Overview

Dataset statistics

Number of variables20
Number of observations35
Missing cells193
Missing cells (%)27.6%
Duplicate rows1
Duplicate rows (%)2.9%
Total size in memory5.6 KiB
Average record size in memory164.8 B

Variable types

Unsupported1
Text18
DateTime1

Dataset

Description2022-07-08
Author주민등록인구통계
URLhttps://bigdata.gwangju.go.kr/usr/dataSet/getDataDetailView.rd?dataSetUncd=DS000201926

Alerts

Unnamed: 12 has constant value ""Constant
Dataset has 1 (2.9%) duplicate rowsDuplicates
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 10:04:32.501767
Analysis finished2024-02-10 10:04:39.124448
Duration6.62 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
2024-02-10T10:04:39.370174image/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%
2024-02-10T10:04:40.359218image/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
2024-02-10T10:04:40.812962image/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%
2024-02-10T10:04:41.700097image/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
2024-02-10T10:04:42.098675image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length9
Mean length3.4166667
Min length1

Characters and Unicode

Total characters41
Distinct characters20
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)16.7%

Sample

1st row광주광역시 동구
2nd row2022.06 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%
2022.06 1
7.1%
현재 1
7.1%
2024-02-10T10:04:43.033851image/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 3
 
7.3%
2
 
4.9%
0 2
 
4.9%
2
 
4.9%
2
 
4.9%
2
 
4.9%
Other values (10) 12
29.3%

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 3
50.0%
0 2
33.3%
6 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 3
30.0%
0 2
20.0%
6 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 3
30.0%
0 2
20.0%
6 1
 
10.0%
. 1
 
10.0%

Unnamed: 4
Text

MISSING 

Distinct2
Distinct (%)50.0%
Missing31
Missing (%)88.6%
Memory size412.0 B
2024-02-10T10:04:43.407452image/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%
2024-02-10T10:04:44.250195image/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
2024-02-10T10:04:44.656282image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length6
Mean length3
Min length1

Characters and Unicode

Total characters99
Distinct characters15
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique27 ?
Unique (%)81.8%

Sample

1st row합 계
2nd row52,540
3rd row102,936
4th row879
5th row97
ValueCountFrequency (%)
0 4
 
11.8%
425 2
 
5.9%
1,348 1
 
2.9%
873 1
 
2.9%
103,233 1
 
2.9%
52,717 1
 
2.9%
6 1
 
2.9%
297 1
 
2.9%
177 1
 
2.9%
4 1
 
2.9%
Other values (20) 20
58.8%
2024-02-10T10:04:45.679053image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 10
10.1%
5 10
10.1%
3 10
10.1%
7 10
10.1%
1 9
9.1%
6 9
9.1%
0 8
8.1%
9 8
8.1%
4 7
7.1%
8 7
7.1%
Other values (5) 11
11.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 88
88.9%
Other Punctuation 6
 
6.1%
Space Separator 2
 
2.0%
Other Letter 2
 
2.0%
Dash Punctuation 1
 
1.0%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
2 10
11.4%
5 10
11.4%
3 10
11.4%
7 10
11.4%
1 9
10.2%
6 9
10.2%
0 8
9.1%
9 8
9.1%
4 7
8.0%
8 7
8.0%
Other Letter
ValueCountFrequency (%)
1
50.0%
1
50.0%
Other Punctuation
ValueCountFrequency (%)
, 6
100.0%
Space Separator
ValueCountFrequency (%)
2
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 97
98.0%
Hangul 2
 
2.0%

Most frequent character per script

Common
ValueCountFrequency (%)
2 10
10.3%
5 10
10.3%
3 10
10.3%
7 10
10.3%
1 9
9.3%
6 9
9.3%
0 8
8.2%
9 8
8.2%
4 7
7.2%
8 7
7.2%
Other values (3) 9
9.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 97
98.0%
Hangul 2
 
2.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
2 10
10.3%
5 10
10.3%
3 10
10.3%
7 10
10.3%
1 9
9.3%
6 9
9.3%
0 8
8.2%
9 8
8.2%
4 7
7.2%
8 7
7.2%
Other values (3) 9
9.3%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 6
Text

