Overview

Dataset statistics

Number of variables13
Number of observations30
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.4 KiB
Average record size in memory114.4 B

Variable types

Categorical3
Text4
Numeric6

Dataset

Description해당연도 각 세목별 예산액, 수납액, 불납결손액 현황. 1)추가경정예산을 반영한 수치. 2)자진납부세액에 고지결정세액을 합한 금액에서 환급결정금액을 차감. 3)국세청 공공데이터 "국세청_소관_세수_현황"과 일치. ※ 단위: 백만원
URLhttps://www.data.go.kr/data/15113671/fileData.do

Alerts

구분1 has constant value ""Constant
예산액 is highly overall correlated with 징수결정액 and 3 other fieldsHigh correlation
징수결정액 is highly overall correlated with 예산액 and 3 other fieldsHigh correlation
수납액 is highly overall correlated with 예산액 and 2 other fieldsHigh correlation
불납결손액 is highly overall correlated with 예산액 and 2 other fieldsHigh correlation
미수납액 is highly overall correlated with 예산액 and 3 other fieldsHigh correlation
구분2 is highly overall correlated with 구분3High correlation
구분3 is highly overall correlated with 구분2High correlation
구분2 is highly imbalanced (68.6%)Imbalance
수납액 has unique valuesUnique
예산액 has 4 (13.3%) zerosZeros
징수결정액 has 3 (10.0%) zerosZeros
불납결손액 has 11 (36.7%) zerosZeros
미수납액 has 5 (16.7%) zerosZeros
비율(수납액_예산액) has 4 (13.3%) zerosZeros

Reproduction

Analysis started2023-12-12 12:58:36.760192
Analysis finished2023-12-12 12:58:41.083297
Duration4.32 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

구분1
Categorical

CONSTANT 

Distinct1
Distinct (%)3.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
국세청 총징수액
30 

Length

Max length8
Median length8
Mean length8
Min length8

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row국세청 총징수액
2nd row국세청 총징수액
3rd row국세청 총징수액
4th row국세청 총징수액
5th row국세청 총징수액

Common Values

ValueCountFrequency (%)
국세청 총징수액 30
100.0%

Length

2023-12-12T21:58:41.190425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:58:41.337540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국세청 30
50.0%
총징수액 30
50.0%

구분2
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)13.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
국세청세수
27 
지방소비세 이체액
 
1
근로 자녀장려금
 
1
물납세액
 
1

Length

Max length9
Median length5
Mean length5.2
Min length4

Unique

Unique3 ?
Unique (%)10.0%

Sample

1st row지방소비세 이체액
2nd row근로 자녀장려금
3rd row물납세액
4th row국세청세수
5th row국세청세수

Common Values

ValueCountFrequency (%)
국세청세수 27
90.0%
지방소비세 이체액 1
 
3.3%
근로 자녀장려금 1
 
3.3%
물납세액 1
 
3.3%

Length

2023-12-12T21:58:41.460445image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:58:41.577417image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국세청세수 27
84.4%
지방소비세 1
 
3.1%
이체액 1
 
3.1%
근로 1
 
3.1%
자녀장려금 1
 
3.1%
물납세액 1
 
3.1%

구분3
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)30.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
내국세
22 
지방소비세 이체액
 
1
근로 자녀장려금
 
1
물납세액
 
1
교통 에너지 환경세
 
1
Other values (4)
4 

Length

Max length10
Median length3
Mean length3.8333333
Min length3

Unique

Unique8 ?
Unique (%)26.7%

Sample

1st row지방소비세 이체액
2nd row근로 자녀장려금
3rd row물납세액
4th row내국세
5th row내국세

Common Values

ValueCountFrequency (%)
내국세 22
73.3%
지방소비세 이체액 1
 
3.3%
근로 자녀장려금 1
 
3.3%
물납세액 1
 
3.3%
교통 에너지 환경세 1
 
3.3%
방위세 1
 
3.3%
교육세 1
 
3.3%
농어촌특별세 1
 
3.3%
종합부동산세 1
 
3.3%

Length

2023-12-12T21:58:41.706117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:58:41.838070image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
내국세 22
64.7%
지방소비세 1
 
