Overview

Dataset statistics

Number of variables9
Number of observations35
Missing cells5
Missing cells (%)1.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory2.8 KiB
Average record size in memory81.8 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 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 5 (14.3%) missing valuesMissing
감면금액 has unique valuesUnique
부과금액 has 5 (14.3%) zerosZeros
비과세감면율 has 5 (14.3%) zerosZeros

Reproduction

Analysis started2023-12-10 23:00:27.035884
Analysis finished2023-12-10 23:00:28.678113
Duration1.64 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
창원시
35 

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 35
100.0%

Length

2023-12-11T08:00:28.735434image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:28.824932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
창원시 35
100.0%

시군구명
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
의창구
7 
성산구
7 
마산합포구
7 
마산회원구
7 
진해구
7 

Length

Max length5
Median length3
Mean length3.8
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
의창구 7
20.0%
성산구 7
20.0%
마산합포구 7
20.0%
마산회원구 7
20.0%
진해구 7
20.0%

Length

2023-12-11T08:00:28.926770image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:29.039250image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
의창구 7
20.0%
성산구 7
20.0%
마산합포구 7
20.0%
마산회원구 7
20.0%
진해구 7
20.0%

자치단체코드
Categorical

HIGH CORRELATION 

Distinct5
Distinct (%)14.3%
Missing0
Missing (%)0.0%
Memory size412.0 B
48121
7 
48123
7 
48125
7 
48127
7 
48129
7 

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
20.0%
48123 7
20.0%
48125 7
20.0%
48127 7
20.0%
48129 7
20.0%

Length

2023-12-11T08:00:29.141602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:29.231927image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
48121 7
20.0%
48123 7
20.0%
48125 7
20.0%
48127 7
20.0%
48129 7
20.0%

세목명
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)20.0%
Missing0
Missing (%)0.0%
Memory size412.0 B
등록세
5 
재산세
5 
주민세
5 
취득세
5 
자동차세
5 
Other values (2)
10 

Length

Max length7
Median length3
Mean length4
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
등록세 5
14.3%
재산세 5
14.3%
주민세 5
14.3%
취득세 5
14.3%
자동차세 5
14.3%
등록면허세 5
14.3%
지역자원시설세 5
14.3%

Length

2023-12-11T08:00:29.340385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:29.454503image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록세 5
14.3%
재산세 5
14.3%
주민세 5
14.3%
취득세 5
14.3%
자동차세 5
14.3%
등록면허세 5
14.3%
지역자원시설세 5
14.3%

과세년도
Categorical

CONSTANT 

Distinct1
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size412.0 B
2021
35 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2021
2nd row2021
3rd row2021
4th row2021
5th row2021

Common Values

ValueCountFrequency (%)
2021 35
100.0%

Length

2023-12-11T08:00:29.566674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T08:00:29.655783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2021 35
100.0%

비과세금액
Real number (ℝ)

HIGH CORRELATION  MISSING 

Distinct30
Distinct (%)100.0%
Missing5
Missing (%)14.3%
Infinite0
Infinite (%)0.0%
Mean4.1436098 × 109
Minimum2600000
Maximum3.079721 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T08:00:29.754355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2600000
5-th percentile5288950
Q143440250
median2.188565 × 108
Q34.3348538 × 109
95-th percentile2.2141856 × 1010
Maximum3.079721 × 1010
Range3.079461 × 1010
Interquartile range (IQR)4.2914135 × 109

Descriptive statistics

Standard deviation8.0199123 × 109
Coefficient of variation (CV)1.9354893
Kurtosis4.5561436
Mean4.1436098 × 109
Median Absolute Deviation (MAD)2.119665 × 108
Skewness2.2572244
Sum1.243083 × 1011
Variance6.4318994 × 1019
MonotonicityNot monotonic
2023-12-11T08:00:29.857968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
46981000 1
 
2.9%
572321000 1
 
2.9%
26395000 1
 
2.9%
88983000 1
 
2.9%
1361249000 1
 
2.9%
12379000 1
 
2.9%
30797210000 1
 
2.9%
313739000 1
 
2.9%
16650000 1
 
2.9%
81485000 1
 
2.9%
Other values (20) 20
57.1%
(Missing) 5
 
14.3%
ValueCountFrequency (%)
2600000 1
2.9%
5239000 1
2.9%
5350000 1
2.9%
8430000 1
2.9%
12379000 1
2.9%
16650000 1
2.9%
26395000 1
2.9%
42260000 1
2.9%
46981000 1
2.9%
81037000 1
2.9%
ValueCountFrequency (%)
30797210000 1
2.9%
25562281000 1
2.9%
17961336000 1
2.9%
13896928000 1
2.9%
11993172000 1
2.9%
6912000000 1
2.9%
6158914000 1
2.9%
5307004000 1
2.9%
1418403000 1
2.9%
1361249000 1
2.9%

