Overview

Dataset statistics

Number of variables19
Number of observations31
Missing cells14
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.1 KiB
Average record size in memory169.3 B

Variable types

Categorical4
Text2
Numeric13

Dataset

Description인천광역시 기업형슈퍼마켓의 주간 가격동향을 알 수 있습니다.(구분, 품목, 규격및단가, 금주가격 등)항목 을 제공합니다.* 참고사이트 : https://www.price.go.kr/tprice/portal/dailynecessitypriceinfo/priceiteminfo/getPriceItemInfoList.do
Author인천광역시
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15053080&srcSe=7661IVAWM27C61E190

Alerts

금주가격(평균_원) is highly overall correlated with 전주가격(평균_원) and 10 other fieldsHigh correlation
전주가격(평균_원) is highly overall correlated with 금주가격(평균_원) and 10 other fieldsHigh correlation
등락가격(원) is highly overall correlated with 등락율(퍼센트) and 1 other fieldsHigh correlation
등락율(퍼센트) is highly overall correlated with 등락가격(원)High correlation
최고가(원) is highly overall correlated with 금주가격(평균_원) and 9 other fieldsHigh correlation
최저가(원) is highly overall correlated with 금주가격(평균_원) and 9 other fieldsHigh correlation
동구(홈플러스익스프레스) is highly overall correlated with 금주가격(평균_원) and 10 other fieldsHigh correlation
미추홀구(홈플러스익스프레스) is highly overall correlated with 금주가격(평균_원) and 10 other fieldsHigh correlation
연수구(롯데슈퍼연수점) is highly overall correlated with 금주가격(평균_원) and 9 other fieldsHigh correlation
남동구(롯데슈퍼고잔점) is highly overall correlated with 금주가격(평균_원) and 9 other fieldsHigh correlation
부평구(지에스슈퍼산곡점) is highly overall correlated with 금주가격(평균_원) and 9 other fieldsHigh correlation
계양구(지에스슈퍼박촌점) is highly overall correlated with 금주가격(평균_원) and 9 other fieldsHigh correlation
서구(롯데슈퍼신현동) is highly overall correlated with 금주가격(평균_원) and 10 other fieldsHigh correlation
구분 is highly overall correlated with 금주가격(평균_원) and 5 other fieldsHigh correlation
동구(홈플러스익스프레스) has 4 (12.9%) missing valuesMissing
미추홀구(홈플러스익스프레스) has 7 (22.6%) missing valuesMissing
남동구(롯데슈퍼고잔점) has 1 (3.2%) missing valuesMissing
부평구(지에스슈퍼산곡점) has 1 (3.2%) missing valuesMissing
계양구(지에스슈퍼박촌점) has 1 (3.2%) missing valuesMissing
품 목 has unique valuesUnique
규격 및 단위 has unique valuesUnique
금주가격(평균_원) has unique valuesUnique
전주가격(평균_원) has unique valuesUnique
등락가격(원) has 7 (22.6%) zerosZeros
등락율(퍼센트) has 7 (22.6%) zerosZeros

Reproduction

Analysis started2024-01-28 11:16:12.255290
Analysis finished2024-01-28 11:16:25.605070
Duration13.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
농축수산물
18 
가공식품
10 
공산품

Length

Max length5
Median length5
Mean length4.483871
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농축수산물
2nd row농축수산물
3rd row농축수산물
4th row농축수산물
5th row농축수산물

Common Values

ValueCountFrequency (%)
농축수산물 18
58.1%
가공식품 10
32.3%
공산품 3
 
9.7%

Length

2024-01-28T20:16:25.663052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:16:25.749647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농축수산물 18
58.1%
가공식품 10
32.3%
공산품 3
 
9.7%

품 목
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-01-28T20:16:25.892926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3225806
Min length1

Characters and Unicode

Total characters72
Distinct characters54
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row
2nd row콩나물
3rd row마늘
4th row양파
5th row대파
ValueCountFrequency (%)
1
 
3.2%
고춧가루 1
 
3.2%
샴푸 1
 
3.2%
세제 1
 
3.2%
소주 1
 
3.2%
고추장 1
 
3.2%
두부 1
 
3.2%
분유 1
 
3.2%
1
 
3.2%
라면 1
 
3.2%
Other values (21) 21
67.7%
2024-01-28T20:16:26.179144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6
 
8.3%
3
 
4.2%
3
 
4.2%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
Other values (44) 46
63.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 72
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
6
 
8.3%
3
 
4.2%
3
 
4.2%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
Other values (44) 46
63.9%

Most occurring scripts

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
6
 
8.3%
3
 
4.2%
3
 
4.2%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
Other values (44) 46
63.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 72
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
6
 
8.3%
3
 
4.2%
3
 
4.2%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
2
 
2.8%
Other values (44) 46
63.9%

규격 및 단위
Text

UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size380.0 B
2024-01-28T20:16:26.363950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length14
Mean length11.516129
Min length6

Characters and Unicode

Total characters357
Distinct characters159
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks5 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)100.0%

Sample

1st row임금님표이천쌀/10kg
2nd row풀무원국산콩무농약콩나물/200g
3rd row깐마늘(중품)100g
4th row양파중망/1망
5th row흙대파/1단
ValueCountFrequency (%)
임금님표이천쌀/10kg 1
 
