Overview

Dataset statistics

Number of variables16
Number of observations64
Missing cells19
Missing cells (%)1.9%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory8.9 KiB
Average record size in memory143.1 B

Variable types

Text2
Numeric13
Categorical1

Dataset

Description외식 및 스포츠센터 이용료, 숙박료 등의 대구시의 개인서비스요금 가격동향을 파악하여 정리했으며 지역의 주요 전통시장 8곳을 중심으로 조사하였음.
Author대구광역시
URLhttps://www.data.go.kr/data/15126895/fileData.do

Alerts

4월24일 평균 가격(원) is highly overall correlated with 4월29일 평균 가격(원) and 10 other fieldsHigh correlation
4월29일 평균 가격(원) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
등락율(퍼센트) is highly overall correlated with 등락가격(원)High correlation
최고가(원) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
최저가(원) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
남문시장(중구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
동구시장(동구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
서문시장(중구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
봉덕시장(남구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
칠성시장(북구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
수성시장(수성구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
서남시장(달서구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
팔달시장(북구) is highly overall correlated with 4월24일 평균 가격(원) and 10 other fieldsHigh correlation
등락가격(원) is highly overall correlated with 등락율(퍼센트)High correlation
등락가격(원) is highly imbalanced (68.9%)Imbalance
남문시장(중구) has 3 (4.7%) missing valuesMissing
동구시장(동구) has 3 (4.7%) missing valuesMissing
서문시장(중구) has 4 (6.2%) missing valuesMissing
봉덕시장(남구) has 1 (1.6%) missing valuesMissing
칠성시장(북구) has 2 (3.1%) missing valuesMissing
수성시장(수성구) has 1 (1.6%) missing valuesMissing
서남시장(달서구) has 2 (3.1%) missing valuesMissing
팔달시장(북구) has 3 (4.7%) missing valuesMissing
품 목 has unique valuesUnique
등락율(퍼센트) has 55 (85.9%) zerosZeros

Reproduction

Analysis started2024-05-04 07:38:22.520199
Analysis finished2024-05-04 07:39:24.064373
Duration1 minute and 1.54 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품 목
Text

UNIQUE 

Distinct64
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-05-04T07:39:24.420564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.9375
Min length2

Characters and Unicode

Total characters316
Distinct characters126
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique64 ?
Unique (%)100.0%

Sample

1st row곰탕
2nd row냉면
3rd row비빔밥
4th row갈비탕
5th row삼계탕
ValueCountFrequency (%)
곰탕 1
 
1.6%
냉면 1
 
1.6%
세탁료 1
 
1.6%
노래방이용료 1
 
1.6%
pc방이용료 1
 
1.6%
영화관람료 1
 
1.6%
사진촬영료 1
 
1.6%
사진인화료 1
 
1.6%
방송수신료 1
 
1.6%
대입학원비(단과 1
 
1.6%
Other values (54) 54
84.4%
2024-05-04T07:39:25.519747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
22
 
7.0%
( 17
 
5.4%
) 17
 
5.4%
14
 
4.4%
11
 
3.5%
11
 
3.5%
8
 
2.5%
8
 
2.5%
8
 
2.5%
7
 
2.2%
Other values (116) 193
61.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 272
86.1%
Open Punctuation 17
 
5.4%
Close Punctuation 17
 
5.4%
Uppercase Letter 8
 
2.5%
Decimal Number 2
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
22
 
8.1%
14
 
5.1%
11
 
4.0%
11
 
4.0%
8
 
2.9%
8
 
2.9%
8
 
2.9%
7
 
2.6%
7
 
2.6%
6
 
2.2%
Other values (108) 170
62.5%
Uppercase Letter
ValueCountFrequency (%)
P 3
37.5%
G 2
25.0%
L 2
25.0%
C 1
 
12.5%
Decimal Number
ValueCountFrequency (%)
1 1
50.0%
2 1
50.0%
Open Punctuation
ValueCountFrequency (%)
( 17
100.0%
Close Punctuation
ValueCountFrequency (%)
) 17
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 272
86.1%
Common 36
 
11.4%
Latin 8
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
22
 
8.1%
14
 
5.1%
11
 
4.0%
11
 
4.0%
8
 
2.9%
8
 
2.9%
8
 
2.9%
7
 
2.6%
7
 
2.6%
6
 
2.2%
Other values (108) 170
62.5%
Common
ValueCountFrequency (%)
( 17
47.2%
) 17
47.2%
1 1
 
2.8%
2 1
 
2.8%
Latin
ValueCountFrequency (%)
P 3
37.5%
G 2
25.0%
L 2
25.0%
C 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 272
86.1%
ASCII 44
 
13.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
22
 
8.1%
14
 
5.1%
11
 
4.0%
11
 
4.0%
8
 
2.9%
8
 
2.9%
8
 
2.9%
7
 
2.6%
7
 
2.6%
6
 
2.2%
Other values (108) 170
62.5%
ASCII
ValueCountFrequency (%)
( 17
38.6%
) 17
38.6%
P 3
 
6.8%
G 2
 
4.5%
L 2
 
4.5%
1 1
 
2.3%
2 1
 
2.3%
C 1
 
2.3%
Distinct50
Distinct (%)78.1%
Missing0
Missing (%)0.0%
Memory size644.0 B
2024-05-04T07:39:26.083760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length28
Median length19
Mean length10.53125
Min length3

