Overview

Dataset statistics

Number of variables9
Number of observations34
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.7 KiB
Average record size in memory81.9 B

Variable types

Categorical5
Numeric4

Dataset

Description지방세 금액 중 비과세·감면액이 차지하는 비율
Author경상남도 창원시
URLhttps://bigdata.gyeongnam.go.kr/index.gn?menuCd=DOM_000000114002001000&publicdatapk=15078691

Alerts

시도명 has constant value ""Constant
과세년도 has constant value ""Constant
시군구명 is highly overall correlated with 자치단체코드High correlation
자치단체코드 is highly overall correlated with 시군구명High correlation
비과세금액 is highly overall correlated with 감면금액 and 2 other fieldsHigh correlation
감면금액 is highly overall correlated with 비과세금액 and 1 other fieldsHigh correlation
부과금액 is highly overall correlated with 비과세금액 and 1 other fieldsHigh correlation
비과세감면율 is highly overall correlated with 비과세금액High correlation
감면금액 has unique valuesUnique
부과금액 has unique valuesUnique
비과세금액 has 4 (11.8%) zerosZeros

Reproduction

Analysis started2023-12-10 23:00:32.275822
Analysis finished2023-12-10 23:00:34.012021
Duration1.74 second
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
창원시
34 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
창원시 34
100.0%

Length

2023-12-11T08:00:34.084673image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:34.183998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창원시 34
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
의창구
7 
성산구
7 
마산합포구
7 
마산회원구
7 
진해구
6 

Length

Max length5
Median length3
Mean length3.8235294
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의창구 7
20.6%
성산구 7
20.6%
마산합포구 7
20.6%
마산회원구 7
20.6%
진해구 6
17.6%

Length

2023-12-11T08:00:34.314445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:34.430892image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의창구 7
20.6%
성산구 7
20.6%
마산합포구 7
20.6%
마산회원구 7
20.6%
진해구 6
17.6%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Memory size404.0 B
48121
7 
48123
7 
48125
7 
48127
7 
48129
6 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row48121
2nd row48121
3rd row48121
4th row48121
5th row48121

Common Values

ValueCountFrequency (%)
48121 7
20.6%
48123 7
20.6%
48125 7
20.6%
48127 7
20.6%
48129 6
17.6%

Length

2023-12-11T08:00:34.536202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:34.656340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48121 7
20.6%
48123 7
20.6%
48125 7
20.6%
48127 7
20.6%
48129 6
17.6%

세목명
Categorical

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

Length

Max length7
Median length3
Mean length4.0294118
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

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

Length

2023-12-11T08:00:34.794655image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:34.916532image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
재산세 5
14.7%
주민세 5
14.7%
취득세 5
14.7%
자동차세 5
14.7%
등록면허세 5
14.7%
지역자원시설세 5
14.7%
등록세 4
11.8%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size404.0 B
2020
34 

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 34
100.0%

Length

2023-12-11T08:00:35.035460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:35.132583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 34
100.0%

비과세금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)91.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.2267803 × 109
Minimum0
Maximum2.9084066 × 1010
Zeros4
Zeros (%)11.8%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T08:00:35.221694image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q114137250
median1.303435 × 108
Q38.8998625 × 108
95-th percentile1.911282 × 1010
Maximum2.9084066 × 1010
Range2.9084066 × 1010
Interquartile range (IQR)8.75849 × 108

Descriptive statistics

Standard deviation7.1477414 × 109
Coefficient of variation (CV)2.2151311
Kurtosis5.8538646
Mean3.2267803 × 109
Median Absolute Deviation (MAD)1.303435 × 108
Skewness2.5266083
Sum1.0971053 × 1011
Variance5.1090207 × 1019
MonotonicityNot monotonic
2023-12-11T08:00:35.357640image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 4
 
