Overview

Dataset statistics

Number of variables20
Number of observations35
Missing cells193
Missing cells (%)27.6%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory5.6 KiB
Average record size in memory164.8 B

Variable types

Unsupported1
Text18
DateTime1

Dataset

Description2023-06-23
Author주민등록인구통계
URLhttps://bigdata.gwangju.go.kr/usr/dataSet/getDataDetailView.rd?dataSetUncd=DS000201926

Alerts

Unnamed: 12 has constant value ""Constant
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: 5 has 2 (5.7%) 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: 0 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2024-02-10 10:00:03.181383
Analysis finished2024-02-10 10:00:13.710065
Duration10.53 seconds
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
2024-02-10T10:00:14.020100image/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%
2024-02-10T10:00:15.036420image/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
2024-02-10T10:00:15.653970image/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%
2024-02-10T10:00:16.546099image/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
2024-02-10T10:00:16.919488image/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 동구
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%
동구 1
7.1%
2023.05 1
7.1%
현재 1
7.1%
2024-02-10T10:00:17.786957image/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
2024-02-10T10:00:18.242088image/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%
2024-02-10T10:00:19.031944image/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
Text

MISSING 

Distinct29
Distinct (%)87.9%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:19.578816image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3.030303
Min length1

Characters and Unicode

Total characters100
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

Unique26 ?
Unique (%)78.8%

Sample

1st row합 계
2nd row54,667
3rd row106,443
4th row703
5th row107
ValueCountFrequency (%)
0 3
 
8.8%
1 2
 
5.9%
283 2
 
5.9%
578 1
 
2.9%
617 1
 
2.9%
709 1
 
2.9%
106,595 1
 
2.9%
54,805 1
 
2.9%
6 1
 
2.9%
152 1
 
2.9%
Other values (20) 20
58.8%
2024-02-10T10:00:21.279243image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 16
16.0%
0 13
13.0%
6 13
13.0%
5 10
10.0%
4 10
10.0%
3 8
8.0%
8 7
7.0%
7 7
7.0%
, 6
 
6.0%
2 3
 
3.0%
Other values (4) 7
7.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 90
90.0%
Other Punctuation 6
 
6.0%
Space Separator 2
 
2.0%
Other Letter 2
 
2.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 16
17.8%
0 13
14.4%
6 13
14.4%
5 10
11.1%
4 10
11.1%
3 8
8.9%
8 7
7.8%
7 7
7.8%
2 3
 
3.3%
9 3
 
3.3%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 98
98.0%
Hangul 2
 
2.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 16
16.3%
0 13
13.3%
6 13
13.3%
5 10
10.2%
4 10
10.2%
3 8
8.2%
8 7
7.1%
7 7
7.1%
, 6
 
6.1%
2 3
 
3.1%
Other values (2) 5
 
5.1%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 98
98.0%
Hangul 2
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 16
16.3%
0 13
13.3%
6 13
13.3%
5 10
10.2%
4 10
10.2%
3 8
8.2%
8 7
7.1%
7 7
7.1%
, 6
 
6.1%
2 3
 
3.1%
Other values (2) 5
 
5.1%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 6
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:21.799596image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.0909091
Min length1

Characters and Unicode

Total characters69
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

Unique19 ?
Unique (%)57.6%

Sample

1st row충장동
2nd row4,137
3rd row5,260
4th row36
5th row6
ValueCountFrequency (%)
0 7
21.2%
56 3
 
9.1%
6 2
 
6.1%
1 2
 
6.1%
28 1
 
3.0%
36 1
 
3.0%
49 1
 
3.0%
5,316 1
 
3.0%
4,180 1
 
3.0%
43 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T10:00:23.060624image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12
17.4%
6 10
14.5%
1 9
13.0%
5 7
10.1%
3 7
10.1%
2 5
7.2%
, 4
 
5.8%
4 4
 
5.8%
7 3
 
4.3%
8 3
 
4.3%
Other values (4) 5
7.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
89.9%
Other Punctuation 4
 
