Overview

Dataset statistics

Number of variables25
Number of observations35
Missing cells201
Missing cells (%)23.0%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory7.0 KiB
Average record size in memory204.8 B

Variable types

Unsupported1
Text22
Categorical1
DateTime1

Dataset

Description2023-10-12
Author주민등록인구통계
URLhttps://bigdata.gwangju.go.kr/usr/dataSet/getDataDetailView.rd?dataSetUncd=DS000201928

Alerts

Unnamed: 12 has constant value ""Constant
Dataset has 1 (2.9%) duplicate rowsDuplicates
Unnamed: 5 is highly imbalanced (68.4%)Imbalance
Unnamed: 0 has 35 (100.0%) missing valuesMissing
인구이동보고서(1호) has 19 (54.3%) missing valuesMissing
Unnamed: 2 has 24 (68.6%) missing valuesMissing
Unnamed: 3 has 23 (65.7%) missing valuesMissing
Unnamed: 4 has 31 (88.6%) missing valuesMissing
Unnamed: 6 has 2 (5.7%) missing valuesMissing
Unnamed: 7 has 2 (5.7%) missing valuesMissing
Unnamed: 8 has 2 (5.7%) missing valuesMissing
Unnamed: 9 has 2 (5.7%) missing valuesMissing
Unnamed: 10 has 1 (2.9%) missing valuesMissing
Unnamed: 11 has 2 (5.7%) missing valuesMissing
Unnamed: 12 has 34 (97.1%) missing valuesMissing
Unnamed: 13 has 2 (5.7%) missing valuesMissing
Unnamed: 14 has 2 (5.7%) missing valuesMissing
Unnamed: 15 has 2 (5.7%) missing valuesMissing
Unnamed: 16 has 2 (5.7%) missing valuesMissing
Unnamed: 17 has 2 (5.7%) missing valuesMissing
Unnamed: 18 has 2 (5.7%) missing valuesMissing
Unnamed: 19 has 2 (5.7%) missing valuesMissing
Unnamed: 20 has 2 (5.7%) missing valuesMissing
Unnamed: 21 has 2 (5.7%) missing valuesMissing
Unnamed: 22 has 2 (5.7%) missing valuesMissing
Unnamed: 23 has 2 (5.7%) missing valuesMissing
Unnamed: 24 has 2 (5.7%) missing valuesMissing
Unnamed: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-10 07:24:56.033591
Analysis finished2024-02-10 07:24:57.482445
Duration1.45 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

Unnamed: 0
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing35
Missing (%)100.0%
Memory size447.0 B
Distinct16
Distinct (%)100.0%
Missing19
Missing (%)54.3%
Memory size412.0 B
2024-02-10T07:24:57.910069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length10.5
Mean length7.875
Min length5

Characters and Unicode

Total characters126
Distinct characters43
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

Unique16 ?
Unique (%)100.0%

Sample

1st row행정기관 :
2nd row작성기준 :
3rd row시, 군, 구(읍면동)
4th row전월말세대수
5th row전월말인구수
ValueCountFrequency (%)
2
 
7.7%
2
 
7.7%
2
 
7.7%
금월말거주불명자수 1
 
3.8%
금월말인구수 1
 
3.8%
금월말세대수 1
 
3.8%
거주불명자수증감 1
 
3.8%
인구수증감 1
 
3.8%
세대수증감 1
 
3.8%
1
 
3.8%
Other values (13) 13
50.0%
2024-02-10T07:25:00.152743image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
12
 
9.5%
11
 
8.7%
8
 
6.3%
8
 
6.3%
5
 
4.0%
5
 
4.0%
4
 
3.2%
4
 
3.2%
4
 
3.2%
4
 
3.2%
Other values (33) 61
48.4%

Most occurring categories

ValueCountFrequency (%)
Other Letter 104
82.5%
Control 12
 
9.5%
Space Separator 4
 
3.2%
Other Punctuation 4
 
3.2%
Open Punctuation 1
 
0.8%
Close Punctuation 1
 
0.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
11
 
10.6%
8
 
7.7%
8
 
7.7%
5
 
4.8%
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
Other values (27) 47
45.2%
Other Punctuation
ValueCountFrequency (%)
, 2
50.0%
: 2
50.0%
Control
ValueCountFrequency (%)
12
100.0%
Space Separator
ValueCountFrequency (%)
4
100.0%
Open Punctuation
ValueCountFrequency (%)
( 1
100.0%
Close Punctuation
ValueCountFrequency (%)
) 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 104
82.5%
Common 22
 
17.5%

Most frequent character per script

Hangul
ValueCountFrequency (%)
11
 
10.6%
8
 
7.7%
8
 
7.7%
5
 
4.8%
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
Other values (27) 47
45.2%
Common
ValueCountFrequency (%)
12
54.5%
4
 
18.2%
, 2
 
9.1%
: 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 104
82.5%
ASCII 22
 
17.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
12
54.5%
4
 
18.2%
, 2
 
9.1%
: 2
 
9.1%
( 1
 
4.5%
) 1
 
4.5%
Hangul
ValueCountFrequency (%)
11
 
10.6%
8
 
7.7%
8
 
7.7%
5
 
4.8%
5
 
4.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
4
 
3.8%
Other values (27) 47
45.2%

Unnamed: 2
Text

MISSING 

Distinct9
Distinct (%)81.8%
Missing24
Missing (%)68.6%
Memory size412.0 B
2024-02-10T07:25:01.243668image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length2
Mean length2.3636364
Min length2

Characters and Unicode

Total characters26
Distinct characters17
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

Unique7 ?
Unique (%)63.6%

Sample

1st row전 입
2nd row복귀
3rd row출생
4th row등록
5th row국외
ValueCountFrequency (%)
국외 2
15.4%
기타 2
15.4%
2
15.4%
1
7.7%
복귀 1
7.7%
출생 1
7.7%
등록 1
7.7%
1
7.7%
사망 1
7.7%
말소 1
7.7%
2024-02-10T07:25:03.012634image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
15.4%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
2
 
