Overview

Dataset statistics

Number of variables15
Number of observations41
Missing cells14
Missing cells (%)2.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory132.2 B

Variable types

Categorical3
Text2
Numeric9
DateTime1

Dataset

Description대전광역시 중구의 장바구니 물가동향 정보에 대한 데이터로 농산물,축산물,공산품,수산물 품목 정보를 제공합니다.
Author대전광역시 중구
URLhttps://www.data.go.kr/data/15008334/fileData.do

Alerts

관리기관 has constant value ""Constant
연락처 has constant value ""Constant
데이터기준일 has constant value ""Constant
평균가(단위_원) is highly overall correlated with 홈플러스 and 7 other fieldsHigh correlation
홈플러스 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
하나로마트 is highly overall correlated with 평균가(단위_원)High correlation
문창시장 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
산성시장 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
유천시장 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
태평시장 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
오류시장 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
부사동 is highly overall correlated with 평균가(단위_원) and 6 other fieldsHigh correlation
홈플러스 has 5 (12.2%) missing valuesMissing
하나로마트 has 1 (2.4%) missing valuesMissing
문창시장 has 1 (2.4%) missing valuesMissing
산성시장 has 1 (2.4%) missing valuesMissing
오류시장 has 4 (9.8%) missing valuesMissing
부사동 has 2 (4.9%) missing valuesMissing
조사기준 has unique valuesUnique
평균가(단위_원) has unique valuesUnique

Reproduction

Analysis started2023-12-16 15:58:01.245737
Analysis finished2023-12-16 15:58:48.490629
Duration47.24 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct4
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size460.0 B
농산물
16 
공산품
16 
수산물
5 
축산물
4 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
농산물 16
39.0%
공산품 16
39.0%
수산물 5
 
12.2%
축산물 4
 
9.8%

Length

2023-12-16T15:58:48.849395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:58:49.526721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
농산물 16
39.0%
공산품 16
39.0%
수산물 5
 
12.2%
축산물 4
 
9.8%

품목
Text

Distinct38
Distinct (%)92.7%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-16T15:58:50.343442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length2
Mean length2.2439024
Min length1

Characters and Unicode

Total characters92
Distinct characters69
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

Unique35 ?
Unique (%)85.4%

Sample

1st row쌀
2nd row쌀
3rd row콩
4th row참깨
5th row참깨
ValueCountFrequency (%)
쌀 2
 
4.9%
참깨 2
 
4.9%
두부 2
 
4.9%
우유 1
 
2.4%
라면 1
 
2.4%
김 1
 
2.4%
참기름 1
 
2.4%
분말커피 1
 
2.4%
멸치 1
 
2.4%
밀가루 1
 
2.4%
Other values (28) 28
68.3%
2023-12-16T15:58:52.093252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
기 4
 
4.3%
고 3
 
3.3%
참 3
 
3.3%
유 3
 
3.3%
주 3
 
3.3%
쌀 2
 
2.2%
분 2
 
2.2%
동 2
 
2.2%
두 2
 
2.2%
지 2
 
2.2%
Other values (59) 66
71.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 90
97.8%
Close Punctuation 1
 
1.1%
Open Punctuation 1
 
1.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 4
 
4.4%
고 3
 
3.3%
참 3
 
3.3%
유 3
 
3.3%
주 3
 
3.3%
쌀 2
 
2.2%
분 2
 
2.2%
동 2
 
2.2%
두 2
 
2.2%
지 2
 
2.2%
Other values (57) 64
71.1%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 90
97.8%
Common 2
 
2.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 4
 
4.4%
고 3
 
3.3%
참 3
 
3.3%
유 3
 
3.3%
주 3
 
3.3%
쌀 2
 
2.2%
분 2
 
2.2%
동 2
 
2.2%
두 2
 
2.2%
지 2
 
2.2%
Other values (57) 64
71.1%
Common
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 90
97.8%
ASCII 2
 
