Overview

Dataset statistics

Number of variables6
Number of observations41
Missing cells3
Missing cells (%)1.2%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory55.2 B

Variable types

Categorical1
Numeric4
Text1

Dataset

Description충청남도의 수출입에 대한 데이터로 20대 품목별 품목명, 금액(백만달러), 전년대비 증감율 및 비중 등의 현황을 제공
URLhttps://www.data.go.kr/data/15045140/fileData.do

Alerts

순위 is highly overall correlated with 금액 and 1 other fieldsHigh correlation
금액 is highly overall correlated with 순위 and 1 other fieldsHigh correlation
비중 is highly overall correlated with 순위 and 1 other fieldsHigh correlation
금액 has 1 (2.4%) missing valuesMissing
증감율 has 1 (2.4%) missing valuesMissing
비중 has 1 (2.4%) missing valuesMissing
증감율 has 1 (2.4%) zerosZeros

Reproduction

Analysis started2023-12-12 12:50:03.057135
Analysis finished2023-12-12 12:50:05.109939
Duration2.05 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)4.9%
Missing0
Missing (%)0.0%
Memory size460.0 B
충남
21 
전국
20 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충남 21
51.2%
전국 20
48.8%

Length

2023-12-12T21:50:05.169865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:50:05.263137image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충남 21
51.2%
전국 20
48.8%

순위
Real number (ℝ)

HIGH CORRELATION 

Distinct21
Distinct (%)51.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.756098
Minimum1
Maximum21
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T21:50:05.361221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q16
median11
Q316
95-th percentile20
Maximum21
Range20
Interquartile range (IQR)10

Descriptive statistics

Standard deviation5.9949165
Coefficient of variation (CV)0.55735052
Kurtosis-1.1951878
Mean10.756098
Median Absolute Deviation (MAD)5
Skewness0.0064004529
Sum441
Variance35.939024
MonotonicityNot monotonic
2023-12-12T21:50:05.484848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
1 2
 
4.9%
2 2
 
4.9%
20 2
 
4.9%
19 2
 
4.9%
18 2
 
4.9%
17 2
 
4.9%
16 2
 
4.9%
15 2
 
4.9%
14 2
 
4.9%
13 2
 
4.9%
Other values (11) 21
51.2%
ValueCountFrequency (%)
1 2
4.9%
2 2
4.9%
3 2
4.9%
4 2
4.9%
5 2
4.9%
6 2
4.9%
7 2
4.9%
8 2
4.9%
9 2
4.9%
10 2
4.9%
ValueCountFrequency (%)
21 1
2.4%
20 2
4.9%
19 2
4.9%
18 2
4.9%
17 2
4.9%
16 2
4.9%
15 2
4.9%
14 2
4.9%
13 2
4.9%
12 2
4.9%

품목명
Text

Distinct30
Distinct (%)73.2%
Missing0
Missing (%)0.0%
Memory size460.0 B
2023-12-12T21:50:05.685663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length8
Mean length4.1219512
Min length1

Characters and Unicode

Total characters169
Distinct characters80
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)46.3%

Sample

1st row원유
2nd row유연탄
3rd row나프타
4th row정밀화학원료
5th rowLNG
ValueCountFrequency (%)
원유 2
 
4.7%
나프타 2
 
4.7%
자동차부품 2
 
4.7%
사료 2
 
4.7%
반도체제조용장비 2
 
4.7%
프로판 2
 
4.7%
유연탄 2
 
4.7%
금속광물 2
 
4.7%
lng 2
 
4.7%
정밀화학원료 2
 
4.7%
Other values (22) 23
53.5%
2023-12-12T21:50:06.017404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
품 7
 
4.1%
유 6
 
3.6%
프 5
 
3.0%
자 5
 
3.0%
동 5
 
3.0%
로 4
 
2.4%
광 4
 
2.4%
반 4
 
2.4%
도 4
 
2.4%
체 4
 
2.4%
Other values (70) 121
71.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 159
94.1%
Uppercase Letter 7
 
