Overview

Dataset statistics

Number of variables9
Number of observations57
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.4 KiB
Average record size in memory79.2 B

Variable types

Text3
Numeric5
DateTime1

Dataset

Description경기도 성남시 장바구니(생필품)관련 물가정보로 품목, 백화점, 대형마트, 전통시장, 금주가격, 이번주가격, 증감률 등의 항목을 제공합니다.
Author경기도 성남시
URLhttps://www.data.go.kr/data/3071011/fileData.do

Alerts

데이터기준일자 has constant value ""Constant
백화점 is highly overall correlated with 대형마트 and 3 other fieldsHigh correlation
대형마트 is highly overall correlated with 백화점 and 3 other fieldsHigh correlation
전통시장 is highly overall correlated with 백화점 and 3 other fieldsHigh correlation
금주가격 is highly overall correlated with 백화점 and 3 other fieldsHigh correlation
지난주가격 is highly overall correlated with 백화점 and 3 other fieldsHigh correlation
품목 has unique valuesUnique
대형마트 has unique valuesUnique
금주가격 has unique valuesUnique
지난주가격 has unique valuesUnique
백화점 has 2 (3.5%) zerosZeros
대형마트 has 1 (1.8%) zerosZeros
전통시장 has 4 (7.0%) zerosZeros
금주가격 has 1 (1.8%) zerosZeros
지난주가격 has 1 (1.8%) zerosZeros

Reproduction

Analysis started2024-03-14 17:10:09.601469
Analysis finished2024-03-14 17:10:17.078804
Duration7.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

UNIQUE 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size584.0 B
2024-03-15T02:10:17.905831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length2.754386
Min length1

Characters and Unicode

Total characters157
Distinct characters103
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

Unique57 ?
Unique (%)100.0%

Sample

1st row쌀
2nd row무
3rd row배추
4th row대파
5th row오이
ValueCountFrequency (%)
쌀 1
 
1.8%
마른멸치 1
 
1.8%
라면 1
 
1.8%
두부 1
 
1.8%
햄 1
 
1.8%
참치캔 1
 
1.8%
우유 1
 
1.8%
밀가루 1
 
1.8%
설탕 1
 
1.8%
식용류 1
 
1.8%
Other values (47) 47
82.5%
2024-03-15T02:10:19.063753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
고 8
 
5.1%
기 6
 
3.8%
치 5
 
3.2%
장 4
 
2.5%
유 4
 
2.5%
추 4
 
2.5%
) 3
 
1.9%
제 3
 
1.9%
가 3
 
1.9%
루 3
 
1.9%
Other values (93) 114
72.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 151
96.2%
Close Punctuation 3
 
1.9%
Open Punctuation 3
 
1.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
고 8
 
5.3%
기 6
 
4.0%
치 5
 
3.3%
장 4
 
2.6%
유 4
 
2.6%
추 4
 
2.6%
제 3
 
2.0%
가 3
 
2.0%
루 3
 
2.0%
참 3
 
2.0%
Other values (91) 108
71.5%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 151
96.2%
Common 6
 
3.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
고 8
 
5.3%
기 6
 
4.0%
치 5
 
3.3%
장 4
 
2.6%
유 4
 
2.6%
추 4
 
2.6%
제 3
 
2.0%
가 3
 
2.0%
루 3
 
2.0%
참 3
 
2.0%
Other values (91) 108
71.5%
Common
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 151
96.2%
ASCII 6
 
3.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
고 8
 
5.3%
기 6
 
4.0%
치 5
 
3.3%
장 4
 
2.6%
유 4
 
2.6%
추 4
 
2.6%
제 3
 
2.0%
가 3
 
2.0%
루 3
 
2.0%
참 3
 
2.0%
Other values (91) 108
71.5%
ASCII
ValueCountFrequency (%)
) 3
50.0%
( 3
50.0%

규격
Text

Distinct55
Distinct (%)96.5%
Missing0
Missing (%)0.0%
Memory size584.0 B
2024-03-15T02:10:20.166660image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length11.719298
Min length2

Characters and Unicode

Total characters668
Distinct characters157
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

Unique53 ?
Unique (%)93.0%

Sample

1st row경기미10kg
2nd row1kg (잎 없는것 1개)
3rd row2kg (중간크기 1포기)
4th row1단 0.8-1kg
5th row다다기오기, 1개
ValueCountFrequency (%)
100g 9
 