2.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
기 4
 
4.4%
고 3
 
3.3%
참 3
 
3.3%
유 3
 
3.3%
주 3
 
3.3%
쌀 2
 
2.2%
분 2
 
2.2%
동 2
 
2.2%
두 2
 
2.2%
지 2
 
2.2%
Other values (57) 64
71.1%
ASCII
ValueCountFrequency (%)
) 1
50.0%
( 1
50.0%

조사기준
Text

UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-16T15:58:52.945820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length10.780488
Min length3

Characters and Unicode

Total characters442
Distinct characters127
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique41 ?
Unique (%)100.0%

Sample

1st row청풍명월(20kg)
2nd row일반정미(20kg)
3rd row국산, 백태 1kg
4th row국산 500g
5th row수입산, 500g
ValueCountFrequency (%)
1kg 7
 
7.2%
국산 5
 
5.2%
1병 5
 
5.2%
중품 4
 
4.1%
300g 3
 
3.1%
500g 3
 
3.1%
수입 2
 
2.1%
600g 2
 
2.1%
수입콩 2
 
2.1%
2kg 2
 
2.1%
Other values (59) 62
63.9%
2023-12-16T15:58:54.683681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
56
 
12.7%
0 39
 
8.8%
1 29
 
6.6%
g 27
 
6.1%
, 19
 
4.3%
k 12
 
2.7%
2 9
 
2.0%
5 9
 
2.0%
3 9
 
2.0%
m 7
 
1.6%
Other values (117) 226
51.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 189
42.8%
Decimal Number 107
24.2%
Space Separator 56
 
12.7%
Lowercase Letter 52
 
11.8%
Other Punctuation 21
 
4.8%
Open Punctuation 6
 
1.4%
Close Punctuation 6
 
1.4%
Uppercase Letter 4
 
0.9%
Letter Number 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
국 7
 
3.7%
산 7
 
3.7%
리 6
 
3.2%
수 6
 
3.2%
병 5
 
2.6%
중 5
 
2.6%
입 5
 
2.6%
정 5
 
2.6%
백 4
 
2.1%
개 4
 
2.1%
Other values (95) 135
71.4%
Decimal Number
ValueCountFrequency (%)
0 39
36.4%
1 29
27.1%
2 9
 
8.4%
5 9
 
8.4%
3 9
 
8.4%
6 4
 
3.7%
4 4
 
3.7%
7 2
 
1.9%
8 2
 
1.9%
Lowercase Letter
ValueCountFrequency (%)
g 27
51.9%
k 12
23.1%
m 7
 
13.5%
l 3
 
5.8%
c 3
 
5.8%
Other Punctuation
ValueCountFrequency (%)
, 19
90.5%
. 2
 
9.5%
Uppercase Letter
ValueCountFrequency (%)
L 3
75.0%
O 1
 
25.0%
Space Separator
ValueCountFrequency (%)
56
100.0%
Open Punctuation
ValueCountFrequency (%)
( 6
100.0%
Close Punctuation
ValueCountFrequency (%)
) 6
100.0%
Letter Number
ValueCountFrequency (%)
ⅹ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 196
44.3%
Hangul 189
42.8%
Latin 57
 
12.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
국 7
 
3.7%
산 7
 
3.7%
리 6
 
3.2%
수 6
 
3.2%
병 5
 
2.6%
중 5
 
2.6%
입 5
 
2.6%
정 5
 
2.6%
백 4
 
2.1%
개 4
 
2.1%
Other values (95) 135
71.4%
Common
ValueCountFrequency (%)
56
28.6%
0 39
19.9%
1 29
14.8%
, 19
 
