Overview

Dataset statistics

Number of variables10
Number of observations32
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory91.1 B

Variable types

Numeric5
Categorical5

Dataset

Description충청북도 충주시 2019~2021년 지방세 과세현황(연번, 시도명, 시군구명, 자치단체코드, 과세연도, 세목명, 과세건수, 과세금액, 비과세 감면 건수, 비과세 감면 금액
URLhttps://www.data.go.kr/data/15117552/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
연번 is highly overall correlated with 과세년도High correlation
과세건수 is highly overall correlated with 비과세 감면 건수 and 1 other fieldsHigh correlation
과세금액 is highly overall correlated with 세목명High correlation
비과세 감면 건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세 감면 금액 is highly overall correlated with 비과세 감면 건수 and 1 other fieldsHigh correlation
과세년도 is highly overall correlated with 연번High correlation
세목명 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
연번 has unique valuesUnique
과세건수 has unique valuesUnique
과세금액 has unique valuesUnique
비과세 감면 건수 has 8 (25.0%) zerosZeros
비과세 감면 금액 has 8 (25.0%) zerosZeros

Reproduction

Analysis started2023-12-12 12:01:38.219649
Analysis finished2023-12-12 12:01:41.428691
Duration3.21 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.5
Minimum1
Maximum32
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T21:01:41.507822image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.55
Q18.75
median16.5
Q324.25
95-th percentile30.45
Maximum32
Range31
Interquartile range (IQR)15.5

Descriptive statistics

Standard deviation9.3808315
Coefficient of variation (CV)0.56853524
Kurtosis-1.2
Mean16.5
Median Absolute Deviation (MAD)8
Skewness0
Sum528
Variance88
MonotonicityStrictly increasing
2023-12-12T21:01:41.678011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
1 1
 
3.1%
18 1
 
3.1%
32 1
 
3.1%
31 1
 
3.1%
30 1
 
3.1%
29 1
 
3.1%
28 1
 
3.1%
27 1
 
3.1%
26 1
 
3.1%
25 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
1 1
3.1%
2 1
3.1%
3 1
3.1%
4 1
3.1%
5 1
3.1%
6 1
3.1%
7 1
3.1%
8 1
3.1%
9 1
3.1%
10 1
3.1%
ValueCountFrequency (%)
32 1
3.1%
31 1
3.1%
30 1
3.1%
29 1
3.1%
28 1
3.1%
27 1
3.1%
26 1
3.1%
25 1
3.1%
24 1
3.1%
23 1
3.1%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
충청북도
32 

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 32
100.0%

Length

2023-12-12T21:01:41.802314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:01:41.900063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충청북도 32
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
충주시
32 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
충주시 32
100.0%

Length

2023-12-12T21:01:42.005533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:01:42.105278image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
충주시 32
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)3.1%
Missing0
Missing (%)0.0%
Memory size388.0 B
43130
32 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row43130
2nd row43130
3rd row43130
4th row43130
5th row43130

Common Values

ValueCountFrequency (%)
43130 32
100.0%

Length

2023-12-12T21:01:42.229735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:01:42.410748image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
43130 32
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
2020
11 
2021
11 
2019
10 

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 (%)
2020 11
34.4%
2021 11
34.4%
2019 10
31.2%

Length

2023-12-12T21:01:42.542885image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:01:42.696851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 11
34.4%
2021 11
34.4%
2019 10
31.2%

세목명
Categorical

HIGH CORRELATION 

Distinct11
Distinct (%)34.4%
Missing0
Missing (%)0.0%
Memory size388.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (6)
17 

Length

Max length7
Median length5
Mean length4.15625
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row취득세
2nd row등록세
3rd row주민세
4th row재산세
5th row자동차세

Common Values

ValueCountFrequency (%)
취득세 3
9.4%
등록세 3
9.4%
주민세 3
9.4%
재산세 3
9.4%
자동차세 3
9.4%
담배소비세 3
9.4%
등록면허세 3
9.4%
지역자원시설세 3
9.4%
지방소득세 3
9.4%
교육세 3
9.4%

Length

2023-12-12T21:01:42.837652image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
9.4%
등록세 3
9.4%
주민세 3
9.4%
재산세 3
9.4%
자동차세 3
9.4%
담배소비세 3
9.4%
등록면허세 3
9.4%
지역자원시설세 3
9.4%
지방소득세 3
9.4%
교육세 3
9.4%

과세건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean116646.75
Minimum6
Maximum495880
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T21:01:42.996896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum6
5-th percentile49.35
Q126586
median88080
Q3168527.25
95-th percentile478788.15
Maximum495880
Range495874
Interquartile range (IQR)141941.25

