Overview

Dataset statistics

Number of variables11
Number of observations41
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.0 KiB
Average record size in memory99.2 B

Variable types

Categorical5
Numeric6

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공함으로써 지자체의 재정자주도, 재정자립도를 산출하는 기초 및 납세 협력도, 조세 순응도를 확인하는 자료로 활용할 수 있도록 함.
Author경기도 포천시
URLhttps://www.data.go.kr/data/15080052/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 5 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 5 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 11 (26.8%) zerosZeros
수납급액 has 11 (26.8%) zerosZeros
환급금액 has 14 (34.1%) zerosZeros
결손금액 has 16 (39.0%) zerosZeros
미수납 금액 has 14 (34.1%) zerosZeros
징수율 has 11 (26.8%) zerosZeros

Reproduction

Analysis started2023-12-12 16:09:17.543912
Analysis finished2023-12-12 16:09:21.110345
Duration3.57 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
경기도
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 41
100.0%

Length

2023-12-13T01:09:21.172667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:21.267316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 41
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
포천시
41 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
포천시 41
100.0%

Length

2023-12-13T01:09:21.352054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:21.435641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
포천시 41
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Memory size460.0 B
41650
41 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41650
2nd row41650
3rd row41650
4th row41650
5th row41650

Common Values

ValueCountFrequency (%)
41650 41
100.0%

Length

2023-12-13T01:09:21.524253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:21.626639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41650 41
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.3%
Missing0
Missing (%)0.0%
Memory size460.0 B
2017
14 
2018
14 
2019
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

Length

2023-12-13T01:09:21.724687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T01:09:21.817783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 14
34.1%
2018 14
34.1%
2019 13
31.7%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)34.1%
Missing0
Missing (%)0.0%
Memory size460.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (9)
26 

Length

Max length7
Median length5
Mean length4.3902439
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row도축세
2nd row레저세
3rd row재산세
4th row주민세
5th row취득세

Common Values

ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

Length

2023-12-13T01:09:21.961626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.3%
재산세 3
 
7.3%
주민세 3
 
7.3%
취득세 3
 
7.3%
자동차세 3
 
7.3%
과년도수입 3
 
7.3%
담배소비세 3
 
7.3%
도시계획세 3
 
7.3%
등록면허세 3
 
7.3%
지방교육세 3
 
7.3%
Other values (4) 11
26.8%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0081571 × 1010
Minimum0
Maximum8.1698195 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T01:09:22.072403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median1.7549865 × 1010
Q33.4074647 × 1010
95-th percentile7.394777 × 1010
Maximum8.1698195 × 1010
Range8.1698195 × 1010
Interquartile range (IQR)3.4074647 × 1010

Descriptive statistics

Standard deviation2.1755298 × 1010
Coefficient of variation (CV)1.0833464
Kurtosis1.5517275
Mean2.0081571 × 1010
Median Absolute Deviation (MAD)1.7549865 × 1010
Skewness1.3407523
Sum8.2334442 × 1011
Variance4.7329299 × 1020
MonotonicityNot monotonic
2023-12-13T01:09:22.194929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
36975331000 1
 
2.4%
7906295000 1
 
2.4%
37061126000 1
 
2.4%
25038676000 1
 
2.4%
7994913000 1
 
2.4%
17584177000 1
 
2.4%
18929588000 1
 
2.4%
31313398000 1
 
2.4%
73947770000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
5239756000 1
 
2.4%
5423509000 1
 
2.4%
5755638000 1
 
2.4%
7103160000 1
 
2.4%
7294444000 1
 
2.4%
7906295000 1
 
2.4%
7947361000 1
 
2.4%
7994913000 1
 
2.4%
8515046000 1
 
2.4%
ValueCountFrequency (%)
81698195000 1
2.4%
77673440000 1
2.4%
73947770000 1
2.4%
44655221000 1
2.4%
40861483000 1
2.4%
38627231000 1
2.4%
37352242000 1
2.4%
37061126000 1
2.4%
36975331000 1
2.4%
36033464000 1
2.4%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)75.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8325069 × 1010
Minimum0
Maximum8.0981578 × 1010
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T01:09:22.327465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7.966189 × 109
Q33.0481849 × 1010
95-th percentile7.3417565 × 1010
Maximum8.0981578 × 1010
Range8.0981578 × 1010
Interquartile range (IQR)3.0481849 × 1010