3.2%
중품/1kg 1
 
3.2%
엘라스틴마린콜라겐볼륨샴푸/780㎖ 1
 
3.2%
비트(가루형_리필)/4kg 1
 
3.2%
참이슬프레시/1병_360㎖ 1
 
3.2%
해찬들우리쌀로만든태양초골드/1kg 1
 
3.2%
풀무원국산콩두부(찌개용)/380g 1
 
3.2%
남양xo/800g/1단계 1
 
3.2%
삼립정통크림빵(3개입 1
 
3.2%
신라면/5개입 1
 
3.2%
Other values (21) 21
67.7%
2024-01-28T20:16:26.665681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 28
 
7.8%
0 27
 
7.6%
1 23
 
6.4%
g 18
 
5.0%
3 9
 
2.5%
k 8
 
2.2%
) 7
 
2.0%
( 7
 
2.0%
5 6
 
1.7%
5
 
1.4%
Other values (149) 219
61.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
52.9%
Decimal Number 78
21.8%
Lowercase Letter 32
 
9.0%
Other Punctuation 30
 
8.4%
Close Punctuation 7
 
2.0%
Open Punctuation 7
 
2.0%
Connector Punctuation 4
 
1.1%
Math Symbol 4
 
1.1%
Uppercase Letter 4
 
1.1%
Other Symbol 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
 
2.6%
4
 
2.1%
4
 
2.1%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
Other values (122) 154
81.5%
Decimal Number
ValueCountFrequency (%)
0 27
34.6%
1 23
29.5%
3 9
 
11.5%
5 6
 
7.7%
2 4
 
5.1%
8 4
 
5.1%
9 2
 
2.6%
7 1
 
1.3%
4 1
 
1.3%
6 1
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
g 18
56.2%
k 8
25.0%
2
 
6.2%
m 2
 
6.2%
c 2
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
X 1
25.0%
O 1
25.0%
J 1
25.0%
C 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 28
93.3%
. 2
 
6.7%
Math Symbol
ValueCountFrequency (%)
~ 3
75.0%
1
 
25.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 7
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 4
100.0%
Other Symbol
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 189
52.9%
Common 134
37.5%
Latin 34
 
9.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
 
2.6%
4
 
2.1%
4
 
2.1%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
Other values (122) 154
81.5%
Common
ValueCountFrequency (%)
/ 28
20.9%
0 27
20.1%
1 23
17.2%
3 9
 
6.7%
) 7
 
5.2%
( 7
 
5.2%
5 6
 
4.5%
2 4
 
3.0%
8 4
 
3.0%
_ 4
 
3.0%
Other values (9) 15
11.2%
Latin
ValueCountFrequency (%)
g 18
52.9%
k 8
23.5%
m 2
 
5.9%
c 2
 
5.9%
X 1
 
2.9%
O 1
 
2.9%
J 1
 
2.9%
C 1
 
2.9%

Most occurring blocks

ValueCountFrequency (%)
Hangul 189
52.9%
ASCII 163
45.7%
Letterlike Symbols 2
 
0.6%
CJK Compat 2
 
0.6%
Math Operators 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 28
17.2%
0 27
16.6%
1 23
14.1%
g 18
11.0%
3 9
 
5.5%
k 8
 
4.9%
) 7
 
4.3%
( 7
 
4.3%
5 6
 
3.7%
2 4
 
2.5%
Other values (14) 26
16.0%
Hangul
ValueCountFrequency (%)
5
 
2.6%
4
 
2.1%
4
 
2.1%
4
 
2.1%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
3
 
1.6%
Other values (122) 154
81.5%
Letterlike Symbols
ValueCountFrequency (%)
2
100.0%
CJK Compat
ValueCountFrequency (%)
2
100.0%
Math Operators
ValueCountFrequency (%)
1
100.0%

금주가격(평균_원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7818.9908
Minimum551
Maximum40225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:26.772120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum551
5-th percentile1065
Q11798
median3392.8571
Q37724.5
95-th percentile33100
Maximum40225
Range39674
Interquartile range (IQR)5926.5

Descriptive statistics

Standard deviation10487.326
Coefficient of variation (CV)1.3412634
Kurtosis4.3756495
Mean7818.9908
Median Absolute Deviation (MAD)2109.8571
Skewness2.2241762
Sum242388.71
Variance1.0998401 × 108
MonotonicityNot monotonic
2024-01-28T20:16:26.872357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
39300.0 1
 
3.2%
1403.0 1
 
3.2%
22457.14286 1
 
3.2%
9352.0 1
 
3.2%
13880.0 1
 
3.2%
1253.0 1
 
3.2%
12550.0 1
 
3.2%
3476.0 1
 
3.2%
26900.0 1
 
3.2%
1790.0 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
551.0 1
3.2%
1050.0 1
3.2%
1080.0 1
3.2%
1253.0 1
3.2%
1283.0 1
3.2%
1403.0 1
3.2%
1604.0 1
3.2%
1790.0 1
3.2%
1806.0 1
3.2%
1992.0 1
3.2%
ValueCountFrequency (%)
40225.0 1
3.2%
39300.0 1
3.2%
26900.0 1
3.2%
22457.14286 1
3.2%
13880.0 1
3.2%
12550.0 1
3.2%
9352.0 1
3.2%
8033.0 1
3.2%
7416.0 1
3.2%
7354.0 1
3.2%

전주가격(평균_원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct31
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7675.4654
Minimum536
Maximum40225
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:26.971117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum536
5-th percentile969
Q11724
median3388.5714
Q37902.5
95-th percentile33171.5
Maximum40225
Range39689
Interquartile range (IQR)6178.5

Descriptive statistics

Standard deviation10369.48
Coefficient of variation (CV)1.3509904
Kurtosis4.8703764
Mean7675.4654
Median Absolute Deviation (MAD)1929.5714
Skewness2.3202972
Sum237939.43
Variance1.0752612 × 108
MonotonicityNot monotonic
2024-01-28T20:16:27.066474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
39443.0 1
 
