Overview

Dataset statistics

Number of variables6
Number of observations38
Missing cells24
Missing cells (%)10.5%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.1 KiB
Average record size in memory55.5 B

Variable types

Text1
Categorical1
Numeric4

Dataset

Description산업단지별 수출실적 및 전월대비 증감률, 누계 등 변동사항에 대해 국가산업단지 산업동향정보를 월별로 제공하고 있습니다.
Author한국산업단지공단
URLhttps://www.data.go.kr/data/15085892/fileData.do

Alerts

구분 has constant value ""Constant
당월(백만달러) is highly overall correlated with 전월(백만달러) and 1 other fieldsHigh correlation
전월(백만달러) is highly overall correlated with 당월(백만달러) and 1 other fieldsHigh correlation
2023누계(백만달러) is highly overall correlated with 당월(백만달러) and 1 other fieldsHigh correlation
당월(백만달러) has 5 (13.2%) missing valuesMissing
전월(백만달러) has 5 (13.2%) missing valuesMissing
2023누계(백만달러) has 5 (13.2%) missing valuesMissing
증감률(전월대비)(퍼센트) has 9 (23.7%) missing valuesMissing
산업단지 has unique valuesUnique
당월(백만달러) has 4 (10.5%) zerosZeros
전월(백만달러) has 4 (10.5%) zerosZeros
2023누계(백만달러) has 4 (10.5%) zerosZeros
증감률(전월대비)(퍼센트) has 1 (2.6%) zerosZeros

Reproduction

Analysis started2024-04-06 08:11:22.700893
Analysis finished2024-04-06 08:11:27.317011
Duration4.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

산업단지
Text

UNIQUE 

Distinct38
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size436.0 B
2024-04-06T17:11:27.692854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length3.2631579
Min length2

Characters and Unicode

Total characters124
Distinct characters79
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique38 ?
Unique (%)100.0%

Sample

1st row서울
2nd row녹산
3rd row대구
4th row남동
5th row부평
ValueCountFrequency (%)
서울 1
 
2.6%
대불(외 1
 
2.6%
진해 1
 
2.6%
국가식품클러스터(외 1
 
2.6%
군산 1
 
2.6%
군산2 1
 
2.6%
익산 1
 
2.6%
광양 1
 
2.6%
대불 1
 
2.6%
구미 1
 
2.6%
Other values (28) 28
73.7%
2024-04-06T17:11:28.303852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 7
 
5.6%
항 4
 
3.2%
구 3
 
2.4%
미 3
 
2.4%
포 3
 
2.4%
주 3
 
2.4%
) 3
 
2.4%
대 3
 
2.4%
( 3
 
2.4%
외 3
 
2.4%
Other values (69) 89
71.8%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
91.9%
Close Punctuation 3
 
2.4%
Open Punctuation 3
 
2.4%
Uppercase Letter 3
 
2.4%
Decimal Number 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 7
 
6.1%
항 4
 
3.5%
구 3
 
2.6%
미 3
 
2.6%
포 3
 
2.6%
주 3
 
2.6%
대 3
 
2.6%
외 3
 
2.6%
스 2
 
1.8%
양 2
 
1.8%
Other values (63) 81
71.1%
Uppercase Letter
ValueCountFrequency (%)
M 1
33.3%
T 1
33.3%
V 1
33.3%
Close Punctuation
ValueCountFrequency (%)
) 3
100.0%
Open Punctuation
ValueCountFrequency (%)
( 3
100.0%
Decimal Number
ValueCountFrequency (%)
2 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
91.9%
Common 7
 
5.6%
Latin 3
 
2.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 7
 
6.1%
항 4
 
3.5%
구 3
 
2.6%
미 3
 
2.6%
포 3
 
2.6%
주 3
 
2.6%
대 3
 
2.6%
외 3
 
2.6%
스 2
 
1.8%
양 2
 
1.8%
Other values (63) 81
71.1%
Common
ValueCountFrequency (%)
) 3
42.9%
( 3
42.9%
2 1
 
14.3%
Latin
ValueCountFrequency (%)
M 1
33.3%
T 1
33.3%
V 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 113
91.1%
ASCII 10
 
8.1%
Compat Jamo 1
 
0.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 7
 
6.2%
항 4
 
3.5%
구 3
 
2.7%
미 3
 
2.7%
포 3
 
2.7%
주 3
 
2.7%
대 3
 
2.7%
외 3
 
2.7%
스 2
 
1.8%
양 2
 
1.8%
Other values (62) 80
70.8%
ASCII
ValueCountFrequency (%)
) 3
30.0%
( 3
30.0%
2 1
 
10.0%
M 1
 
10.0%
T 1
 
10.0%
V 1
 
10.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

구분
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size436.0 B
국가
38 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
국가 38
100.0%

Length

2024-04-06T17:11:28.565095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-06T17:11:28.761934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국가 38
100.0%

당월(백만달러)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)90.9%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean541.75814
Minimum0
Maximum5119.6326
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:11:28.981740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.9866706
median101.9886
Q3447.88587
95-th percentile2432.2821
Maximum5119.6326
Range5119.6326
Interquartile range (IQR)439.8992