11.8%
23118351000 1
 
2.9%
576106000 1
 
2.9%
26813000 1
 
2.9%
89647000 1
 
2.9%
923261000 1
 
2.9%
13250000 1
 
2.9%
29084066000 1
 
2.9%
310520000 1
 
2.9%
16799000 1
 
2.9%
Other values (21) 21
61.8%
ValueCountFrequency (%)
0 4
11.8%
200000 1
 
2.9%
4780000 1
 
2.9%
7826000 1
 
2.9%
9756000 1
 
2.9%
13250000 1
 
2.9%
16799000 1
 
2.9%
26813000 1
 
2.9%
29426000 1
 
2.9%
36499000 1
 
2.9%
ValueCountFrequency (%)
29084066000 1
2.9%
23118351000 1
2.9%
16955996000 1
2.9%
13249587000 1
2.9%
11659546000 1
2.9%
7102712000 1
2.9%
2425615000 1
2.9%
1283602000 1
2.9%
923261000 1
2.9%
790162000 1
2.9%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.8017235 × 109
Minimum1099000
Maximum2.1209509 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T08:00:35.507895image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1099000
5-th percentile13885050
Q11.0720725 × 108
median2.837965 × 108
Q31.9870585 × 109
95-th percentile1.7808587 × 1010
Maximum2.1209509 × 1010
Range2.120841 × 1010
Interquartile range (IQR)1.8798512 × 109

Descriptive statistics

Standard deviation5.5597242 × 109
Coefficient of variation (CV)1.9843943
Kurtosis5.3005778
Mean2.8017235 × 109
Median Absolute Deviation (MAD)2.71408 × 108
Skewness2.4898318
Sum9.52586 × 1010
Variance3.0910533 × 1019
MonotonicityNot monotonic
2023-12-11T08:00:35.641174image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
19118000 1
 
2.9%
52189000 1
 
2.9%
163237000 1
 
2.9%
1099000 1
 
2.9%
2079520000 1
 
2.9%
248834000 1
 
2.9%
21209509000 1
 
2.9%
975075000 1
 
2.9%
166784000 1
 
2.9%
699750000 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
1099000 1
2.9%
7400000 1
2.9%
17377000 1
2.9%
19118000 1
2.9%
44916000 1
2.9%
52189000 1
2.9%
57918000 1
2.9%
71093000 1
2.9%
98219000 1
2.9%
134172000 1
2.9%
ValueCountFrequency (%)
21209509000 1
2.9%
18592848000 1
2.9%
17386293000 1
2.9%
9750094000 1
2.9%
6903452000 1
2.9%
3642141000 1
2.9%
3410435000 1
2.9%
2781971000 1
2.9%
2079520000 1
2.9%
1709674000 1
2.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct34
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.9199855 × 1010
Minimum24146000
Maximum1.94051 × 1011
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T08:00:35.768343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum24146000
5-th percentile34560450
Q13.9190342 × 109
median1.1703678 × 1010
Q33.6581184 × 1010
95-th percentile8.7112381 × 1010
Maximum1.94051 × 1011
Range1.9402685 × 1011
Interquartile range (IQR)3.266215 × 1010

Descriptive statistics

Standard deviation3.9224139 × 1010
Coefficient of variation (CV)1.3432991
Kurtosis8.7983185
Mean2.9199855 × 1010
Median Absolute Deviation (MAD)1.1669266 × 1010
Skewness2.605969
Sum9.9279507 × 1011
Variance1.5385331 × 1021
MonotonicityNot monotonic
2023-12-11T08:00:35.899340image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=34)
ValueCountFrequency (%)
79687000 1
 
2.9%
3828752000 1
 
2.9%
4083618000 1
 
2.9%
24146000 1
 
2.9%
23404954000 1
 
2.9%
5169039000 1
 
2.9%
65915545000 1
 
2.9%
36178523000 1
 
2.9%
3555987000 1
 
2.9%
36651274000 1
 
2.9%
Other values (24) 24
70.6%
ValueCountFrequency (%)
24146000 1
2.9%
33915000 1
2.9%
34908000 1
2.9%
79687000 1
2.9%
3295955000 1
2.9%
3555987000 1
2.9%
3563690000 1
2.9%
3828752000 1
2.9%
3864173000 1
2.9%
4083618000 1
2.9%
ValueCountFrequency (%)
194051000000 1
2.9%
103501000000 1
2.9%
78287740000 1
2.9%
68493602000 1
2.9%
65915545000 1
2.9%
60229103000 1
2.9%
51429167000 1
2.9%
40198532000 1
2.9%
36651274000 1
2.9%
36370914000 1
2.9%

