Overview

Dataset statistics

Number of variables12
Number of observations28
Missing cells1
Missing cells (%)0.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.0 KiB
Average record size in memory108.7 B

Variable types

Categorical5
Text1
Numeric6

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공합니다. (지자체의 재정자주도,재정자립도를 산출하는 기초 및 납세 협력도 및 조세 순응도를 확인하는 자료로 활용)
Author전라남도 광양시
URLhttps://www.data.go.kr/data/15078867/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 is highly overall correlated with 과세년도High correlation
과세년도 is highly overall correlated with 데이터기준일High correlation
부과금액 is highly overall correlated with 수납급액 and 2 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액High correlation
환급금액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
결손금액 is highly overall correlated with 미수납 금액High correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 미수납 금액High correlation
징수율 has 1 (3.6%) missing valuesMissing
부과금액 has unique valuesUnique
수납급액 has unique valuesUnique
부과금액 has 1 (3.6%) zerosZeros
수납급액 has 1 (3.6%) zerosZeros
환급금액 has 5 (17.9%) zerosZeros
결손금액 has 17 (60.7%) zerosZeros
미수납 금액 has 8 (28.6%) zerosZeros
징수율 has 1 (3.6%) zerosZeros

Reproduction

Analysis started2023-12-12 14:29:16.023291
Analysis finished2023-12-12 14:29:20.792511
Duration4.77 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
전라남도
28 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전라남도
2nd row전라남도
3rd row전라남도
4th row전라남도
5th row전라남도

Common Values

ValueCountFrequency (%)
전라남도 28
100.0%

Length

2023-12-12T23:29:20.873069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:20.982552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 28
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
광양시
28 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row광양시
2nd row광양시
3rd row광양시
4th row광양시
5th row광양시

Common Values

ValueCountFrequency (%)
광양시 28
100.0%

Length

2023-12-12T23:29:21.093353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:21.178166image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
광양시 28
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.6%
Missing0
Missing (%)0.0%
Memory size356.0 B
46230
28 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46230
2nd row46230
3rd row46230
4th row46230
5th row46230

Common Values

ValueCountFrequency (%)
46230 28
100.0%

Length

2023-12-12T23:29:21.262712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:21.368017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46230 28
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
2022
14 
2021
14 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022
2nd row2022
3rd row2022
4th row2022
5th row2022

Common Values

ValueCountFrequency (%)
2022 14
50.0%
2021 14
50.0%

Length

2023-12-12T23:29:21.482441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:21.574240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022 14
50.0%
2021 14
50.0%

세목명
Text

Distinct14
Distinct (%)50.0%
Missing0
Missing (%)0.0%
Memory size356.0 B
2023-12-12T23:29:21.720619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.2142857
Min length3

Characters and Unicode

Total characters118
Distinct characters34
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row과년도
2nd row취득세
3rd row등록면허세
4th row레저세
5th row지역자원시설세
ValueCountFrequency (%)
과년도 2
 
7.1%
취득세 2
 
7.1%
등록면허세 2
 
7.1%
레저세 2
 
7.1%
지역자원시설세 2
 
7.1%
교육세 2
 
7.1%
담배소비세 2
 
7.1%
지방소비세 2
 
7.1%
주민세 2
 
7.1%
지방소득세 2
 
7.1%
Other values (4) 8
28.6%
2023-12-12T23:29:22.049783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 26
22.0%
지 8
 
6.8%
소 6
 
5.1%
비 4
 
3.4%
득 4
 
3.4%
등 4
 
3.4%
록 4
 
3.4%
방 4
 
3.4%
주 4
 
3.4%
자 4
 
3.4%
Other values (24) 50
42.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 114
96.6%
Open Punctuation 2
 
1.7%
Close Punctuation 2
 
1.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 26
22.8%
지 8
 
7.0%
소 6
 
5.3%
비 4
 
3.5%
득 4
 
3.5%
등 4
 
3.5%
록 4
 
3.5%
방 4
 
3.5%
주 4
 
3.5%
자 4
 
3.5%
Other values (22) 46
40.4%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 114
96.6%
Common 4
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 26
22.8%
지 8
 
