Overview

Dataset statistics

Number of variables20
Number of observations9419
Missing cells9431
Missing cells (%)5.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.4 MiB
Average record size in memory160.0 B

Variable types

Unsupported1
Text14
Categorical5

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21705/F/1/datasetView.do

Alerts

Unnamed: 2 is highly overall correlated with Unnamed: 3 and 3 other fieldsHigh correlation
Unnamed: 3 is highly overall correlated with Unnamed: 2 and 3 other fieldsHigh correlation
Unnamed: 18 is highly overall correlated with Unnamed: 2 and 3 other fieldsHigh correlation
Unnamed: 19 is highly overall correlated with Unnamed: 2 and 3 other fieldsHigh correlation
Unnamed: 17 is highly overall correlated with Unnamed: 2 and 3 other fieldsHigh correlation
Unnamed: 3 is highly imbalanced (56.7%)Imbalance
Unnamed: 17 is highly imbalanced (99.8%)Imbalance
Unnamed: 18 is highly imbalanced (56.7%)Imbalance
Unnamed: 19 is highly imbalanced (56.7%)Imbalance
Unnamed: 16 has 9417 (> 99.9%) missing valuesMissing
유동인구_관찰조사_2009 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 09:16:32.498377
Analysis finished2023-12-11 09:16:35.101051
Duration2.6 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유동인구_관찰조사_2009
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
Distinct1187
Distinct (%)12.6%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:35.367961image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.4376725
Min length6

Characters and Unicode

Total characters60630
Distinct characters30
Distinct categories5 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row조사지점코드
2nd rowEXAMIN_SPOT_CD
3rd row01-033
4th row01-033
5th row01-033
ValueCountFrequency (%)
01-3009 16
 
0.2%
01-208 16
 
0.2%
22-3021 16
 
0.2%
11-3045 16
 
0.2%
11-3014 16
 
0.2%
23-376 16
 
0.2%
06-070 16
 
0.2%
05-031 16
 
0.2%
02-1172 16
 
0.2%
17-047 16
 
0.2%
Other values (1177) 9258
98.3%
2023-12-11T18:16:35.803725image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 10742
17.7%
1 9954
16.4%
- 9416
15.5%
2 8744
14.4%
3 5372
8.9%
4 3518
 
5.8%
5 3094
 
5.1%
7 2526
 
4.2%
6 2518
 
4.2%
9 2422
 
4.0%
Other values (20) 2324
 
3.8%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 51194
84.4%
Dash Punctuation 9416
 
15.5%
Uppercase Letter 12
 
< 0.1%
Other Letter 6
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
M 1
8.3%
C 1
8.3%
T 1
8.3%
O 1
8.3%
P 1
8.3%
S 1
8.3%
N 1
8.3%
I 1
8.3%
A 1
8.3%
X 1
8.3%
Other values (2) 2
16.7%
Decimal Number
ValueCountFrequency (%)
0 10742
21.0%
1 9954
19.4%
2 8744
17.1%
3 5372
10.5%
4 3518
 
6.9%
5 3094
 
6.0%
7 2526
 
4.9%
6 2518
 
4.9%
9 2422
 
4.7%
8 2304
 
4.5%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Dash Punctuation
ValueCountFrequency (%)
- 9416
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60612
> 99.9%
Latin 12
 
< 0.1%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 10742
17.7%
1 9954
16.4%
- 9416
15.5%
2 8744
14.4%
3 5372
8.9%
4 3518
 
5.8%
5 3094
 
5.1%
7 2526
 
4.2%
6 2518
 
4.2%
9 2422
 
4.0%
Other values (2) 2306
 
3.8%
Latin
ValueCountFrequency (%)
M 1
8.3%
C 1
8.3%
T 1
8.3%
O 1
8.3%
P 1
8.3%
S 1
8.3%
N 1
8.3%
I 1
8.3%
A 1
8.3%
X 1
8.3%
Other values (2) 2
16.7%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60624
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 10742
17.7%
1 9954
16.4%
- 9416
15.5%
2 8744
14.4%
3 5372
8.9%
4 3518
 
5.8%
5 3094
 
5.1%
7 2526
 
4.2%
6 2518
 
4.2%
9 2422
 
4.0%
Other values (14) 2318
 
3.8%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Unnamed: 2
Categorical

HIGH CORRELATION 

Distinct27
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
0907
 
402
0926
 
400
1102
 
400
1107
 
400
1026
 
400
Other values (22)
7417 

Length

Max length10
Median length4
Mean length4.000637
Min length4

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row조사일자
3rd rowEXAMIN_DAY
4th row1102
5th row1102

Common Values

ValueCountFrequency (%)
0907 402
 
4.3%
0926 400
 
4.2%
1102 400
 
4.2%
1107 400
 
4.2%
1026 400
 
4.2%
1031 400
 
4.2%
1123 400
 
4.2%
1128 400
 
4.2%
1116 400
 
4.2%
0905 400
 
4.2%
Other values (17) 5417
57.5%

Length

2023-12-11T18:16:35.949256image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
0907 402
 
4.3%
1121 400
 
4.2%
1017 400
 
4.2%
0921 400
 
4.2%
0928 400
 
4.2%
0914 400
 
4.2%
1010 400
 
4.2%
1005 400
 
4.2%
0912 400
 
4.2%
0926 400
 
4.2%
Other values (17) 5417
57.5%

Unnamed: 3
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
4710 
4706 
<NA>
 
1
조사요일
 
1
EXAMIN_DATE
 
1

Length

Max length11
Median length1
Mean length1.0016987
Min length1

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row조사요일
3rd rowEXAMIN_DATE
4th row
5th row

