Overview

Dataset statistics

Number of variables48
Number of observations10000
Missing cells1500
Missing cells (%)0.3%
Duplicate rows21
Duplicate rows (%)0.2%
Total size in memory3.9 MiB
Average record size in memory412.0 B

Variable types

Numeric15
Text11
Categorical18
DateTime1
Boolean3

Dataset

Description지방세ARS간편납부시스템으로 납부한 지방세 납부 통계(고지구분, 수납금액, 카드사, 세목명, 납기일자 등)
URLhttps://www.data.go.kr/data/15062556/fileData.do

Alerts

Dataset has 21 (0.2%) duplicate rowsDuplicates
세외수입금액 is highly imbalanced (99.3%)Imbalance
할부기간 is highly imbalanced (56.1%)Imbalance
수수료 is highly imbalanced (99.3%)Imbalance
결제에러 is highly imbalanced (97.2%)Imbalance
나이스페이결제에러 is highly imbalanced (98.5%)Imbalance
휴대폰수수료금액 is highly imbalanced (98.1%)Imbalance
선택납부여부 is highly imbalanced (97.1%)Imbalance
수납실패횟수 is highly imbalanced (97.5%)Imbalance
시군구코드 is highly imbalanced (98.0%)Imbalance
시군구명 is highly imbalanced (98.7%)Imbalance
세목명 is highly imbalanced (51.3%)Imbalance
농특본세 is highly imbalanced (98.4%)Imbalance
도시가산금 is highly imbalanced (95.9%)Imbalance
농특가산금 is highly imbalanced (97.8%)Imbalance
결의여부 is highly imbalanced (63.1%)Imbalance
압류여부 is highly imbalanced (63.2%)Imbalance
분납여부 is highly imbalanced (99.5%)Imbalance
징수일자 has 729 (7.3%) missing valuesMissing
도시잔액 is highly skewed (γ1 = 35.66428393)Skewed
공동잔액 is highly skewed (γ1 = 26.12066077)Skewed
농특잔액 is highly skewed (γ1 = 72.87021682)Skewed
도시본세 is highly skewed (γ1 = 35.93225107)Skewed
가산금 is highly skewed (γ1 = 51.13319289)Skewed
공동가산금 is highly skewed (γ1 = 36.52880897)Skewed
교육가산금 is highly skewed (γ1 = 29.0462991)Skewed
가산금합 is highly skewed (γ1 = 48.09986839)Skewed
도시잔액 has 9959 (99.6%) zerosZeros
공동잔액 has 6560 (65.6%) zerosZeros
교육잔액 has 666 (6.7%) zerosZeros
농특잔액 has 9950 (99.5%) zerosZeros
도시본세 has 9959 (99.6%) zerosZeros
가산금 has 7281 (72.8%) zerosZeros
공동가산금 has 9326 (93.3%) zerosZeros
교육가산금 has 7481 (74.8%) zerosZeros
가산금합 has 7281 (72.8%) zerosZeros

Reproduction

Analysis started2023-12-12 20:18:18.533554
Analysis finished2023-12-12 20:18:20.610126
Duration2.08 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

헤더시퀸스번호
Real number (ℝ)

Distinct8503
Distinct (%)85.2%
Missing19
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean154575.54
Minimum141843
Maximum167832
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:20.677115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum141843
5-th percentile143065
Q1148338
median154802
Q3160998
95-th percentile165911
Maximum167832
Range25989
Interquartile range (IQR)12660

Descriptive statistics

Standard deviation7417.724
Coefficient of variation (CV)0.047987695
Kurtosis-1.1769622
Mean154575.54
Median Absolute Deviation (MAD)6311
Skewness-0.016812026
Sum1.5428185 × 109
Variance55022629
MonotonicityNot monotonic
2023-12-13T05:18:20.841320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
144352 8
 
0.1%
167828 8
 
0.1%
143121 8
 
0.1%
165493 7
 
0.1%
159584 7
 
0.1%
165965 7
 
0.1%
164237 7
 
0.1%
155951 7
 
0.1%
165726 6
 
0.1%
166833 6
 
0.1%
Other values (8493) 9910
99.1%
(Missing) 19
 
0.2%
ValueCountFrequency (%)
141843 2
< 0.1%
141852 1
 
< 0.1%
141856 2
< 0.1%
141859 1
 
< 0.1%
141861 4
< 0.1%
141866 1
 
< 0.1%
141871 1
 
< 0.1%
141873 1
 
< 0.1%
141874 1
 
< 0.1%
141878 1
 
< 0.1%
ValueCountFrequency (%)
167832 2
 
< 0.1%
167828 8
0.1%
167817 1
 
< 0.1%
167813 3
 
< 0.1%
167809 1
 
< 0.1%
167796 2
 
< 0.1%
167789 1
 
< 0.1%
167786 1
 
< 0.1%
167783 2
 
< 0.1%
167781 1
 
< 0.1%

수납금액
Real number (ℝ)

Distinct5024
Distinct (%)50.3%
Missing19
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean310490.8
Minimum2480
Maximum16880200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:21.033702image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum2480
5-th percentile11000
Q194460
median171510
Q3316470
95-th percentile942220
Maximum16880200
Range16877720
Interquartile range (IQR)222010

Descriptive statistics

Standard deviation598360.32
Coefficient of variation (CV)1.9271435
Kurtosis167.00329
Mean310490.8
Median Absolute Deviation (MAD)92700
Skewness9.9473001
Sum3.0990086 × 109
Variance3.5803507 × 1011
MonotonicityNot monotonic
2023-12-13T05:18:21.239418image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11000 607
 
6.1%
11330 109
 
1.1%
129870 86
 
0.9%
72380 32
 
0.3%
145410 31
 
0.3%
55000 31
 
0.3%
133750 26
 
0.3%
220440 26
 
0.3%
172640 25
 
0.2%
66000 24
 
0.2%
Other values (5014) 8984
89.8%
ValueCountFrequency (%)
2480 1
< 0.1%
2500 1
< 0.1%
2540 1
< 0.1%
2700 1
< 0.1%
2820 1
< 0.1%
3140 1
< 0.1%
3580 1
< 0.1%
3600 1
< 0.1%
3660 1
< 0.1%
3670 1
< 0.1%
ValueCountFrequency (%)
16880200 1
 
< 0.1%
13253940 1
 
< 0.1%
13103370 1
 
< 0.1%
12629160 1
 
< 0.1%
10962580 1
 
< 0.1%
8619910 1
 
< 0.1%
8550560 4
< 0.1%
7376550 1
 
< 0.1%
6639000 3
< 0.1%
6514830 1
 
< 0.1%
Distinct5034
Distinct (%)50.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:18:21.706148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length6
Mean length5.7771
Min length4

Characters and Unicode

Total characters57771
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2969 ?
Unique (%)29.7%

Sample

1st row11000
2nd row1962720
3rd row703430
4th row193420
5th row168830
ValueCountFrequency (%)
11000 607
 
6.1%
11330 109
 
1.1%
129870 86
 
0.9%
72380 32
 
0.3%
145410 31
 
0.3%
55000 31
 
0.3%
133750 26
 
0.3%
220440 26
 
0.3%
172640 25
 
0.2%
66000 24
 
0.2%
Other values (5024) 9003
90.0%
2023-12-13T05:18:22.300113image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14962
25.9%
1 8078
14.0%
2 5895
 
10.2%
3 4930
 
8.5%
4 4359
 
7.5%
5 3965
 
6.9%
8 3900
 
6.8%
9 3892
 
6.7%
6 3877
 
6.7%
7 3875
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57733
99.9%
Dash Punctuation 38
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14962
25.9%
1 8078
14.0%
2 5895
 
10.2%
3 4930
 
8.5%
4 4359
 
7.6%
5 3965
 
6.9%
8 3900
 
6.8%
9 3892
 
6.7%
6 3877
 
6.7%
7 3875
 
6.7%
Dash Punctuation
ValueCountFrequency (%)
- 38
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 57771
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14962
25.9%
1 8078
14.0%
2 5895
 
10.2%
3 4930
 
8.5%
4 4359
 
7.5%
5 3965
 
6.9%
8 3900
 
6.8%
9 3892
 
6.7%
6 3877
 
6.7%
7 3875
 
6.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 57771
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14962
25.9%
1 8078
14.0%
2 5895
 
10.2%
3 4930
 
8.5%
4 4359
 
7.5%
5 3965
 
6.9%
8 3900
 
6.8%
9 3892
 
6.7%
6 3877
 
6.7%
7 3875
 
6.7%

세외수입금액
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9981 
2022-11-07
 
4
2023-03-06
 
3
2023-01-05
 
3
2023-06-05
 
2
Other values (6)
 
7

Length

Max length10
Median length1
Mean length1.0171
Min length1

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9981
99.8%
2022-11-07 4
 
< 0.1%
2023-03-06 3
 
< 0.1%
2023-01-05 3
 
< 0.1%
2023-06-05 2
 
< 0.1%
2023-02-06 2
 
< 0.1%
2022-07-05 1
 
< 0.1%
2023-04-05 1
 
< 0.1%
2022-10-05 1
 
< 0.1%
2023-07-05 1
 
< 0.1%

Length

2023-12-13T05:18:22.509735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 9981
99.8%
2022-11-07 4
 
< 0.1%
2023-03-06 3
 
< 0.1%
2023-01-05 3
 
< 0.1%
2023-06-05 2
 
< 0.1%
2023-02-06 2
 
< 0.1%
2022-07-05 1
 
< 0.1%
2023-04-05 1
 
< 0.1%
2022-10-05 1
 
< 0.1%
2023-07-05 1
 
< 0.1%

할부기간
Categorical

IMBALANCE 

Distinct29
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
5029 
3
2551 
6
679 
2
617 
5
 
