Overview

Dataset statistics

Number of variables15
Number of observations27
Missing cells14
Missing cells (%)3.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.6 KiB
Average record size in memory135.9 B

Variable types

Categorical2
Text2
Numeric11

Dataset

Description인천광역시 계양구 주요업소별 물가 정보(품목, 금주가격, 전주가격, 등락구분, 등락가격, 등락율, 업소명 등)
Author인천광역시 계양구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=3072904&srcSe=7661IVAWM27C61E190

Alerts

금주가격(원) is highly overall correlated with 전주가격(원) and 8 other fieldsHigh correlation
전주가격(원) is highly overall correlated with 금주가격(원) and 8 other fieldsHigh correlation
등락율(퍼센트) is highly overall correlated with 등락구분High correlation
롯데마트계양점 is highly overall correlated with 금주가격(원) and 8 other fieldsHigh correlation
이마트 is highly overall correlated with 금주가격(원) and 7 other fieldsHigh correlation
홈플러스계산점 is highly overall correlated with 금주가격(원) and 7 other fieldsHigh correlation
홈플러스작전점 is highly overall correlated with 금주가격(원) and 8 other fieldsHigh correlation
계산시장 is highly overall correlated with 금주가격(원) and 7 other fieldsHigh correlation
작전시장 is highly overall correlated with 금주가격(원) and 7 other fieldsHigh correlation
계양산전통시장 is highly overall correlated with 금주가격(원) and 7 other fieldsHigh correlation
구분 is highly overall correlated with 금주가격(원) and 3 other fieldsHigh correlation
등락구분 is highly overall correlated with 등락율(퍼센트)High correlation
롯데마트계양점 has 3 (11.1%) missing valuesMissing
이마트 has 4 (14.8%) missing valuesMissing
홈플러스계산점 has 1 (3.7%) missing valuesMissing
홈플러스작전점 has 1 (3.7%) missing valuesMissing
계산시장 has 2 (7.4%) missing valuesMissing
작전시장 has 1 (3.7%) missing valuesMissing
계양산전통시장 has 2 (7.4%) missing valuesMissing
품 목 has unique valuesUnique
금주가격(원) has unique valuesUnique
전주가격(원) has unique valuesUnique
등락가격(원) has 15 (55.6%) zerosZeros
등락율(퍼센트) has 15 (55.6%) zerosZeros

Reproduction

Analysis started2024-03-18 04:07:58.753267
Analysis finished2024-03-18 04:08:11.274821
Duration12.52 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
농축산물(14종)
14 
가공식품(10종)
10 
공산품(3종)

Length

Max length9
Median length9
Mean length8.7777778
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row농축산물(14종)
2nd row농축산물(14종)
3rd row농축산물(14종)
4th row농축산물(14종)
5th row농축산물(14종)

Common Values

ValueCountFrequency (%)
농축산물(14종) 14
51.9%
가공식품(10종) 10
37.0%
공산품(3종) 3
 
11.1%

Length

2024-03-18T13:08:11.362679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:08:11.484412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농축산물(14종 14
51.9%
가공식품(10종 10
37.0%
공산품(3종 3
 
11.1%

품 목
Text

UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-18T13:08:11.732962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length6.3703704
Min length1

Characters and Unicode

Total characters172
Distinct characters52
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

Unique27 ?
Unique (%)100.0%

Sample

1st row
2nd row콩 나 물
3rd row마 늘
4th row양 파
5th row대 파
ValueCountFrequency (%)
3
 
5.4%
3
 
5.4%
2
 
3.6%
2
 
3.6%
2
 
3.6%
1
 
1.8%
1
 
1.8%
1
 
1.8%
1
 
1.8%
1
 
1.8%
Other values (39) 39
69.6%
2024-03-18T13:08:12.086758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
110
64.0%
4
 
2.3%
3
 
1.7%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
2
 
1.2%
1
 
0.6%
Other values (42) 42
 
24.4%

Most occurring categories

ValueCountFrequency (%)
Space Separator 110
64.0%
Other Letter 62
36.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
 
6.5%
3
 
4.8%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
1
 
1.6%
1
 
1.6%
Other values (41) 41
66.1%
Space Separator
ValueCountFrequency (%)
110
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 110
64.0%
Hangul 62
36.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
 
