Overview

Dataset statistics

Number of variables19
Number of observations24
Missing cells63
Missing cells (%)13.8%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory3.7 KiB
Average record size in memory157.5 B

Variable types

Text19

Dataset

Description복사본03232016년지역별RD투자현황-공공데이터
Author전라북도
URLhttps://www.bigdatahub.go.kr/opendata/dataSet/detail.nm?contentId=37&rlik=49451aebf056b486&serviceId=202423

Alerts

Unnamed: 0 has 4 (16.7%) missing valuesMissing
2016년 지역별 R&D 투자현황 has 4 (16.7%) missing valuesMissing
Unnamed: 2 has 3 (12.5%) missing valuesMissing
Unnamed: 3 has 5 (20.8%) missing valuesMissing
Unnamed: 4 has 3 (12.5%) missing valuesMissing
Unnamed: 5 has 4 (16.7%) missing valuesMissing
Unnamed: 6 has 3 (12.5%) missing valuesMissing
Unnamed: 7 has 4 (16.7%) missing valuesMissing
Unnamed: 8 has 3 (12.5%) missing valuesMissing
Unnamed: 9 has 4 (16.7%) missing valuesMissing
Unnamed: 10 has 3 (12.5%) missing valuesMissing
Unnamed: 11 has 4 (16.7%) missing valuesMissing
Unnamed: 12 has 3 (12.5%) missing valuesMissing
구분 has 2 (8.3%) missing valuesMissing
국비 has 1 (4.2%) missing valuesMissing
지방비 has 3 (12.5%) missing valuesMissing
국비+지방비 has 4 (16.7%) missing valuesMissing
지방비/ (국비+지방비) has 3 (12.5%) missing valuesMissing
지방비/국비 has 3 (12.5%) missing valuesMissing

Reproduction

Analysis started2024-03-14 01:08:28.730989
Analysis finished2024-03-14 01:08:29.970391
Duration1.24 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:30.073978image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.15
Min length2

Characters and Unicode

Total characters43
Distinct characters28
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

Unique20 ?
Unique (%)100.0%

Sample

1st row구분
2nd row단위:억원
3rd row서울
4th row인천
5th row경기
ValueCountFrequency (%)
구분 1
 
5.0%
단위:억원 1
 
5.0%
세종 1
 
5.0%
제주 1
 
5.0%
경남 1
 
5.0%
경북 1
 
5.0%
전남 1
 
5.0%
전북 1
 
5.0%
충남 1
 
5.0%
충북 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T10:08:30.326375image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3
 
7.0%
3
 
7.0%
3
 
7.0%
3
 
7.0%
2
 
4.7%
2
 
4.7%
2
 
4.7%
2
 
4.7%
2
 
4.7%
2
 
4.7%
Other values (18) 19
44.2%

Most occurring categories

ValueCountFrequency (%)
Other Letter 42
97.7%
Other Punctuation 1
 
2.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (17) 18
42.9%
Other Punctuation
ValueCountFrequency (%)
: 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 42
97.7%
Common 1
 
2.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (17) 18
42.9%
Common
ValueCountFrequency (%)
: 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 42
97.7%
ASCII 1
 
2.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
3
 
7.1%
3
 
7.1%
3
 
7.1%
3
 
7.1%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
2
 
4.8%
Other values (17) 18
42.9%
ASCII
ValueCountFrequency (%)
: 1
100.0%
Distinct20
Distinct (%)100.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:30.495003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length5.2
Min length4

Characters and Unicode

Total characters104
Distinct characters19
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

Unique20 ?
Unique (%)100.0%

Sample

1st row 국비(a)
2nd row 현금
3rd row35,925
4th row4,385
5th row23,740
ValueCountFrequency (%)
국비(a 1
 
5.0%
현금 1
 
5.0%
4170 1
 
5.0%
1410 1
 
5.0%
9,721 1
 
5.0%
6,165 1
 
5.0%
3,057 1
 
5.0%
6,712 1
 
5.0%
4,843 1
 
5.0%
4,962 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T10:08:30.792444image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
, 16
15.4%
5 12
11.5%
6 11
10.6%
1 11
10.6%
4 9
8.7%
3 8
7.7%
2 8
7.7%
7 7
6.7%
9 4
 
3.8%
0 4
 
3.8%
Other values (9) 14
13.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
74.0%
Other Punctuation 16
 
15.4%
Space Separator 4
 
3.8%
Other Letter 4
 
3.8%
Lowercase Letter 1
 
1.0%
Close Punctuation 1
 
1.0%
Open Punctuation 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12
15.6%
6 11
14.3%
1 11
14.3%
4 9
11.7%
3 8
10.4%
2 8
10.4%
7 7
9.1%
9 4
 
5.2%
0 4
 
5.2%
8 3
 
3.9%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 16
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 99
95.2%
Hangul 4
 
3.8%
Latin 1
 
1.0%

Most frequent character per script

Common
ValueCountFrequency (%)
, 16
16.2%
5 12
12.1%
6 11
11.1%
1 11
11.1%
4 9
9.1%
3 8
8.1%
2 8
8.1%
7 7
7.1%
9 4
 
4.0%
0 4
 
4.0%
Other values (4) 9
9.1%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Latin
ValueCountFrequency (%)
a 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 100
96.2%
Hangul 4
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
, 16
16.0%
5 12
12.0%
6 11
11.0%
1 11
11.0%
4 9
9.0%
3 8
8.0%
2 8
8.0%
7 7
7.0%
9 4
 
4.0%
0 4
 
4.0%
Other values (5) 10
10.0%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Unnamed: 2
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:30.943802image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.1428571
Min length1

Characters and Unicode

Total characters66
Distinct characters21
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

