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

Description2023-06-23
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 07:09:57.284288
Analysis finished2024-02-10 07:09:58.900779
Duration1.62 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
2024-02-10T07:09:59.309216image/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%
2024-02-10T07:10:00.493422image/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
2024-02-10T07:10:00.886013image/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%
2024-02-10T07:10:01.851374image/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
2024-02-10T07:10:02.347998image/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 row2023.05 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%
2023.05 1
7.1%
현재 1
7.1%
2024-02-10T07:10:03.225212image/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%
5 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%
5 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%
5 1
 
10.0%
. 1
 
10.0%

Unnamed: 4
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing31
Missing (%)88.6%
Memory size412.0 B
2024-02-10T07:10:03.598104image/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%
2024-02-10T07:10:04.550445image/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

2024-02-10T07:10:05.041954image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-02-10T07:10:05.623812image/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 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:05.967726image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length1
Mean length1.9090909
Min length1

Characters and Unicode

Total characters63
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

Unique21 ?
Unique (%)63.6%

Sample

1st row양동
2nd row1,961
3rd row3,380
4th row25
5th row3
ValueCountFrequency (%)
0 8
24.2%
3 4
 
12.1%
16 2
 
6.1%
3,357 1
 
3.0%
1,945 1
 
3.0%
1 1
 
3.0%
23 1
 
3.0%
5 1
 
3.0%
7 1
 
3.0%
27 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T07:10:07.664181image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

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

Most occurring categories

ValueCountFrequency (%)
Decimal Number 55
87.3%
Other Punctuation 4
 
6.3%
Dash Punctuation 2
 
3.2%
Other Letter 2
 
3.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
18.2%
3 10
18.2%
2 8
14.5%
1 8
14.5%
6 4
 
7.3%
5 4
 
7.3%
8 3
 
5.5%
4 3
 
5.5%
7 3
 
5.5%
9 2
 
3.6%
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 61
96.8%
Hangul 2
 
3.2%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
16.4%
3 10
16.4%
2 8
13.1%
1 8
13.1%
, 4
 
6.6%
6 4
 
6.6%
5 4
 
6.6%
8 3
 
4.9%
4 3
 
4.9%
7 3
 
4.9%
Other values (2) 4
 
6.6%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61
96.8%
Hangul 2
 
3.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
16.4%
3 10
16.4%
2 8
13.1%
1 8
13.1%
, 4
 
6.6%
6 4
 
6.6%
5 4
 
6.6%
8 3
 
4.9%
4 3
 
4.9%
7 3
 
4.9%
Other values (2) 4
 
6.6%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 7
Text

MISSING 

Distinct21
Distinct (%)63.6%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:08.478847image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.9393939
Min length1

Characters and Unicode

Total characters64
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

Unique17 ?
Unique (%)51.5%

Sample

1st row양3동
2nd row2,135
3rd row4,355
4th row30
5th row1
ValueCountFrequency (%)
0 10
30.3%
1 2
 
6.1%
13 2
 
6.1%
30 2
 
6.1%
22 1
 
3.0%
41 1
 
3.0%
2,137 1
 
3.0%
17 1
 
3.0%
2 1
 
3.0%
25 1
 
3.0%
Other values (11) 11
33.3%
2024-02-10T07:10:09.946981image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12
18.8%
1 12
18.8%
3 11
17.2%
2 8
12.5%
4 5
7.8%
, 4
 
6.2%
5 4
 
6.2%
8 2
 
3.1%
7 2
 
3.1%
1
 
1.6%
Other values (3) 3
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57
89.1%
Other Punctuation 4
 
6.2%
Other Letter 2
 
3.1%
Dash Punctuation 1
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
21.1%
1 12
21.1%
3 11
19.3%
2 8
14.0%
4 5
8.8%
5 4
 
7.0%
8 2
 
3.5%
7 2
 
3.5%
9 1
 
1.8%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 62
96.9%
Hangul 2
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
19.4%
1 12
19.4%
3 11
17.7%
2 8
12.9%
4 5
8.1%
, 4
 
6.5%
5 4
 
6.5%
8 2
 
3.2%
7 2
 
3.2%
9 1
 
1.6%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
96.9%
Hangul 2
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12
19.4%
1 12
19.4%
3 11
17.7%
2 8
12.9%
4 5
8.1%
, 4
 