Common Values

ValueCountFrequency (%)
4710
50.0%
4706
50.0%
<NA> 1
 
< 0.1%
조사요일 1
 
< 0.1%
EXAMIN_DATE 1
 
< 0.1%

Length

2023-12-11T18:16:36.052239image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:16:36.142778image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
4710
50.0%
4706
50.0%
na 1
 
< 0.1%
조사요일 1
 
< 0.1%
examin_date 1
 
< 0.1%
Distinct324
Distinct (%)3.4%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:36.464427image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length15
Median length4
Mean length4.0013803
Min length4

Characters and Unicode

Total characters37685
Distinct characters25
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique143 ?
Unique (%)1.5%

Sample

1st row조사시작시간
2nd rowEXAMIN_START_TM
3rd row0850
4th row1230
5th row1830
ValueCountFrequency (%)
0800 489
 
5.2%
1900 487
 
5.2%
1300 483
 
5.1%
1230 405
 
4.3%
1700 362
 
3.8%
0900 325
 
3.5%
1500 325
 
3.5%
1100 279
 
3.0%
1400 250
 
2.7%
1000 250
 
2.7%
Other values (314) 5763
61.2%
2023-12-11T18:16:36.934906image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 13621
36.1%
1 8893
23.6%
5 3917
 
10.4%
3 2914
 
7.7%
9 1904
 
5.1%
2 1800
 
4.8%
4 1611
 
4.3%
8 1484
 
3.9%
6 797
 
2.1%
7 723
 
1.9%
Other values (15) 21
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37664
99.9%
Uppercase Letter 13
 
< 0.1%
Other Letter 6
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 13621
36.2%
1 8893
23.6%
5 3917
 
10.4%
3 2914
 
7.7%
9 1904
 
5.1%
2 1800
 
4.8%
4 1611
 
4.3%
8 1484
 
3.9%
6 797
 
2.1%
7 723
 
1.9%
Uppercase Letter
ValueCountFrequency (%)
T 3
23.1%
A 2
15.4%
M 2
15.4%
E 1
 
7.7%
X 1
 
7.7%
I 1
 
7.7%
N 1
 
7.7%
S 1
 
7.7%
R 1
 
7.7%
Other Letter
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37666
99.9%
Latin 13
 
< 0.1%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 13621
36.2%
1 8893
23.6%
5 3917
 
10.4%
3 2914
 
7.7%
9 1904
 
5.1%
2 1800
 
4.8%
4 1611
 
4.3%
8 1484
 
3.9%
6 797
 
2.1%
7 723
 
1.9%
Latin
ValueCountFrequency (%)
T 3
23.1%
A 2
15.4%
M 2
15.4%
E 1
 
7.7%
X 1
 
7.7%
I 1
 
7.7%
N 1
 
7.7%
S 1
 
7.7%
R 1
 
7.7%
Hangul
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37679
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 13621
36.2%
1 8893
23.6%
5 3917
 
10.4%
3 2914
 
7.7%
9 1904
 
5.1%
2 1800
 
4.8%
4 1611
 
4.3%
8 1484
 
3.9%
6 797
 
2.1%
7 723
 
1.9%
Other values (10) 15
 
< 0.1%
Hangul
ValueCountFrequency (%)
2
33.3%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Distinct324
Distinct (%)3.4%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:37.274628image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length4
Mean length4.001168
Min length4

Characters and Unicode

Total characters37683
Distinct characters25
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique144 ?
Unique (%)1.5%

Sample

1st row조사완료시간
2nd rowEXAMIN_END_TM
3rd row0915
4th row1255
5th row1855
ValueCountFrequency (%)
0825 489
 
5.2%
1925 487
 
5.2%
1325 483
 
5.1%
1255 405
 
4.3%
1725 363
 
3.9%
0925 325
 
3.5%
1525 325
 
3.5%
1125 279
 
3.0%
1025 250
 
2.7%
1425 250
 
2.7%
Other values (314) 5762
61.2%
2023-12-11T18:16:37.720996image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
5 9527
25.3%
1 8897
23.6%
0 5806
15.4%
2 4627
12.3%
3 2365
 
6.3%
9 1961
 
5.2%
4 1618
 
4.3%
8 1220
 
3.2%
7 886
 
2.4%
6 757
 
2.0%
Other values (15) 19
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 37664
99.9%
Uppercase Letter 11
 
< 0.1%
Other Letter 6
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
5 9527
25.3%
1 8897
23.6%
0 5806
15.4%
2 4627
12.3%
3 2365
 
6.3%
9 1961
 
5.2%
4 1618
 
4.3%
8 1220
 
3.2%
7 886
 
2.4%
6 757
 
2.0%
Uppercase Letter
ValueCountFrequency (%)
E 2
18.2%
M 2
18.2%
N 2
18.2%
A 1
9.1%
T 1
9.1%
D 1
9.1%
I 1
9.1%
X 1
9.1%
Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 37666
> 99.9%
Latin 11
 
< 0.1%
Hangul 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
5 9527
25.3%
1 8897
23.6%
0 5806
15.4%
2 4627
12.3%
3 2365
 
6.3%
9 1961
 
5.2%
4 1618
 
4.3%
8 1220
 
3.2%
7 886
 
2.4%
6 757
 
2.0%
Latin
ValueCountFrequency (%)
E 2
18.2%
M 2
18.2%
N 2
18.2%
A 1
9.1%
T 1
9.1%
D 1
9.1%
I 1
9.1%
X 1
9.1%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 37677
> 99.9%
Hangul 6
 
