Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells4
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory81.9 B

Variable types

Categorical5
Numeric4

Dataset

Description인천광역시 서구 2017년도부터 2021년도까지 세목별 비과세금액, 감면금액, 부과금액, 비과세감면율을 포함하고 있습니다.
Author인천광역시 서구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15078587&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 2 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액 and 3 other fieldsHigh correlation
세목명 is highly overall correlated with 비과세감면율High correlation
비과세금액 has 4 (11.8%) missing valuesMissing
감면금액 has unique valuesUnique
비과세금액 has 1 (2.9%) zerosZeros
부과금액 has 4 (11.8%) zerosZeros
비과세감면율 has 4 (11.8%) zerosZeros

Reproduction

Analysis started2024-01-28 05:30:48.426492
Analysis finished2024-01-28 05:30:50.496085
Duration2.07 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
인천광역시
34 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
인천광역시 34
100.0%

Length

2024-01-28T14:30:50.547017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:50.629970image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 34
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
서구
34 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
서구 34
100.0%

Length

2024-01-28T14:30:50.715940image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:50.792393image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
서구 34
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
28260
34 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28260
2nd row28260
3rd row28260
4th row28260
5th row28260

Common Values

ValueCountFrequency (%)
28260 34
100.0%

Length

2024-01-28T14:30:50.873081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:50.953075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28260 34
100.0%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)20.6%
Missing0
Missing (%)0.0%
Memory size404.0 B
등록세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (2)
9 

Length

Max length7
Median length3
Mean length3.9117647
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 5
14.7%
재산세 5
14.7%
주민세 5
14.7%
취득세 5
14.7%
자동차세 5
14.7%
등록면허세 5
14.7%
지역자원시설세 4
11.8%

Length

2024-01-28T14:30:51.039936image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:51.138012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 5
14.7%
재산세 5
14.7%
주민세 5
14.7%
취득세 5
14.7%
자동차세 5
14.7%
등록면허세 5
14.7%
지역자원시설세 4
11.8%

과세년도
Categorical

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
2017
7 
2018
7 
2019
7 
2021
7 
2020
6 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2017
2nd row2017
3rd row2017
4th row2017
5th row2017

Common Values

ValueCountFrequency (%)
2017 7
20.6%
2018 7
20.6%
2019 7
20.6%
2021 7
20.6%
2020 6
17.6%

Length

2024-01-28T14:30:51.243590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-28T14:30:51.332369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 7
20.6%
2018 7
20.6%
2019 7
20.6%
2021 7
20.6%
2020 6
17.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING  ZEROS 

Distinct30
Distinct (%)100.0%
Missing4
Missing (%)11.8%
Infinite0
Infinite (%)0.0%
Mean1.6256002 × 1010
Minimum0
Maximum8.1548877 × 1010
Zeros1
Zeros (%)2.9%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-28T14:30:51.436535image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2948700
Q129643750
median4.826225 × 108
Q31.6136342 × 1010
95-th percentile7.7333345 × 1010
Maximum8.1548877 × 1010
Range8.1548877 × 1010
Interquartile range (IQR)1.6106698 × 1010

Descriptive statistics

Standard deviation2.8641089 × 1010
Coefficient of variation (CV)1.7618777
Kurtosis0.92241504
Mean1.6256002 × 1010
Median Absolute Deviation (MAD)4.7711081 × 108
Skewness1.6069466
Sum4.8768007 × 1011
Variance8.2031196 × 1020
MonotonicityNot monotonic
2024-01-28T14:30:51.545959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
12725000 1
 
