Overview

Dataset statistics

Number of variables11
Number of observations926
Missing cells140
Missing cells (%)1.4%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory79.7 KiB
Average record size in memory88.1 B

Variable types

Unsupported2
Text7
Categorical2

Dataset

Description비영리민간단체_등록현황20165월말
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202755

Alerts

Unnamed: 9 is highly overall correlated with Unnamed: 8High correlation
Unnamed: 8 is highly overall correlated with Unnamed: 9High correlation
Unnamed: 9 is highly imbalanced (54.6%)Imbalance
Unnamed: 6 has 33 (3.6%) missing valuesMissing
Unnamed: 7 has 106 (11.4%) missing valuesMissing
Unnamed: 1 has unique valuesUnique
비영리민간단체 등록 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-03-13 23:54:55.042186
Analysis finished2024-03-13 23:54:56.164922
Duration1.12 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

비영리민간단체 등록
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size7.4 KiB

Unnamed: 1
Text

UNIQUE 

Distinct926
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2024-03-14T08:54:56.304517image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length40
Median length30
Mean length16.006479
Min length12

Characters and Unicode

Total characters14822
Distinct characters26
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

Unique926 ?
Unique (%)100.0%

Sample

1st row등록번호 (구등록번호)
2nd row2000-1-전라북도-1(1)
3rd row2000-1-전라북도-2(3)
4th row2000-1-전라북도-3(4)
5th row2000-1-전라북도-4(5)
ValueCountFrequency (%)
등록번호 1
 
0.1%
2009-1-전라북도-8 1
 
0.1%
2009-1-전라북도-10(116 1
 
0.1%
2009-1-전라북도-11 1
 
0.1%
2009-1-전라북도-12 1
 
0.1%
2009-1-전라북도-13 1
 
0.1%
2009-1-전라북도-14 1
 
0.1%
2009-1-전라북도-15 1
 
0.1%
2009-1-전라북도-16 1
 
0.1%
2009-1-전라북도-19 1
 
0.1%
Other values (917) 917
98.9%
2024-03-14T08:54:56.636646image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 2792
18.8%
0 2031
13.7%
1 1740
11.7%
2 1415
9.5%
도 928
 
6.3%
북 928
 
6.3%
전 928
 
6.3%
라 928
 
6.3%
3 467
 
3.2%
) 394
 
2.7%
Other values (16) 2271
15.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 7514
50.7%
Other Letter 3727
25.1%
Dash Punctuation 2792
 
18.8%
Close Punctuation 394
 
2.7%
Open Punctuation 394
 
2.7%
Control 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
도 928
24.9%
북 928
24.9%
전 928
24.9%
라 928
24.9%
호 4
 
0.1%
제 2
 
0.1%
등 2
 
0.1%
록 2
 
0.1%
번 2
 
0.1%
구 1
 
< 0.1%
Other values (2) 2
 
0.1%
Decimal Number
ValueCountFrequency (%)
0 2031
27.0%
1 1740
23.2%
2 1415
18.8%
3 467
 
6.2%
4 338
 
4.5%
5 314
 
4.2%
7 306
 
4.1%
9 306
 
4.1%
8 300
 
4.0%
6 297
 
4.0%
Dash Punctuation
ValueCountFrequency (%)
- 2792
100.0%
Close Punctuation
ValueCountFrequency (%)
) 394
100.0%
Open Punctuation
ValueCountFrequency (%)
( 394
100.0%
Control
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11095
74.9%
Hangul 3727
 
25.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 2792
25.2%
0 2031
18.3%
1 1740
15.7%
2 1415
12.8%
3 467
 
4.2%
) 394
 
3.6%
( 394
 
3.6%
4 338
 
3.0%
5 314
 
2.8%
7 306
 
2.8%
Other values (4) 904
 
8.1%
Hangul
ValueCountFrequency (%)
도 928
24.9%
북 928
24.9%
전 928
24.9%
라 928
24.9%
호 4
 
0.1%
제 2
 
0.1%
등 2
 
0.1%
록 2
 
0.1%
번 2
 
0.1%
구 1
 
< 0.1%
Other values (2) 2
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11095
74.9%
Hangul 3727
 
25.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 2792
25.2%
0 2031
18.3%
1 1740
15.7%
2 1415
12.8%
3 467
 