9.7%
2 9
 
4.6%
5 9
 
4.6%
3 9
 
4.6%
( 6
 
3.1%
) 6
 
3.1%
6 4
 
2.0%
Other values (4) 10
 
5.1%
Latin
ValueCountFrequency (%)
g 27
47.4%
k 12
21.1%
m 7
 
12.3%
L 3
 
5.3%
l 3
 
5.3%
c 3
 
5.3%
O 1
 
1.8%
ⅹ 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
ASCII 252
57.0%
Hangul 189
42.8%
Number Forms 1
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
56
22.2%
0 39
15.5%
1 29
11.5%
g 27
10.7%
, 19
 
7.5%
k 12
 
4.8%
2 9
 
3.6%
5 9
 
3.6%
3 9
 
3.6%
m 7
 
2.8%
Other values (11) 36
14.3%
Hangul
ValueCountFrequency (%)
국 7
 
3.7%
산 7
 
3.7%
리 6
 
3.2%
수 6
 
3.2%
병 5
 
2.6%
중 5
 
2.6%
입 5
 
2.6%
정 5
 
2.6%
백 4
 
2.1%
개 4
 
2.1%
Other values (95) 135
71.4%
Number Forms
ValueCountFrequency (%)
ⅹ 1
100.0%

평균가(단위_원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct41
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11264.098
Minimum1593
Maximum62274
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:58:55.577950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1593
5-th percentile2065
Q13223
median7227
Q310567
95-th percentile56129
Maximum62274
Range60681
Interquartile range (IQR)7344

Descriptive statistics

Standard deviation14570.325
Coefficient of variation (CV)1.2935191
Kurtosis6.4595214
Mean11264.098
Median Absolute Deviation (MAD)3830
Skewness2.6206352
Sum461828
Variance2.1229437 × 108
MonotonicityNot monotonic
2023-12-16T15:58:56.505176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
56167 1
 
2.4%
7996 1
 
2.4%
7554 1
 
2.4%
7883 1
 
2.4%
2154 1
 
2.4%
2360 1
 
2.4%
3397 1
 
2.4%
2396 1
 
2.4%
2065 1
 
2.4%
9430 1
 
2.4%
Other values (31) 31
75.6%
ValueCountFrequency (%)
1593 1
2.4%
1809 1
2.4%
2065 1
2.4%
2154 1
2.4%
2196 1
2.4%
2358 1
2.4%
2360 1
2.4%
2396 1
2.4%
2682 1
2.4%
2904 1
2.4%
ValueCountFrequency (%)
62274 1
2.4%
56167 1
2.4%
56129 1
2.4%
25424 1
2.4%
22354 1
2.4%
20952 1
2.4%
19729 1
2.4%
13054 1
2.4%
12676 1
2.4%
10984 1
2.4%

홈플러스
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct34
Distinct (%)94.4%
Missing5
Missing (%)12.2%
Infinite0
Infinite (%)0.0%
Mean10467.583
Minimum780
Maximum70500
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:58:57.423294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum780
5-th percentile1237.5
Q12262.5
median4250
Q310592.5
95-th percentile35575
Maximum70500
Range69720
Interquartile range (IQR)8330

Descriptive statistics

Standard deviation15536.75
Coefficient of variation (CV)1.4842729
Kurtosis9.5011709
Mean10467.583
Median Absolute Deviation (MAD)2935
Skewness3.0229359
Sum376833
Variance2.413906 × 108
MonotonicityNot monotonic
2023-12-16T15:58:58.332354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
4000 3
 
7.3%
15890 1
 
2.4%
2350 1
 
2.4%
1700 1
 
2.4%
1490 1
 
2.4%
780 1
 
2.4%
8480 1
 
2.4%
9590 1
 
2.4%
1380 1
 
2.4%
10900 1
 
2.4%
Other values (24) 24
58.5%
(Missing) 5
 
12.2%
ValueCountFrequency (%)
780 1
2.4%
1200 1
2.4%
1250 1
2.4%
1380 1
2.4%
1490 1
2.4%
1700 1
2.4%
1890 1
2.4%
1900 1
2.4%
2000 1
2.4%
2350 1
2.4%
ValueCountFrequency (%)
70500 1
2.4%
64900 1
2.4%
25800 1
2.4%
22740 1
2.4%
20400 1
2.4%
19930 1
2.4%
15890 1
2.4%
10940 1
2.4%
10900 1
2.4%
10490 1
2.4%