7.2%
10g 4
 
3.2%
1개 4
 
3.2%
국산 3
 
2.4%
중간크기 3
 
2.4%
1kg 3
 
2.4%
600g 3
 
2.4%
1단 2
 
1.6%
1병 2
 
1.6%
1개(300g 2
 
1.6%
Other values (87) 90
72.0%
2024-03-15T02:10:21.882121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 88
 
13.2%
1 68
 
10.2%
68
 
10.2%
g 45
 
6.7%
) 18
 
2.7%
( 18
 
2.7%
m 18
 
2.7%
k 15
 
2.2%
l 15
 
2.2%
/ 13
 
1.9%
Other values (147) 302
45.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 234
35.0%
Decimal Number 197
29.5%
Lowercase Letter 99
14.8%
Space Separator 68
 
10.2%
Other Punctuation 28
 
4.2%
Close Punctuation 18
 
2.7%
Open Punctuation 18
 
2.7%
Uppercase Letter 4
 
0.6%
Dash Punctuation 2
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
기 10
 
4.3%
리 10
 
4.3%
개 8
 
3.4%
다 5
 
2.1%
중 5
 
2.1%
산 5
 
2.1%
스 5
 
2.1%
마 5
 
2.1%
순 4
 
1.7%
크 4
 
1.7%
Other values (119) 173
73.9%
Decimal Number
ValueCountFrequency (%)
0 88
44.7%
1 68
34.5%
2 9
 
4.6%
3 8
 
4.1%
6 7
 
3.6%
5 7
 
3.6%
8 3
 
1.5%
9 3
 
1.5%
4 3
 
1.5%
7 1
 
0.5%
Lowercase Letter
ValueCountFrequency (%)
g 45
45.5%
m 18
 
18.2%
k 15
 
15.2%
l 15
 
15.2%
c 4
 
4.0%
p 1
 
1.0%
ℓ 1
 
1.0%
Uppercase Letter
ValueCountFrequency (%)
F 1
25.0%
T 1
25.0%
E 1
25.0%
P 1
25.0%
Other Punctuation
ValueCountFrequency (%)
/ 13
46.4%
, 8
28.6%
. 7
25.0%
Space Separator
ValueCountFrequency (%)
68
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 332
49.7%
Hangul 234
35.0%
Latin 102
 
15.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
기 10
 
4.3%
리 10
 
4.3%
개 8
 
3.4%
다 5
 
2.1%
중 5
 
2.1%
산 5
 
2.1%
스 5
 
2.1%
마 5
 
2.1%
순 4
 
1.7%
크 4
 
1.7%
Other values (119) 173
73.9%
Common
ValueCountFrequency (%)
0 88
26.5%
1 68
20.5%
68
20.5%
) 18
 
5.4%
( 18
 
5.4%
/ 13
 
3.9%
2 9
 
2.7%
3 8
 
2.4%
, 8
 
2.4%
. 7
 
2.1%
Other values (8) 27
 
8.1%
Latin
ValueCountFrequency (%)
g 45
44.1%
m 18
 
17.6%
k 15
 
14.7%
l 15
 
14.7%
c 4
 
3.9%
F 1
 
1.0%
p 1
 
1.0%
T 1
 
1.0%
E 1
 
1.0%
P 1
 
1.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 433
64.8%
Hangul 234
35.0%
Letterlike Symbols 1
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 88
20.3%
1 68
15.7%
68
15.7%
g 45
10.4%
) 18
 
4.2%
( 18
 
4.2%
m 18
 
4.2%
k 15
 
3.5%
l 15
 
3.5%
/ 13
 
3.0%
Other values (17) 67
15.5%
Hangul
ValueCountFrequency (%)
기 10
 
4.3%
리 10
 
4.3%
개 8
 
3.4%
다 5
 
2.1%
중 5
 
2.1%
산 5
 
2.1%
스 5
 
2.1%
마 5
 
2.1%
순 4
 
1.7%
크 4
 
1.7%
Other values (119) 173
73.9%
Letterlike Symbols
ValueCountFrequency (%)
ℓ 1
100.0%

