Overview

Dataset statistics

Number of variables9
Number of observations27
Missing cells35
Missing cells (%)14.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.2 KiB
Average record size in memory82.9 B

Variable types

Categorical2
Text1
Numeric6

Dataset

Description2022년도 계룡시 지방세 징수현황에 관한 데이터로서, 지방세목별 부과금액, 징수액, 불납결손액, 과오납반환액, 미수액, 징수율에 관한 공공데이터를 제공합니다.
URLhttps://www.data.go.kr/data/15093904/fileData.do

Alerts

세목 has constant value ""Constant
조정누계액 is highly overall correlated with 징수누계액 and 3 other fieldsHigh correlation
징수누계액 is highly overall correlated with 조정누계액 and 1 other fieldsHigh correlation
정리보류액등누계 is highly overall correlated with 미수액High correlation
미수액 is highly overall correlated with 조정누계액 and 2 other fieldsHigh correlation
환급누계액 is highly overall correlated with 조정누계액 and 1 other fieldsHigh correlation
징수율 is highly overall correlated with 조정누계액 and 1 other fieldsHigh correlation
징수누계액 has 5 (18.5%) missing valuesMissing
정리보류액등누계 has 18 (66.7%) missing valuesMissing
미수액 has 4 (14.8%) missing valuesMissing
환급누계액 has 8 (29.6%) missing valuesMissing
조정누계액 has unique valuesUnique
징수율 has 5 (18.5%) zerosZeros

Reproduction

Analysis started2023-12-12 20:05:01.249868
Analysis finished2023-12-12 20:05:06.729966
Duration5.48 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분
Categorical

Distinct2
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size348.0 B
시세
14 
도세
13 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
시세 14
51.9%
도세 13
48.1%

Length

2023-12-13T05:05:06.792445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:05:06.906894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
시세 14
51.9%
도세 13
48.1%

대표세목
Text

Distinct16
Distinct (%)59.3%
Missing0
Missing (%)0.0%
Memory size348.0 B
2023-12-13T05:05:07.078913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length3
Mean length4.037037
Min length3

Characters and Unicode

Total characters109
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique8 ?
Unique (%)29.6%

Sample

1st row취득세
2nd row등록면허세
3rd row레저세
4th row지역자원시설세
5th row교육세
ValueCountFrequency (%)
취득세 3
11.1%
주민세 3
11.1%
재산세 3
11.1%
등록면허세 2
 
7.4%
지역자원시설세 2
 
7.4%
교육세 2
 
7.4%
지방소득세 2
 
7.4%
자동차세 2
 
7.4%
레저세 1
 
3.7%
담배소비세 1
 
3.7%
Other values (6) 6
22.2%
2023-12-13T05:05:07.444412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 27
24.8%
지 6
 
5.5%
득 5
 
4.6%
소 4
 
3.7%
시 4
 
3.7%
자 4
 
3.7%
취 3
 
2.8%
동 3
 
2.8%
설 3
 
2.8%
허 3
 
2.8%
Other values (25) 47
43.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 109
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 27
24.8%
지 6
 
5.5%
득 5
 
4.6%
소 4
 
3.7%
시 4
 
3.7%
자 4
 
3.7%
취 3
 
2.8%
동 3
 
2.8%
설 3
 
2.8%
허 3
 
2.8%
Other values (25) 47
43.1%

Most occurring scripts

ValueCountFrequency (%)
Hangul 109
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 27
24.8%
지 6
 
5.5%
득 5
 
4.6%
소 4
 
3.7%
시 4
 
3.7%
자 4
 
3.7%
취 3
 
2.8%
동 3
 
2.8%
설 3
 
2.8%
허 3
 
2.8%
Other values (25) 47
43.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 109
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 27
24.8%
지 6
 
5.5%
득 5
 
4.6%
소 4
 
3.7%
시 4
 
3.7%
자 4
 
3.7%
취 3
 
2.8%
동 3
 
2.8%
설 3
 
2.8%
허 3
 
2.8%
Other values (25) 47
43.1%

세목
Categorical

CONSTANT 

Distinct1
Distinct (%)3.7%
Missing0
Missing (%)0.0%
Memory size348.0 B
소계
27 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
소계 27
100.0%

Length

2023-12-13T05:05:07.593406image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:05:07.694875image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
소계 27
100.0%

조정누계액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct27
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.0654361 × 109
Minimum34640
Maximum1.79549 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:05:07.788377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34640
5-th percentile159959
Q15239860
median1.0842835 × 108
Q31.6579313 × 109
95-th percentile8.2934203 × 109
Maximum1.79549 × 1010
Range1.7954866 × 1010
Interquartile range (IQR)1.6526915 × 109