MISSING 

Distinct23
Distinct (%)69.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:46.087749image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
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 row충장동
2nd row3,764
3rd row4,874
4th row84
5th row7
ValueCountFrequency (%)
0 7
21.2%
51 2
 
6.1%
84 2
 
6.1%
7 2
 
6.1%
5 2
 
6.1%
1 1
 
3.0%
104 1
 
3.0%
3,762 1
 
3.0%
8 1
 
3.0%
2 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T10:04:46.890260image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 11
16.2%
0 8
11.8%
7 8
11.8%
5 6
8.8%
1 6
8.8%
8 5
7.4%
2 5
7.4%
6 5
7.4%
, 4
 
5.9%
3 4
 
5.9%
Other values (5) 6
8.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 59
86.8%
Other Punctuation 4
 
5.9%
Other Letter 3
 
4.4%
Dash Punctuation 2
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
4 11
18.6%
0 8
13.6%
7 8
13.6%
5 6
10.2%
1 6
10.2%
8 5
8.5%
2 5
8.5%
6 5
8.5%
3 4
 
6.8%
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 65
95.6%
Hangul 3
 
4.4%

Most frequent character per script

Common
ValueCountFrequency (%)
4 11
16.9%
0 8
12.3%
7 8
12.3%
5 6
9.2%
1 6
9.2%
8 5
7.7%
2 5
7.7%
6 5
7.7%
, 4
 
6.2%
3 4
 
6.2%
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 (%)
4 11
16.9%
0 8
12.3%
7 8
12.3%
5 6
9.2%
1 6
9.2%
8 5
7.7%
2 5
7.7%
6 5
7.7%
, 4
 
6.2%
3 4
 
6.2%
Other values (2) 3
 
4.6%
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
2024-02-10T10:04:47.334599image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
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

Unique21 ?
Unique (%)63.6%

Sample

1st row동명동
2nd row2,458
3rd row3,840
4th row113
5th row7
ValueCountFrequency (%)
0 7
21.2%
5 3
 
9.1%
19 2
 
6.1%
14 1
 
3.0%
113 1
 
3.0%
23 1
 
3.0%
112 1
 
3.0%
3,845 1
 
3.0%
2,463 1
 
3.0%
1 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:04:48.247568image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 9
13.4%
1 9
13.4%
2 9
13.4%
3 8
11.9%
5 7
10.4%
4 6
9.0%
8 5
7.5%
, 4
6.0%
9 3
 
4.5%
2
 
3.0%
Other values (4) 5
7.5%

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 (%)
0 9
15.3%
1 9
15.3%
2 9
15.3%
3 8
13.6%
5 7
11.9%
4 6
10.2%
8 5
8.5%
9 3
 
5.1%
6 2
 
3.4%
7 1
 
1.7%
Other Letter
ValueCountFrequency (%)
2
66.7%
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 (%)
0 9
14.1%
1 9
14.1%
2 9
14.1%
3 8
12.5%
5 7
10.9%
4 6
9.4%
8 5
7.8%
, 4
6.2%
9 3
 
4.7%
6 2
 
3.1%
Other values (2) 2
 
3.1%
Hangul
ValueCountFrequency (%)
2
66.7%
1
33.3%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 9
14.1%
1 9
14.1%
2 9
14.1%
3 8
12.5%
5 7
10.9%
4 6
9.4%
8 5
7.8%
, 4
6.2%
9 3
 
4.7%
6 2
 
3.1%
Other values (2) 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
2024-02-10T10:04:48.645507image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length2
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

Unique21 ?
Unique (%)63.6%

Sample

1st row계림1동
2nd row5,950
3rd row10,875
4th row96
5th row12
ValueCountFrequency (%)
0 7
21.2%
12 3
 
9.1%
96 2
 
6.1%
72 1
 
3.0%
54 1
 
3.0%
10,847 1
 
3.0%
5,937 1
 
3.0%
2 1
 
3.0%
28 1
 
3.0%
13 1
 
3.0%
Other values (14) 14
42.4%
2024-02-10T10:04:49.735205image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 11
14.3%
0 10
13.0%
2 9
11.7%
7 7
9.1%
5 7
9.1%
9 6
7.8%
3 6
7.8%
4 5
6.5%
, 4
 