Descriptive statistics

Standard deviation2.1549627 × 1010
Coefficient of variation (CV)1.1759643
Kurtosis1.9938952
Mean1.8325069 × 1010
Median Absolute Deviation (MAD)7.966189 × 109
Skewness1.510185
Sum7.5132782 × 1011
Variance4.6438642 × 1020
MonotonicityNot monotonic
2023-12-13T01:09:22.802781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 11
26.8%
35580242000 1
 
2.4%
7714849000 1
 
2.4%
34661607000 1
 
2.4%
23971909000 1
 
2.4%
7966189000 1
 
2.4%
17584177000 1
 
2.4%
3775016000 1
 
2.4%
28626247000 1
 
2.4%
73417565000 1
 
2.4%
Other values (21) 21
51.2%
ValueCountFrequency (%)
0 11
26.8%
3472442000 1
 
2.4%
3775016000 1
 
2.4%
4931512000 1
 
2.4%
5087884000 1
 
2.4%
5414703000 1
 
2.4%
7084582000 1
 
2.4%
7115472000 1
 
2.4%
7714849000 1
 
2.4%
7788362000 1
 
2.4%
ValueCountFrequency (%)
80981578000 1
2.4%
76866978000 1
2.4%
73417565000 1
2.4%
42357791000 1
2.4%
39389722000 1
2.4%
37192341000 1
2.4%
35580242000 1
2.4%
34661607000 1
2.4%
34216696000 1
2.4%
33299288000 1
2.4%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1172824 × 108
Minimum0
Maximum3.239472 × 109
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T01:09:22.918320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median14110000
Q32.5556 × 108
95-th percentile1.631058 × 109
Maximum3.239472 × 109
Range3.239472 × 109
Interquartile range (IQR)2.5556 × 108

Descriptive statistics

Standard deviation6.9694975 × 108
Coefficient of variation (CV)2.2357607
Kurtosis9.8034148
Mean3.1172824 × 108
Median Absolute Deviation (MAD)14110000
Skewness3.0857442
Sum1.2780858 × 1010
Variance4.8573896 × 1017
MonotonicityNot monotonic
2023-12-13T01:09:23.030311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
51421000 1
 
2.4%
2138000 1
 
2.4%
909116000 1
 
2.4%
122936000 1
 
2.4%
28801000 1
 
2.4%
2627822000 1
 
2.4%
256807000 1
 
2.4%
550087000 1
 
2.4%
5944000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
1541000 1
 
2.4%
2026000 1
 
2.4%
2138000 1
 
2.4%
3563000 1
 
2.4%
5944000 1
 
2.4%
8256000 1
 
2.4%
14110000 1
 
2.4%
19166000 1
 
2.4%
23028000 1
 
2.4%
ValueCountFrequency (%)
3239472000 1
2.4%
2627822000 1
2.4%
1631058000 1
2.4%
980290000 1
2.4%
909116000 1
2.4%
862602000 1
2.4%
550087000 1
2.4%
474945000 1
2.4%
275126000 1
2.4%
256807000 1
2.4%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)63.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5718485 × 108
Minimum0
Maximum4.142407 × 109
Zeros16
Zeros (%)39.0%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T01:09:23.152570image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median192000
Q315572000
95-th percentile2.285115 × 109
Maximum4.142407 × 109
Range4.142407 × 109
Interquartile range (IQR)15572000