Unique21 ?
Unique (%)100.0%

Sample

1st row 지방정부
2nd row 현금(b)
3rd row181
4th row18
5th row82
ValueCountFrequency (%)
지방정부 1
 
4.8%
48 1
 
4.8%
3,038 1
 
4.8%
1 1
 
4.8%
23 1
 
4.8%
230 1
 
4.8%
444 1
 
4.8%
200 1
 
4.8%
241 1
 
4.8%
154 1
 
4.8%
Other values (11) 11
52.4%
2024-03-14T10:08:31.244319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 8
12.1%
8 8
12.1%
3 8
12.1%
4 7
10.6%
2 7
10.6%
0 6
9.1%
4
 
6.1%
6 3
 
4.5%
7 2
 
3.0%
, 2
 
3.0%
Other values (11) 11
16.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51
77.3%
Other Letter 6
 
9.1%
Space Separator 4
 
6.1%
Other Punctuation 2
 
3.0%
Open Punctuation 1
 
1.5%
Lowercase Letter 1
 
1.5%
Close Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
15.7%
8 8
15.7%
3 8
15.7%
4 7
13.7%
2 7
13.7%
0 6
11.8%
6 3
 
5.9%
7 2
 
3.9%
9 1
 
2.0%
5 1
 
2.0%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59
89.4%
Hangul 6
 
9.1%
Latin 1
 
1.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 8
13.6%
8 8
13.6%
3 8
13.6%
4 7
11.9%
2 7
11.9%
0 6
10.2%
4
6.8%
6 3
 
5.1%
7 2
 
3.4%
, 2
 
3.4%
Other values (4) 4
6.8%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Latin
ValueCountFrequency (%)
b 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60
90.9%
Hangul 6
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 8
13.3%
8 8
13.3%
3 8
13.3%
4 7
11.7%
2 7
11.7%
0 6
10.0%
4
6.7%
6 3
 
5.0%
7 2
 
3.3%
, 2
 
3.3%
Other values (5) 5
8.3%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Unnamed: 3
Text

MISSING 

Distinct15
Distinct (%)78.9%
Missing5
Missing (%)20.8%
Memory size324.0 B
2024-03-14T10:08:31.350809image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length3
Mean length2.2105263
Min length1

Characters and Unicode

Total characters42
Distinct characters12
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique13 ?
Unique (%)68.4%

Sample

1st row 현물
2nd row1
3rd row12
4th row37
5th row29
ValueCountFrequency (%)
4
21.1%
3 2
 
10.5%
현물 1
 
5.3%
1 1
 
5.3%
12 1
 
5.3%
37 1
 
5.3%
29 1
 
5.3%
7 1
 
5.3%
31 1
 
5.3%
11 1
 
5.3%
Other values (5) 5
26.3%
2024-03-14T10:08:31.756753image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
10
23.8%
1 8
19.0%
3 7
16.7%
- 4
 
9.5%
0 3
 
7.1%
2 2
 
4.8%
7 2
 
4.8%
4 2
 
4.8%
1
 
2.4%
1
 
2.4%
Other values (2) 2
 
4.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 26
61.9%
Space Separator 10
 
23.8%
Dash Punctuation 4
 
9.5%
Other Letter 2
 
4.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 8
30.8%
3 7
26.9%
0 3
 
11.5%
2 2
 
7.7%
7 2
 
7.7%
4 2
 
7.7%
9 1
 
3.8%
8 1
 
3.8%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
10
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 40
95.2%
Hangul 2
 
4.8%

Most frequent character per script

Common
ValueCountFrequency (%)
10
25.0%
1 8
20.0%
3 7
17.5%
- 4
 
10.0%
0 3
 
7.5%
2 2
 
5.0%
7 2
 
5.0%
4 2
 
5.0%
9 1
 
2.5%
8 1
 
2.5%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 40
95.2%
Hangul 2
 
4.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
10
25.0%
1 8
20.0%
3 7
17.5%
- 4
 
10.0%
0 3
 
7.5%
2 2
 
5.0%
7 2
 
5.0%
4 2
 
5.0%
9 1
 
2.5%
8 1
 
2.5%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 4
Text

MISSING 

Distinct19
Distinct (%)90.5%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:31.898808image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3333333
Min length1

Characters and Unicode

Total characters49
Distinct characters15
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

Unique17 ?
Unique (%)81.0%

Sample

1st row 대학
2nd row 현금
3rd row201
4th row12
5th row58
ValueCountFrequency (%)
49 2
 
9.5%
12 2
 
9.5%
37 1
 
4.8%
19 1
 
4.8%
826 1
 
4.8%
2 1
 
4.8%
7 1
 
4.8%
23 1
 
4.8%
155 1
 
4.8%
22 1
 
4.8%
Other values (9) 9
42.9%
2024-03-14T10:08:32.168747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10
20.4%
2 10
20.4%
6 4
 
8.2%
4
 
8.2%
4 3
 
6.1%
9 3
 
6.1%
5 3
 
6.1%
8 3
 
6.1%
3 2
 
4.1%
7 2
 
4.1%
Other values (5) 5
10.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 41
83.7%
Space Separator 4
 
8.2%
Other Letter 4
 
8.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
24.4%
2 10
24.4%
6 4
 
9.8%
4 3
 
7.3%
9 3
 
7.3%
5 3
 
7.3%
8 3
 
7.3%
3 2
 
4.9%
7 2
 
4.9%
0 1
 
2.4%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 45
91.8%
Hangul 4
 
8.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
22.2%
2 10
22.2%
6 4
 
8.9%
4
 
8.9%
4 3
 
6.7%
9 3
 
6.7%
5 3
 
6.7%
8 3
 
6.7%
3 2
 
4.4%
7 2
 
4.4%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 45
91.8%
Hangul 4
 
8.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
22.2%
2 10
22.2%
6 4
 
8.9%
4
 
8.9%
4 3
 
6.7%
9 3
 
6.7%
5 3
 
6.7%
8 3
 
6.7%
3 2
 
4.4%
7 2
 
4.4%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Unnamed: 5
Text

MISSING 

Distinct19
Distinct (%)95.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:32.297146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.6
Min length1