7.7%
1
 
3.8%
1
 
3.8%
1
 
3.8%
Other values (7) 7
26.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 22
84.6%
Control 4
 
15.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (6) 6
27.3%
Control
ValueCountFrequency (%)
4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 22
84.6%
Common 4
 
15.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (6) 6
27.3%
Common
ValueCountFrequency (%)
4
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 22
84.6%
ASCII 4
 
15.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4
100.0%
Hangul
ValueCountFrequency (%)
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
2
 
9.1%
1
 
4.5%
1
 
4.5%
1
 
4.5%
1
 
4.5%
Other values (6) 6
27.3%

Unnamed: 3
Text

MISSING 

Distinct7
Distinct (%)58.3%
Missing23
Missing (%)65.7%
Memory size412.0 B
2024-02-10T07:25:03.496873image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.4166667
Min length1

Characters and Unicode

Total characters41
Distinct characters21
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

Unique2 ?
Unique (%)16.7%

Sample

1st row광주광역시 서구
2nd row2023.07 현재
3rd row
4th row남자
5th row여자
ValueCountFrequency (%)
2
14.3%
남자 2
14.3%
여자 2
14.3%
시도내 2
14.3%
시도간 2
14.3%
광주광역시 1
7.1%
서구 1
7.1%
2023.07 1
7.1%
현재 1
7.1%
2024-02-10T07:25:04.464720image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5
 
12.2%
4
 
9.8%
4
 
9.8%
3
 
7.3%
2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
Other values (11) 13
31.7%

Most occurring categories

ValueCountFrequency (%)
Other Letter 31
75.6%
Decimal Number 6
 
14.6%
Space Separator 3
 
7.3%
Other Punctuation 1
 
2.4%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
5
16.1%
4
12.9%
4
12.9%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
1
 
3.2%
Other values (5) 5
16.1%
Decimal Number
ValueCountFrequency (%)
2 2
33.3%
0 2
33.3%
3 1
16.7%
7 1
16.7%
Space Separator
ValueCountFrequency (%)
3
100.0%
Other Punctuation
ValueCountFrequency (%)
. 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 31
75.6%
Common 10
 
24.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
5
16.1%
4
12.9%
4
12.9%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
1
 
3.2%
Other values (5) 5
16.1%
Common
ValueCountFrequency (%)
3
30.0%
2 2
20.0%
0 2
20.0%
3 1
 
10.0%
7 1
 
10.0%
. 1
 
10.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 31
75.6%
ASCII 10
 
24.4%

Most frequent character per block

Hangul
ValueCountFrequency (%)
5
16.1%
4
12.9%
4
12.9%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
2
 
6.5%
1
 
3.2%
Other values (5) 5
16.1%
ASCII
ValueCountFrequency (%)
3
30.0%
2 2
20.0%
0 2
20.0%
3 1
 
10.0%
7 1
 
10.0%
. 1
 
10.0%

Unnamed: 4
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing31
Missing (%)88.6%
Memory size412.0 B
2024-02-10T07:25:04.894470image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length4
Median length4
Mean length4
Min length4

Characters and Unicode

Total characters16
Distinct characters5
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

Unique0 ?
Unique (%)0.0%

Sample

1st row시군구내
2nd row시군구간
3rd row시군구내
4th row시군구간
ValueCountFrequency (%)
시군구내 2
50.0%
시군구간 2
50.0%
2024-02-10T07:25:05.593804image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
2
12.5%
2
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 16
100.0%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
2
12.5%
2
12.5%

Most occurring scripts

ValueCountFrequency (%)
Hangul 16
100.0%

Most frequent character per script

Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
2
12.5%
2
12.5%

Most occurring blocks

ValueCountFrequency (%)
Hangul 16
100.0%

Most frequent character per block

Hangul
ValueCountFrequency (%)
4
25.0%
4
25.0%
4
25.0%
2
12.5%
2
12.5%

Unnamed: 5
Categorical

IMBALANCE 

Distinct3
Distinct (%)8.6%
Missing0
Missing (%)0.0%
Memory size412.0 B
0
32 
<NA>
 
2
합 계
 
1

Length

Max length4
Median length1
Mean length1.2571429
Min length1

Unique

Unique1 ?
Unique (%)2.9%

Sample

1st row<NA>
2nd row<NA>
3rd row합 계
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 32
91.4%
<NA> 2
 
5.7%
합 계 1
 
2.9%

Length

2024-02-10T07:25:06.333028image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-10T07:25:06.660712image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
0 32
88.9%
na 2
 
5.6%
1
 
2.8%
1
 
2.8%

Unnamed: 6
Text

MISSING 

Distinct21
Distinct (%)63.6%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:07.093555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.8484848
Min length1

Characters and Unicode

Total characters61
Distinct characters13
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 (%)45.5%

Sample

1st row양동
2nd row1,941
3rd row3,341
4th row25
5th row3
ValueCountFrequency (%)
0 5
15.2%
1 3
 
9.1%
3 3
 
9.1%
10 3
 
9.1%
4 2
 
6.1%
7 2
 
6.1%
9 2
 
6.1%
12 2
 
6.1%
1,941 1
 
3.0%
3,334 1
 
3.0%
Other values (9) 9
27.3%
2024-02-10T07:25:08.266069image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
19.7%
0 9
14.8%
3 9
14.8%
2 7
11.5%
4 6
9.8%
9 4
 
6.6%
, 4
 
6.6%
7 3
 
4.9%
- 2
 
3.3%
5 2
 
3.3%
Other values (3) 3
 
4.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53
86.9%
Other Punctuation 4
 
6.6%
Dash Punctuation 2
 
3.3%
Other Letter 2
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
22.6%
0 9
17.0%
3 9
17.0%
2 7
13.2%
4 6
11.3%
9 4
 
