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 memory82.7 B

Variable types

Text3
Numeric6

Dataset

Description부산광역시해운대구_물가관리_20220511
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 4 other fieldsHigh correlation
중동(이마트 해운대점) is highly overall correlated with 우동(센텀홈플러스) and 4 other fieldsHigh correlation
좌동(GS수퍼마켓) is highly overall correlated with 우동(센텀홈플러스) and 4 other fieldsHigh correlation
반여2동(골목시장) is highly overall correlated with 우동(센텀홈플러스) and 4 other fieldsHigh correlation
반송동(탑마트) is highly overall correlated with 우동(센텀홈플러스) and 4 other fieldsHigh correlation
재송동(한마음시장) is highly overall correlated with 우동(센텀홈플러스) and 4 other fieldsHigh correlation
품 목 has unique valuesUnique
좌동(GS수퍼마켓) has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:49:27.097930
Analysis finished2023-12-10 16:49:32.456542
Duration5.36 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
2023-12-11T01:49:32.655592image/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%
2023-12-11T01:49:33.145088image/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
2023-12-11T01:49:33.445631image/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%
2023-12-11T01:49:34.014996image/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%
Mean12105.714
Minimum1390
Maximum57900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:34.224753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1390
5-th percentile1514.5
Q13165
median5000
Q311187.5
95-th percentile47299.5
Maximum57900
Range56510
Interquartile range (IQR)8022.5

Descriptive statistics

Standard deviation15327.622
Coefficient of variation (CV)1.2661477
Kurtosis2.9828177
Mean12105.714
Median Absolute Deviation (MAD)3475
Skewness1.9461498
Sum338960
Variance2.3493599 × 108
MonotonicityNot monotonic
2023-12-11T01:49:34.383189image/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%
23900 1
 
3.6%
5990 1
 
3.6%
1610 1
 
3.6%
1390 1
 
3.6%
8780 1
 
3.6%
4600 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 (%)
57900 1
3.6%
49900 1
3.6%
42470 1
3.6%
31800 1
3.6%
23900 1
3.6%
18900 1
3.6%
14750 1
3.6%
10000 2
7.1%
8990 2
7.1%
8780 1
3.6%

우동(농산물시장)
Text

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T01:49:34.606279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length5.3928571
Min length4

Characters and Unicode

Total characters151
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)78.6%

Sample

1st row23190
2nd row47460
3rd row3980
4th row1500
5th row2500
ValueCountFrequency (%)
10000 4
 
14.3%
5000 2
 
7.1%
9900 1
 
3.6%
1,850 1
 
3.6%
1650 1
 
3.6%
10900 1
 
3.6%
5590 1
 
3.6%
2300 1
 
3.6%
7400 1
 
3.6%
990 1
 
3.6%
Other values (14) 14
50.0%
2023-12-11T01:49:35.012576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 64
42.4%
27
17.9%
1 11
 
7.3%
9 11
 
7.3%
5 10
 
6.6%
4 8
 
5.3%
8 4
 
2.6%
2 4
 
2.6%
6 4
 
2.6%
3 3
 
2.0%
Other values (2) 5
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 122
80.8%
Space Separator 27
 
17.9%
Other Punctuation 2
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 64
52.5%
1 11
 
9.0%
9 11
 
9.0%
5 10
 
8.2%
4 8
 
6.6%
8 4
 
3.3%
2 4
 
3.3%
6 4
 
3.3%
3 3
 
2.5%
7 3
 
2.5%
Space Separator
ValueCountFrequency (%)
27
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 151
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 64
42.4%
27
17.9%
1 11
 
7.3%
9 11
 
7.3%
5 10
 
6.6%
4 8
 
5.3%
8 4
 
2.6%
2 4
 
2.6%
6 4
 
2.6%
3 3
 
2.0%
Other values (2) 5
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 151
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 64
42.4%
27
17.9%
1 11
 
7.3%
9 11
 
7.3%
5 10
 
6.6%
4 8
 
5.3%
8 4
 
2.6%
2 4
 
2.6%
6 4
 
2.6%
3 3
 
2.0%
Other values (2) 5
 
3.3%

중동(이마트 해운대점)
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10953.571
Minimum1280
Maximum58100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:35.194226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1280
5-th percentile1476.5
Q13290
median5940
Q311850
95-th percentile41302.25
Maximum58100
Range56820
Interquartile range (IQR)8560