Descriptive statistics

Standard deviation1065.0013
Coefficient of variation (CV)1.9658242
Kurtosis10.763213
Mean541.75814
Median Absolute Deviation (MAD)101.9886
Skewness3.1043616
Sum17878.019
Variance1134227.7
MonotonicityNot monotonic
2024-04-06T17:11:29.227427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 4
 
10.5%
7.9866706 1
 
2.6%
1624.27497 1
 
2.6%
447.8858697 1
 
2.6%
74.05 1
 
2.6%
1548.586554 1
 
2.6%
2898.708283 1
 
2.6%
13.108972 1
 
2.6%
697.915 1
 
2.6%
21.746986 1
 
2.6%
Other values (20) 20
52.6%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
0.0 4
10.5%
0.016 1
 
2.6%
0.121 1
 
2.6%
1.194 1
 
2.6%
1.854878 1
 
2.6%
7.9866706 1
 
2.6%
13.108972 1
 
2.6%
21.746986 1
 
2.6%
26.3369205 1
 
2.6%
45.61151177 1
 
2.6%
ValueCountFrequency (%)
5119.632586 1
2.6%
2898.708283 1
2.6%
2121.331265 1
2.6%
1624.27497 1
2.6%
1548.586554 1
2.6%
697.915 1
2.6%
489.0296688 1
2.6%
465.240683 1
2.6%
447.8858697 1
2.6%
372.55752 1
2.6%

전월(백만달러)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)90.9%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean517.55402
Minimum0
Maximum4888.1733
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:11:29.460726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q110.561
median88.3944
Q3417.63225
95-th percentile2273.0058
Maximum4888.1733
Range4888.1733
Interquartile range (IQR)407.07125

Descriptive statistics

Standard deviation1006.1277
Coefficient of variation (CV)1.9440051
Kurtosis11.315345
Mean517.55402
Median Absolute Deviation (MAD)88.3944
Skewness3.1744412
Sum17079.283
Variance1012292.9
MonotonicityNot monotonic
2024-04-06T17:11:29.716352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 4
 
10.5%
41.167007 1
 
2.6%
1437.181959 1
 
2.6%
417.632245 1
 
2.6%
74.693 1
 
2.6%
1402.992424 1
 
2.6%
2775.260683 1
 
2.6%
10.561 1
 
2.6%
707.288 1
 
2.6%
19.523986 1
 
2.6%
Other values (20) 20
52.6%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
0.0 4
10.5%
0.064 1
 
2.6%
0.121 1
 
2.6%
2.208 1
 
2.6%
6.545983091 1
 
2.6%
10.561 1
 
2.6%
19.523986 1
 
2.6%
26.268788 1
 
2.6%
41.167007 1
 
2.6%
45.63616177 1
 
2.6%
ValueCountFrequency (%)
4888.173267 1
2.6%
2775.260683 1
2.6%
1938.169263 1
2.6%
1437.181959 1
2.6%
1402.992424 1
2.6%
707.288 1
2.6%
473.6817172 1
2.6%
466.328283 1
2.6%
417.632245 1
2.6%
396.56162 1
2.6%

2023누계(백만달러)
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)90.9%
Missing5
Missing (%)13.2%
Infinite0
Infinite (%)0.0%
Mean4592.7046
Minimum0
Maximum45173.687
Zeros4
Zeros (%)10.5%
Negative0
Negative (%)0.0%
Memory size474.0 B
2024-04-06T17:11:29.974438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1107.26908
median887.5536
Q33493.0077
95-th percentile18436.232
Maximum45173.687
Range45173.687
Interquartile range (IQR)3385.7387

Descriptive statistics

Standard deviation9091.6488
Coefficient of variation (CV)1.9795849
Kurtosis12.574533
Mean4592.7046
Median Absolute Deviation (MAD)887.5536
Skewness3.3160092
Sum151559.25
Variance82658078
MonotonicityNot monotonic
2024-04-06T17:11:30.286911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 4
 
10.5%
336.2950522 1
 
2.6%
13609.26941 1
 
2.6%
4220.575934 1
 
2.6%
659.788 1
 
2.6%
13656.79181 1
 
2.6%
23731.73885 1
 
2.6%
107.269077 1
 
2.6%
6514.456 1
 
2.6%
164.939022 1
 
2.6%
Other values (20) 20
52.6%
(Missing) 5
 
13.2%
ValueCountFrequency (%)
0.0 4
10.5%
0.367 1
 
2.6%
1.089 1
 
2.6%
23.059 1
 
2.6%
51.67661364 1
 
2.6%
107.269077 1
 
2.6%
164.939022 1
 
2.6%
266.8380969 1
 
2.6%
336.2950522 1
 
2.6%
423.4182559 1
 
2.6%
ValueCountFrequency (%)
45173.68713 1
2.6%
23731.73885 1
2.6%
14905.89407 1
2.6%
13656.79181 1
2.6%
13609.26941 1
2.6%
6514.456 1
2.6%
4358.515183 1
2.6%
4220.575934 1
2.6%
3493.007746 1
2.6%
3192.017285 1
2.6%

