Overview

Dataset statistics

Number of variables10
Number of observations145
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory12.3 KiB
Average record size in memory86.9 B

Variable types

Categorical6
Numeric4

Dataset

Description최근 3년간 지방세 부과 징수 자료에 따른 지방세 세목별 통계자료를 근거로 연도별 지방세 체납 현황을 추출한 자료에 해당됩니다
Author충청북도 진천군
URLhttps://www.data.go.kr/data/15079493/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액High 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 started2024-03-30 08:00:13.307689
Analysis finished2024-03-30 08:00:21.490106
Duration8.18 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
충청북도
145 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청북도 145
100.0%

Length

2024-03-30T08:00:22.008345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T08:00:22.483425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 145
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
진천군
145 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
진천군 145
100.0%

Length

2024-03-30T08:00:23.004483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T08:00:23.400831image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
진천군 145
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.7%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
43750
145 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43750
2nd row43750
3rd row43750
4th row43750
5th row43750

Common Values

ValueCountFrequency (%)
43750 145
100.0%

Length

2024-03-30T08:00:23.774019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T08:00:24.160763image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43750 145
100.0%

과세년도
Categorical

Distinct4
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
2021
37 
2022
37 
2019
36 
2020
35 

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 (%)
2021 37
25.5%
2022 37
25.5%
2019 36
24.8%
2020 35
24.1%

Length

2024-03-30T08:00:24.499158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T08:00:24.877784image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 37
25.5%
2022 37
25.5%
2019 36
24.8%
2020 35
24.1%

세목명
Categorical

Distinct6
Distinct (%)4.1%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
지방소득세
38 
취득세
34 
재산세
33 
주민세
20 
자동차세
15 

Length

Max length5
Median length3
Mean length3.6965517
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 38
26.2%
취득세 34
23.4%
재산세 33
22.8%
주민세 20
13.8%
자동차세 15
 
10.3%
등록면허세 5
 
3.4%

Length

2024-03-30T08:00:25.345755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-30T08:00:25.807043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 38
26.2%
취득세 34
23.4%
재산세 33
22.8%
주민세 20
13.8%
자동차세 15
 
10.3%
등록면허세 5
 
3.4%

체납액구간
Categorical

Distinct11
Distinct (%)7.6%
Missing0
Missing (%)0.0%
Memory size1.3 KiB
10만원 미만
24 
10만원~30만원미만
21 
30만원~50만원미만
19 
50만원~1백만원미만
19 
1백만원~3백만원미만
16 
Other values (6)
46 

Length

Max length11
Median length11
Mean length10.303448
Min length7

Unique

Unique1 ?
Unique (%)0.7%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 24
16.6%
10만원~30만원미만 21
14.5%
30만원~50만원미만 19
13.1%
50만원~1백만원미만 19
13.1%
1백만원~3백만원미만 16
11.0%
1천만원~3천만원미만 12
8.3%
3백만원~5백만원미만 12
8.3%
5백만원~1천만원미만 12
8.3%
3천만원~5천만원미만 6
 
4.1%
5천만원~1억원미만 3
 
2.1%

Length

2024-03-30T08:00:26.328244image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 24
14.2%
미만 24
14.2%
10만원~30만원미만 21
12.4%
30만원~50만원미만 19
11.2%
50만원~1백만원미만 19
11.2%
1백만원~3백만원미만 16
9.5%
1천만원~3천만원미만 12
7.1%
3백만원~5백만원미만 12
7.1%
5백만원~1천만원미만 12
7.1%
3천만원~5천만원미만 6
 
3.6%
Other values (2) 4
 
2.4%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct73
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean268.91034
Minimum1
Maximum3726
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-30T08:00:26.889130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median12
Q372
95-th percentile1612
Maximum3726
Range3725
Interquartile range (IQR)69

Descriptive statistics

Standard deviation675.13515
Coefficient of variation (CV)2.5106329
Kurtosis11.601744
Mean268.91034
Median Absolute Deviation (MAD)11
Skewness3.3380144
Sum38992
Variance455807.47
MonotonicityNot monotonic
2024-03-30T08:00:27.465452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 18
 