백화점
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)98.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7546.8246
Minimum0
Maximum59000
Zeros2
Zeros (%)3.5%
Negative0
Negative (%)0.0%
Memory size641.0 B
2024-03-15T02:10:22.384201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile143.8
Q1516
median2026
Q35990
95-th percentile37394
Maximum59000
Range59000
Interquartile range (IQR)5474

Descriptive statistics

Standard deviation13426.898
Coefficient of variation (CV)1.7791454
Kurtosis5.4326924
Mean7546.8246
Median Absolute Deviation (MAD)1798
Skewness2.4535464
Sum430169
Variance1.802816 × 108
MonotonicityNot monotonic
2024-03-15T02:10:23.119053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2
 
3.5%
47850 1
 
1.8%
273 1
 
1.8%
936 1
 
1.8%
4466 1
 
1.8%
107 1
 
1.8%
194 1
 
1.8%
315 1
 
1.8%
1860 1
 
1.8%
228 1
 
1.8%
Other values (46) 46
80.7%
ValueCountFrequency (%)
0 2
3.5%
107 1
1.8%
153 1
1.8%
183 1
1.8%
194 1
1.8%
228 1
1.8%
273 1
1.8%
289 1
1.8%
292 1
1.8%
298 1
1.8%
ValueCountFrequency (%)
59000 1
1.8%
48720 1
1.8%
47850 1
1.8%
34780 1
1.8%
31566 1
1.8%
31540 1
1.8%
29666 1
1.8%
18500 1
1.8%
11266 1
1.8%
11092 1
1.8%

대형마트
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4954.4211
Minimum0
Maximum42188
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size641.0 B
2024-03-15T02:10:23.912302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile185.4
Q1495
median1589
Q34168
95-th percentile24550.6
Maximum42188
Range42188
Interquartile range (IQR)3673

Descriptive statistics

Standard deviation8571.4504
Coefficient of variation (CV)1.7300609
Kurtosis7.3524119
Mean4954.4211
Median Absolute Deviation (MAD)1290
Skewness2.6972832
Sum282402
Variance73469761
MonotonicityNot monotonic
2024-03-15T02:10:24.712082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42188 1
 
1.8%
244 1
 
1.8%
824 1
 
1.8%
4663 1
 
1.8%
120 1
 
1.8%
200 1
 
1.8%
299 1
 
1.8%
1986 1
 
1.8%
220 1
 
1.8%
559 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
0 1
1.8%
120 1
1.8%
163 1
1.8%
191 1
1.8%
200 1
1.8%
220 1
1.8%
244 1
1.8%
299 1
1.8%
324 1
1.8%
347 1
1.8%
ValueCountFrequency (%)
42188 1
1.8%
30540 1
1.8%
27153 1
1.8%
23900 1
1.8%
22136 1
1.8%
16533 1
1.8%
16448 1
1.8%
9975 1
1.8%
7811 1
1.8%
6580 1
1.8%

전통시장
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct52
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4032.1754
Minimum0
Maximum38750
Zeros4
Zeros (%)7.0%
Negative0
Negative (%)0.0%
Memory size641.0 B
2024-03-15T02:10:25.213079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1375
median1000
Q34000
95-th percentile20140
Maximum38750
Range38750
Interquartile range (IQR)3625

Descriptive statistics

Standard deviation7086.0029
Coefficient of variation (CV)1.7573647
Kurtosis10.611433
Mean4032.1754
Median Absolute Deviation (MAD)890
Skewness3.0320225
Sum229834
Variance50211437
MonotonicityNot monotonic
2024-03-15T02:10:25.850095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 4
 
7.0%
4000 3
 
5.3%
38750 1
 
1.8%
2125 1
 
1.8%
94 1
 
1.8%
246 1
 
1.8%
332 1
 
1.8%
2435 1
 
1.8%
219 1
 
1.8%
709 1
 
1.8%
Other values (42) 42
73.7%
ValueCountFrequency (%)
0 4
7.0%
94 1
 
1.8%
96 1
 
1.8%
138 1
 
1.8%
198 1
 
1.8%
219 1
 
1.8%
246 1
 
1.8%
277 1
 
1.8%
332 1
 
1.8%
351 1
 
1.8%
ValueCountFrequency (%)
38750 1
1.8%
22250 1
1.8%
21500 1
1.8%
19800 1
1.8%
15000 1
1.8%
12900 1
1.8%
11000 1
1.8%
10000 1
1.8%
7950 1
1.8%
5000 1
1.8%