2.9%
이체액 1
 
2.9%
근로 1
 
2.9%
자녀장려금 1
 
2.9%
물납세액 1
 
2.9%
교통 1
 
2.9%
에너지 1
 
2.9%
환경세 1
 
2.9%
방위세 1
 
2.9%
Other values (3) 3
 
8.8%

구분4
Text

Distinct18
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T21:58:42.044050image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length4.1
Min length2

Characters and Unicode

Total characters123
Distinct characters55
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique16 ?
Unique (%)53.3%

Sample

1st row지방소비세 이체액
2nd row근로 자녀장려금
3rd row물납세액
4th row소득세
5th row소득세
ValueCountFrequency (%)
소득세 12
34.3%
법인세 2
 
5.7%
수입 1
 
2.9%
이체액 1
 
2.9%
지방소비세 1
 
2.9%
증권거래세 1
 
2.9%
농어촌특별세 1
 
2.9%
교육세 1
 
2.9%
방위세 1
 
2.9%
환경세 1
 
2.9%
Other values (13) 13
37.1%
2023-12-12T21:58:42.392990image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 28
22.8%
소 14
 
11.4%
득 12
 
9.8%
5
 
4.1%
지 3
 
2.4%
인 3
 
2.4%
교 2
 
1.6%
부 2
 
1.6%
법 2
 
1.6%
비 2
 
1.6%
Other values (45) 50
40.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 118
95.9%
Space Separator 5
 
4.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 28
23.7%
소 14
 
11.9%
득 12
 
10.2%
지 3
 
2.5%
인 3
 
2.5%
교 2
 
1.7%
부 2
 
1.7%
법 2
 
1.7%
비 2
 
1.7%
별 2
 
1.7%
Other values (44) 48
40.7%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 118
95.9%
Common 5
 
4.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 28
23.7%
소 14
 
11.9%
득 12
 
10.2%
지 3
 
2.5%
인 3
 
2.5%
교 2
 
1.7%
부 2
 
1.7%
법 2
 
1.7%
비 2
 
1.7%
별 2
 
1.7%
Other values (44) 48
40.7%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 118
95.9%
ASCII 5
 
4.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 28
23.7%
소 14
 
11.9%
득 12
 
10.2%
지 3
 
2.5%
인 3
 
2.5%
교 2
 
1.7%
부 2
 
1.7%
법 2
 
1.7%
비 2
 
1.7%
별 2
 
1.7%
Other values (44) 48
40.7%
ASCII
ValueCountFrequency (%)
5
100.0%

구분5
Text

Distinct21
Distinct (%)70.0%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T21:58:42.618117image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length3
Mean length4.2333333
Min length2

Characters and Unicode

Total characters127
Distinct characters59
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)60.0%

Sample

1st row지방소비세 이체액
2nd row근로 자녀장려금
3rd row물납세액
4th row신고분
5th row신고분
ValueCountFrequency (%)
원천분 8
22.9%
신고분 2
 
5.7%
자녀장려금 2
 
5.7%
법인세 2
 
5.7%
수입 1
 
2.9%
이체액 1
 
2.9%
지방소비세 1
 
2.9%
주세 1
 
2.9%
농어촌특별세 1
 
2.9%
교육세 1
 
2.9%
Other values (15) 15
42.9%
2023-12-12T21:58:43.030647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 16
 
12.6%
분 10
 
7.9%
원 8
 
6.3%
천 8
 
6.3%
5
 
3.9%
인 3
 
2.4%
지 3
 
2.4%
려 3
 
2.4%
금 3
 
2.4%
장 3
 
2.4%
Other values (49) 65
51.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 122
96.1%
Space Separator 5
 
3.9%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 16
 
13.1%
분 10
 
8.2%
원 8
 
6.6%
천 8
 
6.6%
인 3
 
2.5%
지 3
 
2.5%
려 3
 
2.5%
금 3
 
2.5%
장 3
 
2.5%
방 2
 
1.6%
Other values (48) 63
51.6%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 122
96.1%
Common 5
 