Characters and Unicode

Total characters674
Distinct characters177
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique43 ?
Unique (%)67.2%

Sample

1st row대중식당, 1그릇
2nd row대중식당, 1그릇
3rd row대중식당, 1그릇
4th row대중식당, 1그릇
5th row대중식당, 1그릇
ValueCountFrequency (%)
대중식당 8
 
6.3%
1그릇 7
 
5.5%
200g 5
 
3.9%
밥제외 5
 
3.9%
중화요리점 4
 
3.1%
일반인 3
 
2.4%
1시간 3
 
2.4%
1l 3
 
2.4%
1인분 3
 
2.4%
영어단과 2
 
1.6%
Other values (74) 84
66.1%
2024-05-04T07:39:27.104759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
85
 
12.6%
, 50
 
7.4%
1 21
 
3.1%
0 17
 
2.5%
15
 
2.2%
14
 
2.1%
14
 
2.1%
13
 
1.9%
2 12
 
1.8%
12
 
1.8%
Other values (167) 421
62.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 426
63.2%
Space Separator 85
 
12.6%
Decimal Number 66
 
9.8%
Other Punctuation 54
 
8.0%
Lowercase Letter 13
 
1.9%
Open Punctuation 10
 
1.5%
Close Punctuation 10
 
1.5%
Uppercase Letter 5
 
0.7%
Math Symbol 5
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
15
 
3.5%
14
 
3.3%
14
 
3.3%
13
 
3.1%
12
 
2.8%
10
 
2.3%
8
 
1.9%
8
 
1.9%
7
 
1.6%
7
 
1.6%
Other values (140) 318
74.6%
Decimal Number
ValueCountFrequency (%)
1 21
31.8%
0 17
25.8%
2 12
18.2%
3 6
 
9.1%
5 4
 
6.1%
4 3
 
4.5%
9 1
 
1.5%
7 1
 
1.5%
6 1
 
1.5%
Lowercase Letter
ValueCountFrequency (%)
g 7
53.8%
k 2
 
15.4%
c 2
 
15.4%
m 1
 
7.7%
l 1
 
7.7%
Other Punctuation
ValueCountFrequency (%)
, 50
92.6%
* 2
 
3.7%
/ 1
 
1.9%
: 1
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
L 3
60.0%
V 1
 
20.0%
T 1
 
20.0%
Math Symbol
ValueCountFrequency (%)
< 2
40.0%
> 2
40.0%
~ 1
20.0%
Space Separator
ValueCountFrequency (%)
85
100.0%
Open Punctuation
ValueCountFrequency (%)
( 10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 426
63.2%
Common 230
34.1%
Latin 18
 
2.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
15
 
3.5%
14
 
3.3%
14
 
3.3%
13
 
3.1%
12
 
2.8%
10
 
2.3%
8
 
1.9%
8
 
1.9%
7
 
1.6%
7
 
1.6%
Other values (140) 318
74.6%
Common
ValueCountFrequency (%)
85
37.0%
, 50
21.7%
1 21
 
9.1%
0 17
 
7.4%
2 12
 
5.2%
( 10
 
4.3%
) 10
 
4.3%
3 6
 
2.6%
5 4
 
1.7%
4 3
 
1.3%
Other values (9) 12
 
5.2%
Latin
ValueCountFrequency (%)
g 7
38.9%
L 3
16.7%
k 2
 
11.1%
c 2
 
11.1%
m 1
 
5.6%
l 1
 
5.6%
V 1
 
5.6%
T 1
 
5.6%

Most occurring blocks

ValueCountFrequency (%)
Hangul 426
63.2%
ASCII 248
36.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
85
34.3%
, 50
20.2%
1 21
 
8.5%
0 17
 
6.9%
2 12
 
4.8%
( 10
 
4.0%
) 10
 
4.0%
g 7
 
2.8%
3 6
 
2.4%
5 4
 
1.6%
Other values (17) 26
 
10.5%
Hangul
ValueCountFrequency (%)
15
 
3.5%
14
 
3.3%
14
 
3.3%
13
 
3.1%
12
 
2.8%
10
 
2.3%
8
 
1.9%
8
 
1.9%
7
 
1.6%
7
 
1.6%
Other values (140) 318
74.6%

4월24일 평균 가격(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct61
Distinct (%)95.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63409.532
Minimum993.125
Maximum1165000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:27.465337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum993.125
5-th percentile2010.975
Q15750
median11375
Q327531.25
95-th percentile303218.75
Maximum1165000
Range1164006.9
Interquartile range (IQR)21781.25

Descriptive statistics

Standard deviation164343.89
Coefficient of variation (CV)2.5917852
Kurtosis32.736668
Mean63409.532
Median Absolute Deviation (MAD)6275
Skewness5.2436804
Sum4058210
Variance2.7008913 × 1010
MonotonicityNot monotonic
2024-05-04T07:39:27.873145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500.0 2
 
