
 
3.6%
1580 1
 
3.6%
11500 1
 
3.6%
5590 1
 
3.6%
2300 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
990 1
3.6%
1000 1
3.6%
1580 1
3.6%
1780 1
3.6%
1980 1
3.6%
2000 1
3.6%
2300 1
3.6%
3500 1
3.6%
3980 1
3.6%
4000 1
3.6%
ValueCountFrequency (%)
60000 1
 
3.6%
47460 1
 
3.6%
45000 1
 
3.6%
23190 1
 
3.6%
20000 1
 
3.6%
14500 1
 
3.6%
11500 1
 
3.6%
10000 4
14.3%
9900 1
 
3.6%
7980 1
 
3.6%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11130.536
Minimum1280
Maximum51800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:23.245141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1325.5
Q13880
median6360
Q314400
95-th percentile41302.25
Maximum51800
Range50520
Interquartile range (IQR)10520

Descriptive statistics

Standard deviation12919.515
Coefficient of variation (CV)1.1607271
Kurtosis5.1384332
Mean11130.536
Median Absolute Deviation (MAD)3987.5
Skewness2.271836
Sum311655
Variance1.6691387 × 108
MonotonicityNot monotonic
2023-12-11T01:49:23.389349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1280 2
 
7.1%
4000 2
 
7.1%
3980 2
 
7.1%
13900 1
 
3.6%
49900 1
 
3.6%
16900 1
 
3.6%
11000 1
 
3.6%
1410 1
 
3.6%
8280 1
 
3.6%
6740 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1280 2
7.1%
1410 1
3.6%
1680 1
3.6%
2280 1
3.6%
2465 1
3.6%
3580 1
3.6%
3980 2
7.1%
4000 2
7.1%
5195 1
3.6%
5900 1
3.6%
ValueCountFrequency (%)
51800 1
3.6%
49900 1
3.6%
25335 1
3.6%
22680 1
3.6%
16900 1
3.6%
15950 1
3.6%
15900 1
3.6%
13900 1
3.6%
11000 1
3.6%
9800 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13829.286
Minimum1430
Maximum71340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:23.524449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile2061
Q14812.5
median6350
Q314800
95-th percentile54645
Maximum71340
Range69910
Interquartile range (IQR)9987.5

Descriptive statistics

Standard deviation17386.634
Coefficient of variation (CV)1.257233
Kurtosis5.3973568
Mean13829.286
Median Absolute Deviation (MAD)3575
Skewness2.3724013
Sum387220
Variance3.0229506 × 108
MonotonicityNot monotonic
2023-12-11T01:49:23.693728image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
24500 1
 
3.6%
62800 1
 
3.6%
13800 1
 
3.6%
9500 1
 
3.6%
5000 1
 
3.6%
4000 1
 
3.6%
1830 1
 
3.6%
1430 1
 
3.6%
8980 1
 
3.6%
5450 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1430 1
3.6%
1830 1
3.6%
2490 1
3.6%
2770 1
3.6%
2780 1
3.6%
4000 1
3.6%
4550 1
3.6%
4900 1
3.6%
4960 1
3.6%
4980 1
3.6%
ValueCountFrequency (%)
71340 1
3.6%
62800 1
3.6%
39500 1
3.6%
24920 1
3.6%
24500 1
3.6%
19800 1
3.6%
17800 1
3.6%
13800 1
3.6%
11800 1
3.6%
10960 1
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9992.8571
Minimum1000
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:23.844640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1372.5
Q14000
median5750
Q310850
95-th percentile33000
Maximum58000
Range57000
Interquartile range (IQR)6850

Descriptive statistics

Standard deviation12323.921
Coefficient of variation (CV)1.233273
Kurtosis9.022897
Mean9992.8571
Median Absolute Deviation (MAD)3250
Skewness2.8709788
Sum279800
Variance1.5187902 × 108
MonotonicityNot monotonic
2023-12-11T01:49:23.985013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5000 4
 
14.3%
4000 2
 
7.1%
9000 2
 
7.1%
10500 1
 
3.6%
58000 1
 
3.6%
8000 1
 
3.6%
1850 1
 
3.6%
1600 1
 
3.6%
6300 1
 
3.6%
4400 1
 
3.6%
Other values (13) 13
46.4%
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%
4000 2
7.1%
4400 1
 
3.6%
5000 4
14.3%
5200 1
 
3.6%
ValueCountFrequency (%)
58000 1
3.6%
40000 1
3.6%
20000 1
3.6%
19200 1
3.6%
13900 1
3.6%
13000 1
3.6%
11900 1
3.6%
10500 1
3.6%
9000 2
7.1%
8000 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11828.214
Minimum1500
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:24.107944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1500
5-th percentile1765.5
Q14000
median5965
Q310600
95-th percentile47135
Maximum58000
Range56500
Interquartile range (IQR)6600

Descriptive statistics

Standard deviation14904.75
Coefficient of variation (CV)1.2601014
Kurtosis3.8985282
Mean11828.214
Median Absolute Deviation (MAD)3035
Skewness2.1874414
Sum331190
Variance2.2215157 × 108
MonotonicityNot monotonic
2023-12-11T01:49:24.254479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 3
 