Descriptive statistics

Standard deviation13758.204
Coefficient of variation (CV)1.2560473
Kurtosis6.1491593
Mean10953.571
Median Absolute Deviation (MAD)4020
Skewness2.468821
Sum306700
Variance1.8928818 × 108
MonotonicityNot monotonic
2023-12-11T01:49:35.375748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4000 2
 
7.1%
17340 1
 
3.6%
22260 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%
1410 1
3.6%
1600 1
3.6%
1840 1
3.6%
1880 1
3.6%
1940 1
3.6%
2690 1
3.6%
3490 1
3.6%
3980 1
3.6%
4000 2
7.1%
ValueCountFrequency (%)
58100 1
3.6%
49900 1
3.6%
25335 1
3.6%
22260 1
3.6%
17340 1
3.6%
16900 1
3.6%
14400 1
3.6%
11000 1
3.6%
9980 1
3.6%
8800 1
3.6%

좌동(GS수퍼마켓)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13423.214
Minimum1430
Maximum66340
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:35.534009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1430
5-th percentile1882.5
Q13995
median5850
Q314550
95-th percentile54645
Maximum66340
Range64910
Interquartile range (IQR)10555

Descriptive statistics

Standard deviation16941.441
Coefficient of variation (CV)1.2621002
Kurtosis4.7453574
Mean13423.214
Median Absolute Deviation (MAD)3715
Skewness2.2606343
Sum375850
Variance2.8701244 × 108
MonotonicityNot monotonic
2023-12-11T01:49:35.710305image/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%
8860 1
 
3.6%
4980 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1430 1
3.6%
1830 1
3.6%
1980 1
3.6%
2070 1
3.6%
2490 1
3.6%
3960 1
3.6%
3980 1
3.6%
4000 1
3.6%
4400 1
3.6%
4550 1
3.6%
ValueCountFrequency (%)
66340 1
3.6%
62800 1
3.6%
39500 1
3.6%
26340 1
3.6%
24500 1
3.6%
19800 1
3.6%
16800 1
3.6%
13800 1
3.6%
11800 1
3.6%
9960 1
3.6%

반여2동(골목시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9998.2143
Minimum1000
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:35.889458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1320
Q14000
median5600
Q310850
95-th percentile34625
Maximum58000
Range57000
Interquartile range (IQR)6850

Descriptive statistics

Standard deviation12634.209
Coefficient of variation (CV)1.2636466
Kurtosis8.4066422
Mean9998.2143
Median Absolute Deviation (MAD)3500
Skewness2.8036984
Sum279950
Variance1.5962324 × 108
MonotonicityNot monotonic
2023-12-11T01:49:36.066456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
5000 4
 
14.3%
4000 2
 
7.1%
10500 1
 
3.6%
58000 1
 
3.6%
9000 1
 
3.6%
8000 1
 
3.6%
1650 1
 
3.6%
1450 1
 
3.6%
6300 1
 
3.6%
4400 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1000 1
 
3.6%
1250 1
 
3.6%
1450 1
 
3.6%
1500 1
 
3.6%
1650 1
 
3.6%
2000 1
 
3.6%
4000 2
7.1%
4400 1
 
3.6%
5000 4
14.3%
5200 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%
Mean12034.643
Minimum1480
Maximum58000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:36.240456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1480
5-th percentile1528
Q13962.5
median5940
Q310600
95-th percentile51715
Maximum58000
Range56520
Interquartile range (IQR)6637.5

Descriptive statistics

Standard deviation15656.89
Coefficient of variation (CV)1.300985
Kurtosis3.8225914
Mean12034.643
Median Absolute Deviation (MAD)3510
Skewness2.1788595
Sum336970
Variance2.451382 × 108
MonotonicityNot monotonic
2023-12-11T01:49:36.398951image/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 (%)
1480 1
3.6%
1500 1
3.6%
1580 1
3.6%
1650 1
3.6%
1980 1
3.6%
2980 1
3.6%
3850 1
3.6%
4000 2
7.1%
4280 1
3.6%
4550 1
3.6%
ValueCountFrequency (%)
58000 1
3.6%
54900 1
3.6%
45800 1
3.6%
29800 1
3.6%
16000 1
3.6%
14500 1
3.6%
12400 1
3.6%
10000 2
7.1%
9000 2
7.1%
7280 1
3.6%

재송동(한마음시장)
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10308.214
Minimum600
Maximum53000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:49:36.560291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum600
5-th percentile1263
Q12225
median4750
Q310750
95-th percentile43925
Maximum53000
Range52400
Interquartile range (IQR)8525

