Overview

Dataset statistics

Number of variables5
Number of observations29
Missing cells28
Missing cells (%)19.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.3 KiB
Average record size in memory47.6 B

Variable types

Categorical1
Numeric3
Text1

Dataset

Description2008~2021년 캠코 자체 채무조정 및 희망모아 프로그램 채무조정 지원 현황(지원자, 지원금액)에 대한 데이터
Author한국자산관리공사
URLhttps://www.data.go.kr/data/15045735/fileData.do

Alerts

비고 has constant value ""Constant
해당연도 is highly overall correlated with 지원금액(억원)High correlation
지원자(천명) is highly overall correlated with 지원금액(억원)High correlation
지원금액(억원) is highly overall correlated with 해당연도 and 1 other fieldsHigh correlation
비고 has 28 (96.6%) missing valuesMissing
지원금액(억원) has unique valuesUnique
지원자(천명) has 2 (6.9%) zerosZeros
지원금액(억원) has 1 (3.4%) zerosZeros

Reproduction

Analysis started2023-12-12 23:43:59.320024
Analysis finished2023-12-12 23:44:00.370011
Duration1.05 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size364.0 B
희망모아
15 
캠코
14 

Length

Max length4
Median length4
Mean length3.0344828
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
희망모아 15
51.7%
캠코 14
48.3%

Length

2023-12-13T08:44:00.445063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T08:44:00.539112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
희망모아 15
51.7%
캠코 14
48.3%

해당연도
Real number (ℝ)

HIGH CORRELATION 

Distinct15
Distinct (%)51.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.2414
Minimum2007
Maximum2021
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T08:44:00.616521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2008
Q12011
median2014
Q32018
95-th percentile2020.6
Maximum2021
Range14
Interquartile range (IQR)7

Descriptive statistics

Standard deviation4.2649332
Coefficient of variation (CV)0.0021173893
Kurtosis-1.1899373
Mean2014.2414
Median Absolute Deviation (MAD)4
Skewness-0.01296439
Sum58413
Variance18.189655
MonotonicityNot monotonic
2023-12-13T08:44:00.724995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
2008 2
 
6.9%
2009 2
 
6.9%
2010 2
 
6.9%
2011 2
 
6.9%
2012 2
 
6.9%
2013 2
 
6.9%
2014 2
 
6.9%
2015 2
 
6.9%
2016 2
 
6.9%
2017 2
 
6.9%
Other values (5) 9
31.0%
ValueCountFrequency (%)
2007 1
3.4%
2008 2
6.9%
2009 2
6.9%
2010 2
6.9%
2011 2
6.9%
2012 2
6.9%
2013 2
6.9%
2014 2
6.9%
2015 2
6.9%
2016 2
6.9%
ValueCountFrequency (%)
2021 2
6.9%
2020 2
6.9%
2019 2
6.9%
2018 2
6.9%
2017 2
6.9%
2016 2
6.9%
2015 2
6.9%
2014 2
6.9%
2013 2
6.9%
2012 2
6.9%

지원자(천명)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)75.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.206897
Minimum0
Maximum303
Zeros2
Zeros (%)6.9%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T08:44:00.857992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0.4
Q17
median16
Q326
95-th percentile79
Maximum303
Range303
Interquartile range (IQR)19

Descriptive statistics

Standard deviation56.132738
Coefficient of variation (CV)1.9219001
Kurtosis21.839739
Mean29.206897
Median Absolute Deviation (MAD)9
Skewness4.4946655
Sum847
Variance3150.8842
MonotonicityNot monotonic
2023-12-13T08:44:00.970560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
7 3
 
10.3%
20 3
 
10.3%
0 2
 
6.9%
8 2
 
6.9%
16 2
 
6.9%
15 1
 
3.4%
46 1
 
3.4%
1 1
 
3.4%
26 1
 
3.4%
37 1
 
3.4%
Other values (12) 12
41.4%
ValueCountFrequency (%)
0 2
6.9%
1 1
 
3.4%
4 1
 
3.4%
6 1
 
3.4%
7 3
10.3%
8 2
6.9%
12 1
 
3.4%
13 1
 
3.4%
14 1
 
3.4%
15 1
 
3.4%
ValueCountFrequency (%)
303 1
3.4%
101 1
3.4%
46 1
3.4%
37 1
3.4%
35 1
3.4%
30 1
3.4%
28 1
3.4%
26 1
3.4%
25 1
3.4%
22 1
3.4%

지원금액(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct29
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2949.9
Minimum0
Maximum23792
Zeros1
Zeros (%)3.4%
Negative0
Negative (%)0.0%
Memory size393.0 B
2023-12-13T08:44:01.112182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile68.86
Q1960
median1844
Q33376
95-th percentile8260.2
Maximum23792
Range23792
Interquartile range (IQR)2416

