Overview

Dataset statistics

Number of variables6
Number of observations187
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.8 KiB
Average record size in memory53.7 B

Variable types

Numeric5
Categorical1

Dataset

Description연도에 따른 지자체별 지하수 부담금 징수계획, 실적, 전년도 대비 증감량, 전년도 대비 증가율에 대한 정보를 제공합니다.
URLhttps://www.data.go.kr/data/15118890/fileData.do

Alerts

계획(백만원) is highly overall correlated with 실적(백만원)High correlation
실적(백만원) is highly overall correlated with 계획(백만원) and 1 other fieldsHigh correlation
전년도대비증감 is highly overall correlated with 전년도대비증감율High correlation
전년도대비증감율 is highly overall correlated with 전년도대비증감High correlation
행정구역 is highly overall correlated with 실적(백만원)High correlation
계획(백만원) has 73 (39.0%) zerosZeros
실적(백만원) has 60 (32.1%) zerosZeros
전년도대비증감 has 61 (32.6%) zerosZeros
전년도대비증감율 has 65 (34.8%) zerosZeros

Reproduction

Analysis started2023-12-12 01:15:27.034994
Analysis finished2023-12-12 01:15:30.976016
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

년도
Real number (ℝ)

Distinct11
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2014
Minimum2009
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T10:15:31.050753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2009
5-th percentile2009
Q12011
median2014
Q32017
95-th percentile2019
Maximum2019
Range10
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.170767
Coefficient of variation (CV)0.001574363
Kurtosis-1.2204747
Mean2014
Median Absolute Deviation (MAD)3
Skewness0
Sum376618
Variance10.053763
MonotonicityDecreasing
2023-12-12T10:15:31.201355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=11)
ValueCountFrequency (%)
2019 17
9.1%
2018 17
9.1%
2017 17
9.1%
2016 17
9.1%
2015 17
9.1%
2014 17
9.1%
2013 17
9.1%
2012 17
9.1%
2011 17
9.1%
2010 17
9.1%
ValueCountFrequency (%)
2009 17
9.1%
2010 17
9.1%
2011 17
9.1%
2012 17
9.1%
2013 17
9.1%
2014 17
9.1%
2015 17
9.1%
2016 17
9.1%
2017 17
9.1%
2018 17
9.1%
ValueCountFrequency (%)
2019 17
9.1%
2018 17
9.1%
2017 17
9.1%
2016 17
9.1%
2015 17
9.1%
2014 17
9.1%
2013 17
9.1%
2012 17
9.1%
2011 17
9.1%
2010 17
9.1%

행정구역
Categorical

HIGH CORRELATION 

Distinct17
Distinct (%)9.1%
Missing0
Missing (%)0.0%
Memory size1.6 KiB
서울특별시
 
11
부산광역시
 
11
대구광역시
 
11
인천광역시
 
11
광주광역시
 
11
Other values (12)
132 

Length

Max length7
Median length5
Mean length4.6470588
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row서울특별시
2nd row부산광역시
3rd row대구광역시
4th row인천광역시
5th row광주광역시

Common Values

ValueCountFrequency (%)
서울특별시 11
 
5.9%
부산광역시 11
 
5.9%
대구광역시 11
 
5.9%
인천광역시 11
 
5.9%
광주광역시 11
 
5.9%
대전광역시 11
 
5.9%
울산광역시 11
 
5.9%
세종특별자치시 11
 
5.9%
경기도 11
 
5.9%
강원도 11
 
5.9%
Other values (7) 77
41.2%

Length

2023-12-12T10:15:31.363982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
서울특별시 11
 
5.9%
강원도 11
 
5.9%
경상남도 11
 
5.9%
경상북도 11
 
5.9%
전라남도 11
 
5.9%
전라북도 11
 
5.9%
충청남도 11
 
5.9%
충청북도 11
 
5.9%
경기도 11
 
5.9%
부산광역시 11
 
5.9%
Other values (7) 77
41.2%

계획(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct111
Distinct (%)59.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean581.50615
Minimum0
Maximum3891
Zeros73
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T10:15:31.534775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median208
Q3772
95-th percentile2570.7
Maximum3891
Range3891
Interquartile range (IQR)772

Descriptive statistics

Standard deviation859.36752
Coefficient of variation (CV)1.4778305
Kurtosis3.9880423
Mean581.50615
Median Absolute Deviation (MAD)208
Skewness2.0165332
Sum108741.65
Variance738512.54
MonotonicityNot monotonic
2023-12-12T10:15:31.738405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 73
39.0%
45.0 2
 
