Overview

Dataset statistics

Number of variables7
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.4 KiB
Average record size in memory64.3 B

Variable types

Categorical3
Numeric4

Dataset

Description전북특별자치도 진안군 2019년부터 2021년까지 지방세 세목별 과세현황 데이터입니다. 각 세목별 과세건수, 금액, 비과세 건수, 금액 정보를 제공합니다.
Author전북특별자치도 진안군
URLhttps://www.data.go.kr/data/15117744/fileData.do

Alerts

시군구 has constant value ""Constant
과세건수(건) is highly overall correlated with 과세금액(천원) and 3 other fieldsHigh correlation
과세금액(천원) is highly overall correlated with 과세건수(건) and 1 other fieldsHigh correlation
비과세건수(건) is highly overall correlated with 과세건수(건) and 2 other fieldsHigh correlation
비과세금액(천원) is highly overall correlated with 과세건수(건) and 1 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수(건) and 2 other fieldsHigh correlation
과세건수(건) has 10 (25.6%) zerosZeros
과세금액(천원) has 10 (25.6%) zerosZeros
비과세건수(건) has 18 (46.2%) zerosZeros
비과세금액(천원) has 18 (46.2%) zerosZeros

Reproduction

Analysis started2024-03-14 20:56:49.727099
Analysis finished2024-03-14 20:56:54.380520
Duration4.65 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시군구
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size440.0 B
전북특별자치도 진안군
39 

Length

Max length11
Median length11
Mean length11
Min length11

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row전북특별자치도 진안군
2nd row전북특별자치도 진안군
3rd row전북특별자치도 진안군
4th row전북특별자치도 진안군
5th row전북특별자치도 진안군

Common Values

ValueCountFrequency (%)
전북특별자치도 진안군 39
100.0%

Length

2024-03-15T05:56:54.585423image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:56:54.825111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
전북특별자치도 39
50.0%
진안군 39
50.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size440.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.3076923
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
담배소비세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

Length

2024-03-15T05:56:55.105578image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 3
 
7.7%
등록세 3
 
7.7%
주민세 3
 
7.7%
재산세 3
 
7.7%
자동차세 3
 
7.7%
레저세 3
 
7.7%
담배소비세 3
 
7.7%
지방소비세 3
 
7.7%
등록면허세 3
 
7.7%
도시계획세 3
 
7.7%
Other values (3) 9
23.1%

과세연도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size440.0 B
2019
13 
2020
13 
2021
13 

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 (%)
2019 13
33.3%
2020 13
33.3%
2021 13
33.3%

Length

2024-03-15T05:56:55.559817image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-15T05:56:55.873806image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2019 13
33.3%
2020 13
33.3%
2021 13
33.3%

과세건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15237.513
Minimum0
Maximum79910
Zeros10
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T05:56:56.357731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median7169
Q313605
95-th percentile72232.3
Maximum79910
Range79910
Interquartile range (IQR)13602

Descriptive statistics

Standard deviation22649.111
Coefficient of variation (CV)1.4864047
Kurtosis2.4039065
Mean15237.513
Median Absolute Deviation (MAD)7163
Skewness1.862592
Sum594263
Variance5.1298223 × 108
MonotonicityNot monotonic
2024-03-15T05:56:56.875993image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 10
25.6%
7169 1
 
2.6%
13668 1
 
2.6%
79910 1
 
2.6%
7848 1
 
2.6%
6963 1
 
2.6%
13385 1
 
2.6%
7 1
 
2.6%
475 1
 
2.6%
20856 1
 
2.6%
Other values (20) 20
51.3%
ValueCountFrequency (%)
0 10
25.6%
6 1
 
2.6%
7 1
 
2.6%
80 1
 
2.6%
273 1
 
2.6%
475 1
 
2.6%
6512 1
 
2.6%
6567 1
 
2.6%
6963 1
 
2.6%
7048 1
 
2.6%
ValueCountFrequency (%)
79910 1
2.6%
76006 1
2.6%
71813 1
2.6%
54117 1
2.6%
53033 1
2.6%
51631 1
2.6%
20856 1
2.6%
20752 1
2.6%
20478 1
2.6%
13668 1
2.6%

