Overview

Dataset statistics

Number of variables15
Number of observations207
Missing cells79
Missing cells (%)2.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory26.2 KiB
Average record size in memory129.6 B

Variable types

Text3
Categorical2
DateTime1
Numeric9

Dataset

Description도매시장 통계연보에 수립되어 있는 전국의 산지 위판장 현황 정보(소재지명,거래실적,물류장비,시설규모,유통주체현황 등)를 제공합니다.
Author한국농수산식품유통공사
URLhttps://www.data.go.kr/data/15088061/fileData.do

Alerts

19년도 거래실적(백만원) is highly overall correlated with 건물면적(제곱미터) and 2 other fieldsHigh correlation
대지규모(제곱미터) is highly overall correlated with 건물면적(제곱미터) and 1 other fieldsHigh correlation
건물면적(제곱미터) is highly overall correlated with 19년도 거래실적(백만원) and 3 other fieldsHigh correlation
경매장규모(제곱미터) is highly overall correlated with 19년도 거래실적(백만원) and 2 other fieldsHigh correlation
경매사 수 is highly overall correlated with 중도매인 수High correlation
중도매인 수 is highly overall correlated with 19년도 거래실적(백만원) and 2 other fieldsHigh correlation
구분 is highly imbalanced (52.3%)Imbalance
19년도 거래실적(백만원) has 39 (18.8%) missing valuesMissing
대지규모(제곱미터) has 11 (5.3%) missing valuesMissing
건물면적(제곱미터) has 15 (7.2%) missing valuesMissing
경매장규모(제곱미터) has 14 (6.8%) missing valuesMissing
디젤지게차 수 has 175 (84.5%) zerosZeros
전동지게차 수 has 174 (84.1%) zerosZeros
선별기 수 has 189 (91.3%) zerosZeros
경매사 수 has 14 (6.8%) zerosZeros
중도매인 수 has 42 (20.3%) zerosZeros

Reproduction

Analysis started2023-12-12 10:51:02.451688
Analysis finished2023-12-12 10:51:18.293689
Duration15.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

조합명
Text

Distinct76
Distinct (%)36.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T19:51:18.698767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length3.1497585
Min length2

Characters and Unicode

Total characters652
Distinct characters104
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique25 ?
Unique (%)12.1%

Sample

1st row경인북부
2nd row옹진
3rd row옹진
4th row인천
5th row인천
ValueCountFrequency (%)
고흥군 8
 
3.9%
거제 7
 
3.4%
경남고성군 7
 
3.4%
남해군 7
 
3.4%
해남군 6
 
2.9%
진도군 5
 
2.4%
구룡포 5
 
2.4%
통영 5
 
2.4%
강원고성군 5
 
2.4%
완도금일 5
 
2.4%
Other values (66) 147
71.0%
2023-12-12T19:51:19.555643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
군 64
 
9.8%
포 26
 
4.0%
남 26
 
4.0%
고 21
 
3.2%
부 20
 
3.1%
도 20
 
3.1%
서 20
 
3.1%
산 19
 
2.9%
해 18
 
2.8%
시 15
 
2.3%
Other values (94) 403
61.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 636
97.5%
Decimal Number 14
 
2.1%
Open Punctuation 1
 
0.2%
Close Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
군 64
 
10.1%
포 26
 
4.1%
남 26
 
4.1%
고 21
 
3.3%
부 20
 
3.1%
도 20
 
3.1%
서 20
 
3.1%
산 19
 
3.0%
해 18
 
2.8%
시 15
 
2.4%
Other values (88) 387
60.8%
Decimal Number
ValueCountFrequency (%)
1 5
35.7%
2 5
35.7%
3 2
 
14.3%
4 2
 
14.3%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 636
97.5%
Common 16
 
2.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
군 64
 
10.1%
포 26
 
4.1%
남 26
 
4.1%
고 21
 
3.3%
부 20
 
3.1%
도 20
 
3.1%
서 20
 
3.1%
산 19
 
3.0%
해 18
 
2.8%
시 15
 
2.4%
Other values (88) 387
60.8%
Common
ValueCountFrequency (%)
1 5
31.2%
2 5
31.2%
3 2
 
