Overview

Dataset statistics

Number of variables11
Number of observations39
Missing cells42
Missing cells (%)9.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.8 KiB
Average record size in memory99.4 B

Variable types

Categorical5
Numeric6

Dataset

Description3년간(2019~2021) 지방세 부과액에 대한 세목별 징수현황 자료로 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액, 징수율 항목을 제공합니다
Author전라남도 나주시
URLhttps://www.data.go.kr/data/15079535/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
부과금액 is highly overall correlated with 수납급액High 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 3 other fieldsHigh correlation
징수율 is highly overall correlated with 결손금액 and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 수납급액 and 1 other fieldsHigh correlation
부과금액 has 7 (17.9%) missing valuesMissing
수납급액 has 7 (17.9%) missing valuesMissing
환급금액 has 7 (17.9%) missing valuesMissing
결손금액 has 7 (17.9%) missing valuesMissing
미수납 금액 has 7 (17.9%) missing valuesMissing
징수율 has 7 (17.9%) missing valuesMissing
환급금액 has 3 (7.7%) zerosZeros
결손금액 has 11 (28.2%) zerosZeros
미수납 금액 has 5 (12.8%) zerosZeros

Reproduction

Analysis started2023-12-12 15:23:55.425783
Analysis finished2023-12-12 15:23:59.179953
Duration3.75 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 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

2023-12-13T00:23:59.231036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:59.315972image/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 size444.0 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

2023-12-13T00:23:59.391643image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:59.475365image/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 size444.0 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

2023-12-13T00:23:59.556466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:59.642886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
46170 39
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
2019
13 
2020
13 
2021
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2019 13
33.3%
2020 13
33.3%
2021 13
33.3%

Length

2023-12-13T00:23:59.748574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T00:23:59.848803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 13
33.3%
2020 13
33.3%
2021 13
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size444.0 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

2023-12-13T00:23:59.967833image/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 

Distinct32
Distinct (%)100.0%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean1.7767106 × 1010
Minimum4.158715 × 109
Maximum5.826368 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:24:00.491686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4.158715 × 109
5-th percentile4.38424 × 109
Q15.6942078 × 109
median1.011269 × 1010
Q32.807986 × 1010
95-th percentile4.6670513 × 1010
Maximum5.826368 × 1010
Range5.4104965 × 1010
Interquartile range (IQR)2.2385653 × 1010

Descriptive statistics

Standard deviation1.5246808 × 1010
Coefficient of variation (CV)0.85814807
Kurtosis0.32380198
Mean1.7767106 × 1010
Median Absolute Deviation (MAD)5.4918785 × 109
Skewness1.1048578
Sum5.6854739 × 1011
Variance2.3246514 × 1020
MonotonicityNot monotonic
2023-12-13T00:24:00.639476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
14091643000 1
 
2.6%
4943197000 1
 
2.6%
10067081000 1
 
2.6%
36877328000 1
 
2.6%
15500633000 1
 
2.6%
4883519000 1
 
2.6%
8033901000 1
 
2.6%
5430417000 1
 
2.6%
27565251000 1
 
2.6%
58263680000 1
 
2.6%
Other values (22) 22
56.4%
(Missing) 7
 
17.9%
ValueCountFrequency (%)
4158715000 1
2.6%
4268465000 1
2.6%
4478965000 1
2.6%
4516876000 1
2.6%
4883519000 1
2.6%
4943197000 1
2.6%
4962978000 1
2.6%
5430417000 1
2.6%
5782138000 1
2.6%
6127221000 1
2.6%
ValueCountFrequency (%)
58263680000 1
2.6%
50954808000 1
2.6%
43165181000 1
2.6%
36877328000 1
2.6%
35833210000 1
2.6%
32465726000 1
2.6%
31818028000 1
2.6%
29623689000 1
2.6%
27565251000 1
2.6%
25754021000 1
2.6%

수납급액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct32
Distinct (%)100.0%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean1.7010027 × 1010
Minimum8.28399 × 108
Maximum5.779393 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:24:00.793239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum8.28399 × 108
5-th percentile2.0805884 × 109
Q14.9321605 × 109
median1.011269 × 1010
Q32.6793882 × 1010
95-th percentile4.626023 × 1010
Maximum5.779393 × 1010
Range5.6965531 × 1010
Interquartile range (IQR)2.1861721 × 1010

