Overview

Dataset statistics

Number of variables16
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory138.4 B

Variable types

Numeric6
Text10

Dataset

Description순천시 소재 마트, 시장의 판매품목별 가격 정보
Author전라남도 순천시
URLhttps://www.data.go.kr/data/15054163/fileData.do

Alerts

마트평균 is highly overall correlated with 하나로마트(연향동)_가격동향(원) and 3 other fieldsHigh correlation
하나로마트(연향동)_가격동향(원) is highly overall correlated with 마트평균 and 3 other fieldsHigh correlation
이마트순천점_가격동향(원) is highly overall correlated with 마트평균 and 3 other fieldsHigh correlation
역전시장_가격동향(원) is highly overall correlated with 마트평균 and 3 other fieldsHigh correlation
시장평균_가격동향(원) is highly overall correlated with 마트평균 and 3 other fieldsHigh correlation
연번 has unique valuesUnique
품목 has unique valuesUnique
마트평균 has unique valuesUnique
하나로마트(연향동)_가격동향(원) has 1 (3.3%) zerosZeros

Reproduction

Analysis started2023-12-12 15:46:52.954011
Analysis finished2023-12-12 15:46:58.342281
Duration5.39 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15.5
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:46:58.433214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.45
Q18.25
median15.5
Q322.75
95-th percentile28.55
Maximum30
Range29
Interquartile range (IQR)14.5

Descriptive statistics

Standard deviation8.8034084
Coefficient of variation (CV)0.56796183
Kurtosis-1.2
Mean15.5
Median Absolute Deviation (MAD)7.5
Skewness0
Sum465
Variance77.5
MonotonicityStrictly increasing
2023-12-13T00:46:58.608195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
1 1
 
3.3%
17 1
 
3.3%
30 1
 
3.3%
29 1
 
3.3%
28 1
 
3.3%
27 1
 
3.3%
26 1
 
3.3%
25 1
 
3.3%
24 1
 
3.3%
23 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1 1
3.3%
2 1
3.3%
3 1
3.3%
4 1
3.3%
5 1
3.3%
6 1
3.3%
7 1
3.3%
8 1
3.3%
9 1
3.3%
10 1
3.3%
ValueCountFrequency (%)
30 1
3.3%
29 1
3.3%
28 1
3.3%
27 1
3.3%
26 1
3.3%
25 1
3.3%
24 1
3.3%
23 1
3.3%
22 1
3.3%
21 1
3.3%

품목
Text

UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:46:58.889288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length11.033333
Min length7

Characters and Unicode

Total characters331
Distinct characters111
Distinct categories8 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique30 ?
Unique (%)100.0%

Sample

1st row쌀 일반미중품(20kg)국내산
2nd row무 상품1개(1kg)
3rd row상추 잎상추500g
4th row열무1단(4kg)
5th row양파1망(1kg)
ValueCountFrequency (%)
1마리 6
 
9.1%
1kg 5
 
7.6%
600g 3
 
4.5%
1
 
1.5%
냉동오징어30cm 1
 
1.5%
30개 1
 
1.5%
한판 1
 
1.5%
조기 1
 
1.5%
15cm 1
 
1.5%
갈치 1
 
1.5%
Other values (45) 45
68.2%
2023-12-13T00:46:59.379690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
36
 
10.9%
1 25
 
7.6%
0 22
 
6.6%
g 19
 
5.7%
k 12
 
3.6%
9
 
2.7%
( 9
 
2.7%
) 9
 
2.7%
8
 
2.4%
5 7
 
2.1%
Other values (101) 175
52.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 157
47.4%
Decimal Number 75
22.7%
Lowercase Letter 41
 
12.4%
Space Separator 36
 
10.9%
Open Punctuation 9
 
2.7%
Close Punctuation 9
 
2.7%
Math Symbol 2
 
0.6%
Other Punctuation 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
9
 
5.7%
8
 
5.1%
7
 
4.5%
7
 
4.5%
7
 
4.5%
6
 
3.8%
6
 
3.8%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (83) 97
61.8%
Decimal Number
ValueCountFrequency (%)
1 25
33.3%
0 22
29.3%
5 7
 
9.3%
3 7
 
9.3%
6 5
 
6.7%
4 4
 
5.3%
2 3
 
4.0%
8 1
 
1.3%
7 1
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
g 19
46.3%
k 12
29.3%
m 5
 
12.2%
c 5
 
12.2%
Space Separator
ValueCountFrequency (%)
36
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 157
47.4%
Common 133
40.2%
Latin 41
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
9
 