5.8%
Other Letter 3
 
4.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
19.4%
6 10
16.1%
1 9
14.5%
5 7
11.3%
3 7
11.3%
2 5
8.1%
4 4
 
6.5%
7 3
 
4.8%
8 3
 
4.8%
9 2
 
3.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
18.2%
6 10
15.2%
1 9
13.6%
5 7
10.6%
3 7
10.6%
2 5
7.6%
, 4
 
6.1%
4 4
 
6.1%
7 3
 
4.5%
8 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 12
18.2%
6 10
15.2%
1 9
13.6%
5 7
10.6%
3 7
10.6%
2 5
7.6%
, 4
 
6.1%
4 4
 
6.1%
7 3
 
4.5%
8 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 7
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:23.687147image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.0606061
Min length1

Characters and Unicode

Total characters68
Distinct characters13
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

Unique19 ?
Unique (%)57.6%

Sample

1st row동명동
2nd row2,431
3rd row3,707
4th row115
5th row7
ValueCountFrequency (%)
0 6
18.2%
1 2
 
6.1%
10 2
 
6.1%
7 2
 
6.1%
26 2
 
6.1%
2 1
 
3.0%
63 1
 
3.0%
16 1
 
3.0%
3,716 1
 
3.0%
2,441 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:00:24.677319image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
20.6%
0 10
14.7%
2 8
11.8%
3 8
11.8%
7 6
8.8%
6 5
 
7.4%
, 4
 
5.9%
4 3
 
4.4%
5 3
 
4.4%
8 2
 
2.9%
Other values (3) 5
 
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 61
89.7%
Other Punctuation 4
 
5.9%
Other Letter 3
 
4.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 14
23.0%
0 10
16.4%
2 8
13.1%
3 8
13.1%
7 6
9.8%
6 5
 
8.2%
4 3
 
4.9%
5 3
 
4.9%
8 2
 
3.3%
9 2
 
3.3%
Other Letter
ValueCountFrequency (%)
2
66.7%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 65
95.6%
Hangul 3
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
21.5%
0 10
15.4%
2 8
12.3%
3 8
12.3%
7 6
9.2%
6 5
 
7.7%
, 4
 
6.2%
4 3
 
4.6%
5 3
 
4.6%
8 2
 
3.1%
Hangul
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65
95.6%
Hangul 3
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 14
21.5%
0 10
15.4%
2 8
12.3%
3 8
12.3%
7 6
9.2%
6 5
 
7.7%
, 4
 
6.2%
4 3
 
4.6%
5 3
 
4.6%
8 2
 
3.1%
Hangul
ValueCountFrequency (%)
2
66.7%
1
33.3%

Unnamed: 8
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:25.172492image/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 row계림1동
2nd row5,819
3rd row10,567
4th row77
5th row12
ValueCountFrequency (%)
0 7
21.2%
12 2
 
6.1%
77 2
 
6.1%
29 2
 
6.1%
53 1
 
3.0%
10,567 1
 
3.0%
121 1
 
3.0%
5,811 1
 
3.0%
6 1
 
3.0%
8 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:00:26.028084image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 14
18.7%
0 11
14.7%
7 9
12.0%
5 9
12.0%
2 7
9.3%
6 5
 
6.7%
9 4
 
5.3%
, 4
 
5.3%
8 3
 
4.0%
3 3
 
4.0%
Other values (5) 6
8.0%

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 14
21.2%
0 11
16.7%
7 9
13.6%
5 9
13.6%
2 7
10.6%
6 5
 
7.6%
9 4
 
6.1%
8 3
 
4.5%
3 3
 
4.5%
4 1
 
1.5%
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 72
96.0%
Hangul 3
 
4.0%

Most frequent character per script

Common
ValueCountFrequency (%)
1 14
19.4%
0 11
15.3%
7 9
12.5%
5 9
12.5%
2 7
9.7%
6 5
 