< 0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
5 9527
25.3%
1 8897
23.6%
0 5806
15.4%
2 4627
12.3%
3 2365
 
6.3%
9 1961
 
5.2%
4 1618
 
4.3%
8 1220
 
3.2%
7 886
 
2.4%
6 757
 
2.0%
Other values (9) 13
 
< 0.1%
Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Distinct145
Distinct (%)1.5%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:37.965372image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.4246124
Min length1

Characters and Unicode

Total characters13417
Distinct characters21
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique35 ?
Unique (%)0.4%

Sample

1st row남자유동인구수
2nd rowMALE
3rd row15
4th row28
5th row16
ValueCountFrequency (%)
3 745
 
7.9%
2 727
 
7.7%
4 677
 
7.2%
5 599
 
6.4%
6 539
 
5.7%
1 535
 
5.7%
7 470
 
5.0%
8 453
 
4.8%
9 406
 
4.3%
10 388
 
4.1%
Other values (135) 3879
41.2%
2023-12-11T18:16:38.340240image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3340
24.9%
2 2041
15.2%
3 1562
11.6%
4 1218
 
9.1%
5 1135
 
8.5%
0 1045
 
7.8%
6 888
 
6.6%
7 799
 
6.0%
8 754
 
5.6%
9 624
 
4.7%
Other values (11) 11
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13406
99.9%
Other Letter 7
 
0.1%
Uppercase Letter 4
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3340
24.9%
2 2041
15.2%
3 1562
11.7%
4 1218
 
9.1%
5 1135
 
8.5%
0 1045
 
7.8%
6 888
 
6.6%
7 799
 
6.0%
8 754
 
5.6%
9 624
 
4.7%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Uppercase Letter
ValueCountFrequency (%)
L 1
25.0%
A 1
25.0%
M 1
25.0%
E 1
25.0%

Most occurring scripts

ValueCountFrequency (%)
Common 13406
99.9%
Hangul 7
 
0.1%
Latin 4
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3340
24.9%
2 2041
15.2%
3 1562
11.7%
4 1218
 
9.1%
5 1135
 
8.5%
0 1045
 
7.8%
6 888
 
6.6%
7 799
 
6.0%
8 754
 
5.6%
9 624
 
4.7%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Latin
ValueCountFrequency (%)
L 1
25.0%
A 1
25.0%
M 1
25.0%
E 1
25.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13410
99.9%
Hangul 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3340
24.9%
2 2041
15.2%
3 1562
11.6%
4 1218
 
9.1%
5 1135
 
8.5%
0 1045
 
7.8%
6 888
 
6.6%
7 799
 
6.0%
8 754
 
5.6%
9 624
 
4.7%
Other values (4) 4
 
< 0.1%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Distinct169
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:38.630059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length7
Median length1
Mean length1.4344872
Min length1

Characters and Unicode

Total characters13510
Distinct characters22
Distinct categories3 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.6%

Sample

1st row여성유동인구수
2nd rowFEMALE
3rd row13
4th row24
5th row23
ValueCountFrequency (%)
2 707
 
7.5%
3 682
 
7.2%
1 622
 
6.6%
4 601
 
6.4%
5 537
 
5.7%
6 515
 
5.5%
8 489
 
5.2%
7 475
 
5.0%
0 456
 
4.8%
9 347
 
3.7%
Other values (159) 3987
42.3%
2023-12-11T18:16:39.057410image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3442
25.5%
2 2076
15.4%
3 1445
10.7%
4 1175
 
8.7%
0 1101
 
8.1%
5 1058
 
7.8%
6 964
 
7.1%
7 832
 
6.2%
8 808
 
6.0%
9 596
 
4.4%
Other values (12) 13
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 13497
99.9%
Other Letter 7
 
0.1%
Uppercase Letter 6
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3442
25.5%
2 2076
15.4%
3 1445
10.7%
4 1175
 
8.7%
0 1101
 
8.2%
5 1058
 
7.8%
6 964
 
7.1%
7 832
 
6.2%
8 808
 
6.0%
9 596
 
4.4%
Other Letter
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Uppercase Letter
ValueCountFrequency (%)
E 2
33.3%
A 1
16.7%
M 1
16.7%
F 1
16.7%
L 1
16.7%

Most occurring scripts

ValueCountFrequency (%)
Common 13497
99.9%
Hangul 7
 
0.1%
Latin 6
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3442
25.5%
2 2076
15.4%
3 1445
10.7%
4 1175
 
8.7%
0 1101
 
8.2%
5 1058
 
7.8%
6 964
 
7.1%
7 832
 
6.2%
8 808
 
6.0%
9 596
 
4.4%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Latin
ValueCountFrequency (%)
E 2
33.3%
A 1
16.7%
M 1
16.7%
F 1
16.7%
L 1
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13503
99.9%
Hangul 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3442
25.5%
2 2076
15.4%
3 1445
10.7%
4 1175
 
8.7%
0 1101
 
8.2%
5 1058
 
7.8%
6 964
 
7.1%
7 832
 
6.2%
8 808
 
6.0%
9 596
 
4.4%
Other values (5) 6
 
< 0.1%
Hangul
ValueCountFrequency (%)
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Distinct83
Distinct (%)0.9%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:39.199598image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.0938628
Min length1

Characters and Unicode

Total characters10302
Distinct characters26
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique31 ?
Unique (%)0.3%

Sample

1st row20세미만유동인구수
2nd rowTWYO_BELO
3rd row0
4th row1
5th row0
ValueCountFrequency (%)
0 3669
39.0%
1 1309
 
