Overview

Dataset statistics

Number of variables16
Number of observations68
Missing cells770
Missing cells (%)70.8%
Duplicate rows6
Duplicate rows (%)8.8%
Total size in memory9.0 KiB
Average record size in memory135.9 B

Variable types

Categorical3
Unsupported10
Text3

Dataset

Description서초구 음식물류 폐기물 수집운반 구역별(1~5구역)에 대한 데이터로 노선 및 수거 요일,시간에 관한 정보 확인이 가능합니다.
Author서울특별시 서초구
URLhttps://www.data.go.kr/data/15077614/fileData.do

Alerts

Dataset has 6 (8.8%) duplicate rowsDuplicates
Unnamed: 12 is highly overall correlated with Unnamed: 3High correlation
Unnamed: 3 is highly overall correlated with Unnamed: 12High correlation
Unnamed: 1 has 44 (64.7%) missing valuesMissing
Unnamed: 2 has 44 (64.7%) missing valuesMissing
Unnamed: 4 has 68 (100.0%) missing valuesMissing
Unnamed: 5 has 68 (100.0%) missing valuesMissing
Unnamed: 6 has 64 (94.1%) missing valuesMissing
Unnamed: 7 has 68 (100.0%) missing valuesMissing
Unnamed: 8 has 68 (100.0%) missing valuesMissing
Unnamed: 9 has 47 (69.1%) missing valuesMissing
Unnamed: 10 has 49 (72.1%) missing valuesMissing
Unnamed: 11 has 49 (72.1%) missing valuesMissing
Unnamed: 13 has 68 (100.0%) missing valuesMissing
Unnamed: 14 has 68 (100.0%) missing valuesMissing
Unnamed: 15 has 65 (95.6%) missing valuesMissing
Unnamed: 1 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 2 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 4 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
Unnamed: 7 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 8 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 10 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 11 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 13 is an unsupported type, check if it needs cleaning or further analysisUnsupported
Unnamed: 14 is an unsupported type, check if it needs cleaning or further analysisUnsupported

Reproduction

Analysis started2023-12-12 07:26:20.565125
Analysis finished2023-12-12 07:26:21.562552
Duration1 second
Software versionydata-profiling vv4.5.1
Download configurationconfig.json

Variables

차량별 수거노선 (○동)
Categorical

Distinct8
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size676.0 B
<NA>
40 
요일구분
 
4
( 월 )요일
 
4
( 화 )요일
 
4
(수 )요일
 
4
Other values (3)
12 

Length

Max length8
Median length4
Mean length5.3529412
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row요일구분
3rd row( 월 )요일
4th row<NA>
5th row( 화 )요일

Common Values

ValueCountFrequency (%)
<NA> 40
58.8%
요일구분 4
 
5.9%
( 월 )요일 4
 
5.9%
( 화 )요일 4
 
5.9%
(수 )요일 4
 
5.9%
( 목 )요일 4
 
5.9%
( 금 )요일 4
 
5.9%
( 토 )요일 4
 
5.9%

Length

2023-12-12T16:26:21.667520image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:26:21.855042image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 40
35.7%
요일 24
21.4%
20
17.9%
요일구분 4
 
3.6%
월 4
 
3.6%
화 4
 
3.6%
수 4
 
3.6%
목 4
 
3.6%
금 4
 
3.6%
토 4
 
3.6%

Unnamed: 1
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing44
Missing (%)64.7%
Memory size676.0 B

Unnamed: 2
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing44
Missing (%)64.7%
Memory size676.0 B

Unnamed: 3
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size676.0 B
<NA>
28 
래미안-롯데캐슬-동사무소-강남대로-나루터길
6 
잠원동지역-한신4지구-나루터로-신반포로
6 
방배동-카페로-동작대로-삼호아파트길-카페골목뒷길
6 
방배본동지역-현대아파트 뒷길-동광로
6 
Other values (3)
16 

Length

Max length26
Median length25
Mean length14.088235
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row주요노선내역
3rd row래미안-롯데캐슬-동사무소-강남대로-나루터길
4th row잠원동지역-한신4지구-나루터로-신반포로
5th row래미안-롯데캐슬-동사무소-강남대로-나루터길

