Overview

Dataset statistics

Number of variables6
Number of observations67
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.5 KiB
Average record size in memory53.0 B

Variable types

Categorical1
Text2
Numeric3

Dataset

Description광양항 배후단지 입주업체 정보에 대한 데이터입니다. 배후단지 구역(동측, 서측 등), 입주기업명, 입주면적, 창고면적, 창고동수 및 주요화물 데이터를 포함하고 있습니다. 데이터는 매년 갱신됩니다.
Author여수광양항만공사
URLhttps://www.data.go.kr/data/15022774/fileData.do

Alerts

창고면적(제곱미터) is highly overall correlated with 창고동수High correlation
창고동수 is highly overall correlated with 창고면적(제곱미터)High correlation
창고면적(제곱미터) has 21 (31.3%) zerosZeros
창고동수 has 21 (31.3%) zerosZeros

Reproduction

Analysis started2023-12-12 08:08:28.034113
Analysis finished2023-12-12 08:08:30.176380
Duration2.14 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구역
Categorical

Distinct10
Distinct (%)14.9%
Missing0
Missing (%)0.0%
Memory size668.0 B
서측
22 
동측 2-2단계
10 
동측3단계
8 
동측 2-1단계
6 
동측용도전환
6 
Other values (5)
15 

Length

Max length8
Median length6
Mean length5.0746269
Min length2

Unique

Unique1 ?
Unique (%)1.5%

Sample

1st row동측 1단계
2nd row동측 1단계
3rd row동측 1단계
4th row동측 1단계
5th row동측 2-1단계

Common Values

ValueCountFrequency (%)
서측 22
32.8%
동측 2-2단계 10
14.9%
동측3단계 8
 
11.9%
동측 2-1단계 6
 
9.0%
동측용도전환 6
 
9.0%
동측 1단계 4
 
6.0%
황금물류센터 4
 
6.0%
서측용도전환 4
 
6.0%
국제물류센터 2
 
3.0%
동측철송장 부근 1
 
1.5%

Length

2023-12-12T17:08:30.283249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:08:30.461475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서측 22
25.0%
동측 20
22.7%
2-2단계 10
11.4%
동측3단계 8
 
9.1%
2-1단계 6
 
6.8%
동측용도전환 6
 
6.8%
1단계 4
 
4.5%
황금물류센터 4
 
4.5%
서측용도전환 4
 
4.5%
국제물류센터 2
 
2.3%
Other values (2) 2
 
2.3%

입주기업
Text

Distinct59
Distinct (%)88.1%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-12T17:08:30.797257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length14
Mean length7.4029851
Min length3

Characters and Unicode

Total characters496
Distinct characters125
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique52 ?
Unique (%)77.6%

Sample

1st row한국파렛트풀㈜
2nd rowCJ 대한통운㈜
3rd row동원로엑스㈜ (위험물)
4th row(주)우인
5th row동원로엑스㈜
ValueCountFrequency (%)
광양시 8
 
9.1%
㈜대평 5
 
5.7%
위험물 5
 
5.7%
제조업 4
 
4.5%
㈜한진 3
 
3.4%
동원로엑스㈜ 3
 
3.4%
ham㈜ 2
 
2.3%
㈜지성 2
 
2.3%
㈜제일로지스 2
 
2.3%
한국파렛트풀㈜ 2
 
2.3%
Other values (48) 52
59.1%
2023-12-12T17:08:31.326504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
㈜ 64
 
12.9%
31
 
6.2%
스 15
 
3.0%
로 14
 
2.8%
양 14
 
2.8%
지 12
 
2.4%
) 12
 
2.4%
( 12
 
2.4%
광 11
 
2.2%
물 10
 
2.0%
Other values (115) 301
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 337
67.9%
Other Symbol 64
 
12.9%
Space Separator 31
 
6.2%
Uppercase Letter 25
 
5.0%
Close Punctuation 19
 
3.8%
Open Punctuation 19
 
3.8%
Dash Punctuation 1
 
0.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
스 15
 
4.5%
로 14
 
4.2%
양 14
 
4.2%
지 12
 
3.6%
광 11
 
3.3%
물 10
 
3.0%
이 10
 
3.0%
시 9
 
2.7%
대 8
 
2.4%
한 8
 
2.4%
Other values (97) 226
67.1%
Uppercase Letter
ValueCountFrequency (%)
C 5
20.0%
A 3
12.0%
H 3
12.0%
K 3
12.0%
E 2
 