하나로마트
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct37
Distinct (%)92.5%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean11012.2
Minimum820
Maximum53000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:58:59.076901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum820
5-th percentile1374.9
Q12185
median8250
Q311030.5
95-th percentile40410
Maximum53000
Range52180
Interquartile range (IQR)8845.5

Descriptive statistics

Standard deviation12966.99
Coefficient of variation (CV)1.1775113
Kurtosis4.0992636
Mean11012.2
Median Absolute Deviation (MAD)5660
Skewness2.0860484
Sum440488
Variance1.6814284 × 108
MonotonicityNot monotonic
2023-12-16T15:58:59.672038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
8900 2
 
4.9%
8500 2
 
4.9%
1780 2
 
4.9%
52000 1
 
2.4%
1540 1
 
2.4%
820 1
 
2.4%
7680 1
 
2.4%
8980 1
 
2.4%
13580 1
 
2.4%
1380 1
 
2.4%
Other values (27) 27
65.9%
ValueCountFrequency (%)
820 1
2.4%
1278 1
2.4%
1380 1
2.4%
1540 1
2.4%
1640 1
2.4%
1650 1
2.4%
1758 1
2.4%
1780 2
4.9%
1900 1
2.4%
2280 1
2.4%
ValueCountFrequency (%)
53000 1
2.4%
52000 1
2.4%
39800 1
2.4%
28800 1
2.4%
27000 1
2.4%
25320 1
2.4%
21250 1
2.4%
14000 1
2.4%
13580 1
2.4%
11122 1
2.4%

문창시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)75.0%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean11046.125
Minimum995
Maximum84000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:59:00.377492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum995
5-th percentile1347.5
Q12437.5
median4750
Q311050
95-th percentile50025
Maximum84000
Range83005
Interquartile range (IQR)8612.5

Descriptive statistics

Standard deviation16939.086
Coefficient of variation (CV)1.5334867
Kurtosis9.9907135
Mean11046.125
Median Absolute Deviation (MAD)3225
Skewness3.0752628
Sum441845
Variance2.8693263 × 108
MonotonicityNot monotonic
2023-12-16T15:59:01.354738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
4000 4
 
9.8%
8000 3
 
7.3%
15000 2
 
4.9%
5000 2
 
4.9%
3000 2
 
4.9%
7500 2
 
4.9%
2000 2
 
4.9%
11800 1
 
2.4%
7950 1
 
2.4%
10800 1
 
2.4%
Other values (20) 20
48.8%
ValueCountFrequency (%)
995 1
2.4%
1300 1
2.4%
1350 1
2.4%
1400 1
2.4%
1500 1
2.4%
1700 1
2.4%
1800 1
2.4%
2000 2
4.9%
2400 1
2.4%
2450 1
2.4%
ValueCountFrequency (%)
84000 1
2.4%
60000 1
2.4%
49500 1
2.4%
26000 1
2.4%
21500 1
2.4%
15500 1
2.4%
15000 2
4.9%
14400 1
2.4%
11800 1
2.4%
10800 1
2.4%

산성시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct27
Distinct (%)67.5%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean12268.75
Minimum900
Maximum60000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:59:01.756617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile1590
Q12700
median7500
Q314000
95-th percentile55225
Maximum60000
Range59100
Interquartile range (IQR)11300

Descriptive statistics

Standard deviation15226.948
Coefficient of variation (CV)1.2411165
Kurtosis4.5993862
Mean12268.75
Median Absolute Deviation (MAD)5500
Skewness2.2581766
Sum490750
Variance2.3185996 × 108
MonotonicityNot monotonic
2023-12-16T15:59:02.209095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
5000 4
 