Common Values

ValueCountFrequency (%)
<NA> 28
41.2%
래미안-롯데캐슬-동사무소-강남대로-나루터길 6
 
8.8%
잠원동지역-한신4지구-나루터로-신반포로 6
 
8.8%
방배동-카페로-동작대로-삼호아파트길-카페골목뒷길 6
 
8.8%
방배본동지역-현대아파트 뒷길-동광로 6
 
8.8%
방배로-동광로-서초로-경신교회길-중앙로-카페로 6
 
8.8%
동사무소길-방배4동뒷골목-방배중앙로-방배로 6
 
8.8%
주요노선내역 4
 
5.9%

Length

2023-12-12T16:26:21.988926image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:26:22.438320image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 28
37.8%
래미안-롯데캐슬-동사무소-강남대로-나루터길 6
 
8.1%
잠원동지역-한신4지구-나루터로-신반포로 6
 
8.1%
방배동-카페로-동작대로-삼호아파트길-카페골목뒷길 6
 
8.1%
방배본동지역-현대아파트 6
 
8.1%
뒷길-동광로 6
 
8.1%
방배로-동광로-서초로-경신교회길-중앙로-카페로 6
 
8.1%
동사무소길-방배4동뒷골목-방배중앙로-방배로 6
 
8.1%
주요노선내역 4
 
5.4%

Unnamed: 4
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

Unnamed: 5
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

Unnamed: 6
Text

MISSING 

Distinct4
Distinct (%)100.0%
Missing64
Missing (%)94.1%
Memory size676.0 B
2023-12-12T16:26:22.622373image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length5
Median length4.5
Mean length4
Min length3

Characters and Unicode

Total characters16
Distinct characters12
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

Unique4 ?
Unique (%)100.0%

Sample

1st row잠원동일부
2nd row방배본동
3rd row방배4동
4th row아파트
ValueCountFrequency (%)
잠원동일부 1
25.0%
방배본동 1
25.0%
방배4동 1
25.0%
아파트 1
25.0%
2023-12-12T16:26:22.936737image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
동 3
18.8%
방 2
12.5%
배 2
12.5%
잠 1
 
6.2%
원 1
 
6.2%
일 1
 
6.2%
부 1
 
6.2%
본 1
 
6.2%
4 1
 
6.2%
아 1
 
6.2%
Other values (2) 2
12.5%

Most occurring categories

ValueCountFrequency (%)
Other Letter 15
93.8%
Decimal Number 1
 
6.2%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 3
20.0%
방 2
13.3%
배 2
13.3%
잠 1
 
6.7%
원 1
 
6.7%
일 1
 
6.7%
부 1
 
6.7%
본 1
 
6.7%
아 1
 
6.7%
파 1
 
6.7%
Decimal Number
ValueCountFrequency (%)
4 1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 15
93.8%
Common 1
 
6.2%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 3
20.0%
방 2
13.3%
배 2
13.3%
잠 1
 
6.7%
원 1
 
6.7%
일 1
 
6.7%
부 1
 
6.7%
본 1
 
6.7%
아 1
 
6.7%
파 1
 
6.7%
Common
ValueCountFrequency (%)
4 1
100.0%

Most occurring blocks

ValueCountFrequency (%)
Hangul 15
93.8%
ASCII 1
 
6.2%

Most frequent character per block

Hangul
ValueCountFrequency (%)
동 3
20.0%
방 2
13.3%
배 2
13.3%
잠 1
 
6.7%
원 1
 
6.7%
일 1
 
6.7%
부 1
 
6.7%
본 1
 
6.7%
아 1
 
6.7%
파 1
 
6.7%
ASCII
ValueCountFrequency (%)
4 1
100.0%

Unnamed: 7
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

Unnamed: 8
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

Unnamed: 9
Text

MISSING 

Distinct11
Distinct (%)52.4%
Missing47
Missing (%)69.1%
Memory size676.0 B
2023-12-12T16:26:23.086451image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length8
Median length8
Mean length7.2380952
Min length4

Characters and Unicode

Total characters152
Distinct characters13
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks2 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4 ?
Unique (%)19.0%

Sample

1st row요일구분
2nd row( 월 )요일
3rd row( 화 )요일
4th row(수 )요일
5th row( 목 )요일
ValueCountFrequency (%)
요일 18
32.7%
16
29.1%
요일구분 3
 