1.1%
1530.0 2
 
1.1%
145.0 2
 
1.1%
499.0 2
 
1.1%
288.0 1
 
0.5%
1698.0 1
 
0.5%
156.0 1
 
0.5%
2571.0 1
 
0.5%
41.0 1
 
0.5%
Other values (101) 101
54.0%
ValueCountFrequency (%)
0.0 73
39.0%
26.0 1
 
0.5%
27.0 1
 
0.5%
28.0 1
 
0.5%
32.0 1
 
0.5%
36.0 1
 
0.5%
38.0 1
 
0.5%
41.0 1
 
0.5%
45.0 2
 
1.1%
48.0 1
 
0.5%
ValueCountFrequency (%)
3891.0 1
0.5%
3823.0 1
0.5%
3740.0 1
0.5%
3680.0 1
0.5%
3528.2 1
0.5%
3260.0 1
0.5%
2610.0 1
0.5%
2596.0 1
0.5%
2582.0 1
0.5%
2571.0 1
0.5%

실적(백만원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct124
Distinct (%)66.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean626.64065
Minimum0
Maximum3794
Zeros60
Zeros (%)32.1%
Negative0
Negative (%)0.0%
Memory size1.8 KiB
2023-12-12T10:15:31.909925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median274
Q3899.5
95-th percentile2463.5
Maximum3794
Range3794
Interquartile range (IQR)899.5

Descriptive statistics

Standard deviation855.41963
Coefficient of variation (CV)1.365088
Kurtosis3.332618
Mean626.64065
Median Absolute Deviation (MAD)274
Skewness1.8679793
Sum117181.8
Variance731742.74
MonotonicityNot monotonic
2023-12-12T10:15:32.087203image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 60
32.1%
474.0 2
 
1.1%
262.0 2
 
1.1%
471.0 2
 
1.1%
734.0 2
 
1.1%
245.0 1
 
0.5%
37.0 1
 
0.5%
3142.0 1
 
0.5%
149.0 1
 
0.5%
1539.0 1
 
0.5%
Other values (114) 114
61.0%
ValueCountFrequency (%)
0.0 60
32.1%
24.0 1
 
0.5%
25.0 1
 
0.5%
26.0 1
 
0.5%
27.0 1
 
0.5%
31.0 1
 
0.5%
36.0 1
 
0.5%
37.0 1
 
0.5%
39.0 1
 
0.5%
41.0 1
 
0.5%
ValueCountFrequency (%)
3794.0 1
0.5%
3711.0 1
0.5%
3649.0 1
0.5%
3588.0 1
0.5%
3448.8 1
0.5%
3230.0 1
0.5%
3142.0 1
0.5%
2507.0 1
0.5%
2501.0 1
0.5%
2474.0 1
0.5%

전년도대비증감
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct97
Distinct (%)51.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42.293055
Minimum-219
Maximum1507
Zeros61
Zeros (%)32.6%
Negative63
Negative (%)33.7%
Memory size1.8 KiB
2023-12-12T10:15:32.313238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-219
5-th percentile-61.7
Q1-5
median0
Q311.5
95-th percentile372.4
Maximum1507
Range1726
Interquartile range (IQR)16.5

Descriptive statistics

Standard deviation164.2229
Coefficient of variation (CV)3.8829757
Kurtosis35.696111
Mean42.293055
Median Absolute Deviation (MAD)7
Skewness4.9586281
Sum7908.8013
Variance26969.162
MonotonicityNot monotonic
2023-12-12T10:15:32.520480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 61
32.6%
-1.0 5
 
2.7%
-5.0 4
 
2.1%
-3.0 4
 
2.1%
6.0 3
 
1.6%
8.0 3
 
1.6%
-2.0 3
 
1.6%
-15.0 2
 
1.1%
-69.0 2
 
1.1%
-6.0 2
 
1.1%
Other values (87) 98
52.4%
ValueCountFrequency (%)
-219.0 1
0.5%
-168.0 1
0.5%
-139.2 1
0.5%
-85.0 1
0.5%
-83.0 1
0.5%
-75.0 1
0.5%
-72.0 1
0.5%
-69.0 2
1.1%
-62.0 1
0.5%
-61.0 2
1.1%
ValueCountFrequency (%)
1507.0 1
0.5%
652.0 1
0.5%
641.0 1
0.5%
510.0 1
0.5%
469.0 1
0.5%
442.0 1
0.5%
416.0 1
0.5%
410.0 1
0.5%
401.0 1
0.5%
397.0 1
0.5%

