Overview

Dataset statistics

Number of variables11
Number of observations102
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.6 KiB
Average record size in memory96.3 B

Variable types

Numeric5
Categorical6

Dataset

Description경상북도 문경시의 체납액 규모별 체납건수에 대한 자료로, 2019년부터 2021년까지 체납구간별로 체납건수 및 체납금액을 제공합니다.
URLhttps://www.data.go.kr/data/15078728/fileData.do

Alerts

시도명 has constant value ""Constant
시군구명 has constant value ""Constant
자치단체코드 has constant value ""Constant
연번 is highly overall correlated with 과세년도High correlation
체납건수 is highly overall correlated with 누적체납건수High correlation
체납금액 is highly overall correlated with 누적체납금액High correlation
누적체납건수 is highly overall correlated with 체납건수High correlation
누적체납금액 is highly overall correlated with 체납금액High correlation
과세년도 is highly overall correlated with 연번High correlation
연번 has unique valuesUnique
체납금액 has unique valuesUnique
누적체납금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 06:03:04.720439
Analysis finished2023-12-12 06:03:07.447845
Duration2.73 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

연번
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean51.5
Minimum1
Maximum102
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T15:03:07.542495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile6.05
Q126.25
median51.5
Q376.75
95-th percentile96.95
Maximum102
Range101
Interquartile range (IQR)50.5

Descriptive statistics

Standard deviation29.588849
Coefficient of variation (CV)0.57454076
Kurtosis-1.2
Mean51.5
Median Absolute Deviation (MAD)25.5
Skewness0
Sum5253
Variance875.5
MonotonicityStrictly increasing
2023-12-12T15:03:07.722789image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1
 
1.0%
66 1
 
1.0%
76 1
 
1.0%
75 1
 
1.0%
74 1
 
1.0%
73 1
 
1.0%
72 1
 
1.0%
71 1
 
1.0%
70 1
 
1.0%
69 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
1 1
1.0%
2 1
1.0%
3 1
1.0%
4 1
1.0%
5 1
1.0%
6 1
1.0%
7 1
1.0%
8 1
1.0%
9 1
1.0%
10 1
1.0%
ValueCountFrequency (%)
102 1
1.0%
101 1
1.0%
100 1
1.0%
99 1
1.0%
98 1
1.0%
97 1
1.0%
96 1
1.0%
95 1
1.0%
94 1
1.0%
93 1
1.0%

시도명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
경상북도
102 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
경상북도 102
100.0%

Length

2023-12-12T15:03:07.884088image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:03:08.022170image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
경상북도 102
100.0%

시군구명
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
문경시
102 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
문경시 102
100.0%

Length

2023-12-12T15:03:08.148130image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:03:08.343402image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
문경시 102
100.0%

자치단체코드
Categorical

CONSTANT 

Distinct1
Distinct (%)1.0%
Missing0
Missing (%)0.0%
Memory size948.0 B
47280
102 

Length

Max length5
Median length5
Mean length5
Min length5

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row47280
2nd row47280
3rd row47280
4th row47280
5th row47280

Common Values

ValueCountFrequency (%)
47280 102
100.0%

Length

2023-12-12T15:03:08.454741image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:03:08.555514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
47280 102
100.0%

과세년도
Categorical

HIGH CORRELATION 

Distinct3
Distinct (%)2.9%
Missing0
Missing (%)0.0%
Memory size948.0 B
2020
35 
2021
35 
2019
32 

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2019
2nd row2019
3rd row2019
4th row2019
5th row2019

Common Values

ValueCountFrequency (%)
2020 35
34.3%
2021 35
34.3%
2019 32
31.4%

Length

2023-12-12T15:03:08.666699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:03:08.781768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2020 35
34.3%
2021 35
34.3%
2019 32
31.4%

세목명
Categorical

Distinct7
Distinct (%)6.9%
Missing0
Missing (%)0.0%
Memory size948.0 B
지방소득세
26 
재산세
25 
취득세
20 
주민세
14 
자동차세
12 
Other values (2)
5 

