Overview

Dataset statistics

Number of variables12
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.1 KiB
Average record size in memory108.4 B

Variable types

Numeric7
Categorical5

Dataset

Description경상북도 지방세 부과액에 대한 세목별 징수현황에 대한 자료로, 2019년부터 2021년까지 부과금액, 수납금액, 환급금액, 결손금액, 미수납금액 및 징수율을 제공합니다.
URLhttps://www.data.go.kr/data/15078725/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
연번 is highly overall correlated with 과세년도High correlation
부과금액 is highly overall correlated with 수납금액 and 3 other fieldsHigh correlation
수납금액 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 수납금액 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 연번High correlation
세목명 is highly overall correlated with 부과금액 and 2 other fieldsHigh correlation
연번 has unique valuesUnique
부과금액 has 7 (17.9%) zerosZeros
수납금액 has 7 (17.9%) zerosZeros
환급금액 has 10 (25.6%) zerosZeros
결손금액 has 25 (64.1%) zerosZeros
미수납 금액 has 12 (30.8%) zerosZeros
징수율 has 7 (17.9%) zerosZeros

Reproduction

Analysis started2023-12-12 22:47:41.665127
Analysis finished2023-12-12 22:47:47.138742
Duration5.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct39
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20
Minimum1
Maximum39
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:47.199306image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.9
Q110.5
median20
Q329.5
95-th percentile37.1
Maximum39
Range38
Interquartile range (IQR)19

Descriptive statistics

Standard deviation11.401754
Coefficient of variation (CV)0.57008771
Kurtosis-1.2
Mean20
Median Absolute Deviation (MAD)10
Skewness0
Sum780
Variance130
MonotonicityStrictly increasing
2023-12-13T07:47:47.320993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=39)
ValueCountFrequency (%)
1 1
 
2.6%
2 1
 
2.6%
23 1
 
2.6%
24 1
 
2.6%
25 1
 
2.6%
26 1
 
2.6%
27 1
 
2.6%
28 1
 
2.6%
29 1
 
2.6%
30 1
 
2.6%
Other values (29) 29
74.4%
ValueCountFrequency (%)
1 1
2.6%
2 1
2.6%
3 1
2.6%
4 1
2.6%
5 1
2.6%
6 1
2.6%
7 1
2.6%
8 1
2.6%
9 1
2.6%
10 1
2.6%
ValueCountFrequency (%)
39 1
2.6%
38 1
2.6%
37 1
2.6%
36 1
2.6%
35 1
2.6%
34 1
2.6%
33 1
2.6%
32 1
2.6%
31 1
2.6%
30 1
2.6%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
경상북도
39 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 39
100.0%

Length

2023-12-13T07:47:47.447919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:47:47.530102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
문경시
39 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문경시 39
100.0%

Length

2023-12-13T07:47:47.647997image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:47:47.751307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문경시 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
47280
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47280
2nd row47280
3rd row47280
4th row47280
5th row47280

Common Values

ValueCountFrequency (%)
47280 39
100.0%

Length

2023-12-13T07:47:47.852194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:47:47.946049image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47280 39
100.0%

과세년도
Categorical

HIGH CORRELATION 

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

2023-12-13T07:47:48.051413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T07:47:48.173168image/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

2023-12-13T07:47:48.321737image/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  ZEROS 

Distinct33
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.0836687 × 109
Minimum0
Maximum1.8110525 × 1010
Zeros7
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:48.439722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.1593195 × 109
median5.251664 × 109
Q38.176317 × 109
95-th percentile1.4525085 × 1010
Maximum1.8110525 × 1010
Range1.8110525 × 1010
Interquartile range (IQR)7.0169975 × 109

Descriptive statistics

Standard deviation4.6957505 × 109
Coefficient of variation (CV)0.92369326
Kurtosis0.47307088
Mean5.0836687 × 109
Median Absolute Deviation (MAD)3.875832 × 109
Skewness0.89535011
Sum1.9826308 × 1011
Variance2.2050073 × 1019
MonotonicityNot monotonic
2023-12-13T07:47:48.568502image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 7
 
17.9%
10001434000 1
 
2.6%
7747776000 1
 
2.6%
8197400000 1
 
2.6%
1170964000 1
 
2.6%
8356651000 1
 
2.6%
1037259000 1
 
2.6%
18110525000 1
 
2.6%
3339763000 1
 
2.6%
7688963000 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
0 7
17.9%
800981000 1
 