증감률(전월대비)(퍼센트)
Real number (ℝ)

MISSING  ZEROS 

Distinct29
Distinct (%)100.0%
Missing9
Missing (%)23.7%
Infinite0
Infinite (%)0.0%
Mean-7.2908145
Minimum-80.599341
Maximum24.126238
Zeros1
Zeros (%)2.6%
Negative14
Negative (%)36.8%
Memory size474.0 B
2024-04-06T17:11:30.559204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-80.599341
5-th percentile-73.66555
Q1-6.0530568
median0
Q37.2440826
95-th percentile14.434636
Maximum24.126238
Range104.72558
Interquartile range (IQR)13.297139

Descriptive statistics

Standard deviation26.982218
Coefficient of variation (CV)-3.7008509
Kurtosis2.604344
Mean-7.2908145
Median Absolute Deviation (MAD)7.2440826
Skewness-1.8445042
Sum-211.43362
Variance728.04008
MonotonicityNot monotonic
2024-04-06T17:11:30.783144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
-6.053056773 1
 
2.6%
13.01804616 1
 
2.6%
7.244082578 1
 
2.6%
-0.860857108 1
 
2.6%
10.37739959 1
 
2.6%
4.448144304 1
 
2.6%
24.12623805 1
 
2.6%
-1.325202746 1
 
2.6%
11.38599464 1
 
2.6%
0.259366744 1
 
2.6%
Other values (19) 19
50.0%
(Missing) 9
23.7%
ValueCountFrequency (%)
-80.59934112 1
2.6%
-75.0 1
2.6%
-71.66387425 1
2.6%
-45.92391304 1
2.6%
-19.28351393 1
2.6%
-17.02486838 1
2.6%
-16.45285131 1
2.6%
-6.053056773 1
2.6%
-1.325202746 1
2.6%
-0.860857108 1
2.6%
ValueCountFrequency (%)
24.12623805 1
2.6%
15.37902854 1
2.6%
13.01804616 1
2.6%
11.38599464 1
2.6%
10.47309673 1
2.6%
10.37739959 1
2.6%
9.450258323 1
2.6%
7.244082578 1
2.6%
7.118714577 1
2.6%
4.735088283 1
2.6%

Interactions

2024-04-06T17:11:26.070704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:23.112732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:23.875651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:25.164438image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:26.267665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:23.340069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:24.142025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:25.381032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:26.429519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:23.536610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:24.351354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:25.613081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:26.584260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:23.716146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:24.942970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-06T17:11:25.847890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-06T17:11:30.953774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
산업단지당월(백만달러)전월(백만달러)2023누계(백만달러)증감률(전월대비)(퍼센트)
산업단지1.0001.0001.0001.0001.000
당월(백만달러)1.0001.0001.0001.0000.000
전월(백만달러)1.0001.0001.0001.0000.000
2023누계(백만달러)1.0001.0001.0001.0000.000
증감률(전월대비)(퍼센트)1.0000.0000.0000.0001.000
2024-04-06T17:11:31.172180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
당월(백만달러)전월(백만달러)2023누계(백만달러)증감률(전월대비)(퍼센트)
당월(백만달러)1.0000.9970.9960.401
전월(백만달러)0.9971.0000.9970.352
2023누계(백만달러)0.9960.9971.0000.371
증감률(전월대비)(퍼센트)0.4010.3520.3711.000

Missing values

2024-04-06T17:11:26.785436image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-06T17:11:27.001424image/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.
2024-04-06T17:11:27.201407image/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

산업단지구분당월(백만달러)전월(백만달러)2023누계(백만달러)증감률(전월대비)(퍼센트)
0서울국가275.472952329.7215482382.798822-16.452851
1녹산국가372.55752396.561623144.477383-6.053057
2대구국가465.240683466.3282833192.017285-0.233226
3남동국가359.963213325.8378953027.66739510.473097
4부평국가45.61151245.636162423.418256-0.054014
5주안국가113.149695113.514457975.185809-0.321335
6광주첨단국가237.620951286.3761062316.496562-17.024868
7빛그린국가1.1942.20823.059-45.923913
8온산국가2121.3312651938.16926314905.894079.450258
9울산ㆍ미포국가5119.6325864888.17326745173.687134.735088
산업단지구분당월(백만달러)전월(백만달러)2023누계(백만달러)증감률(전월대비)(퍼센트)
28여수국가2898.7082832775.26068323731.738854.448144
29구미국가1548.5865541402.99242413656.7918110.3774
30구미(외)국가74.0574.693659.788-0.860857
31포항국가447.88587417.6322454220.5759347.244083
32포항블루밸리국가0.00.00.0<NA>
33경남항공국가<NA><NA><NA><NA>
34밀양나노국가<NA><NA><NA><NA>
35안정국가0.00.00.0<NA>
36진해국가<NA><NA><NA><NA>
37창원국가1624.274971437.18195913609.2694113.018046