Overview

Dataset statistics

Number of variables6
Number of observations10000
Missing cells0
Missing cells (%)0.0%
Duplicate rows4
Duplicate rows (%)< 0.1%
Total size in memory566.4 KiB
Average record size in memory58.0 B

Variable types

Text3
Numeric2
Categorical1

Dataset

Description품목,등급,거래수량,거래단위,해당일자
Author서울시농수산식품공사
URLhttps://data.seoul.go.kr/dataList/OA-13423/S/1/datasetView.do

Alerts

(구분) has constant value ""Constant
Dataset has 4 (< 0.1%) duplicate rowsDuplicates

Reproduction

Analysis started2024-05-11 07:25:03.718328
Analysis finished2024-05-11 07:25:06.294228
Duration2.58 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

Distinct288
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:25:06.686264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.6284
Min length1

Characters and Unicode

Total characters46284
Distinct characters276
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

Unique10 ?
Unique (%)0.1%

Sample

1st row토마토
2nd row호박 고구마
3rd row청양고추
4th row무
5th row감귤 극조생
ValueCountFrequency (%)
딸기 673
 
4.8%
수입 595
 
4.2%
사과 401
 
2.9%
활 273
 
1.9%
국산 258
 
1.8%
시금치 254
 
1.8%
감귤 250
 
1.8%
생표고 225
 
1.6%
양파 223
 
1.6%
고구마 217
 
1.5%
Other values (285) 10675
76.0%
2024-05-11T07:25:07.854285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4044
 
8.7%
추 1401
 
3.0%
) 1162
 
2.5%
( 1162
 
2.5%
기 1045
 
2.3%
고 964
 
2.1%
양 940
 
2.0%
파 833
 
1.8%
수 825
 
1.8%
리 822
 
1.8%
Other values (266) 33086
71.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 39805
86.0%
Space Separator 4044
 
8.7%
Close Punctuation 1162
 
2.5%
Open Punctuation 1162
 
2.5%
Uppercase Letter 111
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
추 1401
 
3.5%
기 1045
 
2.6%
고 964
 
2.4%
양 940
 
2.4%
파 833
 
2.1%
수 825
 
2.1%
리 822
 
2.1%
감 803
 
2.0%
마 709
 
1.8%
일 703
 
1.8%
Other values (260) 30760
77.3%
Uppercase Letter
ValueCountFrequency (%)
M 37
33.3%
B 37
33.3%
A 37
33.3%
Space Separator
ValueCountFrequency (%)
4044
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1162
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1162
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 39805
86.0%
Common 6368
 
13.8%
Latin 111
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
추 1401
 
3.5%
기 1045
 
2.6%
고 964
 
2.4%
양 940
 
2.4%
파 833
 
2.1%
수 825
 
2.1%
리 822
 
2.1%
감 803
 
2.0%
마 709
 
1.8%
일 703
 
1.8%
Other values (260) 30760
77.3%
Common
ValueCountFrequency (%)
4044
63.5%
) 1162
 
18.2%
( 1162
 
18.2%
Latin
ValueCountFrequency (%)
M 37
33.3%
B 37
33.3%
A 37
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 39805
86.0%
ASCII 6479
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4044
62.4%
) 1162
 
17.9%
( 1162
 
17.9%
M 37
 
0.6%
B 37
 
0.6%
A 37
 
0.6%
Hangul
ValueCountFrequency (%)
추 1401
 
3.5%
기 1045
 
2.6%
고 964
 
2.4%
양 940
 
2.4%
파 833
 
2.1%
수 825
 
2.1%
리 822
 
2.1%
감 803
 
2.0%
마 709
 
1.8%
일 703
 
1.8%
Other values (260) 30760
77.3%

등급
Text

Distinct8210
Distinct (%)82.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:25:08.837636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.7153
Min length3

Characters and Unicode

Total characters57153
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique7024 ?
Unique (%)70.2%

Sample

1st row62,739
2nd row7,351
3rd row56,408
4th row11,031
5th row10,755
ValueCountFrequency (%)
26,000 15
 
