Overview

Dataset statistics

Number of variables8
Number of observations137
Missing cells4
Missing cells (%)0.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory9.1 KiB
Average record size in memory68.0 B

Variable types

Text5
Numeric3

Dataset

Description국세청과 그 소속기관 직제시행규칙에 제공되는 세무서별 관할 구역 정보
Author국세청
URLhttps://www.data.go.kr/data/15099881/fileData.do

Alerts

우편번호 is highly overall correlated with 세무서코드High correlation
세무서코드 is highly overall correlated with 우편번호High correlation
팩스번호 has 3 (2.2%) missing valuesMissing
세무서명 has unique valuesUnique
도로명 주소 has unique valuesUnique
전화번호 has unique valuesUnique
세무서코드 has unique valuesUnique
계좌번호 has unique valuesUnique
관할구역 has unique valuesUnique

Reproduction

Analysis started2023-12-12 18:46:18.580206
Analysis finished2023-12-12 18:46:21.053146
Duration2.47 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

세무서명
Text

UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:46:21.353719image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.3357664
Min length5

Characters and Unicode

Total characters731
Distinct characters101
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)100.0%

Sample

1st row서울지방국세청
2nd row강남세무서
3rd row강동세무서
4th row강서세무서
5th row관악세무서
ValueCountFrequency (%)
서울지방국세청 1
 
0.7%
대전지방국세청 1
 
0.7%
정읍세무서 1
 
0.7%
전주세무서 1
 
0.7%
익산세무서 1
 
0.7%
여수세무서 1
 
0.7%
순천세무서 1
 
0.7%
서광주세무서 1
 
0.7%
북전주세무서 1
 
0.7%
북광주세무서 1
 
0.7%
Other values (127) 127
92.7%
2023-12-13T03:46:22.030232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
서 140
19.2%
세 138
18.9%
무 130
17.8%
주 20
 
2.7%
산 19
 
2.6%
천 17
 
2.3%
동 14
 
1.9%
대 13
 
1.8%
부 10
 
1.4%
청 9
 
1.2%
Other values (91) 221
30.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 731
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
서 140
19.2%
세 138
18.9%
무 130
17.8%
주 20
 
2.7%
산 19
 
2.6%
천 17
 
2.3%
동 14
 
1.9%
대 13
 
1.8%
부 10
 
1.4%
청 9
 
1.2%
Other values (91) 221
30.2%

Most occurring scripts

ValueCountFrequency (%)
Hangul 731
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
서 140
19.2%
세 138
18.9%
무 130
17.8%
주 20
 
2.7%
산 19
 
2.6%
천 17
 
2.3%
동 14
 
1.9%
대 13
 
1.8%
부 10
 
1.4%
청 9
 
1.2%
Other values (91) 221
30.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 731
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
서 140
19.2%
세 138
18.9%
무 130
17.8%
주 20
 
2.7%
산 19
 
2.6%
천 17
 
2.3%
동 14
 
1.9%
대 13
 
1.8%
부 10
 
1.4%
청 9
 
1.2%
Other values (91) 221
30.2%

도로명 주소
Text

UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:46:22.518635image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length108
Median length58
Mean length28.182482
Min length14

Characters and Unicode

Total characters3861
Distinct characters262
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)100.0%

Sample

1st row서울특별시 종로구 종로5길 86(수송동)
2nd row서울특별시 강남구 학동로 425(청담동)
3rd row서울특별시 강동구 천호대로 1139(길동)
4th row서울특별시 강서구 마곡서1로 60(마곡동)
5th row서울특별시 관악구 문성로 187(신림동)
ValueCountFrequency (%)
서울특별시 30
 
3.8%
경기도 27
 
3.4%
22
 
2.8%
본관 9
 
1.1%
경북 7
 
0.9%
부산광역시 7
 
0.9%
인천광역시 7
 
0.9%
대구광역시 6
 
0.8%
강원도 6
 
0.8%
충청남도 6
 
0.8%
Other values (559) 662
83.9%
2023-12-13T03:46:23.190253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
656
 
17.0%
1 150
 
3.9%
동 141
 
3.7%
로 140
 
3.6%
시 138
 
3.6%
) 120
 
3.1%
( 120
 
3.1%
구 97
 
2.5%
2 83
 
2.1%
3 80
 
2.1%
Other values (252) 2136
55.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2227
57.7%
Space Separator 656
 
