Overview

Dataset statistics

Number of variables7
Number of observations3363
Missing cells8
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory184.0 KiB
Average record size in memory56.0 B

Variable types

Categorical1
Text6

Dataset

Description2023년 6월 30일 기준 전국 우체국에 대한 정보입니다. 해당 데이터가 보유한 컬럼은 다음과 같습니다. 컬럼명 : 지방우정청명, 총괄국명, 우체국명, 우체국 지번 주소, 우체국 도로명 주소, 우체국 전화번호
URLhttps://www.data.go.kr/data/15070368/fileData.do

Alerts

우체국명 has unique valuesUnique

Reproduction

Analysis started2023-12-12 12:28:34.271897
Analysis finished2023-12-12 12:28:35.735837
Duration1.46 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

지방우정청
Categorical

Distinct9
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
부산지방우정청
576 
경인지방우정청
572 
충청지방우정청
500 
경북지방우정청
439 
서울지방우정청
406 
Other values (4)
870 

Length

Max length7
Median length7
Mean length7
Min length7

Unique

Unique0 ?
Unique (%)0.0%

Sample

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

Common Values

ValueCountFrequency (%)
부산지방우정청 576
17.1%
경인지방우정청 572
17.0%
충청지방우정청 500
14.9%
경북지방우정청 439
13.1%
서울지방우정청 406
12.1%
전남지방우정청 379
11.3%
전북지방우정청 245
7.3%
강원지방우정청 199
 
5.9%
제주지방우정청 47
 
1.4%

Length

2023-12-12T21:28:35.807846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T21:28:36.231874image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
부산지방우정청 576
17.1%
경인지방우정청 572
17.0%
충청지방우정청 500
14.9%
경북지방우정청 439
13.1%
서울지방우정청 406
12.1%
전남지방우정청 379
11.3%
전북지방우정청 245
7.3%
강원지방우정청 199
 
5.9%
제주지방우정청 47
 
1.4%

총괄국명
Text

Distinct248
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T21:28:36.531147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length5
Mean length5.5334523
Min length5

Characters and Unicode

Total characters18609
Distinct characters142
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

Unique26 ?
Unique (%)0.8%

Sample

1st row서울중앙우체국
2nd row서울중앙우체국
3rd row서울중앙우체국
4th row서울중앙우체국
5th row서울중앙우체국
ValueCountFrequency (%)
서울강남우체국 35
 
1.0%
목포우체국 34
 
1.0%
진주우체국 33
 
1.0%
포항우체국 31
 
0.9%
제주우체국 31
 
0.9%
마산우체국 30
 
0.9%
익산우체국 30
 
0.9%
창원우체국 27
 
0.8%
서울송파우체국 27
 
0.8%
평택우체국 27
 
0.8%
Other values (238) 3058
90.9%
2023-12-12T21:28:37.018607image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
우 3363
18.1%
국 3363
18.1%
체 3339
17.9%
서 582
 
3.1%
산 548
 
2.9%
주 457
 
2.5%
울 427
 
2.3%
천 377
 
2.0%
대 258
 
1.4%
성 244
 
1.3%
Other values (132) 5651
30.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 18609
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 3363
18.1%
국 3363
18.1%
체 3339
17.9%
서 582
 
3.1%
산 548
 
2.9%
주 457
 
2.5%
울 427
 
2.3%
천 377
 
2.0%
대 258
 
1.4%
성 244
 
1.3%
Other values (132) 5651
30.4%

Most occurring scripts

ValueCountFrequency (%)
Hangul 18609
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 3363
18.1%
국 3363
18.1%
체 3339
17.9%
서 582
 
3.1%
산 548
 
2.9%
주 457
 
2.5%
울 427
 
2.3%
천 377
 
2.0%
대 258
 
1.4%
성 244
 
1.3%
Other values (132) 5651
30.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 18609
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
우 3363
18.1%
국 3363
18.1%
체 3339
17.9%
서 582
 
3.1%
산 548
 
2.9%
주 457
 
2.5%
울 427
 
2.3%
천 377
 
2.0%
대 258
 
1.4%
성 244
 
1.3%
Other values (132) 5651
30.4%

우체국명
Text

UNIQUE 

Distinct3363
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T21:28:37.278801image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length17
Median length15
Mean length7.7511151
Min length5