6.9%
9 4
 
5.6%
, 4
 
5.6%
8 3
 
4.2%
3 3
 
4.2%
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 11
15.3%
7 9
12.5%
5 9
12.5%
2 7
9.7%
6 5
 
6.9%
9 4
 
5.6%
, 4
 
5.6%
8 3
 
4.2%
3 3
 
4.2%
Other values (2) 3
 
4.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 9
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:26.400733image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
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

Unique20 ?
Unique (%)60.6%

Sample

1st row계림2동
2nd row5,501
3rd row13,070
4th row28
5th row12
ValueCountFrequency (%)
0 7
21.2%
84 2
 
6.1%
39 2
 
6.1%
12 2
 
6.1%
5 1
 
3.0%
83 1
 
3.0%
13,093 1
 
3.0%
5,527 1
 
3.0%
1 1
 
3.0%
23 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:00:27.157304image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13
16.9%
0 12
15.6%
3 9
11.7%
2 8
10.4%
8 6
7.8%
5 6
7.8%
7 5
 
6.5%
4 4
 
5.2%
9 4
 
5.2%
, 4
 
5.2%
Other values (5) 6
7.8%

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 (%)
1 13
18.8%
0 12
17.4%
3 9
13.0%
2 8
11.6%
8 6
8.7%
5 6
8.7%
7 5
 
7.2%
4 4
 
5.8%
9 4
 
5.8%
6 2
 
2.9%
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 74
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
1 13
17.6%
0 12
16.2%
3 9
12.2%
2 8
10.8%
8 6
8.1%
5 6
8.1%
7 5
 
6.8%
4 4
 
5.4%
9 4
 
5.4%
, 4
 
5.4%
Other values (2) 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 13
17.6%
0 12
16.2%
3 9
12.2%
2 8
10.8%
8 6
8.1%
5 6
8.1%
7 5
 
6.8%
4 4
 
5.4%
9 4
 
5.4%
, 4
 
5.4%
Other values (2) 3
 
4.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 10
Text

MISSING 

Distinct25
Distinct (%)73.5%
Missing1
Missing (%)2.9%
Memory size412.0 B
2024-02-10T10:00:27.609644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
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

Unique22 ?
Unique (%)64.7%

Sample

1st row출력일자 :
2nd row산수1동
3rd row4,372
4th row8,151
5th row63
ValueCountFrequency (%)
0 7
20.0%
6 3
 
8.6%
38 2
 
5.7%
94 1
 
2.9%
2 1
 
2.9%
8,165 1
 
2.9%
4,383 1
 
2.9%
1 1
 
2.9%
14 1
 
2.9%
11 1
 
2.9%
Other values (16) 16
45.7%
2024-02-10T10:00:28.389342image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 10
13.2%
0 9
11.8%
3 8
10.5%
4 8
10.5%
6 8
10.5%
8 6
7.9%
5 5
6.6%
2 5
6.6%
, 4
 
5.3%
7 2
 
2.6%
Other values (11) 11
14.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 62
81.6%
Other Letter 7
 
9.2%
Other Punctuation 5
 
6.6%
Space Separator 1
 
1.3%
Dash Punctuation 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 10
16.1%
0 9
14.5%
3 8
12.9%
4 8
12.9%
6 8
12.9%
8 6
9.7%
5 5
8.1%
2 5
8.1%
7 2
 
3.2%
9 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%
Space Separator
ValueCountFrequency (%)
1
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 69
90.8%
Hangul 7
 
9.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 10
14.5%
0 9
13.0%
3 8
11.6%
4 8
11.6%
6 8
11.6%
8 6
8.7%
5 5
7.2%
2 5
7.2%
, 4
 
5.8%
7 2
 
2.9%
Other values (4) 4
 
5.8%
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 (%)
1 10
14.5%
0 9
13.0%
3 8
11.6%
4 8
11.6%
6 8
11.6%
8 6
8.7%
5 5
7.2%
2 5
7.2%
, 4
 