17.0%
Decimal Number 638
 
16.5%
Close Punctuation 121
 
3.1%
Open Punctuation 121
 
3.1%
Other Punctuation 55
 
1.4%
Dash Punctuation 35
 
0.9%
Uppercase Letter 6
 
0.2%
Math Symbol 2
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 141
 
6.3%
로 140
 
6.3%
시 138
 
6.2%
구 97
 
4.4%
도 59
 
2.6%
서 58
 
2.6%
산 53
 
2.4%
대 52
 
2.3%
남 52
 
2.3%
경 51
 
2.3%
Other values (226) 1386
62.2%
Decimal Number
ValueCountFrequency (%)
1 150
23.5%
2 83
13.0%
3 80
12.5%
5 58
 
9.1%
7 54
 
8.5%
4 52
 
8.2%
6 50
 
7.8%
8 41
 
6.4%
9 37
 
5.8%
0 33
 
5.2%
Uppercase Letter
ValueCountFrequency (%)
S 1
16.7%
T 1
16.7%
X 1
16.7%
K 1
16.7%
M 1
16.7%
A 1
16.7%
Other Punctuation
ValueCountFrequency (%)
/ 32
58.2%
: 22
40.0%
. 1
 
1.8%
Close Punctuation
ValueCountFrequency (%)
) 120
99.2%
] 1
 
0.8%
Open Punctuation
ValueCountFrequency (%)
( 120
99.2%
[ 1
 
0.8%
Space Separator
ValueCountFrequency (%)
656
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 35
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2227
57.7%
Common 1628
42.2%
Latin 6
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 141
 
6.3%
로 140
 
6.3%
시 138
 
6.2%
구 97
 
4.4%
도 59
 
2.6%
서 58
 
2.6%
산 53
 
2.4%
대 52
 
2.3%
남 52
 
2.3%
경 51
 
2.3%
Other values (226) 1386
62.2%
Common
ValueCountFrequency (%)
656
40.3%
1 150
 
9.2%
) 120
 
7.4%
( 120
 
7.4%
2 83
 
5.1%
3 80
 
4.9%
5 58
 
3.6%
7 54
 
3.3%
4 52
 
3.2%
6 50
 
3.1%
Other values (10) 205
 
12.6%
Latin
ValueCountFrequency (%)
S 1
16.7%
T 1
16.7%
X 1
16.7%
K 1
16.7%
M 1
16.7%
A 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2227
57.7%
ASCII 1634
42.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
656
40.1%
1 150
 
9.2%
) 120
 
7.3%
( 120
 
7.3%
2 83
 
5.1%
3 80
 
4.9%
5 58
 
3.5%
7 54
 
3.3%
4 52
 
3.2%
6 50
 
3.1%
Other values (16) 211
 
12.9%
Hangul
ValueCountFrequency (%)
동 141
 
6.3%
로 140
 
6.3%
시 138
 
6.2%
구 97
 
4.4%
도 59
 
2.6%
서 58
 
2.6%
산 53
 
2.4%
대 52
 
2.3%
남 52
 
2.3%
경 51
 
2.3%
Other values (226) 1386
62.2%

우편번호
Real number (ℝ)

HIGH CORRELATION 

Distinct133
Distinct (%)97.8%
Missing1
Missing (%)0.7%
Infinite0
Infinite (%)0.0%
Mean28313.824
Minimum1177
Maximum63219
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:46:23.394067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum1177
5-th percentile3382.75
Q111510.75
median26323
Q344358
95-th percentile59178
Maximum63219
Range62042
Interquartile range (IQR)32847.25

Descriptive statistics

Standard deviation18702.116
Coefficient of variation (CV)0.66052951
Kurtosis-1.2384188
Mean28313.824
Median Absolute Deviation (MAD)16039
Skewness0.25155605
Sum3850680
Variance3.4976915 × 108
MonotonicityNot monotonic
2023-12-13T03:46:23.633403image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
6233 3
 
