Overview

Dataset statistics

Number of variables16
Number of observations10000
Missing cells39999
Missing cells (%)25.0%
Duplicate rows73
Duplicate rows (%)0.7%
Total size in memory1.3 MiB
Average record size in memory136.0 B

Variable types

Unsupported2
Text5
Categorical9

Dataset

Description파일 다운로드
Author서울특별시
URLhttps://data.seoul.go.kr/dataList/OA-21706/F/1/datasetView.do

Alerts

Unnamed: 2 has constant value ""Constant
Unnamed: 3 has constant value ""Constant
Unnamed: 11 has constant value ""Constant
Unnamed: 12 has constant value ""Constant
Dataset has 73 (0.7%) duplicate rowsDuplicates
Unnamed: 9 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 14 is highly overall correlated with Unnamed: 4 and 3 other fieldsHigh correlation
Unnamed: 7 is highly overall correlated with Unnamed: 4 and 3 other fieldsHigh correlation
Unnamed: 4 is highly overall correlated with Unnamed: 6 and 7 other fieldsHigh correlation
Unnamed: 10 is highly overall correlated with Unnamed: 4 and 2 other fieldsHigh correlation
Unnamed: 6 is highly overall correlated with Unnamed: 4 and 5 other fieldsHigh correlation
Unnamed: 15 is highly overall correlated with Unnamed: 4 and 7 other fieldsHigh correlation
Unnamed: 8 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 13 is highly overall correlated with Unnamed: 4 and 1 other fieldsHigh correlation
Unnamed: 15 is highly imbalanced (99.8%)Imbalance
Unnamed: 2 has 9999 (> 99.9%) missing valuesMissing
Unnamed: 3 has 9999 (> 99.9%) missing valuesMissing
Unnamed: 11 has 9999 (> 99.9%) missing valuesMissing
Unnamed: 12 has 9999 (> 99.9%) missing valuesMissing
유동인구_속성조사_2012 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 5 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-11 04:01:59.756469
Analysis finished2023-12-11 04:02:02.188606
Duration2.43 seconds
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

유동인구_속성조사_2012
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)< 0.1%
Memory size156.2 KiB

Unnamed: 1
Text

Distinct1001
Distinct (%)10.0%
Missing1
Missing (%)< 0.1%
Memory size156.2 KiB
2023-12-11T13:02:02.592439image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length14
Median length6
Mean length6.0022002
Min length6

Characters and Unicode

Total characters60016
Distinct characters24
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)< 0.1%

Sample

1st row03-148
2nd row14-528
3rd row19-702
4th row14-526
5th row08-331
ValueCountFrequency (%)
10-390 17
 
0.2%
23-862 16
 
0.2%
22-816 16
 
0.2%
21-763 16
 
0.2%
01-053 16
 
0.2%
07-297 16
 
0.2%
21-760 16
 
0.2%
24-965 16
 
0.2%
08-315 15
 
0.2%
06-251 15
 
0.2%
Other values (991) 9840
98.4%
2023-12-11T13:02:03.234334image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
- 9998
16.7%
1 8296
13.8%
2 7359
12.3%
0 7117
11.9%
3 4203
7.0%
4 4106
6.8%
5 4008
6.7%
7 3840
 
6.4%
9 3809
 
6.3%
6 3730
 
6.2%
Other values (14) 3550
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 50004
83.3%
Dash Punctuation 9998
 
16.7%
Uppercase Letter 12
 
< 0.1%
Connector Punctuation 2
 
< 0.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
C 1
8.3%
T 1
8.3%
O 1
8.3%
P 1
8.3%
S 1
8.3%
X 1
8.3%
N 1
8.3%
I 1
8.3%
M 1
8.3%
A 1
8.3%
Other values (2) 2
16.7%
Decimal Number
ValueCountFrequency (%)
1 8296
16.6%
2 7359
14.7%
0 7117
14.2%
3 4203
8.4%
4 4106
8.2%
5 4008
8.0%
7 3840
7.7%
9 3809
7.6%
6 3730
7.5%
8 3536
7.1%
Dash Punctuation
ValueCountFrequency (%)
- 9998
100.0%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 60004
> 99.9%
Latin 12
 
