Overview

Dataset statistics

Number of variables11
Number of observations78
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory7.4 KiB
Average record size in memory97.7 B

Variable types

Categorical4
Numeric7

Dataset

Description지방세 부과액에 대한 세목별 징수현황을 제공
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078676

Alerts

과세년도 has constant value ""Constant
시도명 is highly overall correlated with 자치단체코드 and 2 other fieldsHigh correlation
시군구명 is highly overall correlated with 자치단체코드 and 1 other fieldsHigh correlation
자치단체코드 is highly overall correlated with 시도명 and 1 other fieldsHigh correlation
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 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 부과금액 and 2 other fieldsHigh correlation
부과금액 has 26 (33.3%) zerosZeros
수납급액 has 26 (33.3%) zerosZeros
환급금액 has 31 (39.7%) zerosZeros
결손금액 has 52 (66.7%) zerosZeros
미수납 금액 has 33 (42.3%) zerosZeros
징수율 has 26 (33.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:16:45.642795
Analysis finished2023-12-10 23:16:50.197721
Duration4.55 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

HIGH CORRELATION 

Distinct2
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size756.0 B
창원시
65 
경상남도
13 

Length

Max length4
Median length3
Mean length3.1666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 65
83.3%
경상남도 13
 
16.7%

Length

2023-12-11T08:16:50.255489image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:50.343020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창원시 65
83.3%
경상남도 13
 
16.7%

시군구명
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size756.0 B
성산구
13 
의창구
13 
진해구
13 
마산합포구
13 
마산회원구
13 

Length

Max length5
Median length3
Mean length3.6666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
성산구 13
16.7%
의창구 13
16.7%
진해구 13
16.7%
마산합포구 13
16.7%
마산회원구 13
16.7%
창원시 13
16.7%

Length

2023-12-11T08:16:50.441231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:50.553748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
성산구 13
16.7%
의창구 13
16.7%
진해구 13
16.7%
마산합포구 13
16.7%
마산회원구 13
16.7%
창원시 13
16.7%

자치단체코드
Real number (ℝ)

HIGH CORRELATION 

Distinct6
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean48124.167
Minimum48120
Maximum48129
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:50.653610image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum48120
5-th percentile48120
Q148121
median48124
Q348127
95-th percentile48129
Maximum48129
Range9
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.2047719
Coefficient of variation (CV)6.6593815 × 10-5
Kurtosis-1.3652031
Mean48124.167
Median Absolute Deviation (MAD)3
Skewness0.16085422
Sum3753685
Variance10.270563
MonotonicityNot monotonic
2023-12-11T08:16:50.741035image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
48123 13
16.7%
48121 13
16.7%
48129 13
16.7%
48125 13
16.7%
48127 13
16.7%
48120 13
16.7%
ValueCountFrequency (%)
48120 13
16.7%
48121 13
16.7%
48123 13
16.7%
48125 13
16.7%
48127 13
16.7%
48129 13
16.7%
ValueCountFrequency (%)
48129 13
16.7%
48127 13
16.7%
48125 13
16.7%
48123 13
16.7%
48121 13
16.7%
48120 13
16.7%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)1.3%
Missing0
Missing (%)0.0%
Memory size756.0 B
2021
78 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 78
100.0%

Length

2023-12-11T08:16:50.840052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:16:50.930225image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 78
100.0%

세목명
Categorical

Distinct13
Distinct (%)16.7%
Missing0
Missing (%)0.0%
Memory size756.0 B
레저세
6 
재산세
6 
주민세
6 
취득세
6 
자동차세
6 
Other values (8)
48 

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 6
 
7.7%
재산세 6
 
7.7%
주민세 6
 
7.7%
취득세 6
 
7.7%
자동차세 6
 
7.7%
과년도수입 6
 
7.7%
담배소비세 6
 
7.7%
도시계획세 6
 
7.7%
등록면허세 6
 
7.7%
지방교육세 6
 
7.7%
Other values (3) 18
23.1%

Length

2023-12-11T08:16:51.034167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
레저세 6
 
