Overview

Dataset statistics

Number of variables11
Number of observations26
Missing cells7
Missing cells (%)2.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.6 KiB
Average record size in memory100.9 B

Variable types

Text3
Numeric8

Dataset

Description전라북도 군산시 소재한 대형마트, 일반마트마트별, 재래시장별 물가조사 현황(품목별, 규격단위, 마트, 시장, 등락율 등)
Author전라북도 군산시
URLhttps://www.data.go.kr/data/3080448/fileData.do

Alerts

이마트 is highly overall correlated with 화진마트 and 6 other fieldsHigh correlation
화진마트 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
이마트슈퍼 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
홈마트 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
공설시장 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
주공시장 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
11월셋째주 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
12월셋째주 is highly overall correlated with 이마트 and 6 other fieldsHigh correlation
이마트슈퍼 has 3 (11.5%) missing valuesMissing
홈마트 has 4 (15.4%) missing valuesMissing
품목별 has unique valuesUnique
이마트 has unique valuesUnique
11월셋째주 has unique valuesUnique
12월셋째주 has unique valuesUnique
등락율(퍼센트) has unique valuesUnique

Reproduction

Analysis started2024-03-14 12:45:12.792901
Analysis finished2024-03-14 12:45:28.147698
Duration15.35 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목별
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size336.0 B
2024-03-14T21:45:28.850726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.4230769
Min length1

Characters and Unicode

Total characters63
Distinct characters50
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

Unique26 ?
Unique (%)100.0%

Sample

1st row쌀
2nd row무
3rd row배추
4th row양파
5th row대파
ValueCountFrequency (%)
쌀 1
 
3.8%
무 1
 
3.8%
참기름 1
 
3.8%
식용유 1
 
3.8%
설탕 1
 
3.8%
밀가루 1
 
3.8%
라면 1
 
3.8%
간장 1
 
3.8%
두부 1
 
3.8%
커피(믹스 1
 
3.8%
Other values (16) 16
61.5%
2024-03-14T21:45:29.877751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 4
 
6.3%
고 4
 
6.3%
어 2
 
3.2%
배 2
 
3.2%
라 2
 
3.2%
장 2
 
3.2%
파 2
 
3.2%
주 2
 
3.2%
지 2
 
3.2%
간 1
 
1.6%
Other values (40) 40
63.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 61
96.8%
Close Punctuation 1
 
1.6%
Open Punctuation 1
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 4
 
6.6%
고 4
 
6.6%
어 2
 
3.3%
배 2
 
3.3%
라 2
 
3.3%
장 2
 
3.3%
파 2
 
3.3%
주 2
 
3.3%
지 2
 
3.3%
간 1
 
1.6%
Other values (38) 38
62.3%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 61
96.8%
Common 2
 
3.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 4
 
6.6%
고 4
 
6.6%
어 2
 
3.3%
배 2
 
3.3%
라 2
 
3.3%
장 2
 
3.3%
파 2
 
3.3%
주 2
 
3.3%
지 2
 
3.3%
간 1
 
1.6%
Other values (38) 38
62.3%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 61
96.8%
ASCII 2
 
3.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 4
 
6.6%
고 4
 
6.6%
어 2
 
3.3%
배 2
 
3.3%
라 2
 
3.3%
장 2
 
3.3%
파 2
 
3.3%
주 2
 
3.3%
지 2
 
3.3%
간 1
 
1.6%
Other values (38) 38
62.3%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

규격단위
Text

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size336.0 B
2024-03-14T21:45:30.597027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length11
Mean length9.2692308
Min length3

Characters and Unicode

Total characters241
Distinct characters89
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks6 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)88.5%

Sample

1st row일반미/20kg
2nd row1개(大)
3rd row1포기
4th row1kg
5th row흙대파 1단
ValueCountFrequency (%)
1개(大 3
 