12.5%
4 2
 
12.5%
( 1
 
6.2%
) 1
 
6.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 636
97.5%
ASCII 16
 
2.5%

Most frequent character per block

Hangul
ValueCountFrequency (%)
군 64
 
10.1%
포 26
 
4.1%
남 26
 
4.1%
고 21
 
3.3%
부 20
 
3.1%
도 20
 
3.1%
서 20
 
3.1%
산 19
 
3.0%
해 18
 
2.8%
시 15
 
2.4%
Other values (88) 387
60.8%
ASCII
ValueCountFrequency (%)
1 5
31.2%
2 5
31.2%
3 2
 
12.5%
4 2
 
12.5%
( 1
 
6.2%
) 1
 
6.2%

소재지
Categorical

Distinct11
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
경남
54 
전남
47 
강원
27 
충남
24 
경북
21 
Other values (6)
34 

Length

Max length6
Median length2
Mean length2.0386473
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row인천
2nd row인천
3rd row경기
4th row인천
5th row인천

Common Values

ValueCountFrequency (%)
경남 54
26.1%
전남 47
22.7%
강원 27
13.0%
충남 24
11.6%
경북 21
 
10.1%
부산 9
 
4.3%
제주 9
 
4.3%
인천 6
 
2.9%
전북 6
 
2.9%
경기 2
 
1.0%

Length

2023-12-12T19:51:20.491927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경남 54
26.1%
전남 47
22.7%
강원 27
13.0%
충남 24
11.6%
경북 21
 
10.1%
부산 9
 
4.3%
제주 9
 
4.3%
인천 6
 
2.9%
전북 6
 
2.9%
경기 2
 
1.0%

위판장명칭
Text

Distinct183
Distinct (%)88.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T19:51:21.096189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length2.9516908
Min length2

Characters and Unicode

Total characters611
Distinct characters171
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique174 ?
Unique (%)84.1%

Sample

1st row새우젓산지
2nd row연안
3rd row탄도
4th row연안
5th row소래
ValueCountFrequency (%)
활어 8
 
3.9%
본소 6
 
2.9%
선어 5
 
2.4%
건어물 3
 
1.4%
제2위판 3
 
1.4%
마산지소 2
 
1.0%
활어위판장 2
 
1.0%
연안 2
 
1.0%
건어 2
 
1.0%
현포 1
 
0.5%
Other values (173) 173
83.6%
2023-12-12T19:51:21.883425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
항 39
 
6.4%
어 27
 
4.4%
포 22
 
3.6%
산 21
 
3.4%
소 18
 
2.9%
도 15
 
2.5%
위 13
 
2.1%
동 13
 
2.1%
활 13
 
2.1%
판 12
 
2.0%
Other values (161) 418
68.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 580
94.9%
Open Punctuation 11
 
1.8%
Close Punctuation 11
 
1.8%
Decimal Number 9
 
1.5%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
항 39
 
6.7%
어 27
 
4.7%
포 22
 
3.8%
산 21
 
3.6%
소 18
 
3.1%
도 15
 
2.6%
위 13
 
2.2%
동 13
 
2.2%
활 13
 
2.2%
판 12
 
2.1%
Other values (157) 387
66.7%
Decimal Number
ValueCountFrequency (%)
2 6
66.7%
1 3
33.3%
Open Punctuation
ValueCountFrequency (%)
( 11
100.0%
Close Punctuation
ValueCountFrequency (%)
) 11
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 580
94.9%
Common 31
 
5.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
항 39
 
6.7%
어 27
 
4.7%
포 22
 
3.8%
산 21
 
3.6%
소 18
 
3.1%
도 15
 
2.6%
위 13
 
2.2%
동 13
 
2.2%
활 13
 
2.2%
판 12
 
2.1%
Other values (157) 387
66.7%
Common
ValueCountFrequency (%)
( 11
35.5%
) 11
35.5%
2 6
19.4%
1 3
 
9.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 580
94.9%
ASCII 31
 
5.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
항 39
 
6.7%
어 27
 
4.7%
포 22
 
3.8%
산 21
 
3.6%
소 18
 
3.1%
도 15
 
2.6%
위 13
 
2.2%
동 13
 
2.2%
활 13
 
2.2%
판 12
 
2.1%
Other values (157) 387
66.7%
ASCII
ValueCountFrequency (%)
( 11
35.5%
) 11
35.5%
2 6
19.4%
1 3
 
9.7%

전화번호
Text

Distinct169
Distinct (%)81.6%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
2023-12-12T19:51:22.341185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters2484
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