3.1%
7437.5 2
 
3.1%
5750.0 2
 
3.1%
11250.0 1
 
1.6%
5125.0 1
 
1.6%
8125.0 1
 
1.6%
138750.0 1
 
1.6%
182500.0 1
 
1.6%
136250.0 1
 
1.6%
125000.0 1
 
1.6%
Other values (51) 51
79.7%
ValueCountFrequency (%)
993.125 1
1.6%
1075.0 1
1.6%
1532.5 1
1.6%
1671.0 1
1.6%
3937.5 1
1.6%
4087.5 1
1.6%
4412.5 1
1.6%
4500.0 2
3.1%
4625.0 1
1.6%
4700.0 1
1.6%
ValueCountFrequency (%)
1165000.0 1
1.6%
393750.0 1
1.6%
365000.0 1
1.6%
306875.0 1
1.6%
282500.0 1
1.6%
182500.0 1
1.6%
138750.0 1
1.6%
136250.0 1
1.6%
131682.5 1
1.6%
125000.0 1
1.6%

4월29일 평균 가격(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct60
Distinct (%)93.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean63439.515
Minimum993.125
Maximum1165000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:28.394533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum993.125
5-th percentile2012.675
Q15750
median11375
Q327531.25
95-th percentile303218.75
Maximum1165000
Range1164006.9
Interquartile range (IQR)21781.25

Descriptive statistics

Standard deviation164350.32
Coefficient of variation (CV)2.5906618
Kurtosis32.726991
Mean63439.515
Median Absolute Deviation (MAD)6275
Skewness5.2425516
Sum4060128.9
Variance2.7011029 × 1010
MonotonicityNot monotonic
2024-05-04T07:39:28.909750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4500.0 2
 
3.1%
7437.5 2
 
3.1%
7500.0 2
 
3.1%
5750.0 2
 
3.1%
11250.0 1
 
1.6%
8125.0 1
 
1.6%
138750.0 1
 
1.6%
182500.0 1
 
1.6%
136250.0 1
 
1.6%
125000.0 1
 
1.6%
Other values (50) 50
78.1%
ValueCountFrequency (%)
993.125 1
1.6%
1075.0 1
1.6%
1525.125 1
1.6%
1673.0 1
1.6%
3937.5 1
1.6%
4087.5 1
1.6%
4412.5 1
1.6%
4500.0 2
3.1%
4625.0 1
1.6%
4700.0 1
1.6%
ValueCountFrequency (%)
1165000.0 1
1.6%
393750.0 1
1.6%
365000.0 1
1.6%
306875.0 1
1.6%
282500.0 1
1.6%
182500.0 1
1.6%
138750.0 1
1.6%
136250.0 1
1.6%
132223.75 1
1.6%
125000.0 1
1.6%

등락가격(원)
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct10
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Memory size644.0 B
0
55 
↑125
 
1
↑500
 
1
↑918.75
 
1
↑541.25
 
1
Other values (5)
 
5

Length

Max length17
Median length1
Mean length1.71875
Min length1

Unique

Unique9 ?
Unique (%)14.1%

Sample

1st row0
2nd row0
3rd row0
4th row↑125
5th row↑500

Common Values

ValueCountFrequency (%)
0 55
85.9%
↑125 1
 
1.6%
↑500 1
 
1.6%
↑918.75 1
 
1.6%
↑541.25 1
 
1.6%
↑250 1
 
1.6%
↑2 1
 
1.6%
↓7.375 1
 
1.6%
↓125 1
 
1.6%
↓285.714285714284 1
 
1.6%

Length

2024-05-04T07:39:29.395359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-04T07:39:29.806428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 55
85.9%
↑125 1
 
1.6%
↑500 1
 
1.6%
↑918.75 1
 
1.6%
↑541.25 1
 
1.6%
↑250 1
 
1.6%
↑2 1
 
1.6%
↓7.375 1
 
1.6%
↓125 1
 
1.6%
↓285.714285714284 1
 
1.6%

등락율(퍼센트)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct10
Distinct (%)15.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.0328125
Minimum-2.1
Maximum3.4
Zeros55
Zeros (%)85.9%
Negative3
Negative (%)4.7%
Memory size708.0 B
2024-05-04T07:39:30.123461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.1
5-th percentile0
Q10
median0
Q30
95-th percentile0.485
Maximum3.4
Range5.5
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.57653995
Coefficient of variation (CV)17.570741
Kurtosis21.808897
Mean0.0328125
Median Absolute Deviation (MAD)0
Skewness2.1953801
Sum2.1
Variance0.33239831
MonotonicityNot monotonic
2024-05-04T07:39:30.521180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
0.0 55
85.9%
1.0 1
 
1.6%
3.4 1
 
1.6%
0.9 1
 
1.6%
0.4 1
 
1.6%
0.5 1
 
1.6%
0.1 1
 
1.6%
-0.5 1
 
1.6%
-1.6 1
 
1.6%
-2.1 1
 
1.6%
ValueCountFrequency (%)
-2.1 1
 
1.6%
-1.6 1
 
1.6%
-0.5 1
 
1.6%
0.0 55
85.9%
0.1 1
 
1.6%
0.4 1
 
1.6%
0.5 1
 
1.6%
0.9 1
 
1.6%
1.0 1
 
1.6%
3.4 1
 
1.6%
ValueCountFrequency (%)
3.4 1
 
1.6%
1.0 1
 
1.6%
0.9 1
 
1.6%
0.5 1
 
1.6%
0.4 1
 
1.6%
0.1 1
 
1.6%
0.0 55
85.9%
-0.5 1
 
1.6%
-1.6 1
 
1.6%
-2.1 1
 
1.6%

최고가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct42
Distinct (%)65.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean73407.312
Minimum995
Maximum1240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:30.991458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum995
5-th percentile2044.15
Q16850
median13665
Q330250
95-th percentile387000
Maximum1240000
Range1239005
Interquartile range (IQR)23400