Descriptive statistics

Standard deviation13656.984
Coefficient of variation (CV)1.3248642
Kurtosis5.5047919
Mean10308.214
Median Absolute Deviation (MAD)3310
Skewness2.394564
Sum288630
Variance1.8651322 × 108
MonotonicityNot monotonic
2023-12-11T01:49:36.740568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 3
 
10.7%
9000 2
 
7.1%
15000 2
 
7.1%
22000 1
 
3.6%
10000 1
 
3.6%
1750 1
 
3.6%
1380 1
 
3.6%
8700 1
 
3.6%
4500 1
 
3.6%
1500 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
600 1
3.6%
1200 1
3.6%
1380 1
3.6%
1500 1
3.6%
1700 1
3.6%
1750 1
3.6%
2000 1
3.6%
2300 1
3.6%
2700 1
3.6%
3800 1
3.6%
ValueCountFrequency (%)
53000 1
3.6%
52500 1
3.6%
28000 1
3.6%
22000 1
3.6%
15000 2
7.1%
13000 1
3.6%
10000 1
3.6%
9000 2
7.1%
8700 1
3.6%
7000 1
3.6%

Interactions

2023-12-11T01:49:31.395182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:27.423616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:28.139643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.239499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.967038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.662543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.522093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:27.510473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:28.267009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.351263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.086118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.777575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.625794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:27.597509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:28.785176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.483977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.200897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.897745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.741362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:27.689017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:28.886529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.631092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.351188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.001737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.875008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:27.874042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:28.987814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.747868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.474867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.140556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:32.020649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:28.016542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.103158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:29.851931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:30.574612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:49:31.264392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:49:36.848744image/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 격1.0001.0000.8250.9120.5170.8870.8850.8690.942
우동(센텀홈플러스)1.0000.8251.0000.9300.9430.9590.9160.8780.916
우동(농산물시장)1.0000.9120.9301.0000.9530.8530.8050.8970.937
중동(이마트 해운대점)1.0000.5170.9430.9531.0000.8750.8790.9170.899
좌동(GS수퍼마켓)1.0000.8870.9590.8530.8751.0000.9420.9170.991
반여2동(골목시장)1.0000.8850.9160.8050.8790.9421.0000.9410.917
반송동(탑마트)1.0000.8690.8780.8970.9170.9170.9411.0000.968
재송동(한마음시장)1.0000.9420.9160.9370.8990.9910.9170.9681.000
2023-12-11T01:49:36.991894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우동(센텀홈플러스)중동(이마트 해운대점)좌동(GS수퍼마켓)반여2동(골목시장)반송동(탑마트)재송동(한마음시장)
우동(센텀홈플러스)1.0000.9210.9360.8910.8980.951
중동(이마트 해운대점)0.9211.0000.9540.8570.9410.925
좌동(GS수퍼마켓)0.9360.9541.0000.9260.9340.936
반여2동(골목시장)0.8910.8570.9261.0000.8950.912
반송동(탑마트)0.8980.9410.9340.8951.0000.935
재송동(한마음시장)0.9510.9250.9360.9120.9351.000

Missing values

2023-12-11T01:49:32.184612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:49:32.398078image/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)3kg23900231901734024500105002980022000
1배신고 6㎏31800474602226039500192005800028000
2배추1㎏3990398016003980500042802000
3무1㎏149015001880198010001480600
4대파1㎏(상품)3290250026903960150019802700
5소고기(국산)등심 상등육 500g57900600005810066340425005490052500
6소고기(수입)등심 상등육 500g42470145002533526340119001240015000
7돼지고기삼겹살 500g14750200001440016800139001450013000
8닭고기육계1㎏8990798070409960670072807000
9달 걀특란 10개3890440039804880520038503800
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㎏4600559052004980440046804500
21식용유백설표옥수수기름1.8ℓ87801090082808860630069808700
22소주(소매점)시원소주 360㎖ 1병1390165012801430145015001380
23맥주(소매점)하이트 500㎖ 1병16101,850,14101830165016501750
24소주(외식)시원소주 360㎖ 1병5000400040004000400040004000
25맥주(외식)하이트 500㎖ 1병5000500040005000400040004000
26돼지갈비(외식)200g 정도1000010000110009500800090009000
27삼겹살(외식)200g 정도100001000016900138009000160009000