6.5%
3
 
4.8%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
1
 
1.6%
1
 
1.6%
Other values (41) 41
66.1%
Common
ValueCountFrequency (%)
110
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 110
64.0%
Hangul 62
36.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
110
100.0%
Hangul
ValueCountFrequency (%)
4
 
6.5%
3
 
4.8%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
2
 
3.2%
1
 
1.6%
1
 
1.6%
Other values (41) 41
66.1%
Distinct25
Distinct (%)92.6%
Missing0
Missing (%)0.0%
Memory size348.0 B
2024-03-18T13:08:12.255979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length14
Mean length10.666667
Min length5

Characters and Unicode

Total characters288
Distinct characters91
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

Unique24 ?
Unique (%)88.9%

Sample

1st row 강화쌀 / 20kg
2nd row 400g
3rd row 깐마늘 1kg
4th row 1 kg
5th row 1 kg
ValueCountFrequency (%)
21
28.0%
1 3
 
4.0%
kg 3
 
4.0%
3kg 3
 
4.0%
1kg 3
 
4.0%
400g 2
 
2.7%
1개 2
 
2.7%
500g 2
 
2.7%
2
 
2.7%
비트(리필 1
 
1.3%
Other values (33) 33
44.0%
2024-03-18T13:08:12.649333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
102
35.4%
/ 20
 
6.9%
0 17
 
5.9%
g 16
 
5.6%
1 13
 
4.5%
k 10
 
3.5%
( 4
 
1.4%
) 4
 
1.4%
4 4
 
1.4%
3 4
 
1.4%
Other values (81) 94
32.6%

Most occurring categories

ValueCountFrequency (%)
Space Separator 102
35.4%
Other Letter 80
27.8%
Decimal Number 45
15.6%
Lowercase Letter 29
 
10.1%
Other Punctuation 21
 
7.3%
Open Punctuation 4
 
1.4%
Close Punctuation 4
 
1.4%
Uppercase Letter 2
 
0.7%
Other Symbol 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
1
 
1.2%
1
 
1.2%
Other values (60) 60
75.0%
Decimal Number
ValueCountFrequency (%)
0 17
37.8%
1 13
28.9%
4 4
 
8.9%
3 4
 
8.9%
2 3
 
6.7%
5 2
 
4.4%
6 1
 
2.2%
9 1
 
2.2%
Lowercase Letter
ValueCountFrequency (%)
g 16
55.2%
k 10
34.5%
m 1
 
3.4%
c 1
 
3.4%
1
 
3.4%
Other Punctuation
ValueCountFrequency (%)
/ 20
95.2%
. 1
 
4.8%
Uppercase Letter
ValueCountFrequency (%)
J 1
50.0%
C 1
50.0%
Space Separator
ValueCountFrequency (%)
102
100.0%
Open Punctuation
ValueCountFrequency (%)
( 4
100.0%
Close Punctuation
ValueCountFrequency (%)
) 4
100.0%
Other Symbol
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 178
61.8%
Hangul 80
27.8%
Latin 30
 
10.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
1
 
1.2%
1
 
1.2%
Other values (60) 60
75.0%
Common
ValueCountFrequency (%)
102
57.3%
/ 20
 
11.2%
0 17
 
9.6%
1 13
 
7.3%
( 4
 
2.2%
) 4
 
2.2%
4 4
 
2.2%
3 4
 
2.2%
2 3
 
1.7%
5 2
 
1.1%
Other values (5) 5
 
2.8%
Latin
ValueCountFrequency (%)
g 16
53.3%
k 10
33.3%
m 1
 
3.3%
c 1
 
3.3%
J 1
 
3.3%
C 1
 
3.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 206
71.5%
Hangul 80
 
27.8%
CJK Compat 1
 
0.3%
Letterlike Symbols 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
102
49.5%
/ 20
 
9.7%
0 17
 
8.3%
g 16
 
7.8%
1 13
 
6.3%
k 10
 
4.9%
( 4
 
1.9%
) 4
 
1.9%
4 4
 
1.9%
3 4
 
1.9%
Other values (9) 12
 
5.8%
Hangul
ValueCountFrequency (%)
3
 
3.8%
3
 
3.8%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
2
 
2.5%
1
 
1.2%
1
 
1.2%
Other values (60) 60
75.0%
CJK Compat
ValueCountFrequency (%)
1
100.0%
Letterlike Symbols
ValueCountFrequency (%)
1
100.0%

