Overview

Dataset statistics

Number of variables5
Number of observations34
Missing cells13
Missing cells (%)7.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.5 KiB
Average record size in memory45.9 B

Variable types

Categorical3
Numeric2

Dataset

Description대전광역시 중구 지방세 자동이체 수납현황: 대전광역시 중구에서 부과 된 지방세의 세목별, 매체별 자동이체 수납 현황(정상수납건수, 미처리건수 등)
Author대전광역시 중구
URLhttps://www.data.go.kr/data/15120845/fileData.do

Alerts

건수 is highly overall correlated with 수납금액High correlation
수납금액 is highly overall correlated with 건수High correlation
수납금액 has 13 (38.2%) missing valuesMissing

Reproduction

Analysis started2023-12-12 14:14:18.093220
Analysis finished2023-12-12 14:14:18.885409
Duration0.79 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

세목
Categorical

Distinct9
Distinct (%)26.5%
Missing0
Missing (%)0.0%
Memory size404.0 B
106001 자동차세(자동차)
6 
105301 재산세(건축물)
5 
105305 재산세(주택)
5 
104101 주민세(개인분)
4 
105304 재산세(토지)
4 
Other values (4)
10 

Length

Max length17
Median length16
Mean length15.205882
Min length14

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row104101 주민세(개인분)
2nd row104101 주민세(개인분)
3rd row104101 주민세(개인분)
4th row104101 주민세(개인분)
5th row105301 재산세(건축물)

Common Values

ValueCountFrequency (%)
106001 자동차세(자동차) 6
17.6%
105301 재산세(건축물) 5
14.7%
105305 재산세(주택) 5
14.7%
104101 주민세(개인분) 4
11.8%
105304 재산세(토지) 4
11.8%
114001 등록면허세(면허) 4
11.8%
106002 자동차세(이륜차) 3
8.8%
106003 자동차세(기계장비) 2
 
5.9%
105302 재산세(선박) 1
 
2.9%

Length

2023-12-12T23:14:18.964143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:14:19.110752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
106001 6
 
8.8%
자동차세(자동차 6
 
8.8%
105301 5
 
7.4%
재산세(건축물 5
 
7.4%
105305 5
 
7.4%
재산세(주택 5
 
7.4%
등록면허세(면허 4
 
5.9%
114001 4
 
5.9%
재산세(토지 4
 
5.9%
105304 4
 
5.9%
Other values (8) 20
29.4%

납부유형
Categorical

Distinct2
Distinct (%)5.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
계좌
21 
카드
13 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row계좌
2nd row계좌
3rd row계좌
4th row카드
5th row계좌

Common Values

ValueCountFrequency (%)
계좌 21
61.8%
카드 13
38.2%

Length

2023-12-12T23:14:19.282867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:14:19.386064image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
계좌 21
61.8%
카드 13
38.2%

처리구분
Categorical

Distinct3
Distinct (%)8.8%
Missing0
Missing (%)0.0%
Memory size404.0 B
정상수납
16 
미처리(은행/카드사미처리자료)
13 
과오납(과다수납)
5 

Length

Max length16
Median length9
Mean length9.3235294
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row정상수납
2nd row과오납(과다수납)
3rd row미처리(은행/카드사미처리자료)
4th row정상수납
5th row정상수납

Common Values

ValueCountFrequency (%)
정상수납 16
47.1%
미처리(은행/카드사미처리자료) 13
38.2%
과오납(과다수납) 5
 
14.7%

Length

2023-12-12T23:14:19.521446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T23:14:19.653905image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
정상수납 16
47.1%
미처리(은행/카드사미처리자료 13
38.2%
과오납(과다수납 5
 
14.7%

건수
Real number (ℝ)

HIGH CORRELATION 

Distinct24
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean525.05882
Minimum1
Maximum7198
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T23:14:19.771059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11.5
median26.5
Q3294.25
95-th percentile3043.15
Maximum7198
Range7197
Interquartile range (IQR)292.75

Descriptive statistics

Standard deviation1399.9933
Coefficient of variation (CV)2.6663551
Kurtosis16.424419
Mean525.05882
Median Absolute Deviation (MAD)25.5
Skewness3.8834431
Sum17852
Variance1959981.1
MonotonicityNot monotonic
2023-12-12T23:14:19.906824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
1 9
26.5%
3 2
 
5.9%
9 2
 
5.9%
3426 1
 
2.9%
297 1
 
2.9%
48 1
 
2.9%
66 1
 
2.9%
586 1
 
2.9%
16 1
 
2.9%
13 1
 
2.9%
Other values (14) 14
41.2%
ValueCountFrequency (%)
1 9
26.5%
3 2
 
5.9%
4 1
 
2.9%
9 2
 
5.9%
13 1
 
2.9%
16 1
 
2.9%
21 1
 
2.9%
32 1
 
2.9%
40 1
 
2.9%
48 1
 
2.9%
ValueCountFrequency (%)
7198 1
2.9%
3426 1
2.9%
2837 1
2.9%
959 1
2.9%
807 1
2.9%
586 1
2.9%
383 1
2.9%
382 1
2.9%
297 1
2.9%
286 1
2.9%

