Overview

Dataset statistics

Number of variables12
Number of observations26
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory109.1 B

Variable types

Categorical3
Text1
Numeric8

Dataset

Description연도별 남북협력기금 사업별 지원 현황을 나타내는 데이터이며, 1991-2015까지의 남북협력기금 현황에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/3040410/fileData.do

Alerts

1991-2008 누적 is highly overall correlated with 2009 and 4 other fieldsHigh correlation
2009 is highly overall correlated with 1991-2008 누적 and 5 other fieldsHigh correlation
2010 is highly overall correlated with 1991-2008 누적 and 5 other fieldsHigh correlation
2011 is highly overall correlated with 1991-2008 누적 and 6 other fieldsHigh correlation
2012 is highly overall correlated with 1991-2008 누적 and 5 other fieldsHigh correlation
2013 is highly overall correlated with 2009 and 5 other fieldsHigh correlation
2014 is highly overall correlated with 1991-2008 누적 and 5 other fieldsHigh correlation
2015 is highly overall correlated with 2011 and 1 other fieldsHigh correlation
대구분 is highly overall correlated with 중구분High correlation
중구분 is highly overall correlated with 대구분High correlation
1991-2008 누적 has unique valuesUnique
2009 has 10 (38.5%) zerosZeros
2010 has 10 (38.5%) zerosZeros
2011 has 6 (23.1%) zerosZeros
2012 has 10 (38.5%) zerosZeros
2013 has 10 (38.5%) zerosZeros
2014 has 6 (23.1%) zerosZeros
2015 has 14 (53.8%) zerosZeros

Reproduction

Analysis started2023-12-12 07:16:06.017142
Analysis finished2023-12-12 07:16:12.814591
Duration6.8 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

대구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
경상사업
16 
융자사업
10 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상사업 16
61.5%
융자사업 10
38.5%

Length

2023-12-12T16:16:12.879183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:16:12.982663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상사업 16
61.5%
융자사업 10
38.5%

중구분
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)26.9%
Missing0
Missing (%)0.0%
Memory size340.0 B
남북경제협력(융자)
8 
남북사회문화교류
4 
인도적사업
4 
남북경제협력
4 
기타
2 
Other values (2)
4 

Length

Max length10
Median length8
Mean length7.3846154
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row남북사회문화교류
2nd row남북사회문화교류
3rd row남북사회문화교류
4th row남북사회문화교류
5th row인도적사업

Common Values

ValueCountFrequency (%)
남북경제협력(융자) 8
30.8%
남북사회문화교류 4
15.4%
인도적사업 4
15.4%
남북경제협력 4
15.4%
기타 2
 
7.7%
인도적사업(융자) 2
 
7.7%
대북경수로사업 2
 
7.7%

Length

2023-12-12T16:16:13.082557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:16:13.190699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남북경제협력(융자 8
30.8%
남북사회문화교류 4
15.4%
인도적사업 4
15.4%
남북경제협력 4
15.4%
기타 2
 
7.7%
인도적사업(융자 2
 
7.7%
대북경수로사업 2
 
7.7%

자금종류
Text

Distinct13
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size340.0 B
2023-12-12T16:16:13.409673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length11
Mean length8.6923077
Min length6

Characters and Unicode

Total characters226
Distinct characters46
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

Unique0 ?
Unique (%)0.0%

Sample

1st row인적왕래지원
2nd row인적왕래지원
3rd row사회문화교류지원
4th row사회문화교류지원
5th row이산가족교류 지원
ValueCountFrequency (%)
대출 4
 
11.1%
인적왕래지원 2
 
5.6%
인도적사업(융자 2
 
5.6%
민족공동체회복지원 2
 
5.6%
경협사업자금대출 2
 
5.6%
교역자금대출 2
 
5.6%
자금대출 2
 
5.6%
교역경협사업 2
 
5.6%
사회문화분야협력기반조성 2
 
5.6%
사회문화교류지원 2
 
5.6%
Other values (7) 14
38.9%
2023-12-12T16:16:13.677891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
사 14
 
6.2%
협 10
 
4.4%
10
 
4.4%
출 10
 
4.4%
대 10
 
4.4%
지 10
 
4.4%
원 10
 
4.4%
교 10
 
4.4%
경 10
 
4.4%
업 10
 
4.4%
Other values (36) 122
54.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 212
93.8%
Space Separator 10
 