5.7%
8
 
5.1%
7
 
4.5%
7
 
4.5%
7
 
4.5%
6
 
3.8%
6
 
3.8%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (83) 97
61.8%
Common
ValueCountFrequency (%)
36
27.1%
1 25
18.8%
0 22
16.5%
( 9
 
6.8%
) 9
 
6.8%
5 7
 
5.3%
3 7
 
5.3%
6 5
 
3.8%
4 4
 
3.0%
2 3
 
2.3%
Other values (4) 6
 
4.5%
Latin
ValueCountFrequency (%)
g 19
46.3%
k 12
29.3%
m 5
 
12.2%
c 5
 
12.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 174
52.6%
Hangul 157
47.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
36
20.7%
1 25
14.4%
0 22
12.6%
g 19
10.9%
k 12
 
6.9%
( 9
 
5.2%
) 9
 
5.2%
5 7
 
4.0%
3 7
 
4.0%
m 5
 
2.9%
Other values (8) 23
13.2%
Hangul
ValueCountFrequency (%)
9
 
5.7%
8
 
5.1%
7
 
4.5%
7
 
4.5%
7
 
4.5%
6
 
3.8%
6
 
3.8%
4
 
2.5%
3
 
1.9%
3
 
1.9%
Other values (83) 97
61.8%

마트평균
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8211.1
Minimum1604
Maximum51093
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:46:59.612309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1604
5-th percentile1724.95
Q12429.5
median3700
Q37216.75
95-th percentile35682.9
Maximum51093
Range49489
Interquartile range (IQR)4787.25

Descriptive statistics

Standard deviation11878.793
Coefficient of variation (CV)1.446675
Kurtosis7.629871
Mean8211.1
Median Absolute Deviation (MAD)1778
Skewness2.8076372
Sum246333
Variance1.4110572 × 108
MonotonicityNot monotonic
2023-12-13T00:46:59.797508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
44088 1
 
3.3%
11780 1
 
3.3%
4204 1
 
3.3%
15700 1
 
3.3%
5314 1
 
3.3%
3534 1
 
3.3%
3151 1
 
3.3%
1666 1
 
3.3%
2380 1
 
3.3%
2578 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
1604 1
3.3%
1666 1
3.3%
1797 1
3.3%
1863 1
3.3%
1878 1
3.3%
1914 1
3.3%
1930 1
3.3%
2380 1
3.3%
2578 1
3.3%
3151 1
3.3%
ValueCountFrequency (%)
51093 1
3.3%
44088 1
3.3%
25410 1
3.3%
15700 1
3.3%
11864 1
3.3%
11780 1
3.3%
8664 1
3.3%
7704 1
3.3%
5755 1
3.3%
5314 1
3.3%
Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:00.037292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1666667
Min length4

Characters and Unicode

Total characters125
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)70.0%

Sample

1st row39900
2nd row1990
3rd row1980
4th row판매안함
5th row3490
ValueCountFrequency (%)
판매안함 3
 
10.0%
1990 2
 
6.7%
2990 2
 
6.7%
1590 2
 
6.7%
4990 1
 
3.3%
39900 1
 
3.3%
6360 1
 
3.3%
18900 1
 
3.3%
6950 1
 
3.3%
3060 1
 
3.3%
Other values (15) 15
50.0%
2023-12-13T00:47:00.434620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 33
26.4%
9 22
17.6%
1 11
 
8.8%
3 10
 
8.0%
5 9
 
7.2%
4 7
 
5.6%
2 6
 
4.8%
8 6
 
4.8%
6 5
 
4.0%
7 4
 
3.2%
Other values (4) 12
 
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 113
90.4%
Other Letter 12
 
9.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 33
29.2%
9 22
19.5%
1 11
 
9.7%
3 10
 
8.8%
5 9
 
8.0%
4 7
 
6.2%
2 6
 
5.3%
8 6
 
5.3%
6 5
 
4.4%
7 4
 
3.5%
Other Letter
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 113
90.4%
Hangul 12
 
9.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 33
29.2%
9 22
19.5%
1 11
 
9.7%
3 10
 
8.8%
5 9
 
8.0%
4 7
 
6.2%
2 6
 
5.3%
8 6
 
5.3%
6 5
 
4.4%
7 4
 
3.5%
Hangul
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 113
90.4%
Hangul 12
 
9.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 33
29.2%
9 22
19.5%
1 11
 
9.7%
3 10
 
8.8%
5 9
 
8.0%
4 7
 
6.2%
2 6
 
5.3%
8 6
 
5.3%
6 5
 
4.4%
7 4
 
3.5%
Hangul
ValueCountFrequency (%)
3
25.0%
3
25.0%
3
25.0%
3
25.0%

하나로마트(연향동)_가격동향(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7339.6667
Minimum0
Maximum59400
Zeros1
Zeros (%)3.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:47:00.608293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile1167.5
Q11962.5
median3500
Q35000
95-th percentile30425
Maximum59400
Range59400
Interquartile range (IQR)3037.5

