Overview

Dataset statistics

Number of variables11
Number of observations80
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.6 KiB
Average record size in memory97.7 B

Variable types

Categorical4
Numeric7

Dataset

Description본 데이터는 경상남도 합천군의 년도별 지방세 징수현황으로 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율 등의 정보를 제공하고 있습니다.
URLhttps://www.data.go.kr/data/15089299/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 3 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 3 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 환급금액 and 1 other fieldsHigh correlation
징수율 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 has 16 (20.0%) zerosZeros
수납급액 has 16 (20.0%) zerosZeros
환급금액 has 23 (28.7%) zerosZeros
결손금액 has 37 (46.2%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 16 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 12:11:03.667479
Analysis finished2023-12-12 12:11:09.284172
Duration5.62 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
경상남도
80 

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 80
100.0%

Length

2023-12-12T21:11:09.356375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:11:09.483974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상남도 80
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
합천군
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
합천군 80
100.0%

Length

2023-12-12T21:11:09.592670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:11:09.697314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
합천군 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
48890
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48890
2nd row48890
3rd row48890
4th row48890
5th row48890

Common Values

ValueCountFrequency (%)
48890 80
100.0%

Length

2023-12-12T21:11:09.823684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:11:09.950697image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48890 80
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)7.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.45
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T21:11:10.049703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2019
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.7276603
Coefficient of variation (CV)0.00085551031
Kurtosis-1.2889692
Mean2019.45
Median Absolute Deviation (MAD)1.5
Skewness0.041238905
Sum161556
Variance2.9848101
MonotonicityIncreasing
2023-12-12T21:11:10.169129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2017 14
17.5%
2018 14
17.5%
2019 13
16.2%
2020 13
16.2%
2021 13
16.2%
2022 13
16.2%
ValueCountFrequency (%)
2022 13
16.2%
2021 13
16.2%
2020 13
16.2%
2019 13
16.2%
2018 14
17.5%
2017 14
17.5%

세목명
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)17.5%
Missing0
Missing (%)0.0%
Memory size772.0 B
레저세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (9)
50 

Length

Max length7
Median length5
Mean length4.425
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

Length

2023-12-12T21:11:10.330143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 6
 
7.5%
재산세 6
 
7.5%
주민세 6
 
7.5%
취득세 6
 
7.5%
자동차세 6
 
7.5%
과년도수입 6
 
7.5%
담배소비세 6
 
7.5%
도시계획세 6
 
7.5%
등록면허세 6
 
7.5%
지방교육세 6
 
7.5%
Other values (4) 20
25.0%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0859038 × 109
Minimum-6.93374 × 108
Maximum1.35987 × 1010
Zeros16
Zeros (%)20.0%
Negative1
Negative (%)1.2%
Memory size852.0 B
2023-12-12T21:11:10.493990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-6.93374 × 108
5-th percentile0
Q15.6534925 × 108
median1.1583155 × 109
Q34.5699995 × 109
95-th percentile1.0905477 × 1010
Maximum1.35987 × 1010
Range1.4292074 × 1010
Interquartile range (IQR)4.0046502 × 109

Descriptive statistics

Standard deviation3.5235652 × 109
Coefficient of variation (CV)1.141826
Kurtosis0.69648883
Mean3.0859038 × 109
Median Absolute Deviation (MAD)1.2855755 × 109
Skewness1.2437908
Sum2.4687231 × 1011
Variance1.2415511 × 1019
MonotonicityNot monotonic
2023-12-12T21:11:10.705569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
3719101000 1
 
1.2%
998440000 1
 
1.2%
2767393000 1
 
1.2%
1018499000 1
 
1.2%
3523067000 1
 
1.2%
4778368000 1
 
1.2%
9641700000 1
 
1.2%
679215000 1
 
1.2%
4274627000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
-693374000 1
 