Characters and Unicode

Total characters52
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique18 ?
Unique (%)90.0%

Sample

1st row 현물
2nd row447
3rd row29
4th row268
5th row123
ValueCountFrequency (%)
49 2
 
10.0%
129 1
 
5.0%
현물 1
 
5.0%
1,709 1
 
5.0%
2 1
 
5.0%
13 1
 
5.0%
64 1
 
5.0%
92 1
 
5.0%
58 1
 
5.0%
43 1
 
5.0%
Other values (9) 9
45.0%
2024-03-14T10:08:32.564962image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 7
13.5%
9 7
13.5%
1 7
13.5%
2 6
11.5%
3 5
9.6%
7 3
5.8%
6 3
5.8%
8 3
5.8%
5 3
5.8%
, 2
 
3.8%
Other values (4) 6
11.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 46
88.5%
Other Punctuation 2
 
3.8%
Space Separator 2
 
3.8%
Other Letter 2
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 7
15.2%
9 7
15.2%
1 7
15.2%
2 6
13.0%
3 5
10.9%
7 3
6.5%
6 3
6.5%
8 3
6.5%
5 3
6.5%
0 2
 
4.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
96.2%
Hangul 2
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 7
14.0%
9 7
14.0%
1 7
14.0%
2 6
12.0%
3 5
10.0%
7 3
6.0%
6 3
6.0%
8 3
6.0%
5 3
6.0%
, 2
 
4.0%
Other values (2) 4
8.0%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
96.2%
Hangul 2
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 7
14.0%
9 7
14.0%
1 7
14.0%
2 6
12.0%
3 5
10.0%
7 3
6.0%
6 3
6.0%
8 3
6.0%
5 3
6.0%
, 2
 
4.0%
Other values (2) 4
8.0%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 6
Text

MISSING 

Distinct18
Distinct (%)85.7%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:32.723584image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length5
Mean length2.8571429
Min length1

Characters and Unicode

Total characters60
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique15 ?
Unique (%)71.4%

Sample

1st row 대기업/중견기업
2nd row 현금
3rd row519
4th row36
5th row582
ValueCountFrequency (%)
2 2
 
9.5%
49 2
 
9.5%
2,097 2
 
9.5%
대기업/중견기업 1
 
4.8%
36 1
 
4.8%
582 1
 
4.8%
324 1
 
4.8%
519 1
 
4.8%
44 1
 
4.8%
53 1
 
4.8%
Other values (8) 8
38.1%
2024-03-14T10:08:32.982364image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 9
15.0%
4 7
11.7%
9 6
10.0%
1 5
 
8.3%
5 5
 
8.3%
4
 
6.7%
3 4
 
6.7%
7 2
 
3.3%
0 2
 
3.3%
, 2
 
3.3%
Other values (10) 14
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 44
73.3%
Other Letter 9
 
15.0%
Space Separator 4
 
6.7%
Other Punctuation 3
 
5.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 9
20.5%
4 7
15.9%
9 6
13.6%
1 5
11.4%
5 5
11.4%
3 4
9.1%
7 2
 
4.5%
0 2
 
4.5%
6 2
 
4.5%
8 2
 
4.5%
Other Letter
ValueCountFrequency (%)
2
22.2%
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Other Punctuation
ValueCountFrequency (%)
, 2
66.7%
/ 1
33.3%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 51
85.0%
Hangul 9
 
15.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 9
17.6%
4 7
13.7%
9 6
11.8%
1 5
9.8%
5 5
9.8%
4
7.8%
3 4
7.8%
7 2
 
3.9%
0 2
 
3.9%
, 2
 
3.9%
Other values (3) 5
9.8%
Hangul
ValueCountFrequency (%)
2
22.2%
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 51
85.0%
Hangul 9
 
15.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 9
17.6%
4 7
13.7%
9 6
11.8%
1 5
9.8%
5 5
9.8%
4
7.8%
3 4
7.8%
7 2
 
3.9%
0 2
 
3.9%
, 2
 
3.9%
Other values (3) 5
9.8%
Hangul
ValueCountFrequency (%)
2
22.2%
2
22.2%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Unnamed: 7
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:33.134350image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length3
Min length1

Characters and Unicode

Total characters60
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row 현물
2nd row1,130
3rd row131
4th row1,415
5th row682
ValueCountFrequency (%)
현물 1
 
5.0%
1,130 1
 
5.0%
4,929 1
 
5.0%
7 1
 
5.0%
4 1
 
5.0%
367 1
 
5.0%
382 1
 
5.0%
28 1
 
5.0%
55 1
 
5.0%
198 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T10:08:33.452676image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
20.0%
4 7
11.7%
3 6
10.0%
8 6
10.0%
, 4
 
6.7%
0 4
 
6.7%
5 4
 
6.7%
2 4
 
6.7%
9 4
 
6.7%
7 3
 
5.0%
Other values (4) 6
10.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 52
86.7%
Other Punctuation 4
 
6.7%
Space Separator 2
 
3.3%
Other Letter 2
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
23.1%
4 7
13.5%
3 6
11.5%
8 6
11.5%
0 4
 
7.7%
5 4
 
7.7%
2 4
 
7.7%
9 4
 
7.7%
7 3
 
5.8%
6 2
 
3.8%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 58
96.7%
Hangul 2
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
20.7%
4 7
12.1%
3 6
10.3%
8 6
10.3%
, 4
 