금주가격(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6327.963
Minimum856
Maximum29193
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:12.808803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum856
5-th percentile1049.2
Q11831
median3034
Q39063
95-th percentile18033.4
Maximum29193
Range28337
Interquartile range (IQR)7232

Descriptive statistics

Standard deviation6803.3483
Coefficient of variation (CV)1.0751245
Kurtosis3.8866557
Mean6327.963
Median Absolute Deviation (MAD)1893
Skewness1.8956321
Sum170855
Variance46285548
MonotonicityNot monotonic
2024-03-18T13:08:12.980887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
19771 1
 
3.7%
1115 1
 
3.7%
29193 1
 
3.7%
6729 1
 
3.7%
10247 1
 
3.7%
1446 1
 
3.7%
13979 1
 
3.7%
2494 1
 
3.7%
13157 1
 
3.7%
1570 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
856 1
3.7%
1021 1
3.7%
1115 1
3.7%
1260 1
3.7%
1446 1
3.7%
1570 1
3.7%
1773 1
3.7%
1889 1
3.7%
2194 1
3.7%
2494 1
3.7%
ValueCountFrequency (%)
29193 1
3.7%
19771 1
3.7%
13979 1
3.7%
13157 1
3.7%
12941 1
3.7%
12271 1
3.7%
10247 1
3.7%
7879 1
3.7%
6729 1
3.7%
5996 1
3.7%

전주가격(원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6317.2593
Minimum856
Maximum28778
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:13.087980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum856
5-th percentile1049.9
Q11670.5
median3022
Q39063
95-th percentile18133.5
Maximum28778
Range27922
Interquartile range (IQR)7392.5

Descriptive statistics

Standard deviation6762.2761
Coefficient of variation (CV)1.0704446
Kurtosis3.6662574
Mean6317.2593
Median Absolute Deviation (MAD)1905
Skewness1.8590615
Sum170566
Variance45728378
MonotonicityNot monotonic
2024-03-18T13:08:13.186168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
19914 1
 
3.7%
1115 1
 
3.7%
28778 1
 
3.7%
6729 1
 
3.7%
10247 1
 
3.7%
1446 1
 
3.7%
13979 1
 
3.7%
2423 1
 
3.7%
13157 1
 
3.7%
1571 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
856 1
3.7%
1022 1
3.7%
1115 1
3.7%
1260 1
3.7%
1446 1
3.7%
1571 1
3.7%
1634 1
3.7%
1707 1
3.7%
2194 1
3.7%
2423 1
3.7%
ValueCountFrequency (%)
28778 1
3.7%
19914 1
3.7%
13979 1
3.7%
13157 1
3.7%
13111 1
3.7%
12056 1
3.7%
10247 1
3.7%
7879 1
3.7%
6729 1
3.7%
5996 1
3.7%

등락구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)11.1%
Missing0
Missing (%)0.0%
Memory size348.0 B
<NA>
14 

Length

Max length4
Median length4
Mean length2.5555556
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row
2nd row<NA>
3rd row
4th row
5th row

Common Values

ValueCountFrequency (%)
<NA> 14
51.9%
7
25.9%
6
22.2%

Length

2024-03-18T13:08:13.295470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T13:08:13.386167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 14
51.9%
7
25.9%
6
22.2%

등락가격(원)
Real number (ℝ)

ZEROS 

Distinct13
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73
Minimum0
Maximum414
Zeros15
Zeros (%)55.6%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:13.464383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3136
95-th percentile287.8
Maximum414
Range414
Interquartile range (IQR)136

Descriptive statistics

Standard deviation110.47415
Coefficient of variation (CV)1.5133446
Kurtosis2.5821197
Mean73
Median Absolute Deviation (MAD)0
Skewness1.671633
Sum1971
Variance12204.538
MonotonicityNot monotonic
2024-03-18T13:08:13.551845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0 15
55.6%
143 1
 