Characters and Unicode

Total characters26067
Distinct characters404
Distinct categories7 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3363 ?
Unique (%)100.0%

Sample

1st row서울중앙우체국
2nd row서울을지로4가우체국
3rd row서울태평로우체국
4th row서울퇴계로5가우체국
5th row서울역전우체국
ValueCountFrequency (%)
영월우체국 2
 
0.1%
평창우체국 2
 
0.1%
서울중앙우체국 1
 
< 0.1%
나주금천우체국 1
 
< 0.1%
왕곡우체국 1
 
< 0.1%
병영우체국 1
 
< 0.1%
세지우체국 1
 
< 0.1%
반남우체국 1
 
< 0.1%
나주동강우체국 1
 
< 0.1%
다도우체국 1
 
< 0.1%
Other values (3353) 3353
99.6%
2023-12-12T21:28:37.736293image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
우 3369
 
12.9%
국 3310
 
12.7%
체 2452
 
9.4%
동 1211
 
4.6%
편 882
 
3.4%
취 805
 
3.1%
급 803
 
3.1%
산 656
 
2.5%
서 509
 
2.0%
울 441
 
1.7%
Other values (394) 11629
44.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25590
98.2%
Decimal Number 459
 
1.8%
Uppercase Letter 13
 
< 0.1%
Space Separator 2
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%
Dash Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
우 3369
 
13.2%
국 3310
 
12.9%
체 2452
 
9.6%
동 1211
 
4.7%
편 882
 
3.4%
취 805
 
3.1%
급 803
 
3.1%
산 656
 
2.6%
서 509
 
2.0%
울 441
 
1.7%
Other values (374) 11152
43.6%
Decimal Number
ValueCountFrequency (%)
2 113
24.6%
1 110
24.0%
3 82
17.9%
0 32
 
7.0%
4 28
 
6.1%
5 23
 
5.0%
6 21
 
4.6%
7 20
 
4.4%
8 15
 
3.3%
9 15
 
3.3%
Uppercase Letter
ValueCountFrequency (%)
A 3
23.1%
S 2
15.4%
L 2
15.4%
I 2
15.4%
D 2
15.4%
G 2
15.4%
Space Separator
ValueCountFrequency (%)
2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25590
98.2%
Common 464
 
1.8%
Latin 13
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
우 3369
 
13.2%
국 3310
 
12.9%
체 2452
 
9.6%
동 1211
 
4.7%
편 882
 
3.4%
취 805
 
3.1%
급 803
 
3.1%
산 656
 
2.6%
서 509
 
2.0%
울 441
 
1.7%
Other values (374) 11152
43.6%
Common
ValueCountFrequency (%)
2 113
24.4%
1 110
23.7%
3 82
17.7%
0 32
 
6.9%
4 28
 
6.0%
5 23
 
5.0%
6 21
 
4.5%
7 20
 
4.3%
8 15
 
3.2%
9 15
 
3.2%
Other values (4) 5
 
1.1%
Latin
ValueCountFrequency (%)
A 3
23.1%
S 2
15.4%
L 2
15.4%
I 2
15.4%
D 2
15.4%
G 2
15.4%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25590
98.2%
ASCII 477
 
1.8%

Most frequent character per block

Hangul
ValueCountFrequency (%)
우 3369
 
13.2%
국 3310
 
12.9%
체 2452
 
9.6%
동 1211
 
4.7%
편 882
 
3.4%
취 805
 
3.1%
급 803
 
3.1%
산 656
 
2.6%
서 509
 
2.0%
울 441
 
1.7%
Other values (374) 11152
43.6%
ASCII
ValueCountFrequency (%)
2 113
23.7%
1 110
23.1%
3 82
17.2%
0 32
 
6.7%
4 28
 
5.9%
5 23
 
4.8%
6 21
 
4.4%
7 20
 
4.2%
8 15
 
3.1%
9 15
 
3.1%
Other values (10) 18
 
3.8%

우편번호
Text

Distinct3327
Distinct (%)98.9%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T21:28:38.188040image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length5
Mean length4.8962236
Min length4

