Overview

Dataset statistics

Number of variables4
Number of observations266
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory35.5 B

Variable types

Numeric3
Categorical1

Dataset

Description한국에너지재단에서 시행하는 저소득층 에너지효율개선사업 지원통계에 대한 연도별 및 시도별 지원금액, 가구수 통계
Author재단법인한국에너지재단
URLhttps://www.data.go.kr/data/15106689/fileData.do

Alerts

지원금 is highly overall correlated with 가구수High correlation
가구수 is highly overall correlated with 지원금High correlation
지원금 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:13:40.541389
Analysis finished2023-12-12 06:13:41.767809
Duration1.23 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

Distinct16
Distinct (%)6.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014.6128
Minimum2007
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:13:41.825163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2007
5-th percentile2007
Q12011
median2015
Q32019
95-th percentile2022
Maximum2022
Range15
Interquartile range (IQR)8

Descriptive statistics

Standard deviation4.600704
Coefficient of variation (CV)0.0022836667
Kurtosis-1.1953557
Mean2014.6128
Median Absolute Deviation (MAD)4
Skewness-0.034581757
Sum535887
Variance21.166478
MonotonicityIncreasing
2023-12-12T15:13:41.933736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2015 17
 
6.4%
2017 17
 
6.4%
2022 17
 
6.4%
2021 17
 
6.4%
2020 17
 
6.4%
2019 17
 
6.4%
2013 17
 
6.4%
2014 17
 
6.4%
2018 17
 
6.4%
2016 17
 
6.4%
Other values (6) 96
36.1%
ValueCountFrequency (%)
2007 16
6.0%
2008 16
6.0%
2009 16
6.0%
2010 16
6.0%
2011 16
6.0%
2012 16
6.0%
2013 17
6.4%
2014 17
6.4%
2015 17
6.4%
2016 17
6.4%
ValueCountFrequency (%)
2022 17
6.4%
2021 17
6.4%
2020 17
6.4%
2019 17
6.4%
2018 17
6.4%
2017 17
6.4%
2016 17
6.4%
2015 17
6.4%
2014 17
6.4%
2013 17
6.4%

지역
Categorical

Distinct17
Distinct (%)6.4%
Missing0
Missing (%)0.0%
Memory size2.2 KiB
서울
 
16
부산
 
16
대구
 
16
인천
 
16
광주
 
16
Other values (12)
186 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울
2nd row부산
3rd row대구
4th row인천
5th row광주

Common Values

ValueCountFrequency (%)
서울 16
 
6.0%
부산 16
 
6.0%
대구 16
 
6.0%
인천 16
 
6.0%
광주 16
 
6.0%
대전 16
 
6.0%
울산 16
 
6.0%
경기 16
 
6.0%
강원 16
 
6.0%
충북 16
 
6.0%
Other values (7) 106
39.8%

Length

2023-12-12T15:13:42.065044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울 16
 
6.0%
충북 16
 
6.0%
제주 16
 
6.0%
경남 16
 
6.0%
경북 16
 
6.0%
전남 16
 
6.0%
전북 16
 
6.0%
충남 16
 
6.0%
강원 16
 
6.0%
부산 16
 
6.0%
Other values (7) 106
39.8%

지원금
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct266
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.645859 × 109
Minimum32000000
Maximum1.6574 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:13:42.204556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum32000000
5-th percentile2.2426021 × 108
Q19.6672588 × 108
median1.9424043 × 109
Q33.6735873 × 109
95-th percentile7.2104024 × 109
Maximum1.6574 × 1010
Range1.6542 × 1010
Interquartile range (IQR)2.7068614 × 109

Descriptive statistics

Standard deviation2.2777611 × 109
Coefficient of variation (CV)0.86087772
Kurtosis4.9776104
Mean2.645859 × 109
Median Absolute Deviation (MAD)1.2337965 × 109
Skewness1.6912289
Sum7.037985 × 1011
Variance5.1881957 × 1018
MonotonicityNot monotonic
2023-12-12T15:13:42.341036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1801000000 1
 