7.0%
소 6
 
5.3%
비 4
 
3.5%
득 4
 
3.5%
등 4
 
3.5%
록 4
 
3.5%
방 4
 
3.5%
주 4
 
3.5%
자 4
 
3.5%
Other values (22) 46
40.4%
Common
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 114
96.6%
ASCII 4
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 26
22.8%
지 8
 
7.0%
소 6
 
5.3%
비 4
 
3.5%
득 4
 
3.5%
등 4
 
3.5%
록 4
 
3.5%
방 4
 
3.5%
주 4
 
3.5%
자 4
 
3.5%
Other values (22) 46
40.4%
ASCII
ValueCountFrequency (%)
( 2
50.0%
) 2
50.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6210146 × 1010
Minimum0
Maximum1.47375 × 1011
Zeros1
Zeros (%)3.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T23:29:22.215874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37633910
Q15.8320843 × 109
median1.2199493 × 1010
Q33.6672422 × 1010
95-th percentile9.3837948 × 1010
Maximum1.47375 × 1011
Range1.47375 × 1011
Interquartile range (IQR)3.0840337 × 1010

Descriptive statistics

Standard deviation3.4963979 × 1010
Coefficient of variation (CV)1.3339864
Kurtosis4.7716006
Mean2.6210146 × 1010
Median Absolute Deviation (MAD)1.1616267 × 1010
Skewness2.1425659
Sum7.3388408 × 1011
Variance1.2224798 × 1021
MonotonicityNot monotonic
2023-12-12T23:29:22.353207image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
8537526700 1
 
3.6%
83130676960 1
 
3.6%
56441510 1
 
3.6%
76066390 1
 
3.6%
42177560900 1
 
3.6%
36668898480 1
 
3.6%
70586689220 1
 
3.6%
15061091320 1
 
3.6%
12270517000 1
 
3.6%
11904529140 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
0 1
3.6%
27506740 1
3.6%
56441510 1
3.6%
76066390 1
3.6%
79334680 1
3.6%
85309450 1
3.6%
1335719250 1
3.6%
7330872590 1
3.6%
8132649550 1
3.6%
8537526700 1
3.6%
ValueCountFrequency (%)
147375000000 1
3.6%
99603401720 1
3.6%
83130676960 1
3.6%
70586689220 1
3.6%
42177560900 1
3.6%
39539844270 1
3.6%
36682990760 1
3.6%
36668898480 1
3.6%
23317843500 1
3.6%
21377282550 1
3.6%

수납급액
Real number (ℝ)

HIGH CORRELATION  UNIQUE  ZEROS 

Distinct28
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5390592 × 1010
Minimum-4.9696382 × 109
Maximum1.46481 × 1011
Zeros1
Zeros (%)3.6%
Negative1
Negative (%)3.6%
Memory size384.0 B
2023-12-12T23:29:22.478248image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4.9696382 × 109
5-th percentile8561917
Q19.338265 × 108
median1.2199493 × 1010
Q33.5360747 × 1010
95-th percentile9.3492438 × 1010
Maximum1.46481 × 1011
Range1.5145064 × 1011
Interquartile range (IQR)3.4426921 × 1010

Descriptive statistics

Standard deviation3.504793 × 1010
Coefficient of variation (CV)1.3803511
Kurtosis4.7172254
Mean2.5390592 × 1010
Median Absolute Deviation (MAD)1.2117171 × 1010
Skewness2.1274653
Sum7.1093657 × 1011
Variance1.2283574 × 1021
MonotonicityNot monotonic
2023-12-12T23:29:22.610577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
1216665510 1
 
