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

Description2017년부터 2022년까지 서천군 지방세 부과액대비 징수현황에 대한 자료, 부과액, 수납금액, 환급금, 결손금액, 미수납금액 및 징수율 현황입니다
URLhttps://www.data.go.kr/data/15080473/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 2 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 2 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 3 other fieldsHigh correlation
징수율 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 미수납 금액 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 38 (47.5%) zerosZeros
미수납 금액 has 26 (32.5%) zerosZeros
징수율 has 16 (20.0%) zerosZeros

Reproduction

Analysis started2023-12-12 06:09:58.055203
Analysis finished2023-12-12 06:10:04.204383
Duration6.15 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
충청남도
80 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 80
100.0%

Length

2023-12-12T15:10:04.284302image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:10:04.408023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 80
100.0%

시군구명
Categorical

CONSTANT 

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

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서천군 80
100.0%

Length

2023-12-12T15:10:04.541655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:10:04.649677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서천군 80
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.2%
Missing0
Missing (%)0.0%
Memory size772.0 B
44770
80 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44770
2nd row44770
3rd row44770
4th row44770
5th row44770

Common Values

ValueCountFrequency (%)
44770 80
100.0%

Length

2023-12-12T15:10:04.786634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:10:04.927614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44770 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
2023-12-12T15:10:05.056494image/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
2023-12-12T15:10:05.194998image/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

2023-12-12T15:10:05.333142image/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%
Mean4.6717774 × 109
Minimum0
Maximum3.7975004 × 1010
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:10:05.506013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.0142378 × 109
median2.753233 × 109
Q37.3484452 × 109
95-th percentile1.3413967 × 1010
Maximum3.7975004 × 1010
Range3.7975004 × 1010
Interquartile range (IQR)6.3342075 × 109

Descriptive statistics

Standard deviation5.7333259 × 109
Coefficient of variation (CV)1.2272258
Kurtosis13.963114
Mean4.6717774 × 109
Median Absolute Deviation (MAD)2.753233 × 109
Skewness2.9939808
Sum3.737422 × 1011
Variance3.2871025 × 1019
MonotonicityNot monotonic
2023-12-12T15:10:05.681035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
6118785000 1
 
1.2%
1840586000 1
 
1.2%
4605933000 1
 
1.2%
1584474000 1
 
1.2%
5604928000 1
 
1.2%
7676860000 1
 
1.2%
7557100000 1
 
1.2%
972504000 1
 
1.2%
6777592000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
29250000 1
 
1.2%
814323000 1
 
1.2%
869240000 1
 
1.2%
972504000 1
 
1.2%
1028149000 1
 
1.2%
1077374000 1
 
1.2%
1175249000 1
 
1.2%
1228950000 1
 
1.2%
1311099000 1
 
1.2%
ValueCountFrequency (%)
37975004000 1
1.2%
20889475000 1
1.2%
15665717000 1
1.2%
14936037000 1
1.2%
13333858000 1
1.2%
12887476000 1
1.2%
11516755000 1
1.2%
10784848000 1
1.2%
9240270000 1
1.2%
8665054000 1
1.2%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct65
Distinct (%)81.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.5018246 × 109
Minimum0
Maximum3.7954719 × 1010
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:10:05.847229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q18.0366075 × 108
median2.6668875 × 109
Q36.9832325 × 109
95-th percentile1.3316834 × 1010
Maximum3.7954719 × 1010
Range3.7954719 × 1010
Interquartile range (IQR)6.1795718 × 109

Descriptive statistics

Standard deviation5.7336708 × 109
Coefficient of variation (CV)1.2736326
Kurtosis14.276347
Mean4.5018246 × 109
Median Absolute Deviation (MAD)2.6668875 × 109
Skewness3.0441085
Sum3.6014597 × 1011
Variance3.2874981 × 1019
MonotonicityNot monotonic
2023-12-12T15:10:06.059190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 16
 
20.0%
5967534000 1
 
1.2%
860906000 1
 
1.2%
4605933000 1
 
1.2%
1577200000 1
 
1.2%
5453564000 1
 
1.2%
7440384000 1
 
1.2%
7557100000 1
 
1.2%
947195000 1
 
1.2%
6606300000 1
 
1.2%
Other values (55) 55
68.8%
ValueCountFrequency (%)
0 16
20.0%
29250000 1
 
1.2%
531251000 1
 
1.2%
575464000 1
 
1.2%
789929000 1
 
1.2%
808238000 1
 
1.2%
847548000 1
 
1.2%
860906000 1
 
1.2%
867860000 1
 
1.2%
875701000 1
 
1.2%
ValueCountFrequency (%)
37954719000 1
1.2%
20854906000 1
1.2%
15555617000 1
1.2%
14937300000 1
1.2%
13231546000 1
1.2%
12867514000 1
1.2%
11516755000 1
1.2%
10235638000 1
1.2%
8694205000 1
1.2%
8306246000 1
1.2%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct58
Distinct (%)72.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59859212
Minimum0
Maximum6.05998 × 108
Zeros23
Zeros (%)28.7%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:10:06.207895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3828000
Q358820750
95-th percentile3.20673 × 108
Maximum6.05998 × 108
Range6.05998 × 108
Interquartile range (IQR)58820750