4.2%
) 394
 
3.6%
( 394
 
3.6%
4 338
 
3.0%
5 314
 
2.8%
7 306
 
2.8%
Other values (4) 904
 
8.1%
Hangul
ValueCountFrequency (%)
도 928
24.9%
북 928
24.9%
전 928
24.9%
라 928
24.9%
호 4
 
0.1%
제 2
 
0.1%
등 2
 
0.1%
록 2
 
0.1%
번 2
 
0.1%
구 1
 
< 0.1%
Other values (2) 2
 
0.1%

Unnamed: 2
Text

Distinct925
Distinct (%)99.9%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2024-03-14T08:54:56.825582image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length26
Median length22
Mean length10.762419
Min length3

Characters and Unicode

Total characters9966
Distinct characters437
Distinct categories9 ?
Distinct scripts3 ?
Distinct blocks3 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique924 ?
Unique (%)99.8%

Sample

1st row단체명칭
2nd row한국여성소비자연합 전주.전북지회
3rd row자연보호 전주시협의회
4th row자연보호 전라북도협의회
5th row전주여성의전화
ValueCountFrequency (%)
바르게살기운동 15
 
1.2%
전북지부 14
 
1.1%
한국농업경영인 14
 
1.1%
전라북도지부 9
 
0.7%
전라북도 8
 
0.6%
자연보호 7
 
0.5%
전주 7
 
0.5%
익산지부 6
 
0.5%
한국자유총연맹 5
 
0.4%
강살리기 5
 
0.4%
Other values (1095) 1208
93.1%
2024-03-14T08:54:57.137407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
회 530
 
5.3%
전 417
 
4.2%
373
 
3.7%
북 277
 
2.8%
지 253
 
2.5%
연 220
 
2.2%
사 205
 
2.1%
부 175
 
1.8%
협 174
 
1.7%
국 168
 
1.7%
Other values (427) 7174
72.0%

Most occurring categories

ValueCountFrequency (%)
Other Letter 9381
94.1%
Space Separator 373
 
3.7%
Uppercase Letter 83
 
0.8%
Decimal Number 42
 
0.4%
Close Punctuation 39
 
0.4%
Other Punctuation 21
 
0.2%
Lowercase Letter 12
 
0.1%
Open Punctuation 11
 
0.1%
Dash Punctuation 4
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
회 530
 
5.6%
전 417
 
4.4%
북 277
 
3.0%
지 253
 
2.7%
연 220
 
2.3%
사 205
 
2.2%
부 175
 
1.9%
협 174
 
1.9%
국 168
 
1.8%
한 164
 
1.7%
Other values (386) 6798
72.5%
Uppercase Letter
ValueCountFrequency (%)
C 15
18.1%
A 14
16.9%
Y 11
13.3%
M 9
10.8%
O 5
 
6.0%
W 5
 
6.0%
H 4
 
4.8%
N 4
 
4.8%
G 4
 
4.8%
B 3
 
3.6%
Other values (6) 9
10.8%
Decimal Number
ValueCountFrequency (%)
2 12
28.6%
1 8
19.0%
5 7
16.7%
6 6
14.3%
4 4
 
9.5%
0 2
 
4.8%
3 2
 
4.8%
8 1
 
2.4%
Lowercase Letter
ValueCountFrequency (%)
a 2
16.7%
e 2
16.7%
y 2
16.7%
p 2
16.7%
t 1
8.3%
g 1
8.3%
n 1
8.3%
o 1
8.3%
Other Punctuation
ValueCountFrequency (%)
. 14
66.7%
· 5
 
23.8%
, 2
 
9.5%
Close Punctuation
ValueCountFrequency (%)
) 38
97.4%
] 1
 
2.6%
Open Punctuation
ValueCountFrequency (%)
( 10
90.9%
[ 1
 
9.1%
Space Separator
ValueCountFrequency (%)
373
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 9381
94.1%
Common 490
 
4.9%
Latin 95
 
1.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
회 530
 
5.6%
전 417
 
4.4%
북 277
 
3.0%
지 253
 
2.7%
연 220
 
2.3%
사 205
 
2.2%
부 175
 
1.9%
협 174
 
1.9%
국 168
 
1.8%
한 164
 
1.7%
Other values (386) 6798
72.5%
Latin
ValueCountFrequency (%)
C 15
15.8%
A 14
14.7%
Y 11
11.6%
M 9
9.5%
O 5
 