3.2%
1500.0 1
 
3.2%
20700.0 1
 
3.2%
9352.0 1
 
3.2%
11880.0 1
 
3.2%
1253.0 1
 
3.2%
11168.0 1
 
3.2%
3562.0 1
 
3.2%
26900.0 1
 
3.2%
1790.0 1
 
3.2%
Other values (21) 21
67.7%
ValueCountFrequency (%)
536.0 1
3.2%
888.0 1
3.2%
1050.0 1
3.2%
1253.0 1
3.2%
1459.0 1
3.2%
1500.0 1
3.2%
1600.0 1
3.2%
1658.0 1
3.2%
1790.0 1
3.2%
1987.0 1
3.2%
ValueCountFrequency (%)
40225.0 1
3.2%
39443.0 1
3.2%
26900.0 1
3.2%
20700.0 1
3.2%
11880.0 1
3.2%
11168.0 1
3.2%
9352.0 1
3.2%
8451.0 1
3.2%
7354.0 1
3.2%
7153.0 1
3.2%

등락
Categorical

Distinct3
Distinct (%)9.7%
Missing0
Missing (%)0.0%
Memory size380.0 B
상승
13 
하락
11 
<NA>

Length

Max length4
Median length2
Mean length2.4516129
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row하락
2nd row하락
3rd row상승
4th row하락
5th row하락

Common Values

ValueCountFrequency (%)
상승 13
41.9%
하락 11
35.5%
<NA> 7
22.6%

Length

2024-01-28T20:16:27.184035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:16:27.265073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
상승 13
41.9%
하락 11
35.5%
na 7
22.6%

등락가격(원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)71.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean271.71582
Minimum0
Maximum2000
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:27.341766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.2857143
median85.714286
Q3297.97619
95-th percentile1569.7143
Maximum2000
Range2000
Interquartile range (IQR)293.69048

Descriptive statistics

Standard deviation505.86293
Coefficient of variation (CV)1.8617353
Kurtosis6.2460694
Mean271.71582
Median Absolute Deviation (MAD)85.714286
Skewness2.6205079
Sum8423.1905
Variance255897.3
MonotonicityNot monotonic
2024-01-28T20:16:27.433685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0.0 7
22.6%
4.285714286 4
 
12.9%
143.0 1
 
3.2%
40.0 1
 
3.2%
1757.142857 1
 
3.2%
2000.0 1
 
3.2%
1382.285714 1
 
3.2%
85.71428571 1
 
3.2%
14.28571429 1
 
3.2%
7.142857143 1
 
3.2%
Other values (12) 12
38.7%
ValueCountFrequency (%)
0.0 7
22.6%
4.285714286 4
12.9%
7.142857143 1
 
3.2%
14.28571429 1
 
3.2%
21.42857143 1
 
3.2%
40.0 1
 
3.2%
85.71428571 1
 
3.2%
96.57142857 1
 
3.2%
134.6666667 1
 
3.2%
143.0 1
 
3.2%
ValueCountFrequency (%)
2000.0 1
3.2%
1757.142857 1
3.2%
1382.285714 1
3.2%
438.0 1
3.2%
418.0 1
3.2%
385.7142857 1
3.2%
337.1428571 1
3.2%
333.5714286 1
3.2%
262.3809524 1
3.2%
191.8571429 1
3.2%

등락율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)67.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.3064516
Minimum0
Maximum21.6
Zeros7
Zeros (%)22.6%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:27.532656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.1
median2.4
Q38.8
95-th percentile18.45
Maximum21.6
Range21.6
Interquartile range (IQR)8.7

Descriptive statistics

Standard deviation6.6263079
Coefficient of variation (CV)1.2487267
Kurtosis0.16910394
Mean5.3064516
Median Absolute Deviation (MAD)2.4
Skewness1.1327167
Sum164.5
Variance43.907957
MonotonicityNot monotonic
2024-01-28T20:16:27.619164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0.0 7
22.6%
0.1 3
 
9.7%
0.3 2
 
6.5%
8.5 2
 
6.5%
0.4 1
 
3.2%
6.4 1
 
3.2%
16.8 1
 
3.2%
12.4 1
 
3.2%
2.4 1
 
3.2%
2.7 1
 
3.2%
Other values (11) 11
35.5%
ValueCountFrequency (%)
0.0 7
22.6%
0.1 3
9.7%
0.3 2
 
6.5%
0.4 1
 
3.2%
0.6 1
 
3.2%
0.9 1
 
3.2%
2.4 1
 
3.2%
2.7 1
 
3.2%
3.7 1
 
3.2%
4.5 1
 
3.2%
ValueCountFrequency (%)
21.6 1
3.2%
20.1 1
3.2%
16.8 1
3.2%
15.7 1
3.2%
12.4 1
3.2%
12.3 1
3.2%
12.0 1
3.2%
9.1 1
3.2%
8.5 2
6.5%
6.4 1
3.2%
Distinct7
Distinct (%)22.6%
Missing0
Missing (%)0.0%
Memory size380.0 B
동구
14 
연수구
부평구
계양구
미추홀구
Other values (2)

Length

Max length4
Median length3
Mean length2.5806452
Min length2

Unique

Unique2 ?
Unique (%)6.5%

Sample

1st row동구
2nd row동구
3rd row서구
4th row동구
5th row미추홀구

Common Values

ValueCountFrequency (%)
동구 14
45.2%
연수구 5
 
16.1%
부평구 4
 
12.9%
계양구 4
 
12.9%
미추홀구 2
 
6.5%
서구 1
 
3.2%
남동구 1
 
3.2%

Length

2024-01-28T20:16:27.938325image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:16:28.029863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동구 14
45.2%
연수구 5
 
