Overview

Dataset statistics

Number of variables10
Number of observations40
Missing cells1
Missing cells (%)0.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory89.3 B

Variable types

Categorical2
Text2
Numeric6

Dataset

Description농축산물, 수산물, 가공식품, 공산품의 물가데이터 기반의 경기도 연천군 소비자물가 현황 정보를 조회할 수 있도록 제공하는 데이터입니다.
Author경기도 연천군
URLhttps://www.data.go.kr/data/3046268/fileData.do

Alerts

기준일자 has constant value ""Constant
하나로마트(전곡) is highly overall correlated with 롯데슈퍼(전곡) and 4 other fieldsHigh correlation
롯데슈퍼(전곡) is highly overall correlated with 하나로마트(전곡) and 4 other fieldsHigh correlation
전곡재래시장(전곡) is highly overall correlated with 하나로마트(전곡) and 4 other fieldsHigh correlation
하나로마트(연천) is highly overall correlated with 하나로마트(전곡) and 4 other fieldsHigh correlation
연천마트(연천) is highly overall correlated with 하나로마트(전곡) and 4 other fieldsHigh correlation
K-마트(신서) is highly overall correlated with 하나로마트(전곡) and 4 other fieldsHigh correlation
전곡재래시장(전곡) has 1 (2.5%) missing valuesMissing
품목 has unique valuesUnique
하나로마트(전곡) has 1 (2.5%) zerosZeros
롯데슈퍼(전곡) has 4 (10.0%) zerosZeros
전곡재래시장(전곡) has 1 (2.5%) zerosZeros
연천마트(연천) has 4 (10.0%) zerosZeros
K-마트(신서) has 1 (2.5%) zerosZeros

Reproduction

Analysis started2023-12-12 00:16:33.856950
Analysis finished2023-12-12 00:16:38.049446
Duration4.19 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct5
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
농산물
16 
가공식품
10 
수산물
5 
공산품
5 
축산물
4 

Length

Max length4
Median length3
Mean length3.25
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 16
40.0%
가공식품 10
25.0%
수산물 5
 
12.5%
공산품 5
 
12.5%
축산물 4
 
10.0%

Length

2023-12-12T09:16:38.126205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:38.244136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 16
40.0%
가공식품 10
25.0%
수산물 5
 
12.5%
공산품 5
 
12.5%
축산물 4
 
10.0%

품목
Text

UNIQUE 

Distinct40
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T09:16:38.493999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.6
Min length1

Characters and Unicode

Total characters104
Distinct characters73
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique40 ?
Unique (%)100.0%

Sample

1st row쌀
2nd row배추
3rd row무
4th row대파
5th row양파
ValueCountFrequency (%)
쌀 1
 
2.5%
배추 1
 
2.5%
맥주 1
 
2.5%
물오징어 1
 
2.5%
동태 1
 
2.5%
김 1
 
2.5%
두부 1
 
2.5%
참기름 1
 
2.5%
식용유 1
 
2.5%
소주 1
 
2.5%
Other values (30) 30
75.0%
2023-12-12T09:16:38.904378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 6
 
5.8%
기 4
 
3.8%
추 4
 
3.8%
유 4
 
3.8%
용 3
 
2.9%
어 2
 
1.9%
주 2
 
1.9%
배 2
 
1.9%
우 2
 
1.9%
소 2
 
1.9%
Other values (63) 73
70.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 102
98.1%
Open Punctuation 1
 
1.0%
Close Punctuation 1
 
1.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 6
 
5.9%
기 4
 
3.9%
추 4
 
3.9%
유 4
 
3.9%
용 3
 
2.9%
어 2
 
2.0%
주 2
 
2.0%
배 2
 
2.0%
우 2
 
2.0%
소 2
 
2.0%
Other values (61) 71
69.6%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 102
98.1%
Common 2
 
1.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 6
 
5.9%
기 4
 
3.9%
추 4
 
3.9%
유 4
 
3.9%
용 3
 
2.9%
어 2
 
2.0%
주 2
 
2.0%
배 2
 
2.0%
우 2
 
2.0%
소 2
 
2.0%
Other values (61) 71
69.6%
Common
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 102
98.1%
ASCII 2
 
1.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 6
 
5.9%
기 4
 
3.9%
추 4
 
3.9%
유 4
 
3.9%
용 3
 
2.9%
어 2
 
2.0%
주 2
 
2.0%
배 2
 
2.0%
우 2
 
2.0%
소 2
 
2.0%
Other values (61) 71
69.6%
ASCII
ValueCountFrequency (%)
( 1
50.0%
) 1
50.0%