Descriptive statistics

Standard deviation136523.59
Coefficient of variation (CV)1.170402
Kurtosis3.1314074
Mean116646.75
Median Absolute Deviation (MAD)81441.5
Skewness1.8635757
Sum3732696
Variance1.863869 × 1010
MonotonicityNot monotonic
2023-12-12T21:01:43.439239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
35199 1
 
3.1%
88490 1
 
3.1%
495880 1
 
3.1%
75533 1
 
3.1%
92405 1
 
3.1%
96570 1
 
3.1%
7 1
 
3.1%
487 1
 
3.1%
174126 1
 
3.1%
204772 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
6 1
3.1%
7 1
3.1%
84 1
3.1%
282 1
3.1%
487 1
3.1%
532 1
3.1%
592 1
3.1%
747 1
3.1%
35199 1
3.1%
36963 1
3.1%
ValueCountFrequency (%)
495880 1
3.1%
485788 1
3.1%
473061 1
3.1%
204772 1
3.1%
204628 1
3.1%
195722 1
3.1%
174126 1
3.1%
171510 1
3.1%
167533 1
3.1%
102441 1
3.1%

과세금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct32
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.5588175 × 1010
Minimum70789430
Maximum8.9166293 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T21:01:43.613907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum70789430
5-th percentile1.0299287 × 108
Q17.0762018 × 109
median1.7301959 × 1010
Q33.7087343 × 1010
95-th percentile7.9446922 × 1010
Maximum8.9166293 × 1010
Range8.9095503 × 1010
Interquartile range (IQR)3.0011141 × 1010

Descriptive statistics

Standard deviation2.3851114 × 1010
Coefficient of variation (CV)0.9321147
Kurtosis1.1935343
Mean2.5588175 × 1010
Median Absolute Deviation (MAD)1.1149666 × 1010
Skewness1.2708746
Sum8.1882161 × 1011
Variance5.6887566 × 1020
MonotonicityNot monotonic
2023-12-12T21:01:43.773211image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=32)
ValueCountFrequency (%)
83527578700 1
 
3.1%
6354889650 1
 
3.1%
25191533050 1
 
3.1%
49282925410 1
 
3.1%
7187240480 1
 
3.1%
7077075410 1
 
3.1%
13402008000 1
 
3.1%
17199275030 1
 
3.1%
38816021270 1
 
3.1%
36314191350 1
 
3.1%
Other values (22) 22
68.8%
ValueCountFrequency (%)
70789430 1
3.1%
86965280 1
3.1%
116106360 1
3.1%
6055633890 1
3.1%
6248951550 1
3.1%
6354889650 1
3.1%
6574075080 1
3.1%
7073580910 1
3.1%
7077075410 1
3.1%
7187240480 1
3.1%
ValueCountFrequency (%)
89166292510 1
3.1%
83527578700 1
3.1%
76108203420 1
3.1%
49282925410 1
3.1%
44602559260 1
3.1%
44427616570 1
3.1%
39318537850 1
3.1%
38816021270 1
3.1%
36511116440 1
3.1%
36314191350 1
3.1%

비과세 감면 건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11605.031
Minimum0
Maximum61254
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T21:01:43.899434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q116.5
median4374
Q312960.75
95-th percentile56252
Maximum61254
Range61254
Interquartile range (IQR)12944.25

Descriptive statistics

Standard deviation17357.809
Coefficient of variation (CV)1.4957141
Kurtosis2.9371305
Mean11605.031
Median Absolute Deviation (MAD)4374
Skewness1.9093903
Sum371361
Variance3.0129352 × 108
MonotonicityNot monotonic
2023-12-12T21:01:44.051953image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8
25.0%
9974 2
 
6.2%
16065 2
 
6.2%
56252 2
 
6.2%
26750 2
 
6.2%
8377 2
 
6.2%
4108 2
 
6.2%
78 2
 
6.2%
22 2
 
6.2%
29491 1
 
3.1%
Other values (7) 7
21.9%
ValueCountFrequency (%)
0 8
25.0%
22 2
 
6.2%
28 1
 
3.1%
78 2
 
6.2%
98 1
 
3.1%
4108 2
 
6.2%
4640 1
 
3.1%
8377 2
 
6.2%
9440 1
 
3.1%
9974 2
 
6.2%
ValueCountFrequency (%)
61254 1
3.1%
56252 2
6.2%
29491 1
3.1%
26750 2
6.2%
16065 2
6.2%
11926 1
3.1%
11232 1
3.1%
9974 2
6.2%
9440 1
3.1%
8377 2
6.2%

비과세 감면 금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct17
Distinct (%)53.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.0810584 × 109
Minimum0
Maximum2.3800575 × 1010
Zeros8
Zeros (%)25.0%
Negative0
Negative (%)0.0%
Memory size420.0 B
2023-12-12T21:01:44.189960image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14740
median1.2965684 × 108
Q31.2899171 × 109
95-th percentile2.3099537 × 1010
Maximum2.3800575 × 1010
Range2.3800575 × 1010
Interquartile range (IQR)1.2899124 × 109