5.5%
목 3
 
5.5%
금 3
 
5.5%
월 3
 
5.5%
화 3
 
5.5%
수 3
 
5.5%
토 3
 
5.5%
2023-12-12T16:26:23.371307image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
50
32.9%
요 21
13.8%
일 21
13.8%
( 18
 
11.8%
) 18
 
11.8%
구 3
 
2.0%
분 3
 
2.0%
목 3
 
2.0%
금 3
 
2.0%
월 3
 
2.0%
Other values (3) 9
 
5.9%

Most occurring categories

ValueCountFrequency (%)
Other Letter 66
43.4%
Space Separator 50
32.9%
Open Punctuation 18
 
11.8%
Close Punctuation 18
 
11.8%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
요 21
31.8%
일 21
31.8%
구 3
 
4.5%
분 3
 
4.5%
목 3
 
4.5%
금 3
 
4.5%
월 3
 
4.5%
화 3
 
4.5%
수 3
 
4.5%
토 3
 
4.5%
Space Separator
ValueCountFrequency (%)
50
100.0%
Open Punctuation
ValueCountFrequency (%)
( 18
100.0%
Close Punctuation
ValueCountFrequency (%)
) 18
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 86
56.6%
Hangul 66
43.4%

Most frequent character per script

Hangul
ValueCountFrequency (%)
요 21
31.8%
일 21
31.8%
구 3
 
4.5%
분 3
 
4.5%
목 3
 
4.5%
금 3
 
4.5%
월 3
 
4.5%
화 3
 
4.5%
수 3
 
4.5%
토 3
 
4.5%
Common
ValueCountFrequency (%)
50
58.1%
( 18
 
20.9%
) 18
 
20.9%

Most occurring blocks

ValueCountFrequency (%)
ASCII 86
56.6%
Hangul 66
43.4%

Most frequent character per block

ASCII
ValueCountFrequency (%)
50
58.1%
( 18
 
20.9%
) 18
 
20.9%
Hangul
ValueCountFrequency (%)
요 21
31.8%
일 21
31.8%
구 3
 
4.5%
분 3
 
4.5%
목 3
 
4.5%
금 3
 
4.5%
월 3
 
4.5%
화 3
 
4.5%
수 3
 
4.5%
토 3
 
4.5%

Unnamed: 10
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing49
Missing (%)72.1%
Memory size676.0 B

Unnamed: 11
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing49
Missing (%)72.1%
Memory size676.0 B

Unnamed: 12
Categorical

HIGH CORRELATION 

Distinct8
Distinct (%)11.8%
Missing0
Missing (%)0.0%
Memory size676.0 B
<NA>
33 
경남아파트-신반포상가-신반포로-서래로
6 
반포4동지역-남산교회-612번지-사평대로
6 
동광로-동광단지-함지박사거리-방배로-
6 
효령로-방배동지역-방배로-방배중앙로
6 
Other values (3)
11 

Length

Max length22
Median length21
Mean length11.352941
Min length4

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row<NA>
2nd row주요노선내역
3rd row경남아파트-신반포상가-신반포로-서래로
4th row반포4동지역-남산교회-612번지-사평대로
5th row경남아파트-신반포상가-신반포로-서래로

Common Values

ValueCountFrequency (%)
<NA> 33
48.5%
경남아파트-신반포상가-신반포로-서래로 6
 
8.8%
반포4동지역-남산교회-612번지-사평대로 6
 
8.8%
동광로-동광단지-함지박사거리-방배로- 6
 
8.8%
효령로-방배동지역-방배로-방배중앙로 6
 
8.8%
음식물 수거방식변경으로 인한 신축아파트 4
 
5.9%
아크로리버파크,신반포자이 4
 
5.9%
주요노선내역 3
 
4.4%

Length

2023-12-12T16:26:23.528718image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2023-12-12T16:26:23.660887image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
ValueCountFrequency (%)
na 33
41.2%
경남아파트-신반포상가-신반포로-서래로 6
 