Descriptive statistics

Standard deviation12428.285
Coefficient of variation (CV)1.6933037
Kurtosis12.050287
Mean7339.6667
Median Absolute Deviation (MAD)1700
Skewness3.4217003
Sum220190
Variance1.5446227 × 108
MonotonicityNot monotonic
2023-12-13T00:47:00.796371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2900 3
 
10.0%
3500 2
 
6.7%
1800 2
 
6.7%
3900 2
 
6.7%
4400 2
 
6.7%
41000 1
 
3.3%
5200 1
 
3.3%
17500 1
 
3.3%
3450 1
 
3.3%
3170 1
 
3.3%
Other values (14) 14
46.7%
ValueCountFrequency (%)
0 1
 
3.3%
1100 1
 
3.3%
1250 1
 
3.3%
1550 1
 
3.3%
1680 1
 
3.3%
1700 1
 
3.3%
1800 2
6.7%
2450 1
 
3.3%
2900 3
10.0%
3170 1
 
3.3%
ValueCountFrequency (%)
59400 1
3.3%
41000 1
3.3%
17500 1
3.3%
11340 1
3.3%
11000 1
3.3%
7900 1
3.3%
6800 1
3.3%
5200 1
3.3%
4400 2
6.7%
4000 1
3.3%
Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:01.056254image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2333333
Min length4

Characters and Unicode

Total characters127
Distinct characters14
Distinct categories2 ?
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 (%)93.3%

Sample

1st row59800
2nd row판매안함
3rd row7700
4th row2250
5th row3480
ValueCountFrequency (%)
1590 2
 
6.7%
59800 1
 
3.3%
17940 1
 
3.3%
12800 1
 
3.3%
5950 1
 
3.3%
3430 1
 
3.3%
3150 1
 
3.3%
2290 1
 
3.3%
4000 1
 
3.3%
1690 1
 
3.3%
Other values (19) 19
63.3%
2023-12-13T00:47:01.453930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
30.7%
9 16
12.6%
1 14
 
11.0%
4 11
 
8.7%
5 10
 
7.9%
2 8
 
6.3%
3 8
 
6.3%
7 7
 
5.5%
8 7
 
5.5%
6 3
 
2.4%
Other values (4) 4
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123
96.9%
Other Letter 4
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 39
31.7%
9 16
13.0%
1 14
 
11.4%
4 11
 
8.9%
5 10
 
8.1%
2 8
 
6.5%
3 8
 
6.5%
7 7
 
5.7%
8 7
 
5.7%
6 3
 
2.4%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123
96.9%
Hangul 4
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 39
31.7%
9 16
13.0%
1 14
 
11.4%
4 11
 
8.9%
5 10
 
8.1%
2 8
 
6.5%
3 8
 
6.5%
7 7
 
5.7%
8 7
 
5.7%
6 3
 
2.4%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123
96.9%
Hangul 4
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39
31.7%
9 16
13.0%
1 14
 
11.4%
4 11
 
8.9%
5 10
 
8.1%
2 8
 
6.5%
3 8
 
6.5%
7 7
 
5.7%
8 7
 
5.7%
6 3
 
2.4%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

이마트순천점_가격동향(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8628
Minimum1050
Maximum52200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:47:01.619961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1050
5-th percentile1303
Q12247.5
median4095
Q37640
95-th percentile37621
Maximum52200
Range51150
Interquartile range (IQR)5392.5

Descriptive statistics

Standard deviation12224.402
Coefficient of variation (CV)1.4168292
Kurtosis6.4410109
Mean8628
Median Absolute Deviation (MAD)2145
Skewness2.6019513
Sum258840
Variance1.4943601 × 108
MonotonicityNot monotonic
2023-12-13T00:47:01.794915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2980 2
 
6.7%
5980 2
 
6.7%
41500 1
 
3.3%
12420 1
 
3.3%
4290 1
 
3.3%
13800 1
 
3.3%
4860 1
 
3.3%
3060 1
 
3.3%
3150 1
 
3.3%
1630 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
1050 1
3.3%
1150 1
3.3%
1490 1
3.3%
1630 1
3.3%
1900 1
3.3%
1920 1
3.3%
1980 1
3.3%
2230 1
3.3%
2300 1
3.3%
2380 1
3.3%
ValueCountFrequency (%)
52200 1
3.3%
41500 1
3.3%
32880 1
3.3%
14600 1
3.3%
13800 1
3.3%
12420 1
3.3%
10530 1
3.3%
7920 1
3.3%
6800 1
3.3%
5990 1
3.3%
Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:02.034328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length4
Mean length4.3333333
Min length4