8.0%
J 2
 
8.0%
T 2
 
8.0%
M 2
 
8.0%
W 1
 
4.0%
P 1
 
4.0%
Close Punctuation
ValueCountFrequency (%)
) 12
63.2%
] 7
36.8%
Open Punctuation
ValueCountFrequency (%)
( 12
63.2%
[ 7
36.8%
Other Symbol
ValueCountFrequency (%)
㈜ 64
100.0%
Space Separator
ValueCountFrequency (%)
31
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 401
80.8%
Common 70
 
14.1%
Latin 25
 
5.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
㈜ 64
 
16.0%
스 15
 
3.7%
로 14
 
3.5%
양 14
 
3.5%
지 12
 
3.0%
광 11
 
2.7%
물 10
 
2.5%
이 10
 
2.5%
시 9
 
2.2%
대 8
 
2.0%
Other values (98) 234
58.4%
Latin
ValueCountFrequency (%)
C 5
20.0%
A 3
12.0%
H 3
12.0%
K 3
12.0%
E 2
 
8.0%
J 2
 
8.0%
T 2
 
8.0%
M 2
 
8.0%
W 1
 
4.0%
P 1
 
4.0%
Common
ValueCountFrequency (%)
31
44.3%
) 12
 
17.1%
( 12
 
17.1%
] 7
 
10.0%
[ 7
 
10.0%
- 1
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 337
67.9%
ASCII 95
 
19.2%
None 64
 
12.9%

Most frequent character per block

None
ValueCountFrequency (%)
㈜ 64
100.0%
ASCII
ValueCountFrequency (%)
31
32.6%
) 12
 
12.6%
( 12
 
12.6%
] 7
 
7.4%
[ 7
 
7.4%
C 5
 
5.3%
A 3
 
3.2%
H 3
 
3.2%
K 3
 
3.2%
E 2
 
2.1%
Other values (7) 10
 
10.5%
Hangul
ValueCountFrequency (%)
스 15
 
4.5%
로 14
 
4.2%
양 14
 
4.2%
지 12
 
3.6%
광 11
 
3.3%
물 10
 
3.0%
이 10
 
3.0%
시 9
 
2.7%
대 8
 
2.4%
한 8
 
2.4%
Other values (97) 226
67.1%

입주면적(제곱미터)
Real number (ℝ)

Distinct62
Distinct (%)92.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean34874.537
Minimum5107
Maximum133775
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T17:08:31.512494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum5107
5-th percentile6998.3
Q116548
median24782
Q343527.5
95-th percentile95262.7
Maximum133775
Range128668
Interquartile range (IQR)26979.5

Descriptive statistics

Standard deviation27111.779
Coefficient of variation (CV)0.777409
Kurtosis2.4789812
Mean34874.537
Median Absolute Deviation (MAD)10677
Skewness1.5825698
Sum2336594
Variance7.3504857 × 108
MonotonicityNot monotonic
2023-12-12T17:08:31.722207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31919 2
 
3.0%
16500 2
 
3.0%
19000 2
 
3.0%
39516 2
 
3.0%
37473 2
 
3.0%
33191 1
 
1.5%
40216 1
 
1.5%
49699 1
 
1.5%
73000 1
 
1.5%
65801 1
 
1.5%
Other values (52) 52
77.6%
ValueCountFrequency (%)
5107 1
1.5%
5796 1
1.5%
6547 1
1.5%
6647 1
1.5%
7818 1
1.5%
8601 1
1.5%
10162 1
1.5%
13358 1
1.5%
13368 1
1.5%
14105 1
1.5%
ValueCountFrequency (%)
133775 1
1.5%
106405 1
1.5%
102440 1
1.5%
99097 1
1.5%
86316 1
1.5%
75052 1
1.5%
73000 1
1.5%
70024 1
1.5%
66019 1
1.5%
65930 1
1.5%

창고면적(제곱미터)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)68.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8987.3134
Minimum0
Maximum75127
Zeros21
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T17:08:31.927562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5674
Q312591.5
95-th percentile27844.4
Maximum75127
Range75127
Interquartile range (IQR)12591.5

Descriptive statistics

Standard deviation12164.087
Coefficient of variation (CV)1.3534731
Kurtosis13.134489
Mean8987.3134
Median Absolute Deviation (MAD)5674
Skewness3.0370749
Sum602150
Variance1.4796502 × 108
MonotonicityNot monotonic
2023-12-12T17:08:32.120005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 21
31.3%
4858 2
 