3.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 16
 
13.1%
분 10
 
8.2%
원 8
 
6.6%
천 8
 
6.6%
인 3
 
2.5%
지 3
 
2.5%
려 3
 
2.5%
금 3
 
2.5%
장 3
 
2.5%
방 2
 
1.6%
Other values (48) 63
51.6%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 122
96.1%
ASCII 5
 
3.9%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 16
 
13.1%
분 10
 
8.2%
원 8
 
6.6%
천 8
 
6.6%
인 3
 
2.5%
지 3
 
2.5%
려 3
 
2.5%
금 3
 
2.5%
장 3
 
2.5%
방 2
 
1.6%
Other values (48) 63
51.6%
ASCII
ValueCountFrequency (%)
5
100.0%

구분6
Text

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T21:58:43.285421image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9.5
Mean length4.9
Min length2

Characters and Unicode

Total characters147
Distinct characters65
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)86.7%

Sample

1st row지방소비세 이체액
2nd row근로 자녀장려금
3rd row물납세액
4th row종합소득세
5th row양도소득세
ValueCountFrequency (%)
법인세 2
 
5.7%
자녀장려금 2
 
5.7%
근로소득세 2
 
5.7%
농어촌특별세 1
 
2.9%
교육세 1
 
2.9%
방위세 1
 
2.9%
환경세 1
 
2.9%
에너지 1
 
2.9%
부가가치세 1
 
2.9%
수입 1
 
2.9%
Other values (22) 22
62.9%
2023-12-12T21:58:43.723919image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 26
 
17.7%
소 12
 
8.2%
득 10
 
6.8%
5
 
3.4%
근 4
 
2.7%
로 4
 
2.7%
금 4
 
2.7%
장 3
 
2.0%
지 3
 
2.0%
려 3
 
2.0%
Other values (55) 73
49.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 142
96.6%
Space Separator 5
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 26
 
18.3%
소 12
 
8.5%
득 10
 
7.0%
근 4
 
2.8%
로 4
 
2.8%
금 4
 
2.8%
장 3
 
2.1%
지 3
 
2.1%
려 3
 
2.1%
자 3
 
2.1%
Other values (54) 70
49.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 142
96.6%
Common 5
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 26
 
18.3%
소 12
 
8.5%
득 10
 
7.0%
근 4
 
2.8%
로 4
 
2.8%
금 4
 
2.8%
장 3
 
2.1%
지 3
 
2.1%
려 3
 
2.1%
자 3
 
2.1%
Other values (54) 70
49.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 142
96.6%
ASCII 5
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 26
 
18.3%
소 12
 
8.5%
득 10
 
7.0%
근 4
 
2.8%
로 4
 
2.8%
금 4
 
2.8%
장 3
 
2.1%
지 3
 
2.1%
려 3
 
2.1%
자 3
 
2.1%
Other values (54) 70
49.3%
ASCII
ValueCountFrequency (%)
5
100.0%

구분7
Text

Distinct29
Distinct (%)96.7%
Missing0
Missing (%)0.0%
Memory size372.0 B
2023-12-12T21:58:43.972835image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length4.8333333
Min length2

Characters and Unicode

Total characters145
Distinct characters70
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique28 ?
Unique (%)93.3%

Sample

1st row지방소비세 이체액
2nd row근로 자녀장려금
3rd row물납세액
4th row종합소득세
5th row양도소득세
ValueCountFrequency (%)
원천분 2
 
5.7%
자녀장려금 2
 
5.7%
신고분 1
 
2.9%
농어촌특별세 1
 
2.9%
교육세 1
 
2.9%
방위세 1
 
2.9%
환경세 1
 
2.9%
에너지 1
 
2.9%
증여세 1
 
2.9%
수입 1
 
2.9%
Other values (23) 23
65.7%
2023-12-12T21:58:44.403898image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
세 23
 