16.1%
부평구 4
 
12.9%
계양구 4
 
12.9%
미추홀구 2
 
6.5%
서구 1
 
3.2%
남동구 1
 
3.2%

최고가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9456.2258
Minimum699
Maximum49950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:28.124373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum699
5-th percentile1155
Q12640
median4290
Q39485
95-th percentile35550
Maximum49950
Range49251
Interquartile range (IQR)6845

Descriptive statistics

Standard deviation12156.754
Coefficient of variation (CV)1.285582
Kurtosis4.5290776
Mean9456.2258
Median Absolute Deviation (MAD)2600
Skewness2.2007613
Sum293143
Variance1.4778666 × 108
MonotonicityNot monotonic
2024-01-28T20:16:28.215501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2990 3
 
9.7%
1690 2
 
6.5%
42900 1
 
3.2%
5540 1
 
3.2%
28200 1
 
3.2%
13000 1
 
3.2%
19800 1
 
3.2%
1260 1
 
3.2%
15200 1
 
3.2%
4290 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
699 1
 
3.2%
1050 1
 
3.2%
1260 1
 
3.2%
1690 2
6.5%
1790 1
 
3.2%
2317 1
 
3.2%
2590 1
 
3.2%
2690 1
 
3.2%
2990 3
9.7%
3327 1
 
3.2%
ValueCountFrequency (%)
49950 1
3.2%
42900 1
3.2%
28200 1
3.2%
26900 1
3.2%
19800 1
3.2%
15200 1
3.2%
13000 1
3.2%
9980 1
3.2%
8990 1
3.2%
8800 1
3.2%
Distinct6
Distinct (%)19.4%
Missing0
Missing (%)0.0%
Memory size380.0 B
연수구
10 
계양구
동구
남동구
서구

Length

Max length3
Median length3
Mean length2.7419355
Min length2

Unique

Unique1 ?
Unique (%)3.2%

Sample

1st row연수구
2nd row계양구
3rd row연수구
4th row남동구
5th row연수구

Common Values

ValueCountFrequency (%)
연수구 10
32.3%
계양구 9
29.0%
동구 6
19.4%
남동구 3
 
9.7%
서구 2
 
6.5%
부평구 1
 
3.2%

Length

2024-01-28T20:16:28.304895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T20:16:28.384870image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
연수구 10
32.3%
계양구 9
29.0%
동구 6
19.4%
남동구 3
 
9.7%
서구 2
 
6.5%
부평구 1
 
3.2%

최저가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)93.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6241.4516
Minimum460
Maximum34900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:28.475520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile587.5
Q11198.5
median2843
Q35990
95-th percentile29400
Maximum34900
Range34440
Interquartile range (IQR)4791.5

Descriptive statistics

Standard deviation9011.0978
Coefficient of variation (CV)1.4437503
Kurtosis4.7850014
Mean6241.4516
Median Absolute Deviation (MAD)2093
Skewness2.3606938
Sum193485
Variance81199884
MonotonicityNot monotonic
2024-01-28T20:16:28.565823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5990 2
 
6.5%
4990 2
 
6.5%
34900 1
 
3.2%
5300 1
 
3.2%
16900 1
 
3.2%
6500 1
 
3.2%
8580 1
 
3.2%
1210 1
 
3.2%
7390 1
 
3.2%
2843 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
460 1
3.2%
499 1
3.2%
676 1
3.2%
750 1
3.2%
990 1
3.2%
1000 1
3.2%
1050 1
3.2%
1187 1
3.2%
1210 1
3.2%
1450 1
3.2%
ValueCountFrequency (%)
34900 1
3.2%
31900 1
3.2%
26900 1
3.2%
16900 1
3.2%
8580 1
3.2%
7390 1
3.2%
6500 1
3.2%
5990 2
6.5%
5300 1
3.2%
4990 2
6.5%

동구(홈플러스익스프레스)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct25
Distinct (%)92.6%
Missing4
Missing (%)12.9%
Infinite0
Infinite (%)0.0%
Mean8271.963
Minimum500
Maximum49950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:28.666619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile1107.6
Q11740
median2990
Q36490
95-th percentile37800
Maximum49950
Range49450
Interquartile range (IQR)4750

Descriptive statistics

Standard deviation12546.66
Coefficient of variation (CV)1.5167694
Kurtosis5.6117302
Mean8271.963
Median Absolute Deviation (MAD)1540
Skewness2.4628574
Sum223343
Variance1.5741867 × 108
MonotonicityNot monotonic
2024-01-28T20:16:28.763113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
2990 2
 
6.5%
1690 2
 
6.5%
4990 1
 
3.2%
25900 1
 
3.2%
19800 1
 
3.2%
1260 1
 
3.2%
14990 1
 
3.2%
4290 1
 
3.2%
2590 1
 
3.2%
3450 1
 
3.2%
Other values (15) 15
48.4%
(Missing) 4
 
12.9%
ValueCountFrequency (%)
500 1
3.2%
1050 1
3.2%
1242 1
3.2%
1260 1
3.2%
1450 1
3.2%
1690 2
6.5%
1790 1
3.2%
2317 1
3.2%
2590 1
3.2%
2650 1
3.2%
ValueCountFrequency (%)
49950 1
3.2%
42900 1
3.2%
25900 1
3.2%
19800 1
3.2%
14990 1
3.2%
9267 1
3.2%
6990 1
3.2%
5990 1
3.2%
4990 1
3.2%
4500 1
3.2%