3.0%
44175 1
 
1.5%
1729 1
 
1.5%
25337 1
 
1.5%
5367 1
 
1.5%
5021 1
 
1.5%
10225 1
 
1.5%
15109 1
 
1.5%
19548 1
 
1.5%
Other values (36) 36
53.7%
ValueCountFrequency (%)
0 21
31.3%
775 1
 
1.5%
1729 1
 
1.5%
2619 1
 
1.5%
2631 1
 
1.5%
4080 1
 
1.5%
4500 1
 
1.5%
4858 2
 
3.0%
5021 1
 
1.5%
5121 1
 
1.5%
ValueCountFrequency (%)
75127 1
1.5%
44175 1
1.5%
33086 1
1.5%
28919 1
1.5%
25337 1
1.5%
23135 1
1.5%
19548 1
1.5%
19546 1
1.5%
18696 1
1.5%
17382 1
1.5%

창고동수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct9
Distinct (%)13.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.2537313
Minimum0
Maximum9
Zeros21
Zeros (%)31.3%
Negative0
Negative (%)0.0%
Memory size735.0 B
2023-12-12T17:08:32.268559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q34
95-th percentile6
Maximum9
Range9
Interquartile range (IQR)4

Descriptive statistics

Standard deviation2.1485956
Coefficient of variation (CV)0.95335037
Kurtosis0.32295227
Mean2.2537313
Median Absolute Deviation (MAD)2
Skewness0.82187785
Sum151
Variance4.6164631
MonotonicityNot monotonic
2023-12-12T17:08:32.387509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
0 21
31.3%
3 12
17.9%
4 10
14.9%
1 8
 
11.9%
2 8
 
11.9%
6 3
 
4.5%
7 2
 
3.0%
5 2
 
3.0%
9 1
 
1.5%
ValueCountFrequency (%)
0 21
31.3%
1 8
 
11.9%
2 8
 
11.9%
3 12
17.9%
4 10
14.9%
5 2
 
3.0%
6 3
 
4.5%
7 2
 
3.0%
9 1
 
1.5%
ValueCountFrequency (%)
9 1
 
1.5%
7 2
 
3.0%
6 3
 
4.5%
5 2
 
3.0%
4 10
14.9%
3 12
17.9%
2 8
 
11.9%
1 8
 
11.9%
0 21
31.3%

주요화물
Text

Distinct50
Distinct (%)74.6%
Missing0
Missing (%)0.0%
Memory size668.0 B
2023-12-12T17:08:32.617160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length9
Mean length4.9701493
Min length2

Characters and Unicode

Total characters333
Distinct characters102
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

Unique38 ?
Unique (%)56.7%

Sample

1st row인조대리석
2nd rowLME,코일
3rd row고지,사료
4th row제지, 사료
5th row곡물,사료
ValueCountFrequency (%)
사료 9
 
9.9%
화학제품 6
 
6.6%
등 5
 
5.5%
비철금속 4
 
4.4%
제지 4
 
4.4%
위험물 3
 
3.3%
우드펠릿 3
 
3.3%
건초 3
 
3.3%
수지 2
 
2.2%
합성고무소재 2
 
2.2%
Other values (42) 50
54.9%
2023-12-12T17:08:33.016057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
24
 
7.2%
, 24
 
7.2%
제 14
 
4.2%
품 13
 
3.9%
료 13
 
3.9%
사 12
 
3.6%
지 11
 
3.3%
L 7
 
2.1%
물 7
 
2.1%
화 6
 
1.8%
Other values (92) 202
60.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 255
76.6%
Uppercase Letter 29
 
8.7%
Space Separator 24
 
7.2%
Other Punctuation 24
 
7.2%
Decimal Number 1
 
0.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
제 14
 
5.5%
품 13
 
5.1%
료 13
 
5.1%
사 12
 
4.7%
지 11
 
4.3%
물 7
 
2.7%
화 6
 
2.4%
수 6
 
2.4%
재 6
 
2.4%
철 6
 
2.4%
Other values (79) 161
63.1%
Uppercase Letter
ValueCountFrequency (%)
L 7
24.1%
M 5
17.2%
D 5
17.2%
E 3
10.3%
K 2
 