2.9%
867876000 1
 
2.9%
5747000 1
 
2.9%
246564000 1
 
2.9%
15223963000 1
 
2.9%
2775000 1
 
2.9%
81548877000 1
 
2.9%
5470380 1
 
2.9%
215276140 1
 
2.9%
40941116550 1
 
2.9%
Other values (20) 20
58.8%
(Missing) 4
 
11.8%
ValueCountFrequency (%)
0 1
2.9%
2775000 1
2.9%
3161000 1
2.9%
5470380 1
2.9%
5553000 1
2.9%
5747000 1
2.9%
6450000 1
2.9%
12725000 1
2.9%
80400000 1
2.9%
81290000 1
2.9%
ValueCountFrequency (%)
81548877000 1
2.9%
77562775000 1
2.9%
77052930210 1
2.9%
73328922000 1
2.9%
71527636000 1
2.9%
40941116550 1
2.9%
19274967000 1
2.9%
16440468000 1
2.9%
15223963000 1
2.9%
7792473000 1
2.9%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.6783705 × 1010
Minimum82789000
Maximum1.0610353 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-28T14:30:51.668861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum82789000
5-th percentile99393050
Q11.5102459 × 108
median4.09391 × 108
Q31.8971238 × 1010
95-th percentile9.0141361 × 1010
Maximum1.0610353 × 1011
Range1.0602074 × 1011
Interquartile range (IQR)1.8820213 × 1010

Descriptive statistics

Standard deviation3.1160178 × 1010
Coefficient of variation (CV)1.8565733
Kurtosis2.5426193
Mean1.6783705 × 1010
Median Absolute Deviation (MAD)3.260795 × 108
Skewness1.968162
Sum5.7064597 × 1011
Variance9.7095669 × 1020
MonotonicityNot monotonic
2024-01-28T14:30:51.780354image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1553186000 1
 
2.9%
112988590 1
 
2.9%
152115000 1
 
2.9%
150661120 1
 
2.9%
18454253440 1
 
2.9%
129456810 1
 
2.9%
84825452830 1
 
2.9%
4457821680 1
 
2.9%
301778000 1
 
2.9%
4404426000 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
82789000 1
2.9%
83834000 1
2.9%
107771000 1
2.9%
107824000 1
2.9%
112988590 1
2.9%
116869000 1
2.9%
129456810 1
2.9%
137270000 1
2.9%
150661120 1
2.9%
152115000 1
2.9%
ValueCountFrequency (%)
106103534000 1
2.9%
91476261000 1
2.9%
89422569000 1
2.9%
84825452830 1
2.9%
65716271000 1
2.9%
25487633000 1
2.9%
25166522000 1
2.9%
20152009000 1
2.9%
19143566000 1
2.9%
18454253440 1
2.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.0030954 × 1011
Minimum0
Maximum5.2762215 × 1011
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-28T14:30:51.890368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.916637 × 1010
median2.86075 × 1010
Q31.53264 × 1011
95-th percentile3.8445095 × 1011
Maximum5.2762215 × 1011
Range5.2762215 × 1011
Interquartile range (IQR)1.3409763 × 1011

Descriptive statistics

Standard deviation1.392145 × 1011
Coefficient of variation (CV)1.387849
Kurtosis1.9672178
Mean1.0030954 × 1011
Median Absolute Deviation (MAD)1.9325838 × 1010
Skewness1.6901453
Sum3.4105244 × 1012
Variance1.9380676 × 1022
MonotonicityNot monotonic
2024-01-28T14:30:52.002322image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
11.8%
146109000000 1
 
2.9%
28470970000 1
 
2.9%
28744030000 1
 
2.9%
50035197000 1
 
2.9%
527622151000 1
 
2.9%
20274375000 1
 
2.9%
189585026000 1
 
2.9%
29099999550 1
 
2.9%
60715118890 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
0 4
11.8%
191110490 1
 
2.9%
15966307000 1
 
2.9%
17004873000 1
 
2.9%
18402181000 1
 
2.9%
19005735000 1
 
2.9%
19648275510 1
 
2.9%
20274375000 1
 
2.9%
20944896000 1
 
2.9%
22881422000 1
 
2.9%
ValueCountFrequency (%)
527622151000 1
2.9%
386737000000 1
2.9%
383220000000 1
2.9%
358883000000 1
2.9%
312075000000 1
2.9%
220687000000 1
2.9%
189585026000 1
2.9%
177304000000 1
2.9%
155649000000 1
2.9%
146109000000 1
2.9%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)88.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean16.833529
Minimum0
Maximum78.83
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2024-01-28T14:30:52.106276image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10.69
median4.82
Q326.9
95-th percentile66.14
Maximum78.83
Range78.83
Interquartile range (IQR)26.21