미추홀구(홈플러스익스프레스)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)91.7%
Missing7
Missing (%)22.6%
Infinite0
Infinite (%)0.0%
Mean6006.25
Minimum500
Maximum39900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:28.861246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum500
5-th percentile835.8
Q11735
median2940
Q35128.75
95-th percentile23923.5
Maximum39900
Range39400
Interquartile range (IQR)3393.75

Descriptive statistics

Standard deviation9034.2173
Coefficient of variation (CV)1.5041361
Kurtosis9.0728754
Mean6006.25
Median Absolute Deviation (MAD)1525
Skewness2.9588611
Sum144150
Variance81617082
MonotonicityNot monotonic
2024-01-28T20:16:28.951129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2990 3
 
9.7%
4990 1
 
3.2%
25500 1
 
3.2%
1260 1
 
3.2%
14990 1
 
3.2%
2890 1
 
3.2%
2590 1
 
3.2%
3450 1
 
3.2%
1050 1
 
3.2%
4500 1
 
3.2%
Other values (12) 12
38.7%
(Missing) 7
22.6%
ValueCountFrequency (%)
500 1
3.2%
798 1
3.2%
1050 1
3.2%
1260 1
3.2%
1450 1
3.2%
1690 1
3.2%
1750 1
3.2%
2317 1
3.2%
2380 1
3.2%
2590 1
3.2%
ValueCountFrequency (%)
39900 1
 
3.2%
25500 1
 
3.2%
14990 1
 
3.2%
8990 1
 
3.2%
5990 1
 
3.2%
5545 1
 
3.2%
4990 1
 
3.2%
4500 1
 
3.2%
3450 1
 
3.2%
2990 3
9.7%

연수구(롯데슈퍼연수점)
Real number (ℝ)

HIGH CORRELATION 

Distinct30
Distinct (%)96.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7517.0323
Minimum499
Maximum34900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:29.055594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum499
5-th percentile844.5
Q11470
median3290
Q36990
95-th percentile29400
Maximum34900
Range34401
Interquartile range (IQR)5520

Descriptive statistics

Standard deviation9620.4364
Coefficient of variation (CV)1.2798184
Kurtosis2.2741582
Mean7517.0323
Median Absolute Deviation (MAD)2030
Skewness1.8152689
Sum233028
Variance92552797
MonotonicityNot monotonic
2024-01-28T20:16:29.165584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
5990 2
 
6.5%
34900 1
 
3.2%
7990 1
 
3.2%
24900 1
 
3.2%
13000 1
 
3.2%
15900 1
 
3.2%
1260 1
 
3.2%
15200 1
 
3.2%
3290 1
 
3.2%
26900 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
499 1
3.2%
699 1
3.2%
990 1
3.2%
1000 1
3.2%
1050 1
3.2%
1260 1
3.2%
1290 1
3.2%
1350 1
3.2%
1590 1
3.2%
1690 1
3.2%
ValueCountFrequency (%)
34900 1
3.2%
31900 1
3.2%
26900 1
3.2%
24900 1
3.2%
15900 1
3.2%
15200 1
3.2%
13000 1
3.2%
7990 1
3.2%
5990 2
6.5%
5090 1
3.2%

남동구(롯데슈퍼고잔점)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean7806.8667
Minimum460
Maximum43000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:29.268875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum460
5-th percentile859.7
Q11745
median3685
Q37862.5
95-th percentile31300
Maximum43000
Range42540
Interquartile range (IQR)6117.5

Descriptive statistics

Standard deviation10332.979
Coefficient of variation (CV)1.3235757
Kurtosis4.998452
Mean7806.8667
Median Absolute Deviation (MAD)2410
Skewness2.2876893
Sum234206
Variance1.0677046 × 108
MonotonicityNot monotonic
2024-01-28T20:16:29.358670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
34900 1
 
3.2%
43000 1
 
3.2%
17900 1
 
3.2%
7360 1
 
3.2%
13200 1
 
3.2%
1260 1
 
3.2%
13900 1
 
3.2%
3990 1
 
3.2%
26900 1
 
3.2%
3380 1
 
3.2%
Other values (20) 20
64.5%
ValueCountFrequency (%)
460 1
3.2%
704 1
3.2%
1050 1
3.2%
1260 1
3.2%
1290 1
3.2%
1352 1
3.2%
1690 1
3.2%
1730 1
3.2%
1790 1
3.2%
1990 1
3.2%
ValueCountFrequency (%)
43000 1
3.2%
34900 1
3.2%
26900 1
3.2%
17900 1
3.2%
13900 1
3.2%
13200 1
3.2%
8800 1
3.2%
7990 1
3.2%
7480 1
3.2%
7360 1
3.2%

부평구(지에스슈퍼산곡점)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)96.7%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean7411.4333
Minimum498
Maximum49800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:29.448800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum498
5-th percentile867.25
Q11865
median3330
Q37730
95-th percentile31145
Maximum49800
Range49302
Interquartile range (IQR)5865

Descriptive statistics

Standard deviation11317.805
Coefficient of variation (CV)1.5270738
Kurtosis9.1336921
Mean7411.4333
Median Absolute Deviation (MAD)2245
Skewness3.0239705
Sum222343
Variance1.2809272 × 108
MonotonicityNot monotonic
2024-01-28T20:16:29.543101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1980 2
 
