Overview

Dataset statistics

Number of variables8
Number of observations133
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.2 KiB
Average record size in memory71.0 B

Variable types

Categorical1
Text1
Numeric6

Dataset

Description2022년말 기준 세무서별 누계 체납액 현황임 누계체납 건수(금액)은 정리중체납 건수(금액)과 정리보류 건수(금액)을 합한 금액임
URLhttps://www.data.go.kr/data/15113736/fileData.do

Alerts

누계체납 건수 is highly overall correlated with 누계체납 금액 and 4 other fieldsHigh correlation
누계체납 금액 is highly overall correlated with 누계체납 건수 and 4 other fieldsHigh correlation
정리중체납 건수 is highly overall correlated with 누계체납 건수 and 4 other fieldsHigh correlation
정리중체납 금액 is highly overall correlated with 누계체납 건수 and 4 other fieldsHigh correlation
정리보류 건수 is highly overall correlated with 누계체납 건수 and 4 other fieldsHigh correlation
정리보류 금액 is highly overall correlated with 누계체납 건수 and 4 other fieldsHigh correlation
세무서 has unique valuesUnique
누계체납 건수 has unique valuesUnique
누계체납 금액 has unique valuesUnique
정리중체납 건수 has unique valuesUnique
정리보류 건수 has unique valuesUnique
정리보류 금액 has unique valuesUnique

Reproduction

Analysis started2023-12-12 10:43:21.397410
Analysis finished2023-12-12 10:43:26.831951
Duration5.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지방청
Categorical

Distinct6
Distinct (%)4.5%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
부산청
33 
서울청
28 
중부청
25 
대전청
17 
인천청
15 

Length

Max length3
Median length3
Mean length3
Min length3

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산청 33
24.8%
서울청 28
21.1%
중부청 25
18.8%
대전청 17
12.8%
인천청 15
11.3%
광주청 15
11.3%

Length

2023-12-12T19:43:26.912327image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T19:43:27.050606image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산청 33
24.8%
서울청 28
21.1%
중부청 25
18.8%
대전청 17
12.8%
인천청 15
11.3%
광주청 15
11.3%

세무서
Text

UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-12T19:43:27.509602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.2481203
Min length2

Characters and Unicode

Total characters299
Distinct characters98
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique133 ?
Unique (%)100.0%

Sample

1st row종로
2nd row남대문
3rd row마포
4th row용산
5th row영등포
ValueCountFrequency (%)
종로 1
 
0.8%
세종 1
 
0.8%
여수 1
 
0.8%
순천 1
 
0.8%
해남 1
 
0.8%
나주 1
 
0.8%
목포 1
 
0.8%
광산 1
 
0.8%
서광주 1
 
0.8%
북광주 1
 
0.8%
Other values (123) 123
92.5%
2023-12-12T19:43:28.110743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
산 20
 
6.7%
주 19
 
6.4%
동 16
 
5.4%
천 14
 
4.7%
대 11
 
3.7%
부 10
 
3.3%
서 10
 
3.3%
남 9
 
3.0%
양 8
 
2.7%
성 8
 
2.7%
Other values (88) 174
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 299
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
산 20
 
6.7%
주 19
 
6.4%
동 16
 
5.4%
천 14
 
4.7%
대 11
 
3.7%
부 10
 
3.3%
서 10
 
3.3%
남 9
 
3.0%
양 8
 
2.7%
성 8
 
2.7%
Other values (88) 174
58.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 299
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
산 20
 
6.7%
주 19
 
6.4%
동 16
 
5.4%
천 14
 
4.7%
대 11
 
3.7%
부 10
 
3.3%
서 10
 
3.3%
남 9
 
3.0%
양 8
 
2.7%
성 8
 
2.7%
Other values (88) 174
58.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 299
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
산 20
 