13.9%
2 1096
 
11.6%
3 820
 
8.7%
4 487
 
5.2%
5 367
 
3.9%
6 307
 
3.3%
7 213
 
2.3%
8 174
 
1.8%
9 118
 
1.3%
Other values (73) 858
 
9.1%
2023-12-11T18:16:39.477648image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 3831
37.2%
1 2065
20.0%
2 1368
 
13.3%
3 946
 
9.2%
4 579
 
5.6%
5 476
 
4.6%
6 370
 
3.6%
7 271
 
2.6%
8 231
 
2.2%
9 148
 
1.4%
Other values (16) 17
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10285
99.8%
Uppercase Letter 8
 
0.1%
Other Letter 8
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 3831
37.2%
1 2065
20.1%
2 1368
 
13.3%
3 946
 
9.2%
4 579
 
5.6%
5 476
 
4.6%
6 370
 
3.6%
7 271
 
2.6%
8 231
 
2.2%
9 148
 
1.4%
Other Letter
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Uppercase Letter
ValueCountFrequency (%)
O 2
25.0%
E 1
12.5%
B 1
12.5%
T 1
12.5%
Y 1
12.5%
W 1
12.5%
L 1
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10286
99.8%
Latin 8
 
0.1%
Hangul 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 3831
37.2%
1 2065
20.1%
2 1368
 
13.3%
3 946
 
9.2%
4 579
 
5.6%
5 476
 
4.6%
6 370
 
3.6%
7 271
 
2.6%
8 231
 
2.2%
9 148
 
1.4%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Latin
ValueCountFrequency (%)
O 2
25.0%
E 1
12.5%
B 1
12.5%
T 1
12.5%
Y 1
12.5%
W 1
12.5%
L 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10294
99.9%
Hangul 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 3831
37.2%
1 2065
20.1%
2 1368
 
13.3%
3 946
 
9.2%
4 579
 
5.6%
5 476
 
4.6%
6 370
 
3.6%
7 271
 
2.6%
8 231
 
2.2%
9 148
 
1.4%
Other values (8) 9
 
0.1%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Distinct172
Distinct (%)1.8%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:39.715012image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.3346783
Min length1

Characters and Unicode

Total characters12570
Distinct characters23
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique53 ?
Unique (%)0.6%

Sample

1st row20대30대유동인구수
2nd rowTWNT_THRTS
3rd row20
4th row35
5th row55
ValueCountFrequency (%)
2 946
 
10.0%
1 910
 
9.7%
3 885
 
9.4%
0 771
 
8.2%
4 736
 
7.8%
5 603
 
6.4%
6 479
 
5.1%
7 417
 
4.4%
8 370
 
3.9%
10 297
 
3.2%
Other values (162) 3004
31.9%
2023-12-11T18:16:40.064613image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3050
24.3%
2 1932
15.4%
3 1529
12.2%
0 1334
10.6%
4 1155
 
9.2%
5 1009
 
8.0%
6 783
 
6.2%
7 686
 
5.5%
8 614
 
4.9%
9 461
 
3.7%
Other values (13) 17
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 12553
99.9%
Uppercase Letter 9
 
0.1%
Other Letter 7
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3050
24.3%
2 1932
15.4%
3 1529
12.2%
0 1334
10.6%
4 1155
 
9.2%
5 1009
 
8.0%
6 783
 
6.2%
7 686
 
5.5%
8 614
 
4.9%
9 461
 
3.7%
Uppercase Letter
ValueCountFrequency (%)
T 4
44.4%
W 1
 
11.1%
R 1
 
11.1%
H 1
 
11.1%
N 1
 
11.1%
S 1
 
11.1%
Other Letter
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 12554
99.9%
Latin 9
 
0.1%
Hangul 7
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3050
24.3%
2 1932
15.4%
3 1529
12.2%
0 1334
10.6%
4 1155
 
9.2%
5 1009
 
8.0%
6 783
 
6.2%
7 686
 
5.5%
8 614
 
4.9%
9 461
 
3.7%
Latin
ValueCountFrequency (%)
T 4
44.4%
W 1
 
11.1%
R 1
 
11.1%
H 1
 
11.1%
N 1
 
11.1%
S 1
 
11.1%
Hangul
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 12563
99.9%
Hangul 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3050
24.3%
2 1932
15.4%
3 1529
12.2%
0 1334
10.6%
4 1155
 
9.2%
5 1009
 
8.0%
6 783
 
6.2%
7 686
 
5.5%
8 614
 
4.9%
9 461
 
3.7%
Other values (7) 10
 
0.1%
Hangul
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Distinct93
Distinct (%)1.0%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:40.294312image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length1
Mean length1.2532385
Min length1

Characters and Unicode

Total characters11803
Distinct characters21
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique23 ?
Unique (%)0.2%

Sample

1st row40대50대유동인구수
2nd rowFRTS_FFTS
3rd row7
4th row14
5th row16
ValueCountFrequency (%)
2 1142
12.1%
3 1000
10.6%
1 967
10.3%
4 844
 
9.0%
5 700
 
7.4%
0 663
 
7.0%
6 555
 
5.9%
7 468
 
5.0%
8 413
 
4.4%
10 350
 
3.7%
Other values (83) 2316
24.6%
2023-12-11T18:16:40.736490image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2857
24.2%
2 1903
16.1%
3 1459
12.4%
0 1174
9.9%
4 1075
 
9.1%
5 975
 
8.3%
6 745
 
6.3%
7 622
 
5.3%
8 563
 
4.8%
9 414
 
3.5%
Other values (11) 16
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11787
99.9%
Uppercase Letter 8
 