6.5%
42800 1
 
3.2%
8500 1
 
3.2%
16900 1
 
3.2%
6900 1
 
3.2%
9900 1
 
3.2%
1260 1
 
3.2%
13780 1
 
3.2%
3280 1
 
3.2%
2580 1
 
3.2%
Other values (19) 19
61.3%
ValueCountFrequency (%)
498 1
3.2%
775 1
3.2%
980 1
3.2%
1050 1
3.2%
1260 1
3.2%
1650 1
3.2%
1700 1
3.2%
1860 1
3.2%
1880 1
3.2%
1980 2
6.5%
ValueCountFrequency (%)
49800 1
3.2%
42800 1
3.2%
16900 1
3.2%
13780 1
3.2%
9900 1
3.2%
8630 1
3.2%
8500 1
3.2%
7980 1
3.2%
6980 1
3.2%
6900 1
3.2%

계양구(지에스슈퍼박촌점)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct28
Distinct (%)93.3%
Missing1
Missing (%)3.2%
Infinite0
Infinite (%)0.0%
Mean6757.1333
Minimum498
Maximum42800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:29.638706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum498
5-th percentile608.35
Q11192.75
median3580
Q37167.5
95-th percentile31830
Maximum42800
Range42302
Interquartile range (IQR)5974.75

Descriptive statistics

Standard deviation10217.706
Coefficient of variation (CV)1.5121362
Kurtosis6.5143755
Mean6757.1333
Median Absolute Deviation (MAD)2555
Skewness2.6419995
Sum202714
Variance1.0440152 × 108
MonotonicityNot monotonic
2024-01-28T20:16:29.723941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1980 2
 
6.5%
1000 2
 
6.5%
42800 1
 
3.2%
5300 1
 
3.2%
28200 1
 
3.2%
6500 1
 
3.2%
8580 1
 
3.2%
1210 1
 
3.2%
7390 1
 
3.2%
3750 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
498 1
3.2%
553 1
3.2%
676 1
3.2%
750 1
3.2%
1000 2
6.5%
1050 1
3.2%
1187 1
3.2%
1210 1
3.2%
1610 1
3.2%
1780 1
3.2%
ValueCountFrequency (%)
42800 1
3.2%
34800 1
3.2%
28200 1
3.2%
9980 1
3.2%
8580 1
3.2%
7980 1
3.2%
7480 1
3.2%
7390 1
3.2%
6500 1
3.2%
5310 1
3.2%

서구(롯데슈퍼신현동)
Real number (ℝ)

HIGH CORRELATION 

Distinct28
Distinct (%)90.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7420.871
Minimum699
Maximum36900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size411.0 B
2024-01-28T20:16:29.829928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum699
5-th percentile1020
Q12140
median2990
Q37795
95-th percentile29400
Maximum36900
Range36201
Interquartile range (IQR)5655

Descriptive statistics

Standard deviation9282.6045
Coefficient of variation (CV)1.250878
Kurtosis3.7638834
Mean7420.871
Median Absolute Deviation (MAD)1785
Skewness2.0834587
Sum230047
Variance86166746
MonotonicityNot monotonic
2024-01-28T20:16:29.918353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
2990 3
 
9.7%
6990 2
 
6.5%
36900 1
 
3.2%
1690 1
 
3.2%
17900 1
 
3.2%
13000 1
 
3.2%
15900 1
 
3.2%
1260 1
 
3.2%
7600 1
 
3.2%
2843 1
 
3.2%
Other values (18) 18
58.1%
ValueCountFrequency (%)
699 1
3.2%
990 1
3.2%
1050 1
3.2%
1205 1
3.2%
1260 1
3.2%
1290 1
3.2%
1690 1
3.2%
1990 1
3.2%
2290 1
3.2%
2590 1
3.2%
ValueCountFrequency (%)
36900 1
3.2%
31900 1
3.2%
26900 1
3.2%
17900 1
3.2%
15900 1
3.2%
13000 1
3.2%
8990 1
3.2%
7990 1
3.2%
7600 1
3.2%
6990 2
6.5%

