Overview

Dataset statistics

Number of variables10
Number of observations212
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory17.9 KiB
Average record size in memory86.6 B

Variable types

Categorical5
Numeric5

Dataset

Description지방세 체납현황(과세연도, 세목명, 체납액구간, 체납건수, 체납금액, 누적체납건수, 누적체납금액 등) 정보 공개
Author경기도 동두천시
URLhttps://www.data.go.kr/data/15079184/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
체납금액 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액High correlation
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2024-03-16 04:18:54.462266
Analysis finished2024-03-16 04:19:01.299930
Duration6.84 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
경기도
212 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 212
100.0%

Length

2024-03-16T13:19:01.389702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:19:01.544103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 212
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
동두천시
212 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
동두천시 212
100.0%

Length

2024-03-16T13:19:01.707752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:19:01.851500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
동두천시 212
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.5%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
41250
212 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41250
2nd row41250
3rd row41250
4th row41250
5th row41250

Common Values

ValueCountFrequency (%)
41250 212
100.0%

Length

2024-03-16T13:19:01.999699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:19:02.176084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41250 212
100.0%

과세연도
Real number (ℝ)

Distinct6
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.6415
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-16T13:19:02.385518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.6845474
Coefficient of variation (CV)0.0008340824
Kurtosis-1.2194097
Mean2019.6415
Median Absolute Deviation (MAD)1
Skewness-0.1123885
Sum428164
Variance2.8377001
MonotonicityIncreasing
2024-03-16T13:19:02.588819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 39
18.4%
2021 38
17.9%
2022 38
17.9%
2019 34
16.0%
2018 33
15.6%
2017 30
14.2%
ValueCountFrequency (%)
2017 30
14.2%
2018 33
15.6%
2019 34
16.0%
2020 39
18.4%
2021 38
17.9%
2022 38
17.9%
ValueCountFrequency (%)
2022 38
17.9%
2021 38
17.9%
2020 39
18.4%
2019 34
16.0%
2018 33
15.6%
2017 30
14.2%

세목명
Categorical

Distinct7
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
지방소득세
53 
재산세
49 
취득세
42 
주민세
28 
자동차세
23 
Other values (2)
17 

Length

Max length7
Median length3
Mean length3.8349057
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row등록면허세
2nd row등록면허세
3rd row자동차세
4th row자동차세
5th row자동차세

Common Values

ValueCountFrequency (%)
지방소득세 53
25.0%
재산세 49
23.1%
취득세 42
19.8%
주민세 28
13.2%
자동차세 23
10.8%
등록면허세 10
 
4.7%
지역자원시설세 7
 
3.3%

Length

2024-03-16T13:19:02.772469image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-16T13:19:02.977480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 53
25.0%
재산세 49
23.1%
취득세 42
19.8%
주민세 28
13.2%
자동차세 23
10.8%
등록면허세 10
 
4.7%
지역자원시설세 7
 
3.3%

체납액구간
Categorical

Distinct11
Distinct (%)5.2%
Missing0
Missing (%)0.0%
Memory size1.8 KiB
10만원 미만
40 
10만원~30만원미만
33 
30만원~50만원미만
28 
50만원~1백만원미만
28 
1백만원~3백만원미만
24 
Other values (6)
59 

Length

Max length11
Median length11
Mean length10.212264
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row1천만원~3천만원미만
3rd row10만원 미만
4th row10만원~30만원미만
5th row30만원~50만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 40
18.9%
10만원~30만원미만 33
15.6%
30만원~50만원미만 28
13.2%
50만원~1백만원미만 28
13.2%
1백만원~3백만원미만 24
11.3%
1천만원~3천만원미만 18
8.5%
3백만원~5백만원미만 17
8.0%
5백만원~1천만원미만 15
 
7.1%
3천만원~5천만원미만 4
 
1.9%
5천만원~1억원미만 3
 
1.4%

Length

2024-03-16T13:19:03.216295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 40
15.9%
미만 40
15.9%
10만원~30만원미만 33
13.1%
30만원~50만원미만 28
11.1%
50만원~1백만원미만 28
11.1%
1백만원~3백만원미만 24
9.5%
1천만원~3천만원미만 18
7.1%
3백만원~5백만원미만 17
6.7%
5백만원~1천만원미만 15
 
6.0%
3천만원~5천만원미만 4
 
1.6%
Other values (2) 5
 
2.0%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct97
Distinct (%)45.8%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean291.08491
Minimum1
Maximum6239
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-16T13:19:03.527346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median13.5
Q378.25
95-th percentile1625.4
Maximum6239
Range6238
Interquartile range (IQR)74.25

