Overview

Dataset statistics

Number of variables11
Number of observations72
Missing cells42
Missing cells (%)5.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory6.7 KiB
Average record size in memory95.8 B

Variable types

Categorical6
Numeric4
DateTime1

Dataset

Description상기 데이터는 연도별 체납액 규모별 체납 건수를 납세자 유형별로 제공하여 체납정책 수립시 기초자료로 활용할 수 있도록 제공함
Author충청남도
URLhttps://alldam.chungnam.go.kr/index.chungnam?menuCd=DOM_000000201001001001&st=&cds=&orgCd=&apiType=&isOpen=Y&pageIndex=348&beforeMenuCd=DOM_000000201001001000&publicdatapk=15079977

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일자 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수 High correlation
체납금액 is highly overall correlated with 누적체납금액 High correlation
누적체납건수 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 체납금액 and 1 other fieldsHigh correlation
체납건수 has 19 (26.4%) missing valuesMissing
체납금액 has 19 (26.4%) missing valuesMissing
누적체납건수 has 2 (2.8%) missing valuesMissing
누적체납금액 has 2 (2.8%) missing valuesMissing

Reproduction

Analysis started2024-01-09 22:16:35.051216
Analysis finished2024-01-09 22:16:36.692872
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
충청남도
72 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충청남도 72
100.0%

Length

2024-01-10T07:16:36.742155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:36.816395image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청남도 72
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
부여군
72 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부여군 72
100.0%

Length

2024-01-10T07:16:36.891013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:36.962373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부여군 72
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
44760
72 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row44760
2nd row44760
3rd row44760
4th row44760
5th row44760

Common Values

ValueCountFrequency (%)
44760 72
100.0%

Length

2024-01-10T07:16:37.060729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:37.156483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
44760 72
100.0%

과세년도
Categorical

Distinct2
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size708.0 B
2021
36 
2022
36 

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 36
50.0%
2022 36
50.0%

Length

2024-01-10T07:16:37.258083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:37.362264image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 36
50.0%
2022 36
50.0%

세목명
Categorical

Distinct6
Distinct (%)8.3%
Missing0
Missing (%)0.0%
Memory size708.0 B
지방소득세
18 
취득세
18 
재산세
16 
주민세
10 
자동차세
8 

Length

Max length5
Median length3
Mean length3.6666667
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 18
25.0%
취득세 18
25.0%
재산세 16
22.2%
주민세 10
13.9%
자동차세 8
11.1%
등록면허세 2
 
2.8%

Length

2024-01-10T07:16:37.476603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:37.600951image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 18
25.0%
취득세 18
25.0%
재산세 16
22.2%
주민세 10
13.9%
자동차세 8
11.1%
등록면허세 2
 
2.8%

체납액구간
Categorical

Distinct9
Distinct (%)12.5%
Missing0
Missing (%)0.0%
Memory size708.0 B
10만원 미만
12 
10만원~30만원미만
10 
30만원~50만원미만
10 
50만원~1백만원미만
10 
1백만원~3백만원미만
8 
Other values (4)
22 

Length

Max length11
Median length11
Mean length10.055556
Min length6

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 12
16.7%
10만원~30만원미만 10
13.9%
30만원~50만원미만 10
13.9%
50만원~1백만원미만 10
13.9%
1백만원~3백만원미만 8
11.1%
3백만원~5백만원미만 6
8.3%
5백만원~1천만원미만 6
8.3%
1천만원~3천만원미만 6
8.3%
3천만원이상 4
 
5.6%

Length

2024-01-10T07:16:37.727590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-10T07:16:37.856092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 12
14.3%
미만 12
14.3%
10만원~30만원미만 10
11.9%
30만원~50만원미만 10
11.9%
50만원~1백만원미만 10
11.9%
1백만원~3백만원미만 8
9.5%
3백만원~5백만원미만 6
7.1%
5백만원~1천만원미만 6
7.1%
1천만원~3천만원미만 6
7.1%
3천만원이상 4
 
4.8%

체납건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct26
Distinct (%)49.1%
Missing19
Missing (%)26.4%
Infinite0
Infinite (%)0.0%
Mean110.01887
Minimum1
Maximum1452
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-01-10T07:16:37.973912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median6
Q343
95-th percentile703.2
Maximum1452
Range1451
Interquartile range (IQR)41

Descriptive statistics

Standard deviation283.43067
Coefficient of variation (CV)2.5762005
Kurtosis12.243941
Mean110.01887
Median Absolute Deviation (MAD)5
Skewness3.4547588
Sum5831
Variance80332.942
MonotonicityNot monotonic
2024-01-10T07:16:38.065969image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
1 10
13.9%
2 6
 