Descriptive statistics

Standard deviation1.5268964 × 1010
Coefficient of variation (CV)0.8976449
Kurtosis0.4319427
Mean1.7010027 × 1010
Median Absolute Deviation (MAD)5.88575 × 109
Skewness1.1085294
Sum5.4432086 × 1011
Variance2.3314126 × 1020
MonotonicityNot monotonic
2023-12-13T00:24:00.954536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
13560070000 1
 
2.6%
4750344000 1
 
2.6%
10067081000 1
 
2.6%
35880763000 1
 
2.6%
14939763000 1
 
2.6%
4870260000 1
 
2.6%
8033901000 1
 
2.6%
1572939000 1
 
2.6%
26182363000 1
 
2.6%
57793930000 1
 
2.6%
Other values (22) 22
56.4%
(Missing) 7
 
17.9%
ValueCountFrequency (%)
828399000 1
2.6%
1572939000 1
2.6%
2495938000 1
2.6%
4113110000 1
2.6%
4340771000 1
2.6%
4466301000 1
2.6%
4750344000 1
2.6%
4870260000 1
2.6%
4952794000 1
2.6%
6000676000 1
2.6%
ValueCountFrequency (%)
57793930000 1
2.6%
50690878000 1
2.6%
42635155000 1
2.6%
35880763000 1
2.6%
34796802000 1
2.6%
31073424000 1
2.6%
31067135000 1
2.6%
28628438000 1
2.6%
26182363000 1
2.6%
24827702000 1
2.6%

환급금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)93.8%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean6.9059412 × 108
Minimum0
Maximum5.077267 × 109
Zeros3
Zeros (%)7.7%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:24:01.117156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11963500
median51440500
Q33.7161 × 108
95-th percentile3.9747969 × 109
Maximum5.077267 × 109
Range5.077267 × 109
Interquartile range (IQR)3.696465 × 108

Descriptive statistics

Standard deviation1.3828935 × 109
Coefficient of variation (CV)2.0024692
Kurtosis3.7121194
Mean6.9059412 × 108
Median Absolute Deviation (MAD)51361500
Skewness2.1874097
Sum2.2099012 × 1010
Variance1.9123944 × 1018
MonotonicityNot monotonic
2023-12-13T00:24:01.284746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 3
 
7.7%
24972000 1
 
2.6%
987000 1
 
2.6%
4156507000 1
 
2.6%
113586000 1
 
2.6%
77561000 1
 
2.6%
158000 1
 
2.6%
1898937000 1
 
2.6%
174460000 1
 
2.6%
595845000 1
 
2.6%
Other values (20) 20
51.3%
(Missing) 7
 
17.9%
ValueCountFrequency (%)
0 3
7.7%
158000 1
 
2.6%
854000 1
 
2.6%
987000 1
 
2.6%
1009000 1
 
2.6%
1620000 1
 
2.6%
2078000 1
 
2.6%
8988000 1
 
2.6%
9976000 1
 
2.6%
11154000 1
 
2.6%
ValueCountFrequency (%)
5077267000 1
2.6%
4156507000 1
2.6%
3826125000 1
2.6%
3176299000 1
2.6%
1898937000 1
2.6%
1515285000 1
2.6%
595845000 1
2.6%
525714000 1
2.6%
320242000 1
2.6%
182006000 1
2.6%

결손금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct22
Distinct (%)68.8%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean86545500
Minimum0
Maximum1.196237 × 109
Zeros11
Zeros (%)28.2%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:24:01.463486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median224000
Q31238250
95-th percentile7.4450875 × 108
Maximum1.196237 × 109
Range1.196237 × 109
Interquartile range (IQR)1238250

Descriptive statistics

Standard deviation2.7349922 × 108
Coefficient of variation (CV)3.1601784
Kurtosis10.073089
Mean86545500
Median Absolute Deviation (MAD)224000
Skewness3.2591999
Sum2.769456 × 109
Variance7.4801825 × 1016
MonotonicityNot monotonic
2023-12-13T00:24:01.622962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 11
28.2%
704929000 1
 