2.2%
5506 2
 
1.5%
3151 1
 
0.7%
54096 1
 
0.7%
58262 1
 
0.7%
55741 1
 
0.7%
58723 1
 
0.7%
61238 1
 
0.7%
54937 1
 
0.7%
61969 1
 
0.7%
Other values (123) 123
89.8%
ValueCountFrequency (%)
1177 1
0.7%
1415 1
0.7%
2118 1
0.7%
2489 1
0.7%
2863 1
0.7%
3133 1
0.7%
3151 1
0.7%
3460 1
0.7%
3629 1
0.7%
4090 1
0.7%
ValueCountFrequency (%)
63219 1
0.7%
62232 1
0.7%
61969 1
0.7%
61484 1
0.7%
61238 1
0.7%
61011 1
0.7%
59631 1
0.7%
59027 1
0.7%
58723 1
0.7%
58262 1
0.7%

전화번호
Text

UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:46:23.998735image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.948905
Min length11

Characters and Unicode

Total characters1637
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)100.0%

Sample

1st row02-2114-2200
2nd row02-519-4200
3rd row02-2224-0200
4th row02-2630-4200
5th row02-2173-4200
ValueCountFrequency (%)
02-2114-2200 1
 
0.7%
062-380-5200 1
 
0.7%
062-236-7200 1
 
0.7%
063-530-1200 1
 
0.7%
063-250-0200 1
 
0.7%
063-840-0200 1
 
0.7%
061-688-0200 1
 
0.7%
061-720-0200 1
 
0.7%
042-615-2200 1
 
0.7%
061-530-6200 1
 
0.7%
Other values (128) 128
92.8%
2023-12-13T03:46:24.527367image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 526
32.1%
- 267
16.3%
2 247
15.1%
3 114
 
7.0%
5 89
 
5.4%
1 84
 
5.1%
4 80
 
4.9%
6 76
 
4.6%
9 60
 
3.7%
7 43
 
2.6%
Other values (4) 51
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1361
83.1%
Dash Punctuation 267
 
16.3%
Close Punctuation 7
 
0.4%
Open Punctuation 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 526
38.6%
2 247
18.1%
3 114
 
8.4%
5 89
 
6.5%
1 84
 
6.2%
4 80
 
5.9%
6 76
 
5.6%
9 60
 
4.4%
7 43
 
3.2%
8 42
 
3.1%
Dash Punctuation
ValueCountFrequency (%)
- 267
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1637
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 526
32.1%
- 267
16.3%
2 247
15.1%
3 114
 
7.0%
5 89
 
5.4%
1 84
 
5.1%
4 80
 
4.9%
6 76
 
4.6%
9 60
 
3.7%
7 43
 
2.6%
Other values (4) 51
 
3.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1637
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 526
32.1%
- 267
16.3%
2 247
15.1%
3 114
 
7.0%
5 89
 
5.4%
1 84
 
5.1%
4 80
 
4.9%
6 76
 
4.6%
9 60
 
3.7%
7 43
 
2.6%
Other values (4) 51
 
3.1%

팩스번호
Text

MISSING 

Distinct134
Distinct (%)100.0%
Missing3
Missing (%)2.2%
Memory size1.2 KiB
2023-12-13T03:46:24.875370image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length11.865672
Min length11

Characters and Unicode

Total characters1590
Distinct characters14
Distinct categories5 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique134 ?
Unique (%)100.0%

Sample

1st row02-722-0528
2nd row02-512-3917
3rd row02-489-3251
4th row02-2679-8777
5th row02-2173-4269
ValueCountFrequency (%)
02-563-8030 1
 
0.7%
062-716-7215 1
 
0.7%
062-225-4701 1
 
0.7%
063-470-3249 1
 
0.7%
061-332-8583 1
 
0.7%
063-632-7302 1
 
0.7%
061-244-5915 1
 
0.7%
062-716-7280 1
 
0.7%
062-716-7260 1
 
0.7%
053-661-7052 1
 
0.7%
Other values (125) 125
92.6%
2023-12-13T03:46:25.562132image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 261
16.4%
0 226
14.2%
2 156
9.8%
3 152
9.6%
1 147
9.2%
5 139
8.7%
6 121
7.6%
4 120
7.5%
7 97
 
6.1%
8 81
 
5.1%
Other values (4) 90
 
5.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 1320
83.0%
Dash Punctuation 261
 
16.4%
Close Punctuation 7
 
0.4%
Open Punctuation 1
 
0.1%
Space Separator 1
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 226
17.1%
2 156
11.8%
3 152
11.5%
1 147
11.1%
5 139
10.5%
6 121
9.2%
4 120
9.1%
7 97
7.3%
8 81
 
