Overview

Dataset statistics

Number of variables4
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory38.4 B

Variable types

Numeric3
Categorical1

Dataset

Description한국보훈복지의료공단 광주보훈병원에서 제공하는 년도별 국가유공자 진료인원 데이터로 년도,구분,자연인,연인원 순으로 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15067087/fileData.do

Alerts

자연인 is highly overall correlated with 구분High correlation
연인원 is highly overall correlated with 구분High correlation
구분 is highly overall correlated with 자연인 and 1 other fieldsHigh correlation
연인원 has 3 (7.7%) zerosZeros

Reproduction

Analysis started2023-12-12 22:27:47.238208
Analysis finished2023-12-12 22:27:48.211896
Duration0.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2016
Minimum2010
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:27:48.260929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2010
5-th percentile2010
Q12013
median2016
Q32019
95-th percentile2022
Maximum2022
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.79057
Coefficient of variation (CV)0.0018802431
Kurtosis-1.2145002
Mean2016
Median Absolute Deviation (MAD)3
Skewness0
Sum78624
Variance14.368421
MonotonicityIncreasing
2023-12-13T07:27:48.377108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=13)
ValueCountFrequency (%)
2010 3
 
7.7%
2011 3
 
7.7%
2012 3
 
7.7%
2013 3
 
7.7%
2014 3
 
7.7%
2015 3
 
7.7%
2016 3
 
7.7%
2017 3
 
7.7%
2018 3
 
7.7%
2019 3
 
7.7%
Other values (3) 9
23.1%
ValueCountFrequency (%)
2010 3
7.7%
2011 3
7.7%
2012 3
7.7%
2013 3
7.7%
2014 3
7.7%
2015 3
7.7%
2016 3
7.7%
2017 3
7.7%
2018 3
7.7%
2019 3
7.7%
ValueCountFrequency (%)
2022 3
7.7%
2021 3
7.7%
2020 3
7.7%
2019 3
7.7%
2018 3
7.7%
2017 3
7.7%
2016 3
7.7%
2015 3
7.7%
2014 3
7.7%
2013 3
7.7%

구분
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
감면
13 
국비
13 
일반
13 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row감면
2nd row국비
3rd row일반
4th row감면
5th row국비

Common Values

ValueCountFrequency (%)
감면 13
33.3%
국비 13
33.3%
일반 13
33.3%

Length

2023-12-13T07:27:48.526125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:27:48.642055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
감면 13
33.3%
국비 13
33.3%
일반 13
33.3%

자연인
Real number (ℝ)

HIGH CORRELATION 

Distinct38
Distinct (%)97.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16037.385
Minimum1
Maximum27958
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:27:48.743788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.8
Q114548
median15773
Q319571
95-th percentile23139.7
Maximum27958
Range27957
Interquartile range (IQR)5023

Descriptive statistics

Standard deviation5930.994
Coefficient of variation (CV)0.36982302
Kurtosis2.584821
Mean16037.385
Median Absolute Deviation (MAD)1323
Skewness-1.1226063
Sum625458
Variance35176690
MonotonicityNot monotonic
2023-12-13T07:27:48.892257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
1 2
 
5.1%
14575 1
 
2.6%
23072 1
 
2.6%
16620 1
 
2.6%
14524 1
 
2.6%
22822 1
 
2.6%
16419 1
 
2.6%
14572 1
 
2.6%
23749 1
 
2.6%
13459 1
 
2.6%
Other values (28) 28
71.8%
ValueCountFrequency (%)
1 2
5.1%
3 1
2.6%
12857 1
2.6%
13344 1
2.6%
13459 1
2.6%
13460 1
2.6%
14450 1
2.6%
14478 1
2.6%
14524 1
2.6%
14572 1
2.6%
ValueCountFrequency (%)
27958 1
2.6%
23749 1
2.6%
23072 1
2.6%
22938 1
2.6%
22822 1
2.6%
22408 1
2.6%
22354 1
2.6%
21938 1
2.6%
20049 1
2.6%
19687 1
2.6%

연인원
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct37
Distinct (%)94.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean214520.67
Minimum0
Maximum437805
Zeros3
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:27:49.011956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1112951
median176272
Q3375151.5
95-th percentile429328.6
Maximum437805
Range437805
Interquartile range (IQR)262200.5

Descriptive statistics

Standard deviation141230.05
Coefficient of variation (CV)0.65835172
Kurtosis-1.1004545
Mean214520.67
Median Absolute Deviation (MAD)71920
Skewness0.50759782
Sum8366306
Variance1.9945927 × 1010
MonotonicityNot monotonic
2023-12-13T07:27:49.137367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=37)
ValueCountFrequency (%)
0 3
 
7.7%
175811 1
 
2.6%
114167 1
 
2.6%
199390 1
 
2.6%
429314 1
 
2.6%
115131 1
 
2.6%
198171 1
 
2.6%
423627 1
 
2.6%
116970 1
 
2.6%
379919 1
 
2.6%
Other values (27) 27
69.2%
ValueCountFrequency (%)
0 3
7.7%
99129 1
 
2.6%
102363 1
 
2.6%
102438 1
 
2.6%
103677 1
 
2.6%
104352 1
 
2.6%
109254 1
 
2.6%
111855 1
 
2.6%
114047 1
 
2.6%
114167 1
 
2.6%
ValueCountFrequency (%)
437805 1
2.6%
429460 1
2.6%
429314 1
2.6%
428764 1
2.6%
427128 1
2.6%
426529 1
2.6%
423674 1
2.6%
423627 1
2.6%
417355 1
2.6%
379919 1
2.6%

Interactions

2023-12-13T07:27:47.831269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.368345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.609253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.905191image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.469748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.694473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.976783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.542884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:27:47.764006image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:27:49.228670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도구분자연인연인원
년도1.0000.0000.0000.000
구분0.0001.0000.7080.935
자연인0.0000.7081.0000.935
연인원0.0000.9350.9351.000
2023-12-13T07:27:49.317984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도자연인연인원구분
년도1.0000.0940.1800.000
자연인0.0941.000-0.3270.587
연인원0.180-0.3271.0000.899
구분0.0000.5870.8991.000

Missing values

2023-12-13T07:27:48.081747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:27:48.181547image/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

년도구분자연인연인원
02010감면10
12010국비10
22010일반30
32011감면14939168306
42011국비14478417355
52011일반19455102438
62012감면15773175678
72012국비15393437805
82012일반19241103677
92013감면15910178824
년도구분자연인연인원
292019일반23749116970
302020감면14575175811
312020국비13459379919
322020일반2004999129
332021감면14450176272
342021국비13344370384
352021일반27958124418
362022감면13460140837
372022국비12857318449
382022일반21938102363