4.4%
Close Punctuation 2
 
0.9%
Open Punctuation 2
 
0.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 14
 
6.6%
협 10
 
4.7%
출 10
 
4.7%
대 10
 
4.7%
지 10
 
4.7%
원 10
 
4.7%
교 10
 
4.7%
경 10
 
4.7%
업 10
 
4.7%
자 8
 
3.8%
Other values (33) 110
51.9%
Space Separator
ValueCountFrequency (%)
10
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 212
93.8%
Common 14
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 14
 
6.6%
협 10
 
4.7%
출 10
 
4.7%
대 10
 
4.7%
지 10
 
4.7%
원 10
 
4.7%
교 10
 
4.7%
경 10
 
4.7%
업 10
 
4.7%
자 8
 
3.8%
Other values (33) 110
51.9%
Common
ValueCountFrequency (%)
10
71.4%
) 2
 
14.3%
( 2
 
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 212
93.8%
ASCII 14
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
사 14
 
6.6%
협 10
 
4.7%
출 10
 
4.7%
대 10
 
4.7%
지 10
 
4.7%
원 10
 
4.7%
교 10
 
4.7%
경 10
 
4.7%
업 10
 
4.7%
자 8
 
3.8%
Other values (33) 110
51.9%
ASCII
ValueCountFrequency (%)
10
71.4%
) 2
 
14.3%
( 2
 
14.3%

기준
Categorical

Distinct2
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size340.0 B
금액(백만원)
13 
건수
13 

Length

Max length7
Median length4.5
Mean length4.5
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row금액(백만원)
2nd row건수
3rd row금액(백만원)
4th row건수
5th row금액(백만원)

Common Values

ValueCountFrequency (%)
금액(백만원) 13
50.0%
건수 13
50.0%

Length

2023-12-12T16:16:13.798190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:16:13.887246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
금액(백만원 13
50.0%
건수 13
50.0%

1991-2008 누적
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct26
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean219050.81
Minimum1
Maximum1537475
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:13.993627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4.75
Q169.75
median852.5
Q3191559.5
95-th percentile1265202
Maximum1537475
Range1537474
Interquartile range (IQR)191489.75

Descriptive statistics

Standard deviation436264.76
Coefficient of variation (CV)1.9916145
Kurtosis3.9193181
Mean219050.81
Median Absolute Deviation (MAD)847
Skewness2.1976234
Sum5695321
Variance1.9032694 × 1011
MonotonicityNot monotonic
2023-12-12T16:16:14.138001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
39907 1
 
3.8%
784199 1
 
3.8%
7 1
 
3.8%
1374393 1
 
3.8%
27 1
 
3.8%
337817 1
 
3.8%
82 1
 
3.8%
228195 1
 
3.8%
310 1
 
3.8%
55280 1
 
3.8%
Other values (16) 16
61.5%
ValueCountFrequency (%)
1 1
3.8%
4 1
3.8%
7 1
3.8%
10 1
3.8%
27 1
3.8%
56 1
3.8%
66 1
3.8%
81 1
3.8%
82 1
3.8%
110 1
3.8%
ValueCountFrequency (%)
1537475 1
3.8%
1374393 1
3.8%
937629 1
3.8%
784199 1
3.8%
337817 1
3.8%
283475 1
3.8%
228195 1
3.8%
81653 1
3.8%
55280 1
3.8%
39907 1
3.8%

2009
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4443.6538
Minimum0
Maximum41461
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:14.252635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.5
Q32809.75
95-th percentile25879.25
Maximum41461
Range41461
Interquartile range (IQR)2809.75

Descriptive statistics

Standard deviation10026.404
Coefficient of variation (CV)2.2563422
Kurtosis8.108186
Mean4443.6538
Median Absolute Deviation (MAD)3.5
Skewness2.8413107
Sum115535
Variance1.0052877 × 108
MonotonicityNot monotonic
2023-12-12T16:16:14.354965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 10
38.5%
2 2
 
7.7%
15416 1
 
3.8%
8596 1
 
3.8%
1 1
 
3.8%
7000 1
 
3.8%
17 1
 
3.8%
8416 1
 
3.8%
18 1
 
3.8%
13 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
0 10
38.5%
1 1
 
3.8%
2 2
 
7.7%
5 1
 
3.8%
13 1
 
3.8%
17 1
 
3.8%
18 1
 
3.8%
40 1
 
3.8%
2152 1
 
3.8%
3029 1
 
3.8%
ValueCountFrequency (%)
41461 1
3.8%
29367 1
3.8%
15416 1
3.8%
8596 1
3.8%
8416 1
3.8%
7000 1
3.8%
3029 1
3.8%
2152 1
3.8%
40 1
3.8%
18 1
3.8%

2010
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct16
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4932.6538
Minimum0
Maximum41569
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:14.447406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median8.5
Q32086
95-th percentile28231.25
Maximum41569
Range41569
Interquartile range (IQR)2086