6.5%
5 4
 
6.5%
8 2
 
3.2%
7 2
 
3.2%
9 1
 
1.6%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 8
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:10.454846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3333333
Min length1

Characters and Unicode

Total characters77
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

Unique21 ?
Unique (%)63.6%

Sample

1st row농성1동
2nd row6,573
3rd row11,430
4th row43
5th row6
ValueCountFrequency (%)
0 8
24.2%
43 2
 
6.1%
6 2
 
6.1%
24 1
 
3.0%
11,430 1
 
3.0%
54 1
 
3.0%
6,622 1
 
3.0%
108 1
 
3.0%
49 1
 
3.0%
7 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:10:11.210213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
18.2%
0 13
16.9%
6 8
10.4%
4 8
10.4%
2 8
10.4%
3 5
 
6.5%
5 5
 
6.5%
, 4
 
5.2%
8 4
 
5.2%
7 3
 
3.9%
Other values (4) 5
 
6.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 70
90.9%
Other Punctuation 4
 
5.2%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
20.0%
0 13
18.6%
6 8
11.4%
4 8
11.4%
2 8
11.4%
3 5
 
7.1%
5 5
 
7.1%
8 4
 
5.7%
7 3
 
4.3%
9 2
 
2.9%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 74
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
18.9%
0 13
17.6%
6 8
10.8%
4 8
10.8%
2 8
10.8%
3 5
 
6.8%
5 5
 
6.8%
, 4
 
5.4%
8 4
 
5.4%
7 3
 
4.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
18.9%
0 13
17.6%
6 8
10.8%
4 8
10.8%
2 8
10.8%
3 5
 
6.8%
5 5
 
6.8%
, 4
 
5.4%
8 4
 
5.4%
7 3
 
4.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 9
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:11.737484image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
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

Unique21 ?
Unique (%)63.6%

Sample

1st row농성2동
2nd row2,861
3rd row4,576
4th row44
5th row0
ValueCountFrequency (%)
0 10
30.3%
30 2
 
6.1%
60 1
 
3.0%
4,573 1
 
3.0%
2,871 1
 
3.0%
4 1
 
3.0%
3 1
 
3.0%
10 1
 
3.0%
2 1
 
3.0%
22 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T07:10:12.736758image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 16
23.2%
2 10
14.5%
1 6
 
8.7%
4 6
 
8.7%
3 5
 
7.2%
, 4
 
5.8%
8 4
 
5.8%
6 4
 
5.8%
5 4
 
5.8%
7 4
 
5.8%
Other values (5) 6
 
8.7%

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 (%)
0 16
26.7%
2 10
16.7%
1 6
 
10.0%
4 6
 
10.0%
3 5
 
8.3%
8 4
 
6.7%
6 4
 
6.7%
5 4
 
6.7%
7 4
 
6.7%
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 (%)
- 2
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 16
24.2%
2 10
15.2%
1 6
 
9.1%
4 6
 
9.1%
3 5
 
7.6%
, 4
 
6.1%
8 4
 
6.1%
6 4
 
6.1%
5 4
 
6.1%
7 4
 
6.1%
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 16
24.2%
2 10
15.2%
1 6
 
9.1%
4 6
 
9.1%
3 5
 
7.6%
, 4
 
6.1%
8 4
 
6.1%
6 4
 
6.1%
5 4
 
6.1%
7 4
 
6.1%
Other values (2) 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 10
Text

MISSING 

Distinct27
Distinct (%)79.4%
Missing1
Missing (%)2.9%
Memory size412.0 B
2024-02-10T07:10:13.117086image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5.5
Mean length2.2352941
Min length1

Characters and Unicode

Total characters76
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

Unique26 ?
Unique (%)76.5%

Sample

1st row출력일자 :
2nd row광천동
3rd row3,982
4th row7,470
5th row63
ValueCountFrequency (%)
0 8
22.9%
1 2
 
5.7%
29 2
 
5.7%
출력일자 1
 
2.9%
31 1
 
2.9%
62 1
 
2.9%
7,441 1
 
2.9%
3,983 1
 
2.9%
8 1
 
2.9%
28 1
 
2.9%
Other values (16) 16
45.7%
2024-02-10T07:10:13.976923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 11
14.5%
0 10
13.2%
1 9
11.8%
3 7
9.2%
4 5
 
6.6%
7 5
 
6.6%
8 5
 
6.6%
9 4
 
5.3%
, 4
 
5.3%
6 3
 
3.9%
Other values (11) 13
17.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
80.3%
Other Letter 7
 