6.9%
0 4
 
6.9%
5 4
 
6.9%
2 4
 
6.9%
9 4
 
6.9%
7 3
 
5.2%
Other values (2) 4
 
6.9%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 58
96.7%
Hangul 2
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
20.7%
4 7
12.1%
3 6
10.3%
8 6
10.3%
, 4
 
6.9%
0 4
 
6.9%
5 4
 
6.9%
2 4
 
6.9%
9 4
 
6.9%
7 3
 
5.2%
Other values (2) 4
 
6.9%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 8
Text

MISSING 

Distinct20
Distinct (%)95.2%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:33.581392image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.9047619
Min length1

Characters and Unicode

Total characters61
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)90.5%

Sample

1st row 중소기업
2nd row 현금
3rd row584
4th row94
5th row844
ValueCountFrequency (%)
112 2
 
9.5%
43 1
 
4.8%
78 1
 
4.8%
3,333 1
 
4.8%
1 1
 
4.8%
15 1
 
4.8%
120 1
 
4.8%
653 1
 
4.8%
40 1
 
4.8%
96 1
 
4.8%
Other values (10) 10
47.6%
2024-03-14T10:08:33.801401image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10
16.4%
3 10
16.4%
4 8
13.1%
2 5
8.2%
4
 
6.6%
5 4
 
6.6%
8 4
 
6.6%
7 2
 
3.3%
0 2
 
3.3%
6 2
 
3.3%
Other values (8) 10
16.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 49
80.3%
Other Letter 6
 
9.8%
Space Separator 4
 
6.6%
Other Punctuation 2
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
20.4%
3 10
20.4%
4 8
16.3%
2 5
10.2%
5 4
 
8.2%
8 4
 
8.2%
7 2
 
4.1%
0 2
 
4.1%
6 2
 
4.1%
9 2
 
4.1%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 55
90.2%
Hangul 6
 
9.8%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
18.2%
3 10
18.2%
4 8
14.5%
2 5
9.1%
4
 
7.3%
5 4
 
7.3%
8 4
 
7.3%
7 2
 
3.6%
0 2
 
3.6%
6 2
 
3.6%
Other values (2) 4
 
7.3%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 55
90.2%
Hangul 6
 
9.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
18.2%
3 10
18.2%
4 8
14.5%
2 5
9.1%
4
 
7.3%
5 4
 
7.3%
8 4
 
7.3%
7 2
 
3.6%
0 2
 
3.6%
6 2
 
3.6%
Other values (2) 4
 
7.3%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Unnamed: 9
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:33.958836image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.5
Min length1

Characters and Unicode

Total characters70
Distinct characters14
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row 현물
2nd row2,267
3rd row355
4th row3,066
5th row1,555
ValueCountFrequency (%)
현물 1
 
5.0%
2,267 1
 
5.0%
11,112 1
 
5.0%
6 1
 
5.0%
90 1
 
5.0%
513 1
 
5.0%
436 1
 
5.0%
159 1
 
5.0%
257 1
 
5.0%
588 1
 
5.0%
Other values (10) 10
50.0%
2024-03-14T10:08:34.227308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
20.0%
5 10
14.3%
6 8
11.4%
3 7
10.0%
2 6
8.6%
, 5
 
7.1%
7 4
 
5.7%
4 4
 
5.7%
8 4
 
5.7%
2
 
2.9%
Other values (4) 6
8.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
87.1%
Other Punctuation 5
 
7.1%
Space Separator 2
 
2.9%
Other Letter 2
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
23.0%
5 10
16.4%
6 8
13.1%
3 7
11.5%
2 6
9.8%
7 4
 
6.6%
4 4
 
6.6%
8 4
 
6.6%
0 2
 
3.3%
9 2
 
3.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 5
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 68
97.1%
Hangul 2
 
2.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
20.6%
5 10
14.7%
6 8
11.8%
3 7
10.3%
2 6
8.8%
, 5
 
7.4%
7 4
 
5.9%
4 4
 
5.9%
8 4
 
5.9%
2
 
2.9%
Other values (2) 4
 
5.9%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68
97.1%
Hangul 2
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
20.6%
5 10
14.7%
6 8
11.8%
3 7
10.3%
2 6
8.8%
, 5
 
7.4%
7 4
 
5.9%
4 4
 
5.9%
8 4
 
5.9%
2
 
2.9%
Other values (2) 4
 
5.9%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 10
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:34.381619image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.7142857
Min length1

Characters and Unicode

Total characters57
Distinct characters15
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

Unique21 ?
Unique (%)100.0%

Sample

1st row 기타
2nd row 현금
3rd row4,647
4th row47
5th row223
ValueCountFrequency (%)
기타 1
 
4.8%
22 1
 
4.8%
5,674 1
 
4.8%
1
 
4.8%
3 1
 
4.8%
151 1
 
4.8%
67 1
 
4.8%
14 1
 
4.8%
41 1
 
4.8%
69 1
 
4.8%
Other values (11) 11
52.4%
2024-03-14T10:08:34.691277image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 8
14.0%
4 7
12.3%
2 7
12.3%
6
10.5%
1 6
10.5%
7 5
8.8%
5 4
7.0%
, 3
 
5.3%
3 3
 
5.3%
9 3
 
5.3%
Other values (5) 5
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43
75.4%
Space Separator 6
 
10.5%
Other Letter 4
 
7.0%
Other Punctuation 3
 
5.3%
Dash Punctuation 1
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
6 8
18.6%
4 7
16.3%
2 7
16.3%
1 6
14.0%
7 5
11.6%
5 4
9.3%
3 3
 