498
Other values (24)
626 

Length

Max length10
Median length1
Mean length1.0412
Min length1

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row0
2nd row2
3rd row0
4th row5
5th row3

Common Values

ValueCountFrequency (%)
0 5029
50.3%
3 2551
25.5%
6 679
 
6.8%
2 617
 
6.2%
5 498
 
5.0%
4 181
 
1.8%
10 161
 
1.6%
7 144
 
1.4%
12 80
 
0.8%
8 25
 
0.2%
Other values (19) 35
 
0.4%

Length

2023-12-13T05:18:22.675198image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 5029
50.3%
3 2551
25.5%
6 679
 
6.8%
2 617
 
6.2%
5 498
 
5.0%
4 181
 
1.8%
10 161
 
1.6%
7 144
 
1.4%
12 80
 
0.8%
8 25
 
0.2%
Other values (19) 35
 
0.4%

유효기간
Real number (ℝ)

Distinct97
Distinct (%)1.0%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean2537.594
Minimum285
Maximum7608
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:22.884613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum285
5-th percentile2304
Q12408
median2512
Q32701
95-th percentile2709
Maximum7608
Range7323
Interquartile range (IQR)293

Descriptive statistics

Standard deviation187.08211
Coefficient of variation (CV)0.073724209
Kurtosis141.58981
Mean2537.594
Median Absolute Deviation (MAD)104
Skewness5.2923434
Sum25312500
Variance34999.715
MonotonicityNot monotonic
2023-12-13T05:18:23.091400image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
2706 312
 
3.1%
2705 270
 
2.7%
2608 247
 
2.5%
2704 246
 
2.5%
2602 237
 
2.4%
2701 234
 
2.3%
2603 229
 
2.3%
2702 229
 
2.3%
2707 228
 
2.3%
2703 202
 
2.0%
Other values (87) 7541
75.4%
ValueCountFrequency (%)
285 1
 
< 0.1%
431 3
 
< 0.1%
710 2
 
< 0.1%
862 1
 
< 0.1%
1442 1
 
< 0.1%
2207 10
 
0.1%
2208 24
0.2%
2209 26
0.3%
2210 33
0.3%
2211 36
0.4%
ValueCountFrequency (%)
7608 1
< 0.1%
6811 1
< 0.1%
5947 1
< 0.1%
5843 1
< 0.1%
5741 2
< 0.1%
5724 1
< 0.1%
5315 1
< 0.1%
4532 1
< 0.1%
3593 1
< 0.1%
3438 1
< 0.1%

발급카드사
Categorical

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
국민
1803 
신한
1740 
삼성
1654 
현대
1522 
BC
1165 
Other values (4)
2116 

Length

Max length2
Median length2
Mean length1.9981
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row현대
2nd row삼성
3rd row삼성
4th row현대
5th row신한

Common Values

ValueCountFrequency (%)
국민 1803
18.0%
신한 1740
17.4%
삼성 1654
16.5%
현대 1522
15.2%
BC 1165
11.7%
농협 879
8.8%
롯데 787
7.9%
외환 431
 
4.3%
Y 19
 
0.2%

Length

2023-12-13T05:18:23.312068image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:23.479955image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
국민 1803
18.0%
신한 1740
17.4%
삼성 1654
16.5%
현대 1522
15.2%
bc 1165
11.7%
농협 879
8.8%
롯데 787
7.9%
외환 431
 
4.3%
y 19
 
0.2%

수수료
Categorical

IMBALANCE 

Distinct19
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9981 
2022-12-09
 
2
2022-10-19
 
1
2022-06-16
 
1
2023-02-28
 
1
Other values (14)
 
14

Length

Max length10
Median length1
Mean length1.0171
Min length1

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9981
99.8%
2022-12-09 2
 
< 0.1%
2022-10-19 1
 
< 0.1%
2022-06-16 1
 
< 0.1%
2023-02-28 1
 
< 0.1%
2022-10-18 1
 
< 0.1%
2023-05-18 1
 
< 0.1%
2023-01-04 1
 
< 0.1%
2023-02-16 1
 
< 0.1%
2023-03-16 1
 
< 0.1%
Other values (9) 9
 
0.1%

Length

2023-12-13T05:18:23.635887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 9981
99.8%
2022-12-09 2
 
< 0.1%
2022-09-06 1
 
< 0.1%
2022-10-26 1
 
< 0.1%
2022-12-30 1
 
< 0.1%
2023-05-22 1
 
< 0.1%
2023-01-22 1
 
< 0.1%
2022-07-03 1
 
< 0.1%
2023-06-01 1
 
< 0.1%
2022-10-17 1
 
< 0.1%
Other values (9) 9
 
0.1%
Distinct342
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:18:23.957224image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9829
Min length1

Characters and Unicode

Total characters99829
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st row2022-08-21
2nd row2023-02-23
3rd row2022-09-29
4th row2022-06-30
5th row2022-06-30
ValueCountFrequency (%)
2022-09-30 398
 
4.0%
2022-08-01 395
 
4.0%
2022-06-30 311
 
3.1%
2023-01-02 228
 
2.3%
2022-09-29 227
 
2.3%
2022-09-28 158
 
1.6%
2022-06-29 153
 
1.5%
2022-09-27 151
 
1.5%
2022-07-29 150
 
1.5%
2022-09-16 132
 
1.3%
Other values (332) 7697
77.0%
2023-12-13T05:18:24.497705image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 34058
34.1%
0 21981
22.0%
- 19962
20.0%
1 6936
 
6.9%
3 4331
 
4.3%
9 3344
 
3.3%
7 2775
 
2.8%
8 2570
 
2.6%
6 2296
 
2.3%
5 910
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79867
80.0%
Dash Punctuation 19962
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 34058
42.6%
0 21981
27.5%
1 6936
 
8.7%
3 4331
 
5.4%
9 3344
 
4.2%
7 2775
 
3.5%
8 2570
 
3.2%
6 2296
 
2.9%
5 910
 
1.1%
4 666
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 19962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99829
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 34058
34.1%
0 21981
22.0%
- 19962
20.0%
1 6936
 
6.9%
3 4331
 
4.3%
9 3344
 
3.3%
7 2775
 
2.8%
8 2570
 
2.6%
6 2296
 
2.3%
5 910
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99829
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 34058
34.1%
0 21981
22.0%
- 19962
20.0%
1 6936
 
6.9%
3 4331
 
4.3%
9 3344
 
3.3%
7 2775
 
2.8%
8 2570
 
2.6%
6 2296
 
2.3%
5 910
 
0.9%
Distinct342
Distinct (%)3.4%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2023-12-13T05:18:24.841752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9905
Min length5

Characters and Unicode

Total characters99905
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)0.3%

Sample

1st row2022-08-21
2nd row2023-02-23
3rd row2022-09-29
4th row2022-06-30
5th row2022-06-30
ValueCountFrequency (%)
2022-09-30 398
 
4.0%
2022-08-01 395
 
4.0%
2022-06-30 311
 
3.1%
2023-01-02 228
 
2.3%
2022-09-29 227
 
2.3%
2022-09-28 158
 
1.6%
2022-06-29 153
 
1.5%
2022-09-27 151
 
1.5%
2022-07-29 150
 
1.5%
2022-09-16 132
 
1.3%
Other values (332) 7697
77.0%
2023-12-13T05:18:25.398010image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 34058
34.1%
0 21981
22.0%
- 19962
20.0%
1 6955
 
7.0%
3 4350
 
4.4%
9 3344
 
3.3%
7 2775
 
2.8%
8 2570
 
2.6%
6 2315
 
2.3%
5 910
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79943
80.0%
Dash Punctuation 19962
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 34058
42.6%
0 21981
27.5%
1 6955
 
8.7%
3 4350
 
5.4%
9 3344
 
4.2%
7 2775
 
3.5%
8 2570
 
3.2%
6 2315
 
2.9%
5 910
 
1.1%
4 685
 
0.9%
Dash Punctuation
ValueCountFrequency (%)
- 19962
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99905
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 34058
34.1%
0 21981
22.0%
- 19962
20.0%
1 6955
 
7.0%
3 4350
 
4.4%
9 3344
 
3.3%
7 2775
 
2.8%
8 2570
 
2.6%
6 2315
 
2.3%
5 910
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99905
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 34058
34.1%
0 21981
22.0%
- 19962
20.0%
1 6955
 
7.0%
3 4350
 
4.4%
9 3344
 
3.3%
7 2775
 
2.8%
8 2570
 
2.6%
6 2315
 
2.3%
5 910
 
0.9%

결제에러
Categorical

IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
카드 결제 성공
9956 
No Information
 
25
경기도 양주시
 
19

Length

Max length14
Median length8
Mean length8.0131
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row카드 결제 성공
2nd row카드 결제 성공
3rd row카드 결제 성공
4th row카드 결제 성공
5th row카드 결제 성공

Common Values

ValueCountFrequency (%)
카드 결제 성공 9956
99.6%
No Information 25
 
0.2%
경기도 양주시 19
 
0.2%

Length

2023-12-13T05:18:25.578364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:25.720867image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
카드 9956
33.2%
결제 9956
33.2%
성공 9956
33.2%
no 25
 
0.1%
information 25
 
0.1%
경기도 19
 
0.1%
양주시 19
 
0.1%

나이스페이결제에러
Categorical

IMBALANCE 

Distinct11
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
카드 결제 성공
9956 
<NA>
 
25
2022-12-07
 
6
2022-08-03
 
4
2022-06-06
 
3
Other values (6)
 
6

Length

Max length10
Median length8
Mean length7.9938
Min length4

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row카드 결제 성공
2nd row카드 결제 성공
3rd row카드 결제 성공
4th row카드 결제 성공
5th row카드 결제 성공