2.6%
1037259000 1
 
2.6%
1147675000 1
 
2.6%
1170964000 1
 
2.6%
1187357000 1
 
2.6%
1299083000 1
 
2.6%
1375832000 1
 
2.6%
1422518000 1
 
2.6%
1523058000 1
 
2.6%
ValueCountFrequency (%)
18110525000 1
2.6%
15909991000 1
2.6%
14371207000 1
2.6%
10001434000 1
2.6%
9574830000 1
2.6%
9483644000 1
2.6%
8560711000 1
2.6%
8356651000 1
2.6%
8211330000 1
2.6%
8197400000 1
2.6%

수납금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct33
Distinct (%)84.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.7851219 × 109
Minimum0
Maximum1.7987107 × 1010
Zeros7
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:48.700504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q19.05525 × 108
median5.251664 × 109
Q37.9702785 × 109
95-th percentile1.4503197 × 1010
Maximum1.7987107 × 1010
Range1.7987107 × 1010
Interquartile range (IQR)7.0647535 × 109

Descriptive statistics

Standard deviation4.7413473 × 109
Coefficient of variation (CV)0.99085194
Kurtosis0.50490146
Mean4.7851219 × 109
Median Absolute Deviation (MAD)4.097372 × 109
Skewness0.95682125
Sum1.8661976 × 1011
Variance2.2480375 × 1019
MonotonicityNot monotonic
2023-12-13T07:47:48.854735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
0 7
 
17.9%
9490639000 1
 
2.6%
7520514000 1
 
2.6%
8197400000 1
 
2.6%
1128241000 1
 
2.6%
8134430000 1
 
2.6%
1007333000 1
 
2.6%
17987107000 1
 
2.6%
854969000 1
 
2.6%
7473169000 1
 
2.6%
Other values (23) 23
59.0%
ValueCountFrequency (%)
0 7
17.9%
744140000 1
 
2.6%
789662000 1
 
2.6%
854969000 1
 
2.6%
956081000 1
 
2.6%
1007333000 1
 
2.6%
1108976000 1
 
2.6%
1128241000 1
 
2.6%
1154292000 1
 
2.6%
1258142000 1
 
2.6%
ValueCountFrequency (%)
17987107000 1
2.6%
15837588000 1
2.6%
14354931000 1
2.6%
9490639000 1
2.6%
9363748000 1
2.6%
8983447000 1
2.6%
8211330000 1
2.6%
8197400000 1
2.6%
8134430000 1
2.6%
8051828000 1
2.6%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57067538
Minimum0
Maximum3.75956 × 108
Zeros10
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:48.990073image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14000
median6522000
Q392541500
95-th percentile2.409726 × 108
Maximum3.75956 × 108
Range3.75956 × 108
Interquartile range (IQR)92537500

Descriptive statistics

Standard deviation91374101
Coefficient of variation (CV)1.6011572
Kurtosis3.4364977
Mean57067538
Median Absolute Deviation (MAD)6522000
Skewness1.9100199
Sum2.225634 × 109
Variance8.3492263 × 1015
MonotonicityNot monotonic
2023-12-13T07:47:49.119599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 10
25.6%
8169000 1
 
2.6%
7040000 1
 
2.6%
290640000 1
 
2.6%
45979000 1
 
2.6%
2314000 1
 
2.6%
114000 1
 
2.6%
235454000 1
 
2.6%
144318000 1
 
2.6%
129611000 1
 
2.6%
Other values (20) 20
51.3%
ValueCountFrequency (%)
0 10
25.6%
8000 1
 
2.6%
114000 1
 
2.6%
779000 1
 
2.6%
993000 1
 
2.6%
1280000 1
 
2.6%
2204000 1
 
2.6%
2297000 1
 
2.6%
2314000 1
 
2.6%
3878000 1
 
2.6%
ValueCountFrequency (%)
375956000 1
2.6%
290640000 1
2.6%
235454000 1
2.6%
186822000 1
2.6%
156585000 1
2.6%
148588000 1
2.6%
144318000 1
2.6%
134844000 1
2.6%
129611000 1
2.6%
110561000 1
2.6%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct15
Distinct (%)38.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean38830487
Minimum0
Maximum6.29973 × 108
Zeros25
Zeros (%)64.1%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:49.247898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q377000
95-th percentile4.187303 × 108
Maximum6.29973 × 108
Range6.29973 × 108
Interquartile range (IQR)77000