Descriptive statistics

Standard deviation10735.495
Coefficient of variation (CV)2.1764136
Kurtosis5.6412806
Mean4932.6538
Median Absolute Deviation (MAD)8.5
Skewness2.4633751
Sum128249
Variance1.1525085 × 108
MonotonicityNot monotonic
2023-12-12T16:16:14.538574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
0 10
38.5%
3 2
 
7.7%
196 1
 
3.8%
1 1
 
3.8%
3703 1
 
3.8%
27 1
 
3.8%
10326 1
 
3.8%
169 1
 
3.8%
31243 1
 
3.8%
41569 1
 
3.8%
Other values (6) 6
23.1%
ValueCountFrequency (%)
0 10
38.5%
1 1
 
3.8%
3 2
 
7.7%
14 1
 
3.8%
17 1
 
3.8%
27 1
 
3.8%
169 1
 
3.8%
196 1
 
3.8%
1987 1
 
3.8%
2119 1
 
3.8%
ValueCountFrequency (%)
41569 1
3.8%
31243 1
3.8%
19196 1
3.8%
17676 1
3.8%
10326 1
3.8%
3703 1
3.8%
2119 1
3.8%
1987 1
3.8%
196 1
3.8%
169 1
3.8%

2011
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1935.8846
Minimum0
Maximum12442
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:14.647233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median9.5
Q32372.5
95-th percentile9503.75
Maximum12442
Range12442
Interquartile range (IQR)2371.25

Descriptive statistics

Standard deviation3516.6551
Coefficient of variation (CV)1.8165623
Kurtosis2.7224258
Mean1935.8846
Median Absolute Deviation (MAD)9.5
Skewness1.8716393
Sum50333
Variance12366863
MonotonicityNot monotonic
2023-12-12T16:16:15.038207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6
23.1%
2 3
 
11.5%
30 1
 
3.8%
5489 1
 
3.8%
3 1
 
3.8%
1729 1
 
3.8%
61 1
 
3.8%
5761 1
 
3.8%
64 1
 
3.8%
7490 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 6
23.1%
1 1
 
3.8%
2 3
11.5%
3 1
 
3.8%
4 1
 
3.8%
6 1
 
3.8%
13 1
 
3.8%
30 1
 
3.8%
61 1
 
3.8%
64 1
 
3.8%
ValueCountFrequency (%)
12442 1
3.8%
10175 1
3.8%
7490 1
3.8%
5761 1
3.8%
5489 1
3.8%
4377 1
3.8%
2587 1
3.8%
1729 1
3.8%
95 1
3.8%
64 1
3.8%

2012
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)57.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3380.0385
Minimum0
Maximum42109
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:15.154642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2
Q31863.5
95-th percentile17432.75
Maximum42109
Range42109
Interquartile range (IQR)1863.5

Descriptive statistics

Standard deviation9098.2104
Coefficient of variation (CV)2.6917476
Kurtosis13.819551
Mean3380.0385
Median Absolute Deviation (MAD)2
Skewness3.5793104
Sum87881
Variance82777433
MonotonicityNot monotonic
2023-12-12T16:16:15.256174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 10
38.5%
2 3
 
11.5%
2338 1
 
3.8%
1 1
 
3.8%
440 1
 
3.8%
2385 1
 
3.8%
42109 1
 
3.8%
18 1
 
3.8%
18282 1
 
3.8%
99 1
 
3.8%
Other values (5) 5
19.2%
ValueCountFrequency (%)
0 10
38.5%
1 1
 
3.8%
2 3
 
11.5%
5 1
 
3.8%
18 1
 
3.8%
94 1
 
3.8%
99 1
 
3.8%
440 1
 
3.8%
2338 1
 
3.8%
2385 1
 
3.8%
ValueCountFrequency (%)
42109 1
3.8%
18282 1
3.8%
14885 1
3.8%
3822 1
3.8%
3397 1
3.8%
2385 1
3.8%
2338 1
3.8%
440 1
3.8%
99 1
3.8%
94 1
3.8%

2013
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct14
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13525.923
Minimum0
Maximum177144
Zeros10
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:15.374337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3
Q32069.75
95-th percentile55549
Maximum177144
Range177144
Interquartile range (IQR)2069.75

Descriptive statistics

Standard deviation36906.422
Coefficient of variation (CV)2.7285695
Kurtosis16.443064
Mean13525.923
Median Absolute Deviation (MAD)3
Skewness3.8650025
Sum351674
Variance1.362084 × 109
MonotonicityNot monotonic
2023-12-12T16:16:15.507194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
0 10
38.5%
3 2
 