10.7%
일반미/20kg 1
 
3.6%
백설표/320㎖(진한 1
 
3.6%
백설표/1.8ℓ/콩기름 1
 
3.6%
백설표/정백당/1kg 1
 
3.6%
백설표/중력분/1kg 1
 
3.6%
신라면/120g/1봉지 1
 
3.6%
햇살담은조림간장/1.7ℓ 1
 
3.6%
1모/300g/수입산 1
 
3.6%
인스턴트/100p 1
 
3.6%
Other values (16) 16
57.1%
2024-03-14T21:45:31.646612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
/ 28
 
11.6%
0 24
 
10.0%
1 23
 
9.5%
g 12
 
5.0%
( 6
 
2.5%
) 6
 
2.5%
5 5
 
2.1%
3 5
 
2.1%
k 5
 
2.1%
2 5
 
2.1%
Other values (79) 122
50.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 92
38.2%
Decimal Number 69
28.6%
Other Punctuation 31
 
12.9%
Lowercase Letter 28
 
11.6%
Open Punctuation 6
 
2.5%
Close Punctuation 6
 
2.5%
Other Symbol 3
 
1.2%
Uppercase Letter 3
 
1.2%
Space Separator 2
 
0.8%
Math Symbol 1
 
0.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
백 5
 
5.4%
설 4
 
4.3%
표 4
 
4.3%
개 4
 
4.3%
마 3
 
3.3%
리 3
 
3.3%
大 3
 
3.3%
동 2
 
2.2%
냉 2
 
2.2%
병 2
 
2.2%
Other values (54) 60
65.2%
Decimal Number
ValueCountFrequency (%)
0 24
34.8%
1 23
33.3%
5 5
 
7.2%
3 5
 
7.2%
2 5
 
7.2%
6 3
 
4.3%
8 2
 
2.9%
7 1
 
1.4%
4 1
 
1.4%
Lowercase Letter
ValueCountFrequency (%)
g 12
42.9%
k 5
17.9%
m 4
 
14.3%
ℓ 3
 
10.7%
c 3
 
10.7%
p 1
 
3.6%
Uppercase Letter
ValueCountFrequency (%)
P 1
33.3%
E 1
33.3%
T 1
33.3%
Other Punctuation
ValueCountFrequency (%)
/ 28
90.3%
. 3
 
9.7%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Other Symbol
ValueCountFrequency (%)
㎖ 3
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Math Symbol
ValueCountFrequency (%)
× 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 121
50.2%
Hangul 89
36.9%
Latin 28
 
11.6%
Han 3
 
1.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
백 5
 
5.6%
설 4
 
4.5%
표 4
 
4.5%
개 4
 
4.5%
마 3
 
3.4%
리 3
 
3.4%
동 2
 
2.2%
냉 2
 
2.2%
병 2
 
2.2%
라 2
 
2.2%
Other values (53) 58
65.2%
Common
ValueCountFrequency (%)
/ 28
23.1%
0 24
19.8%
1 23
19.0%
( 6
 
5.0%
) 6
 
5.0%
5 5
 
4.1%
3 5
 
4.1%
2 5
 
4.1%
6 3
 
2.5%
ℓ 3
 
2.5%
Other values (7) 13
10.7%
Latin
ValueCountFrequency (%)
g 12
42.9%
k 5
17.9%
m 4
 
14.3%
c 3
 
10.7%
p 1
 
3.6%
P 1
 
3.6%
E 1
 
3.6%
T 1
 
3.6%
Han
ValueCountFrequency (%)
大 3
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 142
58.9%
Hangul 89
36.9%
Letterlike Symbols 3
 
1.2%
CJK Compat 3
 
1.2%
CJK 3
 
1.2%
None 1
 
0.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
/ 28
19.7%
0 24
16.9%
1 23
16.2%
g 12
8.5%
( 6
 
