Overview

Dataset statistics

Number of variables7
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.5 KiB
Average record size in memory66.4 B

Variable types

Numeric7

Dataset

Description국세 징세비 현황에 대한 데이터로 년도별 국세청세수(억원), 징세비(백만원), 정원(명), 1인당세수(백만원), 1인당징세비(백만원), 세수 100원당 징세비(원) 현황에 대하여 제공합니다.
URLhttps://www.data.go.kr/data/15070680/fileData.do

Alerts

연도 is highly overall correlated with 국세청세수(억원) and 5 other fieldsHigh correlation
국세청세수(억원) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
징세비(백만원) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
정원(명) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
1인당세수(백만원) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
1인당징세비(백만원) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
세수 100원당 징세비(원) is highly overall correlated with 연도 and 5 other fieldsHigh correlation
연도 has unique valuesUnique
국세청세수(억원) has unique valuesUnique
징세비(백만원) has unique valuesUnique
1인당세수(백만원) has unique valuesUnique

Reproduction

Analysis started2023-12-12 19:47:57.823282
Analysis finished2023-12-12 19:48:04.462756
Duration6.64 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연도
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2003
Minimum1984
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:04.527755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1984
5-th percentile1985.9
Q11993.5
median2003
Q32012.5
95-th percentile2020.1
Maximum2022
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.0056923386
Kurtosis-1.2
Mean2003
Median Absolute Deviation (MAD)10
Skewness0
Sum78117
Variance130
MonotonicityStrictly increasing
2023-12-13T04:48:04.667503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1984 1
 
2.6%
1985 1
 
2.6%
2006 1
 
2.6%
2007 1
 
2.6%
2008 1
 
2.6%
2009 1
 
2.6%
2010 1
 
2.6%
2011 1
 
2.6%
2012 1
 
2.6%
2013 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1984 1
2.6%
1985 1
2.6%
1986 1
2.6%
1987 1
2.6%
1988 1
2.6%
1989 1
2.6%
1990 1
2.6%
1991 1
2.6%
1992 1
2.6%
1993 1
2.6%
ValueCountFrequency (%)
2022 1
2.6%
2021 1
2.6%
2020 1
2.6%
2019 1
2.6%
2018 1
2.6%
2017 1
2.6%
2016 1
2.6%
2015 1
2.6%
2014 1
2.6%
2013 1
2.6%

국세청세수(억원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1264926.4
Minimum79649
Maximum3842495
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:04.833686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum79649
5-th percentile99832.6
Q1400326
median1070486
Q31911639.5
95-th percentile2894185.7
Maximum3842495
Range3762846
Interquartile range (IQR)1511313.5

Descriptive statistics

Standard deviation1007617.1
Coefficient of variation (CV)0.79658166
Kurtosis-0.23359777
Mean1264926.4
Median Absolute Deviation (MAD)749633
Skewness0.74883106
Sum49332128
Variance1.0152923 × 1012
MonotonicityNot monotonic
2023-12-13T04:48:04.982427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
79649 1
 
2.6%
89416 1
 
2.6%
1302609 1
 
2.6%
1530628 1
 
2.6%
1575286 1
 
2.6%
1543305 1
 
2.6%
1660149 1
 
2.6%
1801532 1
 
2.6%
1920926 1
 
2.6%
1902353 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
79649 1
2.6%
89416 1
2.6%
100990 1
2.6%
120159 1
2.6%
150838 1
2.6%
180780 1
2.6%
226778 1
2.6%
269854 1
2.6%
320853 1
2.6%
363747 1
2.6%
ValueCountFrequency (%)
3842495 1
2.6%
3344714 1
2.6%
2844127 1
2.6%
2835355 1
2.6%
2772753 1
2.6%
2555932 1
2.6%
2333291 1
2.6%
2081615 1
2.6%
1957271 1
2.6%
1920926 1
2.6%

징세비(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean898446.31
Minimum91492
Maximum1900294
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:05.121228image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum91492
5-th percentile110679.1
Q1370834
median879652
Q31365063.5
95-th percentile1745772
Maximum1900294
Range1808802
Interquartile range (IQR)994229.5

Descriptive statistics

Standard deviation572531.79
Coefficient of variation (CV)0.63724653
Kurtosis-1.3535254
Mean898446.31
Median Absolute Deviation (MAD)486058
Skewness0.094386767
Sum35039406
Variance3.2779265 × 1011
MonotonicityNot monotonic
2023-12-13T04:48:05.266001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
91492 1
 