5.8%
7 2
 
2.9%
Other values (4) 4
 
5.8%
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 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:28.783347image/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

Unique19 ?
Unique (%)57.6%

Sample

1st row산수2동
2nd row4,715
3rd row9,996
4th row26
5th row6
ValueCountFrequency (%)
0 6
18.2%
26 3
 
9.1%
1 3
 
9.1%
6 3
 
9.1%
54 1
 
3.0%
106 1
 
3.0%
4,714 1
 
3.0%
39 1
 
3.0%
34 1
 
3.0%
50 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T10:00:29.663424image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9
12.9%
1 9
12.9%
6 8
11.4%
2 8
11.4%
4 7
10.0%
5 6
8.6%
9 6
8.6%
, 4
5.7%
7 4
5.7%
3 3
 
4.3%
Other values (5) 6
8.6%

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 9
14.8%
1 9
14.8%
6 8
13.1%
2 8
13.1%
4 7
11.5%
5 6
9.8%
9 6
9.8%
7 4
6.6%
3 3
 
4.9%
8 1
 
1.6%
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 67
95.7%
Hangul 3
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
0 9
13.4%
1 9
13.4%
6 8
11.9%
2 8
11.9%
4 7
10.4%
5 6
9.0%
9 6
9.0%
, 4
6.0%
7 4
6.0%
3 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 67
95.7%
Hangul 3
 
4.3%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9
13.4%
1 9
13.4%
6 8
11.9%
2 8
11.9%
4 7
10.4%
5 6
9.0%
9 6
9.0%
, 4
6.0%
7 4
6.0%
3 3
 
4.5%
Other values (2) 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
Minimum2023-06-02 00:00:00
Maximum2023-06-02 00:00:00
2024-02-10T10:00:29.979555image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-10T10:00:30.244103image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Unnamed: 13
Text

MISSING 

Distinct20
Distinct (%)60.6%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:30.539562image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.030303
Min length1

Characters and Unicode

Total characters67
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 row지산1동
2nd row2,433
3rd row4,122
4th row42
5th row3
ValueCountFrequency (%)
0 9
27.3%
3 3
 
9.1%
42 2
 
6.1%
19 2
 
6.1%
4,122 2
 
6.1%
지산1동 1
 
3.0%
67 1
 
3.0%
36 1
 
3.0%
31 1
 
3.0%
15 1
 
3.0%
Other values (10) 10
30.3%
2024-02-10T10:00:31.423426image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 13
19.4%
0 10
14.9%
3 10
14.9%
1 8
11.9%
4 7
10.4%
6 5
 
7.5%
, 4
 
6.0%
9 2
 
3.0%
7 2
 
3.0%
5 2
 
3.0%
Other values (4) 4
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
88.1%
Other Punctuation 4
 
6.0%
Other Letter 3
 
4.5%
Dash Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 13
22.0%
0 10
16.9%
3 10
16.9%
1 8
13.6%
4 7
11.9%
6 5
 
8.5%
9 2
 
3.4%
7 2
 
3.4%
5 2
 
3.4%
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 64
95.5%
Hangul 3
 
4.5%

Most frequent character per script

Common
ValueCountFrequency (%)
2 13
20.3%
0 10
15.6%
3 10
15.6%
1 8
12.5%
4 7
10.9%
6 5
 
7.8%
, 4
 
6.2%
9 2
 
3.1%
7 2
 
3.1%
5 2
 
3.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 64
95.5%
Hangul 3
 
4.5%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 13
20.3%
0 10
15.6%
3 10
15.6%
1 8
12.5%
4 7
10.9%
6 5
 
7.8%
, 4
 
6.2%
9 2
 
3.1%
7 2
 
3.1%
5 2
 
3.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 14
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:31.887365image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length1.969697
Min length1

Characters and Unicode

Total characters65
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 row지산2동
2nd row2,379
3rd row4,336
4th row10
5th row6
ValueCountFrequency (%)
0 7
21.2%
4 2
 