금주가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5790.5263
Minimum0
Maximum42929
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size641.0 B
2024-03-15T02:10:26.358994image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile158
Q1532
median1662
Q34708
95-th percentile28512.4
Maximum42929
Range42929
Interquartile range (IQR)4176

Descriptive statistics

Standard deviation10025.903
Coefficient of variation (CV)1.7314321
Kurtosis5.8372218
Mean5790.5263
Median Absolute Deviation (MAD)1424
Skewness2.517461
Sum330060
Variance1.0051873 × 108
MonotonicityNot monotonic
2024-03-15T02:10:27.020993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
42929 1
 
1.8%
238 1
 
1.8%
877 1
 
1.8%
4593 1
 
1.8%
107 1
 
1.8%
213 1
 
1.8%
315 1
 
1.8%
2093 1
 
1.8%
222 1
 
1.8%
594 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
0 1
1.8%
107 1
1.8%
146 1
1.8%
161 1
1.8%
213 1
1.8%
222 1
1.8%
238 1
1.8%
297 1
1.8%
315 1
1.8%
334 1
1.8%
ValueCountFrequency (%)
42929 1
1.8%
41450 1
1.8%
33586 1
1.8%
27244 1
1.8%
25317 1
1.8%
20324 1
1.8%
19038 1
1.8%
14491 1
1.8%
9178 1
1.8%
8951 1
1.8%

지난주가격
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct57
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5978.8421
Minimum0
Maximum44025
Zeros1
Zeros (%)1.8%
Negative0
Negative (%)0.0%
Memory size641.0 B
2024-03-15T02:10:27.438445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile157.4
Q1525
median1651
Q34707
95-th percentile31962.6
Maximum44025
Range44025
Interquartile range (IQR)4182

Descriptive statistics

Standard deviation10423.333
Coefficient of variation (CV)1.7433699
Kurtosis5.3022353
Mean5978.8421
Median Absolute Deviation (MAD)1369
Skewness2.446606
Sum340794
Variance1.0864588 × 108
MonotonicityNot monotonic
2024-03-15T02:10:27.881619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
44025 1
 
1.8%
238 1
 
1.8%
879 1
 
1.8%
4593 1
 
1.8%
107 1
 
1.8%
213 1
 
1.8%
315 1
 
1.8%
2107 1
 
1.8%
222 1
 
1.8%
590 1
 
1.8%
Other values (47) 47
82.5%
ValueCountFrequency (%)
0 1
1.8%
107 1
1.8%
147 1
1.8%
160 1
1.8%
213 1
1.8%
222 1
1.8%
238 1
1.8%
297 1
1.8%
315 1
1.8%
316 1
1.8%
ValueCountFrequency (%)
44025 1
1.8%
40200 1
1.8%
36909 1
1.8%
30726 1
1.8%
25595 1
1.8%
21140 1
1.8%
19038 1
1.8%
17246 1
1.8%
10017 1
1.8%
8657 1
1.8%

증감률
Text

Distinct40
Distinct (%)70.2%
Missing0
Missing (%)0.0%
Memory size584.0 B
2024-03-15T02:10:28.522145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.3157895
Min length1

Characters and Unicode

Total characters189
Distinct characters12
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

Unique39 ?
Unique (%)68.4%

Sample

1st row-2.49
2nd row-2.15
3rd row3.89
4th row3.72
5th row16.29
ValueCountFrequency (%)
0 18
31.6%
0.68 2
 
3.5%
8.38 2
 
3.5%
0.23 1
 
1.8%
0.66 1
 
1.8%
3.4 1
 
1.8%
0.02 1
 
1.8%
15.97 1
 
1.8%
11.85 1
 
1.8%
0.25 1
 
1.8%
Other values (28) 28
49.1%
2024-03-15T02:10:29.615829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 37
19.6%
0 35
18.5%
- 19
10.1%
1 16
8.5%
3 14
 
7.4%
2 12
 
6.3%
8 12
 
6.3%
6 11
 
5.8%
5 11
 
5.8%
7 9
 
4.8%
Other values (2) 13
 
6.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 133
70.4%
Other Punctuation 37
 