< 0.1%

Most frequent character per script

Common
ValueCountFrequency (%)
- 9998
16.7%
1 8296
13.8%
2 7359
12.3%
0 7117
11.9%
3 4203
7.0%
4 4106
6.8%
5 4008
6.7%
7 3840
 
6.4%
9 3809
 
6.3%
6 3730
 
6.2%
Other values (2) 3538
 
5.9%
Latin
ValueCountFrequency (%)
C 1
8.3%
T 1
8.3%
O 1
8.3%
P 1
8.3%
S 1
8.3%
X 1
8.3%
N 1
8.3%
I 1
8.3%
M 1
8.3%
A 1
8.3%
Other values (2) 2
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 60016
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
- 9998
16.7%
1 8296
13.8%
2 7359
12.3%
0 7117
11.9%
3 4203
7.0%
4 4106
6.8%
5 4008
6.7%
7 3840
 
6.4%
9 3809
 
6.3%
6 3730
 
6.2%
Other values (14) 3550
 
5.9%

Unnamed: 2
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-11T13:02:03.435308image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters10
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEXAMIN_DAY
ValueCountFrequency (%)
examin_day 1
100.0%
2023-12-11T13:02:03.739571image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
A 2
20.0%
E 1
10.0%
X 1
10.0%
M 1
10.0%
I 1
10.0%
N 1
10.0%
_ 1
10.0%
D 1
10.0%
Y 1
10.0%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 9
90.0%
Connector Punctuation 1
 
10.0%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
A 2
22.2%
E 1
11.1%
X 1
11.1%
M 1
11.1%
I 1
11.1%
N 1
11.1%
D 1
11.1%
Y 1
11.1%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 9
90.0%
Common 1
 
10.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
A 2
22.2%
E 1
11.1%
X 1
11.1%
M 1
11.1%
I 1
11.1%
N 1
11.1%
D 1
11.1%
Y 1
11.1%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 10
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
A 2
20.0%
E 1
10.0%
X 1
10.0%
M 1
10.0%
I 1
10.0%
N 1
10.0%
_ 1
10.0%
D 1
10.0%
Y 1
10.0%

Unnamed: 3
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-11T13:02:03.932292image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters9
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowEXAMIN_DATE
ValueCountFrequency (%)
examin_date 1
100.0%
2023-12-11T13:02:04.279493image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
E 2
18.2%
A 2
18.2%
X 1
9.1%
M 1
9.1%
I 1
9.1%
N 1
9.1%
_ 1
9.1%
D 1
9.1%
T 1
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10
90.9%
Connector Punctuation 1
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
E 2
20.0%
A 2
20.0%
X 1
10.0%
M 1
10.0%
I 1
10.0%
N 1
10.0%
D 1
10.0%
T 1
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10
90.9%
Common 1
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
E 2
20.0%
A 2
20.0%
X 1
10.0%
M 1
10.0%
I 1
10.0%
N 1
10.0%
D 1
10.0%
T 1
10.0%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
E 2
18.2%
A 2
18.2%
X 1
9.1%
M 1
9.1%
I 1
9.1%
N 1
9.1%
_ 1
9.1%
D 1
9.1%
T 1
9.1%

Unnamed: 4
Categorical

HIGH CORRELATION 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
여자
5083 
남자
4915 
MW_MN_SE
 
1
<NA>
 
1

Length

Max length8
Median length2
Mean length2.0008
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row여자
2nd row여자
3rd row남자
4th row여자
5th row여자

Common Values

ValueCountFrequency (%)
여자 5083
50.8%
남자 4915
49.1%
MW_MN_SE 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T13:02:04.446533image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:02:04.589713image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
여자 5083
50.8%
남자 4915
49.1%
mw_mn_se 1
 