7.7%
재산세 6
 
7.7%
주민세 6
 
7.7%
취득세 6
 
7.7%
자동차세 6
 
7.7%
과년도수입 6
 
7.7%
담배소비세 6
 
7.7%
도시계획세 6
 
7.7%
등록면허세 6
 
7.7%
지방교육세 6
 
7.7%
Other values (3) 18
23.1%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.9245155 × 1010
Minimum0
Maximum1.3035295 × 1011
Zeros26
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:51.140431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6.852282 × 109
Q32.5758728 × 1010
95-th percentile7.0170995 × 1010
Maximum1.3035295 × 1011
Range1.3035295 × 1011
Interquartile range (IQR)2.5758728 × 1010

Descriptive statistics

Standard deviation2.8915558 × 1010
Coefficient of variation (CV)1.502485
Kurtosis4.4967031
Mean1.9245155 × 1010
Median Absolute Deviation (MAD)6.852282 × 109
Skewness2.1054986
Sum1.5011221 × 1012
Variance8.3610947 × 1020
MonotonicityNot monotonic
2023-12-11T08:16:51.259849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
33.3%
7133124000 1
 
1.3%
1754221000 1
 
1.3%
22108045000 1
 
1.3%
1924326000 1
 
1.3%
5546543000 1
 
1.3%
26599055000 1
 
1.3%
3720829000 1
 
1.3%
70535567000 1
 
1.3%
37549243000 1
 
1.3%
Other values (43) 43
55.1%
ValueCountFrequency (%)
0 26
33.3%
1754221000 1
 
1.3%
1909691000 1
 
1.3%
1924326000 1
 
1.3%
2142111000 1
 
1.3%
3424400000 1
 
1.3%
3618264000 1
 
1.3%
3720829000 1
 
1.3%
4080518000 1
 
1.3%
4151197000 1
 
1.3%
ValueCountFrequency (%)
130352947000 1
1.3%
119379057000 1
1.3%
115380514000 1
1.3%
70535567000 1
1.3%
70106659000 1
1.3%
69613287000 1
1.3%
66331330000 1
1.3%
65620678000 1
1.3%
61658005000 1
1.3%
53340059000 1
1.3%

수납급액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct53
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.8276008 × 1010
Minimum0
Maximum1.3027083 × 1011
Zeros26
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:51.373551image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3.977114 × 109
Q32.4825691 × 1010
95-th percentile7.0155255 × 1010
Maximum1.3027083 × 1011
Range1.3027083 × 1011
Interquartile range (IQR)2.4825691 × 1010

Descriptive statistics

Standard deviation2.8888114 × 1010
Coefficient of variation (CV)1.5806577
Kurtosis4.6371319
Mean1.8276008 × 1010
Median Absolute Deviation (MAD)3.977114 × 109
Skewness2.1458636
Sum1.4255287 × 1012
Variance8.3452314 × 1020
MonotonicityNot monotonic
2023-12-11T08:16:51.488937image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 26
33.3%
7133124000 1
 
1.3%
1754221000 1
 
1.3%
21186252000 1
 
1.3%
1924326000 1
 
1.3%
5370373000 1
 
1.3%
25765686000 1
 
1.3%
3590925000 1
 
1.3%
70430633000 1
 
1.3%
35882428000 1
 
1.3%
Other values (43) 43
55.1%
ValueCountFrequency (%)
0 26
33.3%
644133000 1
 
1.3%
1326615000 1
 
1.3%
1754221000 1
 
1.3%
1877270000 1
 
1.3%
1909691000 1
 
1.3%
1924326000 1
 
1.3%
2101554000 1
 
1.3%
2118457000 1
 
1.3%
2142111000 1
 
1.3%
ValueCountFrequency (%)
130270832000 1
1.3%
117828283000 1
1.3%
115109352000 1
1.3%
70430633000 1
1.3%
70106659000 1
1.3%
69569076000 1
1.3%
65014208000 1
1.3%
63788379000 1
1.3%
60850936000 1
1.3%
52283837000 1
1.3%