8.3%
3 5
 
6.9%
6 4
 
5.6%
11 3
 
4.2%
4 2
 
2.8%
7 2
 
2.8%
9 2
 
2.8%
15 2
 
2.8%
43 1
 
1.4%
Other values (16) 16
22.2%
(Missing) 19
26.4%
ValueCountFrequency (%)
1 10
13.9%
2 6
8.3%
3 5
6.9%
4 2
 
2.8%
5 1
 
1.4%
6 4
 
5.6%
7 2
 
2.8%
8 1
 
1.4%
9 2
 
2.8%
11 3
 
4.2%
ValueCountFrequency (%)
1452 1
1.4%
1075 1
1.4%
930 1
1.4%
552 1
1.4%
360 1
1.4%
329 1
1.4%
224 1
1.4%
221 1
1.4%
139 1
1.4%
93 1
1.4%

체납금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct52
Distinct (%)98.1%
Missing19
Missing (%)26.4%
Infinite0
Infinite (%)0.0%
Mean14226245
Minimum49400
Maximum89779960
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-01-10T07:16:38.169988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum49400
5-th percentile637846
Q11807270
median5207100
Q317019650
95-th percentile68164148
Maximum89779960
Range89730560
Interquartile range (IQR)15212380

Descriptive statistics

Standard deviation21770012
Coefficient of variation (CV)1.5302711
Kurtosis5.4648202
Mean14226245
Median Absolute Deviation (MAD)4290100
Skewness2.4124719
Sum7.5399101 × 108
Variance4.7393344 × 1014
MonotonicityNot monotonic
2024-01-10T07:16:38.280911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
89779960 2
 
2.8%
1225000 1
 
1.4%
14174560 1
 
1.4%
61315800 1
 
1.4%
5207100 1
 
1.4%
26323920 1
 
1.4%
14610560 1
 
1.4%
4385300 1
 
1.4%
1957490 1
 
1.4%
10808690 1
 
1.4%
Other values (42) 42
58.3%
(Missing) 19
26.4%
ValueCountFrequency (%)
49400 1
1.4%
211420 1
1.4%
433150 1
1.4%
774310 1
1.4%
819750 1
1.4%
860870 1
1.4%
917000 1
1.4%
1180100 1
1.4%
1197210 1
1.4%
1225000 1
1.4%
ValueCountFrequency (%)
89779960 2
2.8%
78436670 1
1.4%
61315800 1
1.4%
51434060 1
1.4%
36268070 1
1.4%
26323920 1
1.4%
25215680 1
1.4%
25167900 1
1.4%
24522410 1
1.4%
18935600 1
1.4%

누적체납건수
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct44
Distinct (%)62.9%
Missing2
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean697.8
Minimum1
Maximum13232
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-01-10T07:16:38.384400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q14
median12.5
Q3110.75
95-th percentile3666.55
Maximum13232
Range13231
Interquartile range (IQR)106.75

Descriptive statistics

Standard deviation2266.4507
Coefficient of variation (CV)3.2479947
Kurtosis21.27979
Mean697.8
Median Absolute Deviation (MAD)11.5
Skewness4.5149512
Sum48846
Variance5136798.8
MonotonicityNot monotonic
2024-01-10T07:16:38.487375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 10
 
13.9%
4 6
 
8.3%
2 3
 
4.2%
3 3
 
4.2%
5 3
 
4.2%
6 3
 
4.2%
11 3
 
4.2%
13 2
 
2.8%
12 2
 
2.8%
9 1
 
1.4%
Other values (34) 34
47.2%
(Missing) 2
 
2.8%
ValueCountFrequency (%)
1 10
13.9%
2 3
 
4.2%
3 3
 
4.2%
4 6
8.3%
5 3
 
4.2%
6 3
 
4.2%
9 1
 
1.4%
10 1
 
1.4%
11 3
 
4.2%
12 2
 
2.8%
ValueCountFrequency (%)
13232 1
1.4%
11780 1
1.4%
5776 1
1.4%
4846 1
1.4%
2225 1
1.4%
1912 1
1.4%
1896 1
1.4%
1552 1
1.4%
1030 1
1.4%
942 1
1.4%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct59
Distinct (%)84.3%
Missing2
Missing (%)2.8%
Infinite0
Infinite (%)0.0%
Mean58043783
Minimum433150
Maximum3.2298084 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size780.0 B
2024-01-10T07:16:38.589636image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum433150
5-th percentile545410
Q15322780
median22694735
Q386318245
95-th percentile2.2746633 × 108
Maximum3.2298084 × 108
Range3.2254769 × 108
Interquartile range (IQR)80995465

Descriptive statistics

Standard deviation74754703
Coefficient of variation (CV)1.287902
Kurtosis2.8445138
Mean58043783
Median Absolute Deviation (MAD)21405810
Skewness1.7554097
Sum4.0630648 × 109
Variance5.5882657 × 1015
MonotonicityNot monotonic
2024-01-10T07:16:38.693691image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
7859160 2
 