Unique143 ?
Unique (%)69.1%

Sample

1st row032-932-5840
2nd row032-883-8159
3rd row032-886-4358
4th row032-886-9723
5th row032-446-6393
ValueCountFrequency (%)
033-632-0024 5
 
2.4%
055-673-4165 4
 
1.9%
054-791-5310 3
 
1.4%
061-553-7206 3
 
1.4%
033-573-5632 3
 
1.4%
055-867-6526 3
 
1.4%
054-787-1337 3
 
1.4%
033-633-3315 3
 
1.4%
033-572-1015 3
 
1.4%
054-732-5002 2
 
1.0%
Other values (159) 175
84.5%
2023-12-12T19:51:23.155961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 414
16.7%
0 348
14.0%
5 323
13.0%
3 259
10.4%
6 224
9.0%
1 222
8.9%
2 188
7.6%
4 182
7.3%
7 148
 
6.0%
8 112
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 2070
83.3%
Dash Punctuation 414
 
16.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 348
16.8%
5 323
15.6%
3 259
12.5%
6 224
10.8%
1 222
10.7%
2 188
9.1%
4 182
8.8%
7 148
7.1%
8 112
 
5.4%
9 64
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 414
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 2484
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 414
16.7%
0 348
14.0%
5 323
13.0%
3 259
10.4%
6 224
9.0%
1 222
8.9%
2 188
7.6%
4 182
7.3%
7 148
 
6.0%
8 112
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2484
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 414
16.7%
0 348
14.0%
5 323
13.0%
3 259
10.4%
6 224
9.0%
1 222
8.9%
2 188
7.6%
4 182
7.3%
7 148
 
6.0%
8 112
 
4.5%

개장일
Date

Distinct194
Distinct (%)93.7%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
Minimum1961-04-12 00:00:00
Maximum2019-11-26 00:00:00
2023-12-12T19:51:23.457526image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:23.771951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

19년도 거래실적(백만원)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct168
Distinct (%)100.0%
Missing39
Missing (%)18.8%
Infinite0
Infinite (%)0.0%
Mean22982.292
Minimum5
Maximum192881
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:24.052972image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5
5-th percentile272.2
Q14338.5
median10887
Q329962
95-th percentile83719.95
Maximum192881
Range192876
Interquartile range (IQR)25623.5

Descriptive statistics

Standard deviation31429.738
Coefficient of variation (CV)1.3675633
Kurtosis9.5429812
Mean22982.292
Median Absolute Deviation (MAD)8877
Skewness2.7568866
Sum3861025
Variance9.8782842 × 108
MonotonicityNot monotonic
2023-12-12T19:51:24.342987image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1263 1
 
0.5%
82 1
 
0.5%
2330 1
 
0.5%
8828 1
 
0.5%
2077 1
 
0.5%
200 1
 
0.5%
184 1
 
0.5%
85 1
 
0.5%
52370 1
 
0.5%
54300 1
 
0.5%
Other values (158) 158
76.3%
(Missing) 39
 
18.8%
ValueCountFrequency (%)
5 1
0.5%
7 1
0.5%
82 1
0.5%
85 1
0.5%
166 1
0.5%
173 1
0.5%
184 1
0.5%
200 1
0.5%
226 1
0.5%
358 1
0.5%
ValueCountFrequency (%)
192881 1
0.5%
181731 1
0.5%
139765 1
0.5%
116153 1
0.5%
111477 1
0.5%
111469 1
0.5%
102976 1
0.5%
89726 1
0.5%
86430 1
0.5%
78687 1
0.5%

구분
Categorical

IMBALANCE 

Distinct5
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size1.7 KiB
개방형
153 
폐쇄형
38 
물양장
 
14
미사용
 
1
개방형
 
1

Length

Max length4
Median length3
Mean length3.0048309
Min length3

Unique

Unique2 ?
Unique (%)1.0%

Sample

1st row개방형
2nd row폐쇄형
3rd row폐쇄형
4th row폐쇄형
5th row폐쇄형

Common Values

ValueCountFrequency (%)
개방형 153
73.9%
폐쇄형 38
 
18.4%
물양장 14
 
6.8%
미사용 1
 
0.5%
개방형 1
 
0.5%

Length

2023-12-12T19:51:24.623617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:51:24.855106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개방형 154
74.4%
폐쇄형 38
 