Descriptive statistics

Standard deviation4.0828485 × 109
Coefficient of variation (CV)1.9767489
Kurtosis8.351872
Mean2.0654361 × 109
Median Absolute Deviation (MAD)1.0829735 × 108
Skewness2.7101711
Sum5.5766775 × 1010
Variance1.6669652 × 1019
MonotonicityNot monotonic
2023-12-13T05:05:07.951965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
17954900310 1
 
3.7%
1032277640 1
 
3.7%
227530 1
 
3.7%
336370 1
 
3.7%
464410 1
 
3.7%
16634970 1
 
3.7%
34640 1
 
3.7%
131000 1
 
3.7%
4308320 1
 
3.7%
352355920 1
 
3.7%
Other values (17) 17
63.0%
ValueCountFrequency (%)
34640 1
3.7%
131000 1
3.7%
227530 1
3.7%
336370 1
3.7%
464410 1
3.7%
2525280 1
3.7%
4308320 1
3.7%
6171400 1
3.7%
12144500 1
3.7%
15107270 1
3.7%
ValueCountFrequency (%)
17954900310 1
3.7%
8606605400 1
3.7%
7562654930 1
3.7%
6423696650 1
3.7%
5314057910 1
3.7%
4253080820 1
3.7%
2283585030 1
3.7%
1032277640 1
3.7%
703977840 1
3.7%
484155470 1
3.7%

징수누계액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct22
Distinct (%)100.0%
Missing5
Missing (%)18.5%
Infinite0
Infinite (%)0.0%
Mean2.4794724 × 109
Minimum-51019570
Maximum1.7950229 × 1010
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)7.4%
Memory size375.0 B
2023-12-13T05:05:08.113257image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-51019570
5-th percentile-1098793
Q17682612.5
median2.9976812 × 108
Q33.6829106 × 109
95-th percentile8.3202528 × 109
Maximum1.7950229 × 1010
Range1.8001248 × 1010
Interquartile range (IQR)3.675228 × 109

Descriptive statistics

Standard deviation4.3807478 × 109
Coefficient of variation (CV)1.7668064
Kurtosis6.8202346
Mean2.4794724 × 109
Median Absolute Deviation (MAD)3.0034215 × 108
Skewness2.4441965
Sum5.4548392 × 1010
Variance1.9190951 × 1019
MonotonicityNot monotonic
2023-12-13T05:05:08.266122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
6654300 1
 
3.7%
1493860 1
 
3.7%
9040 1
 
3.7%
258408430 1
 
3.7%
341127810 1
 
3.7%
-51019570 1
 
3.7%
10767550 1
 
3.7%
125880390 1
 
3.7%
11621150 1
 
3.7%
-1157100 1
 
3.7%
Other values (12) 12
44.4%
(Missing) 5
18.5%
ValueCountFrequency (%)
-51019570 1
3.7%
-1157100 1
3.7%
9040 1
3.7%
1493860 1
3.7%
2525280 1
3.7%
6654300 1
3.7%
10767550 1
3.7%
11621150 1
3.7%
21733290 1
3.7%
125880390 1
3.7%
ValueCountFrequency (%)
17950228550 1
3.7%
8360126350 1
3.7%
7562654930 1
3.7%
6122765480 1
3.7%
5204145220 1
3.7%
4149352510 1
3.7%
2283585030 1
3.7%
1031118020 1
3.7%
695649930 1
3.7%
460721820 1
3.7%

정리보류액등누계
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct9
Distinct (%)100.0%
Missing18
Missing (%)66.7%
Infinite0
Infinite (%)0.0%
Mean1035852.2
Minimum11880
Maximum5696950
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:05:08.402947image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum11880
5-th percentile15072
Q130450
median181900
Q31132000
95-th percentile3946890
Maximum5696950
Range5685070
Interquartile range (IQR)1101550

Descriptive statistics

Standard deviation1825310.7
Coefficient of variation (CV)1.7621342
Kurtosis6.9319725
Mean1035852.2
Median Absolute Deviation (MAD)170020
Skewness2.551068
Sum9322670
Variance3.331759 × 1012
MonotonicityNot monotonic
2023-12-13T05:05:08.521352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=9)
ValueCountFrequency (%)
5696950 1
 
3.7%
19860 1
 
3.7%
1321800 1
 
3.7%
1132000 1
 
3.7%
889460 1
 
3.7%
30450 1
 
3.7%
181900 1
 
3.7%
11880 1
 
3.7%
38370 1
 
3.7%
(Missing) 18
66.7%
ValueCountFrequency (%)
11880 1
3.7%
19860 1
3.7%
30450 1
3.7%
38370 1
3.7%
181900 1
3.7%
889460 1
3.7%
1132000 1
3.7%
1321800 1
3.7%
5696950 1
3.7%
ValueCountFrequency (%)
5696950 1
3.7%
1321800 1
3.7%
1132000 1
3.7%
889460 1
3.7%
181900 1
3.7%
38370 1
3.7%
30450 1
3.7%
19860 1
3.7%
11880 1
3.7%