Descriptive statistics

Standard deviation181633.39
Coefficient of variation (CV)2.4743228
Kurtosis27.516573
Mean73407.312
Median Absolute Deviation (MAD)7715
Skewness4.7726788
Sum4698068
Variance3.2990687 × 1010
MonotonicityNot monotonic
2024-05-04T07:39:31.507949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
9000 5
 
7.8%
8500 5
 
7.8%
5000 4
 
6.2%
14000 3
 
4.7%
15000 3
 
4.7%
20000 2
 
3.1%
25000 2
 
3.1%
12000 2
 
3.1%
150000 2
 
3.1%
6000 2
 
3.1%
Other values (32) 34
53.1%
ValueCountFrequency (%)
995 1
 
1.6%
1200 1
 
1.6%
1574 1
 
1.6%
1699 1
 
1.6%
4000 1
 
1.6%
4600 1
 
1.6%
4700 1
 
1.6%
5000 4
6.2%
5500 1
 
1.6%
5900 1
 
1.6%
ValueCountFrequency (%)
1240000 1
1.6%
450000 2
3.1%
390000 1
1.6%
370000 1
1.6%
220000 1
1.6%
155160 1
1.6%
150000 2
3.1%
143000 1
1.6%
140000 1
1.6%
129350 1
1.6%

최저가(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean54492.031
Minimum987
Maximum1090000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:31.956719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum987
5-th percentile1520.7
Q15000
median9300
Q323625
95-th percentile237750
Maximum1090000
Range1089013
Interquartile range (IQR)18625

Descriptive statistics

Standard deviation149952.92
Coefficient of variation (CV)2.751832
Kurtosis37.028503
Mean54492.031
Median Absolute Deviation (MAD)5300
Skewness5.63398
Sum3487490
Variance2.2485877 × 1010
MonotonicityNot monotonic
2024-05-04T07:39:32.403359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
7000 5
 
7.8%
5000 5
 
7.8%
10000 4
 
6.2%
6000 3
 
4.7%
4000 3
 
4.7%
6500 2
 
3.1%
12000 2
 
3.1%
15000 2
 
3.1%
3500 2
 
3.1%
14000 2
 
3.1%
Other values (34) 34
53.1%
ValueCountFrequency (%)
987 1
 
1.6%
1000 1
 
1.6%
1485 1
 
1.6%
1500 1
 
1.6%
1638 1
 
1.6%
3500 2
3.1%
3600 1
 
1.6%
4000 3
4.7%
4500 1
 
1.6%
4700 1
 
1.6%
ValueCountFrequency (%)
1090000 1
1.6%
350000 1
1.6%
300000 1
1.6%
240000 1
1.6%
225000 1
1.6%
160000 1
1.6%
130000 1
1.6%
120000 1
1.6%
110000 1
1.6%
96230 1
1.6%

남문시장(중구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)68.9%
Missing3
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean46272.066
Minimum993
Maximum400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:32.834639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum993
5-th percentile1699
Q15000
median12000
Q325000
95-th percentile250000
Maximum400000
Range399007
Interquartile range (IQR)20000

Descriptive statistics

Standard deviation84941.961
Coefficient of variation (CV)1.8357071
Kurtosis6.7242757
Mean46272.066
Median Absolute Deviation (MAD)7000
Skewness2.6262705
Sum2822596
Variance7.2151368 × 109
MonotonicityNot monotonic
2024-05-04T07:39:33.336155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
4500 5
 
7.8%
8000 4
 
6.2%
5000 4
 
6.2%
7000 3
 
4.7%
15000 3
 
4.7%
6000 2
 
3.1%
150000 2
 
3.1%
120000 2
 
3.1%
12000 2
 
3.1%
16000 2
 
3.1%
Other values (32) 32
50.0%
(Missing) 3
 
4.7%
ValueCountFrequency (%)
993 1
 
1.6%
1200 1
 
1.6%
1574 1
 
1.6%
1699 1
 
1.6%
3600 1
 
1.6%
4000 1
 
1.6%
4500 5
7.8%
4700 1
 
1.6%
5000 4
6.2%
5500 1
 
1.6%
ValueCountFrequency (%)
400000 1
1.6%
320000 1
1.6%
300000 1
1.6%
250000 1
1.6%
180000 1
1.6%
150000 2
3.1%
136170 1
1.6%
120000 2
3.1%
105180 1
1.6%
56000 1
1.6%

동구시장(동구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)63.9%
Missing3
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean46695.59
Minimum994
Maximum450000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:33.800498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum994
5-th percentile1698
Q15600
median12000
Q325000
95-th percentile290000
Maximum450000
Range449006
Interquartile range (IQR)19400

Descriptive statistics

Standard deviation90983.792
Coefficient of variation (CV)1.9484451
Kurtosis8.8039949
Mean46695.59
Median Absolute Deviation (MAD)7000
Skewness2.9651591
Sum2848431
Variance8.2780504 × 109
MonotonicityNot monotonic
2024-05-04T07:39:34.257599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
4500 3
 
