Overview

Dataset statistics

Number of variables10
Number of observations248
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory21.0 KiB
Average record size in memory86.5 B

Variable types

Categorical5
Numeric5

Dataset

Description체납액 규모별, 세목별, 건수 및 금액, 연간 누적 건수 및 금액 등을 납세자 유형별로 제공함으로써 체납정책 수립시 기초자료로 활용하고자 합니다.
Author부산광역시 해운대구
URLhttps://www.data.go.kr/data/15078945/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
체납건수 is highly overall correlated with 누적체납건수 and 1 other fieldsHigh correlation
체납금액 is highly overall correlated with 누적체납금액 and 1 other fieldsHigh correlation
누적체납건수 is highly overall correlated with 체납건수 and 1 other fieldsHigh correlation
누적체납금액 is highly overall correlated with 체납건수 and 2 other fieldsHigh correlation
체납액구간 is highly overall correlated with 체납금액High correlation
체납금액 has unique valuesUnique

Reproduction

Analysis started2024-04-21 01:50:24.711194
Analysis finished2024-04-21 01:50:29.033445
Duration4.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
부산광역시
248 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산광역시 248
100.0%

Length

2024-04-21T10:50:29.100235image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:29.181823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산광역시 248
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
해운대구
248 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
해운대구 248
100.0%

Length

2024-04-21T10:50:29.275015image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:29.367356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
해운대구 248
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)0.4%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
26350
248 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row26350
2nd row26350
3rd row26350
4th row26350
5th row26350

Common Values

ValueCountFrequency (%)
26350 248
100.0%

Length

2024-04-21T10:50:29.461193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:29.615045image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
26350 248
100.0%

과세년도
Real number (ℝ)

Distinct6
Distinct (%)2.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2019.7944
Minimum2017
Maximum2022
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-21T10:50:29.722918image/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 deviation1.6647234
Coefficient of variation (CV)0.00082420441
Kurtosis-1.1404295
Mean2019.7944
Median Absolute Deviation (MAD)1
Skewness-0.24288706
Sum500909
Variance2.771304
MonotonicityIncreasing
2024-04-21T10:50:29.866408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=6)
ValueCountFrequency (%)
2021 49
19.8%
2022 49
19.8%
2020 47
19.0%
2019 39
15.7%
2018 33
13.3%
2017 31
12.5%
ValueCountFrequency (%)
2017 31
12.5%
2018 33
13.3%
2019 39
15.7%
2020 47
19.0%
2021 49
19.8%
2022 49
19.8%
ValueCountFrequency (%)
2022 49
19.8%
2021 49
19.8%
2020 47
19.0%
2019 39
15.7%
2018 33
13.3%
2017 31
12.5%

세목명
Categorical

Distinct7
Distinct (%)2.8%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
지방소득세
59 
재산세
53 
취득세
52 
주민세
35 
자동차세
24 
Other values (2)
25 

Length

Max length7
Median length3
Mean length3.8951613
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 59
23.8%
재산세 53
21.4%
취득세 52
21.0%
주민세 35
14.1%
자동차세 24
9.7%
지역자원시설세 15
 
6.0%
등록면허세 10
 
4.0%

Length

2024-04-21T10:50:30.025075image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-21T10:50:30.311690image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 59
23.8%
재산세 53
21.4%
취득세 52
21.0%
주민세 35
14.1%
자동차세 24
9.7%
지역자원시설세 15
 
6.0%
등록면허세 10
 
4.0%

체납액구간
Categorical

HIGH CORRELATION 

Distinct14
Distinct (%)5.6%
Missing0
Missing (%)0.0%
Memory size2.1 KiB
10만원 미만
42 
30만원~50만원미만
36 
10만원~30만원미만
33 
50만원~1백만원미만
32 
1백만원~3백만원미만
24 
Other values (9)
81 

Length

Max length11
Median length11
Mean length10.181452
Min length6

Unique

Unique1 ?
Unique (%)0.4%

Sample

1st row10만원 미만
2nd row10만원 미만
3rd row10만원~30만원미만
4th row30만원~50만원미만
5th row50만원~1백만원미만

Common Values

ValueCountFrequency (%)
10만원 미만 42
16.9%
30만원~50만원미만 36
14.5%
10만원~30만원미만 33
13.3%
50만원~1백만원미만 32
12.9%
1백만원~3백만원미만 24
9.7%
3백만원~5백만원미만 20
8.1%
5백만원~1천만원미만 18
7.3%
1천만원~3천만원미만 12
 
4.8%
3천만원~5천만원미만 9
 
3.6%
5천만원~1억원미만 8
 
3.2%
Other values (4) 14
 
5.6%

Length

2024-04-21T10:50:30.439376image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
10만원 42
14.5%
미만 42
14.5%
30만원~50만원미만 36
12.4%
10만원~30만원미만 33
11.4%
50만원~1백만원미만 32
11.0%
1백만원~3백만원미만 24
8.3%
3백만원~5백만원미만 20
6.9%
5백만원~1천만원미만 18
6.2%
1천만원~3천만원미만 12
 