Descriptive statistics

Standard deviation1.1867847 × 108
Coefficient of variation (CV)1.9826266
Kurtosis7.7785357
Mean59859212
Median Absolute Deviation (MAD)3828000
Skewness2.7393532
Sum4.788737 × 109
Variance1.4084579 × 1016
MonotonicityNot monotonic
2023-12-12T15:10:06.371470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 23
28.7%
7634000 1
 
1.2%
341136000 1
 
1.2%
4354000 1
 
1.2%
3302000 1
 
1.2%
34144000 1
 
1.2%
256570000 1
 
1.2%
230000 1
 
1.2%
48156000 1
 
1.2%
2676000 1
 
1.2%
Other values (48) 48
60.0%
ValueCountFrequency (%)
0 23
28.7%
4000 1
 
1.2%
18000 1
 
1.2%
70000 1
 
1.2%
87000 1
 
1.2%
113000 1
 
1.2%
147000 1
 
1.2%
230000 1
 
1.2%
240000 1
 
1.2%
380000 1
 
1.2%
ValueCountFrequency (%)
605998000 1
1.2%
478545000 1
1.2%
442007000 1
1.2%
341136000 1
1.2%
319596000 1
1.2%
273064000 1
1.2%
266997000 1
1.2%
256570000 1
1.2%
172168000 1
1.2%
164846000 1
1.2%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)52.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15471875
Minimum0
Maximum3.79367 × 108
Zeros38
Zeros (%)47.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:10:06.544616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median17500
Q3219250
95-th percentile1.4709015 × 108
Maximum3.79367 × 108
Range3.79367 × 108
Interquartile range (IQR)219250

Descriptive statistics

Standard deviation56931240
Coefficient of variation (CV)3.67966
Kurtosis22.804924
Mean15471875
Median Absolute Deviation (MAD)17500
Skewness4.4900429
Sum1.23775 × 109
Variance3.2411661 × 1015
MonotonicityNot monotonic
2023-12-12T15:10:06.717776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 38
47.5%
70000 2
 
2.5%
261000 1
 
1.2%
319000 1
 
1.2%
247000 1
 
1.2%
189515000 1
 
1.2%
40000 1
 
1.2%
118000 1
 
1.2%
153000 1
 
1.2%
20000 1
 
1.2%
Other values (32) 32
40.0%
ValueCountFrequency (%)
0 38
47.5%
8000 1
 
1.2%
15000 1
 
1.2%
20000 1
 
1.2%
25000 1
 
1.2%
40000 1
 
1.2%
41000 1
 
1.2%
46000 1
 
1.2%
49000 1
 
1.2%
50000 1
 
1.2%
ValueCountFrequency (%)
379367000 1
1.2%
189515000 1
1.2%
165967000 1
1.2%
158645000 1
1.2%
146482000 1
1.2%
141866000 1
1.2%
30922000 1
1.2%
7052000 1
1.2%
6946000 1
1.2%
3232000 1
1.2%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct55
Distinct (%)68.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.545125 × 108
Minimum0
Maximum9.99684 × 108
Zeros26
Zeros (%)32.5%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:10:06.883653image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median24841500
Q31.8966875 × 108
95-th percentile7.9354125 × 108
Maximum9.99684 × 108
Range9.99684 × 108
Interquartile range (IQR)1.8966875 × 108

Descriptive statistics

Standard deviation2.4824803 × 108
Coefficient of variation (CV)1.6066534
Kurtosis3.5252917
Mean1.545125 × 108
Median Absolute Deviation (MAD)24841500
Skewness2.0290732
Sum1.2361 × 1010
Variance6.1627086 × 1016
MonotonicityNot monotonic
2023-12-12T15:10:07.035397image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
32.5%
148854000 1
 
1.2%
34569000 1
 
1.2%
423661000 1
 
1.2%
790165000 1
 
1.2%
7234000 1
 
1.2%
151246000 1
 
1.2%
236323000 1
 
1.2%
25289000 1
 
1.2%
171292000 1
 
1.2%
Other values (45) 45
56.2%
ValueCountFrequency (%)
0 26
32.5%
1263000 1
 
1.2%
3432000 1
 
1.2%
3570000 1
 
1.2%
4937000 1
 
1.2%
6699000 1
 
1.2%
7234000 1
 
1.2%
19962000 1
 
1.2%
19967000 1
 
1.2%
20285000 1
 
1.2%
ValueCountFrequency (%)
999684000 1
1.2%
986492000 1
1.2%
875917000 1
1.2%
857690000 1
1.2%
790165000 1
1.2%
685446000 1
1.2%
548693000 1
1.2%
545474000 1
1.2%
477760000 1
1.2%
426705000 1
1.2%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct56
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.114375
Minimum0
Maximum100.01
Zeros16
Zeros (%)20.0%
Negative0
Negative (%)0.0%
Memory size852.0 B
2023-12-12T15:10:07.212502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q146.5375
median97.07
Q399.2475
95-th percentile100
Maximum100.01
Range100.01
Interquartile range (IQR)52.71