5.3%
W 5
 
5.3%
H 4
 
4.2%
N 4
 
4.2%
G 4
 
4.2%
B 3
 
3.2%
Other values (14) 21
22.1%
Common
ValueCountFrequency (%)
373
76.1%
) 38
 
7.8%
. 14
 
2.9%
2 12
 
2.4%
( 10
 
2.0%
1 8
 
1.6%
5 7
 
1.4%
6 6
 
1.2%
· 5
 
1.0%
- 4
 
0.8%
Other values (7) 13
 
2.7%

Most occurring blocks

ValueCountFrequency (%)
Hangul 9381
94.1%
ASCII 580
 
5.8%
None 5
 
0.1%

Most frequent character per block

Hangul
ValueCountFrequency (%)
회 530
 
5.6%
전 417
 
4.4%
북 277
 
3.0%
지 253
 
2.7%
연 220
 
2.3%
사 205
 
2.2%
부 175
 
1.9%
협 174
 
1.9%
국 168
 
1.8%
한 164
 
1.7%
Other values (386) 6798
72.5%
ASCII
ValueCountFrequency (%)
373
64.3%
) 38
 
6.6%
C 15
 
2.6%
A 14
 
2.4%
. 14
 
2.4%
2 12
 
2.1%
Y 11
 
1.9%
( 10
 
1.7%
M 9
 
1.6%
1 8
 
1.4%
Other values (30) 76
 
13.1%
None
ValueCountFrequency (%)
· 5
100.0%

Unnamed: 3
Text

Distinct876
Distinct (%)94.7%
Missing1
Missing (%)0.1%
Memory size7.4 KiB
2024-03-14T08:54:57.390284image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length26.536216
Min length8

Characters and Unicode

Total characters24546
Distinct characters305
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

Unique839 ?
Unique (%)90.7%

Sample

1st row사무소의 소재지
2nd row전라북도 전주시 완산구 전룡4길 8 (서신동)
3rd row전라북도 전주시덕진구 금암동 금암2동 1587-76
4th row전라북도 전주시 덕진구 기린대로 451 (덕진동1가)
5th row전라북도 전주시 완산구 효자로 338, 3호 (중화산동2가)
ValueCountFrequency (%)
전라북도 924
 
19.1%
전주시완산구 179
 
3.7%
익산시 142
 
2.9%
전주시 134
 
2.8%
전주시덕진구 104
 
2.1%
완산구 90
 
1.9%
군산시 75
 
1.5%
1호 56
 
1.2%
정읍시 50
 
1.0%
덕진구 44
 
0.9%
Other values (1441) 3045
62.9%
2024-03-14T08:54:57.766321image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5240
21.3%
전 1382
 
5.6%
북 970
 
4.0%
도 945
 
3.8%
라 943
 
3.8%
1 908
 
3.7%
동 809
 
3.3%
시 761
 
3.1%
산 582
 
2.4%
2 579
 
2.4%
Other values (295) 11427
46.6%

Most occurring categories

ValueCountFrequency (%)
Other Letter 14477
59.0%
Space Separator 5240
 
21.3%
Decimal Number 3915
 
15.9%
Dash Punctuation 276
 
1.1%
Close Punctuation 264
 
1.1%
Open Punctuation 263
 
1.1%
Other Punctuation 89
 
0.4%
Uppercase Letter 11
 
< 0.1%
Math Symbol 10
 
< 0.1%
Lowercase Letter 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
전 1382
 
9.5%
북 970
 
6.7%
도 945
 
6.5%
라 943
 
6.5%
동 809
 
5.6%
시 761
 
5.3%
산 582
 
4.0%
주 500
 
3.5%
지 461
 
3.2%
구 433
 
3.0%
Other values (271) 6691
46.2%
Decimal Number
ValueCountFrequency (%)
1 908
23.2%
2 579
14.8%
3 410
10.5%
4 408
10.4%
5 316
 