Interactions

2024-01-28T20:16:24.256655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:12.940876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.899650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.061445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.968597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.800548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.706593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.828143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.674508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.584198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.486860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.310939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.387320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.326813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.016312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.974890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.136567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.037322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.874643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.774883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.898772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.747574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.656608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.553230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.378956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.456030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.397762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.095015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.058120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.210974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.105198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.954544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.845251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.971435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.819023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.728633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.619012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.446982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.524537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.472775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.168561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.364772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.280228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.172416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.036189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.913768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.040251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.897927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.803525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.689024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.527701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.593301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.541136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.232024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.426174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.351467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.235741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.094966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.974200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.099994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.961108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.863930image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.744691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.594038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.651356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.618551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.297096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.489210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.414006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.311108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.152745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.034985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.159273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.026677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.933374image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.803722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.655567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.709996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.705695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.388302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.558189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.484608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.377149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.216841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.101220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.233335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.096434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.005242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.868096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.720387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.777466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.773415image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.470760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.622517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.548117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.436672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.275899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.168802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.295236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.160099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.074953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.930234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.779002image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.834849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.844568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.548327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.693285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.619834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.503449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.345710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.242477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.365393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.235393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.146962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.008786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.083799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.903130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.913854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.616384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.764879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.691331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.566087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.427627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.309694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.431095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.310827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.215978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.082443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.148784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.981524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.979150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.686058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.827247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.758860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.623249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.496691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.381530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.488741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.384569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.278774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.136775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.206331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.071690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:25.043869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.761090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.905183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.826741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.681890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.571501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.683337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.548593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.452693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.348562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.192733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.264948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.135639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:25.104994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:13.827731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:14.980668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:15.895712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:16.738008image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:17.637281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:18.742779image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:19.608906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:20.513519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:21.417536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:22.247462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:23.323388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T20:16:24.191077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T20:16:30.005027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분품 목규격 및 단위금주가격(평균_원)전주가격(평균_원)등락등락가격(원)등락율(퍼센트)최고가(지역)최고가(원)최저가(지역)최저가(원)동구(홈플러스익스프레스)미추홀구(홈플러스익스프레스)연수구(롯데슈퍼연수점)남동구(롯데슈퍼고잔점)부평구(지에스슈퍼산곡점)계양구(지에스슈퍼박촌점)서구(롯데슈퍼신현동)
구분1.0001.0001.0000.6890.8850.2460.8610.3130.0000.6370.7580.7490.7620.9240.5830.6480.7050.6240.765
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규격 및 단위1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
금주가격(평균_원)0.6891.0001.0001.0000.9800.2350.7110.0000.3750.9710.5470.9810.9570.9640.9910.9450.9750.9610.941
전주가격(평균_원)0.8851.0001.0000.9801.0000.1260.8550.0000.4610.9190.8180.9960.9410.9450.9200.9810.9200.8710.923
등락0.2461.0001.0000.2350.1261.0000.0000.4630.0000.0000.0000.3210.0000.6640.2160.0000.0000.4670.403
등락가격(원)0.8611.0001.0000.7110.8550.0001.0000.7030.2310.7230.7770.8940.8790.9320.5850.8620.9060.8450.482
등락율(퍼센트)0.3131.0001.0000.0000.0000.4630.7031.0000.4250.0000.4780.0000.5250.3170.0000.0000.0000.0000.000
최고가(지역)0.0001.0001.0000.3750.4610.0000.2310.4251.0000.0690.0860.0000.0000.6000.6440.0000.0000.0000.330
최고가(원)0.6371.0001.0000.9710.9190.0000.7230.0000.0691.0000.0000.8651.0000.9310.9740.9650.9500.9100.922
최저가(지역)0.7581.0001.0000.5470.8180.0000.7770.4780.0860.0001.0000.7790.4440.6510.4190.4780.7320.7290.000
최저가(원)0.7491.0001.0000.9810.9960.3210.8940.0000.0000.8650.7791.0000.9411.0000.9220.9860.9070.8670.909
동구(홈플러스익스프레스)0.7621.0001.0000.9570.9410.0000.8790.5250.0001.0000.4440.9411.0000.9510.9280.9990.9680.9280.922
미추홀구(홈플러스익스프레스)0.9241.0001.0000.9640.9450.6640.9320.3170.6000.9310.6511.0000.9511.0000.9880.9440.9450.9960.884
연수구(롯데슈퍼연수점)0.5831.0001.0000.9910.9200.2160.5850.0000.6440.9740.4190.9220.9280.9881.0000.8790.8740.8090.895
남동구(롯데슈퍼고잔점)0.6481.0001.0000.9450.9810.0000.8620.0000.0000.9650.4780.9860.9990.9440.8791.0001.0000.9140.982
부평구(지에스슈퍼산곡점)0.7051.0001.0000.9750.9200.0000.9060.0000.0000.9500.7320.9070.9680.9450.8741.0001.0000.9870.889
계양구(지에스슈퍼박촌점)0.6241.0001.0000.9610.8710.4670.8450.0000.0000.9100.7290.8670.9280.9960.8090.9140.9871.0000.912
서구(롯데슈퍼신현동)0.7651.0001.0000.9410.9230.4030.4820.0000.3300.9220.0000.9090.9220.8840.8950.9820.8890.9121.000
2024-01-28T20:16:30.142834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
최고가(지역)최저가(지역)등락구분
최고가(지역)1.0000.0000.0000.000
최저가(지역)0.0001.0000.0000.407
등락0.0000.0001.0000.388
구분0.0000.4070.3881.000
2024-01-28T20:16:30.230033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
금주가격(평균_원)전주가격(평균_원)등락가격(원)등락율(퍼센트)최고가(원)최저가(원)동구(홈플러스익스프레스)미추홀구(홈플러스익스프레스)연수구(롯데슈퍼연수점)남동구(롯데슈퍼고잔점)부평구(지에스슈퍼산곡점)계양구(지에스슈퍼박촌점)서구(롯데슈퍼신현동)구분등락최고가(지역)최저가(지역)
금주가격(평균_원)1.0000.9960.142-0.1330.9740.9720.9820.9640.9750.9870.9770.9600.9510.5530.2030.0000.363
전주가격(평균_원)0.9961.0000.131-0.1510.9720.9680.9780.9750.9700.9860.9830.9530.9400.5590.0000.2340.435
등락가격(원)0.1420.1311.0000.9230.2200.0710.3290.3100.0840.0700.2030.2300.1550.5250.0000.1050.378
등락율(퍼센트)-0.133-0.1510.9231.000-0.038-0.210-0.002-0.008-0.186-0.220-0.109-0.097-0.0810.1680.2770.2190.262
최고가(원)0.9740.9720.220-0.0381.0000.9260.9500.9290.9360.9590.9530.9210.9680.4890.0000.0000.006
최저가(원)0.9720.9680.071-0.2100.9261.0000.9330.9140.9740.9690.9530.9770.9280.4040.3030.0000.394
동구(홈플러스익스프레스)0.9820.9780.329-0.0020.9500.9331.0000.9470.9580.9680.9520.9320.9190.6310.0000.0000.282
미추홀구(홈플러스익스프레스)0.9640.9750.310-0.0080.9290.9140.9471.0000.9010.9610.9680.9040.8690.6180.3490.3450.293
연수구(롯데슈퍼연수점)0.9750.9700.084-0.1860.9360.9740.9580.9011.0000.9770.9420.9460.9400.4360.1680.3660.237
남동구(롯데슈퍼고잔점)0.9870.9860.070-0.2200.9590.9690.9680.9610.9771.0000.9870.9400.9380.4690.0000.0000.287
부평구(지에스슈퍼산곡점)0.9770.9830.203-0.1090.9530.9530.9520.9680.9420.9871.0000.9420.9030.3600.0000.0000.351
계양구(지에스슈퍼박촌점)0.9600.9530.230-0.0970.9210.9770.9320.9040.9460.9400.9421.0000.9130.2970.4820.0000.339
서구(롯데슈퍼신현동)0.9510.9400.155-0.0810.9680.9280.9190.8690.9400.9380.9030.9131.0000.6080.4160.0850.000
구분0.5530.5590.5250.1680.4890.4040.6310.6180.4360.4690.3600.2970.6081.0000.3880.0000.407
등락0.2030.0000.0000.2770.0000.3030.0000.3490.1680.0000.0000.4820.4160.3881.0000.0000.000
최고가(지역)0.0000.2340.1050.2190.0000.0000.0000.3450.3660.0000.0000.0000.0850.0000.0001.0000.000
최저가(지역)0.3630.4350.3780.2620.0060.3940.2820.2930.2370.2870.3510.3390.0000.4070.0000.0001.000

