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

Description경상남도 거창군 지방세 징수현황에 대한 데이터로 과세년도, 세목명, 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율 항목을 제공합니다.
URLhttps://www.data.go.kr/data/15079224/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 4 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 1 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 부과금액 and 3 other fieldsHigh correlation
징수율 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 4 other fieldsHigh correlation
부과금액 has 16 (20.0%) zerosZeros
수납급액 has 16 (20.0%) zerosZeros
환급금액 has 23 (28.7%) zerosZeros
결손금액 has 35 (43.8%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 16 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:29:08.757713
Analysis finished2023-12-12 06:29:14.404997
Duration5.65 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

2023-12-12T15:29:14.494917image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:29:14.650206image/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
거창군
80 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
거창군 80
100.0%

Length

2023-12-12T15:29:14.800993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:29:14.912240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
거창군 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
48880
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48880
2nd row48880
3rd row48880
4th row48880
5th row48880

Common Values

ValueCountFrequency (%)
48880 80
100.0%

Length

2023-12-12T15:29:15.050744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:29:15.164778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48880 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
2023-12-12T15:29:15.296635image/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
2023-12-12T15:29:15.445549image/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

2023-12-12T15:29:15.610613image/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%
Mean4.359296 × 109
Minimum0
Maximum1.9208463 × 1010
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:29:15.762947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.451805 × 108
median1.594201 × 109
Q36.2521918 × 109
95-th percentile1.6545568 × 1010
Maximum1.9208463 × 1010
Range1.9208463 × 1010
Interquartile range (IQR)5.3070112 × 109

Descriptive statistics

Standard deviation4.9262355 × 109
Coefficient of variation (CV)1.130053
Kurtosis1.9089349
Mean4.359296 × 109
Median Absolute Deviation (MAD)1.594201 × 109
Skewness1.4890094
Sum3.4874368 × 1011
Variance2.4267796 × 1019
MonotonicityNot monotonic
2023-12-12T15:29:16.301739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
5493902000 1
 
1.2%
1496130000 1
 
1.2%
4138694000 1
 
1.2%
1283450000 1
 
1.2%
5377549000 1
 
1.2%
7175553000 1
 
1.2%
5376600000 1
 
1.2%
943961000 1
 
1.2%
6513581000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
33598000 1
 
1.2%
765944000 1
 
1.2%
913142000 1
 
1.2%
943961000 1
 
1.2%
945587000 1
 
1.2%
977747000 1
 
1.2%
985619000 1
 
1.2%
991741000 1
 
1.2%
1091524000 1
 
1.2%
ValueCountFrequency (%)
19208463000 1
1.2%
19181784000 1
1.2%
18935443000 1
1.2%
17574816000 1
1.2%
16491397000 1
1.2%
12949698000 1
1.2%
10771066000 1
1.2%
10564068000 1
1.2%
10320834000 1
1.2%
10168298000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.2142401 × 109
Minimum0
Maximum1.912377 × 1010
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:29:16.483659image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q17.2960075 × 108
median1.316136 × 109
Q36.1239188 × 109
95-th percentile1.6399047 × 1010
Maximum1.912377 × 1010
Range1.912377 × 1010
Interquartile range (IQR)5.394318 × 109

Descriptive statistics

Standard deviation4.8958729 × 109
Coefficient of variation (CV)1.1617451
Kurtosis2.0863792
Mean4.2142401 × 109
Median Absolute Deviation (MAD)1.316136 × 109
Skewness1.529531
Sum3.3713921 × 1011
Variance2.3969571 × 1019
MonotonicityNot monotonic
2023-12-12T15:29:16.659691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
5351726000 1
 
1.2%
704052000 1
 
1.2%
4138694000 1
 
1.2%
1281328000 1
 
1.2%
5233057000 1
 
1.2%
6676372000 1
 
1.2%
5376600000 1
 
1.2%
922910000 1
 
1.2%
6384301000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
33598000 1
 
1.2%
354591000 1
 
1.2%
704052000 1
 
1.2%
708897000 1
 
1.2%
736502000 1
 
1.2%
747765000 1
 
1.2%
775759000 1
 
1.2%
782760000 1
 
1.2%
873510000 1
 
1.2%
ValueCountFrequency (%)
19123770000 1
1.2%
19084699000 1
1.2%
18918937000 1
1.2%
17554453000 1
1.2%
16338236000 1
1.2%
12729157000 1
1.2%
10333887000 1
1.2%
10145380000 1
1.2%
10106969000 1
1.2%
9901580000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean47063912
Minimum0
Maximum6.52163 × 108
Zeros23
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:29:16.839941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3181000
Q358947250
95-th percentile2.131794 × 108
Maximum6.52163 × 108
Range6.52163 × 108
Interquartile range (IQR)58947250