9.8%
10000 3
 
7.3%
2000 3
 
7.3%
15000 2
 
4.9%
1850 2
 
4.9%
8000 2
 
4.9%
7000 2
 
4.9%
14000 2
 
4.9%
2700 2
 
4.9%
900 1
 
2.4%
Other values (17) 17
41.5%
ValueCountFrequency (%)
900 1
 
2.4%
1400 1
 
2.4%
1600 1
 
2.4%
1650 1
 
2.4%
1850 2
4.9%
2000 3
7.3%
2700 2
4.9%
3000 1
 
2.4%
4000 1
 
2.4%
4200 1
 
2.4%
ValueCountFrequency (%)
60000 1
2.4%
59500 1
2.4%
55000 1
2.4%
37000 1
2.4%
28000 1
2.4%
20000 1
2.4%
17000 1
2.4%
15000 2
4.9%
14000 2
4.9%
13000 1
2.4%

유천시장
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10840.488
Minimum900
Maximum72000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:59:02.705945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile1600
Q12400
median5500
Q311950
95-th percentile60000
Maximum72000
Range71100
Interquartile range (IQR)9550

Descriptive statistics

Standard deviation16163.262
Coefficient of variation (CV)1.4910087
Kurtosis7.7520795
Mean10840.488
Median Absolute Deviation (MAD)3400
Skewness2.851122
Sum444460
Variance2.6125103 × 108
MonotonicityNot monotonic
2023-12-16T15:59:03.341657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
3000 4
 
9.8%
60000 2
 
4.9%
2300 2
 
4.9%
12000 2
 
4.9%
1600 2
 
4.9%
2000 2
 
4.9%
5000 2
 
4.9%
7000 2
 
4.9%
2500 1
 
2.4%
2400 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
900 1
 
2.4%
1500 1
 
2.4%
1600 2
4.9%
1650 1
 
2.4%
2000 2
4.9%
2100 1
 
2.4%
2300 2
4.9%
2400 1
 
2.4%
2500 1
 
2.4%
3000 4
9.8%
ValueCountFrequency (%)
72000 1
2.4%
60000 2
4.9%
25000 1
2.4%
20000 1
2.4%
17500 1
2.4%
14800 1
2.4%
13860 1
2.4%
12000 2
4.9%
11950 1
2.4%
10000 1
2.4%

태평시장
Real number (ℝ)

HIGH CORRELATION 

Distinct32
Distinct (%)78.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10669.756
Minimum900
Maximum68000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:59:04.053683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum900
5-th percentile1500
Q12700
median5000
Q310000
95-th percentile59000
Maximum68000
Range67100
Interquartile range (IQR)7300

Descriptive statistics

Standard deviation15697.494
Coefficient of variation (CV)1.471214
Kurtosis7.4426985
Mean10669.756
Median Absolute Deviation (MAD)2900
Skewness2.8119569
Sum437460
Variance2.4641133 × 108
MonotonicityNot monotonic
2023-12-16T15:59:04.733772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
3000 5
 
12.2%
7000 3
 
7.3%
9000 2
 
4.9%
10000 2
 
4.9%
2500 2
 
4.9%
59000 1
 
2.4%
11600 1
 
2.4%
7500 1
 
2.4%
6500 1
 
2.4%
16500 1
 
2.4%
Other values (22) 22
53.7%
ValueCountFrequency (%)
900 1
2.4%
1000 1
2.4%
1500 1
2.4%
1550 1
2.4%
1650 1
2.4%
2000 1
2.4%
2100 1
2.4%
2200 1
2.4%
2500 2
4.9%
2700 1
2.4%
ValueCountFrequency (%)
68000 1
2.4%
60000 1
2.4%
59000 1
2.4%
25000 1
2.4%
20000 1
2.4%
16500 1
2.4%
13860 1
2.4%
13800 1
2.4%
13000 1
2.4%
11600 1
2.4%