6.7%
주 19
 
6.4%
동 16
 
5.4%
천 14
 
4.7%
대 11
 
3.7%
부 10
 
3.3%
서 10
 
3.3%
남 9
 
3.0%
양 8
 
2.7%
성 8
 
2.7%
Other values (88) 174
58.2%

누계체납 건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean37534.226
Minimum4440
Maximum100494
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:43:28.320689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum4440
5-th percentile7022.6
Q124506
median34130
Q348640
95-th percentile77041
Maximum100494
Range96054
Interquartile range (IQR)24134

Descriptive statistics

Standard deviation20530.557
Coefficient of variation (CV)0.5469823
Kurtosis0.099885702
Mean37534.226
Median Absolute Deviation (MAD)11911
Skewness0.62328035
Sum4992052
Variance4.2150377 × 108
MonotonicityNot monotonic
2023-12-12T19:43:28.526258image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
28399 1
 
0.8%
36262 1
 
0.8%
16262 1
 
0.8%
34126 1
 
0.8%
10225 1
 
0.8%
11445 1
 
0.8%
28803 1
 
0.8%
37123 1
 
0.8%
26345 1
 
0.8%
37413 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
4440 1
0.8%
5024 1
0.8%
6073 1
0.8%
6731 1
0.8%
6856 1
0.8%
6874 1
0.8%
6977 1
0.8%
7053 1
0.8%
7762 1
0.8%
8352 1
0.8%
ValueCountFrequency (%)
100494 1
0.8%
87131 1
0.8%
85841 1
0.8%
82516 1
0.8%
82510 1
0.8%
77888 1
0.8%
77047 1
0.8%
77037 1
0.8%
76455 1
0.8%
75683 1
0.8%

누계체납 금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7707.8195
Minimum534
Maximum23042
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:43:29.066454image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum534
5-th percentile1064.8
Q14470
median6663
Q310295
95-th percentile19018.2
Maximum23042
Range22508
Interquartile range (IQR)5825

Descriptive statistics

Standard deviation5195.3188
Coefficient of variation (CV)0.67403223
Kurtosis1.2639873
Mean7707.8195
Median Absolute Deviation (MAD)2921
Skewness1.1266019
Sum1025140
Variance26991337
MonotonicityNot monotonic
2023-12-12T19:43:29.276282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10295 1
 
0.8%
6240 1
 
0.8%
2345 1
 
0.8%
4974 1
 
0.8%
1762 1
 
0.8%
1869 1
 
0.8%
5681 1
 
0.8%
5628 1
 
0.8%
4680 1
 
0.8%
6025 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
534 1
0.8%
892 1
0.8%
929 1
0.8%
931 1
0.8%
939 1
0.8%
1018 1
0.8%
1045 1
0.8%
1078 1
0.8%
1280 1
0.8%
1319 1
0.8%
ValueCountFrequency (%)
23042 1
0.8%
22806 1
0.8%
22565 1
0.8%
22386 1
0.8%
22286 1
0.8%
21723 1
0.8%
21501 1
0.8%
17363 1
0.8%
15852 1
0.8%
15714 1
0.8%

정리중체납 건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean19683.677
Minimum2464
Maximum58315
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:43:29.458098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2464
5-th percentile4011.2
Q112809
median17503
Q325273
95-th percentile41943.6
Maximum58315
Range55851
Interquartile range (IQR)12464

Descriptive statistics

Standard deviation11308.426
Coefficient of variation (CV)0.5745078
Kurtosis0.59220134
Mean19683.677
Median Absolute Deviation (MAD)6081
Skewness0.85074859
Sum2617929
Variance1.278805 × 108
MonotonicityNot monotonic
2023-12-12T19:43:29.647584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12920 1
 
