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_20221108
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 16:48:36.516377
Analysis finished2023-12-10 16:48:42.313747
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
2023-12-11T01:48:42.454996image/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%
2023-12-11T01:48:42.848782image/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
2023-12-11T01:48:43.063534image/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%
2023-12-11T01:48:43.565164image/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%
Mean10061.786
Minimum1260
Maximum54550
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:43.834055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1260
5-th percentile1502
Q12942.5
median4995
Q310200
95-th percentile42025
Maximum54550
Range53290
Interquartile range (IQR)7257.5

Descriptive statistics

Standard deviation13365.99
Coefficient of variation (CV)1.3283914
Kurtosis6.1715978
Mean10061.786
Median Absolute Deviation (MAD)3125
Skewness2.5306696
Sum281730
Variance1.7864968 × 108
MonotonicityNot monotonic
2023-12-11T01:48:43.993893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10000 2
 
7.1%
5000 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%
4990 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1260 1
3.6%
1390 1
3.6%
1710 1
3.6%
1750 1
3.6%
1990 1
3.6%
2370 1
3.6%
2800 1
3.6%
2990 1
3.6%
3300 1
3.6%
3590 1
3.6%
ValueCountFrequency (%)
54550 1
3.6%
49900 1
3.6%
27400 1
3.6%
17900 1
3.6%
17750 1
3.6%
12950 1
3.6%
10800 1
3.6%
10000 2
7.1%
8490 1
3.6%
5490 1
3.6%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12069.643
Minimum1380
Maximum53900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:44.200092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1380
5-th percentile1739
Q13712.5
median6687.5
Q313275
95-th percentile42871.75
Maximum53900
Range52520
Interquartile range (IQR)9562.5

Descriptive statistics

Standard deviation13436.233
Coefficient of variation (CV)1.1132254
Kurtosis4.177231
Mean12069.643
Median Absolute Deviation (MAD)4020
Skewness2.0779636
Sum337950
Variance1.8053236 × 108
MonotonicityNot monotonic
2023-12-11T01:48:44.391152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 2
 
7.1%
20850 1
 
3.6%
29940 1
 
3.6%
23080 1
 
3.6%
14400 1
 
3.6%
1550 1
 
3.6%
1380 1
 
3.6%
10800 1
 
3.6%
9060 1
 
3.6%
5195 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1380 1
3.6%
1550 1
3.6%
2090 1
3.6%
2760 1
3.6%
2770 1
3.6%
2850 1
3.6%
3510 1
3.6%
3780 1
3.6%
3980 1
3.6%
4950 1
3.6%
ValueCountFrequency (%)
53900 1
3.6%
49835 1
3.6%
29940 1
3.6%
23080 1
3.6%
21950 1
3.6%
20850 1
3.6%
14400 1
3.6%
12900 1
3.6%
12800 1
3.6%
11900 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15268.929
Minimum1430
Maximum75000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:44.583581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile1882.5
Q14560
median5965
Q314650
95-th percentile59170
Maximum75000
Range73570
Interquartile range (IQR)10090

Descriptive statistics

Standard deviation19633.512
Coefficient of variation (CV)1.2858474
Kurtosis3.0977217
Mean15268.929
Median Absolute Deviation (MAD)3675
Skewness1.996583
Sum427530
Variance3.854748 × 108
MonotonicityNot monotonic
2023-12-11T01:48:44.770753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 2
 
7.1%
39800 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%
5450 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1430 1
3.6%
1830 1
3.6%
1980 1
3.6%
2770 1
3.6%
3480 1
3.6%
3980 1
3.6%
4500 1
3.6%
4580 1
3.6%
4960 1
3.6%
5000 2
7.1%
ValueCountFrequency (%)
75000 1
3.6%
59800 1
3.6%
58000 1
3.6%
39800 1
3.6%
34500 1
3.6%
19800 1
3.6%
14800 1
3.6%
14600 1
3.6%
11000 1
3.6%
9980 1
3.6%

송정동
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9980.7143
Minimum1010
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:44.973382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1010
5-th percentile1422
Q12785
median5850
Q310000
95-th percentile38000
Maximum59800
Range58790
Interquartile range (IQR)7215