전년도대비증감율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct103
Distinct (%)55.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.5695187
Minimum-28.2
Maximum243.6
Zeros65
Zeros (%)34.8%
Negative60
Negative (%)32.1%
Memory size1.8 KiB
2023-12-12T10:15:32.714579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-28.2
5-th percentile-8.01
Q1-2
median0
Q33.65
95-th percentile45.01
Maximum243.6
Range271.8
Interquartile range (IQR)5.65

Descriptive statistics

Standard deviation33.619802
Coefficient of variation (CV)3.9231843
Kurtosis26.511537
Mean8.5695187
Median Absolute Deviation (MAD)2.2
Skewness4.8725973
Sum1602.5
Variance1130.2911
MonotonicityNot monotonic
2023-12-12T10:15:32.899630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 65
34.8%
-1.0 3
 
1.6%
-2.8 3
 
1.6%
-2.0 3
 
1.6%
-5.0 2
 
1.1%
-1.3 2
 
1.1%
-6.8 2
 
1.1%
10.9 2
 
1.1%
-3.9 2
 
1.1%
-11.4 2
 
1.1%
Other values (93) 101
54.0%
ValueCountFrequency (%)
-28.2 1
0.5%
-16.1 1
0.5%
-13.9 1
0.5%
-11.4 2
1.1%
-10.8 1
0.5%
-10.5 1
0.5%
-10.2 1
0.5%
-10.1 1
0.5%
-8.1 1
0.5%
-7.8 1
0.5%
ValueCountFrequency (%)
243.6 1
0.5%
221.0 1
0.5%
179.0 1
0.5%
151.6 1
0.5%
145.9 1
0.5%
98.3 1
0.5%
66.5 1
0.5%
55.9 1
0.5%
53.0 1
0.5%
48.7 1
0.5%

Interactions

2023-12-12T10:15:29.825981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.304567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.876001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.524509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.178815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.933843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.423689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.985820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.648466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.303121image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:30.079931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.550233image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.124916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.778625image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.449643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:30.194798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.672155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.267951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.924624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.586060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:30.613504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:27.787932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:28.397721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.044465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T10:15:29.717589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T10:15:33.021473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도행정구역계획(백만원)실적(백만원)전년도대비증감전년도대비증감율
년도1.0000.0000.0810.0000.0000.145
행정구역0.0001.0000.8140.8450.4080.430
계획(백만원)0.0810.8141.0000.9960.6780.264
실적(백만원)0.0000.8450.9961.0000.6740.212
전년도대비증감0.0000.4080.6780.6741.0000.734
전년도대비증감율0.1450.4300.2640.2120.7341.000
2023-12-12T10:15:33.170953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
년도계획(백만원)실적(백만원)전년도대비증감전년도대비증감율행정구역
년도1.000-0.0710.140-0.349-0.3240.000
계획(백만원)-0.0711.0000.8710.1630.1930.485
실적(백만원)0.1400.8711.0000.0260.0970.533
전년도대비증감-0.3490.1630.0261.0000.9570.191
전년도대비증감율-0.3240.1930.0970.9571.0000.190
행정구역0.0000.4850.5330.1910.1901.000

Missing values

2023-12-12T10:15:30.746344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T10:15:30.907906image/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

년도행정구역계획(백만원)실적(백만원)전년도대비증감전년도대비증감율
02019서울특별시0.0156.0-9.0-5.0
12019부산광역시0.01291.0-75.0-5.0
22019대구광역시0.0959.0-43.0-4.0
32019인천광역시0.0204.013.07.0
42019광주광역시0.00.00.00.0
52019대전광역시0.0322.0-3.0-1.0
62019울산광역시0.0572.0-42.0-7.0
72019세종특별자치시0.00.00.00.0
82019경기도0.02413.0-7.00.0
92019강원도0.01129.0-1.00.0
년도행정구역계획(백만원)실적(백만원)전년도대비증감전년도대비증감율
1772009세종특별자치시0.00.00.00.0
1782009경기도1195.01106.0258.030.4
1792009강원도353.0347.0246.0243.6
1802009충청북도419.0398.0274.0221.0
1812009충청남도692.0668.0140.026.5
1822009전라북도0.00.00.00.0
1832009전라남도45.041.08.024.2
1842009경상북도0.00.00.00.0
1852009경상남도463.0442.0442.00.0
1862009제주특별자치도0.00.00.00.0