Overview

Dataset statistics

Number of variables10
Number of observations28
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory92.7 B

Variable types

Numeric8
Text2

Dataset

Description부산광역시해운대구_물가관리_20210618
Author부산광역시 해운대구
URLhttp://data.busan.go.kr/dataSet/detail.nm?contentId=10&publicdatapk=15063783

Alerts

GS수퍼 is highly overall correlated with 농산물시장 and 5 other fieldsHigh correlation
농산물시장 is highly overall correlated with GS수퍼 and 5 other fieldsHigh correlation
반여2동시장 is highly overall correlated with GS수퍼 and 5 other fieldsHigh correlation
반여3동시장 is highly overall correlated with GS수퍼 and 5 other fieldsHigh correlation
센텀홈플러스 is highly overall correlated with GS수퍼 and 5 other fieldsHigh correlation
중동이마트 is highly overall correlated with GS수퍼 and 5 other fieldsHigh correlation
탑마트반송점 is highly overall correlated with GS수퍼 and 5 other fieldsHigh correlation
연번 has unique valuesUnique
품목 has unique valuesUnique

Reproduction

Analysis started2023-12-10 16:50:31.459347
Analysis finished2023-12-10 16:50:39.219321
Duration7.76 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14.5
Minimum1
Maximum28
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:39.282064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.35
Q17.75
median14.5
Q321.25
95-th percentile26.65
Maximum28
Range27
Interquartile range (IQR)13.5

Descriptive statistics

Standard deviation8.2259751
Coefficient of variation (CV)0.56730863
Kurtosis-1.2
Mean14.5
Median Absolute Deviation (MAD)7
Skewness0
Sum406
Variance67.666667
MonotonicityStrictly increasing
2023-12-11T01:50:39.407460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1 1
 
3.6%
16 1
 
3.6%
28 1
 
3.6%
27 1
 
3.6%
26 1
 
3.6%
25 1
 
3.6%
24 1
 
3.6%
23 1
 
3.6%
22 1
 
3.6%
21 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
1 1
3.6%
2 1
3.6%
3 1
3.6%
4 1
3.6%
5 1
3.6%
6 1
3.6%
7 1
3.6%
8 1
3.6%
9 1
3.6%
10 1
3.6%
ValueCountFrequency (%)
28 1
3.6%
27 1
3.6%
26 1
3.6%
25 1
3.6%
24 1
3.6%
23 1
3.6%
22 1
3.6%
21 1
3.6%
20 1
3.6%
19 1
3.6%

품목
Text

UNIQUE 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T01:50:39.729442image/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%
2023-12-11T01:50:40.099964image/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%

규격
Text

Distinct22
Distinct (%)78.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-11T01:50:40.388802image/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%
2023-12-11T01:50:40.918096image/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%

GS수퍼
Real number (ℝ)

HIGH CORRELATION 

Distinct27
Distinct (%)96.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15045
Minimum1240
Maximum66500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:41.088497image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1240
5-th percentile1567
Q13987.5
median6500
Q313600
95-th percentile61070
Maximum66500
Range65260
Interquartile range (IQR)9612.5

Descriptive statistics

Standard deviation19672.783
Coefficient of variation (CV)1.307596
Kurtosis2.1491049
Mean15045
Median Absolute Deviation (MAD)4015
Skewness1.8518254
Sum421260
Variance3.8701837 × 108
MonotonicityNot monotonic
2023-12-11T01:50:41.228738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
4000 2
 
7.1%
53000 1
 
3.6%
56000 1
 
3.6%
13200 1
 
3.6%
8500 1
 
3.6%
1560 1
 
3.6%
1240 1
 
3.6%
8180 1
 
3.6%
3950 1
 
3.6%
4550 1
 
3.6%
Other values (17) 17
60.7%
ValueCountFrequency (%)
1240 1
3.6%
1560 1
3.6%
1580 1
3.6%
1980 1
3.6%
1990 1
3.6%
2980 1
3.6%
3950 1
3.6%
4000 2
7.1%
4550 1
3.6%
4980 1
3.6%
ValueCountFrequency (%)
66500 1
3.6%
63800 1
3.6%
56000 1
3.6%
53000 1
3.6%
25000 1
3.6%
24800 1
3.6%
14800 1
3.6%
13200 1
3.6%
11800 1
3.6%
9750 1
3.6%

