Overview

Dataset statistics

Number of variables10
Number of observations53
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory4.6 KiB
Average record size in memory88.5 B

Variable types

Categorical5
Numeric4
DateTime1

Dataset

Description인천광역시 남동구 지방세과세현황에 대한 데이터로 (과세년도, 세목명, 과세건수, 과세금액, 비과세건수, 비과세금액, 데이터기준일) 등을 제공합니다.
Author인천광역시 남동구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15079227&srcSe=7661IVAWM27C61E190

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
데이터기준일 has constant value ""Constant
과세건수 is highly overall correlated with 세목명High correlation
과세금액 is highly overall correlated with 세목명High correlation
비과세건수 is highly overall correlated with 비과세금액High correlation
비과세금액 is highly overall correlated with 비과세건수High correlation
세목명 is highly overall correlated with 과세건수 and 1 other fieldsHigh correlation
과세건수 has 11 (20.8%) zerosZeros
과세금액 has 11 (20.8%) zerosZeros
비과세건수 has 16 (30.2%) zerosZeros
비과세금액 has 18 (34.0%) zerosZeros

Reproduction

Analysis started2024-03-18 02:03:12.473811
Analysis finished2024-03-18 02:03:15.443674
Duration2.97 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
인천광역시
53 

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 53
100.0%

Length

2024-03-18T11:03:15.499382image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:03:15.575948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 53
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
남동구
53 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
남동구 53
100.0%

Length

2024-03-18T11:03:15.650030image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:03:15.721985image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
남동구 53
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
28200
53 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28200
2nd row28200
3rd row28200
4th row28200
5th row28200

Common Values

ValueCountFrequency (%)
28200 53
100.0%

Length

2024-03-18T11:03:15.807991image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:03:15.883782image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28200 53
100.0%

과세년도
Categorical

Distinct5
Distinct (%)9.4%
Missing0
Missing (%)0.0%
Memory size556.0 B
2017
13 
2021
12 
2020
10 
2018
9 
2019
9 

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 13
24.5%
2021 12
22.6%
2020 10
18.9%
2018 9
17.0%
2019 9
17.0%

Length

2024-03-18T11:03:15.982010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-18T11:03:16.103434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2017 13
24.5%
2021 12
22.6%
2020 10
18.9%
2018 9
17.0%
2019 9
17.0%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)24.5%
Missing0
Missing (%)0.0%
Memory size556.0 B
취득세
5 
주민세
5 
재산세
5 
자동차세
5 
등록면허세
5 
Other values (8)
28 

Length

Max length7
Median length5
Mean length4.1132075
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
취득세 5
9.4%
주민세 5
9.4%
재산세 5
9.4%
자동차세 5
9.4%
등록면허세 5
9.4%
지역자원시설세 5
9.4%
지방소득세 5
9.4%
교육세 5
9.4%
등록세 4
7.5%
지방소비세 3
5.7%
Other values (3) 6
11.3%

Length

2024-03-18T11:03:16.217946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
취득세 5
9.4%
주민세 5
9.4%
재산세 5
9.4%
자동차세 5
9.4%
등록면허세 5
9.4%
지역자원시설세 5
9.4%
지방소득세 5
9.4%
교육세 5
9.4%
등록세 4
7.5%
지방소비세 3
5.7%
Other values (3) 6
11.3%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct42
Distinct (%)79.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean265878.51
Minimum0
Maximum1101250
Zeros11
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-03-18T11:03:16.323114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q182677
median221319
Q3315483
95-th percentile1063536.8
Maximum1101250
Range1101250
Interquartile range (IQR)232806

Descriptive statistics

Standard deviation296079.78
Coefficient of variation (CV)1.1135905
Kurtosis2.9031523
Mean265878.51
Median Absolute Deviation (MAD)128343
Skewness1.8113715
Sum14091561
Variance8.7663236 × 1010
MonotonicityNot monotonic
2024-03-18T11:03:16.430738image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=42)
ValueCountFrequency (%)
0 11
 