18.4%
물양장 14
 
6.8%
미사용 1
 
0.5%

대지규모(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct191
Distinct (%)97.4%
Missing11
Missing (%)5.3%
Infinite0
Infinite (%)0.0%
Mean3698.9796
Minimum50
Maximum38625
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:25.192668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile217.5
Q1675.75
median1967
Q34286
95-th percentile13938.25
Maximum38625
Range38575
Interquartile range (IQR)3610.25

Descriptive statistics

Standard deviation5703.7364
Coefficient of variation (CV)1.5419756
Kurtosis15.615442
Mean3698.9796
Median Absolute Deviation (MAD)1396
Skewness3.6256579
Sum725000
Variance32532609
MonotonicityNot monotonic
2023-12-12T19:51:25.521346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
917 2
 
1.0%
638 2
 
1.0%
3315 2
 
1.0%
597 2
 
1.0%
4577 2
 
1.0%
1066 1
 
0.5%
370 1
 
0.5%
2994 1
 
0.5%
14962 1
 
0.5%
1800 1
 
0.5%
Other values (181) 181
87.4%
(Missing) 11
 
5.3%
ValueCountFrequency (%)
50 1
0.5%
100 1
0.5%
126 1
0.5%
132 1
0.5%
154 1
0.5%
156 1
0.5%
162 1
0.5%
175 1
0.5%
181 1
0.5%
198 1
0.5%
ValueCountFrequency (%)
38625 1
0.5%
34528 1
0.5%
33153 1
0.5%
25450 1
0.5%
20965 1
0.5%
20666 1
0.5%
20420 1
0.5%
17295 1
0.5%
16466 1
0.5%
14962 1
0.5%

건물면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct182
Distinct (%)94.8%
Missing15
Missing (%)7.2%
Infinite0
Infinite (%)0.0%
Mean1618.6771
Minimum50
Maximum33900
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:25.810474image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum50
5-th percentile125.6
Q1335.5
median754
Q31711.5
95-th percentile5498.75
Maximum33900
Range33850
Interquartile range (IQR)1376

Descriptive statistics

Standard deviation2999.9495
Coefficient of variation (CV)1.8533342
Kurtosis70.879214
Mean1618.6771
Median Absolute Deviation (MAD)519
Skewness7.1656313
Sum310786
Variance8999697.3
MonotonicityNot monotonic
2023-12-12T19:51:26.123903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
167 3
 
1.4%
599 3
 
1.4%
132 2
 
1.0%
1113 2
 
1.0%
50 2
 
1.0%
396 2
 
1.0%
232 2
 
1.0%
750 2
 
1.0%
4622 1
 
0.5%
10289 1
 
0.5%
Other values (172) 172
83.1%
(Missing) 15
 
7.2%
ValueCountFrequency (%)
50 2
1.0%
62 1
0.5%
82 1
0.5%
89 1
0.5%
92 1
0.5%
100 1
0.5%
102 1
0.5%
116 1
0.5%
119 1
0.5%
131 1
0.5%
ValueCountFrequency (%)
33900 1
0.5%
10289 1
0.5%
9861 1
0.5%
9753 1
0.5%
8780 1
0.5%
7908 1
0.5%
7210 1
0.5%
6609 1
0.5%
6281 1
0.5%
5958 1
0.5%

경매장규모(제곱미터)
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct173
Distinct (%)89.6%
Missing14
Missing (%)6.8%
Infinite0
Infinite (%)0.0%
Mean906.21762
Minimum36
Maximum7830
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:26.405847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum36
5-th percentile93.2
Q1253
median538
Q31079
95-th percentile2948
Maximum7830
Range7794
Interquartile range (IQR)826

Descriptive statistics

Standard deviation1095.2313
Coefficient of variation (CV)1.2085743
Kurtosis13.352899
Mean906.21762
Median Absolute Deviation (MAD)357
Skewness3.1393785
Sum174900
Variance1199531.7
MonotonicityNot monotonic
2023-12-12T19:51:26.760312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 4
 
1.9%
50 3
 
1.4%
240 3
 
1.4%
167 2
 
1.0%
333 2
 
1.0%
132 2
 
1.0%
246 2
 
1.0%
978 2
 
1.0%
655 2
 
1.0%
165 2
 
1.0%
Other values (163) 169
81.6%
(Missing) 14
 
6.8%
ValueCountFrequency (%)
36 1
 
0.5%
50 3
1.4%
58 1
 
0.5%
76 1
 
0.5%
78 1
 
0.5%
82 1
 
0.5%
89 1
 
0.5%
92 1
 
0.5%
94 1
 
0.5%
100 4
1.9%
ValueCountFrequency (%)
7830 1
0.5%
6852 1
0.5%
5049 1
0.5%
4622 1
0.5%
4114 1
0.5%
3656 1
0.5%
3199 1
0.5%
3166 1
0.5%
3102 1
0.5%
3080 1
0.5%