4.1%
Space Separator 2
 
1.2%
Dash Punctuation 1
 
0.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
품 7
 
4.4%
유 6
 
3.8%
프 5
 
3.1%
자 5
 
3.1%
동 5
 
3.1%
로 4
 
2.5%
광 4
 
2.5%
반 4
 
2.5%
도 4
 
2.5%
체 4
 
2.5%
Other values (64) 111
69.8%
Uppercase Letter
ValueCountFrequency (%)
N 2
28.6%
L 2
28.6%
G 2
28.6%
C 1
14.3%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 159
94.1%
Latin 7
 
4.1%
Common 3
 
1.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
품 7
 
4.4%
유 6
 
3.8%
프 5
 
3.1%
자 5
 
3.1%
동 5
 
3.1%
로 4
 
2.5%
광 4
 
2.5%
반 4
 
2.5%
도 4
 
2.5%
체 4
 
2.5%
Other values (64) 111
69.8%
Latin
ValueCountFrequency (%)
N 2
28.6%
L 2
28.6%
G 2
28.6%
C 1
14.3%
Common
ValueCountFrequency (%)
2
66.7%
- 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 159
94.1%
ASCII 10
 
5.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
품 7
 
4.4%
유 6
 
3.8%
프 5
 
3.1%
자 5
 
3.1%
동 5
 
3.1%
로 4
 
2.5%
광 4
 
2.5%
반 4
 
2.5%
도 4
 
2.5%
체 4
 
2.5%
Other values (64) 111
69.8%
ASCII
ValueCountFrequency (%)
2
20.0%
N 2
20.0%
L 2
20.0%
G 2
20.0%
- 1
10.0%
C 1
10.0%

금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct40
Distinct (%)100.0%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean4016.025
Minimum71
Maximum36302
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T21:50:06.180535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum71
5-th percentile81.5
Q1198.75
median1897.5
Q34651.75
95-th percentile11238.35
Maximum36302
Range36231
Interquartile range (IQR)4453

Descriptive statistics

Standard deviation6704.3423
Coefficient of variation (CV)1.6693976
Kurtosis13.901928
Mean4016.025
Median Absolute Deviation (MAD)1751.5
Skewness3.3633028
Sum160641
Variance44948206
MonotonicityNot monotonic
2023-12-12T21:50:06.347703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=40)
ValueCountFrequency (%)
7951 1
 
2.4%
19833 1
 
2.4%
10500 1
 
2.4%
9072 1
 
2.4%
8152 1
 
2.4%
7679 1
 
2.4%
7502 1
 
2.4%
6523 1
 
2.4%
4028 1
 
2.4%
3822 1
 
2.4%
Other values (30) 30
73.2%
ValueCountFrequency (%)
71 1
2.4%
72 1
2.4%
82 1
2.4%
96 1
2.4%
105 1
2.4%
112 1
2.4%
126 1
2.4%
136 1
2.4%
156 1
2.4%
192 1
2.4%
ValueCountFrequency (%)
36302 1
2.4%
19833 1
2.4%
10786 1
2.4%
10500 1
2.4%
9072 1
2.4%
8152 1
2.4%
7951 1
2.4%
7679 1
2.4%
7502 1
2.4%
6523 1
2.4%

증감율
Real number (ℝ)

MISSING  ZEROS 

Distinct39
Distinct (%)97.5%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean-3.2275
Minimum-63.4
Maximum105.3
Zeros1
Zeros (%)2.4%
Negative24
Negative (%)58.5%
Memory size501.0 B
2023-12-12T21:50:06.510868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-63.4
5-th percentile-33.915
Q1-18.625
median-10.25
Q310.725
95-th percentile32.85
Maximum105.3
Range168.7
Interquartile range (IQR)29.35

Descriptive statistics

Standard deviation27.388028
Coefficient of variation (CV)-8.4858337
Kurtosis5.4863319
Mean-3.2275
Median Absolute Deviation (MAD)15.15
Skewness1.4648136
Sum-129.1
Variance750.1041
MonotonicityNot monotonic
2023-12-12T21:50:07.058811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
-17.9 2
 