비과세감면율
Real number (ℝ)

HIGH CORRELATION 

Distinct22
Distinct (%)64.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17.529412
Minimum1
Maximum85
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size438.0 B
2023-12-11T08:00:36.021958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q14
median10.5
Q321.25
95-th percentile59.7
Maximum85
Range84
Interquartile range (IQR)17.25

Descriptive statistics

Standard deviation20.825735
Coefficient of variation (CV)1.1880453
Kurtosis2.6104075
Mean17.529412
Median Absolute Deviation (MAD)7.5
Skewness1.7786821
Sum596
Variance433.71123
MonotonicityNot monotonic
2023-12-11T08:00:36.121195image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
2 4
 
11.8%
5 4
 
11.8%
3 3
 
8.8%
12 3
 
8.8%
13 2
 
5.9%
4 2
 
5.9%
61 1
 
2.9%
11 1
 
2.9%
85 1
 
2.9%
33 1
 
2.9%
Other values (12) 12
35.3%
ValueCountFrequency (%)
1 1
 
2.9%
2 4
11.8%
3 3
8.8%
4 2
5.9%
5 4
11.8%
6 1
 
2.9%
8 1
 
2.9%
10 1
 
2.9%
11 1
 
2.9%
12 3
8.8%
ValueCountFrequency (%)
85 1
2.9%
61 1
2.9%
59 1
2.9%
52 1
2.9%
50 1
2.9%
34 1
2.9%
33 1
2.9%
24 1
2.9%
22 1
2.9%
19 1
2.9%

Interactions

2023-12-11T08:00:33.513582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:32.563114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:32.898786image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.193476image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.587080image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:32.651232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:32.973781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.277988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.652430image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:32.726032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.046683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.353132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.741091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:32.817711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.129240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:33.440646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:00:36.192633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명비과세금액감면금액부과금액비과세감면율
시군구명1.0001.0000.0000.0000.0000.0000.000
자치단체코드1.0001.0000.0000.0000.0000.0000.000
세목명0.0000.0001.0000.4190.5830.7650.668
비과세금액0.0000.0000.4191.0000.7500.7240.841
감면금액0.0000.0000.5830.7501.0000.8460.547
부과금액0.0000.0000.7650.7240.8461.0000.522
비과세감면율0.0000.0000.6680.8410.5470.5221.000
2023-12-11T08:00:36.286893image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명
시군구명1.0001.0000.000
자치단체코드1.0001.0000.000
세목명0.0000.0001.000
2023-12-11T08:00:36.388990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율시군구명자치단체코드세목명
비과세금액1.0000.6800.6980.5180.0000.0000.249
감면금액0.6801.0000.8950.3210.0000.0000.390
부과금액0.6980.8951.0000.1260.0000.0000.363
비과세감면율0.5180.3210.1261.0000.0000.0000.423
시군구명0.0000.0000.0000.0001.0001.0000.000
자치단체코드0.0000.0000.0000.0001.0001.0000.000
세목명0.2490.3900.3630.4230.0000.0001.000

Missing values

2023-12-11T08:00:33.838911image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:00:33.965644image/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
0창원시의창구48121등록세20200191180007968700024
1창원시의창구48121재산세20202311835100036421410005142916700052
2창원시의창구48121주민세2020782600043805200076549230006
3창원시의창구48121취득세202071027120001859284800019405100000013
4창원시의창구48121자동차세20201001960001688183000350582260005
5창원시의창구48121등록면허세202036499000238490000102638010003
6창원시의창구48121지역자원시설세2020633647000174677000668902600012
7창원시성산구48123등록세20200173770003490800050
8창원시성산구48123재산세20201695599600034104350006022910300034
9창원시성산구48123주민세2020200000318759000263514510001
시도명시군구명자치단체코드세목명과세년도비과세금액감면금액부과금액비과세감면율
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