Overview

Dataset statistics

Number of variables5
Number of observations36
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.6 KiB
Average record size in memory44.7 B

Variable types

Text3
Categorical1
Numeric1

Dataset

Description연도별 남북협력기금 재원 조성 현황. 연도별 남북협력기금 재원 조성 현황에 대한 데이터로 1991년부터 2015년까지의 정보를 제공합니다.
Author한국수출입은행
URLhttps://www.data.go.kr/data/3040273/fileData.do

Alerts

운용수익 등 is highly overall correlated with 정부외출연금High correlation
정부외출연금 is highly overall correlated with 운용수익 등High correlation
연 도 has unique valuesUnique
운용수익 등 has unique valuesUnique

Reproduction

Analysis started2023-12-12 09:47:30.188340
Analysis finished2023-12-12 09:47:30.727880
Duration0.54 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 도
Text

UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T18:47:30.874983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length4
Mean length5.4166667
Min length4

Characters and Unicode

Total characters195
Distinct characters13
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

Unique36 ?
Unique (%)100.0%

Sample

1st row1991
2nd row1992
3rd row1993
4th row1994
5th row1995
ValueCountFrequency (%)
2015년 12
25.0%
2010 1
 
2.1%
3월 1
 
2.1%
2011 1
 
2.1%
2012 1
 
2.1%
2013 1
 
2.1%
2014 1
 
2.1%
1월 1
 
2.1%
2월 1
 
2.1%
1991 1
 
2.1%
Other values (27) 27
56.2%
2023-12-12T18:47:31.245610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 40
20.5%
1 34
17.4%
2 32
16.4%
9 21
10.8%
5 15
 
7.7%
년 12
 
6.2%
12
 
6.2%
월 12
 
6.2%
3 4
 
2.1%
4 4
 
2.1%
Other values (3) 9
 
4.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 159
81.5%
Other Letter 24
 
12.3%
Space Separator 12
 
6.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 40
25.2%
1 34
21.4%
2 32
20.1%
9 21
13.2%
5 15
 
9.4%
3 4
 
2.5%
4 4
 
2.5%
6 3
 
1.9%
7 3
 
1.9%
8 3
 
1.9%
Other Letter
ValueCountFrequency (%)
년 12
50.0%
월 12
50.0%
Space Separator
ValueCountFrequency (%)
12
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 171
87.7%
Hangul 24
 
12.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 40
23.4%
1 34
19.9%
2 32
18.7%
9 21
12.3%
5 15
 
8.8%
12
 
7.0%
3 4
 
2.3%
4 4
 
2.3%
6 3
 
1.8%
7 3
 
1.8%
Hangul
ValueCountFrequency (%)
년 12
50.0%
월 12
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 171
87.7%
Hangul 24
 
12.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 40
23.4%
1 34
19.9%
2 32
18.7%
9 21
12.3%
5 15
 
8.8%
12
 
7.0%
3 4
 
2.3%
4 4
 
2.3%
6 3
 
1.8%
7 3
 
1.8%
Hangul
ValueCountFrequency (%)
년 12
50.0%
월 12
50.0%

정부출연금
Text

Distinct18
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T18:47:31.401691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.9166667
Min length1

Characters and Unicode

Total characters141
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

Unique13 ?
Unique (%)36.1%

Sample

1st row25000
2nd row40000
3rd row40000
4th row40000
5th row240000
ValueCountFrequency (%)
13
36.1%
500000 3
 
8.3%
40000 3
 
8.3%
100000 2
 
5.6%
650000 2
 
5.6%
34300 1
 
2.8%
25000 1
 
2.8%
105500 1
 
2.8%
34567 1
 
2.8%
12153 1
 
2.8%
Other values (8) 8
22.2%
2023-12-12T18:47:31.709040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 76
53.9%
- 13
 
9.2%
5 11
 
7.8%
1 11
 
7.8%
4 9
 
6.4%
3 7
 
5.0%
2 5
 
3.5%
6 3
 
2.1%
7 2
 
1.4%
9 2
 
1.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 128
90.8%
Dash Punctuation 13
 
9.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 76
59.4%
5 11
 
8.6%
1 11
 
8.6%
4 9
 
7.0%
3 7
 
5.5%
2 5
 
3.9%
6 3
 
2.3%
7 2
 
1.6%
9 2
 
1.6%
8 2
 
1.6%
Dash Punctuation
ValueCountFrequency (%)
- 13
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 141
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 76
53.9%
- 13
 
9.2%
5 11
 
7.8%
1 11
 
7.8%
4 9
 
6.4%
3 7
 
5.0%
2 5
 
3.5%
6 3
 
2.1%
7 2
 
1.4%
9 2
 
1.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 141
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 76
53.9%
- 13
 
9.2%
5 11
 
7.8%
1 11
 
7.8%
4 9
 
6.4%
3 7
 
5.0%
2 5
 
3.5%
6 3
 
2.1%
7 2
 
1.4%
9 2
 
1.4%

정부외출연금
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)47.2%
Missing0
Missing (%)0.0%
Memory size420.0 B
-
13 
1
4 
3
3 
2
2 
0
2 
Other values (12)
12 

