Overview

Dataset statistics

Number of variables5
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory48.1 B

Variable types

Numeric4
Categorical1

Dataset

Description음주폐해예방 관련 지표- 음주폐해 > 알코올 사용에 의한 정신 및 행동 장애 사망자수 및 구성비(성별) 지표데이터를 제공합니다.
Author한국건강증진개발원
URLhttps://www.data.go.kr/data/15050210/fileData.do

Alerts

계 is highly overall correlated with 남자 and 2 other fieldsHigh correlation
남자 is highly overall correlated with 계 and 2 other fieldsHigh correlation
여자 is highly overall correlated with 계 and 2 other fieldsHigh correlation
구분 is highly overall correlated with 계 and 2 other fieldsHigh correlation

Reproduction

Analysis started2023-12-12 08:58:17.011006
Analysis finished2023-12-12 08:58:19.954337
Duration2.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct16
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2012.5
Minimum2005
Maximum2020
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:58:20.045225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2005
5-th percentile2005.55
Q12008.75
median2012.5
Q32016.25
95-th percentile2019.45
Maximum2020
Range15
Interquartile range (IQR)7.5

Descriptive statistics

Standard deviation4.6835333
Coefficient of variation (CV)0.0023272215
Kurtosis-1.2083083
Mean2012.5
Median Absolute Deviation (MAD)4
Skewness0
Sum64400
Variance21.935484
MonotonicityIncreasing
2023-12-12T17:58:20.238259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2005 2
 
6.2%
2014 2
 
6.2%
2020 2
 
6.2%
2019 2
 
6.2%
2018 2
 
6.2%
2017 2
 
6.2%
2016 2
 
6.2%
2015 2
 
6.2%
2013 2
 
6.2%
2006 2
 
6.2%
Other values (6) 12
37.5%
ValueCountFrequency (%)
2005 2
6.2%
2006 2
6.2%
2007 2
6.2%
2008 2
6.2%
2009 2
6.2%
2010 2
6.2%
2011 2
6.2%
2012 2
6.2%
2013 2
6.2%
2014 2
6.2%
ValueCountFrequency (%)
2020 2
6.2%
2019 2
6.2%
2018 2
6.2%
2017 2
6.2%
2016 2
6.2%
2015 2
6.2%
2014 2
6.2%
2013 2
6.2%
2012 2
6.2%
2011 2
6.2%

구분
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)6.2%
Missing0
Missing (%)0.0%
Memory size388.0 B
사망자수
16 
비율
16 

Length

Max length4
Median length3
Mean length3
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row사망자수
2nd row비율
3rd row사망자수
4th row비율
5th row사망자수

Common Values

ValueCountFrequency (%)
사망자수 16
50.0%
비율 16
50.0%

Length

2023-12-12T17:58:20.411505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T17:58:20.578226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사망자수 16
50.0%
비율 16
50.0%

계
Real number (ℝ)

HIGH CORRELATION 

Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean481.40625
Minimum100
Maximum1089
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:58:20.729405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile100
Q1100
median409.5
Q3847.5
95-th percentile1044.95
Maximum1089
Range989
Interquartile range (IQR)747.5

Descriptive statistics

Standard deviation397.49651
Coefficient of variation (CV)0.82569869
Kurtosis-1.8762081
Mean481.40625
Median Absolute Deviation (MAD)309.5
Skewness0.15973373
Sum15405
Variance158003.47
MonotonicityNot monotonic
2023-12-12T17:58:20.924377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100 16
50.0%
1040 1
 
3.1%
1089 1
 
3.1%
916 1
 
3.1%
940 1
 
3.1%
984 1
 
3.1%
846 1
 
3.1%
783 1
 
3.1%
747 1
 
3.1%
728 1
 
3.1%
Other values (7) 7
21.9%
ValueCountFrequency (%)
100 16
50.0%
719 1
 
3.1%
721 1
 
3.1%
728 1
 
3.1%
747 1
 
3.1%
749 1
 
3.1%
766 1
 
3.1%
783 1
 
3.1%
846 1
 
3.1%
852 1
 
3.1%
ValueCountFrequency (%)
1089 1
3.1%
1051 1
3.1%
1040 1
3.1%
984 1
3.1%
940 1
3.1%
916 1
3.1%
874 1
3.1%
852 1
3.1%
846 1
3.1%
783 1
3.1%

남자
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean438.25062
Minimum88.98
Maximum969
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:58:21.083579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum88.98
5-th percentile89.076
Q191.5275
median370.54
Q3763.75
95-th percentile963.9
Maximum969
Range880.02
Interquartile range (IQR)672.2225