7.5%
7 3
 
5.7%
5 2
 
3.8%
6 1
 
1.9%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59
96.7%
Hangul 2
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
20.3%
0 9
15.3%
3 9
15.3%
2 7
11.9%
4 6
10.2%
9 4
 
6.8%
, 4
 
6.8%
7 3
 
5.1%
- 2
 
3.4%
5 2
 
3.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59
96.7%
Hangul 2
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
20.3%
0 9
15.3%
3 9
15.3%
2 7
11.9%
4 6
10.2%
9 4
 
6.8%
, 4
 
6.8%
7 3
 
5.1%
- 2
 
3.4%
5 2
 
3.4%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 7
Text

MISSING 

Distinct20
Distinct (%)60.6%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:08.684110image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.8484848
Min length1

Characters and Unicode

Total characters61
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

Unique15 ?
Unique (%)45.5%

Sample

1st row양3동
2nd row2,137
3rd row4,339
4th row30
5th row1
ValueCountFrequency (%)
0 10
30.3%
5 3
 
9.1%
30 2
 
6.1%
1 2
 
6.1%
6 2
 
6.1%
10 1
 
3.0%
2,132 1
 
3.0%
15 1
 
3.0%
16 1
 
3.0%
26 1
 
3.0%
Other values (9) 9
27.3%
2024-02-10T07:25:09.656259image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13
21.3%
3 9
14.8%
1 9
14.8%
2 6
9.8%
5 4
 
6.6%
6 4
 
6.6%
, 4
 
6.6%
4 3
 
4.9%
- 2
 
3.3%
7 2
 
3.3%
Other values (4) 5
 
8.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 53
86.9%
Other Punctuation 4
 
6.6%
Dash Punctuation 2
 
3.3%
Other Letter 2
 
3.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
24.5%
3 9
17.0%
1 9
17.0%
2 6
11.3%
5 4
 
7.5%
6 4
 
7.5%
4 3
 
5.7%
7 2
 
3.8%
9 2
 
3.8%
8 1
 
1.9%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 59
96.7%
Hangul 2
 
3.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
22.0%
3 9
15.3%
1 9
15.3%
2 6
10.2%
5 4
 
6.8%
6 4
 
6.8%
, 4
 
6.8%
4 3
 
5.1%
- 2
 
3.4%
7 2
 
3.4%
Other values (2) 3
 
5.1%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 59
96.7%
Hangul 2
 
3.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
22.0%
3 9
15.3%
1 9
15.3%
2 6
10.2%
5 4
 
6.8%
6 4
 
6.8%
, 4
 
6.8%
4 3
 
5.1%
- 2
 
3.4%
7 2
 
3.4%
Other values (2) 3
 
5.1%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 8
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:09.979487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2727273
Min length1

Characters and Unicode

Total characters75
Distinct characters15
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 (%)63.6%

Sample

1st row농성1동
2nd row6,638
3rd row11,542
4th row41
5th row6
ValueCountFrequency (%)
0 7
21.2%
7 3
 
9.1%
54 2
 
6.1%
57 1
 
3.0%
41 1
 
3.0%
60 1
 
3.0%
11,608 1
 
3.0%
6,683 1
 
3.0%
1 1
 
3.0%
66 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:25:10.910295image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 15
20.0%
1 12
16.0%
6 9
12.0%
5 7
9.3%
4 7
9.3%
7 5
 
6.7%
3 5
 
6.7%
, 4
 
5.3%
8 4
 
5.3%
2 2
 
2.7%
Other values (5) 5
 
6.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
89.3%
Other Punctuation 4
 
5.3%
Other Letter 3
 
4.0%
Dash Punctuation 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 15
22.4%
1 12
17.9%
6 9
13.4%
5 7
10.4%
4 7
10.4%
7 5
 
7.5%
3 5
 
7.5%
8 4
 
6.0%
2 2
 
3.0%
9 1
 
1.5%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
96.0%
Hangul 3
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 15
20.8%
1 12
16.7%
6 9
12.5%
5 7
9.7%
4 7
9.7%
7 5
 
6.9%
3 5
 
6.9%
, 4
 
5.6%
8 4
 
5.6%
2 2
 
2.8%
Other values (2) 2
 
2.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
96.0%
Hangul 3
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 15
20.8%
1 12
16.7%
6 9
12.5%
5 7
9.7%
4 7
9.7%
7 5
 
6.9%
3 5
 
6.9%
, 4
 
5.6%
8 4
 
5.6%
2 2
 
2.8%
Other values (2) 2
 
2.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 9
Text

MISSING 

Distinct22
Distinct (%)66.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:11.303871image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.0909091
Min length1

Characters and Unicode

Total characters69
Distinct characters15
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 (%)60.6%

Sample

1st row농성2동
2nd row2,878
3rd row4,571
4th row36
5th row0
ValueCountFrequency (%)
0 10
30.3%
17 4
 
12.1%
22 1
 
3.0%
18 1
 
3.0%
4,554 1
 
3.0%
2,870 1
 
3.0%
3 1
 
3.0%
8 1
 
3.0%
5 1
 
3.0%
15 1
 
3.0%
Other values (11) 11
33.3%
2024-02-10T07:25:12.223407image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11
15.9%
7 8
11.6%
2 8
11.6%
1 7
10.1%
5 7
10.1%
8 6
8.7%
3 5
7.2%
, 4
 
5.8%
4 4
 
5.8%
- 3
 
4.3%
Other values (5) 6
8.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
85.5%
Other Punctuation 4
 
5.8%
Dash Punctuation 3
 
4.3%
Other Letter 3
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11
18.6%
7 8
13.6%
2 8
13.6%
1 7
11.9%
5 7
11.9%
8 6
10.2%
3 5
8.5%
4 4
 