5.2%
8 4
 
5.2%
Other values (5) 8
10.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
87.0%
Other Punctuation 4
 
5.2%
Dash Punctuation 3
 
3.9%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 11
16.4%
0 10
14.9%
2 9
13.4%
7 7
10.4%
5 7
10.4%
9 6
9.0%
3 6
9.0%
4 5
7.5%
8 4
 
6.0%
6 2
 
3.0%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
1 11
14.9%
0 10
13.5%
2 9
12.2%
7 7
9.5%
5 7
9.5%
9 6
8.1%
3 6
8.1%
4 5
6.8%
, 4
 
5.4%
8 4
 
5.4%
Other values (2) 5
6.8%
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 11
14.9%
0 10
13.5%
2 9
12.2%
7 7
9.5%
5 7
9.5%
9 6
8.1%
3 6
8.1%
4 5
6.8%
, 4
 
5.4%
8 4
 
5.4%
Other values (2) 5
6.8%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 9
Text

MISSING 

Distinct22
Distinct (%)66.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:50.141968image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.0606061
Min length1

Characters and Unicode

Total characters68
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

Unique17 ?
Unique (%)51.5%

Sample

1st row계림2동
2nd row4,145
3rd row9,742
4th row44
5th row12
ValueCountFrequency (%)
0 8
24.2%
12 2
 
6.1%
9,742 2
 
6.1%
44 2
 
6.1%
14 2
 
6.1%
11 1
 
3.0%
60 1
 
3.0%
3 1
 
3.0%
5 1
 
3.0%
22 1
 
3.0%
Other values (12) 12
36.4%
2024-02-10T10:04:50.992721image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
4 13
19.1%
0 10
14.7%
2 10
14.7%
1 9
13.2%
3 6
8.8%
, 4
 
5.9%
7 3
 
4.4%
6 3
 
4.4%
5 3
 
4.4%
9 2
 
2.9%
Other values (4) 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 (%)
4 13
21.3%
0 10
16.4%
2 10
16.4%
1 9
14.8%
3 6
9.8%
7 3
 
4.9%
6 3
 
4.9%
5 3
 
4.9%
9 2
 
3.3%
8 2
 
3.3%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
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 (%)
4 13
20.0%
0 10
15.4%
2 10
15.4%
1 9
13.8%
3 6
9.2%
, 4
 
6.2%
7 3
 
4.6%
6 3
 
4.6%
5 3
 
4.6%
9 2
 
3.1%
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 (%)
4 13
20.0%
0 10
15.4%
2 10
15.4%
1 9
13.8%
3 6
9.2%
, 4
 
6.2%
7 3
 
4.6%
6 3
 
4.6%
5 3
 
4.6%
9 2
 
3.1%
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
2024-02-10T10:04:51.367078image/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

Unique21 ?
Unique (%)61.8%

Sample

1st row출력일자 :
2nd row산수1동
3rd row4,471
4th row8,489
5th row65
ValueCountFrequency (%)
0 7
20.0%
26 2
 
5.7%
65 2
 
5.7%
6 2
 
5.7%
1
 
2.9%
86 1
 
2.9%
4,461 1
 
2.9%
18 1
 
2.9%
10 1
 
2.9%
1 1
 
2.9%
Other values (16) 16
45.7%
2024-02-10T10:04:52.175440image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13
17.1%
4 12
15.8%
0 9
11.8%
6 8
10.5%
8 5
 
6.6%
, 4
 
5.3%
7 4
 
5.3%
5 3
 
3.9%
2 3
 
3.9%
3 3
 
3.9%
Other values (11) 12
15.8%

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 (%)
1 13
21.3%
4 12
19.7%
0 9
14.8%
6 8
13.1%
8 5
 
8.2%
7 4
 
6.6%
5 3
 
4.9%
2 3
 
4.9%
3 3
 
4.9%
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%
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 (%)
1 13
18.8%
4 12
17.4%
0 9
13.0%
6 8
11.6%
8 5
 