7.0%
9 3
 
7.0%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Space Separator
ValueCountFrequency (%)
6
100.0%
Other Punctuation
ValueCountFrequency (%)
, 3
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 53
93.0%
Hangul 4
 
7.0%

Most frequent character per script

Common
ValueCountFrequency (%)
6 8
15.1%
4 7
13.2%
2 7
13.2%
6
11.3%
1 6
11.3%
7 5
9.4%
5 4
7.5%
, 3
 
5.7%
3 3
 
5.7%
9 3
 
5.7%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 53
93.0%
Hangul 4
 
7.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 8
15.1%
4 7
13.2%
2 7
13.2%
6
11.3%
1 6
11.3%
7 5
9.4%
5 4
7.5%
, 3
 
5.7%
3 3
 
5.7%
9 3
 
5.7%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Unnamed: 11
Text

MISSING 

Distinct18
Distinct (%)90.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:34.828564image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.6
Min length1

Characters and Unicode

Total characters52
Distinct characters15
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

Unique16 ?
Unique (%)80.0%

Sample

1st row 현물
2nd row544
3rd row20
4th row276
5th row241
ValueCountFrequency (%)
1,740 2
 
10.0%
23 2
 
10.0%
70 1
 
5.0%
1
 
5.0%
9 1
 
5.0%
46 1
 
5.0%
111 1
 
5.0%
55 1
 
5.0%
544 1
 
5.0%
43 1
 
5.0%
Other values (8) 8
40.0%
2024-03-14T10:08:35.105495image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 8
15.4%
1 6
11.5%
0 6
11.5%
7 5
9.6%
2 5
9.6%
6 5
9.6%
4
7.7%
3 3
 
5.8%
5 3
 
5.8%
, 2
 
3.8%
Other values (5) 5
9.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 43
82.7%
Space Separator 4
 
7.7%
Other Punctuation 2
 
3.8%
Other Letter 2
 
3.8%
Dash Punctuation 1
 
1.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 8
18.6%
1 6
14.0%
0 6
14.0%
7 5
11.6%
2 5
11.6%
6 5
11.6%
3 3
 
7.0%
5 3
 
7.0%
8 1
 
2.3%
9 1
 
2.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 50
96.2%
Hangul 2
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
4 8
16.0%
1 6
12.0%
0 6
12.0%
7 5
10.0%
2 5
10.0%
6 5
10.0%
4
8.0%
3 3
 
6.0%
5 3
 
6.0%
, 2
 
4.0%
Other values (3) 3
 
6.0%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 50
96.2%
Hangul 2
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 8
16.0%
1 6
12.0%
0 6
12.0%
7 5
10.0%
2 5
10.0%
6 5
10.0%
4
8.0%
3 3
 
6.0%
5 3
 
6.0%
, 2
 
4.0%
Other values (3) 3
 
6.0%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 12
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:35.269304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length3.6190476
Min length1

Characters and Unicode

Total characters76
Distinct characters16
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row 합계
2nd row 현금
3rd row6,135
4th row207
5th row1,789
ValueCountFrequency (%)
합계 1
 
4.8%
231 1
 
4.8%
14,968 1
 
4.8%
6 1
 
4.8%
49 1
 
4.8%
716 1
 
4.8%
1,473 1
 
4.8%
303 1
 
4.8%
414 1
 
4.8%
451 1
 
4.8%
Other values (11) 11
52.4%
2024-03-14T10:08:35.560560image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15
19.7%
3 10
13.2%
4 8
10.5%
6 6
 
7.9%
, 6
 
7.9%
7 5
 
6.6%
9 5
 
6.6%
4
 
5.3%
5 4
 
5.3%
2 4
 
5.3%
Other values (6) 9
11.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
81.6%
Other Punctuation 6
 
7.9%
Space Separator 4
 
5.3%
Other Letter 4
 
5.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
24.2%
3 10
16.1%
4 8
12.9%
6 6
 
9.7%
7 5
 
8.1%
9 5
 
8.1%
5 4
 
6.5%
2 4
 
6.5%
0 3
 
4.8%
8 2
 
3.2%
Other Letter
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
94.7%
Hangul 4
 
5.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
20.8%
3 10
13.9%
4 8
11.1%
6 6
 
8.3%
, 6
 
8.3%
7 5
 
6.9%
9 5
 
6.9%
4
 
5.6%
5 4
 
5.6%
2 4
 
5.6%
Other values (2) 5
 
6.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
94.7%
Hangul 4
 
5.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
20.8%
3 10
13.9%
4 8
11.1%
6 6
 
8.3%
, 6
 
8.3%
7 5
 
6.9%
9 5
 
6.9%
4
 
5.6%
5 4
 
5.6%
2 4
 
5.6%
Other values (2) 5
 
6.9%
Hangul
ValueCountFrequency (%)
1
25.0%
1
25.0%
1
25.0%
1
25.0%

구분
Text

MISSING 

Distinct22
Distinct (%)100.0%
Missing2
Missing (%)8.3%
Memory size324.0 B
2024-03-14T10:08:35.728868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length3.6363636
Min length2

Characters and Unicode

Total characters80
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique22 ?
Unique (%)100.0%

Sample

1st row전국
2nd row전북
3rd row 현물
4th row4,389
5th row547
ValueCountFrequency (%)
401 1
 
4.5%
현물 1
 
4.5%
249 1
 
4.5%
19,791 1
 
4.5%
15 1
 
4.5%
117 1
 
4.5%
1,007 1
 
4.5%
1,164 1
 
4.5%
303 1
 
4.5%
408 1
 
4.5%
Other values (12) 12
54.5%
2024-03-14T10:08:35.991156image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
15.0%
4 9
11.2%
7 8
10.0%
9 8
10.0%
, 7
8.8%
2 6
7.5%
0 6
7.5%
6 5
6.2%
3 5
6.2%
5 4
 