3.7%
215 1
 
3.7%
109 1
 
3.7%
133 1
 
3.7%
181 1
 
3.7%
319 1
 
3.7%
139 1
 
3.7%
170 1
 
3.7%
76 1
 
3.7%
Other values (3) 3
 
11.1%
ValueCountFrequency (%)
0 15
55.6%
1 1
 
3.7%
71 1
 
3.7%
76 1
 
3.7%
109 1
 
3.7%
133 1
 
3.7%
139 1
 
3.7%
143 1
 
3.7%
170 1
 
3.7%
181 1
 
3.7%
ValueCountFrequency (%)
414 1
3.7%
319 1
3.7%
215 1
3.7%
181 1
3.7%
170 1
3.7%
143 1
3.7%
139 1
3.7%
133 1
3.7%
109 1
3.7%
76 1
3.7%

등락율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct13
Distinct (%)48.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.36296296
Minimum-10.7
Maximum10.6
Zeros15
Zeros (%)55.6%
Negative6
Negative (%)22.2%
Memory size375.0 B
2024-03-18T13:08:13.640287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-10.7
5-th percentile-3.41
Q10
median0
Q30
95-th percentile7.06
Maximum10.6
Range21.3
Interquartile range (IQR)0

Descriptive statistics

Standard deviation3.6465103
Coefficient of variation (CV)10.046508
Kurtosis5.0873652
Mean0.36296296
Median Absolute Deviation (MAD)0
Skewness0.18241525
Sum9.8
Variance13.297037
MonotonicityNot monotonic
2024-03-18T13:08:13.727588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
0.0 15
55.6%
-0.7 1
 
3.7%
1.8 1
 
3.7%
3.7 1
 
3.7%
-3.8 1
 
3.7%
10.6 1
 
3.7%
-10.7 1
 
3.7%
8.5 1
 
3.7%
-1.3 1
 
3.7%
-2.5 1
 
3.7%
Other values (3) 3
 
11.1%
ValueCountFrequency (%)
-10.7 1
 
3.7%
-3.8 1
 
3.7%
-2.5 1
 
3.7%
-1.3 1
 
3.7%
-0.7 1
 
3.7%
-0.1 1
 
3.7%
0.0 15
55.6%
1.4 1
 
3.7%
1.8 1
 
3.7%
2.9 1
 
3.7%
ValueCountFrequency (%)
10.6 1
 
3.7%
8.5 1
 
3.7%
3.7 1
 
3.7%
2.9 1
 
3.7%
1.8 1
 
3.7%
1.4 1
 
3.7%
0.0 15
55.6%
-0.1 1
 
3.7%
-0.7 1
 
3.7%
-1.3 1
 
3.7%

롯데마트계양점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)95.8%
Missing3
Missing (%)11.1%
Infinite0
Infinite (%)0.0%
Mean6468.5417
Minimum820
Maximum27000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:13.934213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum820
5-th percentile984.5
Q11890
median3080
Q36932.5
95-th percentile22550
Maximum27000
Range26180
Interquartile range (IQR)5042.5

Descriptive statistics

Standard deviation7447.1766
Coefficient of variation (CV)1.1512914
Kurtosis1.9472093
Mean6468.5417
Median Absolute Deviation (MAD)1577
Skewness1.7027863
Sum155245
Variance55460439
MonotonicityNot monotonic
2024-03-18T13:08:14.057719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1890 2
 
7.4%
5580 1
 
3.7%
27000 1
 
3.7%
1380 1
 
3.7%
18300 1
 
3.7%
2500 1
 
3.7%
23300 1
 
3.7%
2770 1
 
3.7%
820 1
 
3.7%
1180 1
 
3.7%
Other values (13) 13
48.1%
(Missing) 3
 
11.1%
ValueCountFrequency (%)
820 1
3.7%
950 1
3.7%
1180 1
3.7%
1380 1
3.7%
1626 1
3.7%
1890 2
7.4%
2000 1
3.7%
2445 1
3.7%
2500 1
3.7%
2590 1
3.7%
ValueCountFrequency (%)
27000 1
3.7%
23300 1
3.7%
18300 1
3.7%
15966 1
3.7%
11650 1
3.7%
10990 1
3.7%
5580 1
3.7%
5180 1
3.7%
4128 1
3.7%
3990 1
3.7%

이마트
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)95.7%
Missing4
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean7521.3913
Minimum820
Maximum34000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:14.144472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum820
5-th percentile958
Q11673.5
median2980
Q39690
95-th percentile32410
Maximum34000
Range33180
Interquartile range (IQR)8016.5