6.8%
6 2
 
3.4%
9 1
 
1.7%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66
95.7%
Hangul 3
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11
16.7%
7 8
12.1%
2 8
12.1%
1 7
10.6%
5 7
10.6%
8 6
9.1%
3 5
7.6%
, 4
 
6.1%
4 4
 
6.1%
- 3
 
4.5%
Other values (2) 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
95.7%
Hangul 3
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11
16.7%
7 8
12.1%
2 8
12.1%
1 7
10.6%
5 7
10.6%
8 6
9.1%
3 5
7.6%
, 4
 
6.1%
4 4
 
6.1%
- 3
 
4.5%
Other values (2) 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 10
Text

MISSING 

Distinct24
Distinct (%)70.6%
Missing1
Missing (%)2.9%
Memory size412.0 B
2024-02-10T07:25:12.725774image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2647059
Min length1

Characters and Unicode

Total characters77
Distinct characters21
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 (%)61.8%

Sample

1st row출력일자 :
2nd row광천동
3rd row3,971
4th row7,427
5th row61
ValueCountFrequency (%)
0 8
22.9%
12 3
 
8.6%
46 2
 
5.7%
1
 
2.9%
36 1
 
2.9%
7,388 1
 
2.9%
3,948 1
 
2.9%
4 1
 
2.9%
39 1
 
2.9%
23 1
 
2.9%
Other values (15) 15
42.9%
2024-02-10T07:25:13.560731image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 11
14.3%
3 9
11.7%
1 9
11.7%
0 8
10.4%
4 5
6.5%
6 5
6.5%
7 5
6.5%
9 5
6.5%
, 4
 
5.2%
8 4
 
5.2%
Other values (11) 12
15.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
80.5%
Other Letter 7
 
9.1%
Other Punctuation 5
 
6.5%
Dash Punctuation 2
 
2.6%
Space Separator 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11
17.7%
3 9
14.5%
1 9
14.5%
0 8
12.9%
4 5
8.1%
6 5
8.1%
7 5
8.1%
9 5
8.1%
8 4
 
6.5%
5 1
 
1.6%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Other Punctuation
ValueCountFrequency (%)
, 4
80.0%
: 1
 
20.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70
90.9%
Hangul 7
 
9.1%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11
15.7%
3 9
12.9%
1 9
12.9%
0 8
11.4%
4 5
7.1%
6 5
7.1%
7 5
7.1%
9 5
7.1%
, 4
 
5.7%
8 4
 
5.7%
Other values (4) 5
7.1%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70
90.9%
Hangul 7
 
9.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11
15.7%
3 9
12.9%
1 9
12.9%
0 8
11.4%
4 5
7.1%
6 5
7.1%
7 5
7.1%
9 5
7.1%
, 4
 
5.7%
8 4
 
5.7%
Other values (4) 5
7.1%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Unnamed: 11
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:14.026026image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.1212121
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 (%)60.6%

Sample

1st row유덕동
2nd row4,840
3rd row10,567
4th row18
5th row3
ValueCountFrequency (%)
0 7
21.2%
3 2
 
6.1%
18 2
 
6.1%
30 2
 
6.1%
44 1
 
3.0%
10,567 1
 
3.0%
70 1
 
3.0%
4,838 1
 
3.0%
12 1
 
3.0%
2 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:25:14.828104image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14
20.0%
1 8
11.4%
2 8
11.4%
4 7
10.0%
3 6
8.6%
8 6
8.6%
5 5
 
7.1%
, 4
 
5.7%
7 4
 
5.7%
6 3
 
4.3%
Other values (4) 5
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
87.1%
Other Punctuation 4
 
5.7%
Other Letter 3
 
4.3%
Dash Punctuation 2
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
23.0%
1 8
13.1%
2 8
13.1%
4 7
11.5%
3 6
9.8%
8 6
9.8%
5 5
 
8.2%
7 4
 
6.6%
6 3
 
4.9%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 67
95.7%
Hangul 3
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
20.9%
1 8
11.9%
2 8
11.9%
4 7
10.4%
3 6
9.0%
8 6
9.0%
5 5
 
7.5%
, 4
 
6.0%
7 4
 
6.0%
6 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 67
95.7%
Hangul 3
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
20.9%
1 8
11.9%
2 8
11.9%
4 7
10.4%
3 6
9.0%
8 6
9.0%
5 5
 
7.5%
, 4
 
6.0%
7 4
 
6.0%
6 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 12
Date

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing34
Missing (%)97.1%
Memory size412.0 B
Minimum2023-08-01 00:00:00
Maximum2023-08-01 00:00:00
2024-02-10T07:25:15.343202image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-10T07:25:15.674562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Unnamed: 13
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:15.985355image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.4848485
Min length1

Characters and Unicode

Total characters82
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

Unique22 ?
Unique (%)66.7%

Sample

1st row치평동
2nd row13,653
3rd row29,334
4th row55
5th row15
ValueCountFrequency (%)
0 7
21.2%
57 2
 
6.1%
15 2
 
6.1%
7 1
 
3.0%
259 1
 
3.0%
13,707 1
 
3.0%
2 1
 
3.0%
49 1
 
3.0%
54 1
 
3.0%
8 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T07:25:16.926849image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
13.4%
3 11
13.4%
0 10
12.2%
5 10
12.2%
2 9
11.0%
7 7
8.5%
9 6
7.3%
4 6
7.3%
, 4
 
4.9%
8 3
 
3.7%
Other values (4) 5
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 75
91.5%
Other Punctuation 4
 
4.9%
Other Letter 3
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
14.7%
3 11
14.7%
0 10
13.3%
5 10
13.3%
2 9
12.0%
7 7
9.3%
9 6
8.0%
4 6
8.0%
8 3
 
4.0%
6 2
 
2.7%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79
96.3%
Hangul 3
 
3.7%

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
13.9%
3 11
13.9%
0 10
12.7%
5 10
12.7%
2 9
11.4%
7 7
8.9%
9 6
7.6%
4 6
7.6%
, 4
 