< 0.1%
na 1
 
< 0.1%

Unnamed: 5
Unsupported

REJECTED  UNSUPPORTED 

Missing1
Missing (%)< 0.1%
Memory size156.2 KiB

Unnamed: 6
Categorical

HIGH CORRELATION 

Distinct6
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
오전7시30분~11시
3317 
오후2시~5시
2432 
오후5시~8시
2425 
오전11시~오후2시
1824 
EXAMIN_TMZON_TEXT
 
1

Length

Max length17
Median length11
Mean length8.8747
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row오후5시~8시
2nd row오전11시~오후2시
3rd row오전7시30분~11시
4th row오후5시~8시
5th row오후2시~5시

Common Values

ValueCountFrequency (%)
오전7시30분~11시 3317
33.2%
오후2시~5시 2432
24.3%
오후5시~8시 2425
24.2%
오전11시~오후2시 1824
18.2%
EXAMIN_TMZON_TEXT 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T13:02:04.754019image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:02:04.916976image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
오전7시30분~11시 3317
33.2%
오후2시~5시 2432
24.3%
오후5시~8시 2425
24.2%
오전11시~오후2시 1824
18.2%
examin_tmzon_text 1
 
< 0.1%
na 1
 
< 0.1%

Unnamed: 7
Categorical

HIGH CORRELATION 

Distinct9
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
20~29세
2132 
50~59세
2073 
40~49세
1872 
30~39세
1764 
60세 이상
1441 
Other values (4)
718 

Length

Max length8
Median length6
Mean length5.972
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row20~29세
2nd row50~59세
3rd row40~49세
4th row20~29세
5th row50~59세

Common Values

ValueCountFrequency (%)
20~29세 2132
21.3%
50~59세 2073
20.7%
40~49세 1872
18.7%
30~39세 1764
17.6%
60세 이상 1441
14.4%
15~19세 646
 
6.5%
미상 70
 
0.7%
AGRDE_CN 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T13:02:05.112330image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:02:05.270603image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
20~29세 2132
18.6%
50~59세 2073
18.1%
40~49세 1872
16.4%
30~39세 1764
15.4%
60세 1441
12.6%
이상 1441
12.6%
15~19세 646
 
5.6%
미상 70
 
0.6%
agrde_cn 1
 
< 0.1%
na 1
 
< 0.1%

Unnamed: 8
Categorical

HIGH CORRELATION 

Distinct28
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
노원구
 
708
기타지역
 
626
송파구
 
526
중랑구
 
478
영등포구
 
464
Other values (23)
7198 

Length

Max length9
Median length3
Mean length3.1524
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row용산구
2nd row마포구
3rd row금천구
4th row마포구
5th row성북구

Common Values

ValueCountFrequency (%)
노원구 708
 
7.1%
기타지역 626
 
6.3%
송파구 526
 
5.3%
중랑구 478
 
4.8%
영등포구 464
 
4.6%
구로구 455
 
4.5%
은평구 444
 
4.4%
강서구 421
 
4.2%
성북구 401
 
4.0%
양천구 399
 
4.0%
Other values (18) 5078
50.8%

Length

2023-12-11T13:02:05.467861image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
노원구 708
 
7.1%
기타지역 626
 
6.3%
송파구 526
 
5.3%
중랑구 478
 
4.8%
영등포구 464
 
4.6%
구로구 455
 
4.5%
은평구 444
 
4.4%
강서구 421
 
4.2%
성북구 401
 
4.0%
양천구 399
 
4.0%
Other values (18) 5078
50.8%

Unnamed: 9
Categorical

HIGH CORRELATION 

Distinct15
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등)
1604 
업무관련
1565 
여가/오락/친교/모임/식사(회식,음료 포함)
1359 
출근
1161 
물건을 사려고
891 
Other values (10)
3420 

Length

Max length31
Median length20
Mean length14.2065
Min length2

Unique

Unique3 ?
Unique (%)< 0.1%

Sample

1st row여가/오락/친교/모임/식사(회식,음료 포함)
2nd row여가/오락/친교/모임/식사(회식,음료 포함)
3rd row업무관련
4th row그냥 걸으려고(운동,산책,기분전환 등)
5th row귀가/퇴근/하교