기준규격 및 단위
Text

Distinct34
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-12-12T09:16:39.119428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length16
Median length13
Mean length7.85
Min length2

Characters and Unicode

Total characters314
Distinct characters91
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique32 ?
Unique (%)80.0%

Sample

1st row일반미(20kg)
2nd row1포기(1.5kg)
3rd row재래종(중)
4th row1단
5th row작은망(1kg)
ValueCountFrequency (%)
1마리(중 5
 
11.4%
400g 3
 
6.8%
백설표(3kg 1
 
2.3%
살구맛사지(120g 1
 
2.3%
페리오(140g 1
 
2.3%
깨끗한나라30m(30개 1
 
2.3%
맥심(175g 1
 
2.3%
서울우유(1000ml 1
 
2.3%
계량김(100장 1
 
2.3%
농심신라면(120g 1
 
2.3%
Other values (28) 28
63.6%
2023-12-12T09:16:39.556553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 39
 
12.4%
( 31
 
9.9%
) 31
 
9.9%
1 25
 
8.0%
g 20
 
6.4%
중 9
 
2.9%
3 8
 
2.5%
5 7
 
2.2%
개 7
 
2.2%
m 7
 
2.2%
Other values (81) 130
41.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 110
35.0%
Decimal Number 94
29.9%
Lowercase Letter 40
 
12.7%
Open Punctuation 31
 
9.9%
Close Punctuation 31
 
9.9%
Space Separator 4
 
1.3%
Other Punctuation 4
 
1.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
중 9
 
8.2%
개 7
 
6.4%
리 7
 
6.4%
마 6
 
5.5%
표 4
 
3.6%
심 3
 
2.7%
오 2
 
1.8%
조 2
 
1.8%
백 2
 
1.8%
라 2
 
1.8%
Other values (62) 66
60.0%
Decimal Number
ValueCountFrequency (%)
0 39
41.5%
1 25
26.6%
3 8
 
8.5%
5 7
 
7.4%
4 5
 
5.3%
2 5
 
5.3%
6 3
 
3.2%
7 1
 
1.1%
8 1
 
1.1%
Lowercase Letter
ValueCountFrequency (%)
g 20
50.0%
m 7
 
17.5%
l 6
 
15.0%
k 6
 
15.0%
ℓ 1
 
2.5%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
. 2
50.0%
Open Punctuation
ValueCountFrequency (%)
( 31
100.0%
Close Punctuation
ValueCountFrequency (%)
) 31
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 165
52.5%
Hangul 110
35.0%
Latin 39
 
12.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
중 9
 
8.2%
개 7
 
6.4%
리 7
 
6.4%
마 6
 
5.5%
표 4
 
3.6%
심 3
 
2.7%
오 2
 
1.8%
조 2
 
1.8%
백 2
 
1.8%
라 2
 
1.8%
Other values (62) 66
60.0%
Common
ValueCountFrequency (%)
0 39
23.6%
( 31
18.8%
) 31
18.8%
1 25
15.2%
3 8
 
4.8%
5 7
 
4.2%
4 5
 
3.0%
2 5
 
3.0%
4
 
2.4%
6 3
 
1.8%
Other values (5) 7
 
4.2%
Latin
ValueCountFrequency (%)
g 20
51.3%
m 7
 
17.9%
l 6
 
15.4%
k 6
 
15.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 203
64.6%
Hangul 110
35.0%
Letterlike Symbols 1
 
0.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 39
19.2%
( 31
15.3%
) 31
15.3%
1 25
12.3%
g 20
9.9%
3 8
 
3.9%
5 7
 
3.4%
m 7
 
3.4%
l 6
 
3.0%
k 6
 
3.0%
Other values (8) 23
11.3%
Hangul
ValueCountFrequency (%)
중 9
 
8.2%
개 7
 
6.4%
리 7
 
6.4%
마 6
 
5.5%
표 4
 
3.6%
심 3
 
2.7%
오 2
 
1.8%
조 2
 
1.8%
백 2
 
1.8%
라 2
 
1.8%
Other values (62) 66
60.0%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

하나로마트(전곡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8349.75
Minimum0
Maximum69000
Zeros1
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:16:39.724894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile732.2
Q11693.75
median3990
Q37500
95-th percentile22205
Maximum69000
Range69000
Interquartile range (IQR)5806.25