5.1%
8 3
 
3.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79
96.3%
Hangul 3
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 11
13.9%
3 11
13.9%
0 10
12.7%
5 10
12.7%
2 9
11.4%
7 7
8.9%
9 6
7.6%
4 6
7.6%
, 4
 
5.1%
8 3
 
3.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 14
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:17.400408image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.6060606
Min length1

Characters and Unicode

Total characters86
Distinct characters15
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 (%)69.7%

Sample

1st row상무1동
2nd row12,253
3rd row24,151
4th row151
5th row12
ValueCountFrequency (%)
0 6
 
18.2%
4 2
 
6.1%
12 2
 
6.1%
110 1
 
3.0%
151 1
 
3.0%
143 1
 
3.0%
24,094 1
 
3.0%
12,257 1
 
3.0%
3 1
 
3.0%
57 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:25:18.543120image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 19
22.1%
2 13
15.1%
0 12
14.0%
4 7
 
8.1%
7 6
 
7.0%
3 5
 
5.8%
5 5
 
5.8%
6 4
 
4.7%
, 4
 
4.7%
9 3
 
3.5%
Other values (5) 8
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
89.5%
Other Punctuation 4
 
4.7%
Other Letter 3
 
3.5%
Dash Punctuation 2
 
2.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 19
24.7%
2 13
16.9%
0 12
15.6%
4 7
 
9.1%
7 6
 
7.8%
3 5
 
6.5%
5 5
 
6.5%
6 4
 
5.2%
9 3
 
3.9%
8 3
 
3.9%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 83
96.5%
Hangul 3
 
3.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 19
22.9%
2 13
15.7%
0 12
14.5%
4 7
 
8.4%
7 6
 
7.2%
3 5
 
6.0%
5 5
 
6.0%
6 4
 
4.8%
, 4
 
4.8%
9 3
 
3.6%
Other values (2) 5
 
6.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 83
96.5%
Hangul 3
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 19
22.9%
2 13
15.7%
0 12
14.5%
4 7
 
8.4%
7 6
 
7.2%
3 5
 
6.0%
5 5
 
6.0%
6 4
 
4.8%
, 4
 
4.8%
9 3
 
3.6%
Other values (2) 5
 
6.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 15
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:18.966956image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.4848485
Min length1

Characters and Unicode

Total characters82
Distinct characters15
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

Unique24 ?
Unique (%)72.7%

Sample

1st row상무2동
2nd row13,022
3rd row22,837
4th row108
5th row8
ValueCountFrequency (%)
0 7
21.2%
8 2
 
6.1%
111 1
 
3.0%
22,847 1
 
3.0%
13,038 1
 
3.0%
4 1
 
3.0%
10 1
 
3.0%
16 1
 
3.0%
25 1
 
3.0%
84 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:25:19.941051image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14
17.1%
0 12
14.6%
1 12
14.6%
8 11
13.4%
3 8
9.8%
7 6
7.3%
4 5
 
6.1%
, 4
 
4.9%
6 3
 
3.7%
9 2
 
2.4%
Other values (5) 5
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
90.2%
Other Punctuation 4
 
4.9%
Other Letter 3
 
3.7%
Dash Punctuation 1
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
18.9%
0 12
16.2%
1 12
16.2%
8 11
14.9%
3 8
10.8%
7 6
8.1%
4 5
 
6.8%
6 3
 
4.1%
9 2
 
2.7%
5 1
 
1.4%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 79
96.3%
Hangul 3
 
3.7%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
17.7%
0 12
15.2%
1 12
15.2%
8 11
13.9%
3 8
10.1%
7 6
7.6%
4 5
 
6.3%
, 4
 
5.1%
6 3
 
3.8%
9 2
 
2.5%
Other values (2) 2
 
2.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 79
96.3%
Hangul 3
 
3.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 14
17.7%
0 12
15.2%
1 12
15.2%
8 11
13.9%
3 8
10.1%
7 6
7.6%
4 5
 
6.3%
, 4
 
5.1%
6 3
 
3.8%
9 2
 
2.5%
Other values (2) 2
 
2.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 16
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:20.375686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2424242
Min length1

Characters and Unicode

Total characters74
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 (%)60.6%

Sample

1st row화정1동
2nd row8,756
3rd row15,715
4th row47
5th row5
ValueCountFrequency (%)
0 8
24.2%
5 4
 
12.1%
8,756 2
 
6.1%
111 1
 
3.0%
15,715 1
 
3.0%
196 1
 
3.0%
15,710 1
 
3.0%
1 1
 
3.0%
70 1
 
3.0%
66 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T07:25:21.228633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13
17.6%
0 12
16.2%
5 11
14.9%
6 9
12.2%
7 8
10.8%
8 4
 
5.4%
, 4
 
5.4%
3 3
 
4.1%
9 3
 
4.1%
4 2
 
2.7%
Other values (4) 5
 
6.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65
87.8%
Other Punctuation 4
 
5.4%
Other Letter 3
 
4.1%
Dash Punctuation 2
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
20.0%
0 12
18.5%
5 11
16.9%
6 9
13.8%
7 8
12.3%
8 4
 
6.2%
3 3
 
4.6%
9 3
 
4.6%
4 2
 
3.1%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 71
95.9%
Hangul 3
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13
18.3%
0 12
16.9%
5 11
15.5%
6 9
12.7%
7 8
11.3%
8 4
 
5.6%
, 4
 
5.6%
3 3
 
4.2%
9 3
 
4.2%
4 2
 
2.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 71
95.9%
Hangul 3
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 13
18.3%
0 12
16.9%
5 11
15.5%
6 9
12.7%
7 8
11.3%
8 4
 
5.6%
, 4
 
5.6%
3 3
 
4.2%
9 3
 
4.2%
4 2
 
2.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 17
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:21.656433image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2727273
Min length1