7.2%
, 4
 
5.8%
7 4
 
5.8%
5 3
 
4.3%
2 3
 
4.3%
3 3
 
4.3%
Other values (4) 5
 
7.2%
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 13
18.8%
4 12
17.4%
0 9
13.0%
6 8
11.6%
8 5
 
7.2%
, 4
 
5.8%
7 4
 
5.8%
5 3
 
4.3%
2 3
 
4.3%
3 3
 
4.3%
Other values (4) 5
 
7.2%
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 

Distinct25
Distinct (%)75.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:52.581723image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
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

Unique21 ?
Unique (%)63.6%

Sample

1st row산수2동
2nd row4,893
3rd row10,492
4th row36
5th row5
ValueCountFrequency (%)
0 6
18.2%
32 2
 
6.1%
1 2
 
6.1%
6 2
 
6.1%
28 2
 
6.1%
5 2
 
6.1%
88 1
 
3.0%
57 1
 
3.0%
37 1
 
3.0%
10,464 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T10:04:53.880664image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
8 10
13.9%
0 9
12.5%
4 9
12.5%
3 7
9.7%
2 7
9.7%
1 6
8.3%
5 5
6.9%
, 4
 
5.6%
6 4
 
5.6%
9 3
 
4.2%
Other values (5) 8
11.1%

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 (%)
8 10
15.9%
0 9
14.3%
4 9
14.3%
3 7
11.1%
2 7
11.1%
1 6
9.5%
5 5
7.9%
6 4
 
6.3%
9 3
 
4.8%
7 3
 
4.8%
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 (%)
8 10
14.5%
0 9
13.0%
4 9
13.0%
3 7
10.1%
2 7
10.1%
1 6
8.7%
5 5
7.2%
, 4
 
5.8%
6 4
 
5.8%
9 3
 
4.3%
Other values (2) 5
7.2%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
8 10
14.5%
0 9
13.0%
4 9
13.0%
3 7
10.1%
2 7
10.1%
1 6
8.7%
5 5
7.2%
, 4
 
5.8%
6 4
 
5.8%
9 3
 
4.3%
Other values (2) 5
7.2%
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
Minimum2022-07-04 00:00:00
Maximum2022-07-04 00:00:00
2024-02-10T10:04:54.552515image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
2024-02-10T10:04:54.949945image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram with fixed size bins (bins=1)

Unnamed: 13
Text

MISSING 

Distinct22
Distinct (%)66.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:55.612558image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2.1212121
Min length1

Characters and Unicode

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

Unique

Unique17 ?
Unique (%)51.5%

Sample

1st row지산1동
2nd row2,436
3rd row4,223
4th row42
5th row2
ValueCountFrequency (%)
0 7
21.2%
33 3
 
9.1%
13 2
 
6.1%
1 2
 
6.1%
2 2
 
6.1%
20 2
 
6.1%
4,210 1
 
3.0%
2,425 1
 
3.0%
11 1
 
3.0%
3 1
 
3.0%
Other values (11) 11
33.3%
2024-02-10T10:04:56.812413image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
2 14
20.0%
3 12
17.1%
0 11
15.7%
1 9
12.9%
4 6
8.6%
, 4
 
5.7%
5 4
 
5.7%
6 3
 
4.3%
- 2
 
2.9%
1
 
1.4%
Other values (4) 4
 
5.7%

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 (%)
2 14
23.0%
3 12
19.7%
0 11
18.0%
1 9
14.8%
4 6
9.8%
5 4
 
6.6%
6 3
 
4.9%
8 1
 
1.6%
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 67
95.7%
Hangul 3
 
4.3%

Most frequent character per script

Common
ValueCountFrequency (%)
2 14
20.9%
3 12
17.9%
0 11
16.4%
1 9
13.4%
4 6
9.0%
, 4
 
6.0%
5 4
 
6.0%
6 3
 
4.5%
- 2
 
3.0%
8 1
 
1.5%
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 (%)
2 14
20.9%
3 12
17.9%
0 11
16.4%
1 9
13.4%
4 6
9.0%
, 4
 
