Overview

Dataset statistics

Number of variables11
Number of observations39
Missing cells24
Missing cells (%)5.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory99.3 B

Variable types

Categorical5
Numeric6

Dataset

Description3년간(2020~2022) 지방세 부과액에 대한 세목별 징수현황 자료로 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율 항목을 제공합니다.
Author전라남도 나주시
URLhttps://www.data.go.kr/data/15126702/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 1 other fieldsHigh correlation
수납급액 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
환급금액 is highly overall correlated with 결손금액 and 1 other fieldsHigh correlation
결손금액 is highly overall correlated with 환급금액 and 2 other fieldsHigh correlation
미수납 금액 is highly overall correlated with 환급금액 and 2 other fieldsHigh correlation
징수율 is highly overall correlated with 결손금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 부과금액 and 1 other fieldsHigh correlation
부과금액 has 4 (10.3%) missing valuesMissing
수납급액 has 4 (10.3%) missing valuesMissing
환급금액 has 4 (10.3%) missing valuesMissing
결손금액 has 4 (10.3%) missing valuesMissing
미수납 금액 has 4 (10.3%) missing valuesMissing
징수율 has 4 (10.3%) missing valuesMissing
부과금액 has 1 (2.6%) zerosZeros
수납급액 has 1 (2.6%) zerosZeros
환급금액 has 5 (12.8%) zerosZeros
결손금액 has 15 (38.5%) zerosZeros
미수납 금액 has 8 (20.5%) zerosZeros
징수율 has 1 (2.6%) zerosZeros

Reproduction

Analysis started2024-04-21 08:01:39.531348
Analysis finished2024-04-21 08:01:50.195617
Duration10.66 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
전라남도
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
전라남도 39
100.0%

Length

2024-04-21T17:01:50.389094image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:01:50.687905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전라남도 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
나주시
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
나주시 39
100.0%

Length

2024-04-21T17:01:50.993814image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:01:51.294190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
나주시 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
46170
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row46170
2nd row46170
3rd row46170
4th row46170
5th row46170

Common Values

ValueCountFrequency (%)
46170 39
100.0%

Length

2024-04-21T17:01:51.605382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:01:51.903157image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46170 39
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size440.0 B
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

Length

2024-04-21T17:01:52.213558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T17:01:52.522828image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size440.0 B
레저세
3 
재산세
3 
주민세
3 
취득세
3 
자동차세
3 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.4615385
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
레저세 3
 
7.7%
재산세 3
 
7.7%
주민세 3
 
7.7%
취득세 3
 
7.7%
자동차세 3
 
7.7%
과년도수입 3
 
7.7%
담배소비세 3
 
7.7%
도시계획세 3
 
7.7%
등록면허세 3
 
7.7%
지방교육세 3
 
7.7%
Other values (3) 9
23.1%

Length

2024-04-21T17:01:52.907958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 3
 
7.7%
재산세 3
 
7.7%
주민세 3
 
7.7%
취득세 3
 
7.7%
자동차세 3
 
7.7%
과년도수입 3
 
7.7%
담배소비세 3
 
7.7%
도시계획세 3
 
7.7%
등록면허세 3
 
7.7%
지방교육세 3
 
7.7%
Other values (3) 9
23.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)100.0%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1.7867361 × 1010
Minimum0
Maximum5.9486155 × 1010
Zeros1
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-04-21T17:01:53.281489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.9299648 × 109
Q15.366277 × 109
median1.0067081 × 1010
Q32.6659636 × 1010
95-th percentile5.3587786 × 1010
Maximum5.9486155 × 1010
Range5.9486155 × 1010
Interquartile range (IQR)2.1293359 × 1010

Descriptive statistics

Standard deviation1.6920243 × 1010
Coefficient of variation (CV)0.94699171
Kurtosis0.56324621
Mean1.7867361 × 1010
Median Absolute Deviation (MAD)5.470897 × 109
Skewness1.2436133
Sum6.2535764 × 1011
Variance2.8629462 × 1020
MonotonicityNot monotonic
2024-04-21T17:01:53.690770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
6331017000 1
 