1.2%
0 16
20.0%
25270000 1
 
1.2%
539358000 1
 
1.2%
556194000 1
 
1.2%
568401000 1
 
1.2%
586065000 1
 
1.2%
586402000 1
 
1.2%
599212000 1
 
1.2%
610129000 1
 
1.2%
ValueCountFrequency (%)
13598700000 1
1.2%
11796898000 1
1.2%
11597434000 1
1.2%
10915237000 1
1.2%
10904963000 1
1.2%
10083504000 1
1.2%
9939500000 1
1.2%
9641700000 1
1.2%
9574777000 1
1.2%
8412467000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9829582 × 109
Minimum-1.179948 × 109
Maximum1.35987 × 1010
Zeros16
Zeros (%)20.0%
Negative1
Negative (%)1.2%
Memory size852.0 B
2023-12-12T21:11:10.883142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-1.179948 × 109
5-th percentile0
Q13.2058875 × 108
median9.884205 × 108
Q34.220702 × 109
95-th percentile1.0868842 × 1010
Maximum1.35987 × 1010
Range1.4778648 × 1010
Interquartile range (IQR)3.9001132 × 109

Descriptive statistics

Standard deviation3.524657 × 109
Coefficient of variation (CV)1.1815978
Kurtosis0.80928665
Mean2.9829582 × 109
Median Absolute Deviation (MAD)1.2855755 × 109
Skewness1.2744286
Sum2.3863666 × 1011
Variance1.2423207 × 1019
MonotonicityNot monotonic
2023-12-12T21:11:11.109690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
3645476000 1
 
1.2%
383293000 1
 
1.2%
2767393000 1
 
1.2%
1015333000 1
 
1.2%
3447948000 1
 
1.2%
4569438000 1
 
1.2%
9641700000 1
 
1.2%
657641000 1
 
1.2%
4153687000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
-1179948000 1
 
1.2%
0 16
20.0%
25270000 1
 
1.2%
61736000 1
 
1.2%
242675000 1
 
1.2%
346560000 1
 
1.2%
383293000 1
 
1.2%
521885000 1
 
1.2%
537198000 1
 
1.2%
548854000 1
 
1.2%
ValueCountFrequency (%)
13598700000 1
1.2%
11785253000 1
1.2%
11576823000 1
1.2%
10890612000 1
1.2%
10867696000 1
1.2%
9998899000 1
1.2%
9900485000 1
1.2%
9641700000 1
1.2%
9574777000 1
1.2%
8150018000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60874588
Minimum0
Maximum1.675334 × 109
Zeros23
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T21:11:11.309013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2381500
Q361808500
95-th percentile1.809688 × 108
Maximum1.675334 × 109
Range1.675334 × 109
Interquartile range (IQR)61808500

Descriptive statistics

Standard deviation1.966056 × 108
Coefficient of variation (CV)3.2296826
Kurtosis59.16891
Mean60874588
Median Absolute Deviation (MAD)2381500
Skewness7.2817007
Sum4.869967 × 109
Variance3.8653761 × 1016
MonotonicityNot monotonic
2023-12-12T21:11:11.485555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
28.7%
11093000 1
 
1.2%
204240000 1
 
1.2%
3199000 1
 
1.2%
3807000 1
 
1.2%
33216000 1
 
1.2%
178195000 1
 
1.2%
475000 1
 
1.2%
4154000 1
 
1.2%
154000 1
 
1.2%
Other values (48) 48
60.0%
ValueCountFrequency (%)
0 23
28.7%
3000 1
 
1.2%
57000 1
 
1.2%
59000 1
 
1.2%
64000 1
 
1.2%
81000 1
 
1.2%
97000 1
 
1.2%
154000 1
 
1.2%
161000 1
 
1.2%
472000 1
 
1.2%
ValueCountFrequency (%)
1675334000 1
1.2%
420701000 1
1.2%
270491000 1
1.2%
204240000 1
1.2%
179744000 1
1.2%
178195000 1
1.2%
172842000 1
1.2%
149520000 1
1.2%
143297000 1
1.2%
119965000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13844675
Minimum0
Maximum2.7771 × 108
Zeros37
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T21:11:12.005658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median12000
Q3500000
95-th percentile1.1834465 × 108
Maximum2.7771 × 108
Range2.7771 × 108
Interquartile range (IQR)500000