Length

Max length7
Median length3
Mean length3.7647059
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
지방소득세 26
25.5%
재산세 25
24.5%
취득세 20
19.6%
주민세 14
13.7%
자동차세 12
11.8%
등록면허세 3
 
2.9%
지역자원시설세 2
 
2.0%

Length

2023-12-12T15:03:08.935582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:03:09.085847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
지방소득세 26
25.5%
재산세 25
24.5%
취득세 20
19.6%
주민세 14
13.7%
자동차세 12
11.8%
등록면허세 3
 
2.9%
지역자원시설세 2
 
2.0%

체납액구간
Categorical

Distinct10
Distinct (%)9.8%
Missing0
Missing (%)0.0%
Memory size948.0 B
10만원 미만
19 
10만원~30만원미만
15 
30만원~50만원미만
15 
50만원~1백만원미만
13 
1백만원~3백만원미만
11 
Other values (5)
29 

Length

Max length11
Median length11
Mean length10.245098
Min length7

Unique

Unique1 ?
Unique (%)1.0%

Sample

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

Common Values

ValueCountFrequency (%)
10만원 미만 19
18.6%
10만원~30만원미만 15
14.7%
30만원~50만원미만 15
14.7%
50만원~1백만원미만 13
12.7%
1백만원~3백만원미만 11
10.8%
3백만원~5백만원미만 8
7.8%
5백만원~1천만원미만 8
7.8%
1천만원~3천만원미만 7
 
6.9%
3천만원~5천만원미만 5
 
4.9%
5천만원~1억원미만 1
 
1.0%

Length

2023-12-12T15:03:09.223771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T15:03:09.379273image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
10만원 19
15.7%
미만 19
15.7%
10만원~30만원미만 15
12.4%
30만원~50만원미만 15
12.4%
50만원~1백만원미만 13
10.7%
1백만원~3백만원미만 11
9.1%
3백만원~5백만원미만 8
6.6%
5백만원~1천만원미만 8
6.6%
1천만원~3천만원미만 7
 
5.8%
3천만원~5천만원미만 5
 
4.1%

체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct44
Distinct (%)43.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean148.88235
Minimum1
Maximum2600
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T15:03:09.546731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q12
median5.5
Q335
95-th percentile643.3
Maximum2600
Range2599
Interquartile range (IQR)33

Descriptive statistics

Standard deviation449.91225
Coefficient of variation (CV)3.0219314
Kurtosis19.271478
Mean148.88235
Median Absolute Deviation (MAD)4.5
Skewness4.3070876
Sum15186
Variance202421.04
MonotonicityNot monotonic
2023-12-12T15:03:09.696568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=44)
ValueCountFrequency (%)
1 25
24.5%
2 10
 
9.8%
3 8
 
7.8%
5 6
 
5.9%
19 3
 
2.9%
6 3
 
2.9%
15 3
 
2.9%
36 2
 
2.0%
4 2
 
2.0%
7 2
 
2.0%
Other values (34) 38
37.3%
ValueCountFrequency (%)
1 25
24.5%
2 10
 
9.8%
3 8
 
7.8%
4 2
 
2.0%
5 6
 
5.9%
6 3
 
2.9%
7 2
 
2.0%
9 2
 
2.0%
10 2
 
2.0%
13 1
 
1.0%
ValueCountFrequency (%)
2600 1
1.0%
2413 1
1.0%
2330 1
1.0%
1222 1
1.0%
1061 1
1.0%
646 1
1.0%
592 1
1.0%
537 1
1.0%
494 1
1.0%
445 1
1.0%

체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean18930005
Minimum4360
Maximum92399910
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T15:03:09.850222image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4360
5-th percentile313316
Q12143070
median10386335
Q327289788
95-th percentile67979510
Maximum92399910
Range92395550
Interquartile range (IQR)25146718