9.2%
Other Punctuation 5
 
6.6%
Dash Punctuation 2
 
2.6%
Space Separator 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 11
18.0%
0 10
16.4%
1 9
14.8%
3 7
11.5%
4 5
8.2%
7 5
8.2%
8 5
8.2%
9 4
 
6.6%
6 3
 
4.9%
5 2
 
3.3%
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 69
90.8%
Hangul 7
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
2 11
15.9%
0 10
14.5%
1 9
13.0%
3 7
10.1%
4 5
7.2%
7 5
7.2%
8 5
7.2%
9 4
 
5.8%
, 4
 
5.8%
6 3
 
4.3%
Other values (4) 6
8.7%
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 69
90.8%
Hangul 7
 
9.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 11
15.9%
0 10
14.5%
1 9
13.0%
3 7
10.1%
4 5
7.2%
7 5
7.2%
8 5
7.2%
9 4
 
5.8%
, 4
 
5.8%
6 3
 
4.3%
Other values (4) 6
8.7%
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 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:14.399908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.1515152
Min length1

Characters and Unicode

Total characters71
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 row유덕동
2nd row4,852
3rd row10,589
4th row22
5th row3
ValueCountFrequency (%)
0 6
 
18.2%
32 2
 
6.1%
2 2
 
6.1%
3 2
 
6.1%
31 1
 
3.0%
53 1
 
3.0%
10,576 1
 
3.0%
4,845 1
 
3.0%
13 1
 
3.0%
7 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T07:10:15.281945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 11
15.5%
0 10
14.1%
2 9
12.7%
4 8
11.3%
1 7
9.9%
8 6
8.5%
5 5
7.0%
, 4
 
5.6%
- 3
 
4.2%
9 2
 
2.8%
Other values (5) 6
8.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
85.9%
Other Punctuation 4
 
5.6%
Dash Punctuation 3
 
4.2%
Other Letter 3
 
4.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 11
18.0%
0 10
16.4%
2 9
14.8%
4 8
13.1%
1 7
11.5%
8 6
9.8%
5 5
8.2%
9 2
 
3.3%
7 2
 
3.3%
6 1
 
1.6%
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 68
95.8%
Hangul 3
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
3 11
16.2%
0 10
14.7%
2 9
13.2%
4 8
11.8%
1 7
10.3%
8 6
8.8%
5 5
7.4%
, 4
 
5.9%
- 3
 
4.4%
9 2
 
2.9%
Other values (2) 3
 
4.4%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 68
95.8%
Hangul 3
 
4.2%

Most frequent character per block

ASCII
ValueCountFrequency (%)
3 11
16.2%
0 10
14.7%
2 9
13.2%
4 8
11.8%
1 7
10.3%
8 6
8.8%
5 5
7.4%
, 4
 
5.9%
- 3
 
4.4%
9 2
 
2.9%
Other values (2) 3
 
4.4%
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
Minimum2023-06-01 00:00:00
Maximum2023-06-01 00:00:00
2024-02-10T07:10:15.648105image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-10T07:10:16.197368image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Unnamed: 13
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:16.610173image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
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

Unique24 ?
Unique (%)72.7%

Sample

1st row치평동
2nd row13,641
3rd row29,392
4th row58
5th row18
ValueCountFrequency (%)
0 7
21.2%
161 2
 
6.1%
167 1
 
3.0%
57 1
 
3.0%
29,377 1
 
3.0%
13,650 1
 
3.0%
1 1
 
3.0%
15 1
 
3.0%
9 1
 
3.0%
13 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:10:17.483005image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 23
26.7%
0 9
 
10.5%
3 9
 
10.5%
6 8
 
9.3%
2 6
 
7.0%
5 6
 
7.0%
9 5
 
5.8%
7 5
 
5.8%
8 4
 
4.7%
, 4
 
4.7%
Other values (5) 7
 
8.1%

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 23
29.9%
0 9
 
11.7%
3 9
 
11.7%
6 8
 
10.4%
2 6
 
7.8%
5 6
 
7.8%
9 5
 
6.5%
7 5
 
6.5%
8 4
 
5.2%
4 2
 
2.6%
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 23
27.7%
0 9
 
10.8%
3 9
 
10.8%
6 8
 
9.6%
2 6
 
7.2%
5 6
 
7.2%
9 5
 
6.0%
7 5
 
6.0%
8 4
 
4.8%
, 4
 
4.8%
Other values (2) 4
 
4.8%
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 23
27.7%
0 9
 
10.8%
3 9
 
10.8%
6 8
 
9.6%
2 6
 
7.2%
5 6
 
7.2%
9 5
 
6.0%
7 5
 
6.0%
8 4
 
4.8%
, 4
 
4.8%
Other values (2) 4
 
4.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 14
Text

MISSING 

Distinct27
Distinct (%)81.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:17.911090image/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