4.1%
3천만원~5천만원미만 9
 
3.1%
Other values (5) 22
7.6%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct120
Distinct (%)48.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean827.69758
Minimum1
Maximum20058
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-21T10:50:30.602343image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q13
median13
Q3218.25
95-th percentile4271.25
Maximum20058
Range20057
Interquartile range (IQR)215.25

Descriptive statistics

Standard deviation2641.0795
Coefficient of variation (CV)3.190875
Kurtosis30.573991
Mean827.69758
Median Absolute Deviation (MAD)12
Skewness5.187731
Sum205269
Variance6975301.1
MonotonicityNot monotonic
2024-04-21T10:50:30.755123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 43
 
17.3%
3 17
 
6.9%
2 15
 
6.0%
4 13
 
5.2%
5 9
 
3.6%
6 6
 
2.4%
13 5
 
2.0%
7 5
 
2.0%
9 4
 
1.6%
11 3
 
1.2%
Other values (110) 128
51.6%
ValueCountFrequency (%)
1 43
17.3%
2 15
 
6.0%
3 17
 
6.9%
4 13
 
5.2%
5 9
 
3.6%
6 6
 
2.4%
7 5
 
2.0%
9 4
 
1.6%
10 2
 
0.8%
11 3
 
1.2%
ValueCountFrequency (%)
20058 1
0.4%
19300 1
0.4%
18459 1
0.4%
13201 1
0.4%
11023 1
0.4%
8953 1
0.4%
7346 1
0.4%
6838 1
0.4%
6674 1
0.4%
4952 1
0.4%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct248
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1.640344 × 108
Minimum16190
Maximum5.7387601 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-21T10:50:30.918768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum16190
5-th percentile434464
Q15436385
median47094275
Q31.9216666 × 108
95-th percentile6.3764752 × 108
Maximum5.7387601 × 109
Range5.738744 × 109
Interquartile range (IQR)1.8673028 × 108

Descriptive statistics

Standard deviation4.1072752 × 108
Coefficient of variation (CV)2.503911
Kurtosis138.06321
Mean1.640344 × 108
Median Absolute Deviation (MAD)45891410
Skewness10.398693
Sum4.068053 × 1010
Variance1.686971 × 1017
MonotonicityNot monotonic
2024-04-21T10:50:31.059859image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22192960 1
 
0.4%
11410060 1
 
0.4%
333308700 1
 
0.4%
331381530 1
 
0.4%
293811230 1
 
0.4%
88099070 1
 
0.4%
82190240 1
 
0.4%
291382350 1
 
0.4%
65013780 1
 
0.4%
505391990 1
 
0.4%
Other values (238) 238
96.0%
ValueCountFrequency (%)
16190 1
0.4%
22130 1
0.4%
74400 1
0.4%
185350 1
0.4%
194110 1
0.4%
223260 1
0.4%
244800 1
0.4%
257400 1
0.4%
370800 1
0.4%
383170 1
0.4%
ValueCountFrequency (%)
5738760140 1
0.4%
935617870 1
0.4%
856359430 1
0.4%
838447240 1
0.4%
793643580 1
0.4%
776625140 1
0.4%
766740660 1
0.4%
759874420 1
0.4%
743147160 1
0.4%
731948280 1
0.4%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct162
Distinct (%)65.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2701.8468
Minimum1
Maximum67774
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-21T10:50:31.198909image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q16
median51
Q3566.5
95-th percentile16424.8
Maximum67774
Range67773
Interquartile range (IQR)560.5

Descriptive statistics

Standard deviation8905.9749
Coefficient of variation (CV)3.2962546
Kurtosis31.19204
Mean2701.8468
Median Absolute Deviation (MAD)50
Skewness5.2480402
Sum670058
Variance79316390
MonotonicityNot monotonic
2024-04-21T10:50:31.319124image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 23
 
9.3%
2 12
 
4.8%
6 9
 
3.6%
3 9
 
3.6%
4 6
 
2.4%
18 4
 
1.6%
5 4
 
1.6%
25 3
 
1.2%
13 3
 
1.2%
19 3
 
1.2%
Other values (152) 172
69.4%
ValueCountFrequency (%)
1 23
9.3%
2 12
4.8%
3 9
 
3.6%
4 6
 
2.4%
5 4
 
1.6%
6 9
 
3.6%
7 3
 
1.2%
8 1
 
0.4%
10 2
 
0.8%
11 1
 
0.4%
ValueCountFrequency (%)
67774 1
0.4%
65694 1
0.4%
60811 1
0.4%
49315 1
0.4%
36114 1
0.4%
25091 1
0.4%
21503 1
0.4%
19985 1
0.4%
19891 1
0.4%
18009 1
0.4%

누적체납금액
Real number (ℝ)