Common Values

ValueCountFrequency (%)
카드 결제 성공 9956
99.6%
<NA> 25
 
0.2%
2022-12-07 6
 
0.1%
2022-08-03 4
 
< 0.1%
2022-06-06 3
 
< 0.1%
2021-08-05 1
 
< 0.1%
2022-06-02 1
 
< 0.1%
2023-01-01 1
 
< 0.1%
2022-07-03 1
 
< 0.1%
2022-01-07 1
 
< 0.1%

Length

2023-12-13T05:18:25.861229image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
카드 9956
33.3%
결제 9956
33.3%
성공 9956
33.3%
na 25
 
0.1%
2022-12-07 6
 
< 0.1%
2022-08-03 4
 
< 0.1%
2022-06-06 3
 
< 0.1%
2021-08-05 1
 
< 0.1%
2022-06-02 1
 
< 0.1%
2023-01-01 1
 
< 0.1%
Other values (3) 3
 
< 0.1%

이체일자
Categorical

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-08-16
2129 
2022-10-13
2071 
2022-07-13
1258 
2022-09-13
1180 
2023-01-13
1162 
Other values (18)
2200 

Length

Max length10
Median length10
Mean length9.985
Min length4

Unique

Unique6 ?
Unique (%)0.1%

Sample

1st row2022-09-13
2nd row2023-03-13
3rd row2022-10-13
4th row2022-07-13
5th row2022-07-13

Common Values

ValueCountFrequency (%)
2022-08-16 2129
21.3%
2022-10-13 2071
20.7%
2022-07-13 1258
12.6%
2022-09-13 1180
11.8%
2023-01-13 1162
11.6%
2023-02-13 765
 
7.6%
2022-11-14 296
 
3.0%
2023-03-13 279
 
2.8%
2023-04-13 229
 
2.3%
2022-12-13 226
 
2.3%
Other values (13) 405
 
4.0%

Length

2023-12-13T05:18:26.002811image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-08-16 2129
21.3%
2022-10-13 2071
20.7%
2022-07-13 1258
12.6%
2022-09-13 1180
11.8%
2023-01-13 1162
11.6%
2023-02-13 765
 
7.6%
2022-11-14 296
 
3.0%
2023-03-13 279
 
2.8%
2023-04-13 229
 
2.3%
2022-12-13 226
 
2.3%
Other values (13) 405
 
4.0%
Distinct22
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2022-08-16
2129 
2022-10-13
2071 
2022-07-13
1258 
2022-09-13
1180 
2023-01-13
1162 
Other values (17)
2200 

Length

Max length10
Median length10
Mean length9.985
Min length4

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st row2022-09-13
2nd row2023-03-13
3rd row2022-10-13
4th row2022-07-13
5th row2022-07-13

Common Values

ValueCountFrequency (%)
2022-08-16 2129
21.3%
2022-10-13 2071
20.7%
2022-07-13 1258
12.6%
2022-09-13 1180
11.8%
2023-01-13 1162
11.6%
2023-02-13 765
 
7.6%
2022-11-14 296
 
3.0%
2023-03-13 279
 
2.8%
2023-04-13 229
 
2.3%
2022-12-13 226
 
2.3%
Other values (12) 405
 
4.0%

Length

2023-12-13T05:18:26.150809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
2022-08-16 2129
21.3%
2022-10-13 2071
20.7%
2022-07-13 1258
12.6%
2022-09-13 1180
11.8%
2023-01-13 1162
11.6%
2023-02-13 765
 
7.6%
2022-11-14 296
 
3.0%
2023-03-13 279
 
2.8%
2023-04-13 229
 
2.3%
2022-12-13 226
 
2.3%
Other values (12) 405
 
4.0%
Distinct251
Distinct (%)2.5%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
Minimum2021-09-30 00:00:00
Maximum2023-06-09 00:00:00
2023-12-13T05:18:26.298711image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T05:18:26.437608image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)

휴대폰수수료금액
Categorical

IMBALANCE 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9956 
<NA>
 
25
106001
 
10
104101
 
5
114001
 
2

Length

Max length6
Median length1
Mean length1.017
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9956
99.6%
<NA> 25
 
0.2%
106001 10
 
0.1%
104101 5
 
0.1%
114001 2
 
< 0.1%
105305 2
 
< 0.1%

Length

2023-12-13T05:18:26.571043image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:26.709315image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9956
99.6%
na 25
 
0.2%
106001 10
 
0.1%
104101 5
 
< 0.1%
114001 2
 
< 0.1%
105305 2
 
< 0.1%

선택납부여부
Categorical

IMBALANCE 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
Y
9921 
N
 
35
<NA>
 
25
자동차세(자동차)
 
10
주민세(개인분)
 
5
Other values (2)
 
4

Length

Max length9
Median length1
Mean length1.0218
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
Y 9921
99.2%
N 35
 
0.4%
<NA> 25
 
0.2%
자동차세(자동차) 10
 
0.1%
주민세(개인분) 5
 
0.1%
등록면허세(면허) 2
 
< 0.1%
재산세(주택) 2
 
< 0.1%

Length

2023-12-13T05:18:26.846670image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:26.976032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
y 9921
99.2%
n 35
 
0.4%
na 25
 
0.2%
자동차세(자동차 10
 
0.1%
주민세(개인분 5
 
< 0.1%
등록면허세(면허 2
 
< 0.1%
재산세(주택 2
 
< 0.1%
Distinct355
Distinct (%)3.6%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:27.262193image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9910777
Min length4

Characters and Unicode

Total characters99661
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique39 ?
Unique (%)0.4%

Sample

1st row2022-08-21
2nd row2023-02-23
3rd row2022-09-29
4th row2022-06-30
5th row2022-06-30
ValueCountFrequency (%)
2022-09-30 398
 
4.0%
2022-08-01 395
 
4.0%
2022-06-30 311
 
3.1%
2023-01-02 228
 
2.3%
2022-09-29 227
 
2.3%
2022-09-28 158
 
1.6%
2022-06-29 153
 
1.5%
2022-09-27 151
 
1.5%
2022-07-29 150
 
1.5%
2022-09-16 132
 
1.3%
Other values (345) 7672
76.9%
2023-12-13T05:18:27.756038image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 33992
34.1%
0 21936
22.0%
- 19912
20.0%
1 6936
 
7.0%
3 4321
 
4.3%
9 3349
 
3.4%
7 2781
 
2.8%
8 2569
 
2.6%
6 2284
 
2.3%
5 910
 
0.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79749
80.0%
Dash Punctuation 19912
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 33992
42.6%
0 21936
27.5%
1 6936
 
8.7%
3 4321
 
5.4%
9 3349
 
4.2%
7 2781
 
3.5%
8 2569
 
3.2%
6 2284
 
2.9%
5 910
 
1.1%
4 671
 
0.8%
Dash Punctuation
ValueCountFrequency (%)
- 19912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99661
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 33992
34.1%
0 21936
22.0%
- 19912
20.0%
1 6936
 
7.0%
3 4321
 
4.3%
9 3349
 
3.4%
7 2781
 
2.8%
8 2569
 
2.6%
6 2284
 
2.3%
5 910
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99661
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 33992
34.1%
0 21936
22.0%
- 19912
20.0%
1 6936
 
7.0%
3 4321
 
4.3%
9 3349
 
3.4%
7 2781
 
2.8%
8 2569
 
2.6%
6 2284
 
2.3%
5 910
 
0.9%

수납실패횟수
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9975 
<NA>
 
25

Length

Max length4
Median length1
Mean length1.0075
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9975
99.8%
<NA> 25
 
0.2%

Length

2023-12-13T05:18:27.929231image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:28.048311image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9975
99.8%
na 25
 
0.2%

시군구코드
Categorical

IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
41630
9956 
<NA>
 
25
0
 
17
9420
 
1
35630
 
1

Length

Max length5
Median length5
Mean length4.9906
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row41630
2nd row41630
3rd row41630
4th row41630
5th row41630

Common Values

ValueCountFrequency (%)
41630 9956
99.6%
<NA> 25
 
0.2%
0 17
 
0.2%
9420 1
 
< 0.1%
35630 1
 
< 0.1%

Length

2023-12-13T05:18:28.200209image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:28.380942image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
41630 9956
99.6%
na 25
 
0.2%
0 17
 
0.2%
9420 1
 
< 0.1%
35630 1
 
< 0.1%

시군구명
Categorical

IMBALANCE 

Distinct14
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
경기도 양주시
9956 
<NA>
 
25
1030
 
5
0
 
3
38960
 
2
Other values (9)
 
9

Length

Max length7
Median length7
Mean length6.9869
Min length1

Unique

Unique9 ?
Unique (%)0.1%

Sample

1st row경기도 양주시
2nd row경기도 양주시
3rd row경기도 양주시
4th row경기도 양주시
5th row경기도 양주시

Common Values

ValueCountFrequency (%)
경기도 양주시 9956
99.6%
<NA> 25
 
0.2%
1030 5
 
0.1%
0 3
 
< 0.1%
38960 2
 
< 0.1%
30750 1
 
< 0.1%
21010 1
 
< 0.1%
46220 1
 
< 0.1%
5720 1
 
< 0.1%
51630 1
 
< 0.1%
Other values (4) 4
 
< 0.1%

Length

2023-12-13T05:18:28.536974image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
경기도 9956
49.9%
양주시 9956
49.9%
na 25
 
0.1%
1030 5
 
< 0.1%
0 3
 
< 0.1%
38960 2
 
< 0.1%
30750 1
 
< 0.1%
21010 1
 
< 0.1%
46220 1
 
< 0.1%
5720 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
Distinct233
Distinct (%)2.3%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:28.854731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9828571
Min length1