12.4%
2 12
 
8.3%
4 10
 
6.9%
3 7
 
4.8%
7 5
 
3.4%
5 4
 
2.8%
6 4
 
2.8%
12 3
 
2.1%
15 3
 
2.1%
9 3
 
2.1%
Other values (63) 76
52.4%
ValueCountFrequency (%)
1 18
12.4%
2 12
8.3%
3 7
 
4.8%
4 10
6.9%
5 4
 
2.8%
6 4
 
2.8%
7 5
 
3.4%
8 3
 
2.1%
9 3
 
2.1%
10 2
 
1.4%
ValueCountFrequency (%)
3726 1
0.7%
3581 1
0.7%
3228 1
0.7%
2697 1
0.7%
2209 1
0.7%
2069 1
0.7%
1824 1
0.7%
1617 1
0.7%
1592 1
0.7%
1549 1
0.7%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean42433094
Minimum160680
Maximum2.4913104 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-30T08:00:27.962353image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160680
5-th percentile499564
Q17283240
median27966630
Q361786050
95-th percentile1.4878101 × 108
Maximum2.4913104 × 108
Range2.4897036 × 108
Interquartile range (IQR)54502810

Descriptive statistics

Standard deviation49825375
Coefficient of variation (CV)1.1742103
Kurtosis5.5233793
Mean42433094
Median Absolute Deviation (MAD)23987800
Skewness2.1861107
Sum6.1527986 × 109
Variance2.482568 × 1015
MonotonicityNot monotonic
2024-03-30T08:00:28.494373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2721820 1
 
0.7%
56408760 1
 
0.7%
243429920 1
 
0.7%
118123530 1
 
0.7%
27966630 1
 
0.7%
61789540 1
 
0.7%
160551760 1
 
0.7%
36721560 1
 
0.7%
104005300 1
 
0.7%
494450 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
160680 1
0.7%
174250 1
0.7%
211830 1
0.7%
288140 1
0.7%
341820 1
0.7%
416670 1
0.7%
461540 1
0.7%
494450 1
0.7%
520020 1
0.7%
611480 1
0.7%
ValueCountFrequency (%)
249131040 1
0.7%
243429920 1
0.7%
235776630 1
0.7%
208348950 1
0.7%
201901710 1
0.7%
185756240 1
0.7%
160551760 1
0.7%
156445380 1
0.7%
118123530 1
0.7%
110779310 1
0.7%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct98
Distinct (%)67.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean878.33793
Minimum1
Maximum10546
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-30T08:00:28.956852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q111
median36
Q3228
95-th percentile6454.8
Maximum10546
Range10545
Interquartile range (IQR)217

Descriptive statistics

Standard deviation2185.3601
Coefficient of variation (CV)2.488063
Kurtosis7.208467
Mean878.33793
Median Absolute Deviation (MAD)32
Skewness2.8349324
Sum127359
Variance4775798.9
MonotonicityNot monotonic
2024-03-30T08:00:29.395216image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8 8
 
5.5%
2 8
 
5.5%
23 7
 
4.8%
6 5
 
3.4%
1 4
 
2.8%
18 4
 
2.8%
5 3
 
2.1%
14 3
 
2.1%
16 3
 
2.1%
9 3
 
2.1%
Other values (88) 97
66.9%
ValueCountFrequency (%)
1 4
2.8%
2 8
5.5%
4 2
 
1.4%
5 3
 
2.1%
6 5
3.4%
7 1
 
0.7%
8 8
5.5%
9 3
 
2.1%
10 1
 
0.7%
11 2
 
1.4%
ValueCountFrequency (%)
10546 1
0.7%
10130 1
0.7%
9257 1
0.7%
8301 1
0.7%
6820 1
0.7%
6658 1
0.7%
6533 1
0.7%
6501 1
0.7%
6270 1
0.7%
5832 1
0.7%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct145
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.2012804 × 108
Minimum160680
Maximum1.117537 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.4 KiB
2024-03-30T08:00:29.937747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum160680
5-th percentile2991280
Q116924380
median71426940
Q31.5352897 × 108
95-th percentile3.4361873 × 108
Maximum1.117537 × 109
Range1.1173763 × 109
Interquartile range (IQR)1.3660459 × 108

Descriptive statistics

Standard deviation1.7986473 × 108
Coefficient of variation (CV)1.4972752
Kurtosis17.152517
Mean1.2012804 × 108
Median Absolute Deviation (MAD)57396260
Skewness3.795838
Sum1.7418565 × 1010
Variance3.235132 × 1016
MonotonicityNot monotonic
2024-03-30T08:00:30.368591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6201850 1
 