오류시장
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct29
Distinct (%)78.4%
Missing4
Missing (%)9.8%
Infinite0
Infinite (%)0.0%
Mean10957.568
Minimum1000
Maximum65000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:59:05.393003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1000
5-th percentile1640
Q12500
median5000
Q39600
95-th percentile58400
Maximum65000
Range64000
Interquartile range (IQR)7100

Descriptive statistics

Standard deviation16163.794
Coefficient of variation (CV)1.4751261
Kurtosis6.1094296
Mean10957.568
Median Absolute Deviation (MAD)3000
Skewness2.6302071
Sum405430
Variance2.6126823 × 108
MonotonicityNot monotonic
2023-12-16T15:59:06.010778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
5000 4
 
9.8%
2500 4
 
9.8%
3000 2
 
4.9%
2000 2
 
4.9%
5480 1
 
2.4%
9600 1
 
2.4%
32000 1
 
2.4%
4200 1
 
2.4%
12000 1
 
2.4%
2200 1
 
2.4%
Other values (19) 19
46.3%
(Missing) 4
 
9.8%
ValueCountFrequency (%)
1000 1
 
2.4%
1600 1
 
2.4%
1650 1
 
2.4%
1800 1
 
2.4%
2000 2
4.9%
2200 1
 
2.4%
2500 4
9.8%
2700 1
 
2.4%
3000 2
4.9%
4200 1
 
2.4%
ValueCountFrequency (%)
65000 1
2.4%
60000 1
2.4%
58000 1
2.4%
32000 1
2.4%
15900 1
2.4%
15000 1
2.4%
12500 1
2.4%
12000 1
2.4%
11000 1
2.4%
9600 1
2.4%

부사동
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)82.1%
Missing2
Missing (%)4.9%
Infinite0
Infinite (%)0.0%
Mean11356.231
Minimum995
Maximum72000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-16T15:59:06.834548image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum995
5-th percentile1335
Q12450
median5000
Q313400
95-th percentile50700
Maximum72000
Range71005
Interquartile range (IQR)10950

Descriptive statistics

Standard deviation15758.869
Coefficient of variation (CV)1.3876849
Kurtosis6.9162098
Mean11356.231
Median Absolute Deviation (MAD)3250
Skewness2.6178756
Sum442893
Variance2.4834197 × 108
MonotonicityNot monotonic
2023-12-16T15:59:07.961231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
15000 3
 
7.3%
8000 2
 
4.9%
2450 2
 
4.9%
12000 2
 
4.9%
2000 2
 
4.9%
3000 2
 
4.9%
28500 1
 
2.4%
1750 1
 
2.4%
3300 1
 
2.4%
1450 1
 
2.4%
Other values (22) 22
53.7%
(Missing) 2
 
4.9%
ValueCountFrequency (%)
995 1
2.4%
1200 1
2.4%
1350 1
2.4%
1450 1
2.4%
1500 1
2.4%
1750 1
2.4%
1800 1
2.4%
2000 2
4.9%
2450 2
4.9%
2548 1
2.4%
ValueCountFrequency (%)
72000 1
 
2.4%
57000 1
 
2.4%
50000 1
 
2.4%
28500 1
 
2.4%
25000 1
 
2.4%
16000 1
 
2.4%
15000 3
7.3%
14800 1
 
2.4%
12000 2
4.9%
10000 1
 
2.4%

관리기관
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
대전광역시 중구 일자리경제과
41 

Length

Max length15
Median length15
Mean length15
Min length15

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row대전광역시 중구 일자리경제과
2nd row대전광역시 중구 일자리경제과
3rd row대전광역시 중구 일자리경제과
4th row대전광역시 중구 일자리경제과
5th row대전광역시 중구 일자리경제과