2.6%
102796 1
 
2.6%
1023822 1
 
2.6%
1081983 1
 
2.6%
1239631 1
 
2.6%
1300741 1
 
2.6%
1341752 1
 
2.6%
1364417 1
 
2.6%
1339749 1
 
2.6%
1365710 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
91492 1
2.6%
102796 1
2.6%
111555 1
2.6%
123042 1
2.6%
146629 1
2.6%
174116 1
2.6%
210614 1
2.6%
267149 1
2.6%
313866 1
2.6%
347860 1
2.6%
ValueCountFrequency (%)
1900294 1
2.6%
1800663 1
2.6%
1739673 1
2.6%
1712157 1
2.6%
1638833 1
2.6%
1592674 1
2.6%
1520167 1
2.6%
1480552 1
2.6%
1462947 1
2.6%
1365710 1
2.6%

정원(명)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16417.128
Minimum12462
Maximum21584
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:05.409105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum12462
5-th percentile12602.8
Q114896
median15318
Q318806
95-th percentile20258.8
Maximum21584
Range9122
Interquartile range (IQR)3910

Descriptive statistics

Standard deviation2622.2955
Coefficient of variation (CV)0.15972924
Kurtosis-1.1580928
Mean16417.128
Median Absolute Deviation (MAD)2705
Skewness0.1759191
Sum640268
Variance6876433.6
MonotonicityNot monotonic
2023-12-13T04:48:05.553130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
18341 2
 
5.1%
15153 2
 
5.1%
12613 2
 
5.1%
14094 2
 
5.1%
14986 2
 
5.1%
15158 2
 
5.1%
18765 1
 
2.6%
18797 1
 
2.6%
18815 1
 
2.6%
18917 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
12462 1
2.6%
12592 1
2.6%
12604 1
2.6%
12613 2
5.1%
13099 1
2.6%
14094 2
5.1%
14502 1
2.6%
14806 1
2.6%
14986 2
5.1%
15145 1
2.6%
ValueCountFrequency (%)
21584 1
2.6%
20932 1
2.6%
20184 1
2.6%
19947 1
2.6%
19414 1
2.6%
19131 1
2.6%
18951 1
2.6%
18917 1
2.6%
18901 1
2.6%
18815 1
2.6%

1인당세수(백만원)
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6990.6923
Minimum639
Maximum17803
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:05.667300image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum639
5-th percentile791.9
Q12686
median7062
Q310165
95-th percentile14742.4
Maximum17803
Range17164
Interquartile range (IQR)7479

Descriptive statistics

Standard deviation4785.717
Coefficient of variation (CV)0.68458413
Kurtosis-0.73877868
Mean6990.6923
Median Absolute Deviation (MAD)3609
Skewness0.43294701
Sum272637
Variance22903088
MonotonicityNot monotonic
2023-12-13T04:48:05.785546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
639 1
 
2.6%
710 1
 
2.6%
7948 1
 
2.6%
8336 1
 
2.6%
8589 1
 
2.6%
8415 1
 
2.6%
8952 1
 
2.6%
9600 1
 
2.6%
10219 1
 
2.6%
10111 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
639 1
2.6%
710 1
2.6%
801 1
2.6%
953 1
2.6%
1196 1
2.6%
1380 1
2.6%
1609 1
2.6%
1914 1
2.6%
2212 1
2.6%
2457 1
2.6%
ValueCountFrequency (%)
17803 1
2.6%
15979 1
2.6%
14605 1
2.6%
14258 1
2.6%
13737 1
2.6%
13360 1
2.6%
12345 1
2.6%
10984 1
2.6%
10347 1
2.6%
10219 1
2.6%

1인당징세비(백만원)
Real number (ℝ)

HIGH CORRELATION 

Distinct33
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean50.666667
Minimum7
Maximum88
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:05.910285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile8.9
Q124.5
median58
Q373
95-th percentile86
Maximum88
Range81
Interquartile range (IQR)48.5

Descriptive statistics

Standard deviation27.291153
Coefficient of variation (CV)0.53864117
Kurtosis-1.3968835
Mean50.666667
Median Absolute Deviation (MAD)22
Skewness-0.25413006
Sum1976
Variance744.80702
MonotonicityNot monotonic
2023-12-13T04:48:06.034091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
86 3
 
7.7%
62 2
 
5.1%
73 2
 
5.1%
38 2
 
5.1%
71 2
 
5.1%
63 1
 
2.6%
59 1
 
2.6%
68 1
 
2.6%
72 1
 
2.6%
7 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
12 1
2.6%
13 1
2.6%
15 1
2.6%
19 1
2.6%
22 1
2.6%
23 1
2.6%
ValueCountFrequency (%)
88 1
 
2.6%
86 3
7.7%
84 1
 
2.6%
83 1
 
2.6%
80 1
 
2.6%
78 1
 
2.6%
77 1
 
2.6%
73 2
5.1%
72 1
 
2.6%
71 2
5.1%

세수 100원당 징세비(원)
Real number (ℝ)