미수액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct23
Distinct (%)100.0%
Missing4
Missing (%)14.8%
Infinite0
Infinite (%)0.0%
Mean52567841
Minimum34640
Maximum3.0093117 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:05:08.652294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum34640
5-th percentile101275
Q11395585
median5490200
Q388274270
95-th percentile2.3777594 × 108
Maximum3.0093117 × 108
Range3.0089653 × 108
Interquartile range (IQR)86878685

Descriptive statistics

Standard deviation83665057
Coefficient of variation (CV)1.5915635
Kurtosis3.1931316
Mean52567841
Median Absolute Deviation (MAD)5398690
Skewness1.9172183
Sum1.2090603 × 109
Variance6.9998418 × 1015
MonotonicityNot monotonic
2023-12-13T05:05:08.805614image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
1159620 1
 
3.7%
189160 1
 
3.7%
336370 1
 
3.7%
452530 1
 
3.7%
14959210 1
 
3.7%
34640 1
 
3.7%
91510 1
 
3.7%
4308320 1
 
3.7%
93058030 1
 
3.7%
83490510 1
 
3.7%
Other values (13) 13
48.1%
(Missing) 4
 
14.8%
ValueCountFrequency (%)
34640 1
3.7%
91510 1
3.7%
189160 1
3.7%
336370 1
3.7%
452530 1
3.7%
1159620 1
3.7%
1631550 1
3.7%
4308320 1
3.7%
4339720 1
3.7%
4671760 1
3.7%
ValueCountFrequency (%)
300931170 1
3.7%
246479050 1
3.7%
159447920 1
3.7%
109912690 1
3.7%
103728310 1
3.7%
93058030 1
3.7%
83490510 1
3.7%
37190600 1
3.7%
23433650 1
3.7%
14959210 1
3.7%

환급누계액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct19
Distinct (%)100.0%
Missing8
Missing (%)29.6%
Infinite0
Infinite (%)0.0%
Mean27565588
Minimum1930
Maximum2.0719997 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size375.0 B
2023-12-13T05:05:08.986634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1930
5-th percentile10516
Q1313530
median2776290
Q324885860
95-th percentile1.1363001 × 108
Maximum2.0719997 × 108
Range2.0719804 × 108
Interquartile range (IQR)24572330

Descriptive statistics

Standard deviation53503465
Coefficient of variation (CV)1.9409513
Kurtosis6.7798893
Mean27565588
Median Absolute Deviation (MAD)2711870
Skewness2.5246152
Sum5.2374618 × 108
Variance2.8626207 × 1015
MonotonicityNot monotonic
2023-12-13T05:05:09.137453image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
1871170 1
 
3.7%
11470 1
 
3.7%
3137340 1
 
3.7%
2776290 1
 
3.7%
86187300 1
 
3.7%
266200 1
 
3.7%
3095450 1
 
3.7%
42590 1
 
3.7%
1481820 1
 
3.7%
59540390 1
 
3.7%
Other values (9) 9
33.3%
(Missing) 8
29.6%
ValueCountFrequency (%)
1930 1
3.7%
11470 1
3.7%
42590 1
3.7%
64420 1
3.7%
266200 1
3.7%
360860 1
3.7%
927870 1
3.7%
1481820 1
3.7%
1871170 1
3.7%
2776290 1
3.7%
ValueCountFrequency (%)
207199970 1
3.7%
103233350 1
3.7%
86187300 1
3.7%
59540390 1
3.7%
32485730 1
3.7%
17285990 1
3.7%
3776040 1
3.7%
3137340 1
3.7%
3095450 1
3.7%
2776290 1
3.7%

징수율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)70.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean57.637037
Minimum-47.1
Maximum100
Zeros5
Zeros (%)18.5%
Negative2
Negative (%)7.4%
Memory size375.0 B
2023-12-13T05:05:09.263973image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-47.1
5-th percentile-13.09
Q13.45
median76.6
Q398.35
95-th percentile100
Maximum100
Range147.1
Interquartile range (IQR)94.9

Descriptive statistics

Standard deviation47.966953
Coefficient of variation (CV)0.83222448
Kurtosis-1.0368434
Mean57.637037
Median Absolute Deviation (MAD)23.4
Skewness-0.73347579
Sum1556.2
Variance2300.8286
MonotonicityNot monotonic
2023-12-13T05:05:09.439410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
100.0 5
18.5%
0.0 5
18.5%
99.9 1
 