4.2%
) 6
 
4.2%
5 5
 
3.5%
3 5
 
3.5%
k 5
 
3.5%
2 5
 
3.5%
Other values (12) 23
16.2%
Hangul
ValueCountFrequency (%)
백 5
 
5.6%
설 4
 
4.5%
표 4
 
4.5%
개 4
 
4.5%
마 3
 
3.4%
리 3
 
3.4%
동 2
 
2.2%
냉 2
 
2.2%
병 2
 
2.2%
라 2
 
2.2%
Other values (53) 58
65.2%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 3
100.0%
CJK Compat
ValueCountFrequency (%)
㎖ 3
100.0%
CJK
ValueCountFrequency (%)
大 3
100.0%
None
ValueCountFrequency (%)
× 1
100.0%

이마트
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8009.8846
Minimum780
Maximum59800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:32.020019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum780
5-th percentile1055
Q12080
median4810
Q38855
95-th percentile17545
Maximum59800
Range59020
Interquartile range (IQR)6775

Descriptive statistics

Standard deviation11619.601
Coefficient of variation (CV)1.4506578
Kurtosis16.773151
Mean8009.8846
Median Absolute Deviation (MAD)3038.5
Skewness3.8077435
Sum208257
Variance1.3501514 × 108
MonotonicityNot monotonic
2024-03-14T21:45:32.399966image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
59800 1
 
3.8%
1380 1
 
3.8%
17900 1
 
3.8%
11480 1
 
3.8%
8480 1
 
3.8%
2380 1
 
3.8%
2350 1
 
3.8%
780 1
 
3.8%
7200 1
 
3.8%
990 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
780 1
3.8%
990 1
3.8%
1250 1
3.8%
1380 1
3.8%
1660 1
3.8%
1883 1
3.8%
1990 1
3.8%
2350 1
3.8%
2380 1
3.8%
2489 1
3.8%
ValueCountFrequency (%)
59800 1
3.8%
17900 1
3.8%
16480 1
3.8%
13380 1
3.8%
11480 1
3.8%
10980 1
3.8%
8980 1
3.8%
8480 1
3.8%
7450 1
3.8%
7210 1
3.8%

화진마트
Real number (ℝ)

HIGH CORRELATION 

Distinct25
Distinct (%)96.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8904.6154
Minimum780
Maximum63900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:32.774133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum780
5-th percentile1087.5
Q12020
median3875
Q311085
95-th percentile22150
Maximum63900
Range63120
Interquartile range (IQR)9065

Descriptive statistics

Standard deviation12790.315
Coefficient of variation (CV)1.4363692
Kurtosis14.157885
Mean8904.6154
Median Absolute Deviation (MAD)2690
Skewness3.4496467
Sum231520
Variance1.6359216 × 108
MonotonicityNot monotonic
2024-03-14T21:45:33.068399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
1900 2
 
7.7%
63900 1
 
3.8%
1380 1
 
3.8%
22900 1
 
3.8%
11480 1
 
3.8%
7100 1
 
3.8%
2380 1
 
3.8%
780 1
 
3.8%
9000 1
 
3.8%
990 1
 
3.8%
Other values (15) 15
57.7%
ValueCountFrequency (%)
780 1
3.8%
990 1
3.8%
1380 1
3.8%
1600 1
3.8%
1680 1
3.8%
1900 2
7.7%
2380 1
3.8%
2605 1
3.8%
2650 1
3.8%
2690 1
3.8%
ValueCountFrequency (%)
63900 1
3.8%
22900 1
3.8%
19900 1
3.8%
16480 1
3.8%
13310 1
3.8%
12785 1
3.8%
11480 1
3.8%
9900 1
3.8%
9000 1
3.8%
7490 1
3.8%

이마트슈퍼
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)91.3%
Missing3
Missing (%)11.5%
Infinite0
Infinite (%)0.0%
Mean9509.1304
Minimum780
Maximum79900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:33.288147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum780
5-th percentile1483
Q12105
median3980
Q39190
95-th percentile24190
Maximum79900
Range79120
Interquartile range (IQR)7085