농산물시장
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10877.143
Minimum990
Maximum69800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:41.367064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum990
5-th percentile1000
Q12257.5
median4765
Q312900
95-th percentile43500
Maximum69800
Range68810
Interquartile range (IQR)10642.5

Descriptive statistics

Standard deviation15825.708
Coefficient of variation (CV)1.4549508
Kurtosis8.1699557
Mean10877.143
Median Absolute Deviation (MAD)3225
Skewness2.8112956
Sum304560
Variance2.5045302 × 108
MonotonicityNot monotonic
2023-12-11T01:50:41.497237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1000 2
 
7.1%
4000 2
 
7.1%
12900 2
 
7.1%
22000 1
 
3.6%
69800 1
 
3.6%
15000 1
 
3.6%
10000 1
 
3.6%
1650 1
 
3.6%
1350 1
 
3.6%
7950 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
990 1
3.6%
1000 2
7.1%
1350 1
3.6%
1500 1
3.6%
1650 1
3.6%
1980 1
3.6%
2350 1
3.6%
3000 1
3.6%
3900 1
3.6%
3980 1
3.6%
ValueCountFrequency (%)
69800 1
3.6%
54000 1
3.6%
24000 1
3.6%
22000 1
3.6%
15000 1
3.6%
14900 1
3.6%
12900 2
7.1%
10000 1
3.6%
7950 1
3.6%
7900 1
3.6%

반여2동시장
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10302.179
Minimum1
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:41.614440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1302.5
Q14000
median5490
Q310350
95-th percentile42450
Maximum59800
Range59799
Interquartile range (IQR)6350

Descriptive statistics

Standard deviation14116.669
Coefficient of variation (CV)1.3702605
Kurtosis7.9581673
Mean10302.179
Median Absolute Deviation (MAD)3240
Skewness2.8479767
Sum288461
Variance1.9928034 × 108
MonotonicityNot monotonic
2023-12-11T01:50:41.734886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
4000 3
 
10.7%
5000 3
 
10.7%
9000 1
 
3.6%
59800 1
 
3.6%
8000 1
 
3.6%
7000 1
 
3.6%
1980 1
 
3.6%
1400 1
 
3.6%
4850 1
 
3.6%
4400 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
1 1
 
3.6%
1250 1
 
3.6%
1400 1
 
3.6%
1980 1
 
3.6%
2000 1
 
3.6%
2500 1
 
3.6%
4000 3
10.7%
4400 1
 
3.6%
4850 1
 
3.6%
5000 3
10.7%
ValueCountFrequency (%)
59800 1
3.6%
54000 1
3.6%
21000 1
3.6%
20000 1
3.6%
12400 1
3.6%
12000 1
3.6%
11400 1
3.6%
10000 1
3.6%
9000 1
3.6%
8000 1
3.6%

반여3동시장
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10073.571
Minimum700
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:41.866358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum700
5-th percentile1000
Q11875
median5250
Q311200
95-th percentile44100
Maximum60000
Range59300
Interquartile range (IQR)9325

Descriptive statistics

Standard deviation14394.828
Coefficient of variation (CV)1.4289697
Kurtosis6.2842309
Mean10073.571
Median Absolute Deviation (MAD)3750
Skewness2.5672263
Sum282060
Variance2.0721108 × 108
MonotonicityNot monotonic
2023-12-11T01:50:42.030428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1500 2
 
7.1%
4000 2
 
7.1%
1000 2
 
7.1%
12000 1
 
3.6%
7000 1
 
3.6%
11800 1
 
3.6%
5900 1
 
3.6%
1440 1
 
3.6%
1250 1
 
3.6%
7980 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
700 1
3.6%
1000 2
7.1%
1250 1
3.6%
1440 1
3.6%
1500 2
7.1%
2000 1
3.6%
3290 1
3.6%
3700 1
3.6%
3800 1
3.6%
4000 2
7.1%
ValueCountFrequency (%)
60000 1
3.6%
49000 1
3.6%
35000 1
3.6%
15000 1
3.6%
14000 1
3.6%
12000 1
3.6%
11800 1
3.6%
11000 1
3.6%
8000 1
3.6%
7980 1
3.6%