Descriptive statistics

Standard deviation21821513
Coefficient of variation (CV)1.1527473
Kurtosis1.9157398
Mean18930005
Median Absolute Deviation (MAD)9852085
Skewness1.5281732
Sum1.9308606 × 109
Variance4.7617845 × 1014
MonotonicityNot monotonic
2023-12-12T15:03:10.032067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2229530 1
 
1.0%
6352410 1
 
1.0%
30489330 1
 
1.0%
61564740 1
 
1.0%
46749070 1
 
1.0%
55747480 1
 
1.0%
514360 1
 
1.0%
5347470 1
 
1.0%
77358150 1
 
1.0%
27608760 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
4360 1
1.0%
10730 1
1.0%
111970 1
1.0%
243360 1
1.0%
266730 1
1.0%
313050 1
1.0%
318370 1
1.0%
361990 1
1.0%
365040 1
1.0%
395190 1
1.0%
ValueCountFrequency (%)
92399910 1
1.0%
85010420 1
1.0%
85000000 1
1.0%
77358150 1
1.0%
71894070 1
1.0%
68317130 1
1.0%
61564740 1
1.0%
55753870 1
1.0%
55747480 1
1.0%
50193870 1
1.0%

누적체납건수
Real number (ℝ)

HIGH CORRELATION 

Distinct65
Distinct (%)63.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean447.2451
Minimum1
Maximum6521
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T15:03:10.196800image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2.05
Q110.25
median20.5
Q389.5
95-th percentile2748.55
Maximum6521
Range6520
Interquartile range (IQR)79.25

Descriptive statistics

Standard deviation1189.9162
Coefficient of variation (CV)2.6605461
Kurtosis11.845103
Mean447.2451
Median Absolute Deviation (MAD)17.5
Skewness3.3869555
Sum45619
Variance1415900.6
MonotonicityNot monotonic
2023-12-12T15:03:10.375964image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
3 6
 
5.9%
14 4
 
3.9%
15 4
 
3.9%
5 4
 
3.9%
18 3
 
2.9%
6 3
 
2.9%
12 3
 
2.9%
2 3
 
2.9%
21 3
 
2.9%
1 3
 
2.9%
Other values (55) 66
64.7%
ValueCountFrequency (%)
1 3
2.9%
2 3
2.9%
3 6
5.9%
4 2
 
2.0%
5 4
3.9%
6 3
2.9%
7 2
 
2.0%
8 1
 
1.0%
10 2
 
2.0%
11 3
2.9%
ValueCountFrequency (%)
6521 1
1.0%
5897 1
1.0%
4682 1
1.0%
3484 1
1.0%
3483 1
1.0%
2751 1
1.0%
2702 1
1.0%
2637 1
1.0%
2489 1
1.0%
2257 1
1.0%

누적체납금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct102
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64894498
Minimum4360
Maximum4.5195288 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.0 KiB
2023-12-12T15:03:10.613613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4360
5-th percentile1817856.5
Q17913842.5
median32870830
Q392396022
95-th percentile2.1154641 × 108
Maximum4.5195288 × 108
Range4.5194852 × 108
Interquartile range (IQR)84482180

Descriptive statistics

Standard deviation84607594
Coefficient of variation (CV)1.3037715
Kurtosis8.0098329
Mean64894498
Median Absolute Deviation (MAD)28659590
Skewness2.5432705
Sum6.6192388 × 109
Variance7.158445 × 1015
MonotonicityNot monotonic
2023-12-12T15:03:11.137156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6678030 1
 
1.0%
18447810 1
 
1.0%
47886350 1
 
1.0%
152289070 1
 
1.0%
111706900 1
 
1.0%
119739320 1
 
1.0%
2686810 1
 
1.0%
28307360 1
 
1.0%
438827230 1
 
1.0%
104591240 1
 
1.0%
Other values (92) 92
90.2%
ValueCountFrequency (%)
4360 1
1.0%
10730 1
1.0%
580100 1
1.0%
614420 1
1.0%
893150 1
1.0%
1794650 1
1.0%
2258780 1
1.0%
2301990 1
1.0%
2686810 1
1.0%
2707550 1
1.0%
ValueCountFrequency (%)
451952880 1
1.0%
438827230 1
1.0%
366942460 1
1.0%
245872610 1
1.0%
240685780 1
1.0%
212283860 1
1.0%
197534910 1
1.0%
179809720 1
1.0%
154228480 1
1.0%
152289070 1
1.0%