Characters and Unicode

Total characters16466
Distinct characters17
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3291 ?
Unique (%)97.9%

Sample

1st row4535
2nd row4549
3rd row4512
4th row4618
5th row4509
ValueCountFrequency (%)
임시청사 8
 
0.2%
12249 2
 
0.1%
41504 2
 
0.1%
50311 2
 
0.1%
50635 2
 
0.1%
26050 2
 
0.1%
4386 2
 
0.1%
37684 2
 
0.1%
14041 2
 
0.1%
25517 2
 
0.1%
Other values (3318) 3345
99.2%
2023-12-12T21:28:38.893583image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 2236
13.6%
1 2053
12.5%
2 1988
12.1%
3 1969
12.0%
4 1821
11.1%
0 1526
9.3%
6 1423
8.6%
7 1241
7.5%
8 1135
6.9%
9 1018
6.2%
Other values (7) 56
 
0.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 16410
99.7%
Other Letter 32
 
0.2%
Open Punctuation 8
 
< 0.1%
Close Punctuation 8
 
< 0.1%
Space Separator 8
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 2236
13.6%
1 2053
12.5%
2 1988
12.1%
3 1969
12.0%
4 1821
11.1%
0 1526
9.3%
6 1423
8.7%
7 1241
7.6%
8 1135
6.9%
9 1018
6.2%
Other Letter
ValueCountFrequency (%)
임 8
25.0%
시 8
25.0%
청 8
25.0%
사 8
25.0%
Open Punctuation
ValueCountFrequency (%)
( 8
100.0%
Close Punctuation
ValueCountFrequency (%)
) 8
100.0%
Space Separator
ValueCountFrequency (%)
8
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 16434
99.8%
Hangul 32
 
0.2%

Most frequent character per script

Common
ValueCountFrequency (%)
5 2236
13.6%
1 2053
12.5%
2 1988
12.1%
3 1969
12.0%
4 1821
11.1%
0 1526
9.3%
6 1423
8.7%
7 1241
7.6%
8 1135
6.9%
9 1018
6.2%
Other values (3) 24
 
0.1%
Hangul
ValueCountFrequency (%)
임 8
25.0%
시 8
25.0%
청 8
25.0%
사 8
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 16434
99.8%
Hangul 32
 
0.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 2236
13.6%
1 2053
12.5%
2 1988
12.1%
3 1969
12.0%
4 1821
11.1%
0 1526
9.3%
6 1423
8.7%
7 1241
7.6%
8 1135
6.9%
9 1018
6.2%
Other values (3) 24
 
0.1%
Hangul
ValueCountFrequency (%)
임 8
25.0%
시 8
25.0%
청 8
25.0%
사 8
25.0%

주소(지번)
Text

Distinct3356
Distinct (%)99.8%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T21:28:39.434983image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length94
Median length47
Mean length21.423431
Min length13

Characters and Unicode

Total characters72047
Distinct characters447
Distinct categories10 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3349 ?
Unique (%)99.6%

Sample

1st row서울특별시 중구 충무로1가 21-1
2nd row서울특별시 중구 을지로4가 312-1
3rd row서울특별시 중구 봉래동1가 5-36 우리빌딩 2층
4th row서울특별시 중구 장충동2가 173-3
5th row서울특별시 중구 봉래동2가 123
ValueCountFrequency (%)
경기도 469
 
3.0%
서울특별시 405
 
2.6%
경상북도 333
 
2.1%
경상남도 329
 
2.1%
전라남도 297
 
1.9%
전라북도 244
 
1.5%
충청남도 227
 
1.4%
강원도 199
 
1.3%
부산광역시 192
 
1.2%
충청북도 162
 
1.0%
Other values (7202) 12961
81.9%
2023-12-12T21:28:40.176740image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12979
 
18.0%
1 2824
 
3.9%
- 2675
 
3.7%
시 2609
 
3.6%
도 2511
 
3.5%
동 2263
 
3.1%
2 1931
 
2.7%
구 1638
 
2.3%
3 1627
 
2.3%
리 1593
 
2.2%
Other values (437) 39397
54.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 41947
58.2%
Decimal Number 14004
 