Descriptive statistics

Standard deviation40.196683
Coefficient of variation (CV)0.54236014
Kurtosis-0.44679982
Mean74.114375
Median Absolute Deviation (MAD)2.68
Skewness-1.1892277
Sum5929.15
Variance1615.7733
MonotonicityNot monotonic
2023-12-12T15:10:07.397661image/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%
99.72 1
 
1.2%
99.83 1
 
1.2%
94.32 1
 
1.2%
46.77 1
 
1.2%
99.54 1
 
1.2%
97.3 1
 
1.2%
96.92 1
 
1.2%
97.4 1
 
1.2%
Other values (46) 46
57.5%
ValueCountFrequency (%)
0.0 16
20.0%
31.55 1
 
1.2%
36.54 1
 
1.2%
38.62 1
 
1.2%
45.84 1
 
1.2%
46.77 1
 
1.2%
49.28 1
 
1.2%
94.09 1
 
1.2%
94.25 1
 
1.2%
94.32 1
 
1.2%
ValueCountFrequency (%)
100.01 1
 
1.2%
100.0 10
12.5%
99.95 1
 
1.2%
99.85 1
 
1.2%
99.83 1
 
1.2%
99.74 1
 
1.2%
99.72 1
 
1.2%
99.54 1
 
1.2%
99.52 1
 
1.2%
99.38 1
 
1.2%

Interactions

2023-12-12T15:10:03.178112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:58.420127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.177457image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.924274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.834884image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.525830image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.523355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.275258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:58.508440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.280318image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.025153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.950001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.621286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.603067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.386075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:58.656665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.373052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.143679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.061976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.056252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.686454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.483403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:58.784357image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.483902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.261337image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.177432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.163515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.776168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.584271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:58.878227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.601430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.381626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.264947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.261261image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.867439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.705901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:58.968804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.720054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.519622image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.360182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.354480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.003108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.803494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.088996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:09:59.832159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:00.689054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:01.441476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:02.446367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:10:03.089562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:10:07.519611image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8580.8490.8450.6730.8260.883
부과금액0.0000.8581.0001.0000.7030.0000.4770.315
수납급액0.0000.8491.0001.0000.7100.0000.4410.315
환급금액0.0000.8450.7030.7101.0000.7840.8650.784
결손금액0.0000.6730.0000.0000.7841.0000.8940.917
미수납 금액0.0000.8260.4770.4410.8650.8941.0000.879
징수율0.0000.8830.3150.3150.7840.9170.8791.000
2023-12-12T15:10:07.992767image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도부과금액수납급액환급금액결손금액미수납 금액징수율세목명
과세년도1.0000.1830.1860.099-0.126-0.0030.2750.000
부과금액0.1831.0000.9860.6170.2310.5290.4800.477
수납급액0.1860.9861.0000.5520.1760.4560.5430.465
환급금액0.0990.6170.5521.0000.6350.8520.0920.459
결손금액-0.1260.2310.1760.6351.0000.811-0.1600.419
미수납 금액-0.0030.5290.4560.8520.8111.000-0.1310.504
징수율0.2750.4800.5430.092-0.160-0.1311.0000.676
세목명0.0000.4770.4650.4590.4190.5040.6761.000

Missing values

2023-12-12T15:10:03.943752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:10:04.133141image/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
0충청남도서천군447702017도축세000000.0
1충청남도서천군447702017레저세000000.0
2충청남도서천군447702017재산세49520860004796180000957000705200014885400096.85
3충청남도서천군447702017주민세161526700015783880005720002580003662100097.72
4충청남도서천군447702017취득세133338580001323154600094786000010231200099.23
5충청남도서천군447702017자동차세924027000086942050005178700059100054547400094.09
6충청남도서천군447702017과년도수입224691100086786000027306400037936700099968400038.62
7충청남도서천군447702017담배소비세41244950004124495000000100.0
8충청남도서천군447702017도시계획세000000.0
9충청남도서천군447702017등록면허세10773740001070660000516100015000669900099.38
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
70충청남도서천군447702022취득세14936037000149373000006213800001263000100.01
71충청남도서천군447702022자동차세741799800069911900005327500010300042670500094.25
72충청남도서천군447702022과년도수입157502000057546400044200700014186600085769000036.54
73충청남도서천군447702022담배소비세41751640004175164000400000100.0
74충청남도서천군447702022도시계획세000000.0
75충청남도서천군447702022등록면허세13515250001348044000646000049000343200099.74
76충청남도서천군447702022지방교육세54607960005304172000197180007000015655400097.13
77충청남도서천군447702022지방소득세84778520008306246000164846000017160600097.98
78충청남도서천군447702022지방소비세1151675500011516755000000100.0
79충청남도서천군447702022지역자원시설세3259555000323249300024000002706200099.17