Characters and Unicode

Total characters99579
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique111 ?
Unique (%)1.1%

Sample

1st row2022-08-03
2nd row2022-05-07
3rd row2022-09-03
4th row2022-06-06
5th row2022-06-06
ValueCountFrequency (%)
2022-07-03 2112
21.2%
2022-06-06 1878
18.8%
2022-12-07 1551
15.5%
2022-09-02 1444
14.5%
2022-08-03 1187
11.9%
2022-09-03 550
 
5.5%
2023-01-08 339
 
3.4%
2023-01-06 117
 
1.2%
2021-12-06 56
 
0.6%
2021-08-05 24
 
0.2%
Other values (223) 717
 
7.2%
2023-12-13T05:18:29.364880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 32261
32.4%
0 27983
28.1%
- 19912
20.0%
3 4687
 
4.7%
6 4110
 
4.1%
7 3835
 
3.9%
1 2706
 
2.7%
9 2126
 
2.1%
8 1659
 
1.7%
5 201
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79667
80.0%
Dash Punctuation 19912
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 32261
40.5%
0 27983
35.1%
3 4687
 
5.9%
6 4110
 
5.2%
7 3835
 
4.8%
1 2706
 
3.4%
9 2126
 
2.7%
8 1659
 
2.1%
5 201
 
0.3%
4 99
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99579
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 32261
32.4%
0 27983
28.1%
- 19912
20.0%
3 4687
 
4.7%
6 4110
 
4.1%
7 3835
 
3.9%
1 2706
 
2.7%
9 2126
 
2.1%
8 1659
 
1.7%
5 201
 
0.2%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 32261
32.4%
0 27983
28.1%
- 19912
20.0%
3 4687
 
4.7%
6 4110
 
4.1%
7 3835
 
3.9%
1 2706
 
2.7%
9 2126
 
2.1%
8 1659
 
1.7%
5 201
 
0.2%

징수일자
Text

MISSING 

Distinct114
Distinct (%)1.2%
Missing729
Missing (%)7.3%
Memory size156.2 KiB
2023-12-13T05:18:29.634838image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9902923
Min length4

Characters and Unicode

Total characters92620
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique57 ?
Unique (%)0.6%

Sample

1st row2022-08-03
2nd row2022-05-08
3rd row2022-09-03
4th row2022-06-06
5th row2022-06-06
ValueCountFrequency (%)
2022-07-04 2112
22.8%
2022-09-03 1993
21.5%
2022-06-06 1878
20.3%
2022-12-08 1554
16.8%
2022-08-03 1119
12.1%
2023-01-06 118
 
1.3%
2021-12-06 56
 
0.6%
2021-08-05 24
 
0.3%
2022-09-12 23
 
0.2%
2022-11-08 22
 
0.2%
Other values (104) 372
 
4.0%
2023-12-13T05:18:30.051239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 29060
31.4%
0 26080
28.2%
- 18504
20.0%
6 4054
 
4.4%
3 3353
 
3.6%
8 2820
 
3.0%
7 2204
 
2.4%
4 2198
 
2.4%
1 2187
 
2.4%
9 2088
 
2.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74116
80.0%
Dash Punctuation 18504
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29060
39.2%
0 26080
35.2%
6 4054
 
5.5%
3 3353
 
4.5%
8 2820
 
3.8%
7 2204
 
3.0%
4 2198
 
3.0%
1 2187
 
3.0%
9 2088
 
2.8%
5 72
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 18504
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 92620
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29060
31.4%
0 26080
28.2%
- 18504
20.0%
6 4054
 
4.4%
3 3353
 
3.6%
8 2820
 
3.0%
7 2204
 
2.4%
4 2198
 
2.4%
1 2187
 
2.4%
9 2088
 
2.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 92620
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29060
31.4%
0 26080
28.2%
- 18504
20.0%
6 4054
 
4.4%
3 3353
 
3.6%
8 2820
 
3.0%
7 2204
 
2.4%
4 2198
 
2.4%
1 2187
 
2.4%
9 2088
 
2.3%
Distinct121
Distinct (%)1.2%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:30.286339image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9828571
Min length1

Characters and Unicode

Total characters99579
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique69 ?
Unique (%)0.7%

Sample

1st row2022-08-31
2nd row2022-05-31
3rd row2022-09-30
4th row2022-06-30
5th row2022-06-30
ValueCountFrequency (%)
2022-07-31 2133
21.4%
2022-09-30 2040
20.5%
2022-06-30 1927
19.3%
2022-12-31 1583
15.9%
2022-08-31 1246
12.5%
2023-01-31 546
 
5.5%
2021-12-31 57
 
0.6%
2023-03-31 37
 
0.4%
2023-05-31 31
 
0.3%
2022-10-31 31
 
0.3%
Other values (111) 344
 
3.4%
2023-12-13T05:18:30.673667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 30680
30.8%
0 22383
22.5%
- 19912
20.0%
3 10630
 
10.7%
1 8310
 
8.3%
7 2175
 
2.2%
9 2076
 
2.1%
6 1982
 
2.0%
8 1317
 
1.3%
5 64
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79667
80.0%
Dash Punctuation 19912
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 30680
38.5%
0 22383
28.1%
3 10630
 
13.3%
1 8310
 
10.4%
7 2175
 
2.7%
9 2076
 
2.6%
6 1982
 
2.5%
8 1317
 
1.7%
5 64
 
0.1%
4 50
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99579
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 30680
30.8%
0 22383
22.5%
- 19912
20.0%
3 10630
 
10.7%
1 8310
 
8.3%
7 2175
 
2.2%
9 2076
 
2.1%
6 1982
 
2.0%
8 1317
 
1.3%
5 64
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99579
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 30680
30.8%
0 22383
22.5%
- 19912
20.0%
3 10630
 
10.7%
1 8310
 
8.3%
7 2175
 
2.2%
9 2076
 
2.1%
6 1982
 
2.0%
8 1317
 
1.3%
5 64
 
0.1%
Distinct123
Distinct (%)1.2%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:30.875818image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length9.9835589
Min length1

Characters and Unicode

Total characters99586
Distinct characters11
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique74 ?
Unique (%)0.7%

Sample

1st row2022-08-31
2nd row2023-02-28
3rd row2022-09-30
4th row2022-06-30
5th row2022-06-30
ValueCountFrequency (%)
2022-07-31 2481
24.9%
2022-09-30 2084
20.9%
2022-08-31 1230
12.3%
2022-06-30 1188
11.9%
2022-12-31 1084
10.9%
2023-01-31 1001
10.0%
2022-10-31 349
 
3.5%
2023-02-28 78
 
0.8%
2022-01-31 55
 
0.6%
2023-03-31 49
 
0.5%
Other values (113) 376
 
3.8%
2023-12-13T05:18:31.594032image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 29808
29.9%
0 22242
22.3%
- 19912
20.0%
3 11110
 
11.2%
1 9145
 
9.2%
7 2529
 
2.5%
9 2139
 
2.1%
8 1363
 
1.4%
6 1218
 
1.2%
5 72
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 79674
80.0%
Dash Punctuation 19912
 
20.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 29808
37.4%
0 22242
27.9%
3 11110
 
13.9%
1 9145
 
11.5%
7 2529
 
3.2%
9 2139
 
2.7%
8 1363
 
1.7%
6 1218
 
1.5%
5 72
 
0.1%
4 48
 
0.1%
Dash Punctuation
ValueCountFrequency (%)
- 19912
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99586
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 29808
29.9%
0 22242
22.3%
- 19912
20.0%
3 11110
 
11.2%
1 9145
 
9.2%
7 2529
 
2.5%
9 2139
 
2.1%
8 1363
 
1.4%
6 1218
 
1.2%
5 72
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 99586
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 29808
29.9%
0 22242
22.3%
- 19912
20.0%
3 11110
 
11.2%
1 9145
 
9.2%
7 2529
 
2.5%
9 2139
 
2.1%
8 1363
 
1.4%
6 1218
 
1.2%
5 72
 
0.1%

세목코드
Real number (ℝ)

Distinct33
Distinct (%)0.3%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean106089.48
Minimum0
Maximum140004
Zeros3
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:31.760630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile104101
Q1105305
median105305
Q3106001
95-th percentile106001
Maximum140004
Range140004
Interquartile range (IQR)696

Descriptive statistics

Standard deviation6395.3939
Coefficient of variation (CV)0.060283018
Kurtosis97.091408
Mean106089.48
Median Absolute Deviation (MAD)696
Skewness-2.3935671
Sum1.0582425 × 109
Variance40901063
MonotonicityNot monotonic
2023-12-13T05:18:31.939182image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=33)
ValueCountFrequency (%)
106001 4120
41.2%
105305 3340
33.4%
104101 1143
 
11.4%
105304 557
 
5.6%
105301 242
 
2.4%
140001 144
 
1.4%
114001 136
 
1.4%
104102 94
 
0.9%
101502 64
 
0.6%
140004 43
 
0.4%
Other values (23) 92
 
0.9%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
0 3
< 0.1%
1000 5
0.1%
5560 1
 
< 0.1%
11500 1
 
< 0.1%
20370 1
 
< 0.1%
29860 1
 
< 0.1%
35020 1
 
< 0.1%
35960 1
 
< 0.1%
37710 1
 
< 0.1%
38960 2
 
< 0.1%
ValueCountFrequency (%)
140004 43
 
0.4%
140003 2
 
< 0.1%
140002 14
 
0.1%
140001 144
 
1.4%
135001 6
 
0.1%
114001 136
 
1.4%
106003 6
 
0.1%
106002 15
 
0.1%
106001 4120
41.2%
105305 3340
33.4%

세목명
Categorical

IMBALANCE 

Distinct23
Distinct (%)0.2%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
자동차세(자동차)
4120 
재산세(주택)
3340 
주민세(개인분)
1143 
재산세(토지)
557 
재산세(건축물)
 