3.6%
83025465330 1
 
3.6%
49821120 1
 
3.6%
76066390 1
 
3.6%
40747524860 1
 
3.6%
35645281190 1
 
3.6%
69572852580 1
 
3.6%
14889567160 1
 
3.6%
12270517000 1
 
3.6%
11904529140 1
 
3.6%
Other values (18) 18
64.3%
ValueCountFrequency (%)
-4969638190 1
3.6%
0 1
3.6%
24462620 1
3.6%
49821120 1
3.6%
76066390 1
3.6%
79334680 1
3.6%
85309450 1
3.6%
1216665510 1
3.6%
7321545700 1
3.6%
8123886910 1
3.6%
ValueCountFrequency (%)
146481000000 1
3.6%
99128500100 1
3.6%
83025465330 1
3.6%
69572852580 1
3.6%
40747524860 1
3.6%
38228370320 1
3.6%
35645281190 1
3.6%
35265902670 1
3.6%
22731620010 1
3.6%
20816982430 1
3.6%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)85.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6122459 × 108
Minimum0
Maximum8.3770828 × 109
Zeros5
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T23:29:22.756784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1464122.5
median17391910
Q32.7492916 × 108
95-th percentile1.8501671 × 109
Maximum8.3770828 × 109
Range8.3770828 × 109
Interquartile range (IQR)2.7446504 × 108

Descriptive statistics

Standard deviation1.6176697 × 109
Coefficient of variation (CV)2.8823927
Kurtosis21.897204
Mean5.6122459 × 108
Median Absolute Deviation (MAD)17391910
Skewness4.5104847
Sum1.5714289 × 1010
Variance2.6168552 × 1018
MonotonicityNot monotonic
2023-12-12T23:29:22.882931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 5
 
17.9%
1814846540 1
 
3.6%
1869185870 1
 
3.6%
1097150 1
 
3.6%
1475080 1
 
3.6%
216511770 1
 
3.6%
267401990 1
 
3.6%
909245740 1
 
3.6%
20926830 1
 
3.6%
228950 1
 
3.6%
Other values (14) 14
50.0%
ValueCountFrequency (%)
0 5
17.9%
228950 1
 
3.6%
304770 1
 
3.6%
517240 1
 
3.6%
528720 1
 
3.6%
535040 1
 
3.6%
660630 1
 
3.6%
1097150 1
 
3.6%
1475080 1
 
3.6%
14725650 1
 
3.6%
ValueCountFrequency (%)
8377082780 1
3.6%
1869185870 1
3.6%
1814846540 1
3.6%
1100985030 1
3.6%
909245740 1
3.6%
444889520 1
3.6%
297510670 1
3.6%
267401990 1
3.6%
241323050 1
3.6%
216511770 1
3.6%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct12
Distinct (%)42.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean88707739
Minimum0
Maximum1.5901803 × 109
Zeros17
Zeros (%)60.7%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T23:29:23.015469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3958700
95-th percentile5.4149977 × 108
Maximum1.5901803 × 109
Range1.5901803 × 109
Interquartile range (IQR)958700

Descriptive statistics

Standard deviation3.2890203 × 108
Coefficient of variation (CV)3.7077039
Kurtosis17.613819
Mean88707739
Median Absolute Deviation (MAD)0
Skewness4.1379371
Sum2.4838167 × 109
Variance1.0817654 × 1017
MonotonicityNot monotonic
2023-12-12T23:29:23.184618image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 17
60.7%
1590180260 1
 
3.6%
84960 1
 
3.6%
1642720 1
 
3.6%
1100630 1
 
3.6%
1270430 1
 
3.6%
107641120 1
 
3.6%
5411070 1
 
3.6%
448870 1
 
3.6%
775115960 1
 
3.6%
Other values (2) 2
 
7.1%
ValueCountFrequency (%)
0 17
60.7%
9270 1
 
3.6%
84960 1
 
3.6%
448870 1
 
3.6%
911390 1
 
3.6%
1100630 1
 
3.6%
1270430 1
 
3.6%
1642720 1
 
3.6%
5411070 1
 
3.6%
107641120 1
 
3.6%
ValueCountFrequency (%)
1590180260 1
3.6%
775115960 1
3.6%
107641120 1
3.6%
5411070 1
3.6%
1642720 1
3.6%
1270430 1
3.6%
1100630 1
3.6%
911390 1
3.6%
448870 1
3.6%
84960 1
3.6%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct21
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.3087427 × 108
Minimum0
Maximum5.7306809 × 109
Zeros8
Zeros (%)28.6%
Negative0
Negative (%)0.0%
Memory size384.0 B
2023-12-12T23:29:23.330094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median88836315
Q38.4359226 × 108
95-th percentile4.0951696 × 109
Maximum5.7306809 × 109
Range5.7306809 × 109
Interquartile range (IQR)8.4359226 × 108