Descriptive statistics

Standard deviation858.13053
Coefficient of variation (CV)2.948042
Kurtosis25.481928
Mean291.08491
Median Absolute Deviation (MAD)11.5
Skewness4.7050003
Sum61710
Variance736388
MonotonicityNot monotonic
2024-03-16T13:19:03.878885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23
 
10.8%
3 16
 
7.5%
2 12
 
5.7%
5 12
 
5.7%
7 7
 
3.3%
6 7
 
3.3%
4 6
 
2.8%
9 5
 
2.4%
12 5
 
2.4%
14 5
 
2.4%
Other values (87) 114
53.8%
ValueCountFrequency (%)
1 23
10.8%
2 12
5.7%
3 16
7.5%
4 6
 
2.8%
5 12
5.7%
6 7
 
3.3%
7 7
 
3.3%
8 2
 
0.9%
9 5
 
2.4%
10 4
 
1.9%
ValueCountFrequency (%)
6239 1
0.5%
5966 1
0.5%
5299 1
0.5%
3128 1
0.5%
3098 1
0.5%
2927 1
0.5%
2821 1
0.5%
2204 1
0.5%
1711 1
0.5%
1671 1
0.5%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct212
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51751794
Minimum29660
Maximum3.9778181 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-16T13:19:04.130122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum29660
5-th percentile338450
Q13563380
median20949550
Q368971840
95-th percentile2.0651923 × 108
Maximum3.9778181 × 108
Range3.9775215 × 108
Interquartile range (IQR)65408460

Descriptive statistics

Standard deviation72058699
Coefficient of variation (CV)1.3923904
Kurtosis4.9655837
Mean51751794
Median Absolute Deviation (MAD)19878440
Skewness2.1302539
Sum1.097138 × 1010
Variance5.1924561 × 1015
MonotonicityNot monotonic
2024-03-16T13:19:04.396161image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1698000 1
 
0.5%
2129240 1
 
0.5%
11770050 1
 
0.5%
115870 1
 
0.5%
75853940 1
 
0.5%
261503940 1
 
0.5%
23821900 1
 
0.5%
1836490 1
 
0.5%
173667070 1
 
0.5%
137302780 1
 
0.5%
Other values (202) 202
95.3%
ValueCountFrequency (%)
29660 1
0.5%
77950 1
0.5%
80410 1
0.5%
102420 1
0.5%
115870 1
0.5%
153550 1
0.5%
197290 1
0.5%
257830 1
0.5%
301380 1
0.5%
313600 1
0.5%
ValueCountFrequency (%)
397781810 1
0.5%
374949970 1
0.5%
287665580 1
0.5%
280682420 1
0.5%
261503940 1
0.5%
260654890 1
0.5%
256899570 1
0.5%
246090660 1
0.5%
221352580 1
0.5%
218854600 1
0.5%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct143
Distinct (%)67.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean925.15094
Minimum1
Maximum16216
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-16T13:19:04.670732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile4
Q119
median58.5
Q3215.25
95-th percentile6181.15
Maximum16216
Range16215
Interquartile range (IQR)196.25

Descriptive statistics

Standard deviation2486.0059
Coefficient of variation (CV)2.6871355
Kurtosis16.420974
Mean925.15094
Median Absolute Deviation (MAD)49
Skewness3.8098439
Sum196132
Variance6180225.6
MonotonicityNot monotonic
2024-03-16T13:19:04.933738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4 7
 
3.3%
1 5
 
2.4%
30 4
 
1.9%
2 4
 
1.9%
12 4
 
1.9%
14 4
 
1.9%
9 4
 
1.9%
77 3
 
1.4%
23 3
 
1.4%
27 3
 
1.4%
Other values (133) 171
80.7%
ValueCountFrequency (%)
1 5
2.4%
2 4
1.9%
3 1
 
0.5%
4 7
3.3%
5 3
1.4%
6 1
 
0.5%
7 2
 
0.9%
8 2
 
0.9%
9 4
1.9%
10 1
 
0.5%
ValueCountFrequency (%)
16216 1
0.5%
15710 1
0.5%
13686 1
0.5%
10250 1
0.5%
8327 1
0.5%
7906 1
0.5%
7658 1
0.5%
7450 1
0.5%
7152 1
0.5%
7047 1
0.5%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct212
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.4599093 × 108
Minimum115870
Maximum1.285639 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.0 KiB
2024-03-16T13:19:05.190569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum115870
5-th percentile2824302.5
Q117382510
median64026125
Q32.0131566 × 108
95-th percentile4.7508529 × 108
Maximum1.285639 × 109
Range1.2855231 × 109
Interquartile range (IQR)1.8393316 × 108