Characters and Unicode

Total characters75
Distinct characters15
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

Unique24 ?
Unique (%)72.7%

Sample

1st row화정2동
2nd row7,931
3rd row19,903
4th row27
5th row15
ValueCountFrequency (%)
0 7
21.2%
15 2
 
6.1%
70 1
 
3.0%
19,891 1
 
3.0%
7,939 1
 
3.0%
1 1
 
3.0%
12 1
 
3.0%
8 1
 
3.0%
11 1
 
3.0%
51 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:25:22.608916image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
18.7%
0 13
17.3%
9 9
12.0%
5 7
9.3%
7 6
8.0%
3 6
8.0%
, 4
 
5.3%
2 4
 
5.3%
8 4
 
5.3%
4 2
 
2.7%
Other values (5) 6
8.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
89.3%
Other Punctuation 4
 
5.3%
Other Letter 3
 
4.0%
Dash Punctuation 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
20.9%
0 13
19.4%
9 9
13.4%
5 7
10.4%
7 6
9.0%
3 6
9.0%
2 4
 
6.0%
8 4
 
6.0%
4 2
 
3.0%
6 2
 
3.0%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
96.0%
Hangul 3
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
19.4%
0 13
18.1%
9 9
12.5%
5 7
9.7%
7 6
8.3%
3 6
8.3%
, 4
 
5.6%
2 4
 
5.6%
8 4
 
5.6%
4 2
 
2.8%
Other values (2) 3
 
4.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
96.0%
Hangul 3
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
19.4%
0 13
18.1%
9 9
12.5%
5 7
9.7%
7 6
8.3%
3 6
8.3%
, 4
 
5.6%
2 4
 
5.6%
8 4
 
5.6%
4 2
 
2.8%
Other values (2) 3
 
4.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 18
Text

MISSING 

Distinct22
Distinct (%)66.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:22.985465image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length2.1818182
Min length1

Characters and Unicode

Total characters72
Distinct characters15
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 (%)54.5%

Sample

1st row화정3동
2nd row4,458
3rd row9,398
4th row31
5th row11
ValueCountFrequency (%)
0 8
24.2%
11 3
 
9.1%
24 3
 
9.1%
31 2
 
6.1%
5 1
 
3.0%
9,398 1
 
3.0%
68 1
 
3.0%
4,452 1
 
3.0%
7 1
 
3.0%
6 1
 
3.0%
Other values (11) 11
33.3%
2024-02-10T07:25:23.968912image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10
13.9%
2 9
12.5%
4 9
12.5%
0 8
11.1%
3 7
9.7%
7 6
8.3%
, 4
 
5.6%
8 4
 
5.6%
9 4
 
5.6%
5 3
 
4.2%
Other values (5) 8
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
86.1%
Other Punctuation 4
 
5.6%
Dash Punctuation 3
 
4.2%
Other Letter 3
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
16.1%
2 9
14.5%
4 9
14.5%
0 8
12.9%
3 7
11.3%
7 6
9.7%
8 4
 
6.5%
9 4
 
6.5%
5 3
 
4.8%
6 2
 
3.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
95.8%
Hangul 3
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
14.5%
2 9
13.0%
4 9
13.0%
0 8
11.6%
3 7
10.1%
7 6
8.7%
, 4
 
5.8%
8 4
 
5.8%
9 4
 
5.8%
5 3
 
4.3%
Other values (2) 5
7.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 69
95.8%
Hangul 3
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
14.5%
2 9
13.0%
4 9
13.0%
0 8
11.6%
3 7
10.1%
7 6
8.7%
, 4
 
5.8%
8 4
 
5.8%
9 4
 
5.8%
5 3
 
4.3%
Other values (2) 5
7.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 19
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:24.515590image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
Mean length2.2727273
Min length1

Characters and Unicode

Total characters75
Distinct characters15
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 (%)60.6%

Sample

1st row화정4동
2nd row8,199
3rd row19,477
4th row21
5th row13
ValueCountFrequency (%)
0 7
21.2%
8,199 2
 
6.1%
13 2
 
6.1%
53 2
 
6.1%
78 1
 
3.0%
21 1
 
3.0%
159 1
 
3.0%
19,469 1
 
3.0%
1 1
 
3.0%
8 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:25:25.384559image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
16.0%
0 11
14.7%
9 8
10.7%
7 8
10.7%
4 7
9.3%
5 5
6.7%
3 5
6.7%
8 5
6.7%
, 4
 
5.3%
2 3
 
4.0%
Other values (5) 7
9.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
88.0%
Other Punctuation 4
 
5.3%
Other Letter 3
 
4.0%
Dash Punctuation 2
 
2.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
18.2%
0 11
16.7%
9 8
12.1%
7 8
12.1%
4 7
10.6%
5 5
7.6%
3 5
7.6%
8 5
7.6%
2 3
 
4.5%
6 2
 
3.0%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
96.0%
Hangul 3
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
16.7%
0 11
15.3%
9 8
11.1%
7 8
11.1%
4 7
9.7%
5 5
6.9%
3 5
6.9%
8 5
6.9%
, 4
 
5.6%
2 3
 
4.2%
Other values (2) 4
 
5.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
96.0%
Hangul 3
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
16.7%
0 11
15.3%
9 8
11.1%
7 8
11.1%
4 7
9.7%
5 5
6.9%
3 5
6.9%
8 5
6.9%
, 4
 
5.6%
2 3
 
4.2%
Other values (2) 4
 
5.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 20
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:25.821098image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2
Min length1

Characters and Unicode

Total characters66
Distinct characters15
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 (%)66.7%

Sample

1st row서창동
2nd row2,653
3rd row5,690
4th row8
5th row4
ValueCountFrequency (%)
0 8
24.2%
4 3
 