Descriptive statistics

Standard deviation94178728
Coefficient of variation (CV)2.0010816
Kurtosis21.319319
Mean47063912
Median Absolute Deviation (MAD)3181000
Skewness3.9372233
Sum3.765113 × 109
Variance8.8696329 × 1015
MonotonicityNot monotonic
2023-12-12T15:29:17.005814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
28.7%
2166000 1
 
1.2%
256045000 1
 
1.2%
4844000 1
 
1.2%
3748000 1
 
1.2%
30921000 1
 
1.2%
230477000 1
 
1.2%
490000 1
 
1.2%
4744000 1
 
1.2%
2113000 1
 
1.2%
Other values (48) 48
60.0%
ValueCountFrequency (%)
0 23
28.7%
4000 1
 
1.2%
86000 1
 
1.2%
206000 1
 
1.2%
349000 1
 
1.2%
366000 1
 
1.2%
424000 1
 
1.2%
460000 1
 
1.2%
490000 1
 
1.2%
706000 1
 
1.2%
ValueCountFrequency (%)
652163000 1
1.2%
256045000 1
1.2%
255851000 1
1.2%
230477000 1
1.2%
212269000 1
1.2%
175758000 1
1.2%
166818000 1
1.2%
150471000 1
1.2%
150027000 1
1.2%
125128000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)57.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19630638
Minimum0
Maximum2.61875 × 108
Zeros35
Zeros (%)43.8%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:29:17.161418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median69000
Q31511000
95-th percentile1.7258325 × 108
Maximum2.61875 × 108
Range2.61875 × 108
Interquartile range (IQR)1511000

Descriptive statistics

Standard deviation58899081
Coefficient of variation (CV)3.0003652
Kurtosis9.5349
Mean19630638
Median Absolute Deviation (MAD)69000
Skewness3.2445165
Sum1.570451 × 109
Variance3.4691018 × 1015
MonotonicityNot monotonic
2023-12-12T15:29:17.307637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 35
43.8%
5000 1
 
1.2%
2974000 1
 
1.2%
225009000 1
 
1.2%
42000 1
 
1.2%
944000 1
 
1.2%
146693000 1
 
1.2%
10000 1
 
1.2%
482000 1
 
1.2%
1664000 1
 
1.2%
Other values (36) 36
45.0%
ValueCountFrequency (%)
0 35
43.8%
5000 1
 
1.2%
10000 1
 
1.2%
28000 1
 
1.2%
42000 1
 
1.2%
65000 1
 
1.2%
73000 1
 
1.2%
96000 1
 
1.2%
230000 1
 
1.2%
269000 1
 
1.2%
ValueCountFrequency (%)
261875000 1
1.2%
250243000 1
1.2%
238236000 1
1.2%
225009000 1
1.2%
169824000 1
1.2%
146693000 1
1.2%
93824000 1
1.2%
66812000 1
1.2%
37706000 1
1.2%
30816000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2542526 × 108
Minimum0
Maximum7.41213 × 108
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:29:17.516042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median26249500
Q31.5321025 × 108
95-th percentile5.4103995 × 108
Maximum7.41213 × 108
Range7.41213 × 108
Interquartile range (IQR)1.5321025 × 108

Descriptive statistics

Standard deviation1.8431255 × 108
Coefficient of variation (CV)1.469501
Kurtosis1.6908257
Mean1.2542526 × 108
Median Absolute Deviation (MAD)26249500
Skewness1.6363149
Sum1.0034021 × 1010
Variance3.3971115 × 1016
MonotonicityNot monotonic
2023-12-12T15:29:17.664171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
125556000 1
 
1.2%
20363000 1
 
1.2%
434205000 1
 
1.2%
567069000 1
 
1.2%
2080000 1
 
1.2%
143548000 1
 
1.2%
352488000 1
 
1.2%
21051000 1
 
1.2%
129270000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
1498000 1
 
1.2%
1714000 1
 
1.2%
1725000 1
 
1.2%
1884000 1
 
1.2%
1906000 1
 
1.2%
2080000 1
 
1.2%
16506000 1
 
1.2%
17227000 1
 
1.2%
17445000 1
 
1.2%
ValueCountFrequency (%)
741213000 1
1.2%
621355000 1
1.2%
570273000 1
1.2%
567069000 1
1.2%
539670000 1
1.2%
488202000 1
1.2%
486378000 1
1.2%
471012000 1
1.2%
447392000 1
1.2%
434205000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)53.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.444625
Minimum0
Maximum100
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:29:17.819841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q148.535
median97
Q399.0175
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)50.4825