19.6%
Dash Punctuation 19
 
10.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 35
26.3%
1 16
12.0%
3 14
 
10.5%
2 12
 
9.0%
8 12
 
9.0%
6 11
 
8.3%
5 11
 
8.3%
7 9
 
6.8%
9 8
 
6.0%
4 5
 
3.8%
Other Punctuation
ValueCountFrequency (%)
. 37
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 189
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
. 37
19.6%
0 35
18.5%
- 19
10.1%
1 16
8.5%
3 14
 
7.4%
2 12
 
6.3%
8 12
 
6.3%
6 11
 
5.8%
5 11
 
5.8%
7 9
 
4.8%
Other values (2) 13
 
6.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 189
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 37
19.6%
0 35
18.5%
- 19
10.1%
1 16
8.5%
3 14
 
7.4%
2 12
 
6.3%
8 12
 
6.3%
6 11
 
5.8%
5 11
 
5.8%
7 9
 
4.8%
Other values (2) 13
 
6.9%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Memory size584.0 B
Minimum2024-01-11 00:00:00
Maximum2024-01-11 00:00:00
2024-03-15T02:10:29.961471image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:30.255303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-15T02:10:15.170524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:10.278599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:12.095766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:13.306404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:14.221555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:15.406375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:10.567515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:12.347535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:13.464386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:14.494725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:15.639608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:10.861650image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:12.652150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:13.612025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:14.646122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:15.895149image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:11.506934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:12.915701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:13.783138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:14.805731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:16.120753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:11.740021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:13.167842image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:13.954976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T02:10:15.034877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T02:10:30.478767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품목규격백화점대형마트전통시장금주가격지난주가격증감률
품목1.0001.0001.0001.0001.0001.0001.0001.000
규격1.0001.0001.0001.0001.0001.0001.0000.962
백화점1.0001.0001.0000.9320.9280.9680.9710.975
대형마트1.0001.0000.9321.0000.9010.9670.9660.972
전통시장1.0001.0000.9280.9011.0000.9660.9490.942
금주가격1.0001.0000.9680.9670.9661.0000.9990.974
지난주가격1.0001.0000.9710.9660.9490.9991.0000.974
증감률1.0000.9620.9750.9720.9420.9740.9741.000
2024-03-15T02:10:30.774709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
백화점대형마트전통시장금주가격지난주가격
백화점1.0000.9440.8650.9550.950
대형마트0.9441.0000.8500.9940.994
전통시장0.8650.8501.0000.8480.846
금주가격0.9550.9940.8481.0000.999
지난주가격0.9500.9940.8460.9991.000

Missing values

2024-03-15T02:10:16.449619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T02:10:16.908275image/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

품목규격백화점대형마트전통시장금주가격지난주가격증감률데이터기준일자
0쌀경기미10kg4785042188387504292944025-2.492024-01-11
1무1kg (잎 없는것 1개)22931643179019081950-2.152024-01-11
2배추2kg (중간크기 1포기)599029353350409139383.892024-01-11
3대파1단 0.8-1kg562341684000459744323.722024-01-11
4오이다다기오기, 1개2166157011501628140016.292024-01-11
5감자중간크기 100g7265624205695258.382024-01-11
6호박애호박 1개 (중간크기 400g)3100216518902385204616.572024-01-11
7풋고추100g214014431000152715041.532024-01-11
8상추적상추100g2973158995018371874-1.972024-01-11
9콩나물상품100g74869370071371302024-01-11
품목규격백화점대형마트전통시장금주가격지난주가격증감률데이터기준일자
47혼합조미료쇠고기다시다 골드400g/ 10g36138239738038002024-01-11
48참기름320ml/10ml29232427729729702024-01-11
49고추장순창1kg/ 100g119717719701312131202024-01-11
50된장순창1kg/ 100g6328077457287260.282024-01-11
51가루비누비트3.2kg/ 100g2983553513343165.72024-01-11
52치약2080파워쉴드 10g15319196146147-0.682024-01-11
53샴푸엘라스틴 600g/100ml1076135411751201120102024-01-11
54섬유유연제피존(리필) 2100ml/100ml1831631381611600.622024-01-11
55부엌용세제자연퐁 1.2kg/100ml35744556945745702024-01-11
56종이기저귀하기스 중형 46p/팬티1개677632065465402024-01-11