0.4%
3800659053 1
 
0.4%
1440280271 1
 
0.4%
492257782 1
 
0.4%
41399230 1
 
0.4%
6857251077 1
 
0.4%
2276693223 1
 
0.4%
1326825911 1
 
0.4%
1519340295 1
 
0.4%
1559521483 1
 
0.4%
Other values (256) 256
96.2%
ValueCountFrequency (%)
32000000 1
0.4%
32519698 1
0.4%
33035173 1
0.4%
41399230 1
0.4%
52888280 1
0.4%
54949337 1
0.4%
59729366 1
0.4%
74365822 1
0.4%
104000000 1
0.4%
113866746 1
0.4%
ValueCountFrequency (%)
16574000000 1
0.4%
9627931190 1
0.4%
8826529420 1
0.4%
8645484058 1
0.4%
8438990449 1
0.4%
8366641776 1
0.4%
8175227338 1
0.4%
7773667081 1
0.4%
7716296180 1
0.4%
7656589183 1
0.4%

가구수
Real number (ℝ)

HIGH CORRELATION 

Distinct258
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2201.7331
Minimum17
Maximum12406
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.5 KiB
2023-12-12T15:13:42.511763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum17
5-th percentile207.75
Q1908.5
median1791
Q33029
95-th percentile5795.5
Maximum12406
Range12389
Interquartile range (IQR)2120.5

Descriptive statistics

Standard deviation1765.744
Coefficient of variation (CV)0.80197916
Kurtosis5.05648
Mean2201.7331
Median Absolute Deviation (MAD)994.5
Skewness1.7377079
Sum585661
Variance3117852
MonotonicityNot monotonic
2023-12-12T15:13:42.671339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1840 2
 
0.8%
26 2
 
0.8%
3824 2
 
0.8%
27 2
 
0.8%
1777 2
 
0.8%
1478 2
 
0.8%
2392 2
 
0.8%
1273 2
 
0.8%
2687 1
 
0.4%
2443 1
 
0.4%
Other values (248) 248
93.2%
ValueCountFrequency (%)
17 1
0.4%
23 1
0.4%
26 2
0.8%
27 2
0.8%
32 1
0.4%
84 1
0.4%
87 1
0.4%
89 1
0.4%
115 1
0.4%
117 1
0.4%
ValueCountFrequency (%)
12406 1
0.4%
9102 1
0.4%
8252 1
0.4%
7472 1
0.4%
7451 1
0.4%
7256 1
0.4%
6884 1
0.4%
6585 1
0.4%
6489 1
0.4%
6021 1
0.4%

Interactions

2023-12-12T15:13:41.322742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:40.686686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:41.015170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:41.418015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:40.792418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:41.135537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:41.526195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:40.907969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:13:41.230076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:13:42.764094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지역지원금가구수
연도1.0000.0000.3140.231
지역0.0001.0000.5840.660
지원금0.3140.5841.0000.774
가구수0.2310.6600.7741.000
2023-12-12T15:13:42.856065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도지원금가구수지역
연도1.0000.443-0.0820.000
지원금0.4431.0000.7880.305
가구수-0.0820.7881.0000.329
지역0.0000.3050.3291.000

Missing values

2023-12-12T15:13:41.651808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:13:41.735257image/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

연도지역지원금가구수
02007서울18010000003641
12007부산9870000002310
22007대구5830000001291
32007인천312000000617
42007광주335000000657
52007대전337000000553
62007울산145000000260
72007경기9910000001777
82007강원346000000515
92007충북299000000355
연도지역지원금가구수
2562022세종3251969823
2572022경기84389904494768
2582022강원61574113413143
2592022충북30153012931612
2602022충남29592742131623
2612022전북36672415192054
2622022전남58446213123017
2632022경북77736670814096
2642022경남70031285263738
2652022제주343258475213