과세금액(천원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1813506.1
Minimum0
Maximum6893200
Zeros10
Zeros (%)25.6%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T05:56:57.379824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q1121507.5
median1293801
Q32346644.5
95-th percentile6652336.3
Maximum6893200
Range6893200
Interquartile range (IQR)2225137

Descriptive statistics

Standard deviation2152299.1
Coefficient of variation (CV)1.1868166
Kurtosis0.67602997
Mean1813506.1
Median Absolute Deviation (MAD)1075595
Skewness1.2996954
Sum70726736
Variance4.6323912 × 1012
MonotonicityNot monotonic
2024-03-15T05:56:57.904743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 10
25.6%
5769230 1
 
2.6%
531515 1
 
2.6%
2047835 1
 
2.6%
3001653 1
 
2.6%
282061 1
 
2.6%
505177 1
 
2.6%
6892495 1
 
2.6%
1376324 1
 
2.6%
3806512 1
 
2.6%
Other values (20) 20
51.3%
ValueCountFrequency (%)
0 10
25.6%
243015 1
 
2.6%
259145 1
 
2.6%
282061 1
 
2.6%
391725 1
 
2.6%
403329 1
 
2.6%
426095 1
 
2.6%
501283 1
 
2.6%
505177 1
 
2.6%
531515 1
 
2.6%
ValueCountFrequency (%)
6893200 1
2.6%
6892495 1
2.6%
6625652 1
2.6%
6380989 1
2.6%
5769230 1
2.6%
3806512 1
2.6%
3753486 1
2.6%
3563242 1
2.6%
3001653 1
2.6%
2369396 1
2.6%

비과세건수(건)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2193
Minimum0
Maximum19038
Zeros18
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T05:56:58.261807image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median52
Q32230
95-th percentile17414
Maximum19038
Range19038
Interquartile range (IQR)2230

Descriptive statistics

Standard deviation4800.2002
Coefficient of variation (CV)2.1888738
Kurtosis8.1291969
Mean2193
Median Absolute Deviation (MAD)52
Skewness3.0040537
Sum85527
Variance23041922
MonotonicityNot monotonic
2024-03-15T05:56:58.672628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 18
46.2%
1385 1
 
2.6%
2587 1
 
2.6%
66 1
 
2.6%
795 1
 
2.6%
2762 1
 
2.6%
3389 1
 
2.6%
19038 1
 
2.6%
2255 1
 
2.6%
1415 1
 
2.6%
Other values (12) 12
30.8%
ValueCountFrequency (%)
0 18
46.2%
29 1
 
2.6%
52 1
 
2.6%
66 1
 
2.6%
764 1
 
2.6%
792 1
 
2.6%
795 1
 
2.6%
1339 1
 
2.6%
1385 1
 
2.6%
1415 1
 
2.6%
ValueCountFrequency (%)
19038 1
2.6%
17990 1
2.6%
17350 1
2.6%
3389 1
2.6%
3176 1
2.6%
3097 1
2.6%
2935 1
2.6%
2762 1
2.6%
2587 1
2.6%
2255 1
2.6%

비과세금액(천원)
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct22
Distinct (%)56.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean412905.26
Minimum0
Maximum3948657
Zeros18
Zeros (%)46.2%
Negative0
Negative (%)0.0%
Memory size479.0 B
2024-03-15T05:56:59.103509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median4
Q3131077.5
95-th percentile2472151.5
Maximum3948657
Range3948657
Interquartile range (IQR)131077.5

Descriptive statistics

Standard deviation946430.69
Coefficient of variation (CV)2.2921255
Kurtosis5.2648081
Mean412905.26
Median Absolute Deviation (MAD)4
Skewness2.4434193
Sum16103305
Variance8.9573104 × 1011
MonotonicityNot monotonic
2024-03-15T05:56:59.403420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 18
46.2%
3948657 1
 