Unique25 ?
Unique (%)75.8%

Sample

1st row상무1동
2nd row12,258
3rd row24,194
4th row157
5th row11
ValueCountFrequency (%)
0 6
 
18.2%
152 2
 
6.1%
1 1
 
3.0%
312 1
 
3.0%
24,177 1
 
3.0%
12,266 1
 
3.0%
5 1
 
3.0%
17 1
 
3.0%
8 1
 
3.0%
9 1
 
3.0%
Other values (17) 17
51.5%
2024-02-10T07:10:18.739722image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 22
25.6%
2 13
15.1%
0 9
10.5%
9 6
 
7.0%
6 6
 
7.0%
5 5
 
5.8%
4 5
 
5.8%
, 4
 
4.7%
8 4
 
4.7%
7 4
 
4.7%
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 22
28.6%
2 13
16.9%
0 9
11.7%
9 6
 
7.8%
6 6
 
7.8%
5 5
 
6.5%
4 5
 
6.5%
8 4
 
5.2%
7 4
 
5.2%
3 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 22
26.5%
2 13
15.7%
0 9
10.8%
9 6
 
7.2%
6 6
 
7.2%
5 5
 
6.0%
4 5
 
6.0%
, 4
 
4.8%
8 4
 
4.8%
7 4
 
4.8%
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 22
26.5%
2 13
15.7%
0 9
10.8%
9 6
 
7.2%
6 6
 
7.2%
5 5
 
6.0%
4 5
 
6.0%
, 4
 
4.8%
8 4
 
4.8%
7 4
 
4.8%
Other values (2) 5
 
6.0%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 15
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:19.255783image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.5757576
Min length1

Characters and Unicode

Total characters85
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 row상무2동
2nd row12,995
3rd row22,926
4th row103
5th row8
ValueCountFrequency (%)
0 5
 
15.2%
1 3
 
9.1%
8 2
 
6.1%
80 2
 
6.1%
115 1
 
3.0%
103 1
 
3.0%
172 1
 
3.0%
22,844 1
 
3.0%
12,992 1
 
3.0%
82 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T07:10:20.210054image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16
18.8%
2 14
16.5%
0 12
14.1%
8 7
8.2%
3 7
8.2%
6 6
 
7.1%
9 6
 
7.1%
, 4
 
4.7%
4 4
 
4.7%
5 3
 
3.5%
Other values (5) 6
 
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 76
89.4%
Other Punctuation 4
 
4.7%
Other Letter 3
 
3.5%
Dash Punctuation 2
 
2.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
21.1%
2 14
18.4%
0 12
15.8%
8 7
9.2%
3 7
9.2%
6 6
 
7.9%
9 6
 
7.9%
4 4
 
5.3%
5 3
 
3.9%
7 1
 
1.3%
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 82
96.5%
Hangul 3
 
3.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
19.5%
2 14
17.1%
0 12
14.6%
8 7
8.5%
3 7
8.5%
6 6
 
7.3%
9 6
 
7.3%
, 4
 
4.9%
4 4
 
4.9%
5 3
 
3.7%
Other values (2) 3
 
3.7%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82
96.5%
Hangul 3
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
19.5%
2 14
17.1%
0 12
14.6%
8 7
8.5%
3 7
8.5%
6 6
 
7.3%
9 6
 
7.3%
, 4
 
4.9%
4 4
 
4.9%
5 3
 
3.7%
Other values (2) 3
 
3.7%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 16
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:20.595197image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3030303
Min length1

Characters and Unicode

Total characters76
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 row화정1동
2nd row8,707
3rd row15,673
4th row45
5th row6
ValueCountFrequency (%)
0 7
21.2%
5 2
 