Descriptive statistics

Standard deviation8.1678731 × 108
Coefficient of variation (CV)3.1758764
Kurtosis14.418232
Mean2.5718485 × 108
Median Absolute Deviation (MAD)192000
Skewness3.7566652
Sum1.0544579 × 1010
Variance6.671415 × 1017
MonotonicityNot monotonic
2023-12-13T01:09:23.274386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 16
39.0%
2506657000 1
 
2.4%
67000 1
 
2.4%
152511000 1
 
2.4%
326000 1
 
2.4%
59000 1
 
2.4%
2285115000 1
 
2.4%
936000 1
 
2.4%
41000 1
 
2.4%
443000 1
 
2.4%
Other values (16) 16
39.0%
ValueCountFrequency (%)
0 16
39.0%
41000 1
 
2.4%
59000 1
 
2.4%
67000 1
 
2.4%
155000 1
 
2.4%
192000 1
 
2.4%
257000 1
 
2.4%
326000 1
 
2.4%
425000 1
 
2.4%
443000 1
 
2.4%
ValueCountFrequency (%)
4142407000 1
2.4%
2506657000 1
2.4%
2285115000 1
2.4%
600775000 1
2.4%
573343000 1
2.4%
152511000 1
2.4%
107575000 1
2.4%
66181000 1
2.4%
58574000 1
2.4%
18392000 1
2.4%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)68.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4993176 × 109
Minimum0
Maximum1.2869457 × 1010
Zeros14
Zeros (%)34.1%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T01:09:23.440187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.07819 × 108
Q31.433692 × 109
95-th percentile1.0741155 × 1010
Maximum1.2869457 × 1010
Range1.2869457 × 1010
Interquartile range (IQR)1.433692 × 109

Descriptive statistics

Standard deviation3.0579973 × 109
Coefficient of variation (CV)2.0395927
Kurtosis8.1917961
Mean1.4993176 × 109
Median Absolute Deviation (MAD)3.07819 × 108
Skewness2.9583819
Sum6.1472023 × 1010
Variance9.3513472 × 1018
MonotonicityNot monotonic
2023-12-13T01:09:23.576809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 14
34.1%
23172000 1
 
2.4%
191379000 1
 
2.4%
2247008000 1
 
2.4%
1066441000 1
 
2.4%
28665000 1
 
2.4%
12869457000 1
 
2.4%
2686215000 1
 
2.4%
530205000 1
 
2.4%
340894000 1
 
2.4%
Other values (18) 18
43.9%
ValueCountFrequency (%)
0 14
34.1%
18386000 1
 
2.4%
23172000 1
 
2.4%
28665000 1
 
2.4%
158999000 1
 
2.4%
163400000 1
 
2.4%
191379000 1
 
2.4%
307819000 1
 
2.4%
335470000 1
 
2.4%
340894000 1
 
2.4%
ValueCountFrequency (%)
12869457000 1
2.4%
11570766000 1
2.4%
10741155000 1
2.4%
3019455000 1
2.4%
2730166000 1
2.4%
2686215000 1
2.4%
2534771000 1
2.4%
2295116000 1
2.4%
2247008000 1
2.4%
1471318000 1
2.4%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)70.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean65.322927
Minimum0
Maximum100
Zeros11
Zeros (%)26.8%
Negative0
Negative (%)0.0%
Memory size501.0 B
2023-12-13T01:09:23.693381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median94.08
Q397.58
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)97.58

Descriptive statistics

Standard deviation44.184574
Coefficient of variation (CV)0.67640224
Kurtosis-1.4415822
Mean65.322927
Median Absolute Deviation (MAD)5.2
Skewness-0.74591657
Sum2678.24
Variance1952.2766
MonotonicityNot monotonic
2023-12-13T01:09:23.817441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 11
26.8%
100.0 3
 