4.7%
12000 3
 
4.7%
130000 3
 
4.7%
8000 3
 
4.7%
4000 3
 
4.7%
6000 3
 
4.7%
5000 3
 
4.7%
15000 3
 
4.7%
7000 3
 
4.7%
13000 3
 
4.7%
Other values (29) 31
48.4%
(Missing) 3
 
4.7%
ValueCountFrequency (%)
994 1
 
1.6%
1200 1
 
1.6%
1569 1
 
1.6%
1698 1
 
1.6%
4000 3
4.7%
4500 3
4.7%
4700 1
 
1.6%
5000 3
4.7%
5500 1
 
1.6%
5600 1
 
1.6%
ValueCountFrequency (%)
450000 1
 
1.6%
350000 1
 
1.6%
320000 1
 
1.6%
290000 1
 
1.6%
170000 1
 
1.6%
130000 3
4.7%
96230 1
 
1.6%
95000 1
 
1.6%
77480 1
 
1.6%
56000 1
 
1.6%

서문시장(중구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct38
Distinct (%)63.3%
Missing4
Missing (%)6.2%
Infinite0
Infinite (%)0.0%
Mean45864.567
Minimum994
Maximum400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:34.771428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum994
5-th percentile1637.5
Q16000
median10000
Q325300
95-th percentile252500
Maximum400000
Range399006
Interquartile range (IQR)19300

Descriptive statistics

Standard deviation83861.513
Coefficient of variation (CV)1.8284597
Kurtosis6.8862633
Mean45864.567
Median Absolute Deviation (MAD)5500
Skewness2.6440564
Sum2751874
Variance7.0327533 × 109
MonotonicityNot monotonic
2024-05-04T07:39:35.496958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
8000 6
 
9.4%
4500 5
 
7.8%
16000 3
 
4.7%
6000 3
 
4.7%
10000 3
 
4.7%
11000 2
 
3.1%
300000 2
 
3.1%
5000 2
 
3.1%
14000 2
 
3.1%
140000 2
 
3.1%
Other values (28) 30
46.9%
(Missing) 4
 
6.2%
ValueCountFrequency (%)
994 1
 
1.6%
1000 1
 
1.6%
1495 1
 
1.6%
1645 1
 
1.6%
4000 1
 
1.6%
4500 5
7.8%
4700 1
 
1.6%
5000 2
 
3.1%
5900 1
 
1.6%
6000 3
4.7%
ValueCountFrequency (%)
400000 1
1.6%
300000 2
3.1%
250000 1
1.6%
170000 1
1.6%
150000 1
1.6%
140000 2
3.1%
131250 1
1.6%
101290 1
1.6%
88000 1
1.6%
55000 1
1.6%

봉덕시장(남구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct41
Distinct (%)65.1%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean62038.714
Minimum995
Maximum1090000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:36.146677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum995
5-th percentile1543.6
Q16000
median12000
Q332500
95-th percentile292500
Maximum1090000
Range1089005
Interquartile range (IQR)26500

Descriptive statistics

Standard deviation157382.59
Coefficient of variation (CV)2.5368447
Kurtosis30.07474
Mean62038.714
Median Absolute Deviation (MAD)7000
Skewness5.028626
Sum3908439
Variance2.4769278 × 1010
MonotonicityNot monotonic
2024-05-04T07:39:36.699573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
15000 5
 
7.8%
5000 5
 
7.8%
8000 4
 
6.2%
6000 4
 
6.2%
7000 3
 
4.7%
12000 3
 
4.7%
130000 2
 
3.1%
40000 2
 
3.1%
400000 2
 
3.1%
25000 2
 
3.1%
Other values (31) 31
48.4%
ValueCountFrequency (%)
995 1
 
1.6%
1200 1
 
1.6%
1500 1
 
1.6%
1529 1
 
1.6%
1675 1
 
1.6%
3500 1
 
1.6%
4000 1
 
1.6%
4500 1
 
1.6%
4700 1
 
1.6%
5000 5
7.8%
ValueCountFrequency (%)
1090000 1
1.6%
400000 2
3.1%
300000 1
1.6%
225000 1
1.6%
160000 1
1.6%
140000 1
1.6%
139870 1
1.6%
130000 2
3.1%
116870 1
1.6%
59000 1
1.6%

칠성시장(북구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)67.7%
Missing2
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean47079.355
Minimum987
Maximum350000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:37.347056image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum987
5-th percentile1756.1
Q16250
median10500
Q321125
95-th percentile296000
Maximum350000
Range349013
Interquartile range (IQR)14875

Descriptive statistics

Standard deviation86541.076
Coefficient of variation (CV)1.8381959
Kurtosis5.795733
Mean47079.355
Median Absolute Deviation (MAD)6000
Skewness2.5548574
Sum2918920
Variance7.4893578 × 109
MonotonicityNot monotonic
2024-05-04T07:39:37.940031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
9000 5
 