Missing values

2024-01-28T20:16:25.211249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T20:16:25.407414image/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.
2024-01-28T20:16:25.537053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

구분품 목규격 및 단위금주가격(평균_원)전주가격(평균_원)등락등락가격(원)등락율(퍼센트)최고가(지역)최고가(원)최저가(지역)최저가(원)동구(홈플러스익스프레스)미추홀구(홈플러스익스프레스)연수구(롯데슈퍼연수점)남동구(롯데슈퍼고잔점)부평구(지에스슈퍼산곡점)계양구(지에스슈퍼박촌점)서구(롯데슈퍼신현동)
0농축수산물임금님표이천쌀/10kg39300.039443.0하락143.00.4동구42900연수구3490042900399003490034900428004280036900
1농축수산물콩나물풀무원국산콩무농약콩나물/200g1403.01500.0하락96.5714296.4동구1790계양구676179017501350135217006761205
2농축수산물마늘깐마늘(중품)100g1080.0888.0상승191.85714321.6서구2990연수구49912427984997047755532990
3농축수산물양파양파중망/1망2393.02730.0하락337.14285712.3동구2990남동구17302990299024901730198019802590
4농축수산물대파흙대파/1단1806.01987.0하락181.4285719.1미추홀구2990연수구10001690299010001990198010001990
5농축수산물재래종_잎없음15~20cm1283.01459.0하락175.71428612.0동구1690계양구75016901690129012909807501290
6농축수산물배추통배추/1포기2853.02988.0하락134.6666674.5부평구3980계양구19802790<NA>27902790398019802790
7농축수산물사과5~12개/2.5~3kg7416.07153.0상승262.3809523.7미추홀구8990연수구59906990899059907990698079806990
8농축수산물고등어1마리/35∼38cm3226.02788.0상승438.015.7계양구4980남동구2500<NA><NA>29802500268049802990
9농축수산물멸치국물용멸치/100g2476.02498.0하락21.4285710.9동구3327계양구11873327238029902580188011872990
구분품 목규격 및 단위금주가격(평균_원)전주가격(평균_원)등락등락가격(원)등락율(퍼센트)최고가(지역)최고가(원)최저가(지역)최저가(원)동구(홈플러스익스프레스)미추홀구(홈플러스익스프레스)연수구(롯데슈퍼연수점)남동구(롯데슈퍼고잔점)부평구(지에스슈퍼산곡점)계양구(지에스슈퍼박촌점)서구(롯데슈퍼신현동)
21가공식품스넥과자새우깡(소)/1봉지_90g1050.01050.0<NA>0.00.0동구1050동구10501050105010501050105010501050
22가공식품라면신라면/5개입3392.8571433388.571429상승4.2857140.1동구3450서구33003450345033803380338034103300
23가공식품삼립정통크림빵(3개입)1790.01790.0<NA>0.00.0동구2590연수구99025902590990<NA>25801000990
24가공식품분유남양XO/800g/1단계26900.026900.0<NA>0.00.0연수구26900연수구26900<NA><NA>2690026900<NA><NA>26900
25가공식품두부풀무원국산콩두부(찌개용)/380g3476.03562.0하락85.7142862.4동구4290서구28434290289032903990328037502843
26가공식품고추장해찬들우리쌀로만든태양초골드/1kg12550.011168.0상승1382.28571412.4연수구15200계양구7390149901499015200139001378073907600
27가공식품소주참이슬프레시/1병_360㎖1253.01253.0<NA>0.00.0동구1260계양구12101260126012601260126012101260
28공산품세제비트(가루형_리필)/4kg13880.011880.0상승2000.016.8동구19800계양구858019800<NA>15900132009900858015900
29공산품샴푸엘라스틴마린콜라겐볼륨샴푸/780㎖9352.09352.0<NA>0.00.0연수구13000계양구6500<NA><NA>1300073606900650013000
30공산품화장지크리닉스데코소프트/30롤22457.1428620700.0상승1757.1428578.5계양구28200부평구1690025900255002490017900169002820017900