6.1%
9 81
 
6.1%
Dash Punctuation
ValueCountFrequency (%)
- 261
100.0%
Close Punctuation
ValueCountFrequency (%)
) 7
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 1590
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
- 261
16.4%
0 226
14.2%
2 156
9.8%
3 152
9.6%
1 147
9.2%
5 139
8.7%
6 121
7.6%
4 120
7.5%
7 97
 
6.1%
8 81
 
5.1%
Other values (4) 90
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 1590
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 261
16.4%
0 226
14.2%
2 156
9.8%
3 152
9.6%
1 147
9.2%
5 139
8.7%
6 121
7.6%
4 120
7.5%
7 97
 
6.1%
8 81
 
5.1%
Other values (4) 90
 
5.7%

세무서코드
Real number (ℝ)

HIGH CORRELATION  UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean315.64234
Minimum100
Maximum800
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:46:25.807281image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum100
5-th percentile108.8
Q1144
median300
Q3500
95-th percentile615.2
Maximum800
Range700
Interquartile range (IQR)356

Descriptive statistics

Standard deviation178.69296
Coefficient of variation (CV)0.56612481
Kurtosis-0.95056642
Mean315.64234
Median Absolute Deviation (MAD)157
Skewness0.54726515
Sum43243
Variance31931.173
MonotonicityNot monotonic
2023-12-13T03:46:25.998616image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
100 1
 
0.7%
409 1
 
0.7%
419 1
 
0.7%
408 1
 
0.7%
401 1
 
0.7%
412 1
 
0.7%
407 1
 
0.7%
411 1
 
0.7%
418 1
 
0.7%
500 1
 
0.7%
Other values (127) 127
92.7%
ValueCountFrequency (%)
100 1
0.7%
101 1
0.7%
104 1
0.7%
105 1
0.7%
106 1
0.7%
107 1
0.7%
108 1
0.7%
109 1
0.7%
110 1
0.7%
113 1
0.7%
ValueCountFrequency (%)
800 1
0.7%
624 1
0.7%
623 1
0.7%
621 1
0.7%
620 1
0.7%
617 1
0.7%
616 1
0.7%
615 1
0.7%
613 1
0.7%
612 1
0.7%

계좌번호
Real number (ℝ)

UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean150287.59
Minimum165
Maximum950435
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size1.3 KiB
2023-12-13T03:46:26.223615image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Quantile statistics

Minimum165
5-th percentile1590.4
Q123744
median50144
Q3130381
95-th percentile920302.6
Maximum950435
Range950270
Interquartile range (IQR)106637

Descriptive statistics

Standard deviation266612.28
Coefficient of variation (CV)1.7740139
Kurtosis4.5255885
Mean150287.59
Median Absolute Deviation (MAD)38304
Skewness2.4677589
Sum20589400
Variance7.1082106 × 1010
MonotonicityNot monotonic
2023-12-13T03:46:26.472759image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
11895 1
 
0.7%
60671 1
 
0.7%
27313 1
 
0.7%
60639 1
 
0.7%
70399 1
 
0.7%
60642 1
 
0.7%
70412 1
 
0.7%
50144 1
 
0.7%
2862 1
 
0.7%
40756 1
 
0.7%
Other values (127) 127
92.7%
ValueCountFrequency (%)
165 1
0.7%
178 1
0.7%
181 1
0.7%
602 1
0.7%
1562 1
0.7%
1575 1
0.7%
1588 1
0.7%
1591 1
0.7%
1601 1
0.7%
2846 1
0.7%
ValueCountFrequency (%)
950435 1
0.7%
950419 1
0.7%
935188 1
0.7%
930170 1
0.7%
930167 1
0.7%
930154 1
0.7%
920313 1
0.7%
920300 1
0.7%
910378 1
0.7%
910365 1
0.7%

관할구역
Text

UNIQUE 

Distinct137
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size1.2 KiB
2023-12-13T03:46:27.087732image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length192
Median length101
Mean length26.49635
Min length7

Characters and Unicode

Total characters3630
Distinct characters229
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique137 ?
Unique (%)100.0%