감면금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct35
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.6253043 × 109
Minimum726000
Maximum2.2501881 × 1010
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T08:00:29.977414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum726000
5-th percentile4754500
Q141700000
median1.54309 × 108
Q31.949449 × 109
95-th percentile1.5509324 × 1010
Maximum2.2501881 × 1010
Range2.2501155 × 1010
Interquartile range (IQR)1.907749 × 109

Descriptive statistics

Standard deviation5.4977508 × 109
Coefficient of variation (CV)2.0941384
Kurtosis6.8378708
Mean2.6253043 × 109
Median Absolute Deviation (MAD)1.48857 × 108
Skewness2.7042728
Sum9.1885652 × 1010
Variance3.0225263 × 1019
MonotonicityNot monotonic
2023-12-11T08:00:30.088949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=35)
ValueCountFrequency (%)
5452000 1
 
2.9%
3978171000 1
 
2.9%
154309000 1
 
2.9%
153278000 1
 
2.9%
2052711000 1
 
2.9%
38127000 1
 
2.9%
13629797000 1
 
2.9%
956175000 1
 
2.9%
45903000 1
 
2.9%
156750000 1
 
2.9%
Other values (25) 25
71.4%
ValueCountFrequency (%)
726000 1
2.9%
3127000 1
2.9%
5452000 1
2.9%
16550000 1
2.9%
21975000 1
2.9%
24580000 1
2.9%
32669000 1
2.9%
38127000 1
2.9%
39997000 1
2.9%
43403000 1
2.9%
ValueCountFrequency (%)
22501881000 1
2.9%
19894888000 1
2.9%
13629797000 1
2.9%
9837947000 1
2.9%
6340727000 1
2.9%
3978171000 1
2.9%
2835213000 1
2.9%
2640044000 1
2.9%
2052711000 1
2.9%
1846187000 1
2.9%

부과금액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.7269736 × 1010
Minimum0
Maximum1.3035295 × 1011
Zeros5
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T08:00:30.201394image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.1158575 × 109
median9.733543 × 109
Q33.9741552 × 1010
95-th percentile8.3989051 × 1010
Maximum1.3035295 × 1011
Range1.3035295 × 1011
Interquartile range (IQR)3.5625694 × 1010

Descriptive statistics

Standard deviation3.2718276 × 1010
Coefficient of variation (CV)1.1998017
Kurtosis2.5325286
Mean2.7269736 × 1010
Median Absolute Deviation (MAD)9.733543 × 109
Skewness1.6235383
Sum9.5444077 × 1011
Variance1.0704856 × 1021
MonotonicityNot monotonic
2023-12-11T08:00:30.324835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0 5
 
14.3%
53340059000 1
 
2.9%
5546543000 1
 
2.9%
7721254000 1
 
2.9%
24459010000 1
 
2.9%
69613287000 1
 
2.9%
4080518000 1
 
2.9%
37955427000 1
 
2.9%
3618264000 1
 
2.9%
3424400000 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
0 5
14.3%
3424400000 1
 
2.9%
3618264000 1
 
2.9%
3720829000 1
 
2.9%
4080518000 1
 
2.9%
4151197000 1
 
2.9%
4472377000 1
 
2.9%
5546543000 1
 
2.9%
5855386000 1
 
2.9%
6571440000 1
 
2.9%
ValueCountFrequency (%)
130352947000 1
2.9%
115380514000 1
2.9%
70535567000 1
2.9%
69613287000 1
2.9%
65620678000 1
2.9%
61658005000 1
2.9%
53340059000 1
2.9%
45828297000 1
2.9%
41527676000 1
2.9%
37955427000 1
2.9%

비과세감면율
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct31
Distinct (%)88.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13.690286
Minimum0
Maximum86
Zeros5
Zeros (%)14.3%
Negative0
Negative (%)0.0%
Memory size447.0 B
2023-12-11T08:00:30.454558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q11.13
median3.31
Q316.165
95-th percentile60.266
Maximum86
Range86
Interquartile range (IQR)15.035