7.7%
55549 2
 
7.7%
104 2
 
7.7%
2045 1
 
3.8%
1 1
 
3.8%
2078 1
 
3.8%
13251 1
 
3.8%
2 1
 
3.8%
26622 1
 
3.8%
Other values (4) 4
 
15.4%
ValueCountFrequency (%)
0 10
38.5%
1 1
 
3.8%
2 1
 
3.8%
3 2
 
7.7%
14 1
 
3.8%
104 2
 
7.7%
111 1
 
3.8%
2045 1
 
3.8%
2078 1
 
3.8%
13251 1
 
3.8%
ValueCountFrequency (%)
177144 1
3.8%
55549 2
7.7%
26622 1
3.8%
19094 1
3.8%
13251 1
3.8%
2078 1
3.8%
2045 1
3.8%
111 1
3.8%
104 2
7.7%
14 1
3.8%

2014
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)73.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4104.4615
Minimum0
Maximum44082
Zeros6
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:15.649626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.25
median17.5
Q32956.5
95-th percentile18251.75
Maximum44082
Range44082
Interquartile range (IQR)2955.25

Descriptive statistics

Standard deviation9753.0403
Coefficient of variation (CV)2.3762046
Kurtosis11.453596
Mean4104.4615
Median Absolute Deviation (MAD)17.5
Skewness3.2171238
Sum106716
Variance95121795
MonotonicityNot monotonic
2023-12-12T16:16:15.779912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 6
23.1%
4 2
 
7.7%
2 2
 
7.7%
19000 1
 
3.8%
1 1
 
3.8%
3289 1
 
3.8%
40 1
 
3.8%
16007 1
 
3.8%
5 1
 
3.8%
2993 1
 
3.8%
Other values (9) 9
34.6%
ValueCountFrequency (%)
0 6
23.1%
1 1
 
3.8%
2 2
 
7.7%
4 2
 
7.7%
5 1
 
3.8%
10 1
 
3.8%
25 1
 
3.8%
40 1
 
3.8%
45 1
 
3.8%
52 1
 
3.8%
ValueCountFrequency (%)
44082 1
3.8%
19000 1
3.8%
16007 1
3.8%
14765 1
3.8%
3289 1
3.8%
3083 1
3.8%
2993 1
3.8%
2847 1
3.8%
460 1
3.8%
52 1
3.8%

2015
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)46.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2131.5
Minimum0
Maximum28219
Zeros14
Zeros (%)53.8%
Negative0
Negative (%)0.0%
Memory size366.0 B
2023-12-12T16:16:15.898649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q329
95-th percentile10629
Maximum28219
Range28219
Interquartile range (IQR)29

Descriptive statistics

Standard deviation6010.0227
Coefficient of variation (CV)2.8196213
Kurtosis15.08155
Mean2131.5
Median Absolute Deviation (MAD)0
Skewness3.7224921
Sum55419
Variance36120373
MonotonicityNot monotonic
2023-12-12T16:16:16.011919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 14
53.8%
2 2
 
7.7%
6135 1
 
3.8%
3 1
 
3.8%
5241 1
 
3.8%
5 1
 
3.8%
12127 1
 
3.8%
32 1
 
3.8%
28219 1
 
3.8%
20 1
 
3.8%
Other values (2) 2
 
7.7%
ValueCountFrequency (%)
0 14
53.8%
2 2
 
7.7%
3 1
 
3.8%
5 1
 
3.8%
20 1
 
3.8%
32 1
 
3.8%
77 1
 
3.8%
3556 1
 
3.8%
5241 1
 
3.8%
6135 1
 
3.8%
ValueCountFrequency (%)
28219 1
3.8%
12127 1
3.8%
6135 1
3.8%
5241 1
3.8%
3556 1
3.8%
77 1
3.8%
32 1
3.8%
20 1
3.8%
5 1
3.8%
3 1
3.8%