Descriptive statistics

Standard deviation4532.7792
Coefficient of variation (CV)1.5365874
Kurtosis16.769314
Mean2949.9
Median Absolute Deviation (MAD)1377
Skewness3.8218245
Sum85547.1
Variance20546087
MonotonicityNot monotonic
2023-12-13T08:44:01.247397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
1260.0 1
 
3.4%
3050.0 1
 
3.4%
0.0 1
 
3.4%
0.1 1
 
3.4%
172.0 1
 
3.4%
984.0 1
 
3.4%
1678.0 1
 
3.4%
1967.0 1
 
3.4%
1912.0 1
 
3.4%
2165.0 1
 
3.4%
Other values (19) 19
65.5%
ValueCountFrequency (%)
0.0 1
3.4%
0.1 1
3.4%
172.0 1
3.4%
403.0 1
3.4%
436.0 1
3.4%
448.0 1
3.4%
467.0 1
3.4%
960.0 1
3.4%
984.0 1
3.4%
1260.0 1
3.4%
ValueCountFrequency (%)
23792.0 1
3.4%
10159.0 1
3.4%
5412.0 1
3.4%
4849.0 1
3.4%
4497.0 1
3.4%
3905.0 1
3.4%
3400.0 1
3.4%
3376.0 1
3.4%
3050.0 1
3.4%
2379.0 1
3.4%

비고
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing28
Missing (%)96.6%
Memory size364.0 B
2023-12-13T08:44:01.382694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length12
Mean length12
Min length12

Characters and Unicode

Total characters12
Distinct characters10
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

Unique1 ?
Unique (%)100.0%

Sample

1st row2008년 이전 누적값
ValueCountFrequency (%)
2008년 1
33.3%
이전 1
33.3%
누적값 1
33.3%
2023-12-13T08:44:01.629375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2
16.7%
2
16.7%
2 1
8.3%
8 1
8.3%
년 1
8.3%
이 1
8.3%
전 1
8.3%
누 1
8.3%
적 1
8.3%
값 1
8.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
50.0%
Decimal Number 4
33.3%
Space Separator 2
 
16.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
년 1
16.7%
이 1
16.7%
전 1
16.7%
누 1
16.7%
적 1
16.7%
값 1
16.7%
Decimal Number
ValueCountFrequency (%)
0 2
50.0%
2 1
25.0%
8 1
25.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 6
50.0%
Hangul 6
50.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
년 1
16.7%
이 1
16.7%
전 1
16.7%
누 1
16.7%
적 1
16.7%
값 1
16.7%
Common
ValueCountFrequency (%)
0 2
33.3%
2
33.3%
2 1
16.7%
8 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 6
50.0%
Hangul 6
50.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2
33.3%
2
33.3%
2 1
16.7%
8 1
16.7%
Hangul
ValueCountFrequency (%)
년 1
16.7%
이 1
16.7%
전 1
16.7%
누 1
16.7%
적 1
16.7%
값 1
16.7%

Interactions

2023-12-13T08:43:59.971859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:43:59.450867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:43:59.723047image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:44:00.058811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:43:59.537934image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:43:59.816908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:44:00.140301image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:43:59.636571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T08:43:59.899786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T08:44:01.734855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분해당연도지원자(천명)지원금액(억원)
구분1.0000.0000.0000.100
해당연도0.0001.0000.5190.641
지원자(천명)0.0000.5191.0000.918
지원금액(억원)0.1000.6410.9181.000
2023-12-13T08:44:01.827140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
해당연도지원자(천명)지원금액(억원)구분
해당연도1.000-0.362-0.7520.000
지원자(천명)-0.3621.0000.7260.000
지원금액(억원)-0.7520.7261.0000.090
구분0.0000.0000.0901.000

Missing values

2023-12-13T08:44:00.230169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T08:44:00.332336image/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

구분해당연도지원자(천명)지원금액(억원)비고
0캠코2008151260.0<NA>
1캠코2009143050.0<NA>
2캠코2010123905.0<NA>
3캠코201181844.0<NA>
4캠코201272039.0<NA>
5캠코2013133376.0<NA>
6캠코20144467.0<NA>
7캠코20156403.0<NA>
8캠코20168448.0<NA>
9캠코20177436.0<NA>
구분해당연도지원자(천명)지원금액(억원)비고
19희망모아2012162379.0<NA>
20희망모아2013375412.0<NA>
21희망모아2014262165.0<NA>
22희망모아2015201912.0<NA>
23희망모아2016201967.0<NA>
24희망모아2017161678.0<NA>
25희망모아20187984.0<NA>
26희망모아20191172.0<NA>
27희망모아202000.1<NA>
28희망모아202100.0<NA>