Descriptive statistics

Standard deviation16441.733
Coefficient of variation (CV)1.7290469
Kurtosis16.690574
Mean9509.1304
Median Absolute Deviation (MAD)2290
Skewness3.9014778
Sum218710
Variance2.7033057 × 108
MonotonicityNot monotonic
2024-03-14T21:45:33.506082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
3000 2
 
7.7%
1980 2
 
7.7%
17800 1
 
3.8%
24900 1
 
3.8%
11980 1
 
3.8%
9480 1
 
3.8%
3180 1
 
3.8%
2000 1
 
3.8%
780 1
 
3.8%
12900 1
 
3.8%
Other values (11) 11
42.3%
(Missing) 3
 
11.5%
ValueCountFrequency (%)
780 1
3.8%
1460 1
3.8%
1690 1
3.8%
1980 2
7.7%
2000 1
3.8%
2210 1
3.8%
3000 2
7.7%
3180 1
3.8%
3950 1
3.8%
3980 1
3.8%
ValueCountFrequency (%)
79900 1
3.8%
24900 1
3.8%
17800 1
3.8%
12900 1
3.8%
11980 1
3.8%
9480 1
3.8%
8900 1
3.8%
7550 1
3.8%
6490 1
3.8%
4980 1
3.8%

홈마트
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing4
Missing (%)15.4%
Infinite0
Infinite (%)0.0%
Mean8278.6364
Minimum890
Maximum69800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:33.723839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum890
5-th percentile1357.5
Q12062.5
median3530
Q38450
95-th percentile23135
Maximum69800
Range68910
Interquartile range (IQR)6387.5

Descriptive statistics

Standard deviation14657.946
Coefficient of variation (CV)1.7705749
Kurtosis16.299978
Mean8278.6364
Median Absolute Deviation (MAD)1855
Skewness3.8870198
Sum182130
Variance2.1485538 × 108
MonotonicityNot monotonic
2024-03-14T21:45:33.942944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
1850 1
 
3.8%
23800 1
 
3.8%
10500 1
 
3.8%
8980 1
 
3.8%
2490 1
 
3.8%
2280 1
 
3.8%
890 1
 
3.8%
9800 1
 
3.8%
1350 1
 
3.8%
4680 1
 
3.8%
Other values (12) 12
46.2%
(Missing) 4
 
15.4%
ValueCountFrequency (%)
890 1
3.8%
1350 1
3.8%
1500 1
3.8%
1850 1
3.8%
1980 1
3.8%
1990 1
3.8%
2280 1
3.8%
2490 1
3.8%
2650 1
3.8%
3000 1
3.8%
ValueCountFrequency (%)
69800 1
3.8%
23800 1
3.8%
10500 1
3.8%
9800 1
3.8%
8980 1
3.8%
8500 1
3.8%
8300 1
3.8%
6980 1
3.8%
4680 1
3.8%
3750 1
3.8%

공설시장
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8364.5
Minimum760
Maximum64000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:34.315251image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum760
5-th percentile1125
Q12312.5
median3391.5
Q37468.75
95-th percentile23625
Maximum64000
Range63240
Interquartile range (IQR)5156.25

Descriptive statistics

Standard deviation12848.787
Coefficient of variation (CV)1.5361093
Kurtosis14.703492
Mean8364.5
Median Absolute Deviation (MAD)2141.5
Skewness3.5822205
Sum217477
Variance1.6509132 × 108
MonotonicityNot monotonic
2024-03-14T21:45:34.696573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
3267 2
 