10.7%
10000 2
 
7.1%
9000 2
 
7.1%
29800 1
 
3.6%
47800 1
 
3.6%
16000 1
 
3.6%
1650 1
 
3.6%
1500 1
 
3.6%
7480 1
 
3.6%
5350 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1500 1
 
3.6%
1650 1
 
3.6%
1980 1
 
3.6%
2580 1
 
3.6%
2980 1
 
3.6%
3280 1
 
3.6%
4000 3
10.7%
4280 1
 
3.6%
4550 1
 
3.6%
4900 1
 
3.6%
ValueCountFrequency (%)
58000 1
3.6%
47800 1
3.6%
45900 1
3.6%
29800 1
3.6%
16000 1
3.6%
12500 1
3.6%
12400 1
3.6%
10000 2
7.1%
9000 2
7.1%
7480 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9565.3571
Minimum800
Maximum50000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:24.408245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum800
5-th percentile1263
Q12225
median4800
Q39625
95-th percentile37650
Maximum50000
Range49200
Interquartile range (IQR)7400

Descriptive statistics

Standard deviation12201.832
Coefficient of variation (CV)1.2756275
Kurtosis5.7192471
Mean9565.3571
Median Absolute Deviation (MAD)3250
Skewness2.4116267
Sum267830
Variance1.4888471 × 108
MonotonicityNot monotonic
2023-12-11T01:49:24.580411image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4000 3
 
10.7%
9000 2
 
7.1%
21000 1
 
3.6%
24000 1
 
3.6%
1750 1
 
3.6%
1380 1
 
3.6%
8700 1
 
3.6%
4600 1
 
3.6%
1500 1
 
3.6%
1200 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
800 1
3.6%
1200 1
3.6%
1380 1
3.6%
1500 1
3.6%
1600 1
3.6%
1750 1
3.6%
2000 1
3.6%
2300 1
3.6%
2500 1
3.6%
3800 1
3.6%
ValueCountFrequency (%)
50000 1
3.6%
45000 1
3.6%
24000 1
3.6%
21000 1
3.6%
15000 1
3.6%
12000 1
3.6%
11500 1
3.6%
9000 2
7.1%
8700 1
3.6%
8000 1
3.6%

Interactions

2023-12-11T01:49:19.651152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:13.364492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.179238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.172019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.205057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:17.249138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.602979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.788475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:13.483929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.330220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.301651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.350734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:17.381341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.761240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.939700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:13.626225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.444723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.457570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.512435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:17.526057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.898238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.082327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:13.745533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.553010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.603060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.676932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:17.638998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.074109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.207642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:13.851063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.686997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.745756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.831645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.153547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.236325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.332469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:13.942253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.885167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.882715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.969120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.302504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.383504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:20.454329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:14.056406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:15.028030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:16.040215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:17.111376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:18.454318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:19.510381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:49:24.692103image/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 격1.0001.0000.9010.5870.0000.8590.8930.6880.869
우동(센텀홈플러스)1.0000.9011.0000.9440.8320.9660.9020.9310.966
우동(농산물시장)1.0000.5870.9441.0000.9440.9130.9160.8600.972
중동(이마트 해운대점)1.0000.0000.8320.9441.0000.8440.8570.8810.951
좌동(GS수퍼마켓)1.0000.8590.9660.9130.8441.0000.8890.9780.942
반여2동(골목시장)1.0000.8930.9020.9160.8570.8891.0000.8650.954
반송동(탑마트)1.0000.6880.9310.8600.8810.9780.8651.0000.926
재송동(한마음시장)1.0000.8690.9660.9720.9510.9420.9540.9261.000
2023-12-11T01:49:24.845587image/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)1.0000.9390.8970.9110.8490.9000.954
우동(농산물시장)0.9391.0000.9260.9060.8330.9400.938
중동(이마트 해운대점)0.8970.9261.0000.9210.8440.9310.922
좌동(GS수퍼마켓)0.9110.9060.9211.0000.9290.9160.932
반여2동(골목시장)0.8490.8330.8440.9291.0000.8870.905
반송동(탑마트)0.9000.9400.9310.9160.8871.0000.936
재송동(한마음시장)0.9540.9380.9220.9320.9050.9361.000

Missing values

2023-12-11T01:49:20.610234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:49:20.838899image/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)3kg23900231901390024500105002980021000
1배신고 6㎏31800474602268039500192005800024000
2배추1㎏3990398012804980500042802000
3무1㎏219020002280278010001980800
4대파1㎏(상품)3190100035804960250025802500
5소고기(국산)등심 상등육 500g54550600005180071340400004590045000
6소고기(수입)등심 상등육 500g17450145002533524920119001240011500
7돼지고기삼겹살 500g13950200001590017800139001250012000
8닭고기육계1㎏89907980848010960670065807200
9달 걀특란 10개3890440039805080520040003800
품 목규 격우동(센텀홈플러스)우동(농산물시장)중동(이마트 해운대점)좌동(GS수퍼마켓)반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏8990740059008600650049804000
19두부500g 판두부(포장두부 420g) 1모1790230051954550125045501500
20밀가루백설표 중력분1등2.5㎏4600559067405450440053504600
21식용유백설표옥수수기름1.8ℓ87801150082808980630074808700
22소주(소매점)시원소주 360㎖ 1병1390158012801430160015001380
23맥주(소매점)하이트 500㎖ 1병1610178014101830185016501750
24소주(외식)시원소주 360㎖ 1병5000400040004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500040005000400040004000
26돼지갈비(외식)200g 정도1000010000110009500800090009000
27삼겹살(외식)200g 정도100001000016900138009000160009000