7.3%
99.72 1
 
2.4%
97.58 1
 
2.4%
93.53 1
 
2.4%
95.74 1
 
2.4%
99.64 1
 
2.4%
19.94 1
 
2.4%
91.42 1
 
2.4%
99.28 1
 
2.4%
Other values (19) 19
46.3%
ValueCountFrequency (%)
0.0 11
26.8%
19.79 1
 
2.4%
19.94 1
 
2.4%
37.15 1
 
2.4%
89.46 1
 
2.4%
91.42 1
 
2.4%
91.61 1
 
2.4%
92.41 1
 
2.4%
93.53 1
 
2.4%
93.81 1
 
2.4%
ValueCountFrequency (%)
100.0 3
7.3%
99.74 1
 
2.4%
99.72 1
 
2.4%
99.64 1
 
2.4%
99.28 1
 
2.4%
99.12 1
 
2.4%
98.96 1
 
2.4%
98.0 1
 
2.4%
97.58 1
 
2.4%
97.55 1
 
2.4%

Interactions

2023-12-13T01:09:20.358794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:17.842968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.332936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.877822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.399778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.867568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.443091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:17.930457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.429692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.963058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.502007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.962644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.523224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.019396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.514473image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.048722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.583686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.045434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.610094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.105076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.599569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.136540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.656688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.130437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.683823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.172907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.679845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.227463image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.723643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.204697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.760136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.255389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:18.785314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.311691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:19.798252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T01:09:20.284843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T01:09:23.952771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9730.9450.7960.6090.8320.827
부과금액0.0000.9731.0000.9700.7670.3360.6750.487
수납급액0.0000.9450.9701.0000.7830.3950.7440.644
환급금액0.0000.7960.7670.7831.0000.9630.8270.848
결손금액0.0000.6090.3360.3950.9631.0000.9580.973
미수납 금액0.0000.8320.6750.7440.8270.9581.0000.914
징수율0.0000.8270.4870.6440.8480.9730.9141.000
2023-12-13T01:09:24.099704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T01:09:24.190340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9770.7700.6770.7540.5730.0000.670
수납급액0.9771.0000.6680.5830.6530.6540.0000.588
환급금액0.7700.6681.0000.8760.9240.2480.0000.370
결손금액0.6770.5830.8761.0000.8970.1760.0000.311
미수납 금액0.7540.6530.9240.8971.0000.1550.0000.536
징수율0.5730.6540.2480.1760.1551.0000.0000.529
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6700.5880.3700.3110.5360.5290.0001.000

Missing values

2023-12-13T01:09:20.875065image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T01:09:21.053725image/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 금액징수율
0경기도포천시416502017도축세000000.0
1경기도포천시416502017레저세000000.0
2경기도포천시416502017재산세36975331000355802420001411000058574000133651500096.23
3경기도포천시416502017주민세52397560004931512000825600042500030781900094.12
4경기도포천시416502017취득세816981950008098157800027512600010757500060904200099.12
5경기도포천시416502017자동차세44655221000423577910002255740002314000229511600094.86
6경기도포천시416502017과년도수입236813890008797827000163105800041424070001074115500037.15
7경기도포천시416502017담배소비세1897972400018979724000000100.0
8경기도포천시416502017도시계획세000000.0
9경기도포천시416502017등록면허세71031600007084582000230280001920001838600099.74
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
31경기도포천시416502019취득세7394777000073417565000550087000053020500099.28
32경기도포천시416502019자동차세3131339800028626247000256807000936000268621500091.42
33경기도포천시416502019과년도수입189295880003775016000262782200022851150001286945700019.94
34경기도포천시416502019담배소비세1758417700017584177000000100.0
35경기도포천시416502019도시계획세000000.0
36경기도포천시416502019등록면허세7994913000796618900028801000590002866500099.64
37경기도포천시416502019지방교육세2503867600023971909000122936000326000106644100095.74
38경기도포천시416502019지방소득세3706112600034661607000909116000152511000224700800093.53
39경기도포천시416502019지방소비세000000.0
40경기도포천시416502019지역자원시설세7906295000771484900021380006700019137900097.58