6.0%
5 4
 
6.0%
6 3
 
4.5%
- 2
 
3.0%
8 1
 
1.5%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 14
Text

MISSING 

Distinct21
Distinct (%)63.6%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:57.292480image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
Mean length2
Min length1

Characters and Unicode

Total characters66
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 row지산2동
2nd row2,411
3rd row4,462
4th row41
5th row7
ValueCountFrequency (%)
0 8
24.2%
20 2
 
6.1%
30 2
 
6.1%
41 2
 
6.1%
7 2
 
6.1%
1 2
 
6.1%
2 2
 
6.1%
지산2동 1
 
3.0%
60 1
 
3.0%
6 1
 
3.0%
Other values (10) 10
30.3%
2024-02-10T10:04:58.279948image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14
21.2%
2 11
16.7%
4 10
15.2%
1 8
12.1%
6 5
 
7.6%
3 4
 
6.1%
7 4
 
6.1%
, 4
 
6.1%
- 1
 
1.5%
1
 
1.5%
Other values (4) 4
 
6.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 58
87.9%
Other Punctuation 4
 
6.1%
Other Letter 3
 
4.5%
Dash Punctuation 1
 
1.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
24.1%
2 11
19.0%
4 10
17.2%
1 8
13.8%
6 5
 
8.6%
3 4
 
6.9%
7 4
 
6.9%
5 1
 
1.7%
8 1
 
1.7%
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 63
95.5%
Hangul 3
 
4.5%

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
22.2%
2 11
17.5%
4 10
15.9%
1 8
12.7%
6 5
 
7.9%
3 4
 
6.3%
7 4
 
6.3%
, 4
 
6.3%
- 1
 
1.6%
5 1
 
1.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
22.2%
2 11
17.5%
4 10
15.9%
1 8
12.7%
6 5
 
7.9%
3 4
 
6.3%
7 4
 
6.3%
, 4
 
6.3%
- 1
 
1.6%
5 1
 
1.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 15
Text

MISSING 

Distinct20
Distinct (%)60.6%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:58.603598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.030303
Min length1

Characters and Unicode

Total characters67
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

Unique14 ?
Unique (%)42.4%

Sample

1st row서남동
2nd row2,243
3rd row3,012
4th row63
5th row3
ValueCountFrequency (%)
0 8
24.2%
20 3
 
9.1%
3 2
 
6.1%
2 2
 
6.1%
33 2
 
6.1%
63 2
 
6.1%
58 1
 
3.0%
82 1
 
3.0%
37 1
 
3.0%
45 1
 
3.0%
Other values (10) 10
30.3%
2024-02-10T10:04:59.599458image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 16
23.9%
0 14
20.9%
2 14
20.9%
5 4
 
6.0%
, 4
 
6.0%
6 3
 
4.5%
1 3
 
4.5%
4 2
 
3.0%
8 2
 
3.0%
9 1
 
1.5%
Other values (4) 4
 
6.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
89.6%
Other Punctuation 4
 
6.0%
Other Letter 3
 
4.5%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 16
26.7%
0 14
23.3%
2 14
23.3%
5 4
 
6.7%
6 3
 
5.0%
1 3
 
5.0%
4 2
 
3.3%
8 2
 
3.3%
9 1
 
1.7%
7 1
 
1.7%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
3 16
25.0%
0 14
21.9%
2 14
21.9%
5 4
 
6.2%
, 4
 
6.2%
6 3
 
4.7%
1 3
 
4.7%
4 2
 
3.1%
8 2
 
3.1%
9 1
 
1.6%
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 (%)
3 16
25.0%
0 14
21.9%
2 14
21.9%
5 4
 
6.2%
, 4
 
6.2%
6 3
 
4.7%
1 3
 
4.7%
4 2
 
3.1%
8 2
 
3.1%
9 1
 
1.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 16
Text

MISSING 

Distinct24
Distinct (%)72.7%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:04:59.966928image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length3
Mean length2.0606061
Min length1

Characters and Unicode

Total characters68
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 row3,623
3rd row7,697
4th row77
5th row8
ValueCountFrequency (%)
0 8
24.2%
77 2
 