Length

Max length4
Median length1
Mean length1.5277778
Min length1

Unique

Unique12 ?
Unique (%)33.3%

Sample

1st row-
2nd row0
3rd row3
4th row1
5th row119

Common Values

ValueCountFrequency (%)
- 13
36.1%
1 4
 
11.1%
3 3
 
8.3%
2 2
 
5.6%
0 2
 
5.6%
78 1
 
2.8%
119 1
 
2.8%
132 1
 
2.8%
288 1
 
2.8%
542 1
 
2.8%
Other values (7) 7
19.4%

Length

2023-12-12T18:47:31.884710image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
13
36.1%
1 4
 
11.1%
3 3
 
8.3%
2 2
 
5.6%
0 2
 
5.6%
1080 1
 
2.8%
33 1
 
2.8%
56 1
 
2.8%
52 1
 
2.8%
15 1
 
2.8%
Other values (7) 7
19.4%

공자기금예수금
Text

Distinct20
Distinct (%)55.6%
Missing0
Missing (%)0.0%
Memory size420.0 B
2023-12-12T18:47:32.042032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length3.6388889
Min length1

Characters and Unicode

Total characters131
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

Unique18 ?
Unique (%)50.0%

Sample

1st row-
2nd row-
3rd row-
4th row-
5th row-
ValueCountFrequency (%)
16
44.4%
310000 2
 
5.6%
875000 1
 
2.8%
34500 1
 
2.8%
16000 1
 
2.8%
30000 1
 
2.8%
228600 1
 
2.8%
530000 1
 
2.8%
400000 1
 
2.8%
104400 1
 
2.8%
Other values (10) 10
27.8%
2023-12-12T18:47:32.367386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 58
44.3%
- 16
 
12.2%
1 10
 
7.6%
5 10
 
7.6%
4 10
 
7.6%
3 7
 
5.3%
8 7
 
5.3%
2 5
 
3.8%
6 3
 
2.3%
9 3
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 115
87.8%
Dash Punctuation 16
 
12.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 58
50.4%
1 10
 
8.7%
5 10
 
8.7%
4 10
 
8.7%
3 7
 
6.1%
8 7
 
6.1%
2 5
 
4.3%
6 3
 
2.6%
9 3
 
2.6%
7 2
 
1.7%
Dash Punctuation
ValueCountFrequency (%)
- 16
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 131
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 58
44.3%
- 16
 
12.2%
1 10
 
7.6%
5 10
 
7.6%
4 10
 
7.6%
3 7
 
5.3%
8 7
 
5.3%
2 5
 
3.8%
6 3
 
2.3%
9 3
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 131
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 58
44.3%
- 16
 
12.2%
1 10
 
7.6%
5 10
 
7.6%
4 10
 
7.6%
3 7
 
5.3%
8 7
 
5.3%
2 5
 
3.8%
6 3
 
2.3%
9 3
 
2.3%

운용수익 등
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct36
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean33237.75
Minimum237
Maximum173431
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size456.0 B
2023-12-12T18:47:32.538096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum237
5-th percentile1707.75
Q14735.5
median20711
Q349314.5
95-th percentile83671.75
Maximum173431
Range173194
Interquartile range (IQR)44579

Descriptive statistics

Standard deviation36252.316
Coefficient of variation (CV)1.090697
Kurtosis4.9999528
Mean33237.75
Median Absolute Deviation (MAD)18656
Skewness1.8416783
Sum1196559
Variance1.3142304 × 109
MonotonicityNot monotonic
2023-12-12T18:47:32.684508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
237 1
 
2.8%
74070 1
 
2.8%
69384 1
 
2.8%
173431 1
 
2.8%
70131 1
 
2.8%
1464 1
 
2.8%
6030 1
 
2.8%
1789 1
 
2.8%
3393 1
 
2.8%
10643 1
 
2.8%
Other values (26) 26
72.2%
ValueCountFrequency (%)
237 1
2.8%
1464 1
2.8%
1789 1
2.8%
2321 1
2.8%
2506 1
2.8%
2614 1
2.8%
3393 1
2.8%
3597 1
2.8%
4608 1
2.8%
4778 1
2.8%
ValueCountFrequency (%)
173431 1
2.8%
89305 1
2.8%
81794 1
2.8%
74070 1
2.8%
70131 1
2.8%
69384 1
2.8%
68896 1
2.8%
56994 1
2.8%
50243 1
2.8%
49005 1
2.8%

Interactions

2023-12-12T18:47:30.389891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T18:47:32.788169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 도정부출연금정부외출연금공자기금예수금운용수익 등
연 도1.0001.0001.0001.0001.000
정부출연금1.0001.0000.7730.9290.620
정부외출연금1.0000.7731.0000.9170.884
공자기금예수금1.0000.9290.9171.0000.955
운용수익 등1.0000.6200.8840.9551.000
2023-12-12T18:47:32.904847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
운용수익 등정부외출연금
운용수익 등1.0000.540
정부외출연금0.5401.000

Missing values

2023-12-12T18:47:30.550373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T18:47:30.663145image/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

연 도정부출연금정부외출연금공자기금예수금운용수익 등
0199125000--237
11992400000-5118
21993400003-4778
31994400001-9387
41995240000119-14589
51996100000132-18409
6199750000288-27874
71998---40280
81999-314983123013
9200010000054225485281794
연 도정부출연금정부외출연금공자기금예수금운용수익 등
262015년 3월34300-300001789
272015년 4월---3393
282015년 5월---10643
292015년 6월12153-160008535
302015년 7월---2614
312015년 8월---2321
322015년 9월34567-345004608
332015년 10월---2506
342015년 11월-1-3597
352015년 12월121832371000015320