19.4%
Space Separator 12979
 
18.0%
Dash Punctuation 2675
 
3.7%
Close Punctuation 180
 
0.2%
Open Punctuation 179
 
0.2%
Other Punctuation 39
 
0.1%
Uppercase Letter 24
 
< 0.1%
Connector Punctuation 18
 
< 0.1%
Math Symbol 2
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
시 2609
 
6.2%
도 2511
 
6.0%
동 2263
 
5.4%
구 1638
 
3.9%
리 1593
 
3.8%
남 1274
 
3.0%
면 1234
 
2.9%
경 1224
 
2.9%
산 1063
 
2.5%
군 975
 
2.3%
Other values (402) 25563
60.9%
Uppercase Letter
ValueCountFrequency (%)
A 4
16.7%
I 4
16.7%
C 3
12.5%
B 3
12.5%
N 2
8.3%
K 2
8.3%
T 1
 
4.2%
P 1
 
4.2%
D 1
 
4.2%
S 1
 
4.2%
Other values (2) 2
8.3%
Decimal Number
ValueCountFrequency (%)
1 2824
20.2%
2 1931
13.8%
3 1627
11.6%
4 1385
9.9%
5 1281
9.1%
6 1151
8.2%
7 1049
 
7.5%
8 940
 
6.7%
0 937
 
6.7%
9 879
 
6.3%
Other Punctuation
ValueCountFrequency (%)
, 29
74.4%
. 7
 
17.9%
* 1
 
2.6%
' 1
 
2.6%
/ 1
 
2.6%
Close Punctuation
ValueCountFrequency (%)
) 163
90.6%
] 17
 
9.4%
Open Punctuation
ValueCountFrequency (%)
( 162
90.5%
[ 17
 
9.5%
Space Separator
ValueCountFrequency (%)
12979
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2675
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 18
100.0%
Math Symbol
ValueCountFrequency (%)
~ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 41947
58.2%
Common 30076
41.7%
Latin 24
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
시 2609
 
6.2%
도 2511
 
6.0%
동 2263
 
5.4%
구 1638
 
3.9%
리 1593
 
3.8%
남 1274
 
3.0%
면 1234
 
2.9%
경 1224
 
2.9%
산 1063
 
2.5%
군 975
 
2.3%
Other values (402) 25563
60.9%
Common
ValueCountFrequency (%)
12979
43.2%
1 2824
 
9.4%
- 2675
 
8.9%
2 1931
 
6.4%
3 1627
 
5.4%
4 1385
 
4.6%
5 1281
 
4.3%
6 1151
 
3.8%
7 1049
 
3.5%
8 940
 
3.1%
Other values (13) 2234
 
7.4%
Latin
ValueCountFrequency (%)
A 4
16.7%
I 4
16.7%
C 3
12.5%
B 3
12.5%
N 2
8.3%
K 2
8.3%
T 1
 
4.2%
P 1
 
4.2%
D 1
 
4.2%
S 1
 
4.2%
Other values (2) 2
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 41947
58.2%
ASCII 30100
41.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12979
43.1%
1 2824
 
9.4%
- 2675
 
8.9%
2 1931
 
6.4%
3 1627
 
5.4%
4 1385
 
4.6%
5 1281
 
4.3%
6 1151
 
3.8%
7 1049
 
3.5%
8 940
 
3.1%
Other values (25) 2258
 
7.5%
Hangul
ValueCountFrequency (%)
시 2609
 
6.2%
도 2511
 
6.0%
동 2263
 
5.4%
구 1638
 
3.9%
리 1593
 
3.8%
남 1274
 
3.0%
면 1234
 
2.9%
경 1224
 
2.9%
산 1063
 
2.5%
군 975
 
2.3%
Other values (402) 25563
60.9%

주소(도로명)
Text

Distinct3346
Distinct (%)99.7%
Missing8
Missing (%)0.2%
Memory size26.4 KiB
2023-12-12T21:28:40.614286image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length98
Median length51
Mean length22.453353
Min length10

Characters and Unicode

Total characters75331
Distinct characters540
Distinct categories10 ?
Distinct scripts4 ?
Distinct blocks4 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3339 ?
Unique (%)99.5%