6.1%
4 2
 
6.1%
19 2
 
6.1%
64 1
 
3.0%
7,697 1
 
3.0%
50 1
 
3.0%
7,662 1
 
3.0%
3,619 1
 
3.0%
35 1
 
3.0%
Other values (13) 13
39.4%
2024-02-10T10:05:00.802238image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 11
16.2%
6 9
13.2%
2 8
11.8%
7 7
10.3%
3 7
10.3%
1 5
7.4%
9 5
7.4%
5 4
 
5.9%
, 4
 
5.9%
4 3
 
4.4%
Other values (4) 5
7.4%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 60
88.2%
Other Punctuation 4
 
5.9%
Dash Punctuation 2
 
2.9%
Other Letter 2
 
2.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 11
18.3%
6 9
15.0%
2 8
13.3%
7 7
11.7%
3 7
11.7%
1 5
8.3%
9 5
8.3%
5 4
 
6.7%
4 3
 
5.0%
8 1
 
1.7%
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 66
97.1%
Hangul 2
 
2.9%

Most frequent character per script

Common
ValueCountFrequency (%)
0 11
16.7%
6 9
13.6%
2 8
12.1%
7 7
10.6%
3 7
10.6%
1 5
7.6%
9 5
7.6%
5 4
 
6.1%
, 4
 
6.1%
4 3
 
4.5%
Other values (2) 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 66
97.1%
Hangul 2
 
2.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 11
16.7%
6 9
13.6%
2 8
12.1%
7 7
10.6%
3 7
10.6%
1 5
7.6%
9 5
7.6%
5 4
 
6.1%
, 4
 
6.1%
4 3
 
4.5%
Other values (2) 3
 
4.5%
Hangul
ValueCountFrequency (%)
1
50.0%
1
50.0%

Unnamed: 17
Text

MISSING 

Distinct27
Distinct (%)81.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:05:01.200650image/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

Unique24 ?
Unique (%)72.7%

Sample

1st row학운동
2nd row5,262
3rd row11,379
4th row67
5th row14
ValueCountFrequency (%)
0 5
 
15.2%
1 2
 
6.1%
2 2
 
6.1%
14 2
 
6.1%
78 1
 
3.0%
67 1
 
3.0%
11,379 1
 
3.0%
11,322 1
 
3.0%
5,250 1
 
3.0%
57 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T10:05:02.194880image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 13
16.9%
0 10
13.0%
5 9
11.7%
2 8
10.4%
3 7
9.1%
4 6
7.8%
7 5
 
6.5%
6 5
 
6.5%
, 4
 
5.2%
8 3
 
3.9%
Other values (5) 7
9.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 67
87.0%
Other Punctuation 4
 
5.2%
Dash Punctuation 3
 
3.9%
Other Letter 3
 
3.9%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 13
19.4%
0 10
14.9%
5 9
13.4%
2 8
11.9%
3 7
10.4%
4 6
9.0%
7 5
 
7.5%
6 5
 
7.5%
8 3
 
4.5%
9 1
 
1.5%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
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 10
13.5%
5 9
12.2%
2 8
10.8%
3 7
9.5%
4 6
8.1%
7 5
 
6.8%
6 5
 
6.8%
, 4
 
5.4%
8 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 (%)
1 13
17.6%
0 10
13.5%
5 9
12.2%
2 8
10.8%
3 7
9.5%
4 6
8.1%
7 5
 
6.8%
6 5
 
6.8%
, 4
 
5.4%
8 3
 
4.1%
Other values (2) 4
 
5.4%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 18
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:05:02.661307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4
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 row지원1동
2nd row3,674
3rd row7,773
4th row25
5th row6
ValueCountFrequency (%)
0 7
21.2%
2 2
 
6.1%
7 2
 
6.1%
15 1
 
3.0%
7,773 1
 
3.0%
25 1
 
3.0%
8,328 1
 
3.0%
3,912 1
 
3.0%
555 1
 
3.0%
238 1
 
3.0%
Other values (15) 15
45.5%
2024-02-10T10:05:03.596797image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
3 13
17.1%
2 10
13.2%
0 8
10.5%
7 8
10.5%
4 7
9.2%
1 6
7.9%
8 5
 