5.0%
Other values (7) 10
12.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65
81.2%
Other Punctuation 7
 
8.8%
Other Letter 6
 
7.5%
Space Separator 2
 
2.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
18.5%
4 9
13.8%
7 8
12.3%
9 8
12.3%
2 6
9.2%
0 6
9.2%
6 5
7.7%
3 5
7.7%
5 4
 
6.2%
8 2
 
3.1%
Other Letter
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 7
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74
92.5%
Hangul 6
 
7.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
16.2%
4 9
12.2%
7 8
10.8%
9 8
10.8%
, 7
9.5%
2 6
8.1%
0 6
8.1%
6 5
6.8%
3 5
6.8%
5 4
 
5.4%
Other values (2) 4
 
5.4%
Hangul
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74
92.5%
Hangul 6
 
7.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
16.2%
4 9
12.2%
7 8
10.8%
9 8
10.8%
, 7
9.5%
2 6
8.1%
0 6
8.1%
6 5
6.8%
3 5
6.8%
5 4
 
5.4%
Other values (2) 4
 
5.4%
Hangul
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

국비
Text

MISSING 

Distinct23
Distinct (%)100.0%
Missing1
Missing (%)4.2%
Memory size324.0 B
2024-03-14T10:08:36.148192image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length4.3478261
Min length2

Characters and Unicode

Total characters100
Distinct characters17
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)100.0%

Sample

1st row183,356
2nd row6,712
3rd row합계
4th row현금+현물
5th row10,524
ValueCountFrequency (%)
183,356 1
 
4.3%
378 1
 
4.3%
34,759 1
 
4.3%
21 1
 
4.3%
166 1
 
4.3%
1,723 1
 
4.3%
2,637 1
 
4.3%
606 1
 
4.3%
822 1
 
4.3%
1,447 1
 
4.3%
Other values (13) 13
56.5%
2024-03-14T10:08:36.454519image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13
13.0%
, 13
13.0%
7 12
12.0%
3 9
9.0%
6 9
9.0%
5 8
8.0%
2 8
8.0%
4 8
8.0%
8 6
6.0%
0 5
 
5.0%
Other values (7) 9
9.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 80
80.0%
Other Punctuation 13
 
13.0%
Other Letter 6
 
6.0%
Math Symbol 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
16.2%
7 12
15.0%
3 9
11.2%
6 9
11.2%
5 8
10.0%
2 8
10.0%
4 8
10.0%
8 6
7.5%
0 5
 
6.2%
9 2
 
2.5%
Other Letter
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Other Punctuation
ValueCountFrequency (%)
, 13
100.0%
Math Symbol
ValueCountFrequency (%)
+ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 94
94.0%
Hangul 6
 
6.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13
13.8%
, 13
13.8%
7 12
12.8%
3 9
9.6%
6 9
9.6%
5 8
8.5%
2 8
8.5%
4 8
8.5%
8 6
6.4%
0 5
 
5.3%
Other values (2) 3
 
3.2%
Hangul
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 94
94.0%
Hangul 6
 
6.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13
13.8%
, 13
13.8%
7 12
12.8%
3 9
9.6%
6 9
9.6%
5 8
8.5%
2 8
8.5%
4 8
8.5%
8 6
6.4%
0 5
 
5.3%
Other values (2) 3
 
3.2%
Hangul
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

지방비
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:36.616565image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length5
Mean length5.3809524
Min length3

Characters and Unicode

Total characters113
Distinct characters23
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

Unique21 ?
Unique (%)100.0%

Sample

1st row3,041
2nd row241
3rd row지방비/(국비+지방비)
4th rowb/(a+b)
5th row0.47%
ValueCountFrequency (%)
3,041 1
 
4.8%
2.84 1
 
4.8%
17.21 1
 
4.8%
2.69 1
 
4.8%
2.86 1
 
4.8%
6.44 1
 
4.8%
9.69 1
 
4.8%
3.47 1
 
4.8%
3.20 1
 
4.8%
1.24 1
 
4.8%
Other values (11) 11
52.4%
2024-03-14T10:08:36.889959image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
% 17
15.0%
. 17
15.0%
4 11
9.7%
1 9
 
8.0%
0 7
 
6.2%
2 7
 
6.2%
7 7
 
6.2%
6 6
 
5.3%
3 4
 
3.5%
9 4
 
3.5%
Other values (13) 24
21.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
52.2%
Other Punctuation 37
32.7%
Other Letter 8
 
7.1%
Lowercase Letter 3
 
2.7%
Math Symbol 2
 
1.8%
Close Punctuation 2
 
1.8%
Open Punctuation 2
 
1.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 11
18.6%
1 9
15.3%
0 7
11.9%
2 7
11.9%
7 7
11.9%
6 6
10.2%
3 4
 
6.8%
9 4
 
6.8%
5 2
 
3.4%
8 2
 
3.4%
Other Punctuation
ValueCountFrequency (%)
% 17
45.9%
. 17
45.9%
/ 2
 
5.4%
, 1
 
2.7%
Other Letter
ValueCountFrequency (%)
3
37.5%
2
25.0%
2
25.0%
1
 
12.5%
Lowercase Letter
ValueCountFrequency (%)
b 2
66.7%
a 1
33.3%
Math Symbol
ValueCountFrequency (%)
+ 2
100.0%
Close Punctuation
ValueCountFrequency (%)
) 2
100.0%
Open Punctuation
ValueCountFrequency (%)
( 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 102
90.3%
Hangul 8
 