9.1%
5,690 1
 
3.0%
8 1
 
3.0%
5,676 1
 
3.0%
2,658 1
 
3.0%
1 1
 
3.0%
14 1
 
3.0%
5 1
 
3.0%
16 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:25:26.926086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10
15.2%
4 8
12.1%
2 8
12.1%
1 8
12.1%
6 7
10.6%
5 6
9.1%
, 4
 
6.1%
8 3
 
4.5%
7 3
 
4.5%
3 2
 
3.0%
Other values (5) 7
10.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57
86.4%
Other Punctuation 4
 
6.1%
Other Letter 3
 
4.5%
Dash Punctuation 2
 
3.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
17.5%
4 8
14.0%
2 8
14.0%
1 8
14.0%
6 7
12.3%
5 6
10.5%
8 3
 
5.3%
7 3
 
5.3%
3 2
 
3.5%
9 2
 
3.5%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 63
95.5%
Hangul 3
 
4.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
15.9%
4 8
12.7%
2 8
12.7%
1 8
12.7%
6 7
11.1%
5 6
9.5%
, 4
 
6.3%
8 3
 
4.8%
7 3
 
4.8%
3 2
 
3.2%
Other values (2) 4
 
6.3%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63
95.5%
Hangul 3
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
15.9%
4 8
12.7%
2 8
12.7%
1 8
12.7%
6 7
11.1%
5 6
9.5%
, 4
 
6.3%
8 3
 
4.8%
7 3
 
4.8%
3 2
 
3.2%
Other values (2) 4
 
6.3%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 21
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:27.301572image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.2121212
Min length1

Characters and Unicode

Total characters73
Distinct characters15
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 (%)66.7%

Sample

1st row금호1동
2nd row8,908
3rd row19,695
4th row29
5th row4
ValueCountFrequency (%)
0 7
21.2%
4 2
 
6.1%
29 2
 
6.1%
5 1
 
3.0%
152 1
 
3.0%
8,914 1
 
3.0%
26 1
 
3.0%
6 1
 
3.0%
13 1
 
3.0%
67 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T07:25:28.406752image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10
13.7%
9 9
12.3%
0 8
11.0%
6 8
11.0%
4 7
9.6%
2 5
6.8%
8 5
6.8%
3 5
6.8%
, 4
 
5.5%
5 4
 
5.5%
Other values (5) 8
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 65
89.0%
Other Punctuation 4
 
5.5%
Other Letter 3
 
4.1%
Dash Punctuation 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
15.4%
9 9
13.8%
0 8
12.3%
6 8
12.3%
4 7
10.8%
2 5
7.7%
8 5
7.7%
3 5
7.7%
5 4
 
6.2%
7 4
 
6.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 70
95.9%
Hangul 3
 
4.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
14.3%
9 9
12.9%
0 8
11.4%
6 8
11.4%
4 7
10.0%
2 5
7.1%
8 5
7.1%
3 5
7.1%
, 4
 
5.7%
5 4
 
5.7%
Other values (2) 5
7.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 70
95.9%
Hangul 3
 
4.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 10
14.3%
9 9
12.9%
0 8
11.4%
6 8
11.4%
4 7
10.0%
2 5
7.1%
8 5
7.1%
3 5
7.1%
, 4
 
5.7%
5 4
 
5.7%
Other values (2) 5
7.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 22
Text

MISSING 

Distinct22
Distinct (%)66.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:28.765084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.2727273
Min length1

Characters and Unicode

Total characters75
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

Unique18 ?
Unique (%)54.5%

Sample

1st row금호2동
2nd row10,511
3rd row27,303
4th row21
5th row11
ValueCountFrequency (%)
0 9
27.3%
21 2
 
6.1%
11 2
 
6.1%
10,511 2
 
6.1%
71 1
 
3.0%
150 1
 
3.0%
34 1
 
3.0%
6 1
 
3.0%
50 1
 
3.0%
52 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T07:25:29.712236image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 15
20.0%
0 14
18.7%
2 8
10.7%
5 6
 
8.0%
8 6
 
8.0%
6 6
 
8.0%
3 5
 
6.7%
, 4
 
5.3%
7 4
 
5.3%
4 3
 
4.0%
Other values (4) 4
 
5.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
90.7%
Other Punctuation 4
 
5.3%
Other Letter 3
 
4.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 15
22.1%
0 14
20.6%
2 8
11.8%
5 6
 
8.8%
8 6
 
8.8%
6 6
 
8.8%
3 5
 
7.4%
7 4
 
5.9%
4 3
 
4.4%
9 1
 
1.5%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 72
96.0%
Hangul 3
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 15
20.8%
0 14
19.4%
2 8
11.1%
5 6
 
8.3%
8 6
 
8.3%
6 6
 
8.3%
3 5
 
6.9%
, 4
 
5.6%
7 4
 
5.6%
4 3
 
4.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 72
96.0%
Hangul 3
 
4.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 15
20.8%
0 14
19.4%
2 8
11.1%
5 6
 
8.3%
8 6
 
8.3%
6 6
 
8.3%
3 5
 
6.9%
, 4
 
5.6%
7 4
 
5.6%
4 3
 
4.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 23
Text

MISSING 

Distinct27
Distinct (%)81.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:30.114530image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.5151515
Min length1

Characters and Unicode

Total characters83
Distinct characters15
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

Unique24 ?
Unique (%)72.7%

Sample

1st row풍암동
2nd row15,091
3rd row35,199
4th row78
5th row22
ValueCountFrequency (%)
0 5
 
15.2%
95 2
 
6.1%
2 2
 
6.1%
22 2
 
6.1%
1 1
 
3.0%
156 1
 
3.0%
35,126 1
 
3.0%
15,094 1
 
3.0%
73 1
 
3.0%
3 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:25:30.955501image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
16.9%
2 13
15.7%
0 11
13.3%
3 9
10.8%
9 7
8.4%
5 7
8.4%
6 6
7.2%
, 4
 