Descriptive statistics

Standard deviation1.4641326 × 109
Coefficient of variation (CV)2.003262
Kurtosis8.3164343
Mean7.3087427 × 108
Median Absolute Deviation (MAD)88836315
Skewness2.9302881
Sum2.046448 × 1010
Variance2.1436843 × 1018
MonotonicityNot monotonic
2023-12-12T23:29:23.505122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 8
28.6%
5730680930 1
 
3.6%
105211630 1
 
3.6%
6620390 1
 
3.6%
1430036040 1
 
3.6%
1023617290 1
 
3.6%
1012925250 1
 
3.6%
171524160 1
 
3.6%
560300120 1
 
3.6%
72461000 1
 
3.6%
Other values (11) 11
39.3%
ValueCountFrequency (%)
0 8
28.6%
3044120 1
 
3.6%
6620390 1
 
3.6%
8677680 1
 
3.6%
9317620 1
 
3.6%
56444630 1
 
3.6%
72461000 1
 
3.6%
105211630 1
 
3.6%
171524160 1
 
3.6%
173502650 1
 
3.6%
ValueCountFrequency (%)
5730680930 1
3.6%
5530241480 1
3.6%
1430036040 1
3.6%
1416639220 1
3.6%
1306062880 1
3.6%
1023617290 1
3.6%
1012925250 1
3.6%
787147930 1
3.6%
585122860 1
3.6%
560300120 1
3.6%

징수율
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct19
Distinct (%)70.4%
Missing1
Missing (%)3.6%
Infinite0
Infinite (%)0.0%
Mean73.87037
Minimum-372.1
Maximum100
Zeros1
Zeros (%)3.6%
Negative1
Negative (%)3.6%
Memory size384.0 B
2023-12-12T23:29:23.685036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-372.1
5-th percentile4.29
Q196.65
median98.9
Q399.9
95-th percentile100
Maximum100
Range472.1
Interquartile range (IQR)3.25

Descriptive statistics

Standard deviation92.427443
Coefficient of variation (CV)1.2512113
Kurtosis22.921415
Mean73.87037
Median Absolute Deviation (MAD)1.1
Skewness-4.679992
Sum1994.5
Variance8542.8322
MonotonicityNot monotonic
2023-12-12T23:29:23.839481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
100.0 6
21.4%
99.9 3
 
10.7%
99.4 2
 
7.1%
14.3 1
 
3.6%
0.0 1
 
3.6%
88.3 1
 
3.6%
96.6 1
 
3.6%
97.2 1
 
3.6%
98.6 1
 
3.6%
98.9 1
 
3.6%
Other values (9) 9
32.1%
ValueCountFrequency (%)
-372.1 1
3.6%
0.0 1
3.6%
14.3 1
3.6%
88.3 1
3.6%
88.9 1
3.6%
96.1 1
3.6%
96.6 1
3.6%
96.7 1
3.6%
97.2 1
3.6%
97.4 1
3.6%
ValueCountFrequency (%)
100.0 6
21.4%
99.9 3
10.7%
99.6 1
 
3.6%
99.5 1
 
3.6%
99.4 2
 
7.1%
98.9 1
 
3.6%
98.6 1
 
3.6%
98.5 1
 
3.6%
97.5 1
 
3.6%
97.4 1
 
3.6%

데이터기준일
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)7.1%
Missing0
Missing (%)0.0%
Memory size356.0 B
2022-12-30
14 
2021-12-30
14 

Length

Max length10
Median length10
Mean length10
Min length10

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2022-12-30
2nd row2022-12-30
3rd row2022-12-30
4th row2022-12-30
5th row2022-12-30

Common Values

ValueCountFrequency (%)
2022-12-30 14
50.0%
2021-12-30 14
50.0%

Length

2023-12-12T23:29:23.975039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:29:24.090962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2022-12-30 14
50.0%
2021-12-30 14
50.0%