Common Values

ValueCountFrequency (%)
대전광역시 중구 일자리경제과 41
100.0%

Length

2023-12-16T15:59:08.747187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:59:09.361471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
대전광역시 41
33.3%
중구 41
33.3%
일자리경제과 41
33.3%

연락처
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
042-606-7224
41 

Length

Max length12
Median length12
Mean length12
Min length12

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row042-606-7224
2nd row042-606-7224
3rd row042-606-7224
4th row042-606-7224
5th row042-606-7224

Common Values

ValueCountFrequency (%)
042-606-7224 41
100.0%

Length

2023-12-16T15:59:09.927284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-16T15:59:10.610302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
042-606-7224 41
100.0%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
Minimum2023-11-30 00:00:00
Maximum2023-11-30 00:00:00
2023-12-16T15:59:11.252984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:59:12.465579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2023-12-16T15:58:41.111092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:03.934463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:08.617652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:12.825700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:16.543564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:20.736004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:25.353054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:30.032810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:34.525582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:41.390429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:04.407933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:09.230216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:13.237913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:16.963711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:21.159116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:25.901653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:30.521571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:35.060119image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:41.808784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:04.949651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:09.509108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:13.628723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:17.506023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:21.563745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:26.369750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:30.959539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:35.540422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:42.310571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:05.311824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:10.050810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:14.049414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:17.928324image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:22.370557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:26.733594image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:31.473144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:36.396528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:42.676977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:05.910831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:10.608780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:14.348029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:18.268006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:22.874019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:27.274167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:31.883329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:37.079166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:43.265126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:06.432387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:11.159204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:14.708750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:18.741496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:23.449888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:27.714443image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:32.354899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:37.813942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:43.725692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:06.916795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:11.661581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:15.089193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:19.303796image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:23.941329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:28.102298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:32.956999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:39.256671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:44.457645image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:07.414094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:12.185607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:15.527099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:19.768179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:24.534357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:28.594624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:33.394599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:39.984969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:45.085491image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:08.103223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:12.552739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:16.127819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:20.352506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:24.909273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:29.185272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:33.905547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-16T15:58:40.419944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-16T15:59:13.229005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분품목조사기준평균가(단위_원)홈플러스하나로마트문창시장산성시장유천시장태평시장오류시장부사동
구분1.0001.0001.0000.2470.0130.3290.0000.0000.0000.1060.3870.000
품목1.0001.0001.0000.9531.0001.0000.0000.6450.9860.9860.0000.000
조사기준1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
평균가(단위_원)0.2470.9531.0001.0000.7910.7520.9080.8130.9770.9720.9300.958
홈플러스0.0131.0001.0000.7911.0000.5970.7340.9730.9560.9560.9610.904
하나로마트0.3291.0001.0000.7520.5971.0000.7750.8990.6010.6080.4590.679
문창시장0.0000.0001.0000.9080.7340.7751.0000.9080.9040.9040.9190.993
산성시장0.0000.6451.0000.8130.9730.8990.9081.0000.8300.8360.9560.796
유천시장0.0000.9861.0000.9770.9560.6010.9040.8301.0000.9990.9480.887
태평시장0.1060.9861.0000.9720.9560.6080.9040.8360.9991.0000.9430.887
오류시장0.3870.0001.0000.9300.9610.4590.9190.9560.9480.9431.0000.871
부사동0.0000.0001.0000.9580.9040.6790.9930.7960.8870.8870.8711.000
2023-12-16T15:59:14.405636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
평균가(단위_원)홈플러스하나로마트문창시장산성시장유천시장태평시장오류시장부사동구분
평균가(단위_원)1.0000.9080.5260.9140.9470.9370.9310.7440.9240.152
홈플러스0.9081.0000.2860.9100.9350.9190.9130.7790.8830.025
하나로마트0.5260.2861.0000.2560.4100.3640.3600.1320.3470.125
문창시장0.9140.9100.2561.0000.9350.9430.8990.8010.9760.000
산성시장0.9470.9350.4100.9351.0000.9170.9240.7950.9180.000
유천시장0.9370.9190.3640.9430.9171.0000.9750.7670.9290.000
태평시장0.9310.9130.3600.8990.9240.9751.0000.7550.8800.071
오류시장0.7440.7790.1320.8010.7950.7670.7551.0000.8070.242
부사동0.9240.8830.3470.9760.9180.9290.8800.8071.0000.000
구분0.1520.0250.1250.0000.0000.0000.0710.2420.0001.000