6.1%
45 2
 
6.1%
91 1
 
3.0%
15,673 1
 
3.0%
6 1
 
3.0%
8,755 1
 
3.0%
61 1
 
3.0%
48 1
 
3.0%
3 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T07:10:21.606727image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 12
15.8%
1 12
15.8%
0 10
13.2%
8 8
10.5%
7 7
9.2%
4 6
7.9%
3 5
6.6%
2 4
 
5.3%
, 4
 
5.3%
6 3
 
3.9%
Other values (4) 5
6.6%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69
90.8%
Other Punctuation 4
 
5.3%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 12
17.4%
1 12
17.4%
0 10
14.5%
8 8
11.6%
7 7
10.1%
4 6
8.7%
3 5
7.2%
2 4
 
5.8%
6 3
 
4.3%
9 2
 
2.9%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
5 12
16.4%
1 12
16.4%
0 10
13.7%
8 8
11.0%
7 7
9.6%
4 6
8.2%
3 5
6.8%
2 4
 
5.5%
, 4
 
5.5%
6 3
 
4.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 12
16.4%
1 12
16.4%
0 10
13.7%
8 8
11.0%
7 7
9.6%
4 6
8.2%
3 5
6.8%
2 4
 
5.5%
, 4
 
5.5%
6 3
 
4.1%
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
2024-02-10T07:10:22.037747image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3030303
Min length1

Characters and Unicode

Total characters76
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 row7,920
3rd row19,930
4th row29
5th row15
ValueCountFrequency (%)
0 7
21.2%
15 2
 
6.1%
98 1
 
3.0%
19,917 1
 
3.0%
7,924 1
 
3.0%
1 1
 
3.0%
13 1
 
3.0%
4 1
 
3.0%
10 1
 
3.0%
49 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:10:22.799569image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11
14.5%
1 11
14.5%
9 11
14.5%
8 7
9.2%
2 7
9.2%
4 6
7.9%
7 5
6.6%
5 4
 
5.3%
, 4
 
5.3%
6 3
 
3.9%
Other values (5) 7
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
88.2%
Other Punctuation 4
 
5.3%
Other Letter 3
 
3.9%
Dash Punctuation 2
 
2.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11
16.4%
1 11
16.4%
9 11
16.4%
8 7
10.4%
2 7
10.4%
4 6
9.0%
7 5
7.5%
5 4
 
6.0%
6 3
 
4.5%
3 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 73
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11
15.1%
1 11
15.1%
9 11
15.1%
8 7
9.6%
2 7
9.6%
4 6
8.2%
7 5
6.8%
5 4
 
5.5%
, 4
 
5.5%
6 3
 
4.1%
Other values (2) 4
 
5.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11
15.1%
1 11
15.1%
9 11
15.1%
8 7
9.6%
2 7
9.6%
4 6
8.2%
7 5
6.8%
5 4
 
5.5%
, 4
 
5.5%
6 3
 
4.1%
Other values (2) 4
 
5.5%
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
2024-02-10T07:10:23.225558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.1212121
Min length1

Characters and Unicode

Total characters70
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

Unique17 ?
Unique (%)51.5%

Sample

1st row화정3동
2nd row4,461
3rd row9,442
4th row30
5th row11
ValueCountFrequency (%)
0 8
24.2%
11 3
 
9.1%
30 2
 
6.1%
27 2
 
6.1%
4,461 2
 
6.1%
46 1
 
3.0%
1 1
 
3.0%
3 1
 
3.0%
25 1
 
3.0%
28 1
 
3.0%
Other values (11) 11
33.3%
2024-02-10T07:10:24.220624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12
17.1%
1 11
15.7%
4 9
12.9%
2 9
12.9%
3 8
11.4%
, 4
 
5.7%
6 4
 
5.7%
9 3
 
4.3%
8 3
 
4.3%
7 2
 
2.9%
Other values (5) 5
7.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
88.6%
Other Punctuation 4
 
5.7%
Other Letter 3
 
4.3%
Dash Punctuation 1
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
19.4%
1 11
17.7%
4 9
14.5%
2 9
14.5%
3 8
12.9%
6 4
 
6.5%
9 3
 
4.8%
8 3
 
4.8%
7 2
 
3.2%
5 1
 
1.6%
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 67
95.7%
Hangul 3
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
17.9%
1 11
16.4%
4 9
13.4%
2 9
13.4%
3 8
11.9%
, 4
 
6.0%
6 4
 
6.0%
9 3
 
4.5%
8 3
 
4.5%
7 2
 
3.0%
Other values (2) 2
 
3.0%
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 12
17.9%
1 11
16.4%
4 9
13.4%
2 9
13.4%
3 8
11.9%
, 4
 