Descriptive statistics

Standard deviation1.3796474 × 108
Coefficient of variation (CV)3.5530004
Kurtosis11.567129
Mean38830487
Median Absolute Deviation (MAD)0
Skewness3.5207093
Sum1.514389 × 109
Variance1.9034268 × 1016
MonotonicityNot monotonic
2023-12-13T07:47:49.380058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=15)
ValueCountFrequency (%)
0 25
64.1%
3341000 1
 
2.6%
93000 1
 
2.6%
61000 1
 
2.6%
629973000 1
 
2.6%
668000 1
 
2.6%
1107000 1
 
2.6%
2379000 1
 
2.6%
168000 1
 
2.6%
413863000 1
 
2.6%
Other values (5) 5
 
12.8%
ValueCountFrequency (%)
0 25
64.1%
19000 1
 
2.6%
30000 1
 
2.6%
50000 1
 
2.6%
61000 1
 
2.6%
93000 1
 
2.6%
101000 1
 
2.6%
168000 1
 
2.6%
668000 1
 
2.6%
1107000 1
 
2.6%
ValueCountFrequency (%)
629973000 1
2.6%
462536000 1
2.6%
413863000 1
2.6%
3341000 1
2.6%
2379000 1
2.6%
1107000 1
2.6%
668000 1
2.6%
168000 1
2.6%
101000 1
2.6%
93000 1
2.6%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)71.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5971628 × 108
Minimum0
Maximum2.117629 × 109
Zeros12
Zeros (%)30.8%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:49.548334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median36320000
Q32.17337 × 108
95-th percentile2.0229257 × 109
Maximum2.117629 × 109
Range2.117629 × 109
Interquartile range (IQR)2.17337 × 108

Descriptive statistics

Standard deviation5.4598568 × 108
Coefficient of variation (CV)2.1022389
Kurtosis7.6601276
Mean2.5971628 × 108
Median Absolute Deviation (MAD)36320000
Skewness2.916978
Sum1.0128935 × 1010
Variance2.9810036 × 1017
MonotonicityNot monotonic
2023-12-13T07:47:49.679807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 12
30.8%
4496000 1
 
2.6%
33065000 1
 
2.6%
211082000 1
 
2.6%
186008000 1
 
2.6%
5112000 1
 
2.6%
2022258000 1
 
2.6%
510694000 1
 
2.6%
123418000 1
 
2.6%
29926000 1
 
2.6%
Other values (18) 18
46.2%
ValueCountFrequency (%)
0 12
30.8%
3806000 1
 
2.6%
4496000 1
 
2.6%
5112000 1
 
2.6%
11319000 1
 
2.6%
16276000 1
 
2.6%
29926000 1
 
2.6%
33065000 1
 
2.6%
36320000 1
 
2.6%
40848000 1
 
2.6%
ValueCountFrequency (%)
2117629000 1
2.6%
2028935000 1
2.6%
2022258000 1
2.6%
510694000 1
2.6%
508715000 1
2.6%
500136000 1
2.6%
324782000 1
2.6%
266505000 1
2.6%
227262000 1
2.6%
222221000 1
2.6%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct29
Distinct (%)74.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.666667
Minimum0
Maximum100
Zeros7
Zeros (%)17.9%
Negative0
Negative (%)0.0%
Memory size483.0 B
2023-12-13T07:47:49.814530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q160.255
median97.01
Q399.43
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)39.175

Descriptive statistics

Standard deviation40.430031
Coefficient of variation (CV)0.54147363
Kurtosis-0.42961039
Mean74.666667
Median Absolute Deviation (MAD)2.63
Skewness-1.2245114
Sum2912
Variance1634.5874
MonotonicityNot monotonic
2023-12-13T07:47:49.931865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
0.0 7
 