Missing values

2023-12-16T15:58:45.784284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-16T15:58:47.154669image/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.
2023-12-16T15:58:47.991505image/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

구분품목조사기준평균가(단위_원)홈플러스하나로마트문창시장산성시장유천시장태평시장오류시장부사동관리기관연락처데이터기준일
0농산물쌀청풍명월(20kg)56167<NA>52000495005950060000590006500050000대전광역시 중구 일자리경제과042-606-72242023-11-30
1농산물쌀일반정미(20kg)561296490053000600005500060000600005800057000대전광역시 중구 일자리경제과042-606-72242023-11-30
2농산물콩국산, 백태 1kg109841993089001500013000120009000700015000대전광역시 중구 일자리경제과042-606-72242023-11-30
3농산물참깨국산 500g20952<NA>27000215002000012000130001500025000대전광역시 중구 일자리경제과042-606-72242023-11-30
4농산물참깨수입산, 500g69587745<NA>5000100007000700050004800대전광역시 중구 일자리경제과042-606-72242023-11-30
5농산물사과부사 400g1개, 중품322340004060300070001500150030003000대전광역시 중구 일자리경제과042-606-72242023-11-30
6농산물배신고배 600g1개, 중품422940005100400050003000300030004000대전광역시 중구 일자리경제과042-606-72242023-11-30
7농산물감귤100g 정도 10개760579901112230001400060001000050003200대전광역시 중구 일자리경제과042-606-72242023-11-30
8농산물밤굵은것 1kg9894<NA>9470750010000100001000050008000대전광역시 중구 일자리경제과042-606-72242023-11-30
9농산물무통무우 2kg235824901278200020002500300020002000대전광역시 중구 일자리경제과042-606-72242023-11-30
구분품목조사기준평균가(단위_원)홈플러스하나로마트문창시장산성시장유천시장태평시장오류시장부사동관리기관연락처데이터기준일
31공산품참기름오뚜기320ml 1병7996959013801080010900580065001100012000대전광역시 중구 일자리경제과042-606-72242023-11-30
32공산품분말커피동서맥심175g 1병1267615890154015000140001750016500830014800대전광역시 중구 일자리경제과042-606-72242023-11-30
33공산품소주O2린 360ml, 1병2904138011000140016001600155016001450대전광역시 중구 일자리경제과042-606-72242023-11-30
34공산품맥주카스 500ml 1병5191189025320170018502300165022001750대전광역시 중구 일자리경제과042-606-72242023-11-30
35공산품청주백화수복, 1.8L 1병10567109403680118001200011950116001200012000대전광역시 중구 일자리경제과042-606-72242023-11-30
36공산품화장지크리넥스 35mⅹ24롤25424<NA>2680260002800025000250003200028500대전광역시 중구 일자리경제과042-606-72242023-11-30
37공산품빵삼립 식빵 750g96323490398003500<NA>350050002500<NA>대전광역시 중구 일자리경제과042-606-72242023-11-30
38공산품우유매일우유 1000L403129709900245030003000320054802548대전광역시 중구 일자리경제과042-606-72242023-11-30
39공산품분유남양임페리얼 3단계223542580021250<NA>370001386013860<NA><NA>대전광역시 중구 일자리경제과042-606-72242023-11-30
40공산품세제애경 스파크 3kg82935950800010000150005000450096009850대전광역시 중구 일자리경제과042-606-72242023-11-30