0.1%
Other Letter 7
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2857
24.2%
2 1903
16.1%
3 1459
12.4%
0 1174
10.0%
4 1075
 
9.1%
5 975
 
8.3%
6 745
 
6.3%
7 622
 
5.3%
8 563
 
4.8%
9 414
 
3.5%
Other Letter
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Uppercase Letter
ValueCountFrequency (%)
F 3
37.5%
S 2
25.0%
T 2
25.0%
R 1
 
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11788
99.9%
Latin 8
 
0.1%
Hangul 7
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2857
24.2%
2 1903
16.1%
3 1459
12.4%
0 1174
10.0%
4 1075
 
9.1%
5 975
 
8.3%
6 745
 
6.3%
7 622
 
5.3%
8 563
 
4.8%
9 414
 
3.5%
Hangul
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Latin
ValueCountFrequency (%)
F 3
37.5%
S 2
25.0%
T 2
25.0%
R 1
 
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11796
99.9%
Hangul 7
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2857
24.2%
2 1903
16.1%
3 1459
12.4%
0 1174
10.0%
4 1075
 
9.1%
5 975
 
8.3%
6 745
 
6.3%
7 622
 
5.3%
8 563
 
4.8%
9 414
 
3.5%
Other values (5) 9
 
0.1%
Hangul
ValueCountFrequency (%)
2
28.6%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
1
14.3%
Distinct56
Distinct (%)0.6%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:40.927268image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length1
Mean length1.0659376
Min length1

Characters and Unicode

Total characters10039
Distinct characters27
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)0.2%

Sample

1st row60대이상유동인구수
2nd rowSXTS_ABOVE
3rd row0
4th row3
5th row4
ValueCountFrequency (%)
0 2763
29.3%
1 1857
19.7%
2 1440
15.3%
3 938
 
10.0%
4 606
 
6.4%
5 376
 
4.0%
6 310
 
3.3%
7 222
 
2.4%
8 178
 
1.9%
9 126
 
1.3%
Other values (46) 602
 
6.4%
2023-12-11T18:16:41.227067image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 2892
28.8%
1 2422
24.1%
2 1615
16.1%
3 1047
 
10.4%
4 653
 
6.5%
5 428
 
4.3%
6 354
 
3.5%
7 256
 
2.6%
8 207
 
2.1%
9 147
 
1.5%
Other values (17) 18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10021
99.8%
Uppercase Letter 9
 
0.1%
Other Letter 8
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 2892
28.9%
1 2422
24.2%
2 1615
16.1%
3 1047
 
10.4%
4 653
 
6.5%
5 428
 
4.3%
6 354
 
3.5%
7 256
 
2.6%
8 207
 
2.1%
9 147
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
S 2
22.2%
E 1
11.1%
V 1
11.1%
O 1
11.1%
B 1
11.1%
A 1
11.1%
T 1
11.1%
X 1
11.1%
Other Letter
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10022
99.8%
Latin 9
 
0.1%
Hangul 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
0 2892
28.9%
1 2422
24.2%
2 1615
16.1%
3 1047
 
10.4%
4 653
 
6.5%
5 428
 
4.3%
6 354
 
3.5%
7 256
 
2.6%
8 207
 
2.1%
9 147
 
1.5%
Latin
ValueCountFrequency (%)
S 2
22.2%
E 1
11.1%
V 1
11.1%
O 1
11.1%
B 1
11.1%
A 1
11.1%
T 1
11.1%
X 1
11.1%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10031
99.9%
Hangul 8
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 2892
28.8%
1 2422
24.1%
2 1615
16.1%
3 1047
 
10.4%
4 653
 
6.5%
5 428
 
4.3%
6 354
 
3.5%
7 256
 
2.6%
8 207
 
2.1%
9 147
 
1.5%
Other values (9) 10
 
0.1%
Hangul
ValueCountFrequency (%)
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
1
12.5%
Distinct106
Distinct (%)1.1%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:41.426133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length9
Median length1
Mean length1.1930346
Min length1

Characters and Unicode

Total characters11236
Distinct characters28
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique29 ?
Unique (%)0.3%

Sample

1st row정장착용유동인구수
2nd rowSUIT_WEAR
3rd row6
4th row28
5th row4
ValueCountFrequency (%)
0 2233
23.7%
1 1299
13.8%
2 1166
12.4%
3 832
 
8.8%
4 599
 
6.4%
5 416
 
4.4%
6 367
 
3.9%
7 274
 
2.9%
8 242
 
2.6%
9 204
 
2.2%
Other values (96) 1786
19.0%
2023-12-11T18:16:41.802631image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2652
23.6%
0 2520
22.4%
2 1778
15.8%
3 1226
10.9%
4 823
 
7.3%
5 612
 
5.4%
6 514
 
4.6%
7 422
 
3.8%
8 375
 
3.3%
9 296
 
2.6%
Other values (18) 18
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 11218
99.8%
Other Letter 9
 
0.1%
Uppercase Letter 8
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2652
23.6%
0 2520
22.5%
2 1778
15.8%
3 1226
10.9%
4 823
 
7.3%
5 612
 
5.5%
6 514
 
4.6%
7 422
 
3.8%
8 375
 
3.3%
9 296
 
2.6%
Other Letter
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Uppercase Letter
ValueCountFrequency (%)
A 1
12.5%
E 1
12.5%
W 1
12.5%
S 1
12.5%
T 1
12.5%
I 1
12.5%
U 1
12.5%
R 1
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 11219
99.8%
Hangul 9
 
0.1%
Latin 8
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2652
23.6%
0 2520
22.5%
2 1778
15.8%
3 1226
10.9%
4 823
 