Descriptive statistics

Standard deviation21.189196
Coefficient of variation (CV)1.5477541
Kurtosis3.8427734
Mean13.690286
Median Absolute Deviation (MAD)3.31
Skewness2.0776891
Sum479.16
Variance448.98204
MonotonicityNot monotonic
2023-12-11T08:00:30.572126image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=31)
ValueCountFrequency (%)
0.0 5
 
14.3%
55.38 1
 
2.9%
11.37 1
 
2.9%
0.86 1
 
2.9%
3.31 1
 
2.9%
11.06 1
 
2.9%
1.37 1
 
2.9%
86.0 1
 
2.9%
13.0 1
 
2.9%
1.83 1
 
2.9%
Other values (21) 21
60.0%
ValueCountFrequency (%)
0.0 5
14.3%
0.08 1
 
2.9%
0.53 1
 
2.9%
0.86 1
 
2.9%
0.89 1
 
2.9%
1.37 1
 
2.9%
1.83 1
 
2.9%
2.04 1
 
2.9%
2.07 1
 
2.9%
2.32 1
 
2.9%
ValueCountFrequency (%)
86.0 1
2.9%
62.17 1
2.9%
59.45 1
2.9%
55.38 1
2.9%
33.73 1
2.9%
24.84 1
2.9%
23.75 1
2.9%
22.93 1
2.9%
19.33 1
2.9%
13.0 1
2.9%

Interactions

2023-12-11T08:00:28.171576image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.308409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.605311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.892036image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:28.241702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.384860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.682840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.965348image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:28.306600image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.461392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.752692image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:28.032361image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:28.375946image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.536661image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:27.829906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-11T08:00:28.105081image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-11T08:00:30.966742image/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
세목명0.0000.0001.0000.4890.7100.7100.897
비과세금액0.0000.0000.4891.0000.7950.9070.940
감면금액0.0000.0000.7100.7951.0000.8790.831
부과금액0.0000.0000.7100.9070.8791.0000.694
비과세감면율0.0000.0000.8970.9400.8310.6941.000
2023-12-11T08:00:31.070967image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
시군구명자치단체코드세목명
시군구명1.0001.0000.000
자치단체코드1.0001.0000.000
세목명0.0000.0001.000
2023-12-11T08:00:31.177834image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
비과세금액감면금액부과금액비과세감면율시군구명자치단체코드세목명
비과세금액1.0000.7740.5660.9350.0000.0000.286
감면금액0.7741.0000.8370.8430.0000.0000.317
부과금액0.5660.8371.0000.6720.0000.0000.468
비과세감면율0.9350.8430.6721.0000.0000.0000.529
시군구명0.0000.0000.0000.0001.0001.0000.000
자치단체코드0.0000.0000.0000.0001.0001.0000.000
세목명0.2860.3170.4680.5290.0000.0001.000

Missing values

2023-12-11T08:00:28.493584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T08:00:28.634279image/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
0창원시의창구48121등록세2021<NA>545200000.0
1창원시의창구48121재산세20212556228100039781710005334005900055.38
2창원시의창구48121주민세202153500003266900072057110000.53
3창원시의창구48121취득세202161589140002250188100011538051400024.84
4창원시의창구48121자동차세20211000930001551137000364669260004.53
5창원시의창구48121등록면허세2021523900021922900065714400003.42
6창원시의창구48121지역자원시설세2021670944000152470000721945600011.41
7창원시성산구48123등록세2021<NA>1655000000.0
8창원시성산구48123재산세20211796133600028352130006165800500033.73
9창원시성산구48123주민세2021260000021975000301082780000.08
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25창원시마산회원구48127자동차세202181485000956175000415276760002.5
26창원시마산회원구48127등록면허세2021166500004590300034244000001.83
27창원시마산회원구48127지역자원시설세2021313739000156750000361826400013.0
28창원시진해구48129등록세2021<NA>312700000.0
29창원시진해구48129재산세20213079721000018461870003795542700086.0
30창원시진해구48129주민세2021123790004340300040805180001.37
31창원시진해구48129취득세2021136124900063407270006961328700011.06
32창원시진해구48129자동차세202188983000719425000244590100003.31
33창원시진해구48129등록면허세2021263950003999700077212540000.86
34창원시진해구48129지역자원시설세202157232100058439000554654300011.37