Common Values

ValueCountFrequency (%)
개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등) 1604
16.0%
업무관련 1565
15.7%
여가/오락/친교/모임/식사(회식,음료 포함) 1359
13.6%
출근 1161
11.6%
물건을 사려고 891
8.9%
귀가/퇴근/하교 877
8.8%
그냥 걸으려고(운동,산책,기분전환 등) 722
7.2%
역/정류장/교통수단 이용 595
 
5.9%
학업관련(학원/도서관) 496
 
5.0%
누군가를 데리러(또는 데려다 주러) 314
 
3.1%
Other values (5) 416
 
4.2%

Length

2023-12-11T13:02:05.663737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
등 2326
13.4%
개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 1604
 
9.3%
업무관련 1565
 
9.0%
여가/오락/친교/모임/식사(회식,음료 1359
 
7.8%
포함 1359
 
7.8%
출근 1161
 
6.7%
물건을 891
 
5.1%
사려고 891
 
5.1%
귀가/퇴근/하교 877
 
5.1%
그냥 722
 
4.2%
Other values (17) 4576
26.4%

Unnamed: 10
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
매일
3498 
주 3~5회
2971 
주 1~2회
1691 
월 1~2회
1033 
오늘 처음
426 
Other values (3)
381 

Length

Max length8
Median length6
Mean length4.634
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row매일
2nd row매일
3rd row월 1~2회
4th row주 3~5회
5th row매일

Common Values

ValueCountFrequency (%)
매일 3498
35.0%
주 3~5회 2971
29.7%
주 1~2회 1691
16.9%
월 1~2회 1033
 
10.3%
오늘 처음 426
 
4.3%
6개월 1~3회 379
 
3.8%
VISIT_FQ 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T13:02:05.807185image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:02:05.975908image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
주 4662
28.3%
매일 3498
21.2%
3~5회 2971
18.0%
1~2회 2724
16.5%
월 1033
 
6.3%
오늘 426
 
2.6%
처음 426
 
2.6%
6개월 379
 
2.3%
1~3회 379
 
2.3%
visit_fq 1
 
< 0.1%

Unnamed: 11
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-11T13:02:06.172274image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length13
Median length13
Mean length13
Min length13

Characters and Unicode

Total characters13
Distinct characters7
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowCNCDNT_MAN_NM
ValueCountFrequency (%)
cncdnt_man_nm 1
100.0%
2023-12-11T13:02:06.472270image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
N 4
30.8%
C 2
15.4%
_ 2
15.4%
M 2
15.4%
D 1
 
7.7%
T 1
 
7.7%
A 1
 
7.7%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 11
84.6%
Connector Punctuation 2
 
15.4%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
N 4
36.4%
C 2
18.2%
M 2
18.2%
D 1
 
9.1%
T 1
 
9.1%
A 1
 
9.1%
Connector Punctuation
ValueCountFrequency (%)
_ 2
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 11
84.6%
Common 2
 
15.4%

Most frequent character per script

Latin
ValueCountFrequency (%)
N 4
36.4%
C 2
18.2%
M 2
18.2%
D 1
 
9.1%
T 1
 
9.1%
A 1
 
9.1%
Common
ValueCountFrequency (%)
_ 2
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 13
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
N 4
30.8%
C 2
15.4%
_ 2
15.4%
M 2
15.4%
D 1
 
7.7%
T 1
 
7.7%
A 1
 
7.7%

Unnamed: 12
Text

CONSTANT  MISSING 

Distinct1
Distinct (%)100.0%
Missing9999
Missing (%)> 99.9%
Memory size156.2 KiB
2023-12-11T13:02:06.637133image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length11
Min length11

Characters and Unicode

Total characters11
Distinct characters8
Distinct categories2 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique1 ?
Unique (%)100.0%

Sample

1st rowTRNSPORT_MN
ValueCountFrequency (%)
trnsport_mn 1
100.0%
2023-12-11T13:02:06.968550image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
T 2
18.2%
R 2
18.2%
N 2
18.2%
S 1
9.1%
P 1
9.1%
O 1
9.1%
_ 1
9.1%
M 1
9.1%