센텀홈플러스
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12540.357
Minimum1230
Maximum59900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:42.148904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1230
5-th percentile1414
Q13297.5
median4740
Q310737.5
95-th percentile50580
Maximum59900
Range58670
Interquartile range (IQR)7440

Descriptive statistics

Standard deviation17135.193
Coefficient of variation (CV)1.3664039
Kurtosis2.5605959
Mean12540.357
Median Absolute Deviation (MAD)3170
Skewness1.9756419
Sum351130
Variance2.9361483 × 108
MonotonicityNot monotonic
2023-12-11T01:50:42.256213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
4000 2
 
7.1%
3990 2
 
7.1%
51000 1
 
3.6%
59900 1
 
3.6%
15380 1
 
3.6%
10000 1
 
3.6%
1440 1
 
3.6%
1230 1
 
3.6%
7690 1
 
3.6%
3290 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1230 1
3.6%
1400 1
3.6%
1440 1
3.6%
1650 1
3.6%
1790 1
3.6%
2430 1
3.6%
3290 1
3.6%
3300 1
3.6%
3490 1
3.6%
3990 2
7.1%
ValueCountFrequency (%)
59900 1
3.6%
51000 1
3.6%
49800 1
3.6%
48950 1
3.6%
15570 1
3.6%
15380 1
3.6%
12950 1
3.6%
10000 1
3.6%
9980 1
3.6%
8990 1
3.6%

중동이마트
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)89.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11770.714
Minimum750
Maximum58900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:42.368726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum750
5-th percentile1060
Q13916.25
median4980
Q312000
95-th percentile51512.5
Maximum58900
Range58150
Interquartile range (IQR)8083.75

Descriptive statistics

Standard deviation15730.131
Coefficient of variation (CV)1.3363786
Kurtosis4.1432823
Mean11770.714
Median Absolute Deviation (MAD)3332.5
Skewness2.1941905
Sum329580
Variance2.4743702 × 108
MonotonicityNot monotonic
2023-12-11T01:50:42.486557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
12000 2
 
7.1%
4000 2
 
7.1%
4980 2
 
7.1%
21360 1
 
3.6%
58900 1
 
3.6%
1410 1
 
3.6%
1190 1
 
3.6%
6480 1
 
3.6%
4190 1
 
3.6%
4700 1
 
3.6%
Other values (15) 15
53.6%
ValueCountFrequency (%)
750 1
3.6%
990 1
3.6%
1190 1
3.6%
1410 1
3.6%
1640 1
3.6%
1655 1
3.6%
3725 1
3.6%
3980 1
3.6%
4000 2
7.1%
4190 1
3.6%
ValueCountFrequency (%)
58900 1
3.6%
56500 1
3.6%
42250 1
3.6%
24000 1
3.6%
21360 1
3.6%
16900 1
3.6%
12000 2
7.1%
11880 1
3.6%
7500 1
3.6%
7160 1
3.6%

탑마트반송점
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)92.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9634.7857
Minimum1
Maximum49800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-11T01:50:42.597906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile377.95
Q12980
median4110
Q311825
95-th percentile42200
Maximum49800
Range49799
Interquartile range (IQR)8845

Descriptive statistics

Standard deviation12884.25
Coefficient of variation (CV)1.3372638
Kurtosis4.655107
Mean9634.7857
Median Absolute Deviation (MAD)3450
Skewness2.3033679
Sum269774
Variance1.660039 × 108
MonotonicityNot monotonic
2023-12-11T01:50:42.722440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
2980 2
 
7.1%
4000 2
 
7.1%
37000 1
 
3.6%
49800 1
 
3.6%
13000 1
 
3.6%
8000 1
 
3.6%
3500 1
 
3.6%
1600 1
 
3.6%
1300 1
 
3.6%
7980 1
 
3.6%
Other values (16) 16
57.1%
ValueCountFrequency (%)
1 1
3.6%
43 1
3.6%
1000 1
3.6%
1300 1
3.6%
1600 1
3.6%
2100 1
3.6%
2980 2
7.1%
3000 1
3.6%
3200 1
3.6%
3500 1
3.6%
ValueCountFrequency (%)
49800 1
3.6%
45000 1
3.6%
37000 1
3.6%
13900 1
3.6%
13000 1
3.6%
12500 1
3.6%
11900 1
3.6%
11800 1
3.6%
8500 1
3.6%
8000 1
3.6%