HIGH CORRELATION 

Distinct247
Distinct (%)99.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.0334243 × 108
Minimum148210
Maximum5.7387601 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size2.3 KiB
2024-04-21T10:50:31.444735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum148210
5-th percentile1073539
Q123289860
median96649910
Q34.1258993 × 108
95-th percentile9.6393024 × 108
Maximum5.7387601 × 109
Range5.7386119 × 109
Interquartile range (IQR)3.8930007 × 108

Descriptive statistics

Standard deviation5.705712 × 108
Coefficient of variation (CV)1.8809475
Kurtosis38.842459
Mean3.0334243 × 108
Median Absolute Deviation (MAD)90951245
Skewness5.2383133
Sum7.5228923 × 1010
Variance3.255515 × 1017
MonotonicityNot monotonic
2024-04-21T10:50:31.582617image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
834300 2
 
0.8%
11410060 1
 
0.4%
574878870 1
 
0.4%
437348580 1
 
0.4%
430663350 1
 
0.4%
154991120 1
 
0.4%
82190240 1
 
0.4%
447556590 1
 
0.4%
106427750 1
 
0.4%
1450488260 1
 
0.4%
Other values (237) 237
95.6%
ValueCountFrequency (%)
148210 1
0.4%
170340 1
0.4%
244740 1
0.4%
338210 1
0.4%
392110 1
0.4%
467800 1
0.4%
500580 1
0.4%
759150 1
0.4%
834300 2
0.8%
915690 1
0.4%
ValueCountFrequency (%)
5738760140 1
0.4%
3377107820 1
0.4%
2948384350 1
0.4%
2600482680 1
0.4%
2358720030 1
0.4%
1762035440 1
0.4%
1467465360 1
0.4%
1450488260 1
0.4%
1398657000 1
0.4%
1276269400 1
0.4%

Interactions

2024-04-21T10:50:28.249914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.220150image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.818512image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.279025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.713495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:28.350872image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.353265image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.905001image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.361341image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.802327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:28.465460image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.567729image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.995472image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.448227image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.945055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:28.562139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.650052image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.085901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.541708image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:28.043820image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:28.689703image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:26.736013image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.181102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:27.625932image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-04-21T10:50:28.146840image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2024-04-21T10:50:31.705153image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
과세년도1.0000.0000.0000.0000.1160.1850.036
세목명0.0001.0000.2600.3940.1120.4030.467
체납액구간0.0000.2601.0000.2370.9060.0000.812
체납건수0.0000.3940.2371.0000.3120.9850.735
체납금액0.1160.1120.9060.3121.0000.1110.878
누적체납건수0.1850.4030.0000.9850.1111.0000.701
누적체납금액0.0360.4670.8120.7350.8780.7011.000
2024-04-21T10:50:31.818793image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명체납액구간
세목명1.0000.097
체납액구간0.0971.000
2024-04-21T10:50:31.917546image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
과세년도체납건수체납금액누적체납건수누적체납금액세목명체납액구간
과세년도1.0000.0500.313-0.0630.2480.0000.000
체납건수0.0501.0000.4950.9430.6250.2230.104
체납금액0.3130.4951.0000.3070.9550.0760.801
누적체납건수-0.0630.9430.3071.0000.5020.2280.000
누적체납금액0.2480.6250.9550.5021.0000.1790.437
세목명0.0000.2230.0760.2280.1791.0000.097
체납액구간0.0000.1040.8010.0000.4370.0971.000

Missing values

2024-04-21T10:50:28.820292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-21T10:50:28.970177image/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

시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
0부산광역시해운대구263502017등록면허세10만원 미만62722192960150751259540
1부산광역시해운대구263502017자동차세10만원 미만1595701799308768389726940
2부산광역시해운대구263502017자동차세10만원~30만원미만208434625488078651276269400
3부산광역시해운대구263502017자동차세30만원~50만원미만9432051980294102340520
4부산광역시해운대구263502017자동차세50만원~1백만원미만1161473305031566290
5부산광역시해운대구263502017재산세10만원 미만1942627505207298222404790
6부산광역시해운대구263502017재산세10만원~30만원미만26843951330873137278270
7부산광역시해운대구263502017재산세1백만원~3백만원미만20268248306385352290
8부산광역시해운대구263502017재산세30만원~50만원미만30109048109634833710
9부산광역시해운대구263502017재산세50만원~1백만원미만27180708807047442820
시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
238부산광역시해운대구263502022취득세10억원이상1573876014015738760140
239부산광역시해운대구263502022취득세1백만원~3백만원미만588022001629853500
240부산광역시해운대구263502022취득세1억원~3억원미만35988973503598897350
241부산광역시해운대구263502022취득세1천만원~3천만원미만236655190236655190
242부산광역시해운대구263502022취득세30만원~50만원미만62357080259523160
243부산광역시해운대구263502022취득세3백만원~5백만원미만416568020623300700
244부산광역시해운대구263502022취득세3천만원~5천만원미만274085310274085310
245부산광역시해운대구263502022취득세50만원~1백만원미만537323403727807570
246부산광역시해운대구263502022취득세5백만원~1천만원미만1551028015510280
247부산광역시해운대구263502022취득세5천만원~1억원미만185663560185663560