Overview

Dataset statistics

Number of variables4
Number of observations23
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory960.0 B
Average record size in memory41.7 B

Variable types

Numeric4

Dataset

Description근로복지공단의 산재 진폐요양환자 관련정보입니다.(2000~2022) (연도, 병상수, 입원진료, 통원진료)
URLhttps://www.data.go.kr/data/3077246/fileData.do

Alerts

연 도 is highly overall correlated with 병상수(개) and 2 other fieldsHigh correlation
병상수(개) is highly overall correlated with 연 도 and 2 other fieldsHigh correlation
진 료 구 분_입원(명) is highly overall correlated with 연 도 and 2 other fieldsHigh correlation
진 료 구 분_통원(명) is highly overall correlated with 연 도 and 2 other fieldsHigh correlation
연 도 has unique valuesUnique
진 료 구 분_입원(명) has unique valuesUnique

Reproduction

Analysis started2023-12-12 16:48:50.499785
Analysis finished2023-12-12 16:48:52.631284
Duration2.13 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연 도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2011
Minimum2000
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:48:52.704180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2001.1
Q12005.5
median2011
Q32016.5
95-th percentile2020.9
Maximum2022
Range22
Interquartile range (IQR)11

Descriptive statistics

Standard deviation6.78233
Coefficient of variation (CV)0.0033726156
Kurtosis-1.2
Mean2011
Median Absolute Deviation (MAD)6
Skewness0
Sum46253
Variance46
MonotonicityStrictly increasing
2023-12-13T01:48:52.855741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2000 1
 
4.3%
2001 1
 
4.3%
2022 1
 
4.3%
2021 1
 
4.3%
2020 1
 
4.3%
2019 1
 
4.3%
2018 1
 
4.3%
2017 1
 
4.3%
2016 1
 
4.3%
2015 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
2000 1
4.3%
2001 1
4.3%
2002 1
4.3%
2003 1
4.3%
2004 1
4.3%
2005 1
4.3%
2006 1
4.3%
2007 1
4.3%
2008 1
4.3%
2009 1
4.3%
ValueCountFrequency (%)
2022 1
4.3%
2021 1
4.3%
2020 1
4.3%
2019 1
4.3%
2018 1
4.3%
2017 1
4.3%
2016 1
4.3%
2015 1
4.3%
2014 1
4.3%
2013 1
4.3%

병상수(개)
Real number (ℝ)

HIGH CORRELATION 

Distinct19
Distinct (%)82.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3265.2174
Minimum1722
Maximum4765
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:48:53.012028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1722
5-th percentile1734.4
Q12835
median3242
Q33773.5
95-th percentile4357.1
Maximum4765
Range3043
Interquartile range (IQR)938.5

Descriptive statistics

Standard deviation820.35103
Coefficient of variation (CV)0.25123933
Kurtosis-0.1654072
Mean3265.2174
Median Absolute Deviation (MAD)425
Skewness-0.26607812
Sum75100
Variance672975.81
MonotonicityNot monotonic
2023-12-13T01:48:53.148674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
2992 3
 
13.0%
2835 2
 
8.7%
1722 2
 
8.7%
3242 1
 
4.3%
1846 1
 
4.3%
3226 1
 
4.3%
2817 1
 
4.3%
2716 1
 
4.3%
3425 1
 
4.3%
3435 1
 
4.3%
Other values (9) 9
39.1%
ValueCountFrequency (%)
1722 2
8.7%
1846 1
 
4.3%
2716 1
 
4.3%
2817 1
 
4.3%
2835 2
8.7%
2992 3
13.0%
3226 1
 
4.3%
3242 1
 
4.3%
3412 1
 
4.3%
3425 1
 
4.3%
ValueCountFrequency (%)
4765 1
4.3%
4366 1
4.3%
4277 1
4.3%
4267 1
4.3%
4151 1
4.3%
3779 1
4.3%
3768 1
4.3%
3518 1
4.3%
3435 1
4.3%
3425 1
4.3%

진 료 구 분_입원(명)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct23
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2403.8696
Minimum1148
Maximum3211
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:48:53.271723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1148
5-th percentile1272.8
Q11927
median2515
Q32918
95-th percentile3189.6
Maximum3211
Range2063
Interquartile range (IQR)991