8.1%
6 304
 
7.8%
0 292
 
7.5%
7 256
 
6.5%
8 222
 
5.7%
9 220
 
5.6%
Uppercase Letter
ValueCountFrequency (%)
C 3
27.3%
A 2
18.2%
M 2
18.2%
Y 2
18.2%
G 1
 
9.1%
F 1
 
9.1%
Other Punctuation
ValueCountFrequency (%)
, 87
97.8%
@ 2
 
2.2%
Space Separator
ValueCountFrequency (%)
5240
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 276
100.0%
Close Punctuation
ValueCountFrequency (%)
) 264
100.0%
Open Punctuation
ValueCountFrequency (%)
( 263
100.0%
Math Symbol
ValueCountFrequency (%)
~ 10
100.0%
Lowercase Letter
ValueCountFrequency (%)
e 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 14477
59.0%
Common 10057
41.0%
Latin 12
 
< 0.1%

Most frequent character per script

Hangul
ValueCountFrequency (%)
전 1382
 
9.5%
북 970
 
6.7%
도 945
 
6.5%
라 943
 
6.5%
동 809
 
5.6%
시 761
 
5.3%
산 582
 
4.0%
주 500
 
3.5%
지 461
 
3.2%
구 433
 
3.0%
Other values (271) 6691
46.2%
Common
ValueCountFrequency (%)
5240
52.1%
1 908
 
9.0%
2 579
 
5.8%
3 410
 
4.1%
4 408
 
4.1%
5 316
 
3.1%
6 304
 
3.0%
0 292
 
2.9%
- 276
 
2.7%
) 264
 
2.6%
Other values (7) 1060
 
10.5%
Latin
ValueCountFrequency (%)
C 3
25.0%
A 2
16.7%
M 2
16.7%
Y 2
16.7%
G 1
 
8.3%
e 1
 
8.3%
F 1
 
8.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 14477
59.0%
ASCII 10069
41.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5240
52.0%
1 908
 
9.0%
2 579
 
5.8%
3 410
 
4.1%
4 408
 
4.1%
5 316
 
3.1%
6 304
 
3.0%
0 292
 
2.9%
- 276
 
2.7%
) 264
 
2.6%
Other values (14) 1072
 
10.6%
Hangul
ValueCountFrequency (%)
전 1382
 
9.5%
북 970
 
6.7%
도 945
 
6.5%
라 943
 
6.5%
동 809
 
5.6%
시 761
 
5.3%
산 582
 
4.0%
주 500
 
3.5%
지 461
 
3.2%
구 433
 
3.0%
Other values (271) 6691
46.2%

Unnamed: 4
Text

Distinct855
Distinct (%)92.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2024-03-14T08:54:58.095664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length3
Mean length3.0777538
Min length2

Characters and Unicode

Total characters2850
Distinct characters208
Distinct categories8 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique797 ?
Unique (%)86.1%

Sample

1st row대표자성명
2nd row정순례
3rd row강찬원
4th row진창환
5th row조 숙
ValueCountFrequency (%)
김영구 4
 
0.4%
외 4
 
0.4%
김재승 3
 
0.3%
김종영 3
 
0.3%
서주상 3
 
0.3%
김택천 3
 
0.3%
박재만 3
 
0.3%
김양옥 3
 
0.3%
여형일 3
 
0.3%
김완자 3
 
0.3%
Other values (866) 919
96.6%
2024-03-14T08:54:58.531101image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
김 201
 
7.1%
이 132
 
4.6%
정 92
 
3.2%
영 91
 
3.2%
박 59
 
2.1%
희 52
 
1.8%
최 50
 
1.8%
수 49
 
1.7%
순 48
 
1.7%
현 41
 
1.4%
Other values (198) 2035
71.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 2805
98.4%
Space Separator 26
 
0.9%
Decimal Number 8
 
0.3%
Other Punctuation 7
 
0.2%
Dash Punctuation 1
 
< 0.1%
Math Symbol 1
 
< 0.1%
Close Punctuation 1
 
< 0.1%
Open Punctuation 1
 
< 0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
김 201
 
7.2%
이 132
 
4.7%
정 92
 
3.3%
영 91
 
3.2%
박 59
 
2.1%
희 52
 
1.9%
최 50
 
1.8%
수 49
 
1.7%
순 48
 
1.7%
현 41
 
1.5%
Other values (187) 1990
70.9%
Decimal Number
ValueCountFrequency (%)
1 3
37.5%
4 2
25.0%
3 2
25.0%
2 1
 