Most occurring categories

ValueCountFrequency (%)
Uppercase Letter 10
90.9%
Connector Punctuation 1
 
9.1%

Most frequent character per category

Uppercase Letter
ValueCountFrequency (%)
T 2
20.0%
R 2
20.0%
N 2
20.0%
S 1
10.0%
P 1
10.0%
O 1
10.0%
M 1
10.0%
Connector Punctuation
ValueCountFrequency (%)
_ 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 10
90.9%
Common 1
 
9.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
T 2
20.0%
R 2
20.0%
N 2
20.0%
S 1
10.0%
P 1
10.0%
O 1
10.0%
M 1
10.0%
Common
ValueCountFrequency (%)
_ 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
T 2
18.2%
R 2
18.2%
N 2
18.2%
S 1
9.1%
P 1
9.1%
O 1
9.1%
_ 1
9.1%
M 1
9.1%

Unnamed: 13
Categorical

HIGH CORRELATION 

Distinct7
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
보통
5032 
약간 만족
1904 
약간 불만족
1646 
매우 만족
895 
매우 불만족
521 
Other values (2)
 
2

Length

Max length10
Median length2
Mean length3.7075
Min length2

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row보통
2nd row약간 불만족
3rd row보통
4th row매우 만족
5th row약간 불만족

Common Values

ValueCountFrequency (%)
보통 5032
50.3%
약간 만족 1904
 
19.0%
약간 불만족 1646
 
16.5%
매우 만족 895
 
8.9%
매우 불만족 521
 
5.2%
WALK_ENVRN 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T13:02:07.159206image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:02:07.315595image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
보통 5032
33.6%
약간 3550
23.7%
만족 2799
18.7%
불만족 2167
14.5%
매우 1416
 
9.5%
walk_envrn 1
 
< 0.1%
na 1
 
< 0.1%

Unnamed: 14
Categorical

HIGH CORRELATION 

Distinct13
Distinct (%)0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
(전업)주부
2082 
(대)학생
1707 
사무/기술직
1505 
자영업
1018 
판매/서비스직
998 
Other values (8)
2690 

Length

Max length7
Median length6
Mean length5.533
Min length3

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row(전업)주부
2nd row무직/기타
3rd row전문/자유직
4th row(전업)주부
5th row일용/작업직

Common Values

ValueCountFrequency (%)
(전업)주부 2082
20.8%
(대)학생 1707
17.1%
사무/기술직 1505
15.0%
자영업 1018
10.2%
판매/서비스직 998
10.0%
무직/기타 852
8.5%
전문/자유직 781
 
7.8%
일용/작업직 499
 
5.0%
경영/관리직 327
 
3.3%
생산/운수직 211
 
2.1%
Other values (3) 20
 
0.2%

Length

2023-12-11T13:02:07.449923image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
전업)주부 2082
20.8%
대)학생 1707
17.1%
사무/기술직 1505
15.0%
자영업 1018
10.2%
판매/서비스직 998
10.0%
무직/기타 852
8.5%
전문/자유직 781
 
7.8%
일용/작업직 499
 
5.0%
경영/관리직 327
 
3.3%
생산/운수직 211
 
2.1%
Other values (3) 20
 
0.2%

Unnamed: 15
Categorical

HIGH CORRELATION  IMBALANCE 

Distinct3
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size156.2 KiB
2012
9998 
YEAR
 
1
<NA>
 
1

Length

Max length4
Median length4
Mean length4
Min length4

Unique

Unique2 ?
Unique (%)< 0.1%

Sample

1st row2012
2nd row2012
3rd row2012
4th row2012
5th row2012

Common Values

ValueCountFrequency (%)
2012 9998
> 99.9%
YEAR 1
 
< 0.1%
<NA> 1
 
< 0.1%

Length

2023-12-11T13:02:07.599066image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-11T13:02:07.719059image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
2012 9998
> 99.9%
year 1
 