7.8%
8500 4
 
6.2%
4500 4
 
6.2%
20000 4
 
6.2%
12000 4
 
6.2%
10000 2
 
3.1%
7000 2
 
3.1%
5000 2
 
3.1%
350000 2
 
3.1%
51000 1
 
1.6%
Other values (32) 32
50.0%
(Missing) 2
 
3.1%
ValueCountFrequency (%)
987 1
 
1.6%
1000 1
 
1.6%
1485 1
 
1.6%
1638 1
 
1.6%
4000 1
 
1.6%
4200 1
 
1.6%
4500 4
6.2%
4700 1
 
1.6%
4900 1
 
1.6%
5000 2
3.1%
ValueCountFrequency (%)
350000 2
3.1%
320000 1
1.6%
300000 1
1.6%
220000 1
1.6%
150000 1
1.6%
135150 1
1.6%
130000 1
1.6%
120000 1
1.6%
110000 1
1.6%
99500 1
1.6%

수성시장(수성구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)63.5%
Missing1
Missing (%)1.6%
Infinite0
Infinite (%)0.0%
Mean69513.048
Minimum994
Maximum1240000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:38.595109image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum994
5-th percentile1911.1
Q16200
median11000
Q330850
95-th percentile379000
Maximum1240000
Range1239006
Interquartile range (IQR)24650

Descriptive statistics

Standard deviation180050.57
Coefficient of variation (CV)2.5901694
Kurtosis29.425805
Mean69513.048
Median Absolute Deviation (MAD)6000
Skewness4.9667478
Sum4379322
Variance3.2418208 × 1010
MonotonicityNot monotonic
2024-05-04T07:39:39.416017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
8000 6
 
9.4%
4500 5
 
7.8%
15000 4
 
6.2%
16000 3
 
4.7%
5000 3
 
4.7%
9000 3
 
4.7%
13000 2
 
3.1%
12000 2
 
3.1%
140000 2
 
3.1%
7000 2
 
3.1%
Other values (30) 31
48.4%
ValueCountFrequency (%)
994 1
 
1.6%
1000 1
 
1.6%
1539 1
 
1.6%
1679 1
 
1.6%
4000 1
 
1.6%
4500 5
7.8%
4700 1
 
1.6%
5000 3
4.7%
5500 1
 
1.6%
6000 1
 
1.6%
ValueCountFrequency (%)
1240000 1
1.6%
450000 1
1.6%
390000 1
1.6%
380000 1
1.6%
370000 1
1.6%
170000 1
1.6%
140000 2
3.1%
130000 1
1.6%
125350 1
1.6%
118760 1
1.6%

서남시장(달서구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct42
Distinct (%)67.7%
Missing2
Missing (%)3.1%
Infinite0
Infinite (%)0.0%
Mean46205.71
Minimum994
Maximum400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:39.917265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum994
5-th percentile1741.75
Q15625
median10000
Q321125
95-th percentile238000
Maximum400000
Range399006
Interquartile range (IQR)15500

Descriptive statistics

Standard deviation87061.67
Coefficient of variation (CV)1.8842189
Kurtosis7.3068534
Mean46205.71
Median Absolute Deviation (MAD)5500
Skewness2.7371727
Sum2864754
Variance7.5797344 × 109
MonotonicityNot monotonic
2024-05-04T07:39:40.557717image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
7000 7
 
10.9%
10000 4
 
6.2%
15000 3
 
4.7%
4500 3
 
4.7%
5000 3
 
4.7%
18000 2
 
3.1%
14000 2
 
3.1%
8000 2
 
3.1%
16000 2
 
3.1%
20000 2
 
3.1%
Other values (32) 32
50.0%
(Missing) 2
 
3.1%
ValueCountFrequency (%)
994 1
 
1.6%
1000 1
 
1.6%
1515 1
 
1.6%
1665 1
 
1.6%
3200 1
 
1.6%
4000 1
 
1.6%
4200 1
 
1.6%
4500 3
4.7%
4600 1
 
1.6%
4700 1
 
1.6%
ValueCountFrequency (%)
400000 1
1.6%
370000 1
1.6%
300000 1
1.6%
240000 1
1.6%
200000 1
1.6%
155160 1
1.6%
140000 1
1.6%
130000 1
1.6%
129350 1
1.6%
120000 1
1.6%

팔달시장(북구)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct39
Distinct (%)63.9%
Missing3
Missing (%)4.7%
Infinite0
Infinite (%)0.0%
Mean46849.574
Minimum994
Maximum400000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size708.0 B
2024-05-04T07:39:40.997914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum994
5-th percentile1685
Q15500
median11000
Q325000
95-th percentile260000
Maximum400000
Range399006
Interquartile range (IQR)19500

Descriptive statistics

Standard deviation87184.038
Coefficient of variation (CV)1.8609356
Kurtosis6.7277866
Mean46849.574
Median Absolute Deviation (MAD)6300
Skewness2.6432345
Sum2857824
Variance7.6010566 × 109
MonotonicityNot monotonic
2024-05-04T07:39:41.573463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
7000 4
 
6.2%
5000 4
 
6.2%
8000 3
 
4.7%
12000 3
 
4.7%
18000 3
 
4.7%
6500 2
 
3.1%
130000 2
 
3.1%
4000 2
 
3.1%
4500 2
 
3.1%
11000 2
 
3.1%
Other values (29) 34
53.1%
(Missing) 3
 
4.7%
ValueCountFrequency (%)
994 1
 
1.6%
1000 1
 
1.6%
1495 1
 
1.6%
1685 1
 
1.6%
3500 1
 
1.6%
4000 2
3.1%
4500 2
3.1%
4700 1
 
1.6%
5000 4
6.2%
5500 2
3.1%
ValueCountFrequency (%)
400000 1
1.6%
350000 1
1.6%
300000 1
1.6%
260000 1
1.6%
190000 1
1.6%
143000 1
1.6%
138610 1
1.6%
130000 2
3.1%
120000 1
1.6%
117480 1
1.6%