12.5%
Other Punctuation
ValueCountFrequency (%)
, 6
85.7%
. 1
 
14.3%
Space Separator
ValueCountFrequency (%)
26
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%
Math Symbol
ValueCountFrequency (%)
> 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 2805
98.4%
Common 45
 
1.6%

Most frequent character per script

Hangul
ValueCountFrequency (%)
김 201
 
7.2%
이 132
 
4.7%
정 92
 
3.3%
영 91
 
3.2%
박 59
 
2.1%
희 52
 
1.9%
최 50
 
1.8%
수 49
 
1.7%
순 48
 
1.7%
현 41
 
1.5%
Other values (187) 1990
70.9%
Common
ValueCountFrequency (%)
26
57.8%
, 6
 
13.3%
1 3
 
6.7%
4 2
 
4.4%
3 2
 
4.4%
- 1
 
2.2%
> 1
 
2.2%
) 1
 
2.2%
2 1
 
2.2%
. 1
 
2.2%

Most occurring blocks

ValueCountFrequency (%)
Hangul 2805
98.4%
ASCII 45
 
1.6%

Most frequent character per block

Hangul
ValueCountFrequency (%)
김 201
 
7.2%
이 132
 
4.7%
정 92
 
3.3%
영 91
 
3.2%
박 59
 
2.1%
희 52
 
1.9%
최 50
 
1.8%
수 49
 
1.7%
순 48
 
1.7%
현 41
 
1.5%
Other values (187) 1990
70.9%
ASCII
ValueCountFrequency (%)
26
57.8%
, 6
 
13.3%
1 3
 
6.7%
4 2
 
4.4%
3 2
 
4.4%
- 1
 
2.2%
> 1
 
2.2%
) 1
 
2.2%
2 1
 
2.2%
. 1
 
2.2%

Unnamed: 5
Text

Distinct654
Distinct (%)70.6%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
2024-03-14T08:54:58.787961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.9967603
Min length5

Characters and Unicode

Total characters7405
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique523 ?
Unique (%)56.5%

Sample

1st row등록연월일
2nd row00/04/19
3rd row00/04/20
4th row00/04/20
5th row00/04/26
ValueCountFrequency (%)
00/05/16 55
 
5.9%
00/04/27 21
 
2.3%
00/05/17 11
 
1.2%
00/10/02 11
 
1.2%
00/05/18 7
 
0.8%
01/03/27 5
 
0.5%
12/05/16 5
 
0.5%
04/02/20 5
 
0.5%
01/03/30 4
 
0.4%
05/04/22 4
 
0.4%
Other values (644) 798
86.2%
2024-03-14T08:54:59.179631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 1955
26.4%
/ 1850
25.0%
1 1121
15.1%
2 631
 
8.5%
3 317
 
4.3%
5 298
 
4.0%
7 268
 
3.6%
9 250
 
3.4%
6 246
 
3.3%
4 242
 
3.3%
Other values (6) 227
 
3.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 5550
74.9%
Other Punctuation 1850
 
25.0%
Other Letter 5
 
0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 1955
35.2%
1 1121
20.2%
2 631
 
11.4%
3 317
 
5.7%
5 298
 
5.4%
7 268
 
4.8%
9 250
 
4.5%
6 246
 
4.4%
4 242
 
4.4%
8 222
 
4.0%
Other Letter
ValueCountFrequency (%)
등 1
20.0%
록 1
20.0%
연 1
20.0%
월 1
20.0%
일 1
20.0%
Other Punctuation
ValueCountFrequency (%)
/ 1850
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 7400
99.9%
Hangul 5
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 1955
26.4%
/ 1850
25.0%
1 1121
15.1%
2 631
 
8.5%
3 317
 
4.3%
5 298
 
4.0%
7 268
 
3.6%
9 250
 
3.4%
6 246
 
3.3%
4 242
 
3.3%
Hangul
ValueCountFrequency (%)
등 1
20.0%
록 1
20.0%
연 1
20.0%
월 1
20.0%
일 1
20.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 7400
99.9%
Hangul 5
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 1955
26.4%
/ 1850
25.0%
1 1121
15.1%
2 631
 