0.1%
15,000 15
 
0.1%
16,000 14
 
0.1%
28,000 14
 
0.1%
9,000 13
 
0.1%
13,000 13
 
0.1%
24,000 12
 
0.1%
25,000 10
 
0.1%
10,000 10
 
0.1%
20,000 9
 
0.1%
Other values (8200) 9875
98.8%
2024-05-11T07:25:10.458672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 9925
17.4%
1 7024
12.3%
2 5406
9.5%
0 4999
8.7%
3 4982
8.7%
4 4565
8.0%
5 4476
7.8%
6 4119
7.2%
7 4095
7.2%
8 3834
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 47228
82.6%
Other Punctuation 9925
 
17.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 7024
14.9%
2 5406
11.4%
0 4999
10.6%
3 4982
10.5%
4 4565
9.7%
5 4476
9.5%
6 4119
8.7%
7 4095
8.7%
8 3834
8.1%
9 3728
7.9%
Other Punctuation
ValueCountFrequency (%)
, 9925
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57153
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 9925
17.4%
1 7024
12.3%
2 5406
9.5%
0 4999
8.7%
3 4982
8.7%
4 4565
8.0%
5 4476
7.8%
6 4119
7.2%
7 4095
7.2%
8 3834
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57153
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 9925
17.4%
1 7024
12.3%
2 5406
9.5%
0 4999
8.7%
3 4982
8.7%
4 4565
8.0%
5 4476
7.8%
6 4119
7.2%
7 4095
7.2%
8 3834
 
6.7%

거래수량
Text

Distinct3907
Distinct (%)39.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2024-05-11T07:25:11.442487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length6
Mean length5.8178
Min length3

Characters and Unicode

Total characters58178
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1195 ?
Unique (%)11.9%

Sample

1st row65,922
2nd row9,165
3rd row146,183
4th row11,531
5th row11,878
ValueCountFrequency (%)
9,339 11
 
0.1%
14,256 10
 
0.1%
38,091 10
 
0.1%
33,000 10
 
0.1%
40,000 9
 
0.1%
27,032 9
 
0.1%
14,985 9
 
0.1%
16,778 8
 
0.1%
30,847 8
 
0.1%
43,396 8
 
0.1%
Other values (3897) 9908
99.1%
2024-05-11T07:25:13.002726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 9961
17.1%
1 6925
11.9%
2 5795
10.0%
3 5253
9.0%
4 4710
8.1%
5 4556
7.8%
0 4259
7.3%
7 4257
7.3%
6 4249
7.3%
8 4185
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 48217
82.9%
Other Punctuation 9961
 
17.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 6925
14.4%
2 5795
12.0%
3 5253
10.9%
4 4710
9.8%
5 4556
9.4%
0 4259
8.8%
7 4257
8.8%
6 4249
8.8%
8 4185
8.7%
9 4028
8.4%
Other Punctuation
ValueCountFrequency (%)
, 9961
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58178
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 9961
17.1%
1 6925
11.9%
2 5795
10.0%
3 5253
9.0%
4 4710
8.1%
5 4556
7.8%
0 4259
7.3%
7 4257
7.3%
6 4249
7.3%
8 4185
7.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58178
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 9961
17.1%
1 6925
11.9%
2 5795
10.0%
3 5253
9.0%
4 4710
8.1%
5 4556
7.8%
0 4259
7.3%
7 4257
7.3%
6 4249
7.3%
8 4185
7.2%

거래단위
Real number (ℝ)

Distinct87
Distinct (%)0.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean80.8608
Minimum10
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:25:13.506662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum10
5-th percentile51
Q171
median85
Q394
95-th percentile99
Maximum100
Range90
Interquartile range (IQR)23

Descriptive statistics

Standard deviation15.570288
Coefficient of variation (CV)0.19255669
Kurtosis0.5523607
Mean80.8608
Median Absolute Deviation (MAD)10
Skewness-0.96350129
Sum808608
Variance242.43387
MonotonicityNot monotonic
2024-05-11T07:25:14.011132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
99 448
 
4.5%
98 416
 
4.2%
97 394
 
3.9%
96 378
 
3.8%
95 359
 
3.6%
94 359
 
3.6%
93 332
 
3.3%
90 304
 
3.0%
91 285
 
2.9%
88 281
 
2.8%
Other values (77) 6444
64.4%
ValueCountFrequency (%)
10 1
 
< 0.1%
14 1
 
< 0.1%
16 1
 
< 0.1%
17 3
 
< 0.1%
18 1
 
< 0.1%
19 2
 
< 0.1%
20 4
< 0.1%
21 8
0.1%
22 7
0.1%
23 3
 
< 0.1%
ValueCountFrequency (%)
100 225
2.2%
99 448
4.5%
98 416
4.2%
97 394
3.9%
96 378
3.8%
95 359
3.6%
94 359
3.6%
93 332
3.3%
92 280
2.8%
91 285
2.9%