15.9%
소 10
 
6.9%
득 8
 
5.5%
5
 
3.4%
분 4
 
2.8%
금 4
 
2.8%
합 3
 
2.1%
자 3
 
2.1%
장 3
 
2.1%
려 3
 
2.1%
Other values (60) 79
54.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 140
96.6%
Space Separator 5
 
3.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
세 23
 
16.4%
소 10
 
7.1%
득 8
 
5.7%
분 4
 
2.9%
금 4
 
2.9%
합 3
 
2.1%
자 3
 
2.1%
장 3
 
2.1%
려 3
 
2.1%
지 3
 
2.1%
Other values (59) 76
54.3%
Space Separator
ValueCountFrequency (%)
5
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 140
96.6%
Common 5
 
3.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
세 23
 
16.4%
소 10
 
7.1%
득 8
 
5.7%
분 4
 
2.9%
금 4
 
2.9%
합 3
 
2.1%
자 3
 
2.1%
장 3
 
2.1%
려 3
 
2.1%
지 3
 
2.1%
Other values (59) 76
54.3%
Common
ValueCountFrequency (%)
5
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 140
96.6%
ASCII 5
 
3.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
세 23
 
16.4%
소 10
 
7.1%
득 8
 
5.7%
분 4
 
2.9%
금 4
 
2.9%
합 3
 
2.1%
자 3
 
2.1%
장 3
 
2.1%
려 3
 
2.1%
지 3
 
2.1%
Other values (59) 76
54.3%
ASCII
ValueCountFrequency (%)
5
100.0%

예산액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct27
Distinct (%)90.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12838947
Minimum-4871800
Maximum87638900
Zeros4
Zeros (%)13.3%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-12T21:58:44.556781image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4871800
5-th percentile-317845
Q1456750
median4537800
Q39761450
95-th percentile71141355
Maximum87638900
Range92510700
Interquartile range (IQR)9304700

Descriptive statistics

Standard deviation23058710
Coefficient of variation (CV)1.795997
Kurtosis5.1992839
Mean12838947
Median Absolute Deviation (MAD)4334300
Skewness2.4295113
Sum3.851684 × 108
Variance5.317041 × 1014
MonotonicityNot monotonic
2023-12-12T21:58:44.713241image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=27)
ValueCountFrequency (%)
0 4
 
13.3%
23706600 1
 
3.3%
8620400 1
 
3.3%
5260100 1
 
3.3%
4726600 1
 
3.3%
10902200 1
 
3.3%
6154900 1
 
3.3%
948000 1
 
3.3%
7538000 1
 
3.3%
3737400 1
 
3.3%
Other values (17) 17
56.7%
ValueCountFrequency (%)
-4871800 1
 
3.3%
-577900 1
 
3.3%
0 4
13.3%
114000 1
 
3.3%
293000 1
 
3.3%
948000 1
 
3.3%
1360000 1
 
3.3%
1883000 1
 
3.3%
2647000 1
 
3.3%
3579000 1
 
3.3%
ValueCountFrequency (%)
87638900 1
3.3%
79323300 1
3.3%
61141200 1
3.3%
34222800 1
3.3%
23706600 1
3.3%
16427300 1
3.3%
10902200 1
3.3%
10141800 1
3.3%
8620400 1
3.3%
8222900 1
3.3%

징수결정액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct28
Distinct (%)93.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean14681944
Minimum-4500892
Maximum90056061
Zeros3
Zeros (%)10.0%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-12T21:58:44.862159image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4500892
5-th percentile-273465.5
Q1474502
median4449010
Q310785388
95-th percentile76077162
Maximum90056061
Range94556953
Interquartile range (IQR)10310886

Descriptive statistics

Standard deviation24969002
Coefficient of variation (CV)1.7006606
Kurtosis4.1351102
Mean14681944
Median Absolute Deviation (MAD)4387729
Skewness2.1927314
Sum4.4045831 × 108
Variance6.2345108 × 1014
MonotonicityNot monotonic
2023-12-12T21:58:44.979739image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=28)
ValueCountFrequency (%)
0 3
 
