Overview

Dataset statistics

Number of variables10
Number of observations39
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory89.4 B

Variable types

Categorical5
Numeric4
DateTime1

Dataset

Description인천광역시 중구에서 부과되는 지방세에 대한 데이터로 건수 및 금액을 연도별 각 세목별(취득세, 등록면허세, 주민세, 재산세, 자동차세 등)로 제공합니다.
Author인천광역시 중구
URLhttps://data.incheon.go.kr/findData/publicDataDetail?dataId=15079304&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 과세금액 and 3 other fieldsHigh correlation
과세금액 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
비과세건수 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
비과세금액 is highly overall correlated with 과세건수 and 3 other fieldsHigh correlation
세목명 is highly overall correlated with 과세건수 and 2 other fieldsHigh correlation
과세건수 has 9 (23.1%) zerosZeros
과세금액 has 9 (23.1%) zerosZeros
비과세건수 has 15 (38.5%) zerosZeros
비과세금액 has 16 (41.0%) zerosZeros

Reproduction

Analysis started2024-01-28 16:13:28.601744
Analysis finished2024-01-28 16:13:30.103884
Duration1.5 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
인천광역시
39 

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 39
100.0%

Length

2024-01-29T01:13:30.159686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:13:30.236627image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
인천광역시 39
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
중구
39 

Length

Max length2
Median length2
Mean length2
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
중구 39
100.0%

Length

2024-01-29T01:13:30.326529image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:13:30.407536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
중구 39
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
28110
39 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row28110
2nd row28110
3rd row28110
4th row28110
5th row28110

Common Values

ValueCountFrequency (%)
28110 39
100.0%

Length

2024-01-29T01:13:30.485897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:13:30.561510image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
28110 39
100.0%

과세년도
Categorical

Distinct3
Distinct (%)7.7%
Missing0
Missing (%)0.0%
Memory size444.0 B
2020
13 
2021
13 
2022
13 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2020
2nd row2020
3rd row2020
4th row2020
5th row2020

Common Values

ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

Length

2024-01-29T01:13:30.632694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-01-29T01:13:30.709115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 13
33.3%
2021 13
33.3%
2022 13
33.3%

세목명
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)33.3%
Missing0
Missing (%)0.0%
Memory size444.0 B
취득세
3 
등록세
3 
주민세
3 
재산세
3 
자동차세
3 
Other values (8)
24 

Length

Max length7
Median length5
Mean length4.1538462
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

2024-01-29T01:13:30.804232image/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%

과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct30
Distinct (%)76.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean79154.59
Minimum0
Maximum409139
Zeros9
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-29T01:13:30.905939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median67912
Q3119078
95-th percentile371021.8
Maximum409139
Range409139
Interquartile range (IQR)119075

Descriptive statistics

Standard deviation103269.39
Coefficient of variation (CV)1.3046544
Kurtosis4.3061653
Mean79154.59
Median Absolute Deviation (MAD)65621
Skewness2.0396882
Sum3087029
Variance1.0664566 × 1010
MonotonicityNot monotonic
2024-01-29T01:13:31.005373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
0 9
23.1%
3 2
 
5.1%
31407 1
 
2.6%
123226 1
 
2.6%
409139 1
 
2.6%
98290 1
 
2.6%
133533 1
 
2.6%
94217 1
 
2.6%
7 1
 
2.6%
106 1
 
2.6%
Other values (20) 20
51.3%
ValueCountFrequency (%)
0 9
23.1%
3 2
 
5.1%
6 1
 
2.6%
7 1
 
2.6%
20 1
 
2.6%
106 1
 
2.6%
29879 1
 
2.6%
30214 1
 
2.6%
31407 1
 
2.6%
67487 1
 
2.6%
ValueCountFrequency (%)
409139 1
2.6%
380362 1
2.6%
369984 1
2.6%
139342 1
2.6%
133533 1
2.6%
127553 1
2.6%
123226 1
2.6%
121459 1
2.6%
120646 1
2.6%
119661 1
2.6%