6.1%
1 2
 
6.1%
10 2
 
6.1%
23 1
 
3.0%
47 1
 
3.0%
9 1
 
3.0%
4,340 1
 
3.0%
2,383 1
 
3.0%
2 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:00:32.780996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10
15.4%
1 10
15.4%
3 10
15.4%
2 8
12.3%
4 7
10.8%
, 4
 
6.2%
7 3
 
4.6%
9 3
 
4.6%
8 3
 
4.6%
6 2
 
3.1%
Other values (5) 5
7.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 57
87.7%
Other Punctuation 4
 
6.2%
Other Letter 3
 
4.6%
Dash Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 10
17.5%
1 10
17.5%
3 10
17.5%
2 8
14.0%
4 7
12.3%
7 3
 
5.3%
9 3
 
5.3%
8 3
 
5.3%
6 2
 
3.5%
5 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 62
95.4%
Hangul 3
 
4.6%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10
16.1%
1 10
16.1%
3 10
16.1%
2 8
12.9%
4 7
11.3%
, 4
 
6.5%
7 3
 
4.8%
9 3
 
4.8%
8 3
 
4.8%
6 2
 
3.2%
Other values (2) 2
 
3.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 62
95.4%
Hangul 3
 
4.6%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10
16.1%
1 10
16.1%
3 10
16.1%
2 8
12.9%
4 7
11.3%
, 4
 
6.5%
7 3
 
4.8%
9 3
 
4.8%
8 3
 
4.8%
6 2
 
3.2%
Other values (2) 2
 
3.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 15
Text

MISSING 

Distinct22
Distinct (%)66.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:33.075253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2
Min length1

Characters and Unicode

Total characters66
Distinct characters13
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 row서남동
2nd row2,243
3rd row2,953
4th row53
5th row3
ValueCountFrequency (%)
0 8
24.2%
3 3
 
9.1%
20 2
 
6.1%
21 2
 
6.1%
13 1
 
3.0%
53 1
 
3.0%
56 1
 
3.0%
2,956 1
 
3.0%
2,255 1
 
3.0%
1 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T10:00:34.115630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14
21.2%
0 10
15.2%
1 9
13.6%
5 9
13.6%
3 8
12.1%
, 4
 
6.1%
9 3
 
4.5%
7 2
 
3.0%
4 2
 
3.0%
6 2
 
3.0%
Other values (3) 3
 
4.5%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
89.4%
Other Punctuation 4
 
6.1%
Other Letter 3
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 14
23.7%
0 10
16.9%
1 9
15.3%
5 9
15.3%
3 8
13.6%
9 3
 
5.1%
7 2
 
3.4%
4 2
 
3.4%
6 2
 
3.4%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
22.2%
0 10
15.9%
1 9
14.3%
5 9
14.3%
3 8
12.7%
, 4
 
6.3%
9 3
 
4.8%
7 2
 
3.2%
4 2
 
3.2%
6 2
 
3.2%
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 (%)
2 14
22.2%
0 10
15.9%
1 9
14.3%
5 9
14.3%
3 8
12.7%
, 4
 
6.3%
9 3
 
4.8%
7 2
 
3.2%
4 2
 
3.2%
6 2
 
3.2%
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
2024-02-10T10:00:34.554232image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length2
Mean length1.969697
Min length1

Characters and Unicode

Total characters65
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 row학동
2nd row3,530
3rd row7,409
4th row79
5th row9
ValueCountFrequency (%)
0 6
18.2%
9 2
 
6.1%
4 2
 
6.1%
5 2
 
6.1%
79 2
 
6.1%
31 1
 
3.0%
44 1
 
3.0%
7,404 1
 
3.0%
3,532 1
 
3.0%
2 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:00:35.381238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 12
18.5%
3 11
16.9%
0 10
15.4%
9 6
9.2%
2 5
7.7%
7 4
 