디젤지게차 수
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.39613527
Minimum0
Maximum11
Zeros175
Zeros (%)84.5%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:27.011227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3247401
Coefficient of variation (CV)3.3441611
Kurtosis30.001966
Mean0.39613527
Median Absolute Deviation (MAD)0
Skewness5.0465112
Sum82
Variance1.7549364
MonotonicityNot monotonic
2023-12-12T19:51:27.229088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 175
84.5%
1 15
 
7.2%
2 7
 
3.4%
3 4
 
1.9%
7 2
 
1.0%
4 2
 
1.0%
8 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
0 175
84.5%
1 15
 
7.2%
2 7
 
3.4%
3 4
 
1.9%
4 2
 
1.0%
7 2
 
1.0%
8 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
11 1
 
0.5%
8 1
 
0.5%
7 2
 
1.0%
4 2
 
1.0%
3 4
 
1.9%
2 7
 
3.4%
1 15
 
7.2%
0 175
84.5%

전동지게차 수
Real number (ℝ)

ZEROS 

Distinct8
Distinct (%)3.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.42028986
Minimum0
Maximum11
Zeros174
Zeros (%)84.1%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:27.472283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile3
Maximum11
Range11
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.3042775
Coefficient of variation (CV)3.1032809
Kurtosis28.959004
Mean0.42028986
Median Absolute Deviation (MAD)0
Skewness4.8023606
Sum87
Variance1.7011397
MonotonicityNot monotonic
2023-12-12T19:51:27.684011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
0 174
84.1%
1 11
 
5.3%
2 10
 
4.8%
3 6
 
2.9%
4 3
 
1.4%
11 1
 
0.5%
8 1
 
0.5%
7 1
 
0.5%
ValueCountFrequency (%)
0 174
84.1%
1 11
 
5.3%
2 10
 
4.8%
3 6
 
2.9%
4 3
 
1.4%
7 1
 
0.5%
8 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
11 1
 
0.5%
8 1
 
0.5%
7 1
 
0.5%
4 3
 
1.4%
3 6
 
2.9%
2 10
 
4.8%
1 11
 
5.3%
0 174
84.1%

선별기 수
Real number (ℝ)

ZEROS 

Distinct7
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.29468599
Minimum0
Maximum10
Zeros189
Zeros (%)91.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:27.934877image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile2
Maximum10
Range10
Interquartile range (IQR)0

Descriptive statistics

Standard deviation1.1554517
Coefficient of variation (CV)3.9209592
Kurtosis30.843288
Mean0.29468599
Median Absolute Deviation (MAD)0
Skewness5.0858937
Sum61
Variance1.3350687
MonotonicityNot monotonic
2023-12-12T19:51:28.150627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
0 189
91.3%
2 5
 
2.4%
4 5
 
2.4%
1 3
 
1.4%
3 2
 
1.0%
6 2
 
1.0%
10 1
 
0.5%
ValueCountFrequency (%)
0 189
91.3%
1 3
 
1.4%
2 5
 
2.4%
3 2
 
1.0%
4 5
 
2.4%
6 2
 
1.0%
10 1
 
0.5%
ValueCountFrequency (%)
10 1
 
0.5%
6 2
 
1.0%
4 5
 
2.4%
3 2
 
1.0%
2 5
 
2.4%
1 3
 
1.4%
0 189
91.3%

경매사 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct11
Distinct (%)5.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8067633
Minimum0
Maximum11
Zeros14
Zeros (%)6.8%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:28.379821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11
median1
Q32
95-th percentile4
Maximum11
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.5922606
Coefficient of variation (CV)0.88127793
Kurtosis11.275429
Mean1.8067633
Median Absolute Deviation (MAD)1
Skewness2.9655043
Sum374
Variance2.5352938
MonotonicityNot monotonic
2023-12-12T19:51:28.591522image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
1 95
45.9%
2 65
31.4%
3 18
 