환급금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct48
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9420083 × 108
Minimum0
Maximum5.138508 × 109
Zeros31
Zeros (%)39.7%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:51.600862image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5370000
Q32.578315 × 108
95-th percentile2.1976191 × 109
Maximum5.138508 × 109
Range5.138508 × 109
Interquartile range (IQR)2.578315 × 108

Descriptive statistics

Standard deviation9.4290131 × 108
Coefficient of variation (CV)2.3919313
Kurtosis11.976817
Mean3.9420083 × 108
Median Absolute Deviation (MAD)5370000
Skewness3.36322
Sum3.0747665 × 1010
Variance8.8906288 × 1017
MonotonicityNot monotonic
2023-12-11T08:16:51.722112image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=48)
ValueCountFrequency (%)
0 31
39.7%
2698000 1
 
1.3%
847028000 1
 
1.3%
427301000 1
 
1.3%
863178000 1
 
1.3%
3689000 1
 
1.3%
14735000 1
 
1.3%
915000 1
 
1.3%
379149000 1
 
1.3%
213358000 1
 
1.3%
Other values (38) 38
48.7%
ValueCountFrequency (%)
0 31
39.7%
915000 1
 
1.3%
1278000 1
 
1.3%
2097000 1
 
1.3%
2698000 1
 
1.3%
2997000 1
 
1.3%
3556000 1
 
1.3%
3689000 1
 
1.3%
5124000 1
 
1.3%
5616000 1
 
1.3%
ValueCountFrequency (%)
5138508000 1
1.3%
3942556000 1
1.3%
3891422000 1
1.3%
2350722000 1
1.3%
2170601000 1
1.3%
1921383000 1
1.3%
1636603000 1
1.3%
1473458000 1
1.3%
1007372000 1
1.3%
863178000 1
1.3%

결손금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)34.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0636247 × 108
Minimum0
Maximum2.659322 × 109
Zeros52
Zeros (%)66.7%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:51.819623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q394750
95-th percentile8.9451355 × 108
Maximum2.659322 × 109
Range2.659322 × 109
Interquartile range (IQR)94750

Descriptive statistics

Standard deviation4.1835962 × 108
Coefficient of variation (CV)3.9333385
Kurtosis21.478757
Mean1.0636247 × 108
Median Absolute Deviation (MAD)0
Skewness4.4970872
Sum8.296273 × 109
Variance1.7502477 × 1017
MonotonicityNot monotonic
2023-12-11T08:16:51.925872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 52
66.7%
17620000 1
 
1.3%
646000 1
 
1.3%
12676000 1
 
1.3%
393000 1
 
1.3%
1436404000 1
 
1.3%
438000 1
 
1.3%
103000 1
 
1.3%
2236000 1
 
1.3%
3000 1
 
1.3%
Other values (17) 17
 
21.8%
ValueCountFrequency (%)
0 52
66.7%
3000 1
 
1.3%
10000 1
 
1.3%
11000 1
 
1.3%
18000 1
 
1.3%
59000 1
 
1.3%
70000 1
 
1.3%
103000 1
 
1.3%
284000 1
 
1.3%
393000 1
 
1.3%
ValueCountFrequency (%)
2659322000 1
1.3%
1656675000 1
1.3%
1436404000 1
1.3%
1297445000 1
1.3%
823408000 1
1.3%
341600000 1
1.3%
26503000 1
1.3%
17620000 1
1.3%
16008000 1
1.3%
12676000 1
1.3%