6.0%
6 4
 
6.0%
9 3
 
4.5%
8 3
 
4.5%
7 2
 
3.0%
Other values (2) 2
 
3.0%
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
2024-02-10T07:10:24.677647image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3030303
Min length1

Characters and Unicode

Total characters76
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

Unique21 ?
Unique (%)63.6%

Sample

1st row화정4동
2nd row8,148
3rd row19,388
4th row23
5th row16
ValueCountFrequency (%)
0 7
21.2%
23 3
 
9.1%
15 2
 
6.1%
28 1
 
3.0%
19,388 1
 
3.0%
64 1
 
3.0%
8,171 1
 
3.0%
57 1
 
3.0%
1 1
 
3.0%
8,148 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:10:25.558385image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
15.8%
0 9
11.8%
8 9
11.8%
2 7
9.2%
4 7
9.2%
3 5
6.6%
5 5
6.6%
6 5
6.6%
7 5
6.6%
9 5
6.6%
Other values (4) 7
9.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69
90.8%
Other Punctuation 4
 
5.3%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
17.4%
0 9
13.0%
8 9
13.0%
2 7
10.1%
4 7
10.1%
3 5
7.2%
5 5
7.2%
6 5
7.2%
7 5
7.2%
9 5
7.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 73
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
16.4%
0 9
12.3%
8 9
12.3%
2 7
9.6%
4 7
9.6%
3 5
6.8%
5 5
6.8%
6 5
6.8%
7 5
6.8%
9 5
6.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
16.4%
0 9
12.3%
8 9
12.3%
2 7
9.6%
4 7
9.6%
3 5
6.8%
5 5
6.8%
6 5
6.8%
7 5
6.8%
9 5
6.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 20
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:25.991686image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length1.9393939
Min length1

Characters and Unicode

Total characters64
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

Unique19 ?
Unique (%)57.6%

Sample

1st row서창동
2nd row2,655
3rd row5,677
4th row8
5th row4
ValueCountFrequency (%)
0 8
24.2%
8 2
 
6.1%
23 2
 
6.1%
1 2
 
6.1%
4 2
 
6.1%
48 1
 
3.0%
2,655 1
 
3.0%
5,677 1
 
3.0%
25 1
 
3.0%
2,654 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T07:10:26.853100image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 10
15.6%
2 9
14.1%
0 8
12.5%
1 7
10.9%
4 6
9.4%
6 6
9.4%
8 4
 
6.2%
, 4
 
6.2%
3 3
 
4.7%
7 2
 
3.1%
Other values (5) 5
7.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 56
87.5%
Other Punctuation 4
 
6.2%
Other Letter 3
 
4.7%
Dash Punctuation 1
 
1.6%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 10
17.9%
2 9
16.1%
0 8
14.3%
1 7
12.5%
4 6
10.7%
6 6
10.7%
8 4
 
7.1%
3 3
 
5.4%
7 2
 
3.6%
9 1
 
1.8%
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 61
95.3%
Hangul 3
 
4.7%

Most frequent character per script

Common
ValueCountFrequency (%)
5 10
16.4%
2 9
14.8%
0 8
13.1%
1 7
11.5%
4 6
9.8%
6 6
9.8%
8 4
 
6.6%
, 4
 
6.6%
3 3
 
4.9%
7 2
 
3.3%
Other values (2) 2
 
3.3%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 61
95.3%
Hangul 3
 
4.7%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 10
16.4%
2 9
14.8%
0 8
13.1%
1 7
11.5%
4 6
9.8%
6 6
9.8%
8 4
 
6.6%
, 4
 
6.6%
3 3
 
4.9%
7 2
 
3.3%
Other values (2) 2
 
3.3%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 21
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:27.240009image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3030303
Min length1

Characters and Unicode

Total characters76
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 row금호1동
2nd row8,926
3rd row19,798
4th row31
5th row4
ValueCountFrequency (%)
0 7
21.2%
4 3
 
9.1%
31 2
 
6.1%
55 1
 
3.0%
88 1
 
3.0%
19,730 1
 
3.0%
8,909 1
 
3.0%
1 1
 
3.0%
68 1
 
3.0%
17 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T07:10:28.105633image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
15.8%
0 11
14.5%
9 9
11.8%
8 7
9.2%
4 6
7.9%
3 6
7.9%
5 5
6.6%
7 4
 