8.7%
0 14
 
6.8%
4 6
 
2.9%
8 3
 
1.4%
9 2
 
1.0%
6 1
 
0.5%
7 1
 
0.5%
11 1
 
0.5%
ValueCountFrequency (%)
0 14
 
6.8%
1 95
45.9%
2 65
31.4%
3 18
 
8.7%
4 6
 
2.9%
5 1
 
0.5%
6 1
 
0.5%
7 1
 
0.5%
8 3
 
1.4%
9 2
 
1.0%
ValueCountFrequency (%)
11 1
 
0.5%
9 2
 
1.0%
8 3
 
1.4%
7 1
 
0.5%
6 1
 
0.5%
5 1
 
0.5%
4 6
 
2.9%
3 18
 
8.7%
2 65
31.4%
1 95
45.9%

중도매인 수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)25.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.89372
Minimum0
Maximum130
Zeros42
Zeros (%)20.3%
Negative0
Negative (%)0.0%
Memory size1.9 KiB
2023-12-12T19:51:28.835161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median13
Q327
95-th percentile51
Maximum130
Range130
Interquartile range (IQR)23

Descriptive statistics

Standard deviation18.124925
Coefficient of variation (CV)1.012921
Kurtosis7.2031072
Mean17.89372
Median Absolute Deviation (MAD)12
Skewness1.9416555
Sum3704
Variance328.51292
MonotonicityNot monotonic
2023-12-12T19:51:29.085441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 42
 
20.3%
18 8
 
3.9%
12 8
 
3.9%
19 7
 
3.4%
13 7
 
3.4%
8 6
 
2.9%
9 5
 
2.4%
10 5
 
2.4%
27 5
 
2.4%
17 5
 
2.4%
Other values (43) 109
52.7%
ValueCountFrequency (%)
0 42
20.3%
1 4
 
1.9%
2 1
 
0.5%
3 4
 
1.9%
4 5
 
2.4%
5 3
 
1.4%
6 5
 
2.4%
7 4
 
1.9%
8 6
 
2.9%
9 5
 
2.4%
ValueCountFrequency (%)
130 1
 
0.5%
86 1
 
0.5%
76 1
 
0.5%
64 1
 
0.5%
62 1
 
0.5%
58 1
 
0.5%
52 2
1.0%
51 4
1.9%
47 2
1.0%
46 1
 
0.5%

Interactions

2023-12-12T19:51:15.458593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:03.444134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.825919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:06.246978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.609336image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:09.524640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:11.173576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.593243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.959723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:15.668347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:03.605719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.994924image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:06.418833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.763760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:09.816028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:11.336175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.754370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:14.113606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:15.976193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:03.792802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:05.152333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:06.613422image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.911333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:10.129129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:11.517326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.918834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:14.257903image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:16.195206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:03.941439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:05.305773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:06.773800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:08.054661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:10.375389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:11.688401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.081237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:14.453333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:16.353265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.107212image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:05.468297image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:06.941529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:08.597806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:10.570642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:11.869034image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.248607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:14.602537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:16.473641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.249915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:05.622047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.068550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:08.746470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:10.688069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.036928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.377551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:14.704260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:16.664283image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.392372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:05.785458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.192715image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:08.915065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:10.801512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.168542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.532363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:14.850711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:16.841837image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.528732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:05.941562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.318086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:09.078552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:10.909020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.291803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.674320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:15.043067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:17.181978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:04.667719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:06.093778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:07.475177image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:09.281621image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:11.044971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:12.427921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:13.800198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:51:15.214775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:51:29.299397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조합명소재지19년도 거래실적(백만원)구분대지규모(제곱미터)건물면적(제곱미터)경매장규모(제곱미터)디젤지게차 수전동지게차 수선별기 수경매사 수중도매인 수
조합명1.0000.9970.9340.9370.6860.6280.7350.7950.8500.7930.8610.880
소재지0.9971.0000.4650.6590.4580.1660.1770.3550.2460.3860.6020.311
19년도 거래실적(백만원)0.9340.4651.0000.2530.5210.6130.7590.7130.5000.7580.7760.731
구분0.9370.6590.2531.0000.0000.0000.0000.0000.6410.2460.0000.152
대지규모(제곱미터)0.6860.4580.5210.0001.0000.7600.8770.0000.0000.0000.7440.783
건물면적(제곱미터)0.6280.1660.6130.0000.7601.0000.7780.5640.6750.0000.8930.730
경매장규모(제곱미터)0.7350.1770.7590.0000.8770.7781.0000.4900.4970.1890.7110.717
디젤지게차 수0.7950.3550.7130.0000.0000.5640.4901.0000.7840.8440.5590.591
전동지게차 수0.8500.2460.5000.6410.0000.6750.4970.7841.0000.6870.3980.409
선별기 수0.7930.3860.7580.2460.0000.0000.1890.8440.6871.0000.6330.518
경매사 수0.8610.6020.7760.0000.7440.8930.7110.5590.3980.6331.0000.740
중도매인 수0.8800.3110.7310.1520.7830.7300.7170.5910.4090.5180.7401.000
2023-12-12T19:51:29.574813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
소재지구분
소재지1.0000.431
구분0.4311.000
2023-12-12T19:51:29.756600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
19년도 거래실적(백만원)대지규모(제곱미터)건물면적(제곱미터)경매장규모(제곱미터)디젤지게차 수전동지게차 수선별기 수경매사 수중도매인 수소재지구분
19년도 거래실적(백만원)1.0000.4540.5710.5440.3380.2520.2380.4730.7590.2290.160
대지규모(제곱미터)0.4541.0000.6780.6620.1710.2850.0830.2870.3590.2250.000
건물면적(제곱미터)0.5710.6781.0000.8550.2770.3110.1890.4880.5200.0890.000
경매장규모(제곱미터)0.5440.6620.8551.0000.2910.2710.1760.4520.4770.0780.000
디젤지게차 수0.3380.1710.2770.2911.0000.3770.2930.2850.2900.1810.000
전동지게차 수0.2520.2850.3110.2710.3771.0000.2520.1970.2820.1210.481
선별기 수0.2380.0830.1890.1760.2930.2521.0000.2570.2370.1980.171
경매사 수0.4730.2870.4880.4520.2850.1970.2571.0000.5040.3060.000
중도매인 수0.7590.3590.5200.4770.2900.2820.2370.5041.0000.1270.085
소재지0.2290.2250.0890.0780.1810.1210.1980.3060.1271.0000.431
구분0.1600.0000.0000.0000.0000.4810.1710.0000.0850.4311.000