17.9%
100.0 5
 
12.8%
99.7 1
 
2.6%
97.22 1
 
2.6%
97.8 1
 
2.6%
97.16 1
 
2.6%
99.64 1
 
2.6%
25.6 1
 
2.6%
94.89 1
 
2.6%
99.32 1
 
2.6%
Other values (19) 19
48.7%
ValueCountFrequency (%)
0.0 7
17.9%
22.72 1
 
2.6%
25.6 1
 
2.6%
26.45 1
 
2.6%
94.06 1
 
2.6%
94.73 1
 
2.6%
94.89 1
 
2.6%
95.69 1
 
2.6%
96.35 1
 
2.6%
96.63 1
 
2.6%
ValueCountFrequency (%)
100.0 5
12.8%
99.89 1
 
2.6%
99.72 1
 
2.6%
99.7 1
 
2.6%
99.64 1
 
2.6%
99.54 1
 
2.6%
99.32 1
 
2.6%
98.59 1
 
2.6%
97.8 1
 
2.6%
97.34 1
 
2.6%

Interactions

2023-12-13T07:47:45.981244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:41.986845image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.623106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.315143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.055627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.683445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.365593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:46.070595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.089720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.735257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.414466image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.146382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.793290image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.457116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:46.180795image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.179164image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.837270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.505803image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.236031image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.900631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.548493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:46.288450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.263892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.921483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.617171image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.329841image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.008889image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.637713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:46.667111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.351476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.030329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.734106image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.414589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.097299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.719951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:46.741143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.448051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.143451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.827099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.504148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.185475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.811794image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:46.813862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:42.535702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.224855image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:43.941138image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:44.595665image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.265379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T07:47:45.907735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T07:47:50.037288image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명부과금액수납금액환급금액결손금액미수납 금액징수율
연번1.0000.9390.0000.0000.0000.2400.3060.0000.000
과세년도0.9391.0000.0000.0000.0000.0000.0000.0000.000
세목명0.0000.0001.0000.8820.8090.7380.3210.9790.968
부과금액0.0000.0000.8821.0000.9860.7630.5680.8710.955
수납금액0.0000.0000.8090.9861.0000.7410.0000.6860.079
환급금액0.2400.0000.7380.7630.7411.0000.9700.9600.766
결손금액0.3060.0000.3210.5680.0000.9701.0000.8650.652
미수납 금액0.0000.0000.9790.8710.6860.9600.8651.0000.674
징수율0.0000.0000.9680.9550.0790.7660.6520.6741.000
2023-12-13T07:47:50.164291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-13T07:47:50.245750image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번부과금액수납금액환급금액결손금액미수납 금액징수율과세년도세목명
연번1.0000.1140.1370.098-0.224-0.0210.2130.8270.000
부과금액0.1141.0000.9690.6300.1620.5350.4970.0000.595
수납금액0.1370.9691.0000.5140.0380.4080.5920.0000.489
환급금액0.0980.6300.5141.0000.5070.8600.0760.0000.403
결손금액-0.2240.1620.0380.5071.0000.686-0.2920.0000.138
미수납 금액-0.0210.5350.4080.8600.6861.000-0.1600.0000.828
징수율0.2130.4970.5920.076-0.292-0.1601.0000.0000.812
과세년도0.8270.0000.0000.0000.0000.0000.0001.0000.000
세목명0.0000.5950.4890.4030.1380.8280.8120.0001.000

Missing values

2023-12-13T07:47:46.930515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T07:47:47.084606image/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

연번시도명시군구명자치단체코드과세년도세목명부과금액수납금액환급금액결손금액미수납 금액징수율
01경상북도문경시472802019레저세000000.0
12경상북도문경시472802019재산세768896300074731690003878000334100021245300097.19
23경상북도문경시472802019주민세129908300012581420002297000930004084800096.85
34경상북도문경시472802019취득세14371207000143549310004081200001627600099.89
45경상북도문경시472802019자동차세948364400089834470001105610006100050013600094.73
56경상북도문경시472802019과년도수입3614989000956081000156585000629973000202893500026.45
67경상북도문경시472802019담배소비세52516640005251664000000100.0
78경상북도문경시472802019도시계획세000000.0
89경상북도문경시472802019등록면허세1375832000137202600095610000380600099.72
910경상북도문경시472802019지방교육세602965900058492140003448000066800017977700097.01
연번시도명시군구명자치단체코드과세년도세목명부과금액수납금액환급금액결손금액미수납 금액징수율
2930경상북도문경시472802021취득세1811052500017987107000129611000012341800099.32
3031경상북도문경시472802021자동차세10001434000949063900014431800010100051069400094.89
3132경상북도문경시472802021과년도수입3339763000854969000235454000462536000202225800025.6
3233경상북도문경시472802021담배소비세5395349000539534900011400000100.0
3334경상북도문경시472802021도시계획세000000.0
3435경상북도문경시472802021등록면허세14225180001417387000231400019000511200099.64
3536경상북도문경시472802021지방교육세65568450006370807000459790003000018600800097.16
3637경상북도문경시472802021지방소득세95748300009363748000290640000021108200097.8
3738경상북도문경시472802021지방소비세82113300008211330000000100.0
3839경상북도문경시472802021지역자원시설세11873570001154292000704000003306500097.22