미수납 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct46
Distinct (%)59.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.6278444 × 108
Minimum0
Maximum1.274399 × 1010
Zeros33
Zeros (%)42.3%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:52.061439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median63163000
Q37.6321375 × 108
95-th percentile6.1930965 × 109
Maximum1.274399 × 1010
Range1.274399 × 1010
Interquartile range (IQR)7.6321375 × 108

Descriptive statistics

Standard deviation2.1013811 × 109
Coefficient of variation (CV)2.4355807
Kurtosis16.014161
Mean8.6278444 × 108
Median Absolute Deviation (MAD)63163000
Skewness3.8082637
Sum6.7297186 × 1010
Variance4.4158025 × 1018
MonotonicityNot monotonic
2023-12-11T08:16:52.173605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=46)
ValueCountFrequency (%)
0 33
42.3%
784062000 1
 
1.3%
921793000 1
 
1.3%
176170000 1
 
1.3%
833369000 1
 
1.3%
129894000 1
 
1.3%
104934000 1
 
1.3%
1666815000 1
 
1.3%
6422860000 1
 
1.3%
17609000 1
 
1.3%
Other values (36) 36
46.2%
ValueCountFrequency (%)
0 33
42.3%
12934000 1
 
1.3%
17468000 1
 
1.3%
17609000 1
 
1.3%
23143000 1
 
1.3%
23984000 1
 
1.3%
44211000 1
 
1.3%
82115000 1
 
1.3%
104934000 1
 
1.3%
106993000 1
 
1.3%
ValueCountFrequency (%)
12743990000 1
1.3%
8725626000 1
1.3%
6544711000 1
1.3%
6422860000 1
1.3%
6152550000 1
1.3%
2516448000 1
1.3%
2484325000 1
1.3%
2180440000 1
1.3%
2128921000 1
1.3%
1666815000 1
1.3%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct44
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean59.897692
Minimum0
Maximum100
Zeros26
Zeros (%)33.3%
Negative0
Negative (%)0.0%
Memory size834.0 B
2023-12-11T08:16:52.279706image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median95.835
Q398.69
95-th percentile100
Maximum100
Range100
Interquartile range (IQR)98.69

Descriptive statistics

Standard deviation47.159942
Coefficient of variation (CV)0.78734156
Kurtosis-1.826388
Mean59.897692
Median Absolute Deviation (MAD)4.135
Skewness-0.4425988
Sum4672.02
Variance2224.0602
MonotonicityNot monotonic
2023-12-11T08:16:52.621605image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
0.0 26
33.3%
100.0 7
 