과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)79.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.1257248 × 1010
Minimum0
Maximum2.4403146 × 1011
Zeros9
Zeros (%)23.1%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-29T01:13:31.104058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12.48127 × 108
median1.316677 × 1010
Q34.5538203 × 1010
95-th percentile1.4497593 × 1011
Maximum2.4403146 × 1011
Range2.4403146 × 1011
Interquartile range (IQR)4.5290076 × 1010

Descriptive statistics

Standard deviation6.3041438 × 1010
Coefficient of variation (CV)1.5280088
Kurtosis3.1019969
Mean4.1257248 × 1010
Median Absolute Deviation (MAD)1.316677 × 1010
Skewness1.8967334
Sum1.6090327 × 1012
Variance3.9742229 × 1021
MonotonicityNot monotonic
2024-01-29T01:13:31.205668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 9
23.1%
131502535000 1
 
2.6%
48344497000 1
 
2.6%
115296334000 1
 
2.6%
16262713000 1
 
2.6%
11998687000 1
 
2.6%
2575693000 1
 
2.6%
5945555000 1
 
2.6%
13905334000 1
 
2.6%
135711109000 1
 
2.6%
Other values (21) 21
53.8%
ValueCountFrequency (%)
0 9
23.1%
236254000 1
 
2.6%
260000000 1
 
2.6%
1169000000 1
 
2.6%
1392504000 1
 
2.6%
2575693000 1
 
2.6%
5945555000 1
 
2.6%
9672465000 1
 
2.6%
11486880000 1
 
2.6%
11998687000 1
 
2.6%
ValueCountFrequency (%)
244031465000 1
2.6%
228359288000 1
2.6%
135711109000 1
2.6%
131502535000 1
2.6%
127771506000 1
2.6%
123727441000 1
2.6%
115296334000 1
2.6%
101281686000 1
2.6%
79989967000 1
2.6%
48344497000 1
2.6%

비과세건수
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5915.3077
Minimum0
Maximum47709
Zeros15
Zeros (%)38.5%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-29T01:13:31.325383image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median6
Q37442.5
95-th percentile34189.9
Maximum47709
Range47709
Interquartile range (IQR)7442.5

Descriptive statistics

Standard deviation11121.586
Coefficient of variation (CV)1.8801365
Kurtosis7.0573323
Mean5915.3077
Median Absolute Deviation (MAD)6
Skewness2.6760254
Sum230697
Variance1.2368967 × 108
MonotonicityNot monotonic
2024-01-29T01:13:31.434235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 15
38.5%
2 2
 
5.1%
5964 1
 
2.6%
3217 1
 
2.6%
83 1
 
2.6%
3588 1
 
2.6%
9073 1
 
2.6%
14139 1
 
2.6%
47709 1
 
2.6%
10793 1
 
2.6%
Other values (14) 14
35.9%
ValueCountFrequency (%)
0 15
38.5%
2 2
 
5.1%
3 1
 
2.6%
5 1
 
2.6%
6 1
 
2.6%
83 1
 
2.6%
2195 1
 
2.6%
2928 1
 
2.6%
3160 1
 
2.6%
3217 1
 
2.6%
ValueCountFrequency (%)
47709 1
2.6%
40642 1
2.6%
33473 1
2.6%
14139 1
2.6%
13141 1
2.6%
12077 1
2.6%
10793 1
2.6%
9073 1
2.6%
9044 1
2.6%
8921 1
2.6%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct24
Distinct (%)61.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.9989954 × 109
Minimum0
Maximum4.9074863 × 1010
Zeros16
Zeros (%)41.0%
Negative0
Negative (%)0.0%
Memory size483.0 B
2024-01-29T01:13:31.775604image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median3413000
Q35.238535 × 108
95-th percentile4.4307972 × 1010
Maximum4.9074863 × 1010
Range4.9074863 × 1010
Interquartile range (IQR)5.238535 × 108