4.9%
-11.0 1
 
2.4%
43.3 1
 
2.4%
-11.5 1
 
2.4%
9.6 1
 
2.4%
-28.6 1
 
2.4%
-20.8 1
 
2.4%
-14.0 1
 
2.4%
6.6 1
 
2.4%
105.3 1
 
2.4%
Other values (29) 29
70.7%
ValueCountFrequency (%)
-63.4 1
2.4%
-39.9 1
2.4%
-33.6 1
2.4%
-30.1 1
2.4%
-28.6 1
2.4%
-25.0 1
2.4%
-22.6 1
2.4%
-22.2 1
2.4%
-22.1 1
2.4%
-20.8 1
2.4%
ValueCountFrequency (%)
105.3 1
2.4%
43.3 1
2.4%
32.3 1
2.4%
26.1 1
2.4%
19.9 1
2.4%
16.9 1
2.4%
16.7 1
2.4%
14.0 1
2.4%
13.5 1
2.4%
11.1 1
2.4%

비중
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)57.5%
Missing1
Missing (%)2.4%
Infinite0
Infinite (%)0.0%
Mean3.39
Minimum0.4
Maximum43.4
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-12T21:50:07.193097image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.4
5-th percentile0.4
Q10.7
median1.1
Q32.9
95-th percentile12.235
Maximum43.4
Range43
Interquartile range (IQR)2.2

Descriptive statistics

Standard deviation7.1279803
Coefficient of variation (CV)2.102649
Kurtosis26.632732
Mean3.39
Median Absolute Deviation (MAD)0.5
Skewness4.8701509
Sum135.6
Variance50.808103
MonotonicityNot monotonic
2023-12-12T21:50:07.330545image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0.7 4
 
9.8%
0.6 4
 
9.8%
1.1 3
 
7.3%
1.0 3
 
7.3%
0.4 3
 
7.3%
0.8 2
 
4.9%
1.4 2
 
4.9%
2.9 2
 
4.9%
1.3 2
 
4.9%
2.7 2
 
4.9%
Other values (13) 13
31.7%
ValueCountFrequency (%)
0.4 3
7.3%
0.5 1
 
2.4%
0.6 4
9.8%
0.7 4
9.8%
0.8 2
4.9%
0.9 1
 
2.4%
1.0 3
7.3%
1.1 3
7.3%
1.3 2
4.9%
1.4 2
4.9%
ValueCountFrequency (%)
43.4 1
2.4%
12.9 1
2.4%
12.2 1
2.4%
8.6 1
2.4%
7.1 1
2.4%
4.5 1
2.4%
3.8 1
2.4%
3.7 1
2.4%
3.2 1
2.4%
2.9 2
4.9%

Interactions

2023-12-12T21:50:04.510701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.290482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.685538image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.093716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.601031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.387805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.789244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.189093image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.686225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.490819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.893624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.317981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.766133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.586330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:03.997873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:50:04.415205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:50:07.429574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분순위품목명금액증감율비중
구분1.0000.0000.0000.3900.0000.000
순위0.0001.0000.8020.6180.0000.550
품목명0.0000.8021.0000.0000.3150.000
금액0.3900.6180.0001.0000.0000.617
증감율0.0000.0000.3150.0001.0000.000
비중0.0000.5500.0000.6170.0001.000
2023-12-12T21:50:07.585381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
순위금액증감율비중구분
순위1.000-0.590-0.239-0.9680.000
금액-0.5901.0000.2570.7240.454
증감율-0.2390.2571.0000.2840.000
비중-0.9680.7240.2841.0000.000
구분0.0000.4540.0000.0001.000

Missing values

2023-12-12T21:50:04.870159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:50:04.969007image/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.
2023-12-12T21:50:05.060476image/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

구분순위품목명금액증감율비중
0충남1원유7951-11.043.4
1충남2유연탄2232-0.812.2
2충남3나프타157010.68.6
3충남4정밀화학원료82916.74.5
4충남5LNG524-9.52.9
5충남6금속광물294-22.11.6
6충남7의약품2345.31.3
7충남8슬랩231-25.01.3
8충남9벙커-C유20714.01.1
9충남10와이어하네스20111.11.1
구분순위품목명금액증감율비중
31전국11축전지3822105.31.4
32전국12철광3149-17.31.1
33전국13자동차부품279513.51.0
34전국14자동차272826.11.0
35전국15하이브리드자동차236019.90.8
36전국16동광2339-22.20.8
37전국17사료20970.00.7
38전국18금속광물1976-6.40.7
39전국19프로판1819-30.10.6
40전국20쇠고기1758-11.30.6