242
Other values (18)
598 

Length

Max length11
Median length10
Mean length8.0821
Min length1

Unique

Unique4 ?
Unique (%)< 0.1%

Sample

1st row주민세(개인분)
2nd row지방소득세(종합소득)
3rd row재산세(토지)
4th row자동차세(자동차)
5th row자동차세(자동차)

Common Values

ValueCountFrequency (%)
자동차세(자동차) 4120
41.2%
재산세(주택) 3340
33.4%
주민세(개인분) 1143
 
11.4%
재산세(토지) 557
 
5.6%
재산세(건축물) 242
 
2.4%
지방소득세(종합소득) 144
 
1.4%
등록면허세(면허) 136
 
1.4%
주민세(사업소분) 94
 
0.9%
취득세(차량) 64
 
0.6%
지방소득세(특별징수) 43
 
0.4%
Other values (13) 117
 
1.2%

Length

2023-12-13T05:18:32.147083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
자동차세(자동차 4120
41.2%
재산세(주택 3340
33.4%
주민세(개인분 1143
 
11.4%
재산세(토지 557
 
5.6%
재산세(건축물 242
 
2.4%
지방소득세(종합소득 144
 
1.4%
등록면허세(면허 136
 
1.4%
주민세(사업소분 94
 
0.9%
취득세(차량 64
 
0.6%
지방소득세(특별징수 43
 
0.4%
Other values (13) 117
 
1.2%

본세잔액
Real number (ℝ)

Distinct3960
Distinct (%)39.7%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean161263.87
Minimum0
Maximum14754610
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:32.323667image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10000
Q155640
median95870
Q3159520
95-th percentile356526
Maximum14754610
Range14754610
Interquartile range (IQR)103880

Descriptive statistics

Standard deviation441761.05
Coefficient of variation (CV)2.7393677
Kurtosis344.85184
Mean161263.87
Median Absolute Deviation (MAD)46570
Skewness15.279956
Sum1.6086071 × 109
Variance1.9515282 × 1011
MonotonicityNot monotonic
2023-12-13T05:18:32.514040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 713
 
7.1%
10300 420
 
4.2%
99900 113
 
1.1%
102890 79
 
0.8%
50000 76
 
0.8%
27070 49
 
0.5%
55680 46
 
0.5%
111860 44
 
0.4%
15000 36
 
0.4%
149620 35
 
0.4%
Other values (3950) 8364
83.6%
ValueCountFrequency (%)
0 2
< 0.1%
10 1
< 0.1%
230 1
< 0.1%
900 1
< 0.1%
1480 1
< 0.1%
1780 1
< 0.1%
2000 1
< 0.1%
2020 1
< 0.1%
2060 2
< 0.1%
2070 2
< 0.1%
ValueCountFrequency (%)
14754610 1
< 0.1%
12052780 1
< 0.1%
11744010 1
< 0.1%
10919480 1
< 0.1%
10245650 1
< 0.1%
9135490 1
< 0.1%
8493910 1
< 0.1%
8110540 1
< 0.1%
6387630 1
< 0.1%
5538010 1
< 0.1%

도시잔액
Real number (ℝ)

SKEWED  ZEROS 

Distinct12
Distinct (%)0.1%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean3.3794486
Minimum0
Maximum5010
Zeros9959
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:32.694323image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum5010
Range5010
Interquartile range (IQR)0

Descriptive statistics

Standard deviation108.9695
Coefficient of variation (CV)32.244756
Kurtosis1329.4795
Mean3.3794486
Median Absolute Deviation (MAD)0
Skewness35.664284
Sum33710
Variance11874.351
MonotonicityNot monotonic
2023-12-13T05:18:32.836732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 9959
99.6%
300 5
 
0.1%
270 2
 
< 0.1%
2980 1
 
< 0.1%
2130 1
 
< 0.1%
4480 1
 
< 0.1%
2500 1
 
< 0.1%
5010 1
 
< 0.1%
3770 1
 
< 0.1%
3500 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
0 9959
99.6%
270 2
 
< 0.1%
300 5
 
0.1%
2130 1
 
< 0.1%
2500 1
 
< 0.1%
2980 1
 
< 0.1%
3500 1
 
< 0.1%
3590 1
 
< 0.1%
3710 1
 
< 0.1%
3770 1
 
< 0.1%
ValueCountFrequency (%)
5010 1
 
< 0.1%
4480 1
 
< 0.1%
3770 1
 
< 0.1%
3710 1
 
< 0.1%
3590 1
 
< 0.1%
3500 1
 
< 0.1%
2980 1
 
< 0.1%
2500 1
 
< 0.1%
2130 1
 
< 0.1%
300 5
0.1%

공동잔액
Real number (ℝ)

SKEWED  ZEROS 

Distinct1019
Distinct (%)10.2%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean7295.3534
Minimum0
Maximum2012570
Zeros6560
Zeros (%)65.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:33.011619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q37810
95-th percentile22290
Maximum2012570
Range2012570
Interquartile range (IQR)7810

Descriptive statistics

Standard deviation40920.114
Coefficient of variation (CV)5.6090653
Kurtosis1018.0593
Mean7295.3534
Median Absolute Deviation (MAD)0
Skewness26.120661
Sum72771150
Variance1.6744557 × 109
MonotonicityNot monotonic
2023-12-13T05:18:33.210965image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 6560
65.6%
11640 47
 
0.5%
22290 38
 
0.4%
8170 36
 
0.4%
8160 35
 
0.4%
23290 31
 
0.3%
6410 30
 
0.3%
6830 28
 
0.3%
5500 27
 
0.3%
10800 23
 
0.2%
Other values (1009) 3120
31.2%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
0 6560
65.6%
340 1
 
< 0.1%
2010 1
 
< 0.1%
2060 1
 
< 0.1%
2120 1
 
< 0.1%
2200 1
 
< 0.1%
2410 2
 
< 0.1%
2470 1
 
< 0.1%
2510 1
 
< 0.1%
2540 1
 
< 0.1%
ValueCountFrequency (%)
2012570 1
< 0.1%
1784540 1
< 0.1%
871370 1
< 0.1%
816350 1
< 0.1%
714760 1
< 0.1%
665950 1
< 0.1%
538050 1
< 0.1%
528950 1
< 0.1%
526870 1
< 0.1%
500220 2
< 0.1%

교육잔액
Real number (ℝ)

ZEROS 

Distinct2629
Distinct (%)26.4%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean26361.359
Minimum0
Maximum2183890
Zeros666
Zeros (%)6.7%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:33.409269image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13770
median11380
Q335910
95-th percentile77150
Maximum2183890
Range2183890
Interquartile range (IQR)32140

Descriptive statistics

Standard deviation58040.722
Coefficient of variation (CV)2.2017348
Kurtosis600.48066
Mean26361.359
Median Absolute Deviation (MAD)10380
Skewness19.253749
Sum2.6295456 × 108
Variance3.3687254 × 109
MonotonicityNot monotonic
2023-12-13T05:18:33.572689image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1000 731
 
7.3%
0 666
 
6.7%
1030 422
 
4.2%
29970 117
 
1.2%
5000 80
 
0.8%
30860 79
 
0.8%
16700 49
 
0.5%
33550 44
 
0.4%
7210 41
 
0.4%
4890 39
 
0.4%
Other values (2619) 7707
77.1%
ValueCountFrequency (%)
0 666
6.7%
30 1
 
< 0.1%
110 1
 
< 0.1%
260 1
 
< 0.1%
270 1
 
< 0.1%
290 1
 
< 0.1%
300 1
 
< 0.1%
400 1
 
< 0.1%
410 4
 
< 0.1%
420 1
 
< 0.1%
ValueCountFrequency (%)
2183890 1
< 0.1%
2125590 1
< 0.1%
1947670 1
< 0.1%
1827090 1
< 0.1%
988920 1
< 0.1%
873450 1
< 0.1%
730150 1
< 0.1%
671080 1
< 0.1%
638000 1
< 0.1%
637740 1
< 0.1%

농특잔액
Real number (ℝ)

SKEWED  ZEROS 

Distinct22
Distinct (%)0.2%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean198.9203
Minimum0
Maximum838850
Zeros9950
Zeros (%)99.5%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:33.744785image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum838850
Range838850
Interquartile range (IQR)0

Descriptive statistics

Standard deviation9643.8873
Coefficient of variation (CV)48.481162
Kurtosis5969.9739
Mean198.9203
Median Absolute Deviation (MAD)0
Skewness72.870217
Sum1984230
Variance93004562
MonotonicityNot monotonic
2023-12-13T05:18:33.870199image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=22)
ValueCountFrequency (%)
0 9950
99.5%
30 5
 
0.1%
890 1
 
< 0.1%
86380 1
 
< 0.1%
138510 1
 
< 0.1%
340 1
 
< 0.1%
1070 1
 
< 0.1%
1950 1
 
< 0.1%
1050 1
 
< 0.1%
1130 1
 
< 0.1%
Other values (12) 12
 
0.1%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
0 9950
99.5%
30 5
 
0.1%
160 1
 
< 0.1%
340 1
 
< 0.1%
640 1
 
< 0.1%
890 1
 
< 0.1%
1050 1
 
< 0.1%
1070 1
 
< 0.1%
1130 1
 
< 0.1%
1340 1
 
< 0.1%
ValueCountFrequency (%)
838850 1
< 0.1%
369900 1
< 0.1%
178000 1
< 0.1%
138510 1
< 0.1%
98800 1
< 0.1%
92500 1
< 0.1%
86380 1
< 0.1%
74030 1
< 0.1%
66920 1
< 0.1%
30120 1
< 0.1%

