Overview

Dataset statistics

Number of variables11
Number of observations40
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.9 KiB
Average record size in memory99.3 B

Variable types

Categorical5
Numeric6

Dataset

Description경상남도 사천시 지방세 징수 현황(2018 ~ 2020년)에 대한 데이터로 지방세 부과액에 대한 세목별 징수 현황을 제공합니다.
Author경상남도 사천시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15079515

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액 and 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
환급금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
징수율 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
부과금액 has 9 (22.5%) zerosZeros
수납급액 has 9 (22.5%) zerosZeros
환급금액 has 11 (27.5%) zerosZeros
결손금액 has 18 (45.0%) zerosZeros
미수납 금액 has 13 (32.5%) zerosZeros
징수율 has 9 (22.5%) zerosZeros

Reproduction

Analysis started2023-12-11 00:20:27.792347
Analysis finished2023-12-11 00:20:31.730543
Duration3.94 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
경상남도
40 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상남도 40
100.0%

Length

2023-12-11T09:20:31.795239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:31.888995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 40
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
사천시
40 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
사천시 40
100.0%

Length

2023-12-11T09:20:31.967127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:32.041932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
사천시 40
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
48240
40 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48240
2nd row48240
3rd row48240
4th row48240
5th row48240

Common Values

ValueCountFrequency (%)
48240 40
100.0%

Length

2023-12-11T09:20:32.121048image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:32.209229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48240 40
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Memory size452.0 B
2018
14 
2019
13 
2020
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2018
2nd row2018
3rd row2018
4th row2018
5th row2018

Common Values

ValueCountFrequency (%)
2018 14
35.0%
2019 13
32.5%
2020 13
32.5%

Length

2023-12-11T09:20:32.301697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T09:20:32.398711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2018 14
35.0%
2019 13
32.5%
2020 13
32.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)35.0%
Missing0
Missing (%)0.0%
Memory size452.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (9)
25 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique1 ?
Unique (%)2.5%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 3
 
7.5%
재산세 3
 
7.5%
주민세 3
 
7.5%
취득세 3
 
7.5%
자동차세 3
 
7.5%
과년도수입 3
 
7.5%
담배소비세 3
 
7.5%
도시계획세 3
 
7.5%
등록면허세 3
 
7.5%
지방교육세 3
 
7.5%
Other values (4) 10
25.0%

Length

2023-12-11T09:20:32.503395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.5%
재산세 3
 
7.5%
주민세 3
 
7.5%
취득세 3
 
7.5%
자동차세 3
 
7.5%
과년도수입 3
 
7.5%
담배소비세 3
 
7.5%
도시계획세 3
 
7.5%
등록면허세 3
 
7.5%
지방교육세 3
 
7.5%
Other values (4) 10
25.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.1137845 × 1010
Minimum0
Maximum4.7919021 × 1010
Zeros9
Zeros (%)22.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:20:32.618013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.542994 × 109
median6.2136285 × 109
Q31.6549921 × 1010
95-th percentile3.5170837 × 1010
Maximum4.7919021 × 1010
Range4.7919021 × 1010
Interquartile range (IQR)1.4006927 × 1010

Descriptive statistics

Standard deviation1.2355407 × 1010
Coefficient of variation (CV)1.1093176
Kurtosis1.8439281
Mean1.1137845 × 1010
Median Absolute Deviation (MAD)6.2136285 × 109
Skewness1.4739659
Sum4.4551379 × 1011
Variance1.5265608 × 1020
MonotonicityNot monotonic
2023-12-11T09:20:32.782744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 9
22.5%
3717398000 1
 