< 0.1%
na 1
 
< 0.1%

Correlations

2023-12-11T13:02:08.070253image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 13Unnamed: 14Unnamed: 15
Unnamed: 41.0000.7180.7890.9160.8560.7690.9410.9711.000
Unnamed: 60.7181.0000.6750.7660.7890.6620.6400.7171.000
Unnamed: 70.7890.6751.0000.7140.7360.6390.6650.8261.000
Unnamed: 80.9160.7660.7141.0000.6840.7400.7560.6911.000
Unnamed: 90.8560.7890.7360.6841.0000.8190.7100.7541.000
Unnamed: 100.7690.6620.6390.7400.8191.0000.6400.6761.000
Unnamed: 130.9410.6400.6650.7560.7100.6401.0000.8051.000
Unnamed: 140.9710.7170.8260.6910.7540.6760.8051.0001.000
Unnamed: 151.0001.0001.0001.0001.0001.0001.0001.0001.000
2023-12-11T13:02:08.223869image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 9Unnamed: 14Unnamed: 7Unnamed: 4Unnamed: 10Unnamed: 6Unnamed: 15Unnamed: 8Unnamed: 13
Unnamed: 91.0000.4040.4380.7320.4520.5630.9990.2810.448
Unnamed: 140.4041.0000.5300.8020.4130.5020.9990.3060.450
Unnamed: 70.4380.5301.0000.7070.4110.5001.0000.3810.448
Unnamed: 40.7320.8020.7071.0000.7070.7071.0000.7080.707
Unnamed: 100.4520.4130.4110.7071.0000.5051.0000.4210.449
Unnamed: 60.5630.5020.5000.7070.5051.0001.0000.5000.500
Unnamed: 150.9990.9991.0001.0001.0001.0001.0000.9991.000
Unnamed: 80.2810.3060.3810.7080.4210.5000.9991.0000.454
Unnamed: 130.4480.4500.4480.7070.4490.5001.0000.4541.000
2023-12-11T13:02:08.374129image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Unnamed: 4Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 13Unnamed: 14Unnamed: 15
Unnamed: 41.0000.7070.7070.7080.7320.7070.7070.8021.000
Unnamed: 60.7071.0000.5000.5000.5630.5050.5000.5021.000
Unnamed: 70.7070.5001.0000.3810.4380.4110.4480.5301.000
Unnamed: 80.7080.5000.3811.0000.2810.4210.4540.3060.999
Unnamed: 90.7320.5630.4380.2811.0000.4520.4480.4040.999
Unnamed: 100.7070.5050.4110.4210.4521.0000.4490.4131.000
Unnamed: 130.7070.5000.4480.4540.4480.4491.0000.4501.000
Unnamed: 140.8020.5020.5300.3060.4040.4130.4501.0000.999
Unnamed: 151.0001.0001.0000.9990.9991.0001.0000.9991.000