Descriptive statistics

Standard deviation22.855908
Coefficient of variation (CV)1.3577609
Kurtosis0.93409273
Mean16.833529
Median Absolute Deviation (MAD)4.82
Skewness1.4259877
Sum572.34
Variance522.39253
MonotonicityNot monotonic
2024-01-28T14:30:52.225220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0.0 4
 
11.8%
0.69 2
 
5.9%
10.11 1
 
2.9%
3.64 1
 
2.9%
0.4 1
 
2.9%
8.47 1
 
2.9%
23.0 1
 
2.9%
53.64 1
 
2.9%
0.41 1
 
2.9%
7.7 1
 
2.9%
Other values (20) 20
58.8%
ValueCountFrequency (%)
0.0 4
11.8%
0.4 1
 
2.9%
0.41 1
 
2.9%
0.5 1
 
2.9%
0.56 1
 
2.9%
0.69 2
5.9%
0.92 1
 
2.9%
1.02 1
 
2.9%
1.1 1
 
2.9%
1.51 1
 
2.9%
ValueCountFrequency (%)
78.83 1
2.9%
66.4 1
2.9%
66.0 1
2.9%
53.64 1
2.9%
52.15 1
2.9%
43.28 1
2.9%
32.52 1
2.9%
28.9 1
2.9%
27.09 1
2.9%
26.33 1
2.9%

Interactions

2024-01-28T14:30:49.953183image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:48.678223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.002565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.628809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:50.034187image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:48.771392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.094865image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.717959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:50.107391image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:48.845755image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.183017image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.795559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:50.176042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:48.922749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.259018image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-28T14:30:49.867686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-28T14:30:52.306512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도비과세금액감면금액부과금액비과세감면율
세목명1.0000.0000.6260.8290.6380.785
과세년도0.0001.0000.0000.0000.0000.000
비과세금액0.6260.0001.0000.9160.9230.955
감면금액0.8290.0000.9161.0000.9600.949
부과금액0.6380.0000.9230.9601.0000.979
비과세감면율0.7850.0000.9550.9490.9791.000
2024-01-28T14:30:52.408672image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-01-28T14:30:52.492939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율세목명과세년도
비과세금액1.0000.7920.7880.7370.4190.000
감면금액0.7921.0000.7390.6770.4300.000
부과금액0.7880.7391.0000.7300.3800.000
비과세감면율0.7370.6770.7301.0000.5460.000
세목명0.4190.4300.3800.5461.0000.000
과세년도0.0000.0000.0000.0000.0001.000

Missing values

2024-01-28T14:30:50.299698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-28T14:30:50.444846image/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

시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
0인천광역시서구28260등록세2017<NA>155318600000.0
1인천광역시서구28260재산세2017715276360002548763300014610900000066.4
2인천광역시서구28260주민세20178040000082789000159663070001.02
3인천광역시서구28260취득세2017164404680006571627100031207500000026.33
4인천광역시서구28260자동차세2017258899800018590750004085855700010.89
5인천광역시서구28260등록면허세20173161000205364000190057350001.1
6인천광역시서구28260지역자원시설세2017718681000273402000209448960004.74
7인천광역시서구28260등록세2018<NA>35504900000.0
8인천광역시서구28260재산세2018775627750002516652200015564900000066.0
9인천광역시서구28260주민세201881290000176083000170048730001.51
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
24인천광역시서구28260취득세2020409411165508482545283038673700000032.52
25인천광역시서구28260자동차세20202152761404457821680607151188907.7
26인천광역시서구28260등록면허세20205470380112988590290999995500.41
27인천광역시서구28260등록세2021<NA>30177800000.0
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