2.5%
3172988000 1
 
2.5%
10326700000 1
 
2.5%
28997441000 1
 
2.5%
13539985000 1
 
2.5%
3599685000 1
 
2.5%
10146660000 1
 
2.5%
1373477000 1
 
2.5%
16484251000 1
 
2.5%
Other values (22) 22
55.0%
ValueCountFrequency (%)
0 9
22.5%
1373477000 1
 
2.5%
2932833000 1
 
2.5%
3055611000 1
 
2.5%
3172988000 1
 
2.5%
3599685000 1
 
2.5%
3717398000 1
 
2.5%
4217352000 1
 
2.5%
4794852000 1
 
2.5%
4846847000 1
 
2.5%
ValueCountFrequency (%)
47919021000 1
2.5%
45379562000 1
2.5%
34633536000 1
2.5%
28997441000 1
2.5%
28929671000 1
2.5%
27365720000 1
2.5%
18960292000 1
2.5%
18311761000 1
2.5%
17314661000 1
2.5%
16746932000 1
2.5%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct32
Distinct (%)80.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0581427 × 1010
Minimum-2.737354 × 109
Maximum4.7782806 × 1010
Zeros9
Zeros (%)22.5%
Negative1
Negative (%)2.5%
Memory size492.0 B
2023-12-11T09:20:32.909745image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-2.737354 × 109
5-th percentile0
Q15.64456 × 108
median6.144753 × 109
Q31.5596203 × 1010
95-th percentile3.4966907 × 1010
Maximum4.7782806 × 1010
Range5.052016 × 1010
Interquartile range (IQR)1.5031747 × 1010

Descriptive statistics

Standard deviation1.239234 × 1010
Coefficient of variation (CV)1.1711407
Kurtosis1.9607951
Mean1.0581427 × 1010
Median Absolute Deviation (MAD)6.144753 × 109
Skewness1.4885489
Sum4.2325708 × 1011
Variance1.535701 × 1020
MonotonicityNot monotonic
2023-12-11T09:20:33.016705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
0 9
22.5%
3707685000 1
 
2.5%
3082428000 1
 
2.5%
10326700000 1
 
2.5%
27966950000 1
 
2.5%
13150201000 1
 
2.5%
3591099000 1
 
2.5%
10146660000 1
 
2.5%
-2737354000 1
 
2.5%
15547325000 1
 
2.5%
Other values (22) 22
55.0%
ValueCountFrequency (%)
-2737354000 1
 
2.5%
0 9
22.5%
752608000 1
 
2.5%
900669000 1
 
2.5%
2845476000 1
 
2.5%
2968389000 1
 
2.5%
3082428000 1
 
2.5%
3591099000 1
 
2.5%
3707685000 1
 
2.5%
4208532000 1
 
2.5%
ValueCountFrequency (%)
47782806000 1
2.5%
45153438000 1
2.5%
34430774000 1
2.5%
28029567000 1
2.5%
27966950000 1
2.5%
26529350000 1
2.5%
18192985000 1
2.5%
17656456000 1
2.5%
16685442000 1
2.5%
15742838000 1
2.5%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)75.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.1268935 × 108
Minimum0
Maximum4.997241 × 109
Zeros11
Zeros (%)27.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:20:33.126074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median7555000
Q31.007375 × 108
95-th percentile1.8093757 × 109
Maximum4.997241 × 109
Range4.997241 × 109
Interquartile range (IQR)1.007375 × 108

Descriptive statistics

Standard deviation8.856142 × 108
Coefficient of variation (CV)2.8322493
Kurtosis20.86336
Mean3.1268935 × 108
Median Absolute Deviation (MAD)7555000
Skewness4.3086416
Sum1.2507574 × 1010
Variance7.8431251 × 1017
MonotonicityNot monotonic
2023-12-11T09:20:33.248417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 11
27.5%
12086000 1
 
2.5%
579000 1
 
2.5%
735634000 1
 
2.5%
71919000 1
 
2.5%
30002000 1
 
2.5%
1238000 1
 
2.5%
4997241000 1
 
2.5%
111779000 1
 
2.5%
369632000 1
 
2.5%
Other values (20) 20
50.0%
ValueCountFrequency (%)
0 11
27.5%
78000 1
 
2.5%
259000 1
 
2.5%
579000 1
 
2.5%
907000 1
 
2.5%
1238000 1
 
2.5%
2181000 1
 
2.5%
4116000 1
 
2.5%
4314000 1
 
2.5%
7513000 1
 
2.5%
ValueCountFrequency (%)
4997241000 1
2.5%
2038054000 1
2.5%
1797340000 1
2.5%
1040928000 1
2.5%
735634000 1
2.5%
524786000 1
2.5%
369632000 1
2.5%
167543000 1
2.5%
154660000 1
2.5%
111779000 1
2.5%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct23
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean91759075
Minimum0
Maximum1.056216 × 109
Zeros18
Zeros (%)45.0%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:20:33.399205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median38000
Q31440500
95-th percentile9.340552 × 108
Maximum1.056216 × 109
Range1.056216 × 109
Interquartile range (IQR)1440500