Descriptive statistics

Standard deviation39.960225
Coefficient of variation (CV)0.53677784
Kurtosis-0.35560438
Mean74.444625
Median Absolute Deviation (MAD)2.835
Skewness-1.2222694
Sum5955.57
Variance1596.8196
MonotonicityNot monotonic
2023-12-12T15:29:17.980128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0.0 16
20.0%
100.0 13
 
16.2%
97.0 5
 
6.2%
98.0 5
 
6.2%
99.88 2
 
2.5%
98.3 2
 
2.5%
54.4 1
 
1.2%
93.04 1
 
1.2%
97.67 1
 
1.2%
95.66 1
 
1.2%
Other values (33) 33
41.2%
ValueCountFrequency (%)
0.0 16
20.0%
31.0 1
 
1.2%
45.0 1
 
1.2%
47.06 1
 
1.2%
48.25 1
 
1.2%
48.63 1
 
1.2%
54.4 1
 
1.2%
93.04 1
 
1.2%
95.0 1
 
1.2%
95.03 1
 
1.2%
ValueCountFrequency (%)
100.0 13
16.2%
99.88 2
 
2.5%
99.85 1
 
1.2%
99.84 1
 
1.2%
99.83 1
 
1.2%
99.7 1
 
1.2%
99.07 1
 
1.2%
99.0 1
 
1.2%
98.3 2
 
2.5%
98.0 5
 
6.2%

Interactions

2023-12-12T15:29:13.421808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.025000image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.874464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.648350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.341487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.947381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.605896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.507815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.093914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.963420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.749624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.429989image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.038079image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.714969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.615831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.204194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.108890image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.851469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.512501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.143399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.912597image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.724975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.281884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.210806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.952799image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.603725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.240086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.027173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.814462image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.356687image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.314320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.065040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.692915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.323821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.123285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.911636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.718106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.418329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.165606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.770932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.408289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.209138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:14.031822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:09.799395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:10.529258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.260295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:11.862846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:12.521389image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:29:13.299347image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:29:18.080498image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8650.8550.7730.5660.8300.808
부과금액0.0000.8651.0001.0000.4580.7580.7220.089
수납급액0.0000.8551.0001.0000.4300.7630.7400.089
환급금액0.0000.7730.4580.4301.0000.8480.9770.936
결손금액0.0000.5660.7580.7630.8481.0000.8760.914
미수납 금액0.0000.8300.7220.7400.9770.8761.0000.965
징수율0.0000.8080.0890.0890.9360.9140.9651.000
2023-12-12T15:29:18.225573image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1760.1750.079-0.0500.0220.2330.000
부과금액0.1761.0000.9730.6780.4040.6020.4880.594
수납급액0.1750.9731.0000.5770.3050.4980.5700.576
환급금액0.0790.6780.5771.0000.7740.9080.0660.508
결손금액-0.0500.4040.3050.7741.0000.862-0.1310.275
미수납 금액0.0220.6020.4980.9080.8621.000-0.0940.511
징수율0.2330.4880.5700.066-0.131-0.0941.0000.552
세목명0.0000.5940.5760.5080.2750.5110.5521.000

Missing values

2023-12-12T15:29:14.158605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:29:14.342387image/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
0경상남도거창군488802017도축세000000.0
1경상남도거창군488802017레저세000000.0
2경상남도거창군488802017재산세50944200004967990000300700087400012555600097.52
3경상남도거창군488802017주민세98561900095204900045630007930003277700096.59
4경상남도거창군488802017취득세164913970001633823600055811000015316100099.07
5경상남도거창군488802017자동차세105640680001014538000083372000221500041647300096.04
6경상남도거창군488802017과년도수입143896200078276000015002700016982400048637800054.4
7경상남도거창군488802017담배소비세44301210004430121000000100.0
8경상남도거창군488802017도시계획세000000.0
9경상남도거창군488802017등록면허세11165650001114755000365100096000171400099.84
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70경상남도거창군488802022취득세192084630001908469900093925000012376400099.0
71경상남도거창군488802022자동차세9205601000875636500093313000184400044739200095.0
72경상남도거창군488802022과년도수입158049500070889700025585100025024300062135500045.0
73경상남도거창군488802022담배소비세42996120004299612000400000100.0
74경상남도거창군488802022도시계획세000000.0
75경상남도거창군488802022등록면허세129707300012951390004194000280001906000100.0
76경상남도거창군488802022지방교육세555521700054012930002975300056600015335800097.0
77경상남도거창군488802022지방소득세834063700080846930002122690003081600022512800097.0
78경상남도거창군488802022지방소비세1010696900010106969000000100.0
79경상남도거창군488802022지역자원시설세991741000974449000366000650001722700098.0