Descriptive statistics

Standard deviation14607.651
Coefficient of variation (CV)1.7494716
Kurtosis12.947915
Mean8349.75
Median Absolute Deviation (MAD)2655
Skewness3.5656523
Sum333990
Variance2.1338346 × 108
MonotonicityNot monotonic
2023-12-12T09:16:39.882951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
7500 2
 
5.0%
1980 2
 
5.0%
5000 2
 
5.0%
2400 2
 
5.0%
736 1
 
2.5%
7980 1
 
2.5%
7580 1
 
2.5%
0 1
 
2.5%
1390 1
 
2.5%
1540 1
 
2.5%
Other values (26) 26
65.0%
ValueCountFrequency (%)
0 1
2.5%
660 1
2.5%
736 1
2.5%
1050 1
2.5%
1063 1
2.5%
1180 1
2.5%
1196 1
2.5%
1280 1
2.5%
1390 1
2.5%
1540 1
2.5%
ValueCountFrequency (%)
69000 1
2.5%
66000 1
2.5%
19900 1
2.5%
17700 1
2.5%
16200 1
2.5%
15900 1
2.5%
12800 1
2.5%
7980 1
2.5%
7580 1
2.5%
7500 2
5.0%

롯데슈퍼(전곡)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)82.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8014.9
Minimum0
Maximum53940
Zeros4
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:16:40.043031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11557.5
median3992.5
Q38237.5
95-th percentile27938
Maximum53940
Range53940
Interquartile range (IQR)6680

Descriptive statistics

Standard deviation11808.815
Coefficient of variation (CV)1.4733577
Kurtosis7.5523848
Mean8014.9
Median Absolute Deviation (MAD)2517.5
Skewness2.6944424
Sum320596
Variance1.3944811 × 108
MonotonicityNot monotonic
2023-12-12T09:16:40.206207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 4
 
10.0%
1490 3
 
7.5%
2990 2
 
5.0%
8990 2
 
5.0%
46900 1
 
2.5%
1710 1
 
2.5%
11900 1
 
2.5%
2500 1
 
2.5%
8980 1
 
2.5%
1460 1
 
2.5%
Other values (23) 23
57.5%
ValueCountFrequency (%)
0 4
10.0%
478 1
 
2.5%
820 1
 
2.5%
1460 1
 
2.5%
1490 3
7.5%
1580 1
 
2.5%
1710 1
 
2.5%
1790 1
 
2.5%
1998 1
 
2.5%
2490 1
 
2.5%
ValueCountFrequency (%)
53940 1
2.5%
46900 1
2.5%
26940 1
2.5%
23900 1
2.5%
22900 1
2.5%
15980 1
2.5%
11900 1
2.5%
8990 2
5.0%
8980 1
2.5%
7990 1
2.5%

전곡재래시장(전곡)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)48.7%
Missing1
Missing (%)2.5%
Infinite0
Infinite (%)0.0%
Mean6851.2821
Minimum0
Maximum66000
Zeros1
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:16:40.336652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile770
Q11600
median3000
Q38000
95-th percentile18200
Maximum66000
Range66000
Interquartile range (IQR)6400

Descriptive statistics

Standard deviation11019.43
Coefficient of variation (CV)1.6083748
Kurtosis22.507104
Mean6851.2821
Median Absolute Deviation (MAD)2000
Skewness4.3498898
Sum267200
Variance1.2142783 × 108
MonotonicityNot monotonic
2023-12-12T09:16:40.469910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
3000 5
12.5%
1500 4
10.0%
8000 4
10.0%
2000 3
 
7.5%
1000 3
 
7.5%
4000 3
 
7.5%
17000 2
 
5.0%
11000 2
 
5.0%
1700 2
 
5.0%
5000 2
 
5.0%
Other values (9) 9
22.5%
ValueCountFrequency (%)
0 1
 
2.5%
500 1
 
2.5%
800 1
 
2.5%
1000 3
7.5%
1500 4
10.0%
1700 2
 
5.0%
2000 3
7.5%
3000 5
12.5%
4000 3
7.5%
5000 2
 
5.0%
ValueCountFrequency (%)
66000 1
 
2.5%
20000 1
 
2.5%
18000 1
 
2.5%
17000 2
5.0%
11000 2
5.0%
8000 4
10.0%
7000 1
 
2.5%
6000 1
 
2.5%
5500 1
 
2.5%
5000 2
5.0%

하나로마트(연천)
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)85.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7749.25
Minimum450
Maximum78000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:16:40.605243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum450
5-th percentile716
Q11857.5
median3575
Q37000
95-th percentile20660
Maximum78000
Range77550
Interquartile range (IQR)5142.5