Descriptive statistics

Standard deviation44412400
Coefficient of variation (CV)3.2079048
Kurtosis17.425543
Mean13844675
Median Absolute Deviation (MAD)12000
Skewness3.9510278
Sum1.107574 × 109
Variance1.9724613 × 1015
MonotonicityNot monotonic
2023-12-12T21:11:12.191012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 37
46.2%
12000 2
 
2.5%
82000 2
 
2.5%
9017000 1
 
1.2%
33000 1
 
1.2%
41263000 1
 
1.2%
62000 1
 
1.2%
692000 1
 
1.2%
180000 1
 
1.2%
10000 1
 
1.2%
Other values (32) 32
40.0%
ValueCountFrequency (%)
0 37
46.2%
9000 1
 
1.2%
10000 1
 
1.2%
12000 2
 
2.5%
19000 1
 
1.2%
21000 1
 
1.2%
33000 1
 
1.2%
41000 1
 
1.2%
60000 1
 
1.2%
62000 1
 
1.2%
ValueCountFrequency (%)
277710000 1
1.2%
148461000 1
1.2%
137964000 1
1.2%
134222000 1
1.2%
117509000 1
1.2%
111116000 1
1.2%
81468000 1
1.2%
41263000 1
1.2%
12858000 1
1.2%
9017000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean89100988
Minimum0
Maximum5.98467 × 108
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T21:11:12.394290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median18749000
Q399504500
95-th percentile4.173494 × 108
Maximum5.98467 × 108
Range5.98467 × 108
Interquartile range (IQR)99504500

Descriptive statistics

Standard deviation1.4428908 × 108
Coefficient of variation (CV)1.6193881
Kurtosis3.7999405
Mean89100988
Median Absolute Deviation (MAD)18749000
Skewness2.0888705
Sum7.128079 × 109
Variance2.0819339 × 1016
MonotonicityNot monotonic
2023-12-12T21:11:12.604276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
81741000 1
 
1.2%
39015000 1
 
1.2%
202025000 1
 
1.2%
573884000 1
 
1.2%
3166000 1
 
1.2%
75057000 1
 
1.2%
208238000 1
 
1.2%
21394000 1
 
1.2%
120940000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
3075000 1
 
1.2%
3166000 1
 
1.2%
3397000 1
 
1.2%
3442000 1
 
1.2%
4171000 1
 
1.2%
8679000 1
 
1.2%
10304000 1
 
1.2%
11645000 1
 
1.2%
12194000 1
 
1.2%
ValueCountFrequency (%)
598467000 1
1.2%
573884000 1
1.2%
523776000 1
1.2%
492977000 1
1.2%
413369000 1
1.2%
401090000 1
1.2%
375458000 1
1.2%
304302000 1
1.2%
269064000 1
1.2%
260521000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean75.050625
Minimum0
Maximum170.17
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T21:11:12.840657image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q144.7375
median97.16
Q399.5625
95-th percentile100
Maximum170.17
Range170.17
Interquartile range (IQR)54.825

Descriptive statistics

Standard deviation42.080641
Coefficient of variation (CV)0.56069674
Kurtosis-0.34751488
Mean75.050625
Median Absolute Deviation (MAD)2.455
Skewness-0.99934354
Sum6004.05
Variance1770.7804
MonotonicityNot monotonic
2023-12-12T21:11:13.052252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0.0 16
 