Descriptive statistics

Standard deviation647.40372
Coefficient of variation (CV)0.26931732
Kurtosis-0.87032228
Mean2403.8696
Median Absolute Deviation (MAD)522
Skewness-0.53401423
Sum55289
Variance419131.57
MonotonicityNot monotonic
2023-12-13T01:48:53.464702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
2430 1
 
4.3%
2628 1
 
4.3%
1148 1
 
4.3%
1256 1
 
4.3%
1424 1
 
4.3%
1637 1
 
4.3%
1731 1
 
4.3%
1861 1
 
4.3%
1993 1
 
4.3%
2118 1
 
4.3%
Other values (13) 13
56.5%
ValueCountFrequency (%)
1148 1
4.3%
1256 1
4.3%
1424 1
4.3%
1637 1
4.3%
1731 1
4.3%
1861 1
4.3%
1993 1
4.3%
2118 1
4.3%
2294 1
4.3%
2421 1
4.3%
ValueCountFrequency (%)
3211 1
4.3%
3192 1
4.3%
3168 1
4.3%
3093 1
4.3%
3076 1
4.3%
2944 1
4.3%
2892 1
4.3%
2855 1
4.3%
2775 1
4.3%
2628 1
4.3%

진 료 구 분_통원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)95.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean648.34783
Minimum308
Maximum1001
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size339.0 B
2023-12-13T01:48:53.605307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum308
5-th percentile343.5
Q1465.5
median656
Q3820.5
95-th percentile981.3
Maximum1001
Range693
Interquartile range (IQR)355

Descriptive statistics

Standard deviation219.11861
Coefficient of variation (CV)0.33796459
Kurtosis-1.351946
Mean648.34783
Median Absolute Deviation (MAD)175
Skewness-0.009317094
Sum14912
Variance48012.964
MonotonicityNot monotonic
2023-12-13T01:48:53.751005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
810 2
 
8.7%
308 1
 
4.3%
738 1
 
4.3%
1001 1
 
4.3%
989 1
 
4.3%
912 1
 
4.3%
831 1
 
4.3%
852 1
 
4.3%
872 1
 
4.3%
785 1
 
4.3%
Other values (12) 12
52.2%
ValueCountFrequency (%)
308 1
4.3%
340 1
4.3%
375 1
4.3%
387 1
4.3%
396 1
4.3%
422 1
4.3%
509 1
4.3%
536 1
4.3%
542 1
4.3%
546 1
4.3%
ValueCountFrequency (%)
1001 1
4.3%
989 1
4.3%
912 1
4.3%
872 1
4.3%
852 1
4.3%
831 1
4.3%
810 2
8.7%
785 1
4.3%
747 1
4.3%
738 1
4.3%

Interactions

2023-12-13T01:48:52.019810image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:50.595713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:50.930096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.285397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:52.118279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:50.669866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.013032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.379281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:52.228168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:50.753759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.098230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.845176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:52.323150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:50.838695image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.184441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:48:51.917102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:48:53.861412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 도병상수(개)진 료 구 분_입원(명)진 료 구 분_통원(명)
연 도1.0000.7830.9530.757
병상수(개)0.7831.0000.6070.617
진 료 구 분_입원(명)0.9530.6071.0000.727
진 료 구 분_통원(명)0.7570.6170.7271.000
2023-12-13T01:48:53.955060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연 도병상수(개)진 료 구 분_입원(명)진 료 구 분_통원(명)
연 도1.000-0.527-0.7920.979
병상수(개)-0.5271.0000.880-0.536
진 료 구 분_입원(명)-0.7920.8801.000-0.768
진 료 구 분_통원(명)0.979-0.536-0.7681.000

Missing values

2023-12-13T01:48:52.502527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:48:52.595000image/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 도병상수(개)진 료 구 분_입원(명)진 료 구 분_통원(명)
0200028352430308
1200128352628340
2200232422775396
3200335182892387
4200437792944375
5200537683076422
6200642673093509
7200742773211542
8200847653192548
9200943663168546
연 도병상수(개)진 료 구 분_입원(명)진 료 구 분_통원(명)
13201334252421747
14201427162294785
15201528172118810
16201629921993872
17201729921861852
18201829921731810
19201932261637831
20202018461424912
21202117221256989
222022172211481001