7.7%
1500 2
 
7.7%
64000 1
 
3.8%
3450 1
 
3.8%
25000 1
 
3.8%
15500 1
 
3.8%
6900 1
 
3.8%
2800 1
 
3.8%
2200 1
 
3.8%
760 1
 
3.8%
Other values (14) 14
53.8%
ValueCountFrequency (%)
760 1
3.8%
1000 1
3.8%
1500 2
7.7%
1800 1
3.8%
2000 1
3.8%
2200 1
3.8%
2650 1
3.8%
2800 1
3.8%
3000 1
3.8%
3267 2
7.7%
ValueCountFrequency (%)
64000 1
3.8%
25000 1
3.8%
19500 1
3.8%
15500 1
3.8%
13900 1
3.8%
7900 1
3.8%
7500 1
3.8%
7375 1
3.8%
7200 1
3.8%
6900 1
3.8%

주공시장
Real number (ℝ)

HIGH CORRELATION 

Distinct23
Distinct (%)88.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8622.6923
Minimum840
Maximum70500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:35.056431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum840
5-th percentile1400
Q12867.5
median5000
Q38550
95-th percentile24750
Maximum70500
Range69660
Interquartile range (IQR)5682.5

Descriptive statistics

Standard deviation13814.403
Coefficient of variation (CV)1.6020985
Kurtosis17.296512
Mean8622.6923
Median Absolute Deviation (MAD)2700
Skewness3.9631821
Sum224190
Variance1.9083772 × 108
MonotonicityNot monotonic
2024-03-14T21:45:35.444716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
5000 4
 
15.4%
70500 1
 
3.8%
3300 1
 
3.8%
27700 1
 
3.8%
10400 1
 
3.8%
8800 1
 
3.8%
3000 1
 
3.8%
2100 1
 
3.8%
840 1
 
3.8%
9500 1
 
3.8%
Other values (13) 13
50.0%
ValueCountFrequency (%)
840 1
3.8%
1350 1
3.8%
1550 1
3.8%
1850 1
3.8%
2100 1
3.8%
2400 1
3.8%
2830 1
3.8%
2980 1
3.8%
3000 1
3.8%
3300 1
3.8%
ValueCountFrequency (%)
70500 1
3.8%
27700 1
3.8%
15900 1
3.8%
10400 1
3.8%
10000 1
3.8%
9500 1
3.8%
8800 1
3.8%
7800 1
3.8%
7300 1
3.8%
5900 1
3.8%

11월셋째주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8724
Minimum798
Maximum67480
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:35.815900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum798
5-th percentile1264
Q12559
median4116.5
Q38752
95-th percentile21566.25
Maximum67480
Range66682
Interquartile range (IQR)6193

Descriptive statistics

Standard deviation13173.691
Coefficient of variation (CV)1.5100517
Kurtosis16.853743
Mean8724
Median Absolute Deviation (MAD)2792.5
Skewness3.8450023
Sum226824
Variance1.7354614 × 108
MonotonicityNot monotonic
2024-03-14T21:45:36.212589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
67480 1
 
3.8%
1444 1
 
3.8%
22900 1
 
3.8%
12188 1
 
3.8%
8188 1
 
3.8%
2646 1
 
3.8%
2146 1
 
3.8%
798 1
 
3.8%
8940 1
 
3.8%
1204 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
798 1
3.8%
1204 1
3.8%
1444 1
3.8%
1664 1
3.8%
1794 1
3.8%
2146 1
3.8%
2530 1
3.8%
2646 1
3.8%
2804 1
3.8%
3135 1
3.8%
ValueCountFrequency (%)
67480 1
3.8%
22900 1
3.8%
17565 1
3.8%
12752 1
3.8%
12223 1
3.8%
12188 1
3.8%
8940 1
3.8%
8188 1
3.8%
8054 1
3.8%
7861 1
3.8%

12월셋째주
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8675.8462
Minimum805
Maximum67983
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size362.0 B
2024-03-14T21:45:36.795347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum805
5-th percentile1286.5
Q12718
median4122
Q38847.25
95-th percentile22083
Maximum67983
Range67178
Interquartile range (IQR)6129.25