20.0%
100.0 10
 
12.5%
99.57 1
 
1.2%
99.61 1
 
1.2%
96.78 1
 
1.2%
38.39 1
 
1.2%
99.69 1
 
1.2%
97.87 1
 
1.2%
95.63 1
 
1.2%
96.82 1
 
1.2%
Other values (46) 46
57.5%
ValueCountFrequency (%)
0.0 16
20.0%
7.73 1
 
1.2%
26.94 1
 
1.2%
38.39 1
 
1.2%
38.67 1
 
1.2%
46.76 1
 
1.2%
87.41 1
 
1.2%
93.34 1
 
1.2%
94.93 1
 
1.2%
95.63 1
 
1.2%
ValueCountFrequency (%)
170.17 1
 
1.2%
100.0 10
12.5%
99.9 1
 
1.2%
99.87 1
 
1.2%
99.82 1
 
1.2%
99.69 1
 
1.2%
99.63 1
 
1.2%
99.62 1
 
1.2%
99.61 1
 
1.2%
99.58 1
 
1.2%

Interactions

2023-12-12T21:11:08.243541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:03.987141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.735533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.422534image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.369591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.024025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.620631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.339061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.079662image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.833957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.514359image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.482373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.101261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.721929image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.443729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.208153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.938640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.612010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.598042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.181921image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.805185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.557432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.331451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.044446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.995970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.682658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.308396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.891971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.682670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.430477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.159199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.086318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.763704image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.390666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.980404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.787867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.512512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.235095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.183156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.848535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.464501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.062344image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.882441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:04.627539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:05.323331image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.272450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:06.942529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:07.544584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:11:08.145634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:11:13.177334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8850.8950.6170.4580.7770.767
부과금액0.0000.8851.0000.9970.4030.0000.7220.567
수납급액0.0000.8950.9971.0000.0000.0000.6820.706
환급금액0.0000.6170.4030.0001.0000.7420.7660.807
결손금액0.0000.4580.0000.0000.7421.0000.8290.710
미수납 금액0.0000.7770.7220.6820.7660.8291.0000.760
징수율0.0000.7670.5670.7060.8070.7100.7601.000
2023-12-12T21:11:13.295917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.2070.2020.1130.0670.0080.2150.000
부과금액0.2071.0000.9860.5790.2340.4870.5430.597
수납급액0.2020.9861.0000.5110.1800.4220.5840.627
환급금액0.1130.5790.5111.0000.7060.8960.1620.370
결손금액0.0670.2340.1800.7061.0000.824-0.0900.227
미수납 금액0.0080.4870.4220.8960.8241.000-0.0250.453
징수율0.2150.5430.5840.162-0.090-0.0251.0000.500
세목명0.0000.5970.6270.3700.2270.4530.5001.000

Missing values

2023-12-12T21:11:09.027383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:11:09.205100image/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
0경상남도합천군488902017도축세000000.0
1경상남도합천군488902017레저세000000.0
2경상남도합천군488902017재산세33628580003281117000117400008174100097.57
3경상남도합천군488902017주민세556194000537198000525000820001891400096.58
4경상남도합천군488902017취득세11597434000115768230004891700002061100099.82
5경상남도합천군488902017자동차세8412467000815001800082414000192800026052100096.88
6경상남도합천군488902017과년도수입-693374000-11799480001675334000111116000375458000170.17
7경상남도합천군488902017담배소비세27103120002710312000000100.0
8경상남도합천군488902017도시계획세000000.0
9경상남도합천군488902017등록면허세91515700091175100032890009000339700099.63
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70경상남도합천군488902022취득세109049630001089061200011996500001435100099.87
71경상남도합천군488902022자동차세574077100056170730009084500075800012294000097.85
72경상남도합천군488902022과년도수입90067300024267500027049100013422200052377600026.94
73경상남도합천군488902022담배소비세28839400002883940000300000100.0
74경상남도합천군488902022도시계획세000000.0
75경상남도합천군488902022등록면허세833730000830197000200000091000344200099.58
76경상남도합천군488902022지방교육세370744900036547740003642500017160005095900098.58
77경상남도합천군488902022지방소득세61797530005866434000149520000901700030430200094.93
78경상남도합천군488902022지방소비세1359870000013598700000000100.0
79경상남도합천군488902022지역자원시설세65926800064047500047200084890001030400097.15