Overview

Dataset statistics

Number of variables3
Number of observations186
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.9 KiB
Average record size in memory26.7 B

Variable types

Text1
Numeric2

Dataset

Description광주광역시 각화동농산물도매시장에서 제공하는 품목별 농산물 거래실적 정보로 품목명, 거래량(kg), 거래금액(원)을 제공합니다.
Author광주광역시
URLhttps://www.data.go.kr/data/3043742/fileData.do

Alerts

거래물량(kg) is highly overall correlated with 거래금액(원)High correlation
거래금액(원) is highly overall correlated with 거래물량(kg)High correlation
품목 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:41:24.461630
Analysis finished2023-12-12 10:41:25.318009
Duration0.86 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

품목
Text

UNIQUE 

Distinct186
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
2023-12-12T19:41:25.653052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length2.9569892
Min length1

Characters and Unicode

Total characters550
Distinct characters215
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

Unique186 ?
Unique (%)100.0%

Sample

1st row무
2nd row배추
3rd row양파
4th row고구마
5th row감자
ValueCountFrequency (%)
무 1
 
0.5%
케일 1
 
0.5%
용과 1
 
0.5%
여주 1
 
0.5%
보리순 1
 
0.5%
메밀순 1
 
0.5%
수삼 1
 
0.5%
탄제린 1
 
0.5%
마 1
 
0.5%
모시대 1
 
0.5%
Other values (176) 176
94.6%
2023-12-12T19:41:26.193892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
리 20
 
3.6%
추 20
 
3.6%
고 15
 
2.7%
나 14
 
2.5%
파 10
 
1.8%
) 9
 
1.6%
( 9
 
1.6%
배 9
 
1.6%
무 9
 
1.6%
양 9
 
1.6%
Other values (205) 426
77.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 532
96.7%
Close Punctuation 9
 
1.6%
Open Punctuation 9
 
1.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
리 20
 
3.8%
추 20
 
3.8%
고 15
 
2.8%
나 14
 
2.6%
파 10
 
1.9%
배 9
 
1.7%
무 9
 
1.7%
양 9
 
1.7%
물 9
 
1.7%
자 8
 
1.5%
Other values (203) 409
76.9%
Close Punctuation
ValueCountFrequency (%)
) 9
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 532
96.7%
Common 18
 
3.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
리 20
 
3.8%
추 20
 
3.8%
고 15
 
2.8%
나 14
 
2.6%
파 10
 
1.9%
배 9
 
1.7%
무 9
 
1.7%
양 9
 
1.7%
물 9
 
1.7%
자 8
 
1.5%
Other values (203) 409
76.9%
Common
ValueCountFrequency (%)
) 9
50.0%
( 9
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 532
96.7%
ASCII 18
 
3.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
리 20
 
3.8%
추 20
 
3.8%
고 15
 
2.8%
나 14
 
2.6%
파 10
 
1.9%
배 9
 
1.7%
무 9
 
1.7%
양 9
 
1.7%
물 9
 
1.7%
자 8
 
1.5%
Other values (203) 409
76.9%
ASCII
ValueCountFrequency (%)
) 9
50.0%
( 9
50.0%

거래물량(kg)
Real number (ℝ)

HIGH CORRELATION 

Distinct184
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1182985.8
Minimum2
Maximum19257894
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T19:41:26.376320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile57.125
Q13888.25
median47082.75
Q3740765.4
95-th percentile5992627.2
Maximum19257894
Range19257892
Interquartile range (IQR)736877.15

Descriptive statistics

Standard deviation2946371.2
Coefficient of variation (CV)2.4906227
Kurtosis19.433993
Mean1182985.8
Median Absolute Deviation (MAD)47012.5
Skewness4.1153336
Sum2.2003535 × 108
Variance8.6811032 × 1012
MonotonicityDecreasing
2023-12-12T19:41:26.856742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6.0 2
 