Sample

1st row서울특별시 중구 소공로 70 (층무로1가)
2nd row서울특별시 중구 을지로 154-2 (을지로4가)
3rd row서울특별시 중구 칠패로 42 우리빌딩 2층 (봉래동1가)
4th row서울특별시 중구 퇴계로 270 (장충동2가)
5th row서울특별시 중구 통일로 21 (봉래동2가)
ValueCountFrequency (%)
경기도 446
 
2.6%
서울특별시 401
 
2.4%
경상북도 332
 
2.0%
경상남도 326
 
1.9%
전라남도 295
 
1.7%
전북 239
 
1.4%
충청남도 227
 
1.3%
강원도 193
 
1.1%
부산광역시 192
 
1.1%
충청북도 162
 
1.0%
Other values (6820) 14153
83.4%
2023-12-12T21:28:41.257452image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
13835
 
18.4%
로 2823
 
3.7%
시 2636
 
3.5%
1 2352
 
3.1%
도 2248
 
3.0%
동 1701
 
2.3%
구 1666
 
2.2%
2 1438
 
1.9%
( 1414
 
1.9%
) 1413
 
1.9%
Other values (530) 43805
58.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 47404
62.9%
Space Separator 13835
 
18.4%
Decimal Number 10646
 
14.1%
Open Punctuation 1421
 
1.9%
Close Punctuation 1420
 
1.9%
Dash Punctuation 418
 
0.6%
Other Punctuation 148
 
0.2%
Uppercase Letter 28
 
< 0.1%
Connector Punctuation 8
 
< 0.1%
Math Symbol 3
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
로 2823
 
6.0%
시 2636
 
5.6%
도 2248
 
4.7%
동 1701
 
3.6%
구 1666
 
3.5%
남 1336
 
2.8%
경 1257
 
2.7%
면 1216
 
2.6%
산 1107
 
2.3%
길 1074
 
2.3%
Other values (493) 30340
64.0%
Uppercase Letter
ValueCountFrequency (%)
B 6
21.4%
C 4
14.3%
A 4
14.3%
I 3
10.7%
N 2
 
7.1%
K 2
 
7.1%
H 1
 
3.6%
T 1
 
3.6%
R 1
 
3.6%
P 1
 
3.6%
Other values (3) 3
10.7%
Decimal Number
ValueCountFrequency (%)
1 2352
22.1%
2 1438
13.5%
3 1150
10.8%
4 970
9.1%
5 901
 
8.5%
7 833
 
7.8%
6 819
 
7.7%
0 778
 
7.3%
8 712
 
6.7%
9 693
 
6.5%
Other Punctuation
ValueCountFrequency (%)
, 131
88.5%
. 12
 
8.1%
· 2
 
1.4%
@ 1
 
0.7%
/ 1
 
0.7%
' 1
 
0.7%
Open Punctuation
ValueCountFrequency (%)
( 1414
99.5%
[ 7
 
0.5%
Close Punctuation
ValueCountFrequency (%)
) 1413
99.5%
] 7
 
0.5%
Space Separator
ValueCountFrequency (%)
13835
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 418
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 8
100.0%
Math Symbol
ValueCountFrequency (%)
~ 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 47403
62.9%
Common 27899
37.0%
Latin 28
 
< 0.1%
Han 1
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
로 2823
 
6.0%
시 2636
 
5.6%
도 2248
 
4.7%
동 1701
 
3.6%
구 1666
 
3.5%
남 1336
 
2.8%
경 1257
 
2.7%
면 1216
 
2.6%
산 1107
 
2.3%
길 1074
 
2.3%
Other values (492) 30339
64.0%
Common
ValueCountFrequency (%)
13835
49.6%
1 2352
 
8.4%
2 1438
 
5.2%
( 1414
 
5.1%
) 1413
 
5.1%
3 1150
 
4.1%
4 970
 
3.5%
5 901
 
3.2%
7 833
 
3.0%
6 819
 
2.9%
Other values (14) 2774
 
9.9%
Latin
ValueCountFrequency (%)
B 6
21.4%
C 4
14.3%
A 4
14.3%
I 3
10.7%
N 2
 
7.1%
K 2
 
7.1%
H 1
 
3.6%
T 1
 
3.6%
R 1
 
3.6%
P 1
 
3.6%
Other values (3) 3
10.7%
Han
ValueCountFrequency (%)
內 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 47403
62.9%
ASCII 27925
37.1%
None 2
 