본세
Real number (ℝ)

Distinct3538
Distinct (%)35.5%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean159858.79
Minimum0
Maximum14754610
Zeros22
Zeros (%)0.2%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:34.039141image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10000
Q155140
median95080
Q3156780
95-th percentile351338
Maximum14754610
Range14754610
Interquartile range (IQR)101640

Descriptive statistics

Standard deviation437084.18
Coefficient of variation (CV)2.7341893
Kurtosis345.98313
Mean159858.79
Median Absolute Deviation (MAD)45680
Skewness15.241735
Sum1.5945914 × 109
Variance1.9104258 × 1011
MonotonicityNot monotonic
2023-12-13T05:18:34.234446image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10000 1132
 
11.3%
99900 192
 
1.9%
50000 90
 
0.9%
27070 80
 
0.8%
55680 76
 
0.8%
111860 63
 
0.6%
99550 60
 
0.6%
15000 60
 
0.6%
149620 50
 
0.5%
99750 48
 
0.5%
Other values (3528) 8124
81.2%
ValueCountFrequency (%)
0 22
0.2%
10 1
 
< 0.1%
900 1
 
< 0.1%
1480 1
 
< 0.1%
1780 1
 
< 0.1%
2000 1
 
< 0.1%
2020 1
 
< 0.1%
2060 2
 
< 0.1%
2070 2
 
< 0.1%
2090 1
 
< 0.1%
ValueCountFrequency (%)
14754610 1
< 0.1%
11744010 1
< 0.1%
11701730 1
< 0.1%
10919480 1
< 0.1%
10245650 1
< 0.1%
9135490 1
< 0.1%
7701730 1
< 0.1%
7533410 1
< 0.1%
6387630 1
< 0.1%
5538010 1
< 0.1%

도시본세
Real number (ℝ)

SKEWED  ZEROS 

Distinct12
Distinct (%)0.1%
Missing25
Missing (%)0.2%
Infinite0
Infinite (%)0.0%
Mean4.3388471
Minimum0
Maximum6510
Zeros9959
Zeros (%)99.6%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:34.360375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile0
Maximum6510
Range6510
Interquartile range (IQR)0

Descriptive statistics

Standard deviation141.68492
Coefficient of variation (CV)32.654969
Kurtosis1347.573
Mean4.3388471
Median Absolute Deviation (MAD)0
Skewness35.932251
Sum43280
Variance20074.615
MonotonicityNot monotonic
2023-12-13T05:18:34.470292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=12)
ValueCountFrequency (%)
0 9959
99.6%
330 5
 
0.1%
270 2
 
< 0.1%
3870 1
 
< 0.1%
2770 1
 
< 0.1%
5820 1
 
< 0.1%
2930 1
 
< 0.1%
6510 1
 
< 0.1%
4900 1
 
< 0.1%
4550 1
 
< 0.1%
Other values (2) 2
 
< 0.1%
(Missing) 25
 
0.2%
ValueCountFrequency (%)
0 9959
99.6%
270 2
 
< 0.1%
330 5
 
0.1%
2770 1
 
< 0.1%
2930 1
 
< 0.1%
3870 1
 
< 0.1%
4550 1
 
< 0.1%
4660 1
 
< 0.1%
4900 1
 
< 0.1%
5080 1
 
< 0.1%
ValueCountFrequency (%)
6510 1
 
< 0.1%
5820 1
 
< 0.1%
5080 1
 
< 0.1%
4900 1
 
< 0.1%
4660 1
 
< 0.1%
4550 1
 
< 0.1%
3870 1
 
< 0.1%
2930 1
 
< 0.1%
2770 1
 
< 0.1%
330 5
0.1%
Distinct871
Distinct (%)8.7%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:34.832351image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length2.2180451
Min length1

Characters and Unicode

Total characters22125
Distinct characters13
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

Unique418 ?
Unique (%)4.2%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0
ValueCountFrequency (%)
0 6542
65.6%
8160 54
 
0.5%
11640 52
 
0.5%
8170 41
 
0.4%
22290 41
 
0.4%
7740 39
 
0.4%
5500 35
 
0.4%
6410 35
 
0.4%
6830 34
 
0.3%
23290 31
 
0.3%
Other values (861) 3071
30.8%
2023-12-13T05:18:35.380823image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11016
49.8%
1 2204
 
10.0%
8 1214
 
5.5%
6 1201
 
5.4%
2 1184
 
5.4%
7 1176
 
5.3%
4 1166
 
5.3%
5 1023
 
4.6%
9 960
 
4.3%
3 943
 
4.3%
Other values (3) 38
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 22087
99.8%
Other Letter 38
 
0.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11016
49.9%
1 2204
 
10.0%
8 1214
 
5.5%
6 1201
 
5.4%
2 1184
 
5.4%
7 1176
 
5.3%
4 1166
 
5.3%
5 1023
 
4.6%
9 960
 
4.3%
3 943
 
4.3%
Other Letter
ValueCountFrequency (%)
19
50.0%
16
42.1%
3
 
7.9%

Most occurring scripts

ValueCountFrequency (%)
Common 22087
99.8%
Hangul 38
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11016
49.9%
1 2204
 
10.0%
8 1214
 
5.5%
6 1201
 
5.4%
2 1184
 
5.4%
7 1176
 
5.3%
4 1166
 
5.3%
5 1023
 
4.6%
9 960
 
4.3%
3 943
 
4.3%
Hangul
ValueCountFrequency (%)
19
50.0%
16
42.1%
3
 
7.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 22087
99.8%
Hangul 38
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11016
49.9%
1 2204
 
10.0%
8 1214
 
5.5%
6 1201
 
5.4%
2 1184
 
5.4%
7 1176
 
5.3%
4 1166
 
5.3%
5 1023
 
4.6%
9 960
 
4.3%
3 943
 
4.3%
Hangul
ValueCountFrequency (%)
19
50.0%
16
42.1%
3
 
7.9%
Distinct2385
Distinct (%)23.9%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:35.743589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.3459649
Min length1

Characters and Unicode

Total characters43351
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1287 ?
Unique (%)12.9%

Sample

1st row1000
2nd row0
3rd row59410
4th row28160
5th row38960
ValueCountFrequency (%)
1000 1150
 
11.5%
0 658
 
6.6%
29970 196
 
2.0%
5000 97
 
1.0%
16700 79
 
0.8%
29860 68
 
0.7%
33550 63
 
0.6%
44880 50
 
0.5%
50870 48
 
0.5%
39840 48
 
0.5%
Other values (2375) 7518
75.4%
2023-12-13T05:18:36.298555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14649
33.8%
1 4853
 
11.2%
3 3275
 
7.6%
9 3199
 
7.4%
5 3141
 
7.2%
2 3073
 
7.1%
4 3049
 
7.0%
7 2834
 
6.5%
6 2660
 
6.1%
8 2599
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43332
> 99.9%
Uppercase Letter 19
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14649
33.8%
1 4853
 
11.2%
3 3275
 
7.6%
9 3199
 
7.4%
5 3141
 
7.2%
2 3073
 
7.1%
4 3049
 
7.0%
7 2834
 
6.5%
6 2660
 
6.1%
8 2599
 
6.0%
Uppercase Letter
ValueCountFrequency (%)
Y 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 43332
> 99.9%
Latin 19
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14649
33.8%
1 4853
 
11.2%
3 3275
 
7.6%
9 3199
 
7.4%
5 3141
 
7.2%
2 3073
 
7.1%
4 3049
 
7.0%
7 2834
 
6.5%
6 2660
 
6.1%
8 2599
 
6.0%
Latin
ValueCountFrequency (%)
Y 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 43351
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14649
33.8%
1 4853
 
11.2%
3 3275
 
7.6%
9 3199
 
7.4%
5 3141
 
7.2%
2 3073
 
7.1%
4 3049
 
7.0%
7 2834
 
6.5%
6 2660
 
6.1%
8 2599
 
6.0%

농특본세
Categorical

IMBALANCE 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9945 
<NA>
 
25
N
 
14
Y
 
5
369900
 
1
Other values (10)
 
10

Length

Max length6
Median length1
Mean length1.0122
Min length1

Unique

Unique11 ?
Unique (%)0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9945
99.5%
<NA> 25
 
0.2%
N 14
 
0.1%
Y 5
 
0.1%
369900 1
 
< 0.1%
74030 1
 
< 0.1%
178000 1
 
< 0.1%
89810 1
 
< 0.1%
66920 1
 
< 0.1%
30120 1
 
< 0.1%
Other values (5) 5
 
0.1%

Length

2023-12-13T05:18:36.443765image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0 9945
99.5%
na 25
 
0.2%
n 14
 
0.1%
y 5
 
< 0.1%
369900 1
 
< 0.1%
74030 1
 
< 0.1%
178000 1
 
< 0.1%
89810 1
 
< 0.1%
66920 1
 
< 0.1%
30120 1
 
< 0.1%
Other values (5) 5
 
< 0.1%
Distinct3916
Distinct (%)39.3%
Missing25
Missing (%)0.2%
Memory size156.2 KiB
2023-12-13T05:18:36.800054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length6
Mean length5.5610025
Min length1

Characters and Unicode

Total characters55471
Distinct characters11
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2663 ?
Unique (%)26.7%

Sample

1st row11000
2nd row1769010
3rd row564420
4th row122030
5th row168830
ValueCountFrequency (%)
11000 1132
 
11.3%
129870 192
 
1.9%
55000 89
 
0.9%
27070 80
 
0.8%
72380 75
 
0.8%
145410 63
 
0.6%
15000 60
 
0.6%
129410 60
 
0.6%
194500 50
 
0.5%
220440 48
 
0.5%
Other values (3906) 8126
81.5%
2023-12-13T05:18:37.325004image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15791
28.5%
1 8798
15.9%
2 5405
 