Interactions

2024-05-04T07:39:17.153245image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:24.962393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:29.157528image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:33.773761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:37.543179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:42.220181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:47.083199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:51.749166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:56.443203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:00.624247image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:04.759670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:09.395288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:12.562663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:17.566429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:25.346818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:29.738819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:34.030210image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:37.965263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:42.600933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:47.360289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:52.177322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:56.801157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:00.805106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:05.205989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:09.682364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:12.853419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:17.963715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:25.731470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:30.068968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:34.388825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:38.311235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:42.915604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:47.649775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:52.449949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:57.364866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:01.074490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:05.610197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:09.982655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:13.193749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:18.354662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:25.971900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:30.431607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:34.628999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:38.830108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:43.273434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:47.947312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:52.711059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:57.841676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:01.321956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:05.938822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:10.282561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:13.559177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:18.674559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:26.289441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:30.924734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:34.890563image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:39.346762image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:43.533606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:48.420496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:52.948822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:58.108281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:01.594263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:06.738054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:10.554202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:13.924194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:19.119942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:26.638623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:31.304293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:35.140901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:39.699023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:43.777929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:48.768042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:53.274167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:58.407509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:01.878228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:07.070574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:10.812754image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:14.230116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:19.532230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:26.957708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:31.667896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:35.412114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:40.067007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:44.176799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:49.175128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:53.686136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:58.695936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:02.184366image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:07.387048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:11.023156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:14.574383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:19.885831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:27.264050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:31.969078image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:35.686095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:40.393261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:44.612323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:49.516520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:54.099671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:58.949291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:02.499222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:07.669031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:11.261938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:14.962622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:20.199864image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:27.625904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:32.265429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:36.001959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:40.752115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:45.019547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:49.867889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:54.589272image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:59.260065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:02.856159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:07.958668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:11.458868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:15.377977image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:20.520678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:27.877379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:32.549606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:36.234769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:41.000771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:45.311156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:50.284633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:54.943885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:59.561986image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:03.161276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:08.248520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:11.619649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:15.664479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:21.084090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:28.186154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:32.949590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:36.484599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:41.351714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:45.727010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:50.621936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:55.248456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:59.837554image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:03.587933image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:08.525903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:11.804485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:16.025999image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:21.443260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:28.516194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:33.210729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:36.742079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:41.633570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:46.231498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:50.927161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:55.587719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:00.091657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:03.954110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:08.816158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:12.038766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:16.289855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:21.852027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:28.845539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:33.492379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:37.048840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:41.947607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:46.785729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:51.343574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:38:55.985040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:00.372104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:04.326483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:09.112713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:12.278118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-04T07:39:16.712876image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-04T07:39:41.963067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
품 목규격 및 단위4월24일 평균 가격(원)4월29일 평균 가격(원)등락가격(원)등락율(퍼센트)최고가(원)최저가(원)남문시장(중구)동구시장(동구)서문시장(중구)봉덕시장(남구)칠성시장(북구)수성시장(수성구)서남시장(달서구)팔달시장(북구)
품 목1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
규격 및 단위1.0001.0001.0001.0000.0000.0001.0001.0001.0001.0000.9901.0000.9991.0000.9991.000
4월24일 평균 가격(원)1.0001.0001.0001.0000.0000.0000.9910.9930.9910.9050.9951.0000.9840.9960.9990.962
4월29일 평균 가격(원)1.0001.0001.0001.0000.0000.0000.9910.9930.9910.9050.9951.0000.9840.9960.9990.962
등락가격(원)1.0000.0000.0000.0001.0001.0000.0000.0000.2720.0000.3430.0000.5210.0000.0000.544
등락율(퍼센트)1.0000.0000.0000.0001.0001.0000.0000.0000.0000.0000.0000.0000.2220.0000.0000.000
최고가(원)1.0001.0000.9910.9910.0000.0001.0000.9811.0000.9701.0000.9941.0000.9991.0001.000
최저가(원)1.0001.0000.9930.9930.0000.0000.9811.0000.9970.8630.9990.9900.9650.9880.9790.949
남문시장(중구)1.0001.0000.9910.9910.2720.0001.0000.9971.0000.9340.9960.9910.9861.0000.9820.971
동구시장(동구)1.0001.0000.9050.9050.0000.0000.9700.8630.9341.0000.9380.8920.9460.9620.9330.970
서문시장(중구)1.0000.9900.9950.9950.3430.0001.0000.9990.9960.9381.0000.9920.9871.0000.9850.958
봉덕시장(남구)1.0001.0001.0001.0000.0000.0000.9940.9900.9910.8920.9921.0000.9850.9931.0000.975
칠성시장(북구)1.0000.9990.9840.9840.5210.2221.0000.9650.9860.9460.9870.9851.0000.9990.9820.926
수성시장(수성구)1.0001.0000.9960.9960.0000.0000.9990.9881.0000.9621.0000.9930.9991.0000.9990.962
서남시장(달서구)1.0000.9990.9990.9990.0000.0001.0000.9790.9820.9330.9851.0000.9820.9991.0000.956
팔달시장(북구)1.0001.0000.9620.9620.5440.0001.0000.9490.9710.9700.9580.9750.9260.9620.9561.000
2024-05-04T07:39:42.315422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
4월24일 평균 가격(원)4월29일 평균 가격(원)등락율(퍼센트)최고가(원)최저가(원)남문시장(중구)동구시장(동구)서문시장(중구)봉덕시장(남구)칠성시장(북구)수성시장(수성구)서남시장(달서구)팔달시장(북구)등락가격(원)
4월24일 평균 가격(원)1.0001.0000.1840.9920.9890.9860.9860.9950.9890.9890.9900.9930.9920.000
4월29일 평균 가격(원)1.0001.0000.1860.9920.9890.9860.9860.9950.9890.9890.9900.9930.9920.000
등락율(퍼센트)0.1840.1861.0000.2020.1880.1990.1930.2060.1880.2030.2200.1950.1830.965
최고가(원)0.9920.9920.2021.0000.9730.9710.9770.9920.9830.9920.9870.9920.9850.000
최저가(원)0.9890.9890.1880.9731.0000.9880.9760.9850.9830.9730.9770.9810.9770.000
남문시장(중구)0.9860.9860.1990.9710.9881.0000.9820.9850.9750.9640.9710.9730.9710.131
동구시장(동구)0.9860.9860.1930.9770.9760.9821.0000.9850.9800.9660.9670.9760.9840.000
서문시장(중구)0.9950.9950.2060.9920.9850.9850.9851.0000.9800.9940.9910.9920.9870.167
봉덕시장(남구)0.9890.9890.1880.9830.9830.9750.9800.9801.0000.9710.9780.9860.9860.000
칠성시장(북구)0.9890.9890.2030.9920.9730.9640.9660.9940.9711.0000.9870.9890.9810.275
수성시장(수성구)0.9900.9900.2200.9870.9770.9710.9670.9910.9780.9871.0000.9840.9840.000
서남시장(달서구)0.9930.9930.1950.9920.9810.9730.9760.9920.9860.9890.9841.0000.9870.000
팔달시장(북구)0.9920.9920.1830.9850.9770.9710.9840.9870.9860.9810.9840.9871.0000.281
등락가격(원)0.0000.0000.9650.0000.0000.1310.0000.1670.0000.2750.0000.0000.2811.000