1.1%
20.0 2
 
1.1%
19257894.5 1
 
0.5%
7716.4 1
 
0.5%
14589.0 1
 
0.5%
14535.0 1
 
0.5%
10500.0 1
 
0.5%
10089.0 1
 
0.5%
9542.0 1
 
0.5%
9155.0 1
 
0.5%
Other values (174) 174
93.5%
ValueCountFrequency (%)
2.0 1
0.5%
4.0 1
0.5%
6.0 2
1.1%
16.0 1
0.5%
20.0 2
1.1%
30.0 1
0.5%
36.0 1
0.5%
44.0 1
0.5%
96.5 1
0.5%
115.0 1
0.5%
ValueCountFrequency (%)
19257894.5 1
0.5%
18482331.3 1
0.5%
17504456.0 1
0.5%
13304354.0 1
0.5%
9017984.0 1
0.5%
8939506.3 1
0.5%
8266807.8 1
0.5%
7314088.5 1
0.5%
7082430.5 1
0.5%
6086151.7 1
0.5%

거래금액(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct185
Distinct (%)99.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.4101957 × 109
Minimum9000
Maximum2.2726967 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T19:41:27.008235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum9000
5-th percentile127050
Q110423625
median2.3955544 × 108
Q32.0435819 × 109
95-th percentile1.3516833 × 1010
Maximum2.2726967 × 1010
Range2.2726958 × 1010
Interquartile range (IQR)2.0331583 × 109

Descriptive statistics

Standard deviation4.6947954 × 109
Coefficient of variation (CV)1.9478898
Kurtosis5.8976539
Mean2.4101957 × 109
Median Absolute Deviation (MAD)2.3887388 × 108
Skewness2.4932582
Sum4.4829639 × 1011
Variance2.2041104 × 1019
MonotonicityNot monotonic
2023-12-12T19:41:27.161467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
20000 2
 
1.1%
12932666000 1
 
0.5%
39815500 1
 
0.5%
39227500 1
 
0.5%
29031600 1
 
0.5%
102000000 1
 
0.5%
51891000 1
 
0.5%
24983480 1
 
0.5%
23914950 1
 
0.5%
124803000 1
 
0.5%
Other values (175) 175
94.1%
ValueCountFrequency (%)
9000 1
0.5%
11000 1
0.5%
15000 1
0.5%
20000 2
1.1%
24000 1
0.5%
66900 1
0.5%
68300 1
0.5%
119000 1
0.5%
121900 1
0.5%
142500 1
0.5%
ValueCountFrequency (%)
22726967032 1
0.5%
22101066200 1
0.5%
21460165600 1
0.5%
19338208200 1
0.5%
17297008325 1
0.5%
15741915500 1
0.5%
15612459400 1
0.5%
14623631230 1
0.5%
14243288100 1
0.5%
13624881810 1
0.5%

Interactions

2023-12-12T19:41:24.906114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:41:24.651889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:41:25.038058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:41:24.780863image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:41:27.255839image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래물량(kg)거래금액(원)
거래물량(kg)1.0000.853
거래금액(원)0.8531.000
2023-12-12T19:41:27.348635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
거래물량(kg)거래금액(원)
거래물량(kg)1.0000.979
거래금액(원)0.9791.000

Missing values

2023-12-12T19:41:25.184914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:41:25.277592image/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

품목거래물량(kg)거래금액(원)
0무19257894.512932666000
1배추18482331.321460165600
2양파17504456.013624881810
3고구마13304354.019338208200
4감자9017984.014243288100
5수박8939506.315612459400
6사과8266807.822726967032
7감귤7314088.517297008325
8양배추7082430.53453510800
9호박6086151.711849548511
품목거래물량(kg)거래금액(원)
176비파44.0330000
177돌나물36.066900
178절임식품30.09000
179옻20.0164400
180초석잠20.020000
181신선초16.024000
182파래6.0121900
183고라비6.020000
184가죽나물4.015000
185오가피2.011000