6.9%
C 2
 
6.9%
F 2
 
6.9%
A 1
 
3.4%
B 1
 
3.4%
P 1
 
3.4%
Space Separator
ValueCountFrequency (%)
24
100.0%
Other Punctuation
ValueCountFrequency (%)
, 24
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 255
76.6%
Common 49
 
14.7%
Latin 29
 
8.7%

Most frequent character per script

Hangul
ValueCountFrequency (%)
제 14
 
5.5%
품 13
 
5.1%
료 13
 
5.1%
사 12
 
4.7%
지 11
 
4.3%
물 7
 
2.7%
화 6
 
2.4%
수 6
 
2.4%
재 6
 
2.4%
철 6
 
2.4%
Other values (79) 161
63.1%
Latin
ValueCountFrequency (%)
L 7
24.1%
M 5
17.2%
D 5
17.2%
E 3
10.3%
K 2
 
6.9%
C 2
 
6.9%
F 2
 
6.9%
A 1
 
3.4%
B 1
 
3.4%
P 1
 
3.4%
Common
ValueCountFrequency (%)
24
49.0%
, 24
49.0%
2 1
 
2.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 255
76.6%
ASCII 78
 
23.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
24
30.8%
, 24
30.8%
L 7
 
9.0%
M 5
 
6.4%
D 5
 
6.4%
E 3
 
3.8%
K 2
 
2.6%
C 2
 
2.6%
F 2
 
2.6%
A 1
 
1.3%
Other values (3) 3
 
3.8%
Hangul
ValueCountFrequency (%)
제 14
 
5.5%
품 13
 
5.1%
료 13
 
5.1%
사 12
 
4.7%
지 11
 
4.3%
물 7
 
2.7%
화 6
 
2.4%
수 6
 
2.4%
재 6
 
2.4%
철 6
 
2.4%
Other values (79) 161
63.1%

Interactions

2023-12-12T17:08:29.286831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:28.520603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:28.913376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:29.401669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:28.649756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:29.037333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:29.495755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:28.809993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:08:29.174807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:08:33.144136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구역입주기업입주면적(제곱미터)창고면적(제곱미터)창고동수주요화물
구역1.0000.4570.2110.3320.4700.704
입주기업0.4571.0000.9450.1810.7440.995
입주면적(제곱미터)0.2110.9451.0000.5820.5310.831
창고면적(제곱미터)0.3320.1810.5821.0000.8160.751
창고동수0.4700.7440.5310.8161.0000.000
주요화물0.7040.9950.8310.7510.0001.000
2023-12-12T17:08:33.273721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
입주면적(제곱미터)창고면적(제곱미터)창고동수구역
입주면적(제곱미터)1.0000.4920.4650.100
창고면적(제곱미터)0.4921.0000.8390.164
창고동수0.4650.8391.0000.229
구역0.1000.1640.2291.000

Missing values

2023-12-12T17:08:29.638340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:08:29.783879image/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

구역입주기업입주면적(제곱미터)창고면적(제곱미터)창고동수주요화물
0동측 1단계한국파렛트풀㈜99097441757인조대리석
1동측 1단계CJ 대한통운㈜37736231353LME,코일
2동측 1단계동원로엑스㈜ (위험물)3006999251고지,사료
3동측 1단계(주)우인1600773203제지, 사료
4동측 2-1단계동원로엑스㈜65930195464곡물,사료
5동측 2-1단계㈜한진49194173826화학제품
6동측철송장 부근㈜대평 냉동창고21640131894농산물,과일
7동측 2-1단계㈜대평 위험물 [위험물]1446926193MDF 등
8동측 2-1단계동방광양물류센터㈜3304287892후판,마그네슘
9동측 2-1단계㈜KCTC102440289193조사료 등
구역입주기업입주면적(제곱미터)창고면적(제곱미터)창고동수주요화물
57서측㈜연우3191900제지, 사료
58서측㈜킹톱스3191996003의약외품
59서측㈜세양지엘시3747300화학제품, 자동차
60서측㈜더원로지스3951600컨테이너, 사료
61서측태웅글로벌㈜39516002차전지, KD
62서측국양로지텍㈜13377500합성수지, 건초
63서측용도전환㈜이에스꼬레아664700선용품
64서측용도전환㈜대평579600위험물
65서측용도전환㈜지성1997100식수
66서측용도전환㈜어울림860100비철금속, 석재