Missing values

2023-12-12T19:51:17.459917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:51:17.872674image/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.
2023-12-12T19:51:18.143471image/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

조합명소재지위판장명칭전화번호개장일19년도 거래실적(백만원)구분대지규모(제곱미터)건물면적(제곱미터)경매장규모(제곱미터)디젤지게차 수전동지게차 수선별기 수경매사 수중도매인 수
0경인북부인천새우젓산지032-932-58402011-07-0110632개방형485942462196020319
1옹진인천연안032-883-81591992-09-0158120폐쇄형432925121516000444
2옹진경기탄도032-886-43582019-05-28<NA>폐쇄형1729551951900027
3인천인천연안032-886-97231992-05-0147548폐쇄형880698613199120321
4인천인천소래032-446-63931986-12-0716910폐쇄형402215121272000413
5영흥인천영흥032-886-43302003-08-0312179개방형1652972495001229
6경기남부경기궁평항031-357-81612011-02-2819729폐쇄형40001507600000338
7강원고성군강원거진항033-682-20741995-05-0413506개방형592350750703017
8강원고성군강원대진항033-682-57081998-12-1812140개방형50250250200015
9강원고성군강원아야진항033-633-33151993-12-318733개방형10084170170010113
조합명소재지위판장명칭전화번호개장일19년도 거래실적(백만원)구분대지규모(제곱미터)건물면적(제곱미터)경매장규모(제곱미터)디젤지게차 수전동지게차 수선별기 수경매사 수중도매인 수
197모슬포제주활어064-794-05531993-11-1530547개방형20751107928730251
198성산포제주본소064-782-31361988-02-2078687개방형924651233166002247
199성산포제주제2위판064-782-31362011-08-10<NA>개방형13381338127800420
200제주시제주본소064-720-31511975-09-2458586개방형8964580718074251
201추자도제주추자항064-742-81921990-12-109512개방형992327327402417
202추자도제주신양항064-742-81922001-10-10<NA>개방형4738184416500000
203한림제주한림제1위판064-795-05321998-11-30139765개방형21582509250911410962
204한림제주한림제2위판064-795-05322012-07-28<NA>폐쇄형164663996193600090
205보령(무창포어촌계)충남활어위판장041-936-35101961-04-121312개방형66049024000016
206수협중앙회인천인천032-890-45921973-05-0114744개방형100100100120321