7.3%
5 612
 
5.5%
6 514
 
4.6%
7 422
 
3.8%
8 375
 
3.3%
9 296
 
2.6%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Latin
ValueCountFrequency (%)
A 1
12.5%
E 1
12.5%
W 1
12.5%
S 1
12.5%
T 1
12.5%
I 1
12.5%
U 1
12.5%
R 1
12.5%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11227
99.9%
Hangul 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2652
23.6%
0 2520
22.4%
2 1778
15.8%
3 1226
10.9%
4 823
 
7.3%
5 612
 
5.5%
6 514
 
4.6%
7 422
 
3.8%
8 375
 
3.3%
9 296
 
2.6%
Other values (9) 9
 
0.1%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Distinct209
Distinct (%)2.2%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:42.160102image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length2
Mean length1.5820769
Min length1

Characters and Unicode

Total characters14900
Distinct characters28
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique68 ?
Unique (%)0.7%

Sample

1st row캐주얼착용유동인구수
2nd rowCSL_WEAR
3rd row9
4th row27
5th row29
ValueCountFrequency (%)
4 532
 
5.6%
3 505
 
5.4%
2 484
 
5.1%
5 480
 
5.1%
6 457
 
4.9%
7 456
 
4.8%
8 408
 
4.3%
9 375
 
4.0%
10 341
 
3.6%
11 306
 
3.2%
Other values (199) 5074
53.9%
2023-12-11T18:16:42.616683image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3586
24.1%
2 2306
15.5%
3 1669
11.2%
4 1327
 
8.9%
5 1226
 
8.2%
6 1077
 
7.2%
7 1008
 
6.8%
0 980
 
6.6%
8 922
 
6.2%
9 781
 
5.2%
Other values (18) 18
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14882
99.9%
Other Letter 10
 
0.1%
Uppercase Letter 7
 
< 0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3586
24.1%
2 2306
15.5%
3 1669
11.2%
4 1327
 
8.9%
5 1226
 
8.2%
6 1077
 
7.2%
7 1008
 
6.8%
0 980
 
6.6%
8 922
 
6.2%
9 781
 
5.2%
Other Letter
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Uppercase Letter
ValueCountFrequency (%)
W 1
14.3%
R 1
14.3%
A 1
14.3%
E 1
14.3%
L 1
14.3%
S 1
14.3%
C 1
14.3%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14883
99.9%
Hangul 10
 
0.1%
Latin 7
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3586
24.1%
2 2306
15.5%
3 1669
11.2%
4 1327
 
8.9%
5 1226
 
8.2%
6 1077
 
7.2%
7 1008
 
6.8%
0 980
 
6.6%
8 922
 
6.2%
9 781
 
5.2%
Hangul
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Latin
ValueCountFrequency (%)
W 1
14.3%
R 1
14.3%
A 1
14.3%
E 1
14.3%
L 1
14.3%
S 1
14.3%
C 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14890
99.9%
Hangul 10
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3586
24.1%
2 2306
15.5%
3 1669
11.2%
4 1327
 
8.9%
5 1226
 
8.2%
6 1077
 
7.2%
7 1008
 
6.8%
0 980
 
6.6%
8 922
 
6.2%
9 781
 
5.2%
Other values (8) 8
 
0.1%
Hangul
ValueCountFrequency (%)
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
1
10.0%
Distinct109
Distinct (%)1.2%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:42.845846image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length12
Median length1
Mean length1.1667021
Min length1

Characters and Unicode

Total characters10988
Distinct characters29
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique36 ?
Unique (%)0.4%

Sample

1st row물건소지유동인구수
2nd rowTHING_POSSES
3rd row3
4th row16
5th row1
ValueCountFrequency (%)
0 1776
18.9%
1 1441
15.3%
2 1312
13.9%
3 929
9.9%
4 713
7.6%
5 501
 
5.3%
6 422
 
4.5%
7 344
 
3.7%
8 270
 
2.9%
9 183
 
1.9%
Other values (99) 1527
16.2%
2023-12-11T18:16:43.241891image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 2684
24.4%
0 2033
18.5%
2 1795
16.3%
3 1228
11.2%
4 891
 
8.1%
5 681
 
6.2%
6 527
 
4.8%
7 465
 
4.2%
8 392
 
3.6%
9 271
 
2.5%
Other values (19) 21
 
0.2%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 10967
99.8%
Uppercase Letter 11
 
0.1%
Other Letter 9
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 2684
24.5%
0 2033
18.5%
2 1795
16.4%
3 1228
11.2%
4 891
 
8.1%
5 681
 
6.2%
6 527
 
4.8%
7 465
 
4.2%
8 392
 
3.6%
9 271
 
2.5%
Uppercase Letter
ValueCountFrequency (%)
S 3
27.3%
O 1
 
9.1%
P 1
 
9.1%
T 1
 
9.1%
G 1
 
9.1%
N 1
 
9.1%
I 1
 
9.1%
H 1
 
9.1%
E 1
 
9.1%
Other Letter
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 10968
99.8%
Latin 11
 
0.1%
Hangul 9
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 2684
24.5%
0 2033
18.5%
2 1795
16.4%
3 1228
11.2%
4 891
 
8.1%
5 681
 
6.2%
6 527
 
4.8%
7 465
 
4.2%
8 392
 
3.6%
9 271
 
2.5%
Latin
ValueCountFrequency (%)
S 3
27.3%
O 1
 
9.1%
P 1
 
9.1%
T 1
 
9.1%
G 1
 
9.1%
N 1
 
9.1%
I 1
 
9.1%
H 1
 
9.1%
E 1
 
9.1%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10979
99.9%
Hangul 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 2684
24.4%
0 2033
18.5%
2 1795
16.3%
3 1228
11.2%
4 891
 