8.5%
3 317
 
4.3%
5 298
 
4.0%
7 268
 
3.6%
9 250
 
3.4%
6 246
 
3.3%
4 242
 
3.3%
Hangul
ValueCountFrequency (%)
등 1
20.0%
록 1
20.0%
연 1
20.0%
월 1
20.0%
일 1
20.0%

Unnamed: 6
Text

MISSING 

Distinct866
Distinct (%)97.0%
Missing33
Missing (%)3.6%
Memory size7.4 KiB
2024-03-14T08:54:59.427069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length303
Median length137
Mean length39.015677
Min length4

Characters and Unicode

Total characters34841
Distinct characters540
Distinct categories13 ?
Distinct scripts3 ?
Distinct blocks7 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique847 ?
Unique (%)94.8%

Sample

1st row주된사업
2nd row소비자 고발센타 운영
3rd row환경보전(자연보호 운동)
4th row환경보전(자연보호 운동)
5th row여성의 인권과 복지증진, 위기의 여성상담
ValueCountFrequency (%)
및 586
 
7.2%
등 299
 
3.7%
위한 247
 
3.0%
229
 
2.8%
사업 162
 
2.0%
교육 89
 
1.1%
○ 82
 
1.0%
활동 68
 
0.8%
청소년 62
 
0.8%
운영 56
 
0.7%
Other values (3326) 6301
77.0%
2024-03-14T08:54:59.818058image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
7000
 
20.1%
사 1116
 
3.2%
, 670
 
1.9%
및 642
 
1.8%
업 624
 
1.8%
613
 
1.8%
지 606
 
1.7%
동 518
 
1.5%
한 507
 
1.5%
의 463
 
1.3%
Other values (530) 22082
63.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 25449
73.0%
Space Separator 7000
 
20.1%
Other Punctuation 1023
 
2.9%
Control 613
 
1.8%
Decimal Number 248
 
0.7%
Dash Punctuation 242
 
0.7%
Other Symbol 84
 
0.2%
Open Punctuation 56
 
0.2%
Close Punctuation 55
 
0.2%
Lowercase Letter 33
 
0.1%
Other values (3) 38
 
0.1%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
사 1116
 
4.4%
및 642
 
2.5%
업 624
 
2.5%
지 606
 
2.4%
동 518
 
2.0%
한 507
 
2.0%
의 463
 
1.8%
교 440
 
1.7%
활 429
 
1.7%
육 378
 
1.5%
Other values (492) 19726
77.5%
Decimal Number
ValueCountFrequency (%)
2 48
19.4%
1 48
19.4%
3 42
16.9%
4 38
15.3%
0 31
12.5%
5 16
 
6.5%
6 12
 
4.8%
7 7
 
2.8%
8 3
 
1.2%
9 3
 
1.2%
Other Punctuation
ValueCountFrequency (%)
, 670
65.5%
. 289
28.3%
· 35
 
3.4%
? 23
 
2.2%
: 2
 
0.2%
' 2
 
0.2%
& 1
 
0.1%
/ 1
 
0.1%
Uppercase Letter
ValueCountFrequency (%)
H 9
39.1%
O 8
34.8%
M 2
 
8.7%
A 1
 
4.3%
E 1
 
4.3%
G 1
 
4.3%
N 1
 
4.3%
Other Number
ValueCountFrequency (%)
② 3
25.0%
④ 3
25.0%
③ 3
25.0%
① 3
25.0%
Modifier Symbol
ValueCountFrequency (%)
˙ 2
66.7%
` 1
33.3%
Space Separator
ValueCountFrequency (%)
7000
100.0%
Control
ValueCountFrequency (%)
613
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 242
100.0%
Other Symbol
ValueCountFrequency (%)
○ 84
100.0%
Open Punctuation
ValueCountFrequency (%)
( 56
100.0%
Close Punctuation
ValueCountFrequency (%)
) 55
100.0%
Lowercase Letter
ValueCountFrequency (%)
o 33
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 25449
73.0%
Common 9336
 