2.6%
11654000 1
 
2.6%
477000 1
 
2.6%
237000 1
 
2.6%
1196237000 1
 
2.6%
1848000 1
 
2.6%
147000 1
 
2.6%
39735000 1
 
2.6%
244000 1
 
2.6%
Other values (12) 12
30.8%
(Missing) 7
17.9%
ValueCountFrequency (%)
0 11
28.2%
93000 1
 
2.6%
125000 1
 
2.6%
134000 1
 
2.6%
147000 1
 
2.6%
211000 1
 
2.6%
237000 1
 
2.6%
244000 1
 
2.6%
477000 1
 
2.6%
491000 1
 
2.6%
ValueCountFrequency (%)
1196237000 1
2.6%
792884000 1
2.6%
704929000 1
2.6%
39735000 1
2.6%
15631000 1
2.6%
11654000 1
2.6%
1848000 1
2.6%
1440000 1
2.6%
1171000 1
2.6%
696000 1
2.6%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct28
Distinct (%)87.5%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean6.7053344 × 108
Minimum0
Maximum2.661241 × 109
Zeros5
Zeros (%)12.8%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:24:01.783798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q198063750
median4.99888 × 108
Q39.956065 × 108
95-th percentile2.552748 × 109
Maximum2.661241 × 109
Range2.661241 × 109
Interquartile range (IQR)8.9754275 × 108

Descriptive statistics

Standard deviation7.7095367 × 108
Coefficient of variation (CV)1.1497617
Kurtosis1.5759317
Mean6.7053344 × 108
Median Absolute Deviation (MAD)4.871505 × 108
Skewness1.4515956
Sum2.145707 × 1010
Variance5.9436957 × 1017
MonotonicityNot monotonic
2023-12-13T00:24:01.957060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 5
 
12.8%
10059000 1
 
2.6%
192853000 1
 
2.6%
984911000 1
 
2.6%
560393000 1
 
2.6%
13022000 1
 
2.6%
2661241000 1
 
2.6%
1381040000 1
 
2.6%
469750000 1
 
2.6%
176678000 1
 
2.6%
Other values (18) 18
46.2%
(Missing) 7
 
17.9%
ValueCountFrequency (%)
0 5
12.8%
10059000 1
 
2.6%
12453000 1
 
2.6%
13022000 1
 
2.6%
126411000 1
 
2.6%
136788000 1
 
2.6%
154659000 1
 
2.6%
175958000 1
 
2.6%
176678000 1
 
2.6%
192853000 1
 
2.6%
ValueCountFrequency (%)
2661241000 1
2.6%
2625387000 1
2.6%
2493316000 1
2.6%
1397420000 1
2.6%
1381040000 1
2.6%
1312504000 1
2.6%
1012345000 1
2.6%
996673000 1
2.6%
995251000 1
2.6%
984911000 1
2.6%

징수율
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct18
Distinct (%)56.2%
Missing7
Missing (%)17.9%
Infinite0
Infinite (%)0.0%
Mean91.36875
Minimum20
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T00:24:02.110725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum20
5-th percentile36.81
Q195.975
median97
Q399.175
95-th percentile100
Maximum100
Range80
Interquartile range (IQR)3.2

Descriptive statistics

Standard deviation20.113411
Coefficient of variation (CV)0.22013447
Kurtosis7.8173054
Mean91.36875
Median Absolute Deviation (MAD)1.65
Skewness-2.9978307
Sum2923.8
Variance404.54931
MonotonicityNot monotonic
2023-12-13T00:24:02.248407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=18)
ValueCountFrequency (%)
100.0 7
17.9%
96.0 5
12.8%
97.0 3
 