0.7%
235944630 1
 
0.7%
243429920 1
 
0.7%
227511170 1
 
0.7%
52784090 1
 
0.7%
233928620 1
 
0.7%
199867450 1
 
0.7%
133904010 1
 
0.7%
296977240 1
 
0.7%
1473730 1
 
0.7%
Other values (135) 135
93.1%
ValueCountFrequency (%)
160680 1
0.7%
1071000 1
0.7%
1166740 1
0.7%
1359140 1
0.7%
1473730 1
0.7%
1719420 1
0.7%
2841190 1
0.7%
2936690 1
0.7%
3209640 1
0.7%
3400020 1
0.7%
ValueCountFrequency (%)
1117536970 1
0.7%
1110840820 1
0.7%
998941180 1
0.7%
881760340 1
0.7%
440717600 1
0.7%
388539500 1
0.7%
373772920 1
0.7%
353543910 1
0.7%
303918030 1
0.7%
296977240 1
0.7%

Interactions

2024-03-30T08:00:18.854756image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:14.120029image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:15.639634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:17.342409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:19.228485image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:14.562135image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:16.046585image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:17.721277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:19.590001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:14.944152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:16.420426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:18.029579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:19.976815image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:15.247713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:16.775077image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-30T08:00:18.444950image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-30T08:00:30.659760image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.0000.0000.109
세목명0.0001.0000.0000.5290.5290.4590.446
체납액구간0.0000.0001.0000.2530.5200.0000.223
체납건수0.0000.5290.2531.0000.7680.9950.700
체납금액0.0000.5290.5200.7681.0000.6090.855
누적체납건수0.0000.4590.0000.9950.6091.0000.683
누적체납금액0.1090.4460.2230.7000.8550.6831.000
2024-03-30T08:00:31.070005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납액구간과세년도세목명
체납액구간1.0000.0000.000
과세년도0.0001.0000.000
세목명0.0000.0001.000
2024-03-30T08:00:31.329204image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.4070.9610.4430.0000.2910.114
체납금액0.4071.0000.2980.9510.0000.2940.263
누적체납건수0.9610.2981.0000.3880.0000.2450.000
누적체납금액0.4430.9510.3881.0000.0770.2850.103
과세년도0.0000.0000.0000.0771.0000.0000.000
세목명0.2910.2940.2450.2850.0001.0000.000
체납액구간0.1140.2630.0000.1030.0000.0001.000

Missing values

2024-03-30T08:00:20.497209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-30T08:00:21.230315image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0충청북도진천군437502019등록면허세10만원 미만23527218205146201850
1충청북도진천군437502019등록면허세10만원~30만원미만11606801160680
2충청북도진천군437502019자동차세10만원 미만1110538481204162195743150
3충청북도진천군437502019자동차세10만원~30만원미만14572491310405165881760340
4충청북도진천군437502019자동차세30만원~50만원미만953233447022877447800
5충청북도진천군437502019재산세10만원 미만1824349612505604116351880
6충청북도진천군437502019재산세10만원~30만원미만1953231745055388087140
7충청북도진천군437502019재산세1백만원~3백만원미만294379564068109548370
8충청북도진천군437502019재산세1천만원~3천만원미만110318010221790270
9충청북도진천군437502019재산세30만원~50만원미만2077629104115438650
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
135충청북도진천군437502022지방소득세5백만원~1천만원미만1511077931055388539500
136충청북도진천군437502022지방소득세5천만원~1억원미만1848618702141486850
137충청북도진천군437502022취득세10만원 미만9416670281166740
138충청북도진천군437502022취득세10만원~30만원미만6941720254708760
139충청북도진천군437502022취득세1백만원~3백만원미만7126285803153600280
140충청북도진천군437502022취득세1천만원~3천만원미만79874357014206730610
141충청북도진천군437502022취득세30만원~50만원미만3106695082841190
142충청북도진천군437502022취득세3백만원~5백만원미만13978830624878900
143충청북도진천군437502022취득세50만원~1백만원미만429940202315884620
144충청북도진천군437502022취득세5백만원~1천만원미만219291580537394540