Descriptive statistics

Standard deviation13244.472
Coefficient of variation (CV)1.5265914
Kurtosis17.186797
Mean8675.8462
Median Absolute Deviation (MAD)2777
Skewness3.9027364
Sum225572
Variance1.7541604 × 108
MonotonicityNot monotonic
2024-03-14T21:45:37.185095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
67983 1
 
3.8%
1462 1
 
3.8%
23700 1
 
3.8%
11890 1
 
3.8%
8290 1
 
3.8%
2705 1
 
3.8%
2138 1
 
3.8%
805 1
 
3.8%
9033 1
 
3.8%
1228 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
805 1
3.8%
1228 1
3.8%
1462 1
3.8%
1787 1
3.8%
1803 1
3.8%
2138 1
3.8%
2705 1
3.8%
2757 1
3.8%
3188 1
3.8%
3320 1
3.8%
ValueCountFrequency (%)
67983 1
3.8%
23700 1
3.8%
17232 1
3.8%
12064 1
3.8%
11890 1
3.8%
10344 1
3.8%
9033 1
3.8%
8290 1
3.8%
8012 1
3.8%
7328 1
3.8%

등락율(퍼센트)
Text

UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size336.0 B
2024-03-14T21:45:37.999614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.5
Min length5

Characters and Unicode

Total characters143
Distinct characters13
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)100.0%

Sample

1st row0.00%
2nd row7.37%
3rd row9.91%
4th row8.96%
5th row-1.29%
ValueCountFrequency (%)
0.00 1
 
3.8%
7.37 1
 
3.8%
2.45 1
 
3.8%
1.25 1
 
3.8%
2.23 1
 
3.8%
0.36 1
 
3.8%
0.88 1
 
3.8%
1.04 1
 
3.8%
2.02 1
 
3.8%
1.90 1
 
3.8%
Other values (16) 16
61.5%
2024-03-14T21:45:39.246659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 26
18.2%
% 26
18.2%
0 14
9.8%
2 13
9.1%
1 12
8.4%
- 11
7.7%
5 10
 
7.0%
9 8
 
5.6%
3 7
 
4.9%
8 6
 
4.2%
Other values (3) 10
 
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
55.9%
Other Punctuation 52
36.4%
Dash Punctuation 11
 
7.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
17.5%
2 13
16.2%
1 12
15.0%
5 10
12.5%
9 8
10.0%
3 7
8.8%
8 6
7.5%
7 4
 
5.0%
4 4
 
5.0%
6 2
 
2.5%
Other Punctuation
ValueCountFrequency (%)
. 26
50.0%
% 26
50.0%
Dash Punctuation
ValueCountFrequency (%)
- 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 143
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 26
18.2%
% 26
18.2%
0 14
9.8%
2 13
9.1%
1 12
8.4%
- 11
7.7%
5 10
 
7.0%
9 8
 
5.6%
3 7
 
4.9%
8 6
 
4.2%
Other values (3) 10
 
7.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 143
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 26
18.2%
% 26
18.2%
0 14
9.8%
2 13
9.1%
1 12
8.4%
- 11
7.7%
5 10
 