26.8%
Latin 56
 
0.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
사 1116
 
4.4%
및 642
 
2.5%
업 624
 
2.5%
지 606
 
2.4%
동 518
 
2.0%
한 507
 
2.0%
의 463
 
1.8%
교 440
 
1.7%
활 429
 
1.7%
육 378
 
1.5%
Other values (492) 19726
77.5%
Common
ValueCountFrequency (%)
7000
75.0%
, 670
 
7.2%
613
 
6.6%
. 289
 
3.1%
- 242
 
2.6%
○ 84
 
0.9%
( 56
 
0.6%
) 55
 
0.6%
2 48
 
0.5%
1 48
 
0.5%
Other values (20) 231
 
2.5%
Latin
ValueCountFrequency (%)
o 33
58.9%
H 9
 
16.1%
O 8
 
14.3%
M 2
 
3.6%
A 1
 
1.8%
E 1
 
1.8%
G 1
 
1.8%
N 1
 
1.8%

Most occurring blocks

ValueCountFrequency (%)
Hangul 25421
73.0%
ASCII 9259
 
26.6%
Geometric Shapes 84
 
0.2%
None 35
 
0.1%
Compat Jamo 28
 
0.1%
Enclosed Alphanum 12
 
< 0.1%
Modifier Letters 2
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
7000
75.6%
, 670
 
7.2%
613
 
6.6%
. 289
 
3.1%
- 242
 
2.6%
( 56
 
0.6%
) 55
 
0.6%
2 48
 
0.5%
1 48
 
0.5%
3 42
 
0.5%
Other values (21) 196
 
2.1%
Hangul
ValueCountFrequency (%)
사 1116
 
4.4%
및 642
 
2.5%
업 624
 
2.5%
지 606
 
2.4%
동 518
 
2.0%
한 507
 
2.0%
의 463
 
1.8%
교 440
 
1.7%
활 429
 
1.7%
육 378
 
1.5%
Other values (490) 19698
77.5%
Geometric Shapes
ValueCountFrequency (%)
○ 84
100.0%
None
ValueCountFrequency (%)
· 35
100.0%
Compat Jamo
ValueCountFrequency (%)
ㅇ 27
96.4%
ㆍ 1
 
3.6%
Enclosed Alphanum
ValueCountFrequency (%)
② 3
25.0%
④ 3
25.0%
③ 3
25.0%
① 3
25.0%
Modifier Letters
ValueCountFrequency (%)
˙ 2
100.0%

Unnamed: 7
Text

MISSING 

Distinct765
Distinct (%)93.3%
Missing106
Missing (%)11.4%
Memory size7.4 KiB
2024-03-14T08:55:00.013213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length12
Mean length12
Min length3

Characters and Unicode

Total characters9840
Distinct characters14
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique719 ?
Unique (%)87.7%

Sample

1st row연락처
2nd row063-282-9898
3rd row063-278-2332
4th row063-278-2332
5th row063-287-7324
ValueCountFrequency (%)
063-271-5454 4
 
0.5%
063-625-5433 3
 
0.4%
063-433-6556 3
 
0.4%
063-232-5561 3
 
0.4%
063-226-9861 3
 
0.4%
063-838-6500 3
 
0.4%
063-232-8100 3
 
0.4%
063-277-1234 3
 
0.4%
063-856-9698 2
 
0.2%
063-462-8744 2
 
0.2%
Other values (755) 791
96.5%
2024-03-14T08:55:00.383215image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 1638
16.6%
3 1518
15.4%
6 1306
13.3%
0 1295
13.2%
2 997
10.1%
5 707
7.2%
4 600
 
6.1%
8 580
 
5.9%
1 533
 
5.4%
7 394
 
4.0%
Other values (4) 272
 
2.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 8199
83.3%
Dash Punctuation 1638
 
16.6%
Other Letter 3
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 1518
18.5%
6 1306
15.9%
0 1295
15.8%
2 997
12.2%
5 707
8.6%
4 600
 
7.3%
8 580
 
7.1%
1 533
 
6.5%
7 394
 
4.8%
9 269
 
3.3%
Other Letter
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%
Dash Punctuation
ValueCountFrequency (%)
- 1638
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 9837
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 1638
16.7%
3 1518
15.4%
6 1306
13.3%
0 1295
13.2%
2 997
10.1%
5 707
7.2%
4 600
 