2.6%
10067081000 1
 
2.6%
4943197000 1
 
2.6%
62881000 1
 
2.6%
31773897000 1
 
2.6%
7447736000 1
 
2.6%
59486155000 1
 
2.6%
23935783000 1
 
2.6%
6698819000 1
 
2.6%
8428606000 1
 
2.6%
Other values (25) 25
64.1%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 1
2.6%
62881000 1
2.6%
4158715000 1
2.6%
4516876000 1
2.6%
4596184000 1
2.6%
4883519000 1
2.6%
4943197000 1
2.6%
4962978000 1
2.6%
5302137000 1
2.6%
5430417000 1
2.6%
ValueCountFrequency (%)
59486155000 1
2.6%
58263680000 1
2.6%
51583831000 1
2.6%
50954808000 1
2.6%
36877328000 1
2.6%
35833210000 1
2.6%
31773897000 1
2.6%
29623689000 1
2.6%
27565251000 1
2.6%
25754021000 1
2.6%

수납급액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct35
Distinct (%)100.0%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1.7087558 × 1010
Minimum0
Maximum5.8746778 × 1010
Zeros1
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-04-21T17:01:54.078179image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile5.987436 × 108
Q14.911527 × 109
median1.0067081 × 1010
Q32.5505032 × 1010
95-th percentile5.2821794 × 1010
Maximum5.8746778 × 1010
Range5.8746778 × 1010
Interquartile range (IQR)2.0593506 × 1010

Descriptive statistics

Standard deviation1.6828837 × 1010
Coefficient of variation (CV)0.98485911
Kurtosis0.63709833
Mean1.7087558 × 1010
Median Absolute Deviation (MAD)5.72631 × 109
Skewness1.2486724
Sum5.9806452 × 1011
Variance2.8320975 × 1020
MonotonicityNot monotonic
2024-04-21T17:01:54.480039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
6194136000 1
 
2.6%
10067081000 1
 
2.6%
4750344000 1
 
2.6%
62881000 1
 
2.6%
30348772000 1
 
2.6%
7274217000 1
 
2.6%
58746778000 1
 
2.6%
22552774000 1
 
2.6%
2439862000 1
 
2.6%
8428606000 1
 
2.6%
Other values (25) 25
64.1%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 1
2.6%
62881000 1
2.6%
828399000 1
2.6%
1572939000 1
2.6%
2439862000 1
2.6%
4340771000 1
2.6%
4580706000 1
2.6%
4750344000 1
2.6%
4870260000 1
2.6%
4952794000 1
2.6%
ValueCountFrequency (%)
58746778000 1
2.6%
57793930000 1
2.6%
50690878000 1
2.6%
49518409000 1
2.6%
35880763000 1
2.6%
34796802000 1
2.6%
30348772000 1
2.6%
28628438000 1
2.6%
26182363000 1
2.6%
24827702000 1
2.6%

환급금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct31
Distinct (%)88.6%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean5.7538949 × 108
Minimum0
Maximum4.156507 × 109
Zeros5
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-04-21T17:01:54.872461image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1998000
median12134000
Q32.155605 × 108
95-th percentile3.6777999 × 109
Maximum4.156507 × 109
Range4.156507 × 109
Interquartile range (IQR)2.145625 × 108

Descriptive statistics

Standard deviation1.202843 × 109
Coefficient of variation (CV)2.0904849
Kurtosis3.6341643
Mean5.7538949 × 108
Median Absolute Deviation (MAD)12134000
Skewness2.2262795
Sum2.0138632 × 1010
Variance1.4468313 × 1018
MonotonicityNot monotonic
2024-04-21T17:01:55.180238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 5
 
12.8%
77561000 1
 
2.6%
592000 1
 
2.6%
3614232000 1
 
2.6%
60038000 1
 
2.6%
11746000 1
 
2.6%
7000 1
 
2.6%
1136444000 1
 
2.6%
157352000 1
 
2.6%
249115000 1
 
2.6%
Other values (21) 21
53.8%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 5
12.8%
7000 1
 
2.6%
158000 1
 
2.6%
592000 1
 
2.6%
987000 1
 
2.6%
1009000 1
 
2.6%
1620000 1
 
2.6%
5127000 1
 
2.6%
8988000 1
 
2.6%
9050000 1
 
2.6%
ValueCountFrequency (%)
4156507000 1
2.6%
3826125000 1
2.6%
3614232000 1
2.6%
3176299000 1
2.6%
1898937000 1
2.6%
1136444000 1
2.6%
595845000 1
2.6%
525714000 1
2.6%
249115000 1
2.6%
182006000 1
2.6%