2.6%
71718 1
 
2.6%
9 1
 
2.6%
131540 1
 
2.6%
42560 1
 
2.6%
130615 1
 
2.6%
2734866 1
 
2.6%
152828 1
 
2.6%
1859202 1
 
2.6%
Other values (12) 12
30.8%
ValueCountFrequency (%)
0 18
46.2%
2 1
 
2.6%
4 1
 
2.6%
9 1
 
2.6%
26947 1
 
2.6%
37450 1
 
2.6%
42560 1
 
2.6%
62527 1
 
2.6%
71718 1
 
2.6%
77867 1
 
2.6%
ValueCountFrequency (%)
3948657 1
2.6%
2734866 1
2.6%
2442961 1
2.6%
2237478 1
2.6%
1859202 1
2.6%
1747736 1
2.6%
152828 1
2.6%
143094 1
2.6%
135651 1
2.6%
131540 1
2.6%

Interactions

2024-03-15T05:56:52.997608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:49.979579image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:50.924270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:51.785379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:53.345082image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:50.253452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:51.093362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:52.267060image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:53.569979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:50.495576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:51.252122image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:52.460444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:53.738189image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:50.677442image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:51.514111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-15T05:56:52.721345image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-15T05:56:59.627641image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세연도과세건수(건)과세금액(천원)비과세건수(건)비과세금액(천원)
세목명1.0000.0000.9400.9101.0000.746
과세연도0.0001.0000.0000.0000.0000.000
과세건수(건)0.9400.0001.0000.7380.9990.530
과세금액(천원)0.9100.0000.7381.0000.5890.754
비과세건수(건)1.0000.0000.9990.5891.0000.697
비과세금액(천원)0.7460.0000.5300.7540.6971.000
2024-03-15T05:56:59.919063image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세연도세목명
과세연도1.0000.000
세목명0.0001.000
2024-03-15T05:57:00.267792image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수(건)과세금액(천원)비과세건수(건)비과세금액(천원)세목명과세연도
과세건수(건)1.0000.5850.7910.7010.7270.000
과세금액(천원)0.5851.0000.3590.4170.6630.000
비과세건수(건)0.7910.3591.0000.9190.8500.000
비과세금액(천원)0.7010.4170.9191.0000.4540.000
세목명0.7270.6630.8500.4541.0000.000
과세연도0.0000.0000.0000.0000.0001.000

Missing values

2024-03-15T05:56:53.962170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-15T05:56:54.230365image/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

시군구세목명과세연도과세건수(건)과세금액(천원)비과세건수(건)비과세금액(천원)
0전북특별자치도 진안군취득세20197169576923013853948657
1전북특별자치도 진안군등록세20190000
2전북특별자치도 진안군주민세201913292391725293537450
3전북특별자치도 진안군재산세2019516311863419173502237478
4전북특별자치도 진안군자동차세20192047837534863097143094
5전북특별자치도 진안군레저세20190000
6전북특별자치도 진안군담배소비세201980129380100
7전북특별자치도 진안군지방소비세20190000
8전북특별자치도 진안군등록면허세201912685501283220562527
9전북특별자치도 진안군도시계획세20190000
시군구세목명과세연도과세건수(건)과세금액(천원)비과세건수(건)비과세금액(천원)
29전북특별자치도 진안군재산세2021541172102599190382734866
30전북특별자치도 진안군자동차세20212085638065123389130615
31전북특별자치도 진안군레저세20210000
32전북특별자치도 진안군담배소비세2021475137632400
33전북특별자치도 진안군지방소비세20217689249500
34전북특별자치도 진안군등록면허세202113385505177276242560
35전북특별자치도 진안군도시계획세20210000
36전북특별자치도 진안군지역자원시설세20216963282061795131540
37전북특별자치도 진안군지방소득세20217848300165300
38전북특별자치도 진안군지방교육세2021799102047835669