7.5%
반포4동지역-남산교회-612번지-사평대로 6
 
7.5%
동광로-동광단지-함지박사거리-방배로 6
 
7.5%
효령로-방배동지역-방배로-방배중앙로 6
 
7.5%
음식물 4
 
5.0%
수거방식변경으로 4
 
5.0%
인한 4
 
5.0%
신축아파트 4
 
5.0%
아크로리버파크,신반포자이 4
 
5.0%

Unnamed: 13
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

Unnamed: 14
Unsupported

MISSING  REJECTED  UNSUPPORTED 

Missing68
Missing (%)100.0%
Memory size744.0 B

Unnamed: 15
Text

MISSING 

Distinct3
Distinct (%)100.0%
Missing65
Missing (%)95.6%
Memory size676.0 B
2023-12-12T16:26:23.822998image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Length

Max length11
Median length4
Mean length6
Min length3

Characters and Unicode

Total characters18
Distinct characters16
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

Unique3 ?
Unique (%)100.0%

Sample

1st row반포2.3.4동 일부
2nd row방배1동
3rd row아파트
ValueCountFrequency (%)
반포2.3.4동 1
25.0%
일부 1
25.0%
방배1동 1
25.0%
아파트 1
25.0%
2023-12-12T16:26:24.132194image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
. 2
 
11.1%
동 2
 
11.1%
반 1
 
5.6%
포 1
 
5.6%
2 1
 
5.6%
3 1
 
5.6%
4 1
 
5.6%
1
 
5.6%
일 1
 
5.6%
부 1
 
5.6%
Other values (6) 6
33.3%

Most occurring categories

ValueCountFrequency (%)
Other Letter 11
61.1%
Decimal Number 4
 
22.2%
Other Punctuation 2
 
11.1%
Space Separator 1
 
5.6%

Most frequent character per category

Other Letter
ValueCountFrequency (%)
동 2
18.2%
반 1
9.1%
포 1
9.1%
일 1
9.1%
부 1
9.1%
방 1
9.1%
배 1
9.1%
아 1
9.1%
파 1
9.1%
트 1
9.1%
Decimal Number
ValueCountFrequency (%)
2 1
25.0%
3 1
25.0%
4 1
25.0%
1 1
25.0%
Other Punctuation
ValueCountFrequency (%)
. 2
100.0%
Space Separator
ValueCountFrequency (%)
1
100.0%

Most occurring scripts

ValueCountFrequency (%)
Hangul 11
61.1%
Common 7
38.9%

Most frequent character per script

Hangul
ValueCountFrequency (%)
동 2
18.2%
반 1
9.1%
포 1
9.1%
일 1
9.1%
부 1
9.1%
방 1
9.1%
배 1
9.1%
아 1
9.1%
파 1
9.1%
트 1
9.1%
Common
ValueCountFrequency (%)
. 2
28.6%
2 1
14.3%
3 1
14.3%
4 1
14.3%
1
14.3%
1 1
14.3%

Most occurring blocks

ValueCountFrequency (%)
Hangul 11
61.1%
ASCII 7
38.9%

Most frequent character per block

ASCII
ValueCountFrequency (%)
. 2
28.6%
2 1
14.3%
3 1
14.3%
4 1
14.3%
1
14.3%
1 1
14.3%
Hangul
ValueCountFrequency (%)
동 2
18.2%
반 1
9.1%
포 1
9.1%
일 1
9.1%
부 1
9.1%
방 1
9.1%
배 1
9.1%
아 1
9.1%
파 1
9.1%
트 1
9.1%

Correlations

2023-12-12T16:26:24.214139image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량별 수거노선 (○동)Unnamed: 3Unnamed: 6Unnamed: 9Unnamed: 12Unnamed: 15
차량별 수거노선 (○동)1.0000.404NaN1.0000.396NaN
Unnamed: 30.4041.000NaN0.5781.000NaN
Unnamed: 6NaNNaN1.000NaNNaN1.000
Unnamed: 91.0000.578NaN1.0000.578NaN
Unnamed: 120.3961.000NaN0.5781.000NaN
Unnamed: 15NaNNaN1.000NaNNaN1.000
2023-12-12T16:26:24.322154image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량별 수거노선 (○동)Unnamed: 12Unnamed: 3
차량별 수거노선 (○동)1.0000.2110.236
Unnamed: 120.2111.0001.000
Unnamed: 30.2361.0001.000
2023-12-12T16:26:24.406882image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
차량별 수거노선 (○동)Unnamed: 3Unnamed: 12
차량별 수거노선 (○동)1.0000.2360.211
Unnamed: 30.2361.0001.000
Unnamed: 120.2111.0001.000