10.0%
7978178 1
 
3.3%
7960830 1
 
3.3%
5853866 1
 
3.3%
4705115 1
 
3.3%
964 1
 
3.3%
11141720 1
 
3.3%
44429325 1
 
3.3%
801247 1
 
3.3%
6309736 1
 
3.3%
Other values (18) 18
60.0%
ValueCountFrequency (%)
-4500892 1
 
3.3%
-497210 1
 
3.3%
0 3
10.0%
964 1
 
3.3%
121598 1
 
3.3%
365587 1
 
3.3%
801247 1
 
3.3%
1635537 1
 
3.3%
2264615 1
 
3.3%
2636188 1
 
3.3%
ValueCountFrequency (%)
90056061 1
3.3%
88784429 1
3.3%
60546059 1
3.3%
44429325 1
3.3%
33908016 1
3.3%
30129047 1
3.3%
16598721 1
3.3%
11141720 1
3.3%
9716393 1
3.3%
7978178 1
3.3%

수납액
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct30
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean13877267
Minimum-4503625
Maximum86984423
Zeros0
Zeros (%)0.0%
Negative2
Negative (%)6.7%
Memory size402.0 B
2023-12-12T21:58:45.116074image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum-4503625
5-th percentile-273341.25
Q11786909.5
median5324868.5
Q310666842
95-th percentile72006598
Maximum86984423
Range91488048
Interquartile range (IQR)8879932.8

Descriptive statistics

Standard deviation23059047
Coefficient of variation (CV)1.6616419
Kurtosis4.8945658
Mean13877267
Median Absolute Deviation (MAD)4259734.5
Skewness2.3453982
Sum4.16318 × 108
Variance5.3171966 × 1014
MonotonicityNot monotonic
2023-12-12T21:58:45.254577image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=30)
ValueCountFrequency (%)
26984404 1
 
3.3%
16585937 1
 
3.3%
6798810 1
 
3.3%
5648403 1
 
3.3%
4643472 1
 
3.3%
886 1
 
3.3%
11116375 1
 
3.3%
7315526 1
 
3.3%
798775 1
 
3.3%
6302867 1
 
3.3%
Other values (20) 20
66.7%
ValueCountFrequency (%)
-4503625 1
3.3%
-497709 1
3.3%
886 1
3.3%
82794 1
3.3%
121598 1
3.3%
365587 1
3.3%
798775 1
3.3%
1630138 1
3.3%
2257224 1
3.3%
2627693 1
3.3%
ValueCountFrequency (%)
86984423 1
3.3%
81626608 1
3.3%
60248808 1
3.3%
32233279 1
3.3%
26984404 1
3.3%
26011631 1
3.3%
16585937 1
3.3%
11116375 1
3.3%
9318244 1
3.3%
7611302 1
3.3%

불납결손액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct19
Distinct (%)63.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean60647.3
Minimum0
Maximum1776791
Zeros11
Zeros (%)36.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T21:58:45.381681image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median9
Q32017.25
95-th percentile9588.7
Maximum1776791
Range1776791
Interquartile range (IQR)2017.25

Descriptive statistics

Standard deviation324140.09
Coefficient of variation (CV)5.3446746
Kurtosis29.994963
Mean60647.3
Median Absolute Deviation (MAD)9
Skewness5.4765596
Sum1819419
Variance1.050668 × 1011
MonotonicityNot monotonic
2023-12-12T21:58:45.521176image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=19)
ValueCountFrequency (%)
0 11
36.7%
3 2
 
6.7%
16 1
 
3.3%
6393 1
 
3.3%
1562 1
 
3.3%
2169 1
 
3.3%
4 1
 
3.3%
3395 1
 
3.3%
1776791 1
 
3.3%
125 1
 
3.3%
Other values (9) 9
30.0%
ValueCountFrequency (%)
0 11
36.7%
3 2
 
6.7%
4 1
 
3.3%
6 1
 
3.3%
12 1
 
3.3%
16 1
 
3.3%
45 1
 
3.3%
48 1
 
3.3%
125 1
 
3.3%
143 1
 
3.3%
ValueCountFrequency (%)
1776791 1
3.3%
10900 1
3.3%
7986 1
3.3%
6393 1
3.3%
6192 1
3.3%
3626 1
3.3%
3395 1
3.3%
2169 1
3.3%
1562 1
3.3%
143 1
3.3%