8.1%
5 681
 
6.2%
6 527
 
4.8%
7 465
 
4.2%
8 392
 
3.6%
9 271
 
2.5%
Other values (10) 12
 
0.1%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Distinct210
Distinct (%)2.2%
Missing1
Missing (%)< 0.1%
Memory size73.7 KiB
2023-12-11T18:16:43.574851image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length2
Mean length1.5871735
Min length1

Characters and Unicode

Total characters14948
Distinct characters30
Distinct categories4 ?
Distinct scripts3 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique73 ?
Unique (%)0.8%

Sample

1st row빈손통행유동인구수
2nd rowEMTHD_PASNG
3rd row14
4th row53
5th row55
ValueCountFrequency (%)
5 495
 
5.3%
2 495
 
5.3%
4 478
 
5.1%
3 475
 
5.0%
6 428
 
4.5%
7 414
 
4.4%
8 384
 
4.1%
9 356
 
3.8%
10 345
 
3.7%
1 333
 
3.5%
Other values (200) 5215
55.4%
2023-12-11T18:16:44.010092image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 3640
24.4%
2 2322
15.5%
3 1675
11.2%
4 1301
 
8.7%
5 1276
 
8.5%
0 1068
 
7.1%
6 1065
 
7.1%
7 943
 
6.3%
8 850
 
5.7%
9 788
 
5.3%
Other values (20) 20
 
0.1%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 14928
99.9%
Uppercase Letter 10
 
0.1%
Other Letter 9
 
0.1%
Connector Punctuation 1
 
< 0.1%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 3640
24.4%
2 2322
15.6%
3 1675
11.2%
4 1301
 
8.7%
5 1276
 
8.5%
0 1068
 
7.2%
6 1065
 
7.1%
7 943
 
6.3%
8 850
 
5.7%
9 788
 
5.3%
Uppercase Letter
ValueCountFrequency (%)
M 1
10.0%
N 1
10.0%
S 1
10.0%
A 1
10.0%
P 1
10.0%
D 1
10.0%
H 1
10.0%
T 1
10.0%
E 1
10.0%
G 1
10.0%
Other Letter
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 14929
99.9%
Latin 10
 
0.1%
Hangul 9
 
0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
1 3640
24.4%
2 2322
15.6%
3 1675
11.2%
4 1301
 
8.7%
5 1276
 
8.5%
0 1068
 
7.2%
6 1065
 
7.1%
7 943
 
6.3%
8 850
 
5.7%
9 788
 
5.3%
Latin
ValueCountFrequency (%)
M 1
10.0%
N 1
10.0%
S 1
10.0%
A 1
10.0%
P 1
10.0%
D 1
10.0%
H 1
10.0%
T 1
10.0%
E 1
10.0%
G 1
10.0%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Most occurring blocks

ValueCountFrequency (%)
ASCII 14939
99.9%
Hangul 9
 
0.1%

Most frequent character per block

ASCII
ValueCountFrequency (%)
1 3640
24.4%
2 2322
15.5%
3 1675
11.2%
4 1301
 
8.7%
5 1276
 
8.5%
0 1068
 
7.1%
6 1065
 
7.1%
7 943
 
6.3%
8 850
 
5.7%
9 788
 
5.3%
Other values (11) 11
 
0.1%
Hangul
ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%

Unnamed: 16
Text

MISSING 

Distinct2
Distinct (%)100.0%
Missing9417
Missing (%)> 99.9%
Memory size73.7 KiB
2023-12-11T18:16:44.163116image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length6
Median length4.5
Mean length4.5
Min length3

Characters and Unicode

Total characters9
Distinct characters9
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

Unique2 ?
Unique (%)100.0%

Sample

1st row기타특이사항
2nd rowETC
ValueCountFrequency (%)
기타특이사항 1
50.0%
etc 1
50.0%
2023-12-11T18:16:44.756644image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
1
11.1%
E 1
11.1%
T 1
11.1%
C 1
11.1%

Most occurring categories

ValueCountFrequency (%)
Other Letter 6
66.7%
Uppercase Letter 3
33.3%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Uppercase Letter
ValueCountFrequency (%)
E 1
33.3%
T 1
33.3%
C 1
33.3%

Most occurring scripts

ValueCountFrequency (%)
Hangul 6
66.7%
Latin 3
33.3%

Most frequent character per script

Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
Latin
ValueCountFrequency (%)
E 1
33.3%
T 1
33.3%
C 1
33.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 6
66.7%
ASCII 3
33.3%

Most frequent character per block

Hangul
ValueCountFrequency (%)
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
1
16.7%
ASCII
ValueCountFrequency (%)
E 1
33.3%
T 1
33.3%
C 1
33.3%

Unnamed: 17
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
2009
9416 
<NA>
 
1
년도
 
1
YEAR
 
1

Length

Max length4
Median length4
Mean length3.9997877
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row년도
3rd rowYEAR
4th row2009
5th row2009

Common Values

ValueCountFrequency (%)
2009 9416
> 99.9%
<NA> 1
 
< 0.1%
년도 1
 
< 0.1%
YEAR 1
 
< 0.1%

Length

2023-12-11T18:16:44.890163image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:16:45.013146image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2009 9416
> 99.9%
na 1
 
< 0.1%
년도 1
 
< 0.1%
year 1
 
< 0.1%

Unnamed: 18
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
주중
4710 
주말
4706 
<NA>
 