Descriptive statistics

Standard deviation9678.838
Coefficient of variation (CV)1.2868414
Kurtosis3.4305725
Mean7521.3913
Median Absolute Deviation (MAD)1950
Skewness1.9969453
Sum172992
Variance93679905
MonotonicityNot monotonic
2024-03-18T13:08:14.237531image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2480 2
 
7.4%
950 1
 
3.7%
34000 1
 
3.7%
11900 1
 
3.7%
1380 1
 
3.7%
9000 1
 
3.7%
19000 1
 
3.7%
820 1
 
3.7%
1180 1
 
3.7%
1880 1
 
3.7%
Other values (12) 12
44.4%
(Missing) 4
 
14.8%
ValueCountFrequency (%)
820 1
3.7%
950 1
3.7%
1030 1
3.7%
1180 1
3.7%
1380 1
3.7%
1497 1
3.7%
1850 1
3.7%
1880 1
3.7%
2160 1
3.7%
2480 2
7.4%
ValueCountFrequency (%)
34000 1
3.7%
33900 1
3.7%
19000 1
3.7%
14950 1
3.7%
11900 1
3.7%
10380 1
3.7%
9000 1
3.7%
6880 1
3.7%
5580 1
3.7%
3495 1
3.7%

홈플러스계산점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)92.3%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean6427.3077
Minimum820
Maximum24900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:14.335476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum820
5-th percentile897.5
Q11907.5
median3650
Q39340
95-th percentile21750
Maximum24900
Range24080
Interquartile range (IQR)7432.5

Descriptive statistics

Standard deviation6733.5474
Coefficient of variation (CV)1.0476467
Kurtosis2.0152479
Mean6427.3077
Median Absolute Deviation (MAD)2100
Skewness1.6443173
Sum167110
Variance45340660
MonotonicityNot monotonic
2024-03-18T13:08:14.758835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2790 2
 
7.4%
11900 2
 
7.4%
880 1
 
3.7%
1880 1
 
3.7%
22900 1
 
3.7%
10900 1
 
3.7%
1380 1
 
3.7%
18300 1
 
3.7%
2490 1
 
3.7%
24900 1
 
3.7%
Other values (14) 14
51.9%
ValueCountFrequency (%)
820 1
3.7%
880 1
3.7%
950 1
3.7%
1180 1
3.7%
1380 1
3.7%
1790 1
3.7%
1880 1
3.7%
1990 1
3.7%
2490 1
3.7%
2790 2
7.4%
ValueCountFrequency (%)
24900 1
3.7%
22900 1
3.7%
18300 1
3.7%
11900 2
7.4%
10900 1
3.7%
9990 1
3.7%
7390 1
3.7%
5580 1
3.7%
5180 1
3.7%
4490 1
3.7%

홈플러스작전점
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)100.0%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean7002.6923
Minimum820
Maximum34000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:14.860983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum820
5-th percentile897.5
Q11907.5
median3155
Q39340
95-th percentile23250
Maximum34000
Range33180
Interquartile range (IQR)7432.5

Descriptive statistics

Standard deviation8212.9812
Coefficient of variation (CV)1.1728319
Kurtosis4.0533967
Mean7002.6923
Median Absolute Deviation (MAD)2000
Skewness2.0160869
Sum182070
Variance67453060
MonotonicityNot monotonic
2024-03-18T13:08:14.982735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
880 1
 
3.7%
1880 1
 
3.7%
34000 1
 
3.7%
11900 1
 
3.7%
12900 1
 
3.7%
1380 1
 
3.7%
18300 1
 
3.7%
2490 1
 
3.7%
24900 1
 
3.7%
2780 1
 
3.7%
Other values (16) 16
59.3%
ValueCountFrequency (%)
820 1
3.7%
880 1
3.7%
950 1
3.7%
1180 1
3.7%
1380 1
3.7%
1790 1
3.7%
1880 1
3.7%
1990 1
3.7%
2490 1
3.7%
2780 1
3.7%
ValueCountFrequency (%)
34000 1
3.7%
24900 1
3.7%
18300 1
3.7%
14990 1
3.7%
12900 1
3.7%
11900 1
3.7%
9990 1
3.7%
7390 1
3.7%
5580 1
3.7%
5180 1
3.7%