Descriptive statistics

Standard deviation2.7031139 × 108
Coefficient of variation (CV)2.9458819
Kurtosis8.8147379
Mean91759075
Median Absolute Deviation (MAD)38000
Skewness3.1548031
Sum3.670363 × 109
Variance7.306825 × 1016
MonotonicityNot monotonic
2023-12-11T09:20:33.508738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 18
45.0%
1345000 1
 
2.5%
981000 1
 
2.5%
185765000 1
 
2.5%
1727000 1
 
2.5%
48000 1
 
2.5%
928727000 1
 
2.5%
478000 1
 
2.5%
10000 1
 
2.5%
7916000 1
 
2.5%
Other values (13) 13
32.5%
ValueCountFrequency (%)
0 18
45.0%
10000 1
 
2.5%
28000 1
 
2.5%
48000 1
 
2.5%
76000 1
 
2.5%
112000 1
 
2.5%
166000 1
 
2.5%
207000 1
 
2.5%
433000 1
 
2.5%
448000 1
 
2.5%
ValueCountFrequency (%)
1056216000 1
2.5%
1035291000 1
2.5%
928727000 1
2.5%
303051000 1
2.5%
185765000 1
2.5%
74302000 1
2.5%
67273000 1
2.5%
7916000 1
2.5%
5763000 1
2.5%
1727000 1
2.5%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.6465885 × 108
Minimum0
Maximum3.182104 × 109
Zeros13
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size492.0 B
2023-12-11T09:20:33.611844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median87017000
Q36.3468 × 108
95-th percentile2.914644 × 109
Maximum3.182104 × 109
Range3.182104 × 109
Interquartile range (IQR)6.3468 × 108

Descriptive statistics

Standard deviation8.1066057 × 108
Coefficient of variation (CV)1.744636
Kurtosis5.8942503
Mean4.6465885 × 108
Median Absolute Deviation (MAD)87017000
Skewness2.5004319
Sum1.8586354 × 1010
Variance6.5717056 × 1017
MonotonicityNot monotonic
2023-12-11T09:20:33.717342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 13
32.5%
9685000 1
 
2.5%
89579000 1
 
2.5%
844726000 1
 
2.5%
388057000 1
 
2.5%
8538000 1
 
2.5%
3182104000 1
 
2.5%
936448000 1
 
2.5%
136215000 1
 
2.5%
61377000 1
 
2.5%
Other values (18) 18
45.0%
ValueCountFrequency (%)
0 13
32.5%
8538000 1
 
2.5%
8820000 1
 
2.5%
9685000 1
 
2.5%
61377000 1
 
2.5%
76364000 1
 
2.5%
83061000 1
 
2.5%
86924000 1
 
2.5%
87110000 1
 
2.5%
89579000 1
 
2.5%
ValueCountFrequency (%)
3182104000 1
2.5%
2986028000 1
2.5%
2910887000 1
2.5%
1092958000 1
2.5%
1003928000 1
2.5%
936448000 1
2.5%
844726000 1
2.5%
762068000 1
2.5%
759391000 1
2.5%
655098000 1
2.5%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.2905
Minimum-199.3
Maximum100
Zeros9
Zeros (%)22.5%
Negative1
Negative (%)2.5%
Memory size492.0 B
2023-12-11T09:20:33.830347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-199.3
5-th percentile0
Q111.775
median96.72
Q399.11
95-th percentile100
Maximum100
Range299.3
Interquartile range (IQR)87.335

Descriptive statistics

Standard deviation60.459543
Coefficient of variation (CV)0.94041177
Kurtosis8.0067467
Mean64.2905
Median Absolute Deviation (MAD)3.01
Skewness-2.425214
Sum2571.62
Variance3655.3564
MonotonicityNot monotonic
2023-12-11T09:20:33.935907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0.0 9
22.5%
100.0 4
 