Characters and Unicode

Total characters130
Distinct characters17
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)73.3%

Sample

1st row41000
2nd row2050
3rd row1780
4th row4400
5th row1450
ValueCountFrequency (%)
2800 2
 
6.7%
판매안함 2
 
6.7%
4400 2
 
6.7%
1450 2
 
6.7%
41000 1
 
3.3%
47000 1
 
3.3%
14500 1
 
3.3%
4800 1
 
3.3%
4100 1
 
3.3%
3170 1
 
3.3%
Other values (16) 16
53.3%
2023-12-13T00:47:02.389013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 49
37.7%
5 14
 
10.8%
4 13
 
10.0%
1 12
 
9.2%
8 9
 
6.9%
3 7
 
5.4%
2 6
 
4.6%
7 4
 
3.1%
9 3
 
2.3%
6 2
 
1.5%
Other values (7) 11
 
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 119
91.5%
Other Letter 8
 
6.2%
Open Punctuation 1
 
0.8%
Lowercase Letter 1
 
0.8%
Close Punctuation 1
 
0.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 49
41.2%
5 14
 
11.8%
4 13
 
10.9%
1 12
 
10.1%
8 9
 
7.6%
3 7
 
5.9%
2 6
 
5.0%
7 4
 
3.4%
9 3
 
2.5%
6 2
 
1.7%
Other Letter
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
2
25.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
g 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
93.1%
Hangul 8
 
6.2%
Latin 1
 
0.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 49
40.5%
5 14
 
11.6%
4 13
 
10.7%
1 12
 
9.9%
8 9
 
7.4%
3 7
 
5.8%
2 6
 
5.0%
7 4
 
3.3%
9 3
 
2.5%
6 2
 
1.7%
Other values (2) 2
 
1.7%
Hangul
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
2
25.0%
Latin
ValueCountFrequency (%)
g 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 122
93.8%
Hangul 8
 
6.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 49
40.2%
5 14
 
11.5%
4 13
 
10.7%
1 12
 
9.8%
8 9
 
7.4%
3 7
 
5.7%
2 6
 
4.9%
7 4
 
3.3%
9 3
 
2.5%
6 2
 
1.6%
Other values (3) 3
 
2.5%
Hangul
ValueCountFrequency (%)
2
25.0%
2
25.0%
2
25.0%
2
25.0%
Distinct24
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:02.616840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1666667
Min length4

Characters and Unicode

Total characters125
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)70.0%

Sample

1st row44500
2nd row2300
3rd row3100
4th row판매안함
5th row1500
ValueCountFrequency (%)
판매안함 4
 
13.3%
1500 3
 
10.0%
6000 2
 
6.7%
9000 1
 
3.3%
44500 1
 
3.3%
4900 1
 
3.3%
15500 1
 
3.3%
4500 1
 
3.3%
4220 1
 
3.3%
3300 1
 
3.3%
Other values (14) 14
46.7%
2023-12-13T00:47:02.981787image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 57
45.6%
1 11
 
8.8%
5 11
 
8.8%
4 8
 
6.4%
3 6
 
4.8%
2 6
 
4.8%
8 5
 
4.0%
4
 
3.2%
4
 
3.2%
4
 
3.2%
Other values (4) 9
 
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 109
87.2%
Other Letter 16
 
12.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 57
52.3%
1 11
 
10.1%
5 11
 
10.1%
4 8
 
7.3%
3 6
 
5.5%
2 6
 
5.5%
8 5
 
4.6%
6 2
 
1.8%
9 2
 
1.8%
7 1
 
0.9%
Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 109
87.2%
Hangul 16
 
12.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 57
52.3%
1 11
 
10.1%
5 11
 
10.1%
4 8
 
7.3%
3 6
 
5.5%
2 6
 
5.5%
8 5
 
4.6%
6 2
 
1.8%
9 2
 
1.8%
7 1
 
0.9%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 109
87.2%
Hangul 16
 
12.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 57
52.3%
1 11
 
10.1%
5 11
 
10.1%
4 8
 
7.3%
3 6
 
5.5%
2 6
 
5.5%
8 5
 
4.6%
6 2
 
1.8%
9 2
 
1.8%
7 1
 
0.9%
Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
4
25.0%
Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:03.185315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0333333
Min length1