Descriptive statistics

Standard deviation13267.133
Coefficient of variation (CV)1.3292769
Kurtosis8.2244398
Mean9980.7143
Median Absolute Deviation (MAD)4050
Skewness2.8095791
Sum279460
Variance1.7601682 × 108
MonotonicityNot monotonic
2023-12-11T01:48:45.160938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
10000 2
 
7.1%
9900 2
 
7.1%
15000 1
 
3.6%
59800 1
 
3.6%
5000 1
 
3.6%
4000 1
 
3.6%
1800 1
 
3.6%
1580 1
 
3.6%
7980 1
 
3.6%
6200 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1010 1
3.6%
1380 1
3.6%
1500 1
3.6%
1580 1
3.6%
1600 1
3.6%
1800 1
3.6%
2200 1
3.6%
2980 1
3.6%
3350 1
3.6%
3980 1
3.6%
ValueCountFrequency (%)
59800 1
3.6%
45000 1
3.6%
25000 1
3.6%
15000 1
3.6%
14500 1
3.6%
11800 1
3.6%
10000 2
7.1%
9900 2
7.1%
7980 1
3.6%
7500 1
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10551.786
Minimum1500
Maximum54800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:45.351327image/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 deviation12072.125
Coefficient of variation (CV)1.1440836
Kurtosis6.8196866
Mean10551.786
Median Absolute Deviation (MAD)3650
Skewness2.5064405
Sum295450
Variance1.457362 × 108
MonotonicityNot monotonic
2023-12-11T01:48:45.555129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4000 2
 
7.1%
5000 2
 
7.1%
10000 2
 
7.1%
10500 1
 
3.6%
54800 1
 
3.6%
9000 1
 
3.6%
8000 1
 
3.6%
1850 1
 
3.6%
1600 1
 
3.6%
8200 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1500 1
3.6%
1600 1
3.6%
1850 1
3.6%
2000 1
3.6%
2500 1
3.6%
3000 1
3.6%
3200 1
3.6%
4000 2
7.1%
4200 1
3.6%
5000 2
7.1%
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 1
3.6%

반송동(탑마트)
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10929.286
Minimum1400
Maximum54800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:45.740474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1400
5-th percentile1720
Q13987.5
median5865
Q311225
95-th percentile38942.5
Maximum54800
Range53400
Interquartile range (IQR)7237.5

Descriptive statistics

Standard deviation12996.478
Coefficient of variation (CV)1.1891424
Kurtosis4.722259
Mean10929.286
Median Absolute Deviation (MAD)3510
Skewness2.2385885
Sum306020
Variance1.6890843 × 108
MonotonicityNot monotonic
2023-12-11T01:48:45.914694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
4000 3
 
10.7%
1980 2
 
7.1%
24000 1
 
3.6%
6580 1
 
3.6%
11000 1
 
3.6%
9500 1
 
3.6%
1580 1
 
3.6%
1400 1
 
3.6%
8480 1
 
3.6%
5350 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
1400 1
 
3.6%
1580 1
 
3.6%
1980 2
7.1%
2480 1
 
3.6%
2980 1
 
3.6%
3950 1
 
3.6%
4000 3
10.7%
4500 1
 
3.6%
4550 1
 
3.6%
5280 1
 
3.6%
ValueCountFrequency (%)
54800 1
3.6%
39450 1
3.6%
38000 1
3.6%
24000 1
3.6%
16000 1
3.6%
15000 1
3.6%
11900 1
3.6%
11000 1
3.6%
10000 1
3.6%
9500 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10092.143
Minimum1200
Maximum55000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:48:46.120965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1200
5-th percentile1422
Q13000
median4300
Q312375
95-th percentile36025
Maximum55000
Range53800
Interquartile range (IQR)9375

Descriptive statistics

Standard deviation12485.023
Coefficient of variation (CV)1.2371033
Kurtosis6.6490928
Mean10092.143
Median Absolute Deviation (MAD)2750
Skewness2.5041221
Sum282580
Variance1.558758 × 108
MonotonicityNot monotonic
2023-12-11T01:48:46.331960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
4000 5
17.9%
15000 3
 