6.6%
5 5
 
6.6%
6 4
 
5.3%
, 4
 
5.3%
Other values (5) 6
7.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 68
89.5%
Other Punctuation 4
 
5.3%
Other Letter 3
 
3.9%
Dash Punctuation 1
 
1.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
3 13
19.1%
2 10
14.7%
0 8
11.8%
7 8
11.8%
4 7
10.3%
1 6
8.8%
8 5
 
7.4%
5 5
 
7.4%
6 4
 
5.9%
9 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 73
96.1%
Hangul 3
 
3.9%

Most frequent character per script

Common
ValueCountFrequency (%)
3 13
17.8%
2 10
13.7%
0 8
11.0%
7 8
11.0%
4 7
9.6%
1 6
8.2%
8 5
 
6.8%
5 5
 
6.8%
6 4
 
5.5%
, 4
 
5.5%
Other values (2) 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 (%)
3 13
17.8%
2 10
13.7%
0 8
11.0%
7 8
11.0%
4 7
9.6%
1 6
8.2%
8 5
 
6.8%
5 5
 
6.8%
6 4
 
5.5%
, 4
 
5.5%
Other values (2) 3
 
4.1%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Unnamed: 19
Text

MISSING 

Distinct26
Distinct (%)78.8%
Missing2
Missing (%)5.7%
Memory size412.0 B
2024-02-10T10:05:03.958995image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length5
Mean length2.4848485
Min length1

Characters and Unicode

Total characters82
Distinct characters15
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique24 ?
Unique (%)72.7%

Sample

1st row지원2동
2nd row7,210
3rd row16,078
4th row126
5th row8
ValueCountFrequency (%)
0 7
21.2%
8 2
 
6.1%
134 1
 
3.0%
15,980 1
 
3.0%
7,180 1
 
3.0%
1 1
 
3.0%
98 1
 
3.0%
30 1
 
3.0%
6 1
 
3.0%
76 1
 
3.0%
Other values (16) 16
48.5%
2024-02-10T10:05:04.844688image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 14
17.1%
1 14
17.1%
8 9
11.0%
2 7
8.5%
7 6
7.3%
6 6
7.3%
9 6
7.3%
3 4
 
4.9%
, 4
 
4.9%
4 3
 
3.7%
Other values (5) 9
11.0%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 72
87.8%
Other Punctuation 4
 
4.9%
Dash Punctuation 3
 
3.7%
Other Letter 3
 
3.7%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 14
19.4%
1 14
19.4%
8 9
12.5%
2 7
9.7%
7 6
8.3%
6 6
8.3%
9 6
8.3%
3 4
 
5.6%
4 3
 
4.2%
5 3
 
4.2%
Other Letter
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%
Other Punctuation
ValueCountFrequency (%)
, 4
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

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

Most frequent character per script

Common
ValueCountFrequency (%)
0 14
17.7%
1 14
17.7%
8 9
11.4%
2 7
8.9%
7 6
7.6%
6 6
7.6%
9 6
7.6%
3 4
 
5.1%
, 4
 
5.1%
4 3
 
3.8%
Other values (2) 6
7.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Most occurring blocks

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

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 14
17.7%
1 14
17.7%
8 9
11.4%
2 7
8.9%
7 6
7.6%
6 6
7.6%
9 6
7.6%
3 4
 
5.1%
, 4
 
5.1%
4 3
 
3.8%
Other values (2) 6
7.6%
Hangul
ValueCountFrequency (%)
1
33.3%
1
33.3%
1
33.3%