5.3%
, 4
 
5.3%
6 4
 
5.3%
Other values (5) 8
10.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 66
86.8%
Other Punctuation 4
 
5.3%
Dash Punctuation 3
 
3.9%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
18.2%
0 11
16.7%
9 9
13.6%
8 7
10.6%
4 6
9.1%
3 6
9.1%
5 5
7.6%
7 4
 
6.1%
6 4
 
6.1%
2 2
 
3.0%
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 73
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
16.4%
0 11
15.1%
9 9
12.3%
8 7
9.6%
4 6
8.2%
3 6
8.2%
5 5
6.8%
7 4
 
5.5%
, 4
 
5.5%
6 4
 
5.5%
Other values (2) 5
6.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 73
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 12
16.4%
0 11
15.1%
9 9
12.3%
8 7
9.6%
4 6
8.2%
3 6
8.2%
5 5
6.8%
7 4
 
5.5%
, 4
 
5.5%
6 4
 
5.5%
Other values (2) 5
6.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 22
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:28.532249image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4
Mean length2.3333333
Min length1

Characters and Unicode

Total characters77
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 row금호2동
2nd row10,492
3rd row27,361
4th row21
5th row10
ValueCountFrequency (%)
0 8
24.2%
21 2
 
6.1%
106 1
 
3.0%
200 1
 
3.0%
27,323 1
 
3.0%
10,500 1
 
3.0%
38 1
 
3.0%
8 1
 
3.0%
9 1
 
3.0%
75 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T07:10:29.359674image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 17
22.1%
1 10
13.0%
2 9
11.7%
5 7
9.1%
6 7
9.1%
3 5
 
6.5%
, 4
 
5.2%
7 4
 
5.2%
8 4
 
5.2%
4 3
 
3.9%
Other values (5) 7
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 69
89.6%
Other Punctuation 4
 
5.2%
Other Letter 3
 
3.9%
Dash Punctuation 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 17
24.6%
1 10
14.5%
2 9
13.0%
5 7
10.1%
6 7
10.1%
3 5
 
7.2%
7 4
 
5.8%
8 4
 
5.8%
4 3
 
4.3%
9 3
 
4.3%
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 74
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 17
23.0%
1 10
13.5%
2 9
12.2%
5 7
9.5%
6 7
9.5%
3 5
 
6.8%
, 4
 
5.4%
7 4
 
5.4%
8 4
 
5.4%
4 3
 
4.1%
Other values (2) 4
 
5.4%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 74
96.1%
Hangul 3
 
3.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 17
23.0%
1 10
13.5%
2 9
12.2%
5 7
9.5%
6 7
9.5%
3 5
 
6.8%
, 4
 
5.4%
7 4
 
5.4%
8 4
 
5.4%
4 3
 
4.1%
Other values (2) 4
 
5.4%
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
2024-02-10T07:10:29.819076image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length3
Mean length2.5757576
Min length1

Characters and Unicode

Total characters85
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

Unique24 ?
Unique (%)72.7%

Sample

1st row풍암동
2nd row15,086
3rd row35,307
4th row78
5th row22
ValueCountFrequency (%)
0 5
 
15.2%
2 2
 
6.1%
22 2
 
6.1%
171 1
 
3.0%
78 1
 
3.0%
35,307 1
 
3.0%
35,252 1
 
3.0%
15,091 1
 
3.0%
55 1
 
3.0%
5 1
 
3.0%
Other values (17) 17
51.5%
2024-02-10T07:10:30.784486image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 18
21.2%
0 14
16.5%
2 11
12.9%
5 11
12.9%
3 9
10.6%
8 4
 
4.7%
, 4
 
4.7%
6 4
 
4.7%
7 3
 
3.5%
9 3
 
3.5%
Other values (4) 4
 
4.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 77
90.6%
Other Punctuation 4
 
4.7%
Other Letter 3
 
3.5%
Dash Punctuation 1
 
1.2%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 18
23.4%
0 14
18.2%
2 11
14.3%
5 11
14.3%
3 9
11.7%
8 4
 
5.2%
6 4
 
5.2%
7 3
 
3.9%
9 3
 
3.9%
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 82
96.5%
Hangul 3
 
3.5%

Most frequent character per script

Common
ValueCountFrequency (%)
1 18
22.0%
0 14
17.1%
2 11
13.4%
5 11
13.4%
3 9
11.0%
8 4
 