Characters and Unicode

Total characters121
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)76.7%

Sample

1st row44000
2nd row1600
3rd row3900
4th row4000
5th row1300
ValueCountFrequency (%)
3500 3
 
10.0%
6500 2
 
6.7%
1600 2
 
6.7%
1000 1
 
3.3%
44000 1
 
3.3%
4200 1
 
3.3%
17800 1
 
3.3%
4350 1
 
3.3%
3550 1
 
3.3%
3350 1
 
3.3%
Other values (16) 16
53.3%
2023-12-13T00:47:03.553555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58
47.9%
5 14
 
11.6%
3 11
 
9.1%
1 9
 
7.4%
4 6
 
5.0%
6 5
 
4.1%
2 5
 
4.1%
9 4
 
3.3%
8 4
 
3.3%
1
 
0.8%
Other values (4) 4
 
3.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117
96.7%
Other Letter 4
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58
49.6%
5 14
 
12.0%
3 11
 
9.4%
1 9
 
7.7%
4 6
 
5.1%
6 5
 
4.3%
2 5
 
4.3%
9 4
 
3.4%
8 4
 
3.4%
7 1
 
0.9%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117
96.7%
Hangul 4
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58
49.6%
5 14
 
12.0%
3 11
 
9.4%
1 9
 
7.7%
4 6
 
5.1%
6 5
 
4.3%
2 5
 
4.3%
9 4
 
3.4%
8 4
 
3.4%
7 1
 
0.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117
96.7%
Hangul 4
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58
49.6%
5 14
 
12.0%
3 11
 
9.4%
1 9
 
7.7%
4 6
 
5.1%
6 5
 
4.3%
2 5
 
4.3%
9 4
 
3.4%
8 4
 
3.4%
7 1
 
0.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:03.790851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.2333333
Min length4

Characters and Unicode

Total characters127
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)86.7%

Sample

1st row41000
2nd row1780
3rd row3400
4th row2980
5th row1480
ValueCountFrequency (%)
3400 2
 
6.7%
10800 2
 
6.7%
41000 1
 
3.3%
판매안함 1
 
3.3%
14800 1
 
3.3%
6700 1
 
3.3%
3170 1
 
3.3%
1650 1
 
3.3%
2700 1
 
3.3%
2000 1
 
3.3%
Other values (18) 18
60.0%
2023-12-13T00:47:04.570158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 56
44.1%
1 13
 
10.2%
2 12
 
9.4%
4 11
 
8.7%
8 10
 
7.9%
3 5
 
3.9%
7 5
 
3.9%
5 5
 
3.9%
6 5
 
3.9%
9 1
 
0.8%
Other values (4) 4
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 123
96.9%
Other Letter 4
 
3.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 56
45.5%
1 13
 
10.6%
2 12
 
9.8%
4 11
 
8.9%
8 10
 
8.1%
3 5
 
4.1%
7 5
 
4.1%
5 5
 
4.1%
6 5
 
4.1%
9 1
 
0.8%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 123
96.9%
Hangul 4
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 56
45.5%
1 13
 
10.6%
2 12
 
9.8%
4 11
 
8.9%
8 10
 
8.1%
3 5
 
4.1%
7 5
 
4.1%
5 5
 
4.1%
6 5
 
4.1%
9 1
 
0.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 123
96.9%
Hangul 4
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 56
45.5%
1 13
 
10.6%
2 12
 
9.8%
4 11
 
8.9%
8 10
 
8.1%
3 5
 
4.1%
7 5
 
4.1%
5 5
 
4.1%
6 5
 
4.1%
9 1
 
0.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:04.794499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1333333
Min length4

Characters and Unicode

Total characters124
Distinct characters13
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row41000
2nd row2000
3rd row2000
4th row3000
5th row1000
ValueCountFrequency (%)
3000 5
16.7%
2000 5
16.7%
1000 4
13.3%
4500 2
 
6.7%
4000 2
 
6.7%
42000 1
 
3.3%
41000 1
 
3.3%
5000 1
 
3.3%
5500 1
 
3.3%
7000 1
 
3.3%
Other values (7) 7
23.3%
2023-12-13T00:47:05.193248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 82
66.1%
1 8
 
6.5%
5 7
 
5.6%
3 6
 
4.8%
2 6
 
4.8%
4 6
 
4.8%
7 2
 
1.6%
9 2
 
1.6%
6 1
 
0.8%
1
 
0.8%
Other values (3) 3
 
2.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 120
96.8%
Other Letter 4
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 82
68.3%
1 8
 
6.7%
5 7
 
5.8%
3 6
 
5.0%
2 6
 
5.0%
4 6
 
5.0%
7 2
 
1.7%
9 2
 
1.7%
6 1
 
0.8%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 120
96.8%
Hangul 4
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 82
68.3%
1 8
 