Descriptive statistics

Standard deviation1.301847 × 1010
Coefficient of variation (CV)2.6042173
Kurtosis6.7070912
Mean4.9989954 × 109
Median Absolute Deviation (MAD)3413000
Skewness2.7700203
Sum1.9496082 × 1011
Variance1.6948057 × 1020
MonotonicityNot monotonic
2024-01-29T01:13:31.902588image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=24)
ValueCountFrequency (%)
0 16
41.0%
16366126000 1
 
2.6%
117716000 1
 
2.6%
20000 1
 
2.6%
736267000 1
 
2.6%
67411000 1
 
2.6%
526468000 1
 
2.6%
49074863000 1
 
2.6%
52825000 1
 
2.6%
9081000 1
 
2.6%
Other values (14) 14
35.9%
ValueCountFrequency (%)
0 16
41.0%
1000 1
 
2.6%
20000 1
 
2.6%
3394000 1
 
2.6%
3413000 1
 
2.6%
9081000 1
 
2.6%
39181000 1
 
2.6%
52825000 1
 
2.6%
67411000 1
 
2.6%
82170000 1
 
2.6%
ValueCountFrequency (%)
49074863000 1
2.6%
47000775000 1
2.6%
44008772000 1
2.6%
17573803000 1
2.6%
16782505000 1
2.6%
16366126000 1
2.6%
736267000 1
2.6%
689485000 1
2.6%
680786000 1
2.6%
526468000 1
2.6%

데이터기준일
Date

CONSTANT 

Distinct1
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size444.0 B
Minimum2022-12-31 00:00:00
Maximum2022-12-31 00:00:00
2024-01-29T01:13:31.991556image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:32.057683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Interactions

2024-01-29T01:13:29.619242image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:28.838896image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.089468image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.365775image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.678910image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:28.894566image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.155897image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.422790image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.746824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:28.966709image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.221735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.489004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.811090image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.029764image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.302140image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-01-29T01:13:29.551788image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-01-29T01:13:32.113574image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명과세건수과세금액비과세건수비과세금액
과세년도1.0000.0000.0000.0000.0000.000
세목명0.0001.0000.9440.9110.7930.868
과세건수0.0000.9441.0000.6770.4490.419
과세금액0.0000.9110.6771.0000.7200.803
비과세건수0.0000.7930.4490.7201.0000.972
비과세금액0.0000.8680.4190.8030.9721.000
2024-01-29T01:13:32.190292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도
세목명1.0000.000
과세년도0.0001.000
2024-01-29T01:13:32.260742image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세건수과세금액비과세건수비과세금액과세년도세목명
과세건수1.0000.7270.6420.5440.0000.756
과세금액0.7271.0000.5770.5820.0000.665
비과세건수0.6420.5771.0000.9090.0000.481
비과세금액0.5440.5820.9091.0000.0000.617
과세년도0.0000.0000.0000.0001.0000.000
세목명0.7560.6650.4810.6170.0001.000

Missing values

2024-01-29T01:13:29.916319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-01-29T01:13:30.057308image/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
0인천광역시중구281102020취득세314071315025350005964163661260002022-12-31
1인천광역시중구281102020등록세00234130002022-12-31
2인천광역시중구281102020주민세67912187324230009044821700002022-12-31
3인천광역시중구281102020재산세12064612777150600033473440087720002022-12-31
4인천광역시중구281102020자동차세11966112844673000120775212390002022-12-31
5인천광역시중구281102020레저세201392504000002022-12-31
6인천광역시중구281102020담배소비세00002022-12-31
7인천광역시중구281102020지방소비세31169000000002022-12-31
8인천광역시중구281102020등록면허세84991967246500021951033520002022-12-31
9인천광역시중구281102020도시계획세00002022-12-31
시도명시군구명자치단체코드과세년도세목명과세건수과세금액비과세건수비과세금액데이터기준일
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