6.2%
, 4
 
6.2%
5 4
 
6.2%
1 3
 
4.6%
8 2
 
3.1%
Other values (4) 4
 
6.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58
89.2%
Other Punctuation 4
 
6.2%
Other Letter 2
 
3.1%
Dash Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 12
20.7%
3 11
19.0%
0 10
17.2%
9 6
10.3%
2 5
8.6%
7 4
 
6.9%
5 4
 
6.9%
1 3
 
5.2%
8 2
 
3.4%
6 1
 
1.7%
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 63
96.9%
Hangul 2
 
3.1%

Most frequent character per script

Common
ValueCountFrequency (%)
4 12
19.0%
3 11
17.5%
0 10
15.9%
9 6
9.5%
2 5
7.9%
7 4
 
6.3%
, 4
 
6.3%
5 4
 
6.3%
1 3
 
4.8%
8 2
 
3.2%
Other values (2) 2
 
3.2%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 63
96.9%
Hangul 2
 
3.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
4 12
19.0%
3 11
17.5%
0 10
15.9%
9 6
9.5%
2 5
7.9%
7 4
 
6.3%
, 4
 
6.3%
5 4
 
6.3%
1 3
 
4.8%
8 2
 
3.2%
Other values (2) 2
 
3.2%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 17
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:35.830190image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
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

Unique19 ?
Unique (%)57.6%

Sample

1st row학운동
2nd row5,192
3rd row11,155
4th row44
5th row22
ValueCountFrequency (%)
0 8
24.2%
22 2
 
6.1%
44 2
 
6.1%
31 2
 
6.1%
25 1
 
3.0%
11,155 1
 
3.0%
46 1
 
3.0%
5,186 1
 
3.0%
23 1
 
3.0%
6 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T10:00:36.672213image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 12
16.7%
3 10
13.9%
2 9
12.5%
0 8
11.1%
4 6
8.3%
5 6
8.3%
6 5
6.9%
8 4
 
5.6%
, 4
 
5.6%
9 2
 
2.8%
Other values (5) 6
8.3%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 63
87.5%
Other Punctuation 4
 
5.6%
Other Letter 3
 
4.2%
Dash Punctuation 2
 
2.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 12
19.0%
3 10
15.9%
2 9
14.3%
0 8
12.7%
4 6
9.5%
5 6
9.5%
6 5
7.9%
8 4
 
6.3%
9 2
 
3.2%
7 1
 
1.6%
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 69
95.8%
Hangul 3
 
4.2%

Most frequent character per script

Common
ValueCountFrequency (%)
1 12
17.4%
3 10
14.5%
2 9
13.0%
0 8
11.6%
4 6
8.7%
5 6
8.7%
6 5
7.2%
8 4
 
5.8%
, 4
 
5.8%
9 2
 
2.9%
Other values (2) 3
 
4.3%
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 12
17.4%
3 10
14.5%
2 9
13.0%
0 8
11.6%
4 6
8.7%
5 6
8.7%
6 5
7.2%
8 4
 
5.8%
, 4
 
5.8%
9 2
 
2.9%
Other values (2) 3
 
4.3%
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
2024-02-10T10:00:37.091868image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.0606061
Min length1

Characters and Unicode

Total characters68
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 row지원1동
2nd row4,231
3rd row9,055
4th row21
5th row8
ValueCountFrequency (%)
0 7
21.2%
8 4
 
12.1%
37 2
 
6.1%
20 2
 
6.1%
15 1
 
3.0%
88 1
 
3.0%
4,245 1
 
3.0%
1 1
 
3.0%
14 1
 
3.0%
4 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T10:00:37.982022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 12
17.6%
4 8
11.8%
3 7
10.3%
2 7
10.3%
1 7
10.3%
8 6
8.8%
5 5
7.4%
, 4
 
5.9%
9 4
 
5.9%
7 2
 
2.9%
Other values (5) 6
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
88.2%
Other Punctuation 4
 