결손금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct21
Distinct (%)60.0%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean1.0126194 × 108
Minimum0
Maximum1.220584 × 109
Zeros15
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-04-21T17:01:55.391546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median125000
Q31196500
95-th percentile8.523214 × 108
Maximum1.220584 × 109
Range1.220584 × 109
Interquartile range (IQR)1196500

Descriptive statistics

Standard deviation3.0429264 × 108
Coefficient of variation (CV)3.005005
Kurtosis9.5856865
Mean1.0126194 × 108
Median Absolute Deviation (MAD)125000
Skewness3.2236714
Sum3.544168 × 109
Variance9.2594013 × 1016
MonotonicityNot monotonic
2024-04-21T17:01:55.726040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
0 15
38.5%
477000 1
 
2.6%
285948000 1
 
2.6%
3807000 1
 
2.6%
239000 1
 
2.6%
1220584000 1
 
2.6%
263000 1
 
2.6%
76503000 1
 
2.6%
62000 1
 
2.6%
11654000 1
 
2.6%
Other values (11) 11
28.2%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 15
38.5%
62000 1
 
2.6%
93000 1
 
2.6%
125000 1
 
2.6%
147000 1
 
2.6%
237000 1
 
2.6%
239000 1
 
2.6%
244000 1
 
2.6%
263000 1
 
2.6%
477000 1
 
2.6%
ValueCountFrequency (%)
1220584000 1
2.6%
1196237000 1
2.6%
704929000 1
2.6%
285948000 1
2.6%
76503000 1
2.6%
39735000 1
2.6%
11654000 1
2.6%
3807000 1
2.6%
1848000 1
2.6%
545000 1
2.6%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)80.0%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean6.7854146 × 108
Minimum0
Maximum3.038373 × 109
Zeros8
Zeros (%)20.5%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-04-21T17:01:56.101472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q111540500
median2.6393 × 108
Q39.95962 × 108
95-th percentile2.6361432 × 109
Maximum3.038373 × 109
Range3.038373 × 109
Interquartile range (IQR)9.844215 × 108

Descriptive statistics

Standard deviation8.3258574 × 108
Coefficient of variation (CV)1.2270227
Kurtosis1.5351538
Mean6.7854146 × 108
Median Absolute Deviation (MAD)2.6393 × 108
Skewness1.4541633
Sum2.3748951 × 1010
Variance6.9319901 × 1017
MonotonicityNot monotonic
2024-04-21T17:01:56.506676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 8
20.5%
13022000 1
 
2.6%
224787000 1
 
2.6%
1779474000 1
 
2.6%
633281000 1
 
2.6%
15239000 1
 
2.6%
3038373000 1
 
2.6%
1382746000 1
 
2.6%
662874000 1
 
2.6%
173457000 1
 
2.6%
Other values (18) 18
46.2%
(Missing) 4
 
10.3%
ValueCountFrequency (%)
0 8
20.5%
10059000 1
 
2.6%
13022000 1
 
2.6%
15239000 1
 
2.6%
136788000 1
 
2.6%
173457000 1
 
2.6%
175958000 1
 
2.6%
176678000 1
 
2.6%
192853000 1
 
2.6%
224787000 1
 
2.6%
ValueCountFrequency (%)
3038373000 1
2.6%
2661241000 1
2.6%
2625387000 1
2.6%
1779474000 1
2.6%
1425125000 1
2.6%
1382746000 1
2.6%
1381040000 1
2.6%
1312504000 1
2.6%
996673000 1
2.6%
995251000 1
2.6%

징수율
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct17
Distinct (%)48.6%
Missing4
Missing (%)10.3%
Infinite0
Infinite (%)0.0%
Mean88.942857
Minimum0
Maximum100
Zeros1
Zeros (%)2.6%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-04-21T17:01:57.110565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile26.3
Q195.88
median97
Q399.83
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)3.95

Descriptive statistics

Standard deviation25.142494
Coefficient of variation (CV)0.28268143
Kurtosis6.018504
Mean88.942857
Median Absolute Deviation (MAD)2
Skewness-2.6848015
Sum3113
Variance632.14501
MonotonicityNot monotonic
2024-04-21T17:01:57.505144image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
100.0 9
23.1%
96.0 7
17.9%
97.0 3
 