20.8%
3 2
 
3.8%
333423 1
 
1.9%
205356 1
 
1.9%
1072415 1
 
1.9%
107787 1
 
1.9%
229591 1
 
1.9%
279238 1
 
1.9%
474304 1
 
1.9%
180162 1
 
1.9%
Other values (32) 32
60.4%
ValueCountFrequency (%)
0 11
20.8%
3 2
 
3.8%
82677 1
 
1.9%
90192 1
 
1.9%
91463 1
 
1.9%
92976 1
 
1.9%
107787 1
 
1.9%
166139 1
 
1.9%
167414 1
 
1.9%
168159 1
 
1.9%
ValueCountFrequency (%)
1101250 1
1.9%
1090111 1
1.9%
1072415 1
1.9%
1057618 1
1.9%
1021096 1
1.9%
474304 1
1.9%
464180 1
1.9%
454589 1
1.9%
441518 1
1.9%
424454 1
1.9%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct43
Distinct (%)81.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6.3225952 × 1010
Minimum0
Maximum3.27 × 1011
Zeros11
Zeros (%)20.8%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-03-18T11:03:16.537776image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.4869667 × 1010
median1.8609871 × 1010
Q39.8008118 × 1010
95-th percentile2.662966 × 1011
Maximum3.27 × 1011
Range3.27 × 1011
Interquartile range (IQR)8.3138451 × 1010

Descriptive statistics

Standard deviation8.3257848 × 1010
Coefficient of variation (CV)1.3168303
Kurtosis2.7481504
Mean6.3225952 × 1010
Median Absolute Deviation (MAD)1.8609871 × 1010
Skewness1.8208367
Sum3.3509755 × 1012
Variance6.9318693 × 1021
MonotonicityNot monotonic
2024-03-18T11:03:16.645190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=43)
ValueCountFrequency (%)
0 11
 
20.8%
266929000000 1
 
1.9%
44200639000 1
 
1.9%
304000000000 1
 
1.9%
17904642000 1
 
1.9%
107000000000 1
 
1.9%
63046303000 1
 
1.9%
2520000000 1
 
1.9%
16911436000 1
 
1.9%
15674354000 1
 
1.9%
Other values (33) 33
62.3%
ValueCountFrequency (%)
0 11
20.8%
2360000000 1
 
1.9%
2520000000 1
 
1.9%
14869667000 1
 
1.9%
15162670000 1
 
1.9%
15336788000 1
 
1.9%
15362471000 1
 
1.9%
15626804000 1
 
1.9%
15664025000 1
 
1.9%
15674354000 1
 
1.9%
ValueCountFrequency (%)
327000000000 1
1.9%
304000000000 1
1.9%
266929000000 1
1.9%
265875000000 1
1.9%
248772000000 1
1.9%
152000000000 1
1.9%
130000000000 1
1.9%
123138000000 1
1.9%
120915000000 1
1.9%
120115000000 1
1.9%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct38
Distinct (%)71.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean15513.113
Minimum0
Maximum75038
Zeros16
Zeros (%)30.2%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-03-18T11:03:16.904907image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median5347
Q323326
95-th percentile60821.4
Maximum75038
Range75038
Interquartile range (IQR)23326

Descriptive statistics

Standard deviation21159.119
Coefficient of variation (CV)1.3639505
Kurtosis0.76018917
Mean15513.113
Median Absolute Deviation (MAD)5347
Skewness1.3495131
Sum822195
Variance4.477083 × 108
MonotonicityNot monotonic
2024-03-18T11:03:17.022504image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=38)
ValueCountFrequency (%)
0 16
30.2%
22546 1
 