6.7%
5 7
 
5.8%
3 6
 
5.0%
2 6
 
5.0%
4 6
 
5.0%
7 2
 
1.7%
9 2
 
1.7%
6 1
 
0.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 120
96.8%
Hangul 4
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 82
68.3%
1 8
 
6.7%
5 7
 
5.8%
3 6
 
5.0%
2 6
 
5.0%
4 6
 
5.0%
7 2
 
1.7%
9 2
 
1.7%
6 1
 
0.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct16
Distinct (%)53.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:05.417310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.0333333
Min length1

Characters and Unicode

Total characters121
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique10 ?
Unique (%)33.3%

Sample

1st row42000
2nd row1800
3rd row2000
4th row5000
5th row1000
ValueCountFrequency (%)
2000 4
13.3%
2500 4
13.3%
5000 3
10.0%
1000 3
10.0%
3000 3
10.0%
4000 3
10.0%
42000 1
 
3.3%
1800 1
 
3.3%
1500 1
 
3.3%
10000 1
 
3.3%
Other values (6) 6
20.0%
2023-12-13T00:47:05.808684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 77
63.6%
2 11
 
9.1%
5 10
 
8.3%
1 8
 
6.6%
4 7
 
5.8%
3 3
 
2.5%
8 1
 
0.8%
1
 
0.8%
1
 
0.8%
1
 
0.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 117
96.7%
Other Letter 4
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 77
65.8%
2 11
 
9.4%
5 10
 
8.5%
1 8
 
6.8%
4 7
 
6.0%
3 3
 
2.6%
8 1
 
0.9%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 117
96.7%
Hangul 4
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 77
65.8%
2 11
 
9.4%
5 10
 
8.5%
1 8
 
6.8%
4 7
 
6.0%
3 3
 
2.6%
8 1
 
0.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 117
96.7%
Hangul 4
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 77
65.8%
2 11
 
9.4%
5 10
 
8.5%
1 8
 
6.8%
4 7
 
6.0%
3 3
 
2.6%
8 1
 
0.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Distinct17
Distinct (%)56.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-13T00:47:06.013068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length4.1666667
Min length4

Characters and Unicode

Total characters125
Distinct characters14
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique12 ?
Unique (%)40.0%

Sample

1st row46000
2nd row2000
3rd row2000
4th row5000
5th row2000
ValueCountFrequency (%)
2000 7
23.3%
5000 4
13.3%
3000 3
10.0%
2500 2
 
6.7%
1500 2
 
6.7%
판매안함 1
 
3.3%
46000 1
 
3.3%
13500 1
 
3.3%
4800 1
 
3.3%
3300 1
 
3.3%
Other values (7) 7
23.3%
2023-12-13T00:47:06.336996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 78
62.4%
5 11
 
8.8%
2 10
 
8.0%
3 8
 
6.4%
1 6
 
4.8%
4 4
 
3.2%
7 1
 
0.8%
9 1
 
0.8%
1
 
0.8%
1
 
0.8%
Other values (4) 4
 
3.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 121
96.8%
Other Letter 4
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 78
64.5%
5 11
 
9.1%
2 10
 
8.3%
3 8
 
6.6%
1 6
 
5.0%
4 4
 
3.3%
7 1
 
0.8%
9 1
 
0.8%
8 1
 
0.8%
6 1
 
0.8%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
96.8%
Hangul 4
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 78
64.5%
5 11
 
9.1%
2 10
 
8.3%
3 8
 
6.6%
1 6
 
5.0%
4 4
 
3.3%
7 1
 
0.8%
9 1
 
0.8%
8 1
 
0.8%
6 1
 
0.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 121
96.8%
Hangul 4
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 78
64.5%
5 11
 
9.1%
2 10
 
8.3%
3 8
 
6.6%
1 6
 
5.0%
4 4
 
3.3%
7 1
 
0.8%
9 1
 
0.8%
8 1
 
0.8%
6 1
 
0.8%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

역전시장_가격동향(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct20
Distinct (%)66.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7492.6667
Minimum1000
Maximum52000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:47:06.508601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1500
Q12500
median3950
Q35500
95-th percentile33675
Maximum52000
Range51000
Interquartile range (IQR)3000

Descriptive statistics

Standard deviation12298.577
Coefficient of variation (CV)1.6414152
Kurtosis10.606803
Mean7492.6667
Median Absolute Deviation (MAD)1550
Skewness3.3423695
Sum224780
Variance1.5125499 × 108
MonotonicityNot monotonic
2023-12-13T00:47:06.689628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=20)
ValueCountFrequency (%)
2500 4
 