7.0%
9 8
 
5.6%
3 7
 
4.9%
8 6
 
4.2%
Other values (3) 10
 
7.0%

Interactions

2024-03-14T21:45:26.188260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:13.280528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:15.312807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:17.331033image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:19.444062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:21.501868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:23.197369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:24.854048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:26.340709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:13.528690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:15.561801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:17.585880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:19.697246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:21.745618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:23.346543image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:25.002517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:26.495239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:13.779587image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:15.806571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:17.853539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:19.951767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:21.992561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:23.568378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:25.153628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:26.663230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:14.038270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:16.068413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:18.120900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:20.217824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:22.251284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:23.754934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:25.319648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:26.818784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:14.298724image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:16.320751image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:18.382876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:20.473639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:22.509664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:23.916758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:25.473850image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:26.971715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:14.546102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:16.570086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:18.642825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:20.728007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:22.734887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:24.077408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:25.627288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:27.218423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:14.801558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:16.823045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:18.908520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:20.985147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:22.888991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:24.438309image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:25.784412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:27.373987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:15.058136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:17.078303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:19.178065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:21.244852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:23.041319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:24.608799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-14T21:45:26.031172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-14T21:45:39.517793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목별규격단위이마트화진마트이마트슈퍼홈마트공설시장주공시장11월셋째주12월셋째주등락율(퍼센트)
품목별1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규격단위1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
이마트1.0001.0001.0000.8150.9050.9840.8660.8320.8790.8531.000
화진마트1.0001.0000.8151.0000.9940.9950.9450.9830.9860.9811.000
이마트슈퍼1.0001.0000.9050.9941.0001.0000.9720.9990.9991.0001.000
홈마트1.0001.0000.9840.9951.0001.0000.8760.9951.0001.0001.000
공설시장1.0001.0000.8660.9450.9720.8761.0000.9600.9780.9721.000
주공시장1.0001.0000.8320.9830.9990.9950.9601.0000.9970.9981.000
11월셋째주1.0001.0000.8790.9860.9991.0000.9780.9971.0001.0001.000
12월셋째주1.0001.0000.8530.9811.0001.0000.9720.9981.0001.0001.000
등락율(퍼센트)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2024-03-14T21:45:39.847454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
이마트화진마트이마트슈퍼홈마트공설시장주공시장11월셋째주12월셋째주
이마트1.0000.9470.9020.8860.9430.8440.9630.954
화진마트0.9471.0000.8950.9410.9280.8410.9590.956
이마트슈퍼0.9020.8951.0000.9570.9410.9410.9680.974
홈마트0.8860.9410.9571.0000.9530.9180.9580.979
공설시장0.9430.9280.9410.9531.0000.8450.9710.970
주공시장0.8440.8410.9410.9180.8451.0000.8740.901
11월셋째주0.9630.9590.9680.9580.9710.8741.0000.995
12월셋째주0.9540.9560.9740.9790.9700.9010.9951.000

Missing values

2024-03-14T21:45:27.592882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T21:45:27.882473image/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.
2024-03-14T21:45:28.064205image/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

품목별규격단위이마트화진마트이마트슈퍼홈마트공설시장주공시장11월셋째주12월셋째주등락율(퍼센트)
0쌀일반미/20kg59800639007990069800640007050067480679830.00%
1무1개(大)125016001980199015002400166417877.37%
2배추1포기188319004980358033335000313534469.91%
3양파1kg248933002210265020003890253027578.96%
4대파흙대파 1단45802605398019803000298032293188-1.29%
5사과1개(大)397526503000348032674300327434455.22%
6배1개(大)549044506490375032675000468947411.10%
7쇠고기한우등심/100g1338013310<NA>83001390028301222310344-15.37%
8돼지고기삼겹살/100g2680269030003000265059002804332018.40%
9닭고기육계/1kg89805990890085007900780080548012-0.53%
품목별규격단위이마트화진마트이마트슈퍼홈마트공설시장주공시장11월셋째주12월셋째주등락율(퍼센트)
16콜라코카콜라/PET/1.8ℓ19902980462046803450330035443503-1.15%
17커피(믹스)가루형 인스턴트/100p164801648017800<NA>19500159001756517232-1.90%
18두부1모/300g/수입산9909901690135010001350120412282.02%
19간장햇살담은조림간장/1.7ℓ7200900012900980058009500894090331.04%
20라면신라면/120g/1봉지7807807808907608407988050.88%
21밀가루백설표/중력분/1kg23501900200022802200210021462138-0.36%
22설탕백설표/정백당/1kg238023803180249028003000264627052.23%
23식용유백설표/1.8ℓ/콩기름848071009480898069008800818882901.25%
24참기름백설표/320㎖(진한)1148011480119801050015500104001218811890-2.45%
25화장지60m×24롤17900229002490023800250002770022900237003.49%