계산시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct24
Distinct (%)96.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean7182.52
Minimum860
Maximum38000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:15.110476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum860
5-th percentile1020
Q11666
median2650
Q38300
95-th percentile28099.2
Maximum38000
Range37140
Interquartile range (IQR)6634

Descriptive statistics

Standard deviation9254.9429
Coefficient of variation (CV)1.288537
Kurtosis5.7428814
Mean7182.52
Median Absolute Deviation (MAD)1650
Skewness2.3920979
Sum179563
Variance85653968
MonotonicityNot monotonic
2024-03-18T13:08:15.216453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
2500 2
 
7.4%
1100 1
 
3.7%
31249 1
 
3.7%
8300 1
 
3.7%
12250 1
 
3.7%
1550 1
 
3.7%
11150 1
 
3.7%
860 1
 
3.7%
1300 1
 
3.7%
6500 1
 
3.7%
Other values (14) 14
51.9%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
860 1
3.7%
1000 1
3.7%
1100 1
3.7%
1300 1
3.7%
1500 1
3.7%
1550 1
3.7%
1666 1
3.7%
2038 1
3.7%
2333 1
3.7%
2467 1
3.7%
ValueCountFrequency (%)
38000 1
3.7%
31249 1
3.7%
15500 1
3.7%
12250 1
3.7%
11150 1
3.7%
11000 1
3.7%
8300 1
3.7%
7300 1
3.7%
6500 1
3.7%
6350 1
3.7%

작전시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)80.8%
Missing1
Missing (%)3.7%
Infinite0
Infinite (%)0.0%
Mean7028.7308
Minimum950
Maximum33500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:15.314965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum950
5-th percentile1210
Q12000
median2990
Q37650
95-th percentile28150
Maximum33500
Range32550
Interquartile range (IQR)5650

Descriptive statistics

Standard deviation8686.8976
Coefficient of variation (CV)1.2359127
Kurtosis5.304738
Mean7028.7308
Median Absolute Deviation (MAD)1720
Skewness2.3342909
Sum182747
Variance75462191
MonotonicityNot monotonic
2024-03-18T13:08:15.413834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
2000 3
 
11.1%
13000 2
 
7.4%
3000 2
 
7.4%
2500 2
 
7.4%
33500 1
 
3.7%
1400 1
 
3.7%
33200 1
 
3.7%
7500 1
 
3.7%
12777 1
 
3.7%
1500 1
 
3.7%
Other values (11) 11
40.7%
ValueCountFrequency (%)
950 1
 
3.7%
1150 1
 
3.7%
1390 1
 
3.7%
1400 1
 
3.7%
1500 1
 
3.7%
2000 3
11.1%
2500 2
7.4%
2650 1
 
3.7%
2800 1
 
3.7%
2980 1
 
3.7%
ValueCountFrequency (%)
33500 1
3.7%
33200 1
3.7%
13000 2
7.4%
12777 1
3.7%
11800 1
3.7%
7700 1
3.7%
7500 1
3.7%
7200 1
3.7%
6250 1
3.7%
5000 1
3.7%

계양산전통시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)76.0%
Missing2
Missing (%)7.4%
Infinite0
Infinite (%)0.0%
Mean6410.64
Minimum900
Maximum33000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2024-03-18T13:08:15.523720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile1020
Q12000
median2650
Q37500
95-th percentile20233.4
Maximum33000
Range32100
Interquartile range (IQR)5500

Descriptive statistics

Standard deviation7500.2103
Coefficient of variation (CV)1.1699628
Kurtosis6.2037809
Mean6410.64
Median Absolute Deviation (MAD)1550
Skewness2.3455128
Sum160266
Variance56253154
MonotonicityNot monotonic
2024-03-18T13:08:15.617029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
11000 3
 
11.1%
7500 2
 
7.4%
2000 2
 
7.4%
4000 2
 
7.4%
2500 2
 
7.4%
2333 1
 
3.7%
1666 1
 
3.7%
13167 1
 
3.7%
2650 1
 
3.7%
1000 1
 
3.7%
Other values (9) 9
33.3%
(Missing) 2
 
7.4%
ValueCountFrequency (%)
900 1
3.7%
1000 1
3.7%
1100 1
3.7%
1400 1
3.7%
1550 1
3.7%
1666 1
3.7%
2000 2
7.4%
2200 1
3.7%
2333 1
3.7%
2500 2
7.4%
ValueCountFrequency (%)
33000 1
 