5.9%
Other Letter 3
 
4.4%
Dash Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 12
20.0%
4 8
13.3%
3 7
11.7%
2 7
11.7%
1 7
11.7%
8 6
10.0%
5 5
8.3%
9 4
 
6.7%
7 2
 
3.3%
6 2
 
3.3%
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 65
95.6%
Hangul 3
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
0 12
18.5%
4 8
12.3%
3 7
10.8%
2 7
10.8%
1 7
10.8%
8 6
9.2%
5 5
7.7%
, 4
 
6.2%
9 4
 
6.2%
7 2
 
3.1%
Other values (2) 3
 
4.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 65
95.6%
Hangul 3
 
4.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 12
18.5%
4 8
12.3%
3 7
10.8%
2 7
10.8%
1 7
10.8%
8 6
9.2%
5 5
7.7%
, 4
 
6.2%
9 4
 
6.2%
7 2
 
3.1%
Other values (2) 3
 
4.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 19
Text

MISSING 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:00:38.368984image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.3939394
Min length1

Characters and Unicode

Total characters79
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

Unique23 ?
Unique (%)69.7%

Sample

1st row지원2동
2nd row7,684
3rd row16,662
4th row109
5th row7
ValueCountFrequency (%)
0 7
21.2%
7 3
 
9.1%
65 1
 
3.0%
132 1
 
3.0%
16,770 1
 
3.0%
7,722 1
 
3.0%
1 1
 
3.0%
108 1
 
3.0%
38 1
 
3.0%
63 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T10:00:39.169022image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13
16.5%
1 13
16.5%
7 11
13.9%
6 10
12.7%
2 10
12.7%
3 6
7.6%
8 4
 
5.1%
, 4
 
5.1%
9 2
 
2.5%
5 2
 
2.5%
Other values (4) 4
 
5.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
91.1%
Other Punctuation 4
 
5.1%
Other Letter 3
 
3.8%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13
18.1%
1 13
18.1%
7 11
15.3%
6 10
13.9%
2 10
13.9%
3 6
8.3%
8 4
 
5.6%
9 2
 
2.8%
5 2
 
2.8%
4 1
 
1.4%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 76
96.2%
Hangul 3
 
3.8%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13
17.1%
1 13
17.1%
7 11
14.5%
6 10
13.2%
2 10
13.2%
3 6
7.9%
8 4
 
5.3%
, 4
 
5.3%
9 2
 
2.6%
5 2
 
2.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 76
96.2%
Hangul 3
 
3.8%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13
17.1%
1 13
17.1%
7 11
14.5%
6 10
13.2%
2 10
13.2%
3 6
7.9%
8 4
 