Missing values

2023-12-11T13:02:01.318405image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-11T13:02:01.582560image/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.
2023-12-11T13:02:01.881279image/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_속성조사_2012Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
132401328803-148<NA><NA>여자4오후5시~8시20~29세용산구여가/오락/친교/모임/식사(회식,음료 포함)매일<NA><NA>보통(전업)주부2012
5147515514-528<NA><NA>여자2오전11시~오후2시50~59세마포구여가/오락/친교/모임/식사(회식,음료 포함)매일<NA><NA>약간 불만족무직/기타2012
9947999519-702<NA><NA>남자1오전7시30분~11시40~49세금천구업무관련월 1~2회<NA><NA>보통전문/자유직2012
109421099014-526<NA><NA>여자4오후5시~8시20~29세마포구그냥 걸으려고(운동,산책,기분전환 등)주 3~5회<NA><NA>매우 만족(전업)주부2012
145591462708-331<NA><NA>여자3오후2시~5시50~59세성북구귀가/퇴근/하교매일<NA><NA>약간 불만족일용/작업직2012
113291137725-998<NA><NA>여자4오후5시~8시30~39세강동구귀가/퇴근/하교주 3~5회<NA><NA>매우 만족사무/기술직2012
7350736822-793<NA><NA>여자3오후2시~5시50~59세서초구출근매일<NA><NA>약간 만족판매/서비스직2012
9587963512-481<NA><NA>남자2오전11시~오후2시20~29세은평구그냥 걸으려고(운동,산책,기분전환 등)주 3~5회<NA><NA>약간 만족(대)학생2012
5075508314-536<NA><NA>남자1오전7시30분~11시30~39세마포구출근매일<NA><NA>보통사무/기술직2012
175991769716-607<NA><NA>남자3오후2시~5시15~19세강서구개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등)주 1~2회<NA><NA>보통(대)학생2012
유동인구_속성조사_2012Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
112341128211-406<NA><NA>남자1오전7시30분~11시50~59세노원구물건을 사려고주 1~2회<NA><NA>보통자영업2012
4678468621-776<NA><NA>여자4오후5시~8시30~39세관악구개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등)주 3~5회<NA><NA>보통(전업)주부2012
101751022323-873<NA><NA>남자1오전7시30분~11시60세 이상강남구여가/오락/친교/모임/식사(회식,음료 포함)주 3~5회<NA><NA>보통전문/자유직2012
52452224-962<NA><NA>남자1오전7시30분~11시20~29세송파구역/정류장/교통수단 이용월 1~2회<NA><NA>보통(대)학생2012
9211925915-573<NA><NA>남자3오후2시~5시60세 이상양천구그냥 걸으려고(운동,산책,기분전환 등)매일<NA><NA>보통무직/기타2012
3541354907-300<NA><NA>남자3오후2시~5시50~59세중랑구귀가/퇴근/하교매일<NA><NA>보통일용/작업직2012
181211821921-763<NA><NA>여자4오후5시~8시50~59세영등포구개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등)월 1~2회<NA><NA>약간 만족판매/서비스직2012
8406842402-074<NA><NA>여자1오전7시30분~11시15~19세중구역/정류장/교통수단 이용오늘 처음<NA><NA>약간 불만족(대)학생2012
151591522714-530<NA><NA>남자4오후5시~8시60세 이상마포구개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등)주 3~5회<NA><NA>약간 불만족생산/운수직2012
132621331003-150<NA><NA>남자4오후5시~8시30~39세은평구출근매일<NA><NA>매우 불만족자영업2012

Duplicate rows

Most frequently occurring

Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15# duplicates
2313-490<NA><NA>여자오후2시~5시50~59세서대문구그냥 걸으려고(운동,산책,기분전환 등)매일<NA><NA>보통(전업)주부20123
2913-509<NA><NA>남자오후2시~5시15~19세서대문구귀가/퇴근/하교매일<NA><NA>보통(대)학생20123
4920-737<NA><NA>남자오전7시30분~11시20~29세동작구학업관련(학원/도서관)매일<NA><NA>보통무직/기타20123
6524-923<NA><NA>남자오전7시30분~11시60세 이상송파구그냥 걸으려고(운동,산책,기분전환 등)매일<NA><NA>보통무직/기타20123
001-060<NA><NA>여자오전11시~오후2시60세 이상관악구개인용무/집안일(병원,은행,관공서,종교활동,봉사활동 등)월 1~2회<NA><NA>매우 만족무직/기타20122
102-119<NA><NA>남자오후5시~8시60세 이상중구업무관련주 3~5회<NA><NA>약간 만족자영업20122
202-139<NA><NA>여자오후2시~5시60세 이상중구그냥 걸으려고(운동,산책,기분전환 등)주 3~5회<NA><NA>매우 불만족무직/기타20122
305-205<NA><NA>남자오후2시~5시50~59세광진구업무관련매일<NA><NA>보통생산/운수직20122
405-226<NA><NA>여자오후5시~8시20~29세광진구여가/오락/친교/모임/식사(회식,음료 포함)주 3~5회<NA><NA>매우 만족전문/자유직20122
506-239<NA><NA>여자오후5시~8시30~39세동대문구귀가/퇴근/하교매일<NA><NA>보통사무/기술직20122