1
주중주말구분
 
1
EXAMIN_WDAY_WEND
 
1

Length

Max length16
Median length2
Mean length2.0021234
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row주중주말구분
3rd rowEXAMIN_WDAY_WEND
4th row주중
5th row주중

Common Values

ValueCountFrequency (%)
주중 4710
50.0%
주말 4706
50.0%
<NA> 1
 
< 0.1%
주중주말구분 1
 
< 0.1%
EXAMIN_WDAY_WEND 1
 
< 0.1%

Length

2023-12-11T18:16:45.121654image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:16:45.226275image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주중 4710
50.0%
주말 4706
50.0%
na 1
 
< 0.1%
주중주말구분 1
 
< 0.1%
examin_wday_wend 1
 
< 0.1%

Unnamed: 19
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct5
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size73.7 KiB
오전
4710 
오후
4706 
<NA>
 
1
오전오후구분
 
1
EXAMIN_AM_PM
 
1

Length

Max length12
Median length2
Mean length2.0016987
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row<NA>
2nd row오전오후구분
3rd rowEXAMIN_AM_PM
4th row오전
5th row오전

Common Values

ValueCountFrequency (%)
오전 4710
50.0%
오후 4706
50.0%
<NA> 1
 
< 0.1%
오전오후구분 1
 
< 0.1%
EXAMIN_AM_PM 1
 
< 0.1%

Length

2023-12-11T18:16:45.352129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T18:16:45.453992image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오전 4710
50.0%
오후 4706
50.0%
na 1
 
< 0.1%
오전오후구분 1
 
< 0.1%
examin_am_pm 1
 
< 0.1%

Correlations

2023-12-11T18:16:45.546671image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 3Unnamed: 8Unnamed: 10Unnamed: 11Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
Unnamed: 21.0001.0000.8110.8130.8000.0001.0001.0000.946
Unnamed: 31.0001.0000.9620.9630.9710.0001.0001.0000.982
Unnamed: 80.8110.9621.0000.9500.9130.0001.0000.9620.964
Unnamed: 100.8130.9630.9501.0000.9680.0001.0000.9630.963
Unnamed: 110.8000.9710.9130.9681.0000.0001.0000.9710.971
Unnamed: 160.0000.0000.0000.0000.0001.0000.0000.0000.000
Unnamed: 171.0001.0001.0001.0001.0000.0001.0001.0001.000
Unnamed: 181.0001.0000.9620.9630.9710.0001.0001.0000.982
Unnamed: 190.9460.9820.9640.9630.9710.0001.0000.9821.000
2023-12-11T18:16:45.685812image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 3Unnamed: 18Unnamed: 19Unnamed: 17
Unnamed: 21.0000.9990.9990.8150.999
Unnamed: 30.9991.0001.0000.8161.000
Unnamed: 180.9991.0001.0000.8161.000
Unnamed: 190.8150.8160.8161.0001.000
Unnamed: 170.9991.0001.0001.0001.000
2023-12-11T18:16:45.797915image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 2Unnamed: 3Unnamed: 17Unnamed: 18Unnamed: 19
Unnamed: 21.0000.9990.9990.9990.815
Unnamed: 30.9991.0001.0001.0000.816
Unnamed: 170.9991.0001.0001.0001.000
Unnamed: 180.9991.0001.0001.0000.816
Unnamed: 190.8150.8161.0000.8161.000

Missing values

2023-12-11T18:16:34.257487image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T18:16:34.539156image/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.
2023-12-11T18:16:34.848988image/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

유동인구_관찰조사_2009Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
0NaN<NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA><NA>
1ID관찰조사조사지점코드조사일자조사요일조사시작시간조사완료시간남자유동인구수여성유동인구수20세미만유동인구수20대30대유동인구수40대50대유동인구수60대이상유동인구수정장착용유동인구수캐주얼착용유동인구수물건소지유동인구수빈손통행유동인구수기타특이사항년도주중주말구분오전오후구분
2ID_OBSERV_EXAMINEXAMIN_SPOT_CDEXAMIN_DAYEXAMIN_DATEEXAMIN_START_TMEXAMIN_END_TMMALEFEMALETWYO_BELOTWNT_THRTSFRTS_FFTSSXTS_ABOVESUIT_WEARCSL_WEARTHING_POSSESEMTHD_PASNGETCYEAREXAMIN_WDAY_WENDEXAMIN_AM_PM
3143201-03311020850091515130207069314<NA>2009주중오전
4143201-033110212301255282413514328271653<NA>2009주중오전
5143401-0331102183018551623055164429155<NA>2009주중오후
6143401-03311021900192526270352801342078<NA>2009주중오후
7143601-0331107104511101170942416312<NA>2009주말오전
8143601-03311071230125515190296215271147<NA>2009주말오전
9143801-033110717201745411834555461722240<NA>2009주말오후
유동인구_관찰조사_2009Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15Unnamed: 16Unnamed: 17Unnamed: 18Unnamed: 19
940931625-82009261402142712053202304<NA>2009주말오후
941031625-8200926170217272120391017218<NA>2009주말오후
941131825-8220921104011052301303715<NA>2009주중오전
941231825-8220921131013359102124295213<NA>2009주중오전
9413156525-822092115151540280780518114<NA>2009주중오후
9414156525-822092119352000892640951012<NA>2009주중오후
941532225-8220926102010452421310704<NA>2009주말오전
941632225-82209261300132523231216010<NA>2009주말오전
941732425-82209261405143061111991317412<NA>2009주말오후
941832425-822092619001925540490221216<NA>2009주말오후