7.7%
99.0 2
 
5.1%
98.0 2
 
5.1%
29.0 1
 
2.6%
95.0 1
 
2.6%
20.0 1
 
2.6%
94.0 1
 
2.6%
95.9 1
 
2.6%
Other values (8) 8
20.5%
(Missing) 7
17.9%
ValueCountFrequency (%)
20.0 1
 
2.6%
29.0 1
 
2.6%
43.2 1
 
2.6%
94.0 1
 
2.6%
95.0 1
 
2.6%
95.5 1
 
2.6%
95.7 1
 
2.6%
95.9 1
 
2.6%
96.0 5
12.8%
96.4 1
 
2.6%
ValueCountFrequency (%)
100.0 7
17.9%
99.7 1
 
2.6%
99.0 2
 
5.1%
98.8 1
 
2.6%
98.0 2
 
5.1%
97.9 1
 
2.6%
97.7 1
 
2.6%
97.0 3
7.7%
96.4 1
 
2.6%
96.0 5
12.8%

Interactions

2023-12-13T00:23:58.378303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:55.776519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.298074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.830658image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.341797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.923888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.461091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:55.861186image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.389128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.925448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.440467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.995744image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.533700image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:55.953117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.475854image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.011714image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.557441image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.073975image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.609541image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.031127image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.580017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.082100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.662041image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.145246image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.684747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.110209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.663113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.170923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.755712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.215636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.761609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.206025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:56.745844image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.253129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:57.835122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T00:23:58.301027image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T00:24:02.419219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
과세년도1.0000.0000.1410.0000.0000.0190.0000.019
세목명0.0001.0000.7230.8440.5610.2830.8840.283
부과금액0.1410.7231.0000.9900.7230.0000.7030.000
수납급액0.0000.8440.9901.0000.7360.0000.8360.000
환급금액0.0000.5610.7230.7361.0001.0000.5301.000
결손금액0.0190.2830.0000.0001.0001.0000.5631.000
미수납 금액0.0000.8840.7030.8360.5300.5631.0000.563
징수율0.0190.2830.0000.0001.0001.0000.5631.000
2023-12-13T00:24:02.578533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명
과세년도1.0000.000
세목명0.0001.000
2023-12-13T00:24:02.680112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
부과금액수납급액환급금액결손금액미수납 금액징수율과세년도세목명
부과금액1.0000.9810.427-0.0420.3180.0030.0000.404
수납급액0.9811.0000.365-0.1100.2170.0960.0000.557
환급금액0.4270.3651.0000.6470.739-0.4600.0000.265
결손금액-0.042-0.1100.6471.0000.753-0.7340.0000.109
미수납 금액0.3180.2170.7390.7531.000-0.8760.0000.643
징수율0.0030.096-0.460-0.734-0.8761.0000.0000.109
과세년도0.0000.0000.0000.0000.0000.0001.0000.000
세목명0.4040.5570.2650.1090.6430.1090.0001.000

Missing values

2023-12-13T00:23:58.875387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T00:23:59.002915image/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.
2023-12-13T00:23:59.118222image/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
0전라남도나주시461702019레저세<NA><NA><NA><NA><NA><NA>
1전라남도나주시461702019재산세2479485400023781069000261680001440000101234500095.9
2전라남도나주시461702019주민세61272210006000676000207800013400012641100097.9
3전라남도나주시461702019취득세4316518100042635155000320242000053002600098.8
4전라남도나주시461702019자동차세32465726000310671350001621620001171000139742000095.7
5전라남도나주시461702019과년도수입578213800024959380001515285000792884000249331600043.2
6전라남도나주시461702019담배소비세73718800007371880000000100.0
7전라남도나주시461702019도시계획세<NA><NA><NA><NA><NA><NA>
8전라남도나주시461702019등록면허세44789650004466301000233140002110001245300099.7
9전라남도나주시461702019지방교육세13043167000124547680007671300052700058787200095.5
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
29전라남도나주시461702021취득세5826368000057793930000595845000046975000099.0
30전라남도나주시461702021자동차세27565251000261823630001744600001848000138104000095.0
31전라남도나주시461702021과년도수입5430417000157293900018989370001196237000266124100029.0
32전라남도나주시461702021담배소비세8033901000803390100015800000100.0
33전라남도나주시461702021도시계획세<NA><NA><NA><NA><NA><NA>
34전라남도나주시461702021등록면허세488351900048702600007756100023700013022000100.0
35전라남도나주시461702021지방교육세155006330001493976300011358600047700056039300096.0
36전라남도나주시461702021지방소득세368773280003588076300041565070001165400098491100097.0
37전라남도나주시461702021지방소비세1006708100010067081000000100.0
38전라남도나주시461702021지역자원시설세49431970004750344000987000019285300096.0