< 0.1%
CJK 1
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
13835
49.5%
1 2352
 
8.4%
2 1438
 
5.1%
( 1414
 
5.1%
) 1413
 
5.1%
3 1150
 
4.1%
4 970
 
3.5%
5 901
 
3.2%
7 833
 
3.0%
6 819
 
2.9%
Other values (26) 2800
 
10.0%
Hangul
ValueCountFrequency (%)
로 2823
 
6.0%
시 2636
 
5.6%
도 2248
 
4.7%
동 1701
 
3.6%
구 1666
 
3.5%
남 1336
 
2.8%
경 1257
 
2.7%
면 1216
 
2.6%
산 1107
 
2.3%
길 1074
 
2.3%
Other values (492) 30339
64.0%
None
ValueCountFrequency (%)
· 2
100.0%
CJK
ValueCountFrequency (%)
內 1
100.0%

전화번호
Text

Distinct3360
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size26.4 KiB
2023-12-12T21:28:41.618378image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length19
Median length12
Mean length11.930717
Min length9

Characters and Unicode

Total characters40123
Distinct characters18
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique3357 ?
Unique (%)99.8%

Sample

1st row02)6450-1114
2nd row02)2273-6505
3rd row02)3783-7025
4th row02)2273-6508
5th row02)757-1140
ValueCountFrequency (%)
임시청사 8
 
0.2%
061)853-6002 2
 
0.1%
061)275-9788 2
 
0.1%
062-231-0280 2
 
0.1%
064)764-1117 1
 
< 0.1%
061)337-1002 1
 
< 0.1%
061)335-6061 1
 
< 0.1%
061)336-1002 1
 
< 0.1%
061)432-1002 1
 
< 0.1%
02)6450-1114 1
 
< 0.1%
Other values (3351) 3351
99.4%
2023-12-12T21:28:42.154483image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 8817
22.0%
2 3914
9.8%
3 3876
9.7%
5 3725
9.3%
- 3503
 
8.7%
1 3364
 
8.4%
) 3222
 
8.0%
4 2831
 
7.1%
6 2425
 
6.0%
7 1704
 
4.2%
Other values (8) 2742
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 33341
83.1%
Dash Punctuation 3503
 
8.7%
Close Punctuation 3222
 
8.0%
Other Letter 32
 
0.1%
Space Separator 16
 
< 0.1%
Open Punctuation 9
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 8817
26.4%
2 3914
11.7%
3 3876
11.6%
5 3725
11.2%
1 3364
 
10.1%
4 2831
 
8.5%
6 2425
 
7.3%
7 1704
 
5.1%
8 1607
 
4.8%
9 1078
 
3.2%
Other Letter
ValueCountFrequency (%)
임 8
25.0%
시 8
25.0%
청 8
25.0%
사 8
25.0%
Dash Punctuation
ValueCountFrequency (%)
- 3503
100.0%
Close Punctuation
ValueCountFrequency (%)
) 3222
100.0%
Space Separator
ValueCountFrequency (%)
16
100.0%
Open Punctuation
ValueCountFrequency (%)
( 9
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40091
99.9%
Hangul 32
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 8817
22.0%
2 3914
9.8%
3 3876
9.7%
5 3725
9.3%
- 3503
 
8.7%
1 3364
 
8.4%
) 3222
 
8.0%
4 2831
 
7.1%
6 2425
 
6.0%
7 1704
 
4.3%
Other values (4) 2710
 
6.8%
Hangul
ValueCountFrequency (%)
임 8
25.0%
시 8
25.0%
청 8
25.0%
사 8
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40091
99.9%
Hangul 32
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 8817
22.0%
2 3914
9.8%
3 3876
9.7%
5 3725
9.3%
- 3503
 
8.7%
1 3364
 
8.4%
) 3222
 
8.0%
4 2831
 
7.1%
6 2425
 
6.0%
7 1704
 
4.3%
Other values (4) 2710
 
6.8%
Hangul
ValueCountFrequency (%)
임 8
25.0%
시 8
25.0%
청 8
25.0%
사 8
25.0%

Missing values

2023-12-12T21:28:35.532437image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T21:28:35.672917image/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