5.3%
, 4
 
5.3%
9 2
 
2.6%
5 2
 
2.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Correlations

2024-02-10T10:00:39.539591image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
인구이동보고서(1호)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
인구이동보고서(1호)1.0000.0001.000NaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 20.0001.000NaNNaN0.8641.0000.9200.9251.0001.0001.0000.8421.0001.0000.9251.0001.0001.000
Unnamed: 31.000NaN1.000NaN1.0001.0000.7901.0000.7901.0001.0001.0001.0001.0001.0001.0000.7901.000
Unnamed: 4NaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0000.0001.0001.0001.0001.0001.0001.000
Unnamed: 51.0000.8641.0001.0001.0000.9880.9930.9780.9930.9890.9810.9890.9930.9870.9900.9880.9950.990
Unnamed: 61.0001.0001.0001.0000.9881.0000.9810.9810.9880.9850.9870.9780.9820.9940.9860.9950.9830.989
Unnamed: 71.0000.9200.7901.0000.9930.9811.0000.9780.9910.9920.9610.9700.9680.9820.9800.9660.9800.985
Unnamed: 81.0000.9251.0001.0000.9780.9810.9781.0000.9940.9970.9880.9830.9780.9760.9840.9950.9760.987
Unnamed: 91.0001.0000.7901.0000.9930.9880.9910.9941.0000.9980.9800.9640.9870.9900.9870.9890.9880.995
Unnamed: 101.0001.0001.0001.0000.9890.9850.9920.9970.9981.0000.9880.9830.9870.9890.9870.9870.9871.000
Unnamed: 111.0001.0001.0001.0000.9810.9870.9610.9880.9800.9881.0000.9840.9760.9600.9830.9970.9680.994
Unnamed: 131.0000.8421.0000.0000.9890.9780.9700.9830.9640.9830.9841.0000.9790.9710.9950.9860.9730.993
Unnamed: 141.0001.0001.0001.0000.9930.9820.9680.9780.9870.9870.9760.9791.0000.9870.9940.9880.9900.997
Unnamed: 151.0001.0001.0001.0000.9870.9940.9820.9760.9900.9890.9600.9710.9871.0000.9880.9820.9960.994
Unnamed: 161.0000.9251.0001.0000.9900.9860.9800.9840.9870.9870.9830.9950.9940.9881.0000.9920.9870.997
Unnamed: 171.0001.0001.0001.0000.9880.9950.9660.9950.9890.9870.9970.9860.9880.9820.9921.0000.9790.994
Unnamed: 181.0001.0000.7901.0000.9950.9830.9800.9760.9880.9870.9680.9730.9900.9960.9870.9791.0000.995
Unnamed: 191.0001.0001.0001.0000.9900.9890.9850.9870.9951.0000.9940.9930.9970.9940.9970.9940.9951.000

Missing values

2024-02-10T10:00:11.253901image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-10T10:00:12.030630image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.
2024-02-10T10:00:12.853409image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
The correlation heatmap measures nullity correlation: how strongly the presence or absence of one variable affects the presence of another.

Sample

Unnamed: 0인구이동보고서(1호)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
0<NA>행정기관 :<NA>광주광역시 동구<NA><NA><NA><NA><NA><NA>출력일자 :<NA>2023.06.02<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>
2<NA>시, 군, 구(읍면동)<NA><NA><NA>합 계충장동동명동계림1동계림2동산수1동산수2동<NA>지산1동지산2동서남동학동학운동지원1동지원2동
3<NA>전월말세대수<NA><NA><NA>54,6674,1372,4315,8195,5014,3724,715<NA>2,4332,3792,2433,5305,1924,2317,684
4<NA>전월말인구수<NA><NA><NA>106,4435,2603,70710,56713,0708,1519,996<NA>4,1224,3362,9537,40911,1559,05516,662
5<NA>전월말거주불명자수<NA><NA><NA>7033611577286326<NA>421053794421109
6<NA>전월말재외국민등록자수<NA><NA><NA>10767121266<NA>36392287
7<NA>증 가 요 인전 입<NA>1,3661626312118911271<NA>675256797392229
8<NA><NA><NA>남자<NA>656723067845726<NA>363129363843107
9<NA><NA><NA>여자<NA>7109033541055545<NA>312127433549122
Unnamed: 0인구이동보고서(1호)Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
25<NA><NA>말소<NA><NA>4120001<NA>0000000
26<NA><NA>국외<NA><NA>0000000<NA>0000000
27<NA><NA>기타<NA><NA>1001000<NA>0000000
28<NA>세대수증감<NA><NA><NA>1384310-82611-1<NA>-74122-61438
29<NA>인구수증감<NA><NA><NA>152569-62314-39<NA>043-5-238108
30<NA>거주불명자수증감<NA><NA><NA>6130-1-10<NA>0-1140-11
31<NA>금월말세대수<NA><NA><NA>54,8054,1802,4415,8115,5274,3834,714<NA>2,4262,3832,2553,5325,1864,2457,722
32<NA>금월말인구수<NA><NA><NA>106,5955,3163,71610,56113,0938,1659,957<NA>4,1224,3402,9567,40411,1329,06316,770
33<NA>금월말거주불명자수<NA><NA><NA>7093711877276226<NA>42954834420110
34<NA>금월말재외국민등록자수<NA><NA><NA>10867121266<NA>37392287