7.1%
Latin 3
 
2.7%

Most frequent character per script

Common
ValueCountFrequency (%)
% 17
16.7%
. 17
16.7%
4 11
10.8%
1 9
8.8%
0 7
6.9%
2 7
6.9%
7 7
6.9%
6 6
 
5.9%
3 4
 
3.9%
9 4
 
3.9%
Other values (7) 13
12.7%
Hangul
ValueCountFrequency (%)
3
37.5%
2
25.0%
2
25.0%
1
 
12.5%
Latin
ValueCountFrequency (%)
b 2
66.7%
a 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 105
92.9%
Hangul 8
 
7.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
% 17
16.2%
. 17
16.2%
4 11
10.5%
1 9
8.6%
0 7
6.7%
2 7
6.7%
7 7
6.7%
6 6
 
5.7%
3 4
 
3.8%
9 4
 
3.8%
Other values (9) 16
15.2%
Hangul
ValueCountFrequency (%)
3
37.5%
2
25.0%
2
25.0%
1
 
12.5%

국비+지방비
Text

MISSING 

Distinct20
Distinct (%)100.0%
Missing4
Missing (%)16.7%
Memory size324.0 B
2024-03-14T10:08:37.045602image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length7
Mean length2.25
Min length1

Characters and Unicode

Total characters45
Distinct characters18
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique20 ?
Unique (%)100.0%

Sample

1st row164,934
2nd row6,953
3rd row지방비 매칭비중
4th row15
5th row16
ValueCountFrequency (%)
6 1
 
4.8%
164,934 1
 
4.8%
1 1
 
4.8%
10 1
 
4.8%
8 1
 
4.8%
5 1
 
4.8%
2 1
 
4.8%
3 1
 
4.8%
7 1
 
4.8%
12 1
 
4.8%
Other values (11) 11
52.4%
2024-03-14T10:08:37.330425image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
24.4%
6 4
 
8.9%
3 4
 
8.9%
4 4
 
8.9%
9 3
 
6.7%
5 3
 
6.7%
7 2
 
4.4%
, 2
 
4.4%
2
 
4.4%
2 2
 
4.4%
Other values (8) 8
17.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 35
77.8%
Other Letter 7
 
15.6%
Other Punctuation 2
 
4.4%
Space Separator 1
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
31.4%
6 4
 
11.4%
3 4
 
11.4%
4 4
 
11.4%
9 3
 
8.6%
5 3
 
8.6%
7 2
 
5.7%
2 2
 
5.7%
8 1
 
2.9%
0 1
 
2.9%
Other Letter
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 38
84.4%
Hangul 7
 
15.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
28.9%
6 4
 
10.5%
3 4
 
10.5%
4 4
 
10.5%
9 3
 
7.9%
5 3
 
7.9%
7 2
 
5.3%
, 2
 
5.3%
2 2
 
5.3%
1
 
2.6%
Other values (2) 2
 
5.3%
Hangul
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 38
84.4%
Hangul 7
 
15.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
28.9%
6 4
 
10.5%
3 4
 
10.5%
4 4
 
10.5%
9 3
 
7.9%
5 3
 
7.9%
7 2
 
5.3%
, 2
 
5.3%
2 2
 
5.3%
1
 
2.6%
Other values (2) 2
 
5.3%
Hangul
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:37.484219image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length5
Mean length2.047619
Min length1

Characters and Unicode

Total characters43
Distinct characters20
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row1.84%
2nd row3.47%
3rd row국비지원 순위
4th rowa
5th row2
ValueCountFrequency (%)
1.84 1
 
4.5%
국비지원 1
 
4.5%
15 1
 
4.5%
16 1
 
4.5%
4 1
 
4.5%
5 1
 
4.5%
14 1
 
4.5%
12 1
 
4.5%
8 1
 
4.5%
10 1
 
4.5%
Other values (12) 12
54.5%
2024-03-14T10:08:37.758143image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
25.6%
4 4
 
9.3%
7 3
 
7.0%
3 3
 
7.0%
. 2
 
4.7%
6 2
 
4.7%
2 2
 
4.7%
5 2
 
4.7%
% 2
 
4.7%
8 2
 
4.7%
Other values (10) 10
23.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
72.1%
Other Letter 6
 
14.0%
Other Punctuation 4
 
9.3%
Space Separator 1
 
2.3%
Lowercase Letter 1
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
35.5%
4 4
 
12.9%
7 3
 
9.7%
3 3
 
9.7%
6 2
 
6.5%
2 2
 
6.5%
5 2
 
6.5%
8 2
 
6.5%
9 1
 
3.2%
0 1
 
3.2%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
% 2
50.0%
Space Separator
ValueCountFrequency (%)
1
100.0%
Lowercase Letter
ValueCountFrequency (%)
a 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36
83.7%
Hangul 6
 
14.0%
Latin 1
 
2.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
30.6%
4 4
 
11.1%
7 3
 
8.3%
3 3
 
8.3%
. 2
 
5.6%
6 2
 
5.6%
2 2
 
5.6%
5 2
 
5.6%
% 2
 
5.6%
8 2
 
5.6%
Other values (3) 3
 
8.3%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Latin
ValueCountFrequency (%)
a 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37
86.0%
Hangul 6
 
14.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
29.7%
4 4
 
10.8%
7 3
 
8.1%
3 3
 
8.1%
. 2
 
5.4%
6 2
 
5.4%
2 2
 
5.4%
5 2
 
5.4%
% 2
 
5.4%
8 2
 
5.4%
Other values (4) 4
 
10.8%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

지방비/국비
Text

MISSING 

Distinct21
Distinct (%)100.0%
Missing3
Missing (%)12.5%
Memory size324.0 B
2024-03-14T10:08:37.937148image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length5
Mean length2.1428571
Min length1

Characters and Unicode

Total characters45
Distinct characters21
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique21 ?
Unique (%)100.0%