9.7%
7 3854
 
6.9%
8 3794
 
6.8%
3 3693
 
6.7%
4 3653
 
6.6%
9 3598
 
6.5%
5 3454
 
6.2%
6 3412
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55452
> 99.9%
Uppercase Letter 19
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15791
28.5%
1 8798
15.9%
2 5405
 
9.7%
7 3854
 
7.0%
8 3794
 
6.8%
3 3693
 
6.7%
4 3653
 
6.6%
9 3598
 
6.5%
5 3454
 
6.2%
6 3412
 
6.2%
Uppercase Letter
ValueCountFrequency (%)
N 19
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55452
> 99.9%
Latin 19
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15791
28.5%
1 8798
15.9%
2 5405
 
9.7%
7 3854
 
7.0%
8 3794
 
6.8%
3 3693
 
6.7%
4 3653
 
6.6%
9 3598
 
6.5%
5 3454
 
6.2%
6 3412
 
6.2%
Latin
ValueCountFrequency (%)
N 19
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55471
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15791
28.5%
1 8798
15.9%
2 5405
 
9.7%
7 3854
 
6.9%
8 3794
 
6.8%
3 3693
 
6.7%
4 3653
 
6.6%
9 3598
 
6.5%
5 3454
 
6.2%
6 3412
 
6.2%

가산금
Real number (ℝ)

SKEWED  ZEROS 

Distinct580
Distinct (%)5.8%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1227.9751
Minimum0
Maximum960500
Zeros7281
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:37.518699image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3300
95-th percentile5010
Maximum960500
Range960500
Interquartile range (IQR)300

Descriptive statistics

Standard deviation13018.892
Coefficient of variation (CV)10.601919
Kurtosis3274.6509
Mean1227.9751
Median Absolute Deviation (MAD)0
Skewness51.133193
Sum12225720
Variance1.6949155 × 108
MonotonicityNot monotonic
2023-12-13T05:18:37.658230image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7281
72.8%
300 420
 
4.2%
2990 102
 
1.0%
3890 42
 
0.4%
1670 40
 
0.4%
2980 38
 
0.4%
810 32
 
0.3%
3290 28
 
0.3%
5390 27
 
0.3%
3590 26
 
0.3%
Other values (570) 1920
 
19.2%
(Missing) 44
 
0.4%
ValueCountFrequency (%)
0 7281
72.8%
60 1
 
< 0.1%
70 3
 
< 0.1%
80 3
 
< 0.1%
90 1
 
< 0.1%
110 3
 
< 0.1%
120 1
 
< 0.1%
130 4
 
< 0.1%
140 9
 
0.1%
150 4
 
< 0.1%
ValueCountFrequency (%)
960500 1
< 0.1%
436570 1
< 0.1%
408810 1
< 0.1%
351050 1
< 0.1%
312100 1
< 0.1%
159210 1
< 0.1%
157850 1
< 0.1%
148450 1
< 0.1%
101500 1
< 0.1%
98460 1
< 0.1%

도시가산금
Categorical

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9956 
<NA>
 
44

Length

Max length4
Median length1
Mean length1.0132
Min length1

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9956
99.6%
<NA> 44
 
0.4%

Length

2023-12-13T05:18:37.809326image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:37.909142image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9956
99.6%
na 44
 
0.4%

공동가산금
Real number (ℝ)

SKEWED  ZEROS 

Distinct97
Distinct (%)1.0%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean38.889112
Minimum0
Maximum28610
Zeros9326
Zeros (%)93.3%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:38.025500image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile200
Maximum28610
Range28610
Interquartile range (IQR)0

Descriptive statistics

Standard deviation556.4709
Coefficient of variation (CV)14.30917
Kurtosis1503.6265
Mean38.889112
Median Absolute Deviation (MAD)0
Skewness36.528809
Sum387180
Variance309659.86
MonotonicityNot monotonic
2023-12-13T05:18:38.205482image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 9326
93.3%
240 36
 
0.4%
230 28
 
0.3%
200 28
 
0.3%
250 28
 
0.3%
220 24
 
0.2%
160 23
 
0.2%
350 22
 
0.2%
340 22
 
0.2%
190 21
 
0.2%
Other values (87) 398
 
4.0%
(Missing) 44
 
0.4%
ValueCountFrequency (%)
0 9326
93.3%
70 2
 
< 0.1%
90 1
 
< 0.1%
100 1
 
< 0.1%
110 4
 
< 0.1%
120 2
 
< 0.1%
130 6
 
0.1%
140 9
 
0.1%
150 8
 
0.1%
160 23
 
0.2%
ValueCountFrequency (%)
28610 1
< 0.1%
24890 1
< 0.1%
18190 1
< 0.1%
17960 1
< 0.1%
16580 1
< 0.1%
15710 1
< 0.1%
12440 1
< 0.1%
11420 1
< 0.1%
5980 1
< 0.1%
5360 1
< 0.1%

교육가산금
Real number (ℝ)

SKEWED  ZEROS 

Distinct226
Distinct (%)2.3%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean193.82382
Minimum0
Maximum44430
Zeros7481
Zeros (%)74.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:38.356685image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q30
95-th percentile1340
Maximum44430
Range44430
Interquartile range (IQR)0

Descriptive statistics

Standard deviation778.59237
Coefficient of variation (CV)4.0170107
Kurtosis1397.194
Mean193.82382
Median Absolute Deviation (MAD)0
Skewness29.046299
Sum1929710
Variance606206.08
MonotonicityNot monotonic
2023-12-13T05:18:38.505055image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7481
74.8%
30 424
 
4.2%
890 136
 
1.4%
150 48
 
0.5%
500 47
 
0.5%
120 43
 
0.4%
1160 42
 
0.4%
140 41
 
0.4%
90 39
 
0.4%
100 38
 
0.4%
Other values (216) 1617
 
16.2%
(Missing) 44
 
0.4%
ValueCountFrequency (%)
0 7481
74.8%
10 4
 
< 0.1%
20 3
 
< 0.1%
30 424
 
4.2%
40 8
 
0.1%
50 6
 
0.1%
60 3
 
< 0.1%
70 12
 
0.1%
80 25
 
0.2%
90 39
 
0.4%
ValueCountFrequency (%)
44430 1
< 0.1%
31560 1
< 0.1%
23050 1
< 0.1%
13380 1
< 0.1%
8600 1
< 0.1%
8080 1
< 0.1%
6230 1
< 0.1%
5930 1
< 0.1%
5540 1
< 0.1%
4960 1
< 0.1%

농특가산금
Categorical

IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
0
9954 
<NA>
 
44
2690
 
1
50
 
1

Length

Max length4
Median length1
Mean length1.0136
Min length1

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 9954
99.5%
<NA> 44
 
0.4%
2690 1
 
< 0.1%
50 1
 
< 0.1%

Length

2023-12-13T05:18:38.641223image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:38.746567image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 9954
99.5%
na 44
 
0.4%
2690 1
 
< 0.1%
50 1
 
< 0.1%

가산금합
Real number (ℝ)

SKEWED  ZEROS 

Distinct617
Distinct (%)6.2%
Missing44
Missing (%)0.4%
Infinite0
Infinite (%)0.0%
Mean1460.9632
Minimum0
Maximum960500
Zeros7281
Zeros (%)72.8%
Negative0
Negative (%)0.0%
Memory size166.0 KiB
2023-12-13T05:18:38.896384image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0
Q3330
95-th percentile6420
Maximum960500
Range960500
Interquartile range (IQR)330

Descriptive statistics

Standard deviation13369.348
Coefficient of variation (CV)9.1510503
Kurtosis2965.7193
Mean1460.9632
Median Absolute Deviation (MAD)0
Skewness48.099868
Sum14545350
Variance1.7873947 × 108
MonotonicityNot monotonic
2023-12-13T05:18:39.093181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 7281
72.8%
330 421
 
4.2%
3880 99
 
1.0%
2170 43
 
0.4%
5050 42
 
0.4%
3870 38
 
0.4%
810 31
 
0.3%
4270 26
 
0.3%
450 25
 
0.2%
4660 25
 
0.2%
Other values (607) 1925
 
19.2%
(Missing) 44
 
0.4%
ValueCountFrequency (%)
0 7281
72.8%
60 1
 
< 0.1%
70 2
 
< 0.1%
80 2
 
< 0.1%
90 2
 
< 0.1%
100 1
 
< 0.1%
110 1
 
< 0.1%
120 1
 
< 0.1%
130 4
 
< 0.1%
140 11
 
0.1%
ValueCountFrequency (%)
960500 1
< 0.1%
436570 1
< 0.1%
408810 1
< 0.1%
356530 1
< 0.1%
351050 1
< 0.1%
189410 1
< 0.1%
171500 1
< 0.1%
159210 1
< 0.1%
114880 1
< 0.1%
103270 1
< 0.1%

고지구분
Categorical

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
미납
6563 
체납
2689 
자납
704 
<NA>
 
44

Length

Max length4
Median length2
Mean length2.0088
Min length2

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row미납
2nd row체납
3rd row미납
4th row미납
5th row미납

Common Values

ValueCountFrequency (%)
미납 6563
65.6%
체납 2689
26.9%
자납 704
 
7.0%
<NA> 44
 
0.4%

Length

2023-12-13T05:18:39.243683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-13T05:18:39.686488image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
미납 6563
65.6%
체납 2689
26.9%
자납 704
 
7.0%
na 44
 
0.4%

결의여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing44
Missing (%)0.4%
Memory size97.7 KiB
True
9252 
False
 