지방우정청총괄국명우체국명우편번호주소(지번)주소(도로명)전화번호
0서울지방우정청서울중앙우체국서울중앙우체국4535서울특별시 중구 충무로1가 21-1서울특별시 중구 소공로 70 (층무로1가)02)6450-1114
1서울지방우정청서울중앙우체국서울을지로4가우체국4549서울특별시 중구 을지로4가 312-1서울특별시 중구 을지로 154-2 (을지로4가)02)2273-6505
2서울지방우정청서울중앙우체국서울태평로우체국4512서울특별시 중구 봉래동1가 5-36 우리빌딩 2층서울특별시 중구 칠패로 42 우리빌딩 2층 (봉래동1가)02)3783-7025
3서울지방우정청서울중앙우체국서울퇴계로5가우체국4618서울특별시 중구 장충동2가 173-3서울특별시 중구 퇴계로 270 (장충동2가)02)2273-6508
4서울지방우정청서울중앙우체국서울역전우체국4509서울특별시 중구 봉래동2가 123서울특별시 중구 통일로 21 (봉래동2가)02)757-1140
5서울지방우정청서울중앙우체국서울신당동우체국4590서울특별시 중구 신당동 369-36서울특별시 중구 다산로 128-8 (신당동)02)2236-0606
6서울지방우정청서울중앙우체국서울흥인동우체국4569서울특별시 중구 흥인동 113-1서울특별시 중구 다산로 253 (흥인동)02)2236-0500
7서울지방우정청서울중앙우체국서울소공동우체국4526서울특별시 중구 북창동 70-1서울특별시 중구 남대문로1길 34 (북창동)02)757-2660
8서울지방우정청서울중앙우체국서울충무로2가우체국4553서울특별시 중구 충무로2가 49-17서울특별시 중구 삼일대로6길 5 (충무로2가)02)2278-0037
9서울지방우정청서울중앙우체국서울회현동우편취급국4635서울특별시 중구 남창동 190-11서울특별시 중구 퇴계로6길 18 (남창동)02)756-3377
지방우정청총괄국명우체국명우편번호주소(지번)주소(도로명)전화번호
3353제주지방우정청서귀포우체국서귀포예래동우체국63538제주특별자치도 서귀포시 하예동 15-3제주특별자치도 서귀포시 하예로 58064)738-6204
3354제주지방우정청서귀포우체국서귀포중문동우체국63545제주특별자치도 서귀포시 중문동 2087-2제주특별자치도 서귀포시 천제연로 175064)738-5204
3355제주지방우정청서귀포우체국서귀포중앙동우체국63591제주특별자치도 서귀포시 서귀동 293-3제주특별자치도 서귀포시 중정로 57064)762-0014
3356제주지방우정청서귀포우체국서귀포효돈동우체국63606제주특별자치도 서귀포시 하효동 152-3제주특별자치도 서귀포시 효돈로 160064)767-0002
3357제주지방우정청서귀포우체국성산포우체국63643제주특별자치도 서귀포시 성산읍 성산리 182-2제주특별자치도 서귀포시 성산읍 성산중앙로 42064)782-2205
3358제주지방우정청서귀포우체국안덕우체국63530제주특별자치도 서귀포시 안덕면 화순리 1069-5제주특별자치도 서귀포시 안덕면 화순로 109064)794-9002
3359제주지방우정청서귀포우체국표선우체국63629제주특별자치도 서귀포시 표선면 표선리 694-4제주특별자치도 서귀포시 표선면 표선동서로 245064)787-0002
3360제주지방우정청서귀포우체국제주남원우체국63621제주특별자치도 서귀포시 남원읍 남원리 1349-1제주특별자치도 서귀포시 남원읍 태위로 632064)764-1117
3361제주지방우정청서귀포우체국중문관광단지우편취급국63535제주특별자치도 서귀포시 색달동 2864-36제주특별자치도 서귀포시 중문관광로 110번길 15064)738-4979
3362제주지방우정청제주우편집중국제주우편집중국63087제주특별자치도 제주시 노형동 707제주특별자치도 제주시 1100로 3307064-710-5200