Overview

Dataset statistics

Number of variables9
Number of observations70
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.3 KiB
Average record size in memory77.9 B

Variable types

Categorical6
Numeric3

Dataset

Description미환급 유형별 미환급금 현황 및 연간 누적률 제공에 대한 데이터로 세목명, 과세년도, 미환급유형, 납세자유형, 당해미환급건수,당해미환급금액
URLhttps://www.data.go.kr/data/15078409/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
당해미환급건수 is highly overall correlated with 당해미환급금액High correlation
당해미환급금액 is highly overall correlated with 당해미환급건수High correlation
당해미환급금액 has unique valuesUnique

Reproduction

Analysis started2023-12-11 22:57:55.965413
Analysis finished2023-12-11 22:57:57.000560
Duration1.04 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size692.0 B
경기도
70 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경기도 70
100.0%

Length

2023-12-12T07:57:57.066032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:57.159222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경기도 70
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size692.0 B
여주시
70 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
여주시 70
100.0%

Length

2023-12-12T07:57:57.235774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:57.307316image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여주시 70
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.4%
Missing0
Missing (%)0.0%
Memory size692.0 B
41670
70 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row41670
2nd row41670
3rd row41670
4th row41670
5th row41670

Common Values

ValueCountFrequency (%)
41670 70
100.0%

Length

2023-12-12T07:57:57.386266image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:57.463323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41670 70
100.0%

세목명
Categorical

Distinct6
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size692.0 B
자동차세
26 
지방소득세
20 
재산세
11 
주민세
6 
등록면허세
4 

Length

Max length5
Median length4
Mean length4.0571429
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row자동차세
2nd row자동차세
3rd row자동차세
4th row재산세
5th row재산세

Common Values

ValueCountFrequency (%)
자동차세 26
37.1%
지방소득세 20
28.6%
재산세 11
15.7%
주민세 6
 
8.6%
등록면허세 4
 
5.7%
취득세 3
 
4.3%

Length

2023-12-12T07:57:57.550976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:57.650128image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
자동차세 26
37.1%
지방소득세 20
28.6%
재산세 11
15.7%
주민세 6
 
8.6%
등록면허세 4
 
5.7%
취득세 3
 
4.3%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.5857
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T07:57:57.735494image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2017
5-th percentile2017
Q12018
median2020
Q32021
95-th percentile2022
Maximum2022
Range5
Interquartile range (IQR)3

Descriptive statistics

Standard deviation1.5463173
Coefficient of variation (CV)0.00076566066
Kurtosis-0.94714221
Mean2019.5857
Median Absolute Deviation (MAD)1
Skewness-0.1895201
Sum141371
Variance2.3910973
MonotonicityIncreasing
2023-12-12T07:57:57.818920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2020 19
27.1%
2021 13
18.6%
2019 11
15.7%
2018 10
14.3%
2017 9
12.9%
2022 8
11.4%
ValueCountFrequency (%)
2017 9
12.9%
2018 10
14.3%
2019 11
15.7%
2020 19
27.1%
2021 13
18.6%
2022 8
11.4%
ValueCountFrequency (%)
2022 8
11.4%
2021 13
18.6%
2020 19
27.1%
2019 11
15.7%
2018 10
14.3%
2017 9
12.9%

미환급유형
Categorical

Distinct11
Distinct (%)15.7%
Missing0
Missing (%)0.0%
Memory size692.0 B
신규
42 
사망
9 
주소불명
5 
주소불명
 
4
신규
 
2
Other values (6)
8 

Length

Max length9
Median length2
Mean length2.8428571
Min length2

Unique

Unique4 ?
Unique (%)5.7%

Sample

1st row사망
2nd row신규
3rd row신규
4th row신규
5th row신규

Common Values

ValueCountFrequency (%)
신규 42
60.0%
사망 9
 
12.9%
주소불명 5
 
7.1%
주소불명 4
 
5.7%
신규 2
 
2.9%
신규 2
 
2.9%
거주불명등록 2
 
2.9%
송달분미수령 1
 
1.4%
폐업 또는 부도 1
 
1.4%
주소불명 1
 
1.4%

Length

2023-12-12T07:57:57.916707image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
신규 46
62.2%
주소불명 10
 
13.5%
사망 9
 
12.2%
거주불명등록 2
 
2.7%
폐업 2
 
2.7%
또는 2
 
2.7%
송달분미수령 1
 
1.4%
부도 1
 
1.4%
반송 1
 
1.4%

납세자유형
Categorical

Distinct3
Distinct (%)4.3%
Missing0
Missing (%)0.0%
Memory size692.0 B
개인
41 
법인
24 
사망자
5 

Length

Max length3
Median length2
Mean length2.0714286
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row개인
2nd row개인
3rd row법인
4th row개인
5th row법인

Common Values

ValueCountFrequency (%)
개인 41
58.6%
법인 24
34.3%
사망자 5
 
7.1%

Length

2023-12-12T07:57:58.011766image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T07:57:58.089493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
개인 41
58.6%
법인 24
34.3%
사망자 5
 
7.1%

당해미환급건수
Real number (ℝ)

HIGH CORRELATION 

Distinct34
Distinct (%)48.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean69.857143
Minimum1
Maximum771
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T07:57:58.172317image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median5
Q320.75
95-th percentile529.4
Maximum771
Range770
Interquartile range (IQR)19.75