6.1%
8 580
 
5.9%
1 533
 
5.4%
7 394
 
4.0%
Hangul
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 9837
> 99.9%
Hangul 3
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 1638
16.7%
3 1518
15.4%
6 1306
13.3%
0 1295
13.2%
2 997
10.1%
5 707
7.2%
4 600
 
6.1%
8 580
 
5.9%
1 533
 
5.4%
7 394
 
4.0%
Hangul
ValueCountFrequency (%)
연 1
33.3%
락 1
33.3%
처 1
33.3%

Unnamed: 8
Categorical

HIGH CORRELATION 

Distinct46
Distinct (%)5.0%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
문화예술과
169 
여성청소년과
138 
대외협력과
104 
노인장애인복지과
80 
행정지원관실
76 
Other values (41)
359 

Length

Max length13
Median length5
Mean length5.6220302
Min length3

Unique

Unique22 ?
Unique (%)2.4%

Sample

1st row주관과
2nd row여성청소년과
3rd row환경보전과
4th row환경보전과
5th row여성청소년과

Common Values

ValueCountFrequency (%)
문화예술과 169
18.3%
여성청소년과 138
14.9%
대외협력과 104
11.2%
노인장애인복지과 80
8.6%
행정지원관실 76
8.2%
환경보전과 74
8.0%
다문화교류과 42
 
4.5%
미래농업과 34
 
3.7%
사회복지과 30
 
3.2%
보건의료과 22
 
2.4%
Other values (36) 157
17.0%

Length

2024-03-14T08:55:00.502450image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
문화예술과 169
18.2%
여성청소년과 138
14.9%
대외협력과 104
11.2%
노인장애인복지과 80
8.6%
행정지원관실 76
8.2%
환경보전과 74
8.0%
다문화교류과 42
 
4.5%
미래농업과 34
 
3.7%
사회복지과 30
 
3.2%
보건의료과 22
 
2.4%
Other values (37) 159
17.1%

Unnamed: 9
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size7.4 KiB
등록
746 
<NA>
179 
등록구분 (신규,변경)
 
1

Length

Max length12
Median length2
Mean length2.3974082
Min length2

Unique

Unique1 ?
Unique (%)0.1%

Sample

1st row등록구분 (신규,변경)
2nd row등록
3rd row등록
4th row<NA>
5th row<NA>

Common Values

ValueCountFrequency (%)
등록 746
80.6%
<NA> 179
 
19.3%
등록구분 (신규,변경) 1
 
0.1%

Length

2024-03-14T08:55:00.593173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-03-14T08:55:00.692083image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
등록 746
80.5%
na 179
 
19.3%
등록구분 1
 
0.1%
신규,변경 1
 
0.1%

Unnamed: 10
Unsupported

REJECTED  UNSUPPORTED 

Missing0
Missing (%)0.0%
Memory size7.4 KiB

Correlations

2024-03-14T08:55:00.762824image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 8Unnamed: 9
Unnamed: 81.0001.000
Unnamed: 91.0001.000
2024-03-14T08:55:00.841009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 9Unnamed: 8
Unnamed: 91.0000.973
Unnamed: 80.9731.000
2024-03-14T08:55:01.156219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 8Unnamed: 9
Unnamed: 81.0000.973
Unnamed: 90.9731.000

Missing values

2024-03-14T08:54:55.815495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T08:54:55.999696image/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.
2024-03-14T08:54:56.106143image/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

비영리민간단체 등록Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
0연번등록번호 (구등록번호)단체명칭사무소의 소재지대표자성명등록연월일주된사업연락처주관과등록구분 (신규,변경)회원수
112000-1-전라북도-1(1)한국여성소비자연합 전주.전북지회전라북도 전주시 완산구 전룡4길 8 (서신동)정순례00/04/19소비자 고발센타 운영063-282-9898여성청소년과등록2300
222000-1-전라북도-2(3)자연보호 전주시협의회전라북도 전주시덕진구 금암동 금암2동 1587-76강찬원00/04/20환경보전(자연보호 운동)063-278-2332환경보전과등록150
332000-1-전라북도-3(4)자연보호 전라북도협의회전라북도 전주시 덕진구 기린대로 451 (덕진동1가)진창환00/04/20환경보전(자연보호 운동)063-278-2332환경보전과<NA>1893
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비영리민간단체 등록Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10
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