미수납액
Real number (ℝ)

HIGH CORRELATION  ZEROS 

Distinct26
Distinct (%)86.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1812980.5
Minimum0
Maximum35337008
Zeros5
Zeros (%)16.7%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T21:58:45.665198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12534.25
median17367
Q3367495.5
95-th percentile6479739
Maximum35337008
Range35337008
Interquartile range (IQR)364961.25

Descriptive statistics

Standard deviation6554285.1
Coefficient of variation (CV)3.6151989
Kurtosis25.744969
Mean1812980.5
Median Absolute Deviation (MAD)17367
Skewness4.9664942
Sum54389415
Variance4.2958653 × 1013
MonotonicityNot monotonic
2023-12-12T21:58:45.817562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=26)
ValueCountFrequency (%)
0 5
 
16.7%
367702 1
 
3.3%
1155627 1
 
3.3%
203901 1
 
3.3%
59474 1
 
3.3%
74 1
 
3.3%
21950 1
 
3.3%
35337008 1
 
3.3%
2472 1
 
3.3%
6866 1
 
3.3%
Other values (16) 16
53.3%
ValueCountFrequency (%)
0 5
16.7%
74 1
 
3.3%
496 1
 
3.3%
2472 1
 
3.3%
2721 1
 
3.3%
5393 1
 
3.3%
6866 1
 
3.3%
7391 1
 
3.3%
8495 1
 
3.3%
9738 1
 
3.3%
ValueCountFrequency (%)
35337008 1
3.3%
8421467 1
3.3%
4106516 1
3.3%
1793814 1
3.3%
1671111 1
3.3%
1155627 1
3.3%
398133 1
3.3%
367702 1
3.3%
366876 1
3.3%
297206 1
3.3%

비율(수납액_예산액)
Real number (ℝ)

ZEROS 

Distinct25
Distinct (%)83.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean87.16
Minimum0
Maximum124.8
Zeros4
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size402.0 B
2023-12-12T21:58:46.235398image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q185.2
median98.8
Q3105.75
95-th percentile119.9
Maximum124.8
Range124.8
Interquartile range (IQR)20.55

Descriptive statistics

Standard deviation36.578622
Coefficient of variation (CV)0.41967212
Kurtosis2.2923571
Mean87.16
Median Absolute Deviation (MAD)9.75
Skewness-1.8301863
Sum2614.8
Variance1337.9956
MonotonicityNot monotonic
2023-12-12T21:58:46.356188image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=25)
ValueCountFrequency (%)
0.0 4
 
13.3%
99.3 2
 
6.7%
119.9 2
 
6.7%
84.9 1
 
3.3%
78.9 1
 
3.3%
107.4 1
 
3.3%
98.2 1
 
3.3%
102.0 1
 
3.3%
118.9 1
 
3.3%
84.3 1
 
3.3%
Other values (15) 15
50.0%
ValueCountFrequency (%)
0.0 4
13.3%
78.9 1
 
3.3%
83.6 1
 
3.3%
84.3 1
 
3.3%
84.9 1
 
3.3%
86.1 1
 
3.3%
91.9 1
 
3.3%
92.4 1
 
3.3%
94.2 1
 
3.3%
95.6 1
 
3.3%
ValueCountFrequency (%)
124.8 1
3.3%
119.9 2
6.7%
118.9 1
3.3%
114.5 1
3.3%
109.7 1
3.3%
107.4 1
3.3%
106.7 1
3.3%
102.9 1
3.3%
102.0 1
3.3%
101.0 1
3.3%