1.9%
2982 1
 
1.9%
5 1
 
1.9%
19519 1
 
1.9%
28 1
 
1.9%
27721 1
 
1.9%
64212 1
 
1.9%
47565 1
 
1.9%
8568 1
 
1.9%
Other values (28) 28
52.8%
ValueCountFrequency (%)
0 16
30.2%
4 1
 
1.9%
5 1
 
1.9%
6 1
 
1.9%
26 1
 
1.9%
28 1
 
1.9%
39 1
 
1.9%
42 1
 
1.9%
2632 1
 
1.9%
2982 1
 
1.9%
ValueCountFrequency (%)
75038 1
1.9%
68030 1
1.9%
64212 1
1.9%
58561 1
1.9%
55088 1
1.9%
47898 1
1.9%
47565 1
1.9%
43039 1
1.9%
41193 1
1.9%
36914 1
1.9%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct36
Distinct (%)67.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean8.1205355 × 109
Minimum0
Maximum4.6550882 × 1010
Zeros18
Zeros (%)34.0%
Negative0
Negative (%)0.0%
Memory size609.0 B
2024-03-18T11:03:17.147547image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median2.54901 × 108
Q32.330721 × 109
95-th percentile4.2009532 × 1010
Maximum4.6550882 × 1010
Range4.6550882 × 1010
Interquartile range (IQR)2.330721 × 109

Descriptive statistics

Standard deviation1.5741218 × 1010
Coefficient of variation (CV)1.9384459
Kurtosis0.9570728
Mean8.1205355 × 109
Median Absolute Deviation (MAD)2.54901 × 108
Skewness1.6735231
Sum4.3038838 × 1011
Variance2.4778596 × 1020
MonotonicityNot monotonic
2024-03-18T11:03:17.260777image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=36)
ValueCountFrequency (%)
0 18
34.0%
40098647000 1
 
1.9%
7705607000 1
 
1.9%
813732000 1
 
1.9%
1000 1
 
1.9%
34706213000 1
 
1.9%
39225000 1
 
1.9%
244674000 1
 
1.9%
46550882000 1
 
1.9%
2256646000 1
 
1.9%
Other values (26) 26
49.1%
ValueCountFrequency (%)
0 18
34.0%
1000 1
 
1.9%
37602000 1
 
1.9%
39225000 1
 
1.9%
47283000 1
 
1.9%
54278000 1
 
1.9%
120300000 1
 
1.9%
209917000 1
 
1.9%
244674000 1
 
1.9%
254901000 1
 
1.9%
ValueCountFrequency (%)
46550882000 1
1.9%
45317092000 1
1.9%
43453804000 1
1.9%
41046684000 1
1.9%
40430492000 1
1.9%
40098647000 1
1.9%
39785302000 1
1.9%
38769403000 1
1.9%
34706213000 1
1.9%
30934481000 1
1.9%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)1.9%
Missing0
Missing (%)0.0%
Memory size556.0 B
Minimum2023-02-17 00:00:00
Maximum2023-02-17 00:00:00
2024-03-18T11:03:17.356490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:17.437263image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-03-18T11:03:14.949226image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:13.884197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.359294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.643040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:15.015564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.011182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.435941image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.720160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:15.096205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.218677image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.505719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.801827image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:15.175612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.294992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.574746image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-03-18T11:03:14.872479image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-03-18T11:03:17.494369image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9540.8250.6160.640
과세건수0.0000.9541.0000.6260.6050.000
과세금액0.0000.8250.6261.0000.7050.769
비과세건수0.0000.6160.6050.7051.0000.774
비과세금액0.0000.6400.0000.7690.7741.000
2024-03-18T11:03:17.582703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-03-18T11:03:17.657537image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.4500.3960.2620.0000.791
과세금액0.4501.0000.4260.4650.0000.530
비과세건수0.3960.4261.0000.8920.0000.291
비과세금액0.2620.4650.8921.0000.0000.348
과세년도0.0000.0000.0000.0001.0000.000
세목명0.7910.5300.2910.3480.0001.000

Missing values

2024-03-18T11:03:15.265639image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-18T11:03:15.391776image/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

시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액데이터기준일
0인천광역시남동구282002017취득세9146326692900000022546400986470002023-02-17
1인천광역시남동구282002017등록세0042472830002023-02-17
2인천광역시남동구282002017주민세23852217375268000136012099170002023-02-17
3인천광역시남동구282002017재산세2441029248062500023326397853020002023-02-17
4인천광역시남동구282002017자동차세424454551213460004119324851480002023-02-17
5인천광역시남동구282002017레저세00002023-02-17
6인천광역시남동구282002017담배소비세00002023-02-17
7인천광역시남동구282002017지방소비세00002023-02-17
8인천광역시남동구282002017등록면허세1661391637062300053479358890002023-02-17
9인천광역시남동구282002017도시계획세00002023-02-17
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