704
(Missing)
 
44
ValueCountFrequency (%)
True 9252
92.5%
False 704
 
7.0%
(Missing) 44
 
0.4%
2023-12-13T05:18:39.790623image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

압류여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing44
Missing (%)0.4%
Memory size97.7 KiB
False
9254 
True
 
702
(Missing)
 
44
ValueCountFrequency (%)
False 9254
92.5%
True 702
 
7.0%
(Missing) 44
 
0.4%
2023-12-13T05:18:39.893115image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

분납여부
Boolean

IMBALANCE 

Distinct2
Distinct (%)< 0.1%
Missing44
Missing (%)0.4%
Memory size97.7 KiB
False
9952 
True
 
4
(Missing)
 
44
ValueCountFrequency (%)
False 9952
99.5%
True 4
 
< 0.1%
(Missing) 44
 
0.4%
2023-12-13T05:18:39.990022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Sample

헤더시퀸스번호수납금액지방세금액세외수입금액할부기간유효기간발급카드사수수료등록일자거래일자결제에러나이스페이결제에러이체일자나이스페이이체일자회계일자휴대폰수수료금액선택납부여부수정일자수납실패횟수시군구코드시군구명부과일자징수일자최초납기가산납기세목코드세목명본세잔액도시잔액공동잔액교육잔액농특잔액본세도시본세공동본세교육본세농특본세본세합가산금도시가산금공동가산금교육가산금농특가산금가산금합고지구분결의여부압류여부분납여부
74131521111100011000002405현대02022-08-212022-08-21카드 결제 성공카드 결제 성공2022-09-132022-09-132022-08-220Y2022-08-21041630경기도 양주시2022-08-032022-08-032022-08-312022-08-31104101주민세(개인분)10000001000010000001000011000000000미납YNN
1728616552619627201962720022703삼성02023-02-232023-02-23카드 결제 성공카드 결제 성공2023-03-132023-03-132023-02-230Y2023-02-23041630경기도 양주시2022-05-072022-05-082022-05-312023-02-28140001지방소득세(종합소득)192822000001769010000017690101592100000159210체납YYN
11431157414703430703430002502삼성02022-09-292022-09-29카드 결제 성공카드 결제 성공2022-10-132022-10-132022-09-290Y2022-09-29041630경기도 양주시2022-09-032022-09-032022-09-302022-09-30105304재산세(토지)5050100059410050501000594100564420000000미납YNN
2166144458193420193420052508현대02022-06-302022-06-30카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-300Y2022-06-30041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)93870002816009387000281600122030000000미납YNN
1972144248168830168830032408신한02022-06-302022-06-30카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-300Y2022-06-30041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)1298700038960012987000389600168830000000미납YNN
152561628086986069860032610삼성02023-01-022023-01-02카드 결제 성공카드 결제 성공2023-01-132023-01-132023-01-020Y2023-01-02041630경기도 양주시2022-12-072022-12-082022-12-312022-12-31106001자동차세(자동차)5374000161200537400016120069860000000미납YNN
12237158336390160390160002505현대02022-09-302022-09-30카드 결제 성공카드 결제 성공2022-10-132022-10-132022-09-300Y2022-09-30041630경기도 양주시2022-09-022022-09-032022-09-302022-09-30105305재산세(주택)222730035340213600222730035340213600279430000000미납YNN
177241662191545015450002707외환02023-03-242023-03-24카드 결제 성공카드 결제 성공2023-04-132023-04-132023-03-240Y2023-03-24041630경기도 양주시2023-01-062023-01-062023-01-312023-02-28114001등록면허세(면허)154500000150000000150004500000450체납YNN
15065162568424770424770052408롯데02023-01-022023-01-02카드 결제 성공카드 결제 성공2023-01-132023-01-132023-01-020Y2023-01-02041630경기도 양주시2022-12-072022-12-082022-12-312022-12-31106001자동차세(자동차)99550002986009955000298600129410000000미납YNN
2620144998149760149760032507국민02022-07-072022-07-07카드 결제 성공카드 결제 성공2022-08-162022-08-162022-07-070Y2022-07-07041630경기도 양주시2022-06-062022-06-062022-06-302022-07-31106001자동차세(자동차)1152100034550011186000335500145410335000100004350체납YNN
헤더시퀸스번호수납금액지방세금액세외수입금액할부기간유효기간발급카드사수수료등록일자거래일자결제에러나이스페이결제에러이체일자나이스페이이체일자회계일자휴대폰수수료금액선택납부여부수정일자수납실패횟수시군구코드시군구명부과일자징수일자최초납기가산납기세목코드세목명본세잔액도시잔액공동잔액교육잔액농특잔액본세도시본세공동본세교육본세농특본세본세합가산금도시가산금공동가산금교육가산금농특가산금가산금합고지구분결의여부압류여부분납여부
79711530165834058340022412외환02022-08-282022-08-28카드 결제 성공카드 결제 성공2022-09-132022-09-132022-08-290Y2022-08-28041630경기도 양주시2022-06-062022-06-062022-06-302022-07-31106001자동차세(자동차)448800013460043580001307005665013000039001690체납YNN
21691444638686086860002610신한02022-06-302022-06-30카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-300Y2022-06-30041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)6682000200400668200020040086860000000미납YNN
73491520411100011000002704롯데02022-08-202022-08-20카드 결제 성공카드 결제 성공2022-09-132022-09-132022-08-220Y2022-08-20041630경기도 양주시2022-08-032022-08-032022-08-312022-08-31104101주민세(개인분)10000001000010000001000011000000000미납YNN
10605156414119570119570002404현대02022-09-262022-09-26카드 결제 성공카드 결제 성공2022-10-132022-10-132022-09-260Y2022-09-26041630경기도 양주시2022-09-022022-09-032022-09-302022-09-30105305재산세(주택)98560012420859009856001242085900119570000000미납YNN
9111429947641076410002409농협02022-06-232022-06-23카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-230Y2022-06-23041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)27070000027070000027070000000미납YNN
3628146294122570122570002607삼성02022-07-192022-07-19카드 결제 성공카드 결제 성공2022-08-162022-08-162022-07-190Y2022-07-19041630경기도 양주시2022-07-032022-07-042022-07-312022-07-31105305재산세(주택)1049100108506810010491001085068100122570000000미납YNN
16287164183764840764840002802BC02023-01-262023-01-26카드 결제 성공카드 결제 성공2023-02-132023-02-132023-01-260Y2023-01-26041630경기도 양주시2023-01-08<NA>2023-01-312023-01-31106001자동차세(자동차)1863400055900018634000559000242240000000자납NNN
16183164013198960198960062410국민02023-01-242023-01-24카드 결제 성공카드 결제 성공2023-02-132023-02-132023-01-250Y2023-01-24041630경기도 양주시2022-12-072022-12-082022-12-312023-01-31106001자동차세(자동차)1530500045910014860000445800193180445000133005780체납YNN
3445146070177310177310052305농협02022-07-182022-07-18카드 결제 성공카드 결제 성공2022-08-162022-08-162022-07-180Y2022-07-18041630경기도 양주시2022-05-19<NA>2022-08-312022-08-31140001지방소득세(종합소득)69400000069400000069400000000자납NNN
1462143669285070285070032404삼성02022-06-282022-06-28카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-280Y2022-06-28041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)1098900032960010989000329600142850000000미납YNN

Duplicate rows

Most frequently occurring

헤더시퀸스번호수납금액지방세금액세외수입금액할부기간유효기간발급카드사수수료등록일자거래일자결제에러나이스페이결제에러이체일자나이스페이이체일자회계일자휴대폰수수료금액선택납부여부수정일자수납실패횟수시군구코드시군구명부과일자징수일자최초납기가산납기세목코드세목명본세잔액도시잔액공동잔액교육잔액농특잔액본세도시본세공동본세교육본세농특본세본세합가산금도시가산금공동가산금교육가산금농특가산금가산금합고지구분결의여부압류여부분납여부# duplicates
5144352901780901780002501신한02022-06-302022-06-30카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-300Y2022-06-30041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)66500000066500000066500000000미납YNN5
2143896504080504080002608국민02022-06-292022-06-29카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-290Y2022-06-29041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)27070000027070000027070000000미납YNN3
6144385755850755850002602국민02022-06-302022-06-30카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-300Y2022-06-30041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)27070000027070000027070000000미납YNN3
13164190597620597620032412국민02023-01-262023-01-26카드 결제 성공카드 결제 성공2023-02-132023-02-132023-01-260Y2023-01-26041630경기도 양주시2023-01-062023-01-062023-01-312023-01-31114001등록면허세(면허)18000000018000000018000000000미납YNN3
1716506111993011993000<NA>삼성02023-02-062023-02-06No Information<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>3
0142271194520194520032511신한02022-06-172022-06-17카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-170Y2022-06-17041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)61750000061750000061750000000미납YNN2
1142892707700707700062504외환02022-06-222022-06-22카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-220Y2022-06-22041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)2172300065160021723000651600282390000000미납YNN2
3143952290930290930002703국민02022-06-292022-06-29카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-290Y2022-06-29041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)27070000027070000027070000000미납YNN2
4144352901780901780002501신한02022-06-302022-06-30카드 결제 성공카드 결제 성공2022-07-132022-07-132022-06-300Y2022-06-30041630경기도 양주시2022-06-062022-06-062022-06-302022-06-30106001자동차세(자동차)47500000047500000047500000000미납YNN2
7151337587000587000052510국민02022-08-162022-08-16카드 결제 성공카드 결제 성공2022-09-132022-09-132022-08-160Y2022-08-16041630경기도 양주시2022-06-062022-06-062022-06-302022-07-31106001자동차세(자동차)1888400056650018334000550000238340550000165007150체납YNN2