HIGH CORRELATION 

Distinct29
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.82794872
Minimum0.49
Maximum1.15
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T04:48:06.166673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0.49
5-th percentile0.576
Q10.715
median0.84
Q30.93
95-th percentile1.105
Maximum1.15
Range0.66
Interquartile range (IQR)0.215

Descriptive statistics

Standard deviation0.15950878
Coefficient of variation (CV)0.19265538
Kurtosis-0.27026146
Mean0.82794872
Median Absolute Deviation (MAD)0.12
Skewness-0.041199492
Sum32.29
Variance0.02544305
MonotonicityNot monotonic
2023-12-13T04:48:06.306179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.91 3
 
7.7%
1.15 2
 
5.1%
0.71 2
 
5.1%
0.79 2
 
5.1%
0.85 2
 
5.1%
0.84 2
 
5.1%
0.81 2
 
5.1%
0.93 2
 
5.1%
0.96 2
 
5.1%
0.98 1
 
2.6%
Other values (19) 19
48.7%
ValueCountFrequency (%)
0.49 1
2.6%
0.54 1
2.6%
0.58 1
2.6%
0.6 1
2.6%
0.62 1
2.6%
0.63 1
2.6%
0.65 1
2.6%
0.7 1
2.6%
0.71 2
5.1%
0.72 1
2.6%
ValueCountFrequency (%)
1.15 2
5.1%
1.1 1
 
2.6%
1.02 1
 
2.6%
0.99 1
 
2.6%
0.98 1
 
2.6%
0.97 1
 
2.6%
0.96 2
5.1%
0.93 2
5.1%
0.91 3
7.7%
0.9 1
 
2.6%

Interactions

2023-12-13T04:48:03.532079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.029377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.743961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.667154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.524232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.341562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.298773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:03.630481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.126419image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.861267image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.797501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.658118image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.478825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.445722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:03.735141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.253479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.017154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.929044image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.778993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.614108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.604470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:03.819117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.373320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.154482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.044448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.877669image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.780857image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.736980image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:03.917141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.455680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.269845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.158144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.984615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.876945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.839349image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:04.023199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.557117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.394582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.292546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.094166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.006830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.950176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:04.143609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:58.643929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:47:59.541743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:00.412169image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:01.212993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:02.147496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T04:48:03.079279image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T04:48:06.396413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도국세청세수(억원)징세비(백만원)정원(명)1인당세수(백만원)1인당징세비(백만원)세수 100원당 징세비(원)
연도1.0000.9510.9680.8990.9600.9600.907
국세청세수(억원)0.9511.0000.9640.8400.9830.9060.914
징세비(백만원)0.9680.9641.0000.8490.9430.9690.908
정원(명)0.8990.8400.8491.0000.8100.7450.764
1인당세수(백만원)0.9600.9830.9430.8101.0000.9230.912
1인당징세비(백만원)0.9600.9060.9690.7450.9231.0000.877
세수 100원당 징세비(원)0.9070.9140.9080.7640.9120.8771.000
2023-12-13T04:48:06.543195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연도국세청세수(억원)징세비(백만원)정원(명)1인당세수(백만원)1인당징세비(백만원)세수 100원당 징세비(원)
연도1.0000.9990.9990.9850.9990.997-0.969
국세청세수(억원)0.9991.0000.9980.9841.0000.996-0.972
징세비(백만원)0.9990.9981.0000.9850.9980.998-0.966
정원(명)0.9850.9840.9851.0000.9840.981-0.948
1인당세수(백만원)0.9991.0000.9980.9841.0000.995-0.972
1인당징세비(백만원)0.9970.9960.9980.9810.9951.000-0.957
세수 100원당 징세비(원)-0.969-0.972-0.966-0.948-0.972-0.9571.000

Missing values

2023-12-13T04:48:04.294487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T04:48:04.412770image/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

연도국세청세수(억원)징세비(백만원)정원(명)1인당세수(백만원)1인당징세비(백만원)세수 100원당 징세비(원)
0198479649914921246263971.15
11985894161027961259271081.15
219861009901115551260480191.1
3198712015912304212613953101.02
41988150838146629126131196120.97
51989180780174116130991380130.96
61990226778210614140941609150.93
71991269854267149140941914190.99
81992320853313866145022212220.98
91993363747347860148062457230.96
연도국세청세수(억원)징세비(백만원)정원(명)1인당세수(백만원)1인당징세비(백만원)세수 100원당 징세비(원)
292013190235313657101881510111730.72
302014195727114629471891710347770.75
312015208161514805521895110984780.71
322016233329115201671890112345800.65
332017255593215926741913113360830.62
342018283535516388331941414605840.58
352019284412717121571994714258860.6
362020277275317396732018413737860.63
372021334471418006632093215979860.54
382022384249519002942158417803880.49