Sample

1st row서울특별시 전체
2nd row서울특별시 강남구 중 신사동/ 논현동/ 압구정동/ 청담동
3rd row서울특별시 강동구
4th row서울특별시 강서구
5th row서울특별시 관악구
ValueCountFrequency (%)
서울특별시 29
 
4.0%
경기도 25
 
3.4%
중 17
 
2.3%
경상북도 10
 
1.4%
제외 9
 
1.2%
충청남도 9
 
1.2%
부산광역시 9
 
1.2%
전라남도 8
 
1.1%
강원도 8
 
1.1%
경상남도 8
 
1.1%
Other values (475) 596
81.9%
2023-12-13T03:46:28.050773image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
620
 
17.1%
/ 360
 
9.9%
동 185
 
5.1%
시 156
 
4.3%
구 117
 
3.2%
도 104
 
2.9%
군 95
 
2.6%
서 62
 
1.7%
남 62
 
1.7%
천 58
 
1.6%
Other values (219) 1811
49.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2440
67.2%
Space Separator 620
 
17.1%
Other Punctuation 420
 
11.6%
Decimal Number 95
 
2.6%
Open Punctuation 28
 
0.8%
Close Punctuation 27
 
0.7%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 185
 
7.6%
시 156
 
6.4%
구 117
 
4.8%
도 104
 
4.3%
군 95
 
3.9%
서 62
 
2.5%
남 62
 
2.5%
천 58
 
2.4%
산 53
 
2.2%
경 50
 
2.0%
Other values (201) 1498
61.4%
Decimal Number
ValueCountFrequency (%)
2 29
30.5%
1 27
28.4%
3 10
 
10.5%
5 8
 
8.4%
4 7
 
7.4%
6 4
 
4.2%
9 4
 
4.2%
7 3
 
3.2%
8 3
 
3.2%
Other Punctuation
ValueCountFrequency (%)
/ 360
85.7%
· 50
 
11.9%
: 6
 
1.4%
. 3
 
0.7%
* 1
 
0.2%
Open Punctuation
ValueCountFrequency (%)
( 27
96.4%
[ 1
 
3.6%
Space Separator
ValueCountFrequency (%)
620
100.0%
Close Punctuation
ValueCountFrequency (%)
) 27
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2440
67.2%
Common 1190
32.8%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 185
 
7.6%
시 156
 
6.4%
구 117
 
4.8%
도 104
 
4.3%
군 95
 
3.9%
서 62
 
2.5%
남 62
 
2.5%
천 58
 
2.4%
산 53
 
2.2%
경 50
 
2.0%
Other values (201) 1498
61.4%
Common
ValueCountFrequency (%)
620
52.1%
/ 360
30.3%
· 50
 
4.2%
2 29
 
2.4%
) 27
 
2.3%
( 27
 
2.3%
1 27
 
2.3%
3 10
 
0.8%
5 8
 
0.7%
4 7
 
0.6%
Other values (8) 25
 
2.1%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2439
67.2%
ASCII 1140
31.4%
None 50
 
1.4%
Compat Jamo 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
620
54.4%
/ 360
31.6%
2 29
 
2.5%
) 27
 
2.4%
( 27
 
2.4%
1 27
 
2.4%
3 10
 
0.9%
5 8
 
0.7%
4 7
 
0.6%
: 6
 
0.5%
Other values (7) 19
 
1.7%
Hangul
ValueCountFrequency (%)
동 185
 
7.6%
시 156
 
6.4%
구 117
 
4.8%
도 104
 
4.3%
군 95
 
3.9%
서 62
 
2.5%
남 62
 
2.5%
천 58
 
2.4%
산 53
 
2.2%
경 50
 
2.1%
Other values (200) 1497
61.4%
None
ValueCountFrequency (%)
· 50
100.0%
Compat Jamo
ValueCountFrequency (%)
ㆍ 1
100.0%

Interactions

2023-12-13T03:46:20.074480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:19.159544image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:19.620294image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:20.217362image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:19.323162image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:19.771123image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:20.381518image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:19.489492image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2023-12-13T03:46:19.922103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Correlations

2023-12-13T03:46:28.754920image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호세무서코드계좌번호
우편번호1.0000.8520.517
세무서코드0.8521.0000.422
계좌번호0.5170.4221.000
2023-12-13T03:46:28.908949image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
우편번호세무서코드계좌번호
우편번호1.0000.8190.335
세무서코드0.8191.0000.255
계좌번호0.3350.2551.000