Descriptive statistics

Standard deviation14920.873
Coefficient of variation (CV)1.9254603
Kurtosis15.910359
Mean7749.25
Median Absolute Deviation (MAD)2130
Skewness3.9386053
Sum309970
Variance2.2263246 × 108
MonotonicityNot monotonic
2023-12-12T09:16:40.759243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
2200 3
 
7.5%
7000 2
 
5.0%
1980 2
 
5.0%
4000 2
 
5.0%
2000 2
 
5.0%
720 1
 
2.5%
7980 1
 
2.5%
1390 1
 
2.5%
1540 1
 
2.5%
5080 1
 
2.5%
Other values (24) 24
60.0%
ValueCountFrequency (%)
450 1
2.5%
640 1
2.5%
720 1
2.5%
950 1
2.5%
1000 1
2.5%
1050 1
2.5%
1390 1
2.5%
1500 1
2.5%
1540 1
2.5%
1580 1
2.5%
ValueCountFrequency (%)
78000 1
2.5%
59800 1
2.5%
18600 1
2.5%
15900 1
2.5%
12900 1
2.5%
8480 1
2.5%
8300 1
2.5%
7980 1
2.5%
7500 1
2.5%
7000 2
5.0%

연천마트(연천)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7458.5
Minimum0
Maximum67000
Zeros4
Zeros (%)10.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:16:40.919938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11125
median3100
Q37425
95-th percentile22000
Maximum67000
Range67000
Interquartile range (IQR)6300

Descriptive statistics

Standard deviation13837.373
Coefficient of variation (CV)1.8552488
Kurtosis13.279793
Mean7458.5
Median Absolute Deviation (MAD)2390
Skewness3.6039917
Sum298340
Variance1.914729 × 108
MonotonicityNot monotonic
2023-12-12T09:16:41.105575image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
10.0%
720 2
 
5.0%
1200 2
 
5.0%
4500 2
 
5.0%
8300 2
 
5.0%
1000 2
 
5.0%
5700 2
 
5.0%
1800 1
 
2.5%
2200 1
 
2.5%
1700 1
 
2.5%
Other values (21) 21
52.5%
ValueCountFrequency (%)
0 4
10.0%
600 1
 
2.5%
720 2
5.0%
1000 2
5.0%
1050 1
 
2.5%
1150 1
 
2.5%
1200 2
5.0%
1700 1
 
2.5%
1800 1
 
2.5%
2000 1
 
2.5%
ValueCountFrequency (%)
67000 1
2.5%
60000 1
2.5%
20000 1
2.5%
16000 1
2.5%
14000 1
2.5%
12600 1
2.5%
8300 2
5.0%
8000 1
2.5%
7500 1
2.5%
7400 1
2.5%

K-마트(신서)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)77.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7815
Minimum0
Maximum65000
Zeros1
Zeros (%)2.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-12T09:16:41.247179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile600
Q11500
median4000
Q37725
95-th percentile22350
Maximum65000
Range65000
Interquartile range (IQR)6225

Descriptive statistics

Standard deviation12553.139
Coefficient of variation (CV)1.6062878
Kurtosis12.883142
Mean7815
Median Absolute Deviation (MAD)2650
Skewness3.4378526
Sum312600
Variance1.5758131 × 108
MonotonicityNot monotonic
2023-12-12T09:16:41.392132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
4500 3
 
7.5%
7700 2
 
5.0%
1500 2
 
5.0%
6300 2
 
5.0%
5000 2
 
5.0%
600 2
 
5.0%
1300 2
 
5.0%
4000 2
 
5.0%
1700 1
 
2.5%
1900 1
 
2.5%
Other values (21) 21
52.5%
ValueCountFrequency (%)
0 1
2.5%
600 2
5.0%
900 1
2.5%
1000 1
2.5%
1200 1
2.5%
1300 2
5.0%
1400 1
2.5%
1500 2
5.0%
1700 1
2.5%
1900 1
2.5%
ValueCountFrequency (%)
65000 1
2.5%
48000 1
2.5%
21000 1
2.5%
19800 1
2.5%
16000 1
2.5%
15000 1
2.5%
11700 1
2.5%
8300 1
2.5%
8000 1
2.5%
7800 1
2.5%

기준일자
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2023-09-11
40 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2023-09-11
2nd row2023-09-11
3rd row2023-09-11
4th row2023-09-11
5th row2023-09-11