Descriptive statistics

Standard deviation361.6053
Coefficient of variation (CV)0.82511075
Kurtosis-1.8842067
Mean438.25062
Median Absolute Deviation (MAD)281.495
Skewness0.15549585
Sum14024.02
Variance130758.39
MonotonicityNot monotonic
2023-12-12T17:58:21.275969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
689.0 2
 
6.2%
963.0 1
 
3.1%
89.01 1
 
3.1%
88.98 1
 
3.1%
969.0 1
 
3.1%
90.07 1
 
3.1%
825.0 1
 
3.1%
89.78 1
 
3.1%
844.0 1
 
3.1%
90.04 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
88.98 1
3.1%
89.01 1
3.1%
89.13 1
3.1%
89.78 1
3.1%
90.04 1
3.1%
90.07 1
3.1%
90.55 1
3.1%
90.68 1
3.1%
91.81 1
3.1%
91.82 1
3.1%
ValueCountFrequency (%)
969.0 1
3.1%
965.0 1
3.1%
963.0 1
3.1%
886.0 1
3.1%
844.0 1
3.1%
825.0 1
3.1%
809.0 1
3.1%
793.0 1
3.1%
754.0 1
3.1%
710.0 1
3.1%

여자
Real number (ℝ)

HIGH CORRELATION 

Distinct31
Distinct (%)96.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean43.175938
Minimum6.92
Maximum120
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T17:58:21.446747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6.92
5-th percentile7.364
Q19.095
median33.51
Q374.75
95-th percentile96.9
Maximum120
Range113.08
Interquartile range (IQR)65.655

Descriptive statistics

Standard deviation37.073619
Coefficient of variation (CV)0.8586639
Kurtosis-1.3881957
Mean43.175938
Median Absolute Deviation (MAD)25.785
Skewness0.39620854
Sum1381.63
Variance1374.4532
MonotonicityNot monotonic
2023-12-12T17:58:21.654098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
59.0 2
 
6.2%
77.0 1
 
3.1%
58.0 1
 
3.1%
11.02 1
 
3.1%
120.0 1
 
3.1%
9.93 1
 
3.1%
91.0 1
 
3.1%
10.21 1
 
3.1%
96.0 1
 
3.1%
9.96 1
 
3.1%
Other values (21) 21
65.6%
ValueCountFrequency (%)
6.92 1
3.1%
7.32 1
3.1%
7.4 1
3.1%
7.44 1
3.1%
8.01 1
3.1%
8.18 1
3.1%
8.19 1
3.1%
8.42 1
3.1%
9.32 1
3.1%
9.45 1
3.1%
ValueCountFrequency (%)
120.0 1
3.1%
98.0 1
3.1%
96.0 1
3.1%
92.0 1
3.1%
91.0 1
3.1%
86.0 1
3.1%
80.0 1
3.1%
77.0 1
3.1%
74.0 1
3.1%
67.0 1
3.1%

Interactions

2023-12-12T17:58:19.114509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.292540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.961123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.590470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:19.258753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.508865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.129683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.769403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:19.366428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.635434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.277945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.879535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:19.486805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:17.832867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.438525image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T17:58:18.995022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T17:58:21.798597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도구분계남자여자
연도1.0000.0000.0000.0000.000
구분0.0001.0001.0001.0001.000
계0.0001.0001.0000.9720.840
남자0.0001.0000.9721.0000.772
여자0.0001.0000.8400.7721.000
2023-12-12T17:58:21.927050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도계남자여자구분
연도1.0000.027-0.1760.3560.000
계0.0271.0000.9340.8890.949
남자-0.1760.9341.0000.7030.949
여자0.3560.8890.7031.0000.913
구분0.0000.9490.9490.9131.000

Missing values

2023-12-12T17:58:19.669326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T17:58:19.877441image/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

연도구분계남자여자
02005사망자수1040963.077.0
12005비율10092.67.4
22006사망자수1051965.086.0
32006비율10091.828.18
42007사망자수874809.065.0
52007비율10092.567.44
62008사망자수852793.059.0
72008비율10093.086.92
82009사망자수749689.060.0
92009비율10091.998.01
연도구분계남자여자
222016사망자수846754.092.0
232016비율10089.1310.87
242017사망자수984886.098.0
252017비율10090.049.96
262018사망자수940844.096.0
272018비율10089.7810.21
282019사망자수916825.091.0
292019비율10090.079.93
302020사망자수1089969.0120.0
312020비율10088.9811.02