10.0%
97.15 2
 
5.0%
15.7 1
 
2.5%
96.45 1
 
2.5%
97.12 1
 
2.5%
99.76 1
 
2.5%
-199.3 1
 
2.5%
94.32 1
 
2.5%
99.72 1
 
2.5%
Other values (18) 18
45.0%
ValueCountFrequency (%)
-199.3 1
 
2.5%
0.0 9
22.5%
15.7 1
 
2.5%
18.58 1
 
2.5%
93.16 1
 
2.5%
94.0 1
 
2.5%
94.32 1
 
2.5%
95.95 1
 
2.5%
96.37 1
 
2.5%
96.42 1
 
2.5%
ValueCountFrequency (%)
100.0 4
10.0%
99.79 1
 
2.5%
99.76 1
 
2.5%
99.74 1
 
2.5%
99.72 1
 
2.5%
99.5 1
 
2.5%
99.41 1
 
2.5%
99.01 1
 
2.5%
98.78 1
 
2.5%
98.55 1
 
2.5%

Interactions

2023-12-11T09:20:31.050941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.115644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.682879image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.235736image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.788651image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.584649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:31.125663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.207428image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.775271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.325505image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.885760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.658612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:31.215533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.290747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.862496image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.418036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.979358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.746059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:31.288172image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.377808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.942195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.503832image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.058361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.826437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:31.361076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.476677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.035444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.596893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.150834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.896432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:31.436508image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:28.584833image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.139408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:29.692074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.241778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T09:20:30.976057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T09:20:34.012444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8950.9000.5820.5920.9230.974
부과금액0.0000.8951.0000.9460.7810.5950.6670.592
수납급액0.0000.9000.9461.0000.8120.6370.6920.882
환급금액0.0000.5820.7810.8121.0000.9010.6300.696
결손금액0.0000.5920.5950.6370.9011.0000.8690.658
미수납 금액0.0000.9230.6670.6920.6300.8691.0000.733
징수율0.0000.9740.5920.8820.6960.6580.7331.000
2023-12-11T09:20:34.108244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-11T09:20:34.185675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9820.6760.4110.6110.4770.0000.608
수납급액0.9821.0000.5700.3170.5060.5290.0000.621
환급금액0.6760.5701.0000.6680.8370.1720.0000.277
결손금액0.4110.3170.6681.0000.794-0.0540.0000.296
미수납 금액0.6110.5060.8370.7941.000-0.0880.0000.681
징수율0.4770.5290.172-0.054-0.0881.0000.0000.571
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.6080.6210.2770.2960.6810.5710.0001.000

Missing values

2023-12-11T09:20:31.542656image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T09:20:31.681494image/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
0경상남도사천시482402018도축세000000.0
1경상남도사천시482402018레저세000000.0
2경상남도사천시482402018재산세17314661000166854420004116000134500062787400096.37
3경상남도사천시482402018주민세575181400056683050002590004480008306100098.55
4경상남도사천시482402018취득세45379562000451534380001546600006727300015885100099.5
5경상남도사천시482402018자동차세1597927800014886320000938680000109295800093.16
6경상남도사천시482402018과년도수입484684700090066900017973400001035291000291088700018.58
7경상남도사천시482402018담배소비세95466880009546688000000100.0
8경상남도사천시482402018도시계획세000000.0
9경상남도사천시482402018등록면허세4217352000420853200075970000882000099.79
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
30경상남도사천시482402020취득세4791902100047782806000369632000013621500099.72
31경상남도사천시482402020자동차세164842510001554732500011177900047800093644800094.32
32경상남도사천시482402020과년도수입1373477000-273735400049972410009287270003182104000-199.3
33경상남도사천시482402020담배소비세1014666000010146660000123800000100.0
34경상남도사천시482402020도시계획세000000.0
35경상남도사천시482402020등록면허세359968500035910990003000200048000853800099.76
36경상남도사천시482402020지방교육세135399850001315020100071919000172700038805700097.12
37경상남도사천시482402020지방소득세289974410002796695000073563400018576500084472600096.45
38경상남도사천시482402020지방소비세1032670000010326700000000100.0
39경상남도사천시482402020지역자원시설세317298800030824280005790009810008957900097.15