0.8%
17426 1
 
0.8%
8136 1
 
0.8%
18132 1
 
0.8%
5612 1
 
0.8%
4836 1
 
0.8%
11422 1
 
0.8%
20709 1
 
0.8%
13844 1
 
0.8%
21810 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
2464 1
0.8%
2692 1
0.8%
3646 1
0.8%
3707 1
0.8%
3790 1
0.8%
3802 1
0.8%
3980 1
0.8%
4032 1
0.8%
4185 1
0.8%
4400 1
0.8%
ValueCountFrequency (%)
58315 1
0.8%
48944 1
0.8%
48288 1
0.8%
44830 1
0.8%
43255 1
0.8%
43075 1
0.8%
42036 1
0.8%
41882 1
0.8%
41628 1
0.8%
41453 1
0.8%

정리중체납 금액
Real number (ℝ)

HIGH CORRELATION 

Distinct129
Distinct (%)97.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1170.4737
Minimum103
Maximum4007
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:43:29.851612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum103
5-th percentile165.2
Q1618
median1003
Q31495
95-th percentile2773.6
Maximum4007
Range3904
Interquartile range (IQR)877

Descriptive statistics

Standard deviation810.46194
Coefficient of variation (CV)0.69242218
Kurtosis1.1773575
Mean1170.4737
Median Absolute Deviation (MAD)428
Skewness1.1471499
Sum155673
Variance656848.55
MonotonicityNot monotonic
2023-12-12T19:43:30.050450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
914 2
 
1.5%
304 2
 
1.5%
176 2
 
1.5%
1088 2
 
1.5%
125 1
 
0.8%
557 1
 
0.8%
646 1
 
0.8%
575 1
 
0.8%
302 1
 
0.8%
1275 1
 
0.8%
Other values (119) 119
89.5%
ValueCountFrequency (%)
103 1
0.8%
112 1
0.8%
125 1
0.8%
141 1
0.8%
145 1
0.8%
147 1
0.8%
149 1
0.8%
176 2
1.5%
190 1
0.8%
206 1
0.8%
ValueCountFrequency (%)
4007 1
0.8%
3595 1
0.8%
3380 1
0.8%
3198 1
0.8%
3105 1
0.8%
3038 1
0.8%
3013 1
0.8%
2614 1
0.8%
2567 1
0.8%
2564 1
0.8%

정리보류 건수
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean17850.549
Minimum1748
Maximum45678
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:43:30.251314image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1748
5-th percentile3325.2
Q111185
median17140
Q322771
95-th percentile34925.4
Maximum45678
Range43930
Interquartile range (IQR)11586

Descriptive statistics

Standard deviation9581.8871
Coefficient of variation (CV)0.53678389
Kurtosis-0.099176618
Mean17850.549
Median Absolute Deviation (MAD)5800
Skewness0.48697482
Sum2374123
Variance91812561
MonotonicityNot monotonic
2023-12-12T19:43:30.443356image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15479 1
 
0.8%
18836 1
 
0.8%
8126 1
 
0.8%
15994 1
 
0.8%
4613 1
 
0.8%
6609 1
 
0.8%
17381 1
 
0.8%
16414 1
 
0.8%
12501 1
 
0.8%
15603 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
1748 1
0.8%
2271 1
0.8%
2560 1
0.8%
2751 1
0.8%
2824 1
0.8%
3084 1
0.8%
3270 1
0.8%
3362 1
0.8%
3407 1
0.8%
4167 1
0.8%
ValueCountFrequency (%)
45678 1
0.8%
42179 1
0.8%
40474 1
0.8%
36897 1
0.8%
36612 1
0.8%
35155 1
0.8%
34950 1
0.8%
34909 1
0.8%
34228 1
0.8%
33990 1
0.8%

정리보류 금액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct133
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6537.3459
Minimum431
Maximum20643
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-12T19:43:30.628146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum431
5-th percentile917
Q13667
median5560
Q38800
95-th percentile16479
Maximum20643
Range20212
Interquartile range (IQR)5133

Descriptive statistics

Standard deviation4448.8152
Coefficient of variation (CV)0.68052316
Kurtosis1.4125049
Mean6537.3459
Median Absolute Deviation (MAD)2686
Skewness1.1621935
Sum869467
Variance19791957
MonotonicityNot monotonic
2023-12-12T19:43:30.843168image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9381 1
 