9.0%
98.69 2
 
2.6%
99.94 2
 
2.6%
99.76 2
 
2.6%
96.35 1
 
1.3%
96.51 1
 
1.3%
99.85 1
 
1.3%
95.56 1
 
1.3%
21.4 1
 
1.3%
Other values (34) 34
43.6%
ValueCountFrequency (%)
0.0 26
33.3%
5.84 1
 
1.3%
10.86 1
 
1.3%
14.88 1
 
1.3%
21.4 1
 
1.3%
22.33 1
 
1.3%
91.09 1
 
1.3%
93.19 1
 
1.3%
94.88 1
 
1.3%
95.27 1
 
1.3%
ValueCountFrequency (%)
100.0 7
9.0%
99.94 2
 
2.6%
99.85 1
 
1.3%
99.77 1
 
1.3%
99.76 2
 
2.6%
99.64 1
 
1.3%
99.62 1
 
1.3%
99.61 1
 
1.3%
99.36 1
 
1.3%
99.08 1
 
1.3%

Interactions

2023-12-11T08:16:49.391676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:45.989556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.516045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.052874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.573396image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.343553image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.855572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.474488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.074609image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.606167image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.128206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.641637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.417286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.929901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.571221image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.151785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.686595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.200579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.745883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.495634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.008696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.671907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.230363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.756846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.274271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.830150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.571062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.088925image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.757429image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.299947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.828310image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.345866image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.899393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.643377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.160737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.836805image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.370825image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.912135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.422350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.969224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.708952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.237848image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.915467image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.445642image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:46.985741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:47.503122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.278883image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:48.787218image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:16:49.314329image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:16:52.693007image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명자치단체코드세목명부과금액수납급액환급금액결손금액미수납 금액징수율
시도명1.0001.000NaN0.0000.2080.1770.0000.0000.0000.802
시군구명1.0001.0001.0000.0000.1530.0660.0000.0360.0000.465
자치단체코드NaN1.0001.0000.0000.0000.0000.0000.0270.0000.000
세목명0.0000.0000.0001.0000.5880.5580.5210.2180.6800.701
부과금액0.2080.1530.0000.5881.0000.9960.3560.2440.0730.424
수납급액0.1770.0660.0000.5580.9961.0000.3290.0000.0000.367
환급금액0.0000.0000.0000.5210.3560.3291.0000.9840.8610.750
결손금액0.0000.0360.0270.2180.2440.0000.9841.0000.9430.870
미수납 금액0.0000.0000.0000.6800.0730.0000.8610.9431.0000.926
징수율0.8020.4650.0000.7010.4240.3670.7500.8700.9261.000
2023-12-11T08:16:52.796195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시도명시군구명세목명
시도명1.0000.9730.000
시군구명0.9731.0000.000
세목명0.0000.0001.000
2023-12-11T08:16:52.868822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
자치단체코드부과금액수납급액환급금액결손금액미수납 금액징수율시도명시군구명세목명
자치단체코드1.0000.2000.2140.2790.1140.2710.2410.9731.0000.000
부과금액0.2001.0000.9740.8430.4520.7760.6830.1460.0760.300
수납급액0.2140.9741.0000.7560.3530.6840.7520.1230.0070.278
환급금액0.2790.8430.7561.0000.6350.9020.4400.0000.0000.258
결손금액0.1140.4520.3530.6351.0000.7090.1310.0000.0000.087
미수납 금액0.2710.7760.6840.9020.7091.0000.2950.0000.0000.398
징수율0.2410.6830.7520.4400.1310.2951.0000.5860.3110.462
시도명0.9730.1460.1230.0000.0000.0000.5861.0000.9730.000
시군구명1.0000.0760.0070.0000.0000.0000.3110.9731.0000.000
세목명0.0000.3000.2780.2580.0870.3980.4620.0000.0001.000

Missing values

2023-12-11T08:16:50.017953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:16:50.144436image/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
0창원시성산구481232021레저세71331240007133124000269800000100.0
1창원시성산구481232021재산세6165800500060850936000676010001762000078944900098.69
2창원시성산구481232021주민세30108278000299144040009030000019387400099.36
3창원시성산구481232021취득세13035294700013027083200027088700008211500099.94
4창원시성산구481232021자동차세45828297000436993760007697270000212892100095.35
5창원시성산구481232021과년도수입110264340006441330005138508000165667500087256260005.84
6창원시성산구481232021담배소비세000000.0
7창원시성산구481232021도시계획세000000.0
8창원시성산구481232021등록면허세973354300097104000002490900002314300099.76
9창원시성산구481232021지방교육세2619196700025435612000258341000225900075409600097.11
시도명시군구명자치단체코드과세년도세목명부과금액수납급액환급금액결손금액미수납 금액징수율
68경상남도창원시481202021취득세000000.0
69경상남도창원시481202021자동차세000000.0
70경상남도창원시481202021과년도수입000000.0
71경상남도창원시481202021담배소비세000000.0
72경상남도창원시481202021도시계획세000000.0
73경상남도창원시481202021등록면허세000000.0
74경상남도창원시481202021지방교육세000000.0
75경상남도창원시481202021지방소득세000000.0
76경상남도창원시481202021지방소비세000000.0
77경상남도창원시481202021지역자원시설세000000.0