Interactions

2023-12-12T21:58:40.156945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.229139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.676679image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.502160image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.033220image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.642939image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:40.248630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.293540image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.747988image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.580637image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.113114image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.719155image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:40.328913image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.364323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.833557image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.656623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.220612image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.799099image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:40.407062image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.447761image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.926328image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.738019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.320976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.876663image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:40.499448image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.522152image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.034649image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.861190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.429607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.968303image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:40.612506image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:37.602298image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.409705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:38.956771image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:39.524133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-12T21:58:40.057111image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-12T21:58:46.449819image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분2구분3구분4구분5구분6구분7예산액징수결정액수납액불납결손액미수납액비율(수납액_예산액)
구분21.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.417
구분31.0001.0001.0001.0001.0001.0000.0000.0000.0000.0000.0000.558
구분41.0001.0001.0001.0001.0000.9870.0000.0000.0001.0000.7490.685
구분51.0001.0001.0001.0001.0000.9910.0000.0000.0001.0000.8780.769
구분61.0001.0001.0001.0001.0000.9920.0000.0000.0001.0001.0000.844
구분71.0001.0000.9870.9910.9921.0000.0000.0000.0001.0001.0000.915
예산액0.0000.0000.0000.0000.0000.0001.0000.9880.9960.0000.7490.000
징수결정액0.0000.0000.0000.0000.0000.0000.9881.0000.9671.0000.9920.000
수납액0.0000.0000.0000.0000.0000.0000.9960.9671.0000.0000.5580.000
불납결손액0.0000.0001.0001.0001.0001.0000.0001.0000.0001.0001.0000.174
미수납액0.0000.0000.7490.8781.0001.0000.7490.9920.5581.0001.0000.043
비율(수납액_예산액)0.4170.5580.6850.7690.8440.9150.0000.0000.0000.1740.0431.000
2023-12-12T21:58:46.576131image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
구분3구분2
구분31.0000.899
구분20.8991.000
2023-12-12T21:58:46.651902image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
예산액징수결정액수납액불납결손액미수납액비율(수납액_예산액)구분2구분3
예산액1.0000.9760.8550.6130.8540.2490.0000.000
징수결정액0.9761.0000.8410.6550.8930.3220.0000.000
수납액0.8550.8411.0000.4910.7030.0670.0000.000
불납결손액0.6130.6550.4911.0000.8000.2150.0000.000
미수납액0.8540.8930.7030.8001.0000.2870.0000.000
비율(수납액_예산액)0.2490.3220.0670.2150.2871.0000.3390.319
구분20.0000.0000.0000.0000.0000.3391.0000.899
구분30.0000.0000.0000.0000.0000.3190.8991.000

Missing values

2023-12-12T21:58:40.764544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:58:41.005126image/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

구분1구분2구분3구분4구분5구분6구분7예산액징수결정액수납액불납결손액미수납액비율(수납액_예산액)
0국세청 총징수액지방소비세 이체액지방소비세 이체액지방소비세 이체액지방소비세 이체액지방소비세 이체액지방소비세 이체액0026984404000.0
1국세청 총징수액근로 자녀장려금근로 자녀장려금근로 자녀장려금근로 자녀장려금근로 자녀장려금근로 자녀장려금005001334000.0
2국세청 총징수액물납세액물납세액물납세액물납세액물납세액물납세액0082794000.0
3국세청 총징수액국세청세수내국세소득세신고분종합소득세종합소득세237066003012904726011631109004106516109.7
4국세청 총징수액국세청세수내국세소득세신고분양도소득세양도소득세3422280033908016322332793626167111194.2
5국세청 총징수액국세청세수내국세소득세원천분이자소득세이자소득세2647000263618826276930849599.3
6국세청 총징수액국세청세수내국세소득세원천분배당소득세배당소득세4349000416748141577430973895.6
7국세청 총징수액국세청세수내국세소득세원천분사업소득세사업소득세3579000419290540962294896628114.5
8국세청 총징수액국세청세수내국세소득세원천분근로소득세원천분6114120060546059602488084529720698.5
9국세청 총징수액국세청세수내국세소득세원천분근로소득세납세조합분11400012159812159800106.7
구분1구분2구분3구분4구분5구분6구분7예산액징수결정액수납액불납결손액미수납액비율(수납액_예산액)
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