(구분)
Categorical

CONSTANT 

Distinct1
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
DOWN
10000 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
DOWN 10000
100.0%

Length

2024-05-11T07:25:14.503774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-05-11T07:25:14.820837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
down 10000
100.0%

해당일자
Real number (ℝ)

Distinct181
Distinct (%)1.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20237153
Minimum20231101
Maximum20240510
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2024-05-11T07:25:15.366124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20231101
5-th percentile20231108
Q120231208
median20240211
Q320240407
95-th percentile20240503
Maximum20240510
Range9409
Interquartile range (IQR)9199

Descriptive statistics

Standard deviation4357.4686
Coefficient of variation (CV)0.00021532024
Kurtosis-1.5755948
Mean20237153
Median Absolute Deviation (MAD)208
Skewness-0.64963576
Sum2.0237153 × 1011
Variance18987532
MonotonicityNot monotonic
2024-05-11T07:25:16.156130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20240419 98
 
1.0%
20231116 94
 
0.9%
20240427 90
 
0.9%
20240414 90
 
0.9%
20240423 90
 
0.9%
20231108 89
 
0.9%
20240425 88
 
0.9%
20240422 88
 
0.9%
20240428 87
 
0.9%
20240409 87
 
0.9%
Other values (171) 9099
91.0%
ValueCountFrequency (%)
20231101 31
 
0.3%
20231102 61
0.6%
20231103 66
0.7%
20231104 76
0.8%
20231105 82
0.8%
20231106 70
0.7%
20231107 81
0.8%
20231108 89
0.9%
20231109 77
0.8%
20231110 81
0.8%
ValueCountFrequency (%)
20240510 56
0.6%
20240509 80
0.8%
20240508 71
0.7%
20240507 77
0.8%
20240506 76
0.8%
20240505 70
0.7%
20240504 67
0.7%
20240503 61
0.6%
20240502 63
0.6%
20240501 64
0.6%

Interactions

2024-05-11T07:25:05.052902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:25:04.542049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:25:05.328973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-05-11T07:25:04.792702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-05-11T07:25:16.645532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래단위해당일자
거래단위1.0000.145
해당일자0.1451.000
2024-05-11T07:25:16.901234image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래단위해당일자
거래단위1.0000.003
해당일자0.0031.000

Missing values

2024-05-11T07:25:05.849441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-05-11T07:25:06.174081image/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

품목등급거래수량거래단위(구분)해당일자
21506토마토62,73965,92295DOWN20240412
91451호박 고구마7,3519,16580DOWN20231112
13493청양고추56,408146,18339DOWN20240422
9358무11,03111,53196DOWN20240426
92562감귤 극조생10,75511,87891DOWN20231111
80697늙은호박1,1011,23489DOWN20231127
9347망고 수입33,60934,25098DOWN20240426
8194칼리플라워24,78532,06577DOWN20240428
17031겉홍합12,62712,88598DOWN20240417
37461노랑 파프리카50,09751,85297DOWN20240311
품목등급거래수량거래단위(구분)해당일자
88225상추7,47413,89554DOWN20231116
40290봄동배추13,33922,11960DOWN20240304
5157양송이11,62013,26188DOWN20240503
32900수박(일반)2,5703,22580DOWN20240323
78975배추얼갈이6,2138,12476DOWN20231129
44386활 민어(자연)6,2477,46284DOWN20240223
50215생강74,66776,03298DOWN20240207
37627느타리버섯7,3139,29779DOWN20240311
71755(선)갈치86,63691,03195DOWN20231215
6972오이맛고추51,09290,04257DOWN20240429

Duplicate rows

Most frequently occurring

품목등급거래수량거래단위(구분)해당일자# duplicates
0마늘 쫑 수입22,11125,88585DOWN202405102
1마늘 쫑 수입31,65233,00096DOWN202401292
2마늘 쫑 수입31,81033,00096DOWN202401262
3배 신고15,00015,40097DOWN202311162