Descriptive statistics

Standard deviation8.1318247 × 109
Coefficient of variation (CV)1.9925774
Kurtosis1.416328
Mean4.0810584 × 109
Median Absolute Deviation (MAD)1.2965684 × 108
Skewness1.773928
Sum1.3059387 × 1011
Variance6.6126573 × 1019
MonotonicityNot monotonic
2023-12-12T21:01:44.343801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=17)
ValueCountFrequency (%)
0 8
25.0%
23800574870 2
 
6.2%
129656840 2
 
6.2%
16946361930 2
 
6.2%
1299509750 2
 
6.2%
257998370 2
 
6.2%
707296820 2
 
6.2%
6320 2
 
6.2%
25823930 2
 
6.2%
1286719540 1
 
3.1%
Other values (7) 7
21.9%
ValueCountFrequency (%)
0 8
25.0%
6320 2
 
6.2%
8940 1
 
3.1%
5559650 1
 
3.1%
25823930 2
 
6.2%
70997810 1
 
3.1%
129656840 2
 
6.2%
257998370 2
 
6.2%
595070300 1
 
3.1%
707296820 2
 
6.2%
ValueCountFrequency (%)
23800574870 2
6.2%
22525961190 1
3.1%
19000309160 1
3.1%
16946361930 2
6.2%
1299509750 2
6.2%
1286719540 1
3.1%
774784090 1
3.1%
707296820 2
6.2%
595070300 1
3.1%
257998370 2
6.2%

Interactions

2023-12-12T21:01:40.673707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:38.585740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.097527image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.614705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.128125image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.793299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:38.707201image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.201274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.736542image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.235057image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.871677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:38.818826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.298173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.840271image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.343524image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.963253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:38.902962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.398798image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.937780image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.449981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:41.051561image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.004175image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:39.508612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.031804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:01:40.559176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:01:44.448617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명과세건수과세금액비과세 감면 건수비과세 감면 금액
연번1.0000.9580.0000.0000.0000.0000.000
과세년도0.9581.0000.0000.0000.0000.0000.000
세목명0.0000.0001.0000.9720.9020.9651.000
과세건수0.0000.0000.9721.0000.7880.8010.884
과세금액0.0000.0000.9020.7881.0000.7050.907
비과세 감면 건수0.0000.0000.9650.8010.7051.0000.783
비과세 감면 금액0.0000.0001.0000.8840.9070.7831.000
2023-12-12T21:01:44.620823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2023-12-12T21:01:44.710358image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세건수과세금액비과세 감면 건수비과세 감면 금액과세년도세목명
연번1.0000.1110.084-0.158-0.2140.8330.000
과세건수0.1111.0000.1810.6650.4080.0000.830
과세금액0.0840.1811.0000.1760.2310.0000.682
비과세 감면 건수-0.1580.6650.1761.0000.8710.0000.809
비과세 감면 금액-0.2140.4080.2310.8711.0000.0000.851
과세년도0.8330.0000.0000.0000.0001.0000.000
세목명0.0000.8300.6820.8090.8510.0001.000

Missing values

2023-12-12T21:01:41.190404image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:01:41.356866image/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

연번시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세 감면 건수비과세 감면 금액
01충청북도충주시431302019취득세3519983527578700997423800574870
12충청북도충주시431302019등록세592869652802225823930
23충청북도충주시431302019주민세100515657407508016065129656840
34충청북도충주시431302019재산세195722333453189105625216946361930
45충청북도충주시431302019자동차세16753336511116440267501299509750
56충청북도충주시431302019담배소비세841638035548000
67충청북도충주시431302019등록면허세8697460556338908377257998370
78충청북도충주시431302019지역자원시설세8767062489515504108707296820
89충청북도충주시431302019지방소득세562884460255926000
910충청북도충주시431302019교육세47306123690170080786320
연번시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세 감면 건수비과세 감면 금액
2223충청북도충주시431302021등록세747116106360285559650
2324충청북도충주시431302021주민세10244174425203601123270997810
2425충청북도충주시431302021재산세204772363141913506125419000309160
2526충청북도충주시431302021자동차세17412638816021270294911286719540
2627충청북도충주시431302021담배소비세4871719927503000
2728충청북도충주시431302021지방소비세71340200800000
2829충청북도충주시431302021등록면허세9657070770754109440595070300
2930충청북도충주시431302021지역자원시설세9240571872404804640774784090
3031충청북도충주시431302021지방소득세755334928292541000
3132충청북도충주시431302021교육세49588025191533050988940