수납금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct21
Distinct (%)100.0%
Missing13
Missing (%)38.2%
Infinite0
Infinite (%)0.0%
Mean1.1056181 × 108
Minimum4790
Maximum9.2826877 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-12T23:14:20.041641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4790
5-th percentile5520
Q173440
median7784730
Q363117000
95-th percentile4.5755866 × 108
Maximum9.2826877 × 108
Range9.2826398 × 108
Interquartile range (IQR)63043560

Descriptive statistics

Standard deviation2.284742 × 108
Coefficient of variation (CV)2.066484
Kurtosis8.1461629
Mean1.1056181 × 108
Median Absolute Deviation (MAD)7779210
Skewness2.7636683
Sum2.3217979 × 109
Variance5.220046 × 1016
MonotonicityNot monotonic
2023-12-12T23:14:20.174539image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=21)
ValueCountFrequency (%)
12000 1
 
2.9%
63117000 1
 
2.9%
123139600 1
 
2.9%
828730 1
 
2.9%
13340 1
 
2.9%
206550 1
 
2.9%
73440 1
 
2.9%
28195860 1
 
2.9%
56320 1
 
2.9%
396322430 1
 
2.9%
Other values (11) 11
32.4%
(Missing) 13
38.2%
ValueCountFrequency (%)
4790 1
2.9%
5520 1
2.9%
12000 1
2.9%
13340 1
2.9%
56320 1
2.9%
73440 1
2.9%
206550 1
2.9%
828730 1
2.9%
1137080 1
2.9%
1818500 1
2.9%
ValueCountFrequency (%)
928268770 1
2.9%
457558660 1
2.9%
396322430 1
2.9%
221722240 1
2.9%
123139600 1
2.9%
63117000 1
2.9%
40932500 1
2.9%
40672230 1
2.9%
28195860 1
2.9%
9927620 1
2.9%

Interactions

2023-12-12T23:14:18.493343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:18.306516image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:18.592734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T23:14:18.399369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T23:14:20.279532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목납부유형처리구분건수수납금액
세목1.0000.0000.0000.0000.000
납부유형0.0001.0000.0000.0000.136
처리구분0.0000.0001.0000.0000.000
건수0.0000.0000.0001.0000.946
수납금액0.0000.1360.0000.9461.000
2023-12-12T23:14:20.372926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
납부유형세목처리구분
납부유형1.0000.0000.000
세목0.0001.0000.000
처리구분0.0000.0001.000
2023-12-12T23:14:20.475023image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
건수수납금액세목납부유형처리구분
건수1.0000.9450.0000.0000.000
수납금액0.9451.0000.0000.0000.000
세목0.0000.0001.0000.0000.000
납부유형0.0000.0000.0001.0000.000
처리구분0.0000.0000.0000.0001.000

Missing values

2023-12-12T23:14:18.723273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T23:14:18.840758image/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

세목납부유형처리구분건수수납금액
0104101 주민세(개인분)계좌정상수납342640932500
1104101 주민세(개인분)계좌과오납(과다수납)112000
2104101 주민세(개인분)계좌미처리(은행/카드사미처리자료)286<NA>
3104101 주민세(개인분)카드정상수납1561818500
4105301 재산세(건축물)계좌정상수납807221722240
5105301 재산세(건축물)계좌과오납(과다수납)15520
6105301 재산세(건축물)계좌미처리(은행/카드사미처리자료)40<NA>
7105301 재산세(건축물)카드정상수납217784730
8105301 재산세(건축물)카드미처리(은행/카드사미처리자료)1<NA>
9105302 재산세(선박)계좌정상수납14790
세목납부유형처리구분건수수납금액
24106001 자동차세(자동차)카드미처리(은행/카드사미처리자료)9<NA>
25106002 자동차세(이륜차)계좌정상수납13206550
26106002 자동차세(이륜차)계좌미처리(은행/카드사미처리자료)1<NA>
27106002 자동차세(이륜차)카드정상수납113340
28106003 자동차세(기계장비)계좌정상수납16828730
29106003 자동차세(기계장비)계좌미처리(은행/카드사미처리자료)3<NA>
30114001 등록면허세(면허)계좌정상수납586123139600
31114001 등록면허세(면허)계좌미처리(은행/카드사미처리자료)66<NA>
32114001 등록면허세(면허)카드정상수납4863117000
33114001 등록면허세(면허)카드미처리(은행/카드사미처리자료)1<NA>