10.7%
3000 2
 
7.1%
18000 1
 
3.6%
1200 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 (11) 11
39.3%
ValueCountFrequency (%)
1200 1
 
3.6%
1380 1
 
3.6%
1500 1
 
3.6%
1800 1
 
3.6%
2000 1
 
3.6%
2100 1
 
3.6%
3000 2
 
7.1%
3700 1
 
3.6%
4000 5
17.9%
4600 1
 
3.6%
ValueCountFrequency (%)
55000 1
 
3.6%
42500 1
 
3.6%
24000 1
 
3.6%
18000 1
 
3.6%
15000 3
10.7%
11500 1
 
3.6%
10800 1
 
3.6%
10000 1
 
3.6%
8000 1
 
3.6%
7000 1
 
3.6%

Interactions

2023-12-11T01:48:41.178486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:36.824795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.478961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.383930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.043186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.717764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.405643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.350680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:36.907477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.586585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.466215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.136454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.800989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.509671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.477617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.002666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.661904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.558885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.248933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.875812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.608705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.621404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.094037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.751926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.656274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.341581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.969848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.726596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.729409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.188066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.834544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.761587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.440933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.084240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.823295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.834231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.277597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.912425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.852541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.527293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.183168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.909653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.989217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.390998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:37.989471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:38.949408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:39.617256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:40.289167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:48:41.059929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:48:46.791610image/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 격1.0001.0000.9220.7060.7990.8310.7230.8970.833
우동(센텀홈플러스)1.0000.9221.0000.9080.8430.9760.8330.8550.842
중동(이마트 해운대점)1.0000.7060.9081.0000.9370.9160.8770.9420.963
좌동(GS수퍼마켓)1.0000.7990.8430.9371.0000.9020.9230.9830.979
송정동1.0000.8310.9760.9160.9021.0000.8930.9510.968
반여2동(골목시장)1.0000.7230.8330.8770.9230.8931.0000.9500.963
반송동(탑마트)1.0000.8970.8550.9420.9830.9510.9501.0000.996
재송동(한마음시장)1.0000.8330.8420.9630.9790.9680.9630.9961.000
2023-12-11T01:48:46.982075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.8200.8340.8070.6980.7980.890
중동(이마트 해운대점)0.8201.0000.9240.9540.8120.9320.928
좌동(GS수퍼마켓)0.8340.9241.0000.9280.9170.9480.943
송정동0.8070.9540.9281.0000.8420.9270.951
반여2동(골목시장)0.6980.8120.9170.8421.0000.8930.832
반송동(탑마트)0.7980.9320.9480.9270.8931.0000.940
재송동(한마음시장)0.8900.9280.9430.9510.8320.9401.000

Missing values

2023-12-11T01:48:42.115706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:48:42.256896image/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)3kg17900208503980015000105002400018000
1배신고 6㎏27400299405800025000192003800024000
2배추1㎏5490276039801600300045004000
3무1㎏2990285034801500150029802000
4대파1㎏(상품)3590351049601380250024803000
5소고기(국산)등심 상등육 500g54550498357500045000400003945042500
6소고기(수입)등심 상등육 500g17750219503450014500119001500015000
7돼지고기삼겹살 500g1295012900148009900139001190011500
8닭고기육계1㎏8490818099805900670063806500
9달 걀특란 10개3890398054803980420039504000
품 목규 격우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)송정동반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
18고춧가루0.1㎏49903780860033502400052803700
19두부500g 판두부(포장두부 420g) 1모1990519545802200320045501500
20밀가루백설표 중력분1등2.5㎏4600906054506200540053504600
21식용유백설표옥수수기름1.8ℓ1080010800978079808200848010800
22소주(소매점)시원소주 360㎖ 1병1390138014301580160014001380
23맥주(소매점)하이트 500㎖ 1병1710155018301800185015801800
24소주(외식)시원소주 360㎖ 1병5000500045004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500050005000400040004000
26돼지갈비(외식)200g 정도10000144009500100008000950010000
27삼겹살(외식)200g 정도1000023080146001000090001100015000