7.7%
99.0 2
 
5.1%
98.0 2
 
5.1%
94.0 1
 
2.6%
20.0 1
 
2.6%
95.0 1
 
2.6%
29.0 1
 
2.6%
97.67 1
 
2.6%
Other values (7) 7
17.9%
(Missing) 4
10.3%
ValueCountFrequency (%)
0.0 1
 
2.6%
20.0 1
 
2.6%
29.0 1
 
2.6%
36.42 1
 
2.6%
94.0 1
 
2.6%
94.22 1
 
2.6%
95.0 1
 
2.6%
95.51 1
 
2.6%
95.76 1
 
2.6%
96.0 7
17.9%
ValueCountFrequency (%)
100.0 9
23.1%
99.66 1
 
2.6%
99.0 2
 
5.1%
98.76 1
 
2.6%
98.0 2
 
5.1%
97.67 1
 
2.6%
97.0 3
 
7.7%
96.0 7
17.9%
95.76 1
 
2.6%
95.51 1
 
2.6%

Interactions

2024-04-21T17:01:47.576820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:40.014164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:41.489215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:42.969018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:44.606134image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:46.086709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:47.823971image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:40.261588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:41.739289image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:43.213268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:44.857114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:46.340399image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:48.068160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:40.507402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:41.986285image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:43.455549image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:45.110889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:46.590973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:48.302666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:40.744176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:42.224873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:43.683057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:45.349095image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:46.831129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:48.548918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:40.995314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:42.475813image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:43.931040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:45.590195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:47.083165image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:48.798560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:41.249017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:42.728853image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:44.174521image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:45.844904image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T17:01:47.334569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T17:01:57.808823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.8570.8590.6640.5800.8000.857
부과금액0.0000.8571.0000.9940.6570.0000.7590.000
수납급액0.0000.8590.9941.0000.5620.0230.7430.000
환급금액0.0000.6640.6570.5621.0001.0000.8720.850
결손금액0.0000.5800.0000.0231.0001.0000.9840.908
미수납 금액0.0000.8000.7590.7430.8720.9841.0000.970
징수율0.0000.8570.0000.0000.8500.9080.9701.000
2024-04-21T17:01:58.094378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2024-04-21T17:01:58.340773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9840.4590.1730.4280.0010.0000.542
수납급액0.9841.0000.4020.0980.3420.0720.0000.545
환급금액0.4590.4021.0000.7820.790-0.4090.0000.326
결손금액0.1730.0980.7821.0000.721-0.5170.0000.304
미수납 금액0.4280.3420.7900.7211.000-0.7520.0000.467
징수율0.0010.072-0.409-0.517-0.7521.0000.0000.408
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.5420.5450.3260.3040.4670.4080.0001.000

Missing values

2024-04-21T17:01:49.144117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T17:01:49.639200image/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.
2024-04-21T17:01:49.996938image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
0전라남도나주시461702020레저세<NA><NA><NA><NA><NA><NA>
1전라남도나주시461702020재산세2575402100024827702000997600049100092582800096.0
2전라남도나주시461702020주민세63310170006194136000121340009300013678800098.0
3전라남도나주시461702020취득세5095480800050690878000525714000026393000099.0
4전라남도나주시461702020자동차세2217077300020857724000182006000545000131250400094.0
5전라남도나주시461702020과년도수입41587150008283990003176299000704929000262538700020.0
6전라남도나주시461702020담배소비세78781340007878134000898800000100.0
7전라남도나주시461702020도시계획세<NA><NA><NA><NA><NA><NA>
8전라남도나주시461702020등록면허세496297800049527940002497200012500010059000100.0
9전라남도나주시461702020지방교육세14091643000135600700009689100024400053132900096.0
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
29전라남도나주시461702022취득세59486155000587467780002491150007650300066287400098.76
30전라남도나주시461702022자동차세2393578300022552774000157352000263000138274600094.22
31전라남도나주시461702022과년도수입6698819000243986200011364440001220584000303837300036.42
32전라남도나주시461702022담배소비세84286060008428606000700000100.0
33전라남도나주시461702022도시계획세000000.0
34전라남도나주시461702022등록면허세45961840004580706000117460002390001523900099.66
35전라남도나주시461702022지방교육세159316230001529453500060038000380700063328100096.0
36전라남도나주시461702022지방소득세51583831000495184090003614232000285948000177947400096.0
37전라남도나주시461702022지방소비세1487822900014878229000000100.0
38전라남도나주시461702022지역자원시설세53021370005077350000592000022478700095.76