Interactions

2023-12-11T01:50:38.290294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:31.802671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.585099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.270542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.933009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.875814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.170583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:37.459710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.382549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:31.882058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.682264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.347063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.032294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:35.065209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.288186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:37.563659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.480599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:31.975425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.791934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.432267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.120997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:35.272358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.416513image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:37.646523image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.587019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.085812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.875550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.516405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.229339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:35.428654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.533809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:37.754031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.678745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.188475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.958003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.602425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.346746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:35.584515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.674678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:37.867517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.753808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.296806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.037705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.682639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.465297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:35.733599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.795158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.001185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.838144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.399693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.116969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.757963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.600450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:35.881339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.909480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.098574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.926820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:32.502475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.199153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:33.851727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:34.711557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:36.026938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:37.360262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T01:50:38.204178image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T01:50:42.822078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번품목규격GS수퍼농산물시장반여2동시장반여3동시장센텀홈플러스중동이마트탑마트반송점
연번1.0001.0000.9450.0000.4520.3320.0000.1310.0000.429
품목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규격0.9451.0001.0000.9490.8000.8500.7560.4180.8820.937
GS수퍼0.0001.0000.9491.0000.8360.8250.8860.7220.9200.823
농산물시장0.4521.0000.8000.8361.0000.7450.9760.8680.9450.781
반여2동시장0.3321.0000.8500.8250.7451.0000.8040.8790.8080.771
반여3동시장0.0001.0000.7560.8860.9760.8041.0000.7790.9390.744
센텀홈플러스0.1311.0000.4180.7220.8680.8790.7791.0000.7490.889
중동이마트0.0001.0000.8820.9200.9450.8080.9390.7491.0000.689
탑마트반송점0.4291.0000.9370.8230.7810.7710.7440.8890.6891.000
2023-12-11T01:50:42.964137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번GS수퍼농산물시장반여2동시장반여3동시장센텀홈플러스중동이마트탑마트반송점
연번1.000-0.396-0.243-0.305-0.258-0.281-0.173-0.122
GS수퍼-0.3961.0000.8540.8750.9300.9040.9190.701
농산물시장-0.2430.8541.0000.8170.8960.8340.8410.769
반여2동시장-0.3050.8750.8171.0000.8860.8300.8850.692
반여3동시장-0.2580.9300.8960.8861.0000.9220.8810.765
센텀홈플러스-0.2810.9040.8340.8300.9221.0000.8990.712
중동이마트-0.1730.9190.8410.8850.8810.8991.0000.710
탑마트반송점-0.1220.7010.7690.6920.7650.7120.7101.000

Missing values

2023-12-11T01:50:39.031677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T01:50:39.166898image/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

연번품목규격GS수퍼농산물시장반여2동시장반여3동시장센텀홈플러스중동이마트탑마트반송점
01사과부사(1개 300g이상)3kg5300022000900012000510002136037000
12배신고 6㎏56000240002100035000498004225045000
23배추1㎏298049804000150033009903200
34무1㎏15801000170016507501
45대파1㎏(상품)8840198025005000449037252980
56소고기(국산)등심 상등육 500g66500540005400049000489505650043
67소고기(수입)등심 상등육 500g25000129001240014000155702400013900
78돼지고기삼겹살 500g14800149001140011000129501690011900
89닭고기육계1㎏9750698060005700799049804590
910달 걀특란 10개5280398059803700399044803980
연번품목규격GS수퍼농산물시장반여2동시장반여3동시장센텀홈플러스중동이마트탑마트반송점
1819고춧가루0.1㎏6400100065003800899075002100
1920두부500g 판두부(포장두부 420g) 1모4550235012501000179047003000
2021밀가루백설표 중력분1등2.5㎏3950455044003290329041904220
2122식용유백설표옥수수기름1.8ℓ8180795048507980769064807980
2223소주(소매점)시원소주 360㎖ 1병1240135014001250123011901300
2324맥주(소매점)하이트 500㎖ 1병1560165019801440144014101600
2425소주(외식)시원소주 360㎖ 1병4000400040004000400040003500
2526맥주(외식)하이트 500㎖ 1병4000400040004000400040004000
2627돼지갈비(외식)200g 정도8500100007000590010000120008000
2728삼겹살(외식)200g 정도1320015000800011800153801200013000