3.7%
22000 1
 
3.7%
13167 1
 
3.7%
11000 3
11.1%
7500 2
7.4%
6200 1
 
3.7%
6100 1
 
3.7%
4000 2
7.4%
2650 1
 
3.7%
2500 2
7.4%

Interactions

2024-03-18T13:08:09.598652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:00.646034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.590493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.399418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.255149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.098813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.100292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.989667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.784269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.660021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.504025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.701414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:00.794151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.673281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.471498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.328526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.177654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.186876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.069247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.858873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.730633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.573642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.790804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:00.874625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.744221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.547140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.399285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.251291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.261611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.144616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.931890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.801924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.644433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.875310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:00.943604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.810369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.623249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.459569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.311927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.327308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.215347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.001614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.867446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.708841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.963091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.012985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.874959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.684402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.523252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.578958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.410378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.289660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.073109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.934292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.011636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:10.051882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.083797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.940114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.755458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.594580image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.641109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.497326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.354721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.149685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.026385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.076279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:10.168672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.156770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.015050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.868103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.706233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.711022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.571260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.424902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.252206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.127734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.154418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:10.277300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.225528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.109310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.966412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.787608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.775115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.642459image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.494294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.350138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.192100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.222774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:10.392667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.322838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.185964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.042601image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.869089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.854261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.715586image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.584693image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.423564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.274058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.300072image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:10.497489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.398398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.258945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.112674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.946643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.920858image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.799577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.654615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.501992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.360869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.392746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:10.601837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:01.491140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:02.331971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:03.187403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:04.031196image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.017284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:05.900023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:06.723044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:07.590956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:08.434405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T13:08:09.503505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T13:08:15.701142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분품 목규격 및 단위금주가격(원)전주가격(원)등락구분등락가격(원)등락율(퍼센트)롯데마트계양점이마트홈플러스계산점홈플러스작전점계산시장작전시장계양산전통시장
구분1.0001.0001.0000.6850.6850.0000.4730.0000.7600.5900.5270.7580.6770.3040.784
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규격 및 단위1.0001.0001.0001.0001.0000.3390.0000.0000.9761.0000.9700.9781.0001.0001.000
금주가격(원)0.6851.0001.0001.0001.0000.0000.3490.0000.9210.9710.7530.8590.9180.9491.000
전주가격(원)0.6851.0001.0001.0001.0000.0000.3490.0000.9210.9710.7530.8590.9180.9491.000
등락구분0.0001.0000.3390.0000.0001.0000.0000.9570.0000.0000.0000.0000.0000.0000.000
등락가격(원)0.4731.0000.0000.3490.3490.0001.0000.8220.7900.6560.3810.8020.6150.1250.493
등락율(퍼센트)0.0001.0000.0000.0000.0000.9570.8221.0000.0000.0000.0000.0000.0000.0000.000
롯데마트계양점0.7601.0000.9760.9210.9210.0000.7900.0001.0000.9760.9680.9700.9450.9360.914
이마트0.5901.0001.0000.9710.9710.0000.6560.0000.9761.0000.9610.9690.9620.8500.956
홈플러스계산점0.5271.0000.9700.7530.7530.0000.3810.0000.9680.9611.0000.9310.8460.8310.849
홈플러스작전점0.7581.0000.9780.8590.8590.0000.8020.0000.9700.9690.9311.0000.8690.7960.820
계산시장0.6771.0001.0000.9180.9180.0000.6150.0000.9450.9620.8460.8691.0000.8770.982
작전시장0.3041.0001.0000.9490.9490.0000.1250.0000.9360.8500.8310.7960.8771.0000.951
계양산전통시장0.7841.0001.0001.0001.0000.0000.4930.0000.9140.9560.8490.8200.9820.9511.000
2024-03-18T13:08:15.829886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
등락구분구분
등락구분1.0000.000
구분0.0001.000
2024-03-18T13:08:15.904222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
금주가격(원)전주가격(원)등락가격(원)등락율(퍼센트)롯데마트계양점이마트홈플러스계산점홈플러스작전점계산시장작전시장계양산전통시장구분등락구분
금주가격(원)1.0000.9960.233-0.0820.9710.9840.9730.9810.9590.9570.9730.5320.000
전주가격(원)0.9961.0000.257-0.1290.9700.9800.9710.9770.9660.9560.9820.5320.000
등락가격(원)0.2330.2571.0000.0380.2780.3760.1300.1460.2770.3160.3480.2880.000
등락율(퍼센트)-0.082-0.1290.0381.000-0.057-0.101-0.087-0.063-0.191-0.108-0.1870.0000.641
롯데마트계양점0.9710.9700.278-0.0571.0000.9840.9680.9710.9360.9420.9600.5770.000
이마트0.9840.9800.376-0.1010.9841.0000.9600.9680.9620.9810.9810.4160.000
홈플러스계산점0.9730.9710.130-0.0870.9680.9601.0000.9960.9260.9150.9460.3780.000
홈플러스작전점0.9810.9770.146-0.0630.9710.9680.9961.0000.9330.9250.9490.5810.000
계산시장0.9590.9660.277-0.1910.9360.9620.9260.9331.0000.9800.9860.3300.000
작전시장0.9570.9560.316-0.1080.9420.9810.9150.9250.9801.0000.9830.2230.000
계양산전통시장0.9730.9820.348-0.1870.9600.9810.9460.9490.9860.9831.0000.4120.000
구분0.5320.5320.2880.0000.5770.4160.3780.5810.3300.2230.4121.0000.000
등락구분0.0000.0000.0000.6410.0000.0000.0000.0000.0000.0000.0000.0001.000