Interactions

2023-12-12T15:03:06.826038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.103606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.509262image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.962698image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.407979image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.908647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.188420image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.600091image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.049723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.484894image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.980145image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.264590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.683733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.182240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.585958image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:07.047608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.340356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.777277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.267307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.668593image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:07.120716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.423180image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:05.873914image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.340158image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T15:03:06.751014image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T15:03:11.252352image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
연번1.0000.9520.7230.0000.0000.2850.2410.157
과세년도0.9521.0000.0000.0000.0000.0000.0500.000
세목명0.7230.0001.0000.0000.3680.4180.5970.444
체납액구간0.0000.0000.0001.0000.1950.7130.0000.542
체납건수0.0000.0000.3680.1951.0000.5610.9580.615
체납금액0.2850.0000.4180.7130.5611.0000.6210.814
누적체납건수0.2410.0500.5970.0000.9580.6211.0000.651
누적체납금액0.1570.0000.4440.5420.6150.8140.6511.000
2023-12-12T15:03:11.379256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
세목명과세년도체납액구간
세목명1.0000.0000.000
과세년도0.0001.0000.000
체납액구간0.0000.0001.000
2023-12-12T15:03:11.489565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
연번체납건수체납금액누적체납건수누적체납금액과세년도세목명체납액구간
연번1.000-0.189-0.033-0.195-0.0080.9120.4680.000
체납건수-0.1891.0000.3640.9420.3320.0000.2270.097
체납금액-0.0330.3641.0000.3010.9410.0000.2260.291
누적체납건수-0.1950.9420.3011.0000.3510.0220.2430.000
누적체납금액-0.0080.3320.9410.3511.0000.0000.2570.292
과세년도0.9120.0000.0000.0220.0001.0000.0000.000
세목명0.4680.2270.2260.2430.2570.0001.0000.000
체납액구간0.0000.0970.2910.0000.2920.0000.0001.000

Missing values

2023-12-12T15:03:07.223918image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T15:03:07.389696image/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

연번시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
01경상북도문경시472802019등록면허세10만원 미만13422295304116678030
12경상북도문경시472802019자동차세10만원 미만37816558620211089616740
23경상북도문경시472802019자동차세10만원~30만원미만537923999102257366942460
34경상북도문경시472802019자동차세30만원~50만원미만2588301506823994360
45경상북도문경시472802019자동차세50만원~1백만원미만155414073835440
56경상북도문경시472802019재산세10만원 미만122218329670348460110100
67경상북도문경시472802019재산세10만원~30만원미만1182053950029749068640
78경상북도문경시472802019재산세1백만원~3백만원미만19280557705286648610
89경상북도문경시472802019재산세1천만원~3천만원미만111250630221266630
910경상북도문경시472802019재산세30만원~50만원미만19687273010436241330
연번시도명시군구명자치단체코드과세년도세목명체납액구간체납건수체납금액누적체납건수누적체납금액
9293경상북도문경시472802021지방소득세5백만원~1천만원미만74709540022154228480
9394경상북도문경시472802021지역자원시설세10만원 미만310730310730
9495경상북도문경시472802021취득세10만원 미만726673019614420
9596경상북도문경시472802021취득세10만원~30만원미만2365040142736100
9697경상북도문경시472802021취득세1백만원~3백만원미만112341401423046540
9798경상북도문경시472802021취득세1천만원~3천만원미만120000000347263370
9899경상북도문경시472802021취득세30만원~50만원미만275685051794650
99100경상북도문경시472802021취득세3백만원~5백만원미만13281760414035050
100101경상북도문경시472802021취득세50만원~1백만원미만2134256063768300
101102경상북도문경시472802021취득세5천만원~1억원미만1850000002148978100