Common Values

ValueCountFrequency (%)
2023-09-11 40
100.0%

Length

2023-12-12T09:16:41.516353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T09:16:41.621717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2023-09-11 40
100.0%

Interactions

2023-12-12T09:16:37.215587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.207707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.726025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.234561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.692480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.540439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:37.311444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.288632image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.812021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.311038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.774029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.633113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:37.392558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.371275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.899204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.383582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.855294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.747919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:37.479154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.451739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.993731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.464511image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.249399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.849017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:37.572474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.537051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.083614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.544186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.345789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.971024image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:37.676083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:34.629062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.160381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:35.624882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:36.444100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T09:16:37.080038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T09:16:41.694445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분품목기준규격 및 단위하나로마트(전곡)롯데슈퍼(전곡)전곡재래시장(전곡)하나로마트(연천)연천마트(연천)K-마트(신서)
구분1.0001.0000.9060.1560.2800.6580.5340.6580.406
품목1.0001.0001.0001.0001.0001.0001.0001.0001.000
기준규격 및 단위0.9061.0001.0000.7850.9610.8711.0000.9790.982
하나로마트(전곡)0.1561.0000.7851.0000.9320.8240.7500.7700.913
롯데슈퍼(전곡)0.2801.0000.9610.9321.0000.8370.8550.9060.986
전곡재래시장(전곡)0.6581.0000.8710.8240.8371.0000.9290.9600.874
하나로마트(연천)0.5341.0001.0000.7500.8550.9291.0000.9750.887
연천마트(연천)0.6581.0000.9790.7700.9060.9600.9751.0000.966
K-마트(신서)0.4061.0000.9820.9130.9860.8740.8870.9661.000
2023-12-12T09:16:41.843345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
하나로마트(전곡)롯데슈퍼(전곡)전곡재래시장(전곡)하나로마트(연천)연천마트(연천)K-마트(신서)구분
하나로마트(전곡)1.0000.9120.7390.8340.6640.7490.115
롯데슈퍼(전곡)0.9121.0000.7190.8010.7390.7610.182
전곡재래시장(전곡)0.7390.7191.0000.7780.5840.6930.294
하나로마트(연천)0.8340.8010.7781.0000.7210.8370.223
연천마트(연천)0.6640.7390.5840.7211.0000.8600.295
K-마트(신서)0.7490.7610.6930.8370.8601.0000.280
구분0.1150.1820.2940.2230.2950.2801.000

Missing values

2023-12-12T09:16:37.811203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T09:16:37.983979image/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

구분품목기준규격 및 단위하나로마트(전곡)롯데슈퍼(전곡)전곡재래시장(전곡)하나로마트(연천)연천마트(연천)K-마트(신서)기준일자
0농산물쌀일반미(20kg)660004690005980067000650002023-09-11
1농산물배추1포기(1.5kg)4000399040003850450050002023-09-11
2농산물무재래종(중)1980179020001980120015002023-09-11
3농산물대파1단1980299030001980230025002023-09-11
4농산물양파작은망(1kg)2980299020002200200039002023-09-11
5농산물오이1개(조선)6604785004506006002023-09-11
6농산물감자1kg5000499040003300450050002023-09-11
7농산물애호박1개(400g)128014901000950100013002023-09-11
8농산물풋고추400g5200640030005500520040002023-09-11
9농산물상추400g2400249020002000320028002023-09-11
구분품목기준규격 및 단위하나로마트(전곡)롯데슈퍼(전곡)전곡재래시장(전곡)하나로마트(연천)연천마트(연천)K-마트(신서)기준일자
30가공식품설탕백설표(3kg)5080509055005080550063002023-09-11
31가공식품간장샘표양조(500ml)7500899080008300830077002023-09-11
32가공식품라면농심신라면(120g)7368208007207209002023-09-11
33가공식품우유서울우유(1000ml)27102790<NA>2750280029002023-09-11
34가공식품커피맥심(175g)654069901100070007400117002023-09-11
35공산품화장지깨끗한나라30m(30개)1990023900200001590016000210002023-09-11
36공산품치약페리오(140g)1180158015001580105014002023-09-11
37공산품세안용비누살구맛사지(120g)1050015001050115020002023-09-11
38공산품세탁용비누무궁화 표백비누(250g)1063010001000100012002023-09-11
39공산품섬유유연제피죤 (3100ml, 리필)5490670030005100570045002023-09-11