13.3%
1500 3
 
10.0%
5000 3
 
10.0%
5500 2
 
6.7%
3500 2
 
6.7%
2000 2
 
6.7%
52000 1
 
3.3%
4500 1
 
3.3%
4400 1
 
3.3%
12500 1
 
3.3%
Other values (10) 10
33.3%
ValueCountFrequency (%)
1000 1
 
3.3%
1500 3
10.0%
1800 1
 
3.3%
2000 2
6.7%
2500 4
13.3%
2800 1
 
3.3%
3000 1
 
3.3%
3500 2
6.7%
4400 1
 
3.3%
4500 1
 
3.3%
ValueCountFrequency (%)
52000 1
 
3.3%
51000 1
 
3.3%
12500 1
 
3.3%
10080 1
 
3.3%
9900 1
 
3.3%
9500 1
 
3.3%
6000 1
 
3.3%
5500 2
6.7%
5000 3
10.0%
4800 1
 
3.3%

시장평균_가격동향(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6965.1333
Minimum1125
Maximum45250
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-13T00:47:06.845763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1125
5-th percentile1306.25
Q12075
median3600
Q35056.25
95-th percentile31020.65
Maximum45250
Range44125
Interquartile range (IQR)2981.25

Descriptive statistics

Standard deviation10772.479
Coefficient of variation (CV)1.5466292
Kurtosis9.6138455
Mean6965.1333
Median Absolute Deviation (MAD)1525
Skewness3.1627177
Sum208954
Variance1.160463 × 108
MonotonicityNot monotonic
2023-12-13T00:47:06.979418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1875 2
 
6.7%
2500 2
 
6.7%
2000 2
 
6.7%
2075 2
 
6.7%
45250 1
 
3.3%
11445 1
 
3.3%
4225 1
 
3.3%
14750 1
 
3.3%
5075 1
 
3.3%
4113 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
1125 1
3.3%
1250 1
3.3%
1375 1
3.3%
1875 2
6.7%
2000 2
6.7%
2075 2
6.7%
2375 1
3.3%
2500 2
6.7%
2950 1
3.3%
3125 1
3.3%
ValueCountFrequency (%)
45250 1
3.3%
44333 1
3.3%
14750 1
3.3%
11445 1
3.3%
9900 1
3.3%
9750 1
3.3%
5500 1
3.3%
5075 1
3.3%
5000 1
3.3%
4750 1
3.3%

Interactions

2023-12-13T00:46:57.263230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:53.734616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:54.772598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.425974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.068277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.643127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.360412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:53.880872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:54.912341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.538300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.151011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.734427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.476299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:53.998621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.023804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.659595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.236250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.835638image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.566715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:54.426217image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.137434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.766376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.342364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.945749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.651966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:54.547163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.237433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.878154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.442255image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.054860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.752369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:54.658715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.328157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:55.982138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:56.541851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:46:57.158812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:47:07.103510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번품목마트평균홈플러스(풍덕점)_가격동향(원)하나로마트(연향동)_가격동향(원)홈플러스(순천점)_가격동향(원)이마트순천점_가격동향(원)순 천원예농협_가격동향(원)빅스토아(용당점)_가격동향(원)㈜참마트_가격동향(원)순천농협파머스마켓_가격동향(원)웃장_가격동향(원)아랫장_가격동향(원)중앙시장_가격동향(원)역전시장_가격동향(원)시장평균_가격동향(원)
연번1.0001.0000.0000.6230.4670.9270.0000.6930.7520.6930.8490.6520.6410.5740.4230.000
품목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
마트평균0.0001.0001.0000.8770.9131.0000.9940.8720.8230.9581.0001.0000.9630.9640.9140.810
홈플러스(풍덕점)_가격동향(원)0.6231.0000.8771.0001.0001.0000.6600.9170.9560.8510.8720.9240.8810.9490.9600.922
하나로마트(연향동)_가격동향(원)0.4671.0000.9131.0001.0001.0000.8611.0001.0000.9731.0001.0000.9670.9710.8620.960
홈플러스(순천점)_가격동향(원)0.9271.0001.0001.0001.0001.0001.0000.9610.9480.9610.9710.9510.9830.9831.0001.000
이마트순천점_가격동향(원)0.0001.0000.9940.6600.8611.0001.0000.8920.6600.9651.0000.9710.9670.9620.9120.810
순 천원예농협_가격동향(원)0.6931.0000.8720.9171.0000.9610.8921.0000.9710.8880.8970.9780.9340.9140.9350.967
빅스토아(용당점)_가격동향(원)0.7521.0000.8230.9561.0000.9480.6600.9711.0000.9400.8360.9600.9040.9110.9460.905
㈜참마트_가격동향(원)0.6931.0000.9580.8510.9730.9610.9650.8880.9401.0000.8970.9500.9720.9891.0001.000
순천농협파머스마켓_가격동향(원)0.8491.0001.0000.8721.0000.9711.0000.8970.8360.8971.0000.8270.0000.4251.0000.000
웃장_가격동향(원)0.6521.0001.0000.9241.0000.9510.9710.9780.9600.9500.8271.0000.9090.9671.0001.000
아랫장_가격동향(원)0.6411.0000.9630.8810.9670.9830.9670.9340.9040.9720.0000.9091.0000.9431.0001.000
중앙시장_가격동향(원)0.5741.0000.9640.9490.9710.9830.9620.9140.9110.9890.4250.9670.9431.0001.0001.000
역전시장_가격동향(원)0.4231.0000.9140.9600.8621.0000.9120.9350.9461.0001.0001.0001.0001.0001.0001.000
시장평균_가격동향(원)0.0001.0000.8100.9220.9601.0000.8100.9670.9051.0000.0001.0001.0001.0001.0001.000
2023-12-13T00:47:07.335963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번마트평균하나로마트(연향동)_가격동향(원)이마트순천점_가격동향(원)역전시장_가격동향(원)시장평균_가격동향(원)
연번1.0000.0640.0860.0210.0720.162
마트평균0.0641.0000.8020.9090.8900.959
하나로마트(연향동)_가격동향(원)0.0860.8021.0000.7200.7330.787
이마트순천점_가격동향(원)0.0210.9090.7201.0000.7830.900
역전시장_가격동향(원)0.0720.8900.7330.7831.0000.936
시장평균_가격동향(원)0.1620.9590.7870.9000.9361.000