Missing values

2023-12-12T16:26:20.984716image/svg+xmlMatplotlib v3.7.2, https://matplotlib.org/
A simple visualization of nullity by column.
2023-12-12T16:26:21.235888image/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.
2023-12-12T16:26:21.423918image/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

차량별 수거노선 (○동)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
0<NA>NaNNaN<NA><NA><NA>잠원동일부<NA><NA><NA>NaNNaN<NA><NA><NA>반포2.3.4동 일부
1요일구분시작시간종료시간주요노선내역<NA><NA><NA><NA><NA>요일구분시작시간종료시간주요노선내역<NA><NA><NA>
2( 월 )요일01:00:0010:00:00래미안-롯데캐슬-동사무소-강남대로-나루터길<NA><NA><NA><NA><NA>( 월 )요일00:00:0009:30:00경남아파트-신반포상가-신반포로-서래로<NA><NA><NA>
3<NA>NaNNaN잠원동지역-한신4지구-나루터로-신반포로<NA><NA><NA><NA><NA><NA>NaNNaN반포4동지역-남산교회-612번지-사평대로<NA><NA><NA>
4( 화 )요일01:00:0009:30:00래미안-롯데캐슬-동사무소-강남대로-나루터길<NA><NA><NA><NA><NA>( 화 )요일00:00:0009:00:00경남아파트-신반포상가-신반포로-서래로<NA><NA><NA>
5<NA>NaNNaN잠원동지역-한신4지구-나루터로-신반포로<NA><NA><NA><NA><NA><NA>NaNNaN반포4동지역-남산교회-612번지-사평대로<NA><NA><NA>
6(수 )요일01:00:0010:30:00래미안-롯데캐슬-동사무소-강남대로-나루터길<NA><NA><NA><NA><NA>(수 )요일00:00:0009:00:00경남아파트-신반포상가-신반포로-서래로<NA><NA><NA>
7<NA>NaNNaN잠원동지역-한신4지구-나루터로-신반포로<NA><NA><NA><NA><NA><NA>NaNNaN반포4동지역-남산교회-612번지-사평대로<NA><NA><NA>
8( 목 )요일01:00:0010:30:00래미안-롯데캐슬-동사무소-강남대로-나루터길<NA><NA><NA><NA><NA>( 목 )요일00:00:0009:00:00경남아파트-신반포상가-신반포로-서래로<NA><NA><NA>
9<NA>NaNNaN잠원동지역-한신4지구-나루터로-신반포로<NA><NA><NA><NA><NA><NA>NaNNaN반포4동지역-남산교회-612번지-사평대로<NA><NA><NA>
차량별 수거노선 (○동)Unnamed: 1Unnamed: 2Unnamed: 3Unnamed: 4Unnamed: 5Unnamed: 6Unnamed: 7Unnamed: 8Unnamed: 9Unnamed: 10Unnamed: 11Unnamed: 12Unnamed: 13Unnamed: 14Unnamed: 15
58<NA>NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
59( 화 )요일10:00:0013:00:00<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
60<NA>NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
61(수 )요일NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
62<NA>NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
63( 목 )요일NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
64<NA>NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
65( 금 )요일10:00:0013:00:00<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
66<NA>NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>
67( 토 )요일NaNNaN<NA><NA><NA><NA><NA><NA><NA>NaNNaN<NA><NA><NA><NA>

Duplicate rows

Most frequently occurring

차량별 수거노선 (○동)Unnamed: 3Unnamed: 6Unnamed: 9Unnamed: 12Unnamed: 15# duplicates
5<NA><NA><NA><NA><NA><NA>18
3<NA>방배본동지역-현대아파트 뒷길-동광로<NA><NA>효령로-방배동지역-방배로-방배중앙로<NA>6
4<NA>잠원동지역-한신4지구-나루터로-신반포로<NA><NA>반포4동지역-남산교회-612번지-사평대로<NA>6
1<NA>동사무소길-방배4동뒷골목-방배중앙로-방배로<NA><NA>아크로리버파크,신반포자이<NA>4
0요일구분주요노선내역<NA>요일구분주요노선내역<NA>3
2<NA>동사무소길-방배4동뒷골목-방배중앙로-방배로<NA><NA><NA><NA>2