Correlations

2024-02-10T10:05:05.201634image/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: 20.0001.000NaNNaN0.9400.8371.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 31.000NaN1.000NaN1.0001.0001.0001.0000.7900.8541.0000.7900.7901.0001.0001.0001.0001.000
Unnamed: 4NaNNaNNaN1.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.0001.000
Unnamed: 51.0000.9401.0001.0001.0000.9880.9940.9890.9910.9930.9900.9870.9850.9830.9890.9920.9910.991
Unnamed: 61.0000.8371.0001.0000.9881.0000.9610.9830.9760.9870.9790.9760.9920.9880.9890.9930.9890.993
Unnamed: 71.0001.0001.0001.0000.9940.9611.0000.9930.9700.9900.9800.9800.9870.9810.9970.9930.9940.994
Unnamed: 81.0001.0001.0001.0000.9890.9830.9931.0000.9760.9940.9840.9890.9870.9860.9941.0001.0001.000
Unnamed: 91.0001.0000.7901.0000.9910.9760.9700.9761.0000.9880.9820.9800.9810.9730.9850.9960.9840.993
Unnamed: 101.0001.0000.8541.0000.9930.9870.9900.9940.9881.0000.9780.9820.9950.9930.9980.9970.9870.994
Unnamed: 111.0001.0001.0001.0000.9900.9790.9800.9840.9820.9781.0000.9850.9870.9830.9870.9930.9900.992
Unnamed: 131.0001.0000.7901.0000.9870.9760.9800.9890.9800.9820.9851.0000.9760.9790.9851.0000.9971.000
Unnamed: 141.0001.0000.7901.0000.9850.9920.9870.9870.9810.9950.9870.9761.0000.9801.0001.0000.9941.000
Unnamed: 151.0001.0001.0001.0000.9830.9880.9810.9860.9730.9930.9830.9790.9801.0000.9910.9970.9920.995
Unnamed: 161.0001.0001.0001.0000.9890.9890.9970.9940.9850.9980.9870.9851.0000.9911.0000.9930.9940.994
Unnamed: 171.0001.0001.0001.0000.9920.9930.9931.0000.9960.9970.9931.0001.0000.9970.9931.0000.9961.000
Unnamed: 181.0001.0001.0001.0000.9910.9890.9941.0000.9840.9870.9900.9970.9940.9920.9940.9961.0001.000
Unnamed: 191.0001.0001.0001.0000.9910.9930.9941.0000.9930.9940.9921.0001.0000.9950.9941.0001.0001.000

Missing values

2024-02-10T10:04:36.546011image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-02-10T10:04:37.429107image/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.
2024-02-10T10:04:38.330353image/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>2022.07.04<NA><NA><NA><NA><NA><NA><NA>
1<NA>작성기준 :<NA>2022.06 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>52,5403,7642,4585,9504,1454,4714,893<NA>2,4362,4112,2433,6235,2623,6747,210
4<NA>전월말인구수<NA><NA><NA>102,9364,8743,84010,8759,7428,48910,492<NA>4,2234,4623,0127,69711,3797,77316,078
5<NA>전월말거주불명자수<NA><NA><NA>8798411396446536<NA>424163776725126
6<NA>전월말재외국민등록자수<NA><NA><NA>9777121265<NA>27381468
7<NA>증 가 요 인전 입<NA>1,651955896607188<NA>55608266104637179
8<NA><NA><NA>남자<NA>824513055243148<NA>283037364632484
9<NA><NA><NA>여자<NA>827442841364040<NA>273045305831395
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>4000010<NA>0020100
26<NA><NA>국외<NA><NA>0000000<NA>0000000
27<NA><NA>기타<NA><NA>0000000<NA>0000000
28<NA>세대수증감<NA><NA><NA>177-25-133-10-6<NA>-11-120-4-12238-30
29<NA>인구수증감<NA><NA><NA>297-85-280-18-28<NA>-13220-35-57555-98
30<NA>거주불명자수증감<NA><NA><NA>-60-1-2001<NA>1000-2-2-1
31<NA>금월말세대수<NA><NA><NA>52,7173,7622,4635,9374,1484,4614,887<NA>2,4252,4102,2633,6195,2503,9127,180
32<NA>금월말인구수<NA><NA><NA>103,2334,8663,84510,8479,7428,47110,464<NA>4,2104,4643,0327,66211,3228,32815,980
33<NA>금월말거주불명자수<NA><NA><NA>8738411294446537<NA>434163776523125
34<NA>금월말재외국민등록자수<NA><NA><NA>9976121266<NA>27391478

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: 19# duplicates
0<NA>국외<NA><NA>0000000<NA>00000002