Missing values

2023-12-13T00:46:57.936003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:46:58.230376image/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

연번품목마트평균홈플러스(풍덕점)_가격동향(원)하나로마트(연향동)_가격동향(원)홈플러스(순천점)_가격동향(원)이마트순천점_가격동향(원)순 천원예농협_가격동향(원)빅스토아(용당점)_가격동향(원)㈜참마트_가격동향(원)순천농협파머스마켓_가격동향(원)웃장_가격동향(원)아랫장_가격동향(원)중앙시장_가격동향(원)역전시장_가격동향(원)시장평균_가격동향(원)
01쌀 일반미중품(20kg)국내산4408839900410005980041500410004450044000410004100042000460005200045250
12무 상품1개(1kg)191419901700판매안함1980205023001600178020001800200025002075
23상추 잎상추500g33831980290077002300178031003900340020002000200015001875
34열무1단(4kg)4175판매안함3500225079204400판매안함4000298030005000500050004500
45양파1망(1kg)19303490125034801490145015001300148010001000200015001375
56오이 상품 3개1604177018001770192013501500980174010001500150010001250
67풋고추(1kg)86649930680010000105306050850065001100060005000500060005500
78마늘상품 1kg118641373011000119801460098001100012000108009000100001050095009750
89감자상품 1kg33913500290038003900295040003500258030002500200025002500
910배추1포기 3kg40102990400029902980432048005500450040003000300055003875
연번품목마트평균홈플러스(풍덕점)_가격동향(원)하나로마트(연향동)_가격동향(원)홈플러스(순천점)_가격동향(원)이마트순천점_가격동향(원)순 천원예농협_가격동향(원)빅스토아(용당점)_가격동향(원)㈜참마트_가격동향(원)순천농협파머스마켓_가격동향(원)웃장_가격동향(원)아랫장_가격동향(원)중앙시장_가격동향(원)역전시장_가격동향(원)시장평균_가격동향(원)
2021갈치 60cm 1마리770449907900425059908500900090001200070004000500030004750
2122명태 40cm 1마리34093380350016906800280025003800280030002000250025002500
2223고등어 30cm 1마리25782990290040002380285010002500200020002500250025002375
2324냉동오징어30cm 1마리23802290245022901050판매안함판매안함3500270020002000200020002000
2425설탕 정백당 1kg16661590155015901630165018201850165020002000200020002000
2526라면신라면 5개 1봉31512750317031503150317033003350317035003000330035003325
2627밀가루중력분 2.5kg35343060345034303060410042203550340045004250290048004113
2728식용유 1.8리터53146950440059504860480045004350670055004500480055005075
2829화장지 24롤1570018900175001280013800145001550017800148001900014000135001250014750
2930부탄가스4개 1묶음42044550390041904290395038004150480045004000400044004225