3.7%
9.0 1
 
3.7%
6.9 1
 
3.7%
73.3 1
 
3.7%
80.1 1
 
3.7%
-47.1 1
 
3.7%
71.3 1
 
3.7%
76.6 1
 
3.7%
Other values (9) 9
33.3%
ValueCountFrequency (%)
-47.1 1
 
3.7%
-18.7 1
 
3.7%
0.0 5
18.5%
6.9 1
 
3.7%
9.0 1
 
3.7%
54.8 1
 
3.7%
68.2 1
 
3.7%
71.3 1
 
3.7%
73.3 1
 
3.7%
76.6 1
 
3.7%
ValueCountFrequency (%)
100.0 5
18.5%
99.9 1
 
3.7%
98.8 1
 
3.7%
97.9 1
 
3.7%
97.6 1
 
3.7%
97.1 1
 
3.7%
95.3 1
 
3.7%
95.2 1
 
3.7%
80.1 1
 
3.7%
76.6 1
 
3.7%

Interactions

2023-12-13T05:05:05.260214image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:01.558604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.192475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.873335image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.597139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:04.412274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.400377image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:01.663388image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.335264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.976852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.717386image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:04.573345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.533271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:01.773307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.454287image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.094931image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.818819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:04.776957image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.639770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:01.858368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.552341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.218103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.943577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:04.948476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.757444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:01.963652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.676901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.324672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:04.103102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.059023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.865895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.064579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:02.774068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:03.461055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:04.245619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:05:05.147800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T05:05:09.569742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분대표세목조정누계액징수누계액정리보류액등누계미수액환급누계액징수율
구분1.0001.0000.0000.3660.6410.0000.2270.000
대표세목1.0001.0000.5040.7391.0000.0600.3630.000
조정누계액0.0000.5041.0001.000NaN0.8330.9830.000
징수누계액0.3660.7391.0001.000NaN0.8220.9960.000
정리보류액등누계0.6411.000NaNNaN1.0000.944NaN0.944
미수액0.0000.0600.8330.8220.9441.0000.7340.504
환급누계액0.2270.3630.9830.996NaN0.7341.0000.000
징수율0.0000.0000.0000.0000.9440.5040.0001.000
2023-12-13T05:05:09.748252image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
조정누계액징수누계액정리보류액등누계미수액환급누계액징수율구분
조정누계액1.0000.9480.4830.7630.5090.7060.000
징수누계액0.9481.0000.0360.3770.4160.7000.131
정리보류액등누계0.4830.0361.0000.5170.4290.2430.312
미수액0.7630.3770.5171.0000.6370.4520.000
환급누계액0.5090.4160.4290.6371.000-0.0230.203
징수율0.7060.7000.2430.452-0.0231.0000.000
구분0.0000.1310.3120.0000.2030.0001.000

Missing values

2023-12-13T05:05:06.033370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T05:05:06.200592image/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.
2023-12-13T05:05:06.671175image/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

구분대표세목세목조정누계액징수누계액정리보류액등누계미수액환급누계액징수율
0도세취득세소계1795490031017950228550<NA>467176059540390100.0
1도세등록면허세소계10322776401031118020<NA>1159620187117099.9
2도세레저세소계2173329021733290<NA><NA><NA>100.0
3도세지역자원시설세소계703977840695649930<NA>832791036086098.8
4도세교육세소계42530808204149352510<NA>1037283103248573097.6
5시세담배소비세소계22835850302283585030<NA><NA>1930100.0
6시세지방소비세소계75626549307562654930<NA><NA><NA>100.0
7시세주민세소계484155470460721820<NA>2343365092787095.2
8시세지방소득세소계86066054008360126350<NA>24647905020719997097.1
9시세재산세소계53140579105204145220<NA>109912690377604097.9
구분대표세목세목조정누계액징수누계액정리보류액등누계미수액환급누계액징수율
17시세지방소득세소계108428350-51019570<NA>15944792086187300-47.1
18시세재산세소계425750320341127810113200083490510277629080.1
19시세자동차세소계35235592025840843088946093058030313734073.3
20도세취득세소계4308320<NA><NA>4308320<NA>0.0
21도세면허세소계13100090403045091510<NA>6.9
22도세공동시설세소계34640<NA><NA>34640<NA>0.0
23시세주민세소계16634970149386018190014959210114709.0
24시세재산세소계464410<NA>11880452530<NA>0.0
25시세도시계획세소계336370<NA><NA>336370<NA>0.0
26시세종합토지세소계227530<NA>38370189160<NA>0.0