Missing values

2024-05-04T07:39:22.504899image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-04T07:39:23.418602image/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.
2024-05-04T07:39:23.820786image/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

품 목규격 및 단위4월24일 평균 가격(원)4월29일 평균 가격(원)등락가격(원)등락율(퍼센트)최고가(원)최저가(원)남문시장(중구)동구시장(동구)서문시장(중구)봉덕시장(남구)칠성시장(북구)수성시장(수성구)서남시장(달서구)팔달시장(북구)
0곰탕대중식당, 1그릇11250.011250.000.014000100001200010000100001400010000130001000011000
1냉면대중식당, 1그릇8750.08750.000.01200070001200080008000800090001000080007000
2비빔밥대중식당, 1그릇7437.57437.500.08500700070007000800070008500800070007000
3갈비탕대중식당, 1그릇12750.012875.0↑1251.015500100001550013000140001200012500130001300010000
4삼계탕대중식당, 1그릇14687.515187.5↑5003.416500140001600015000140001650016000150001500014000
5김치찌개대중식당, 1그릇7812.57812.500.08500700080007000800080008500800070008000
6된장찌개대중식당, 1그릇7437.57437.500.08500600080007000800070008500800070006000
7불고기쇠고기 200g, 밥제외17297.517297.500.020000140001538014000180001500020000160002000020000
8등심(외식)쇠고기 200g, 밥제외40750.040750.000.046000300003000044000440004000044000420004600036000
9돼지갈비(외식)돼지갈비 200g, 밥제외11953.7511953.7500.013330100001333012000110001330012000120001000012000
품 목규격 및 단위4월24일 평균 가격(원)4월29일 평균 가격(원)등락가격(원)등락율(퍼센트)최고가(원)최저가(원)남문시장(중구)동구시장(동구)서문시장(중구)봉덕시장(남구)칠성시장(북구)수성시장(수성구)서남시장(달서구)팔달시장(북구)
54휘발유무연휘발유,주유소판매, 1L1671.01673.0↑20.11699163816991698164516751638167916651685
55경유주유소판매, 1L1532.51525.125↓7.375-0.51574148515741569149515291485153915151495
56LPG(자동차용)주유소판매, 1L993.125993.12500.0995987993994994995987994994994
57숙박료(호텔)관광호텔3성급 더블침대101250.0101250.000.01430005900012000095000880005900013000011000065000143000
58숙박료(여관)갑류온돌방, 욕실부설41250.041250.000.050000350004500040000400004000040000500004000035000
59이용료성인중급, 1회10125.010125.000.014000700010000140001000011000900070001000010000
60미용료성인여자, 중급11500.011500.000.0140006000600013000120001200012000100001400013000
61목욕료성인대중탕7625.07500.0↓125-1.68500650080008000650085007500650070008000
62찜질방이용료성인주간13571.4285713285.71429↓285.714285714284-2.115000110001500012000130001300014000<NA>1500011000
63택배이용료2kg이하(타지역) <우체국택배>5000.05000.000.05000500050005000500050005000500050005000