Missing values

2024-03-18T13:08:10.751626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T13:08:10.970472image/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.
2024-03-18T13:08:11.159371image/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(원)전주가격(원)등락구분등락가격(원)등락율(퍼센트)롯데마트계양점이마트홈플러스계산점홈플러스작전점계산시장작전시장계양산전통시장
0농축산물(14종)강화쌀 / 20kg1977119914143-0.7<NA>33900<NA><NA>380003350033000
1농축산물(14종)콩 나 물400g11151115<NA>00.016261030880880100013901000
2농축산물(14종)마 늘깐마늘 1kg12271120562151.8159661495099909990110001300011000
3농축산물(14종)양 파1 kg303429251093.73730322033103310233330002333
4농축산물(14종)대 파1 kg33703503133-3.84128298044904490250025002500
5농축산물(14종)1 kg1889170718110.61890185019901990150020002000
6농축산물(14종)배 추통배추 / 3kg26672986319-10.72590<NA>27902790350030004000
7농축산물(14종)사 과개당 / 300g177316341398.52000149717901790166620001666
8농축산물(14종)고 등 어1마리 / 40cm34273427<NA>00.03990349530003000500050004000
9농축산물(14종)멸 치다시멸치 / 400g27022702<NA>00.02445216044404360203820002000
구분품 목규격 및 단위금주가격(원)전주가격(원)등락구분등락가격(원)등락율(퍼센트)롯데마트계양점이마트홈플러스계산점홈플러스작전점계산시장작전시장계양산전통시장
17가공식품(10종)스낵과자새우깡 (소) / 1봉지12601260<NA>00.01180118011801180130014001400
18가공식품(10종)라 면신라면 / 1개856856<NA>00.0820820820820860950900
19가공식품(10종)삼립식빵 (소 ) / 1개157015711-0.12770<NA>27902780<NA>2650<NA>
20가공식품(10종)분 유남양분유1315713157<NA>00.023300190002490024900<NA><NA><NA>
21가공식품(10종)두 부손두부 / 1모24942423712.92500248024902490250025002500
22가공식품(10종)고 추 장해찬들 / 1kg1397913979<NA>00.01830090001830018300111501180011000
23가공식품(10종)소 주참이슬 / 1병14461446<NA>00.01380138013801380155015001550
24공산품(3종)세 제비트(리필) / 3kg1024710247<NA>00.0<NA>119001090012900122501277711000
25공산품(3종)샴 푸엘라스틴 / 600g67296729<NA>00.0<NA><NA>1190011900830075007500
26공산품(3종)화 장 지뽀삐 / 24롤29193287784141.427000340002290034000312493320022000