Interactions

2023-12-12T23:29:19.567880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:16.402200image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.094367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.659581image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.257179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.915107image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:19.995838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:16.503461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.194527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.750290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.376968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:19.041063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:20.091333image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:16.620500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.293133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.851128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.485743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:19.154402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:20.170490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:16.739969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.375337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.947467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.585878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:19.263037image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:20.279199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:16.889795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.474138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.041328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.706705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:19.368108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:20.379982image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.012620image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:17.575168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.161821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:18.812944image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:29:19.473042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:29:24.194688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
과세년도1.0000.0000.0000.0000.0000.0000.0000.0000.993
세목명0.0001.0000.9060.7990.8040.4190.9441.0000.000
부과금액0.0000.9061.0000.9680.7360.0000.6870.0000.000
수납급액0.0000.7990.9681.0000.8620.0000.8540.0000.000
환급금액0.0000.8040.7360.8621.0000.7840.8910.7650.000
결손금액0.0000.4190.0000.0000.7841.0000.6411.0000.000
미수납 금액0.0000.9440.6870.8540.8910.6411.0000.6430.000
징수율0.0001.0000.0000.0000.7651.0000.6431.0000.000
데이터기준일0.9930.0000.0000.0000.0000.0000.0000.0001.000
2023-12-12T23:29:24.319809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
데이터기준일과세년도
데이터기준일1.0000.926
과세년도0.9261.000
2023-12-12T23:29:24.405552image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도데이터기준일
부과금액1.0000.9860.5730.1830.5500.0870.0000.000
수납급액0.9861.0000.4920.0970.4610.1430.0000.000
환급금액0.5730.4921.0000.4700.816-0.3420.0000.000
결손금액0.1830.0970.4701.0000.616-0.3450.0000.000
미수납 금액0.5500.4610.8160.6161.000-0.6390.0000.000
징수율0.0870.143-0.342-0.345-0.6391.0000.0000.000
과세년도0.0000.0000.0000.0000.0000.0001.0000.926
데이터기준일0.0000.0000.0000.0000.0000.0000.9261.000

Missing values

2023-12-12T23:29:20.523714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:29:20.724446image/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

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
0전라남도광양시462302022과년도8537526700121666551018148465401590180260573068093014.32022-12-30
1전라남도광양시462302022취득세9960340172099128500100444889520047490162099.52022-12-30
2전라남도광양시462302022등록면허세813264955081238869102005817084960867768099.92022-12-30
3전라남도광양시462302022레저세7933468079334680000<NA>2022-12-30
4전라남도광양시462302022지역자원시설세1181131334011636167970517240164272017350265098.52022-12-30
5전라남도광양시462302022교육세233178435002273162001086736400110063058512286097.52022-12-30
6전라남도광양시462302022담배소비세121284688301212846883053504000100.02022-12-30
7전라남도광양시462302022지방소비세1698803086016988030860000100.02022-12-30
8전라남도광양시462302022주민세154878370201543012196066063012704305644463099.62022-12-30
9전라남도광양시462302022지방소득세147375000000146481000000110098503010764112078714793099.42022-12-30
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율데이터기준일
18전라남도광양시462302021지역자원시설세121106791101203821811030477007246100099.42021-12-30
19전라남도광양시462302021교육세2137728255020816982430297510670056030012097.42021-12-30
20전라남도광양시462302021담배소비세119045291401190452914022895000100.02021-12-30
21전라남도광양시462302021지방소비세1227051700012270517000000100.02021-12-30
22전라남도광양시462302021주민세150610913201488956716020926830017152416098.92021-12-30
23전라남도광양시462302021지방소득세7058668922069572852580909245740911390101292525098.62021-12-30
24전라남도광양시462302021재산세36668898480356452811902674019900102361729097.22021-12-30
25전라남도광양시462302021자동차세42177560900407475248602165117700143003604096.62021-12-30
26전라남도광양시462302021등록세7606639076066390147508000100.02021-12-30
27전라남도광양시462302021주민세(폐지)564415104982112010971500662039088.32021-12-30