Sample

1st row1.88%
2nd row3.59%
3rd row순지방비 매칭순위
4th rowb
5th row9
ValueCountFrequency (%)
1.88 1
 
4.5%
순지방비 1
 
4.5%
2 1
 
4.5%
15 1
 
4.5%
6 1
 
4.5%
1 1
 
4.5%
7 1
 
4.5%
4 1
 
4.5%
10 1
 
4.5%
14 1
 
4.5%
Other values (12) 12
54.5%
2024-03-14T10:08:38.195589image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
24.4%
8 3
 
6.7%
3 3
 
6.7%
5 3
 
6.7%
. 2
 
4.4%
4 2
 
4.4%
2 2
 
4.4%
6 2
 
4.4%
9 2
 
4.4%
7 2
 
4.4%
Other values (11) 13
28.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 31
68.9%
Other Letter 8
 
17.8%
Other Punctuation 4
 
8.9%
Lowercase Letter 1
 
2.2%
Space Separator 1
 
2.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
35.5%
8 3
 
9.7%
3 3
 
9.7%
5 3
 
9.7%
4 2
 
6.5%
2 2
 
6.5%
6 2
 
6.5%
9 2
 
6.5%
7 2
 
6.5%
0 1
 
3.2%
Other Letter
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Other Punctuation
ValueCountFrequency (%)
. 2
50.0%
% 2
50.0%
Lowercase Letter
ValueCountFrequency (%)
b 1
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 36
80.0%
Hangul 8
 
17.8%
Latin 1
 
2.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
30.6%
8 3
 
8.3%
3 3
 
8.3%
5 3
 
8.3%
. 2
 
5.6%
4 2
 
5.6%
2 2
 
5.6%
6 2
 
5.6%
9 2
 
5.6%
7 2
 
5.6%
Other values (3) 4
 
11.1%
Hangul
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Latin
ValueCountFrequency (%)
b 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37
82.2%
Hangul 8
 
17.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
29.7%
8 3
 
8.1%
3 3
 
8.1%
5 3
 
8.1%
. 2
 
5.4%
4 2
 
5.4%
2 2
 
5.4%
6 2
 
5.4%
9 2
 
5.4%
7 2
 
5.4%
Other values (4) 5
13.5%
Hangul
ValueCountFrequency (%)
2
25.0%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Correlations

2024-03-14T10:08:38.279499image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 02016년 지역별 R&D 투자현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12구분국비지방비국비+지방비지방비/ (국비+지방비)지방비/국비
Unnamed: 01.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
2016년 지역별 R&D 투자현황1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 21.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.0001.0001.0001.0000.8990.8990.9141.0001.0001.0001.0000.9141.0001.0001.0001.0001.0001.0001.000
Unnamed: 41.0001.0001.0000.8991.0000.9820.8961.0000.9691.0001.0000.9231.0001.0001.0001.0001.0001.0001.000
Unnamed: 51.0001.0001.0000.8990.9821.0000.9251.0000.9801.0001.0000.9391.0001.0001.0001.0001.0001.0001.000
Unnamed: 61.0001.0001.0000.9140.8960.9251.0001.0000.9631.0001.0000.9791.0001.0001.0001.0001.0001.0001.000
Unnamed: 71.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 81.0001.0001.0001.0000.9690.9800.9631.0001.0001.0001.0000.9391.0001.0001.0001.0001.0001.0001.000
Unnamed: 91.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 101.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 111.0001.0001.0000.9140.9230.9390.9791.0000.9391.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 121.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
구분1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
국비1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지방비1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
국비+지방비1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지방비/\n(국비+지방비)1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
지방비/국비1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000

Missing values

2024-03-14T10:08:29.395674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-03-14T10:08:29.590024image/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.
2024-03-14T10:08:29.778359image/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

Unnamed: 02016년 지역별 R&D 투자현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12구분국비지방비국비+지방비지방비/ (국비+지방비)지방비/국비
0<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>전국183,3563,041164,9341.84%1.88%
1<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>전북6,7122416,9533.47%3.59%
2<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
3구분국비(a)지방정부<NA>대학<NA>대기업/중견기업<NA>중소기업<NA>기타<NA>합계<NA>합계지방비/(국비+지방비)지방비 매칭비중국비지원 순위순지방비 매칭순위
4단위:억원현금현금(b)현물현금현물현금현물현금현물현금현물현금현물현금+현물b/(a+b)<NA>ab
5서울35,92518112014475191,1305842,2674,6475446,1354,38910,5240.47%1529
6인천4,38518121229361319435547202075477540.45%16916
7경기23,7408237582685821,4158443,0662232761,7895,0626,8510.31%17312
8대전56,11527829261233246823111,5552142411,1532,6313,7840.59%1415
9부산6,57293<NA>49113498111246531643347231,0571.77%11611
Unnamed: 02016년 지역별 R&D 투자현황Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12구분국비지방비국비+지방비지방비/ (국비+지방비)지방비/국비
14충북4,96248314949341087832622602315748051.24%121014
15충남4,84315411371294919814258869704519961,4473.20%7810
16전북6,7122414124924559625741434144088223.47%3124
17전남3,0572003225828284015914553033036069.69%2147
18경북6,16544414315592156382653436671111,4731,1642,6376.44%551
19경남9,721230182364192367120513151467161,0071,7232.86%846
20제주141023-71314159039491171662.69%101615
21세종41701-222716--615211.06%131717
22합계183,3563,0383018261,7092,0974,9293,33311,1125,6741,74014,96819,79134,759<NA><NA><NA><NA>
23<NA><NA>3,0413008241,7082,0974,9303,33411,1145,6751,74014,97219,79234,764<NA><NA><NA><NA>