Missing values

2023-12-13T03:46:20.583456image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-13T03:46:20.840179image/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.
2023-12-13T03:46:20.985139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

세무서명도로명 주소우편번호전화번호팩스번호세무서코드계좌번호관할구역
0서울지방국세청서울특별시 종로구 종로5길 86(수송동)315102-2114-220002-722-052810011895서울특별시 전체
1강남세무서서울특별시 강남구 학동로 425(청담동)606802-519-420002-512-3917211180616서울특별시 강남구 중 신사동/ 논현동/ 압구정동/ 청담동
2강동세무서서울특별시 강동구 천호대로 1139(길동)535502-2224-020002-489-3251212180629서울특별시 강동구
3강서세무서서울특별시 강서구 마곡서1로 60(마곡동)779902-2630-420002-2679-877710912027서울특별시 강서구
4관악세무서서울특별시 관악구 문성로 187(신림동)877302-2173-420002-2173-426914524675서울특별시 관악구
5구로세무서서울특별시 영등포구 경인로 778(문래동 1가)736302-2630-720002-2679-639411311756서울특별시 구로구
6금천세무서본관 : 서울특별시 금천구 시흥대로152길 11-21(독산동)/ 조사과 : 서울특별시 관악구 남부순환로 1369 (신림동)관악농협 하나로마트 5층853602-850-420002-861-147511914371서울특별시 금천구
7남대문세무서서울특별시 중구 삼일대로 340(저동1가) 나라키움저동빌딩455102-2260-020002-755-711410411785서울특별시 중구 중 남대문로 1·3·4·5가/ 을지로 1·2·3·4·5가/ 주교동/ 삼각동/ 수하동/ 장교동/ 수표동/ 저동 1·2가/ 입정동/ 산림동/ 무교동/ 다동/ 북창동/ 남창동/ 봉래동 1·2가/ 회현동 1·2·3가/ 소공동/ 태평로 1·2가/ 서소문동/ 정동/ 순화동/ 의주로 1·2가/ 중림동/ 만리동 1·2가/ 충정로 1가
8노원세무서서울특별시 도봉구 노해로69길 14 (창동)141502-3499-020002-992-14852171562서울특별시 노원구/ 도봉구 중 창동
9도봉세무서서울특별시 강북구 도봉로 117 (미아동)117702-944-020002-984-258021011811서울특별시 강북구/ 도봉구(창동 제외)
세무서명도로명 주소우편번호전화번호팩스번호세무서코드계좌번호관할구역
127서부산세무서부산광역시 서구 대영로 10 (서대신동2가 288-2)49228051-250-6200051-241-700460330546부산광역시 서구/ 사하구
128수영세무서부산광역시 수영구 남천동로 19번길 28 (남천동)48306051-620-9200051-621-259361730478부산광역시 남구/ 수영구
129양산세무서경상남도 양산시 물금읍 증산역로135/ 9층/ 10층(가촌리1296-1)50653055-389-6200055-389-660262426194경상남도 양산시
130울산세무서울산광역시 남구 갈밭로 4944715052-259-0200052-266-2135610160021울산광역시 남구/ 울산광역시 울주군 중 온산읍·온양읍·청량면·웅촌면·서생면
131제주세무서제주특별자치도 제주시 청사로 59(도남동)63219064-720-5200064-724-1107616120171제주특별자치도 전체
132중부산세무서부산광역시 중구 흑교로 64 (보수동1가)48962051-240-0200051-241-600960230562부산광역시 중구/ 영도구
133진주세무서경상남도 진주시 진주대로908번길 15(칠암동)52724055-751-0200055-753-9009613950435경상남도 진주시/ 사천시/ 산청군/ 하동군/ 남해군
134창원세무서경남 창원시 성산구 중앙대로105 STX 오션타워51515055-239-0200055-287-1394609140669경상남도 창원시 성산구/ 의창구/ 진해구
135통영세무서경남 통영시 무전5길 20-9 (무전동)53036055-640-7200055-644-1814612140708경상남도 통영시/ 거제시/ 고성군* 거제지서 안내는 화면 하단에 있습니다.
136해운대세무서부산광역시 해운대구 좌동순환로 17(좌동) 해운대세무서48084051-660-9200051-660-961062325470부산광역시 해운대구