2.8%
5322780 2
 
2.8%
87323600 2
 
2.8%
5867000 2
 
2.8%
7559380 2
 
2.8%
2867500 2
 
2.8%
1199250 2
 
2.8%
545410 2
 
2.8%
4800160 2
 
2.8%
10681380 2
 
2.8%
Other values (49) 50
69.4%
ValueCountFrequency (%)
433150 1
1.4%
514360 2
2.8%
545410 2
2.8%
1199250 2
2.8%
1378600 1
1.4%
1737210 1
1.4%
1895240 1
1.4%
2239470 1
1.4%
2867500 2
2.8%
3120240 1
1.4%
ValueCountFrequency (%)
322980840 1
1.4%
281196290 1
1.4%
261665040 1
1.4%
254872370 1
1.4%
193970070 1
1.4%
175034470 1
1.4%
169434950 1
1.4%
154824390 1
1.4%
147012410 1
1.4%
130693750 1
1.4%

데이터기준일자
Date

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size708.0 B
Minimum2023-09-30 00:00:00
Maximum2023-09-30 00:00:00
2024-01-10T07:16:38.775423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:39.070464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-10T07:16:36.121546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.332402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.604088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.852914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:36.196703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.402081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.669371image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.923266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:36.263253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.463449image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.726351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.985442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:36.336269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.534558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:35.789590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-10T07:16:36.053689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-10T07:16:39.125821image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.1950.3150.0000.000
세목명0.0001.0000.0000.4610.2160.6540.000
체납액구간0.0000.0001.0000.0000.7430.0000.280
체납건수0.1950.4610.0001.0000.6001.0000.785
체납금액0.3150.2160.7430.6001.0000.4810.822
누적체납건수0.0000.6540.0001.0000.4811.0000.800
누적체납금액0.0000.0000.2800.7850.8220.8001.000
2024-01-10T07:16:39.208852image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납액구간세목명
과세년도1.0000.0000.000
체납액구간0.0001.0000.000
세목명0.0000.0001.000
2024-01-10T07:16:39.279367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
체납건수1.0000.2910.9220.4140.1930.2890.000
체납금액0.2911.0000.2450.8830.2250.1130.475
누적체납건수0.9220.2451.0000.5730.0000.2850.000
누적체납금액0.4140.8830.5731.0000.0000.0000.131
과세년도0.1930.2250.0000.0001.0000.0000.000
세목명0.2890.1130.2850.0000.0001.0000.000
체납액구간0.0000.4750.0000.1310.0000.0001.000

Missing values

2024-01-10T07:16:36.434477image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-10T07:16:36.552612image/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.
2024-01-10T07:16:36.643532image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
0충청남도부여군447602021등록면허세10만원 미만93160830056486530202023-09-30
1충청남도부여군447602021자동차세10만원 미만22194241401896759262602023-09-30
2충청남도부여군447602021자동차세10만원~30만원미만2243626807015522616650402023-09-30
3충청남도부여군447602021자동차세30만원~50만원미만7226382064216956402023-09-30
4충청남도부여군447602021자동차세50만원~1백만원미만<NA><NA>15143602023-09-30
5충청남도부여군447602021재산세10만원 미만107517610670117802548723702023-09-30
6충청남도부여군447602021재산세10만원~30만원미만4370720509421548243902023-09-30
7충청남도부여군447602021재산세30만원~50만원미만72497930149557351702023-09-30
8충청남도부여군447602021재산세50만원~1백만원미만32120220107742907102023-09-30
9충청남도부여군447602021재산세1백만원~3백만원미만913922640721198850602023-09-30
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액데이터기준일자
62충청남도부여군447602022지방소득세3천만원이상18977996031470124102023-09-30
63충청남도부여군447602022취득세10만원 미만<NA><NA>135454102023-09-30
64충청남도부여군447602022취득세10만원~30만원미만<NA><NA>611992502023-09-30
65충청남도부여군447602022취득세30만원~50만원미만143315014331502023-09-30
66충청남도부여군447602022취득세50만원~1백만원미만<NA><NA>428675002023-09-30
67충청남도부여군447602022취득세1백만원~3백만원미만<NA><NA>578591602023-09-30
68충청남도부여군447602022취득세3백만원~5백만원미만<NA><NA>275593802023-09-30
69충청남도부여군447602022취득세5백만원~1천만원미만<NA><NA>158670002023-09-30
70충청남도부여군447602022취득세1천만원~3천만원미만<NA><NA>4873236002023-09-30
71충청남도부여군447602022취득세3천만원이상1897799601897799602023-09-30