Descriptive statistics

Standard deviation1.9887784 × 108
Coefficient of variation (CV)1.3622616
Kurtosis10.333585
Mean1.4599093 × 108
Median Absolute Deviation (MAD)59137785
Skewness2.7784641
Sum3.0950077 × 1010
Variance3.9552396 × 1016
MonotonicityNot monotonic
2024-03-16T13:19:05.816039image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
4739840 1
 
0.5%
33639260 1
 
0.5%
23992910 1
 
0.5%
115870 1
 
0.5%
256314040 1
 
0.5%
1189937990 1
 
0.5%
100811650 1
 
0.5%
14612350 1
 
0.5%
431037760 1
 
0.5%
316829550 1
 
0.5%
Other values (202) 202
95.3%
ValueCountFrequency (%)
115870 1
0.5%
173900 1
0.5%
311810 1
0.5%
506420 1
0.5%
566850 1
0.5%
816290 1
0.5%
1272840 1
0.5%
1586440 1
0.5%
1644020 1
0.5%
1953080 1
0.5%
ValueCountFrequency (%)
1285639010 1
0.5%
1189937990 1
0.5%
1028739440 1
0.5%
890937360 1
0.5%
782648780 1
0.5%
699914820 1
0.5%
648471610 1
0.5%
545764620 1
0.5%
534954180 1
0.5%
502882320 1
0.5%

Interactions

2024-03-16T13:18:59.852626image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:55.042991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.649154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.854452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.903829image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.075068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:55.236117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.810348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.031087image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.136537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.265291image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.044341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.999696image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.180949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.278361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.432307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.233485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.278737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.426560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.487772image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:19:00.657701image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:56.478437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:57.612108image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:58.638654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-16T13:18:59.662712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-16T13:19:05.971886image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세연도1.0000.0000.0000.0000.0000.0000.000
세목명0.0001.0000.2650.3970.3810.3960.390
체납액구간0.0000.2651.0000.0730.6830.0000.449
체납건수0.0000.3970.0731.0000.5730.9480.500
체납금액0.0000.3810.6830.5731.0000.6680.924
누적체납건수0.0000.3960.0000.9480.6681.0000.751
누적체납금액0.0000.3900.4490.5000.9240.7511.000
2024-03-16T13:19:06.149503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간
세목명1.0000.132
체납액구간0.1321.000
2024-03-16T13:19:06.309185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세연도1.0000.1100.2050.0280.1330.0000.000
체납건수0.1101.0000.5080.9170.4880.2250.030
체납금액0.2050.5081.0000.3100.9510.2120.392
누적체납건수0.0280.9170.3101.0000.3680.2200.000
누적체납금액0.1330.4880.9510.3681.0000.2180.219
세목명0.0000.2250.2120.2200.2181.0000.132
체납액구간0.0000.0300.3920.0000.2190.1321.000

Missing values

2024-03-16T13:19:00.915086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-16T13:19:01.207480image/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

시도명시군구명자치단체코드과세연도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0경기도동두천시412502017등록면허세10만원 미만7816980002354739840
1경기도동두천시412502017등록면허세1천만원~3천만원미만113114800113114800
2경기도동두천시412502017자동차세10만원 미만460209764703039140036600
3경기도동두천시412502017자동차세10만원~30만원미만526870041003960648471610
4경기도동두천시412502017자동차세30만원~50만원미만22759042015353188550
5경기도동두천시412502017자동차세50만원~1백만원미만317927202415159520
6경기도동두천시412502017재산세10만원 미만639301732802893131704100
7경기도동두천시412502017재산세10만원~30만원미만1392022736061593087980
8경기도동두천시412502017재산세1백만원~3백만원미만121910148086153037480
9경기도동두천시412502017재산세1천만원~3천만원미만110504640460669340
시도명시군구명자치단체코드과세연도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
202경기도동두천시412502022지방소득세5백만원~1천만원미만2316814297056390265710
203경기도동두천시412502022지방소득세5천만원~1억원미만42806824204280682420
204경기도동두천시412502022지역자원시설세10만원 미만22966014311810
205경기도동두천시412502022취득세10만원 미만9346910471644020
206경기도동두천시412502022취득세10만원~30만원미만71590580254952570
207경기도동두천시412502022취득세1백만원~3백만원미만11184516602239757900
208경기도동두천시412502022취득세30만원~50만원미만31210670113928400
209경기도동두천시412502022취득세3백만원~5백만원미만13254780726104860
210경기도동두천시412502022취득세50만원~1백만원미만533744101511015460
211경기도동두천시412502022취득세5백만원~1천만원미만213882900428306090