0.8%
5396 1
 
0.8%
1946 1
 
0.8%
3971 1
 
0.8%
1370 1
 
0.8%
1565 1
 
0.8%
5085 1
 
0.8%
4441 1
 
0.8%
3616 1
 
0.8%
4750 1
 
0.8%
Other values (123) 123
92.5%
ValueCountFrequency (%)
431 1
0.8%
763 1
0.8%
780 1
0.8%
788 1
0.8%
806 1
0.8%
873 1
0.8%
896 1
0.8%
931 1
0.8%
1074 1
0.8%
1143 1
0.8%
ValueCountFrequency (%)
20643 1
0.8%
20004 1
0.8%
19006 1
0.8%
18970 1
0.8%
18710 1
0.8%
18303 1
0.8%
18279 1
0.8%
15279 1
0.8%
13381 1
0.8%
13357 1
0.8%

Interactions

2023-12-12T19:43:25.675363image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:21.807481image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.569435image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.325536image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.119769image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.931666image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.813509image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:21.924635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.685475image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.463431image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.236432image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.055346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.941826image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.050978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.794464image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.598773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.353020image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.159100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:26.080025image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.210843image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.933185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.737667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.495076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.286414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:26.249624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.354860image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.080906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.885963image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.644101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.434387image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:26.374768image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:22.443381image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:23.193294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.001734image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:24.780952image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T19:43:25.546414image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T19:43:30.977448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
지방청누계체납 건수누계체납 금액정리중체납 건수정리중체납 금액정리보류 건수정리보류 금액
지방청1.0000.4460.4670.4420.4350.4300.500
누계체납 건수0.4461.0000.8050.9740.9000.9690.921
누계체납 금액0.4670.8051.0000.7740.8220.7970.957
정리중체납 건수0.4420.9740.7741.0000.9050.9230.901
정리중체납 금액0.4350.9000.8220.9051.0000.8510.925
정리보류 건수0.4300.9690.7970.9230.8511.0000.894
정리보류 금액0.5000.9210.9570.9010.9250.8941.000
2023-12-12T19:43:31.155151image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
누계체납 건수누계체납 금액정리중체납 건수정리중체납 금액정리보류 건수정리보류 금액지방청
누계체납 건수1.0000.8940.9880.8950.9790.8810.248
누계체납 금액0.8941.0000.8860.9350.8790.9970.248
정리중체납 건수0.9880.8861.0000.9150.9400.8670.244
정리중체납 금액0.8950.9350.9151.0000.8460.9110.241
정리보류 건수0.9790.8790.9400.8461.0000.8740.238
정리보류 금액0.8810.9970.8670.9110.8741.0000.284
지방청0.2480.2480.2440.2410.2380.2841.000

Missing values

2023-12-12T19:43:26.594136image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T19:43:26.765458image/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

지방청세무서누계체납 건수누계체납 금액정리중체납 건수정리중체납 금액정리보류 건수정리보류 금액
0서울청종로283991029512920914154799381
1서울청남대문151985127772958874694539
2서울청마포518939800279881495239058305
3서울청용산29318121171542115181389710599
4서울청영등포5147210626242061542272669084
5서울청동작347648472171291177176357295
6서울청강서506239501305891632200347869
7서울청서대문20235557210295129399404279
8서울청은평29363487315884751134794122
9서울청구로481439584217161184264278400
지방청세무서누계체납 건수누계체납 금액정리중체납 건수정리중체납 금액정리보류 건수정리보류 금액
123부산청거창7762107844001473362931
124부산청통영410697579207681282203016297
125부산청진주38485665520607897178785758
126부산청해운대332606864191801467140805397
127부산청김해7788811983448302062330589921
128부산청양산32566433217151721154153611
129부산청제주6304411905432553105197898800
130부산청수영31068675316708907143605846
131부산청동울산486407319257001133229406186
132부산청금정32450518618077872143734314