Descriptive statistics

Standard deviation173.53906
Coefficient of variation (CV)2.4841992
Kurtosis9.2112674
Mean69.857143
Median Absolute Deviation (MAD)4
Skewness3.1488807
Sum4890
Variance30115.805
MonotonicityNot monotonic
2023-12-12T07:57:58.274096image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
1 19
27.1%
2 8
 
11.4%
3 6
 
8.6%
16 3
 
4.3%
4 2
 
2.9%
17 2
 
2.9%
8 2
 
2.9%
6 2
 
2.9%
57 1
 
1.4%
104 1
 
1.4%
Other values (24) 24
34.3%
ValueCountFrequency (%)
1 19
27.1%
2 8
11.4%
3 6
 
8.6%
4 2
 
2.9%
6 2
 
2.9%
7 1
 
1.4%
8 2
 
2.9%
9 1
 
1.4%
10 1
 
1.4%
13 1
 
1.4%
ValueCountFrequency (%)
771 1
1.4%
764 1
1.4%
657 1
1.4%
623 1
1.4%
415 1
1.4%
354 1
1.4%
198 1
1.4%
145 1
1.4%
120 1
1.4%
110 1
1.4%

당해미환급금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct70
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1613229.7
Minimum100
Maximum17888470
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size762.0 B
2023-12-12T07:57:58.383314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile2827
Q138090
median183670
Q3810440
95-th percentile9858542.5
Maximum17888470
Range17888370
Interquartile range (IQR)772350

Descriptive statistics

Standard deviation3688034.6
Coefficient of variation (CV)2.2861187
Kurtosis10.091147
Mean1613229.7
Median Absolute Deviation (MAD)178805
Skewness3.2119191
Sum1.1292608 × 108
Variance1.3601599 × 1013
MonotonicityNot monotonic
2023-12-12T07:57:58.528976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
8990 1
 
1.4%
47470 1
 
1.4%
9920 1
 
1.4%
17888470 1
 
1.4%
2061710 1
 
1.4%
216550 1
 
1.4%
151430 1
 
1.4%
3975010 1
 
1.4%
53510 1
 
1.4%
108790 1
 
1.4%
Other values (60) 60
85.7%
ValueCountFrequency (%)
100 1
1.4%
1680 1
1.4%
2280 1
1.4%
2530 1
1.4%
3190 1
1.4%
3210 1
1.4%
4650 1
1.4%
5080 1
1.4%
6400 1
1.4%
6850 1
1.4%
ValueCountFrequency (%)
17888470 1
1.4%
15552520 1
1.4%
14681510 1
1.4%
12934720 1
1.4%
6098770 1
1.4%
5885020 1
1.4%
5103420 1
1.4%
4576370 1
1.4%
3975010 1
1.4%
3692440 1
1.4%

Interactions

2023-12-12T07:57:56.611878image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.202379image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.403114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.676680image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.262644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.471439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.748888image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.332414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T07:57:56.540223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T07:57:58.619412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액
세목명1.0000.0000.0000.0000.0000.000
과세년도0.0001.0000.0000.0900.0690.167
미환급유형0.0000.0001.0000.6510.4360.582
납세자유형0.0000.0900.6511.0000.0000.000
당해미환급건수0.0000.0690.4360.0001.0000.970
당해미환급금액0.0000.1670.5820.0000.9701.000
2023-12-12T07:57:58.705684image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명납세자유형미환급유형
세목명1.0000.0000.000
납세자유형0.0001.0000.456
미환급유형0.0000.4561.000
2023-12-12T07:57:58.806061image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도당해미환급건수당해미환급금액세목명미환급유형납세자유형
과세년도1.0000.2320.3140.0000.0000.000
당해미환급건수0.2321.0000.8350.0000.2210.000
당해미환급금액0.3140.8351.0000.0000.3210.000
세목명0.0000.0000.0001.0000.0000.000
미환급유형0.0000.2210.3210.0001.0000.456
납세자유형0.0000.0000.0000.0000.4561.000

Missing values

2023-12-12T07:57:56.853299image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T07:57:56.950661image/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

시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액
0경기도여주시41670자동차세2017사망개인18990
1경기도여주시41670자동차세2017신규개인701645810
2경기도여주시41670자동차세2017신규법인16156060
3경기도여주시41670재산세2017신규개인15526020
4경기도여주시41670재산세2017신규법인22280
5경기도여주시41670주민세2017신규개인26400
6경기도여주시41670지방소득세2017신규개인21954050
7경기도여주시41670지방소득세2017신규법인1100
8경기도여주시41670취득세2017신규개인16850
9경기도여주시41670자동차세2018사망개인2100360
시도명시군구명자치단체코드세목명과세년도미환급유형납세자유형당해미환급건수당해미환급금액
60경기도여주시41670지방소득세2021거주불명등록개인137080
61경기도여주시41670지방소득세2021사망사망자277800
62경기도여주시41670자동차세2022신규법인1104576370
63경기도여주시41670자동차세2022신규개인76415552520
64경기도여주시41670자동차세2022거주불명등록개인1769020
65경기도여주시41670재산세2022신규개인1985103420
66경기도여주시41670지방소득세2022신규법인182267950
67경기도여주시41670지방소득세2022신규개인4156098770
68경기도여주시41670지방소득세2022주소불명개인9337300
69경기도여주시41670취득세2022신규개인4689670