Interactions

2023-12-12T16:16:11.808300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:06.476440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.313897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.025416image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.888389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.609237image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.318620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.102886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.915273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:06.634774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.424307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.103473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.975629image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.701748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.478341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.197678image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:12.003922image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:06.730806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.509882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.169452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.053749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.768106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.563577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.271789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:12.099756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:06.816193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.596327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.238232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.132001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.835172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.645561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.355725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:12.193606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:06.920883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.695030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.314865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.215656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.923226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.740417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.455050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:12.287867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.002354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.787992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.378500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.301770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.040472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.833278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.540507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:12.391909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.108021image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.874323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.452429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.438801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.146786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.939151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.643900image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:12.465225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.194610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:07.948118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:08.814688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:09.525780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:10.232089image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.020345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T16:16:11.725955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T16:16:16.112518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대구분중구분자금종류기준1991-2008 누적2009201020112012201320142015
대구분1.0001.0001.0000.0000.3400.5440.2640.4420.3060.3380.0000.000
중구분1.0001.0001.0000.0000.5850.0000.0000.0000.0000.0000.0000.000
자금종류1.0001.0001.0000.0000.2530.2480.2310.2420.2960.0000.2090.231
기준0.0000.0000.0001.0000.3950.5050.2580.6450.1970.5450.4080.258
1991-2008 누적0.3400.5850.2530.3951.0000.8900.7370.8410.7170.8380.8790.822
20090.5440.0000.2480.5050.8901.0000.9401.0000.9700.8321.0000.786
20100.2640.0000.2310.2580.7370.9401.0000.9390.8930.6150.8580.720
20110.4420.0000.2420.6450.8411.0000.9391.0000.9910.9981.0000.913
20120.3060.0000.2960.1970.7170.9700.8930.9911.0000.8340.9760.540
20130.3380.0000.0000.5450.8380.8320.6150.9980.8341.0000.8910.399
20140.0000.0000.2090.4080.8791.0000.8581.0000.9760.8911.0000.700
20150.0000.0000.2310.2580.8220.7860.7200.9130.5400.3990.7001.000
2023-12-12T16:16:16.276570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
대구분기준중구분
대구분1.0000.0000.890
기준0.0001.0000.000
중구분0.8900.0001.000
2023-12-12T16:16:16.384152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
1991-2008 누적2009201020112012201320142015대구분중구분기준
1991-2008 누적1.0000.5360.5200.5420.5090.3380.5520.2410.3110.2120.366
20090.5361.0000.9750.8210.9750.6120.8050.5000.3500.0000.323
20100.5200.9751.0000.8210.9860.5900.8200.3630.2920.0000.285
20110.5420.8210.8211.0000.8230.7450.9590.5180.2700.0000.414
20120.5090.9750.9860.8231.0000.6090.8250.3910.1830.0000.104
20130.3380.6120.5900.7450.6091.0000.7790.6140.2050.0000.351
20140.5520.8050.8200.9590.8250.7791.0000.4560.0000.0000.254
20150.2410.5000.3630.5180.3910.6140.4561.0000.0000.0000.285
대구분0.3110.3500.2920.2700.1830.2050.0000.0001.0000.8900.000
중구분0.2120.0000.0000.0000.0000.0000.0000.0000.8901.0000.000
기준0.3660.3230.2850.4140.1040.3510.2540.2850.0000.0001.000

Missing values

2023-12-12T16:16:12.581802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:16:12.748348image/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

대구분중구분자금종류기준1991-2008 누적2009201020112012201320142015
0경상사업남북사회문화교류인적왕래지원금액(백만원)399070030004600
1경상사업남북사회문화교류인적왕래지원건수660010020
2경상사업남북사회문화교류사회문화교류지원금액(백만원)310693029211925872338204528476135
3경상사업남북사회문화교류사회문화교류지원건수812321123
4경상사업인도적사업이산가족교류 지원금액(백만원)816532152198795440207830835241
5경상사업인도적사업이산가족교류 지원건수565322345
6경상사업인도적사업인도적 지원사업금액(백만원)15374752936719196101752385132511476512127
7경상사업인도적사업인도적 지원사업건수4374017422432
8경상사업남북경제협력경제분야협력기반조성금액(백만원)93762941461176761244242109266224408228219
9경상사업남북경제협력경제분야협력기반조성건수11013141318142520
대구분중구분자금종류기준1991-2008 누적2009201020112012201320142015
16융자사업남북경제협력(융자)교역경협사업 자금대출금액(백만원)283475154164156974901828255549190000
17융자사업남북경제협력(융자)교역경협사업 자금대출건수292181966499104450
18융자사업남북경제협력(융자)교역자금대출금액(백만원)55280841631243576114885029930
19융자사업남북경제협력(융자)교역자금대출건수310171696194050
20융자사업남북경제협력(융자)경협사업자금대출금액(백만원)2281957000103261729339755549160070
21융자사업남북경제협력(융자)경협사업자금대출건수8212735104400
22융자사업남북경제협력(융자)민족공동체회복지원 대출금액(백만원)33781785963703548938221909432893556
23융자사업남북경제협력(융자)민족공동체회복지원 대출건수272122312
24융자사업대북경수로사업경수로사업 대출금액(백만원)13743930000000
25융자사업대북경수로사업경수로사업 대출건수70000000