4.8%
7 4
 
4.8%
8 2
 
2.4%
Other values (5) 6
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 74
89.2%
Other Punctuation 4
 
4.8%
Other Letter 3
 
3.6%
Dash Punctuation 2
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
18.9%
2 13
17.6%
0 11
14.9%
3 9
12.2%
9 7
9.5%
5 7
9.5%
6 6
8.1%
7 4
 
5.4%
8 2
 
2.7%
4 1
 
1.4%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 80
96.4%
Hangul 3
 
3.6%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
17.5%
2 13
16.2%
0 11
13.8%
3 9
11.2%
9 7
8.8%
5 7
8.8%
6 6
7.5%
, 4
 
5.0%
7 4
 
5.0%
8 2
 
2.5%
Other values (2) 3
 
3.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 80
96.4%
Hangul 3
 
3.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
17.5%
2 13
16.2%
0 11
13.8%
3 9
11.2%
9 7
8.8%
5 7
8.8%
6 6
7.5%
, 4
 
5.0%
7 4
 
5.0%
8 2
 
2.5%
Other values (2) 3
 
3.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 24
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:25:31.365003image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.0909091
Min length1

Characters and Unicode

Total characters69
Distinct characters13
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 (%)54.5%

Sample

1st row동천동
2nd row6,345
3rd row15,611
4th row15
5th row9
ValueCountFrequency (%)
0 7
21.2%
9 2
 
6.1%
95 2
 
6.1%
39 2
 
6.1%
42 2
 
6.1%
5 1
 
3.0%
15,610 1
 
3.0%
6,355 1
 
3.0%
2 1
 
3.0%
1 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T07:25:32.308514image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 13
18.8%
1 10
14.5%
0 9
13.0%
3 7
10.1%
9 6
8.7%
6 6
8.7%
4 4
 
5.8%
2 4
 
5.8%
, 4
 
5.8%
2
 
2.9%
Other values (3) 4
 
5.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
87.0%
Other Punctuation 4
 
5.8%
Other Letter 3
 
4.3%
Dash Punctuation 2
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 13
21.7%
1 10
16.7%
0 9
15.0%
3 7
11.7%
9 6
10.0%
6 6
10.0%
4 4
 
6.7%
2 4
 
6.7%
8 1
 
1.7%
Other Letter
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 66
95.7%
Hangul 3
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
5 13
19.7%
1 10
15.2%
0 9
13.6%
3 7
10.6%
9 6
9.1%
6 6
9.1%
4 4
 
6.1%
2 4
 
6.1%
, 4
 
6.1%
- 2
 
3.0%
Hangul
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
95.7%
Hangul 3
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 13
19.7%
1 10
15.2%
0 9
13.6%
3 7
10.6%
9 6
9.1%
6 6
9.1%
4 4
 
6.1%
2 4
 
6.1%
, 4
 
6.1%
- 2
 
3.0%
Hangul
ValueCountFrequency (%)
2
66.7%
1
33.3%

Sample

Unnamed: 0인구이동보고서(1호)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24
0<NA>행정기관 :<NA>광주광역시 서구<NA><NA><NA><NA><NA><NA>출력일자 :<NA>2023.08.01<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1<NA>작성기준 :<NA>2023.07 현재<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
2<NA>시, 군, 구(읍면동)<NA><NA><NA>합 계양동양3동농성1동농성2동광천동유덕동<NA>치평동상무1동상무2동화정1동화정2동화정3동화정4동서창동금호1동금호2동풍암동동천동
3<NA>전월말세대수<NA><NA><NA>01,9412,1376,6382,8783,9714,840<NA>13,65312,25313,0228,7567,9314,4588,1992,6538,90810,51115,0916,345
4<NA>전월말인구수<NA><NA><NA>03,3414,33911,5424,5717,42710,567<NA>29,33424,15122,83715,71519,9039,39819,4775,69019,69527,30335,19915,611
5<NA>전월말거주불명자수<NA><NA><NA>0253041366118<NA>5515110847273121829217815
6<NA>전월말재외국민등록자수<NA><NA><NA>03160123<NA>1512851511134411229
7<NA>증 가 요 인전 입<NA>02021201434670<NA>309266262193140681504413318222795
8<NA><NA><NA>남자<NA>0109101262740<NA>1441391338769377724619613239
9<NA><NA><NA>여자<NA>01012100171930<NA>1651271291067131732072869556
Unnamed: 0인구이동보고서(1호)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24
25<NA><NA>말소<NA><NA>0200000<NA>010000000000
26<NA><NA>국외<NA><NA>0000000<NA>000000000000
27<NA><NA>기타<NA><NA>0000000<NA>000000000000
28<NA>세대수증감<NA><NA><NA>0-4-545-8-23-2<NA>5441608-60560310
29<NA>인구수증감<NA><NA><NA>0-7-566-17-39-12<NA>49-5710-5-12-11-8-14-2634-73-1
30<NA>거주불명자수증감<NA><NA><NA>010-1-340<NA>2-3-4-11-7-1-100-2-2
31<NA>금월말세대수<NA><NA><NA>01,9372,1326,6832,8703,9484,838<NA>13,70712,25713,0388,7567,9394,4528,1992,6588,91410,51115,0946,355
32<NA>금월말인구수<NA><NA><NA>03,3344,33411,6084,5547,38810,555<NA>29,38324,09422,84715,71019,8919,38719,4695,67619,66927,33735,12615,610
33<NA>금월말거주불명자수<NA><NA><NA>0263040336518<NA>5714810446282420729217613
34<NA>금월말재외국민등록자수<NA><NA><NA>03170123<NA>1512851511134411229

Duplicate rows

Most frequently occurring

인구이동보고서(1호)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19Unnamed: 20Unnamed: 21Unnamed: 22Unnamed: 23Unnamed: 24# duplicates
0<NA>기타<NA><NA>0000000<NA>0000000000002