4.9%
, 4
 
4.9%
6 4
 
4.9%
7 3
 
3.7%
9 3
 
3.7%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 82
96.5%
Hangul 3
 
3.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 18
22.0%
0 14
17.1%
2 11
13.4%
5 11
13.4%
3 9
11.0%
8 4
 
4.9%
, 4
 
4.9%
6 4
 
4.9%
7 3
 
3.7%
9 3
 
3.7%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 24
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T07:10:31.165412image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.1818182
Min length1

Characters and Unicode

Total characters72
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

Unique24 ?
Unique (%)72.7%

Sample

1st row동천동
2nd row6,347
3rd row15,686
4th row16
5th row6
ValueCountFrequency (%)
0 7
21.2%
8 2
 
6.1%
132 1
 
3.0%
15,635 1
 
3.0%
6,340 1
 
3.0%
1 1
 
3.0%
51 1
 
3.0%
7 1
 
3.0%
41 1
 
3.0%
56 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T07:10:32.105346image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
6 12
16.7%
1 10
13.9%
0 8
11.1%
3 8
11.1%
5 7
9.7%
4 5
6.9%
8 4
 
5.6%
, 4
 
5.6%
7 4
 
5.6%
2 3
 
4.2%
Other values (4) 7
9.7%

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 (%)
6 12
19.4%
1 10
16.1%
0 8
12.9%
3 8
12.9%
5 7
11.3%
4 5
8.1%
8 4
 
6.5%
7 4
 
6.5%
2 3
 
4.8%
9 1
 
1.6%
Other Letter
ValueCountFrequency (%)
2
66.7%
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 (%)
6 12
17.4%
1 10
14.5%
0 8
11.6%
3 8
11.6%
5 7
10.1%
4 5
7.2%
8 4
 
5.8%
, 4
 
5.8%
7 4
 
5.8%
2 3
 
4.3%
Other values (2) 4
 
5.8%
Hangul
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
6 12
17.4%
1 10
14.5%
0 8
11.6%
3 8
11.6%
5 7
10.1%
4 5
7.2%
8 4
 
5.8%
, 4
 
5.8%
7 4
 
5.8%
2 3
 
4.3%
Other values (2) 4
 
5.8%
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>2023.06.01<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1<NA>작성기준 :<NA>2023.05 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>01,9612,1356,5732,8613,9824,852<NA>13,64112,25812,9958,7077,9204,4618,1482,6558,92610,49215,0866,347
4<NA>전월말인구수<NA><NA><NA>03,3804,35511,4304,5767,47010,589<NA>29,39224,19422,92615,67319,9309,44219,3885,67719,79827,36135,30715,686
5<NA>전월말거주불명자수<NA><NA><NA>0253043446322<NA>5815710345293023831217816
6<NA>전월말재외국민등록자수<NA><NA><NA>03160133<NA>1811861511164410226
7<NA>증 가 요 인전 입<NA>02024226596182<NA>316299266251171692065511116628286
8<NA><NA><NA>남자<NA>01211110323738<NA>155161135123823810726528015247
9<NA><NA><NA>여자<NA>0813116272444<NA>16113813112889319929598613039
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>0000000<NA>001000100000
26<NA><NA>국외<NA><NA>0000000<NA>000000000000
27<NA><NA>기타<NA><NA>0000000<NA>000000000000
28<NA>세대수증감<NA><NA><NA>0-16249101-7<NA>98-3484023-1-1785-7
29<NA>인구수증감<NA><NA><NA>0-23-17108-3-29-13<NA>-15-17-8261-13-11575-68-38-55-51
30<NA>거주불명자수증감<NA><NA><NA>0100-4-1-2<NA>-1-510-1000-102-1
31<NA>금월말세대수<NA><NA><NA>01,9452,1376,6222,8713,9834,845<NA>13,65012,26612,9928,7557,9244,4618,1712,6548,90910,50015,0916,340
32<NA>금월말인구수<NA><NA><NA>03,3574,33811,5384,5737,44110,576<NA>29,37724,17722,84415,73419,9179,43119,4455,68219,73027,32335,25215,635
33<NA>금월말거주불명자수<NA><NA><NA>0263043406220<NA>5715210445283023830218015
34<NA>금월말재외국민등록자수<NA><NA><NA>03160123<NA>1712851511154411228

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